esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical Properties of Sandstone Cement-Stabilized Macadam
## 4 Conclusions  
In this paper, sandstone is utilized as a coarse aggregate for CSM, and the mechanical properties and influential elements of the SCSM and CSM are evaluated by laboratory tests. The results lead to the following conclusions:  
1. The results show that the cement content and curing age are factors affecting the ITS and UCS. The mechanical properties of the SCSM blend increase with the cement dosage and curing period, similar to the CSM mixture.
2. The strength of the SCSM blend is significantly lower than the strength of the CSM blend. The cause of this phenomenon may be the differences in the properties of the parent rock, including the porosity, crushing value and compressive strength. It may also be due to the weak bonding at the interface between the sandstone and cement.
3. Both the UCS and ITS of the SCSM and CSM blends are affected by frost action. However, the strength degradation amplitude of the SCSM blend caused by freeze-thaw effect is larger than that of the CSM blend. The degradation amplitude increased with increasing cement content, and the curing age has little effect on the amplitude.
4. The properties of the SCSM, including the UCS, ITS, softening coefficient and frost resistance coefficient, meet the requirements of low-grade roads.  
In view of the above conclusions, sandstone can be used for road base construction. Furthermore, applying sandstone to the actual construction of on-site resource utilization will bring suitable economic and environmental benefits.  
All authors contributed equally to this work. All authors wrote, reviewed and commended on the manuscript. All authors have read and approved the final manuscript.  
This study is sponsored by the National Social Science Foundation of China (Grand No. 16 CJY028), Transportation Technology Project of Shaanxi Province (Grand No. 15-06k) and the Fundamental Research Funds for the Central Universities (Grand No. 300102238303, 300102239617).  
The authors declare no conflict of interest.
## 4 Case Studies  
### Piers of Gaoleiro Buildings (Portugal)  
Two masonry piers of a Portuguese building typology (Gaioleiro buildings) were selected and modelled, aiming to evaluate first the performance of the numerical tools at the structural element level. Gaoleiro buildings, built in Portugal during the late 19 th and early 20 th centuries, typically consist of four to six levels [26,71]. The interstory height varies between 2.7 and 3.7 m, with the ground floor usually being higher than the other levels. To provide ventilation and light, these buildings have central or lateral shafts (Figure 4). The foundations and facades are built using rubble masonry, while lateral walls are often made of brick [71]. The thickness of the facades ranges from 0.50 to 1.00 m on the ground floor, and reduces as the elevation increases [72]. The inner walls can be made of brick (walls parallel to the facades) or timber planks with plaster (walls perpendicular to the facades). Floors are flexible diaphragms composed of timber joists and timber planks, or metallic profiles and jack arches. Joists and metallic profiles are set perpendicular to the facades with a spacing of 0.35-0.40 m, and are simply supported on the walls. As for the roofs, they are simple timber structures that cannot be considered trusses [71; 72].  
Figure 4c highlights two specific priors of the facade selected to be subjected to IP dynamic actions, aiming to induce shear-diagonal and flexural failures. Pier 1, which is located on the ground floor, is 1.90 \(\times\) 1.80 \(\times\) 0.70 m (length \(\times\) height \(\times\) thickness). Due to its geometry and loading condition, shear-diagonal failure is likely to occur in this panel. On the other hand, Pier 2, at the top of the facade, measures 1.55 \(\times\) 2.55 \(\times\) 0.45 m. This panel is used to simulate flexural damage since it is slender, and its level of compression is considerably lower than that of Pier 1. For the simulation, equivalent masses were placed on top of the panels to represent the weight of the structural elements they carry. The normal stress applied at the top of Pier 1 and Pier 2 is equal to 328.75 kPa and 21 kPa, respectively. The numerical models were generated in Abaqus [23], using quadrilateral plane stress elements with a characteristic length of 0.05 m. The elements have the real thickness of the piers. The mechanical properties of the masonry are detailed in Section 4.
# Considering the Life-Cycle Cost of Distributed Energy-Storage Planning in Distribution Grids
###### Abstract  
In the face of the radical revolution of energy systems, there is a gradually held consensus regarding the adoption of distributed renewable energy resources, represented by Photovoltaic (PV) and wind generation. Consequently, the distributed Energy Storage Systems (ESSs) have become increasingly important in the distribution networks, as they provide the arbitrage and ancillary services. Determining the optimal installation site and the capacity of the distributed ESSs will defer the network reinforcements, reduce the investment of ESSs, and improve the reliability, flexibility, and efficiency of distribution grids. In order to investigate the optimal ESS configuration and to solve voltage fluctuations brought by the increased penetration of PV, in this study a two-stage heuristic planning strategy has been proposed, which considers both the economic operation and the lifetime of the distributed ESSs, to determine the optimal sitting and sizing of the ESSs, in the distribution grids. The first stage decides the optimal installation site and the economic scheduling of the ESSs, aiming to minimize the fabricating cost of the distributed ESSs and the network losses. Based on the output of the first stage, the second stage planning is further delivered to achieve the optimal ESS capacity, considering the Life-Cycle Cost (LCC) minimization. Finally, the feasibility and effectiveness of the proposed method is verified on a typical distribution case study network.  
distributed renewable energy resources; distributed energy storage system; genetic algorithm; simulated annealing; life-cycle cost +
Footnote †: journal: Applied Sciences
## 3 Results
### Approximate Techniques
#### 3.2.2 Rank Ordered Centroid (ROC) Technique  
The Rank Ordered Centroid Technique assumes knowledge of the ordinal ranking of preference attributes with no other supporting quantitative information on how much more important one attribute is relative to the others [80]. As a consequence of this assumption, it is assumed that the weights are uniformly distributed on the simplex of rank ordered weights [78].  
The Rank Ordered Centroid Technique is comprised of the following two steps: (1) rank order attributes and establish rank indices, and (2) calculate the rank ordered centroid for each attribute.  
ROC Step 1: Rank Order Attributes and Establish Rank Indices  
Consider a finite set of N attributes or criteria deemed relevant by an individual or group of experts to a particular decision problem. This first step asks users to agree on a rank ordering of these attributes according to their relative contribution to the expert's overall preference within an additive utility (or value) function framework. A number of approaches exist to assist in holistic ranking, the most popular being pairwise ranking [81]. The resultant ranking is from most important to least important, where the index \(i=1\) is assigned to the most important attribute, and the index \(i=N\) is assigned to the least important attribute.  
For example, consider the typical choice problem centered on which automobile to purchase. The output from this step would be a rank ordering of these preference attributes from most to least important, as shown in Table 12.  
ROC Step 2: Calculate the Rank Ordered Centroid for Each Attribute  
The Rank Ordered Centroid Technique assigns to each of \(N\) rank ordered attributes a weight \(w_{i}\) according to Equation (5).  
\[wi=(1/N)\ \sum_{k=i}^{N}\frac{1}{K} \tag{5}\]  
where, again, the attributes are ordered from most important (\(i=1\)) to least important (\(i=N\)).  
In our car buying example, the weights assigned to each attribute can be calculated as shown in Table 13. Note that the predefined formula used in the approximate techniques limits the number of weights available for assignment. Thus, while we can see the ordinality  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Abbreviation** & **Criteria** & **Ordinal Ranking with Index** \\ \hline (P) & Purchase Price & \(i=1\) \\ (R) & Reliability & \(i=2\) \\ (S) & Safety & \(i=3\) \\ (A) & Attractiveness & \(i=4\) \\ (G) & Gas Mileage & \(i=5\) \\ \hline \hline \end{tabular}
\end{table}
Table 12: Ordinal criteria ranking.  
of weight preferences remains, the magnitude of weights and distance between them has changed. This is the tradeoff that a decision maker must make; is more control over weights preferred or not? If so, using a ratio assignment technique provides more control. If time is more crucial or if decision makers are not as informed regarding the problem, an approximate technique may prove more appropriate.
###### Abstract  
The records of 24,797 traffic accidents (9039 involving fatalities or severe injury) during rainy conditions from 2007 to 2017 in Seoul, South Korea, were used to analyze the spatial distribution of the traffic accidents and rainfall events based on radar and gauge rainfall data. According to the spatial correspondence analysis between rainfall distribution and accident locations for localized and stratiform rain events, radar data in a two-dimensional grid (250 by 250 m) of 10 min temporal resolution benefited the localized rainfall distribution concerning the accident location. The relative accident rate (RAR) from radar data, which was used as a quantitative reference value for the effect of rainfall on traffic accidents, was about 18% higher than that from gauge rainfall. The radar data more clearly classified the number of traffic accidents during rainy conditions because its spatial distribution was more precise for all accidents. In addition, the RAR estimation of accidents involving fatalities and severe injury during rainfall could provide information on the district in which traffic accidents increase due to rainfall. The study results support the adoption of radar-derived rainfall data to analyze the influence of rainfall on accidents and the development of more accurate risk-assessment tools for drivers and planners.  
radar; rain gauge; relative accident rate; relative accident risk; road traffic accidents +
Footnote †: journal: Applied sciences
# A Thermal Transport Study of Branched Carbon Nanotubes with Cross and T-Junctions
## 2 Methodology
To avoid rigid body motion (i.e., translational movement and rotation) of the branched CNTs, all of the branches were fixed at their ends for a length of 0.5 nm adjacent to a 5 nm temperature-controlled region. The temperatures in the hot and cold regions were controlled at 300 \(\pm\) 30 K by using the Langevin thermostat setting. To observe the thermal flow, a hot region was created at the end of branch 1 of the branched CNTs whereas cold regions were created at all of the other branch ends. The NVE ensemble was implemented in the free area of branched CNTs for 1 ns to enable the heat current to reach a steady state. All of the results were collected in the following 1 ns. Each model was simulated and averaged six times with different initial velocities. In the branched CNT with cross-junctions, the heat current flowed from branch 1 into the other three branches (branches 2, 3, and 4; Figure 4a). To investigate the heat flow behavior inside this branched CNT, we defined the heat current ratio beyond the junction as follows:  
\[R_{i}=\frac{I_{i}}{\sum I_{i}}\,\ i=2\ \sim 4 \tag{1}\]  
where \(I_{i}\) is the power removed from the temperature-controlled region of branch \(i\). The heat current ratio for the T-junctions was similarly defined as that in Equation (1). Figure 3: Atomic configurations of two cross-junctions: (**a**) cross-junction 1 and (**b**) cross-junction 2 (P, H, and O represent pentagons, heptagons, and octagons, respectively).
# A Palladium-Deposited Molybdenum Disulfide-Based Hydrogen Sensor at Room Temperature
###### Abstract  
Recently, hydrogen (H\({}_{2}\)) energy has attracted attention among eco-friendly energy sources because H\({}_{2}\) energy is eco-friendly, energy-efficient, and abundant in nature. However, when the concentration of H\({}_{2}\) in the atmosphere is more than 4%, H\({}_{2}\) has a risk of explosion. H\({}_{2}\) is a colorless, stateless, and odorless gas that is difficult to detect with human senses. Therefore, developing an optimized hydrogen sensor is essential. Palladium (Pd) has good reactivity to hydrogen. Molybdenum disulfide (MoS\({}_{2}\)) has high carrier mobility, sensitive reactivity to toxic gases, and high surface-area-to-volume ratio. Therefore, we proposed hydrogen sensors that use Pd and MoS\({}_{2}\). The main fabrication processes include MoS\({}_{2}\) deposition through CVD and Pd deposition through DC sputtering. In this study, we utilized Pd and MoS\({}_{2}\) to enable sensing at room temperature. By optimizing the Pd to a nanoparticle structure with an expansive surface area of 4 nm, we achieved a fast response time of 4-5 s and an enhanced yield through a simplified structure. Hydrogen sensors inherently exhibit sensitivity to various environmental factors. To address these challenges, technologies such as machine learning can be incorporated. Emphasizing low-power consumption and various application compatibilities becomes pivotal to promoting commercialization across diverse industries.  
hydrogen; palladium; molybdenum disulfide; hydrogen sensor; room temperature +
Footnote †: journal: Applied Sciences
## 2 Transmission Characteristics
The deviation is less than 1.0% when \(n=2\), so it is enough to use the second-order joint approximating spline filter in practice. In the three-dimensional surface measurement, a method similar to the separability of the Gaussian filter is used to realize a two-dimensional approximating spline filter. Two groups of one-dimensional approximating spline filters process the original data along the \(x\)-axis and \(y\)-axis. First, the filter is applied \(m\) times in the \(x\)-direction (the same as the original data row number), and then \(n\) times in the \(y\)-direction (the same as the original  
Figure 4: The maximum deviation of the transmission characteristics between the Gaussian filter and the approximating spline filter for different cascade orders. Figure 3: (**a**) Transmission characteristic and (**b**) transmission characteristic deviation of Gaussian Figure 1 and std-approximating spline filter (\(\beta=0.62524\)). data column number) [29]. Two-dimensional approximating spline filtering characteristics are defined as follows:  
\[G(\omega)=\frac{1}{\left\{1+2\tau\mu\left(1-\cos\omega_{x}\right)+4\mu\left(1- \cos\omega_{x}\right)^{2}\right\}\times\left\{1+2\tau\mu\left(1-\cos\omega_{y} \right)+4\mu\left(1-\cos\omega_{y}\right)^{2}\right\}} \tag{3}\]  
The anisotropy of the normal spline filter in orthogonal direction limits its application in 3D surface roughness evaluation. A two-dimensional isotropic spline filter can be built by superimposing a one-dimensional cascade approximating spline filter in the \(x\) and \(y\) directions, and its amplitude-frequency characteristics are close to those of the Gaussian filter. In the one-dimensional cascade approximating filter, when \(n=2\), the maximum deviation is less than 1.0%, which represents an ideal balance between the approximation accuracy and the computational efficiency. Similarly, as shown in Figure 5, when \(n=2\), the maximum deviation between the amplitude-frequency characteristics of the two-dimensional cascaded approximating spline filter and the two-dimensional Gaussian filter is less than 1.5%, which can meet most of the application requirements.
## Materials and Methods
### Simulation Procedure  
#### 2.4.1 Material Modeling  
To compare the experimental results, the same abrasive material (Silicon carbide) and workpiece material (Inconel 625) were utilized in the simulations. The Mie-Gruneisen Equation of state [20] was employed to describe the initial elastic response of the materials. The strength and damage behavior of silicon carbide was modeled with the Johnson-Holmquist material model [21]. The Johnson-Cook model was employed to model the strength behavior of Inconel 625 [22]. In the Johnson-Cook model [23], the von Mises stress, \(\sigma\), is expressed as  
\[\sigma=\Big{[}A+B\varepsilon^{t}\Big{]}[1+C\ln\dot{\varepsilon}^{*}][1-T^{*m}] \tag{1}\]  
where \(\varepsilon\) is the equivalent plastic strain, \(\dot{\varepsilon}^{*}=\dot{\varepsilon}/\dot{\varepsilon}_{0}\) is the dimensionless plastic strain rate for \(\dot{\varepsilon}_{0}=1.0\) s\({}^{-1}\), and \(T^{*}\) is the homologous temperature where \(T^{*}=(T-T_{\rm room})/(T_{\rm melt}-T_{\rm room})\). The five material constants are \(A\), \(B\), \(t\), \(C\), and \(m\), where \(A\) is the yield stress, \(B\) and \(t\) represent the effects of strain hardening, and \(C\) is the strain rate constant. The expression in the first set of brackets gives the stress as a function of strain for \(\dot{\varepsilon}^{*}=1.0\) and \(T^{*}=0\). The expressions in the second and third set of brackets represent the effects of strain rate and temperature, respectively.  
The important parameters for simulations are summarized in Table 2.
# A TCP Acceleration Algorithm for Aerospace-Ground Service Networks
## 3 Transmission Acceleration Using BoostTCP
### Implementation Architecture  
The BoostTCP consists of several modules, as shown in Figure 2. The modules are explained in the following:  
* Learning state machine: This is an information and control hub of BoostTCP, which accumulates knowledge about network paths and makes real-time decisions about the transmission of specific connections, such as the rate at which data are transmitted and the timing of data retransmission;
* Traffic monitor: This module extracts and learns the external features of each TCP flow and records and maintains the learning state machine;
* Packet loss monitor: This module monitors packet loss and determines the most probable cause of data loss using a learning state machine, for instance, whether the loss is caused by simple random packet drops or network congestion;
* Congestion controller: This controller executes the core congestion control logic based on a learning state machine;
* Exception handler: This module leverages knowledge of the learning state machines to identify flaws in peer TCP stacks or certain devices along the data transmission path, such as security detection devices. This module is used to detect specific characteristics of TCP to ensure maximum acceleration. Exception handlers also contribute to the knowledge accumulation of learning state machines;
* Window controller: This controller calculates the size of the TCP broadcast window and balances incoming packets from the LAN and WAN sides;
* Resource manager: This module tracks and controls system resources, including memory and computing power, and dynamically balances system resource consumption across all active TCP flows. The knowledge of learning state machines is the input to resource management.  
Figure 2: Schematic diagram of the BoostTCP modules.
## 1 Introduction
[PERSON], in his dissertation [8], proposed a new physics-based technique to derive an analytical expression for the PDF of the irradiance, by taking into consideration the turbulence and aperture averaging effects and taking into account parameters directly related to measurable atmospheric parameters. The Log-Normal and Gamma-Gamma distributions have been utilized in [9] to derive closed-form expressions for the average capacity of an FSO link. An experimental work has been presented in [10] that provides a methodology for modeling laser beam's intensity in a maritime environment and the corresponding PDF has been computed by using field measurements. [PERSON], in his dissertation [11], proposed the exponentiated Weibull (EW) distribution for modeling the irradiance fluctuations in FSO links under atmospheric turbulence and taking into consideration the aperture averaging effect. Motivated by the above, the main contribution of this paper is to propose a methodology of comparing different theoretical distribution models, for any turbulence strength regime. In doing so, a large experimentally measured dataset of received signal strength of an FSO link in the maritime environment is utilized and different theoretical PDF models are fitted to the entire dataset histogram. The Kullback-Leibler (KL) and Jensen-Shannon (JS) divergences are initially employed to compare five theoretical models and the best fit is determined by the absolute value of the corresponding divergence. Additionally, the Pearson family of probability distribution functions, consisting of seven types of continuous probability distributions, is utilized to determine the type that best fits to experimental data. The rest of the paper is organized as follows. Section 1 provides the theoretical background of the atmospheric turbulence and channel modeling. Section 2 describes the experimental setup and the data acquisition process. Section 3 provides the theoretical background of the KL and JS divergence, the Pearson distribution family and the model comparison results, whereas Section 4 concludes the paper.
## 3 Parameter Optimization and Performance Test
### Net Dropping Seedling Duration Measurement Test
#### 3.1.3 Experimental Results and Analysis  
The test results are shown in Table 1, and according to Table 1, the following conclusion were drawn:  
1. Due to the differences between the individual bot seedlings, the net seedling dropping duration of different seedlings at the same position was inconsistent and fluctuated within a certain range and was mainly affected by the state of the pot. Some potted seedlings were broken during the pick-up process, and the energy loss during collision and friction was different, which affected the drop rate. The pot seedlings of the No. 1, 2, 4, and 5 positions were in contact with the inner wall of the seedling dropping cylinder, and there were more uncertainties in the seedling dropping process, so the coefficient of variation was larger. The potted seedlings at the No. 3 position had less uncertainty in the drop process, and the coefficient of variation was smaller.
2. The dropping duration of the pot seedlings at the No. 1, 2, 4, and 5 positions was similar, with the average drop duration being about 0.37 s, and the average net dropping duration at the No. 3 position was 0.32 s. The reason for the large difference at the No. 3 position was that there was almost no contact with the inner wall of the seedling dropping cylinder during the seedling dropping process, while collision and friction occurred at the inner wall of the drop tube during the dropping process at the other positions resulting in energy loss and a longer drop trajectory than at the No. 3 position.
3. The dropping trajectories of the pot seedlings at the No. 1, 2, 4, and 5 positions were different, but the net dropping duration was similar, indicating that the collision height, collision angle and friction distance in the two dropping trajectories had little effect on the energy loss during the dropping process under the experimental conditions.