esa-sceva/satcom-chunk-collection huggingface dataset:
# Pure Electric Sweeper Performance Analysis and Test Verification of Dust Extraction Port
## 3 Numerical Simulation Results and Analysis
### Simulation and Experimental Comparison Analysis of Flow Field Characteristics  
Figure 16 gives a comparison between the simulated and experimental outlet wind pressure results under different negative pressures. As can be seen from the figure, the trend of the simulated results is basically the same as that of the experimental results, with the wind pressure in the central area of the outlet being greater than that in the peripheral area. There is an error between the data obtained from the simulation and the data obtained from the test, and the magnitude of the error can be calculated according to Equation (12); the maximum error is 9.5%, which is within the allowable error range of 10% mentioned in the literature [9].  
Figure 17 gives the simulation results of the inlet air speed at different negative pressures compared with the test. As can be seen from the figure, the air speed at the centre of the inlet is significantly greater than that at the edge of the area, and the trend of the simulation results is basically the same as the test results. Here the maximum error calculated by Equation (12) is 9.2%, which is also within the 10% error tolerance. Combined with Figure 13, it can be seen that the space inside the dust suction port is nearly closed, and some areas even form airflow vortices, which indicates that the airflow changes very rapidly. The main reason for this error is that the locations of the measurement points in the simulation do not exactly coincide with the locations of the measurement points in the test, thus causing deviations. The reason why the air velocity and pressure at the centre of the inlet and outlet are higher than at the edges is that the presence of the inner baffle causes a swirl of airflow at the edge of the exhaust pipe, with less pressure at the centre of the swirl. At the same time, due to the structure of the suction inlet, the incoming airflow converges to the centre of the inlet, so the airflow velocity at the centre is higher than at the edges. In addition, the presence of swirls in the airflow has a different effect on the airflow at the left and right ends of the suction port, resulting in an asymmetrical velocity distribution.  
Figure 16: Comparison between simulation and test of outlet air pressure at different negative pressures: (**a**) comparison of outlet air pressure at 2400 Pa; (**b**) comparison of outlet air pressure at 2800 Pa; (**c**) comparison of outlet air pressure at 3200 Pa.
# Application of Graph Theory for Blockchain Technologies
## 5 Graph Theory Application in Blockchain Systems
### Simulating Blockchain Networks
#### 5.4.3 Protocol Testing  
Analyzing consensus propagation across node communication graphs under various conditions vets the performance of experimental protocols before finalization [89]. Figure 21 represents a blockchain peer network graph, which serves as a basis for protocol testing of consensus algorithms where nodes represent peers in the blockchain network. Edges depict the communication links between these peers.  
**Assumption 20**.:
* _The blockchain peer network is modeled as a communication graph_ \(G(V,E)\)_._
* _Consensus requires agreement propagation among peers._  
**Application Statement 20**.:
* _Simulating consensus algorithms on graph models of blockchain peer networks under various conditions facilitates the performance evaluation of proposed protocols before finalization._  
Proof.:
1. Model peer network as graph \(G(V,E)\), capturing node connections.
2. Implement consensus protocol logic for state transitions of \(G\)'s nodes and edges.
3. Test consensus algorithm on \(G\) under different conditions of failures, delays and partitions.
4. Compare simulation metrics like agreement times, message complexity and partitions tolerated to analyze protocol progress and limitations.  
Figure 21: Protocol testing.  
This allows comprehensive benchmarking of any consensus mechanism under realistic scenarios. Graph-based blockchain network simulations enable the systematic testing of consensus protocols for standardization and live deployment.
## 5 Empirical Results
### Type of Multifractality  
Another contribution of this study is to identify the type of multifractality presented in the ship traffic flow data. We performed the same analysis on the randomly shuffled series of the original ship traffic flow sequences. The randomly shuffled sequences were obtained by shuffling the original ship flow sequences. The shuffled sequences remained with the same fluctuation distributions, though it destroyed any temporal correlations in the original data.  
The process of shuffling can be depicted as the three steps presented by [39]. Firstly, \((p,q)\) pairs are generated from random integer numbers with \(p,q\leq N\), where \(N\) is the length of the original time series. Secondly, \(p\) and \(q\) entries are swapped with each other. Finally, the above two steps are repeated \(N=20\) times to ensure that the original series is fully shuffled.  
We shuffled the ship flow sequences and calculated \(H_{\text{shuffle}}(q)\). As seen in Figure 8, the \(H_{\text{shuffle}}(q)\) is approximately 0.5 for most of the ship flow sequences except for the weekly ship flow sequences of Singapore. The shuffled ship flow sequences with \(H_{\text{shuffle}}(q)\) of about 0.5, indicating the multifractality of these ship flow sequences, are caused by different fluctuations in correlations of small and large scales. However, the multifractality of the weekly ship flow sequences of Singapore port is caused by a broadening of the probability density function. This result is consistent with the results shown in Figure 3. Therefore, the multifractality presented in the ship flow sequences of container ports are due to the correlation properties as well as the probability density function of the ship flow series.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Ship Flow Sequences**} & \multicolumn{3}{c}{**Time Scale**} \\ \cline{2-4}  & **Daily** & **Weekly** & **Monthly** \\ \hline Shanghai & 0.296 & 0.999 & 0.459 \\ Singapore & 0.342 & 2.004 & 0.984 \\ Rotterdam & 0.182 & 1.643 & 0.383 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The strength of multifractality for ship flow sequences.  
Figure 8: _Cont_.
## 3 Method
### Point Sampling from the UAV Imaging Model  
Given the focal length \(f\), the coordinate of the camera center in the global coordinate system \((X_{s},Y_{S},Z_{S})\), the coordinate of the ground point in the global coordinate system \((X,Y,Z)\), and the corresponding image point \((x,y)\), with the rotation matrix being written as \([[a_{1},a_{2},a_{3}]^{T},[b_{1},b_{2},a_{3}]^{T},[c_{1},c_{2},c_{3}]^{T}]\), the relationship of \((X,Y,Z)\) and \((x,y)\) can be built according to the colinear condition equation \(F_{j}\), which is formulated as follows:  
Figure 2: **Main workflow of the multitask UAV-NeRF. We used traditional photogrammetry methods to sample the NeRF point, and we then used a geometric imaging model to perform radiation correction and decoding. “MLP” represents the multilayer perceptrons that were used to decode the different radiation information.**  
\[\begin{split} X-X_{S}=(Z-Z_{S})\frac{a_{1}x+a_{2}y-a_{3}f}{c_{1}x+c_{2} y-c_{3}f},\\ Y-Y_{S}=(Z-Z_{S})\frac{b_{1}x+b_{2}y-b_{3}f}{c_{1}x+c_{2}y-c_{3}f}. \end{split} \tag{5}\]  
As described in NeRF [7], the rays originate from the optical center of the camera, and then the points are sampled in the range of the near and far bounds. However, the camera optical center is far away from the ground points for UAV images, which makes the point sampling and volume rendering of the whole ray computationally redundant. To solve this issue, we estimated the positions of the start and end sampling points in each ray by manually setting the minimum height \(h_{\text{min}}\) and maximum height \(h_{\text{max}}\) for each scene in the global coordinate system. Specifically, the start point \(\mathbf{x}_{\text{start}}\) and the end point \(\mathbf{x}_{\text{end}}\) for each ray were estimated according to Equation (6), which is as follows:  
\[\mathbf{X}_{\text{start}}=F_{j}(\mathbf{p},h_{\text{max}})_{\text{UTM}}\quad \mathbf{X}_{\text{end}}=F_{j}(\mathbf{p},h_{\text{min}})_{\text{UTM}}, \tag{6}\]  
where the subscript \"UTM\" indicates that the 3D points returned by \(F_{i}\) were converted into the UTM coordinate system, thus enabling it to work within a Cartesian reference system and ensuring geographical operation.  
Then, the initial sampling point \(\mathbf{o}\) and the direction vector \(\mathbf{d}\) for the ray \(\mathbf{r}(t)=\mathbf{o}+t\mathbf{d}\) intersecting with pixel \(\mathbf{p}\) in the image can be written as follows:  
\[\mathbf{o}=\mathbf{x}_{\text{start}};\quad\mathbf{d}=\frac{\mathbf{x}_{\text {end}}-\mathbf{x}_{\text{start}}}{\left\|\mathbf{x}_{\text{end}}-\mathbf{x}_ {\text{start}}\right\|_{2}}. \tag{7}\]
## 4 Concept
### Data Processing  
The \"Data processing\" phase includes the actual work with the data. The phase begins with the gathering of raw data from the data sources via the developed interfaces in the \"Data acquisition\" task. The delivered raw data are checked and prepared in \"Data preprocessing\" with respect to their data quality before they are analyzed in the task \"Contextual data analysis\" with respect to the physical questions in context. Finally, in \"Use of the ML application\" the results of the ML applications are fed back into the CPPS and fulfill the required task in the CPPS. The tasks described below serve to separate the physical effects of the CPPS from disturbances in the data flow.
# State of the Art in LP-WAN Solutions for Industrial IoT Services
## 5 Discussion (All that Glitters Is Not Gold)
Regarding security, for mission-critical or high-security applications, the use of private data storage or servers would be more convenient than using third-party (e.g., LoRaWAN, Sigfox, _etc._) servers. In the last case, the data owner could lose control of the information management process; this could be risky or even unacceptable in certain applications. Additionally, as the ISM bands are freely accessible, they are vulnerable to a broad range of security threats; therefore, including extra functionality to support the functions of confidentiality, authentication, authorization, or even accounting would be very welcome. Of course, all these new features would be against the edge-device's power consumption, so a balance between the edge-nodes; functionality and energy use would be necessary. Besides these important issues more focused on the network's technical insights, the business model emerges as another key challenge for taking advantage against the competitors. Having the best technological solution does not always lead to success. For example, we have seen that ultra-narrow-band technology presents a series of drawbacks in comparison with other modulation schemes that offer better connectivity. However, Sigfox seems to be very attractive to potential customers due to its simplicity and its higher degree of deployment. It is on this last point where LP-WAN companies have to make the biggest economical effort and some of them have focusedon different specific regions. While Sigfox seems to be more focused, for the moment, in Europe, with several countries fully covered, LoRa-WAN and Ingenu are focused on the North American market. Regarding territory coverage plans, they are commonly designed regarding the territory's population; thus, the major urban areas are usually mostly covered but there is often a lack of connectivity in rural sites. Precisely, many big factories and farms are isolated in these emplacements, so quasi-dedicated base stations will be needed to provide services to these customers. To sum up, we are currently in a highly dynamic scenario, with all the different platforms positioning themselves in the market. The diverse technological and business solutions offered by each of them will determine their success or failure, but there is no doubt that the LP-WAN is a rising technology that will play an important role in the forthcoming expansion of IIoT services.
# Helping CNAs Generate CVSS Scores Faster and More Confidently Using XAI
## Appendix A Common Vulnerability Scoring System
* If Scope is Unchanged, then Base Score \(=\) \(\lceil\min(\text{Impact}+\text{Exploitability},10)\rceil\). * If Scope is Changed, then Base Score \(=\)\(\lceil 1.08\times(\text{Impact}+\text{Exploitability}),10\rceil\). * Impact: The Impact subscore (ISC) is determined as follows:
* If Scope is Unchanged, then ISC \(=\) 6.42 \(\times\) ISC Base. * If Scope is Changed, then ISC \(=\) 7.52 \(\times\)\((\text{ISC Base}-0.029)-3.25\times(\text{ISC Base}\times 0.9731-0.02)\)13. Where ISC Base can be calculated as follows: ISC Base \(=\)\(1-[(1-\text{Impact Conf})\times(1-\text{Impact Integ})\times(1-\text{Impact Avail})]\). * Exploitability: Exploitability \(=\) 8.22 \(\times\) AttackVector \(\times\) AttackComplexity \(\times\) PrivilegesReq \(\times\) UserInteraction. * Temporal: The Temporal Score is calculated using the following formula: Temporal Score \(=\) Base Score \(\times\) ExploitCodeMaturity \(\times\) RemediationLevel \(\times\) ReportConfidence. * Environmental: The Environmental Score is determined based on the Modified Impact Sub score:
* If Modified Impact Sub score \(\leq\) 0, then Environmental Score \(=\)
* If Modified Scope is Unchanged, then Environmental Score \(=\) \(\lceil\min(\text{M.Impact}+\text{M.Exploitability},10)\rceil\times\text{ ExploitCodeMaturity}\times\) RemediationLevel \(\times\) ReportConfidence. * If Modified Scope is Changed, then Environmental Score \(=\) \(\lceil 1.08\times(\text{M.Impact}+\text{M.Exploitability}),10\rceil\times\text{ ExploitCodeMaturity}\times\) RemediationLevel \(\times\) ReportConfidence. * Modified Impact: The Modified Impact Subscore (ISC Modified) is calculated as follows:
* If Modified Scope is Unchanged, then ISC Modified \(=\) 6.42 \(\times\) ISC Modified. * If Modified Scope is Changed, then ISC Modified \(=\) 7.52 \(\times\)\((\text{ISC Modified}-0.029)-3.25\times(\text{ISC Modified}\times 0.9731-0.02)\)13. with \[\text{ISC Modified}=\min[1-(1-\text{M.Impact}\times\text{CR})]\] \[\times(1-\text{M.Integ}\times\text{IR})\times(1-\text{M.Avail}\times\text{ AR}),0.915. * Modified Exploitability: The Modified Exploitability sub score is calculated as follows: Modified Exploitability \(=\) 8.22 \(\times\) M.AttackVector \(\times\) M.AttackComplexity \(\times\) M.PrivilegesReq \(\times\) M.UserInteraction. Note: The \"Round up\" function is defined as the smallest number, specified to one decimal place, that is equal to or higher than its input. For example, Round up(4.02) is 4.1, and Round up(4.00) is 4.0.
## 4 Results and Discussions
### Recommendations and Future Directions  
The findings of this study open up intriguing possibilities and introduce a new set of questions that warrant exploration in subsequent research. One avenue for further investigation involves enhancing the core functionality of ML-DProSA by integrating additional machine learning models. Specifically, incorporating word embedding models could contribute to delineating relationships between items within the context of grocery store product arrangements. This extension aims to refine cluster relationships among items, facilitating the inclusion of unclustered or poorly clustered items into groups that exhibit significant semantic similarities.  
Furthermore, recommender system models present another avenue for exploration. These models could play a pivotal role in predictive recommendation by discerning the consumer type of a grocery shopper based on their shopping list's content. Subsequently, the model could cross-reference this information with a dataset of preferences, ultimately enhancing the algorithm's sorting capabilities and improving the traversal path for more accurate recommendations. This multifaceted approach of leveraging word embedding models for cluster refinement and recommender system models for predictive recommendation on top of the currently established clustering and sorting algorithms holds promise for advancing the capabilities and effectiveness of ML-DProSA in the realm of grocery shopping optimization.  
However, the applicability of this algorithm extends beyond grocery stores. ML-DProSA's optimization capabilities can have a substantial impact in other settings with multiple items or objects arranged in rows of shelves and storage units. An immediate application that comes to mind is the optimization of warehouse management. If ML-DProSA is further developed to such a point where it is highly accurate and reliable, it has the potential to simplify the logistical challenges associated with tracking and managing large warehouses. This could empower personnel to navigate warehouse layouts more effectively.
###### Abstract  
The severity of vertical seismic ground motions is often factored into design regulations as a component of their horizontal counterparts. Furthermore, most design codes, including Eurocode 8, ignore the impact of local soil on vertical spectra. This paper investigates vertical pseudo-absolute acceleration spectral estimates, as well as the ratios of spectral estimates for strong motion in vertical and horizontal directions, for low to medium seismicity regions with deep local soil and deep geological sediments beneath the local soil. The case study region encompasses the city of Osijek in Croatia. New regional frequency-dependent empirical scaling equations are derived for the vertical spectra. According to these equations, for a 0.3 s spectral amplitude at deep soils atop deep geological sediments compared to the rock sites, the maximum amplification is 1.48 times. The spectra of vertical components of various real strong motions recorded in the surrounding region are compared to the empirical vertical response spectra. The new empirical equations are used to construct a Uniform Hazard Spectra for Osijek. The ratios of vertical to horizontal Uniform Hazard Spectra are generated, examined, and compared to Eurocode 8 recommendations. All the results show that local soil and deep geology conditions have a significant impact on vertical ground motions. The results also show that for deep soils atop deep geological strata, Eurocode 8 can underestimate the vertical to horizontal spectral ratios by a factor of three for Type 2 spectra while overestimating them by a factor of two for Type 1 spectra.  
vertical ground motion; pseudo-absolute acceleration spectra; low to medium seismicity; deep local soil; deep geological sediments; Eurocode 8 2021  
18 June 2021  
1 Faculty of Technical Sciences, University of Novi Sad, Tng [PERSON] 6, 21000 Novi Sad, Serbia; [EMAIL_ADDRESS]
# Research on Synthetic Aperture Radar Processing for the Spaceborne Sliding Spotlight Mode
## 4 Convolution Backprojection Algorithm for Sliding Spotlight SAR
### Simulation Results  
The simulation parameters are shown in Table 2, and the distribution maps of 121 simulation points are given in Figure 9. The space between each points is 400 m, and the imaging area is \(4000\) m \(\times\)\(4000\) m. The radar echo signal after matching filter and motion compensation is a two dimensional matrix of \(4096\times 4096\) points. The result of fast imaging simulation is shown in Figure 10 with different segmentation levels.  
Figure 8: Flow chart of fast realization based on Quadtree sub-image recursive segmentation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline Carrier Frequency & 5.4 GHz & Wavelength & 0.056 m \\ Signal bandwidth & 240 MHz & Sampling frequency & 300 MHz \\ Height of platform & 750 Km & Pulse width & 20 us \\ Incidence angle & 30\({}^{\circ}\) & Size of sub-images & 4 \(\times\) 4–128 \(\times\) 128 \\ Azimuth size of antenna & 7.5 m & Type of window & None \\ Range resolution & 1.0 m & Azimuth resolution & 1.2 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 10: Imaging simulation result. (**a**) Divided to 128 \(\times\) 128 points; (**b**) Divided to 64 \(\times\) 64 points; (**c**) Divided to 32 \(\times\) 32 points; (**d**) Divided to 16 \(\times\) 16 points; (**e**) Divided to 8 \(\times\) 8 points; (**f**) Divided to 4 \(\times\) 4 points.  
Figure 9: Simulation point targets distribution.  
From the above, it can be seen that all the images are well focused in each level. But each segmentation level is equivalent to re-sampling and cumulative error is inevitable, so the number of segmentation levels is a compromise between image quality and computation.
## 4 Experiments
### Experimental Results
#### 4.4.1 Training Results  
As illustrated in Figure 4, the plot depicts the variation of the loss function over training epochs. From this training loss curve, it is evident that during the initial stages of training, the loss rapidly decreased, indicating effective learning from the data and a significant reduction in prediction error due to parameter adjustments. As training progressed, the rate of loss reduction gradually slowed down, and the curve tended to stabilize, indicating convergence of the model to a stable state. Although the loss curve exhibited some fluctuations, these may be attributed to data noise or suboptimal settings of optimization parameters. Toward the end of 1000 epochs, the loss function appeared to approach a plateau, suggesting that the model was nearing its performance limit. Overall, the variation in the loss function curve demonstrates stable learning and gradual convergence during model training, providing positive prospects for performance enhancement.  
4.2 Hyperparameter Tuning Experiment for the Number of Layers in the Self-Temporal Compression Mechanism Calculation Module  
The number of layers in the Self-temporal Compression Mechanism calculation module significantly affects the model's prediction performance. To determine the optimal hyperparameter for this module, we conducted a tuning experiment on the number of layers. The experimental results are shown in Table 8.  
From the experimental results, it can be observed that the model achieved optimal performance when the module had two layers, with an accuracy of 86.3%. The module contains a large number of learnable parameters, and the model's fitting capability increases with more layers. When the module had only one layer, the model's fitting ability was insufficient, resulting in an accuracy of 83.6%. However, when the number of layers exceeded two, the enhanced network complexity led to overly strong learning capability, causing the model to overfit and its prediction performance to decline.