esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Optimal Selection of CBM Development Blocks
### Optimal Selection Results and Discussions  
A comprehensive evaluation was performed according to the multi-index gray relation analysis optimization model, and the results are shown in Table 5. Table 5 shows that the ranking of the target blocks from the most to least optimal membership degree is as follows: Block 2 (0.8936), Block 1 (0.7500), Block 4 (0.5123), Block 3 (0.2808), and Block 5 (0.1112). It shows that the development potential of target blocks decreases from the Sanjiao block to the Fanzhuang-Zhengzhuang block, Daning-Jixian block, Gujiao block, and Fengrun block.  
As shown in Figure 3, in terms of the overall change trend, the average daily gas production per well and the optimal membership degree obtained by the GRA optimization model of the five target blocks show good consistency. However, there is a small difference between the Gujiao block and the Fengrun block in the consistency of average daily gas production per well and optimal membership degree. The difference may be due to the low development level of these two blocks and the small number of CBM wells, which fail to accurately reflect the CBM production capacity of the block.  
The above analysis shows that the GRA optimization model based on interval value can be applied to the production potential optimization evaluation of CBM blocks with large changes in reservoir parameters under complex conditions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Target Block** & **Block 1** & **Block 2** & **Block 3** & **Block 4** & **Block 5** \\ \hline Optimal membership degree & 0.7500 & 0.8936 & 0.2808 & 0.5123 & 0.1112 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of gray correlation evaluation of CBM blocks.
## Materials and Methods
### Methods Validation  
The analytical performance of the AChE assay was fully evaluated in terms of repeatability, trueness and limits of detection (LODs). Strawberry and lettuce samples were spiked with carbofuran or carbofuran-3-hydroxy or dichlorvos. In the case of carbofuran, repeatability and trueness testing were performed in six-fold at two concentration levels (0.020 and 0.100 mg Kg\({}^{-1}\)). Aliquots of the control extracts were used to prepare matrix matched calibration standards at 0.005, 0.010, 0.020, 0.050, 0.100, 0.250 and 0.500 mg Kg\({}^{-1}\) for the enzyme assay. In terms of carbofuran-3-hydroxy and dichlorvos, repeatability and trueness testing were performed at 0.300 and 1.20 mg Kg\({}^{-1}\) as they were weaker inhibitors compared to carbofuran. The calibration range was also different for these two compounds, in detail 0.078, 0.150, 0.300, 0.600, 1.20, 2.5 and 5.0 mg Kg\({}^{-1}\). The LODs were calculated using the formula  
LOD enzyme assay = meanblank \(-\) 3.3 \(\times\) SDblank, \(n\) = 6 blank samplesTo investigate the assay quantification capability in the presence of multiple residues, as well as to monitor their toxicity potential, the widely accepted toxic unit (TU) approach was applied in matrix matched calibration curves containing two analytes [29]. The calibration range for each analyte was based on the IC\({}_{50}\) obtained in the matrix matched calibration curve. In detail:  
- Three points below the IC\({}_{50}\) value, IC\({}_{50}\)/8, IC\({}_{50}\)/4, IC\({}_{50}\)/2;  
- One at the IC\({}_{50}\);  
- Three points above the IC\({}_{50}\), 2 \(\times\) IC\({}_{50}\), 4 \(\times\) IC\({}_{50}\), 8 \(\times\) IC\({}_{50}\).  
Moreover, a TU is the sum of the toxic contributions of each component in the mixture. The calculation formula for TU is the following:  
\[\text{TU}=\text{IC}_{50}\text{ A(mix)/IC}_{50}\text{ A (individual)}+\text{IC}_{50}\text{ B (mix)/IC}_{50}\text{ B (individual)},\]  
where A and B are pesticides, IC\({}_{50}\) (mix) is the effect of each component in the binary mixture and IC\({}_{50}\) (individual) is the individually induced inhibitory effect, at the IC\({}_{50}\) level. The TU model can inform about the joint inhibitory effect type, with TU equals to one meaning additive effect, in other words 0.5 TUA + 0.5 TUB = 1 TUA + B. Alternatively, TU \(<\) 1 indicates synergistic effect whilst TU \(>\)1 shows antagonism between the inhibitors.  
Finally, an accredited LC-MS/MS method was also applied for the same samples to compare the assay performance. Repeatability and trueness were also tested at the same conditions as for the enzyme assay and matrix matched calibration standards were prepared at 0.001, 0.002, 0.005, 0.010, 0.020, 0.050 and 0.100 mg Kg\({}^{-1}\). The LODs were calculated using the formula.  
LOD LC-MS/MS = SDregression/slope \(\times\) 3.3.
# Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks
## 1 Introduction
### Contributions  
Our research tackles the aforementioned challenge through the following key contributions:  
* ANN-based BER Estimation: We propose a novel model utilizing artificial neural networks to predict the Bit Error Rate (BER) based on the characteristics of nearby buildings.
* Incorporation of Environmental Characteristics: Our model integrates building information (\(X_{\text{min}}\), \(X_{\text{max}}\), \(Y_{\text{min}}\), \(Y_{\text{max}}\)) and the physical attributes of the environment, including the antenna parameters of vehicles, as reflected in the NS-3 physical layer.
* Data Collection: We employ Network Simulator 3 (NS-3) alongside the SUMO mobility simulator to extract real-world geospatial data from the OpenStreetMap geographic database, capturing the 2D geometric structure of buildings for our ANN.
* Algorithm Assessment: We assess the efficacy of ten distinct learning algorithms for our ANN using five pivotal metrics, highlighting the superior performance of the Conjugate Gradient With Powell/Beale Restarts (CGB) learning algorithm.
* Enhanced Communication Reliability: Our research augments the reliability of communication in urban VANET contexts, especially for emergency vehicles, by leveraging supervised learning and ANNs to predict signal degradation and optimize routing decisions.  
The remainder of this paper is organized as follows: Section 2 provides a comprehensive literature review, focusing on relevant research in the field. Section 3 introduces artificial neural networks, highlighting their significance and application in our study. In Section 4, we detail the step-by-step process employed to develop the BER estimation model. Subsequently, Section 5 presents the obtained results, accompanied by a meticulous analysis. Finally, Section 6 concludes the paper, summarizing the key findings and discussing potential avenues for future research.
# Optimization of Coverage and Capacity Using Smart Antennae
## 3 Methodology
### Coverage Model  
The calculation of the coverage rate for traditional omni-directional antennas involves determining the radius within which the base station can transmit at its maximum power. The area of the circle with this radius is then calculated to represent the coverage area. However, for intelligent adaptive directional antennas, the coverage area varies depending  
\begin{table}
\begin{tabular}{c c} \hline \(S_{i}\) & \(S_{i}\) (\(i=1\), \(2\), \(\ldots\), \(n\)) Denote \(n\) Small Cells \\ \hline \(X_{j}^{i}\) & The antenna beam j (j = 1, 2, \(\ldots\), m) of small cell \(S_{i}\) \\ \(R_{j}^{i}\) & The coverage area that user k can be served by \(S_{i}\) under the smart antenna \(X_{j}^{i}\) \\ \(P_{max}\) & Maximum transmission power of each small cell \\ \(P_{min}\) & Minimum transmission power of each small cell \\ \(B\) & Bandwidth of the channel \\ \(t_{S_{i}}\) & Number of users covered by \(S_{i}\) \\ \(B_{k}\) & \(B_{k}=\frac{B}{t_{S_{i}}}\), the bandwidth that user k can use \\ \(K\) & Number of total users \\ \(p_{i}\) & The transmission power of small cell \(S_{i}\) \\ PL & Path loss \\ \(d_{k,S_{i}}\) & The distance between user k and small cell \(S_{i}\) \\ \(\gamma\) & Path loss exponent (\(\gamma\geq 2\)) \\ \hline \end{tabular}
\end{table}
Table 1: System parameter settings.  
Figure 5: Smart directional antennae reduce interference while maintaining coverage.  
on which antenna directions are activated or deactivated. Therefore, the coverage rate [15] calculation in this study is represented by Formula (1), as follows:  
\[\text{Coverage rate}=\frac{1}{K}\sum_{k=1}^{K}u_{k} \tag{1}\]  
\[u_{k}=\left\{\begin{array}{cc}1,&\text{\it user }k\text{\it under the coverage area }R_{j}^{i}\\ &0,&\text{\it otherwise}\end{array}\right.\]  
where \(u_{k}\) equals 1 if \(u_{k}\) is within the coverage range (denoted as \(R_{j}^{i}\)) of the antenna in the direction where \(X_{j}^{i}=1\) for \(S_{i}\). We can use the signal-to-interference-plus-noise ratio \(SINR_{k}\geq\theta\) of \(u_{k}\) to determine whether it is covered by \(S_{i}\). The coverage rate is calculated as follows: (number of UE serviced by the base station/number of UE unable to connect to the base station) * 100%.
## 5 Testing and Navigation
### Path Planning and Weather Routing  
Given the starting and finishing line defined by the Microtransat organization, it is interesting to note that the northern route from the northernmost point of the starting line to the northernmost point of the finish line is 50% shorter than the southern route. This gives a tremendous advantage to the northern path. SBmet, which is the only boat so far to ever complete the Microtransat Challenge in the unmanned category, chose the northern route. However, sending the boat to the north point of the starting line as well as recovering it at the northern point of the finish line requires a significant budget and means. In 2015,the Breizh Tigresse was launched just a few miles from Halifax. In comparison, Sealeon was launched from Cape Breton, which resulted in a significant gain of about 140 NM. Additionally, this last option avoided the marine traffic from Halifax. Both routes meet off Sable Island at the edge of the continental shelf. Then, both routes follow the north option depicted in Figure 5 and described in Section 5.2. Off the Grand Banks of Newfoundland, the route follows the great circle route up to a target point located on the finish line in the west of Brest, France.  
Weather routing aims to define the optimal route and to estimate the energy budget needed to complete the Microtransat Challenge [28]. Since the maximum forecast prediction period available is 16 days, it is necessary that the rest of the journey is predicted using pilot charts. The simulation was performed with Opencpn software. This software includesa simple Velocity Prediction Program (VPP) that gives a rough assessment of our design. The simulation results were consistent with the performance measured at sea for the Breizh Tigresse. The resulting polar speed diagram with respect to True Wind Angle (TWA) and True Wind Speed (TWS) is shown in Table 6. The weather routing showed that the wind appeared to be strongest on the northern route. Correspondingly, the average boat speed was lower on the southern route. However, simulation results were quite close (1.47 vs. 1.43 knots in boat speed and 7.23 vs. 7.19 knots in wind velocity). As such, the route was defined to avoid traffic lanes and fishing zones as much as possible.  
It is interesting to compare the routing analysis with the performance that was observed at sea. These speed predictions are rather pessimistic compared to the measures at sea. Indeed, during the first week of the 2015 challenge, the Breizh Tigresse achieved an average speed about 1.70 knots. However, the averaged velocity made good (VMG) was 1.40 knots, which is quite close to the predictions. This loss of 18% between the actual boat velocity and the velocity projected on the aimed direction (i.e., VMG) should be considered in future designs. This is relevant for a better energy budget prediction and can also contribute to a more accurate control system.
## 5 Results
### Performance of multiDecompose on Benchmark Problems  
We use 8 widely used MOP and DTLZ test instances (listed in Table 1) to compare multiDecompose with NSGA-II and MOEA/D, two representative multi-objective algorithms. NSGA-II and MOEA/D are implemented by the pymoo library [37] with default parameters, except that the population size is set to be 200 for all test instances. Additionally, in multiDecompose, we set \(H=50,\ \epsilon=10^{12},\ tol=0.005\). Then, a set of experiments is carried out to provide the comparison between these three algorithms in terms of Pareto front (_PF_) and the most popular quality indicator, hypervolume (_HV_) [38]. Both these algorithms have been run 30 times independently for each test instance.  
Figure 3: Procedure of multiDecompose algorithm.  
The \(PFs\) of the three algorithms on the MOP and DTLZ test instances are summarized in Figures 4 and 5, respectively. It is clear from the two figures that, as to the quantity and uniformness of final solutions, multiDecompose is superior to the other two algorithms for all test instances, and even illustrates an overwhelming advantage on the DTLZ instances. NSGA-II shows a mediocre performance while MOEA/D finds few non-dominated solutions under a specific number of function evaluations in most test instances. Furthermore, from the perspective of the number of function evaluations, multiDecompose converges much faster than NSGA-II and MOEA/D on all test instances.  
Table 2 presents the mean of \(HV\)-metric value of final solutions obtained by each algorithm on each test instance. It is obvious that our algorithm also performs splendidly on each test instance in terms of \(HV\)-metric, i.e., our algorithm maintains the maximum \(HV\)-metric value for all test instances. NSGA-II is close behind, while MEAD/D performs the worst. It can be inferred from the aforementioned facts that multiDecompose can achieve a better performance over NSGA-II and MOEA/D in terms of the solution quality and convergence speed on the 2-objective and 3-objective optimization problems.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **multiDecompose** & **NSGA-II** & **MOEA/D** \\ \hline MOP1 & 13494.20024 & 12157.93554 & 8265.419011 \\ MOP2 & 0.30459570 & 0.302570066 & 0.296286100 \\ MOP3 & 360.3988959 & 360.3714609 & 354.1858850 \\ MOP4 & 27.37402078 & 27.35540827 & 26.62617847 \\ DTLZ1 & 4.947239949 & 4.937894772 & 4.071856060 \\ DTLZ2 & 0.506487643 & 0.447724955 & 0.123985535 \\ DTLZ3 & 31.63621196 & 30.60244867 & 23.59450020 \\ DTLZ4 & 0.547895448 & 0.506647045 & 0.073129642 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average \(HV\) value of the solutions obtained by multiDecompose, NSGA-II and MOEA/D.  
Figure 5: \(P\)s of multiDecompose, NSGA-II and MOEA/D on all DTLZ instances where \(n\_ev\) denotes the number of the function evaluations.
De-synchronization attacks can contradict this property, e.g., by blocking protocol messages or forcing protocols parties to update their shared secret values to different values, where the protocols' parties do not authenticate each other any more and availability of service is destroyed. Many protocols have been proposed in the literature [12; 13; 14; 15] that have attempted to address CIA security principles, but unfortunately, there have been several reports of attacks [16; 17; 18; 19; 20; 21; 22; 23] against them that indicate they have failed to provide the desired security. Hence, efforts to design a secure protocol are still ongoing and the new attacks that are developing provide designers with new insight on how to (not) design a protocol. In this way, these attacks and security analyses have contributed to the development of the protocols. **Our contributions:** The contributions of this paper are summarized as follows:  
* We show that the ULRAS protocol [24], a protocol which has been designed based on rotation function, is not secure and fixing the security problem by any particular mode of rotation function may not be possible. * An improved protocol named UEAP has also been proposed using lightweight encryption functions in which the ULRAS protocol's security pitfalls are solved. * The security proof of the UEAP protocol has been done through an informal way and also a formal way through Scyther tool. In fact, in this paper, we show that the ULRAS protocol, consistent with the SASI protocol [12] and the Gossamer protocol [13], is not secure. Precisely, we present a de-synchronization attack against ULRAS protocol. Hence, employing it in any application is not recommended. In this regard, by using the ULRAS protocol as an example, we show that designing a secure protocol using only the rotation operation without the use of cryptography primitives is not possible. **Paper's organization:** The rest of this paper is structured as follows: Section 2 introduces required preliminaries including a brief review of rotation-based RFID authentication protocols and the explanation of the ULRAS protocol. We present the security analysis of the protocol in Section 3. We proposed an improved protocol in Section 4 and its security evaluation is explained in Section 4.1. Finally, we conclude the paper in Section 5.
## 2 Experimental Apparatus and Measurement Principle
For a U-shaped cavity, the non-vertical reflection in the folded mirror produces slightly different phase shifts between the S-polarized light and the P-polarized light. The optical cavity forms two stable, separated resonant modes in the s-polarization direction and p-polarization direction, respectively. The simultaneous presence of two resonant modes reduces the stability of the transmitted light field [17]. A linear polarizer is set between the collimator and mirror M3, which ensures S-polarized light injects into the cavity. During the absorption spectrum measurement, we set the working temperature of the DFB laser at 21 \({}^{\circ}\)C and the scanning range of injection current at 40-120 mA. The DFB semiconductor laser can realize continuous scanning in the range of 1510-1512 nm calibrated by the wavelength meter (WA-1500-NIR, EXFO Burleigh, Quebec City, QC, Canada). The PZT modulates the cavity length by a triangular wave signal with an amplitude of 200 V and a frequency of 20 Hz. When resonance occurs, PDAI (PDALOCS-E, Thorlabs, Newton, NJ, USA) detects part of the transmitted signal and the high-speed acquisition card acquires this signal. The measurement program takes the peak intensity of the resonance signal corresponding to the scanning current as the cavity enhancement spectrum signal. The PDA4 obtains the real-time laser intensity and it is used as a reference to eliminate the effects caused by variations in laser power. According to the multi-beam interference theory and the Beer-Lambert law, the molecular absorption coefficient in the cavity can be obtained as Equation (3) [18]:  
\[\alpha(\lambda)=\frac{1-R_{eff}(\lambda)}{L}(\sqrt{\frac{I_{0}(\lambda)}{I( \lambda)}}-1) \tag{3}\]  
where \(\alpha\) represents the gas absorption coefficient; \(R_{eff}\) denotes the effective reflectivity calculated by the loss of \"empty cavity\"; \(L\) represents the cavity length; and \(I\) and \(I_{0}\) are the transmitted light intensities in the presence and absence of gas absorption, respectively. Figure 1: Experimental device structure diagram of U-shaped CEAS. M1–M4: mirrors; DFB: distributed feedback semiconductor laser; P1: line polarizer; PDA: photodetector; PZT: piezoelectric ceramic transducer. According to Equations (2) and (3) and the HITRAN database [19], we can obtain the concentration of absorbing gas molecules. Unlike conventional CEAS systems, the spectral resolution of the system is not limited by the free spectral range (FSR) when performing spectral scanning. In our experimental system, the PZT mounted on M4 (shown in Figure 1) can precisely modulate the cavity length to realize cavity resonance. The spectral resolution is mainly determined by the tuning resolution of the DFB semiconductor laser. Thus, our system improves the spectral resolution to 0.003 cm\({}^{-1}\).
# Post-Quantum Key Exchange and Subscriber Identity Encryption in 5G Using ML-KEM (Kyber) 1
## 5 Our PQC Incorporation and Implementation  
We implement PQC in the 4G/5G context for empirical performance analyses and validations. This section describes our implementation scope, i.e., using the 5G information fields and simulating the participating entities and their SUCI protocol. Our implementation follows the standardized 5G protocol because we focus on the 5G application, and our implementation and experimentation focus on the KEM involving public-key cryptography (requiring the PQC transition for quantum resistance) and not on the symmetric cryptographic operations.
## 3 Benchmark Results
### Digital Signature Algorithms
#### 3.5.1 Temporal Costs
The column labelled with a \(\Delta\) shows the performance increase of the algorithm as the length in \(KB\) of the message becomes bigger. Thus BB, which is the algorithm with a highest \(\Delta\) in the signing operation, is the most efficient algorithm for signing large documents (from tens KB) with a performance of \(20.24\ MB/s\) for files of 1 M. Contrary, for signing small documents, the fastest algorithm is DSA-512. The fastest algorithm for performing a signature validation is RSA-512 regardless the size of the document. Comparing with the throughput of symmetric algorithms (see Figure 2), results show that a cryptographic operation on big data files over 400 KB can be more efficient using asymmetric algorithms than symmetric ones.
# A Bibliometric Analysis and Visualization of In-Vehicle Communication Protocols
## 3 Results
### Co-Authorship Analysis
#### 3.3.2 Highly Cited Publications  
The major publications in connected vehicle technologies and cybersecurity highlight dominant communications systems, artificial intelligence, and intrusion detection technologies. Table 2 shows highly cited publications. One area of focus is optimizing systems through AI, examined by [PERSON] et al. (2019) [PERSON] et al. (2018) [PERSON] et al. (2018) [PERSON] et al. (2018) in deep reinforcement learning to concern edge computing, offloading, and resource allocation, which seeks to meet the increasing data processing needs in real-time vehicular networks. These papers attest to the academic discourse while advancing the development of connected vehicle ecosystems.  
Another important cybersecurity topic is in-vehicle intrusion detection systems. Scholars have shown great concern regarding the protection of vehicular networks as cyber threats are on the rise. [PERSON] et al. (2018) [PERSON] et al. (2018) developed novel IDS methods based on GANs and remote frame analysis in this context. Moreover, [PERSON] et al. (2018) [PERSON] et al. (2018) proposed Volt-ageIDS, which focuses on communications at the lower levels. In addition, [PERSON] et al.'s (2019) and [PERSON] et al.'s (2020) work on survey ID in cybersecurity, with no explicit focus, serve as other reference works for primary researchers by bringing together knowledge of different ID approaches and challenges in cybersecurity [PERSON] et al. (2018). Documenting these gaps, which stem from the existing literature, shines a light on focusing on system performance in parallel to robust cybersecurity, marking the fact that there is a gap that the publications jointly depict.  
Regarding the most cited countries in the field, China, Korea, and the United States, in particular, focus on applying artificial intelligence (AI) and machine learning (ML) to vehicles. This is clear in highly cited papers from these countries dealing with deep reinforcement learning for node-edge computation on vehicles, intrusion detection systems (IDS), and mobility-aware caching. For instance, substantial research performed by authors from China and the United States showcases advanced ML-based dynamic resource allocation and cybersecurity on vehicles. These countries have been strategically directing their research efforts towards AI-augmented vehicle innovation. It is no longer news that countries are rushing towards developing smart vehicles and adaptive vehicular networks, and these countries have marked their position as frontrunners in transportation research.  
Figure 7: Country co-authorship network.