esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Implemented Example Use Cases and Configuration Details
### Platooning  
The platooning application (the application concept is depicted in Figure 5) helps the equipped vehicles to maintain a short leading gap to the preceding vehicle. The short gap between vehicles offers several benefits, including reduced air drag or better utilization of the roads [38]. In the scope of the platooning applications, we consider the lateral control (i.e., vehicle steering) to be solved, and we focus solely on the longitudinal control. We used our previous work [39] to select the proper control algorithm for this implementation. The gap size and the throttle control depend on the control algorithm running on the vehicles.  
Figure 4: Urban traffic with platooning service use case.  
Figure 3: Road works warning app configuration.  
In the scope of this work, we did not implement any platooning-specific or management-type communication. Instead of dedicated platooning messages, we used CAMs to transfer speed, position, and other relevant dynamic data as inputs for the individual C-ACC algorithms and maintain vehicle platoons. In this example implementation, if an equipped vehicle detected a vehicle in front (practically in a bounding box) by means of CAMs or sensory data, it engaged its platooning application. This meant that the longitudinal control was passed to the platooning application instead of SUMO's regular driver model. For this, we also prohibited the SUMO-controlled lane changes of the cars. In practice, this behavior meant that if a vehicle saw another vehicle that it could follow, it started to accelerate and then gently slowed down to follow the partner smoothly. In Figure 6, the vehicles denoted with red color are the so-called follower vehicles, and the yellow ones are either platoon leaders or vehicles not in platoons. In this work, we model the platooning application without Platooning Control Messages; however, the solution can be extended with such messages. The advantage of this solution is its simplicity, while the drawback is that we cannot explicitly control the platoon membership. For example, the vehicles will not change lanes to form a platoon.
## 1 Introduction
On top of this, SAR interferometry (InSAR) is a mature technique that exploits the backscattering coefficients as well as the phase difference between two SAR observations acquired from similar acquisition geometry [23; 24; 25]. The spatio-temporal similarity of interferometric phase is a measure of stability of backscattering characteristics from the targets (crops, ground surface). If the targets are affected by certain events so that their backscattering characteristics are modified, the spatio-temporal similarity between two image acquisitions decreases. The technique that exploits these similarity levels to characterize changes on the ground is called coherent change detection (CCD) [26; 27; 28]. InSAR gained recognition in the early 1990s and has been widely used in mapping land surface deformation and displacement over the past two decades [29; 30; 31; 32]. Unlike the past generations of SAR systems, e.g., ERS-1/2 and ENVISAT ASAR, the Sentinel-1 constellation consists of two C-band SAR satellites with the advantage of shorter revisit time for its interferometry mode (regular revisit time 12 days with one satellite, and 6 days with both satellites), and the data is freely available [33; 34]. The shorter revisiting cycle allows for more accurate determination of the timing of change. Past applications of InSAR have focused on the detection of land deformation, landslides, and infrastructure stability where there is no significant vegetation cover in the areas under study. More recently, researchers started applying the CCD technique to wetland studies by taking advantage of the SAR specular reflectance separating open surface water and land surface, and also the double-bounce backscattering mechanism present in flooded vegetation [35; 36; 37]. In comparison with wetland studies however, the study of croplands using InSAR will be more challenging due to the larger variability of crops and events to be detected, and similar backscattering mechanisms of different crops [38; 39; 40]. Recently, a study was conducted by [PERSON] et al. [40] to determine crop harvest status in northern Kazakhstan using Sentinel-1 SAR data through the detection of a changing InSAR coherence level and the decrease of VH, assuming that harvest transformed a crop field into a bare soil condition. The objective of this study is to detect field activities that can induce plant structural changes, such as field preparation for seeding and crop harvest. Using time-series Sentinel-1 data, this current research focused on the determination of crop seeding and harvest dates over an agricultural region in northern Ontario, Canada. The intentions are twofold: (1) to evaluate the performance of coherent and incoherent SAR change detection techniques in detecting the occurrence of agricultural field activities, and (2) to explore the capability of time-series C-band SAR data acquired by the Sentinel-1 satellites for field-scale change detection.
###### Abstract  
Since the atmosphere has a strong scattering effect on ultraviolet light, the transmission of non-line-of-sight (NLOS) signals can be realized in the atmosphere. In previous articles, ultraviolet (UV) light atmospheric scattering has been characterized by many scattering models based on spot light sources with uniformly distributed light intensity. In order to explore the role of light sources in atmospheric transmission, this work proposed a UV light atmospheric transport model under different types of light source, including light-emitting diode (LED), laser, and ordinary light sources, based on the Monte Carlo point probability method. The simulation of the light source in the proposed model is a departure from the use of a light source with uniform intensity distribution in previous articles. The atmospheric transmission efficiency of different light sources was calculated and compared with the data of existing models. The simulation results showed that the type of light source can significantly change the shape of the received signal and the received energy density. The Monte Carlo (MC) point probability method dramatically reduced the calculation time and the number of photons. The transmission characteristics of different ultraviolet light sources in the atmosphere provide a theoretical foundation for the design of ultraviolet detection and near-ultraviolet signal communication in the future.  
photon scattering; UV communication; Monte Carlo method; pulse response; non-line-of-sight +
Footnote †: journal: Applied Physics Letters
## 2 System Model
### DF Transmission Protocol  
Communication between nodes S and D is performed in two time slots \(T_{1}\) and \(T_{2}\) for the first and second hops, respectively. Relays only harvest energy during \(T_{1}\) because of the increased complexity of the node if power is collected and released at the same time. Node S converts the RF signal vector \(r\) with electrical power \(\rho\) to an optical signal by employing subcarrier intensity modulation (SIM). The dc bias \(A\in[A_{\text{min}},A_{max}]\) is added to the RF signal to ensure that the optical signal is non-negative, where \(A_{min}\) and \(A_{max}\) are the minimum and maximum values for the DC bias, respectively. Let \(P_{\text{s}}\) indicate the electrical power of node S used to transmit the optical signal vector \(s_{1}\). Then, the optical signal vector can be written as [11]:  
\[s_{1}=\sqrt{P_{\text{s}}}[\delta r+A] \tag{4}\]  
where \(\delta\) is the electro-optical conversion coefficient. To avoid clipping problems caused by nonlinearity of semiconductor lasers, the following constraints should be met \(\delta\)[11]:  
\[\delta\leq min\bigg{(}\frac{A-A_{min}}{\rho},\frac{A_{max}-A}{\rho}\bigg{)} \tag{5}\]  
The electrical signal at the output of the PD can be expressed as  
\[y_{FSO}=h_{FSO}s_{1}+n_{FSO} \tag{6}\]  
where \(h_{FSO}=\big{(}P_{\text{S}}^{2}\eta^{2}/\sigma_{FSO}^{2}\big{)}I_{FSO}\) is the channel coefficient of the S-D link, where \(\eta\) is the photoelectric conversion coefficient. The noise in FSO communication systems is expressed as \(\eta_{FSO}\). \(\eta_{FSO}\) is caused by circuit noise as well as high-intensity background lighting, and it is often modeled as zero mean additive white Gaussian noise. \(\sigma_{FSO}^{2}\) is the variance in the additive white Gaussian noise with a mean of zero.
# Research on the Characteristics of Seepage Failure in the Surrounding Rock (Coal) of the Goafs
## 1 Introduction  
The study of waterproof coal pillars dates back to the 1950s, when B,L,C,  
are critical for enhancing deep-seated coalbed methane extraction. Scholars such as [PERSON] et al. [17], [PERSON] et al. [18], and [PERSON] et al. [19] have also demonstrated that moisture content significantly impacts the mechanical properties of coal, which decrease progressively as moisture increases.  
In practice, the softening of coal pillars due to water exposure has also been observed. [PERSON] et al. [5], in their research on coal pillar protection in goafs, found that the tensile and compressive strengths of coal samples from the No. 7 coal seam in the Pengzhuang Mine decreased significantly after three weeks of groundwater immersion, before stabilizing. In acidic environments within old goafs, the coal pillar strength reduction coefficient can reach as low as 0.6 [20]. Based on these observations, researchers have modified the original coal pillar size calculation formula to account for a weakening coefficient. [PERSON] et al. [21] and [PERSON] et al. [22] analyzed the influence of groundwater seepage on coal seam strength, highlighting that the seepage-stress coupling effect is a critical factor in determining coal pillar stability. [PERSON] et al. [23] added that the critical size of coal pillars is influenced by factors such as the depth of coal seam burial, as well as the structure and characteristics of the roof and floor strata. [PERSON] et al. [22] and [PERSON] et al. [24] also noted that seepage can erode fissure channels, thereby weakening coal pillar integrity, while [PERSON] et al. [25] and [PERSON] et al. [26] pointed out that mining-induced fissures in the overburden rock can significantly alter the mechanical properties of the coal seam when exposed to water.  
Despite the wealth of research on coal pillar stability, most studies have focused on roadways or coal pillars within mining faces, with fewer investigations into the effects of water seepage in water-filled goat areas. Goaf areas with accumulated water present a unique challenge, as coal pillars exposed to water for extended periods can become unstable, potentially leading to the collapse of mining sections or the rapid inflow of water from goat areas into other parts of the mine. These situations pose severe economic and safety risks.  
Given these challenges, it is essential to further explore the mechanisms of seepage damage in coal pillars within water-filled goat areas. Understanding the interaction between water and rock in these conditions is crucial for developing a scientific basis for determining the appropriate size and characteristics of waterproof coal pillars, thus ensuring the safety and stability of mining operations. This study aims to provide theoretical support for the evaluation and prevention of water-related hazards in coal mines, particularly in the context of water-rock interactions in goat areas.
# An Intelligent Condition Monitoring Approach for
## 5 Conclusions  
In this paper, an intelligent condition monitoring approach consisting of WPT-based feature extraction and hybrid HMM-ANN based fault detection steps was proposed. The proposed approach  
\begin{table}
\begin{tabular}{l l r r r r r r r r r r r r r r r r r r r r r r} \hline \hline
**States** & **State No.** & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & \\ \hline Normal & 1 & 1 & 3 & 2 & 1 & 1 & 1 & 1 & 1 & 1 & 2 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & – & – \\ Fault 2 & 2 & 2 & 4 & 2 & 2 & 2 & 4 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & \(-\) & – & – & – \\ Fault 3 & 3 & 3 & 3 & 3 & 3 & 3 & 3 & 3 & 3 & 2 & 3 & 3 & 2 & 3 & 2 & 3 & 3 & 3 & 3 & 3 & 2 & 3 & 3 \\ Fault 4 & 4 & 4 & 4 & 4 & 4 & 4 & 4 & 4 & 4 & 4 & 4 & 4 & 4 & 4 & 4 & 4 & \(-\) & \(-\) & \(-\) & \(-\) & – & – & – \\ \hline \hline \end{tabular} Total samples for validation: 68, correct predictions: 59, accuracy rate: 86.76%.  
\end{table}
Table 7: Comparison of health states and HMM outputs.  
Figure 17: Comparison of HMM, ANN and HMM–ANN.  
\begin{table}
\begin{tabular}{l r r r r r r r r r r r r r r r r r r r r r} \hline \hline
**States** & **State No.** & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & \\ \hline Normal & 1 & & 4 & 3 & 3 & 4 & 4 & 4 & 1 & 1 & 1 & 4 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & – & – \\ Fault 2 & 2 & & 4 & 2 & 2 & 2 & 2 & 4 & 4 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 4 & 2 & – & – & – & – \\ Fault 3 & 3 & 3 & 3 & 3 & 4 & 3 & 3 & 3 & 4 & 4 & 4 & 3 & 2 & 3 & 4 & 4 & 3 & 3 & 3 & 3 & 3 & 4 & 3 \\ Fault 4 & 4 & & 2 & 4 & 4 & 2 & 1 & 4 & 2 & 4 & 2 & 4 & 4 & 2 & 2 & 4 & – & – & – & – & – \\ \hline \hline \end{tabular} Total samples for validation: 68, correct predictions: 42, accuracy rate: 61.76%.  
\end{table}
Table 8: Comparisons of health states and ANN outputs.  
was validated using the noise signals, which were acquired from shearing machines under the lab environments. The verification results showed that the extracted features of the working noises can well characterize the health states of shearing machine, the hybrid model which combined the advantage on dynamic-modeling of HMM and pattern recognition of ANN is effective--the accuracy rate is significantly higher than that of the isolated HMM or ANN model.  
[PERSON] designed the research work, analyzed the data, and finished the deduction procedure under the supervision of [PERSON]. The text of this paper was written by [PERSON], and was checked and revised by [PERSON].  
This paper is supported by the Double First-class Project on Nuclear Safety Management Science and Engineering of University of South China and the Major Projects of Science and Technology in Hunan Province (No. 2012 FJ1007). The authors would like to thank the University of South China and their colleagues for their support and contributions during the development of this work.  
The authors declare no conflicts of interest.
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 3 Mobility with UAV Technology  
The term \"handover\" refers to the process of switching from one cell to another while maintaining connectivity. Which is a core part of mobility management, if not the most important part. Beginning with the mobility management and HO concepts, which are regarded as the most important terms for understanding wireless technology in general, this section provides the reader with a comprehensive organized overview of mobility management with UAV technology. Following this conceptual review, we introduce mobility and HO in 3D for a variety of scenarios that correspond to UAV flights through space. This will provide the reader with a solid foundation for understanding the key components of the wireless network infrastructure that supports UAVs [45; 46; 47].
# A Systematic Review of the Trends and Advances in IFC Schema Extensions for BIM Interoperability
## 5 Evaluation of IFC Schema Extension Cases
### Analysis of Extension Cases via Domains and Sectors  
Table 2 presents the number of IFC schema extension cases categorized into domains and sectors. A total of 20 of the cases were in the architecture domain, while the other 46 cases were in the infrastructure domain. The number of IFC schema extension cases for the infrastructure domain was more than double that of those in the architecture domain. The difference reflects the historical need of the industry to extend the IFC schema for representing products, processes, and related concepts in the infrastructure domain.  
Figure 7: Trends of academic efforts for IFC schema extension.  
Conversely, it also reflects the lack of representation for the infrastructure domain in the IFCs, which only started being partially addressed with version 4.0.  
Within the architecture domain, most extensions targeted the general building sector, followed by a few extensions for MEP. The extension cases for infrastructure mainly focused on the tunnels, bridges, and roads sectors, followed by more specific sectors such as dams and harbors, as well as specific subsectors such as foundations and earthworks.
## 1 Introduction
For instance, they can reduce the amount of wastewater and provide an easy-handling separation. Although these are high-performing catalysts, they require special effort to achieve their full commercial potential. Mixed metal oxides as MgO/La\({}_{2}\)O\({}_{3}\), CaO/MgO, and MgO/Al\({}_{2}\)O\({}_{3}\) have been widely used as basic catalysts for the production of GC. Their surface area and composition can be adjusted easily. These catalysts have higher catalytic activity than that of a single component [15; 16; 17; 18]. Zeolites modified by ion exchange of alkaline cations have also emerged as interesting solid bases [19; 20]. They are known to catalyze reactions that require a base site, such as transesterification reactions. The base strength of an alkali ion exchanged zeolite increases with an increasing electropositivity of the exchange cation, and subsequently the product yield. There is considerable interest in the development of catalysts based on clays. With regard to their environmental aspect,these materials are one of the most user-friendly solid catalysts. Clays (in particular, smectites) are successfully used in many organic reactions [21]. Transcarbonation has been successfully driven with residues, such as fuel ash [22], with regard to esterification, mineral acids, metal exchangers and heteropolyacid-supported montmorillonite [23,24]. To follow glycerol carbonate production, [PERSON] et al. used HPLC [25]. [PERSON] et al. applied FTIR analysis to quantify GC when its concentration is around 70 to 75% in a mixture of glycerol and GC [26]. Herein, we report the preparation, characterization, and catalytic activity of Tunisian smectite in the transcarbonation reaction of glycerol and ethylene carbonate to glycerol carbonate and ethylene glycol in solventless conditions. The smectite is fully characterized in terms of BET porosimetry, SEM-EDS, FTIR and DRX, while the reaction is followed qualitatively by \({}^{1}\)H-NMR and, in a quantitative manner, by ion exclusion HPLC (High-performance liquid chromatography). Data are collected from runs performed at diverse catalyst concentrations, reagent molar ratios and temperatures. Subsequently, a step-by-step proposal, fitting and validation methodology is applied to obtain a kinetic model adequate for a wide experimental range. Finally, the stability of the catalyst is studied in depth in a wide temperature interval for four cycles, applying the aforementioned kinetic model now featuring an added first-order deactivation to deepen into the mechanisms underlying the smectite deactivation.
## 1 Introduction
stated that higher torque does not ensure the uniform pressure distribution because of the bending of end plates [12]. For PEMWE, [PERSON] et al. [3] studied the in situ experimental characterizations of clamping pressure effects. A graphite block is stiffer than a titanium block for loading uniform pressure to the MEAs, which results in homogeneous local current density [13]. Similar to the additional hydraulic compression in PEMFC, the pneumatical compression of the end plates in PEMWE stacks enhances the uniformity of local current density [14]. Non-uniform distribution of current density can result in a temperature difference and significant degradation of MEAs [15]. [PERSON] et al. [16] have an end plate design patent for a high-pressure electrolytic module that maintains a uniform pressure with the backing plate. [PERSON] et al. investigated the relationship between pressure distribution and local current density with the operando method [17]. The highly compressed region exhibits a high current density with low ohmic resistance. [PERSON] et al. compared circular PEMWE stacks with 5 and 10 cells [18]. The further analysis of clamping pressure distribution in the stacks with different cell numbers is necessary to investigate the performance difference between the stacks. Although the previous studies conducted PEMFC and PEMVE end plate research, few papers have studied high pressure sealing end plates with weight reduction. Comparing the square and the circular end plates is necessary to ensure the uniform pressure distribution at each cell in the stacks. In this study, stress, deformation analysis, and topology optimization are performed to reduce the weight of circular and square end plates. After the topology optimization, design parameters, such as removing the regions of upper, lower, or side parts, were derived. Stress and deformation analyses were conducted with newly designed end plates. Strain simulation and validating experiments of dummy cells was performed to verify clamping pressure uniformity between cells by insertion of pressure sensing films into the middle and end cells of the stack. The square end plate experiences more stress than the circular end plate in topology optimization with weight reduction. In contrast, the clamping pressure between cells in the square end plate stacks is uniform similarly to cells of the circular end plate stacks based on FEA.
## 4 Results
### Robustness Check  
#### 4.3.1 Random Forest with Country Fixed Effects  
To account for unobserved structural differences between EU member states, we extended the random forest model by introducing country fixed effects using one-hot encoding of the geo variable. In addition, we retained year as a continuous numeric predictor to capture temporal trends in cloud adoption over the study period. This enhanced specification allowed the model to jointly estimate the impact of structural geographic variation and the progression of digital transformation over time.  
The inclusion of country identifiers resulted in a clear improvement in predictive performance. On the test set, the model achieved a root mean squared error (RMSE) of 0.421, a mean absolute error (MAE) of 0.321, and an R\({}^{2}\) of 0.864, outperforming the tuned model without fixed effects, which had an RMSE of 0.437, MAE of 0.327, and R\({}^{2}\) of 0.853. This improvement demonstrates that incorporating country-specific heterogeneity via dummy variables, along with a continuous temporal trend, enhances the model's ability to generalize and capture complex variation in cloud adoption across EU member states.  
The updated variable importance scores are shown in Table 5. Consistent with previous models, ICT specialists and broadband access remained the most influential predictors, with %IncMSE values of 28.18% and 20.12%, respectively. The year variable also emerged as highly predictive (14.27%), indicating a clear upward trend in cloud adoption over time. In addition, several country dummies, i.e., most notably Luxembourg (geo_LU) and Bulgaria (geo_BG), ranked among the top predictors, each contributing over 8-13% to model accuracy. This highlights the significance of national context, beyond economic or digital infrastructure measures alone.  
A visual summary of these scores is provided in Figure 3, which reinforces the dominant role of digital infrastructure and skills while also illustrating the added explanatory power of temporal and geographic indicators. The inclusion of fixed effects did not displace core digital predictors but rather enriched the model's context awareness.  
Figure 2: Variable importance scores from the tuned random forest model on Panel A (no fixed effects). Source: Authors’ estimations in R software Version 2024.12.1+563.