esa-sceva/satcom-chunk-collection huggingface dataset:
# State of Health Prediction for Lithium-Ion Batteries Using Transformer-LSTM Fusion Model
## 2 Extraction and Selection of Multidimensional Features
### Feature Extraction  
We selected a charging and discharging interval from the original charge and discharge cycle data, and focuses on analyzing the changes in voltage, current, energy, and temperature in these intervals to extract features that can effectively characterize battery degradation. The variation curves for voltage, current, energy, and temperature in the charging interval during a certain cycle after normalization are shown in Figure 3a. As the number of cycles increases, the rising time of the charging voltage curve gradually shortens, and the time required to reach the cut-off voltage continues to decrease (shown in Figure 3b). The charging current curve also decreases earlier as the cycle progresses (shown in Figure 3c). At the same time, the charging energy curve shows a gradual convex trend (shown in Figure 3d); however, the charging temperature curve does not show a significant change in trend (shown in Figure 3e). These changes indicate that the information within the voltage, current, energy, and temperature curves can accurately reflect the battery's degradation process and performance changes over time.  
By analyzing these regularly changing curves, the key features reflecting battery degradation can be effectively extracted. From the original charge and discharge measurement data, we extracted the time domain, frequency domain; and time dimension features of the voltage, energy; and temperature curves, totaling 102 direct or indirect features, some of which are shown in Figure 3f. These features cover many aspects of information, including time dimension features, such as constant-current charging time and constant voltage charging time; time domain features, such as the difference, maximum slope, average value, variance, standard deviation, curve integral, root mean square value, root amplitude, skewness, kurtosis, peak factor, margin factor, pulse factor; and waveform factor of the voltage, energy; and temperature curves; and frequency domain features, such as center of gravity frequency, average frequency, frequency variance, frequency standard deviation; and frequency root mean square. These features cover the important dynamic information of batteries during the charge and discharge process, and provide solid data support for further accurate prediction of battery SOH.  
Figure 2: Lithium battery capacity–degradation curve of MIT dataset.
# Safety Evaluation of a RC Structure with Multiple Openings under High Water Depth Inundations
## 4 Numerical Simulation  
### Description of the Numerical Model  
Numerical analysis was performed to simulate the hydrostatic pressure test discussed in Section 3.2 and to examine the behaviour of the prototype building under flowing water. A commercial package, TNO DIANA, was used [54]. The specimen was discretised with 4403 nodes and 4187 four-node quadrilateral shell elements (Figure 7a), considering the recent studies of RC structures [55]. The model was composed of glass blocks, laminated glass, PMMA plates and RC walls. The windows were discretised as laminated glass and PMMA plates. The double-glazed windows were considered to be single-pane ones as the analyses focused on the behaviour of the panes contacting water. Only the PMMA plates were considered for the window frames since it was presumed that the movement of the sashes and casings were governed by the PMMA plates (see, Figure 2f). As for the operable windows, hinged connections were adopted to the boundaries between the plates and walls and to those between the plates and panes, taking into account the state of panes and plates as shown in Figure 2f. The model was fully constrained at the bottoms of the structure. Geometrical and material nonlinearities were considered. Sensitivity analysis was performed to study the influences of the mesh size.  
Figure 6: Waterproof measures: (**a**) fixed window, (**b**) operable window, (**c**) door sill, state after applying the waterproof measures (**d**,**e**) door and (**f**) west windows.  
The material parameters are presented in Table 2. For RC and glass blocks, typical properties were considered [56; 57; 58]. The values of laminated glass were determined, taking into account the material characterisation tests discussed in Section 3.1 and publications on laminated glass [59; 60; 61; 62; 63; 64]. As for The values of PMMA was determined, considering the material characterisation tests presented in Section 3.1 and recent experimental studies of PMMA [63; 64]. As for the door, the modulus of elasticity was selected, considering the used materials as discussed in Section 2.2.  
As for failure criteria, a smeared crack model was adopted. Rotating total strain-based crack model was adopted to RC, glass blocks, laminated glass and PMMA. This model considers the orientation of cracks co-rotates with the axes of principal strains [65; 66]. In other words, it is based on the coaxial stress-strain approach that evaluates the stress-strain relationships in the principal directions of strains. As for uniaxial behaviour, linear tension softening was adopted to RC. Tensile fracture energy was equal to 100 N/m. Parabolic hardening was considered in compression. Compressive fracture energy was 30,000 N/m, taking into account recent publications [56; 57; 58]. Brittle behaviour was adopted to glass blocks, laminated glass and PMMA in tension, based on experimental studies [62; 63]. Constant hardening was considered for the three materials in compression [64; 67]. As for the door, linear elasticity was adopted.
# Taxonomy of Scheduling Problems with Learning and Deterioration Effects
## 3 Results
### Solution Methods
#### 3.4.2 Heuristics Leading to Optimal Solutions  
Even though the general case of the scheduling problem with learning and deterioration effect belongs to the family of NP-hard problems, research has also focused on finding special cases for which optimal solutions can be achieved in polynomial or pseudo-polynomial time. It is worth noting that with the beginning of the study of scheduling problems with learning and deterioration effects, some of the first approaches that address this issue suggested pseudo-polynomial and polynomial algorithms. For example, the paper of [PERSON] [94] introduces polynomial solutions to the problem into single and parallel machine environments. This category is typically called approximation algorithms and is an intermediary point between exact methods and heuristics. In this case, the algorithms known as \"heuristics with a guarantee\" or \"not-quite-exact\" algorithms ensure the possibility of finding the optimal solution in certain cases [98]. This category also includes work that deals with dominance rules and defines upper and lower bounds, with almost 74.7%.  
Some of the methods included in this category are based on the branch and bound (B&B) algorithm, based on dispatching rules or specific algorithms adapted to each problem.  
Figure 10: Solution method vs. system configuration.  
Another method, rarely used but also reported, is the bounds-based nested partition (BBNP) method applied by [PERSON] et al. [99] and [PERSON] [100], and studies related to worst-case and lower-bound definitions are also addressed. Despite the high frequency of use of heuristics leading to optimal solutions, these methods are not suitable for solving large-instance problems, or situations with specific characteristics of human behavior such as experience, fatigue, forgetting, etc., that constitute real problems (due to the uncertainty involved).
## 2 Theory  
### Four-Point Method for Phase Determination in LIT  
LIT is one of the most widely used TT techniques [22]. LIT is a technology that simultaneously stimulates sinusoidal heat as a heat source across the entire surface of the object to be inspected and senses the IR signal emitted from the surface with an IR camera. Using sinusoidal waveforms results in the ability to preserve the shape and frequency, while the only deviation from the reference wave will be the magnitude and phase delay [23]. Defect detection is possible by demodulating this phase change and comparing and analyzing the difference between the sound area and the defective area. LIT modulates the heat source that stimulates the object in the form of a harmonic function and synchronizes the detection element to demodulate the phase change of the harmonic function. As a result of extracting phase changes using lock-in technology, even minute temperature changes on the surface can be detected even at low sampling, reducing the impact of uneven surface emissivity. In addition, inspectors can obtain good results by controlling the influence of environmental factors such as ambient temperature, measurement angle, and wind speed [24].  
The phase shift method is a technique used to determine phase maps from different thermography raw images. The four-point method is one of the phase shift methods. It is calculated using the phase angle data of each pixel, then saved in a 2D matrix format, and displayed as a phase image. Figure 2 shows the processing process of the four-point method in LIT. If S\({}_{1}\), S\({}_{2}\), S\({}_{3}\), and S\({}_{4}\) are four equidistant thermal images, then the phase (\(\varnothing\)) data can be expressed by Equation (1) [24; 25].  
\[\mathcal{O}\{\mathrm{x},\mathrm{y}\}=\tan^{-1}(\frac{S_{1}-S_{3}}{S_{2}-S_{4}}) \tag{1}\]
## 3 Proposed Scheme
### SDN Controller  
The SDN controllers play a critical role in managing the mobility of network devices and users in a network. Here are some of the ways in which SDN controllers are important in mobility:  
1. Centralized control: SDN controllers provide a centralized point of control for network devices, enabling network administrators to manage and control network traffic in real-time. This is particularly important in a mobile network where devices are constantly moving and changing their location.
2. Dynamic routing: In SDN, the network topology can be changed dynamically, allowing network administrators to route traffic to the most efficient path. This is essential in a mobile network where devices are moving in and out of coverage areas and need to be rerouted to ensure seamless connectivity.
3. Traffic engineering: SDN controllers allow for fine-grained traffic engineering, enabling network administrators to direct traffic to specific devices or areas of the network. This is important in a mobile network where devices are moving in and out of coverage areas and need to be directed to the most optimal path.
4. Network segmentation: SDN controllers allow for network segmentation, which enables network administrators to isolate network traffic from different devices and users. This is important in a mobile network where devices are constantly moving and may be carrying sensitive data.
5. QoS control: SDN controllers allow for the implementation of quality of service (QoS) policies, which enable network administrators to prioritize traffic based on its importance. This is important in a mobile network where devices are constantly competing for bandwidth and need to be prioritized based on their importance.  
Figure 4: Software-defined mobility architecture for NDN.  
In a nutshell, SDN controllers are essential in managing the mobility of network devices and users in a network. They provide a centralized point of control, dynamic routing, fine-grained traffic engineering, network segmentation, and QoS control, all of which are critical in a mobile network.  
The SDN controller has a precise understanding of the MC's status. As a result, the SDN controller determines the routing parameter in support of user mobility in relation to the introduction of a certain protocol. The southbound API, also known as the OpenFlow protocol, is how the controller, who has a comprehensive view of the network, can understand the state of the underlying network. OpenFlow v3.0 has been utilized in this study.
## 1 Introduction
### Vision of Tactile Internet  
The International Telecommunication Union (ITU-R) initiated a program in early 2012 to establish \"International Mobile Telecommunication (IMT) for 2020 and Beyond\", laying the way for 5G research activities around the world. Figure 2[12] illustrates the 5G Roadmap. The International Telecommunications Union (ITU) has a long history of producing radio interface standards for mobile communications. IMT-2000 and IMT-Advanced are part of the International Mobile Telecommunications (IMT) framework of standards, which encompasses 3G and 4G industry perspectives and will continue to evolve as 5G with IMT-2020. The ITU multi-stakeholder framework's reliability ensures a positive outcome for the global telecommunications community.  
Figure 1: Evolution of mobile network technologies.  
The TI, according to one ITU-T Technology Watch Report [13], has taken a quantum leap forward. Tactile and haptic sensations will be enabled by TI's high availability and security, ultra-fast reaction times, and carrier-grade reliability, adding new dimensions to human-machine contact (HMI) [14]. The 5G technology vision envisions 1000-fold increases in area capacity, 10 Gb/s peak data rates, and connections for at least 100 billion devices to achieve this goal. The main challenge of 5G wireless access and core network architectures is to enable new machine-centric use cases that are currently unsupported by cellular networks. Additionally, the TI is characterized by the following technological capabilities [12]:  
* Ultra-low latency; 1 ms and below latency (as in round-trip-time/round-trip delay)
* Ultra-high availability; 99.999% availability
* Ultra-secure end-to-end (E2E) communications
* Persistent very high bandwidth capability (>1 Gbps)
* Bandwidth: data rates of 100 MB/s on average
* Capacity: up to 1 million devices per square kilometer
* Reliability: 99.999% network reliability
* Mobility: Seamless transfer between radio nodes up to 500 km/h
* Battery Life: up to ten (10) years battery life for low power (IoT) devices  
However, it has to be mentioned that the TI should be able to highlight the difference between humans and machines. This should be used in situations where there is a high demand for machines and little interference from humans. Machines should be used to supplement humans rather than to replace them [15].
# The Accuracy Assessment of Lithospheric Density Models
## 1 Introduction
In spite of these practical and theoretical limitations, the development of synthetic density models that more or less realistically approximate the Earth's shape and inner density structure is essential for a more comprehensive assessment of gravimetric forward and inverse modeling techniques (than those based on using simplistic, typically geometric density models). Moreover, input data uncertainties should be known in order to realistically assess the accuracy of synthetic density models. To inspect the possibilities of refining the Earth's synthetic density model, the accuracy of published lithospheric density models is assessed based on a novel approach presented in this study. Whereas the accuracy of crustal density models has been partially inspected, a robust analysis of lithospheric mantle density models is absent. This novel approach allows us to formulate the error propagation and consequently provide rough error estimates in gravimetric forward modeling that are attributed to lithospheric density and geometry uncertainties. Gravimetric forward modeling is applied to determine a long-wavelength geoidal geometry that is corrected for the gravitational contributions of lithospheric density and thickness variations. Expressions for gravimetric forward modeling are presented in Section 2 and then used to derive the error analysis in Section 3. Numerical results are briefly presented in Section 4, and the accuracy of the results is discussed in Section 5. Major findings are concluded in Section 6.
# Estimation of Chlorophyll Content in Wheat Based on Optimal Spectral Index
## 2 Materials and Methods  
### Study Area  
The study area is located at the Xinxiang site of the Chinese Academy of Agricultural Sciences (35.2\({}^{\circ}\) N, 113.8\({}^{\circ}\) E), with a plot area of 3.0 m \(\times\) 1.4 m, as shown in Figure 1. Each plot has a separator of 0.2 m in the east-west direction and 1.0 m in the north-south direction during the winter wheat growing season between 2020 and 2021. The wheat variety was Zhengmai 369 [19], and the soil type was fluvial soil. The natural conditions of all plots in the experiment were completely the same; the same standard field operations and management, such as fertilization, watering, insect control, weeding, seedling leveling, etc., were implemented on each plot. Xinxiang City experiences a mild continental monsoon climate with four distinct seasons, and the seasonal characteristics are obvious. The spring is dry and windy, the summer is hot and rainy, and the average temperature difference between day and night in autumn is large. The winter is cold, with scarce rain and snowfall. The average precipitation and the inequality of distribution are mainly concentrated from June to September, and the annual precipitation generally accounts for about 75% of the national precipitation, which is suitable for the growth of wheat (Figure 1).
# Process Optimization and Wear Performance of Plasma-Cladding Fe5 Coatings on Rotary Tillage Blades
## 3 Results and Discussion  
### Process Optimization of Plasma Cladding  
#### 3.1.1 Experimental Design  
To obtain a wear-resistant coating with excellent comprehensive performance, the Box-Behnken method was employed in Design-Expert 13 for experimental design [26]. The factors and levels to be optimized were determined considering the adjustable range of equipment parameters and the results of preliminary experiments, as shown in Table 3. The wear mass of the cladding layer was used as the evaluation index. The experimental design and results, along with the analysis of variance (ANOVA) of the regression model, are presented in Tables 4 and 5, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Cladding Current** & **Cladding Speed** & **Powder Feeding** & **Cladding Distance** \\
**(A)** & **(mm/s)** & **Rate (g/min)** & **(mm)** \\ \hline
140–180 & 10–35 & 15–25 & 5–15 \\ \hline \end{tabular}
\end{table}
Table 3: Parameters of the plasma-cladding process.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Test No.** & **Cladding** & **Cladding** & **Powder** & **Cladding** & **Wear Loss** \\
**Current A** & **Speed B** & **Feeding Rate** & **Distance D** & **(mg)** \\
**(A)** & **(mm/s)** & **C (g/min)** & **(mm)** & **(mm)** & **(mg)** \\ \hline  
\hline \end{tabular}
\end{table}
Table 4: Experimental design and results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Source** & **Sum of** & **Degree of** & **Mean Square** & **F-Values** & **\(p\)-Values** & **Significance** \\  & **Squares** & **Freedom** & **Mean Square** & **F-Values** & **\(p\)-Values** & **Significance** \\ \hline Model & 6192.84 & 14 & 442.35 & 16.41 & \textless{}0.0001 & **\(\ast\) \\ A & 349.49 & 1 & 349.49 & 12.97 & 0.0036 & **\(\ast\) \\ B & 38.38 & 1 & 38.38 & 1.42 & 0.2558 & \\ C & 2532.00 & 1 & 2532.00 & 93.94 & \textless{}0.0001 & **\(\ast\) \\ D & 1331.20 & 1 & 1331.20 & 49.39 & \textless{}0.0001 & **\(\ast\) \\ AB & 64.24 & 1 & 64.24 & 2.38 & 0.1486 & \\ AC & 272.91 & 1 & 272.91 & 10.13 & 0.0079 & **\(\ast\) \\ AD & 75.95 & 1 & 75.95 & 2.82 & 0.1190 & \\ BC & 105.06 & 1 & 105.06 & 3.90 & 0.0718 & \\ BD & 256.16 & 1 & 256.16 & 9.50 & 0.0095 & **\(\ast\) \\ CD & 15.09 & 1 & 15.09 & 0.5600 & 0.4687 & \\ A2 & 4.73 & 1 & 4.73 & 0.1755 & 0.6827 & \\ B2 & 172.93 & 1 & 172.93 & 6.42 & 0.0263 & \(\ast\) \\ C2 & 220.19 & 1 & 220.19 & 8.17 & 0.0144 & \\ D2 & 1009.86 & 1 & 1009.86 & 37.47 & \textless{}0.0001 & **\(\ast\) \\ Residual & 323.44 & 12 & 26.95 & & \\ Lack of Fit & 305.62 & 10 & 30.56 & 3.43 & 0.2468 & \\ Pure Error & 17.83 & 2 & 8.91 & & \\ Cor Total & 6516.29 & 26 & & & & \\ \hline \end{tabular} Note. \(\ast\) indicates a significant effect (\(p<0.05\)); ** indicates an extremely significant effect (\(p<0.01\)).  
\end{table}
Table 5: ANOVA of the regression model.
# Method for Wind-Solar-Load Extreme Scenario Generation Based on an Improved InfoGAN
## 2 Extreme Scenario Feature Selection for Power Systems
### Extreme Weather Events  
In recent years, under the influence of global climate change, extreme weather events have exhibited a trend characterized by \"increased frequency, higher occurrence rates, greater severity, and simultaneous occurrences of multiple extreme weather events\". These changes have significantly constrained the stability and reliability of renewable energy generation within power systems due to wind and solar resources and climate risks, thereby threatening the security of power supply [22; 23]. Currently, research on extreme scenarios in power systems primarily defines these scenarios based on climate conditions and their associated impacts on production and daily life. For instance, extremely high temperatures and drought conditions pose severe challenges to the power supply assurance of systems heavily reliant on hydropower [24; 25], while typhoon conditions lead to a rapid increase in wind power generation, exerting significant pressure on the integration of renewable energy [26].  
To further analyze the extreme scenarios faced by power systems in ensuring supply and renewable energy integration, this study introduces extreme meteorological factors as classification criteria [1]. Given that wind and solar power generation are significantly influenced by weather, meteorological conditions, and natural disasters, extreme scenarios such as prolonged high temperatures, extreme cold waves, typhoons, and conditions of no sunlight and no wind are regarded as typical extreme scenarios within power systems due to their broad impact and high occurrence frequency [22; 27]. Therefore, this paper classifies extreme scenarios into five categories: prolonged high temperatures, extreme cold waves, typhoons, no sunlight, and no wind. Additionally, based on the \"Extreme Low Temperature and Cooling Detection Indicators\" released by China [28], we define the specific criteria for extreme scenarios as shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Extreme Weather Events** & **Selection of Features** \\ \hline Long-term high-temperature & Temperature A, duration T1 \\ \hline Extreme cold weather & Temperature B, duration T2 \\ \hline Typhoon weather & Air velocity C, rate of increase in wind turbine power T3 \\ \hline No wind for a long time & Air velocity D, duration T4 \\ \hline Prolonged absence of solar energy & Radiation intensity E, duration T5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definition of extreme scenarios.