esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Method
### Six-Minute Time-Averaged EIRP (\(EIRP_{\mathrm{act}}\))  
By combining knowledge of the time-averaged transmit power, \(P_{\mathrm{avg}}\), with the average gain, \(G_{\mathrm{avg}}(\varphi,\theta)\), the six-minute time-averaged actual EIRP was determined according to:  
\[EIRP_{\mathrm{act}}(\varphi,\theta,t)=G_{\mathrm{avg}}\left(\varphi,\theta,t \right)\times P_{\mathrm{avg}}(t) \tag{1}\]  
\(EIRP_{\mathrm{act}}\) provides a measure of the spatial distribution of the time-averaged transmit power, and it is therefore directly related to EMF exposure. Equation (1) implicitly assumes the time-averaged power \(P_{\mathrm{avg}}\) to be equally distributed among the beam configurations reported within the same six-minute interval. In addition, while \(P_{\mathrm{avg}}\) provides a measure of the total transmit power, including the broadcast channel, \(G_{\mathrm{avg}}\) is determined based on the antenna pattern only used for data traffic. Since the antenna gain for the broadcast signals is several dB lower than the peak antenna gain for the traffic beams,  
Figure 2: (**a**) Antenna radiation patterns for four beam configurations (in different colors) in the azimuth plane (\(\theta=90^{\circ}\)). The values in the polar plot are normalized to the peak gain of the broadside beam and presented in a logarithmic scale in dB. Only a subset of the available BS beams is shown. (**b**) Reference system with respect to the antenna.  
\(EIRP_{\text{act}}\) provides a conservative estimate of the time-averaged EIRP. In any case, the broadcast signal in 5G NR only counts for a very small amount of the BS maximum transmit power (the resource elements allocated for transmission of the synchronization signal block are typically lower than 0.1% of the total available [6]).  
The value of \(P_{\text{avg}}\) at time \(t\) was approximated by the largest six-minute averaged power sample reported in the corresponding fifteen-minute interval as extracted from the network. With reference to the fifteen-minute interval in Figure 1, the selected \(P_{\text{avg}}\) would be 23.75% (i.e., the center value of the largest bin, for which at least one sample was observed). This is a conservative assumption, since for many instances of \(t\) within the fifteen minutes, the six-minute time-averaged transmit power could be lower (e.g., in Figure 1 most of the samples belong to the bin corresponding to a power of 0 to 2.5%). As shown in Section 3, this assumption might lead to a large overestimate of \(EIRP_{\text{act}}\).
## 9 Conclusions  
This paper summarized the latest update on the advanced RF-EH technology. The paper presented a general outline of RF-EHNs, specifically the design, methods, and current applications. The study also reviewed the background issues regarding the circuit design and its implementations. A wide range of design issues associated with the resource allocation in RF-EHNs has also been addressed. The practical challenges and future directions in RF-EH techniques were also given. This novel technology would play a crucial part in replacing batteries in the foreseeable future. The abundantly available RF electromagnetic waves in the environment are harmless to the human body since they are able to penetrate through the soft tissues without inflicting any damage. Despite the progressive achievements over the years, various improvements remain to be further optimized to enhance the RF-Eh technology, including increasing the operational range, minimizing the transmission loss, optimizing the PCE, and reducing the system dimensions. In short, future studies should be focused on developing antennas with the capability to operate across multiple frequencies comprising 2.3 GHz (Wi-MAX), 2.4 GHz (WLAN), 2.6 GHz (LTE/4G), as well as 5.2 GHz (WLAN). Further studies should also consider improving the rectenna's direct current (DC) voltage to provide the optimum transfer of power.  
Conceptualization, H.H.I. and S.S.A.-B.; methodology, H.H.I. and S.S.A.-B.; software, H.H.I. and S.S.A.-B.; validation, H.H.I. and S.S.A.-B.; formal analysis, H.H.I. and S.S.A.-B.; investigation, H.H.I., M.J.S., S.S.A.-B. and M.T.I.; resources, H.H.I., M.J.S., S.S.A.-B. and M.T.I.; data curation H.H.I. and S.S.A.-B.; writing--original draft preparation, H.H.I. and S.S.A.-B.; writing--review and editing, M.J.S.; S.S.A.-B., S.K.I., M.T.I., A.A. and M.S.I.; visualization, H.H.I. and S.S.A.-B.; supervision, M.J.S., S.S.A.-B. and M.T.I.; project administration, M.J.S. and S.S.A.-B. All authors have read and agreed to the published version of the manuscript. This work was supported by the Research University Grant, Universiti Kebangsaan Malaysia, Gearan Universiti Penyelidikan (GUP), code: GUP-2021-026. The authors declare no conflict of interest.
## 4 Analysis Results
### Example of a Prediction of the Peak Equivalent Displacement  
The structural model shown here as an example is Model-Tf44. Figure 18 shows the nonlinear properties of the equivalent SDOF model calculated according to Section 2.2.2.  
In the results shown in Figure 18b,c, the \(V_{\Delta E\mu 1}u^{*}\)-\(T_{1 eff}\) curve was constructed and overlaid with the bidirectional \(V_{\Delta E}\) spectrum. Figure 19 shows the prediction of the peak equivalent displacements of the first two modes.  
Figure 17: Horizontal response spectra of the recorded ground motions: (**a**) Elastic acceleration response spectrum (damping ratio: 0.05); (**b**) elastic velocity response spectrum (damping ratio: 0.05); (**c**) elastic total input energy spectrum (damping ratio: 0.10).  
Figure 18: Nonlinear properties of the equivalent single-degree-of-freedom (SDOF) model representing the first modal response (Model-Tf44): (**a**) The \(A_{1U}{}^{*}\)-\(D_{1U}{}^{*}\) relationship; (**b**) the \(T_{1 eff}{}^{*}\)-\(D_{1U}{}^{*}\) relationship; (**c**) the \(V_{\Delta E\mu 1}u^{*}\)-\(D_{1U}{}^{*}\) relationship.  
The predicted peak was obtained as the intersection point of the \(V_{\Delta E\mu U}\)\({}^{*}\)-\(T_{1 eff}\) curve according to the pushover analysis results and the bidirectional \(V_{\Delta E}\) spectrum, as shown on the left side of Figure 19a. Then, the peak equivalent displacement \(D_{1U}\)\({}^{*}\)\({}_{\max}\) was predicted from the \(V_{\Delta E\mu U}\)\({}^{*}\)-\(D_{1U}\)\({}^{*}\)\({}_{\max}\) curve. The prediction of the peak equivalent displacement of the second mode, \(D_{2V}\)\({}^{*}\)\({}_{\max}\), can be made using the same procedure as that of \(D_{1U}\)\({}^{*}\)\({}_{\max}\), as shown in Figure 19b. Note that for the predictions of \(D_{1U}\)\({}^{*}\)\({}_{\max}\) and \(D_{2V}\)\({}^{*}\)\({}_{\max}\), the same \(V_{\Delta E}\) spectrum (bidirectional \(V_{\Delta E}\) spectrum) was used.
# Interference Suppression in EEG Dipole Source Localization through Reduced-Rank Beamforming
## 2 Materials and Methods
### EEG Data
#### 2.6.1 Simulated Data
Figure 4 shows an example of such ROI including the sources' positions (\(\mathbf{\theta}\)) we used in our simulations.
# Cooperative Standoff Tracking of Moving Targets Using Modified Lyapunov Vector Field Guidance
## 5 Experiments and Results
### Numerical Simulation
#### 5.1.3 Phase-Keeping in Cooperative Standoff Tracking  
In order to demonstrate the performance of the phase keeping algorithm, a team of three UAVs maintain the same desired distance, circumnavigating around a stationary target. The cruising speed of the UAV is 20 m/s, its flight speed is restricted within 13 m/s \(\leq v_{uw}\leq\) 25 m/s, and the acceleration constraint is \(-2\) m/s\({}^{2}\leq a_{uw}\leq\) 2 m/s\({}^{2}\). The desired standoff distance is 400 m.  
Figure 12: Trajectory and position estimation of the moving target.  
Figure 13: Position estimation error versus time.  
Figure 15a shows three UAVs approaching the loiter circle. We utilized the standoff tracking algorithm presented above with the same parameter on the proposed phase keeping method and the method proposed by [PERSON] etc. [7]. It is obvious that three UAVs stick to the desired circle and maintain the desired standoff distance. Figure 15b,c shows the velocity of vehicles and phase differences between adjoining UAVs versus time. Compared with the reference method, the proposed algorithm settles down to the cruising speed faster, and the velocity does not change back and forth. The phase difference can converge to the desired value in a shorter time.  
From the experiment results, we can conclude that whether in velocity or in phase difference, the proposed controller shows shorter stabilization time and less overshot.
# Decision Support Models for Operations and Maintenance for Offshore Wind Farms: A Review
## 4 Modeling Techniques
### Types of Models  
#### 4.2.1 Discrete Event Simulation  
Discrete event simulation (DES) models a system as a discrete sequence of events and is explained in Nance [78]. The simulation jumps from one event to the next and leaves out the time in between those state changes. The model is inherently stochastic and depends on basic random number generation. DES can be understood as a discrete form of a dynamic stochastic model. DES has very low computational requirements since only events are simulated and not the time in between. An example of a discrete event model is illustrated in Figure 1, showing the events happening on ground between airplane landing and take-off. In the framework of maintenance scheduling, events can be the failure or repair of turbines that change the status from \"running\" to \"stopped\" and back, respectively. DES provides a very basic framework for modeling maintenance. DES is very simple and can be implemented in most programming languages--even by inexperienced programmers and researchers. However, DES does not have the means to include different scheduling methods and complex interaction of input variables, such as wind speeds and downtime. Therefore, DES has a limited use for the wind industry and research in maintenance scheduling of offshore wind farms. Due to its simplicity it can act as a \"stepping-stone\" for explaining the problem of maintenance scheduling to untrained personnel or students. However, to develop a decision support tool beneficial for operators or maintenance providers, one should consider other--more complex- types of models.  
Figure 1: A simplified illustration of a discrete event model, showing possible events taking place at an airport between the arrival and departure of an airplane.
## 6 Conclusions and Future Works  
In this paper, a game theory-based real-time fault-tolerant routing protocol called GTRF has been proposed. GTRF consists of two parts: a VA model and a jumping transmission model. In the VA model, we design the auction process and some rules for nodes to obey. We proved that the auction situation the satisfies Nash Equilibrium if every node bids its actual energy. Then the jumping transmission model is proposed which applies a jumping manner when a void region is detected or the remaining time ratio of a message falls below a threshold. We also prove that GTRF can meet real-time requirements. Finally, extensive simulations are conducted to show the advantages of GTRF. These simulation results show that GTRF can not only efficiently balance the energy cost of the network, prolonging the lifetime, but also utilize fewer control packets in achieving real-time transmission. As part of our future work, we would like to extend GTRF in the data aggregation area. It is also valuable to research a multi-level VA model, which can not only further conserve energy, but also relieve the burden of the sink nodes.  
Figure 13: Comparison of transmission delay after transmitting (**a**) 1000; (**b**) 2000; (**c**) 5000 and (**d**) 10,000 packets under different routing protocols.
## 1 Introduction
### Wearable Hand Device  
Conventional gesture recognition is conducted through wearable hand devices like data gloves. It is a wearable sensor input device of human-computer interaction applied for hand motion capture. When users wear the gloves and perform some hand gestures or sign language, the data gloves can grasp the critical information of the hand gesture, such as position, orientation, and configuration, and transfer them to the computer. Then, the finger and hand motion is accurately transformed into real-time virtual information. A feed-forward neural network for gesture recognition can train through adopting the collected data. The sensors of data gloves play a vital role in acquiring gesture-related information. Without sensors in data gloves, the hand motions will not be interpreted into digital data and transmitted to the host computer for further analysis. The performance of sensors may affect the degree of precision of the selected information, which directly influences hand gesture recognition accuracy and completeness. The recognition workflow of data glove is indicated by the following procedures. Initially, users wear the data gloves and display different gestures in the data acquisition phase. The embedded sensors can sense the flexing point of each joint of fingers and deliver the collected data to the microcontroller to capture gestures. Those input signals will then be processed and become proper signals for feature extraction. After extracting the data, the processed data are transmitted to a gesture recognition and hand tracking system for training the algorithm, getting the recognition rate, or others. Lastly, the hand gestures are realized and translated into corresponding meanings. There are pros and cons of wearable hand devices. The data gloves can measure the parameters of the hand and finger directly. They are easy to use for acquiring helpful information with fast processing speed. However, using the data gloves for hand gesture recognition may obtain several limitations. The data gloves are time consuming with regard to dynamic gesture recognition. The recognition accuracy is highly dependent on the data collection process, which cannot obtain a stable recognition performance. If the data collection procedure is conducted well, the recognition accuracy will be higher and vice versa. Furthermore, this approach suffers from slow enforcement, which may not be appropriate for real-time interactions. In addition, data gloves are immersive devices that may result in an inconvenient operation and be less comfortable worn by the users. Data gloves are unsuitable for long-distance control and MR-based applications [52; 53; 54].
## 3 The Proposed Method
### Joint Method Based on Hough Transform of RSPWVD  
From the above analysis, not only WVD but also RSPWVD can be combined with Hough transform. However, WVD suffers from severe cross-term interference. As a result, it will affect the result of the Hough transform. Therefore, the Hough transform of RSPWVD is proposed [23]. The general expression of the Hough transform of the time-frequency transform can be written as  
\[TFH_{X}(f_{0},k)=\int_{-\infty}^{\infty}TF_{X}(t,f_{0}+kt)dt \tag{18}\]  
where \(TFH_{X}(f_{0},k)\) is the TF distribution of the analytical signal \(X_{a}(t)\). From Equation (10), the chirp interference in the TF plane is written as polar coordinates as well as Cartesian coordinates. Therefore, Equation (11) can be written as  
\[TFH_{X}(\rho,\theta)=\int_{-\infty}^{\infty}TF_{X}(t,f(\rho,\theta))dt \tag{19}\]  
where \(f(\rho,\theta)\) is the instantaneous frequency of the chirp interference in polar coordinates. Equation (18) as well as Equation (19) depicts that the line of the TF plane becomes a peak at point \((\rho,\theta)\) of the parameter plane by Hough transform. Conversely, a peak in the parameter plane represents a line in the TF plane and can be used to estimate the parameter of the line. As a result, this can be used to detect and identify chirp interferences. The joint method based on the Hough transform of RSPWVD is displayed as Figure 3 and is based on the following steps.  
map \(X_{a}(t)\) onto the TF plane by computing its RSPWVD  
\[X_{a}(t)\leftrightarrow RSPWVD_{X}(t,f) \tag{20}\]  
2. separate the data of \(RSPWVD_{X}(t,f)\) into two sets and map the set above the primary threshold onto the Hough transform.  
\[RSPWVD_{X}(t,f)\leftrightarrow TFH_{X}(\rho,\theta) \tag{21}\]  
3. search the peaks of \(TFH_{X}(\rho,\theta)\); if a peak exceeds the secondary threshold, record its point value \((\rho,\theta)\)  
4. estimate the parameter \((f_{0},k)\) by the point value \((\rho,\theta)\)  
\[TFH_{X}(\rho,\theta)\leftrightarrow TFH_{X}(f_{0},k) \tag{22}\]
## 4 Results and Discussion  
In this section, the performance of the proposed BPNN-SP-PID method is validated through simulation and experiments. First, according to the mathematical model deduced in Section 2, a simulation block diagram of the system was built in MATLAB Simulink. Three types of controllers (PID, BPNN-PID and BPNN-SP-PID) were tested and compared. Then, an experimental prototype was built, and the force response curves under step input and sinusoid input were collected. Finally, a comprehensive discussion was made based on the results of the simulation and experiment.
###### Abstract  
Landslide sources and runout features of typical natural terrain landslides can be observed from a geotechnical perspective. Landslide sources are the major area of occurrences, whereas runout signatures reveal the subsequent phenomena caused by unstable gravity. Remotely sensed landslide detection generally includes runout areas, unless these results have been excluded manually through detailed comparison with stereo aerial photos and other auxiliary data. Areas detected using remotely sensed landslide detection can be referred to as \"landslide-affected\" areas. The runout areas should be separated from landslide-affected areas when upgrading landslide detections into a landslide inventory to avoid unreliable results caused by impure samples. A supervised data mining procedure was developed to separate landslide sources and runout areas based on four topographic attributes derived from a 10-m digital elevation model with a random forest algorithm and cost-sensitive analysis. This approach was compared with commonly used methods, namely support vector machine (SVM) and logistic regression (LR). The Typhoon Morakot event in the Laonong River watershed, southern Taiwan, was modeled. The developed models constructed using the limited training data sets could separate landslide source and runout signatures verified using the polygon and area constraint-based datasets. Furthermore, the performance of developed models outperformed SVM and LR algorithms, achieving over 80% overall accuracy, area under the curve of the receiver operating characteristic, user's accuracy, and producer's accuracy in most cases. The agreement of quantitative evaluations between the area sizes of inventory polygons for training and the predicted targets was also observed when applying the supervised modeling strategy.  
Keywords:data mining; landslide detection; landslide inventory; Typhoon Morakot +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Hyperspectral Data Acquisition and Processing
#### 2.2.2 Winter Wheat Canopy Imaging Hyperspectral Image Data  
The UHD185 airborne imaging spectrometer, developed and manufactured by Cubert, Ulm, Germany, was used to obtain hyperspectral images. The spectrometer can capture 125 spectral bands within the range of 450-950 nm in just 1/1000 s. It has a sampling interval of 4 nm and a spectral resolution of 8 nm. Additionally, it can simultaneously capture a hyperspectral image of 50 \(\times\) 50 pixels and a grayscale image of 1000 \(\times\) 1000 pixels using the gaze imaging principle.  
The UHD185 hyperspectral imaging system utilizes the VH-2 octocopter UAV, manufactured by Northern Skyway Airlines. The flight altitude was adjusted to 100 m, ensuring an 80% overlap in the direction of travel and a 60% overlap on the sides. The spectrometer lens was chosen with a focal length of 25 mm, resulting in a field of view of around 13\({}^{\circ}\). The obtained image had a ground resolution of about 32 cm, while the grayscale image had a ground resolution of about 1.6 cm.  
The hyperspectral data were exported by integrating the image data with the cube data using the Cube-Pilot software [[https://mirragin.com.au/member-profile/135/](https://mirragin.com.au/member-profile/135/)]([https://mirragin.com.au/member-profile/135/](https://mirragin.com.au/member-profile/135/)) (CubePliot PTY Ltd., Breakwater, Australia) that comes with the UHD185 camera, and then the images were combined using Agisoft PhotoScan software (1.4.5 version, Agisoft LLC, Saint Petersburg, Russia). The region of interest (ROI) for each plot was created using ENVI 5.1 software. The average reflectance spectral value within the ROI was then retrieved as the spectral reflectance of the wheat canopy in the plot using the IDL development environment. The hyperspectral image processing process is shown in Figure 2.
## 5 Conclusions  
An experimental investigation aimed at investigating the relationship between wet and dry dynamic modulus of elasticity MoE\({}_{\text{dyn}}\) in Sardinian maritime pine has been carried. MoE\({}_{\text{dyn}}\) of Sardinian maritime pine boards both in the wet and dry conditions has been determined from measurements of the velocity of sonic waves propagating from one end of the boards to the other. The correlations between the parameters involved have been studied.  
The following conclusions can be drawn.  
* In the passage from the wet to the dry state, mass and density undergo an average reduction of 23%, whereas sonic velocity and MoE\({}_{\text{dyn}}\) show an increase of about 18% and 8%, respectively.
* The inverse dependence of MoE\({}_{\text{dyn}}\) with respect to density and sonic velocity and the greater weight of velocity in the variation of MoE\({}_{\text{dyn}}\) from wet to dry state are highlighted.
* The linear regression analysis for predicting MoE\({}_{\text{dry}}\) from MoE\({}_{\text{wet}}\) points out the good level of correlation between the two parameters, being the determination coefficient R\({}^{2}\) about 0.8, and allows to implement a relationship to estimate MoE\({}_{\text{dyn}}\) in the dry condition starting from MoE\({}_{\text{dyn}}\) measured in wet condition.
* A two-variable regression model, assuming \(\rho_{\text{wet}}\) and V\({}_{\text{wet}}\) as predictive parameters and MoE\({}_{\text{dry}}\) as the predicted feature, gives a determination coefficient R\({}^{2}\) of about 0.76, and allows to implement a straightforward relationship to estimate MoE\({}_{\text{dyn}}\) in the dry condition starting from density and propagation velocity measured directly on wet boards, bypassing MoE\({}_{\text{dyn}}\) computing in the wet condition.  
Further research will exploit the relationship between the dynamic MoE\({}_{\text{wet}}\) derived from sonic velocity measurements on wet boards and static strength, stiffness, and material defects, in order to verify the feasibility of Sardinian maritime pine grading since in the wet condition.  
This research was funded by Sardegna Ricerche--POR Sardegna FESR 2014/2020--project Cluster Top-Down \"PLES--Local Products for Sustainable Building. Development of Eco-Sustainable Building Solutions for Energy Efficient Walls and Slabs\".  
Not applicable.  
The data presented in this study are available upon request to the author.  
The author declares no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.