esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Feature Selection Approaches  
Relations between feature selection and classification processes in intrusion detection systems can be seen in Figure 1. First, raw data are collected by using relevant packet analyzer tools. Second, the preprocessing stage and feature selection phase take place. Finally, model learning and testing phases take place when the learning process is per formed (Figure 1(1.1)). During the learning process, the feature selection process plays an important role. Feature selection methods are divided into three main categories including the filter approach, wrapper approach, and embedded approach. In the filter approach, the more significant features are selected by using a heuristics search [18]. The selection is performed only once before the learning process takes place (Figure 1(1.2)). This approach is fast and scalable, and selected features can be used for various classifiers. However, if the selection is made once and independently of the classifier, it may lead to poor classification performance. On the other hand, in the wrapper approach, the feature selection and classification processes work together [19]. Subsets of properties are generated to represent the features, and classification is performed by using these subfeatures (Figure 1(1.3)). The most ideal subfeature group is selected according to the model performance. Due to the interaction between the feature subset search and model selection, generally higher accuracy and a greater consideration of dependencies between features can be counted among the advantages of the wrapper approach. On the other hand, the disadvantages of these approaches are that the learning process is slower, computationally more expensive, and has the risk of over-learning. In the embedded approach, the feature selection method and the classifier work together [20], but the features are selected during the learning process (Figure 1(1.4)). The embedded approach interacts with classifiers and requires less computation time when compared to the wrapper approach.
An issue with using indicators to describe environmental processes is that they are difficult to develop for complex biological phenomena. The intricacies of bloom events and their associated relationship within food webs, as well as the lack of spatially and temporally extensive data, make assessing ES complicated [17; 27; 28]. Among other indicators for assessing ES, ranging from acidification and beach litter to seal reproduction and trout abundance, is the pre-core indicator Cyanobacterial Bloom Index (CyaBI) [4; 29]. The CyaBI consists of two inputs: the remote sensing (RS) data of bloom parameters and in situ cyanobacterial biomass [17]. Other indices, outside of HELCOM, have also been used in the Baltic Sea to assess bloom events: Cyanobacterial Surface Accumulation (CSA) [17] and the Phytoplankton Intensity Index [30], among others [8; 31; 32]. Cyanobacterial methods for setting target levels and assessing the blooms typically make use of chlorophyll-a concentrations (chl-a) [28]. Bloom assessment methods, such as CSA, the CyaBI, and the Phytoplankton Intensity Index, have been shown to be applicable to different open sea areas of the Baltic Sea [17; 30]. Additionally, band differences for the bands of the Ocean and Land Colour Instrument (OLCI) and bands of the Moderate Resolution Imaging Spectroradiometer (MODIS) were shown to agree with the in situ data in the Baltic Sea and could be used for the detection of cyanobacteria blooms [33]. Other approaches, such as hyperspectral satellites [34] or unmanned aerial vehicles (UAVs) [35; 36], have also been used in other parts of the world. However, to our knowledge, no previous research has applied these methods to the Estonian coast. The Estonian coastal waters are optically complex and heavily influenced by colored dissolved organic matter (CDOM) and total suspended matter (TSM) and are typically shallow, which have made it an optically difficult area to study via satellites. When estimating a baseline for ES, it is important to consider that the Baltic Sea has historically seen phytoplankton blooms even before human intervention. As highlighted by HELCOM, a threshold ES should therefore not be set as \"no blooms present\" but rather should ensure that blooms are not severe and do not cause economic and ecological harm. Simultaneously, the assessment should be in accordance with the sustainable use of sea resources by humans [37]. Previously, a time-series analysis based on the satellite data of cyanobacterial blooms in the Baltic Sea has been conducted by, e.g., Kahru and Elmgren [8], where the idea of a lack of a bloom-free period was solidified. The main objective of this research was to test various indices used to monitor the ES of Estonian coastal areas in regard to cyanobacteria biomass and to assess the applicability of input parameters such as the chl-a, turbidity, and cyanobacteria biomass, derived from Sentinel-3 OLCI data products. Through the use of indices, an assessment of the current cyanobacterial biomass in the Baltic Sea during the summer bloom events from 2016 to 2022 using satellite data was made.
## 2 Theory
### Wave Equations
\(\phi\), \(\varphi_{1}\), \(\varphi_{2}\), and \(\varphi_{3}\) are unknown functions related only to \(r\). Let  
\[ \begin{array}{rl}h_{1}=\varphi_{2}+\varphi_{3}\\ h_{2}=\varphi_{2}-\varphi_{3}\\ h_{3}=\varphi_{1}\end{array}  \tag{10}\]  
Substituting Equation (9) into Equations (5) and (6), and dropping \(e^{i(k_{z}z-\omega t)}\), after a series of mathematical transformations, the equation of a wave propagating in an infinitely long, homogeneous, and isotropic elastic tube wall can be obtained as  
\[ \begin{array}{rl}\Big{(}\frac{\partial^{2}}{\partial r^{2}}+\frac{1}{r} \frac{\partial}{\partial r}-\frac{n^{2}}{r^{2}}+\frac{\omega^{2}}{\varepsilon _{1}^{2}}\Big{)}\phi=k_{z}^{2}\phi\\ \Big{(}\frac{\partial^{2}}{\partial r^{2}}+\frac{1}{r}\frac{\partial}{\partial r }-\frac{(n-1)^{2}}{r^{2}}+\frac{\omega^{2}}{\varepsilon_{2}^{2}}\Big{)}h_{1} =k_{z}^{2}h_{1}\\ \Big{(}\frac{\partial^{2}}{\partial r^{2}}+\frac{1}{r}\frac{\partial}{\partial r }-\frac{(n+1)^{2}}{r^{2}}+\frac{\omega^{2}}{\varepsilon_{2}^{2}}\Big{)}h_{2} =k_{z}^{2}h_{2}\\ \Big{(}\frac{\partial^{2}}{\partial r^{2}}+\frac{1}{r}\frac{\partial}{\partial r }-\frac{n^{2}}{r^{2}}+\frac{\omega^{2}}{\varepsilon_{2}^{2}}\Big{)}h_{3}=k_{z }^{2}h_{3}\end{array}  \tag{11}\]  
The wave equation in the fluid is  
\[\
abla^{2}\Phi_{fl}=\frac{1}{c_{fl}^{2}}\frac{\partial^{2}\Phi_{fl}}{\partial t ^{2}} \tag{12}\]  
where the subscript \(fl\) represents the fluid and \(c_{fl}\) is the sound velocity of the fluid in the tube. The displacement potential function \(\Phi_{fl}\) is  
\[\Phi_{fl}=\phi_{fl}\cos n\theta e^{i(k_{z}z-\omega t)} \tag{13}\]  
Substituting Equation (13) into Equation (12), the equation of an acoustic wave propagating in the fluid is obtained as  
\[\Bigg{(}\frac{\partial^{2}}{\partial r^{2}}+\frac{1}{r}\frac{\partial}{ \partial r}-\frac{n^{2}}{r^{2}}+\frac{\omega^{2}}{c_{fl}^{2}}\Bigg{)}\phi_{fl} =k_{z}^{2}\phi_{fl} \tag{14}\]
## 2 Data Processing and Network Description
### Relation Network
#### 2.3.3 Relation Metric Module  
The relation metric module is responsible for computing similarity scores between the test samples and each class. It consists of multiple convolutional and pooling layers, which effectively learn the similarity and dissimilarity features among different classes. By adaptively adjusting the similarity and dissimilarity between classes, the relation metric module can better adapt to different datasets and task requirements. The specific network structure is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Type** & **Output Shape** \\ \hline Input & & 3 \(\times\) 84 \(\times\) 84 \\ \hline \multirow{6}{*}{1} & Conv2d & 64 \(\times\) 82 \(\times\) 82 \\  & BatchNorm2d & 64 \(\times\) 82 \(\times\) 82 \\  & ReLU & 64 \(\times\) 82 \(\times\) 82 \\  & MaxPool2d & 64 \(\times\) 41 \(\times\) 41 \\  & SEBlock & 64 \(\times\) 41 \(\times\) 41 \\ \hline \multirow{6}{*}{2} & Conv2d & 64 \(\times\) 39 \(\times\) 39 \\  & BatchNorm2d & 64 \(\times\) 39 \(\times\) 39 \\  & ReLU & 64 \(\times\) 39 \(\times\) 39 \\  & MaxPool2d & 64 \(\times\) 19 \(\times\) 19 \\ \hline \multirow{6}{*}{3} & SEBlock & 64 \(\times\) 19 \(\times\) 19 \\  & Conv2d & 64 \(\times\) 19 \(\times\) 19 \\  & BatchNorm2d & 64 \(\times\) 19 \(\times\) 19 \\  & ReLU & 64 \(\times\) 19 \(\times\) 19 \\  & SEBlock & 64 \(\times\) 19 \(\times\) 19 \\ \hline \multirow{6}{*}{4} & Conv2d & 64 \(\times\) 19 \(\times\) 19 \\  & BatchNorm2d & 64 \(\times\) 19 \(\times\) 19 \\ \cline{1-1}  & ReLU & 64 \(\times\) 19 \(\times\) 19 \\ \cline{1-1}  & SEBlock & 64 \(\times\) 19 \(\times\) 19 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structure of the feature embedding module.  
The output of the relation metric module is a similarity score, which represents the similarity between the test sample and its corresponding class. By sorting the scores, the most similar class to the test sample can be identified, thus achieving the classification task.
# Shot Classification of Field Sports Videos Using AlexNet Convolutional Neural Network
## 2 Proposed Method
### AlexNet Architecture  
In the proposed work, we employed the Alexnet CNN deep learning architecture [23] for shot classification in field sports videos. The network is deeper than standard CNN with five convolution layers followed by three maximum pooling layers. Dropout of 0.5% is applied on the fully connected layers to avoid over fitting of the data. The architecture consists of the following components:  
* 1 Convolution with 11 \(\times\) 11 kernel size (1 CONV)
* Rectified Linear Unit Layer Activation (RELU)  
Figure 1: Proposed framework for shot classification.  
* Response Normalization Layer
* 1 Maximum Pooling (4 \(\times\) 4 kernel)
* 2 Convolution with 5 \(\times\) 5 kernel size (2 CONV)
* Rectified Linear Unit Layer (RELU)
* Response Normalization Layer
* 2 Maximum Pooling (3 \(\times\) 3)
* 3 Convolution with 3 \(\times\) 3 kernel size (3 CONV)
* Rectified Linear Unit Layer Activation (RELU)
* 4 Convolution with 3 \(\times\) 3 kernel size
* Rectified Linear Unit Layer Activation (RELU)
* 3 Maximum Pooling (3 \(\times\) 3)
* Fully Connected Layer (4096 nodes)
* Rectified Linear Unit Layer Activation (RELU)
* Fully Connected Layer (4096 nodes)
* Rectified Linear Unit Layer (RELU)
* Soft-max out  
The proposed AlexNet CNN architecture is presented in Figure 2. In the proposed research work, image input layer is defined as a pre-processing layer where the input frames are down-sampled from 640 \(\times\) 480 to 227 \(\times\) 227 in terms of spatial resolution to reduce the computational cost of our deep learning framework. The proposed system uses five convolutional (CONV) layers followed by three pooling layers (POOL) and Rectified Linear Unit (RELU). For the first convolutional layer, 96 kernels of relatively large size 11 \(\times\) 11 \(\times\) 3 are used. For the second convolutional layer, 256 kernels of size 5 \(\times\) 5 are used. For the third, fourth, and fifth layers, 384 kernels of size 3 \(\times\) 3 are used. Each convolutional layer generates a feature map. The feature maps of first, second and fifth convolutional layer are used in combination with pooling layers of 3 \(\times\) 3 and stride of 2 \(\times\) 2. The framework is comprised of eight layered architecture with 4096 nodes. This generates the trainable feature maps, i.e., feature extraction phenomena are performed in these layers. These feature maps are subjected to fully connected layers (FC) and then Soft-max activation is performed to determine the classification probabilities used by the final output classification layer. These classification probabilities in the Soft-max layer can make categories up to 1000 different classes, but in our dataset, we have four classes.  
Figure 2: AlexNet convolution neural network architecture of the proposed framework.
## 3 Method and Materials
### BiLSTM Layer  
When the time is \(t\), the calculation formula for the state update of each LSTM unit is given by Equations (6)-(11):  
\[f_{t}=\sigma\Big{(}w_{f}\cdot[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{6}\]  
\[i_{t}=\sigma(w_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{7}\]  
\[\widetilde{c}_{t}=\tanh(w_{c}\cdot[h_{t-1},x_{t}]+b_{c}) \tag{8}\]  
\[c_{t}=f_{t}\circ c_{t-1}+i_{t}\circ\widetilde{c}_{t} \tag{9}\]  
\[o_{t}=\sigma(w_{o}\cdot[h_{t-1},x_{t}+b_{o}]) \tag{10}\]  
\[h_{t}=o_{t}\circ\tanh(c_{t}) \tag{11}\]  
where \(\sigma\) represents the activation Sigmoid function; \(\circ\) represents the corresponding multiplication of homologous elements; [] represents the connection of two vectors; \(x_{t}\) represents the LSTM input vector at time \(t\); \(h_{t}\) and \(h_{t-1}\) represent the hidden states at time \(t\) and \((t-1)\), respectively; \(f_{t},\ i_{t},\ c_{t},\ o_{t}\) represent the values of the forget gate, input gate, memory cell, and output gate; \(w_{f},\ w_{i},\ w_{c},\ w_{o}\) represent the weight matrix of the forget gate, input gate, memory cell, and output gate; \(b_{f},\ b_{i},\ b_{c},\ b_{o}\) represent the bias terms of the forget gate, input gate, memory cell and output gate.  
Equation (6) is the calculation process for the forget gate, which determines how much of the memory cell state at the previous moment is saved at the current time; Equation (7) is the operation process of the input gate, which determines which input vectors \(x_{t}\) are stored in the unit state \(c_{t}\) at the current moment; Equations (8) and (9) calculate temporary cell states \(\widetilde{c}_{t}\) and \(c_{t}\) at the current time, respectively; Equation (10) is the calculation process of the output gate, which controls how much the cell state \(c_{t}\) outputs to the current output value \(h_{t}\) of the LSTM; Equation (11) calculates the hidden layer state \(h_{t}\) at the current time using the calculation results of the output gate and the unit state \(c_{t}\).  
The dynamic word vector obtained from ERNIE was input into the BiLSTM layer. Then, we obtained the forward and backward hidden layer vector sums using the forward and backward modules, respectively. Next, both vector sums are spliced to obtain the hidden layer vector, As shown in Equation (12), \(V\) represents the set of hidden layer vectors at all times, as shown in Equation (13):  
\[v_{t}=\left[\overset{\rightarrow}{h}_{t},\overset{\leftarrow}{h}_{t}\right] \tag{12}\]  
\[V=[v_{1},v_{2},v_{3},\ldots,v_{t}] \tag{13}\]
## 3 Methods
### Winding Angle Detection Method
#### 3.2.2 Binarization  
Binarization segmentation will cause the omission of useful information if the threshold value is not selected reasonably, and in the grayscale image obtained in this paper, there are only two kinds of objects: wires and gaps, which do not contain other information. According to this characteristic, a method to determine the threshold segmentation is proposed, which can get a more ideal binarized image. The threshold selection method is as follows:  
Calculate the average gray value of the whole image \(aver_{gray}\):  
\[aver_{gray}=\frac{1}{h\times w}\sum_{i=1}^{h}\sum_{j=1}^{w}f(i,j) \tag{5}\]  
For the reason that there are more dark pixels in the image, \(1/2\) of the average gray value is taken as the segmentation threshold after the sample test to better distinguish the wires from the gaps. Each figure in Figure 10 is: (a) adaptive binarization, (b) the original image is obtained as a binarized image based on the above threshold, and (c) the slice method grayscale image is based on the binarized image obtained from the above threshold, respectively. From Figure 10, we can see that the image obtained by the slicing method is more evenly distributed, even though there is still some noise interference, but it does not affect the overall recognition effect, and the binarization noise can be eliminated by using the image opening operation.  
Figure 10: Compare different binary images: (**a**) adaptive binarization, (**b**) the original image is obtained as a binarized image based on the above threshold, (**c**) the slice method grayscale map is based on the binarized image obtained from the above threshold.  
Figure 9: Comparison of different slice values: (**a**) origin gray image; (**b**) \(n\) equal 20; (**c**) \(n\) equal 40; (**d**) \(n\) equal 80.  
In the above-binarized image, the white stripes correspond to the wires, and the black stripes correspond to the gaps. Ideally, the wires are wound compactly, and each black stripe obtained should be of equal width, but in practice, there are various unavoidable disturbances that can cause some interference with the width of the stripes.
## 4 Next Forwarding Node Selection in UWSNs  
In this section, next forwarding node selection for UWSNs is reviewed. Firstly, the impact of next forwarding node selection on the design of routing techniques is investigated. Secondly, the related literature is critically reviewed following a proposed taxonomy. Thirdly, a comparative investigation is performed, focusing on the strengths and weaknesses of the techniques considered.
## 4 Conclusions  
Our study successfully employed the BDULSTM method for 3D segmentation of soil CT images, leveraging the combined strengths of U-Net and CLSTM networks to process image sequences and improve segmentation performance. Experiments on soil CT images in various freeze-thaw scenarios yielded the following conclusions:  
(1) The performance of BDULSTM varies depending on the number of sequence images used. The maximum performance is achieved when utilizing seven images.  
(2) BDULSTM effectively captures the 3D structure of soil pores by processing sequential images. When compared to other models, BDULSTM demonstrates superior performance in pore segmentation.  
(3) The model complexity, floating-point operations (FLOPs), and memory requirements of BDULSTM remain reasonably low, thereby expanding the practical application of the BDULSTM method and facilitating its operation on low-power devices.  
When processing complex 3D data that heavily depend on spatial structures, BDULSTM may not perform as well as 3D convolutional networks. However, if computational resources are extremely limited, using BDULSTM could be a reasonable compromise. In conclusion, BDULSTM has shown promising capabilities in segmenting 3D soil pore structures within CT images, and it lays a solid groundwork for further 3D pore structure analysis in soil science.  
L.L.: investigation, writing--original draft, software. Q.H.: validation, writing--review and editing. Y.Z. ([PERSON]): writing--review and editing. Y.Z. ([PERSON]): conceptualization, methodology, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (32071838), the National Natural Science Youth Foundation of China (32101590), and the Special Fund for Beijing Common Construction Project. And The APC was funded by the National Natural.  
Not applicable.  
Dataset available on request from the authors.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Models** & **Parameters (M)** & **FLOPs (G)** & **Memory (M)** \\ \hline CLSTM & 0.55 & 10.85 & 32.87 \\ U-Net & 7.41 & 11.60 & 202.75 \\ U-Net–LSTM & 51.90 & 38.47 & 442.50 \\ BDULSTM & 50.82 & 32.87 & 409.50 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of number of parameters, FLOPs, and memory requirements for various models.
# Path Diversity Improved Opportunistic Routing for Underwater Sensor Networks
## 3 Path Diversity Improved Opportunistic Routing
### Candidates Selection Algorithm for PRCS
Then, whether node \(c\) or \(d\) could be a candidate member depends on the depth, successful packet delivery probability and the path correlation with the existing candidate forwarding set \(C_{s}=\{a,b\}\). An example of one-hop candidate set selection is illustrated in Figure 3. The black triangle is the sender node, the red ring represents its neighbor node, and the green square shows the node with highest NADV value. Obviously, the candidate nodes selected by EPA metric are limited in a specific space, represented by blue-x. The candidate nodes chosen by PICS algorithm are scattered within the communication range of each other, represented by the black square. The candidate nodes selected by PRCS algorithm could get a larger coverage area, represented by the pink ring. In the sender-based candidates selection scheme, the candidate priority is always based on NADV [14; 32]. A dynamic priority is applied in this paper. We take the sequence that neighbor node adds to the candidate set as its priority \(\lambda\). When candidates with higher priority fail to receive packets, the candidate member that has a higher path correlation with the previous forwarders will often fail to forward. The next forwarder which not only has a higher NADV value but also has a lower path correlation with the previous forwarders will have a higher priority. Node priority information is also embedded in the packet header. Figure 2: Candidates selection diagram—an example.
## 3 Results and Discussion
### Construction of Complex Ecospatial Network
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Number**} & \multicolumn{1}{c}{**Primary Impact Factor**} & \multicolumn{1}{c}{**Secondary Impact Factor**} & \multicolumn{1}{c}{**Classification**} & \multicolumn{1}{c}{**Resistance Value**} \\ \hline  & & & \(<\)500 & 1 \\  & & & 500–1000 & 3 \\  & & DEM (m) & 1000–2000 & 5 \\  & & & 2000–3000 & 7 \\
1 & Topography & & \(>\)3000 & 9 \\ \cline{3-5}  & & & \(<\)2 & 1 \\  & & & 2–6 & 3 \\  & & Slope (\({}^{\circ}\)) & 6–15 & 5 \\  & & & 15–25 & 7 \\  & & & \(>\)25 & 9 \\ \hline  & & & \(<\)0 & 9 \\
2 & Vegetation cover & NDVI & 0–0.2 & 7 \\  & & & 0.2–0.4 & 5 \\  & & & 0.4–0.6 & 3 \\  & & & \(>\)0.6 & 1 \\ \hline  & & & \(<\)0 & 9 \\
3 & Hydrological distribution & & 0–0.3 & 7 \\  & & & 0.3–0.6 & 5 \\  & & & 0.6–0.8 & 3 \\  & & & 0.8–1.0 & 1 \\ \hline  & & & Woodland & 1 \\
4 & & & & \\
55\({}^{\circ}\) 07\({}^{\circ}\)  
The ecological nodes and corridors in the study area are mainly distributed in the central and eastern parts, indicating that the ecological corridors in these regions are more complex in trajectory and have good ecological circulation. The distribution of ecological nodes in the northern and western regions is relatively sparse. The distance between ecological nodes in the west is greater, and the corridors are longer; in other areas, the ecological nodes are relatively closer, and the corridors are shorter.