esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussion  
### Model Developed with Original Data  
In our novel ML model, about 1300 fatigue test results from the 29 paper data sets used by [PERSON] et al. [4] were collected and organized. For training and testing of the model, 90% of the total data was used for training and 10% was used for testing.  
* Total data sets: 1298 data sets;
* Training data sets: 1169 data sets;
* Test data sets: 129 data sets.  
Figure 11: Parameters of the proposed AdaBoost model [79].  
Figure 10: Parameters of the proposed gradient boosting model [79].  
The four ML models (random forest, neural network, gradient boosting, and AdaBoost) were run, and the results of training and testing for each model are shown in Table 8a,b below. Using the same data sets, [PERSON] et al. [4] reported that the MSE and correlation coefficient (r) from the random forest model are 0.44 and 0.85, respectively. The determination coefficient (\(R^{2}\)) in that case was about 0.723. In this study, excluding the sustained strength of the concrete variable, which was originally treated as the seventh input variable in the work of [PERSON] et al. [4], resulted in improving the MSE and \(R^{2}\) values. Moreover, Table 8a,b shows that the gradient boosting model with the value of the minimum error and a high \(R^{2}\) value is indicates high accuracy in predicting outcomes. Additionally, [PERSON] et al. [4] reported that the MSE and r values from the typical traditional regression fatigue formulae (represented as \(S\)-\(N\)-\(T\)-\(R\)) in terms of \(R\), \(S_{\text{max}}\), and rate of loading (\(T\)) were 1.46 and 0.50, respectively.
# High Performance GAA SNWT with a Triangular Cross Section: Simulation and Experiments
###### Abstract  
In this paper, we present a gate-all-around silicon nanowire transistor (GAA SNWT) with a triangular cross section by simulation and experiments. Through the TCAD simulation, it was found that with the same nanowire width, the triangular cross-sectional SNWT was superior to the circular or quadrate one in terms of the subthreshold swing, on/off ratio, and SCE immunity, which resulted from the smallest equivalent distance from the nanowire center to the surface in triangular SNWTs. Following this, we fabricated triangular cross-sectional GAA SNWTs with a nanowire width down to 20 nm by TMAH wet etching. This process featured its self-stopped etching behavior on a silicon (1 1 1) crystal plane, which made the triangular cross section smooth and controllable. The fabricated triangular SNWT showed an excellent performance with a large Ion/Ioff ratio (\(\sim\)10\({}^{\circ}\)), low SS (85 mV/dec), and preferable DIBL (63 mV/V). Finally, the surface roughness mobility of the fabricated device at a low temperature was also extracted to confirm the benefit of a stable cross section.  
silicon nanowire; triangular cross section; simulation; experiment; TMAH; low temperature; mobility Article
# Discoloration Mechanisms of Natural Rubber and Its Control
## 5 Discoloration Mechanism of Natural Rubber
### Enzymatic Browning
#### 5.1.3 Effect of Ethephon Stimulation on Polyphenols Content  
Stimulation-based low-intensity harvesting (LIH) systems are accepted worldwide as an agronomic tool to overcome the high cost of production, worker scarcity, and low economic lifespan of trees in rubber plantation industries [37]. Discoloration due to ethephon stimulation has been reported [34]; however, there is no evidence for the effect of ethephon concentration on the color of crepe rubber. In 1976, [PERSON] observed that ethephon could stimulate an increase in the phenol content in the latex exuding from the rubber trees, resulting in slight discoloration of NR [33].  
Stimulation by ethephon greatly reduces latex PPO activity, which is correlated positively with plugging index, which results in an increase of phenolic substances in stimulated latex. A significant increase in phenolic content reported (after first & second tapping) after ethephon treatment is due to a decrease in phenol-oxidase activity [35; 38]. [PERSON] et al. have shown a definite lowering of \(o\)-diphenyl oxidase activity in latices at the first tapping after stimulation of the chaste tree [27].  
The ethephon increased the thiol content of the latex, which acted as an antioxidant by reacting with phenolic compounds to form colorless products. It is evident from the results that with increasing ethephon concentration up to 3%, the Lovibond color index is reduced, with the simultaneous increase in thiol concentration. The lighter color observed in rubber with ethephon treatment may prevent phenolic discoloration with thiol compounds. Therefore, thiol concentration above a critical level allows permanent protection against enzymatic browning [39]. However, this level may often be dependent on the physiological condition of the tree [18].  
However, there was no significant difference (\(p>0.05\)) in Lovibond color among the harvesting systems [40]. [PERSON] [41] observed that unidentified constituents (whether polyphenols or polyphenol oxides) were rapidly oxidized by a non-enzymatic process immediately after ammoniation of latex. It was concluded that non-rubber constituents were oxidized. The preservation of natural rubber latex with ammonia is still commonly used processing process in the industry. Therefore, the discoloration caused by the appropriate amount of ethephon stimulation is not significant.
# Conductive Ink Printed Fabric Antenna with Aperture Feeding Technique
## 2 Material Selection and Pre-Processing
### Screen-Printing of Conductive Ink  
This technique is widely known in the textile printing industry as it affords large-scale printing and fast production on an economic scale. It is achieved by pressing conductive ink over a meshed screen onto the base fabric which can therefore achieve high print resolution and uniform electrical conductivity based on several factors. The screen mesh size, the number of print cycles, screening speed, the viscosity of conductive ink, as well as the curing procedure, are some factors that determine the outcome of the print quality [12; 13; 14].  
After careful research into these findings and our investigations presented in [4; 9], the screen-printing properties used were 70-mesh openings per 1 inch (pixel/in) with each opening being \(\approx\) 280 \(\times\) 280 \(\upmu\)m\({}^{2}\) and mesh wire diameter of \(\approx\) 75 \(\upmu\)m. A high mesh resolution however offered better surface roughness with less viscous conductive ink. Using a silver-based conductive ink (Electroda 479 SS) from Henkel Co. [15] which has a viscosity of 12,000 mPas(cP) and sheet resistivity of 0.02 \(\Omega\)/sq/mil, the ink's bulk conductivity was deduced at 1.97 MS/m (bulk conductivity of copper = 58.7 MS/m).  
Although lower than copper, this value was high enough to achieve comparable gain performance as shown in [9]. The course silver particles present in the Electrode 479 SS ink or paste makes it more conductive and thus more viscous.  
With this high viscosity, a lower resolution mesh of 70 pixels/inch will perform better than a higher resolution as a highly viscous ink paste permeates a wider aperture mesh more evenly. The number of printing layers was also limited to two as presented in [3] and [4] to be the best number for screen-printed conductive fabrics. These however also depend on several factors such as the mesh size, curing time, and ink viscosity among others. Printing more than two layers on the 70 pixel/in mesh did not increase the electrical performance of the conductive fabric [4]. The final material takes about 24 to 48 h to completely cure and be ready for use as a conductive textile sheet.
## 2 Localization Solution
### Working Principle  
The core principle of this localization system revolves around measuring the time differences (\(\Delta t\)) between the arrivals of signals from pairs of beacons at the robot's receivers. These time differences can be interpreted as hyperbolic loci in space, known as isochrones (from Greek \"iso-\" meaning \"equal\" and \"chronos\" meaning \"time\"), which represent all points where the time difference between signals from two beacons remains constant.  
All possible solutions for a given time difference lie on a hyperbolic function in space. The isochrones are a visualization of this hyperbolic space, represented as color gradients in the figures. By analyzing the intersections of these isochrones from multiple pairs of beacons, the system can determine the robot's precise location with high accuracy.  
To better illustrate this concept, Figures 2 and 3 provide visual representations of the hyperbolic function through color gradients. Each color in the gradient corresponds to a specific time difference value, effectively representing the function across the operational area. These visualizations offer valuable insight into the geometric relationships that underpin the system's functionality and demonstrate how time differences translate into spatial locations.  
When a pair of beacons emit ultrasound signals simultaneously, the robot's receivers detect the arrival times of these signals. The system computes the time difference (\(\Delta t\)) between the signal arrivals, placing the robot on an isochrone that represents all possible locations where this time difference could be observed. The intersection of isochrones from multiple beacon pairs provides the robot's precise location.  
Figure 3 shows the complete isochrone plot for all beacon pairs. Each color represents isochrones for a different beacon pair, illustrating how time differences vary across the operational area. The dense network of isochrones demonstrates the rich information available from just three beacons, enabling precise localization.  
Figure 4 focuses on the specific isochrones that pass through the robot's position. This visualization clearly shows how the intersection of these isochrones from different beacon  
Figure 3: All isochrones for beacon pairs. Each color represents a different beacon pair, showing how time differences vary across the area.  
Figure 2: Time difference plots for pairs of beacons. The color gradients represent the hyperbolic function, with isochrones in (\(\Delta t\)) 1 millisecond increments. The x- and y-axes represent spatial coordinates within the operational area.  
pairs determines the robot's exact location. Each colored line represents the isochrone from a beacon pair that corresponds to the time difference measured by the robot.  
These visualizations highlight the effectiveness of the time difference approach. They demonstrate how this method achieves accurate localization by leveraging the geometric relationships between the beacons and the robot.
## 2 Methods
### GNSS Tracking Networks
#### 2.1.2 IGS Tracking Network  
The IGS, as important component of the International Association of Geodesy (IAG), has been providing high-quality GNSS raw data for analysis centers and global research institutes. The MGEX pilot project has been established by the IGS to track and collect multi-GNSS signals, including the BDS, Galileo and QZSS systems, as well as modernized GPS and GLONASS satellites. The hourly data from about three hundred sites have been collected by the IGS data archive centers, e.g., Crustal Dynamics Data Information System (CDDIS) and Institut National de l'Information Geographique et Forestiere (IGN).  
In this contribution, 21 iGMAS stations and 100 IGS stations are chosen for hourly updated ultrarapid POD study, and station distribution is demonstrated in Figure 1. The observation data from 1 June 2021 to 31 May 2022 are processed.
## 3 Proposed Approach
### Evaluation of Model Performance  
#### 3.10.1 Ap  
In object detection, Average Precision (AP) is commonly used as a numerical metric of accuracy. It represents the average precision across recall values ranging from 0 to 1, evaluated at various Intersection-over-Union (IOU) thresholds. Here, IOU refers to the overlap area between predicted and ground-truth bounding boxes, divided by the total area covered by both boxes. AP is computed as shown in Equation (9):  
\[AP=\sum_{i=1}^{n}p(i)\Delta r(i) \tag{9}\]  
where \(n\) denotes the total number of detected bounding boxes; \(i\) is the rank of a particular detection; \(p(i)\) indicates the precision from the first to the i-th detection; and \(\Delta r(i)\) represents the change in recall from the (\(i-1\))th to the \(i\)-th detection [13].  
In this study, performance was evaluated at AP50--requiring a minimum IOU of 0.5--and AP50:95, which averages precision across IOU thresholds from 0.5 to 0.95. In the context of building detection and segmentation, an IOU of at least 0.5 indicates that the predicted bounding box or mask substantially overlaps the true building, enabling reliable dimension measurements while still allowing for minor localization errors.
## 1 Introduction
The Tianshan Mountains are one of the most active seismic regions on the Chinese mainland, as testified by the occurrence of 15 earthquakes of \(M\geq 7\) leading to surface rupture. As an internal block of the Tianshan region, the Yultuz Basin is surrounded by multiple active late Quaternary faults, which were associated with the 2012 Xinyuan-Heijing \(M_{\rm S}\) 6.6 earthquake and the 1944 Xinyuan \(M\) 7\(\frac{1}{4}\) earthquake. The Kalawenguquan fault is known to be the northern edge of the Nalati fault and controls the northwestern edge of the Yultuz Basin. It is about 400 km long, spanning 360 km in Xinjiang, China, and about 40 km in Kazakhstan (Figure 1b). This left-lateral strike-slip fault has an average strike of N64\({}^{\circ}\)E and dips 60-80\({}^{\circ}\) to the south [37]. Previous studies characterized Kalawenguquan fault activity using geological and geomorphological features such as alluvial fans, river terraces, and gullies [38; 39]. Using remote sensing multi-source data, such as Gaofen-2 images, Landsat 8 satellite images, and aerial high-resolution images, [PERSON] [37] evaluated the geometry and seismic hazard posed by the Kalawenguquan fault. However, geometric structure, sliding rate, and fault activity of the Kalawenguquan fault have not yet been comprehensively understood. [PERSON] et al. [40] conducted systematic research on the middle segment (F1-3 in Figure 1b) of the Kalawenguquan fault using high-resolution remote sensing image interpretation, geological field surveys, geomorphological surveys, drone aerial surveys, trench excavation, and the dating of trench samples with the radiocarbon-14 dating method. The results indicated that the F1-3 segment of the Kalawenguquan fault has been an active fault since the Holocene. Furthermore, the Kalawenguquan fault was the seismogenic structure of the 2007 \(M_{\rm S}\) 5.9 Tekesi earthquake, but its accumulated energy was not fully released [41]. There is still a possibility of large earthquakes occurring on this fault in the future. Due to abundant water resources, infrastructures such as reservoirs, tunnels, pipelines, and other facilities have been planned to be built in the Kalawenguquan region. The Kalawenguquan fault is about 43 km away from Tekesi City and 60 km away from Zhaosu City, and it crosses the Yining-Akesu Railway. Therefore, reasonable evaluations of ground motion parameters for near-field large earthquakes have important engineering significance for the seismic safety of major infrastructures and transportation routes and forthe urban seismic planning of cities in Tekesi and Zhaosu. In the current study, we designed a multi-scheme simulation and used the stochastic finite-fault method to consider the uncertainty of various modeling parameters for the F1-3 fault.
## 3 Results
### Spectral Dimensionality Reduction  
PCA is a commonly used method that reduces the dimensionality of linear data via linear variation to achieve the extraction of the original data. It supports a small number of unrelated variables (principal components) to describe the data and retains as much useful information as possible [36]. The PCA algorithm is used to reduce the dimension of the 330 bands of the ASD spectral data and the GF5 AHSI spectral data of the sample. To facilitate the subsequent image inversion of the sample area, the number of principal components of the spectral data from two different sources is taken as 10. Table 3 shows the contribution and cumulative contribution of the spectral principal components after dimensionality reduction.
## 3 Proposed Methodology
### Performance Evaluation Metrics  
The performance parameters applied for evaluating the effectiveness of the proposed scheme were _accuracy, recall, precision, F-measure, mAP_, and the classification rate (_Error Rate_).  
\[Accuracy=\frac{TN+TP}{(FP+TP+FN+TN)} \tag{4}\]  
\[Precision=\frac{TP}{(FP+TP)} \tag{5}\]  
\[Recall=\frac{TP}{(TP+FN)} \tag{6}\]  
\[F-measure=\frac{(1+\beta^{2})\times precision\times recall}{\beta^{2}\times precision +recall} \tag{7}\]  
\[TPR=\frac{TP}{(TP+FN)} \tag{8}\]  
\[FPR=\frac{FP}{(TN+FP)} \tag{9}\]  
\[ErrorRate=\frac{FN+FP}{(TP+TN+FP+FN)} \tag{10}\]  
\[mAP=\frac{1}{N}\sum_{i=1}^{N}AP_{i} \tag{11}\]
# Study of System Integral Energy Efficiency of a Hybrid Pneumatic Power System
## 3 Results
### Exhaust Pressure and Temperature of the ICE  
The exhaust pressure was measured by the pressure sensor, in the case of different volumes, in the butterfly valve (intake throttle) of the ICE, as shown in Figure 2. The horizontal axis (load percentage) is the volume of the butterfly valve (intake throttle) of the ICE. It changed from 0\({}^{\circ}\) at the closing position to 90\({}^{\circ}\) at the maximum position. The vertical axis (exhaust pressure) is relative pressure (not including the atmosphere). Figure 2 shows that the exhaust pressure depended on the intake of the ICE and was linear. When the volume was increased from 0% to 90%, the exhaust pressure rose regularly from 14 to 34 Psig.  
The exhaust temperature was measured by the temperature sensor, in the case of different volumes, in the butterfly valve (intake throttle) of the ICE, as shown in Figure 3. The horizontal axis (load percentage) is the volume of the butterfly valve (intake throttle) of the ICE. It changed from 0\({}^{\circ}\) at the closing position to 90\({}^{\circ}\) at the maximum position. The vertical axis shows the exhaust temperature in \({}^{\circ}\)C. Following Figure 3, we can see that the exhaust temperature increased linearly with the intake throttle of ICE. When the intake valve of the ICE changed from 0% to 30%, the temperature of the exhaust rose slightly from 100 \({}^{\circ}\)C to 170 \({}^{\circ}\)C. After that, from 30% to 90% of the volume of intake throttle, the temperature increased from 170 \({}^{\circ}\)C to 450 \({}^{\circ}\)C, at a faster rate than before.
# Current Applications of Ultrasound in Fruit and Vegetables Osmotic Dehydration Processes
## 4 Ultrasound-Assisted Osmotic Dehydration  
The application of ultrasound in the OD process is of great interest for researchers and food industries, since it offers the possibility to be used both prior the OD (as a pretreatment) and during the OD process (ultrasound-assisted osmotic dehydration (UOD)). Due to the cavitation phenomenon and continuous compression and decompression of the tissue, US leads to the creation of microchannels, which facilitate the mass transfer between the product and osmotic solution [32; 70; 75; 98].
# A Multi-Gateway Behaviour Study for Traffic-Oriented LoRaWAN Deployment
## 2 Fundamentals of LoRaWAN Technology
### LoRa (Long Range)  
The term LoRa or LoRa RF (long-range radio frequency) refers to the technology owned by SEMTECH [5]. Its CSS modulation allows linear broadband frequencies, making the signal robust to channel noise. In addition, thanks to the orthogonal channels, communications with different data rates do not interfere with each other, which presents a significant advantage in overcoming interferences. At the PHY level, LoRa offers the possibility of optional choices to transmitters from five parameters, namely: the Central Frequency (\(CF\)), Transmission Power (\(TP\)), Spreading Factor (\(SF\)), Bandwidth (\(BW\)), and Coding Rate (\(CR\)). However, the signal range and transmission bit rate (\(R_{b}\)) in a LoRa network depend on the used combination of these parameters and are defined by the following expression:  
\[R_{b}=SF*\frac{CR}{\frac{2^{SF}}{BW}}=\frac{SF*CR*BW}{2^{SF}}\,(\text{bits/s}) \tag{1}\]The spreading factor \(SF\) is equal to the number of bits per symbol. We can deduce from Equation (1) the modulation speed noted \(R_{s}\) (symbol rate) and expressed in baud in Equation (2):  
\[R_{s}=\frac{BW}{2^{SF}}*CR\,\text{(Bauds)} \tag{2}\]  
Depending on the combination of these parameters, the LoRa modules allow obtaining 28 different flow rate values from the transmission between 0.3 kbits/s and 11 kbits/s. For example, Figure 2 shows the impact of these parameters on the bit rate (\(R_{b}\)) and symbol rate (\(R_{s}\)). In addition, this figure illustrates how the LoRa communication range and throughput (for the 868 MHz band) are impacted by the bandwidth values, the signal's output power, and the spreading factor used.  
Although LoRa has advantages over competing technologies, its simple ALOHA channel access method is considered the main weak point for possible traffic-oriented scalability. For this channel access method, a station can transmit whenever it wants and waits for an acknowledgement (ACK) (in the confirmed traffic case). The terminal retransmits its frame if no ACK has been received from the network server after a fixed period. The corresponding information is corrupted and lost whenever two or more packets collide. Thus, the packets in question must be retransmitted, leading to additional bandwidth use and a reduction in the network's capacity.  
Figure 1: LoRaWAN network architecture.  
Figure 2: LoRaWAN bit rate variation according to \(SF\) and \(BW\) parameters [5].