esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Grid-Based Rainfall Products and Water Balances over the Mekong River Basin
## 3 Methods  
Owing to the two specific objectives of this study, the following two methods were used:  
1. [label=(0)]
2. Statistical evaluation metrics were employed for evaluating the performance of the GPPs compared with the \"actual\" precipitation patterns derived from the gauge-based rainfall observations.
3. The SWAT model was used to investigate how accurately the GPPs are able to model hydrologic processes, while statistical metrics (i.e., the Nash-Sutcliffe efficiency coefficient (NSE), percent bias (PBIAS), and coefficient of determination (R\({}^{2}\))) were used to evaluate the model's performance, followed by the SWAT model to analyze the water balance components in each sub-region of the MRB.
# Standing Wave Field Distribution in Graded-Index Antireflection Coatings
## 2 Theory and Model
Here, \(\overset{\rightarrow}{E}_{0}^{+}\), \(\overset{\rightarrow}{E}_{0}^{-}\) are the electric field amplitude of incident wave and reflect wave at the surface of coating, respectively.
## 5 Lower Model Based on the Satisfaction Index and Total Social Cost
### Sizing Model Based on the Hybrid Method  
A hybrid method based on queuing theory and the greedy algorithm is employed to determine the capacity of the charging stations. After determining the optimal path, we can obtain specific data about the mileage that has been traveled when she/he arrives at a charging station to recharge. The congestion can be reflected by the system density \(\rho_{i}\) by utilizing queuing theory. A greedy algorithm is used to allocate a certain number of chargers at each station based on the relative system density of stations without a prior knowledge of the arrival rate of each station. Therefore, as is shown in Equation (12), the linear function related to the number of EVs \(n_{ev,i}\) served at the charging station \(i\) is adopted to describe the arrival rate \(\lambda_{i}\) of the charging station \(i\). When it comes to the calculation of the capacity, \(n_{ev,i}\) could be used to replace \(\lambda_{i}\) which would reflect the system density of each charging station correctly:  
\[\lambda_{i}=f_{2}(n_{ev,i}), \tag{12}\]  
The service rate \(\mu_{i}\) of the charging station \(i\) can be obtained via Equation (13). We denote the number of chargers at the charging station \(i\) as \(c_{i}\), then the system density \(\rho_{i}\) can be calculated by Equation (14):  
\[\mu_{i}=\frac{1}{t_{c,i}}, \tag{13}\]  
\[\rho_{i}=\frac{\lambda_{i}}{c_{i}\mu_{i}}, \tag{14}\]  
\[t_{c,i}=t_{fast}\frac{d^{\prime}_{acc,i}}{R}, \tag{15}\]  
Figure 10: The flowchart of the lower model.  
\[d^{\prime}_{acc,i}=\frac{1}{n_{ev,i}}\sum_{j=1}^{n_{ev,i}}d^{\prime}_{i,j}, \tag{16}\]  
where \(t_{c,i}\) is the average charging time at the charging station \(i\) which is related to the SOC of EVs when they arrive at the charging station \(i\) and it could be obtained via Equation (15). \(t_{inst}\) is the time required for a full fast charge when the EV runs out of power. \(d^{\prime}_{acc,i}\) denotes the average mileage that has been traveled from the previous charging station to the newly-arrived charging station \(i\), which can be obtained via Equation (16). \(R\) is the maximum travel range of EVs. \(d^{\prime}_{i,j}\) represents the mileage that has been traveled from the previous charging station to the newly-arrived charging station \(i\). \(n_{ev,i}\) is the number of EVs charged at the charging station \(i\).  
In the hybrid method, the total number of fast chargers for the planning area \(C_{av}\) is fixed and often much greater than the number of chargers needed. The chargers are allocated according to the system density of each charging station, that is, we fix one charger at the charging station with the highest system density until all the chargers are set. The number of charging stations is recorded as \(p\) and the flowchart of sizing with the hybrid method is shown in Figure 11.
## 2 Related Work
However, data hiding may lead to potential video quality degradation and additional computational complexity. Several schemes proposed in [35] encrypt video content by scrambling the IPMs of intra-coded macroblocks. Exclusive OR (XOR) is used to offer data security. The authors of [36] provide a review of existing and encryption techniques for H.264/AVC video encoders, which are categorized on the basis of the stages where the encryption is applied: before compression, through compression, and after compression. These encryption techniques are based on IPMs, residual data, MVDs based on XOR operation, transformation, and the entropy coding process. The existing literature suggests numerous schemes of error recovery for enhancing video quality that involve complex mathematical computations and result in high computational cost. The method proposed in this work involves a bit-inversion mechanism that detects the error bit from the bitstream and flips it to recover the error and improve video quality at the receiving end. The proposed technique additionally merges the error recovery process with syntax-based encryption to preserve the confidentiality and integrity of videos for improved user experience.
## 4 Performance Evaluation  
The simulation adopts the Opportunistic Network Environment (ONE) to test in the Health Capability Maturity Model (HCMM) [30]. In our work, the Predict and Forward algorithm will be compared with the other three algorithms: EIMST routing algorithm (effective information transmission based on socialization nodes) [12], Epidemic routing algorithm [27] and Spray and Wait routing algorithm [20]. The EIMST routing algorithm is the latest strategy for OppNet and the other two algorithms are traditional methods in OppNet.  
In this experimentation, the algorithm \(FunkSVD\) is trained and tested in the mathematical tool MATLAB. According to the experimental results with different simulation settings, the \(FunkSVD\) method shows the best performance when the regularization coefficient \(\lambda\) is 0.02 and the constant \(\theta\) is 0.1, so the parameters \(\lambda\) and \(\theta\) are initialized to 0.02 and 0.1 in the Predict and Forward scheme. Moreover, the other parameters are set as follows. The simulation area is 3000 m \(\times\) 3000 m and there are 1000 nodes (the value of \(n\) is 1000) in the communication area. Each node in the network includes six different node attributes and the destination profile consists of 50 nodes which satisfy the message delivery function. The simulation time is set as 1-12 h, the initial value for a period of time \(t\) is 40 min and the maximum moving range of each node is 20 m\({}^{2}\). The total number of Prelearn messages is 100 and the time to create a Prelearn message is 1-10 s. Each Prelearn message occupies 100 Kb storage space. During the Predict phase, the Prelearn message always keeps a single copy to route nodes in the network. With the movement speed of 1-4 m/s, each node in the network has the cache spaces of 50 Mb and carries 10 data packets. In order to improve the readability of the simulation settings, we build Table 1 to introduce the simulation environment briefly. In the experimental results, we focus on the following parameters:
## 2 Related Works
### Channel Attention Mechanism and Spatial Attention Mechanism  
The feature layer extracted from the backbone network contains rich semantic information, but the semantic information contained in the feature map at different levels is different, and the contribution to target detection is also different. YOLOv7-tiny's neck structure integrates all levels, which can improve the target detection ability, but not all levels of information have the same contribution to target detection, and redundant information may even mislead the network to use effective information.  
The attention mechanism can adjust the weight of the fused information, enhance the attention of the neural network to useful information, and inhibit the attention to invalid information. In order to enable the detector to divide attention when detecting different targets, the attention mechanism thus improves the perception of useful information.  
As shown in Figure 3, the CBAM attention mechanism can perceive both channel attention (CA) and spatial attention (SA) [30]. The CA module is similar to SENet (Squeeze-and-Excitation Network), which adds the feature extraction method of maxpool on the basis of SENet and uses the channel relationship between features to generate the channel attention map. The SA module utilizes the spatial relationship between features, parallels the average pooling and maximum pooling operations along the channel axis, and obtains the attention map through a convolution layer after connection.  
In this paper, the CBAM attention mechanism is applied to multi-scale feature fusion, and the fused feature map is adjusted.  
Figure 2: The architecture of MobileViT.
## 6 Experimental Results and Discussion
### Result and Discussion
#### 6.1.1 Classification by All Features  
As mentioned in the previous parts, we extracted 96 features from a region of interest (ROI). They consisted of many characteristics of each kind of liver disease. In this experiment, SVM was used to discriminate the diseases by using each kind of feature (GLCM or GLRLM) and using the two kinds (GLCM and GLRLM) together. Table 4 shows the results of classification in this case.  
As seen from Table 4, the classification results without applying any feature selection method of SVM are not accurate enough, except using the model that included GLCM and SVM, whose highest accuracy rate is 75.75%. The other combinations of SVM obtain detection rates of 54.53% and 61%.  
This result shows that both GLCM and GLRLM features can be applied to classification of HCC and liver abscess; however, GLRLM seems to contain more noise than GLCM. The classification can adapt to the redundant features in both GLCM and GLRLM; however, SVM's performance is affected significantly. In the next section, the classification was conducted by applying sequential forward selection.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**GLCM (52 Features)** & **GLRLM (44 Features)** & **GLCM + GLRLM (96 Features)** \\ \hline
75.75\% & 54.53\% & 61\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy rate without applying selection method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**PredictionCondition** & **Positive** & **Negative** \\ \hline
**Positive** & True Positive & False Positive \\ \hline
**Negative** & False Negative & True Negative \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix.
# Structural Information Reconstruction of Distorted Underwater Images Using Image Registration
## 2 Methods
### Lucky Patches Fusion  
At first, the input frames are subdivided into a group of smaller patches with the same size. The overlap area of each pair of neighboring patches is chosen to 50% [21]. Then the K-means algorithm is applied to cluster patches into two groups in accordance with warp intensity. Finally, the less warped patches within each patch sequence, are picked out and fused into a single frame, while the rest patches are discarded.  
The warp intensity is quantified by the structure similarity index (SSIM) [26], which represents the deviation of the patch \(I\) from the mean \(M\) of the current patch sequence. The SSIM can be described as  
\[\text{SSIM}(I,M)=\frac{(2\mu_{I}\mu_{M}+c_{1})(2\sigma_{IM}+c_{2})}{(\mu_{I} ^{2}+\mu_{M}^{2}+c_{1})(\sigma_{I}^{2}+\sigma_{M}^{2}+c_{2})} \tag{1}\]  
Here \(\mu_{I},\mu_{M},\sigma_{IM},\sigma_{I},\sigma_{M}\) represents local means, standard deviations, and cross-covariance for \(I,M\), respectively. \(c_{1},c_{2}\) are constants. In the fusion step of each patch sequence, the patch group with higher SSIM value is preserved and averaged to a patch. All the fused patches are stitched together to form an entire frame, and a two-dimensional weighted Hanning window [14] is used to mitigate the boundary effects of every patch.
###### Abstract  
Exact evaluation of the degradation levels in bearing defects is one of the most essential works in bearing condition monitoring. This paper proposed an efficient evaluation method using a deep neural network (DNN) for correct prediction of degradation levels of bearings under different crack size conditions. An envelope technique was first used to capture the characteristic fault frequencies from acoustic emission (AE) signals of bearing defects. Accordingly, a health-related indicator (HI) calculation was performed on the collected envelope power spectrum (EPS) signals using a Gaussian window method to estimate the fault severities of bearings that served as an appropriate dataset for DNN training. The proposed DNN was then trained for effective prediction of bearing degradation using the Adam optimization-based backpropagation algorithm, in which the synaptic weights were optimally initialized by the Xavier initialization method. The effectiveness of the proposed degradation prediction approach was evaluated through different crack size experiments (3, 6, and 12 mm) of bearing faults.  
Keywords:degradation levels; bearing defects; AE signal; DNN; envelope spectrum analysis +
Footnote †: journal: Applied Sciences
## 4 Training and Evaluation of Threat-Detection AI Model
### Theoretical Background to Object-Detection Models
#### 4.1.2 DETR with Improved deNoising anchOr Boxes (DINO)  
DINO introduces a groundbreaking approach to end-to-end object detection, surpassing previous DETR-like models in both performance and efficiency [27]. At the core of DINO's architecture are several novel techniques designed to enhance training effectiveness and prediction accuracy. One key innovation is the contrastive denoising training method, which aims to minimize a composite loss function consisting of various reconstruction losses (such as the l1 and GIOU losses for box regression) and focal losses for classification. This loss function is formulated to penalize false positives and encourage the model to distinguish between useful anchor boxes and noise. By incorporating positive and negative samples of ground truth boxes with different noise scales, DINO effectively learns to reject useless anchors and make more accurate predictions.  
Another crucial aspect of DINO's architecture is the mixed query selection strategy, which optimizes the initialization of anchor boxes while maintaining learnable content queries. This strategy is mathematically represented by the set of queries \(Q=\{q_{1},q_{2},\dots,q_{n}\}\), where \(Q\) denotes the set of queries and \(q_{i}\) represents the \(i\)-th query. By selecting initial anchor boxes as positional queries from the output of the encoder and keeping content queries learnable, DINO leverages spatial priors while still allowing flexibility in learning content features. This approach enhances the model's ability to capture spatial relationships and improve prediction accuracy.  
Furthermore, DINO incorporates a novel look forward twice scheme for box prediction, which iteratively refines the initial boxes and predicted offsets to improve prediction quality. This scheme involves updating the parameters based on both the current and future layers, as represented by the equation \(\Delta b_{i}=\text{Layer}_{i}(b_{i-1})\), where \(b^{\prime}=\text{Update}(b_{i-1},\Delta b_{i})\), \(b^{\prime}_{i}\) represents the refined box, \(b_{i-1}\) denotes the initial box, and \(\Delta b_{i}\) denotes the predicted offset. By iteratively improving both the initial boxes and the predicted offsets, DINO achieves more accurate and reliable predictions, particularly in scenarios with complex spatial relationships or small objects. Figure 4 shows the architecture of the DINO model.  
Figure 4: Architecture of the DINO model [28].
## 3 Algorithmic Challenges in Complex Environments
### Algorithm Adaptability and Robustness  
In the observation of urban residential vacancy rate, the algorithm faces many challenges in a complex environment, among which the adaptability and robustness of the algorithm are particularly important [92].  
Adaptability is reflected in the ability of the algorithm to automatically adjust model parameters according to the environmental characteristics of different cities and regions to achieve accurate prediction of residential vacancy rates [93]. This requires algorithm designers to fully consider various influencing factors and design algorithms with adaptive capabilities. On the one hand, the algorithm needs to be able to automatically identify and learn the patterns in the data and capture the potential spatial correlation; on the other hand, the algorithm also needs to have strong generalization capabilities and be able to quickly adapt and give accurate prediction results when faced with new observation data.  
Robustness refers to the ability of an algorithm to maintain stable performance in the face of outliers, noise and missing data [94]. In the actual process of observing urban residential vacancy rates, data quality issues are difficult to avoid, such as data collection errors and incomplete information, which will have a great impact on the performance of the algorithm. Therefore, it is crucial to improve the robustness of the algorithm. To achieve this goal, algorithm designers can use a variety of technical means, such as weighted sampling, data cleaning and model regularization, to reduce the impact of abnormal data on model performance. In addition, in view of the characteristics of the complex environment in the observation of urban residential vacancy rates, algorithm designers also need to pay attention to the following points: first, the algorithm should have the ability to analyze spatiotemporal correlations, and fully consider the dynamic changes of residential vacancy rates in time and space to improve the accuracy of predictions; second, the algorithm should be able to integrate multi-source data, such as remote sensing images, demographic data and socioeconomic indicators, so as to comprehensively capture the factors affecting residential vacancy rates; algorithm design should take into account computational efficiency to meet the needs of real-time processing of large-scale urban data.  
In the face of the challenges of a complex environment, the adaptability and robustness of the urban residential vacancy rate observation algorithm are crucial [95]. By continuously optimizing the algorithm design and improving its adaptability and robustness, accurate observation of urban residential vacancy rates can be achieved, providing strong support for policy makers and urban planners. At the same time, this is also an important direction for future algorithm research, which requires joint efforts from academia and industry to meet the ever-changing needs of urban development.
# LightR: A Fault-Tolerant Wavelength-Routed Optical Networks-on-Chip Topology +
## 2 Background
### State-of-the-Art WRONoC Topologies  
For each master-slave pair that requires communication, state-of-the-art WRONoC topologies construct one fixed signal path [6, 16, 17, 18, 23]. In addition, most topologies, such as \(\lambda\)-router [17], Snake [23], and GWOR [18], use CSEs with two identical MRRs, where each MRR is designed to be on-resonance with one signal, which corresponds to one signal path.  
Figure 5(a) shows the logic scheme of a \(4\times 4\)\(\lambda\)-router, which consists of six CSEs. For example, the MRRs of the top-left CSE are on-resonance with the signals from \(m_{1}\) to \(s_{3}\) and from \(m_{2}\) to \(s_{4}\), respectively.  
Figure 5(b) shows the logic scheme of a \(4\times 4\) Hash [16], which uses PSEs instead of CSEs. In the Hash, the MRR of a PSE is configured to be on-resonance with two signals. For example, the signals from \(m_{1}\) to \(s_{4}\) and from \(m_{2}\) to \(s_{3}\), represented by red lines in Figure 5(b), are coupled to the MRR of the top-left PSE.
# Climate-Smart Agro-Hydroological Model for a Large Scale Rice Irrigation Scheme in Malaysia
## Materials and Methods
### Computation of Available Discharges for Irrigation Supply  
The HEC-RAS model, which is a computer program that models the hydraulics of water flow through natural rivers and other channels, was adopted in the present study. HEC-RAS 5.0 with the Geospatial Hydrologic Modeling extension (Arc-Hydro and HEC-GeoRAS) was used. Prior to using the model, a DEM raster of the study area was downloaded from DIVA-GIS, and converted to a triangular irregular network (TIN) within Arc-GIS. The TIN was used to establish the river system connectivity, cross section data, reach length, etc., using HEC-GeoRAS. The overall cross section traits of channels were extracted and finally exported into the HEC-RAS model. The earlier simulated streamflow at the SKC by the HEC-HMS model was used as the input hydrograph to the HEC-RAS model for flow routing in the river.  
HEC-RAS software is capable of performing one-dimensional (1-D) steady- and unsteady-flow simulations. The unsteady, gradually varied flow simulation model is dependent on finite difference solutions of the Saint-Venant equations as presented in Equations (7) and (8) [35]:  
\[\frac{\partial\text{A}}{\partial\text{t}}+\frac{\partial\text{Q}}{\partial \text{x}}=0, \tag{7}\]  
\[\frac{\partial\text{Q}}{\partial\text{t}}+\frac{\partial\left(\frac{\text{Q} ^{2}}{\text{A}}\right)}{\partial\text{x}}+\text{gA}\frac{\partial\text{H}}{ \partial\text{x}}+\text{gA}(\text{S}_{\text{o}}-\text{S}_{\text{f}})=0, \tag{8}\]  
where A = cross-sectional area normal to the flow (m\({}^{2}\)); Q = discharge (m\({}^{3}\)/s); g = acceleration due to gravity (m/s\({}^{2}\)); H = elevation of the water surface above a specified datum, also called stage (m); S\({}_{\text{o}}\) = bed slope (%); S\({}_{\text{f}}\) = energy slope (%); t = temporal coordinate; and x = longitudinal coordinate.  
The most sensitive calibration parameter required by the HEC-RAS model is channel resistance, specifically, Manning's n. Its values can be extracted with the aid of a land use map of the area using HEC-GeoRAS. However, due to insufficient land use data, covering all of the river area, n values presented by another study [36] were used as a first estimate of the appropriate channel resistance and adjusted through calibration. Because of irregular and limited flow data for the downstream side, located at the inlet of the main canal, reliable data for four years (from 2001 to 2004) were used for model evaluation. The model was calibrated by comparing between the simulated and observedvalues using the 2001-2003 daily discharge records of the river. Finally, the calibrated model was validated using the 2004 daily discharge records.