esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Uncertainty Propagation and Correlation Analysis  
Uncertainty propagation and correlation analyses were performed on the quantities of interest: scalar analysis, one-dimensional functional analysis, and three-dimensional functional analysis. The quantities of interest are represented by the following function:  
\[P=P(m,t,n)\]  
where the sample number is  
\[n=1\ldots N\]  
and the node number is  
\[m=1\ldots M.\]  
Here, \(N\) is equal to 100, and M is equal to 511, 568. The timestep variable is represented by  
\[t=t_{0},t_{1}\ldots t_{f}\]  
where \(t_{f}\) is the final timestep. A Python script was written to automate the preprocessing, simulation job, and postprocessing of each case for DAKOTA.  
Spatial averages and ensemble averages were applied to the results of uncertainty propagation. Spatial average is defined in Equation (1) as the statistical average over the functional phase-space (space and time). The standard deviation over this functional phase-space is calculated by Equation (2).  
\[\hat{P}(m,t)=\frac{1}{N}\sum_{n=1}^{N}P(m,t,n) \tag{1}\]  
\[\hat{\sigma}_{P}=\sqrt{\frac{\left(P-\hat{\rho}\right)^{2}}{N-1}} \tag{2}\]  
A correlation analysis using Spearman rank correlation coefficient (SRCC) is conducted to nonparametrically measure the strength and direction of association between the two ranked variables, i.e., the input and output data. The SRCCs capture the monotonic correlation between any input and output of interest. The input data considered for this study are the parameters through which uncertainties are propagated, and they are the delayed neutron precursor (DNP), delayed precursor decay constant (\(\lambda\)), liquid fuel density (\(\rho\)), liquid fuel dynamic viscosity (\(\mu\)), heat transfer coefficient (HTC), pump momentum (PM), specific heat (\(C_{p}\)), and thermal expansion (\(\beta_{th}\)). The output parameters of interest for this study are temperature and velocity. The correlation analysis study is conducted at the three different levels also investigated for the uncertainty propagation (scalar, one-dimensional, and three-dimensional) to understand how the correlations change with time and space. At the scalar level, the correlations are computed between the input data and the ensemble averaged system average/maximum of the output quantities of interest is computed at the final timestep. The one-dimensional functional correlation analysis focuses on how the correlations between the input and output data evolves with time. This functional study is important as it gives more information about the significance of the parameters before and after the transient.
## 2 Overview of the Study Area
### Characteristics of Strata and Coal Seams  
The main strata in the study area from old to new are the Upper Triassic Yongping Formation (T\({}_{3}\)y), the Lower Jurassic Fuxian Formation (J\({}_{1}\)f), the Middle Jurassic Yan'an Formation (J\({}_{2}\)y), the Zhiluo Formation (J\({}_{2}\)z), and the Anding Formation (J\({}_{2}\)a). Lower Cretaceous Luohe Formation (K\({}_{1}\)I), Neogene, and Quaternary. Yan'an Formation is the coal-bearing strata in the study area, generally containing 3-5 layers of mineable coal seams, of which 2\({}^{-2}\) coal seams are the main coal seams. The buried depth is between 180 and 316.36 m, the average buried depth is 300 m, the thickness is between 0.26 and 12.16 m, the average thickness is 6.5 m, the dip angle is less than 3\({}^{\circ}\), and it is nearly horizontal. A large number of boreholes in the study area revealed that: First of all, the macroscopic layered structure of 2\({}^{-2}\) coal seam overburden is most widely distributed in the type of \"sand layer-red soil layer-bedrock layer\". Secondly, the overlying bedrock of 2\({}^{-2}\) coal seam is mainly composed of sandstone and a small amount of mudstone interbedded structure,  
Figure 1: Location of study area. and there are two distinct sections of upper and lower characteristics, that is, the upper characteristic section is \"sandy mudstone-fine sandstone\" interbedded structure; the lower characteristic section is an \"siltstone-fine grained sandstone\" interbedded structure; the sand-mud ratio of the overlying bedrock of the \(2^{-2}\) coal seam is generally 60-80%, while the sandstone is dominant. The number of sandstone layers is at least five layers, up to 35 layers, and the thickness is generally between 3 and 35 m. There are four layers of sandy mudstone, and the thickness is generally between 10 and 30 m. Thirdly, sandy clay or laterite is developed on the overlying bedrock of \(2^{-2}\) coal seam, with an average thickness of 20 m, and is covered by modern aeolian sand with an average thickness of 30 m. The physical and hydraulic parameters of various rock and soil layers are shown in Table 1. There are aquifers in the upper and lower parts of the study area (see Figure 2), and the roof and floor of the coal seam are fine-grained sandstone and siltstone aouifuge. Under natural conditions, the aouifuge separates the coal seam from the aquifer, but there is a huge head difference under the condition of coal seam mining. The aouifuge will become a weak permeable layer or even a partially permeable layer, so that the groundwater in the aquifer will flow to the mine. Figure 2: Hydrogeological profile of the study area.
# Revisiting Low-Resolution Images Retrieval with Attention Mechanism and Contrastive Learning
## 3 Results
### Quantitative Results  
We save the model's weights that attain the lowest loss value or the highest accuracy for the validation set in the case of using the auxiliary classification loss. The model's weights are loaded into the corresponding model to extract embedded vectors for performing image search in the retrieval phase. The search strategy used in our study is simply an exhaustive search using \(L_{2}\) similarity. Note that the embedded vectors are normalized in a unit sphere so that the similarity can be calculated simply by the dot product. For quantitative evaluation, the embedded vectors from the test set are used to build a gallery and embedded vectors extracted from the validation set act as queries. It is rational to apply a more effective searching strategy rather than an exhaustive comparison using L2 for image retrieval; however, we focus on improving the searching space and consider effective search methods as future works. The matching score for a query is measured by ranking recall as follows.  
\[R\@k(x)=\left\{\begin{array}{ll}1&\quad\left|\{z\in R_{k}|y(z)=y(x)\}\right| >0\\ 0&\quad\left|\{z\in R_{k}|y(z)=y(x)\}\right|=0\end{array}\right., \tag{11}\]  
where \(x\) is a query image and \(R_{k}\) is a set of top k retrieval results. The main focus of this experiment is to calculate the average recall value over queries from the validation set, and such a higher recall value shows better performance. In addition, we also calculate the mean average precision (mAP) for comparison purposes.  
Most experiments were carried out with a basic setting as follows: the ViT-B-16 model is fine-tuned and used to perform image retrieval on the CUB200-2011 dataset with supervised contrastive loss function (\(\alpha=0\)), auxiliary classification loss (\(\beta=1\)), and triplet loss function (\(\gamma=1\)); the embedding dimension is set to 128, and the representation vector is  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Name** & **Params** & **FLOPS** & **Top-1 (\%)** \\ \hline ViT-B-16 & 86 M & 16.85 G & 84.15 \\ BiT-M-R152x4 & 68 M & 68.59 M & 85.39 \\ Efficient-B7 & 66 M & 87.07 M & 84.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters count, FLOPS, and ImageNet Top-1 accuracy for ViT, BiT, and EfficientNet. The parameters and FLOPS are reported by “thop” library given an image of size \(224\times 224\times 3\).  
extracted from the class token. First, we show the performance of the proposed framework using different datasets. Then we make an ablation study about the effectiveness of the backbone networks, the number of embedding dimensions, and loss components in the subsequent sections.
## 3 Experimental Results
### Initialization of Weights  
The essence of the deep learning model training process consists of updating the weights. Each parameter must have an appropriate initial value for network training. Poor initialization parameters can cause gradient propagation problems and reduce training speed, while good initialization parameters can speed up convergence and are more likely to find better solutions. In addition, the output of the middle layer of the model is intransparent, and the influence of the previous weights on the output of subsequent neurons is not unique. Even with such advanced parameter passing and updating, an inappropriate initialization of weights can cause laborious parameter learning and even the output loss gradient of the layer activation function to explode or vanish during forward propagation of the deep neural network. In either case, if the loss gradient is too large or too small, it cannot effectively backpropagate, and even if it can backpropagate, it takes longer for the network to reach convergence.  
To ensure that low-frequency classes can be learned during the training process, weights were initialized according to the proportion of pixels occupied by each class in all images. The higher the proportion, the lower the initial weight of the class. Artificial adjustments were then made according to the results of the generated images. For example, if the class \"cracks\" was under-predicted, it was multiplied by a coefficient greater than one in the initial weight. Therefore, many different initial weights were tested. The best combination of initial weights in comparison with the unweighted combination is shown in Table 3. In the case that the backbone was \"Xception\", the result after adjustment was significantly improved. The initial weights from left to right stand for the weld metal, background, pores, cracks and seam shape defects.
# A Novel Piezoelectric Ceramic Actuator with Scissoring Composite Vibration for Medical Applications
## 5 Conclusions  
In summary, we have designed and evaluated a novel scissoring composite actuator to efficiently convert longitudinal vibration into scissoring-type vibration. The detailed mechanism of the longitudinal to transverse conversion of the vibration has been systematically demonstrated by structural analysis and mechanical modeling. Moreover, a variety of finite element modeling (FEM) methods, such as Eigen frequency analysis, harmonic analysis and transient analysis, have been carried out to demonstrate the effectiveness of the proposed actuator on excitation of scissoring vibration mode. The simulation results revealed that the as-designed actuator could excite stable scissoring vibration mode and supply more efficient symmetrical oblique elliptical vibration at the beam tip. Furthermore, a prototype of the as-designed actuator has been manufactured and experimentally characterized by using a laser vibrometer test. The scanning test revealed that the longitudinal driving vibration could be efficiently converted into scissoring vibration at the distal tip of the actuator in real environment, confirming the superior mode conversion performance. It can be seen that with this new structure, the actuator has the potential to be optimized with different sizes by modifying its structural parameters to meet the demands in practical medical applications, which will probably be studied in the future for specific medical applications.  
The following are available online at [[http://www.mdpi.com/2076-3417/9/21/4637/s1](http://www.mdpi.com/2076-3417/9/21/4637/s1)]([http://www.mdpi.com/2076-3417/9/21/4637/s1](http://www.mdpi.com/2076-3417/9/21/4637/s1)); Video SI: Laser vibrometer test.  
Conceptualization, J.Y., Q.Z. and T.X.; methodology, J.Y. and T.X.; mathematical modeling, J.Y.; finite element analysis, J.Y. and Q.Z.; experiment, J.Y. and T.X.; investigation, J.Y.; data curation, J.Y., Q.Z. and T.X.; writing-original draft preparation, J.Y., Q.Z. and T.X.; writing-review and editing, J.Y. and Q.Z.; visualization, J.Y.; supervision, J.Y. and T.X.; project administration, J.Y. and T.X.; funding acquisition, J.Y.  
This research was funded by National Natural Science Foundation of China (Grant number: 51865021) and Scientific Research Foundation from Kunming University of Science and Technology (Grant number: 1411909207).  
The authors declare that there is no conflict of interest regarding the publication of this paper.  
Figure 11: The transverse amplitude of the tip points at point A and B.
## 3 Results
### VOC Profile of C.depressa Fruit Edible Parts of Different Origins
In contrast, the esters (methyl butanoate, methyl hexanoate, methyl octanoate, ethyl acetate, ethyl butanoate, ethyl hexanoate, and ethyl octanoate) were located in the PC1-positive/PC2-positive quadrant near the PC2 axis, whereas the aldehydes (hexanal, heptanal, octanal, and nonanal) were located in the PC1-positive/PC2-negative quadrant. OPLS-DA was subsequently performed on the VOCs in the edible parts of fruits of different origins (Figure 5e,f). The plot illustrates the contributions of aldehyde compounds (hexanal and heptanal) to Kagoshima accessions, ester compounds (methyl butanoate, methyl hexanoate, and ethyl hexanoate) to Okinawa accessions, and monoterpene and sesquiterpene compounds to Taiwanese accessions (Figure 5f). In addition, the volcano plots allowed for the distinction of VOCs in the fruits of Kagoshima, Okinawa, and Taiwan origins (Figure 6). As for the VOCs found to be distinguishable between Okinawan and Kagoshima origins, Okinawan accessions were found to be significantly higher in 13 VOCs, which included esters and monoterpene hydrocarbons such as (\(Z\))-\(\beta\)-codimene, methyl octanoate, methyl hexanoate, \(\alpha\)-pinene, and \(\beta\)-pinene (\(\log_{2}\)FC = 3.08, 2.30, 2.24, 2.15, and 2.14, respectively), while Kagoshima fruits were found to be significantly higher in aldehydes such as hexanal, octanal, heptanal, and nonanal (\(\log_{2}\)FC = 1.61, 1.48, 1.38, and 1.03, respectively) (Figure 6a, Supplementary Table S4). On the other hand, regarding Okinawan and Taiwanese accessions, esters and aldehydes of methyl butanoate, hexanal, methyl hexanoate, methyl octanoate, and ethyl butanoate (\(\log_{2}\)FC = 4.37, 3.62, 3.57, 3.21, and 2.92, respectively) were found to be significantly higher in Okinawan accessions, while Taiwanese fruits were characterized with monoterpenes such as alloocimene, terpinolene, fenchol, \(\alpha\)-terpinene, and \(\gamma\)-terpinene (\(\log_{2}\)FC = 5.83, 5.79, 5.68, 5.30, and 5.09, respectively) (Figure 6b, Supplementary Table S4). In addition, for the VOCs distinguishable between Kagoshima and Taiwanese accessions, five VOCs (hexanal, heptanal, methyl butanoate, ethyl butanoate, and octanal: \(\log_{2}\)FC = 5.23, 4.12, 2.48, 2.06, and 1.66, respectively) were richer in Kagoshima fruits, while monoterpene hydrocarbons (terpinolene, \(\gamma\)-terpinolene, \(\beta\)-myrcence, \(\beta\)-pinene, and (\(Z\))-\(\beta\)-codimene: \(\log_{2}\)FC = 7.37, 7.02, 6.94, 6.81, and 6.72 respectively) were more abundant in Taiwanese accessions (Figure 6c, Supplementary Table S4).
## 2 Materials and Methods
### BCI Competition Dataset (Dataset 2)  
Dataset IIa of BCI competition IV (2008) [60]. This dataset contains 22 channels at a 250 Hz sampling rate. Nine participants (A01-A09) performed left hand, right hand, foot, and tongue motor imagery. This dataset has two sessions recorded on two different days, each with six runs of 48 trials (12 trials per class), yielding 288 trials per session (72 trials per class). The first session was used as the training set and the second as the test set. In this study, only the first session of each subject is used to train and evaluate for the number of trials as close to that of one measurement of the self-acquired dataset as possible, hence it would be easier for comparison between datasets. Furthermore, only three tasks (left hand, right hand, and feet) are performed in this study. Consequently, each subject in this dataset has 216 trials in total, before any artifacts removal, equally divided into three classes. Please visit the following website for further information: [[http://www.bbci.de/competition/IV/](http://www.bbci.de/competition/IV/)]([http://www.bbci.de/competition/IV/](http://www.bbci.de/competition/IV/)) (accessed on 10 June 2021).  
There are a few key differences between the author's self-acquired and BCI Competition datasets:  
* Firstly, the data acquisition paradigms are slightly different in terms of timing.
* Secondly, the number of measurements of the self-acquired dataset are more and occurred on different days while there is only one measurement for each subject in BCI Competition. That results in a different number of trials for each subject and possibly affects the outcomes.
# Sub-Milliwatt Transceiver IC for Transcutaneous Communication of an Intracortical Visual Prosthesis
## 5 Results
### Non-Coherent BPSK Receiver
#### 5.2.2 Link Budget for Downlink  
Similarly to the uplink case, it is important to analyze in a link budget how the receiver would fit with the transmit/receive coils, while taking into account 3-7 mm transcutaneous communication with the transmitter on the outside. The BPSK receiver sensitivity is about 10-50 mV, determined by its comparator, at the first stage. Furthermore, from our coil and transmitter simulation, a voltage level of 200-2500 mV could reach the receiver, depending on the coil design and distance [21]. Typical coil sizes range from 15 mm to 40 mm in diameter depending on the size desired. The coupled inductive link from the coils acts as a low-power voltage transformer, boosting the voltage around resonance frequency it is designed for [34]. Typical coil dimensions range from 10 mm to 50 mm in diameter to ensure that the coil self-resonance frequency is much higher than our operating frequency. In addition, these coils sizes result in a tolerable misalignment of several mm. However, here we connect directly without coils to deliver a 200 mV level into the receiver. From a sensitivity of 50 mV and a possible coil output of 200-2500 mV, the link will be closed with a 12-26 dB excess margin for downlink when the coupled coil response has the desired bandwidth of about 2 MHz, as investigated in [21]. BER performance assumes that the \(<\)10\({}^{-3}\) target is reached; otherwise, the receiver power level can be adjusted from the external transmitter and coil design, considering the 12-26 dB excess margin. Finally, error detection and protection methods such as linear and convolutional codes could further increase the link margin.
# Enhanced Gradient-Based Local Feature Descriptors by Saliency Map for Egocentric Action Recognition
## 4 Experiments and Results
### Experiments with Different Resolutions, Block Sizes, and Feature Encoding Methods  
In this experiment, SMLFD (including SM-HOG, SM-HMG, SM-HOF, SM-MBHx, and SM-MBHy) was applied for comparison with GROILFD [5] (covering GROI-HOG, GROI-HMG, GROI-HOF, GROI-MBHx, and GROI-MBHy). Videos with the same down-scaled resolution (by a factor of 6), as reported in [5], were used in this experiment as the default. Thus, each video in the dataset has a uniform resolution of 320 \(\times\) 180 pixels. Firstly, the values of the feature extraction time \(T^{E}\) were studied. Tables 2 and 6 show the speed of extracting SMLFD was significantly boosted by at least 40-fold (denoted as 40\(\times\)). When using a block size of 16-by-16 spacial pixels by 6 temporal frames (denoted as [16 \(\times\) 16 \(\times\) 6]) for the feature extraction, SM-HOG, SM-HMG, SM-HOF, SM-MBHx, and SM-MBHy were boosted by 40\(\times\), 41\(\times\), 43\(\times\), 41\(\times\), and 41\(\times\), respectively. When using a block size of [32 \(\times\) 32 \(\times\) 6], SM-HOG, SM-HMG, SM-HOF, SM-MBHx, and SM-MBHy were boosted by 41\(\times\), 43\(\times\), 45\(\times\), 43\(\times\), and 43\(\times\), respectively. Because GROILFD extracts the sparse features, SMLFD feature extraction needs more computational time than GROILFD. However, VLAD and FV feature encoding methods consume similar amounts of time for feature encoding. In terms of accuracy, the performance of SMLFD was improved significantly compared with that using the original resolution. Furthermore, SMLFD consistently outperformed GROILFD with the down-scaled dataset, because SMLFD constructed dense features. To investigate the trade-off between performance and time complexity, the features encoded with a smaller number of visual words were studied. Table 2 shows that SMLFD generally had better accuracy when using smaller block sizes (i.e., [4 \(\times\) 4 \(\times\) 6], [8 \(\times\) 8 \(\times\) 6], and [16 \(\times\) 16 \(\times\) 6]), while GROILFD outperformed when using larger ones. Therefore, this indicates that SMLFD is a better candidate for low-resolution videos.
# ISAC towards 6G Satellite-Terrestrial Communications: Principles, Status, and Prospects
## 4 Development Trend and Key Technology of ISAC towards 6G ISTNs
### Critical Techniques in Integrated Design  
The construction of an integrated satellite-terrestrial system with ISAC will face multiple challenges. We need to explore critical technologies including interference cancellation, distributed cooperative sensing, frame structure design, waveform design, and beam enhancement.
## 2 Understanding of Polyurethane with Water Expansion Properties  
### Fabrication of Superabsorbent Polymers and Water Absorption Principle  
The superabsorbent polymer incorporated into the polyurethane with water expansion properties is a powdered polymer with an average particle size of 10-220 \(\upmu\)m, a pH of 5.5-6.5, and a specific gravity of 0.6-0.8. Unlike general water-soluble polymers, it does not dissolve in water, it has the ability to absorb water in amounts up to hundreds of times its own weight, and it can retain water even under external pressure [11]. The superabsorbent polymer is fabricated by the polymerization of acrylic acid and partially neutralized acrylic acid through an initiator. The properties of the superabsorbent polymer are summarized in Table 1, and the molecular structure is shown in Figure 2[12].  
As shown in Figure 3, the superabsorbent polymer is a material with a network structure. It has a large number of hydrophilic groups, such as carboxyl (-COOH), at the ends and absorbs water molecules through ion-water interactions via ionization. The principle is that due to the network structure of the carbon chain, the absorbed water molecules are trapped in it, and the polymer expands further due to the repulsion of negative (-) charges to absorb more water molecules [13; 14].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Unit** & **Property** \\ \hline Particle size & \(\upmu\)m & 10–220 \\ \hline pH & - & 5.5–6.5 (at 5.0 g/L 0.9\% NaCl solution) \\ \hline Specific gravity & g/cm\({}^{2}\) & 0.6–0.8 (apparent density) \\ \hline Decomposition temperature & \({}^{\circ}\)C & 200 \({}^{\circ}\)C or higher \\ \hline \hline \end{tabular}
\end{table}
Table 1: The properties of superabsorbent polymers.  
Figure 2: The molecular structure of superabsorbent polymers.