esa-sceva/satcom-chunk-collection huggingface dataset:
# Multilevel Regularization Method for Building Outlines
###### Abstract  
Extraction of vectorized building outlines from high-resolution remote sensing images is highly useful for various application fields, such as map creation and urban planning. However, this process is often complicated by external factors, such as trees and shadows, which cause issues, such as excessive node redundancy, jagged lines, and unclear corner points. In this study, a multilevel regularization method was designed for building outlines, including the \"overall-local-detail\" levels. First, overall regularization was performed by combining the minimum bounding rectangle of the building outline with the Hausdorff distance method. Next, based on the convex hull of the building outline and the distribution characteristics of nodes along the outline, the building outline was divided into multiple line chains and classified for local regularization. Finally, the details of the building outline were processed, with the parallel and perpendicular characteristics enhanced to obtain the final regularization results. The experimental results showed that the proposed method effectively enhances the edge representation accuracy of building outlines and significantly improves the accuracy and regularity of building edges. Furthermore, it strengthens the orthogonal characteristics of building outlines, providing more accurate representations of true building outlines.  
high-resolution remote sensing image; jagged building outline; multilevel regularization +
Footnote †: journal: Applied Sciences
# Improvement of ID3 Algorithm Based on Simplified Information Entropy and Coordination Degree
## Improved ID3 Algorithm
### Variety Bias Problem and the Solution
#### 3.2.2 The Selection Process of the Optimal Attribution Based on the Improved Method  
In order to solve the variety bias problem, different weights are introduced into each attribution based on Equation (17). Each weight equals the reciprocal of the length of different values in the corresponding attribution. The computation process is listed in the following:  
\[Gain(D,a_{i}) = M_{i}(\frac{2 pn}{p+n}-\sum_{i=1}^{V}\frac{2p_{i}n_{i}}{p_{i}+n_{i} }), \tag{17}\] \[Gain(D,Number) = M_{1}(\frac{2 pn}{p+n}-\sum_{i=1}^{V}\frac{2p_{i}n_{i}}{p_{i}+n_{ i}})=0.5,\] (18) \[Gain(D,Color) = M_{2}(\frac{2 pn}{p+n}-\sum_{i=1}^{V}\frac{2p_{i}n_{i}}{p_{i}+n_{ i}})=0.3,\] (19) \[Gain(D,Body\;Shape) = M_{3}(\frac{2 pn}{p+n}-\sum_{i=1}^{V}\frac{2p_{i}n_{i}}{p_{i}+n_{ i}})=0.5,\] (20) \[Gain(D,Hair\;Type) = M_{4}(\frac{2 pn}{p+n}-\sum_{i=1}^{V}\frac{2p_{i}n_{i}}{p_{i}+n_{ i}})=0.5. \tag{21}\]  
The attribution that has the biggest information gain is the optimal attribution. If there is more than one property with the maximum information gain value, the optimal property will be randomly selected from these candidate attributions. The attribution \"Body Shape\" is finally randomly selected as the optimal attribution in Matlab, the decision tree model based on the improved method is presented in Figure 4.  
With the help of Equation (8), a decision tree model will be quickly built and the problem of the variety bias problem will be avoided. This paper introduced the coordination degree theory to improve the feature of split points for acquiring a more concise decision tree model because the decision tree size is hard to control.
# A Magnetotelluric Signal Acquisition and Monitoring System Based on a Cloud Platform
## 6 Results  
The acquisition and monitoring system (CMT) produced by this study is shown in Figure 21. The system is equipped with 5-channel synchronous acquisition capabilities (Ex, Ey, Hx, Hy, and Hz), with an operating bandwidth covering DC to 1 kHz, and providing four adjustable gain steps of 0.1/1/16/64. The instrument adopts a 32-bit high-precision ADC, realizes a 160 dB total dynamic range, supports three sampling rates (15 Hz/150 Hz/2400 Hz) for parallel acquisition, and integrates a real-time digital filtering function. Its key technical indicators include a 30 ns GPS synchronization accuracy and less than 2.5 W stand-alone power consumption, supporting Ethernet/WiFi dual-mode measurement and control, and innovative compatibility with universal rechargeable power supply, reflecting the design features of low power consumption and high integration. The data processing part of the system utilizes razorback, an open-source geomagnetic data processing library in Python. The system passed server stress testing and was further tested at the PetroChina base in Bazhou, Hebei, and achieved good experimental results.
## 3 Quality Assessment of the BIC/B2a Signals from BDS-3 Operational Satellites
### Observation Models for Combined BDS-3, GPS, and Galileo RTK
#### 4.1.2 Differential ISBs Estimation with Tightly Combined Model (TCM)  
The TCM assumes a single-reference satellite for all the tracked constellations. When inter-system double-differenced observations are created between different constellations, additional phase and code differential ISBs are present and should be carefully considered. The double-differenced observation equation corresponding to TCM with ISBs estimation is given by [17; 18; 36]:  
\[\begin{split}\phi_{br,i}^{1_{G}\varsigma_{G}}&= \rho_{br}^{1_{G}\varsigma_{G}}+\lambda_{i}N_{br,i}^{1_{G}\varsigma_{G}}+ \varepsilon_{br,i}^{1_{G}\varsigma_{G}}\\ \phi_{br,i}^{1_{G}\varsigma_{*}}&=\rho_{br}^{1_{G} \varsigma_{*}}+\lambda_{i}(N_{br,i}^{1_{*}s_{*}}+\overline{\delta_{br,i}^{G_{* }}}^{*})+\varepsilon_{br,i}^{1_{G}s_{*}}\\ P_{br,i}^{1_{G}\varsigma_{G}}&=\rho_{br}^{1_{G} \varsigma_{*}}+\varepsilon_{br,i}^{1_{G}\varsigma_{G}}\\ P_{br,i}^{1_{G}\varsigma_{*}}&=\rho_{br}^{1_{*}s_{*} }+d_{br,i}^{\varsigma_{*}}+\varepsilon_{br,i}^{1_{G}\varsigma_{*}}\end{split} \tag{4}\]  
where \(s_{G}\geq 2_{G}\) and \(s_{*}\geq 1_{*}\), \(\overline{\delta_{br,i}^{G_{*}}}=\delta_{br,i}^{\varsigma_{*}}+N_{br,i}^{1_{ *}1_{*}}\) is the lumped phase ISB parameter, which is composed of the phase ISB parameter and double-differenced ambiguity between reference satellites of GPS and constellation *, and \(d_{br,i}^{G_{*}}\) is the code ISB parameter. According to Equation (4), the phase and code ISBs can be estimated separately for each overlapping frequency and each system pair for single-epoch or multi-epoch solutions.
# Two-Stage Deep Learning Method for Breast Cancer Detection Using High-Resolution Mammogram Images
###### Abstract  
Breast cancer screening and detection using high-resolution mammographic images have always been a difficult task in computer vision due to the presence of very small yet clinically significant abnormal growths in breast masses. The size difference between such masses and the overall mammogram image as well as difficulty in distinguishing intra-class features of the Breast Imaging Reporting and Database System (BI-RADS) categories creates challenges for accurate diagnosis. To obtain near-optimal results, object detection models should be improved by directly focusing on breast cancer detection. In this work, we propose a new two-stage deep learning method. In the first stage, the breast area is extracted from the mammogram and small square patches are generated to narrow down the region of interest (RoI). In the second stage, breast masses are detected and classified into BI-RADS categories. To improve the classification accuracy for intra-classes, we design an effective tumor classification model and combine its results with the detection model's classification scores. Experiments conducted on the newly collected high-resolution mammography dataset demonstrate our two-stage method outperforms the original Faster R-CNN model by improving mean average precision (mAP) from 0.85 to 0.94. In addition, comparisons with existing works on a popular INbreast dataset validate the performance of our two-stage model.  
breast cancer detection; optimization; BI-RADS; breast cancer diagnosis; deep learning Article
## 2 Materials and Methods
### Overall Methodology
#### 2.5.4 Hydrologic Performance of the Developed Models  
The hydrologic performance, accuracy, and efficiency of the developed models were assessed based on the streamflow rates. These discharges were simulated based on different SbPPs, and GbGPPs were compared against the observed streamflow. The accuracy of the simulated discharge was found based on the Coefficient of Determination (R\({}^{2}\)) and the Nash-Sutcliffe Efficiency (NSE). The mathematical formulations for R\({}^{2}\) and NSE are given in Equations (2) and (3), respectively.  
\[\mathrm{R}^{2}=\left[\frac{\sum_{i=1}^{n}(\mathrm{O}_{i}-\mathrm{O}_{\mathrm{ mean}})\times\left(\mathrm{S}_{i}-\mathrm{S}_{\mathrm{mean}}\right)}{\sqrt{ \sum_{i=1}^{n}(\mathrm{O}_{i}-\mathrm{O}_{\mathrm{mean}})^{2}\times\sum_{i=1} ^{n}(\mathrm{S}_{i}-\mathrm{S}_{\mathrm{mean}})^{2}}}\right]^{2} \tag{2}\]  
\[\mathrm{NSE}=1-\left(\frac{\sum_{i=1}^{n}(\mathrm{S}_{i}-\mathrm{O}_{i})^{2}}{ \sum_{i=1}^{n}(\mathrm{O}_{i}-\mathrm{O}_{\mathrm{mean}})^{2}}\right) \tag{3}\]  
where O\({}_{i}\) stands for observed while S\({}_{i}\) stands for simulated discharges. O\({}_{\mathrm{mean}}\) and S\({}_{\mathrm{mean}}\) stands for average of observed flow and simulated flow, respectively.
# Recent Strategies for Environmental Remediation of Organochlorine Pesticides
## 5 Photocatalysts for Pesticides Degradation
### Titanium Oxide-Based Photocatalyst for Pesticide Degradation
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Photocatalysts**} & \multicolumn{2}{c}{**Organochlorine and Other**} & \multirow{2}{*}{**Solar rad. Used**} & \multirow{2}{*}{**Ref.**} \\  & **Pollutants Destroyed** & & \\ \hline F and N co-doped TiO\({}_{2}\) & Representative pesticide & UV & [77] \\ \hline Nanocrystalline & & & \\ \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)/TiO\({}_{2}\) nanoparticles & & & \\ \hline GO-TiO\({}_{2}\) & Atrazine, isoproturon, alachlor and & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\ \hline Titanium oxide and & & & \\   lanthanum oxide composite & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\ \hline \hline \end{tabular} \begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{\begin{tabular}{c} **Carbon-doped TiO\({}_{2}\)** \\ on zeolite \\ \end{tabular} } & Atrazine and others (and 18 drugs) & UV & [81] \\ \hline \hline \multicolumn{1}{c}{\begin{tabular}{c} **TiO\({}_{2}\)** + HZSM-5 \\ zeolite combinator \\ \end{tabular} } & Parachlorophenol & UV & [82] \\ \hline \hline \multicolumn{1}{c}{\begin{tabular}{c} **TiO\({}_{2}\)** on zeolite support \\ (activated carbon, silica \\ and zeolite) \\ \end{tabular} } & 3,5-dichloro-\(N\)-(3-methyl-1-butyn-3-yl) & UV & [84] \\ \hline \hline \multicolumn{1}{c}{Silver-doped TiO\({}_{2}\)} & Acetamiprid & Visible & [85] \\ \hline \hline \multicolumn{1}{c}{\begin{tabular}{c} nano-TiO\({}_{2}\) coated film \\ \end{tabular} } & \begin{tabular}{c} Cypermethrin, dicofol and \(\alpha\)-, \(\beta\)-, \(\gamma\)-, \(\gamma\)-, \\ \(\delta\)-hexachlorobenzene (BHC) \\ \end{tabular} & UV & [86] \\ \hline \hline \multicolumn{1}{c}{\begin{tabular}{c} GO/TiO\({}_{2}\)/Ag nanotube \\ \end{tabular} } & 2-chlorophenol and (dyes) & UV & [87] \\ \hline \hline \multicolumn{1}{c}{\begin{tabular}{c} mesoporous GO-Fe\({}_{3}\)O\({}_{4}\)/TiO\({}_{2}\) \\ \end{tabular} } & Fenitrothion & Visible & [88] \\ \hline \hline \multicolumn{1}{c}{\begin{tabular}{c} TiO\({}_{2}\)/ZnO \\ \end{tabular} } & Trichoroethanonic acid & UV & [89] \\ \hline \hline \multirow{3}{*}{\begin{tabular}{c} Aqueous TiO\({}_{2}\) suspension \\ \end{tabular} } & Irgaro, prometryn, cyanazine, & & \\  & propazine, atrazine and other & UV & [90] \\ \cline{1-1}  & pesticides & & \\ \hline \hline \multicolumn{1}{c}{\begin{tabular}{c} TiO\({}_{2}\) \\ \end{tabular} } & Dichlorvos in gas phase & UV & [76] \\ \hline \hline \multicolumn{1}{c}{\
\begin{tabular}{c} Nitrogen-doped \\ anatase TiO\({}_{2}\) \\ \end{tabular} } & Endosulfan from soil & UV & [91] \\ \hline \hline \multicolumn{1}{c}{
\begin{tabular}{c} TiO\({}_{2}\) and oxygen system \\ \end{tabular} } & Lindane and DDT & UV & [92] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: TiO\({}_{2}\)-based photocatalysts and the pesticides removed.
## 2 Background
### Nelder-Mead Algorithm
#### 2.2.1 Advantages of the Nelder-Mead Algorithm  
Some advantages of the Nelder-Mead algorithm are:  
1. [label=0.,ref=0]
2. **Simplicity:** The Nelder-Mead algorithm is relatively easy to understand and implement compared to more complex optimization methods.
3. **No derivative information required:** The algorithm does not rely on derivative information, making it suitable for optimizing functions that are not easily differentiable or when computing derivatives is computationally expensive.
4. **Convergence in certain cases:** The Nelder-Mead algorithm can converge quickly for low-dimensional problems with smooth, convex objective functions.
5. **Robustness:** It is relatively robust against noisy or imperfect function evaluations.
# Buzz Tweet Classification Based on Text and Image Features of Tweets Using Multi-Task Learning
## 3 Materials and Methods
### Multi-Task Learning of RTs, Likes, and Buzz Classes  
In the proposed method, the input consists of text and image features, and a neural network is trained to predict the score of the number of RTs and the number of likes, which are further converted into four patterns of numerical values in the quartile range, and into the binary value of buzz/non-buzz (buzz class).  
Both the number of RTs and the number of likes tend to increase with the size of the buzz phenomenon, but the difference between the two values depends on factors such as the number of followers and the user's name recognition, so they are trained as separate prediction targets.  
In multi-task learning, learning efficiency and prediction accuracy are expected to improve more than learning task-specific models [29]. The network for multitask learning is illustrated in Figure 1. The same loss function, mean square error (MSE), is used for both RTs and likes. The concatenation layer is used as the sharing layer. Batch normalization is applied to the input layer.  
Figure 1: Multi-task learning to predict the number of likes/RTs and the buzz class.  
The architectures of the multitask and single-task networks are displayed in Figure 2. In both networks, a dropout function was applied to the third layer. The dropout rate was assumed to be 0.3. In the layer that outputs the buzz class, the softmax function was used as the activation function, and in the other output layers, the tanh (hyperbolic tangent function) was used. This is because normalizing the scores of the number of likes and RTs in the quartile range may cause the numbers to become negative. Activation functions other than the output layer are shown in Figure 2.
## 2 Principle of the Proposed Electrical Equalization
First, \(w_{p}^{F}\) and \(w_{q}^{B}\) are represented by the vector \(W(k)\) as  
\[W(k)=[w_{-\frac{N}{2}}^{F}\dots w_{0}^{F}\dots w_{\frac{N}{2}-1}^{F}w_{1}^{B} w_{2}^{B}\dots w_{M}^{B}] \tag{5}\]  
Afterward, the \(W(k)\) of the MA-DFE and PA-DFE can be updated as follows:  
\[W(k)_{\textit{MA-DFE}}=W(k-1)+G(k)e(k)y^{*}(k) \tag{6}\]  
\[W(k)_{\textit{PA-DFE}}=W(k-1)+G(k)e^{*}(k) \tag{7}\]  
Figure 2: Schematic diagram of MA-DFE and PA-DFE structures (where arg(\(\cdot\)) denotes phase extraction, \(|\cdot|\) denotes magnitude extraction, the blue dashed line represents the error function of MA-DFE, and the red dashed line represents the error function of PA-DFE). where \(()^{*}\) denotes complex conjugation, and \(G(k)\) is a gain vector. Let  
\[ \begin{array}{c}U(k)=\left[x\left(k+\frac{N}{2}\right)\ \ldots\ x(0)\ \ldots\ x\left(k-\frac{N}{2}+1\right)\ \ \ d(k-1)e^{i\theta(k-1)}\right.\\ \left.\ldots\ d(k-M)e^{i\theta(k-M)}\right]^{T}\end{array}  \tag{8}\]  
\(G(k)\) is specifically computed as  
\[G(k)=\frac{P(k-1)U(k)}{\lambda+U^{H}(k)P(k-1)U(k)} \tag{9}\]  
where \(()^{H}\) denotes the conjugate transpose, and \(\lambda\) is the forgetting factor (\(\lambda\in(0,1]\)). \(P(k)\) is an inverse matrix, updated as follows:  
\[P(k)=\frac{1}{\lambda}[P(k-1)-G(k)U^{H}(k)P(k-1)] \tag{10}\]  
In the MA-DFE and PA-DFE equalization process, there are two stages. The initial stage involves the training convergence process, where the decision value \(d(k)\) directly corresponds to the training sequence:  
\[d(k)=Training(k) \tag{11}\]  
where the training sequence is an ideal PAM4 signal. By continuously training and iterating, the error function \(e(k)\) converges, thereby obtaining the optimal tap coefficients for the equalizer. Following the convergence of the error function, the second stage commences. During this stage, the decision value \(d(k)\) is determined by the magnitude of the equalized signal  
\[d(k)=\left|y(k)\right| \tag{12}\]  
It should be emphasized that after the tap coefficients are updated in the first stage, the error function has already converged, so the tap coefficients are not updated in the second stage.
# Atmospheric Effects on OFDM Wireless Links Operating in the Millimeter Wave Regime
## 1 Introduction
Section 6 presents simulations for MMW communication scenarios. The results of the simulation are presented in the form of a constellation diagram and BER performance graph for different atmospheric conditions, focusing on rainfall rate effect (Section 7). The novel approach of predicting phase distortions due to the dispersion of the millimeter waves propagating in the atmospheric medium is presented in Section 8. It is shown that by employing an adaptive phase compensator, the bit error rate can be considerably reduced.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 4 Network Flow Programming Models
### Special Classes of NFPs
#### 4.2.6 Minimum Spanning Tree (MST)  
This type of problems aims to find the set of arcs in a graph network that connect all nodes such that the sum of the arc lengths is minimized. Such a group should contain no loop, obviously. For a network of \(n\) nodes, a _spanning tree_ would be a group of \(n-1\) arcs that connect all nodes of the network and contain no loops. A spanning tree of minimum length is a _minimum spanning tree_.  
_[PERSON]'s_ algorithm is a greedy algorithm that finds a minimum spanning tree for a weighted undirected graph. Informally speaking, the way it works is as follows:  
1. Initialize a tree with a single vertex, chosen arbitrarily from the graph.
2. Grow the tree by one edge: of the edges that connect the tree to vertices not yet in the tree, find the minimum-weight edge, and transfer it to the tree.
3. Repeat step 2 until all vertices are in the tree.  
The time complexity of [PERSON]'s algorithm depends on the data structures used for the graph and for ordering the edges by weight, which can be done using a priority queue. Table 2 shows the typical choices [33; 34], where \(V\) and \(E\) are the vertices and the edges of the graph respectively.  
**Example for a Possible Application to WSN**: MST can be used to solve the problem of finding the best routes to take when a single source sensor node wants to broadcast a message to a group of sensor nodes (sinks) in the network where each non-source node is both a receiving node and a relaying node. If the (directed) edges' costs represent the energy that need to be expended on the link, the MST would give us the solution of how to broadcast a message to a set of nodes with the minimum overall energy.
## 3 Results and Discussion
### Bulk Density  
The effect of the three independent variables on the density of the extrudates is presented in Figure 2. The response surfaces showed that the rise in the feed moisture content and the amount of chokeberry pomace led to an increase in the \(BD\), as the effect of feed moisture was higher. The working screw speed had an insignificant effect on the \(BD\) (Figure 1). Figure 2 shows that the effect of the amount of chokeberry pomace was more pronounced at high level of feed moisture (20%) than at low level (14%). The feed moisture had more pronounced influence on the density when the amount of the chokeberry pomace was at high level (20%) than at low level (10%). It also can be seen that the effect of the investigated factors on the density was positive, while the effect of the same factors on the expansion was negative. This inversely proportional relationship between the expansion and the density is in agreement with the experimental data for other extruded products [35; 36]. Feed moisture is a key factor in the bulk density of extrudate [37]. According to [PERSON] et al. [34], the increased feed moisture during extrusion provokes molecular transformation of amylopectin leading to a less elastic melt, reduced expansion and increased density. According to [PERSON] et al. [38], the addition of fruits affects the degree of gelatinization and therefore the degree of expansion and the bulk density. These authors related the increase in the \(BD\) of extrudates to the presence of sugar and soluble fiber absorbing more moisture, leading to a decrease in the expansion and increase in the density. [PERSON] et al. [39] also found that the increase in the amount of grape pomace led to a rise in the \(BD\) of extrudates obtained with a twin-screw extruder. Similar results were obtained from [PERSON] et al. [32] for pineapple pomace using a single screw extruder.  
Figure 2: _Cont._
# Image Classification of Parcel Boxes under the Underground Logistics System Using CNN MobileNet
## 3 Methodology
### Concept of CNN  
Object recognition, classification and detection are chiefly conducted using a CNN algorithm based on deep-learning [13]. A typical CNN algorithm consists of three different layers stacked together: a convolutional layer, pooling layer, and fully connected layer [14]. A convolutional layer is used to extract image features and automatically learn the representations of image features. It performs a 3D convolution with several kernels and a given step size to produce feature maps from the input image [15]. Compared to other neural networks of a similar type, CNN has much fewer parameters and connections. Therefore, it is more efficient to train a CNN model, which has an excellent performance. CNN algorithms vary with regard to building convolutional and pooling layers and training a neural network [16].  
A convolutional layer multiplies the elements corresponding to each input channel while moving the weight filter at regular intervals. Then, the result of adding those values  
\begin{table}
\begin{tabular}{c c c c c} \hline
is sequentially stored in the output. The mathematical expression for this convolution process can be defined as Equation (1) [17]:  
\[O_{x,y,n}=\sum_{c=1}^{3}\sum_{j=1}^{3}\sum_{i=1}^{3}K_{i,j,c,n}\cdot F_{x+i-1,y+ j-1,c} \tag{1}\]  
where \(O\) is output, \(K\) is filter, \(F\) is input, and \(i\) and \(j\) represent the horizontal and vertical lengths of the filter, respectively. In addition, \(x\) and \(y\) represent the horizontal and vertical lengths of input and output, respectively, \(c\) represents the input channel and \(n\) is the output channel.  
The general structure of the CNN matches a part of the input image rather than the whole image, as shown in Figure 3, and a 3 \(\times\) 3 grid is used to extract features. After the convolution process is complete, the max pooling is called to shrink the image stack. Window size is defined for max pooling. Then, the window is filtered across all of the images, and the max pooling reduces the dimension of each functional map. The normalization process of CNN is generally performed by a rectified linear unit (ReLU). This ReLU process increases the non-linearity property of the model. A fully connected layer, which is the next-performing layer, can be observed in the CNN and is called the classifier [12].