esa-sceva/satcom-chunk-collection huggingface dataset:
# Research Progress of Zero-Busbar Technology Based on Heterojunction Photovoltaic Modules
## 2 Materials and Methods
### Materials  
Solar cells: Soldering-based 0 BB cells and IFC-based 0 BB cells, as shown in Figures 3 and 4, respectively, differ in terms of pattern design. The printing pattern of a soldering-based 0 BB cell is denoted as pattern 1, and the printing pattern of an IFC-based 0 BB cell is denoted as pattern 2. The abovementioned HJT solar cells (Trina Solar, Changzhou, China) both have dimensions of 210 mm \(\times\) 105 mm and solder ribbon contact areas (SRCAs) for 24 ribbons. As busbar lines are eliminated due to the 0 BB cell design and fingers are printed with a 50% Ag@Cu mixture paste for both patterns, silver consumption is reduced. Compared with a soldering-based SMBB cell design (with busbars and fingers both made of silver paste), the soldering-based 0 BB design shows a 39.3% decrease in silver consumption per watt, while the IFC-based 0 BB design shows a 59.8% decrease in silver consumption per watt.  
Solder ribbons: The solder ribbons used in PV modules consist of copper wire and a tin alloy solder coating. The low-temperature solders used in this study were as follows: Sn43 Pb43 Bi14 (melting range: 136-167 \({}^{\circ}\)C), denoted as Bi14; Sn37 Pb42 Bi21 (melting range: 136-158 \({}^{\circ}\)C), denoted as Bi21; and Sn32 Pb42 Bi26 (melting range: 136-143 \({}^{\circ}\)C), denoted as Bi26. The abovementioned ribbons have a diameter of 0.25 mm. The ribbons all come from Yourbest New-Type Materials Co., Ltd., Suzhou, China.  
Encapsulation system: From the front surface to the rear surface, the soldering-based 0 BB/SMBB modules consist of the following: glass, an ethylene-vinyl acetate (EVA) film  
Figure 1: The production process of soldering-based 0 BB technology.  
Figure 3: Soldering-based 0 BB solar cell printing pattern (partial).  
Figure 2: The production process of IFC-based 0 BB technology.  
with a light conversion additive, solar cell strings, an EVA film, and glass. From the front surface to the rear surface, the IFC-based 0 BB modules consist of the following: glass, an EVA film with a light conversion additive, a carrier film, solar cell strings, carrier films, an EVA film, and glass. All of the modules are sealed with butyl rubber.  
The abovementioned glass is double-coated with an anti-reflection (AR) coating, and the carrier films are pre-curing EVA films. The front EVA film has a down-conversion function with high transmittance and high adhesion, and the rear EVA film is a normal film with high transmittance and high adhesion. EVA films, carrier films and butyl rubber all come from First Applied Material Co., Ltd., Hangzhou, China. Glasses come from Xinyi Solar, Co., Ltd., Wuhu, China.
# An Overview of Emergency Communication Networks
## 5 Sea Ad Hoc Network (SANET)
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **MANET** & **VANET** & **FANET** & **SANET** \\ \hline Number of nodes & high & high & low & low \\ \hline Propagation model & on the ground & on the ground & in the air & on the sea \\ \hline Topology & ad hoc and random & \begin{tabular}{c} star with roadside \\ infrastructure and ad \\ hoc among vehicles \\ \end{tabular} & \begin{tabular}{c} star and mesh (with \\ base station, ad hoc \\ among UAVs) \\ \end{tabular} & \begin{tabular}{c} star and mesh (with \\ base station, ad hoc \\ among ships) \\ \end{tabular} \\ \hline Topology change & \begin{tabular}{c} dynamic, \\ unpredictable, and low \\ \end{tabular} & average speed & fast & medium \\ \hline Line of sight & \begin{tabular}{c} not available for \\ all cases \\ \end{tabular} & available in some cases & available in most cases & available in some cases \\ \hline Power consumes & low & high & \begin{tabular}{c} high for mini-UAV, \\ small UAV not needed \\ \end{tabular} & high \\ \hline Mobility & low & high & very high & medium \\ \hline Mobility model & random & regular & \begin{tabular}{c} regular under the \\ condition \\ \end{tabular} & random \\ \hline Main functionality & \begin{tabular}{c} real-time \\ communication \\ \end{tabular} & \begin{tabular}{c} real-time \\ communication \\ \end{tabular} & \begin{tabular}{c} real-time \\ communication \\ \end{tabular} & \
\begin{tabular}{c} real-time \\ communication \\ \end{tabular} \\ \hline Computational power & limited & average & high & high \\ \hline Density & low & high & very low & high \\ \hline Localization & GPS & GPS, DGPS, AGPS &
\begin{tabular}{c} IMU, GPS, \\ DGPS, AGPS \\ \end{tabular} & GPS, DGPS \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of ad hoc networks. Figure 6: Structure diagram of SANET.
## 4 Experimentation and Results  
To implement the deep learning experiments Anaconda 5.3.1. [33], Keras library [34], OpenCV library [35], and Python 3.7.3. programming languages are used. All the experiments were run on the Hercules cluster in our PDS lab [pds.ucdenver.edu] on GPU Tesla P100-SXM2 [36].  
To test the SAFEPA system, we used an Intel Core i7-8750 CPU (2.20 GHz, 8 GB RAM), with a Windows 10 64-bit operating system.  
This study describes three main experiments that were conducted to evaluate the proposed SAFEPA system.  
* In Experiment 1, SAFEPA and three models called VGG16, ResNet50, and Ensemble described in [17] were trained and tested on a subset of the UNBC-McMaster dataset to measure their performance on a pain assessment task and compare them to the recent study in [3, 22, 23].
* In Experiment 2, the SAFEPA model was extended to recognize seven facial expressions on a subset of KDEF containing only frontal facial images and compare their performance with four recent studies in [17, 24, 25, 26]. As another but related experiment, FEAPAS was separately trained and tested on the JAFFE dataset (gray scaled frontal face images) and its results were compared with the state-of-the-art models: VGG16, ResNet50, Ensemble, and HOG, which are described in [17] and [25], respectively. All models were run on both KDEF and JAFFE datasets.
* To assess the model's ability to generalize, experiment 3 was conducted by running the SAFEPA system which is described in Figure 8 on the BioVid dataset that consists of 6900 videos with various poses and measured the accuracy and average processing time.
* Subsequently, we proceeded to conduct ablation tests as a follow-up to the three experiments.
## 6 Conclusions  
There are many analytical, numerical and analytical-numerical methods to solve thin plates. Most of them are based on the theory of thin plates (see Section 2). Method presented in the paper is one of them. The essence of the method is as follows:  
* to obtain with high accuracy particular solution of equilibrium equations at the separate surface nodes using boundary collocation method,
* to generate set of the initial points at the coordinate axis and then their distribution at the plate contour,
* to write boundary conditions at each edge nodes; number of these nodes always corresponds to number of unknown parameter of the model,
* to solve system of boundary equations,
* to calculate the desired magnitudes,
* to prepare the results.  
The major findings of this research are summarized in the following:  
* The mathematical model of thin isotropic plates is constructed. Basic shape and force functions are introduced.
* Within the model equilibrium equations are performed exactly.
* Expansion of the deflection within the area of the plate is obtained as sum of shape and force functions multiplied by unknown parameters.
* Displacements, slopes, moments and shearing forces are obtained using method of automatic differentiation. Union of presented relations create mathematical model of the plate.
* Within constructed model, a method to solving plate of arbitrary geometry have been suggested.
* All operations are performed automatically with author's program.
* Effectiveness of method were illustrated at the examples of rectangular plates with various boundary conditions. Obtained results have been compared with numerical ones computed in ABAQUS package.
* With help of suggested method three kinds of the rectangular plates, namely: plate with two opposite edges simply supported and the other free; plate simply supported and clamped at the contour were solved. It has been shown that suggested method performs all boundary conditions. Finite element method performs exactly only kinematic ones. Static boundary conditions are performed with less accuracy.  
It is shown that chosen direction is actual and important from a practical point of view.  
Conceptualization, M.D; methodology, M.D. and K.R.; software, K.R.; validation, M.D.; writing-original draft preparation, M.D.; writing-review and editing, M.D. and K.R.; visualization, K.R.; supervision, M.D.; project administration, M.D.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Data Processing and Parameter Determination
### Processing Sample Data  
In the investigated area, observational data from four monitoring points were chosen as the source for subsidence prediction. A subset of the data is seen in Table 1. Taking the data from monitoring point 1 as an example, according to the sample division strategy in Table 2, six consecutive data points were used as input, with the seventh data point serving as the target output, resulting in a total of 77 independent samples. The initial 67 data sets were utilized for training purposes, and the subsequent 10 sets served as test samples. To avoid the instability caused by anomalous sample data, the divided data were normalized. The processed training and testing samples are detailed in Tables 3 and 4, respectively. Although this data partitioning strategy might extend the training duration of the model, it effectively preserves the temporal characteristics of the data, maximizes data utilization, and reduces the risk of overfitting. Due to space limitations, only a portion of the data are displayed in the table.  
\begin{table}
\begin{tabular}{c r r r r} \hline \multicolumn{1}{c}{\multirow{2}{*}{**Time**}} & \multicolumn{4}{c}{**Subsidence Value (mm)**} \\ \cline{2-5}  & **1** & **2** & **3** & **4** \\ \hline
4 January 2023 & \(-\)0.87 & 0.42 & \(-\)0.08 & 0.03 \\
5 January 2023 & \(0.14\) & 0.10 & 0.31 & 0.26 \\
6 January 2023 & \(-\)1.09 & \(-\)0.73 & 0.03 & \(-\)0.21 \\
7 January 2023 & \(-\)0.71 & \(-\)0.32 & \(-\)0.92 & 0.39 \\
8 January 2023 & \(0.33\) & 0.28 & \(-\)0.53 & \(-\)0.98 \\
9 January 2023 & \(-\)0.62 & \(-\)0.94 & \(-\)0.88 & 0.36 \\
10 January 2023 & \(-\)0.48 & \(-\)0.37 & 0.43 & \(-\)0.83 \\
11 January 2023 & \(-\)0.88 & 0.37 & \(-\)0.59 & \(-\)0.92 \\
12 January 2023 & \(-\)0.33 & \(-\)0.34 & \(-\)1.03 & \(-\)0.01 \\  & & & & \\
22 March 2023 & \(-\)0.15 & \(-\)0.56 & 0.03 & \(-\)0.32 \\
23 March 2023 & \(0.25\) & \(-\)0.66 & \(-\)0.3 & \(-\)0.72 \\
24 March 2023 & \(-\)0.41 & \(-\)0.02 & 0.30 & 0.07 \\
25 March 2023 & \(-\)0.16 & \(-\)0.22 & \(-\)0.34 & 0.36 \\
26 March 2023 & \(-\)0.65 & \(-\)0.41 & \(-\)0.33 & \(-\)0.37 \\ \hline \end{tabular}
\end{table}
Table 1: Subsidence value of the monitoring point.  
\begin{table}
\begin{tabular}{c c c} \hline \multicolumn{1}{c}{\multirow{2}{*}{**N Input**}} & \multicolumn{2}{c}{**M Output**} \\ \hline \(\chi_{1},\chi_{2},\ldots,\chi_{\rm N}\) & \(\chi_{\rm N+1}\) & \\ \(\chi_{2},\chi_{3},\ldots,\chi_{\rm N+1}\) & \(\chi_{\rm N+2}\) & \\ \(\ldots\) & & \\ \(\chi_{\rm K},\chi_{\rm K+1},\ldots,\chi_{\rm N+K-1}\) & \(\chi_{\rm N+K}\) & \\ \hline \end{tabular}
\end{table}
Table 2: Sample division strategy.
# A Digital Twin for Intelligent Transportation Systems in Interurban Scenarios
## 6 Performance Evaluation Results
### Results of Pollution Use Case Evaluation
#### 6.2.1 VSL in the Incident Section  
This subsection presents the pollution use case results for simulations without any scaling factor, focusing on the impact of variable speed limits, with strategies from 120 km/h to 50 km/h. In Figure 11, the results of the North direction's total values for average speed and deviation, intensity, travel time, CO, NO\({}_{\text{x}}\), and PM\({}_{\text{2.5}}\) are plotted when applying the different strategies to North's section N_5.  
Figure 10: Simulation results for incident A and the re-routing strategy.  
As can be observed, as the speed limit decreases, the average speed in the northbound direction of the highway decreases too, as expected. For the default strategy, the average speed is not 120 km/h due to the nature of the vehicles and the presence of other types of vehicles, like trucks, which typically travel at speeds between 80 and 90 km/h, thereby lowering the average speed. The speed deviation remains quite constant, with values between 0 and 10 km/h. The intensity graph hardly shows any differences for the variable speed limits. On the other hand, since vehicles are closer together at lower speeds and take longer to reach their destinations, total travel time increases accordingly. Pollutant indicators, however, do not exhibit the same behavior. In general, all three metrics show similar trends, decreasing as the average speed is reduced. However, CO reaches its minimum at the lowest speed, while NOx and PM2.5 achieve their minimums for the 70 km/h and 60 km/h speed limits, respectively, before increasing again as the speed limit continues to decrease.  
These varying behaviors make it difficult to develop a strategy that benefits all parameters. Therefore, an intermediate approach is often adopted, prioritizing the pollution KPIs while reducing the total travel time, if possible, as it is the most representative indicator among the ones related to traffic performance. In this case, for example, possible choices could include the 70 km/h or 60 km/h strategies. Since the 70 km/h strategy does not considerably increase pollution levels and results in lower total travel time than the 60 km/h option, it could be considered the final decision.
###### Abstract  
The in situ detection of trace heavy metal is very important for human health and environmental protection. In this paper, a novel and stable nebulized discharge excited by an alternating current (AC) power supply at atmospheric pressure is employed to detect the trace metal copper by atomic emission spectrometry. Different from the previous experiments in which a conductive object was wrapped around a pneumatic nebulizer directly as a discharge electrode. Plasma is generated near needle electrodes and aerosol is introduced from above the electrode gap by a pneumatic nebulizer, which avoid damage to the fragile device. The effects of applied voltage, gas flow rate, pH value of liquid, and concentration of organic addition agents on the emission intensity of Cu I (3d\({}^{10}\)4p-3d\({}^{10}\)4s, 324.75 nm) are investigated for the purpose of optimizing the experiment conditions. For studying the discharge characteristics and understanding the mechanisms of metal atomic excitation, the waveforms of applied voltage and discharge current are measured, and the vibrational and rotational temperature are calculated by the spectra of N\({}_{2}\) (C\({}^{3}\)T\({}_{\mathrm{th}}\)-B\({}^{7}\)T\({}_{\mathrm{fg}}\), \(\Delta\upsilon=-2\)). In addition, gas temperature evolution of nebulized discharge is acquired and it is found that the emission intensity of Cu I (3d\({}^{10}\)4p-3d\({}^{10}\)4s, 324.75 nm) can be affected by applied voltage, gas flow rate, pH value of liquid, and concentration of organic addition agents. An optimized experimental condition of nebulized discharge for Cu detection is 3.59 of the pH, 5.6 kV of applied voltage, 1.68 L/min of Ar flow rate, and 2% of the ethanol. Under this condition, the limit of detection (LOD) of Cu can reach up to 0.083 mg/L.  
heavy metal detection; atomic emission spectrometry; nebulized discharge; atmospheric pressure discharge plasma; plasma gas temperature Article
## 4 Simulation Study  
In this section, several cases are simulated to confirm the performance of the proposed method. Firstly, three parameters \(\lambda_{0}\), \(\lambda_{1}\) and \(\rho\) of the Algorithm 1 should be determined. The parameters \(\lambda_{0},\lambda_{1}\in[0,1]\) are related to the ratio of sparse and low-rank component. The more sparse and low-rank \(\mathbf{X}\) is, the larger \(\lambda_{0}\) and \(\lambda_{1}\) are. The parameter \(\rho>1\) is related to the convergence speed and guarantee convergence, which has little impact on the extraction performance. Thus, \(\lambda_{0}=0.29\), \(\lambda_{1}=0.2\) and \(\rho=1.5\) is adopted in this paper  
Figure 2: The flowchart of the proposed extraction method.  
to achieve a better performance and proper speed for fault feature extraction in OFSC. The simulations in this section are carried out by MATLAB R2019b on Intel(R) Core(TM) i7-7700 CPU @ 3.60 GHz system with 16 GB-RAM. Note that there are few relevant studies focusing on the fault feature extraction in the cyclic order-frequency domain. Inspired by refs. [21,25] in the time-frequency domain, the proposed method is compared with methods imposing the single sparsity constraint and single low-rankness constraint on noisy OFSCs. Then, the outer and inner race fault (following the Equations (6) and (7)) is simulated in this section as two specific cases.
## 5 Hardware Implementation Analysis  
In this section, we present the hardware-based experimental results for the proposed RIS-assisted secret key-extraction method, and evaluate the effectiveness of the proposed optimization approach for configuring the RIS.
# Recent Advances in the Targeting of Head and Neck Cancer Stem Cells
## 3 Identification of CSC  
Since CSCs may play an important role in the prognosis of cancer and therapeutic strategy, the precise identification of CSC subpopulations is considered essential for a more accurate characterization of patient subtypes and ultimately more personalized and efficient therapeutic approaches. The first proof for the existence of CSCs comes from the study by [PERSON] and [PERSON], who identified a small subpopulation of leukemic cells in the bone marrow samples of AML patients, displaying the immature CD34\({}^{+}\) CD38\({}^{-}\) phenotype and the functional capacity to recapitulate the tumor of origin upon serial transplantation in immunocompromised mice [10]. Since then, the possibility of CSC identification based on the expression of specific stem cell-related markers has paved the way for their detection in multiple types of solid tumors, including HNSCC [11].  
In HNSCC, several different markers such as CD44 and CD133, side populations, and aldehyde dehydrogenase (ALDH) activity have been used to identify and isolate highly tumorigenic cells with reduced sensitivity to chemo- and radiotherapy. However, despite significant efforts made to identify a unique marker for CSCs in HNSCC, the specificity of markers as well as their restriction to CSCs still remains the major challenge [59; 60].
## 4 Discussion
### Future Outlook and Research Directions
#### 4.6.2 In the Context of Life Cycle Analysis Research  
[PERSON], [PERSON] [67] highlighted the use of corn, cassava, and corn cob as feedstocks. Future research should investigate the use of non-food biomass and waste materials to address food security concerns and broaden the array of usable feedstocks. The potential of FT fuels, especially when integrated with corn ethanol production, is showcased. Continued research in techno-economic evaluation of innovative pathways and integration strategies is necessary [68]. With a study showing potential WTW GHG emissions reduction of at least 92% using electrofuels, it is evident that the future of aviation fuel might lean heavily on these innovations [55]. More detailed studies into optimising e-fuel production are warranted. A study focusing on PtL production highlighted its potential, especially for the aviation sector [69]. Further research into the scale-up, optimisation, and commercial viability of PtL processes is a promising direction. The study [61] hinted at the increasing competitiveness of the methanol production process in the mid-term future. Future research could delve deeper into market trends, economic feasibility, and cost structures. While GHG emissions remain central, the phenomenon of contrail-induced cloudiness highlightsthe importance of addressing broader climate impacts. Further studies into the atmospheric and climate effects of different SAFs and their production processes are essential [65].
## 2 DC Microgrid System Based on Distributed PV Technology  
### Principle of Distributed PV Microgrid System Proposed  
The block diagram of a distributed PV microgrid system is shown in Figure 1, which mainly includes the PV power generation system, circuit system, and various DC appliances. The common drawback of renewable energy sources is their intermittency [29], so this project considers the inclusion of PV batteries to reduce the intermittency of the system. After that, the PV system includes PV panels, an MPPT tracking system [30], DC detector, the circuit system includes boost, drive, buck, and an STM32 control system. This control method was selected for this project to achieve control of the boost and buck circuits because the inherent control algorithm of PWM maximizes the energy delivered to the battery pack, thus increasing its battery life [31; 32].  
The basic working principle is that the entire microgrid system sends out DC power through the PV system, which is boosted by the boost circuit in the circuit system, and outputs different voltages through the STM32-controlled pulse width duty cycle (PWM) to meet the voltage requirements of different DC appliances.
## 3 Feature-Selection Method and Application
### Symmetrical Uncertainty (SU)  
#### 3.2.1 Information Entropy  
Information entropy is the average amount of information contained in a message, which was proposed by [PERSON] in 1948 [PERSON] (1948). Among them, entropy is generally understood as a measure of uncertainty rather than certainty. Because the more random the message, the greater the entropy value, which also explains the probability distribution of the sample in information theory. The reason for using the logarithm of probability distribution as the information measure is that it is additive. The formula for entropy is shown in (5). Moreover, the information gain is shown in (6).  
\[H(t)=-k\sum\limits_{i}^{N}P(x_{i})I(x_{i}) \tag{5}\]  
\[I(X)=\sum\limits_{i}^{N}\ln P(x_{i}) \tag{6}\]  
Among them, \(P\) is the probability mass function of \(x\), \(k\) is a proportional constant corresponding to the selected metric, and \(i\) is the information body of \(x\).
# Decision-Making Framework for Aviation Safety in Predictive Maintenance Strategies
## 3 Results
### MCDA-Based Decision-Making Framework for PM Implementation
#### 3.3.3 Case Studies
High-criticality components, such as engines or landing gear, often require thresholds of 0.80-0.85 due to their safety importance. Medium-criticality components, like hydraulic systems, may have thresholds around 0.70-0.75, while low-criticality components, such as interior systems, might be evaluated with thresholds of 0.60-0.65. Applying this approach, a general threshold of 0.75 was used in the example provided. Using Formula (1), the suitability score for both components can be defined:  
\[P_{engine}=0.9225,\ P_{avionics}=0.635.\]  
The turbofan engine surpasses a typical threshold (\(T=0.75\)), making it highly suitable for PM. Its gradual degradation, critical impact on flight safety, and the ability to monitor and collect data continuously make it an ideal candidate for predictive maintenance. The FMC unit falls below the established threshold, indicating that it is not suitable for predictive maintenance. The unpredictability of failures, lack of sufficient data for predictive modeling, and high costs of adaptation make PM impractical for avionics.