esa-sceva/satcom-chunk-collection huggingface dataset:
# Resource Allocation in Wireless Powered IoT System: A Mean Field Stackelberg Game-Based Approach
## 3 System Model and Problem Formulation
### Stackelberg Game Framework  
In our proposed wireless powered IoT system, there are one dedicated HAP and \(N\) SNs. In the downlink scenario, the HAP controls its power level for energy transfer. In the uplink scenario, each SN controls its power level for information transmission. As each SN uses the energy from the HAP for information transmission, the power level for energy transfer can significantly affect the performance of the SNs. Then the relationships between the HAP and the SNs can be considered as a Stackelberg game, more specifically, it can be considered as a one-leader-many-followers Stackelberg game. The HAP works as the leader, where the SNs are the followers. The Stackelberg game is composed by two parts, the leader-level game and the followers-level game, respectively, as shown in Figure 3.  
(1) Leader-level game: As the HAP can significantly affect the performance of the SNs, it is considered as the leader. The HAP will transfer the energy to the SNs based on its own aspiration, and announces its strategy of power level for energy transfer to the SNs. Then the HAP can affect the SNs on their strategies for information transmission. Once the SNs make decisions on power level for information transmission, the HAP can re-adjust its energy transfer strategy to get more utilities.  
(2) Follower-level game: As the SNs are affect by the HAP, they can be considered as the followers of the game. The SNs control their power for information transmission under the HAP's energy transfer strategy, by playing a Stackelberg game.
## 2 Materials and Methods
### Closed-Circuit Cable-Pulley System Transmission Backlash and Grasping Force Estimation  
When the speed of the cable moving along the pulley is greater than that of the pulley moving around its support shaft, the cable will exhibit elastic slip along the driven pulley, since the pre-tensioned cable \(T\) and the cable \(B\) are, respectively, fixed to the driving pulley (point \(C^{t}\) and point \(C^{b}\)) and to the driven pulley (point \(D\)); there is no elastic slip along the driving pulley and the driven pulley, and the elongation of the cable along the driving pulley and the driven pulley can be ignored.  
It is assumed that the pretension of cable \(T\) and cable \(B\) are \(T^{t}_{pre}\) and \(T^{b}_{pre}\), respectively; when there is no external exerted torque, the tensions of cable \(T\) and cable \(B\) on both sides of the pulley are, respectively:  
\[\left\{\begin{array}{l}T^{t}_{0.1}=T^{t}_{1,2}=\cdots=T^{t}_{4,5}=T^{t}_{pre }\\ T^{b}_{0.1}=T^{b}_{1,2}=\cdots=T^{b}_{4,5}=T^{b}_{pre}\end{array}\right., \tag{31}\]  
Assume that the driving pulley exerted external torque and rotates counterclockwise; ignoring the moment of inertia of the cable-pulley system, the following formulas can be obtained as:  
\[M_{in}=(T^{t}_{0,1}-T^{b}_{0,1})R_{0}, \tag{32}\]  
\[M_{out}=(T^{t}_{4,5}-T^{b}_{4,5})R_{5}, \tag{33}\]  
Defining \(\eta\in\{t,b\}\), \(\lambda^{\eta}_{j}=\lambda(\rho^{\eta}_{j},\mu_{j},\theta^{\eta}_{s,j})\), \(\rho^{\eta}_{j}=r^{\eta}_{j}/r\), \(K^{\eta}_{i,0}=1\), \(K^{\eta}_{i,m}=\prod_{j=i}^{m}\lambda^{\eta}_{j}\), and from Equation (12), the following can be obtained as:  
\[\left\{\begin{array}{l}T^{t}_{i:l+1}=\left\{\begin{array}{l}T^{t}_{0,1}/K^ {t}_{1,i},\,if\text{sgn}(\dot{\theta}_{in})=1\\ T^{0}_{0,1}K^{t}_{1,i},\,\,other\\ T^{b}_{i:l+1}=\left\{\begin{array}{l}T^{b}_{0,1}K^{b}_{1,i},\,if\text{sgn}( \dot{\theta}_{in})=1\\ T^{b}_{0,1}/K^{b}_{1,i},\,other\end{array}\right.,\end{array}\right.\end{array}\right. \tag{34}\]  
Figure 6: Force transfer characteristics of closed-loop cable-pulley.  
Substituting Equation (34) into Equation (33), the following can be formulated as:  
\[\left\{\begin{array}{l}M_{out}=\left(T_{0,1}^{t}/K_{1,4}^{t}-T_{0,1}^{b}K_{1,4}^{ b}\right)R_{5},\;if\text{sgn}(\dot{\theta}_{in})=1\\ M_{out}=\left(T_{0,1}^{t}K_{1,4}^{t}-T_{0,1}^{b}/K_{1,4}^{b}\right)R_{5},\; other\end{array}\right., \tag{35}\]  
According to the geometric constraint relationship of the closed-circuit cable-pulley system, the following formulas can be yielded as:  
\[\Delta L^{t}+\Delta L^{b}=0, \tag{36}\]  
\[\Delta\theta_{out}=\Delta L^{t}/R_{5}=-\Delta L^{b}/R_{5}, \tag{37}\]  
\[\Delta L^{t}=\theta_{in}R_{0}-\theta_{out}R_{5}\;\Rightarrow\;\theta_{out}= \theta_{in}R_{0}/R_{5}-\Delta\theta_{out}, \tag{38}\]  
where \(\Delta L^{t}\) and \(\Delta L^{b}\) are the total elongation of cable \(T\) and cable \(B\), respectively.
## 2 Materials and Methods
### Recurrent Network with Long Short-Term Memory Cell  
Recurrent networks are a group of neural networks which is developed to support the dealing of the temporal aspects in the data [31]. This is achieved while conditioning the hidden state from a time instance \(t\) not only on the input for time \(t\) but also on the hidden state at the time instance \(t-1\). In simple recurrent networks also called Elman networks, three matrices are shared over all time instances. A matrix \(W\) which stores weights connecting input \(x_{t}\) and hidden state \(h_{t}\), a matrix \(U\) containing the weights between the hidden states of subsequent time instances and the matrix \(V\) which transforms the hidden states to the network output. Accordingly, the hidden state \(h_{t}\) is obtained from the application of an activation function \(g\) on the weighted input and previous hidden state (2). The network output \(y_{t}\) is calculated using an activation function \(f\) on the weighted hidden state \(h_{t}\) as specified in (3).  
\[h_{t}=g(Uh_{t-1}+Wx_{t}) \tag{2}\]  
\[y_{t}=f(Vh_{t}) \tag{3}\]  
One of the problems of Elman networks is that information inserted early time instances may become lost at later ones. A solution to that problem is Long Short-Term Memory (LSTM) cells which can filter out information not required at further time steps and keep the one that may be needed later on. This is achieved by adding a recurrent context layer and several weight matrices and gates in combination with the usage of the sigmoid activation function as presented in Figure 4.  
Figure 3: Correlation between temperature and load on subsequent days in May 2017 for the New York City data.  
A Long Short-Term Memory cell consists of the forget gate (4), the input gate (5), the cell update gate (6) and the output gate (7). The context \(C_{t}\) is obtained from the sum of the pointwise multiplication (\(\otimes\)) of the context from time instance \(t-1\) and the forget gate and the cell update gate and the input gate (8). The output for the time instance \(y_{t}\) is calculated through the pointwise multiplication of the context at time instance \(t\) and the output gate (9).  
\[f_{t}=\sigma(U^{f}x_{t}+W^{f}h_{t-1}) \tag{4}\] \[i_{t}=\sigma(U^{i}x_{t}+W^{i}h_{t-1})\] (5) \[\hat{C}_{t}=tanh(U^{s}x_{t}+W^{s}h_{t-1})\] (6) \[o_{t}=\sigma(U^{p}x_{t}+W^{o}h_{t-1})\] (7) \[C_{t}=\sigma(C_{t-1}\otimes f_{t}+\hat{C}_{t}\otimes i_{t})\] (8) \[h_{t}=y_{t}=tanh(C_{t}\otimes o_{t}) \tag{9}\]
# Estimating Maize Yield from 2001 to 2019 in the North China Plain Using a Satellite-Based Method
## 2 Materials and Methodology
### Datasets and Preprocessing
#### 2.2.1 Maize-Related Data  
The statistical data for the county-level maize yields (measured in kg/ha), utilized in this study, were taken from the Agricultural Yearbook (stats.gov.cn, accessed on 30 October 2022) and covered the years 2000 to 2019 (a few counties have data gaps of a year or more). The following values were deemed outliers: (1) values beyond the range of biophysically viable yields; (2) values from between 2000 and 2019 that were two standard deviations above or below the mean [36; 37]. If a value is missing from a time series and only one value is missing, the average of the two years before and after is used to fill in the gap; if there  
Figure 1: The main summer maize planting areas in this study.  
are two consecutive missing years, the most recent year's data is used; and if missing for three consecutive years, the data is deemed to be missing. To predict crop yields, it is also critical to accurately identify the planting area. The planting area's dynamic change was considered. From 2000 to 2019, China's annual maize planting pixels at 1 km resolution, from [PERSON] et al. [38], were utilized for mask generation.
# Strategies and Challenges in Detecting XSS Vulnerabilities Using an Innovative Cookie Collector
## 1 Introduction
### Limitations of Traditional Detection Methods  
XSS vulnerabilities continue to evolve, overwhelming the capabilities of traditional detection methods that have critical limitations in one key dimension: blindness to third-party cookies. A total of 64% of XSS attacks in educational environments (2024) exploited cross-domain federated authentication cookies, where WAFs do not inspect SameSite/HttpOnly [8] attributes. Tools such as Burp Suite only capture primary cookies, omitting 71% of cross-domain interactions. Sandboxing mechanisms fail to track cookie streams in parallel tabs.
# Evaluation of Orbital Drift Effect on Proba-V Surface Reflectances Time Series
## 4 Results  
### Analysis of Predicted SZA Drift  
The predicted evolution of SZA was computed following Equation (1) and it is presented in Figure 4 as a function of date and latitude since start of Proba-V mission. In this figure, the absolute SZA changes with respect to a sun-synchronous orbit are also presented, where the overpass time of the stable orbit corresponds to the one reached after launch (10:45 a.m.). The SZA evolution shows periodic oscillations, following the seasonal cycle, super-imposed to a latitude-dependent positive drift induced by the orbital decay. The observed drift in SZA is higher around the tropical and sub-tropical regions during hemispherical summer time and lower at higher latitudes and for wintertime conditions. This behavior is expected and was verified also for AVHRR-NOAA drifting orbits [4]. The deviations from a stable sun-synchronous orbit start to exceed 10 degrees during 2018, as a result of the stronger decay of LTDN after the first 4 years of the mission. Illumination conditions continue to degrade during the last 2 years of operations, with predicted SZA increasing up to 20 degrees in July 2020, when Proba-V Operational Phase was terminated. The predicted changes of SZA throughout the mission were verified against real observations (see Supplementary Materials, Section S.1). Despite a slight over-estimation of the simulated orbital drift effect (up to 3\({}^{\circ}\) in SZA), the adopted empirical formula (Equation (1)) provides a sufficiently accurate prediction of the intra and inter-annual changes of illumination conditions along the mission lifetime.
## 5 Experiment Verification  
To validate the effectiveness of the proposed loosening detection method, impact experiments were conducted utilizing a bolted flange connection structure (Figure 21), as shown in Section 3. The DH5922D vibration signal acquisition system was used to acquire the acceleration response data. The preload was measured with bolt pressure sensors to ensure the same tension, and the impact load was applied with a force hammer. The bolt grade in the experiment was 12.9. In the experiment, due to the limitations of the experimental conditions, the bolt arrangement was only six bolts, which were evenly arranged. Hammering experiments in loose and unloose conditions were performed to validate the suggested approach, and the acceleration signals of the vibration were obtained. In the experiment, consistent with the finite-element method, 20 strikes were performed under each bolt preload state to simulate the continuous pulse load condition. The magnitude of the force was between 700 and 1000 N. A total of 60 strikes were performed; 70% of the data formed the training group, and 30% of the data formed the verification group.  
The experimental data are shown in Table 4. The proportion of prediction accuracy is defined as the number of correct predictions over the total number of each case. The method proposed in this paper, based on the VMD decomposition and PE construction of feature vectors to predict the bolt preload state, had an overall accuracy of 77.78% in the experiment. The accuracy in the experiment was lower than that in the finite-element, but the magnitude was limited. There are many reasons for the error in the impact experiment, such as the unavoidable difference between the actual sampling rate and the implicit dynamics calculation, which means that the impact pulse width cannot be completely consistent, and that there was interference in the acceleration signal acquisition. The contact surfaces were all flat in the finite element model, and only the linear friction relationship on the ideal contact surfaces was addressed. However, real experimental settings involve complicated friction interactions as well as intricate local warpage and wear conditions at the connecting contact surfaces. Even under the influence of these error factors, the method proposed in this paper still maintains a certain accuracy, verifying its effectiveness. As shown in Table 4, the experiment also demonstrates that the prediction accuracy when using VMD for feature vector construction, as in this method, is higher than that of CEEMD.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Prediction Accuracy**} & **Feature Vector by VMD and PE** & **Feature Vector by CEEMD** \\ \hline No loosening (5000 N) & 50\% & 50\% \\ Slight loosening (3000 N) & 83.3\% & 50\% \\ Serious loosening (300 N) & 100\% & 83.3\% \\ Total & 77.78\% & 61.11\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Prediction accuracy under the experiments.  
Figure 21: Experimental equipment.
# Path Difference Optimization of 5G Millimeter Wave Communication Networks in Malaysia
## 3 Rain Attenuation
The path reduction factor (or path attenuation) is estimated as the difference between the received signal strength under clear skies and the same under rainy conditions. Therefore, the total signal attenuation due to rainfall is  
\[\alpha=K.A^{\beta}.R.p_{r}, \tag{3}\]  
while the path reduction factor of the ITU-R model can be expressed as [43]:  
\[p_{r}=\frac{1}{0.477R^{0.633}A_{0.01}^{0.073\beta}f^{0.123}-10.579[1-exp(-0.02 41R)]} \tag{4}\]  
where \(R\) is the path difference between the transmitter and receiver in \(km\) and \(f\) is the operating frequency in GHz. The path loss due to rainfall can then be estimated by substituting Equation (4) into Equation (3).
## 3 Results
### Machine-Learning-Based Modeling Result
#### 3.2.2 Result 2--Field-Data-Based Machine Learning  
In Section 3.2.1, the selected RF and ANN methods were shown to have significantly recovered the synthetic physics-based dataset. This section presents the results of the application of six ML models tested on field-measured data, as shown in Figure 5. The field data received were large, and in.txt format. The data were converted to Excel format and imported through the Pandas/Python library to perform appropriate data pre-processing. Initially, information about the dataset was explored to evaluate datatypes (object or float) and check whether an unrecorded dataset existed, as well as the presence of outliers. Data cleaning was then performed. Cross-correlation of the dataset was also evaluated to select the input features to be modeled with the target dataset.  
The ML-based prediction model and the actual measured values were used for analysis of the statistical accuracy. For this, a linear regression model was derived, based on the model-predicted and actual values. Figures 22-27 show the ML model prediction and ML model linear trendline, given as:  
\[\text{ML Model}=\text{a.Measured data}+\text{b}\]  
Figure 26: (**a**) Scatter plot of 70% training tripping-out data vs. LSTM model prediction. (**b**) Scatter plot of 30% test tripping-out data vs. LSTM model prediction.  
Figure 24: (**a**) Scatter plot of 70% training tripping-out data vs. LightGBM model prediction. (**b**) Scatter plot of 30% test tripping-out data vs. LightGBM model prediction.  
Figure 25: (**a**) Scatter plot of 70% training tripping-out data vs. XGBoost model prediction. (**b**) Scatter plot of 30% test tripping-out data vs. XGBoost model prediction.  
Table 11 shows the ML model's accuracy performance with the training and test results displayed in Figures 22-27. The model predictions exhibited a high R\({}^{2}\) score and a minimum mean sum square error. The results demonstrate the potential application of machine learning modeling for the swab and surge field dataset.
## 4 Digital Watermarking System for JPEGs
### JPEG Localisation and Dataset Overview  
#### 4.2.1 JPEGs Within Forensic Disk Images  
Forensic disk images contain a wide range of file types. This research concentrates on JPEG files as a proof-of-concept for watermarking individual files without altering the overall disk image structure. The system uses JPEG markers to accurately locate and isolate these files within the disk image.  
**JPEG Markers:** The system identifies JPEG images using the FFD8 FF start and FFD9 end markers to preserve the structure of the disk image during watermarking. The byte sequences FFD8 FF (start of image, SOI) and FFD9 (end of image, EOI) are defined by the JPEG ISO/IEC 10918-1 standard; forensic tools universally rely on the same markers to locate JPEG segments within raw byte streams [60; 61]. This process modifies only JPEG data and leaves non-JPEG data intact. To maintain forensic compliance, the system skips and logs corrupt or fragmented JPEGs. Comprehensive logging records all actions and anomalies throughout the watermarking process.
# A Hierarchical Sparse Discriminant Autoencoder for Bearing Fault Diagnosis
## 1 Introduction
The optimal parameter optimization model was obtained through a genetic algorithm, and high diagnostic accuracy was achieved. [PERSON] et al. [30] introduced the gray wolf optimization algorithm to optimize the key parameter smoothing factor of the model to obtain an ideal classification model. The results show that this method can achieve an effective diagnosis of bearing faults under different working conditions under a small sample training set. The above research shows that selecting appropriate hyperparameter optimization algorithms for different network structures can effectively improve the performance of the network model. This study fully considers the sparsity, classification and hyperparameter selection of autoencoders and proposes a hierarchical sparse discriminant autoencoder (HSDAE) method. The proposed HSDAE method can improve the feature extraction performance of autoencoders from the aspects of network sparsity and classification and is used for fault diagnosis of rotating components under complex working conditions. A novel hierarchical sparse strategy was used to enhance the sparse connection between deep network layers, and at the same time, was combined with particle swarm optimization to obtain the optimal sparse hyperparameters to improve network sparsity. Class aggregation and class separability strategy were used as discriminative distance to enhance the classification ability of the network, thereby enhancing the feature extraction ability of the improved autoencoder. The proposed method was compared and analyzed with a variety of existing similar methods, and the results show the superiority of the proposed method. The main innovations and contributions are as follows:  
1. In this paper, a novel semi-supervised autoencoder (hierarchical sparse discriminant autoencoder) is proposed to extract features for fault diagnosis;2. A novel hierarchical sparsity strategy is proposed to enhance the sparsity of autoencoder networks, combining class aggregation and class separability strategy to improve feature extraction performance;
3. Experimental comparative analysis verifies that the proposed method can achieve reliable fault diagnosis for rotating parts under complex working conditions;  
The rest of this article is organized as follows: Firstly, Section 2 introduces the basic principles of stacked sparse autoencoders and particle swarm optimization algorithms. Secondly, Section 3 introduces the improvement of the original method in this study and the specific process of the proposed method. Section 4 then introduces the experimental results and verifies the effectiveness of the proposed method on the CWRU bearing data set and private-bearing data set. Finally, Section 5 is the conclusion.
# A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition
## 4 Related Datasets
### Scene Text Recognition Datasets
The dataset is labelled for text detection, recognition and spotting tasks. **ICDAR2019--LSVT**[59] is a large dataset containing photos captured from different mobile phones in the streets of China. There are 50,000 fully annotated images and 400,000 training images with weak annotations. Photos are considered fully annotated when text instances are transcribed and the corresponding ground-truth location is also provided. On the other hand, they are weakly annotated when they only have the transcription of the text of interest in these images. The properties of the aforementioned scene text datasets are summarised in Table 3. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Datasets** & **\#Training / Test Images** & **\#Test Instances** & **Annotation** & **Orientation** & **Curved** & **Language** \\ \hline MJSynth [57] & \(\sim\)8.9 M & \(\sim\)8.9 M & Word & Multi-oriented & Not curved & English \\ SynthTel [58] & \(\sim\)800 k & \(\sim\)8 M & Test-string / Word/ Character & Multi-oriented & Not curved & English \\ IISX-words [63] & 308/740 & 2000/2000 & Word/ Character & Horizontal & Not curved & English \\ Street View Text [64] & 101/249 & 211/514 & Word & Horizontal & Not curved & English \\ ICDAR2003 [65] & 288/251 & 1156/1110 & Word/ Character & Horizontal & Not curved & English \\ ICDAR2013 [66] & 420/141 & 3564/1439 & Word/ Character & Horizontal & Not curved & English \\ Char/\({}^{4}\)[67] & - & \(\sim\)78,000 & Character & Horizontal & Few curved & Latin \\ ICDAR2015 [68] & 1000/500 & 4468/2077 & Word & Multi-oriented & Few curved & English \\ MSRA-TDS00 [60] & 300/200 & - & Word & Multi-oriented & Few curved & Chinese and English \\ SVT Perspective [69] & 238 & 639 & Word & Multi-oriented & Few curved & English \\ VinTest [72] & 2000 & \(\sim\)56,000 & Word & Multi-oriented & Few curved & Vietnamese \\ CUTERS0 [61] & 80 & 288 & Word & Multi-oriented & Curved & English \\ COCO-Teat [70] & 43,686/20,000 & 118,309/27,550 & Word & Multi-oriented & Curved & English \\ Total-Text [71] & 1555/300 & 111,666/293 & Word & Multi-oriented & Curved & English \\ SCUT-CNN1500 [62] & 1000/500 & 7863/3088 & Word & Multi-oriented & Curved & Chinese and English \\ ICDAR2017\(-\)MLT [73] & 900/9000 & 84,686/87/619 & Word & Multi-oriented & Curved & 9 Languages \\ ICDAR2019\(-\)MLT [73] & 10,000/10,000 & 89,177/102,462 & Word & Multi-oriented & Curved & 10 Languages \\ ICDAR2019\(-\)Art [74] & 6603/4563 & 50,029/48,426 & Word & Multi-oriented & Curved & Chinese and English \\ ICDAR2019\(-\)LSVT [59] & 430,000/20,000 & - & Word & Multi-oriented & Curved & Chinese and English \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison between Scene Text Datasets.