esa-sceva/satcom-chunk-collection huggingface dataset:
# OTFS-Based Handover Triggering in UAV Networks
## 8 Conclusions  
In this paper, we have investigated the problem of handover triggering in UAV-to-ground communication networks, where the UAVs and/or ground entities are flying/moving too fast. Instead of the conventional highly time-varying handover-triggering schemes based on RSS or FT channel gains using OFDM modulation, we introduced a handover strategy based on the estimated DD channel gains using OTFS modulation. This is motivated by the fact that DD channel gains are stable and constant over the whole OTFS symbol duration. By mathematical analysis, we proved that channel estimation errors using LS or MMSE have marginal effects on the spectrum efficiency performance of the proposed scheme. By numerical analysis, we proved the high potency of the proposed scheme over RSS and OFDM in terms of handover overhead, average throughput, and ping-pong ratio under different scenarios. Also, we bound the effect of channel estimation error on the performance of the proposed scheme via numerical simulations. The numerical results revealed the high efficiency of the proposed scheme over other benchmarks, where it outperforms their average throughput performance tens of times in ultra-high-speed scenarios. While this paper focuses on the UAV-to-ground scenario, the proposed OTFS-band handover approach is adaptable to any highly dynamic communication applications without requiring any modifications. Also, the DD channel framework facilitates optimization of the UAV trajectory by dynamically adjusting its velocity and direction to align with the motion of the ground entity. This approach significantly mitigates the frequency of handover requests, thereby enhancing handover performance. Nevertheless, further investigation is warranted to comprehensively address this aspect, which is deferred to future research endeavors.  
Conceptualization, E.M.M. and H.S.H.; methodology, E.M.M. software, E.M.M.; validation, E.M.M., M.A.A. and S.H.; formal analysis, E.M.M.; investigation, E.M.M.; resources, E.M.M. and M.A.A.; writing--original draft preparation, E.M.M.; writing--review and editing, H.S.H., M.A.A. and S.H.; visualization, S.H.; project administration, E.M.M.; funding acquisition, E.M.M. and H.S.H. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to Prince Sattam bin Abdulaziz University for funding this research work through the project number (PSAU/2024/01/30944).  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
Figure 18: Effect of channel estimation error on the OTFS average throughput.
# Improving a Street-Based Geocoding Algorithm Using Machine Learning Techniques
## 2 Overview of Geocoding and Its Advancement
### General Steps for Geocoding  
As one of the primary functions of a GIS [19], geocoding works to assign positions, such as latitude and longitude, into textual addresses using a reference database [3; 5; 10; 15; 19]. These addresses are attached to datasets that are applicable across various applications domains; hence by geocoding, a bridge between spatial and attribute data is established [20]. This association to the geographic information enables not just visual display through accurate maps, but also the application of more in-depth spatial analysis [21; 22]. This integration strengthens the function of GIS as a vital tool across various fields of interest such as urban planning and management [13], human activity and movement studies [5], health [9; 23; 24], emergency dispatching [7], traffic accidents [12], and management of administrative data [4].  
Generally, the geocoding process includes three steps--parsing, matching, and locating, as shown in Figure 1[5]. Previous studies have presented analogous procedures that mostly aim to accept textual input, perform a preprocessing step to this input, and perform a match to a database to return coordinate value pairs for position [8; 22; 25]. Parsing converts unstructured or semi-structured input addresses into structured ones. This process is crucial in giving out precise location, even if input addresses or even the address databases are imprecise and vague [3]. Input addresses may have problems matching due to their unstructured forms. Thus it is essential to capture meaningful units of addresses and enhance the quality of these address elements, which is critical in improving geocoding accuracy [22]. Matching compares and links the structured input addresses to an address reference database. The address reference database includes the information on the addressing system to be matched with the input addresses. Locating finds coordinates based on the results from the matching process.
# Gaussian Process Modeling of Specular Multipath Components
## 1 Introduction
Ray tracing simulations make an attempt to solving this challenge [22, 23, 24]. However, ultimately, this approach needs exact models of the building materials which makes it infeasible for online use in channel prediction. In this paper, we use a data-driven approach to solve this issue. A mirror-source model is used to describe the arrival times and arrival/departure angles of the SMCs, which can be obtained by using a SLAM algorithm as in References [8, 9]. We then apply Gaussian process regression (GPR) as a tool to model the amplitudes of the SMCs. That is, GPR is used to learn the joint effect of the antenna patterns and the material properties of the environment. Our prime aim is on validating this concept based on experimental data. In order to simplify the involved mathematics as much as possible, we select a scenario with a constant polarization state, no antenna coupling, and we restrict the modeling to horizontal characteristics, that is, we simplify the approach to a 2D geometric model. This reduces the amplitude model to a model that depends only on the angle of arrival (or departure) of the SMCs in the horizontal plane. An accurate amplitude model of SMCs leads to the possibility of predicting the amplitude of SMCs very accurately. The potential use could be in performance prediction, where an accurate amplitude model of SMCs may be related to the channel capacity at a certain receiver position [25]. In References [11, 26], it was shown that SLAM using position-related multipath parameters (delay and AoA) can be significantly improved in robustness if the multipath components amplitude information is also used. The contributions of this paper are the following:  
* We derive a Gaussian process regression model for describing the spatial non-stationarity of SMC amplitudes in indoor environments. * We validate the prediction capability of the GP based real measurements acquired in an indoor environment. * We show that the GP is capable of modeling the angle-dependencies of the SMC amplitudes. The remainder of the paper is structured as follows: In Section 2 we introduce the system and signal models. Section 3 presents the GP regression and the methods for quality evaluation. Section 4 reports numerical results using real measurements. Section 5 concludes the paper.
## 3 Results
### Tomato Organic Fraction  
The \({}^{1}\)H and \({}^{13}\)C NMR assignments of Bamano, King Creole, DulceMiel, Sugarland, and Fiaschetta tomatoes extracts in CDCl\({}_{3}\)/CD\({}_{3}\)OH was obtained by 2D experiments (Figures S4-S6). In Table 3, the metabolites identified in the tomatoes organic fraction are reported along with the assignment of \({}^{1}\)H and corresponding \({}^{13}\)C resonances. Previously, the \({}^{1}\)H NMR spectra assignment of organic fraction from lyophilized cherry tomatoes (Naomi and Shiren cultivars) in CDCl\({}_{3}\) has been reported [9; 14]. Taking into account different plant material, a different extraction procedure, and a different solvent (CDCl\({}_{3}\)/CD\({}_{3}\)OH instead of CDCl\({}_{3}\)) used in the present study, a new \({}^{1}\)H NMR spectra assignment was required. In the upfield spectral region, the CH\({}_{3}\)-18 signals of \(\beta\)-sitosterol at 0.66 ppm (I\({}_{\beta\text{-}\text{SIT}}\)), and stigmasterol at 0.68 ppm (I\({}_{\text{SITG}}\)) were detected and quantified (Table 3 and Figures 3 and 4). The resonance centered at 2.30 ppm (I\({}_{\text{FA}}\)) was ascribed to \(\alpha\)-CH\({}_{2}\) of all fatty acid chains. Resonances at 2.73 (I\({}_{\text{DI}}\)) and 2.77 ppm (I\({}_{\text{TRI}}\)) were ascribed to diallylic CH\({}_{2}\) of linoleic and linoleic acids. The multiplet at 5.31 ppm (I\({}_{\text{UNS}}\)) belongs to double bond CH groups of all unsaturated fatty acids. The results of quantitative analysis, reported as histograms in Figure 4, pointed out some interesting differences between the investigated tomatoes cultivars. The amount of \(\beta\)-sitosterol changed from 3.1% (Fiaschetta) to 5.9% (Bamano), whereas stigmasterol ranged from 0.7% in King Creole to 1.8% in Bamano cultivar. The total concentration of unsaturated fatty acids (UFA) is very close in all the selected tomato fruits; it varies from 51.7% in Bamano to 65.8% in Fiaschetta. In terms of mono-unsaturated fatty acids (MUFA), Fiaschetta cultivar was found to be the richest one (24.7%), whereas Sugarland
## 7 Discussion
### Future Directions and Research Opportunities
#### 7.2.2 Advanced Analytics and AI  
The integration of advanced analytics, particularly artificial intelligence (AI) and machine learning (ML), represents a promising direction for the future of DTs in transportation. While current DT frameworks primarily focus on real-time monitoring and basic predictive modeling, future systems could leverage advanced analytics to provide deeper insights and optimize transportation networks throughout their entire lifecycle.  
In the planning and design phase, AI algorithms can analyze complex datasets, such as historical traffic patterns and environmental conditions, to optimize infrastructure designs and simulate future scenarios. During the construction phase, advanced analytics can enhance real-time monitoring of construction progress, identifying potential issues early and improving efficiency. As infrastructure enters the operation and maintenance phase, machine learning models can predict infrastructure wear and tear, optimize maintenance schedules, and improve traffic management. For example, AI can analyze complex traffic patterns to reduce congestion and enhance safety. Finally, in the decommissioning phase, predictive analytics can assess the environmental and operational impacts of decommissioning activities, ensuring sustainable and efficient processes.  
By training machine learning models on large datasets, DTs can enhance their predictive capabilities, enabling more accurate forecasts of congestion, accidents, and maintenance needs [128]. This would allow transportation management to become more proactive, thereby reducing downtime and improving safety.  
Moreover, integrating big data analytics into DTs can support long-term decision-making for urban planners and policymakers. By analyzing data from diverse sources--such as historical traffic patterns, socio-economic factors, and environmental conditions--DTs can simulate future scenarios and assess the impacts of various policy measures. However, implementing advanced analytics in DT systems requires significant computational resources and sophisticated data processing techniques. Continued research is needed to optimize these technologies for real-time applications, ensuring they can deliver actionable insights efficiently.
## 4 Experimental Results
### Skeleton Curve  
The skeleton curves for the IAP and GIAP specimens can be obtained from the hysteresis curves, as shown in Figure 13(a). The skeleton curves decrease when \(d\) exceeds +25 mm for the IAP specimen and when \(d\) exceeds +90 mm for the GIAP specimen, which indicates the failure of the specimen. It is demonstrated that the load-bearing capacity of the GIAP specimen-soil system may be underestimated if the girder is not considered. The determination process of the Park method [51] is, as shown in Figure 13(b), the following: (1) A horizontal line is drawn at 75% of the maximum load-bearing capacity (\(F_{\text{max}}\)), intersecting the skeleton curve at Point \(A\). The horizontal coordinate of Point \(A\) is defined as the elastic displacement (\(d_{A}\)). (2) A line is extended from the origin through Point \(A\) to intersect the horizontal line at the peak of the skeleton curve at Point \(B\). (3) A vertical line is drawnthrough Point \(B\) to intersect the skeleton curve at Point \(C\). The vertical coordinate of Point \(C\) is taken as the yield load (\(F_{y}\)) and the horizontal coordinate as the yield displacement (\(d_{y}\)). The ultimate load (\(F_{u}\)) is defined as 0.85 \(F_{\text{max}}\), with the corresponding horizontal coordinate as the ultimate displacement (\(d_{u}\)). As mentioned in Section 3.3, the soil-abutment interaction and soil-pile interaction should be considered under a positive \(d\), and only the soil-pile interaction should be considered under a negative \(d\). As a result, the \(F_{y}\) and \(F_{u}\) of the GIAP specimen-soil system under a positive \(d\) are significantly larger than those under a negative \(d\). Therefore, only the parameters for the skeleton curves under a positive \(d\) were analyzed. The \(F_{y}\), \(F_{u}\), elastic displacement range (0\(\sim\)\(d_{A}\)), and elastoplastic displacement range (\(d_{A}\)\(\sim\)\(d_{y}\)) for the GIAP specimen, as determined using the Park method [51] with the same parameters for the skeleton curve for the IAP specimen [36], under a positive \(d\) are listed in Table 2. It can be observed that the \(F_{y}\), \(F_{u}\), and \(d_{u}\) for the GIAP specimen are larger than those of the IAP specimen. The elastic stage of the skeleton curve for the GIAP specimen (0\(\sim\)37.7 mm) is larger than that for the IAP specimen (0\(\sim\)12 mm). Therefore, the load-bearing capacity and elastic range of the skeleton curve for the GIAP specimen are underestimated if the girder is not considered.
## 3 Results and Discussion
### Optimization of SPE Procedures
#### 3.2.1 Effect of Sample pH  
Before conducting the experiment, we selected the sample pH of 1, 3, 5, and 7 for investigating its effect on recoveries of all pharmaceuticals. After loading the sample, the cartridges were washed with 4 mL of H\({}_{2}\)O/MeOH (1:1). In the pH range around pKa, small changes in pH provide major changes in ionization and retention. Higher pH above their pKa values leads to an increase in the degree of ionization of acidic targets, making the acidic drug mainly retained by ion-exchange mechanism. As the sample pH decreases, the degree of ionization of the acidic drug also decreases, but at this time the acidic target can be well retained on the column by hydrophobic interaction. In conclusion, in the entire pH range, the two complement interaction mechanisms can retain the acidic analytes well, in whatever form they exist. This was confirmed by the fact that the recoveries of all analytes varied little with the changes in the investigated pH range. As shown in Figure 5, most surface water samples are roughly neutral. Therefore, in the subsequent experiments, there was no need to adjust the sample pH before loading it on the SPE column.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pharmaceutical** & **CAS Number** & **Structure** & **pKa** & **Log P** & **Water Solubility (mg L\({}^{-1}\))** \\ \hline \hline \multirow{2}{*}{nifumic acid} & \multirow{2}{*}{4394-00-7} & \multirow{2}{*}{1.68} & \multirow{2}{*}{4.43} & \multirow{2}{*}{19} \\  & & & & & \\ \cline{1-1}
## 3 Results
### Latency Sensibility Analysis
#### 3.2.1 Causes  
The holographic table recreates the 3D sense of depth by exploiting the stereoscopic principle, like the basic way the human eyes work. The user is provided with a pair of slightly different images, and the 3D is then recreated by the user's brain perspective. Given its functioning, the system presents a critical sensibility on the application latency: the images provided to the user require a constant and precise update to maintain a feasible 3D model from the user's perspective.  
Figure 8: Placeholder system for warping across models. The Rai control center in Monza, Italy.  
High-fidelity surveys have the best visual performance but with a non-negligible cost: their size and level of details represent a computational cost that can result in worse performances for the application. Furthermore, their computational cost is concurrent with the holographic table for rendering, making the 3D model not stable and accurate, resulting in visual artifacts causing motion sickness to the user.
# Performance Assessment of Multi-GNSS PPP Ambiguity Resolution with LEO-Augmentation
## 2 LEO Constellation Design and Ground Observation Simulation  
In this study, two LEO constellations were designed: L and M, both of which were Walker constellations [28]. L consisted of 70 satellites distributed in six orbital planes, with an orbital altitude of 1000 km and an orbital inclination of 90\({}^{\circ}\). M consisted of 90 satellites distributed in 10 orbital planes, with an orbital altitude of 1000 km and an orbital inclination of 60\({}^{\circ}\). The main difference between the two LEO constellations was their orbital inclinations. Figure 1 shows the daily average number of visible satellites of the LEO constellation on 1 January 2022, at a cutoff elevation angle of 7\({}^{\circ}\), with L, M, and L+M from top to bottom. The number of visible satellites from the L constellation in high latitudes was much greater than that in middle and low latitudes, because the L constellation is a polar orbit constellation with an inclination of 90\({}^{\circ}\). The M constellation compensated for the lack of visible satellites in the low and middle latitudes of the L constellation, but it had no coverage in the polar regions, which was also caused by orbital inclination.  
This study conducted LEO ground-observation simulations for all stations in the MGEX observation network. The simulation data included two frequencies, L1 (1575.42 MHz) and L2 (1227.60 MHz). Errors, such as satellite clock offsets, receiver clock offsets, relativistic effect, phase wind-up, tides, satellite and receiver antenna phase center offsets and variations, tropospheric delay, Earth rotation, and measurement noise, were considered. The dry and wet components of the tropospheric delay in the zenith direction were calculated using the Saastamoinen model [29], and the GMF projection function [30] was used to calculate the total tropospheric delay in the signal propagation direction. The measurement noise of the pseudorange and the carrier phase were 0.5 and 0.003 m, respectively.
# Literature Review on Hybrid Evolutionary Approaches for Feature Selection
## 3 A Brief Survey
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Paper** & **Year** & **Aim** & **Experimental Evaluation** & **Assessment Metrics** \\ \hline P12 [75] & 2019 & This article presents a hybrid GWOCSA that efficiently blends the strengths of both GWO and the crowd search optimizer (CSO) to provide optimal solutions for the most efficient global operation. & The experimental findings suggest that the GWOCSA has improved fitness optimization and performed at a higher convergence speed compared to the other FS methodologies to solve the FS problem and achieved more satisfactory optimization results in fewer iterations. This demonstrates the potential of the model to solve difficult issues in real-world large datasets. & The outcomes from testing the HBBEPSO demonstrate the possibility of using the recommended hybrid strategy to determine the ideal variable combination. & MF, BF, WF, STD, average SR, and average FSc. \\ \hline P14 [77] & 2018 & This article utilizes a hybrid method of discrete PSO and the SFLA to reduce the feature dimension and choose optimal parameter subsets. & The simulation outputs indicate that the suggested hybrid approach is good enough to provide an optimized attribute subset and obtain a high CA values. & CA, PR, and RE. \\ \hline P15 [78] & 2017 & The DE operators are used as local search techniques in this work to address the difficulties in SCA. & The outcomes of the execution conclude that the new technique will function better than the alternatives on the basis of success metrics and predictive analysis. & CA, NSF, MF, BF, and WF. \\ \hline P16 [79] & 2017 & In this paper, hybridized frameworks are introduced to construct FS models based on the WOA. & The proposed hybrid models combine SA with WOA. The derived experimentation results have shown that the performance and the capacity of the hybrid WOA approach for choosing the most informational features and for searching the feature space are improved compared with individual wrapper approaches. \\ \hline P17 [6] & 2016 & This article presents a hybrid evolutionary optimization technique called the artificial bee colony-particle swarm optimization (ABC-PSO) for optimum selection and retrieval of features. & The findings show that the overall efficiency of the method is very good and the suggested hybrid method is better suited for voice recognition upon implementing it in the MATLAB working platform. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._
# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 2 Related Work
### Fall Detection Technologies: A Classification Based on AI Paradigms
#### 2.1.1 System Classification by Computational Paradigms
(2025), who developed a hybrid model combining YOLOv8s, a high-performance object detection network, with AlphaPose, a human skeleton tracking system that estimates joint positions [32]. This integration enhanced detection accuracy by simultaneously analyzing visual input and body posture, illustrating the considerable potential of multimodal systems for improving monitoring reliability. Furthermore, such systems can incorporate voice interaction components or conversational AI, enabling real-time confirmation or dismissal of detected events. This additional validation mechanism not only improves accuracy but also enhances user trust and facilitates acceptance of the technology in care environments.