esa-sceva/satcom-chunk-collection huggingface dataset:
# Framework for Video Steganography Using Integer Wavelet Transform and JPEG Compression
## 3 Proposed Method
### Concealing and Retrieval Algorithm  
The concealing and retrieval process of the secret message is shown in the Algorithm 1. IWT can decompose the given signal up to N- levels. N is the length of the given signal. Every time during decomposition IWT eliminates the noise from the signal and calculates the refined part of the signal as well. Noisy parts of the signal are used for embedding secret information. In the given algorithm, the input is taken in the form of secret image and cover video file. The file is decomposed into frame using OpenCV. Some of the suitable frames are selected to hide the secret message randomly. On the other side, JPEG compression is applied on secret image so that the concealing capacity can be improved. JPEG compression work on the chrominance information so that changes are not visible to the third person.  
IWT do the actual embedding of secret message before that secret message is decomposed into binary values. Then these values are embedded randomly into frames. The process repeats itself until the complete secret message is not embedded in cover frames. After the embedding of the complete secret message stego file is created, the stego file travels through the web to the destination.  
Retrieval of the secret message is done on the receiver side. For retrieval of secret messages, the reverse process of information embedding takes place. The complete secret message is retrieved because of the integer and reversible nature of the IWT technique. IWT helps in fast calculation resulting into low cost and high robustness. JPEG compression also provides the high concealing capacity and good visual quality of stego file.
## 5 Conclusions  
In this article, we proposed an effective unsupervised outlier detection method based on quantiles and skewness coefficients called ISOD. ISOD can be mainly divided into three stages: (1) the construction of the empirical cumulative distribution function; (2) the computation of the quantiles and skewness coefficients of each dimension; (3) summarizing the degree of anomalies in each dimension and ultimately obtaining the outlier score for each data point. After these stages, the method finally obtains the outlier scores.  
The experimental results derived from applying the ISOD algorithm to 10 benchmark datasets show that the ISOD method has great competitive and promising performance in comparison to the state-of-the-art baseline anomaly detection algorithms. In addition to achieving better experimental results, the ISOD algorithm also has high interpretability and scalability, as explained in Section 4.  
Based on Sections 3.4 and 4.3.1, it is clear that the ISOD algorithm does not require labeled data and that it is an unsupervised anomaly detection algorithm. At the same time, it has good scalability and can obtain good performance with ultra-high dimensional datasets. Finally, this algorithm is theoretically guaranteed to have high interpretability.  
Conceptualization, Y.H.; funding acquisition, W.L.; methodology, Y.H.; project administration, Y.H.; software, S.L., Y.G. and W.C.; supervision, W.L.; validation, S.L., Y.G. and W.C.; writing--original draft, Y.H.; writing--review and editing, Y.H. and W.L. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Natural Science Foundation of China (No. 61862011), the Guangxi Natural Science Foundation (No.2019 GXNSFGA245004), and the Innovation Project of Guangxi Graduate Education (No.YCBZ2023128).  
Not applicable.  
The data presented in this study are available in article.  
The authors declare no conflict of interest.  
Figure 1: The running times for the ISOD algorithm on 10 benchmark datasets.(Larger dot mean longer running time).
# Image Quality Assessment for Gibbs Ringing Reduction
###### Abstract
We therefore test the corrected definition of entropy in this paper. Huber loss, also known as the smooth \(\ell^{1}\) loss, behaves similar to \(\ell_{e}^{0}\) (and somewhat similar to mean squared error). It deliberately exaggerates the impact of large errors. MAE, also known as the \(\ell^{1}\) loss, measures the average of the sum of absolute differences between the reconstructions and the ground truth. Neither of these two metrics have been used previously in relation to Gibbs ringing and are primarily known as machine learning loss functions used in robust regression. In this paper, we discuss the advantages and drawbacks of using different metrics to evaluate the reconstruction methods using three different test images. This discussion is essential to place the choice of loss function for Gibbs ringing suppression networks on a robust evidence base. Gibbs ringing is largest close to discontinuities (edges) in an image. We therefore speculate that applying metrics to a limited region near edges could enhance their sensitivity to ringing, which could overcome the problems we identified previously. Not only region of interest could be a sensible approach for classical approaches, learning-based approaches could also be potentially useful. Although an attention-guided CNN model has not been proposed for Gibbs ringing reduction, it has been proposed for image denoising [20]. In this paper, we introduce a three-step method for identifying the region of interest for Gibbs ringing removal. We apply metrics on that region of interest in order to determine if SSIM, MS-SSIM \(\ell_{e}^{0}\), Huber loss, MAE, entropy or any of the metrics we tested in [17] are improved by this approach. For both Huber loss and \(\ell_{e}^{0}\), there is a threshold which divides large errors from small. We have previously used the mean error of the unfiltered Fourier reconstruction to identify this threshold. However, that choice was for convenience. In this paper, we aim to place the determination of the threshold on firmer ground. The structure of this paper is as follows. In Section 2, we introduce three test images with increasing complexity. In Section 3, we investigate and compare candidate metrics of image ringing and blur. In Section 4, we apply the metrics to a region of interest around edges instead of to the whole image. In Section 5, we investigate the effect of varying the error threshold in Huber loss and \(\ell_{e}^{0}\). Finally, we present our conclusions.
# Smart Mobility with Big Data: Approaches, Applications, and Challenges
## 4 Application of Smart Mobility with Big Data
### Visualization and Monitoring  
Visualization of data analysis results is important because it is intuitive and clear to non-experts. The importance of data visualization is emphasized as the amount of data increases and becomes more complex. Visually checking data analysis results has advantages in terms of understanding and time efficiency. According to [70], the speed of information processing varies greatly from one sense to another and compared to a computer system.  
For example, the authors of [71] developed a visualization and analysis method for transportation data, exploiting taxi and bus GPS and bicycle-sharing data. The proposed application is a python package, TransBigData, which was used by data science researchers and engineers, and through spatiotemporal transportation data analysis, government and other enterprises can support efficient and reliable management decisions. Furthermore, the possibility of motivating and improving traffic operation highway safety by exploiting real-time microwave vehicle detection system (MVDS) data was studied in [12].  
The authors of another study [17] presented a Java-based traffic information observation (TDO) tool to filter and visualize historical traffic big data. The authors of [72] established a solution to create a platform for pothole detection and road monitoring, implementing road observers using IMU sensors attached to embedded systems and proposing smart environmental monitoring and analysis, an IoT-based monitoring platform system, and an application that transmits real-time big data. The authors of [73] proposed a system that uses information visualization techniques to analyze urban traffic data and the impact of emissions on urban air quality. The study showed that citizens and public agencies can use effective visualization to identify trends, detect congested road sections at certain times, and visualize the correlation between road traffic and air pollution for system users. The authors of [74] used an ANN with fuzzy logic to determine the remaining construction life (LCL) of a permanent magnet (PM) by mapping the source swell and the time at which the electric vehicle is driven and outputting the external temperature, twisting temperature, the moment of component lubrication, and the split weakening of the responsible magnetic field in the context of connected vehicles. The authors also proposed a monitoring system whereby cloud computing, traffic data, and intelligent transportation systems (ITS) can improve PHEV energy management.  
Finally, the authors of [75] analyzed road traffic and pollution data and applied algorithms that leverage the MapReduce framework running on Hadoop clusters. Results were collected, and the path with the least pollution was calculated and visualized.
## 2 STATCOM Configuration and Operation  
The novelty of this paper is the presence of binary weighted transformers (1:1 & 2:1 turns ratio's) integrated seven level VSC based STATCOM with a single DC-Link capacitance and firing angle equation (which is newly developed for VSC with a low total harmonic distortion (THD) value). The turns ratio of binary weighted transformers is 1:2:4:......2n\({}^{-1}\), which is suitable for multilevel (like seven level, fifteen level, thirty-one level......) VSC based STATCOM. The switching angles are designed for multilevel VSC based STATCOM from a novel firing equation with minimum THD. It can be implemented for various dynamic conditions of reactive power changes and system voltage changes. The performance of the seven level VSC based STATCOM is presented under different dynamic conditions with switching on inductive and capacitive loads.
# Multi Stage Retrieval for Web Search During Crisis
## 3 Materials and Methods
### Multi Stage Retrieval  
Hybrid retrievers combine the strengths of both lexical and dense search methods. In our pipeline (Figure 5), these search methods are used sequentially: first, a lexical search selects the top-n elements for each query, then the selected documents of all queries are aggregated into a filtered corpus, and then a dense search further refines this selection to the top-m elements (where n > m). Each search step comprises a retrieval step (i.e., BM25 or Dense Retriever), followed by a cutoff and a light cross-encoder to rerank the documents, followed by another cutoff, as shown in Figure 5. This way, we reduce the documents we must deal with step by step. The reranker in lexical search ranks the most relevant documents higher and prevents them from being excluded by the second cutoff of the lexical search step. Hence, only the most relevant subset of documents returned by the first phase is provided as input to the dense search.  
In the lexical search step, after applying BM25, we selected the top 200 for each query. This threshold was chosen based on empirical observation across development to balance computational costs and performance. After the reranking, we select the top 60 documents for each query to pass them to the next stage. This was done to balance the execution time of the dense search step while maintaining a high recall in the final result. After the lexical search, the filtered corpus aggregates 60 documents times the number of queries in \(Q\) in the worst case (duplicates are removed). The dense retriever selects the top 200 (or the top 10% of the corpus size if it is less than 200). This conditional threshold ensures scalability. Moreover, in a small data corpus, it prevents overfitting, while in a large corpus, it ensures computational feasibility. After the final reranking, we keep only the top 10% of the documents. This threshold was determined through ablation experiments to balance retrieval precision and semantic diversity.  
The reranking of the dense search is essential, as its score is used to construct the final summary, which is detailed in the subsequent section.  
This hybrid approach allows us to initially leverage a fast BM25 for broad filtering, followed by a more precise reranking of a subset using a general-purpose cross-encoder. We then employ a crisis-pre-trained model for dense retrieval and rerank a small document set.  
While developing a robust reranker specifically for crisis-related documents is challenging, this method enables us to harness the strengths of both a general, powerful reranker and a model with embeddings tailored to the task at hand.
# SCM-IoT: An Approach for Internet of Things Services Integration and Coordination
## 3 Methodology
### Platform Independent Computational Model  
The SCM-IoT model was formally described through CPNs, allowing for the possibility of rigorous verification of some of its properties to be visualized, analyzed, validated, and enabled. In the CPNs shown in Figure 2 of Section 3.2.1, the notebook is represented by location H, accessible to three transitions U, N, and C that represent the update, subscription for notification, and subscription for collection services, respectively, as described in the conceptual model.  
The content of the notebook corresponds to the marking of place H, given by its current marking with the multiset expression \"1\" (PAS, NO, BAJ, NO, NO, NO, NO, NO) ++ 1\" (SAL, YES, BAJ, NO, NO, NO, NO, NO, NO) ++... \". This expression is taken from the case study presented below. The expression consists of the sum of several tuples representing tokens, each one multiplied by their corresponding multiplicities, i.e., the number of times it appears repeated in the multi-set. Thus, the token \"(PAS, NO, BAJ, NO, NO, NO, NO, NO)\" appears only once in the book. The expression \"(u, \(p\), T, h, g, c, v, A)\", annotated on the input arc of transition U to place H, declaratively indicates how the values are assigned to the variables that appear in this expression. For the token given before, the values are assigned sequentially in the order in which they appear from left to right, so the list of assignments is \"u = PAS, \(p\) = NO, T = LOW, h = NO, g = NO, c = NO, v = NO, A = NO\". The  
Figure 2: Update service behavior. In (**a**), transition U is enabled by request on place \(P\). In (**b**), the effect of changing the notebook contents of place H is shown.  
variable names that appear to the left of each assignment can be used in logical conditions such as \"\(p=\text{SI}\)\". According to the SCM-IoT model, the tokens represent the notebook pages, and the data separated by commas and grouped by parentheses represent the fields. The number of elements in the multiset, including duplicates, is circled within the marked place, as shown in Figure 2a.  
The editor services associated with the three transitions U, N, and C, mentioned above, are described in detail below.
# Hybrid TOA/RSS Range-Based Localization with Self-Calibration in Asynchronous Wireless Networks
## 3 Proposed Localization Algorithm with Self-Calibration
### TOA/RSS Parameters Estimation Using Self-Calibration
The new system in Equation (27) can then be solved by resorting to a LS approach:  
\[ \begin{bmatrix}\delta x_{b}\\ \delta y_{b}\\ \delta d_{b}\end{bmatrix} =\left[ \begin{array}{ccc}\dfrac{x_{1}-x_{b}}{\rho _{1,b}-d_{b}}&\dfrac{y_{1}-y_{b}}{\rho_{1,b}-d_{b}}&1\\ \dfrac{x_{2}-x_{b}}{\rho_{2,b}-d_{b}}&\dfrac{y_{2}-y_{b}}{\rho_{2,b}-d_{b}}&1 \\ \vdots&\vdots&\vdots\\ \dfrac{x_{N}-x_{b}}{\rho_{N,b}-d_{b}}&\dfrac{y_{N}-y_{b}}{\rho_{N,b}-d_{b}}&1 \end{array} \right]^{+}\left[ \begin{array}{c}\delta\rho_{1,b}\\ \delta\rho_{2,b}\\ \vdots\\ \delta\rho_{N,b}\end{array} \right], \tag{28}\]  
where the obtained solutions are used to update the estimates of the sought parameters \(x_{b}\) and \(d_{b}\). More precisely, the algorithm computes the difference \(\Delta_{i}\) between the pseudoranges \(\rho_{i,b}\) calculated on the basis of the new guesses \([(x_{b}+\delta x_{b})\ (y_{b}+\delta y_{b})\ (d_{b}+\delta d_{b})]^{T}\) and the estimated \(\hat{\rho}_{i,b}\); then, \(\Delta_{i}\) is set as the new \(\delta\rho_{i,b}\) for the next iteration. An error metric is evaluated from the absolute values of \(\delta x_{b}\), \(\delta y_{b}\) and \(\delta d_{b}\), namely \(\epsilon=\sqrt{\delta x_{b}^{2}+\delta y_{b}^{2}+\delta d_{b}^{2}}\). The algorithm stops when \(\epsilon\) is lower than a predefined threshold or when a maximum number of allowed iterations has been reached. The ILS algorithm is widely-used in commercial GNSS receivers (e.g., GPS) to retrieve the user position from a set of pseudoranges measured with respect to all the visible satellites. Remarkably, such an iterative procedure is robust against the problem of local minima and is able to achieve fast convergence (usually, less than 10 iterations are required). It should be noticed that the outcomes of this last calibration step include, in addition to the estimated blind clock offset \(\hat{t}_{b}=\hat{d}_{b}/c\), also an estimate of the unknown blind position \(\hat{x}_{b}\). However, we do not consider the latter as the ultimate estimate since it has been computed on the basis of the sole TOA information, which is known to provide very poor localization performance in some operational scenarios, in particular at short distances and for narrowband signals (and in fact motivates research on hybrid positioning). Therefore, in the following we will consider \(\hat{x}_{b}\) as a coarse initial estimate that will be used only to initialize a different ILS procedure employed in the final localization step, as described in the next section.
## 4 Discussion  
### Particulate Shape and Size  
Particle morphology can be identified at larger scales, such as that of the particle itself, as spherical, rounded, blocky, bulky, platy, elliptical, elongated, and so on. On a smaller scale, texture is essential because it reflects local roughness properties such as surface smoothness, roundness of edges and corners, and asperities. As shown in Figure 14, there is no direct correlation between particle size and particle shape.  
Polydispersity, a key concept in materials science and chemistry, refers to the distribution of particles with varying sizes or masses within a sample. Unlike monodisperse systems, where all particles are of the same size, polydisperse systems are characterized by non-uniform particles. It significantly influences the physical properties and behaviour of materials like soil samples, polymers, and colloids [59]. A shear test was conducted on glass beads of two different sizes. Each sample had a monodisperse size. Despite both samples having the same shape regularity, valued at 1, it was observed that the larger beads, with a D\({}_{50}\) value of 1.77, exhibited higher shear strength at normal stresses of 25, 50, and 100 kPa, as shown in Figure 15. This was in comparison to the finer beads, which had a D\({}_{50}\) value of 0.89. Therefore, we can conclude that larger particles can induce higher shear strength compared to finer particles.  
Figure 14: Mean particle size versus the regularity.  
\begin{table}
\begin{tabular}{c|c c c} \hline \hline  & **Training Database** & **Testing Database** & **10-Fold CV** \\ \hline
**Observations** & 36 & 10 & 46 \\ _MAE_ & 3.79 & 5.68 & 9.83 \\ _RMSE_ & 6.55 & 7.37 & 15.8 \\ _RMSLE_ & 0.07 & 0.09 & 0.19 \\ _R\({}^{2}\)_ & 0.98 & 0.97 & 0.90 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The performance of RFR to predict shear strength.
## 4 Results and Discussion
### Experimental Setup and Evaluation Metrics
#### 4.1.3 Evaluation Metrics  
The performance of the model was assessed using several key evaluation metrics, including total profit gain, average reward, strategy stability (standard deviation of actions),the coefficient of determination (\(R^{2}\)) for the reward-price relationship, the Sharpe ratio, and maximum drawdown.  
\[\text{Total Profit Gain}=\frac{P_{\text{final}}-P_{\text{initial}}}{P_{\text{ initial}}}\times 100\% \tag{27}\]  
\[\text{Average Reward}=\frac{1}{N_{1}}\sum_{t=1}^{N_{1}}r_{t} \tag{28}\]  
\[\text{Std. of Actions}=\sqrt{\frac{1}{N_{1}}\sum_{t=1}^{N_{1}}(a_{t}-\overline{ a})^{2}} \tag{29}\]  
\[R^{2}=1-\frac{\sum_{t=1}^{N_{2}}(r_{t}-\hat{r}_{t})^{2}}{\sum_{t=1}^{N_{2}}(r_ {t}-\overline{r})^{2}}, \tag{30}\]  
\[\text{Sharpe Ratio}=\frac{\overline{r}-r_{f}}{\sigma_{r}} \tag{31}\]  
\[\text{Max Drawdown}=\max_{t\in[1,T]}\left(\frac{\max_{\tau\in[1,t]}P_{\tau}-P _{t}}{\max_{\tau\in[1,t]}P_{\tau}}\right) \tag{32}\]  
where \(P_{\text{final}}\) represents the total profit obtained by the model at the end of training and \(P_{\text{initial}}\) is the total profit at the initial state. The variable \(r_{t}\) denotes the immediate reward at time \(t\), \(N_{1}\) is the total number of training steps, \(a_{t}\) refers to the action taken at time \(t\), and \(\overline{a}\) is the average of all actions taken. In addition, \(\hat{r}_{t}\) is the model's predicted reward, \(\overline{r}\) is the mean of the actual rewards, and \(N_{2}\) is the total number of data points. \(r_{f}\) is the baseline risk-free reward (set to 0 in our experiments for simplicity), and \(\sigma_{r}\) is the standard deviation of the reward sequence. \(P_{t}\) denotes the cumulative profit at time \(t\). The total profit gain measures the percentage increase in the cumulative profit achieved through the optimized pricing policy relative to the initial profit level. The average reward quantifies the mean reward obtained per decision step, reflecting the effectiveness of the learned strategy throughout the training process. Strategy stability, represented by the standard deviation of actions, evaluates the consistency of the pricing decisions, particularly under changing market conditions. The \(R^{2}\) metric assesses the correlation between the predicted pricing strategies and actual revenue, where higher \(R^{2}\) values indicate a stronger alignment between the model's pricing outputs and real-world revenue patterns. The Sharpe ratio measures the average excess reward earned per unit of reward volatility, indicating the risk-adjusted efficiency of the pricing strategy throughout the decision-making process. And the maximum drawdown quantifies the worst-case decline from a historical peak in the cumulative profit, reflecting the extent of the short-term loss risk encountered during strategy execution.
# Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems
## 2 Vehicle Modeling
### Sensors Models
#### 2.3.2 Accelerometers  
An accelerometer is a device that measures acceleration, i.e., the rate of change of velocity of the vehicle in its own instantaneous reference frame. They are modeled as a random noise and bias of 0.001 ms\({}^{-2}\) which resembles real performance of these sensors. Because they provide the acceleration vector expressed in body axes, velocity vector expressed in body triad can be obtained after integration of each of its components along time.  
In addition, the magnitudes obtained from accelerometers will be used in the gravity vector estimation approach. As it is explained in the following sections, the velocity vector module is required to estimate it.