esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Future Research Directions
### Moving Target Tracking  
Moving targets can be considered as intelligent agents and this characteristic often increases the tracking difficulty. On the other hand, sensor nodes only have limited wireless communication capability. Therefore, there is a need to find a way to cooperate efficiently in target estimation.  
[PERSON] [108] applied a zero-sum game approach to the estimation of target position. The minimax filter is constructed to minimize the estimation error under the worst case noise, so it is robust to the adversary tracking difficulty imposed by moving targets. The work also developed a distributed version of the minimax filter with an improved performance. A sensor node communicates with its neighbors that are located in the range of wireless communication in the distributed minimax filter.  
A further investigation of a moving-target tracking problem with sensor networks was reported. Each sensor can observe the target by a sensor and estimate the target position by a processor. In this game, the tracking of the adversary moving target can use a strategy to maximize the estimation error. Moreover, how to track a moving target by taking the advantages of large spatial coverage, robustness, and cooperation is still challenging. [PERSON] (2008) formulated this target estimation problem as a zero-sum game and used a minimax filter to estimate the target position in addition to the distributed Kalman filter which had been one of the popular approaches for estimating the position of a moving target.
## 3 Results and Analysis  
### Analysis of Heat Transfer  
During solution mining, thermal convection and conduction arise from the temperature difference between the injected freshwater, the fluid within the cavern, and the cavern walls, impacting fluid flow, the brine concentration, and the salt rock wall's dissolution rate. Figure 11 illustrates the directions of thermal conduction and fluid flow. The injected fluid induces forced convection, creating a shearing effect between the fluids with temperature differences, thus aligning the forced thermal convection direction perpendicularly to the fluid flow direction in the cavern.  
Forced convection impacts brine concentration distribution, initiating transportation and diffusion towards equilibrium. This process, accelerated by forced thermal convection, aligns with the brine diffusion direction, making it perpendicular to the brine concentration gradient, as shown in Figure 12. Such convection hastens brine transportation and dissolution, with higher wall temperatures increasing the salt rock dissolution rates, thus accelerating cavity construction. The cavity expansion caused by different formation temperatures were compared, as shown in Figure 13. A constant injection fluid temperature alongside higher formation temperatures and greater temperature differences leads to faster cavity construction rates and increased expansion heights.  
Figure 11: Diagram of the thermal convection direction and fluid flow direction of brine in the cavity. (The blue arrows represent thermal convection, and the red arrows represent fluid flow.)  
Figure 10: Data comparison of concentration and cavern volume evolutions with leaching time.
# Characterization of Light-To-Frequency Converter for Visible Light Communication Systems
## 2 VLC and LTF System Model
### Light-To-Frequency Model  
An LTF reduces and simplifies the signal acquisition process coming from a light source because its output can be sent directly to be processed to a microcontroller for data processing [10]. As a result, traditional systems using ADCs can be seen as an additional option on the list. In some low-cost cases, complex ADCs are not a good choice as they can be oversized for low speed applications, and this is the result of all the related subsystems inside of an ADC-like antialiasing filter, sampler, quantization, and encoder. The LTF in data processing quantifies light intensity variations in terms of frequency, through a current-to-frequency (CTF) converter [11; 15].  
An LTF generates a train of pulses with a constant duty cycle (50%) and a frequency that is a function of the irradiance incident light signal:  
\[f_{0}=f_{D}+(R_{e})(E(\lambda)) \tag{6}\]  
From Equation (6), it can be observed that the output frequency of the LTF \(f_{0}\) is proportional to the irradiance of the perceived light \(E(\lambda)\), and when no power is detected, the LTF has a constant frequency \(f_{D}\), which is called dark frequency. \(R_{e}\) is the LTF responsivity in a certain wavelength \(\lambda\) and the associated units are \(\mathrm{Hz}/\left(\mu\mathrm{W/cm^{2}}\right)\). The irradiance is related to the surface area \(A_{r}\) of the LTF converter through the expression \(E(\lambda)=\frac{P_{m}}{A_{r}}\) measured in \(\mu\mathrm{W/cm^{2}}\)[1; 11].  
The dark frequency value, \(f_{D}\), results from the leak current produced by the semiconductor material and is affected by the overall system temperature [13; 14].  
Given that LTF output corresponds with a pulse train with variable frequency, it is important to keep in mind the different available techniques to measure it; therefore, a selection criterion of the technique will depend on the resolution and speed of the electronic interface used [14]. Thus, if a high resolution embedded system is required and time response is not too demanding, frequency counting or an accumulation of pulses can be used; if frequency is high and a high speed electronic interface is needed for measurement, given the rapid change of the light intensity, the period measurement technique is the more suitable solution [14; 15]. The period measurement demands a reference clock signal with a frequency greater than the signal of interest. In the case of the TCS3200 LTF sensor, the output signal possesses frequencies between 10 Hz and 780 kHz; hence, this guides the choice of a low-cost embedded system, because almost every single chip on the market has an equipped timer with a reference signal in the order of MHz [16]. As quoted, the period measurement technique for the scenario of optical wireless communications is properly considered, given that these systems call for online processing to decrease the overall link latency.
## 3 Model Construction and Underlying Assumptions
[50] noted that most investigations have utilized the UTAUT model in its entirety or only a subset thereof, frequently omitting the moderators. The reason for this is that previous studies on the use of UTAUT for online learning have not produced large differences in data with moderators compared to data without moderators. The objective of the current study is to examine the sustainable learning intention, and based on the generalization of the user experience factors in the previous section, the modified model is shown in Figure 2.  
Based on the adapted research model, the following twelve hypotheses (H1-H12) were developed and tested:  
**Hypothesis 1 (H1):**: _Career development factors positively influence employees' performance expectancy for online learning._  
**Hypothesis 2 (H2):**: _Business needs factors positively influence employees' performance expectancy for online learning._  
**Hypothesis 3 (H3):**: _Self-management factors positively influence employees' effort expectancy for online learning._  
**Hypothesis 4 (H4):**: _Learning experience positively influences employees' effort expectancy for online learning._  
**Hypothesis 5 (H5):**: _Company atmosphere factors are positively related to social influence on employees' online learning._  
Figure 2: Research model after adapting UTAUT. **Hypothesis 6 (H6):**: _External pressure factors are positively related to social influence on employees' online learning._
**Hypothesis 7 (H7):**: _Company support is positively related to facilitating conditions on employees' online learning._
**Hypothesis 8 (H8):**: _Course quality is positively related to facilitating conditions on employees' online learning._
**Hypothesis 9 (H9):**: _Employee performance expectancy is positively related to employees' behavioral intention to learn sustainability._
**Hypothesis 10 (H10):**: _Employee effort expectancy is positively related to employees' behavioral intention to learn sustainability._
**Hypothesis 11 (H11):**: _Employees' perceived social influence is positively related to employees' behavioral intention to learn sustainability._
**Hypothesis 12 (H12):**: _Facilitating conditions of the employee experience are positively related to employees' behavioral intention to learn sustainability._
## 3 Results and Discussion
### Performance of Crystallization
Figure 3 shows the image series occurring during the process that GNs are added to PP, and cooled from a melted state at 200 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min increments until it reaches 130 \({}^{\circ}\)C, where it is kept. The presence of GNs can be seen in the melted state image in Figure 3a. Figure 3b is the image photographed close to 130 \({}^{\circ}\)C, at which point the molecules start to nucleate and PP starts to nucleate the surrounding GNs. Figure 3c,d show that the spherocrystals continuously enlarge. Due to the nano-scale that GNs are at, the combination of GNs results in numerous nucleating sites in PP. Therefore, there is a great amount of spherocrystals of PP, which easily collide with adjacent spherocrystals, and as a result, spherocrystals have a small size and low completeness. Figure 4 shows the image series occurring during the process in which CF was added to PP and cooled from a melted state at 200 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min increments until it reached 130 \({}^{\circ}\)C, where it was kept. The black part shown in Figure 4a is CF; Figure 4b shows that the molecular chains of PP nucleate and the spherocrystals enlarge along the CF; Figure 4c,d shows ever-growing spherocrystals and a clear transrystallization. Compared to the combination of GNs, the combination of CF makes spherocrystals larger and more complete. \begin{table}
\begin{tabular}{c c c c c c} \hline wt\% & \(T_{\text{c}}\) (\({}^{\circ}\)C) & \(T_{\text{onset}}\) (\({}^{\circ}\)C) & \(T_{\text{onset}}\)-\(T_{\text{c}}\) (\({  
Figures 3 and 4 show that using GNs or CF as the nucleating agent facilitates the crystallization of PP. The combination of GNs results in a smaller spherocrystal size but a poorer completeness. Conversely, the combination of CF results in a larger spherocrystal size but a better completeness.
# Real-Time Penetration Recognition Based on Voltage Signal in K-TIG Welding
## 3 Experimental Results and Discussion
### Complete and Incomplete Joint Penetration under Non-Steady State  
Notwithstanding the successful acquisition of complete joint penetration and incomplete joint penetration welding seams in Section 3.2, the welding parameters employed are not the same, making it challenging to determine whether the characteristic signal strength is correlated to the welding melt state or independent of the welding parameters. Consequently, this section ensures that the welding parameters remain unaltered while the welding material undergoes a sudden shift in the welding melt state owing to thermal deformation and other factors by selecting the welding parameters at the edge of the welding window: a welding current of 500 A and a welding speed of 240 mm/min. Figure 7 presents the experimental parameters and the morphology of the front and back sides of the welding seams after the experiment, respectively, and depicts the power spectral tracking graph of the calculated characteristic signal. Evidently, as the welding reaches the middle section of the welding seam and the welding melt state changes from complete joint penetration to incomplete joint penetration, the collected characteristic signal strength drops steeply and then stabilizes within a low-intensity interval. This observation confirms that the strength of the characteristic signal is indeed related to the welding melt state and is not unaffected by the unchanged welding parameters.  
By monitoring the strength of the characteristic signal, it is possible to judge the welding melt state due to the unique characteristics of the signal. This paper proposes the possibility of achieving closed-loop control and ensuring welding quality stability by  
Figure 7: Complete and incomplete joint penetration under the same welding condition.  
continuously adjusting the welding parameters to maintain the strength of the characteristic signal in the complete joint penetration interval or returning to the complete joint penetration state when incomplete joint penetration occurs. However, this assumption is based on two prerequisites. Firstly, when the welding melt state changes from complete joint penetration to incomplete joint penetration and the strength of the characteristic signal drops, adjusting the welding parameters to return to the complete joint penetration state will lead to a rise in the strength of the characteristic signal. Secondly, adjusting the welding parameters will not significantly impact the strength of the characteristic signal.
###### Abstract  
On 10 August 2016 China launched the GF-3, its first C-band polarimetric synthetic aperture radar (SAR) satellite, which was put into operation at the end of January, 2017. GF-3 polarimetric SAR has many advantages such as high resolution and multi-polarization imaging capabilities. Polarimetric SAR can fully characterize the backscatter property of targets, and thus it is of great interest to explore the physical scattering mechanisms of terrain types, which is very important in interpreting polarimetric SAR imagery and for its further usages in Earth observations. In this paper, focusing on target scattering characterization and feature extraction, we generalize the \(\Delta\alpha_{\rm B}/\alpha_{\rm B}\) method, which was proposed under the reflection symmetric assumption, for the general backscatter process to account for both the reflection symmetry and asymmetry cases. Then, we evaluate the performances of physical scattering mechanism analysis methods for GF-3 polarimetric SAR imagery. Radarsat-2 data acquired over the same area is used for cross validation. Results show that GF-3 polarimetric SAR data has great potential for target characterization, especially for ocean area observation.  
GF-3 satellite; radar polarimetry; synthetic aperture radar (SAR); physical scattering mechanism +
Footnote †: journal: Sensors
## 5 Conclusions  
In this paper, we proposed the multicast approach named ILDM to avoid core overload and achieve traffic load balancing. In ILDM, each multicast service is identified by a globally unique MSEID, and the mapping between the MSEID and the NAs of multicast tree nodes is stored in the NRS. To avoid core overload and reduce traffic aggregation on the core, we design dynamic core management and selection mechanism. Routers can register with NRS as candidate cores. NRS maintains the state of each candidate core and dynamically assigns a low-load core to each multicast service. Furthermore, we designed a path state-aware multicast tree node selection mechanism. The multicast tree node with the best path state is selected for each receiver to join the multicast service. By using low-load links more efficiently, traffic aggregation is alleviated. The simulation results showed that our multicast approach has the following advantages compared with other ST-based multicast approaches: (1) it reduced the load of each core and achieve load balancing; (2) it reduced the number of join requests that each router needs to handle; (3) it reduced link load and avoid traffic aggregation; and (4) it reduced the number of multicast states per router.  
In future work, we will consider the location of the core, sources, and receivers when selecting the core for each multicast service. We will also study the impact of node failures, link failures, and packet loss on multicast communications and improve the reliability of ILDM to meet the high-reliability requirements in some scenarios (e.g., Vehicle Networking). Moreover, we will evaluate our proposed multicast approach on a testbed.  
Conceptualization, B.L. and J.W.; methodology, B.L. and J.W.; software, B.L.; writing--original draft preparation, B.L.; writing--review and editing, B.L. and J.W.; supervision, J.W.; project administration, J.W.; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Strategic Leadership Project of Chinese Academy of Sciences: SEANET Technology Standardization Research System Development (Project No. XDC02070100).  
Data Availability StatementData sharing not applicable.  
The authors declare no conflict of interest.
## 4 Simulation results
### Results
* Mode 3: The damping is approximately 2.0... 2.1 (taut string) and approximately 1.3... 1.9 (FR and FSC) times greater than the damping due to the passive damper being optimized to modes 1 to 4. * Mode 4: The damping is approximately 2.3... 2.5 (taut string) and approximately 1.2... 2.1 (FR and FSC) times greater than the damping due to the passive damper being optimized to modes 1 to 4. * The tracking of the desired semi-active force is limited by the minimum and maximum viscous coefficients of the controllable damper. The according force displacement curves due to the minimum and maximum quadratic viscous forces are depicted in Figures 8 and 9 for the simulations of mode 1 and 4. It is observed that \(c_{\rm max}\) mainly constrains the force tracking accuracy for lower frequency modes while \(c_{\rm min}\) mainly limits the force tracking accuracy for higher frequency modes. This is explained by the fact that the according limiting control forces are in proportion to the square of the damper relative velocity, see (21). Hence, how precisely the desired semi-active force can be tracked by the semi-active damper with control force limitations due to \(c_{\rm min}\) and \(c_{\rm max}\) depends on mode number, which makes the interpretation of the obtained cable damping results for modes 3 and 4 difficult. Figure 8: Simulated clipped VDNS with control force constraints for mode 1: (**a**) force displacement curves and (**b**) force velocity curves during steady state conditions (before simulated free decay response). Figure 9: Simulated clipped VDNS with control force constraints for mode 4: (**a**) force displacement curves and (**b**) force velocity curves during steady state conditions (before simulated free decay response).
## 2 Materials and Methods
### Emission Factor Database  
For simulation purposes, it is necessary to transform numerical input attributes into outputs. However, for functionality purposes, it is not required to link SustainSIM to online databases; instead, a local database of emission factors included in the algorithm's attributes will suffice.  
Tables 3-5 presents emission factors converted into units used in the European Union, sourced from the GHG Protocol calculation tool database and the European Commission's database of electricity consumption emission factors for Europe mentioned earlier. Standardized unit values were used for converting emission factors into units commonly applied within the EU.  
**Example of Factor Conversion:**  
Given values for gasoline mmBtu/gallon = 0.125 (million British thermal units per 3.7854 L)  
Attributes will be assigned a level according to the following conceptual map (Figure 4), where the highest level represents the number of tons of CO\({}_{2}\) emissions for a given object, Scope, activity, etc.  
Figure 4: Concept map of the algorithm and attribute hierarchy in the algorithmic model.  
that defines a large number of relationships between variables based on hierarchical structuring (Figure 5) when designing logical relationships between variables and time. The basic time unit is defined as one hour, and the map is structured accordingly, incorporating unit conversions per hour.  
The algorithm map visualizes the processes of CO2 generation and reduction in the context of engineering companies. On the left side of the map, the attributes related to CO2 generation are hierarchically arranged, including stationary and mobile combustion (Scope 1) and electricity consumption by machinery (Scope 2). For each fuel type, recorded data include the volume of fuel consumed, number of operating hours, fuel consumption per hour, and emission factor. Additionally, the model tracks operating time, utilization rate, and maximum power capacity to calculate total electricity consumption and resulting CO2 emissions.  
On the right side of the map, CO2 reduction methods are displayed, such as the use of electric vehicles, heat pumps, and solar panels. These methods incorporate factors such as energy consumption, electricity emission factor, and performance coefficient, which are essential for calculating emission reductions.  
The map comprehensively visualizes the data flow and relationships between various variables that are essential for the quantification and optimization of the carbon footprint in engineering companies and for designing the proposed model. Connections between databases also play a crucial role and cannot be overlooked.  
We considered other modeling approaches such as regression models and black-box simulations; however, these methods often mask the internal logic of CO2 interactions and make it difficult to visualize how individual factors contribute to emissions and reductions.
# VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication Security
## 2 Related Work
### Key Considerations  
The following were the key considerations for our study:  
* Inference time: We considered the importance of low-latency processing to meet real-time detection requirements in V2X environments.
* Detection accuracy: We focused on designing a model capable of identifying anomalies without relying on labeled attack data.
* Unsupervised problem: The solution was developed under an unsupervised setting to ensure practicality, given the difficulty of obtaining labeled anomalies in real-world V2X data.
* Deployability: We aimed for lightweight and modular architecture that is suitable for deployment on resource-constrained devices, such as OBUs and RSUs.