esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast 3D Reconstruction of UAV Images Based on Neural Radiance Field
## 3 Experiments and Results
### Analysis and Comparison of Experimental Results
#### 3.2.1 Model Reconstruction Effect  
Based on Figure 6, a meticulous visual scrutiny of the image reveals that the outcomes of the reconstruction process, as facilitated by the neural radiation field method, evince a heightened lucidity in encapsulating the architectural perimeters and vertices of the edifice. Nevertheless, a modicum of inconsequential noise subsists, which is conjecturally ascribable to the potential overexposure incurred during the image acquisition phase. In the case of Dataset 1, the resultant reconstructions stemming from the classical methodology lay bare noteworthy lacunae within the models, predominantly stemming from occlusions induced by the overhanging eaves of the structure. In stark contradistinction, the employment of the neural radiance field approach conspicuously obviates any semblance of such omissions. Dataset 2 exposes the proclivity of the conventionally adopted technique to yield a substantially uneven surface, thereby engendering a blurred demarcation of the building's contours, particularly pronounced in regions like the spire, where conspicuous morphological distortions manifest. In contradistinction, the neural radiance field paradigm evinces a remarkable efficacy in effecting precise reconstruction of the architectural outlines, even when contending with delicate configurations such as lightning rods. A comprehensive analysis of Dataset 3 serves to underscore that the neural  
Figure 5: Camera pose visualization.  
radiation field modality engenders superior architectural contours and a more seamlessly rendered surface reconstruction.  
In Figure 7, based on detailed comparisons, it is clearly shown that our method produces reconstructed objects that are more complete with almost no missing parts. Additionally, the object outlines are sharper, and the surfaces are smoother. In comparison to our method, commonly used solutions like the open-source software Colmap and the commercial software Pix4 dmapper result in reconstructed objects with rough surfaces and missing components.  
Figure 6: Comparison of reconstruction results of different methods.
# Efficient Coverage Path Planning for a Drone in an Urban Environment
## 6 Performance Evaluation
### Experimental Setting  
To develop and test the proposed algorithm, a simulation environment of a drone flying in an urban area was created using the AirSim simulator [35] built on Unreal Engine [36]. For a detailed description and visual examples of the simulator, the reader may refer to [12].  
The algorithm performance analysis is based on a 3D Digital Surface Model (DSM) with an area of \(\mathcal{S}=1500\times 1500\) [m\({}^{2}\)] generated by an AirSim simulation [12]. The DSM allows us to determine a priori the obstacle locations (e.g., buildings) within the 2D occupancy grid as a function of the drone's altitude. The drone's velocity was set to a constant value of \(10\frac{\text{m}}{\text{s}}\), and the image footprint was calculated for the down-facing camera with a FOV of \(70^{\circ}\) vertically and \(50^{\circ}\) horizontally. The flight altitude was set to 20 m, 40 m, 70 m, 100 m or 150 m, and the searching area was restricted to \(\mathcal{S}\), \(\frac{1}{2}\mathcal{S}\), \(\frac{1}{4}\mathcal{S}\), \(\frac{1}{8}\mathcal{S}\), or \(\frac{1}{16}\mathcal{S}\). The time constraint was set to 300 s as an estimation of the time required for a drone flying at 150 m to cover a \(1000\times 1000\) [m\({}^{2}\)] dense urban area, assuming 50% of it was free space. It is worth mentioning that the suggested multi-resolution approach, with five iterations of 300 s each, requires 24% less time than a single drone scan of the same area at 20 m.  
All algorithms examined were implemented in Python and executed on a PC with a 3.0 GHz Intel Core i5 CPU (Intel Corporation, Santa Clara, CA, USA) and 8 GB RAM.
# Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
## Methodology
### Evaluation Platform  
Extensive range of simulations has been performed by MATLAB to evaluate the performance of MOCHs in comparison with Sleep-awake Energy Efficient Distributed (SEED) clustering [11], ABC [9], Zonal Based Routing (ZBR) [27] and CEEC [15] in terms of First Node Dies (FND), Half Nodes Die (HND), Last Node Dies (LND), throughput of the network, the lifetime of the network, number of CHs per round, and the transmission time of the network. The selected methods for comparison purpose are very recent in the literature of WSN and functioning of these protocols is some way associated with our proposed model. We preferred CEEC [15] and ABC [9] for comparison because the CH selection and cluster formation process of ABC are centralized and controlled by the BS. Moreover, both the protocols are trying to overcome the problem of control overhead. Despite the fact that, SEED and ZBR are selected for comparison with our proposed model for the reason that both the protocols have good cluster stability and good energy management. The average results included in these simulations have 90% confidence interval which is acquired after running the simulation 5 times. The performance comparison also reveals that the MOCHs is better than SEED [11], ABC [9], ZBR [27] and CEEC [15] in terms of energy efficiency and the network throughput.
# Channel Estimation Based on Statistical Frames and Confidence Level in OFDM Systems
## 6 Simulation Results
### The Performance in Dynamic Multipath Channels
However, the SER curves are not smooth with Doppler spread equal to 20 Hz, 60 Hz and 80 Hz since the Doppler spread brings interference between adjacent OFDM signals. The 16 QAM utilizing sixteen signals constellations for mapping may cause interference between constellation points under deep fading channels. As the SNR increases, the SER curves still represent a declining trend. The SER performance curves show that the proposed confidence level channel estimation method for the CP-OFDM system is able to provide significant SNR gains under dynamic frequency selective channels.
# An Explainable Artificial Intelligence Approach for Detecting Empathy in Textual Communication
###### Abstract  
Empathy is a necessary component of human communication. However, it has been largely ignored in favor of other concepts such as emotion and feeling in Affective computing. Research that has been carried out regarding empathy in computer science lacks a method of measuring empathy based on psychological research. Likewise, it does not present an avenue for expanding knowledge regarding this concept. We provide a comprehensive study on the nature of empathy and a method for detecting it in textual communication. We measured empathy present in conversations from a database through volunteers and psychological research. Subsequently, we made use of a pattern-based classification algorithm to predict the Empathy levels in each conversation. Our research contributions are: the Empathy score, a metric for measuring empathy in texts; Empathetic Conversations, a database containing conversations with their respective Empathy score; and our results. We show that an explicative pattern-based approach (PBC4 cip) is, to date, the best approach for detecting empathy in texts. This is by measuring performance in both nominal and ordinal metrics. We found a statistically significant difference in performance for our approach and other algorithms with lower performance. In addition, we show the advantages of interpretability by our model in contrast to other approaches. This is one of the first approaches to measuring empathy in texts, and we expect it to be useful for future research.  
empathy; natural language processing; pattern-based classification; affective computing; databases +
Footnote †: journal: Applied Sciences
# An Asymptotic Cyclicity Analysis of Live Autonomous Timed Event Graphs
## 3 Transformation from a TEG to Its Precedence Graph
Therefore, we can make a TEG in which every pair of transitions is connected by one place at most. In conclusion, if we apply Rules 1-4 repeatedly to a live autonomous TEG, we can achieve a TEG equivalent to the original and eventually have the properties of a precedent graph. Figure 8 illustrates the process of deriving a precedence graph from Figure 1 while repeatedly applying transformation rules. For better understanding, the newly created place in each step is highlighted in gray. First, parallel places \(p_{23}\) and \(p_{23}^{\prime}\) are merged using Rule 4. In the next step, places with multiple tokens \(p_{21}\) and \(p_{43}\) are divided into corresponding places by Rule 1: (\(p_{21}\Rightarrow p_{21-0}\), \(p_{21-1}\) and \(p_{21-2}\)) and (\(p_{43}\Rightarrow p_{43-0}\) and \(p_{43-1}\)). Now, there are currently three places without tokens, \(p_{12}\), \(p_{34}\), and \(p_{41}\), so we must remove them using Rules 2 and 3. The upstream transition of each of places \(p_{12}\) and \(p_{32}\) has only one downstream place, so Rule 2 is applied. For \(p_{12}\), the upstream transition \(t_{1}\) is removed, and the place \(p_{12}\) is concatenated to \(p_{21-2}\) and \(p_{41}\), which belong to the upstream places of \(t_{1}\). Like \(p_{12}\), \(p_{34}\) is concatenated with \(p_{43-1}\) and \(p_{23}|p_{23}^{\prime}\) by removing Transition \(t_{3}\). Now, there only remains one place without tokens, \(p_{41}p_{12}\). Its upstream transition is \(t_{4-0}\), which has two downstream places, \(p_{41}p_{12}\) and \(p_{43-0}\). After Rule 3 is applied, two new places are created by concatenating \(p_{41}p_{12}\) with the upstream places of \(t_{4-0}\), (\(p_{23}|p_{23}^{\prime})p_{34}\) and \(p_{43-1}p_{34}\), respectively. The TEG in the last step of Figure 8 has the precedence graph's properties and is isomorphic with the precedence graph in Figure 2.
# Ultrafast Mid-IR Laser Pulses Generation via Chirp Manipulated Optical Parametric Amplification
## 3 Theory on Broadband Pumped OPA  
In order to further broaden the bandwidth obtained from DC-OPA, the broad bandwidth of a comparatively lower energy Ti:sapphire laser can be helpful in such a case. In this section, we discuss the physics in the DC-OPA system pumped with a spectrally broadened pulse from a multi-plate system [109]. We attend to and focus upon how the spectral-temporal manipulation of broadband pump and seed pulses affect the gain mechanism during the parametric amplification.  
The OPA process is in principle the amplification of the initial signal and idler pulses through the three-wave interaction. Therefore, the achievable output bandwidths are limited by the initial bandwidths of the seed and idler pulses. The initial signal bandwidth is defined by the input seed continuum, while the initial idler bandwidth is the bandwidth obtained in the very beginning of the amplification. In an infinitely thin layer of the nonlinear crystal, the idler pulse is produced as the DFG happens between the input pump and seed pulses. The idler bandwidth in such a crystal is not affected by the phase-matching condition but by the present pump and seed wavelengths. In further propagation through the nonlinear crystal, the gain bandwidth of both signal and idler pulses is influenced by the phase-matching condition between the three interacting pulses.  
Regarding DC-OPA, the instantaneous wavelengths of the initial pump and seed can be individually manipulated, which gives us more degrees of freedom compared to conventional OPCPA, as the pump chirp is variable as well as the seed. Consequently, the system performance is largely dependent on the instantaneous wavelengths of the input pump and seed pulses. In the following, we will discuss the gain mechanism in the chirp manipulated OPA for signal and idler pulses respectively.
## 4 Methodology
### Data Filtering
#### 4.3.3 Ant Colony Techniques for Optimization (ACO)  
[PERSON] and colleagues introduced the Ant System (AS) algorithm in the early 1990s as a cutting-edge meta-heuristic technique for resolving combinatorial optimization issues  
Figure 4: An Intrusion Detection Model with Binary Symmetry [48].  
that drew inspiration from nature. The first problem to be solved by the algorithm was the traveling salesperson problem. Recently, this has been improved and modified to enhance its usefulness and apply it to other optimization problems. Ant Colony System (ACS), AS-rank, MAX-MIN, and AS are the upgraded versions of AS [49].  
Ant Colony Optimization mimics the cooperative and adaptive mechanisms of natural ant behavior on an individual level. Real ants' foraging habits inspire ACO [49]. We based our computational paradigm for the ACO algorithm on actual ant colonies and their functioning. Our goal was to involve as many constructive computational ants as possible. Each ant follows the generated solution, which relies on its dynamic memory structure recording outcomes from past trials.  
Ethologists' findings derived the paradigm regarding the pheromone trail system ants employ to communicate information regarding the most direct paths to food. Upon finding a previously constructed road, an ant can identify it, decide to follow it with a high chance of success, and reinforce the route with its pheromones. A solitary ant, on the other hand, travels around practically at random. What appears is a type of autocatalytic process whereby a track becomes more appealing to ants to follow the more they follow it. As a result, a positive feedback loop defines the process where the likelihood of selecting a path increases in proportion to the number of ants that have already selected that path. The ACO family of algorithms draws inspiration from the abovementioned mechanism [50].  
It is also possible to consider a FS period before the training phase. Which traits are more discriminative than others is determined by the FS process. It removes duplicated and unnecessary functionality, improving system performance. Generally speaking, FS is a rare IDS operation. Nonetheless, several researchers have conducted their experiments using various FS techniques. This suggests that FS may raise IDS classification accuracy. The problem in using FS lies in finding a minimal feature subset of size \(S\) (\(S<N\)) given a feature set of size \(N\) while maintaining the prior accuracy. As a result, the FS problem needs a solution path [50; 51].  
A partial solution, sometimes referred to as a subset, suggests that there is no connection between the elements or characteristics of the solution, and the decision of which element to add to the partial solution after the previous one needs not be influenced [50; 51]. FS problems can occasionally have solutions of varying proportions. Redefining the use of the ACO representation graph is the first stage in FS employing ACO.  
Using an Ant Colony Optimization algorithm, we selected the attributes shown in Table 5.
# GRACE/GFO and Swarm Observation Analysis of the 2023-2024 Extreme Drought in the Amazon River Basin
###### Abstract  
The Amazon River Basin (ARB) experienced an extreme drought from summer 2023 to spring 2024, driven by complex interactions among multiple climatic and environmental factors. A detailed investigation into this drought is crucial in understanding the entire process of the drought. Here, we employ drought indices derived from the Gravity Recovery and Climate Experiment (GRACE), GRACE Follow-On (GFO), and Swarm missions to reconstruct the drought's progression, combined with reanalysis datasets and extreme-climate indices to analyze atmospheric and hydrological mechanisms. Our findings reveal a six-month drought from September 2023, reaching a drought peak of \(-1.29\) and a drought severity of \(-5.62\), with its epicenter migrating systematically from the northwestern to southeastern basin, spatially mirroring the 2015-2016 extreme drought pattern. Reduced precipitation and abnormal warming were the direct causes, which were closely linked to the 2023 El Nino event. This event disrupted atmospheric vertical movements. These changes led to abnormally strong sinking motions over the basin, which interacted synergistically with anomalies in land cover types caused by deforestation, triggering this extreme drought. This study provides spatiotemporal drought diagnostics valuable for hydrological forecasting and climate adaptation planning.  
GRACE/GFO; Swarm; regional drought; extreme climate; atmospheric circulation; water vapor transport 2023
# Omni Wheel Arrangement Evaluation Method Using Velocity Moments
## 3 Omni Wheel Arrangement Evaluation Method Using Velocity Moments
### Estimation of Straight-Line Direction  
Even if unbalanced, we assume any Omni wheel robot of any wheel arrangement can go straight in at least a particular direction \(X^{{}^{\prime}}\), where the angle from the \(X\) axis to the \(X^{{}^{\prime}}\) axis is \(\phi\). Since the ground speed of each wheel when moving straight ahead in the \(X^{{}^{\prime}}\) axis is obtained by the wheel's orientation from the \(X^{{}^{\prime}}\) axis, the equation of balance for the velocity moment is expressed as follow:  
\[\sum^{n}-V_{x}\cos(\theta_{i}-\phi)l_{i}\sin(\theta_{i}-\beta_{i})=0 \tag{9}\]  
To Solve the above equation for \(\phi\), \(\phi\) is obtained as follow:  
\[\phi=\arctan\biggl{(}-\frac{\sum^{n}\cos(\theta_{i})l_{i}\sin(\theta_{i}- \beta_{i})}{\sum^{n}\sin(\theta_{i})l_{i}\sin(\theta_{i}-\beta_{i})}\biggr{)} \tag{10}\]  
Thus, the robot's straight-line direction \(\phi\) can be estimated by the ratio of the sum of the velocity moments in the x- and y-directions. Since Equation (10) is valid for any number of wheels, it can be applied to any wheel arrangement. In other words, even a robot that cannot move normally due to motor failure may be controlled to move straight ahead.  
The minimum number of wheels for this straight-line estimation is two since Equation (9) only needs to be satisfied. Moreover, since this method does not consider the friction caused by the failed wheel, the failed wheel must not interfere with the robot's movement, as in the case of a wheel falling off. Therefore, this method does not apply to all robot failures, but it may help to increase the number of measures that can be taken in the event of a failure.
## 5 Conclusions and Future Work  
The importance of connectivity, coverage and energy efficiency in wireless sensor networks cannot be overstated. Sensor node failure can result in major issues such as network partitioning and coverage loss. As a result, connection, coverage and energy efficiency must all be addressed appropriately, and nodes must be capable of resolving these issues on their own. A revolutionary approach suggested in this paper provides a solution to these problems. By utilizing jumping nodes, it is possible to provide coverage as well as connection restoration in a seamless manner. The proposed algorithm's methodology makes it an energy-efficient solution. Simulations have confirmed and endorsed the proposed algorithm as an energy efficient option. The results of simulations reveal that, when compared to SNR and PACR algorithms, the suggested technique has a shorter average distance between nodes. Furthermore, these results suggest that the proposed technique enhances the field coverage percentage reduction when compared to the other two algorithms. Finally, the results reveal that the suggested technique requires less messages to operate than the other two techniques evaluated, SNR and PACR. Because jumping nodes are part of the network, the cost of these advantages is that a larger number of nodes are required to cover a given area of interest. Even so, for mission-critical applications, it is cost-effective. The in-depth investigation will be finished in the future.  
Conceptualization, R.M.G., M.A.K. and J.K.; methodology, K.M. and I.B.; software, H.A. and N.J; validation; J.K. and K.M.; formal analysis, M.A.K. and I.B.; investigation, I.B.; resources, H.A. and N.J.; data curation, R.M.G. and H.A.; writing--original draft preparation, K.M. and I.B.; writing--review and editing, K.M.; and I.B.; visualization, H.A.; supervision, J.K., M.A.K. and R.M.G.; project administration, H.A.; funding acquisition, J.K. and M.A.K. All authors have read and agreed to the published version of the manuscript.  
Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R138), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
**Data Availability Statement:** No data were used to support this study. We have conducted the simulations to evaluate the performance of proposed protocol. However, any query about the research conducted for this paper is highly appreciated and can be requested from the principal author ([PERSON] ([EMAIL_ADDRESS])) upon request.  
Figure 5: The number of nodes messages overhead.  
**Acknowledgments:** The authors sincerely appreciate the support from Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R138), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
## 3 Result and Discussion
### Comparison with the Previous Study
#### 3.4.2 Comparison of Failure Mode  
A comparison of the failure modes of different configurations of multi-cell and single-cell panels was also carried out for panels with and without infill. It was found that local buckling was the primary failure mode in hollow cell panels irrespective of the cell number. However, there was limited buckling under the loading points for the infill panels. The infill panels reported tearing and debonding of the sheet corners due to the slippage of hardened infill material from the steel in the panels during the test, which was not the case for the hollow panels. Despite wear and tear on the sheet surface, a significant load was transferred and held by the concrete until the flexural failure of the slab specimens containing SCGC infill. Major failure modes observed in infill panels and panels without infill are highlighted in Figure 15.  
Furthermore, an immediate buckling and thereby failure of the panel specimens were observed in the hollow single-cell panels due to a lack of effective load transfer between the top and bottom skins. Moreover, there was no separation between the individual cells in both hollow and infill multi-cell panels, indicating that the overlap of the sheets at each cell joint plays a significant role in the structural integrity of the specimens and, thereby, efficient load distribution. It is implied that infill material such as the SCGC has assisted the efficient load transfer between top and bottom skins, thereby offering higher load resistance and preventing the premature failure of the panel specimens.  
Figure 14: Summary of ultimate load capacity of various configurations of test panels.