esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Control Strategy  
Optimally adjusting the controller parameters improves system performance and ensures a successful response in changing system conditions. While various methods are used to adjust the controller parameters, they can be grouped as theoretical, experimental and with the help of artificial intelligence. While the controller parameters can be calculated with theoretical methods using the data obtained at the output of the system, this situation may not provide the appropriate control success in case of situations such as the system being under external influence. When finding these parameters experimentally, while successful results can be produced, the time spent can be high, and there is absolutely no guarantee that good results can be achieved. Utilizing artificial-intelligence methods is another method of overcoming these problems. There is no guarantee that the optimum solution will be found with the use of these techniques, but the time taken to find the ideal parameters for the expected result in the system output is reduced. In this study, an optimized PID controller was designed for route-tracking control of the FCAGV and I controller for voltage-tracking control of DC/DC converters. While PID controllers can be easily applied in linear systems and systems with a stable progress, it is important to determine the parameters correctly in order to give successful results in the control of uncertain, time-varying, complex and nonlinear systems [49; 50]. In such cases, when artificial-intelligence techniques are not used, it becomes time-consuming and difficult to reach optimum values. Combining the PID method with other artificial-intelligence methods to create more efficient control systems gives successful and sufficient results in the control of many systems. The PID controller combined with the optimization theories  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Property** & **Value** \\ \hline Model & 24 V DC PEMFC \\ Nominal and maximum power & 1.26 KW-2 KW \\ Voltage at 0A and 1A & V(0A) = 42 V, V(1A) = 35 V \\ Nominal working point & \(\text{I}_{\text{nom}}=52\) A, V\({}_{\text{nom}}=24.23\) V \\ Max. operation point & \(\text{I}_{\text{end}}\) = 100 A, V\({}_{\text{nom}}\) = 20 V \\ Cell number & 42 \\ Stack efficiency & 46\% \\ Working temperature & 55 \({}^{\circ}\)C \\ Nominal air outlet rate & 2400 lpm \\ Nominal supply pressure & Fuel: 1.5 bar, air: 1 bar \\ Nominal composition & 99.95\% H\({}_{2}\), 2\% O\({}_{2}\), 1\% H\({}_{2}\)O \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specification of PEMFC [48].  
such as particle swarm optimization (PSO) [51], genetic algorithms (GAs) [52] gray wolf optimization (GWO) [53] and artificial bee colonies (ABCs) [54] can facilitate a more flexible, robust and improved control performance. For this reason, the parameters of controllers and DC/DC converters were adjusted according to process conditions using the PSO algorithm in this study to increase the route/voltage-tracking performance and power consumption efficiency.
# Temporal Logic Planning and Receding Horizon Control for Signal Source Localization
## 3 Temporal Logic Planning
### Particle Filter  
It should be noted that all trajectories that satisfy the LTL formula can be obtained based on the product automaton \(P\) in an offline fashion for any initial cells. However, the robot is not only required to orderly visit each region but also is required to locate the corresponding signal sources in each region. Hence, the possible positions of signal sources need to be labeled in the product automaton \(P\). In order to estimate the positions of signal sources, the particle filter can be used [21].  
Since signal sources are unknown, particles are distributed evenly in the whole searching space at the beginning. The number of particles is \(n\) and the position of the \(i\)th particle at \(k\) time is \(p_{k}^{i}\). Through the above signal strength model (2), the corresponding signal strength value of each particle can be calculated. The specific formula is as follows:  
\[y_{k}^{i}=\lfloor Z_{k}^{i}-\sqrt{R}*randn\rfloor \tag{5}\]  
where \(Z_{k}^{i}\) is the signal strength received by the robot at time \(k\) when the \(i\)th particle is regarded as the signal source and can be calculated by (2); \(R\) is the observed noise; and \(randn\) is a random number that satisfies the normal distribution.  
After obtaining the real observation value \(Z_{k}\), we calculate the weight of each particle as follows:  
\[\omega_{k}^{i}=\frac{1}{\sqrt{2\pi R}}\exp\left(-\frac{(y_{k}^{i}-Z_{k}^{i})^ {2}}{2R}\right) \tag{6}\]  
Then, we normalize the particle weight as:  
\[\tilde{\omega}_{k}^{i}=\left.\omega_{k}^{i}\right/\sum_{i=1}^{n}\omega_{k}^{i} \tag{7}\]  
According to the calculated weight, the new particle swarm is obtained by resampling. Low weight particles are sampled with low probability, and high weight particles are sampled with high probability. According to the new particle population, the estimated location of the signal source is given by  
\[\overline{p}_{k}=\left.\sum_{i=1}^{n}p_{k}^{i}\right/n \tag{8}\]  
Repeat (5)-(8) after receiving a new signal strength observation. Finally, the estimation position of a signal source will converge to the corresponding real position.  
If the searching area is divided into four regions in terms of the LTL formula (4), \(\Delta_{A}\), \(\Delta_{B}\), \(\Delta_{C}\), and \(\Delta_{D}\) are employed to represent the node set of each region where each element in the set is also a set consisting of the states in the automaton \(T\) with the states in the automaton \(B\) and is described by \(\Delta_{Aj},j=1,\ldots\). It is worth mentioning that these elements in sets \(\Delta_{A}\), \(\Delta_{B}\), \(\Delta_{C}\), or \(\Delta_{D}\), may change over time. Therefore, one can use \(\Delta_{A}\), \(\Delta_{B}\), \(\Delta_{C}\), and \(\Delta_{D}\), and the product automaton as a path monitor to guarantee that the generated path meets (4) and passes through the acceptable nodes.
# IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data
## 2 Experimental Design
### IAQ Monitoring Study  
The IAQ monitoring session in an individual apartment consisted of measuring indoor air parameters in the kitchen and the living room in parallel. The measurement point located in the kitchen (kitchen zone) was meant to provide the measurement data representing the overall kitchen atmosphere. Therefore, it was situated in the area designated for food preparation and consumption but at a distance from the cooker, spice storage area, and window/vent. The measurement point located in the living room (living room zone) was meant to provide the measurement data representing the actual living room atmosphere that people are exposed to. Therefore, it was situated centrally at the height of the breathing zone of a sitting person. One multi-sensor device was operated at each measurement point. One exemplar of the sensor device was used in the kitchen of all the flats included in this study, and another exemplar was used in the living room of all the apartments. Five apartments were examined in sequence. The monitoring session in a single apartment lasted at least seven days.
# Efficient Multi-UAV Path Planning for Collaborative Area Search Operations
## 5 Evaluation
### Performance Comparison of MUCS-GD Algorithm and MUCS-BSAE Algorithm
Based on using the MUCS-BSAE algorithm to assign the working path for each UAV, the time comparison graph for completing the search of the whole working area is shown. In Figure 12, we can see that the efficiency of the \"square wave signal\" curve path is very different compared to the other search paths. Working on the same number of UAVs, the working time of the \"square wave signal\" curve path is significantly lower than for the other search paths. It also shows better performance at all speeds compared to the other search paths. Moreover, when using the MTSP path as the UAV flight path, the MTSP path is the most time-consuming path at all flight speeds. From Figure 12, we can see that the curve path algorithm using the \"square wave signal\" exhibits better performance than the other search paths. Figure 11: Comparison of MUCS-GD based on different routes for a square area of \((16D\times 16D)\)m\({}^{2}\). It can be seen that the \"square wave signal\" curve path is better than the other search paths among the above search paths. Based on the \"square wave signal\" curve path, through Figure 13, it is easy to observe that the performance of the MUCS-BSAE algorithm is significantly better than that of the MUCS-GD algorithm. The performance is 14.4%, 25.8%, 23.0% and 17.7% better than the MUCS-GD algorithm for speeds of 5 m/s, 10 m/s, 15 m/s and 20 m/s, respectively.
## 4 Experimental Analysis and Results
### Using String Matching to Understand Tag Changes  
Using the notation from earlier in the paper each object \(P\) has \(n\) versions \((0,(n-1))\). Then \(T_{i}\) is the set of tags (keys, values) assigned to \(P\) at version \(i\). Then \(T_{i}=\{t_{0}^{i},t_{1}^{i},\ldots,t_{n-1}^{i}\}\) where \(t_{0}^{i}\) is the set of tags (possibly empty) at the first version of \(P\) and \(t_{n-1}^{i}\) is the set of tags (possibly empty) at the final version of \(P\). For each object with \(3\) or more changes to the \"name\" tag attribute we clustered the assigned name tags into chronological groups and then compared the transformation of tags into one another using two well known string matching metrics to quantify how similar the name tags were. The Levenshtein distance is defined as the minimal number of characters you have to replace, insert or delete to transform from one string to another [48]. The JaroWinkler distance [49] is a similar metric used mostly for duplicate detection in databases. The metric is normalized such that \(0\) equates to no similarity and \(1\) is an exact match between the two strings. In Figure 3 we show a plot of the mean Levenshtein distance against the mean JaroWinkler distance for \(412\) objects. Most objects are clustered around a mean Levenshtein distance of \(10\) and mean JaroWinkler distance of \(0.5\) which indicates that the changes from one name tag to the next name tag are substantially different. This is potentially caused by contributors: spelling placenames incorrectly, providing local variations on official placenames, incorrect naming of streets, performing correction of placename spellings.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Version** & **User** & **HIGHWAY** & **Edited on** \\ \hline \(1\) & \(7{,}070\) & Trunk & 15/11/09 \\ \(4\) & \(20{,}510\) & Construction & 16/11/09 \\ \(5\) & \(7{,}070\) & Trunk & 17/11/09 \\ \(7\) & \(19{,}889\) & Construction & 17/11/09 \\ \(10\) & \(7{,}070\) & Trunk & 17/11/09 \\ \(11\) & \(19{,}889\) & Construction & 17/11/09 \\ \(12\) & \(7{,}070\) & Trunk & 19/11/09 \\ \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\ \(78\) & \(206{,}986\) & Construction & 19/12/09 \\ \(79\) & \(7{,}070\) & Trunk & 20/12/09 \\ \(80\) & \(206{,}986\) & Construction & 20/12/09 \\ \(81\) & \(210{,}596\) & Trunk & 20/12/09 \\ \(88\) & \(145{,}231\) & Construction & 16/02/11 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Example of a “tag war” among OSM contributors in Germany. The value of the HIGHWAY tag representing a road feature is frequently reverted back by its original contributor. The OSM-ID of this feature is \(23,704,273\).
# Adaptive Speech Separation Based on Beamforming and Frequency Domain-Independent Component Analysis
## 4 Simulations and Experiment  
### Simulation Set-up  
To evaluate the proposed speech separation algorithm, a series of simulations was conducted. Voice propagation in a multi-channel environment was simulated by calculating the impulse response between the sound source and each microphone and convolving the impulse response with the sound source signal to obtain the voice signal received by each microphone. The image-source model (ISM) [28; 29] is used to calculate the impulse response of indoor speech propagation and signal convolution. For the multiple sound sources, each sound source was individually convoluted and generated, and then the signals were summed at each receiving microphone. The program was coded and ran in Matlab. The computer configuration is CPU: i7-5700, GPU: Nvidia 630, and 8 GB memory. Configurations for the proposed method are shown in Table 1.  
Figure 4 depicts the considered indoor environment, which consists of Room A with dimensions \(10\) m \(\times\) 10 m \(\times\) 4 m, Room B with \(6\) m \(\times\) 4 m \(\times\) 3 m and reverberation time T60 = 0.3 s.  
The proposed method was evaluated for two, three, and four sound sources distributed according to Figure 5 and measured the improved signal-to-interference ratio (SIR) to estimate the algorithm performance.  
\begin{table}
\begin{tabular}{c c} \hline Sampling frequency & \(16\) kHz \\ \hline Frame length & \(1024\) points \\ \hline Frame overlap & \(75\%\) \\ \hline Frequency resolution & \(15.6\) Hz \\ \hline Window functions & Hanning \\ \hline Localization algorithm & \\ \hline Frequency band \(\Omega\) & \(200\) Hz \\ \hline Frequency band overlap & \(100\) Hz \\ \hline Threshold for the normalized energy cross-correlation & \(0.8\) \\ \hline FDICA & \\ \hline Threshold for nonlinear correlation & \(0.0001\) \\ \hline Step size of iteration & \(0.1\) \\ \hline \end{tabular}
\end{table}
Table 1: Configurations for proposed method.  
Figure 4: Indoor environment for evaluation of speech separation.  
The SIR of the sound source \(i\) at the receiving microphone \(j\) is given by  
\[Input\text{SIR}_{i}=10\log_{10}\frac{\sum_{t}\abs{x_{i}(t)}^{2}}{\sum_{t}\abs{ \sum_{k\
eq i}x_{jk}(t)}^{2}} \tag{31}\]  
and the SIR after separation is given by  
\[Output\text{SIR}_{i}=10\log_{10}\frac{\sum_{t}\abs{x_{i}(t)}^{2}}{\sum_{t}\abs{ \sum_{k\
eq i}x_{ik}(t)}^{2}} \tag{32}\]  
where \(y_{ii}\) is the component of sound source \(i\) and \(y_{ik}\) is the residual component from the other sound sources after separation. The SIR improvement defines the algorithm performance as  
\[Impro\text{SIR}_{i}=Output\text{SIR}_{i}-Input\text{SIR}_{i} \tag{33}\]  
where the input SIR is calculated from the signal received by the microphone at the array center. Note that this performance measure only reflects the degree of separation considering the signal power, rather than the quality and completeness of the recovered signal.
## 4 Location Estimation-Based Congestion-Aware Routing Protocol (LECAR)--Detailed Proposal  
We propose a novel routing protocol, LECAR, that estimates UAVs' location using the path planner and incorporates the results in the routing decision. As a DTN-based routing protocol, LECAR works in a single-copy unicast fashion. The protocol aims to ensure minimum resource usage and energy consumption, while maintaining a high packet delivery ratio.  
In this section, we briefly describe LECAR in detail. We define the data tables that LECAR maintains for routing decisions, the packet format it shares with neighbors, and the hello message mechanism. Then, we describe the routing decision process of LECAR.
## 2 Conventional Channel Estimation Methods
### Pilot-Based Channel Estimation Method
#### 2.1.4 LMMSE Channel Estimation  
As expressed in Equation (10), the inverse of the matrix needs to be recalculated when the pilot signal \(\mathbf{X}\) changes, making the computation more difficult. The LMMSE algorithm is a simplified version of the MMSE algorithm by replacing \(\big{(}\mathbf{X}\mathbf{X}^{\text{H}}\big{)}^{-1}\) with its expectation \(\text{E}\Big{\{}\big{(}\mathbf{X}\mathbf{X}^{\text{H}}\big{)}^{-1}\Big{\}}\). Furthermore, the SNR is defined as \(SNR=\text{E}\Big{\{}|x|^{2}\Big{\}}/\sigma^{2}\), so that there is:  
\[\hat{\mathbf{h}}_{\text{LMMSE}}=\mathbf{R}_{hh}\bigg{(}\mathbf{R}_{hh}+\frac {\beta}{SNR}\mathbf{I}\bigg{)}^{-1}\hat{\mathbf{h}}_{\text{LS}} \tag{11}\]  
where \(\beta=\text{E}\Big{\{}|x|^{2}\Big{\}}\text{E}\Big{\{}1/|x|^{2}\Big{\}}\) is the modulation type parameter. For instance, \(\beta=1\) corresponds to QPSK modulation, and \(\beta=17/9\) corresponds to 16 QAM modulation. If both \(\mathbf{R}_{hh}\) and \(SNR\) are known, only one matrix inverse operation is performed, effectively reducing computational complexity.
# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 5 Recent MM Techniques and 5G Architecture
### Design Aspects of 5G Related MM Mechanisms
Adaptability Parameter Considerations The following parameters of adaptability fulfill the criteria for MM mechanisms for 5G and future networks in accordance with our understanding and review of the literature. These are: 1. AL2: This parameter can fulfill the adaptability criteria by providing handover, and 3 GPP ProSec, accordingly. 2. AL3: This parameter can fulfill the criteria of adaptability by considering the context of the occupant with the help of network slicing. 3. AL4: This parameter can fulfill adaptability criteria by making connections to various access points (APs) through IPv6 multi-homing provision and MR-DC. 4. AL5: This parameter can fulfill the criteria of adaptability by providing granularity of service offered to maintain per-user mobility level, the capability to support on-demand MM, and per-user PDU sessions with the help of network slicing provision. 3. Extensibility Parameter Considerations The following parameters of extensibility fulfill the criteria for the MM mechanism for 5G and future networks. These are: 1. EL2 and EL3: These parameters can fulfill the criteria of extensibility by the provision of both control plane and data plan-related strategies at the core, access, and edge network concerning MM's point of view. 2. EL4: This parameter can fulfill the criteria of extensibility owing to the standardization, hence, no hindrances occur in the deployment, implementation, and integration of RATs. 3. EL5: This parameter can fulfill the criteria of extensibility owing to the granularity in service per mobility level. 4. Power Saving Parameter Considerations The following parameters of power-saving (battery optimization) fulfill the criteria for MM mechanisms for 5G and future networks. These are: 1. DL1: This parameter can fulfill the criteria of power-saving owing to the addition of other micros that open for the options of offloading the traffic among the nodes, and every single node can move to the idle state for an extended period. 2. DL2: This parameter can fulfill the criteria of power-saving owing to the permission for both more profound and more extended periods of sleep when there is minimal or no continuity in data transmissions. 3. PL5: This parameter can fulfill the power-saving criteria by placing the base station into a sleep state with no traffic. Hence, switching off hardware components will ultimately save energy.
## 5 Experimental Verification
It is apparent that the fault detection time for the proposed SDRE method in experimental implementation is 96 ms and for the differential method proposed in [15], it is 114 ms. Additionally, load voltage when using the SDRE observer protection is restored faster and has a smaller transient-current. The load voltage attains a small value without protection. Due to the fast fault detection and consequent prevention of huge rises in the fault current, the SDRE observer protection leads to a lower current in the freewheeling path rather than the differential protection method, i.e., reduced protective system expenses and the conceivability  
Figure 19: Electrical scheme of the testbed experimental prototype. Figure 20: Residual current of \(C_{1}\) capacitor during the fault occurrence. of utilizing the simpler, smaller diodes. Figure 22 demonstrates the voltage of IGBT breakers with snubber attendance in differential and SDRE observer modes. Exploiting the SDRE observer protection led to a reduction in the pick of voltage on the IGBT breakers, which permitted the utilization of breakers with lower nominal insulation voltages. In addition, by taking advantage of the SDRE observer controller, IGBTs tolerated lower transient voltages that caused a reduction in the possibility of IGBT breaker failure. The implementation results of the SDRE observer protection algorithm prove the usefulness of this study in terms of practical applications. The proposed schemes are applicable and more feasible, efficient, fast, and accurate than those of the differential algorithm. Consequently, the proposed approach can also be implemented in real-scale different dc islanded configurations.
# Sign Language Recognition Using Two-Stream Convolutional Neural Networks with Wi-Fi Signals
## 2 Materials and Methods
### Singular Value Decomposition  
In linear algebra, singular value decomposition (SVD) is the factorization of real or complex matrices [24]. SVD is a decomposition method that can be applied to any matrix. There is always SVD for any matrix A, as shown in Equation (2):  
\[A=U\Sigma V^{\mathrm{T}} \tag{2}\]  
Assuming that \(A\) is an \(m\times n\) real or complex matrix, the obtained \(U\) is an \(m\times m\) square matrix, and the orthogonal vector in \(U\) is called a left singular vector. \(\Sigma\) is an \(m\times n\) rectangular diagonal matrix. Except for the diagonal elements, all elements of \(\Sigma\) are 0. The elements on the diagonal are called singular values. \(V^{\mathrm{T}}\) is the transposed matrix of \(V\), which is an \(n\times n\) square matrix. The orthogonal vector is called the right singular value vector.  
Generally speaking, the values on the \(\Sigma\) are in descending order [24]. The larger the value, the higher the importance of the dimension. We can choose the top singular values to approximate the matrix. This way not only extracts important features from the data, but also simplifies the data and eliminates noise and redundancy. The number of singular values depends on various factors, such as different datasets, recognition methods, and temporal and spatial characteristics.
# Benchmarking of Load Forecasting Methods Using Residential Smart Meter Data
## 2 Materials and Methods
### Forecasting Approaches  
As mentioned before, due to a considerable amount of noise in the consumption time series for each individual consumer, and in order to create methodologies to deal with a large number of individual end-users, it is strongly recommended to create forecasting models simultaneously accurate, straightforward, non-time-consuming and even explainable. This research uses Multivariate Adaptive Regression Splines, Random Forest and Artificial Neural Networks.