esa-sceva/satcom-chunk-collection huggingface dataset:
# Research Method for Ship Engine Fault Diagnosis Based on Multi-Head Graph Attention Feature Fusion
## 2 Related Work and Theory
### Data Topology Construction
#### 2.1.3 Feature Graph Fusion  
Multi-scale feature fusion is mainly used to fuse the probabilistic graph structure and rank-order graph structure constructed earlier and fully excavate the intrinsic feature connection between each graph structure. The more widely used feature fusion methods are early fusion [27], late fusion, and attention feature fusion. Attention feature fusion is able to effectively deal with multimodal data, and it has strong adaptability [28; 29]. Late fusion can capture the temporal correlation and interaction effects between data sources well and has better performance in time series tasks [30]. However, the above two methods have the shortcomings of a complex model structure, a high demand for computational resources, and a high cost of training time when the number of samples is small [31]. Therefore, for the discrete small-sample ship engine failure sample set, this paper chooses early fusion [32] as the method of fusion of the multi-graph structure, sets the feature weight \(W_{i}\) and neighbor threshold \(T\) of each graph to construct the fusion graph structure, and its formula is as follows:  
\[A_{F}=\sum\limits_{i=1}^{K}W_{i}Nol(A_{i}), \tag{10}\]  
\[Nol(x)=\frac{x-\min(x)}{\max(x)-\min(x)}, \tag{11}\]  
\[\mathbf{A_{\mathrm{T}}}=\begin{bmatrix}a_{11}&a_{12}&\cdots&a_{1N}\\ a_{21}&a_{22}&\cdots&a_{2N}\\ \vdots&\vdots&\ddots&\vdots\\ a_{N1}&a_{N2}&\cdots&a_{NN}\end{bmatrix}, \tag{12}\]  
\[a_{ij}=\begin{bmatrix}1,&A_{F}^{ij}\geq T\\ 0,&A_{F}^{ij}<T^{\prime}\end{bmatrix} \tag{13}\]  
where \(A_{F}\) is the fusion feature matrix; \(Nol(A_{i})\) represents the feature matrix of each graph after normalization; \(A_{i}\) is the original feature matrix; \(A_{T}\) signifies the adjacency matrix after multi-graph fusion; and \(a_{ij}\) denotes the elements of the adjacency matrix in the \(i\)th row and \(j\)th column of \(A_{F}\) after applying the adjacency threshold \(T\).  
The larger the value of the element \(A_{F}^{ij}\) in \(A_{F}\), the stronger the similarity between node \(i\) and node \(j\). How to set the threshold \(T\) to define the adjacency matrix \(A_{T}\) is the key. In thispaper, we use quartiles to determine the threshold. The calculated \(A_{F}^{ij}\) values are arranged from small to large and divided into four equal parts. The three values in the numerical split point position are defined as Q1, Q2, and Q3, and Q3 is set to be the critical value with \(T\). Compare \(A_{F}^{ij}\) with \(T\). When \(A_{F}^{ij}\) is greater than \(T\), it means that node \(i\) and node \(j\) are connected, and \(a_{ij}=1\). When \(A_{F}^{ij}\) is smaller than \(T\), it means that \(i\)-node and \(j\)-node are not connected, then \(a_{ij}=0\).
## 1 Introduction
Therefore, the goal of our study is to investigate the changes in the mechanical and microstructural properties of ordinary Portland cement (OPC) blended with TEOS/PVP NFs, as well as versions modified by carbon nanotubes (CNT-TEOS/PVP NFs). To the best of our knowledge, silica is TEOS/PVP's primary ingredient. Where PVP is a carrier matrix and TEOS is a precursor to silica [21]. Therefore, it is anticipated that adding TEOS/PVP NFs to cement will result in more polaronic reactions. These nanofibers can, therefore, efficiently raise the amount of hydration products in the matrix, enhancing its mechanical characteristics and toughness. According to the findings of earlier research [11], CNTs have demonstrated their value in enhancing the tensile strength of cement paste when combined with nylon 66 nanofibers. To continue this strategy, CNTs have been introduced to TEOS/PVP NFs to examine their impact on the pastes' tensile strength. Positive results regarding the mechanical properties of cement can be considered as a premise for improving the quality of this material, reducing the amount of cement used and thereby minimizing its environmental impacts. The nanofibers were fabricated by the electrospinning process [17; 18; 22; 23; 24; 25] and blended directly into cement powder [11; 12]. In our study, we modified TEOS/PVP NFs using multi-walled carbon nanotubes (MWCNTs), and we conducted the ultrasonication process while preparing the polymer solution to break the strong van der Waals forces that cause the agglomeration of carbon nanotubes (CNTs) [26; 27; 28; 29]. Above all, the primary goal of this study is to examine the mechanical and microstructural characteristics of cementitious materials blended with TEOS/PVP NFs and CNT-TEOS/PVP NFs. Our mechanical strength test results showed how our proposed electrospun nanofibers affected some key properties of cement. We also carried out microstructure analyses, such as the field emission transmission electron microscope (FE-TEM) and the field emission scanning electron microscope (FE-SEM), to confirm the changes in the nanofibers' morphology as well as the matrix's microstructure. Furthermore, we performed energy-dispersive spectroscopy (EDS) and thermogravimetric analysis (TGA) to examine the variation in hydration-product proportions.
## 3 Results and Discussion
### Primary and Secondary VOC Emissions
#### 3.2.3 Comparison with the Emission Data from the NRC Database  
Numerous studies reporting VOC emissions from various materials can be found in the literature, whereas comprehensive databases, such as the NRC (the National Research Council Canada) database, developed based on the measurements of emissions from a broad range of indoor materials in chambers, are still limited. For example, the VOC emission rates (assumed to be at a steady state) determined in this study for the wood-based panels and the carpet, with or without ozone exposure, were compared, and the ranges of the life-time-averaged emission rates were determined using the NRC database [56], as shown in Table 7. In the NRC database, ozone was not intentionally induced into the emission chamber, and the samples for OSB, plywood, pine, and carpet were six, one, three, and seven, respectively. The eight chemicals listed in Table 7 were common compounds measured during both studies.
# Precoded Generalized Spatial Modulation for Downlink MIMO Transmissions in Beyond 5G Networks
## 3 Transmitter and Receiver Structure  
In this section the transmitter and receiver structures' design will be addressed. The receiver design will be based on the alternating direction method of the multipliers (ADMM), which will be explained further in Section 3.2.
# Co-Operative Binary Bat Optimizer with Rough Set Reducts for Text Feature Selection
## 5 Results and Discussion
### Results on Non-English Datasets
#### 5.3.1 Results of Malay Datasets
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Class** & **Q-BBA\({}_{\text{LHS}}\)** & **Q-BBA\({}_{\text{CO}}\)** & **\#F** & **\#F-BBA\({}_{\text{LHS}}\)** & **\#F-BBA\({}_{\text{CO}}\)** & **R-BBA\({}_{\text{LHS}}\)** & **R-BBA\({}_{\text{CO}}\)** \\ \hline Addin & 0.416 & 0.483 & 3925 & 315 & 256 & 91.97 & 93.48 \\ Bignes & 0.536 & 0.547 & 2564 & 335 & 184 & 86.93 & 92.82 \\ Dekotaman & 0.564 & 0.58 & 3396 & 358 & 314 & 89.46 & 90.75 \\ Global & 0.667 & 0.638 & 2052 & 259 & 237 & 87.38 & 88.45 \\ Hiburan & 0.645 & 0.651 & 2947 & 356 & 330 & 87.92 & 88.80 \\ Pendidikan & 0.655 & 0.685 & 3284 & 365 & 303 & 88.89 & 90.77 \\ Santai & 0.517 & 0.592 & 3940 & 428 & 402 & 89.14 & 89.80 \\ Sihat & 0.662 & 0.688 & 3943 & 382 & 374 & 90.31 & 90.51 \\ Sukan & 0.406 & 0.489 & 2041 & 363 & 327 & 82.21 & 83.98 \\ Teknologi & 0.537 & 0.669 & 2818 & 201 & 194 & 92.87 & 93.12 \\ Vroom & 0.616 & 0.668 & 3150 & 362 & 342 & 88.51 & 89.14 \\ \hline \end{tabular}
\end{table} 
Table 17: Characteristics of the selected feature sets in Harian Metro dataset, where Q indicates the quality of feature set, #F indicates to number of features, R indicates reduction rate (%). \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Dataset** & **Metric** & **Classifier** & **No FS** & **Chi-Square** & **BBA\({}_{\text{LHS}}\)** & **BBA\({}_{\text{CO}}\)** \\ \hline \multirow{8}{*}{Mix-DS} & Micro & NB & 76.32 & 84.32 & 88.50 & 89.34 \\  & average F1 & SVM & 76.01 & 84.6 & 88.04 & 88.72 \\  & & KNN & 75.94 & 84.07 & 87.77 & 88.24 \\ \cline{2-6}  & Macro & NB & 73.40 & 83.58 & 86.61 & 88.42 \\  & average F1 & SVM & 74.72 & 82.15 & 86.98 & 87.38 \\  & & KNN & 73.35 & 82.42 & 86.35 & 87.47 \\ \hline \multirow{4}{*}{Harian-Metro dataset} & Micro & NB & 68.20 & 78.94 & 82.38 & 83.56 \\  & average F1 & SVM & 67.38 & 79.19 & 82.16 & 83.71 \\ \cline{1-1} \cline{2-6}  & Micro & KNN & 66.77 & 78.51 & 81.64 & 83.18 \\ \cline{1-1} \cline{2-6}  & Macro & NB & 67.49 & 76.09 & 81.10 & 83.61 \\ \cline{1-1}  & average F1 & SVM & 66.29 & 77.13 & 80.86 & 82.16 \\ \cline{1-1} \cline{2-6}  & & KNN & 66.02 & 75.84 & 80.55 & 81.98 \\ \hline \end{tabular}
\end{table} 
Table 18: Classification results achieved by using the original dataset, the feature sets selected by Chi-square, BBA\({}_{\text{LHS}}\) and BBA\({}_{\text{CO}}\).
## 2 Materials and Methods
### Data Analysis  
The questionnaires were transferred into a digital format and analyzed using descriptive statistical methods. Technology affinity was assessed based on the description of the Affinity for Technology Interaction Scale [16]. The responses were first coded on a six-point Likert scale (completely disagree = 1,..., completely agree = 6), negatively worded items were reversed, and a mean score for every participant was computed. Additionally, mean scores for both patients and therapists were calculated for the seven categories. Similarly, the usability questionnaires were translated, and mean values for each patient and therapist were calculated. Subsequently, the overall means of the categories for patients and therapists, with the standard deviation, were calculated. All data analysis was performed using Microsoft Excel (Microsoft Excel Version 2108, Microsoft, Redmond, WA, USA).  
Qualitative data were digitalized and translated into English. The content was categorized depending on the particular questions either in certain aspects (e.g., the expected usage of face-to-face or synchronous therapy) or positive or negative associations with the usage of the technology platform. The results of the qualitative questionnaires were then summarized. Data analysis was performed using MAXQDA, (MAXQDA Version 24, VERBI GmbH, Berlin, Germany).
# A Hybrid IVFF-AHP and Deep Reinforcement Learning Framework for an ATM Location and Routing Problem
## 3 The Proposed Methodology
### Clustering
#### 3.1.1 K-Means  
In the K-means algorithm, one of the most common clustering methods, the data is divided into k-data groups by including each data point in the cluster with the closest mean [25]. First, the k centroids of the data are calculated, and in the next step, each data point is directed from the relevant data point to the cluster with the nearest centroid. Different distance metrics are used while finding the closest centroid; the most widely used method is the Euclidean distance. After clustering, the center of gravity of each cluster is recalculated. Considering this center, the new Euclidean distance between each data point and the center is calculated, and the points with the minimum Euclidean distance in the clusters are determined. Each cluster is defined together with the data in the cluster and its centroid. The centroid of each cluster is the point where the sum of the distances to all the data in the related cluster is minimized. In this case, K-means can be defined as an iterative and non-hierarchical algorithm in which the sum of the distances of the data in the cluster from the cluster center is minimized for each cluster. The minimum square error can stop the algorithm, or the optimum set can be determined by trial and error.
# Modelica-Based Energy Management of PEMFC Hybrid Power System of Vehicle
## 4 Simulation and Evaluation
### Stack Temperature Control  
During the startup of the fuel cell stack, the temperature is relatively low. At this stage, circulating cooling water is still required to prevent the occurrence of local overheating in the fuel cell during actual operation, even when the average stack temperature remains low. In this study, a PID controller is used to regulate the speed of the circulating cooling water pump, thereby controlling the stack's temperature.  
In the HWFET test, the temperature variation curves of the battery stack under different cooling water flow rates, based on the battery stack power obtained using the SM-MPC method, are depicted in Figure 11. Obviously, as the cooling water flow rate increases, the overall temperature curve of the battery stack shifts downward. To ensure that the battery stack operates within an appropriate range, the cooling water flow rate is limited to below 0.5 kg/s here, in order to reduce water pump power consumption while maintaining the battery stack temperature.  
PID controller parameters are set to \(K_{p}=100\), \(K_{i}=10\), and \(K_{d}=0.1\) without tuning. The test results under the HWFET driving condition are shown in Figure 12.  
From the figure, it can be seen that the fuel cell stack reaches 55 \({}^{\circ}\)C at 170.2 s, at which point the cooling-water mass flow increases. During this period, the vehicle reaches its maximum power demand at 742.6 s, and consequently, the stack temperature subsequently rises to a maximum of 60.8 \({}^{\circ}\)C. The control process of the PID controller is relatively stable, with the temperature error maintained within \(\pm\)5.8 \({}^{\circ}\)C.  
The power and speed of the circulating water pump are shown in Figure 13. It can be observed that the pump's speed fluctuates rather rapidly for most of the time. The speed easily reaches the preset upper limit and may drop abruptly to zero. Consequently,  
Figure 11: Temperature variations of the battery stack under different cooling water flow rates during the HWFET test.  
Figure 12: Temperature variation of the fuel cell stack under the PID controller.  
although the fuel cell stack temperature remains stable in this experiment, the temperature range being maintained is excessively large. This leads to frequent start-stop operations of the water pump, indicating that further improvements in the performance of the control algorithm are still required. In addition, the power consumption of the water pump is negligible compared to the overall power generation of the hybrid system. Therefore, when modeling high-power fuel cell hybrid systems, more auxiliary components can be considered to refine the system modeling process.
# Evaluation of Non-Encapsulated and Microencapsulated Lactic Acid Bacteria
## 3 Results
### pH Low Tolerance  
The tolerance of _Lactobacillus_ isolates confirmed and identified by 16S rRNA gene sequence analysis was tested for two levels of pH resistance (pH 2.0 and 3.0) during 3 h of incubation at 37 \({}^{\circ}\)C, in anaerobic conditions. The bacterial populations of identified strains (IBNA 26, IBNA 64, IBNA 29, IBNA 37, IBNA 33, and IBNA 41) were between 8-11 Log10 in the fermentation medium with pH 6.5 \(\pm\) 0.2. The low pH values of the fermentation medium (2.0 and 3.0) decreased the viable counts of confirmed _Lactobacillus_ strains, involving a low resistance (Table 3).  
After measuring the viability at pH 3.0, all confirmed LAB strains were able to survive during 3 h at 37 \({}^{\circ}\)C (\(\geq\)58.53%). As shown in Table 3, at pH 2.0 after 3 h, the strains survivability compared with environment without acidic condition, involved a decline for IBNA 26 (0.00%), IBNA 37 (23.82%), and IBNA 29 (10.22%). It was found to have an excellent survival rate at pH 2.0 for IBNA 41 (74.98%), IBNA 33 (65.90%), and IBNA 64 (37.69%).
## 6 Analysis and Discussion of the Results
#### 6.2.2 CO Concentration  
From Figure 14, it is possible to note that the CO levels corresponding to the BEB fire are always higher than those associated with the burning ICEB, in both cases of ventilation investigated.  
By considering the scenario of natural ventilation, Figure 14 also shows that when the BEB catches fire, the CO concentrations are above the threshold value of 1200 ppm [44] at a distance from the fire center in the range of 40-365 m downstream and 30-360 m upstream of the bus in flames, with a peak value of about 2410 ppm and 2190 ppm downstream and upstream of the burning bus, respectively. In contrast, in the event of the ICEB fire, the CO concentrations are above 1200 ppm at a distance from the fire center in the range of 70-330 m downstream and 70-320 m upstream of the bus in flames, achieving a peak value of about 2015 ppm and 2000 ppm downstream and upstream of the burning bus,  
Figure 13: Spatial distributions related to the HF concentration at the height of breathing along the escape route downstream and upstream of the bus in flames for both BEB and ICEB fires, in both cases of ventilation investigated.  
respectively. Therefore, these results confirm the previous ones concerning the inability of natural ventilation alone to prevent the back-layering phenomenon.  
From Figure 14, it can also be seen that when the BEB or ICEB catches fire, the longitudinal mechanical ventilation efficiently pushes the CO concentrations toward Portal \(B\), so that the CO levels are very low (almost zero) upstream of the bus in flames and higher downstream of the fire, reaching values exceeding the acceptability limit only immediately downstream of the burning bus. The peak value of the CO level detected during the BEB fire is much higher than that measured for the burning ICEB (i.e., about 2790 ppm against approximately 2390 ppm).
# Fully Metallic Additively Manufactured Monopulse Horn Array Antenna in Ka-Band
## 3 Manufacturing  
To validate the design of the proposed horn array difference pattern antenna, incorporating various components such as waveguides, power dividers, twisters, and horn antennas, a decision was made to manufacture and test a monolithic version. AM LPBF was the technology employed for fabrication. This technology involves the deposition of metallic powder layers, around 30 \(\upmu\)m, on a building plate, followed by selective melting using a laser beam as a heat source to achieve the desired structure. This procedure is repeated for the different layers until the final part is manufactured. It is worth mentioning that it is not possible to manufacture the design with any subtractive technique in a monolithic piece.  
The resulting monolithic piece integrates the horn antenna array with the feeding network, utilizing solely aluminum alloy AlSi10 Mg with an electrical conductivity of \(\sigma\) = 1.68 \(\times\) 10\({}^{7}\) S/m [34]. The main advantage of using this method is that it eliminates the need for subsequent assembly and coating steps, streamlining production from two or three steps to just one, thereby reducing potential errors and enhancing device performance. The complexity of the design, including twisted sections, is challenging to produce even in multiple parts via traditional methods, particularly in terms of a difference pattern antenna, where the phase distribution along the aperture of the component is a critical aspect. Since LPBF was chosen from the outset, the antenna design was tailored to accommodate  
Figure 2: The difference pattern horn array antenna: (**a**) an exploded view of the vacuum design and (**b**) a rendering of the mechanical design.  
process constraints. For instance, mechanical surfaces in the 4 \(\times\) 4 horn antenna array were designed with no inclination exceeding 45\({}^{\circ}\) to align with manufacturing capabilities. Additionally, managing high temperatures during production is critical to prevent thermal deformations that could impact the electromagnetic response. Thermal optimization was employed during cooling using multiple thicknesses to mitigate potential deformations.  
After manufacturing the monolithic piece using aluminum alloy AlSi10 Mg, post-processing steps were initiated. Initially, the piece was detached from the printing bed along with the manufacturing supports. Subsequently, the supports were removed, and the piece underwent thorough cleaning. To further refine the surface, sandblasting was performed using glass microspheres. Finally, a machining cut was applied to the flange area, where an additional thickness of 500 \(\upmu\)m had been incorporated to ensure proper contact with the measuring equipment. Overall, a surface roughness of 5-6 \(\upmu\)m as-built is expected, and it is reduced to 3-4 \(\upmu\)m after sandblasting. Figure 3 depicts photographs of the additively manufactured difference pattern horn array antenna in aluminum alloy AlSi10 Mg. The overall size of the array is 41.5 \(\times\) 41.5 mm\({}^{2}\), with a weight of 52.3 gr.
# First-Principles Study of the Contact Resistance at 2D Metal/2D Semiconductor Heterojunctions
## 1 Introduction  
Two-dimensional (2D) materials are currently attracting a lot of attention, both fundamentally and technologically [1; 2; 3; 4; 5; 6; 7]. Concerning their possible use in field-effect transistors, these materials would enable to scale the channel thickness down to the atomic level, resulting in an optimized electrostatic control of the devices. In addition, heterostructures based on 2D materials, formed by their van der Waals stacking or via the formation of 2D lateral heterojunctions, show promise for the realization of novel devices with unique electronic, optoelectronic or magnetic properties [8; 9; 10; 11; 12; 13].  
However, the high contact resistance (R\({}_{\text{c}}\)W) in these 2D material-based devices is still a major issue [14; 15]. Typical contact resistances at various metal/2D semiconductor interfaces are in the range of 10\({}^{3}\)-10\({}^{5}\)\(\Omega\cdot\upmu\)m, severely limiting the device performances; R\({}_{\text{c}}\)W should be indeed reduced to about 100 \(\Omega\cdot\upmu\)m in field-effect devices [14]. Promising interfaces, based on lateral 2D metal/2D semiconductor heterojunctions, were realized recently [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. Very interestingly, low R\({}_{\text{c}}\)W values, in the range of 100-200 \(\Omega\cdot\upmu\)m, were reported at edge contacts formed between the metallic 1T-MoS\({}_{2}\) phase and the semiconducting 2H-MoS\({}_{2}\) phase [16; 20].  
We discuss here recent theoretical results on the contact resistance at lateral heterojunctions between graphene or 1T-MoS\({}_{2}\) with 2H-MoS\({}_{2}\) monolayers, obtained from first-principles simulations. The computed contact resistance at graphene/2H-MoS\({}_{2}\) junctions varies between 10\({}^{2}\) and 10\({}^{4}\)\(\Omega\cdot\upmu\)m and strongly depends on the edge contact symmetry (zigzag or armchair) and termination (Mo and/or S terminated); these results are correlated to the presence or absence of dangling bond defects and/or polar bonds at/near the interface. On the other hand, the contact resistance at pristine 1T/2H-MoScontacts weakly depends on the edge contact symmetry, and it is about 3-4 \(\times\) 10\({}^{4}\)\(\Omega\)-\(\mu\)m, due to the large electron energy barrier at the interface. The functionalization of metallic 1T-MoS2, by various adsorbates, was found to decrease the contact resistance by about two orders of magnitude.