esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Sound Intensity Scaling Theory Based on Beamforming Technology  
The output of beamforming reflects the sound pressure contribution of the sound source on the plane of the microphone array. [PERSON] [21] scales the beamforming output result by constructing a scaling factor to ensure that the integral of the scaled output in the main lobe area is equal to the sound power radiated by the sound source on the side of the microphone array.  
The scaled beamforming result has sound intensity meaning, and defined as \"scaling sound intensity\", the coefficient is defined as \"sound intensity scaling factor\" \(\alpha\)[21], and it satisfies:  
\[\left\{\begin{array}{l}B_{\rm I}(R,\varphi)=\alpha\cdot|B(R,\varphi)|^{2}\\ \\ W_{h}=\int_{0}^{R1}\int_{0}^{2\pi}\alpha\cdot|B(R,\varphi)|_{2}RdR\varphi \end{array}\right. \tag{3}\]  
In Formula (3), the \((R,\varphi)\) polar coordinate system is used to describe each focus grid point on the sound source calculation plane, \(R\) is the distance from the focus grid point to the intersection of the sound source calculation plane and the \(Z\)-axis, and \(\varphi\) is the azimuth angle. \(B_{\rm I}(R,\varphi)\) is the sound intensity after scaling the beamforming output result, and \(W_{h}\) point source on the side of the microphone array.  
By constructing a fixed sound source recognition model and mathematical derivation, the sound intensity scaling factor \(\alpha\) is obtained by:  
\[\alpha=\frac{1}{16\Gamma}\frac{(kD)^{2}}{\rho c}\approx\frac{5.88}{\rho c}\left( \frac{D}{\lambda}\right)^{2} \tag{4}\]  
\(D\) is the array diameter, m. \(\rho\) is the air density, kg/m\({}^{3}\). \(\lambda\) is the wavelength, m. \(\alpha\) is the sound intensity scaling factor.  
The integral of the scaled sound intensity in the main lobe area is the sound power of the side hemisphere of the sound array, and the sound power \(W_{h}\) is [21]:  
\[W_{h}=\alpha\cdot 32\pi\left(\frac{|P_{e}|L}{kD}\right)^{2}\cdot 0.419 \tag{5}\]  
\(L\) is the sound source depth, m. \(|P_{e}|\)is the effective amplitude of the microphone signal. \(k\) is the wave number in the propagation direction of the plane wave.  
According to Equation (5), it is necessary to obtain the depth of the sound source to calculate the sound power of the moving sound source. In this paper, the stereo vision  
Figure 2: Principle of active infrared stereo vision technology.  
method is introduced to obtain the three-dimensional positioning of the sound source, then the sound power can be estimated.
## 3 Apparent Reflectance Error Analysis and Its Predictive Power
### Spectral Distortions in the Apparent Reflectance as Quality Indicator of the Atmospheric Correction
Afterwards, the remaining 70% of the DB was used to _test_ the retrieved outputs (\(\Delta\widehat{AOT}\), \(\widehat{\Delta\alpha}\) and \(\widehat{\Delta g}\)) versus their reference counterparts (\(\Delta\)AOT, \(\Delta\alpha\) and \(\Delta g\)). For clarification, all steps concerning the DB simulation, regression training and testing are summarized in Figure 10.  
Figure 10: Database (DB) generation process: TOA radiance simulation (1 st) and atmospheric correction process (2 nd). DB multivariate linear regression training (3 rd) and validation (4 th) processes. Grey-shaded boxes correspond to the multivariate linear regression inputs. Regarding the predictive power of the \(\rho_{app}\) to correct the estimation of the aerosols optical properties retrieval, Figure 11 summarizes the results obtained for the O\({}_{2}\)-B and the O\({}_{2}\)-A absorption bands. For both oxygen regions, the applied method reduces in 75% of the validation cases the errors below 0.5%, 8% and 3% for aerosol parameters AOT, \(\alpha\) and \(g\), respectively. As expected, and in agreement with the results in Section 3.1, the predictions of the \(\widehat{\Delta\alpha}\) coefficient are much less sensitive than predictions of the \(\widehat{\Delta\alpha}\) and the \(\widehat{\Delta g}\) parameters. In addition, results suggest that the linear regression predicts the over-/under-estimation of the aerosol optical properties precise enough despite including different input values of WV and surface pressure. However, to quantify this effect, we computed the same statistics as showed in Figure 11, but only considering one WV and surface pressure value in the DB (not shown). As expected, obtained results were in 75% of the validation cases slightly better than in the full DB case, deriving relative errors below 0.2%, 6% and 2% for aerosol parameters AOT, \(\alpha\) and \(g\), respectively.
# A Computation Offloading Strategy in LEO Constellation Edge Cloud Network
## 3 System Model
### Compute Offloading Mechanism  
In LEO-ECN, the edge computing orchestrator has the global view of the entire LEO cloud network system, including the network resource view and computing resource view. The network resource view contains topology and routing information of the entire system, which changes regularly and predictably with the relative motion of satellites. The computing resource view contains the computing resource usage and remaining status of all satellites, which changes with the previous and current amounts of offloading tasks. The orchestrator, as the control center of the entire system, can obtain the latest global status information in real time.  
When the user needs to offload the computing task, the satellite terminal will send the computing offloading request to the orchestrator. The orchestrator summarizes all computing requests \(\mathcal{W}\) at the current time \(t\) and runs the optimal algorithm combining with the network resources and computing resources at the current moment to make the optimal offloading decision, determine the satellite nodes for each user to provide computing service, which will be announced to each satellite terminal, and then the satellite terminal sends the task data to the service satellite arranged by the orchestrator. After the calculation is completed, the service satellite sends response data to the satellite terminal, and finally completes the task of offloading. The main process is shown in Figure 3.  
Considering the autonomous operation of the whole system and the reduction in signaling delay, the orchestrator, as a control node, also can be distributed on satellite nodes with high performance, and status information can be synchronized between control nodes through distributed consistency protocol. On the other hand, compared with virtual machines, containers can make more efficient use of the limited computing resources of the space-borne MEC server, and computing services are provided by enabling the space-borne MEC server to instantiate containers.  
Figure 2: The LEO-ECN scenario.
## 1 Introduction
Additionally, the ensemble design for SynthSecureNet is carefully tuned and optimized so that it achieves the best performance in terms of both accuracy and speed. ResNet50V2 is a widely used solution that is ideal for capturing the details and complex patterns in the input data, while MobileNetV2 is designed for specific tasks for edge computing applications, and it has a very effective and simple architecture [23; 24]. This combination allows SynthSecureNet to work quicker and achieve better results than other bagging methods that require speed at the expense of accuracy or vice versa. The architecture also implements the strategy for feature fusion and multi-level classification better than the original model so that it can recognize violent actions more intricately and effectively distinguish different actions of violence. SynthSecureNet has several new qualities that are absent in other violence detection systems. First, it presents a dual-architectural framework that ensures improved flexibility to different surveillance setups, whether they refer to high-resolution urban footage or low-quality video streams from remote locations [21; 22]. The fact that the architecture can work effectively on edge devices like smart cameras or mobile units is a step forward because it eliminates the time delay in transmitting data to centralized servers for processing [13]. This hybrid alert system ensures that critical alerts are delivered in real time even in environments where connectivity is limited. The proposed model was trained using a large dataset. Overall, these advancements facilitate the application of SynthSecureNet as a complete predictive system that can comprehensively provide real-time violence detection on CCTV systems for addressing the missing pieces in prior ensemble architectures.
# Rule-Based Scan-to-BIM Mapping Pipeline in the Plumbing System
## 3 Scan-to-BIM Mapping Scenario Analysis  
### Pipeline Concept  
The concept of a pipeline refers to a structure or a set of data processing elements in which the output of the prior processing step is connected to the input of the next. These connected pipeline steps become a standardized process that can be easily replicated for other projects with only minor modifications in regular and formal parameters.  
Since multiple predefined pipelines can be executed collaboratively, performance can be much improved by processing a large amount of data in parallel. If a pipeline well determines a standardized input/output format, parameters, and logistics for execution steps, reusability rates can be improved by modifying the parameters of the predefined pipeline according to the purpose of use. This can be a meaningful way to increase productivity in Scan-to-BIM projects with many repetitive tasks. Figure 1 shows a conceptual diagram of the Scan-to-BIM Mapping Pipeline proposed in this study.  
It is important to analyze a Scan-to-BIM use-case scenario and define its processing steps in designing a pipeline structure. Through this, elements constituting a process can be mapped to the pipeline components. Each step in the process transforms the data through a variety of tools, techniques, and manual interventions, and sends it to the next step. At this stage, a modeler determines steps for automation and analyzes what data inputs/outputs and operation parameters are required for the pipeline structure. Data input/output types and operation parameters can be defined as generalizable rules. Scan-to-BIM rules can be stored in a library through predefined pipelines and parameters, allowing them to be reused in similar use-cases.  
A scenario analysis of Scan-to-BIM use-cases, process component definition, data input/output format, and parameter normalization studies is discussed in the following subsections.
# Snow Depth Variations in Svalbard Derived from GNSS Interferometric Reflectometry
## 1 Introduction
The results show that there is little difference between the two methods and the SNR observations, but the estimation results for the dual-frequency carrier phase exhibit higher accuracy and higher data utilization [25]. Generally, GNSS observations are greatly affected by the noise power, which cannot be ignored in snow depth estimation with SNR observations. In addition, the effects of snow accumulation and melting as well as the impact of meteorological observations on the snow depth estimation need to be further investigated, because they can cause differences in the reflection characteristics of the snow surface. In this paper, an improved method is proposed that involves performing wavelet decomposition before Lomb Scargle periodogram (LSP) spectrum analysis to reduce the influence of noise power and improve the estimation accuracy; the results were verified by field work. The snow depth in Ny-Alesund, Svalbard, over the 2014-2018 period was retrieved from GNSS-IR. To better understand the relationship between the estimation method and snow-surface characteristics, the accuracy of the snow depth estimation during the accumulation, melting and stabilization periods were compared, and the impact of rainfall on snow depth estimation was analyzed with meteorological observations.
## 6 Empirical Research on New Heterogeneous Traffic Control Mechanisms
### Testing and Validation of Control Mechanisms in Simulation Environments
#### 6.1.2 Simulation Research on Intersection Collaborative Optimization  
As key nodes in urban traffic networks, intersections are particularly important for control optimization. In heterogeneous traffic environments, the collaborative optimization of signal control, vehicle trajectories, and spatial resources has become a research hotspot.  
Multi-level control architectures are effective methods for handling the complexity of intersection collaborative optimization. [PERSON] et al. [65] and [PERSON] et al. [66] proposed collaborative control frameworks with different hierarchical structures. The two-level hierarchical framework developed by [PERSON] et al. optimizes signal timing and vehicle arrival times at the upper level, and CAV trajectories at the lower level, achieving dual optimization of delay and energy consumption while maintaining computational efficiency. [PERSON] et al. incorporated spatial resources (lane configurations) into the collaborative framework, achieving three-dimensional collaborative optimization of signal timing, lane configuration, and vehicle trajectories. These two frameworks differ in structure but both demonstrate the advantages of hierarchical optimization in balancing computational complexity and control effectiveness.  
Platoon-based control methods that use leading CAVs to influence following vehicles are another effective heterogeneous traffic control strategy. The \"1 + n\" mixed platoon concept proposed by [PERSON] et al., which influences n following human-driven vehicles through one leading CAV, achieves resource utilization maximization [67]. The research conducted rigorous stability and controllability analysis on the linearized dynamics model of mixed platoons, proving the superiority of this method compared to single CAV trajectory optimization, especially under medium to low CAV penetration rates.  
For low penetration rate environments, trajectory data-based adaptive control strategies also show practical value. [PERSON] et al. developed two simple yet effective adaptive control strategies relying only on sample delay and stop count data, avoiding dependence on expensive physical detection infrastructure [68]. This method validated the feasibility of low-cost sensing and control schemes in transitional period heterogeneous traffic management.  
Synthesizing these simulation research results, we can identify the following commonalities: (1) collaborative optimization methods for heterogeneous traffic significantly outperform traditional control methods under various conditions; (2) hierarchical control architectures and platoon-based methods are effective approaches to addressing the complexity of mixed environments; (3) even under resource-limited conditions, simplified adaptive strategies can bring significant improvements. These findings provide practical guidance for the design of intersection control systems in heterogeneous traffic environments.
# Vulnerability of Satellite Quantum Key Distribution to Disruption from Ground-Based Lasers
## 4 Discussion
### Countermeasures  
Our analysis has identified a number of countermeasures or design considerations a satellite QKD system could take to mitigate or reduce the impact of disruption due to ground-based laser terminals. Some of these design considerations are likely to be incorporated on improved satellite QKD systems in the future [8; 17], and we discuss how they impact this form of disruption.
## 3 Results
### Surrounding Rock Excavatability Classification Based on Rock-Breaking Data
#### 3.1.3 Establishment of TOPSIS-Based Surrounding Rock Excavatability Classification Criteria
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Excavatability Grade** & _BQ_ & _SER_/(**m\({}^{3}\cdot\)rev\({}^{-1}\cdot\)cutter\(\cdot\)kN\({}^{-1}\))** & _PR_/(**m/h**) & **Excavatability Description** \\ \hline L-1 & \textless{}200 & \textgreater{}4.6 & \textgreater{}4.6 & excellent \\ L-2 & 325–200 & 4.6–2.3 & 4.6–3.7 & good \\ L-3 & 397–325 & 2.3–1.6 & 3.7–3.2 & fair \\ L-4 & 507–397 & 1.6–0.9 & 3.2–2.4 & poor \\ L-5 & 657–507 & 0.9–0.4 & 2.4–1.3 & very poor \\ L-6 & \textgreater{}657 & \textless{}0.4 & \textless{}1.3 & extremely poor \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Surrounding rock excavatability classification based on _SER_ and _PR_.
## 3 Generation of the Initial Data Set
### Correlation Analysis  
The quality and diversity of the calculated data have a decisive effect on the accuracy of the artificial intelligence model [22]. The Pearson correlation coefficient (_PCC_) was introduced to determine the relationships between the COP (\(Y\)) and geological and operational parameters (\(X\)). According to the paired data of COP and operational parameters (\(x_{1}\),\(y_{1}\)), \(\ldots\) (\(x_{n}\), \(y_{n}\)), _PCC_ can be calculated as follows [23]:  
\[PCC=\frac{\sum_{i=1}^{n}{(y_{i}-\overline{y})(x_{i}-\overline{x})}}{\sqrt{\sum_ {i=1}^{n}{(y_{i}-\overline{y})^{2}(x_{i}-\overline{x})^{2}}}} \tag{1}\]  
where \(n\) is the number of the paired data in the initial data set; \(x_{i}\) and \(y_{i}\) are the data points of operational parameters and _COP_, respectively, in the initial data set; \(\overline{x}=\frac{1}{n}\sum\limits_{i=1}^{n}{x_{i}}\), and analogously for \(\overline{y}\). If the _PCC_ is equal to 1, \(X\) and \(Y\) are linearly related. If the _PCC_ is equal to 0, \(X\) and \(Y\) are not correlated.  
It is clear from Table 2 that the liquid production rate, oil saturation, and reservoir permeability have strong correlations with the COP. The porosity, well spacing, well distance, steam temperature, and initial reservoir temperature have a medium relation with the COP. However, the _PCC_ values between the soaking time, steam injection volume, steam injection rate, steam quality, and COP are less than 0.01, indicating that these parameters are weakly related to the COP. The correlation results are in line with the reality of horizontal CSS well production. Therefore, the selected input variables were the liquid production rate, reservoir permeability, oil saturation, porosity, well length, well spacing, steam temperature, and reservoir initial temperature.
## 7 Theoretical and Practical Implications  
This study presents a new approach by comparing different geometric LED array configurations, focusing on SNR and BER performances in V2V systems. From a theoretical perspective, this study provides fundamental insights into the impact of varying LED geometries (such as honeycomb, hexagonal, octagonal, and square arrays) on the efficiency and communication range of VLC systems. Our theoretical comparisons show that the honeycomb array provides the best SNR performance, with a communication range improvement of 19% a at 1 cm spacing and 16% at a 1 mm spacing, compared to a single-LED setup, indicating that geometry plays an important role in maximizing communication performance under ideal conditions. Our study also highlights how geometric optimization can be used to improve communication systems without changing the total power of LED arrays.  
From a practical perspective, this research provides a solid foundation for future experimental studies. It is suggested that adapting LED array geometries has the potential to improve V2V communication systems. Although the focus is on simulation data, it provides a theoretical framework for comparing different arrays in outdoor environments. This has the capacity to improve V2V communication performance, especially in scenarios of high-speed vehicles. It is a fact that even a few centimeters of communication distance gain in traffic will speed up access to critical data and enable drivers or autonomous systems to make quick decisions. For example, according to studies conducted by the National Highway Traffic Safety Administration (NHHSA), even millisecond delays can cause an increase in accidents [44]. The increase in communication distance provided by the honeycomb array can increase safety between vehicles by providing more reliable data transmission during critical time periods.  
We demonstrated that our proposed transmitter design performs robustly even when the sizes of the LED arrays are decreased drastically. We reduced the sizes of the LED arrays such that our largest array is smaller than one of the smallest industrial headlight modules, Samsung's PixCell LED [42; 45]. This result emphasizes the versatility of our design, which not only adapts well to compact dimensions, but also allows automotive manufacturers the flexibility to tailor lighting solutions to meet their specific size and aesthetic requirements while ensuring effective communication ranges.
# Analysis of Load Transfer and the Law of Deformation within a Pile-Supported Reinforced Embankment
## 3 Model Test Results
### Pile-Subsoil Stress Ratio  
The average soil pressure at the top of the pile caps divided by the average soil pressure above the subsoil is known as the pile-subsoil stress ratio, which represents the features of pile-subsoil load efficiency. It is also a crucial indicator for embankment-settlement calculation, foundation-bearing capacity, and stability analysis. Figure 9 depicts the curve for the pile-subsoil stress ration.  
1. Before the relative height of the embankment (\(h/(s-a)\) reached 2.4, the pile-subsoil stress ration grew quickly, and after that it tended to remain constant.
2. As cohesiveness rises, the pile-subsoil stress ratio decreases under the embankment of the identical height.  
Figure 8: Effect of filling cohesion on pile-subsoil differential settlement.  
Figure 9: Effect of filling cohesion on pile-subsoil stress ratio, relative height of the embankment is \(h/(s-a)\).  
The effect of cohesion on the stress distribution in the embankment is what causes the pile-subsoil stress ratio decreases as filling cohesion increases. Furthermore, the settlement and deformation of the embankment are also affected by the filling cohesion, which changes the load distribution at the embankment's bottom. The slip surfaces are formed, in the embankment filling, because of the pile-subsoil different settlement. The of the active soil's self-weight load is transmitted to the adjacent and transverse soil, and the pile-soil stress ratio is greater than 1, because of the friction and occlusion between the soil particles. The increase in filling cohesiveness increases the particle contact force and the embankment's integrality. Therefore, pile-subsoil different settlements and the relative movement between the particles are weakened, and less load is transmitted to the pile caps. The pile-subsoil stress ratio reduces as a result.