esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Method
### Calculation Steps of the Fractal Dimension  
The original gravity anomaly data are discretized and meshed into matrix grids with the size of 100 \(\times\) 50 by the method of Kriging before processing. The regional variable, variogram and semivariance are analyzed by the Kriging interpolation algorithm, which is the best and most unbiased estimation after the comparison with Nearest Neighbors and Inverse Distance to a Power methods. As such, the contour map of the gravity anomaly and the surface location of the geological body are shown in Figure 2. The vertical profile of a geological body is also presented. Based on the above assumption, the anomaly value \(\Delta g(i,j)\) is the functional value at the point \((i,j)\) created from the difference of density in three-dimensional space. In order to calculate the values of the grids, a sliding window with fixed size of 3 \(\times\) 3 is created, as seen in Figures 1a and 2. Then, the method of surface variance statistics for the processing of those data in the window is applied to obtain the fractal dimensions from the south-north direction, the east-west direction, the southeast-northwest direction, and the northeast-southwest direction, respectively. Two of the adjacent points and the first and third points in the same direction will be calculated in order to acquire their variances and distances, which are all projected into the log-log coordinate system. After the curve fitting operation, assign the target to point \((i,j)\) as the slope of this fitted line. The final fractal dimension D is achieved by Equation (4). In order to eliminate the directional influence of the calculation, the average, maximum and minimum are captured as the target value of the point \((i,j)\). Then, the sliding window will be moved in proper sequence to make sure that all of the matrix grids participate in the calculation. An equal row or column is added when the sliding window locates the boundary of the matrix grid, that is to say, the size of matrix is changed to 102 \(\times\) 52. All of the calculated fractal dimensions are formulated into a matrix, and are shown in the contour maps.  
Figure 2: A sliding window for a 3 \(\times\) 3 matrix of variance statistics for fractal surfaces. (**a**) Southnorth direction; (**b**) eastwest direction; (**c**) southeasternnorthwest direction; (**d**) northeastsouthwest direction. The red arrows represent the direction of movement during calculation.
## 2 RBM Vector Representation
### RBM Vector Extraction  
An RBM model is assigned to each test speaker after the adaptation step. The visible to hidden weight matrices along with their corresponding bias vectors of the adapted RBMs are concatenated in order to generate a higher dimensional speaker vector. These are referred to as RBM supervectors. After this, a PCA whitening with dimensionality reduction is applied to the RBM supervectors in order to generate the lower dimensional RBM vectors. The PCA whitening transforms the original data to the principal component space which de-correlates the data components. The PCA is trained with the RBM supervectors extracted from the background speakers' utterances and is applied to the RBM supervectors of the test speakers. All the RBM supervectors are mean-normalized before subjecting to PCA whitening and dimensionality reduction. The extracted RBM vectors are supposed to convey enough speaker-specific information, which can discriminate different speakers.  
Figure 2 shows a visualization of a pair of RBM vectors (top and bottom) extracted from different utterances of two different speakers randomly selected from the test audios. From the Figure, it is clear that the two RBM vectors extracted for Speaker 1 look similar but are different from those extracted for Speaker 2. Similarly, the two RBM vectors extracted for Speaker 2 look similar but are different from those extracted for Speaker 1. In our previous work [22], it has been shown that the RBM vector extracted in this way is successful in learning speaker-specific information in a speaker verification task. Thus, we make an effort to make use of the RBM vector in the tasks of speaker clustering and speaker tracking.  
Figure 2: _Cont._
## 3 Methodology
### Flowchart for Proposed System  
The real-time shopping drift identification system utilizes the YOLOv5 and DeepSORT algorithms described in this figure, moving forward with its global design and process. The process starts with a high-definition video camera capturing the retail environment  
\begin{table}
\begin{tabular}{c c c} \hline
**N** & **Components** & **Description** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{Surveillance Cameras} & The mission of the component is to take \\  & & high-quality video recordings from critical store \\  & & locations to watch consumer behavior and \\  & & product sales. \\ \hline \multirow{3}{*}{2} & \multirow{3}{*}{Data Processing Unit} & The task of the component is to maintain and \\  & & structure streams of video to provide an \\  & & accessible format for effective object detection. \\ \hline \multirow{3}{*}{3} & YOLOv5 Object Detection & Its goal is to detect and classify objects in video \\  & & modules, and output box boundaries and labels \\  & & for the identified objects. \\ \hline \multirow{3}{*}{4} & \multirow{3}{*}{DeepSORT Tracking Module} & The task of the component is to monitor detected \\  & & objects between frames, give individual IDs, and \\  & & handle actions and obstructions. \\ \hline \multirow{3}{*}{5} & \multirow{3}{*}{Data Analysis and} & The mission of the component is to examine \\  & & monitoring information to detect movements \\  & & and tendencies and offer representations and \\  & & analyses for insight. \\ \hline \multirow{3}{*}{6} & \multirow{3}{*}{Store Management System} & The task of the component is to incorporate \\  & & understanding through data analysis to enhance \\ \cline{1-1}  & & Integration \\ \cline{1-1}  & & store organization, inventory, and \\ \cline{1-1}  & & marketing strategies. \\ \hline \end{tabular}
\end{table}
Table 2: The description of the elements of the system.  
Figure 4: The data flow diagram for this system.  
interpreted by the surveillance cameras. After that, the video is supplied to the Data Processing Unit, which extracts and stabilizes the videotape to prepare for analysis. YOLOv5 is used to detect objects and recognize and categorize customers and products. The systems handle detection deficiency by repeating and correcting errors. A conditional check decides if the detection is successful. If it is, the discovered objects are processed. Subsequently, DeepSORT performs a similar conditional check to ensure the success of overseeing the identified items throughout the sequence. The Data Analysis and Display interface analyzes and displays successful tracking outcomes. The insights are finally integrated into the shop management system to maximize store operations and strategies. To provide robustness to the system's operation by addressing any problems with detection or tracking, the flowchart also includes a conditional path for error management, as shown in Figure 5. Each step in the flowchart is described in Table 3.  
Figure 5: Flowchart of the real-time retail tendency detection system.
## 5 Experimental Validation and Benchmarks
### Applied Solutions in Real-World Networks
#### 5.2.2 AI-Driven Traffic Management in Smart Cities  
Urban traffic management is a complex problem that benefits from AI's ability to process large datasets and make real-time decisions. In this application, an AI-based traffic prediction system was integrated with the city's traffic light control system to optimize vehicle flow during peak hours.  
The system utilized a combination of DL for predicting traffic patterns and RL for dynamically adjusting the traffic light schedules based on real-time traffic conditions. The model was trained on historical traffic data and real-time vehicle counts collected from various sensors deployed throughout the city.  
The AI-driven system resulted in the following:  
* Congestion Reduction: Traffic congestion was reduced by 15%, significantly improving vehicle flow during rush hours.
* Efficiency Improvement: The system enhanced overall traffic flow efficiency by 10%, reducing the average commute time for vehicles by approximately 5 min.
* Energy Savings: The optimization of traffic light cycles also led to a reduction in fuel consumption and emissions, saving the city around 5% in energy usage during peak periods.  
Challenges: The integration of AI into existing infrastructure presented several challenges, including the need to handle real-time data streams, ensure low latency for traffic signal adjustments and integrate the system with legacy traffic management platforms. The solution to these challenges involved implementing a hybrid model that combined AI with traditional rule-based systems for fallback and redundancy, ensuring continuous operation even in case of model failure.
## 4 Conclusions  
From the perspective of spatial spillover, SBM-GML is adopted to measure the level of agricultural green development based on the input and output indicators with the provincial panel data from the years 2000 to 2020 and the spatial spillover effect and heterogeneity analysis of the impact of environmental regulation and financial support for agriculture on agricultural green development are further empirically tested. The research results are summarized as follows:  
First, the level of agricultural green development in China shows a rising trend year by year, which is consistent with previous research ([PERSON] et al., 2022) [9]. Moreover, it shows spatial characteristics of high in the east and low in the west. Second, environmental regulation would inhibit agricultural green development in both local and adjacent regions, which is contrary to the impact of financial support for agriculture. However, the inhibitory effect of environmental regulation is gradually decreased, while the positive effect of financial support for agriculture is constantly increased with time. Third, the dual effect of environmental regulation and financial support for agriculture shows a negative spatial spillover effect on agricultural green development, while the significance coefficient shows a decreasing trend. Fourth, with the continuous improvement of the systemization of agricultural green development, the impact of environmental regulation and financial support for agriculture on agricultural green development is increasingly enhanced. Meanwhile, the spatial spillover effect is the most significant in the east-middle region while insignificant in the east-west region and west-middle region, which is consistent with the research of [PERSON] et al. [46].
## 3 Q2: What Are the Challenges of the Current UIoT System
### Security Challenges of UIoT
#### 3.3.2 Data Privacy  
In the UIoT environment, data privacy is extremely important since it can handle sensitive data in naval applications such as secret operations, identity sharing, enemy submarine tracking, etc. Since the UIoT environment is harsh, it is difficult to apply the privacy methods of terrestrial IoT environments such as k-anonymity, l-diversity, t-closeness and differential privacy to the UIoT environment. Therefore, the attackers can steal private data from UIoT devices.
# Opportunistic and On-Demand Network Coding-Based Solutions for LPWAN Forwarding
## 3 Challenges and Motivation
### Bottle Neck in Opportunistic Forwarding  
Opportunistic forwarding only improves transmission reliability between the sender and intermediate forwarder. However, packet forwarding from the relay to the gateway may fail, creating a long queue in the relay that causes a bottleneck problem, especially if the relay requires to retransmit the packets loss in the next feedback message. Therefore, we employ a network coding approach in the relay that can increase network throughput and reliability via encoding and decoding multiple packets.
# Determination of a Complete Conversion Model for Gasification of Lignite Char
## 3 Experimental
### Data Preparation and Evaluation  
For each measurement, weight and temperature were recorded with a sampling rate of 300 Hz. The char conversion is calculated depending on starting and final mass for every measurement according to Equation (10).  
\[X(m)=\frac{m_{0}-m}{m_{0}-m_{ash}} \tag{10}\]  
The char conversion during the experiment is represented over temperature and time, as well as dX/dt over temperature. To reduce the measurement noise, the signal is smoothed with a first order Savitzky-Golay-Filter [21]. The integration time for the Savitzky-Golay-Filter was obtained based on the method described by [PERSON] et al. [22] through minimizing the Allan variance for each measurement. The standard deviation for the char reaction rate after filtering is in the range of \(3.6\times 10^{-3}\) min\({}^{-1}\) and \(15.6\times 10^{-3}\) min\({}^{-1}\) with the mean at \(8.5\times 10^{-3}\) min\({}^{-1}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Config. No.** & **Sample Mass (mg)** & **Flow Rate CO\({}_{2}\) (mL/min)** \\ \hline
1 & 2.5 & 20 \\
2 & 5 & 20 \\
3 & 10 & 20 \\
4 & 13 & 20 \\
5 & 20 & 20 \\
6 & 45 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experiment configurations for determination of the sample mass influence; N\({}_{2}\) was added to yield a total gas flow of yield a total gas flow of 100 mL/min.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Config. No.** & **Flow Rate CO\({}_{2}\) (mL/min)** & **Flow Rate CO (mL/min)** \\ \hline
1 & 20 & 0 \\
2 & 20 & 24 \\
3 & 25 & 0 \\
4 & 25 & 22.5 \\
5 & 33 & 0 \\
6 & 33 & 20 \\
7 & 50 & 0 \\
8 & 50 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experiment configurations for CO\({}_{2}\) gasification; N\({}_{2}\) was added to yield a total gas flow of 100 mL/min.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Config. No.** & **Flow Rate H\({}_{2}\)O (mL/min)** & **Flow Rate H\({}_{2}\) (mL/min)** \\ \hline
1 & 20 & 0 \\
2 & 20 & 35 \\
3 & 25 & 0 \\
5 & 33 & 0 \\
6 & 33 & 0 \\
7 & 50 & 0 \\
8 & 50 & 35 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experiment configurations for steam gasification; N\({}_{2}\) was added to yield a total gas flow of 100 mL/min.  
The models investigated in this paper were fitted to the measurement with the nonlinear least-squares method and the parameters of the models were calculated by minimizing the objective function OF, Equation (11).  
\[OF=\sum_{i=1}^{n}\left(\left(\frac{dX}{dt}\right)_{exp,i}-\left(\frac{dX}{dt} \right)_{calc,i}\right)^{2} \tag{11}\]
## 6 Discussion  
Continuous advances have been made in the multimedia industry. Revolutionary developments in multimedia content have been bringing new experiences to the audience, and 360\({}^{\circ}\) multimedia has provided a completely new experience to viewers. In this study, an authoring system was proposed to increase the throughput of 360\({}^{\circ}\) multimedia content in terms of providing interesting experiences. The detailed description of the proposed system and experimental results presented in the previous sections proved the potential of a 360\({}^{\circ}\) video in providing multiple interesting experiences to viewers. This study supported that unlike the existing 360\({}^{\circ}\) cinematography approaches in the literature review, the proposed system offered the generation of multiple experiences from a single 360\({}^{\circ}\) video. This aspect of the system provides an answer to RQ1 and RQ2 that multiple experiences can be generated from a single 360\({}^{\circ}\) video. Here, an important point to notice is that the number of experiences generated depends on the content of a video. In response to RQ3, a user survey proved that users found it easy to discover interesting information in a 360\({}^{\circ}\) environment by following the experience of others. In support of RQ4, it was observed that the simple object tracker with some additional modules proved to have good performance in tracking objects in 360\({}^{\circ}\) videos. The tracking of objects turned out to be effective in reducing the effort for authoring 360\({}^{\circ}\) video tours, which provided evidence in favor of RQ5. As supported by the results of the user survey, transferring of the 360\({}^{\circ}\) experiences in VR using the adaptive focus assistance technique ensured minimum loss of information while directing the viewer towards an ROI, which provides sufficient proof of RQ6.
## 4 Conclusions  
In this paper, the main objective was to build up an approach for the reduction of free and total gossypol content in cottonseed flour to safe levels that reduces the toxicity and improves its nutritional benefits for human consumption. In order to this, the effect of different independent parameters viz. acetone levels, time of extraction, liquid-solid ratio, and the number of extraction cycles on free gossypol and total gossypol reduction were analyzed. The calculated values obtained from RSM-based models and the measured values from the experimental investigations followed the same trend. This refers to a good correlation between input values and output responses. Experimental results indicate that the maximum reduction in free gossypol obtained was 99.3%, and total gossypol reduction obtained was 89.2%. The optimized parameters for a better reduction in free gossypol and total gossypol percentage were 86.3% acetone, time of extractions 30 min, liquid-solid ration 10:1, and the number of extraction cycles 4. The value of r-square was found above 0.9, which indicates that the developed models are suitable and reliable for predicting free and total gossypol reduction percentage. The study concluded that two parameters, namely the number of extraction cycles and liquid, solid ratio influenced significant free gossypol reduction and total gossypol reduction; however, time of extraction is found to be least significant. In the developed process, despite the reduction of free and total gossypol in CSKP, the gossypol is recovered, which has much industrial and medical significance. The resulting low gossypol cottonseed flour can be used as a protein-rich food supplement for human beings.  
Conceptualization, M.S. and V.S.; Supervision, M.S.; Resources, M.S.; Methodology, V.M. and M.K.; Investigation, V.S.; writing--original draft preparation, V.S., D.J., and V.M.; Software, M.K., S.C., and M.M.; writing--review and editing, S.C. and M.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors are highly thankful to [PERSON] (Principal Scientist, ICARCIROCIT, Nagpur) for providing necessary infrastructural, laboratory support, and testing facilities. The authors would like to thank the University of Kiel and Schleswig-Holstein for the support through the OA program.  
The authors confirm that there are no conflicts of interest or personal financial interests associated with the outcome of this investigation.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Output** & **Acetone (\%)** & **Time of Extraction(min)** & **LSR** & **No. of Extraction Cycles** & **Experimental Result** & **Predicted Result** & **Error** \\ \hline
**Free gossypol** & 86.38 & 30 & 10 & 4 & 99.3 & 99.79 & 0.49 \\
**reduction (\%)** & 86.38 & 30 & 10 & 4 & 89 & 89.34 & 0.34 \\ \hline \end{tabular}
\end{table}
Table 7: Validation test results of optimized parameters.
## 2 Numerical Simulation
### Material Model
#### 2.2.3 Concrete Damage Plasticity Model  
The protective structure of the hydrogen storage facility and tunnel lining was composed of concrete and steel reinforcements embedded within the concrete. The elastoplastic behavior of the material was evaluated using the Abaqus Concrete Damage Plasticity (CDP) model [34]. The CDP model is defined for the stress-strain relationship as follows:  
\[\sigma_{c}=(1-d_{c})D_{0}^{pl}:(\varepsilon-\varepsilon_{c}^{pl}) \tag{8}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Geological** & **Depth** & **Unit** & **Cohesion** & **Internal** & **Dynamic** & **Dynamic** \\
**Stratum** & **(m)** & **Weight** & **(kN/m3)** & **(kPa)** & **Angle** & **Moolus** & **Poisson’s** \\  & & & & **(o)** & **(MPa)** & **Ratio** \\ \hline Silty sand & 2.0 & 18.5 & 5 & 27 & 205 & 0.395 \\ \hline Gravel sand & 2.5 & 18 & 3 & 28 & 275 & 0.417 \\ \hline Silty sand & 1.5 & 19 & 24 & 30 & 788 & 0.407 \\ \hline Soft rock & 3.5 & 23 & 400 & 34 & 4600 & 0.357 \\ \hline Hard rock & 15.5 & 26 & 2500 & 42 & 12,918 & 0.242 \\ \hline Soft rock & 3.1 & 22 & 350 & 31 & 4600 & 0.357 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties used in the numerical simulation model.  
Figure 7: The 3D numerical model simulated by ABAQUS (a quarter of the field).  
\[\sigma_{t}=(1-d_{t})D_{0}^{el}:(\varepsilon-\varepsilon_{t}^{pl}) \tag{9}\]  
where tension and compression are denoted by subscripts \(t\) and \(c\), respectively, and where \(\sigma_{t}\) represents the tensile stress vector and \(\sigma_{c}\) represents the compressive stress vector. Plastic strain for tension and compression is expressed as \(\varepsilon_{t}^{pl}\) and \(\varepsilon_{c}^{pl}\), respectively, and \(d_{t}\) and \(d_{c}\) are variables representing damage in tension and compression as a function of plastic strain, respectively. The damage variable is zero when there is no damage. \(D_{0}^{el}\) refers to the initial elastic modulus before the material is damaged. Tables 3 and 4 show the input parameters related to the CDP model, and this study used the stress-strain relationship and damage-strain behavior for the compression of the M30-grade concrete material. The data were obtained from Ref. [35].