esa-sceva/satcom-chunk-collection huggingface dataset:
# Reduced Complexity Sequential Digital Predistortion Technique for 5G Applications
###### Abstract
Finally, extended correction bandwidth is achieved without the need for offline characterization of the static nonlinearity of the device under test (DUT). In fact, the use of static predistortion function derived from offline measurements (as reported in prior work) represents a significant drawback for several reasons. First, there is a need to pre-calculate a large set of memoryless predistortion functions that would take into account the variation of the operating conditions of the PA mainly due to the variations of the average power as well as the signal statistics. Secondly, the PA is likely to experience long-term drifts of the bias point, operating temperature, etc. While some of these changes (such as bias drifts) are unobservable, their impact on the DPD performance translates into a degradation of the adjacent channel leakage ratio. Taking into account the signal variations as well as the operating conditions, variations will require an unrealistically large set of LUTs to be pre-built offline. Moreover, from an implementation point of view, there is a need to determine which of the memoryless LUTs needs to be applied as the static predistortion function. This requires an additional processing block that would select the most suitable LUT predistorter based on the knowledge of the signal characteristics, operating conditions, etc. Accordingly, the novelty in the proposed predistorter is related to the following aspects:  
* First, the predistortion function is performed in a sequential manner through two distinct characterization steps. * Second, a single characterization hardware loop is used for both characterization steps. * Third, a relaxed sampling rate is used in the feedback path for acquisition of the power amplifier's output. * Fourth, the computational complexity overhead due to the proposed technique is minimized through the use of coarse delay alignment when identifying the second predistortion sub-function. * Fifth, the use of sequential identification process minimizes the computational complexity associated with the signal reconstruction to a simple re-sampling operation. The above-mentioned five aspects cohesively allow for substantial improvements in digital predistortion systems that makes it possible to achieve satisfactory linearity performance over a correction bandwidth much larger than the observation bandwidth of the feedback path without the computational and hardware complexity drawbacks observed in previously reported work. In Section 2, the proposed S-DPD system is introduced and thoroughly discussed. In Section 3, the S-DPD system is applied for the linearization of an inverse Class-F power amplifier prototype. The computational complexity reduction through coarse delay alignment is investigated in Section 4. The conclusions are reported in Section 5.
# Single-Lens Imaging Spectral Restoration Method Based on Gradient Prior Information Optimization
Building on the previous research and achievements of our research group, the TV regularization term was selected for constrained solving, and this study mainly focuses on optimizing and improving the TV regularization term. The proposed algorithm is based on gradient prior information optimization and enhances similarity to the original image, ultimately improving the quality of spectral restoration. In comparison to unoptimized gradient prior information, the proposed algorithm stands out in terms of restoration quality. The feasibility of this algorithm was validated using publicly available remote sensing datasets and actual captured images.
# A Novel Measurement Standard for Surface Roughness on Involute Gears
## 2 Methods for the Analysis of Surface Roughness on Involutes
### Measurement Systems  
Profilometer measurements are carried out by using the _High Resolution Topo Scan_ (HRTS) that has been developed at PTB. It is traced back to the SI functioning as the reference profilometer. The HRTS is capable of providing roughness measurements with measurement uncertainties (MU) in the Nanometer range [25]. The HRTS profiler moves a stylus with diamond tip along a straight line. The stylus is controlled to follow the topography of a scanned surface. Scanning a gear tooth with the profilometric measurement principle then results in a data set of equidistantly sampled lateral positions along a chord line of the tooth's involute shape and the belly of the involute shaped as vertical height values.  
The _P40_--_measurement system_ (P40) by the KLINGELNBERG AG [26] is capable of measuring roughness profiles on gear flanks within a gear measurement regime. A diamond tip is moved along the surface of the gear flank as the gear is turned. This ensures that the measuring probe is orientated perpendicular to the surface for the entire measured line. The recorded coordinates are distributed equally in length of roll \(l=r_{\rm b}\xi\), although they are displayed in the arc length dimension.  
The two differently operating measurement system deliver very different profile data. While the HRTS delivers a data set displaying the involute shape, the P40 delivers a profile that does not contain that shape. This requires that, for the profilometer, a form removal is to be performed in order to obtain the primary profile \(z^{\rm P}\).  
The sampling intervals differ as well. While the sampling intervals of the profilometer are equidistant with respect to a chord close to the surface of the tooth, the sampling intervals of the P40 are not. They are regularly distributed along roll angle \(\xi\) instead.  
For both types of instruments, a separation of the larger scale components of the texture, the waviness and the small scale texture features is realised by applying a Gaussian filter or a Gaussian regression filter.
## 13 Near-Field-Based 5G Sub-6 GHz Array Antenna Diagnosis Using Transfer Learning  
In this paper [12], the method for near-field-based 5G sub-6 GHz array antenna diagnosis is proposed using transfer learning. The case for normal or abnormal operation of the array system and the failure of a specific port are assorted with a classification network. Moreover, a regression network is employed to predict the amplitude and phase information of the excited signal to the array antenna. Additionally, several near-field samples are applied to the regression network to accelerate the diagnosis. To verify the proposed method, the fabricated 4 \(\times\) 4 array antenna is measured, and the results show that the trained network can diagnose 29 of 30 measurement scenarios.  
4 Axial-Symmetric Diffraction Radiation Antenna with a Very Narrow Funnel-Shaped Directional Diagram  
In this paper [13], the reliable modeling and analysis is introduced for the axially symmetric radiators with a very sharp funnel-shaped radiation pattern. The Smith-Purcell coherent radiation is obtained by such a formed diagram. The surface wave of the dielectric radiator is swept out by the exponential curve with a concentric periodic grating. The required radiation pattern is generated by the fundamental harmonic which propagates without attenuation in a direction of symmetry axis of the radiator.  
Machine Learning Technique to Improve an Impedance Matching Characteristic of a Bent Monopole Antenna  
In this paper [14], the bent wire monopole antenna designed using a machine learning method, is proposed. They are validated their machine learning model trained with deep neural network (DNN) by calculating the mean squared error and R-squared score. The authors also verify the operating principle of the by analyzing the equivalent circuits corresponding to their structures.
## 4 Discussion
This finding supports research by [PERSON] et al., who indicated that the combination of splint use with behavioral and physiotherapy interventions could maximize benefits for patients with bruxism and TMD [37]. Finally, in the HG, significant improvements were only observed in PSQI after the use of the occlusal splint, while other variables, such as JFLS-8, GAD-7, and WHO-5, did not show statistically significant changes. This suggests that while the use of splints may enhance certain aspects of sleep in healthy individuals, their impact on other aspects of well-being and mandibular function is minimal in the absence of TMD or evident bruxism. These findings align with studies highlighting the importance of combining splint use with additional interventions for those without apparent mandibular or bruxism-related issues. Although WHO-5 scores slightly decreased in this group, the change was not statistically significant. This may reflect a lack of perceived benefit from the splint in asymptomatic individuals or discomfort from using an unnecessary device. While occlusal splints are generally safe, minor side effects such as occlusal changes or patient dissatisfaction may occur, particularly when no clinical indication is present. On the other hand, we concur with the statements made by [PERSON] et al., in their study, which highlights that for some patients, using the splint at night represents a safe and relatively effective management approach to reduce the frequency and intensity of bruxism [21]. Finally, it is important to acknowledge some limitations of this study. Specifically, the first limitation is the small sample size of registered patients. Another limitation is that bruxism was assessed using self-reported questionnaires rather than instrumental diagnostic tools, such as polysomnography or electromyography. This may lead to an overestimation of bruxism prevalence, and future studies should aim to validate self-reported data with objective measures. The assessment of well-being was based on self-reported questionnaires, and its improvements may be indirectly related to pain reduction rather than a direct effect of the intervention. Future research should explore additional dimensions of well-being beyond self-reported questionnaires. Moreover, there is a lack of a control group that did not take part in an intervention, since this research was conducted in a clinical context in which every patient that comes to the clinic seeks treatment. A quasi-experimental design was chosen for ethical reasons, as withholding treatment in a clinical setting was not appropriate. While this limits causal inference, it reflects real-world practice. Additionally, the duration of the follow-up was limited to one month. Although this timeframe was chosen based on previous studies suggesting early symptom improvements, it is possible that a longer follow-up (e.g., three to six months) could reveal different patterns of response or sustained benefits. We also acknowledge that expectancy or placebo effects may have influenced some subjective outcomes, such as perceived anxiety or well-being.
# IEC-61108-7 SBAS Standard for Shipborne Receivers: Preliminary Testing Validation Activities +
## 4 Conclusions and Recommendations  
In the framework of the new IEC-61108-7 standard, a comprehensive set of test cases was defined according to the specifications set out in the standard. These test cases were supplemented with actual test results from SBAS-enabled shipborne receivers, demonstrating how adherence to the requirements is assessed. This initiative aims to assist shipborne receiver manufacturers in adopting the IEC-61108-7 standard and help accredited test centers in developing the certification procedures for SBAS shipborne receivers. A broad test campaign was conducted by ESSP, JRC, and EUSPA to evaluate the IEC-61108-7 standard for shipborne receivers. The outcomes of this campaign confirmed the feasibility of the tests described in the standard. The definition and implementation of the test cases were found to be well designed and executed. Notably, the test results demonstrated that the receivers could provide sub-meter accuracy in both static and dynamic conditions, as well as maintaining their performance in the presence of broadband interference. The performance under various dynamic conditions, including changes in speed and course, fell within the accepted thresholds of accuracy.  
Figure 8: Example results for the SBAS switch with different service provider IDs (TC-016).  
Figure 7: Example results for the COG/SOG accuracy test (TC-011).  
In conclusion, the preliminary testing and validation activities have confirmed that the IEC-61108-7 standard offers a comprehensive and reliable framework for assessing the performance of shipborne SBAS receivers. Future work may focus on expanding the range of devices tested and supporting standardization activities for dual-frequency multi-constellation (DFMC) SBAS systems.  
Methodology, G.F., J.F., J.T., E.L. and C.G.; software, G.F., J.F., J.T. and C.G.; formal analysis, G.F., J.F., J.T., E.L. and C.G.; writing--original draft preparation, G.F., J.T. and C.G.; visualization, J.T.; supervision, E.C. and S.P.; project administration, E.C. and S.P. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by EUSPA, grant number GSA/CD/09/19 SC1.  
Not applicable.  
The datasets presented in this article are not available because the receiver manufacturers involved in this study did not give written consent for their data to be shared publicly; therefore, due to the sensitive nature of the research, the supporting data are not available.  
Authors [PERSON] and [PERSON] were employed by the company European Satellite Services Provider (ESSP). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Materials and Methods
### Contrast Normalization  
Contrast normalization using z-score has been used as part of image preprocessing to normalize images [36]. Slides can vary in thickness, which affects the amount of light that is transmitted or reflected.  
Normalization using the minimum and maximum values of the image is not a good choice, as there are often various artifacts in the slides that can strongly distort normalization. For this reason, fit-based normalization to the Gaussian distribution model was used for the histogram. The slide histograms are a Gaussian mixture [37] with two peaks plus asymmetry, but this type of normalization is less sensitive to outliers, that is, problem of min-max normalization. An example of normalization is shown in Figure 5. The following formulas are used for normalization for 'uint8' data type:  
\[Y(x,y)=\frac{255}{6}\left(\frac{X(x,y)-mean(X)}{std(X)}+3\right), \tag{1}\]  
where \(x\) and \(y\) are pixel coordinates, \(X\) is the input pixel value, and \(Y\) is the output pixel value.  
Figure 5: Exemplary pair of images and histograms with min–max and Gaussian (z–score) normalizations (the red vertical line represents the average on histograms).
# Optimization of Fuzzy Logic Controller Used for a Differential Drive Wheeled Mobile Robot
## 2 Problem Description
### Design of the Initial Fuzzy Logic Controller  
The movement of the differential drive wheeled mobile robot heading to the waypoint (\(W\)) is depicted in Figure 4.  
Figure 4: The differential drive wheeled mobile robot and the waypoint parameters.  
From Figure 4 we get:  
\[\Delta D=\sqrt{\left(x_{W}-x_{Q}\right)^{2}+\left(y_{W}-y_{Q}\right)^{2}} \tag{5}\]  
\[\Delta\theta=\theta_{R}-\theta_{W} \tag{6}\]  
\[\tan\theta_{W}=\frac{y_{W}-y_{Q}}{x_{W}-x_{Q}} \tag{7}\]  
The \(\Delta D\) is the distance and the \(\Delta\theta\) is the angle that is the difference between the current robot's position and the waypoint's position.  
An FLC will be designed to guide the DDWMR from the current robot's position \(Q(x_{Q},y_{Q})\) to the waypoint \(W(x_{W},y_{W})\). The general structure of a fuzzy logic controller is shown in Figure 5[30, 31, 32].  
The block diagram of the FLC used for the DDWMR is shown in Figure 6. For this FLC, two inputs (\(\Delta D\) and \(\Delta\theta\)) are used. Outputs of this FLC are the angular velocity of the left and the right wheels (\(\omega_{l}\), \(\omega_{r}\)). These outputs are transformed into DDWMR inputs according to Equations (8) and (9).  
\[v=\frac{1}{2}(v_{r}+v_{l}) \tag{8}\]  
\[\dot{\theta}=\omega=\frac{1}{b}(v_{r}-v_{l}) \tag{9}\]  
The FLC used for the DDWMR in this study is constructed as follows:
# Monitoring Perishable Commodities Using Cellular IoT:
## 2 Materials and Methods  
### System Architecture  
The architecture of the complete solution designed to record the values of the environmental parameters can be seen in the block diagram in Figure 1. The designed instrumentation system would be placed in the area where the parameters are to be monitored, such as the climatic chamber of a refrigerated truck or a logistic warehouse, and it would record the parameters measured by the sensors.  
The core of the device was centered on an ESP32-D0 WDQ6 from Espressif Systems (Bibo Road, 690, Shanghai, China) [52], a System-On-Chip (SoC) widely used in electronic development, with flexible programming and at a very economical price. Sensors communicated with the ESP32 microcontroller, which stored the data in its flash memory and sent the data wirelessly to the 1 NCE (Sternengasse, 14, Cologne, Germany) [53] operator through the SIM7000G module from SIMCom Wireless Solutions Limited (Jinzhong Road, 633, Shanghai, China) [54] via NB-IoT communication. NB-IoT was selected because it had lower power consumption than other technologies such as LTE-M. Also, UDP was used as a protocol because of its lightness. The communication between the microcontroller and the SIM7000G, which incorporated the operator's SIM card, was carried out through AT commands.  
Subsequently, the operator's data were accessed from a server through an interface programmed in Node-RED [55], which allowed for using the operator's REST API, and the data were stored in a database created with InfluxDB [56] and Grafana [57], which could be accessed and visualized from any device through the Internet. These were hosted in a Virtual Private Server (located in the UPCT). In the following sections, the components and solutions adopted will be detailed.
# Monitoring the Health and Residence Conditions of Elderly People, Using LoRa and The Things Network
## 5 Discussion and Results  
The use of devices to monitor people using low-cost and long-range technology, including LoRa networks, as demonstrated here, allows specific sensors to be used for data collection and eventually the detection of abnormal events by users in their homes. Several authors have studied the use of these sensors. Still, we focused our attention on the security issues of the elderly in remote areas--places where mobile networks do not exist or have low coverage. This presents a solution that may use, for the benefit of affected populations, the technological potential installed in wind farms, making use of the internet connection for the LoRa Gateway connection, thereby ensuring the coverage of this type of communication network.  
One of the sensors that we believe to be of great relevance, as mentioned in the chapter on materials and methods, is the accelerometer, which allows detecting falls or other events related to sudden movements. Thus, to analyze the accuracy of the ADXL335 sensor used, we performed the statistical analysis of the data obtained. A predictive algorithm was subsequently applied to predict, based on historical data, when an abnormal event of user positioning (for example, a fall) may occur.  
Figure 7: Example of the result obtained in the query from our C# standalone application, in JSON format.  
To demonstrate that it is possible to apply machine learning techniques to the collected data, we created a model that we tested with a data set. The model is not robust enough to be used in a real context, but it serves as a proof of concept. It is possible to develop models with greater and better precision, drastically reducing the possibility of the occurrence of false positives and false negatives.  
Training data were collected in a way that corresponds to the normal upright position of the human body. First, the accelerometer was calibrated, using the sketch provided by the ADXL335 programming library, detecting the maximum and minimum values for each axis. Subsequently, the accelerometer was moved to simulate human walking in various directions, and the minimum and maximum limit values were determined for the three axes. They correspond to the possible positions of the body and are classified as \"Normal position\", \"Fallen back\", \"Fallen front\", \"Fallen to the right\", \"Fallen to the left\".  
As for the test data, the accelerometer within the prototype inside a duly stowed backpack and pre-defined courses were followed with different falls simulations. Then, the obtained data were classified, and the column concerning the class was removed. In this way, we were able to correctly know which data corresponded to the respective category.
# Evaluating the Robot Inclusivity of Buildings Based on Surface Unevenness
## 4 Experimental Validation
### Test Site 2: Printing Room  
The printing room had a skewed trapezoidal floor plan and a divider wall at the centre. Obstacles in the form of raised rubber mats and cardboard sheets were placed to simulate ground bumps and infrastructure such as ground power outlet covers and raised platforms that the robot can traverse over with a risk to its localization capabilities.  
The generated LiDAR map of the printing room and the path taken by the robot is shown in Figure 6a. The corresponding uneveness of the surface (_U_), measured using Algorithm 1, was plotted onto the map as depicted in Figure 6b. The resultant RIL-SU heatmap for this site is presented in Figure 6c.  
To validate the RIL-SU heatmap, three different zones with different RIL-SU values were selected in the site similar to the previous experiment. The selected zones are represented as 'L4', 'L5', and 'L6' in Figure 6b,c. The robot was made to pass through the selected locations at its nominal speed and also made to run multiple times around each zone. At location 'L4', there were no external materials placed on the floor. Hence, the uneveness of the surface was low, which is shown in Figure 6b. While experimenting at L4 in Figure 7, the Meerkat robot mapping and localization were precise, and there were no navigation issues, as seen in Figure 7a, which indicates the robot's high inclusivity in this zone. This validates the High RIL in Figure 6c.  
Location 'L5' was chosen where medium-raised rubber mats were placed, as seen in Figure 7b. Thus, a \(U\) value of around 2 was observed (see unevenness heatmap in Figure 6b). As a result of this moderately elevated \(U\), the RIL of that location was tagged as 'Moderate' (see Figure 6c). The Meerkat robot mapping and localization were precise, and there were no navigation issues, which indicates the robot's high inclusivity level in that zone. The robot laser scan initially did not show any drift, but after traversing through that zone multiple times over the period, there were observations of accumulated drift, leading to the medium inclusivity, which validates the RIL in Figure 6c.  
Finally, at location 'L6' in Figure 7c, there were multiple uneven bumpy tiles placed in this zone. The \(U\) in this zone was higher (see Figure 6b), and the inclusivity was tagged as low (see Figure 6c). The Meerkat robot laser scan drifted by a higher range once the robot tried to traverse across this zone (see Figure 7). It implies the robot's ineffectiveness in reaching its destination coordinates indicating 'Low' inclusivity. The results are in agreement with the generated RIL-SU heatmap.  
Figure 6: (**a**) A 2D LiDAR map of the printing room and the path taken by the audit robot; (**b**) surface map of the printing room; (**c**) RIL-SU colour map of the printing room.