esa-sceva/satcom-chunk-collection huggingface dataset:
# Line Beam Scanning-Based Ultra-Fast THz Imaging Platform
## 2 Platform Design and Construction
Both the source/sensor chip and the elliptical cylindrical reflector with the size of 380 mm \(\times\) 600 mm as shown in Figure 2C were fixed at the bottom of the mechanical system, and the scanning mirror with the size of 403 mm \(\times\) 792 mm combining with the rotation motor (Kollmogen KBMS 43H03, USA, Radford) and the encoder (Heidenhain RCN 2380, Germany, Traunreut) was designed as the scanning section. The encoder chosen had 26-bit circular grating with a precision of \(\pm\)5\"; the rotation motor selected was frameless, which can obtain the rotation range larger than \(\pm\)9\({}^{\circ}\). Considering the weight of the scanning mirror was less than 250 N required by rotation motor, it is proved that the scanning section could realize the line beam scanning with high precision and a wide range. Moreover, with the quantitative analysis on the vibration mode of the whole mechanical system, its inherent frequency is around 47.7 Hz, much higher than that of the scanning mirror as 2.5 Hz; therefore, no resonance occurred during line beam scanning. In addition, the whole THz imaging platform including the source/sensor part and the mechanical part uses the power supply of 220 V AC, and the total power of the whole system is -2500 W.  
Figure 1: The optical system of the line beam scanning-based ultra-fast THz imaging platform. **(A)** Framework of the optical system; **(B)** the source/sensor chip; **(C)** the elliptical cylindrical reflector; **(D)** the calculated intensity distribution at the working plane; **(E,F)** the calculated sectional intensity distributions along the x and y axes. With the platform design and construction in both optical and mechanical systems, the main optimization on the THz imaging platform includes (1) optimizing the optical system composed of the elliptical cylindrical reflector and the scanning mirror to pursue high imaging resolution; (2) lightening the mechanical structure, and (3) precisely controlling the scanning using the rotation motor and the encoder to improve the mechanical system. After the design and construction of both the optical and mechanical systems, the performance of the line beam scanning-based ultra-fast THz imaging platform was tested, and it was then adopted for practical applications for safe screening on the human body, which are both described in the following section.
## 3 Results  
### Isolation and Identification of the PET-Degrading Bacteria S. malondialdehyde PRS8  
Initially, 24 bacterial strains were screened for PET biodegradation from the plastic-contaminated soil. By the end of the experiment, only three bacterial strains, designated PRS3, PRS6, and PRS8, survived and grew very well during the rigorous screening period while utilizing PET as the sole carbon source. The strain PRS8 was selected for further study based on its rapid growth in the presence of PET.  
PRS8 grew as yellowish, pointed, and small colonies on nutrient agar plates. The microscopy was found to be gram-negative. The biochemical characterization yielded positive results for citrate, urease, and catalase. The gene sequencing of 165 rRNA revealed the closest alignment to the phylum Pseudomonas, genus _Stenotrophomonas_. A phylogenetic tree constructed using the neighbor-joining method presented a 99% similarity of PRS8 to _Stenotrophomonas maltophilia_. The sequence was submitted to the NCBI database under the accession number OK510364 (Figure 1).
# Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning
## 5 Evaluation and Results  
In this section, we conduct an in-depth evaluation of our GPS spoofing attack detection system using datasets from the three missions. Initially, we assess the models' performance on each mission independently using the Monte Carlo cross-validation technique, which involves running 50 randomized train-test splits.  
For each case, the dataset is randomly divided into 70% for training and 30% for testing, taking into account variations in dataset size and class distribution. A feature importance analysis is also conducted on each dataset; this step allows identifying the most significant variables and discarding irrelevant features, consequently reducing the training overhead. However, it is important to highlight that stratified sampling is employed during the train-test split, ensuring a more balanced representation of each class in both subsets. Subsequently, all mission records are merged to create a unified dataset, enabling further evaluation of the model's generalization capability. Finally, we compare the performance of our proposed models before and after feature selection.  
The evaluation metrics used to measure the performance of our models are described as follows:  
\[\text{Accuracy} =\frac{\sum_{i=1}^{C}TP_{i}}{\sum_{i=1}^{C}(TP_{i}+FP_{i}+FN_{i})}, \tag{2}\] \[\text{Precision} =\frac{1}{C}\sum_{i=1}^{C}\frac{TP_{i}}{TP_{i}+FP_{i}}\] (3) \[\text{Recall} =\frac{1}{C}\sum_{i=1}^{C}\frac{TP_{i}}{TP_{i}+FN_{i}},\] (4) \[\text{F1-Score} =\frac{1}{C}\sum_{i=1}^{C}\frac{2\cdot\text{Precision}_{i}\cdot \text{Recall}_{i}}{\text{Precision}_{i}+\text{Recall}_{i}}, \tag{5}\]  
where C is the total number of classes. Accuracy measures the proportion of correct predictions made by the model throughout the dataset. Precision measures the correctness of positive predictions. Recall indicates how well the model finds all positive predictions. F1-score measures the harmonic mean of precision and recall. Since our contribution involves classifying four categories of GPS signals, we use macro-averaging to compute the evaluation metrics. This method treats all classes equally, including minorities, which is suitable for class-imbalanced datasets. Therefore, each metric is calculated independently for all classes, and then the results are averaged. Using these metrics is essential to confirm that the models do not overfit and maintain consistent performance across different training and testing sets in multiclass classification tasks.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **Hyperparameters** \\ \hline Decision Tree & criterion: ‘gini’, min\_samples\_split: 2, random\_state: None \\ \hline Random Forest & criterion: ‘gini’, max\_features: ‘sqrt’, min\_samples\_split: 2, n\_estimators: 100, random\_state: None \\ \hline XGBoost & objective: ‘multi-softmax’, num\_class: 4 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Hyperparameter settings of the proposed models.
## 5 Results and Analysis
### Limitations  
This study limited its scope to four question types from the Korean Hanja Proficiency Test that emphasize the evaluation of Sino-Korean words and four-character idioms with broader semantic meanings, rather than focusing on the meanings of individual Hanja characters. Accordingly, the dataset used for the study was curated to align with these four question types, and the experiments were conducted using approximately 200 items per type. This constraint inherently limits the scale and diversity of the dataset.  
In addition, the experiments were conducted exclusively on closed large language models. Future research should consider conducting comparative evaluations using open-source large language models on the same dataset to examine potential differences in performance.
###### Abstract  
A significant challenge of visible-light communication systems (VLC) is to overcome their limited converge area in non-line-of-sight (NLOS) transmission. To tackle this problem, for the first time, a real-time high-speed dual-hop VLC system based on blue micro-light-emitting diodes (micro-LED) is proposed and experimentally demonstrated. Benefiting from the advantage of high electrical-to-optical (E-O) bandwidth of the micro-LED, the frequency-response measurements show that the 3-dB modulation bandwidth of 2 m free-space single-hop link is 880 MHz, and the dual-hop system can reach to 715 MHz over a 4 m communication distance. We then investigated the communication performance of our proposed single-hop and dual-hop systems. The real-time waveforms are analyzed at different positions of the dual-hop link and eye diagrams at the receiving terminal are captured for evaluation. Furthermore, the bit error rate (BER) at the target node is measured. The results demonstrate that a 1.1 Gbps on-off keying (OOK) signal with a BER less than the forward-error-correction (FEC) limit could be achieved over a 4 m NLOS free-space link. This work shows that the proposed dual-hop system based on a micro-LED can meet the requirements for most indoor NLOS-transmission scenarios.  
Keywords:visible-light communication (VLC); micro-LED; dual-hop transmission; non-line-of-sight (NLOS); receive-forward +
Footnote †: journal: _Photonics_
# Automated Methodology for Dependability Evaluation of Wireless Visual Sensor Networks
## 3 Problem Formulation and Background
### Integration of Models
The events at the bottom of the tree are referred as basic events and must be assigned to battery, hardware and link dependability functions (reliability or availability functions, according to the evaluation interest), resulting from the respective CTMCs' evaluation. Figure 9 shows the logical gates configuration for each failure condition in order to represent the events' dependency in an FT structure. Figure 9a indicates that the application fails (the TOP event is assessed equals to _true_) if one or more given combination of essential nodes (\(fc\_Comb_{i}\)) fail. According to Figure 9b, a combination of nodes fails if all paths (\(fc\_Path_{i}\)) that connect the sink node to each node in the combination fail. As presented in Figure 9c, a path fails if any link (\(lk_{i}\)) or device (\(fc\_Dev_{i}\)) in the path from the device to sink node fails. Finally, a device (sensor node) fails if its hardware (_hw_) fails or if its battery discharges (\(bt\)), as shown in Figure 9d. System dependability evaluation is considered to be, in the last stage, a Fault Tree analysis, which requires the evaluation of how the basic events (availability functions) associated with each network elements interact between them. In this context, the next section presents the methodology to automate the modeling process and to integrate the dependability evaluation with the SHARPE tool.
## 2 Background
### SHARAD Data Products
#### 2.2.2 Introduction to SHARAD Data Products  
SHARAD tracks on our target areas are identified (see Table 2) using the interfaces established on Planetary Data System (PDS, [[https://ode.rsl.wustl.edu/mars/indexproductsearch.aspx](https://ode.rsl.wustl.edu/mars/indexproductsearch.aspx)]([https://ode.rsl.wustl.edu/mars/indexproductsearch.aspx](https://ode.rsl.wustl.edu/mars/indexproductsearch.aspx))). The SHARAD tracks are footprints of the signals collected by the sensor. They consist of varying along-track lengths. The data obtained from the PDS comprises of a radargram image file, which is a real-valued image of the radar backscatter power, with the vertical and horizontal axis representing the time delay and along-track samples, respectively. The along-track samples have a spacing of 128 pixels per degree (nearly 460 m) and are associated with latitude-longitude pairs, spacecraft position, planetary radius, and other information contained in a geometry file. The round-trip delay samples in the vertical axis are uniform at 0.0375 microseconds per pixel. The coordinate system is planetocentric with longitude positive toward the east [49]. On-board pre-summing is usually performed with typical pre-summing factors like 4 or 8, to control data volume transmission to Earth [50]. The degree of pre-summing affects only the Doppler frequency bandwidth (in Hz) of the Doppler spectrum. Doppler processing narrows the along-track resolution by filtering out signals that return to the sensor with more than a specified Doppler shift. Echoes from areas perpendicular to the flight path at this band of Doppler components are characterized only by round-trip delay, so reflections from features far from the ground track can appear as apparent subsurface returns and are depicted as clutters [51].  
Since the data processing algorithm is not feasible to implement into in-house S/W, the CO-SHARPS [52] processing boutique is employed for radar signal processing. We describe the CO-SHARPS processing parameters in Section 3.1.
## 1 Introduction
The time difference of arrival (TDOA) method is used to detect the precise position of a PUEA, proposed by [17]. SUs in the CRN cooperate with each other based on TDOA values. [PERSON]. [18] proposed the localization technique to detect the attacker in the CRN. The technique aims to detect the PUEA not only inside but also outside the network with high accuracy. Localization error is reduced by using particle swarm optimization (PSO), novel bat algorithm (NBA), and the modified particle swarm optimization (MPSO) based on the TDOA technique [19]. MPSO has higher performance than the other two. Ref. [20] uses a K nearest neighbor classifier (KNN) algorithm to identify PUEAs and grouped all malicious nodes together. Data encryption is used to maintain network security, but this technique has an overhead of training the classifier, which is time-consuming and costly. Property identification-based detecting with dual threshold value has been proposed by [21] to avoid spectrum degradation. If the sensed signal has greater value than the upper threshold or lesser value than the lower threshold, then it is reflected as a PU signal, but if the signal value lies in between the two thresholds, then that signal is recognized as an attacker. To make detection of signals more accurate, a game model is used along with thresholds. In this approach, it is very critical to carefully choose the two thresholds in order to make the optimal decision. The Extreme Machine Learning (EML) algorithm and Time-Distance with Signal Strength Evaluation (TDSE) algorithm are used by [22] to detect and prevent PUEAs. TDSE is used to detect the malicious node in the CRN, whereas EML decides whether the detected node is an attacker or not. The detecting ability of the proposed algorithms is high, and it reduces the total delay in the network. This proposed approach, however, has a high complexity rate in mobile networks. Our main contributions in this work are mentioned below:An efficient method is proposed to diminish the PUEA efficiently. A TDOA-based localization approach is used along with the DE algorithm. Intervention during the spectrum sensing process is reduced, which increases bandwidth usage. The fitness function is minimized to correctly estimate the attacker's position. Correct detection probability is increased. False detection probability is decreased. The outline of the paper is given below. Section 2 provides materials and methods. Section 3 includes the results. Section 4 shows the discussion of the results. Section 5 concludes this research work.
## 3 Evidential Reasoning Algorithm
### Input Data Analysis
#### 3.4.2 Exploitation Time as Input Data  
When evaluating a system component based on its age, it can start from the reliability function over time \(R(t)\). To determine the value of this function, it is necessary to know the fault statistics of such a or a similar component. A characteristic parameter is the intensity of the fault \(\lambda(t)\), i.e., the probability that the component will malfunction at a given point in time \(t\). The reliability function can be described by an exponential function of time and fault intensity.  
\[R(t)=\mathrm{e}^{-\int_{0}^{t}\lambda\mathrm{dt}} \tag{33}\]  
In practice, the intensity of the fault is constant during most of the exploitation time of the component and is often expressed as the mean time to failure (MTTF):  
\[\mathrm{MTTF}=\frac{1}{\lambda} \tag{34}\]  
If \(\lambda\) is constant, the reliability function simplifies to  
\[R(t)=\mathrm{e}^{-\lambda\mathrm{t}} \tag{35}\]  
The reliability interval \(R(t)\in[0,1]\) can be split into \(N\) intervals which can be assigned qualitative ratings \(H_{n}\), \(n=1,\,\ldots,\,N\).  
The mean value of each class \(R_{n}\) is transcribed to the time axis:  
\[R_{n}=\mathrm{e}^{-\lambda\mathrm{t}_{n}} \tag{36}\]  
\[t_{n}=-\frac{1}{\lambda}\ln R_{n} \tag{37}\]For a known component age \(t\), qualitative grade \(H_{n}\) is added. The confidence rating is higher as time \(t\) approaches the middle of interval \(t_{n}\). Consequently, each interval \(n=1\),..., \(N\) is associated with a normal distribution with expectancy \(\mu_{n}=t_{n}\):  
\[\text{b}_{n}=\frac{1}{\sigma_{n}\sqrt{2\pi}}e^{-\frac{(t-t_{n})^{2}}{2\sigma_{n }^{2}}} \tag{38}\]  
Standard deviation \(\sigma_{n}\) is chosen so that the following conditions are met:  
\[b_{n}(t_{n})=\text{R}_{n} \tag{39}\]  
\[\sigma_{n}=\frac{1}{R_{n}\sqrt{2\pi}} \tag{40}\]  
As with the previous method, the overall confidence rating is normalized according to the equation:  
\[\beta_{n}=\frac{b_{n}}{\sum_{n=1}^{N}b_{n}}\cdot(1-\beta_{H}) \tag{41}\]  
where \(\beta_{H}\) represents the measurement ambiguity, which can arise as a consequence of disregarding certain factors such as specific component age or the exact amount of malfunction occurrence on such devices.
## 4 Numerical Results
### Impact of the \(q_{l}\) on \(S_{A}\)*, \(S_{Ar}\)*, \(p\)*, and F(T)  
In this subsection, the we investigate the impact of the limited quantity of carbon emission, \(q_{l}\), on the optimal stock capacity for new products, \(S_{A}\)*, reconditioned products, \(S_{Ar}\)*, returned products percentage, \(p\), and the total profit, \(F(T)\). Thus, we vary the value of \(q_{l}\), and by using the optimization  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(W\) & **Lower** & **Upper** & **_Standard Deviation_** & \(S_{A}\)* & \(S_{Ar}\)* & \(p\)* & **F(T)** \\ \hline
**50** & **25** & **75** & **25** & **1029** & **411** & **50\%** & **6.0735 \(\times\) 10\({}^{10}\)** \\
100 & 50 & 150 & 50 & 1241 & 617 & 50\% & \(6.07324\times 10^{10}\) \\
500 & 250 & 750 & 250 & 1641 & 747 & 50\% & \(5.11676\times 10^{10}\) \\
750 & 375 & 1025 & 375 & 2394 & 783 & 50\% & \(4.57892\times 10^{10}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Impact of length of the mandatory carbon period \(W\) on \(S_{A}\)*, \(S_{Ar}\)*, \(p\)*, and \(F(T)\).  
algorithm, we determine the corresponding \(S_{A}\)*, \(S_{Ar}\)*, \(p\)*, and \(F(T)\). The simulations' results are illustrated in Table 4. In order to improve the visualization of the obtained results in the analyzed part, we add the Figure 7 to the presented Table.  
As it observed, when the limited quantity of carbon increases, the profit slightly increases, but both \(S_{A}\)* and \(S_{Ar}\)* decrease (see Figure 7). In fact, when the limited quantity of carbon emission is high, the production either for the new or reconditioned products are less limited, and then both types of products are more available, thus, both demands \(d_{A}(t)\) and \(d_{Ar}(t)\) are better satisfied. Moreover, when both types of products are available, the stocking decreases as the demands are satisfied. However, when the limited quantity of carbon emission is low, the production either for the new or reconditioned products are very limited. Thus, both types of products are less available and then it shall increase the storing to satisfy the demands. This study allows a firm manager to propose optimal decisions on the manufacturing, reconditioning, stocking, and quantity of returned worn products when the government imposes a limited quantity of carbon emission.
## 3 Experiments and Results
### Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Methods** & **MAE** & **MSE** & \(R^{2}\) & **MAER** & **MSER** \\ \hline \multirow{4}{*}{Scenario 1} & LR & 55.32 & 6352.48 & \(-\)0.45 & 1.28 & 3.38 \\  & DT & 53.56 & 12,792.09 & \(-\)1.92 & 1.24 & 6.80 \\ \cline{1-1}  & RF & **36.11** & **2335.73** & **0.47** & **0.84** & **1.24** \\ \cline{1-1}  & SVR & 38.82 & 5141.12 & \(-\)0.17 & 0.90 & 2.73 \\ \cline{1-1}  & MLP & 51.73 & 7995.99 & \(-\)0.83 & 1.19 & 4.25 \\ \hline \end{tabular}
\end{table} 
Table 5: Performance of the evaluated ML regression methods in the estimation of the number of locusts (Scenarios 1, 2 and 3). Figure 3 shows the graphical performance of the best ML approach (RF) in all the scenarios considered. It is significant that the algorithm is able to accurately locate the greatest peaks in locust presence. There are some issues due to false positives, mainly in Scenario 3, as can be seen in the figure. In Scenario 1, there is a main peak in presence, which the RF algorithm is able to locate. In Scenario 2, there are many more peaks over time, which are also located by the algorithm in an efficient way. Scenario 3 is formed by fewer samples, with important peaks, which are located, though false positives appear, as mentioned before. The scatter plot graphs reveal that the RF algorithm works well in Scenario 1. In Scenarios 2 and 3, its performance is poorer, and false-positive results can be seen specifically in Scenario 3, probably due to the small number of samples in this scenario. These results corroborate the idea that the regression problem is much more difficult than the classification task, where the lack of data has a major effect on the performance of the regressors considered. In spite of this, we have shown that it is possible to obtain a reasonable prediction of the number of locust individuals at each sighting location with ML techniques. Figure 3: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Methods** & **MAE** & **MSE** & \(\mathbf{R^{2}}\) & **MAER** & **MSER** \\ \hline \multirow{4}{*}{Scenario 2} & LR & 157.70 & 56,348.88 & 0.45 & 0.73 & 1.20 \\  & DT & 185.55 & 78,636.24 & 0.23 & 0.86 & 1.67 \\  & RF & **157.59** & **54,067.41** & **0.47** & **0.73** & **1.15** \\  & SVR & 189.52 & 117,055.81 & \(-\)0.15 & 0.87 & 2.49 \\  & MLP & 170.91 & 69,203.28 & 0.32 & 0.79 & 1.47 \\ \hline \multirow{4}{*}{Scenario 3} & LR & 143.24 & 41,401.31 & \(-\)0.09 & 1.78 & 6.38 \\  & DT & **89.89** & 29,965.90 & 0.21 & **1.12** & 4.62 \\ \cline{1-1}  & RF & 97.58 & **22,440.73** & **0.41** & 1.21 & **3.46** \\ \cline{1-1}  & SVR & 94.54 & 44,932.54 & \(-\)0.18 & 1.17 & 6.93 \\ \cline{1-1}  & MLP & 101.34 & 32,342.32 & 0.15 & 1.26 & 4.99 \\ \hline \end{tabular}
\end{table} 
Table 5: _Cont._