esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Transformer Health Index Estimation Model
### Health Index Model Based on Scoring Algorithm  
HI was computed based on a scoring algorithm. It is a conventional method that utilizes the weighting and ranking techniques to a list of condition parameters data, followed by conversion to scores from a predefined grade range. The scores were later aggregated into a single quantitative value. This method was employed due to its flexibility with the available data and it was the most commonly used by the utilities [1; 2; 3; 4; 5; 6; 43].  
The condition parameters data were retrieved from the CBM database and the on-site physical conditions. The scoring and weighting algorithm is defined based on the technical guidelines, historical database, and fundamental theory. Expert decision and failure rate record are typically used to describe the appropriate weightages [1]. The procedures for determining the scores and weightage based on the different input parameters can be found in [1]. The final \(HI\) that was used in this study was computed according to Equation (23), which was adopted and modified from Equation (22) [1; 43].  
\[HI=60\%\times\frac{\sum_{j=1}^{21}K_{j}HIF_{j}}{\sum_{j=1}^{21}4K_{j}}+40\% \times\frac{\sum_{j=22}^{24}K_{j}HI_{j}}{\sum_{j=22}^{24}4K_{j}} \tag{22}\]  
The updated \(HI\) formula omitted the contribution factors from transformers (60%) and tap changer (40%) derived from CIGRE WG 12-05 [44]. Because this study only considered three parameters, namely dissolved gases, oil quality, and furfural condition parameters of transformers data.  
\[HI=\frac{\sum_{j=1}^{3}K_{j}HIF_{j}}{\sum_{j=1}^{3}4K_{j}}=\frac{K_{DGA}HIF_{ DGA}+K_{QQA}HIF_{OQA}+K_{FCA}HI_{FCA}}{4K_{DGA}+4K_{QQA}+4K_{FCA}} \tag{23}\]where \(K\) is the coefficient that is assigned to the respective factor and \(\mathit{HIF}\) is the score of each factor. In this case, \(K_{DGA}=10\), \(K_{\mathrm{OQA}}=8\), and \(K_{\mathrm{FCA}}=6\), respectively, and \(\mathit{HIF}\) is the rating (A, B, C, D, E) that is converted to a factor between 4 and 0 [1]. Finally, the HIs were categorized, as per Table 11 in [1], and grouped into discrete categories, from \"very good\" to \"very poor\", which correspond to transformer condition and interpretations.
# Quantitative Ultrasound Texture Analysis to Assess the Spastic Muscles in Stroke Patients
## 4 Discussion
### Limitations  
This study is subject to several limitations. First, due to the small number of subjects, it may reduce the strength of correlation between tested variables and hinder the consistency of the significant differences between bilateral BBM in different planes and radius. In addition, a previous study reported that EI is affected by sex and muscle type, we only recruited males with stroke to prevent the influence of gray intensity. Therefore, sex, as well as age and duration from stroke onset, should be investigated in a larger population to confirm the validity of these results and their generalizability. Secondly, the histopathological assessment was not performed to confirm post-stroke muscle quality. Thirdly, electromyography was not used to monitor muscle relaxation. Fourthly, as the primary purpose of this study was to verify the usefulness of this method in stroke patients, healthy individuals were not assessed. Lastly, we did not test the repeatability of LBP eductexture analysis; however, several studies have reported that echo texture analysis is a repeatable and valid method for quantifying skeletal muscle characteristics [30]. There are still no standard test methods for assessing muscle texture. As texture factors can be influenced by system factors (ultrasonic setting and probe tilt angle) and personal factors (age, sex, muscle type, and disease type), standardized procedures are needed.  
Conceptualization, writing-original draft preparation, P.-T.L.; writing-review and editing, supervision, C.T.-S.C.; Methodology, project administration, T.-S.W. All authors have read and agreed to the published version of the manuscript. This research was funded by Changhua Christian hospital, grant number: 061203.  
The authors declare no conflict of interest.
# Solder Joint Defect Detection in the Connectors Using Improved Faster-RCNN Algorithm
## 2 Materials and Methods
### Important Improvements  
In order to detect solder joint defects, we welded 625 solder joint samples and completed the images collection. In terms of algorithm, we used the open source TensorFlow version of Faster-RCNN algorithm on the network to train and test the test images. We found that the automatic detection of solder joint defects could not be realized with obvious effect, and a large number of undetected solder joint defects would lead to insufficient product performance. After comprehensive analysis, we believe that the main reasons are as follows: 1. There are too few training samples, so the network does not converge well after training; 2. The depth of the model is insufficient, and the ability to extract solder joint features of the connector is insufficient; 3. The scales of the default anchor do not match the size of the solder joint, and the positioning is not accurate, so the features are not fully learned by the network. Therefore, the following three improvement strategies are proposed, including data augmentation, k-means clustering to generate anchor boxes, and transfer learning with ResNet-101.
## 3 Methodology
### Optimanzed
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Stage**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{2}{c}{**Avg.**} & \multicolumn{2}{c}{**Mean**} & \multirow{2}{*}{**Avg.**} & \multicolumn{2}{c}{**Mean**} & \multirow{2}{*}{**Avg.**} & \multirow{2}{*}{**Squarero**} & \multirow{2}{*}{**(MSE)**} & \multirow{2}{*}{**Squarero**} & \multirow{2}{*}{**Avg.**} \\  & & & & **Aksololololumn{2}{c}{**Error (MAE)**} & & & & & **(RMSE)** & \\ \hline \multirow{4}{*}{FFNN-PSO} & 1 & 97.78 & & 0.0275 & & 0.031 & & 0.176 & \multirow{4}{*}{0.177} \\  & 2 & 98.15 & 98.0275 & & 0.0210 & & 0.028 & & 0.167 & \multirow{4}{*}{0.177} \\  & 3 & 98.16 & & 0.0256 & & 0.0319 & & 0.0318 & & 0.177 \\  & 4 & 98 & & 0.0287 & & & 0.0365 & & 0.191 & \\ \hline \multirow{4}{*}{FFNN-PSO} & 1 & 98.95 & & 0.016 & & 0.0168 & & 0.129 & \multirow{4}{*}{0.150} \\  & 2 & 97.94 & & 0.028 & & 0.0266 & & 0.137 & \multirow{4}{*}{0.150} \\  & 3 & 98.45 & & 0.018 & & 0.0216 & & 0.0253 & & 0.147 \\  & 4 & 97.76 & & 0.0176 & & 0.0363 & & 0.190 & \\ \hline \multicolumn{2}{c}{AEC} & \multicolumn{2}{c}{52.8} & \multicolumn{2}{c}{52.8\%} & \multicolumn{2}{c}{37.108} & \multicolumn{2}{c}{37.3} & \multicolumn{2}{c}{\(7.48\times 10^{3}\)} & \multicolumn{2}{c}{\(7.48\times 10^{3}\)} & \multicolumn{2}{c}{86.488} & \multicolumn{2}{c}{86.488} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The final average results of the performance of the proposed models. Figure 6: The effect of epoch. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Amount** \\ \hline Number of layers & 4 \\ Neurons & 40, 30, 10, 10 \\ Train Metric & MSE \\ Training Optimizer & PSO \\ Epochs & 100 \\ MSE goal & \(1\times 10^{-100}\) \\ Training Set & 70\% \\ Validation Set & 20\% \\ Test Set & 10\% \\ Minimum Gradience & \(1\times 10^{-200}\) \\ Maximum Training Time & \(0.5\times 60\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: FFNN-PSO model configurations and parameters. Finally, we make a comparison that shows some comparisons between this study and the literature studies of performance. Comparing our best method with other studies, the accuracy outperforms every single method (see Table 5).
# Virtual Network Function Embedding under Nodal Outage Using Deep Q-Learning
## 4 Deep Reinforcement Learning
### Reward Function  
The environment rewards the agent only if the selected action enables a successful placement of the VNFs and VLs; otherwise, a penalty is assigned to the agent. We have considered two types of rewards: local and global rewards. The local reward is given when the agent provides a satisfactory substrate node per VNF and substrate link per VL. The global reward is given when a VNF-FG is successfully deployed (i.e., a continuous path between the head and end VNFs is defined subject to latency and other constraints).  
In the literature, most researchers considered the Shortest-Distance algorithm to establish the path between the VNFs; adopting this technique will cause biases leading to high congestion on a few links, which will generate a prolonged link delay. This method will not be feasible for delay-sensitive applications. Thus, we adopted an approach where the path per VNF-FG is established based on the upper bound of the latency. We have set this limit to 30 ms in our analyses. Thus, the reward function is constructed based on the deployment of the VNFs and VLs onto the satisfying substrate nodes and links, respectively. In addition, the reward function ensures that the communication delay between the two VNFs should not exceed the upper bound of the latency. The algorithm for the placement of link is provided in Algorithm 3. Algorithm 2 summarises our model for VNF-FG embedding. We have examined the performance of this model under several scenarios like different network topology, nodal capacity, core capacity, and diverse nodal-outage levels. Thus, we have generated several results based on the network conditions, some of them being presented in the next section.
## 2 Method  
### Rayleigh-Sommerfeld Integral Model  
As shown in Figure 1, the transducer with arbitrary shape is located in the complete radiating surface \(S\). According to the Huygens principle, each point on \(S\) can be regarded as a vibration source radiating spherical waves outward, and the acoustic field at any point in space is the combined result of propagation in space in the form of a spherical wave at each point of the source plane, which can be expressed as Rayleigh integral [16]:  
\[p(x,y,z)=\frac{\mathrm{i}\rho c}{\lambda}\int_{s}u\frac{\mathrm{e}^{-\mathrm{ i}r}}{r}ds \tag{1}\]  
where \(\rho\) is the density of the medium, \(c\) is the phase velocity of the sound waves, \(u\) is the velocity amplitude of the piston, \(\lambda\) is the wavelength, \(k\) is the wavenumber, and \(r\) is the distance between the sampling point (\(x\), \(y\), \(z\)) in space and an elemental area \(\Delta S\) of the radiation surface \(S\).  
In most cases, Rayleigh integrals are difficult to find analytical solutions. Numerical processes are introduced into the calculation. The point source method is the most commonly used numerical calculation method. Divide the surface of the sound source into an infinite number of small facets. When the facets are small enough, any facet can be regarded as a point sound source. By superimposing the sound field distribution at each point of the source plane in space, we can obtain the distribution in the whole space. This method is suitable for calculating the free sound field generated in the acousto-optic crystal by a transducer of any shape. The expression is given by:  
\[p(x,y,z)=\frac{|ycu}{\lambda}\sum\limits_{n=1}^{N}\frac{e^{-y\sqrt{(x-x_{n})^{ 2}+(y-y_{n})^{2}+z}}}{\sqrt{(x-x_{n})^{2}+(y-y_{n})^{2}+z}} \tag{2}\]  
where the location of the sound source point is denoted by \((x_{n},y_{n},0)\), and \(N\) is the number of sampling points in the sound source. For uniformly excited rectangular sources, the complex surface velocity \(u\) is the same for all points.
# Oil Spill Monitoring of Shipborne Radar Image Features Using SVM and Local Adaptive Threshold
## 2 Materials and Methods  
### Materials  
The shipborne radar images (Figure 1) were acquired during a clean-up mission (Figure 2) on 21 July 2010, of the teaching-training ship Yukun (Figure 3) of Dalian Maritime University. The monitoring radius in the image is 0.75 of a nautical mile (NM). The image resolution is 1024\(\times\)1024 pixels. The image processing and analysis platform is MATLAB 2014a. The parameters of the shipborne radar are shown in Table 1.  
Figure 1: Original radar images, where (**a–d**) were acquired at 23:19:58, 23:20:27, 23:21:03, and 23:21:09, respectively.  
Figure 3: Hardware architecture showing (**a**) the installation position of the radar antenna and the (**b**) radar data acquisition system.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Product type & Sperry Marine B.V. \\ Band & X-band \\ Detection distance & 0.5/0.75/1.5.3.6.12 NM \\ Image size & \(1024\times 1024\) \\ Antenna type & Waveguide split antenna \\ Polarization mode & Horizontal \\ Horizontal detection angle & \(360^{\circ}\) \\ Rotation speed & 28–45 revolutions/min \\ Length of antenna & 8 ft \\ Pulse repetition frequency & 3000 Hz/1800 Hz/785 Hz \\ Pulse width & 50 ns/250 ns/750 ns \\ Data acquisition period & 2 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the shipborne radar.  
Figure 2: Clean-up mission. The red line indicates the departure route and the green line indicates the return route.
# The Use of Rapeseed Husks to Remove Acidic and Basic Dyes from Aquatic Solutions
## 3 Results and Discussion
### The Kinetics of Dye Sorption onto RH
Data**} & \multirow{2}{*}{**Equilibrium Time**} \\ \cline{3-4} \cline{6-9}  & & \(k_{1}\) & & \(q_{e,e,e,e}\) & & \(R^{2}\) & \(k_{2}\) & \(q_{e,e,e}\) & \\  & **[mg/L]** & **[1/min]** & **[mg/g]** & **-** & **[g/mg:min]** & **[mg/g]** & **-** & **[mg/g]** & **[min]** \\ \hline \multirow{3}{*}{AR18} & 50 & 0.0453 & 8.01 & 0.9953 & 0.0072 & 8.89 & 0.9964 & 8.14 & 150 \\  & 100 & 0.0497 & 14.97 & 0.9846 & 0.0044 & 16.48 & 0.9993 & 15.41 & 1  
The sorption of dyes onto RH was also described using the intraparticular diffusion model (Table 3, Figure 4). Graphs plotted from the obtained experimental data suggest that dye sorption onto the tested sorbent proceeded in three main phases. In the first, most intense but relatively short phase, the dye molecules diffused from the solution to the sorbent's area and occupied the most accessible active sites on the RH surface. When most sorption centers had been saturated on the sorbent's surface, phase two began. In this phase, the dye molecules probably began to occupy active sites located in more difficult-to-reach sites of RH, such as pores. This phase was characterized by great competition between dye molecules for free active centers, which, when combined with a small number of active sites, translated into a lower sorption intensity. When most sorbent's sorption centers had been saturated with dye particles, phase three began--the least intense and the longest in most cases. It consisted in the slow saturation of the last active sites in the pores of RH [54]. After the third phase, the systems entered into a state of sorption equilibrium. Figure 4: Intraparticular diffusion model of the sorption of: (**a**) AR18, (**b**) AY23, (**c**) BR46, and (**d**) BV10 onto RH (average + range). The values of k\({}_{\rm d1}\), k\({}_{\rm d2}\) and k\({}_{\rm d3}\) constants determined from the intraparticular diffusion model correlated strongly with the initial dye concentration. This confirms the relatively low affinity of the tested dyes to the RH functional groups. The longer duration of the first sorption phase of basic dyes compared to the acidic dyes could be due to the very strong electrostatic interaction of the negatively charged functional groups of AR18 and AY23 with the positively charged RH surface at pH = 2. In the second phase, when RH pores were saturated with dye molecules, the alkaline dyes already had a clear advantage over the acidic dyes due to their smaller particle sizes. This was mainly manifested in the longer duration of this phase.
# Effect of Target Size, Location, and Input Method on Interaction in Immersive Virtual Reality
## 4 Results
### Error Rate  
We examined how the error rate (ER) varied with each experimental condition. ER of the large target was 0.35% and 0.65% in the small target. The chi-squared test was performed to determine whether these differences were statistically significant. The results showed that the size of the target was not a significant factor in the ER (\(p\) = 0.057; \(\alpha\) = 0.05). Additionally, the manual-selection method showed ER of 0.90%, and the gaze-selection method was 0.10%. ER was about nine times different, depending on the selection method. As in the chi-squared test, the two target selection methods were analyzed to be significant factors for the ER (\(p\) < 0.001).  
Figure 5: Sequence of target selection.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Large Size** & **Small Size** & **Total** \\ \hline Manual selection & 1948 (\(\pm\)859) & 2316 (\(\pm\)997) & 2132 (\(\pm\)944) \\ Gaze selection & 2697 (\(\pm\)919) & 2719 (\(\pm\)885) & 2708 (\(\pm\)902) \\ Total & 2322 (\(\pm\)961) & 2518 (\(\pm\)963) & 2420 (\(\pm\)967) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The task completion time and standard deviation according to the target size and selection method. (unit: ms).
# A Review: Image Processing Techniques' Roles towards Energy-Efficient and Secure IoT
### Contributions and Article Organization  
#### 1.4.1 Contributions  
This article has addressed a variety of contributions, which can be summed up as follows:  
1. It examines a variety of publications and research studies that discuss the designing and building of IoT applications and systems that make use of image-processing techniques. These papers and studies involve the topic of designing and building IoT applications and systems;
2. It highlights a variety of contributions made by image processing techniques towards other concerns that IoT applications typically face and find difficult to overcome, such as the energy consumption made by IoT systems and the security of IoT data. These issues include: (1) The energy consumption made by IoT systems; and (2) The security of IoT data;
3. It highlights several different types of IoT applications that depend on image processing techniques, such as image processing-based IoT monitoring applications, image processing-based IoT security applications, image processing-based IoT location detection applications, IoT safety applications, and IoT healthcare applications;
4. The process of object detection is extremely important and significant since it has an impact on the decision that is made. There won't be much of a focus on it if there isn't an accurate edge detection mechanism, though, because so many reviewed publications have used it to finish the jobs they needed to. This article highlights and reviews a number of IoT applications that rely on the object detection process, which is considered crucial to perform such IoT-related tasks as monitoring, classification, and recognition. The applications are discussed in detail in this article;
5. It uses the PRISMA 2020 method for systematic reviews in order to emphasize precise steps on the extraction technique of the publications and how that strategy has been performed for various cascade-step phases;It analyzes the reviewed articles according to various criteria;
* It discusses a number of important aspects that made it possible for image processing to act as a connection between us and an intelligent and safe IoT environment;
* It suggests a mind map highlighting the most important IoT systems-related issues/concerns and their solutions utilizing image processing techniques; to contribute to future research for potential improvements in this regard;
* It provides the readers with a number of significant future trends that need to be taken into consideration by potentially interested researchers and designers.
## 3 System Architecture and Key Technologies  
The system architecture is designed around heterogeneous cloud management, dynamic data aggregation, and three-dimensional visual analysis, creating a clear, hierarchical, and highly integrated framework, as shown in Figure 1. The system employs heterogeneous cloud resource management and adaptive scheduling strategies to address the challenges of multi-source data access and the optimization of computational resource allocation. These strategies dynamically allocate computing resources across different cloud environments to achieve efficient data processing and the optimal utilization of computational resources, thereby reducing latency and enhancing processing capabilities. The dynamic aggregation method for heterogeneous geological data integrates and converts data from various sources, enabling seamless coupling and consistent data representation across multiple scales and sources. To meet the demands of three-dimensional visual analysis, the system introduces an optimized rendering strategy to improve the rendering efficiency and interaction performance of large-scale geological data, ensuring that users experience smooth operations and quick response times during the interactive visualization of complex geological data.
# Digital Twins in Solar Farms: An Approach through Time Series and Deep Learning
## 4 Methodology
### Architecture of the Digital Twin  
To obtain the DT, we propose the use of an autoencoder architecture. This choice is due to characteristics of this type of architecture to encode the signals, which allows a better understanding of the relationships between the signals to obtain the model. For this, a combination of different layers was used in the architecture as follows:  
* Convolutional Conv1D Layer: the objective of this layer is to extract the most significant characteristics from the data set.
* Convolutional Conv1 dTranspose Layer: the objective of this layer is to return to the configuration of the input, keeping the characteristics learned by the previous layer.
* LSTM Recurrent Layer: By using this layer, it is intended that the network can learn the temporal relationships that are established between a multivariate data set.
* RepeatVector reorganization layer: This layer is used as an adapter that allows the Encoder output to be concatenated with the Decoder input.
* LSTM Recurrent Layer: By using this layer, it is intended that the network can decode the temporal relationships established between a multivariate data set.
* TimeDistributed Recurring Layer: This layer is used as a container for a Dense layer so that each weight can produce an output at each instant of time (time step).  
The combination of Conv1D and LSTM layers allows mixing characteristics of the time series (Conv1D) and the time relationships between the different series. Table 1 shows the characteristics of the proposed autoencoder.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Layers** & **Characteristics** \\ \hline \multirow{5}{*}{Encoder} & Input & Shape: time steps x metrics \\ \cline{2-3}  & \multirow{5}{*}{Convolutional1D} & Number of filters: 64 \\  & & Kernel size: 7 \\  & & Regularization function: L2 = 0.0001 \\  & & Activation function: ’relu’ \\ \cline{2-3}  & \multirow{2}{*}{Dropout} & Frequency: 0.2 \\ \cline{2-3}  & & Transpose Conv1D & Number of filters: metrics \\  & & Kernel size: 7 \\ \cline{2-3}  & \multirow{2}{*}{LSTM} & Number of layers: 2 \\  & & Number of output units: 128 and 64 \\  & & Activation function: ’relu’ \\ \cline{2-3}  & \multirow{2}{*}{Repeat Vector} & Repetition factor: metrics \\ \hline \multirow{3}{*}{Decoder} & \multirow{3}{*}{LSTM} & Number of layers: 2 \\  & & Number of output units: 64 and 128 \\ \cline{1-1}  & & Activation function: ’relu’ and ’softmax’ \\ \cline{1-1} \cline{2-3}  & \multicolumn{2}{c}{Time Distributed} & Layer: Dense (Number of Units: metrics) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the Autoencoder.  
By using the DT, it is proposed to obtain the reconstruction of the system signals, as well as its reconstruction error, to be able to establish using this reconstruction error as a threshold for detecting anomalies.