esa-sceva/satcom-chunk-collection huggingface dataset:
# The Study of Multi-Stage Cold Forming Process for the Manufacture of Relief Valve Regulating Nuts
## 4 Conclusions  
In this study, a multi-stage cold-forming process for the manufacturing of relief valve regulating nuts was carried out both numerically and experimentally. The forming process through five stages included the preparation and centering for backward extrusion, backward extrusion over a die pin, upsetting, backward extrusion over the moving punch, and piercing. The cold-forming experiments were carried out on a 20-tonne universal testing machine using the designed forming dies. The finite element code of DEFORM-3D was used for the numerical simulation analysis of the five-stage cold forming. The formability of the workpiece was investigated, such as the effect on the forming force response, maximum forming force, effective stress and effective strain distribution, metal flow patterns, and strength.  
A comparison between the forming forces obtained in the forming experiment and the numerical simulation results for the five-stage cold forming showed that the forming force responses were in good agreement. For the maximum forming force and forming energy, the fourth stage of the backward extrusion over the moving punch at the upper face was the largest among the five stages. The total maximum forming forces from the first to the fifth stages were numerically 440.9 kN and experimentally 449.4 kN, and the total forming energies were numerically about 1.174 kJ and experimentally about 1.458 kJ. The numerical simulations and experimental results were in good agreement. The numerically simulated effective strain distributions were consistent with the experimentally tested hardness distribution. Highly compacted grain flow lines also resulted in higher hardness. The overall hardness of the workpiece (average 207.6 HV) formed by five-stage cold forming increased by 31% compared to the initial billet. The hardness of the workpiece increased with the forming stages, and the strain-hardening effect was obvious. The strength of the workpiece was significantly increased by the five-stage cold forming. This effect can be applied to replace medium carbon or alloy steels with low carbon steels if not heat treated. This study provides an effective reference for designers.  
Conceptualization, C.-C.-Y. and C.-H.L.; methodology, C.-C.Y.; validation, C.-C.-Y.; investigation, C.-C.Y. and C.-H.L.; resources, C.-H.L.; data curation, C.-H.L.; writing--original draft preparation, C.-C.Y.; writing--review and editing, C.-C.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Acknowledgments:** The authors would like to acknowledge the support of Jie Shuen Industrial Co., Ltd., Kaohsiung, Taiwan, for providing the materials to carry out the forming experimental work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Fault Injection Emulation for Systems in FPGAs: Tools, Techniques and Methodology, a Tutorial
###### Abstract  
Communication systems that work in jeopardized environments such as space are affected by soft errors that can cause malfunctions in the behavior of the circuits such as, for example, single event upsets (SEUs) or multiple bit upsets (MBUs). In order to avoid this erroneous functioning, this kind of systems are usually protected using redundant logic such as triple modular redundancy (TMR) or error correction codes (ECCs). After the implementation of the protected modules, the communication modules must be tested to assess the achieved reliability. These tests could be driven into accelerator facilities through ionization processes or they can be performed using fault injection tools based on software simulation such as the SEUs simulation tool (SST), or based on field-programmable gate array (FPGA) emulation like the one described in this work. In this paper, a tutorial for the setup of a fault injection emulation platform based on the Xilinx soft error mitigation (SEM) intellectual property (IP) controller is depicted step by step, showing a complete cycle. To illustrate this procedure, an online repository with a complete project and a step-by-step guide is provided, using as device under test a classical communication component such as a finite impulse response (FIR) filter. Finally, the integration of the automatic configuration memory error-injection (ACME) tool to speed up the fault injection process is explained in detail at the end of the paper.  
communication modules; emulation; fault injection debugger; FIR filter; SEM IP; SEU; Xilinx
## 5 EI-RNN Optimization and Analysis
### EI-RNN Compression
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Compression Hyperparameters**} & \multicolumn{5}{c}{**Evaluation Metrics**} & \multicolumn{5}{c}{**Description**} \\ \hline
**uRank** & **wRank** & **sU** & **sW** &
\begin{tabular}{c} **Model\_Size** \\ **(KB)** \\ \end{tabular} & **train\_acc** & **val\_acc** & **test\_acc** & **F1 Score** & \\ \hline - & - & 1 & 1 & 2.03 & 0.95 & 0.95 & 0.94 & 0.94 & Baseline \\ \hline
8 & 8 & 1 & 1 & 2.25 & 0.93 & 0.93 & 0.935 & 0.93 & Low rank parameterization (LRP) \\ \hline - & - & 0.9 & 0.9 & 2.03 & 0.94 & 0.94 & 0.93 & 0.94 & Hard thresholding and sparse retraining (HTSR) \\ \hline
12 & 12 & 0.8 & 0.8 & 3.20 & 0.94 & 0.95 & 0.932 & 0.9346 & Combination of both LRP and HTSR \\ \hline
8 & 8 & 0.9 & 0.9 & 1.88 & 0.93 & 0.93 & 0.92 & 0.92 & Our choice for performance and memory savings \\ \hline
16 & 16 & 0.5 & 0.5 & 4.03 & 0.94 & 0.94 & 0.92 & 0.94 & Performance-efficient \\ \hline
4 & 4 & 0.8 & 0.8 & 1.3 & 0.91 & 0.91 & 0.91 & 0.91 & Memory-efficient \\ \hline
10 & 10 & 0.5 & 0.5 & 2.76 & 0.92 & 0.91 & 0.91 & 0.919 & Other trials \\ \hline
12 & 12 & 0.4 & 0.4 & 2.61 & 0.93 & 0.94 & 0.92 & 0.93 & Other trails \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Compression techniques and hyperparameter checks for Fast Recurrent Neural Network or FastRNN (with 16 hidden units, 32 batch size, 300 epochs, root mean square (or RMS Prop optimizer) on SPHAR dataset for performance and memory optimization.
## 6 Simulation Results  
This section is divided into three subsections. In the first and second subsections, the performance of the proposed HABC in real-world engineering problems is evaluated. In the third subsection, the performance of the proposed HABC-DCNN is evaluated. To evaluate the performance of HABC, five well-known and state-of-the-art competitive algorithms called PSO, BBO, ABC, orchard algorithm (OA) [22], and grey wolf optimizer (GWO) [43] are used. To evaluate the performance of HABC-DCNN, two DL architectures called long short-term memory (LSTM) and recurrent neural network (RNN), and three ML models from previous studies are used. All algorithms were coded in MATLAB.  
Meta-heuristic algorithms require calibration of various parameters. Thus, it is crucial to identify the optimal parameter combination prior to evaluating the algorithm's performance. The paper employs a trial-and-error approach to adjust parameter calibration, where each parameter is tested with different values while keeping other variables constant. The fitness function is the primary criterion used to measure and calibrate the algorithm's parameters. Although a large number of values were tested for each calibration parameter, only a limited number of instances were selected and are presented in Table 1.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Algorithm** & **Parameter** & **Value** \\ \hline \multirow{6}{*}{HABC} & The global movement rate (\(\gamma\)) & 0.14 \\ \cline{2-3}  & The probability range for migrating into each gene & [0, 1] \\ \cline{2-3}  & Maximum emigration (I) and immigration (E) coefficient & 1 \\ \cline{2-3}  & Number of onlooker bees & 80 \\ \cline{2-3}  & Number of employed bees & 120 \\ \cline{2-3}  & Number of scout bees (population size) & 150 \\ \cline{2-3}  & Iteration & 300 \\ \hline \multirow{6}{*}{OA} & N\_high & 50 \\ \cline{2-3}  & N\_low & 40 \\ \cline{2-3}  & N\_trans & 60 \\ \cline{2-3}  & \(\alpha\) & 0.7 \\ \cline{2-3}  & \(\beta\) & 0.3 \\ \cline{2-3}  & Population size & 150 \\ \cline{2-3}  & Iteration & 300 \\ \hline \multirow{6}{*}{GWO} & C & 0.7 \\ \cline{2-3}  & A & 0.3 \\ \cline{2-3}  & \(\alpha\) & [0, 2] \\ \cline{2-3}  & Population size & 150 \\ \cline{2-3}  & Iteration & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter setting by trial-and-error method.  
Figure 5: An example of bee definition for DCNN training.  
Table 1 displays the optimal parameter values for HABC and other algorithms. The results indicate that a global movement rate of 0.14 yielded the best outcome, as larger values resulted in reduced convergence. The migration probability range of [0, 1] produced the most favorable results. Additionally, the ideal numbers of onlooker and employed bees were determined to be 80 and 120, respectively. The algorithm initially failed to identify an appropriate solution with a population of 40 scout bees, but increasing the population to 110 bees improved the fitness functions of the algorithm. Ultimately, with a population of 150 bees, the algorithm was able to identify superior solutions.
## 3 Numerical Examples  
This section uses benchmark examples to verify the precision and distinguishability of the FD-WENO-URLBFS scheme, comparing it with LF, ROE, and LBFS schemes. The examples include density perturbations, Shock-tube problems, Shu-Osher problems, blast wave problems, several configurations of the Riemann problem, double Mach reflections, and KH instability problems. All schemes use WENO5 and WENO7 schemes to reconstruct interface flow variables, respectively. Unless otherwise stated, all test cases use third-order TVD Runge-Kutta time integration.
## 6 Experimental Results
### Schedulability Test  
Figure 6 shows the success ratio of all algorithms when varying callback deadlines. For each deadline interval \([a,b]\), we run all algorithms on sets of 1000 callbacks with a cardinality of 100 callbacks per set and a CPU utilization factor of 90%.  
Figure 6 shows that the proposed algorithm significantly outperforms both the RMS and the GBFS algorithms in terms of the success ratio. We observe that the ratio decreases as the deadlines become shorter (stressed system). RMS algorithms quickly fail to schedule callbacks when the proposed algorithm continues to provide feasible solutions under tight deadlines. However, LL provides the same success ratio as the proposed algorithm, since it applies the same schedulability test.
## 1 Introduction
ANNs are proficient in the recognition of complicated characteristics using numerous parameters [41] and are frequently applied in the structural and stratigraphic identification and interpretation of seismic features [50]. Fault attributes are accompanied and followed by an ant-tracking attribute to interpret the discontinuities of the seismic amplitude [11; 51; 32; 52]. Ant-colony optimization (ACO) is an efficient method for detecting small scale faults and fractures within the reservoirs, and it is more reliable, accurate, and intuitive than other methods [12; 53; 54; 55]. Ant-tracking technology is a proficient tool in interpreting and augmenting the production of deployed fields [53]. The identification of small scale faults plays a vital role in detecting promising well-locations and explaining injection contradictions [8]. The small scale fault structure helps in identifying remaining reserves by analyzing fracture development using the ant-tracking method [56]. The application of an ant-colony optimization algorithm conveys the basis for the future development and exploration of remaining hydrocarbons, and it also gives a reference for analyzing the micro-scale faults and fractures of similar reservoirs. Multiple studies have been carried out to explore and classify reservoir hydrocarbons. Still, the vast majority remains unexplored due to insufficient attention to comprehend the fracture network of reservoir sands. Several authors have studied thin sections to illustrate the presence of fractures in their study areas [57; 58]. In some scenarios of these exploration ventures, the geophysicists and geologists have had solitary seismic data for interpretation. Due to the absence of well logs, image logs, and core data in these situations, unconventional seismic attributes and unsupervised neural networks play a vital role in characterizing fault and fracture networks by using conventional seismic data. Therefore, the present research aimed to provide an innovative workflow of data conditioning by using integrated geostatistical filtering for the dip and azimuth calculation, as well as structural filtering for the enhancement of structural features such as faults and fractures. Moreover, the study also provides an advanced workflow using the multi-attribute analysis, neural networks, and artificial ant-tracking for the identification of fracture parameters. Additionally, the current study also aimed to identify the small scale fault and network of fractures in reservoir sands using 3D seismic data within the Sawan gas field of the Lower Indus Basin, which has been previously overlooked. The methods applied in the study for the delineation of the small scale faults and fractures incorporated the combined use of data conditioning, multi-attribute analysis (using dip, curvature, variance, similarity, thinned-fault likelihood, fracture density, and fracture proximity), ANNs, and the ACO approach. The applied ant-colony optimization algorithm was followed by the automatic fault extraction technology to reveal the dip azimuth, dip angle, fracture length, and surface area of the fractures.
# Graph Representation-Based Deep Multi-View Semantic Similarity Learning Model for Recommendation
## 3 Problem Definition
### User-Based Collaborative Filtering Recommendation Algorithm  
The user-based collaborative filtering algorithm assumes that similar users have similar preferences, and the procedure is: (1) find users similar to the current active users by some kind of similarity metric; (2) use the weighted average of similar users' ratings of the target item to predict the current active users' ratings of the target item. Adopting appropriate metrics to calculate the similarity between users is key to user-based collaborative filtering algorithms. Typical similarity metrics include cosine similarity, Pearson's correlation coefficient, and adjusted cosine similarity. For example, according to the cosine similarity metric, the similarity between users \(u,v\) is defined as:  
\[sim(u,v)=cos(u,v)=\frac{\sum_{i}\gamma_{ui}\times\gamma_{vi}}{\sqrt{\sum_{i} \gamma_{ui}^{2}}\times\sqrt{\sum_{i}\gamma_{vi}^{2}}}, \tag{1}\]  
where \(i\) is the item that users \(u,v\) have jointly rated.  
After the user-based collaborative filtering algorithm calculates the similarity between users according to some similarity metric, it finds the set of users that are most similar to the currently active users. Then, predict user \(u\)'s rating of item \(j\) according to Equation (2).  
\[\gamma_{uj}^{R}=\gamma_{iu}+\frac{\sum_{v\in D}sim(u,v)\times(\gamma_{vj}- \gamma_{v})}{|\sum_{v\in D}sim(u,v)|}, \tag{2}\]where \(\vec{\gamma_{u}}\) denotes the average rating of user \(u\) for all rated items,\(\vec{\tau_{v}}\) denotes the average rating of user \(v\) for all rated items, and \(D\) denotes the set of users most similar to user \(u\).  
Finally, the user-based collaborative filtering algorithm recommends the \(K\) items with the highest prediction scores to the current user.
# Landslide Displacement Prediction Based on Variational Mode Decomposition and GA-Elman Model
## 4 Application Research and Method Comparison
### Prediction of Random Displacement  
After the random item component dataset was substituted into the model, and the operation results were generated, as shown in Figure 10a-d.  
The evaluation indices in Table 5 show that, as the data in the training set increased, the model prediction accuracy went through a trend of increasing, then decreasing, and then increasing again. The best operation results were generated by Model 4, i.e., when the training set data volume was 80 weeks. Here, MAPE was 13.73%, MSE was 0.0007, RMSE was 0.0261, and R\({}^{2}\) was 0.9765. Moreover, the random displacement term accounted for a small proportion in the total displacement, and the influencing factors were difficult to identify.  
Figure 10: Random displacement prediction results of different training sets: (**a**) prediction results of Model 1; (**b**) prediction results of Model 2; (**c**) prediction results of Model 3; (**d**) prediction results of Model 4.
# Genomic and Metabolic Characteristics of the Pathogenicity in Pseudomonas aeruginosa
## 2 Pseudomonas aeruginosa
### Bacterial Pathogenesis  
_P. aeruginosa_ is adaptable to different environments and metabolically versatile, covering a wide variety of habitats, including the human body, soil, water, hospital environment, drains, but also other water-rich locations (swimming pools), as well as non-householdenvironments (such as river water), and can also be isolated from vegetables [12; 13; 14; 15]. This is due to the minimum nutritional requirements necessary for the growth of this bacterium and its ability to survive under a variety of environmental and physical conditions [11; 14]. The dissemination of new infections that come from the environment and hospitals requires special attention in order to minimize the chance of nosocomial infection by this pathogen [13; 16].  
The ubiquity of _P. aeruginosa_ in nature comes from its natural ability to use several mechanisms to resist against other organisms. For example, when using the type III secretion system to directly inject cytotoxic effector proteins into the cytosol of eukaryotic cells, a polysaccharide-encased community (a biofilm) of _P. aeruginosa_ can be formed and this community can resist to predation by protozoa [17; 18].  
The infection potential of _P. aeruginosa_ comes in part from the presence of virulence factors and the ability to metabolize many antibiotics (encoded in genes that are collectively organized in genomic islands), consequently, promoting infectivity potential [19; 20; 21]. In general, virulence factors can be classified as cell- associated or extracellular virulence factors [22; 23], both being secreted via secretion systems. Cell-associated virulence factors are important for the bacterial colonization process and they are secreted and bound to the cell envelope (inner or outer membrane) [18]. On the other hand, extracellular virulence factors are released by _P. aeruginosa_ after colonization and they are secreted into the extracellular medium (exoproteins) [18].
# Enhanced Routing Algorithm Based on Reinforcement Machine Learning--A Case of VoIP Service
## 3 The Proposed e-RLRP Algorithm
### Algorithm Used in the e-RLRP to Reduce the Overhead
To this end, there is a function named Update Neighbors File responsible for updating the list of neighbors every time a Hello from a new node is received. And there is a other function named Check Expired Neighbors that checks if a Hello message has been received from neighbors every 7 s, if a neighbor is 7 s or more without sending a Hello, it is removed from the list because it is out of reach. This interval of time was defined experimentally in Reference [40]. In case, a new neighbor is detected or an existing one is lost, it will be considered that there is mobility in the network. In the proposed e-RLRP, when mobility occurs, the \(BI\) value will be reduced to a lower limit called \(BI\) Lower Limit (\(BI_{\text{LL}}\)), the algorithm waits for a new time interval, sends a message and restarts the process. If a mobility event does not occur, the \(BI\) parameter will be increased with the Adjustment Factor (AF) parameter respecting the upper limit called \(BI\) Upper Limit (\(BI_{\text{UL}}\)). When the time defined by \(BI\) is reached, a Hello message  
Figure 2: Scheme of the dynamic adjustment algorithm proposed. will be sent and the process is restarted. Hence, the frequency of sending Hello messages is adjusted according to the mobility of the network  
It is worth mentioning that the proposed dynamic adjustment is not based on RL, because RL uses more computational resources.
## 3 Results and Discussion
### Mass and Energy Yield of the Biopellets by Torrefaction  
The mass and energy yields of torrefied biomass are important factors to consider when designing and operating torrefaction reactors and when evaluating the potential of torrefied biomass as a fuel for different applications. The mass yield of the torrefied biomass is typically between 50% and 90%, depending on the torrefaction conditions (temperature, time, and atmosphere). Energy yield is typically between 70% and 95%, depending on the torrefaction conditions and the type of woody biomass. Figure 10 shows the mass yield and energy yield of different woody biomass following torrefaction. HW and CO have mass yields in the 60-70% range and energy yields in the 80-90% range. TA and MT have mass yields of about 60% and energy yields of over 95%. This suggests that TA and MT could be used to produce a high-quality torrefied biomass fuel with excellent combustion properties. TA and MT are both high in cellulose content, as indicated in Table 1. This particular component is relatively susceptible to breakdown during torrefaction, contributing to a lower mass yield and a higher energy yield than HW and CO. Similar findings have been reported by [PERSON] et al. (2011), who observed that the mass and energy yields of torrefied biomass are highly dependent on the feedstock type and torrefaction conditions [28].  
Figure 10: Mass and energy yield of the prepared biopellets by torrefaction.  
Figure 9: Calorific value of the prepared biopellets before and after torrefaction.