esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Tool Implementation and Results
### Fuzzy Control System  
3.2.1. Fuzzification  
The first step, as described in the methodology, is the identification of linguistic judgment of each KPIs on the basis of their final score value. The fuzzy range of each green KPIs is defined and shown in Table 6. The fuzzy range of the remaining dimensions (lean, agile, resilient) are shown in Appendix B (Tables A7-A9).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**KPI** &
\begin{tabular}{c} **Linguistic** \\ **Judgement** \\ \end{tabular} & **a** & **b** & **c** & **d** \\ \hline \multirow{4}{*}{Cost of waste} & Very low & 0 & 0 & 20,000 & 20,000 \\  & Low & 15,000 & 20,000 & 25,000 & 30,000 \\  & High & 25,000 & 30,000 & 35,000 & 40,000 \\  & Very high & 40,000 & 42,000 & 50,000 & 50,000 \\ \hline \multirow{4}{*}{Cost of raw materials} & Very low & 0 & 0 & 100,000 & 200,000 \\  & Low & 100,000 & 200,000 & 300,000 & 400,000 \\  & High & 300,000 & 400,000 & 500,000 & 600,000 \\  & Very high & 500,000 & 600,000 & 1000.00 & 1000.00 \\ \hline \multirow{4}{*}{Energy consumption} & Very low & 0 & 0 & 7000 & 9000 \\  & Low & 7000 & 9000 & 15,000 & 20,000 \\  & High & 15,000 & 20,000 & 25,000 & 30,000 \\  & Very high & 25,000 & 30,000 & 50,000 & 50,000 \\ \hline \multirow{4}{*}{Sanitation costs} & Very low & 0 & 0 & 10,000 & 15,000 \\  & Low & 10,000 & 15,000 & 20,000 & 25,000 \\  & High & 20,000 & 25,000 & 30,000 & 35,000 \\  & Very high & 30,000 & 35,000 & 50,000 & 50,000 \\ \hline \multirow{4}{*}{Waste index} & Very high & 0\% & 0\% & 50\% & 55\% \\  & High & 50\% & 55\% & 70\% & 75\% \\ \cline{1-1}  & Low & 70\% & 75\% & 88\% & 95\% \\ \cline{1-1}  & Very low & 88\% & 95\% & 100\% & 100\% \\ \hline \hline \end{tabular} On the basis of the fuzzy range and the final score, the linguistic judgment and the membership degree are defined, as shown in Table 7 and Appendix C (Tables A10-A12).  
\end{table}
Table 7: Linguistic judgment and membership degree of green KPIs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**KPIs** & **Linguistic Judgment** & **Membership Degree** \\ \hline Cost of raw materials & Very low & 1 \\ Use of renewable materials & Low & 1 \\  & High & 0 \\ Suppliers with environmental certifications & Very high & 1 \\ Water consumption & Low & 0.83 \\  & Very low & 0.17 \\ Energy consumed produced from fossil and renewable resources & High & 1 \\ Energy consumption & Low & 0.6 \\  & High & 0.4 \\ Human capital & High & 0.97 \\  & Very high & 0.03 \\ Waste index & Very high & 1 \\ Compliant products & High & 0 \\  & Very high & 1 \\ Number of smart tasks & High & 1 \\ Sanitation costs & Low & 0.6 \\  & High & 0.4 \\ CO\({}_{2}\) emission & Low & 1 \\ Fuel consumption & Very low & 1 \\ Route efficiency & High & 0.71 \\  & Very high & 0.29 \\ Vehicle load rate & Low & 0 \\  & High & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._
# Extended Spatial-Index LED Modulation for Optical MIMO-OFDM Wireless Communication
## 3 Simulation Results and Computational Complexity
### Achievable Data Rate (\(R\))  
The achievable data rate \((R)\) of the proposed ESI-LED modulation scheme (3) under the assumption of very high SNR values is evaluated and compared with both the GLIM [31] and FGIS [33] rates at different \(n_{t}\). Here, a modulation of 4-QAM is presumed to be applied on this test. As Figure 3 illustrates, the GLIM can not work when the number of transmit LEDs below four, since it does not have a sufficient number of unique LED indices to include all four potential likelihoods for OFDM\({}_{td}\) signs information, while the proposed ESI and the FGIS [33] are working with only three LEDs. Moreover, Figure 3 shows that the proposed ESI scheme achieves a semi-nonlinear incremental relationship in its \(R\) with respect to the \(n_{t}\), while the FGIS achieves linear increment. Hence, for \(n_{t}=9\), ESI gains SE of 11 bpcu, while GLIM and FGIS attain only 4 and 8 (i.e., \(log_{2}(M)+n_{t}-3\)[33]) bpcu, respectively. Therefore, the proposed ESI can provide a higher SE value compared to the inferior SE value provided by the GLIM or FGIS.
# A Delay-Aware and Reliable Data Aggregation for Cyber-Physical Sensing
## 4 Design of the TSR-DARDA Scheme
### Discussion on Parameters Selection
#### 4.3.1 The Outmost Tier Width \(d_{k}\)  
We know that the goal of the proposed TSR-DARDA algorithm is to enable nodes to simultaneously transmit as much data as possible under the assumption that the wireless channels between two adjacent tiers are independent across links. In order to realize the interference free transmission between two adjacent tiers, multiple transmission frequencies are required. In addition, in order to satisfy that all non-collecting nodes within each cell could transfer data packets directly to collecting nodes in the same cell, the maximum spherical distance \(l_{m}\) of cell in the tier \(T_{m}\) with most tier width \(d_{m}\) needs to satisfy the following equation to finish the distributed data aggregation within each cell:  
\[l_{m}\leq\Re \tag{18}\]  
Under this condition, all non-collecting nodes within each cell could transfer data packets directly to collecting nodes in the same cell. According to Section 4.2.1, assume \(d_{m}=f_{1}(d_{k})=f_{1}(\delta r)\). And the sum of \(d_{k},d_{k-1},...,d_{m}\) is denoted by \(f_{2}(\delta r)\). Besides, as shown in Figure 4, \(l_{m}\) can be calculated by the following formula:  
\[l_{m}=R\cdot\arccos[\cos\frac{r}{R\cos\frac{f_{1}(\delta r)}{R}}\cdot\cos \frac{f_{2}(\delta r)-f_{1}(\delta r)}{R}\cdot\cos\frac{f_{2}(\delta r)}{R}+ \sin\frac{f_{2}(\delta r)-f_{1}(\delta r)}{R}\cdot\sin\frac{f_{2}(\delta r)}{R}] \tag{19}\]  
Therefore, we can get the following result:  
\[\arccos[\cos\frac{r}{R\cos\frac{f_{1}(\delta r)}{R}}\cdot\cos\frac{f_{2}( \delta r)-f_{1}(\delta r)}{R}\cdot\cos\frac{f_{2}(\delta r)}{R}+\sin\frac{f_{ 2}(\delta r)-f_{1}(\delta r)}{R}\cdot\sin\frac{f_{2}(\delta r)}{R}]\leq\frac{ \Re}{R} \tag{20}\]  
Under the given values except \(\delta\), we can obtain the parameter \(\delta\) range and the outmost tier width range \(d_{k}=\delta r\) to solve the above inequality.
# Opportunistic Sensor Data Collection with Bluetooth Low Energy
## 5 Evaluation
### Energy Cost  
In the previous two subsections, we have observed that the advertisement-based approach allows to achieve a greater sensor node lifetime (i.e., a lower sensor node energy consumption) than the connection-based one, while the latter provides a greater data transfer capacity. However, for a meaningful analysis, these two performance parameters should be related to each other, to determine the energy cost (in terms of Joules per collected bit) for the two approaches.  
Figure 22 provides the energy cost for the two approaches, as a function of _advInterval_, and for \(T_{contact}\) values of 150 s and 45 s. In addition, non-zero BER values have been evaluated in some cases to illustrate influence of BER on the energy cost. The energy consumed is determined by first calculating the power consumed (i.e., the average current consumption, computed in Section 5.1, multiplied by the battery voltage assumed, 3 V), and dividing it by the period between two consecutive contacts of one day.  
As shown in Figure 22, for low _advInterval_ values, the energy cost of the connection-based approach is 1-3 orders of magnitude below that of the advertisement-based approach. The energy cost of the latter tends to increase with _advInterval_, since sleep periods increase and during these no data is collected, but energy is still consumed. However, the energy cost of the connection-based approach decreases significantly with _advInterval_, since there is a large amount of data collected during the contact, while the dominant component in the energy consumed is still the rest of the day, during which the sensor node is advertising its connectability, and the device tends to be mostly in sleep mode (as shown by the average current consumption trend with _advInterval_ in Figure 14). The duration of sleep periods increases with _advInterval_.  
Figure 22 also shows how non-zero values of BER affect the energy cost for both the advertisement-based and the connection-based approaches. In the former, channel errors reduce the amount of collected data, but do not alter the energy spent in advertisement transmission. Therefore, the energy cost increases with BER, although as found in the amount of collected data analysis, energy cost increase is only significant for BER greater than \(10^{-4}\). In the connection-based approach, channel errors reduce the energy spent by the sensor node, but reduce to a much greater extent the amount of collected data during a connection. In fact, the sensor node consumes a significant amount of energy in periods between contacts (to perform advertisement tasks), while data can only be collected during contact intervals.  
Figure 21: Influence of _convInterval_ on the maximum amount of collected data per contact interval, for the connection-based approach, for different BER values, for _advInterval_\(=0.02\) s and \(T_{contact}=150\) s.
## 2 Theoretical Approach
### Initial and Boundary Conditions
Figure 2: Schematic diagram of tunnel entrance hoods: (**a**) no tunnel entrance hood; (**b**) oblique enlarged tunnel entrance hood; (**c**) expanded equal section non-uniform opening holes tunnel entrance hood. The initial condition for the one-dimensional flow model is the airflow velocity is zero, and standard atmospheric pressure is the initial pressure. The exit boundary conditions are defined as atmospheric boundary conditions. In light of this, the research idea of our team was to use different methods to solve different regions according to the generation process of micro-pressure waves. As shown in Figure 4, for the initial compression wave generation stage, based on the IDDES SST \(k-\omega\) turbulence model, a three-dimensional numerical model was used to transiently simulate the initial compression wave induced by a 400 km/h high-speed train entering a tunnel. As the compression waves transition from three-dimensional characteristics to one-dimensional plane waves, the initial compression wave propagation process in tunnels of different lengths is simulated by a one-dimensional compressible unsteady non-isentropic flow model. Here, we select the waveform of the compression waves at the 7D (one-dimensional characteristics) measurement point as the entrance boundary conditions for the one-dimensional model. The improved MOC utilizes the initial conditions to determine the flow parameters on the initial time (\(Z_{0}\)). In the improved MOC, the Riemann variables, known at \(Z\) time grid points, cannot determine the Riemann variables at the boundary points for the \(Z+\Delta Z\) time. Utilizing boundary conditions enables the computation of the Riemann variable at boundary points. The reasonableness of the boundary conditions directly affects the accuracy of the numerical computation results. Additionally, for both the basic MOC and the improved MOC, the roles of the initial conditions and boundary conditions are the same.
## 5 Conclusions  
The satellite-derived surface moisture (STR, Ts) and vegetation status (NDVI) are appropriate measures to distinguish between irrigated and non-irrigated pixels within farmlands. The use of the STR, which is based on Band 7 of Landsat-8 (the optical spectral range), with the NDVI, demonstrated successful mapping of irrigated landcover for the summer season of 2020-2021 in CGID. The results of this optical approach of classification were found to be comparable with those of the thermal approach, where Ts\(-\)Ta is used with the NDVI for classification. These results have broader implications in terms of irrigated landcover assessment, as the use of satellite imagery in these applications may not necessarily be limited to sensors with either microwave or thermal bands. The impact of this is a greater freedom, both spatially and temporally, to develop this information.  
**Author Contributions:** Conceptualization, M.A. and A.M.; methodology, M.A.; validation, A.M., M.A. and K.S.; formal analysis, M.A.; resources, A.M.; writing--original draft preparation, M.A.; writing--review and editing, K.S. and M.A.; project administration, A.M. and M.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The data sets are not available due to privacy reasons for individual farm holders.  
**Acknowledgments:** The authors are thankful to the leadership team of Agriculture Victoria Research for their support of the work.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 1 Introduction
### Our Study and Contributions  
This study reviews and groups previous studies on agricultural tasks recently performed with UAVs. As a result of the importance of agricultural applications with UAVs, review and survey studies have also increased. The increasing use of UAVs in precision agriculture and various UAV solutions were presented by [PERSON] et al. [29]. [PERSON], [PERSON], [PERSON] and [PERSON] [4] discussed potential UAV applications in precision agriculture, and information was given on 20 precision agriculture-UAV studies conducted in the past. [PERSON], [PERSON], [PERSON] and [PERSON] [24] explained the characteristics, control and design of agricultural UAVs and then touched on UAV applications in agriculture such as mapping, spraying, planting, crop monitoring, irrigation and pest detection. [PERSON] and [PERSON] [25] discussed multiple UAV systems in agriculture and emphasized that the performance of a multiple UAV system is significantly superior to a single UAV system. [PERSON], [PERSON] and [PERSON] [3] explained precision agriculture practices with UAVs, types of UAVs used, data collection and data-processing methods and limitations.  
This study focuses on the survey of UAV studies and divides agricultural tasks into two steps. In the first, outdoor UAV studies are discussed, and in the second, UAV applications in the greenhouse (indoor) are investigated, unlike previous studies. Attention is drawn to the necessity and importance of the use of UAVs for precision agriculture tasks in greenhouses. This study emphasizes that the number of studies on UAVs in the greenhouse is low and offers a simultaneous localization and mapping (SLAM) proposal for indoor (greenhouse) autonomous UAVs as a solution.  
The fact that the use of UAVs for agricultural purposes is a field that has progressed very rapidly makes survey and review studies important. The important contributions of this study can be summarized as follows:  
* Agricultural practices carried out with UAVs recently, mostly in 2020, are extensively discussed.
* UAV agricultural applications are discussed in two categories, i.e., indoor and outdoor environments.
* The importance, necessity and inadequacy of greenhouse UAV missions are emphasized.
* The importance of SLAM for autonomous agricultural UAV solutions in the greenhouse is explained.  
The remainder of this manuscript is organized as follows. In Section 2, information is presented about the different agricultural tasks performed with UAVs in open fields, and previous agricultural studies are discussed. Section 3 highlights the necessity and lack of UAV solutions in the greenhouse and discusses previous UAV studies carried out in the greenhouse. Section 4 states that the SLAM problem is vital for performing autonomous missions with a UAV inside the greenhouse. Finally, Section 5 discusses the entire manuscript and concludes the study.
## 3 Sample Preparation and Methodology
### Testing Procedure
#### 3.2.2 UCS Test  
The UCS samples were prepared with controlled density approach. The results obtained from stage 1 (i.e., compaction) were then used to prepare UCS samples. The test was conducted in accordance with ASTM 2166 [22]. While the utmost care was taken to achieve those values in practical laboratory conditions, after checking, OMC was reported \(\pm\)0.25% and MDD was reported \(\pm\)0.5%. In order to eliminate the size effect, the ratio of height to diameter kept at 2. The diameter of samples were 50 mm and the height was 100 mm. The effect of sulphate attack on the sample was considered by running UCS tests for mixtures before and after exposure to sulphate. The sulphate attack solution was prepared in our laboratory by sulphate concentration of 42.2 g/L which forms \(MgSO_{4}\). The effect of adding SD on the improvement of \(q_{u}\) was evaluated by using Equation (1). This equation has been widely used in previous work to assess the mechanical effects of mixing soil with different agents. [18]  
\[IMP(\%)=\frac{q_{u,SD}-q_{u,PC}}{q_{u,PC}}\times 100\% \tag{1}\]  
where \(IMP\) = percentage of the improvement; \(q_{u}\), \(SD\) = peak UCS value for SD included mixture; and \(q_{u}\), \(PC\) = peak UCS value for the PC treated mixture before addition of the SD.  
Figure 3: Cured mixture (7 days) of the bentonite-slag-cement (**a**) 0%, (**b**) 3%, (**c**) 5%, (**d**) 10% and (**e**) 15% after sulphate attack.
# Application of Nanofluids in CO\({}_{2}\) Absorption: A Review
## 4 Classification of Nanofluids Based on Base Liquid
### Water-Based Nanofluid
The CO\({}_{2}\) absorption performance of different nanofluids from gas streams was also experimentally studied to determine the influence of different nanoparticles (Al\({}_{2}\)O\({}_{3}\), SiO\({}_{2}\), Fe\({}_{3}\)O\({}_{4}\), and CNT), in low concentrations, on the improvement of CO\({}_{2}\) absorption. It was revealed that unlike Al\({}_{2}\)O\({}_{3}\) and SiO\({}_{2}\) which have better CO\({}_{2}\) absorption performance at high concentrations, CNT and Fe\({}_{3}\)O\({}_{4}\) nanoparticles better enhance CO\({}_{2}\) absorption at low concentrations [84; 102]. In general, when water is used as the base fluid in absorption processes, hydrophobic nanoparticles have better dispersion and a higher rate of collisions with CO\({}_{2}\) bubbles; consequently, the rate of CO\({}_{2}\) bubble cracking and as a result, the mass transfer between the two phases increases [111].
# Lightweight Path Recovery in IPv6 Internet-of-Things Systems
###### Abstract
By fusing spatial and temporal information, LiPa can maintain high recovery performance even in those network scenarios with low node activity, massive nodes, and increased network dynamics. The contributions of our work are the following:  
* We propose a lightweight path recovery scheme that explores the potential correlations of routing paths between different nodes using the node information of the parent and grandparent. * We propose a modulated observation window size for path recovery based on the activity level of the nodes. This enables effective path recovery with appropriate computational effort for network scenarios with different traffic sizes. * We propose new algorithms and obtain good performance in simulation experiments, compared to the state-of-the-art. In addition, we analyze the performance improvement of each highlight in the algorithm. The rest of the paper is organized as follows. Section 2 discusses the related works. Section 3 describes the path reconstruction problem and system model in this paper. Section 4 presents the proposed algorithms. Section 5 analyzes the recovery performance of our work and two related works. Finally, Section 6 concludes this paper and gives future research directions.
## 3 Results
### Comparison of Different ML Model Performances
#### 3.3.2 Comparison of Model Performances in RO1 Site  
Based on the results of Section 3.3.1, optimal input variable combinations (bolded font in Table 3) were selected for four ML methods to validate the model performance using the RO1 site, while data from three NE sites were used for training. The input variable combination of A0 was considered the control combination. The ML methods provided accurate estimates for GPP in the RO1 site, providing sufficient evidence of successful parameter settings in Section 3.3.1 and ensuring the adequate generalization of the model (Figure 7 for RF and Figure 8 for SVM, CNN, and ELM). The slope was close to 1 and the intercept was close to 0. The URMSE was also computed and applied to further analyze the effect of SLN and NMP on each of the models. The ELM model performed best (NSE = 0.765, RMSE = 2.549 gC\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\), Bias = 0.621 gC\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\) and CV = 0.219), and the performance of the SVM model was rated as the second best (NSE = 0.764, RMSE = 2.556 gC\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\), Bias = 0.523 gC\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\) and CV = 0.219) when the variables SLN and NMP were not taken into account (A0 combination). Despite the integration of SLN and NMP leading to reduced accuracy in both SVM and ELM, as shown by the evaluation metrics in Figure 8, the decline is not substantial. Additionally, Figure 8b,d demonstrate that data points are more tightly clustered to the fitting line. In addition, in Table 4, it could be also concluded that not all URMSEs of each model varied consistently with RMSE. For both SVM and ELM, their URMSE decreased with the increase in RMSE. Thus, the effectiveness of the SVM and ELM algorithms was still evident for site-to-site validation. The consideration of NMP brought CNN the best accuracy (NSE = 0.766, RMSE = 2.539 gC\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\), Bias = 0.781 gC\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\) and CV = 0.218), and its URMSE also decreased. The accuracy of RF significantly increased when considering SLN and NMP simultaneously, as shown in Figure 7d, with its URMSE reaching the second lowest level. Moreover, all URMSE values decreased compared to the RMSE, and ELM had the lowest URMSE. Therefore, one the one hand, based on the NSE, RMSE, and CV, the additional input variables of SLN and NMP supported the RF and CNN algorithms in enhancing the accuracy. One the other hand, according to the URMSE, all the ML models maintained robustness with the supplement of SLN and NMP.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & & \multicolumn{2}{c}{**NSE**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**Bias**} & \multicolumn{2}{c}{**CV**} \\ \cline{3-10}  & & \(\mu\) & \(\sigma\) & \(\mu\) & \(\sigma\) & \(\mu\) & \(\sigma\) & \(\mu\) & \(\sigma\) \\ \hline \multirow{4}{*}{RF} & A0 & 0.9574 & 0.0021 & 1.8671 & 0.0432 & \(-\)0.0174 & 0.0621 & 0.1805 & 0