esa-sceva/satcom-chunk-collection huggingface dataset:
# GIS-Based Evaluation Method for Accessibility of Social Infrastructure Facilities
## 3 Methodology
### Accessibility Index  
The accessibility index is mainly used to evaluate the overall accessibility of a region. For example, [PERSON] et al. created buffers along with road networks to develop a neighbor destination accessibility index for urban facilities and services in four cities in New Zealand and developed an accessibility index using the weighting facilities in the buffer [22]. [PERSON] et al. computed the accessibility index for the transit point of public transportation. They divided the entire region into cells and defined the average distance of the accessible transfer points from the cell's center point within the cell as the accessibility index [17]. [PERSON] et al. calculated the accessibility index for a comparative study of public service accessibility in Warsaw, Poland. The accessibility index was calculated by giving one point for kindergartens, two points for elementary schools, and three for high schools [23]. [PERSON] and [PERSON] divided the accessible distance range into three categories: shorter than 800 m, 800 m-1200 m, and longer than 1200 m to calculate the accessibility index considering the population of the census blocks [24]. [PERSON] et al. computed the accessibility index with population proportion and distance for spatial equity of urban facilities [25]. [PERSON] et al. defined access to sustainable transport from university index using total nodes and routes weighted by attractiveness at service areas [26].  
\[AI_{i}=\frac{\sum p_{ij}\times t_{min}}{P} \tag{1}\]  
This study defines the accessibility index (AI) as shown in Equation (1). AI in a neighborhood is calculated using Equation (1), where \(AI_{i}\) is the AI of \(i\)th-neighborhood in a city, \(P\) is the total population of the city, \(p_{ij}\) is the population of the \(j\)th residential block (RB) in \(i\)th-neighborhood, and \(t_{min}\) is the travel time between the RB and nearest social infrastructure facility. Thus, AI represents the population-weighted expected travel time (mathematical expectation) required to reach a social infrastructure facility in a town. For example, when AI for a library of a town is 4.3 min, the town's citizens need an average of 4.3 min to get to a library.
# Pilot-Assisted OFDM for Underwater Acoustic Communication
## 6 Conclusions  
We investigated the BER performance of CP-OFDM in underwater acoustic (UWA) channels by employing a pilot-based channel estimation and two equalizers, namely, least squares (LS) and zero forcing (ZF) equalizers. The underwater channel model used in this work was based on Rician shadowing. Through this model, we evaluated the performance of a system in UWA channels more realistically. This model also enabled us to adjust the various channel parameters such as fading, Doppler shift, and ambient noise. The main conclusions obtained after extensively simulating the proposed system by varying several system parameters were: (1) the best BER performance was achieved if the number of pilots was kept as one-fourth the number of subcarriers; (2) the BER performance degraded if the energy of the pilots increased whilst keeping the overall symbol energy constant; (3) an acceptable BER performance was observed when using the LS or ZF equalizer; (4) the performance of the ZF equalizer was marginally better than that of the LS equalizer; (5) increasing the number of subcarriers deteriorated the BER performance because of an increased ICI; and (6) an acceptable error performance was observed even at a transmitter-receiver distance of 1250 m with both equalizers. This work could be extended by employing MMSE with soft channel estimates and a decision feedback equalizer (DFE) in a UWA environment. The proposed technique will be further evaluated using a Bellhop core model and/or a Water Mark simulator [43; 44]. For trials in a local lake, we plan to implement the proposed model in a GNU radio [45] for Raspberry Pi Zero [46].  
[PERSON] and [PERSON] conceived the idea and the block diagram of the system. [PERSON] and [PERSON] implemented the model in MATLAB, ran the simulations, and wrote the manuscript. [PERSON] conceived and designed the UWA channel model used and contributed to the discussion section. [PERSON] and [PERSON] reviewed the final version of the manuscript. All authors have read and agreed to the published version of the manuscript.  
Figure 9: BER of CP-OFDM when using the LS and ZF equalizers as a function of the number of subcarriers with a transmitter-receiver distance of 500 m and the number of pilots one-fourth the number of subcarriers.  
**Funding:** This research was funded by a grant from the Spanish Ministry of Science and Innovation through the project \"NAUTILUS; Swarms of underwater autonomous vehicles guided by artificial intelligence: its time has come\" (PID2020-112502 RB / AEI / 10.13039/501100011033).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Acknowledgments:** The authors express their gratitude to the Escuela Tecnica Superior de Ingenieria de Telecomunicacion and the Instituto de Ingenieria Oceanica, Universidad de Malaga, Malaga, Spain. We would also like to thank the College of Computer and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Methodology
### Performance Metrics  
To measure the performance of the subset dataset, generated after feature selection optimization, we followed [PERSON] et al. [28] performance metrics' method, who used the insider detection rate and insider detection precision. As mentioned in Section 3.4, the subset dataset is fitted to LOF to detect anomalies and we measure the performance of the results for each subset dataset tested, to find the optimal subset. The subset dataset that produces the best precision will be selected as the optimal one. Insider detection rate (DR) and precision can be determined by using Equations (1) and (2).  
\[DR=\frac{TP}{TP+FN} \tag{1}\]  
\[Precision=\frac{TP}{TP+FP} \tag{2}\]  
In Equations (1) and (2) True Positive (TP), represents the number of true malicious users detected, False Positive (FP) represents the number of normal users that were detected as malicious, and False Negative (FN) represents the malicious users that were not detected as malicious, but falsely considered as normal users.
# Time-Series Prediction of Failures in an Industrial Assembly Line Using Artificial Learning
## 3 Materials and Methods  
The method followed is shown in Figure 1 schematically.  
Figure 1: Schematic representation of prediction workflow.  
Various AL techniques are appraised for modelling and prediction. Their hyper-parameters are determined by trial and error and suggestions collected from the literature. Creating the models is based on optimisation, so-called _training_.  
Preliminary analyses of the dataset are conducted to characterise its properties, including temporal dependencies such as lags. Model performance is evaluated through error metrics, regression analysis, error histograms, and variance assessments. The dataset is partitioned into two segments: the initial 80% is allocated for network training, testing, and validation. Within this subset, validation and testing portions are employed to assess and refine the model's generalisation capacity, rather than for predictive purposes. The remaining 20% of the dataset, corresponding to the most recent temporal observations, is reserved for prediction. This enables the evaluation of machine-learning (ML) performance on data extending beyond the temporal scope of the training period, referred to as the \"horizon,\" which signifies future time points outside the available dataset. In this context, the terms \"network\" and \"model\" are used interchangeably, as both denote systems governed by underlying mathematical functions. All computational experiments are executed on a system equipped with 8 GB RAM and a 2.8 GHz quad-core CPU. Processing is confined to CPU resources, with one time-series analysed across six distinct AL approaches, resulting in six cases.
## 5 Proposed Method
### Description
#### 5.1.1.2 Fuzzy Inference  
The second step in fuzzy logic processing is fuzzy inference. A rule base, comprising of the range of rules consisting of fuzzy outputs corresponding to SNR and PDPT fuzzy inputs, was formed using the opinion of experts with rich theoretical and practical experience in jamming and counter jamming disciplines of information warfare. The rule base was further refined by vetting the system outputs by the experts. The rule base is given as follows:  
1. If SNR is LOW and PDPT is LOW then JI is HIGH.
2. If SNR is LOW and PDPT is MEDIUM then JI is HIGH.
3. If SNR is LOW and PDPT is HIGH then JI is HIGH.
4. If SNR is MEDIUM and PDPT is LOW then JI is LOW.
5. If SNR is MEDIUM and PDPT is MEDIUM then JI is MEDIUM.
6. If SNR is MEDIUM and PDPT is HIGH then JI is HIGH.
7. If SNR is HIGH and PDPT is LOW then JI is NO.
8. If SNR is HIGH and PDPT is MEDIUM then JI is LOW.
9. If SNR is HIGH and PDPT is HIGH then JI is MEDIUM.  
Figure 4: Input-output surface corresponding to the membership values of inputs (SNR, PDPT) and output (JI).  
Figure 3: Graphical representation of the trapezoidal function for the output, jamming index (JI).  
Relations obtained from the rule base are interpreted using the minimum operator 'and'. The outputs obtained from the rule base are interpreted using maximum operator 'or'. The overall input-output surface corresponding to the above membership functions, values of variables, and rule base is depicted in Figure 4.
## 3 Results and Discussion
### Effect of Pyrolysis Temperature on Biochar Yield and Physicochemical Characteristics
#### 3.1.3 Elemental Composition of Biochar Samples  
The high ash content in the algal biomass was also reflected in the elemental content of the biochar, identified primarily in the total carbon content (Table 1). In general, an increase in the pyrolysis temperature generated an increase in the carbon content and decreases in the total hydrogen and nitrogen contents [15, 17]. However, in our study, we only found a negative relationship between the pyrolysis temperature and the N content of biochars, which decreased from 3.15% to 1.58% with increasing temperature (Table 1).  
The degree of carbonization (H:C = 1.51) of the BC450 sample was lower than that of the other biochar samples (Table 1), likely due to a higher hydrogen content, lower carbon content, and higher presence of alkaline metal hydroxides. During the pyrolysis process, the greatest mass loss occurred between \(\sim\)200 and \(\sim\)350 \({}^{\circ}\)C (depending on the pyrolysis rate), where organic C slowly volatilized in the form of CO and CO\({}_{2}\), thus decreasing the organic matter content and increasing the percentage of inorganic matter [12, 29]. Then, this inorganic matter entered a carbonization process, where alkaline metal hydroxides and/or metal carbonates are formed [17, 30]. Above \(\sim\)350 \({}^{\circ}\)C, a slower decomposition of stable organic and inorganic matter occurs, forming metal oxides that can become part of the aromatic structure of the biochar or be volatilized [17, 27, 30].
## 2 Materials and Methods
### Leaf Segmentation  
#### 2.3.1 \(k\)-Means  
The concept of the developed algorithm is demonstrated in the workflow shown in Figure 2. The k-means algorithm has been widely used to group, classify, and segment clusters of point data [30]. This algorithm was used in this study to implement leaf PCD segmentation. The leaf PCDs in a specific region were segmented into small groups, each of which depicted either a single leaf PCD or part of a leaf PCD, based on the initial given parameter \(k\). The parameter \(k\) is used for deciding the number of segmentations. Specifying a larger \(k\)-value leads to a cluster of PCDs in the space to be segmented into smaller pieces of points groups. The method for deciding the optimal \(k\)-value for the \(k\)-means algorithm will be discussed later, in Section 2.3.3.  
Figure 2: Workflow of leaf angle estimates.  
The \(k\)-means involves iteratively exploring the centroid of every cluster and then picking and regrouping the near points into a new group. Figure 3a displays the changes in the class boundary and cluster centroid between the \(n\)th and \((n+1)\)th iterations. As shown, in the \(n\)th iteration, point p was closer to the centroid of the leaf a. However, it was grouped with leaf b (\(D_{a}<D_{b}\)), which did not lead to the smallest sum of the distance between the points and the cluster's centroid within the group. Therefore, the leaf PCDs were reorganized to group the p point with leaf a, where \(\sum\) Di \(>\sum\) D'i, and the iteration ended in \(\sum\) D'i, which was the smallest. For instance, assuming that \(\sum\) D'i in the \((n+1)\)th and \((n+2)\)th iterations were equal, the iteration stopped at the \((n+1)\)th iteration because the cluster in the \((n+1)\)th iteration was the smallest.
## 3 Results and Discussion
### Morphological Change of Si Micropillars during Electrochemical Cycling
The high amount of O is likely due to the oxidation of anode when it was transferred from the glove box to the SEM chamber for characterization. The detection of only a small amount of P (1.37 wt %) indicates that only a tiny amount of electrolyte residue is left on the Si anode. Since Li has very low energy of characteristic radiation, it cannot be detected in the EDX spectrum. The EDX result evidences that an SEI layer has formed on the top layer of the silicon anode after 100 cycles. A schematic model (Figure 7) is used to illustrate the morphological change of Si micropillars during electrochemical cycling. Si micropillars expanded in the first few cycles, but the pillar structure is discernible (Figure 7b,c). SEI layers formed on the surfaces of micropillars due to the high surface area. However, owing to limited space among micropillars, they merged with each other and formed an island-like structure (Figure 7d). After long-term cycling, an additional SEI layer formed on the  
Figure 6: (**a**) EDX spectrum of Si anode after 100 cycles; (**b**) EDX maps of O, C, Si, F, and P on the Si anode. top of the anode, and the anode became a flat and thick film with microsized cracks (Figure 7e). The accumulation of SEI products in the space among the micropillars as well as on the top layer of micropillars participates in the capacity decay by obstructing Li\({}^{+}\) from accessing the Si. Finally, the effects of waste Si slurry thickness on the electrochemical cycling performance are investigated. As shown in Figure 8, the morphologies of the anode surfaces produced from different slurry layer thicknesses became very similar after 100 cycles. The independent micropillar structure had changed into a thick film structure, regardless of the initial diameters of the micropillars. This explains the insignificant discrepancy of the capacity of micropillars with different diameters at the later period of cycling. However, by comparing Figure 8a-c, it is clear that by using a thinner Si slurry thickness to generate thinner Si micropillars, the number of surface cracks on the anode surface could be greatly reduced. The reduction of crack generation indicates a longer service life of a LIB anode. Figure 8: Morphologies of anode surfaces after 100 cycles. Si micropillars are produced by laser irradiation on slurry layers of different thicknesses: (**a**,**d**) 8, (**b**,**e**) 13, and (**c**,**f**) 20 \(\upmu\)m. (**a–c**) are in low magnification. (**d–f**) are in high magnification. Figure 7: Schematic of morphological change of Si micropillars during the charge/discharge process: (**a**) initial situation, (**b**) pillar expansion and SEI formation, (**c**) growth of pillar and SEI layer, (**d**) formation of island structure, (**e**) formation of thick film with cracks.
# MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication
## 4 Simulation Results and Discussions
### Delay Comparison  
Figure 5 illustrates that the proposed approach possesses consistent low delay values compared to other schemes with respect to the device's speed. End-to-end delay is when a packet is required to reach its destination comprising several nodes taking part in the route. Each packet must wait in a queue of the nodes for a specific time until the node starts processing the packet and propagates towards the next node in the selected path. Queuing delay is a significant part of the overall delay in situations where the congestion on the network is high and nodes in the system are operating at their full queue length capacity.  
In this condition, the selection of nodes based on their queue length is more important than the mobility and battery level of the nodes. The stated schemes (MP-OLSRv2 and MEQSA-OLSRv2) do not base their forwarding decision on the queue length and relay, which leads to the generation of multiple paths toward the destination and does not positively affect the delay comparison, as some packets would be trapped in the network on nodes with larger queue length. Until they are processed in the queue or they are resent by the source node, they tend to increase the amount of time to be delivered to the destination. The delay performance achieved by the MBMQA scheme is 33% lower as compared to MP-OLSRv2 and 19% lower as compared to MEQSA-OLSRv2, which is obtained at the maximum device speed of 60 m/s. Moreover, MEQSA-OLSRv2 exhibited better performance with respect to MP-OLSRv2 as a result of analyzing the battery level of the nodes and its effect on the forwarding path. Nodes with a low amount of battery will eventually be exhausted and will lose the existing packets in the queue of the nodes. These packets need to be re-transmitted by the source nodes, which increases the overall time of the process.
## 2 Materials and Methods
### Data Processing  
Raw accelerometer data with removed gravitational forces were acquired from each WIMU and downloaded via SPRO software (Realtrack Systems, Almeria, Spain, [[https://www.hudl.com/en_gb/products/wimu](https://www.hudl.com/en_gb/products/wimu)]([https://www.hudl.com/en_gb/products/wimu](https://www.hudl.com/en_gb/products/wimu))). The high-frequency noise of the signals was removed using a 2 nd-order low-pass Butterworth filter with a cut-off frequency of 120 Hz. Data from the first 10 s were dismissed for each 30 s trial to ensure a stationary vibration signal. A 5 s window of the signal from the 10 s mark was selected and processed to obtain the analysed parameters [16].
# A Novel Method of Emotion Recognition from Multi-Band EEG Topology Maps Based on ERENet
## 3 Experiments and Results
### Experimental Environment and Experimental Settings  
The proposed model is implemented based on the TensorFlow framework and deployed on a NVIDIA GeForce RTX 2060 GPU. The binary cross entropy is employed as the loss function of the model, and an Adam optimizer is applied to minimize the loss function with an initial learning rate of 1 \(\times\) 10\({}^{-3}\) and a decay value of 5 \(\times\) 10\({}^{-4}\). In addition, the batch size and epoch are set to 100 and 100, respectively.
# A Survey of Information Extraction Based on Deep Learning
## 2 Entity Relationship Extraction Based on Deep Learning
### Supervised Learning--Joint Extraction
#### 2.2.1 Joint Extraction Based on Parameter Sharing  
In the parameter sharing-based approach, by allowing two or more tasks to share the coding layer, it enables both entity recognition and relationship extraction to update the parameters of the shared coding layer during training, thus, finding the optimal parameters for the global task. The parameter sharing-based approach can effectively improve the error accumulation propagation problem and the problem of ignoring the intrinsic connection and dependency between two sub-tasks in the pipeline approach, and improve the robustness of the model [12]. At the same time, the number of parameters and the complexity of the model are reduced to achieve model light weighting while keeping the model performance unchanged. [PERSON] et al. [48] showed that cross-layer parameter sharing has better performance than the standard transformer in aspects, such as language modeling. [PERSON] et al. [49] combined the parameter-sharing transformer with the standard [PERSON] et al. [50] proposed efficient neural architecture search (ENAS), which achieves speedup by sharing weights for all sub-models and avoiding training from zero. Considering the long-distance relationship between entity labels, [PERSON] et al. proposed a hybrid neural network model without manual features to jointly extract entities and their relationships, using a bi-directional encoder-decoder LSTM (BiLSTM) module for entity extraction, and then passing entity context information to the CNN module for relationship extraction [51]. This method considers the long-range relationships between entities, but there are some relationships that are ignored and need further research to improve the recall rate. In addition, the connection between the two modules, named entity recognition and relationship extraction, needs further research to obtain better performance.
# Data Spaces in Manufacturing and Supply Chains: A Review and Insights from European Initiatives
## 2 Materials and Methods
### Analysis of European Data Space Initiatives  
In parallel with the literature review, a document-based analysis was conducted to examine major European initiatives' structure, maturity, and strategic orientation related to data spaces in manufacturing and supply chains. To identify and select relevant projects, we first retrieved the complete list of European data space initiatives from the International Data Spaces Association (IDSA) Data Spaces Radar repository. Each initiative entry and its official project website were screened to confirm that the project explicitly addressed data space challenges in manufacturing or supply chains. In addition, we performed targeted Scopus searches using project names (e.g., \"Catena-X\", \"Gaia-X\") to collect peer-reviewed publications describing their data space implementations. Any documents or studies that mentioned a project but did not explicitly describe its data space features or use cases were excluded. This dual-track approach--combining repository screening with bibliographic validation--ensures that our analysis focuses solely on initiatives with a clear, documented commitment to data space development. The analysis then examined the following:  
* **Maturity levels:** the five-stage model (Exploratory, Preparatory, Implementation, Operational, Scaling) captures the lifecycle progression of initiatives, indicating their readiness and deployment status.
* **Architectural foundations:** Identifying the use of frameworks like IDS or Gaia-X allows assessment of technical alignment with recognized standards and interoperability models.
* **Design principles:** core principles such as data sovereignty, interoperability, and trust reflect the governance philosophy and foundational values shaping each initiative.
* **Funding sources:** analyzing the role of EU, national, and private funding highlights the degree of public investment and institutional prioritization of data spaces.
* **Business models:** classification into patterns like joint innovation or marketplace structures provides insight into the economic rationale and collaboration modes behind the initiatives.
* **Geographic distribution:** mapping projects by country enables the identification of regional leadership, policy alignment, and cross-country engagement in data space development.  
Finally, cross-mapping was conducted to connect academic publications with the real-world initiatives they referenced or studied. This provided insight into the degree of alignment between theory and practice and identified which initiatives are most embedded in scholarly discourse. This enabled the synthesis of content presented in Section 3.3.