esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Using an encoder-decoder structure with custom attention mechanisms [13], our model effectively learns complex patterns from limited data while maintaining the computational efficiency needed for real-time applications. The incorporation of stochastic processes allows our model to represent trajectories as probability distributions over possible future states, naturally handling uncertainty while providing richer information for downstream applications [14]. This stochastic framework is complemented by regularization terms derived from Lyapunov stability theory [15], ensuring that predicted trajectories converge to established patterns while accommodating reasonable deviations consistent with real-world operations. Furthermore, our multilayer attention mechanism explicitly models the influence of weather conditions [16], allowing dynamic adjustments to predictions based on changing environmental factors. The validation of our approach uses the TrajAir dataset collected from Pittsburgh-Butler Regional Airport (KBTP), providing a comprehensive testbed with diverse flight operations and weather conditions. The results demonstrate that our stochastic deep learning approach significantly outperforms both traditional physics-based models and deterministic machine learning methods in terms of prediction accuracy, robustness to environmental variations, and computational efficiency. The contributions of this paper can be summarized as follows:  
* We propose a deep learning architecture based on stochastic processes that generates trajectory distributions rather than single paths, effectively quantifying uncertainty in general aviation trajectory prediction. * We integrate Lyapunov stability theory-based regularization with a multilayer attention mechanism for weather factors and a comprehensive loss function, creating a model that maintains stable convergence to flight patterns while effectively quantifying uncertainty and responding to environmental variations critical to general aviation operations. * We validate our approach using real-world data from non-towered airports, demonstrating practical applicability and significant performance improvements over existing methods. The remainder of this paper is organized as follows. Section 2 reviews related work on trajectory prediction and uncertainty quantification. Section 3 details our methodology, including stochastic process modeling, deep learning architecture, and custom loss functions. Section 4 describes the experimental setup, evaluation metrics and comparative analysis. Finally, Section 5 discusses the implications, limitations, and directions for future research.
## 4 Collaborative Spectrum Detection Algorithm  
Multiple users perform collaborative detection and send their detection information to the fusion center. The fusion center fuses the received information and then makes a final decision based on the decision rules. It can alleviate the hidden terminal problem of single-user energy detection. Collaborative spectrum detection models can be divided into three categories: (1) Centralized collaborative spectrum detection [31]: The fusion center adjudicates the detection information sent by the sensing users and later returns the results to each sensing user. (2) Distributed collaborative spectrum detection [32]: Each node senses independently and exchanges sensing information to self-determine which bands can be accessed. The third model is (3) relay-forward co-operative spectrum detection [33]. The commonly used collaborative detection algorithms are mainly the centralized collaborative detection algorithm and the distributed collaborative detection algorithm. However, the distributed collaborative detection algorithm requires data exchange between nodes, which increases the system overhead and renders the algorithm difficult to use, so this paper uses the centralized collaborative detection algorithm.  
Generally, there are two fusion rules for the fusion center: (1) Soft merge: The fusion center directly processes the original sensory information from the sensing user. Commonly used collaboration criteria are equal gain combining, maximum ratio combining, and selective combining. (2) Hard merge: The user judges the signal and sends the result to the fusion center for fusion. The commonly used judgment rules are as follows: (1) AND criterion: If the detection results of all users are H\({}_{1}\) (band occupied), the decision is H\({}_{1}\). (2) OR criterion: If the detection result of one user is H\({}_{1}\), the judgment is H\({}_{1}\). (3) K out of N criterion: As long as the detection result of K users is H\({}_{1}\), the judgment is H\({}_{1}\).  
When single-threshold collaborative detection with different decision rules is used, the false alarm probability and detection probability are expressed as follows:  
\[\rm{P_{d-and}}=\prod_{j=1}^{N}P_{di} \tag{13}\]  
\[\rm{P_{f-and}}=\prod_{j=1}^{N}P_{fi} \tag{14}\]  
\[\rm{P_{d-or}}=1-\prod_{j=1}^{N}(1-P_{di}) \tag{15}\]  
\[\rm{P_{f-or}}=1-\prod_{j=1}^{N}(1-P_{fi}) \tag{16}\]
# Adaptive Vector Quantization for Lossy Compression of Image Sequences +
## 2 The Adaptive Vector Quantization (AVQ) Algorithm  
As we mentioned above, one of the main characteristics of the AVQ algorithm is to combine the features of a dictionary-based scheme (i.e., LZ2 scheme) and the capacity of the vector quantization methodology to accurately approximate data. The AVQ algorithm is essentially the first application of an LZ-based strategy to still images [7]. In fact, this algorithm does not reduce the two-dimensional data to one-dimensional data (as in [8]), but it operates effectively on the two-dimensional data.  
The basic idea behind the AVQ algorithm is to subdivide the input image in variable-sized blocks. Each one of these latter blocks is substituted by an index, which represents a _pointer_ to a similar block (according to a given distortion measure), stored into the dictionary. During the encoding, the AVQ algorithm dynamically updates the dictionary \(D\), by adding new blocks to it. The new blocks are derived from the already processed blocks and from the already coded parts of the input data. These new blocks are essential to continue the encoding of the input image, since they can be used for the encoding of the not yet processed parts of the input image.  
As we will see in Section 2.1, in which we focus on the details related to the logical architecture of the AVQ algorithm, all the rules (e.g., the rules for the adding of new blocks to the dictionary, the rules  
Figure 1: The usage of the VQ by an encoder (**a**) and by a decoder (**b**).  
for the computation of the distortion measures, etc.) can be denoted as _heuristics_, while all the data structures used by the AVQ algorithm (e.g., the dictionary, etc.) are denoted as _components_.  
Subsequently, in Section 2.2, we explain the key concepts and the technical aspects related to the compression and the decompression stages of the AVQ algorithm.
## 3 Design of a Lightweight Distributed File System Based on RDMA Communication Mode
### Design of a Lightweight Distributed File System Based on RDMA Communication Mode
Another thread is mainly responsible for data interaction with the Client, called the S-C process. When the Client initiates a request, it needs to use this thread to respond to the Client's request and upload or download file data transmission. In addition, the S-C process will communicate with other storage in the same group, especially when files need to be synchronized. 3. RDMA communication module  
The RDMA communication module is the core communication and underlying message management module. By changing the file, the RDMA module can cover the original TCP and UDP communication methods and can assume the communication function of the entire system. During the initialization process, the physical memory is preregistered through the _ibv_reg_mr()_ function, that is, the memory management is handed over to the RDMA module. The process of this module when transmitting data is as follows: First, the Client module initiates a data request, which requires the bottom layer to start an RDMA Tracking server. At the same time, the Tracker will let the remote receiving node start an RDMA Client. A queue pair and a completion queue are established through the _ibv_create_cq()_ and _ibv_create_pq()_ functions, complete initialization, and a connection is established. The relationship needs to be installed before the client communicates with the storage. After the connection is established, the Client and storage can transfer files. Once the data transfer is complete, you can notify the end of the data transfer and consume the corresponding elements in the completion queue.
# Seismic Vulnerability Assessment of Historical Masonry Buildings in Croatian Coastal Area
## 4 Discussion
It is worth mentioning that the damage evaluation of the building was performed via static nonlinear analysis. Thus, the numerical damage patterns for the collapse acceleration do not necessarily comply with the damage to the structure caused by a real earthquake for the same acceleration. Numerical collapse is realized when the critical criteria for shear, compression, tension, and/or bending failure are achieved. The number of elements with realized failure criteria depends on the characteristics of the building, and can affect the damage intensity and damage pattern. Additionally, the results of the analysis depend on the material mechanical properties of the buildings, which are unknown. In fact, the mechanical properties were obtained from the literature and were reduced by a confidence factor equal to 1.35 according to Eurocode 8-3 [48]. The limitations of pushover analysis and the selection of control points could also influence the results in the case of horizontal and vertical irregularities. Despite these approximations and assumptions, the obtained results provide a better insight into individual seismic vulnerability, the capacity and expected damage of the buildings subjected to certain seismic actions, than the insights that can be reached by simply associating buildings to a class and then determining vulnerability and damage for the whole class. The results have important operational outcomes in terms of the planning and management activities for the investigated site. They can be used to determine priorities in rehabilitation and in the provision of funds for the reconstruction of the buildings. Future efforts will be devoted to developing vulnerability and damage curves for the whole settlement, which includes not only the historical center with stone masonry buildings, but also newer buildings at the periphery, dating from the beginning of the 20 th century to the present day.
## 3 Results
The TF values in most of the sites was more than 1, indicating the plant's efficient ability to transport metal in the upper parts of the plant. TF for Zn is R-S \(\geq\) to S-L \(>\) L-F. Zn translocation is decreased in floral parts so that floral parts may receive a lower quantity of Zn compared to Pb and Cu and Cd. The highest Cu translocation was from R-S followed by the S-L, and less translocation is for L-F. Cd was found to be less mobile compared to Pb, Zn and Cu. Cd translocation was found to be higher for L-F. The ANOVA result was significant, indicating significant differences in translocation among sites and plant parts. In many cases, the translocation factor was more than 1, indicating the ability of _C. procera_ to accumulate and translocate high quantities of metal and potentially be used in phytoremediation practices. Principal component analysis (PCA) shows the relationship of plant-soil HM concentration, bioaccumulation, and translocation with soil physiochemical properties (Figure 4). The PCA biplot of samples and environmental data show a distinct scattering that was found to be impacted mostly by metal concentration of soil (Zn, Pb, and Cu), soil texture \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**HMs**} & \multicolumn{3}{c}{**BAF (S/R)**} & \multirow{2}{*}{**F-Value**} & \multirow{2}{*}{**\(p\)-Value**} & \multicolumn{3}{c}{**BAF (S/P)**} & \multirow{2}{*}{**F-Value**} \\ \cline{2-2} \cline{5-10}  & **Roadside** & & & & & & **Roadside** & & **Urban** & **Rural** & \multirow{2}{*}{**F-Value**} \\ \cline{2-2} \cline{5-10}  & **Roadside** & & & & & & & **Roadside** & & **Urban** & **Rural** & \\ \hline Zn & 3.64 \(\pm\) 0.07 \({}^{\ast}\) & 1.38 \(\pm\) 0.07 \({}^{\ast}\) & 0.43 \(\pm\) 0.06 \({}^{\ast}\) & 1.64 & 0.17 & 1.56 \(\pm\) 0.08 \({}^{\ast}\) & 0.41 \(\pm\) 0.05 \({}^{\ast}\) & 0.46 \(\pm\) 0.04 \({}^{\ast}\) & 7.90 & 0.003 \({}^{\ast\ast\ast}\) \\ Cu & 1.1 \(\pm\) 0.14 \({}^{\ast}\) & 2.37 \(\pm\) 0.03 \({}^{\ast}\) & 0.38 \(\pm\) 0.03 \({}^{\ast}\) & 1.23 & 0.37 & 0.79 \(\pm\) 0.06 \({}^{\ast}\) & 0.40 \(\pm\) 0.03 \({}^{\ast}\) & 0.45 \(\pm\) 0.02 \({}^{\ast}\) & 1.28 & 0.34 \\ Pb & 1.83 \(\pm\) 0.27 \({}^{\ast}\) & 0.71 \(\pm\) 0.13 \({}^{\ast}\) & 1.75 \(\pm\) 0.08 \({}^{\ast}\) & 0.49 & 0.68 & 0.62 \(\pm\) 0.07 & 0.55 \(\pm\) 0.06 \({}^{\ast}\) & 0.99 \(\pm\) 0.07 \({}^{\ast}\) & 4.86 & 0.02 \({}^{\ast}\) \\ Cd & 0.47 \(\pm\) 0.05 \({}^{\ast}\) & 0.67 \(\pm\) 0.07 \({}^{\ast}\) & 0.80 \(\pm\) 0.07 \({}^{\ast}\) & 0.58 & 0.43 & 0.44 \(\pm\) 0.04 \({}^{\ast}\) & 0.56 \(\pm\) 0.03 \({}^{\ast}\) & 0.71 \(\pm\) 0.05 \({}^{\ast}\) & 7.98 & 0.002 \({}^{\ast\ast\ast}\) \\ \hline \hline \end{tabular} Note: \({}^{\ast\ast}\) (\(p<0.001\)), \({}^{\ast\ast}\) (\(p<0.05\)); different superscript letter (\({}^{\ast}\)+) show significant differences; BAF (bioaccumulation factor); S/R (Soil to root); S/P (soil to whole plant tissue).  
\end{table} 
Table 4: Bioaccumulation factor of HMs from soil to root and soil to _Calotropis procera_ intact-plant.
## 2 Materials and Methods
### Data Collection
#### 2.2.2 LAI Acquisition  
Destructive sampling was used to collect rice LAI data in different plots. Repeated destructive sampling was carried out after RGB image acquisition. Four representative plants from each plot were brought back to the laboratory. The samples were separately divided into leaves and straw, and ten leaves were randomly selected from each sample. Middle segments of the selected leaves 8 cm in length were used to estimate leaf area (LA) by measuring the length and height. After the leaf area measurements, the middle segments of the selected leaves were initially dried for 30 min at 105 \({}^{\circ}\)C and then dried at 80 \({}^{\circ}\)C to a constant weight to acquire the dry matter quantity (DM\({}_{1}\)). The specific leaf area (SLA) was calculated using Equation (1).  
\[SLA=LA/DM_{1} \tag{1}\]  
where \(LA\) is the total leaf area of ten leaves; \(SLA\) is the specific leaf area in cm\({}^{2}\)/g; and \(DM_{1}\) is the dry matter quantity of ten leaves (g).  
The remaining leaf blades were used to measure dry matter (\(DM_{2}\)). The leaf dry biomass of each plot (w) was obtained by summing \(DM_{1}\) and DM\({}_{2}\). LAI was calculated using Equation (2).  
\[LAI=1/4\times W\times SLA\times N\times 10^{-4} \tag{2}\]  
where \(LAI\) is the total leaf area per plot; \(W\) is the leaf dry biomass of each plot; and \(N\) is the number of rice plants in a surface of 1 m\({}^{2}\).
_applied_  
_sciences_  
_Article_  
**A Geospatial Approach to Energy Planning in Aid of Just Energy Transition in Small Island Communities in the Philippines**  
**[PERSON] 1,2,*\({}^{\copyright}\), [PERSON] 1,3,4\({}^{\copyright}\), [PERSON] 1,5 and [PERSON] 1,3,6\({}^{\copyright}\)**  
1 Engineering Graduate Program, School of Engineering, University of San Carlos, Cebu City 6000, Philippines; 1 [PERSON]_ADDRESS] (L.L.); [EMAIL_ADDRESS] (LD.F.T.); [EMAIL_ADDRESS] (E.M.Q.)  
2 Department of Electrical Engineering, Palawan State University, Puerto Princresa 5300, Philippines  
3 Center for Research in Energy Systems and Technologies, School of Engineering, University of San Carlos, Cebu City 6000, Philippines  
4 Department of Industrial Engineering, University of San Carlos, Cebu City 6000, Philippines  
5 Department of Chemical Engineering, University of San Carlos, Cebu City 6000, Philippines  
6 Department of Electrical and Electronics Engineering, University of San Carlos, Cebu City 6000, Philippines  
* Correspondence: [EMAIL_ADDRESS]  
**Abstract:** Providing electricity in off-grid island communities is a big challenge, exacerbated by the high cost of transporting fossil fuels and the non-viability of extending grid connections. Installing renewable energy systems in these areas is deemed a practical solution, especially supporting just energy transitions in these communities. However, the lack of information about resource availability and the most suitable locations hinders effective planning. This paper aims to determine the sufficiency of available renewable energy sources to meet the electricity demand of off-grid island communities. It is achieved through a three-phased approach: (1) an assessment stage; (2) geospatial analysis; and (3) technical potential estimation. The approach is applied in three island communities in Palawan, namely Araceli, Balabac, and Cuyo, where a diesel power plant currently provides electricity to its households and commercial/institutional establishments. The results indicate that the three islands can be powered by 3, 1.5, and 11 MW solar photovoltaic farms, respectively, which is sufficient to meet the projected demand until 2030. The approach can be helpful, especially for off-grid island communities, as they plan to provide universal electricity access using renewable energy sources.  
**Keywords:** energy access; load forecasting; renewable energy; resource mapping; GIS; multicriteria decision analysis; analytic hierarchy process
## 4 Results and Discussion
### Influence of the Operational Parameters
#### 4.2.1 Effects of HTF (Heat Transfer Fluid) Inlet Temperature  
The temporal evolution of the liquid fraction within the shell and coil tube ice storage tank is plotted in Figure 8 for four different HTF inlet temperatures of ethylene glycol, namely 5, 7.5, 10, and 12.5 \({}^{\circ}\)C for a volumetric flowrate V = 2 L/min. As it can be clearly seen, by increasing the inlet temperature from 5 to 7.5 \({}^{\circ}\)C, the melting time decreases intensely, by about 25%. By increasing it further from 7.5 to 10 \({}^{\circ}\)C, the melting time reduction is about 33%. The melting time continues to decrease by about 23% when T\({}_{\text{inlet}}\) increases from 10 to 12.5 \({}^{\circ}\)C.  
To understand the effect of the HTF inlet temperature on the melting process, the contours of liquid fraction for the shell-and-tube ice storage with two different inlet temperature (7.5 and 10 \({}^{\circ}\)C) are depicted in Figure 9. As illustrated, for melting of 25% of PCM, the heat transfer process is only governed by the conduction mechanism so that the contours of the both models are the same. After some time, the effect of natural convection in the PCM plays an important role (melting of 50-100% PCM) so that the differences between the models appears obviously. Finally, the system with higher inlet temperature melts faster.  
Figure 7: Stable and unstable structures of thermal layers developed around the coil tube for T\({}_{\text{inlet}}\) = 5 \({}^{\circ}\)C and V = 4 L/min at \(t\) = 250 min (**a**) and 500 min (**b**).
# Multi-Source Data Fusion for Vehicle Maintenance Project Prediction
## 2 Related Work
### Traditional Time Series Prediction Methods  
Traditional time series analysis centers on establishing parametric models, determining model parameters, and utilizing the resolved models for future predictions. Exponential smoothing, introduced by [PERSON] (2016), forecasts future values by assigning weights to historical values. The moving average method, devised by [PERSON] (2016), computes the mean within a specific time window to forecast future values. Building on these methods, [PERSON] developed the extensively utilized ARIMA model [PERSON] (2016). ARIMA amalgamates autoregressive and moving average models to capture data trends, seasonal variations, and noise characteristics. Prior to prediction, the observed value series is subjected to tests for smoothness, white noise evaluation, and assessments of the auto-correlation coefficient and partial autocorrelation coefficient. Although traditional methods excel in addressing straightforward time series prediction problems, they encounter challenges in managing high-dimensional time series data with intricate dependencies and nonlinearities.
# Virtualized MME Design for IoT Support in 5G Systems
## 2 Background
### Virtualization
We will further assume that the virtualized EPC entities are deployed using a cloud provider service, such as Amazon's Elastic Computing Cloud (EC2), in which the computing resources can be dynamically scaled in or out on demand and with a pay-as-you-go price scheme. For example, Amazon EC2 charges a per-hour price for the service use [25]. Additionally, a relevant aspect in cloud services such as EC2 is that the time required to obtain and boot a new server instance can be in the range of tens of seconds to minutes [26].
# Preparation and Modification of Biochar Materials and their Application in Soil Remediation
## 4 Application of Biochar in Soil Remediation
### Potential Risk of Biochar  
Although biochar has great advantages in remediation of soil pollution, improvement of soil quality, increase of crop yield, and reduction of greenhouse gas emissions, these studies are all short-term and the long-term effects of biochar on soil are still ambiguous. Therefore, in order to make better use of biochar and reduce its possible risks, the long-term effects and risk assessment of biochar on soil should be paid more attention. Studies have shown that, although the application of biochar improved soil quality and crop yield, biochar reduced the efficacy of herbicide and increased weed growth by 200% [129]. The reduction of herbicide efficacy must increase the use of herbicide, which may increase the residual concentration of herbicide in the soil and cause more serious pollution to the soil. In addition, biochar, which is mostly prepared from crop waste, may contain heavy metals on its own and could release pollutants if it gets into the soil. Due to the weathering and aging, biochar would undergo physical, chemical, and biological degradation. Finally, it would form colloids, nanoparticles, and smaller fragments that alter the microbial community in the soil. However, the interaction between these components of biochar and soil, the internal mechanism of microbial transformation and geochemical circulation still need to be further studied [130].