esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Effects of Twist Number on the Mechanical Properties of Yarns  
Figure 4 shows that the twisting process provides functional composite yarns with greater tenacity, as compared to the tenacity of the control group (i.e., pure bamboo charcoal yarns and quick-dry yarns). Conversely, the wrapping process provided functional composite yarns with greater tenacity, as compared to the tenacity of the control group. Bamboo charcoal yarns had a low tenacity and elongation at the break, according to Table 3 and Figure 5. The wrapping and twisting process improved the extensibility of the yarns. Therefore, regardless of whether it was wrapped yarns or  
twisted yarns, the elongation at the break of the functional composite yarns was proportional to the twist number of the yarns.  
Moreover, the tenacity of functional composite yarns that were processed with a wrapping process showed a remarkable decrease when the twist number increases. For wrapped yarns, the wrap material could only enwrap the core, but it was primarily the core that bears the majority of the exerted force. Therefore, variations in twist number did not have a considerable influence on the tenacity of the wrapped yarns [26]. Figures 1B and 2B are the structural diagrams of twisted yarns and wrapped yarns. The wrapped yarns were composed of bamboo charcoal yarn and quick-dry yarn alternatively as the core and sheath. A low twist number was unable to enwrap the core completely. After the breakage of the core, a large amount of sheath was possible to slip. A high twist number compactly enwrapped the core, decreasing the slip level of sheath. The twisted yarns were composed of bamboo charcoal yarns and quick-dry yarns with a spiral structure. The higher the twist number, the denser the spiral structure. When a tensile force was exerted, the twisted yarns exhibited a structural slip where the spiral structures were presented. As a result, increasing the twist number improved the elongation of the twisted yarns. However, a high twist number also increased the contact area between the two yarns, as was the case with the friction. Therefore, a high twist number inhibited the slip of structure of twisted yarns, which eventually caused a lower elongation at the break, more than that of the wrapped yarns.  
Figure 4: Tenacity of functional composite yarns in relation to the administration of a wrapping/ twisting process as well as the twist number. The error bar indicates the Standard deviation (SD). Q: Quick-dry, and B: Bamboo charcoal.  
Figure 5: Elongation of functional composite yarns in relation to the administration of a wrapping/ twisting process as well as the twist number. The error bar indicates the SD.
## Data and Methods
### Multiple Linear Regression
\begin{table}
\begin{tabular}{c c c c} \hline
**ID** & **Indicators** & **Definitions** & **Units** \\ \hline TN10p & Cool nights & Percentage of days when TN \textless{} 10 th percentile & Days \\ \hline TX10p & Cool days & Percentage of days when TX \textless{} 10 th percentile & Days \\ \hline TN90p & Warm nights & Percentage of days when TN \textgreater{} 90 th percentile & Days \\ \hline TX90p & Warm days & Percentage of days when TX \textgreater{} 90 th percentile & Days \\ \hline WSDI & Warm spell duration indicator & Annual count of days with at least 6 consecutive days when TX \textgreater{} 90 th percentile & Days \\ \hline CSDI & Cold spell duration indicator & Annual count of days with at least 6 consecutive days when TN \textless{} 10 th percentile & Days \\ \hline SDII & Simple precipitation intensity index & Annual total precipitation divided by the number of wet days (defined as daily \(p\geq 1.0\) mm) in the year & mm days\({}^{-1}\) \\ \hline R10 & Moderate precipitation days & Annual count of days when daily \(p\geq 10\) mm & Days \\ \hline R20 & Heavy precipitation days & Annual count of days when daily \(p\geq 20\) mm & Days \\ \hline R50 & Extremely heavy precipitation days & Annual count of days when daily \(p\geq 50\) mm & Days \\ \hline CDD & Consecutive dry days & Maximum number of consecutive days with a daily \(p<1\) mm & Days \\ \hline CWD & Consecutive wet days & Maximum number of consecutive days with a daily \(p\geq 1\) mm & Days \\ \hline R95p & Very wet days & Annual total \(p\) when daily \(p>95\)th percentile & mm \\ \hline R99p & Extremely wet days & Annual total \(p\) when daily \(p>99\)th percentile & mm \\ \hline PRCPTOT & Annual total wet-day precipitation & Annual total \(p\) in wet days (daily \(p\geq 1\) mm) & mm \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._correlation between ecosystem and climate indices. Based on the regression coefficients of the MLR, a relative contribution rate (\(\eta_{i}\)) of each predictor was expressed as:  
\[\eta_{i}=\frac{|\beta_{i}|}{\sum_{i=1}^{n}|\beta_{i}|} \tag{3}\]
## 3 Discussion
### Evolution of Slip Systems
#### 3.3.2 Analysis of Schmid Factors
Therefore, the recalculated SFs were shown in Figure 13. By comparing Figures 11 and 13, it was found that SFs overall were higher in Figure 13 than that in Figure 11, especially for the values of prismatic slip systems and pyramidal\(<\)a\(>\) slip systems. Figure 12: **(a)** Schematic diagram of major principal stress; **(b)**\(\varepsilon=0.7\%\); **(c)**\(\varepsilon=2.4\%\); **(d)**\(\varepsilon=7.8\%\) effective strain increment fields in the local and the direction of major principal stress. Figure 13: Distribution of recalculated SFs at macro strains = 0.7%, 0.9%, 2.4%, 4.0%, 7.8%, 15.5%. The results proved the assumption that some grains of the \(\alpha\) phase were deviated from uniaxial stress at the early stage of deformation, which resulted in the deviation of the local stress from the primary uniaxial tensile direction. Figure 12b-d shows the direction of major principal stress and the effective strain increment fields at macro strains of 0.7%, 2.4%, and 7.8% (the effective strain increment was defined as the increment of effective strain of elements for every increase of 0.1% of macro strain and to show the effect of current step). As shown in Figure 12b, when the macro strain reached 0.7%, the grains of the \(\alpha\) phase deviated from uniaxial stress, while the grains of the \(\beta\) phase kept the uniaxial stress. This was mainly due to the fact that the effective strain increment was concentrated on the grains of the \(\alpha\) phase, resulting in the inconsistent deformation and the grain interactions, which forced the grains to deviate from the uniaxial stress. Then, as shown in Figure 12c, the effective strain increment of the \(\beta\) phase increased and the direction of major principal stress slightly changed, while it was found that the SFs of the activated slip systems in the \(\beta\) phase were in the range of 0.4-0.5 (see Figure 11). As shown in Figure 12d, the deviation of the \(\beta\) phase further increased, while SFs smaller than 0.1 appeared (see Figure 11), which meant the deviation influenced the activation of slip systems in the \(\beta\) phase.
# Delineation of the Flooded Areas in Urban Environments Based on a Simplified Approach
## 3 Case Study and Mathematical Model
### Analysed Rainfall Events  
For calibration and validation purposes, two important rainfall events were used:  
* The rainfall on 12-13 of September 2013 (Figure 8), with a total amount of precipitation of 67.8 mm. The estimated frequency of the 2013 event (based on IDF curves for Tulcea meteorological station) was close to 1:10.  
The maximum rainfall values were recorded around 7:10 a.m. on 13 September 2013 (at the end of event). Precipitation prior to the maximum affects the initial humidity in the study area, leading to the increase of the runoff coefficient.  
* The rainfall from 14 June 2016 (Figure 9) had a total duration of approx. 3.5 h (between 11:00 a.m. and 14:30). The maximum intensity was recorded around 11:50 a.m., at the beginning of the event. The total amount of precipitation (approx. 25.6 mm) was relatively low compared to the situation of 12-13 September 2013, which raises questions about the proper functioning or maintenance of the sewerage system in Tulcea. The estimated frequency of the 2016 event (based on the same IDF curves) was close to 1:2.  
Figure 8: The 12–13 September 2013 rainfall event: (**a**) hyetograph; (**b**) rainfall mass curve.
# Sand and Dust Storms: Impact Mitigation
## 6 Conclusions  
The environmental effects associated with wind erosion and the movement of sand and dust through the atmosphere are numerous and wide-ranging, and include significant hazards to human populations in drylands and beyond. We conservatively estimate, using a simple review of peer-reviewed published sources, that 77% of all parties to the UNCCD are affected directly by SDS issues and 23% of all parties can be classified as SDS source areas.  
Although many SDS hazards are well-known, the processes involved and their impacts are not all equally well-understood. Several areas deserve a greater research effort. Our knowledge and understanding of desert dust source regions, processes of emission and transport, remain incomplete, which means that our attempts to model desert dust movement and provide adequate forecasts that can help to mitigate impacts are imperfect. Similarly, assessments of the spatial distributions of vulnerability to SDS effects, on human health and various forms of economic activity, are scarce, a deficiency that undermines the development and implementation of effective mitigation measures. Action that can help to mitigate the health impacts of atmospheric dust also relies upon an understanding of how dust exposure is linked to various ailments and many of these causal links remain unclear.  
Assessing damage caused by SDS in economic terms supplies critical input to policy development, an input that is gaining greater importance as risk management becomes the dominant approach of hazard mitigation policies. In this case, we note that there have been relatively few attempts to assess the economic burden of SDS impacts. Such assessments are of particular importance for those impacts that are transboundary in nature.  
Policies designed to mitigate the impacts of wind erosion, particularly in agricultural areas, are among the most developed in certain parts of the world. These policies, and the mitigation methods they have advocated, illustrate valuable lessons--of both success and failure--for those countries seeking to develop such policies anew. Policies designed to mitigate the wider impacts of SDS, including many that are transboundary, have a shorter history, but further development and wider implementation of such policies has been advocated because of the recent marked increase in wind erosion and associated dust storms in several parts of the world. This paper has been designed to add a small additional momentum to that process of policy development.  
The following is available online at www.mdpi.com/2071-1050/9/6/1053/s1, Table S1: UNCCD parties affected by SDS.  
**Acknowledgments:** We recognize the support of UNCCD for portions of the research reported here. The paper has benefitted from comments made by three anonymous reviewers and an academic editor.  
**Author Contributions:** [PERSON] and [PERSON] conceived this paper; [PERSON] wrote the bulk of it, with critical inputs from [PERSON], who also designed and drew the figures.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Materials and Methods
### Features
#### 3.3.10 Network Protocol  
**Feature feasibility:** The protocols used in the ICS network should be strictly defined and limited. Appearance of new protocols in the traffic indicate serious change in the network. If the networking protocol is identified using only port number, as is typical with Wireshark, the detection may be incorrect. Many systems use non-standard port addresses to conduct their network activity.  
**Our data:** Network protocol using port numbers was not a feasible feature in this environment. The Promise RAID controller messages render it difficult to use as explained earlier in Section 3.1. More advanced protocol identification might change the feature to be highly usable.
## 3 ML-Based Routing Algorithms in WSNs
### Distributed-Regression-Based Routing Schemes  
Distributed regression aims to find a fit function \(\overset{\wedge}{f}(t)\) to best fit the real sensor measure function \(f(t)\). The fit function \(\overset{\wedge}{f}(t)\) can be expressed as:  
\[\overset{\wedge}{f}(t)\ =\sum_{i}w_{i}h_{i}\ (t), \tag{4}\]  
where \(h_{i}(t)\) is a basis function to minimize square sense, and \(w_{i}\) is the basis function coefficient. Pick the weight vector \(w^{*}\) that minimizes the squared error, we have  
\[w^{*}=\underset{w}{\text{argmin}}\frac{1}{s}\sum_{j=1}^{n}\left(f(t_{j})- \overset{\wedge}{f}(t_{j})\right)^{2}, \tag{5}\]  
where \(s\) is the size of the whole sample set \(S\).  
To improve the routing performance, an efficient sensor data modeling framework (ESDFM) was presented in [64]. In this distributed framework, the nodes optimally fit a global function to match their local measurement in a collaborative way, and the model executes a kernel linear regression in the form of a weighted sum of local basis functions, which can map training samples into feature space to facilitate data manipulation. Once the model is started, all nodes can answer queries in the local scope, or users on the external network can efficiently obtain the model parameters from the nodes. Multiple sensors in the proposed framework are highly correlated, which can minimize the communication cost used to detect sensor data structures. This distributed regression framework includes three layers: the routing layer, which builds a spanning tree so that neighboring nodes have high-quality communication links; the connection tree layer, which sends messages between neighboring nodes in the routing tree to enforce the intersection property; and the regression layer, which optimizes the regression estimation by sending information about the coefficients of the basis functions. Distributed regression achieves energy efficiency by optimizing the transmitted data and routing, thus extending the lifecycle of the WSN.  
Figure 7: The proposed ML-based system model.  
ESDFM provides a key inspiration to utilize ML in routing design in WSNs, achieving good fitting results and less overhead in the learning phase. However, it still faces some challenges, such as the inability to learn nonlinear and complex functions.
# Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters
## 1 Introduction
Section 3 provides a statistical analysis of the relationships established between the backscattered radar signal and the surface parameters (soil and vegetation). Section 4 discusses the performance of the different backscattering models for bare and covered soils. Our conclusions are provided in Section 5.
## 3 The Proposed Approach
### Maintenance Optimization
#### 3.2.2 Solutions Evaluation  
Each genetic algorithm's candidate solution, which results in a set of times and intervention levels for preventive maintenance, is associated with a total cost along with a fixed user-defined planning horizon, obtained by the sum of preventive and corrective maintenance costs for all cycles. Denoting by \(C_{PM}(T_{j},s_{j})\) and \(C_{CM}\) the preventive maintenance and corrective maintenance costs, respectively, the expected total maintenance cost \(C_{TOT}\) can be given by Equation (8).  
\[C_{TOT}\big{(}T_{j},\ s_{j},c\big{)}=\sum_{j=1}^{c}\Big{[}C_{PM}\big{(}T_{j},s _{j}\big{)}+E\Big{[}N(t)_{j}\Big{]}*C_{CM}\Big{]} \tag{8}\]  
in which \(E\Big{[}N(t)_{j}\Big{]}\) is the expected number of failures in the cycle \(j\) and \(s_{j}\) is the intervention level of the \(j\)-th PM.  
The expected number of failures in each cycle, \(E\Big{[}N(t)_{j}\Big{]}\), is also modified according to the average function \(\mu(t)\), which represents the average number of failures as a function of time. Thus, Equation (8) must be rewritten as Equation (9).  
\[C_{TOT}\big{(}T_{j},\ s_{j},c\big{)}=\sum_{j=1}^{c}\big{[}C_{PM}\big{(}T_{j},s _{j}\big{)}+\big{[}\mu\big{(}T_{j}\big{)}-\mu\big{(}T_{j-1}\big{)}\big{]}*C_{ CM}\big{]} \tag{9}\]  
The sum of the costs for all maintenance cycles is used as a measure to determine the optimal values of \(T_{j}\) and \(s_{j}\) as well as the optimal number of PM activities.
###### Abstract
Regarding the mechanical parameters of the rock mass in the influence area of the cross-tunneling project, some scholars have determined the rock mass reduction parameters based on photo images and the information of the over-advanced geological prediction by using the Hoek-Brown criterion. [PERSON] et al. [31] established a more complete system of the comprehensive tunnel overrun geological forecasting method and also established a comprehensive grading evaluation system for adverse geological conditions in tunnels. [PERSON] et al. [32] used the method of combining overpass geological prediction and investigations into the palm face surrounding the rock to obtain the Hoek-Brown parameters of the tunnel's surrounding rock, and then analyzed the stability of the surrounding rock in the fracture zone through FLAC3D numerical simulation. [PERSON] et al. [33] firstly determined the Hoek-Brown strength parameters using on-site investigation and the empirical checklist method and then simulated the excavation process of the three-step method and the double sidewall guideway method by combining FLAC3D. The results show that the double sidewall guideway method can better control the surrounding rock deformation and is suitable for tunnel sections with more broken surrounding rock. [PERSON] et al. [34] corrected the parameters'mb' and's', based on the Hoek-Brown strength criterion, from which the equivalent M-C strength parameters of anisotropic side-slope rock mass were further calculated; the reasonableness of the parameters was tested using engineering examples. [PERSON] et al. [35] summarized the current research status of tunnel overtopping prediction technology using the drill-and-blast method and proposed a four-stage all-process integrated overtopping geological prediction system for tunnels. To summarize, there are some results on the mechanical response characteristics of new tunnels with static excavation and blasting excavation, but usually the static and dynamic loads are only considered individually and there are fewer cases that consider both at the same time; in particular, there is a lack of research cases on the tunneling of double-line highway tunnels diagonally passing through existing railways. Therefore, this paper relies on the tunnel project of the new double-line Shiqian Highway Tunnel passing under the Hurong Railway at an oblique angle; adopts the method of over-advance geological prediction and investigation into the palm face surrounding the rock; determines the rock's mechanical parameters of the cross-tunneling project influence area, considering the coupling effect of dynamic and static loads through the quantization of disturbance system and Hoek-Brown criterion; establishes an engineering-scale finite element numerical model; and reveals the mechanical parameters of the double-line highway tunnel passing through the existing railway tunnel under the influence of dynamic and static loads.
## 1 Introduction
This effect becomes more prominent in overhanging regions with large inclination angles, which means that the laser beam passes over a section of the powder layer under which only loose powder is present, leading to poorer surface quality due to stair-case effects or sag and cross formations [5; 17]. Another similar approach is the fitted-ellipse method applied to the stack of images extracted from the scanned CT volume to determine the internal channel roughness [7]. However, the geometrical shape of the channel imposes a limitation on the length of the section on which the analysis can be performed and therefore hinders the applicability of the method to different geometries. In this paper, the algorithm for \"wall thickness analysis\" in VSGtudio MAX 3.3 (Volume Graphics GmbH, Germany) is proposed to evaluate the geometrical mean diameter (\(d_{mom}\)), the functional equivalent diameter (\(d_{\textit{eq}}\)) and the _msth_ of internal channels without the necessity of the alignment between the actual and nominal geometry to avoid dimensional measurement uncertainties, as discussed in [13; 14]. These data could be used as essential feedback in the design phase for the evaluation of shape compensation methods to achieve near-net shapes close to the nominal dimensions. In contrast, \(d_{\textit{eq}}\), for example, can be correlated with \(d_{h}\). To validate the proposed methodology, the \(d_{\textit{eq}}\) values obtained from the wall thickness analysis are compared with the \(d_{h}\) determined with incompressible experimental flow tests performed in the turbulent flow regime. Darcy-Weisbach and Colebrook-White equations are used to evaluate the experimental \(d_{h}\) and \(A_{\textit{eff}}\). Section 2 presents the components used in this study and the methodologies involved in performing the CT scan and the wall thickness analysis to determine \(d_{\textit{eq}}\) and _msth_. In addition, this section describes the experimental flow test arrangement and the governing equations to determine \(d_{h}\). Section 3 presents the results obtained from the wall thickness analysis and the experimental flow tests. The results are compared and discussed to validate the proposed methodology, leading to the conclusions, and suggestions for future work are described in Section 4.