esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Analysis
### Model Performance Evaluation
#### 4.2.2 Homologous Function Search  
A homologous function search involves identifying functions within a designated pool that share homology with a query function. The performance of GMN+ and the baseline methods was evaluated using HR-K and NDCG-K. For each function in the test set, we constructed a pool consisting of one randomly selected homologous function version and 100 non-homologous functions to assess the similarity rankings of the homologous function relative to the pool. The results are shown in Figure 7. The horizontal axes represent the number of top results evaluated, denoted as K, and the vertical axes give the Hit Rate (HR) and Normalized Discounted Cumulative Gain (NDCG), respectively. As can be seen from Figure 7, for K \(\leq\) 40, the HR values for GMN+ surpassed those of the baseline methods, whereas for K \(>\) 40, the HR values for all methodologies converged toward 1. Similarly, GMN+ also achieved the highest NDCG-K. These results demonstrate the superior capability of GMN+ in homologous function search tasks compared to baseline methods. The distinct advantage of GMN+ over baseline methods arises from its advanced modeling of both structural and semantic aspects of code. GMN+ enhances homologous function search by effectively integrating various data modalities, such as the code structure and the semantic aspects of instructions, particularly by incorporating instruction type information into the model.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**O0-O1**} & \multicolumn{3}{c}{**O0-O3**} & \multicolumn{3}{c}{**O1-O2**} & \multicolumn{3}{c}{**O0-O1-O2-O3**} \\ \cline{2-11}  & **Recall** & **Precision** & **F1** & **Recall** & **Precision** & **F1** & **Recall** & **Precision** & **F1** & **Recall** & **Precision** & **F1** \\ \hline FIT & 0.9379 & 0.8769 & 0.9074 & 0.7033 & 0.8469 & 0.7685 & 0.9224 & **0.9681** & **0.9446** & 0.8418 & 0.8605 & 0.8511 \\ GMN & 0.8221 & 0.8791 & 0.8696 & 0.8541 & 0.8623 & 0.8582 & 0.9192 & 0.9312 & 0.9252 & 0.8894 & **0.9161** & 0.9025 \\ SAFE & 0.8881 & 0.8185 & 0.8519 & 0.7973 & 0.7668 & 0.7818 & 0.8972 & 0.8988 & 0.8981 & 0.8603 & 0.8174 & 0.8383 \\
**GMN+\((\text{ours})\)** & **0.9601** & **0.9265** & **0.9431** & **0.9343** & **0.8888** & **0.9111** & **0.9468** & 0.9417 & 0.9442 & **0.9373** & 0.9004 & **0.9185** \\ \hline \hline \end{tabular} Note. Values in bold font are the optimal values for each column.  
\end{table}
Table 8: Results of the similarity detection task across optimization levels.  
Figure 6: Comparison of ROC curves for different methods across optimization levels.
# DRL-Based Load-Balancing Routing Scheme for 6G Space-Air-Ground Integrated Networks
## 2 Related Work
### Machine-Learning-Based Satellite Routing  
The complex satellite network environment and dynamic inter-satellite links make the satellite routing algorithm difficult to calculate. Reinforcement learning has been widely used in various new industries because of its unique ability to deal with sequential decision-making problems [34]. The content caching problem was investigated in [35]. The Q-learning algorithm was utilized in the cloud content distribution systems. In [36], congested links in the Internet of Things (IoT) were identified using Q-learning to increase the fault tolerance rate. Similarly, Q-learning was used in [37] to improve the throughput of wireless sensor networks (WSNs) and solve the problem of the energy consumption of devices. In [38], to solve the optimal allocation problem of cache, bandwidth, and other resources in the Internet of Vehicles, they used deep reinforcement learning to solve the model. The authors of [39] designed a centralized satellite routing algorithm for the space-ground integrated network using the Deep Deterministic Policy Gradient (DDPG) [40] in machine learning [41,42]. The decision center of the proposed strategy was set on the ground. The decision center obtained the traffic information of the whole network in real time and sent the routing information to the relevant satellites after making a decision. However, the drawback of this strategy was that the delay of the satellite communication was very large, and the transmission routing decision could not be made in time. The network burden was increased; so, it was not suitable for large-scale use. A routing algorithm based on Multiagent Deep Deterministic Policy Gradient (MADDPG) [43] was proposed in [44], which was a routing strategy that was deployed on each satellite after centralized training and solved part of the problems of the above centralized routing algorithm. The centralized training method had limitations in acquiring sufficient data, which became increasingly difficult as the network size and deep reinforcement learning algorithm training complexity increased. To address the dynamic load-balancing challenges in the LEO satellite network, this paper introduces a DQN-based load-balancing routing algorithm for LEO satellites.
## 2 Materials and Methods
### Cross-Validation and Performance Metrics  
The model proposed in this work was validated using k-fold cross-validation, wherein the dataset was divided into k subsets. In this process, each subset serves as a test set once, while the others are used as training sets, ensuring unbiased validation of the model's performance.
## 4 Proposed Collision Warning System and V2V Communications
### Overall System Model  
For autonomous vehicles, it is necessary for the embedded collision warning system to quickly perceive the surrounding environment and make decisions. In Figure 5, we illustrate an overall architecture of the proposed collision warning system. We summarize the components of the system as follows. Each vehicle obtains raw data from the equipped sensors such as cameras and radars. Through the object detection process, the object class and its confidences are extracted from the raw data. The trajectory information such as location and velocity are tagged to the object information in real-time. Next, we propose a compact message format for communications to achieve low latency. The encapsulated messages is shared via V2V communication, which provides high data rates and reliability such as DSRC. Next, the aforementioned data fusion algorithms are applied to determine the states of the detected objects. Based on them, the future trajectories of the detected objects are estimated by Kalman filter with cosine effect compensation [59; 60]. From the estimated trajectories, the proposed system warns an imminent collision. In this paper, we mainly consider collision warning protocol of the system in detail.  
Figure 5: Proposed collision warning system.
# Radio Channel Modelling for VHF System Operating in the Offshore Wind Farms Propagation Environment
## 2 Methods
### Itu-R BT.1893-1 Model
#### 2.1.2 Wind Turbine Impact Studies on Radiocommunication Systems--Model ITU-R BT.1893-1 Annex 2
In the case of wind turbines, the maximum frequency\(f_{Bmax}\) corresponds to the situation where \(\phi_{r}=0^{\circ}\) and the turbine blades rotate in the transmitting antenna-turbine-receiving antenna plane. In order to actually take into account the different wind conditions (which are likely to apply to different receiver locations), it is recommended that the three values of the Power Spectral Density (\(S_{high}\), \(S_{medium}\), \(S_{low}\)) be determined below. The case of high variability of conditions (\(S_{high}\)(\(f\))) represents the most pessimistic case. \[S_{high}(f)= \begin{cases}19.7\exp\Big{(}4.5\cdot\frac{f}{f_{Bmax}}\Big{)}-38.0 &-0.9\cdot f_{Bmax}\leq f<0\\ \delta(f)&f=0\\ 21.4\text{exp}\Big{(}-4.8\cdot\frac{f}{f_{Bmax}}\Big{)}-38.1&0<f\leq 0.9\cdot f_{ Bmax}\end{cases} , \tag{10a}\] \[S_{medium}(f)= \begin{cases}22.0\exp\Big{(}6.1\cdot\frac{f}{f_{Bmax}}\Big{)}-30. 4&-0.7\cdot f_{Bmax}\leq f<0\\ \delta(f)&f=0\\ 25.1\text{exp}\Big{(}-8.7\cdot\frac{f}{f_{Bmax}}\Big{)}-29.5&0<f\leq 0.6\cdot f_{ Bmax}\end{cases} ,\] (10b) \[S_{low}(f)= \begin{cases}22.9\exp\Big{(}17.9\cdot\frac{f}{f_{Bmax}}\Big{)}-24.9 &-0.3\cdot f_{Bmax}\leq f<0\\ \delta(f)&f=0\\ 23.2\text{exp}\Big{(}-17.6\cdot\frac{f}{f_{Bmax}}\Big{)}-25.0&0<f\leq 0.3\cdot f_{ Bmax}\end{cases} . \tag{10c}\]  
\(f_{Bmax}\)--defined by the Formula (9);  
\(\delta(f)\)--is the Dirac delta function, defined as follows:  
\[\delta(f)= \begin{cases}0,&f\
eq 0\\ +\infty,&f=0\end{cases} . \tag{11}\]
# Design of Three-Dimensional Electrical Impedance Tomography System for Rock Samples
## 4 Measurement System
Each of the two relay boards contains 192 relays, with each relay having an interface that connects to an external electrode. The opening and closing of the electrode channels depend on the switching status of the relays. Due to the large number of relays and the limited number of pins on the STM32 chip, the MCP23017 chips are used to expand the IO ports.
## 2 Related Work
### Instruction Tracing  
Arm CoreSight [17] is another microarchitectural monitoring technology available for off-the-shelf processors. Arm CoreSight is a debug and trace technology inside Arm's System-on-Chip (SoC). Its Program Trace Macrocell [18] collects instruction traces by meansof tracing key instructions, such as branch, exception, and synchronization. These traces can be accessed via the bus or debug port. To the best of our knowledge, there is no microarchitectural attack detection using Arm CoreSight but some papers mention its use for research works in Dynamic Information Flow Tracking (DIFT) [19] and code reuse detection [20]. The proposed approaches consist of capturing representative traces inside the processor, then decompressing and analyzing these traces inside the FPGA. Compared with hardware event counters, Arm CoreSight provides much fine-grained information about the software's execution, and the analysis can proceed in parallel inside the FPGA to reduce the execution time of the detection process. However, limited by the bus structure, the FPGA can only obtain little information when running continuous analysis.  
Similar hardware monitoring techniques that target the fine-grained instruction and data trace of the processor have also been proposed in different academic research works. Several tag-based monitoring techniques for DIFT have been notably proposed. The Raksha architecture [21] consists in modifying the pipeline to include tag related memory, tag propagation, and verification logic, while FlexCore [22] and Harmoni [23] use a dedicated component. Trace-based monitoring techniques have also been proposed to allow collection and analysis of the execution trace. LBA [24] performs the analysis on a general-purpose core; PHMon [25] performs the analysis on a coprocessor; while REHAD [26] describes a solution to detect Flush + Reload attack by means of a dedicated component. Among these works, only FlexCore and REHAD discuss the use of reconfigurable hardware for flexible detection logic structure, and only REHAD targets microarchitectural attack detection. However, even in REHAD--the detection algorithm design phase--the generality of the approach for several classes of attacks and an exhaustive evaluation of the attack detection are not provided.
# The Impact of Automated Vehicles on Road and Intersection Capacity
## 3 The Homogeneous Road Traffic Flow-Density Relationship
### Theoretical Derivation
\[h_{dC}=h_{n}=l+s_{0}+t_{c}v_{b}, \tag{25}\]  
\[K_{C}=\frac{1000}{h_{dC}}, \tag{26}\]  
\[K_{I}=\frac{1000\sqrt{1-\left(\frac{v_{b}}{v_{0}}\right)^{4}}}{s_{0}+v_{b}T+l \sqrt{1-\left(\frac{v_{b}}{v_{0}}\right)^{4}}}, \tag{18}\]0  
\[K_{I}=\frac{1000\sqrt{1-\left(\frac{v_{b}}{v_{0}}\right)^{4}}}{s_{0}+v_{b}T+l \sqrt{1-\left(\frac{v_{b}}{v_{0}}\right)^{4}}}, \tag{18}\]1  
where: \(h_{dC}\) and \(h_{n}\) represent the distance between the heads of CACC autonomous driving vehicles in the balanced state, \(v_{b}\) represents the balanced-state velocity of a CACC autonomous vehicle with a value range of \([1,v_{0})\), \(s_{0}\) represents the minimum expected (safety) workshop distance of a CACC autonomous vehicle, \(t_{c}\) represents the expected (minimum) headway of a CACC autonomous vehicle, \(l\) represents the length of a CACC autonomous vehicle, \(K_{C}\) represents the lane density of a CACC autonomous vehicle at equilibrium speed; \(h_{tC}\) represents the time headway of a CACC autonomous vehicle in the equilibrium state, and \(Q_{C}\) represents the traffic volume of a CACC autonomous vehicle in an equilibrium state. According to the calculation formula of the basic traffic flow diagram model of the three vehicles, the density-flow relationship between manually driven vehicles and autonomous vehicles, can be obtained by constantly changing the vehicle's equilibrium velocity \(v_{b}\) (from 1 to 33 in turn), as shown in \[K_{I}=\frac{1000\sqrt{1-\left(\frac{v_{b}}{v_{0}}\right)^{4}}}{s_{0}+v_{b}T+l \sqrt{1-\left(\frac{v_{b}}{v_{0}}\right)^{4}}}, \tag{18}\]2  
As can be seen from the chart above:  
1. The black solid line in the figure represents the IDM manually driven vehicle \(v_{f}\). 2. The flow-density curve of the IDM manually driven vehicles is a quadratic function, while the flow-density curve of the ACC and the CACC autonomous vehicles, is a linear function. This is because the following model selected, is of linear structure. Different from the flow-density curve of the IDM, it only has a crowded area on the right side of the diagram. By extending the intersection of the black solid line with the maximum flow point of the ACC and the CACC, the uncrowded areas of the ACC and the CACC flow density maps, can be obtained. 3. In the flow-density diagram, the maximum flow can be approximated as road capacity, or basic saturated flow rate. 4. When the road is full of ACC vehicles, its theoretical maximum capacity is 1.48 times that of manual vehicles; When the road is full of CACC vehicles, its theoretical maximum capacity is 2.42 times that of manual vehicles. 5. Compared with ACC vehicles, CACC vehicles demonstrate greater improvement on road capacity.
# Numerical Simulation of Natural Gas/Hydrogen Combustion in a Novel Laboratory Combestor
## 1 Introduction
[PERSON] and [PERSON] [33] experimentally investigated CH\({}_{4}\)/H\({}_{2}\) flames in a swirl-stabilized burner operating either under partial premixed conditions or MILD combustion while maintaining constant thermal power. Their experiments revealed a gradual increase in chemiluminescence intensity and a narrowing of the flame with hydrogen enrichment up to 40% (vol.) for both combustion regimes. NO emission increased in the conventional combustion regime but decreased under distributed combustion as the hydrogen content increased. The present work reports a computational investigation of NG/H\({}_{2}\) combustion in a swirl burner in a new combustor configuration. A unique feature of this combustor is the injection of secondary air through a set of holes placed circumferentially, near the exit of the combustor, in a direction opposite to the swirl burner, as sketched in Figure 1. The motivation to use this configuration is two-fold. First, the secondary air has a cooling effect for the walls, which is beneficial in gas turbine combustors. Second, air-staging is expected to contribute to reducing NO\({}_{x}\) emissions, which tend to increase when hydrogen is added to natural gas.
# A 100-Gb/s PAM-4 DSP in 28-nm CMOS for Sredes Receiver
## 2 Architecture of DSP  
Figure 1 shows the block diagram of the implemented DSP. It contains a clock data recovery (CDR) engine, a 16-tap FFE, a 1-tap DFE, and two LMS engines. The system mainly consists of two parts. One is the data path, which is composed of FFE and DFE in series and is used to balance the received data so as to recover a clear eye map [7]. The tap coefficients of FFE and DFE are provided by their respective LMS algorithms to achieve the purpose of adaptive balancing. The other part is the clock recovery pathway, which is implemented by the CDR module. It is used to process the received data so as to recover the correct sampling points, and then feed back to the ADC for sampling [8]. A small FFE module is integrated internally to provide the variables required by the algorithm to the CDR module. As shown in Figure 1, the quantized 8-bit data of the ADC are respectively applied to the CDR and FFE, where the former is adopted to adjust the sampling clock to its optimal point using a Mueller Mueller phase detector (MM-PD), and the latter is employed to compensate for the long-tail inter symbol interference (ISI). In particular, the CDR adopts a simplified 8-tap FFE data path instead of the 16-tap data path in the FFE structure, so that the phase information can be output as soon as possible in order to reduce the loop delay, thereby optimizing the bandwidth and phase margin of the CDR. The putative 1-tap DFE is linked to the FFE output to make further data balancing and data decisions. The 16-tap FFE and 1-tap DFE utilize separate LMS engines to continuously update the tap weights, where the bandwidth of the DFE loop is designed to be much smaller than the FFE loop to ensure their convergence [9].
# Pyrophyllite: An Economic Mineral for Different Industrial Applications
## 3 Pyrophyllite's Industrial Applications
### Pyrophyllite as a Raw Material  
#### 3.2.1 Ceramic Industry  
Clay minerals are the most important components in ceramic products. Moreover, several ceramic products can be prepared using different types of clay with different proportions in the mixture and different firing temperatures [59]. Since pyrophyllite becomes mullite under firing temperatures, which affords beneficial technical properties, pyrophyllite is used as a component in certain ceramic products. Ceramic products containing pyrophyllite include tiles, sanitary ware, white ware, and electrical components (e.g., insulators, vacuum gaskets, resistors, and transducers) [12; 48]. The use of pyrophyllite in the ceramic industry improves mechanical properties, translucence, chemical resistance, resistance to thermal shocks; this mineral also provides high dielectric strength and promotes the crack-free glazing of the finished products. Further, pyrophyllite complements the silica in raw-material mixtures. Moreover, pyrophyllite's ability to heat quickly and convert into mullite at a lower temperature (1100 \({}^{\circ}\)C) enables faster firing cycles than similar materials [21].  
According to the industrial specifications of ceramics, the percentages of impurities such as iron oxide, titanium oxide, and alkalis should remain within the permissible percentages because of their deleterious effects on products (Table 5) [51; 60]. Iron and titanium also have a coloring effect on the final product. In addition, iron oxide with titanium forms a low-melting iron-titanate glass, which causes increased porosity in the final product through the formation of blisters [3; 21; 61]. Alkalis are also deleterious because alkalis in the form of carbonates combine with silica under firing temperatures to form water-soluble silicates. Additionally, alkali ions are electrically conductive [62].
## 3 Results
### Wettability, Polarization Curves, and SBF Corrosion Tests
The diffusion coefficient of Mg2+ ions in the PHB protective membrane can be evaluated using the standard time lag equation [58; 59]:  
\[\text{D}_{\text{Mg}}^{2+}=l^{2}/6\text{ t}_{\text{lag}} \tag{2}\]  
where tlag is the time lag (1.9 days), \(l\) is the membrane thickness (5.2 \(\pm\) 0.6 \(\times\) 10\({}^{-3}\) cm), and DMg2+ is the resulting diffusion coefficient, which in our testing condition is \(2.7\pm 0.5\times 10^{-11}\) cm2/s. This Mg2+ ion diffusion coefficient, which accounts for the mutual cation and anion self-diffusion coefficients \(D+\) and \(D-\) and solubilities in the polymer, is coherent with the values observed for highly crystalline and hydrophobic polymers [60] such is PHB polymer. Due to the charge neutrality, the Mg2+ cation diffusion in the PHB bulk coating toward the external SBF solution is also dependent on the counterdiffusion of the anionic species (especially the Cl- ions that are directly involved in the metal corrosion process). As previously emphasized when analyzing the potentiometric polarization curves of the electrochemical characterization, the very low natural corrosion current and the shift of natural corrosion potential of the PHB-coated alloy towards the anodic direction indicate that the polymer film principally acts as a physical barrier that strongly hinders electrolyte anion migration from the external SBF corroding solution to the metal.
## 4 Conclusions  
In this study, detailed experimental investigations of kerosene and surrogate flames were performed using different measuring techniques. The temperature, main species and short-chain HCs, PAHs and soot volume fraction were resolved for kerosene Jet A-1 and a two component surrogate at different operating conditions.  
The results show that the kerosene and surrogate flames do not differ significantly in the main flame structure in terms of temperature and main species profiles. However, they differ significantly in terms of soot volume fraction. These differences are due to the complex chemistry of the PAH formation. Numerical modeling showed that the variations in fuel composition has little impact on the species profiles of shorter hydrocarbons and larger differences are expected in the range of PAH.  
It remains to be noted that none of the chemical mechanisms used here can correctly predict the species important for soot formation (e.g., acetylene and benzene). It is well known that the mechanism used has a large impact on the soot modeling results [10]. Therefore, the prediction accuracy of soot emission from real aircraft engines would benefit from improved chemical reaction mechanisms for kerosene.  
The following are available online at [[https://www.mdpi.com/article/10_390/app11114796/s1](https://www.mdpi.com/article/10_390/app11114796/s1)]([https://www.mdpi.com/article/10_390/app11114796/s1](https://www.mdpi.com/article/10_390/app11114796/s1)), Table S1: Experimental results of kerosene flames, Table S2: Experimental results of surrogate flames.  
Conceptualization, T.v.L., M.M.S., S.S., B.S., D.T. and N.Z.; methodology, T.v.L., S.S. and M.M.S.; data curation, T.v.L., S.S. and M.M.S.; writing--original draft preparation, T.v.L., M.M.S., S.S. and B.S.; writing--review and editing, T.v.L., M.M.S., S.S. and B.S.; visualization, T.v.L.; and supervision, B.S., D.T. and N.Z. All authors have read and agreed to the published version of the manuscript.  
The present research work contributes to the MTET program, Resource and Energy Efficiency, Anthropogenic Carbon Cycle (38.05.01) of Helmholtz Association.  
**Data Availability Statement:** Experimental Data presented in this study is available as Supplementary Materials.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 8: Comparison of numerically calculated species profiles for different mixture compositions—surrogate with trimethylbenzene (TMB) as well as surrogate with toluene.