esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of CuTi Alloy for Applications as Non-Sparking Material
###### Abstract  
A new non-sparking metallic material, Cu-Ti, with applications in potentially explosive environments is proposed as an alternative to CuBe, to reduce the processing and toxic effects of Be. Using high-purity Cu and Ti materials, a Cu (\(\sim\)3-4 wt%) Ti alloy with good chemical and structural homogeneity was fabricated in an induction furnace under an Ar atmosphere. The hot-rolled material was tested in an explosive gas mixture (10% H\({}_{2}\) or 6.5% CH\({}_{4}\)) under extremely severe wear tests for 15,000 cycles, and no hot sparks were produced to ignite the medium. The material was investigated as hot-rolled plates (600 s at 950 \({}^{\circ}\)C and 10% reduction). The microstructures and surface of the wear test samples were investigated by light optical microscopy (LOM) and scanning electron microscopy (SEM). The chemical compositions were determined by energy dispersive spectrometry (EDS). The corrosion behavior was studied using electrochemical techniques: open-circuit, linear, and cyclic potentiometry in saline electrolyte solutions. The mechanical properties, such as microhardness and friction coefficient, were determined using UMT equipment. The results showed that the alloy is suitable for applications requiring non-ignition properties, with good hot rolling deformability and chemical composition homogeneity. Regarding the corrosion analysis and mechanical properties of the experimental CuTi alloy, minor differences were observed between the cast- and hot-rolled material.  
on-spark material; aggressive wear test; corrosion resistance Article
# Improved GA-LNS Algorithm for Solving Vehicle Path Problems Considering Carbon Emissions
## 4 Improved Genetic Algorithm Stage
### Improving the Genetic Algorithm Phase
#### 4.1.2 Initial Population Generation  
The effectiveness of genetic algorithms is greatly influenced by the quality of the starting solution. A high-quality initial solution facilitates faster convergence towards the optimal outcome and minimizes the number of required iterations, ultimately improving overall efficiency. The minimum cost nearest neighbor (NNC) method, originally introduced by [PERSON] (1965), is a constructive algorithm that produces relatively high-quality feasible solutions and has been utilized in the context of the Vehicle Routing Problem (VRP). This research utilizes the NNC method to address the Vehicle Routing Problem with Time Windows (VRPTW), and is enhanced as follows:  
Step 1: determine the distances from the distribution center to each customer and identify the closest customer to serve as the starting point for the vehicle route.  
Step 2: Calculate the distances from the remaining customers to the most recent customer in the ongoing route, arrange these distances in ascending order, and systematically evaluate their insertion based on the vehicle's capacity and the customers' time window constraints. Incorporate the highest-ranking customer point that satisfies these conditions into the current route.  
Step 3: Iterate Step 2. If no appropriate customer points are found, deploy an additional vehicle. Upon completing visits to all customer points, finalize the calculations.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AMI & Advanced Metering Interface \\ AS & Ancillary Services \\ BBPLC & Broad Band PLC \\ CSS & Chirp Spread Spectrum \\ CT & Current Transformer \\ DER & Distributed Energy Resources \\ DMS & Distribution Management System \\ DR & Demand Response \\ DSO & Distribution System Operator \\ DSSS & Direct Sequence Spread Spectrum \\ eMBB & Enhanced Mobile Broadband \\ EMS & Energy Management System \\ ESS & Energy Storage System \\ EV & Electric Vehicle \\ SG & Fifth-Generation \\ FMI & Functional Mockup Interface \\ FSK & Frequency Shifting Key \\ GEO & Geostationary Earth Orbits \\ GOOSE & Generic Object-Oriented Substation Event \\ GSM & Global System for Mobile \\ FAN & Field Area Network \\ HAN & Home Area Network \\ HD & High-Definition \\ HEMS & Home Energy Management System \\ IEC & International Electrotechnical Commission \\ IED & Intelligent Electronic Devices \\ IEEE & Institute of Electrical and Electronics Engineers \\ IoT & Internet of Things \\ ISM & Industrial, Scientific, and Medical \\ ITU & International Telecommunication Union \\ LAN & Local Area Network \\ LEO & Low Earth Orbits \\ LPWAN & Low Power WAN \\ LTE & Long-Term Evolution \\ MAS & Multi-Agent System \\ MEO & Medium Earth Orbit \\ MG & Micro-Grid \\ MGMS & Micro-Grid Management System \\ MIMO & Multiple-Input Multiple-Output \\ MMS & Manufacturing Message Specification \\ mMTC & Massive Machine Type Communications \\ mm-Wave & Millimeter-Wave \\ NBPLC & Narrow Band PLC \\ OSI & Open System Interaction \\ PCC & Point of Common Coupling \\ PLC & Power Line Carrier \\ PMU & Phasor Measurements Units \\ P2P & Peer to Peer \\ \end{tabular}
# Semantic Publication of Agricultural Scientific Literature Using Property Graphs
## 4 Results
### Use Case 2: Annotations in the Same Context  
The graph model used for annotating the scientific articles provides a paragraph level granularity, that is, annotations are associated with a concrete paragraph in an article. Thus, it is possible to retrieve annotations that usually appear in the same context (paragraph). This is important because it enables interesting features such as identifying related concepts that could be used for disambiguation.  
For instance, the annotation SIGMA FACTORS appears in 3 paragraphs. Two of these paragraphs are part of the article with PubMed ID 28948393, while the other one appear in the article 25244327 (see Query 20). All these paragraphs have the following annotations in common: FACTORS, GENES, TRANSCRIPTION, SIGMA FACTORS and EXPRESSION (see Query 21). In this case, we have a context for SIGMA FACTORS conformed by genetic concepts. Thus, all these annotations can be classified in the  
Figure 10: Word cloud with the forty most frequent annotations representing the cluster of articles with similar documents.  
Figure 9: Estimation of the optimal number of clusters by using the average silhouette width.  
same domain. This could help to disambiguate the annotation EXPRESSION, for example, which is referring to gene expression in this context, but could act as synonym of formula or equation in other contexts. This is exemplified in the following paragraph, which was taken from the article with PubMed ID 30486880 (see Query 22):  
In this **expression**, Nur indicates the total number of plant species used to extract a certain color and Nt refers to the number of species simultaneously approved by all informants for dyeing a certain color. ICF values range between 0 and 1, with 0 indicating the highest level of informant consent and 1 the lowest. To quantify the use frequency of certain species [[CR22]], the below formula was adopted.  
The annotations found in this paragraph are the following: SPECIES, DYEING, NUMBER OF, NUMBER OF PLANT, FORMULA, EXPRESSION, TOTAL, NUR, TO EXTRACT, COLOR, PLANT SPECIES, NUMBER, PLANT, CONSENT, FREQUENCY, LEVEL, RANGE, VALUES and ICF. In this case, this context does not contain any genetic concept but mathematical terms like FORMULA, NUMBER OF or FREQUENCY, which can help to define the meaning of EXPRESSION in a mathematical domain.
# Improved PR Control Without Load Current Sensors and Phase-Locked Loops for APFs
## 5 Experimental Verification
### Analysis of Traditional PR Control Experiments  
Figure 11 shows the current and voltage waveforms of the grid under the traditional PR control algorithm in a steady state. Figure 11a shows the voltage and current waveforms of phase A. After the filter is put into operation, the current basically achieves unity power factor operation, indicating that the controller has a certain steady-state tracking capability. Figure 11b shows the three-phase grid current. The overall trend is close to sinusoidal, but obvious high-frequency ripple and asymmetry can be seen, indicating that the proposed method still has large current fluctuations in the steady state; the THD reaches 6.2%. As shown in Figure 11c, the current spectrum still contains multiple low-order harmonic components. This result shows that in the steady state of the grid, the traditional PR control algorithm can guarantee the power quality to a certain extent.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Supply voltage and frequency & \(e_{g}=380\) V(rms), \(f=50\) Hz \\  & \(L_{f}=0.0022\) H \\ Active power filter circuit & \(C=1500\) uF, \(U_{dc}=600\) V \\ Nonlinear load & \(R_{L}=120\) O \\ Sampling frequency & \(f_{s}=20\) kHz \\ Switching frequency & \(f_{sw}=20\) kHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: System parameters for experiment.  
Figure 10: Experimental platform.  
Figure 11: Steady-state situation of conventional PR control: (a) phase A grid voltage and current; (b) grid current; (c) steady-state current FFT; (d) phase A current mutation.  
To assess transient performance, this study drops the load from 120 \(\Omega\) to 80 \(\Omega\) at t = a to simulate the load mutation. Figure 11d shows that the grid current exhibits no significant overshoot during the transition, and the waveform stabilizes within approximately one cycle (20 ms). Although minor fluctuations are present during the transient, the system successfully returns to steady operation without triggering protective responses. Overall, the traditional PR control method demonstrates basic compensation capability under dynamic conditions. However, it requires multiple PR controllers for different harmonics, a PLL for phase tracking, and load-side current sensors to generate the compensation reference. These hardware and computational requirements increase the complexity and reduce the flexibility of the system, especially in fast-changing grid conditions.  
In general, the traditional PR control can achieve basic current tracking and harmonic compensation under steady-state conditions, but there are still problems such as steady-state error, and the transient response speed can be improved. At the same time, the proposed method relies on the PLL to extract the current phase and requires the use of a load-side current sensor, which is not conducive to economic efficiency.
## 3 Distributed Target Tracking Systems
### Phase IV: Sub-Cluster Member Reselection and Leader Handoff  
This phase performs the sub-cluster member reseelection and leader handoff task, which aims to maintain tracking stability. The conditions for initiating the leader handoff procedure are:  
* The distance between the reference location of the sub-cluster member \(P_{i}\) (\(\forall i\in M\)) and the fused target position estimate \(f(j)\) exceeds the handoff threshold value at time step \(j\). That is, \(d(P_{i},f(j))>\Re\), \(\forall i\in M\). * Due to the movement of the target, the number of \(Position\) messages or expected active sensors are less than the desired value. That is, \(N_{P}<n\) or \(N_{E}<n\). For condition 1, \(\Re=\beta\cdot R\), where \(R\) is the radio transmission range and \(0<\beta<1\). For condition 2, a threshold value \(\triangle R\) with \(\triangle R=\delta\cdot R\) (\(0<\delta<1\)) for dynamically measuring the number of valid sub-cluster members is applied. Denote \(P_{L_{id}}\) as the location of leader \(L_{id}\) and \(B_{L_{id}}\) as the leader handoff boundary (the red dashed line in Figures 8 and 9) of leader \(L_{id}\), which is a circle of radius \(\triangle R\) centered at \(O_{L_{id}}\). Note that \(O_{L_{id}}\) is the center of gravity of the bounding box, which is derived by the estimated target position, the positions of the sub-cluster members, and the bounding-box algorithm [24]. Thus, \(N_{P}\) is the number of estimates from the sub-cluster members and \(N_{E}\) can be computed as the number of active sensors which satisfy \(d(O_{L_{id}},P_{i})\leq\triangle R\), where \(P_{i}\) is the location of active sensor \(i\). Figure 8: An example of adaptively updating the sub-cluster members with \(\triangle R\). Figure 9: An example of leader handoff procedure. Figure 8 presents an example of adaptively updating the sub-cluster members with \(\triangle R\). Observe that at time step 20, two sub-cluster members are with tracking responsibility (Figure 8(left)). However, at time step 21, one sub-cluster member becomes an inactive sensor due to the target movement (Figure 8(right)). Thus, the leader will assign an active sensor which is located inside \(B_{L_{id}}\) to become
# Secure PD-NOMA with Multi-User Cooperation and User Clustering in Both Uplink and Downlink PD-NOMA
## 2 System Model for Multi-User Downlink and Uplink PD-NOMA
### Multi-User Uplink PD-NOMA  
In the multi-user uplink PD-NOMA, each user transmits signals at their maximum power of \(P_{i}\) towards the gNodeB. However, each user experiences different channel conditions and hence has different channel gains. A multi-user uplink PD-NOMA system is shown in Figure 2, where four users communicate to gNodeB with the same power and different channel gains. It is understood that PD-NOMA serves users at different power levels, but here all the users have the same transmit power. The users' signals are combined over the air because they are utilizing the same spectrum at the same time. The received composite signal at the gNodeB is given by  
\[X=\sum_{i=1}^{4}\sqrt{P_{i}}h_{i}x_{i}(t), \tag{7}\]  
where \(P_{i}\) is the maximum transmit power and \(h_{i}\) is the channel gain for the \(i\)th user. We know that the channel gain order is: \(h_{1}>h_{2}>h_{3}>h_{4}\), and all the users are transmitting at their maximum power. As a result, the signal of the nearby user \(R_{1}\) with channel gain \(h_{1}\) is attenuated less and has more power remaining in comparison to the signal of the other users. The decoding of the signals at the gNodeB is: \(R_{1}\) signal is decoded first and is used as a SIC term to decode the \(R_{2}\) signal. Similarly, \(R_{1}\) and \(R_{2}\) signals are eliminated using SIC to detect the \(R_{3}\) signal, while \(R_{1}\), \(R_{2}\), and \(R_{3}\) signals are eliminated using SIC to detect the \(R_{4}\) signal.  
The achieved data rate in the case of a four-user uplink PD-NOMA is given by [35].  
\[R_{i}=log_{2}\Big{(}1+\frac{P_{i}|h_{i}|^{2}}{\sum_{j=i+1}^{4}P_{j}|h_{j}|^{2}+N_{ i}}\Big{)},\qquad\forall\quad i=1,2,3,4. \tag{8}\]  
The main difference between the uplink and the downlink PD-NOMA is the SIC order. In the downlink, the far or weak user data is decoded first, while in the uplink the near or strong user data is decoded first. However, the main theme of the PD-NOMA is preserved in the sense that it decodes the signals based on the power level. In both the uplink and downlink, the signals having the highest power will be decoded first at each receiver. In the downlink, the strong users receive interference-free transmissions upon the successful elimination of the weak user signals through the SIC scheme. While in the uplink, weak users' signals are once decoded upon the elimination of interference from all the strong users.
## 4 Inversion and Verification of Mechanical Parameters  
### Acquisition of Dataset  
In this paper, the results of the sensitivity analysis are employed to understand the impact weight of environmental factors and rock mass parameters on various evaluation indices of the tunnel, as well as the comprehensive impact weight. In the inversion study, the environmental factors of the research tunnel section are determined, and the basis for inversion relies on displacement monitoring data. The mechanical parameters of the rock mass are key in reflecting displacement changes. Therefore, elasticity modulus, Poisson ratio, cohesion, and internal friction angle were selected as the parameters for inversion. Based on the geological data of CX22 section in the RBSR tunnel, the essential mechanical properties of the surrounding rock in the study area were selected. Such a selection of training samples aligned with the specified range of pivotal mechanical properties of the rock surrounding the tunnel (detailed in Table 4). From Table 4, E varies between 0.5 and  
Figure 7: \(S_{1}\) and ST of seven factors. (**a**) Crown displacement; (**b**) B-C convergence; (**c**) D–E convergence; (**d**) maximum principal stress; (**e**) minimum principal stress; (**f**) proportion of plastic zone; (**g**) sum of \(S_{1}\) and \(S_{T}\).  
5 GPa, while \(\upmu\) varies from 0.27 to 0.35. c, and \(\varphi\) values vary from 0.1 to 0.7 MPa and 22\({}^{\circ}\) to 33\({}^{\circ}\), respectively.  
In this study, we employed the Latin Hypercube Sampling (LHS) method to generate a set of 1000 samples within the range of parameters [44,45]. Figure 8 illustrates the distribution of samples, demonstrating a comprehensive coverage within the defined range. Moreover, the average values of the 1000 samples for each parameter were close to the specified range's mean values (elasticity modulus: 2.743 GPa, Poisson ratio: 0.31, cohesion: 0.4 MPa, and internal friction angle: 27.5\({}^{\circ}\)). Consequently, it is evident that the outcomes of this sampling method can serve as a foundation for the establishment of a metamodel.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Input Variables** & **Items** & **Unit** & **Ranges** & **Mean** \\ \hline Elasticity modulus & E & GPa & (0.5, 5) & 2.75 \\ Poisson ratio & \(\upmu\) & - & (0.27, 0.35) & 0.31 \\ Cohesion & c & MPa & (0.1, 0.7) & 0.4 \\ Internal friction angle & \(\varphi\) & \(\circ\) & (22, 33) & 27.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Input parameters involved in the inversion of the operational tunnel.  
Figure 8: Scatter plots and distribution histogram of 1000 samples for each parameter using the LHS method. (**a**) Elasticity modulus; (**b**) Poisson ratio; (**c**) cohesion; (**d**) internal friction angle.  
A total of 1000 parameter samples were sequentially put into the finite element model to calculate the displacement of the surrounding rock at five monitoring points. The data were subsequently summarized. Figure 9 shows the results of numerical simulation for 1000 samples. It is evident that the vast majority of results in the output samples fall within the range of 2 to 30 mm.
# Angular Trajectory Design for MR-SPS Using Bezier Shaping Approach
## 4 Simulation Results
### Angular Trajectory around Solstice  
Assume that the MR-SPS is on orbit at 18 June 2025 and the maximum torque and minimum torque are set as \(\mathbf{T}_{\rm{cmax}}=\left[80\quad 80\quad 80\right]^{T}\) N \(\cdot\) m and \(\mathbf{T}_{\rm{cmin}}=\left[-80\quad-80\quad-80\right]^{T}\) N \(\cdot\) m. The optimal angular trajectory of \(\gamma\) and \(\theta_{j}\) generated by the proposed Bezier approach and the ideal angular trajectory of \(\gamma\) and \(\theta_{j}\) are shown in Figure 3. It can be obtained that the optimal angular trajectory generated by the Bezier approach is very close to one ideal trajectory because the rate of change of the rotation angle is always small and the control effort is enough to track the ideal trajectory. The average energy receiving efficiency is 99.97% which is very close to 100%.  
The control torque history is shown in Figure 4. It can be observed that all the components of \(\mathbf{T}_{\rm{c}}\) satisfy the constraints, and the peak value of \(\mathbf{T}_{\rm{c}}\) of the ideal trajectory is 150 N\(\cdot\)m and is larger than that of the optimal trajectory which is 80 N\(\cdot\)m. Although the peak value of control torque is less for the optimal trajectory, the loss of the efficiency is negligible. The value of the component \(T_{cy}\) is much less than the other components. The histories of \(\tau_{\rm{sc1}}\) and \(\tau_{\rm{sc2}}\) are almost the same due to the symmetrical structure and the history of \(\tau_{\rm{scj}}\) of the optimal trajectory and ideal trajectory is similar. If the constraints are less strict, the ideal trajectory can also be applied.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline \(l_{a}\) & \(\rm{diag}\ (4.4\times 10^{9},1\times 10^{8},4.4\times 10^{9})\ kg\cdot m^{2}\) \\ \hline \(I_{sj}\) & \(\rm{diag}\ (1.88\times 10^{8},\ 2.08\times 10^{7},2.08\times 10^{8})\ kg\cdot m^{2}\) \\ \hline \(m_{a}\) & \(8\times 10^{4}\ kg\) \\ \hline \(m_{sj}\) & \(2.5\times 10^{4}\ kg\) \\ \hline \(\mathbf{r}_{\rm{c1}}\) & \(\left[0\quad 230\quad-30\right]^{T}\) m \\ \hline \(\mathbf{r}_{\rm{c2}}\) & \(\left[0\quad-230\quad-30\right]^{T}\) m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the MR-SPS.  
Figure 3: Angular trajectory around solstice. (**a**) Angular trajectory of \(\gamma\); (**b**) angular trajectory of \(\theta_{j}\).
# A Robust and Efficient Method for Effective Facial Keypoint Detection
## 4 Experimental Results
### Main Results
#### 4.2.6 Experimental Results of Different Pruning Rates  
Pruning rate refers to the proportion of parameters that are removed from the model. Pruning can reduce the computational load and the number of parameters, thereby improving the operational efficiency of the model. To further study the effect of pruning techniques on model performance, we set different pruning rates r for comparative experiments. By comparing the model's performance on the verification set under various pruning rates, we evaluated the impact on model accuracy and computational efficiency to identify the optimal pruning rate.  
\[r\in\{10\%,30\%,50\%,70\%,90\%\} \tag{11}\]  
With the pruning rate \(r=10\%\), the MSE of the model on the verification set was 0.0438. As the pruning rate increased, computational efficiency improved, but model accuracy decreased. When the pruning rate \(r=90\%\), the MSE increased to 0.0478, resulting in a precision loss of 9.13% compared to the model without pruning. The experimental results  
Figure 4: The results of regression and heatmap under different weight ratios.  
Figure 5: Distillation and pruning loss weight ratio results.  
presented in Figure 6 indicate that a low pruning rate (e.g., 10%) has minimal impact on model accuracy, but the improvement in computational efficiency is limited. Conversely, a high pruning rate (e.g., 90%) significantly reduces the computational load but also leads to a substantial loss in accuracy.