esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient Connected-Coverage Scheme in Wireless Sensor Networks
## 6 Experimental Simulation
### MCCA Algorithm Simulation Results
#### 6.1.2 Results and Analysis  
Figure 6 shows a simulation scenario of uniform deployment, with a total of 400 sensor nodes and 64 MTPs set up. The black circles represent the sensing ranges of the sensor nodes, and the red stars represent MTPs. Figure 7 shows the coverage of the sensor nodes after 15 iterations. Only 35 sensor nodes need to be activated to cover all MTPs, in order to avoid the problem of redundant coverage.  
Figure 5: Flow chart of the combination of two algorithms.  
Figure 6: Distribution of 400 uniform nodes and 64 monitoring target points.  
Figure 8 shows the distribution of 400 random nodes and 64 MTPs. The coverage area of adjacent nodes overlaps with each other, which leads to the redundant coverage and energy waste. To extend the network life, the node scheduling should be optimized under the premise of ensuring coverage quality. Figure 9 shows the coverage condition of sensor nodes after 15 iterations by our algorithm. Only 37 sensor nodes are needed to be activated to cover all MTPs and the problem of the redundant coverage can be avoided.  
Figure 8: Distribution of 400 random nodes and 64 monitoring target points.  
Figure 7: The coverage diagram after the 15 th iteration.  
Figure 9: The coverage diagram after the 15 th iteration.  
Next, in order to evaluate the performance of the MCCA algorithm for reducing redundant sensor nodes, we compared it with I-GA (improved genetic algorithm) [10] and traditional GA.The same parameters are used for all algorithms.  
Figure 10 shows the number of sensor nodes that need to be activated for different numbers of MTPs. As the number of MTPs increases, the number of activated sensor nodes also increases. However, the number of sensor nodes selected to work in the MCCA algorithm proposed in this paper is lower than that of I-GA and traditional GA. It can be seen from the figure that, in order to cover 60 target points, the MCCA algorithm selects 23 sensor nodes, while I-GA and traditional GA require 27 and 47, respectively.
## 5 Comparison and Validation
### Comparison with NASA's Operational OCO-2 L2 Product  
In this section, we compare the same two months (April and August 2015) of post-filtered and bias corrected FOCAL v06 XCO\({}_{2}\) results with NASA's operational OCO-2 L2 product. Comparing Figure 14 with Figure 13 (top) shows similar large scale temporal and spatial patterns and also the relative enhancement in the anthropogenic source regions of East Asia in April are similar. The most obvious difference is that the NASA product has about three times more soundings. The primary reason for this is the inherently poor throughput (11%) of the MODIS based cloud screening of the preprocessor (see discussion in Section 2).  
Analyzing only the same soundings in both data sets and considering the column averaging kernels, the NASA product has on average 0.7 ppm larger values than FOCAL which is (due to the used color table) most noticeable in the northern hemisphere of Figure 14 (right). The standard deviation of the difference is 1.1 ppm. As done in the last section, we separate the systematic mismatch from the stochastic mismatch by analyzing grid boxes with more than 100 co-locations. The standard deviation of the stochastic and the systematic mismatch amounts 0.91 ppm and 0.83 ppm, respectively. It is no surprise, that the stochastic mismatch is smaller than expected from the combined  
Figure 14: NASA v7.3.05b monthly mean XCO\({}_{2}\) at \(5^{\circ}\times 5^{\circ}\). **Left**: April 2015. **Right**: August 2015.  
Figure 13: Monthly mean XCO\({}_{2}\) at \(5^{\circ}\times 5^{\circ}\). (**Top**) FOCAL v06. (**Bottom**) CAMS v15r4 sampled as FOCAL. (**Left**) April 2015. (**Right**) August 2015.  
reported uncertainties because both data products base on the same L1b input data including the same noise spectra.
## 3 Two-Stage Stochastic Scheduling Model for the Hybrid AC/DC Distribution Network
### Objective Function  
The day-ahead scheduling cost includes the generating cost of gas-fired units in the first stage and the power purchasing cost, penalty cost of power fluctuation from the substation, network loss cost, load shedding, and wind curtailment cost:  
\[\begin{array}{ll}\min&\sum\limits_{t}\sum\limits_{i}c^{G}P_{i,t}^{G}+\sum \limits_{s}\pi_{s}\sum\limits_{t}c^{L}(\sum\limits_{i\in\mathcal{N}_{\text{AC} }}3L_{i,t,s}^{\text{Cur}}+\sum\limits_{i\in\mathcal{N}_{\text{DC}}}L_{i,t,s}^ {\text{Cur}})+\\ &\sum\limits_{s}\pi_{s}\sum\limits_{t}c^{W}(\sum\limits_{i\in\mathcal{N}_{ \text{AC}}}3W_{i,t,s}^{\text{Cur}}+\sum\limits_{i\in\mathcal{N}_{\text{DC}}} W_{i,t}^{\text{Cur}})+\\ &\sum\limits_{s}\pi_{s}\sum\limits_{t}c^{\text{Loss}(\sum\limits_{k\in \mathcal{L}_{\text{AC}}}3{(I_{k,t,s}^{\text{AC}})^{2}r_{k}}+\sum\limits_{k\in \mathcal{L}_{\text{DC}}}{(I_{k,t,s}^{\text{DC}})^{2}r_{k}})}+\\ &\sum\limits_{s}\pi_{s}\sum\limits_{t}\sum\limits_{i\in\mathcal{N}_{\text{BD} }}{(C_{t}^{\text{Sub}}P_{i,t,s}^{\text{Sub}}+c^{\text{Ramp}}|P_{i,t,s}^{ \text{Sub}}-P_{i,t-1,s}^{\text{Sub}}|)}\end{array} \tag{32}\]  
where \(c^{G}\) is the unit generating cost of gas-fired units; \(c^{L}\) is the load shedding price; \(c^{W}\) is the wind curtailment price; \(c^{\text{Loss}}\) is the network loss price; \(c_{t}^{\text{Sub}}\) is the electricity price of the main grid at time \(t\); \(c^{\text{Ramp}}\) is the penalty price for power fluctuation; \(\Omega^{\text{Sub}}\) is the node set of substation; and \(\pi_{s}\) is the occurrence probability of the wind power scenario, \(s\).
## 2 Overview of Cyber Threats in EM
### Sensing Technologies  
Several EM tools for planning and decision making require reliable real-time information gathered through different sensing technologies. Here, we include satellite-based sensing, focussing on Global Positioning Systems (GPS), and terrestrial sensing with an emphasis on wireless sensor networks.
# Combining InSAR and GNSS to Track Magma Transport at Bastic Volcano
## 6 Discussion
### Combining InSAR and GNSS for Complementary Spatial and Temporal Information
#### 6.2.3 GNSS Temporal Inversion Solves the Conundrum of Non Unique Static Inversion  
In this study, we also showed that GNSS time series data help to choose between equally likely models determined from InSAR. Regardless of whether ascending and descending InSAR data have equal weighting or weighting coming from the data amplitudes or whether GNSS data were used or not, two families of equally well fitting models were obtained from the inversion of data covering the whole eruption (Table 1). These non unique results arise from inherent uncertainties pertaining to the data and the models. Here, we show that GNSS time series data can resolve the ambiguity between these families. In fact, the family of sources corresponding to a sill that turns into a dike leads to a source consistent with the displacement time series from GNSS and to a more meaningful propagation of the magma: magma starts beneath the summit and moves towards the ground surface in a way that is compatible with observations at this volcano [58]. The other family of sources corresponding to a curved eastward dipping intrusion is less consistent with the displacement time series and leads to magma going downward, accumulating at depth and never reaching the surface.
# A Geographical Heuristic Routing Protocol for VANETs
###### Abstract  
Vehicular ad hoc networks (VANETs) leverage the communication system of Intelligent Transportation Systems (ITS). Recently, Delay-Tolerant Network (DTN) routing protocols have increased their popularity among the research community for being used in non-safety VANET applications and services like traffic reporting. Vehicular DTN protocols use geographical and local information to make forwarding decisions. However, current proposals only consider the selection of the best candidate based on a local-search. In this paper, we propose a generic Geographical Heuristic Routing (GHR) protocol that can be applied to any DTN geographical routing protocol that makes forwarding decisions hop by hop. GHR includes in its operation adaptations simulated annealing and Tabu-search meta-heuristics, which have largely been used to improve local-search results in discrete optimization. We include a complete performance evaluation of GHR in a multi-hop VANET simulation scenario for a reporting service. Our study analyzes all of the meaningful configurations of GHR and offers a statistical analysis of our findings by means of MANOVA tests. Our results indicate that the use of a Tabu list contributes to improving the packet delivery ratio by around 5% to 10%. Moreover, if Tabu is used, then the simulated annealing routing strategy gets a better performance than the selection of the best node used with carry and forwarding (default operation).  
heuristic optimization; routing protocols; VANET 161567; doi:10.3390/s16101567
# Research on Lateral Bearing Behavior of Spliced Helical Piles with the SPH Method
###### Abstract  
The length of a spliced pile is 2 m assembled from an original spiral pile using a connector. The whole pile is the structure of the upper straight pipe and the lower spiral. The pile-soil model is established with FEM-SPH by LS-DYNA to simulate and analyze the characteristics of the spliced piles. When the helical pile is subjected to a horizontal load, the pile rotates around the point of rotation, and the contact force position of the soil in the model is as expected. During the process of pile driving, the soil forms an inverted cone stress-area, and the maximum particle stress area near the pile tip and the ground surface is 400 Kpa, which is highly concentrated. When loaded laterally, the area of the interaction stress of the soil particles is divided into three regions: the stress effect region; the transition region; and the critical region. Then, \(7^{\circ}\) is defined as the ultimate horizontal bearing-capacity of the spliced pile, and the numerical simulation of the horizontal bearing-capacity fundamentally matches the test results. The simulation model realizes the transition from the pile installation to the lateral loading, predicts the ultimate horizontal bearing-capacity, and analyzes the stress distribution of the soil particles and the time-development of the soil displacement.  
FEM-SPH; spliced pile; lateral bearing capacity; stress effect region; installation; time-development soil +
Footnote †: journal: Applied Sciences
# An Exploit Traffic Detection Method Based on Reverse Shell
## 2 Background
### Reverse Shell
#### 2.2.2 Reverse Shell Communication Behavior Model  
The network communication process of TCP-based reverse shells usually includes three stages: connection establishment, reverse shell, and command interaction. At the same stage, traffic of different types of reverse shells may show various characteristics. For example, in the connection establishment phase, a non-encrypted reverse shell only needs the three-way handshake, while an encrypted reverse shell additionally needs an encryption protocol negotiation process. After establishing the connection, a system shell directly enters the command interaction stage, when the target machine returns the system shell information. while an advanced shell, such as a Meterperer shell, usually delivers the payload in segments first and then returns an advanced shell after it runs on the target machine.  
With Metasploit penetration testing tools [20] in the Linux/x64/Meterpreter/reverse_tcp module as an example, as Figure 1 shows, in the connection establishment stage, the target machine establishes the TCP connection through the three-way handshake. Then, it enters the shell return stage. Since the Meterperer belongs to staged attack payloads, attackers first send a small piece of payload to the target, named stage0, and then upload stage1 through stage0. stage1 and other payloads gradually hand over the control of the shell and finally return a multi-functional Meterperer shell to the attacker. The process is similar to the \"a little horse introduces a big horse\" in web penetration. Finally, in the command interaction stage, the attacker sends the command to the control terminal, and the target machine receives and returns the result to the attacker. There is a relatively long interval between the packet carrying a command and the next packet due to the thought time indispensable for the attacker to decide what to enter.
## 1 Introduction
Characterizing the direct effect of ionic silver is important as Ag\({}^{+}\) is known to be persistent, accumulate, and can become highly toxic to organisms, including plants [11]. Transcriptomic analysis has illustrated that Ag\({}^{+}\) inflicts greater observable damage to membranes and DNA than intact AgNPs [21]. Overall, as the size of the AgNP becomes closer to ionic silver, the negative effects increase [11]. Furthermore, Ag\({}^{+}\) is known to compete with copper (Cu\({}^{+}\)) for the binding of plastocyanin from the photosynthetic electron transport chain and can either reduce or inactivate the important energy-producing process [22]. The use of Ag\({}^{+}\) may adversely affect plant growth in spaceflight but the choice of a substrate-based or substrate-free growth system and silver concentration could mitigate the more extreme effects. One reason for this is due to the high cation-exchange capacity of arcillite that allows for the adsorption of positively charged ions due to negatively charged surfaces. We expected that arcillite as a substrate with high cation-exchange capacity would act like biochar, which holds a lower cation-exchange capacity, in its ability to diminish the uptake of toxic metals in plants by immobilizing the metal across the surface of the substrate via ion exchange [23; 24]. To that end, it remained to be determined what dosage of Ag\({}^{+}\) is acceptable for plant irrigation water under ISS-like environmental conditions (~3000 ppm carbon dioxide) and whether Ag\({}^{+}\) will need to be removed from potable water before use as space crop irrigation water. To answer this, we asked at what concentration does silver-treated water influence the growth, nutrient content, and microbiome of a space crop grown on an arcillite (calcinated clay) substrate or without a substrate (hydroponically)?
# Ambient Environmental Parameter Estimation for Reliable Diffusive Molecular Communications
## 2 System Model of Diffusive Molecular Communications
### Effect of Transmitter Molecule Volume on CIR  
Figure 5 shows the system model with the transmitter having a finite volume. It is assumed that molecule \(A\) is released from the surface of a spherical transmitting molecule with radius \(a_{TX}\). Hence, it is considered as homogeneous regardless of the position on the surface of the transmitting molecule, and the emission amount is the same as that in the case of a point source. Given that molecule \(A\) is released instantaneously at the start time of the symbol section, it is necessary to change the initial condition (4) of the diffusion equation as follows:  
\[\begin{split}& P_{A}^{V_{TX}}(r,t\to 0|r_{0})\\ &=\left\{\begin{array}{ll}\frac{1}{4\pi r_{0}^{2}}\frac{\sqrt{a _{TX}^{2}-(r-r_{0})^{2}}}{2a_{TX}^{2}}&\text{if }r_{0}-a_{TX}\leq r\leq r_{0}+a_{TX}\\ 0&\text{otherwise}\end{array}\right.\end{split} \tag{11}\]  
In the equation, the release of the signaling molecule from the transmitting molecule is represented based on the volume.  
Figure 4: System model with heterogeneous temperature distribution.  
Figure 3: System model with homogeneous temperature distribution.
# Exploring Synergy of Denoising and Distillation: Novel Method for Efficient Adversarial Defense
## 3 Distillation and deNoise Network(DiNo-Net)
### Adversarial Robustness Training
#### 3.1.2 Feature Denoising for Student Model  
The final defense mechanism of the student model relies on a denoising module, as depicted in Figure 3. Unlike the adversarial robustness provided by the teacher model, the denoising feature directly mitigates the perturbations of the adversarial examples. Previous shortcomings in treating all inputs identically during the denoising process have led to suboptimal classification outcomes. [PERSON] et al. [44] proposed a solution involving adversarial training prior to denoising, which resulted in effective perturbation removal without impairing the classification of the original inputs. The efficacy of this approach is evident in Figures 4B and 5B, which show a smoother loss surface around the ground truth compared with the untrained denoising process shown in Figures 4A and 5A. An increase in the r-value signifies a less sensitive response to adversarial attacks and feature denoising. Despite the additional computational demand for adversarial training, it enables effective defense through feature denoising.  
Figure 4C shows how the DiNo-Net expands the loss surface around the optimal point, mitigating the impact of denoising on the accuracy of the original image. The narrow loss surface near the decision boundary does not hinder the effective removal of adversarial example perturbations, as shown in Figure 5C. Denoising through knowledge distillation, as shown in Figure 5C, achieves a more efficient and steady defensive performance than adversarial training, as depicted in Figure 5B, by enhancing ground-truth smoothing. Additionally, our approach does not involve direct adversarial training, as is the case in [48].  
Figure 4: Original input loss surface by defensive techniques.  
Figure 5: Adversarial example loss surface by defensive techniques.  
DiNo-Net effectively learns robustness indirectly through distillation. The subsequent sections detail methods for effectively merging these two defense mechanisms.
# The Seepage and Soil Plug Formation in Suction Caisons in Sand Using Visual Tests
## 3 Test Results and Discussion
### Soil Plug and Settlement Formation  
During suction penetration, a part of the soil in the caissons gets into the open-ended hollow caisson cavity, forming a soil plug [23]. The final soil plugs in the caisson at the buried depth of 5 cm (caused by the caisson) are shown in Figure 8. The test results show that there is no soil plug in the caissons with small suction applied during suction penetration. The maximum heights of soil plugs in the suction caisson models with buried depths of 5, 10, and 15 cm are 0.169, 0.085, and 0.087 times the buried depths, respectively. It was observed that the suction caused settlement of the surrounding soils outside the caisson. The maximum soil settlement appeared a certain distance from the outer wall of a suction caisson due to caisson-soil friction.  
Figure 9 shows the curves of the heights of soil plug \(h_{\text{sp}}\) and of soil settlement \(h_{\text{settlement}}\) outside the caisson. Other test conditions not drawn in Figure 9 did not have the obvious soil plug and soil settlement around the suction caisson model. There is no obvious soil settlement outside the suction caisson with a buried depth of 15 cm. The height of soil plug \(h_{\text{sp}}\) increases with the increase of \(x\) as a result of the distribution of seepage velocity, which is greater as it moves toward the wall of suction caissons.  
Figure 8: The soil plug in the caisson.  
Figure 7: The hydraulic gradient with \(x/D=0.2675\) and \(h/D=1.25\).  
The volume of soil plug \(V_{\text{sp}}\) and that of soil settlement \(V_{\text{settlement}}\) were measured by using the volume formula after obtaining the size of the soil plug and settlement from the images. The comparison between the volume of soil plug \(V_{\text{sp}}\) and that of soil settlement \(V_{\text{settlement}}\) in tests is shown in Table 3. The soil plug consists of the soil heaved by seepage force into the caisson, and the soil from outside into the caisson, leading to soil settlement around the foundation. It can be seen that the influence of settlement outside the caisson on the soil plug decreases as the buried depth rises. The soil plug is mainly influenced by the soil from outside into the caisson, and \(V_{\text{settlement}}/V_{\text{sp}}=1.581\) due to the decrease in the void ratio of soil around the caisson caused by the seepage force during suction penetration when \(h=5\) cm and \(S=1.0\) kPa. The influence of the soil heaved by the soil plug increases with the increase of \(h\). The volume of soil heaved begins to rise as the velocity of seepage reaches a certain value.
## 5 Discussion  
Overall, the proposed model performed well in terms of accuracy, speed, computational load, and memory footprint balance. It was observed that GLDD-YOLOv5 has higher adaptability and more capability in defect detection in a complex environment and under different lighting conditions compared to existing state-of-the-art models. It also showed relatively higher accuracy in bounding box localisation.  
The GLDD-YOLOv5 model proposed in this study provides a 4.6% improvement in accuracy (mAP50) compared to YOLOv5s, while improving the computational cost by 44% with 8.4 GFLOPs, the number of parameters by 45% with 3.96 M, and the file size by 26% with 10.7 MB. Although it shows a 6% decrease in inference speed (from 175 FPS to 163.9 FPS), this is minimal compared to other models. It produced a total carbon footprint of 0.145 gCO2e in the inference phase with the test dataset.  
Evaluation of the carbon footprint in the study revealed that optimisation of the models is critical not only in terms of computational efficiency but also in terms of sustainability. The proposed model provides a perspective on developing environmentally friendly, effective, efficient, and fast object detection solutions in resource-limited environments such as edge devices.  
In light of these evaluations, GLDD-YOLOv5 performed better than other models in the field of industrial defect detection and managed to increase the accuracy rate without sacrificing too much speed. GLDD-YOLOv5 appears to be a particularly high-performance and efficient solution.  
Figure 10: Comparison of detection results on a custom dataset: (a) YOLOv5s detection results; (b) YOLOv5-P34 GhostECA detection results.  
Nevertheless, there were some limitations to the study. Due to the ongoing operation of the industrial plant, the available was limited in scope and focused on specific defect types. Deployment of the defect detection system will facilitate expansion of the learning capacity of the model by diversifying the dataset to cover a wider range of data. Furthermore, evaluation of the model's performance on different datasets was limited, as there is no open dataset serving a similar purpose in the existing literature. Future studies will investigate the generalisation capability of the model by evaluating it with various datasets. In addition, some complex backgrounds, which are not always possible under physical conditions, as well as limitations in adjusting camera viewing angles, which are necessary for optimal conditions, may lead to off-target false detections. The performance of the models can be further optimised in future studies by applying more lightweight techniques such as quantisation [84], model pruning [60,85] and information distillation [86].