esa-sceva/satcom-chunk-collection huggingface dataset:
# Security Challenges of Location Privacy in VANETs and State-of-the-Art Solutions: A Survey
## 2 Background
### Threats in VANETs  
By construction, road safety has been a significant concern since the advent of the automobile in 1885. With the introduction of VANET, vehicles and drivers are now vulnerable to cyber security threats, compromising network access, anonymity, stability and data confidentiality, as well as physical safety for passengers and facilities. We studied the existing literature and categorized the attacks on VANETs based on their characteristics, and compiled them into the following table. The table explains the threats that VANETs can face. We used the table parameter as follows: whether the attack type is centralized or not, attack effect the privacy of individual node, Certificate Authority (CA) is used or not, whether the solution has support for routing protocol, the cryptographic algorithm is used and how efficient the detection. Table 1 below shows security attacks against VANETs and their countermeasure.  
Many surveys are available in the literature they explain the types of attacks pertaining to VANETs. However, we categorized these attacks in tabular form based on various attributes such as whether the scheme has support for CA, attacks types are centralized or decentralized, and how VANETs nodes react during attacks. In most of the attacks the privacy of nodes is preserved while most of the attacks target those areas where no routing support is available. Most of the attacks can be traced because they are reporting back to the concerned authority such as RSU or any other Trusted Third Party (TTP). Therefore, there is a need to further investigate and analyze the state-of-the-art in location privacy in VANET and discuss their pros and cons accordingly.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Solution** & **Attack** & **Centralized** & \begin{tabular}{l} **Privacy** \\ **of Node** \\ \end{tabular} & \begin{tabular}{l} **CA** \\ **Used** \\ \end{tabular} & \begin{tabular}{l} **Routing** \\ **Protocol** \\ \end{tabular} & \
\begin{tabular}{l} **Crypto-Algo** \\ **Activation** \\ \end{tabular} &
\begin{tabular}{l} **Remote-** \\ **Activation** \\ \end{tabular} \\ \hline
[11] & Tracking & Yes & Yes & Yes & No & Yes & Yes & Good \\
[12] & DoS & No & Yes & Yes & Yes & No & No & Limitations \\
[13] & Sybil & No & Yes & Yes & No & Yes & No & Good \\
[14] & MitM & No & No & Yes & No & Yes & Yes & Good \\
[15] & Replay & No & Yes & Yes & No & Yes & Yes & Good \\
[16] & Spam & Yes & Yes & Yes & No & Yes & Yes & Effective \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between VANETs location privacy schemes.
## 3 Connected Dominating Set
### Lower Bound  
We prove a lower bound for the approximation factor of local algorithms for connected dominating set in the setting of location aware nodes depending on the locality distance of the algorithm.  
**Theorem 6**.: _Let \(\mathcal{A}\) be a local algorithm for connected dominating set in the setting of location aware nodes. Let \(k\) be the locality distance of \(\mathcal{A}\). Then the approximation ratio which \(\mathcal{A}\) achieves is bounded from below by \(1+1/k\)._  
Proof.: Similar to the proof of Theorem 2 we construct graphs in which \(\mathcal{A}\) has to take the same decisions in a certain vertex \(v\). Our construction consists of a cycle of vertices with \(2k+2\) vertices as shown in Figure 8. Denote this cycle by \(G\). Note that \(G\) is indeed a unit disk graph. Denote by \(CD\) the connected dominating set which \(\mathcal{A}\) computes for \(G\). Now let \(v\) be a vertex. Denote by \(G_{v}\) the graph which we obtain by removing the opposite vertex of \(v\) from \(G\) (see Figure 8). As \(\mathcal{A}\) is a local algorithm with locality distance \(k\) it must take the same decision about assigning \(v\) to the connected dominating set in \(G\) and \(G_{v}\). Denote by \(CD_{v}\) the connected dominating set which is computed for \(G_{v}\) by \(\mathcal{A}\). As not assigning \(v\) to \(CD_{v}\) would result in \(CD_{v}\) being disconnected, we conclude that \(v\in CD_{v}\) and \(v\in CD\). With the same reasoning we can show that all vertices in \(G\) must be in \(CD\). So \(|CD|=2k+2\). But an optimal connected dominating set for \(G\) has only \(2k\) vertices. So the performance ratio of \(\mathcal{A}\) is bounded from below by \(\frac{2k+2}{2k}=1+\frac{1}{k}\).  
Figure 8: Construction for the lower bound for a local algorithm for connected dominating set for \(k=2\).  
**Corollary 3**.: _Let \(\mathcal{A}\) be a local algorithm for connected dominating set in the setting of location aware nodes with locality distance \(2\). Then the approximation ratio which \(\mathcal{A}\) achieves is bounded from below by \(1.5\)._
## 3 Filtering Method
### Principle of Filtering Method
In Figure 5, the LMF of a defect is within a certain range. To accurately identify and reconstruct a defect, the LMF should be measured as completely as possible. However, at a distance from the defect, the LMF is usually weak and difficult to distinguish from interference. The boundary of LMF in this paper is that the absolute value of LMF reduces to 10% of the maximum in the z direction. Beyond this range, the LMF is relatively small and negligible. According to Formula (1), if z = z0 unchanged, H\({}_{\text{z}}\)(x, z0) is the maximum when x = -\(\sqrt{\text{a}^{2}+z_{0}^{2}}\). The leakage magnetic field distributes in 2L centered on the defect. According to Equation (4), L is calculated by Equation (6). \[\ln\frac{\left[\left(-\text{L}-\text{a}\right)^{2}+z_{0}^{2}\right]}{\left[ \left(-\text{L}+\text{a}\right)^{2}+z_{0}^{2}\right]}=\frac{1}{10}\ln\frac{ \left[\left(-\sqrt{\text{a}^{2}+z_{0}^{2}}-\text{a}\right)^{2}+z_{0}^{2} \right]}{\left[\left(-\sqrt{\text{a}^{2}+z_{0}^{2}}+\text{a}\right)^{2}+z_{0} ^{2}\right]} \tag{6}\]  
The detection speed is v m/s, and the sampling speed is s point/s. J\({}_{0}\) is a sampling point in the z direction. If the distance between j\({}_{0}\) and the center of a defect is less than L + \(\sqrt{\text{a}^{2}+z_{0}^{2}}\), there will be a maximum point in the x direction and a minimum point in the z direction from sampling point j\({}_{0}\) - B to j\({}_{0}\) + B.  
\[\text{B}=\frac{\left(\text{L}+\sqrt{\text{a}^{2}+z_{0}^{2}}\right)\text{s}}{ \text{V}} \tag{7}\]  
Figure 5: Magnetic field distribution of defects in x and z directions of different sizes. The absolute value of the difference between the output signals of the two sensors is large at the maximum point, so it is beneficial to find the reference signal. The absolute value of the difference between the outputs of two sensors in sampling point j is denoted by aj. When sampling point j0 is processed, aj0 \(-\) B to aj0 + B are calculated first. Secondly, the maximum in aj0 \(-\) B to aj0 + B is found. The maximum of ith pair of sensors is denoted by MAXi. At last, the pair of sensors with the smallest MAXi (i = 1,2,...,N) is found and their output is taken as the reference signal.
# Task Assignment of the Improved Contract Net Protocol under a Multi-Agent System
## 2 Improvement of the Contract Net Protocol Model
### The Agent Model  
For a task assignment under the MAS system, the participation of two kinds of agents and the public environment are needed, including a manager agent and contractor agent. The manager agent is the manager who formulates the bidding task and evaluates the winning bidding node. It is mainly responsible for the decomposition of tasks, the formulation and publication of tenders, the collection of tenders, the judgment of winning bids and the signing of contracts, as well as the supervision of task execution and effect evaluation. The contractor agent is mainly responsible for bidding according to its own working status and ability. In the whole contract negotiation process, the role of the agent does not need to be pre-defined, any agent can become a manager by releasing tasks and can be a contractor by participating in bidding activities. We first needed to model the agent [11; 12; 13; 14; 15]. The basic structure of a single agent is shown in Figure 1 below.  
When acting as a manager agent, each agent's knowledge base, processing and decision center and communication coordinator play an effective role, while when acting as a contractor agent, the task actuator also runs. The knowledge base stores historical assignment information, task related information, agent status and capability information. The processing and decision-making center can decouple tasks, publishing tenders and assigning tasks when acting as a manager agent. According to the information of the bidding task, the contractor agent can feedback costs, loads and the other information of executing the task. After the contractor agent signs the contract with the manager agent, the task actuator can perform corresponding tasks according to the decision of the processing and decision-making center. The communication coordinator can publish or receive information to communicate between nodes.
# Concept Drift Adaptation with Incremental-Decremental SVM
###### Abstract  
Data classification in streams where the underlying distribution changes over time is known to be difficult. This problem--known as concept drift detection--involves two aspects: (i) detecting the concept drift and (ii) adapting the classifier. Online training only considers the most recent samples; they form the so-called shifting window. Dynamic adaptation to concept drift is performed by varying the width of the window. Defining an online Support Vector Machine (SVM) classifier able to cope with concept drift by dynamically changing the window size and avoiding retraining from scratch is currently an open problem. We introduce the Adaptive Incremental-Decremental SVM (AIDSVM), a model that adjusts the shifting window width using the Hoeffding statistical test. We evaluate AIDSVM performance on both synthetic and real-world drift datasets. Experiments show a significant accuracy improvement when encountering concept drift, compared with similar drift detection models defined in the literature. The AIDSVM is efficient, since it is not retrained from scratch after the shifting window slides.  
support vector machines; concept drift; incremental learning +
Footnote †: journal: Applied Sciences
# Recharging Schedule for Mitigating Data Loss in Wireless Rechargeable Sensor Network
## 4 A Heuristic Algorithm
### Tour Improvement  
It is possible to insert some unselected vertices into the tour \(P\) constructed by the LKH algorithm in the last step, whose distance does not exceed \(L\). For each vertex \(v_{i}\in V\backslash P\), let's define its insertion cost as \(c_{i}^{{}^{\prime\prime}}=\min_{v_{x},v_{y}\in P}c_{ix}+c_{iy}-c_{xy}\), where \(v_{x},v_{y}\) are adjacent vertices in \(P\). If \(C_{\text{LKH}}(P)+c_{i}^{{}^{\prime\prime}}\leq L\), then vertex \(v_{i}\) is feasible for adding to the tour. Find the feasible vertex in a set \(V\backslash P\) with highest reward-to-insertion-cost, which is computed as \(\hat{f}_{i}/c^{{}^{\prime\prime}}\), and then insert it into the tour at the location with lowest insertion cost. Repeat this procedure until no more feasible vertices in \(V\backslash P\) can be inserted. The detailed algorithm is described in Algorithm 3.
```
0:\(G=(V,E)\), initial charging tour \(P\), traveling distance constraint \(L\);
0: An improved charging tour \(P\)
1:repeat
2:\(R:=\phi\)
3:for each vertex \(v_{i}\in V\) and \(v_{i}\
otin P\)do
4:\(c_{i}^{{}^{\prime\prime}}:=\min_{v_{x},v_{y}\in P}c_{ix}+c_{iy}-c_{xy}\)
5:if\(c_{i}^{{}^{\prime\prime}}+C_{LKH}(P)\leq L\)then
6:\(R(i,P):=\hat{r}_{i}/c_{i}^{{}^{\prime\prime}}\)
7:\(R:=R\cup\{R(i,P)\}\)
8:else
9: continue
10:endif
11:endfor
12:\(v_{i}:=\arg\max_{R(i,P)\in R}R(i,P)\)
13: insert \(v_{i}\) into \(P\) at the location with minimum insertion cost
14:until no more feasible vertices in \(V\backslash P\)
15:return\(P\)
```  
**Algorithm 3** Tour improvement.
## 9 Conclusions  
This paper introduced a dual-hop decode-and-forward relay-assisted communication system that combines terahertz and VLC. In this system, the first hop utilizes a THz channel to transmit information from the UAV (source node) to the relay node. The relay node decodes the received signal and retransmits it through VLC inside the building. This work presented the derivation of closed-form expressions for E2E BER with and without the presence of pointing errors, which were validated through simulations results.  
Moreover, this study also conducted a comparative analysis of the proposed system with FSO-VLC and RF-VLC in terms of BER and outage probability. The results indicated the superior performance of THz-VLC compared to RF-VLC and FSO-VLC. Varying the VLC link distance while maintaining a constant distance between the source and relay indicated that integrating VLC with THz offers more system stability compared to FSO-VLC and RF-VLC due to the resilient nature of THz at higher SNRs.  
These findings underscore the importance of considering factors such as path loss, pointing errors, and molecular absorption when deploying UAV-assisted THz-VLC system. Such considerations are paramount for optimizing performance and ensuring reliable communication in practical deployment scenarios.  
Conceptualization, H.R. and D.N.K.J.; Methodology, H.R. and D.N.K.J.; Validation, H.R.; Formal analysis, H.R.; Investigation, H.R.; Resources, H.R. and D.N.K.J.; Data curation, H.R.; Writing--review & editing, H.R. and D.N.K.J.; Supervision, D.N.K.J.; Project administration, D.N.K.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported, in part, by the Sri Lanka Institute of Information technology via grant PVC(R&I)/RG/2024/12, by the COFAC--Cooperativa de Formacao e Animacao Cultural, C.R.L. (University of Lusofona University), via project PortuLight (COFAC/ILIND/COPELABS/2/2023), by the national funds through FCT--Fundacao para a Ciencia e a Tecnologia - as part the grant AIEE-UAV(no. 2022.03897.PTDC), CEECINST/00002/2021/CP2788/CT0003 and COPELABS (no. UIDB/04111/2020) and by the Scheme for Promotion of Academic & Research Collaboration (SPARC), Government of India, via grant no. SPARC/2024-2025/NXTG/P3524.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 4 Ecosystem
### Enabling Technologies
#### 4.3.1 DNA Computing  
DNA, formally known as Deoxyribonucleic Acid, is the biological substance that forms our genetic code. DNA can be broken down into four pieces known as ATCG, or formally Adenine, Thymine, Cytosine, and Guanine [178]. This concept can be used in encryption by encoding the data using this structure and leveraging existing DNA technology and sequences to manipulate it [179]. This encoding scheme has many advantages such as immense keyspace [180], high storage space for data [181], and high parallelism for algorithms [182].  
The most common way to implement DNA concepts in image encryption is to pair it with a chaos source that aids in choosing random DNA coding rules or sequences [179]. Many algorithms with varying sources of chaos have been proposed. These include the logistic map [183; 184; 185; 186], sine chaotic map [182], sine-Henon alteration map [185], cellular automata [187], optical chaos [188], etc.  
The encryption algorithms themselves also employ a wide range techniques. These include encryption then transmission [188], using DNA sequences as a one-time pad [184], the Vigenere cipher [189], matrix subdivision [186], splitting color streams [190], the use of the Chinese remainder theorem [191], and the use of reconfigurable hardware called FPGA.  
Several more unique approaches have also been proposed. One such proposal was by [PERSON] et al. [178], where DNA mutations were implemented to improve the randomness of DNA algorithms. A different approach by [PERSON] and [PERSON] [192] utilizes DNA pseudo-symbols where the standard DNA sequence of four characters (ATCG) is expanded to use eight pseudo-symbols.  
A particularly interesting proposal was also made by [PERSON] et al. [193], where the concept of the castle piece from the game Chess (also called the rook) was used. These authors argued that simply mixing chaos and DNA is not enough, and to more effectivelyscramble and confuse the pixels of the image, random movements of the castle piece were simulated on a chessboard with dimensions equivalent to the image.  
Statistical and security analyses of several algorithms based on DNA encoding and chaos have been performed; one examples is [PERSON] et al. [194]. In this study, it was found that existing algorithms are vulnerable, and choosing the plaintext strategically can reveal some or all secret parameters. Another research proposal by [PERSON] et al. [181] takes a graphical approach to analyzing the security of algorithms. The provided GUI allows for configuring the secret key, encrypting a chosen image, and viewing the statistical analysis.
## 6 Experimental Verification
### On-Machine Tool Wear Monitoring and Auto-Compensation  
In this experiment, the two functions, on-machine auto-monitoring and auto-compensation of tool wear, were used in the production line of GlobalTek Co. to verify the effectiveness and feasibility of the two functions. Except for the tolerances (T03: \(\pm\)6 \(\upmu\)m; T04: \(\pm\)7.5 \(\upmu\)m), the same machining conditions as shown in Table 1 were used. Another 7643 components were tested in this experiment. With use of the proposed system, the tool wear of each cut was predicted and auto-compensated to the CNC controller of the turning machine for the next component.  
Figures 22 and 23 show the statistics of the dimension inspection results made with the air gauge instruments. It can be seen that all the machined components matched the requirement for dimension tolerance. It was also noted that most of the parts had dimension errors of about 2-3 \(\upmu\)m. Because of the tool wear, some parts had larger dimension errors of about 4-5 \(\upmu\)m, but they all still met the tolerance requirement. The results showed that the two functions (on-machine auto-monitoring and auto-compensation of tool wear) can provide appropriate prediction and compensation of tool wear for each cutting process.  
According to the verification results, it showed that the proposed method is suitable for on-line monitoring/compensation for a mass production line. The proposed method is mainly based on the correlation between the variation of cutting vibration signals (detected by the vibration sensors), cutter wear, and machining errors. However, very light tool wear causes very little change in cutting vibration, which it may not be possible to clearly detect using a vibration sensor if the sensitivity of the sensor used is not fine enough. Therefore, the possible limitation of this method is the sensitivity of the selected vibration sensors. The better the sensors, the higher the cost. Thus, the vibration sensors should be selected based on the requirements of the machining process.
# A Faster Algorithm for Reducing the Computational Complexity of Convolutional Neural Networks
## 2 Related Work
### Strassen Algorithm  
Suppose there are two matrices A and B, and matrix C is the product of A and B. The numbers of the elements in both rows and columns of A, B and C are even. We can partition A, B and C into block matrices of equal sizes as follows:  
\[\mathrm{A}=\left[\begin{array}{cc}\mathrm{A}_{1,1}&\mathrm{A}_{1,2}\\ \mathrm{A}_{2,1}&\mathrm{A}_{2,2}\end{array}\right],\mathrm{B}=\left[\begin{array} []{cc}\mathrm{B}_{1,1}&\mathrm{B}_{1,2}\\ \mathrm{B}_{2,1}&\mathrm{B}_{2,2}\end{array}\right],\mathrm{C}=\left[\begin{array} []{cc}\mathrm{C}_{1,1}&\mathrm{C}_{1,2}\\ \mathrm{C}_{2,1}&\mathrm{C}_{2,2}\end{array}\right] \tag{17}\]  
According to the conventional matrix multiplication algorithm, we then have Equation (18).  
\[\mathrm{C}=\mathrm{A}\times\mathrm{B}=\left[\begin{array}{cc}\mathrm{A}_{1, 1}\mathrm{B}_{1,1}+\mathrm{A}_{1,2}\mathrm{B}_{2,1}&\mathrm{A}_{1,1}\mathrm{B }_{1,2}+\mathrm{A}_{1,2}\mathrm{B}_{2,2}\\ \mathrm{A}_{2,1}\mathrm{B}_{1,1}+\mathrm{A}_{2,2}\mathrm{B}_{2,1}&\mathrm{A}_{ 2,1}\mathrm{B}_{1,2}+\mathrm{A}_{2,2}\mathrm{B}_{2,2}\end{array}\right] \tag{18}\]  
As Equation (18) shows, we need 8 multiplications and 4 additions to complete matrix C. The Strassen algorithm can be used to reduce the number of multiplications [23]. The process of the Strassen algorithm is shown as follows:  
\[\mathrm{I}=(\mathrm{A}_{1,1}+\mathrm{A}_{2,2})\times(\mathrm{B}_{1,1}+\mathrm{ B}_{2,2}), \tag{19}\]  
\[\mathrm{II}=(\mathrm{A}_{2,1}+\mathrm{A}_{2,2})\times\mathrm{B}_{1,1}, \tag{20}\]  
\[\mathrm{III}=\mathrm{A}_{1,1}\times(\mathrm{B}_{1,2}-\mathrm{B}_{2,2}), \tag{21}\]  
\[\mathrm{IV}=\mathrm{A}_{2,2}\times(\mathrm{B}_{2,1}-\mathrm{B}_{1,1}), \tag{22}\]  
\[\mathrm{V}=(\mathrm{A}_{1,1}+\mathrm{A}_{2,2})\times\mathrm{B}_{2,2}, \tag{23}\]  
\[\mathrm{VI}=(\mathrm{A}_{2,1}-\mathrm{A}_{1,1})\times(\mathrm{B}_{1,1}+\mathrm{ B}_{1,2}), \tag{24}\]  
\[\mathrm{VII}=(\mathrm{A}_{1,2}-\mathrm{A}_{2,2})\times(\mathrm{B}_{2,1}+\mathrm{ B}_{2,2}), \tag{25}\]\[\mathrm{C}_{1,1}=\mathrm{I}+\mathrm{I}V-\mathrm{V}+\mathrm{VII}, \tag{26}\]  
\[\mathrm{C}_{1,2}=\mathrm{III}+\mathrm{V}, \tag{27}\]  
\[\mathrm{C}_{2,1}=\mathrm{II}+\mathrm{I}V, \tag{28}\]  
\[\mathrm{C}_{2,2}=\mathrm{I}-\mathrm{II}+\mathrm{III}+\mathrm{VI}, \tag{29}\]  
where I, II, III, IV, V, VI, VII are temporary matrices. The whole process requires 7 multiplications and 18 additions. It reduces the number of multiplications from 8 to 7 without changing the computational results. More multiplications can be saved by using the Strassen algorithm recursively, as long as the numbers of rows and columns of the submatrices are even. If we use N recursions of the Strassen algorithm, then it can save \(1-(7/8)^{\mathrm{N}}\) multiplications. The Strassen algorithm is suitable for the special convolutional matrix in Equation (4) [24]. Therefore, we can use the Strassen algorithm to handle a convolutional matrix.