esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Effect of Surcharge Magnitudes
#### 4.2.4 Subgrade Reaction  
Figure 17 illustrates the non-dimensional subgrade reaction profiles under different surcharge magnitudes. As anticipated, the subgrade reaction increases with the surcharge loads. The subgrade reactions increase first and then decrease, and the maximum subgrade reactions appear at \(z_{\rm{a}}=1.1\) and 0.75 in the cases of \(\beta=-20^{\circ}\) and \(-10^{\circ}\), respectively. While the surcharge load varies from 34.375 to 84.375, 134.375, and 184.375 kPa, the maximum non-dimensional subgrade reaction increases from 0.01 to 0.04, 0.08, and 0.13 in the case of \(\beta=-20^{\circ}\) and varies from 0.03 to 0.08, 0.13 and 0.19 in the case of \(\beta=-10^{\circ}\), respectively.  
Figure 16: Non-dimensional earth pressure profiles under different surcharge magnitudes: (**a**) \(\beta=-20^{\circ}\); (**b**) \(\beta=-10^{\circ}\).  
Figure 15: Non-dimensional bending moment profiles under different surcharge magnitudes: (**a**) \(\beta=-20^{\circ}\); (**b**) \(\beta=-10^{\circ}\).  
For an intuitive illustration of the influence of surcharge magnitudes on pile performance, Table 5 summarizes the maximum responses (including the maximums of pile deflection, bending moment, earth pressure, and subgrade reaction) under different surcharge magnitudes in the cases of \(\beta=-20^{\circ}\) and \(-10^{\circ}\).
## 3 Selection and Construction of Thin Chip and Cape Seal Surfacings
### Binder Selection  
#### 3.2.1 General  
Binder selection as part of any surfacing and the performance thereof in terms of the known parameters of the pavement structure are of importance. The early-life performance of thin chip seals is closely related to strict adherence to weather tolerances during construction (temperature and wet weather) [27; 28]. It follows that the performance of thin chip seals is traditionally linked to good quality control during construction. Over and above  
Figure 2: Experimental layout of three different chip and Cape seal surfacings constructed for Accelerated Pavement Testing (APT) and evaluation.  
additional considerations, only the relevant data in terms of this paper are repeated in terms of road surface temperature requirements. According to best practice [27; 28], binder application may only be placed under the following surface temperature conditions:  
* Hot binders with 0% solvents: 25 \({}^{\circ}\)C and rising
* Emulsions: 10 \({}^{\circ}\)C and rising  
The surface temperature can be reduced by 1 \({}^{\circ}\)C for each percentage point of a low flash-point solvent (LFS) (e.g., paraffin) added. For single and double seals with an elastomer-modified binder (S-E1 and S-E2) [11], a maximum of 4% LFS is recommended, while for a bitumen-rubber (S-R1) [11] binder, a maximum of 8% high flash-point solvent (HFS) is recommended.  
Lower temperatures will normally lead to a rapid drop in binder temperatures, an increase in binder stiffness and a resultant increase in the risk of binder-chip adhesion failure [11]. Adhesion failure is the most common problem experienced with chip seals, with an associated risk during winter periods or under challenging construction conditions, resulting in a drop in binder temperature before spreading of the stone can be done. Trafficking of newly laid chip seals, before orientation of the stone has taken place at temperatures below 20 \({}^{\circ}\)C, will often also result in an increase in stone loss. It follows that any developments leading to the ease of construction tolerances will be beneficial for the successful use of chip seals under challenging conditions.
# A Novel Singleton Giant Phage Yong-XC31 Lytic to the _Pyropia_ Pathogen _Vibrio mediterranean_
###### Abstract  
_Vibrio mediterranean_ 117-T6 is extensively pathogenic to several _Pyropia_ species, leading to the death of conchoelis. In this study, the first _V. mediterranean_ phage (named _Vibrio_ phage Yong-XC31, abbreviated as Yong-XC31) was isolated. Yong-XC31 is a giant phage containing an icosahedral head about 113 nm in diameter and a contractible tail about 219 nm in length. The latent period of Yong-XC31 is 30 min, and burst size is 64,227. Adsorption rate of Yong-XC31 to _V. mediterranean_ 117-T6 can reach 93.8% in 2 min. The phage genome consisted of a linear, double-stranded 290,532 bp DNA molecule with a G + C content of 45.87%. Bioinformatic analyses predicted 318 open reading frames (ORFs), 80 of which had no similarity to protein sequences in current (26 January 2021) public databases. Yong-XC31 shared the highest pair-wise average nucleotide identity (ANI) value of 58.65% (below the \(\geq\)95% boundary to define a species) and the highest nucleotide sequence similarity of 11.71% (below the \(>\)50% boundary to define a genus) with the closest related phage. In the proteomic tree based on genome-wide sequence similarities, Yong-XC31 and three unclassified giant phages clustered in a monophyletic clade independently between the family Drexlervridae and Herelevridae. Results demonstrated Yong-XC31 as a new evolutionary lineage of phage. We propose a new phage family in Caudovirales order. This study provides new insights and fundamental data for the study and application of giant phages.  
_Vibrio mediterranean_; giant phage; complete genome
## 3 Simulation and Analysis
### Number of Reflections at Each Epoch for a Single LEO GNSS-R Observatory  
We use the percentages of the time to describe the overall percentage of the total number of times N (a number range from 0 to 16) GNSS satellites are available for one LEO GNSS-R observatory. The percentages of the time that the different numbers of reflected signals are available for the GPS only and four-system GNSS cases at the L1 and L2 frequencies are shown in Figure 6, respectively. The yellow bars represent the case where only the GPS is available and the blue bars represent the case where the four-system GNSS is available. From Figure 6a, we can determine that for more than 20% of the time for one LEO satellite, there is no observation at the L1 frequency in the GPS-only case. When all signals from the four systems are used, this value falls to almost 1%. In CYGNSS, the receivers are designed to be able to receive and process four parallel reflections; however, if the antenna beamwidths are adopted as per this work, then for the majority of the time, one observatory cannot receive four reflections, and this will result in channels being wasted at most times. In the multi-GNSS-R case, a given GNSS-R LEO satellite can receive the reflected signals from at least four GNSS satellites for more than 70% of the time.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**GPS** & **BDS** & **GLONASS** & **GALILEO** \\ \hline L1: 1575.42 MHz & B1: 1561.098 MHz & L1: 1602.5625 MHz & L1: 1575.42 MHz \\ \hline
12: 1227.6 MHz & B2: 1207.52 MHz & L2: 1246.4375 MHz & E5b: 1207.14 MHz \\ L5: 1176.45 MHz & B3: 1208.52 MHz & L2: 1246.4375 MHz & E5a: 1176.45 MHz \\ \hline \hline \end{tabular}
\end{table}
Table 4: Signal frequencies for the four main GNSS systems.  
Figure 6: Percentage of time where the different GNSS satellite numbers are available in one epoch for a given GNSS-R LEO satellite at GPS L1 (**a**) and GPS L2 (**b**) frequency with peak gain.  
Consideration of Figure 6b shows that because the beamwidth of L2 is wider than that of L1, more of the L2 reflected signals can be received. In the GPS-only case at the L2 frequency, there is at least one observation for one LEO for approximately 96% of the time. For more than 90% of the time, one LEO satellite can receive at least six GNSS specular reflected signals when the four-system GNSS reflections are available. However, the corresponding value for L1 is just 30% because its beamwidth is narrower. Therefore, enlargement of the beamwidth of the GNSS-R instrument's LHCP antenna will be effective in helping to capture more of the GNSS reflected signals.
# RAU-Net++: River Channel Extraction Methods for Remote Sensing Images of Cold and Arid Regions
## 3 Methods
### Feature Extraction  
#### 3.2.1 Backbone Network  
Currently, VGG networks are widely used in semantic segmentation, target detection, and other computer vision tasks. The simplicity and superior robustness of the VGG16 network structure make it one of the classic models in the field of deep learning. The RAU-Net++ network proposed in this paper uses the first 13 layers of VGG16 as the backbone network for feature extraction of the river. The first 13 layers of VGG16 consist of 5 blocks, and the overall network structure is 2-2-3-3-3, which consists of 13 3 \(\times\) 3 convolutional blocks (as shown in Table 2).  
The input river image is a 564-channel false-color remote sensing image of 512 pixels \(\times\) 512 pixels \(\times\) 3. The convolution kernels in the 13 convolutional layers of VGG16 are all 3 \(\times\) 3 in size, which can be divided into five groups of convolutional layers according to the number of channels. The convolutional layers and the maximum pooling layer downsampling stacking are used to extract the river channel features so as to expand the receptive field, in which the maximum pooling uses a 2 \(\times\) 2 sized small pooling kernel; the activation function is Relu. The convolutional layers consist of five sets with channel numbers 64, 128, 256, 512, and 512, respectively. Through four rounds of downsampling, the input river remote sensing image is condensed into a 32 pixels \(\times\) 32 pixels \(\times\) 512 size output image, effectively capturing the essential features of the river in the remote sensing image.
## 2 Background
### Comparison between WSNs and WBASNs  
(1) Node Identification  
Node identification refers to the process of assigning a unique identification ID to a node. The IDs have local significance in WSNs and WBASNs. It is expected that the number of deployed nodes in WBASNs is less than in WSNs, where hundreds of nodes are deployed. Hence, fewer numbers of bits are used to identify the WBASN nodes. A fewer number of bits as a node identification reduces the processing time and energy consumed.  
(2) Node Size  
In WBASNs, smaller-size sensor nodes are used, whereas, in WSNs, large-size nodes can be used according to the requirement of the scenario. In WBASNs, two types of sensor nodes are used, i.e., implanted and wearable. The size of implanted nodes is very small. Figure 4 shows different implanted and skin sensor nodes. Their purpose is to read text and convert the words into voice signals.  
Figure 4: Smart skin sensors and finger-implanted sensors [49].  
Figure 3: Block diagram of the PAN system.  
(3) Network Size Usually, a WBASN consists of a limited number of nodes, which vary between 6 to 12, whereas a WSN is made of hundreds of nodes. In WBASNs, the transmission range is selected according to the height of the human body (up to a few meters), and all the sensor nodes send data to a BAN coordinator (BANC) that transmits the data to the destination. In WSNs, the transmission range can be 100 m, and a dedicated node cluster head is used as the coordinator. Overall, the network area of WBASNs is several meters due to its low transmission range, and it requires low transmission power, which is not harmful to body tissues. In WSNs, the network area is hundreds of meters due to its high transmission range and requires high power for transmission.  
(4) Limited Resources In WBASNs, the size of sensor nodes is tiny, and they have limited resources in terms of bandwidth, energy source, processing speed and memory; in contrast, in WSNs, due to the bigger size of the nodes, the resources are not as limited as in WBASNs.  
(5) Mobility As WBASNs are deployed on the human body, internal and external body mobility creates complexity. Hence, the protocols need to support mobility in WBASNs. In WSNs, the network structure is usually static.
# Measurement and Analysis on Magnetic Field Influence of Substation for Magnetic Shielding Device
## 4 Test Results and Discussion
### Magnetic Field near substation 2
Based on the analysis of DC magnetic field and fluctuating magnetic field, when substation 2 is used, the construction site of the magnetic shielding device should be at least 60 m away from the radius of substation 2.
# A Study of Constitutive Model of Rock Damage under the Joint Effect of Load and Moisture
## 4 The Damage Constitutive Model of Water Containing Sandrock Specimens  
### The Impact of Moisture on the Specim Damage  
According to the previous mechanical tests, under different stress paths, the elastic modulus of sandstone specimens decreases linearly with the increase in water content. Therefore, the elastic modulus \(E\) can be used as a damage variable to characterize the effect of water content on the mechanical properties of sandstone specimens. Assuming that the damage value of the dry sandstone specimen is 0, the fitting function of the elastic modulus of the sandstone specimen and the water content is presented below.  
\[E=A\omega_{t}+B \tag{25}\]  
In Formula (25), \(A\) and \(B\) are constants, obtained from experimental statistics.  
The elastic modules at different moisture contents are normalized, and the continuity factor can be defined.  
\[\omega=\frac{E_{\omega_{t}}}{E_{0}} \tag{26}\]  
In Formula (26), \(E\) (\(\omega_{t}\)) is the elastic module at the water content of \(\omega_{t}\). According to the test results, \(E_{0}=15.375\) GPa, \(E_{0-10}=20.090\) GPa, \(E_{0-20}=21.749\) GPa, \(E_{0-30}=18.475\) GPa.  
Therefore, the damage in the submerged sandrock specimen is:  
\[D(\omega)=1-\omega=1-\frac{Aw_{t}+B}{E_{0}} \tag{27}\]  
Based on the test results, the damage variance curve of the water-containing sandstone specimen under different stress paths is drawn as shown in Figure 8. As shown in Figure 8, with the increase in water content, the damage to rock samples increases sharply. When the water content reaches the limit, the damage value reaches 1, with no more accumulation. Meanwhile, with the increase of the confining pressure, the rate of damage evolution slows down, indicating that the ability of rock to resist damage can be strengthened with the increase of confining pressure.  
Figure 8: The damage variance curve of water-containing sandstone under different stress paths.
# Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles
## 2 Requirement Analysis
### Detecting Range and Field of View  
Some ranging systems, as for instance laser scanners or visual systems, have line-of-sight characteristics, meaning that they can only measure the position of neighbors, to whom they have direct visibility. They can be easily obstructed by other vehicles, buildings or the surrounding topography. These systems have also a technical range limitation, related to the limited transmit power or sensor sensitivity. Additionally, they also have a limited field of view, defined by the opening angles in azimuth and elevation in which they can scan the environment. To overcome this limitation, multiple instances of a sensor need to be placed around the vehicle to get a 360 degree view of the environment. With V2V communication and omnidirectional antennas, an all-around perception of the environment is achieved.
## 2 Methods
### Feature Extraction Methods
The complete list of the tested feature extraction methods is presented in Table 2. From all demonstrated feature extraction methods, some do not require any data-specific training, and others do. Therefore, we believe the easier to adapt are the ones that do not require data-specific training. Unfortunately, at the same time, these methods provide poorer results. If not specified otherwise, the pretrained models were downloaded with the Transformers [54] and Flair [55] Python modules. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method Name Adopted** & **Additional Description** & **Data-Specific** & **Method for Obtaining** & **Source** \\
**in This Study** & **Additional Description** & **Adaptability** & **Tweet-Level Embeddings** & **Source** \\ \hline \multirow{4}{*}{Term Frequency} & Top 300 features selected & & & \\  & according to a mutual & & & \\  & information method & & & \\  & implemented in the Python & & & \\  & sci-kit learn module & & & \\ \hline \multirow{4}{*}{SEANCE} & Lexicon-based method, & & & \\  & “Sentiment analysis and & & & \\  & social cognition engine” & & & \\ \hline \multirow{4}{*}{LIWC} & Lexicon-based method, & & & \\  & “Linguistic inquiry and word & & & \\  & count” & & & \\ \hline \multirow{2}{*}{[PERSON]} & Tiny version of BERT, model & & & \\  & version “base-v2 pooled” & & & \\ \hline \multirow{4}{*}{[PERSON]} & “Distilled” [58] version of & & & \\  & BERT pre-trained to output & & & \\  & sentence-level embeddings, & & & \\  & model version “base-nli-stsb- & & & \\  & mean-tokenspouled” & & & \\ \hline \multirow{4}{*}{[PERSON]} & Robustly pre-trained BERT & & & \\  & ready to output & & & \\  & sentence-level embeddings, & & & \\  & model version & & \\  & “roberta-large-nli-stsb- & & & \\  & tokenspooled” & & & \\ \hline \multirow{4}{*}{Fasttext LSTM} & Token embeddings from & & & \\  & Fasttext, model version & & & \\  & “en-crawl” converted by an & & & \\ \cline{1-1}  & LSTM into tweet-level & & & \\ \cline{1-1}  & embeddings & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of the feature extraction methods used in our study.
## 3 Materials and Methods
### Gene Expression Analysis  
The mining of _C. erythraea_ leaf transcriptome database [59] has resulted in the identification of nucleotide sequences for SGs biosynthetic pathway genes. Primer pairs for qPCR analysis of 10 SGs metabolic pathway genes (_CeGPPS_, _CeGES_, _CeG8O_, _Ce8 HGO_, two _IS_ candidates--_CeIS1_ and _CeIS2_, _CeIO_, _Ce7 DLGT_, _Ce7 DLH2_, _CeLAMT_, and _CeSLS_) were designed while using Primer3 Plus software [66], as described by [4]. One more primer pair was designed for _C. erythraea_ elongation factor 1 (_CeEF1_), which was used as an endogenous control in qPCR analysis, as well as primer pairs for six transcription factors: _CeCOI1_, _CeJAZ1_, _CeBIS1_, _CeMYC2_, _CeJAM2_, and _CeJAM3_ (Table S1).  
The total RNA was extracted from the collected leaf samples while using a modified protocol of [67]. Approximately 150 mg of each sample was used for RNA isolation, and 1 \(\upmu\)g of isolated RNA was treated with DNase I (ThermoFisher Scientific, Waltham, MA, USA) at 37 \({}^{\circ}\)C for 30 min. UV absorption spectrophotometer (Agilent 8453 spectrophotometer, Agilent Technologies, Waldbron, Germany) and Qubit 3.0 fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) were used to determine the total RNA quantity and to test RNA quality. cDNA was synthesized from 300 ng of total RNA while using RevertAid(tm) First Strand cDNA kit (Thermo Fisher Scientific, Waltham, MA, USA) and oligo-(dT) primers. SYBR Green I (Maxima SYBR Green/ROX Kit, Thermo Scientific, Waltham, MA, USA) was used for the qPCR analysis in a Light cycler QuantStudio 3 (ThermoFisher Scientific, Waltham, MA, USA), according to the manufacturer's instructions. Thermocycler conditions were 95 \({}^{\circ}\)C for 10 min.; 40 cycles of 95 \({}^{\circ}\)C for 15 s; 60 \({}^{\circ}\)C for 30 s; 72 \({}^{\circ}\)C for 30 s; and, final extension at 72 \({}^{\circ}\)C for 10 min. The expression levels of targeted genes were calculated according to the 2-\(\uplambda\)Ct method [68], whereby EF1 gene expression was used as the endogenous control, since this gene showed minimal variation across the control and stress conditions. All of the results are represented as mean value +/- SE from three biological replicates.
## 4 Data Analysis and Results
### Sunlight Analysis  
Sunlight analysis started with selecting the building and the surrounding area, then selecting Display Shadows. We set the time range of shadow display at 9 a.m. to 17 p.m., dragging the timeline to observe the shadow changes. Figure 11 shows the solar orbit orthography of the planned area using the Cartesian coordinate system, and Figure 12 shows the sphere shadow diagram, where the x-coordinate is the height angle of the sun, and the y-coordinate is the azimuth angle of the sun, which could quickly denote the position of the sun anytime and anywhere, analyze the shading relationship between adjacent buildings, and obtain the shadow map of buildings at any time.  
Figure 13 animated the annual shadow changes of the building, enabling a comprehensive analysis to avoid potential mistakes. According to the annual shadow map, light-loving plants were cultivated in areas with more solar radiation throughout the year. Large areas of evergreen trees were planted to provide shading where the sunshine accumulated in intensity. In places where the sunshine accumulation was less, deciduous trees were planted, leaving people to enjoy the sunshine in winter. The shadow shielding analysis on multiple buildings was carried out by setting a bench in the shade to create a more comfortable and livable atmosphere, and then the height, position, and window area were adjusted to provide the rooms with sufficient light and ventilation.  
Figure 11: Solar orbit orthography and sphere shadow diagram of the planned area.  
Figure 12: Sphere shadow diagram of the planned area.  
Figure 14: Annual incident solar radiation.  
Figure 13: Animated annual shadow changes of the building.  
Figures 16 and 17 present the optimal orientation diagram, where the arrow pointing to 157.5 represents the best orientation, and the arrow pointing to 67.5 represents the worst orientation. The blue circle represents the amount of solar radiation in the coldest three months, the red circle represents the amount of solar radiation in the hottest three months, and the green circle represents the average radiation dose throughout the year. Good positioning could provide thermal comfort and energy efficiency. In the north temperate zone, the southward direction allowed the building to take full advantage of the sun and blocked the main wind direction in winter, providing the best thermal conditions for the southern facade. The green line represents the direction in which the buildings obtain the most solar radiation in winter, the thin red line represents the direction in which buildings obtain the most solar radiation in summer, and the yellow line represents the optimal orientation after comprehensive consideration.