esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
The peak current is around 22 A, and the SC module is charged from 12.3 V to 12.9 V. The lower value of the SC module voltage at the end of the second cycle reflects losses in the system, mainly in the bidirectional DC/DC converter. To properly test the SC module, it was constantly charged and discharged with a current of 30 A to monitor the cell balance. Cell balance is the most important factor in series-connected cells because if the cell voltage drifts during cycles, the weaker cells can be damaged by the overvoltage condition. The test was performed for 150 s (10 periods of operation), during which time, cell voltages and temperature were monitored. The pack voltage and current can be seen in Figure 16.  
Figure 14: Unregulated bus/accumulator current limited to 0 A.  
Figure 15: SC module voltage and current, where accumulator current was set to be 0 A in both ways. The SC module was constantly charged and discharged from around 1.5 V to 13 V, with a current of 30 A. Individual cell voltages can be seen in Figure 17.  
The I\({}_{\text{chrg}}\) = 5 A is relevant to the one-cell series group. Because the pack has six series groups, the total charging current of the pack is 30 A, as shown in Figure 16. As can be seen, the cell balancing is excellent, and individual cell voltages are not drifting from each other. This proves a good match for the supercapacitors. Finally, the temperature of the module was monitored. The following Figure 18 shows the temperature before the cycle measurement. Figure 16: Periodical charging and discharging of SC module. Figure 11:0 Individual cell voltages during periodical testing. The average temperature is around 25 \({}^{\circ}\)C before the test. After the test, the maximum temperature reached 47 \({}^{\circ}\)C, which is within the tolerance provided by the manufacturer; see Figure 11:1 This temperature rise is mainly caused by the internal ESR of the supercapacitors and the fact that a current of around 30 A was flowing through the module. The average power loss in the SC module was around 11 W. This experiment was designed to measure the absolute limit of the developed modules. In practical application, the modules will not be cycled so often.
## 2 Materials and Methods
### The Multi-Body Model
#### 2.1.1 Definition of Shank and Foot Segments  
The model consists of two rigid links, that is, the shank and the foot, whose dimensions were selected from average data. Although the geometry does not actually contribute to the calculations in the model, due to the rigid-body approximation, a prismatic solid body was associated with each segment, with the dimensions presented in Table 1. The center of mass of each link was coincident with the centroid of each prismatic solid body. The geometry was necessary to help in the selection of four points of interest for each segment that corresponds to four reflective markers located on the skin of a subject. The location of the markers was defined arbitrarily on the frontal surface of the shank and on the top surface of the foot, as shown in Figure 1a. Those positions defined a default configuration of each marker set (or cluster). In this configuration, the longitudinal distance between proximal and distal markers was set equal to half the length of the corresponding segment, whereas the lateral distance between each couple of lateral and medial markers was set equal to the total width of the corresponding segment. Medial and lateral positions refer to a right lower limb. The markers' positioning followed a similar configuration used by the authors in a previous study, in which rigid plastic shells were used to limit rigid and non-rigid cluster deformation during in vivo analyses [19].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Dimension** & **Value (cm)** \\ \hline Shank length & 40 \\ Foot length & 24 \\ Shank width 1  & 10 \\ Foot width & 9 \\ Foot thickness & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Shank and foot segment dimensions.  
Among each marker cluster, one of the markers was placed out of the plane including the other three markers to account for any variability in marker positioning that normally occurs during experimentation. Then, the scattering of the four markers belonging to each cluster was governed by a multiplicative factor (\(k_{\rm S}\) for the shank, \(k_{\rm F}\) for the foot) that was used to change the distance between each marker and the corresponding cluster centroid as compared to the default configuration (\(k_{\rm S}=k_{\rm F}=1\)). These factors (or gains) respectively affected the location of each marker in the frontal plane of the shank or on the top surface of the foot.  
Figure 1: The methodology for the generation of noisy marker trajectories: (**a**) two views of the multi-body model, with markers on the shank (red) and foot (blue) highlighted, as well as the joint reference frame at the ankle (cyan); (**b**) the angular motion of the knee and ankle joints; (**c**) the linear motion of the ankle AoR; (**d**) downsampled and noisy marker trajectories in the global coordinate system. The plots refer to a generic tri-planar (6 DoFs) motion configuration.
## 4 Results and Discussion
Figure 11: Voltage response of different sensors located at distances of 0–200 cm from infrared power source. Note that the cutoff frequency is equal to 400 Hz in every case. Figure 10: Voltage responsivity of sensors with substrate thicknesses of 1, 100, 200, 300, 400 and 500 \(\upmu\)m and cutoff frequencies in the range of 0–3500 Hz. Figure 12 shows the experimental results obtained for the output voltages of the various sensors at a cutoff frequency of 400 Hz and the simulation results obtained for the temperature variation within the corresponding sensing layers. In general, a good agreement is observed between the two sets of results, thereby confirming the validity of Equation (6) in Section 2.
## 2 Materials and Methods
### Remotely Sensed Data Collection and Pre-Processing
We also calculated the normalized difference vegetation index (NDVI) [29] for the UAV orthomosaic and the PS and S2 images. The NDVI was chosen in this study due to being the most widely used spectral vegetation index for vegetation monitoring applications in precision agriculture [7; 30] and its long history, simplicity, and dependence on readily available spectral bands (R and NIR) [31]. Figure 2: Spectral (bandwidth and central wavelength) characteristics of UAV orthomosaic, PlanetScope, and Sentinel-2A imagery. A flowchart, Figure 3, illustrates the main steps of our proposed spatial imputation methodology for filling in the missing parts in UAV orthomosaics using the RF algorithm and ancillary data derived from satellite image.
# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 3 ITS Sensing
### Aerial Sensing for ITS  
Aerial sensing using drones, i.e., unmanned aerial vehicles (UAVs), has been performed in the military for years and recently has become increasingly explored in civil applications, such as agriculture, transportation, good delivery, and security. Automation and smartness of surface traffic cannot be fulfilled with ground transportation itself. UAV extends the functionality of existing ground transportation systems with its high mobility, top-view perspective, wide view range, and autonomous operation [181]. UAV's role is envisaged in many ITS scenarios, such as flying accident report agents [182], traffic enforcement [183], traffic monitoring [184], and vehicle navigation [185]. While there are regulations to be completed and practical challenges to be addressed, such as safety concerns, privacy issues, and short battery life problem, UAV's applications in ITS is envisioned to be one step forward towards transportation network automation [181]. On the road user side, UAV extends the functionality of ground transportation systems by detecting vehicles, pedestrians, and cyclists from the top view, which has a wider view range and better view angle (no occlusion) than surveillance cameras and onboard cameras. UAV also detects road users' interactions and traffic theory-based parameters, thereby supporting applications in traffic management and user experience improvement.
## 4 Finite Element Analysis
### Frequency Analysis  
This section is devoted to the numerical analysis of the example that was analytically solved in Section 3.2. The problem was resolved using a Frequency step to compute the natural frequencies following _Lanczos eigensolver_[30]. The FEM analysis was carried out excluding the acoustic structural coupling due to the problem conditions. The eigenvectors were normalized by the displacement. The displacement normalization is a technique to represent mode shapes in a modal analysis where the peak amplitude is normalized to a value of 1. This is a common method to signify mode shapes not only using commercial tools but also for general purposes while representing an analytical solution. The eigenvector normalization allows scaling up or down the mode shape amplitude and hence does not affect frequencies.  
Regarding the second harmonic mode, it reflects a rotation about the \(x\)-axis according to the coordinate system presented in Figure 8. The suspension system is established by four springs, which vertically vibrate with a 180-degree phase shift. It must be mentioned that the third harmonic mode accounted for a higher frequency due to a rotation that occurred along the diagonal of the corners. As a conclusion, it is possible to state that the natural frequencies are independent of the displacement imposed.  
Figure 10 shows the magnitude displacement profile obtained on the second and third natural frequencies if the imposed displacement is \(10\;(\mathrm{mm})\) on Node 1. As shown in Figure 10a, the suspension compresses and extends vertically, while the third frequency experienced a rotation along the diagonal direction as depicted in Figure 10b. This behaviour should be similar for different displacement conditions. Therefore, it is feasible to predict the shape of the structural response of the model if subjected to such displacement imposition.
# A Survey on Penetration Path Planning in Automated Penetration Testing
###### Abstract  
Penetration Testing (PT) is an effective proactive security technique that simulates hacker attacks to identify vulnerabilities in networks or systems. However, traditional PT relies on specialized experience and costs extraordinary time and effort. With the advancement of artificial intelligence technologies, automated PT has emerged as a promising solution, attracting attention from researchers increasingly. In automated PT, penetration path planning is a core task that involves selecting the optimal attack paths to maximize the overall efficiency and success rate of the testing process. Recent years have seen significant progress in the field of penetration path planning, with diverse methods being proposed. This survey aims to comprehensively examine and summarize the research findings in this domain. Our work first outlines the background and challenges of penetration path planning and establishes the framework for research methods. It then provides a detailed analysis of existing studies from three key aspects: penetration path planning models, penetration path planning methods, and simulation environments. Finally, this survey offers insights into the future development trends of penetration path planning in PT. This paper aims to provide comprehensive references for academia and industry, promoting further research and application of automated PT path planning methods.  
automated penetration testing; penetration path planning; planning models; planning methods; cybersecurity threats +
Footnote †: journal: Applied sciences
## 7 Conclusions and Outlook  
This study analyzes the performance of soil-bentonite vertical cut-off walls used as underground pollutant barriers. Key conclusions and recommendations are as follows:1. Construction methods and applicable conditions for soil-bentonite vertical cut-off walls are summarized, with bentonite-based materials being most common. New materials could enhance research on heavy metal-contaminated site remediation.
2. Current research on single metal pollution's impact on chemical compatibility is no longer representative. More attention should be given to composite pollution and long-term exposure effects.
3. Research on vertical cut-off wall materials in heavy metal pollution and dry-wet cycle environments is insufficient. Modified materials show potential but need more field verification. Environmental factors can affect material structure and impermeability, so further research is needed for long-term effectiveness.
4. Longevity evaluation of vertical cut-off walls typically relies on contaminant transport calculations. However, real-world conditions are more complex, requiring in-depth studies on wall performance. In situ testing is essential but lacks long-term data. Future research should focus on long-term in situ testing to better inform design and construction.
5. Future technological advances to improve soil bentonite vertical containment walls were outlined, such as smart monitoring systems, contamination-resistant materials and VR/AR assessment tools.  
The failure of vertical cut-off walls has significant environmental impacts, making the identification of the most effective life evaluation program crucial for pollutant containment.  
Conceptualization, Y.Z.; methodology, K.W.; software, K.W.; validation, K.W. and Y.Z.; formal analysis, K.W.; investigation, K.W.; resources, Y.Z.; data curation, K.W.; writing--original draft preparation, K.W.; writing--review and editing, Y.Z.; visualization, K.W.; supervision, Y.Z.; project administration, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Natural Science Foundation of China: 42207172; University Natural Science Research Project of Anhui Province: 2023 AH030037.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Thanks to [PERSON] and [PERSON] for their support in providing the experimental platform for the completion of this paper.  
The authors declare no conflicts of interest.
## 3 Model Description and Formulation
### A Mathematical Formulation for the New Truck Appointment System  
We use the following notations to formulate a mathematical model:  
**Indices and parameters**  
\(i\): Index for a task type, \(i\in\{out(bound),in(bound)\}\)  
\(j\): Index for a yard block, \(j\ =\ 1,2,\cdots,J\)  
\(k\): Index for a time window, \(k\ =\ 1,2,\cdots,K\)  
\(n\): Index for a transport company (forwarder), \(n\ =\ 1,2,\cdots,N\)  
\(r^{n}\): The rehandling rate required during inbound operations by the transport company (forwarder) \(n,0\ \leq\ r^{n}\ \leq\ 1\)  
\(t\): The tier where an inbound container is stored  
\(T\): Maximum number of storage tiers of a yard block  
\(w_{ij}^{n}\): The number of tasks for the transport company (forwarder) \(n\) of task type \(i\) assigned to the yard block \(j\)  
\(v_{k}^{n}\): The number of trucks from the transport company (forwarder) \(n\) available in the time window \(k\)  
\(B_{jk}\): Maximum number of tasks for a yard crane (TC) in the yard block \(j\) and the time window \(k\)  
\(s_{ijk}\): The number of loading/discharging operations of a container of task type \(i\) assigned to the yard block \(j\) and the time window \(k\)  
\(g_{k}\): Maximum number of trucks that can pass through the gate in time window \(k\)  
\(\alpha\): Transport company's weight  
\(\beta\): Terminal operator's weight  
**Decision variables**  
\(X_{ijk}^{n}\): The number of trucks of task type \(i\) assigned by the transport company (forwarder) \(n\) to the yard block \(j\) and the time window \(k\) (from the perspective of the terminal operator we apply this variable as a parameter.)  
\(Y_{jk}\): The number of tasks a truck can reserve allocated by the terminal operator for the yard block \(j\) and the time window \(k\) (from the perspective of the transport company (forwarder), we apply this variable as a parameter).
## 2 Reduced Concept Lattice
### Attribute Reduction Based on the Concept Lattice  
Suppose there is a decision table [35; 36]: \(S=(E,C\cup D,\{\mathrm{V}_{a}|a\in C\cup D\},\{\mathrm{F}_{a}|a\in C\cup D\})\), in which \(E\) is the set of objects, \(C\) is the set of condition attributes, \(C=\{C_{1},C_{2},\cdots,C_{l}\}\), \(D\) is the set of decision attributes, \(V_{a}\) is the range of attribute \(a\), and \(F_{a}\) is the mapping from object \(E\) to domain \(V_{a}\). In addition, when multiple decision attributes are contained in \(D\), we can convert it to its equivalent form so that a decision attribute includes multiple threshold values. To simplify the purpose, we assume that \(D=\{d\}\).  
Let \(G=\underset{a\in\mathbb{C}\cup D}{\cup}V_{a}\), \(I=\left\{(e,e(a))|e\in E,a\in C\cup\left\{d\right\}\right\}\), then \((E,G,I)\) is the formal context corresponding to the decision table. To facilitate the narrative, element \(g\) of \(\underset{a\in\mathbb{C}\cup D}{\cup}V_{a}\) denotes the attribute of the formal context \((E,G,I)\), attribute for short; element \(g\) of \(\underset{a\in\mathbb{C}}{\cup}V_{a}\) denotes the condition attribute, abbreviating set \(\underset{a\in\mathbb{C}}{\cup}V_{a}\) to \(V_{C}\); and element \(g\) of \(\underset{a\in D}{\cup}V_{a}\) is denoted the decision attribute, abbreviating set \(\underset{a\in D}{\cup}V_{a}\) to \(V_{D}\). If two object concepts \((A_{1},B_{1})\) and \((A_{2},B_{2})\) share the same parent concept \((A,B)\), and the connotation of the parent concept \((A,B)\) contains no decision attributes, however it satisfies  
\[V_{D}\cap B_{1}\
eq\phi\lor V_{D}\cap B_{2}\
eq\phi \tag{2}\]  
then we say that the public parent concept \((A,B)\) is the discriminable concept of concept \((A_{1},B_{1})\) and \((A_{2},B_{2})\). For concept \((A,B)\), if the condition attribute \(C_{l}\) of the original decision table satisfies \(V_{C_{l}}\cap B_{2}=\phi\), then the set of all condition attributes \(C_{l}\) that satisfy this condition is called the loss attribute of concept \((A,B)\) with respect to the original decision table.  
The attribute reduction method based on the concept lattice aims to construct a complete concept lattice, find the discriminable concepts and loss attributes within the lattice, and delete the set containing loss attributes from the power set of condition attributes, finally obtaining the reducible attribute set. The attribute set contains all combinations of condition attribute types.
###### Abstract  
This paper presents the results of a test cycle of two types of silty sands (_siSa_) with different contents of fine fractions. Fine fractions are understood as soil grains with a grain diameter of less than 63 um (as the sum of silt and clay fractions). The soils tested had a content of fine fractions of \(f_{Si+C,1,1}=15.14\%\) and \(f_{Si+C,2}=20.48\%\), respectively, before the study. Changes in the content of these fractions after the experiments were analyzed. These experiments consisted of dynamic bar projectile impact loading, and a split Hopkinson pressure bar (SHPB) test stand was used in the study. Changes in the granulometric composition of the silty sands studied were carried out in a laser particle size analyzer, allowing measurement of fractional content in the grain size range from 0.01 um to 3500 um. As a result, a summary of changes in soil grain size curves in the range of fine fractions was compiled. Repeated trends were observed in the changes in the granulometric composition of the soil samples as a function of the moisture content of the soil sample (\(w_{1}=0\%\), \(w_{2}=5\%\), \(w_{3}=10\%\), and \(w_{4}=15\%\)) and the impact velocity of the loading bar projectile for SHPB pneumatic launcher pressures (\(p_{1}=1.2\) bar \(\to\)\(v_{1}=12.76\) m/s, \(p_{2}=1.8\) bar \(\to\)\(v_{2}=17.69\) m/s and \(p_{3}=2.4\) bar \(\to\)\(v_{3}=21.32\) m/s). The influence of the initial moisture content of the investigated soil on the value of the optimum moisture content obtained during its dynamic compaction was discussed. The trend in the behavior of the change in the granulometric composition of the tested samples was determined, taking the value of the initial moisture content of the soil in relation to the optimum moisture content of the reference sample as a reference. The largest percentage change in granulometric composition through an increase in the value of the silt and clay fraction relative to the reference sample \(f_{Si+C}\) for both types of silty sand tested occurs for the same moisture content variant \(w_{2}=5\%\)-for soil \(f_{Si+C,1}=15.14\%\) there is an increase in the fine fraction of 11.08% and for soil/\(f_{Si+C,2}=20.48\%\) there is an increase in the fine fraction of 15.17%. In general, it can be seen that more silty soil is more strongly susceptible to the phenomenon of grain crushing for moisture content \(w_{1}=0\%\) and \(w_{2}=5\%\) less than its optimum moisture content \(w_{opt,1}=8.70\%\). In contrast, less silty soil is more susceptible to the phenomenon of grain crushing for moisture contents \(w_{3}=10\%\) and \(w_{4}=15\%\) greater than its optimum moisture content \(w_{opt,2}=9.20\%\). The presented dynamic physical phenomenon of soil behavior is crucial during explosive and impact impacts on structures made of soil, e.g., as ground protection layers.  
Keywords:granulometric composition; grain crushing; laser particle size analyzer; silty sand; soil; high strain rate; split Hopkinson pressure bar +
Footnote †: journal: Applied Sciences
###### Abstract  
Fengyun-3F (FY-3F) satellite was launched in 2023 with a MicroWave Temperature Sounder (MWTS) and a MicroWave Humidity Sounder (MWHS) onboard. This study evaluates the in-orbit performances of these two instruments and compares them with similar instruments onboard FY-3E and NOAA-20 satellites. It is found that the polarization of FY-3F MWHS at channel 1 is different from FY-3E from the quasi-horizontal to quasi-vertical, whereas the rest of the channels are revised to quasi-horizontal polarization. FY-3F MWTS performance at the upper air channels is, in general, better than FY-3E MWTS, with 0.3 K smaller in biases (O-B) and 0.13 K lower in standard deviation. The striping noise between FY-3E and 3F MWHS is similar in magnitude for most of the channels. The FY-3F can form a satellite constellation with the FY-3E and NOAA-20, enabling better monitoring of many weather events, such as typhoons and hurricanes, through the use of all three satellites. Using the Global-Scene Dependent Atmospheric Retrieval Testbed (GSDART), Typhoon Yagi warm cores are retrieved from both MWTS/MWHS and ATMS. It is shown the warm core structures of Typhoon Yagi are consistent with the three satellites in terms of their magnitudes and locations.  
F 3F satellite; microwave sounding instruments; tropical cyclone (TC); 1 DVAR; warm core 2024164546[[https://doi.org/10.3390/rs16234546](https://doi.org/10.3390/rs16234546)]([https://doi.org/10.3390/rs16234546](https://doi.org/10.3390/rs16234546))