Patent Publication Number: US-2022236244-A1

Title: Gas sensing device and method for determining a calibrated measurement value for a concentration of a target gas

Description:
This application claims the benefit of European Patent Application No. 21153012, filed on Jan. 22, 2021, which application is hereby incorporated herein by reference. 
     TECHNICAL FIELD 
     Examples of the present disclosure relate to a gas sensing device, in particular a chemoresistor gas sensing device, for sensing a target gas. Further examples relate to a method for determining a calibrated measurement value for a concentration of a target gas. Some examples relate to a method for calibrating a gas sensing device. 
     BACKGROUND 
     Chemoresistive gas sensors, like metal-oxide- (MOX), polymer- or graphene-based gas sensors, often exhibit variations of their properties from device to device since the deposition of the sensing layer is usually difficult to control, therefore leading, for instance, to different sensing area. Due to these variations those sensors are usually calibrated one by one, exposing them to certain concentrations of the target gases and storing the calibration data into the sensor itself (e.g. in ASIC or μC registers). This calibration data will be used by the software to estimate the concentration of the target gases, e.g. with polynomial fitting or adjusting a model of the sensor. 
     SUMMARY 
     In view of the state of the art, a concept for a gas sensing device would be desirable, which provides an improved trade-off between a time- and cost-efficient calibration of the gas sensing device and a high accuracy of the gas sensing device in determining a concentration of the target gas. 
     Examples of the present disclosure provide a gas sensing device for sensing a target gas. The gas sensing device comprises a sensing unit for sensing the target gas. The sensing unit is configured for providing a measurement signal based on a concentration of the target gas in an environment of the gas sensing device. The gas sensing device further comprises a signal calibration unit which is configured for determining a calibrated measurement value based on the measurement signal and further based on a calibration model. The calibration model is based on calibration data of a plurality of test sensor units having the same type as the sensor unit. 
     Further examples of the present disclosure provide a method for determining a calibrated measurement value for a concentration of the target gas. The method comprises a step of obtaining a measurement signal based on the concentration of the target gas. The method further comprises a step of determining the calibrated measurement value based on the measurement signal and based on a calibration model. The calibration model is based on calibration data of a plurality of test sensor units having the same type as the sensor unit. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments of the present disclosure are described in more detail below with respect to the figures, among which: 
         FIG. 1  illustrates an example of a gas sensing device; 
         FIG. 2  illustrates an example of a method for obtaining the calibration model and an example of a method for calibrating the gas sensing device; 
         FIG. 3  illustrates an example of pre-selecting the test sensing units; 
         FIG. 4  illustrates an example of testing a functional state of the gas sensing device; 
         FIGS. 5A, 5B, and 5C  show examples of concentration profiles of NO 2  and O 3 ; 
         FIGS. 6A and 6B  show examples of temperature and humidity profiles; 
         FIG. 7  illustrates an example of evaluating an operational condition of the gas sensing device; 
         FIG. 8  illustrates an example for re-determining a baseline value of the sending unit; 
         FIG. 9  illustrates an example for initializing a recovery sequence; 
         FIG. 10  illustrates a flowchart of an example of a method for obtaining a calibrated measurement value of the gas sensing device; and 
         FIGS. 11A and 11B  illustrate the accuracy of the calibration model according to an example. 
     
    
    
     DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS 
     In the following, embodiments are discussed in detail, however, it should be appreciated that the embodiments provide many applicable concepts that can be embodied in a wide variety of sensing devices and calibration thereof. The specific embodiments discussed are merely illustrative of specific ways to implement and use the present concept, and do not limit the scope of the embodiments. In the following description, a plurality of details is set forth to provide a more thorough explanation of embodiments of the disclosure. However, it will be apparent to one skilled in the art that other embodiments may be practiced without these specific details. In other instances, well-known structures and devices are shown in form of a block diagram rather than in detail in order to avoid obscuring examples described herein. In addition, features of the different embodiments described herein may be combined with each other, unless specifically noted otherwise. 
     In the following description of embodiments, the same or similar elements or elements that have the same functionality are provided with the same reference sign or are identified with the same name, and a repeated description of elements provided with the same reference number or being identified with the same name is typically omitted. Hence, descriptions provided for elements having the same or similar reference numbers or being identified with the same names are mutually exchangeable or may be applied to one another in the different embodiments. Features shown in dashed lines are optional features. 
       FIG. 1  illustrates an example of a gas sensing device  100  for sensing a target gas. The gas sensing device  100  comprises a sensing unit  110  for sensing the target gas. The sensing unit  110  is configured for providing a measurement signal  112  based on a concentration of the target gas in an environment of the gas sensing device. The gas sensing device  100  further comprises a signal calibration unit  130 . The signal calibration unit  130  is configured for determining a calibrated measurement value  132  based on the measurement signal  112  and based on a calibration model  120 . The calibration model  120  is based on calibration data of a plurality of test sensor units having the same type as the sensor unit. 
     Examples of the present disclosure rely on the idea to determine the calibrated measurement value  132  for the concentration of the target gas by using the calibration model  120  which is based on calibration data of a plurality of test sensor units. As the plurality of test sensor units which have the same type as the sensor unit  110  may be representative of characteristics of the sensor unit  110  a calibration for the sensor unit  110  may at least partially be predicted from the calibration model  120 . Therefore, an individual calibration of the sensor unit  110  may be avoided, or at least an extent to which the sensor unit  110  has to be calibrated individually may be reduced. Nevertheless, a high accuracy in the determination of the calibrated measurement value  112  may be achieved, as characteristics of the sensor unit  110  may be derived from the calibration model  120 . In particular, as the calibration model  120  is based on a plurality of test sensing units, the calibration model  120  may be able to compensate for manufacturing variations between individual sensing units. Calibration of single devices is an expensive process usually done on package level which may drastically increases the price of the final product. Therefore, avoiding an individual calibration of the sensor unit  110  allows for a time- and cost-efficient manufacturing process of the gas sensing device  100 . For example, the gas sensing device  100  is for monitoring outdoor and/or indoor air quality. Calibration costs for such gas sensors may represent a relevant part of the total cost. The disclosed concept reduces the calibration costs and therefore the overall costs of the gas sensing device. 
     Furthermore, during its lifetime, a gas sensing device, for example a chemoresistive sensor, is may be exposed to an event that temporarily or permanently modifies the original behavior of the gas sensing device. Therefore, determining the concentration of the target gas based on a device-individual calibration may reduce the accuracy for determining the concentration of the target gas after a change of the behavior of the gas sensing device. In the case of chemoresistive gas sensing devices, such an event may for example be an exposure to a high concentration of a gas which is adsorbed by the sensing unit, for example at a sensing layer of the sensing unit, and thus occupies all the adsorption sites. The adsorption may decrease the sensitivity of the sensor, or may degrade the sensing material due to poisoning or oxidation of the surface (for instance when the sensor is exposed to high concentration of O 3 ). Using the calibration model  120  has the advantage, that the determination of the calibrated measurement value  132  includes calibration data of sensing units which are different from the sensing unit  110 , thus providing a broad knowledge base which may allow to cope with a change of the behavior of the sensing unit  110 . Thus, the gas sensing device  100  may persistently determine the calibrated measurement value  132  with a high accuracy even without a recalibration after manufacturing. 
     As the calibration model  120  does not necessarily rely on calibration data of the sensing unit  110 , it may further be updated without the need to subject the gas sensing device  100  to a recalibration procedure. For example, calibration data for updating the calibration model may be obtained from test sensing units or further sensing units. 
     In examples, the gas sensing device  100  is a chemoresistive gas sensing device. For example, the sensing unit  110  may comprise a chemoresistive sensing layer a surface region of which is exposed to the environment of the gas sensing device  100 , and which may change its resistivity upon adsorption of gas molecules, in particular gas molecules of the target gas, at the surface reason of the sensing layer. 
     For example, the measurement signal  112  provided by the sensing unit  110  may be a digital or an analog signal. The measurement signal  112  may represent a resistivity of a sensing layer of the sensing unit  110  which depends on the concentration of the target gas in the environment of the sensing unit  110 . The calibrated measurement signal  132  may represent the concentration of the target gas, and derive from the measurement signal  112  by using the calibration model  120 . 
     For example, the calibration model  120  is stored on a data storage of the gas sensing device  100 . The signal calibration unit  130  may be a signal processor of the gas sensing device  100  or may represent a process performed on a signal processor the gas sensing device  100 . 
     In examples, the calibration model  120  is a statistical model which is trained by supervised learning techniques using the calibration data. Training the calibration model  120  using the calibration data of the plurality of test sensing units allows for an accurate determination of the calibrated measurement value  132  even if the calibration model  120  does not include calibration data of the sensing unit  110 . 
     For example, the calibration model  120  is built using data obtained from a characterization of a statistically relevant amount of test sensing units. 
     The test sensing units may be selected so that the plurality of test sensing units is statistically representative of the sensing unit  110 . For example, the test sensing units may be manufactured equivalently to the sensing unit  110 . 
     In examples, the calibration data includes measurement signal values of the test sensor units. The measurement signal values of the test sensor units are acquired during a plurality of calibration measurement sequences. During the calibration measurement sequences, test sensor units of the plurality of test sensor units are exposed to a sequence of varying environmental conditions. The environmental conditions are characterized at least by one or more environmental parameters including a concentration of the target gas. For example, the environmental parameters may further include one or more of temperature, humidity, pressure, concentrations of one or more further gases, and further parameters. Generating the calibration data using a plurality of environmental parameters may increase a robustness of the calibration model. The calibration data may optionally include values for the environmental parameters during the sequence of varying environmental conditions. That is, the sequence of varying environmental parameters may follow a specific sequence of values of the environmental parameters. For example, the sequences of varying environmental parameters may follow sequences of varying concentrations of the target gas, while one or more further environmental parameters are kept constant during the sequence. 
     Including measurement signal values of the test sensing units obtained during the sequence of varying environmental conditions into the calibration model allows for an accurate determination of the calibrated measurement value  132  over a wide range of concentrations and of values of further environmental conditions. Comparing the behavior of the sensing unit  110  with the calibration model  120  which is based on measurement signal values of the test sensing units obtained during the sequence of varying concentrations may provide for a precise prediction of the calibrated measurement value  132 . 
     In examples, the calibration data of the test sensor units includes respective baseline values of the test sensor units. For example, a baseline value may represent a value of the measurement signal, that is a measurement signal value, which is measured in a condition in which the sensing unit is exposed to an environment in which the concentration of the target gas is zero or substantially zero. The base line value may be measured particularly easy but may provide at least a hint on basic characteristics of the sensing unit. Thus, the baseline value may allow for a classification of the sensing unit. In combination with measurement signal values of the test sensor units acquired during the plurality of calibration measurement sequences, the calibration model  120  may allow for, or may include, a prediction of a correlation between the baseline value and a behavior of the sensing unit for different concentrations of the target gas. 
     In examples, the gas sensing device  100  further comprises a further sensing unit  110 ′ for sensing a further target gas. The test sensor units of the plurality of test sensor units may have the same types as the sensing unit and the further sensing unit  110 ′. The calibration data may include measurement signal values of the test sensor units acquired during a plurality of calibration measurement sequences in which the test sensor units of the plurality of test sensor units are exposed to a sequence of varying concentrations of at least one of the target gas and the further target gas. For example, in the calibration measurement sequences, the test sensor units of the plurality of test sensor units are exposed to a sequence of varying environmental conditions which are characterized at least by one or more of the environmental parameters, which may include at least one of the concentrations of the target gas and the further target gas. 
     The further sensing unit  110 ′ may have a different sensitivity to the target gas than the sensing unit  110 . In examples, the signal calibration unit  130  may be configured for determining the calibrated measurement value  132  based on the measurement signal  112  and a further measurement signal  112 ′ provided by the further sensing unit  110 ′. Optionally, the signal calibration unit  130  may determine a further calibrated measurement value which represents the concentration of a further target gas in the environment of the gas sensing device  100 . According to these examples, the calibration model  120  may further be based on calibration data of a plurality of test sensor units having the type of the further sensing unit  110 ′. In examples, the calibration model  120  is based on calibration data of a plurality of test gas sensing devices having the same type as the gas sensing device  100 . That is, the test gas sensing devices may comprise the same types of sensing units as the gas sensing device  100 . 
     Having the sensing unit  110  and the further sensing unit  110 ′ allows for comparing the measurement signal  112  and the further measurement signal  112 ′, for example by considering a cross-correlation of the measurement signal  112  and the further measurement signal  112 ′. Therefore, the calibrated measurement value  132  may be determined more accurately. Further, a comparison of the measurement signal  112  and the further measurement signal  112 ′ allows to detect a malfunction of one of the sensing unit  110  and the further sensing unit  110 ′. 
     In examples, the gas sensing device  100  further comprises a data storage holding a baseline value, i.e. a baseline value for the sensing unit  110 . The signal calibration unit  130  may use the baseline value for determining the calibrated measurement value  132 . 
     The baseline value may serve as an input for the determination of the calibrated measurement value  132  using the calibration model  120 . The baseline value of the sensing unit  110  may be measured of the manufacturing of the gas sensing device  100  for the sensing unit  110 . In examples, the baseline value may be updated during operation of the gas sensing device  100 . This example is advantageously implemented in combination with the feature that the calibration data for the calibration model  120  comprises baseline values of the test sensing units. Thus, the baseline value of the sensing unit  110  allows for a classification of the sensing unit  110  relative to the test sensing units, so that the calibration model  120  allows for an accurate prediction of the calibrated measurement value  132  based on the calibration data of the test sensing units. As the baseline value may be measured without exposing the sensing unit  110  to a specific concentration of the target gas, the measurement of the baseline value of the sensing unit  110  may be easy, fast and cost-efficient. Further, as it may be possible to predict a time period within which the concentration of the target gas in the environment of the gas sensing device  100  during operation is low or zero, it may be possible to update the baseline value, so that a change of a characteristic of the sensing unit  110  may be considered in the determination of the calibrated measurement value  132  without a recalibration of the sensing unit  110  under laboratory conditions. 
       FIG. 2  illustrates an example of a method  250  for obtaining the calibration model  120  in combination with an example of a method  240  for calibrating the gas sensing device  100 . The method  250  may represent a process to generate a common model, the calibration model  120 , which model may be used for a plurality of gas sensing devices such as the gas sensing device  100 . The method  240  may represent a quick calibration (e.g. just baseline) that is done on each of a plurality of manufactured gas sensing device. The methods  240  and  250  may be implemented independently from each other. 
     The method  250  comprises a characterization  252  of a test sensor unit. The characterization  252  comprises a step  253  of reading out the measurement signal of the test sensing unit, for example in a condition in which none of one or more of target gases of the test sensing unit is present in the environment of the test sensing units. That is, the baseline value of the test sensing unit may be measured. In a step  254  of the characterization  252 , the measurement signal of the test sensing unit is evaluated with respect to one or more criteria, so as to decide whether the test sensing unit is to be used for determining the calibration model one  120  or not. For example, if the baseline of the test sensing unit is out of spec, for example higher or lower than a specific threshold, or if noise of the measurement signal of the test sensing unit is higher or lower than a specific threshold, the test sensing unit may be excluded from the determination of the calibration model  120 . In other words, outliers of test sensing units may be excluded. It is noted that steps  253  and  254  are optional. 
     In a step  256  of the characterization  252 , the test sensing unit is characterized, for example by exposing the test sensing unit to the plurality of calibration measurement sequences. For example, step  256  may include to acquire measurement signal values of the test sensing unit with and without background gases such as the target gas. For example, the calibration measurement sequences may be performed at different environmental conditions. The environmental condition for one of the calibration measurement sequences may be characterized by specific values of a set of environmental parameters, including one or more of the concentration of the target gas, temperature, relative humidity (RH), pressure, and further parameters. In examples, in step  256 , a plurality of test sensing units, e.g. hundreds of test sensing units, may be characterized in parallel. 
     In other words, the test sensing units may be exposed to several realistic profiles of target gases (e.g. including the target gas and one or more further target gases of the gas sensing device  100 ) in known background conditions, e.g. synthetic air, and fixed humidity, temperature and pressure. Alternatively or additionally, the test sensing unit may be exposed to multiple realistic profiles including target gases and background gases (e.g. NO 2 , O 3 , CO, SnO 2 , NO, CH 4  in the case of outdoor environment and NH 3 , TVOCs, CO 2  in case of indoor environment). For example, the test sensing unit may be exposed to a respective realistic profiles of indoor environments and outdoor environments. A profile may refer to a temporal evolution of the concentration of one or more parameters such as the concentration of the target gas. Alternatively or additionally, the test sensing unit is exposed to one or more target gasses and different ambient temperatures and humidities. Alternatively or additionally, the test sensing unit is exposed to one or more target gasses and different (realistic) pressures. 
     The characterization  252  is performed for the plurality of test sensing units so as to obtain calibration data  257 . In other words, a statistically relevant amount of samples (e.g. &gt;100 devices) is characterized, for example by using a calibrated measurement set up. That is, for example concentrations of the target gas and further ambient conditions are known during the characterization procedure  252 . 
     In a step  258  of the methods  250 , the calibration model  120  is trained by using the calibration data of the plurality of test sensing units measured during step  256 . Optionally, the baseline value measured in step  253  services an additional input for training the calibration model  120 . In other words, the calibration data obtained in step  252  is used to train and to generate an average model which is able to predict the concentration of the target gases independently from small manufacturing variations. For example, a dimensionality of the calibration model  120  may depend on the amount of features being extracted by the various sensor fields of the device. For example, the sensor unit  110  and optional one or more further sensing units  110 ′ may be referred to as sensor fields of the gas sensing device  100 . The damage nullity of the calibration model  120  may further depend on a sampling time of the output and the number of target gases. 
     In other words, the method  250  is for building up a model of the response of the sensing unit  110  using supervised learning techniques, the model being robust against small manufacturing variations. Therefore, an individual calibration of the single chips, e.g. the sensing unit  110 , with the target gases may not be required. 
     The method  240  for calibrating the gas sensing device  100  comprises a step  243  of reading out the measurement signal of the sensing unit  110 , which may be performed after an assembly of the gas sensing device  100 . Similar to step  253  of method  250 , the step  243  may be performed in the absence of target gases in the environment of the gas sensing device  100 . Thus, step  253  may yield the baseline value  244  of the sensing unit  110 . In other words, of the assembly of a bit gas sensing unit, a short readout on the clean air may performed to measure the baseline of the sensor. In step  245 , the baseline value  244  is evaluated with respect to one or more criteria so as to decide whether to discard the sensing unit are not. The criteria applied in step  245  may be same criteria as applied in step  254  of method  250 . If the sensing unit  110  is not discarded, in step  246  the baseline value  244  is stored in the gas sensing device, e.g. on a data storage such as an ASIC of μC registers. In step  247 , the calibration model  120  obtained by methods  250  is stored in the gas sensing device  100 . 
     The common acquired model, that is the calibration model  120 , may be stored in a firmware of the gas sensing device  100  and further gas sensing devices of the same type, and may be used by the firm that to predict the concentration of the target gases. 
     In examples, the baseline value  244  (or the sensor baseline) is the only sensor specific calibration data (i.e. the only calibration data obtained from the gas sensing device  100 ) which is needed for determining the calibrated measurement value  132  of the gas sensing device  100 . 
     Calibrating the gas sensing device  100  using the methods  250  and  240  may drastically reduce the calibration cost since a single model may be used for a plurality of gas sensing devices like the gas sensing device  100 . That means, that no individual calibration with different target gases and concentrations is required. Just a short read-out under synthetic air may be necessary. The cost of the calibration setup will be drastically lower (no mass flow controllers, pipes, gas bottles required, special room with adequate ventilation, etc.) and the cost of the calibration itself will decrease thanks to a shorter calibration time. 
       FIG. 3  illustrates an example of step  254  of evaluating the baseline of the test sensing units. For example, readout of the baseline values of the test sensing units in step  253  may be performed at a specific temperature such as 25° C. In a step  361 , an R value of the measured baseline may be tested against a target value or against a target range. For example, it may be tested in step  361 , whether the R value is within or without a specific target range. For example, a test sensing units may be regarded as defect, if the R value is higher or lower by more than a relative threshold, e.g. 20%, of a target value. In a step  362 , a noise of the measured baseline may be compared to a target value. In examples, a test sensing unit may be regarded as defect, if the noise of the baseline exceeds a threshold, e.g. is more than 20% over a target value. If both steps  361  and  362  indicate that a test sensing unit is not defect, the test sensing unit may be used for generating calibration data for the calibration model  120 . In other words, the board block diagram of  FIG. 3  illustrates how outliers may be identified and excluded from the characterization in order to build a robust calibration model. 
       FIGS. 11A and 11B  provide an example of how the calibration model  120  trained on a first group of sensing devices effectively generalizes to a second group of sensing devices, which has not been used for training the calibration model  120 .  FIG. 11A  shows calibrated measurement values for the concentration of NO 2  and O 3  (Pred NO 2 , Pred O 3 ) determined from measurement signals of the first group of sensing devices in comparison with concentrations of the respective gases (True NO 2 , True O 3 ).  FIG. 11B  shows the corresponding data determined from measurement signals of the second group of sensing devices. Please note that sensors not in spec have been preliminary discarded according to the method in  FIG. 2  and  FIG. 3 . 
     Continuing with the description of  FIG. 1 , in examples, the gas sensing device  100  further comprises means for determining an operational condition of the gas sensing device  100 . Further, the gas sensor device  100  may comprise means for switching the calibration model in response to a change of the operational condition. 
     The means for determining the operational condition and the means for switching the calibration model may, for example, be provided by the means  134  which may optionally be part of the signal calibration unit  130  as indicated in  FIG. 1 . Alternatively, the means  134  may be separate means, e.g. comprising one or more of a signal processor, a communication interface, a sensing device. 
     The operational condition may include one or more of a malfunctioning of the sensing unit  110  and/or the further sensing unit  110 ′, a geolocation of the gas sensing device  100 , and an indication about whether the gas sensing device  100  is located indoor or outdoor. For example, the means  134  for determining the operational condition may comprise an interface for receiving an indication of the operational condition, such as the geolocation. The means  134  may be configured for determining the operational condition, e.g. by sensing the geolocation. In other examples, the means  134  for determining the operational condition may comprise a signal processor for evaluating the concentration of the target gas as determined by the gas sensing device  100 . For example, based on a knowledge about typical concentration profiles of the target gas (i.e. an evolution of the concentration of the target gas over time), the gas sensing device  100  may infer the operational condition, such as the geolocation or the indoor/outdoor location. Also, the gas sensing device may infer a functional state of the sensing unit  110 ,  110 ′ from the evaluation of the concentration of the target gas. E.g., the functional state may indicate whether the sensing unit  110  is malfunctioning. Switching the calibration model in response to a change of the operational condition, for example by selecting a calibration model which is representative of the operational condition of the gas sensing device  100 , may ensure that the calibration model  120  is suitable for the operational condition of the gas sensing device  100 . Thus, an accurate determination of the calibrated measurement value  132  may be granted even after a change of the operational condition. 
     In other words, examples of the disclosure may implement methods for ensuring long term validity of the acquired calibration model, the methods comprising one or more of (1) defect detection and elimination of malfunctioning sensors, (2) recalibration of the model as part of a maintenance or upgrade procedure, (3) a cleaning protocol to restore the sensor properties and thus preserve the sensitivity of the sensor and thus the validity of the acquired model over a longer period of time. These methods may be triggered by an internal mechanism of the gas sensing device  100  that collects internal statistics on the sensor signals (including the predictions from the estimation algorithm, i.e. the calibrated measurement values) and decides accordingly whether a different or improved calibration model is needed. Alternatively, if some form of (sporadic) connectivity to a monitoring station or similar device is available, then the additional information available (e.g. geographical data) from the external device could also be used to start the procedures (1)-(3). The examples of the gas sensing device  100  described in the following may optionally implement one or more of the method steps (1)-(3). 
     In examples, the gas sensing device  100  comprises a further sensing unit  110 ′ for sensing a further target gas, the further sensing unit  110 ′ being configured for providing a further measurement signal  112 ′ based on a concentration of the further target gas in the environment of the gas sensing device  100 . According to this example, the signal calibration unit  130  is configured for determining the calibrated measurement value  132  based on the measurement signal  112  and the further measurement signal  112 ′. Additionally, the gas sensing device  100  further comprises means for evaluating the further measurement signal  112 ′ so as to decide whether the further sensing unit  110 ′ is in a malfunctioning state. If the variation of the further measurement signal  112 ′ indicates, that the further sensing unit  110 ′ is in a malfunctioning state, the signal calibration unit  130  may determine the calibrated measurement value  132  independent of the further measurement signal  112 ′. Additionally or alternatively, if the evaluation of the further measurement signal  112 ′ indicates, that the further sensing unit  110 ′ is in a malfunctioning state, the signal calibration unit  130  may use, as the calibration model  120 , a further calibration model which is independent of calibration data from test sensing units of the type of the further sensing unit  110 ′. Optionally, the means for evaluating the further measurement signal  112 ′ may be provided by the means  134 . 
     In other words, if the further sensing unit  110 ′ is malfunctioning, the further measurement signal  112 ′ may be excluded from the determination of the calibrated measurement value  132 . Alternatively or additionally, the signal calibration unit  130  may switch the calibration model  120  and apply as the calibration model  120  a further calibration model. The further calibration model may be built such that it allows for a determination of the calibrated measurement value  132  independent of the further measurement signal  112 ′. For that purpose, the further calibration model is independent of calibration data of test sensing units of the type of the further sensing unit  110 ′. In other words, the further calibration model may be a reduced model which excludes the malfunctioning further sensing unit  110 ′ from the determination of the calibrated measurement value  132 . Thus, an outlying sensing unit may be eliminated from the determination of the calibrated measurement value  132 . Determining the calibrated measurement signal  132  independent of the further measurement signal  112 ′ and/or calibration data from test sensing units of the type of the further sensing unit  110 ′ may allow for a reliable determination of the calibrated measurement value  132  in case of a malfunctioning of the further sensing unit  110 ′. 
     In other words, during the lifetime of a device malfunctioning can occur due to damage or defect. This can happen, for instance, if the sensing layer presents defects or damages, e.g. irregularities of the sensing layer or scratches in the sensing layers that are not detected during a characterization of the gas sensing device on wafer level (e.g. scratches cause during pre-assembly and assembly), or if the MEMS shows some defects, e.g. delamination of some metal lines or broken membranes, or if the bonding wires are damaged or the adhesion of the bond was poor (for instance due to surface contamination). In such cases, the sensor will either not respond or deliver values which are way above or below the expected ranges for the concentration ranges and dynamics the sensor has been calibrated for. For example, it may be tested whether the noise level of the raw signal, e.g. the measurement signal  112 , is higher than the levels experienced in the lab. Additionally or alternatively, it may be tested whether one or both of the sensitivity and the derivative are lower or higher than the values experienced in the lab. For testing these criteria, the raw signal may be evaluated for a prolonged amount of time, e.g. over several days. These criteria may be used for testing a functional state of a sensing unit, that is to determine whether the tested sensing unit is malfunctioning or not. 
       FIG. 4  illustrates a flowchart of an example of a method  470  for selecting a calibration model in dependence on a functional state of a tested sensing unit of the gas sensing device  100 . Examples of the signal calibration unit  130  of  FIG. 1  may perform method  470 . During operation of the gas sensing device  100 , the measurement signal of the tested sensing unit, e.g. the further sensing unit  110 ′, is a read out in a step  471 , so as to obtain measurement signal values  472 . In a step  473 , the measurement signal values  472  are used for evaluating the sensitivity of the tested sensing unit, e.g. by testing the sensitivity with respect to one or more thresholds. In a step  474 , the measurement signal values  472  are used for evaluating a derivative of the measurement signal or of the sensitivity of the measurement signal, e.g. by testing the derivative with respect to one or more thresholds. In step  475 , a cross-correlation between the measurement signal of the tested sensing unit and one or more of the sensing units such as the sensing unit  110  is evaluated, e.g. the testing the cross-correlation against the one or more thresholds. The testing of the sensitivity in step  473 , the derivative in step  474 , and the cross-correlation in step  475  may, for example, be performed by using respective thresholds. For example, the sensitivity and its derivative and the noise level may be compared to pre-stored thresholds T_Noise_sens or T_Max_sens (for the sensitivity) and T_Noise_d and T_Max_d (for the derivative). Similarly, it has been observed that the presence of an interfering background gas can also cause a specific sensor field, i.e. sensor unit, to react to it more than the other fields and behave in an unexpected way. Since the responses from the sensor fields to various concentration events tend to be quite similar, monitoring the correlation among them over time provides an additional indicator on the status of the sensor. Sensor cross-correlation could be defined as 
     
       
         
           
             
               
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                       p 
                     
                   
                 
               
             
           
         
       
     
     where x r  and x p  indicate the normalized response at sensor r and p, respectively, at different moments in time and n is the window size being used to calculate the cross-correlation. 
     If any of the steps  473 ,  474 ,  475  indicates, that the tested sensing unit is malfunctioning, the method  470  may proceed with step  476  of replacing the calibration model  120  with a further calibration model, e.g. a reduced model. The reduced model may be obtained from calibration data measured under laboratory conditions, e.g. as described with respect to the method  250 . For example, the reduced model may exclude calibration data of test sensing units of the type of the malfunctioning tested sensing unit. If none of the steps  473 ,  474 ,  475  indicates a malfunctioning of the tested sensing unit, the existing calibration model  120  may be kept, cf. step  477 . In a step  478 , the calibration model  120  may be used for determining the calibrated measurement value  132  on the basis of the measurement signal  112 . Each of the steps  473 ,  474 ,  475  may be optional, so that the method for  70  may also be implemented without one or more of the steps  473 ,  474 ,  475 . Further, in alternative implementations, the calibration model may be kept, if a selection, but not necessary all of the steps  473 ,  474 ,  475  do not indicate a malfunctioning of the test sensing unit. For example, step  476  is performed, if two or more of the steps  473 ,  474 ,  475  indicate a malfunctioning of the tested sensing unit. 
     The further calibration model on the reduced calibration model may be stored in a data storage and the gas sensing device  100  or may be provided to the gas sensing device  100  via a communication interface, for example over the air. For example, the gas sensing device  100  may be configured for requesting the further calibration model in response to the finding that the tested sensing unit, e.g. the further sensing unit  110 ′, is malfunctioning. 
     In other words, according to examples, the original calibration model may be replaced with a reduced model which excludes the malfunctioning sensor fields and their related features. This reduced model could be pre-stored on the device memory or, if some form of connectivity is available even sporadically, a new model in memory can be transferred and stored on the device over the air (OTA). 
     Continuing in the description of  FIG. 1 , according to examples, the gas sensing device  100  further comprises means for obtaining information about a location, e.g. a geolocation or an indoor/outdoor location, of the gas sensing device  100 . According to these examples, the signal calibration unit  130  is configured to use, as the calibration model  120  a calibration model which is based on calibration data which is representative of the location. Optionally, the means for obtaining information about the location may be provided by the means  134 . 
     For example, the calibration data which is representative of the location may have been obtained by exposing the plurality of test sensing units to profiles, e.g. concentration profiles, which are characteristic are representative for the location. 
     For example, during operation, the gas sensing device  100  may obtain information on the geographic location of the gas sensing device  100  (e.g. urban or rural, latitude, altitude, etc.). A geolocation may be associated with specific characteristics of the gas profiles (or patterns), such as typical ranges for the concentration, dominant gases in the mixture, relative dynamics. For example, the typical range of concentration of the main pollutants may vary a lot from city to city, depending for instance on the population density, the local culture and habits. 
       FIGS. 5A, 5B, and 5C  show exemplary plots of the NO 2  concentration  502 , the O 3  concentration  501  and the CO concentration  503  (in ppb) monitored for five weeks in three different cities: Beijing ( FIG. 5A ), Munich ( FIG. 5B ), and Puno ( FIG. 5C ). It can be observed from the graphs that in Beijing there are peaks of O 3  up to 120 ppb, while the NO 2  concentration didn&#39;t increase more than 40 ppb during the monitoring period. The CO levels in the same cities reached peaks of 1200 ppb. In Munich, on the other side, the variation of NO 2  and O 3  were shift to lower concentrations, with maximum values around 70 ppb for O 3  and 50 ppb for NO 2  and CO concentration below 300 ppb. If we look at smaller cities with very low population density, like Puno, we see that the range for NO 2  is even more compressed (with maximum values of 10 ppb) and maximum values for O 3  below 60 ppb, the CO concentration is below 200 ppb. Additionally, the temperature and relative humidity can also vary a lot from region to region. An example of temperature and relative humidity for five weeks in Munich, Beijing and Puno are shown in  FIGS. 6A and 6B . 
     In examples, the signal calibration unit  130  may select the calibration model  120  out of a plurality of calibration models, representing the differences and the different range of pollutants described exemplarily by  FIGS. 5A-5C  and  FIGS. 6A-6B , e.g. one calibration model to predict the target gas concentrations in big cities in Asia, a second calibration model to predict the gas concentration in big cities in Europe, a third calibration model for cities with very low population density or countryside. Selecting a calibration model according to the geolocation of the gas sensing device  100  may improve the accuracy in the determination of the calibrated measurement value  132 . 
     In other words, the existing pre-defined model, e.g. the calibration model  120  stored in the gas sensing device after manufacturing, may be replaced by a new one (the further calibration model), which may be the result of a more accurate training process where the additional information on the gas behavior has been accounted for. For example, this new model could be transferred over the air to the device, if some sporadic connectivity is available, or it could be activated from an already available pool of models stored in memory once the geographic location or the specific characteristics of the patterns are identified. 
     In examples, the gas sensing device  100  comprises means for characterizing an environment of the gas sensing device  100  by evaluating an evolution, e.g. a temporal evolution, of the measurement signal  112  during a period of time. The signal calibration unit  130  may be configured to select, as the calibration model  120 , a calibration model on the basis of the characterization of the environment of the gas sensing device  100 . Optionally, the means for characterizing the environment of the gas sensing device may be provided by the means  134 . 
     In other words, the sensor algorithm could also keep track of the gas estimates (e.g. ranges and rate of variation) over a certain period of time and select a more accurate model with better matched gas concentration ranges. 
       FIG. 7  shows a flow chart of an example for a method  780  for selecting the calibration model  120  in dependence on the location of the gas sensing device  100  and/or in dependence on an evolution of the measurement signal  112 . For example, the method  718  may be performed by the signal calibration unit  130  of  FIG. 1 . During operation of the gas sensing device  100 , in step  781  of the method  780 , the measurement signal  112  is acquired. In step  782  of the method  780 , the evolution of the acquired measurement signal  112  is compared with historic data, for example with data every presenting evolution of the measurement signal  112  in earlier time period. Thus, a change of the sensor characteristics may be detected, and a type of the change of the sensor characteristics may be classified, so as to select a suitable calibration model. Alternatively or additionally, in step  782 , and information on the location, e.g. a geographic location, of the gas sensing device  100  is used for selecting a calibration model. Alternatively or additionally, in step  782 , and evolution of the measurement signal  112  is evaluated so as to characterize the environment of the gas sensing device  100 . Subsequently, a calibration model which matches the environment of the gas sensing device  100  may be selected. If an information about the location of the gas sensing device  100  is available, or if historic data is available, in step  783  the calibration model  120  may be replaced with a more accurate calibration model, for example a calibration model which better matches the operational condition of the gas sensing device  100 . If no better matching calibration model is available, or if no information on the location and/or historic data and/or information on possible environment of the gas sensing device  100  is available, step  784  of keeping the currently used calibration model may be performed. 
     In examples, the gas sensing device  100  comprises means  136  for receiving a further calibration model  120 ′ and the signal calibration unit  130  is configured to use the further calibration model  120 ′ as the calibration model  120 . 
     For example, the means  136  for receiving the further calibration model include a communication interface. The further calibration model  120 ′ may provide for any of the calibration models to which the gas sensing unit  110  may switch in response to the operational condition. Having the means for receiving the further calibration model allows for adapting the calibration model  120  according to the operational condition of the gas sensing device  100 , for example according to the functional state of the sensing unit  110 , according to the location of the gas sensing device  100  or according to an evolution of the concentration of the target gas as described with respect to  FIG. 4  to  FIG. 7 , without imposing higher requirements regarding memory capacity on the gas sensing device  100 . 
     According to further examples, the gas sensing device comprises a data storage  138  holding a plurality of calibration models. According to this example, the signal calibration unit  130  is configured to select the calibration model  120  for the determination of the calibrated measurement value  132  from the plurality of calibration models. 
     The plurality of calibration models may provide the calibration model to which the gas sensing device  100  may switch in response to the operational condition. Having the plurality of calibration models stored in the gas sensing device  100  has the advantage, that the calibration model  120  may be adapted even if the gas sensing device  100  has no connectivity, e.g. to a server. Thus, a reliable operation of the gas sensing device may be granted. 
       FIG. 8  shows a block diagram of an example of an operation scheme  890 , which may be executed by the gas sensing device  100 , for example the signal calibration unit  130  of  FIG. 1 . According to this example, the gas sensing device  100  comprises means, e.g. the signal calibration unit  130 , for detecting, in a step  893 , whether an exposure  892  of the sensor unit  110  to the target gas exceeds a first threshold. Further, the gas sensing device  100  may comprise means for determining, in a step  894 , a baseline value  844  of the sensing unit  110  if the exposure of the sensor unit  110  to the target gas exceeds the first threshold. According to this example, the signal calibration unit  130  is configured to use the determined baseline value  844  as an input for the calibration model  120  so as to determine, in a step  839 , the calibrated measurement value  132 . 
     For example, during operation of the gas sensing device  100 , molecules of the target gas, e.g. O 3  or NO 2 , may be adsorbed at the sensing unit  110 . As described above, adsorbed gas molecules may decrease the sensitivity of the sensing unit  110 , such changing the state of the sensing unit  110 . The exposure  892  to the target gas may be an indication for an amount of the adsorbed gas molecules at the sensing unit  110 . 
     Optionally, the operation scheme  890  comprises a step  890  one of determining the exposure  892  of the sensor unit to the target gas. The exposure  892  may be a measure for a concentration of the target gas to which the sensor unit  110  is exposed, for example during a specific period of time. For example, the exposure  892  may be determined by evaluating the measurement signal  112  or a plurality of calibrated measurement values  132  determined during a period of time, starting for example after a startup of the gas sensing device  100  or after a cleaning of the sensing unit  110 . For example, the exposure  892  may be determined by integrating the concentration of the target gas over time. 
     The baseline value  844  may be determined as described with respect to the baseline value  244  of  FIG. 2 . For example, the baseline value  844  may be used to replace the baseline value  244  determined after manufacturing, or may be used for replacing a currently used baseline value. As described with respect to  FIG. 2 , the baseline value may be in indicator for classifying the sensing unit  110  with respect to the plurality of test sensing units. Thus, updating the baseline value allows for adapting the determination of the calibrated measurement value  132  to a current state of the sensing unit  110 . Thus, a high reliability of the determination of the calibrated measurement value  132  in situations in which the state of the sensing unit  110  changes may be achieved. 
     In other words, as mentioned above, it can happen that during operation the sensor properties change in a reversible manner, for example when this is exposed to large concentration of gases such as O 3  which are not quickly desorbed thus masking the presence of other gases (such as NO 2 ) and hinder their accurate estimation. For a relatively low amount of adsorbed molecules, the molecules may be desorbed from the sensing unit  110  by heating the sensing unit  110 , as will be described with respect to  FIG. 9 , such restoring an original state of the sensing unit  110 . In the case in which a long exposure of the sensor to high concentration of NO 2  and O 3  is detected, a complete sensor recovery might not be possible. Under these circumstances the sensing unit may be recalibrated using a local baseline (i.e. determining the baseline value  844 ). For example, the baseline value  844  may be determined during a time when minimum concentration of NO 2  and O 3  are detected (this could typically happen around 5 a.m.). In this way an overestimation of the gas concentration can be avoided. 
       FIG. 9  shows a block diagram of an example of an operation scheme  990  which may be executed by the gas sensing device  100 , for example by the signal calibration unit  130  of  FIG. 1 . According to this example, the gas sensing device  100  comprises means for detecting, in a step  995 , whether an exposure  992  of the gas sensing device  100  to the target gas exceeds a second threshold. The gas sensing device  100  may further comprising means for initializing a recovery sequence  999 , if the exposure of the gas sensing device to the target gas exceeds the second threshold. 
     The exposure  992  to the target gas may be determined according to the exposure  982  to the target gas. The recovery sequence  999  may comprise a heating of the sending unit  110  or of a sensing layer of the sensing unit  110 . By heating the sensing unit  110 , molecules may be desorbed from the sensing unit  110 . In other words, the recovery sequence may clean the sensing unit  110 , for example with a short heating pulse at very high temperature (e.g. &gt;200° C.). The heating pulse may enhance the desorption of gas molecules, restoring the original sensitivity of the sensor so that the existing acquired model can be further used. Thus, the operation scheme  890  and the operation scheme  990  of  FIG. 8  and  FIG. 9  may be combined. For example, the second threshold may be lower than the first threshold. Thus, for a relatively low exposure to the target gas, exceeding the second threshold, the recovery sequence  999  may be performed, and for a relatively high exposure to the target gas, exceeding the first threshold, the baseline value may be re-determined. 
     Optionally, the operation scheme  990  comprises the step  891  of determining the exposure  992 . Step  891  may comprise a readout of the measurement signal  112  and a determination of a plurality of calibrated measurement values  132  based on the measurement signal  112 . 
     Optionally, the operation scheme  990  comprises a step  997  of determining whether the exposure  992  of the sensing unit  110  to the target gas is below a third threshold. Step  979  may be performed, if step  995  indicates that the exposure  992  is above the second threshold. Performing the recovery sequence  999  may result in a degradation of the sensing layer of the sensing unit  110 , if the concentration of a specific gas, for example O 3 , which may be the target gas, is high. Thus, the gas sensing device  100  may be configured to initialize the recovery sequence  999  if the exposure  992  exceeds the second threshold and is below the third threshold. In other words, the decision on when to trigger the recovery sequence  999 , may be based on the estimate of the dominant gas provided by the algorithm, i.e. an indication for the exposure  992  to the target gas. For instance, after a high O 3  concentration is detected (&gt;T_O 3 _mx), the estimates of the other gases are suspended until the estimated concentration goes down to a known value (T_O 3 _ox) at which it is possible to heat the sensor without damaging it. 
     Optionally, if step  995  indicates, that the exposure  992  is above the second threshold, the calibrated measurement value  132  is discarded or skipped, as indicated by step  996 . Otherwise the calibrated measurement value  132  may be kept. For a high exposure  992 , the concentration of the target gas may be overestimated by the calibrated measurement value  132 . Thus, discarding the calibrated measurement value  132  in this case grants a reliable output of the gas sensing device  100 . 
       FIG. 10  shows a flowchart of an example of a method  1000  for determining a calibrated measurement value  132  for a concentration of the target gas. The method  1000  comprises a step  1001  of obtaining a measurement signal  112  based on the concentration of the target gas. The method  100  further comprises a step  1002  of determining the calibrated measurement value  132  based on the measurement signal  112  and based on a calibration model  120 , wherein the calibration model  120  is based on calibration data of a plurality of test sensor units having the same type as the sensor unit  110 . 
     In the following, further examples of the disclosure are described. 
     An example according to the disclosure provides a gas sensing device  100  for sensing a target gas, comprising: 
     a sensing unit  110  for sensing the target gas, the sensing unit being configured for providing a measurement signal  112  based on a concentration of the target gas in an environment of the gas sensing device  100 , 
     a signal calibration unit  130 , configured for determining a calibrated measurement value  132  based on the measurement signal  112  and based on a calibration model  120 , 
     wherein the calibration model  120  is based on calibration data of a plurality of test sensor units having the same type as the sensor unit. 
     According to an example, the calibration data  257  includes measurement signal values of the test sensor units acquired during a plurality of calibration measurement sequences in which test sensor units of the plurality of test sensor units are exposed  256  to a sequence of varying environmental conditions, wherein the environmental conditions are characterized at least by one or more environmental parameters including a concentration of the target gas. 
     According to an example, the calibration model  120  is a statistical model which is trained  258  by supervised learning techniques using the calibration data  257 . 
     According to an example, the calibration data  257  of the test sensor units includes respective baseline values of the test sensor units. 
     According to an example, the gas sensing device  100  comprises a further sensing unit for sensing a further target gas, and the test sensor units of the plurality of test sensor units have the same types as the sensing unit and the further sensing unit. Further, the calibration data  257  includes measurement signal values of the test sensor units acquired during a plurality of calibration measurement sequences in which the test sensor units of the plurality of test sensor units are exposed  256  to a sequence of varying concentrations of at least one of the target gas and the further target gas. 
     According to an example, the gas sensing device  100  further comprises a data storage holding a baseline value  244 , and wherein the signal calibration unit  130  is configured for using the baseline value  244  for determining the calibrated measurement value  132 . 
     According to an example, the gas sensing device  100  further comprises: 
     means for determining an operational condition of the gas sensing device  100 , and 
     means for switching the calibration model  120  in response to a change of the operational condition. 
     According to an example, the gas sensing device  100  comprises a further sensing unit  110 ′ for sensing a further target gas, the further sensing unit being configured for providing a further measurement signal  112 ′ based on a concentration of the further target gas in the environment of the gas sensing device  100 , wherein the signal calibration unit is configured for determining the calibrated measurement value  132  based on the measurement signal  112  and the further measurement signal  112 ′, and wherein the gas sensing device  100  further comprises: 
     means for evaluating  472 ,  473 ,  474  the further measurement signal  112 ′ so as to decide whether the further sensing unit  110 ′ is in a malfunctioning state, 
     and the signal calibration unit  130  is configured for, if the evaluation of the further measurement signal indicates, that the further sensing unit is in a malfunctioning state, 
     determining the calibrated measurement value  132  independent of the further measurement signal, and/or 
     using  476 , as the calibration model  120 , a further calibration model which is independent of calibration data from test sensing units of the type of the further sensing unit. 
     According to an example, the gas sensing device  100  further comprises means for obtaining an information about a location of the gas sensing device  100 , 
     and the signal calibration unit  130  is configured to use  478 , as the calibration model  120 , a calibration model which is based on calibration data which is representative of the location. 
     According to an example, the gas sensing device  100  further comprises means for characterizing an environment of the gas sensing device  100  by evaluating an evolution of the measurement signal during a period of time, 
     and the signal calibration unit  130  is configured to select, as the calibration model  120 , a calibration model on the basis of the characterization of the environment of the gas sensing device  100 . 
     According to an example, the gas sensing device  100  further comprises means  136  for receiving a further calibration model, and wherein the signal calibration unit is configured to use the further calibration model as the calibration model. 
     According to an example, the gas sensing device  100  further comprises a data storage  138  holding a plurality of calibration models, wherein the signal calibration unit is configured to select the calibration model for the determination of the calibrated measurement value  132  from the plurality of calibration models. 
     According to an example, the gas sensing device  100  further comprises means for detecting  893  whether an exposure of the sensor unit to the target gas exceeds a first threshold, and 
     means for determining  894  a baseline value of the sensing unit if the exposure of the sensor unit to the target gas exceeds the first threshold, and 
     the signal calibration unit  130  is configured to use the determined baseline value as an input for the calibration model so as to determine the calibrated measurement value  132 . 
     According to an example, the gas sensing device  100  further comprises means for detecting  995  whether an exposure of the gas sensing device  100  to the target gas exceeds a second threshold, and 
     the gas sensing device  100  comprises means for initializing a recovery sequence  999 , if the exposure of the gas sensing device  100  to the target gas exceeds the second threshold. 
     According to an example, the gas sensing device  100  is a chemoresistive gas sensing device. 
     A further example of the disclosure provides a method  1000  for determining a calibrated measurement value  132  for a concentration of a target gas, the method comprising: 
     obtaining  1001  a measurement signal  112  based on the concentration of the target gas, 
     determining  1002  the calibrated measurement value  132  based on the measurement signal  112  and based on a calibration model  120 , 
     wherein the calibration model  120  is based on calibration data of a plurality of test sensor units having the same type as the sensor unit  110 . 
     According to an example, the test sensor units of the plurality of test sensor units have the same types as the sensing unit and a further sensing unit, and the calibration data  257  includes measurement signal values of the test sensor units acquired during a plurality of calibration measurement sequences in which the test sensor units of the plurality of test sensor units are exposed  256  to a sequence of varying environmental conditions, wherein the environmental conditions are characterized at least by one or more environmental parameters including a concentration of the target gas. 
     According to an example, wherein the determining  1002  of the calibrated measurement value  132  comprises using a baseline value  244 , e.g. of the sensing unit  110  of the gas sensing device  100 . 
     According to an example, the method  1000  further comprises steps of: 
     determining an operational condition of the gas sensing device  100 , and 
     switching the calibration model  120  in response to a change of the operational condition. 
     According to an example, the method  1000  further comprises obtaining a further measurement signal  112 ′ from a further measurement unit  110 ′, and wherein the method  1000  comprises determining the calibrated measurement value  132  based on the measurement signal  112  and the further measurement signal  112 ′, and wherein the method  1000  further comprises: 
     evaluating  472 ,  473 ,  474  the further measurement signal  112 ′ so as to decide whether the further sensing unit  110 ′ is in a malfunctioning state, 
     if the evaluation of the further measurement signal indicates, that the further sensing unit  110 ′ is in a malfunctioning state, 
     determining the calibrated measurement value  132  independent of the further measurement signal  112 ′, and/or 
     using  476 , as the calibration model  120 , a further calibration model  120 ′ which is independent of calibration data from test sensing units of the type of the further sensing unit. 
     According to an example, the method  1000  further comprises obtaining an information about a location of the gas sensing device  100 , and using  478 , as the calibration model  120 , a calibration model which is based on calibration data which is representative of the location. 
     According to an example, the method  1000  further comprises characterizing an environment of the gas sensing device  100  by evaluating an evolution of the measurement signal during a period of time, and 
     selecting, as the calibration model  120 , a calibration model on the basis of the characterization of the environment of the gas sensing device  100 . 
     According to an example, the method  1000  further comprises receiving a further calibration model, and using the further calibration model as the calibration model. 
     According to an example, the method  1000  further comprises selecting the calibration model for the determination of the calibrated measurement value  132  from a plurality of calibration models. 
     According to an example, the method  1000  further comprises 
     detecting  893  whether an exposure of the gas sensing device  100  to the target gas exceeds a first threshold, 
     determining  894  a baseline value, e.g. for a sensing unit of the gas sensing device, if the exposure of the sensor unit to the target gas exceeds the first threshold, and 
     using the determined baseline value as an input for the calibration model  120  so as to determine the calibrated measurement value  132 . 
     According to an example, the method  1000  further comprises detecting  995  whether an exposure  992  of the gas sensing device  100  to the target gas exceeds a second threshold, and initializing a recovery sequence  999 , if the exposure of the gas sensing device  100  to the target gas exceeds the second threshold. 
     Although some aspects have been described as features in the context of an apparatus it is clear that such a description may also be regarded as a description of corresponding features of a method. Although some aspects have been described as features in the context of a method, it is clear that such a description may also be regarded as a description of corresponding features concerning the functionality of an apparatus. 
     Some or all of the method steps may be executed by (or using) a hardware apparatus, like for example, a microprocessor, a programmable computer or an electronic circuit. In some embodiments, one or more of the most important method steps may be executed by such an apparatus. 
     Depending on certain implementation requirements, embodiments of the invention can be implemented in hardware or in software or at least partially in hardware or at least partially in software. The implementation can be performed using a digital storage medium, for example a floppy disk, a DVD, a Blu-Ray, a CD, a ROM, a PROM, an EPROM, an EEPROM or a FLASH memory, having electronically readable control signals stored thereon, which cooperate (or are capable of cooperating) with a programmable computer system such that the respective method is performed. Therefore, the digital storage medium may be computer readable. 
     Some embodiments according to the invention comprise a data carrier having electronically readable control signals, which are capable of cooperating with a programmable computer system, such that one of the methods described herein is performed. 
     Generally, embodiments of the present invention can be implemented as a computer program product with a program code, the program code being operative for performing one of the methods when the computer program product runs on a computer. The program code may for example be stored on a machine readable carrier. 
     Other embodiments comprise the computer program for performing one of the methods described herein, stored on a machine readable carrier. 
     In other words, an embodiment of the inventive method is, therefore, a computer program having a program code for performing one of the methods described herein, when the computer program runs on a computer. 
     A further embodiment of the inventive methods is, therefore, a data carrier (or a digital storage medium, or a computer-readable medium) comprising, recorded thereon, the computer program for performing one of the methods described herein. The data carrier, the digital storage medium or the recorded medium are typically tangible and/or non-transitory. 
     A further embodiment of the inventive method is, therefore, a data stream or a sequence of signals representing the computer program for performing one of the methods described herein. The data stream or the sequence of signals may for example be configured to be transferred via a data communication connection, for example via the Internet. 
     A further embodiment comprises a processing means, for example a computer, or a programmable logic device, configured to or adapted to perform one of the methods described herein. 
     A further embodiment comprises a computer having installed thereon the computer program for performing one of the methods described herein. 
     A further embodiment according to the invention comprises an apparatus or a system configured to transfer (for example, electronically or optically) a computer program for performing one of the methods described herein to a receiver. The receiver may, for example, be a computer, a mobile device, a memory device or the like. The apparatus or system may, for example, comprise a file server for transferring the computer program to the receiver. 
     In some embodiments, a programmable logic device (for example a field programmable gate array) may be used to perform some or all of the functionalities of the methods described herein. In some embodiments, a field programmable gate array may cooperate with a microprocessor in order to perform one of the methods described herein. Generally, the methods are preferably performed by any hardware apparatus. 
     The apparatus described herein may be implemented using a hardware apparatus, or using a computer, or using a combination of a hardware apparatus and a computer. 
     The methods described herein may be performed using a hardware apparatus, or using a computer, or using a combination of a hardware apparatus and a computer. 
     In the foregoing Detailed Description, it can be seen that various features are grouped together in examples for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed examples require more features than are expressly recited in each claim. Rather, as the following claims reflect, subject matter may lie in less than all features of a single disclosed example. Thus the following claims are hereby incorporated into the Detailed Description, where each claim may stand on its own as a separate example. While each claim may stand on its own as a separate example, it is to be noted that, although a dependent claim may refer in the claims to a specific combination with one or more other claims, other examples may also include a combination of the dependent claim with the subject matter of each other dependent claim or a combination of each feature with other dependent or independent claims. Such combinations are proposed herein unless it is stated that a specific combination is not intended. Furthermore, it is intended to include also features of a claim to any other independent claim even if this claim is not directly made dependent to the independent claim. 
     The above described embodiments are merely illustrative for the principles of the present disclosure. It is understood that modifications and variations of the arrangements and the details described herein will be apparent to others skilled in the art. It is the intent, therefore, to be limited only by the scope of the pending patent claims and not by the specific details presented by way of description and explanation of the embodiments herein.