esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Related Work
On the other hand, there are limited studies in the literature that deal with PQD classification using their images. In the study by [PERSON] et al. [27], a transfer learning approach is used for classifying PQDs through image classification. VGG-16 is used as a pre-trained model to classify five PQDs through scalograms. The results confirmed the efficacy of the proposed strategy. In another work [28], the authors developed a deep transfer learning framework for PQD classification. A small dataset containing voltage waveform images was used to train the model and classify four different types of PQDs. With the limited training dataset, the model achieved high accuracy for PQD classification. A signal transformation from 1-D to two-dimensional (2-D) time-frequency images is performed in [29]. A CNN-based deep learning model is employed to classify sixteen different types of PQDs. Deep learning models are extremely important for generalization and problem learning. Additionally, the generalization ability of a single classification model can be enhanced by combining multiple classification models via an ensemble approach. The idea of this approach is to enhance the accuracy and stability of the overall model, thereby achieving better performance. Some studies [30; 31; 32] have employed an ensemble approach to classify various PQDs. However, ensembles of shallow learners are trained using PQD signal data instead of deep learning models trained on PQD image data. The stated work reveals that using an ensemble approach with deep learning models is a promising way to improve the accuracy, robustness, and generalization ability of the PQD classification model. This strategy has the potential to simplify the training process, reducing complexity and time while maintaining a high classification performance. This paper proposes a voting ensemble model that utilizes the potential of pertained DCNNs by using time-frequency images of various types of PQDs as training samples. The proposed approach decreases the complexity of the training course and achieves high classification performance.
## 3 Soil-Pile Analytical Model
### System of Equations  
Each pile slices undergo an absolute offset (\(u_{i}\)), as a result of the application of the external load (\(Q_{pile}\)) and capping layer weight (\(W_{pile}\)). \(u_{i}\) is a positive downward movement [16]. At the same time, soil slices undergo a downward movement (\(w_{i}\)) as a result of the load application (\(Q_{soil}\)) and associated capping weight (\(W_{soil}\)) pile-soil interaction depends on the relative displacement between them (\(u_{i}\)-\(w_{i}\)). The following equation system solves the mathematical problem.  
For the pile  
\[Q_{pile}+W_{pile}=Rf_{1}(u_{1}-w_{1})+K_{1}(u_{1}-u_{2})-P_{1} \tag{21}\]  
\[0=Rf_{2}(u_{2}-w_{2})+K_{2}(u_{2}-u_{3})-K_{1}(u_{1}-u_{2})-P_{2} \tag{22}\]  
\[0=Rf_{3}(u_{3}-w_{3})+K_{3}(u_{3}-u_{4})-K_{2}(u_{2}-u_{3})-P_{3} \tag{23}\]  
\[0=Rf_{1}(u_{i}-w_{i})+K_{i}(u_{i}-u_{i+1})-K_{i-1}(u_{i-1}-u_{i})-P_{i} \tag{24}\]  
\[0=Rp(u_{n}-w_{n})+Rf_{n}(u_{n}-w_{n})+K_{n}(u_{n})-K_{n-1}(u_{n-1}-u_{n})-P_{n} \tag{25}\]  
\(K_{i}\) is the pile stiffness that takes the value \(K_{i}\) = EA/L.  
where:  
E: pile deformation modulus.  
A: pile cross-section area.  
L: slice length.  
On the other hand, for the soil slices interacting with the pile:  
\[Q_{soil}+W_{soil}=-Rf_{1}(u_{1}-w_{1})+K_{soil1}(w_{1}-w_{2}) \tag{26}\]  
\[0=-Rf_{2}(u_{2}-w_{2})+K_{soil2}(w_{2}-w_{3})-K_{soil1}(w_{1}-w_{2}) \tag{27}\]  
\[0=-Rf_{3}(u_{3}-w_{3})+K_{soil3}(w_{3}-w_{4})-K  
The solution of the previous system provides the seeking relationship between applied external pressure F0 and displacement at the top pile (\(u_{1}\)). Since skin friction \(R\!f\!i(u_{i})\) and base resistance \(R_{p}(u_{n})\) are functions of displacement, the system is non-linear, and it has to be used as an iterative algorithm to reach the solution.
## 3 The Proposed Approach of SCDNN
### Scdnn  
The SCDNN model includes three steps, and the general task in each step is described as follows and shown in Figure 2.  
Step 1: The dataset is divided into two components: training and testing datasets. The training dataset is clustered by SC and this output is regarded as the training subset labelled Training Datasets 1-\(k\). The SC centres from the training dataset clustering process are stored to serve as initialisation cluster centre for generating the testing dataset clusters. Because intrusion data features indicate  
Figure 2: The SCDNN flow chart is divided into three steps and shows each process in detail.  
similar attributes of each type in the raw dataset, points in the training dataset with similar features are aligned into groups and regarded as the same subset. In order to improve SCDNN performance, different cluster numbers and values of _sigma_ are considered. The number of clusters ranged from 2 to 6 and _sigma_ from 0.1 to 1.0. The samples are assigned to one cluster by similarity. The minimum distance from a data point to each cluster centre is measured by Algorithm 1 and each point is assigned to a cluster. The training subsets generated by clusters are given as input to the DNNs. In order to train the different subsets, the number of DNNs is equal to the number of data subsets. The architecture of each DNN consists of five layers: two hidden layers, one input layer, one softmax layer and one output layer. In this paper, the two hidden layers learn features from each training subset and the top layer is a five-dimensional output vector. Each training subset which are generated from the \(k\)th cluster centre by SC algorithm in clustering process, regarded as input data to feed into the \(k\)th DNN, respectively. These trained sub-DNN models are marked as sub-DNN 1 through sub-DNN \(k\).  
Step 2: The testing dataset, which has been divided from the raw dataset, is used to generate \(k\) datasets. The previous cluster centres obtained from SC cluster in Step 1, are regarded as the initialization cluster centres of the SC algorithm in this step. The test dataset which are divided by SC cluster, are regarded testing subsets. These subsets are denoted as Test 1 through Test \(k\).  
Step 3: The \(k\) testing data subsets are fed into \(k\) sub-DNNs, which were completed by the \(k\) training data subsets in Step 1. The output of each sub-DNN is integrated as the final output and employed to analyse positive detection rates. Then, a confusion matrix is used to analyse the detection performance of the five classifications: Normal, DoS, Probe, U2R and R2L.
# FF-PCA-LDA: Intelligent Feature Fusion Based PCA-LDA Classification System for Plant Leaf Diseases
## 3 Material and Methods
### Handcrafted Feature Extraction
#### 3.3.2 Local Binary Patterns  
LBPs use the spatial information of an image pixel within a certain radii. Binary patterns are generated by employing a radius which depicts the texture of neighboring pixels by considering the central pixel as a threshold [26]. LBPs have better representation capability to cater to the affected area of leaf disease. A total of 256 LBP features are obtained by selecting a radius of 1, 2, and 3 pixels, empirically. Details of the LBP can be found in [27].  
The following expressions are used to obtain LBP features:  
\[LBP_{R,p}(x,y)=\sum_{p=1}^{p-1}s(i_{p}-i_{c})2^{p} \tag{11}\]  
where \(i_{p},i_{c}\), and \(R\) represent the pixel, its spatial information, and its radius, respectively. The pictorial representation of the LBP is shown in Figure 3.  
The function \(s\) in Equation (11) can be defined as:  
\[s(x)=\begin{cases}1,&ifx\geq 0\\ 0,&\text{otherwise}\end{cases} \tag{12}\]
# Modeling Hourly Soil Temperature Using Deep BiLSTM Neural Network
## 2 Data
It is generally believed that the absolute value of r is 0.0-0.2 indicates an extremely weak or no correlation between \(x\) and \(y\), 0.2-0.4 indicates a weak correlation, 0.4-0.6 indicates a moderate correlation, 0.6-0.8 indicates a strong correlation, and 0.8-1.0 indicates an extremely strong correlation. \[\mathrm{r}=\frac{\mathrm{n}\sum_{i=1}^{n}x_{i}y_{i}-\sum_{i=1}^{n}x_{i}\sum_{i= 1}^{n}y_{i}}{\sqrt{\mathrm{n}\sum_{i=1}^{n}x_{i}^{2}-\left(\sum_{i=1}^{n}x_{i} \right)^{2}}\sqrt{\mathrm{n}\sum_{i=1}^{n}y_{i}^{2}-\left(\sum_{i=1}^{n}y_{i} \right)^{2}}} \tag{1}\]  
Figure 2 shows the correlation coefficient between the ST amplitude and meteorological parameters for the different depths, in which the correlation coefficient greater than 0 indicates a positive correlation between \(x\) and \(y\), and less than 0 indicates a negative correlation. The parameters appearing between -0.2 and 0.2 are month, day of the month, air temperature observed, wind direction average, wind speed maximum, relative humidity, and dew point temperature, indicating that these parameters have no correlation or have extremely weak correlation with ST amplitude for most depths. Figure 3 shows the correlation coefficients between the daily average ST and the meteorological parameters for the different depths. It can be seen that, except for the wind direction average, wind speed maximum, and relative humidity, the other parameters are moderately or strongly correlated with the daily average ST for most depths. Figure 2: Correlation coefficients of (x) daily weather parameters and (y) soil temperature (ST) amplitude for different depths. Through the analysis of Figures 2 and 3, we delete the parameter in which the absolute value of correlation coefficient is less than 0.2, and choose eight parameters as the input vector for predicting ST amplitude. For the forecasting task of daily average ST, eleven parameters are selected as input. Details of the input parameters are shown in Table 2, in which meteorological parameters are collected by sensors at intervals of one hour.
## 4 Experimental Results
### Individual Evaluation
#### 4.2.4 Comparison with the State-Of-The-Art
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{CID:IQ} & \multicolumn{4}{c}{VDID2014} \\ \hline  & \multicolumn{2}{c}{**50 cm (2.5”H)**} & \multicolumn{2}{c}{**100 cm (5”H)**} & \multicolumn{2}{c}{**4”H**} & \multicolumn{2}{c}{**6”H**} \\ \hline
**Metric** & **PCC** & **SROCC** & **PCC** & **SROCC** & **PCC** & **SROCC** & **PCC** & **SROCC** \\ \hline  & \multicolumn{4}{c}{**Full-Reference**} \\ \hline  
ur method (NR)** & **0.870** & **0.867** & **0.870** & **0.846** & 0.932 & 0.907 & 0.940 & 0.922 \\ \hline \end{tabular}
\end{table} 
Table 8: Pearson (PCC) and Spearman (SROCC) correlation coefficients between the predicted quality scores and the subjective ones for each viewing distance of CID:IQ and VDID2014. Highest values per category are shown in bold and the highest one has a grey background. \begin{table}
\begin{tabular}{l c c} \hline
**Method** & **PCC** & **SROCC** \\ \hline
**PSNR** & 0.636 & 0.635 \\ \hline
**PSNR-HVS-M (FR)** & 0.696 & 0.686 \\ \hline
**PSNR-HA (FR)** & 0.694 & 0.694 \\ \hline
**C-PSNR-HA (FR)** & 0.737 & 0.740 \\ \hline
**C-PSNR-HVS-M (FR)** & 0.744 & 0.742 \\ \hline
**SSIM** & 0.623 & 0.680 \\ \hline
**CSSIM (FR)** & 0.798 & 0.793 \\ \hline \end{tabular}
\end{table} 
Table 9: PCC and SROCC values whatever the viewing distance computed on CID:IQ.
## 5 Conclusions  
It has been demonstrated that it is possible to design and construct a contactless, easy-to-use application that allows contactless access to medical images and videoconferencing while maintaining complete sterility in the operating room. The system's essential elements are justified economically and in terms of implementation. The proposed mobile solution can be quickly transferred to another operating room if necessary. The ORTHO_Kinect_OR application and the Kinect motion capture sensor as an optical tracking technology are suitable for contactless computer use in the OR, medical image retrieval from the PACS, its analysis, and videoconferencing use for telementoring and teleeducational purposes in the OR. In the future, the developed application will be helpful for the remote control of all programs used in the OR, including electronic medical records.  
Conceptualization, W.M.G., T.M. and R.S.; methodology, W.M.G.; software, T.M. and R.S.; validation, W.M.G. and R.S.; formal analysis, W.M.G., T.M. and R.S.; investigation, W.M.G.; resources, W.M.G. and R.S.; data curation, W.M.G.; writing--original draft preparation, W.M.G., T.M. and R.S.; writing--review and editing, W.M.G. and R.S.; visualization, W.M.G., T.M. and R.S.; supervision, W.M.G. and R.S.; project administration, W.M.G. and R.S.; funding acquisition, W.M.G. and R.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the Medical University of Warsaw for the project entitled \"A three-dimensional computer system for designing the functional evaluation of orthopedic equipment in patients with disorders of statics and kinematics of the body in the course of civilization diseases of the musculoskeletal system\" (Protocol code KB 22/2012; date of approval 17 January 2012).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 2 Problem Definition
### Objective Functions  
In this optimization problem, two objectives are considered. The first is the number of GSs, which should be minimized, while the second objective is the maximization observing rate to reach \(k\)-heavy coverage. The objective function can be defined as  
\[\left\{\begin{array}{l}C_{1}=\underset{k}{\min}X_{k}(1)=\underset{k}{\min}( n_{GS}^{k})\\ C_{2}=\underset{k\_Heavy\_Coverage}{\max}(R_{obs})\end{array}\right., \tag{2}\]  
where \(R_{obs}\) is the observing rate, which can be expressed as  
\[R_{obs}=\underset{j\in All\_satellites}{\min}\left(\frac{\Delta t\left(n_{visible\_GS}^{j}>=k_{Heavy\_Coverage}\right)}{\Delta t_{ observation}}\right), \tag{3}\]  
where \(n_{visible\_GS}^{j}\) refers to the number of GSs that are visible to satellite \(j\), \(k_{Heavy\_Coverage}\) denotes the minimum number of GSs that are visible to each satellite at any epoch (in this study, this minimum should be three), \(\Delta t\) is the duration of time where satellite \(j\) is visible by at least \(k_{Heavy\_Coverage}\) GSs and \(\Delta t_{observation}\) is the whole time duration of observation.  
In this study, k-HC is set to 3 in order to reach a coverage multiplicity of at least three GSs. Therefore, the parameter \(k_{Heavy\_Coverage}\) is fixed throughout the entire study.  
Figure 2: Variable (chromosome) structure.  
As multi-objective optimization processes tend to minimize the values of the objectives, matching the fitness function to the minimization objective is required. For the observing rate objective, the non-observing rate is defined, which represents the length of time that each satellite is not visible to k GSs during each epoch, and it is defined as the complementary rate to the observing rate as follows:  
\[R_{Non-obs}=1-R_{obs}. \tag{4}\]  
Therefore, the new objective function is expressed as  
\[\left\{\begin{array}{l}C_{1}=\underset{k}{\min}X_{k}(1)=\underset{k}{\min}( n_{\mathrm{CS}}^{k})\\ \widetilde{C}_{2}=\underset{k\_Heurry_Coerage}{\min}(R_{Non-obs})\end{array} \right.. \tag{5}\]
## 3 Experimental Results and Analysis
### The Effects of Solid Volume Concentration on the Rheological Behavior of Debris Flows
Coarse and fine particles adhere to each other, forming mud films on the surface of coarse particles. Collisions and compression occur between solid particles, and the internal friction of the slurry gradually increases, leading to an increase in the yield stress. Considering physical and chemical properties such as particle size distribution, clay content, mineral and elemental composition, the influence of clay mineral content is also an important factor that cannot be ignored [24]. This is mainly because clay minerals themselves have significant properties such as a large specific surface area, a double electric layer, hydrophilicity, and surface features like cation adsorption. Regarding the clay minerals in the raw materials of the debris flow from Jiangjiagou in this study, the main ones are illite and chlorite (Table 1). These typical clay minerals are superior to non-typical clay minerals in terms of specific surface area, hydrophilicity, expansibility, and surface adsorption capacity. Although a significant solid-liquid phase separation is prone to occur in the debris flow slurry at low volume concentrations, as the solid volume concentration increases, the proportion of clay minerals gradually rises, leading to an eventual continuous enhancement in capturing pore water. Hence, a debris flow with strong fluidity and viscosity can form. Consequently, clay minerals are also crucial factors in the establishment of a strong structure, great stability, and minimal sedimentation in debris flows. Furthermore, experiments have also shown that there is a significant shear-thinning phenomenon in debris flows, and the shear-thinning is more pronounced at low shear rates than at high shear rates, greatly reducing the viscous resistance of debris flows. Therefore, when the sediment stagnant in a channel bed is triggered according to its rheological properties, the flow velocity may suddenly increase. The head of the debris flow is disturbed, which hinders the formation of a mesh structure in the slurry, enhancing the shear-thinning performance and reducing resistance; however, the flow at the tail of the debris flow is small, which weakens the disturbance, allowing for the developing of a good mesh structure and easily leading to a shear-thickening behavior, thereby increasing the movement resistance.
# Particle Swarm Optimization Based on a Novel Evaluation of Diversity
## 3 Pso-Ed
### The Main Idea of PSO-ED  
Given a dimension \(D\) (\(D=10\) or \(30\) for CEC2015), the flowchart of the PSO-ED is shown in Figure 1. PSO-ED has four main modules: exploration degree evaluation, an adaptive update of inertia weight, normal and disturbance update modes, and swarm reinitialization.  
As mentioned above, the core problem of particle swarm optimization algorithm is how to balance the exploration and exploitation abilities of the swarm. To address this problem, in Module 1, we develop a technique of exploration degree based on the diversity of the swarm in the exploration, exploitation, and convergence states. In module 2, with the help of exploration degree, we adaptively update the inertial weight of the swarm to ensure that the swarm has a larger exploration degree in the exploration state and a smaller one in the convergence state. In Module 3, we give two update modes (normal and disturbance update modes) for each particle according to its fitness, so as to achieve the global optimum quickly. In Module 4, to help the swarm escape from local traps, a novel swarm reinitialization scheme is proposed.  
For ease of description, we summarize the parameters used in the rest of the paper in Table 1. The definition of each critical parameter is also given when it first appears in the context.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Quantity** \\ \hline \(t\) & The index of the current iteration \\ \(x_{i}\) & The position of particle \(i\), and \(x_{i}\in[x_{lb},x_{ub}]\) where \(x_{lb}\) and \(x_{ub}\) are lower and upper bounds of the position \\ \(v_{i}\) & The velocity of particle \(i\), and \(v_{i}\in[-v_{ub},v_{ub}]\) where \(v_{ub}\) is the upper bound of the velocity \\ \(w,c_{1},c_{2},c_{3}\) & The inertial weight and accelerations for PSO respectively, and \(c_{3}\) is defined in Section 3.4 \\ \(D_{i},\ P_{g},\ P_{t}\) & The position of _Phest_, _Gbest_ and _Tbest_, where _Tbest_ denotes the best position over all historical rounds of evolutions \\ \(N_{s}\) & The size of the swarm \\ \(D\) & The dimension of the problem space \\ \(N_{total}\) & The total number of iterations \\ \(S\) & The evolution state of the swarm, and \(S\in\{e_{r},\ e_{i},\ e_{c}\}\) where \(e_{r},\ e_{i},\ e_{c}\) represents the Exploration state, the \\  & Exploitation state, and the Convergence state, respectively. \\ \(N_{state}\) & The number of generations of state maintenance, and \(N_{state}\in[N_{smit},N_{smax}]\) where \(N_{smit}\) and \(N_{smax}\) are the \\  & minimum and maximum generations of state maintenance \\ \(E_{r}\) & The average evolution rate \\ \(N_{ud}\) & The number of undeveloped generations, and \(N_{ud}<N_{smax}\) where \(N_{uamx}\) is the Maximum consecutive undeveloped generations \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nomenclature.
# Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter
## 4 Results and Discussions  
The proposed sensor fusion algorithm is demonstrated in a relatively open environment, which allows for uninterrupted satellite signal and individualized GNSS localization. The acquisition frequency for GNSS data is 1 Hz, while the IMU data are acquired at a frequency of 100 Hz; the smooth dimension \(L\) is selected as 10. These parameters provide acceptable conditions for analyzing the accuracy of combined GNSS/IMU localization, even in the event of GNSS signal loss, as demonstrated by the trajectory shown in Figure 2. In order to evaluate the localization accuracy of the GNSS/IMU combined localization process, the simulation involves artificially inducing the loss of lock by introducing exaggerated errors to the GNSS satellite observation information based on raw GNSS data.
## 7 Future Directions
### Channel Modeling  
Channel parameters are one of the major concerns that may influence the performance of the 5G WCS and include HO decisions and mobility management protocols. Therefore, to analyze, build, and optimize mobility management in 5G HetNets, adequate channel modeling plays a key role. Accurate channel modeling enables efficient allocation of radio resources, intelligent beamforming, and antenna selection, ensuring seamless HOs and an improved user experience. We believe that stochastically and deterministically based wireless channel modeling can optimize mobile data traffic between the sender and receiver, especially in high-speed moving objects. Path loss, signal fading, and multiple component parameters can be estimated and minimized through channel modeling, thus achieving seamless connectivity among mobile users.
## 1 Introduction  
Over the past few years, free-space optical (FSO) communications have attracted considerable attention, becoming an interesting alternative for next-generation broadband. Compared with conventional radio-frequency (RF) systems, the FSO system has the advantages of license-free transmissions, large bandwidth, simple and inexpensive implementation, and high-rate transmission capability [1, 2, 3].  
Despite these appealing features, there are also some inevitable challenges hampering the system performance when transmitting through the atmosphere. Typical degradations include atmospheric turbulence-induced fading, geometrical loss and pointing errors [4]. As a result, the transmission distance and system performance are limited significantly. To overcome this limitation, relaying techniques are investigated by dividing a long link into several short links [5]. The utilization of relays can also remove the requirement that the source node and the destination node be in line of sight.