esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[11] recently presented glycogen (from mussel cooking wastewater) and chitin [12] (from squid pen and prawn exoskeleton) as precursors for producing carbons with a high porosity and specific surface area, up to 3.3 nm and 1526 m\({}^{2}\) g\({}^{-1}\), respectively. Glycogen-based carbon presented an associated specific capacitance of 657 F g\({}^{-1}\) at a current density of 1 A g\({}^{-1}\), at 30 \({}^{\circ}\)C, with 99% retention after 1000 cycles. Several precursors have been studied for producing carbon materials, from crab shells [13], fish scales [14; 15; 16], prawn shells [17], and fish bones [18], among others. In this work, the authors employ a one-step carbonization process to transform blue shark biopolymers (chondroitin sulfate and gelatine) recovered from wastes into porous carbon materials. Commercial chondroit sulfate will be used for comparison. Morphological characterization techniques and electrochemical evaluation, including specific capacitance and cycling stability, will determine their suitability for high-performance energy storage devices. The effect of time and temperature on carbonization was studied. The best-resulting parameters (1 h at 1000 \({}^{\circ}\)C) for the three studied materials were chosen to proceed with the characterization of the materials. The maximum specific surface area and pore size for blue shark chondro sulfate, gelatine, and commercial chondroit sulfate are 135.24 m\({}^{2}\) g\({}^{-1}\) and 1.44 nm, 30.32 m\({}^{2}\) g\({}^{-1}\) and 0.87 nm, and 76.11 m\({}^{2}\) g\({}^{-1}\) and 1.11 nm, respectively. The effect of ball-milling on these materials' surface area and porosity was also evaluated. Ball-milling, a mechanical activation technique, has been proven effective in creating porous carbon materials with controlled pore structures and increased surface area. This technique can be sustainable for synthesizing porous carbon materials with tailored structures and enhanced electrochemical properties. The obtained results showed an optimal specific surface area and pore size at different ball-milling times; however, in the three materials, there is an increase in both parameters, up to 260 m\({}^{2}\) g\({}^{-1}\) and 1.55 nm for blue shark chondroit sulfate, 33.56 m\({}^{2}\) g\({}^{-1}\) and 0.91 nm for blue shark gelatine, and 125 m\({}^{2}\) g\({}^{-1}\) and 1.26 nm for commercial chondro sulfate. This increase in area and pore size is recognizes as promoting the increase in capacitance, as already presented by [PERSON] et al. [19] and evidenced in this work, with the best-resulting ball-milled samples subjected to the electrochemical analysis and the three studied samples showing an increase in capacitance (40 F g\({}^{-1}\) to 122 F g\({}^{-1}\) for blue shark chondroit sulfate, 7 to 49 F g\({}^{-1}\) for blue shark gelatine, and 10 to 25 F g\({}^{-1}\) for commercial chondroit sulfate).
# Securing Smart Grid Enabled Home Area Networks with Retro-Reflective Visible Light Communication
## 3 Numerical and Experimental Results
### Latency Evaluation  
Due to the bandwidth limitation of the Retro-VLC uplink, the key exchange process may incur additional delay. To better understand the performance of the proposed heterogeneous network, experiments are conducted to measure the key exchange latency for different uplink data rates. The results are shown in Figure 8. To measure the time elapsed between the key request and the reception of the encryption key, we first use Linux Wondershaper [25] to manually set the uplink and downlink data rates for the purpose of emulating the Retro-VLC uplink and conventional VLC downlink. For each test, the downlink is always set to 10 Mbps and the uplink rate is changed from 2 Kbps to 1 Mbps. The key exchange process is initiated by a TCP three-way handshake, followed by a key request sent from the host to the gateway. The process is stopped with a key file of 100 bytes [26] sent from the gateway to the host, which includes the Pairwise Transient Key (PTK) and the Group Temporal Key (GTK) for unicast and multicast traffic encryption, respectively. Timestamps of all the transmitted and received TCP packets at the host side are captured by Wireshark and the key exchange latency is determined between the first and the last packets of the entire process. The results in Figure 8 exhibit an inverse proportional relationship between the uplink data rate and the key exchange latency. Using 4-QAM signals and unoptimized pixel configuration, the Pi-cell LCD shutter-based Retro-VLC tag can roughly achieve 1 Kbps uplink data rate. With data extrapolation, we can expect that the time cost of the key exchange at a 1 Kbps uplink rate is 1.6789 s. The latency could be significantly improved with a customized Retro-VLC medium access control (MAC) protocol and a larger bandwidth of optical modulators.
# BGP Neighbor Trust Establishment Mechanism Based on the Bargaining Game
## 5 Simulation and Efficiency Analysis
### Correctness  
Correctness means that the detection algorithm can effectively describe whether the trusted neighbor's behaviors meet the negotiation agreement. The AS can judge malicious /inactive neighbors by the detection result. The experimental scene settings are as follows: the neighbor trust establishment requester \(AS_{1}\) and the neighbor trust establishment agree \(AS_{2}\) have successfully established a trusted neighbor relationship through the BNTE-BG mechanism, and \(AS_{2}\) has forwarded data \(T=7\) times. Negotiation agreement is \(X^{succ}=\left\{x_{1}^{succ},x_{2}^{succ},x_{3}^{succ},x_{4}^{succ},x_{5}^{ succ}\right\}=\left[50,0.1,15,60,300\right]\). R = 1, J = 200 ms, D = 500 ms, \(\theta=0.3\). Table 1 shows the data set collected by \(AS_{1}\).  
Figure 2 shows the changes in \(AS_{2}\)'s behavioral trusts, which are (1, 1, 0.8331, 0.8729, 0.4998, 0.3126, 0.2557). In the first and second forwardings, \(AS_{2}\)'s service fully meets the negotiation agreement, and the behavioral trusts are 1. In the third, fourth, and fifth forwardings, the service provided by \(AS_{2}\) could not fully meet the negotiation agreement. Among them, in the third and fourth forwardings, the service provided by \(AS_{2}\) is not much different from the negotiation agreement, and \(AS_{2}\)'s behavioral trusts are greater than 0.8. In the fifth forwarding, the \(AS_{2}\)'s service is too far away from the negotiation agreement, and the behavioral trust is less than 0.5. In the sixth and seventh forwardings, the \(AS_{2}\)'s service completely deviates from the negotiation agreement, and the behavioral trusts are only about 0.3. During the entire period, the quality of services provided by \(AS_{2}\) gradually declined, and \(AS_{2}\)'s behavioral trusts also gradually decreased. The results show that our detection algorithm can effectively characterize the behavior of \(AS_{2}\). When \(AS_{1}\) detects that the sixth and seventh time's behavior trusts are too low, it could issue a warning to \(AS_{2}\) to further verify whether it is a malicious neighbor. The \(AS_{1}\) can also analyze the bandwidth and packet loss rate of the sixth and seventh forwarding to determine whether the \(AS_{2}\) is a malicious/inactive neighbor. Due to the limited length of this paper, no more experiments will be carried out.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Serial Number** & **Bandwidth/G** & **Packet Loss Rate** & **Jitter/ms** & **Delay/ms** \\ \hline
1 & 50 & 0 & 10 & 40 \\
2 & 52 & 0.05 & 13 & 45 \\
3 & 53 & 0.2 & 25 & 70 \\
4 & 40 & 0.1 & 16 & 50 \\
5 & 45 & 0.5 & 113 & 172 \\
6 & 7 & 0.7 & 148 & 230 \\
7 & 5 & 0.9 & 156 & 389 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data set collected by \(AS_{1}\).  
Figure 2: The behavioral trust of \(AS_{2}\).
# Analysis of the Efficiency of Slope Evacuation for Dense Crowds in Urban Street-Type Public Spaces
## 3 Risk Analysis of Crowd Gathering on Slope in Urban Street Spaces
### Analysis of Factors Affecting Pedestrian Movement Speed  
#### 3.3.1 Effect of Crowd Density on the Speed of Pedestrian Movement  
The Pathfinder Technical Guidance Manual states that the maximum speed at which a pedestrian can move and the density of people in the current environment combine to influence the speed of pedestrian movement \(v\)[32].  
\[v=v_{\text{max}}\cdot v_{f(\rho)} \tag{2}\]  
In Equation (2), \(v_{\text{max}}\) is the maximum pedestrian movement speed, m/s; \(\rho\) is the current crowd density in the evacuation environment, pedestrians/m\({}^{2}\).  
\(v_{f(\rho)}\) is a speed fraction as a function of density as follows:  
\[v_{f(\rho)}=\begin{cases}1&,\quad\rho<0.55\\ \text{max}\Big{[}0.15v_{\text{max}},\frac{1}{0.85}(1-0.266\rho)\Big{]}&,\quad \rho\geq 0.55\end{cases} \tag{3}\]
# A Simulation of Contact Graph Routing for Mars-Earth Data Communication
## 2 Model of CGR
### Routing Management for the Combination of Satellite and DSN Antennas  
For \(j=3,\ldots,6\) and \(k=7,8,9\), the data will be sent from satellite \(j\) to DSN-antenna \(k\) if \(se_{jk}(t)=1\). There are two situations, as follows.  
1. \(rs_{ij}(t)=1\) and \(se_{jk}(t)=1\), so data are delivered smoothly from Mars to Earth in the same time.
2. When there is only \(se_{jk}(t)=1\) and there are data being stored at satellite \(j\) from the previous time interval(s). The first situation needs to find which \(j\) is being considered. If \(x(1,r,d,t)=1\) and \(ids(i)\leq r<ids(i+1)\), we invert Equation (12) as follows: \[j=r+i-ids(i).\] (13)  
In the second situation, we cannot use variable \(x\), because it does not discriminate between stored and carried-through data. The stored data at time \(t\) can be a collection of data from a few minutes before the current time \(t\). We look for all indices \(j\) satisfying \(storsats(j,t)>0\). The stored data are once more divided into packets with a maximum size of 256 KBytes per packet. The total number of these packets is added to the number of data originally being handled at time \(t\), which is \(n_{d}(t)\).
# Pathological Digital Biomarkers: Validation and Application
## 3 Validation of Pathological Digital Biomarker
### Preamalytic Validation  
A whole-slide image (WSI) is a critical procedure for digitizing data when using pathological digital biomarkers in diagnostics [17]. As a result, in order to apply a pathological  
Figure 1: Validation of pathological digital biomarker.  
digital biomarker in clinical practice, the accuracy of the WSI must be guaranteed [18]. This is predicated on the assumption that glass slides have been precisely scanned and duplicated using a scanner [19]. [PERSON] et al. gathered data on the failure rates of scanned digital images before and after the use of quality control (QC) measures for routine scanning. The QC failure rate of WSIs was 20%. Glass slides and WSIs were subjected to strict quality control techniques three months later. The failure rate of images was dramatically lowered to 2% [20].  
The WSI quality control consists of prescan QC, real-time QC, and postscan QC processes [20]. Before scanning, it should be checked if the glass slides are not damaged, stained or dried properly, clean and free of ink markings, that the covers are properly positioned, and that there are no air bubbles [21]. Barcode detection failures, tissue detection failures, macro focus image failures, and image quality errors are all checked by scanners [20]. After the scanning, image thumbnails are checked to ensure that all of the tissues on the glass slide have been digitized [22].  
The College of American Pathologists published a guideline for the validation of whole-slide imaging systems in pathology for diagnostic purposes. Three recommendations and nine statements of good practice were included in this guideline. According to the guideline, the validation process should include at least 60 cases for one application or use case (e.g., hematoxylin-eosin-stained sections of fixed tissue, frozen sections, hematology) that reflect the spectrum and complexity of specimen types and diagnoses likely to be encountered during routine practice [23]. Current histology QC requirements allow for a wide heterogeneity in preanalytical and analytical artifacts across and within labs [24; 25]. Artifact severity and quality vary. Some influence the glass slide (pen markings, filth, bubbles), while others affect the tissue (folds, thickness, stain intensity) or the scanning process (for example, differences in focus or the gridding effect of the WSI). While pathologists learn to interpret using their usual stain methodology and read artifacts on a glass slide, tissue section, or digital picture, computers must adapt to such heterogeneity [26]. Computer-aided algorithms must be generalizable across datasets for digital pathology measurement, categorization, and prognostication [27]. The variability in WSI training sets may be an advantage for generalizability; however, an algorithm's success is tied to analytics control and dataset homogeneity [28].
# Numerical Simulation of Thermo-Hydro-Mechanical Coupling of Model Test for Nuclear Waste Disposal
## 2 THM Coupling Model
### Mass Conservation Equation
#### 2.2.3 Gas Mass Conservation Equation  
Gas is a mixture of dry air and water vapor, including dry air dissolved in water. The mixed gas satisfies the ideal state equation. According to the generalized conservation of mass equation, it is assumed that the change in dry air quality dissolved in water is relatedto the dry air pressure on the water surface; the gas mass conservation equation is obtained as [43; 44]  
\[\begin{array}{l}\
abla\cdot\left[\rho_{a}\frac{k_{lr}k}{k_{lr}}(\
abla p_{l}- \rho_{l}g)+\rho_{g}\frac{k_{pr}k}{k_{g}}(\
abla p_{g}+\rho_{g}g)+\rho_{g}k_{gT} \
abla T\right]\\ =n\big{\{}H\rho_{a}\big{(}S_{cap}+c_{sp}\big{)}+\rho_{g}\big{[}(1-S)c_{sp}-c_{ sp}\big{]}\big{\}}\frac{\partial p_{g}}{dt}-n\epsilon_{sp}\big{(}H\rho_{a}-\rho_{g} \big{)}\frac{\partial p_{l}}{\partial t}\\ -nHS\rho_{a}c_{ap}\frac{\partial p_{l}}{\partial t}+n\big{\{}\rho_{a}H(\beta_{ tT}-S\beta_{aT})-\rho_{g}\big{[}(1-S)\beta_{gT}+\beta_{sT}\big{]}\big{\}}\frac{ \partial T}{\partial t}\\ +\big{[}HS\rho_{a}+\rho_{g}(1-S)\big{]}\frac{\partial m}{\partial t}+n[HS\rho_ {a}+nS\rho_{l}]\frac{\partial c_{sp}}{\partial t}-\rho_{l}\omega(p_{sp}-p_{v}) \end{array} \tag{4}\]  
where \(v_{gr}\) is the intrinsic flow rate of gas relative to the solid phase, \(k_{lr}\) is the relative permeability of the liquid phase, \(k_{gr}\) is the gas-phase relative permeability, \(k_{gT}\) is the thermal driving coefficient of mixed gas, \(\mu_{g}\) is the dynamic viscosity coefficient of gas, \(\rho_{g}\) is the intrinsic density of mixed gas, \(c_{sp}\) is the compressibility factor, \(\beta_{gT}\) is the thermal expansion coefficient of mixed gas, \(c_{\rm ap}\) is the dry air compression coefficient, \(\beta_{aT}\) is the dry air thermal expansion coefficient, \(H\) is the Henry solubility, and \(\rho_{l}\) and \(\rho_{a}\) are the densities of water and dry air, respectively.
## 4 Full-Scale PCS Response Analysis and Optimization Under Traffic Load
### Structural Optimization Based on the Numerical Design
In contrast, Cases 2 and 3, where additional prestressed rebars are placed on the outer beam, further decrease the high-stress area, improving the overall uniformity of stress distribution and reducing the maximum stress. These cases show only a small high-stress region at the bottom of the inner beam-column joint (Figure 17c,d). This suggests that positioning prestressed rebars on both the inner side and the outer beam-column joint is reduced, but a concentrated stress zone still exists (Figure 17c). Figure 16: Concrete stress distribution under external eccentric load. Figure 17: Concrete stress distribution under internal eccentric load. upper inner and outer sides of the beam significantly improves the uniformity of stress distribution under internal eccentric loading. Figure 18 illustrates that the inclusion of prestressed rebars significantly lowers stress levels at both the bottom of the outer beam-column joint and the tensile zone at the inner beam-column joint under full loading. In Case 1, with prestressed rebars only on the inner side, the maximum stress is reduced to 2.8 MPa (Figure 18b), but the stress in the high-stress area remains notable. In Case 2, the further optimization of prestressed rebar placement lowers the maximum stress to 2.7 MPa (Figure 18c), alleviating stress concentration and achieving a more uniform distribution of tensile stress, although some significant stress remains at the outer beam-column joint. In Case 3, the addition of prestressed rebars at the bottom of the inner beam further reduces maximum stress to 2.6 MPa (Figure 18d), completely eliminating the high-stress area at the outer beam-column joint. These findings demonstrate that positioning prestressed rebars on the upper inner and outer surfaces, as well as the bottom inner beam, optimizes stress distribution under full loading. The results above demonstrate that placing prestressed rebars on the top inner and outer surfaces of the cantilever beam (Case 2) significantly improves stress distribution under both internal and external eccentric loading.
# A Tuning Method for Speed Tracking Controller Parameters of Autonomous Vehicles
## 4 Simulation Results and Discussion
### Experimental Results Analysis
This proves that the PID controller parameter optimization algorithm proposed in this study not only significantly improves the speed tracking accuracy of the PID controller but also greatly enhances the dynamic response capability of the system, allowing the autonomous vehicle to maintain stable and precise control across all speed ranges. Therefore, based on this series of simulation results, it can be concluded that the proposed controller parameter optimization algorithm enables the autonomous vehicle to maintain excellent speed tracking performance under complex driving conditions while ensuring good longitudinal control stability. This provides strong technical support for the practical application of autonomous vehicles in dealing with complex road conditions and dynamic changes and lays a solid foundation for the future development of intelligent transportation systems.
## 6 Convergence Speed of the Algorithm and the Localization Error of Unknown Nodes  
Experimental analysis was performed under four different network simulation conditions to prove the effectiveness of the convergence speed of the proposed algorithm and the localization error of each unknown node. The four network topologies are square random topology, square grid topology, C-shaped random topology, and C-shaped grid topology. Experimental results are shown in Figures 10 and 11. The size of the experimental area is \(100\times 100\) m\({}^{2}\).  
Figure 10: (**a**) Effect of the Iteration parameter on ALE in square random topology; (**b**) Effect of the Iteration parameter on ALE in a square grid topology; (**c**) Effect of the Iteration parameter on ALE in C-Shaped random topology; (**d**) Effect of the Iteration parameter on ALE in C-Shaped grid topology.
## 5 Securing ITS Applications: A Practical Case Study  
A careful analysis of the ITS performance and security requirements reveals that these two categories of design challenges are sometimes contradictory. On the one hand, vehicular communications should be efficient and provide real-time performance (_cf._ critical latency in Table 1), but on the other hand, extra processing and messages overheads are required to ensure the security of these communications (as discussed in Section 4). Furthermore, there is still a lack of experimental deployment and performance evaluation of existing standards under realistic conditions, especially regarding the security of V2X communications. In order to elaborate more on this issue, this section analyzes a detailed application case study related to the collision risk warning (CRW) safety application, where vehicles periodically exchange ITS safety messages (or beacons) to detect and prevent the risk of collision between two or more vehicles. In this context, existing standards (_i.e._, IEEE 1609 [7] and ETSI TS ITS [10]) recommend the usage of digital signature algorithms (DSA) to ensure the authenticity, integrity and non-repudiation of the exchanged messages.  
Without loss of generality, the case study is analyzed in light of the European ETSI ITS reference architecture [10], even though a similar analysis can be made using the IEEE 1609 ITS standard [7; 33]. To the best knowledge of the authors, there have been very few works [22; 23] that have attempted to evaluate the ETSI ITS security layer. The first implementation was described in [22], where some weaknesses in the early version of the standard were identified and discussed; whereas some preliminary cryptographic performance indicators were presented in [23]. In the following, we extend these works by describing a new implementation of the ETSI TS 103 097 [24] security layer and its integration into an existing standard-compliant V2X platform [90]. Various elliptic curve digital signature algorithms (ECDSA) are then experimentally evaluated for signing and verifying ITS safety messages. Finally, lessons learned are provided to enhance the future versions of the standard and to highlight future interesting research directions.
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 2 Grid Planning
### Economic, Financial, and Asset Management Planning
#### 2.5.1 Economic and Financial Planning  
The financial sustainability of grids emphasizes equitable cost sharing, using cost-benefit approaches and strategic investments. Digital simulations that merge performance with economic factors [56] utilize tools like sequential Monte Carlo simulation and three-phase power flow to balance service quality with economic efficiency. A multi-level planning strategy within clustered microgrids in Ref. [106] focuses on optimizing energy coordination using advanced optimization and DR for efficient energy transaction management. The role of decentralized energy storage is explored in [163], which highlights its importance in enhancing system reliability and cost efficiency, particularly with RES variability. Additionally, an incentive-based model for smart distribution systems in [164] employs MILP to manage uncertainties in system demand and distributed generation output, showcasing how digital tools can support economic strategies in power distribution.
## 3 Model Checking CTL Properties  
As we have seen in the introduction, the branching time logic CTL focuses on the possible future behaviors of a system, given by the different successors of the current state, instead of a single execution as in LTL and \(\omega\)-regular properties. CTL has two kinds of formulas: state formulas and path formulas. State formulas, as the name suggests, refer to the properties that the single states satisfy: basic facts represented by atomic propositions, conjunctions and negations of states formulas, and quantified path formulas, where the quantification is given by either the universal (\(\forall\)) or the existential (\(\exists\)) quantifier. Path formulas, instead, refer to what happens to the states that are visited along a single paths. This \"state-based\" nature of CTL makes the CTL model checking problem, i.e., whether a system satisfies a given CTL formula, rather easy and intuitive, which results in the development of efficient algorithms; see, e.g., [26; 27] for more details. The quantum counterpart of CTL, QCTL [44], replaces the path quantifiers \(\forall\) and \(\exists\) with a single quantum quantifier \(\mathbb{Q}_{\prec\mathcal{E}}\), which accumulates the super-operators corresponding to the paths satisfying the given path formula, and then compares it with \(\mathcal{E}\). This is similar to the probabilistic CTL, where the path quantifiers are replaced by the probabilistic operator \(\mathbb{P}_{\sim p}\) computing the overall probability of the satisfying paths and comparing it with \(p\).