esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology Architecture
### Seismic Noise Filtration (SNF)  
The presence of micro-seisms in SP1 constituted a regular data pattern in the seismic instrumentation called stochastic seismic noise that leads to false alarms and poor data integrity. The first step was to skim or extract the seismic noise of the first 500 samples by applying the Savitzky-Golay filter (savgol_filter) in MicroPython as a signal. The savgol_filter (signal, windowsize, order of polynomial) operates at the embedded hardware level. In the Savitzky-Golay filter in MicroPython, the SGBIN data SP1 consists of a time series set of S {x} yj, tj, tj-1} with points (j = 1,..., n), where x is an independent variable at tj, and yj is an observed value at delayed tj-1. They are treated with a set of m-xy convolution coefficients, Ci, according to the expression  
\[y_{t}=\frac{1}{h}\Bigg{(}\sum_{i=-(\frac{m-1}{2})}^{\frac{m-1}{2}}a_{i}x_{t+i} \Bigg{)} \tag{3}\]  
In the triangular moving average (TMA), i.e., the function smoothTriangle (noisy signal, degree of filter) at the PC level, two simple moving averages (SMA) are computed on top of each other in order to gain more weight to nearer (adjacent) points. This means that first our SMAi are computed and then a TMAi for the window size \(n\) is computed as:  
\[\text{TMA}_{i}=(\text{SMA}_{i}+\text{SMA}_{i+1}+\ldots+\text{SMA}_{i+n})/n \tag{4}\]  
The generated filtered time series array (FTSA0) was subtracted from the FTSA of the original signal, i.e., 500 samples from t0 to t499, and difference was saved in the second array called seismic noise array (SNA0).  
\[\text{SNA}=\text{SP1}-\text{FTSA} \tag{5}\]  
Later on, this SNA was sent to SGBIN as a feedback tuning coefficient to be subtracted from the transmitted signal of SGBIN to reduce the level-2 computation complexity.  
\[\text{SP2}=\text{SP1}-\text{SNA} \tag{6}\]  
This technique reduced the filtration cost as well as the expected computation cost of SWEDA in the next phases.  
Figure 3: Demonstration of GMAC Terrain Normalization performed by smart geospatial bi-axial inclinometer nodes (SGBINs) as: (**a**) normal SGBIN clusters placement; (**b**) normalization of SGBINs’ outputs.  
In Figure 4, we have two plots, seismic signal with noise and filtered signal. We averaged all the data samples from two sites and considered only 10,000 samples. For feature extraction, we had to filter meaningless information out of the 10,000 samples. In a noisy signal, the magnitude of the angles \(1.5^{\circ}\) and \(-0.5^{\circ}\) are the values that lead to false alarms and wrong detection of seismic events. The correct values are in the range between \(0.3^{\circ}\) and \(-0.3^{\circ}\), which need to be processed. The kurtosis value and S/L-Kurt algorithm [39] can be used here for microseismic signals.
# Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans
## 3 Datasets
### Ct-Org  
The CT-ORG dataset contains 140 CT scans with 3D annotations for lungs, bones, liver, kidneys, and bladder, with some scans also labeling the brain. It includes diverse imaging conditions and patients with liver lesions and metastatic diseases. Of the scans, 131 are standalone CTs and 9 are PET-CT components. The dataset is divided into 119 training and 21 testing scans. While lungs and bones in the training set were segmented automatically using morphological techniques, all other organs were segmented manually, primarily using ITK-SNAP with semi-automatic active contouring. The dataset also incorporates images from the LiTS challenge, supporting advancements in multi-class organ segmentation and algorithm benchmarking [38].  
**Evaluation Metrics**  
Commonly used metrics in medical image processing are as follows: for segmentation--Dice coefficient, IoU, Hausdorff distance; for classification--accuracy, precision, recall, F1-score; and for detection--sensitivity, specificity, AUROC.  
**Segmentation Metrics**  
* Dice Coefficient: \[\text{Dice Coefficient}=\frac{2\cdot|A\cap B|}{|A|+|B|}\]
* Intersection over Union (IoU): \[\text{IoU}=\frac{|A\cap B|}{|A\cup B|}\]
* Hausdorff Distance: \[d_{H}(A,B)=\text{max}\left\{\sup_{a\in A}\inf_{b\in B}\|a-b\|,\sup_{b\in B}\inf _{a\in A}\|b-a\|\right\}\]  
**Classification Metrics**  
* Accuracy: \[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{FN}+ \text{TN}}\]Precision: \[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\]
* Recall (Sensitivity): \[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\]
* F1-Score: \[\text{F1-Score}=2\cdot\frac{\text{Precision}\cdot\text{Recall}}{\text{Precision}+ \text{Recall}}\] **Detection Metrics**
* Sensitivity: \[\text{Sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}}\]
* Specificity: \[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}}\]
* AUC-ROC: Represents the area under the receiver operating characteristic curve, plotting: \[\text{TPR}=\frac{\text{TP}}{\text{TP}+\text{FN}},\quad\text{TPR}=\frac{\text {FP}}{\text{FP}+\text{TN}}\] \[\text{AUC}=\sum_{i=1}^{n-1}(\text{FPR}_{i+1}-\text{FPR}_{i})\cdot\frac{\text {TPR}_{i+1}+\text{TPR}_{i}}{2}\]  
Incorporating explainable AI (XAI) techniques, such as Grad-CAM [39], SHAP [40], and LIME [41], can enhance the interpretability of deep learning models used for abdominal organ segmentation.
# Trends from 30-Year Observations of Downward Solar Irradiance in Thessaloniki, Greece
## 3 Results
### Long-Term SDR Trends by Season  
Similarly to the long term trends from daily means of SDR discussed above, we have calculated the trend for the three sky conditions and for each season of the year using the corresponding mean monthly anomalies (Figure 5 and Table 2). Table 2 also contains the \(2\sigma\) standard error, the Pearson's correlation coefficient R, and the corresponding \(p\)-value. The winter linear trends generally exhibit the largest R values, ranging between 0.54 and 0.60%/y. For all-sky conditions, the trend in SDR in winter is the largest (0.7%/y), followed by the trend in autumn (0.42%/y, a value close to the long-term trend), both statistically significant at the 95% confidence level. In spring and summer, the trends are much smaller and of lesser statistical significance. These seasonal differences indicate a possible relation of the trends in SDR to trends in clouds during winter and autumn. For clear skies, the trend in winter is 0.4%/y and is associated with the decreasing trend in aerosol optical depth [32]. Moreover, it is almost half of that for all skies, which is another indication of a decreasing trend in cloud optical thickness. In other seasons, the clear-sky trend is very small (below 0.1%/y). Finally, for cloudy skies, the winter trend is the largest (0.76%/y) and greater than for all skies, followed by a much smaller trend in autumn (0.19%/y).  
The trends under clear- and cloudy-sky conditions are in the same direction, and it would be expected that their sum is similar to the all-sky trend. This does not happen, especially for winter, likely due to the way the monthly means for clear and cloudy skies were calculated. Daily means were calculated only when at least 60% of the clear- or cloudy-sky data were available (see Section 2.2).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sky Condition** & **Season** & **Trend** & **Trend S.E.** & **Pearson** & **Trend** \\  & & **[\%/year]** & **(2\(\sigma\))** & **Correl.** & **_p_-**Value** \\ \hline \multirow{4}{*}{All skies} & Winter & 0.70 & 0.43 & 0.54 & 0.003 \\  & Spring & 0.11 & 0.24 & 0.17 & 0.371 \\  & Summer & 0.11 & 0.15 & 0.25 & 0.175 \\  & Autumn & 0.42 & 0.30 & 0.47 & 0.009 \\ \hline \multirow{4}{*}{Clear skies} & Winter & 0.40 & 0.20 & 0.60 & 0.001 \\  & Spring & 0.06 & 0.17 & 0.13 & 0.497 \\ \cline{1-1}  & Summer & \(-\)0.05 & 0.06 & \(-\)0.30 & 0.106 \\ \cline{1-1}  & Autumn & 0.05 & 0.12 & 0.17 & 0.366 \\ \hline \hline \end{tabular}
\end{table}
Table 2: SDR linear trends of monthly anomalies for each season of the year and related statistical parameters.  
Figure 4: Long-term trends of daily SDR as a function of SZA for (**a**) all-sky, (**b**) clear-sky and (**c**) cloudy-sky conditions, separately for morning and afternoon periods. Solid shapes represent statistically significant trends (\(p<0.005\)).
## 3 Threat Detection System for the Security of Critical Infrastructures
### Software Architecture of the Threat Detection Framework
#### 3.3.1 2D Video Tracking and Re-Identification  
The main feature of the 2D Video system is person re-identification (Re-ID), a technology that can match people based on similarity in different cameras. In particular, the technology allows one to automatically transfer person tracking from one camera to another, even if the field of views of the cameras are not overlapping. The method has been recently evolved to work in a privacy-preserving way with anonymized faces [24], and some experiments have shown that the precision and recall values can be higher than 99% [23].  
The Re-ID technology is especially relevant to support fusion of sensors at different locations [4]. In INSTEAD, re-identification supports the C&C in relating the sensor output of a person at one location with the sensor output of the same person at another location.
# Shaping 1,2,4-Triazolium Fluorinated Ionic Liquid Crystals
## 3 Results and Discussion  
### Synthesis of ILCs  
The synthesis of the ILCs followed a previously reported synthetic route in which 1,2,4-oxadiazole (**3**) was obtained from the reaction between amidoxime (**2**), previously formed, and the selected perfluoroacyl chloride (Scheme 2). The obtained oxadiazole precursor then underwent a nucleophilic attack by methylhydrazine and, through the ANRORC rearrangements (Addition of a Nucleophile, Ring-Opening, Ring-Closure), the 1,2,4-triazoleole intermediate (**4**) was formed [27; 28; 29]. The proposed mechanism of this rearrangement comprised an initial attack of the nucleophile at the C(5) position of the 1,2,4-oxadiazole, involving the less-hindered NH\({}_{2}\) end of methylhydrazine. The nucleophile attack caused the ring opening. This was followed by a subsequent intramolecular nucleophilic attack at the (C3) position, leading to triazole formation after coming out of the leaving group (Scheme 3) [30]. Thus obtained, the 1,2,4-triazoleole (**4**) underwent demethylation with BBr\({}_{3}\) to form intermediate (**5**), which was then subjected to alkylation with long chain alkyl bromides or iodides, giving rise to the corresponding dialkyloxy-phenyl-1,2,4-triazoleoles, **Ph-Tri-(n)2**, in a good yield and with a high purity. Finally, the obtainedtriazoles were alkylated with methyl trifluoromethanesulfonate in toluene giving rise to trifluoromethanesulfonate 1,2,4-triazolium salts [**Ph-Tri-(n)\({}_{2}\)**][**OTf**] (Scheme 2). According to previous studies, the methylation for 3,5-disubstituted-1,2,4- triazoles occurred at a sterically less-hindered and the more nucleophilic \(N(4)\) site [23].  
Trifluoromethanesulfonate 1,2,4-triazolium salts showed the characteristic triflate signal at \(-\)79.9 ppm in the \({}^{19}\)F NMR spectrum. All F signals can be distinguished contrary to what observed for the \({}^{19}\)F NMR spectrum of the corresponding neutral precursor, **Ph-Tri-(n)\({}_{2}\)**, in which two fluorine peaks were superimposed at \(-\)122.5 (Figures S6 and S7 of Supplementary Materials).
## 2 Experimental Model Tests
### Isolation Layer  
The isolation layer material is a rubber-sand mixture. Standard quartz sand with a particle size ranging from 0.5 mm to 1.0 mm was adopted. The rubber has a particle size of 420 \(\upmu\)m. According to American Society Testing and Materials (ASTM) specification [48], this material is classified as granulated or particulate rubber. Rubber content (\(RC\)) is defined as the mass ratio of rubber to sand to assess rubber's effect on the rubber-sand isolation layer. According to the method presented by the literature [49; 50], rubber-sand samples with different rubber content were prepared. The material's elastic modulus (\(E\)) and damping ratio (\(D_{r}\)) were calculated by the method of the literature [51]. Figure 4 shows that the rubber-sand mixture samples have a decreasing elastic modulus and a monotony-increasing damping ratio with increasing rubber content. When the rubber content is too low, the damping of the material is reduced, and the effect of energy absorption and shock absorption cannot be achieved; while the rubber content is too high, the stiffness of the material is too low to bear the essential load-bearing capacity. Based on the available experimental studies, a 5% rubber content was proven effective in reducing the strain response of the pipe [36]. Therefore, a rubber-sand mixture with a 5% content of rubber was selected to fabricate the isolation layer in this paper.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Dimensions** & **Similarity Relation** & **Similarity Ratio** \\ \hline Geometry & L & \(C_{L}\) & 1/20 \\ Density & ML\({}^{-3}\) & \(C_{\rho}\) & 1/30 \\ Young’s modulus & ML\({}^{-1}\)T\({}^{-2}\) & \(C_{E}\) & 1/1.25 \\ Strain & - & \(C_{\epsilon}\) & 1 \\ Acceleration & LT\({}^{-2}\) & \(C_{\epsilon}C_{E}/(C_{L}C_{\rho})\) & 1/1.2 \\ Time & T & \(C_{L}/C_{A}{}^{-1/2}\) & 1/4.08 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Similitude relations for the shaking table test.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multicolumn{3}{c}{**Elastic Module/MPa**} & \multicolumn{3}{c}{**Density/g/cm\({}^{3}\)**} \\ \cline{2-6}  & \(C_{E}\) & **Prototype** & **Model** & \(C_{\rho}\) & **Prototype** & **Model** \\ \hline Strongly weathered tracbyte & & 6500 & 208 & & 2.40 & 1.92 \\ Weakly weathered tracbyte & & 6500 & 208 & & 1/1.25 & 2.40 & 1.92 \\ Fault zone & & 2000 & 64 & & 2.20 & 1.76 \\ Tunnel lining & & 30,000 & 1000 & & 2.5 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical properties of the prototype and model.  
A biaxial geogrid mold with a mesh size of 40 mm was used as a skeleton to prefabricate the isolation layer easily. Then, the standard quartz sand and rubber granules with a mass ratio of 190:10, i.e., 5% rubber content, were added into the biaxial geogrid molds. Thus, the rubber-sand isolation layer was prefabricated in the lab. The mechanical properties of the prefabricated isolation layer are listed in Table 3. The prefabricated isolation layer was assembled between the fault and the tunnel and made to fit tightly (Figure 5) for the shaking table test.
# Enhancing the SRAM PUF with an XOR Gate
## 4 Results
### PUF Metrics
#### 4.3.2 Uniqueness  
To assess the uniqueness of the PUF, the \(HD_{inter}\) (see Section 2.2) was calculated using five different configurations of the SRAM XOR PUF. Both filtered and unfiltered CRPs were used to compute the \(HD_{inter}\) for each of the five configurations. The final \(HD_{inter}\) value was obtained by averaging the results. Filtered CRPs were obtained by excluding flaky cells, while unfiltered CRPs included all cells. The testing was conducted at various temperatures, and the results are shown in Table 3.  
These findings demonstrate the impressive uniqueness metrics of the SRAM PUF design and devices. The \(HD_{inter}\) values are consistently close to the ideal value of 50%, indicating high uniqueness. Importantly, these metrics remain consistent and are not significantly affected by temperature variations or filtering.
# Reinforced Concrete Building with IED Detonation: Test and Simulation
## 4 Results and Discussion
### Acceleration Signals
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test** & **Explosive** & **\#Sensor** & **Measured Acceleration (g)** & **Model Acceleration (g)** & **Difference (\%)** \\ \hline \multirow{3}{*}{T0} & \multirow{3}{*}{PG2} & A2 & 44.4 & 26.3 & 40.77 \\  & & A3 & 77.1 & 57.8 & 25.03 \\  & & A4 & 29.6 & 30.2 & \(-\)1.94 \\ \hline \multirow{3}{*}{T1} & \multirow{3}{*}{BP} & A1 & 207.2 & 152.8 & 26.25 \\  & & A3 & 699.3 & 416.5 & 40.44 \\  & & A4 & 69.2 & 79.9 & \(-\)15.49 \\ \hline \multirow{3}{*}{T2} & \multirow{3}{*}{BP} & A1 & 105.2 & 75.4 & 28.30 \\  & & A3 & 430.3 & 403.6 & 6.21 \\  & & A5 & 298.8 & 354.8 & \(-\)18.76 \\ \hline \multirow{3}{*}{T3} & \multirow{3}{*}{ANFO} & A1 & 1048.9 & 852.4 & 18.73 \\ \hline \multirow{3}{*}{T4} & \multirow{3}{*}{ANFO} & A1 & 1113.6 & 928.3 & 16.64 \\  & & A3 & 1048.2 & 809.9 & 22.74 \\  & & A4 & 259.8 & 349.4 & \(-\)34.50 \\  & & A5 & 1646.4 & 1056.0  
Despite the increase in the explosive load, the maximum accelerations recorded on the roof slightly decreased, contrary to the rest of the sensors. This was especially noticeable in the case of test T7, with 7 kg of PG2. This may be a consequence of the decrease in the stiffness of the structure due to the accumulated damage after performing the tests consecutively without intermediate reinforcement or support actions on the structure. The extra stiffness provided by the double reinforcement used in the roof slab may have lost its effect after so many tests, but it was probably the one that prevented an earlier collapse of the structure. To reinforce this idea, in the model (Figure 11), it is easy to observe how stress accumulations occur in the window and in the joints of the roof slab with the walls, and therefore, these are the areas that withstood the most stresses throughout the tests. The sequence of images shows how in two seconds the stresses produced in the structure have already stabilized. It can be seen in Figure 8 how the effect of the detonation is only 4 ms when the sequence of images in Figure 11 is every second. It can also be observed that after the T7 test, the stress state of the structure is considerably higher than before. Figure 11: Effective stress (scale in Pa) of trial T7. The first image with time zero corresponds to the stress state when the T7 test load is detonated in the model. Figure 10: Accelerations from tests T2, T4, T6 and T7, in position A5 (see Figure 1) and their comparison with LS-DYNA.
# Energy-Efficient Internet of Drones Path-Planning Study Using Meta-Heuristic Algorithms
###### Abstract  
The increasing popularity of unmanned aerial vehicles (UAVs), commonly known as drones, in various fields is primarily due to their agility, quick deployment, flexibility, and excellent mobility. Particularly, the Internet of Drones (IoD)--a networked UAV system--has gained broad-spectrum attention for its potential applications. However, threat-prone environments, characterized by obstacles, pose a challenge to the safety of drones. One of the key challenges in IoD formation is path planning, which involves determining optimal paths for all UAVs while avoiding obstacles and other constraints. Limited battery life is another challenge that limits the operation time of UAVs. To address these issues, drones require efficient collision avoidance and energy-efficient strategies for effective path planning. This study focuses on using meta-heuristic algorithms, recognized for their robust global optimization capabilities, to solve the UAV path-planning problem. We model the path-planning problem as an optimization problem that aims to minimize energy consumption while considering the threats posed by obstacles. Through extensive simulations, this research compares the effectiveness of particle swarm optimization (PSO), improved PSO (IPSO), comprehensively improved PSO (CIIPSO), the artificial bee colony (ABC), and the genetic algorithm (GA) in optimizing the IoD's path planning in obstacle-dense environments. Different performance metrics have been considered, such as path optimality, energy consumption, straight line rate (SLR), and relative percentage deviation (RPD). Moreover, a nondeterministic test is applied, and a one-way ANOVA test is obtained to validate the results for different algorithms. Results indicate IPSO's superior performance in terms of IoD formation stability, convergence speed, and path length efficiency, albeit with a longer run time compared to PSO and ABC.  
Internet of Drones; obstacle avoidance; path planning; energy consumption; UAV; convergence speed; PSO; CIPSO; genetic algorithm; ABC +
Footnote †: journal: Applied sciences
## 2 Expressions Correction and Monte Carlo Verification
Lastly, in some applications, it is desirable to know the radial distribution of the transmitted photons. Example applications include the analysis of the angular distribution of the transmitted photons [13] and the analysis of the spatial resolution of the transmittance measurements [14]. Equation (1) from the RWT provides an analytical expression for the radial distribution of the transmitted photons. However, in order to apply Equation (1) in practical systems, it is necessary to understand several aspects of this equation. First, what is the application range of it in terms of the OD? Equation (1) is derived based on the assumption of large OD, and it is desirable to have a quantitative understanding of the valid range of this assumption. Second, how to extend Equation (1) to anisotropic media? Equation (1) is developed for isotropic media (_i.e._, g = 0), however many practical systems involve scatters which are anisotropic. Thus it is desirable to extend Equation (1) to  
Figure 2: Comparison of transmittance predicted by Equation (A4\({}^{\prime}\)) [12] with results of Monte Carlo simulations. anisotropic media. Past work has investigated the use of various scaling laws for such extension [10, 11], and here we examined these scaling laws specifically for the radial distribution. To understand these questions, Monte Carlo simulations were performed to obtain the radial distribution of transmitted photons and representative results were summarized and compared to Equation (1) in Figures 1 and 3. Panel (a) and (b) of Figure 3 show the results obtained for isotropic media, and it can seen that Equation (1) agrees well with the Monte Carlo simulation even for relative small OD (L is defined as OD/\(\sqrt{2}\) in these figures). For the rest of the results, the scaling law \(\Sigma_{s}^{{}^{\prime}}=(1-g)\Sigma_{s}\big{/}\sqrt{1+g}\) was applied. As can be seen from Panel (c) and (d) of Figure 3, at intermediate g, Equation (1) also agrees well with the Monte Carlo simulations even at relatively small OD under this scaling law. However, as shown in Figure 1, the discrepancy increases as g increases and decreases as OD increases. At g = 0.77, acceptable agreement begins at L = 7; and at g = 0.92, it begins at L = 30. These trends were observed in all our comparisons with various gs, and it suggested that the applicable range of the RWT can be quantified by an effective OD defined as OD \(\Sigma_{s}^{{}^{\prime}}\). The agreement between the RWT and Monte Carlo simulation improves with increasing effective OD.