esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Calculation of Transient Thermal Stress in Surge Lines with Thermal Stratification Region
## 3 Fast Calculation of Thermal Stress Based on Green's Function Method
### Division of Thermal Zones and Working Conditions
In dividing the thermal zone of the thermal stratification region, some experts divide the thermal stratification region into two thermal zones, and the temperature of the upper zone is taken as the temperature of the fluid from the pressurized, and the temperature of the lower zone is taken as the temperature of the fluid from the hot leg. However, this kind of division of thermal zones is too simple to describe the temperature field of thermal stratification, and the error in the temperature field will cause large errors in the calculation of thermal stress. Therefore, a much more accurate description of the thermal zone is needed. Referring to the thermal stratification monitoring work for the surge line of the Paks Nuclear Power Plant [9], seven thermocouples were installed on the outer wall of the surge line. In order to accurately describe the fluid temperature in the thermal stratification region, all temperatures at the seven measuring points were used to divide the thermal stratification region into six thermal zones, which were named zone 4-9. The division of the thermal zone of the surge line is shown in Figure 6.  
Figure 1:0 The division of thermal zones of the surge line. (**a**) Turbulent penetration region. (**b**) Thermal stratification region. During the operation of a nuclear power plant, the working conditions are very complicated. The stratification state and thermal hydraulic parameters in the surge line are different under different working conditions. Based on the power plant operation experience, seven flow cases were set according to the presence or absence of thermal stratification, the flow rates of the hot leg and the outlet of the pressurized. Among the seven flow cases, case 3 is a start-up condition, and the thermal stratification phenomenon is most obvious under this flow case. Cases 6 and 7 are accident conditions, which correspond to the power off of the main pump and the low flow rate in the hot leg. Generally, such an accident occurs when the hot leg flow rate drops below one-tenth of the rated rate \(Q_{n}\). The judgments and description of every flow cases is shown in Figure 1:1  
When determining the wall-fluid convective heat transfer coefficient of each thermal zone, the fluid temperature and flow rate should be considered. When the flow rate is low, it is natural convective heat transfer controlled. When the flow rate is high enough, it is forced convective heat transfer controlled. In the turbulent penetration region, spiral convective heat transfer should be more suitable. So, convective heat transfer coefficients should be determined carefully with suitable empirical equations and thermal hydraulic parameters [15]. In this paper, the start-up condition (case 3) is taken as an example, and the calculation results of the convective heat transfer coefficient of each hot zone under this condition is shown in Figure 1:2
# Risk-Based Selection of Inspection Method for External Post-Tensioning System of Bridges
## 2 Literature Review
### Defining and Classifying Applicable Inspection Methods
#### 2.2.3 Infrared Thermography  
Thermal energy emissions that exit the surface under investigation are translated into a temperature map via infrared thermography, an imaging method. The photos created provide information about the temperature gradients that were noticed.  
Delamination and voids function as thermal barriers for heat emitted from concrete, making this an efficient NDE approach; Figure 5[9] shows the photo that was taken by infrared thermography and the corresponding result. However, because infrared thermography devices are extremely reliant on ambient temperature conditions, doing this testing can be problematic. The best results are obtained at the time of day when the temperature fluctuates the greatest. Over the last few decades, infrared thermography has evolved into valuable equipment widely known for its capacity to detect surface faults in concrete structures. It may be mounted on a vehicle for 360-degree tunnel inspections or used with handheld cameras. Active or passive systems are the two types of infrared thermography devices. Passive infrared systems are non-contact technologies that use the sun's heat at different times of day when the environment is warming or cooling to provide temperature gradients for thermal inspection. The application of the heat source is the only difference between active and passive infrared systems. In active infrared thermography, a heater is used to warm a specific part of the structure in a controlled setting [50].  
Figure 4: Sounding used for spot inspection.  
Figure 5: Photo captured by infrared thermography with interpretation [9].
## 5 Performance Evaluation
### Setup  
In this work, the Contiki-NG operating system, an open source platform for IoT which is a fork of contiki [25], was chosen. We measure the performance of OSCAR by comparing it to Orchestra. In practice, the RPL rank information is retrieved via the contiki system. The algorithm in this work does not modify the RPL protocol. The contiki system integrates the RPL protocol and provides facilities to retrieve the necessary information from RPL every five packets. The size of the EB slotframe and the broadcast slotframe remains unchanged with 397 and 31 slots, respectively. The unicast slotframe, on the other hand, has a size of 6 slots for OSCAR and 16 slots for Orchestra. For OSCAR, this means that if we take a portion made up of 36 slots with a size 6 for the unicast slotframe, class 0 will benefit from 6 slots out of the 36 slots, i.e., the next 6 slotframes, so 1 slot every 6 slots. Classes 1-4 will, respectively, obtain on average 1 slot every 7.2, 9, 12, and 18 slots. Finally, the last class 5 will have 1 slot out of the 36 slots. In our experiments, we have deployed a network composed of 11 CC2650 Launchpad boards representing a root and 10 nodes distributed in a tree topology. The root is located with a rank equal to 0. The transmit power is set to 5 dBm where one node acts as the border router. We generate an upward traffic of 1 pkts/min in a convergeast way where each node sends 2000 packets to the root, which means that all nodes forward packets to the sink. Each scenario was performed at least twice for both experiments and simulations to ensure that the results were repeatable. A small variation between repetitions of 2 to 5% was observed for all of the scenarios presented in this work. Depending on the number of nodes in the network, we considered that a unicast slotframe is made up of 6 timeslots. The duration of each timeslot is fixed at 10 ms as indicated in the TSCH 802.15.4e standard. All the tests we have done compare performance between our architecture and orchestra in terms of reliability, low energy consumption, and latency. The goal is to briefly test the behavior of our architecture and its implementation in a wireless sensors network.
## 3 Testing Conditions  
### Polymeric Bearings  
A prototype PB considered in this research is made up of a polymer cylinder measuring 28 mm in diameter and 28 mm high with a centrally located hole (14 mm in diameter) into which a pin-ended steel core (6 mm in diameter) is inserted. Two additional steel anchor plates measuring 30 mm in diameter and 15 mm high were mounted at both ends of the polymer cylinder. The total height of the PB is 58 mm. The basic components of the prototype PB are shown in Figures 4 and 5. The prototype solution ensures that the steel core sustains vertical forces, while the polymer cylinder is subjected to shearing. In that way, the steel core prevents the polymer cylinder from carrying vertical loads that could possibly lead to undesirable bulging of the bearing. The maximum horizontal displacement of the PB is about 100% of its total height, which--when compared to HDRBs and LRBs--is a relatively high value. More importantly, the damping coefficient of the PBs calculated based on the experimentally obtained hysteresis loops ranges from 20% to nearly 50%, while, for most commonly used seismic isolators, the damping ratio is approximately from 10% to 20%.  
It should be underlined that, due to uniaxial (horizontal) shaking table used in experimental study (see Section 3.2), the PBs were intentionally constructed as one-dimensional prototype devices capable to support only small experimental models. A full scale multi-dimensional version of the PB to be used for real structures has also been proposed and already submitted to the Polish Patent Department.  
Figure 3: DMA results for all excitation frequencies in the temperature range from \(-20\) to 40 \({}^{\circ}\)C.
# A Novel System of Mixed RF/FSO UAV Communication Based on MRR and RIS by Adopting Hybrid Modulation
On this basis, considering the atmospheric turbulence and pointing error, the outage probability, average BER, and average channel capacity expressions of the mixed RF/FSO system based on MRR and RIS are derived. The remainder of this paper is organized as follows: Section 2 presents the mixed RF/FSO UAV dual-hop system based on MRR and RIS. Section 3 further derives closed expressions for the end-to-end outage probability, average BER, and average channel capacity of the system. Then in Section 4, numerical results are provided. Finally, Section 5 offers some concluding remarks.
# Vegetation Dynamics and Recovery Potential in Arid and Semi-Arid Northwest China
## 4 Materials and Methods
### Data Collection and Processing  
#### 4.2.1 Vegetation Data  
The NDVI dataset employed in this investigation was sourced from the 5 km resolution monthly NDVI grid dataset of China, accessible via the National Science and Technology Resource Shared Service Platform--National Earth System Science Data Center ([[http://www.geodata.cn](http://www.geodata.cn)]([http://www.geodata.cn](http://www.geodata.cn)), accessed on 1 March 2023). The temporal scope encompasses the years 1982 to 2019. This dataset was meticulously generated through the processes of monthly synthesis, mosaicing, and cropping, leveraging the NASA AVHRR CDR NDVI V5 version, which offers day-to-day data. The Advanced Very High Resolution Radiometer (AVHRR), a pivotal instrumentation component of the NOAA satellite series, serves as the primary means of observation. Being noteworthy for its extended revisit cycle, the AVHRR allows for daily monitoring of actual vegetation dynamics, capturing the intricate interplay between human activities and natural influences on vegetation evolution. To mitigate the impact of atmospheric variables such as cloud cover, aerosols, cloud shadows, viewing angles, solar altitude, and extreme monthly values, this study employs the Maximum Value Composition (MVC) methodology. This approach selects the maximum NDVI value from  
Figure 14: Study area (**a**) and distribution map of land use (**b**). Note: this study used the national standard map of China, with the Map Approval Number China_GS (2020)4619 without any modifications.  
each month to represent the annual NDVI data. Subsequently, the ENVI tool was employed to compute vegetation cover.  
FVC is usually constructed using an index to construct a dimidiate pixel model, which is the simplest and most commonly used model for linear pixel decomposition assuming that the image pixel is composed of two components, non-vegetation and vegetation, and the spectral information is linearly combined by them. The FVC value of each pixel is obtained by calculating the proportion of vegetation for the pixel, and the formulae are as follows [57]:  
\[FVC=(NDVI-NDVI_{min})/(NDVI_{max}-NDVI_{min}) \tag{1}\]  
where \(FVC\) is the vegetation cover; \(NDVI\) is the vegetation index of the requested image; \(NDVI_{max}\) and \(NDVI_{min}\) are the maximum and minimum values of \(NDVI\) in the study area, respectively; and the vegetation cover was calculated using the ENVI tool with a 95% confidence interval and a 5% vegetation index.
# Performance Analysis of Dual-Hop AF Relaying with Non-Linear/Linear Energy Harvesting
## Appendix C Calculation of \(A(z)\)  
In (19), substituting (14), \(A(z)\) is written as  
\[A(z)=\frac{a}{2}\int_{0}^{P_{th}}\!\left[1-\frac{1}{\sqrt{C^{ \prime}+\frac{\bar{g}^{\prime}}{W}}}\right]f_{W}(w)\mathrm{d}w, \tag{107}\]where \(C^{\prime}=(1+z\bar{g})/z\bar{g}\), \(B^{\prime}=1/\bar{g}\), and \(f_{\rm W}(w)\) is given in (23). Noting that \(A(z)\) given in (28) is a function with parameter \(z\) and applying the same procedure from (A2) to (A8), we have  
\[A(z)=\frac{a}{2}(E_{2}-F_{2}(z)), \tag{29}\]  
where  
\[E_{2}=\int_{0}^{P_{\rm fib}}f_{\rm W}(w){\rm d}w=I\gamma(m_{w},P_{ \rm fib}\phi_{w})/\phi_{w}^{m_{w}} \tag{30}\]  
and  
\[F_{2}(z)=\frac{I}{\sqrt{B^{\prime}}}\big{[}M^{\prime}(z)+N^{ \prime}(z)\big{]}. \tag{31}\]  
In (31),  
\[M^{\prime}(z) =G_{2}^{m_{w}+0.5}\sum_{k=0}^{\rm Y}(0.5)_{k}\frac{(-1)^{k}}{k!} \frac{\Gamma(1)\Gamma(m_{w}+k+0.5)}{\Gamma(m_{w}+k+1.5)}\] \[\times{}_{1}F_{1}(m_{w}+k+0.5;m_{w}+k+1.5;-\phi_{w}G_{2}) \tag{32}\]  
and  
\[N^{\prime}(z)=\sum_{j=0}^{\rm Y}(0.5)_{j}\frac{(-1)^{j}(\Gamma(m _{w}-j,\phi_{w}G_{2})-\Gamma(m_{w}-j,\phi_{w}P_{\rm fib}))}{j!G_{2}^{-(j+0.5) }\phi_{w}^{(m_{w}-j)}}, \tag{33}\]  
where \(G_{2}=B^{\prime}/C^{\prime}\). Please note that \(E_{2}\) is independent of \(z\) in (29).
# The Design and Implementation of a Novel Open Source Massive Deployment System
## 2 Related Works  
The OS and applications in a physical machine are the basis for all of the computing scenarios. Therefore, many related works have been conducted concerning system deployment. At least three methods were proposed to deploy a system: (1) installing OS and applications from scratch with an automated installation and configuration program (hereafter referred to as \"automated installation\") [11]; (2) restoring previously saved files from a disk (hereafter referred to as \"differential update\") [12]; and, (3) restoring an image of the previously saved file system (hereafter referred to as \"file system imaging\") [13]. These three methods have specific advantages and disadvantages. The major advantage of the automated installation method is the flexibility to decide which packages will be installed. The system can also be configured in the deployment processes. The major disadvantage of this method is the comparatively longer time that is required. By using the automated installation method, every computer must be installed from scratch. The major advantage of the differential update method is the efficiency in file synchronization and the consumption of network bandwidth. However, the file system must be created before the system deployment starts so that files can be copied. Furthermore, not all of the file systems can be created when the OS is different. Therefore, the differential update method can support a very limited number of OSs. The major advantage of the file system imaging method is the highest efficiency of deployment. However, the method lacks flexibility because all of the systems being deployed are the same. Due to the different pros and cons that are associated with each deployment method, system administrators must choose the most appropriate one when deploying systems.
# Physics-Based Data Augmentation Enables Accurate Machine Learning Prediction of Melt Pool Geometry
## 2 Materials and Methods
### Machine Learning Modeling and Optimization
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Hyperparameters** & **Before Data Enhancement** & **Data Enhancement** \\ \hline RF & n\_estimators & 372 & 148 \\  & max\_depth & 6 & 191 \\  & min\_samples\_split & 2 & 2 \\  & min\_samples\_leaf & 1 & 1 \\ \hline XGBoost & n\_estimators & 1213 & 1066 \\  & learning\_rate & 0.1 & 0.3 \\  & max\_depth & 4 & 3 \\  & min\_child\_weight & 1 & 1 \\  & Subsample & 0.8 & 0.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Specific parameter selection of Random Forest and XGBoost models in Bayesian optimization. The calculation of these three evaluation metrics is shown below:  
\[R^{2}=1-\sum_{i=1}^{n}\hat{(Y_{i}-\overline{Y})}^{2}/\sum_{i=1}^{n}\left(Y_{i}- \overline{Y}\right)^{2} \tag{6}\]  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\hat{\left|Y_{i}-\overline{Y}_{i}\right|} \tag{7}\]  
\[RMSE=\sqrt{\frac{\sum\limits_{i=1}^{n}\hat{(Y_{i}-Y_{i})}^{2}}{n}} \tag{8}\]  
where Y is the actual value; \(\overline{Y}\) the mean of the actual value; \(\hat{Y}\) is the predicted value; and n is the number of test samples.
# A Framework for Proactive Resource Provisioning in IaaS Clouds
## 3 System Framework
### Critical Task Scheduling Algorithm  
In the current Spark system, the default task scheduling algorithm is efficient but intuitive. If there is only one Spark system executing, all resources are allocated to this Spark system, and the default algorithm simply schedules stages using the First-In-First-Out (FIFO) mechanism. If there are multiple Spark systems executing simultaneously, another mechanism is applied to make sure that all Spark systems get fair resource usages. In other words, the default task scheduling algorithm treats all stages ready for execution as equivalent. When a Spark system is informed that an available resource is allocated to it, the ready stage with the smallest id would be picked for execution first. However, in order to shorten the overall processing time, a stage belonging to the critical path should be given higher priority for execution first. Apparently, the default task scheduling algorithm in the Spark system cannot identify the stage belonging to the critical path, hence its overall processing time could probably be improved further.  
In the proposed framework, a proactive critical task scheduling algorithm is designed to generate an appropriate sequence to execute stages without violating any data dependencies. The main idea is to execute stages belonging to critical path early, and the goal is to achieve shorter processing time. Figure 5 shows the pseudo-code of proposed proactive critical task scheduling algorithm. When a Spark system starts to execute an application, the execution pattern could be found in the profiled log. Based on data dependencies between stages, each stage \(S_{i}\) would be designated two values; \(Rank(S_{i})\) and \(ET(S_{i})\). \(Rank(S_{i})\) is designated to 0 if stage \(S_{i}\) has no successors. For a stage \(S_{i}\) with successors, its \(Rank(S_{i})\) is designated to the maximum \(Rank(S_{j})\) of successors \(S_{j}\) plus one. After the above pre-process, the stage has no predecessor and belongs to the critical path will be designated to maximum rank value. \(ET(S_{i})\) is set to the longest execution time of a task in stage \(S_{i}\).  
Figure 4: Pseudo-code of resource estimating algorithm.  
In Figure 5, stages whose predecessors have all been assigned are collected in a ready set. At first, the ready set would contain stages without any predecessors in the given RDD DAG. A loop would then repeat \(N\) times, while in each iteration one stage is selected. In each iteration, this task scheduling algorithm finds stages with the maximum rank in the ready set. If there is more than one stage with maximum rank, only stages with maximum \(ET\) values would be retained. If there is still more than one stage remaining, the stage with smallest id would be selected in this iteration. After \(N\) iterations, an appropriate sequence to execute all stages in the given RDD DAG would be generated.
# Review of Indoor Positioning: Radio Wave Technology
###### Abstract  
The indoor positioning system (IPS) is becoming increasing important in accurately determining the locations of objects by the utilization of micro-electro-mechanical-systems (MEMS) involving smartphone sensors, embedded sources, mapping localizations, and wireless communication networks. Generally, a global positioning system (GPS) may not be effective in servicing the reality of a complex indoor environment, due to the limitations of the line-of-sight (LoS) path from the satellite. Different techniques have been used in indoor localization services (ILSs) in order to solve particular issues, such as multipath environments, the energy inefficiency of long-term battery usage, intensive labour and the resources of offline information collection and the estimation of accumulated positioning errors. Moreover, advanced algorithms, machine learning, and valuable algorithms have given rise to effective ways in determining indoor locations. This paper presents a comprehensive review on the positioning algorithms for indoors, based on advances reported in radio wave, infrared, visible light, sound, and magnetic field technologies. The traditional ranging parameters in addition to advanced parameters such as channel state information (CSI), reference signal received power (RSRP), and reference signal received quality (RSRQ) are also presented for distance estimation in localization systems. In summary, the recent advanced algorithms can offer precise positioning behaviour for an unknown environment in indoor locations.  
indoor positioning; radio wave technologies; triangulation; fingerprinting; machine learning; ultrasonic sensor
## 2 Vibration Signal Processing Methods
### Multi-Domain Feature Extraction and Sensitive Feature Selection  
#### 2.2.1 Multi-Domain Feature Extraction  
Time, frequency, and time-frequency domain features are extracted [35], as shown in Table 1, where numbers 1-7 represent the time domain feature expressions, and numbers 8-13 represent the frequency domain feature expressions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Serial Number** & **Name** & **Feature Expression** \\ \hline
1 & Mean & \(p_{1}=\frac{\sum\limits_{i=1}^{N}x_{i}}{N}\) \\
2 & Standard Deviation & \(p_{2}=\sqrt{\frac{\sum\limits_{i=1}^{N}\left(x_{i}-p_{1}\right)^{2}}{N}}\) \\
3 & Variance & \(p_{3}=\frac{\sum\limits_{i=1}^{N}\left(x_{i}-p_{1}\right)^{2}}{N-1}\) \\
4 & Skewness & \(p_{4}=\frac{\sum\limits_{i=1}^{N}x_{i}^{3}}{N}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Feature expressions.  
Figure 2: Wavelet decomposition principle.  
For the extraction of time-frequency domain features, the db6 function is used as the wavelet basis function, and the vibration signal is decomposed using three-layer wavelet packet decomposition to obtain \(2^{3}=8\) seed frequency bands. The energy characteristics of these eight seed frequency bands are calculated to obtain the wavelet energy entropy [44]:  
\[WEE=-\sum\limits_{i_{o}=1}^{8}{(\frac{E_{i_{o}}}{E}\cdot\log(\frac{E_{i_{o}}}{E}))} \tag{3}\]  
where \(E_{i_{o}}\) (where \(i_{o}\) denotes the \(i_{o}\)-th sub-frequency band) represents the energy characteristic of the \(i_{o}\)-th sub-frequency band, and \(E\) represents the total energy of all eight sub-frequency bands combined.  
The energy ratio of each sub-frequency band is calculated using the following formula [44]:  
\[P_{i_{0}}=\frac{E_{i_{0}}}{E}\times 100\% \tag{4}\]  
This allows us to estimate the energy ratios corresponding to different frequency sub-bands: p\({}_{14}\)(3,0), p\({}_{15}\)(3,1), p\({}_{16}\)(3,2), p\({}_{17}\)(3,3), p\({}_{18}\)(3,4), p\({}_{19}\)(3,5), p\({}_{20}\)(3,6), and p\({}_{21}\)(3,7) [44].