esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Soil Particle Size on the Engineering Properties and Microstructure of a Red Clay
## 4 Conclusions  
In this paper, red clay from Guilin of various particle sizes and dry densities were subjected to low-temperature nitrogen adsorption tests, UCS tests, and WD cycle crack quantitative tests to study the effects of particle size on the engineering properties and the microstructure. The results are as follows:  
(1) The SSA obtained by the Langmuir adsorption isotherm equation via the low-temperature nitrogen adsorption test is about 2.5 times that given by the BET adsorption isotherm equation. The external surface area of the red clay accounts for 93-95% of the total surface area. The pore size ranges from 1.7 to 15 nm, and the pores are mainly tiny. In addition, the low-temperature nitrogen adsorption test determines the internal and external surface areas of the soil particles lattice, which cannot reflect the relationship between the particle size of the red clay and the SSA.  
(2) The failure modes of red clay soil samples of different particle sizes are a split failure and shear failure when subjected to unconfined compression tests. When the particle size is 0.25-2 mm, the soil sample surface expands from the bottom upwards, and when the size is 5 mm, cracks present from the middle of the soil sample, appearing as a shear failure. The stress-strain curves are all strain-softening.  
(3) Through the WD cycle crack quantitative test, we found that cracks occur in all the red clay soil samples after a certain number of cycles, and the soil samples with a particle size of 0.25 mm are the most likely to have cracks. After quantification, we found that red clay's total porosity and fractal dimension increase with the increase in particle size, and the total porosity will reach the maximum in the fourth cycle. The higher the dry density, the smaller the total porosity and fractal dimension of red clay.  
Figure 20: The fitting curve of dry density and fractal dimension.  
(4) In real construction projects, the impact of particle size and dry density on the strength of red clay should be considered, as well as the impact of extreme weather, such as the rainfall and high temperatures in Guilin, on the cracks of red clay.  
[PERSON] and [PERSON] wrote the manuscript; [PERSON] conducted experiments and analyzed the data; [PERSON] provided suggestions for the research method and revised the article completely. All authors have read and agreed to the published version of the manuscript.  
This project was financially supported by the National Key Research and Development Project (2017 YFC1503102) and the National Natural Science Foundation of China (Grant Nos. 51874065 and U1903112).  
Not applicable.  
The authors declare no conflict of interest.
###### Abstract  
In recent years, Smart Farming (SF) and Precision Agriculture (PA) have attracted attention from both the agriculture industry as well as the research community. Altogether, SF and PA aim to help farmers use inputs (such as fertilizers and pesticides) more efficiently through using Internet of Things (IoT) devices, but in doing so, they create new security threats that can defeat this purpose in the absence of adequate awareness and proper countermeasures. A survey on different security-related challenges is required to raise awareness and pave they way for further research in this area. In this paper, we first itemize the security aspects of SF and PA. Next, we review the types of cyber attacks that can violate each of these aspects. Accordingly, we present a taxonomy on cyber-threats to SF and PA on the basis of their relations to different stages of Cyber-Kill Chain (CKC). Among cyber-threats, we choose Advanced Persistent Threats (APTs) for further study. Finally, we studied related risk mitigation strategies and countermeasure, and developed a future road map for further study in this area. This paper's main contribution is a categorization of security threats within the SF/PA areas and provide a taxonomy of security threats for SF environments so that we may detect the behavior of APT attacks and any other security threat in SF and PA environments.  
smart farming; precision agriculture; cyber-threats; advanced persistent threats; cyber-kill chain; security threats +
Footnote †: journal: Applied Sciences
# Dual-Band Rectifier Circuit Design for IoT Communication in 5G Systems
## 3 Design Framework  
### Substrate Selection  
The selection of the proper substrate is a crucial part of the design process for rectifiers. A substrate with a low dielectric constant and low dielectric loss is required. We choose the \"RT/Duroid 5880\" substrate, which has a copper thickness of 0.035 mm, a dielectric loss tangent of 0.0009, a thickness of 0.508 mm, and a dielectric constant of 2.2. All of these features indicate that this substrate is suitable for high-frequency applications, such as the proposed dual-band rectifier that operates at 3.5 GHz and 5 GHz [55].
# Visibility Assessment of New Photovoltaic Power Plants in Areas with Special Landscape Value
## 2 Methodology
### Global Accumulated Perception Time  
Finally, taking into account all the observation points or nodes \(n\) of route \(k\) and the number of observers \(NOY_{k}\) travelling through each route, the cumulative value of \(APT_{i,k}\) in cell \(i\), will be given by the expression (6),  
\[APT_{i,k}=NOY_{k}\sum_{n=1}^{N_{n}}(fhv_{i,n}\cdot w_{i,n}\cdot t_{n})(1+Isk_{i, n})\qquad(\text{hours}/\text{year}) \tag{6}\]  
where \(N_{n}\) represents the total number of nodes or observation points on route \(k\).  
This process is repeated sequentially, analysing all possible routes (roads, path or trails used by the potential observers) in the area under study, so that for each cell \(i\), the result of adding the \(APT_{i,k}\) values of each route \(k\) is the global value of the accumulated perception time (\(GAP_{i}\)), as shown in (7),  
\[GAPT_{i}=\sum_{k=1}^{N_{k}}APT_{i,k}\quad(\text{hours}/\text{year}) \tag{7}\]  
where \(N_{k}\) represents the total number of routes in the study area.  
The \(GAPT_{i}\) values in each of the cells in the studied area are collected in a raster dataset, which can be visually analysed in the form of a map. In this way, it is possible to easily visualize those areas with a lower \(GAPT\) value which are therefore candidates for the installation of PV plants under an objective visibility criterion. Note that the purpose of the proposed methodology is to be able to choose the place within the study area where a new PV plant with certain characteristics can be built with the least observability by visitors who move along the routes in that area.  
This methodology can be applied to tourist areas or areas with unique qualities where, due to their location or their relationship with the landscape, it is necessary to determine those sites where the presence of new PV plants would be less harmful in terms of the visual impact. A similar case is its application to areas close to historical sites and landmarks, since PV facilities visible from these locations can have a negative impact on the perception of their cultural value [39]. The visual impact in places with a great cultural heritage has become a controversial topic [40] and, in general, the methodology proposed in this work can be used for evaluating the construction of new PV plants in locations that, due to their cultural heritage and their cultural, historical or landscape value, have to be preserved [41].
## 2 Physical Layer-Based Full-Duplex Underwater Acoustic Communication Systems
### Comparison of Different FD-Physical Layer-Based UWAC Systems  
As shown in Table 1, this section provides a detailed comparison of various FD physical layer-based UWAC systems. We also provide a brief overview and implementation of these protocols in different environments, as well as their performance comparison with respect to various metrics such as spectral quality, data rate, BER, transmission range, and SNR.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{2}{*}{**System Type**} & \multirow{2}{*}{**Name/Ref.**} & \multirow{2}{*}{**Modulation**} & \multirow{2}{*}{**Implementation**} & \multicolumn{4}{c}{**Performance Metrics**} \\ \cline{5-10}  & & & & **Spectral Efficiency** & **BER** & **Transmission Range** & **SNR** & **Data Rate** \\ \hline \multirow{2}{*}{Cooperative OFDM} & AUA-CCS [40] & OFDM & Simulation & High & Low & NA & Low & High \\ \cline{2-10}  & A-DTWRS [19] & OFDM & Simulation & Moderate & High & NA & High & Low \\ \hline \multirow{7}{*}{OFDM-IBFD} & HSIC [41] & OFDM & Deep sea water & Enhance & NA & Very low & NA & NA \\ \cline{2-10}  & TRICE [22] & OFDM & Deep sea water & Moderate & Low & High & low & NA \\ \cline{2-10}  & ML-SC-IBFD [23] & OFDM & Pool water & Enhance & Low & Medium & Moderate & NA \\ \cline{2-10}  & A-IBFD [24] & OFDM & Pool water & Moderate & High & Medium & High & NA \\ \cline{2-10}  & ASIC [44] & OFDM & Shallow water & NA & NA & NA & NA & NA \\ \cline{2-10}  & RLS adaptive filter [25] & BPSK & Water Tank & Very low & NA & Low & NA & Low \\ \cline{2-10}  & ANE [60] & BPSK & Water Tank & Low & NA & Very Low & NA & Low \\ \cline{2-10}  & FSK-UWAC [29] & FSK & Laboratory set-up & High & NA & High & NA & Moderate \\ \hline \multirow{2}{*}{Spread spectrum} & Hybrid-SS [33] & DSSS & Shallow water & High & NA & High & High & Medium \\ \hline \end{tabular}
\end{table}
Table 1: Analysis of physical layer-based UWAC systems.
# Testing Multi-Frequency Low-Cost GNSS Receivers for Geodetic Monitoring Purposes
## 3 Results and Discussions
### Positional Quality Assessment
Table 7 represents the results of the ANOVA test, where the differences from Table 6 are analyzed. ANOVA test was performed with a significance level of 5%. The results of the test show that the null hypothesis was rejected for all three components and may confirm that using different GNSS instrument types (geodetic vs. low-cost) will significantly affect estimated baseline components [12]. The difference is again much larger for the height component. Again, the reason for this may be in uncalibrated low-cost GNSS antenna.
# Ground Subsidence Analysis in Tianjin (China) Based on Sentinel-1A Data Using MT-InSAR Methods
## 4 Discussion
### Recent Ground Subsidence Characteristics and Its Influencing Factors in Tianjin
The production process, especially electroplating, requires a large amount of water, mostly derived from underground mining. [PERSON] et al. [28] indicated that the shallow groundwater in Wangqingtuo town is relatively poor, and when it is excessively withdrawn, the surface formed by alluvial/diluvial sediments is prone to uneven subsidence. In our study, the town is still located in the high subsidence area, and its average subsidence rate in the past two years has exceeded -50 mm/year. Field investigations have found that long-term ground subsidence has caused some buildings to sink, and local residents have had to move away (Figure 5b,c).
# Ultra-Wideband Compact Fractal Antenna for WiMAX, WLAN, C and X Band Applications
## 2 Antenna Design
### Analysis of the Suggested Antenna Parametric  
The proposed fractal antenna has a wideband operation and improved gain. These parameters were reached through a parametric study using HFSS electromagnetic solver high-frequency structure simulator. By changing just one parameter while holding the other parameters constant, the suggested antenna performance was tested.  
A. Effect of the rayon, R  
The antenna patch radius is crucial in determining how much the resonant frequencies vary. Figure 4 shows the suggested structure's impact on the reflection coefficient using three different rayons. The R is modified from 11 mm to 13 mm with a 1 mm step. The impact of the radius on the antenna performance is clearly observed by analyzing Figure 4. There is a reduction in the frequency range covered by the antenna. Also, a band shift is noted from 4 GHz to 10 GHz. However, a wide band is achieved for R = 12 mm.
# Unsupervised Image Segmentation on 2D Echocardiogram
## Appendix A Appendix A Appendix A.1 Quality Assessment of Selected vs. Non-Selected Videos
### Ablation Study  
Ablation studies were conducted to evaluate the influence of individual components of the loss function-reconstruction (_rec_), contour (_contour_), and similarity (_sim_). By systematically adjusting the weight of each component, both individually and in combination, we aimed to determine the relative contribution of each term to the overall model performance.  
As shown in Figures 12 and 12, several configurations resulted in low validation loss values, particularly those with the reconstruction component weighted highly (e.g., \(rec=1\), \(cont=0\), \(sim=0\)), which reached a minimum loss value of 0.000026. However, these configurations often failed to segment the images accurately, producing a single color as the segmented output, representing only one class for the entire image, which is a sign of overfitting. An exception was observed when using a combination of two loss components, which resulted in a higher validation loss but still failed due to excessive noise in the segmentation results.  
The configuration using \(rec=0.15\), \(cont=0.32\), and \(sim=0.53\) led to a better segmentation result. This configuration leverages the reconstruction component as a regularization parameter to preserve the overall shape of the structures in the image, while the similarity component groups pixels with similar characteristics. The contour component, in turn, helps define clearer boundaries between regions. As a result, this combination produced more accurate segmentations, with well-defined boundaries and preserved structural integrity in the output, and it has the highest loss in our ablation test, as shown in Figure A5.  
Furthermore, the validation loss behavior of this configuration, as shown in Figure A5, is a strong indication that the model is not overfitting, unlike other combinations that achieve a much lower loss by setting most pixels to the same class and rapidly reducing the loss over time. This balance of components ensures that the model generalizes better and maintains a more precise segmentation
## 4 The Seismic Performance Parameters of RCS Composite Joints Under Floor Effect Are Studied
### The Influence of Floor Thickness on Seismic Performance of Nodes  
In order to study the influence of floor thickness on the seismic performance of nodes, this section takes node FJD-0 with a 100 mm floor thickness as the basis, takes floor thickness as the parameter, and designs node FJD-5 and FJD-6 with 110 mm and 120 mm floor thicknesses, respectively, which are consistent with the original design in other parameters, and conducts a comparative analysis of the seismic performance.
## 4 Seismic Vulnerability of Wall Pier Girder Bridges
### Seismic Vulnerability of Wall Pier Bridges  
Seismic vulnerability is generally defined as the probability that a component or structure will reach a specific damage state at a given ground motion intensity measure IM. The specific expression is \(P[D\geq C\big{|}IM]\), where, \(C\) denotes the seismic capacity and is a property of the structure itself; \(D\) denotes the earthquake demand, which is obtained through the dynamic time-history analysis and is related to the ground motion intensity. It is assumed that the seismic fragility models of the wall pier bridge and its components follow a logarithmic normal distribution, as shown by the following equation:  
\[P[D\geq C|IM]=\Phi\left[\frac{\ln(IM)-\ln(m)}{\beta}\right] \tag{9}\]  
where m and \(\beta\) denote the median value and logarithmic standard deviation of the seismic vulnerability mode respectively l. Meanwhile, the bridge system is regarded as a series system to obtain the seismic fragility parameters m and \(\beta\) of the vulnerable components and the bridge system by maximum  
Figure 8: Mechanics models of bridge components. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of Ground** & **Types** & **Fault Distance (R)** &
\begin{tabular}{c} **Intensity** \\ **Magnitude** \\ \end{tabular} & **PGV/PGA** \\ \hline near field & pulse type & 0–20 km & 6–8 & \textgreater{}0.15 \\ far field & non-pulse type & 20–100 km & 6–8 & \textless{}0.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Ground motion selection criteria. likelihood estimation. The specific calculation results are shown in Table 9. The fragility curves of different components and bridge system are shown in Figure 9 below:  
It can be seen from Figure 9(a) that the damage probabilities of the piers under various ground motion intensities are higher than that of the bearings and the abutments. When the ground motion intensity is less than 0.16 g, the damage probability of the piers in transverse direction is higher than that of the pier in out-of-plane direction, the bearing and the abutment. The main reason is that the tensile strength of concrete is low and cracks are likely to appear under the action of ground motions. It can be seen from Figure 9(b) that under various ground motion intensities, the damage probability of the bearings is higher than that of other components and the in-plane damage probability of the pier is the lowest. The rule of the damage probability of each component in Figure 9(c) is similar to that of Figure 9(b). It can be inferred from Figure 9(d) that when the ground motion intensity is higher than 0.3 g, the damage probability of the bearings and of the bridge system increases rapidly with the increase of the ground motion intensity, while the damage probability of other components increases slowly. Based on the four graphs (a) to (d) in Figure 9, it can be found that the wall piers perform well under the action of earthquake, and the wall piers have the lowest damage probability in the medium
## 1 Introduction  
Deep learning models are widely employed in natural language processing [1; 2], image recognition [3], etc. In addition, the performance of deep learning models depends on the number of annotated datasets [4], especially in specific fields, deep learning models are more dependent on annotated datasets. Hence, few annotated datasets are applied for NLP in the medical field, which leads to the poor performance of many deep learning models [5]. With the development of the Internet of Things [6; 7] and the soundness of intelligent medical systems [8; 9], data augmentation that aims to generate a new dataset is proposed to enhance the accuracy of deep learning models in different tasks of natural language processing (NLP).  
Automatic data augmentation is first utilized in computer vision to train more efficient models [10], especially for small datasets in different domains. However, in the medical field, most medical knowledge is recorded in text data, such as electronic medical records. Although data augmentation can augment complex texts, there are many problems in the field of Chinese medicine. Firstly, image data augmentation cannot be applied to natural language tasks because natural language is discrete [11]. Secondly, traditional text data augmentation ignores the semantic information in sentences, which leads to the degradation of the semantic stability of contexts and labels. Third, medical texts contain a large number of specialized vocabularies [12], and existing data augmentation cannot be exploited to enhance the specialized vocabularies. Therefore, we propose a novel data augmentation called medical data augmentation (MDA) that can effectively identify medical words and keep the semantics of the sentence the same.  
In this paper, we propose MDA based on medical knowledge graph for Chinese medical texts. First, a large amount of medical knowledge is collected and stored as triplets to construct a medical knowledge graph, which can be utilized to identify medical keywords in sentences. Moreover, based on the medical knowledge graph, the MDA is applied to augment the text data and enhance the performance of deep learning models. In the end, the experimental results of the MDA are compared with different models on three downstream application tasks.  
In summary, the contributions of our work can be summarized as follows:  
1. We propose a medical data augmentation (MDA) method, which can effectively remedy the problem of semantic stability in the Chinese medical field;
2. We experiment on NLP with two tasks: Named entity recognition (NER) and relational classification (RC). The MDA outperforms the traditional text data augmentation in terms of F1-score, and the MDA can increase the diversity of the augmented data;  
The rest of the paper is organized as follows: Section 2 introduces some related work on data augmentation. In Section 3, the MDA is described in detail. In Section 4, the performance of the MDA is evaluated on the four different datasets for the NER task and RC task, and several analyses are presented. Finally, we give some conclusions and discuss future research directions in Section 5.
## 5 Experimental Progress  
In this section, recent experimental progress in NRRS based on a test satellite constellation is summarized. Since there are no inter-satellite links in the test satellite constellation, it is still unable to build a complete NRRS architecture. This experiment mainly verifies parts of the NRRS services, i.e., user demand collection and data distribution through the satellite Internet.
## 3 Results and Discussion
### Cement Mortars  
The compressive strength of mortars was investigated for several replacement levels: 20, 30 and 40 wt%. These are compared to a CEM I reference in Figure 9. A strength drop approximately equal to the replacement level is observed. The addition of limestone (L) slightly improved the contribution of the SPL to compressive strength, as the mixture with 30 wt% SPL and 10 wt% limestone showed higher strength values than the mixture with 40 wt% of SPL. However, the limestone addition was not as effective as observed in the LC3 system [13]. This is expected, as calcined clays show a much higher reactivity than what is observed in this paper for SPL.  
The strength evolution was studied in more detail for the samples including 30 wt% SPL and including 30 wt% SPL and 10 or 15 wt% limestone in Figure 10. A strength measurement after 90 days was added and the relative strength was calculated (% = strength sample/strength reference sample \(\times\) 100%). The strength increased significantly after 28 days for the two samples containing SPL and this increase occurred at a higher rate than the reference CEM I. This becomes more clear when observing the calculated relative strengths. The relative strength at an early age (2 and 7 days) lies below the dotted line representing the replacement level, i.e., below 70% strength for 30 wt% SPL and below 55% strength for 30 wt% SPL and 15 wt% limestone. This could be associated with the retarding effect of the SPL on cement hydration, causing a delay in the strength development. The relative strength of the sample including limestone reached its replacement  
Figure 9: Compressive strength evolution of mortars using several replacement levels of spent pot lining aluminosilicates (SPL) as supplementary cementitious material (**a**). The influence of the addition of limestone (L) to mixtures with 30 wt% of SPL is shown in (**b**).  
level at 7 days, showing again the benefit of the limestone addition. At 28 and 90 days, the benefit of the SPL as SCM became clear. The samples with 30 wt% of SPL reached 80% of the strength of the reference at 28 days and 85% of the strength of the reference at 90 days. The continued increase of the relative strength shows a similar behavior to coal fly ashes and other common pozzolanic materials, which are currently used in cement.