esa-sceva/satcom-chunk-collection huggingface dataset:
# Natural Radioactivity and Radon Exhalation from Building Materials in Underground Parking Lots
## 3 Results and Discussion
### Evaluation of Radiological Hazard Parameters in Building Materials  
#### 3.3.1 Radium Equivalent Activity  
The natural radioactivity of construction materials is usually determined from the content of natural radionuclides \({}^{40}\)K, \({}^{232}\)Th, and \({}^{226}\)Ra. The distribution of these radionuclides in building materials samples under investigation is not uniform; therefore, a radium equivalent activity index (\(Ra_{\text{eq}}\)) was introduced. \(Ra_{\text{eq}}\) is an index used to represent the activity concentrations of \({}^{40}\)K, \({}^{232}\)Th, and \({}^{226}\)Ra by a single quantity, which takes into account the radiation hazards associated with them. \(Ra_{\text{eq}}\) is calculated according to the following Equation (6):  
\[Ra_{\text{eq}}=C_{\text{Ra}}+1.43C_{\text{Th}}+0.077C_{\text{K}} \tag{6}\]  
The calculated values of \(Ra_{\text{eq}}\) for all types of building materials ranged from 37.01 to 185.05 Bq kg\({}^{-1}\), which is less than the limit of 370 Bq kg\({}^{-1}\)[17; 31; 32; 33]. This value is defined by the Organization for Economic Cooperation and Development to keep the external dose below 1.5 mSv y\({}^{-1}\). The highest value of \(Ra_{\text{eq}}\) is estimated in ferroc concrete samples (Table 3).  
The results show that \(Ra_{\text{eq}}\) of different building materials can vary significantly. This is important in choosing the appropriate materials for building constructions. Actually, \(Ra_{\text{eq}}\) of the same type of building materials can have large variations. Therefore, it is important to identify their radioactivity levels before using building materials.
## 4 Experiment Simulation and Data Analysis
### Analysis of Complementary Characteristics of Hybrid Algorithms  
In order to verify the reliability of the proposed model, the actual operation load data of a certain province are used as data set 1, the data from 0:00 on 1 January 2016 to 18:00 on 26 May 2018 are used as the training data set and a total of 50 h of data from 19:00 on 26 May to 20:00 on 28 May 2018 is used as the test data set; the sampling frequency is 1 h, and the iterative prediction is performed 6 h in advance.  
The GEFCom2012 data are used as data set 2. The load data from 0:00 on 1 January 2009 to 23:45 on 25 January 2009 are used as the training data set and the load data at 23:45 on 7 February 2009 are used as the test set; the sampling frequency is 15 min. The iterative prediction is carried out 2 h in advance. The GRU network and TCN network parameters used, respectively, in the experiment are determined by training and testing. The parameters of the two neural networks are shown in Table 2.  
Figure 8 shows the load forecasting results of the eight IMF components in data set 1 using the GRU model and the TCN model, respectively. For the low-frequency load components, the error between actual and forecasting data based on the GRU network is smaller than that of the TCN forecasting model. However, the prediction accuracy of GRU will decrease with the frequency increase such as IMF5 to IMF 8.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **GRU** & **TCN** \\ \hline hidden layer & 3 & 3 \\ batch size & 128 & 128 \\ epoch & 100 & 100 \\ learning rate & 0.01 & 0.01 \\ dropout & 0.1/0.2 & 0.1/0.2 \\ activation function & relu & relu \\ dilation rate & - & 1/2/4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of neural networks.  
Figure 8: The forecasting results of GRU and TCN, respectively, in the high-frequency component.  
Furthermore, the GRU model and the TCN model are used for the load forecasting of high-frequency components and low-frequency components in data set 2. The results are shown in Figures 9 and 10, respectively. It can be seen that in the low-frequency component, the forecasting result of the GRU model is more accurate than that of the TCN model. Figure 10 shows that the GRU model has a larger prediction error when the time-series data fluctuate too fast.
## 4 Enabling Technologies for FSO Communication
### Hybrid FSO Systems
#### 4.11.2 Architecture of Hybrid FSO Systems  
Hybrid FSO systems typically combine FSO links with RF, mmWave, or THz links to create a robust communication network. In this system, the efficiency and reliability of signal transmission are significantly influenced by the switching configurations employed. This section delves into two primary switching architectures: soft-switching and hard-switching. Soft-switching aims to minimize losses and electromagnetic interference by ensuring smoother transitions between states, thereby enhancing system performance and longevity. Conversely, hard-switching, characterized by abrupt transitions, can lead to increased efficiency under certain conditions but may also introduce greater stress on system components. Understanding the nuances of these configurations is crucial for optimizing FSO systems for various operational scenarios. Figure 15 illustrates two potential setups for a hybrid FSO/RF link.  
Figure 15: Hybrid FSO switching configurations: (**a**) soft-switching and (**b**) hard-switching.  
Hard-Switching Configurations  
One switching approach is a switch-over hybrid technique that employs hard-switching between the FSO and alternative links to make use of their complementary natures [430; 431]. In this approach, as shown in Figure 15a, the Tx and Rx collaboratively decide between the FSO and an alternative channel for data transmission. The FSO link is chosen only when the channel conditions allow for reliable communication; otherwise, all data are transmitted via the alternative channel [432; 433]. In this regard, commercially available hybrid solutions simply use the RF link as a hot-standby backup for the FSO link, which is to be used only when the FSO channel is inoperative [434]. It is important to note that the Tx and Rx need to be coordinated through feedback to choose the appropriate channel for transmission. One major drawback of hard-switching is that one link remains idle at any given time. In reality, scintillation or loss in the sensitive FSO channel leads to infrequent selection of that link. Consequently, once the RF link is chosen, the channel capacity of the FSO link goes unused [432]. Also, this method results in frequent switching between the FSO and alternative links [429; 430].
## 3 Results and Discussion
### Thermal Behavior  
The thermal behavior of the process was analyzed by recording the temperatures at different locations on the base plate. Three thermocouples were positioned on the top side of the plate at distances of 10 mm (T1), 15 mm (T2) and 20 mm (T3) from the weld centerline, whereas two thermocouples were placed at the bottom of the plate (T4), one at the corner of the plate, and the other 5 mm (T5) away from the weld centerline. The schematics of positioning the thermocouples on the top and bottom of the plate is presented in Figure 2. The peak temperatures were measured and graphed against EN ratio at Ia of 175 A in Figure 4. For an EN ratio of 0%, the filler wire acted as an anode and BM acted as a cathode during the complete pulse duration. The resultant temperatures behaved similarly to the usual pattern on DC-pulsed GMAW. However, for an EN ratio of 30% and 50%, the filler wire switched its polarity in each cycle of the pulse. Slightly higher peak temperatures were observed at 30% EN ratio. For an EN ratio of 30%, the filler wires acted as 30% cathode and BM  
Figure 3: Current pulses at different EN (electrode negative) ratios.  
Figure 2: **(a)** Experimental schematics used in the welding tests **(b)** position of thermocouples on base plate.  
acted as a 70% anode of the total pulse duration. The cumulative droplet and arc heat generated as a result of ion bombardment in the arc plasma provided a higher amount of heat to the workpiece. Almost 50 \({}^{\circ}\)C higher temperatures were recorded at an EN ratio of 30%. For an EN ratio of 50%, the filler wire and BM equally acted as a 50% anode and cathode of the total pulse duration. At a 50% EN ratio, the EP duration was kept the same as in the case of 30%; however, the EN duration was increased to get the 50% EN ratio. The change in the EN duration resulted in the change of heat generation behavior in the arc column. The longer EN duration resulted in the higher temperature gradient away from the weld centerline. As a result of cooling effect on joint sides, low temperatures were recorded at 50% EN. From the above findings, it may be concluded that the heat input in the pulsed AC-GMAW process can be controlled by changing the amount of the electrode negative cycle. Figure 5 shows the recorded temperature with respect to time at Ia of 250 A and 50% EN ratio. A rapid increase in temperature (T4) at the bottom corner of the joint was observed, which is equal to the melting point of the SS400. Deeper penetration was expected due to very high temperatures at the centerline of welding joints.  
Figure 4: Peak temperatures at current 175 A.  
Figure 5: Thermocouple temperatures at current 250 A and 50% EN ratio.
# Manifolds-Based Low-Rank Dictionary Pair Learning for Efficient Set-Based Video Recognition
## 2 Related Works
### Dictionary Learning  
Dictionary learning has been widely studies and applied for many artificial intelligence tasks, such as image classification, image compression, image denoising, etc. To date, many dictionary learning methods have been developed, which can be categorized into unsupervised and discriminative dictionary learning methods.
## 6 Numerical Results
### Probability of Error  
In order to analyze the probability of error performance for the proposed IFP, we perform the simulations under two parts. In the first part, we study the stand-alone performance of the IFP precoder by observing the probability of error behavior at the relay node. Further, in the second part, we analyze the effect of the implementation of CF with IFP on the overall end-to-end system from source to destination.
# Demystifying DFT-Based Harmonic Phase Estimation, Transformation, and Synthesis
## 2 Robust DFT-Based Phase Estimation of Individual Sinusoids
### ODFT and the Rectangular Window
When the results in Figure 8 are compared to the results in Figure 5, it can be seen that no major differences exist, as expected (essentially because the same window is used), although the results in Figure 8 are slightly better. This is explained by the fact that the ODFT implicitly performs a small frequency modulation (i.e., an upshift in frequency by \(\pi/N\)) of the input signal, which improves the frequency separation between the two Dirichlet kernels, and this slightly reduces the mutual interference due to leakage.
# Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories
## 7 Conclusions  
This paper highlighted the routing problems in TSNs. Different from traditional bus-based real-time communication networks, TSNs have two main routing problems: those for TT flows and those for non-TT flows. To address the specific communication needs of smart factories, we classified network data from smart factories and analyzed the communication requirements and transmission mechanisms of various types of traffic. By mathematically modeling the problem, we transformed the routing problem into an MTSP problem and a load-balanced multi-priority route assignment problem. For the MTSP problem, we proposed an improved algorithm named GATTRP. For the multi-priority route assignment problem, we proposed an improved algorithm named AACO. The simulation results showed that transforming the traversal problem of TT flows into an MTSP can effectively reduce the traversal cycle time and that the GATTRP proposed in this paper has a strong convergence performance. For non-TT flows, the AACO proposed in this paper is more comprehensive and exhibits an excellent performance and better results. However, there were also some limitations in this paper. For upper-layer industrial applications, the transmission mechanisms and requirements of AVB flows and BE flows, which belong to the same non-TT flow type, are not exactly the same. If they can be distinguished for refined routing, the the routing performance could be further improved. In the future, we will focus on improving the routing algorithms for AVB flows.  
Conceptualization, Z.Y. and Y.L.; methodology, Z.Y. and Y.L.; software, Y.L.; validation, Z.Y., Y.L., and Y.M.; investigation, Y.L. and H.Y.; data curation, Y.M. and F.X.; writing--original draft preparation, Y.L.; writing--review and editing, Z.Y., Y.L., G.H., and Y.B.; visualization, Y.M. and F.X.; supervision, Z.Y., Y.L., and H.Y.; project administration, Z.Y., G.H., and Y.B.; funding acquisition, Z.Y. and G.H. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the National Key R&D Program of China under grant number 2017 YFE0125300.
## Nomenclature  
\begin{tabular}{l l} \(\mathrm{c_{P}}\) & Specific heat capacity, kJ/kgK \\ \(\mathrm{C_{0}}\) & Investment cost, EUR \\ h & Specific enthalpy, kJ/kg \\ h\(\mathrm{in}\) & Convection coefficient inside the building, W/m\({}^{2}\) K \\ h\(\mathrm{out}\) & Heat convection coefficient with outdoor air, W/m\({}^{2}\) K \\ Hu & Lower heating value of the oil, kJ/kg \\ k & Thermal conductivity, W/mK \\ k\(\mathrm{cl}\) & Electricity cost, EUR/kWh\(\mathrm{el}\) \\ k\(\mathrm{oil}\) & Oil cost, EUR/kWh\(\mathrm{th}\) \\ L & Thickness of material in a structural element, m \\ LCC & Life cycle cost, EUR \\ m & Mass flow rate, kg/s \\ M & Number of layers in every structural element \\ N & Lifetime of the project, years \\ NS & Net savings, EUR \\ p & Pressure, bar \\ \(\mathrm{P_{d}}\) & Electricity demand on the compressor, kW \\ PE & Primary energy, kWh \\ \(\mathrm{Q_{b}}\) & Boiler energy demand, kW \\ \(\mathrm{Q_{heat}}\) & Heating demand, kW \\ \(\mathrm{Q_{in}}\) & Heat input in the heat pump from the ambient, kW \\ r & Discount factor, \% \\ s & Specific entropy, kJ/kgK \\ SPP & Simple payback period, years \\ T & Temperature, \({}^{\circ}\)C \\ U & Thermal transmittance, W/m\({}^{2}\) K \\ \(\mathrm{Y_{CO2}}\) & Yearly CO\({}_{2}\) emissions, kg\(\mathrm{c_{02}}\)/year \\
**Greek Symbols** & \\ \(\mathrm{\eta_{b}}\) & Boiler efficiency \\ \(\mathrm{\eta_{is}}\) & Isentropic efficiency \\ \(\mathrm{\rho}\) & Density, kg/m\({}^{3}\) \\
**Subscripts** & \\ am & Ambient \\ b & Boiler \\ Bitzer & Data from the Bitzer manufacturer \\ cond & Condenser \\ evap & Evaporator \\ HP & Heat pump \\ is & Isentropic \\
**Abbreviations** & \\ ACH & Air changes per hour \\ COP & Coefficient of performance \\ GWP & Global warming potential \\ ODP & Ozone depleting potential \\ SCOP & Seasonal coefficient of performance \\ \end{tabular}
## 2 Materials and Methods
### Background of SC Techniques
#### 2.2.2 Ensemble Approach  
Ensemble learning (EL) is an advanced numerical modelling technique which uses various models usually termed \"weak learners\", which are trained to solve the same problem and which are consolidated to generate more reliable results. The main underlying assumption is that if weak models are perfectly merged, then the results will be more accurate and the consolidated model is likely to be more robust. Employing ensembles to conduct predictions may be justified due to their remarkable capability to generalize outcomes and decrease model variation, due to their lower susceptibility to data noise [67]. In this paper, two primary ensemble techniques including bagging and boosting were employed. Boosting is a method applied to resolve classification and regression problems. Boosting techniques primarily focus on bias mitigation and on enhancing the model's suitability to data. Compared with bagging techniques that primarily decrease diversity, boosting is a technique that involves sequentially fitting various weak learners in a highly adaptive process. Every model in the series is applied, providing higher importance to observations in the dataset examined by the earlier models in the series. Each novel model concentrates on fitting the most challenging measurements and the outcome is an intense learner with a lower bias.  
Bagging, or bootstrap aggregating, is one of the most established methods for generating ensemble models [67]. While this technique was primarily developed to solve classification problems, it was later employed to analyze regression data. Bagging is defined by producing and refitting various samples, by utilizing the bootstrap method from an identical set of data. As a result, it is likely to create many different trees for an identical predictor and employ them for creating an aggregate forecast. The prediction can be achieved by voting or by an average for classification and regression problems [68]. This prediction creates various samples for an identical set. The benefit of utilizing this method for ensemble creation is that it minimizes any error in the baseline predictors. The prediction accuracy of the bagging technique can present its predictive performance measures related to calculations of cross-validation and measures of testing sets [69].
# Battery Sizing for Mild P2 HEVs Considering the Battery Pack Thermal Limitations
## 2 Vehicle Performance Modelling
### Battery Performance and Electro-Thermal Model
#### 2.5.3 The Battery Thermal Model  
The thermal model is developed from contributions of the voltage losses that are computed from the dynamic voltage model of Equation (12). Based on lumped parameter assumption [39], the thermal model is designed based on heat transfer by convection and by neglecting the conduction heat transfer component according to Equation (14). The discrete-time battery surface temperature \(\theta_{surf}\) is computed considering the power losses, \(P_{loss}\) that is derived from the voltage losses, as the heat source.  
\[\theta_{surf}(k+1)=\theta_{surf}(k)+\frac{T_{s}}{c_{p}\cdot m_{cell}}\bigg{(} P_{loss}(k)-\frac{\theta_{surf}(k)-\theta_{amb}}{R_{conv}}\bigg{)} \tag{14}\]  
\(c_{p}\) is the battery specific heat capacity; \(m_{cell}\) is the cell mass; \(P_{loss}\) is the power is computed based on the voltage losses; \(\theta_{amb}\) is the ambient temperature; \(T_{s}\) is the sampling time; \(R_{conv}\) is the thermal resistance by convection. The thermal parameters are reported in Table 2.
# Traffic Conflict Prediction for Sharp Turns on Mountain Roads Based on Driver Behavior Patterns
## 2 Materials and Methods
### Data Collection Environment and Procedures
#### 2.1.3 Extraction of Traffic Conflict Data  
To obtain traffic conflict data for the experimental section, six observation points were established along the X412 County Road from Lishu Village to Damao Village, encompassing diverse road geometries, including sharp curve sections (radii < 60 m), gentle curve sections (radii between 60 m and 100 m), slope sections, and tunnel sections. Each observation point was staffed with two professional observers and equipped with a high-definition camera (SONY AX700, Tokyo, Japan). The study conducted continuous observations over three months, maintaining daily 12 h observation periods (8:00-20:00), accumulating 2000 h of total observation time. All observations strictly adhered to the international standard for Traffic Conflict Technique (TICT) [34], documenting comprehensive data, including vehicle conflict time, location, type, and severity level, while maintaining video recordings of key areas and high-risk events. Through the integration of manual records and video analysis methods, the study collected detailed traffic conflict data encompassing 960 valid conflict events, including rear-end collisions, head-on conflicts, and lane-changing conflicts, with severity levels ranging from minor to major. The extensive database of traffic conflicts, categorized by both type and severity, provides a robust foundation for analyzing the correlation between driving behavior and traffic conflicts in this academic research investigation.
### Non-Parametric Techniques for BOC Signals  
Typically, the most widely used multipath mitigation techniques are non-parametric techniques. They avoid false locking on the secondary peaks of BOC signals by using original code delay discriminators or designing particular local reference code waveforms for correlation.  
Code Correlation Reference Waveforms (CCRW) [6] is a representative of this type. In a broad sense, shaping correlator [7], BOC-Gated-PRN [8; 9; 10], gating functions (GF) [1] and even Narrow Correlator(tm) [11] and double-delta correlation variants (high resolution correlator (HRC), strobe correlator, etc.) [12; 13; 14; 15] all belong to this family. For example, if the incoming signal is correlated with a local specially designed CCRW, we can get a discriminator curve equivalent to that of the Narrow Correlator(tm). Nevertheless, these techniques have to carefully design different local reference PRN sequences for each specific type of BOC signals, which shows low generality [16]. For example, the shaping correlator method, developed for BOC(1,1) modulation, is not applicable to other high-order BOC signals [7]. BOC-Gated-PRN is just effective on BOC(\(n\),\(n\)) signals and MBOC signal, but not effective for other signals [8; 9]. Another kind of CCRW, named as gating functions, can be used on BOC(\(n\),\(n\)) and BOC(\(2n\),\(n\)) signals, but it still cannot process arbitrary BOC(\(m\),\(n\)) signals, such as BOC(14,2) [1]. Besides, it is also worth to mention some other well-known limitations, namely degraded carrier-to-noise ratio and poor tracking dynamic performance, which significantly limit the applicability of such methods [7].
## 3 Results and Discussion  
### Compressive Strength  
The compressive strength of all the geopolymer specimens was determined using a monotonic compression test, and their graphs were plotted in Figure 2A. The highest strength was observed for GP5 and GP7 specimens. The compressive strength of GP3, GP5, and GP7 specimens was 21.13%, 27.19%, and 31.79%, respectively, higher than that of the reference (GP0) specimen. The possible reason for the increase in the compressive strengths could be the synergistic effect of nanosilica particles and PVA fibers in the matrixes. Soluble silicate plays an important role in the geopolymerization process. It gives the aqueous phase of the geopolymeric system with the soluble-silicate species which is crucial for the formation of oligomer and polycondensation. Due to the incorporation of nanosilica, the extensive aluminosilicates dissolution rate increases with the increased Si and Al species in the aqueous phase of geopolymers, gradually altering the chemical system from a monosilicate chain to a species with greater rings and more complex network, resulting in 3-dimensional polymeric structures and an increased compressive strength of the resulting geopolymeric materials [32]. Nanosilica could also refine the pores, making the geopolymeric network dense and compact, with consequent improvement in strength. Moreover, the specimens with PVA fibers may have controlled the propagation of cracks because of the bridging effect. In addition, due to their texture and surface reaction with geopolymer products and an alkali activator solution, heat was produced, which increased the internal curing temperature. The rise in temperature accelerated the geopolymerization process, which in turns strengthened the geopolymeric gel enhanced the mechanical strength [25; 33]. It was observed that an increase of fibers improved the compressive strength of specimens. Previous studies also reported the similar findings [34; 35]. [PERSON] et al. [36] found that, as the content of PVA fibers increased, the compressive and flexural strengths were also increased. They concluded that the compressive strength of the specimen, with larger amounts of PVA fibers (2% vol.), was 72.7% higher than that of the control sample (without fiber) after 28 days of curing. It was noticed that the PVA fibers reinforced geopolymer specimens showed ductile failure after a maximum load by retaining their original shapes. Similar trends were also observed in previous work [25].