esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Study
### Simulation Results  
In this subsection, we first present simulation results that show the end-to-end PDR gain in a simple network configuration. Then, in order to evaluate the network performance of RoCoMAR in a more  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Simulation time & 600 s \\ Mobility & RPGM model \\ Simulation area & 1,000 m \(\times\) 1,000 m \\ Group area radius & 400 m \\ Transmission range & 225 m \\ Traffic source & MPEG-4 Video Streaming (CBR/UDP) \\ Packet size & 1,500 bytes \\ Routing protocols & RoCoMAR, AODV, OLSR, DSR, GFR \\ MAC layer & IEEE 802.11 DCF \\ Physical layer IEEE & 802.11g \\ Maximum Bit rate & 54 Mbps \\ Fading model & Ricean fading with K = 10 \\ Number of nodes & 25 \\ Number of RNs & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters and Values.  
realistic environment, we compare the performance of RoCoMAR with those of other routing protocols using the mobility model presented in Section 5.2.
# A Precise Modeling Method for PMSM Servo System of Spacecraft
## 4 Experimental Testing and Validation
### Nonlinear Identification
#### 4.1.2 Dead-Zone Backlash Parameter Identification  
The backlash is usually measured by measuring the angular position difference in the forward and reverse transition process when the steering gear is stopped. In this paper, another method is adopted, that is, the position feedback of commutation time is observed in the process of motor motion. Because of the backlash, the position feedback will have a larger delay in reversing, and the backlash of the system can be calculated by measuring this delay angle. The actual operation process is as follows.  
Start the motor, when the motor is running steadily this indicates that the servo system has eliminated the unidirectional backlash. Immediately give the reverse angle command to reverse the motor. Due to the existence of transmission backlash, the actuator's reverse angle lags behind the given angle. The width of the transmission backlash can be determined by comparing and analyzing the given angle and the actual feedback angle. The result of the backlash test is shown in Figure 10. According to the test results, the maximum backlash of the servo system output is 0.2300\({}^{\circ}\), and the average backlash is 0.1923\({}^{\circ}\). To evaluate the consistency of the test results, the standard deviation of these data is calculated to be 0.0193, which is within the acceptable range, indicating a good consistency of data. So, the backlash 2\(b\) = 0.1923\({}^{\circ}\), and then the unilateral backlash \(b\) = 0.0962\({}^{\circ}\).
## 3 Experimental Results and Discussion  
A schematic of our experimental setup is shown in Figure 2. Two telecom laser diodes (Toptica DL Pro and MOT/DL Pro) emitting wavelengths at about 1.5 \(\upmu\)m provided narrow linewidth radiation (tens of kHz on a millisecond timescale), tunable over more than 10 THz by means of a stepping-motor-controlled external cavity. The two monochromatic sources seeded two distinct fiber amplifiers (A1 and A2 in Figure 2). Amplifier A1 (IPG Photonics EAR-10K-C-SF, power up to 11 W) operates between 1540 nm and 1565 nm wavelength, correspondingly to a tunability of about 3 THz. For the other amplifier (A2), two models can be used, operating in two different narrow bands: 1573-1577 nm (IPG Photonics EAR-7K-1575-SF, power up to 7 W) and 1603-1607 nm (IPG Photonics EAR-3K-1605-SF, power up to 3 W), respectively. The two available combinations allow for generation in the 0.97-4.57 THz range and in the 4.54-8.12 THz range, respectively, while the DFG process transfers the spectral properties of the seed telecom lasers to the THz domain.  
The two amplified laser beams are overlapped by a non-polarizing beam splitter and focalized on the input facet of a 5% MgO-doped LN crystal plate (HCP Photonics) by means of a 4 mm focal lens. The crystal plate, having a size of 5 \(\times\) 8 \(\times\) 10 mm\({}^{3}\), contains two planar waveguides (not confining light in the \(z\) direction) and nine channel waveguides, ion-planted on the whole of the side which is 1 cm long.  
The THz radiation, extracted by means of a 45\({}^{\circ}\) cut, high-resistivity silicon (HR-Si) prism, is collimated by a cylindrical mirror and focalized on a room-temperature, Golay-type detector (Tydex GC-1P) with a noise-equivalent power of about \(10^{-10}\frac{W}{\sqrt{Hz}}\) at 10 Hz, and with a calibrated responsivity of 20 kV/W. A 10 Hz mechanical modulation on one of the pump beams, and a fast detector (an InGaAs photodiode with 150 MHz bandwidth--Thorlabs PDA10 CF-EC) placed at the waveguide output allowed us to achieve real-time monitoring of the coupled mode powers \(P_{1}\) and \(P_{2}\), and retrieve the nonlinear generation efficiency.
# Efficient GPU Implementation of the McMurchie-Davidson Method for Shell-Based ERI Computations
## 1 Introduction
### Contributions  
Our previous related work [23] investigated the acceleration of the McMurchie-Davidson (MD) method and developed a parallel algorithm optimized for efficient GPU implementation. This paper builds upon [23] and further extends the GPU acceleration frameworks. Our previous study [23] successfully improved the evaluation efficiency of the recurrence relation (Equation (22)) by employing a batch-based computational algorithm. However, two major issues remained, which we address in this study:  
1. Although the batch-based approach enhanced the efficiency of evaluating the recurrence relation (Equation (22)), there was computational redundancy, as the Boys function needed to be evaluated every time a GTO-ERI within a Basis-ERI was computed.
2. In the previous implementation, each ERI calculation was assigned to a single CUDA block. This assignment strategy led to inefficient utilization of computational resources, particularly for ERI calculations with low computational cost, where the available computational resources within a CUDA block could not be fully utilized, increasing execution time.  
The related approaches in MD acceleration, as shown in Table 1, focus on different aspects/configurations of GPU-enabled MD acceleration. For instance, [PERSON] and [PERSON] [18; 19] focus on the most efficient computation of the Fock matrix and the effective screening of integrals, [PERSON] et al. [20] focus on the automated generation of (symbolic) kernels to evaluate integrals, each of which can be tailored to GPU hardware, [PERSON] and [PERSON] [21] focus on how to find an optimal order of ERIs by (recursive) search, and how to enable the cooperating threads to reduce register use, and [PERSON] et al. [22] focus on (serial) screening by CPU and the thread-based integral evaluations. Compared to the aforementioned related works (as summarized in Table 1), and departing from particular aspects/configurations of MD acceleration, our approach is a generalized algorithm capable of computing GTO-ERIs for arbitrary azimuthal quantum numbers, provided that the intermediate terms can be stored. To this end, our approach capitalizes on the use of shells, batches, and triple-buffering of the shared memory, and efficient block and thread mapping strategies in CUDA, thus exploring the overall effective parallelism frontiers. The following points briefly describe the key contributions of this paper.
## 5 Discussions
### Proposed Research Roadmap and Future Directions
#### 5.6.1 Timeline of Core Technology Maturity  
This section presents the evolutionary timelines of key technologies that play a critical role in IoE security, including Blockchain [8; 73; 178], AI [25; 59; 164], Quantum-Resilient Cryptography [18; 54; 180], and IoE infrastructures [107].  
Figure 11 presents a strategic timeline projecting how advanced technologies will evolve over the years to address the security challenges in IoE systems.  
For the 2025-2028 period, it is assumed that the systems will still be in a relatively immature phase, characterized by resource-constrained devices. Therefore, security solutions will focus on lightweight and practical technologies such as optimized blockchain algorithms [178; 179], PQC-compatible digital signatures [35; 52; 180], and FL methods that enable local model training without compromising data privacy [23; 44; 170]. During this phase, reactive and on-site security mechanisms like Edge AI-based anomaly detection [25; 59; 145] will also become prominent.  
In the 2029-2032 period, infrastructures are expected to mature, and interactions between devices will intensify. At this stage, new requirements such as interoperability and resilience against quantum threats will emerge. Consequently, more complex, secure, and flexible solutions will be implemented, including QKD [18], post-quantum blockchain architectures [54], context-aware access control [84; 160; 171], and interoperable AI-based security systems [144; 164]. Additionally, technologies like cross-chain privacy protocols and secure multi-party computation (SMPC) will gain importance during this period [47; 175; 176].  
By the 2033-2036+ period, it is assumed that the systems will evolve into self-protecting and self-learning structures rather than merely reactive ones. Security will transition towards architectures that are based on zero-trust principles [161; 164], capable of autonomous threat detection and mitigation [144; 145], resistant to quantum attacks [54; 81], and supported by semantic AI for proactive and self-managing security frameworks [136; 166].  
Thus, the figure represents not only a technological sequence but also the structural transformation of security in parallel with the maturation of system requirements. Table 21 illustrates the strategic roadmap for advancing security technologies in IoE ecosystems.  
Figure 11: Progressive roadmap of IoE security systems from early integration to autonomous quantum-resilient models.  
This roadmap outlines the evolving priorities in IoE security technologies between 2025 and 2036, focusing on scalable, interoperable, and quantum-resilient solutions aligned with FL, zero-trust architectures, and semantic AI.
## 2 Sample Preparation and Test Methods
### Test Equipment  
In previous studies, the rock pore water pressure only came from axial hydraulic loading, ignoring the influence of the hydraulic envelope pressure, which is not consistent with the complex hydraulic environment in which the actual rock engineering is located [17; 18; 19]. To overcome this shortcoming, hydraulic loading equipment was self-developed to account for hydraulic loads in all directions. The equipment mainly consisted of a hydraulic loading system, a pressure vessel, and a data acquisition system, as shown in Figure 2. In the hydraulic loading system, the water tank was a container for storing water, the low-pressure water was converted into high-pressure water through a booster pump (maximum output stress 2.5 MPa), and the pressure regulator was used to set the maximum output pressure of the booster system. The cylindrical pressure vessel (inner diameter 320 mm, height 250 mm) provided space for the rock hydraulic tests. The top cover and the cylinder block were connected by 8 bolts. The top cover was equipped with a pressure booster hole, a pressure relief hole, a pressure gauge hole, and a wire hole. The pressurized hole as connected to the pressure-increasing valve. The pressure relief hole was connected to a pressure-relief valve. The outlet end of the hydraulic loading system was connected to the pressure-increasing valve of the pressure vessel through a high-pressure water pipe. The pressure gauge hole is controlled by the pointer gauge. The wire hole was used to pass the sensor wires. All the holes were sealed to ensure that no water could flow out of the container through the holes. The data acquisition system consisted of strain gauges, water pressure sensors, and a data acquisition instrument. The PC could receive test data in real time. This data acquisition system was connected to the pressure vessel via sensor leads.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Sample No.** & **Dry Density (g/cm\({}^{3}\))** & **Saturation Density (g/cm\({}^{3}\))** & **Porosity (\%)** \\ \hline S1 & 2.810 & 2.827 & 1.76 \\ S2 & 2.730 & 2.743 & 1.53 \\ S3 & 2.841 & 2.854 & 1.29 \\ S4 & 2.613 & 2.629 & 1.25 \\ \hline \end{tabular}
\end{table}
Table 1: Basic physical parameters of the limestone samples.  
Figure 2: Schematic diagram of the test equipment.
## 3 Results  
After the removal of variables using VIF and the feature selection procedure using BorutaShap, a total of 13 variables were used during the modelling procedure. The model showed a predictive accuracy of \(89.6\%\pm 3.1\%\), correctly classifying 516 out of 576 games that resulted in a win or loss (precision: 88.9%; recall: 90.1%, f1-score: 89.5%; Figure 2). In Table 2, the misclassifications are further specified in relation to total goal difference.  
The most important predictors of the model are presented in Figure 3. As an illustration of the possibilities of local explanations using TreeExplainer, two individual game predictions are presented in Figure 4a,b.  
Figure 3: Feature importance based on SHAP-values. On the left side, the mean absolute SHAP-values are depicted, to illustrate global feature importance. On the right side, the local explanation summary shows the direction of the relationship between a variable and game outcome. Positive SHAP-values are indicative of winning, while negative SHAP-values are indicative of losing. As demonstrated by the colorbar, higher values are shown in red, while lower values are shown in blue. As an example, this shows that positive differences between total shots on target from the attacking penalty box between teams are associated with winning, while negative differences are associated with losing.  
Figure 2: Confusion matrix showing the true and predicted results.  
Figure 4: Two waterfall plots examining local game predictions, showing the contribution of each variable to the prediction. The variable name is preceded by the value of the particular variable. Below the x-axis, the baseline value (EI(\(\chi\))) is displayed, indicative of the expected value of the model evaluated on the background dataset. The SHAP-values of each variable are summed to match the model output with all variables included. Positive SHAP-values push the model to predict a win, while negative SHAP-values push the model to predict a loss.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Goal Difference**} & \multicolumn{2}{c}{**Games (n = 576)**} & \multicolumn{2}{c}{**Misclassifications (n = 60)**} \\ \cline{2-5}  & **Total** & **\%** & **Total** & **\%** \\ \hline
1 & 302 & 52.4\% & 43 & 71.7\% \\
2 & 160 & 27.8\% & 16 & 26.7\% \\
3 & 69 & 12.0\% & 1 & 1.7\% \\
4 & 33 & 5.7\% & 0 & 0\% \\
5 & 8 & 1.4\% & 0 & 0\% \\
6 & 3 & 0.5\% & 0 & 0\% \\
7 & 1 & 0.2\% & 0 & 0\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: An overview on the total and relative number of games and misclassifications for each goal difference.
# A Joining Procedure and Synchronization for TSCH-RPL Wireless Sensor Networks
## 4 Joining Procedure and Time Synchronization Problems
### Common Configurations, the Proposal, and Involved Parameters  
The analysis of the network joining mechanisms offers a list of involved parameters that can affect, either positively or negatively, the process. Tuning a parameter in order to favour a desired trait--for instance, joining speed--can result in an impracticable amount of energy consumption.  
Identified variables that can influence the results, and serve as input for different experiments and simulations, are:  
* the number of channels used during transmission;
* the number of channels used for scanning the medium before synchronization;
* the EB (beacon) period, be it fixed or variable; and
* the RPL control message (DIS, DIO) period.  
The control message traffic rate policies, which are represented by the EB and RPL periods, are commonly found under two configurations: the RPL trickle timer, as seen in Section 4.3, and the fixed EB period, as seen in Section 4.1. These are the options that are recommended by the Contiki OS and the IEEE802.15.4e standard with the minimal 6 TiSCH RFC, respectively.  
Figure 5: A combination of TSCH and RPL.  
A proposal for a new configuration, based on the best traits of these two configurations and called \"Custom Trickle Timer\", allows nodes to increase or decrease the EB period at a certain moment. A detailed description is introduced in the results section. Since many of the proposed configuration's parameters are derived from the common configurations' results, it is better to explain it after characterizing the performance of the \"Fixed EB\" and the \"RPL trickle timer\".
# Game theory-based Routing for Wireless Sensor Networks: A Comparative Survey
## 3 Game Theory-Based Routing Protocols in WSNs
### Routing Protocols Based on Cooperative Games
#### 3.1.4 Probabilistic Routing Scheme Based on Game Theory (PRGT)
The other is social selfishness (SS), which denotes the unwillingness of the nodes to forward messages to the nodes that are not friendly. In OppNets, the energy and buffer of the nodes are pretty much limited because of the overhearing issues. Therefore, SCS is prominent in OppNets because the limited energy and buffer size of the nodes compel them to deal with their own messages only. SCS is mathematically defined using the following equation:  
\[SCS=(\alpha.\frac{E_{r}(t)}{E_{s}}+\ \beta.\frac{B_{r}(t)}{B_{s}}+1)^{-1}, \tag{9}\]  
where \(E_{r}(t)\) is the remaining energy if node \(r\) at time \(t\). Similarly, \(B_{r}\)(t) is the remaining buffer of node \(r\) at time \(t\). \(\alpha\) and \(\beta\) are the influence weights of energy and buffer on the node's SCS, respectively. SS is a common feature among the nodes in OppNets. In [44], the proposed PRGT protocol was compared with some classical routing algorithms (epidemic routing, SprayAndWait) in terms of message delivery ratio, latency, and overhead ratio. PRGT showed a better performance in terms of all the metrics. Technical strengths:  
* It could successfully deal with the selfishness of the nodes and build cooperation for better routing to save energy;
* The mathematical formulation for the bargaining game is sound and in detail. It will be convenient for real life implementation with the sensor networks. Technical weaknesses:
* Proposed protocol requires a centralized scheme. Not suitable for distributed architecture. Applications:
* Applicable for a scenario where there is a reliable backbone for the Internet. Not suitable for deployment during natural disasters. Probable future improvements:
* More routing metrics can be introduced for performance analysis.
# System-Theoretic Process Analysis Based on SysML/MARTE and NuSMV
## 4 Discussion
### About the Definition of Loss Scenario  
In STPA-SN, loss scenario is defined as the process in which the system emerges UCA and system-level hazard with the involvement of causal factors. In HSTPA, loss scenario is defined as describing the causal factors that lead to UCAs and to hazards.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**HSTPA** & **STPA-SN** \\ \hline Step 1: Define the purpose of the analysis & Step 1: Define the purpose of the analysis. No major modification. \\ \hline Step 2: Model the control structure: & Step 2: Construct the system model: (a) Boxes and lines are used to describe the hierarchical control structure between the components, including the components and the commands and feedbacks between components. (b) The process model needs to be built exclusively and mainly consists of static information. & Step 3: Identify the unsafe control actions (a) If the system (a) Define UCA as a state and define UCA with the CA received controlled process or the one issued by the controller. (b) Extract the information required for UCA identification from the boxes and lines model in step 2. \\ \hline Step 4: Identify the loss scenarios & Step 4: Identify the loss scenarios (a) The loss scenario is defined as describing the causal factors that lead to UCAs and to hazards. (b) Loss scenarios are identified manually. & Step 4: Identify the loss scenarios (a) The loss scenario is defined as the process in which the system emerges UCA and system-level hazard with the involvement of causal factors. (b) SysML state machine diagrams are automatically converted into the NuSMV model, through which the loss scenarios are automatically obtained. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparisons between STPA-SN and HSTPA.  
As shown in Section 3.4.4, STPA-SN can offer a loss scenario with a complete process. However, HSTPA does not have this capability, just pointing out possible causal factors [1] (pp. 42-53). Hence, the loss scenario by STPA-SN is more rigorous and specific than the one by HSTPA, which benefits the design.  
If a loss scenario is identified by STPA-SN, the system designer can accurately analyze the causality and rapidly decide the system modification scheme. After modification, the updated system model can soon be verified by STPA-SN, until no loss scenario is identified.
## 3 Results
### Case Study in Slovakia
#### 3.1.5 Determination of Arrival and Departure Train Sequence Interval Times  
The calculations of individual train sequence interval times are based on the procedure for determining these operating sequence interval times provided in the internal ZSR regulation DP 1 (internal regulation of the Slovak railway infrastructure manager: \"Determination operating intervals and subsequent intermediates\"). Since this regulation does not recognize the ETCS L3 currently, we need to model the calculations to suit this system. Since we assume that the speed and kilometer profile will be maintained and only modifications related to the train control system will take place on the track, unmodified train driving times are used in the calculation [36; 37].  
The calculated stopping distances for trains are a prerequisite when determining the calculation method. These calculations are performed based on standard procedures and are subsequently shown in Table 2. However, these values are greater than the actual technical possibilities for stopping the train. In our investigation, this does not interfere as this reserve includes possible deviations from the perfect braking of the train [36].  
In determining the subsequent train sequence interval times, we therefore use established train driving times and the calculated stopping distance of the trains, as follows [33]. For a sequence of equally fast trains:  
\[I_{o}^{RR}=\frac{t_{of\text{ }the\text{ }train\text{ }2}^{stopping\text{ }distance}+I_{(train\text{ }1)}}{V(train)}*0.06+T_{dispatching\text{ }time\text{ }of\text{ }the\text{ }train\text{ }[\text{min}]} \tag{1}\]  
For a sequence of the first fast-second slow train:  
\[I_{o}^{RP}=\frac{t_{of\text{ }the\text{ }train\text{ }2}^{stopping\text{ }distance}+I_{(train\text{ }1)}}{V(train\text{ }1)}*0.06+T_{dispatching\text{ }time\text{ }of\text{ }the\text{ }train\text{ }[\text{min}]} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Train Type** & **Ex** & **R** & **Os** & **Pn** & **Mn** \\ \hline \hline Braking distance & 1.375 & 633 & 395 & 466 & 223 \\ \hline \end{tabular}
\end{table}
Table 2: Determined braking distances for ETCS L3 level (m).  
Figure 5: ZSR 130 route marked in red on the map.  
For a sequence of the first slow-second fast train:  
\[l_{o}^{PR}=t_{(slow\ train\ running)}+I_{o}^{RR}-f_{j(fast\ train\ running)}[ \text{min}] \tag{3}\]  
The particular symbols used that require explanation are as follows (the others are explained directly in the formulas):  
\[V_{(i)}\text{train speed of the }i-\text{train }[\text{km}/\text{h}];\]  
\[\text{l}_{()}\text{-train length }of\ the\ i-train\ [\text{m}];\]  
\[t_{j(1\ or\ 2)}^{j}\text{-running time of the }i-\text{train }[\text{min}];\]  
\[l_{o}^{ij}\text{-train sequence interval time }for\ i-j\ train\ sequence[\text{min}];\]  
\[I_{p}\text{-arrival headway time of the }i-\text{train }[\text{min}].\]