esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setting and Results
### Experimental Settings and Evaluation Matrix  
To verify the ANN search performance of the proposed TOCEH method, many state-of-the-art hashing methods, including locality sensitive hashing (LSH) [14], spectral hashing (SH) [17], iterative quantization hashing method (ITQ) [13], k-means hashing (KMH) [12], partial randomness hashing (PRH) [23], deep variational binaries (DVB) [39], deep hashing (DH) [40], DeepBit [41], deep Cauchy hashing (DCH) [11] and twin-bottle neck hashing (TBH) [10] are utilized as the baseline methods. LSH [14], SH [17], ITQ [13] and KMH [12] belong to the shallow methods. During the ANN search experiments, we extract the content information from RS images by AlexNet and the features are represented as 4096-dimension float point data. Then, these shallow hashing methods map the 4096-dimension featuresinto the compact Hamming space and achieve the ANN search task according to the Hamming distance. DCH [11], TBH [10], DVB [39], DH [40], DeepBit [41] and the proposed TOCEH are deep learning hashing methods. They directly generate the RS image's binary feature using an end-to-end mechanism.  
The training process and comparative experiments are conducted on a high-performance computer with GPU Tesla T4 16 GB, CPU Intel Xeon 6242R 3.10 GHz and 64 GB RAM.  
To evaluate the ANN search performance, two widely used standards, mean average precision (mAP) and recall curves, are employed in this paper.  
The recall curve represents the fraction of the positive samples that are successfully retrieved. The definition of recall is shown in Equation (16). #(\(\cdot\)) returns the number of samples.  
\[recall=\frac{\#(retrieved\ positive\ samples)}{\#(all\ positive\ samples)} \tag{16}\]  
Mean average precision value expresses the return rate of positive samples as defined in Equation (17). \(|total|\) is the total number of retrieved samples. K\({}_{1}\) returns the number of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **UCMD** & **SAT4** & **SAT6** \\ \hline Class Number & 21 & 4 & 6 \\ Image Size & 256 \(\times\) 256 & 28 \(\times\) 28 & 28 \(\times\) 28 \\ Dataset Size & 2100 & 500,000 & 405,000 \\ Training Set & 1470 & 400,000 & 360,000 \\ Query Set & 630 & 100,000 & 45,000 \\ Ground Truth & 100 & 1000 & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics and several parameter settings of three datasets.  
Figure 4: Sample images of the UCMD dataset.  
Figure 5: Sample images of the SAT-4 dataset.  
Figure 6: Sample images of the SAT-6 dataset.  
positive samples of the \(i\)-th query sample. _rank(\(j\))_ is the ranking number of the \(j\)-th positive sample in the retrieved results.  
\[mAP=\frac{1}{|total|}\sum_{i=1}^{|total|}\frac{1}{K_{i}}\sum_{j=1}^{K_{i}}\frac{ j}{rank(j)} \tag{17}\]
# Developing Machine Learning-Based Models for Railway Inspection
## 4 Deep Learning-Based Methods
### Deep Learning Networks  
Convolutional neural networks have achieved groundbreaking performance in image recognition and there are numerous variants of CNN architectures reported in the literature. In [27], several deep learning networks were empirically studied for the task of univariate and multivariate time series classification. These studies suggest ResNet [30; 31] and fully convolutional networks (FCN) [28] achieve better performances than the others. Therefore, in this paper ResNet and FCN are investigated. The same network structures of ResNet and FCN presented in [28] are adopted. They are reconfigured for bivariate time series classification. Figure 5 shows their structures.  
FCN is built with three convolution blocks followed by a global average pooling layer and a soft-max layer. The basic convolution block is a convolutional layer followed by a batch normalization (BN) layer and a ReLU activation layer. The kernel sizes are \(\{8\times 2,\,5\times 2,\,3\times 2\}\) and filter sizes are \(\{128,256,128\}\) respectively to the three convolution blocks (Figure 5a). ResNet is one of the most popular architectures in various computer vision tasks. ResNet extends the neural networks to a very deep structure by adding a shortcut connection between consecutive convolutional layers. These additional shortcut connections link the output of residual blocks to their inputs thus enable the flow of the gradient directly through these connections. In this structure, there are three residual blocks with a shortcut connection, a global average pooling layer, and a soft-max layer. A basic unit of a residual block is composed of a convolutional layer followed by a batch  
Figure 5: Network structures of FCN and ResNet.  
normalization (BN) layer and a ReLU activation layer. There are three basic units in each residual block with the kernel sizes of \(\{8\times 2,5\times 2,3\times 2\}\). The filter sizes of these three residual blocks are set as [64; 128; 128] respectively (Figure 5b).  
The input to the networks is a bivariate time series of acceleration data of 'right' and 'left' sides; the output of the network is probability distributions over three class labels of {\"None-joint\", \"Left-joint\", \"Right-joint\"} for three circumstances: no joint detected, a joint on \"left\" side, and a joint on \"right\" side. The class label is determined with the largest probability. These models are implemented in Python taking advantage of the deep learning package \"Keras\".
# Research and Implementation of High-Efficiency and Low-Complexity LDPC Coding Algorithm
## 4 LDPC Decoding Algorithm
### Min-Sum Algorithm(MSA)  
The MSA algorithm, a simplified form of the LLR-BP algorithm, performs the following simplification during the check nodes update [22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32]:  
\[\Lambda_{mn}^{(k)}=\left(\underset{n^{\prime}\in N(m)\backslash n}{\Pi}sign \Big{(}\lambda_{mn^{\prime}}^{(k-1)}\Big{)}\right)\left(\underset{n^{\prime} \in N(m)\backslash n}{\min}\Big{|}\lambda_{mn^{\prime}}^{(k-1)}\Big{|}\right) \tag{34}\]The MSA algorithm detours the implementation of function \(\Phi(x)\) by solving the minimum value and performing the additive operation, which, to some extent, reduces the implementation complexity of decoding. This paper uses the MSA algorithm as the decoding implementation algorithm, with its implementation flow depicted in Figure 3.
# Review on Electrical Impedance Tomography: Artificial Intelligence Methods and its Applications
## 6 Other Applications of EIT  
Along with the EIT research, focus on impedance imaging in the physiological and medical field there has been significant research in the applications of process control. In 1980, Professor [PERSON] presented impedance imaging as a process tomography and put his efforts into bringing the process and UK medical tomography groups on to the same platform. The joint venture and collaboration helped to promote these systems to develop in both research fields and resulted in the development of capacitive and inductive tomographies. The limitations are different in the process control as compared to that in medical since for liquids like oil and water, more than 200 frame rates per second need to be calculated at a time. The most relevant example is the calculations of the fast fluid flow in rigid pipes. Large fixed electrodes could be positioned around the pipe as capacitive tomography systems are practical in case of rigid pipes and the electrodes are not in contact with the flow of the liquid.  
EIT is also used to identify small defects such as cracks or voids in metals and nondestructive testing for detecting corrosion. Since its early usage, the system has progressed remarkably with its technology as well as in its usage. Different researchers have discussed some other applications of the EIT. [PERSON] et al. [100] have outlined the application of EIT in non-destructive material testing. They have proposed an EIT-based reconstruction algorithm, demonstrating its usage for finding cracks in electrically conductive objects. The algorithm presented found to be proficient in finding a defect precisely.  
[PERSON] et al. [101] have presented a resistivity reconstruction technique that depends upon the magnetic detection of EIT to calculate the topology and breakpoints of the grounding grid. They have highlighted the efficiency of the proposed method by applying it on some a mathematical instance that confirms that the suggested approach can be implemented in finding the breakpoints and topology of the grounding grids.
## 2 Materials and Methods
### Mathematical Definition and Application of Bhattacharyya Distance  
#### 2.6.1 Color Extraction and the K-Means Algorithm  
First, the primary colors in the background are extracted using the K-means algorithm. The mathematical formulas behind the K-means algorithm mainly involve two steps:  
Assignment Step: Each data point (in this case, pixels in the image) is assigned to the nearest cluster center. The distance is usually calculated using the Euclidean distance. Here, \(Si\) is the ith cluster, \(xp\) is the data point, and \(\mu i\) and \(\mu j\) are the cluster centers. The Euclidean norm is denoted by \(\|.\|\):  
\[Si=\{xp:\mid\;xp-\mu i\;\mid\;2\leq\mid\;xp-\mu j\;\mid\;2 for\;all1\leq j\leq k\} \tag{1}\]  
Update Step: The centers of the clusters are recalculated, usually by taking the mean of all points in the cluster. Here, \(\mid\;Si\;\mid\;\) is the number of data points in cluster \(Si\), and \(xj\) is a data point in \(Si\):  
\[\mu_{i}=\frac{1}{\mid S_{i}\mid}\sum_{x_{j}\in S_{i}}x_{j} \tag{2}\]  
Figure 10: Technological route.  
Value of K (n_clusters): This is the number of \"clusters\" (i.e., primary colors) to be found in the K-means algorithm. Here, it is set to 10. That is, the algorithm will divide all pixels in the image into 10 clusters, each represented by a primary color.  
\[\text{Kmeans}=\text{KMeans}(\text{n\_clusters}=10)\]  
Data Points (pixels): The line pixels = img_reshape(\(-\)1, 3) in the code transforms the image into an \(N\times 3\) matrix, where \(N\) is the number of pixels in the image, and 3 corresponds to BGR(Blue, Green, Red).  
In computational practice, it is observed that the K-means clustering algorithm, when applied to a singular image across multiple iterations, may yield marginally varying outcomes due to its stochastic nature. However, these variations are typically negligible, allowing the initial computational results to be retained with a high degree of confidence. This consistency underscores the algorithm's robustness for image analysis applications where reproducibility is paramount.  
Cluster Centers (colors): The line colors = kmeans. cluster centers_ retrieves the centers of each cluster, which are the BGR values of each primary color. Based on the above algorithm,,we are given the BGR values for ten different colors and their corresponding percentages from Figure 11, as shown in Figure 12.
## 5 Research Findings
### RQ1: What Techniques Are Utilized to Abuse the CPLS as C&C Communication Channels?
#### 5.3.3 API Verification with CAPTCHA  
[PERSON] et al. [114] present API Verifier, a tool that utilizes CAPTCHA verification to determine if an API call to a social media platform is from a human or a bot. This verification process aims to prevent automated bot actions by requiring human authentication.
# A Microfluidic Approach for Biosensing DNA within Forensics
## 3 Biosensing of DNA
### Next Generation Sequencing
#### 3.2.3 Nanopore  
A single DNA molecule can be read by the use of nanopores. This can be accomplished without the need for amplification or expensive fluorescent labels [31]. The detection principle of nanopore based systems depends on the ion current, which is generated when a charged molecule, such as a DNA molecule, passes through a nanoscale pore in a membrane resulting in a change of the ionic current. The four different bases produce four distinct current levels, which can be used for sequencing the DNA strand [31; 32].
# Fault Controls on Hydrocarbon Migration--An Example from the Southwestern Pearl River Mouth Basin
## 4 Results
### Hydrocarbon Sealing Faults (F1-F6)  
#### 4.1.1 Fault Structure  
The east-west trending and northward dipping F1 fault acts as the sealing fault for the northern part of Field A. In cross-section, the fault is steeply dipping, significantly offsets horizons and cuts downwards into the Late Eocene Wenchang Formation, and extends upwards to the Late Miocene Yuehai Formation. The F2 exhibits a similar geometry to the F1 fault, with a near east-west trend and northward dip, and serves as the sealing fault for the southern part of Field A (Figure 4). The F2 normal fault has a gentler dip angle and is of a smaller scale compared to the F1 fault. It cuts downwards into the Wenchang Formation and extends upwards to the first member of the Early Miocene Zhujiang Formation (N\({}_{1}\)\(z_{1}\)).  
Figure 4: Interpreted seismic profiles of the sealing faults F1–F6 and the potential hydrocarbon bounded target faults F7–F10. F1–F10 are indicated in blue. Relative gas reservoirs in red and oil reservoirs in green are shown in the seismic profiles. For the location of profiles, see Figure 3.  
Field B is bounded by the F3 and F4 faults. The F3 fault trends northwest and dips northeast, while the F4 fault is near east-west trending and dips southward. In the cross-section, both the F3 and F4 faults exhibit a listric geometry with relatively gentle dip angles (Figure 4). The F4 fault is located at a greater depth compared to the F3 fault, which cuts downwards into the Wenchang Formation and extends upwards to the second member of the Zhujiang Formation (N\({}_{1}\)z\({}_{2}\)). The F3 fault offsets a small upper segment of the F4 fault. The F4 fault can cut downwards into the Enping Formation and extend upwards to the bottom of the Yuehai Formation.  
Field C  
In Field C, the F5 and F6 faults act as the main sealing faults, dividing the oil field into several segments. The F5 fault trends northwest and dips northeast, while the F6 fault is near east-west trending and dips northward. In the cross-section, both the F5 and F6 faults exhibit a listric geometry with relatively gentle dip angles. The fault scale of the F5 is smaller than that of the F6. The F5 fault cuts downwards into the Enping Formation and extends upwards to the N\({}_{1}\)z\({}_{1}\) strata, while the F6 fault cuts deeply into the Wenchang Formation and can extend upwards to the Hanjiang Formation.
## 7 Conclusions  
An adaptive data transmission algorithm for underwater wireless sensor networks, EP-ADTA, is proposed in this paper. The end-edge-cloud architecture is used to define the underwater wireless sensor networks, and the agent is used to define the network nodes. All the monitoring data that enter the edge network are compressed and preprocessed using the edge prediction algorithm, EP-ARMA, and only the prediction parameters and correction data are transmitted in the network transmission process. The selection of relay nodes and the adjustment of transmission accuracy are controlled by the reinforcement learning algorithm, RL-ADTA. The algorithm comprehensively considers the location, energy, and channel delay of the transmission node, and sends the data packets with appropriate accuracy to the best relay node. By comprehensively considering the functions and requirements of all layers and using the method of edge computing to compress the demand of actual node data communication, the algorithm realizes more efficient and reliable communication in underwater wireless sensor networks. Simulation experiments show that EP-ADTA can meet the needs of underwater monitoring applications having different levels of data acquisition accuracy, and can effectively compress the actual amount of data transmitted. Compared with the state-of-the-art algorithms, EP-ADTA (0.1) reduces transmission delay by 9%, reduces energy consumption by 44%, and improves the packet-sending success rate by 35%. With the development of underwater network computing power, the underwater nodes will gain more edge computing functions, which will achieve more efficient and reliable data communication.  
Conceptualization, B.W., K.B. and H.L.; methodology, B.W. and K.B.; software, B.W.; validation, B.W. and M.Z.; resources, B.W.; data curation, B.W.; writing--original draft preparation, B.W. and F.Z.; writing--review and editing, B.W.; funding acquisition, M.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 52071153.
# Kinematic Modelling and Experimental Validation of a Foldable Pneumatic Soft Manipulator
## 5 Conclusions  
In this paper, a design of a foldable pneumatic soft manipulator composed of pneumatic actuators and inflatable straight arms was presented. The pneumatic actuators were driven by pressured air and the inflatable straight arms could switch between the contraction state and the extended state. The foldability of the soft manipulator was realized by the inflatable straight arms. Based on this design, the kinematic model of one foldable pneumatic module was proposed. From the numerical results of the shape deformation and the workspace, it can be determined that the prediction of the proposed model was reasonable. In order to validate the design and the theoretical model, an experiment system including a prototype of pneumatic soft module, a pneumatic control system, and an electronic total station was developed. Through the experiment, the repeatability of the pneumatic soft module was tested and the model prediction accuracy was validated. It can be determined that the performance of the pneumatic soft module was stable, especially in the condition of two bellows inflated. The prediction of the shape deformation by the proposed model was validated by the experimental results, although the error still existed obviously under some conditions. Based on the modelling method presented in this paper, the kinematic model of the soft manipulator composed of several pneumatic modules could be developed. Due to the advantage of the foldability, the foldable pneumatic soft manipulator will show a wide application prospect in the aerospace field in the future. In our future work, a soft manipulator composed of three pneumatic modules will be designed and developed. The experiment will be conducted based on the air floating platform, which can provide a free moving environment for the planar motion of the soft manipulator.  
Conceptualization, X.Z. and Y.H.; Formal analysis, H.L. and Y.C.; Funding acquisition, X.Z. and X.C.; Investigation, Y.C.; Methodology, Z.L., X.Z. and Y.H.; Validation, H.L.; Writing--original draft, Z.L.; Writing--review & editing, Y.H. and X.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Postdoctoral Research Foundation of China and the Natural Science Foundation of China, under grant numbers 11702320, 91741107, and 11725211.  
The authors declare no conflict of interest.
# Effect of Leaf Grade on Taste and Aroma of Shaken Hunan Black Tea
## 1 Introduction  
Black tea has become one of the most popular beverages in the world because of its unique taste and aroma [1]. Hunan black tea, a representative Chinese black tea, is renowned for its distinctive nectar aroma and sweet taste [2; 3; 4]. The formation of these qualities in Hunan black tea is influenced by the process and raw materials. In recent years, innovative technology has been introduced to improve the quality of Hunan black tea, specifically through the incorporation of the shaking process during withering, which is the first and crucial step [5]. Shaking involves continuously squeezing, rubbing, and colliding the fresh leaf blades, leading to noticeable modifications in leaf morphology, color, aroma, and biochemical composition [6]. These alterations ultimately contribute to the enhancement of taste and aroma [7; 8; 9].  
The quality of Hunan black tea is influenced by the leaf grade, which determines the raw material quality. Maturity affects the quality of black tea by regulating the metabolites mainly through the types and proportions of components contained in fresh leaves [10]. Studies have shown that the leaf grades had a significant impact on the quality of ancientblack tea [11]. Furthermore, research by [PERSON] found notable variations in the quality of standard samples of Hunan black tea with different leaf grades [12]. It is worth noting that the shaking process requires tea leaves to have a certain maturity. Only tea leaves that have reached a certain level of maturity can undergo optimal metabolic processes during shaking, resulting in the production of volatile compounds that enhance the aroma of the tea leaves [13]. Therefore, the grade of fresh leaves plays a crucial role in determining the quality of Hunan black tea produced through shaking. However, there is a lack of research on the impact of different fresh leaf grades on the quality of shaken black tea and the corresponding patterns of change.  
This study utilized sensory evaluation, spectrophotometry (SP), high-performance liquid chromatography (HPLC), and headspace solid-phase microextraction/gas chromatography-mass spectrometry (HS-SPME/GC-MS) techniques to investigate the impact of varying leaf grades on the quality of shaken Hunan black tea. We analyzed the main taste substances and volatile compounds of shaken Hunan black tea made from different grades of fresh leaves. The objective was to determine the optimal maturity levels and further examine the patterns of change and differential components in shaken Hunan black tea at different grade levels. The findings aim to provide theoretical support for production and practical applications.
## 4 Test Results and Analysis  
The Spectrum Two Fourier transform infrared spectrometer produced by Perkin-Elmer was used for the test. The front surface test result is shown in Figure 15, and the passband transmittance is quite different from the theoretical design. After analysis, it may be due to the fact that graphite is often used as the bushing of the crucible for Ge film deposition and the use of graphite bushing for a long time will cause pollution, resulting in fogging of the film, poor roughness, absorption, or scattering. Figure 16 is a comparison chart of fogging and non-fogging of the lens under the illumination of a strong light. As shown in the figure, the left side is the unfogged lens and the right side is the fogged lens.  
Considering the effect of roughness on scattering, the relationship is as follows:  
\[S_{R}=R_{0}\Big{(}\frac{4\pi\sigma n_{0}}{\lambda}\Big{)}^{2} \tag{13}\]  
\[S_{T}=T_{0}\Big{[}\frac{2\pi\sigma}{\lambda}\Big{(}n_{f}-n_{o}\Big{)}\Big{]}^{2} \tag{14}\]  
\[TIS=S_{R}+S_{T} \tag{15}\]  
Among them, \(\sigma\) represents the surface roughness, \(S_{R}\) represents the reflected scattered light, \(S_{T}\) represents the transmitted scattered light, and TIS represents the total scattering. It can be seen that the intensity of the scattered light is proportional to the surface roughness. Therefore, in order to improve the passband transmittance of the filter, it is necessary to reduce the influence of its surface roughness. The impurity particles embedded in the film will increase the scattering and absorption of the film, so it is advised to try to choose a process that produces fewer impurity particles. However, during electron gun deposition, graphite will also be bombarded by electron beams and deposited into the film [20], so Ge is directly put into a water-cooled crucible, and the transmission ripple in the two passbands is compared as shown in Figure 17.  
By comparison, it is found that the transmittance of Ge directly placed in the crucible is higher than that deposited with graphite bushing, and the transmittance curve of the final prepared double-sided passband is shown in Figure 18.  
The average transmittance is 90.5% in 3-3.5 \(\upmu\)m and 94.24% in 6.5-8.2 \(\upmu\)m. The cut-off depth is above OD3 in the range of 1.6-2.95 \(\upmu\)m, 3.55-6.45 \(\upmu\)m, and 8.3-10 \(\upmu\)m, which meets the application requirements.  
Figure 16: Comparison between fogging and non-fogging of the film layer.