esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis on Influence Factors of Seismic Performance
### Material Strength of Energy Dissipation Ring  
When the axial compression ratio of prestress is 0.05, the initial prestress is 52 kN; when the axial compression ratio of prestress is 0.15, the initial prestress is 155 kN.  
Comparing the hysteretic curve and skeleton curve of the PSCFST pier with different material strengths of the energy dissipation ring (as shows in Figure 17), it can be found that with the increase in material strength of the energy dissipation ring from Q195 to Q345, the hysteretic curve and skeleton curve of the pier gradually increase, with a more obvious flag shape. This is due to the increase in the strength of the connecting members between segments, which makes the integrity of the pier better, the ultimate bearing capacity of the pier gradually increases, and the overall stiffness of the pier has been significantly improved.  
In the elastic stage of the first loading, it can be seen that the hysteretic curves and skeleton curves of PSCFST pier with different material strengths of energy dissipation rings basically coincide, indicating that the overall stiffness has nothing to do with the material strength when it does not enter the damage stage at the beginning. The slip section of unloading is basically the same, indicating that the influence of material strength on unloading stiffness is also relatively small.  
As shows in Figure 18, with the increase in the material strength of the energy dissipation ring, the energy dissipation capacity of the PSCFST pier has been significantly improved, which can be seen from the final cumulative energy dissipation: Q195 corresponds to 3726.7 kN\(\cdot\)mm and Q345 corresponds to 5036.45 kN\(\cdot\)mm, up 26 percent.  
Comparing the friction energy dissipation curves of PSCFST pier with different energy dissipation ring material strengths (as shows in Figure 19), it can be seen that the friction energy dissipation of pier is inversely proportional to the material strength within 20 s before loading, but with the increase in energy dissipation ring damage, the friction in the concrete-filled steel tubular sections between segments gradually increases. In the later stage of loading, it can be seen that the friction energy dissipation in the model with the Q345 energy dissipation ring material increases faster. With the increase in the material strength of the energy dissipation ring, the friction loss also increases.  
Figure 17: Hysteretic curve and skeleton curve under different energy dissipation ring strength. (**a**) Hysteretic curve. (**b**) Skeleton curve.
# The Interface of Privacy and Data Security in Automated City Shuttles: The GDPR Analysis
## 1 Introduction
Additionally, the ACS data privacy risks entail further regulations such as the ePrivacy [10] and the Network and Information Security (NIS) directives [11; 12]. Those directives urge to take the appropriate and proportionate measures for a high level of information systems' security to prevent and minimize the impact of cyber incidents and attacks. For the purpose of our work, we constrain our analysis to explore only the GDPR implications. The present article provides the following contributions:  
1. An extensive analysis on how the GDPR discusses the principles of data processing, the rights of data subjects, and roles and responsibilities of the stakeholders (data controllers, data processors, sub-processors, etc.) before, during, and after the processing of personal data collected from the ACS. Figure 1: Example of an automated city shuttle (Geneva, Switzerland). Categorization of the main privacy-preserving techniques that are applicable to the ACS environment. * Presentation of the gaps between the legal definitions and technological implementation of privacy-preserving schemes recommended by the GDPR, which are mainly pseudonymization and anonymization techniques. * Investigation, through interdisciplinary efforts, into the shortcomings and pitfalls of the GDPR data processing principles in protecting personal data within the complex ACS context. * This article addresses the following research questions:  
**RQ 1**. : _According to the GDPR, what are the rights of data subjects that data controllers have to take into consideration before initiating any processing of collected data from the ACS ecosystem?_  
**RQ 2**. : _What is the attribution of role for each stakeholder involved in the ACS landscape? What are their respective responsibilities based on the GDPR?_  
**RQ 3**. : _What are the relevant privacy-preserving techniques to protect personal data within the ACS environment and how do each guarantee personal information protection in line with the GDPR? Would the techniques recommended by the GDPR be enough for an optimal protection?_  
The remainder of this paper is structured as follows: Section 2 discusses the related work and makes a comparison of the present work with already published efforts. Section 3 delivers a thorough review of the most crucial GDPR regulatory effects while applied to the automated driving ecosystem. Section 4 overviews the gaps between technological and regulatory disciplines in terms of implementation of mitigation techniques to data privacy risks related to the ACS deployment. Section 5 offers concluding remarks and future work orientation about lawful processing of personal data within the ACS environment.
## 3 Proposed Testbed for Cybersecurity Analysis of Nuclear Power Plants
### ScadaBR HMI and Historian  
ScadaBR [24] is an open-source supervisory system that presents several features expected by our testbed in order to reproduce a situation close to the industry practice in a virtual environment. In particular: visualization of automation data in real time; construction of graphical screens; and continuous recording of variable changing values in a database. This last function, also called Historian or Datalogger, is provided by the relational database management system (RDBMS) linked to the main program. It is fundamental to enable deeper studies based on the analysis of data captured over long periods of time, such as, for example, the creation of datasets for IDS automation research through the training of ML algorithms. In the version we used, the supervisory links to an Apache Derby RDBMS by default. However, most ScadaBR users migrate the application to use the MySQL [31] manager instead, which would provide performance and stability gains to the Historian in real applications. We repeated the procedure in our testbed and, in order to facilitate the manipulation of this database, we also installed the MySQL Workbench [27] graphical tool in the same VM.  
Regarding the choice of variables to be monitored by ScadaBR, it is important to recognize that the version of ANS that was employed continuously provides the values of 153 different input and output (IO) variables (sensors, actuators, setpoints, and commands). These are grouped into 19 subsystems distributed among the three main circuits found in PWR reactors (primary, secondary, and tertiary). Thus, any practical study involving cyber-attacks against the ANS and evaluation of its impacts must commence by selecting a relevant subset of this universe.  
Besides the desired participation of the nuclear reactor (RX) and its reactivity control variables, our choice of subsystems of ANS to be included in the ScadaBR HMI was guided by their close relationship to those that are likely to be mostly affected in the cyber-attack scenario planned as a validation test for the proposed framework. To this end, we determined that the attack would primarily involve the condenser cooling pump (CCP). This system in the tertiary circuit is responsible for controlling the balance between steam and water in the condenser (CD) linked to the output of the power plant's electrical generator driving turbine (TB). Its improper operation could, in the extreme, cause the interruption of the turbine (TB) operation, and indirectly of the nuclear reactor. Another feature that makes this subsystem attractive for an insider is that it is usually physically located outside the nuclear island.  
These considerations led to the development of a HMI consisting in the numerical and graphical screens shown in Figures 9 and 10. In those, we have just linked the variables associated with the chosen subsystems: main control; condenser cooling (CC); condenser (CD); turbine (TB); and reactor (RX). Their characteristics are described in greater detail in the experimental section of this paper.
# Determination of Point-to-Point 3D Routing Algorithm Using LiDAR Data for Noise Prediction
## 4 Results and Discussions  
_Efficiency of Algorithm_  
The efficiency of algorithm is tested using two verification schemes. _One_: checking whether the routes determined to be the principal routes (for propagation of sound (noise)) are indeed the principal routes. It is understood [40] that the principal routes carry the maximum sound energy during propagation. Further, the principal routes need to be shortest to carry maximum energy and become principal. Thus, all the determined routes are checked whether they are the shortest in their respective planes. _Two_: If the routing  
Figure 13: “S” is source and “D” is destination and R’ is the new reflection point found on wall of building.  
algorithm indeed offers the optimal routes, then the efficiency of noise modeling (where the routes are used for noise prediction) would improve significantly. Thus, the noise levels are predicted for the RGIPT campus and verified with ground recorded data of noise level to determine the improvement of accuracy for noise prediction.
# Energy Management for a Power-Split Plug-In Hybrid Electric Vehicle Based on Reinforcement Learning
## 4 Simulation and Result
It can be recognized that the EMS based on the RL algorithm is capable of controlling the range of the battery power variation smaller than that of the CD/CS method, and the RL method can restrict the maximum battery discharge power. Here we can conclude that the EMS based on the RL control strategy can protect the battery and extend the battery life to some extent. Figure 11 shows the SOC curve under different driving cycles. The initial SOC is supposed to be 90%, and the minimum SOC threshold is 30%. Compared with the results of the CD/CS scheme, the SOC downward trend based on the RL method is more smoothly. Figure 12 illustrates the fuel consumption under four driving cycles. According to Figures 11 and 12, we can find that the optimized control strategy does not take effect completely in the entire cycle, and works before the battery SOC drops to a certain value. Even so, the proposed algorithm can still effectively reduce the fuel consumption. To further discover improvements of the RL based strategy, the engine operating points for both RL based method and the CD/CS method under four driving cycles are depicted in Figure 13. It can be obviously found that by implementing the RL based algorithm, the engine working efficiency is higher than 30% in most cases. Compared with the CD/CS strategy, the proposed method can make the engine working points more densely in the high efficiency area. Moreover, it can be noticed that based on the RL based method, the majority of engine working points gather near the optimal operating line,
# Development of a Simulation Model for HMT of a 50 kW Class Agricultural Tractor
## 2 Materials and Methods
### Hydrostatic Circuit  
The layout of the HSU is shown in Figure 2. In this study, a fixed hydraulic motor was selected in the hydrostatic circuit for the lower cost and simplification of the control process. The circuit was mainly composed of two basic hydraulic components, a variable displacement pump, and a fix displacement motor. The gear ratio of the engine and pump is shown in Equation (1), considering the volumetric efficiency, and the rotational speed of the output shaft is calculated in Equation (2). In this transmission, the pump input shaft is driven by the engine through a pair of gears. The following layout explains the concept form of the circuit. The complete transmission circuit is more complex, including a flushing valve for cooling, a reservoir for leakage oil, and a charge circuit for the whole loop. In this hydrostatic circuit, when the engine output speed is constant, the output shaft speed of the motor is determined by the coefficient of the variable displacement pump. The motor output speed changes from \(-n_{\text{max}}\) to \(n_{\text{max}}\) as the coefficient varies from \(-1\) to \(1\).  
\[i_{p}=-\frac{n_{e}}{n_{p}} \tag{1}\]  
where \(i_{p}\) is the gear ratio of the engine and pump, \(n_{\text{p}}\) is the rotational speed of the pump (r/min), and \(n_{\text{e}}\) is the rated rotational speed of the engine (r/min).  
\[n_{out}=-\frac{n_{m}}{i_{m}}=\frac{n_{e}V_{p}\eta_{v,p}\eta_{v,m}\varepsilon}{ i_{p}i_{m}V_{m}} \tag{2}\]  
where \(n_{out}\) is the rotational speed of the output shaft (r/min), \(i_{m}\) the is the gear ratio of the motor and output shaft, \(n_{m}\) is the rotational speed of the motor (r/min), \(V_{p}\) is the displacement of the pump (cc/rev), \(\varepsilon\) is the coefficient of the variable pump with a range of \(-1\) to \(1\), \(V_{m}\) is the displacement of the  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Item** & **Specification** \\ \hline \multirow{5}{*}{Engine} & Length \(\times\) Width \(\times\) Height (mm) & \(648\times 560\times 685\) \\  & Weight (kg) & 237 \\  & Bore/Stroke (mm) & 92/110 \\  & Displacement (L) & 2.9 \\  & Number of cylinders & 4 \\  & Max. Power (kW \%/min) & 55.4 @2200 \\  & Max. torque (Nm \%/min) & 260 @1800 \\ \hline \multirow{5}{*}{HSU} & Displacement (cc/min) & Pump & 0–42 (Variable) \\  & & Motor & 42 (Fixed) \\  & Weight (kg) & 30 \\  & Max. speed (r/min) & 3200 \\  & Oil & ISO VG46 \\  & Relief valve (MPa) & 34.3 \\  & Charge pump (cc/min) & 11.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the engine and hydrostatic unit (HSU) of the HMT tractor prototype.  
Figure 1: Schematic diagram of the prototype of hydro mechanical transmission (HMT).  
motor (cc/rev), \(\eta_{v,p}\) is the volumetric efficiency of the pump (%), and \(\eta_{v,m}\) is the volumetric efficiency of the motor (%).
# A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data
## 4 Results  
### Results of the BFE Approach Implemented in AUTOWADE  
The intermediate results of the various steps of the BFE approach and the final map are shown in Figure 12 for the Modena test site (EMSR359, Table 2). It can be noted that the output map (panel d) is basically derived from the maps of TDWA (panel e) and TDFA (panel f). The maps of CDWA and CDFA, derived from the ISODATA, correspond to the red pixels in panels b,c. As expected, the maps of CDWA and CDFA are very noisy. Hence the need to combine the CDWA with the reference water mask to select the objects actually corresponding to water bodies (MCDWA). Similarly, the CDFA is modified to select the objects that contain open water, but do not contain permanent water bodies (MCDFA).
## 4 Challenges and Future Research Directions
### Experimental Implementations  
UAV-enabled ITSs represents the future of transportation systems and remain an emerging research domain. Despite significant advancements, real-life implementation is still under investigation. The lack of comprehensive information, the internal and external uncertainties, and the absence of extensive protocols make it challenging to identify the specific obstacles to UAV integration in future ITSs. To uncover potential risks, future research should focus on experimental implementations of UAV applications within ITSs. Conducting thorough research and experiments before full-scale implementation will pave the way for successful deployment and expand the possibilities for UAV-enabled ITSs.  
In addition to the potential research areas discussed in this section, numerous other avenues exist for enhancing the large-scale implementation of UAV-based ITSs. As technology continues to evolve, addressing these additional areas could significantly advance the capabilities and effectiveness of UAV-based ITSs.
## 2 Preliminaries  
### Mathematical Model of Multi-PMSMs System  
The mathematical model of the multi-PMSM system is described as follows:  
\[\left\{\begin{array}{l}\dot{\omega}_{r,i}=-\frac{B_{i}}{l_{i}}\omega_{r,i}- \frac{n_{p}}{l_{i}}T_{L,i}+\frac{1.5n_{p}^{2}\psi_{f,i}}{l_{i}}i_{q,i}\\ \dot{i}_{d,i}=-\frac{R_{i}}{l_{d}}i_{d,i}+\omega_{r,i}i_{q,i}+\frac{1}{L_{i}}u_ {d,i}\\ \dot{i}_{q,i}=-\omega_{r,i}i_{d,i}-\frac{R_{q,i}}{L_{i}}i_{q,i}-\frac{\psi_{f, i}}{L_{i}}u_{r,i}+\frac{1}{L_{i}}u_{q,i}\end{array}\right. \tag{1}\]  
\(\omega_{r,i}\) represents the electrical angular velocity; \(\psi_{f,i}\) is the permanent magnet flux chain; \(n_{p}\) is the pole pairs; \(L_{i}\) is the dq-axis inductance; \(B_{i}\) represents the friction coefficient; \(T_{L,i}\) represents the load torque; \(J_{i}\) represents the rotor inertia; \(u_{d,i}\), \(u_{q,i}\), \(i_{d,i}\), \(i_{q,i}\) are the dq-axis voltages and currents; \(R_{s,i}\) represents the stator resistance.
## 5 Discussion
### Soil Contamination Assessment
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline
**Transect** & **Soil Sample** & **Be** & **V** & **Cr** & **Co** & **Ni** & **Cu** & **Zn** & **As** & **Se** & **Cd** & **Sn** & **Sb** & **T1** & **Pb** \\ \hline \multicolumn{11}{c}{**Core 2 (yellow zone)**} \\  
*Core 7 (tidal channel)** & & & & & & & & & & & & & & & \\
0–30 cm & 0.79 & 2.19 & 1.37 & 0.89 & 0.64 & **64.04** & 8.29 & **368.6** & 2.30 & 2.02 & 1.66 & **132.5** & 10.09 & **46.32** \\
30–60 cm & 0.54 & 1.10 & 0.94 & 0.42 & 0.43 & **51.84** & 4.72 & **271.1** & 1.53 & 6.90 & 0.95 & **76.92** & 6.87 & 25.18 \\ \hline \multicolumn{11}{c}{**Core 2 (yellow zone)**} \\
0–20 cm & 0.72 & 0.68 & 1.72 & 15.25 & 2.21 & 26.00 & 35.43 & **137.6** & 5.63 & **45.96** & 2.93 & **195.2** & **60.36** & **260.7** \\
20–35 cm & 0.52 & 0.69 & 1.39 & **132.8** & 4.74 & **106.5** & **157.9** & **270.7** & 35.93 & **224.6** & 9.52 & **1578** & **494.4** & **1893** \\
35–45 cm & 0.89 & 0.94 & 0.95 & 0.83 & 0.49 & 6.44 & 3.77 & 14.44 & 0.61 & 6.01 & 0.59 & 2.59 & 1.51 & 3.13 \\
**Core 3 (yellow zone)** & & & & & & & & & & & & & & & & \\
0–30 cm & 1.00 & 0.91 & 1.42 & 1.54 & 1.20 & 7.70 & 8.97 & **41.95** & 1.03 & 9.02 & 0.43 & **41.65** & 18.77 & 19.93 \\
30–45 cm & 1.09 & 1.01 & 3.92 & 4.68 & 4.08 & 25.73 & 37.62 & **162.7** & 2.61 & 32.19 Given the polymetallic nature of the contamination, a quantitative evaluation of the multi-element soil anomalies was made based on the pollution load index of [PERSON] et al. [24] by taking the seven highest enriched elements (As, Cd, Cu, Pb, Sb, Tl, and Zn) and deriving the seventh root of the seven contamination factors multiplied together (Equation (2)). The _PLI_ values were highly variable both laterally and vertically over the investigated zones (Figure 9). In fact, the metal loading of the soil differed noticeably from one sampling site to another, and even within the same core. The highest metal loading was recorded at the yellow zone immediately adjacent to the sulphide heaps, not only in the topsoil  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
(_PLI_ = 23.6) but also in the subsoil (_PLI_ = 26.0) due to the deposition of pyrite grains carried from the stockpiles by past runoff events. Jarosite seems to be responsible for the high _PLI_ values observed in the furthest yellow zone. On the contrary, the _PLI_ values recorded at the deepest sampling levels of the distal areas were close to unity, except for the jarosite-rich channel infill, where the _PLI_ values were between 6.3 and 10.5.
# DNNs Based Computation Offloading for LEO Satellite Edge Computing
## 4 Problem Formulation  
We define the system utility function \(J\) as the weighted sum of energy consumption and delay required by the processing tasks:  
\[J(I,\mathbf{x},\mathbf{b})=\sum_{n=1}^{N}\Bigg{(}\sum_{m=1}^{M}\Big{(}E_{nm}^{\text{ local}}(1-x_{nm})+E_{nm}^{\text{edge}}x_{nm}\Big{)}+\varphi\max\Big{\{}T_{n}^{ \text{local}},T_{n}^{\text{edge}}\Big{\}}\Big{)}, \tag{8}\]  
where \(\varphi\) represents the weight between the energy consumption and delay required by the processing tasks in the system utility function. By setting parameter \(\varphi\), we can more intuitively express the relationship between delay and energy consumption. The optimization objective of DDOA algorithm [31] is Delay-Energy Weighted Sum (DEWS) metric: \(DEWS:\
abla=\omega T+(1-\omega)E\), where \(\omega\in(0,1)\) is a weighting factor. In this paper, apart from considering the total delay of each user's task processing as the maximum value of each user's total local processing delay and each user's total edge processing delay, the relationship between \(J\) and \(\
abla\) is : \(J=1/(1-\omega)\times\
abla\), and the relation between \(\phi\) and \(\omega\) is \(\phi=\omega/(1-\omega)\). If the optimization objective \(J\) in this paper is multiplied by \((1-\omega)\), it will be the same as the optimization objective \(DEWS\) proposed in [31].  
We formulate the problem of energy consumption and delay for joint optimization tasks as the problem of minimizing the utility function of the system as:  
\[(\mathrm{P1}):J^{*}(I)=\min_{\mathbf{x},\mathbf{b}}\quad J(I,\mathbf{x},\mathbf{b}) \tag{9}\]  
\[\text{subject to }\sum_{n=1}^{N}b_{n}\leq B, \tag{9a}\]  
\[b_{n}\geq 0,\forall n\in N, \tag{9b}\]  
\[x_{nm}\in\{0,1\}. \tag{9c}\]  
Constraint (9a) states that the total uplink bandwidth allocation for all users cannot exceed the maximum bandwidth \(B\). Constraint (9b) indicates that the bandwidth \(b_{n}\) allocated to each user is greater than or equal to 0. Constraint (9c) represents a binary constraint on offloading decision \(x_{nm}\).
## 6 The Local Scheduler
#### Estimating the Execution Time  
An important factor in the scheduling decision is accurately estimating the expected execution time of a given function. To achieve this, we perform controlled profiling on the four different applications used in our evaluation, as described in Section 8.2. We assume that input size and required memory are variables provided by the user. For each function, we collect execution time data across various input sizes and memory configurations in a controlled environment (e.g., consistent CPU shares, warm starts only). This data is then used to train a regression model. Assuming input size and memory as predictors, each function is modeled using a simple linear regression of the form: Y: as a function of input size (\(X_{1}\)) and memory (\(X_{2}\)):  
\[Y=\beta_{0}+\beta_{1}X_{1}+\beta_{2}X_{2}+\varepsilon \tag{12}\]  
\(\beta_{0}\): Baseline execution time (intercept).  
\(\beta_{1}\): Time per KB of input (slope for \(X_{1}\)).  
\(\beta_{2}\): Time saved per MB of memory (Slope for \(X_{2}\)).  
\(\varepsilon\): Residual error.  
By combining offline profiling with linear regression, we can reliably estimate the execution times of repetitive serverless tasks, allowing the buffer-aware scheduler to optimize resource utilization within the invoker.  
```
1:\(Y_{prediction}\)
2:L (final scheduling map within Invoker \(I\)).
3:foreach Request \(r\) in \(Y_{prediction}\)do
4: Get all active containers
5: Calculate buffer vector \(V\)
6: Calculate expected execution \(ex\)
7:if\(V+ex\) < \(SLA\)
8:\(Max_{V}\gets Min_{ex}\)
9:endif
10:else
11: Cold-Start \(\leftarrow\)\(Min_{ex}\)
12:endforeach
13:end
14:end
```  
**Algorithm 1**Local Scheduler  
**Input:**\(Y_{prediction}\)  
**Output:**\(L\) (final scheduling map within Invoker \(I\)).