esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization and Water Content Estimation Method of Living Plant Leaves Using Terahertz Waves
## 3 Results and Discussion
### Estimating Leaf Water Content  
In this study, WC in leaves was observed by determining the physical parameters such as weight and thickness. Referring to the weight of leaves, initially on the first day, the time duration between the two weight measurements were maintained from two to three hours. On the second day, this was extended to four hours and finally, on the third and fourth day, it was increased to 6 hours. It was noted that there was significant decrease in the weights of some leaves as shown in Figure 6 on day 1, i.e., basil, baby-leaf and pea-shoot, whereas, other leaves displayed a slow decreasing trend in weight loss of leaves as days progressed.  
This clearly indicated that the moisture in leaves evaporated more rapidly on the day 1 and 2 compared to day 3 and 4, thereby creating more air cavities in the leaves. To assess the variation of leaf WC during the leaf's water evaporation process, the measurements were translated into WC using [17; 32],  
\[WC=\frac{W_{\text{time}}-W_{\text{dry}}}{W_{\text{fresh}}}\times 100 \tag{4}\]  
where \(W_{\text{fresh}}\) is the weight of the fresh leaf, \(W_{\text{time}}\) is the weight of a leaf measured over time and \(W_{\text{dry}}\) is the weight of a dry leaf. In the beginning, the WC loss observed between the two hours on day 1 was found in the range of 5% to 22%. At the end of the investigation on day 4, this loss increased during the natural evaporation of leaf moisture and was established in the range of 83.12% to 99.33%. The obtained percentages loss of WC can be validated with Figure 6a. Considering this discussion, it also showed a significant correlation with the real part of the permittivity which was the highest on  
Figure 5: Correlation of permittivity with loss of WC in leaves.  
Figure 6: Change in the physical properties of leaves with time.  
the first day when the weight of leaves was considerably high compared with the fourth day as shown in Figure 4. The thickness of all the leaves was carefully determined to avoid any excess pressure to the samples that would cause disturbances in the morphological structure of the leaves, changing the dielectric properties of the samples as a result. As seen in Figure 5(b), the thickness of leaves was considerably higher on day 1, implying a greater WC in fresh leaves compared to day 4 when mostly, all leaves were dried out. From this significant and meaningful observation, it was concluded that dehydration of leaves with passing days affected the thicknesses to a substantial degree. On day 4, some leaves stayed invariant or slight changes occurred in the thickness of leaves i.e., coriander and spinach as shown in Figure 5(a). These transformations in the thickness of leaves evidently showed that WC in coriander and spinach leaves had evaporated to the maximum on day 4 and no further variations could be observed in thickness of leaves.
## 2 Access to Android Raw GNSS Measurements and GNSS Observation Generation  
Since releasing the Nougat version of the Android system (Version 7) in 2016, the users have access to the raw GNSS measurements through the new location API consisting of two classes, GNSSMeasurement class and GNSSClock class. However, the users still need to extract the typical GNSS observations, such as pseudorange, carrier-phase and Doppler observations, from the raw data logged in the two classes. A list of raw measurements of Android 7 Location API in GNSSClock and GNSSMeasurement classes can be found in [20; 40]. How to convert the raw measurements logged through the android.location API to the GNSS observations (i.e., pseudorange, carrier-phase and Doppler observations) has been provided in details in the white paper published by the European GNSS Agency's (GSA) [40]. Table 1 gives a list of available GNSS logger Android applications and their output formats.  
A brief explanation about how to generate the pseudorange, carrier-phase and Doppler observations is provided in continue.
# Systematic Review: Malware Detection and Classification in Cybersecurity
## 4 Discussion
### Comprehensive Analysis of Malware Detection Techniques  
The statistical analysis of the reviewed literature underscores the dominance of machine learning and deep learning techniques, which together account for 75% of the studies. These techniques are essential for adapting to new threats and detecting complex behaviors that traditional methods struggle to identify. In Table 5, Distribution of the most researched techniques in malware detection.  
Machine learning approaches, which comprise 45% of the studies, excel in adapting to changing patterns. Techniques like optimizable decision trees, paired with AdaBoost, provide high accuracy (98.84%) in processing large datasets without relying on predefined signatures [83]. Meanwhile, deep learning, particularly CNNs such as EfficientNet, is notable for its ability to handle complex malware threats like zero-day malware, achieving  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Technique** & **Percentage of Studies (\%)** \\ \hline Machine Learning & 45 \\ Deep Learning & 30 \\ Computer Vision & 15 \\ Static Analysis & 5 \\ Dynamic Analysis & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Distribution of the most researched techniques in malware detection.  
an accuracy of 95.7% on Android devices [34]. Although these methods require significant computational resources, their precision in detecting sophisticated threats is unparalleled.  
Computer vision, featured in 15% of studies, represents an emerging approach that transforms malware binaries into images for analysis. This offers a novel way to detect malware that might evade signature-based detection [79]. While still in development, these techniques show promise in identifying hidden threats in seemingly benign data.  
Finally, traditional static and dynamic analysis, though less prevalent (10% each), complement modern techniques by providing insights into malware's internal structure and behavior. Static analysis identifies known signatures, while dynamic analysis detects real-time activities, offering a holistic view of malware behavior [83].
# The Design and FPGA-Based Implementation of a Stream Cipher Based on a Secure Chaotic Generator
## 4 Performance Analysis of the Proposed SCbSC  
In this section, we first give the hardware metrics obtained by the proposed SCbSC system and compare them with those of some published systems. Then, and we assess its security against a known cryptanalytic analysis.
# Simulation Study of Thermal-Mechanical Coupling Fretting Wear of Ti-6 Al-4V Alloy
## 2 Numerical Calculation Method
### Modified Energy Wear Model  
The conventional energy model does not include temperature effects. In order to consider the effect of temperature on wear, a wear coefficient dependent on temperature T is used. The modified wear depth expression is  
\[\mathrm{h(x,t,T)}=\mathrm{K_{E}(T)}\int\limits_{0}^{t}q(x,t)ds(x,t) \tag{5}\]  
where \(\mathrm{K_{E}(T)}\) is the energy wear coefficient dependent on temperature.  
The energy wear coefficient \(\mathrm{K_{E}(T)}\) is an unknown parameter. Based on the equations of the energy wear model and the Archard wear model [9], it can be deduced that the following relationship exists between the wear coefficients corresponding to the two models [14]  
\[\mathrm{K_{A}}=\mathrm{\mu K_{E}} \tag{6}\]  
where \(\mathrm{K_{A}}\) is the wear coefficient of the Archard wear model and \(\mathrm{\mu}\) is the friction coefficient between the two contact surfaces.  
As mentioned earlier, temperature affects the friction coefficient, and lower temperatures lead to larger friction coefficients. According to the results of the fretting wear experiments on Ti-6 Al-4V [20; 21], it is known that the friction coefficient is linearly related to the temperature below 300 \({}^{\circ}\)C, which is expressed as  
\[\mathrm{\mu(T)}=0.8-8.84\times 10^{-4}(T-25) \tag{7}\]  
where \(\mathrm{\mu(T)}\) is the coefficient of friction dependent on temperature.  
Similarly, temperature-dependent wear coefficients for the Archard model can be obtained from the Ti-6 Al-4V wear tests performed by [PERSON] et al. [30]. Below the critical transition temperature (around 350 \({}^{\circ}\)C), the Archard wear coefficient increases linearly with temperature, which is expressed as  
\[\mathrm{K_{A}(T)}=3.06\times 10^{-7}+5\times 10^{-10}(T-25) \tag{8}\]  
where \(\mathrm{K_{A}(T)}\) is the Archard wear coefficient dependent on temperature.  
Combining Equations (6)-(8) to obtain the energy wear coefficient dependent on temperature gives  
\[\mathrm{K_{E}(T)}=\frac{\mathrm{K_{A}(T)}}{\mu(T)}=\frac{3.06\times 10^{-7}+5 \times 10^{-10}(T-25)}{0.8-8.84\times 10^{-4}(T-25)} \tag{9}\]  
In order to write the wear subroutine more conveniently, Equation (9) is fitted using the least squares method, and the fitted expression is  
\[\mathrm{K_{E}(T)}=1.95\times 10^{-12}\mathrm{T^{2}}+8.51\times 10^{-10}\mathrm{ T}+3.62\times 10^{-7} \tag{10}\]  
The fitting effect is shown in Figure 1.
## 5 Results and Analyses
### Experimental Analyses
#### 5.2.4 Comparative Discussion and Complexity Analysis  
The experimental results demonstrate that CLEAR significantly outperforms existing state-of-the-art methods on the CodRED benchmark. When compared to the strongest baseline REIC [11], CLEAR achieves a 3.40% absolute improvement in F1 score and 2.90% in AUC with the RoBERTa backbone. These gains primarily stem from two key innovations: relation-aware context filtering and the Path Distance-Weighted Attention mechanism.  
Compared to ECRIM's entity-centered filtering [8], our relation-aware approach preserves critical contextual cues (e.g., verbs like \"purchased\" for ownership relations) that are often omitted by entity-only methods. This aligns with findings from [PERSON] et al. [12] showing that relation semantics improve evidence selection, but CLEAR extends this  
Figure 3: Attention matrix heatmaps from our model.  
through dynamic relation-candidate weighting. The Path Distance-Weighted Attention mechanism addresses limitations in PILOT's uniform path weighting [12], significantly reducing attention noise, as shown in the attention heatmap (Figure 3).  
Regarding model complexity, CLEAR introduces \(O(N^{2})\) operations for path-pair distance calculation (where N is the number of paths), but this is offset by three efficiency optimizations:  
1. **Input compression:** Relation-aware filtering reduces the average input length from 4900 to 512 tokens (89% reduction), decreasing encoder computation by 7.8x.
2. **Sparse interaction:** The cross-path entity matrix activates only 34% of possible path-pair connections on average.
3. **Parallelization:** Distance calculations are batch-processed on a GPU, adding only 22 ms latency per sample.  
The total inference time averages 1.8 s per sample, remaining practical compared to REIC (1.5 s) and ECRIM (1.2 s) while providing superior accuracy. This complexity-accuracy tradeoff is consistent with [PERSON] et al. [13], but CLEAR achieves a 12% higher F1 score (68.42% vs. 56-60%) at similar computational costs.  
Further comparison with LLM-based approaches reveals CLEAR's efficiency advantage: while GPT-4 achieves a 61.3% F1 score on CodRED in our tests, it requires an estimated 48x more computation (1.8T vs. 355M parameters). Our specialized architecture demonstrates that targeted innovations in cross-document reasoning can outperform general-purpose LLMs while maintaining computational feasibility.
# On Mitigating the Effects of Multipath on GNSS Using Environmental Context Detection
## 2 Performance Evaluation of a Standard Multi-Constellation GNSS Receiver
### Experimental Setup and Data Collection  
The experimental setup and test vehicle used for the performance evaluation are shown in Figure 2. The high-precision antenna (PolaNt Choke Ring B3/E6) was mounted on the roof of the vehicle and connected to a PolaRx55 multi-frequency, multi-constellation GNSS receiver. The PolaRx55 can acquire and track signals from all four constellations, i.e., GPS, BeiDou, Galileo and GLONASS. The field experiment was performed by driving the vehicle along the route shown in Figure 1. The vehicle started and ended its journey at the Sukkur IBA University with a total observation period of 80 min. The GNSS data was logged at a rate of 1 Hz. The technical specifications of the equipment, i.e., the receiver, antenna and supported frequency bands, are given in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site** & **Location** & \
\begin{tabular}{c} **Rout Length** \\ **[km]** \\ \end{tabular} &
\begin{tabular}{c} **Time** \\ **[min]** \\ \end{tabular} & \multicolumn{1}{c}{**Characteristics**} \\ \hline P1 & N-65 Highway & 4.0 & 02–10 & Mostly open-sky views \\ P2 & Lloyd Barrage Sukkur & 1.6 & 14–22 & Partial obstruction \\ P3 & Center of City & 4.0 & 37–62 & Severe obstruction \\ P4 & Shikarpur Road & 5.0 & 65–80 & Mostly open-sky views \\ \hline \hline \end{tabular}
\end{table}
Table 1: The route length, observation time and signal-reception characteristics of the observation windows P1, P2, P3 and P4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment** & **Antenna** & **Receiver** & **Constellations** & \
\begin{tabular}{c} **Experiment** \\ **Duration** \\ \end{tabular} &
\begin{tabular}{c} **Antenna** \\ **Height** \\ \end{tabular} \\ \hline Exp1 & PolaNt Choke & Sepentrio & G+R+E+C 1 & 11-09-2021 & 1675 mm \\ (without CAN) & Ring B3/E6 & PolaRx5S & & 80 (min) & \\ Exp2 & PolaNt Choke & Sepentrio & G+R+E+C & 18-09-2021 & 1675 mm \\ (with CAN) & Ring B3/E6 & PolaRx5S & & 80 (min) & \\ \hline \hline \end{tabular} \({}^{1}\) G = GPS, R = GLONASS, E = Galileo, C = BeiDou.  
\end{table}
Table 2: Equipment, duration, constellations and the antenna height for the two experiments.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Equipment** & **Description/Technical Specification** \\ \hline PolaNt Choke-Ring & Polanet Choke ring is a high-precision geodetic multi-frequency \\ B3/E6 Antenna & multi-constellation antenna with the capability of receiving \\ navigation signals from the entire GNSS Spectrum \\ \hline Sepentrio PolaRx5S & Multi-constellation and Multi-frequency GNSS Receiver \\ Hardware Channels & 544 \\ Sampling Rate & Up to 100 Hz \\ Supported Frequency bands & GPS (L1 CA-1575.42 MHz, L2P-1227.60 MHz, L5-1176.45 MHz), \\ GLONASS (L1 CA-1602 MHz, L2C-1246 MHz, L3 OC-1202 MHz), \\ GAILLEO (E1-1575.4 MHz,E5-1176.46 MHz,AltBoc-1191.79 MHz), \\ BEIDOU (B11-1561.9 MHz, B2I-1207.14 MHz, B3-1267.52 MHz). \\ \hline \hline \end{tabular}
\end{table}
Table 3: Technical specifications of the equipment used.
## 3 Result and Discussion
### Multispectral Data Processing
#### 3.1.1 Verification of Regenerative Areas Using Vegetation Indices  
Image processing was performed using the ArcGIS program. The min, max, and avg by date of observation of the vegetation indices (NDVI, EVI, and SAVI) were obtained. The calculated values are shown in Table 3. On 26 March 2019, the average pixel value before the fire occurred was positive (+), indicating that vegetation was actively growing before the fire occurred. The average pixel value on 20 April 2019, after the fire, was close to zero for most of the vegetation indices, indicating an absence of vegetation. The average pixel value on 8 August 2019, four months after the fire, increased compared to the value immediately after the fire, indicating that vegetation had regenerated during the four months since the fire. Furthermore, the difference between the average pixel value of the pre-fire image and the post-fire image was positive, which confirmed the vegetation's average regeneration in the subtracted image.  
Figure 3: Images derived from the vegetation indices (**a**) right after the fire, (**b**) four months later, and (**c**) the difference (**b**,**a**).
# Viability of ABO Blood Typing with ATR-FTIR Spectroscopy
## 3 Results and Discussion
### Naive Bayesian Classifier
Table 5 shows the classification results both for NBC and a random classification, which are significantly different in every case with \(p<0.002\) according to a Student's \(t\)-test. The A and B samples have a higher success rate with NBC classification than with a random one, although O shows slightly worse results. This may be because the lack of antigens in the surface reduces the spectroscopic characteristics, enabling the differentiation. Furthermore, LDA classification with only three classes provides a better true positive ratio for every class. It is interesting to note that, both with LDA and NBC, the incorporation of the AB samples reduces the true positive ratio of the model.
## 4 Problem Formulation  
The task completion delay of UE \(i\) can be expressed as  
\[\begin{split}& U_{i}=x_{i,0}D_{i,0}+x_{i,S}D_{i,S}+x_{i,\text{B}}D_{ i,\text{B}}\\ &=x_{i,0}\frac{c_{i}a_{i}}{f_{i,0}}+x_{i,S}\bigg{(}\frac{a_{i}}{r _{i,S}}+\frac{2d_{i,S}}{c}+\frac{c_{i}a_{i}}{f_{i,\text{S}}}\bigg{)}+x_{i, \text{B}}\bigg{(}\frac{a_{i}}{r_{i,\text{S}}}+\frac{2d_{i,\text{S}}}{c}+\frac {a_{i}}{r_{\text{S,G}}}+\frac{2d_{\text{S,G}}}{c}+\frac{c_{i}a_{i}}{f_{i,\text{ B}}}\bigg{)}.\end{split} \tag{6}\]  
The completion delay of all UEs' tasks can be expressed as \(U=\sum_{i\in\mathcal{N}}U_{i}\). The optimization objective of this paper is to minimize the completion delay of all UEs' tasks. The optimization variables are the task offloading decisions of UEs, i.e., \(x_{i,0}\), \(x_{i,S}\), \(x_{i,\text{B}}\) and the computing resource allocation of the LEO satellite's MEC server and the BS's MEC server, i.e., \(f_{i,\text{S}}\), \(f_{i,\text{B}}\). The optimization problem \(\mathcal{P}\) is as follows.  
\[\min_{\left\{\begin{subarray}{c}x_{i,0},x_{i,\text{S}},x_{i,\text{B}},\\ f_{i,\text{S}},f_{i,\text{B}}\end{subarray}\right\}}U \tag{7a}\] \[\text{s.t.}\ \sum_{i\in\mathcal{N}}x_{i,\text{S}}f_{i,\text{S}}\leq F_{ \text{S}},\] (7b) \[\sum_{i\in\mathcal{N}}x_{i,\text{B}}f_{i,\text{B}}\leq F_{\text{B}},\] (7c) \[0\leq f_{i,\text{S}}\leq F_{\text{S}},\forall i\in\mathcal{N},\] (7d) \[0\leq f_{i,\text{B}}\leq F_{\text{B}},\forall i\in\mathcal{N},\] (7e) \[x_{i,0}+x_{i,S}+x_{i,\text{B}}=1,\forall i\in\mathcal{N},\] (7f) \[x_{i,0},x_{i,S},x_{i,\text{B}}\in\{0,1\},\forall i\in\mathcal{N}. \tag{7g}\]  
Equation (7a) is the objective function. (7b) represents that computing resources allocated to task \(i\) cannot surpass the resources owned by the LEO satellite's MEC server, i.e., \(F_{\text{S}}\). (7c) shows that computing resources allocated to task \(i\) cannot exceed the resources owned by the BS's MEC server, i.e., \(F_{\text{B}}\). (7d) and (7e) mean that the MEC server's computingresource allocation for task \(i\) is nonnegative. (\(\mathcal{T}\)) denotes that only one strategy is selected to complete task \(i\). (\(\mathcal{T}\)) means \(x_{i,0}\), \(x_{i,S}\), and \(x_{i,B}\) are 0 or 1.
## 3 Results and Discussion
### Sleep Stage Scores  
As shown in Table 3, the confusion matrix is calculated by summing the scored values of all test data. Each row represents the number of samples classified by the expert, and each column represents the number of epochs predicted by the model. The table also shows the accuracy, recall, and F1 score for each category. According to the confusion matrix, the values in the diagonal position are higher than the other values, which proves that the NAMRTNet model is adequate. In addition, the score of F1 can reach 92.0 in stage W, 89.4 in stage N2, and 88.6 in stage N3. Due to the similarity of the signal characteristics of the N2 and REM periods, it may need to be more accurate in the classification process. The REM stage F1 score is 83.5, and these four stages are above 80. We need to care that the  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**K-Folds**} & \multicolumn{3}{c}{**Overall Performance**} & \multicolumn{5}{c}{**Per-Class Performance**} \\  & **Acc** & **MF1** & \(\mathbf{\kappa}\) & **W** & **N1** & **N2** & **N3** & **REM** \\ \hline
10 & 83.7 & 76.4 & 0.77 & 90.7 & 35.8 & 88.9 & 90.4 & 86.8 \\
15 & 84.8 & 77.9 & 0.793 & 90.3 & 39.9 & 88.9 & 86.3 & 83.9 \\
20 & 86.2 & 79.8 & 0.808 & 92.0 & 45.5 & 89.4 & 88.6 & 83.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The 10, 15, and 20-fold cross-validation results.  
Figure 9: (**a**) ResNet + NAM + LSTM (improved ResNet 34) confusion. (**b**) ResNet + NAM + TCN (improved ResNet 34) confusion.  
N1 stage score is relatively low. First of all, from Table 1, it can be seen that the number of N1 stage samples is only 2804, which is far less than other stages. In addition, for N1, as a transition period, there will be many confusing features before and after the transition period, which causes great difficulties for staging.
## 3 Methodology
### Analysis Methods  
#### 3.2.1 Comparing Alarm Usage among Wake-Up Tasks (RQ1)  
To compare the alarm usage among the four wake-up tasks, each user's usage variables were aggregated for each task. To check the existence of statistically significant differences in alarm usage among the wake-up task, we conducted a Kruskal-Wallis \(H\) test, a non-parametric hypothesis test for a multigroup comparison. By considering the large size of each group, we also calculated the effect sizes to evaluate the magnitude of the differences [24]. We considered \(e^{2}\), the effect size for the Kruskal-Wallis \(H\) test. A prior study [25] suggested that \(e^{2}=0.01\) be considered a small effect size, 0.04 represents a moderate effect size, 0.16 a relatively strong effect size, and 0.46 a strong effect size.  
In cases where the comparison test shows a moderate or strong effect size under the prior studies' guideline [24], we conducted the Dunn test as a post-hoc test to identify the detailed relationship between the two tasks and calculated [PERSON]'s \(d\) as the effect size, which represent the magnitude of the mean difference. [PERSON] [26] provided rules of thumb for interpreting \(d\), suggesting that an \(d\) of \(|0.2|\) represents a small size, \(|0.5|\) represents a medium size, and \(|0.8|\) represents a large effect size. We used 0.001 as the level of significance for all the hypothesis tests and conducted a Bonferroni correction method for the post-hoc tests.