esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Design of Compliant Structures  
Compliant structures must constrain the relative positions of the guiding rings in the resting state, while enabling finger flexion and promoting passive extension. Since the cable is routed in the palmar side, the compliant elements can be placed in the dorsal face. The compliant dorsal structures are designed based on a ribbon kirigami-inspired geometry [33]. A planar structure composed of elementary cells interconnected by segments arranged in a repeating configuration was developed to allow flexion and provide controlled mechanical compliance, making it suitable for applications requiring flexible yet structurally organized components. This solution can be effectively developed by exploiting the possibilities enabled by additive manufacturing technologies like Fused Deposition Modeling (FDM), that allows accurate control of the material deposition [34; 35].  
Figure 2 shows the structure of the compliant dorsal link proposed in this work. While in the kirigami structure a continuous ribbon of material is modified through strategic cut patterns and properly fold, we create strategic holes with AM to create interconnected elementary cells according to the pattern presented in the figure.  
Among possible alternative designs, we choose this one based on previous evidence [34] that compared four different geometries and identified the proposed one as the best compromise between axial rigidity and bending. The structure shows a series of repeated cutouts that create elementary repetitive cells. Figure 2 highlights the main geometrical features that influence the mechanical behavior of the structure: \(H_{s}\) is the length of an elementary cell, \(d\) is the space between adjacent cells, \(2r\) is the cut width, and \(w\) is the ribbon thickness.  
Figure 1: (**a**) Fingers geometric model in a flexed configuration, with representative zoomed-in view of the force decomposition; Joints are represented with different markers: a square for the MCP joint, a star for the PIP joint, and a triangle for the DIP joint; (**b**) Anthropometric measurements highlighted: hand breadth (HB), hand length (HL), palm height (\(h_{p}\)), \(D_{i}\) diameter at i-th phalanx; (**c**) finger in a rest configuration, with representative zoomed-in view of the distance between two consecutive anchor points.  
A parametric CAD model was developed to allow customization of the presented target geometry. The length of the elementary cell was parametrized considering the optimal spacing derived from the optimization process, i.e., \(H_{l}\), the number of elementary cells \(q\), and the distance \(d\) between cells, as shown in Equation (5).  
\[H_{s}=\frac{H_{l}-(n-1)\cdot d}{n} \tag{5}\]  
where \(H_{l}\) is defined as  
\[H_{l}(i)=a_{i}(1-k_{i}^{opt})+a_{i+1}k_{i+1}^{opt}-s_{i}^{opt} \tag{6}\]  
with \(i\) range from 1 to \(q-1\).  
The cut width was set to 25% of \(H_{s}\) to allow for flexion between the elementary cells and address the manufacturing constraints related to FDM. These constraints also prevent the design of structures that would be too thin to be reliably fabricated using FDM technology.
## 1 Introduction
### Initial Cost  
The costs associated with material supply and processing must be considered. For example, a lack of infrastructure for recovering and processing the requisite materials may increase initial costs.
# Sound Event Detection Using Derivative Features in Deep Neural Networks
###### Abstract  
We propose using derivative features for sound event detection based on deep neural networks. As input to the networks, we used log-mel-filterbank and its first and second derivative features for each frame of the audio signal. Two deep neural networks were used to evaluate the effectiveness of these derivative features. Specifically, a convolutional recurrent neural network (CRNN) was constructed by combining a convolutional neural network and a recurrent neural networks (RNN) followed by a feed-forward neural network (FNN) acting as a classification layer. In addition, a mean-teacher model based on an attention CRNN was used. Both models had an average pooling layer at the output so that weakly labeled and unlabeled audio data may be used during model training. Under the various training conditions, depending on the neural network architecture and training set, the use of derivative features resulted in a consistent performance improvement by using the derivative features. Experiments on audio data from the Detection and Classification of Acoustic Scenes and Events 2018 and 2019 challenges indicated that a maximum relative improvement of 16.9% was obtained in terms of the F-score.  
sound event detection; convolutional recurrent neural network; derivative features; mean-teacher model; attention model +
Footnote †: journal: Applied Sciences
# Development of a Stock Price Prediction Framework for Intelligent Media and Technical Analysis
## 2 Materials and Methods
### Methodology
#### 2.3.3 Technical Analysis  
Mean Squared Error  
\[\text{MSE}(y,\hat{y})=\frac{1}{n_{\text{samples}}}\sum_{i=0}^{n_{\text{samples} }-1}\ (y_{i}-\hat{y}_{i})^{2} \tag{1}\]  
MSE = mean squared error  
\(n\) = number of data points  
\(y_{i}\) = observed values  
\(\hat{y}_{i}\) = predicted values  
The present paper collected stock price data from 1 January 2012 to 1 December 2019 on Vodacom Group Limited. Pandas-DataReader, a Pandas Python library, pulls all this stock data from Yahoo finance with the tag VODJO [27]. The DataReader pulled Vodacom's opening price, high price, low price, volume, and closing price. The closing price was extracted from this data to make up the univariate dataset. The second dataset is comprised of three technical indicators: the three moving average, MACD, and Bollinger Bands. These indicators were considered due to their popularity in the equity trading space, as most traders use them. This is an excellent way to mimic what traders would use to enhance their decision making. This collected a total of 2040 rows and 17 columns. The data collected using the Python Pandas DataReader consisted of 2040 rows. The DataReader does most of the hard work in making sure the data is imported in a friendly format, ready for the modelling stage.  
Before modelling, the last stage was data transformation to both the univariate and multivariate datasets. The present paper scaled the data using the sklearn MinMaxScaler Python library, which scales the data between zero and one.  
The experiment on technical analysis extracted features from Vodacom using three technical indicators: the three moving average, MACD, and Bollinger Bands. In total, the 3 technical indicators yielded a total of 11 features.
## 4 Operation of the System Implemented
### Polling  
Table 2 sketches the procedure followed to have a Central Node obtain the information gathered by the two _Smart Modules_ in its tower.  
_Smart Modules_ sleep between two transmissions in order to reduce power consumption. Information provided during start-up (such as number of panels in the tower or information to send in each transmission) allows them to determine how long they can sleep so that they can wake up in time to meet the request from their Central Node.  
The stream containing the information sent by the panels to the Central Node includes a header, number of data bytes in the stream, number of panel sending the information, values measured (in a pre-established order) and checksum:  
\begin{tabular}{|c|c|c|c|c|c|c|} \hline Header & N. bytes & Panel ID & Val.1 & Val.2 & Chksum \\ \hline \end{tabular}  
A similar procedure is followed by each of the Central Nodes to send the information to the _Monitoring Center_. In this case (two PV panels associated to the Central Node) the stream could look like this:  
\begin{tabular}{|c|c|c|c|c|c|c|c|} \hline Hdr & No. bytes & Tower ID & Panel ID & Val 1 & Val 2 & Panel ID & Val 1 & Val 2 & Chk sum \\ \hline \end{tabular}  
\begin{table} \end{table}
Table 2: Polling during normal operation.
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 4 Security and Resilient Architecture
### Attack Detection in Smart Grid
#### 4.3.2 Chi-Squared Detector  
Then, the estimated value along with the original value are all fed into the \(\chi^{2}\) detector to make judgments. \(\chi^{2}\) detector is a proven effective and widely used detecting method in smart grids. Combined with the Kalman filter, the \(\chi^{2}\) detector can easily detect both the DoS attack and random attacks by comparing the estimate value to the original value [26; 82]. It can be calculated as [26]  
\[g_{k}=\frac{r_{k}^{T}r_{k}}{R_{ii}} \tag{10}\]where \(r_{k}\) is the Gaussian-distributed residuals, \(g_{k}\) is \(\chi^{2}\) distributed. In this circumstance, the \(\chi^{2}\) detector is effective under the Kalman filter framework, which has high noise tolerance [26; 83]. The goal is to compare \(g_{k}\) value to the threshold, which is provided by the standard \(\chi^{2}\) table, to detect potential intrusion [9; 26]. However, some FDI attacks like zero-dynamics attacks will adjust their attack vectors according to the target to bypass the \(\chi^{2}\) detector [24].
## 5 Methods, Results and Discussion
### 2D Trajectory Validation
The tests in this section demonstrate the DAA can execute guided planar motions (trajectories) thanks to the adapted A* pathfinding algorithm and the developed feed-forward control strategy. Figure 14: (**a**) 2D displacement of the conveyed object aiming the points (0,\(-\)258.8), (295.7,\(-\)258.8), (295.7,0), (0,0) [values in μm] following the developed A* trajectory algorithm result. (**b**) Theoretical-experimental comparison for the rectangular trajectory decomposed in displacement step and correction steps for each axis. Figure 15: Bi-dimensional trajectory forming the Universite de Technologie de Compiegne acronym on a 2 \(\times\) 2 DAA.
# Challenges and Solution Directions of Microservice Architectures: A Systematic Literature Review
## 5 Identified Challenges and Solution Directions
### RQ2. What Are the Identified Solution Directions?
#### 5.2.2 Data Management and Consistency  
Study [S] focuses on the management of distributed transactions. In this study, a multi-agent-based framework is proposed to coordinate distributed transactions of the system. This solution is based on agents associated with a particular microservice, eventual consistency, saga patterns [16], and a semi-orchestrated asynchronous model, and provides a decoupled autonomous layer to the application to simplify the microservice interactions.  
Study [AH] introduces the backup, availability, and/or consistency (BAC) theorem to be used in backing up microservice. This theorem indicates that practitioners have to pick up two out of three items which are backup, availability, or consistency. This theorem, inspired by the CAP theorem [17], claims that it is not possible to satisfy both availability and consistency at the same time in backing up microservices.  
Study [C] provides a Synapse framework supporting independent services to share data with each other through clean APIs. This study addresses the problem of complex service groups that do not have a consistent, manageable structure and operate on each other's data. Synapse provides a transparent data propagation layer by using the model-view-controller (MVC) framework and object/relational mappings (ORMs). Synapse has been implemented for Ruby on Rails and has shown that it provides good performance and scalability.  
In sum, the identified primary studies indicate that it is difficult to manage data consistency, backup data, and data synchronization. For distributed transaction management, the identified solution proposes a multi-agent-based framework. For backing up, the identified solution proposes a novel BAC theorem. Finally, a solution comprehensive framework is proposed to be used in data synchronization.
## 3 Results  
### Accuracy and Uncertainty Analysis  
The two-color pyrometry temperature measurement method is used to measure nine standard temperatures in the range of 600-1000 \({}^{\circ}\)C in the blackbody furnace, and the average value of the nine measured temperatures is taken as the measurement result. In Table 2, the temperature measurement error = \(\mid\) two-color temperature \(-\) blackbody temperature \(\mid\) / blackbody temperature \(\times\) 100%. The method, in the temperature range of 600-1000 \({}^{\circ}\)C, shows that the measurement error is basically within 2.5%, and the range of error is 0.61-2.31%. Figure 11 gives the temperature curves and measurement errors for nine temperature points, with the measurement errors in the high-temperature section all exceeding 1.2%. This may be due to the fact that the camera, which has an 8-bit pixel depth, increases the nonlinearity of the camera's response as the temperature rises. Additionally, with the emergence of image noise, the temperature measurement error gradually increases.  
Figure 10: The relationship between \(G_{2}\) and \(M_{2}\).  
Figure 9: Monochrome image of a blackbody cavity at 768 nm from 600 \({}^{\circ}\)C to 1000 \({}^{\circ}\)C.  
A logarithmic differentiation method has been used to evaluate temperature uncertainty, on the basis of Equation (8). The main sources of uncertainty lie in the gray body hypothesis, in the interference filters, and in the conversion procedure from spectral radiance to gray scale levels [46]. Table 3 shows the values of each parameter at 600-1000 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Sampling** \\ **Point** \\ \end{tabular} & \(G_{1}\) & \(G_{2}\) & \begin{tabular}{c} **Blackbody** \\ **Temperature/\({}^{\circ}\)C** \\ \end{tabular} & \
\begin{tabular}{c} **Two-Color** \\ **Pyrometry** \\ **Temperature/\({}^{\circ}\)C** \\ \end{tabular} &
\begin{tabular}{c} **Temperature** \\ **Measurement** \\ **Error/\%** \\ \end{tabular} \\ \hline
1 & 6 & 9.1 & 600 & 605.63 & 0.94 \\
2 & 6.29 & 10.5 & 650 & 653.96 & 0.61 \\
3 & 6.7 & 11.89 & 700 & 706.26 & 0.89 \\
4 & 8.26 & 16.68 & 750 & 764.38 & 1.92 \\
5 & 11 & 24 & 800 & 812.61 & 1.58 \\
6 & 17.37 & 39.6 & 850 & 866.47 & 1.94 \\
7 & 32 & 72.89 & 900 & 920.76 & 2.31 \\
8 & 55.77 & 122.5 & 950 & 970.68 & 2.18 \\
9 & 102 & 216.51 & 1000 & 1012.63 & 1.26 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Error analysis of two-color pyrometry.  
Figure 11: Temperature measurement error curve.  
The \(\frac{G_{1}-b_{1}}{G_{2}-b_{2}}\) is replaced by \(N_{G_{1},G_{2},b_{1},b_{2}}\), where \(K_{\lambda_{1},\lambda_{2}}=\frac{k_{2}}{k_{1}}\), then we named \(F\):  
\[F=\ln\frac{G_{1}-b_{1}}{G_{2}-b_{2}}+\ln\frac{k_{2}}{k_{1}}+5\ln\frac{\lambda_{ 1}}{\lambda_{2}}=\ln(N_{G_{1},G_{2},b_{1},b_{2}}K_{\lambda_{1},\lambda_{2}} \frac{\lambda_{1}{}^{5}}{\lambda_{2}^{5}}), \tag{9}\]
# Automatic Detection of Ballast Unevenness Using Deep Neural Network
###### Abstract
Both systems were mounted on a mobile inspection trolley and used to measure ballast geometry. The test was carried out on a section of rail track comprising a steel rail, concrete sleepers and ballast as structural support. The experiment showed that a low-cost LiDAR is able to determine ballast geometry but with slightly less accuracy than a traditional static LiDAR. Ref. [16] used a laser scanner together with classification methods to detect elements of railway tunnels based on the point cloud. Paper [17] used close-range photogrammetry to evaluate ballast particle degradation. The authors took into consideration the morphology of the ballast (e.g., shape, angularity and surface texture). Another article [18] used an algorithm for the extraction of vegetation and local muddy areas from ballast images. The rapid development of both hardware and image-processing algorithms has led to the development of visual methods for railroad track diagnostics. In particular, the appearance of the paper [19] on the application of a deep convolutional network for object detection was a breakthrough in the field of machine learning. Since then, many papers on deep neural networks have been published [20; 21; 22; 23; 24; 25; 26]. The authors used deep learning networks in an algorithm that allows for the detection of irregularities in the ballast. These irregularities are determined relative to the surface of the sleepers. The detection of sleepers is performed using deep learning networks such as Mask R-CNN and YOLACT, which are used for image segmentation. The images used by the algorithm were captured by a laser triangulation system placed on a rail inspection vehicle managed by Polish railway lines. It allows for the continuous inspection of the track both during the day and the night and it provides immunity to electromagnetic interference, which eliminates the aforementioned limitations. Our contributions are:  
* We propose a simple criterion for the evaluation of the condition of the ballast based on the measurement of its unevenness in relation to the level of the sleeper;* We treat ballast irregularity detection as an instance segmentation process. We examine two instance segmentation algorithms, Mask R-CNN and YOLACT, in terms of their application to ballast irregularity detection;
* The use of instance segmentation makes it possible to determine the unevenness of the ballast individually relative to each sleeper;
* We use segmentation-related metrics--mAP (Mean Average Precision), IoU (Inter-section over Union) and Pixel Accuracy--to evaluate the quality of the detection of ballast irregularities.
# GIS-Based Site Suitability Analysis for Solar Power Systems in Mongolia
## 3 Methods
### Rating Criteria  
Because the PV site suitability of Mongolia was analyzed through comparison, this study assigned rated values of 100, 75, 50, and 25 for seven criteria on a relative scale, where the conditions at the applicable site were evaluated as good, fair, low, or poor, respectively (Table 3). The class ranges were mostly set at equal intervals between the good and poor ratings.  
PV systems require at least 4 kWh/m\({}^{2}\)/day of solar irradiance for economic use, owing to the efficiency of PV systems in the sunlight zone [16]. Many previous studies on site suitability analysis for PV systems considered that slopes > 3% are typically unsuitable for PV system installation, as panels will shade adjacent rows, reducing the PV system's efficiency, and thereby impacting economic viability [9; 15; 18]. Therefore, in this study, areas with slopes < 3% and located in the south, southeast, or southwest were highly rated. In addition, areas with distances of < 25 km from main roads and power transmission lines were rated as good sites for installing PV power plants.
## 5 Lattice Boltzmann Methods (LBMs)
In this context, various refinement approaches (_uniform and non-uniform grid refinement_) have been implemented in the LBM framework such as adaptive mesh refinement (AMR) [140], multi-grid approach [141], multi-domain approach [142] and the so-called non-uniform staggered Cartesian grid (NSCG) approach [143]. In their pioneering work, [PERSON] et al. [144] implemented an LBM algorithm with a non-uniform mesh and found an excellent agreement with previous experimental and numerical results. Due to the general physical description of the Boltzmann transport equation, a whole series of Lattice Boltzmann methods have been proposed to simulate various processes. On this basis, software tools such as Power FLOW and Open LB have been developed to simulate practical engineering problems. Initially, the LBM was designed to deal with isothermal flows. To broaden the applications, the LBM has also been extended to simulations of thermal flows in porous media, whether or not involving PCMs, etc. There are three typical collision operators in the LBM context: the single relaxation time (SRT) collision operator, the two relaxation time (TRT) collision operator, and the multiple relaxation time (MRT) collision operator. In terms of numerical accuracy and stability, these three operators obviously differ from each other [145]. However, and unsurprisingly, it is the SRT model that is best known for its simplicity and ease of implementation [134].Nevertheless, the accuracy of the velocity distribution may be impaired when the single relaxation time increases [145], and non-physical artifacts may occur in case of complex geometries [146]. To have a better precision and numerical stability, the MRT collision operator is a judicious alternative which tunes each relaxation parameter based on a comprehensive asymptotic analysis [147; 148]. However, its implementation is more complicated, and the multiple relaxation times selection is a difficult point. The intermediate TRT model is as simple as the SRT while exhibiting the advantages of the MRT model in terms of accuracy and stability [145]. It uses two relaxation times to relax the particle distributions, one fixed and the other tunable [149; 150]. In the following, a brief description is presented to introduce these schemes coupled with direct forcing to deal with sensible and latent heat transfer processes in porous media. Generally, the discrete LB equation with external forces can be written as:  
\[\underbrace{f_{i}/g_{i}(x+e_{i}\delta t,t+\delta t)-f_{i}/g_{i}(x,t)}_{streaming }=\underbrace{\Omega_{i}(f_{i}/g_{i})}_{collision\ operator}+\underbrace{\delta t \cdot F_{ei}/S_{i}}_{force\ term/Source\ term} \tag{2}\]  
where \(f_{i}/g_{i}\) is the fluid distribution function for the dynamic (\(f_{i}\)) and thermal (\(g_{i}\)) fields, respectively, with the discrete velocity \(e_{i}\) and \(F_{ei}/S_{i}\), the second right term represents external forces (_or source terms_) depending upon the governing equations considered. Note that the general flowchart of any LBM based on Equation (2) is depicted in Figure 17.
## 6 Discussion and Limitations
### Limitations  
Despite the above findings, there are some limitations to this study. On the one hand, Weibo, which includes location information on inferiority, was used in this study. Location information is voluntarily shared by users. Due to various reasons, the users only selectively share part of their location data when they are posting content, so the data are sparse. This leads to difficulty in mining the spatial behavioral patterns of the individuals who experience inferiority. In addition, the acquisition of the data in this study was based on keywords, so many posts related to inferiority that did not include keywords were not collected. Furthermore, the selection of independent variables was not rigorous. In the future, we will establish specific spatial statistics or data mining methods to determine the scope of the influence of different types of influencing factors and the degree of the regional impact. Optimal models and methods should be adopted to simulate the spatial and temporal variation trend of inferiority and to reveal the temporal and spatial differences of inferiority on the practitioners in different types of occupations.  
Figure 7: The spatial coefficient distribution for manufacturing practitioners from 2012 to 2017 based on the GWR model. (Note: A positive coefficient indicates a positive association between an increasing proportion of manufacturing practitioners (N_MP) and more inferiority, while a negative coefficient indicates the opposite).