esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Procedures  
Soccer players carried global positioning system (GPS) devices (WIMU PROTM, RealTrack System, Almeria, Spain) operating at a sampling frequency of 10 Hz. The technology used to collect the GPS data had been previously validated and was shown to be reliable for monitoring soccer players [23]. Participants wore a fitted body vest, and the GPS device was inserted in a purpose-built harness prior to the SSGs. Before being placed on the players, the GPS devices were calibrated and synchronized following the manufacturer's recommendations. The procedure was as follows: (a) turn on the devices, (b) wait approximately 30 s after turning them on, (c) press the button to start recording once the device's operating system is initialized and (d) analyze the data obtained from the devices using SPROTM software (RealTrack Systems, Almeria, Spain). SPROTM software calculates the external load indicator used in our study. Player load is derived from triaxial accelerometers (\(x\), \(y\) and \(z\)), which are used to evaluate neuromuscular load in different athletes.  
\[PL_{n}=\sqrt{\frac{\left(x_{n}-x_{n-1}\right)^{2}+\left(y_{n}-y_{n-1}\right)^{ 2}+\left(z_{n}-z_{n-1}\right)^{2}}{100}}\]  
\[Accumulated\;PL=\sum_{n=0}^{m}PL_{n}\times 0.01\]
# Prediction-Based Energy Saving Mechanism in 3 GPP NB-IoT Networks
## 2 NB-IoT
### NB-IoT Key Technologies
#### 2.2.7. CP CIoT EPS Optimization  
The existing EPS is optimized for high-end services, including multimedia, voice, and Internet access. Thus, transmitting the IoT packet that is small and infrequent is inefficient through this system. Specifically, a connection setup is always required regardless of the packet size, and the EPS supports only IP packets with large overhead. Consequently, EPS optimization has been applied for the NB-IoT on both control and user plane aspects. In this paper, we focus on optimization for the control plane. The CP CIoT EPS optimization is designed to allow a piggyback application packet into the NAS PDU to reduce the radio resource usage when establishing the user-plane radio bearer. First, a CIoT-serving gateway node (C-SGN) that is a logical node and plays the role of mobility management entity (MME), a packet data network gateway (P-GW), and a serving gateway (S-GW) are used to reduce the network signaling overhead. In addition, encryption and header compression can be processed on the MME, and data transmission does not require the access stratum (AS) setup. Both AS security and user-plane radio bearer setup are mandatory in LTE networks. Thus, at least 9 messages are required before the transmission of the first application packet, as shown Figure 7a. On the other hand, the NB-IoT CP CIoT EPS optimization allows to transmit application packets after 4 messages, as shown in Figure 7b.  
Figure 5: User equipment (UE) power state: (**a**) discontinuous reception (DRX); (**b**) extended discontinuous reception (eDRX).  
Figure 6: UE power state in power saving mode (PSM).
# Attention-Guided Wireless Channel Modeling and Generating
## Appendix A  
We provide 20 sets of data for the first transmitting antenna and the first receiving antenna under the first delay spread. These data encompass several important dimensions, including the real part, imaginary part, magnitude, and phase. By presenting these detailed datasets, we aim to offer readers a more comprehensive and in-depth perspective, thereby facilitating a better understanding of the performance and characteristics of the model we have proposed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Real** & **Imaginary** & **Magnitude** & **Phase** \\ \hline \(-0.340249985\) & \(-0.129639252\) & \(0.364110407\) & \(-2.777561828\) \\ \(-0.807743013\) & \(-0.105123891\) & \(0.814554975\) & \(-3.012174847\) \\ \(-0.74634701\) & \(1.830609924\) & \(1.976908332\) & \(1.9579264\) \\ \(2.955234469\) & \(0.259716469\) & \(2.966624919\) & \(0.087658327\) \\ \(-0.052083001\) & \(-0.844535693\) & \(0.846140163\) & \(-1.632388895\) \\ \(-0.276836008\) & \(-0.213868771\) & \(0.349825709\) & \(-2.48381706\) \\ \(0.472559686\) & \(2.263887156\) & \(2.31268193\) & \(1.365012962\) \\ \(1.951065774\) & \(-1.359609416\) & \(2.378065478\) & \(-0.608611925\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The first 20 sets of data for the first transmitting antenna and the first receiving antenna under the first delay spread.
###### Abstract  
For the degree of orderly arrangement of the molecular chains at the interface of nanocomposites, the static and sheared polyethylene (PE)/carbon nanotube (CNT) models and the sheared pure PE model were constructed, and molecular simulation experiments were carried out in comparison. The micro-mechanism of molecular chains orientation, synergistically induced by the carbon nanotube and shear flow in injection molding, was discussed by analyzing the radius of gyration, molecular chain motion, conformation evolution of molecular chains, bond orientation parameter, interface binding energy and atom distribution. The results show that, for the static composite system, the conformation adjustment of PE molecular chains induced by CNT is limited due to the hindrance from the surrounding chains. Thus, the orientation and radius of gyration of molecular chains increase slightly. For the sheared pure PE system, the orientation induced by shear flow is unstable. After the cessation of shear, the molecular chains undergo intense thermal movement and relax quickly. The disorientation is obvious, and the radius of gyration decreases considerably. It is worth noting that for the sheared composite system, shear flow and the CNT have a synergistic effect on the orientation of the molecular chains, which is due to the adsorption effect of the CNT on shear-induced oriented chains and the inhibition effect of CNT on the relaxation of shear-induced oriented chains. Thus, the orientation stability of PE chains is greatly improved, and interface crystallization is promoted. Moreover, because of the more regular arrangement of molecular chains in the sheared composite system, more H atoms and C atoms are close to the surface of the CNT, which increases the van der Waals force, and correspondingly increases the interface binding energy.  
molecular chains orientation; synergistic effect; micro-mechanism; injection molding 1
## 4 Conclusions  
In this paper, a lemniscate-compensated layout is proposed to enhance the coverage efficiency of the underwater LED array in the UOWC system. Based on the MC simulation method, the received optical power distribution and BER performance of the proposed system are analyzed under three typical seawater environments. At 5 m, 7 m, and 9 m transmission depths, the PPD values of the lemniscate-compensated LED layout are 0.79, 0.66, and 0.65, respectively, which can achieve a more stable power distribution of the received optical power. Through the analysis of RMSE results, the proposed LED layout shows better uniformity under three typical seawater environments. Furthermore, in clean and coastal seawater, the BER of the proposed LED layout demonstrates obvious advantages at the same transmission depth. In turbid harbor seawater, with the deterioration of seawater quality, the transmission distance of the system is limited. However, the proposed LED layout still achieves better BER performance. Furthermore, to verify the proposed LED array coverage model, an experimental verification system can be established in a real seawater environment to improve the underwater coverage performance of the LED array, which is of great significance to ensure the high-speed interconnection of underwater Internet of Things devices at short and medium ranges.  
Figure 8: The BER performance of the three layouts in (**a**) clean seawater, (**b**) coastal seawater, and (**c**) harbor seawater.  
**Author Contributions:** Conceptualization, A.L. and H.Y.; formal analysis, A.L., H.Y. and H.Z.; methodology, A.L.; software, A.L., H.Y. and Y.Y.; validation, A.L. and Y.W.; writing--original draft, A.L., H.Y. and H.Z.; writing--review and editing, A.L. and Y.W.; visualization, A.L. and H.Y.; supervision, A.L.; project administration, A.L. and Y.W.; funding acquisition, A.L. and Y.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported in part by the Fundamental Research Funds for the Central Universities under Grant 3132023240, and the National Natural Science Foundation of China under Grants 62176037 and 62205045.  
**Data Availability Statement:** The data presented in this paper are available after contacting the corresponding author.  
**Acknowledgments:** The authors would like to thank the anonymous reviewers for their careful reading and valuable comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Experiments and Functioning Tests
### Microrobot Pest Detection Test  
This test aims to verify the functionality of microrobots for pest detection in narrow spacesThe ESP32-CAM mounted on the HEXBUG nano performs a visual inspection while a CNN ML algorithm linked directly through the wireless network makes a prediction of the presence of a rat in the image. By training the model with a large dataset of labeled images including the original images and captured images via the microrobot with their augmentation, the CNN learns to recognize the features of rodents and distinguish them from other objects or backgrounds.
###### Abstract  
Gastrointestinal (GI) diseases constitute a leading problem in the human digestive system. Consequently, several studies have explored automatic classification of GI diseases as a means of minimizing the burden on clinicians and improving patient outcomes, for both diagnostic and treatment purposes. The challenge in using deep learning-based (DL) approaches, specifically a convolutional neural network (CNN), is that spatial information is not fully utilized due to the inherent mechanism of CNNs. This paper proposes the application of spatial factors in improving classification performance. Specifically, we propose a deep CNN-based spatial attention mechanism for the classification of GI diseases, implemented with encoder-decoder layers. To overcome the data imbalance problem, we adapt data-augmentation techniques. A total of 12,147 multi-sited, multi-diseased GI images, drawn from publicly available and private sources, were used to validate the proposed approach. Furthermore, a five-fold cross-validation approach was adopted to minimize inconsistencies in intra- and inter-class variability and to ensure that results were robustly assessed. Our results, compared with other state-of-the-art models in terms of mean accuracy (ResNet50 = 90.28, GoogLeNet = 91.38, DenseNets = 91.60, and baseline = 92.84), demonstrated better outcomes (Precision = 92.8, Recall = 92.7, F1-score = 92.8, and Accuracy = 93.19). We also implemented t-distributed stochastic neighbor embedding (t-SNE) and confusion matrix analysis techniques for better visualization and performance validation. Overall, the results showed that the attention mechanism improved the automatic classification of multi-sited GI disease images. We validated clinical tests based on the proposed method by overcoming previous limitations, with the goal of improving automatic classification accuracy in future work.  
gastrointestinal disease; endoscopic image; lesion classification; computer-aided diagnosis; spatial attention; deep learning +
Footnote †: journal: Medical Imaging
# Fuzzy Clustering Approaches Based on Numerical Optimizations of Modified Objective Functions
## 2 Classical FCM and Modifications on Its Objective Function
### A Spatial Regularization Term  
This modification of the objective function aims at keeping the generated clusters smooth in space. It is involving a spatial regularization term, which would stimulate points close to each other to have similar values of the memberships into the clusters.  
\[J(X,\ U,\ V;\ m,\ C,\ \lambda)=\sum_{i=1}^{n}\sum_{j=1}^{C}\mu_{ij}^{m}d^{2} \big{(}x_{i},\ c_{j}\big{)}+\lambda\sum_{i=1}^{n}\sum_{j=1}^{C}\sum_{k\in N(i) }\Big{|}\mu_{ij}-\mu_{kj}\Big{|}^{2} \tag{6}\]  
Here, \(N(i)\) represents the neighbourhood set of the data instance \(x_{i}\), i.e., the set of points that are in spatial proximity to \(x_{i}\). This may be determined by an additional radius parameter (the radius of proximity) or naturally based on the adjacency concept in the representative data structure, such as adjacent pixels in an image [21]. The coefficient \(\lambda\) controls the strength of the spatial regularization term, thus representing a hyper-parameter that may be tuned. The idea of this modification has been previously used in image analysis, adapting it to the specific scenarios rather than aiming to provide a more general approach [22].
## 3 Results
### Open Transformer Enclosure  
#### 3.1.1 Minimum Flow  
The simulation results presented in Figure 12 pertain to Sensor 66, located in the center of the room, and thus reflect the primary variations in the indoor environment. The results for Sensors 84 and 72 can be found in the Electronic Supplementary Material (Figures S4, S5, S9 and S10). Case O-I features an airflow rate of 1100 m\({}^{3}\)/h. It is evident that the presence of the transformer enclosure significantly alters the indoor thermo-hygrometric conditions, leading to a marked increase in temperature and a decrease in humidity. More  
Figure 11: (**a**) Layout of the open transformer enclosure; (**b**) layout of the confined transformer enclosure.  
over, these findings indicate that the air extraction system is inadequate for keeping the temperature within the operational limits required for the transformers.  
According to the simulation results, the CFD probe placed at the position of Sensor 66 (center of the room) registers an increase in the indoor temperature of about 8 \({}^{\circ}\)C due to the heat produced by the electrical transformer enclosure. The relative humidity decreases up to 22-23% (Figure 12). Figure 13 shows a graphic representation of the indoor temperature and relative humidity values on the section plane placed at the center of the building. The increase in temperature close to the electrical transformer can be noted.  
Figure 13 illustrates the phenomenon of natural stratification, where warm air accumulates near the ceiling above cooler air. The humidity levels within the indoor environment can fluctuate up to 29%, with the lowest values observed near the electrical transformers. This decrease in humidity is associated with the rise in temperature and could lead to moisture rising up from the ground and walls, potentially resulting in saline deposits that may damage the plaster. Additionally, the humidity and temperature plume emanating from the door are visible in Figure 13; this plume is a result of air being drawn in by the extraction fan. Beneath this plume, air circulation is minimal, and the humidity levels are close to zero.
# A Comprehensive Review of Multimodal Analysis in Education
## 4 Applications in Education
### Enhancing Engagement and Learning Performance  
This category encompasses studies that leverage multimodal data to increase students' motivation, participation, and academic outcomes. Applications include systems that provide real-time feedback, adaptive learning environments, and tools to monitor classroom dynamics. These works commonly integrate visual, auditory, and behavioural modalities to assess and stimulate learner activity, with the goal of improving knowledge acquisition, skill development, and retention.  
For instance, [PERSON] et al. [143] conducted a study in a high-school chemistry classroom, demonstrating how multimodal analysis can track student positioning and sensemaking through gestures, verbal cues, and physical interactions. Their findings highlighted the importance of recognising diverse forms of student expression, including gestures, to support sensemaking and participation in scientific practices. Similarly, [PERSON] et al. [125] proposed a virtual reality (VR)-based idea mapping approach for second language learners, using multimodal data (e.g., behavioural logs, gaze tracking, and mouse movement) to identify learning patterns and improve vocabulary acquisition. Their model achieved high engagement rates and demonstrated the benefits of combining multiple data streams for real-time learning feedback.  
\begin{table}
\begin{tabular}{l l} \hline
**Application Category** & **Works** \\ \hline \multirow{4}{*}{Enhancing Engagement and Learning Performance} & [1,5,14,34–36,42,43,46,47,50,54,55,57,62–64,71,72,74,77,79– \\  & 83,85,91,94,95,99,103,106,108,109,111,112,116– \\  & 118,120,122–162] \\ \multirow{4}{*}{Supporting Collaborative Learning and CSCL} & [2,9,10,34,40,44,45,47,48,52,58,59,61,67,68,75,78,99,100, \\  & 104,119,131,163–179] \\ \multirow{4}{*}{Educational Games and Simulation-Based Learning} & [11,12,36–38,40,41,65,70,93,114,180] \\  & [3,4,6,7,15,23,53,56,62,66,69,73,84–89,92,96– \\ \multirow{4}{*}{Multimodal Assessment and Personalisation} & 98,102,107,110,115,181] \\  & [8,10,49,60,7,6,90,99,101,105,113,116,145,182] \\ \multirow{4}{*}{Monitoring Emotional and Cognitive States} & [8,10,49,60,7,6,90,99,101,105,113,116,145,182] \\  & [13,18,39,51,116,183,184] \\ \multirow{4}{*}{Special Needs and Inclusive Education} & [7,25,77,108,185,186] \\  & \multicolumn{1}{c}{} \\ \end{tabular}
\end{table}
Table 6: Categorisation of reviewed papers by application domain.
## Abstract  
The migration of radionuclides from radioactive waste into the environment poses a public safety concern. Thus, the long-term safety assessment for near-surface disposal sites for radioactive waste in South Korea entails providing reasonable assurance that the annual radiation dose exposure from radionuclide release from the waste repository into the biosphere will not exceed the regulatory limit of 0.1 mSv/yr. At the first near-surface disposal site in Gyeongju, concrete was a crucial component of the engineered barriers designed to contain radionuclides within the disposal site. The ability of concrete to retain radioactive waste within the disposal site is attributed to its high sorption capacity for radionuclides. However, research has shown that the degradation of concrete can affect its radionuclide retention capabilities, which are defined by sorption properties of distribution (Kd) and diffusion (Dj) coefficient parameters. As a result, changes in sorption properties may lead to radionuclides migrating out of the disposal vault. In light of the geochemical deterioration of engineered concrete barriers, this study assesses the long-term safety of near-surface disposal sites. To simulate the impact of concrete degradation on radionuclide migration, we employed RESRAD-OFFSETITE's extended source-term features, which can model the release of radionuclides from radioactive waste shielded by concrete barriers. Using carefully screened published sorption data of four radionuclides (\({}^{14}\)C, \({}^{137}\)Cs, \({}^{90}\)Sr and \({}^{99}\)Tc) in different stages of concrete degradation, the results indicated that released radioactivity during the most degraded state of concrete will result in a maximum radiation exposure dose of \(1.4\times 10^{-8}\) mSv/yr from \({}^{99}\)Tc which is below the permissible limit of 0.1 mSv per year, thus demonstrating that concrete is a reliable component of the engineered designed barriers for near-surface disposal facilities.  
caption = [color=black]0.05
## 2 Materials and Methods  
### Experimental Site and Data Collection  
This field experiment was conducted during the maize vegetation season at a 23-hectare field belonging to Szechenyi Istvan University, located in Mosonmagyarovar, Hungary. The site lies within the Little Hungarian Plain, an alluvial plain of the Leitha River, characterized by relatively flat topography with a slight slope of approximately 5% and elevation ranging from 133 to 138 m above sea level. This gradient allows for natural surface drainage while minimizing erosion risks. The region experiences a temperate climate with moderate precipitation, typical of Central Europe. During the study period, from May to October, total precipitation was approximately 400 mm, and the average air temperature ranged from 18.5 \({}^{\circ}\)C to 21.3 \({}^{\circ}\)C. Based on USDA soil classification taxonomy [30] and laboratory particle size distribution analysis using the soil texture triangle, three primary soil textures were identified in the field: loam, sandy loam, and silt loam (Figure 1). These soil textures represent more than 60% of the soils in this region [29]. The field is an alluvial plain of the Leitha River located within the little Hungarian plain and is characterized by relatively flat topography. The field has a slight slope of 5%, with elevation varying between 133 and 138 m above sea level. This slight gradient allows natural surface drainage while minimizing erosion risks. For this study, two soil textures--loam and silt loam--were selected for detailed analysis. Their specific physical and chemical properties are presented in Table 1. The field is managed under a cereal-based crop rotation system, which includes winter wheat, spring barley, maize, and soybeans.  
Figure 1: Soil texture in the 23 ha maize field in Mosonmagyarovar, Hungary, with GPS points of the sampling locations.
# Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data
## 3 Methodology
### Data Preprocessing  
The Himawari-8 data downloaded from the JAXA were in the 'NetCDF' format, therefore, data preprocessing was needed prior to operational use. This included converting the format, writing geographic information, clipping and re-projecting onto the Geocentric Datum of Australia (GDA) 1994 South Australia Lambert coordinates.
## 3 Installation Methods and Circuit Protection
### Installation Methods of Steel Arch Strain Gauge
In order to facilitate on-site installation and data recording, strain gauges were fitted to the steel arch at the desired position, as shown in Figure 16a, and the flange was used to connect the wires in series, as shown in Figure 16b. This method effectively reduced problems related to long fusion connection time and poor operability. After the docking was completed, the polyurethane foam was sealed to protect the circuit, as shown in Figure 16c. After all steps were completed, the respective sensor wavelengths across the appropriate time frame were recorded, and the recorded wavelengths were used as the initial wavelengths for subsequent data analysis, as shown in Figure 16d. 4. Multiple optical cables were connected to the FBG wireless demodulator. First, the hook was welded, as shown in Figure 17a. Subsequently, these components were protected by galvanized steel tubes and reflective strips (Figure 17b). The protective effect was strong enough that the influence of blasting impact and stone crushing was avoided. In addition, the reflective strip provided a good reflective effect and prevented the excavator from tearing the line due to operation during slag removal.
## 2 Experimental
### Characterization of ACRM  
The ACRM powder that was obtained after annealing underwent a comprehensive characterization process. The first step involved using a Panalytical XPert MPD/DY636 powder X-ray diffractometer (PXRD), which was operated using CuK\(\alpha\) radiation (\(\lambda=0.15418\) nm), with a voltage of 45 kV and a current of 20 mA. The pre- and post-reaction ACRM powder underwent scanning for 20 in the range of 20\({}^{\circ}\) to 90\({}^{\circ}\), with a recording time of 5 s. Additionally, to analyze the particle morphology of the ACRM particles, Scanning (SEM, Nova Nano 450, FEI) and Transmission Electron Microscopes (TEM, Tecnai G2 S Twin FEG, FEI) were utilized.