esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Digit Image Processing
### Image Preprocessing  
As outlined in the Introduction, TMs exhibit significant variability in digit formats, fonts, colors, and sizes. To address this diversity and ensure more uniform data input  
Figure 5: View of metering device through camera lens of the edge device used for creation of datasets.  
for our ML model, our system employs a series of preprocessing techniques designed to standardize the captured images.  
The first step in image preprocessing, taken by the camera, is to crop the image and extract single digits, which, in this case, fit into a 16 \(\times\) 24 frame, as shown in Figure 6. This proves to be a huge reduction of the input instance size.  
Next, a transformation is applied to convert the grayscale image to a black-and-white image, which further decreases the image size by a factor of 8, as each pixel can be represented by a single bit instead of a byte. The initial approach to this transformation was to compare each pixel value to a fixed threshold and classify it as black or white accordingly. However, due to variations in lighting conditions, it is impossible to establish a single threshold value that would produce meaningful output in all cases.  
A better way to transform images to black and white is by applying the K-means clustering algorithm (\(k=2\)). As shown in Figure 7 (middle part), the algorithm makes a solid distinction between black and white areas in the image.  
Figure 6: Conversion to B/W image format using a fixed threshold: TH = 128 (**left**), TH = 192 (**right**).  
Figure 7: Initial image taken using camera on edge device, intermediate image after B/W conversion and the final image without artifacts.  
The remaining issue to be solved is the removal of white artifacts, which are parts of the figures above or below the one that needs to be detected. That is accomplished by utilizing a coloring algorithm, which paints all the disjoint white areas in the picture in different colors. In the example shown in Figure 7, the coloring algorithm will color the white areas in three different colors, as there are three disjoint areas. However, as the lower two areas are both parts of the figure that need to be detected, we apply an additional step, which is to merge any two differently colored areas that have pixels touching in corners. Then, we recognize the biggest colored area and eliminate all the smaller ones as artifacts. What remains is the actual image of the figure centered both horizontally and vertically (right-hand side of Figure 7), which concludes the preprocessing part.  
The full sequence of preprocessing operations is described in Algorithm 1.  
```
0: Cropped \(16\times 24\) part of the grayscale image containing digit
0: Clean B/W digit image without artifacts  Convert to B/W by running k-means clustering (\(k=2\)) on grayscale image  Color disjoint white areas in different colors  Merge differently colored areas that have pixels touching in corners  Identify the dominant color  Paint all the dominant-colored pixels white  Paint all the rest black
```  
**Algorithm 1** Image preprocessing algorithm
## 3 Materials and Methods
### Moving-Bed Tests (ADCP-BT)  
The ADCP-BT method, also called the kinematic model, computes bedload transport rate based on Equation (1), where \(v_{MB}\) is the average moving-bed velocity, \(\delta_{b}\) is the thickness of the bedload active layer and \(c_{b}\) is the sediment concentration in the bedload layer [27; 28; 29].  
\[q_{b,\ ADCP}=v_{MB}\cdot\delta_{b}\cdot c_{b} \tag{1}\]  
Moving-bed tests (MB) were performed with ADCP to measure \(v_{MB}\), using the bottom tracking feature. Measurements were carried out at the same verticals where HS samples were collected. Tests lasted 5 min.  
The ADCP-BT method was applied following two different approaches: (1) based on [PERSON] [17], hereafter called ADCP-BTe; and (2) based on [PERSON] [27], hereafter called ADCP-BTvr. In the ADCP-BTe method, the thickness of the bedload layer (\(\delta_{b,e}\)) in each vertical was adopted as twice the median diameter (\(d_{50}\)) of HS samples (Equation (2) [17]) and the sediment concentration in the bedload layer (\(c_{b,e}\)) followed Equation (3), both classical formulae from the literature [1]. In the ADCP-BTvr method, \(\delta_{b,vr}\) and \(c_{b,vr}\) were computed based on the formulations proposed by [PERSON] [27], (Equations (4)-(8)). Density was adopted as \(\rho_{s}=2650\) kg/m\({}^{3}\) and porosity as \(p=0.4\) (usual values for sand [1]).  
\[\delta_{b,e}=2\times d_{50} \tag{2}\]  
\[c_{b,e}=(1-p)\times\rho_{s} \tag{3}\]  
\[\delta_{b,vr}=0.3\times d_{50}\times D_{*}^{0.7}\times T^{0.5} \tag{4}\]\[D_{*}=d_{50}\times\left[\frac{(s-1)g}{v^{2}}\right]^{\frac{1}{3}} \tag{5}\]  
\[T=\frac{\left(u_{*}^{\prime}\right)^{2}-\left(u_{*cr}\right)^{2}}{\left(u_{*cr} \right)^{2}} \tag{6}\]  
\[u_{*}^{\prime}=\frac{\overline{u}}{5.75\times\log\left(\frac{12h}{3d_{00}} \right)} \tag{7}\]  
\[\frac{c_{b,vr}}{c_{0}}=0.18\times\frac{T}{D_{*}} \tag{8}\]  
where \(\rho_{s}\) is the density of sand (kg/m\({}^{3}\)); \(p\) is the porosity of sand (dimensionless); \(D_{*}\) is the dimensionless particle parameter; \(T\) is the transport stage parameter (or excess shear parameter) (dimensionless); \(v\) is the kinematic viscosity of water (m\({}^{2}\)/s); \(u_{*}^{\prime}\) is the bed shear velocity related to grains (m/s); \(u_{*cr}\) is the critical bed shear velocity (m/s) according to [PERSON]; \(\overline{u}\) is the depth-averaged flow velocity (m/s); \(h\) is the water depth (m); \(d_{00}\) represents the grain size (m) of the superficial bed sediment for which 90% of the particles are lower in weight; \(c_{0}\) is the maximum (bed) concentration (=0.65).  
Due to the arrangement of the equipment on the boat, it was not possible to track exactly the same point (in each vertical) with the ADCP and the Helley-Smith sampler. This contributes to the uncertainties related to the sampling procedures and the variability of the results. However, the distance between both devices was kept the shortest as possible, not exceeding 3 m. The results of MB tests were used to estimate unit bedload transport rates at the 6 verticals and then integrate them along the cross-section.
# A Comprehensive Hybrid Deep Learning Approach for Accurate Status Predicting of Hydropower Units
## 3 Methodology
### Dual-Path Memory Integration for Extracting Adjacent Temporal Features
The activation function \(\tanh\) is used to calculate the cell state \(C_{l,t}\) at the current time step, while \(C_{l,t-1}\) denotes the cell state from the previous time step. \(F_{l,t}\) and \(I_{l,t}\) are the outputs from the forget and input gates, respectively. The dot symbol \(\cdot\) denotes matrix multiplication, while the symbol \(\circ\) represents the Hadamard product. \(W_{c}\) and \(b_{c}\) represent the weights and biases associated with the cell state computation. \(X_{l,t}\), \(H_{l,t-1}\), \(C_{l,t-1}\), \(H_{l,t}\), and \(C_{l,t}\) correspond to \(H_{2,t-2}\), \(H_{2,t-1}\), \(C_{2,t-1}\), \(H_{3,t-2}\), and \(C_{2,t-2}\) in Figure 4, respectively. The functions of the forget gate, input gate, and output gate are critical for the controlled flow of information within the REST-LSTM. These gates are computed using Equations (13)-(15):  
\[F_{l,t}=\sigma\Big{(}W_{f}\cdot[H_{l,t-1},\ X_{l,t}]+b_{f}\Big{)} \tag{13}\]  
\[I_{l,t}=\sigma(W_{i}\cdot[H_{l,t-1},\ X_{l,t}]+b_{i}) \tag{14}\]  
\[g_{r}=\sigma(W_{r}\cdot[h_{3b,t-1},h_{2,t}]+b_{r}) \tag{7}\]0  
where \(\sigma\) stands for the sigmoid activation function, which helps in modulating the flow through each gate. The weights and biases for each gate are denoted by \(W_{f}\), \(W_{i}\), and \(W_{o}\) and \(b_{f}\), \(b_{i}\), and \(b_{o}\), respectively. After processing through the parallel structure of the DPMI module, the features extracted from the time series data are forwarded to an adaptive output module.
# Assessing the Performance of the Multi-Beam Echo-Sounder Bathymetric Uncertainty Prediction Model
## 2 Modeling and Measuring Bathymetric Uncertainties
### Determining the Bathymetric Uncertainties from the Measurements  
The uncertainty prediction model is based on the assumption of a flat seafloor. Therefore, when comparing the modeled uncertainties with those measurements, areas have to be selected with minimum variation in the water depth. Still, small changes in water depth might be present. To mitigate the contribution of these small variations to the measured uncertainties such that they are comparable to the modeled uncertainties, the approach described in the following is taken.  
Measurements in a discrete surface patch on the bottom \(d_{i}=f(X_{V_{i}},Y_{V_{i}}),\ i=1,2,...,N_{\text{hits}}\) (with \(N_{\text{hits}}\) the number of soundings in a surface patch) are considered. The surface patch includes a few angles around the central beam angle and a few consecutive pings. \(X_{V_{i}}\), \(Y_{V_{i}}\), and \(d_{i}\) indicate the across-track and along-tack coordinates in the vessel frame along with the depth of the \(i^{\text{th}}\) sounding located in the surface patch, respectively. The surface patch is modeled assuming either a plane (with three unknowns) or bi-quadratic polynomial (with six unknowns). Thus, we have [27]:  
\[d=f(X_{V},Y_{V})=a_{0}+a_{X_{V}}X_{V}+a_{Y_{V}}Y_{V}+(a_{X_{V}}^{\prime}X_{V}^ {2}+a_{Y_{V}}^{\prime}Y_{V}^{2}+a_{X_{V},Y_{V}}^{\prime}X_{V}Y_{V})\;, \tag{2}\]  
where the term in the parentheses is used when considering a bi-quadratic polynomial and discarded otherwise. The unknown parameters can be derived from the least-squares method [28].  
The least-squares estimate of the unknown parameters \(\mathbf{x}=\left[a_{0},a_{X_{V}},a_{Y_{V}},(a_{X_{V}}^{\prime},a_{Y_{V}}^{\prime},a_{X_{V},Y_{V}}^{\prime})\right]^{T}\) for a linear model of observation equations, \(\mathbb{E}\left\{\mathbf{y}\right\}=\mathbf{A}\mathbf{x}\), with \(\mathbb{E}\) the expectation operator, \(\mathbf{A}\) the design matrix, and \(\mathbf{y}\) the vector containing the observations  
where **A** is of the size \(N_{\text{hits}}\times 3(6)\) with its \(i^{\text{th}}\) row being \(\textbf{A}_{i}=\left[1,X_{V_{i}},Y_{V_{i}},(X_{V_{i}^{2}}^{2},Y_{V_{i}^{2}}^{2}, X_{V_{i}}Y_{V_{i}})\right]\). \(\textbf{Q}_{y}=\sigma_{y}^{2}\textbf{I}\) is the covariance matrix of \(\boldsymbol{y}\), with \(\sigma_{y}^{2}\) the variance of the data and **I** an identity matrix. For this special structure of covariance matrix (independent and identically distributed errors), Equation (3) is simplified to \(\boldsymbol{\hat{x}}=(\textbf{A}^{T}\textbf{A})^{-1}\textbf{A}^{T}\boldsymbol {y}\). The least-squares estimate of the variance component is:  
\[\hat{\sigma}_{y}^{2}=\frac{\hat{e}^{T}\hat{e}}{N_{\text{hits}}-3(6)}\, \tag{4}\]  
with \(\hat{e}=\textbf{A}\hat{\boldsymbol{x}}-\boldsymbol{y}\) the \(N_{\text{hits}}\)-vector of the least-squares residuals [28]. The square root of the variance component gives the estimate of the standard deviation of the depth measurements for which the potential remaining presence of the slopes has been accounted.
# Image Recognition and Classification of Farmland Pests Based on Improved Yolox-Tiny Algorithm
###### Abstract  
In order to rapidly detect pest types in farmland and mitigate their adverse effects on agricultural production, we proposed an improved Yolox-tiny-based target detection method for farmland pests. This method enhances the detection accuracy of farmland pests by limiting downsampling and incorporating the Convolution Block Attention Module (CBAM). In the experiments, images of pests common to seven types of farmland and particularly harmful to crops were processed through the original Yolox-tiny model after preprocessing and partial target expansion for comparative training and testing. The results indicate that the improved Yolox-tiny model increased the average precision by 7.18%, from 63.55% to 70.73%, demonstrating enhanced precision in detecting farmland pest targets compared to the original model.  
target detection; farmland pests; convolution block attention mechanism; convolutional neural network +
Footnote †: journal: Applied sciences
## 1 Introduction
Therefore, by harnessing context-awareness and environment-adaptive features, VLC systems can counteract signal degradation, enhance resilience, and maximize throughput, bringing VLC technology closer to deployment in real-life applications.
## 2 Materials and Methods
### Orange Data Mining Application and Image Management
Figure 4.0  
where \(\hat{y}\) is the predicted output, \(X\) is the input feature vector, and \(\theta\) represents the set of all network parameters (weights and biases). Training a neural network involves finding the optimal parameters \(\theta\) that minimize a loss function \(L(\hat{y},\ y)\), which measures the difference between the predicted output \(\hat{y}\) and the true output \(y\). This is typically achieved through backpropagation, where the gradients of the loss function with respect to the network parameters are computed using the chain rule, and the parameters are updated via an optimization algorithm such as stochastic gradient descent (SGD); see Equation (11). Figure 4.1  
where \(\eta\) is the learning rate. To determine which neural network model fits best for a particular task, one must evaluate several architectures and hyperparameter configurations. Key factors to consider include the number of hidden layers, the number of neurons per layer, the choice of activation functions, and the regularization techniques (e.g., dropout, L2 regularization) to prevent overfitting. Model selection can be systematically approached by dividing the dataset into training, validation, and test sets. The training set is used to fit the model, the validation set is used to tune hyperparameters and select the best model, and the test set provides an unbiased evaluation of the model's performance. Cross-validation techniques can further enhance the robustness of model selection by averaging performance metrics over multiple folds of the data. Finally, metrics such as accuracy, precision, recall, F1-score, or mean squared error (depending on the task) are used to compare the performance of different models. The model with the best performance on the validation or test set is considered the best fit for the given problem. The models employed by Orange Data Mining utilize a neural network to ascertain the optimal model, with the logistic regression widget selected to facilitate this determination. The tree algorithm is a straightforward approach that partitions the data into nodes based on class purity (information gain for categorical and MSE for numeric target variables). The Tree widget, designed in-house, is capable of handling both categorical and numeric datasets. The Neural Network widget utilizes the sklearn Multi-layer Perceptron algorithm, which is capable of learning both non-linear and linear models. [PERSON] is a fast and simple probabilistic classifier based on [PERSON] theorem, assuming feature independence. Subsequently, the confusion matrix provides the number or proportion of instances between the predicted and actual class. The selection of elements from the matrix is then fed into the corresponding instances of the output signal. This allows the user to identify which instances were misclassified and how. Subsequently, an image viewer is incorporated to facilitate observation of the algorithmic identification of images exhibiting defects, as illustrated in Figure 4.2
# Earth-to-Earth Microwave Rain Attenuation Measurements: A Survey On the Recent Literature
## 2 Methodology (Literature Analysis)  
The methodology used to analyze the literature was based on the framework of Search, Appraisal, Synthesis, and Analysis (SALSA) for a literature survey on a systematic map [19]. Initially,both databases and search terms were defined. The databases searched included Science Direct, Scopus, Google Scholar. Additionally, the MDPI, Springer, Wiley, and IEEE engines were searched. The primary search term was \"Rain attenuation\" while secondary research terms including \"measurements\", \"microwave links\" and \"backhaul cellular networks.\" Since the survey is based on recent literature, the time window was defined from 2010 to 2020.  
In the appraisal step, duplicated papers were excluded as well as theoretical or simulation works. Articles that do not fall within the scope of the earth-to-earth measurements and the microwave frequency spectrum were also excluded. Regarding paper quality, papers have been included from high-reputation journal and conferences proceedings. Excluded works were white papers, patents, extended abstracts, technical reports, non-English papers, and presentations.  
Synthesis is typically narrative, while including tabular data, country of measurements, operating frequency, path length, validation methods, and remarks.  
The analysis includes the division of the works into three thematic areas that concern measurements from commercial microwave backhaul links, experimental setups and challenging or limitation issues. Additionally, the analysis for each section is done in chronological order. The four SALSA steps are summarized in Figure 1.  
Finally, it must be mentioned that the type of this review paper is not a systematic review but a literature review with time and scope constraints. In addition, the structure of this paper is based on SALSA methodology.
## 3 Optical Conductivity  
### Drude Model  
Semiclassically, the optical conductivity of monolayer graphene can be described by [29, 38]  
\[\sigma\left(\omega\right)=-\frac{e^{2}}{\pi\hbar^{2}}\int\limits_{-\omega}^{\infty }dE\,\frac{\left|E\right|}{\tau^{-1}\left(E\right)-\mathrm{i}\omega}\frac{df_{ \circ}\left(E\right)}{dE} \tag{6}\]  
where \(\omega\) is the angular frequency of the electric field. Because the behavior of \(\tau^{-1}(E)\) is complicated, as we have shown in Figure 1a, the analytical solution to Equation (6) is difficult to obtain. Alternatively, we can replace \(\tau^{-1}\left(E\right)\) in Equation (6) with an effective energy-independent but frequency-dependent scattering rate \(\tau_{\mathrm{eff}}^{-1}\left(\omega\right)\) to obtain an analytical expression [33]:  
\[\sigma\left(\omega\right)=\frac{e^{2}}{\pi\hbar^{2}}\frac{2k_{\mathrm{B}}T}{ \tau_{\mathrm{eff}}^{-1}\left(\omega\right)-\mathrm{i}\omega}\ln\left[2\cosh \left(\frac{\mu}{2k_{\mathrm{B}}T}\right)\right] \tag{7}\]  
It can be shown that if \(\mu>>k_{\mathrm{B}}T\), \(\tau_{\mathrm{eff}}^{-1}\left(\omega\right)\equiv\tau^{-1}\left(E_{\mathrm{F}}\right)\) and Equation (7) becomes the familiar simple Drude model \(\sigma_{0}\left(1-\mathrm{i}\omega\tau_{\mathrm{eff}}\right)\) with \(\sigma_{0}=e^{2}E_{\mathrm{F}}\tau_{\mathrm{eff}}^{-1}\left/\,\pi\hbar^{2}\), which is commonly used in fitting the experimental data. If the condition \(\mu>>k_{\mathrm{B}}T\) is not valid, \(\tau_{\mathrm{eff}}^{-1}\left(\omega\right)\) has to be calculated numerically. Nevertheless, with \(E_{\mathrm{F}}\geq 100\,\mathrm{meV}\) and \(T=300\) K, it has been shown that \(\tau_{\mathrm{eff}}^{-1}\left(\omega\right)\) is a weak function of frequency, and \(\tau_{\mathrm{eff}}^{-1}\left(\omega\right)\equiv\tau^{-1}\left(E_{\mathrm{F}}\right)\) is a fairly good approximation for Equation (7) [33].
## 3 Localization Algorithm
### The Optimal Reference Selection Scheme
Assume that there are \(M\) ordinary nodes in the network including one reference node which will be selected and \(M-1\) unknown ordinary node. The set of ordinary nodes in relative coordinate system is expressed as \(\hat{\Gamma}(k)\), while \(\hat{\Gamma}_{i}(k)\) is the selected reference node \(i\). Thus, the cost function of the optimal reference selection scheme is expressed as:  
Figure 4: The relative coordinate system. \[ \begin{split}\underset{\text{f}_{i}(k)\in\mathbb{f}(k)}{\text{min}}& \sum_{j=1}^{i-1}\sum_{i=i+1}^{M}b_{ij}(k)^{T}(I-A(k)(A(k)^{T}A(k))^{-1}A(k)^{T })b_{ij}(k)\\ s.t.& A(k){=}\begin{bmatrix}-\hat{x}_{i}(k)&-\hat{y}_{i}(k)&- \hat{z}_{i}(k)\\ 2(\hat{x}_{2}(k)-\hat{x}_{i}(k))&-2\hat{y}_{i}(k)&-2\hat{z}_{i}^{\prime}k\\ 2(\hat{x}_{3}(k)-\hat{x}_{i}(k))&2(\hat{y}_{3}(k)-\hat{y}_{i}^{\prime}k))&-2 \hat{z}_{i}(k)\end{bmatrix}\\ \par-(\hat{x}_{i}(k))^{2}-(y_{i}(k))^{2}-(\hat{z}_{i}(k))^{2}+\Delta d_{1}^{j}( k)\\ \hat{x}_{2}(k)^{2}-(\hat{x}_{i}(k))^{2}-(\hat{y}_{i}(k))^{2}-(\hat{z}_{i}(k))^{2}+ \Delta d_{2}^{j}(k)\\ \hat{x}_{3}(k)^{2}-(\hat{x}_{i}^{\prime}k))^{2}+\hat{y}_{3}(k)^{2}-(\hat{y}_{i }^{\prime}(k))^{2}-(\hat{z}_{i}^{\prime}(k))^{2}+\Delta d_{3}^{j}(k)\end{bmatrix} \\ \Delta d_{n}^{j} =d_{j}^{2}-d_{n}^{2}\quad n=1,2,3\\ \hat{\Gamma}_{i}(k){=}\begin{bmatrix}\hat{x}_{i}(k)\\ \hat{y}_{i}(k)\\ \hat{z}_{i}(k)\end{bmatrix}\end{split}  \tag{5}\]
# A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments
## 6 Time Varying Channels  
With the emergence of high speed vehicles such as high speed trains, one of the targets of 5G is to manage fast mobility communications [76; 77; 78]. Since massive MIMO is a key technology in 5G, and the performance of massive MIMO system is very dependent on accurate channel knowledge, it is important to study the effect of channel errors and channel variations (and, consequently, channel aging effects) on the performance of massive MIMO schemes, especially in the presence of high speed environments [76]. The studies done on massive MIMO systems, considering the channel dispersion type, can be divided into four categories. In the first category the channel is assumed flat fading and time-invariant. In fact the channel is modeled as independent flat fading whose gain remains unchanged during the coherence time and then changes independently. In the second category the channel is time varying flat fading, where the channel coefficients change symbol-by-symbol due to Doppler effects. In the third category the channel is time-invariant frequency selective. In this case the channel has multi-path effects and its coefficients remain unchanged during a block. The fourth category, which is the most probable one in fast moving environments, corresponds to a time-varying, frequency selective channel, where the channel changes within a block due to Doppler effects. In the following we will focus on the performance of massive MIMO systems in the second and the fourth category.