esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of C-S-H Nucleating Agent on Cement Hydration
## 1 Introduction  
In recent years, the use of prefabricated components has become more and more frequent in the construction industry [1; 2; 3]. Compared with traditional buildings, it has the advantages of energy saving, environmental protection and cost reduction [4; 5]. However, in the process of the production of precast components, accelerating the development of early concrete strength and achieving a high turnover of mold is a key issue that needs to be solved urgently in the current precast concrete industry [6]. At present, component factories usually use high-temperature autoclave curing methods to quickly improve the early strength of prefabricated components, thereby improving the turnover efficiency of molds. However, high-temperature autoclave curing has the disadvantages of high energy consumption, heavy environmental pollution, high use cost and impact on the durability and later strength of concrete [7; 8; 9]. This is not friendly to the environment and enterprises. Therefore, seeking a method to improve the early strength of cement-based materials has become a research focus.  
With the further research of various scholars on the process of cement hydration, dissolution and precipitation theory has become the mainstream theory to explain cement hydration, accelerating dissolution or accelerating precipitation has become a new way to explore early-strength admixtures [10; 11; 12]. Traditional concrete early-strength agents have certain application defects. For example, chlorine-based early-strength agents can cause corrosion of steel bars in concrete [13]. Triethanolamine (TEA) can significantly increase the early strength of cement-based materials, but cause a decrease in the flexural strength and late strength of cement [14]. The mixing amount of traditional concrete early-strengthis difficult to determine, and it may cause corrosion damage to concrete products due to alkali-aggregate reaction or excessive sulfate [15]. The C-S-H crystal nucleation early strength agent (CNA) as a nucleation-type early strength agent can provide nucleation sites for hydration products of the initial stage of cement hydration, accelerate the generation of hydration products and promote the early hydration of cement [16; 17; 18].  
In this paper, CNA is prepared by a precipitation method, and then, the effect of CNA on cement hydration is mainly introduced and compared with traditional early strength agent TEA, based on hydration kinetic calculation, resistivity test and BSE analysis methods and combined with fractal theory, which aims to reveal the mechanism of CNA, and provides theoretical support and technical guidance for the practical application of CNA.
# Multi-Relational Graph Convolution Network for Service Recommendation in Mashup Development
## 4 Experiments
### Baseline Methods  
To evaluate the effectiveness of our approach, we compare with seven baseline service recommendation approaches that are designed to recommend services for new mashup development.  
* CF [36]. This is the basic collaborative filtering technique to recommend services by identifying similar mashups. Given a new mashup \(m\), we first calculate its textual description similarity to other existing mashups using the doc2 vec model and cosine similarity measure, and select \(N(m)\) top-similar mashups of \(m\). The recommendation score between \(m\) and a candidate service \(s\) is calculated as: \[score(m,s)=\frac{\sum_{m^{\prime}\in N(m)}sim(m,m^{\prime})\times I(m^{ \prime},s)}{\sum_{m^{\prime}\in N(m)}sim(m,m^{\prime})},\] (7) where \(sim(m,m^{\prime})\) is the cosine similarity between \(m\) and \(m^{\prime}\), \(I(m^{\prime},s)=1\) if \(m^{\prime}\) composes \(s\) and \(0\) otherwise.
* CMF [6]. This approach builds multi-dimensional relationships among mashups, services and topics with a set of coupled matrices, and performs a coupled matrices factorization algorithm to predict unobserved relationships.
* LDA [29]. It learns the representations of textual descriptions of mashups and services with the LDA model and calculates the cosine similarity between mashup request and service contents.
* Doc2 vec [30]. It learns the representations of textual descriptions of mashups and services with the doc2 vec model and calculates the cosine similarity between mashup request and service contents.
* PaSRec [7]. It is a CF-based approach where it builds a heterogeneous information network (HIN) with mashups, services, content, tags, etc., defines meaningful meta-paths, and calculates the similarities between mashups using the meta-path-basedsimilarity measurement. It uses a Bayesian personalized ranking (BPR) algorithm to learn the weights of meta-paths.
* DHSR [18]. It combines collaborative filtering and textual content with a multilayer perceptron to capture the complex invocation relations between mashups and services. It computes semantic similarities between contents with three kinds of feature extractors and incorporates several pre-trained word embeddings.
* MRGCN. It is the proposed model in the paper, in which we implement two variants for functional content modeling: MRGCN_LDA in which LDA is used for content feature representation, and MRGCN_D2V in which Doc2 vec is used for content feature representation.
# Resilient Green Cellular IoT for Landslide Monitoring Using Voice Channels
## 3 VRITHI: System Design and Characteristics
### DoS Attack Scenario in VRITHI  
In this section, we mention the nodes in the deployment and their vulnerability to security attacks. The IoT nodes deployed in our scenario are directly connected to the cellular network, as shown in Figure 3. The IoT nodes have two main components: a sim5300E wireless cellular OEM (original equipment manufacturer) hardware module and a small controller (with only a few bytes of RAM). The wireless module connects the controller to the voice and data connection of cellular provider. This is the typical deployment scenario used in field implementations of IoT testbed without a proxy gateway.  
Figure 3: DoS attack scenario of IoT nodes directly connected to Cellular Network.  
During the node startup, the modem is connected through the voice channel of the cellular provider. However, it does not suffer from DoS attacks at this time due to the non-availability of data connection. Later on, as the controller requires to send data, it requests the modem for Internet connection. Subsequently, the modem obtains an IP address from the cellular provider and gets exposed to the public Internet. The botnets on the Internet scan the IP addresses and possible vulnerable ports open in the modem, resulting in DoS/DDoS attack whenever the ports are not protected.  
In addition, attacks, such as TCP SYN Flood attack, affect the TCP stack, whereas they do not affect the voice connections since the voice communication is handled by GSM stack in the modem. This aspect is employed in the design of VRITHI, where the voice network between the IoT nodes acts as an overlay over the IP network. Without the voice network, it is not possible for other nodes to know whether the peer node is affected by DoS attack or not. The nodes could only find that the peer (affected) node is non-responsive.
## 2 Materials and Methods
### Statistical Analysis  
The analysis of the data was executed using the SPSS software (version 25.0, IBM Corp., Armonk, NY, USA). The normal distribution of the data was assessed using the Kolmogorov-Smirnov test. When the variables followed a parametric distribution, the results were presented as mean \(\pm\) standard deviation, while in the case of a non-parametric distribution, the results were presented as the median (25-75\({}^{\circ}\) percentile). A t-test analysis was used to compare the mean values of each variable for the 3 teams at the same time point, while a paired t-test was used to assess the post-training vs. pre-training differences (\(\Delta\)) of the dependent variables for each team separately. In the cases of non-parametric values, the Wilcoxon test and the Wilcoxon signed-rank test were used accordingly. The magnitude of percentage change was assessed using the Cohen \(d\) effect size (ES) model (<0.1 \(\rightarrow\) trivial effect, 0.1-0.3 \(\rightarrow\) small effect, 0.3-0.5 \(\rightarrow\) moderate effect and >0.5 \(\rightarrow\) large effect). The effects of each team on each dependent variable were evaluated using a two-way analysis of variance with repeated measures across time. The Levene homogeneity of variance test was performed before the ANOVA test to assess the equality of variances of the examined variables. Due to multiple comparisons, a post hoc bonferroni correction was executed in order to correct the type I error and approach the level of significance that was set for the statistical analyses of this study. The correlations between the variables were carried out using the Pearson correlation test for a parametric distribution, and the Spearman correlation test was carried out for a non-parametric distribution. The correlation magnitude was determined according to [PERSON]'s provided guidelines (\(r\) = 0.1-0.3 \(\rightarrow\) small, 0.3-0.5 \(\rightarrow\) medium and >0.5 \(\rightarrow\) large). The effect size for the ANOVA test was evaluated using the \(\eta_{\mathrm{p}}\)\({}^{2}\) model, while all statistical differences between the mean values of the variables were calculated using a \(p\) = 0.05 significance level and a 95% confidence interval (CI).
## 2 Materials and Methods  
### XCT Imaging and Processing  
Due to its non-destructive nature in three-dimension, XCT has evolved into a widely employed technique for visualizing and characterizing geological specimens in three-dimension. The tight rock sample we used in this study was sampled from the Long 7 well of the extension group developed in the center of the Ordos Basin Lake Basin in China which is generally composed of various minerals (such as quartz, feldspar, pyrite, and others) and significant pores and micro-fractures, see Figure 1a. Tomographic datasets, which are series of images based on gray-scale value, were obtained and analyzed using an X-ray Micro-CT-400 made by the Xradia Company. The resolution is 1.0 \(\upmu\)m/voxel, and the voxel size is 1024 \(\times\) 1024 \(\times\) 1024. Some scholars had begun to explore multi-phase segmentation considering multi-minerals [35; 36; 37]; however, the focus of this study was to investigate the impact of micro-fracture characteristics on petrophysical properties. Therefore, the study applied the two-step method to identify the pore and grain space. A non-local means filtering, a nonlinear filter used to enhance the signal-to-noise ratio and preserve the edges between pore and grain phases in XCT images, was utilized. Subsequently, a marker-seeded watershed algorithm [38] was employed to segment the images into pore and grain phases, as shown in Figure 1b. This method depends on the image's intensity field and intensity gradient magnitudes with the seed markers produced using 2D histograms.  
For the convenience of subsequent simulations, a representative elementary volume (REV) should be selected. The REV of the tight samples refers to the smallest unit that can effectively characterize the tight sample's physical properties. The physical properties calculated from the REV of the tight sample are the same as the physical properties of the whole tight sample.  
In this paper, the size of the REV of the tight sample is obtained by analyzing the porosity variation with the tight digital rock size. The specific method is as follows: First, locating a voxel point arbitrarily in the tight digital rock; selecting a cube of a certain variable length at the center of the voxel point and counting its porosity and permeability, gradually  
Figure 1: The sample of tight sandstone. (**a**) is the gray-scale image of the tight sample, (**b**) is the tight digital rock. The red color represents pore space and purple color indicates grain space.  
increasing the side length of the cube and calculating the porosity and permeability of each side length rock cube; making the variation law curve of porosity and permeability with the side length of tight digital rock (see Figure 2); locating different voxel centers and repeating the above steps. However, the minimum rock edge length corresponding to the stabilization of all the curves is the size of the REV. The core voxel size selected after analysis is \(500^{3}\).
# Bounce: A High Performance Satellite-Based Blockchain System
## 6 Experiments, Results, and Analysis
### Merkle Tree Construction Benchmark  
Table 3 shows the construction times on a single node of a Merkle Tree of different sizes. The results indicate that the construction time scales nearly linearly with the number of transactions (each transaction is 392 bytes, the approximate size of a bitcoin transaction). Thus, on a single compute node, one million transaction Merkle Tree can be constructed in under one second.
###### Abstract  
To study the influence of sand particles on the strength and dry-wet cycle characteristics of cement-solidified South China coastal soft clay, a series of cement-solidified South China coastal soft clay samples with different contents and sizes of sand particles were prepared, and unconfined compression strength (UCS) tests, dry-wet cycle tests under seawater and freshwater conditions, scanning electron microscopy tests, and X-ray diffraction tests were carried out. The test results show that the UCS of cement-sand-solidified soil (CS-SS) samples at 7, 14 and 28 d increase as sand particle content increases, and their 28 d strength increase as sand particle size decreases; the average mass loss rate of sand-cement samples ranges from 14% to 61%. The properties of strength and dry-wet cycle durability improve as the sand particle content increases and the sand particle size decreases. Moreover, the anti-dry-wet cycle ability of samples under freshwater conditions is better than those under seawater conditions. The scanning electron microscopy (SEM), X-ray diffraction (XRD), and ion chromatography test results show that the stiffness replacement effect (the high stiffness of sand particles replaces the low stiffness of soft clay), the interface cementation effect between the sand particles and cemented soil, and the microcrack expansion-obstructing effects of sand particles contribute to the improvement of the strength and dry-wet cycle durability of cement-sand solidified South China coastal soft clay.  
coastal soft soil; dry-wet cycle; unconfined compression strength (UCS); sand particle content; sand-cement stabilization
# Impact of Selected Yeast Strains on Quality Parameters of Obtained Sauerkraut
## 2 Materials and Methods
### Determination of Sugars and Organic Acids via HPLC  
HPLC was used to determine the sugars and organic acids in sauerkraut. The Shimadzu (Kyoto, Japan) NEXERA XR chromatograph equipped with a Rezek ROA-Organic Acid H+ (8%) column was used to analyse the organic acids. The sauerkraut homogenate was centrifuged, and the supernatant was filtered using syringe filters (\(\omega\) = 0.45 \(\upmu\)m). To ensure accurate measurements, the samples were diluted five times with distilled water. Organic acids were detected using a UV-Vis detector (\(\lambda\) = 210 nm). To calculate the concentration of each compound, we prepared standard curves for lactic acid, acetic acid, succinic acid, citric acid and oxalic acid (Sigma-Aldrich (St. Louis, MO, USA)) beforehand.  
The analysis of sugars and polyols was conducted using a Shimadzu (Japan) NEXERA XR instrument with an RF-20A refractometric detector and an Asahiapak NH2P-50 4.6 \(\times\) 250 mm Shodex column (Showa Denko Europe, Munich, Germany) maintained at 30 \({}^{\circ}\)C. The mobile phase consisted of an aqueous solution of 70% acetonitrile, and the isocratic elution programme lasted for 16 min at a flow rate of 0.8 mL/min. Standard curves were used to carry out quantitative determinations for glucose, fructose, glycerol, ethanol and mannitol (Sigma-Aldrich).
# Quasi-Static Load Sharing Characteristics of a Planetary Fear Set with Planet Journal Bearings
## 4 Results and Discussion  
### Fundamental Characteristics  
An experimental apparatus was built up for the load sharing factor test of a planetary gear set, as shown in Figure 7. In the experimental apparatus, the drive motor provided various torque for the test box of a planetary gear set through a coupling. The input axis was connected to the sun gear. The maximum range of available operating speeds is 0-5000 r/min, which can be precisely controlled. In the experimental planetary gear set, rolling bearings or journal bearings could be applied to the planets alternatively. For the lubrication needs of bearings, a pressure oil supply system was set up, including specified oil distribution sleeves, planet axles with oil paths, the oil inlet, the oil outlet, the oil filter, and an oil pump. The oil pump is capable of supplying an oil flow rate of 40 L/min, and the maximum oil pressure is about 0.5 MPa. A strain gauge was attached to the stationary ring gear for  
Figure 6: Load sharing factor calculation procedure.  
measuring the deformation of each planetary gear [30] to quantify the load sharing factor. A data acquisition module was employed to collect and transfer the data to a computer for further analysis.  
Figure 8 shows the measured and simulated load sharing factors of a planetary gear set. The maximum sun gear rotation speed used in the experiment was 4000 r/min. As can be seen in the figure, the measured load sharing factor decreased first and then increased along with the increasing rotation speed. The experimental results and the simulated results of the load sharing factors of the planetary gear set with planet journal bearings appeared to have the same trend, along with the changing rotation speeds. The eccentricity ratio of the journal bearings is larger at a lower rotation speed. The large change rate of the load with the eccentricity ratio and the small eccentric angle makes the bearing support stiffness decrease with the increasing speed. However, at a higher rotation speed, the eccentricity ratio becomes smaller. The change rate of the load with the eccentricity ratio also becomes smaller as the eccentric angle becomes larger. As a consequence, the bearing support stiffness increases with the increasing rotation speed. The load sharing factor and the bearing stiffness have the same variation trends with the rotation speed when the load is constant. Obviously, journal bearings affect the variation of the load sharing factor. In addition, the comparison between the applications of rolling bearings and journal bearings in the planetary gear set was also illustrated in the figure. When using planet rolling bearings, the load sharing factors at different rotation speeds were almost the same, which is because the rolling bearing stiffness kept essentially unchanged.  
Figure 8: Measured and simulated results of load sharing factors.  
Figure 7: Experimental apparatus.
# High-Efficiency Navigation of Nonholonomic Mobile Robots Based on Improved Hybrid A* Algorithm
## 3 Simulation and Experiment  
### Simulation of Map Construction  
To evaluate the effectiveness and efficiency of the improved hybrid A* algorithm, a computer simulation was carried out via MATLAB 2018b. The hardware/software environments are listed in Table 1. The following reasonable assumptions were considered in the simulation:  
1. The robot can perform turns within the reaction time, and the turning angle does not exceed its maximum turning angle while moving in the map, without considering factors such as friction and other resistances.
2. The robot's orthographic projection area occupies one quarter of a grid. In a map with an obstacle ratio of 0.2, the value of variable \(\alpha\) is 5, and the value of variable \(\beta\) is 0.2. In a map with an obstacle ratio of 0.3, the value of variable \(\alpha\) is 6, and the value of variable \(\beta\) is 0.3.  
These assumptions fit the definition of our model. The Chebyshev distance from start to finish is 8 m.  
Grid maps with obstacle proportions of 0.2 and 0.3 (the ratio of the obstacle number to the total number of grids) was used as the comparative experiments. The simulation process is the random generation of a grid graph by MATLAB using the A* algorithm and improved algorithm for path planning. Four experiments were carried out on each map with different ratio of obstacles, and the time and length of each planned path were calculated. The grid map used in the simulation was 20 \(\times\) 20, with a size of 0.5 m \(\times\) 0.5 m per grid. The information of the hardware is listed in Table 1. The model of the GPU used in this study is NVIDIA GTX1650 with 896 CUDA cores and the core frequency of 1485 MHz. The SLAM and relevant machine-learning algorithms were processed via CUDA toolkit, and the processing efficiency is directly determined by the core frequency and core number of the GPU. Since GPUs have a large number of cores, which allow for better computation of multiple parallel processes, they can process multiple computations simultaneously. Therefore, the calculation running on a high-performance GPU can have a significantly shorter time compared to those running on the CPU.
## 2 Common Substrates Used in Anaerobic Digestion
### Lipid-Containing Materials  
Lipid materials are a type of waste coming mainly from the food processing industries, slaughterhouses, palm oil industry and grease traps present in different industrial and commercial activities which have this type of collector in their sewage system. This substrate presents an extremely high biogas potential with a value of 1014 m\({}^{3}\)/kg vs. [104], although its addition to a digester needs careful control of organic loading due to problems associated with stratification and the formation of long-chain fatty acids which inhibit methanogenic metabolism [105]. This is particularly true when short hydraulic retention times (HRTs) are applied [106]. Other problems are associated with technical constraints. Several authors have reported pipe clogging and foaming problems in digester gas pipelines along with severe fouling of these lines [107; 108]. If all these problems are overcome, this material could significantly enhance biogas production, almost doubling methane yields with a small supplementation in the feeding mixture [109].  
The digestion of lipids requires hydrolysis into long-chain fatty acids (LCFAs) and glycerol as a first stage. Glycerol is transformed into an intermediate compound (glyceraldehyde 3-phosphate) by a set of enzymes before entering the glycolysis pathway, with this route being more complex than that followed by glucose [110], causing, in some cases, the accumulation of propionic acid. LCFAs are subsequently degraded via \(\beta\)-oxidation, requiring an external electron acceptor for oxidation [111]. Pre-treatments of lipid substrates are considered necessary for enhancing degradation through the increase in surface area and reducing the formation of conglomerates which affects the hydrolysis stage in a negative way [112].  
Several reports have dealt with the successful digestion of lipid-rich wastes, indicating increments in methane production between 10 and 200% [113; 114; 115] and methane yields as high as 999.2 mL CH\({}_{4}\)/g vs. for slaughterhouse wastes [116] obtained from BMP tests. The use of lipids as co-substrates in anaerobic digestion and the application of different strategies to avoid operational problems, such as acclimatization of the microbial consortium and developments of new reactor configurations, could be a potential approach for maximizing the valorization of these streams for producing biomethane [117].