esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods for Testing the Performance of Soil Electrical Conductivity Prototype
#### 2.3.1 Calibration Methods for Prototype  
The calibration test of the soil electrical conductivity prototype was conducted using conductivity calibration solutions produced by Shanghai Leimagnet Instrument Co. At a room temperature of 25 \({}^{\circ}\)C, four calibration solutions with conductivities of 84 \(\mu\)S/cm, 146.5 \(\mu\)S/cm, 1408 \(\mu\)S/cm, and 12.85 mS/cm were contained in 100 mL tall beakers. The prototype probe was fully submerged in each solution, and the output was allowed to stabilize before readings were taken. This process was repeated ten times for each solution, with the average value recorded. After each calibration, the probe was cleaned with anhydrous ethanol and air\(-\)dried before proceeding to the next calibration to prevent contamination of the standard solutions and ensure measurement accuracy. The real part of the impedance (R) of the prototype output and the conductivity of the calibration solution (ECa) were determined, and the fitting function is depicted in Figure 7.  
The relationship between soil electrical conductivity and real part of the impedance is as follows:  
\[EC_{a}=79,387R^{-1.001} \tag{5}\]  
The coefficient of determination R\({}^{2}\) of \(EC_{a}\) is 0.9998, which corroborates that the conductivity of Equation (3) is the reciprocal of the real part of the impedance, with a constant coefficient corresponding to 79,387, which is written into the EEPROM of the prototype to complete the calibration of the conductivity.
# Overrelaxed Sinkhorn-Knopp Algorithm for Regularized Optimal Transport
## 5 Experimental Results  
We compared Algorithm 2 to the SK algorithm on two very different optimal transport settings. In the setting of Figure 6(a), we considered the domain \([0,1]\) discretized into \(100\) samples and the squared Euclidean transport cost on this domain. The marginals were densities made of the sum of a base plateau of height \(0.1\) and another plateau of height and boundaries chosen uniformly in \([0,1]\), subsequently normalized. In the setting of Figure 6(b), the cost was a \(100\times 100\) random matrix with entries uniform in \([0,1]\), and the marginals were uniform.  
Given an estimation of \(1-\eta\), the local convergence rate of the SK algorithm, we define \(\theta_{0}\) as the optimal parameter as given in Proposition 2. For estimating \(\eta\), we followed two strategies. For strategy \"estimated\" (in blue on Figure 7), \(\eta\) was measured by looking at the local convergence rate of the SK algorithm run on another random problem of the same setting and for the same value of \(\varepsilon\). For strategy \"measured\" (in orange on Figure 7), the parameter was set using the local convergence rate of the SK algorithm run on the same problem. Of course, the latter was an unrealistic strategy, but it was interesting to see in our experiments that the \"estimated\" strategy performed almost as well as the \"measured\" one, as shown in Figure 7.  
Figure 7 displays the ratio of the number of iterations required to reach a precision of \(10^{-6}\) on the dual variable \(\alpha\) for the SK algorithm and Algorithm 2. It is worth noting that the complexity per iteration of these algorithms is the same modulo negligible terms, so this ratio is also the runtime ratio (our algorithm can also be parallelized on GPUs just as the SK algorithm). In both experimental settings, for low values of the regularization parameter \(\varepsilon\), the acceleration ratio was above \(20\) with Algorithm 2.  
Figure 7: Speed ratio between the SK algorithm and its accelerated SK-SOR version Algorithm 2 w.r.t. parameter \(\varepsilon\). (**a**) Quadratic cost, random marginals; (**b**) Random cost, uniform marginals.
## 3 Enhanced Mathematical Model with Atmospheric and Environmental Effects
### Channel Capacity vs. Distance with Bandwidth Variations  
Figure 10 illustrates the relationship between channel capacity and distance for three different bandwidths: 10 MHz, 20 MHz, and 50 MHz. Channel capacity is a critical metric for applications that require high data rates, such as Internet of Things (IoT) devices com  
Figure 9: Path loss vs. Tx-Rx separation (low Rx).  
municating over satellite links. As expected, channel capacity increases with bandwidth, highlighting the benefits of allocating wider bandwidths to support high-data-rate requirements. However, the capacity decreases significantly with distance due to the reduction in received power, demonstrating the impact of path loss over extended ranges.  
At shorter distances (within 100 km), the channel capacity is relatively high across all bandwidths. For a 50 MHz bandwidth, the channel capacity reaches approximately 4500 Mbps at close range, while for 20 MHz and 10 MHz bandwidths, the channel capacities are around 2000 Mbps and 1000 Mbps, respectively. This difference emphasizes the strong dependence of channel capacity on bandwidth, as wider bandwidths allow for higher data rates, thereby supporting more demanding applications in close-proximity communications.  
As the distance increases, the channel capacity decreases sharply. For example, at a distance of 500 km, the capacity for 50 MHz has already dropped to around 800 Mbps, while the 20 MHz and 10 MHz bandwidths offer only 300 Mbps and 150 Mbps, respectively. This decline is due to the significant loss in signal strength over longer distances, which reduces the signal-to-noise ratio (SNR) and thereby limits the achievable data rates. This trend suggests that, for satellite-IoT applications requiring high data rates, maintaining adequate channel capacity becomes increasingly challenging as the distance grows.  
Beyond 1000 km, the channel capacities continue to diminish, stabilizing at very low values as the received power falls to levels that cannot support high data rates. At 3000 km, the channel capacities for the 50 MHz, 20 MHz, and 10 MHz bandwidths have dropped to approximately 300 Mbps, 120 Mbps, and 60 Mbps, respectively. These values indicate that, while wider bandwidths initially provide higher capacities, their advantage diminishes over very long distances, where path loss becomes the dominant factor, outweighing the benefits of increased bandwidth.  
The analysis in Figure 10 underscores a key trade-off in satellite-IoT communication design: while higher bandwidths can provide greater channel capacity, their effectiveness is constrained by distance due to signal attenuation. For IoT applications that require extended range, lower bandwidths may be more efficient in terms of power and spectral efficiency, albeit at the cost of reduced data rates. Conversely, for high-data-rate applications within shorter ranges, maximizing bandwidth can significantly enhance capacity, making it suitable for high-throughput IoT and real-time applications in 5G-satellite networks.
# Experimental Research on Bond Behavior between GFR Bars and Stirrups-Confined Concrete
## 2 Testing Program  
### Material Properties  
The GFRP bars in this study were made of unidirectional E-glass fibres and vynilester resin. There are three types of external surfaces of the GFRP bars: helical wrapping, helical wrapping with sand coating, and ribbed (deformations by resin). Pullout tests have also been performed upon steel bars to form a comparison. Figure 1 shows the surface deformation and the characteristics of the bars. The nominal diameters of these bars provided by manufacturers were 8, 12, 16, and 20 mm. Normalised tests were conducted to determine the cross-sectional areas (experimental bar diameter) and mechanical properties (tensile strength and elastic modulus) of the bars, according to ACI 440.3R-04 [41] and CSA S806-02 [29]. Table 1 displays the geometric and mechanical information of the bars.  
In order to analyze the influence of concrete strength on the bond behavior, two different concrete strengths, C1 and C2, were used. Three concrete cubes of 150 mm \(\times\) 150 mm \(\times\) 150 mm were reserved and then cured for 28 days with all specimens being subjected to the same conditions, obtaining an average compressive strength of 28.2 \(\pm\) 0.5 MPa for C1 and 4 47.6 \(\pm\) 0.7 MPa for C2. Table 2 shows the compositions of C1 and C2.
# Finger-Gesture Recognition for Visible Light Communication Systems Using Machine Learning
## 2 VLC Channel Model  
Assume a channel model between a Tx (LED) and an Rx (PD), and consider only the line-of-sight (LOS) path. The channel impulse response of this LOS component is deterministic and given by Equation (1) [36].  
\[h^{LOS}(t)=I(\phi)\frac{g(\psi)A_{PD}}{d^{2}}\delta(t-d/c), \tag{1}\]  
where \(A_{PD}\) is the photo-diode surface area, \(\phi\) is the angle from the Tx to Rx, \(\psi\) is is the angle of incidence with respect to the axis normal to the receiver surface, \(d\) is distance between Tx and Rx, \(c\) is the speed of light, \(g(\psi)\) is the Rx optical gain function, and \(I(\phi)\) is the luminous intensity.  
At the Rx, the received optical power can be expressed as (2).  
\[P_{R}=H(0)P_{E}, \tag{2}\]  
where \(H(0)\) is the channel DC gain, and \(P_{E}\) is the emitted optical intensity.  
It is common to model the emitted signal by a generalized Lambertian pattern, and the DC channel gain can be expressed as [37].  
\[H(0)=\frac{(m+1)A_{PD}}{2\pi d^{2}}\text{cos}^{m}(\phi)T_{s}(\psi)g(\psi)\text {cos}(\psi), \tag{3}\]  
for \(0\leq\psi\leq\Psi_{c}\) where Lambertian order is denoted by (4)  
\[m=\frac{-\text{ln}(2)}{\text{ln}(\text{cos}(\Phi_{1/2}))}, \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Processing** & **Sensor** & **Accuracy (\%)** & **VLC** \\ \hline
[23] & Decision-trees & IR proximity & 98 & No \\
[24] & SVM & Accelerometer & 95 & No \\
[25] & BP-NN & Inertial sensor & 89 & No \\
[26] & LSTM & \(5\times 7\) sensor array & 85 & No \\
[27] & PCA & 32 PDs & 96 & No \\
[28] & KNN & \(3\times 3\) PD array & 48 (single PD) & No \\
[29] & KNN/SVM & IR/VL (PDA100A) & 73(VL@35 cm) & No \\
[30] & RNN & 8 PDs & 99 (10 cm) & No \\
[31] & LSTM & FMCW radar & 98 & No \\
[32] & CNN & RGB Camera & 97 & No \\
[33] & LSTM & RGB/depth Camera & 98 & No \\
[34] & LSTM & RGB Camera (dataset) & 85 & No \\
[35] & DP-LSTM & RGB Camera & 83 & No \\ This work & LSTM & Single PD (low-cost) & 88 & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Gesture recognition systems using machine learning.  
where \(\Phi_{1/2}\) is the semiangle at half-illuminance of the Tx. \(T_{s}(\psi)\) is the optical filter gain, \(\Psi_{c}\) is the Rx field of view (FOV) semi-angle.  
The illuminance at a point on the receiving plane is described by \(\mathrm{I}(\psi)\mathrm{cos}(\psi)/d^{2}\)[38]. The total received power with lens is plotted in Figure 1. This figure shows that the power is greatest directly below the LED and falls off greatest at the corners. The Rx power is sufficiently high in all directions within 2 m of the center, and therefore photo-detectors receive sufficient illuminance in a typical small room or office setting.
## 6 Discussion and Further Improvements
### Calculation Time  
Finally, some considerations about the time were taken to generate predictions. A test was performed to estimate the average time taken to predict various batches of samples by means of the ensemble of 10 DFANN models described above, as is shown in Table 10. Clearly, the execution time was much shorter than only one simulation in EnergyPlus, which was about 5 s, thus making it possible to use these DFANNs for the intended application. The calculation time did not take into account the loading of scaling files and files containing models, adding up to about 3.5 s and performed just once at the program's launch.
###### Abstract  
This study aims to evaluate and optimize the influences of operational factors, including the engine's rotational speed, methane mass, diesel mass, and the duration of injected diesel fuel on the methane/diesel reactivity-controlled compression ignition (RCCI) light-duty engine's performance and emissions by executing the Nondominated Sorting Genetic Algorithm-II (NSGAII). The optimizations aimed to minimize peak firing pressure simultaneously, decrease indicated specific fuel consumption, and reduce tailpipe emissions. It is found that the excess air ratios of (2.22 to 2.37) are the range of feasible results of the RCCI engine, and the power should be less than 0.89 from the maximum design load of the diesel engine when it works without it after treatment. The methane/diesel RCCI engine achieves an indicative thermal efficiency of 51%. The Pareto results from the NSGA algorithm occur on multiple fronts, and there is a tradeoff between power and nitrogen oxide (NOx) in addition to unburned hydrocarbons (UHCs) and carbon monoxide (CO) with NOx emissions. Moreover, EURO IV emissions regulations can occur when using a start of injection (SOI) of \(-35\) CA, a diesel mass of 1.82 mg, a methane mass of 9.74 mg, a diesel injection duration of 2.63 CA, and a rotational speed of 2540 rpm. This accomplished a reduction in indicative specific fuel consumption by 27.8%, higher indicative efficiency by 21.9%, and emissions reductions compared to a conventional diesel engine.  
R Corel engine; optimization; NSGAII; n-heptane; methane; emissions; excess air ratio Article
# PC5-Based Cellular-V2X Evolution and Deployment
## 3 C-V2X Standard and Application Scenarios  
The evolution of C-V2X standardization in the 3 rd Generation Partnership Project (3 GPP) and deployment milestone in 5G Automotive Association (5 GAA) [10] with chipset availability are summarized in Figure 3.
# Analysis of Acoustic Emission (AE) Signals for Quality Monitoring of Laser Lap Microwelding
## 3 System Development and Verification  
The correlation between selected features and bonding quality was analyzed by the scatter index \(J=\frac{R_{c}(k,k)}{R(k,k)}\) that estimates the between-class scatter to within-class scatter ratio. The between-class scatter \(R_{c}\) and within-class scatter \(R\) are defined as follows [23]. The mean of features for each class \(\overline{Y}_{i}(k)\) was obtained using individual features \(Y_{ij}(k)\):  
\[\overline{Y}_{i}(k)=\frac{1}{M_{i}}\sum_{j=1}^{M_{i}}Y_{ij}(k) \tag{1}\]  
where  
\(i=i^{th}\) class  
Figure 3: Setup for welding with (**a**) proper contact and (**b**) loss of contact.  
\begin{table}
\begin{tabular}{l l l} \hline  & **Proper Contact** & **Loss of Contact** \\ \hline
**Torque for screw** & 6 N & 1.5 N \\ \hline
**Extra central clamp** & Yes & No \\ \hline
**Thin paper between workpieces** & No & Yes \\ \hline
**Welding location** & Close to the corner of clamp & Close to central Line \\ \hline \end{tabular}
\end{table}
Table 2: Parameter settings for the generation of proper contact and loss of contact [12].  
\(j=j^{th}\) pattern in a class  
\(k=k^{th}\) feature  
\(M_{i}=\) the number of patterns in class \(C_{i}\).  
The overall system mean \(\overline{Y}\) is determined as follows:  
\[\overline{Y}(k)=\sum_{i=1}^{C}p_{i}\overline{Y}_{i}(k) \tag{2}\]  
where  
\(p_{i}=\) a priori probability of class \(C_{i}\)  
\(C=\) number of classes.  
The within-class scatter is obtained by calculating covariance for each feature as follows:  
\[R_{i}(k)=\frac{1}{M_{i}}\sum_{j=1}^{M_{i}}\big{(}Y_{ij}(k)-\overline{Y}_{ij}(k )\big{)}\big{(}Y_{ij}(k)-\overline{Y}_{ij}(k)\big{)}^{T} \tag{3}\]  
Moreover, the individual-class scatter is defined as follows:  
\[R_{c}(k)=\sum_{i=1}^{C}p_{i}\big{(}\overline{Y}_{i}(k)-\overline{Y}(k)\big{)} \big{(}\overline{Y}_{i}(k)-\overline{Y}(k)\big{)}^{T} \tag{4}\]  
From Equations (3) and (4), the feature selection criterion, a cost function, is defined as follows:  
\[J(k)=\frac{R_{c}(k)}{R(k)} \tag{5}\]  
where  
\[R(k)=\sum_{i=1}^{C}R_{i}(k)\]  
As soon as the features that are closely related to the welding quality were determined, the training AE signal features collected were used to train the HMMs for determining welding quality [24]. Once the models were developed, the unknown condition of welding quality was determined using collected AE signals with selected features and developed HMMs. The schematic of determining the unknown quality condition is illustrated in Figure 4, in which selected AE signal features, other than those used for model development, serve as input data to the system. In the system, to determine the quality, the quality state \(S_{n}\) based on selected features is determined by referring to the model with a large probability value of \(S_{\text{ns}}\) and \(S_{\text{mw}}\) based on Viterbi algorithm.  
Figure 4: Schematic of determining the unknown quality condition by using the developed model.
###### Abstract
Thus, segmentation based on color information is seriously affected by the illumination. In this way, each type of method usually applies to a certain reproductive period. Except for the disadvantages described above, excess dependence on color information will lead to incomplete extraction. The second aspect of extracting leaf coverage from RGB images is based on the classifier. For example, [PERSON] et al. introduced the novel fuzzy c-means approach (FCM), which uses local contextual information and the inherently high inter-pixel correlations to automatically segment images. Experimental results show that the proposed method provides competitive segmentation results compared with other FCM-based methods, and is generally faster [9]. [PERSON] et al. presented an automated object-segmentation approach based on principal pixel analysis and a support vector machine, which effectively segments the entire salient object with reasonable performance and higher speed [10]. Recently, [PERSON] et al. introduced a new segmentation method by combining three technologies: Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. This method is superior for dealing with complex conditions [11]. Moreover, [PERSON] et al. proposed a semantic segmentation of images by using multi-feature extraction and random forest (RF). According to their conclusion, this method offers good performance and accuracy in a small class [12]. Although all the methods mentioned above improve the processing accuracy of specific scenes in image processing, they still have many shortcomings. For example, the color-based segmentation methods are sensitive to changes in light intensity, and so they cannot be regarded as environmentally robust methods. Furthermore, deep-learning (DL) technology requires a large training set and high-performance hardware (e.g., a high graphics processing unit (GPU) frequency) [13]. To resolve these problems, we investigate herein a segmentation algorithm based upon an improved random forest classifier. First, the original UAV remote-sensing image dataset is augmented by three strategies, so that it can meet the requirement of big-data training. To highlight target characteristics, the background of the dataset is removed by using the K-means clustering algorithm. We then extract several image features,including color features and texture features, to describe the differences between the leaf part and the stem part. The improved RF classifier is trained by using the feature matrix and outputs the binary segmentation results. Finally, 4 indicates including \(Q_{seg}\), \(S_{r}\), \(E_{s}\), and mIOU are used to evaluate the segmentation accuracy by comparing the machine recognition results with the manual ground-truth reference images [14]. The rest of this paper is organized as follows: Section 2 briefly introduces the data acquisition and preprocessing, and Section 3 details the proposed algorithm. The experimental result and analysis are given in Sections 4 and 5, respectively, and the conclusion is given in Section 6.
# Fluorescence Hyperspectral Imaging for Early Diagnosis of Heat-Stressed Ginseng Plants
## 2 Materials and Methods
### Data Extraction and Analysis
#### 2.4.2 Analysis of Variance and Waveband Selection  
Hyperspectral imaging involves high dimensionality, broad adjacent wavelengths, and high-resolution spatial data, which increase the amount of information related to the measured number of samples. Selecting optimal wavelengths reduces the complexity of the image-processing pipeline and affords a better understanding of the different bands between our targets. For this purpose, ANOVA was employed to determine the significant wavebands to differentiate between control and stressed ginseng plants. However, because ANOVA is a statistical model that calculates variations between groups of data and provides useful descriptive information, it does not provide information related to the correlation of the groups within the entire computation. For this analysis, the extracted spectral data were divided into calibration (70%) and validation (30%). The control and stressed ginseng plants were calculated through the whole range from 400-750 nm to identify the optimal wavelength that was determined using the F-value of one-way ANOVA for discrimination.
# The Interaction between the LEO Satellite Constellation and the Space Debris Environment
## 4 Analysis of Constellation Collision Maneuver
### Phase 1: Collision Avoidance Maneuver during Climbing  
In order to simplify the calculation process of the model, the satellite orbit in the climbing phase is approximately an elliptical orbit of 300 km \(\times\) 550 km, the semi-major axis is 6803 km, the eccentricity is 0.0184 and the orbit inclination is 53\({}^{\circ}\). The calculation time is 73 days. The calculated results are described by two data, one is detectable, that is, the collision probability and flux of objects above 10 cm (subscript d), and the other is the overall collision probability and flux of space objects, including undetectable objects evolved from the space debris model (subscript w), as shown in Table 6.  
The ACPL value is defined by the engineer as a parameter that is beneficial to the efficient avoidance and maneuver of the satellite. The value range of ACPL in actual operation is comprehensively considered by calculating the avoidance times of the satellite under a certain collision probability level. It can be seen from the data in Table 5 that, although the collision probability of a single satellite at the inclination of the high orbit is larger, the overall collision probability of the orbital layer is smaller due to the influence of the number of satellites. Therefore, when calculating the avoidance times of all satellites in a constellation corresponding to different ACPL values, it is necessary to optimize the average avoidance times of the single satellites in this environment, so as to obtain the avoidance maneuver times corresponding to different ACPL values in this orbital layer.  
Figure 6 shows the variation of the total avoidance times of 1628 Starlink satellites with the ACPL value during the climbing phase. when the ACPL value is \(10^{-6}\), the satellite in this orbital layer needs about 2000 maneuvers to avoid, and a low ACPL value will reduce the operation and efficiency of the satellite. When the ACPL value is \(10^{-4}\), the orbital layer satellite only needs to avoid three times. Figure 7 shows the relationship between the overall collision risk and the number of avoidance. The total collision risk is reduced by 0.2% (Risk Reduction) due to three avoidance maneuvers, and the residual risk of detectable objects and remaining risk of all objects is about 1.5%, indicating that the impact of non-detectable objects with diameters between 6-10 cm on satellite collision risk is small.  
\begin{table}
\begin{tabular}{c c c c} \hline \(ACP_{d}\) & \(ACP_{w}\) & \(Flux_{d}\) **(m\({}^{2}\)/a)** & \(Flux_{w}\) **(m\({}^{2}\)/a)** \\ \hline \(8.22\times 10^{-5}\) & \(8.51\times 10^{-5}\) & \(2.42\times 10^{-6}\) & \(2.56\times 10^{-6}\) \\ \hline \end{tabular}
\end{table}
Table 6: Single satellite annual collision probability (ACP) and space object flux during Phase 1.