esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Safety Analysis Study for Concrete Core Dams
## 1 Introduction
### Proposed Calculation Model
#### 1.1.2 Proposed Method
The relationship between void pressure and permeability coefficient, Table 1, can be used to qualitatively and quantitatively analyze the saturation of soil, which is of certain significance for macroscopic understanding of soil rock seepage. The finite element model of the homogeneous dam is built, which has 3312 elements and 3436 nodes. The calculation results are shown in Figure 2.  
In Figure 3, the pressure head is zero, starting at the upstream water level of 18 m, and the flow line flowing out at the downstream slope nodes of 583, 584, 585, 586, 587, 588, 589, 590, 591, and 545 is called the phreatic line. The left end of this line marks the zero-water pressure point. The seepage calculation, analysis, and evaluation mainly depend on the pressure head in Figure 2. As the three very important factors of seepage, the range, trend, and saturation could be obtained based on Figure 2. Line A in Figure 2 is called the phreatic line. See Figure 3 for the total seepage flow and escape node numbers. The total seepage flow is 87/090 m\({}^{3}\)/d. Based on the basic seepage theory of the earth-rock dam, the dam is divided into multiple areas by a phreatic line for a homogeneous earth dam. The two endpoints of the phreatic line are the dividing points. Divided areas are consisted of upstream wedge, middle section and downstream wedge. The overall seepage state of the homogeneous dam is consistent with our calculation results. Therefore, according to the basic seepage theory of an earth-rock dam, the calculated results are reasonable and can be used to analyze the actual project. \begin{table}
\begin{tabular}{c c} \hline \hline
**Void Pressure Value \(M\) (unit: m)** & **Permeability Coefficient Index \(D\)** \\ \hline \(-\)10.00 & \(-\)4.20 \\ \(-\)9.00 & \(-\)3.80 \\ \(-\)8.00 & \(-\)3.10 \\ \(-\)7.00 & \(-\)2.70 \\ \(-\)6.00 & \(-\)2.300 \\ \(-\)5.00 & \(-\)2.20 \\ \(-\)4.00 & \(-\)1.50 \\ \(-\)3.00 & \(-\)1.30 \\ \(-\
## 6 Simulation Results
### Computational Overhead
#### 6.1.2 Computational Overhead of MACs  
In FESA, each aggregator and its monitoring nodes need to compute \(T+1\) subMACs. Because each subMAC is obtained by first computing a MAC and then selecting some bits of it, forming \(T+1\) subMACs requires the computation of \(T+1\) MACs. Moreover, additional \(2T+1\) MAC computations are needed to verify all the subMACs by forwarding nodes of \(A_{c}\), \(A_{c}\), and neighboring nodes of \(A_{n}\). Hence, FESA totally needs \(3T+2\) MAC computations and \(T+1\) aggregation processes during the whole verification between two consecutive aggregators, while DAA needs total \(4\times(T+1)\) MAC computations and \(T+1\) aggregation processes. The comparison is showed in Table 4.
# Experimental Demonstration of Secure Relay in Quantum Secure Direct Communication Network
## 3 Results  
The QSDC network works at a repetition rate of 16 MHz. For the SPDs, we choose the efficiency to be 10% with a gate width of 1 ns. The dark count rate is about \(1\times 10^{-6}\) per gate. The encrypted messages are transmitted through the quantum channel from [PERSON], to the secure relay, and then to [PERSON]. The delay time at the secure relay is defined as the time interval between the successful reception of the encrypted messages from [PERSON] and the successful transmission of the encrypted messages to [PERSON].  
As shown in Figure 4, the minimum delay time can reach 3.99 ms, which indicates smooth transmission at the secure relay. However, due to noise in the quantum channel, some messages fail to be decoded. In this case, the encrypted messages are sent again, which causes a larger delay time at the secure relay.  
Figure 4: Delay time at the relay.  
In the experiment, two sets of SPDs are used to monitor the QBERs, namely, SPDta0 and SPDta1 monitor the QBERs of the quantum channel between Alice and the relay, and SPDtr0 and SPDtr1 monitor the QBERs of the quantum channel between the relay and Bob. The QBERs are below the error rate threshold, which ensures the security of our QSDC network. The ciphertexts are recovered with the error-correcting code. As shown in Figure 5, the communication rate between Alice and the secure relay is about 2.5 kb/s, and the communication rate between the secure relay and Bob is about 2.6 kb/s. The overall communication rate of the secure relay system is determined by the lower rate. Therefore, the secure relay can support a QSDC communication rate of 2.5 kb/s within a 4 ms time delay.
## 5 Discussion
#### Limitations and Future Work  
This study highlights key limitations that warrant further exploration. The first major challenge involves the need for models to better adapt to organization-specific workflows and regional regulatory nuances, which requires fine-tuning or prompt engineering to align outputs with unique internal documentation practices, organizational policies, and compliance structures. In general, such adaptation may be prohibitively expensive for smaller organizations with limited technical resources; therefore, there is a need for simplified fine-tuning processes or pretrained legal models for GDPR compliance. Moreover, this reliance on static datasets and benchmarks limits the evaluation of how well these models handle dynamic, real-world scenarios, such as rapidly evolving regulatory requirements or cross-jurisdictional compliance contexts.  
Another critical limitation is inconsistent handling of ambiguous legal texts. For example, subtle differences in legal terminology across EU member states can result in outputs that do not meet the compliance requirements of a particular jurisdiction. Solving this problem might involve incorporating external legal knowledge bases, such as taxonomies or ontologies, which provide structured interpretations of ambiguous terms. Second, finer-grained domain-aware embeddings should help narrow the gap between user queries and more refined language in the documentation within the GDPR, so more effective output can be made of it. In fact, these approaches would still be strongly in need of essential research and testing for viability, scalability, and diversifying organizational needs.  
Future work should be conducted on fine-tuning LLMs with GDPR-specific datasets, developing hybrid retrieval techniques that improve the accuracy of ambiguous scenarios, and leveraging adaptive knowledge graphs that can dynamically incorporate new regulatory interpretations. Addressing these challenges will help LLMs evolve into scalable and cost-effective tools that better meet organizational and regulatory requirements, enabling compliance both in structured and complex scenarios.  
Overall, these findings reinforce the viability of LLMs, particularly open-source models such as Qwen2-7B, as transformative tools for GDPR compliance. To fully operationalize these models in legal workflows, organizations and researchers will have to address some key limitations that relate to their adaptability to specific workflows, their ability to deal with ambiguous legal texts, and their scaling for changing regulations. Meeting these challenges through targeted research and development, LLMs can act as that key enabler of scalable and cost-effective compliance in the legal domain.
# Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
## Appendix B Genetic Algorithm Implementation
### Algorithm Steps  
The GA implementation considers an evolutionary process where a series of individuals are evaluated and replaced (or combined) based on their score (according to the objective function). This process is repeated until the algorithm converges to a sub-optimal solution as we illustrated in Algorithm 1. Then, each step can be summarized as follows:  
1. Initial population. An initial collection of \(p\) individuals is created (the population size \(p\) also affects convergence and performance. Experimentally, we found that \(p=200\) provided results that could not be improved in the scenarios tested).
2. Evaluation. Each individual from the current generation is evaluated (and scored) based on the objective function of the problem Equation (9).
3. Selection. A collection of parents is selected from the current generation based on their score and rank, so best individuals are more prone to be chosen as parents.  
Next generation. This step slightly differs from the canonical GA by distinguishing between three different sources of individuals. However, convergence is improved according to [50; 51].  
* Elite individuals. Best 5% individuals from current generation are preserved to the new generation.
* The remaining population (95%) is created as follows:
* Self-reproduction and Mutation (GA_SRM). 20% of the remaining population is created by directly applying a random mutation with probability \(p_{m}^{SRM}\) over selected parents.
* Crossover-and-Mutation (GA_CM). The rest (80%) is created by combining the genes of two parents (crossover) and applying a mutation to these new individuals with probability \(p_{m}^{CM}\).
* Exit criteria: in our implementation, the algorithm ends when the score of the best individual found could not be improved after \(Gen_{\text{max}}\) consecutive generations (observe that \(Gen_{\text{max}}\) has a great impact on performance and convergence. Experimentally, we have found that \(Gen_{\text{max}}=50\) is a good balance between performance and precision) by \(10^{-3}\). Otherwise, the algorithm returns to step 2.
# Towards the Internet of Smart Trains:
## 3 Overview on the Railway Applications Offered by GSM-R
### Railway-Specific Services and Requirements  
Following the last EIRENE specifications, the integrated wireless network should comply with the general and functional requirements under these four categories: Mandatory for Interoperability (MI), Mandatory for the System (M), Optional (O) or Not Applicable (NA), depending on the type of radio. Specifically, the following system services are required [47]:  
* Services: voice, data, and call related features (Table 4).
* Voice Group Call Service (VGCS) conducts group calls between trains or Base Stations (BSs), or between station staff and trackside workers.
* Voice Broadcast Service (VBS) is used to broadcast recorded messages or announce operations to certain groups of trains or BSs. The call set-up required times are shown in Table 5, it shall be achieved in 95% of cases (MI). Furthermore, call set-up times for 99% of cases shall not be more than 1.5 times the required call setup time (MI).
* Functional addressing (FA): a train can be addressed by a number identifying its function.
* Location dependent addressing (LDA): calls from a train can be addressed based on its location.
* Shunting mode for communicating to a group involved in shunting operations.
* Railway specific features [43; 48] include the set-up of urgent or frequent calls through single keystroke or similar; display of functional identity of calling/called party; fast and guaranteed call set-up; seamless communication support for train speeds up to 500 km/h; automatic and manual test modes with fault indications; control over mobile network selection; and control over system configuration.  
EIRENE-compliant mobile devices must guarantee the core requirements specified in SRS, together with network requirements and configuration. Furthermore, in the case of high-speed railways [49], as it is presented in Table 1, speeds of at least 220 km/h shall be managed while enabling speeds over 280 km/h under some circumstances. In general, speeds of 200-220 km/h represent the threshold for upgraded conventional lines. Nonetheless, connectivity has to be guaranteed at a moving speed of 500 km/h, or even more [50].  
QoS mechanisms have to ensure the enhanced Multi-Level Precedence and Pre-emption (eMLPP). Although current networks manage different QoS policies according to the traffic types, QoS for real-time applications shall be checked. QoS control is needed for resource management. Besides, as it will be further explained in the next Sections, strict latency requirements are needed for seamless operation (i.e., train status and location), and the Movement Authority (MA) permission between the in-service train and the control center (i.e., the connection establishment error ratio over one train line should be less than 1% per hour and 99% of ETCS data should have a maximum latency of \(<\)0.5 s [51,52]). Table 6 shows a summary of the main GSM-R QoS parameters jointly with their availability.
## 4 Performance Metrics
### PU2 Packets' Performance Metrics  
The amount of PU2 packets that is blocked and leave per time slot is defined as PU2 packets' blocking rate \(B_{P2}\).  
\[\begin{split} B_{P2}=&\sum_{y=0}^{k+1}(\psi_{y,0,0, 0}\alpha_{11}\alpha_{12}+\psi_{y,0,0,1}\alpha_{11}\alpha_{12})+\sum_{y=1}^{k+1 }[\psi_{y,0,1,0}(f\tilde{x}_{11}\alpha_{12}\tilde{\beta}_{12}+f\alpha_{12} \tilde{\beta}_{12}+f\alpha_{11}\alpha_{12}\beta_{12}+f\alpha_{11}\alpha_{12}) \\ &+\psi_{y,0,1,1}(\bar{\pi}_{11}\alpha_{12}\tilde{\epsilon}_{12}+ \pi\alpha_{12}\tilde{\epsilon}_{12}+\pi\alpha_{11}\alpha_{12}\epsilon_{12}+ \bar{\pi}_{11}\alpha_{12})+\psi_{y,1,0,0}(\alpha_{11}\alpha_{12}\beta_{11}+ \alpha_{12}\tilde{\beta}_{11})\\ &+\psi_{y,1,0,1}(\alpha_{11}\alpha_{12}\epsilon_{11}+\alpha_{12} \epsilon_{11})]\end{split} \tag{22}\]  
The amount of PU2 packets that leave due to interrupted transmission per time slot is defined as PU2 packets' loss rate \(L_{P2}\).  
\[L_{P2}=\sum_{y=1}^{k+1}(\psi_{y,0,1,0}f\alpha_{11}\tilde{\beta}_{12}+\psi_{y, 0,1,1}\pi\alpha_{11}\epsilon_{12}) \tag{23}\]  
The amount of PU2 packets with completed transmission per time slot is defined as PU2 packets' throughput \(T_{P2}\).  
\[T_{P2}=\alpha_{12}-B_{P2}-L_{P2} \tag{24}\]
## 3 MIMO Antenna Configuration Analysis
### Comparison with State-of-the-Art  
A number of works are presented in literature on operation over the 28 GHz spectrum and circular polarization. The dimension, operating frequency, bandwidth, gain, polarization type, ARBW, and antenna type are compared in the Table 2. It can be noted from the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Antenna** \\ **Design** \\ \end{tabular} & \multirow{2}{*}{**Element Placement**} & \begin{tabular}{c} **Operational** \\ **Bandwidth** \\ **(GHz)** \\ \end{tabular} & \begin{tabular}{c} **Transmission** \\ **Co-Efficient** \\ **(dB)** \\ \end{tabular} & \begin{tabular}{c} **Maximum** \\ **Avial Ratio** \\ **Bandwidth** \\ **(GHz)** \\ \end{tabular} & \
\begin{tabular}{c} **Peak Gain** \\ **(dBi)** \\ \end{tabular} &
\begin{tabular}{c} **Radiation** \\ **Efficiency** \\ **(\%)** \\ \end{tabular} \\ \hline Antenna 1 & Parallel placed & 25.25–29.25 & \(<\)\(-\)39 & 2.5 & 8.1 & 98 \\ Antenna 2 & Parallel placed with & 25.4–29.4 & \(<\)\(-\)42 & 3 & 8.6 & 98.2 \\ Antenna 3 & Orthogonal placement & 26–29 & \(<\)\(-\)45 & 3 & 8.2 & 98 \\ Antenna 4 & Inverted placement & 25.5–30 & \(<\)\(-\)40 & 3.1 & 8.75 & 99 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison of various MIMO configurations.  
Figure 17: Comparison among gain and numerically calculated radiation efficiency of MIMO antenna with inverted structure placement of element.  
table that the proposed antenna either has compact size or operating over wideband and high gain, as compared to other listed work. Moreover, the suggested work also offers both LHCP and RHCP with wide ARBW. The mechanism used in this approach is simpler as compared to other work presented in literature.
# Dynamic Monitoring of the Water Flowing Fractured Zone during the Mining Process under a River
## 3 Materials and Methods
### TEM Method and Field Setup  
The working principle of the TEM method is shown in Figure 4. Generally, a current with a certain waveform (generally square wave) is passed through a transmitting coil. A pulsed primary electromagnetic field is generated in front of it. When the current is turned off, the primary electromagnetic field gradually decays and then disappears. The underground geological body generates an induced eddy current, which generates an attenuated secondary field carrying the electrical properties of the geological body. The receiving coil records the induced electromotive force of the secondary field. Then we can analyze the distribution rules of the underground geological body.  
During data processing, late-time apparent resistivities can be calculated from the measured induced electromotive force using the following equation [26]:  
Figure 4: Working principle of the transient electromagnetic (TEM) method.  
\[\rho=\frac{\mu_{0}}{4\pi t}\left(\frac{2\mu_{0}SS_{0}}{5t\cdot\frac{e^{h}(t)}{l_{0}} }\right)^{\frac{5}{3}} \tag{3}\]  
where \(\rho\) is the late-time apparent resistivity, \(\Omega\cdot\mathrm{m}\); \(\mu_{0}\) is magnetic permeability T\(\cdot\mathrm{m}\)/A; \(t\) is time, s; \(S\) is the transmitter loop area, \(\mathrm{m}^{2}\); \(S_{0}\) is the receiver coil area, \(\mathrm{m}^{2}\); \(\epsilon^{h}(t)\) is the measured induced electromotive force, V; \(l_{0}\) is the transmitting current, A.  
The corresponding depths of the late-time apparent resistivities can be calculated using the following equation [27]:  
\[d=\sqrt{\frac{2\rho t}{\mu_{0}}} \tag{4}\]  
To obtain the apparent resistivity distribution of each survey line, Equations (3) and (4) were used to calculate \(\rho\) and \(d\). Through data processing and apparent resistivity mapping, the following results are obtained.  
According to the geological conditions of the 8101 longwall face, the central loop TEM method was adopted to survey the riverbed above the longwall face (Figure 5). The parameters are as follows: the transmitting voltage is 24 V, the transmitting current is 2 A, and the transmitting frequency is 25 HZ (time range of 0.08-8.4 ms). The multiturn transmitter loop is 2.0 m \(\times\) 2.0 m with 64 turns, and the multiturn circular receiver loop is 40 turns with a diameter of 1.0 m. The survey area is a parallelogram with the adjacent sides 300 m and 100 m in length. There are three survey lines in the survey area, with 30 survey points in each line. The point spread is 10 m, and the line spread is 30 m.
## 4 Simulation Results

Table 2: The attributes of the 6 examples. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Maximum R(x) (mm/h)** & \(\tilde{R}\)**(x)** & \(\overset{\alpha}{w}\)**(km)** & \(\tilde{w}\) & **Shape of Rain-Cloud(s)** \\ \hline
1 & 10.07 & 0.7\% & 10.1731 & 1.731\% & Rectangular \\
2 & 10.08 & 0.8\% & 9.8552 & 1.448\% & Trapezoidal \\
3 & 9.965 & 0.35\% & 9.8552 & 1.448\% & Triangular \\
4 & 10.09 & 0.9\% & 2.56 & 2.4\% & Rectangular bimodal \\
5 & 29.9 & 1\% & 9.8552 & 1.448\% & Triangular \\
6 & 49.83 & 1.7\% & 9.9255 & 0.745\% & Triangular \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation result of the 6 examples. Figure 7: Triangular distribution diagrams for \(R(x)=50\) mm/h. (**a**) Given precipitation distribution; (**b**) Simulated precipitation distribution; (**c**) Given precipitation distribution and simulated precipitation distribution; (**d**) Relative error between given and simulated results. **Example 2**. : _The error 8.073% occurs at the junction, \(\delta_{R}-z_{t}\tan\theta\), due to the non-linearity. In the rest of the precipitation area, the relative error of \(\hat{R}(x)\) is less than 3%._  
**Example 3**. : _The error at the junction, \(\hat{x}_{R}-z_{t}\tan\theta\), is 13.47%. In the rest of the precipitation area, maximum relative error of \(\hat{R}(x)\) is 1.7%._  
**Example 4**. : _Due to the non-linearity, fluctuations occur at each junction, which is \(\hat{x}_{R}-n\cdot(z_{t}-z_{0})\)\(\tan\theta\) or \(\hat{x}_{R}-n\cdot z_{t}\tan\theta\), where \(n\) is a natural number. Except the edges and the junctions, \(\hat{R}(x)\) is in the range of \([10.07,\ 10.09]\)\(mm/h\) in the precipitation area, maximum relative error is 0.9%._  
**Example 5**. : _The error at the junction, \(\hat{x}_{R}-z_{t}\tan\theta\), is 12.41%. In the rest of the precipitation area, maximum relative error of \(\hat{R}(x)\) is less than 2%._  
**Example 6**. : _The error of \(\hat{R}(x)\) around the junction \(\hat{x}_{R}-z_{t}\tan\theta\) is 13.12%. In the rest of the precipitation area, maximum relative error of \(\hat{R}(x)\) is 1.8%._
###### Contents  
* 1 Introduction
\t* 1.1 Hydrogen Application and Production
\t* 1.2 The Potential for Hydrogen
\t* 1.3 Recent Developments of Hydrogen Applications in the Transportation Sector
\t* 1.4 Scope
* 2 Hydrogen Properties and Their Implications on Use in Internal Combustion Engine
* 3 Hydrogen Engine Combustion Modes
* 4 Hydrogen Port Fuel Injection
\t* 4.1 Homogeneous Charge Compression Ignition
\t* 4.2 Spark-Ignited Port Fuel Injection  
European countries have developed infrastructure to use natural gas as power and heating sources in buildings. Such infrastructure brings additional benefit from a convenient switch to hydrogen-methane blend for further decarbonization [7]; nevertheless, a technology advancement is required to facilitate the increased share of hydrogen. The universal applicability of hydrogen for modern energy needs has boosted the investment and development of renewable hydrogen production and its related technology by many countries, with China being the largest importer and the US being the largest exporter, as of 2017 [9]. Australia [10,11], Japan [12] and Germany [13] have also devised strategic plans to become major players in the potential future hydrogen economy.  
As of 2016, 96% of the total hydrogen production (i.e., \(\sim\)55 million tonnes per annum [7]) originated from fossil fuels [14]. Apart from the thermo-chemical conversion of coal and oil into hydrogen, steam methane reforming is the most widely adopted method for hydrogen production due to its cost effectiveness [15]. This is achieved by the chemical reaction between purified methane or natural gas and high temperature (i.e., 970-1120 K) and pressure (i.e., 3-25 bar) steam in the presence of a catalyst, which is typically nickel [6]. It should be noted that CO\({}_{2}\) is produced during the steam methane reforming process. Therefore, and due to the fossil feedstock, hydrogen produced through these chemical pathways is not considered renewable.  
On the other hand, electrolysis of water, an electro-chemical process of splitting water into oxygen and hydrogen using electric current, can be considered renewable if the electricity is sourced renewably, such as using hydro, wind or solar power. This method currently accounts for only 4% of the world hydrogen production but it is predicted to expand rapidly to 22% by 2050 [16]. The research community has also been developing other renewable hydrogen production pathways, including photocatalytic hydrogen production, biomass and waste gasification and biological hydrogen production through biomass fermentation, etc. Some of these technologies are expected to mature and to enter commercial scale production by 2030 [17] and will boost the production of hydrogen through renewable routes. Therefore, green hydrogen will become more readily available and find widespread application in energy generation systems and powertrains.
# Detection of Software Security Weakness Using Cross-Language Source Code Representation (CLaSCoRe)
## 4 Experimental Results  
### Learning Scenarios  
The NIST dataset with \"vulnerable\" and \"not vulnerable\" code is used in two scenarios, illustrated in Table 4.  
The \"C# to C#\" scenario considers training various machine learning algorithms on the C# dataset with \"vulnerable\" and \"not vulnerable\" code, to assess the quality of the CLaSCoRe representation. The C# dataset with code samples is split into training and testing sets with both \"vulnerable\" and \"not vulnerable\" code.  
The \"Other to C#\" scenario is basically a zero-shot classifier for C#. It considers the training on datasets written in C, C++, and Java languages in order to detect security vulnerabilities (CWEs) in code written in C#. In this scenario, no C# code snippet is used during the training process.