esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### 3D Representation of the Proposed EDADS-1 and EDADS-2  
In this subsection, a 3D pictorial representation of the proposed schemes (EDADS-1 and EDADS-2) is illustrated with Figures 6 and 7, respectively. To represent the workings of the proposed schemes, we use three features of the standard IEEE 57- and 118-bus systems. In Figure 6, the centroid (indicated in the figure) is calculated using training data consisting of normal and compromised optimal features. The average of the distances of all the samples from the centroid defines a virtual boundary, as shown in Figure 6. The data points lying outside of the virtual boundary are termed anomalous data or outliers. However, in Figure 7, the centroid (indicated in the figure) is calculated on the basis of data labeled as normal only. The distances of all the samples from the centroid are sorted in descending order, and then, the first 10 percent distances (farthest from the centroid) are chosen to calculate their average distance from the centroid. The average distance defines a virtual boundary as shown in Figure 7. The samples lying out of the boundary are considered as outliers. Basic performance metrics used in this work, i.e., accuracy, \(F_{1}\) score and receiver operating characteristic (ROC) curves, are shown in the following subsections.  
Figure 5: Standard IEEE 39-bus system (New England 10-Machine System [38]).
## 4 Results and Discussion
### Subjective Evaluation  
After the experiments, operators were interviewed individually to describe their experience about different force conditions and their perceived performance. In order to compare our results, operators were asked to fill out a questionnaire consisting of seven questions. In every question, they had to choose between the three conditions: no force feedback, realistic force feedback, and exaggerated force feedback.  
Figure 15 shows the questions with the corresponding responses of the operators. Most of the operators agreed that by incorporating haptic feedback in the UAV controls, the mission objectives can be achieved effectively. However, the answer to question 4 shows a controversial situation in which 8 out of 15 operators thought that haptic feedback demanded more effort. When asked about this in the follow-up interview, most of the operators clarified that when they experienced force intermittently, they pushed the control stick away from them, which compromised their performance and required more physical effort to maintain the desired altitude.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & & & \multicolumn{6}{c}{**Performance Parameters**} \\ \hline
**Altitude** & **Force** & **Rise** & **Overshoot** & **Fall** & **Undershoot** & **Settling** & **Mean** & **Best** \\  & **Condition** & **Time** & **Control** & **Time** & **Control** & **Time** & **Error** & **Selection** \\ \hline \multirow{4}{*}{Hold} & No force & \(\bullet\) & \(\bullet\) & & & & *** \\  & Realistic force & & & & & & & *** \\  & Exaggerated & & & & & & & \\  & force & & & & & & & \\ \hline \multirow{4}{*}{Gain} & No force & & & & & & & & \\  & Realistic force & & & & & & & & \\  & Exaggerated & & & & & & & & \\  & force & & & & & & & & \\ \hline \multirow{4}{*}{Descent} & No force & & & & & & & & \\  & Realistic force & & & & & & & & *** \\ \cline{1-1}  & Exaggerated & & & & & & & & \\ \cline{1-1}  & force & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Best force condition on the basis of performance parameters.  
Figure 15: Operator response to subjective questionnaire.
## 5 XAI Analysis for GNSS Signal Disruption Classification
### ML Algorithm Training and Testing  
The proposed model architecture for GNSS signal abnormality classification is depicted in Figure 6. In this work, five types of ML black-box models were employed to classify different types of signal disruptions in GNSS signals. Alongside these, a model explanation framework using LIME and SHAP was developed in parallel, with the sophisticated capabilities of this model explanation being feature contributions, an importance plot, a SHAP force plot, LIME model explanations, the best-suited features only being selected for training, and the redundant features and least important features being eliminated. The evaluation metrics are compared with traditional feature selection and elimination methods Principle Component Analysis (PCA, backward and forward selection methods). The redundant and unimportant features are neglected, and only the most important features are chosen for training. Then, based on the evaluation metrics (accuracy, precision, recall, F1-score, and Receiver Operating Characteristic ROC), the best-performing ML model was selected. Finally, knowing the cause of not predicting a particular class and the significance of the most trustworthy features for predicting the undesirable event would be helpful in analyzing the catastrophic failures in the GNSS ground-station data collection system so that the end user can take precautionary measures based on where exactly the signal quality is degraded and what factors contribute to its abnormal behaviour in the earlier stage before it is subjected to the post-processing stage. The ML models alone failed to capture the relationship between the features when it came to predicting abnormal behaviours in adverse conditions under jamming and spoofing signal attacks. However,  
Figure 5: Boxplot comparison of different disruptions in GNSS signals based on \(p\)-values.  
this XAI framework helped to identify the irregularities, making it possible to investigate and respond quickly based on the explanations associated with the features in safety-critical operations.  
The collected GNSS data have a duration of 40 s. A training-validation-testing split of 64-16-20% is chosen in this work. Each millisecond of data consists of 32,676 samples. We have used 25.6 s of data for training, which is 80% of 32 s of data, and 6.4 s of data are used for the validation phase. The remaining 20% of the total data, with a duration of 8 s, are used for the testing phase.
# Biochar for Wastewater Treatment--Conversion Technologies and Applications
## 2 Conventional Thermal Methods for the Conversion of Feedstock into Carbonaceous Materials
### Hydrothermal Carbonization
[67], the effectiveness of hydrochar obtained from HTC of _Prosopis africana_ shells was compared to that of pyrolytic-biochar to remove Pb and Cd from aqueous solution. Results showed that HTC-hydrochar was more efficient in adsorbing metal ions as compared to biochar from pyrolysis. The same tendency was observed by [PERSON] et al. [68], who reported that hydrochar from pinewood exhibits higher efficiency to adsorb Cu than pyrolysis-biochar prepared from the same feedstock. The higher adsorption of HTC-hydrochar is due to the presence of more functional groups and available activated sites on its surface [67; 68]. The adsorptive capacity of hydrochar could be improved by the addition of some chemical agent (acids, alkalis and surfactants) during the hydrothermal process [69; 70]. [PERSON] et al. [71] used acetic acid and potassium hydroxide as acidic and basic agent to modify the pH of the process water (from 2 to 12) during HTC of wheat straw. The results showed that hydrochar produced at pH 2 has 2.7 times higher surface area than that produced at pH 12 and larger pore volume and pore size.
# Research on Visual Positioning of a Roadheader and Construction of an Environment Map
## 3 Pose Estimation and Optimization of the Roadheader  
### Pose Estimation Scheme  
The Pose estimation scheme of the roadheader (camera) is shown in Figure 4. Specifically, (1) the images of the roadway environment are collected by the airborne RGB-D camera and depth information is obtained; (2) feature extraction and matching are performed on the color images, and 3D matching pairs are formed after depth mapping; (3) combined with the random sample consensus (RANSAC) strategy and the iterative closest point (ICP) strategy, the pose of the roadheader is estimated; (4) the pose graph, based on keyframe and closed-loop constraints, is constructed, and the global pose optimization is carried out to obtain the optimal pose estimation of the roadheader.
# Anti-Jamming Power Control Algorithm for Wireless Communication Systems Based on MPC
## 5 Simulation Results and Analysis
In contrast, the integral state feedback control method shows slower convergence, while the traditional power adaptive method results in a higher bit error rate. Moreover, when jamming pulse intervals are randomly distributed, traditional methods struggle to converge to the specified bit error rate. Figure 6: _Cont._  
As shown in Figure 9, When the prediction interval N = 8 is employed, the resulting error is considerable, and the convergence process is significantly slowed. In contrast, the prediction interval N = 3 yields a more rapid convergence and a smaller error. This is primarily due to the fact that effective predictive time power controllers are capable of anticipating future states, thereby enabling more comprehensive optimization decisions to be made. This contributes to enhancing the stability of the transmission system. Nevertheless, overly lengthy prediction periods may also give rise to a number of issues. Primarily, the precision of the prediction model declines with time, as the unpredictability of the prospective state grows. This may result in the controller making determinations based on erroneous projections, which ultimately diminishes the system's overall performance. Secondly, longer prediction periods elevate the computational burden due to the controller's necessity to address a greater number of future state variables. Figure 6: Comparison of three power control algorithms under burst jamming. (**a**) At t = 2 s, Sudden jamming attacks on communications systems. (**b**) System BER output with different controllers. (**c**) System transmit power with different controllers. Figure 7: Comparison of output variations with weight changes under pulse jamming. (**a**) At t = 2 s, Sudden jamming attacks on communications systems. (**b**) System BER output with different weights. (**c**) System transmit power with different weights. Figure 8: Comparison of three power stability control methods under random pulse jamming. (**a**) Random bursts of jamming to the system. (**b**) System BER output with random burst jamming. (**c**) System transmit power with random burst jamming.
# Security Risk Analysis of LoRaWAN and Future Directions
## Security Challenges of LoRaWAN v1.1
### Trust Related  
_NS_'s are considered as trusted parties to honestly transmit encrypted messages to each end. However;  
* In the specification of LoRaWAN v1.1, following is stated: \"Application payloads are end-to-end encrypted between the end-device and the application server, but they are integrity protected only in a hop-by-hop fashion: one hop between the end-device and the \(NS\), and the other hop between the \(NS\) and the application server\" [16]. As mentioned in [24], this means that, a malicious \(NS\) may be able to alter the content of the data messages in transit, which may even help the \(NS\) to infer some information about the data by observing the reaction of the application end-points to the altered data. As a conclusion to this statement, it can be stated that end-to-end confidentiality and integrity protection between the servers is not warranted or supported by LoRaWAN v1.1.
* While the specification implements a \"federated \(NS\) infrastructure in which network operators are not able to eavesdrop on application data\", there is some trust put on the \(NS\) in that it will not perform selective forwarding attack or act as a sinkhole.
# AUV-Aided Optical--Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning
## 6 Results and Discussion
### Simulation Setup  
To evaluate the proposed scheme, we assume that there are 50 sensor nodes uniformly distributed in an 800 m \(\times\) 800 m square target area. After CHs are designated, data fusion and data compression are performed by CHs. It is assumed that the data types collected and transmitted by the normal sensor nodes are text, records and images, and the amount of data pooled by the CHs is set to be between 10 and 300 packets, with the size of each packet 1024 bits. The AUV starts from the start point \(p_{0}=\) (50, 120) with an initial orientation angle \(\psi_{m,0}=0^{\circ}\) and returns to \(p_{0}\) after collecting data from all the CHs.  
To evaluate the performance of the algorithm, a python 3.8 simulation environment was chosen. The target Q-network and the current Q-network are two-layer fully connected networks with 256 neurons per layer, and we use the ReLU function as the activation function to train both networks using the Adam optimizer. Other simulation parameters and their specific values are provided in Table 1.  
For the sake of performance comparison, the benchmark algorithms are provided as follows.  
* Single Acoustic: The AUV exchanges data utilizing acoustic waves during data collection, and the hovering positions are determined by the UAC radius during the selection process of the steering angle. The AUV trajectories are learned using the DQN algorithm.
* Single Optical: The AUV can exchange data only by selecting optical waves and calculating the AUV hovering locations by means of the UOC radius. The DQN algorithm is used to learn the AUV trajectory.
* Energy Greedy: The AUV performs steering Algorithm 1 and then greedily selects the nodes with the shortest path length in the data collection sequence.
# Convert a Strongly Connected Directed Graph to
## 3 Definitions  
Negation of a set \(H\) is denoted by \(\
eg H\), which means that all elements in \(H\) are negated. Note that \(\
eg\
eg H=H\).  
A literal is normally a Boolean variable--a positive literal; the negation of a Boolean variable is called a negative literal. Examples for literals are: \(a\), \(\
eg a\), \(b\), \(\
eg b\), \(\dots\).  
A clause is a set of literals. A clause set is a set of clauses. A SAT problem is a clause set. An assignment is a set of literals.  
Clauses are interpreted as disjunctions of their literals. Assignments are interpreted as conjunctions of their literals. Clause sets are interpreted as conjunctions of their clauses.  
If a clause or an assignment contains exactly \(k\) literals, then we say it is a \(k\)-clause or a \(k\)-assignment, respectively. A \(k\)-SAT problem is a clause set where its clauses have at most \(k\) literals. A clause from a clause set is a full-length clause if and only if (iff) it contains all variables from the clause set.  
If \(a\) is a literal in clause set \(S\), and \(\
eg a\) is not a literal in \(S\), then we say that \(a\) is a pure literal in \(S\).  
Let \(V\) be the set of variables of a clause set. We say that \(WW\) is the white clause or the white assignment iff \(WW=V\). We say that \(BB\) is the black clause or the black assignment iff \(BB=\
eg V\). For example, if \(V=\{a,b,c\}\), then \(WW=\{a,b,c\}\), and \(BB=\{\
eg a,\
eg b,\
eg c\}\).  
We say that clause \(C\) subsumes clause \(D\) iff \(C\) is a subset of \(D\).  
We say that clause set \(S\) subsumes clause \(C\) iff there is a clause in \(S\) which subsumes \(C\). Formally: \(S\)_subsumes_\(C\iff\exists D(D\in S\wedge D\subseteq C)\).  
We say that assignment \(M\) is a solution for clause set \(S\) iff for all \(C\in S\) we have \(M\cap C\
eq\{\;\}\).  
We say that the clause set \(S\) is a black and white SAT problem iff it has only two solutions, the white assignment (\(WW\)) and the black one (\(BB\)).  
We say that clause sets \(A\) and \(B\) are equivalent iff they have the same set of solutions. We say that clause set \(A\) entails clause set \(B\) iff the set of solutions of \(A\) is a subset of the set of solutions of \(B\); i.e., \(A\) may have no other solutions than \(B\). This notion is denoted by \(A\geq B\). Note that if \(A\)
## 4 Discussion
### Range and Temporal Resolution Analysis  
A fundamental difference between the Colibri micro-lidar and atmospheric elastic lidar systems lies in the achieved range and temporal resolution. Regarding the range resolution of the Colibri micro-lidar, the measurements in Section 3 were not range-averaged and the range resolution remains below 10 cm. No bin averaging technique was necessary and the range resolution is mainly limited by the laser pulse duration, the temporal response of the Si-APD, and the analog-to-digital converter bandwidth. Regarding the temporal resolution, the measurements in Section 3 were temporally-averaged and the temporal resolution remains at 10 cm.  
In addition, the Colibri micro-lidar differs in terms of measurement times. Figure 7 displays single attenuated backscatter profiles at multiple time-resolution (1 ms, 10 ms, 20 ms) to highlight the capabilities of our instrument. While most of the atmospheric lidar systems usually provide time-integrated profiles over more than a few tens of seconds or minutes, the Colibri micro-lidar measures instantaneous aerosol lidar profiles with 1 ms time resolution (reduced to 50 ms after time-averaging to ease the signal processing).  
While most elastic backscatter lidars usually cover a range resolution of tens of meters, the Colibri micro-lidar range and temporal resolution are 10 cm and 1 ms, respectively. These unique features will allow in the future to characterize fast aerosol events with a high temporal resolution with high range-resolution. For instance, problems such as new particle formation or secondary aerosol formation [57] involve rapid changes in aerosol particle size distribution, which are believed to play  
Figure 6: Spatio-temporal (left panel) and 60 s time-averaged (right panel) aerosol number concentration profiles with high range and temporal resolution of fog-oil plumes derived from aerosol backscatter profiles measured by the Colibri micro-lidar.  
important roles for air quality assessment in urban and industrial areas. The study of such processes could greatly benefit from short-range elastic backscatter micro-lidar with high range and temporal resolution.
## 3 Results and Discussion
### Classification Models  
The PCA score images show that there were differences among tea samples from different geographical origins. To further identify the geographical origins of Longjing tea samples, SVM and PLS-DA models were built using the full spectra in the two different spectral ranges. Tables 2 and 3 show the classification results of SVM and PLS-DA models, respectively. As shown in Table 2, close results were obtained by SVM models using full spectra of spectral ranges 1 and 2, and variations could be found in the results of samples from different geographical origins. For the SVM model using full spectra of spectral range 1, the optimal model parameters (C, g) were (256, 1). As shown in Table 1, good classification results were obtained, with a classification accuracy of the calibration set and the prediction set of 94.31% and 88.13%, respectively. For the \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Spectral Range a**} & \multicolumn{8}{c}{**Calibration Set**} & \multicolumn{8}{c}{**Prediction Set**} \\ \cline{2-13}  & \multicolumn{1}{c}{**1 c**} & \multirow{2}{*}{**2**} & \multirow{2}{*}{**3**} & \multirow{2}{*}{**4**} & \multirow{2}{*}{**5**} & \multirow{2}{*}{**6**} & \multicolumn{2}{c}{**Acc. b**} & \multirow{2}{*}{**1**} & \multirow{2}{*}{**2**} & \multirow{2}{*}{**3**} & \multirow{2}{*}{**4**} & \multirow{2}{*}{**5**} & \multirow{2}{*}{**6**} & \multirow{2}{*}{**Acc. (\%)**} \\  & & & & & & & & & & & & & & & & & **(\%)** \\ \hline \multirow{8}{*}{1} & 1 & 764 & 21 & 0 & 8 & 4 & 9 & 94.79 & 240 & 20 & 0 & 3 & 2 & 3 & 89.55 \\  & 2 & 25 & 585 & 1 & 3 & 3 & 3 & 3 & 94.35 & 9 & 147 & 2 & 3 & 0 & 0 & 91.30 \\  & 3 & 1 & 2 & 625 & 6 & 0 & 0 & 98.58 & 2 & 4 & 201 & 2 & 1 & 1 & 95.26 \\  & 4 & 6 & 7 & 1 & 658 & 20 & 3 & 94.68 & 7 & 3 & 0 & 215 & 5 & 1 & 93.07 \\  & 5 & 7 & 3 & 3 & 9 & 804 & 52 & 91.57 & 7 & 3 & 3 & 10 & 242 & 28 & 82.59 \\  & 6 & 9 & 4 & 0 & 4 & 53 & 987 & 93.38 & 4 & 3 & 1 & 1 & 52 & 291 & 82.67 \\  & total & & & & & & 94.31 & & & & & & & & & 88.13 \\ \hline \multirow{8}{*}{2} & 1 & 829 & 19 & 0 & 5 & 2 & 15 & 95.29 & 213 & 55 & 0 & 7 & 2 & 13 & 73.45 \\  & 2 & 17 & 595 & 0 & 13 & 9 & 31 & 89.47 & 4 & 191 & 0 & 8 & 11 & 7 & 86.43 \\  & 3 & 0 & 1 & 580 & 4 & 4 & 0 & 98.47 & 0 & 6 & 164 & 16 & 10 & 0 & 83.67 \\  & 4 & 2 & 16 & 7 & 746 & 4 & 3 & 95.89 & 2 & 8 & 3 & 243 & 3 & 0 & 93.82 \\  & 5 & 1 & 12 & 8 & 15 & 738 & 3 & 94.98 & 3 & 15 & 2 & 5 & 232 & 2 & 89.58 \\  & 6 & 5 & 19 & 0 & 1 & 2 & 1090 & 97.58 & 4 & 9 & 0 & 0 & 2 & 357 & 95.97 \\  & total & & & & & & & 95.45 & & & & & & & & 87.66 \\ \hline \hline \end{tabular}  
* the spectral ranges used for analysis, spectral range 1: 502–947 nm; spectral range 2: 975–1646 nm. b: Acc. represents accuracy. c: 1, 2, 3, 4, 5 and corresponds to Bashan Village, TongWu Village, Meijiawu Village, Tiedian Village, Xinxin Village, and Huijaling Village.  
\end{table} 
Table 2: Confusion matrix of support vector machine (SVM) models using full spectra for spectral range 1 (502–947 nm) and spectral range 2 (975–1646 nm).
# Lunar Satellite Constellations in Frozen Low Orbits
## 6 Conclusions  
The toolkit of techniques and instruments has been presented for the easy and robust design of frozen low lunar orbits and constellations employing such orbits. The Bayesian/Nelder-Mead non-gradient optimization method allows flexibly incorporating different orbit design constraints and at the same time succeeds in satisfying the frozenness condition with high accuracy. The novel compact nomogram, specially developed to visualize the preliminary design of Walker-Mozhaev constellations, has given useful insights regarding geometric configurations capable of providing the lunar surface coverage. The numerical study of different constellation designs has revealed several promising options for the deployment of navigation infrastructure in polar regions of the Moon, which are  
Figure 14: Three-month evolution of the eccentricity vector for a 522 km orbit. Every month is marked by a different color. Two-impulse station-keeping maneuvers are executed on a monthly basis, with the associated eccentricity vector jump being indicated by a red arrow.  
also applicable to the task of global communication. The Lyapunov time estimated for all the considered frozen low orbits shows weak instability, which requires small orbital corrections on a monthly basis.  
Conceptualization, S.T.; methodology, S.T. and M.S.; writing--original draft preparation, S.T.; software, M.S.; investigation, M.S.; writing--review and editing, M.S.; visualization, M.S.; supervision, M.O.; project administration, M.O.; funding acquisition, M.O. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Moscow Center of Fundamental and Applied Mathematics, Agreement with the Ministry of Science and Higher Education of the Russian Federation, No. 075-15-2022-283.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.  
The following abbreviations and acronyms are used in this manuscript:  
\begin{tabular}{l l} BLT & Ballistic lunar transfer \\ CoDe & Constellation Design \\ FSPL & Free-space path loss \\ FTLE & Finite-time Lyapunov exponent \\ LLO & Low lunar orbit \\ MER & Mean-Earth/mean-rotation reference frame \\ PA & Principle axes \\ PDOP & Position dilution of precision \\ RAAN & Right ascension of the ascending node \\ SCRF & Selenocentric celestial reference frame \\ RGT & Repeat ground track orbit \\ UERE & User equivalent range error \\ \end{tabular}