esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation and Experiment
### Test on Time Series in Practical Systems
#### 4.2.2 Test Results  
The experiment is similar to the simulation; the prediction results are obtained from the proposed and contrast methods with different step lengths. The general results are shown in Figures 14 and 15.  
Figure 11: Original practical data used in the experiments. (**a**) Data from food safety fraction ratio. (**b**) Data from SO\({}_{2}\) concentration.  
Figure 12: Seasonal subsequences of the food safety fraction ratio. (**a**) Seasonal subsequences of original practical data. (**b**) Seasonal subsequences of the augmentation set from the food safety fraction ratio.  
Figure 13: Seasonal subsequences of the SO\({}_{2}\) concentration. (**a**) Seasonal subsequences of original practical data. (**b**) Seasonal subsequences of the augmentation set from the SO\({}_{2}\) concentration.  
From Figures 14 and 15, the practical data show pronounced fluctuations without evident periodicity. ARIMA can hardly trace dramatic data changes, especially for the large saltation. LSTM, transformer, and the GRU can only present the general trend, of which the compactness to the true value is low. Compared with the virtual dataset with a relatively simple fitting data trend, transformer's performance is not ideal when fitting the real dataset with a more complex trend. The results of the CI-GRUs and A-CI-GRUs are closer to the benchmark data, although there is a minor backward shifting in the prediction results for SO\({}_{2}\). The corresponding absolute errors are presented in Figures 16 and 17. The factual errors are calculated according to the types of methods and prediction steps, which are listed in Table 5.  
Figures 16 and 17 show a noticeable distinction in various methods. For the error in the five-step method, the A-CI-GRUs reduce the RMSE by 0.54 in the CI-GRUs, 0.24 in transformer, 1.22 in the GRU, 2.68 in LSTM, and 1.99 in ARIMA in the prediction of the food safety fraction ratio. Similarly, the RMSE decreases from 8.69 to 2.87, and MAE reduces from 5.74 to 2.17 in the prediction of the SO\({}_{2}\) concentration. There is also a performance reduction with the prediction step length increasing. The average increment of the RMSE in the A-CI-GRUs is 0.49, whereas it is 1.16, 0.40, 0.66, 1.14, and 0.72 using the other contrast methods. Tables 4 and 5 show that the A-CI-GRUs perform best in the five datasets derived from the RMSE and MAE indicators. This verifies that the proposed prediction framework is practical. In addition, when the backward prediction steps of the model increase, the prediction error increment of the A-CI-GRUs is also smaller than that of the comparison model.  
Figure 14: Prediction results of the fraction ratio in the food safety spot check system.  
Figure 15: Prediction results of the SO\({}_{2}\) concentration in the atmospheric quality monitoring system.
###### Abstract
However, most existing time-domain methods assume idealized pile head boundary conditions, such as fully fixed or fully free, without adequately addressing the flexible constraints commonly encountered in real-world engineering scenarios. Pile head rotation under flexible constraints is a common issue in structures such as offshore jacket foundations, pile-supported wind turbines, and precast tubular piles with inadequate anchorage. Such rotation can occur either within the pile cap or within the superstructure. Even in pile groups with rigid pile head connections, axial deformation of the piles can induce rotation at the pile head [19; 20]. In these cases, the pile head behaves as a flexible restraint, between fully fixed (zero rotation) and fully free conditions, with its rotational stiffness significantly influencing the horizontal load-bearing characteristics of the pile [2021]. [PERSON] et al. [21] modeled the semi-rigid pile head connection using linear springs and analyzed the natural frequencies and relative stiffness of partially embedded piles. [PERSON] et al. [22] investigated the effects of different pile head boundary conditions (fixed and hinged) on the seismic response of piles under harmonic earthquake waves. [PERSON] et al. [23] proposed a simplified calculation method for horizontally loaded piles under varying rotational stiffness using the p-y curve method, while [PERSON] et al. [24] studied the effects of rotational stiffness and yielding moment on the horizontal transient response of piles under Rayleigh wave excitation. This paper proposes a transient dynamic analysis framework for studying the horizontal dynamics of tubular piles in saturated soils with flexible constraints at the pile head. The framework integrates [PERSON]'s equations, the Timoshenko beam model, and a time-domain solution method. By applying the Laplace transform, the differential equations are transformed into algebraic equations, significantly reducing computational complexity. Compared to traditional frequency-domain methods, the time-domain approach captures the dynamic characteristics of transient loads more intuitively, particularly the complex responses during the initial loading phase. Furthermore, the framework incorporates the fluid-solid coupling effects of saturated soils and employs a three-dimensional wave equation to describe the soil's dynamic behavior, providing a more realistic representation of soil-pile interaction dynamics. The objectives of this study are as follows: (1) to propose a time-domain solution method by integrating [PERSON]'s equations and the Timoshenko beam model to analyze the transient horizontal response of tubular piles in saturated soils under flexible constraints at the pile head, (2) to validate the proposed method through numerical simulations and parameter analysis, investigating the effects of key parameters (such as permeability coefficient, pile diameter, and load type) on pile displacement and stress characteristics, and (3) to provide theoretical guidance for the engineering design of pile foundations in complex soil conditions, thereby advancing research in soil-structure dynamics.
## 3 Results and Discussion  
### Numerical Software Validation  
#### 3.1.1 Comparison of Pressure Drop with Experimental Results  
The experimental results on the pressure drop of the RFB single cell for various flow rates were compared with those of the numerical analysis to verify the currently used code. In the numerical analysis, the pressure drop curves were derived based on the analysis results for the flow rates of 0.3, 0.4, 0.6, and 0.8 LPM. Figure 6 shows the experimental and numerical results, wherein both results are in good agreement, as the maximum error is approximately 5.5%.
# Performance Evaluation of a Mesh-Topology LoRa Network
## 6 The ns-3 Simulation Model
### Path-Loss Model  
The work in [35] conducted a large-scale measurement study to quantify the path loss of LoRa networks in urban areas. The study concluded that a log-distance propagation loss model provides good estimations in an urban environment; therefore, it will be the model used in the simulations. The path loss model for the log-distance model is represented by Equation (6):  
\[L=L_{0}+10 nlog_{10}(\frac{d}{d_{0}}) \tag{6}\]  
where \(n\) is the path loss distance, \(d_{0}\) is the reference distance (m), \(L_{0}\) is the path loss at reference distance (dB), \(d\) is the distance (m), and \(L\) is the path loss (dB).  
In all of the simulations performed in this study, a path loss exponent (\(n\)) of 3.76 is used to simulate a dense urban environment. The complex signal propagation characteristics are influenced by buildings, vehicles, and other obstacles that can significantly attenuate signals. The dense urban path loss model accurately accounts for these factors, providing more realistic simulation results and enhancing the reliability of network performance predictions. This assumption is based on the ns-3 model setup in [33], and the same path loss model is used in all simulations of LoRaWAN and LoRaMesh networks. The path-loss model can easily be altered in the simulation model; thus, the results should not be limited to the above proposed model.
## 2 Torsional Thrust Balance Description
### Methodology for the Calculation of the Thrust  
The dynamics of the torsional balance can be described by the harmonic oscillator equation:  
\[\ddot{\theta}(t)+2\zeta\omega_{n}\dot{\theta}(t)+\omega_{n}^{2}\theta(t)=\frac {F_{t}(t)L_{t}}{I}, \tag{1}\]  
\[\zeta=\frac{c}{2\sqrt{I}k}, \tag{2}\]  
\[\omega_{n}=\sqrt{\frac{k}{I}}, \tag{3}\]  
where \(\theta\) is the deflection angle of the arm, \(I\) is the moment of inertia about the rotation axis, \(F_{t}(t)\) is the force produced by the thruster, \(L_{t}\) is the distance between thruster mount position and rotational axis, \(c\) is the damping constant, \(k\) is effective torsional spring rate, \(\zeta\) and \(\omega_{n}\) are the damping ratio and natural frequency, respectively.  
Assume that the thrust generated by the thruster is constant, \(F_{t}(t)=T\), the solution of Equation (1) is:  
Figure 1: Schematic of the torsional thrust balance.  
\[\theta(t)=\frac{TL_{t}}{k}e^{-\zeta\omega_{n}t}\sin(\omega_{n}\sqrt{1-\zeta^{2}}t+ \varphi), \tag{4}\]  
When the arm reaches a steady state in a short period of time, the new position can be found by taking \(t\rightarrow\infty\) into Equation (4):  
\[\theta(t\rightarrow\infty)=\frac{TL_{t}}{k}\Rightarrow T=\frac{k\theta}{L_{t}}, \tag{5}\]  
This equation shows that the thrust \(T\) can be determined by measuring \(\theta\) if \(k\) and \(L_{t}\) are known. However, it is not convenient to directly measure the arm deflection angle \(\theta\). For small deflections, the small angle approximation is valid and the arm deflection \(\theta\) can be determined by measuring the linear displacement at a distance \(L_{s}\) away from the rotational axis:  
\[\theta=x/L_{s}, \tag{6}\]  
The linear displacement can be measured more accurately than the deflection angle; for the instance of \(\theta<4^{\circ}\), the arm position can be determined within 0.1% [5]. The effective torsional spring rate \(k\) is determined by the flexural pivot and thruster power supply wire. Due to the hysteresis of pivot and the wire routing method, \(k\) is not constant and may vary with the arm deflection. Therefore, Equation (5) can be rewritten as:  
\[T=\frac{k(\theta)\theta}{L_{t}}=\frac{k(\frac{x}{L_{s}})\frac{x}{L_{s}}}{L_{t }}=\frac{f(x)}{L_{t}}=F(x), \tag{7}\]  
We can see that determining the thrust comes from function \(F(x)\) and the measured displacement at steady state position \(x\). The function \(f(x)\) can be fitted by exerting a sequence of known force \(F_{e}\) at a certain distance away from the rotational axis and monitoring the corresponding displacement of the arm.
## 4 Strain and Temperature Testing
### Temperature Testing  
The temperature testing of both sensors was performed by putting them inside an oven and testing from room temperature up to a maximum temperature of 225 \({}^{\circ}\)C for two thermal cycles, as shown in Figure 13a. Both sensors were connected to an interrogator (sm130, from Micron Optics) to collect the wavelength and spectrum data. The temperature data were collected using a thermocouple. The trend of the absolute wavelength response of both sensors is similar to that of variation in the temperature, as shown in Figure 13b, where the FP response is plotted on Y-axis and the FBG response on the Y2-axis. The variations in wavelength of both sensors were tracked separately for heating up and cooling down phase, under two thermal cycles. Figure 14a,b shows the sensor response for heating and cooling, respectively, under first temperature cycle. Figure 15a,b shows the corresponding response for the second temperature cycle. LR in all four diagrams stands for linear regression line. Both sensors behave linearly to the temperature with approximately similar sensitivity of 8.85 pm/\({}^{\circ}\)C. The linear regression of the sensor response for both the temperature cycles is shown in Table 4.  
Figure 12: Spectral response of both sensors: (**a**) FP sensor; (**b**) FBG sensor; and (**c**) wavelengths recovered from spectrum.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Wavelength** & **Equation** \\ \hline FBG-Peak & Y = 0.00073 X + 1542.033 nm \\ FP-P1 & Y = 0.00232 X + 1547.095 nm \\ FP-P2 & Y = 0.00233 X + 1547.265 nm \\ FP-d1 & Y = 0.00233 X + 1547.196 nm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Linear Regression of Sensor Wavelength Tracked from Spectrum (Figure 12c).  
Figure 14: Thermal Cycle I: (**a**) heating; and (**b**) cooling.  
Figure 13: Temperature testing: (**a**) thermal cycles; and (**b**) absolute wavelength response.  
Figure 15: Thermal Cycle II: (**a**) heating; and (**b**) cooling.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cycle** & **Phase** & **FP** & **FBG** \\ \hline \multirow{2}{*}{Cycle I} & Heating & Y = (0.00886)X + 1546.89 nm & Y = (0.00881)X + 1541.789 nm \\  & Cooling & Y = (0.00891)X + 1546.852 nm & Y = (0.00883)X + 1541.746 nm \\ \hline \multirow{2}{*}{Cycle II} & Heating & Y = (0.00886)X + 1546.883 nm & Y = (0.00877)X + 1541.773 nm \\  & Cooling & Y = (0.00883)X + 1546.861 nm & Y = (0.00875)X + 1541.751 nm \\ \hline \hline \end{tabular}
\end{table}
Table 4: Linear Regression of Sensor Response to Temperature in Figures 14 and 15.
# Green Spare Parts Evaluation for Hybrid Warehousing and On-Demand Manufacturing
## 2 Materials and Methods
### Physical and Digital Warehouses  
Maintaining a substantial inventory of spare parts is crucial to address failures or replacement needs quickly, especially if the supply lead time is long and the consequence of not having spare parts is very high. On the other hand, having a physical spare parts inventory results in tying up significant capital, adding holding costs to operational costs, adding carbon emissions due to long lead times, and producing waste as some portion of these spare parts often remain untouched [36; 37].  
Additive manufacturing, often known as 3D printing, significantly affects digital warehousing by transforming the way inventory is managed, produced, and stored [11]. It enables the creation of spare parts or products on demand, reducing the need for extensive physical inventory storage. Digital warehouses can store 3D models instead of physical parts, allowing for immediate production when needed. Therefore, it minimizes the space required for storage and decreases the risk of having unused inventory that is tying up capital. In addition, this technology allows for local or in-house production, mitigating the risks associated with supply chain disruptions. Digital warehouses are designed to handle multiple manufacturing processes, including both additive manufacturing and subtractive manufacturing methods such as machining. On-demand warehousing facilitates quick adjustments to inventory levels and adapts to fluctuating customer demands without overcommitting resources [38].  
Deciding whether to invest in a physical or digital warehouse involves evaluating various factors to align with business objectives and needs. First, upfront and ongoing costs associated with physical versus digital warehouses shall be analyzed, including expenses like real estate, construction or technology investment, maintenance, staffing, and operational costs and considering the long-term financial implications of each option. Second, the impacts of unavailability of spare parts on operational efficiency should be estimated and compared for both physical and digital warehouses.
## 3 Methodology  
In order to comprehensively evaluate and rank potential locations for WWSs, this study proposes a novel integrated decision-making approach consisting of two stages as illustrated in Figure 1. The objective of stage A is to evaluate the locations' efficiency based on quantitative indicators. These indicators contain information about energy potential, wave climate, bathymetry, distance to shore and so on. Based on the data of the indicators, a behavioral DEA model is used to determine the locations' efficiency under consideration of decision makers' psychosocial behavior. On the other hand, in stage B, the locations' sustainability is evaluated based on qualitative criteria. At this stage, a group of experts and evaluation criteria are determined. Then, the spherical fuzzy DEMATEL method is applied to calculate the weights of the evaluation criteria. In the next step, the locations' sustainability is evaluated according to the criteria using the spherical fuzzy EDAS method. Finally, the locations' ranking, discussions, and recommendations are provided based on both efficiency and sustainability assessments.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Author** & **Year** & **Data Type** & **Approach** & **Energy Type** & **Applied Country** \\ \hline
1 & [PERSON] et al. [24] & 2021 & Crisp & AHP-MARCOS & Multiple & Turkey \\ \hline
2 & [PERSON] et al. [25] & 2021 & Intuitionistic fuzzy & AHP-TOPSIS & Photovoltaic solar & Turkey \\ \hline
3 & [PERSON] et al. [26] & 2021 & Crisp & GIS-AHP-WLC & Photovoltaic solar & Ecuador \\ \hline
4 & [PERSON] and [PERSON] [27] & 2021 & Crisp & AHP-TOPSIS & Gas flaring & India \\ \hline
5 & [PERSON] et al. [28] & 2022 & Crisp & AHP-GIS & Solar & Turkey \\ \hline
6 & [PERSON] et al. [29] & 2022 &
\begin{tabular}{c} q-rung \\ Orthopair fuzzy \\ \end{tabular} & CoCoSo & Offshore wind & Norway \\ \hline
7 & [PERSON] and [PERSON] et al. [30] & 2022 & Crisp & MAUT-entropy & Bioenergy & Turkey \\ \hline
8 & [PERSON] et al. [31] & 2022 & Interval Fuzzy & GIS-AHP-TOPSIS & Offshore wind & USA \\ \hline
9 & [PERSON] et al. [32] & 2022 &
\begin{tabular}{c} Multiple \\ membership function \\ \end{tabular} & GIS-AHP & Photovoltaic solar & Iran \\ \hline
10 & [PERSON] et al. [15] & 2022 & Triangular fuzzy & \
\begin{tabular}{c} AHP-weighted \\ overlay approach \\ \end{tabular} &
\begin{tabular}{c} Hybrid \\ wind–wave \\ \end{tabular} & China \\ \hline
11 & This study & 2022 & Spherical fuzzy & \
\begin{tabular}{c} B-DEA and \\ DEMATEL-EDAS \\ \end{tabular} &
\begin{tabular}{c} Hybrid \\ wind–wave \\ \end{tabular} & Vietnam \\ \hline \hline \end{tabular}  
* Note: MARCOS (measurement of alternatives and ranking according to compromise solution); TOPSIS (technique for order preference by similarity to ideal solution); WLC (weighted linear combination); CoCoSo (combined compromise solution); MAUT (multi-attribute utility theory); WOA (weighted overlay approach).  
\end{table}
Table 1: MCDM application for renewable energy site selection problem.
## Abbreviations  
\begin{tabular}{l l} Abbreviation & Definition \\ CCA & Clear Channel Assessments \\ CPU & Central Processing Unit \\ CSMA & Carrier Sense Multiple Access with Collision Avoidance \\ IoT & Internet of Things \\ LPL & Low Power Listening \\ LPM & Low Power Mode \\ LW-CCA & Light Weight CCA \\ MAC & Medium Access Control \\ MCU & Microcontroller Unit \\ PDR & Packet Delivery Rate \\ RDC & Radio Duty Cycle \\ RPL & Routing Protocol for LLNs \\ RSSI & Received Signal Strength Indicator \\ UDP & User Datagram Protocol \\ VCC & Voltage at the Common Collector \\ WSN & Wireless Sensor Network \\ \end{tabular}
## 3 Service Model Selection
Third, the facility anomaly detection service includes the following. The building status detection service includes the detection service of building exterior damage (e.g., damage to the exterior wall of a building and deterioration levels), as well as the building structure diagnostics service to detect temperature differences for diagnosis per space and the structure of a building. The precursor fire event detection service in facilities encompasses the detection service for precursor fire events within buildings (e.g., underground electric vehicle charging stations and distribution facilities in multi-use facilities), and the detection service of precursor gas and fire events in facilities that may leak from construction sites, traditional markets, and gas facilities. The detection service of facility accident signs includes the detection service for emissions in business places to detect air pollutants (e.g., carbon, nitrogen oxides, and incombustible gases) emitted from places of businesses, and the detection service of explosives at large events with large crowds to prevent terrorism. Fourth, the disaster prevention anomaly detection service is as follows. The natural disaster prevention detection service includes the detection service for precursor forest fires in forest areas vulnerable to fire (e.g., mountains, forests, and parks), and river flooding detection service to detect river flooding and precursory phenomena in deteriorating weather conditions. Finally, a heat island detection service was derived in response to summer heat waves. Overall, four major services were identified, followed by ten services at the medium classification, and finally, 21 abnormal situation detection services based on thermal cameras (Table 2). \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Major Classification** & **Medium Classification** & **Minor Classification (Service Model)** \\ \hline \multirow{6}{*}{A. Object anomaly detection} & A-1. Preventive detection of traffic accidents at night & A-1-1. Pedestrian detection at night \\ \cline{2-3}  & A-1-2. Nighttime mobility detection \\ \cline{2-3}  & A-2. Detection of night security incidents & A-2-1. Violence-related incident detection \\ \cline{2-3}  & A-2-2. Detection of crime fear \\ \cline{2-3}  & A-2-3. Intrusion status detection \\ \cline{2-3}  & A-3. Pedestrian hazard detection & A-3-1. Detection of dense pedestrian situation \\ \cline{2-3}  & A-3-2. Calculation of cluster population \\ \hline \multirow{6}{*}{B. Road anomaly detection} & B-1. Road weather hazard detection & B-1-1. Road surface temperature detection \\ \cline{2-3}  & B-1-2. Road flooding detection \\ \cline{1-1} \cline{2-3}  & B-1-3. Road ice detection \\ \cline{1-1} \cline{2-3}  & B-2. Road surface hazard detection & B-2-1. Road pothole detection \\ \cline{1-1} \cline{2-3}  & B-2-2. Detection of fallen objects on road \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Service model.