esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Concepts for Inhomogeneous Beam Shaping
### Modification on Focal Length  
An LA homogenizer requires the same aperture dimension and focal length of all lenslets. By differentiating these identical aspects, inhomogeneous illumination patterns can be generated. One possibility is to lengthen the focal lengths of individual lenslet, so that more centralized illumination areas can be generated [14]. Figure 9 illustrates a simple principle of such an optic. \(d\), \(f_{1}\), \(f_{2}\), and \(f_{3}\) have the same meanings as those in Formula (3), \(f_{1}^{\prime}\) denotes the lengthened focal length of the central lenslet, which intends to create the hotspot; \(\beta^{\prime}\) denotes the maximum incident angle of the centralized beamlet on the display surface (the red line to the display surface in the figure),  
Figure 8: Projection and measuring results of the investigation setup. (**a**) The measured luminance image from the LMK Color 5; (**b**) The real projection photo.  
Figure 7: Homogeneity measuring illustration. (**a**) The 13 measuring points defined by ANSI [24; 29]; (**b**) The measuring setup.  
which is not important in this discussion; and, \(H^{\prime}\) denotes the centralized distribution hotspot area resulted from the focal length changing.  
The hotspot area dimension in this illustration can be customized according to the ABCD matrix shown in Formula (7):  
\[\left[\begin{array}{c}\frac{H^{\prime}}{2}\\ \beta^{\prime}\end{array}\right]=\left[\begin{array}{cc}1&f_{3}\\ 0&1\end{array}\right]\left[\begin{array}{cc}1&0\\ -\frac{1}{f_{3}}&1\end{array}\right]\left[\begin{array}{cc}1&f_{1}+f_{2}\\ 0&1\end{array}\right]\left[\begin{array}{cc}1&0\\ -\frac{1}{f_{1}^{\prime}}&1\end{array}\right]\left[\begin{array}{cc}-\frac{ d}{2}\\ 0\end{array}\right] \tag{7}\]  
A validation of this hot spot generation method is accomplished by optical simulation. The optical system is modified from the one presented in Section 4. The simulation model with its result is shown in Figure 10[14]. Both of the optical components in the simulation model are made of PMMA. The system exhibits an optical efficiency of 78.2% (with uncoated surfaces). The distribution that is shown in Figure 10b is quantified to a real headlamp's level, and it is compared with the regulation later.
# Enhancing Photovoltaic Power Predictions with Deep Physical Chain Model
## 3 Results
### Irradiance Decomposition Model  
Like the GHI model, the Transformer Bi-LSTM model for the decomposition process shows a positive trend in its performance.The MAE errors of 118 [\(\mathrm{W/m^{2}}\)], an RMSE of 176 [\(\mathrm{W/m^{2}}\)], and an \(R^{2}\) of 0.12, obtained with \(\mathrm{NWP_{DHI}}\) are taken as a reference. As a reminder, three methodologies of two types are tested: the direct one, which does not use the output of the GHI model, and the indirect one which uses a chain model. Within the latter, two sets are occupied, where their main difference is the use of the Skartveit decomposition model.  
The results, presented in Table 10, demonstrate that both the direct forecasting method and the chain model significantly improve the precision in all cases, particularly at stations 0, 1, and 2. It is important to note that the error at station 3 is significantly reduced, with decrements of approximately 80%.  
The results demonstrate that the Transformer Bi-LSTM model is key to improving forecast accuracy. Incorporating the GHI block and the Skartveit model as additional variables shows a marginal but positive impact on the prediction accuracy. Accuracy is improved at all stations: MAE is reduced to 30 \(\mathrm{W/m^{2}}\), RMSE to 58 \(\mathrm{W/m^{2}}\), and \(R^{2}\) is increased to 0.88.
## 2 Materials and Methods
### Model Application  
#### 2.2.1 Study Catchment Data  
The Bonis catchment (Figure 3) is located (39\({}^{\circ}\)25\({}^{\prime}\)15\({}^{\prime\prime}\) N, 16\({}^{\circ}\)12\({}^{\prime}\)38\({}^{\prime\prime}\) W) in the mountain area of the Calabria region (southern Italy). In particular, it covers an area of 1.39 km\({}^{2}\) of the Sila Plateau (1500 km\({}^{2}\)) with a mean elevation of 1131 m above sea level. The catchment is underlain by acid plutonic rocks [43] characterized as Topic Xerumbreps and Ulpic Haploxeralfs [44]. The forest and rangeland cover around 93% of the total area of the basin, dominated by about 50-60-year-old Calabrian pine (Pinus laricio Poiret). A selective thinning was carried out in 1993, which reduced the stands density by around 55% (38% in low-density stands, 69% in high-density stands). Since 1986 the basin was monitored through the installation of different instruments. Rainfall was measured by three rain gauges (with a tipping bucket), together with temperature measured at three different meteorological stations located at the basin outlet (Outlet: 975 m above sea level a.s.l.) and at representative sites within the north-eastern (Petraella: 1258 m a.s.l.) and southwestern (Don Bruno: 1175 m a.s.l.) parts of the catchment. Runoff was measured at the outlet of the catchment using a gauging structure. In May 2003, a tower for measurement of eddy fluxes was installed at an altitude of 1100 m a.s.l, on a 54-year-old plantation of Laricio pine which allowed the monitoring of other parameters. Although the monitoring period is considered quite long, during the analysis of the collected data (in particular the hourly data), some inhomogeneity of data series and missing data were found because of occasional interruptions in automatic recorders and instrumentation failure [44]. For the missing meteorological data, measurements of the closest meteorological station were used, which in our case is the Cecita station (39\({}^{\circ}\)23\({}^{\prime}\)1.26\({}^{\prime\prime}\) N, 16\({}^{\circ}\)32\({}^{\prime}\)55.01\({}^{\prime\prime}\) E) located a few kilometers from the Bonis catchment and managed by the Multi-Risk Functional Centre of the Regional Agency for Environment Protection of the Calabria Region. The climate is typical of the mountain areas of Calabria, characterized by an average recorded annual precipitation of 915 mm; the mean temperature of the coldest month is about 0.1 \({}^{\circ}\)C and of the hottest month is 18.3 \({}^{\circ}\)C [45]. Dendrological measurements were also carried out through field surveys at this basin, before and after the forest thinning in 1993. These measures allowed to reconstruct the history of the forest at this catchment [46].
## 3 Results and Discussion
### Experimental Analysis of LIBS Signal Changes in Flow Fields
More precise LIBS measurement can be achieved by adjusting the LIBS signal when the flow direction and amount are known. Figure 12: LIBS signal intensity by flow rate for various inlet port directions; (**a**) Ar, (**b**) N\({}_{2}\), and (**c**) He gas. Comparative analysis of the signal strength according to the flow rate of each gas was performed (Figure 14). Under the same conditions, the LIBS signal strength was highest with Ar gas and lowest with He gas. Ar gas raised the temperature of the plasma, and LIBS signal intensity increased accordingly. On the other hand, He gas lowered the temperature of the plasma, and thus the absolute signal intensity was low. The ratio of the increase in signal strength to the increase in gas flow rate can be used to quantify the measurement sensitivity for each gas. N\({}_{2}\) was regarded as an atmospheric gas. For Ar gas, this ratio had a value of 3.03 s/cm\({}^{3}\). In the case of He gas, the ratio was 8.24 s/cm\({}^{3}\) (Figure 15). Considering N\({}_{2}\) as the reference atmospheric environment, the measurement sensitivity for Ar was low and that for He gas was high. Therefore, we concluded that the change in plasma according to gas flow can be measured relatively easily when using He gas. In the overall analysis, the ratio between the magnitude of the LIBS signal and the gas flow rate was reliable within a limited gas flow measurement range. It should be noted that the flow is greatly affected by the external environment in actual applications, and therefore the results should be adjusted to obtain valid experimental values. Figure 13: LIBS spectral line intensity according to the flow rate and trends in signal intensity obtained through modeling; (a) Ar, (b) N\({}_{2}\), and (c) He gas.
## 3 Background
### Kernel Machine Regressions  
KMR is a non-parametric regression approach designed to capture complex, non-linear, and non-additive relationships between predictors and responses, while adjusting for potential confounding factors ([PERSON] et al., 2018 [6]). Unlike traditional parametric regression, which assumes a predefined functional form for the relationship between predictors and the response, KMR allows the model to be more flexible, adapting to the underlying structure of the data without such assumptions. This flexibility is crucial when the relationship between variables is highly non-linear and difficult to model explicitly with standard methods.  
Suppose \(f:\mathbb{R}^{p}\rightarrow\mathbb{R}\) is a function belonging to a function space \(\mathcal{H}_{K}\), which is equipped with a positive semidefinite reproducing kernel \(K:\mathbb{R}^{p}\times\mathbb{R}^{p}\rightarrow\mathbb{R}\). There are two approaches to characterize \(f\). The first approach is to use a basis-function representation, where \(f(x)=\sum_{m=1}^{M}\phi_{m}(x)\eta_{m}\) for some set of basis function \(\{\phi_{m}\}_{m=1}^{M}\) and coefficients \(\{\eta_{m}\}_{m=1}^{M}\). The second approach directly utilizes the positive-definite kernel function \(K(\cdot,\cdot)\), where \(f(x)=\sum_{i=1}^{n}K(x_{i},x)\alpha_{i}\) for a set of coefficients \(\{\alpha_{i}\}_{i=1}^{n}\). [PERSON]'s theorem ([PERSON], 1909 [15]) provides theoretical foundation, ensuring that a kernel function \(K(\cdot,\cdot)\) representing \(f\) implicitly defines a unique function that is spanned by a specific set of orthogonal basis functions used in the basis-function representation of \(f\). For specific examples, see [PERSON] et al. (2007) [16], [PERSON] et al. (2015) [5] and references therein.
# 5G UFMC Scheme Performance with Different Numerologies
## 2 Universal Filtered Multi-Carrier (UFMC)  
Applications that use the 5G systems with the latest requirements that ITU has defined, require, among other aspects, higher data rates, lower latency, and more efficient spectrum usage. The most commonly used modulations that meet these criteria are the filter-bank based multicarrier (FBMC) and the Universal Filtered Multi-Carrier (UFMC) technique. This paper focuses on the new modulation technique known as Universal Filtered Multi-Carrier (UFMC) and compares it with OFDM within a generic framework.  
Figure 1 shows the basic UFMC block diagram that was used in this study. In our study, we followed the idea of FFT demodulation in receiver that was presented in [6; 11]. Using FFT for the demodulation is an ideal candidate for improvement of the UFMC performance in parallel processing environments.  
Figure 1: UFMC transceiver block diagram.  
UFMC is seen as a generalization of the Filtered OFDM modulation. In UFMC, the entire band is filtered in filtered OFDM and individual subcarriers are filtered in FBMC, while groups of subcarriers (subbands) are filtered in UFMC, as seen in Figure 2.  
This subcarrier grouping allows for reducing the filter length (when compared with FBMC). In addition, UFMC can still use QAM, which works with existing MIMO schemes. The basic idea is to use 64 or 265 QAM in order to increase the overall system capacity.  
Regarding the full band of subcarriers (N), it is divided into subbands, each subband holding a fixed number of subcarriers, as seen in Figure 3. Note that not all subbands need to be employed for a given transmission. An N-point IFFT is computed for each subband, while zeros are inserted in the cases of unallocated carriers. Then, each subband is filtered by a filter of length L, and the responses from the different subbands are summed. The filtering is essential in order to reduce the out-of-band spectral emissions. Different filters per subband can be designed. For example, many researchers [12] use windowed-sinc filters with Blackman windowing Functions, while other researchers [13] use Zero Padding Length Design filter techniques.  
In this study, the same filter was used for each subband. A Chebyshev window with parameterized sidelobe attenuation was employed to filter the IFFT output per subband [8,9].  
Figure 3: \(Cont.\)  
Figure 2: OFDM and UFMC different type of waveforms and filter operation.
## 2 Specimen, Experimental Setup, and Acoustic Emission Monitoring
### Experimental Set-Up  
The coated specimen was set with the vessel cover, which was equipped with a sufficient number of coaxial feedthroughs for connecting AE transducers and strain gauges with pre-amplifiers (Figure 1d). The specimen assembly was then placed in the pressure vessel within the loading cell (Figure 1f), which has a capacity of 20,000 kN. First, the specimen was isotropically (hydrostatically) compressed to a confining pressure of 40 MPa. The axial stress was then increased to 75 MPa at a rate of approximately 10 MPa/min under manual operation. Finally, the axial stress was increased at a constant rate of 2.25 MPa/min until failure. The peak axial stress was 220 MPa. When axial loading, the force was controlled by a spring-force balancing system, and the spring was simply compressed by a stepping motor. This loading system allowed dynamic fracturing and a quick stress drop.
# System-Theoretic Process Analysis Based on SysML/MARTE and NuSMV
## 3 STPA-SN Application Example
### Step 3: Identify the Unsafe Control Actions
#### 3.3.3 Give All the Potential UCAs through Traversal  
Each instance of [CA-PR, Type, Context] is considered as a potential UCA (PUCA). A PUCA that contains a time-independent Type is called a time-independent PUCA, and a PUCA that contains a time-dependent Type is called a time-dependent PUCA. There are 64 instances of PUCA, some of which are presented in Table 3.  
\begin{table}
\begin{tabular}{c|c|c|c|c|c} \hline \multicolumn{2}{c|}{**PUCAs**} & \multicolumn{2}{c}{**Analysis Results of PUCAs**} \\ \hline
**ID** & **CA-PR** & **Type** & **Context** & **Analysis** & **A True UCA or Not** \\ \cline{3-5}  & **PhysicalDoor** & **PhysicalObstacle** & **Method** & **A True UCA or Not** \\ \hline
1 & & & PhysicalExisting & Manual & Not UCA \\ \cline{3-5}  & & & PhysicalOpened & analysis & Not UCA \\ \cline{3-5}
2 & & & PhysicalNotExisting & Manual & Not UCA \\ \cline{3-5}
3 & & & PhysicalExisting & Manual & N/A \\ \cline{3-5}
4 & & & PhysicalClosed & analysis & Not UCA \\ \cline{3-5}
5 & & & PhysicalNoExisting & Manual & Not UCA \\ \cline{3-5}
6 & & & PhysicalNoExisting & Manual & Not UCA \\ \cline{3-5}
7 & & & PhysicalClosing & Manual & A true UCA, causing a new system-level \\ \cline{3-5}
8 & & & PhysicalNoExisting & Manual & hazard to be \\ \cline{3-5}
9 & & & PhysicalNoExisting & Manual & identified \\ \cline{3-5}
10 & & & PhysicalOpened & analysis & Not UCA \\ \cline{3-5}
11 & & & PhysicalClosed & Analysis & Not UCA \\ \cline{3-5}
12 & & & PhysicalNoExisting & Manual & Not UCA \\ \cline{3-5}
13 & & & PhysicalOpening & PhysicalExisting & Manual & A true UCA, causing a new system-level \\ \cline{3-5}
13 & & & PhysicalOpening & PhysicalNoExisting & Manual & hazard to be \\ \cline{3-5}
14 & & & PhysicalNoExisting & Manual & Not UCA \\ \cline{3-5}
15 & & & PhysicalClosing & Manual & A true UCA, causing a new system-level \\ \cline{3-5}
16 & & & PhysicalNoExisting & Manual & Not UCA \\ \cline{3-5}
17 & & & PhysicalOpened & PhysicalExisting & Manual & a true UCA, causing a new system-level \\ \cline{3-5}
18 & & & PhysicalNoExisting & Manual & Not UCA \\ \cline{3-5}
19 & & & PhysicalClosed & Analysis & Not UCA \\ \cline{3-5}
20 & & & PhysicalNoExisting & Manual & Not UCA \\ \cline{3-5}
21 & & & PhysicalOpening & PhysicalExisting & Manual & A true UCA, causing a new system-level \\ \cline{3-5}
22 & & & PhysicalNotExisting & Manual & Not UCA \\ \cline{3-5}
23 & & & PhysicalClosing & Manual & A true UCA, causing a new system-level \\ \cline{3-5}
24 & & & PhysicalNoExisting & Manual & Not UCA \\ \hline \end{tabular}
\end{table}
Table 3: PUCAs and their analysis results.
# Wireless Home Energy Management System with Smart Rule-Based Controller
## 4 Results and Discussion
### LED Light  
The LED light was turned on for 12 h of the day during the experiment, and then turned off for the other 12 h. However, the HEM algorithm could reduce the energy usage during the utilization period. Figure 17 compares the power consumption of the LED light without and with the use of the HEM system, as well as the corresponding illuminance variations.  
In Figure 17, the first 12 h correspond to when the LED light was turned on. The light was fully on when the HEM system was not used, while the illuminance was regulated when the system was used. The algorithm turned off the light whenever the room was vacant, as can be observed from the figure. In addition, when the room was occupied, the light was dimmed to 90% of its illuminance capacity during normal electricity pricing hours, but it operated at full capacity during minimum pricing hours. The variation of the illuminance was within the preset preferences in Table 3 during all the scheduling periods, and below the maximum constraint by 10% during non-peak periods, as can be observed from Figure 17b. However, all of the constraints were removed during minimum pricing hours. Figure 18 shows the effects of the HEM algorithm on the daily energy consumption and electricity bill.  
The energy and bill savings achieved by the occupancy principle of the HEM algorithm were remarkable, although there were no savings during the minimum pricing period, because the HEM algorithm allowed for the LED light to operate at full capacity at that time. Overall, the algorithm afforded energy and bill savings of 0.133 kWh and $0.014, respectively, for the day.
## 3 Results and Discussion
### DPPH Radical Scavenging Activity  
The triazole derivatives **1**-**14** were examined for their free scavenging properties by using the DPPH method. This assay was carried out to test their antioxidant abilities and determine their behaviors as proton radical scavengers or hydrogen suppliers. When the DPPH combines with an antioxidant substance (reducing agent), the electron pairing goes off, and the disappearance of solution color is confirmed by a decrease in absorbance (517 nm). Results in Figure 2 illustrated the DPPH radical scavenging activity of 4-(1\(H\)-triazol-1-yl)benzoic acid hybrids **1**-**14** with BHA as standard antioxidant for comparison purpose. The main compounds **1**-**3** showed clear variation in their DPPH radical scavenging activity, where the thioether derivative **1** exhibited the highest radical scavenging effect (89.95 \(\pm\) 0.343%) with IC\({}_{50}\) of 55.59 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, in relation to BHA (95.02 \(\pm\) 0.74%) at the same concentration. Compound **2** came second in its radical scavenging activity scoring (31.47 \(\pm\) 0.165%), while sulfonyl derivative **3** recorded the lowest DPPH radical scavenging activity (16.47 \(\pm\) 0.161%) at the concentration of 100 \(\mathrm{\SIUnitSymbolMicro g}\)/mL.  
**Scheme 1.** The synthetic routes for the 4-(1\(H\)-triazol-1-yl)benzoic acid hybrids **1**-**14**.  
Generally, substitutions in compound **1** resulted in a remarkable decrease in the radical scavenging activity for all thioether derivatives (**4-9, 13**, and **14**). Among them, the 2-hydroxy-5-methoxy product (**5**) exhibited good activity (37.18 \(\pm\) 0.110%), followed by compounds **4**, **6**, **8**, and **9** that demonstrated 27.51 \(\pm\) 0.182, 25.77 \(\pm\) 0.11, 25.61 \(\pm\) 0.17, and 20.49 \(\pm\) 0.16%, respectively. The lowest radical scavenging activity recorded by compounds **7**, **13**, and **14** (18.54 \(\pm\) 0.098, 14.01 \(\pm\) 0.086 and 14.63 \(\pm\) 0.075%, respectively). Concerning the substitutions of compound **2**, an increase in the radical scavenging activity was observed by **11** (41.40 \(\pm\) 0.11%), while compounds **10** and **12** showed a slight decrease in the radical scavenging activity compared to their parent **2** at the same concentration.
## 6 Parameter Dependence of Solutions
### Dependence on Grashof Number, Gr  
The dependence of solutions on the buoyancy parameter, i.e., the Grashof number, Gr, is illustrated in Figures 19-21. The Grashof number represents the driving force for the fluid velocity. In particular, the fluid velocity is driven purely by natural convection effects and hence should remain zero when there is no convection (Gr=0) and otherwise increase with increasing Gr. These observations are illustrated in Figures 20 and 21. As already observed, the fluid temperature is driven largely by the boundary conditions, and hence the parameter Gr, being connected to the fluid velocity, has limited influence on fluid temperature as shown in Figure 19.  
Figure 17: Axial velocity dependence on \(\alpha\) at \(t=5\).  
Figure 18: Transverse velocity dependence on \(\alpha\) at \(t=5\).  
Figure 21: Transverse velocity dependence on Gr at \(t=5\).  
Figure 19: Temperature dependence on Gr at \(t=5\).  
Figure 20: Axial velocity dependence on Gr at \(t=5\).
# A Multi-Path Semantic Segmentation Network Based on Convolutional Attention Guidance
## 2 Related Works  
### Semantic Segmentation  
Semantic segmentation is a fundamental task in computer vision. Since the introduction of fully convolutional networks (FCNs) [1], convolutional neural networks (CNNs) [10; 11; 12; 13] have achieved tremendous success and have become a popular architecture for semantic segmentation. Fully convolutional networks keep pushing forward this field forward via their end-to-end, per-pixel classification paradigms. They capture multi-scale features, incorporate channel attention and introduce self-attention blocks to refine contextual priors. More recently, Transformer-based methods [5; 6; 14; 15; 16] have demonstrated significant potential and have outperformed CNN-based approaches. The general structure of a segmentation network consists of an encoder and a decoder. ResNet [17] and DenseNet [18] are commonly adopted backbones for the encoder. Meanwhile, different decoders are advised for different emphases, including achieving multi-scale receptive fields [12], collecting multi-scale semantics [4; 6; 19], expanding receptive fields [2; 20], enhancing edge features [11] and capturing global contexts [13; 21].