esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The proliferation of electric vehicle (EV) technology is an important step towards a more sustainable future. In the current work, two-layer feed-forward artificial neural-network-based machine learning is applied to design soft sensors to estimate the state of charge (SOC), state of energy (SOE), and power loss (PL) of a formula student electric vehicle (FSEV) battery-pack system. The proposed soft sensors were designed to predict the SOC, SOE, and PL of the EV battery pack on the basis of the input current profile. The input current profile was derived on the basis of the designed vehicle parameters, and formula Bharat track features and guidelines. All developed soft sensors were tested for mean squared error (MSE) and R-squared metrics of the dataset partitions; equations relating the derived and predicted outputs; error histograms of the training, validation, and testing datasets; training state indicators such as gradient, mu, and validation fails; validation performance over successive epochs; and predicted versus derived plots over one lap time. Moreover, the prediction accuracy of the proposed soft sensors was compared against linear or nonlinear regression models and parametric structure models used for system identification such as autoregressive with exogenous variables (ARX), autoregressive moving average with exogenous variables (ARMAX), output error (OE) and Box Jenkins (B). The testing dataset accuracy of the proposed FSEV SOC, SOE, PL soft sensors was 99.96%, 99.96%, and 99.99%, respectively. The proposed soft sensors attained higher prediction accuracy than that of the modelling structures mentioned above. FSEV results also indicated that the SOC and SOE dropped from 97% to 93.5% and 93.8%, respectively, during the running time of 118 s (one lap time). Thus, two-layer feed-forward neural-network-based soft sensors can be applied for the effective monitoring and prediction of SOC, SOE, and PL during the operation of EVs.  
soft sensor; state of charge; state of energy; power loss; neural networks; formula student electric vehicle; battery pack
## 1 Introduction
The time delay data of the optical fiber are safely protected to resist the hidden danger that the information is tampered with. After testing, the developed two-way time transfer via optical fiber equipment realizes the encryption function, in addition to ensuring the accuracy of optical fiber time transfer. The measured stability of time transfer is less than 10.7 ps at 1 s and 7.1 ps at 10 s. The stability of frequency 10 MHz is less than 4.7 \(\times\) 10\({}^{-12}\) at 1 s and 1.1 \(\times\) 10\({}^{-12}\) at 10 s. At the same time, this paper uses three devices to build a ring timing network, which can demonstrate the networked optical fiber time cascade transmission system. The standard deviation of time synchronization measured by the ring timing network is 3.1 \(\times\) 10\({}^{-11}\), and the TDEV is 2.1 \(\times\) 10\({}^{-11}\) at 1 s, which can ensure the safety of the system while the system still has high accuracy.
## 3 Structure of Modular System  
This study intends to develop a modular system for floating OWTs based on MATLAB/SIMULINK in combination with BEM solver and visualization software ParaView as shown in Figure 4. The function of BEM solver is to obtain hydrodynamic coefficients for subsequent 6-DOF motion responses. It can be solved by the boundary element method and by defining the linear free surface. Since BEM solver is established based on the potential flow theory, the influence of viscous force could be ignored. ParaView is an open-source program that combines different codes for visualization of data analysis, and enhances visualization of the flow field as well as 3D modeling.  
The present modular system is composed of four modules, including floater module, coordinate module, motion module and mooring module as indicated in Figure 5. The functions of floater module include calculation capabilities of hydrostatic and hydrodynamic forces. While the particulars like mass, Center of Gravity (COG), inertia moment, hydrodynamic coefficients and dimensions of a floating body are given, wave exciting force, radiation added-mass force, radiation damping force, and restoring force would be calculated. Coordinate module can be used to define earth coordinate system, body-fixed coordinate system, gravity, seawater density, wave spectrum, and simulation time length. Motion module is configured to construct displacements, velocities, and accelerations of single or coupling 6-DOF motion responses. The characteristics of the mooring module are designed to select  
Figure 3: The schematic diagrams of the NREL 5 MW wind turbine mounted on the UMaine TLP with the taut-leg mooring system in different views. (Unit: m).  
the stiffness matrix of the mooring system according to the user's requirement. The mooring load in the time domain can be determined by means of a 6 \(\times\) 6 linear stiffness matrix and pretension force.  
The software ParaView in the modular system is carried out to visualize the flow field between waves and the floating platform. It is open-source software that can be linked with Python, Matlab, and other programs for multiple data analysis. The objects are able to drawn into a dot map, a grid map, etc., according to the user's requirements. Meanwhile, the view angle can also be adjusted in the animation by the user. For example, Figure 6a-d present the snapshots of floating OWTs [1], including OC3-Hywind spar, UMaine-Hywind spar, MIT/NREL TLP and UMaine TLP, influenced by the unidirectional irregular waves with respect to free surface elevation, respectively.  
Figure 4: A flowchart of the modular system.  
Figure 5: The functions in the modular system.
## 5 Conclusions  
This study examines how varying initial electron energies and phases influence radiation characteristics in tightly focused, linearly polarized laser fields. The findings demonstrate that precise control over the driving laser's initial phase enables an effective manipulation of (1) the spatial profile of emitted radiation, and (2) key temporal and spectral properties. Notably, initial phase adjustment serves as a powerful tool for pulse structure modulation, enabling a selective generation of single- or double-peak temporal spectra through controlled phase variation. Meanwhile, the pulse width will appear to change periodically, synchronized with the laser half-period. In the influence on the spectrum, the phase modulation of the harmonic intensity can be realized, which is maximum at \(\phi_{0}=k\pi\) and almost disappears at \(\phi_{0}=(k+1/2)\pi\), enhancing the odd harmonic selectivity, and, at the same time, the reconstruction of the spectral shape is realized. In the linearly polarized laser field, the modulation of the electron initial energy in the harmonic order,intensity, spatial distribution, and time domain also has a significant effect on the harmonic radiation characteristics. As the electron's initial energy increases, the harmonic intensity will rise sharply, and the time-domain pulse width is compressed; at the same time, the compression of the harmonic spatial distribution leads to a more collimated radiation of the high-energy electrons, which is well applied in high-resolution imaging.  
Conceptualization, Y.T.; Methodology, X.Z.; Software, X.Z., Y.Z. and Y.T.; Validation, X.Z., Y.Z. and Y.T.; Formal analysis, X.Z.; Data curation, X.Z.; Writing--original draft preparation, X.Z.; Writing--review and editing, X.Z. and Y.Z.; Visualization, X.Z., Y.Z. and Y.T.; Funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the National Natural Sciences Foundation of China under grants No. 10947170/A05 and No. 11104291, the Natural Science Fund for Colleges and Universities in Jiangsu Province under grant No. 10 KJB140006, the Natural Sciences Foundation of Shanghai under grant No. 11 ZR1441300 and Colleges and Universities in Jiangsu Province under grant No. 10 KJB140006, and the Natural Science Foundation of Nanjing University of Posts and Telecommunications under grant No. 202310293146Y.  
Not applicable.  
The original contributions presented in this study are included in the article; further inquiries can be directed to the corresponding author/s.  
The authors declare no conflict of interest.
## 3 Methodology
### Proposed ML Models
#### 3.7.2 Random Forest  
Random forest is an ensemble learning method celebrated for its resilience and adaptability. By generating numerous decision trees during training, each on a distinct data subset, it mitigates overfitting and enhances predictive accuracy. In classification tasks, the final prediction stems from the mode of classes predicted by individual trees, while in regression, it is derived from their mean prediction. This ensemble approach ensures robustness and stability across diverse datasets. Moreover, random forest's capacity to handle high-dimensional data while maintaining interpretability renders it a widely favored choice in various domains [32].
## 1 Introduction  
Dust accumulation on the photovoltaic (PV) module, which can cause energy power loss in PV power generation of up to 85%, has been observed [1]. The observed PV output efficiency reduction corresponds to a dust accumulation density of 22 g-m\({}^{-2}\), reaching up to 26% [2]. Moreover, it can induce the so-called \"hot-spot\" effect, where the short circuit current of the shaded cells of PV panels raises local heating [3]. For this reason, PV power plants require regular cleaning to wipe the dust from the panels. Due to geographical location [4], the dust might be of sand, silica, red soil, clay, bird droppings, and oil-contaminated soil, which has, in fact, a significant impact on PV cleaning solutions [5]. The state-of-the-art PV cleaning solutions, in general, include natural water/wind cleaning, manual cleaning, mechanical cleaning, electrical screens (EDS), super hydrophobic aircraft, super water jet, and self-cleaning ultrasonic that can be categorized as labor-driven, machine-driven, and coating methods [6]. Among these methods, the utilization of coating techniques incorporating super hydrophilic or superhydrophobic properties appears to offer promising cleaning solutions characterized by low labor and operational costs, minimal water waste,and reduced air pollution. However, they require sufficient precipitation [7] as well as a reduction in solar irradiance absorption, in turn degrading the electrical conversion efficiency. Portable robotic cleaning in relation to mechanical PV cleaning emerges as a reasonable and sustainable solution requiring minimal human intervention and enabling the effective removal of adhered dirt. An automatic cleaning system installed on the PV panel arrays to clean the sites regularly can increase the average annual energy up to 11.1% [8]. A synthesis of the advantages of existing PV cleaning methods is shown in Table 1.
# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 1 Introduction
Therefore, interference is the most significant factor that influences capacity and the QoS provided to end-users [34; 35]. To reduce collisions and time wastage on retransmissions, the system must be able to detect potential interferences in the spectrum and smartly utilize the spectrum [36]. The first stage toward attaining this objective is to identify and classify the existing types of environmental interferences. The most common kinds of interference incorporated with cellular networks are self-interference, inter-user interference, adjacent channel interference, multi-access interference (co-channel interference and multi-user interference), inter-carrier interference, inter-channel interference, intra-channel interference, inter-symbol interference, inter-numerology interference, cross-link interference, inter- and intra-beam interference, multiple-access interference (intra-cell interference and inter-cell interference). Nevertheless, these types of interferences are not the only ones that affect wireless communication networks. Each network is influenced by interference incurred by its deployment and transmission scenario. The main aim of this paper is to explore and highlight the issues of interference that were noticed in various structures and approaches of the B5G network, primarily concentrating on HetNets, Device-to-Device (D2D), Ultra-Dense Networks (UDNs), and Unmanned Aerial Vehicles (UAVs), to acquire novel insights into constructing efficient B5G networks as fast as possible. The remainder of the paper is organized as follows. Section 2 highlights the research motivation and contribution of this work. Section 3 discusses the multiple interference effects in various system models such as HetNet, D2D, UDNs, and UAVs. This includes the scenarios, benefits, interference types, and recent related work for each model. In Section 4, the open issues and future directions are provided. Finally, Section 5 concludes the paper. The structure of this paper is shown in Figure 1.
# Non-Contact High Voltage Measurement in the Online Partial Discharge Monitoring System
## 4 Determining the Voltage Ratio and Measurement Uncertainty
If an element with a very high input impedance is used in the measuring system (in this case, the AD549 voltage follower with an input impedance of \(10^{15}\)\(\Omega\)), then due to the elimination of the influence of the input resistance \(R_{s}\) of the signal acquisition card on the measuring system, Equation (6) for the voltage ratio of the system will be simplified, and then it will take the following form:  
\[U_{1}=\left|\left(\frac{C_{1}+C_{2}}{C_{1}}\right)\cdot U_{2}\right| \tag{9}\]  
Thus, Equation (7) for determining the measurement uncertainty will also be simplified to the following form:  
\[u(U_{1})=\left(\frac{\partial U_{1}}{\partial C_{1}}\right)^{2}u^{2}(C_{1})+ \left(\frac{\partial U_{1}}{\partial C_{2}}\right)^{2}u^{2}(C_{2})+\left( \frac{\partial U_{1}}{\partial U_{2}}\right)^{2}u^{2}(U_{2}) \tag{10}\]  
Limiting the number of variables influencing the uncertainty of voltage determination also allows us to reduce the level of uncertainty, but only to a small extent, because the greatest influence is still the measurement uncertainty of voltage \(U_{2}\), and this does not change.
## 3 Applications
### Parameters of the Design and Materials  
The theory and prototype tests were carried out on the same pipe. The geometric parameters of the adopted pipe are given as Table 1. Key parameters of the materials, involving the concrete, mortar and cylinder, are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Geometric Parameter** & **Value** & **Geometric Parameter** & **Value** \\ \hline Inner diameter of PCCP, \(D_{l}\)/mm & 2000 & Net thickness of mortar coating, \(h_{m}\)/mm & 25 \\ \hline Thickness of core concrete, \(h_{c}\)/mm & 140 & Spacing between each wire, \(l_{s}\)/mm & 22.1 \\ \hline Outer diameter of cylinder, \(D_{y}\)/mm & 2103 & Diameter of wires, \(d_{s}\)/mm & 6 \\ \hline Thickness of cylinder, \(t_{y}\)/mm & 1.5 & Number of layers, \(n\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometric parameters of the embedded concrete pipe (ECP).  
As for the parameters of load, the internal working pressure used was \(P_{w}=0.6\) N/m\({}^{2}\). The internal transient pressure was \(\Delta H_{r}=\max(0.4P_{w},276\) kPa) \(=0.276\) N/mm\({}^{2}\). The internal design pressure was \(P_{d}=P_{w}+\Delta H_{r}=0.876\) MPa \(\approx 0.9\) N/mm\({}^{2}\). The thickness of soil above the top of the pipe was \(H=3\) m. The bedding angle was \(90^{\circ}\). The type of installation was trench-type with a positive projecting embankment. The standard value of the ground stacking load was \(q_{mk}=10\)kN/m\({}^{2}\).  
The parameters of environment are shown as follows: The average relative humidity of the storage environment was 70% RH, the time in outdoor storage was \(t_{1}=270\) d. Burial time after outdoor storage was \(t_{2}=1080\) d.  
Key parameters of the adopted strand are given in Table 3.
# Seismic Response of Shield Tunnel with Double-Layer Lining
## 2 Engineering Background  
The Shiziyang Tunnel is a pivotal project of the Guangzhou-Shenzhen-Hong Kong High-Speed Railway. It passes under the main navigation channel of the Pearl River--the Shiziyang Waterway. The tunnel project has a total length of 10.8 km, with a shield tunneling section spanning 9340 m. It is the first underwater railway shield tunnel in China.  
In the Shiziyang Shield Tunnel, which passes through weak and uneven geological formations at the entrance and exit sections, a double-layer lining structure is adopted due to structural safety concerns (Figure 1). The outer diameter of the shield tunnel is 10.80 m, with a thickness of 0.50 m for the segmental lining. The width of the segmental lining is 2 m. The diameter of the secondary lining is 9.80 m, with a thickness of 0.30 m. There are eight segments in each segmental ring, with 24 bolts connecting them. There are 22 M30 circumferential bolts arranged between two interacting rings (Manufactured by CHINA RAILWAY 12 TH BUREAU GROUP CO., LTD., located in Taiyuan, China). The concrete strength grade for the segmental lining is C50, while the concrete strength grade for the secondary lining is C25.
## 3 Results
### Part 2: Drone Use in Regional Local Government Areas
The geographical distribution of drone usage among the regional councils in NSW, as depicted in Figure 3, indicates a distinct cluster pattern. This pattern suggests that drone uses are more concentrated around certain major regional centres, which act as hubs of technological adoption. One reason for the clustering is that councils are engaging in a knowledge-sharing initiative about technology adoption, specifically in drone adoption. Councils in the vicinity of regional centres like Bathurst and Armidale exhibit a higher use of drones. It could be surmised that if these hubs were to adopt drone technology, they could possibly influence their neighbouring councils, possibly due to shared initiatives, closer collaboration, and the spill over of technological expertise and resources. The clustering also suggests that these centres may have more developed infrastructure or policies supporting drone usage, which could serve as models for other regions. It is also plausible that these regional centres have higher economic activity or more pressing logistical needs that drones help to address, which could explain the higher concentration of drone uses in these areas. The use of drones across the 93 regional councils in NSW is multifaceted and in a state of flux. While some councils are leading the way in using drone technology for a variety of uses, including policy implementation and council engagement, others are still in the nascent stages of adoption. The following discussion presents a dynamic and evolving landscape for drone use in local governance, underscoring the potential for broader adoption and innovation in the future.
## 1 Introduction
For a multidisciplinary system, due to the coupling between disciplines, uncertainty will spread among disciplines, so it is necessary to combine reliability analysis method with multidisciplinary optimization strategy [14], but the direct integration of reliability analysis method and multidisciplinary design optimization (MDO) strategies is inefficient when solving large-scale MDO problems, because when both aleatory and epistemic uncertainties are involved, the procedure of direct integration method will become a nested three-layer loop. Therefore, [PERSON] [15] proposed an efficient uncertainty-based design optimization strategy with random and interval variables for multidisciplinary engineering systems. The method evaluates the uncertainty constraints in the worst case, but it cannot evaluate the impact of interval uncertainties on the design space. For this purpose, the methods proposed in this paper mainly studies from two following aspects: (1) considering the multisource uncertainties, a multidisciplinary reliability comprehensive evaluation index with clear geometric meaning is proposed. It is pointed out that the reliability should be an interval rather than a single value under the condition of aleatory and epistemic uncertainties. The minimum value of the reliability interval is used as the measurement standard, and the interval difference directly reflects the influence of epistemic uncertainties on the limit state function. (2) Based on the comprehensive evaluation index, a decoupling strategy for multidisciplinary reliability approach under multisource uncertainties (MU-DBMRA) is proposed, which divide the three-layer nested loop of MRA process into a sequential monocyclic process, consisting of multidisciplinary probabilistic reliability analysis (MPRA), multidisciplinary convex reliability analysis (MCRA) and multidisciplinary analysis (MDA). Results of example analysis indicate that the proposed method can solve decoupling problems of MRA process in a proper way, and greatly improve efficiency of MRA. This paper is constructed as follows: the first section introduces the related works of uncertainty quantification and PMA principle used in this paper; the second section introduces principle and algorithm flow of the proposed method under multisource uncertainties. Section 3 presents one numerical example and one engineering design example for demonstration. Section 4 concludes and presents possible future work.
## 4 Results and Discussion  
### Nickel Laterite Profile  
Macroscopic analysis of weathering was carried out by evaluating structures visible in cross-section in the pit walls (Figure 4). A similar sequence of separate levels of weathering was found in all the pits investigated. In the uppermost part, to a depth of approximately 0.25 m there is a red-grey soil layer with strongly disturbed structure. Below the overburden layer from an average depth of about 0.25 m to a depth of about 2 m, there is a red laterite (ferruginous cap) with a strongly disturbed structure in relation to the bedrock. Locally, this layer contains small pisolites. At the depth of 2 to 4 m, there is a yellow laterite horizon. The upper zone of this horizon contains a yellow laterite with a strongly disturbed structure. Below, there is a layer of yellow laterite with a partially preserved structure, in which ferruginous pseudomorphes are present. The lower it gets, the more the proportion of bedrock fragments increases. Large fragments of serpentinite or peridotite occur from a depth of 3-7 m below. Locally occurring structures associated with micro-scale diversity in weathering conditions or the bedrock nature are observed in weathering profiles. Siliceous zones and veins were observed in a few pits. In addition, garnetite crusts are visible on serpentinite.  
Figure 4: The Weda Bay laterite profile.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Horizons** & **Average Bulk Density [t/m\({}^{3}\)]** \\ \hline Limonite & 0.99 \\ Transition & 1.11 \\ Saprolite & 1.22 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average bulk density of ore by horizons.