esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adaptive Dempster-Shafer Theory of Evidence Based Trust Model in Multiagent Systems
## 5 Simulation
### Manage Trust Certainty with Entropy  
In Section 4.1, we explained that satisfaction is primarily determined by evidence consisting of positive and negative feedback (or the evaluation) and fluctuation. We utilize the satisfactory degree of \(S_{ij}\) to represent the performance, which could also be interpreted as the number of positive and negative feedback represented by \(r\) and \(s\), respectively. The related evaluation is employed to represent trust with the Dempster-Shafer theory of evidence (Equations (8) and (11)). Much uncertainty caused by randomness, incompleteness, uncertainty, and dynamic environment has been addressed. This direct trust is generally shared with the third-party resource clients to help trust estimation. However, it is essential to decide the certainty of the received or presented evidence. In this paper, we understand certainty with the entropy of the Dempster-Shafer theory of evidence.  
In previous work, some literature assumes that certainty is independent of evidence [15], and a third-party agent provides evidence as well as the relevant certainty towards the evaluation. In terms of the evidence \((r,s)\), [65] map this evidence to a trust represented by \((\frac{r}{r+s+1},\frac{s}{r+s+1},\frac{1}{r+s+1})\), and \(\frac{1}{r+s+1}\) is designated as uncertainty. [PERSON] and [PERSON] used two parameters in the range of 0 and 1 to model all evidence (evaluations) to positive, negative, or neutral by the Dempster-Shafer theory of evidence [27], and neutral is regarded as uncertainty. However, it fails to embody that conflicting evidence (the amount of positive or negative interaction) increases uncertainty. Furthermore, the amount of interaction also has no impact on certainty. Subsequently in [20; 66], certainty is a statistical measure defined on a probability-certainty density function. However, this transformation from evidence to trust neglects implementing an overall analysis of uncertainty caused by a fading factor, lacking information, incompleteness, and randomness. In addition, the work listed above is on the condition that trustees have stable performance. We transform the evidence to trust, providing an overall evaluation appropriate for dynamic agents' trust estimation, and the certainty of the evidence is well-studied thanks to the entropy definition. We reveal that the certainty estimated with entropy fits well with the general properties listed in Section 4.2.3; more details are expressed in the following subsections.
## 4 Results  
In our system, similarity to human perception is the evaluation criterion of system performance. Here, we worked with the staff of a professional QC group to get the accuracy. The formula for calculating the accuracy is defined by Equation (7). To evaluate the performance of our method, we validated our module on a dataset and achieved the final detection accuracy of 96.1%. Through a confusion matrix, we obtained the accuracy and recall rate of the system, and passed the comparison test of the production QC group. Table 4 shows the performance of the model under different augmentation methods of input data and parameters.  
We fused the softmax scores of the two branches at a ratio of 1:1.5. Finally, the overall loss was used to carry out fine-tune [41] the trained network, and high accuracy was obtained. The experiment shows that our model has the ability to distinguish defect-free and defective images in our dataset and achieve higher accuracy than others, which proves the effectiveness of our two-stream module. In Table 5, we compared the results of several other models with identifying local features and global features in the operating status of the stepper motor.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{(**a) Training Setting**} \\ \hline
**Setting** & **Accuracy (\%)** \\ \hline Dropout ratio (0.9) & 91.7 \\ Dropout ratio (0.7) & 93.1 \\ Dropout ratio (0.5) & 93.6 \\ Sum fusion ratio (1:1) & 87.6 \\ Sum fusion ratio (1:2) & 91.6 \\ Sum fusion ratio (1:1.5) & 93.8 \\ Pre-trained & 94.2 \\ Pre-trained + finetuning & 96.1 \\ \hline \multicolumn{2}{c}{(**b) Input Configuration**} \\ \hline
**Configuration** & **Accuracy (\%)** \\ \hline Spectrogram stacking (S = 30) & 87.5 \\ Spectrogram stacking (S = 20) & 88.4 \\ Spectrogram stacking (S = 15) & 91.2 \\ Cropped-jittering & 92.5 \\ Calsse-weight & 94.2 \\ \hline \multicolumn{2}{c}{(**c) Comparison of Models**} \\ \hline
**Method** & **Accuracy (\%)** \\ \hline Improved dense trajectories & 78.2 \\ \hline Temporal CNN & 78.3 \\ \hline Spatial CNN & 81.2 \\ \hline Two-stream (SVM fusion) & 95.8 \\ \hline Two-stream (average fusion) & 93.6 \\ \hline Ours (1:1.5 sum) & 96.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: (**a**) Different accuracy of model based on different dropout ratio, fusion ratio, and method of pre-training; (**b**) different configuration of input data; (**c**) correct detection of each network.  
The detection results of our model on the four kinds of samples are shown in Figure 11. In the process of training, we used the method of classification weights in the final layer to solve the problem of the unbalanced data set. As our content involved the perception of good and bad samples, the accuracy of this model is constituted by true-positive (TP), true-negative (TN), false-positive (FP), and false-negative (FN) values by.  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{7}\]
## 4 Case Study
### Model Results  
#### 4.4.1 Sparse Road Network Topology Models  
By collecting data on node cities and connecting roads in the study area, 14 states (cities/districts) were selected. Using the Space L model, the relationships between nodes and edges were defined to abstract the highway network into a topological structure composed of nodes and edges. In the GIS platform, it is convenient to analyze the topological relationships of geographic entities (topological connections, associations, etc.) to automatically generate a node-road segment association matrix. The attributes of each road segment can be associated through visual graphic editing and table data editing. The road segment table and node table can be stored as the edges and nodes of a directed graph G, respectively, to construct a static road network model. Using functions such as directed  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**O-D** & **Travel Demand** & **O-D** & **Travel Demand** & **O-D** & **Travel Demand** & **O-D** & **Travel Demand** \\ \hline (4,1) & 6000 & (5,1) & 6000 & (6,1) & 4500 & (7,1) & 5000 \\ (4,2) & 6500 & (5,2) & 5500 & (6,2) & 5500 & (7,2) & 6000 \\ (4,3) & 7000 & (5,3) & 6000 & (6,3) & 6500 & (7,3) & 8000 \\ \hline \end{tabular}
\end{table}
Table 4: Travel demand assumptions for OD pairs (pcu/day).  
Figure 14: Simulation process for road network resilience assessment under disaster events.  
graph visualization, a regional highway dual-network topological model of Tibet under normal conditions (without geological disasters) is established, as shown in Figure 15.  
The simplified topological structure of the sparse road network consists of 31 nodes and 52 edges, with a total length of 19,823 km, including highways, national roads, provincial roads, and county roads (Figure 16). The numbers in the figure represent the node IDs. It can be observed that the road segments in the regional network are relatively long, with critical nodes widely distributed. At critical nodes (such as county towns), there are relatively dense small networks. At the mesoscopic level, by using the innovative two-layer topological model, the regional road network can be approximated as nodes, significantly improving the simulation running speed.  
Figure 16: Simplified topology of sparse road network.  
Figure 15: The two-layer structure of the regional sparse road network.
# Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019
## 3 Results
### Spatial Variability of NDVI  
#### 3.2.1 Spatial Distribution and Trend Analysis of NDVI  
The remaining regions were classified into five levels using the equal spacing method in ArcGIS 10.6, into low (0.08-0.25), medium-low (0.25-0.41), medium-0.41-0.58), medium-high (0.58-0.75), and high (0.75-0.92) vegetation cover (Figure 4a) [29]. From the spatial distribution of NDVI levels, the high vegetation cover (0.75-0.92) area was the largest, accounting for 24.95% of the total area. Influenced by the large east-west span of Inner Mongolia, diverse and complex topography, large vertical differences in vegetation growth, and the complex spatial distribution, the NDVI gradually decreased from east to west from a regional perspective. Hulunbuir, the Xing'an League, Tongliao, and Chifeng, locations with forests and typical steppes, recorded high NDVI values, while areas with NDVI values less than 0.41 were mainly in the western Alxa League, Ordos, and Bayannur areas, where the vegetation cover types largely consist of desert and desert steppe.  
Figure 4b shows the results of the [PERSON]'s slope and Mann-Kendall test for annual NDVI. Referring to the grading method of [PERSON] et al. [71], we identified three trend types, \"increased,\" \"stable and constant,\" and \"decreased.\" Increased and decreased represent areas where the rising and falling NDVI trends pass the 0.05 significance level in the Mann-Kendall test, respectively, and we classified the remaining areas with NDVI trends that did not pass the 0.05 significance level into stable and constant. Area increasing NDVI were larger than those with decreasing NDVI, which is 34.65%, while 33.71% of the regions showed stable and constant NDVI. Increased and decreased NDVI mostly occur in eastern Hulunbeier, Xing'an Meng, Tongliao, southern Erdos, and southeastern Chifeng. The areas with stable and constant NDVI were mainly located in Alxa League. Finally, areas where NDVI decreased significantly were concentrated in Baotou, Hohhot, Ulanqab, Xilingol league, and the western part of Hulunbuir.
# Tabu Search Aided Multi-Track Detection Scheme for Bit-Patterned Media Recording
###### Abstract  
A tabu search (TS) aided multi-track detection scheme for bit patterned magnetic recording is proposed. The proposed detection scheme adopts the simplified multi-track detector with a two-dimensional generalized partial response (GPR) target, uses a priori information, and employs a reliability test and a TS detector. The proposed detection scheme processes signals in three stages. First, the single-track detector with a one-dimensional GPR target detects the data for the main-track and side-tracks. Second, the reliability test is performed to detect bits with low reliability in the side-tracks by comparing the distance between the readback signal and the estimated and reconstructed readback signal. If the distance is greater than a specific value, the TS detector is conducted to improve the reliability of bits with low reliability in the side-tracks. Lastly, the simplified multi-track detector detects the main-track data using a priori information from the outputs of the TS or single-track detectors for the side-track data. The proposed and conventional detection schemes are compared in terms of the bit error rate performances. Simulation results show that the proposed detection scheme with slightly additional complexity has more than 3 dB gains compared to the conventional detection schemes.  
bit-patterned media recording; intertrack interference; multi-track detection; tabu search Article
## 2 Materials and Methods
### Measurement and Characterization  
The morphology of the nanoparticles was illustrated by scanning electron microscopy (SEM), model JEOL JSM-7610F at 15 kV. The mesostructure of the silica nanoparticles was illustrated by transmission electron microscopy (TEM), model JEOL JEM-1400 at 100 kV. For infrared spectra, the samples were prepared in potassium bromide pellets at the region of 4000-400 cm\({}^{-1}\) with a resolution of 4 cm\({}^{-1}\). Fourier transform infrared (FT-IR) spectrometer was a PerkinElmer Spectrum BX FTIR. High-resolution XPS spectra of the sample was obtained by JEOL JPS-9030 photoelectron spectrometer, employing mg K\(\alpha\) X-rays. Ultraviolet-visible absorption spectra were obtained by a SpectraMax Plus 384 microplate reader.
# Pull-Out Test and Mechanical Properties Analysis Based on Intelligent Bolt and Internet of Things
## 1 Introduction
The evolution law of bolt force transmission and interface deformation between the anchoring agent and surrounding rock was analyzed by means of a digital speckle test [20]. [PERSON] et al. [21] investigated the weak surrounding rock, resin anchorage, and mine bolt support system using a multi-functional bolt pull-out test system. They revealed the mechanical properties of a coal mine bolt pull-out under varying anchorage lengths. [PERSON] et al. [22] analyzed the impact of the anchoring section length of bolts in weathered rock layers on the ultimate pull-out bearing capacity and the shear stress behavior between the anchoring section and the weathered rock layer through field pull-out tests. The above results greatly enrich the theoretical research and engineering application of rock and soil anchors. However, there are few studies on the intelligent testing of long anchors. The theoretical model lacks on-site long anchor test verification. There are few studies on the anchorage characteristics of long anchors, especially super-long anchors, in engineering. Whether the anchoring force of the super-long anchor rod can meet the design requirements, whether the anchoring length is reasonable, and how the friction resistance plays an effect are still unsolved problems. Due to the complex environment of slope support engineering, it is difficult to calculate the anchoring force of an anchor bolt, and the pull-out test of an anchor bolt is an effective means to analyze the working mechanism of the anchor bolt. Based on this, this paper relies on a high and steep slope and mountain ecological protection and restoration project; it has independently developed an intelligent test system for bolt drawing and collected and analyzed the whole process of drawing data. At the same time, five kinds of long anchor bolts were selected for field pull-out tests. Combined with theoretical analysis, the stress characteristics of anchor bolts under different anchorage lengths were studied, which provided a reference for the design of anchor bolts in loess areas.
# Design of the CIEMAT Corrosion Loop for Liquid Metal Experiments
## 3 CiCLo-C Loop: Requirements and Design
### Flow Meter Calibration  
The flywheel is used to measure the liquid flow rate because the rotation speed, \(n\), is proportional to the flow rate.  
The calibration of the device is based on the thermal balance of the loop. The loop is adjusted to a stationary thermic state. The pump works according to a theoretical characteristic curve in an average rotation speed. All heating zones, except the horizontal one just after the PMP, are deactivated. By adjusting the heating in this zone and the cooling capacity of the coolant, just before the flow meter, a state is established in which all temperatures of the loop are constant. The temperature of the fluid shall be equal to the working temperature of the loop. The present volume flow is calculated with the following Equation [36]:  
\[\dot{V}=\frac{P_{el}}{\rho(\theta)c(\vartheta)(\vartheta_{2}-\vartheta_{1})}\]  
where \(\dot{V}\) is the volume flow rate, \(P_{el}\) is the electrical power of the heaters, \(\rho(\vartheta)\) is the density depending on the fluid temperature (see Figure 8), \(c(\vartheta)\) is the specific heat capacity depending on the fluid temperature (see Figure 9), \(\vartheta_{2}\) and \(\vartheta_{1}\) are the fluid temperatures at the three-point temperature points: after the PMP and before the test section.  
Figure 8: Variation of PbLi density with temperature.  
Figure 7: SEM microscopy image of a 316 stainless steel sample covered by tantalum, showing good adhesion with the substrate and uniform thickness (**a**). Mapping EDX analysis of the coated sample (**b**).  
The measured rotation speed of the flow meter in this state can be related to the calculated flow rate. Thus, the linear characteristic curve of the flow meter using the zero point is calibrated and can be defined for the temperature measured at the thermocouple in the flow meter channel. To calculate the flow meter calibration for other temperatures in the device, the constant \(k\) must be determined by the balance between the accelerating and braking torque at the flywheel [36]:  
\[k=\left(\frac{V(\dot{\theta}_{0})}{2\pi Arn(\theta_{0})}-1\right)\frac{\sigma_ {PbLi}(\dot{\theta}_{0})}{\sigma_{W}(\theta_{0})}\]  
where \(A\) is the cross-section of the flow channel, \(r\) is the radius of the flow meter (45 mm), \(n(\theta_{0})\) is the rotation speed at the calibration temperature, \(\sigma_{w}(\theta_{0})\) is the electrical conductivity of the channel wall at the calibration temperature, and \(\sigma_{PbLi}(\dot{\theta}_{0})\) is the electrical conductivity of fluid at calibration temperature.  
The conductivities of the matching metals are shown in the diagrams in Figures 8 and 9.  
In both cases, the error is given by thermocouples, with a total uncertainty range of up to \(\pm\)2-3% of the reading.
###### Abstract  
The aim of this study was to determine the morphogenetic patterns of the interforminal region from cone beam computed tomography (CBCT) scans of individuals with total mandibular edentulism. CBCT images were obtained from 40 patients with total edentulism who are older (12 men and 28 women; average age of \(69.5\pm 9.4\) years) and who wore a non-implant-supported, lower, removable, total prosthesis. We conducted a two-dimensional (2D) morphogeometric analysis of the Digital Imaging and Communication in Medicine (DICOM) files from the CBCT scans, and five equidistant cross sections were planned. For the three-dimensional (3D) morphogeometric analysis, standard triangular language (STL) files were obtained after segmentation of the interforminal mandibular region, and four anatomical landmarks and their respective curves were digitized. The patterns among the shapes were determined using principal component analysis (PCA) on Morphol software (version 1.07a). The results of the 2D morphogeometric analyses for PCA of the interforminal mandibular paramedian region were PC1 or elongated drop shape, 54.78%; PC2 or wineskin shape, 17.65%; PC3 or pear shape, 11.77%; and PC4 or eggplant shape, 5.71%, and those for PCA of the symbyseal region were PC1 or elongated drop shape, 62.13%; PC2 or ovoid shape, 11.64%; PC3 or triangular shape, 9.71%; and PC4 or tuber shape, 4.96%. The results of the 3D morphogeometric analyses for the interforminal hemimandibular region were PC1, 59.83%; PC2, 10.39%; PC3, 7.67%; and PC4, 5.09%. This study provides relevant information for future clinical guidelines on prosthetics and implants, in addition to proposing the use of new technologies that support diagnosis and treatment in patients with edentulism.  
geometric morphometrics; bone atrophy; mandibular edentulism; cone beam computed tomography; principal component analysis; implant prosthodontics
# Modeling the Power Consumption of Function-Level Code Relocation for Low-Power Embedded Systems
###### Abstract  
The problems associated with the battery life of embedded systems were addressed by focusing on memory components that are heterogeneous and are known to meaningfully affect the power consumption and have not been fully exploited thus far. Our study establishes a model that predicts and orders the efficiency of function-level code relocation. This is based on extensive code profiling that was performed on an actual system to discover the impact and was achieved by using function-level code relocation between the different types of memory, i.e., flash memory and static RAM, to reduce the power consumption. This was accomplished by grouping the assembly instructions to evaluate the distinctive power reduction efficiency depending on function code placement. As a result of the profiling, the efficiency of the function-level code relocation was the lowest at 11.517% for the branch and control groups and the highest at 12.623% for the data processing group. Further, we propose a prior relocation-scoring model to estimate the effective relocation order among functions in a program. To demonstrate the effectiveness of the proposed model, benchmarks in the MiBench benchmark suite were selected as case studies. The experimental results are consistent in terms of the scored outputs produced by the proposed model and measured power reduction efficiencies.  
Keywords:function-level code relocation; prior relocation-scoring; source code insertion; code profiling; low-power; embedded systems +
Footnote †: journal: Applied Sciences
# The Future of the Human-Machine Interface (HMI) in Society 5.0
## 4 Human-Machine Interaction (HMI)
### Brain Computer Interface (BCI)  
A brain-computer interface (BCI) can be realized as a form of the so-called Humanchine by enabling direct communication of the human brain and computer systems [39]. BCIs hold a lot of promise for enabling individuals to control devices and communicate directly with computers using their thoughts. Several approaches have been presented in the literature, with the most commonly applied framework being electroencephalography (EEG) [40]. It is often, however, that these technologies are coupled with other Industry 4.0 technologies, such as eXtended Reality (XR), in order to improve human perception. For example, in [41], a Mixed Reality (MR) framework is proposed in order to facilitate remote user control of robotic arms. However, there are several challenges that need to be overcome to make BCIs more effective and reliable. Some of these challenges include:Signal quality: One of the key challenges in the development of BCIs is obtaining high-quality signals from the human brain. By default, brain signals are weak and can be easily contaminated by noise and interference from other sources, such as muscles and other electronic devices. Therefore, accurate detection and interpretation of brain activity is a challenging task [42];
2. Invasive vs. non-invasive BCIs: The current implementations of BCIs can be divided into two major categories: i) invasive and ii) non-invasive. Invasive BCIs require the implantation of electrodes directly into the brain, while non-invasive BCIs use external sensors to detect brain activity. Despite the fact that invasive BCIs can provide higher-quality signals, they are also riskier and more expensive. On the contrary, non-invasive BCIs are safer for humans and more accessible, at the expense of lower-quality signals [43];
3. Training and calibration: BCIs require substantial effort in terms of training and calibration in order to work effectively. Furthermore, it is imperative for users to learn how to control their brain activity in such a way that can be detected and interpreted by the BCI. As a result, this can be a time-consuming and frustrating process for some users, causing discomfort [44];
4. Limited bandwidth: BCIs often have limited bandwidth, thus allowing only a limited range of brain activity to be detected and interpreted. Therefore, the types of actions that can be controlled using a BCI are still limited [45];
5. Ethical and privacy concerns: Indeed, BCIs have been evidenced to be really useful for the future of human-computer interfaces. However, there are certain ethical and privacy concerns. For example, data ownership legislation needs to be established. Furthermore, issues regarding data misuse and the development of suitable mechanisms to counteract such issues need to be explored [46].