esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Evaluation
### Experiment in University
#### 4.1.2 Discussion and Results
In addition, the participants agreed that they have the intention to use the same kind of AR learning system to observe other astronomy phenomena in the future. Moreover, the participants were satisfied with the R-WOSARLS and thought that such an AR-based learning system could enhance their interest in astronomy learning. The questionnaire asked the participants to state their comments or advice on the proposed R-WOSARLS system without restraint. One participant suggested that it was better to have explanatory contents regarding the specified targeted constellation from the curator while the learners were observing a constellation. Other participants pointed out that if there were a function in the system to recommend a seasonal constellation every day for learners, then this would be invaluable. Three participants mentioned that a multi-language label of the constellation name should be included in the system. One user reported that the size and color of the seasonal constellation triangle or quadrangle should be different from other constellations. Based on this experiment, it can be concluded that the proposed R-WOSARLS is very helpful for university students to study and observe seasonal constellations in a real-world environment.
# Spatial Assessment and Prediction of Urbanization in Maseru Using Earth Observation Data
## 2 Materials and Methods  
### Study Area  
Maseru is the capital city of Lesotho: a mountainous country that is landlocked by South Africa and is considered the commercial hub of the country [25]. The city lies on the country's western border along the Caledon River, bordered by South Africa on the northwestern section (Figure 1). The city is mountainous, with topography ranging from 1482 m to 1905 m, as shown in Figure 1. Maseru has a continental and temperate climate receiving 85% annual rainfall between October and April, averaging 700 mm per year [26]. The city has lowlands and highlands, and temperatures include mild winters and hot summers [26]. The approximate average low temperature of the city is 8 \({}^{\circ}\)C, with a high average temperature of 22.4 \({}^{\circ}\)C [27].  
Figure 1: Map of (**a**) Lesotho, (**b**) A 30 m Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) and (**c**) PlanetScope imagery of Maseru.
# Intelligent Trajectory Prediction Algorithm for Reentry Glide Target Based on Intention Inference
## 6 Simulation
### Validity Analysis of Trajectory Prediction  
In order to illustrate the effectiveness of the proposed method, two cases of trajectory prediction with different no-fly zone situations are selected in this paper. The first is the target flying around the no-fly zones, while the second is the target flying through between the no-fly zones. With an observation time of 150 s, the predicted trajectories are obtained with 100 s, 200 s and 300 s prediction time. Cases 1 and 2 represent the truth trajectories of flying around and passing through, respectively. The prediction effect of case 1 is shown in Figures 15 and 16, while the prediction effect of case 2 is shown in Figures 17 and 18.  
As can be seen from Figure 15, when the prediction time is short, the prediction accuracy is very high. Meanwhile, when the prediction time is longer, the accuracy gradually decreases, which is caused by the accumulation of longitudinal trajectory errors generated by integration. As can be seen from Figure 16, according to the case that the target flies around the no-fly zones, the predicted trajectories based on intention inference in the early stage are more accurate. Because the proposed sign change rule is different from truth guidance logic, as the prediction time increases, the lateral prediction effect may become poor. However, for the long-term trajectory prediction, based on the correct intention  
Figure 14: Effect of intention inference network: (**a**) represent the modified filtering trajectories; (**b**) is the variation of threat value; (**c**) is the variation of time-to-go; (**d**) is the variation of cross-range; (**e**) is the variation of longitudinal range; (**f**) is the variation of importance.  
As can be seen from Figure 17, the variation of longitudinal guidance precision in case 2 is similar to that in case 1, which agrees with the longitudinal guidance variation law of the reentry glide target, that is, longitudinal range is mainly related to the control parameter value. However, as can be seen from Figure 19, the flying through predicted trajectory using the intention inference method has a good effect on short-term and long-term prediction. What is more, for long-term prediction, when intentions are accurately judged, lateral prediction errors do not diverge rapidly, and the prediction error increases slowly. Compared with case 1, the lateral predicted trajectory in case 2 is more accurate. On the other hand, it can be seen from Figures 15 and 17 that, after using the parameter value prediction network, the long-term longitudinal integration error of the target still has a high accuracy for the skip trajectory, and the error can be controlled within 500 m. To further demonstrate the accuracy of the prediction method, the overall test results are analyzed in Section 6.5.
# Piezoelectric Energy Harvesting Prediction and Efficient Management for Industrial Wireless Sensor
## 1 Introduction  
Numerous studies have shown that benefits such as increased productivity, security, efficiency, and even lower costs are due to the automation of most industrial processes [1]. Moreover, these are the objectives of Industry 4.0, which aims to transform all the processes of a factory into an interconnected global system. At the base of automated systems, there are sensors whose function is to transmit information on the system's state at regular time intervals. More specifically, in the industrial environment, the sensors are used in automation systems [2], operation controls, system monitoring (humidity, temperature, fire alarm, dust index, toxic gas, and pollution) [2; 3; 4], and motion control [5].  
The used sensors in the industrial process can be wired or wireless; when these sensors are wireless, they are referred to as Industrial Wireless Sensors (IWSs). When the process to be monitored requires several sensors, the numerous wired connections can be troublesome for the process. For this reason, IWSs have experienced enthusiastic support because of their small dimensions, their ease ofdeployment, their flexibility, and their low cost [6]. These numerous advantages are, unfortunately, accompanied by the issue of their energy supply. Since IWSs are battery-powered, recharging or replacement operations are very costly because they require a cessation of production. The solutions for harvesting ambient energy, which consists of transforming an energy source located in the sensor's immediate environment into electrical energy for its supply, are then considered.  
Many energy harvesting techniques are offered; the difference is likely the primary source used (sun, vibrations, radiofrequency waves, heat, internal light, etc.). Sources such as the sun provide significant amounts of energy that can even be fed back into the national grid; however, the sunshine level is deficient in confined spaces. Recently, radio waves have also been used to recharge IWS batteries [7]. Unfortunately, the amount of ambient radiofrequency energy that can be harvested is limited due to potential health concerns [8]. Concerning the heat, previous work has shown that conversion efficiencies are extremely low [9]. Thus, for industrial applications, vibrations are the most considered energy source to power the IWS [10].
## 4 Conclusions  
In this work, a second-order RC equivalent circuit model in Simulink simulates the charge and discharge behaviors of a lithium battery. The data generated from this equivalent circuit model were used to train several neural network architectures for voltage and SOC prediction. The MHA-BiLSTM demonstrated a superior performance, leveraging multi-head attention to enhance feature extraction and improve the prediction accuracy. In voltage prediction, MHA-BiLSTM achieved an R\({}^{2}\) of 0.9075, RMSE of 0.0567, and MAPE of 0.0095, respectively. For SOC prediction, it led with an R\({}^{2}\) of 0.9554 and RMSE of 0.0419, outperforming LSTM and GRU with a 2.78% and 16.61% reduction in RMSE, respectively. The performance improvements primarily result from the multi-head attention mechanism, which enhances the model's ability to capture complex battery dynamics by focusing on key features and minimizing the cumulative error. This work confirms the feasibility of integrating the second-order RC equivalent circuit model with the MHA-BiLSTM model to provide an effective framework for battery voltage and SOC prediction. This study demonstrates the feasibility of integrating physical modeling with advanced deep learning techniques to achieve accurate and efficient battery state prediction. The proposed framework not only advances the state-of-the-art in battery health monitoring, but also broadens the applicability of data-driven methods by lowering the barriers to data acquisition. Future work will focus on validating the model with experimental data from real-world battery systems and extending its capabilities to predict SOH and RUL.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **MHA-BiLSTM** & **A-BiLSTM** & **LSTM\_A** & **LSTM** & **GRU** \\ \hline Voltage-MSE & **0.000902** & 0.021882 & 0.015968 & 0.016181 & 0.010469 \\ SOC-MSE & **0.002253** & 0.020177 & 0.008896 & 0.017224 & 0.007566 \\ Voltage-MAE & **0.028823** & 0.146407 & 0.097512 & 0.125792 & 0.120779 \\ SOC-MAE & **0.046433** & 0.140330 & 0.091399 & 0.129676 & 0.081371 \\ \hline \hline \end{tabular}
\end{table}
Table 3: MSE and MAE Results of Voltage and SOC for Five Models in the Test Data. The highest the lowest RMSE values in the table are highlighted in bold.  
**Author Contributions:** Conceptualization, Z.L. and H.X.; Methodology, T.L. and J.Q.; Validation, Z.L.; Resources, Z.L. and M.M.; Writing--Original Draft Preparation, H.X.; Writing--Review and Editing, Z.L., T.L. and J.Q.; Supervision, Z.L. and M.M.; Project Administration, Z.L., J.X. and S.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The authors have not been granted authorization to publicly disclose the relevant data.  
**Acknowledgments:** M.M. acknowledges the Shanghai Eastern Talent Plan.  
The authors declare no conflicts of interest.
## 4 Mid-Layer Software EMIDE for User-Friendly Application Development Environment
### Embedded Java Virtual Machine (EJVM)  
EJVM is encouraged for use in the WSN due to the following reasons. Firstly, after the EJVM is applied, the users can program the WSN application by the popular, reusable and robust Java language without the necessity of considering the low-level platform details. Secondly, Java is an object-oriented paradigm, thus the WSN application design, test and maintenance process can be simplified. Thirdly,the Java byte code is platform independent, thus the challenge of programming in the heterogeneous WSN environments can be addressed [25]. Finally, the WSN reprogramming performance can be improved as only the Java application image needs to be updated during the reprogramming process.  
Currently, a set of EJVMs have been developed, including the TinyVM [26], the Darjeeling VM [27], the simpleRTJ [28], the SQuawkVM [29], the nanoVM [30], the Jwik [31] and the Java Card VM [32]. Different JVMs have different features and strike a tradeoff between the VM performance and the resource consumption (Table 5). For all the EJVMs listed in Table 5, the pre-linked mechanism is used for the produce of the application image. After the Java application programs are built, all the separated Java classes will be pre-linked statically to form a single image. Compared to the general-purpose JVMs in which all the classes are loaded dynamically during the run-time, the pre-linked image is less flexible, but it is more appropriate for the resource-constrained WSN nodes. With the pre-linked mechanism, the application image size can be reduced and the underlying EJVM architecture can also be simplified. In some EJVMs [26, 27, 28], a lightweight Java OS is embedded inside. With the Java OS, the multitasking Java application can be achieved by using the Java threads. In other EJVMs [30, 31, 32], no Java OS is developed. With these JVMs, only single-task Java application can be developed.  
Although a user-friendly development environment can be provided to the WSN applications by the EJVMs, the memory consumption of some EJVMs is high [27, 28, 29]. Additionally, the execution efficiency of the Java byte code is lower if compared with the machine code, thus more energy will be consumed during the run-time. Due to these reasons, it is not ideal to apply the EJVM on the high memory and energy-constrained WSN platforms.
## 3 Results and Discussion
### GP and FNNs Models Behavior in Test 2  
The accuracy metrics of Test 2 with different ranges to those used for training are shown in Table 6. The GP model reached 95.35% accuracy, 4.65% error (MAPE); its average error (SEE) from a real output value to a predicted one was 1.8256 watts, which explains 83.99% of output variability (R\({}^{2}\)). The FNNs model achieved 98.21% accuracy (1-MAPE) and 1.79% error (MAPE); the average error (SEE) between a real output value and a predicted one was 0.6776 watts, which explains 97.79% of the output variability (R\({}^{2}\)).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Technique** & **MAE** & **MSE** & **SEE** & **MAPE** & **R\({}^{2}\)** \\ \hline GP & 1.1239 & 2.0691 & 1.4384 & 0.0390 & 0.9267 \\ FNNs & 0.3007 & 0.2330 & 0.4827 & 0.0101 & 0.9934 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Models efficiency metrics for Test 1.  
The two datasets (Test 1 and Test 2) showed that the FNNs model was slightly superior to the GP model in the testing stage, despite the training measures. As shown in Table 6, the error levels for MAE and MSE were lower in FNNs, while their effectiveness explaining the variable output variability or R\({}^{2}\) was superior.  
A comparative response between the obtained GP model output and the desired one (red and blue signals, respectively) is shown in Figure 6a. We plotted AE in Figure 6b, showing a maximum error per sample of 4.6 watts, despite a MAE of 1.4508. The FNNs model response (Figure 7a) exhibited a higher accuracy than the GP model, with a lower accuracy than the GP model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Technique** & **MAE** & **MSE** & **SEE** & **MAPE** & **R\({}^{2}\)** \\ \hline GP & 1.4508 & 3.3328 & 1.8256 & 0.0465 & 0.8399 \\ FNNs & 0.5386 & 0.4591 & 0.6776 & 0.0179 & 0.9779 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Models efficiency metrics for Test 2.  
Figure 4: GP model results in Test 1. (**a**) Data estimated (red) versus energy consumption data measured (blue). (**b**) Absolute error between estimated behavior and measured energy consumption (red).  
Figure 5: FNNs model results in Test 1. (**a**) Data estimated (red) versus energy consumption data measured (blue). (**b**) Absolute error between estimated behavior and measured energy consumption (red).  
MAE of 0.5386, and presented a lower punctual error than the GP model, i.e., almost 1.8 watts (Figure 7b).
# Integrated Design of Moon-to-Earth Transfer Trajectory Considering Re-Entry Constraints
## 5 Numerical Simulation and Analysis
### Design Parameters Analysis
#### 5.2.3 Altitude of Perilune  
The setting of the values of the nominal altitude of the perilune is only involved in the design of the precise transfer trajectory, so the parameter does not affect the distribution of transfer opportunities. However, the altitude of the perilune closely related to the velocity increments is required to precisely transfer trajectories. Figure 11 reveals the relationship between the velocity increment and the perilune altitude. Design parameter settings are the same as in the single transfer trajectory example except for the altitude of perilune.  
The curve in Figure 11 illustrates a negative correlation between the velocity increment and the perilune altitude, which can be explained by the energy difference between orbits. The higher the perilune, the larger the energy of capture orbit. When the probe enters the lunar capture orbit from the higher-energy Moon-to-Earth transfer trajectory, a higher capture orbit energy means a smaller capture velocity increment.
# Framework for Assessing Ethical Aspects of Algorithms and Their Encompassing Socio-Technical System
## 3 Method
### Related Research on Ethical Frameworks
#### 3.2.4 Data Ethics Decision Aid (DEDA)  
Dataschool Utrecht, in cooperation with data analysts from the city, developed DEDA [15] to help data analysts, project managers and policy makers to recognise ethical issues in data projects, data management and data policies. DEDA is comprised of several aspects.  
**Collection of data:**: The algorithm itself should be clear and explainable and the data used should be of good quality.
**Usage of data:**: Attention should be paid to anonymization of the data, and (if applicable) how to visualise the data.
**Storage of data:**: This aspect concerns accessibility and reusability of the data.  
After these three aspects, the framework goes on to more general considerations.
**Responsibility:**: This consideration is not only about responsibility of protocols and communication, but also about prevention of discrimination.
**Transparency:**: It should be evaluated how transparent one can be.
**Privacy:**: DEDA takes a more legal approach to privacy by requiring conducting a Privacy Impact Assessment and having contact with the organisation's privacy officer.
**Bias:**: Questions about the representativeness of samples should be addressed.
**Informed consent:**: Questions like \"Can people opt-out?\" and \"How are they informed?\" should be addressed.  
The DEDA framework is more extensive than the one described above, because it also includes parts that are more related to legal aspects illustrating that legal and ethical concerns are related.
# Minimizing Interference-to-Signal Ratios in Multi-Cell Telecommunication Networks
## 4 Experiments
### Observations (Scenario 1)  
Let us start with comparing the two algorithms (DP-Similarity Clustering and Spectral Clustering) on an arbitrarily chosen clustering problem in the setting of Scenario 1; after that, we turn to a quantitative comparison. Figure 1a shows a randomly and uniformly generated placement of 100 BSs and 200 users. The scaling of the tinf (see Equation (2)) of the solutions provided by Spectral Clustering and Spectral Clustering algorithm as a function of the number of clusters \(M\) is shown in Figure 1b.  
Figure 1: Scenario 1: A demonstration of the output of the two clustering algorithms on 100 BSs and 200 users. Clustering solutions shown for \(M=20\) (so that the clusters are fairly large and visible). Triangles and circles represent BSs and users, respectively. (**a**) A total of 100 BSs and 200 users, randomly and uniformly distributed. (**b**) Comparison of the tinf values of the solution provided by the two algorithms as \(M\) increases; Spectral Clustering failed for \(M=27,30\) because it assigned a couple of users to a cluster without a base station. (**c**) DP-Similarity Clustering into \(M=20\) clusters, Algorithm 2; tinf\(\approx 5.04\). (**d**) Spectral Clustering into \(M=20\) clusters, see [3]; tinf\(\approx 7.12\).  
Figures 2 and 3 compare the performance of the algorithms for \(b=50\) and 200 BSs. The plots correspond to the mean tinf values of the solutions provided by the algorithms over 100 random samples of BSs-user placements.  
Figure 2: Scenario 1: Analyzing the output of DP-Similarity Clustering and Spectral Clustering for \(b=50\) BSs and \(u=200\) users; samples obtained over 100 randomly generated problems. (**a**) For larger \(M\), many of the solutions provided by Spectral Clustering have a cluster that contains some users but zero BSs. (**b**) Mean tinf (see Equation (2)) values as a function of the number of clusters \(M\); the ribbons show the standard deviation over the samples. Note that the failed samples were not included in the sample mean and sample deviation. (**c**) For each \(M=1,\ldots,20\), the figure shows the ratios of those samples where DP-Similarity Clustering and where Spectral Clustering provided the solution with the smaller tinf value. Because of the high number of failures produced by Spectral Clustering for moderately large values of \(M\), this ratio tends to 1 for DP-Similarity Clustering (as \(M\) increases). (**d**) Running times for \(b=50\) BSs and \(M=20\) clusters, as the number of users grows; the \(y\)-axis is logarithmic.
## 2 Multi-Dimensional User Importance Indicator System and Ranking Model
### User Importance Ranking Based on the Improved AHP-EW Method
#### 2.2.2 User Importance Ranking Based on the EW Method  
The indicator can be categorized into positive and negative data. For positive data, a larger value of the indicator data corresponds to higher evaluation; for negative data, a smaller value of the indicator data corresponds to higher evaluation. Therefore, differentindicators must be processed using different standard methods to facilitate subsequent processing. The standardized processing of positive and negative data is as follows:  
\[\mathrm{x}_{ij}=\frac{\mathrm{x}_{ij}-\min\{\mathrm{x}_{1j},\ldots,\mathrm{x}_{ nj}\}}{\max\{\mathrm{x}_{1j},\ldots,\mathrm{x}_{nj}\}-\min\{\mathrm{x}_{1j}, \ldots,\mathrm{x}_{nj}\}} \tag{17}\]  
\[\mathrm{x}^{\prime}_{ij}=\frac{\max\{\mathrm{x}_{1j},\ldots,\mathrm{x}_{nj}\}- \mathrm{x}_{ij}}{\max\{\mathrm{x}_{1j},\ldots,\mathrm{x}_{nj}\}-\min\{\mathrm{x }_{1j},\ldots,\mathrm{x}_{nj}\}} \tag{18}\]  
After standardization, the weight \(p_{ij}\) of the \(i\)th sample under the \(j\)th indicator for that indicator is calculated:  
\[p_{ij}=\frac{x_{ij}}{\sum\limits_{i=1}^{n}x_{ij}},\;i=1,\cdots,n;\;j=1,\cdots,m \tag{19}\]  
The entropy value represents the degree of confusion and the amount of information contained in the indicator; the smaller the entropy value, the more information it contains. And the entropy \(\mathrm{e}_{j}\) of the \(j\)th indicator is calculated as follows:  
\[e_{j}=-k\sum\limits_{i=1}^{n}p_{ij}\ln\bigl{(}p_{ij}\bigr{)},j=1,\cdots,m \tag{20}\]  
where \(k=1/\ln(n)>0\) and satisfies \(\mathrm{e}_{j}\geq 0\).  
The information entropy redundancy \(d_{j}\) is  
\[d_{j}=1-e_{j},j=1,\cdots,m \tag{21}\]  
The weights of the indicators \(\alpha_{j}\) are  
\[\alpha_{j}=\frac{d_{j}}{\sum\limits_{j=1}^{m}d_{j}},j=1,\cdots,m \tag{22}\]