esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Datasets  
#### 2.2.1 Rain Gauge Data  
Generally, the amount of rainfall recorded by ground stations is considered the standard source of rainfall for the validation of satellite-based precipitation products [33]. In the current study, monthly precipitation data from 2003 to 2020 were acquired from 43 meteorological stations of the Pakistan Meteorology Department (PMD). The spatial distribution of the stations is exhibited in Figure 1 and described in Table 1. Missing values in the time series data were filled based on the mean value of the same month from the time series data. The collected precipitation data are evaluated and rectified by the PMD following the World Meteorological Organization (WMO) standards to maintain the quality and reliability of the product [30]. However, the standard normalized homogeneity test (SNHT) was applied to detect outliers and further stability and uniformity.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**S. No**} & \multirow{2}{*}{**Stations**} & \multirow{2}{*}{**Longitude**} & \multirow{2}{*}{**Latitude**} & \multicolumn{2}{c}{**Elevation**} & \multirow{2}{*}{**S. No**} & \multirow{2}{*}{**Stations**} & \multirow{2}{*}{**Longitude**} & \multirow{2}{*}{**Latitude**} & \multicolumn{2}{c}{**Elevation**} \\  & & & & **(m)** & & & & & & **(m)** \\ \hline S1. & Skardu & 75.683 & 35.300 & 2209 & S23. & Larkana & 68.233 & 27.533 & 52 \\ S2. & Astore & 74.900 & 35.333 & 2167 & S24. & Moen-jo-daro & 68.100 & 27.367 & 51 \\ S3. & Chilas & 74.100 & 35.416 & 1251 & S25. & Padidan & 68.133 & 26.850 & 46 \\ S4. & Bunji & 74.633 & 35.666 & 1372 & S26. & Dadu & 67.783 & 26.717 & 38 \\ S5. & Gilgit & 74.333 & 35.916 & 1460 & S27. & Nawabshah & 68.367 & 26.250 & 37 \\ S6. & Gupis & 73.400 & 36.166 & 2155 & S28. & Mirpur\_khas & 69.000 & 25.517 & 15 \\ S7. & Chiral & 71.830 & 35.850 & 4198 & S29. & Hyderabad & 68.417 & 25.383 & 28 \\ S8. & Saidu & 72.350 & 34.730 & 961 & S30. & Karachi & 66.933 & 24.900 & 22 \\ S9. & Peshawar & 71.580 & 34.020 & 359 & S31. & Thata & 67.900 & 24.750 & 15 \\ S10. & Parachinar & 70.080 & 33.900 & 1725 & S32. & Badin & 68.900 & 24.633 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of selected rain gauge stations and their brief description.  
Figure 1: **Left side**: topographical map demonstrating the geographic position of Pakistan, location of selected Pakistan Meteorological Department stations, administrative boundaries of provinces and elevations. **Right side**: map shows four defined regions based on the elevation gradient.
# Steel Surface Defect Detection Based on YOLOv8-TLC
## 4 Experimental Results and Analysis
### Analysis of Realization Results
#### 4.3.1 Training Loss  
The loss function of YOLOv8 consists of three components: classification loss (cls_loss), bounding box regression loss (box_loss), and distribution focal loss (dfl_loss). The classification loss measures the discrepancy between the predicted object classes and the true classes; the bounding box regression loss evaluates the similarity between the predicted bounding boxes and the ground truth boxes, improving the model's localization accuracy and enabling the predicted boxes to better encompass the target objects; the distribution focal loss enhances the accuracy of bounding box regression by modeling the distribution of each coordinate.  
As shown in Figure 9, after approximately 50 epochs of training, significant performance improvements were achieved through substantial weight updates. Subsequently, the rate of decline for each loss gradually slowed and converged, making the detection task more accurate and efficient. The sudden drop in loss during the final 10 epochs was due to the disabling of the mosaic method at this stage.  
Figure 9: Training loss.
## 1 Introduction
The SWAT, SWAT-ANN, and SWAT-MLP were applied to the runoff prediction. Analysis in [PERSON] et al. [17] showed that the Soil and Water Assessment Tool-Whale Optimization Algorithm model was the best, as it was evaluated with four indicators (R2, RMSE, NSE, RE). According to [PERSON] et al. [18], five statistical indexes were employed, which contain RMSE, MAE, MAPE, and MSE and R2 to evaluate the performance of eight runoff prediction models, and the results showed that complete ensemble empirical decomposition with the additive noise-variation mode decomposition-support vector machine (CEEMDAN-VMD-SVM) was the best model. Additionally, multiple linear regression (MLR), the back propagation neural network (BPNN), the Elman neural network (ENN), the particle swarm optimization-support vector machine for regression (PSO-SVR), and a coupling model composed of BPNN, ENN, and PSO-SVR were constructed to predict the annual runoff. The coupling model was considered to have the best predictive performance after the evaluation of the four metrics [19]. Although the previous study on runoff prediction has achieved certain results, there are still several problems, as follows. At present, there is no unified standard and method for the prediction model selection, so a more scientific and reasonable evaluation method for model selection is very necessary. Moreover, the stability of the model prediction performance has not been considered in previous studies, and how to test the stability of the model is still a major challenge for current research. Generally, most of the previous studies focused on runoff in non-karsat areas, but the prediction models based on artificial intelligence and whether they can be applicable for runoff prediction analysis in karst areas needs to be further explored. Therefore, the objective of this study is to construct a suitable monthly runoff prediction model, establish a scientific evaluation system for selecting the model, and test the stability of the optimal model. For these purposes, the following studies will be conducted: firstly, the support vector machine model (SVM), the Elman Neural Network Model (ENN), and multi-model mean model (MMM) will be developed for the runoff prediction. Then, a scoring evaluation system with five indicators will be established to select the best model. Finally, the stability of the models will be tested by the IHA-RVA method with different data structure inputs.
# Community Structure Detection for Directed Networks through Modularity Optimisation
## 2 Iterative Mathematical Programming Model for Modularity Optimisation on Directed Networks
### First Model--MINLP  
Modularity is defined as the number of edges that fall within communities minus the expected number of edges that should fall into communities in a null configuration of the equivalent network with edges being placed at random [3]. The modularity for directed networks can be formulated as below [26]:  
\[Q=\sum_{m}\left(\frac{L_{m}}{L}-\frac{D_{m}^{in}D_{m}^{out}}{L^{2}}\right), \tag{1}\]  
where \(\frac{L_{m}}{L}\) represents the fraction of (weighted) edges that fall into module \(m\), while \(\frac{D_{m}^{in}D_{m}^{out}}{L^{2}}\) is the expected value for module \(m\).  
The sum of weights of edges in module \(m\), \(L_{m}\), is computed as:  
\[L_{m}=\sum_{n,e\in l_{ne}}\beta_{ne}Y_{nm}Y_{em}\quad\forall m, \tag{2}\]  
indicating that an edge pointing from node \(n\) to node \(e\) is included in a module \(m\) if and only if both nodes belong to module \(m\), i.e., \(Y_{nm}=1\) and \(Y_{em}=1\).  
For a given directed network, the sum of weights of edges coming into node \(n\) is denoted as parameter \(d_{n}^{in}\), while the sum of weights of edges pointing from node \(n\) is \(d_{n}^{out}\). For an unweighted network, \(d_{n}^{in}\) and \(d_{n}^{out}\) are simply the in-degree and out-degree of node \(n\). For a module \(m\), the sum of \(d_{n}^{in}\) and \(d_{n}^{out}\) over all nodes belonging to this module (\(Y_{nm}=1\)) are, respectively, calculated as below:  
\[D_{m}^{in}=\sum_{n}d_{n}^{in}Y_{nm}\quad\forall n, \tag{3}\]  
\[D_{m}^{out}=\sum_{n}d_{n}^{out}Y_{nm}\quad\forall n. \tag{4}\]  
We are interested in non-overlapping partitions where each node can only be allocated to exactly one module, and this is modeled via the following constraints:
## 3 State-of-the-Art MIMO-SDR Systems
### Mimo-Sdr Waveforms
#### 3.2.2 OFDM/Cyclic Prefix OFDM (CP-OFDM) Theory Aspects
The channel estimation is the most important block in the receiver. The given output data of this block are given to the equalizer to correct the channel effect. Finally, after P/S conversion, the symbols are de-mapped to bits. The CP insertion and removal blocs are used with cyclic prefix OFDM (CP-OFDM), which widely reduces the ISIs. systems, a circular convolution is performed. Therefore, the received signal \(y\), considering \(R<L_{g}\), will be expressed as:  
\[\left[ \begin{array}{c}y_{0}\\ y_{1}\\ \vdots\\ \vdots\\ \vdots\\ y_{L-1}\end{array} \right]=\left[ \begin{array}{cccccc}c_{0}&0&\cdots&0&c_{N-1}& \cdots&c_{1}\\ \vdots&\ddots&\ddots&&\ddots&\vdots\\ \vdots&&\ddots&\ddots&&\ddots&c_{N-1}\\ c_{N-1}&&&\ddots&\ddots&&0\\ 0&\ddots&&&\ddots&\ddots&\vdots\\ \vdots&\ddots&\ddots&&&&&\ddots&0\\ 0&\cdots&0&c_{N-1}&\cdots&\cdots&c_{0}\end{array} \right]\left[ \begin{array}{c} s_{0}\\ \vdots\\ \vdots\\ \vdots\\ s_{L-1}\end{array} \right]+\eta=Fs+\eta \tag{10}\]  
\(\eta\) refers to the noise term related to the channel. Otherwise, if \(R>L_{g}\), some Inter-Symbol Interferences (ISIs) appear. The matrix \(F\) can be diagonalized by Fourier transform:  
\[\left\{D= \begin{bmatrix}F=E^{-1}DE\\ D_{0}&0&\cdots&0\\ D_{1}&\cdots&0\\ \vdots&&\ddots&\vdots\\ 0&\cdots&0&D_{N-1}\end{bmatrix} \right] \tag{11}\]  
where \(E\) and \(E^{-1}\) are the FFT and IFFT matrices, respectively.
# Spatial Shape-Aware Network for Elongated Target Detection
## 5 Parameter Settings  
Mean Average Precision (mAP): An image may contain multiple object categories, with each target exhibiting significant randomness in its position, distribution, shape, and size in object detection tasks involving non-natural scenes. The goal is to detect every object in the image, regardless of its category, while minimizing false detections. To address these aspects, precision (P) and recall (R) are introduced as key evaluation metrics:  
\[\text{Precision} =\frac{TP}{TP+FP}=\frac{\text{True Positives}}{\text{True Positives + False Positives}} \tag{15}\] \[\text{Recall} =\frac{TP}{TP+FN}=\frac{\text{True Positives}}{\text{True Positives + False Negatives}} \tag{16}\]  
where \(TP\) is True Positive, the correctly detected objects. \(FP\) is False Positive, the incorrectly detected objects. \(FN\) is False Negative, the ground truth objects that were missed. Using two separate metrics to evaluate a model's performance often complicates the quantitative comparison of different models. Therefore, a single comprehensive metric is needed to balance both precision and recall. For a well-trained model, precision and recall typically exhibit an inverse relationship. To address this, a PR curve is constructed with P and R as its axes, and the area under this curve, known as the Average Precision (AP), is used as a unified metric. This approach effectively integrates the influence of both precision and recall on detection performance. The calculation method is as follows:  
\[AP=\int_{0}^{1}P(R)dR \tag{17}\]  
where P(R) is the precision at a given recall level. A single-object detection model may detect multiple categories, so we require an overall evaluation metric that considers the detection performance across all categories. The mean Average Precision (mAP) is defined as  
\[mAP=\frac{\sum_{i=1}^{C}AP_{i}}{C} \tag{18}\]  
where \(AP_{i}\) is the Average Precision for the \(i\)-th category. C is the number of categories.  
Frames Per Second (FPSs): Apart from detection accuracy, a critical performance evaluation metric is obtained by processing the speed of the model. For this purpose, FPSs are introduced to estimate the processing speed of the network. FPSs measure how many images are processed per second by the model, offering a practical indication of real-time performance.
# Overview of the Research Status of Intelligent Water Conservancy Technology System
## 2 Smart Water Conservancy System Based on Digital Twin
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**System Name**} & \multicolumn{1}{c}{**System Content**} \\ \hline Digital twin hydraulic engineering system [16] & Physical layer, data layer, service logic layer, user interaction layer \\ \hline Application research of the “Four Pre-stages” intelligent water conservancy platform for flood control based on digital twin [17] & Data baseboard construction, perception system construction, digital twin model construction, intelligent decision and optimization \\ \hline Digital twin river basin flood control application technology framework [18] & Resource layer, data layer, twin layer, application layer \\ \hline Research on standard specification system of digital twin watershed [19] & Basic commonality, information infrastructure, digital twin platform, business application, security, construction, operation and maintenance \\ \hline Digital twin basin architecture [20] & ABCDMEETS (Artificial intelligence, big data, cloud computing, digital twin, Digital Mainline, model systems engineering, air–Earth integrated network, edge computing, Internet of Things, simulation) digital twin basin architecture \\ \hline Intelligent water conservancy Integrated Management Platform [21] & Physical layer, blockchain layer, interface layer, application layer, regulatory layer \\ \hline Architecture of water conservancy modernization [22] & Big perception stereo system, big network interconnection system, big data cloud center system, brain fusion system, big application system \\ \hline Smart water system framework [23] & Perception layer, network layer, knowledge layer, application layer \\ \hline Theoretical framework of intelligent water network [24] & Construction and key technologies of water physical network, water information network and water management network \\ \hline Real-time runoff prediction system based on digital twin [25] & Physical layer, perception layer, transmission layer, digital layer, decision layer \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related system architecture of smart water conservancy. collection, data processing and data service. The model platform consists of the Hydraulic professional model, Mechanism analysis model, Mathematical-statistical model, Hybrid model and Intelligent algorithm library. The knowledge platform encompasses business planning, expert experience, forecast scheduling and the historical scene. The application layer includes basin flood control, water resources management and allocation, and N services. This optimized content provides a structured overview of the intelligent water conservancy framework, emphasizing the integration of various levels and components to achieve efficient and intelligent water management practices.
# Hybrid Trust Model for Node-Centric Misbehavior Detection in Dynamic Behavior-Homogeneous Clusters
## 8 Performance Analysis  
This section evaluates the effectiveness of the anomaly vehicle determination based on the mixed trust model through simulation. In the experiments, we simulate vehicular network communication scenarios using SUMO 1.6.0, OMNet++, and the Transmission Control Protocol (TCP) interface provided by Veins. Table 2 summarizes the simulation parameters used in our experiments. Figure 7 shows the simulation area, which is the College Bridge intersection extracted from the Open Street Map database, covering an area of approximately 20 km\({}^{2}\).  
Assuming that \(V_{L}=40\) km/h and each vehicle operates for 15 min at random speeds in simulated traffic volumes of 0.05, 0.10, 0.15, 0.20, and 0.25, there are 100, 200, 300, 400, and 500 circulating vehicles in each of these five traffic environments. Vehicle densities of 0.05, 0.15, and 0.25 are defined as low-, medium-, and high-density, respectively. In the simulation, all vehicles are equipped with DSRC for V2V or V2I communication and operate in broadcast mode within a communication transmission range of 100 m, as shown in Figure 8.  
The initial vehicle \(V_{0}\) starts from the Shenning Building and moves using the Krauss vehicle the following model during its journey. Under different traffic densities, vehicles randomly circulate with a maximum travel distance of 10 km and use the shortest path to return to their starting position. In dynamic vehicle clusters, the cluster head vehicles travel at a constant speed of \(V_{L}\pm 10\%\). This section evaluates the effectiveness of the trust model proposed in this chapter through three experiments.
## 3 Results and Discussion
### Torsion Properties
Therefore, BS-4, with two adjacent quadrilaterals, can maintain a certain degree of symmetry while making full use of space to enhance the torsional strength and stability of the tube, making it superior to designs that use hexagons or combinations of different shapes.The torsional strength of BS-4's structure is 18.6% higher than that of BS-1, indicating that it can effectively improve the torsional strength of thin tubes. Figure 6d illustrates the actual torsion performance test results, where the final thin tubes are ultimately broken. The torsion test was conducted at a constant angular displacement rate of 1 degree per second until failure. Table 3 illustrates that the maximum bearing torsional load error between the sample and the simulated value of BS-7 is within 4.6%. The experimental results are consistent with the simulation. The maximum torsional load carrying capacity  
Figure 6: Torsion properties of different BSs. (**a**) Torsional strength between experimental and simulation results for BS-7; (**b**) torsion load-angle simulation curves for different BSs; (**c**) maximum torsional load of different BSs; (**d**) torsion properties test. of BS-2 obtained by simulation is significantly lower than that of other BSs, and the LWN-T value is also the lowest at 0.62 \(\mathrm{N\cdot m/g}\). The difference between the maximum torsional load carrying capacity and LWN-T value of BS-3 and BS-5 is very small. Compared with BS-1, the maximum torsional load carrying capacity and LWN-T value of BS-3 and BS-5 are increased by 0.97% and 2.9%, respectively. The bearing capacity of BS-4, BS-6, and BS-7 outperforms other BSs, and the LWN-T value is likewise higher. The LWN-T value of BS-4 is the highest, at 1.22 \(\mathrm{N\cdot m/g}\), which is 3.3% and 1.6% higher than that of BS-6 and BS-7, respectively. This indicates that BS-4 has good torsional performance across the seven BSs. Notably, the LWN-T value of BS-4 is 17.3% higher than that of BS-3, and the internal structure of BS-4 contains two quadrangles, whereas that of BS-3 is one quadrangle. The LWN-T value of BS-5 is 11.3% higher than that of BS-6, and the internal structure of BS-5 contains two hexagons, while that of BS-3 has one hexagon. This demonstrates that increasing the number of polygons inside the thin tube effectively improves the torsional performance.
## 3 Presentation of Developed Interface
### The System Operator Interface  
The system operator interface is developed as a desktop application. The logging screen of the interface is shown in Figure 10, where the username and password are secured with md5 encoding. It should be noted that secure ssl-c-certified connection might be also used in all data transfer for more secure data transfer.  
A separate menu is provided in order for the system operator to create DR events, as shown in Figure 11. The proposed structure enables the system operator to realize different DR applications for different end user clusters within neighboring regions. The total duration of the DR application is defined in seconds, which enables a more exact timing for the DR event.  
Another tab named as event list is also available, as shown in Figure 12 for monitoring and analyzing all the previous and current events found in the database. This list provides all the required information about the end-users related to the event such as confirmation status, the end user appliances that can be used during the DR event, aggregated flexible power, and participation ratio of certain or all consumers.  
Figure 11: The system operator interface screen for creating new demand response (DR) events: general overview.  
Figure 10: The system operator interface logging screen.
# Multi-Span Tension Control for Printing Systems in Gravure Printed Electronic Equipment
## 3 Design of an ADRC Decoupling Controller for Multi-Span Printing
### Establishing the Decoupling Model
The output control quantities \(u_{01}\), \(u_{02}\), and \(u_{03}\) from NLSEF1, NLSEF2, and NLSEF3 are compensated with the total disturbance estimation quantities \(z_{12}\), \(z_{22}\), and \(z_{32}\), respectively, to obtain \(u_{p1}\), \(u_{p2}\), and \(u_{p3}\) as the final virtual control quantities. The \"compensation factors\" \(b_{01}\), \(b_{02}\), and \(b_{03}\) are the sole parameters in ADRC controllers that are related to the system model, and their values depend on the degree of the compensation required. Figure 4: ADRC decoupling controller for the printing tension system. The discrete algorithm equations for ADRCp1, ADRCp2, and ADRCp3 are the same, as shown below. \[\left\{\begin{array}{l} \begin{array}{l}\epsilon_{\text{i}}(k)=z_{\text{i1}}(k) -T_{\text{pi}}(k)\\ z_{\text{i1}}(k+1)=z_{\text{i1}}(k)+h(z_{\text{i2}}(k)-\beta_{\text{i1}} \epsilon_{\text{i}}(k)+b_{0\text{i}}u_{\text{pi}}(k))\\ z_{\text{i2}}(k+1)=z_{\text{i2}}(k)+h(-\beta_{\text{i2}}fal(\epsilon_{\text{i}} (k),0.5,h))\\ e_{\text{i1}}(k+1)=T_{\text{rpi}}(k+1)-z_{\text{i1}}(k+1)\\ u_{\text{pi}}(k+1)=k_{\text{i}}pfal(e_{\text{i1}}(k+1),0.5,h)-z_{\text{i2}}(k+ 1)/b_{0\text{i}}\end{array} \right. \tag{13}\]  
where i--ADRC controller number (i = 1, 2, 3); \(k\)--discrete number (\(k=0,\ 1,\ 2,\ldots\)); \(h\)--integral step size; \(\beta_{\text{i1}}\) and \(\beta_{\text{i2}}\)--ESO gain coefficients; \(k_{\text{i}p}\)--NLSEF gain coefficient. After solving for the virtual control variables \(u_{p1}\), \(u_{p2}\), and \(u_{p3}\) using Equation (13), further calculate the actual control variables \(\omega_{p1}\), \(\omega_{p2}\), and \(\omega_{p3}\) based on Equation (11). The actual control variables act on the tension system to achieve decoupling control of the multi-span printing tension system.
# Mapping with Pleiades--End-to-End Workflow
## 3 Test Sites and Data Sets
### Test Site Singapore (Singapore)  
This test site covers a part of the island of Singapore, thus mainly consisting of a dense urban environment with very tall buildings. The height is ranging from sea level to about 200 m and the scene covers about 275 km\({}^{2}\). For Singapore terrestrially measured GCPs from Singapore Land Authority and the Tropical Marine Science Institute are available, which are used for sensor model validation and adjustment. However, due to restrictions the GCP heights are rounded to integer values and thus degrade the advantage of highly accurate in-situ measurements in this context. Of the whole set of control points, 16 GCPs are located within the specific stereo scene. Available LiDAR data is under restriction as well and cannot be used within this study.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ID** & **2** & **3** & **4** & **5** & **6** \\ \hline
**1** & 25.2 & 12.2 & 27.5 & 24.0 & 33.9 \\
**2** & & 26.4 & 12.5 & 37.6 & 25.0 \\
**3** & & & 22.7 & 13.0 & 24.7 \\
**4** & & & & 30.3 & 12.6 \\
**5** & & & & & 26.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Convergence angles for all possible stereo pairs of the Ljubljana data set given in degrees.  
Figure 5: The test site Ljubljana (Slovenia): (**a**) topographic map (opentopomap.org (CC-BY-SA)), (**b**) RGB ortho-image and (**c**) relief shaded DSM. (**b,c**) were produced employing the proposed and implemented mapping workflow.