esa-sceva/satcom-chunk-collection huggingface dataset:
# Changes in Biochemical and Volatile Flavor Compounds of Shine Muscat at Different Ripening Stages
###### Abstract  
Changes in the biochemistry and flavor of Shine Muscat grapes at different ripening stages (RS) were analyzed to identify factors affecting these characteristics. The yellowness index values were 45.1, 49.4, and 50.2 in the ripening stage 1 (RS1), ripening stage 2 (RS2), and ripening stage 3 (RS3) groups, respectively, representing the different ripening stages. The yellowness of the grape berries tended to increase with ripening due to the gradual breakdown of chlorophylls and the evolution of carotenoids. The total content of monoterenes, on the other hand, was approximately two-fold higher at RS3 than RS1 and RS2. Moreover, linalool was the most abundant compound contributing to the total content of monoterenes. The highest correlation was observed between the linalool content and \"Brix/acid ratio (r = 0.9981), followed by the monoterene content and \"Brix/acid ratio (r = 0.9933). These findings indicate that changes in the contents of linalool and its oxidized forms may be used as a quality index and an indicator of the timing of harvest for Shine Muscat grapes.  
Keywords:linalool; ripening; Shine Muscat; volatile flavor compounds; \"Brix/acid ratio +
Footnote †: journal: Applied Sciences
## 1 Introduction
The metallic surface of the satellite body is considered as an infinite ground plane of the antenna. Moreover, the patch is tactically designed to pass solar irradiance into the solar panel.
## 2 Digital Twins for Next-Generation Networks
### Digital Twins for Access Networks
#### 2.1.4 Edge Networks  
To achieve 6G ambitions such as ubiquitous connectivity, extremely low latency, and enhanced edge intelligence, Multiple-access edge computing (MEC) plays a crucial role. [PERSON] et al. [35] propose a wireless digital twin edge network model that aims to provide hyper-connected user experience and low-latency edge computing. They tackle the _DT to edge_ association problem concerning the dynamic network states and varying network topology, where DTs can be either twins of user devices or twins of services that users are using. The authors propose to use Deep reinforcement learning (DRL) for digital twin placement and transfer learning for digital twin migration to follow dynamically placed users, thus trying to minimize the latency and energy costs of users in the network.  
In a different context, [PERSON] et al. [36] focus on the integration of MEC offloading with Ultra-reliable low-latency communication (URLLC) and short packet transmission, specifically in the IIoT context. The authors aim to minimize the latency of task offloading by optimizing the user association, transmission power, and processing rate of User equipments (UEs) and edge servers (ESs). To achieve this, a DT of the edge network architecture is constructed, while the wireless communications between UEs and ESs are established via URLLC links, realizing a _DT-empowered URLLC edge network_. In this work, the DT replicates the physical system (hardware information, operating applications, real-time states), optimizes resources, and makes decisions to control the whole system in real time. To achieve this, the DT optimizes a variety of variables, e.g., user association, offloading policies, transmission power, processing rates, energy consumption budget, and computation resources budget of UEs and ESs. Similarly, [PERSON] et al. [37] introduce the use of DTs for intelligently offloading the computing tasks of UEs onto MEC servers. They formulate the problem as an optimization problem with the main objective of minimizing the total digital twin latency by choosing the optimal user association, transmit power, offloading policies, and processing rate of UE and MEC servers.  
Furthermore, [PERSON] et al. [38] presented a solution to address the challenge of minimizing latency in the context of intelligent offloading of IoT devices, assisted by digital twins, onto edge networks deployed with unmanned aerial vehicles (UAVs). The solution takes into consideration the constraints imposed by URLLC links. The problem is formulated as an optimization task that involves jointly optimizing the transmit power, offloading policies, and processing rate of IoT devices and ESs.
## 3 Experiments and Results
### Input Maps Ablation Study
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Fine-Tuned** & **DC (SD)** & **FNR** & **FPR** & **JAC** & **VS** & **AHD (SD)** \\ \hline T1 & **0.8837** (0.0154) & 0.1649 & 0.0596 & 0.7920 & \(-\)0.1193 & **0.1230** (0.0157) \\ \hline FA & 0.8945 (0.0089) & 0.1390 & 0.0677 & 0.8092 & \(-\)0.0795 & 0.1116 (0.0097) \\ \hline MD & 0.8931 (0.0122) & 0.1326 & 0.0776 & 0.8071 & \(-\)0.0615 & 0.1143 (0.0133) \\ \hline RD & 0.8935 (0.0115) & 0.1364 & 0.0724 & 0.8077 & \(-\)0.0715 & 0.1137 (0.0134) \\ \hline AD & 0.8925 (0.0131) & 0.1419 & 0.0680 & 0.8061 & \(-\)0.0829 & 0.1143 (0.0142) \\ \hline FA, T1 & 0.8943 (0.0107) & 0.1373 & 0.0700 & 0.8090 & \(-\)0.0754 & 0.1119 (0.0112) \\ \hline FA, MD & 0.8943 (0.0113) & 0.1327 & 0.0754 & 0.8089 & \(-\)0.0638 & 0.1126 (0.0136) \\ \hline MD, T1 & 0.8882 (0.0136) & 0.1462 & 0.0716 & 0.7992 & \(-\)0.0842 & 0.1185 (0.0132) \\ \hline FA, MD, RD, AD & 0.8904 (0.0100) & 0.1509 & 0.0623 & 0.8026 & \(-\)0.0994 & 0.1158 (0.0105) \\ \hline FA, MD, RD, AD, T1 & **0.8970** (0.0116) & 0.1182 & 0.0853 & 0.8135 & \(-\)0.0367 & **0.1102** (0.0129) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation metrics for each CNN input map combination on the test set: DC—Dice coefficient; FNR—false negative ratio; FPR—false positive ratio; JAC—Jaccard coefficient; VS—volumetric similarity; AHD—average Hausdorff distance (in voxel size). The best and worst DC and AHD results are in **bold**. Figure 9: Dice coefficient and average Hausdorff coefficient for each input map combination evaluated on the test set. Star (\(*\)) denotes statistical significance (\(p<0.05\)) when compared to our best model (all_maps). All p-values were estimated using a two-sided Wilcoxon signed-rank test. those between models that use diffusion maps as input. This implies that any of the tested CNN models that use diffusion maps as input produce different results when compared to a CNN with T1w images as input. Simultaneously, it is apparent that all CNN models using single diffusion maps as input produced mutually similar results.
## 3 Experimental Setup and Evaluation Indicators
### Loss Function  
In PCB soldering defect images, the defect areas occupy a tiny proportion of the entire image, leading to a severe imbalance between positive and negative samples. Moreover, there is also an imbalance in the quantities of different types of defects. The choice of the loss function plays a crucial role in defect segmentation.  
This paper adopts a hybrid loss function combining focal loss and Dice loss to address the issues above. Focal loss enables the model to focus more on defect areas in complex backgrounds, while Dice loss helps to alleviate the problem of poor training performance due to class imbalance. The formulas for the focal loss, Dice loss, and focal-Dice loss are as follows:  
\[L_{focal}=\frac{1}{N}\sum_{i=1}^{N}\left(1-\alpha\right)p_{i}^{\prime\gamma}(1 -p_{i})\log(1-p_{i}^{\prime})-\alpha p_{i}(1-p_{i}^{\prime})^{\gamma}\log(p_{ i}^{\prime}) \tag{8}\]  
\[L_{dice}=1-\frac{2\text{TP}}{2\text{TP}+\text{FP}+\text{FN}} \tag{9}\]  
\[L=L_{focal}+L_{dice} \tag{10}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Version** \\ \hline Operating system & Ubuntu 18.04 \\ GPU & Nvidia RTX 3090 (24 GB) (NVIDIA, Santa Clara, CA, USA) \\ CPU & Xeon(R) Platinum 8255C (Intel, Santa Clara, CA, USA) \\ Programming & Python 39.18 + Pytorch 20.1 + CUDA 11.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental environment.  
where \(p_{i}\) represents the actual pixel value and \(p_{i}^{\prime}\) represents the probability predicted by the model for a defect. \(\alpha\) is the balancing factor for the weighting of the loss values of positive and negative samples. Due to the highly disparate proportions of background pixels and defect pixels in PCB soldering images, \(\alpha\) is set to 0.9. \(\gamma\) adjusts the loss function for easily separable samples, causing the model to pay more attention to difficult samples to improve the performance and robustness on these samples, with \(\gamma\) set to 2.  
In this paper, based on the improved UNet model, experimental results were obtained using the Dice loss, focal loss, CE loss, CE-Dice loss and focal-Dice loss. The results are shown in Table 2. It can be observed from Table 2 that the choice of loss function has a crucial impact on the training effectiveness of the soldering defect segmentation model. The focal-Dice loss effectively overcomes the challenges posed by the tiny proportion of defects in the images and the imbalance in the number of classes.
# Error Analysis of Integrated Absorbance for TDLAS in a Nonuniform Flow Field
## 3 Results and Analysis
### Case 1: \(A_{1}/a_{2}=1\)  
Figure 3 shows the distribution of the absolute value of the IA error with a change in the line damping parameter in two quasi-uniform flow field regions when the IA ratio is fixed at 1 and the normalized coefficient ratio is set at 1:1, 1.5:1, 2:1, and 2.5:1. Considering the physical meanings of the parameters, the different cloud images in Figure 3 represent different temperature ratios of the two quasi-uniform flow field regions. Any small cloud image shows a change in the IA error as the nonuniformity of the static pressure or the absorber concentration in the flow field changes when the temperature ratio of the two quasi-uniform flow field regions is fixed and the IA rate is the same. A comparison of the four cloud images in Figure 3 reveals that a larger temperature ratio of the two quasi-uniform flow fields results in a smaller area being surrounded by the contour \(R=0.01\). In extreme cases, the error generated by the coupling of the nonuniformity and spectroscopic parameters may exceed 0.1.
# Study on the Steepness of Landslide-Induced Wave and Nonlinear Motion of Container Ships
## 5 The Ship Motion Characteristics Caused by Landslide Surges
### The Influence of Navigation Position on Ship's Transverse Motion Characteristics  
A situation in which a ship is sailing is analyzed in a river course and encounters a landslide-induced surge. The influence of the navigation position is analyzed on the ship's transverse motion.  
It can be seen in Figure 13 that the water entry of the landslide body has different effects on the ship's transverse motion characteristics at different positions. The ship transverse curvature changes the fastest at Position #2 of the landslide section. During the 0-20 s period, the landslide-induced surge continues to act on the ship, and the ship's lateral displacement increases from 0 to 16 cm. During the increase, the displacement first increases, then decreases, and then continues to increase. The ship experiences positive relative displacement and negative relative displacement in this period. The ship's transverse curvature is slow, and continuity is strong in Position #1 of the landslide section and Position #3 of the river course, and the high head pressure of the landslide-induced surge increases the risk of ship stability failure. The ship travels in different positions in the river, and the landslide-induced surge interacts with the ship. The ship's yaw motion direction is consistent with the propagation direction of the landslide-induced surge and the ship's own recovery moment causes the ship to move in the opposite direction, which makes the ship's yaw motion change slightly and increase continuously. When the ship stays in the river, the farther the distance between the water entry point of the landslide and the ship, the greater the energy loss of the landslide-induced surge in the process of propagation, and the smaller the damage of the landslide surge to the ship. Because of the steepness of the wave at position #2, the ship has the maximum transverse oscillation at this position.  
By changing the speed and direction of the ship in the Three Gorges Reservoir area, the collision between the ship and the shore in the curved section can be prevented effectively, and the course stability of the ship in the random waves can be further controlled. When passing near the prone geological disaster point, ships in the river should pass quickly along the predetermined route to prevent any capsizing due to sudden landslide or surge. If passing ships release disaster information, ships should cast one or two anchors to further stabilize their position.
# A Broadband Analog Predistortion Linearizer Based on GaAs MMIC for Ka-Band TWTAs
## 2 Analog Predistortion MMIC Topology
### Nonlinear Device Size Selection
Equation (2) also shows that for different-sized diodes under the same process conditions, there are other factors affecting the nonlinear resistance \(R_{\mathrm{d}}\) apart from \(V_{\mathrm{d}}\) and \(I_{s}\). Within the same \(V_{\mathrm{d}}\) variation range, small-sized diodes will exhibit a larger range of nonlinear resistance \(R_{\mathrm{d}}\) changes due to the small \(I_{s}\). An increase in the number of gate electrodes will significantly increase the gate capacity. The selection of the total gate width \(W\times N\) is also critical, as it will directly affect the transconductance and gate capacitance of the diode [1]. Therefore, the GaAs pHEMT process can be used to produce variable-sized diodes, which introduces more variables to the APD circuit design and can achieve more nonlinear characteristics, but the variables are interdependent. Consequently, designs for practical applications rely on simulation software and accurate device models. Independently Tunable Linearizer and Nonlinear Frequency Adjustment Module Design of Analog Predistortion MMIC
## 2 Materials and Methods
### Hardware Platforms and Evaluation Criteria  
The proposed algorithm's performance evaluation was carried out with the following hardware platforms: Intel Core i7-8700 CPU@ 3.2 GHz, with 32 GB memory and a NVIDIA GeForce RTX 2080 Ti (11G video memory) video card, and its software platforms were Ubuntu 18.04, Python 3.6, Tensor Flow-GPU 1.14.0, and Keras 2.0.8.  
The correlation coefficient (\(R^{2}\)) and root mean square error (\(RMSE\)) were used as evaluation criteria for the calibration model. The correlation coefficient \(R^{2}\) indicates the squared correlation between the actual output and the predicted output and can be used to reflect a model's reliability. It can be defined as:  
\[R^{2}=1-\frac{\sum_{i=1}^{N_{T}}(y_{i}-\bar{y}_{i})^{2}}{\sum_{i=1}^{N_{T}}(y_ {i}-\overline{y})^{2}}, \tag{5}\]  
where \(\overline{y}\) is the average of the actual output for the test samples. The closer \(R^{2}\) is to 1, the higher the prediction performance of the calibration model is.  
_RMSE_ can be defined as:  
\[RMSE=\sqrt{\sum_{i=1}^{N_{T}}\frac{\left(y_{i}-\hat{y_{i}}\right)^{2}}{N_{T}}}, \tag{6}\]  
where \(y_{i}\) and \(\hat{y_{i}}\) are the actual and predicted target output values for the ith sample, respectively; and \(N_{T}\) is the number of test samples. _RMSE_ represents the model's accuracy, and a smaller RMSE value indicates higher predictive performance.  
To sum up, a smaller RMSE value and a larger \(R^{2}\) indicate better model performance.
# Semantic Features Based N-Best Rescoring Methods for Automatic Speech Recognition
## 4 N-Best Rescoring with Coordination Scores
### Sentence Coordination Scores
#### 4.1.3 Word-Pair Probability Score Using Word Embedding  
Let \(p_{i,j}\) be the probability that word \(j\) appear in the context of word \(i\). It is apparent that \(p_{i,j}=x_{i,j}/x_{i}\). And according to the last section, the logarithm of \(x_{i,j}\) can be approximate to \(w_{i}^{T}w_{j}\). Thus, \(p_{i,j}\) can be calculated as follows:  
\[p_{i,j}=\frac{x_{i,j}}{x_{i}}=\frac{exp(w_{i}^{T}w_{j})}{\sum_{k=1}^{V}exp(w_ {i}^{T}w_{k})}=softmax(W^{T}w_{i})_{j}, \tag{13}\]  
where \(W=[w_{1},w_{2},\ldots,w_{V}]\) and \((\bullet)_{j}\) refer to the \(j\)-th element of \((\bullet)\). The calculation can be further modified by \(\gamma\) to control the shape of the probability distribution as follows:  
\[p_{i,j}=\frac{x_{i,j}}{x_{i}}=\frac{exp(\gamma w_{i}^{T}w_{j})}{\sum_{k=1}^{V} exp(\gamma w_{i}^{T}w_{k})}=softmax(\gamma W^{T}w_{i})_{j}, \tag{14}\]  
\(\gamma\) is the smoothing factor, which can be regarded as a stretch of all word vectors. \(\gamma\) controls the shape of the probability distribution. When \(\gamma>1\), the probability distribution will be sharpen and when \(\gamma<1\), the distribution will be flatten.  
The word pair probability is similar to the log bi-linear (LBL) language model [4]. It can be regarded as a bi-gram language model which is smoothed by the inner product of the continuous expression for the rare word-pairs. To avoid repetitive computation and accelerate the calculation, we can pre-compute and store all \(x_{i}\). By pre-computing, the probability of a word pair can be calculated in \(O(d)\) time. \(d\) is the length of word vector.  
Usually, the context of a word is defined by the previous two words and the preceding two words of the current word. We use this setting for both word embedding training and word-pair probability prediction. We use the average of conditional probabilities of the current word given words in the context of the current word. The probability can be expressed as follows:  
\[p_{i}=\frac{p_{i-2,i}+p_{i-1,i}+p_{i+1,i}+p_{i+2,i}}{k} \tag{15}\]  
when \(i-2\) or \(i-1\) is less than 0, or \(i+1\), \(i+2\) is larger than the length of the sentence, we use zero as the word-pair probability. And \(k\) is the number of non-zero probabilities of the 4 items. The word probability score for the \(i\)-th word is the logarithm of \(p_{i}\).  
We used a window with exponential weights to emphasize the words near the current word and weaken the effect of words further to the current word. But the result is at a similar level to that of the noncentral rectangular window.
## 3 On-Site Monitoring Arrangement  
### Layout of On-Site Monitoring Points  
According to the site construction conditions, the plane layout of test monitoring points is shown in Figure 2. The numbers 1#-11# are the ground settlement monitoring points, among which 1#-6# are arranged along the jacking axis with an interval of 3 m, and 6#-11# are arranged perpendicular to the jacking axis with an interval of 1.5 m. The monitoring point 1# is 60 m away from the pipe-jacking operation shaft DGJ5. The cross section at the monitoring point 1# is the monitoring section I, and the cross section at the monitoring point 6# is the monitoring section II. \(L_{1}\) and \(L_{2}\) are, respectively, the distance from the excavation face to the monitoring section I and II. \(L_{1}\) and \(L_{2}\) are denoted as negative when the jacking machine arrived before the monitoring section, and the value is denoted as positive when the jacking machine passing the monitoring section.  
Figure 1: Layout plan of working shafts. (**a**) Layout plan of working shafts and jacking construction route; (**b**) lower pipe jacking; (**c**) vertical arrangement of the pipe and soil distribution.  
In order to further explore the internal deformation of soil during pipe-jacking construction, the monitoring points of soil horizontal lateral displacement below the surface are arranged at section I on both sides of the power tunnel, numbered CX1-CX3 using inclinometers. The relative position relationship is shown in Figures 2 and 3.
## 2 Materials and Methods
### Data Source and Processing  
The RS data used in this study were collected from the Google Earth Engine (GEE) cloud-computing platform ([[https://code.earthegine.google.com](https://code.earthegine.google.com)]([https://code.earthegine.google.com](https://code.earthegine.google.com)), accessed on 11 January 2022) [12; 13], and the Sentinel-2 surface reflectance data from 17 July to 22 July 2022 were selected. Only images with no more than 15% cloud coverage were used for the composite. The Sentinel-2 images have a spatial resolution of 10 m. After preprocessing, such as  
Figure 1: The geolocation of the Alar Reclamation Area and the spatial distribution of collected samples.  
radiometric calibration and atmospheric correction, the image map of the research area was cropped to avoid the extraction of non-cotton areas such as water bodies, deserts, buildings, and fruit trees. The accuracy caused interference, so the Google Earth Engine cloud platform was used to classify the images to extract the cotton field planting area and to remove the masks of water bodies (Figure 2b), deserts(Figure 2d), buildings (Figure 2a), fruit trees(Figure 2c), etc. in the research area, as shown in Figure 2.  
Soil samples were collected in the field from 15 July to 24 July 2022, and the sample points were evenly distributed in each company in the reclamation regiment. According to the triangular sampling method [12], three soil samples of 0-15 cm beneath the surface were collected for each point, and the GPS coordinates were measured by Huace S8 (Figure 1). In total, there were 115 field sample points and 345 soil samples. The soil sampling data were placed in the laboratory to dry naturally for one month, and then the three samples of the same sample were ground and evenly mixed into one part. The plant roots and stones in the soil were removed and then sieved with 0.15 mm and 100 mL of distilled water. The solution was prepared according to a water-to-soil ratio of 5:1, it was stirred fully and evenly, it was placed statically for 8-10 h and filtered, and the soil conductivity was measured with the Shanghai Keyou Conductivity Meter DDS-11A.
# A TinyML Approach to Real-Time Snoring Detection in Resource-Constrained Wearables Devices +
## 3 Methods
### Processing  
In this study, Audio Syntiant [15] processing is utilized to classify snoring from non-snoring voices and noises. Audio Syntiant is used to extract time and frequency information from signals. The Syntiant audio processing pipeline is a specialized version of Audio MFE, with additional steps for the Syntiant chip's unique characteristics. Key parameters include frame length, frame stride, filter number, FFT length, low frequency, and high frequency, which define the spectrogram features extracted using Mel-filterbank energy. Pre-emphasis is applied with a specified coefficient. The chip-specific feature extractor is chosen based on the particular Syntiant chip used, ensuring optimal feature generation for the given hardware.  
Syntiant's feature extraction process begins with a pre-emphasis step to amplify high-frequency components. The audio signal is divided into overlapping segments, with the frame length and stride determining the size and spacing of these segments. These parameters influence the extracted speech features. Table 2 provides the specific values used for Audio Syntiant.  
Figure 3: Dataset for snoring and non-snoring sounds.