esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Durability Properties of GPC
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Durability Parameters** & **Relationship with Durability** & **Impact on Durability** & **Remarks** \\ \hline \multirow{3}{*}{Sportivity} & \multirow{3}{*}{Inverse relation} & \multirow{3}{*}{Strong} & Higher rate of sorptivity results in greater capillary rise of water in micro and macro pores of GPC mixture. \\  & & & & Higher the water absorption lower will be the durability of GPC mixture. Higher water absorption rate will reveal the transport mechanism for water in GPC. \\ \hline \multirow{4}{*}{Weight loss} & \multirow{3}{*}{Inverse relation} & \multirow{3}{*}{Strong} & Higher amount of weight loss in specimen will result in deterioration and decrease in durability specially when specimens immersed in sulphuric acid additives, sodium chloride and sodium sulphate + magnesium sulphate solutions. Initially, when these chemical penetrate in concrete the weight of GPC increases. It also cause expansion in concrete volume which will form the micro-racks inside GPC [161]. The expansion mechanism will have a detrimental impact on durability. \\ \hline \multirow{2}{*}{AVPV} & \multirow{2}{*}{Inverse relation} & \multirow{2}{*}{Strong} & Same effect as water absorption and sorptivity of GPC. Increase in AVPV will expose the GPC for environmental impacts and deterioration. \\  & & & Discentious attenuation at voids will enhance the workability and durability in GPC especially exposed to extreme environmental conditions. \\ \hline \multirow{2}{*}{Shrinkage cracks} & \multirow{2}{*}{Inverse relation} & \multirow{2}{*}{Average} & Greater shrinkage cracks will make it susceptible for deterioration. \\  & & & Degradation in compressive, flexural and splitting tensile strength of the GPC specimens exposed to different chemical solutions will indicate the compactness of the GPC. \\ \hline \multirow{4}{*}{Chloride ingress} & \multirow{2}{*}{Inverse relation} & \multirow{2}{*}{Strong} & Chloride ingress in GPC depends both on physical and chemical conditions on which the GPC samples are exposed. Greater chloride ingress in GPC results from greater micro and macro cracks that will further impacts the durability of GPC. \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \end{tabular}
\end{table} 
Table 8: Properties effecting the durability of GPC.
# TFBN: A Cost Effective High Performance Hierarchical Interconnection Network
## 6 Some Generalization
The open issues and challenges have been mentioned earlier. The success of an MPC system is how fast and efficiently it can execute and process a complex task, and the time required for this purpose depends on the execution time and communication and coordination time among the nodes. The execution time is constant, and it depends on the number of cores in the processor and its clock cycles. The execution time is fixed for a particular node of an MPC system. Communication time depends on the number of steps for communication and coordination. Therefore, the total required time is proportional to the number of steps required for communication and coordination. The practical applicability of an interconnection network topology is usually justified by assessing the number of communication and coordination steps required for the benchmark computational intensive problem, viz., fast Fourier transform, solution of a partial differential equation, bitonic merge, finding the maximum, etc. It is believed that the proposed TFBN will result in less communication steps to solve these computational intensive problems because of the low diameter and average distance. A similar phenomenon is observed in our another study [39]. The implementation of a massively parallel computer system is the integration of many chips in the chip level integration to create a node. The interconnection of many nodes in the board level interconnection to make a board, interconnection of many boards in the cabinet level interconnection is to make a cabinet, and finally interconnection of many cabinets in the system level interconnection is to build the MPC system. In the TFBN, Level 1 (BM) is considered as the board, Level-2 is considered as the cabinet, and Levels 3, 4, and 5 are considered as system level interconnection. As mentioned before, Level-4 is interconnected using Level-3 as a sub-net module and similarly Level-5 is interconnected using Level-4 as a sub-net module. In this research, we have considered the static network performance and static cost effective analysis of the proposed TFBN. This is the very first stage of analysis, and, in the next step of this research, we will evaluate the dynamic communication performance by a fit-level simulator.
# Vegetation Loss Measurements for Single Alley Trees in Millimeter-Wave Bands
## 3 Measurement Campaign  
The main motivation to carry out measurements of the foliage loss was to perform measurements between consecutive trees. The measurement scenario is the regular tree alley. We also performed a comparable measurement before the first tree. Then, we also compared our measurement results with statistical models and discussed the scattered-through (based on the RET model) and diffracted components.
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 7 Wireless Technologies in IoV
### Interoperability and Standardization  
Within the IoV in a smart city, effective interoperability and standardization are essential. It involves developing common communication protocols as well as common data formats to allow for frictionless interaction between vehicles and all components of the smart city infrastructure. This venture makes possible a common language of data exchange to enable seamless collaboration and sharing of information. This aims to achieve its goal by standardizing components for integration, thereby reducing integration complexities, ensuring system interoperability the realization of IoVs efficiently in urban mobility.
## 4 Methodology
### Efficient Search Algorithms Based on Transformations and Metaheuristics
#### 4.3.1 Optimal Selection of Technology Set  
The technology diversity problem can be transformed into equivalent formulations, to obtain a more convenient representation that reduces it into a well known NP-hard problem termed as \"The Clique Problem.\" The first step of the transformation is blending the risks and communication protocol matrices into the so-called compatibility matrix \(\mathbf{C}\). Here, the element \(C_{ij}\) in the compatibility matrix \(\mathbf{C}\) is equal to \"1\" if the pair of technologies \(i\) and \(j\) meet both constraints jointly, and is equal to \"0\" otherwise. Figure 3 shows an example of the compatibility matrix for the case depicted in Figure 2.  
Based on the compatibility matrix, the problem is transformed in finding the largest set of jointly compatible technologies. If \(\mathbf{C}\) is interpreted as an adjacency matrix, then it can be represented by a graph, and the above mentioned problem reduces to the well-known Maximum Clique problem. Clique definitions are rooted in social sciences [44] and the problem is part of [PERSON]'s 21 NP-complete problems [45], also in [46] more information is available. A survey concerning maximum clique and related algorithms to so  
Figure 3: Compatibility matrix obtained from the example depicted in Figure 2.  
The simplest equivalent formulation as an integer programming problem, presented in [47] and termed as \"the edge formulation of the problem,\" is used in this work:  
\[\boldsymbol{o}^{*}=\underset{\boldsymbol{o}\in T}{\operatorname{argmax}}\sum_{i=1 }^{N}T_{i}, \tag{12}\]  
subject to:  
\[T_{i}+T_{j}\leq 1 \forall(i,j)\in\overline{E^{\prime}}, \tag{13}\] \[T_{i}\in\{0,1\} \forall i, \tag{14}\]  
where \(G^{\prime}=(V^{\prime},E^{\prime})\) is the graph that transpires from the compatibility matrix, \(\overline{G^{\prime}}=(V^{\prime},\overline{E^{\prime}})\) is the complement graph of \(G^{\prime}\), and \(\overline{E^{\prime}}=\{(i,j)|i,j\in V,i\
eq j\text{ and }(i,j)\
otin E^{\prime}\}\). \(T_{i}\) is a binary variable that indicates if technology \(i\) belongs to the maximum clique.  
The edge formulation given before is also NP-hard; however, it has been implemented in software packages and its execution takes an acceptable amount of time for the problem sizes analyzed here.
# Estimating Carrot Gross Primary Production Using UAV-Based Multispectral Imagery
## 2 Materials and Methods
### Microclimate and EC System Measurements  
The EC Flux tower system was deployed at a farmer's farm, on a plot that has been using conventional tillage practices. The EC technique continuously recorded net carbon ecosystem exchange (NEE) and microclimatic variables. The equipment was installed on 16 August 2021 (6 days post-planting, DPP), and the measurements were taken until the end of the harvest on 20 January 2022 (157 DPP). The EC tower consists of an IRGASON with a gas analyzer of an open path (EC 150, Campbell Scientific, Inc., Logan, UT, USA) and a 3D sonic anemometer (CSAT3A, Campbell Scientific, Inc., Logan, UT, USA). The IGARSON and 3D anemometer are operated using a separate electronic module (EC100, Campbell Scientific, Inc., Logan, UT, USA). The EC tower used here and the configuration of each sensor is described in [35,36].  
NEE Partitioning  
Starting from the premise that NEE = GPP + Rd [37] and according to [38,39], the non-linear Mitscherlich light response function represents the net ecosystem exchange (NEE) as a function of the photosynthetic photon flux density (PPFD). This function was used to partition the diurnal NEE (solar global radiation > 1 W m\({}^{-2}\)) into ecosystem respiration (R\({}_{\text{eco}}\)) and GPP [38,39], as follows:  
\[NEE=-\big{(}A_{gmax}+R_{d}\big{)}\times\bigg{(}1-\exp^{(\frac{-\Phi_{\text{ mol}}I_{\text{inc}}}{A_{gmax}+R_{d}})}\bigg{)}+R_{d} \tag{1}\]  
where \(R_{d}\) is the respiration term [\(\mu_{\text{mol}}\) (CO\({}_{2}\)) m\({}^{-2}\) s\({}^{-1}\)], \(A_{gmax}\) is the CO\({}_{2}\) uptake at light saturation [\(\mu_{\text{mol}}\) (CO\({}_{2}\)) m\({}^{-2}\) s\({}^{-1}\)], \(\Phi\) is the quantum yield efficiency [\(\mu_{\text{mol}}\) (CO\({}_{2}\)) \(\mu_{\text{mol}}\) (photon) \({}^{-1}\)] corresponding to the initial slope of the photosynthetic light response curve, and \(I_{inc}\) is the incident PPFD [\(\mu_{\text{mol}}\) (photon) m\({}^{-2}\) s\({}^{-1}\)]. For each day, the set of parameters were calculated using non-linear regression and a subset of NEE and PPFD data, within a moving window of 14 days, centered on each day. For each daily diurnal half-hour, GPP was estimated by subtracting \(R_{d}\) from the non-linear Mitscherlich light response function, as follows:  
\[\text{GPP}=-\big{(}A_{gmax}+Rd\big{)}\times\ \bigg{(}1-\exp^{(\frac{-\Phi_{ \text{LO}}I_{\text{inc}}}{A_{gmax}+R_{d}})}\bigg{)} \tag{2}\]  
Here, \(R_{\text{eco}}\) was calculated by subtracting the GPP estimated from the measured NEE. Quality control, gap-filling, data postprocessing, energy balance closure, uncertainty analysis, and statistical analysis methods were applied, following the procedures described in [35]. The daily GPP reported here is measured in g C m\({}^{-2}\) day\({}^{-1}\).
## 2 Results and Discussion
### Sampling of Skin Emissions and Secertions  
Four sampling methods (see the Materials and Methods section) for the collection of skin emissions and secretions from the palm were examined. As shown in Table 1, the wiping method (method C), in which the palm surface is wiped with gauze, was found to be the most efficient in collecting 2-nonenal. On the other hand, 2-nonenal was collected by the solution method (method A) using water in a cup and by the pasting method (method D) using gauze taped to the palm but not by the headspace method (method B) using HS-SPME directly in a suction cup. These results indicate that 2-nonenal released from the skin surface is difficult to collect from the headspace at body temperature and that active wiping is more effective than passive sampling, even when the sampling material is in direct contact with the skin. Therefore, the wiping method was used as the sampling method for the collection of skin emissions and secretions in this study. The recovery of 2-nonenal from the gauze soaked with 5 ng of 2-nonenal was about 60% compared to that of 2-nonenal added directly to the vial without gauze.  
To examine the stability of 2-nonenal collected on gauze by the wiping method, 0.1 g of gauze soaked with 100 ng of 2-nonenal was stored in vials at various times and temperatures, and the amount of 2-nonenal remaining on the gauze was determined by HS-SPME/GC-MS. As shown in Table 2, when stored at room temperature, there was almost no decrease in 2-nonenal up to 6 h, but it was found to decrease to 60% at 24 h. However, if the gauze was kept refrigerated at 4 \({}^{\circ}\)C, there was almost no decrease even after 24 h. Therefore, the gauze containing 2-nonenal collected by the wiping method should be kept refrig  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification** & **Sampling Method** &
\begin{tabular}{c} **2-Nonenal Content (ng)** \\ **Mean \(\pm\) SD (\(n\) = 3)** \\ \end{tabular} \\ \hline A & Glass cup sampling & 0.47 \(\pm\) 0.51 \\ B & Direct SPME sampling & N.D. 1 \\ C & Wiping method & 0.90 \(\pm\) 0.13 \\ D & Pasting method & 0.18 \(\pm\) 0.23 \\ \hline \hline \end{tabular} \({}^{\dagger}\) N.D.: not detectable.  
\end{table}
Table 1: Comparison of skin emission sampling methods.  
Figure 3: Typical total ion chromatogram of 2-nonenal obtained by (a) direct injection (200 ng/mL solution, 1 \(\upmu\)L) and HS-SPME (200 ng/mL solution, 100 \(\upmu\)L in 40-mL vial).
## 6 Conclusions  
For the scenario where there are multiple reliable multicast groups in ICN and the cache resources of multicast tree nodes are shared by those groups, this paper designs a dynamic cache allocation mechanism (DCAM) to reduce the overall loss recovery delay of reliable multicast. DCAM considers three factors such as packet loss probability, node depth, and transmission rate, and defines them as NACK weight, distance weight, and rate weight, and finally allocates cache space for multicast groups according to the normalized cache quota weight. We implement this mechanism in NS-2 and evaluate its performance. Combining the DCAM, AARM, and Equal cache allocation mechanisms with LCE, CAPC, ProbCache, and Prob cache strategies respectively, we compare and analyze the loss recovery delay, cache hit ratio, transmission completion time, and overhead. Experimental results show that DCAM can adjust cache allocation results in time according to network changes, and its combinations with various cache strategies outperform other combinations. Based on the cache allocation results of DCAM, the optimal results can be achieved by using CAPC cache strategy, and its recovery delay is 3.2-31.44% lower than other combinations.  
Conceptualization, Y.D., H.N. and X.Z.; Methodology, Y.D., H.N. and X.Z.; Software, Y.D.; Writing--original draft preparation, Y.D.; Writing--review and editing, Y.D., H.N. and X.Z.; Supervision, X.Z.; Project administration, X.Z.; Funding acquisition, H.N. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Strategic Leadership Project of Chinese Academy of Sciences: SEANET Technology Standardization Research System Development (project no. XDC02070100).  
Data Availability Statement: Data sharing not applicable.  
We would like to express our gratitude to [PERSON] and [PERSON] for their meaningful support of this work.  
The authors declare no conflict of interest.
# Numerical Analysis of the Settlement Behavior of Soft Soil Improved with Stone Columns
## 3 FE Analysis Results
### Effect of Crust Layer  
The influence of the crust layer upon the settlement performance is examined for Footings 4 and 6 (Figure 2). The base of Footing 6 is at a depth of 1.2 m, which is the end of the crust layer. The predicted settlement with increasing applied pressure for the two footings under drained and undrained conditions is shown in Figure 13. The calculated settlement with PLAXIS 3D for the two load increments of interest (33 kPa and 70 kPa) is slightly lower for Footing 6 than the one of Footing 4, but the trend reverses for applied pressures greater than 71 kPa and 150 kPa for drained and undrained conditions, respectively. This can be explained as Footing 6 is founded at the base of the crust layer and indicates the sensitivity of the footing depth within the crust layer. The predicted settlement is in good agreement with the measured settlement during the field trials under an applied pressure of 70 kPa.  
The predicted vertical displacements at the ground surface from the centerline of the foundation for average pressures of 33 kPa and 70 kPa for Footing 6 are presented in Figure 14. A comparison with the settlement profile of Footing 4 (Figure 11a), which has an identical stone column length and spacing, reveals a negligible reduction in ground surface settlement for the footing founded on top of the Carse clay layer.
# A Review of Data Mining with Big Data towards Its Applications in the Electronics Industry
## 4 Data Mining with Big Data Applications in the Electronics Industry
### Data Handling
\begin{table}
\begin{tabular}{c c c c} \hline
**Data Preparation** & **Records Obtainment** & **Reference** & **Application** \\ \hline \multirow{4}{*}{Experimental data} & Full factorials & [26; 29; 33; 35; 36; 39; 40; 41; 84; 107] & \\  & Fractional factorial & [27; 114] & Product design improvement and \\  & Orthogonal experiment & [31] & manufacturing process optimization \\  & & Historical records & [28; 30; 32; 34; 37; 38] \\  & & Accumulated records & [43; 44; 45; 56; 57; 58; 59; 64; 65; 66; 67; 66; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77] \\  & & Online measured, traced & [80; 82; 83; 96; 97; 98; 101] & Production process monitoring and \\  & & or monitoring & [85; 86; 87; 88; 99; 100; 82; 83; 84; 85; 86; 87; 89; 100; 88; 101; 89; 102; 103; 104; 105; 106; 107; 108; 109; 100; 101; 102; 103; 104; 105; 106; 107; 108; 109; 100; 101; 103; 104; 106; 107; 108; 109; 110; 111 Data transformation is the process of converting data from one format or structure into another. The pervasive method is normalization for the selected articles but few were conducted based on variance scaling [35; 63], text mining [42; 123], Fisher Z-transformation [44; 47], binary vector transformation [91; 93; 117], Box-Cox transformation [84], and numerical into binary [85]. Dimensionality reduction, as one of the important approaches to data reduction, is to remove the irrelevant and redundant variables to reduce the complexity of analysis and the generated models, and also to improve the efficiency of the whole modeling processes. The widely used approaches from the reviewed articles include regression [34; 44; 45; 83; 86; 89; 110; 126; 127], analysis of variance (ANOVA) [27; 79], GA [55; 83], Las Vegas filter [60; 78], Pearson coefficient [79], Cramer's V correlation coefficients [85; 87], and so on. Clustering [38; 90; 101; 126], aggregation [34; 87], and sampling [82] based approaches were applied to reduce the data numerosity. PCA or the modified PCA [64; 65; 83; 92; 94], and multi-dimensional scaling [84] were employed to compress the representation of the original data. Only a few of the researchers conducted discretization for continuous attributes at the stage of preprocessing.
# Delay-Sensitive Service Provisioning in Software-Defined Low-Earth-Orbit Satellite Networks
## 2 Related Work and Motivation
### Motivation  
With the development of the 6G integrated space-terrestrial network, some important delay-sensitive services are bound to be carried on the LEO satellite network. As mentioned earlier, ensuring service transmission delay in a satellite network requires the cooperation of both the network data plane and control plane. In this paper, we assume that the data plane mainly uses traffic shaping methods to ensure the transmission bandwidth of flows, avoiding long queuing delays caused by node overload. There are already some mature solutions for traffic shaping methods, so our work will mainly focus on establishing the best way to ensure the transmission delay requirements of delay-sensitive services through control plane routing optimization. On the other hand, with the maturity of SDN technology, achieving per-flow routing control in a satellite network based on SDN technology becomes possible. However, the routing optimization of delay-sensitive services in a low-Earth-orbit satellite network based on the SDN architecture still faces the following challenges.  
(1) Knowing how to reduce the overhead of routing handovers. During the entire cycle of satellite topology changes, a flow's path may experience multiple handovers. Although path handover can be achieved by installing flow tables in the SDN architecture, frequent flow table installations will generate a large amount of control overhead. In addition, path handovers may also cause packet reordering and increase delay jitter. Therefore, when calculating routing strategies for flows, it is necessary to minimize the number of path handovers as much as possible. For example, in Figure 1 we will prioritize routing strategy 1 among the three consecutive time slots (slot \(k-1\), slot \(k\), and slot \(k+1\)) because it only requires two handovers compared to three for routing strategy 2.  
(2) Knowing how to achieve load balancing while ensuring the delay constraints of delay-sensitive services. Due to the uneven distribution of population, network traffic in satellite networks often fluctuates and is unbalanced. To avoid the impact of congestion on the transmission delay of delay-sensitive services, it is crucial to achieve maximum network traffic distribution. Therefore, in addition to the cost of path handovers, the routing optimization algorithm also needs to consider achieving load balancing as much as possible while meeting the constraints of flow transmission delay. For example, in the example given in Figure 2, we will prioritize selecting routing strategy 2 because both strategies have the same number of path handovers, but strategy 2 has better load balancing.  
(3) Knowing how to reduce the complexity of the algorithm. In the future, the number of nodes in the LEO satellite network may be large, and the constrained routing optimization problem under dynamic topology is more complex than that under static topology. In this case, low-complexity algorithms need to be designed to provide delay-sensitive services in a short period.