esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Section
### Characterization  
The surface morphology of the prepared materials was characterized by scanning electron microscope (SEM) (Thermo Fisher Scientific, Waltham, MA, USA) and the surface functional groups were analyzed by Fourier-transform infrared (FT-IR) spectroscopy with a scanning range of 4000-500 cm\({}^{-1}\). The crystal structures of prepared materials were characterized by X-ray diffraction (XRD) (Panalytical, Beijing, China)with a PANalytical X'Pert Pro X-ray diffractometer. The electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization measurements were adopted to detect the corrosion protection performance of each coatings in 3.5% NaCl solution, at room temperature. EIS tests were carried out at an CS165 electrochemical workstation (CorrTest Instruments Corp., Ltd., Wuhan, China) with a three-electrode system including the working electrode (steel electrode), the counter electrode (platinum electrode) and reference electrode (Ag/AgCl Electrode). The frequency was in the range of \(10^{5}\)-\(10^{-2}\) Hz with an amplitude of \(\pm\) 5 mV. The potentiodynamic polarization curves were obtained with the sweep rate of 0.5 mV/s. The corrosion resistance test of the coatings was carried out for continuous 30 days with the salt-spray test chamber (FQY-015, Modern Environment Engineering Technique Co., Ltd., Shanghai, China) [35]. The adhesion of the coatings was tested by the cross hatch tester (BGD 502/2, Biugged Laboratory Instruments Co., Ltd., GuangZhou, China). The conductivity was tested with a conductivity tester (ST-2722, Jingge Electronic Co., Ltd., Suzhou, China). The hydrophilicity and hydrophobicity of the coatings were tested by a contact angle measuring instrument (CA-100C, innuo precision instrument Co., Ltd., Shanghai, China).
# Intrusion Detection and Prevention in CoAP Wireless Sensor Networks Using Anomaly Detection
## 4 Experimental Evaluation
### Evaluation Strategy  
The classifiers of our intrusion detection approach are evaluated during the training phase, and in this context we consider fundamental scoring metrics of performance, in particular _Accuracy_, the _Recall (or True Positive Rate)_, _Precision_, _False Positive Rate_ and _F_Measure_, defined as follows:  
\[Recall=\frac{TP}{TP+FN}\]  
\[Accuracy=\frac{TP+TN}{n}\]  
\[Precision=\frac{TP}{TP+FP}\]  
\[False\;Positive\;Rate=\frac{FP}{FP+TP}\]  
\[F\_Measure=2\times\frac{Precision*Recall}{Precision+Recall}\]  
In the previous definitions, _TP_ represents the number of true positives, _TN_ the number of true negatives, _FN_ the number of false negatives, _FP_ the number of false positives and \(n\) the number of observations considered. These performance metrics are evaluated considering confusion matrices and ROC (Receiving Operator Characteristic) curves, both in the multi and binary class approaches, which we proceed to analyze.
## 3 Results and Discussion
### Analysis on Cultural Perception-Duration of Speech  
One-way MANOVA analysis with the four durations as factors and the four emotions as dependent variables showed a statistically significant difference between the different groups of durations (F (12, 87.6) = 2.37, \(p\) = 0.01, [PERSON]'s \(\Lambda\) = 0.475, partial \(\eta\)2 = 0.220). Figure 3 illustrates the overall mean for emotion recognition rate by four Asian ethnic groups in four different durations. Asians found it difficult to perceive emotions in utterances with a duration longer than 0.04 s. A post-hoc analysis revealed that at duration 0.06 s, _happiness_ and _sadness_ were perceived statistically significantly lower than those perceived in other durations (\(p\) < 0.05). _Surprise_ was also perceived to be statistically significantly lower at duration 0.06 s than at duration 0.02 s. The four emotions were perceived best at different durations: _happiness_ at duration 0.03 s with a perception mean of 39.33 (a mark slightly higher than the perception mean at duration 0.04 s (39.15)), _sadness_ at duration 0.04 s (75.83), _surprise_ at duration 0.02 s (42.19), and _anger_ at 0.02 s (37.61).
# Computerized Generation and Finite Element Stress Analysis of Endodontic Rotary Files
## 2 Materials and Methods  
This section describes a new method developed for the computerized generation of endodontic files and the obtention of FE models for their stress analysis. The geometry considered for the endodontic file is shown in Figure 1a. It is defined with respect to a coordinate system \(S_{f}\), whose \(z_{f}\) axis is alined with the theoretical axis of rotation of the endodontic file and its origin \(O_{f}\) is at the tip of the endodontic file. The geometry of the endodontic file is fully parametrized by the following set of parameters:  
* The diameter of the shaft, \(d_{sh}\);
* The diameter of the tip of the active part, \(d_{ap}\);
* The length of the active part, \(L_{ap}\);* The total length of the file, \(L_{total}\);
* The pitch of the active part, \(p_{z}\), which represents the distance between adjacent points in the cutting edge.  
Three different parts can be identified for this endodontic file: the active part, the shaft and the transition part. The active part contains the cutting edges and has an arbitrary cross section (squared, triangular, etc.). The shaft has a circular cross section and connects the active part with the support of the endodontic file (which is not included in this model). Finally, the transition part allows for a smooth transition from the cross section of the active part to the cross section of the shaft. Figure 2 shows an example of the cross section of the endodontic file at each one of these parts.  
Figure 1: Definition of the geometry of the endodontic file (**a**), the raw material (**b**) and the machined material (**c**).  
Figure 2: Cross-sections of the endodontic file at different positions along the \(z_{g}\) axis: active part (**a**), transition area (**b**) and shaft (**c**).  
The computerized generation of this endodontic file is achieved as the intersection of two different geometries: the raw material (Figure 1b) and the machined material (Figure 1c). The raw material corresponds to a geometry of the endodontic file before the cutting edges are machined over it, and its generation is described in Section 2.1. The geometry of the machined material is an auxiliary geometry that is used to generate the cutting edges of the endodontic file, and its generation is described in Section 2.2. The intersection of both geometries and the obtention of the final geometry of the endodontic file is described in Section 2.3.  
Having a computational description of the geometry of the endodontic file allows for an automated generation of FE models for its stress analysis. Section 2.4 provides further details on how to obtain these FE models.
## 1 Introduction
Some defects images of continuous casting slabs also have texture traits, thus we consider introducing GLCM into the feature extraction of continuous casting slabs. Since the features extracted by DNST and GLCM are redundant, we use kernel spectral regression (KSR) [18; 19] technology to remove redundant features. KSR is a kind of manifold learning dimensionality reduction technology, and it casts the problem of learning an embedding function into a regression framework that facilitates efficient computations. The proposed feature extraction approach is named discrete nonseparable shearlet transform gray-level co-occurrence matrix kernel spectral regression (DNST-GLCM-KSR), which combines multi-scale and multi-directional features of DNST with texture features of GLCM and uses KSR to remove redundant features. The DNST-GLCM-KSR approach can improve the defect recognition accuracy of continuous casting slabs and achieved better performance than traditional methods. The novelty of our work lies in introducing DNST into the surface defect recognition of continuous casting slabs with the complex backgrounds, fusing GLCM texture features, and using a suitable dimensionality reduction algorithm KSR, which makes defect recognition easier and more effective. The rest of this paper is organized as follows. In Section 2, the surface defects information of continuous casting slabs is depicted. Section 3 introduces the basic principles of the DNST-GLCM-KSR feature extraction approach. The surface defect recognition algorithm is presented in Section 4. Section 5 describes the experimental results and discussions, followed by conclusions in Section 6.
# Enhanced Scheduling of AI Applications in Multi-Tenant Cloud Using Genetic Optimizations
## 3 Scheduling Optimization by Genetic Algorithms  
The genetic algorithm (GA) is a search heuristic inspired by the natural evolutionary processes of genetics [29]. In this paper, the search domain encompasses the available resource capacities of GPU within each machine in the cluster. The primary objective of the genetic algorithm discussed in this paper is to discover a scheduling solution that optimizes the utilization of GPUs across the entire cluster. To accomplish this goal, our genetic algorithm manages a specific number of solutions, referred to as a population, and evolves this population to find the best solution.  
Our genetic algorithm initiates by generating an initial population randomly. It then chooses a subset of solutions from this population to produce new offspring solutions. As the algorithm progresses through generations, it sustains the population size by replacing some solutions with newly created ones to evolve the population. Ultimately, the solution with the highest fitness value, which will be defined later, within the final population  
Figure 2: Hierarchical structure of jobs, tasks, and task instances in our scheduling.  
is chosen as the resolution to the scheduling issue addressed by the genetic algorithm. Algorithm 2 provides a concise overview of our GA-based scheduling approach.  
Our genetic algorithm operates by taking a list \(V\) containing pairs of <machine, task instance> as its input. Initially, \(V\) is designated as the starting set of solutions, and its fitness value is computed. The algorithm then proceeds with iterative generations to find the most superior solution. Parents are chosen to produce offspring in the mating pool, followed by the execution of a crossover operation. Subsequently, an adaptive mutation is introduced to generate the succeeding set of solutions. Then, the fitness values for the newly generated solutions are evaluated. This iterative process is repeated across multiple generations until the population converges. Finally, the best solution from the generated population is selected to determine which machine within the cluster each task instance will be assigned to. Figure 3 briefly illustrates the overview of our genetic algorithm.  
```
0:valid pair list \(V\)
0:optimized solution \(S\)
1:population P\(\leftarrow\) V
2:calculate_population_fitness(P)
3:while\(num\ \leq\) NUM_OF_GENERATIONSdo
4:\(selects\_parents\_from\_mating\_pool()\)
5:do_singlepoint_crossover()
6:do_adaptive_mutation()
7:\(P\leftarrow\)generate_next_population P\({}^{\prime}\)
8:calculate_population_fitness(P)
9:endwhile
10:picked_machine, picked_taskInstance \(=\) get_best_solution() from P
```  
**Algorithm 2** Genetic Algorithm for Scheduling  
Figure 3: Overview of proposed genetic algorithm.
## 4 Proposed Scheme  
We propose an efficient clustering scheme that comprises of various phases such as CH declaration phase, node association phase, CH-acquaintanceship phase, and CH-friendship phase. In the proposed scheme, we consider a scenario where heterogeneous wireless sensor nodes are randomly deployed in a network. The deployed nodes are further divided into groups named as clusters. Each cluster has one CH node responsible for data collection from its cluster members, data aggregation and data transmission towards the sink node. The nodes declare themselves as CH based on available resources such as residual energy, storage and computational capability.Once the CHs declaration process completes and the CHs are declared, the nodes association process executes in which nodes select the best CH among several potential CH candidates by employing the multi-criteria decision-making technique known as TOPSIS [34]. The proposed scheme also presents a robust mechanism of CH-acquaintanceship and CH-friendship in order to balance the load on the CH and to prolong the lifetime of the CH. In the following subsections, we present all the aforementioned phases in detail.
# Non-Maximum Suppression Performs Later in Multi-Object Tracking
## 4 Results
### Occlusion Problem  
The purpose of postponing NMS to the tracking stage is to prevent the two-stage multi-object tracking method from filtering out the detection boxes useful for tracking in the detection stage and reduce the negative effects of detection on tracking. At the same time, we find that this is also effective for solving the occlusion problem. In multi-object tracking, when objects occluded each other, the occluded object will generate a low-score detection box. The low-score detection box of the occluded target overlaps with the detection frame of the obstacle (people) and is filtered by NMS, which makes the occluded target untraceable. After the NMS is postponed to the tracking stage, the detection box with a low score will be preserved, which effectively solves the problem that the occluded target is lost in tracking. In addition, in ID matching, our method matches the candidate boxes with high score and low score, respectively, which further enhances the matching of low score boxes. Figure 3 shows that our method is effective for the occlusion problem.  
As shown in Figure 3, when NMS acts on the detection stage, the object will be lost when it is blocked. However, when NMS is postponed to the tracking stage, the occluded objects will still be tracked.
# Study on the Effect of Anchor Cable Prestress Loss on Foundation Stability
## 2 Overview of the Project  
### Geological Profile of the Project  
This paper carries out research based on an underground station pit on Qingdao Metro Line 6. The length of the pit is about 287 m, the width is about 21.1-26.4 m, and the depth is about 21.372-22.972 m. In view of the soil-rock combination stratum and the construction requirements of the deep foundation pit of the assembled station, the enclosure structure adopts the scheme of drilled piles + first concrete support + anchor cable. The concrete support can restrain the deformation of the upper weak soil layer well, and at the same time, the pile-anchor support structure has strong safety, convenient construction, and a small demand for construction space, which can guarantee the higher demand for construction space for the main body of the assembly of the station structure. The main buildings nearby are high-rise residential buildings with many underground pipelines.  
The topography of the site is relatively flat. The ground elevation is 7.56-8.81 m. The geomorphological type is erosion and accumulation of river floodplain; the upper stratum is an artificial accumulation layer and a Holocene flood alluvial layer; and the bedrock is mainly Yanshan Late Phyllite. According to the lithological characteristics and physical and mechanical properties, the stratum of the site can be further divided into seven major layers, from top to bottom: plain fill, coarse gravel sand, medium-fine sand, silty clay, highly weathered diabase, moderately weathered diabase, and slightly weathered diabase. The depth of groundwater level in the site is 2.00-3.50 m, the stable water level elevation is 4.37-6.65 m, and the highest water level in 3-5 years is 5.5-8.0 m.
# On Mathematical Modelling of Automated Coverage Optimization in Wireless 5G and beyond Deployments
## 2 Models Developed for Network Coverage and Capacity Problems  
As it was discussed in the Section 1.1, the original mathematical models for the location covering problems cannot be applied to our use-cases. We need to take into account the following two scenarios:  
1. deploying service centres to the new area or reconfiguration of the whole network,
2. deploying additional service centres to the area, where service centres already exist, but do not provide sufficient network capacity.  
Both of these scenarios need to consider the available capacities of service centres and required capacities from customer locations. These considerations are based on the fact that it is not possible to assign an unlimited number of customer locations (user equipment) to the service centres (gNodeB nodes) as the total cell to gNodeB capacity is capped. Especially, in 5G and beyond deployments, the number of connected devices is increasing rapidly due to the evolvability of IoT and other devices. For that reason, there is a need to effectively deploy gNodeB nodes and assign customer capacities. To deal with that issue, the new mathematical models considering gNodeB deployment with the required aspects are proposed.  
Further, the variables and parameters used in the following mathematical models are denoted as follows:  
* \(c_{i},\ i\in I\)--capacity of service centre \(i\),
* \(b_{j},\ j\in J\)--the list of devices from customer location \(j\) that need a service centre,
* \(y_{ij}\in\{0,1\}\)--customer from location \(j\) is assigned or is not assigned to service centre \(i\),
* \(w_{i}\)--expresses the weights of service centre \(i\) (in practice, it represents the gNodeB installation costs).  
The above scenario is illustrated in Figure 3, where \(S_{i}\) represents a service centre with given coverage range. \(L_{j}\) represents customer location, where the colour indicates to which service centre that customer location is assigned.
## 1 Introduction
### Related Studies
#### 1.2.2 CAD Diagnosis Using FCMs  
[PERSON] et al. [12] developed an FCM for the diagnosis of the risk of ischemic stroke. A non-linear Hebbian learning method was applied for FCM training to enhance its efficiency. The dataset included a total of 100 cases. The model achieved an overall accuracy of 93.6 \(\pm\) 4.5% when using 10-fold cross-validation. The performance of the model was significantly improved with the incorporation of expert knowledge and fuzzy logic. For comparison reasons, Support Vector Machine (SVM) and K-nearest neighbors were developed, attaining accuracies of 86% and 80.2%, respectively.  
[PERSON] et al. [13] employed a State Space Advanced FCM (AFCM) model and incorporated a rule-based mechanism to enhance the knowledge of the system and its interpretability for the automatic, non-invasive diagnosis of CAD. The authors enhanced AFCM by utilizing advanced state equations, applied SigmoidN as an activation function, which has been applied in different studies [24], and constructed the proposed model RE-AFCM. A total of 303 patient cases were included in this study, consisting of 116 healthy cases and 187 pathological cases. The authors decided to integrate thirty input concepts that best demonstrate patient status concerning CAD diagnosis along with one output concept. The dataset attributes correspond to the factors influencing the diagnosis of CAD. They concluded that the advanced methodologies increased the performance of the model by 7%. RE-AFCM outperformed traditional FCM and ML algorithms, and SigmoidN performs better than Sigmoid. Regarding the results, RE-AFCM attained 85.47% accuracy, with 89.3% sensitivity, 79.3% specificity, and 87.43% and 82.14% for PPV and NPV, respectively.  
[PERSON] et al. [14] developed an Medical Decision Support System (MDSS), where FCM was applied for the prediction of CAD. This study was an enhancement of previous work, and a total of 303 patient cases were included, including 116 healthy cases and 187 pathological cases. The stenosis of the coronary artery was the only criterion for the labeling of instances. The authors decided to include thirty input concepts that represent patients regarding CAD diagnosis, along with one output concept. The proposed model achieved 78.2% accuracy, 83.96% sensitivity, 68.97% specificity, 81.34% Positive Predictive Value (PPV), and 72.73% Negative Predictive Value (NPV) and outperformed traditional classification algorithms by at least 2%. Based on the fact that the MDSS was not trained on the corresponding dataset, the extracted results are efficient.
# Development of a Caterpillar-Type Walker for the Elderly People
## 3 Results
### Walking Speed for Each Terrain  
Table 8 shows the walking speed when using GTW and CTW in each terrain. The statistical significance of walking speed for each terrain was confirmed through Wilcoxon signed-rank test. In uneven terrain, the walking speed increased by 31% when using CTW compared to GTW. In the obstacle terrain, the walking speed increased by 42% when using CTW compared to GTW. When using CTW, there was a significant increase in walking speed for both uneven terrain and obstacle terrain compared to GTW (\(p<0.05\)). In sloped terrain, in the uphill section at 10 degrees, the walking speed decreased by 22% when using CTW compared to GTW. In the uphill sections at 20 degrees, it decreased by 20% when using CTW compared to GTW. In addition, in the downhill section at 10 degrees,  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Uphill**} & \multicolumn{3}{c}{**Downhill**} \\ \hline  & \(10^{\circ}\) & & \(20^{\circ}\) & & \(10^{\circ}\) & & \(20^{\circ}\) & \\  & **GTW** & **CTW** & **GTW** & **GTW** & **GTW** & **GTW** & **CTW** \\ \hline FDS & \(13.80\pm 2.55\) & \(13.46\pm 3.65\) & \(14.21\pm 3.75\) & \(13.86\pm 2.77\) & \(13.69\pm 1.56\) & \(11.50\pm 1.63\) & \(15.39\pm 2.53\) & \(11.25\pm 2.31\) \\  & (\(12.9,9.99\pm 16.8\)) & (\(12.7,6.7\)-\(18.3\)) & (\(14.0,12.1\)-\(16.0\)) & (\(13.7,11.4\)-\(16.7\)) & (\(13.6,10.8\)-\(15.9\)) & (\(10.0,5.5\)-\(15.4\)) & (\(15.72,18.7\)-\(17.1\)) & (\(10.5,6.8\)-\(14.5\)) \\ \hline BB & \(6.28\pm 1.36\) & \(5.39\pm 1.32\) & \(6.96\pm 1.23\) & \(6.24\pm 1.68\) & \(6.53\pm 2.12\) & \(4.34\pm 1.47\) & \(8.80\pm 1.21\) & \(4.46\pm 1.73\) \\  & (\(5.6,4.7\)-\(8.0\)) & (\(5.1,3.6\)-\(8.6\)) & (\(6.9,4.7\)-\(8.7\)) & (\(5.0,10.6\)-\(22.2\)) & (\(5.5,3.9\)-\(9.7\)) & (\(3.8,2.6\)-\(4.0\)) & (\(8.11,5.5\)-\(12.0\)) & (\(9.3,2.3\)-\(6.4\)) \\ \hline FDP & \(18.36\pm 3.21\) & \(15.19\pm 2.85\) & \(20.09\pm 4.75\) & \(16.57\pm 2.37\) & \(18.57\pm 3.54\) & \(18.08\pm 3.55\) & \(19.67\pm 3.52\) & \(18.98\pm 2.61\) \\  & (\(16.9,15.5\)-\(20.2\)) & (\(13.9,11.3\)-\(18.3\))\({}^{*}\) & (\(20.0,12.7\)-\(25.7\)) & (\(15.8,10.6\)-\(22.2\)) \({}^{*}\) & (\(17.4,14.5\)-\(21.8\)) & (\(16.9,9.9\)-\(23.5\)) & (\(19.4,16.2\)-\(23.0\)) \({}^{*}\  
the walking speed decreased by 26% when using CTW compared to GTW. In the downhill section at 20 degrees, it decreased by 7% when using CTW compared to GTW. There were significant differences in the walking speed between using GTW and CTW in both uphill and downhill sections (\(p<0.05\)).