esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Detection Results for Cyberattack Behaviors
From a mathematical perspective, the synchronous attention mechanism, through its formula design, achieves effective synchronization and integration of information between different data blocks by adjusting the introduction of synchronous signals. This introduction of mathematical mechanisms enhances the model's deep understanding of individual data blocks and improves the model's grasp of the overall data structure and relationships across data blocks. Meanwhile, the synchronous loss function optimizes the management of information synchronicity and task relevance during the model training process through mathematical optimization, ensuring that the model can more accurately and comprehensively identify attack behaviors and characteristics in complex cyberattack detection tasks. Specifically, on the server log dataset, our method successfully identified multiple complex SQL injection attempts by analyzing anomalous SQL query patterns, which some baseline models such as BERT and Transformer failed to detect due to their insufficient context correlation analysis capabilities. Additionally, on the comment dataset, our model used synchronized attention mechanisms to detect XSS attack scripts hidden within normal comments, attacks that are often overlooked by other models due to their covert textual representation. The experimental results and theoretical analysis across different datasets conclude that the proposed method leverages the deep text understanding capabilities of large language models and effectively enhances the performance of detecting cyberattack behaviors through the design of synchronous attention mechanisms and a synchronous loss function. This approach demonstrates a clear advantage, especially in multimodal data processing and the identification of complex attack patterns. These results validate the effectiveness of the proposed method and provide new insights and methodologies for subsequent research in related fields.
## 1 Introduction
Since the amount of phase rotation of the block-by-block estimation scheme due to frequency offsets becomes larger proportional to the number of observed symbols, the proposed method offers excellent robustness against additive noises. To verify the usefulness of the block-by-block estimation method, the mean squared error (MSE) is numerically derived, and the optimal block size \(N_{a}\) that provides the minimum MSE of the proposed scheme is found. From the presented results, it is shown that the block-by-block estimation method saves considerable complexity and achieves accurate estimation performance when compared with the existing estimation method. This paper is structured as follows. Section 2 describes the signal model used in the OFDM-based DVB-C2 system. In Section 3, we introduce the conventional estimation algorithms. In Section 4, an efficient LSE scheme is presented, and the MSE is theoretically obtained in the DVB-C2 system. Section 5 gives the numerical results showing the feasibility of the proposed method. Conclusions are made in Section 6.
## 2 Simulation Models of Human Body and HBC Devices  
### Simulation Conditions  
Signal transmission from a wearable HBC device worn on the wrist to an off-body stationary receiver was selected for analysis in this study. The geometry of the whole model is shown in Figure 1a,b, which shows an enlarged view of the finger touching the receiver electrode.  
Two types of transmitters, one- and two-electrode transmitters, were separately placed 200 mm from the fingertip as shown in Figure 2. The distributions of the strength and direction of the electric field the transmission characteristics were analyzed using these models.  
Simulation software SEMCAD X v14.8.6.1 (SPEAG Software, Zurich, Switzerland) which employs the FDTD method was used for this analysis. Voxel size was 1 mm around the transmitter and the receiver and was gradually increased up to 10 mm with increasing distance from the transmitter and the receiver. Simulation results using coarser voxel size of 2-20 mm and finer voxel size of 0.5-5 mm were evaluated to confirm that the choice of the voxel size was appropriate. Differences in received signal amplitudes and impedances between the transmitter electrodes were less than 3%, and variation in distribution of the electric field was negligible. All the results presented in this paper were obtained using voxel size of 1-10 mm. The simulation space was 1100 mm \(\times\) 635 mm \(\times\) 1810 mm, which was determined by setting 50 mm space between the boundary and the human body and the receiver models. Perfectly matched layer of 13 layers was adopted as the boundary condition.  
Figure 1: Numerical simulation model. (**a**) Full model. (**b**) Detailed view of the finger touching the receiver electrode.  
The signal frequency was set to 10 MHz instead of 21 MHz, which was recommended by the IEEE standard, because this frequency has been used in many previous studies. For example, [PERSON] et al. have reported that the transmission characteristic of HBC was maximized at a frequency of 10 MHz [22]. Details of the human body, transmitter, and receiver models will be presented in the following sections.
## 1 Introduction  
The advancement in vehicular and communication technology facilitates vehicles to organize into a group of units with small inter-vehicle spacing commonly known as a vehicular platoon. In the connected and autonomous vehicles (CAVs) environment, the vehicle platoon is considered as a promising solution for enhancing road safety, improving road traffic efficiency and reducing fuel consumption in recent studies [1; 2; 3; 4; 5]. In addition, many scholars believe that CAVs have the potential to be applied in many scenarios in the future, such as car sharing systems [6; 7; 8], urban transport systems [9] and public transport systems [10; 11]. Therefore, with the increase in the market penetration of CAVs, the research on the formation method and driving optimization of vehicle platoons will continue to be a research hotspot. The design of the formation progress includes the geometric configuration of the platoon, the cruising speed of the platoon and the mutual distance between vehicles in the platoon [12]. In the actual traffic system, the different intelligence levels of vehicles and the topological structure of the information flow between vehicles are very important factors that affect the formation of the platoons. The actual traffic system is a complex system composed of multiple traffic factors. Therefore, it is necessary to comprehensively consider the actual road environment information (e.g., vehicle information, road information) in the formation process of the platoon. Inadequate analysis of the topological structure of information flow in the platoon and a single consideration of traffic environment variables will usually lead to inefficiency of the platoon in terms of traffic efficiency. A graph and safety potential field (G-SPF)-based vehicle platoon formation model is expected to fill this research gap. The purpose of this paper is to investigate the effectiveness potential of vehicle platoon formation using graph and safety potential field theories, along with a driving optimization control strategy on the basis of safety potential field theory.  
The novelty and main contribution of this study lie in the combination of graph theory and potential field theory to achieve platoon formation and driving optimization that can be further implemented in the CAVs control system in the near future. This method is new to the field of platoon control. Compared with previous studies, we innovatively incorporate the concept of the safety potential field to better describe the actual driving risk of vehicles and ensure their absolute safety under the CAVs environment. In addition, our proposed method outperforms the traditional platoon control methods (constant spacing and constant headway) in terms of traffic efficiency.  
The rest of this paper is organized as follows. A brief review of the existing related studies is introduced in Section 2. Section 3 presents the modeling process of the G-SPF platoon formation method. Numerical experiments under different initial states are performed in Section 4. Finally, the conclusions are summarized in Section 5.
## 4 Comparative Analysis with the Existing Online Detection Approaches
### Measurement Entropy  
[PERSON] et al. in [5] also define a kidnapping detector under Monte Carlo Localization in a topological map with recognizable objects/landmarks at each node. In their work, a metric called measurement entropy, is defined as follows  
\[H_{t}(p)=-\sum_{x_{t}^{[y]}}p\left(\mathbf{s}_{t},o_{t},z_{t}\Big{|}x_{t}^{[y]}, m\right)\log p\left(\mathbf{s}_{t},o_{t},z_{t}\Big{|}x_{t}^{[y]},m\right) \tag{10}\]  
where \(\mathbf{s}_{t},\ o_{t},\ z_{t}\) are the distance context to objects at time \(t\), objects seen by the robot at time \(t\), and the features extracted from the objects at time \(t\), respectively. \(x_{t}^{[y]}\) is the state of particle \(y\) at time \(t\) and \(m\) defines the map.  
Figure 3: Comparison of performance between unstandardized 1-D DTW and unstandardized 2-D DTW (**Top**), standardized 1D-DTW and standardized 2-D DTW (**Middle**), and QS1 DTW and QS2 DTW (**Bottom**).  
It can be seen that the term inside the summand in Equation (2) is in fact the weight of particle \(y\) and thus the equation can be described as the sum of a particle's weight entropy, that is  
\[H_{t}(p)=-\sum_{x_{t}^{[y]}}\omega_{t}^{[y]}log\omega_{t}^{[y]} \tag{11}\]  
Kidnapping detection is based on the rise of this measurement entropy, which can be written as  
\[Kidnapped_{t}=\left\{\begin{array}{ll}1&H_{t}(p)\geq\pi\\ 0&Otherwise\end{array}\right. \tag{12}\]  
where \(\pi\) is some constant as a threshold to distinguish kidnapping from normal condition.
# A Comparative Study of Coverage Hole Detection Techniques in Wireless Sensor Networks +
## 2 Methodology
### Reinforcement-Learning-Based Distributed Coverage Hole Recovery Approach  
[PERSON] et al. [20] provide a unique game theory strategy for randomly increasing coverage holes based on reinforcement learning. Sensor nodes can increase coverage holes using only nearby colleagues in the stated likely scenario. Every sensor node chooses the aggregated activity of node movement and detects range adjustment to reduce coverage holes. Contrary to previous methodologies, the simulation findings demonstrate that the proposed strategy may maintain a network that fully accounts for arbitrary destruction.
## 4 Discussion  
The findings of this study lend support to the conclusions of several studies in the major issues discussed below. Like the study of [47] indicating that the energy intensity of the building sector depends on the functionality of the building, not on construction or age of the building, this present study found that the electrical functionality of the building determines the energy consumption and intensity of the building (namely MV switchgear 2 sets, dry type transformer 2 sets 80,000, LV cable 10 m. (XLPE), main distribution board, busduct (MDB-DB), generator (20% of Tr. ), and generator installation). Similarly, like the study of [38] which concluded that estimating the cooling energy demand at the beginning of the condomium design helps reduce the energy consumption of the building, this present study proposed that estimating appropriate sizes of electrical appliances at an early stage can help mitigate the energy usage of the condomiums. This present study suggests downsizing these electrical appliances for energy efficiency because the actual usage lies only at 5.4% to 7.1% of the capacity. The finding of this study is consistent with that of [48], which estimated that 31% of the systems were \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{**Electrical Appliances**} & \multicolumn{2}{c}{**Current Standard Sizes**} & \multicolumn{2}{c}{**Downsized Alternatives**} & \multicolumn{1}{c}{**Diff.**} \\ \cline{3-6}  & & **Size** & **Cost (USD)** & **Size** & **Cost (USD)** & **(USD)** \\ \hline
1 & MV Switchgear 2 Ea & 200 A. 24 kV. & 50,000 & 200 A. 24 kV. & 50,000 & 0.00 \\ \hline
2 & Transformer 2 Ea & 2 \(\times\) 1600 & 146,667 & 2 \(\times\) 630 & 66,667 & 80,000 \\ \hline
3 & LV Cable 10 m. (XLPE) & 2\(\times\) [6 \(\times\) (3 \(\times\) 240/185 N)] & 15,333 & 2\(\times\) [3 \(\times\) (3 \(\times\) 240/185 N)] & 5333 & 10,000 \\ \hline
4 & MDB & 2500 A. & 83,333 & 1000 A. & 46,667 & 36,667 \\ \hline
5 & Busduct (MDB-DB) & 2 \(\times\) 2000 A. & 183,333 & 2 \(\times\) 1000 A. & 93,333 & 90,000 \\ \hline
6 & Generator (20\% of Tr.) & 350 kVA & 70,000 & 130 kVA & 33,333 & 36,667 \\ \hline
7 & Generator Installation & 350 kVA & 16,667 & 130 kVA & 10,000 & 6667 \\ \hline  & Total Cost & 565,333 &
\begin{tabular}{c} Reduced \\ 54\% \\ \end{tabular} & 305,333 & 260,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of two sizes of electrical appliances for cost effectiveness management. oversized and led to excessive peak demand of 41 MW. This study therefore suggests that replacing larger systems with more accurate units appropriate to the optimal capacity has the potential for savings of 81 MW and enables an average system saving of 18% of energy and 20% by sealing pipe leaks and servicing air-conditioning. However, unlike the study of [48], this present study found that the financial value indicates cost-effectiveness, i.e., 54% reduction of the total cost.
## 2 Materials and Methods
### Soil Analyses  
Before sowing and at the head emergence phenological phase (development stage 49), 5 soil sub-samples for each experimental unit were randomly collected on the row spacing at 0-10 cm depth. The sub-samples were thoroughly mixed to obtain a compositeand homogenized soil sample. Soil chemical analyses were carried out according to the methods described by the Italian Ministry of Agricultural, Food and Forestry Policies [40].  
Homogenized soil samples were sieved (<2 mm) to remove plant roots, fauna, and debris and were subjected to community level physiological profiles (CLPP) and rRNA 16S analysis. CLPP was assessed with Biolog EcoPlate(tm) (Biolog Inc., Hayward, CA, USA), following the method described by [PERSON] and [PERSON] [41] and the calculations of AWCD and the Shannon-Weaver index (H) proposed by [PERSON] [42]. For each sample, three independent plates were set up.  
From each sample, genomic DNA was extracted three times utilizing the NucleoSpin(r) Soil kit (Macherey Nagel, Duren, Germany), following the manufacturer's protocol. DNA quantification of the samples was obtained through Qubit dsDNA HS (High Sensitivity) Assay Kit and Qubit Fluorometer (ThermoFisher Scientific, Waltham, MA, USA). The three replications of each sample were pooled in equimolar mixtures and sent to Bio-Fab Research Srl (Roma, Italy) for further processing. DNA was amplified with a specific 16S protocol for the amplification of Bacteria and Archaea, using 300 bp Paired-End 16S community sequencing on the Mi-Seq Illumina platform (Bio-Fab Research, Roma, Italy) [43]. The 16S V3 and V4 region were targeted by gene-specific sequences [44], adding Illumina adapter overhang nucleotide sequences (Forward Primer = 5'-TCGTCGGCAGCGTCAGATGTGTAAAGAGACACCTACGGGNGGCWGCAG; Reverse Primer = 5'-GTCTGGGCTCGGAGATGTATAAGAGACAGGACTACHVGGGATCTAATCC). The bioinformatic workflow was as follow: control of the quality of the reads; count after filtering; assembly of the amplicons; construction of the classifier on the database; taxonomic assignment. The Amplicon Sequence Variant (ASV) clustering was performed with DADA2 plugin by qime2-2020.2 version [45]. The classifier was trained by the fit-classifier-naive-bayes plugin on the specific region of V3-V4 extracted from 16S file of database SILVA 132 release ([[https://www.arb-silva.de/](https://www.arb-silva.de/)]([https://www.arb-silva.de/](https://www.arb-silva.de/)) accessed on 6 March 2021). The similitude chosen for Taxa assignment was performed with a similitude of 97%. Taxonomic assignments were checked with LPSN service ([[https://lpsn.dsmz.de](https://lpsn.dsmz.de)]([https://lpsn.dsmz.de](https://lpsn.dsmz.de)) accessed on 6 March 2021). The Quality Check report is shown in Table S1 of Supplementary material. Diversity indices were calculated using R (R Foundation for Statistical Computing, Vienna, Austria) with the statistical package vegan v2.5-7 [46].
# Redundancy Mitigation Mechanism for Collective Perception in Connected and Autonomous Vehicles
###### Abstract  
Due to poor local range of the perception and object recognition mechanisms used by autonomous vehicles, incorrect decisions can be made, which can jeopardize a fully autonomous operation. A connected and autonomous vehicle should be able to combine its local perception with the perceptions of other vehicles to improve its capability to detect and predict obstacles. Such a collective perception system aims to expand the field of view of autonomous vehicles, augmenting their decision-making process, and as a consequence, increasing driving safety. Regardless of the benefits of a collective perception system, autonomous vehicles must intelligently select which data should be shared with who and when in order to conserve network resources and maintain the overall perception accuracy and time usefulness. In this context, the operational impact and benefits of a redundancy reduction mechanism for collective perception among connected autonomous vehicles are analyzed in this article. Therefore, we propose a reliable redundancy mitigation mechanism for collective perception services to reduce the transmission of inefficient messages, which is called VILE. Knowledge, selection, and perception are the three phases of the cooperative perception process developed in VILE. The results have shown that VILE is able to reduce it the absolute number of redundant objects of 75% and generated packets by up to 55%. Finally, we discuss possible research challenges and trends.  
collective perception; connected and autonomous vehicles; redundancy; V2X communications 2023
# Influence Maximization with Priority in Online Social Networks
## 3 Integrated Greedy Algorithm  
In this section, we first propose Integrated Greedy (IG) Algorithm which is well-known to resolve monotone and sub-modular problems that ensures an lower-bounded of optimization solution. The details of algorithm is described in Algorithm 1. ```
Input: Graph \(G=(V,E)\), \(U\subset V,k\), \(T\) Output: Seed set \(S\), and \(t\)
1.\(S_{1}\leftarrow\emptyset,S_{2}\leftarrow\emptyset\) /* Phase 1: Greedy strategy for prior set */
2.while\(\sigma_{U}(S_{1})<T\)do
3.\(u\leftarrow\arg\max_{v\in V\setminus S_{2}}\left(\sigma_{U}(S_{1}\cup\{v\})- \sigma_{U}(S_{1})\right)\)
4.\(S_{1}\gets S_{1}\cup\{u\}\)
5.end
6.\(t\leftarrow|k|-|S_{1}|\), \(i\gets 0\) /* Phase 2: Greedy strategy for IM within remain budget */
7.while\(i<t\)do
8.\(u\leftarrow\arg\max_{v\in V\setminus S_{2}}\left(\sigma(S_{2}\cup\{v\})- \sigma(S_{2})\right)\)
9.if\(u\in S_{1}\)then
10.\(t\gets t+1\)
11. end if
12.\(S_{2}\gets S_{2}\cup\{u\}\), \(i\gets i+1\)
13.end
14.\(S\gets S_{1}\cup S_{2}\)return\(S,t\);
```  
**Algorithm 1**Integrated Greedy (IG) algorithm  
Assume \(S_{1}\) is the solution of the problem that finds the minimum seed nodes such that the influence on the priority set is greater than threshold \(T\), and \(S_{2}\) is a solution of IM problem. The main idea of this algorithm is to modify the native greedy algorithm [1] by combining two above solutions. The algorithm is divided into two main phases. In the first phase, it tries to find a solution \(S_{1}\) by a greedy strategy (line 2-4). In each iterator, the algorithm chooses a node \(u\) with largest _influence incremental_ to set \(U\) into \(S_{1}\) (line 3-4) until the \(\sigma_{U}(S_{1})\geq T\). Since \(T<k\), \(|S_{1}|\leq T<k\). Denote \(t=k-T\) as the remaining budget (line 6). The algorithm next finds the candidate solution \(S_{2}\) for IM with the remaining budget \(t\) by using a greedy method in the second phase (line 6-10). In each iterator \(i\), it selects a node \(u\) with largest _influence incremental_ (line 7). If \(u\) already belongs to \(S_{1}\), the algorithm increases \(t\) by 1 (line 8-9). This phase ends when the remaining budget \(t\) is exhausted (line 6). Finally, the algorithm returns the solution \(S\) which unites \(S_{1}\) and \(S_{2}\). It is easy to confirm that \(|S|=k\), and \(t>T-k\geq 1\) since \(k>T\). Theorem 1 shows the approximation guarantee of IG algorithm. **Theorem 1**. : IG _algorithm returns \((S,t)\), where \(S\) is a feasible solution and \(t\geq 1\), satisfies:_  
\[\sigma(S)\geq\left(1-\left(1-\frac{1}{k}\right)^{t}\right)\sigma(S^{*})\]  
_The worst-case approximation ratio is obtained when \(t=1\) and it is equal to 1/k._Proof. : Denote \(S^{*}_{\text{IM}}=\{s_{1},s_{2},\ldots,s_{k}\}\) is an optimal solution of IM problem for input data of Algorithm 1 (the graph \(G\) and budget \(k\)). Obviously, we have \(\sigma(S^{*}_{\text{IM}})\geq\sigma(S^{*})\). After ending the second phase, assume that \(S_{2}=\{u_{2}^{1},u_{2}^{2},\ldots,u_{2}^{i}\}\), \(S_{2}^{i}=\{u_{2}^{1},u_{2}^{2},\ldots,u_{2}^{i}\}\), and \(S_{2}^{0}=\emptyset\).
## 4 Suitability Evaluation and Effect Analysis of Non-Conventional Lanes
### Traffic Efficiency Evaluation  
Traffic efficiency is one of the core indicators for evaluating the effectiveness of non-conventional lanes. While non-conventional lanes demonstrate significant effects in improving capacity and reducing delay, different types of non-conventional lanes vary in their efficiency enhancement mechanisms and degrees.  
[PERSON] et al. [62] proposed a lane-based optimization method for signal timing, considering lane markings and signal settings in a unified framework. [PERSON] et al. [63] proposed an integrated model for lane-use and signal-phase designs, showing significant improvements in overall delay, stops, and fuel consumption compared to existing designs. [PERSON] et al. [58] presented an innovative design to increase the capacity of heavily congested intersections through the use of special width approach lanes (SWAL). [PERSON] et al. [64] studied the integrated design and operation of urban arterials with reversible lanes, developing a lane-based optimization model to guide the integrated setting of reversible lanes and other traffic management measures. [PERSON] et al. [65] applied dynamic reversible lane control to signalized diamond interchanges by periodically reversing certain lanes in the internal link, effectively alleviating spillback problems. [PERSON] et al. [66] combined the advantages of tandem control and exit lanes for left-turn (EFL) control, proposing a Combined Tandem and Exit lanes (CTE) design that performs excellently when the left-turn ratio is low to medium (<50%) and queue length is limited (<200 m).  
Existing research primarily focuses on efficiency improvements at individual intersections, with insufficient research on system-wide benefits at the network level. Future work should establish multi-scale efficiency evaluation methods that not only assess the direct benefits at individual intersections but also consider the impacts on adjacent intersections and the entire network. Simultaneously, research is needed on the efficiency characteristics of non-conventional lanes under different traffic states (free flow, saturated flow, oversaturated), providing a basis for developing dynamic control strategies. Furthermore, considering the future connected and automated vehicle environment, the synergistic effects between non-conventional lanes and autonomous vehicles should be studied.