esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Particle Size and Zeta Potential of Nanoemulsion  
After two stages of homogenization at high speed and high pressure, the mean particle size of the emulsions was 106 \(\pm\) 2 nm. The polydispersity index (PDI) was also measured to provide information about the size distribution. The PDI was 0.24 \(\pm\) 0.04, indicating the polydisperse distribution of oil droplets [37] in the emulsion according to ISO standards ISO 22412:2017 [38; 39]. This small average size is due to the good emulsifying properties of lecithin, as it has a high affinity with the essential oil components [40]. Lecithin acts as a ripening inhibitor to prevent droplet growth. Ostwald ripening is a phenomenon that occurs in emulsions and nanoemulsions and results in larger droplets [37].  
The average zeta potential was \(-\)57 \(\pm\) 2 mV, indicating good emulsion stability due to the high zeta potential value. It is reported that a zeta potential value (negative or positive) of more than 30 mV represents a high degree of electrostatic repulsion and high resistance to aggregation, which causes stability in emulsions [41]. Lecithin is a negatively charged phospholipid that can be used to encapsulate insoluble or poorly soluble substances in water with a good encapsulation rate [42]. This result is consistent with the measured zeta potential values, which ranged from \(-\)33.7 to \(-\)58.6 mV, obtained by changing the composition of emulsions prepared with 10% essential oils that were emulsified with 2% soybean lecithin and, as documented by [42].
## 4 Data Modeling
### Integrated Data Set  
A data set for building the risk prediction model was created by integrating the value derived from unstructured text topic modeling and the initially collected numeric data. In addition to the text data, the 243 infrastructure projects ordered by Caltrans include numeric data, which consists of the number of bidders, the number of addendum issues, the number of Notice To Bidders (NTB) changes, the number of drawing changes, the number of specification changes, the number of bid item changes, the number of wage rate changes, and the pre-bid clarification of the project. The pre-bid clarification of the project was determined based on whether or not the project was clarified in advance. The unclarified status of each project was judged according to the following criteria:  
* if the bidders have queried and the owner has not responded within a deadline;  
Figure 5: Topic Modeling Results using Latent Dirichlet Allocation (LDA).  
* if the bidders have requested additional information on unclear information in the bidding document, but the owner has declined; and,
* if the bidders have made further proposals in case of danger and the owner has rejected.  
When a total of 243 data were divided into three groups according to the bid risk percentage, and 89 low risk groups, 77 moderate risk groups, and 77 high risk groups were labeled. Based on the three classified groups, the bid risk prediction models learn the risk level of projects and classify each project into one of the three risk groups. In order to ensure the accuracy of the classification prediction results, it is preferable for the same number of data to be allocated for each group. In this study, the low risk group was assigned slightly more data than the other two groups, but because they did not show a large difference in the entire ratio, the data were analyzed for classification learning.  
Table 2 shows part of the integrated data set. T01-T11 data are obtained from text topic modeling, and N01-N08 data is a numeric data set from bidding information. Each variable is defined as follows:  
* N01 is the number of bidders; N02 indicates whether the project is pre-clarified or unclarified; N03 is the number of NTB changes; N04 is the number of drawings changes; N05 is the number of specification changes; N06 is the number of bid item changes; N07 is the number of wage rate changes; and, N08 is the number of issued addendums issued.  
Examining the variables in Table 2, the value ranges of variables from text modeling and variables from numeric data differ. If the range difference of independent variables is large, learning for classification prediction may not be performed properly. Therefore
# Optimization and Application of Drilling Parameters Based on Gas-Solid Coupling Simulation
## 4 Analysis of Numerical Simulation Results
### Analysis of the Effect of Borehole Diameter on Gas Pressure  
The simulated working conditions are as follows: initial gas pressure 1 Mpa; pumping negative pressure is 25 kPa; and borehole diameters are 120 mm, 153 mm, 203 mm, and 253 mm.  
Figure 4 shows the cloud diagram of gas pressure change around the drill hole when the drill hole diameter is 120 mm, 153 mm, 203 mm, and 253 mm, respectively. Figure 5 shows the variation in effective extraction radius under different drill hole diameters. According to Figures 4 and 5, the maximum effective extraction radius is 4.35 m, 4.58 m, 4.87 m, and 4.90 m when the diameter of the drill hole is 120 mm, 153 mm, 203 mm, and 253 mm, respectively, and the effective extraction radius is the smallest when the diameter of the drill hole is 120 mm and is the maximum when the diameter of the drill hole is 253 mm, which shows a positive correlation between the diameter of the drill hole and effective extraction radius. The effective extraction radius is the smallest when the borehole diameter is 120 mm and is the largest when the borehole diameter is 253 mm, and the positive correlation between the borehole diameter and the effective extraction radius is shown, but when the borehole diameter increases from 203 mm to 253 mm, the effective extraction radius only increases by 0.03 m, and the effective extraction radius has no obvious change.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Extraction Time** & **Effective Extraction Radius** \\ \hline
30 d & 1.39–1.55 \\
50 d & 1.96–2.40 \\
70 d & 2.87–4.38 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effective pumping radius over time.  
Figure 4: _Cont_.  
In addition, in the actual drilling construction and subsequent gas extraction process, the increase in drilling diameter will lead to serious collapse and poor sealing effect [26]; the construction of the 253 mm drill hole needs to be reamed twice, and the reaming will affect the stability of the original drill hole. For the above numerical simulation analysis and the actual situation, the optimal diameter of the drill hole is 203 mm.
## 3 Results and Discussion
### Degree Distribution Characteristics  
The degree distribution is one of the most important indicators for describing the characteristics of the complex network structure. In the existing literature, \(P(k)\) (the distribution function of node degree) or \(P(\geq\)\(k)\) (that of the cumulative degree) was used to describe the degree distribution characteristics of nodes. The former \(P(k)\) is the ratio of the number of nodes with degree \(k\) in the complex network to the number of total nodes. The latter \(P(\geq\)\(k)\) is the ratio of the number of nodes with degrees greater than or equal to \(k\) in the complex network to the number of total nodes. Empirical studies show that a large number of real-world complex networks are characterized by three types of degree distribution of nodes: scale-free properties, wide-scale properties, and single-scale properties. The cumulative distribution was used in this study to describe the degree distribution characteristics of the co-occurrence network of high-frequency words in the bioinformatics literature. The cumulative distribution of network in Figure 2 is shown in Figure 3.  
Figure 3 shows that the cumulative degree distribution curve seems to decline faster at the first stage and slower at the second stage with the increasing of the degree \(k\), indicating that the node degree of the network was scale-free. The scale-free characteristics showed that the connectivity of a small number of nodes in the network were quite large (with a large number of connections), which had a leading role in the operation of the network while most of the nodes had small connections (only a small number of connections).
# Fully Superconducting Josephson Bolometers for Gigahertz Astronomy
###### Abstract  
The origin and the evolution of the universe are concealed in the evanescent diffuse extragalactic background radiation (DEBRA). To reveal these signals, the development of innovative ultra-sensitive bolometers operating in the gigahertz band is required. Here, we review the design and experimental realization of two bias-current-tunable sensors based on one dimensional fully superconducting Josephson junctions: the nanoscale transition edge sensor (nano-TES) and the Josephson escape sensor (JES). In particular, we cover the theoretical basis of the sensors operation, the device fabrication, their experimental electronic and thermal characterization and the deduced detection performance. Indeed, the nano-TES promises a state-of-the-art noise equivalent power (NEP) of about \(5\times 10^{-20}\) W/\(\sqrt{\text{Hz}}\), while the JES active region is expected to show an unprecedented NEP of the order of \(10^{-25}\) W/\(\sqrt{\text{Hz}}\). Therefore, the nano-TES and JES are strong candidates to push radio astronomy to the next level.  
radio astronomy; Josephson effect; transition edge sensor; Josephson escape sensor; nanodevices; gigahertz detection +
Footnote †: journal: _sciences_  
_Review_  
Fully Superconducting Josephson Bolometers for Gigahertz astronomy.  
_Appl. Sci._**2021**, _11_, 746. [[https://doi.org/10.3390/app11020746](https://doi.org/10.3390/app11020746)]([https://doi.org/10.3390/app11020746](https://doi.org/10.3390/app11020746))
## 4 Results and Discussions
### Performance Analysis  
Based on the results of the previous three subsections, it is evident that RNN is outperformed by LSTM and GRU in stock price prediction. The reason is that both LSTM and GRU are advanced versions of traditional RNN and provide more controlling knobs, which control the flow input based on the training weights. This gives LSTM and GRU more flexibility to control the output and thus improve the performance. Another reason is that RNN suffers from the vanishing gradient problem, which may cause RNN to stop being trained if the learning rate is too low. However, with the help of update gate, forget gate, and reset gate, LSTM and GRU can avoid this problem [42]. Moreover, in  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & \(\mathbf{R^{2}}\) & **MAE** & **MAPE** & **RMSE** \\ \hline RNN & 0.90405 & 13.57545 & 5.65341 & 17.83176 \\ \hline LSTM & 0.96641 & 7.83521 & 3.41306 & 10.55002 \\ \hline
**GRU** & 0.98227 & 4.99038 & 2.31463 & 7.66498 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Performance comparison of INTU five-day interval.  
Figure 31: INTU actual vs. GRU predicted for five-day interval.  
Figure 29: INTU actual vs. RNN predicted for five-day interval.  
terms of highly fluctuating data (INTU), although LSTM can closely identify the trend (\(R^{2}\) value is close to GRU), it produces very large errors compared to GRU for all timeframes. Furthermore, in our research, we applied our methods on three types of data (highly fluctuating, moderately fluctuating, and slightly fluctuating) where highly fluctuating data are of the non-stationary type. High \(R^{2}\) value indicates better fit to the regression model, and thus better prediction can be made, which is our ultimate goal in this research work. For non-stationary-type data in our analysis, we achieved very high rate of \(R^{2}\) value for every method in each time frame, which is shown in Tables 10, 11, and 12. It can therefore be said our methods are robust to non- stationary data as well.
###### Abstract
The relationship between the snow wetness, SWE, and backscatter during the snowmelt process is illustrated in Figure 1.  
Certain studies have explored the capability of a SAR to capture the ROD in different regions. Ref. [17] used the SWE data measured from five meteorological stations in the European Alps to validate the ROD captured from Sentinel-1 data during the hydrological year 2016/2017 and 2017/2018. Some scholars also used Sentinel-1 data to capture the ROD in Greenland and the Sierra Nevada mountain range of Spain, respectively. However, their studies both lacked validation through the corresponding ground measurements [28,29]. Ref. [30] explored the elevation-ROD relationship in the Cascade Range of the western United States, revealing the median absolute offset between the ROD extracted from SWE data and that from Sentinel-1 data. The results of experiments conducted by [PERSON] et al. in Canada's La Joule Basin indicate a deviation between the ROD extracted from SWE data and that from Sentinel-1 data ranging from 1 to 18 days [31]. All above-mentioned studies only analyzed the feasibility of SAR data in extracting the ROD under limited years and site conditions. However, the accuracy of the ROD extracted from SAR data is still unclear, especially when the different land cover types are considered, which will have an important impact on snowmelt [32, 33, 34]. In addition, there is currently a lack of analysis on multi-year ROD changes. Current discussions on the influ  
Figure 1: Schematic diagram of the temporal evolution of snow wetness, SWE, and backscatter (modified from [17]). mainly focus on altitude, slope, and aspect, with the influence of snow parameters on the ROD often being overlooked. In addition to exploring the impact of external factors on snowmelt, it is equally important to understand the relationship between the intrinsic parameters of snow and the melt process. This study uses the vertical-vertical (VV) polarization Sentinel-1 data during the snowmelt periods from 2017 to 2023 to extract the ROD in the Sierra Nevada Mountains of the western United States. The ROD extracted from the measured SWE under different land cover types is used to validate the ROD extracted from Sentinel-1 data. Then, we analyzed the interannual variation of the ROD and the impact of the SWE on the ROD. The main innovations of this study include: (i) Quantifying the accuracy of Sentinel-1 in identifying the ROD under different vegetation densities; (ii) Clarifying the interannual variation in the ROD under different land cover types in the study area in the context of climate change in recent years; (iii) Exploring the impact of snow's intrinsic factor, namely the SWE, on the ROD under different land cover types.
# Development of a GNSS Buoy for Monitoring Water Surface elevations in Estuaries and Coastal Areas
## Appendix B
Substituting Equation (19) into Equation (14) to Equation (18), yields the following:  
\[\frac{C_{\eta u}(f)}{C_{\eta\eta}(f)h_{u}(f)}=\int_{-\pi}^{\pi}D(f,\theta) \cos\theta\,d\theta, \tag{20}\]  
\[\Phi_{mn}(f)=\int_{-\pi}^{\pi}I_{m}(f,\theta)I_{n}^{s}(f,\theta)S(f,\theta)d\theta, \tag{12}\]0\[\Phi_{mn}(f)=\int_{-\pi}^{\pi}I_{m}(f,\theta)I_{n}^{s}(f,\theta)S(f,\theta)d\theta, \tag{12}\]1  
\[\Phi_{mn}(f)=\int_{-\pi}^{\pi}I_{m}(f,\theta)I_{n}^{s}(f,\theta)S(f,\theta)d\theta, \tag{12}\]2  
and:  
\[\Phi_{mn}(f)=\int_{-\pi}^{\pi}I_{m}(f,\theta)I_{n}^{s}(f,\theta)S(f,\theta)d\theta, \tag{12}\]3  
where:  
\[\Phi_{mn}(f)=\int_{-\pi}^{\pi}I_{m}(f,\theta)I_{n}^{s}(f,\theta)S(f,\theta)d\theta, \tag{12}\]4  
The directional spreading function can be expressed as the summation of Fourier series:  
\[\Phi_{mn}(f)=\int_{-\pi}^{\pi}I_{m}(f,\theta)I_{n}^{s}(f,\theta)S(f,\theta)d\theta, \tag{12}\]5  
The corresponding Fourier coefficients are as follows:  
\[\Phi_{mn}(f)=\int_{-\pi}^{\pi}I_{m}(f,\theta)I_{n}^{s}(f,\theta)S(f,\theta)d\theta, \tag{12}\]6  
\[\Phi_{mn}(f)=\int_{-\pi}^{\pi}I_{m}(f,\theta)I_{n}^{s}(f,\theta)S(f,\theta)d\theta, \tag{12}\]7  
\[\Phi_{mn}(f)=\int_{-\pi}^{\pi}I_{m}(f,\theta)I_{n}^{s}(f,\theta)S(f,\theta)d\theta, \tag{12}\]8  
and:  
\[\Phi_{mn}(f)=\int_{-\pi}^{\pi}I_{m}(f,\theta)I_{n}^{s}(f,\theta)S(f,\theta)d\theta, \tag{12}\]9  
Based on linear wave theory, the relationships between the \(x\)- and \(y\)- components of the velocities and the water surface elevation are as follows:  
\[I_{m}(f,\theta)=h_{m}(f)\cos^{\alpha}\theta\sin^{\beta}\theta, \tag{13}\]0  
and:  
\[I_{m}(f,\theta)=h_{m}(f)\cos^{\alpha}\theta\sin^{\beta}\theta, \tag{13}\]1  
where \(d\) is the still water depth. By applying the Fourier transform, the auto spectra of \(u\) and \(v\) can be determined as follows:  
\[I_{m}(f,\theta)=h_{m}(f)\cos^{\alpha}\theta\sin^{\beta}\theta, \tag{13}\]2  
and:  
\[I_{m}(f,\theta)=h_{m}(f)\cos^{\alpha}\theta\sin^{\beta}\theta, \tag{13}\]3  
where \(\mathcal{F}\) denotes the Fourier transform. Combining Equations (23) and (24) yields the following:  
\[I_{m}(f,\theta)=h_{m}(f)\cos^{\alpha}\theta\sin^{\beta}\theta, \tag{13}\]4Substituting Equations (B17) and (B25) into Equation (B10) yields the following:  
\[I_{m}(f,\theta)=h_{m}(f)\cos^{\alpha}\theta\sin^{\beta}\theta, \tag{13}\]5 (B26)  
Similarly, substituting Equations (B19) and (B25) into Equation (B11) yields the following:  
\[I_{m}(f,\theta)=h_{m}(f)\cos^{\alpha}\theta\sin^{\beta}\theta, \tag{13}\]6 (B27)  
Utilizing Equations (B12), (B14), (B15) and (B25), Equation (B18) can be rewritten as follows:  
\[I_{m}(f,\theta)=h_{m}(f)\cos^{\alpha}\theta\sin^{\beta}\theta, \tag{13}\]7 (B28)  
Similarly, utilizing Equations (B13), (B15), (B20) and (B25), Equation (B20) can be rewritten as follows:  
\[I_{m}(f,\theta)=h_{m}(f)\cos^{\alpha}\theta\sin^{\beta}\theta, \tag{13}\]8 (B29)  
After \(D(f,\theta)\) is determined, the directional wave spectrum can be calculated using Equation (B9), and the peak wave direction is determined where the maximum PSD occurs.
## 3 LEO Satellite Network Model
### Traffic Model  
As shown in Figure 3, the Earth is divided into geographical areas of \(20^{\circ}\times 10^{\circ}\), and each area is drawn with a satellite \(v_{i}\) and a satellite communication device density index \(SD_{i}\). We choose aircraft AIS (Automatic Identification System) data and ship ADS-B (Automatic Dependent Surveillance-Broadcast) data for high-altitude and marine user distribution. The ADS-B data collects global air traffic information through the onboard ADS-B receiver carried by satellite STU-2C. The AIS data collects global maritime traffic information through the onboard AIS receiver carried by the satellite STU-2B [17].  
Let \(TR_{i,j,t}\) represents the inter-satellite traffic requirement from \(v_{i}\) to \(v_{j}\), which is assumed to be proportional to \(SD_{i}\) and \(SD_{j}\):  
\[TR_{i,j,t}=\frac{\left(a(h_{i,t})\cdot SD_{i}\cdot a(h_{j,t})\cdot SD_{j}\right) ^{\theta}}{d_{i,t}^{\theta}}. \tag{6}\]  
Among them, \(SD_{i}\) and \(SD_{j}\) represent the device density index of the area covered by a satellite node \(v_{i}\) and \(v_{j}\), \(a(h)\) represents the proportion of active equipment in the area served by the satellite at time \(h\), which is shown in Figure 4. \(h_{i,t}\) and \(h_{j,t}\) represent the time in the time zone where satellites \(v_{i}\) and \(v_{j}\) are located at time \(t\), and \(d_{i,j}\) represents the distance between the geographical areas covered by satellites \(v_{i}\) and \(v_{j}\); \(\theta=0.5\) and \(\theta=1.5\)[18].  
Figure 3: Traffic model of 324 satellites.  
Figure 2: LEO satellite network system architecture.  
\(A_{t}\) represents the total amount of data packets in the entire network within one hour. The average amount of data packets per second between satellites \(v_{i}\) and \(v_{j}\)\(\lambda_{i,j,t}\) is  
\[\lambda_{i,j,t}=\frac{TR_{i,j,t}}{\sum_{k=1}^{M\cdot N}\sum_{l=1}^{M\cdot N}TR_{ k,l,t}}\cdot A_{t}\cdot\frac{1}{3600},k\
eq l. \tag{7}\]  
Assuming that the arrival of data packets between \(v_{i}\) and \(v_{j}\) is a Poisson process, the interval between data packet arrivals obeys an exponential distribution with intensity \(\frac{1}{\lambda_{i,j,t}}\):  
\[T_{i,j,t}\,\sim\,\,Exp\Bigg{(}\frac{1}{\lambda_{i,j,t}}\Bigg{)}. \tag{8}\]
# Train Me If You Can: Decentralized Learning on the Deep Edge
###### Abstract
Nevertheless, to the best of the authors' knowledge, the training of an ANN in Arm Cortex-M and RISC-V (RV32 IMCXpulp) MCUs has never been explored. Moving only the inference pass to the edge and confining the training pass to the cloud server still fails the founding principles of FL, imposing the transaction of high volumes of private data over a network. Furthermore, it is not sufficient to tackle the expected overload of the network infrastructure [48; 49; 50], leading to unpredictable latency when edge devices try to adapt to the surrounding environment by model retraining. Under this umbrella, we provide the first public evaluation of the feasibility of ANN training on low-power MCUs, featuring ARM Cortex-M architecture. We propose L-SGD, a lightweight version of stochastic gradient descent (SGD), tuned for maximum speed and minimal memory footprint on this class of MCUs. Previous works on SGD already explore how to tweak this algorithm for improved accuracy [51], training latency [52], or memory footprint [53], but none of these works target the stringent requirements of Arm Cortex-M MCUs. We developed two distinct implementations of SGD: one that operates over 32-bit floating-point weights (L-SGD float-32), and another that operates over quantized 8-bit weights using 16-bit gradients (L-SGD int-8). Both implementations were tested under two different ANN architectures, trained on two different datasets--MNIST [54] and CogDist [2]--representing distinctive fields of smart and low-power applications. For the MNIST dataset, L-SGD (float-32) is 4.20\(\times\) faster than the baseline implementation of SGD, while requiring 2.80% of the memory. L-SGD (int-8) makes the first attempt to evaluate the feasibility of quantized training. Results show that, when training from the scratch, the additional techniques to prevent overflow tend to generate high peaks of memory or timely expensive training steps, ultimately neglecting the purpose of quantization. However, results have shown that these techniques are not necessary for later training iterations when the loss starts to converge. Quantized training shows promising results to perform minor updates, which allows the ML model to adapt to the local data distribution under an FL scenario. In summary, with this work, we make the following contributions:  
* Design and develop an optimization algorithm, tuned for maximum speed and minimum memory footprint, to train an ANN using floating-point gradients;
* Design and develop an optimization algorithm for quantized training of an ANN;
* Evaluate the feasibility of both floating-point and integer-point training on Arm Cortex-M MCUs under an FL scenario.