esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Cross Validation  
To examine access performance metrics and the confusion matrix, the Random Forest classifier was employed for 10-fold cross-validation using 235 samples, 26 predictors, and two classes, \"0\" and \"1\". The greatest number was utilized to determine the best model using accuracy, and mtry = 133 was the final value employed for the model. For an accurate representation of each class and to increase the model's generalizability, a 10-fold cross-validation method with gender-based stratification was employed. To ensure reproducibility, a random seed was used and it was observed that accuracy and Kappa both considerably rose when mtry rose from 2 to 133 and finally 265, suggesting that for the particular dataset and model, choosing more variables at each split improved performance. Figure 9 shows the performance metrics.  
In both train-test split and cross-validation results, the model demonstrated excellent accuracy and Kappa, demonstrating its effective generaliz  
Figure 8: Confusion matrix and statistics for the train–test split and L1, L2 normalization.
## 2 Physical Foundations of X-ray Phase Contrast and Dark-Field Imaging
### Partially Coherent Systems  
To date, we have seen the theoretical case of a coherent monochromatic point-like source. In reality, monochromatic sources are limited to large facilities such as synchrotrons. These also provide small emitting spots and can be considered quasi-coherent. However, more conventional easy-access sources have less advantageous features. Firstly, they are polychromatic; i.e., they emit over a large bandwidth of energies. Secondly, their emitting spots are quite spread out (from 50 to 200 \(\upmu\)m), meaning that they lack spatial coherence. What are the consequences for the image formation? First, the wave-front varies differently for each wavelength, and the intensity of each wavelength is weighted by the source emission spectrum. In the end, the total intensity can be represented as the integral of the propagated wavefront over the source spectrum:  
\[I(x,y)=\int_{\lambda}I_{\lambda}(x,y)w_{\lambda}d\lambda=\int_{\lambda}|\psi_ {\lambda}(x,y)|^{2}w_{\lambda}d\lambda \tag{8}\]  
with \(w_{\lambda}\) being the weight of the wavelength in the source emission spectrum. Then, the source focal spot will induce a blurring of the image. As the photons are not all emitted from one single point, photons hitting one point of the sample will indeed have slightly different incident trajectories, and therefore, not hit the exact same spot on the detector, thereby introducing blurring of the image. This phenomenon can be seen as a convolution of the resulting image with the projected shape of the source on the detector plane, taking into account the distances \(z_{1}\) and \(z_{2}\). These two phenomena result in a blurring of the image that forms due to wave alterations in the sample, making different signal extractions more complicated with such poor coherent sources.  
What are the signals that we are talking about? From those intensity and wave alterations in the sample, three main phenomena can be observed when imaging a sample with X-rays: attenuation, phase shift and small angle scattering (see Figure 1).
# An Ultra-Wideband Frequency Selective Rasorber with Low Infrared Emissivity
## 1 Introduction
Furthermore, the square film of indium tin oxide (ITO) with low infrared emissivity and high radar reflectivity was then applied onto the polyethylene glycol terephthalate (PET) substrate with a high filling rate, resulting in the formation of an infrared shielding layer (IRSL) characterized by low infrared emissivity and high radar transmission. By combining the IRSL with the aforementioned FSR, we successfully achieved a metamaterial possessing both wave transmission capabilities and stealth functions against radar-infrared detection. The proposed ultra-wideband (UWB) FSR exhibits an ultra-wide transmission band (9.01-16.05 GHz, \(S_{21}\geq-1\) dB) and an ultra-wide absorption band (2.52-8.01 GHz, \(S_{11}\leq-10\) dB). Additionally, it possesses a low emissivity value (0.26), enabling the transmission of radar waves and integrating radar-infrared bistealth functionalities.
## 2 The Datasets and Acoustic Features
### Acoustic Features
Roughness is calculated to represent dissonance [34]. * Pitch  
The pitch features extracted in this study refer to all features related to the tone of the sound. The fundamental frequency is defined as the lowest frequency of a harmonic stationary audio signal, which in turn can be qualified as a tonal sound. In music, tonality is a system that organizes the notes of a musical scale according to musical criteria. Moreover, tonality is related to the notion of harmonicity [37]. The harmonic change detection function (HCDF), which is used to represent the change in harmonic content in music, describes the change in harmony between consecutive frames. The harmonic pitch class profile (HPCP) is a 36-dimension vector that represents the intensities of 36 subdivisions of the 12 semitone pitch classes (corresponding to notes from A to G#). * Loudness  
Loudness represents the strength of the music, which is related to the objective amplitude of sound and the subjective psychological perception of hearing [30]. Therefore, the features of loudness were extracted from both objective and subjective perspectives. Dynamic complexity is defined as the average absolute deviation from the global loudness level estimated on the dB scale. It is related to the dynamic range and the amount of fluctuation in loudness. Silence rate describes the proportion of silent segments in the music. Spectral band energy is the energy of each of five subbands spanning the entire spectrum. ERB band energy is the frequency band energy through ERB filter banks. * Rhythm  
Rhythm is a significant feature of music that describes the tempo and beat. The tempo-related features and beat-related features were extracted. The BPM histogram characteristics provide a general tempo perspective and summarize the beat tempos present in music. The beat-loudness band computes the spectrum energy of beats in an audio signal given their beat positions. Danceability estimates the danceability of an audio signal, and the algorithm is derived from the detrended fluctuation analysis (DFA) described in [38].
# CubeSat Mission Scheduling Method Considering Operational Reliability
## 4 CubeSat Scheduling Model
### Objective Function
The objective function is as follows:  
\[maxF=\sum_{i=1}^{3}\lambda_{i}f_{i}^{\prime}=\lambda_{1}\frac{f_{1}-f_{1,min}} {f_{1,max}-f_{1,min}}+\lambda_{2}\frac{f_{2}-f_{2,min}}{f_{2,max}-f_{2,min}}+ \lambda_{3}\frac{f_{3,max}-f_{3}}{f_{3,max}-f_{3,min}} \tag{6}\]  
where \(\lambda_{i}\) represents the weights of the corresponding sub-objective functions and \(\sum_{i=1}^{3}\lambda_{i}=1\); \(f_{i}^{\prime}\) is the normalized result of \(f_{i}\), and \(0\leq f_{i}^{\prime}\leq 1\); \(f_{i,min}\), represents the minimum values of \(f_{i}\); \(f_{i,max}\) represents the maximum values of \(f_{i}\). The selection of \(\lambda_{i}\) will have an impact on the final scheduling result. The larger value of \(\lambda_{1}\) is to improve the task value as the main purpose. The larger values of \(\lambda_{2}\) and \(\lambda_{3}\) are to improve operational reliability. The distribution of weight coefficients reflects the degree of emphasis on different objectives.
# Development of a GNSS Buoy for Monitoring Water Surface elevations in Estuaries and Coastal Areas
## 1 Introduction
Moreover, when the SBAS receiver was additionally equipped with a precision external antenna, it accurately resolved the vertical displacements. However, the researchers were not concerned with tide measurements. [PERSON] et al. [9] utilized GPS buoys along with the precise point positioning (PPP) technique to observe high-frequency sea level variations, which could be identified as waves, meteo-tsunamis, and tides. The PPP technique could be used to overcome limitations of single-point positioning and differential GPS. However, their data were not collected in real-time. [PERSON] et al. [10] installed a single GPS receiver on a buoy to measure ocean-surface waves. They applied a simple high-pass filter algorithm to determine the GPS position; however their GPS receiver did not measure tides. [PERSON] [11] reviewed sea-level-change monitoring systems that use Global Navigation Satellite System (GNSS) technology and suggested that large-scale GNSS buoys could be used to monitor tsunamis, determine tidal levels, calibrate altimeters, and monitor long-term tides. [PERSON] [11] indicated that the GNSS buoy data of sea surface heights should be verified and noted the difficulty of correcting for the tilt of large-scale GNSS buoys. The above studies did not measure real-time tides and waves simultaneously using a buoy in estuaries and coastal areas. Because of advances in GNSS technologies, the current accuracy of elevation measurements is on the centimeter level. Hence, developing a real-time GNSS buoy for measuring tides and waves directly is possible. The development of the GNSS buoy will contribute to the advancement of coastal ocean observing systems [12]. This work aims to develop a GNSS buoy that can observe water surface elevations and provide real-time tide and wave data in estuaries and coastal areas without establishing a RTK reference station. The following are the key features of the GNSS buoy developed in the present study:  
1. Virtual Base Station Real-Time Kinematics (VBS-RTK) technology is used for the meteo-oceanographic observations. 2. The GNSS buoy can receive signals from GPS and global navigation satellite system (GLONASS) and manage L1 and L2 signals. 3. The GNSS buoy is capable of observing tides and waves simultaneously. 4. The tidal datum is not influenced by variations in the Earth's crust. The accuracy of the monitored water surface elevations from the GNSS-buoy was confirmed by comparing the tide and wave data with those obtained from conventional tide gauge and accelerometer-type data buoys, respectively.
## 6 Conclusions  
We have developed a prediction-difference-map (PDM)-based optimal motion planning routine by using Gaussian process regression and well-established control laws for centroidal Voronoi tessellations. By using the PDMs as the density function in our motion planning procedure, we demonstrate that a continuously informed method for optimizing UAV sensor movements is possible. The approach and results outlined in this work demonstrate that PDM-based survey routines can provide actionable information more quickly than traditional uniform survey routines. We also showed that our method provides an estimate of the prediction uncertainty of the unvisited locations, which can be used to establish a quantitative convergence criterion that augments qualitative inspection of the predicted map.  
Using the PDM-based motion planning procedure can improve on the speed of producing contamination maps to provide actionable information in time-sensitive scenarios. Furthermore, because battery life is a major operational limitation for UAV-sensors, PDM-based motion planning can provide a more informative understanding of contamination in a large area for fixed flight times. Another advantage of the PDM-based motion planning procedure is that it does not assume any prior distribution of the contamination but rather dynamically uses the sensor data to produce optimal motion trajectories. Without any assumptions on the prior distribution of the contamination, there should be no dependency on the number of sensors, sensor starting positions, number of contamination sites, the shape/size of the contamination, and the overall scale of the search area; the proposed algorithm will still produce convergent results. PDM-based optimal motion planning is a robust and situation-agnostic approach that can be implemented for any arrangement of number and types of mobile and static sensors.  
Conceptualization, T.H.S.; Data curation, T.H.S.; Formal analysis, T.H.S.; Funding acquisition, T.H.S., D.T.W. and S.F.N.; Investigation, T.H.S.; Methodology, T.H.S., D.T.W. and S.F.N.; Writing--original draft, T.H.S.; Writing--review & editing, D.T.W. and S.F.N. All authors have read and agreed to the published version of the manuscript. This work was supported by the Laboratory Directed Research and Development program of Los Alamos National Laboratory under project number 20190625 PRD2. The authors declare no conflict of interest.
## 5 Measurement Results  
### System of Experiment  
Figure 11 shows the experimental physical test diagram. The experimental test equipment consists of an oscilloscope, which has a bandwidth of 13G, a sampling rate of up to 40 Gb/s, a memory depth of up to 768 Mm, and pulse pattern generator Agilent 81130A, which is used to generate data patterns and clocks, as well as jitter generation modules. The oscilloscope has a full suite of tools for serial data analysis, debugging, verification, and compliance testing. SDA III eye diagram and jitter analysis software can implement serial-data jitter analysis, including jitter separation using the Dual Dirac model; analysis of jitter using the histogram and TIE track; and measurement DCD, RJ, and PJ.
# Color-Based Image Retrieval Using Proximity Space Theory
## 3 Experiment and Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Category Name**} & \multicolumn{2}{c}{**CLD [27]**} & \multicolumn{2}{c}{**Color Moment [28]**} & \multicolumn{2}{c}{**CSD [29]**} & \multicolumn{2}{c}{**Proposed Method**} \\ \cline{2-9}  & **Precision** & **Recall** & **Precision** & **Recall** & **Precision** & **Recall** & **Precision** & **Recall** \\ \hline African Tribes & 0.213 & 0.144 & 0.389 & 0.260 & 0.562 & 0.347 & 0.505 & 0.312 \\ \hline Beaches & 0.529 & 0.283 & 0.295 & 0.169 & 0.288 & 0.153 & 0.442 & 0.209 \\ \hline Buildings & 0.341 & 0.157 & 0.382 & 0.211 & 0.553 & 0.341 & **0.664** & **0.397** \\ \hline Buses & 0.255 & 0.113 & 0.580 & 0.319 & 0.447 & 0.258 & 0.703 & 0.435 \\ \hline Dinosaurs & 0.907 & 0.510 & 0.938 & 0.785 & 0.794 & 0.614 & **0.962** & 0.539 \\ \hline Elephants & 0.477 & 0.235 & 0.491 & 0.302 & 0.630 & 0.260 & **0.706** & **0.339** \\ \hline Flowers & 0.700 & 0.288 & 0.679 & 0.360 & 0.537 & 0.253 & **0.733** & 0.300 \\ \hline Horses & 0.980 & 0.703 & 0.747 & 0.401 & 0.641 & 0.338 & 0.885 & 0.549 \\ \hline Mountains & 0.566 & 0.357 & 0.260 & 0.157 & 0.613 & 0.294 & 0.441 & 0.229 \\ \hline Foods & 0.127 & 0.057 & 0.463 & 0.277 & 0.320 & 0.138 & **0.792** & **0.482** \\ \hline Average Precision & 0.509 & 0.285 & 0.522 & 0.324 & 0.627 & 0.354 & 0.683 & 0.379 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparisons of precision and recall results among CLD, Color Moment, CSD and our Proposed Method. Figure 5: Ten classes on Corel-1000 dataset. Table 3 shows the precision rate of retrieving the Top-20 database images for each query image, while some retrieved images in the dinosaurs category, horses category and foods category are shown in Figure 6.  
Figure 6: Retrieved images for the Top 20: the dinosaurs category (**a**); (**b**) the horses category; and (**c**) the foods category. The query image is the first image in each class.
# Wireless Home Energy Management System with Smart Rule-Based Controller
## 2 Proposed HEM System
### Scheduling Terminal  
The configured XBee microcontroller is connected to a personal computer (PC) via a USB port and functions as a scheduling coordinator. A MATLAB program that is supported by a graphical user interface (GUI) on the main PC enables the user to input the desired comfort levels in terms of the room temperature, fridge temperature, water heater temperature, and illuminance ranges for the minimum, maximum, and normal pricing hours, respectively. The main scheduler also fetches the day-ahead pricing from the website of the utility service that are specified by the user. The day-ahead pricing trends used in the present study were obtained from [24]. The control commands are generated based on information predefined by the user, together with the power consumption information obtained from the smart sockets that are attached to the appliances, the heater outlet water temperature, and the room condition and occupancy information. The commands are in the form of binary decisions sent to specific smart plugs to turn on/off the connected appliances; dimming commands sent to Zigbee-connected dimmers to reduce/increase the light intensity of dimmable LEDs; and, temperature settings sent to Zigbee-connected infra-red (IR) remote controls to set ACs to specific temperatures. Figure 2 shows a screenshot of the scheduling coordinator's GUI.  
The user is able to manually send commands to the house appliances, to adjust the desired comfort level or the power consumption of a specific appliance or to override an automatic scheduling function of a connected device, as can be seen from Figure 2. It can also be clearly seen that the GUI significantly assists the user in making their decisions by graphically showing the power consumption of any specified appliance. The user can also graphically examine the hourly pricing variation and identify the minimum and maximum pricing hours, which is beneficial to decision making.  
Figure 1: System configuration.
# Retrospective Spectrum-Conversion Method Based on Time-Modulated Van Atta Array
## 3 Van Atta Array Design
### The Design of the SPST Switch  
The design of the series SPST switch is shown in Figure 10. The PIN diode D1 is connected in series on the microwave path through the DC blocking capacitors C2 and C3. The cathode of the PIN diode is connected to the ground through the isolation inductor, and the anode is connected to the bias voltage through the LC isolator. A current limiting resistor R1 is connected in series with the bias voltage terminal to protect the PIN diode. In this way, the on-off of the PIN diode can be realized by controlling the bias terminal voltage. To reduce the influence of parasitic parameters, the required capacitance and inductance are all packaged with 0402 SMD. Isolation inductors use high-Q wire-wound inductors.
## Materials and Methods
### Model Parameterization and Validation  
The two components, i.e. _Reref_ for each site and year and the remaining seasonal variation \(f(T,P,W)\) partitioned from the overserved \(RE\), were used to optimize parameters for Equations (4) and (5) separately to better constrain each component of the model. The nonlinear least-squares curve fitting method was utilized to estimate the model parameters. In addition to joint-sites estimation for the two grasslands in two regions, the leave-one-out cross validation method was employed to test the validity of the joint estimation of parameters (i.e., one subtype was excluded at a time, and the data for all other subtypes were used to estimate the parameters for each grassland type). The coefficient of determination (\(R^{2}\)) and root mean square error (RMSE) were chosen to assess the model performance. Regional-scale application is based on PFT-specific parameters retrieved from the entire site-data of alpine grasslands and temperate grasslands to assure most robust parameters.  
To further verify the model improvement by incorporating the moisture effect on both spatial and temporal variability, the \(LSWI_{mean}\) in _Reref_ and _fw_ in \(f(T,P,W)\) were removed, respectively, to test the accuracy of the model. In addition, we also compare some other semi-empirical or process-based models to assess the ability of our model. Specifically, for the \(RE\) estimation at a site scale, we chose the two original models that our model was based on, namely T&P [22] and RECO [27]; for the \(RE\) estimation at a regional scale, an independent regional \(RE\) product simulated by a mainstream dynamic global vegetation model CLM4 CN, derived from [PERSON] et al. [56], was applied.
# A Quad-Band RF Circuit for Enhancement of Energy Harvesting
## 6 Conclusions  
The non-linearity of the Schottky diodes is due to the variation of the input powers or frequency. The non-linearity degrades the RF-DC conversion efficiency significantly. Hence, this work's novelty is to overcome the negative effect of the Schottky diodes' non-linearity issue. Thereby, the following procedures were done: First, the dynamic rectifier input impedance due to the Schottky diode's non-linearity was found at each frequency band by the proposed theoretical model and ADS simulator. A simple impedance matching network by utilizing the Smith chart utility was designed for each frequency band according to the impedance found at each band. Two scenarios to combine the four circuits in one circuit called \"Quad-band circuit\" were adopted to harvest the radiated power of each band of the four frequency bands mentioned in this study (900 MHz, 1800 MHz, 2100 MHz, 2450 MHz). The first scenario introduced a quad-band voltage multiplier circuit with a single receiving antenna and four band-pass filters (BPFs) of elliptic type. The output voltage and conversion efficiency under this approach are 1.662 V and 74.846% for the GSM-900 band, 1.422 V, 66.974% for the GSM-1800 band, 1.333 V, 64.486% for the UMTS 2100 band, and 1.587 V, 61.407% for the Wi-Fi 2.45 band. The second scenario presented a quad-band voltage multiplier circuit with four receiving antennas. The output voltage and conversion efficiency obtained were 1.373 V and 71.194%, 1.421 V and 64.944%, 1.594 V and 62.363%, and 1.896 V and 65.096% for the four frequency bands. The proposed quad-band approach can harvest 2 mW DC power. Hence, harvesting the power of RF waves with high RF-DC conversion efficiency at the four bands under the negative effect of the Schottky diodes' non-linearity conditions is the novelty of this study.  
conceptualization, K.K.S., S.W. and D.A.S.; methodology, K.K.S.; software, K.K.S. and D.A.S.; validation, K.K.S. and D.A.S.; formal analysis, K.K.S. and D.A.S.; investigation, K.K.S. and D.A.S.; resources, K.K.S.; data curation, K.K.S.; writing--original draft preparation, K.K.S.; writing--review and editing, K.K.S., S.W., D.A.S. and S.S.M.G.; visualization, K.K.S.; supervision, S.W.; project administration, S.W.; funding acquisition, S.S.M.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by TAIF UNIVERSITY RESEARCHERS SUPORTING PROJECT, grant number TURSP-2020/34\" and \"The APC was funded by SHERIF GHONEIM\".  
The authors would like to acknowledge the financial support received from Taif University Researchers Supporting Project Number (TURSP-2020/34), Taif University, Taif, Saudi Arabia.  
The authors declare no conflict of interest.