esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
This work suggested a greedy technique for finding a workable DMR and CoDMR scheme with low overall total energy cost, energy balancing, and latency when using binary fields. In [24], [PERSON] proposed a time synchronization solution based on time division long-term evolution (TD-LTE) frame synchronization to improve the accuracy by correcting the delay caused by the radio propagation path. In this work, [PERSON] described a time delay compensation (ATDC) method's system, structure, features, and implementation strategy, as well as timing accuracy. In [25], a compensation technique was proposed that took into account the time-varying delay of wide-area power grid stabilizers. In this task, [PERSON] considered splitting a finite random delay into multiple delay intervals and designed a compensator for each interval. In addition, a predictive-based hierarchical delay compensation method was proposed at the data-processing level to handle delays in a wide-area control system (WACS) power system [26]. Although, as mentioned earlier, much work has been conducted on time delay impact and techniques for minimizing it, most of these analyses have been performed in the steady state of the power grid. None of the studies has looked at the influence of time delay, as well as how to reduce the negative impacts of a delay by using a TSC and TCR approach in the event of a failure to enhance power quality under fault situations. Predicated on this context, this paper presents two methods, a fuzzy logic controller (FLC)-based method and a modified predictor method, to reduce the harmful effects of time delays on power quality management with a combined thyristor switch capacitor (TSC) and a thyristor-controlled reactor (TCR) for reactive power compensation in the situation of a hybrid electric grid comprised of sequential and changeable irradiances with a temperature PV plant. The FLC method was chosen because it can manage the nonlinear behavior, instability, inaccuracy, or fluctuation of input parameters and allows for the incorporation of expert knowledge into control rules [2; 16; 17; 27; 28]. This type of controller works well under shifting system parameters and with moment signals. In termsof reducing the negative impacts of temporal delays, the FLC-based technique outperforms the modified predictor method. Two indices are utilized to evaluate the system's power quality: the voltage indication and the total harmonic distortion (THDu). At several sites in the electrical network, such as the Kigali National Grid (KNG), temporary and permanent faults, both balanced and unbalanced, are investigated. The following is the structure of this paper: Section 2 covers the proposed study's Kigali National Grid power system model. Section 3 discusses communication delays with the combined TSC and TCR controllers. Section 4 discusses ways to reduce communication delays. The simulation findings are described in Section 5. Finally, Section 6 gives this paper's findings.
# Multi-Classifier Feature Fusion-Based Road Detection for Connected Autonomous Vehicles
## 5 Results and Discussion
### Comparative Analysis
The HSV accomplished an important role in dark regions where the image had large grey regions (shadows); furthermore, LBP was used to handle unpredictable weather and lighting conditions. The TLFC-RD method also provided better performance even when it presented with a huge amount of data. For example, the accuracy of TLFC-RD with augmentation was 99.64 % for the KITTI dataset, which was higher than the existing methods. Meanwhile, the runtime of the TLFC-RD method was less compared to all the existing methods, because the deep learning classifiers used in the proposed method are independent of each other. Therefore, precise classification between the drivable and non-drivable areas was achieved by using the TLFC-RD. The detected information was transferred between the vehicles to share information about the road scenarios. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Performances** & **U-Net-DAM** & **TFCN** & **TAAUWN-MV** & **TAAUWN-MVP** & **TLFC-RD** & **TLFC-RD (with Augmentation)** \\ \hline PRE (\%) & 93.42 & 97.21 & 96.07 & 96.42 & 98.10 & 99.01 \\ Sensitivity/Recall (\%) & 96.17 & 95.13 & 97.73 & 97.04 & 98.24 & 98.23 \\ Specificity (\%) & 94.22 & 93.91 & 94.26 & 95.38 & 96.80 & 97.74 \\ Max F (\%) & 95.01 & 95.08 & 95.11 & 94.91 & 96.23 & 98.11 \\ Accuracy (\%) & 94.52 & 98.92 & 97.25 & 95.83 & 99.12 & 99.64 \\ F1 (\%) & 93.55 & 96.20 & 96.14 & 96.11 & 97.92 & 98.37 \\ Run time (s) & 0.26 & 0.31 & 1.01 & 1.18 & 0.13 & 0.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison of TLFC-RD method for the KITTI dataset with the same activation function. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Performances** & **U-Net-DAM** & **TFCN** & **TAAUWN-MV** & **TAAUWN-MVP** & **TLFC-RD** & **TLFC-RD (with Augmentation)** \\ \hline PRE (\%) & 94.86 & 97.71 & NA & NA & 98.10 & 99.01 \\ Sensitivity/Recall (\%) & 96.28 & 96.66 & 97.20 & 96.92 & 98.24 & 98.23 \\ Specificity (\%) & NA & NA & 93.70 & 94.27 & 96.80 & 97.74 \\ Max F (\%) & 95.57 & NA & NA & NA & 96.23 & 98.11 \\ Accuracy (\%) & NA & 99.00 & 96.32 & 96.25 & 99.12 & 99.64 \\ F1 (\%) & NA & 97.18 & NA & NA & 97.92 & 98.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of TLFC-RD method for the KITTI dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Performances** & **TFCN** [18] & **TLFC-RD (K-Fold)** & **TLFC-RD (with Augmentation)** \\ \hline PRE (\%) & 98.55 & 98.93 & 99.25 \\ Sensitivity/ Recall (\%) & 98.44 & 98.72 & 99.04 \\ Accuracy (\%) & 99.11 & 99.24 & 99.71 \\ F1 (\%) & 98.50 & 98.75 & 98.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of TLFC-RD method for the CamVid dataset.
# Detection and Mitigation of RPL Rank and Version Number Attacks in the Internet of Things: SRPL-RP
## The Proposed Protocol
### SRPL-RP Implementation
#### Rank and Version Number Attacks Isolation  
A threshold alert is attached to the DIO messages to implement the isolation feature of malicious nodes from the RPL network for supplemental security. The malicious nodes will be added in the blacklist table to send an alert message to other nodes in the RPL network to skip the malicious nodes. Hence, every node in the RPL network sends DIO messages to other nodes to prevent the malicious nodes from sending DIO messages as is depicted in Algorithm A6, found in the appendix section, and referred to in Figures 1 and 2 in Section 3.
# A Secure and Efficient Three-Factor Authentication Protocol in Global Mobility Networks
## 6 Security Analysis
### Informal Security Analysis
#### 6.1.1 Masquerade Attack  
If a \(MU_{a}\) attempts to impersonate a legal mobile user, \(MU_{a}\) must calculate a request message \(\{M_{1},M_{2},RID_{i},Q_{M}\}\) and response message \(\{M_{4},Q_{MF}\}\) successfully. However, \(MU_{a}\) cannot compute this because \(MU_{a}\) does not know \(MU\)'s real identity \(ID_{MU}\), password \(PW_{MU}\), secret parameters \(X_{i}\), random nonce \(R_{MU}\), and biometrics \(BIO_{i}\). Consequently, the proposed protocol can withstand masquerade attacks because \(MU_{a}\) cannot generate correct messages successfully.
## 4 Results and Discussion
### Cross-Sectional Forces
#### 4.2.3 Normalized Eccentricity  
The eccentricity \(e\) normalized by the segment thickness \(t\) is used to evaluate the effects of the parameters on the sectional forces. The eccentricity \(e\) is defined as:  
\[e=\frac{M_{\rm max}}{N_{\rm assoc}} \tag{8}\]  
where \(M_{\rm max}\) is the maximum moment and \(N_{\rm assoc}\) is the associated axial force at \(M_{\rm max}\). When \(e/t\) is larger than \(1/6\) (\(\cong 0.167\)), tensile stress appears in the segment.  
Figure 13 shows the \(e/t\) for the deep tunnel and the shallow tunnel, respectively. From Figure 13, the following was found:  
Figure 11: Maximum axial force (kN/m).  
e/t is larger than 0.167 at \(k_{\rm n}=10\) MN/m\({}^{3}\) for the deep tunnel and at \(k_{\rm n}<50\) MN/m\({}^{3}\) for the shallow tunnel, while _e/t_ is negligibly small at \(k_{\rm n}\geq 500\) MN/m\({}^{3}\). These are the results of the \(M\) distribution and \(N\) distribution in Figures 7 and 8. This indicates that earth pressure should be considered carefully in soft soils for a shallow tunnel, since tensile stress appears in the lining.
2. As the tangential spring constant, \(k_{\rm t}/k_{\rm n}\), increases from 0 to 1, _e/t_ decreases, but the change is less than 0.05. This indicates that the influence rate of \(k_{\rm t}\) on \(M_{\rm max}\) and \(N_{\rm assoc}\) is similar, and the effect of \(k_{\rm t}\) on _e/t_ is limited.
3. As the load model changes from type 0 to type 1 under _e/t_ > 0.167, _e/t_ increases by about 1.4 times for the deep tunnel and about 1.7 times for the shallow tunnel. This indicates that the load model type (i.e., \(\tau_{0}\)) influences _e/t_ significantly.
4. With larger \(k_{\rm n}\), _e/t_ is drastically reduced. This means that absolute \(M_{\rm max}\) decreases more than \(N_{\rm assoc}\) as \(k_{\rm n}\) increases.
5. The _e/t_ of the shallow tunnel is at most about 1.6 times the _e/t_ of the deep tunnel. This means that absolute \(M_{\rm max}\) decreases less than \(N_{\rm assoc}\) as the overburden depth \(h\) decreases, since the difference of the initial effective earth pressures at the crown and invert is constant, but the initial vertical effective earth pressure decreases as \(h\) decreases.
# A Novel Hybrid Whale-Chimp Optimization Algorithm for Structural Damage Detection
## 5 Conclusions  
In this study, a hybrid whale-chimp optimization algorithm (W-ChOA) is proposed for structural damage detection. Numerical simulations of a two-story rigid frame and a simply supported beam model are adopted to verify the effectiveness of the novel algorithm, and the following conclusions can be drawn:  
1. With these three high-dimensional multimodal test functions, similar conclusions can be drawn. The calculation accuracy and convergence efficiency of the W-ChOA are significantly better than those of the basic ChOA. Test function verification results  
Figure 12: Symmetric damages detection results of the simply supported beam by two chimp-based optimization algorithms. (**a**) 5% noise level. (**b**) 10% noise level.  
show that the hunting mechanism of the whale optimization algorithm and the Sobol sequence introduced in the ChOA are practical and successful.
2. Validated by a two-story rigid frame model and a simply supported beam model, the W-ChOA can effectively identify the damage location and damage degree in both single-damage and multi-damage condition. In cases with extensive noise interference or multi-damage existing in the structure, the high misjudgment rate of the ChOA has been effectively reduced and improved by adopting the W-ChOA.
3. Compared with the ChOA, the proposed W-ChOA has much better performance such as fast convergence speed, high identification accuracy, good case stability, and strong robustness when applied to structural damage detection both in asymmetric damage cases and symmetric damage cases. Due to the excellent performance in different simulations, the W-ChOA is strongly recommended for structural damage detection.
4. For actual structures, the influence of external environmental factors such as temperature and humidity on the modal parameters of the structure is sometimes even greater than the effect of noise. Therefore, the proposed method still needs to consider environmental influences to be better applied to the actual structure. In addition, the parameters of the W-ChOA need to be further refined for appropriate application in the field of structural damage detection.  
Conceptualization, X.L.; Data curation, K.Z.; Funding acquisition, Z.C.; Investigation, T.H.T.C.; Project administration, S.Z. All authors have read and agreed to the published version of the manuscript.  
This work is jointly supported by the National Natural Science Foundation of China (Grant Number U2004184), Training Plan for Young Key Teachers in Colleges and Universities in Henan Province (Grant Number 2021 GJS078), Key Sci-Tech Research Project of Henan, China (202102310272), and Special Joint Research Project of Zhengzhou City and NCWU, China (2021014).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Adopted Assessment Criteria  
Some criteria for the analysis were adopted to assess the safety risks, which directly speak about the braking effectiveness and indirectly about the risk of loss of stability, including jack-knifing. The quantities proving the effectiveness are the braking time, deceleration (acceleration with the opposite sign) in the period of fully developed braking force and the braking distance. The additional criteria may be the circumferential deceleration -\(a_{ki}\) of the wheels (circumferential acceleration \(a_{ki}\) = \(\omega_{ki}\):\(r_{di}\) with the opposite sign) and their circumferential velocities \(v_{ki}\), indicating a tendency to the locking or its occurrence. In the context of the risk of jack-knifing, the first quantity is the horizontal force in the coupling (fifth wheel) \(F_{SA}\). The sign and value of this force are essential criteria for assessing the risk of breaking the set. A positive value of the force \(F_{sA}\) (denoting the situation of the semitrailer \"pressing\" on the tractor) is a significant indication of such a threat. Another quantity is the slip ratio \(s_{i}\) of a given axle. Its high (excessive) value (e.g., close to 1) may indicate a potential lack of control over the vehicle (e.g., locking the steering axle means a  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Axle** & \multicolumn{3}{c}{**Loaded Vehicle**} & \multicolumn{3}{c}{**Empty Vehicle**} \\  & \multicolumn{2}{c}{**Response Time of**} & \multicolumn{2}{c}{**Rise Time**} & \multicolumn{2}{c}{**Response Time of**} & \multicolumn{2}{c}{**Rise Time**} \\  & **Pressure** & **Moment** & **of Moment** & **Pressure** & **Moment** & **of Moment** \\ \hline \(p_{h}\) & 0.05 & - & - & 0.05 & - & - \\ A1 & 0.075 & 0.1 & 0.63 & 0.06 & 0.1 & 0.63 \\ A2 & 0.11 & 0.13 & 0.66 & 0.08 & 0.17 & 0.66 \\ \(p_{m}\) & 0.09 & - & - & - & 0.09 & - & - \\ B2 & 0.155 & 0.17 & 0.58 & 0.155 & 0.17 & 0.74 \\ B2 * & 0.355 & 0.37 & 0.78 & - & - & - \\ \hline \hline \end{tabular} \(p_{h}\)—pressure downstream of the main control valve, \(p_{m}\)—pressure in coupling head, * failure of the semitrailer braking system (longer reaction time)—variant b6.  
\end{table}
Table 3: Response times of pressures and braking moments as well as rise times of braking moments on individual axes, in seconds.  
Figure 9: The course of excitation—given braking torques of the tractor axle and the semitrailer axle for the CE cycle—with the same position of the main control valve adjuster \(\alpha\) = 45\({}^{\circ}\): (**a**) Semitrailer load; (**b**) Semitrailer unladen.  
loss of handling, locking the wheels of the rear axle or the axle of a semitrailer implies a risk of losing longitudinal and lateral stability). The order of locking the wheels of individual axles together with force in the coupling suggests the possibility of jack-knifing or trailer skidding.
# A Novel Hybrid Unsupervised Learning Approach for Enhanced Cybersecurity in the IoT
## 4 Proposed Method for Intrusion Detection and Prevention
### Detection Stage 3: OCSVM + dAE + DBSCAN Clustering  
In this research, DBSCAN is utilized due to its robustness in handling noise and its capability to identify clusters of arbitrary shapes without needing a predefined number of clusters. Moreover, its flexibility in parameter tuning and scalability are essential for enhancing the effectiveness of intrusion detection. This section explains how DBSCAN applies these capabilities to effectively cluster both normal traffic and various attack anomalies.  
The outputs \"zDATAab1\" from OCSVM and \"zDATAab2\" from stage 2 dAE, respectively, contain information about attack patterns, representing points within the output space. DBSCAN operates on these data using the \(\varepsilon\) (epsilon) and MinPts parameters, initiating its clustering process from an unvisited point. The \(\varepsilon\) parameter defines the maximum distance for considering neighborhood points, essentially setting a radius for each point. DBSCAN identifies clusters by retrieving \(\varepsilon\)-neighborhoods, forming clusters if they are sufficiently populated, otherwise classifying as noise. MinPts signifies the minimum number of samples required for a point to be considered a core point, which serves as the central element within dense regions. The fit method assigns clusters based on density, expanding to densely populated \(\varepsilon\)-neighborhoods. This iterative process continues until the entire density-connected cluster is identified. New unvisited points are sequentially processed, potentially revealing additional clusters or noise. DBSCAN optimizes clustering by minimizing the number of clusters from all possible clustering within the dataset. It ensures that each pair of points within a cluster is density-reachable, maintaining the original properties of clusters in terms of \"maximality\" and \"connectivity\".
# The Impact of GCP Chip Distribution on Kompsat-3A RPC Bias Compensation
## 2 Methodology
### GCP Distribution Design  
The mentioned RPCs contain inherent errors, necessitating post-processing to enhance the information's accuracy. This process relies on accurate and well-distributed GCPs across the entire image space, as illustrated in Figure 3a. Biased distributions, as depicted in Figure 3b, are generally less desirable. However, acquiring this information is costly, emphasizing the need for the minimum and efficient utilization of GCPs. Furthermore, information acquisition becomes more limited when the target area is situated in inaccessible regions, such as North Korea.  
Hence, we partitioned the image space into 9 zones, as illustrated in Figure 4. This division resulted in a total of 511 experimental cases, encompassing various distributions: 9 single-zone distributions, 126 cases of five-zone distributions (gC\({}_{5}\)), 84 cases of three-zone distributions (gC\({}_{3}\)), and 1 nine-zone distribution, among others. We proceeded to analyze the impact of these derived GCP distribution scenarios.
# Assessing the Impact of an EV Battery Swapping Station on the Reliability of Distribution Systems
## 2 Overview of Battery Swapping Station
### Structure  
Different from the EV charging station, the charging object of the BSS is the battery; the EV can quickly replace the empty battery in a few minutes and replace it with a fully charged battery; then, the BSS will charge the battery in a centralized manner. This charging and battery swapping mode can ensure the extremely high battery-swapping efficiency of the BSS and EV. At the same time, since the charging behavior can be performed at any time under the control of the scheduling strategy, the economy is improved.  
All batteries equipped in the BSS could be classified into three types, namely full batteries (FB), half-full batteries (HB), and available batteries (AB) [18]. A full battery refers to a battery with an SOC of 0.95, while the SOC of an available battery and a half-full battery are between 0.8 and 0.95 and less than 0.8, respectively. We define the set of half-full batteries at time \(t\) as \(\Omega_{1,t}\), while the set of available and full batteries as \(\Omega_{2,t}\).  
After introducing the running process and classification of batteries, the structure of BSS can be expresses as shown in Figure 1. As we can see, EVs replace HB, which have a different SOC, and these batteries will be charged using an appropriate strategy and become FB or AB if some of them are used to discharge in order to enhance the reliability of the local distribution system.
# Optimizing Healthcare Delivery: A Model for Staffing, Patient Assignment, and Resource Allocation
## 6 Conclusions  
The primary concerns facing the healthcare sector are improving patient care, simplifying healthcare operations, and providing top-notch services at affordable costs. These challenges include general healthcare management, resource allocation, hiring, patient care priorities, and efficient scheduling. Effective staff scheduling, resource allocation, and patient assignments are needed to overcome these difficulties. To this end, we developed an MILP model using the Gurobi optimization solver to address these issues, thereby arriving at the desired results. The model encapsulates healthcare operations such as staff deployment, patient distribution, resource utilization, and monitoring overtime hours to enhance medical care and minimize expenditures.  
Our model was implemented in two distinct scenarios, culminating in optimal solutions. For the first experimental implementation, we achieved an objective value of 844.0; for the second procedure, we obtained an objective value of 539.0. Despite the differing contexts, both performances resulted in a 0.0% gap, signifying an excellent alignment between the top solutions and the theoretical minima of the objective function. This, in turn, underscores the precision and effectiveness of our approach, which builds upon previous studies in healthcare scheduling and management. Furthermore, our model offers healthcare organizations a valuable tool for managing finite resources and proactively reducing patient wait times. This aspect promises to enhance operational efficiency and patient outcomes.  
The academic implications of our study lie in developing a novel MILP model tailored to the unique challenges of healthcare resource allocation and scheduling. By integrating the specific parameters of staff, resources, and patient demands, we contribute to the ongoing academic discourse on healthcare optimization, offering a new perspective and methodological approach. The managerial implications are directly related to the practical application of our model within healthcare settings. Our research offers a systematic and adaptable resource allocation and scheduling framework, allowing healthcare managers to improve patient care, simplify operations, and control costs more effectively. The model provides actionable insights that can be translated into real-world strategies, enhancing operational efficiency and patient satisfaction.
## 2 Models and the Theoretical Methods  
### Quasi-Static REM Structure  
Figure 1a shows the unit lattice structure of the proposed I-shaped REM. Here, \(a\) denotes the leg width of the I-shaped unit lattice structure, i.e., the lattice constant, whereas \(h\), \(d\), and \(e\) denote the waist height, waist thickness, and average leg thickness of the I-shaped unit lattice structure, respectively. The radial periodic arrangement of the unit lattice to form metamaterials is shown in Figure 1b. Figure 1c imposes surface constraints on the Z-direction basal plane (blue represents the constrained surface of the metamaterial structure), thereby constructing a quasi-static metamaterial [31].