esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 2 Literature Overview in VBDA
### Technologies and AI Enhancements in VBDA
#### 2.3.1 Enabling Technologies in VBDA  
Real-time streaming frameworks such as Apache Kafka and Apache Flink play a central role in video data ingestion and analysis. Kafka enables scalable and fault-tolerant pipelines, while Flink supports low-latency, stateful processing with fine-grained event handling [40]. Nevertheless, large-scale VBDA environments continue to face unresolved challenges in adaptive resource allocation and seamless pipeline integration.  
Distributed data processing platforms continue to serve as key infrastructure for handling massive video workloads. Hadoop offers cost-effective long-term storage for archived video but lacks native support for streaming [41]. Spark improves processing speed through in-memory execution, yet is less suited for real-time video stream analytics [42]. Apache Flink, while designed for streaming, still faces deployment limitations in edge-centric and latency-sensitive contexts [11].  
Video data storage systems have also evolved to manage the volume and complexity of multimedia streams. Hadoop HDFS supports durable, large-scale storage but suffers from high query latency [6]. Cloud-based solutions like Amazon S3 provide elastic storage capacity but incur latency and bandwidth costs for real-time access [5]. NoSQL and graph-based databases offer flexible schemas for representing semantic relationships among video elements such as scenes and objects, albeit with trade-offs in consistency guarantees and query performance [43,44].  
To address these limitations, hybrid storage models that combine SQL, NoSQL, and data lakes are under active investigation [45]. AI-driven storage mechanisms, including self-supervised video indexing and adaptive caching, are actively investigated to enhance storage efficiency and minimize retrieval latency [46].
## 4 Proposed Solution Procedure for Harmonic and Interharmonic Detection  
The proposed analysis procedure includes three steps: detection by SSWT, clustering, and estimation. The detection step is designed to implement TFA to harmonic and interharmonic contaminated waveforms, where the parameters for TFA are predetermined. The clustering step is mainly used to identify the exact frequencies of harmonics and interharmonics in the TF domain. Then, the estimation step is to detect the magnitudes of harmonics and interharmonics by implementing inverse SSWT for each of the corresponding frequencies obtained in the clustering step.
# Geometric Characterization of Vines from 3D Point Clouds Obtained with Laser Scanner Systems
## 2 Materials and Methods  
### Proposed Procedure  
Figure 1 shows the workflow of the proposed methodology. Due to the complexity of the shape of the vines and the non-destructive condition of this study, this study starts with an accurate volume calculation of a vine-shaped artificial object (VSAO) to obtain the real volume data of an object of similar geometry to validate the proposed methodology. The VSAO is composed of two PVC pipes (5 cm in diameter) arranged in a \"T\" shape, resembling the trunk and the arms of the vineyards driven on a trellis (Figure 2). The choice of the diameter of the pipes used was made according to the average diameter observed in the vines of the vineyard. Likewise, the dimensions and shape of the artificial object were generated by simulating the geometries of the vines in this vineyard. To calculate the volume of the VSAO, the diameter and length of both cylinders were measured.  
Field data were acquired and processed for a test area with two laser scanning systems: static terrestrial laser scanner (TLS) and mobile mapping system (MMS). Each piece of equipment produced a colored 3D point cloud with accurate geolocation and high density. These point clouds were the input data used to calculate the volume of the VSAO with specifically developed algorithms. These algorithms, which will be fully described in this manuscript, return the volume of the VSAOfrom the point clouds obtained with the different systems. After comparing the calculated volume with the actual volume of the VSAO, a volume accuracy can be determined for each system. This process is called the calibration process. Once calibrated, the methodology and the best algorithm will be applied to a real case study on a vineyard located in the southeast of Spain.
## 4 Results of the Optimizations
### Complete Distributed Generation Solution
The electricity used by the HPs and the cooling energy produced by the ABSs are higher by about 67% and 8%, respectively, when the DCN is adopted. At the same time, the sold electricity and the electrical consumption of the CC are lower by about 17% and 68%, respectively. Nevertheless, the additional operation options allowed by the adoption of the DCN do not lead us to a \begin{table}
\begin{tabular}{l c c c c} \hline  & \
\begin{tabular}{c} **Environmental** \\ **Optimization** \\ \end{tabular} & **70\% Env. Opt.** & **30\% Env. Opt.** &
\begin{tabular}{c} **Economic** \\ **Optimization** \\ \end{tabular} \\ \hline CHP natural gas cost (\(\text{k}\xi\)/y) & 202 & 757 & 1026 & 1242 \\ BOI natural gas cost (\(\text{k}\xi\)/y) & 0 & 7 & 4 & 33 \\ Bugbit electricity cost (\(\text{k}\xi\)/y) & 1474 & 486 & 218 & 38 \\ Sold electricity income (\(\text{k}\xi\)/y) & 126 & 153 & 266 & 340 \\ Photovoltaic incentive (\(\text{k}\xi\)/y) & 75 & 53 & 56 & 66 \\ Operating cost (\(\text{k}\xi\)/y) & 1475 & 1045 & 926 & 908 \\ Maintenance cost (\(\text{k}\xi\)/y) & 10 & 60 & 88 & 107 \\ Total investment cost (\(\text{k}\xi\)/y) & 24,806 & 8114 & 6909 & 5219 \\ Annual investment cost (\(\text{k}\xi\)/y) & 1611 & 680 & 592 & 466 \\
**Total annual cost (\(\text{k}\xi\)/y)** & **3095** & **1785** & **1606** & **1481** \\
**Reduction wrt conv. solution** & \(\mathbf{-18.05\%}\) & \(\mathbf{31.93\%}\) & \(\mathbf{38.75\%}\) & \(\mathbf{43.50\%}\) \\ Electricity emissions (t/y) & 3087 & 1018 & 457 & 80 \\ Sold electricity emissions (t/y) & 197 & 453 & 864 & 1157 \\ Natural gas emissions (t/y) & 403 & 3262 & 4769 & 5974 \\
**Total annual emissions (t/y)** & **3292** & **3827** & **4362** & **4897** \\
**Reduction wrt conv. solution** & \(\mathbf{49.33\%}\) & \(\mathbf{41.10\%}\) & \(\mathbf{32.87\%}\) & \(\mathbf{24.63\%}\) \\ \hline \end{tabular}
\end{table} 
Table 16: Total economic and environmental results of the optimizations—complete distributed generation solution. Figure 15: Pareto front of the optimal complete distributed generation solutions. strong improvement of the environmental, or of the economic performance of the system, but only small improvements by about 1% and 3%, respectively, are obtained. The evaluation of Pareto fronts of the multi-objective optimizations performed allows the best compromise between the minimum annual cost and the minimum annual emissions to be identified for each different heat/cooling integration options. Table 18 summarizes these compromise solutions and compares them with the economic optimum obtained for the conventional and isolated solutions.
# A Dual-Channel End-to-End Speech Enhancement Method Using Complex Operations in the Time Domain
## 3 Experimental Settings
### CFCN Configurations  
The CFCN model is responsible for processing single-channel features after beamforming and estimating the final clean speech. The CFCN configuration is presented in Table 2. The model consists of eight 1D dilated Convs forming a block, and this process is repeated three times; the detailed structure of 1D Conv is provided in Figure 6. Dilated convolution is known for modeling long-term temporal dependencies through large receptive fields. The use of complex operations can convert all Convs into complex ones, but this process does not substantially improve noise reduction performance and may increase computational complexity. A suitable number of complex Convs for balancing performance and complexity is, therefore, crucial.  
In Table 3, we provide SI-SDR results for different numbers of complex Convs in the CFCN, demonstrating that increasing the use of complex Convs cannot always enhance performance. We modified the last 1D dilated Conv of each repetition into a complex one, marked by the gray rectangle in Figure 3, and this configuration was adopted for the CFCN.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer Name** & **Input Size** & **Hyperparameters** \\ \hline complex input & (B, 2, 16,000), (B, 2, 16,000) & - \\ \hline complex shared-LSTM & (B, 2, 100, 160) & (160, 512) \\ \hline complex split-LSTM & (B, 2, 512) & (512, 256) \\ \hline complex split-LSTM & (B, 2, 512) & (512, 256) \\ \hline complex linear & (B, 2, 256) & (256, 25) \\ \hline \hline \end{tabular}
\end{table}
Table 1: CNAB model, where B denotes the batch size and the format of hyperparameters is the size of the input and output channels of the LSTM.
## 5 Experimental Results
### Cnn
#### 5.2.1 Network Layer Selection  
First, this study used a \(1\times 1\) convolution kernel, activation function ReLU, and max pooling to evaluate the performance of different numbers of CNN layers. The snowflake diagrams drawn using SDP were identified from the network models with three layers (one convolution layer, one pooling layer, and one fully connected layer), five layers (two convolution layers, two pooling layers, and one fully connected layer), and to 17 layers (eight convolution layers, eight pooling layers, and one fully connected layer). The test results are shown in Figure 18. The results indicate that the training time increased with the number of network layers. The capability of CNNs for training data fitting was enhanced while the probability of over-fitting increased. Figure 18 indicates that the recognition performance was the best when there were five network layers. When the number of network layers was larger than five, the recognition rate decreased as the number of layers increased, and more training was required. Therefore, this study selected five CNN layers for the experiment.  
Figure 17: CNN architecture of this study.  
Figure 18: Performance test results of CNN models with different numbers of network layers.
# Shifting of Meteorological to Hydrological Drought Risk at Regional Scale
## 5 Conclusions  
This study concludes that the frequency of both hydrological and meteorological droughts increased in the Soan River Basin during the study period. At various time scales, Sub-basin 1 was subjected to more frequent meteorological moderate drought and hydrological drought events. The current study was also designed to investigate the relationship between meteorological and hydrological drought events and SPI and SDI by developing a simple linear function between them. The results of a linear regression between SPI and SDI show an increase in regression coefficients with increasing time scale and became stronger until the ninth month. Climate change and anthropogenic activities (i.e., land use/land cover changes) are the main reasons that cause the variations between these two types of droughts. Moreover, the hydrological drought events commonly lagged 1-3 months (subject to the time scale and sub-basin) from the meteorological drought events. The dissimilarities between these two types of droughts became larger due to  
Figure 8: Temporal Variation of (**a**) Precipitation, (**b**) Runoff depth, and (**c**) Runoff Coefficient at sub-basin 1 and 2.  
climatic variation and might be due to human activities as well. Conclusively, this study provided drought propagation and the basis for long-term drought forecasting and, thus, can be employed for early warning water resources management and as an extension of this current study can be to assess the climate change impacts on hydrological drought at the basin scale.  
Conceptualization, A.N.S. and M.W.; methodology, M.A. and A.A.; software, M.A.; validation, M.W., J.E.L. and M.A.; formal analysis, I.A. and A.A.; investigation, F.u.H.; data curation, A.N.S. and M.W.; writing--original draft preparation, J.E.L.; funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2020S1A5B8103910).  
Not applicable.  
All data is provided in the form of tables and figures.  
The Authors appreciate the NRPU-HEC projects.  
The authors declare no conflict of interest.
# Deep Learning for Predicting Traffic in V2X Networks
## 6 Simulation Results
\begin{table}
\begin{tabular}{c c c c} \hline
**Packets/s** & **MAPE** & **RMSE** & **Processing Time (s)** \\ \hline
4 & 9.86 & 0.5427 & 78 \\ \hline
6 & 10.77 & 0.6321 & 70 \\ \hline
8 & 15.13 & 0.8175 & 71.3 \\ \hline
10 & 20.76 & 1.1675 & 62.6 \\ \hline
12 & 22.13 & 1.2622 & 56.8 \\ \hline
14 & 28.33 & 1.4911 & 117.5 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of prediction accuracy for V2X traffic using RMSE and MAPE. Figure 4: The predicted output at a transmission rate of 6 packets/s. Figure 3: The predicted output at a transmission rate of 4 packets/s. Figure 8 shows that throughput increases over time for both the actual and predicted models at a transmission rate of 14 packets per second. The predicted model increases at time 1 s, decreases until time 4 s, and then increases until time 16 s. While the lowest prediction loss occurs at time 1 s, it is highest at time 2 s to 4 s.  
The trend is for the throughput rate to decrease over time where there is an obvious downward trend in some figures, as in Figures 3-5, and some figures that start with a downward trend and then turn into an upward trend, as in Figures 6-8. If we had a much longer series, we could see that these downward and upward trends are part of a long cycle. These generated trends can go down or up depending on the observation data (DL training dataset) generated by the V2X network. If more packets are lost during transmission (one or more of these packets fails to reach their destination) due to network operation  
Figure 5: The predicted output at a transmission rate of 8 packets/s. Figure 6: The predicted output at a transmission rate of 10 packets/s. issues, such as network congestion or security threats, throughput will decrease over time. However, if there is little or no packet loss, throughput will increase. Thus, based on the observed data, the trend for the predictive model is either up or down over time.
# Enhanced Dual Reversible Data Hiding Using Combined Approaches
## 1 Introduction
To address these issues, we introduce a RDH framework that compresses the HC syndrome representation using AC and subsequently embeds the compressed data through an enhanced EMD method. This combined approach ensures high embedding capacity, efficient data compression, and the reliable recovery of the original cover image. The key contributions of this work are as follows: First, we present a novel reversible data hiding technique that seamlessly integrates Hamming coding, AC, and an enhanced EMD method to address the limitations of existing RDH approaches. Second, we achieve a significant improvement in DH capacity, reaching an embedding rate of 1.5 bpp, while maintaining exceptional image quality, with an average PSNR of 66 dB. Third, leveraging the unique strengths of HC and AC, our method ensures efficient data compression and accurate recovery, making it a promising solution for secure and high-capacity data hiding applications. Fourth, this work demonstrates the practical potential of combining multiple techniques to overcome the individual limitations of existing methods, paving the way for further advancements in RDH technologies. This study focuses on a data hiding technique for bitmap (pixel-based) images, aiming to achieve a high embedding rate while preserving image quality. The proposed method utilizes pixel value-based modification techniques, making it difficult to apply directly to geometrically represented data such as vector graphics (e.g., SVG, AI). Furthermore, the proposed approach is designed for two-dimensional (2D) image data and is not directly applicable to one-dimensional (1D) data, such as audio or sensor data. However, with certain modifications, it may be possible to extend the method to audio data by applying a sample block-based approach or to sensor data by utilizing specific frequency bands. These potential extensions will be considered in future research. The rest of this paper is organized as follows: Section 2 provides a review of Hamming code as a single-error correcting code, AC, and EMD method. Section 3 introduces the proposed dual RDH method, which utilizes Hamming code, AC, and an enhanced EMD method with dual cover images. Section 4 presents the experimental results, and Section 5 concludes the paper with a summary and discussion.
# New Product Short-Term Demands Forecasting with Boxplot-Based Fractional Grey Prediction Model
## 2 Literature Review
### Grey System Model  
[PERSON] first proposed the grey model (GM) [4] in 1982. It aims to handle the issues concerned with uncertain and insufficient information in short-term time-series data. There is still room for improving the prediction accuracy by means of enhancing the basic theories in GM and setting the coefficients. Finding the suitable background values which determine the developing coefficient \(\alpha\) and the grey input \(b\) is another efficient way, where \(\alpha\) is the growth factor and \(b\) is a grey control parameter, the accuracy of the prediction models is rely on these two coefficients.  
In GM (1,1), the newer data changes on the prediction model below the old data, which goes against the concept of the new data priority principle, thus limiting the scope of applying grey predictions. The theory of combining the cumulative generator of the fractional order with the grey prediction model, also named the grey system model, with the fractional order model (FGM), based on the new information priority principle [5], flattens the increase sequence by selecting the appropriate cumulative order, thereby increasing the accuracy of the prediction [7,8]. In the traditional FGM (1,1) model, the background value is formulated as:  
\[z^{(r)}(\kappa)=\frac{1}{2}\Big{[}x^{(r)}(k)+x^{(r)}(k-1)\Big{]} \tag{1}\]  
where \(0<r\leq 1\). The \(r\)-order of the accumulating grey model has following definition and the whitenzian differential Equations (2) and (3)  
\[x^{(r)}(\kappa)-x^{(r)}(\kappa-1)+az^{(r)}(\kappa)=b \tag{2}\]  
\[\frac{dx^{(r)}(t)}{dt}+ax^{(r)}(t)=b \tag{3}\]  
where \(\alpha\) is said to be a developing coefficient and \(b\) the grey input in grey system theory terms; \(x^{(r)}(\kappa)\) is a grey derivative, which maximizes the information density for a given series to be modelled; \(ax^{(r)}(k)\) and \(z^{(r)}(k)\) are the accumulated and the background value in the \(k\)th term, respectively; \(k(=2,3,\ldots,n-1)\) represents the number of terms; and \(\alpha\in(0,1]\) is the weight used to determine where \(z^{(r)}(k)\) would be located between \(x^{(r)}(k-1)\) and \(x^{(r)}(k)\). As the parameters \(\hat{A}=\left[a,b\right]^{T}\) of the fractional order grey model directly influence the utility of the model, the estimation of the parameters act as an important role in improving the simulating performance of the model Equation (4).  
\[x_{p}^{(r)}(\kappa)=\left[x^{(r)}(1)-\frac{b}{a}\right]\,e^{-a(k-1)}(1-e^{a}), \,\,\,k(=2,3,\ldots,n) \tag{4}\]  
then provides prediction values as the output of grey system [6,8].  
Figure 1: Illustration of a box plot.
## 2 Conceptual Model and Research Hypotheses
### Social Influence and Perceived Ease of Use of EESS  
Considering the higher level of complexity of EESS technologies in terms of functionality and use, the first direction in approaching the conceptual model is the impact of social influence on users' perceptions of the ease of use of EESS. This correlation was analyzed in other research to analyze the factors that influence the adoption of assimilated EESS technologies: smart meters [43], electrical vehicles [20], or smart connected objects [13]. Additionally, the correlation between perceived ease of use (PEOU) and behavioral intention to use (BIOU) is included in the initial TAM model [14], in UTAUT links [15], and in UTAUT2 [16], through construct effort expectancy. Therefore, we make the following hypotheses:  
**Hypothesis 1 (H1).**_Social influence (SI)--specifically (a), the adoption of EESS at the level of the network of friends, colleagues, or family (SI_FF), and (b), the promotion of the use of EESS in the public space (SI_PS)--will have positive effects on their perceived ease of use (PEOU)._  
**Hypothesis 2 (H2).**_PEOU will mediate the relationship between (a) SI_FF and (b) SI_PS, and the behavioral intention to use EESS (BIOU)._
# Analytical Model and Feedback Predictor Optimization for Combined Early-HARQ and HARQ
## 3 Experimental Analysis
### Analysis of Model's Tendencies  
We have conducted an extensive comparative analysis of the given analytical model for various SNR values, transmission lengths \(N\), subcode lengths with time shift \(d\) and threshold values \(\theta\). For the experiments in this subsection, we have fixed SNR = 7.0 dB. The maximum transmission time in all the experiments \(T_{N}=\) 84 microslots, or 6 ms, so each of the transmission length scenarios may achieve its own maximum number of transmissions \(T_{c}=\lfloor T_{N}/N\rfloor\). Figures 7 and 8 demonstrate probability of a system being idle and mean spending time for family of curves with various transmission lengths. The legend is consisted of two parameters: \(N\)(indicated by color), and \(d\) (indicated by marker), respectively, which is defined for a specific curve at the plot. The probability of a system being idle is one of the main characteristics that shows how fast the packet can be transmitted and the system goes back in idle state until the next packet arrives. The higher is the probability, the more efficiently the system copes with the packet transmission. In Figure 7 we can see that the system with the smallest transmission length and fastest feedback exchange \(d=0\) shows the best behavior with a slight degradation when increasing \(d\). In general, the given tendency can be visualized for all transmission lengths, which converges to the scenario without e-HARQ prediction (\(\Theta=9\)). In Figure 8 we can see the mean time spending of packet measured in microslots. Indeed, smaller transmission lengths encompasses a higher number of possible transmissions in the given time interval, which results in a faster successful delivery process given the quality of the channel. It is interesting to mention that at small SNRs, the systems with transmission length \(N\) and \(d=0\) outperforms the ones with \(N-1\) and maximum \(d\). This tendency fades away with the improvement of network conditions, e.g., at SNR = 10 dB the curves show the similar behavior, however do not intersect.  
Figure 6: Analytical and imitation models comparison in terms of mean spending time.  
Figure 9 gives us a clear view on prediction errors, such as false negative and false positive ones for \(d=0\). Larger transmission lengths obviously come along with higher FN probabilities in comparison with small \(N\)-systems at low \(\theta\) values, whereas the false positive probabilities have an opposite behavior with smaller lengths deliveries, characterized by the highest prediction error at bigger \(\theta\). The given performance measures show us the tendencies that we exploit further when finding optimized parameters for e-HARQ prediction.  
Figure 8: Mean spending time for families of curves with two parameters: \(N\) (indicated by color), and \(d\) (indicated by marker).  
Figure 7: Probability of a system being idle for families of curves with two parameters: \(N\) (indicated by color), and \(d\) (indicated by marker).