esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Application of a Smart Lighting System Based on Distributed Wireless Sensor Networks
## 3 Materials and Methods
### Lighting Control Based on Rules
#### 3.3.1. Lighting Control Rules for Office Environment
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Stage** & **Time** & & **Rule** \\ \hline
3: On-duty & 9 a.m. to noon & (1) & For luminaire \(n\) in Zones A or C, if \(p_{n}(t)=1\), then \(\sigma_{n}(t)=100\%\). \\  & 9 a.m. to noon & (2) & For luminaire \(n\) in Zones A or C, if \(p_{n}(t)=0\), then \(\sigma_{n}(t)=10\%\). \\  & & (3) & Zone B implements daylight harvesting. \\ \hline
4: Lunch break & noon to 1 p.m. & (1) & \(\sigma_{n}(t)=10\%\) \\ \hline
5: On-duty & 1:30 p.m. to 6:30 p.m. & (1) & For luminaire \(n\) in Zones A or C, if \(p_{n}(t)=1\), then \(\sigma_{n}(t)=100\%\). \\  & & (2) & For luminaire \(n\) in Zones A or C, if \(p_{n}(t)=0\), then \(\sigma_{n}(t)=10\%\). \\  & & (3) & Zone B implements daylight harvesting. \\ \hline
6: Work overtime & 6:30 p.m. to 10 p.m. & (1) & For luminaire \(n\) in Zones A, B, or C, if \(p_{n}(t)=1\), then \(\sigma_{n}(t)=100\%\). \\  & & (2) & For luminaire \(n\) in Zones A, B, or C, if \(p_{n}(t)=0\), then \(\sigma_{n}(t)=0\%\). \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._during the day turning off all luminaires. The second step is to measure illuminance values on a desk when the associated luminaire of the desk is dimmed at different levels and other luminaires are off and illuminance values on the same place when dimming one of its neighbors at different levels and keeping other luminaires off. After all of its neighbors are dimmed in turn, the measurement is transferred to another desk. The PZ and SZ are first determined, and then, values of parameters in Equation (3) are calculated. Moreover, rules in Equation (3) are designed for an office room in Section 4.2, and rules need to be redesigned for different rooms and different buildings.
## 3 Results and Discussion
### Impact of Irrigation on Yield and Crop Water Use Efficiency  
The effect of irrigation levels, as well as irrigation method, was significant on okra yield (\(p\) = 0.05, Figure 5, Table 2). Among the different treatments, the maximum okra yield (10,666 kg ha\({}^{-1}\)) was recorded under flood irrigation with 100% ETc followed by sprinkler irrigation (9876 kg ha\({}^{-1}\)) at a similar ETc level (Figure 5, Table 2). The lowest yield (6086 kg ha\({}^{-1}\)) was recorded under sprinkler irrigation at 25% ETc. Similar observations were recorded for the transplanted rice plants, in which higher transpiration under flood irrigation led to higher yields compared to drip-irrigated rice, which suffered under higher canopy temperatures [30]. Especially during hot summers, canopy and/or air temperatures above 40 \({}^{\circ}\)C might have led to dead flowers, eventually leading to lower okra yields. Sprinkler irrigation is also responsible for exposing crops to sudden temperature fluctuations that can induce temperature shock under high temperature conditions, eventually leading to dead blooms. A significant yield reduction in okra under water stress conditions has also been reported earlier [5], when it was observed that water stress at lower temperatures can lead to lower seed pod weight compared to no water stress at higher temperatures. An earlier study [31] suggested non-significant differences in okra yield among basin, sprinkler, and furrow irrigation methods, though level of irrigation significantly affected fruit yield. Furthermore, irrigating okra plants every seven days was reported to influence morphological parameters, such as plant height, number of leaves, leaf area, and flowering  
Figure 4: Relative water content (**a**), canopy temperature (**b**), soil moisture content (**c**), and relative humidity (**d**) under different irrigation levels observed during okra growing season over two years. F-Flood and S-sprinkler irrigation method at 100, 75, 50, and 25% crop evapotranspiration.  
initiation, which eventually resulted in highest number of fruits per plant and the highest yield [32,33].  
Among the irrigation levels, okra yield at 100% ETc with sprinkler irrigation and 75% ETc with flood irrigation were highly similar. Similarly, water use efficiency was highest (3.3 kg m\({}^{-3}\)) at a 25% ETc level with flood irrigation, followed by the same ETc level with sprinkler irrigation (3.06 kg m\({}^{-3}\)) (Figure 5). At lower levels of irrigation (50 and 25% ETc), water use efficiency was higher in flood irrigation than sprinkler irrigation. Higher water use efficiency for low-frequency irrigation (higher allowable depletion) in flood irrigation was also observed by [31]. Overall, water use efficiency under flood irrigation was higher compared with sprinkler irrigation across all the treatments. Significant effects of irrigation level on fruit diameter and length and water use efficiency of okra under drip irrigation was also reported by [34].
## 1 Introduction
Although static approaches offer a thorough analysis of the code, it can be challenging to detect changes in the application code that might take place while the application is running. On the other hand, the time and performance costs for dynamic approaches are quite high. Hybrid methods have been developed to overcome the disadvantages of both of these methods. The FL-BDE model was developed in this study as a strong and effective malware detection tool to reduce the negative effects of malware on Android operating systems. The proposed model provides us with a structure that interprets and combines the scores of ML-based NN, SVM, DF, LR, BPM and BDT methods in two separate groups of three according to whether the incoming data are voted as benign or malicious applications. The contributions of this study to the literature are summarized as follows:  
* To develop a strong and dynamic model, the results of ML-based approaches with high classification performance are integrated over the FIS, which has an effective inference capacity. * Depending on whether the data obtained through the voting and routing process were voted as benign or malicious, our model combines the results of only three ML-based algorithms. While this allows us to increase the performance of our model by taking advantage of the scoring power of the six methods, it also simplifies the design of the FIS model by reducing the number of inputs. * The model proposed in this study has outperformed comparable studies in the literature, and as a result, it has revealed that it has a strong and dynamic structure. The rest of the study is organized as follows. A brief overview of recent research on the subject is provided in Section 2. The dataset, the proposed model and its features, the ML-based methods and the performance metrics to be employed in the assessment of experiments are described in Section 3. The outcomes of the experiments are discussed along with the literature in the \"Results and Discussion\" section. The last section concludes, along with suggestions for further research on the topic.
## 2 Background
### M-ABC-Based Optimization Algorithm  
The modified artificial bee colony (M-ABC), a swarm intelligence-based technique, was proposed in [17]. The M-ABC is the upgraded version of artificial bee colony optimization, which is a method that mimics the intelligent foraging behavior of honeybee colonies to find the best solution to a problem. It simulates the bees' actions while they search for nectar, and keeps a group of potential answers, referred to as \"bees\", that explore the possible solutions and adjust their positions based on the quality of their findings. This method is particularly effective for solving complex optimization issues that are hard to resolve with traditional optimization methods. This M-ABC algorithm has three types of artificial bees such as onlooker, employed, and scout bee. While onlooker bees pick a source constructed on the employed bee's dance, scout bees oversee discovering new food sources. As a result of being connected to their food supply, the employed bees are shielded against exploitation. The observer bees and the scout bees are not connected to any food source. The primary goals of the fitness function are the best possible classification error and communication effectiveness of the models that are obtained from HSP sites. In order to increase accuracy, the fitness function seeks to reduce classification errors and round consumption.  
In the M-ABC, the scout bee is combined with the firefly algorithm, and the modified technique is a combination of two different metaheuristic optimization techniques, namely, the artificial bee colony (ABC) and the firefly algorithm. The ABC algorithm generates a population of potential solutions, and the firefly algorithm improves the solutions by simulating the flashing behavior of fireflies. This hybrid algorithm is known for its global optimization abilities and has been applied to various optimization problems, showing better results than the original ABC or firefly algorithm alone. It is useful for solving complex optimization problems that are difficult to solve with traditional techniques.
# Measurement of the Resonant Magneto-Optical Kerr Effect Using a Free Electron Laser
## 3 Techniques for Magnetization Dynamics Capture
### Use of linearly-polarized pulses:  
In TR-RMOKE measurements, linearly-polarized light in the EUV \(\sim\) soft X-ray range is used. The polarization state is analyzed through ellipsometry in the polar RMOKE (P-RMOKE) and the longitudinal RMOKE (L-RMOKE). In contrast, TR-XMCD measurements use circularly-polarized light. Most of the TR-XMCD measurements on the femtosecond timescale are conducted using a laser slicing source with circular polarization. However, these measurements suffer from extremely low photon flux when compared with those using SR and FEL sources [60]. Among the FEL facilities that are currently in operation, circularly-polarized light can be obtained in the EUV to soft X-ray range at the FERMI@Elettra and Linac Coherent Light Source (LCLS) [61; 62; 63] facilities. However, to extract the magnetization dynamics, accurate preliminary determination of the degree of circular polarization, which is dependent on the energy, is required. In this sense, measurements using linearly-polarized light are straightforward for use in analysis of the magnetic response from the transient magneto-optical signals. In HHG lasers, circularly-polarized EUV light has recently been produced using various techniques, including use of a phase-shifter and production of two circularly- or linearly-polarized light beams at slightly different wavelengths [64; 65]. However, HHG lasers have smaller output intensities when compared with FELs, and thus, an FEL source is preferred when attempting to detect the dynamics of weak magnetic signals.  
The combination of the RMOKE with an FEL provides another possibility for nonlinear RMOKE signal detection, while the nonlinear regime of XMCD is not expected to be useful. In the visible range, MSHG is often used, as shown in Figure 3. Through a polarization analysis of the SHG signal, a large-scale rotation was observed in the visible range [66]. Because second-order nonlinear signals only appear from systems without inversion symmetry, the nonlinear RMOKE technique can provide element-selective and surface/interface-selective measurements, which is important for investigation of spintronic materials, such as magnetoresistive, spin-valve and magnetic topological insulator systems.
# A Deep Learning Approach to Predict Autism Spectrum Disorder Using Multisite Resting-State fMRI
### Autism Detection Using Structural MRI Data  
Structural MRI studies emphasize the morphometric and volumetric investigation to detect abnormal brain anatomy. [PERSON] et al. (2015) observed that the grey matter volume, the left anterior superior temporal gyrus, and the total brain volume are enlarged in autistic children by 1-2% approximately after conducting a voxel-based morphometry analysis. These findings were not consistent at the adult stage. While [PERSON] et al. (2016) measured total brain volumes and head circumference from 1.5 m coronal MRI scans in 67 autistic subjects and 83 healthy community volunteers within the age range of 8 to 46 years and concluded that no volumetric differences exist between ASD and control brains aged above 12. [PERSON] et al. (2016) concluded that high-functioning autistic subjects showed an enlargement of grey-matter volume but no increase in white-matter and cerebellar volume. In this regard, [PERSON] et al. (2018) also reported increased grey matter volume, specifically in the temporal lobes in autism. On the contrary, an increased white matter and reduced cerebral cortex and hippocampus-amygdala were found in the autistic brain by [PERSON] et al. (2019). Conversely, [PERSON] et al. (2020) observed decreased central white matter volume in autistic subjects performing experiments using MRI data. Thus, all the previous works utilizing structural MRI data failed to reach strong conclusions regarding volumetric changes and presented inconsistent findings regarding grey and white matter volumes in autistic and control brains. The use of a small sample size and limited age range can be regarded as a limitation. In fact, these studies focused more on discovering common patterns among ASD versus control group rather than solving the inherent classification problem with reliable accuracy ([PERSON] et al., 2020).  
However, [PERSON] et al. (2020) presented a promising study to solve the classification problem using only structural MRI data by constructing an individual brain network for each subject and extracting connectivity features between each pair of ROIs (region of interests). Then, these features were ranked by Fisher score computation. The top 3000 features were provided as input to a deep neural network classifier. Results showed a very high accuracy of 90.39% and an area under receiver operating characteristic curve (AUC) score of 97.38% using only 182 subjects from a single site. Accuracy above 0.9 was obtained when only a dozen subjects were considered ([PERSON] et al., 2020), and accuracy deteriorates consequently when a larger dataset is introduced ([PERSON] et al., 2020).
# Phase-Sensitive 2D Motion Estimators Using Frequency Spectra of Ultrasonic Echoes
## 2 Materials and Methods
### Paired 1D Motion Estimators  
[PERSON] et al. proposed a phase-sensitive 2D motion estimator, which estimates the displacement in the direction of interest using the phase shift of an ultrasonic echo by removing the phase shift caused by the displacement in the other direction from the total phase shift [35]. Their method uses the complex analytical signal of the band limited echo signal obtained by the Hilbert transform. They regarded the lateral and axial angular frequencies of the narrow band complex analytical signal as \((\omega_{x},\omega_{z})\) (specific frequencies), which corresponded to the center of the band-pass characteristic of the narrow band filter. In the present study, the concept of the method proposed by [PERSON] et al. was expanded. The method proposed in this section uses 2D frequency spectra of ultrasonic echoes obtained by the 2D Fourier transform. Each component of the 2D frequency spectrum can be regarded as the narrow band complex signal in [PERSON]'s method. The proposed method also combines all the obtained frequency components (narrow band signals) to estimate the 2D displacement. By using the frequency spectra, the inverse Fourier transform in the Hilbert transform can be excluded. The details of the proposed method are described below. Let us define the ultrasonic radio-frequency (RF) echo from the lateral position \(x\) and axial position \(z\) in the \(n\)-th frame by \(s_{n}(x,z)\). The 2D frequency spectrum \(S_{n}(x,z;\omega_{x},\omega_{z})\) of \(s_{n}(x,z)\) is obtained by applying the 2D Fourier transform to \(s_{n}(x,z)\) around the spatial position \((x,z)\) extracted by a spatial window with lateral and axial widths of \((2M+1)\cdot\delta_{x}\) and \((2K+1)\cdot\delta_{z}\), respectively, where \(\delta_{x}\) and \(\delta_{z}\) are lateral and axial sampling intervals of \(s_{n}(x,z)\), respectively, \(M\) and \(K\) are integers determining the lateral and axial sizes of the spatial window, respectively, and \(\omega_{x}\) and \(\omega_{z}\) are lateral and axial angular frequencies, respectively. The 2D frequency spectrum \(S_{n}(x,z;\omega_{x},\omega_{z})\) can be modeled as follows:  
\[S_{n}(x,z;\omega_{x},\omega_{z})=A_{n}(x,z;\omega_{x},\omega_{z})\cdot e^{j( \omega_{x}x+\omega_{z}z)}, \tag{1}\]  
where \(A_{n}(x,z;\omega_{x},\omega_{z})\) is the magnitude of \(S_{n}(x,z;\omega_{x},\omega_{z})\).
## 2 Materials and Methods
### Materials and Equipment  
Citric acid (CA), Ni(NO\({}_{3}\))\({}_{2}\), (NH\({}_{2}\))\({}_{2}\)CO, ethanol, and acetone were purchased from Sigma-Aldrich (Sigma-Aldrich, St. Louis, Missouri, USA). All chemicals were of reagent grade and used without any further purification. The samples were characterized by X-ray diffraction (XRD), using an X' pert pro X-ray diffractometer by Philips (Almelo, The Netherlands), operated with CuK-Alpha radiation. Ultraviolet-visible light (UV-Vis) spectra were acquired using a Varian Cary 50 Scan spectrophotometer (Agilent, Santa Clara, USA). SEM images were acquired with a Zeiss Auriga field-emission scanning electron microscope instrument (Jena, Germany) operating at 7 kV. The energy-dispersive spectroscopy (EDS) maps were obtained by coupling the field-emission scanning electron microscope (SUPRA(tm) 35, Carl Zeiss SMT, Oberkochen, Germany) with energy-dispersive microanalysis (EDS/EDX, INCAx-sight, Model: 7426, Oxford Instruments, Abingdon, Oxfordshire, UK), operating at 20 kV. High-resolution transmission electron microscopy (HR-TEM) was performed on an FEI Tecnai G2 20 high-resolution transmission electron microscope (FEI Company, Hillsboro, USA). The specific surface area and porosity were measured on Micrometrics Instrument Corporation ASAP 2020 (Norcross, Georgia, USA) using N\({}_{2}\) adsorption-desorption isotherms. The methane sensor was assembled according to the scheme in Figure S1 (Supplementary Materials). It allows resistive gas response as a function of temperature and concentration, simultaneously keeping record of the ambient conditions (external temperature and humidity). CQDs@NiO powders were ground in an agate mortar, mixed with deionized water in a ratio of 5:1 until a paste was formed, and then coated onto the ceramic support, which was then connected to four Pt wires through two Au rings. The coated ceramic elements were then dried at 200 \({}^{\circ}\)C for 5 h in air for water to evaporate and to ensure stability. The thickness of the coating was 200 m. The sensor was placed in a chamber of 1 L of volume with its own heating system and temperature control. Synthetic air (Sigma-Aldrich), supplemented with saturated vapor of distilled water, was used both as reference and as diluting gas. All measurements of gas response were made at a relative humidity (RH) of 20%, unless stated otherwise. The resistance was then measured with a signal-to-noise ratio of about 4% at the minimum level and about 2% at the maximum level.
## 4 Implementation of MAFS Traffic Scheduling Algorithm  
### Key Parameter Setting  
The CTN-MADDPG algorithm is deployed on the control plane, and the network state information is first collected by INT and defined as the input data for the network model. The network topology environment is constructed based on the traffic scheduling scenarios in the DCN. The key elements of the CTN-MADDPG algorithm are designed, namely, the state space, action space, and reward function. The trained CTN-MADDPG algorithm can determine the optimal routing path through the current network environment.  
(1) State space: The network state obtained between two terminal hosts through INT. The network state is defined as the load information of the switches in the current link.  
\[s=(o_{1},o_{2},\cdots,o_{n}), \tag{2}\]  
\[o_{i}=\{swidi_{i},qdepth_{i},port\_id_{i}\}, \tag{3}\]  
where \(o_{i}\) represents the \(i\)th observed state, and each observed state is composed of a set of switch identifiers, queue depths, and output port numbers for each switch. It is the global state information obtained after integrating the state information of individual agents.  
(2) Action space: The set of shortest paths that can be scheduled between the source node and the destination node. When selecting an action \(a_{i}\), the \(i\)th path among the \(n\) shortest paths is preferred.  
\[a=(a_{1},a_{2},\cdots,a_{n}), \tag{4}\]  
(3) Reward: The reward value obtained through training the neural network based on the current environment and actions taken. The reward function for each agent is set as follows:  
\[r_{i}=\log t_{i}-\log d_{i},\]where \(t_{i}\) represents the average throughput in the \(i\)th observed state, which focuses on the data transmission capacity of the network. \(d_{i}\) represents the delay in the \(i\)th observed state, which focuses on the time that the current packet experiences in the links. Considering both these two indicators can provide a comprehensive evaluation of the network performance, and the routing strategies based on these indicators can help to optimize the network and improve the overall performance. By using the logarithmic function to converge parameter values to a certain range, the magnitude difference between the two parameters can be narrowed to balance the influence of different parameters, which helps to predict the network performance more accurately.
## 4 Results and Analyses
### Transport Trajectories in Different Seasons
More specifically, Type 2 trajectories have the highest PM\({}_{2.5}\) concentrations, with an average of 148.08 \(\upmu\)g/m\({}^{3}\). This is mostly because these airflow trajectories originate from south-central Hebei and pass over inland cities in northern Shandong. In winter, these areas are in the heating period, leading to increases in anthropogenic pollutant emissions, such as from coal burning [8; 43]. As air masses pass over these areas, they usually carry soil, dust, and pollutants from the inland areas to Weifang. In addition, these airflow trajectories are relatively short and travel slowly, making pollutants not able to diffuse easily. Hence, the average PM\({}_{2.5}\) concentration of these trajectories is relatively high. In contrast, the average concentration of Type 2 trajectories is the lowest, but they are also significantly higher than those in the other seasons, at 82.99 \(\upmu\)g/m\({}^{3}\). These trajectories originate from Siberia and arrive in Weifang through the Mongolian Plateau, Inner Mongolia, Hebei, Beijing, Tianjin, and the Bohai Sea and are relatively long and travel at higher wind speeds. These trajectories pass over the waters of the Bohai Sea, which may slightly facilitate the diffusion and elimination of pollutants. The average PM\({}_{2.5}\) concentration of polluted winter trajectories is 138.61 \(\upmu\)g/m\({}^{3}\). The average PM\({}_{2.5}\) concentration of Type 1 trajectories reaches 158.08 \(\upmu\)g/m\({}^{3}\), which includes not only the external source from southern Hebei Province but also local emissions and is the main pollution source of Weifang City in winter. Overall, in autumn and winter, Weifang is mostly influenced by pollutants from inland areas of Hebei and Shandong. Airflows from these areas often carry particulates emitted from the passed areas. This reflects the knock-on effects of PM\({}_{2.5}\) pollution within Shandong Province. In summer, due to the influences from coastal cities in the east and clean airflows from the ocean, the concentrations of pollutants are lower. In spring, severe pollution in Weifang is closely associated with sandstorms in the arid areas in the west, where loose toposoil and fine sand are blown from dry surfaces.