esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Case Study
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Task Name** & **Degree Centrality** & **Task Name** &
\begin{tabular}{c} **Closeness** \\ **Centrality** \\ \end{tabular} & **Task Name** & **Betweenness Centrality** \\ \hline Task 74 & 0.4000000 & Task 74 & 0.479233227 & Task 74 & 2712.615384615384 \\ Task 81 & 0.38888889 & Task 77 & 0.478214670 & Task 84 & 2702.000000000001 \\ Task 67 & 0.3333333 & Task 71 & 0.477200424 & Task 81 & 2668.384615384646 \\ Task 64 & 0.30000000 & Task 81 & 0.475687104 & Task 64 & 2624.153846153846 \\ Task 71 & 0.30000000 & Task 64 & 0.473684211 & Task 91 & 2624.153846153846 \\ Task 84 & 0.30000000 & Task 87 & 0.469728601 & Task 87 & 2613.538461538462 \\ Task 67 & 0.27777778 & Task 67 & 0.465838509 & Task 71 & 2613.538461538461 \\ Task 61 & 0.24444444 & Task 84 & 0.463917526 & Task 77 & 2592.307692037692 \\ Task 77 & 0.24444444 & Task 61 & 0.458248473 & Task 67 & 2561.435478256821 \\ Task 72 & 0.20000000 & Task 72 & 0.443240123 & Task 64 & 2487.346782493274 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Top 10 tasks based on different centrality measures. The next step is to import the Revit building model and MS Project file into Navisworks to create the dynamic presentation. The screenshot of the animation in Navisworks is illustrated in Figure 14. Through the construction simulation, the construction progress, cost, schedule network analysis information, delay analysis information, and critical path information can be known in real-time in the animation's upper left corner. Additionally, the delay tasks also are identified with different colors. In this figure, the completed tasks are indicated by the color of the building facade, the unfinished tasks are indicated in green, and the red part indicates the delayed tasks. Through this case study, the proposed framework is studied and verified step by step. Firstly, an IFC-based 4D construction management information model is created and expresses the extended construction management information. The graph database Neo4j can also be used to enable this IFC-based information model to be interoperated among different participants. In addition, it verified that the proposed framework could solve the problem that the IFC information model is hard to be modified and updated. Secondly, the graph database-based and IFC-based 4D construction information model is  
Figure 14: Screenshot of animation in Navisworks. Figure 13: Importing construction management information into project. applied to update the data, analyze the schedule network, analyze delays, and dynamically present the 4D construction information model. The final results also prove that the graph database-based automated analysis approaches are practical, which is different from the traditional CPM. In short, the significance of this paper is to innovatively propose an IFC-based and graph data-based information model to solve the difficulties of ineffective data interoperation and unautomated schedule analysis in prefabricated buildings' construction management.
# Upgrading of the L-P Band Cryogenic Receiver of the Sardinia Radio Telescope: A Feasibility Study
## 4 Results and Discussion
### New Filters Requirements for the L-Band Channel  
Regarding the broadest portion of the L-band that the receiver can cover (the original \(-3\) dB bandwidth was 1300-1800 MHz), from the analysis of Figure 10, it emerges that this band can be reduced in order to cut troublesome RFIs that reside on sideband. The comparison between the S21 parameter of the new BPF, obtained with Matlab simulations, and the old BPF (model 5B120-1540/T520-O/O from K&L), installed on the receiver, is shown in Figure 15. Thanks to the features of the new BPF, it is possible to attenuate adequately the mobile communications signals (1810-1880 MHz [48; 49]) and the Italian army radars signals (1310-1370 MHz). In particular, the new filter attenuates of about \(-13\) dB at 1375 MHz, \(-32\) dB at 1342 MHz and \(-63\) dB at 1280 MHz (Italian army radars signals that can be displayed in Figure 10), while the old BPF attenuates of about \(-0.4\) dB, \(-0.4\) dB and \(-1.3\) dB, respectively. Better results are also obtained for mobile communications signals (shown in Figure 10), such as at 1813 MHz and 1845 MHz, where the new filter attenuates the signals of about \(-27\) dB and \(-40\) dB, compared to \(-2\) dB and \(-7\) dB with the old filter attenuation, respectively.  
The analysis of the P-band and L-band channels is conducted considering the broadest bandwidth possible for the receiver, according with the features of the front-end described in Section 2. On the other hand, thanks to the movement of the receiver filter selector block in the APEX room, it is possible to install, at any time, new filters on the receiver that cover a portion of these broadest receiver bandwidths to suite the requirements of the scientific researchers.
## 8 Conclusions  
In this work, the existing model, a BER-based LUT, is replaced by the optimized EFPOC algorithm. We detailed the proposed EFPOC algorithm as an optimized channel selection algorithm and also measured its complexity. Simulations have proved that the proposed algorithm has reduced time in dynamic channel selection. In addition, the overhead is also reduced as the BER is calculated only for the selected channels. From this simulation, it has been concluded that the time utilized by the hardware on implementation can be reduced by the proposed EFPOC dynamic spectrum allocation algorithm. It can be seen from the analysis and from the simulation results that idle licensed PU channels can be chosen by the optimized random channel selection algorithm for sending the data to the destination within the best time. The EFPOC proposal is time efficient; we derive the algorithm complexity and arrive to O(n\({}^{2}\)). This evidently proves that the proposed algorithm is a faster and time-efficient model. The final simulations of the EFPOC algorithm are calculated and improved the network lifetime of the Tier-1 system by up to 46.47%. The EFPOC algorithm improves the energy optimization and the channel allocation with an overall value of 25% for the channel information and provides the OP-BNC user channel quickly. Still, we work with the computation process of channel allocation and improve the time efficiency with the least computation complexity.  
Conceptualization, K.V.M.; methodology, K.V.M.; software, K.V.M.; validation, K.V.M., T.S. and V.K.; formal analysis, K.V.M., T.S.; resources, N.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Connectivity Analysis in VANETs with Dynamic Ranges
## Appendix A  
For two adjacent vehicles nodes to connect, the inter-vehicle distance must be smaller than the communication range. Thus, the probability of connectivity is;  
\[\mathrm{P}^{\mathrm{Nak}}_{\mathrm{c}} \triangleq\mathbb{P}(D\leq R)=1-\mathbb{P}(R<D)\] \[=\int_{0}^{\infty}\mathbb{P}(R<D=x|D=x)f_{D}(x)dx=\int_{0}^{ \infty}\mathbb{P}(R<x)f_{D}(x)dx\] \[=\int_{0}^{\infty}F_{R}(x)f_{D}(x)dx=\int_{0}^{\infty}\frac{ \Gamma(m,m\Delta x^{a})}{\Gamma(m)}\frac{\
u^{2\beta+1}x^{2\beta}e^{-a\
u^{a}x ^{a}}}{\left(\Gamma(\frac{2\beta+1}{v})\right)^{2v}}dx\] \[=\frac{\
u\rho^{1+2\beta}}{\beta^{2}}\sum_{t=0}^{m-1}\frac{\theta t }{\mathrm{f}t}\int_{0}^{\infty}x^{it+n}e^{-\theta x^{a}-\phi x^{a}}dx\overset{d }{=}\mathbf{C}\underbrace{\int_{0}^{\infty}x^{it+n}e^{-\theta x^{a}}}_{*}e^{- \phi x^{a}}dx\] \[\overset{b}{=}\frac{\mathbf{C}}{2\pi j}\int_{c-j\infty}^{c+j \infty}\theta^{-s}\Gamma(s)\underbrace{\int_{0}^{\infty}x^{(-as+at+n)}e^{- \phi x^{a}}dx}_{**}\] \[\overset{c}{=}\frac{\mathbf{C}}{\
u 2\pi j}\int_{c-j\infty}^{c+j \infty}\theta^{-s}\Gamma(s)\underbrace{\Gamma\big{(}\frac{\alpha t+n+1-\alpha s }{v}\big{)}}_{*\ast\ast}\phi^{-\frac{at+n+1-\alpha s}{v}}ds\] \[\overset{d}{=}\frac{\sqrt[2\pi]{(2\pi)^{v-a}(\alpha v^{-1})^{3v-2 }}}{\Gamma\Big{(}\frac{2\beta+1}{v}\Big{)}}\sum_{t=0}^{m-1}\frac{\theta^{t}}{t! }\left(\frac{\alpha}{v}\left(\frac{\Gamma(\frac{2\beta+1}{v})}{\rho}\right)^{v }\right)^{\frac{1}{v}}\times\] \[\frac{1}{2\pi j}\int_{c-j\infty}^{c+j\infty}\Gamma(s)\Gamma \Big{(}\frac{\alpha t+\
u(k+1)-1}{\alpha}-s\Big{)}\left(\frac{1}{\phi}\Big{(} \frac{\
u\phi^{\
u}}{\alpha}\Big{)}^{av^{-1}}\right)^{s}ds\]  
Letting \(\mathbf{C}\) in sub-equation \(P^{Nak}_{c}(a)\) equal \(\frac{\
u\rho^{1+2\beta}}{\beta^{2}}\sum_{t=0}^{m-1}\frac{\theta^{t}}{t!}\). The symbols \((*)\) in sub-equation \((a)\), \((**)\) in sub-equation \((b)\) and \((***)\) in sub-equation \((d)\) capture the solution steps using Mellin transform pair and the procedures used in [29] and properties in [21]. Solving the integral and simplifying sub-equation \(P^{Nak}_{c}(d)\) where \(n=2\beta\), \(\theta=m\Lambda\), and \(\phi=\beta\rho^{v}\) gives (33).
## 1 Introduction
Various studies have been conducted to assess the environmental impacts of technologies employed in maize production [19; 20; 21; 22] and to compare the performance against conventional maize production. Overall, it was found that irrigation, machinery, and fertilizer use impacted most of the environmental impacts and energy use of wheat and maize production [19]. Similar findings were observed [20] in which higher agricultural resources input per unit of grain yield produced an increase in the impact of global warming, acidification, and eutrophication, among others, making conventional planting perform the worst environmentally. The incorporation of the economic and social aspects allows consideration at a holistic level to identify trade-offs and ensure a sustainable solution is selected. Thus, a small number of studies attempted to include these aspects [20; 22; 23; 24; 25] to draw a conclusion that did not sacrifice cost, and that was socially acceptable in an effort to mitigate adverse environmental impacts. LCA, as an international standard method, can support analyses of input and output emissions from production systems proportional to the life cycle of crops or processes and is taken into environmental experts for consideration. Furthermore, the environmental aspects and potential throughout the life cycle of a crop or one step of processed raw material to production (consumption, end of biological practices, recycling, and final disposal) can be investigated. Based on the ISO 14040 standard, LCA contains four portions, including the express purpose, determined inputs and outputs of the system, evaluated environmental impacts, and their interpretation [26]. Thus, LCA can be considered a comprehensive tool to evaluate and compare the environmental performance of laser-induced maize production in bio-stimulated seeds with conventional farming practices. Additionally, no research has been found that surveyed the environmental performance of laser-induced maize production through LCA despite having various studies on the economic and environmental impact of maize production. Using the LCA method, the objectives of this study were to compare different maize production processes using laser technology and conventional techniques to determine the optimum method with the least environmental impact on corn cultivation. SimaPro ver. 9.0, PRe Sustainability, The Netherlands software and the calculation models of CML baseline 2000, University of Leiden, The Netherlands, IMPACT 2002+, Swiss Federal Institute of Technology,--Lausanne (EPFL), Switzerland and ReCiPe 2016, PRe Sustainability, the Netherlands were used for the impact assessment. The final section of this paper discusses the implications and recommendations for policy-making decisions leading to sustainable agricultural practices in Iraq.
## 3 Dataset Establishment
### Correlation Analysis  
The relationships between the four input parameters (i.e., \(\alpha\), \(\beta\), \(d_{\mathrm{max}}\), and \(n\)) and the output parameter (i.e., \(k\)) remain unclear. Directly inputting input parameters that influence the output parameter into the predictive model may obscure the effects of the dominant input parameters and increase the complexity of model training. Therefore, identifying the dominant input parameters can reduce the dimensionality of the sample space while enhancing model training efficiency. Grey relational analysis (GRA), a statistical method for multi-factor analysis, evaluates the degree of correlation between sequences based on the similarity of their curve shapes. The closer the curves, the stronger the correlation between the corresponding sequences, and vice versa. Accordingly, GRA can be employed to determine the dominant input parameters affecting the output parameter. As demonstrated in Figure 2, the grey relational coefficients (\(R\)) between each input parameter and \(k\) are as follows: 0.602 (\(\alpha\)), 0.693 (\(\beta\)), 0.745 (\(d_{\mathrm{max}}\)), and 0.842 (\(n\)), respectively. Generally, a grey relational coefficient (\(R>0.6\)) indicates a strong correlation [41], suggesting that all four input parameters can be considered dominant parameters. Consequently, dimensionality reduction is not required.
## 4 Experiment
### Bit Error Rate for Visible Light Communication  
The underwater optical communication node installed in the ROV was used as the receiver, and the communication node installed in the static underwater sealed cabin WAs used as the transmitter. To test the BER of point-to-point UWVLC under static conditions, each test sent 50,000 data packets with fixed text sequences, and the effective data length in each data packet was 239 bytes. Then receiver calculated the BER, where each symbol was 1 byte, and the RS code was not added during the experiment. Considering that the VLC modem in the ROV will inevitably be disturbed by environmental factors such as the movement of the vehicle, it is difficult to achieve the accuracy of manual alignment of the optical communication link in the actual environment. Therefore, we tested the BER of optical communication with a fixed distance of 7.6 m and the transmitter pointing at different angles. During the experiment, the direction of the receiver was fixed. We used the laser pointer to realize the initial alignment to ensure that the receiver and the transmitter were on the same line, and then gradually increased the offset angle in the direction of the transmitter. The test results are shown in Figure 15.  
Figure 14: Receiver output eye diagram.  
Figure 13: The effective signal strength of LED positive and negative voltages driven by different frequency signals.  
It can be seen that with the increase in the offset angle, the BER gradually increased. This is because the radiation intensity of the LED decreased with the increased offset angle, which reduced the optical power received. At the 5 Mbps data rate, the BER was significantly higher than the other three rates at the same angle. This is because the \(-3\) dB bandwidth of the PD S6801 used in the receiving circuit is low, and the photoelectric conversion efficiency is reduced at 5 MHz frequency. In the actual system, the error rate of the final received information is generally reduced by error correction coding. Under the above experimental conditions, we set the offset angle of the transmitter to 30\({}^{\circ}\), the offset angle of the receiver to 0, and added RS(255, 239) coding to the system. The BER measured in the experiment is as shown in Table 1:  
The experimental results show that the designed UWVLC system can obviously improve the BER when the error rate is low by adding error correction codes. At a transmission rate of 5 Mbps, the system can achieve reliable communication with a BER lower than \(10^{-6}\) within an angular difference of \(\pm 30^{\circ}\) between the transceiver and the transceiver at a distance of at least 7.6 m in clear water.
## 2 Frequency Regulation Model of DFIG
### De-Loading Strategy for FR Reserve Capacity
#### 2.2.3 Operation Point Selection of Pitch Angle  
It is noteworthy that the pitch angle control starts to work at the boundary of low and median WS regions, but the DFIG power output does not increase monotonically with pitch angle here, as shown in Figure 3a, where the current rotor speed keeps at 1.2 p.u. At the boundary WS (9.1 m/s), there are three pitch angle values (\(\beta_{0},\ \beta_{1}\) and \(\beta_{2}\)) that can keep the blade power coefficient \(C_{p}\) to be the value corresponding to \((1-d\%)P_{MPPT}(v_{w})\), that is, \(0.9C_{p,\text{max}}\). It can be seen that when \(\lambda\) decreases with the increase in WS in medium WS region, the change rule of \(C_{p}\) with pitch angle is gradually changing from non-monotonic to monotonic. In order to keep the power output at \((1-d\%)P_{MPPT}(v_{w})\), the pitch angle will be adjusted to keep \(C_{p}\) at \(0.9C_{p,\text{max}}\). However, if \(\beta_{0}\) is chosen as the initial operation point of pitch angle in medium WS region, the pitch angle will jump near 9.3 m/s due to the change in variation trend of \(C_{p}\), as shown in Figure 3b.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**WS Region** & **Division Principle** & **Boundary Value** \\ \hline Start-up \& Low & \(P_{m}(v_{w},0.7,0)=P_{MPPT}(v_{w})\) & 6.7 m/s \\ Low \& Medium & \(P_{m}(v_{w},1.2,0)=(1-d\%)P_{MPPT}(v_{w})\) & 9.1 m/s \\ Medium \& High & \(v_{w}=v_{e}\) & 11.8 m/s \\ High \& Cut-out & \(v_{w}=v_{\text{cut-out}}\) & 25 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: The division of WS (wind speed) regions.  
On the one hand, the frequent adjustment of pitch angle is not recommended considering the life-span and maintenance cost of WT [7]; on the other hand, when the time interval of FR order is small, the pitch angle may not be able to achieve the target value as a result of change in rate limit. Therefore, for the sake of avoiding the sharp change in pitch angle, \(\beta_{2}\) is chosen as the initial operation point of pitch angle control in medium WS region. Moreover, in low WS region, \(\lambda\) always keep at 7.887 through the adjustment of rotor speed; thus, the profile of \(C_{p}\) is the same as 9.1 m/s. In order to avoid the jump of pitch angle at the boundary of low and medium WS regions, the pitch angle should keep operating at \(\beta_{2}\) in low WS region, which can achieve the same power output as \(\beta_{0}\), i.e., 0\({}^{\circ}\).
## 2 Route Generation Methodology
### Lower-Level Path Generation: Ant Colony Optimization (ACO)
#### 2.2.1 Foundation and Basic Principles  
The ACO algorithm was developed for solving discrete optimization problems in a relatively short amount of time [26]. The ACO algorithm falls under the metaheuristic algorithm umbrella. The ACO algorithm uses a simulated colony of ants (search agents) to find near-optimal solutions to complex optimization problems. The ants exploit good solutions through a pheromone update scheme while they explore the solution space through a probability selection stage in graph-like structures.  
The ACO can best be explained through an example. In a graph, the ants begin at a \"nest\" location (start), and they must find the shortest path to the \"food\" location (end).  
Figure 1: Example of a strongly connected graph in a small area with an obstacle.  
Initially, all ants travel along a random path until the food is reached. However, the edges in the shortest path found by the colony are rewarded with a pheromone increase, and the remaining edges undergo pheromone evaporation after each iteration. In the following iterations, the ants are more likely to select edges with higher pheromone levels. This can be visualized in Figure 2.  
The node selection probability can be seen in Equation (1), and the pheromone updating scheme can be seen in Equation (2). In Equation (1), \(p_{j}\) is the probability of travelling to node \(j\) from node \(i\), \(\tau_{ij}\) is the pheromone level on edge \(ij\), \(d_{ij}\) is the distance of edge \(ij\), \(\alpha\) is the pheromone influence factor, \(\beta\) is the distance influence factor, and \(A\) is the set of candidate nodes to visit. In Equation (2), \(L\) is the distance of the best-found solution and \(x_{ij}\) is a binary indicator used to reward edge \(ij\) if it belongs to the best-found path.  
\[p_{j}=\frac{\tau_{ij}^{\alpha}d_{ij}^{-\beta}}{\sum_{k\alpha A}\tau_{ik}d_{ik} ^{-1}} \tag{1}\]  
\[\tau_{ij}=(1-\rho)\tau_{ij}+\frac{1}{L}x_{ij} \tag{2}\]  
As seen in Equation (1), the pheromone levels and edge distance play an important role when it comes to the edge selection stage. As a result, the pheromone and distance influence factors should be carefully tuned to obtain optimal results. Additionally, the ants travel in a stochastic manner. This means that they randomly select the next edge to travel along with a probability that is proportional to the pheromone levels and edge distance.  
Also, the \(L\) parameter in Equation (2) does not have to be the length. This is the fitness of the path created. In some cases, achieving the minimum distance is not the objective of the optimization problem, so this can be changed accordingly.
# Coefficient of Thermal Expansion of RCA Concrete Made by Equivalent Mortar Volume
## 4 Test Results  
### Basic Properties  
The COTE of the SUS-304 specimen, the reference specimen applied to Equation (2) for calculating the COTE of concrete specimens, was measured using a thermomechanical analyzer (TMA). For analysis of the experimental results, the COTE of the mortar, aggregate and cement paste, used for the composite model, was also measured using the TMA. The specimens employed by the TMA had cylindrical shapes of 8 \(\times\) 25 mm. Figure 4 shows photos of the specimens applied to the TMA, and Table 3 shows the results of the COTE measurement.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Specimens**} & \multirow{2}{*}{**Notations**} & \multicolumn{5}{c}{**COTE (\(\times\)10\({}^{-6}\)/C)**} \\ \cline{3-6}  & & **1** & **2** & **3** & **Average** & **Coefficient of Variation (\%)** \\ \hline SUS-304 & \(\alpha_{ref}\) & 15.790 & 15.778 & 15.858 & 15.81 & 0.002 \\ \hline Cement paste & \(\alpha_{p}\) & 19.726 & 21.258 & 18.658 & 19.88 & 1.71 \\ \hline Mortar 1 & \(\alpha_{m}\) & 11.332 & 12.164 & - & 11.75 & 0.35 \\ \hline Mortar 2 & \(\alpha_{m}\) & 12.084 & 12.430 & - & 12.26 & 0.06 \\ \hline NCA & \(\alpha_{e}\) & 7.079 & 7.226 & - & 7.15 & 0.01 \\ \hline RP & \(\alpha_{RP}\) & 8.705 & 9.825 & - & 9.26 & 0.63 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Basic COTE values measured from the TMA.  
Figure 2: Experimental apparatus for measuring COTE and system setup [45].  
Figure 3: Typical temperature cycle applied to concrete specimens [45].  
As pretreatment for analyzing the COTE results of concrete specimens using the composite model, mortar specimens were prepared using the mix applied to mix series 1 and the one applied to mix series 2 and 3, and the elastic modulus of the mortar specimens at the age of 28 days was acquired, as shown in Table 4.