esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Through CST microwave studio, the transmission characteristics of the light in the visible light and infrared band are studied. Based on the two port transmission line theory, the multi cascade calculation and direct simulation of different materials and their thicknesses are carried out. The results show that when the structure contains only transparent substrate materials or only metal materials, the transmission characteristics obtained by direct simulation and cascade calculation are completely consistent. Ulteriorly, when the structure is cascaded by a single layer of metal material and a single layer of transparent substrate material, there is a slight difference between the results of direct simulation and cascade calculation, and the difference in transmittance at the same frequency is about 0.15. To summarize, the comparative study of direct simulation and cascade calculation between different types and thicknesses of materials presents an important reference value for solving problems such as the large thickness transparent substrate in micro/nano lithography technology, which cannot be directly simulated, or the overlong simulation time. On the other hand, a periodic subwavelength metal film filter based on circle hole array is designed and manufactured, and its analysis method accuracy of two port transmission line cascading calculation is verified by comparison. At the same time, a frequency selective optical element whose response band is concentrated in 3.8-4.7 um is realized.  
Conceptualization, K.W., P.L. and X.H.; data curation, K.W.; methodology, K.W.; software, K.W., Y.L., W.L. and Y.X.; project administration, Y.X., P.L. and X.H.; supervision, X.H.; writing--original draft, K.W.; writing--review, X.H.; data analysis, K.W. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Foundation of Hunan Talents (2020 RC3028), the Science Foundation of Hunan Province (2022 JJ20045).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Physico-Chemical Analyses

Table 1: Physico-chemical characteristics (total soluble solids content: TSS, titratable acidity: TA, maturity index: MI, and total polyphenol content: TPC) of original and AT-juices. No information about the pomegranate cultivar was provided in the samples labeling, but these differences may be due to geographical origin, cultivar, and/or different manufacturing procedures. Adding albedo homogenate increased TPC in the juices 1.3-2.7 times. The juices which had an initial lower TPC (samples 981 and 612) were the samples which experimented higher increases, rising values over 2100 mg gallic acid equivalents L\({}^{-1}\). Consequently, maceration of juices with albedo extract could be an interesting option to develop more competitive and healthy products. Sample 707, which had already the highest TPC (\(\sim\)3700 mg gallic acid equivalents L\({}^{-1}\)), rise to more than 5000 mg gallic acid equivalents L\({}^{-1}\) after treatment. [PERSON] _et al._[26] stated a recommended daily intake (RDI) of polyphenols of 1 g day\({}^{-1}\), so the consumption of only 200 mL of this AT-juice will meet the RDI for polyphenols.
## 3 Results and Discussion
### Batter Analysis
#### 3.1.5 FTIR Analysis  
FTIR study was done to evaluate interactions between various components of cake batter. Figure 4 represents the FTIR spectrum of all the cake batter. A broad band in the wavenumber region 3500-3000 cm\({}^{-1}\) corresponds to the O-H stretching vibration of water. The peak at 2922 cm\({}^{-1}\) is related to the lipids and also indicates the formation of lipid-starch complexes [35]. C-H stretching vibrations were confirmed through the spectral peak at 2848 cm\({}^{-1}\)[36]. A sharp peak at 1740 cm\({}^{-1}\) comes from the carbonyl of the ester group. Additionally, the amide I region, which arises from the stretching vibration of C=O of the peptide bond, peaked at 1640 cm\({}^{-1}\)[37]. A wide spectral band centered at 1018 cm\({}^{-1}\) derives from carbohydrates and is related to C-O and C-C stretching and C-O-H bending. The spectral region 1080-950 cm\({}^{-1}\) corresponds to the molecular organization of starch chains [35]. All the samples showed a similar peak positioning except for a few variations in the peak intensity. The peak at 2922 cm\({}^{-1}\) showed the least intensity in batter Sa-C suggesting the reduced formation of starch-lipid complexes in this case.
## 2 Materials and Methods
### Detailed Landcover Map Update Methodology
#### 2.3.1 Conversion Coordinates and Preprocessing  
The coordinates of the spatial data collected then needed to be converted because they were generated by different organizations and in different modalities. All spatial data were placed in the same coordinate system and digitized after being subjected to preprocessing including numerical mapping, coordinate conversion, and boundary matching. High-level landcover map coordinate conversion was performed based on the Global Coordinate System ITRF 2000 TM central origin adopted by the standard national Digital topographic map 2.0.
## 2 Materials and Methods
### Data Pre-Processing  
In the pre-processing stage, we performed three techniques in a row, including missing data handling, data normalization, and data smoothing. If a wavelength of an absorption spectrum was missing, the missing absorbance value was replaced by the average of the absorbance values of the two neighboring wavelengths. Then, standard normal variate correction (i.e., z-score normalization) was applied to every single spectrum of the dataset to eliminate the deviations caused by particle size and scattering, making the NIR data consistent. Eventually, the NIR spectra were streamed through a Savitzky-Golay (SG) filter with a window length of 13 points and a polynomial order of five to smooth the spectra, thereby removing part of the noise [32]. The pre-processing steps were conducted by using the Scipy v1.14.1 library.  
What is especially notable about our dataset is the severe imbalance between the two safety classes. The number of NIR samples belonging to the \"Safe\" class is nearly six times higher than the \"Unsafe\" class. This can cause a classification model to be biased towards the majority class with the \"Safe\" label. To solve this problem, we leveraged the SMOTE technique to handle data imbalance. SMOTE specifically generates new data points for the minority class with the \"Unsafe\" label. It analyzes existing minority data points and generates new ones similar to them. By adding these synthetic samples, SMOTE balances the data, giving the model a better capability to learn the minority class. After applying SMOTE on the training subset, the number of NIR samples belonging to the \"Unsafe\" class is equal to that of the \"Safe\" class. The details of the SMOTE algorithm can be found in [30]. We used the Python package named imbalanced-learn v0.11.0 to perform SMOTE. The synthetic spectrum samples were also normalized and smoothed in the same way as the original ones.
# Monitoring Citrus Soil Moisture and Nutrients Using an IoT Based System
## 3 Design Scheme
### System Hardware Design
#### 3.1.2 Field Master Control Node  
The system uses ARM11-based S3C6410 (YoujianhengtianTechnology Co., Ltd, Shenzhen, China) [23] as the master chip, using FORLINK's OK6410 (Forlinx Embedded Tech. Co., Ltd, Baoding, China) control board module. The GPRS module adopts HUAWEI GTM900C (Huawei Technologies Co., Ltd., Shenzhen, China) and the GPS module uses Trimble 4600 LS single frequency GPS receiver. The receiver is produced by the world's largest GPS manufacturer Trimble Navigation Company (Sunnyvale, CA, USA), and it is the first integrated GPS receiver which has high quality, high precision, high efficiency and low price. The ARM module sent the AT commands to the GTM900C module through the serial port. Through this method, the GTM900C module achieves the corresponding function. 4600 LS receives the geographical coordinates of space and transmits them to the control board through the RS232 serial port. OK6410 connects with the JN5139 GPRS module GTM900 and 4600 LS through the USB serial port. The field controller node is the sink node of the sensor network which monitors the site. It is responsible for managing and maintaining the ZigBee network. The field control unit has a corresponding LCD touch screen, real-time display dynamic changes of soil temperature and humidity, air temperature and humidity in collection points. The structure of the field control node is shown in Figure 4.
###### Abstract  
Long linear constructions, particularly high-speed railway lines, place very high demands on the accuracy of the fundamental geodetic controls on which they are based. In general, national reference coordinate systems possess deformations (relative inconsistencies) due to historical factors--primarily the low accuracy of the measurement methods that were used at the time of their development. The reference coordinate system S-JTSK used in the Czech Republic comprises local deformations that are determined using a modern space geodesy method--the Global Navigation Satellite Systems (GNSS). An analysis of the local deformations revealed that, in the Czech Republic, 99% of the length corrections from the coordinate system deformations are in the interval of \(-1.4\) to \(+1.4\) cm/km with a standard deviation of up to \(1.4\) cm/km. The extreme value in this regard is the length correction from the deformation of \(+2.5\) cm/km and the standard deviation of \(3.6\) cm/km. These values exceed the required accuracy of \(1\) cm/km, and the length corrections from the deformations, together with the length corrections from the used projection, must be taken into account when undertaking surveying work on high-speed railway lines in the Czech Republic (also with the height correction for lengths). The magnitude of the scatter also indicates the distortion of the S-JTSK projection conformality due to the deformations.  
S-JTSK; length correction; coordinate system deformation +
Footnote †: journal: Applied sciences  
Article
## 2 Materials and Methods
### Numerical Model
#### 2.2.1 Mathematical Model  
* Water Movement Model. The mathematical model of water movement in the water level fluctuation zone can be expressed as the Richards equation, as follows:  
\[C(h)\frac{\partial\theta}{\partial t}=\frac{\partial}{\partial z}\bigg{[}K(h) [\frac{\partial h}{\partial t}-\cos(\alpha)]\bigg{]}-S(z,t) \tag{1}\]  
where \(C(h)\) is the water capacity (cm\({}^{-1}\)); \(K(h)\) is the hydraulic conductivity (cm/d); \(h\) is the negative pressure (cm); \(z\) represents the position coordinates in the parallel water flow direction (cm); \(t\) is the time (d); \(\alpha\) is the angle between the water flow direction and the vertical (\({}^{\circ}\)); \(\theta\) is the volumetric water content (cm\({}^{3}\)/cm\({}^{3}\)); and \(S(z,t)\) is the water absorption strength of plant roots (cm\({}^{3}\)/cm\({}^{3}\)\(\cdot\)d\({}^{-1}\)). * Solute transport model in the Vadose zone. The solute transport model only considers the behavioral characteristics of convection, diffusion, adsorption, degradation, etc., and uses the traditional convection-diffusion equation to describe the transport process. The equation is expressed as:  
\[\frac{\partial}{\partial t}(\theta C)=\frac{\partial}{\partial z}(\theta D_{ L}\frac{\partial C}{\partial z}-\
u C)-\rho_{b}\frac{\partial(\rho_{s}K_{L}C)}{ \partial t}-C_{0}\exp(-kt) \tag{2}\]  
where \(C\) is the solute concentration in the liquid phase (mg/L); \(D_{L}\) is the longitudinal dispersion coefficient (cm/d); \(\
u\) is the Darcy flow velocity (cm/d); \(\rho_{b}\) is the soil bulk density (mg/cm); \(\rho_{s}\) is the soil bulk density (mg/cm\({}^{3}\)); \(K_{L}\) is the adsorption distribution  
Figure 3: Time variation of water level. coefficient (cm\({}^{3}\)/g); \(C_{0}\exp(-kt)\) is the source-sink term (cm\({}^{3}\)/cm\({}^{3}\)\(\cdot\)d\({}^{-1}\)); the others are the same as above. * Nitrogen migration and transformation model. The transport model of nitrogen in the soil varies according to the different forms of nitrogen. The transport and transformation process of NH\({}_{4}\)\({}^{+}\) is mainly subjected to adsorption and nitrification. The equation is as follows: \[\left\{ \begin{array}{ll}\frac{\partial(\theta c_{1})}{\partial t}=\frac{ \partial}{\partial z}\left(\theta D_{L}\frac{\partial c_{1}}{\partial z} \right)-\frac{\partial}{\partial z}(Vc_{1})-\rho_{b}\frac{\partial(\rho_{k}c _{1})}{\partial t}-k_{1}\theta c_{1}\\ c_{1}(Z,0)=c_{10}(Z)&0\leq Z\leq L,t=0\\ -\left(\theta D_{L}\frac{\partial c_{1}}{\partial z}-qc_{1}\right)=\varepsilon (t)c_{0}&Z=0,t=0\\ c_{1}(L,t)=c_{1}L&Z=L,t>0\end{array} \right.\] (3) Meanwhile NO\({}_{2}\)\({}^{-}\) and NO\({}_{3}\)\({}^{-}\) are mainly affected by nitrification and denitrification, and the equation is as follows: \[\left\{ \begin{array}{ll}\frac{\partial(\theta c_{1})}{\partial t}=\frac{ \partial}{\partial z}\left(\theta D_{L}\frac{\partial c_{1}}{\partial z} \right)-\frac{\partial}{\partial z}(v_{i}c_{i})-k_{i}\theta c_{i}\\ c_{i}(Z,0)=c_{i0}(Z)&0\leq Z\leq L,t=0\\ c_{i}(0,t)=c_{i0}(t)&Z=0,t>0\\ c_{i}(L,t)=c_{i}L&Z=L,t>0\end{array}
# Least-Squares Estimation of Tangent Space for a Triangular Mesh
## 3 Method  
### Problem  
The objective of this paper is to develop an intuitive but rigorous method for computing an orthonormal tangent space denoted as \(\mathbf{T}_{i}=[\mathbf{t}_{i}|\mathbf{p}_{i}|\mathbf{n}_{i}]\) at every vertex \(i\) of a triangular mesh provided with texture coordinates, as illustrated in Figure 2. The inclusion of the  
Figure 1: Least-squares estimation of rigid transformation between two sets of corresponding points.  
normal vector, \(\mathbf{n}_{i}\), is optional. If \(\mathbf{n}_{i}\) is not provided, it can be computed simultaneously with the tangent vector, \(\mathbf{t}_{i}\), and the bitangent vector, \(\mathbf{b}_{i}\) (see Section 3.2). Conversely, if \(\mathbf{n}_{i}\) is supplied, the computation of \(\mathbf{t}_{i}\) and \(\mathbf{b}_{i}\) should conform to the direction defined by \(\mathbf{n}_{i}\) (see Section 3.3). It is crucial to ensure that the vectors \(\mathbf{t}_{i}\), \(\mathbf{b}_{i}\), and \(\mathbf{n}_{i}\) maintain orthogonality and unit length. This is essential for establishing a consistent local coordinate frame, known as TBN, at the vertex. Furthermore, the TBN frame should exhibit coherence with the frames of neighboring vertices within the mesh.  
Texture mapping often introduces discontinuities where certain edges in a geometric model are duplicated in texture space, each with a different location. These discontinuities, referred to as seams, are illustrated in Figure 2 as magenta lines. Seams are inevitable in the texture mapping process, especially when applying a 2D texture to a closed surface mesh representing a solid object. To address seams, vertex duplication is necessary. The duplicated vertices have the same spatial position but distinct texture coordinates. This concept is similar to vertex duplication at a hard edge, which represents a sharp, distinct boundary or transition between adjacent faces. However, addressing vertex duplicates at a seam requires ensuring a smooth and coherent transition of the tangent space, unlike vertex duplicates at a hard edge. Failing to achieve this smoothness and coherence can result in visible boundaries where abrupt shading changes occur [2]. Initially, we handle vertices without duplication, followed by addressing seams (see Section 3.4).
## 3 Polyphenols Effects on VOCs Release
### Effects on Esters  
Esters are mainly produced by yeasts during alcoholic fermentation. Their concentration and relative proportion are strongly influenced by several fermentation parameters (i.e., oxygen level, fermentation temperature, yeast strain characteristics, yeast assimilable nitrogen levels). From a sensory point of view, they are considered as one of the most important families of compounds lending fruity characters in wines. In most of the GC-O studies present in the literature, esters are included in the list of the compounds with the highest odor active value (OAV) [10].  
The influence of polyphenols on esters release and, in some cases, on their perception, has been investigated on several compounds belonging to this family, as reported in Table 1. Some observations can be made despite the absence of a clear and unique trend that could be explained by the wide range of polarity within the group of esters tested (\(1.26\leq\log P\leq 5.71\)). Around 2.85 seems to be a cut-off \(\log P\) value; indeed, there is a switch of esters behavior, depending on the PPhs levels in the matrix. Esters characterized by lower \(\log P\)s tended to a lower release at smaller PPhs levels, while they were raised at higher ones. This suggests that for poorly hydrophobic esters, there is the prevalence of a retention phenomenon at low PPhs concentrations and a tendency of salting-out effects at higher PPhs concentrations. Alternatively, the release trend of esters characterized by higher \(\log P\)s decreased independently from the PPhs level, suggesting that hydrophobicity represents the main driving force of highly hydrophobic esters release. The only exception to this behavior is the opposite trend observed for ethyl octanoate in oak barrel aged red wine. Both monomers, catechin and gallic acid, tested at 2 g/L, did not affect the release of the more polar esters (ethyl isobutanoate, ethyl butanoate, isoamyl acetate), while the release of the hydrophobic ethyl octanoate decreased in the presence of catechin (\(\log P=1.37\)) but not in the presence of gallic acid (\(\log P=0.59\)), suggesting that the hydrophobicity of PPhs could be significant [66]. In the case of ethyl 2-methyl butanoate, unlike ethyl butanoate, the sensory impact of release differences determined by GC-MS was confirmed by GC-O data. In fact, the olfactometric score increased when the volatile matrix of a white wine was replaced by the volatile extract of an aged red wine [67]. The results from sensory assessment are not completely in line with instrumental ones with ethyl octanoate,ethyl isobutanoate, and ethyl butanoate being perceived as less intense in the presence of catechin at 2 g/L [66].  
Based on results reported above and on the knowledge that esters act synergistically in imparting fruity notes to wine [39], the observed changes of most hydrophobic esters at increased levels of PPhs could have a significant sensory impact on wine fruity aroma. In addition, the observed decreases of isoamyl acetate, a molecule having an important olfactory role in wine, could be significant [39].
# Machine Vision-Based Chinese Walnut Shell-Kernel Recognition and Separation
## 3 Results and Analysis
### Performance Comparison of Several Target Detection Algorithms  
To evaluate the walnut shell-kernel detection effect of the YOLOX network, the walnut shell and kernel training sets were trained based on YOLOv3, SSD [27], and Faster R-CNN  
Figure 4: Training results.  
Figure 5: Detection results of the YOLOX target detection algorithm. (**a**) Detection results of Yunnan Juglans sigillata. (**b**) Detection results of Wen 185.  
target detection algorithms under the same conditions. The number of training epochs was set at 110. Moreover, the performance of the above four detection algorithms was evaluated using the test set. The visual confusion matrix of performance comparison and evaluation metrics of the detection model at IOU = 0.5 are shown in Figure 6 and Table 1, respectively.  
Table 2 shows that the YOLOX model side is basically equivalent to the Faster R-CNN target detection algorithm and is larger than YOLOv3 and SSD target detection algorithms. Compared with YOLOv3, Faster R-CNN, and SSD target detection algorithms, the AP of YOLOX at IOU = 0.50 is increased by 2.1%, 1.3%, and 3.4%, and the AR is increased by 10%, 2.3%, and 9%, respectively. The AP at IOU = 0.50:0.95 is increased by 12.1%, 3.9%, and 9.8%, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Model**} & \multirow{2}{*}{**FLOPs**} & \multirow{2}{*}{**AP\({}_{\text{50}}\)**(\%)**} & \multirow{2}{*}{**AP\({}_{\text{75}}\)**(\%)**} & \multirow{2}{*}{**AP\({}_{\text{8}}\)**(\%)**} & \multirow{2}{*}{**AR(\%)**} \\  & **Size/MB** & & & & & \\ \hline YOLOX & 99 & 351.9 & 96.3 & 92.4 & 80.6 & 84.7 \\ YOLOv3 & 61.53 & 193.87 & 94.2 & 83.7 & 68.5 & 74.7 \\ Faster-RCNN & 98.85 & 427.07 & 95 & 89.1 & 76.7 & 82.4 \\ SSD & 3.04 & 7.02 & 92.9 & 82.6 & 70.9 & 75.7 \\ \hline \end{tabular}
\end{table}
Table 1: Performance comparison of several target detection algorithms.  
Figure 6: Normalized confusion matrices of detection results. (**a**) YOLOX. (**b**) YOLOv3. (**c**) Faster-RCNN. (**d**) SSD.  
The 550 images in the test set were classified according to walnut species, illumination, and shielding conditions. Given the same test condition, the walnut shell-kernel image test sets under different scenes were tested using the above four target detection algorithms. Table 2 presents the test results of different algorithms.  
Table 2 shows that the YOLOX detection model achieves relatively high AP and AR values under different walnut species, illumination, and shielding conditions. The AP and AR values of the YOLOX detection model are equivalent to those of Faster R-CNN and are evidently superior to those of YOLOv3 and SSD models. According to the results, YOLOX can quickly detect walnut shell-kernel in the target detection model. The YOLOX algorithm is a relatively good selection for online walnut shell-kernel separation with considerations to both recognition precision and detection speed.