esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The current distribution of fire stations in Shanghai is largely dependent on administrative divisions and traditional standards, which is not rational. Figure 1: The distribution of fire stations and the extent of the built-up area in Shanghai, 2022. The MCLP model is used to ensure that every region has the right corresponding fire and rescue protection [17; 18]. The P-Center [19] and LSCP [20] aim to identify the minimum distance costs and number of facilities. There is still a significant lack of a model that optimizes both urban and rural coverage while considering the cost-effectiveness of site distribution. The regional grading standards for response times provide the foundation for developing a site distribution model based on risk grades. Hierarchical analyses and Bayesian network models are used widely, relying on available data like geospatial [21] and building [22] information. In China, according to the \"Urban Fire Station Construction Standards\" [23], the direct response time required is 5 min, which is the same as that in the United States, followed by the NFPA 1710 principle [24]. In both the UK and Hong Kong, response times for the highest risk level, which is determined by factors such as population density, land development density, and building characteristics, should not exceed 5 min [25]. This would avoid wastage of firefighting resources and cover all areas as reasonably as possible [26] and is a feasible solution to build a new model. Therefore, this work proposes to develop the traditional MCLP model for the risk classifications of respective areas. A hierarchical method of the model relying on fire response times was proposed and the usage of the model in Shanghai was verified. This approach enabled the meeting of new demand for fire station construction due to the significant increase in coverage areas while effectively utilizing firefighting resources.
# Full-Duplex MAC Protocol for CSMA/CA-Based Single-Hop Wireless Networks
## 3 Proposed FD MAC Protocol
### Three Scenarios for the Protocol  
The three scenarios where the frames are sent by the \(n\) nodes and their corresponding protocol design requirements are discussed as follows:  
First, when only one sender sends the frame, the receiver can receive the frame in the HD mode correctly. No collisions will occur. However, to increase channel utilization, we should require the receiver to send a \"reverse\" frame to the sender at an appropriate time. Second, when three or more senders send their frames simultaneously, any node in the network will receive the transmitted signals from at least two other nodes. Regardless of how the protocol is designed, the collisions must occur. What is needed is to minimize the channel access time wasted by the collisions. Finally, when exactly two senders (A and B) send the frames simultaneously, they have the following four communication modes (Figure 1):  
In Figure 1, the dashed line represents the interference signal, and the solid line represents the desired signal. As shown in Figure 0(a), when the two nodes (A and B) send the frames for each other, the two frames can be received correctly. When only one of the two nodes sends the frame to the other side, as shown in Figure 0(b), only one node (B) can receive the frame correctly. When the two nodes send the frames for the same node or for the other two nodes, as shown in Figure 0(c),d, respectively, the two transmissions will interfere with each other and no receiver can receive the frame correctly. To this end, the protocol design can be divided into two cases. First, for the communication mode as shown in Figure 0(a), we do not need to adjust the FD communication currently in progress. Second, note that  
Figure 1: Four communication modes corresponding to the two nodes sending signals simultaneously.  
the other communication modes either fail to exert the FD capabilities of the nodes (Figure 1b) or fail to produce effective throughput in the network (Figure 1c,d). Therefore, at an appropriate time, we should adjust them to the mode in which the two nodes send the frames for each other. For this problem, the MAC protocol proposed in [5] adds an \"octal\" field to the original RTS frame structure. The content of this field is a random number, which is used to compute a priority with the MAC address of the current node. When two nodes send the request frames simultaneously, the protocol can specify the FD communication according to this priority.
## 3 Results  
Table 1 shows the comparison of the ELO variables between FM and WWC. Moderate and significantly greater TD and TDr were found in WWC (\(p\) = 0.025, ES = \(-\)0.74 and \(p\) = 0.017, ES = \(-\)0.6, respectively). Moreover, greater HSR was observed in WWC matches (\(p\) = 0.088; ES = \(-\)0.43) versus FM.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{2}{c}{**Friendly Match**} & \multicolumn{2}{c}{**Women World Cup**} & \multicolumn{4}{c}{**90\% CI**} \\
**Variables** & **M** & \(\pm\)**SD** & **M** & \(\pm\)**SD** & **ES** & **LL** & **UL** & **_p_** \\ \hline MM (min) & 88.1 & 3.37 & 89.7 & 0.58 & \(-\)0.79 & \(-\)1.84 & 0.26 & 0.200 \\ TD (m) & 9402 & 1044 & 10,054 & 729 & \(-\)0.74 & \(-\)1.24 & \(-\)0.23 & 0.025 \\ HSR (m) & 531 & 166 & 601 & 161 & \(-\)0.43 & \(-\)0.85 & \(-\)0.02 & 0.088 \\ NS (count) & 40.8 & 14.3 & 45.0 & 10.22 & \(-\)0.34 & \(-\)0.84 & 0.16 & 0.244 \\ HSR\({}_{\text{R}}\) (m/min) & 6.03 & 1.94 & 6.7 & 1.81 & \(-\)0.36 & \(-\)0.79 & 0.07 & 0.164 \\ NS\({}_{\text{R}}\) (count/min) & 0.46 & 0.17 & 0.5 & 0.11 & \(-\)0.26 & \(-\)0.76 & 0.23 & 0.353 \\ MV (km/m) & 25.4 & 1.60 & 26.8 & 5.58 & \(-\)0.39 & \(-\)1.42 & 0.63 & 0.499 \\ PL (AU) & 992 & 143 & 942 & 224 & 0.28 & \(-\)0.36 & 0.91 & 0.447 \\ TD\({}_{\text{R}}\) (m/min) & 106 & 9.77 & 111 & 8.61 & \(-\)0.60 & \(-\)0.97 & \(-\)0.22 & 0.017 \\ PL\({}_{\text{R}}\) (AU/min) & 11.3 & 1.60 & 10.5 & 2.44 & 0.41 & \(-\)0.17 & 0.99 & 0.228 \\ \hline \end{tabular}
\end{table}
Table 1: Description and differences of external load between periods.  
Figure 3 shows the intra-subject differences (\(p\) < 0.05) between FM and WWC, for the external load variables, TD (\(p\) = 0.025), HSR (\(p\) = 0.088) and TD\({}_{\rm r}\) (\(p\) = 0.017).  
Figure 4 shows the standardized difference in external load between periods.  
Table 2 shows the comparison of ILO between FM and WWC. In this regard, significantly greater RPE was found for WWC (\(p\) = 0.001; ES = \(-\)1.50). Likewise, significantly better wellness scores were observed after FM, at ST24h, FT48h and MS48h (\(p\) = 0.038; SD = 0.72; \(p\) = 0.066, SD = 0.71; and \(p\) = 0.063; ES = 0.77, respectively).  
Figure 4: Standardized difference of external load between periods. FM friendly match; WWC Women world cup; MM match minutes; TD total distance; HSR high speed running; NS number of sprint; MV maximum velocity; PL player load; R relative to the minutes of the match; AU arbitrary unit; * \(p\) < 0.1.  
Figure 3: Bar chart with significant differences between periods. FM friendly match; WWC Women world cup; (**A**) TD total distance; (**B**) HSR high speed running; (**C**) TD\({}_{\rm r}\) total distance relative to the minutes of the match; * effect size small; ** effect size moderate.  
Figure 5 shows the intra-subject differences (\(p\) < 0.05) between FM and WWC, for the internal load and wellness variables, RPE (\(p\) = 0.001), Stress (\(p\) = 0.038), Fatigue 48 h (\(p\) = 0.066), and Muscle pain (\(p\) = 0.063).
## 3 Theoretical Analysis  
We first present the system model analysis to determine the block orthogonal code's PER, throughput, power consumption and latency. Second, we analyze two strategies to detect the transmitter's direction for mobile networks in the case study. Third, we propose the MAC protocol based on our LDWuR to enhance Wi-Fi power savings in the other case study.
# A High Thermal Conductivity of MgO-H\({}_{2}\)O Nanofluid Prepared by Two-Step Technique
###### Abstract
The synthesis technique was concluded by heating the mixture of molten salt and MgO nanoparticles to 350 \({}^{\circ}\)C and ultrasonication time for 1 h. The effect of nanoparticles on thermal properties was studied with different sizes ranges of 20-100 nm, weight concentrations from 0.5 to 2.0 wt.%, and temperature. The results showed that the enhancement of thermal conductivity of salt with 1 wt.% nano-MgO could be 5.3-11.7%. In an experimental environment, [PERSON] et al. [61] improved the thermal conductivity of a CuO-MgO/H\({}_{2}\)O hybrid nanofluid in an experimental setting. The hybrid nanofluid was synthesized and investigated at various volume fractions (i.e., 0.125-1.25%) of 80% CuO and 20% MgO nanoparticles which were dispersed in water at temperatures ranging from 25 to 50 \({}^{\circ}\)C. The zeta potential measurement indicated that the 0.25% (CuO-MgO)/H\({}_{2}\)O hybrid nanofluid had a good stability of \(-\)45.1 mV. The study results showed that by increasing the concentration ratio, the CuO-MgO/H\({}_{2}\)O hybrid nanofluid's thermal conductivity was clearly enhanced; however, this enhancement began to decline when the concentration was higher than 1 vol.% as a result of nanoparticle precipitation. The two-step approach is often supported by the aid of relatively high temperatures, and/or the use of surfactants, and/or a long ultrasonication treatment time, and/or organic base fluids. Therefore, in the present study, we aimed at preparing MgO-DW nanofluid with efficient thermal properties such as better stability, high thermal conductivity, and low viscosity, without using surfactants or stabilizers, and with environmentally friendly nanofluid at room temperature. Furthermore, the plurality of the concerned investigations has been considered in order to exploit MgO nanopowder as an additive or to synthesize hybrid nanofluid with other nanomaterials, as cited in [62; 63; 64; 65; 66]. However, in this work, pure MgO-DW nanofluid was produced with functional properties. This was achieved by using MgO nanopowder with a unique morphology of feathery or fluffy-like nanostructure, while usually, the nano shape used in nanofluid preparation is spherical particles. The abovementioned aspects are all assumed to be novel, making MgO-DW nanofluid an excellent alternative for heat transfer applications.
## 3 Results
### Data Synthesis
Based on the authors' discoveries, most of the articles have conducted test-retest reliability assessments (n = 29), evaluated the reliability related to measurement conditions involving subjects (n = 37), and scrutinised the equipment's reliability (n = 20). Following this, two types of validity tests were explored in this analysis, specifically criterion and construct validity. Among these, only 14 articles reported performing criterion validity tests, wherein the research compared the employed measurement techniques against the gold standard of measurement methods. None of the articles, however, incorporated the use of construct validity tests in their evaluations. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline  & & \multicolumn{4}{c}{**Reliability**} & \multicolumn{4}{c}{**Validity**} \\ \cline{3-8}
**Studies** & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } & \multicolumn{2}{c}{**Operator Reliability**} & \multicolumn{2}{c}{**Measurement**} \\ \cline{3-8}
**Studies** & & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \cline{2-8}
**Studies** & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \hline [PERSON] et al. [26] & UC & - & - & \(\surd\) & \(\surd\) & UC & - \\ \hline [PERSON] [27] & \(\surd\) & - & - & UC & \(\surd\) & - & - \\ \hline [PERSON] et al. [43] & - & - & - & \(\surd\) & UC & - & - \\ \hline [PERSON] et al. [21] & \(\surd\) & - & - & \(\surd\) & \(\surd\) & \(\surd\) & - \\ \hline [PERSON] et al. [28] & - & - & - & \(\surd\) & \(\surd\) & \(\surd\) & - \\ \hline [PERSON] et al. [44] & - & - & - & - & - & \(\surd\) & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of the selected articles’ evaluation based on reliability and validity of residual limb volume measuring techniques. Order is based on publication year (from oldest to most recent year).
# Clock Ensemble Algorithm Test in the Establishment of Space-Based Time Reference
## 2 Clock Ensemble Algorithm
### Reduced Kalman Clock Ensemble Algorithm  
The generated timescales for the NKT algorithm tend to optimize the long-term stability of the corrected clocks, regardless of their short-term stability [47]. In Kalman filter estimation, the diagonal phase variance entries of the error covariance matrix \(P\) grow without bound [48]. If the phase error variance element of \(P\) is set to zero after each measurement without disturbing the desired output of the Kalman filter, the P matrix can be prevented from becoming out of control, thus giving a more stable calculation. This operation is called x-reduction [16].  
\(P\) is x-reduced, it is shown that  
\[P_{ij}=\begin{bmatrix}0&0\\ 0&p(y_{i},y_{j})\end{bmatrix}\]  
The reduced Kalman timescale, which is designed to minimize the mean squared increments of the corrected clocks instead of their values at one time, produces good results for stability over all available averaging times [48].
# Reliability and Efficiency of Metamodel for Numerical Back Analysis of Tunnel Excavation
## 3 Datasets
### Ground Parameters  
The parameters \(\gamma\), UCS, GSI, m\({}_{i}\), K, and disturbance factor (D) are required to use the Hoek-Brown model as the constitutive model in FLAC. From these six factors the elastic modulus, Poisson ratio, tensile strength, and constants for the rock mass m\({}_{\rm b}\), s, and a, which are required for the numerical analyses, can be calculated [15; 16]. Here, D = 0 was used by assuming that all blasting was performed with very good quality control. Finally, the remaining five factors except D were selected as the dependent variables. Similarly to this selection, [PERSON] [17] had selected tunnel depth, UCS, GSI, m\({}_{\rm i}\), and K as dependent variables to evaluate the behaviour of rock masses at a deep depth. Except for tunnel depth, four factors are the same as the selected dependent variables in this study. The vertical displacement of the tunnel crown was selected as the independent variable since it is the maximum displacement that occurs around a tunnel and utilised for a back analysis during tunnel construction.
## 1 Introduction
### Partnership and Stakeholders  
The 5 GT System was built by a team of engineers from Italian companies (TIM, Azcom Technology, MBI, Sistematica) and a research laboratory (CNIT) at the forefront of innovation projects dedicated to the integration of 5G satellite and cellular networks. In particular, CNIT, which conceived the system and organized the activities, pursues the mission of technology transfer in port and maritime transport areas recognized by relevant grants from national (ASI) and European (ESA, ETSI, etc.) bodies. The technology phase of the 5G SENSOR@SEA project has received support from first-rate stakeholders: the Port Authorities of the Northern Tyrrhenian Sea and the Eastern Adriatic Sea, Eutelsat, and the shipping company Ignazio Messina &C. SpA, which hosted the tests in navigation on its transcontinental liner container ships.
## 3 Results and Discussion  
### Analysis of the Test Results of the Wideband Low-Reflectance Coating  
Due to the small facet size of the RSOA, it is not feasible to directly measure the reflectance of the chip's facet using instruments such as a spectrophotometer or ellipsometer. Therefore, the wideband low-reflectance coating on the RSOA chip's output facet is equivalently characterized by testing the transmittance of K9 glass samples from the same batch. Figure 4 compares the test results of the four-layer Nb\({}_{2}\)O\({}_{5}\)/SiO\({}_{2}\) film system with the simulated results. The blue line in Figure 4 represents the simulated result of the four-layer Nb\({}_{2}\)O\({}_{5}\)/SiO\({}_{2}\) film system, which is derived from the multi-layer film structure model [46]. We incorporate the simulated thickness into the model, where the refractive index is adjusted from that of the incident medium to the refractive index of K9 glass. The reflectivity of this film system on the K9 glass substrate is obtained through calculation. The red line in Figure 4 represents the experimental results of the four-layer Nb\({}_{2}\)O\({}_{5}\)/SiO\({}_{2}\) film system, as obtained from the transmittance curve of the K9 glass sample after coating, and measured using a spectrophotometer. As can be observed in Figure 4, within the wavelength range of 1500-1600 nm, the experimental values for the K9 glass sample closely match the simulated results. The refractive index of the single layer has been calibrated using a spectrophotometer. The thickness of the four-layer Nb\({}_{2}\)O\({}_{5}\)/SiO\({}_{2}\) thin-film system was determined based on the measured red line in Figure 4 according to the multi-layer film structure model [46]. The layer thicknesses are as follows: Nb\({}_{2}\)O\({}_{5}\)/SiO\({}_{2}\)/Nb\({}_{2}\)O\({}_{5}\)/SiO\({}_{2}\) = 223.62 nm/169.11 nm/105.56 nm/342.85 nm. Compared to the simulated values, the deviation in Nb\({}_{2}\)O\({}_{5}\) thickness is negligible, while the deviation in SiO\({}_{2}\) thickness is only 1 nm. As shown in Figure 1b, a thickness precision of 1 nm is sufficient for the fourth layer to meet the required optical performance standards.  
Figure 3: Morphology of the RSOA under microscope.
# Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm
## 3 Results and Discussion
### Real-Time Data Collection and Analysis  
According to Table 4 and Figure 14, the manually measured maize plant heights across the five experimental groups showed distinct distribution patterns. Trials a, b, and c had average maize plant heights concentrated around 129-160 cm (trial b) and 120-160 cm (trial c), respectively, indicating similar growth stages. Trial d had an average plant height of about 167 cm, with a distribution range of 140-200 cm, suggesting a longer growing period compared to trials a, b, and c. Trial e had an average height of approximately 204 cm, with a distribution range of 200-220 cm, indicating a significantly longer growing period than the other trials.  
Combined with the histograms of point cloud-extracted height data from each experiment, the results show that the distribution curve of point cloud-extracted maize heights closely matches the manually measured heights. This further validates that the point cloud extraction method, based on multi-view geometric 3D reconstruction, can accurately reflect maize plant heights with high measurement accuracy and field applicability.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test No.** & **Total** & **Minimum Value** & **Maximum Values** & **Average** & **Range** \\ \hline a & 86 & 80.0 & 232.2 & 150.3471 & 152.2 \\ b & 64 & 75.6 & 164.0 & 129.0714 & 88.4 \\ c & 60 & 64.7 & 178.1 & 134.2102 & 113.4 \\ d & 60 & 109.7 & 204.5 & 167.7627 & 94.8 \\ e & 60 & 156.6 & 232.1 & 204.43 & 75.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistics of measured heights for the five groups of tests.  
Figure 13: Histogram of estimated maize plant height data for five sets of tests using the point cloud extraction method. The heights for each test (**a–e**) were derived from the processed point cloud data generated by the multi-view reconstruction method.
# A Carrier-Based Gardner Timing Synchronization Algorithm for BPSK Signal in Maritime Communication
## 3 Materials and Methods  
The proposed CB-Gardner in this paper is a feedback loop consisting mainly of two interpolators, a timing error detector, a loop filter, and a numerically controlled oscillator. This feedback loop is executed once per symbol to counteract the time-varying Doppler. Figure 3 illustrates the structure and model of the proposed CB-Gardner.  
The fundamental difference between CB-Gardner and Gardner algorithm lies in the error detector, which is mainly reflected in the difference of the location of error detection point and the change of the error detection equation. Error detection in the CB-Gardner can be divided into two steps: finding the error-detection point and calculating the timing error.  
Figure 3: The structure of carrier-based Gardner algorithm.  
Figure 2: Timing error detection schematic of Gardner algorithm.  
An interpolator is also needed before calculating the timing error. Next, we will describe the detailed process of the algorithm.
# A Model-Based Engineering Approach for Evaluating Software-Defined Radio Architecture
## 3 Analytical Models
### Thermal Analysis
\(k\) is the thermal conductivity of the enclosure material in W/(m K), \(T_{AES}\) is the Average Electronic Surface Temperature in \({}^{\circ}\)C, \(t\) is the enclosure thickness in m, \(h_{c}\) is convection heat transfer coefficient in W/(m\({}^{2}\) K), \(T_{a}\) is air temperature inside the CEAE, and \(A\) is the power dissipation area in m\({}^{2}\). The thermal model illustrated in Figure 5b uses a different approach to dissipate the generated heat. It shows that the heat generated by the internal SDR-MM electronic components is first conducted through the enclosure and heat sink, and then dissipated to the CEAE through either natural or forced convection heat transfer. First, the required thermal resistance for a thermal solution (\(R_{the-sol}\)) is calculated by Equation (8) at the given maximum SDR-MM operating temperature (\(T_{max}\)) and maximum estimated power dissipation (\(PD_{max}\)) from all SDR-MM elements. Then, the total resistance, including the calculated thermal solution resistance, and the AEST are calculated using Equations (9) and (7), respectively. \[R_{the-sol}=\left\{\left(T_{max}-T_{a}\right)/PD_{max}\right\}-\left(R_{t,PCB}+2 *R_{t,TIM}+R_{t,enc}\right) \tag{8}\]  
\[R_{total}=R_{PCB}+2*R_{t,TIM}+R_{t,enc}+R_{the-sol} \tag{9}\]  
Figure 5: Thermal management model to predict the Average Electronic Surface Temperature (AEST): (**a**) heat is conducted to the enclosure and then transferred to the CEAE through free convection heat transfer; (**b**) heat is first conducted to the enclosure and heat sink, and then dissipated to the CEAE through either natural or forced convection heat transfer.