esa-sceva/satcom-chunk-collection huggingface dataset:
# Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems
## 1 Introduction
### Contributions  
The main contribution of this scientific research is the application of Machine Learning techniques, i.e., neural network (NN) algorithms, to hybridize GNSS, accelerometer and semiactive laser quadrant photo-detectors signals. The role of the neural networks is to predict the gravity vector to estimate the attitude of the vehicle. Consequently, the advantage of such a hybrid system over the traditional ones, which are usually based on GNSS and IMUs, is the capability of eliminating gyros, which may be expensive and too sensitive for high demanding maneuvers and not reliable at all during some stages of flight.  
The presented approach relies on neural networks and training algorithms to predict the gravity vector in body fixed axes while the vehicle is flying. The three components of the acceleration of the vehicle in body fixed axes are the inputs for the NN. After that, the predicted gravity vector is processed together, by means of a hybridisation algorithm, with velocity and line of sight vector to determine body rotation or attitude.  
To reproduce the flight dynamics of an aerial vehicle, a nonlinear mathematical model is proposed, which considers nonlinear aerodynamic forces and moments and that has been validated to build up realistic conditions for simulation experiments [1; 2]. On top of that, a robust double-input double-output control algorithm is employed to manage coupling among the normal and lateral nonlinear dynamics.  
Note that the presented approach depends on the amount of available data for training, which means stability and convergence may be restricted to the trained mission envelope. However, note that training has been performed for a wide variety of launching, flight, and destination point conditions to resemble realistic settings, i.e., for a comprehensive set of missions. Overall, the methodology results in good enough quality results, even including good response to uncertainty in several conditions and characteristics, i.e., showing good GNC performance. Therefore, the presented research poses a path for a generalized and systematic application of NN/Machine Learning in GNC systems.  
The rest of this paper is organized as follows. In Section 2, the system modeling is described in detail. Section 3 describes the navigation, guidance and control algorithms. Section 4 exposes simulations results. Finally, discussion and conclusions are presented.
# Energy Efficiency Perspectives of PMR Networks +
## 2 Present and Future PMR Systems
### The PMR Evolution through the LTE System
However, the PMR evolution over the LTE system, i.e., TETRA over LTE, represents a big opportunity for current PMR users to increase the efficiency of their own spectrum usage.
## 2 Instrument Design
### Monitoring and Control Systems
#### 2.3.2. Thermal Stabilization  
Microwave radiometers usually work at a constant temperature higher than the outside so that the maximum antenna temperature (with no RFI signals) will be eventually lower than the internal temperature. Moreover, it is easier to perform the temperature stabilization by using heating elements. Hence, MERITXELL radiometer is designed to work at a constant temperature around 45 \({}^{\circ}\)C.  
The temperature control is performed using a 2216 L Eurotherm Proportional Integral Derivative (PID) temperature controller (Eurotherm, Worthing, UK).The temperature inside the radiometer is measured with a PT100 temperature dependent resistance. With this temperature measurement, the PID is able to maintain the temperature inside the radiometer at a determined constant value with the control of a set of heaters and Peltier cells.
## 3 Results and Discussion
### Stability Results  
The equivalent damping (cf. Equation (17)) of the 10 (from a total of 37) modes that turned out to be unstable is shown in Table 2. Modes of vibration of flat discs and by extension of wheels are usually characterized by their number \(m\) of nodal circles and their number \(n\) of nodal diameters, which are related to the stationary points present in each mode of vibration; wheel modes with predominant axial (i.e., out-of-plane) deformation are usually called mLn modes according to these nodal numbers [25]. One can observe that most of the unstable modes identified correspond to 0 Ln modes (without nodal circles), and that the degree of instability decreases with frequency, with mode 0L2 being the most unstable one. From Figure 3, one can observe that the negative damping introduced by the linearization of the friction force alters the damping ratio of the wheel modes with respect to that from the free damped system, turning them unstable or, in the best case, reducing their stability. Although modes 16 and 26 were identified as unstable, their equivalent damping is small compared to the other modes and so is their degree of instability.
## 4 Case Study on the Multiple-Update-Infill Sampling Methods
### Preliminary Description of the Case Studies  
Three case studies (that is, the Friedman model, the borehole model, and the 2D beam frame structure) were performed to evaluate the performances of the proposed SS methods. In surrogate modeling, it is important to normalize the input space (for example, \([-1\ 1]^{p}\) or \([0\ 1]^{p}\)). By doing so, domination by a certain input (that is, with larger magnitude) can be avoided. In other words, normalization of the input space is employed to have an equal effect on the output of all case studies. All multiple-update-infill sampling methods were performed under identical conditions 10 times. At each repetition, the different initial samples were generated, and these initial samples were identically used for all SS methods. To evaluate the performance of the SS method, the same validation samples (the number and their location) were used for each case study. The performance measures werecalculated at each infill stage for all SS methods until the maximum number of the training samples was reached. For searching the infill sample and finding the minimum of the current kriging model in the MED (\(g_{min}^{(n)}\) of Equation (20)), the firefly algorithm was used as an effective global optimizer [27]. The flowchart of the multiple-update-infill sampling methods is tabulated in Table 1.  
This study considers four performance measures as follows: (1) root mean squared error (\(RMSE\)), (2) minimum distance (\(d_{min}\)), (3) pairwise correlation (\(\rho^{2}\)), and (4) computational efficiency (\(T_{norm}\)). The \(RMSE\) aggregates the magnitude of the residuals (that is, squared difference) into a single measure of the prediction accuracy. A smaller \(RMSE\) indicates a higher prediction accuracy, as shown by the following equation:  
\[RMSE=\sqrt{\frac{\left(\mathbf{Y}_{val}-\hat{\mathbf{Y}}_{val}\right)^{T}\left(\mathbf{Y}_ {val}-\hat{\mathbf{Y}}_{val}\right)}{n_{val}}} \tag{27}\]  
where \(\overline{Y}_{val}\) is the average value of \(\mathbf{Y}_{val}\), and \(n_{val}\) is the number of the validation samples. \(d_{min}\) is the minimum value of the pairwise Euclidean distance between pairs of the final training samples (\(\mathbf{X}_{i}^{(final)}\)) as given in Equation (28). Larger values of \(d_{min}\) indicate that the training samples are placed as far apart from each other as possible. In this context, a larger value of \(d_{min}\) indicates efficient sampling performance in terms of the uniformity. \[d_{min}=\min_{x_{i},x_{j}\in\mathbf{X}_{i}^{(final)}}d^{2}\big{(}\mathbf{x}_{i},\mathbf{ x}_{j}\big{)}. \tag{28}\]  
\(\rho^{2}\) evaluates the goodness of the space-filling with respect to pairwise correlations [28]. A higher value of \(\rho^{2}\) indicates that the projection capability in all subspace of the input variables is becoming poor. Therefore, the lower value of \(\rho^{2}\) represents superior uniformity in the input space. \(\rho^{2}\) is calculated as
## 4 Frequency of Errors in PAN Performance and Interpretation  
The frequency of positioning errors when performing PAN has seldom been described in the literature. In a recent review [28], it was reported that PAN have a mean reject rate of 4% due to positioning errors and motion artefacts. On average, one to three errors per PAN can be found in examinations of permanent dentition, including wrong tongue positioning, lack of stabilization of the chin on the chin rest, and a retruded position with respect to the focal trough [17]. According to [PERSON] et al. [16], patient head positioning accounted for the majority of cases that required re-examination. In particular, incorrect head positioning related to the focal trough, lateral rotation, and forward inclination represented the most common errors, and these were each encountered in more than 20% of cases. In a study [20] on 200 PAN, only 36% of cases did not show positioning errors. The presence of radiolucency obscuring the roots of maxillary teeth was observed in 33% of cases, followed by artifacts, reflected and ghost images (17%), backward head inclination (15%), spine overlapping in the mandibular anterior region (10%), and teeth positioned behind or in front of the focal trough (7% and 1%, respectively). Similarly, [PERSON] et al. [29] performed a quality assessment of 315 PANs taken either at general radiology departments or in dental radiology units. They found that only 20% of these PANs could be rated as excellent in the absence of errors in patient preparation, exposure, positioning, and image processing. However, 70% of the examinations were considered diagnostically acceptable, since the errors did not hinder diagnostic performance, whereas only 10% were considered unacceptable. Among the most frequently occurring errors, incorrect patient preparation (especially wrong tongue positioning, overlapping of upper and lower incisors, and the wearing of removable appliances/jewelry) and head positioning (rotation, upper chin position) were the most commonly found. Conversely, motion artefacts and exposure errors were infrequently reported. A randomized, controlled trial by [PERSON] et al. [30] tested the ability of dental students to perform a correct PAN following a computer-assisted learning program, simulation training, or conventional teaching. Errors in the positioning of the horizontal plane occurred in all the three groups. Rotation and positioning errors on the sagittal plane were the most rarely encountered, whereas correct coronal plane positioning was only achieved in the simulation training group. The authors concluded that training significantly improved skills in relation to performing PAN.
## 3 Advanced Control Functionalities for Power-Frequency Regulation
### Droop Controllers Proposed to Be Incorporated in the Smart-Transformer
#### 3.1.1 \(P(f_{MV})\)  
This droop controller is proposed to be integrated in the controller of the ST's MV inverter (depicted in Figure 3) in the \(ESS\)_control_ section, as illustrated in Figure 14. It regulates the active power set-point of the local ESS (described in Section 2.1.4) as a function of the frequency deviations in MV AC network in order to involve the local ESS in the provision of active power-frequency support. In this droop controller, \(f_{MV}\) corresponds to the control quantity \(X\) and \(\Delta IESS_{ref}\) corresponds to the output of the droop controller \(Y\).  
\(\Delta IESS_{ref}\) is added to \(IESS_{ref}\) in order to define the final current set-point for the local ESS. If no local ESS is available or the local ESS has no capacity to respond, the \(P(f_{MV})\) droop offers no contribution for the power-frequency regulation to be provided by the ST. The active power injected or consumed by the local ESS is delivered to the main power system by the ST's MV inverter (described in Section 2.1.1), which is responsible for keeping the power balance in the MV DC bus.  
Figure 12: General curve followed by droop controllers.  
Figure 13: Reaction chain triggered following a frequency disturbance in the MV AC network.
# Water Jacket Systems for Temperature Control of Petri Dish Cell Culture Chambers
## 3 Results and Discussion
### Comparison of Sample 'Transition Period' under Different Dynamic Conditions  
The transient simulations were further extended to analyze the transition period of the sample using different strategies and based on Equation (14), as summarized in Figure 8. Simulations were conducted under the 37 \(\pm\) 8 \({}^{\circ}\)C condition, corresponding to the heating (37 to 45 \({}^{\circ}\)C) and cooling (37 to 29 \({}^{\circ}\)C) cases. The detailed transient temperature contours corresponding to the different cases are presented in Figures S19-S25. No significant differences were observed between the transition period of the heating and cooling cases, which is in line with our findings shown in Figure 7e.  
Based on our results, the strategies can be categorized into three groups according to their effectiveness for shortening the transition period, as discussed below:  
_Group one:_ doubling the flow rate of recirculating water, integrating the heater underneath the Petri dish, and doubling the localized convective heat transfer coefficient at the bottom surface of the Petri dish (by placing a small fan) shortened the transition period by \(\sim\)10%. The integrated heating system described here operates by setting the water inlet temperature to 45.7 \({}^{\circ}\)C, and the heater power to 265 mW, under which the sample temperature reaches 45 \({}^{\circ}\)C under steady state conditions, and the temperature gradient across the Petri dish decreases to 0.5 \({}^{\circ}\)C, as presented in Figure S9. Despite its effectiveness under stable conditions, the integrated heating system is unable to reduce the \"transition period\" of the sample significantly. This suggests a more holistic approach by varying the water inlet temperature and the heater power to reduce the response time of the sample while producing a low temperature gradient across the sample and avoiding overheating of the sample.  
_Group two:_ reducing the sample volume inside the Petri dish to 25%, and using a glass Petri dish instead of a polystyrene dish shortened the \"transition period\" by \(\sim\)20%.  
_Group three:_ reducing the Petri dish diameter to 25 mm shortened the \"transition period\" by \(\sim\)30%, and more interestingly, further reducing the Petri dish diameter to 15 mm shortened the \"transition period\" by \(\sim\)60%.
# Optimal Clustering in Stable Instances Using Combinations of Exact and Noisy Ordinal Queries
## 1 Introduction
### Our Contribution
#### 1.1.1 Our Model  
This paper introduces a natural setting where we can only _compare_ distances, and these comparisons are generally _noisy_; though use some sort of _expensive_ (expert) oracle, we can make sure that certain comparisons are _correctly_ reported. Our model (see Section 2 for formal definitions) captures the following aspects:  
1. Using only _ordinal_ information. Distances or dissimilarities can only be _compared_ and not directly evaluated. One example of such queries is [5; 6; 7] _Which one between \(y\) and \(y^{\prime}\) is more dissimilar to \(x\)?_ Concretely, this means that we can compare \(d(x,y)\) with \(d(x,y^{\prime})\) for some dissimilarity function \(d(\cdot)\). In our model, we allow for comparing arbitrary groups (sum) of pairwise distances.
2. Dealing with _noisy_ results. Comparisons are in general _noisy_ and the resulting error is _persistent_. Due to measurements, we may report the wrong answer with probability bounded by some error probability \(p<1/2\), and repeating the same measurements would lead to the same answer [7; 8; 9]. (In order to import some results from the literature, we shall typically assume \(p<1/16\). All our results automatically extend to larger \(p\) if these prior results in the literature also do.)
3. Allowing _expensive_ operations to remove errors. Errors can be fixed by performing some kind of _expensive_ comparison. In this case, we know the answer is correct, but we would like to _limit_ the total number of such expensive operations (see e.g., [10] for a survey on various practical methods).  
This setting falls into the _dual-mode_ framework in [11], where several optimization problems can be optimally solved using either only low-cost operations or a suitable combination with few high-cost ones. This framework suggests to evaluate the complexity of an algorithm by explicitly counting the low-cost and high-cost operations separately. Without errors, or ignoring that exact operations have high-cost, the problem falls into the class of problems that can be solved only with _ordinal_ operations. Without the aid of high cost operations that is using only noisy comparisons, the problem has been recently studied in the context of active learning under various query and error models (see, e.g., [12; 13] and below for further discussions).
## 2 System Model
### Prosumer PV System Modeling
#### 2.2.2 Temperature Effect on PV Power of Prosumers  
One of the key advantages of using the suggested model compared to standard irradiance-based models is that it takes into account the effect of the temperature and the PV module characteristics on the power of the module. To demonstrate the impact of the temperature on the power output at a fixed irradiance, we implement our model for the module characteristics taken from [32]. The effect of the temperature on the i-u characteristics and the power is given in Figure 2. As is evident from Figure 2a,b, the temperature parameter affects the maximum power of the module.  
Figure 2: Temperature effect on the characteristics of the PV module. **(a)** The i–u characteristics. **(b)** The power characteristics.  
Figure 3 shows the effect of the temperature parameter for a PV system with a rated capacity of 5 kW and a system efficiency of 95% (the parameters for a single module are the same as those used for the results of Figure 2). From the figure, it is evident that the temperature parameter is important to model the PV curves for the prosumers. If unaccounted for, the modeling error for the given system was around 8%. The model using the irradiance parameter only ends up overestimating the PV power for the prosumer by around 0.32 kW, which, over a long-term market operation, would overestimate the prosumer energy available. Therefore, it is essential to use both the irradiance and temperature profiles of the prosumers for modeling the PV power for each P2P market interval. Note that any PV model that uses both the temperature and irradiance parameters (or standard irradiance-based models) for modeling the PV power can be used for the suggested P2P trading platform.
## 5 Discussion
### Practical Application  
Some researchers may argue that the method proposed in this paper still cannot be applied in practice right now. In fact, a method is always being gradually improved. For example, the NISDL proposed in this paper is better than NIPST which was proposed in 2017. In addition, when more diverse data is captured and used for model training, the performance of NISDL will be better.  
Some researchers may argue that the infrared sensor can also be used for measuring skin temperature, so why do we use a vision-based method? In fact, the study [19; 20] focused on thermal comfort measurement with an infrared sensor. However, the measurement accuracy is limited. Beyond this, there are other drawbacks in the infrared based measuring method: (1) Distance. The infrared sensor should be placed close to occupant. (2) Cost. The infrared sensor with high accuracy is expensive and the accuracy of an infrared sensor with low cost is also low. (3) Information is limited. From the perspective of the human sensory system, the infrared sensor is 'touch' and vision-based method is'sight'. The data captured by vision-based methods is much more than that collected by infrared sensor. e.g., human poses can be captured by vision sensor rather than infrared sensor for analyzing human thermal comfort. Based on these three drawbacks, the infrared-based method is difficult to widely apply in practice.  
Furthermore, some other researchers may say that vison-based contactless measuring method may have concerns related to personal privacy. In fact, there are at least two options to protect personal privacy issues: (1) Switch button. Based on this switch button, any customer can choose to accept or reject the implementation of real-time personal service of thermal comfort. 92) Information selection. In future practical applications, only the information about human thermal comfort be processed and saved, while other information will be discarded in real-time. (3) Data protection. In order to avoid data protection issues, processed data related to thermal comfort can be directly transferred to the HVAC system instead of being saved. Therefore, from the perspective of human-centeredness, the NISDL algorithm proposed in this paper is helpful.  
In future practical applications, the processing capacity of HVAC system should be considered. The HVAC system is usually equipped with computer server. The framework proposed in this paper can be embedded on the computer server directly and GPU is required in computer server. Based on occupant number in the building, it is necessary to prepare one or more GPUs. Further, the best ratio between GPU number and occupant number should be validated and tuned in practical application and will not be considered in this paper.
# Theoretical and Experimental Analysis for Cleaning Ice Cores from EtisolTM 140 Drill Liquid
## 3 Experimental Testing
### Ice Core Cleaning
Due to the oily nature of the fluid, drip-drying continues for three days and leaves behind a residual film of approximately 2 g per core section. Drip-drying does not require expensive ventilation systems or higher temperatures, which could compromise the scientific quality of the ice cores. However, if cores are placed in troughs, we suggest placing the troughs in a shelf system with a slope to allow for the fluid to run off along the axis of the core. Although the tests have been simple, they clearly show that the effect of gravitational run-off is the most dominant in fluid loss even after several days in storage. Our tests were not designed to capture such small fluid losses by evaporation or of ice by sublimation as was calculated in Section 2, but our tests confirm that these effects indeed fall well below the one-gram sensitivity of the tests. Experience with ice cores from Greenland drilled with the same fluid has shown us that after one year storage in an open shelf in a snow cave, the cores appear \"dry\" but for a thin film of fluid. Therefore, some mechanical  
Figure 3: Configurations for the experimental tests on real 10 cm ice cores, 55 cm long. decontamination will have to be performed to avoid undesired interferences in chemical analysis, as reported by [PERSON] et al. [19].