esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Driven Modular Vehicle Scheduling in Scenic Areas
## 4 Experimental Results and Analysis
### Sensitivity Analysis of M2 in Different Scenarios
With respect to penalties, the cumulative value of M2 cost savings rises at a slower rate when penalties are constant, with an ultimate cost saving of around USD 80. In contrast, the mean curve rises more rapidly when penalties are varied, leading to final cost savings  
Figure 7: The cost savings of M2 compared to M1 over time steps for different parameter variations. of around USD 200. This aligns with previous findings, showing that variable penalties yield better optimization than fixed ones. Finally, we compare the total cost savings achieved by M2 relative to M1 across 30 simulations and the variations in the vehicle utilization rate. Figures 9 and 10 evaluate the model's performance by calculating the total cost savings of M2 compared to M1 in each simulation and examining the difference in vehicle utilization between M2 and M1, respectively. Overall, across all scenarios, M2 consistently outperforms M1 in both total cost savings and vehicle utilization. Figure 8: The cumulative cost savings of M2 over M1 over time steps for different parameter variations. Specifically, it is evident from Figure 9 that the total cost of each simulation experiment is affected by the penalty. When the penalty changes, the total cost savings from 30 simulations are generally higher than when the penalty remains unchanged, which can save an additional USD 30 to USD 50. And when the load capacity of a single vehicle changes, the total cost savings seem to have little difference. Interestingly, the impact of these two parameters on vehicle utilization is exactly opposite (see Figure 10). Regarding penalties, variations do not significantly affect vehicle utilization. However, substantial differences in utilization are observed under different single-vehicle load conditions. When \(c=12\), the average vehicle utilization rate of M2 can be approximately 2% higher than that of M1, Conversely, when \(c=6\), the maximum vehicle utilization rate of M2 is only 0.6% higher than that of M1. Figure 9: The total cost saved by M2 over M1 in each simulation for different parameter variations. This suggests that total cost is more sensitive to changes in penalties, while vehicle utilization is more influenced by vehicle load capacity. This may be because the M2 strategy, with its flexibility in adjusting fleet length, can more effectively reduce costs when penalties vary. Additionally, with larger vehicle loads, M1 tends to have a higher empty load rate, whereas M2's data-driven strategy better balances travel demand and load, thus enhancing vehicle utilization.
## 4 Test Results and Analysis
### Effect of Filler Content on Slope Stability  
The variation law of slope flow value and test time of asphalt concrete specimens with different filler content and the relationship between slope flow value and filler content are shown in Figures 4 and 5.  
It can be seen from Figure 4 that the slope flow value of asphalt concrete specimens increased with the increase in filler content, and with the increase in filler content, the linear growth stage of slope flow value and test time curve increased greatly. The relationship between the filler content and the slope flow value was further explored. As shown in Figure 5, the slope flow value increased with the increase in the filler content. When the bitumen aggregate ratio was 7.5%, the slope flow value increased obviously. When the filler content increased from 13% to 15%, the slope flow value changed most obviously. When the bitumen aggregate ratio was 7.2%, the slope flow value increased from 0.991 mm to 2.139 mm, which was an increase of 115.7%; when the bitumen aggregate ratio was 7.5%, the slope flow value increased from 1.457 mm to 3.376 mm, with an increase in 131.6%. This was because when the filler content is appropriate, the asphalt mortar mixed with asphalt can better fill the voids generated by the skeleton formed by the aggregate, which increases the viscosity and strength of the asphalt concrete specimen, so that the asphalt concrete specimen has a strong cohesion and high stability to resist the slope. With the increase in filler content, too much filler and asphalt are difficult to mix evenly, and the asphalt mixture is dry and difficult to stir, so that the cohesion of asphalt mortar in asphalt concrete specimens decreases, the slope flow value increases, and the slope stability becomes worse. The test results showed that the filler content affects the slope stability of asphalt concrete, and the unsuitable filler content makes the slope stability worse. Therefore, the filler content in the impervious asphalt concrete panel is not easy to exceed 13%.  
Figure 3: Relationship between gradation index and slope flow value. Different colored symbols represent parallel experiments.
# Reconfigurable Formation Control of Multi-Agents Using Virtual Linkage Approach
## 4 Simulation and Results
### Formation Reconfiguration Using Virtual Linkage
#### 4.2.1 Simulation Setup  
In order to pass through the gallery, a team of nine agents is initially moving in a vertical line formation with a uniform distance 0.1 m, and then reconfigured into arrow formations with varying angles to pass through the gallery. In this scenario, nine agents are designed as the same virtual linkage in Figure 11 using Equations (11) and (12).  
The required trajectory and varying formation shapes to pass through the gallery are predefined as Equations (13)-(15), which can also be provided using planning algorithms [34]. In details, the agents are initialized as a line formation at position \(r_{R}=(0,0)\) with orientation \(q_{R}=0\). The virtual linkage is specified to move with a velocity \(v_{x}=0.3\ m/s\), \(v_{y}=0\ m/s\) to pass through the gallery in 20 s according to the task. That is the position of the virtual linkage holds:  
\[r_{R}=(0.3t,0) \tag{13}\]  
Meanwhile, the attitude of the virtual linkage can be expressed as the angle from the \(x\) direction of the virtual link1 to the world coordination frame \(x\) direction and is also specified as a function of time  
Figure 14: Reconfiguring nine agents into varying formation shapes to go through the gallery.  
\[q_{R}=\left\{\begin{array}{cc}-\frac{\pi}{30}t+\frac{\pi}{2}&t\leq 10\\ \frac{\pi}{30}+\frac{\pi}{6}&10<t<20\end{array}\right. \tag{14}\]  
The virtual joint angle \(\theta_{d}=\theta_{1}\) also obeys the function  
\[\theta_{1}=\left\{\begin{array}{cc}\frac{\pi}{15}t&t\leq 10\\ -\frac{\pi}{15}t+\frac{2\pi}{3}&10<t<20\end{array}\right. \tag{15}\]
## 2 Background and Related Work
### Related Works in Dealing with Dynamic Processes  
A significant body of work exists in the field of developing reduction strategies for high-dimensional data generated via complex physical processes. In fact, linear projection methods are often used to construct such reduced order models from high-dimensional data [19]. Nonlinear counterparts for such settings have been explored in limited settings, for certain types of dynamical systems [20; 21; 22; 23]. In particular, several of these methods have adapted _diffusion maps_[16] to dynamical process data, by considering a fixed length \"time-window\" around each sample of the process data to define a local neighborhood, which can account for moderate noise at short temporal scales. However, most of these methods focus on understanding a single dynamical process, and not necessarily a collection of trajectories obtained by varying the parameter settings.  
One recent work proposes a data-driven method for organizing temporal observations of dynamical systems that depend on the system parameters [24]. In that work, the authors simultaneously compute lower dimensional representations of the data along multiple dimensions, e.g., time, parameter space, initial variable space, etc. An iterative procedure is then applied, wherein every step involves mapping the data into a low-dimensional manifold using the diffusion maps algorithm for each dimension, then recomputing the distances between the observations (for each dimension) through a reconciliation step that utilizes information from other dimensions, and then repeating the process. While, one could adapt the above method for the problem discussed here, it is not designed to provide insights about the sparsely populated regions in the manifold, which is crucial to guide the next round of simulations in our target application.  
Figure 1: High-dimensional process data trajectories generated by a material morphology simulation. Each row corresponds to the instances sampled at different time points from a single trajectory. Each trajectory \(\Gamma_{I}\) corresponds to different variants of the organic thin film fabrication process (described by parameters \(\phi\) and \(\chi\)): \(\Gamma_{1}=\Gamma(\phi=0.6,\chi=2.2)\), \(\Gamma_{2}=\Gamma(\phi=0.6,\chi=3.0)\), \(\Gamma_{3}=\Gamma(\phi=0.5,\chi=3.0)\). Each image is a high-dimensional point capturing material morphology (different colors represent different types of polymer making the material). (Please view in color).
###### Abstract
Thus, MODIS is limited in skill in terms of snow mass estimation although it does an good job of viewing where snow is found on the ground in the absence of dense forest [9; 23]. More recently, active microwave (AMW) synthetic aperture radar (SAR) has been employed for global snow mass detection [24; 25], but two issues remain unsolved: one is the limited repeat overpass (relative to Advanced Microwave Scanning Radiometer (AMSR) or MODIS), and the other is that C-band microwave radiation on, e.g., Sentinel-1, is not as sensitive to snow volume scattering as compared to X-band or as skilled in the detection of shallow snow as compared to Ku-band [26; 27], although recent studies showed its potential at mapping snow mass in mountainous regions [28]. Suffice it to say that the development of such sensors for purposes other than terrestrial snow mass limits the skill of these sensors in the application to global SWE estimation. As a result, the snow science community is discussing the prospect of a future, dedicated spaceborne snow mission which would be the first of its kind [29; 30].
## 5 Experiments
### Practical Evaluation on Resource-Constrained Devices
#### 5.5.1 Response Latency  
The performed latency tests allowed for determining the maximum request load that each SBC can handle without degrading response time. Figures 20 and 21 show the read operation latency obtained for two different SBC devices (Orange Pi One and Raspberry Pi 4, whose main specifications are shown in Table 5) under different workloads. Specifically, Figure 20 shows the results obtained for the Raspberry Pi 4 for up to 1000 requests per second (with a duration of 60 s per each test), while Figure 21 shows the read operation latency obtained for the Orange Pi One when performing up to 50 requests per second.  
As it can be observed in Figure 20, the Raspberry Pi 4 (Raspberry Pi Trading Ltd., Cambridge, UK) offers response times similar to those shown in Figure 12 up to 500 requests per second, despite being the latter figure obtained on a higher performance computer. In the case of the Raspberry 4, after the initial spike (preserved in the figure for the sake of transparency, but due to the behavior of the used measurement tool, as it was previously described in Section 5.2), latency remains under 10 ms up to 600 requests per second. After such a number of requests per second, latency increases significantly with the number of requests except for the 50 th percentile, where it remains low throughout all tests.  
In the case of the Orange Pi One (Shenzhen Xunlong Software Co., Ltd., Shenzhen, China), due to the hardware differences with the Raspberry Pi 4, response times are clearly higher, as it can be seen in Figure 21. For such a SBC, the maximum number of evaluated requests per second was 50 since the latency was notably high, reaching values of up to  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Board** & **SoC** & \begin{tabular}{c} **CPU Clock** \\ **(GHz)** \\ \end{tabular} & **Memory (GB)** & **Ethernet** \\ \hline Raspberry Pi 4 & \
\begin{tabular}{c} Quad-Cortex A72 \\ (ARM64) \\ \end{tabular} & 1.8 & 1 + 1.1 Swap & Gigabit \\ \hline Orange Pi One+ &
\begin{tabular}{c} H6 Quad-core A53 \\ (ARM64) \\ \end{tabular} & 1.8 & 1 + 1.1 Swap & Gigabit \\ \hline \hline \end{tabular}
\end{table}
Table 5: Main characteristics of the tested SBC hardware.  
Figure 19: CPU power consumption for PoW and PoA under stress.  
10 s. In the case of the 50 th percentile, it remains stable under 10 ms until 40 requests per second, exceeding 100 ms for 50 requests per second.  
Figure 21: Latency results when performing up to 50 transactions on the Orange Pi One.  
Figure 20: Latency results when performing up to 1000 transactions on the Raspberry Pi 4.
# Solar Concentration for Wastewaters Remediation: A Review of Materials and Technologies
## 2 Solar Collector Systems Employed for Wastewater Treatment
### Non-Concentrating Systems (NCC)
#### 2.2.1 Inclined Plate Collectors (IPC)  
Inclined plate collectors (IPC) are flat or corrugated panels over which a thin (typically \(<\) 1 mm) laminar flow (usually 0.15-1.0 L/min) of wastewater is sustained. It is probably the simplest  
Figure 5: Fresnel lens transmitter concentrator scheme [60].  
available design and offers the advantage of a large surface to support the photocatalyst material, in a configuration known as \"thin film fixed bed reactor\" (TFFBR). It is to be noted that water heating is not an issue with this design, with temperature typically between 70 and 95 \({}^{\circ}\)C [66]. IPCs are considered to be particularly suitable for small-scale applications [67]. Figure 6 presents an image and a scheme of an IPC.
## 3 Results and Discussion
### Extraction Test  
Extraction tests with SRM 8610 spikes of approximately 150 pg and 308 pg with various extraction times showed recovery yields from 64% to 102%, depending on the purging duration (Table 1). For the 150 pg spikes corresponding to 517 pg L\({}^{-1}\), the highest recovery yields were 86% with the purging time of 120 min while, for the 308 pg spikes (corresponding to 1062 pg L\({}^{-1}\)), the recovery yields reached 102% with the purging time of 120 min. The high recovery yields suggest that the photochemical reduction of Hg\({}^{2+}\) did not take place under our experimental conditions. It should be noted that some amount of Hg\({}^{0}\)aq (2-12% mercury, relative to the amount of Hg\({}^{2+}\)aq) was always observed in the blank corrected results of the Hg\({}^{2+}\)aq spike tests, implying that up to 12% of the Hg\({}^{2+}\)aq may have been unfavorably converted to Hg\({}^{0}\)aq when purged with the mercury-free nitrogen. This bias was not corrected in the uptake data discussed in the following subsection. It should also be noted that poorer recovery yields for the 150 pg spike tests were likely due to the blank subtraction (i.e., smaller sample size and larger influence from the blank subtraction). Based on these results, the 120 min extraction time was chosen for our analysis. As shown later, all the measured masses were similar to or smaller than the 150 pg range. Therefore,corrections for the recovery yield (measured mass divided by 0.86, obtained from the recovery yield test) were made.
## 3 Results and Discussions
### Fee Analysis  
Table 2 shows a summary of the fee obtained (minimum, maximum, and average values) for each scenario considering the neutral \(NPV_{f}\) with the MC methodology used. The fee was lowest in Scenario 3 and highest in Scenario 2. Specifically, the average fee and the fee range for Scenarios 1, 2, and 3 were 10.49 and \(-\)11.43 to 33.02, 22.09 and \(-\)8.64 to 52.09, and 6.38 and \(-\)29.56 to 42.54, respectively, with all in \(\xi\)/tonne of waste.  
In order to include all output values of the simulation, a fee's density and distribution function graph was built for each scenario (Figure 1). Independent of the scenario, the trend of the fee always takes the shape of a Gaussian distribution. The rank class was defined from \(-\)40 to 75 \(\xi\)/tonne to cover the entire set of values assumed by the fees from the MC simulation in all scenarios. The vertical axis Y shows the frequency, in percentage, that the fee assumes the value showed in the horizontal axis X.  
In Scenario 1, the average value of the fee corresponding to values of rank class 15 has the major probability of about 21.26%. It can be noted that, for Scenario 1, P (fee \(>\) -10 \(\xi\)/tonne) is 99.79%, and P (fee \(>\) 30) is 0.42%, approaching 0.  
The same approach was conducted for Scenario 2. In this case, using the same rank classes fixed the values included in rank classes of 20 and 25 to have the major probability of, respectively, 14.34% and 13.79%. Studying the cumulative distribution function, it can be noted that, in Scenario 2, P (fee \(>\) -5) is 99.60% and P (fee \(>\) 50) is 0.19%, approaching 0, for major values of fee.  
Finally, in Scenario 3, the values covered by the rank class 10 have the major frequency of 14.22%. Moreover, with respect to the cumulative distribution function for Scenario 3, it can be seen that P (fee \(>\) -20) is 98.17% and P (fee \(>\) 40) resulting in 0.08% approaching 0 for major values.  
In general, the lower the fee is, the higher the social acceptation is. Considering the fee value obtained for each scenario, the most viable is Scenario 3 with the lowest average fee, followed by Scenario 1, and, in the last position, Scenario 2 with the highest fee.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **FEE (\(NPV_{f}\) = 0)** & \(NPV_{s}\) \\  & **(\(\xi\)/tonne)** & **(\(\xi\)/tonne)** \\  & **Average (Min, Max)** & **Average (Min, Max)** \\ \hline SCENARIO 1 & 10.49 (\(-\)11.43, 33.02) & x \\ SCENARIO 2 & 22.09 (\(-\)8.64, 52.09) & 13.59 (2.49, 24.81) \\ SCENARIO 3 & 6.38 (\(-\)29.56, 42.54) & 16.60 (8.31, 24.81) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fee and social net present value (\(NPV_{s}\)) resulting from Monte Carlo simulation.
# A Hybrid Bald Eagle Search Algorithm for Time Difference of Arrival Localization
## 4 Experimental Design and Analysis
### Performance Evaluation
#### 4.2.4 Analysis of the Convergence Performance of TDOA Based on HBES  
Swarm intelligent optimization algorithms require a certain population size and number of iterations to complete the search for the global optimal solution. Swarm intelligent optimization algorithms with good convergence capability can converge at smaller population sizes or fewer iterations, reducing running time. Experiments were conducted under the condition that the number of base stations was 8 and for 0.6 m varying the population size \(M\). The relationship between RMSE and population size was obtained as shown in Figure 6. The RMSE of SSA, COOT, PSO, CPSO changes significantly with the increase in population size. the RMSE of GWO, BES no longer decreases significantly after the population size reaches 50. The RMSE of HBES tends to be stable after the population size. The RMSE of HBES stabilized after the population size reached 50. It can be seen that CPSO and HBES have better initial solutions than PSO and BES after using chaotic mapping to initialize the populations so that the individuals of the populations can quickly focus on the global optimal solution and search around it, which improves the efficiency of the search. The population size M is set to 30, and the convergence of RMSE of different algorithms was compared under the increasing number of iterations, and the relationship between RMSE and the number of iterations was obtained, as shown in Figure 7. The RMSE of SSA, COOT, and PSO has a steep decline curve, indicating that the algorithms are easily affected by the number of iterations. The HBES algorithm enhances the global search capability through opposition-based learning and Levy flight, allowing the algorithm to find an initial value with a smaller RMSE faster and to calculate the location of the target using fewer iterations. Therefore, applying the HBES algorithm to TDOA localization can achieve smaller population sizes and iterations, and reduce the time taken to compute the results.
# Re-Keying Scheme Revisited: Security Model and Instantiations
## 1 Introduction
### Related Work  
[PERSON] et al. [9] reconsidered the model of re-keying schemes and gave a new concrete scheme based on the hard physical learning problem. They first modeled the re-keying scheme, which assumed that the master secret key is stored as shares, and these shares were updated when the encryption/decryption process was invoked. Divide a secret key into randomized shares is one of the well-known countermeasures against the SCAs. Then, they gave two security models: against the black-box adversaries and the gray-boxones. The first one denotes the security against adversaries who access the inputs and outputs of the re-keying scheme. On the other hand, the second one allows adversaries to see leakages of the scheme in addition to its inputs and outputs. They also proposed a concrete scheme based on the learning parity with leakage (LPL) problem, which can be reduced to the well-known problem, the learning parity with noise (LPN) problem.  
Their models supposed the countermeasure using the shares. Moreover, their scheme is unsuitable for tiny devices since it requires a costly non-volatile write memory for updating shares. On the other hand, our schemes do not use the countermeasure with shares, which were not captured by their model. In addition, our schemes do not require such non-volatile write memory; hence, they are suitable for tiny devices.  
Another device-level approach to enhance the security is also proposed. In [10], [PERSON] et al. proposed a framework that protects data from snooping attacks and improves hardware security. The re-keying scheme can be used as a module to improve the security of this framework.