esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conceptual Solution  
The solution conceptual model showed in Figure 4 is based on combining the ADACOR and the ISA-95 architectures to organize the information interaction in the W-IR cooperative workell. The  
Figure 4: Combined ADACOR and ISA-95 Conceptual Model.  
solution model provides three main services to answer the self-organization problem in the W-IR cooperative workcell. The first service is to \"Plug and Operate\", this means that any worker or IR can dynamically register to or unregister from the workcell during the production process. The second service is to \"Customize and Produce\", this means that the solution adapts the existing number of workers and IR in the workcell to produce any variable number of a new product order. Finally the last service is to \"Assign and Monitor\", this means that the solution model assigns the production tasks to the existing operational resources, then it monitors the overall status of the tasks and the resources to minimize the dwell time and hence obtains the maximum production efficiency.  
The four holon categories of ADACOR are distributed over the ISA-95 levels. Two different types of OHs can be found in level-0 and level-1. These two OHs are a Robot Operational Holon (ROH) and a Worker Operational Holon (WOH). The physical component of an OH locates in the shop floor layer (i.e., level-0) of the ISA-95, while the communication component locates in the process sensing layer (i.e., level-1) of the ISA-95. The input data from the robot controller and the worker User Interface (UI) is acquired by the physical component of an OH in the ISA-95 level-0. These data are passed to the communication component in ISA-95 level-1 for processing. ISA-95 Level-0 and Level-1 interacts together in the same mentioned manner to output the data to the robot controller and the worker UI.  
The process control and supervision layer (i.e., level-2) of the ISA-95 implements other two categories of the ADACOR holons. These two categories are the TH and SH. The TH is responsible for receiving and storing the product orders from the PH. The TH collects different product orders in a single batch order. The product orders within a batch order can be arranged or modified via the TH UI. Finally the TH is responsible for sending the batch order to the SH and control the execution of the product orders. The SH is responsible for continuously receiving the batch orders from the TH, then distributing the product orders all over the available operational resources. In order to do that, the SH should be aware of the number of the available operational resources all the time. Also the SH should be able to follow the overall status of the available operational resources.  
The manufacturing operations management layer (i.e., level-3) of the ISA-95 contains the PH. Similar to the object oriented programming concept, the PH can be customized as many different instances, these instances present the different product orders. The PH provides an UI to customize the features and the quantity of a new product order. Eventually, the PH can send a new product order to the TH before or during the production execution.
# Travel Time Prediction on Long-Distance Road Segments in Thailand
## 3 Materials and Methods
### Baseline Implementation  
Based on the review of the time-series analytic techniques discussed in previous related work, we considered ARIMA [7], SVR [25], LSTM [26], and ConvLSTM [14] for the preliminary analysis. The details of these techniques are described in the following subsections. After this, we conducted an experiment to select which of these techniques would provide the best performance as a baseline in our study. The pre-processed dataset that we adopted and examples of it are shown in Table 1, and the steps of our experiment are described in Section 3.4.
## 3 Results and Discussion  
### Optimized Parameters for ROC Curves  
Equilibrium studies were performed in different containers to determine the amount of time required to achieve optimum detection of the target analytes. For all the containers, 24 h was sufficient for the VOCs to establish equilibrium; however, detection of the analytes of interest can be achieved within 1 h of equilibrium or buildup of the target volatiles. The optimized conditions for the containers of varying volumes are summarized in Table 3. For the three different volume sizes used in this study, the optimum static sampling time was determined to be 10 min for SPME-GC-MS and PSPME-IMS and 1 min for dynamic sampling using PSPME-IMS, with sample sizes ranging from 10-50 mg for both smokeless powders. Experiments using SPME-GC-MS were limited to static headspace extractions for the sampling of quart and gallon can containers and did not include sampling of the large plastic containers due to the impractical nature of sampling large volumes with static SPME sampling (requiring 30 min for the detection of analytes of interest).  
Nitroglycerin (NG) and 2,4-dinitrotoluene (2,4-DNT) were observed to be the most abundant analytes in the headspace of smokeless powders, resulting in detection within 10 min of static extraction with a minimum of 10 mg of smokeless powder present in different containers. Dynamic extractions of 1 min were sufficient for detection of NG and 2,4-DNT from 10 mg of smokeless powders. The sensitivity for detection of DPA was greater in the bench top IMS, resulting in detection of DPA for all the different defined volumes. On the other hand, DPA detection required a minimum of 50 mg of AU smokeless powder in quart and gallon cans for the portable IMS detection system. Detection of DPA was difficult for the large volume containers in which DPA was not detected after 10 min of static sampling but detected within 1 min of a dynamic extraction.  
NG, DPA and 2,4-DNT were successfully detected after a 10 min extraction for all the smokeless powders with as low as 10 mg of smokeless powder in both quart and gallon cans when SPME-GC-MS was used. However, the sampling of large volume containers required longer extraction times (30 min) for detection using the SPME-GC-MS, reducing the true positive detection rates (TPR) for large containers.
# Integrated Photonics for IoT, RoF, and Distributed Fog-Cloud Computing: A Comprehensive Review
## 2 Hierarchical IoT, Fog/Cloud Computing Architecture  
Figure 1 illustrates the hierarchical architecture of an integrated IoT, RoF, Fog, and Cloud computing system. This architecture is composed of three distinct yet interdependent layers, each fulfilling a specific role in the collection, processing, and management of data. At the base is the IoT layer, which includes a wide array of distributed sensing and actuating devices operating in diverse environments. These devices are responsible for real-time data acquisition and control, and serve as the interface between the physical and digital worlds. Above this, the Fog layer acts as an intermediate processing stage located near the edge of the network. It comprises gateways, routers, and localized servers that enable low-latency processing, temporary storage, and timely service delivery. Finally, the Cloud layer represents the centralized data-processing infrastructure, consisting of powerful data centers capable of performing intensive analytics, long-term storage, and resource scaling on demand. Radio-over-Fiber (RoF) technology plays a critical role in this architecture by enabling the transparent, low-latency transmission of high-frequency wireless signals over optical fiber, effectively bridging IoT endpoints with Fog nodes and Cloud infrastructure when needed. By using RoF links, wireless access points and distributed sensors can be interconnected with centralized processing resources while preserving high bandwidth and real-time responsiveness. Together, these layers--interconnected through RoF--form a cohesive and efficient architecture for supporting intelligent, distributed applications in next-generation digital ecosystems.  
Table 1 compares Fog and Cloud computing across several key system requirements, highlighting their complementary roles in distributed computing infrastructures. Fog  
computing prioritizes ultra-low-latency, energy-efficiency, and real-time responsiveness at the network edge--features critical for time-sensitive applications such as industrial automation, autonomous systems, and IoT deployments. In contrast, Cloud computing excels in centralized scalability, high-capacity data-processing, and long-term storage, making it ideal for tasks such as big data analytics and machine learning training. The table also illustrates how communication requirements differ: Fog architectures rely more on wireless and geographically distributed links, whereas Cloud infrastructures are built on high-throughput fiber optic interconnections within and between data centers. Together, these paradigms enable a hierarchical computing model that balances proximity, performance, and scalability.
## 7 The Case Study
### Kinetic Energy Harvesting Materials Assessment
#### 7.4.2 Piezoelectric Materials
To illustrate the potential implementation of PZT-based energy harvesting, the following assumptions are made:  
Energy generated per footstep (piezoelectric): approximately 0.2 Joules. Energy per prayer cycle: approximately 1.5 Joules (due to compression/bending). Average number of visitors per day: 500,000. Prayer cycles per day per visitor: 7. Walking steps per day per visitor: 500. From these assumptions, the calculated daily energy potential is as follows:  
Prayer movement energy: 500,000 visitors \(\times\) 7 cycles/day \(\times\) 1.5 J = 5,250,000 J/day (approximately 1458 Wh/day). Walking energy: 500,000 visitors \(\times\) 500 steps/day \(\times\) 0.2 J = 50,000,000 J/day (approximately 13,889 Wh/day). Thus, the total daily energy (prayer + walking) harvested from piezoelectric mats is approximately 15.3 kWh/day. This output significantly exceeds the energy generated by textile-based triboelectric nanogenerators (t-TENGs), particularly in high-density usage scenarios. Figure 16 illustrates a worshipper performing prayer movements on a piezoelectric smart mat designed to convert mechanical pressure into electricity. The mat consists of three primary layers: a piezoelectric film or crystal layer, a conductive electrode, and a flexible substrate. When pressure is applied during prayer actions such as sujood, piezoelectric charges are generated and harvested as usable electrical energy. Arrows in the diagram indicate the conversion from mechanical stress to electrical output, and an energy meter displays an approximate generated energy value. This configuration is highly suitable for areas with frequent motion, such as prayer zones, providing renewable energy to support mosque operations. Similarly, Figure 17 shows a person walking on a piezoelectric energy-harvesting mat inside a mosque. The mat's labelled layers include a piezoelectric material layer responsive to compression, a conductive electrode layer for charge transfer, and structural support for stability. Each footstep generates measurable electrical voltage, as indicated by directional arrows and energy flow markers. A small chart demonstrates the potential daily energy output derived solely from foot traffic. This technology is particularly well-suited to busy areas such as mosque entrances, Tawaf zones, or corridors where foot traffic is consistently high. Figure 16: Piezoelectric Smart Mat for Energy Harvesting during Prayer. Figure 17: Footstep-Based Energy Generation Using Piezoelectric Mats in Mosque Walkways.
# 3D Point Cloud Completion Method Based on Building Contour Constraint Diffusion Probability Model
## 3 Methods  
The structure of our proposed 3D point cloud generation diffusion probability model based on building contour constraints is shown in Figure 1. Four modules make up the model: _Contour Constraint, Point encoding, Diffusion_, and _Reverse_. The contour constraint module aims to extract the building contour constraint region \(\{C,M\}\) of the missing part of the input point cloud and samples it as dense point clouds \(X_{C}\) and \(X_{M}\). The diffusion module involves gradually adding Gaussian noise to the input original building point cloud over time steps. The point-encoding module is divided into three parts: the first part encodes the input building point cloud into its shape latent code \(h\); the second part encodes the dense point cloud \(X_{C}\) and \(X_{M}\) extracted from the contour constraint, which represents the building's contour constraint region, into its shape latent codes \(C\) and \(M\); and the third step combines the shape latent codes obtained from the first two steps into \(H\). The reverse module involves taking samples from the noise distribution \(p\left(X^{(T)}\right)\sim\mathcal{N}(0,I)\)which is approximated to be similar to the point set \(q\big{(}X^{(T)}\big{)}\). These samples are used as input to a network \(\theta\) to estimate the denoising motion for each point at time step \(t\), thereby reconstructing the overall building point cloud. In this context, the representation of building \(Z\)'s point cloud is denoted as \(X^{(0)}=\big{\{}x_{i}^{(0)}\big{\}}_{i=1}^{N}\).
## 4 Experiment and Result Analysis
### Analysis of Results
Although there are still many false-positive pixels in the image, considering that the unexpected obstacles in this column of images are far away and small in size, and the detection task is very difficult, it can still be said that PF-SSUn achieves the desired detection effect. The image in the right column contains two unexpected obstacles. Both UOD-SS and UOD-Un have many missed pixels, and PF-SSUn fills these missed areas through Bayesian probability fusion. Figure 11: Display of test results.
# D2D-Assisted Adaptive Federated Learning in Energy-Constrained Edge Computing
## 2 Related Work
In [21], the authors investigated wireless-powered FL networks with non-orthogonal multiple access, and they introduced a two-dimensional search algorithm to minimize total energy consumption. In [22], the authors designed an energy-efficient FL scheme over cell-free IoT networks to minimize the energy consumption of IoT devices participating in FL. A wireless-energy-transfer-enabled hierarchical FL framework for heterogeneous networks with a massive multiple-input--multiple-output wireless backhaul was developed in [23]. The authors saved energy costs through FL device association and wireless-transmitted energy management. In [24], the authors focused on renewable-energy-driven FL systems, and they leveraged reinforcement learning to adapt devices to intermittent renewable-energy supplies and to improve communication efficiency. Few studies have investigated D2D-assisted FL mechanisms. In [25], the authors designed a hybrid blockchain architecture to enhance the security and reliability of model parameters sharing between FL D2D pairs, and they improved FL node selection through deep reinforcement learning. In [26], the authors developed a multi-stage hybrid FL approach, which orchestrated the devices on different network layers in a collaborative D2D manner to form consensus on the learning model parameters. The authors in [27] introduced a decentralized FL scheme that took D2D communications and overlapped clustering to enable decentralized device aggregation and to save energy consumption. In [28], the authors focused on the wireless communication efficiency of D2D FL, and they presented theoretical insights into the performance of digital and analog implementations of decentralized stochastic gradient descent in FL systems. To improve the scalability of FI, the authors in [29] proposed a server-less learning approach that leveraged the cooperation of devices to perform data operations inside the network by iterating local computations and mutual interactions. In [30], the authors investigated energy-aware deep neural network model training, and they formulated an energy-aware and D2D-assisted FL problem to minimize the global loss of the training model. Although the aforementioned studies have provided some useful insights and promising paradigms for FL scheduling, key factors, such as the impact of computing power on model accuracy and the correlation of training datasets, as well as the latency constraints of model training, have not yet been well-investigated. Furthermore, how to adaptively formulate D2D pairs between heterogeneous FL clients with complex communication topology remains a critical challenge. To fill this gap, in this paper, we leverage graph theoretic approaches to describe the relationships between FL clients and to obtain the joint schemes of D2D pairing and resource allocation, which improves the model accuracy of FL training.
# Hybrid Multi-Agent GraphRAG for E-Government: Towards a Trustworthy AI Assistant
## 4 Results, E-Governance Use Case and Applications
### Web Search Pipeline Results  
The web search pipeline response, which retrieves up-to-date publicly available information beyond the internal knowledge base, demonstrates strong capability in surfacing real-time perspectives, for example, in our use case, emerging policy instruments and stakeholder reactions, as illustrated in Figure 6. The response integrates multiple high-confidence sources, including institutional sites, news outlets, and expert commentary, to enrich the core understanding of the Clean Industrial Deal with external validation, recent developments, and critical reflections. The output remains consistent with the prompt, as shown in Table 15 in Appendix A, by clearly delineating key pillars of the policy and adding contextual elements such as implementation challenges, funding gaps, and market dynamics.  
Structurally, the response organizes information according to major policy areas (e.g., financing, trade, energy, skills), effectively combining declarative content with strategic context. This, in our example query, supports a broader narrative of how the Clean Industrial Deal is evolving as both a policy framework and a political instrument. Notably, the web pipeline introduces elements that are either missing or underemphasized in internal data pipelines, such as projected investment needs, stakeholder criticisms, and trade-related countermeasures, improving the response's groundedness and realism.  
Moreover, the web output is particularly valuable when policy feasibility, stakeholder alignment, and temporal relevance are critical, especially for forward-looking governance applications. However, while the content is timely and thematically rich, it may vary in reliability depending on the source validity, and it typically lacks the structural formalism found in graph-based retrievals. The web output also does not inherently model relationships or dependencies, relying instead on narrative flow and thematic aggregation.  
Despite these limitations, the web search pipeline contributes a critical external layer to Q&A and, as in our example, to the Clean Industrial Deal narrative. It complements embeddings and graph RAG pipelines by bridging documentation with political, financial, and public sentiment dynamics. For example, this approach adds substantial value in contextualizing EU policy within current global pressures and institutional debates, thereby enhancing the interpretability, credibility, and responsiveness of the Q&A system as a whole.
# Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment
## 3 Preliminaries
### Fuzzy String Matching
#### 3.1.1 Jaro-Winkler  
Jaro-Winkler is a string-matching algorithm that is built upon the Jaro similarity metric, as defined in Equation (1) [44].  
\[Jaro(s_{1},s_{2})=\begin{cases}\frac{1}{3}\times\left(\frac{m}{|s_{1}|}+\frac{m }{|s_{2}|}+\frac{m-t}{m}\right):m>0\\ 0\quad:otherwise\end{cases} \tag{1}\]  
The formula uses the length of the strings being compared, \(|s_{1}|\) and \(|s_{2}|\), along with the number of matching characters, \(m\), and transpositions, \(t\), to calculate a similarity score. To further enhance the performance of the algorithm, [PERSON] introduces a boost factor for equal prefixes, as in Equation (2).  
\[Jaro\;Winkler(s_{1},s_{2})=\begin{cases}Jaro(s_{1},s_{2})+lx\;px(1-Jaro(s_{1},s _{2})):Jaro(s_{1},s_{2})>b_{t}\\ Jaro(s_{1},s_{2})\quad:otherwise\end{cases} \tag{2}\]  
The boost is applied when the Jaro similarity score exceeds a threshold, \(b_{t}\). The length of the common prefix up to a maximum value, \(l_{bound}\), is denoted by \(l\), and \(p\) represents the prefix scale. It is important to note that \(l_{bound}\;p\leq 1\) must be satisfied.
# Potential of Room Acoustic Solver with Plane-Wave Enriched Finite Element Method
## 1 Introduction
### Purpose of This Study  
This study was conducted to discuss the potential of the plane-wave-enriched FEM as a room acoustic solver via performance examination on 2D real-scale room acoustic problems through numerical experiments. This is a preliminary study toward constructing an efficient 3D room acoustic solver. Plane-wave-enriching low-order FEs are used herein to discretize spaces. Local-reaction impedance boundary modeling is used to address the absorption characteristics of sound absorbers such as Helmholtz resonators and porous absorbers. As the main result, we can report whether or not PUFEM can predict wideband frequency responses robustly and accurately from low frequencies to high frequencies with a fine frequency resolution and with a single coarse mesh having a small amount of DOF. Our study also includes a performance comparison against conventional linear FEM having second-order accuracy with respect to dispersion errors. The performance is measured in terms of both the prediction accuracy of sound pressure level, and in terms of the reduction effect of DOF. We use two problems including realistic boundary impedance of sound absorbers commonly used in the room acoustics field. Because the two problems have no analytical solutions, reference solutions are calculated using a fourth-order accurate FEM with fine meshes. The remainder of this study is organized as follows. For reader convenience, PUFEM approximation is briefly introduced in Section 2 where also important aspects related to choosing the numerical parameters are discussed. Section 3 presents examination of the applicability of PUFEM via both the sound field analysis in a single room and a more complex coupled room composed of four rooms. Section 4 concludes this study.