esa-sceva/satcom-chunk-collection huggingface dataset:
# Contact Fatigue State Identification of Specim Based on Heterogeneous Data and Evidence Theory
## 3 Contact Fatigue State Identification
### Contact Fatigue State Identification of Specim  
#### 3.4.1 Vibration Signal Processing  
According to \"YB/T 5345-2014 Rolling Contact Fatigue Test Method for Metallic Materials\", the criteria for judging rolling contact fatigue failure are as follows [30]: 1 The deep peeling area is greater than or equal to 3 mm2; 2 When the pitting is peeled off (concentrated area), the damage with a pitting rate of 15% occurs within a 10 mm2 area. In this paper, aiming at the first criteria, the fatigue state of the sample is divided into three states: normal, medium and failure, which are represented by (0, 1, 2), respectively, as shown in Table 8, where 0.01 mm2 is the visual method for the smallest damage area that can be detected [31].  
For three different states of the specimen, each state contains 1200 sets of sample data, and each set contains 1024 vibration data points. According to the ratio of 9:2:1, the training set, test set and validation set are randomly divided, and each state contains 900 sets of training samples, 200 sets of test samples and 100 sets of validation samples, as shown in Table 9.
## 3 Application of Automatic Optimization System
### Build Automatic Optimization System Framework  
After completing the preliminary work, including physical model modeling, building optimization equations, and recording macro files, the following steps are carried out according to the automatic optimization system framework:  
(1) Initialize the population using the LHS method and pass the chromosome information of each individual containing the position encoding information of 14 heat sources to the NSGA-II algorithm.  
Figure 6: Comparison of sampling results.  
(2) Decode the chromosome information of individuals using NSGA-II, call the MATLAB program through the DOS commands to pass the distribution parameters of 14 heat source positions, and output a new MACRO file.  
(3) Open NX/SST to execute the macro file with the new heat source information, automate the change of the heat source positions on the PCB board in the physical model. Wait and continuously monitor if the NX/SST simulation is completed.  
(4) After the simulation is finished, output temperature values of 100 PCB grid data points. Call a Python program to read the temperature file and process the data, calculate the maximum value of 36 temperature values and the reciprocal of the Mean Squared Error (MSE) as the fitness value in the fitness function.  
(5) Transmit the calculated results back to the optimization algorithm as the fitness value of the individual. After all individuals in the population have obtained fitness values, NSGA-II performs fast non-dominated sorting on the population.  
(6) Through operations such as crossover, mutation, and selection among parent populations, a new generation subpopulation is generated. Perform fast non-dominated sorting on the combined set of parent and offspring populations and select the top 40 high-fitness individuals as the new generation population.  
(7) Repeat the above iterative calculations until the fittest individual in the population is selected. The chromosome encoding information represents the distribution of the optimized 14 heat sources on the PCB board.  
The data flow of files, data, parameters, etc., in each step is shown in Figure 7, and the hyperparameter settings of the NSGA-II algorithm are detailed in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Option** & **Value** \\ \hline Population size & 40 \\ Number of generations & 40 \\ Crossover probability & 0.9 \\ Crossover distribution index & 10.0 \\ Mutation distribution index & 20.0 \\ Max fail runs & 5 \\ Failed run penalty value & 1.0 E30 \\ Failed run objective value & 1.0 E30 \\ Initialization mode & LHS \\ \hline \hline \end{tabular}
\end{table}
Table 4: NSGA-II correlated hyper parameters.  
Figure 7: Data flow linking different pieces of software.  
This section provides a detailed description of the relevant parameters and installation restrictions of the MCM, along with the specific technical processes employed by the automatic system in its application. It particularly focuses on the detailed structure and technical processes of the optimization module within the automatic system.
## 1 Introduction
### Paper Contributions  
To optimize the architecture of the DCNN, we develop a novel hybrid algorithm that combines artificial bee colony (ABC) [20; 21; 22], biogeography-based optimization (BBO) [23; 24; 25; 26; 27; 28], and particle swarm optimization (PSO) techniques. This work proposes a novel approach for improving the detection performance of BC. The proposed algorithm is capable of efficiently searching the vast and complex space of DCNN architectures, identifying the most effective architecture for improving the detection performance of BC. We then train the optimized DCNN using a set of benchmark datasets, and demonstrate the efficacy of our approach by showing that it outperforms existing methods in training benchmark datasets and significantly improves the BER performance of the studied BC system model. Our paper's contributions include proposing a novel approach to improving the detection performance of BC systems, developing a hybrid algorithm for optimizing DCNN architectures, and demonstrating the efficacy of our approach in improving the BER performance of BC systems.  
The major contributions of this paper can be summarized as:  
* We introduce a novel hybrid optimization algorithm named HABC based on ABC, BBO, and PSO, in which new agents such as neighborhood search, the migration of bees, and movement towards the global best are introduced to improve the exploitation and exploration abilities of the ABC algorithm. In the proposed HABC, the migration operator of the BBO is used to improve the exploitation of the algorithm. Moving towards the global best of PSO is also proposed to improve the exploration of the ABC;
* We use HABC to adjust the optimization parameters of the DCNN and significantly enhance the detection accuracy of the DCNN as the BD signal detector;
* Extensive experiments are carried out using both benchmark functions and BC sample data [29]. The results demonstrate that the suggested HABC-DCNN signal detection technique can attain exceptional BER efficiency in comparison with the related works. The simulation results for well-known real-world benchmark datasets show that the proposed HABC algorithm achieves a better performance compared with some other more well-known algorithms.
## 4 Results and Discussions
### Implementation Challenges
#### 4.4.3 Organizational and Human-Centric Resistance  
The implementation of advanced digital technologies is frequently obstructed by internal organizational resistance and the inherent technical and systemic complexities involved. Table 13 provides a comprehensive overview of the organizational and human-centric \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Barrier** & **Sub-barrier** & **Description** & **Reference** \\ \hline Cybersecurity and System Vulnerabilities & & - & Advanced digital systems (CPS, DTs, IoT, XR) face expanding security concerns from equipment failures to software and control vulnerabilities. & [3; 9; 12; 18; 35; 81] \\ Cybersecurity and System Vulnerabilities & & - & Attacks (e.g., ransomware) compromise automated safety systems, risking workers and operations, and causing financial and societal losses. & [7; 8; 9; 52; 53; 81; 85] \\ Cybersecurity and System Vulnerabilities & & - & Complexities include lack of standardized protocols, unprotected remote configs, and integrating socio-technical systems. & [4; 9; 15; 18; 33; 53] \\ Cyber-attacks and attack surface & & - & DTs require high-quality, real-time data, which is difficult to collect and manage, leading to flawed predictions if inaccurate. & [7; 8; 9; 52; 53; 81; 85] \\ \hline Data Management and Protection & Limitations of security measures & - & Advanced systems like VR, XR, and DTs demand high cognitive effort, cause memory retention issues, and increase stress. & [4; 9; 15; 18; 38; 53; 81] \\ Cloud and edge computing trade-offs & - & Cloud computing risks data leakage and is limited by internet reliability & [2; 4; 9; 12; 18; 34; 72] \\ \hline \multirow{7}{*}{Human and Ethical Concerns and trust} & & - & Advanced technologies (XR, wearables, sensors) collect sensitive personal data (biometrics, emotions), raising ethical and privacy concerns regarding autonomy and arsenus (internal and external) & [4; 12; 18; 22; 33; 34; 79] \\ \cline{1-1}  & & - & Monitoring technologies can increase workplace stress due to pressure to share personal data and adapt to new digital environments, impacting employee trust and privacy. & [2; 5; 12; 16; 53; 64; 80] \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Key barriers related to cybersecurity, data management, privacy, and ethical concerns. resistance that hinders the adoption and effectiveness of advanced digital technologies in industrial settings. This resistance is not singular but multifaceted, arising from technical complexities, workforce unpreparedness, and health and safety concerns. Organizational and technical resistance often arises when digital transformation advances faster than the workforce can adapt. New technologies demand new knowledge and skills, which workers may struggle to acquire quickly [5]. Similarly, a shortage of digitally skilled workers is a major challenge that can increase workload pressure (additional strain) on existing employees [85], especially older ones [36], who struggle to acquire the new knowledge and competencies required, leading to increased workload pressure
# A Review on Data-Driven Condition Monitoring of Industrial Equipment
## 2 Data-Driven Condition Monitoring of Industrial Equipment
### Fault Diagnosis and Prediction
#### 2.2.1 Method Based on Shallow Machine Learning
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & **Application** &
\begin{tabular}{c} **Type of** \\ **Equipment** \\ \end{tabular} & **Signal** & **Fault Type** \\ \hline
[60] & Fault diagnosis & \
\begin{tabular}{c} Permanent \\ magnet \\ synchronous \\ motor \\ \end{tabular} &
\begin{tabular}{c} Current, \\ voltage signal \\ and speed \\ signal \\ \end{tabular} & Static and dynamic eccentricity fault \\
[109] & Fault diagnosis & \
\begin{tabular}{c} Induction \\ motor \\ \end{tabular} &
\begin{tabular}{c} Current and \\ voltage signal \\ \end{tabular} & Inter-turn short-circuits, rotor, and bearing faults \\
[118] & Fault diagnosis & \begin{tabular}{c} Induction \\ motor \\ \end{tabular} & \
\begin{tabular}{c} Current and \\ vibration signal \\ \end{tabular} &
\begin{tabular}{c} Stator winding faults \\ \end{tabular} \\
[121] & Fault prognosis & \begin{tabular}{c} Induction \\ motor \\ \end{tabular} & \
\begin{tabular}{c} Vibration and \\ current signal \\ \end{tabular} &
\begin{tabular}{c} Bearing fault, unbalanced rotor fault, bowed rotor fault, \\ rotor misalignment fault, broken-rotor bar fault, phase \\ unbalance and single phasing fault with high resistance, \\ phase unbalance and single phasing fault with low resistance, \\ resistance, stator winding fault with high resistance \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Motor condition monitoring using support vector machine.
## 4 Simulation Results, Theoretical Calculation, and Analysis
### Performance Comparison under Different Transmission Schemes
* If the benchmark of the simulation was the traditional cellular network (Case 1), then the CDF of the latency enhancement gain under different \(\alpha\) and \(\beta\) values (Figure 5a) showed that most of the UE had a large gain. Due to the lack of cooperation node assistance, the latency of the no cooperation node scenario differed from other schemes when the threshold SNR was at a lower level. At the same time, more UE needed to transmit more times before the BS could successfully decode the data. Therefore, the schemes mentioned earlier had more UE with higher latency gain in the obtained CDF curve. * If the benchmark of the simulation was the traditional relay network (Case 2 two combination), then the CDF of the latency enhancement gain under different \(\alpha\) and \(\beta\) values (Figure 5b) indicated that the assistance of cooperation nodes could reduce latency, and thus the increase in the final result was less than the previous one. With the increment of \(\alpha\) and \(\beta\), more UE in the cell chose the multi-path transmission scheme for information transmission. Therefore, there was more UE with higher gain of latency performance. Furthermore, Figure 6 exhibits the gain of different distances under Case 2 with the combination, multi-path transmission, and AUA schemes when \(\alpha=\beta=3\) dB and \(\alpha=\beta=4\) dB. With the increase in \(\alpha\) and \(\beta\), more and more UE in the cell switched from Case 2 with the combination scheme to the multi-path transmission scheme. This range continued to spread from the cooperation nodes' positions to the ends. However, although the larger values of \(\alpha\) and \(\beta\) could result in better latency performance, the performance of energy consumption was quite terrible as the values of \(\alpha\) and \(\beta\) increased, as shown in Figure 7. Therefore, a suitable measurement standard is required to measure the performance of the whole system, which will be discussed in the next section. Figure 5: CDF of latency enhancement gain under different schemes (\(M=1\)). (**a**) Benchmark: Case 1 (traditional cellular network). (**b**) Benchmark: Case 2 (traditional relay network).
## 4 Results and Discussions
### Evaluation of the Monitoring Messaging Methods  
#### 4.1.1 The Message Size  
Monitoring messaging using PROTO instead of PMT results in a smaller serialized message size. This is because message field names and value types do not need to be sent. We analyzed the message sizes for all three messaging method implementations described in Section 3.3 for different numbers of fields in a message. Figure 15 shows the significant difference in message size between the PMT and PROTO methods, which increases with the number of fields in the message. PROTO-ANY and PROTO-BLO generate messages with similar sizes regardless of the number of fields in the message.
# Biomass and Cellulose Dissolution--The Important Issue in Renewable Materials Treatment
## 2 General Characteristics of Lignocellulosic Biomass  
The three main components of lignocellulosic biomass are cellulose (35-83% of dry mass), hemicelluloses (0-30%), and lignin (0-43%) [4; 5; 6]. It also contains small amounts of other molecules such as proteins, pectins, waxes, and minerals. The type and content of these ingredients depend on the type of plant, e.g., grain husks (rice, wheat) and corn cobs  
Figure 1: Possible methods of biomass processing in chemical industry.  
Figure 2: Number of publications for “lignocellulosic biomass” term (according to ScienceDirect).  
are rich in silicon dioxide, while carbonates and phosphates can be commonly found in wood (e.g., willow) [7; 8; 9].  
The content of cellulose, hemicelluloses, and lignin depends on the type of plant, as well as its part, age, and cultivating conditions. Generally, wooden species tend to have a higher amount of lignin compared to green and softer plants such as grass. In Table 1, the content of cellulose, hemicelluloses, and lignin in several examples of different plants is presented.  
Cellulose is a structural component of cell walls and occurs as microfibrils. A higher number of tightly packed cellulose chains increases the mechanical strength, as can be observed in hard biomass (e.g., wood) [21; 22; 23]. Physical resistance is also improved by hemicelluloses, which are responsible for binding cellulose fibers to lignin [24; 25]. The major chemical, enzymatic, and mechanical resistance of plant cells is provided by lignin, which is present in the external part of lignocellulosic [26; 27]. The general structure of lignocellulosic biomass is presented in Figure 3.
## 4 IETF 6 LoWPAN Working Group (IPv6)
### Key Protocols
#### 4.1.1 6 LoWPAN Frames  
RFC 4944 states that all LoWPAN encapsulated datagrams are prefixed with an encapsulation header stack where each header in the header stack contains a header type and zero or more header fields. The LoWPAN header may contain the mesh addressing header, fragmentation header, and IPv6 Header compression header, in that order. Figure 4 shows examples of header stacks that might be used in 6 LoWPAN. These frames are transported inside the frame payload field of an 802.15.4 data MPDU.  
Similar to IPv6, the 6 LoWPAN headers are added when required. The first byte of the encapsulation header, the dispatch byte, identifies the next header. More specifically, the first three bits of the dispatch byte indicate the next header type while the remaining bits are used for different purposes depending on the header type. Table 1 summarizes the different values for the dispatch byte.  
\begin{table}
\begin{tabular}{|c c c|} \hline
**First 3 Bits** & **Header Type** & **Description** \\ \hline \hline
**00x** & NLAP & This is not a 6 LoWPAN frame. This is important for 6 LoWPAN to co-exist with other protocols. The remaining 6 bits are ignored. \\ \hline
**010** & Uncompressed/HC1 & The address type is determined depending on the remaining 5 bits. \\  & Compressed & E.g.: \(\quad\) 00001 = uncompressed IPv6 Address \\  & IPv6 Addressing Header & \(\quad\) 00010 = HC1 Compressed Header \\ \hline
**011** & IPHC Compressed Header & The remaining 5 bits are added to the rest of IPHC compression header to optimize IPv6 header compression \\ \hline
**10x** & Mesh Header & The next header is the mesh header. The last bits are used for other purposes related to mesh-under routing. \\ \hline
**11x** & Fragmentation Header & The next header is a fragment header. The fragment type is determined by the remaining 6 bits. The bit sequence 000 xxx indicates first fragment while 100 xxx indicates Non-first fragments. The last three bits in both types of fragments will be used for other purposes. The other bit sequences are reserved. \\ \hline \end{tabular}
\end{table}
Table 1: Summary of Dispatch Byte values.  
Figure 4: 6 LoWPAN encapsulation header stack examples.
# Forced Circulation of Nitrogen Gas for Accelerated and Eco-Friendly Cooling of Metallic Parts
## 2 Materials and Methods  
### Materials  
The tubular workpiece used in this study was made of THA having 5 wt%Ni-2 wt%Fe binder metals. The as-received powders were de-agglomerated using a vibrating 200 mesh screen. The elemental powders of W, Ni, and Fe were weighed according to the target compositions and blended using a 20-rpm blender for 2.5 h to achieve a homogeneous distribution. The blended tungsten alloy powders were ball-milled for 5 h in a jar filled with 1.0-1.5 inch balls under an argon environment to break up the agglomerates, provide rigorous mixing, and uniformly disperse the elemental powders [43; 44]. After the ball milling, the blended powders are crushed again using a Fitz mill, which committees the powders with blades rotating at 3450 rpm. After the Fitz milling process, the powders were screened using a vibrating 150 mesh screen and blended again by a 20-rpm blender for 1.5 h to obtain a homogeneous powder. The THA powders are difficult to compact due to their high hardness and tendency of minimal deformation under pressure [21]. In this study, milled powders were placed in a flexible rubber mold and subjected to hydrostatic pressure for achieving a more uniform compaction and therefore density. The assembly was then pressurized hydrostatically in a chamber under 195 MPa hydrostatic pressure for 20 s. A tubular powder compact was prepared using the cold isostatic pressing (CIP) method, and liquid phase sintered. Figure 1 shows the process sequence adopted for fabricating the THA tube through the LPS process. The \(D_{i}\), \(D_{o}\), and \(L\) of the tubular workpiece are 0.168 m, 0.204 m, and 0.210 m, respectively.  
The microstructural evolution and mechanical properties of sintered THAs can be adjusted using the post-heat treatment. Particularly, the cooling rate of post-heat treatment must be controlled to enhance the mechanical properties of THSs [24; 25]. A slow cooling in the furnace to room temperature could result in an enhanced growth of tungsten (W) spheroids, determining the decrease of mechanical properties of THAs [21; 22; 23]. Therefore, faster cooling is advantageous to enhance mechanical properties of sintered THAs. In this study, sintered THA has been heat treated and cooled by nitrogen gas to estimate the performance of the proposed nitrogen cooling system.
## 1 Introduction
Third, the broadband absorption (6.0-16.7 GHz, absorptance > 85%) of the absorber is realized by using simple structure and preparation technology. In addition, our absorber has advantages of polarization insensitiveness and wide incidence angle stability. The excellent performance of the absorber has been demonstrated by simulation and experiment, and it promises to be a good candidate for various practical applications, such as the observation windows of communication vehicles, electromagnetic shielding of rooms and electronic toll collection (ETC) systems.
## 3 Fast Hyperbolic Hough Transform
### Hyperbolic Hough Transform
As a result, small measurement errors can lead to significant differences in the estimate, resulting in lower confidence in the location estimate. Notes:  
* In practice, the exact values of the measurement variances are usually not known. In such cases, \(\sigma_{i,1}^{2}\) can serve as an estimate of the true value. If the estimated value is smaller than the true variance then the source position may be outside of the skirt of the generated \"wide hyperbola\" \(H_{i,1}\). On the other side, if the estimated variance is higher than the true value then the source position is safely included inside \(H_{i,1}\). Therefore, in practice, the estimate \(\sigma_{i,1}^{2}\) should be an upper estimate of the true variance. * In the 2.5D case, the shape of \(H_{i,1}\) is not necessarily a hyperbola, but rather a general intersection of a 3-dimensional hyperboloid surface and the plane of the source. Similarly to other variants of Hough transform, the HHT can be evaluated on a grid. The size of the grid has important implications: 1. If the grid size is large, the maximum may be missed; 2. The grid size determines possible accuracy: a smaller grid size can provide higher accuracy; 3. Smaller grid size implies a higher computational cost. Note that if the grid contains \(M\times M\) points, then the number of likelihood function evaluations will be \(M^{2}\), and the search for the maximum will also require \(M^{2}\) (albeit simpler) operations. To improve calculation speed, in [31] the randomized R-HHT was proposed. However, this method faces an obstacle resulting from implication (a): the number of required sampling points to achieve a high probability of finding the global maximum remains high; the improvement, reported in [31], was not better than twofold. The hybrid H-HHT method [31] increases the accuracy and reduces the number of trials, using a fine-grid search in the second phase. In the next subsection, the fast HHT will be introduced, which  
Figure 2: HHT of a scenario with emitter \(E\) (blue cross) and sensors \(S_{1}\) (reference),\(S_{2},\ S_{3}\), and \(S_{4}\) (red crosses), placed on the same plane. Hyperbolas generated by \(S_{i}\) and \(S_{1}\) are denoted by \(H_{i,1}\). (**a**) Source at a position with good GDOP and (**b**) bad GDOP. not only significantly reduces the computational costs but also guarantees that the optimum on the given grid is found.