esa-sceva/satcom-chunk-collection huggingface dataset:
# A Compact Planar Wi-Fi Antenna with Optimized Radiation Patterns for Small UAV Applications
## 2 Antenna Design and Analysis
### Antenna Design for 2.4 GHz  
Figure 7 shows the simulated surface current densities at 2.4 GHz, where the areas with higher surface current densities are the main radiation areas. The two sides of the outer conductor form a pair of L-shaped monopoles, Monopoles E and F. These two monopoles  
Figure 6: The relative power patterns of the vertical infinitesimal dipole, the array factor of Elements 1 and 2, and the total field of the two-element array antenna.  
have the same wavelength, \(\lambda_{L}\), and the lower resonance frequency, f\({}_{L}\), can be approximated by (6).  
\[\text{f}_{L}\cong\frac{c}{\lambda_{L}}=\frac{c}{\left\{\left[W_{1}+\left(W_{2}/2 \right)\right]+\left[\text{L}_{1}+\left(\text{L}_{1}/2\right)\times 2\right] \right\}/0.23}=2.52\text{ GHz}, \tag{6}\]  
Due to the symmetrical design of the architecture, the radiations from the two bottom paths cancel each other out. Due to the electric field that radiates between the inner and outer conductors, the proposed antenna approximates a vertical monopole antenna. In addition, since the cross-sectional area of the inner conductor reference plane is very small, the radiation pattern of the vertical monopole antenna approximates a vertical dipole with an effective length of \(\text{L}_{1}+\left(\text{L}_{1}/2\right)\times 2\), equivalent to \(0.122\)\(\lambda_{L}\). Its maximum radiations are near the horizontal plane.
# Radar-Assisted Multiple Base Station Cooperative mmWave Beam Tracking
## 3 Radar-Assisted Multi-Rsu Cooperative Beam Tracking
### Multi-RSU Cooperative Beam Tracking
As a result, the coordinates of the optimal predicted vehicle position based on multi-RSU cooperation are expressed as  
\[\hat{x}_{k,n}^{co}=\sum_{l=1}^{L}w_{k,l,n}\hat{x}_{k,l,n}^{pre} \tag{39}\]  
\[\hat{y}_{k,n}^{co}=\sum_{l=1}^{L}w_{k,l,n}\hat{y}_{k,l,n}^{pre} \tag{40}\]  
where \(\hat{x}_{k,n}^{co}\) denotes the x-coordinate predicted by ES for vehicle \(k\) at time slot \(n\), similarly for the y-coordinate. Cooperation takes advantage of distributed sensing to bring the predicted location closer to the real vehicle location. The optimal distance and AOD between the \(l\)-th RSU and \(k\)-th vehicle predicted by ES at time slot \(n\) are given by  
\[\hat{d}_{k,l,n}^{co}=\sqrt{\left(\hat{x}_{k,n}^{co}-x_{R_{l}}\right)^{2}+\left( \hat{y}_{k,n}^{co}-y_{R_{l}}\right)^{2}} \tag{41}\]  
\[\hat{\theta}_{k,l,n}^{co}=\tan^{-1}\left(\frac{\hat{y}_{k,n}^{co}-y_{R_{l}}}{ \hat{x}_{k,n}^{co}-x_{R_{l}}}\right) \tag{42}\]  
where \(x_{Rl}\) and \(y_{Rl}\) denote x-coordinate and y-coordinate of the \(l\)-th RSU. \(\hat{d}_{k,l,n}^{co}\) and \(\hat{\theta}_{k,l,n}^{co}\) are more accurate kinematic state variables that are used as input for the EKF prediction at the next time slot. Since multiple RSUs are considered in this paper, switching between RSUs also needs to be addressed. We propose three switching strategies as follows  
* Distance Priority: The RSU with the closest distance to the vehicle is selected as the serving RSU. Although this strategy has a low overhead, its spectral efficiency is also very low. * Reference Signal Receiving Power (RSRP) Priority: Multiple RSUs perform small-range beam scanning separately, and the RSU corresponding to the maximum received RSRP at the vehicle is used as the serving RSU. However, this strategy provides high spectrum efficiency but also a high overhead. * Radar Echo Strength Priority: The RSU with the highest received radar echo strength is selected as the serving RSU. This strategy avoids beam scanning and thus reduces overhead, and also has high spectral efficiency.
# Cognitive Interference Cancellation with Digital Channelizer for Satellite Communication
## 4 Cognitive Interference Cancellation
### Improved Interference Detection
(\(x[k]\) is time-domain input with 1024 samples, \(X[k]\) is the 1024-point FFT output, \(E[k]\) and \(O[k]\) are the 512-point FFT outputs with even- and odd-indexed samples, respectively, and \(W^{k}_{1024}=\exp(\frac{-2\pi k}{1024})\).)
## 3 State-Of-The-Art
### Primary Research in Literature
#### 3.2.4 Other Directions of Research  
Other miscellaneous contributions include simulations, algorithms, theorems, and other applications not classified in the previous groups. These contributions can be useful for the research on V2X, I2X, and P2X communications, because they allow the optimization of parameters on models. An example of a simulation can be found in [112], whose model allows the simulation of intelligent transport systems and the introduction of elements such as traffic lights or roadside stations, among others. As the main advantage, this work provides results on the traffic flow of vehicles, communications, and RF emissions. Another example of outstanding work in algorithms is [113], whose authors present an approach that determines the optimal number of roadside units (RSU) required per area as a function of the vehicle density. Other works in theorems can also be found to provide network stability and scalability based on the topology of the communication flow [114]. In this sense, a novel model has been proposed to solve problems of shared resources by means of graphs, which offers better results than the traditional methods [115]. Finally, another notable work proposed the use of wireless energy in electric vehicles not only to transfer power, but also to support V2I communication in areas of high traffic density and data transfer rate [116].
# Review of Land Surface Albedo: Variance Characteristics, Climate Effect and Management Strategy
## 4 Climate Feedback and Management Strategy for Surface Albedo
### Economic Cost and Management Strategy for Surface Albedo
The use of high-albedo materials further worsens thermal sensation due to further decreased wind speed in addition to its considerable benefits in terms of temperature and energy savings in heat waves; therefore, its application requires a careful evaluation of benefits and side effects [109; 210]. Similarly, pavement materials with high surface albedo have been investigated, where Portland cement concrete slabs show higher surface albedo and lower internal temperatures than asphalt mixture slabs [146]. Moreover, the implementation of urban greenery is a widely adopted mitigation strategy to reduce ambient and surface temperatures, which can then mitigate the UHI effect [235]. In addition, some industry management is also associated with surface albedo. Surface albedo can affect the performance of solar cells (e.g., short-circuit current density, open-circuit voltage, fill factor and output power) [236], and improvements in efficiency of over 50% [237] and 30% [238] have been reported. Specifically, a new solar cell design based on bifacial heterojunction back contact shows an improvement in maximum power density by 8% at 20% albedo compared to the monofacial condition [239]. A simulation of shadow losses of direct normal, diffuse solar radiation and albedo for a very large number of trackers used in photovoltaic systems was calculated, which provides guidance for the usage of these trackers [240]. The UV damage from the combinations of 35 types of grounding materials and 4 types of common photovoltaic backsheets was evaluated theoretically, and it was found that a more accurate evaluation can be obtained by considering the spectral albedo and long-term reliability of photovoltaic systems [241]. The anthropogenic increase in aerosol concentrations since preindustrial times shows a small positive radiative forcing (+0.016 W/m\({}^{2}\)) induced by the reduction in surface albedos and a significantly positive TOA radiative forcing (\(-0.6\) W/m\({}^{2}\)) [241]; the most responsible sectors are pointed out, including the transportation sector in the United States, agricultural burning and transportation in Europe, and the domestic emission sector in Asia. These evaluations can help promote the division of climate responsibility and provide solutions. Climate has been recognized as having direct and indirect impacts on society and the economy, both in the long term and in daily life; therefore, multiagency collaborations are in high demand in future work [242].
# Generation of High Resolution Vegetation Productivity from a Downscaling Method
## 2 Materials and Methods
### Data and Data Processing
#### Meteorological Data  
Meteorological data, including the daily maximum temperature, the daily minimum temperature and the daily mean temperature, were firstly collected from meteorological stations in the study area, and were interpolated to 30 m by using the method of Kriging interpolation. Then eight day mean temperature was obtained by averaging the daily values. Daily solar shortwave radiation with a resolution of 30 m was obtained through a Mountain Microclimate Simulation Model (MT-CLIM) [40], and then eight day mean solar shortwave radiation were obtained by averaging the daily values.
## 2 Materials and Methods
### Classification Methods
#### 2.5.2 K-Nearest Neighbors (KNN)  
K-nearest neighbors (k-NN) is a non-parametric classification method that assigns a class to the data based on the similarity to other data points in the training set, by calculating the distance to the k-nearest data points [42; 43; 44]. The k-parameter in this method determines how many neighbors the data will be compared to. The classification accuracy can be improved by experimentally determining the optimal value of k.
# Detecting Deception from Gaze and Speech Using a Multimodal Attention LSTM-Based Framework
## 2 Materials and Methods
### Feature Extraction
#### 2.2.2 Speech Features  
Figure 1b shows the feature extraction process for speech signals. As in the case of the gaze modality, firstly speech signals were divided into segments of 3 s length with an  
overlap of 2.5 s, obtaining the same number of chunks. Again, masked values were used for padding shorter segments than 3 s.  
For the LSTM-based systems, the speech features consisted of log-mel spectrograms that were extensively used in a variety of speech and audio-based applications, such as depression detection [37], speech intelligibility level classification [33; 34] or environmental sound classification [39]. For each audio segment, log-mel spectrograms were computed by using the Python's package LibROSA [40] as follows: first, the speech signal was divided into short analysis Hamming windows (frames) of 20 ms length with an overlap between adjacent windows of 10 ms. Then, the magnitude of the Short-Time Fourier Transform (STFT) of each window was computed and mapped to the mel-frequency spacing [18] by using an auditory filter bank composed of mel-scaled filters, and later converted to a logarithmic scale. The mel scale is a frequency transformation that emulates the non-equal sensitivity of the human hearing at different frequencies. This way, each speech segment was represented by a temporal sequence \(X_{S}\) with dimension \(N_{S}\times T_{S}\), where \(N_{S}\) is the number of mel filters and \(T_{S}\) is the number of speech frames (note that each acoustic frame corresponds to 10 ms). In this case, \(N_{S}=32\) and \(T_{S}=300\) as it is the number of frames contained in a 3 s segment.  
In the case of the SVM-based systems, a compact representation of the log-mel spectrograms was used, that consisted of the following six statistics: mean, standard deviation, skewness, kurtosis, max and min. This way, the final speech feature set \(X_{S}^{C}\) was obtained, whose dimension was \(N_{S}\times N_{F}\), where \(N_{F}\) is the number of statistics computed (in this case, \(N_{F}=6\), as in the case of the gaze modality).
## 3 Results
### Data Analysis  
Once the spectral library was constructed, the data was analysed in order to build chemometric models using a selection of available pre-processing methods. Initial models (Models from 0.1 to 0.4) were designed to evaluate the impact different pre-processing methods had on their performance during internal cross-validation.  
The first model of this study, 'Model 0.1', was based on spectra from all 480 samples within the library. This model produced cross-validation data that indicated the possibility of using this method for the qualitative discrimination of corrosive substances with an F1 score of 0.818. During cross-validation there were, however, some samples that were misclassified, with the most evident cases being hydrochloric acid samples (where 8% of these were misclassified as sodium hypochlorite) and sulfuric acid (where 4% of the samples were assigned as hydrochloric acid). Since this model included samples of highly concentrated sulfuric acid and sodium hypochlorite prepared only in HDPE bottles, these were filtered out to avoid predictive instability due to an uneven sample distribution between the defined classes. Thus, 'Model 0.2' was created. The performance of Model 0.2 showed little difference compared to Model 0.1, with an F1 score of 0.815, actually indicating an overall slight decrease of performance during cross-validation. A closer inspection of the data revealed that the misclassification seen earlier for sulfuric acid had gone down to 0% and the percentage of samples of hydrochloric acid classified as sodium hypochlorite became 7%. It was also observed that the regions below 800 nm and above 1000 nm of the raw spectra appeared to be noisy; these were, therefore, filtered out for all the other models, which included data only from the region between 800 and 1000 nm. For the next model, 'Model 0.3', this specific wavelength region was used, along with data from all 480 samples. This model showed some classification improvement, with an F1 score of 0.889 and the previously observed misclassification decreasing to 5% for hydrochloric acid and 1% for sulfuric acid. A final model refinement (Model 0.4) involved using the aforementioned wavelength region, as well as filtering out the samples from highly concentrated sulfuric acid and sodium hypochlorite in HDPE bottles. Model 0.4 appeared to have a lower F1 score (0.876) compared to Model 0.3, but the overall percentage of samples misclassified during cross-validation had greatly decreased, with 1% of samples misclassified in the case of hydrochloric acid and 0% for sulfuric acid. This final refined model --hereon referred as 'Model 1'--was therefore selected for initial assessment against the Testing set.
# Real-Time Path Planning for Strategic Missions
## 4 The Description of the Navigation Environment  
To perform the experiments, a \(5\times 5\) square grid map measuring \(3\times 3\) m is used, as shown in Figure 1(a). The black and red dots are the initial \(S_{init}\) and the objective \(S_{g}\) states, respectively. Figure 1(b) indicates what each value of the map and graph means, which is represented in blue.  
It is adopted that if the object occupies the grid space, it is treated as an obstacle. In the process of defining the successor, the center of each cell is assumed as a node. In this paper, the system of 4 nodes is adopted, i.e., 4-neighborhood. To calculate the heuristic function, the Euclidean distance will be used, described by  
\[h(n)=d(n,S_{g})=\sqrt{(x_{n}-x_{S_{g}})^{2}+(y_{n}-y_{S_{g}})^{2}}, \tag{1}\]  
where \(x_{n}\) and \(y_{n}\) represent the current robot position, while \(x_{S_{g}}\) and \(y_{S_{g}}\) are the goal position.
## 4 Conclusions
Last but not least, more GNSS receivers and simulators are being incorporated into the VTTFFTB in order to simulate and study multiple-satellite (\(\geq\)3) real-time formation flight and its applications to multi-scale space weather observation. This will be reported on in the near future. Conceptualization, Y.P. and W.A.S. ; data curation, Y.P. ; formal analysis, Y.P. ; funding acquisition, W.A.S. ; investigation, Y.P. ; methodology, Y.P. ; resources, Y.P. and W.A.S. ; software, Y.P. ; supervision, W.A.S. ; validation, Y.P. ; visualization, Y.P. ; writing--original draft, Y.P. ; writing--review and editing, Y.P. and W.A.S. The VTTFFTB was supported by the AFOSR (Grant # 13-0658-09) and Virginia Tech. **Acknowledgments:** The authors would like to appreciate [PERSON] and [PERSON] for providing useful suggestions. Also, Y.P. is highly appreciative of receiving the International Association of Geodesy (IAG) Travel Grant to present our work at the 2018 International Workshop on GNSS Ionosphere (IWGI2018) in Shanghai, China. **Conflicts of Interest:** The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} VTTFFTB & Virginia Tech Formation Flying Testbed \\ HIL & Hardware-in-the-loop \\ SFF & satellite formation flying \\ MMS & Magnetospheric Multiscale \\ GRACE & Gravity Recovery and Climate Experiment \\ ESA & European Space Agency \\ GNC & guidance, navigation, and control \\ PNT & positioning, navigation, and timing \\ GNSS & Global Navigation Satellite System \\ GPS & Global Positioning System \\ LEO & low Earth orbit \\ TEC & total electron content \\ COSMIC & Constellation Observing System for Meteorology, Ionosphere, and Climate \\ CTECS & Compact Total Electron Content Sensor \\ SPORT & Scintillation Prediction Observations Research Task \\ CERTO & Coherent Electromagnetic Radio Tomography \\ ION-F & Ionospheric Observation Nanosatellite Formation \\ NASA & National Aeronautics and Space Administration \\ DRL & German Aerospace Center \\ _Ne_ & electron density \\ RF & radio frequency \\ USB & Universal Serial Bus \\ EKF & Extended Kalman filter \\ HCW & Hill-Clohesy-Wiltshire \\ S4 & amplitude scintillation index \\ VTEC & vertical TEC \\ DCB & Differential Code Bias \\ COTS & commercial off-the-shell \\ ESF & Equatorial Spread F \\ SR & sounding rocket \\ LT & local time \\ \end{tabular}
# Controlled Fault Current Interruption Scheme for Improved Fault Prediction Accuracy
###### Abstract
The core of the method lies in the utilization of the property that the values of sinusoidal functions are opposite to each other when the phase difference is half a cycle, which determines the independent identification of the parameters of the non-periodic component and the periodic component of the short-circuit current. Specifically, this paper adopts the RLS method to calculate the parameters of the non-periodic components in real time, so as to determine the real-time prediction of the short-circuit current, while the periodic components are calculated using FFT. The advantages of the recursive least squares method can be fully utilized, i.e., it is good at fitting linear data, and of the fast Fourier algorithm, which is good at calculating sinusoidal quantities. The algorithm is tested in MATLAB using simulation data and recorded waveform data, and the results show the advantages of the algorithm in terms of accuracy. After successfully calculating the target breaking point, the sequence of CFI is the key to realize the controlled switching of the short-circuit current. The CFI sequence adopted in most of the current studies is relatively simple, and the main idea is to pre-calculate the opening zero-crossing point before the relay protection action and to determine the waiting time by combining the breaking time with the arc ignition time [18]. However, this type of sequence often does not fully utilize the relay protection response time and waiting time. To address this problem, this paper proposes a double-sampling controlled switching sequence. The first calculation is completed before the relay protection response time, and the target zero-crossing point is obtained through the first sampling calculation. Secondly, on the basis of retaining a certain degree of redundancy, the short-circuit current is continued to be sampled and calculated by utilizing the remaining relay protection response time and waiting time to improve the accuracy of the calculation of the target zero-crossing point, which is obtained in the final iteration result, before finally being combined with the breaking time and the arcing time to calculate the breaking signal issuance time to complete the CFI. Finally, the method proposed in this paper is compared with articles applying least squares and Fourier algorithms, and the results can fully prove the superiority of the method and sequence.
## 4 Characteristics of Different Time Distribution Systems
### Summary of Time Distribution Systems  
All four time distribution systems are network-based and have close interconnections.  
NTP is the simplest time distribution method but offers lower accuracy, in the millisecond range, which limits its application in power grids. It is more suited for non-critical applications. The other three methods--PTP, White Rabbit, and 5G--are all based on PTP with different implementations and performance levels. PTP provides higher accuracy than NTP, reaching the microsecond range with dedicated hardware. Furthermore, 5G can be considered an extension of PTP with similar accuracy but operating over a wireless network. White Rabbit combines Synchronous Ethernet and PTP, utilizing dedicated hardware and optical fiber to achieve the highest accuracy in the nanosecond range. However, it is also the most expensive method.  
A brief summary of the four time distribution systems is shown in Table 3.
###### Abstract  
Understanding metabolic cost through biomechanical data, including ground reaction forces (GRFs) and joint moments, is vital for health, sports, and rehabilitation. The long stabilization time (2-5 min) of indirect calorimetry poses challenges in prolonged tests. This study investigated using artificial neural networks (ANNs) to predict metabolic costs from the GRF and joint moment time series. Data from 20 participants collected over 270 walking trials, including the GRF and joint moments, formed a detailed dataset. Two ANN models were crafted, net\({}_{\text{GRF}}\) for the GRF and net\({}_{\text{Moment}}\) for joint moments, and both underwent training, validation, and testing to validate their predictive accuracy for metabolic cost. Net\({}_{\text{CRF}}\) (six hidden layers, two input delays) showed significant correlations: 0.963 (training), 0.927 (validation), 0.883 (testing), \(p<0.001\). Net\({}_{\text{Moment}}\) (three hidden layers, one input delay) had correlations of 0.920 (training), 0.956 (validation), 0.874 (testing), \(p<0.001\). The models' low mean squared errors reflect their precision. Using Partial Dependence Plots, we demonstrated how gait cycle phases affect metabolic cost predictions, pinpointing key phases. Our findings show that the GRF and joint moments data can accurately predict metabolic costs via ANN models, with net\({}_{\text{GRF}}\) being notably consistent. This emphasizes ANNs' role in biomechanics as a crucial method for estimating metabolic costs, impacting sports science, rehabilitation, assistive technology development, and fostering personalized advancements.  
metabolic cost; artificial neural networks; biomechanics; gait; ground reaction forces; joint moments; human movement analysis +
Footnote †: journal: Applied Sciences