esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Filling Material Strength and Dam Failure Characteristics of Loess Dam
## 2 Materials and Methods  
The soil used for this experiment was taken from a loess accumulation dam in Ansai Nangou, Yan'an city, Shaanxi province, which is ML in the USCS classification of loess. A Bettersize2000 laser particle size distribution instrument was used for particle analysis of dam body soil. The particle grading curve of the sample is shown in Figure 1. Part of the soil was dried to measure its natural moisture content, and the natural weight was calculated by retrieving the ring cutter sample. The basic physical properties of dam body soil are shown in Table 1.  
The triaxial test used GDS unsaturated soil triaxial apparatus. The range of confining pressure controller of the system is 0-1 Mpa, and the axial pressure controller can provide the maximum axial pressure of 5 kN. The sensor and data conversion device of the triaxial test system can automatically collect and output data from various parameters to be tested. The sample required for this instrument is 39.1 mm in diameter and 80 mm in height. It is prepared using the layered compaction method. An equal mass of soil is added four times. In this test, four moisture contents, namely 10%, 15%, 20% and 25%, were used, and the dry densities were 1.4 g/cm\({}^{3}\), 1.5 g/cm\({}^{3}\), 1.6 g/cm\({}^{3}\) and 1.7 g/cm\({}^{3}\), respectively. The samples were subjected to confining pressures of 50 kPa, 100 kPa, 150 kPa, and 200 kPa, respectively.  
The remolded soil samples retrieved from the site were air-dried under natural conditions. After the soil sample was dried, it was put into the oven for drying; the temperature was 105 degrees Celsius, the time was 24 h, and it was weighed before and after drying. The moisture content was obtained after natural air drying. Then, the moisture content of soil samples after natural air drying and the quality of water added to soil samples with moisture contents of 10% and 15% could both be calculated. After adding extra water during the stirring process to achieve the target moisture content, it was left to stand for 24 h before compacting. In this process, the water loss of soil samples with water contents of 10% and 15% during compaction was ignored. For the samples with a water contents of 20% and 25%, due to their high water content, it was impossible to add water first and then compact. Therefore, the soil samples with a water content of 15% were first compacted in layers to achieve the target dry density, and then the pre-determined water content was dripped into the sample with a rubber dropper to achieve the target water content. It was placed in a moisturizing container and stood for 48 h to make the water uniform.  
After the sample preparation was completed, Unconsolidated undrained triaxial shear tests were carried out on the samples. The sample was installed in the GDS static triaxial apparatus, the shear rate and confining pressure were set, and the test was stopped when the shear strain variable reaches 20%.
## 4 Conclusions  
The foremost purpose of this work was to digitize analog gauges, which was achieved by first applying a CNN gauge detection using the available pre-trained models of the TensorFlow API. Some of these models were trained with the target dataset to compare the results and choose the model with the best performance, which achieved a maximum mAP of 92.5% and recall of 94.8%. The following steps were based on conventional computer vision techniques, so after finding the RoI, the gauge dial is segmented using the circle Hough transform, and then the pointer angle is extracted using pixel projection subsequent to the RoI's polar transformation. In the end, the indicating value is determined using the angle method.  
After choosing the methodology, some experiments were made to evaluate the algorithm performance, which resulted in an overall average relative error of 0.95%. The results also showed that bad lighting conditions and angled gauges have a negative impact on the algorithm performance since the average error practically doubled in these conditions. However, the solution proved to have sufficient accuracy for most noncritical applications. Moreover, the robustness evaluation demonstrated that, for wide values range of gamma, contrast, and brightness, the solution has high adaptability; the only exception was the Gaussian noise, which had a significant detriment on the image quality and, in turn, in the algorithm performance. Additionally, a proof-of-concept IoT implementation was developed, and it demonstrated the feasibility of this solution in a real-world application.  
However, the solution has some limitations, such as the small-sized dataset that can contain personal biases and negatively affect the gauge detection even with data augmentation. Furthermore, the final solution is dependent on external information that could be extracted directly from the gauge image, if robust enough. Nevertheless, these limitations will be explored in future work.  
Figure 17: Architecture of the IoT implementation.  
Figure 18: Setup of the IoT application.  
**Author Contributions:** Conceptualization, J.P., J.S., and R.C. ([PERSON]); methodology, J.P. and J.S.; software, J.P.; validation, J.S., R.C. ([PERSON]) and J.M.; investigation, J.P., J.S., and R.C. ([PERSON]); writing--original draft preparation, J.P.; writing--review and editing, J.S, R.C. ([PERSON]) and J.M.; supervision, G.S., J.M., and R.C. ([PERSON]); project administration, A.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors acknowledge the funding of Project POCI-01-0247-FEDER-047091-GRS: Glartek Retrofit Sensors, cofinanced by Programa Operacional Competitividade e Internacionalizacao (COMPETE 2020), through Fundo Europeu de Desenvolvimento Regional (FEDER).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors are grateful to GlarVision S.A for technical support and equipment.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Forecasting the Spread of COVID-19 in Kuwait Using Compartmental and Logistic Regression Models
## 3 Results  
### Forecast of COVID-19 Spread in Kuwait from 24 February 2020 to 19 April 2020  
The logistic regression approach was utilized to fit the confirmed cumulative cases in Kuwait from the start of the outbreak in 24 February 2020 until 19 April 2020 and to predict the short term forecast with the current prevention measures in Kuwait. Simulations were carried out and the estimated parameters are presented in Table 3.  
It can be noted that the logistic regression model resulted in fitting the actual data with a root mean square error (RMSE) of 48 and R\({}^{2}\) of 0.992 as reported in Table 3. The results of the regression of the confirmed cumulative cases are illustrated in Figure 1. The prediction curve highlights an increasing trend in the cumulative cases in the upcoming days in Kuwait and predicts an epidemic size of 4,223. Furthermore, the simulation estimates the epidemic ending phase to start from 15 June 2020. A closer look at Figure 2 along with the parameters of Table 3, shows that the infection rate reaches the peak value on 22 April 2020 with estimated daily cases of 120. In addition, SIR model simulations were used to estimate the reproduction rate of Kuwait and was found to be R\({}_{\rm o}\) = 2.2 which falls within the estimated range reported by WHO [17].  
Short term forecasting of the cumulative cases in Kuwait is presented in Table 4. The confirmed cumulative cases with the predicted cumulative cases between 14 April 2020 until 25 April 2020 were compared and the model error percentage was found to vary between 0.42% and 3.13%.  
Figure 1: Confirmed and predicted cumulative infections in Kuwait from 24 February to 19 April.  
Figure 2: Confirmed infected daily cases and the prediction of infection rate in Kuwait.
## 3 Experiments
### Ablation Studies
#### (3) Study on MFFM  
MFFM is specially designed to further utilize the multi-scale feature information for better FSR performance. In this part, we design three different test models to demonstrate the effectiveness of the MFFM, the results of which are shown in Table 4.  
From the table, it can be observed that: (a) The model without the MFFM performs the worst, which proves the importance of multi-scale features in the image reconstruction process. (b) An add operation or a concatenate layer to fuse multi-scale features does benefit the performance of the FSR model; however, they are too simple to take responsibility for the multi-scale feature fusion part. (c) With the carefully designed MFFM, the model achieves the best performance in terms of PSNR, SSIM, and LPIPS, which proves that a suitable feature fusion strategy such as MFFM can benefit the image reconstruction process.
## 5 Discussion  
As is known, strong geomagnetic disturbances have adverse effects on aerospace activities. For satellites carrying out missions during auroral disturbances, the enhancement of infrared background radiance within the field of view reduces the contrast of target signals, causing interference in target identification processes. Additionally, satellite laser communication systems commonly exploit infrared light for data transmission due to their superior penetration capability and faster data transfer rates. Nevertheless, as communication links traverse the atmosphere, the heightened infrared background radiation induced by auroral disturbances may compromise signal-to-noise ratios, consequently deteriorating communication quality [55]. It is evident from the foregoing conclusions that the amplification of atmospheric limb infrared background radiation due to auroral disturbances reached approximately one order of magnitude. Hence, it is imperative to conduct a quantitative examination of NO 5.3 \(\upmu\)m infrared radiation variations during auroral periods, serving as an early warning metric to mitigate the impact on target identification and enhance the resilience of communication links against interference. The correlation analysis between these radiance variations and the Dst index furnishes data support for the subsequent modeling of NO 5.3 \(\upmu\)m infrared radiation fluctuations during auroral periods.  
Conceptualization, H.W. and C.D.; methodology, H.W., C.D. and F.W.; software, C.D. and F.W.; validation, F.W., S.C. and W.L.; formal analysis, S.C.; investigation, F.W. and C.Z.; resources, F.W.; data curation, F.W. and C.Z.; writing--original draft preparation, F.W.; writing--review and editing, H.W., C.D. and W.L.; visualization, F.W. and S.C.; supervision, C.D.; project administration, H.W. and C.D.; funding acquisition, H.W. and C.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2019 YFA0706004.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
We thank the OMNI ([[https://omniweb.gsfc.nasa.gov/html/ow_data.html](https://omniweb.gsfc.nasa.gov/html/ow_data.html)]([https://omniweb.gsfc.nasa.gov/html/ow_data.html](https://omniweb.gsfc.nasa.gov/html/ow_data.html))) and TIMED/SABER ([[https://saber.gats-inc.com](https://saber.gats-inc.com)]([https://saber.gats-inc.com](https://saber.gats-inc.com))) scientific teams for providing the solar-terrestrial space environmental parameters and radiance data used in this paper. We all thank the funding support provided by the National Key Research and Development Program of China (2019 YFA0706004).  
The authors declare no conflicts of interest.
# The Design and Investigation of Hybrid a Microfluidic Micromizer
## 4 Results and Discussion
### Acoustic Field  
#### 4.1.1 Effect of Micropillar Shapes  
In this section, the characteristics of acoustic fields, such as acoustic pressure and acoustic velocity, are discussed for different micropillar shapes. Figure 6 represents the variation of the acoustic pressure and acoustic velocity for different micropillar shapes. It can be seen in the graphical representations that the presence of micropillars in the microchamber results in a significant increase in acoustic pressure and acoustic velocity. The maximum value of acoustic pressure and acoustic velocity can be observed for the presence of blade-shaped micropillars compared to other shapes. Similarly, the lowest value can be observed without micropillars. On the basis of these observations, it can be examined that micropillars increase the strength of the acoustic field, resulting in enhancing  
Figure 5: Variation of the mixing index with different mesh refinement levels.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mesh Refinement** & \multirow{2}{*}{**Number of Elements**} & \multirow{2}{*}{**Number of Nodes**} & \multirow{2}{*}{**Mixing Index**} \\
**Level** & & & \\ \hline
1 & 32,797 & 17,762 & 0.760 \\
2 & 36,760 & 19,744 & 0.711 \\
3 & 38,622 & 20,675 & 0.783 \\
4 & 41,444 & 22,086 & 0.798 \\
5 & 47,057 & 24,893 & 0.820 \\
6 & 60,397 & 31,563 & 0.821 \\
7 & 76,933 & 39,831 & 0.821 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of the mesh independence study.  
the mixing performance of the microm mixer. Furthermore, the contours of the acoustic pressure and acoustic velocity are shown in Figures 7 and 8. It can be examined in Figure 7 that the acoustic excitation is more localised near the microchamber wall in the absence of micropillars, while with the addition of micropillars, the acoustic field provides more surface area and field strength near the micropillar wall. Similarly, the velocity field is also shown in Figure 8.  
It is evident in Figure 8 that the presence of micropillars generates more streaming velocity near the micropillar wall, resulting in small vertexes, which is helpful in enhancing the mixing performance. Higher streaming can be seen for the microchamber with blade-shaped micropillars. The maximum value of the acoustic field, such as acoustic pressure and acoustic velocity, is observed for blade-shaped micropillars compared to other micropillar shapes. Based on these observations, it can be examined that the inclusion of micropillars, especially the blade shape in the microchamber, can be significantly considered to intensify the mixing of two different heterogeneous fluids.  
Figure 6: Graphical representation of the acoustic pressure and acoustic velocity with different micropillar shapes.  
Figure 7: Acoustic pressure for different micropillar shapes: (**a**) no micropillars; (**b**) circular-shaped micropillars; (**c**) hexagonal-shaped micropillars; (**d**) blade-shaped micropillars.
## 1 Introduction
Second, the chosen fine-tuned model is additionally tested on posts collected from a specific subreddit of 150,000 posts, to define how well the fine-tuned models work against the original versions. Third, we apply the fine-tuned model to pools of the most popular comments and juxtapose the respective summarizations, to see by human assessment whether the model can, in principle, be applied to pooled social media data. However, the fine-tuned models, due to the known limitation of their capacity (they work best with texts or text pools limited to 512 tokens) do not provide for solving real-world analysis of comment threads or discussion outbursts. This is why, fourth, we apply enlarged Transformer-based models to Twitter data in three languages, to discover how enlarged models would work with pooled data and with non-English texts. In particular, we test the selected models on Twitter data of 2015, using the discussion on the _Charlie Hebdo_ massacre on three languages (English, German, and French), and show that, despite the fine-tuning and its satisfactory results for Reddit in English, the models need more work for German and French, while it works well for short English texts. Thus, the novelty of this paper lies in three aspects. First, we fine-tune the models and choose between the LongFormer, Bart, and T5 ones. Second, we test the fine-tuned models on the new Reddit dataset, which can be considered testing on raw data. Third, we test our models on Twitter data on three languages and show that they need additional work for the languages other than English, while short texts provide fewer complications than expected. The remainder of this paper is organized as follows. Section 2 provides a review on deep learning in text summarization studies, including how text summarizations have been applied to Reddit data until now. Section 3 provides the fine-tuning methodology, including the research pipeline and evaluation metrics, as described above. Section 4 provides the results of comparative assessment of the fine-tuned models. Section 5 describes the new real-world dataset, provides examples the resulting summaries for posts and pools of comments, as well as and their human assessment. In the absence of labeled target collections for comment pools, we use post summaries as targets for summarizations of the respective pools of comments. In Section 6, to overcome the limitations of the original models, we apply the enlarged models to Twitter data on three languages. We conclude by putting our results at the background of today's leading research.
## 5 Results and Discussion
### SAR Analysis  
This section presents the study of the RF energy absorption on human tissue generated from the designed antenna, as the proposed design is for wearable smartwatch applications. As per new guidelines of the International Commission for Non-Ionizing Radiation Protection (ICNIRP), the absorption rate is mainly at the skin layer with less than 1 cm for frequencies >6 GHz [23; 24]. However, a three-layer phantom model was developed with skin, fat, and muscle to study the absorption rate, as displayed in Figure 18. The dielectric properties of human tissue vary over the body and the different frequencies. Therefore, the tissue parameters are considered from a verified dataset of the IT'IS foundation [25]. Table 1 presents the dielectric property, electrical conductivity, and density of tissues considered for  
Figure 16: Electric field distribution and radiation patterns at 25.5 GHz with the antenna inside the watch case. (**a**) The XY-plane, (**b**) the XZ-plane, (**c**) the YZ-plane, and (**d**) the radiation pattern on the YZ-plane.  
Figure 15: Reflection coefficient \(\mid\)S\({}_{11}\mid\) antenna response when placed inside the watch case.  
our analysis at 25.5 and 38 GHz. Due to the limitation of computation resources, the watch case is replaced by the watch back case only, which is of a 1 mm thickness and a 23 mm radius. The antenna is placed 2.5 mm above this case. The analysis shows that the SAR is 0.063 and 0.0206 W/kg at 25.5 and 38 GHz, respectively, as displayed in Figure 19. There are two reasons for the low SAR value: (i) The antenna has a full ground plane, and (ii) the antenna is backed by a watch back case that behaves as a reflector for electromagnetic energy. Consequently, the results are satisfactory, suggesting that the antenna is suitable for wearable smartwatch applications.  
Figure 17: Electric field distribution and radiation patterns at 38 GHz with the antenna inside the watch case. (**a**) The XY-plane, (**b**) the XZ-plane, (**c**) the YZ-plane, and (**d**) the radiation pattern on the YZ-plane.  
Figure 18: Phantom model of human tissue to analyze the RF energy penetration.
## 3 Problem Statement and Input Data Representation Methods
### Common Forms of Input Representations
#### 3.2.3 Graph-Based Sequence
[63] used graph-based sequence data to describe traffic flow over a period in urban traffic networks. They define the topological road network as a directed graph, where nodes represent detectors on the roads, and features, such as traffic flow and speed, are added to the nodes. Their study distinguishes between two types of graphs: static adjacency graphs and dynamic adjacency graphs. The static adjacency graph is constructed without relying on prior assumptions, simulating typical long-term spatial dependencies in traffic patterns, while the dynamic adjacency graph addresses short-term local dynamics, allowing relationships between nodes to adjust over time based on observed data. Timestamp information is effectively concentrated onhistorical time information using a multi-head self-attention mechanism. The graphs of consecutive time steps (static and dynamic adjacency graphs) are concatenated to form a graph sequence encompassing both spatial and temporal dimensions. This way, each graph at a time node not only contains the traffic state at that moment but also reflects changes in the relationships between nodes from one time step to the next through the variations in the edges of the dynamic adjacency graph. Importantly, they embed learnable time and space matrices into the input graph-based sequence, ultimately becoming the model's input.
## 4 Results and Discussion: Statistical Approach
### Correlation Analysis  
The first step in assessing whether there is a precise relationship between the UPV readings and each mechanical property is to perform a correlation analysis of the data [52; 53]. In this case, the Pearson correlation coefficient, which indicates whether there is a linear correlation, and the Spearman correlation coefficient, which reflects the existence of monotonic correlations, are shown in Figures 5 and 6, respectively (compressive strength (\(CS\)); modulus of elasticity (\(ME\)); splitting tensile strength (\(STS\)); flexural strength (\(FS\)); ultrasonic pulse velocity (\(UPV\))). Both were calculated at a 95% confidence level.  
Figure 4: Ultrasonic pulse velocity (UPV) of SCC mixes.  
As shown in both figures, there was a strong correlation between UPV and all the mechanical properties with values above 0.70 in all cases. Its positive sign indicated that an increase in any mechanical property led to an increase in the UPV index, as is usual in concrete [14]. The highest correlation was obtained for compressive strength (Pearson correlation of 0.94 and Spearman correlation of 1.00), which showed that UPV is fundamentally related to compressive strength in these types of concrete mixes. It did not follow the usual pattern, as UPV is a property that has usually been linked to the elastic stiffness of concrete [16]. All other mechanical properties showed a similar Pearson correlation with the UPV index, at around 0.75-0.80. However, the Spearman correlations were equal to 0.92 for both modulus of elasticity and flexural strength, although the Spearman correlation was only 0.77 for splitting tensile strength. Thus, in general terms, it can be stated that the UPV index was less strongly related to the tensile-related mechanical properties than to the mechanical properties dependent on the compressive behavior of concrete, including flexural strength. It is important to note that flexural strength is influenced by both the tensile and the compressive strength of concrete [49].  
Comparing both types of correlations shown above, it can be seen that the Spearman correlation values were always higher. A result that points to a monotonic relation between the UPV readings and the mechanical properties, so that, although the linear relation was adequate, the most accurate relation between the UPV readings and every mechanical property was potential. The greatest difference in the value of both correlations was obtained for the modulus of elasticity and flexural strength, each of which showed a particularly noticeable difference in accuracy between the linear and the monotonic relationship.  
Figure 5: Pearson correlation matrix. Correlations between UPV readings and mechanical properties marked within the thick black border.  
Figure 6: Spearman correlation matrix. Correlations between UPV readings and mechanical properties marked within the thick black border.