esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Multi-Agent Framework
### Energy Consumption  
Energy consumption relies on how many packets are being transmitted and the transmission distance of each packet. For a single sensor, say \(v_{i}\), its energy consumption for transmitting a \(k\)-bit message to a receiver, say \(v_{j}\), with a distance \(d\) away can be calculated using Equation (1) [19].  
\[Ene_{T_{i}}(k,d)=k(E_{elec}+\epsilon_{amp}d^{\beta}) \tag{1}\]  
where \(E_{elec}\) represents the energy being dissipated to operate the transmitter or receiver circuitry per bit and \(\epsilon_{amp}\) denotes the energy dissipation of the transmitter power amplifier for transmitting a bit to a receiver with a distance of \(d=1\) unit away, and \(\beta\) is a constant coefficient that denotes the path loss exponent.  
For the receiver, \(v_{j}\), its energy consumption for receiving the \(k\)-bit message is:  
\[Ene_{R_{j}}(k)=kE_{elec} \tag{2}\]  
Thus, for the whole sensor network, its energy consumption in a pre-defined period is the sum of the energy consumption caused by sensor nodes transmitting and receiving data packets in that period.  
Certainly, other factors, such as idle listening, overhearing and synchronisation, can also incur energy consumption. To simplify the model, such factors are not taken into consideration.
## 4 Experimental Setup
### Evaluation Criteria for Assessing Generated Data Quality  
We employ three criteria to assess the quality of the generated data \(G(z)\sim p_{g}\) with respect to test data from the real distribution, \(p_{r}\): maximum mean discrepancy (MMD) [40], principal component analysis (PCA) and marginal distribution histograms. We use the Information Theoretical Estimators Toolbox [41] for computing the MMD between the test and generated distributions. Reference MMD is computed from test data as the average of fifty, randomly-sampled, equally-sized datasets, all conditioned on the same subset of \(y\) values. Every generated or real data set consists of 1000 data points for fair comparison. The radial basis function with \(\sigma=5.0\) is chosen for the kernel and this value is determined by the average variance of the training data and accounts for better sensitivity to small differences between distributions under comparison. Both U-Statistics and V-Statisticsimplementations of MMD conclude to very similar values and we chose the first estimate. Scikit-learn 1.0.1 is used for PCA.
# Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
## 5 Simulation Results
### UC2: Urban Environment
#### 5.2.3 Urban Backhaul Capacity  
Because of the shorter distances mentioned above, the backhaul capacity is 25% larger than in a rural setting. The \(p_{90}\) supported capacity surpasses the 4 Gbps for LP only and 4.4 Gbps for FL. However, for FA, LP, and FL, the actually \(p_{90}\) served capacity is only 1 Gbps, 530 Mbps, and 300 Mbps, respectively. The average number of users served by each EN decreases due to the increased number of serving ENs and the lower likelihood of LoS between nodes. As a result, the FL link efficiency is just 20%.
## 2 Simulation Model
### Simulation of Convergence Effects at Corners  
Several simulations were conducted with variations in K, as shown in Table 2. As the spread of waves is mainly related to the size of the structure, the convergence clouds of waves at the corner for K = 1, 2, 3, 4, and 5 are exemplified in cases when b = 500 mm and W = 100 g of explosive, as shown in Figure 9.  
As observed in the diagram, when K = 1, the converging waves at the corner from the waves reflected at walls A and B and the initial shock waves do not form a noticeable Mach rod phenomenon owing to the short distance between the walls; however, as K increases, the Mach wave on the surface of wall A gradually widens, and a clear Mach rod is observable. As the Mach wave speed is faster than the initial shock wave speed, its wavefront surface gradually flushes with the initial shock wave and surpasses it. Therefore, the high pressure formed near the corner (0, b) comes from the reflection of the Mach wave at the surface of wall B. The above deductions support the theoretical analysis in Section 3.1. For the value of n given in Section 3.1, the simulation shows that when K \(\geq\) 2, the high pressure at the corner (0, b) mainly comes from the positive reflection of Mach waves from wall A to wall B. An example of the spread of the Mach wave and the initial shock wave, when K = 2.4, is shown in Figure 10, where the black dashed line depicts the three-wave line. The high pressure at the corner (0, b) is formed by the positive reflection of the Mach wave.  
Figure 8: Schematic diagram of shock wave wall reflection.  
In previous experiments [2; 3; 4; 5], the peak pressure at points (0, b) and (b, b) was frequently used for comparison. The relationship between the peak pressure at the two measuring points and K when b = 500 mm and W = 100 g and the peak pressure ratio at (0, b) and (b, b) is shown in Figure 11. Figure 11 shows that the value for P (b, b)/P (0, b) tends to be stable when K is in the range of 1 to 2.5, but as K increases, the value for P (b, b)/P (0, b) drops linearly, indicating that the corner convergence effect is weakening.  
To better demonstrate the changes in the corner high-pressure area with K, the peak pressure distribution in the corner at b = 500 mm and W = 100 g when K = 2, K = 3, K = 4, and K = 5 is shown in Figure 12. The graph shows that K significantly influences the corner's high-pressure area. As K increases, the high-pressure area at the corner gradually expands and moves towards the vicinity of the short side center (b, b) and finally disappears.  
Figure 10: The formation process of the convergence effect of shock waves at the corner when K = 2.4.  
Figure 9: The cloud diagram of the convergence effect of shock waves at the corner at different K values when \(b\) = 500 mm and \(W\) = 100 g.
# A Hybrid Metaheuristic for Multi-Objective Scientific Workflow Scheduling in a Cloud Environment
## 4 Proposed Work
### Initialization  
The first task of the proposed optimization model is generating a population of solution candidates, called an ecosystem, using different initialization schemes, where each candidate solution is called an organism. These organisms of the initial population include a schedule generated by the PEFT heuristic, and the remaining schedules are randomly generated under the condition that each organism satisfies all dependencies. The organisms generated by the PEFT heuristic could be used as an approximate endpoint of the Pareto front. The user is required to provide all the necessary inputs, including the size of the ecosystem, the number of VMs, and the number of objective functions. The PEFT heuristic provides guidance to the algorithm that improves the performance of the proposed method and allows for faster convergence to suboptimal solutions. By utilizing the PEFT heuristic, better initial candidate solutions may be obtained. The organisms adjust their position in the solution space through the three phases of the SOS algorithm. Each organism of the ecosystem represents a valid feasible schedule of the entire workflow and an organism's length equals the size of the given workflow. Let \(N\) be the number of organisms, \(n\) be the number of tasks in a given workflow, and \(k\) be the number of VMs for executing the workflow tasks, then the ecosystem is expressed as \(X=[X_{1},X_{2},X_{3},\ldots,X_{N}]\). The position of the \(i^{\text{th}}\) organism, expressed as a vector of the \(1\times n\) element, can be given as \[X_{i}=[x_{i,1},x_{i,2},x_{i,3},\ldots,x_{i,n}] \tag{18}\]  
where \(x_{i,\mu}\in X_{i}\) such that \(1\leq x_{i,\mu}\leq k\). In other words, \(X_{i}\) represents a task-VM mapping scheme of the workflow while preserving the precedence constraints. Table 2 shows an example of an organism \(X_{i}\) for mapping of 10 tasks on 4 VMs. The best position identified by all organisms so far is represented by \(X_{best}\). Each organism of the ecosystem represents a mapping of the tasks of a given workflow to the VMs while keeping the precedence constraints. So, each organism represents a potential solution to the problem at hand in the solution space for the submitted workflow and the proposed algorithm is used to find the optimal solution.
# Forming Difficulty Evaluation for Curved Hull Plates Based on Grey Relational Analysis
## 2 Analyze the Influence Law of Forming Difficulty of Complex Curved Hull Plates
### The Influence of the Remaining Factors on the Forming Difficulty of Complex Curved Hull Plates  
Based on the results of numerical calculations and experimental measurements, the correlation between the forming difficulty of complex curved hull plates and the influencing factors is analyzed.  
With the other influencing factors kept constant, the wider the curved plate, the greater the energy required for deformation, the smaller the resulting deflection deformation, and  
Figure 8: Influence law curve of heating speed on deflection.  
the greater the difficulty of forming; the smaller the radius of curvature, the greater the energy required to deform the plate, the smaller the resulting deflection deformation, and the more difficult it is to form; the greater the deflection of the curved plate to be formed, the greater the energy required to deform the plate and the more difficult it is to form; the more the number of heated lines, the longer the forming time, the more difficult it is to form; the greater the difference of heating lines on both sides of the curved plate, the more difficult it is to form; the smaller the heating line spacing, the more the number of heating lines required, the more difficult it is to form.  
In general, the correlation between the forming difficulty of complex curved hull plates and influencing factors can be divided into two categories, the first category is the influencing factor that shows a negative trend with the difficulty of forming curved plates including plate length, radius of curvature, heating line spacing; the second category is the influencing factor that shows a positive trend with the difficulty of forming curved plates including plate thickness, plate width, deflection, heating line number, number of heating line difference on both sides of the curved plate, heating speed.
## 2 Materials and Methods
### Modeling Dye Adsorption in Fixed-Bed Column
#### 2.5.4 Error Analysis  
To evaluate the good-fitting of the previously mentioned models with the experimental results to describe the adsorption process, an error analysis was performed. The correlation coefficient \(R^{2}\) alone could not be enough to validate the best model, because it only represents the linear fitness between the isotherms and experimental data. Therefore, in this work, error analysis through the sum of squares error (\(SSE\)) (Equation (12)) together with the assessed determination coefficient (\(R^{2}\)) (Equation (13)) from regression analysis were employed to select the model that best fit the adsorption experimental data.  
\[SSE=\sum_{i=1}^{n}(y_{c}-y_{e})_{i}^{2} \tag{12}\]  
\[R^{2}=1-\frac{SSE}{SST}=1-\frac{SSE}{\sum_{i=1}^{n}(y_{e,i}-\overline{y_{e}}) ^{2}} \tag{13}\]  
where \(y_{c}\) is the ratio of effluent to influent dye concentrations calculated using the model, \(y_{e}\) is the ratio of effluent to influent dye concentrations using experimental conditions, \(n\) is the number of experiments, \(i\) is the experiment number and \(SST\) is the total sum of squares [25; 26].
The surface area calculated using adsorption of N\({}_{2}\) and CO\({}_{2}\) at \(-196\)\({}^{\circ}\)C and 0 \({}^{\circ}\)C, respectively, and other textural properties are presented in Table 1. The high S\({}_{\text{BET}}\) and total pore volume of the PSAC tend to greatly facilitate the capacity of dye removal. The average pore diameter determined with the N\({}_{2}\) isotherm data is higher than the highest molecular distance between two extreme atoms of the blue, red, and black dyes, 1.37 nm, 1.28 nm, and 1.53 nm, respectively, which could facilitate pore diffusion [13; 31]. However, the pore size distribution determined with the N\({}_{2}\) and CO\({}_{2}\) adsorption data simultaneously has shown a lower average pore size (Table 1), since CO\({}_{2}\) presented a smaller kinetic diameter than N\({}_{2}\) with high accessibility to the very narrow pores, which may indicate that part of the porous structure is not available for the adsorption of these dyes due to size exclusion and also lead to diffusion limitations [32]. Figure 3 shows the pore size distribution of the PSAC with a broad peak between 2 and 5 nm, corresponding to the mesoporous region and several peaks in the microporous region (less than 2 nm), which can limit dye diffusion. This could be corroborated by the high CO\({}_{2}\) surface area, which reveals the presence of ultramicropores (<0.7 nm) (approximately 30% of the overall volume) that are not available for dye adsorption.
# Cyber Resilience Limitations in Space Systems Design Process: Insights from Space Designers
## 1 Introduction
### Challenges in Achieving Cyber Resilience for Space Infrastructure  
The challenges associated with ensuring cyber resilience in space systems are distinct from those in other critical infrastructure contexts, which is primarily due to the unique operational constraints, environmental factors, and the critical nature of space-based assets [25]. While the concept of cyber resilience applies broadly to any system that relies on cyber resources, space systems face several unique factors that significantly differentiate them from terrestrial counterparts [14].
# On the Impact of Mobility on Battery-Less RF Energy Harvesting System Performance
## 2 Energy Trade-Offs for Mobile Systems  
Our system consists of mobile Radio Frequency (RF) energy harvesting sensor nodes deployed over a large geographical area and powered by dedicated RF power transmitters deployed at strategic locations. As the maximum transmit power of RF transmitters is limited by regulations, the charging range is small relative to distance between RF power transmitters, and the resulting charging network is assumed to be sparse. Whenever a sensor node approaches an RF power transmitter, it starts charging and boots if a sufficient amount of energy is harvested. As the node leaves an area with an RF power transmitter, it keeps operating until it uses up the available energy stored in the capacitor. Each RF charging station modulates a charging signal to embed additional information, such as station ID, which enables the mobile nodes to distinguish between different stations and therefore locations.  
The amount of harvested energy depends on the distance to the charging station, transmit power and the amount of time the sensor spends near the charging station. The available wireless RF power on a receiving antenna of a sensor node is given by the Friis equation:  
\[P_{r}=\eta P_{t}G_{t}G_{r}\Big{(}\frac{\lambda}{4\pi R}\Big{)}^{2} \tag{1}\]  
where \(P_{t}\) is the transmit power, \(G_{t}\) and \(G_{r}\) are antenna gains for the transmitter and receiving antenna, respectively, \(\lambda\) is the wavelength, \(R\) is the distance between a charging station and a sensor node and \(\eta\) is the charging efficiency, which depends on the frequency band and the RF power level. The amount of harvested energy depends on the time the sensor node spends near the RF charging station;our experiments show that a 50-mF capacitor located at a distance of 3 m from a 3-W transmitter charges within 29 s.  
Figure 1 shows a block diagram of a battery-less RF energy harvesting system, which consists of an RF to DC converter, a boost converter and a supercapacitor. As the capacitor voltage varies with the amount of stored energy, a boost converter is used to supply a stable voltage necessary to the sensor node. The amount of energy stored by a supercapacitor is given as:  
\[E=CV^{2}/2 \tag{2}\]  
It is important to note that not all of the stored energy is actually usable because the boost converter requires a minimum voltage, \(V_{min}\), to start up. In fact, modern energy harvesting systems are designed such that the voltage booster activates when the capacitor voltage reaches \(V_{max}\) and keeps operating until the voltage drops to \(V_{min}\), as shown in Figure 2. The hysteresis behaviour ensures that there is sufficient energy to reliably boot the node [7], i.e., \(C(V_{max}^{2}-V_{min}^{2})/2>E_{boot}\). At the same time, it leads to loss of energy in mobile scenarios as explained in the section below.
# A Method for Predicting Radiated Acoustic Field in Shallow Sea Based on Wave Superposition and Ray
## 4 Conclusions  
In this paper, a new method is proposed for the prediction of radiated acoustic field in shallow sea. Ray theory is a high frequency approximation theory with high accuracy and high computational  
Figure 10: Two-dimensional color figure of SPL using combined WSM and FEM (100 Hz). (**a**) Combined WSM; (**b**) FEM.  
efficiency. On the basis of the wave superposition method, the application range of the wave superposition method is expanded by introducing the ray theory. Since the Green's function in sea is a cylindrical function, a high-order superposition is required to ensure accuracy, while the distance between the virtual source surface and the surface of the structure cannot be too close. In addition, the structural surface vibration velocity nodes and the virtual sources should avoid overlapping in the vertical direction as much as possible, which should be paid attention to, while constructing the virtual source surface. After comparison with examples, it is verified that the proposed method is accurate. It can be used to analyze the acoustic radiation of complex structures under water and have potential for the calculation of high-frequency problems.  
Conceptualization, D.S.; methodology, C.Z. and Y.L.; software, Y.L.; validation, C.Z., Y.L. and D.S.; formal analysis, Y.L.; investigation, Y.L.; resources, D.S.; data curation, D.S.; writing--original draft preparation, C.Z. and Y.L.; writing--review and editing, I.U.K.; visualization, Y.L.; supervision, D.S.; project administration, D.S.; funding acquisition, C.Z. and D.S. All authors have read and agreed to the published version of the manuscript. This work was supported by \"the Fundamental Research Funds for the Central Universities, HEU\" (Grant No. 3072019 CF0505) and the Stable Supporting Fund of Acoustic Science and Technology Laboratory (Grant No. JCKYS2019604 SJSJ8009). The authors declare no conflict of interest.
# Detection of Transmission Line Insulator Defects Based on an Improved Lightweight YOLOv4 Model
## 1 Introduction
[25] used the YOLOv4-tiny, a lightweight version of YOLOv4, to identify the bird's nest in transmission lines and towers. [PERSON] et al. [26] used the YOLOV4-tiny and training skills to identify bird species related to transmission line faults. In addition to the above methods, there are various ways to achieve lightweight of CNN, such as using depthwise separable convolution [27] and group convolution [28], using global pooling to replace the fully connected (FC) layers [29], using \(1\times 1\) filter to realize dimensionality reduction [30], etc. According to the current research situation, the lightweight improvements to the model are also made from the aspects of real-time detection and complexity of deployment. This paper proposes a transmission line insulator defect detection method based on the improved YOLOv4 algorithm. A sample set of transmission line insulator images was constructed based on the collected aerial images and a public dataset on the internet, and the image augmentation and preprocessing were carried out on these images. Then, the MobileNet, a lightweight CNN, was used to replace the feature extraction network CSPDarkNet53 of the YOLOv4 detection model. At the same time, the PANet network was improved and the width multiplier was adjusted to further reduce the parameters of the object detection network. A total of 2403 images in the dataset were used to train, validate, and test the improved YOLOv4 model, and the test results were compared with other object detection algorithms. The results show that the method proposed in this paper has high detection accuracy and faster speed, which can provide a reference for real-time detection of transmission line insulator defects.
# Urban Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian Network Fusion
## 4 Results and Discussion
### Houston Flood Case
The pre-event \(\gamma\) of this area is around 0.36, which could be attributed to the existence of trees and anthropogenic activities in the streets around buildings. The co-event \(\gamma\) drops to 0.14, nevertheless, this drop-off is much less significant than that in R#2 and as this area is weakly coherent, \(\gamma\) is not helpful for flood detection in this area. The pre-event \(\sigma^{\circ}\), however, increases from around \(-9.0\) dB to \(-4.8\) dB in co-event \(\sigma^{\circ}\). The wide spaces between buildings and the relatively small \(\phi\) between building orientation and the SAR azimuth direction probably facilitate the enhancement of the double-bounce effect in the co-event acquisition. Thus, \(\sigma^{\circ}\) is more informative than \(\gamma\) for flood detection in this area. R#4 is an underestimated inundation area of dense buildings surrounded by trees. In this area, the pre-event \(\gamma\) drops from around 0.60 to 0.32 in the co-event \(\gamma\), whereas the pre-event \(\sigma^{\circ}\) increases from around \(-7.0\) dB to \(-6.0\) dB in co-event \(\sigma^{\circ}\). The variations in both \(\sigma^{\circ}\) and \(\gamma\) are less considerable than the successfully detected flooded built-up areas, e.g., a 0.28 drop-off of \(\gamma\) compared to a 0.5 drop-off in R#2, a 1 dB increase of \(\sigma^{\circ}\) compared to a 5.2 dB increase in R#2 and a 4.2 dB increase in R#3. Since the buildings, trees and streets are densely distributed, \(\gamma\) is estimated with the mixed structures especially \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Data** & **Precision** & **Recall** & **F1** & **FPR** & **OA (\%)** & \(\kappa\) \\ \hline \multirow{2}{*}{Houston city} & Intensity + Coherence & 0.83 & 0.61 & 0.70 & 0.02 & 94.5 & 0.68 \\ \cline{2-7}  & Intensity & 0.85 & 0.50 & 0.63 & 0.01 & 93.7 & 0.60 \\ \hline \end{tabular}
\end{table} 
Table 5: Quantitative evaluation of the Houston flood case (FPR: false-positive rate, OA: overall accuracy). in medium resolution data such as Sentinel-1, thus attenuating the drop-off in the co-event \(\gamma\). On the other hand, the double-bounce backscattering between floodwater surface and buildings could be obstructed by trees, and the floodwater could also be masked by shadow. Therefore, flood detection in this scenario is challenging especially for medium resolution data (e.g., 10 to 30 m). Figure 3: Flood extent maps of the Houston study area: (**a**) Binary flood extent based on the fusion of \(\sigma^{\circ}\) and \(\gamma\); (**b**) Flood category map of (**a**); (**c**) Binary flood extent based on \(\sigma^{\circ}\) alone; (**d**) Reference flood mask derived from high-resolution aerial photographs provided by the National Oceanic and Atmospheric Administration (NOAA); (**e**) Flood posterior probability conditioned on \(\sigma^{\circ}\); (**f**) Flood posterior probability conditioned on \(\gamma\).