esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Internet of Things (Iot)
#### 2.3.2 Expected Benefits and Challenges IOT  
In case of highways and urban areas, IOT would bring with it several benefits, for example; using cars as smart objects, it will be possible to avoid congestion by monitoring traffic in real time and deploy services that offer traffic routing advice for better traffic management. In addition, parking issues in urban areas can also be mitigated by using smart parking devices systems, based on RFID and sensor technologies that will observe available parking spaces and provide drivers with automated parking advice in real time resulting in improvement of mobility in urban area. Moreover, internet connected sensors may also monitor the flow of automobiles on highways and gather useful information such as numbers of cars at any given point in time and place and their average speed. IOT enabled tools on highways could detect level of carbon dioxide, PM10, etc. and distribute such information to health agencies. Furthermore, connected sensors and other devices could be used in some highway settings for detection of traffic and speed violations and transmit the related data to law enforcement agencies for recognizing the violator or to save details for following accident scene investigation [6].  
As the world becomes more interconnected through the Internet it gives birth to an interesting digital phenomenon in technology world known as the Internet of things (IoT), but this technology brings with it many legal challenges. These challenges may include, but are not limited to, privacy desecrations and security risks [51]. According to [PERSON] et al. [6] and Bandyopadhyay & Sen [52], security is a critical aspect of the widespread adoption of IoT technologies and applications. Stakeholders are unlikely to adopt IoT solutions on a large scale unless system level confidentiality, authenticity and privacy are guaranteed. They further added data confidentiality which signifies an essential issue in IoT setups and that only ratified bodies should be able to access and alter data. The discussion also includes that privacy and trust are very important factors when addressing IoT applications and rules must be demarcated under which, data concerning individual users may be retrieved [51]. Further, privacy issues are addressed as the legal community is required to prepare for this stirring yet alarming digital era and the privacy challenges that will escort its arrival.
# A Lightweight Entropy-Curvature-Based Attention Mechanism for Meningioma Segmentation in MRI Images
## 6 Conclusions  
In this paper, a novel plug-and-play attention module for meningioma segmentation is proposed. This attention mainly includes the following three key components: TSP, EGA, and CFA. TSP compresses the global feature information of 3D meningiomas into three 2D feature maps, achieving dimensionality reduction without loss of information and significantly reducing the computational burden and memory requirements. After dimension reduction, region selection and feature enhancement are performed through EGA. Finally, edge information is enhanced by extracting it through CFA. Experiments were conducted on the meningioma dataset provided by the Department of Neurosurgery at West China Hospital, and its robustness was verified on the BraTS2021 dataset. The results show that ECA has superior performance.  
Conceptualization, Y.G.; Methodology, Y.G., X.X., J.L.; Validation, Y.G.; Formal analysis, Y.G., Y.Y.; Investigation, Y.G., X.X.; Writing--original draft, Y.G.; Writing--review & editing, Y.G., L.Z. ([PERSON]) and J.L.; Visualization, Y.G., L.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Fund for Distinguished Young Scholars (No. 62025601), relevant NSFC scientific departments are responsible for handling applications.  
The study was conducted in accordance with the Declaration of Helsinki, and approved by Ethics Committee on Biomedical Research, West China Hospital of Sichuan University (No. 851 and 4 June 2021).  
Patient consent was waived due to Ethics Committee on Biomedical Research, West China Hospital of Sichuan University has passed the waiver of informed consent.  
Figure 8: Visualization of the heatmap.  
Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The Meningioma Dataset for this work was provided by [PERSON] from the West China Hospital.  
The authors declare that they have no competing financial interests that could affect this research.
## 5 Edge AI for Early Health Prediction
### Prediction of Future Health Outcomes
#### 5.3.1 Predictive Modeling  
Predictive modeling uses data and statistical techniques to create models that can forecast future outcomes or behaviors. In healthcare, predictive modeling can be used for various purposes, such as identifying patients at a high risk of developing chronic diseases or complications [57; 58; 59], optimizing resource allocation and scheduling, improving quality of care and patient satisfaction, reducing costs and waste, and enhancing clinical decision making and diagnosis. Predictive modeling in healthcare requires a multidisciplinary approach that involves data collection, preprocessing, analysis, validation, and deployment. It encompasses various models designed to analyze historical data, identify patterns, and predict future trends. Among the commonly used models are classification, clustering, and time series models. Classification models are frequently employed in healthcare settings to make decisions related to improving patient health, optimizing healthcare services, and detecting health insurance fraud. Clustering models allow for profiling individuals based on characteristics such as age, inpatient admissions, and the risk of emergency hospital admission in the next 12 months. Time series models enable the plotting of observations made over time, such as monthly emergency department admissions or annual healthcare expenditures. Predictive modeling in healthcare faces many challenges, such as data quality, privacy, ethics, and interpretability. Therefore, it is important to ensure that the models are accurate, reliable, transparent, and fair.
# Automatic Detection of Quasi-Periodic Emissions from Satellite Observations by Using DETR Method
(2) A target detection algorithm, called QP-DETR, was proposed to cope with the complex morphological features of QP emissions and the interference from the space electromagnetic environment. (3) QP-DETR was validated on the created QP dataset and the EFD payload ELF band data from June 2023. The experimental results show that QP-DETR achieves a high detection speed and a lightweight model while ensuring recognition accuracy.
Enhancing Heartbeat Classification through Cascading Next Generation and Conventional Reservoir Computing  
[PERSON]  
Electrocardiography (ECG) is a simple and safe tool for detecting heart conditions. Despite the disapora of existing heartbeat classifiers, improvements such as real-time heartbeat identification and patient-independent classification persist. Reservoir computing (RC) based heartbeat classifiers are an emerging computational efficiency solution that is potentially recommended for real-time concerns. However, multiclass patient-independent heartbeat classification using RC-based classifiers has not been considered and constitutes a challenge. This study investigates patient-independent heartbeat classification by leveraging traditional RC and next-generation reservoir computing (NG-RC) solely or in a cascade. Three RCs were investigated for classification tasks: a linear RC featuring linear internal nodes, a nonlinear RC with a nonlinear internal node, and an NG-RC. Each of these has been evaluated independently using either linear ridge regression or multilayer perceptron (MLP) as readout models. Only three classes were considered for classification: the N, V, and S categories. Techniques to deal with the imbalanced nature of the data, such as the synthetic minority oversampling technique (SMOTE) and oversampling by replacement, were used. The MIT-BIH dataset was used to evaluate classification performance. The area under the curve (AUC) criterion was used as an evaluation metric. The NG-RC-based model improves classification performance and mitigates the overfitting issue. It has improved classification performance by 4.18% and 2.31% for the intra-patient and inter-patient paradigms, respectively. By cascading RC and NG-RC, the identification performance of the three heartbeat categories is further enhanced. AUCs of 97.80% and 92.09% were reported for intra- and inter-patient scenarios, respectively. These results suggest promising opportunities to leverage RC technology for multiclass, patient-independent heartbeat recognition.
# An Experimental Study for Swelling Effect on Repairing of Cracks in Fine-Grained Clayey Soils
## 2 Materials and Methods  
The presented article provides a comprehensive study to draw a geo-engineering framework on fine-grained clayey soil self-healing behavior assessment, which is considered a vital element in an earth dam's clay core design. The study contains experimental analysis on compacted clayey soils taken from KalganChay Dam with clay with low plasticity (CL) class and some clay with high plasticity (CH) based on the Unified Soil Classification System (USCS). The dam site was located about 70 km from Tabriz, near Bostanabad, upstream of the Jighar village. A comprehensive sampling was conducted during the field survey. The samples concluded that 90 specimens were prepared for 85 geotechnical tests. All tests were repeated to reduce the test error and evaluate the average values of measurements. In order to determine the soil index properties and geotechnical characteristics of clayey soil, the specimens are prepared, and different soil mechanical tests have been carried out according to the American Society for Testing and Materials (ASTM) standards. The soil index tests concluded that screen-sieve analysis, hydrometry, Atterberg limits, and compaction were used to determine the soil's physiomechanical properties. Swelling potential tests like odometer and consolidation were performed to analyze the studied soil's swelling potential and activity number. All tests were performed on additional samples with 2.5% to 12.5% Bentonite and Kaolin weight percentages. The kaolin used in this study was obtained from the Zonouz Mine, located 20 km northeast of Marand. The product is marketed as SZWMK1. The Bentonite used in the experiments was a product of the Barit Falat Iran Company. In addition, the pinhole test was used to evaluate the self-healing characteristics of clayey soil. This test can be considered a direct approach to the dispersion and colloidal erosion ability of fine-grained clayey soil. The pinhole test is established on distilled water flowing horizontally under a hydraulic head of 50 mm through a 1 mm diameter fracture on the soil specimen.
# Fault Diagnosis in Partially Observable Petri Nets with Quantum Bayesian Learning
## 2 The Problem of Quantum Bayesian Fault Diagnosis for Partially Observable Petri Nets  
### Partially Observable Petri Nets  
**Definition 1**.: _A Partially Observable Petri Net (POPN) is defined as a sextuple: \(POPN=(P,T_{o},T_{u},\textbf{Pre},\textbf{Post},M_{0})\) and \(T=T_{o}\cup T_{u}\). \(P\) is a set of \(m\) places, \(T\) is a set of \(n\) transitions, \(T_{o}\) is a set of observable transitions, and \(T_{u}\) is a set of unobservable transitions. \(\text{Pre}\in\mathbb{N}^{m\times n}\) and \(\text{Post}\in\mathbb{N}^{m\times n}\) are the forward arc matrix and backward arc matrix of transition, respectively. The matrix \(\textbf{C}=\textbf{Post-Pre}\), \(\textbf{C}\in\mathbb{N}^{m\times n}\) is defined as the incidence matrix of the POPN, and \(N\) is a set of non-negative integers. \(M_{0}\) is the initial marking of the POPN._  
Let the marking of the place \(p\) be \(M(p)\), then the necessary and sufficient condition of the logical enablement of transition \(t_{i}\in T\) at marking \(M\) is:  
For any \(p\in\cdot t_{i}\), \(M(p)\geq\text{Pre}(p,t_{i})\cdot t\) is prepositive set of \(t\), \(\cdot t=\{p\in P:\text{Pre}(p,t)>0\}\).  
Transition \(t_{i}\) fires at the marking \(M\), which is marked as \(M[t_{i})\). If \(M^{\prime}=M+\text{C}(\cdot,t_{i})\) after \(t_{i}\) was fired, it is abbreviated as \(M[t_{i})M^{\prime}\). It is called the equation of state of the POPN, and \(C(\cdot,t_{i})\) is the incidence matrix of the transition \(t_{i}\) that is fired.
## 1 Introduction
Two distinct pipelines were compared: the use of the Random Forest (RF) or the eXtreme Gradient Boosting (XGBoost) as a traditional machine learning classifier to perform the classification based on radiomic features extracted from the tumour segmentation; and the use of a Residual Neural network (ResNet) or Hybrid Vision Transformer (ViT) architectures as deep learning (DL) models that receive the segmented tumour, extracts the features and performs the classification. Both 2D and 3D approaches were initiallyemployed for each of these pipelines, and the models were trained on one dataset and then tested on three different test sets. The main contributions of our paper are the following:  
* The classification between adenocarcinoma and non-adenocarcinoma, with the negative class including two distinct histological subtypes;
* A detailed description of the cases excluded from the datasets, along with the pre-processing steps applied to the CT scans, ensuring transparency and reproducibility;
* A comparative analysis of two pipelines, one based on radiomic features and the other on deep features, for each pipeline two different classifiers were compared;
* An exploration of different input forms, specifically 2D slices and 3D volumes from CT scans;
* A cross-cohort study where one public dataset was used for training the models, which were then evaluated in two external public datasets. The structure of the paper is as follows: the Related Work section presents a characterization of the state-of-the-art for the scope of this work (Section 2). The Methodology section presents the datasets used in this work and the experimental pipelines employed (Section 3). The Results section shows the performance obtained by the implemented models and a critical analysis of the results with the presentation of the limitations (Section 4). The conclusion section presents the main conclusions of this work and identifies the future lines of work to address the current limitations (Section 5).
# Multi-Year Monitoring of Ecosystem Metabolism in Two Branches of a Cold-Water Stream
## 3 Results  
### Physical-Chemical Parameters  
Water temperature was significantly different between locations and years (Tables 1 and 2, Figure 2) with the water temperature greater in the North Branch than the South Branch. Overall, the water temperature was more variable in the North Branch than the South Branch (coefficient of variation was 17.4 and 27.8% for the South and North Branches, respectively.) Water depth was also significantly different between locations and years with both locations having the greatest depth in 2019 and the lowest depth in 2018 (Tables 1 and 2). Overall, the water depth was more variable in the North Branch than the South Branch (coefficient of variation 16.8 and 31.9% for the South and North Branches,respectively). Discharge data were only available from the North Branch for 2017 and half of 2019 (Figure 2). Based on comparing these data with those from the South Branch for the same time periods showed that discharge was slightly (but significantly) higher in the South Branch than the North Branch and was higher in 2017 than 2019 for both branches (Tables 1 and 2). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & \multicolumn{3}{c}{**North Branch**} & \multicolumn{3}{c}{**South Branch**} \\ \hline  & **2017** & **2018** & **2019** & **2017** & **2018** & **2019** \\ \hline Depth (m) & 0.33 a (0.001) & 0.19 b (0.001) & 0.35 c (0.001) & 0.29 d (0.001) & 0.27 e (0.001) & 0.37 f (0.001) \\ Discharge (m\({}^{3}\) s\({}^{-1}\)) 1  & 0.23 a (0.001) & — — — & 0.21 b (0.002) & 0.27 c (0.001) & — — \\ Temperature (\({}^{\circ}\)C) 2  & 17.6 a (0.21) & 17.7 b (0.21) & 17.2 c (0.21) & 11.3 d (0.21) & 11.6 e (0.21) & 11.1 f (0.21) \\ Dissolved oxygen (\% saturation) 2  & 95.5 a (0.05) & 93.5 b (0.06) & 93.0 c (0.06) & 96.3 d (0.05) & 96.8 e (0.04) & 95.1 f (0.05) \\ Conductivity & & & & & & \\ (\(\mu\)S cm\({}^{-1}\)) 2  & 451.0 a (2.0) & — — — & — & 436.6 b (2.0) & 427.9 (0.1) & — — \\ Dissolved P & & & & & & \\ (\(\mu\)g L\({}^{-1}\)) 3  & 11.3 a (2.7) & 18.2 a (9.3) & 11.6 a (3.1) & 10.0 a (0.7) & 12.6 a (0.1) & 14.1 a (5.7) \\ Dissolved N & & & & & & \\ (mg L\({}^{-1}\)) 3  & 2.75 a (0.99) & 2.9 a (0.92) & 2.79 a (0.79) & 8.34 b (0.25) & 9.28 b (0.21) & 9.78 b (0.46) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Physical and chemical parameters for the North and South Branches of Valley Creek, Minnesota. Values are least mean squares from mixed-model ANOVAs except for P and N, where values are means. Values in parentheses are standard errors except for N and P, where the values are standard deviations.
## 6 Application Experiment  
### Setup and Evaluation Criteria  
As the evaluation criterion for the results of this experiment, we used recognition accuracy obtained from CPNs as a measure of fitness for optimizing sensor signals. In a report of our earlier study [6], original sensor signals were presented to CPNs to measure the recognition accuracy. This study was conducted to improve the overall system accuracy using GNP as an evolutionary learning approach. From 10 subjects who participated in our earlier study, we selected evaluation targets for this study: six datasets of people for  
Figure 5: Experimentally obtained results of optimizing sensor signals to approximate high-accuracy ideal signals.  
Figure 6: Original and filtered sensor signals.  
whom the recognition accuracy was the lowest [6]. We used leave-one-out cross-validation to evaluate our method when used along with ML and evolutional learning approaches.  
For this experiment, we set \(f_{b}\) as  
\[f_{b}=\sum_{t=0}^{N}\frac{y(i)}{N}\times 100. \tag{16}\]  
Intermediate output \(y(i)\) comprises a binary function as  
\[y(i)=\left\{\begin{array}{ll}1&\text{if}\quad g(t)=l(t),\\ 0&\text{if}\quad g(t)\
eq l(t),\end{array}\right. \tag{17}\]  
where \(g(t)\) is a ground-truth signal at time \(t\).  
For this experiment, we infer that our method can correct original signal features, which include noise for negative effects on accuracy, with preservation of salient features for the recognition of bed-leaving behavior patterns. From six datasets, we used the third, fourth and sixth datasets, which had low accuracies.
# Assessing Digital Technologies' Adoption in Romanian Secondary Schools
## 4 Results
### The Main Effects
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{1}{c}{**Fornell-Larker Criterion**} & \begin{tabular}{c} **Behavioral** \\ **Intention** \\ \end{tabular} & \begin{tabular}{c} **Perceived** \\ **Ease of Use** \\ \end{tabular} & \begin{tabular}{c} **Perceived** \\ **Usefulness** \\ \end{tabular} & \
\begin{tabular}{c} **Teachers’** \\ **Perception** \\ **of Quality** \\ \end{tabular} &
\begin{tabular}{c} **Behavioral** \\ **Intention** \\ \end{tabular} \\ \hline Actual usage & 1.000 & & & & & \\ Behavioral intention & 0.878 & 0.914 & & & \\ Perceived ease of use & 0.881 & 0.854 & 0.883 & & \\ Perceived usefulness & 0.780 & 0.763 & 0.658 & 0.813 & \\ Teachers’ perception of quality & 0.548 & 0.448 & 0.475 & 0.436 & 1.000 \\ \hline \hline \end{tabular} Source: developed by authors using SmartPLS v3.0.  
\end{table} 
Table 5: Discriminant validity. Together, these findings support the theoretical foundations of the Technology Acceptance Model and broaden its relevance to educational contexts. The emphasis on ease of use over usefulness in influencing intention indicates that training and interface design may be key factors for adoption. Meanwhile, the confirmed link from intention to perception of quality to usage suggests that showcasing measurable benefits could further strengthen digital integration in teaching practices. Future research could investigate contextual factors, such as institutional support or subject-specific requirements, to enhance these predictive relationships.
## 2 Background
### Forwarding Information
#### Airtime Link Metric (ALM)  
ALM is used as the default routing metric for IEEE 802.11s. Periodically, a test frame will be sent from a mesh STA to its neighbor mesh STA through a link. The test frame error rate (e.g., the ratio of the failed transmissions to the total transmissions) is calculated over time. Based on the data rate of the link and the frame error rate, the ALM can be calculated as:  
\[C_{a}=\left[O+\frac{B_{t}}{r}\right]\frac{1}{1-e_{f}} \tag{1}\]  
where \(O\) is the overhead caused by the physical layer (PHY); its size varies depending on PHY. \(B_{t}\) is the size of the test frame. Both \(O\) and \(B_{t}\) are constant values. \(r\) is the data rate, and \(e_{f}\) is the frame error rate. Airtime cost is denoted by \(C_{a}\). With these variables, the estimated transmission time over a link can be calculated. This value is used as the metric for the remainder of this paper.  
Note that ALM will be used to calculate the metric of the links, but it is not directly involved in the path formation process. HWMP is the routing protocol that controls the path formation process. By using ALM, the best path can be found based on the expected transmission time and error rate, rather than the hop count. If a large number of mesh STAs are placed relatively near each other in an 802.11s-based WMN, the performance of the network has a high probability of increasing.  
ALM takes both the transmission time and error rate parameters into consideration when computing the link quality (_i.e._, the link metric). Therefore, if HWMP utilizes ALM as the metric, this protocol will be able to route wireless frames more efficiently compared to the hop count-based metric. Of course, scholars have developed a variety of metrics, each with its own capabilities, and these may outperform the default ALM [24]. Examples of these capabilities include load effect sensing, interference reduction, and channel allocation. Each of these metrics considers other parameters in its equation, but the core parameters are still the transmission time and the error rate.
###### Abstract  
This systematic review evaluated machine learning applications for analysing physical activity and behaviour in preschool children using accelerometer data. Following the PRISMA guidelines, we systematically searched PubMed, FECYT, and ProQuest Central databases. Fourteen studies implementing machine learning approaches for preschool accelerometry data were identified and assessed using the MINORS scale. Studies focused on two primary domains: physical activity analysis (\(n=10\)) and sleep monitoring (\(n=4\)). The ActiGraph GT3X+ was predominantly used, with placement varying between the hip and wrist. Random Forest algorithms proved most effective, achieving accuracy rates up to 86.4% in activity classification and 96.2% in sleep prediction. Sampling frequencies (0.25-100 Hz) and epoch lengths (1-60 s) varied considerably across studies. Machine learning applications show promising results for preschool physical activity assessment. However, small sample sizes and methodological inconsistencies limit generalizability. Future research should prioritise larger cohorts, explore multiple sensor integrations, and develop standardised protocols to enhance practical applications.  
technology; preschool; prediction; computer science; education 2025  
A Systematic Review  
1