esa-sceva/satcom-chunk-collection huggingface dataset:
# A Bibliometric Analysis and Visualization of In-Vehicle Communication Protocols
## 3 Results
### Current Status of In-Vehicle Communication Protocol
#### 3.1.3 Distribution of Published Journals  
The bibliometric study reveals a strong dominance of IEEE-affiliated sources in the in-vehicle communication protocols field, detailing the major journals and publications of significant value. Figure 3 presents the top journals in terms of absolute publication counts and their relative proportions. The _IEEE Transactions on Vehicular Technology_ leads with 118 publications (4.04% of the total), followed by _IEEE Access_ with 108 (3.70%) and _IEEE Transactions on Intelligent Transportation Systems_ with 99 (3.39%).  
Including percentage values enhances the analysis by contextualizing journal influence relative to the overall scholarly output in the field. This method ensures that smaller yet high-impact journals are not overlooked in volume-based comparisons.  
As for the rest of the publications, the _IEEE Transactions on Intelligent Transportation Systems_ (99 publications) stands out with profound studies on advanced traffic systems, V2V/V2X communication, and autonomous vehicle communication frameworks. The _IEEE Vehicular Technology Conference_ (67 papers) remains one of the top showcases for new research-directed developments in the ecosystem of vehicular technology. Very useful in connecting the 'ride' industry and academic knowledge regarding automotive engineering standards are the _SAE Technical Papers_ (59 publications). _Significant Sensors_ (50) and _Electronics_ (Switzerland) (44) publications that focus on communication hardware, sensor fusion, and performance of hardware and software in real-time systems. IEEE publications  
Figure 3: Distribution of articles among top journals publishing research on in-vehicle communication protocols. Bars show both the number and percentage of total publications (\(n=2919\)) to contextualize impact relative to dataset size.  
such as the _Internet of Things Journal_ and the _ACM International Conference Proceedings Series_ showcase the intersection of IoT and vehicle communication systems.
## 3 Results and Discussion
### Tensile-Shear Strength  
For the tensile-shear strength, the setting force with a \(p\)-value of 0.001 was the only parameter that was statistically significant at a significance level of \(\alpha=0.05\). By increasing the setting force, the tensile-shear strength linearly increased. In addition, the angular misalignment and the edge offset distance have \(p\)-values between 0.05 and 0.1, and their levels may affects the tensile-shear strength, which means that an angular misalignment of \(\pm 20^{\circ}\) and edge offset distance of \(\pm 5\) mm can affect the tensile shear strength. To achieve consistent tensile-shear strength, these three parameters should be regulated within the ranges narrower than those in this experiment, or they should be considered as control parameters in the experimental design.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{5}{c}{\(p\)**-Value**} \\ \cline{2-6}  & **Poor Fit-Up, G** & **Setting Force, F** & **Pre-Clamping** & **Angular** & **Edge Offset** \\  & **Poor Fit-Up, G** & **Setting Force, F** & **Force, C** & **Misalignment, \(\beta\)** & **Distance, \(\delta\)** \\ \hline Head height & 0.661 & 0.0 * & 0.056 & 0.056 & 0.369 \\ Bottom thickness & 0.013 * & 0.003 * & 0.085 & 0.045 * & 0.008 * \\ Interlock & 0.108 & 0.001 * & 0.135 & 0.277 & 0.136 \\ Tensile-shear strength & 0.184 & 0.001 * & 0.228 & 0.051 & 0.071 \\ \hline \hline \end{tabular}
* \(p\)-value \(\leq 0.05\).  
\end{table}
Table 4: AVOVA results for the experiments with Table 3.  
The tensile-shear strength has a positive Pearson correlation coefficient of 0.762 for the interlock (\(p=0.0\)), and it has negative Pearson correlation coefficients of \(-0.846\) and \(-0.636\) for the head height (\(p=0.0\)) and the bottom thickness (\(p=0.005\)), respectively.
# On IRS-Assisted Covert Communication with a Friendly UAV
## 3 System Optimization
### Reliability of CCS  
When the UAV interferes with [PERSON] in receiving signals, [PERSON] is interfered with as well, which may reduce the covert rate and exert an influence on the reliability of the CCS. According to (9), the outage probability of the covert channel between [PERSON] and [PERSON] is provided by  
\[P_{out}[t] =\text{Pr}(C[t]<R_{b}[t])\] \[=\text{Pr}\left(\frac{P_{a}[t]\left|h_{IB}^{H}[t]\Theta[t]h_{AI }[t]\right|^{2}}{P_{u}[t]\left|h_{IB}[t]\right|^{2}+\sigma_{b}^{2}}<2^{R_{b}[ t]}-1\right)\] \[=\text{Pr}\left(\frac{z}{\left(2^{R_{b}[t]}-1\right)\left|h_{UB}[ t]\right|^{2}}-\frac{\sigma_{b}^{2}}{\left|h_{UB}[t]\right|^{2}}<P_{u}[t]\right)\] \[=\int\frac{z}{\left(2^{R_{b}[t]}-1\right)\left|h_{UB}[t]\right|^ {2}}-\frac{\sigma_{b}^{2}}{\left|h_{UB}[t]\right|^{2}}f_{P_{u}[t]}(x)dx\] \[=1-\frac{z}{\left(2^{R_{b}[t]}-1\right)\hat{P}_{u}[t]\left|h_{UB} [t]\right|^{2}}+\frac{\sigma_{b}^{2}}{\hat{P}_{u}[t]\left|h_{UB}[t]\right|^{2}}, \tag{22}\]  
where \(R_{b}[t]\) is the transmission rate of Bob.  
Considering the upper bound on the outage probability as \(P_{out}^{*}[t]\), i.e., when the outage probability satisfies \(P_{out}[t]\leq P_{out}^{*}[t]\), the reliability of the covert channel between Bob and Alice can be guaranteed. From (22), we know that \(P_{out}[t]\) is monotone increasing with regard to \(R_{b}[t]\). Thus, as the covert rate \(R_{b}[t]\) achieves its maximum value, the outage probability reaches its upper limit \(P_{out}^{*}[t]\). Then, the maximum covert rate is provided by  
\[R_{b}[t]=\text{log}_{2}\Bigg{(}1+\frac{P_{a}[t]|h_{IB}^{H}[t]\Theta[t]h_{AI}[t ]|^{2}}{(1-P_{out}^{*}[t])\hat{P}_{u}[t]|h_{UB}[t]|^{2}+\sigma_{b}^{2}}\Bigg{)}. \tag{23}\]  
To ensure covertness, the UAV is used to jam Willie while maximizing the average received rate of Bob. Therefore, the maximization of the covert rate can be formulated as a joint optimization problem of the UAV trajectory, the IRS phase shift, the transmitting power at Alice's location, and the transmitting power of the UAV. According to (21) and (22), this optimization problem can be expressed as  
\[\max_{C,\Theta,P,\hat{P}_{u}}\frac{1}{T}\sum_{t=1}^{T}R_{b}[t], \tag{24}\] \[s.t.\] \[\xi^{*}[t]\geq 1-\epsilon,\] (25) \[0\leq\theta_{m}[t]\leq 2\pi,\] (26) \[0\leq\hat{P}_{u}[t]\leq\hat{P}_{u,\text{max}}[t],\] (27) \[0\leq P[t]\leq P_{\text{max}}[t],\] (28) \[\|c[t+1]-c[t]\|^{2}\leq D^{2},\] (29) \[c[T]=c_{F},c[1]=c_{A}, \tag{30}\]  
where \(C\triangleq\{c[t],\forall t\}\) and \(\Theta\triangleq\{\theta_{m}[t],\forall m,t\}\); here, (24) is a linear objective function and (29) and (30) are mobility constraints of the UAV with convexity. However, (25) is non-convex about \(C\) and \(\Theta\), which results in this optimization problem not being solved efficiently.
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 7 Open Issues and Future Research Directions
### Performance and Scalability Issues  
Besides the advantages of blockchain scalability and performance issues are still major obstacles in the integrated ecosystems. The scalability issues are mainly based on storage, throughput, and networking.  
* Storage: a massive volume of data transactions such as information storage, management of resources and vehicle transactions are generated due to the integration of blockchain in 5G-based MEC in vehicular networks. In conventional blockchain, all of the nodes store a copy of complete blockchain transactions. As a result, the blockchain bloats and results in a computational and storage load on resource-constrained vehicles [89].
* Throughput: the performance concerns include limited throughput in terms of number of transactions per second, and delays that are caused by additional time to add block transactions into the blockchain [90]. The blockchain has limited block size, and the block generation time grows rapidly due to long waiting time for transactions to be added on the chain that results in limited throughput.
* Network Resource: in the context of integrated blockchain and 5G-based MEC environments, high density networks with large number of heterogeneous and resource constrained devices, consume lots of resources. It might be difficult to fulfill the resource requirements of blockchain in order to accomplish large-scale transaction processing. Moreover, the blockchain consumes large network resources in terms of transmission power, mining, and bandwidth based on the consensus mechanism that results in high latency [91].
* Blockchain Incentive and Penalty Mechanisms: an efficient incentive mechanism should be built so that the incentives are assigned to the miners in a fair manner. Thus, a future direction for research is to develop an efficient and robust incentive mechanism with fair distribution to encourage all parties and miners to engage in the blockchain. In addition, penalty systems are also necessary to discourage any malicious entity from performing harmful activities.  
Besides the above-mentioned issues, a lot of research needs to be done to improve blockchain performance and scalability from various design viewpoints, such as hardware mining [92], hybrid consensus mechanisms [93], off-chain [94], and on-chain [95], solutions, standardization, and regional blockchain design [71].
###### Abstract  
Hyperspectral imaging (HSI) is one of the non-destructive quality assessment methods providing both spatial and spectral information. HSI in food quality and safety can detect the presence of contaminants, adulterants, and quality attributes, such as moisture, ripeness, and microbial spoilage, in a non-destructive manner by analyzing spectral signatures of food components in a wide range of wavelengths with speed and accuracy. However, analyzing HSI data can be quite complicated and time consuming, in addition to needing some special expertise. Artificial intelligence (AI) has shown immense promise in HSI for the assessment of food quality because it is so powerful at coping with irrelevant information, extracting key features, and building calibration models. This review has shown various machine learning (ML) approaches applied to HSI for quality and safety control of foods. It covers the basic concepts of HSI, advanced preprocessing methods, and strategies for wavelength selection and machine learning methods. The application of HSI to AI increases the speed with which food safety and quality can be inspected. This happens through automation in contaminant detection, classification, and prediction of food quality attributes. So, it can enable decisions in real-time by reducing human error at food inspection. This paper outlines their benefits, challenges, and potential improvements while again assessing the validity and practical usability of HSI technologies in developing reliable calibration models for food quality and safety monitoring. The review concludes that HSI integrated with state-of-the-art AI techniques has good potential to significantly improve the assessment of food quality and safety, and that various ML algorithms have their strengths, and contexts in which they are best applied.  
non-destructive methods; machine learning; food processing; digitalization +
Footnote †: journal: Medical Imaging
# Gamma-ray Bursts: 50 Years and Counting!
## 1 Introduction  
Gamma-ray bursts (GRBs) are extraordinary astrophysical events characterized by brief and intense flashes of gamma-ray radiation, representing some of the most energetic phenomena known in the universe. Their enigmatic nature has intrigued the scientific community for decades, prompting extensive research to decipher their origins and the underlying physical processes involved.  
The serendipitous discovery of GRBs traces back to the late 1960s when the secret Vela satellites, designed for nuclear test detection, revealed anomalous gamma-ray emissions from deep space. Following meticulous study and analysis, the first public announcement of these mysterious gamma-ray bursts was made in 1973, marking the inception of a new era in astrophysical exploration.  
This review paper provides a comprehensive historical account of the progression of GRB research, encompassing seven distinctive eras of study.
## 3 Results and Discussion
### Microbiological Quality  
The numbers of all the tested microorganisms were reduced (_p_ < 0.05) by the irradiation treatment (Figure 2) to below the recommended European Union limits (<2.0 Log10 CFU/g _E. coli_, <4.0 Log10 CFU/g _E. coli_, <4.0 Log10 CFU/g _E_oliform and <7.0 Log10 CFU/g _A_CCs) for \"ready-to-eat\" cheese products [49]. Additionally, [PERSON] et al. [17] and [PERSON] et al. [18] reported a significant decrease in the total bacteria counts after the irradiation treatment. [PERSON] et al. [11] further noted that the microbial counts of the irradiated samples decreased with an increase in the irradiation dose from 1 to 5 kGy, with a complete reduction of the coliform counts at 5 kGy. The inactivation of food pathogens by irradiation is due to cellular membrane or DNA damage, causing sublethal injury preventing multiplication and can also cause death [50].  
_Listeria monocytogenes_ are also of importance, as they are frequently reported as post-process contaminants in ready-to-eat food products, including meals, sandwic and cheeses [2; 51]. They can grow at refrigeration temperatures and during aging. For the current study, at 5.0 kGy, all _L. monocytogenes_-positive (16%) samples tested negative. Corresponding with our results, [PERSON] et al. [34] also observed a total reduction in the number of _L. monocytogenes_ at 5 kGy, and [PERSON] et al. [18] reported permanent damage inhibiting further growth even during storage.  
The stage at which the contamination would have occurred is also important. [PERSON] et al. [18] noted that, when milk is contaminated after pasteurisation, even a higher irradiation dose of 4.7 kGy was not enough to eliminate the pathogenic bacterium compared to contamination at the surface of cheese during packaging. At 5.0 kGy, the 500-g samples had significantly higher (1.51 Log10 CFU/g) ACC counts compared to the 250-g (0.79 Log10 CFU/g; Figure 3). However, the coliforms and _E. coli_ counts were not different (_p_ > 0.05) between the 250 and 500-g samples for the 5.0-kGy treatment. These results proved that the irradiation treatment can significantly reduce the microbial counts on retail cheese samples with a size below 500 g without adverse effects of the physicochemical properties. Further studies should focus on using bigger retail sizes and note if the treatment will have the same level of efficacy. An additional sensory analysis should also be done to determine if there are any effects on consumer acceptability.
## 3 Results and Analysis
### DPRS Load Distribution Analysis
#### 3.2.2 Influence of Ambient Temperature  
To examine the influence of the ambient temperature on the load distribution of the DPRS threaded teeth, the load distribution curves of the DPRS at ideal state (without considering thermal deformation) and the ambient temperatures of 20, 70, and 120 \({}^{\circ}\)C are calculated as shown in Figure 14a,b. As the ambient temperature rises, the load on the first few sections of the roller gradually increases, and the load on the threads of the last few sections decreases. The load distributions of the DPRS threaded teeth with temperature on the screw-roller side and the roller-nut side show the same pattern of variation because of the thermal deformation of the DPRS parts caused by environmental changes, which corresponds to an increase in the clearance between the parts and thus a larger load distribution range.  
The results show that ambient temperature significantly affects the DPRS load distribution uniformity. The higher the temperature, the worse the load distribution uniformity. Ref. [14] utilized finite element analysis to investigate the load distribution of the screw under different temperatures. The results exhibited the same variation trend as the findings presented in this section. Furthermore, the same variation trend was also obtained in [17], confirming and validating the accuracy of the analysis through this comparative analysis.  
Figure 13: Temperature rise curve of the screw at different speeds. (**a**) Computational models in this paper; (**b**) The [25] calculation model.
# RF-EMF Exposure near 5G NR Small Cells
## 2 Materials and Methods
### Measurement Equipment  
The first measurement setup (Figure 1) consisted of a spectrum analyzer (SA) of type Rohde & Schwarz (R&S) (Munich, Germany) FSV30 with a frequency range of 10 Hz to 30 GHz combined with an isotropic tri-axial antenna of type Satimo TSEMF26 (further also called a \"probe\") (Microwave Vision Group, Villejust, France). This probe contains three orthogonally arranged antenna elements that are switched electronically, and has a frequency range of 2 GHz to 6 GHz and a dynamic measurement range of 2.5 mV/m to 200 V/m. The FSV30 comes with the'spectrogram' option, which allows a large number of successive measurement samples to be captured. This setup is further called 'FSV setup'.  
The second measurement setup (Figure 2) consisted of a Narda SRM-3006 field strength analyzer (Narda Safety Test Solutions, Pfullingen, Germany) with a frequency range from 9 kHz to 6 GHz combined with an isotropic tri-axial antenna. This probe has a frequency range of 420 MHz to 6 GHz and a dynamic measurement range of 0.14 mV/m to 160 V/m. This setup is further called 'SRM setup'.  
Between the measurement setups, the SRM setup is the most portable and user-friendly and may be more readily available, but its measurement settings are limited. Additionally, a dedicated 5G NR measurement option for code-selective measurements makes this setup even more useful. However, during this research, the option was not yet available.  
Whereas the SRM setup was used for measurements at ground level (1.5 m above the ground) (Figure 2), the FSV setup was simultaneously used for measurements at the height of the base station antenna, with the probe placed on a telescopic pole (Figure 1).  
Figure 1: First type of equipment used in this study to measure the exposure to 5G new radio base stations, consisting of an R&S FSV30 spectrum analyzer controlled by a laptop (both on the (**left**)), and an isotropic tri-axial R&S TS-EMF B2 antenna (on the (**right**), placed on a telescopic pole).
# Quantum Key Distribution Networks: Challenges and Future Research Issues in Security
## 2 Background to Quantum Key Distribution (QKD) Networks
### Quantum Node  
A quantum node is equipped with quantum devices that are necessary for executing the QKD protocol, including qubit generators, qubit measurement devices, and qubit memories. The technologies and types of quantum device adopted depend on the types of QKD protocols (e.g., discrete-variable-based QKD or continuous variable-based QKD).  
Quantum nodes can be categorized into three types according to the function of the quantum node: (1) the repeater node, (2) the access node, and (3) the central control node. The repeater node assists the others in transmitting packets of session keys using a suitable routing path, and the main function of the access node is to provide the API which allows end users/applications to access the session keys. In a specific QKD network (i.e., the client-server architecture), the central control node is the routing server that is responsible for controlling and managing the entire routing table of the QKD network. In addition, because the key generation rate and key service demand are dynamic, it is impossible to maintain a balance between the key supply and demand at all times. Therefore, the  
Figure 1: Framework of the quantum key distribution (QKD) network.  
quantum node must require a buffer (known as the key storage) to store the local keys to enhance the tolerance of the dynamic environment in the QKD network. Protecting local keys in the key storage is an important task. Therefore, an applicable key management mechanism must be adopted to manage and protect key generation, storage, and usage within key storage.
###### Abstract
From the aforementioned studies, it can be concluded that under the conditions of large-scale new energy grid integration, considering the expansion planning of transmission networks and the optimal configuration of energy storage in a coordinated manner can not only enhance the consumption level of new energy but also improve system economic benefits and the utilization rate of transmission equipment [24; 25]. However, most existing studies on coordinated expansion planning of transmission networks and energy storage are static planning [26], which only invests in and configures the planned equipment of transmission lines and energy storage stations once, lacking comprehensive consideration of the phased growth of new energy installations and load demand. This can easily lead to redundant configuration, premature investment, and equipment idleness in the early stages of planning, and insufficient capacity and aging of equipment in the later stages [27]. Therefore, this paper studies the multi-stage coordinated expansion planning of transmission networks and energy storage considering new energy consumption capacity under large-scale new energy grid integration. The remainder of this paper is organized as follows: Chapter 2 reviews the non-line substitution effect of energy storage and analyzes the feasibility of incorporating energy storage into transmission grid planning. Chapter 3 proposes a multi-stage collaborative planning model for transmission networks and energy storage. Chapter 4 uses two case studies to validate the proposed method and analyzes the impact of factors such as energy storage costs and line capacity on the planning results. The main contributions are as follows:  
1. The feasibility of incorporating energy storage into transmission grid planning is analyzed. The collaborative relationship between energy storage configuration and transmission grid planning is clarified, and a framework for the coordinated planning of energy storage and transmission networks is proposed. 2. A multi-stage collaborative planning model for transmission networks and energy storage that considers the acceptance capacity of renewable energy is established. The model aims to minimize the total system cost while considering the mutual influences between different planning stages. 3. The differences between various grid planning methods are explored. The impacts of factors such as energy storage costs and line capacity on the planning results are summarized.
# xLSTM-Based Urban Traffic Flow Prediction for Intelligent Transportation Governance
## 1 Introduction
Through validation using real-world traffic data from Taichung, we demonstrate that xLSTM-based forecasting can significantly improve dispatch response times, reduce the likelihood of secondary incidents, and enhance fuel efficiency in patrol routing. This work presents a scalable and operationally grounded framework for integrating predictive analytics into real-time policing strategies, advancing the broader vision of smart and responsive urban governance [6; 7; 8; 15]. The main contributions of this study are as follows:  
1. Proposing an xLSTM-based traffic forecasting model with matrix-based memory and exponential gating. 2. Achieving 87.3% accuracy with 41.2 ms inference latency for real-time applications. 3. Integrating the forecasting system into proactive police dispatch workflows. 4. Developing a scalable, edge-deployable system architecture for smart city governance.