esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Thermal Radiation Characteristics and the Multi-Point Source Model of Hydrogen Jet Fire
## 4 Results and Discussion
### Flame Size  
The curves of the flame length and the ratio of length to width with different leakage pressures and leakage diameters are shown in Figure 7. In Figure 6(a), it can be seen that for a given leakage diameter, the flame length exhibits an approximately linear relationship with the leakage pressure. As the leakage pressure increases, the flame length increases, and the difference in the flame length for different leakage diameters gradually becomes more pronounced. At a constant leakage pressure, a larger leakage diameter results in a longer flame length. This is because higher leakage pressure and diameter result in a greater hydrogen leakage rate from the leakage hole, leading to a larger jet fire scale.  
The flame length was calculated using the flame Froude number length prediction model and is indicated with the solid line in Figure 6(a). The experimental flame length agrees well with the predicted length from the model, showing a consistent trend and overlap. Therefore, this flame length prediction model can be used to calculate flame lengths under different conditions in the thermal radiation prediction model. Additionally, the relationship between the flame length-to-width ratio and the leakage pressure is shown in Figure 6(b). It is observed that the length-to-width ratio fluctuates within a certain range and is independent of the leakage pressure and diameter. The average ratio obtained from the present experiment was 5.819, consistent with the range of 5-6 reported by [PERSON] et al. [13].
## 5 Experimental Results and Analysis
### PTV Quantitative Evaluation Algorithm
#### 5.3.1 Determination of Characteristic Parameters
To sum up, we set the time threshold of sleep-deprived driving as 4 h.  
Figure 12c shows the probability curve of traffic flow distribution, with a threshold set to 750 based on the curve distribution. The size of road traffic flow directly affects the saturation of road traffic, and the saturation of roads directly affects the occurrence and development of traffic accidents. Figure 12: Statistical distribution of traffic characteristics. (**a**) Vehicle speed distribution. (**b**) Vehicle driving duration distribution. (**c**) Traffic flow distribution. Finally, for different types of vehicles traveling on the expressway at the same speed, the level of danger varies. It is necessary to determine the type of vehicle and conduct a hazard assessment based on different vehicle types. In order to observe the proportion of various types of vehicles driving on the expressway, this article uses data from a province's road network of 6.826061 million sections to statistically analyze the number of different types of vehicles. The results are shown in Table 12. It can be seen that Class 1 passenger cars account for a relatively large proportion, accounting for 82.93% of the total number of vehicles, followed by Class 6 trucks, accounting for 7.02% of the total number. The majority of overall vehicle types are passenger cars and trucks, accounting for 84.33% and 15.52%, respectively, while special operation vehicles only account for 0.13%. Therefore, the larger the number of vehicle types, the greater their danger.
## 4 Discussion
### The Added Value of Robotic Technologies in the Rehabilitation Field  
Robotic devices typically integrate video screens and speakers to provide visual, auditory, or audio-visual feedback. These rehabilitation devices hold significant promise in the field; however, they exhibit notable diversity in terms of their design and training principles, especially when it comes to robots intended to target trunk control and core strength. Among the devices used in the studies included in this review, Spine Balance 3D and Space Balance 3D train trunk control while standing and moving in a 3D space. The controlled tilting of such devices induces different muscular activations based on the direction of movement. For instance, tilting in the anterior direction activates the back muscles, whereas tilting in the posterior direction targets the abdominal muscles. Furthermore, muscles such as the semispinalis, lumbar, and thoracic multifidi are activatedin all tilt directions [130]. However, devices such as hunova, T-chair, and the Trunk Control Robot 3 DBT-33 (TCR) adopt entirely distinct approaches to training. All these devices have seat bases that can be active, passive, or static. In addition, hunova includes an additional platform at the foot level, enabling both standing and sitting exercises, as well as exercises in sitting, with both movable base and movable seat. In addition, as a TCR, it can assist the sit-to-stand movements, as the seat can tilt to facilitate motion. In this review, hunova is the only robotic device that has been used for both stroke and PD subjects.  
It is worth noting that these devices with movable chairs leverage principles similar to those utilized in clinical practice, where cushions are placed under the base of support (both at the seat and feet levels) or physio balls are employed. However, using robotic devices simplifies the process, as therapists can perform all necessary exercises with a single tool, eliminating the need for frequent changes in location or equipment while ensuring a broad differentiation of tasks. All of that is paired with continuous feedback, as these devices include sensors to monitor online subjects' performance. In addition, robotic devices allow for the personalization of training regimens to match the user's specific needs and capabilities, quantitatively monitor training progression, and enhance motivation.
# Comparison of EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) across Multiple Spatial Scales
## Methods  
* Comparison 1: In situ LAI to MODIS LAI (Mc5, Mc6) at 1.0 km.  
Purpose: To explore statistical relationship between upscaled in situ LAI measurements and the latest MODIS algorithm update (Mc6) and check improvements over the previous iteration (Mc5).
* Comparison 2: MODIS LAI (Mc5, Mc6) to EPIC LAI at 12.0 km.  
Purpose: To compare MODIS (Mc5, Mc6) LAI aggregated to 12.0 km to EPIC LAI over a \(3\times 3\) grid centered over the four sites to examine compatibility for use of EPIC at modeling air quality futures.
# Algorithms for Finding Shortest Paths in Networks with Vertex Transfer Penalties
## 2 Shortest Path Problems and Algorithms for Solving Them
### Single Source
A good option is to use a binary heap or self-balancing binary tree since this allows identification of the minimum label in constant time, with look-ups, deletions, and insertions then being performed in logarithmic time. This leads to an overall run time of \(\mathcal{O}((n+m)\lg n)\), which simplifies to \(\mathcal{O}(m\lg n)\) for connected graphs (where \(m\geq n\)). Asymptotically, a further improvement to \(\mathcal{O}(m+n\lg n)\) can also be achieved using a Fibonacci heap for \(Q\), though such structures are often viewed as slow in practice due to their large memory consumption and the high constant factors contained in their operations [8]. As noted at the start of this subsection, one limitation of [PERSON]'s algorithm is its requirement that all edge weights are nonnegative. This is because once a vertex \(v\) has been marked as distinguished, it is assumed that the shortest \(s\)-\(v\)-path has been found and that any path extending this will necessarily have an equal or greater length. However, this property does not hold in graphs featuring negative edge weights. One simple way of dealing with such graphs is to extend DijkSstra so that, in Line 7 of Algorithm 1, rather than only looking at undistinguished neighbours of \(u\), _all_ neighbours of \(u\) are considered. Then, if a distinguished vertex has its label changed, it is reinserted into the priority queue, and is once again marked as undistinguished. However, although this algorithm is correct, unfortunately it exhibits an exponential growth rate in the worst case [4]. Perhaps a better alternative for graphs with negative edge weights is the Bellman-Ford algorithm, which operates in \(\mathcal{O}(nm)\) time [5]. Although this growth rate is higher than [PERSON]'s algorithm, this approach also has the added advantage of being able to detect the presence of negative cycles in the graph. Bellman-Ford can also be augmented with additional data structures to form [PERSON]'s algorithm [9] (also sometimes known as the \"shortest path faster algorithm\"). This augmentation generally results in faster run times than Bellman-Ford, particularly on sparse graphs. The worst case complexity of the method is still \(\mathcal{O}(nm)\), however. In graphs for which all edge weights are relatively small integers, further methods are also known to be efficient in producing shortest path trees. For example, [PERSON] et al. [10] describe an algorithm that runs in \(\mathcal{O}(m+n\sqrt{\lg W})\) time on graphs with nonnegative edge weights, where \(W\) is the value of the largest edge weight in the graph. For graphs with negative edge weights, [PERSON] and [PERSON] [11] have also proposed an algorithm with the slightly higher complexity of \(\mathcal{O}(\sqrt{n}m\lg(nW))\).
# Correlation Analysis of Macro-Micro Parameters of Sandstone Based on PFC3D
## 1 Introduction
[11] established a rigid block model using Voronoi tessellation and simulated sandstone using the SBM model, discovering that this model more accurately represents the mechanical properties of sandstone, including a higher compressive-tensile strength ratio. [PERSON] et al. [12] introduced viscoelastic constitutive relations into PFC simulations, derived the force-displacement equation for the viscoelastic model, and developed related contact failure criteria, making the contact force response and bonding strength between particles more correlated, thus improving simulation accuracy. While the methods mentioned above have made some progress in improving model performance, most research has relied on the PBM model. Therefore, it is of great significance to study how to effectively improve the compressive-tensile strength ratio of the PBM model. Additionally, existing research has relied on trial-and-error methods or empirical formulas during the parameter calibration process, which are inefficient and cannot meet the demands for high-efficiency and accurate simulations. Based on this, this paper takes sandstone as the research subject, uses an improved parallel bond model, and conducts 12 orthogonal experiments on mesoscopic parameters to analyze their influence on macroscopic mechanical properties. This study reveals the quantitative relationships between macro and micro parameters as well as verifies the feasibility of the model and parameters through practical examples.
## 4 Result of a Real Flight Experiment
Therefore, there will be less DD observations at each epoch for the three baselines. It seems that the aforesaid fluctuations are most likely caused by the insufficient number of DD observations at the AIR2 station, especially during the time 20:00-21:00, which can be seen in Figure 10.  
Figure 8: _Cont._  
Figure 8: The time series of baseline length of the three antennas derived from DD, PPP and POP approaches. Both the float as well as fixed solutions are shown for PPP and POP. For clarity, the DD, PPP float, PPP fixed and POP float results are shifted by 0.2, 0.4, 0.6 and 0.8 m, respectively. Figure 9: The satellite visibility tracked by the reference station FD83 (blue) and the rover AIR2 (red). Only 10 satellites are shown here. Receivers losing track of the satellites may introduce biases in the DD solutions. The STD values of the three baselines calculated with DD, PPP and POP float and fixed solutions are shown in Figure 11. It is seen that the DD approach does not work well because only a single reference station is used for such a large region, which is, on average, worse than PPP. The POP generally shows a better performance than PPP both in float and fixed solutions, especially in the results of 0158-SPAN, and the errors in the baseline results are almost within 10 cm. Comparing the STD of float and fixed solutions, ambiguity fixing indeed improved the position accuracy significantly. The time series of the fixed solutions shown in Figure 8 are much flatter than their float solutions, and most of the fluctuations and jumps in the float solutions disappeared. This is also proof that the fluctuations and jumps are caused by the poor observation quality of the rovers. From the beginning, it is very clear that ambiguity fixing can also considerably reduce the convergence time, in this case from 1 h to a half hour, on average. Overall, the POP fixed solution has the best performance, with an STD of 1-3 cm for the three baseline results. The ambiguity fixing is very important for airborne kinematic positioning in the polar regions to overcome fluctuations and jumps caused by poor data quality, since the continuous tracking time is usually short and more signal interruptions could occur due to the high dynamical movement.
# All-Optical Tuning of Fano Resonance for Quasi-BIC and Terahertz Sensing Applications
## 1 Introduction
used optical doping (or optical pumping) methods to induce free-carrier changes in semiconducting dielectric materials in all-dielectric subsurfaces, achieving spectral conversion from quasi-BIC to BIC and amplitude modulation of the transmission spectrum [21; 22]. [PERSON] et al. took advantage of the photosensitive properties of arsenic trisulfide to prepare metasurfaces structures and fine-tune the Fano resonance by light absorption [23]. Taking advantage of the photoconductive properties of the material, the Q factor can be dynamically controlled by adjusting the pump flow rate of external light excitation, which not only has high precision but also has a fast response. It provides a new way for real-time modulate of active metamaterial devices. Therefore, in this paper, a porous metal subsurface is introduced to form a metasurfaces structure, and a structured photosensitive silicon material is introduced into the porous metal structure. Taking advantage of its photoconductive properties, the electron concentration can be tuned from a semiconductor-like to a metal-like by adjusting the electron concentration by an optical field. Through the optimization of the structure, the transformation of the ideal BIC into the Fano quasi-BIC resonance mode with high Q factor is realized in the THz band. Finally, a highly sensitive sensing based on quasi-BIC resonance was proposed by taking advantage of its quasi-BIC resonance properties. The highly sensitive detection of surface area medium thickness and refractive index is achieved, which provides a new method for high sensitivity THz sensing.
# Forward Link Optimization for the Design of VHTS Satellite Networks
## 6 Discussion  
It is important to note that the second scenario has two main different considerations that had an important impact on the obtained results:  
* The service area is smaller than for scenario 1. It means that for the same number of beams the beamwidth is smaller in scenario 2. Therefore, the directivity is greater and the satellite antenna diameter will be larger.
* Rain attenuation is completely different in the two scenarios. This affects the link availability that depends on the selected ModCod and on the service area percentage in VCM.  
If we analyze the cost per Gbps in orbit, in scenario 1, the cost per Gbps in orbit is between 2.63 and 4.34 M\(\mathrm{\SIUnitSymbolCelsius}\), while in scenario 2 the range is from 2.90 to 4.34 M\(\mathrm{\SIUnitSymbolCelsius}\). For the same user \(G\)/\(T\), the cost per Gbps in orbit is lower for higher satellite capacities.  
In scenario 1, for a given user \(G\)/\(T\) increase, the optimal number of beams changes faster than in scenario 2, thus limiting the capacity per beam in scenario 1.  
This methodology minimizes the cost per Gbps in orbit, as shown when the results are compared with the affordability curve presented in [3]. Both scenario 1 and scenario 2 remain below the reference curve, as shown in Figure 14. The reference curve presented in [3] does not take into account launch and ground segment costs. However, in this paper an average launch cost of 100 million euros has been added, plus a fixed cost of 80 million euros as a minimum price for a broadband payload, which is added to the corresponding ground segment cost (see Figure 14).
## 3 Seismic Damage Model
### Seismic Damage Model Considering Biaxial Coupling Load Effect  
In this section, the seismic damage index for the \(X\) and \(Y\) directions are combined to produce a comprehensive seismic damage model that considers the bi-axial load coupling effect. The model parameters of the developed M-Usami model and other two existing models (i.e., Usami model, and Park-Ang model) are first calibrated. The performance of M-Usami model in evaluating the seismic damage evaluation of hollow RC bridge piers subjected to bilateral seismic loading was then compared with that of the other two models.
## 4 Discussion
Several constellation SAR missions have been prepared to be launched in near future. From these operational SAR missions, we might acquire large perpendicular InSAR pairs periodically. A time series of DEMs generated using interferometric pairs with a large perpendicular baseline can be remarkably useful to better understand the dynamics of a glacier surface. The ice budget, which relates to monitoring sea level rises, can be estimated by examination of very sensitive topographic changes using these multi-temporal DEMs.
## 3 The Proposed Vehicle Dataset  
### Dataset Generation Method  
In this paper, a real vehicle dataset in the city of Los Angeles is proposed. The VehDS-LA was generated by utilizing Google Maps and the MATLAB R2021b simulator. The database production process is divided into two main phases, as shown in Figure 8.  
* Phase 1: Creating Driving Routes: This phase was implemented through Google Maps. It includes three steps:
* Step 1: Creating a new map of the city of Los Angeles.
* Step 2: Adding driving routes for all the selected streets (15 streets in this study).
* Step 3: Exporting a Keyhole Markup Language (KMZ) file for each driving route. An example of the contents of a KMZ file is shown in Figure 9.  
Figure 7: The distance between two consecutive geographical points.  
Figure 8: The phases of generating the proposed vehicle DS.  
* Phase 2: Generating the Vehicle Dataset: This phase was performed using the MATLAB simulator. This phase has three steps:
* Step 1: Reading the KMZ files and converting them into structure objects.
* Step 2: Generating extra vehicle samples so that the distance between two samples is small (0.25 m in this study). For each vehicle sample, four features were assigned: (1) latitude coordinate, (2) longitude coordinate, (3) vehicle speed, and (4) vehicle azimuth. The speeds were generated randomly in the range from 10 to 40 km per hour (km/h).
* Step 3: Exporting the proposed VehDS-LA as a comma-separated values (CSV) file.
## 3 Results
### Fault Diagnosis
#### 3.2.3 Squared Envelope Spectrum (SES)  
The Squared Envelope Spectrum is considered a benchmark among the diagnostic techniques and it is attained through signal demodulation, firstly filtering the signal (in the case under examination, with a low-pass filter with cut-off frequency equal to \(f_{s}/16=1280\) Hz), and then obtaining the analytic signal with the Hilbert transform before finally computing the envelope signal as the absolute value of the analytic one and applying the Fourier transform to return to the frequency domain.  
It is known from the literature that squaring the signal at the very beginning of this process (i.e., correlating the signal with itself) is beneficial because it emphasizes the modulation and the clarity of information detectable in the final spectrum (while a squaring in the frequency domain would harmfully blur it). This operation is conveniently introduced for the considered dataset, which is characterized by inner race and rolling element faults, typically modulated by shaft rotation frequency \(f_{r}\) and cage frequency FTF, respectively (Figure 23); as a matter of fact, in Figures 24 and 25 the BPFI and BSF harmonics are accompanied by the characteristic modulation sidebands.  
Figure 23: Typical raw signals and envelope signals from local faults in REBs (from [3]).  
Figure 22: PSD applied to the signal from sensor 6 on bearing 3 (first 49 s).  
Specifically, in Figure 24, regarding bearing 3, the even harmonics of the shaft rotation frequency appear distinctly, while the odd ones emerge from noise only when near to the BPFI harmonics, as their sidebands.  
Truthfully, the BPFI harmonics are not present directly, but indirectly, through the excitation exerted on those of shaft rotation; namely, the spectrum shows frequency lines at 301 Hz and 602 Hz, multiples of \(f_{r}\) = 33.3 Hz, rather than at 297 Hz and 594 Hz, fundamental and second harmonic frequencies of the BPFI; in spite of the closeness of these values, their difference is not negligible or imputable to a mere approximation.  
In Figure 25, about bearing 4, at least the first three harmonics of the BPFO can be detected, not subjected to modulation effects, and so can those of the BSF (centered on the multiple frequencies of 282 Hz instead of the nominal 278 Hz) with sidebands distant from the fault frequencies by the cage frequency, with the left band higher than the right one, unlike the theoretical case (Figure 23).
# A Study of Cellular Traffic Data Prediction by Kernel ELM with Parameter Optimization
## 5 Conclusions  
In mobile network operations, network failures caused by a sudden increase in cellular traffic data demand often occur. Therefore, it is of great practical significance to predict the flow of mobile networks in settings with changing traffic flow for stable network operation and resource scheduling. In order to realize accurate prediction of cellular flow data, this study analyzed the performance of kELM in predicting cellular traffic data with different kernel functions. In the experiment, to determine optimal parameters of kernel function, three metaheuristic optimization algorithms (PSO, MVO, and MFO) were adopted.  
The results showed that kELM optimized by MFO with Gaussian as kernel function had the smallest test set MAPE (11.150%). Moreover, we used ELM, BP, and SVR for flow prediction to verify the performance of kELM. The optimal prediction results of ELM, BP, and SVR for cellular flow data were obtained through a large number of experiments. kELM had a significant advantage in prediction accuracy compared with ELM and BP. Although the prediction accuracy of SVR was as good as that of kELM, the optimization time for SVR was very long, and its prediction efficiency was low.  
We studied an efficient prediction model of mobile traffic based on the kELM algorithm. A commercial street in Huainan was selected to verify the effectiveness of the model through experiments. The proposed kELM-based traffic forecasting method will allow operators to prepare for coping with upcoming congestion and improve service quality. Meanwhile, it could also guide network operators to rationally allocate network resources, effectively saving energy and reducing operating costs.  
Data curation, S.F.; Methodology, W.L.; Supervision, X.Z.; Writing-original draft, H.C.; Writing-review & editing, Z.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National 135 Program \"National Key Research Program\" under Grant 2018 YFF0301000.  
The authors declare no conflict of interest.