esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Test Programs
### Test Programs  
#### 2.2.1 Static Triaxial Test  
First, the cylinder samples (diameter of 39.1 mm, height of 80 mm) with optimum moisture content were prepared according to the impact molding method of the Chinese specifications [31]. They were compacted at the lowest (minimum 96%) compaction standard of high-grade highway subgrade material. Each sample was tightly packaged with plastic wrap to prevent water dispersion and evaporation during the multiple F-T cycles and blocking external water sources. Then, we placed them in a curing box with a temperature of 20 \(\pm\) 2 \({}^{\circ}\)C and a relative humidity greater than 95% for 7 days based on the Test Methods of Materials Stabilized with Inorganic Binders for Highway Engineering of China (JTG E51-2009) [32].  
The researchers [33] showed that when the temperature was lower than \(-\)10 \({}^{\circ}\)C, the strength decreased after F-T cycles tended to be stable. Meanwhile, the temperature changes in seasonally frozen areas were taken into consideration. After the samples were cured, we placed them in a digital refrigerator with a negative constant temperature of \(-\)20 \({}^{\circ}\)C for 24 h. Subsequently, the samples were subjected to thawing at 20 \({}^{\circ}\)C for another 24 h. Due to the physical and mechanical properties of soil tending to be stable after 6-8 F-T cycles [34; 35; 36; 37], the samples were subject to 0, 1, 2, 3, 4, 5, 6, and 8 F-T cycles, respectively. Finally, the TSZ-2S automatic triaxial instrument was used to perform the unconsolidated undrained (UU) triaxial shear tests with 20, 50, and 80 kPa confining pressure. The vertical strain shear rate was 0.8 mm/min during the test. When a shear peak occurred during the test, the shear was stopped when it exceeded 5% of the axial strain; otherwise, the shear continued to 20% of the axial strain [31].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Particle Diameter (mm)** & **5.0** & **2.0** & **1.0** & **0.5** & **0.25** & **0.075** & **0.01** & **0.005** & **0.002** & **0.001** \\ \hline \multicolumn{10}{c}{The percentage of soil particles smaller than the particle size to the total mass of the soil (\%)} \\ \end{tabular}
\end{table}
Table 1: Particle analysis test results of silty soil.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Sample** & **Cement** & **Natural** & **Liquid** & **Plastic** & **Plastic** & **Optimum** & **Maximum** \\
**Content** & **Moisture** & **Liquid** & **Limit w\({}_{\text{P}}\)** & **Plasticity** & **Moisture** & **Dry** \\
**Content** & **(\%)** & **Content w (\%)** & **(\%)** & **(\%)** & **(\%)** & **Index I\({}_{\text{P}}\)** & **Content** & **Density** \\  & & & & & & & & **wopt (\%)** & \(\rho_{\text{d}}\) **(g/cm\({}^{3}\))** \\ \hline S0 & 0 & 5.47 & 22.4 & 14.1 & 8.3 & 8.53 & 2.030 \\ S2 & 2 & 5.47 & 26.6 & 17.6 & 8.9 & 9.69 & 2.042 \\ \hline \end{tabular} Note: the cement content measured by the percentage of cement in the mass of the soil.  
\end{table}
Table 2: The basic physical properties of silty soil. S0, sample with 0% cement content.
# New Insight on the Formation of 2-Aminoacetophenone in White Wines
## 2 Materials and Methods
### Experiments Using Antioxidants and Transition Metal Ions Supplementation
#### 2.2.2 Metal Ions Addition  
A different wine, a Slovenian white blend from 2021 (wine C), was used in 2022 to test the effect of transition metal ions alone and in mixture on the formation of 2-AAP in white wine. Initial concentrations for free sulfite, 2-AAP, Cu, Mn and Fe were 20 mg/L, 0.83 \(\upmu\)g/L, 58 \(\upmu\)g/L, 872 \(\upmu\)g/L and 564 \(\upmu\)g/L, respectively. Transition metals were tested using the same solution freshly prepared as in experiment 2. The following treatments were tested: Fe(II) at 5 mg/L; Fe(III) at 5 mg/L; Fe(II) and Fe(III) (1:1) at 5 mg/L; Fe (II/III) and Mn (II) at 5 mg/L and 4 mg/L, respectively; Cu (II) at 1 mg/L; Mn (II) at 4 mg/L; Cu (II) and Mn (II) at 1 mg/L and 4 mg/L, respectively; Fe(II/III) (1:1), Mn(II) and Cu(II) at 5 mg/L, 4 mg/L and 1 mg/L, respectively.  
Each treatment was prepared in triplicate by adding metal ion solutions in 20 mL vials filled with 18 mL of wine and tightly sealed with PTFE-lined caps. Vials were further exposed to artificial ageing in the oven for 4 days at 40 \({}^{\circ}\)C. Thereafter, vials were stored in the fridge for a few days prior to analysis.
# Intersection-Based Link-Adaptive Beaconless Forwarding in Urban Vehicular Ad-Hoc Networks
## 3 Intersection-Based Link-Adaptive Beaconless Forwarding for City Scenarios
### Routing Protocol Design  
In ILBFC, the data forwarding process is totally beaconless and all the necessary information required for the relay selection process is stored in the header of data packet. The protocol uses the two-way data-ACK packet exchange instead of the four-way RTS-CTS-data-ACK exchange (RTS, Request To Send; CTS, Clear To Send). Here, only the destination node after receiving the data packet sends the acknowledgement (ACK) packet to the last hop node. Specifically, the header contains the following information: sequence number, source address, destination address, previous relaying vehicle address, location and velocity information of previous relaying vehicle. After transmission of the data packet, the node sets the timer for retransmission timeout (\(\gamma_{out}\)). If the node does not receive any confirmation before \(\gamma_{out}\), it will retransmit the data packet. The confirmation for the source and all the intermediate relay nodes, except the last hop, is the reception of the same data packet. For the last-hop relay node, the confirmation is the reception of an ACK packet from the destination. Also, the maximum number of retransmissions for each data packet is fixed to 3, after which the data packet is discarded. The key features of ILBFC are explained next.
###### Abstract  
Earth observation (EO) data can provide large scale, high-resolution, and transferable methodologies to quantify the sprawl and vertical development of cities and are required to inform disaster risk reduction strategies for current and future populations. We synthesize the evolution of Bishkek, Kyrgyzstan, which experiences high seismic hazard, and derive new datasets relevant for seismic risk modeling. First, the urban sprawl of Bishkek (1979-2021) was quantified using built-up area land cover classifications. Second, a change detection methodology was applied to a declassified KeyHole Hexagon (KH-9) and Sentinel-2 satellite image to detect areas of redevelopment within Bishkek. Finally, vertical development was quantified using multi-temporal high-resolution stereo and tri-stereo satellite imagery, which were used in a deep learning workflow to extract buildings footprints and assign building heights. Our results revealed urban growth of 139 km\({}^{2}\) (92%) and redevelopment of -26% (59 km\({}^{2}\)) of the city (1979-2021). The trends of urban growth were not reflected in all the open access global settlement footprint products that were evaluated. Building polygons that were extracted using a deep learning workflow applied to high-resolution tri-stereo (Pleiades) satellite imagery were most accurate (F1 score = 0.70) compared to stereo (WorldView-2) imagery (F1 score = 0.61). Similarly, building heights extracted using a Pleiades-derived digital elevation model were most comparable to independent measurements obtained using ICESat-2 altimetry data and field-measurements (normalized absolute median deviation < 1 m). Across different areas of the city, our analysis suggested rates of building growth in the region of 2000-10,700 buildings per year, which when combined with a trend of urban growth towards active faults highlights the importance of up-to-date building stock exposure data in areas of seismic hazard. Deep learning methodologies applied to high-resolution imagery are a valuable monitoring tool for building stock, especially where country-level or open-source datasets are lacking or incomplete.  
urban growth; deep learning; building footprints; digital elevation models Article
## 2 Research Methods and Data Sources
### Data Sources and Processing  
The data used in this study mainly include basic geographic data, land use data, and socioeconomic statistical data. The basic geographic data were used to show the scope of the area along the Grand Canal and were sourced from the 1:1,000,000 scale national basic geographic database provided by the Ministry of Natural Resources of the People's Republic of China \"[[https://www.webmap.cn/](https://www.webmap.cn/)]([https://www.webmap.cn/](https://www.webmap.cn/)) (accessed on 10 April 2023)\". The land use data used in this study were produced by [PERSON] and [PERSON]'s research team at Wuhan University and are based on the Google Earth Engine cloud platform and use Landsat data for three periods (1991, 2006, and 2021) along the Beijing-Hangzhou Grand Canal. The data resolution is 30 m [15]. ArcGIS 10.7 software was employed to reclassify the land use data into seven categories: cultivated land, construction land, unused land, woodland, grassland, water body, and wetland. When evaluating the value of ecosystem services, it is necessary to determine the economic value corresponding to the standard equivalent factor in the study area. The calculation of economic value relies on three core data for major food crops in the study area: sowing area, unit yield, and market price per unit yield. The data for sowing area and unit yield of food crops are derived from the statistical yearbooks of various provinces in the study area, while the market price per unit yield of food crops is determined based on the annual price list of national agricultural products market in the Yearbook on Food and Strategic Reserves in China.
# A Ka-Band Cylindrical Paneled Reflectarray Antenna
## 3 Implementation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Configuration**} & \multirow{2}{*}{\(\mathbf{f}\) (cm)} & \multirow{2}{*}{\(\mathbf{D}\) (cm)} & \multicolumn{2}{c}{**Gain (dB)**} \\ \cline{3-5}  & & & & **Analytical Model** & **High-Frequency Surface** \\ \hline Parabolic cylindrical reflector & 7.5 & 30 & - & 18.29 \\ Planar reflectarray & 7.5 & 44 & 13.6 & 13.54 \\ Paneled cylindrical reflectarray & 7.5 & 44 & 19.72 & 19.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Gain and efficiency comparison. Figure 3: Comparison of the simulated gain performance for a \(L^{\prime}\times D\) portion of the classical parabolic reflector (dotted line) and of the segmented reflectarray (continuous line). \(L^{\prime}=5\) mm, \(D=30\) cm for the cylindrical parabolic reflector and \(D=44\) mm for the cylindrical reflectarray.
# A Survey of Ledger Technology-Based Databases
## 2 Basic Concepts
Smart contracts are an executable code on blockchain that result in changes in the state of the blockchain. They work as a deterministic program. There are options when it comes to the programming language; for example, Solidity or Java can be used. Smart contracts can be controlled using predefined rules regarding the allowed operations and the state of the database. _Distributed Database vs. Blockchain_  
Many research papers [26; 27; 28; 29] examine the similarities and differences between blockchain and distributed databases. These will be unpacked in this section, considering the ways in which these two technologies with shared similarities in their fundamentals also differ in many aspects. As previously mentioned, blockchain is most often used when there is no trust between nodes, whilst distributed databases are implemented in performance, rather than security, oriented cases. Distributed databases offer better performance as they have higher transaction throughput and lower latency. Blockchain's higher latency is a result of its use of consensus mechanisms, which provide strong consistency, as well as the decentralised structure that is fundamental to blockchain. This structure provides new opportunities for applications despite the decreased performance. An important difference between the two technologies is the deletion and modification of the data; in blockchain, this is impossible as the ledger is immutable. However, in distributed databases, this feature can be enabled, although it is not often put into practice. CAP theorem's [30] basic principles, as introduced 20 years ago, can be used to group distributed databases. CAP comprises of consistency, availability, and partition tolerance; however, it states that all three features cannot be achieved simultaneously. In actual use, partition tolerance is crucial as the system needs to work with arbitrary network failures. Thus, the compromise is between consistency and availability. In the same way that CAP applies to distributed databases, so was DCS theorem [31] proposed to define blockchain technology's property set. DCS consists of decentralisation, consistency, and scalability. Similarly to CAP, only two system properties are available at once of the three. However, in permissioned blockchain, total decentralisation cannot be achieved due to its enterprise-associated characteristics; thus, in these cases, consistency and scalability must be best exploited.
## 6 Advanced Considerations: Environmental Factors in Underwater VLC
### Turbidity  
Turbidity, which refers to the cloudiness or haziness of water caused by suspended particles, is one of the most important environmental factors that affect underwater VLC performance. When turbidity is high, it leads to greater light scattering and absorption, thereby weakening signal strength and reducing communication range. Several studies have highlighted the adverse effects of turbidity on VLC systems. For instance, [PERSON]. [55] debates that the extinction coefficient is an important determinant of VLC link performance, which is dependent on turbidity. [PERSON]. [8] proposes a path loss model based on different water types to account for turbidity variation among them for more realistic performance predictions. The investigation of [56] also confirms that an increase in turbidity decreases the normalized power and increases the BER, which would have consequences in terms of data transmission reliability.  
Other specific studies focusing on turbidity's effect can also be observed in works concerning particular applications of VLC underwater. [PERSON]. [57], for instance, talks about how turbidity affects mean square error (MSE) and peak signal-to-noise ratio (PSNR) video quality metrics in an underwater optical wireless video communication system. [PERSON]. [58] sorts out the water types from pure water of low turbidity to harbor water of high turbidity and demonstrates that with higher turbidity, the distance and quality of communication are substantially reduced due to increased scattering and absorption of light. Ref. [59] further supports this by pointing out that turbulence, usually accompanied by high turbidity, seriously affects signal performance by reducing signal strength and increasing bit error rates.  
Besides horizontal communication links, turbidity also affects vertical VLC systems. Ref. [60] models the underwater link as a series of layers, underlining that variable turbidity at different depths will influence the performance of communication. According to [61], turbidity's effects on signal strength are such that an understanding of them would be very vital in designing robust VLC systems, especially in the vertical configuration. Ref. [62] extends this discussion by modeling the vertical underwater link as a cascaded fading channel, where the fading coefficients associated with different water layers change with varying turbidity levels, thus affecting the overall performance of the communication link.
# Turning Video Resource Management into Cloud Computing
## 3 The Video Resource Management System in HDFS-Based Cloud Computing
### HDFS-Based Architecture of Video Resource Management
#### 3.2.4 Streaming Media Servers  
Streaming media servers are mainly used to provide video storage, playback, and related controls. Reading video data from HDFS clusters according to user requests and sending them to the clients after the streaming processing is the main task of streaming media servers. Streaming media servers that are employed are the Routing Table Maintenance Protocol (RTMP), Red5 and Flowplayer to real-time control video streaming, which includes playback, fast-forward, pause and so on. Interactions between clients and streaming media servers are the main functions of streaming media servers in online video service systems. (1) Client users get video playback resources from streaming media servers through sending video playback requests to the web servers which retrieve video information from MySQL and HDFS servers; (2) Streaming media servers fetch caches and play the video according to the client user's requests; (3) When streaming media servers receive a control command request (such as playback, pause, and fast forward), it controls video streams in terms of commands of users;(4) When video streams are completely transferred or a stop command is sent by users, the streaming media server shuts the connection and ends a playback period.
## 3 System Analysis for a Two-User System
### Performance Evaluation  
For the evaluation of a system capacity, secrecy capacity, secrecy outage probability and throughput are some of the important parameters to prove the authenticity and usefulness of any system. In R-IBFD, the source modulates users' signal on the real component of the 4-QPSK constellation mapping and add complex \(AN\) to make a complex transmission signal. Selected near-user \(Bob_{1}\), amongst K users, forwards the signal of the far-user after adding AN for the R-IBFD cooperative communication. For the selection of relay amongst K relay, a relay selection method is used which is described later in this section.
# Influence of Eco-Friendly Processing Aids on Silica-Based Rubber Composites
###### Abstract  
As eco-friendly \"green tires\" are being developed in the tire industry, conventionally used carbon black is being replaced with silica in rubber compounds. Generally, as a lubricant and dispersing agent, processing aids containing zinc ions have been employed as additives. However, as zinc is a heavy metal, alternative eco-friendly processing aids are required to satisfy worldwide environmental concerns. Furthermore, non-toxic, degradable, and renewable processing aids are required to improve the mechanical properties of the rubber composites. In this study, we evaluated the effects of diverse silica-based processing aids containing hydrocarbon, benzene, and hydroxyl functional groups on the mechanical properties of rubber composites. Among them, rubber composites that used amphiphilic terpene phenol resin (TPR) with hydrophilic silica showed compatibility with the hydrophobic rubber matrix and were revealed to improve the mechanical and fatigue properties. Furthermore, owing to the enhanced dispersion of silica in the rubber matrix, the TPR/styrene butadiene rubber composites exhibited enhanced wet grip and rolling resistance. These results indicated that TPR had multifunctional effects at low levels and has the potential for use as a processing aid in silica-based rubber composites in tire engineering applications.  
terpene phenol resin; processing aids; hydrocarbon resin; alpha methyl styrene resin; alkyl phenol resin; rubber composites +
Footnote †: journal: Applied Sciences
# _M\({}^{2}\)_Gf: Multi-Scale and Multi-Directional Gabor Filters for Image Edge Detection
## 2 Related Work
### The Conversion of RGB Space to CIE L*a*b* Space  
The CIE \(L^{a}\)*\(b\)* color space was published by the International Commission on Illumination in 1976. The CIE \(L^{a}\)*\(b\)* space can better reflect the color difference of objects. Therefore, it has demonstrated an unparalleled representation of visual information and is widely used in various visual tasks [44; 45]. The \(RGB\) color space cannot be directly converted to CIE \(L^{*}a^{*}b^{*}\) color space. The CIE \(XYZ\)[46] color space is required as a medium. The conversion of the \(RGB\) to the CIE \(XYZ\) is  
\[\begin{bmatrix}X\\ Y\\ Z\end{bmatrix}=\begin{bmatrix}0.4124&0.3575&0.1804\\ 0.2128&0.7152&0.0722\\ 0.0193&0.1192&0.9502\end{bmatrix}\begin{bmatrix}R\\ G\\ B\end{bmatrix}. \tag{1}\]  
The conversion of the CIE \(XYZ\) to the CIE \(L^{*}a^{*}b^{*}\) is  
\[L^{*}=116f(Y/Y_{0})-16,\]  
\[a^{*}=500[f(X/X_{0})-f(Y/Y_{0})],\]  
\[b^{*}=200[f(Y/Y_{0})-f(Z/Z_{0})], \tag{2}\]  
\[f(t)=\begin{cases}t^{1/3},&\text{if $t>0.008856$}\\ 7.787t+4/29,&\text{otherwise}\end{cases}, \tag{3}\]  
where \(X_{0}=95.047\), \(Y_{0}=100\) and \(Z_{0}=108.883\), which are the CIE standard illuminator with three color values.  
In Figure 1, the original images, the corresponding gray-scale images and the images of \(L^{*}\), \(a^{*}\) and \(b^{*}\) channels are shown in the first to the fifth rows, respectively. In the picture of \"petal\" in the first column on the left, the area of petals and stems marked by the red circle is not easy to be distinguished in the gray-scale image. But it is easy in the corresponding image in the \(a^{*}\) and \(b^{*}\) channel because of the difference in chromaticity. As the region marked by the red circle in the gray-scale picture of \"Pottery figurine\" shows, the difference between the plant and the back wall in the corresponding gray-scale image is small, which causes it to be covered by the background. However, its overall shape and structure are revealed in the \(b^{*}\) channel of the image. The lines of branches are discontinuous and fuzzy in the gray-scale picture of \"Puppy\", while these lines are completed and the outline is clear in \(b^{*}\) channel of the image. The red flower is overlapped with the background in the region marked by the circle in the picture of \"Cat\". The lines and outlines in the \(a^{*}\) channel of the image are depicted more accurately than in the gray-scale image, owing to chroma. Therefore, using the CIE \(L^{*}a^{*}b^{*}\) color space is very beneficial for extracting detailed information of the edges.  
Figure 1: Four original color images and the corresponding images in different channels. (From top to the bottom, the gray-scale images and the images in \(L^{*}\), \(a^{*}\) and \(b^{*}\) channels are shown in the first row to the fifth row, respectively.)