esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Copy-Move Forgery Detection Based on Fused Features and Density Clustering
## 5 Experimental Results
### Copy-Move Forgery Detection  
This section presents the experimental detection results. The effectiveness of the algorithm was validated by comparing the detection results with the given binary ground truth image in the dataset. The first row of each detection result represents the tampered image, the second row represents the binary ground truth image in the dataset, which indicates the real tampered region, and the third row represents the experimental detection result of the method in this paper. The blue area represents the correctly detected tampered region, the red area represents the falsely detected region, the white area represents the missed detection region, the dark gray area represents the real tampered region, and the light gray area represents the tampered region after boundary post-processing. In order to verify the effectiveness of the algorithm, two comparison algorithms were used to comprehensively evaluate the detection performance of the proposed method. These were the feature point method combining SIFT and KAZE, proposed by [PERSON] et al. [38], followed by the feature extraction method using SURF proposed by [PERSON] et al. [17]. The experimental comparison results are presented in tables or figures.  
Compared with block-based methods, the feature point extraction stage is extremely important in feature point-based methods, especially the number of feature points extracted in smooth areas. In this experiment, we tested SIFT, SURF, A-KAZE, and BRISK. For traditional methods, a contrast threshold does not need to be set in advance during feature extraction, and feature matching can be completed using functions in the OpenCV database. After wrong matches have been eliminated, the set of matched pairs is then counted, as shown in Table 1. We selected photos from the dataset for testing; the images in the first and second columns came from the Ardizzone dataset, and those in the third and fourth columns came from the CoMoFoD dataset. Among them, the first and third columns show occlusion tampering on the original image, and the second and fourth columns show addition tampering on the original image. Specific test photos are shown in Figure 5.  
Table 1 presents the experimental results for the four columns of images in Figure 5, clearly showing that the proposed SURF and A-KAZE feature points have significant advantages compared to other feature points. Compared to some traditional feature points that do not require setting a contrast threshold, a small contrast threshold was set in the method proposed in this paper and SURF and A-KAZE were fused to obtain more matching feature points in the feature extraction stage. Especially for the test image in the first column of Figure 5, where tampering occurs in the smooth area of the sky, only the proposed method detects the correct matching pairs, while other feature extractionmethods do not detect any tampering. This leads to the tampering operations in the smooth area being ignored, resulting in reduced detection effectiveness.
# An Effectively Finite-Tailed Updating for Multiple Object Tracking in Crowd Scenes
## 3 Problem Formulation and the Approach
### Revisiting FairMOT
#### 3.1.4 Updating the Motion Model  
The update method of the motion model uses the Kalman filter. The Kalman filter is an efficient recursive filter (autoregressive filter) which can estimate the state of a dynamic system from a series of incomplete and noisy measurements. The Kalman filter is mainly divided into two steps, prediction and correction. The prediction step is to estimate the current state based on the state at the previous moment, and the correction step is to perform a comprehensive analysis based on the observation of the current state and the estimation at the previous moment to estimate the optimal state value of the system. The complete formula for the prediction step is shown below:  
\[\hat{x}_{k}=A_{k}\hat{x}_{k-1}+B_{k}\vec{u_{k}} \tag{1}\]  
\[P_{k}=A_{k}P_{k-1}A_{k}^{\mathsf{T}}+Q_{k} \tag{2}\]where \(\hat{x}_{k}\) is the system state at time \(k\), and \(\vec{u}_{k}\) is the control vector of the system at time \(k\). The matrix \(A\) relates the state at time step \(k-1\) to the state at step \(k\). Matrix \(B\) is the control matrix. \(P_{k-1}\) represents the correlation between different internal states in \(\hat{x}_{k-1}\), and \(Q_{k}\) represents noise in the prediction process. The update formula of the measurement process is as follows:  
\[K_{k}=P_{k}H_{k}^{\text{T}}\Big{(}H_{k}P_{k}H_{k}^{\text{T}}+R_{k}\Big{)}^{-1} \tag{3}\]  
\[\hat{x_{k}}^{\prime}=\hat{x}_{k}+K_{k}(z_{k}-H_{k}\hat{x}_{k}) \tag{4}\]  
\[P_{k}=(I-K_{k}H_{k})P_{k} \tag{5}\]  
The matrix \(H_{k}\) represents the scale transition from a predicted state to a measured state, and the \(R_{k}\) represents the measurement error at time \(k\). The \(z_{k}\) represents the status value of the actual measurement. The \(K_{k}\) represents the Kalman gain.
# Hybrid LSTM Model to Predict the Level of Air Pollution in Montenegro
###### Abstract  
Air pollution is a critical environmental concern that poses significant health risks and affects multiple aspects of human life. ML algorithms provide promising results for air pollution prediction. In the existing scientific literature, Long Short-Term Memory (LSTM) predictive models, as well as their combination with other statistical and machine learning approaches, have been utilized for air pollution prediction. However, these combined algorithms may not always provide suitable results due to the stochastic nature of the factors that influence air pollution, improper hyperparameter configurations, or inadequate datasets and data characterized by great variability and extreme dispersion. The focus of this paper is applying and comparing the performance of Support Vector Machine and hybrid LSTM regression models for air pollution prediction. To identify optimal hyperparameters for the LSTM model, a hybridization with the Genetic Algorithm is proposed. To mitigate the risk of overfitting, the bagging technique is employed on the best LSTM model. The proposed predicitive model aims to determine the Common Air Quality Index level for the next hour in Niksic, Montenegro. With the hybridization of the LSTM algorithm and by applying the bagging technique, our approach aims to significantly enhance the accuracy and reliability of hourly air pollution prediction. The major contribution of this paper is in the application of advanced machine learning analysis and the combination of the LSTM, Genetic Algorithm, and bagging techniques, which have not been previously employed in the analysis of air pollution in Montenegro. The proposed model will be made available to interested management structures, local governments, national entities, or other relevant institutions, empowering them to make effective pollution level predictions and take appropriate measures.  
air pollution prediction; common air quality index; long short-term memory; support vector machine; genetic algorithm; bagging techniques Article
## 4 Methods
### Loss Function  
We employ a cosine similarity-based loss function to measure the similarity between feature vectors. For a batch of data \(D(a,b,y)\) containing \(N\) samples, where \(a\) and \(b\) represent the feature vectors of two nodes output by the neural network, and \(y\) denotes the true label of the point cloud pair (taking values in \(\{1,-1\}\) to indicate similarity and dissimilarity, respectively), the loss function for the \(i\)-th sample is expressed as follows:  
\[\text{loss}_{i}=\begin{cases}1-\cos(a_{i},b_{i}),&\text{if }y_{i}=1,\\ \max(0,\cos(a_{i},b_{i})-\text{margin}),&\text{if }y_{i}=-1,\end{cases} \tag{12}\]where \(\cos(a_{i},b_{i})\) denotes the cosine similarity between the feature vectors \(a_{i}\) and \(b_{i}\) of sample \(i\), and margin is a predefined margin value set to 0.4.  
The network outputs feature vectors of matched point pairs, with an equal number of positive and negative pairs extracted. The feature similarity between these pairs is computed, and the labels for negative pairs are set to \(-1\), while positive pairs have labels of 1. Given that the number of negative samples far exceeds that of positive samples, we set the ratio of positive to negative samples to 1:8 to improve training effectiveness. Subsequently, the total loss function is obtained by weighted summation based on the total number of positive and negative samples. The weighted loss function is specified as follows:  
\[\text{Loss}=\text{Loss}_{\text{pos}}\cdot\frac{\sum_{i=1}^{n}X_{\text{label},i }}{N}+\text{Loss}_{\text{neg}}\cdot\frac{\sum_{i=1}^{n}X_{\text{mis},i}}{N}, \tag{13}\]  
where \(\text{Loss}_{\text{pos}}\) represents the cross-entropy loss function for positive samples, and \(X_{\text{label},i}\) denotes a pair of labels. Conversely, \(\text{Loss}_{\text{neg}}\) indicates the cross-entropy loss function for negative pairs, and \(X_{\text{mis},i}\) represents a pair of mislabeled point clouds.  
The design of this loss function is simple and easy to optimize, focusing on the directional differences between two feature vectors rather than their specific numerical values. This approach is particularly effective in handling class imbalance problems. Additionally, the loss function is applicable to high-dimensional data, mitigating the impact of dimensionality and thereby enhancing the model's accuracy and robustness.
###### Abstract  
In this work, we have used TiO\({}_{2}\) and SnO\({}_{2}\) layers as electron selective contact (ESC) in n-i-p perovskite solar cells configuration. To study and compare the ion migration kinetics of these ESC, CsFAMAPIBIr and MAPb\({}_{3}\)-based devices were fabricated and characterised in fresh (1 day) and aged (28 days) conditions. Depending on the ESC and perovskite composition, devices reveal a different progression over time in terms of hysteresis and performance. Using transient photovoltage (TPV) and transient photocurrent (TPC) techniques, we studied the kinetics of carrier extraction and recombination, which showed that aged devices present slower recombination kinetics compared to their fresh counterparts, revealing a positive effect of the aging process. Finally, transient of the transient, derived from the TPV technique, discloses that TiO\({}_{2}\) accumulates more charges in the ESC/perovskite interface compared to SnO\({}_{2}\) and that the ion migration kinetics are directly related to the perovskite composition.  
electron selective contact; TiO\({}_{2}\); SnO\({}_{2}\); stability; perovskite; interfacial recombination; transient photovoltage; transient of the transient +
Footnote †: journal:  
and carrier recombination [16, 17]. Based on our previous works [17, 18], where we analysed the behaviour of the TROTR for fresh and aged PSCs, we concluded that the influence of ion migration on carrier lifetime is reduced over the different measurement days, mainly because of oxygen having an influence on both the perovskite and the hole selective contact material (HSC) [17]. In addition, we described different ionic migration behaviours depending on the perovskite composition, being more important in I-based perovskite compared to Br-based perovskites [18].  
In this work, we focused on the influence of the electron selective contact--TiO\({}_{2}\) and SnO\({}_{2}\)--over the ion migration kinetics and carrier recombination in both fresh (1 day) and aged (28 days) devices using different perovskite compositions: methylammonium iodide (MAPbI\({}_{3}\)) and Cs\({}_{0.1}\)(FA\({}_{0.85}\)MA\({}_{0.15}\))\({}_{0.9}\)Pb(I\({}_{0.85}\)Br\({}_{0.15}\))\({}_{3}\), herein referred to as CsFAMAPbIBr (FA = CH(NH\({}_{2}\))\({}_{2}\)\({}^{+}\); MA = CH\({}_{3}\)NH\({}_{3}\)\({}^{+}\)), also known as the triple cation perovskite. We studied the changes in device performance and carrier kinetics over the different measurement days, first, comparing both ESC for the triple cation perovskite, and second, using the SnO\({}_{2}\) as ESC for both perovskite compositions.  
The results obtained revealed that both ESC and perovskite composition have a strong influence on the ionic dynamics that directly impact the stability and the performance in PSCs.
# Multi-Pooled Inception Features for No-Reference Image Quality Assessment
## 4 Conclusions  
In this paper, we introduce a deep framework for NR-IQA, which constructs a feature space relying on multi-level Inception features extracted from pretrained CNNs via GAP layers. Unlike previous deep methods, the proposed approach does not take patches from the input image, but instead treats  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **LIVE In The Wild**[36] \\
**Method** & **PLCC** & **SROCC** \\ \hline BIQI [45] & 0.455 & 0.374 \\ BLIINDS-II [3] & 0.110 & 0.088 \\ BRISQUE [6] & 0.585 & 0.577 \\ CORNIA [46] & 0.660 & 0.638 \\ DIIVINE [2] & 0.482 & 0.435 \\ HOSA [47] & 0.652 & 0.639 \\ SSEQ [4] & 0.282 & 0.240 \\ BosICP [15] & 0.483 & 0.491 \\ CNN [14] & 0.462 & 0.477 \\ DeepBIQ [18] & 0.738 & 0.738 \\ DIQaM-NR [48] & 0.308 & 0.316 \\ WaDIQaM-NR [48] & 0.339 & 0.372 \\ \hline _MultiGAP-NRIQA_ (without fine-tuning) & _0.760_ & _0.764_ \\ _MultiGAP-NRIQA_ & **0.772** & **0.769** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Cross database test. The learning-based NR-IQA methods were trained on the whole KonIQ-10k [33] database and tested on LIVE In the Wild [36] database. The measured PLCC and SROCC values are reported. The best results are shown in **bold** and the second best results are in _italic_.  
Figure 5: Scatter plots showing the ground-truth MOS values against the predicted MOS values on KADID-10k [34] test set.  
the image as a whole and extracts image resolution independent features. As a result, the proposed approach can be easily generalized to any input image size and CNN base architecture. Unlike previous deep methods, we extract multi-level features from the CNN to incorporate both mid-level and high-level deep representations into the feature vector. Furthermore, we pointed out in a detailed parameter study that mid-level features provide significantly more effective descriptors for NR-IQA. Another important observation was that the feature vector containing both mid-level and high-level representations outperforms all feature vectors containing the representation of one level. We also carried out a comparison with other state-of-the-art methods and our approach outperformed the state-of-the-art on the largest available benchmark IQA databases. Moreover, the results were also confirmed in a cross database test. There are many directions for future research. Specifically, we would like to improve the fine-tuning process in order to transfer quality-aware features more effectively into the base CNN. Another direction of future research could be the generalization of the applied feature extraction method to other CNN architectures, such as residual networks.  
The authors thanks the anonymous reviewers for their careful reading of the manuscript and their many insightful comments and suggestions. The authors declare no conflict of interest.
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
## 5 Proposed Scheme-II (Key Recovery System Using Proxy Re-Encryption)
### Med Enforcement Phase  
In this phase, \(Med\) will start recovering the encrypted session key between \(Dev_{A}\) and \(Dev_{B}\) at the request of \(Dev_{A}\) as shown in Figure 11.  
* **Step 1:**\(Dev_{A}\) sends \(KRF\) to \(Med\) to recover the session key \(SK\).  
Figure 11: Med enforcement phase of proposed scheme-II.  
Figure 10: Session key exchange and KRF generation phase of proposed scheme-II.  
* **Step 3:**\(KRC\) re-decrypts \(KRF_{4}^{{}^{\prime}}\) to obtain \(KRF_{4}^{{}^{\prime\prime}}\) using \(sk_{KRC}\) as follows: \[K_{Med-KRC}=H_{3}(Z_{Med}^{d_{KRC}},Z_{Med}^{z_{KRC}},ID_{Med},pk_{Mad},ID_{KRC}, pk_{KRC})\] (54) \[KRF_{4}^{{}^{\prime\prime}} =KRF_{4}^{{}^{\prime}1/H_{4}(KRF_{2}^{{}^{\prime}1/K_{Med-KRC}})}\] (55) \[=(\alpha\cdot\beta)^{H_{4}(V_{Med}^{{}^{\prime}1/H_{4}(KRF_{2}^{{}^{ \prime}1/K_{Med-KRC}})}\] \[=(\alpha\cdot\beta)\] After that, \(KRC\) sends \(KRF_{4}^{{}^{\prime\prime}}\) to \(Med\).
* **Step 4:**\(Med\) decrypts \(KRF_{4}^{{}^{\prime}1}\) to obtain \(SK\) as follows: \[g^{c} =KRF_{4}^{{}^{\prime}1/(y_{Med}+z_{Med})}\] (56) \[=(\alpha\cdot\beta)^{1/(y_{Med}+z_{Med})}\] \[=(Y_{Med}\cdot Z_{Med})^{1/(y_{Med}+z_{Med})}\] \[=g^{c\cdot(y_{Med}+z_{Med})/(y_{Med}+z_{Med})}\] \[(SK\parallel\sigma)=KRF_{3}\oplus H_{2}(g^{c})\] (57) After that, \(Med\) sends \(SK\) to \(Dev_{A}\).
* **Step 5:**\(Dev_{A}\) decrypts the message \(M\) using the obtained \(SK\).  
\[M=Dec_{SK}(C) \tag{58}\]
## 2 Optimal Design Chart  
The current aerodynamic optimal design method consists of two main modules, i.e., a detailed design module and a preliminary optimal design module, and three additional modules, i.e., an input module, a CFD verification module, and an output module (Figure 1).  
The input module mainly consists of the design requirements and design parameters. The design requirements comprise pressure ratio, design mass flow rate, structural constraints, and inlet conditions. Six design parameters are applied, i.e., aspect ratio, solidity, reaction, rotation speed, outlet axial Mach number, and inlet radius ratio.  
The main purpose of the second module is to accurately and rapidly explore the compressor configuration. This module aims at maximizing design adiabatic efficiency while keeping flow mass rate and total pressure ratio unchanged by simultaneously optimizing six types of design parameters. The applied one-dimensional meanline design method consists of widely used empirical models of loss, deviation, incidence, and blockage. To obtain optimized compressor configuration, the compressor design parameters were initialized and then optimized in following iterations.  
It is challenging to catch the global optimal solution for a compressor as a mass of variables and corresponding nonlinearity. The genetic algorithm (GA) [23], as a type of global optimization algorithm, was used the present research.  
The throughflow method used in the detailed design phase was based on the streamline curvature method. The governing equation was solved to obtain the aerodynamic and geometric parameters along the spanwise distribution of each row. The solution was then applied to refine the flow path, guaranteeing the mass flow conservation at the calculation station under consideration.  
The blade section airfoil is used to generate blade geometry. The advanced airfoils, such as the high performance airfoil (HPA) and the controlled diffusion airfoil (CDA), lack reliable and publicly available empirical models. Therefore, the NACA 65-series and double-circular-arc (DCA) airfoils were selected as subsonic and transonic two-dimensional blade section airfoils, respectively. The two-dimensional blade section airfoils were then stacked spanwise along the radial line. The design process ended with output data of design results and CFD verification.  
Figure 1: Optimization flow chart.
## 5 Conclusions  
In this study, based on the finite element method and multi-body dynamics method, a high-speed train-track-bridge coupling system model was established. Calculation and analysis were conducted via programming to explore the influence of unequal-height piers on the dynamic response of a high-speed railway vehicle bridge system. The following conclusion was obtained:  
1. The dynamic response of the system and the safety index of the train generally increase with the increase in the train running speed.
2. Under seismic excitation, the dynamic response of the system is significantly increased, and the lateral dynamic response of the system is more affected by seismic excitation than the vertical response.
3. Compared to equal-height piers, the peak lateral dynamic response of the system with unequal-height piers (gradually increasing) decreases, which is beneficial for stabilizing the vehicle body. The peak lateral dynamic response of the system with unequal-height piers (steep increase in pier height) increases sharply, seriously reducing passenger comfort.
4. The vertical dynamic response of trains under two unequal-height pier conditions increases, and the safety indicators of equal-height piers are significantly better than the two unequal-height pier conditions. The recommended design is the optimal choice for equal-height bridge piers, followed by gradually increasing pier heights, and avoiding steep increases in pier heights.  
Figure 12: Peak response of bridge midspan.  
Figure 13: Train dynamic response.  
**Author Contributions:** Data curation, Z.Z., H.Z., H.H., P.Z. and F.T.; Writing\(-\)original draft, Y.Z.; Writing\(-\)review & editing, L.J. and P.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research work was jointly supported by the China Railway Corporation Limited Science and technology research and development program (2022-major-17), Science and Technology Research and Development Program Project of China railway group limited (2020-Key-02), the National Natural Science Foundation of China (Grant No. 11972379), the Hunan Natural Science Foundation Science and Education Joint Fund Project 2022J60109, Hunan Science Fund for Distinguished Young Scholars (2021 JJ10061).  
**Data Availability Statement:** Underlying research materials related to this paper can be accessed by requesting from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict to interest.
## 4 Simulation  
Numerical simulations are presented to verify the effectiveness and fine performances of the proposed ADP-based robust trajectory-tracking-control scheme (denoted as ADPFTNs) in this section. Firstly, the robust performances of the proposed ADPFTNs in (29) were verified in the case of external disturbances and parameter uncertainties. Then, we demonstrate the superior ability of the proposed ADPFTNs in balancing control performances and cost. The initial position vector was selected as \(\mathbf{\zeta}(0)=[0.1,-0.2,0.131]^{\mathrm{T}}\) m. The desired position trajectory was \(\mathbf{\zeta}_{d}=[0.5\mathrm{sin}(0.6t),0.5\mathrm{cos}(0.6t),0.5+\frac{3t}{50} ]^{\mathrm{T}}\) m. The desired yaw angle was \(\mathbf{\psi}_{d}=0\) rad. The basis function vectors are  
\[\sigma_{1}(\mathbf{e}_{1})=[0.5\mathbf{x}_{e}^{2},0.5\mathbf{z}_{e}^{2},0.5\mathbf{z}_{e}^{2},x _{e},x_{e},y_{e}\mathbf{j}_{e},z_{e}z_{e}]^{\mathrm{T}},\mathbf{\sigma}_{2}(\mathbf{e}_{2} )=[0.5\mathbf{y}_{e}^{2},0.5\mathbf{z}_{e}^{2},0.5\mathbf{y}_{e}^{2},\mathbf{\psi}_{e}\mathbf{\psi} _{e},\theta_{i}\hat{\theta}_{e},\psi_{e}\hat{\psi}_{e}]^{\mathrm{T}}.\]  
The initial weight vectors are  
\[\begin{split} W_{1a}(0)&=[27,26,21,22,24,22]^{ \mathrm{T}},\mathbf{W}_{2a}(0)=[15,16,18,16,18,12]^{\mathrm{T}},\\ W_{1c}(0)&=[28,28,26,23,22,24]^{\mathrm{T}},\mathbf{W}_{2c }(0)=[14,15,15,16,18,24]^{\mathrm{T}}.\end{split}\]  
In addition, the main parameters of the quadrotor UAV are given as \(m_{0}=1.2\) kg, \(g=9.81\) m/s\({}^{2}\), \(J_{0}=\text{diag}\{0.125,0.125,0.25\}\) kg\(\cdot\) m\({}^{2}\), \(J_{r}=0.002\) kg\(\cdot\) m\({}^{2}\), and \(k_{10},k_{20},\cdot\cdot\), \(k_{60}=0.01\).