esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, we undertake a comprehensive performance analysis of the QEFC LEO satellite system. Our simulations show that QEFC LEO satellites encounter significant challenges, including high interference levels and low SINR. To address these issues and achieve ubiquitous coverage, we introduce an approach to optimizing beamwidth to maximize uplink coverage probability. Our proposed scheme aims to enhance SINR by determining the optimal beamwidth configuration that maximizes uplink coverage probability. We compare the performance of our proposed scheme against two existing configurations: the 3 GPP setting and the three-color frequency reuse scheme.  
Simulation results demonstrate that our proposed scheme yields notable improvements in serving power and effectively mitigates interference power compared to conventional configurations. Moreover, our scheme achieves the highest uplink coverage probability, closely approaching the upper bound of achievable uplink coverage probability. In the NTN rural scenario, the proposed scheme maintains the uplink coverage probability above 0.93. By leveraging optimized beamwidths, our proposed scheme offers a promising solution to the challenges faced by QEFC LEO satellite systems, ultimately enhancing network performance and facilitating ubiquitous coverage for users. However, considering the cost implications of the proposed scheme, future studies should explore strategies to maximize performance while operating within budget constraints.  
Conceptualization, C.-T.L. and J.-Y.P.; methodology, C.-T.L.; software, C.-T.L.; validation, C.-T.L.; formal analysis, C.-T.L.; investigation, C.-T.L.; resources, C.-T.L.; data curation, C.-T.L.; writing--original draft preparation, C.-T.L.; writing--review and editing, C.-T.L. and J.-Y.P.; visualization, C.-T.L.; supervision, C.-T.L. and J.-Y.P.; project administration, J.-Y.P.; funding acquisition, J.-Y.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science and Technology Council, Taiwan, R.O.C., under Grants MOST 112-2218-E-305-001 and NSTC 112-2218-E-194-004.  
Data Availability StatementData are available in a publicly accessible repository. The data presented in this study are openly available in FigShare at [[https://doi.org/10.6084/m9.figshare.25497694.v2](https://doi.org/10.6084/m9.figshare.25497694.v2)]([https://doi.org/10.6084/m9.figshare.25497694.v2](https://doi.org/10.6084/m9.figshare.25497694.v2)), accessed on 31 March 2024.  
This research was partly supported by the Advanced Institute of Manufacturing with High-Tech Innovations from the Featurered Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.  
The authors declare no conflict of interest.  
Figure 13: Simulation results of uplink coverage probability with target SINR \(-\)8.61 dB in rural (dashed) and urban (solid) scenario.
## 5 Evaluation of ML and DL Models for Spoofing Detection
CNN achieved the highest performance among the deep learning models, with an accuracy of 83.29% and precision, recall, and F1-scores of 87.21%, 83.27%, and 83.27%, respectively. Its slight improvement over MLP is attributed to its ability to capture spatial relationships and complex dependencies within the feature space. However, this performance came at the cost of a longer training time (4.15 s) and higher memory usage (6.26 MB). Figure 5 visually compares the accuracy, precision, recall, and F1-scores for each model. While the tabulated data offer detailed numerical metrics, this chart emphasizes the consistent strengths of RF and CNN across metrics and the noticeable gap in KNN's performance. The visualization highlights RF's dominance in balancing high accuracy and F1-score, solidifying its position as the best-performing model. Training time analysis revealed significant variations across models. LR and KNN required the least time, making them highly suitable for real-time applications. RF achieved a balance between efficiency and accuracy with moderate training time, while deep learning  
Figure 5: Visual comparison of model performance metrics across ML and DL models. models, particularly CNN and MLP, exhibited longer training times due to their complex architectures. Memory usage analysis underscored the resource efficiency of LR and KNN, which required minimal memory. RF consumed a modest amount of memory despite its complexity. Among the deep learning models, MLP exhibited the highest memory usage due to its fully connected architecture, while CNN demonstrated improved efficiency in memory consumption owing to its convolutional layers. To better understand the contribution of each attribute (RSSI, BL, and altitude) to the model's decision-making, RF feature importance analysis was conducted. The results revealed the following:  
Feature importance analysis, derived from RF as shown in Table 3, identified BL as the most influential attribute, contributing 44.81% to the model's decisions. Altitude followed with 32.22%, and RSSI accounted for 22.86%. These results reinforce the critical role of BL and altitude in detecting spoofing attacks, validating the multi-attribute PLA scheme's effectiveness.
# Design and Analysis of an Efficient Energy Algorithm in Wireless Social Sensor Networks
## 2 Ad Hoc Network Structure and Hybrid Checkpointing Model  
### Mobile Ad Hoc Network Topological Structure  
The mobile ad hoc network is a kind of wireless self-organizing network which has peer to peer movement. Its topological structures are mainly classified to two kinds: flat structures and hierarchical structures [35; 36; 37; 38]. In a flat structure, all network nodes are equal, but the entire network in the hierarchical structure is constituted by subnets which are divided into clusters. Each cluster includes one cluster head and many cluster members. The cluster heads form a higher-level network. Because the scale of the hierarchical structure is not limited and the expandability of this structure is good, this structure has become the favorite development trend of mobile ad hoc networks, therefore, this paper will discuss the checkpointing algorithm under the multi-frequency hierarchical structure.  
Figure 1 is the multi-frequency hierarchical topological structure of an ad hoc network. This structure consists of multiple clusters. Each cluster includes one cluster head (CH) and a limited number of cluster members [39; 40; 41]. The CH\({}_{\text{s}}\) and cluster members are all regular MH\({}_{\text{s}}\). The hosts in one cluster share one frequency to communicate, while the CH\({}_{\text{s}}\) communicate on another frequency. The members in she ame cluster can communicate with each other directly, while the members in different clusters have to communicate by the retransmission of CH\({}_{\text{s}}\).
# Ephemeral Node Identifiers for Enhanced Flow Privacy
## 5 Discussion
### Intra-Flow Privacy  
A \(P_{f}\) value of 1 indicates the best possible outcome for inter-flow identity privacy, but does not provide protection for _intra-flow privacy_. That is, packets within a single transport session can still be correlated based on NID values. A stronger privacy guarantee might ensure that no two packets could be correlated back to the same source, rather than just no two flows. However, this is not possible under the existing network abstraction, which requires the (visible) transport state to be immutable to maintain the end-to-end transport state for a flow.  
A partial solution to this problem has been proposed using QUIC [13]. Under QUIC, a single transport session may be multiplexed over multiple UDP subflows. Each of these subflows is a traditional transport session, so must have immutable transport state, but this can change _between_ subflows. As a result, a single QUIC session can appear as multiple UDP flows on the network. These are reassembled using QUIC session IDs that are cryptographically protected, so cannot be exploited by an attacker. If each UDP subflow has different transport state (such as a different eNID), an attacker would be unable to correlate them to the same QUIC session or source node. In extreme cases, QUIC could send every packet out using a different UDP subflow, providing per-packet unique addressing for maximum intra-flow identity privacy.  
While QUIC's solution has the potential to provide excellent identity privacy protection, it has four key problems:  
1. It is bound by the number of IP addresses a node has: in order to use multiple UDP flows, the node must have multiple public IP addresses. This is typically not true, and might not be possible if, for example, the node was behind a NAT.  
As discussed above, a single node maintaining a large pool of IP addresses introduces performance concerns for the network, which eNIDs can reduce.
3. The application must take care in managing the use of multiple addresses to prevent reuse, especially between sessions. Unlike the in-kernel approach of eNIDs, there is no clear mechanism for applications to access this network-layer state.
4. Applications must be re-engineered and re-deployed to use QUIC, and existing (legacy) applications and transport protocols, such as UDP and TCP, cannot benefit.  
_Note that eNIDs solve all four of these problems._  
QUIC and eNID solutions to privacy are targetted at different problems, and each occupy a different niche: eNIDs provide inter-session privacy for all flows, regardless of transport layer protocol; QUIC's multiplexing provides intra-session privacy for applications that choose to use it, under constraints based on available addresses. When used together, QUIC's solution extends the privacy guarantees further than eNIDs alone, while eNIDs provide the in-kernel mechanism for managing addresses that QUIC requires. So, there is a strong synergy between the two defences, and we expect they can be used together effectively in the future.
## 9 Discussion  
V2X technology is expected to be equipped in all new vehicles by the end of the 2020s, supporting automated driving and safety features. However, it is not considered the sole technology enabling highly automated operation, but rather a safety-redundant component of the automated driving systems. V2X technology acts as an extra set of sensors, influencing the automotive development processes and operational domains. ISO 26262 standardizes the safety integrity of automotive systems, including automated driving, with different Automotive Safety Integrity Levels. While the vehicles produced since 2023 do not rely solely on V2X communication for driving functions, its assessment and integration into the system can enhance the environmental modeling, the decision-making and the actuation in automated driving. This paper explores the requirements, the regulatory challenges, the scalability and the potential impacts of V2X in highly automated and autonomous systems, along with its relevance in non-automotive fields with similar IoT-based information.  
The integration of V2X technology in self-driving vehicles shows great potential for enhancing their capabilities across different levels of autonomy. At lower levels, V2X enables improved driver assistance systems by providing real-time information about the road conditions and the potential hazards. It also facilitates the cooperative perception, where vehicles exchange sensor data to enhance the object recognition and the situational awareness. At higher levels of autonomy, V2X plays a vital role in enabling safe control transitions between the automated systems the and human drivers. It allows the vehicles to communicate intentions, trajectories and sensor data, leading to coordinated behaviors such as merging and platooning, which enhance traffic flow and safety. V2X is crucial for achieving high levels of automation by facilitating critical information exchange between the vehicles and the infrastructure, optimizing the routes and avoiding collisions. Various use cases, including automated valet parking, cooperative platooning and intersection control, demonstrate the practical application of V2X technology in the autonomous driving.
## 3 Results and Discussion
### Classification of Methods
The goal of hybrid methods is to obtain more comprehensive features from multiple perspectives and fuse the advantages of different methods to improve recognition and classification performance. By categorizing and evaluating these methods, we found that each method has its advantages and limitations. Researchers can choose the appropriate method or combine multiple methods according to their specific research objectives and data characteristics to achieve accurate and efficient identification and classification.
# Effects of Process Parameters on the Quality of Suantang Beef
## 2 Materials and Methods
### Volatile Profile Analysis via HS-SPME Combined with GC-MS  
Volatile profile analysis via HS-SPME combined with GC-MS was referred to our previous report [24]. Pegasus HRT 4D Plus instrument (LECO Corporation, St. Joseph, MI, USA) was used to analyze the volatile compounds in ST, optimized ST beef, and boiled beef. Put a total of 5 g equal sample into a 20 mL glass headspace vial and seal it with a spacer. Then 50 \(\times\) 30 \(\mathrm{\SIUnitSymbolMicro m}\) divinylbenzene/carboxyl/poly (dimethylsiloxane) (DVB/CAR/PDMS) coated fibers (Supelco, Inc., Belfonte, PA, USA) were introduced into the top space of the vial at 60 \({}^{\circ}\)C for 30 min. The fibers were then injected into the injection port of Pegasus GC-HRT 4D Plus and desorbed at 250 \({}^{\circ}\)C for 3 min. Volatile compounds were separated by using DB-WAX capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\mathrm{\SIUnitSymbolMicro m}\)). Volatile compounds were separated by SQ 456. The heating procedure is as follows: initial 40 \({}^{\circ}\)C for 3 min, heating up to 150 \({}^{\circ}\)C at the rate of 5 \({}^{\circ}\)C/min, holding 1 min, heating again at the rate of 3 \({}^{\circ}\)C/min to 200 \({}^{\circ}\)C, having 1 min, and finally heating to 230 \({}^{\circ}\)C and keeping 6 min. Helium was used as a carrier gas. GC operates in shunt mode (5:1) with a column flow rate of 6 mL/min. The operating conditions of MS include EI as ionization method, electron energy 70 eV, interface temperature 250 \({}^{\circ}\)C, transmission line temperature 280 \({}^{\circ}\)C, ion source temperature 200 \({}^{\circ}\)C, and quadrupole temperature 150 \({}^{\circ}\)C. Volatile compounds detected in ST, optimized ST beef, and boiled beef using GC-MS were compared with previously published data, the National Institute of Standards and Technology's MS library (search version 1.6), and Wiley library (New York, 320k compound, version 6.0). 2-Methyl-3-heptanone was used as an internal standard for semiquantitative analysis. The relative percentages of the detected peaks were obtained by peak area normalization. The estimated concentration of each volatile in the sample was calculated according to [25]. The odor activity value (OAV)described the contribution of each volatile compound. OAVs were calculated as a ratio of the concentration to the odor detection threshold. Values of odor detection thresholds were derived from the literature data [26]. Compounds with OAVs greater than or equal to 1 were considered essential contributors to odor.
# Routing Protocols for Low Power and Lossy Networks in Internet of Things Applications
###### Abstract  
The emergence of the Internet of Things (IoT) and its applications has taken the attention of several researchers. In an effort to provide interoperability and IPv6 support for the IoT devices, the Internet Engineering Task Force (IETF) proposed the 6 LoWPAN stack. However, the particularities and hardware limitations of networks associated with IoT devices lead to several challenges, mainly for routing protocols. On its stack proposal, IETF standardizes the RPL (IPv6 Routing Protocol for Low-Power and Lossy Networks) as the routing protocol for Low-power and Lossy Networks (LLNs). RPL is a tree-based proactive routing protocol that creates acyclic graphs among the nodes to allow data exchange. Although widely considered and used by current applications, different recent studies have shown its limitations and drawbacks. Among these, it is possible to highlight the weak support of mobility and P2P traffic, restrictions for multicast transmissions, and lousy adaption for dynamic throughput. Motivated by the presented issues, several new solutions have emerged during recent years. The approaches range from the consideration of different routing metrics to an entirely new solution inspired by other routing protocols. In this context, this work aims to present an extensive survey study about routing solutions for IoT/LLN, not limited to RPL enhancements. In the course of the paper, the routing requirements of LLNs, the initial protocols, and the most recent approaches are presented. The IoT routing enhancements are divided according to its main objectives and then studied individually to point out its most important strengths and weaknesses. Furthermore, as the main contribution, this study presents a comprehensive discussion about the considered approaches, identifying the still remaining open issues and suggesting future directions to be recognized by new proposals.  
Internet of Things; low-power and lossy network; LOADng; routing protocol; RPL +
Footnote †: journal: Sensors  
_Sensors_
# A Quantum-Based Beetle Swarm Optimization Algorithm for Numerical Optimization
## 4 Experiment
### Multimodal Unconstrained Optimization
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name** & **Algorithm** & **Best** & **Worst** & **Average** & **Variance** & **Time(s)** \\ \hline \multirow{6}{*}{Ackley} & PIO & 0.0210 & 5.6406 & 2.4490 & 2.5567 & 0.032 \\  & SOA & 0.0620 & 21.3100 & 19.4798 & 19.0403 & 0.021 \\  & GWO & 20.6624 & 21.1627 & 20.9935 & 0.0081 & 0.030 \\  & **QBSO** & **10\({}^{\mathbf{-4}}\)** & **0.0030** & **0.0011** & **10\({}^{\mathbf{-7}}\)** & 0.140 \\  & BSO & 10\({}^{\mathbf{-5}}\) & 6.1147 & 1.9238 & 2.2163 & 0.023 \\  & BAS & 3.753 & 5.506 & 4.399 & 0.1266 & 0.017 \\  & PIO & \(10^{\mathbf{-4}}\) & 0.1750 & 0.0390 & 0.0020 & 0.034 \\  & SOA & \(10^{\mathbf{-5}}\) & 4.4718 & 1.3344 & 0.8192 & 0.022 \\  & GWO & \(10^{\mathbf{-4}}\) & 0.1750 & 0.0390 & 0.0020 & 0.029 \\  & **QBSO** & **10\({}^{\mathbf{-9}}\)** & **10\({}^{\mathbf{-7}}\)** & **10\({}^{\mathbf{-8}}\)** & **10\({}^{\mathbf{-15}}\)** & 0.149 \\  & BSO & 10.792 & 5.4872 & 1.6651 & 0.4517 & 0.134 \\  & BAS & 0.371 & 0.949 & 0.655 & 0.0156 & 0.017 \\  & PIO & 5.7557 & 247.5033 & 138.0620 & 10\({}^{\mathbf{3}}\) & 0.037 \\  & SOA & 0.4156 & 10\({}^{\mathbf{4}}\) & 10\({}^{\mathbf{3}}\) & 10\({}^{\mathbf{7}}\) & 0.017 \\  & GWO & 27.9906 & 143.6525 & 55.9303 & 359.9034 & 0.029 \\  & **QBSO** & **10\({}^{\mathbf{-6}}\)** & **10\({}^{\mathbf{-4}}\)** & **10\({}^{\mathbf{-5}}\)** & **10\({}^{\mathbf{-9}}\)** & 0.143 \\  & BSO & 5.57 & 10\({}^{\mathbf{4}}\) & 10\({}^{\mathbf{3}}\) & 10\({}^{\mathbf{6}}\) & 0.022 \\  & BSO & 82.358 & 10\({}^{\mathbf{2}}\) & 10\({}^{\mathbf{2}}\) & 10\({}^{\mathbf{2}}\) & 0.017 \\  & PIO & 0.0042 & 10\({}^{\mathbf{3}}\) & 10\({}^{\mathbf{5}}\) & 156.2653 & 0.056 \\  & SOA & 0.0084 & 10\({}^{\mathbf{8}}\) & 10\({}^{\mathbf{7}}\) & 10\({}^{\mathbf{16}}\) & 0.030 \\  & GWO & 0.1165 & 1.1138 & 0.3810 & 0.0330 & 0.038 \\  & **QBSO** & **10\({}^{\mathbf{-6}}\)** & **0.0027** & **10\({}^{\mathbf{-4}}\)** & **10\({}^{\mathbf{-7}}\)** & 0.175 \\  & BSO & 0.6687 & 10\({}^{\mathbf{8}}\) & 10\({}^{\mathbf{8}}\) & 10\({}^{\mathbf{16}}\) & 0.046 \\  & BAS & 10\({}^{\mathbf{2}}\) & 10\({}^{\mathbf{2}}\) & 10\({}^{\mathbf{2}}\) & 10\({}^{\mathbf{4}}\) & 0.018 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of the multimodal benchmark function experiments (population size = 30). For algorithm designers, the accuracy and time consumption of the algorithm are difficult to balance. For population-based optimization algorithms, the larger the dimensionality, the larger the population size that needs to be consumed. It was clear from the computational complexity analysis that the time required to maintain accurate optimization results will grow exponentially. Our goal is to try to trade off dimensionality and time for the decision maker, and to handle the high-dimensional optimization problem with the smallest population size. We conducted a performance comparison of the algorithms in different dimensions with the Rastrigin function, and the results are shown in Table 7.
## 2 Problem Formulation  
### Preliminaries  
In this section, a generic system description, covering multi-agent environment, agent control and agent connection, is presented with notations and assumptions as outlined below:  
**Notations.**_Assume that \(R\) is a real number, \(R_{+}\) is a positive real number set, and \(N\) is a non-negative integer set. The \(n\times m\) zero matrix is denoted as \(0_{n\times m}\). The Euclidean vector \(x\) is represented by \(x_{i}(t)=\left[x_{1},\ x_{2},\ x_{3}\ldots\ldots\ldots x_{n}\right]^{T}\in \ R^{n}\) with non-zero elements in Euclidean coordinates representing the positions of the agent. The agent is represented with \(i\), neighbours with \(j\), discrete time with \(t\), local controller \(L_{i}\), global controller \(G\) and broadcast signal \(B\in R\). The function \(f(e,x)\) contains the variables of \(e\) and \(x\)._  
**Assumption 1**.: _The feedback control system presented in Figure 1 has been applied in a broadcast communication environment. The global controller \(G\) will keep broadcasting the scalar value \(\beta(t)\) to agents until consensus is achieved when it reaches its desired target \(x_{d}\). The local controller \(L_{i}\) will determine the updated agent's state position based on the input received by the controller, which consists of random errors and deterministic errors with ET at every discrete time \(t\). The camera will capture and update the state position of agent at \(G\) if there are changes to agent movements. This closed-loop process will repeat until the solution converges where the agent performance achievement of \(P(x(t))\) is equal to 0._  
**Assumption 2**.: _The physical dynamics of homogenous agent \(i\)'s linear discrete time model is represented as_  
\[A_{i}:\ x_{i}(t+1)=x_{i}(t)+u_{i}(t) \tag{1}\]  
_where the initial state location is \(x_{i}(t)\in R^{n}\) and \(u_{i}(t)\) is the input of the controller at time \(t\). The output of the controller, representing the next location of the state agent \(x_{i}(t+1)\), depends on the changes in the input of the controller, also known as the local controller error._  
**Assumption 3**.: _It is assumed that agent \(i\) has its own neighbor set \(j\in N_{i}\) which is strongly linked to the radius as an undirected graph; \(r\). \(r\) represents the radius where a relation can be formed by the agent. In undirected graph theory, \(G_{\mathbb{T}}=(V,E)\) where \(V\) is the vertex that represents the agent and \(E\) is the edge of each vertex representing the connection between agent \(i\) and its neighbours \(j\) or \((i,j)\in E\), as depicted in Figure 2. \(N_{i}=\{(j|(i,j))\}\in E\) denotes a set of all the neighbors of the agent. The representation of the connection/topology of the graph and the number of edges \(E\) for each vertex \(V\) is expressed in matrix form. An adjacency matrix \(A=\left[a_{j}\right]\in R^{N\times N}\) is used to describe the graph topology, where \(a_{ij}=1\) if \((j,i)\in E\) and \(a_{ij}=0\), or otherwise. The degree matrix of the system is defined as \(D=diag\{d_{1},d_{2}..........d_{N}\}\), where \(d_{i}=\sum_{j\in N_{i}}a_{ij}\)._