esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Comparison between EMG Fatigue Thresholds and ventilatory Thresholds  
Our results showed a correspondence between the occurrence of EMG\({}_{\text{TI}}\) and VT\({}_{1}\), on the one hand, and between EMG\({}_{\text{TZ}}\) and VT\({}_{2}\) on the other. This correlation between EMG and ventilatory thresholds could be due to the enhanced activation of respiratory centers triggered by intramuscular accumulation of potassium and hydrogen ions, although this possibility is not based on the present results and thus is speculative. Indeed, it has been suggested that group III-IV muscle afferents are sensitive to changes in muscle acidosis and extracellular potassium concentration [38; 39]. The chemo-sensitive activation of these afferents could elicit a marked stimulation of the respiratory neurones [40; 41], thus leading to an abrupt increase in minute ventilation. Based on this sensorimotor reflex loop, it might be hypothesized that ventilation exhibits a first breakpoint (VT\({}_{1}\)) when an abrupt increase in the number of active motor units (EMG\({}_{\text{TI}}\)) occurs, and a second breakpoint (VT\({}_{2}\)), coinciding with the change in motor unit recruitment from predominantly slow twitch motor units to fast twitch motor units (EMG\({}_{\text{T2}}\)). In agreement with this theory, there are many reports showing a concomitant parallel increase in ventilation and EMG activity during an incremental cycling test [2; 6; 12; 13; 14; 28; 33].  
Alternatively, there are studies suggesting that changes in ventilation observed during incremental exercise are not triggered by accumulation of potassium and hydrogen ions, but that such changes are mediated by an increase in neural activity originating from the subthalamic motor region or indirectly via \(\alpha\)-\(\gamma\) coactivation of motor neurons innervating exercising limbs [42]. Such increase in neural activity may occur in response to the need to progressively recruit fast twitch muscle fibers as exercise intensity is increased. Finally, there are reports suggesting that ventilation variables are not influenced by motor unit recruitment, as evidenced by the dissociation between the occurrence of EMG and ventilation thresholds [2; 43].
## 4 Experimental Results
### Model Complexity and Inference Cost  
To evaluate the practical deployability of the proposed method, we conducted a comparative analysis of model complexity and computation. As shown in Table 7, our method requires 5.624 million parameters and 9.31 GFLOPs, which is efficient than PPAD (6.15 M parameters, 25.6 GFLOPs) and moderately more complex than UOCFR (3.87 M parameters, 8.65 GFLOPs). The results indicate that our model provides a favorable trade-off between computational cost and detection performance, making it suitable for real-time OCT applications.
# Predicting the Pillar Stability of Underground Mines with Random Trees and C4.5 Decision Trees
## 3 Methodology  
There are several data mining and machine learning (ML) algorithms available, among which random tree and C4.5 DT are the most frequently used algorithms for classification [24]. They represent decision trees that use strategies to divide and conquer in form of induction learning. The tree representation in these models helps to classify patterns in datasets, which are organized hierarchically in a collection of inter-connected nodes. The internal nodes evaluate each input attribute/feature in relation to a decision  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**S. No.** & **Mine** & **County** & _W (m)_ & _H (m)_ & _W/H_ & \(\sigma_{\text{use}}\) (MPa)_ & \(\sigma_{\text{p}}\) (MPa)_ & **Pillar Stability** \\ \hline
23 & G-I (Suraka char) & India & 16 & 3.5 & 4.6 & 29 & 4.14 & Stable \\
24 & Lower Kajora (Lachishpuri) & India & 18.3 & 5.1 & 3.6 & 33 & 1.4 & Stable \\
25 & E. Angarapatra (QII) & India & 6 & 2.1 & 2.9 & 19 & 3 & Stable \\
26 & Kathara (Kargali Incline) & India & 9.3 & 3.6 & 2.6 & 40 & 2.34 & Stable \\
27 & Jamadoba 6 and 7 Pits (AVI) & India & 5.8 & 2 & 2.9 & 29 & 7.59 & Stable \\
28 & Singharara (Topjs) & India & 7 & 1.8 & 3.9 & 41 & 5.15 & Stable \\  
\hline \hline \end{tabular} Note: The cases with a ‘*’ sign represent the testing dataset.  
\end{table}
Table 1: _Cont._  
Figure 1: Schematic for typical failure mechanism of a naturally fractured pillar (after [PERSON] et al. [31]): (**a**) disintegration of rock blocks; (**b**) formation of inclined shear fractures transecting the pillar; and (**c**) transgressive fractures.  
constant, thereby deciding what will be the next descending node. The nodes that are considered to be leaves are aimed to identify the instances that reach them as per the corresponding label [35].
# Cognitive Frequency-Hopping Waveform Design for Dual-Function MIMO Radar-Communications System
## 5 Simulation
### Delay-Doppler Resolution Performance  
Figure 6a displays the \(2\times 2\) MIMO radar ambiguity function contours of the original waveform. The delay-Doppler resolution deteriorates owing to the presence of environment noise as shown in Figure 6a. This phenomenon will became worse if the interference scatterers are placed in the vicinity along the line linking the radar target and the antenna. The radar target is considered to be located at the origin of the plan. Figure 6b displays the MIMO radar ambiguity function contours of the optimization waveform by the proposed scheme at the end of 20 iterations. As can be seen  
Figure 4: (**a**) The detection probability for different iterations provided by the proposed scheme; (**b**) The receiver operating characteristics (ROC) for four approaches.  
Figure 5: (**a**) The mean-square error (MSE) of target response estimation subject to power constraint; (**b**) the MSE of target response estimation subject to power and probability constraint.  
from Figure 6a, the target discrimination capability becomes significantly enhanced by the increasing iteration number. Specifically, the noise is suppressed by about 2.5 dB.  
The delay-Doppler resolution, which is the output of matched filter at the radar receiver, is connected to the MIMO radar ambiguity function of the transmit waveforms. The near-ideal thumbtack response would appear if the statistically independent waveforms are employed for emission. We constantly use the optimization waveforms provided by the proposed scheme to match the estimated target response. It is worth noting that the estimated target response is continuously updated at each iteration. At a result, the matching process ensures that the noise interference can be suppressed over the radar channels and further enhance the SNR of the target-scattering signals. Figure 6b illustrates the enhancement in SNR with multiple iteration number. The phenomenon shows the enhanced ability of the proposed dual-function MIMO system to discriminate the target from radar environment and resolve the target's range and velocity.
# Comparative Study of the Presence of Heavy Metals in Edible Vegetable Oils
## 2 Analysis of Publications and Trend of Heavy Metals in Edible Vegetable Oils
### Results of the Analysis of Heavy Metals in Edible Vegetable Oils
#### 2.3.1 Cadmium (Cd)
In this sense, Commission Regulation (EU) 2020/1245 [66] established limit values of Cd in different foods such: as cereals, 0.10 mg/kg; bran, germ, wheat, and rice, 0.20; vegetables and fruits, 0.05; or leafy vegetables 0.10 mg/kg, among other foods. Furthermore, in 2010 the joint FAO/WHO expert committee on Food Additives [69] proposed a maximum tolerable monthly intake of 25 ug/kg of Cd with respect to weight. A year earlier, the EFSA established a tolerable weekly intake of 2.5 ug/kg, a figure significantly lower than that proposed by JECFA [70]. Therefore, in the absence of specific regulations for the presence of Cd in edible vegetable oils, the selected comparative criterion was the most restrictive normative value for the rest of the heavy metals analysed in this review, 0.10 mg/kg (Table 2). In this regard, it is possible to put into perspective those results that deserve mention due to their high concentration in these oils. Table 3 shows both the results that are close to 0.10 mg/kg and the results that significantly exceed it. Due to its proximity, we find, among others, olive oils from Iran and Cyprus with values between 0.094 and 0.097, and 0.09 mg/kg, respectively [53; 60], and rapeseed and sesame oils from Iran with values between 0.098 and 0.099, and between  
Figure 4: Flow of cadmium in soils and plants and ingestion by humans through plants. 0.090 and 0.097 mg/kg [53]. Likewise, among the results that significantly exceed this limit, China rapeseed oil stands out with a value of 0.88 mg/kg [80]; olive oil from Iran and Italy with a value of 4.181 mg/kg [81]; and sunflower oil from India with a value of 0.54 mg/kg [82]. The lack of evidence of toxicity thresholds by international organizations for Cd in oils--being the most analysed heavy metal--shows that oils with high values, such as those mentioned above, are being consumed without any type of restrictions with the aforementioned health risks associated with the prolonged intake of this heavy metal.
# A Survey on Robust Video Watermarking Algorithms for Copyright Protection
## 2 Basic Models of Video Watermarking
### Watermark Embedding  
Watermark embedding is the process which embeds binary strings representing author's information or copyright information into the original video through a specific embedding algorithm. The embedding algorithm must take the balance between invisibility and robustness of the watermark into account. The model of embedding process is shown in Figure 5.  
According to the different embedding positions of watermarks, video watermarking algorithms can be divided into three types: original video-based watermarking algorithm, video watermarking algorithm in encoding process, and video watermarking algorithm after compression. Video watermarking algorithms obtained by these three embedding methods have their own advantages and disadvantages.
## 4 Results
### Performance of Models
determining gout disease status is whether the patient has Creatin with 58.87%, while the least important factor is Eosinophil with 1.11%. Figure 3 shows the ROC curve of the DT model. In tree-based models, the factors affecting the performance of the models are calculated with percentage values, while in the LR model, the co-efficient value is considered. In ANN model, Permutation Importance Score, SHAP (SHapley Additive exPlanations) Values, or LIME (Local Interpretable Model-agnostic Explanations) Explainable values are examined to determine the factors affecting the performance of the model. In this study, the co-efficient value of the factors affecting the performance of the LR model is examined, while the Permutation Importance Score values are examined for the ANN model. Table 11 shows the most important factors determining gout disease status in the logistic regression (LR) model. Accordingly, Creatin was the most important factor determining gout status, while Monocyte was the factor with the lowest effect. Figure 4 shows the ROC curve of the LR model. \begin{table}
\begin{tabular}{c c} \hline
**Feature Name** & **DT Classification (\%)** \\ \hline CREATIN & 0.588792 \\ HAEMOGLOBIN & 0.108985 \\ URIC ACID & 0.065655 \\ HDL & 0.058617 \\ UREA & 0.046587 \\ SEDIMENTATION & 0.023664 \\ LDH & 0.016008 \\ PLT & 0.015976 \\ TOTAL PROTEIN & 0.014848 \\ TRIGLYCERIDE & 0.013363 \\ EOSINOPHIL & 0.011136 \\ \hline \end{tabular}
\end{table} 
Table 10: The most important factors identified with DT. Figure 3: ROC curve of DT. Gouty arthritis (GA) is an inflammatory disease characterized by the accumulation of monosodium urate (MSU) crystals in the joints. This condition is particularly associated with kidney function and metabolic diseases. Urea is a waste product related to protein metabolism that is excreted from the bloodstream by the kidneys. In GA, kidney function declines due to tubulointerstitial nephritis and stone formation. Kidney failure or dysfunction leads to elevated urea levels. Moreover, high urea levels, resulting from impaired kidney function, can cause changes in serum uric acid levels. Therefore, elevated urea  
Figure 5: ROC curve of ANN. Figure 6: Training graphics of ANN. levels in GA may influence the severity of the disease and the frequency of attacks. High urea levels are associated with kidney function and hyperuricemia. Low albumin levels are linked to inflammation and urate accumulation. Urate accumulation reduces nitric oxide, leading to renal ischemia; increases pro-inflammatory cytokines, causing kidney damage; and impairs the kidney's ability to excrete nitrogenous waste products. Glucose levels, on the other hand, contribute to kidney dysfunction by triggering metabolic disorders and inflammatory processes. Examining the results obtained from identifying factors influencing GA using ML models, it has been observed that variables such as urea and uric acid are among the factors affecting model performance. These findings indicate that the models align with real-world findings in determining the factors influencing GA.
# A Survey on Energy Drainage Attacks and Countermeasures in Wireless Sensor Networks
## 3 Energy Drain Attacks in WSNs
### Background of Energy Consumption and Drain  
Before reviewing energy drain attacks, investigating the energy consumption model for the sensor node is crucial. As described before, for components in a node, energy is consumed for the node's activity. Although an energy consumption model is mostly  
Figure 4: Number of papers in published years.  
Figure 3: Number of papers in research areas.  
dependent on characteristics of the sensor node, such as the type of transceiver RF, we consider a general model in which the total energy consumption is equal to the summation of the consumed sensor, computing, and communication energy. Based on previous measurements and analyses [17; 18], we can estimate a portion of the energy consumption on each component in Tables 3 and 4, respectively. Although slight gaps exist between detailed values in the two tables, energy for communications is a larger source of the total energy expenditure than processing and sensing cases.  
Based on the analysis, considerable research on energy is focused on reducing unnecessary energy. Detailed sensing energy can be reduced by deleting unnecessary sensing. Energy efficiency is achieved by either dynamic voltage scaling or system partitioning. Finally, reducing the number of communications as well as the distance of communications is applicable to energy consumption in communications. In [19], as an alternative approach, a WSN-based energy harvesting system was proposed to provide energy for recharging. A new approach to facilitate battery-less energy harvesting techniques in industrial WSNs was presented in [20]. In addition, [PERSON] et al. [21] addressed imbalanced energy issues for topology control and proposed a scale-free topology evolution model by employing a new factor, route-oriented path load. Its main objective is to improve the network load balancing and robustness. To defeat this problem, two approaches, increasing the average path length of the network or sink node near the network center, were suggested. Energy drain can lead to creating a hole in the network. In [22], a hole in a WSN is a region in which several adjacent nodes have failed. When a hole is created, transmitting or sensing data becomes difficult. Holes can be caused by factors such as random node deployment, electronics failure, software errors, and energy depletion.
## 3 Results and Discussion
### Measurement of Properties  
#### 3.2.1 Particle-Size Determination via TEM  
As shown in Figure 2, the particle sizes of ZnO NPs dispersed with and without NOM were analyzed using TEM images. The average size of ZnO NPs was 36.48 \(\pm\) 6.61 nm (\(n\) = 20), and that of ZnO NPs dispersed with NOM was 36.10 \(\pm\) 9.13 nm (\(n\) = 20). However, it is difficult to identify the increase in size resulting from the NOM dispersion using the TEM images. In addition, it was confirmed that the addition of NOM led to the reduced sharpness of the particles, and a thin film of about 2 nm was formed on the particle surface.
## 3 Three-Dimensional Numerical Model
### Limitations  
The first limitation is that the finite element computational model used in this paper is relatively simplified from the actual situation, and the mesh density and computational accuracy may not be sufficient for only approximate numerical solutions. The second limitation is that the paper only conducted a numerical simulation study for this project and lacked field tests or data to validate its numerical results.  
Therefore, the adoption of analytical methods specifically for discontinuous environments and the use of similar indoor tests or field monitoring data to verify the accuracy of the simulation results are topics that need to be further investigated.
# Realistic Texture Mapping of 3D Medical Models Using RGBD Camera for Mixed Reality Applications
## 1 Introduction
The RAYFace (RayMedical, Ray Co., Ltd., Seongnam, Republic of Korea) is a 3D one-shot face-scanning solution developed in 2020 and reached an absolute surface discrepancy of 0.5277 when compared with other facial scanners [36]. Regarding the Intel 3D camera D435 (Intel Corporation, Santa Clara, CA, USA), one of the most popular RGBD sensors, [PERSON] et al. [33] report that there is little research on its reliability. In this study, to collect real external texture information, the Intel D435 3D camera was used. Three-dimensional cameras offer a heightened sense of depth and realism compared to traditional two-dimensional imaging. Unlike standard cameras that record a flat scene representation, 3D cameras create images with depth perception, allowing viewers to experience a more immersive visual environment. The principle behind 3D cameras involves the usage of multiple lenses or sensors to capture the same scene from slightly different perspectives, mimicking the way human eyes perceive depth. These variations in perspective are then processed to create pointclouds, which are three-dimensional representations of the surfaces and structures in the captured scene. Pointclouds are collections of individual data points in 3D space, each point representing a specific location and contributing to the overall depth information of the scene. Often, 3D cameras also integrate standard RGB cameras, allowing the enrichment of pointclouds with the true colours of the objects in the scene. This type of camera is called RGBD camera. This work aimed to register two 3D pointclouds obtained by different techniques (CBCT machine and RGBD camera), apply the real textures' colours collected with RGBDcamera to the external layer of CBCT pointcloud, and then show the result in the real environment using a mixed reality head-mounted display (HMD). The combined use of the two acquisition techniques could make it possible to obtain, in a single 3D model, the advantages of both techniques: fidelity in the representation of the internal tissues thanks to CBCT machine and of the external texture thanks to the RGBD camera. Furthermore, the integration of this model in a mixed reality system would allow the user to interact directly with the digital models through hand gestures, improving the user experience.
## 2 Package Description and Integrated Simulation Methodology  
### Integrated GaN Package Description  
A brief outline of the innovative package will be made in the following [46; 52]. As it was stated in Section 1, these solutions are becoming increasingly used in industry, for Si- and WBG-based power devices [43; 53; 54; 55; 56; 57; 58]. The SiP solution, from STMicroelectronics, presented in this paper has a 100 V breakdown voltage, and it is used in a 48 V-12 V DC-DC converter application. The package has been named 2 SPAK and comprises a half-bridge and the control logic. It allows, if the layout design is carefully addressed, a more compact solution, reducing the contribution of parasitic elements. In this case, via connect GaN metals directly to the pads, reducing parasitic contribution and improving the thermal performance [52]. In addition, the PCB must also be designed in order to minimize parasitic elements, again, for EMI issues [59]. 2 SPAK permits a bulk capacitor position, in PCB, a reduction of length interconnections [52]. In general, oscillations and overshoots must be minimized [60].  
The package analyzed in this work is a bond-wire free one in order to improve thermal performances and reduce parasitic losses. It has an exposed slug on the top. Therefore, top side cooling helps the heat exchange, thereby making it possible to lower the system's thermal resistance or reducing the heatsink size.
## 2 Measuring Method and Measuring System
### Measuring System  
The measuring system contains an optical electric-field measuring unit and a data processing unit. The optical electric-field measuring unit includes optical sensors, a laser source, a laser receiver, and transmission fibers. The output analog signals of the laser receiver are converted into digital signals by a high-speed data acquisition card (HS-DAQ) with 4 acquisition channels and a sampling ratio of 40 MS/s. The digital signals are temporarily stored in the internal storage and further analyzed by the central processor to determine whether a transient process occurs. While the system is triggered by the  
Figure 2: Integrated optical electric-field sensor. (**a**) schematic diagram; (**b**) product photo.  
transient voltage, the transient voltage waveform from 20 ms before to 80 ms after the triggering will be stored into the local storage hard disk. Simultaneously, 400 milliseconds voltage waveform after the triggering time would also be recorded with a sampling ratio of 20 kS/s. All these signals are timed by a GPS module with the error of less than 100 ns. The field measured signal will be transmitted to the central station by a 4G wireless communication module. Thus, the HS-DAQ, the central processor, the internal storage, the hard disk, the GPS module, and the communication module constitute the data processing system. Except for the sensors and optical fibers, all the other devices are integrated into a cabinet or a portable suitcase and powered by an isolated power supply. The general connection diagram of the measuring system is shown in Figure 3.  
Due to the nature of randomness for the lightning strike, the sensors used for measuring lightning transient voltages are installed outdoor for a long term. To protect them from environmental effects, sensor containers are used, which are supported by a grounded metal tube to the same level of the base of adjacent insulators, as shown in Figure 4a. For the measurement of switching transient voltage, the sensors are just supported by an insulating rod temporarily, as shown in Figure 4b. On both arrangements, the clearances between the sensor and the high-voltage conductor are no shorter than the necessary distance specified in [5].