esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Model Design
### Adaptive Time-Stepping Forecasting Method  
In choosing a suitable time step for a model, it is important to not only ensure numerical stability but also to ensure that the time step is sufficiently small to mitigate the risk of missing the peak of a wave at a sampling point. The chosen computation interval provides valuable opportunities to characterize the traffic load, but the value of these opportunities varies over the course of the simulation and should be leveraged accordingly. To further illustrate this point, consider the example of converting analog music to digital, as shown in Figure 3. The wave must be sampled at regular intervals, indicated by the vertical green arrows. A greater number of arrows per sampling point increases the number of interpolation points, resulting in a more defined wave, while fewer arrows provide less definition.  
Is it possible to have too many arrows when sampling a wave? While more arrows lead to higher quality, a point is reached where the increased quality is not discernible by the human ear. In Figure 3, selected areas are highlighted to demonstrate the potential for adaptive time stepping. Adaptive time stepping optimizes arrow placement using a waveform objective function. This can be compared to running a GRU model at a uniform 30 s step, versus advanced time step control that automatically adjusts using the ATS algorithm. The adaptive time stepping reduces sampling without sacrificing accuracy by identifying areas where more information is needed and sets step sizes accordingly.  
It is important to note that regular interval sampling is not required to accurately reproduce the wave later. Rather, identifying instances when greater definition is necessary is key in determining an appropriate sampling strategy. To minimize sampling overhead, one simple adaptive rate strategy is to use a low sampling rate during slow periods and a high rate during fast periods of change. The goal of this adaptive interval is to efficiently sample and reconstruct the traffic curve without compromising prediction accuracy, as would be the case with a constant rate approach.  
In the following section, we examine a typical IoT traffic curve scenario. The curve is divided into slow- and fast-changing periods based on \(T_{\text{max}}^{\text{s}}\) and \(T_{\text{max}}^{f}\), representing the maximum step size. As per [PERSON] sampling theory, the slow-changing zone mandates a step size less than \(2T_{\text{max}}^{\text{s}}\), while the fast-changing zone demands a step size under \(2T_{\text{max}}^{f}\). Subsequently, an average step size of \(S_{ATS}^{avg}\) can then be achieved by  
\[S_{ATS}^{avg}=\frac{T_{s}+T_{f}}{2T_{s}/T_{\text{max}}^{\text{s}}+2T_{f}/T_{ \text{max}}^{f}} \tag{2}\]  
where \(T_{s}\) and \(T_{f}\) denote the duration of slow-changing and fast-changing periods, respectively. Since \(T_{\text{max}}^{\text{s}}>T_{\text{max}}^{f}>T_{\text{max}}\), we deduce \(S_{ATS}^{avg}>2T_{\text{max}}\), which indicates that the ATS approach results in significantly reduced sampling overhead compared to its constant-step-size counterpart.
# GCN-Former: A Method for Action Recognition Using Graph Convolutional Networks and Transformer
## 3 Experiments
### Ablation Experiment  
To demonstrate the effectiveness of the introduced and designed modules, ablation experiments were conducted on the NTU RGB+D dataset. Ablation experiments were conducted using a single-joint modality on the X-Sub benchmark, and the results are shown in Table 3. From the table, replacing the TCN module with the Transformer Block module led to a 1.6% performance improvement. Additionally, after incorporating the CTA attention mechanism, our method outperformed the GCN + TCN approach by 0.5%. This shows that using CTA as an intermediate layer helps preserve local details and transmit them in a form better suited for the Transformer, enabling more efficient feature transformation and smoother data flow.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Action**} & \multicolumn{2}{c}{**ST-GCN**} & \multicolumn{2}{c}{**BlockGCN**} & \multicolumn{2}{c}{**Ours**} \\ \cline{2-7}  & **AUC** & **G-Mean** & **AUC** & **G-Mean** & **AUC** & **G-Mean** \\ \hline Walk & 96.98 & 96.96 & 98.69 & 98.66 & 98.35 & 98.33 \\ Sit Down & 89.37 & 89.21 & 94.29 & 94.14 & 94.42 & 94.24 \\ Standup & 90.05 & 89.92 & 94.52 & 94.49 & 94.17 & 94.12 \\ Pickup & 90.29 & 90.24 & 92.46 & 92.28 & 92.61 & 92.57 \\ Carry & 89.34 & 89.17 & 91.18 & 90.77 & 95.54 & 94.95 \\ Throw & 92.24 & 92.24 & 95.25 & 95.23 & 94.75 & 94.69 \\ Push & 87.69 & 87.18 & 89.41 & 88.92 & 89.26 & 88.86 \\ Pull & 85.73 & 85.39 & 86.74 & 86.35 & 86.83 & 86.51 \\ Wave Hands & 92.56 & 92.44 & 95.23 & 95.16 & 96.18 & 96.09 \\ Clap Hands & 90.65 & 90.16 & 94.72 & 94.58 & 94.95 & 94.62 \\ Average & 90.49 & 90.29 & 93.25 & 93.06 & 93.71 & 93.50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance on the UT-Kinect dataset.  
Comparison of attention mechanisms: We conducted a detailed comparison of attention mechanisms widely applied in graph convolutional networks, with the results shown in Table 4. As shown in the data, methods incorporating attention mechanisms consistently outperform baseline methods, demonstrating the critical role of attention in enhancing the expression of regions of interest. Furthermore, experimental results indicate that our proposed CTA method outperforms other competing approaches. The CTA module, by combining contextual information mining with self-attention learning, effectively enhances the visual representation of 2D feature maps while improving the model's sensitivity to temporal information. This method has demonstrated excellent efficiency and reliability in handling long-sequence data for action classification tasks.  
Number of Convolutional Attention Heads in Multi-Head Attention: We investigated the effectiveness of the initial number of Convolutional Attention heads (h) in our model, with the results shown in Table 5. The findings show that the multi-head attention mechanism improves action classification performance.
## 7 IoMT Healthcare Services and Applications
### Assisted Living (in Home Care)  
Assisted living is a form of long-term care for elderly and disabled individuals who need help with daily activities such as bathing, dressing, and medication. It allows them to remain in their own home while still receiving the care they need. This approach avoids unnecessary hospitalization by enabling patients to stay in a familiar environment [74]. These telepresence and videoconferencing robotic solutions help ensure patient safety and monitoring while also enabling them to maintain contact with family and healthcare professionals [75]. Additionally, these solutions serve to provide educational or therapeutic activities for older individuals without requiring them to learn complex technological systems [76]. Ambient assisted living solutions are designed to be simpler and more intuitive, making them easier to use and understand for those who may not be as familiar with the technology. This enables individuals who may struggle with more complex systems to still access the benefits of technology [77]. The use of AI methodologies for ambient intelligence systems in the healthcare domain helps process the vast amount of monitoring data efficiently and scale effectively with the number of ambient-assisted patients. These technologies will enable healthcare centers to receive automatic alerts for observation and emergency assistance. Cloud and Fog technologies can support healthcare and provide on-demand infrastructure [78,79].
# A Comprehensive Survey for Deep-Learning-Based
## 4 Abnormality Detection with Images over Long Range
### The Application of Satellites in Smart Grid Abnormality Detection
#### 4.1.3 Synthetic Aperture Radar Images  
SAR (synthetic aperture radars) are active imaging sensors that operate in the microwave region of the electromagnetic spectrum [96]. Synthetic aperture radar is based on small antennas that move at a constant speed along the track of a long linear array and radiate coherent signals [97]; the echoes received at different positions are coherently processed to obtain higher-resolution images. It can be divided into two types: focus and non-focus. Unlike traditional optical cameras, synthetic aperture radar has an active microwave sensor, and the obtained images are not affected by the occlusion of clouds and fog, nor are they affected by light intensity and climatic factors. Therefore, they are capable of all-weather earth observation. Furthermore, based on the fact that waves encounter different materials and have different refraction and reflection characteristics, synthetic aperture radar can penetrate the ground surface and even obtain data that is hidden under the vegetation cover. These characteristics give it a wide range of application prospects in civil fields such as agriculture, forestry, water, geology, and natural disasters. It also has unique advantages in the military field, which is the basis for the use of inspection robots for abnormality detection.
# Smart Energy Meters for Smart Grids, an Internet of Things Perspective
## 2 Sensing Techniques
### Electricity Meters  
Electricity is the most common type of energy produced and distributed for consumption by the domestic, industrial, and other sectors owing to its ease of production, distribution, and control, coupled with the economies of scale at play at every level in the electricity business; this also makes it one the most affordable energy solutions worldwide. The electricity network has continually evolved alongside the evolution of the generation techniques and methods, the distribution networks, and the integration of computation and communication technologies into the electricity eco-system, resulting in the modern smart grid concept, which is increasingly being put into the implementation phase at various levels. This conceptual and practical evolution of the electricity network has resulted in the tighter integration of different components across the different domains. Net metering, distributed generation, reactive power injection, grid stability and control, peak shaving, etc., are just few examples of such an integration, which allows the fine-grain control, up to the end user, of consumption and production, and then again up to the main production facility, to optimize the increasingly complex cost functions, such as minimizing the carbon footprint of the water storage facility, etc. Such tight integration and increasing complexity have resulted in the veracity of the measurement techniques for electricity consumption monitoring, resulting in different types of meters at different levels that are now increasingly being called smart grid sensors and include techniques beyond simple domestic meters. In contrast with the other energy networks discussed earlier, the rapid evolution and cross-layer integration of the electricity network warrants a more complex review of the sensing techniques across the network strata as effect of each measured value can propagate across the grid, even to the end user. To systematically review the state of the art in smart grid sensing, we will begin with the basic measurement techniques for currents and voltages and move towards increasing complexity by considering the phasor and temporal nature of the measured quantities. These complexities could lead to the identification of event data, for which specialized sensing techniques exist and will be briefly discussed. Finally, we move on to the actual implementations that exist in the literature. The gap that exists between the reported designs and the required measurements for the smart grids will thus be apparent and point to the future development for smart electric meters.
## 2 Materials and Methods
### Disposable IoT Sensors  
Currently, various technologies have been fused and evolved into IoT technologies. Existing IoT sensors have limitations in price, size, and weight, so it is difficult to precisely monitor a wide range of physical spaces. To solve these problems, technology of disposable IoT that has reduced size, price, and weight and has been constantly studied and developed. In the late 1990s, The University of California developed a very small sensor with a size of 1-2 mm called \"Smart Dust\" [14]. It has a technology that can detect and manage surrounding physical information (temperature, humidity, acceleration, etc.) through a wireless network through disposable IoT sensors (micro sensors). Therefore, it is possible to accurately sense the entire space by using a low-cost micro sensor compared to the existing sensor. In smart dust system using disposable sensors, LEACH is one of the key technologies for effective data collecting. In addition, disposable sensors broadcast collected data without polling process in order to reduce energy consumption and the size of send/receive payloads are relatively small. Disposable IoT sensor can be applied to a wide range of fields, such as weather, national defense, safety of risks, and detection of forest fire, as it can judge parts that are difficult for user to access [15].  
With the advancements of wireless sensor networks, it is anticipated that real time forest fire detection systems can be developed for high precision and accuracy using wireless sensors data. Thousands of disposable sensors can be densely scattered over a disaster-prone area to form a wireless sensor network in a forest [16].  
For monitoring the environment, disposable sensors are distributed in large areas by aerial dispersion using airplanes or dispersion in water stream. Distributed disposable sensors can be dislocated from original point by environmental condition, such as winds or movement of animals, since its size is only a maximum of 1 cm\({}^{2}\), as shown Figure 2.  
Figure 1: Diagram of Wireless Sensor Network (WSN).
## 1 Introduction
However, [PERSON] [27; 28] reported that the size of the hole-plates had slight effect on the flame acceleration when the hole-plate blockage ratio was low, and indicated explosion propagation in a porous medium was governed by the geometric characteristics of the porous media. The dual role between effect of NSSs on flame velocity and explosion-suppression of NSSs on overpressure has not been yet investigated in detail in a confined space. Considering the practical application of NSSs in gas storage, it is worth to investigate the double functions of NSSs on the explosion of gas mixtures. In order to study the effect of NSSs on the propane combustion process in confined space, the explosion experiments of propane-air pre-mixtures with different initial pressures were conducted in a newly designed constant volume combustion bomb (CVCB) with different filling densities of NSSs. Current work demonstrates the following new contributions: (1) The explosion suppression mechanism of NSSs was explained and the time history of in-cylinder pressures was analyzed in confined space. It was found that NSSs promoted pressure rise at the initial propane-air premixed combustion and inhibited the pressure rise in the later reaction period. (2) NSSs with different filling densities were filled in confined space. The explosion overpressure and the explosion time were derived from the pressure-time evolution, which was used to analyze the decay rates of the propane explosion overpressures \(Rp_{\mathrm{max}}\), the rates of average pressure rise \(\Phi\) and the time \(t_{\text{max}}\) to reach explosion overpressure and thus investigated the effects of NSSs on explosion-suppression. (3) Different flame propagation phenomena were all captured by high-speed Schlieren system including flame propagation, corrugated laminar flame, jet flame, corrugated turbulent flame as well as tulip flame under different initial pressures (0.2 MPa, 0.3 MPa and 0.4 MPa). Flame tip velocities were measured by the flame propagation image, and effect of NSSs with different filling densities (21.9 kg/m\({}^{3}\), 38.7 kg/m\({}^{3}\) and 45.1 kg/m\({}^{3}\)) on flame propagation was analyzed at different initial pressures of 0.2\(\sim\)0.4 MPa. The results show that NSSs with filling density of 45.1 kg/m\({}^{3}\) had the least disturbance effect on flame propagation. (4) NSSs suppressed the explosion overpressure in the cylinder, and promoted the flame propagation. In both cases, the NSSs played a dual role. The sup-pression effect of NSSs was affected by both its suppression and promotion effect on the explosion. The results of this work might provide a new insight for the effective prevention of explosion accidents with propane and the development of explosion-suppression products.
# YOLO-PGC: A Tomato Maturity Detection Algorithm Based on Improved YOLOv11
## 2 Related Works
### Vision-Based Agricultural Monitoring  
Researchers have increasingly applied computer vision techniques to agricultural monitoring tasks, particularly for fruit quality assessment. [PERSON] et al. [23] established fundamental methodologies for fruit classification using traditional image processing techniques. As deep learning emerged, [PERSON] and [PERSON] [24] comprehensively reviewed its applications in agriculture, highlighting the transformative potential of Convolutional Neural Networks (CNNs) for crop monitoring. For tomato specific applications, [PERSON] and [PERSON] [25] developed one of the first CNN-based approaches for tomato disease detection, while [PERSON] et al. [26] pioneered work on tomato maturity classification using color features and machine learning algorithms. These early efforts created the foundation for more sophisticated tomato ripeness detection systems.
# SafeNet: SwArm for Earthquake Perturbations Identification Using Deep Learning Networks
## 4 Results
### Considering Unbalanced Datasets  
The actual situation of earthquake issues is usually highly unbalanced, and it is obvious that non-earthquake datasets are always significantly larger than earthquake datasets. To test the real performance of the SafeNet model on unbalanced datasets as well as to investigate whether our proposed method could be applied to earthquake anomaly identification on unbalanced datasets, datasets with the positive to negative ratio of 1:2 (DataSet 14), 1:3 (DataSet 15), 1:4 (DataSet 16), and 1:5 (DataSet 17) were generated (shown in Table 1).  
Table 2 illustrates the performance of the SafeNet model on five datasets (including DataSet 03). Although the method performs most effectively on the dataset with a positive to negative ratio of 1:1 (DataSet 03), the overall performance is similar on the five datasets; for instance, the proposed method has F1 scores around 0.83 (ranging from 0.814 to 0.846) as well as MCC values ranging from 0.661 to 0.717. Figure 10 shows the ROC curves for all three classes, together with a comparison of the performance metrics; we also observed a similar trend of our proposed method's performance on the five datasets, which suggests that the SafeNet model achieves a good performance for pre-seismic perturbation identification on the unbalanced dataset. Although the five unbalanced datasets are different, these results show that our method is less sensitive on the positive to negative ratio, and our method can be used to identify possible electromagnetic preseismic perturbations on an unbalanced dataset, which suggests that it could provide a good realistic performance.
## 1 Introduction
There have only been a few studies conducted on polymer electrolytes that use polyurethane as a substrate for LIBs. Among these, a notable investigation by [PERSON] et al. focused on GPE filled with TiO\({}_{2}\) nanoparticles with good rate capability and mechanical properties. [PERSON] et al. also conducted research on the polyurethane-based electrolytes. However, the ionic conductivity of these electrolytes was found to be lower, with values ranging from 0.3-1.59 \(\times\) 10\({}^{-3}\) S cm\({}^{-1}\)[31; 32]. Despite these drawbacks, researchers propose that the blends of polyurethane acrylate and polyurethane methacrylate improve the mechanical and electrochemical properties of gel polymer electrolytes. This proposition involves the refinement of the ultraviolet (UV)-curing technique and the incorporation of specialized polymers and adjuncts, including inorganic and hybrid compounds. This new approach has a great deal of potential to overcome current obstacles and open up new opportunities for progress [33]. This study introduces a novel category of crosslinked gel polymer electrolyte membranes. These membranes are fabricated from a composition comprising polyurethane acrylate, polyurethane methacrylate, vinyl phosphonic acid (VPA), and bis[2-(methacryloxyoxy) ethyl] phosphate (BMEP). Upon activation with liquid electrolytes, these membranes can serve as gel polymer electrolytes within the context of LIBs. The integration of PUA-PUMA and the phosphate-incorporating constituents is achieved through free-radical polymerization. Free-radical polymerization initiates polymerization by generating free radicals that react with monomers to form polymer chains. These chains grow with the addition of more monomers, resulting in a polymer network [34]. The phenomenon of free-radicalpolymerization, occurring between the acrylate groups and phosphate groups present in VPA and BMEP, engenders the creation of a crosslinked polymeric network. A number of favorable characteristics are displayed by the resulting polymeric network, such as a high crosslinking density and increased resistance to chemical influences. All of these characteristics make it feasible for a variety of uses, most notably in the field of LIBs. The crosslinking process assisted by UV irradiation is remarkable as it gives the membrane a noticeable increase in mechanical strength and flexibility, especially after impregnation with liquid electrolytes. Among the gel polymer electrolytes examined, compositions containing 10% BMEP content are particularly noteworthy. This specific formulation distinctly demonstrates the highest measured ionic conductivity, quantified at a level of 1.83 \(\times\) 10\({}^{-3}\) S cm\({}^{-1}\). This value appears to be significantly greater than the ionic conductivity measurement of the commercially available Celgard2500 separator, which is 0.36 \(\times\) 10\({}^{-3}\) S cm\({}^{-1}\)[35]. The suggested GPE system offers superior lithium-ion conductivity compared to commercial separators, making it a promising choice for flexible LIBs.
## 2 Selected Routing Protocols
A routing update is feasible if its metric is smaller than previously advertised routes. Sequence numbers, generated by the destination node, ensure comparable metrics [45, 46]. Babel discovers neighbors through Hello messages, sent to a multicast address every four seconds, estimating link reception cost (rxcost). I Heard You (IHU) messages, sent every 12 s, determine link bidirectionality and share rxcost without ARP exchanges. Nodes share routing tables in route update messages to discover distant nodes. Updates are sent periodically to a multicast address, with unscheduled updates triggered by significant topology changes, reducing the need for frequent updates (default every 16 s). Feasible updates are used to compute accumulated metrics by adding the link cost [47, 48, 49].