esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Other parameters, like the pressure drop inside the solar collectors, show a significant role in the design of such systems [12]. It has been found that wind speed and airflow rate are the most influent parameters on transpired solar collector performance [13]. Seminal contributions have been made by [PERSON] et al. regarding the influence of the perforation shapes on the absorber plate [14], showing that round holes have lower heat transfer than lobed geometries by about 15%. They also found that non-aligned and non-uniform shapes provide better performance. Although there are many CFD studies providing key elements to understand TSC behaviors, the research remains limited, mainly due to the lack of computing power. TSCs are complex geometries incorporating many small surfaces, such as the holes on the absorber plate which compelled researchers to simplify their numerical models. This paper presents numerical studies performed on a large-scale numerical model, regarding the parametric independence of a Double Skin Transpired Solar Collector (DSTSC). The idea of DSTSC is part of a project aiming to integrate thermal energy storage in TSC [15] with a thermal energy storage (TES) management system. The main idea of the prototype of building-integrated DSTSC, presented in Figure 1 is to use the PCM storage only when the solar energy absorbed by the perforated collector (metal plate) is not covering the heating needs (for example, for the periods when the solar radiation is intermittent). The by-pass system decreases the pressure drop caused by the TES and diminishes the energy consumption of the fan, and thus aims to increase the overall efficiency of the system. The TES management system is based on a system of dampers controlled by a function of the temperature range and heating needs. The performed research is dedicated to less-studied parameters in the literature, like distances between internal structures and the impact of low-conductivity materials. The main purpose of this paper is to present the critical parameters investigated during the concept phase of a Double Skin Transpired Solar Collector prototype. The study addresses three main objectives: the determination of the influence of a second skin inside a TSC and comparison of our prototype with classic TSC to validate the potential for future uses combining with TES integrated in the back-pass; the design of a DSTSC that is easy to integrate in buildings considering lightweight materials, such as aluminum, and reduces the thickness of the collector; and finally, the study of a low-conductivity and affordable plastic-based material.
# A Network-Based Method to Analyze EMI Events of On-Board Signaling System in Railway
## 4 Case Study
### Paths Description  
For the application of the EN model introduced in Sections 2 and 3, this section contains the actual cases of specific EMI events involving the disturbance and coupling. The content of the cases is presented in the subsequent sections. Figure 8 illustrates the paths with red and green lines concerning the interaction paths between the source and equipment.  
The LTE-R (LTE for railway) communications technology will be applied in the future and it has experimented with test lines by the railway group in China. The applicability of LTE-R in the 450 MHz frequency band should be considered according to the existing spectrum resource distribution in China. The transient radiated electric field from the pantograph arcing will have a potential impact on the performance of LTE-R at 450 MHz operation frequency [25]. The path of this case is the red line in Figure 8.  
DMI (Driver-machine Interface unit) displays the information of control order, equipment status, and driving strategies of the train, as well as the fault information to the driver. It is located in the driver room connecting the kernel in OTCS by long cables. So, it could be influenced by the interaction between cables especially the nearby power lines in EMU. The path of this case is the green line in Figure 8.  
Therefore, to verify the proposed modeling method for EMC in OTCS, these paths are used as practical examples in this study.
## 3 Results
### IntEC Model Optimization  
The simulation and verification results of three forest NEP values are shown in Figure 5. As shown in Figure 5, the model simulation NEP is consistent with the trend of the three-flux observation NEP time series. The correlation coefficient is between 0.54 and 0.75. Broadleaf forest has the highest precision (r = 0.75, RMSE = 20.77 gC-m\({}^{-2}\)-yr\({}^{-1}\)). Verification of the results showed that the optimized InTEC model can be used to simulate and predict the spatial distribution of forest NEP in Zhejiang Province.
# A Hierarchical Multi-Label Classification Algorithm for Gene Function Prediction
## 1 Introduction
These days, the Gene Ontology has become the most popular functional classification scheme for gene and protein function prediction studies [21], but only a few HMC methods have been proposed for the GO as its DAG structure is more complicated to handle. In this paper, we deal with these problems based on the GO taxonomy. Figure 1: A fraction of the Gene Ontology taxonomy. For the gene function prediction problem based on GO, a hierarchical multi-label classification algorithm is proposed in this paper. Specifically, we make the following contributions. Negative instances selecting policy and a synthetic minority over-sampling technique (SMOTE) [22] are introduced to preprocess the imbalanced training data sets; in the proposed algorithm, SVM is chosen as the base classifier considering its good performance for binary classification; a two-phase approach based on the Bayesian network is used for combining the outputs of all binary classifiers to guarantee the hierarchy constraint. All of these techniques together lead to the improvement of the final gene function prediction results. Experiments are implemented to evaluate the performance of the proposed algorithm on eight benchmark data sets by comparing it with state-of-the-art algorithms. The experimental results indicate that the algorithm has better performance than the existing algorithms. The rest of this paper is organized as follows. Section 2 revisits the definition of hierarchical multi-label classification and shows the proposed HMC algorithm for the GO. Experimental results on the eight benchmark GO data sets are given in Section 3. The discussion is presented and then, in the last section, the conclusions are introduced.
# Opportunities for Two-Color Experiments in the Soft X-ray Regime at the European XFEL
## 4 Numerical Simulations for Two-Color Generation and Transport
### Comparison of Low-Charge and Fresh-Slice Options  
To compare in more detail the performance of the low-charge and the fresh-slice methods, three additional cases with different combinations of photon energies were considered. As input for the simulations we used the start-to-end-simulated electron beams with 20 and 500 pC charges, both compressed to 5 kA and transported with an average beta function of 30 m. For the fresh-slice simulations we assumed that a single 2m-long corrugated structure module deflects the tail of the beam vertically. The distance of the orbit from the plate was 0.5 mm. The electron beam was assumed to be re-aligned and re-matched for the new lasing window. The results of these numerical simulations are provided in Table 2. The pulse durations correspond to the 2\(\sigma\) spread of the power distribution (in the time domain). A systematic difference in terms of both pulse energy and pulse duration between the two generation methods is observed. On the one hand, typical durations of pulses emitted with the fresh-slice technique (10 fs), are about one order of magnitude longer (installation of additional wakefield modules is expected to decrease the pulse duration, in comparison with the 20 pC low-charge mode (1 fs)). On the other hand, the pulse energies are substantially larger with the fresh-slice operation mode. Also, in simulations we see that the number of active undulator cells affects significantly and non-linearly pulse duration and pulse energy, and is adjusted to obtain comparable energies and durations for both colors.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Generation Method,** & & **420 eV + 280 eV** & **1500 eV + 420 eV** & **2000 eV + 1500 eV** \\ \hline Low charge (20 pC) & 100 \(\upmu\)J, 1 fs + 10 \(\upmu\)J, 1.5 fs & 5 \(\upmu\)J, 1 fs + 130 \(\upmu\)J, 1.3 fs & 20 \(\upmu\)J, 1 fs + 6 \(\upmu\)J, 1 fs \\ Fresh-slice (2 m structure) & 400 \(\upmu\)J, 15 fs + 250 \(\upmu\)J, 15 fs & 100 \(\upmu\)J, 12 fs + 500 \(\upmu\)J, 12 fs & 80 \(\upmu\)J, 6 fs + 50 \(\upmu\)J, 10 fs \\ \hline \hline \end{tabular}
\end{table}
Table 2: Estimated performance of low-charge and fresh-slice methods for generating two-color SASE radiation for three different combinations of photon energies.
## 3 Simulation Analysis of Path Planning Algorithm Based on Improved Genetic Algorithm
### Simulation System  
The simulation platform interface is shown in Figure 3. The simulation interface consists of four parts: the path visualization window, input module, output module, and start button; The path visualization window is used to display the paths planned by the complete coverage path planning algorithm within the work area. The input module is used to set simulation parameters. The output module can output the results of path planning. The start button is used to start running the simulation system.  
In the simulation, path planning is performed on multiple lands of different areas that can verify the adaptability of the proposed full coverage path planning algorithm in handling work areas of different areas. The lands are 1400 m\({}^{2}\), 500 m\({}^{2}\), 150 m\({}^{2}\), 30 m\({}^{2}\), 18 m\({}^{2}\), and 8 m\({}^{2}\) respectively. Due to the operating width of the agricultural robot being 1 m, the areas of 70 \(\times\) 20 m, 50 \(\times\) 10 m, and 30 \(\times\) 5 m can all be considered large-scale operating areas. For the convenience of discussion, the improved genetic algorithm based on the Markov chain is abbreviated as MCGA, and the basic genetic algorithm is abbreviated as GA.
# Electronic and Transport Properties of Epitaxial Graphene on SiC and 3C-SiC/Si: A Review
## 4 Electronic and Transport Properties: Effect of Interactions with the Surroundings
### Epitaxial Graphene-Substrate Interaction
#### Impact of Growth on SiC (0001)
The third transport region is the heavily p-doped outermost layers (\(10^{13}\) cm\({}^{-2}\), 1500 cm\({}^{2}\cdot\)V\({}^{-1}\cdot\)s\({}^{-1}\)) likely due to environmental dopants [14] related to the ambient temperature [90]. [PERSON] et al. determined that the environmental factors cause a p-type behaviour in ambient exposed monolayer and multilayer EG on SiC(000\(\overline{1}\)) [5] (a detailed discussion on the influence of ambient temperature on the transport of EG is given in Section 4.2). [PERSON] et al. remarked that the outer layers of graphene, which are p-type, play a protecting role for the interior graphene layers, preventing influence from the atmosphere [92]. [PERSON] et al. also reported that the natural conductivity state of MEG without the influence of ambient is n-type [5, 84], which is also consistent with the substrate hexagonality argument. The difficulty in understanding the range of doping types reported by others is clarified by [PERSON] since many samples analyzed using a simple one-layer model will tend to be p-type even though n-type results can occur depending upon overall thickness and uniformity. Hence, the variations in reported results are likely due to researchers assuming a simple model of uniform charge density and mobility when analyzing Hall data coupled with sample-to-sample variability. A significant advancement in the transport of EG on 4H-SiC(000\(\overline{1}\)) was achieved when [PERSON] et al. [15] synthesized a monolayer EG on the C-face, which was p-doped \(\sim\)1.27 \(\times\) 10\({}^{12}\) cm\({}^{-2}\) (likely due to environmental doping) with mobility of 20,000 cm\({}^{2}\cdot\)V\({}^{-1}\cdot\)s\({}^{-1}\) at 4 K and -15,000 cm\({}^{2}\cdot\)V\({}^{-1}\cdot\)s\({}^{-1}\) at 300 K [95]. The same group has also demonstrated that the scattering effects due to the substrate (i.e., charge impurity, electron-phonon) are weak. This is related to the absence of a buffer layer, a weak EG-substrate interaction, and no induced charges into the graphene or scattering centres [56, 96]. The values of MLG on 4H-SiC(000\(\overline{1}\)) are an order of magnitude larger than MLG on SiC(0001) and are in line with the values from exfoliated graphene on SiO\({}_{2}\)[97, 98], even though later studies reported two-fold larger mobility of \(\sim\)10\({}^{6}\) cm\({}^{2}\cdot\)V\({}^{-1}\cdot\)s\({}^{-1}\) at a carrier concentration of \(\sim\)10\({}^{8}\) cm\({}^{-2}\) and \(\mathbf{E_{F}}\) within 1 meV for suspended graphene devices [99, 100, 101].
# Energy Consumption Analysis of a Rolling Mechanism Based on a Five-Bow-Shaped-Bar Linkage
## 3 Dynamics and Energy Consumption Analysis
### System Rolling Angle Acceleration Planning  
In this paper, the locomotion of the mechanism is equivalent to pure rolling, i.e., there is no relative slipping between the mechanism and the ground, as shown in Figure 9. The grounded point \(P\) is the instantaneous center of velocity. The locomotion can be considered as rotation around the instantaneous axis that passes through point \(P\) and is perpendicular to the plane of locomotion. The rotational inertia of the mechanism [9] about the instantaneous axis can be denoted as  
\[J_{P}=\sum\limits_{i=1}^{5}\left(J_{i}+m_{i}{d_{i}}^{2}\right) \tag{23}\]  
where \(J_{i}\) denotes the rotational inertia of the bow-shaped module \(i\) about its CoM, and \(d_{i}\) denotes the distance from the CoM of the bow-shaped module \(i\) to the grounded point \(P\)During rolling locomotion, neglecting rolling resistances and considering the effect of the mechanism's CoM, the relationship between gravity and acceleration [9] is written as  
\[J_{P}(\theta_{1},\theta_{2},\phi)\ddot{\phi}=m_{t}\mathrm{g}(X_{c}-R\phi) \tag{24}\]  
According to the research of [PERSON] et al. [8], four types of curves were proposed as dynamic rolling schemes, and the simulation results showed that sinusoid and modified trapezoidal curves were more appropriate for the dynamic rolling of the mechanism. Therefore, the sinusoid and modified trapezoidal curves are used as the dynamic rolling schemes in this paper. To compare and analyze these two schemes, the time spending of the accelerating phase is \(\triangle t=2\) s, and the angular velocity and angular displacement are equal when \(t_{e}=2\) s at the final moment. Since the rolling angle \(\phi\) is in the range of 0 to \(2\pi/5\), the total angular displacement is \(\triangle\phi=2\pi/5\). Furthermore, by setting the angular acceleration to be equal to zero that \(\ddot{\phi}_{0}=\ddot{\phi}_{e}=0\) at both the initial and final moments, the dynamic rolling schemes [8] are shown as below.  
Case (1): sinusoid acceleration  
\[\ddot{\phi}(t)=\frac{\pi\phi_{e}}{{t_{e}}^{2}}\sin(\frac{\pi t}{t_{e}}),\ \ \ \ \ 0\leq t\leq t_{e} \tag{25}\]  
Case (2): modified trapezoidal curve acceleration  
\[\ddot{\phi}(t)=\begin{cases}\frac{6\pi\phi_{e}}{(4+\pi)t_{e}^{2}}\sin(\frac{1 5\pi t}{t_{e}}),&0\leq t<\frac{t_{e}}{3}\\ \frac{6\pi\phi_{e}}{(4+\pi)t_{e}^{2}},&\frac{t_{e}}{3}\leq t<\frac{2t_{e}}{3} \\ \frac{6\pi\phi_{e}}{(4+\pi)t_{e}^{2}}\sin\left(\frac{15\pi t}{t_{e}}-\frac{\pi }{2}\right),&\frac{2t_{e}}{3}\leq t\leq t_{e}\end{cases} \tag{26}\]  
Using the rolling angular acceleration expressed by Equations (25) and (26), the velocity and displacement of the rolling angle can be obtained by the integral method. On this basis, the curves of the angular acceleration \(\ddot{\phi}\) along with the time \(t\) and the curves of the angular velocity \(\dot{\phi}\) along with the time \(t\) are shown in Figure 10, which can meet the requirements of angular velocity and angular displacement above.  
Figure 9: Equivalent rolling motion.
# Size-Dependent Free Vibration of Axially Moving Nanobeams Using [PERSON]'s Two-Phase Integral Model
## 2 Model Equations
Effects of the nonlocal parameter \(\alpha\), volume fraction \(\xi_{1}\) and geometrical parameter \(k_{s}\) on the natural frequency are shown in Figures 7 and 8. The size effect on the first model of natural frequency of the axially moving nonlocal beam is shown in Figure 7a. It shows that the natural frequency increases with the increase of nonlocal parameter \(\alpha\). In Figure 7b, the effect of parameter \(k_{s}\) on the natural frequency of the first mode is shown. It shows that the natural frequency also increases with the increase of \(k_{s}\). In Figure 8, the effect of parameter \(\xi_{1}\) on the natural frequency of the first mode is shown. It can be seen that the natural frequency decreases with the increase of \(\xi_{1}\). It indicates that the integral model will cause larger natural frequency for nanobeams. Figure 7: The natural frequency for different \(\alpha\) and \(k_{s}\), with \(\xi_{1}=0.5,p=0.04\) fixed. (**a**) from top to bottom \(\alpha=80,50,30\); (**b**) from top to bottom \(k_{s}=0.02,0.01,0.005\). From these two figures, we find that the parameters have similar effects on the natural frequencies as those of simply supported boundary condition. However, the natural frequencies of the beam under fixed boundary condition is larger than that of a simply supported boundary condition.
# Reliability Prediction of Mixed-Signal Module Based on Multi-Stress Field Failure Mechanisms
## 4 Hybrid-Precision Circuit Simulation Validation and Stress Analysis
### Development of Behavioral Simulation Models for Integrated Devices Using Verilog
#### 4.1.3 Development of the FPGA Simulation Model  
As the control core of the communication module, the FPGA is closely related to the implementation of its functions. The FPGA simulation circuit is constructed, as shown in Figure 8. To verify FPGA functionality, the test circuit includes not only the FPGA itself but also SRAM. Additionally, it simulates a management unit and a functional module to provide command and telemetry data to the FPGA. To monitor FPGA output, a serial port module is incorporated into the circuit, which converts the FPGA's returned serial data into 8-bit data. During the simulation process, the functional module transmits data to the FPGA. If the FPGA returns the same information to the management module, it is considered that the FPGA simulation model is functioning correctly.  
Figure 8: FPGA simulation test circuit.  
Figure 6: SRAM simulation test circuit.  
Figure 7: Digital signal waveform of SRAM simulation test results.  
The FPGA baud rate is set to 921,600, and a five-cycle data transmission and reception simulation is performed (approximately 22 ms). The telemetry data returned by the FPGA to the management unit are selected as the primary observation metric. The simulation results, as shown in Figure 9, indicate that the FPGA successfully receives and returns the telemetry data \"123456789 abc\" to the management unit. Since the expected data are correctly included in the response, it can be concluded that the FPGA operates as intended.
# Transformer-Based Hybrid Forecasting Model for Multivariate Renewable Energy
## 1 Introduction  
Environmental preservation has been a critical topic in discussing numerous areas of knowledge, mainly due to global warming. According to the World Meteorological Organization [1] at the State of Global Climate of 2020, the 2011-2020 decade had one of the highest average global temperatures. The year 2020 had one of the three highest temperatures of the decade. The reports also observed increases in the temperature of the oceans, sea level, concentration of greenhouse gas, melting of the polar ice caps; in addition to catastrophes such as extreme heat, wildfires, floods, and the record-breaking Atlantic hurricanes season. Climatic causes impact socio-economic development, generate mass migrations and destroy ecosystems, creating threats to safety and health. The concentration of greenhouse gases continued to increase, despite the economic slowdown due to the pandemic lockdown.  
The combustion of fossil fuels is a major and leading cause of air pollution globally [2]. Approximately two-thirds of carbon dioxide emissions come from the burning of non-renewable fossil fuels for the generation and distribution of energy [3], which is the primary means of energy production today. The adoption of renewable energies, especially solar and wind, is rapidly growing and developing worldwide. As renewable energies become more cost-competitive [4; 5], new market designs and policy reforms are constantly created to pursue this new power generation method [6]. For example, the European Union has developed a plan in response to this problem: to cut greenhouse gas emissions by80% compared to the 1990 emission and to produce 100% of the energy needed through renewable energy by 2050 [7].  
The energy efficiency of solar photovoltaic (PV) depends on several factors, such as latitude, season, climatic conditions and local pollution [8]. The wide variation in PV efficiency regarding numerous variables makes it more challenging to estimate the quantity of energy generation in a given time window. Thus, the development of an accurate forecasting model can be a valuable tool for decision support in the use of PV [9; 10; 11].  
Wind energy has drawn more awareness lately because of its benefits, such as low environmental impact and low cost [12]. Thus, wind energy is an essential alternative to fossil fuels. However, due to the wind's volatile nature, one must be careful in choosing where and how to operate such facilities. Wind speed forecasting can directly help system management of wind energy systems management [13; 14].  
In general, in the solar and wind energy area, the forecast horizon [15] can be classified into three categories: short-term, medium-term, and long-term [16]. This paper focuses on the short-term forecast horizon that is useful in the electricity market and renewable energy integrated power management systems, where the forecast can directly affect the decision-making process, monitoring of electricity dispatch, and pricing [17].