esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Models
### Model Architecture
#### 3.1.2 Backbone  
Next the embedding sequence is encoded by the \(L\) transformer layers sequentially. Each layer is composed of a multi-head self-attention sublayer and a position-wise, two-layer fully connected feed-forward network sublayer. The input of a layer is the output of the previous layer. Let \(H_{i}^{B}\) be the output of the \(i\)-th layer; apparently, its input is the output of the \(i-1\)-th layer. The \(i\)-th layer is therefore defined as,  
\[H_{i}^{B}=\text{TransformerLayer}_{i}(H_{i-1}^{B}) \tag{2}\]  
where \(H_{i}^{B}\in R^{|S|\times d}\) is the output of the \(i\)th layer, \(|S|\) is the length of the input sequence, \(d\) is the hidden size of the transformer. For the first layer (bottom layer), the input \(H_{0}^{B}\) is the embedding representation \(E\). Specific implementation of TransformerLayer\({}_{i}(\cdot)\) is described in [7].  
We denote \(H_{n}=H_{L*n/N}^{B}\) as the output representation for the \(n\)th block. To conveniently describe the model, the encoder for each block is defined as,  
\[H_{n}=f_{n}(H_{n-1},\theta_{n}^{t}) \tag{3}\]  
where the function \(f_{n}\) is a stack of \(\frac{L}{N}\) transformer layers, \(\theta_{n}^{t}\) denotes the parameters of the \(\{\frac{(n-1)L}{N}+1,\cdots,\frac{uL}{N}\}\)th transformer layers.  
Following the encoding output of each block is the decision module. The score of the candidate answers in \(n\)th block is calculated by the decision module,  
\[P_{n}(Y|S)=g_{n}(H_{n},\theta_{n}^{d}) \tag{4}\]  
where \(P_{n}(Y|S)\) is the probability distribution over the candidate answer set \(\mathcal{C}\) and \(\theta_{n}^{d}\) is the parameters of the decision function \(g_{n}(H_{n},\theta_{n}^{d})\). The specific implementation of the decision module will be expounded in the Section 3.3.  
Then the predicted answer of this block is determined by the probability,  
\[a_{n}=\operatorname*{argmax}_{c\in\mathcal{C}}P_{n}(c|S) \tag{5}\]  
where \(P_{n}(c|S)\) indicates the predicted probability of the candidate \(c\).
# RMFNet: Redetection Multimodal Fusion Network for RGBT Tracking
## 5 Performance Evaluation
### Ablation Experiments
#### 5.4.3 Efficiency Analysis  
The implementation of RMFNet was based on Python 3.6, PyTorch 1.2.0, Tesla V100 GPU. Finally, we measured the performance and runtime of RMFNet on the RGBT234 and GTOT datasets using MDNet + RGBT [12], MANet [13], DAPNet [1], and CBPNet [2]. As shown in Table 4, the evaluation results demonstrate that RMFNet is superior to other trackers for speed and performance. RMFNet-noAR, which removes the AR module, achieves the highest speed. Notably, under the same conditions we tested MANetz on the RGBT234 and GTOT datasets at 0.88 fps and 0.64 fps, respectively.
# Achieving Excellence in Cyber Fraud Detection: A Hybrid ML+DL Ensemble Approach for Credit Cards
## 4 Method
### Machine Learning Techniques
#### 4.1.2 Random Forest (RF)  
RF is an ensemble method that creates multiple DTs and combines their predictions. Key hyperparameters include n_estimators (number of trees), max_depth (limits tree depth), min_samples_split and min_samples_leaf (control node splitting), and random_state (ensures reproducibility). These parameters help reduce overfitting while capturing complex patterns effectively.
## 2 Methods
### Trajectory Preprocess
#### 2.1.3 Trajectory Simplification  
Simplification algorithms can be classified as either offline or online algorithms, based on whether they require the availability of the full data series [31]. An offline algorithm reduces the size of the trajectory after the trajectory has been fully generated. The DP [32] algorithm, as a typical offline algorithm, is often used to compress AIS data [33; 34; 35; 36]. The online algorithm compresses a trajectory instantly, as an object travels. Typical online algorithms include [37; 38; 39; 40; 41; 42]. Among them, the DOTS algorithm [42] uses Synchronized Euclidean Distance (SED) as a distance error and incremental directed acyclic graph (DAG) to realize online trajectory compression. SED considers the time characteristics of the original trajectory. DAG can generate a top-down compressed path tree. In the \(V_{k+1}\) layer construction of the tree, the new point chooses the point in the \(V_{k}\) layer whose Local Integral Square Synchronized Euclidean Distance is less than the threshold as its parent node. If more than one point satisfies this condition, the minimum Integral Square Synchronized Euclidean Distance should be chosen as the parent. If only one point in the top layer is attached to the bottom layer, the point is output to the simplified trajectory as a sample point. The DOTS algorithm can get the shortest compression path with minimum synchronize position error online and is used to simplify the AIS trajectory in this paper.
# Efficient Small-Object Detection in Underwater Images Using the Enhanced YOLOv8 Network
## 4 Experiments
### Comparisons with Other State-of-the-Art Methods
#### 4.3.1 Results on UTDAC2020
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Method** & **Backbone** & **AP** & **AP\({}_{\text{50}}\)** & \begin{tabular}{c} **Parameters** \\ **(M)** \\ \end{tabular} & \
\begin{tabular}{c} **FLOPs** \\ **(G)** \\ \end{tabular} &
\begin{tabular}{c} **Model Size** \\ **(MB)** \\ \end{tabular} \\ \hline Faster R-CNN w/FPN [29] & ResNet50 & 44.50 & 80.90 & 41.14 & 63.26 & \textasci{} \\ Cascade R-CNN [39] & ResNet50 & 46.60 & 81.50 & 68.94 & 91.06 & \textasci{} \\ RetinaNet [40] & ResNet50 & 43.90 & 80.40 & 36.17 & 52.62 & \textasci{} \\ FCOS [41] & ResNet50 & 43.90 & 81.10 & 31.84 & 50.36 & \textasci{} \\ Deformable DETR [42] & ResNet50 & 46.60 & 84.10 & \textasci{} & \textasci{} & \textasci{} \\ Libra R-CNN [43] & ResNet50 & 45.80 & 82.00 & 41.40 & 63.53 & \textasci{} \\ Dynamic R-CNN [44] & ResNet50 & 45.60 & 80.10 & 41.14 & 63.26 & \textasci{} \\ ATSS [45] & ResNet50 & 46.20 & 82.50 & 31.89 & 51.58 & \textasci{} \\ Boosting R-CNN [6] & ResNet50 & 48.50 & 82.40 & 43.55 & 53.17 & \textasci{} \\ Boosting R-CNN * [6] & ResNet50 & 51.40 & 85.50 & 45.91 & 54.67 & \textasci{} \\ \hline YOLOv8n & Darknet-53 & 49.07 & 82.73 & 3.0 & 8.1 & 6 \\ YOLOv8s & Darknet-53 & 50.50 & 84.73 & 11.1 & 28.4 & 22 \\ YOLOv8m & Darknet-53 & 51.74 & 85.11 & 25.8 & 78.7 & 50 \\ YOLOv8l & Darknet-53 & 51.69 & 84.85 & 43.6 & 164.8 & 84 \\ \hline Ours & FasterNet-T0 & 52.12 & 85.49 & 8.5 & 25.5 & 17 \\ Ours (1280) & FasterNet-T0 & **53.18** & **86.21** & 8.5 & 25.5 & 18 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparisons with different object detectors on UTDAC2020 dataset (The symbol * signifies enhanced version of the model.). Figure 5: Comparison of the detection results of the URPC2020 underwater dataset with YOLOv8s using visualization (**Column Left:** YOLOv8s sets the longer side of the image input to 640 pixels, **Column Middle:** Ours method sets the longer side of the image input to 640 pixels, **Column Right:** Ours method sets the longer side of the image input to 1280 pixels). Additionally, false positives are indicated by yellow boxes, and false negatives are represented by blue boxes.
## 5 Conclusions  
We have created a common livestock detection dataset in Qinghai by utilizing data collected by UAVs. As far as we are aware, this is the first dataset of its kind in this field. The introduction of the CBAM attention mechanism and the CARAFE operator into the model, coupled with the addition of a small object detection layer, aims to enhance the detection performance for livestock. Utilizing this dataset, the CCS-YOLOv8 model secured 84.1% accuracy, 82.2% recall, 84.4% [EMAIL_ADDRESS], 60.3% [EMAIL_ADDRESS], 53.6% [EMAIL_ADDRESS]:0.95, and 83.1% F1-score, showing improvements of 1.1%, 7.9%, 5.8%, 6.6%, 4.8%, and 4.7%, respectively, over the baseline. Relative to other target detection models, the CCS-YOLOv8 model demonstrates excellent performance. By utilizing high-resolution images of livestock in Qinghai collected by UAVs and integrating the CCS-YOLOv8 model, this approach enables rapid detection of the ages and species of livestock. This method offers a new way to effectively manage the number of livestock and control overgrazing, aligning with the needs of modern precision livestock husbandry.  
Conceptualization, C.F.; methodology, C.F. and C.L.; software, C.F., J.N. and P.Y.; validation, Y.H., X.H. and J.N.; formal analysis, X.H. and Y.H.; investigation, C.F., C.L., S.K., P.Y. and X.H.; resources, C.F., S.K. and P.Y.; data curation, J.N. and Y.H.; writing--original draft preparation, C.F.; writing--review and editing, C.F., S.K. and C.L.; supervision, C.L.; project administration, C.L.; funding acquisition, C.L. 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 (grant No. 62166033) and the Basic Research Project of Qinghai Province, China (grant No. 2024-ZJ-788).  
This study and all animal procedures therein were approved by the ethics committee for experimental animals of the Inner Qinghai University (No. qhdx-202306252).  
Not applicable.  
The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.  
The authors thank the High-Performance Computing Center of Qinghai University for its support.  
The authors declare no conflicts of interest.
# Target Detection of Passive Bistatic Radar under the Condition of Impure Reference Signal
## 3 Proposed Target Detection Method under the Condition of Impure Reference Signal
### Reference Signal Purification Method
Given \(\tau_{1}f_{s}\) and \(f_{\text{d1}}\), the subspace of the estimated impure term constructed by the impure reference signal is represented as:  
\[\widetilde{\mathbf{V}}=\mathbf{S}_{\text{ref}}[l-\tau_{1}f_{s}]e^{2\pi f_{ \text{d1}}l/f_{s}} \tag{36}\]  
Replacing \(\mathbf{S}_{\text{sur}}\) with \(\mathbf{S}_{\text{err}}\), the weights of the ECA can be updated as:  
\[\widetilde{\mathbf{W}}=(\widetilde{\mathbf{V}}^{\text{H}}\widetilde{\mathbf{V }})^{-1}\widetilde{\mathbf{V}}^{\text{H}}\mathbf{S}_{\text{err}} \tag{37}\]  
Equation (37) indicates that the residual signal \(\mathbf{S}_{\text{err}}\) after clutter suppression is projected onto the subspace \(\widetilde{\mathbf{V}}\) of the estimated impure term in the reference signal to obtain the energy projection coefficients \(\widetilde{\mathbf{W}}\) of the signal, which matches the impure term in \(\mathbf{S}_{\text{err}}\). Therefore, based on \(\widetilde{\mathbf{W}}\), the impure term in \(\mathbf{S}_{\text{sur}}\) can be estimated as:  
\[(1-\mathbf{W}[1])b_{1}\mathbf{d}[l-\tau_{1}f_{s}]e^{2\pi f_{\text{d1}}l/f_{s}} \approx\widetilde{\mathbf{V}}\widetilde{\mathbf{W}} \tag{38}\]  
After rearranging, (38) is given by:  
\[b_{1}\mathbf{d}[l-\tau_{1}f_{s}]e^{2\pi f_{\text{d1}}l/f_{s}}\approx\frac{ \widetilde{\mathbf{V}}\widetilde{\mathbf{W}}}{1-\mathbf{W}[1]} \tag{39}\]  
The left term of (39) is equal to the impure term introduced by the reference signal in (5), and the right one is the corresponding reconstruction term. Obviously, by subtracting (39) from (5), the purified reference signal can be obtained, which is expressed as:  
\[\widetilde{\mathbf{S}}_{\text{ref}}[l]=\mathbf{S}_{\text{ref}}[l]- \frac{\widetilde{\mathbf{V}}\widetilde{\mathbf{W}}}{1-\mathbf{W}[1]} \tag{40}\] \[=a_{\text{ref}}\mathbf{d}[l]+b_{1}\mathbf{d}[l-\tau_{1}f_{s}]e^{2 \pi f_{\text{d1}}l/f_{s}}-\frac{\widetilde{\mathbf{V}}\widetilde{\mathbf{W}} }{1-\mathbf{W}[1]}+\mathbf{z}_{\text{ref}}[l]\] \[\approx a_{\text{ref}}\mathbf{d}[l]+\mathbf{z}_{\text{ref}}[l]\]From (40), it is easy to see that the purified reference signal and (1) are almost equivalent. Treating (40) as the reference signal for target detection, theoretically normal detection results can be obtained.
# Flow Velocity Computation in Solid-Liquid Two-Phase Flow by a Hybrid Network CNN-RKSVM
###### Abstract  
As an advanced detection technique, electrical resistive tomography (ERT) has been applied to detect the solid-liquid two-phase flow velocity based on available ERT measurements. The flow velocity computation by ERT must depend on the relative algorithms, including both the cross-correlation (CC) principle and convolutional neural networks (CNNs). However, these two types of algorithms have poor accuracy and generalization under complex measuring conditions and various flow patterns. To address this issue, in this paper, a hybrid network is proposed that combines a CNN with a reproducing kernel-based support vector machine (RKSVM) technique. The features hidden in ERT measurements are extracted using the CNN, and then the flow velocity is computed by the RKSVM in a high-dimensional feature space. According to the ERT measurements in an actual experimental platform, the results show that the hybrid network has higher accuracy and generalization ability for flow velocity computation compared with the existing CC, RKSVM, and CNN methods.  
e +
Footnote †: journal: Applied sciences  
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Footnote †: journal: Applied sciences  
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Footnote †: journal: Applied sciences  
method can train the network in advance. More reviews can be found in [16]. Despite progress, these existing CNN-based methods have their individual limitations. First, due to the black-box state of the deep learning process, the generalization of CNNs is unclear and incomprehensive. Second, the computed accuracy of CNNs is often too unstable to satisfy the requirement of flow velocity detection when the flow velocity changes quickly. These problems limit the applicable range of CNNs in applications.  
To address the above problems, we propose a method for flow velocity detection along the existing least square-based support vector machine (LS-SVM) algorithm. LS-SVM is an improved support vector machine (SVM) method proposed by [PERSON] et al. [17] that is widely used in pattern recognition and nonlinear regression due to its simplified computational complexity. LS-SVM uses a reproducing kernel function to map the input to a high-dimensional feature space, which can effectively enhance the generalization of SVM prediction. According to the LS-SVM algorithm, in this paper, we design a hybrid network which combines a CNN with a reproducing kernel-based support vector machine (RKSVM) technique. Firstly, the features hidden in ERT measurements are extracted using the CNN, and then the feature data are mapped to a high-dimensional feature space by a selected reproducing kernel function. Finally, the flow velocity is generally computed by the hybrid network in a high-dimensional feature space. The proposed method is expected to have stronger generalization capabilities and greater accuracy than the typical CC, RKSVM, and CNN methods.
###### Abstract  
The Red River Delta (RRD), including 11 provinces, is one of the four largest rice-growing areas in Vietnam. Tropical storms often occur and cause serious flooding from May to October annually in the RRD, which strongly affects the productivity of the summer-autumn rice, one of two main rice crops. Therefore, the rapid assessment of damaged rice area by flooding inundation is critical for farmers and the government. In this study, we proposed a methodology for quick estimation of rice areas damaged by flooding using Sentinel 1A (S1A) imagery. Firstly, the latest rice map was produced. Then, a Near Real-Time (NRT) flood map, which is estimated from S1A images at the closest time to a flooding event, was generated by excluding the yearly permanent map from the temporal water map. Our experiment was conducted for the assessment of damaged rice area by flooding from the tropical storm named Son-Thin, which happened on 19-21 July 2018. A Support Vector Machine (SVM) classifier was applied on time-series of S1A VV with VH data (VVVH) to obtain a rice map for the winter-spring season of 2018 with 90.5% Overall Accuracy (OA) and 2.37% difference (12,544 ha) from the General Statistics Office (GSO) of Vietnam's reports for the whole region. Then, the Otsu thresholding method was applied for permanent water surface extraction and NRT flood mapping. The estimated damaged area was compared to available provincial and communal statistics for validation and further analysis. Right after the Son-Thin storm, the estimation of inundated rice was approximately 50% of the total rice area in the RRD (271,092 ha). As a result, rice damage level strongly corresponds to the inundation period. In addition, the rice-flooding frequency map over the RRD was estimated to show rice fields suffering a high risk of flooding during the rainy season in the RRD. Our experiment's results highlight the potential of using Synthetic-Aperture Radar (SAR) imagery for fast monitoring and assessment of paddy rice areas affected by flooding at a large scale in the RRD region.  
rice mapping; flood mapping; damage assessment; Red River Delta; Sentinel 1A
## 4 The Design Principles
### Decentralised Decisions  
After establishing the interconnections required to gain information transparency, achieving decentralised decision-making capability is the next expectation in building an Industry 4 system [21]. This, in turn, enables the manufacturing systems aspiring to be or become autonomous or smart. [PERSON] et al. [106] have also outlined a pyramid to support the inherent progression of Industry 4 principles. However, the work related to the top tier in the pyramid received the least contributions in the literature identified, accessed, and reviewed in this research.  
Highly intelligent and flexible manufacturing processes are intended to satisfy fast-changing demands in the market, which in turn require enablers of batch-size-of-one production models for mass individualisation and customisation of the products [3; 4; 26]. [PERSON], [PERSON], and [PERSON] [15] discussed an attempt at employing RFID and data matrix codes in the intralogistics system to individually identify products. However, [PERSON] and [PERSON] [4] believed that the material flows of CPPSs in a smart factory are highly dynamic and changeable, which require decentralised planning and control strategies to be involved.  
It is believed that establishing decentralised decision-making capacity requires the use of certain technologies and enhancing related software and hardware components in manufacturing contexts. Single-board computing units can be employed across the factory floor to enhance the infrastructure for supporting decentralised decision-making strategies [7]. Also, IoT and IIoT are considered to have great potential to contribute to building decentralised control systems in learning factories [4]. Blockchain technology can be used for secure sharing of information along the value chain and product life cycle, to enhance decentralised decision capacity [44]. [PERSON] et al. [37] discussed an application of machine vision in their learning factory to enable a decentralised decision-making strategy for quality control purposes. Also, [PERSON] et al. [107] developed an enhanced manufacturing execution system (MES) to enable a decentralised control strategy for the intralogistics system of their learning factory. Advanced AI algorithms are essential components to boost the performance of decentralised decision making [37; 107].
# Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning
## 2 Materials and Methods
### Data Acquisition and Pre-Processing  
The pre-processing consisted of three basic steps: calculating different spectral indices, creating time series for each life cycle of the organ tree (2017/2018 and 2022/2023), and labeling the data based on ground truth.
# Comparison of the Sensitivity of Various Fibers in Distributed Acoustic Sensing
## 2 Approach
### Samples
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fiber Designation** & **Coating Material** & \(D_{o}\), \(\mu\)**m** & \(D_{i}\), \(\mu\)**m** & \(E\), GPa & \(kL\), N \\ \hline \({}^{\prime\prime}\)Fiber 1\({}^{\prime\prime}\) & Acrylate & 250 & 125 & 2.65 [41] & 98 \\ \hline \multirow{2}{*}{\({}^{\prime\prime}\)Fiber 2\({}^{\prime\prime}\)} & DuPont Tefzel 750 & 900 & 400 & 0.64 [42] & 282 \\  & Covestro DeSolite DSM DF-0009 & 400 & 125 & 0.70 [43] & \\ \hline \multirow{2}{*}{\({}^{\prime\prime}\)Fiber 3\({}^{\prime\prime}\)} & Copper & 163.7 & 157.7 & 117 [44] & 146 \\  & Carbon & 157.7 & 125 & 15.85 [45] & \\ \hline \({}^{\prime\prime}\)Fiber 4\({}^{\prime\prime}\) & Polyimide & 250 & 125 & 2.5 [44] & 92 \\ \hline \({}^{\prime\prime}\)Fiber 5\({}^{\prime\prime}\) & Polyimide & 245–260 & 125 & 2.5 & 87–102 \\ \hline \({}^{\prime\prime}\)Fiber 6\({}^{\prime\prime}\) & Acrylate & 250 & 125 & 2.65 & 98 \\ \hline \({}^{\prime\prime}\)Fiber 7\({}^{\prime\prime}\) & Acrylate & 166 & 80 & 2.65 & 44 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mechanical parameters and the estimated acoustic sensitivity of the FUTs. Figure 2: FUTs’ cross-sections, showing dimensions in \(\mu\)m. White numbers indicate fiber designations for easy reference. As almost all the FUTs are made from the same type of glass and have similar glass cladding diameters, coating differences are the primary distinguishing factor. So, the influence of the glass core and cladding on the Young's modulus was not considered in the study, since it was the same for all FUTs except \"Fiber 7\". In summary, this approach to glass cladding and multilayer coatings has significantly simplified theoretical estimations. Its influence on the results was considered to be acceptable and is discussed in Section 4. According to Table 2, \"Fiber 2\" is expected to exhibit the lowest acoustic sensitivity, while \"Fiber 7\" is anticipated to have the highest sensitivity.
# Calculation of Displacement-Dependent Active Earth Pressure for Deep Excavations in Soft Soil
## 3 Expression for Displacement-Dependent Active Earth Pressure
### Illustrative Example  
A further illustrative study was conducted on an excavation with a width of 50 m and depth of 14.85 m. Therein, 29 m deep bored piles and four levels of struts were adopted as support structures. The unit weight was 18.79 kN/m\({}^{3}\), the cohesion was 17.07 kPa, and the friction angle was 15.61\({}^{\circ}\); other details are shown in Figure 16.  
The internal forces of support structure were calculated using the vertical elastic foundation beam method. According to the simplified method of deformation prediction for excavations [55], the maximum lateral deformation of the support was 36.05 mm, and the deformation at the surface and the support end were 1.80 mm and 3.60 mm, respectively. The earth pressure on support can obtained by the proposed model. According to JGJ120, parameters required for the design are listed in Table 3. The stiffness of soil springs was  
Figure 16: Cross section of the deep excavation.  
Figure 15: Comparison between the predicted earth pressures and the experimental results by [PERSON] [54] According to the analyses, the proposed earth pressure model, which considers displacement, can predict both nonlinear distribution and magnitude. In practice, these results are significant for predicting earth pressure while accounting for support deformation based on the RTC triaxial test.  
calculated by \(k_{i}=m(y_{i}-h)\), where \(m\) is horizontal foundation coefficient, \(y_{i}\) is the depth of the soil spring, and \(h\) is the excavation depth.  
The calculation was implemented using ABAQUS, and the nonlinear earth pressure was conducted by the Dload subroutine. The internal forces obtained from the proposed model were compared with the results calculated from the Rankine active pressure based on JGJ120, as shown in Figures 17 and 18. From the figures, the shear force and bending moment calculated by existing engineering design guidelines are both less than the present method, where the maximum differences of the bending moment and shear force between the two methods are 11.31% and 11.48%, respectively. Thus, the existing design guidelines are unsafe for foundation pit engineering. This issue can be resolved by the proposed method in this study.