esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Facility Grid Division and Walking Time Calculation  
In the classification of urban facilities, the first step is to filter and categorize a total of 387,109 POI points based on their relevance to elderly care services, using the established indicator system. These data cover 9 municipal districts and 172 streets (townships) in Changsha City.  
To analyze the spatial distribution of facilities in greater detail, Changsha was divided into 48,445 grid cells (500 m \(\times\) 500 m) based on typical service radii of elderly care facilities and optimal walking distances for older adults [42]. The Intersect tool in ArcGIS was employed to statistically aggregate facility counts per grid cell by category, generating a decision feature matrix for spatial modeling.  
The walkable life circle was calculated using road network data of Changsha City, based on the Network Dataset module within ArcGIS, to evaluate the distribution of service facilities or resources accessible within specific walking time thresholds. This study uses the average walking speed of the elderly (1.8 miles/h) as the benchmark [74; 75] to calculate the POI facilities accessible within 5-min, 10-min, and 15-min walking time thresholds. This approach quantifies the service coverage level of the living circle around elderly care facilities.
# Beamforming Based on a SSS Angle Estimation Algorithm for 5G NR Networks
## 2 System Model
### Transmitter Model Description  
The transmitter, gNB, equipped with \(N_{TX}\) antennas, is responsible for the generation and transmission of a DL waveform, which is subject to some BM techniques used in the IA procedure for establishment of an effective connection between the gNB and the UE. After that, an SS burst is generated and configured carrying the master information block (MIB) and the demodulation reference signals (DMRS) for the PBCH demodulation. Then each SSB is beamformed within the burst and swept over both azimuth and elevation specified directions within the SS burst's periodicity. This beamformed signal is then transmitted over a spatial-scattering channel, which is presented in the following section, and then processed over each one of the multiple receive-end beams in a dual-end beam-sweeping process [16; 18].
# A Power-Line Communication System Governed by Loop Resonance for Photovoltaic Plant Monitoring
## 4 Discussion  
Within this paper, as a natural continuation of our previous paper [16] where a simple loop communication theory was exposed and tuning circuit was designed, a low-cost practical implementation of this ring topology PLC has been fully developed. An auspicious performance is observed from this PLC system governed by loop resonance. The modulated signal is successfully demodulated and digitally recovered at a 1 MHz carrier, along both communication directions. The circuitry for the implementation of transceivers is extremely simple with a very low-cost microcontroller as the central component and a few external components where the main workload is imposed on the firmware. The configuration described has successfully worked in our TX-RX tests showing a very good performance despite its simplicity and low cost.  
One of the novelties presented is the anti-saturation coil designed to avoid the core saturation related to high DC currents in conventional coils or transformers. This solution resolves saturation effects because of the high DC currents present in PV strings. We have used this component in our system as an AC blocking coil, but its application can be extended to many other situations presented in the previous literature (coils or transformers), or even applications out of the PV scope. An autoresonance frequency has been observed for  
\begin{table}
\begin{tabular}{c c c} \hline \hline
2  
3  
4  
\end{table}
Table 1: Prototype communication parameters.  
Figure 4: Picture of the real circuit used for testing.  
this anti-saturation coil that could find application in transmission/ reception transformers where the primary or secondary binding is made from our proposed component.  
The cost of each PV module board amounted to 5 Euros, and adding the combiner box circuitry (communications board, loop tuning circuit, inverter blocking coils, and bypass capacitors), the cost attained is about 12 Euros per PV module for a 11 modules string, that was not lower than the lowest cost seen in previous literature (4.52 Euros) per PLC board but surpassed significantly the transmission rate performance of previous studies with favorable signal attenuation. Table 2 shows a comparison of the different PV PLC systems found in the literature relative to ours regarding the cost and communication speed achieved (where indicated) or in terms of carrier frequency. The intent is to integrate this developed PLC system with our I-V curve tracing strategies found in [4]. Further studies will be done to characterize the loop and ascertain the performance of its topology over a range of frequencies and varying electrical lengths.
# Measuring Efficiency and Effectiveness of Highway Management in Sustainability
## 3 Data, Methods, and Results  
The study examines whether the two values, effectiveness and efficiency, are compatible and what the determinants of effectiveness are. The examination comprises a three-step process: (1) to measure efficiency; (2) to measure effectiveness with multiple quality factors; and (3) to examine whether the two values have a relation, controlling for other factors. For the purpose of the examination, we employ highway infrastructure cases. The unit of analysis is the state, and the analysis model is a spatial panel regression model. Thus, the data are obtained from the 48 adjoining U.S. states, excluding the non-contiguous states, Hawaii and Alaska, in order to use a spatial panel data model. Data cover 11 years (2000-2008 and 2012-2013). The data sources, Highway Statistics series, have some missing values at one point-in-time. For example, the 2009 data source does not have the information of \"Estimated lane-miles\" (HM-81) and \"Weighted average daily traffic per lane\" (HM-62). The data 2010 does not provide \"Length by ownership\" (HM-10), expenditure for highway (SF-12), pavement roughness (IRI, HM-64), and HM-62. SF-12 is missed in the 2011 data source. Highway Statistics, the Book of States, Comprehensive Annual Financial Reports (CAFR), National Association of State Budget Officers' (NASBO's) State Expenditure Report, and the U.S. census data are the sources of data.
# Improving Conceptual Modeling with Object-Process Methodology Stereotypes
## 2 Literature Review
### Internet of Things (IoT)  
The Internet of Things (IoT) is a vision of a ubiquitous Internet that integrates everyday physical objects via information networks. Such objects must also have the capability to communicate, even in environments where fixed network access infrastructure is weak or nonexistent. The increasing role of software in systems calls for a parallel change in the ways new systems are modeled and developed, and in how the software components of these systems are created and tested alongside the hardware they control.  
Traditionally, systems engineers have not been software experts, and the development of the software embedded in such systems has been the role of specialized software engineers and programmers. This hardware-software development separation has created a chain of activities with systems engineers first defining the system. Only later, the software development could be delegated to software engineers and programmers, who often lack system-level understanding, resulting in software that does not serve the function of the system in the best possible way. When the software is developed, it has to be tested on the hardware of the system and in its operating environment, revealing bugs and problems that must be resolved, causing major rework, delayed project completions, cost overruns, and damage to corporates' reputation.  
The concept of hardware-software co-design is not new [23], but it has been limited almost exclusively to the design of \"embedded systems\"--a narrowly construed concept of developing the hardware of digital circuits hand-in-hand with their associated software. In this specific context, hardware-software co-design involves such issues as concurrent programming and field-programmable gate arrays [24]. The need for a paradigm shift in the development of software-intensive systems is growing hand-in-hand with the intertwined embedding of software within hardware in current advanced \"smart\" systems and almost all future systems, which can increasing be classified as IoT systems.
## 3 Results and Discussion
### Physical-Chemical Analysis of Fruits
#### 3.2.3 Titratable Acidity  
The acidity index is directly related to pH, due to the natural fruit ripening where organic acids are consumed. The decomposition of these acids is used as a respiratory substrate for the synthesis of new compounds, which causes the fruit to become less acidic over time, as shown in Table 2. The barrier properties of the coatings limit the amount of oxygen absorbed by the fruit, consequently reducing respiration and thus decreasing the consumption of organic acids [55].  
The multifactorial statistical analysis showed that the factors (Days/Treatment) have an incidence concerning the response variable with a significant difference at a 95% confidence level. The acidity decreased from day 6, but it could be a result of the concentration of the organic acids in the media as a result of the water loss. The coated samples had a significantly higher amount of organic acids inside the fruit, as can be seen from the acidity measurements. The multiple test ranges between the treatments show that there was a statistically significant difference (\(p<0.05\)) in all the treatments concerning the control. This result demonstrated that the incorporation of the procedures preserves the organic acids inside the fruit, delaying the ripening process, possibly due to the different atmosphere inside the fruit [55].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Time** & **Treatment** & \multicolumn{3}{c}{**pH**} & \multicolumn{3}{c}{**TA (\% Citric Acid)**} & \multicolumn{3}{c}{**TSS (\%Brix)**} & \multicolumn{3}{c}{**Mature Index (\%)**} & \multicolumn{3}{c}{**Damage Index (\%)**} \\ \hline  & Control & 3.65 & \(\pm\) & 0.07 \({}^{\rm
###### Abstract  
Partially guided implant surgery has emerged as a technique that enhances the precision of implant placement while maintaining surgical flexibility. This in vitro experimental study evaluated the influence of the milling cutter drill on the angular and linear deviations of implant placement in synthetic polyurethane bone models using a partially guided surgical protocol. Additionally, the effects of bone density and implant macrogeometry were assessed. A total of 120 Straumann(r) implants (BL, BLT, and BLX) were placed in polyurethane blocks simulating four bone densities (D1-D4). Implant positions were virtually planned with coDiagnostiX(r) (version 10.9) software and executed with or without the use of the milling cutter drill. Deviations between planned and final implant positions were measured at the neck and apex using the software's \"Treatment Evaluation\" tool. The use of the milling cutter drill significantly reduced angular deviation (\(p\) = 0.007), while linear deviations showed no statistically significant differences. Bone density and implant macrogeometry did not significantly affect angular deviation but influenced linear and 3D deviations. Given that angular deviation may compromise prosthetic fit and biomechanical function, the observed reduction is of potential clinical relevance. These findings indicate that the milling cutter drill enhances angular accuracy in partially guided implant surgery and may improve outcomes in anatomically challenging cases. However, the results should be interpreted within the limitations of this in vitro model, including the absence of soft tissue simulation, intraoral constraints, and inter-operator variability.  
Article
# Numerical Investigation of Thermal Efficiency of a Solar Cell
## 2 Materials and Methods
### Numerical Setup  
#### 2.2.1 Grid Independence Test  
In order to study the variation in results because of grid size, several elements throughout the computational domain are varied from 400,000 to 1,300,000. Boundary conditions in all the cases were fixed. The simulation was carried out at a heat input of 326 W/m\({}^{2}\) with a water inlet mass flow rate of 0.025 kg/s. The minimum and maximum face size used in performing grid independence tests were 3.25 \(\times\) 10\({}^{-4}\) and 3.25 \(\times\) 10\({}^{-3}\) (Figure 4); only the number of elements in the computational domain was varied. A commercial tetrahedral mesh is used in a basic solver pack of ANSYS CFX 18.3, ANSYS Inc., Canonsburg, PA, USA.  
As seen in Figure 5, implementing several elements above 1,263,000 shows the same temperature across the outlet. Hence, the use of grid size carrying 1,263,000 number of nodes is optimal for achieving accurate results. The computational time required for convergence using this grid size is 3 h with 12 core of the processor on the server.  
Figure 4: Computational grid: minimum face size = 3.25 \(\times\) 10\({}^{-4}\), maximum face size = 3.25 \(\times\) 10\({}^{-3}\). (**a**) Number of elements 12,000; (**b**) number of elements 1,000,000; (**c**) number of elements 1,263,000.
# Effect of Multiple Reclosing Time Intervals on Axial Vibration of Winding
## 2 Dynamic Model of Winding Axial Vibration
### Solving the Axial Vibration Model
To facilitate the subsequent calculation, Equation (1) is written in matrix form:  
\[\mathbf{M}\big{\{}\ddot{x}(t)\big{\}}+\mathbf{C}\big{\{}\dot{x}(t)\big{\}}+\mathbf{K} \big{\{}x(t)\big{\}}=\big{\{}f(t)\big{\}} \tag{2}\]  
where \(\mathbf{M}\) is the \(n\)-order matrix of disk mass, \(\mathbf{C}\) is the \(n\)-order matrix of damping, \(\mathbf{K}\) is the \(n\)-order matrix of stiffness, \(\big{\{}\ddot{x}(t)\big{\}}\) is the n-dimensional vibration acceleration column vector, \(\big{\{}\dot{x}(t)\big{\}}\) is the vibration \(n\)-dimensional velocity column vector, \(\big{\{}x(t)\big{\}}\) is the \(n\)-dimensional displacement column vector, and \(\big{\{}f(t)\big{\}}\) is the \(n\)-dimensional electric force column vector. Figure 1: Axial mass-damping-spring model of winding. In the case of a transformer short circuit, to facilitate the analysis, only periodic and transient components of the short-circuit current are considered [20]:  
\[I=I_{m}\Big{(}e^{-\frac{p}{L}t}-\cos\omega t\Big{)} \tag{3}\]  
where \(I\) is the steady short circuit current; \(I_{m}\) is the amplitude of the alternating component of short-circuit current; \(L\) is the short circuit equivalent inductance of the system; and \(R\) is short-circuit equivalent resistance of the system [21]. Then, the electromagnetic force \(F\) acting on the winding is  
\[F=\frac{1}{2}pI_{m}^{2}\left(e^{\frac{-2R}{L}t}-2e^{\frac{-R}{L}t}\cos\omega t +\frac{1}{2}\cos 2\omega t+\frac{1}{2}\right) \tag{4}\]  
where \(p\) is the electromagnetic force coefficient and the electromagnetic force frequency is twice \(\omega\), which is the current frequency [22]. The system characteristic formula is  
\[\Big{|}K-\omega_{n}{}^{2}M\Big{|}=0 \tag{5}\]  
where \(\omega_{n}\) is the natural frequency of the \(n\)-order mode shape. The natural frequencies of the \(r\) order are substituted into the eigenvalue problem formulas of the system successively. \[K\{u\}=\omega_{n}{}^{2}M\{u\} \tag{6}\]  
where \(\{u\}\) is the modal row vector.
# Droplets Transmission Mechanism in a Commercial Wide-Body Aircraft Cabin
## 2 Methods
### Numerical Simulation Method
#### 2.2.2 Droplets Model  
The temperature of droplets is determined by the heat balance in the evaporation process. When the influence of radiation is ignored, the heat balance equation of droplets is given as follows.  
\[m_{p}c_{p}\frac{dT_{p}}{dt}=h_{p}A_{p}(T-T_{p})-\frac{dm_{p}}{dt}h_{fg} \tag{5}\]  
where \(m_{p}\) is the mass of droplets, \(c_{p}\) is the heat capacity of droplets, \(T_{p}\) is the temperature of droplets, \(h_{p}\) is the convection heat transfer coefficient between droplets and air, \(A_{p}\) is the surface area of droplets, \(T\) is the temperature of local airflow and \(h_{fg}\) is the latent heat.  
The influence of convection cannot be ignored in the evaporation process since the evaporation rate of droplets entering the aircraft cabin is fast. Therefore, the Convection/Diffusion Controlled Model is selected as the mass transfer model, and the evaporation rate can be expressed as:  
\[\frac{dm_{p}}{dt}=k_{c}A_{p}\rho\ln(1+B_{m}) \tag{6}\]  
where \(k_{c}\) is the mass transfer coefficient, \(\rho\) is the density of gas and \(B_{m}\) is given by:  
\[B_{m}=\frac{C_{s}-C_{\infty}}{1-C_{s}} \tag{7}\]  
where \(C_{s}\) is the mass fraction of water vapor on the particles surface and \(C_{\infty}\) is the water vapor mass fraction in the mainstream.  
The effects of gravity, Saffman force, drag force, buoyancy and thermophoresis force are considered in this paper. According to the force of droplets and [PERSON]'s second law, the motion equation of the particle orbit can be established:  
\[m_{p}\frac{du_{p}}{dt}=F_{G}+F_{D}+F_{T}+F_{S} \tag{8}\]  
where \(m_{p}\) is the mass of droplet, \(u_{p}\) is the motion velocity of droplet, \(F_{G}\) is the gravity and buoyancy, \(F_{D}\) is the drag force, \(F_{T}\) is the thermophoresis force and \(F_{S}\) is the Saffman force.
# Characteristics of GMAW Narrow Gap Welding on the Armor Steel of Combat Vehicles
## 2 Experimental Procedures and Results
### Comparision of Mechanical Properties
#### 2.2.1 Tensile Test  
Tensile tests were conducted to compare the tensile properties of the X-groove and NGW parts. The tensile specimen was prepared in accordance with ASTM E8M-05. The tensile test was conducted under a load of 100 kN with a universal testing machine (Instron 8501, Instron, Norwood, MA, USA). The specimens were loaded at a rate of 1.5 kN/min, as per the ASTM standard.  
The results shown in Table 4 are the average value of five specimens. As a result, the NGW part demonstrated a better yield strength, tensile strength, and elongation percentage than the X-groove part. This is because the total welding heat input energy is lower in NGW than in X-groove, the conventional welding joint, which specifically causes the improved tensile strength and elongation percentage. Additionally, lower welding heat input energy of NGW can be explained by comparison of hardness distribution as shown in Figure 4. Narrow and low hardness values of HAZ (Heat Affected Zone), which provides ductile characteristics, helps to improve tensile strength and elongation in NGW.
## 5 Results and Discussion
### Performance Units
#### 5.2.1 Packet Delivery Ratio (PDR)  
It is defined as the ratio of the number of successfully received packets to the total number of packets sent. PDR is analyzed as defined in Equation (3).  
\[PDR=\frac{\sum_{i=1}^{n}y_{i}}{\eta^{TRF}\sum_{i=1}^{n}z_{i}} \tag{3}\]  
where '\(y_{i}\)' represents the number of packets accepted by hop '\(i\)', '\(z_{i}\)' represents the number of packets diffused by hop '\(i\)', and '\(\eta_{TRF}\)' represents the average number of surrounding hops in the RF dissemination scale. The '\(\eta_{TRF}\)' value is estimated by using vehicular density. As shown in Figure 6, the PDR of MAMC-MAC is compared with the IEEE 802.11 standard and various other protocols. PDR is maximized with the increment in the number of hops. This is because of the rise in the probability of packet delivery. The protocol performs best transferring message for four nodes.
# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
## 6 Proposed Algorithm: Communication Optimisation Integration
### Optimum UV Placement and Great Circle Path
```
0:\(N_{\mathit{v}n}\), \(N_{\mathit{rn}}=n_{\mathit{UAV}}+n_{\mathit{USV}}\); GPS coordinates;
0: CN (closest real nodes vector), CD (closest nodes distances with respect to virtual nodes)
1: Determine the distances between each virtual node \(\mathit{v}n_{i}\) and each real node \(\mathit{rn}_{j}\), \(D=[d_{i,j}]\)\(i\leq N_{\mathit{v}n}\), \(j\leq N_{\mathit{rn}}\)
2: Reshape \(D\) matrix as \(BD_{1\times\left(N_{\mathit{v}n},N_{\mathit{rn}}\right)}\)
3: Sort \(BD\) in ascending order as \(BD_{1}\)
4: Find vector \(bd\) (index positions of \(BD_{1}\) in \(BD\))
5: Initialize closest real nodes vector \(CN_{k}=0\forall k\leq N_{\mathit{v}n}\)
6: Initialize visited real nodes vector \(AT=\emptyset\)
7:for\(i\gets 1\) to \(N_{\mathit{rn}}\)do
8:\(c\leftarrow\lfloor bd_{i}/N_{\mathit{v}n}\rfloor\)
9:\(r\gets bd_{i}\%N_{\mathit{v}n}\)
10:if\(r>0\)then
11:\(n\gets c+1\)
12:else
13:\(r\gets N_{\mathit{rn}}\)
14:\(n\gets c\)
15:endif
16:if\(CN_{\mathit{n}}=0\) & \(r\
otin AT\)then
17:\(CN_{\mathit{n}}\gets r\)
18:\(AT\gets AT\cup\{r\}\)
19:\(CD_{\mathit{n}}\gets d_{\mathit{n},r}\)
20:endif
21:endfor
```  
**Algorithm 1** Closest real UV node algorithm  
Because of the devices' heterogeneity and the influence of distance and height between nodes, the virtual nodes' initial placement will not ensure end-end communication. Consequently, based on the communication graph and optimisation algorithm (Algorithm 2), the optimum solution in terms of distance (latitude, longitude) and height are found. Thus, we started with a hypothetical placement solution assuming that real nodes are placed in the same positions as homologous closest virtual nodes. Algorithm 1 was evaluated, and the results are given in Table 9.  
We observe that for \(n_{UV}=n_{UAV}+n_{USV}\leq 20\), neither the number of UVs nor the distance between WSN and GW does not impact the execution time significantly. The algorithm was also tested for \(n_{UV}=52\), where the execution time increased by 0.0132 s as compared to the scenario when \(n_{UV}=20\), for the same distance of 5 km. When \(n_{UV}=100\), the execution time increases 2.33 times with respect to \(n_{UV}=52\) and 3.68 times with respect to \(n_{UV}=10\). Consequently, we notice that the distance between the gateway and WSN has a lower impact on the time performance than the number of UVs employed in the simulations. The proposed great circle approach together with Algorithm 1 is useful for reducing the search space for the optimal position, determined further using Algorithm 2. If instead of Algorithm 2 proposed further, one would use backtracking algorithm--suitable for solutions to such NP-hard problems, the complexity would increase to \(n\times k\), where n is the number of UVs and k is the number of different positions each of them can have in the entire space.