esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Traditional Power Losses Model  
The traditional and common method is that the on-state current of tubes is multiplied by forward voltage drop based on every switching period, and then these one-switching period losses are multiplied by the switching frequency for the total conduction losses. However, only the phase currents can be measured by the current sensors. Therefore, it is necessary to investigate the relationship between the on-state current of tubes and the phase currents.  
Figure 1 shows the topological structure of three-phase voltage source inverter. Supposing that the PWM period is T and the duty cycle is D, the different directions of phase currents correspond to different switching-on and switching-off of tubes. When a phase current is positive (it is defined as the flow towards the motor), the duration of IGBT in the upper tube is DT, while the duration of FWD in the lower tube is (1-D) T; when a phase current is negative, the duration of FWD in the upper tube is DT, while the duration of IGBT in the lower tube is (1-D) T.  
Considering the flow directions of three phase currents, the conduction losses of IGBT and FWD based on the PWM period are listed in Tables 1 and 2. In the tables, \(I_{A}I_{B}\) and \(I_{C}\) represent the  
Figure 1: Topological structure of three-phase voltage source inverter.  
three-phase currents, which can be measured by the current sensor. The corner mark means the current direction.\(V_{\alpha}\) indicates the forward voltage which can be obtained by the output characteristic curves in the datasheet. \(V_{L}\) represents the dc-link voltage.  
The switching losses of IGBTs and FWDs can be calculated as follows:  
\[P_{igbt\_PWM\_sw}=\frac{1}{T_{PWM}}\Big{(}T_{on}+T_{off}\Big{)} \tag{1}\]  
\[P_{fud\_PWM\_sw}=\frac{1}{T_{PWM}}E_{rec} \tag{2}\]  
where \(T_{on}\) and \(T_{off}\) represent the turn-on and turn-off energy losses per pulse, respectively; \(E_{rec}\) is reverse recovery energy of the FWD. These three values can be obtained through the off-line measurement of the oscilloscope in the double-pulse experiment.  
The power losses of IGBTs and FWDs based on the PWM period are expressed as:  
\[P_{igbt\_PWM\_loss}=P_{igbt\_PWM\_sw}+P_{igbt\_PWM\_cond} \tag{3}\]  
\[P_{fud\_PWM\_loss}=P_{fud\_PWM\_sw}+P_{fud\_PWM\_cond} \tag{4}\]  
The power losses of IGBT modules based on PWM period are indicated as:  
\[P_{PWM\_loss}=6\times\Big{(}P_{igbt\_PWM\_loss}+P_{fud\_PWM\_loss}\Big{)} \tag{5}\]
## 2 Non Destructive Sensing
### Remote Sensing
Non-contact sensing methods for estimating surface moisture cheaply and faster over large areas are being studied in order to replace contact sensing field instruments. The heat generated as water in a leaf freezes can be readily imaged [111], while in extreme cases raised temperatures can be used as a measure of these increased respiration rates [112]. In most cases, however, the heat generated by respiration is too small in quantity to have a detectable effect on leaf temperature. Thermal imagingcan be used to study plant water relations, and specifically stomatal conductance, because a major determinant of leaf temperature is the rate of evaporation or transpiration from the leaf [109]. Scientists have evaluated radio detection and ranging (RADAR) [113], nuclear magnetic resonance (NMR), and microwave transmission [114] as tools for LSW estimation. Leaf surface water can be measured by the reflectance of the canopies, which is influenced by the total amount of water (both surface and internal water) in the sensor's field of view [115]. The thickness of the surface water could be detected by comparing the reflectance measurements of plant canopies with and without surface water [116].
## 3 Results
### Technical Analysis
#### 3.2.2 Fuzzy Logic Controller Features  
During the development of an FLC, researchers must consider several critical aspects: the inputs and outputs, the number of linguistic variables, the type of fuzzy numbers used in the fuzzification block, and the defuzzification method. Additionally, the FLC's rule aggregation strategy, or inference strategy, is a vital consideration. Analyzing the papers from the final set, we found the following about the features of the proposed fuzzy controllers by the researchers:  
1. Number of inputs and outputs: The most used configuration is two inputs and one output, with a percentage of more than 75%, of which [76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87] are typical examples. This aspect is seen clearly in Figure 2, where other configurations that appear in more than one article are one input-one output [88], two inputs-three outputs, two inputs-two outputs, and four inputs-two outputs. 2. Number of linguistic variables: The most popular number of linguistic variables used in the fuzzification stage for the inputs and outputs was seven [78; 79; 82; 88], followed by five and three [76; 80; 83]. Considering the differences between the inputs and outputs, the proposed FLCs had the same number of linguistic variables for both inputs and outputs [76], but there were also FLCs characterized by different numbers of linguistic variables [83; 87]. This aspect can be seen in Table 5. Thus, we introduced both numbers of variables in the assessment. The chart from Figure 3 illustrates the number of linguistic variables. 3. Type of fuzzy numbers (membership functions): The authors extensively used a combination of trapezoidal and triangular membership functions [78; 81; 83; 88], as shown in Figure 4. Close behind are the utilization of the triangular function, the Gauss function [79], and singletons [76]. Other functions used in the fuzzification stage, but rarely, are the bell-shaped, trapeze, and sigmoid functions. Like the number of linguistic variables, we found FLCs that use the same type of function to describe the memberships of the inputs and outputs [86], but also FLCs with different membership functions for the input and output quantities [87]. 4. Defuzzification method: The center of gravity [78; 81; 86] is the defuzzification method normally used to obtain crisp outputs, with a percentage of 41 (55 papers). The other defuzzification methods are bisector, weighed average [76; 77], and singleton, which together appear in 28 articles (20%). Unfortunately, the rest of the 51 papers did not contain clear information about the defuzzification method, even though there were data about other features of the FLC that implied the use of certain defuzzification methods. For example, when using the Mamdani style of inference from MATLAB's Fuzzy Logic Toolbox, the predefined defuzzification method is the centroid--the center of gravity--or, when using the Takagi-Sugeno style of inference, the authors can define corresponding functions to determine the crisp outputs.
# A Low-Complexity Volterra Filtered-Error LMS Algorithm with a Kronecker Product Decomposition
## 3 Proposed Algorithm
### Dvuffelms
```
0:\(L\),\(M\),\(P\),\(\mu\),reference signals \(u(n)\), desired signals \(d(n)\), secondary path \(\mathbf{s}(n)\), adjoint virtual secondary path \(\overline{\mathbf{s}}(n)\) Initialization:
1:for\(p=1\); \(p<P+1\); \(p++\)do
2:for\(q=1\); \(q<p\); \(q++\)do
3:\(\mathbf{w}_{p,q}(0)=[2^{1-q},\;0,\;\cdots\;0]^{T}\)
4:endfor
5:\(\mathbf{w}_{p,p}(0)=[0,\;0,\;\cdots\;0]^{T}\)
6:endfor Iteration:
7:for\(n=0\) to \(END\)do
8:for\(p=1\); \(p<P+1\); \(p++\)do
9:\(y_{p}(n)=1\)
10:for\(q=1\); \(q<p+1\); \(q++\)do
11:\(y_{p}(n)=y_{p}(n)(\mathbf{u}^{T}(n)\mathbf{w}_{p,q}(n))\)
12:endfor
13:endfor
14: calculate the output signal \(y(n)=\sum_{p=0}^{p}y_{p}(n)\)
15: calculate the error signal \(e(n)=d(n)+y(n)*\mathbf{s}(n)\)
16: calculate the filtered-error signal \(e_{f}(n)=e(n)*\overline{\mathbf{s}}(n)\)
17:for\(p=1\); \(p<P+1\); \(p++\)do
18:for\(q=1\); \(q<p+1\); \(q++\)do
19: calculate the auxiliary sub-vector \(\overline{\mu}_{p,q}(n)=\overline{y}_{p,q}(n)\mathbf{u}(n)\) and gain \(\overline{\mu}_{p,q}(n-M)\)
20: update \(\mathbf{w}_{p,q}(n+1)=\mathbf{w}_{p,q}(n)-\widetilde{\mu}_{p}\overline{\mu}_{p,q}(n- M)e_{f}(n)\)
21:endfor
22:endfor
23:endfor Output: error signals \(e(n)\), output signals \(y(n)\), and filter coefficients \(\mathbf{w}_{p,q}(n)\)
```  
**Algorithm 1** Pseudo code of the proposed DVMFELMS algorithm.
## 2 System Description
Figure 2: MD-WFS configuration information: (**a**) the sub-aperture arrangement of MD-WFS, with sub-apertures divided into a hexagonal pattern on the pupil plane; (**b**) schematic diagram of Hartmann image, with sub-apertures forming a rectangular FOV; nine guide regions are selected in each sub-image for multiple-direction wavefront sensing. region pair with a sub-aperture pair. Similarly, all guide regions are taken into consideration. There are three sizes of guide region angular intervals in the horizontal direction, respectively [0 9 18] arcseconds, which can yield two different height measurement grids. The angular interval is 0, corresponding to the data from different sub-apertures of the same guide region. This can only be applied to detecting the overall intensity of atmospheric turbulence. For two measurement grids at different heights, we encrypt the measurement grids based on the method proposed by [PERSON] et al. [24]. Finally, we obtain a 12-layer measurement grid, the corresponding heights of [0 1361 2723 4084 5446 6808 8169 9531 10,893 13,616 16,339 19,062] m.  
The calculation of atmospheric coherence length is mainly based on the DIMM method. For the traditional DIMM method, only one guide region with two sub-apertures is needed. This system utilizes the multi-star region and multi-sub-aperture of the MD-WFS to enhance the accuracy of coherence length measurement. The SLODAR method is primarily employed to measure the intensity distribution information of atmospheric optical turbulence. The measurement system calculates a result every second, which represents the atmospheric conditions at the current moment. To enhance the statistical significance of the results, the most recent 100 results were averaged and displayed on the UPC. Figure 3 depicts the UI of the real-time turbulence intensity and distribution measurement system. The UI is attached to the GLAO control software, and in Figure 3, the UI is enlarged and displayed in the center of the image for easy viewing. The left table of the UI shows the measured heights (in m) and their corresponding atmospheric coherence lengths (in cm) at each height. The right graph area displays the total atmospheric coherence length and the normalized intensity of turbulence (distribution).
## 3 New Generation Sensor Web Enablement
### KVP and SOAP Binding Introduced
The purpose of this functionality is to allow clients to repeatedly obtain sensor data without the need to receive responses which largely contain the same data except for a new timestamp and result value. This is in particular useful in scenarios with restricted bandwidth or processing power. The SOS 2.0 specification redesigns and simplifies the _GetResult_ operation which is used to retrieve pure observation results. In particular, the response from the SOS server containing the results is defined in a more precise way. An additional operation is introduced (_GetResultTemplate_) which returns an exact description of structure and encoding of the results, by making use of SWE Common 2.0 (Section 3.2.1). A functionality added by SOS 2.0 is the insertion of pure observation results through new operations (_InsertResultTemplate_ and _InsertResult_). This allows inserting sensor results into an SOS without the need to repeatedly transmit the entire set of observation metadata. Similar to the result retrieval functionality, this is useful if the communication bandwidth of the client, in this case the sensor data producer, is limited and the other observation metadata is rather static. Also, the capabilities model of the SOS 2.0 has been improved to better support the insertion of new data to the SOS. A new section of the capabilities document (called _insertion capabilities_ section) now states the observation type, result type, feature types, and encodings supported by the SOS server for insertion of observation data. Retrieval of Metadata about Available Data  
In SOS 1.0, questions such as \"which sensors generated observations for certain observed properties\", or \"for which time frames do observations for a particular feature of interest exist\" could not be answered without retrieving the actual observation data by invoking a corresponding _GetObservation_ request. The capabilities document, and the contained observation offerings, only provide information on the temporal bounding box of all observations associated with the offering. This is still true for SOS 2.0. In order to support the described use cases, an extension accompanies the SOS 2.0 specification, which defines the _GetDataAvailability_ operation [79]. The operation enables clients to discover the temporal relationship between given procedures, observed properties and features of interest. The operation contains parameters that can be used by clients to indicate for which period of time these relationships are to be discovered and also to generalize the information about the temporal relationships. The latter is especially useful to decrease the operation's response size but also to display this information as it is described in [80]. The operation replaces and amends the functionality of the _GetFeatureOfInterestTime_ operation that was defined by SOS 1.0.
###### Abstract  
Classical fractional order controller tuning techniques usually consider the frequency domain specifications (phase margin, gain crossover frequency, iso-damping) and are based on knowledge of a process model, as well as solving a system of nonlinear equations to determine the controller parameters. In this paper, a novel auto-tuning method is used to tune a fractional order PI controller. The advantages of the proposed auto-tuning method are two-fold: There is no need for a process model, neither to solve the system of nonlinear equations. The tuning is based on defining a forbidden region in the Nyquist plane using the phase margin requirement and determining the parameters of the fractional order controller such that the loop frequency response remains out of the forbidden region. Additionally, the final controller parameters are those that minimize the difference between the slope of the loop frequency response and the slope of the forbidden region border, to ensure the iso-damping property. To validate the proposed method, a case study has been used consisting of a pick and place movement of an UR10 robot. The experimental results, considering two different robot configurations, demonstrate that the designed fractional order PI controller is indeed robust.  
fractional order control; auto-tuning method; robustness; experimental validation; robot
## 5 Numerical Examples  
We present in this section some examples of the performances of the proposed moment-based estimators for \(S\) and \(N\) and signal-to-noise ratio \(\rho\) in terms of squared bias and variance. We report estimated squared biases and variances obtained through Monte-Carlo simulations and we will find that such numerical results confirm the asymptotic performances derived in Section 4. We also compare them to the corresponding CRLBs derived in Section 3 and we make some comments on the property of efficiency of the proposed estimators, i.e., whether they achieve (asymptotically) the corresponding CRLB, and on the property of consistency, i.e., whether the variance can be made to approach zero as the number of observations \(K\rightarrow\infty\)[7; 13].  
For all proposed estimators we have estimated the squared bias (11) and variance (12) by averaging over \(L=2^{13}=8192\) estimates on a varying number of observations \(K\). The mean of each estimator is estimated by means of a standard sample mean estimator; e.g., for \(\hat{S}\)  
\[E[\hat{S}]\approx\hat{\mu}_{S}=\frac{1}{L}\sum_{l=1}^{L}\hat{S}_{l}, \tag{37}\]  
where \(\hat{S}_{l}\) is a single estimate of \(S\) with \(K\) observations. The squared bias is then obtained from (11), where the estimated mean is used in place of the true mean; e.g., \(b^{2}\left(\hat{S}\right)=\left[E[\hat{S}]-S\right]^{2}\). The variance of estimators is estimated by means of the standard sample variance estimator; e.g., for \(\hat{S}\)  
\[\text{var}(\hat{S})\approx\hat{\sigma}_{S}^{2}=\frac{1}{L-1}\sum_{l=1}^{L}( \hat{S}_{l}-\hat{\mu}_{S})^{2}. \tag{38}\]  
Estimation of squared bias and variance of estimators has been performed at different SNRs that, without loss of generality, are obtained by varying the power of the real sinusoid \(S\) while maintaining fixed noise power \(N=1\). The frequency and phase of the deterministic sinusoid were chosen randomly at the beginning of each simulation.
## 6 Discussion
### Using SCM to Estimate Kd for Performance Assessment
The application of supporting specific Kd estimation in the safety assessment calculation framework; the use of SCM simulation to directly estimate Kd can provide approximate values of parameter ranges. The interpretation and demonstration of the measured data indirectly supports the rationality of the experimentally derived Kd. As for the variation and uncertainty of the geochemical conditions, the sensitivity and uncertainty of the Kd values are analyzed, and the range of Kd is calculated. 2. Guiding the development of the experimental analysis method for obtaining Kd; using the chemical characteristics of surface complexation reactions, we can perform a sensitivity analysis of the environmental conditions for Kd. The experimental design was optimized by the Kd screening calculation in order to establish the most critical localization conditions of the Kd experimental measurement. SCM simulation was applied in order to obtain Kd due to the importance of Kd in safety analysis and the influence of external environmental conditions on the Kd values. Due to the uncertainty and possible variability of the geochemical conditions associated with radioactive disposal sites, as well as the complexity of natural and engineering barriers, it may be impossible to obtain the Kd of nuclides by experimental methods for each region and all geochemical environmental conditions. Therefore, according to the experimental conditions of specific sites, more chemical data may be used to approximate, simplify, or generalize the data analysis to approach the site environmental state in order to establish \begin{table}
\begin{tabular}{c c c c c} \hline
**Nuclide\textbackslash{}Soil** & **Sand** & **Loam** & **Clay** & **Organic** \\ \hline Neptunium & 5 & 25 & 55 & 1200 \\ \hline Plutonium & 550 & 1200 & 5100 & 1900 \\ \hline Thorium & 3200 & 3300 & 5800 & 89,000 \\ \hline Nuclide\textbackslash{}Soil & \multicolumn{4}{c}{Bentonite} \\ \hline Neptunium & \multicolumn{4}{c}{28.6–33 (this study)} \\ \hline \end{tabular}
\end{table} 
Table 12: Comparison of the Kd values (cm\({}^{3}\)/g) estimated in the literature and in this study. confidence in the assessment of the radionuclide adsorption with different Kd values under various conditions through safety analysis cases in a larger spatial and temporal range.
# A Novel Drone Design Based on a Reconfigurable Unmanned Aerial Vehicle for Wildfire Management
## 4 Electronics Design
### RC Controller--Telemetry Module [Herelink HD Video Transmission System V2]  
Before configuring the companion computer, in which smart AI algorithms enable the vehicle to autonomously perform various tasks such as image and video processing, object detection, and monitoring, it was essential to conduct numerous tests in manual mode. These tests served to validate the sufficiency of the equipment and the efficiency of the controller. Furthermore, even during autonomous flight, it is mandatory to be able to control the UAV manually in case of an emergency.  
To operate the UAV manually, a radio control (RC) system is required, allowing the vehicle to be controlled from a handheld transmitter. The RC system consists of a ground-based remote-control unit used by the operator to command the vehicle, and a radio module mounted on the vehicle. This radio module serves as a receiver connected to the flight controller unit, which interprets the operator's commands.  
Herelink (Cube Pilot, Breakwater, Australia) emerges as an integrated solution for seamless compatibility with leading autopilot systems such as the Cube Autopilot, Ardupilot, or PX4. Combining the functionalities of a remote controller, ground station, and wireless digital transmission system, Herelink streamlines essential drone operations into a cohesive unit. Operators benefit from a comprehensive suite of capabilities, including RC control, high-definition video transmission, and telemetry data dissemination, reliably communicated over impressive distances of up to 12 km between the ground station and air unit. The Herelink remote controller features customized Solex TX and QGC applications, tailored to optimize user experience and operational efficiency. Both the air unit and ground station feature an integrated eight-core system-on-chip (SOC), laying a robust foundation for the development and implementation of custom applications. Its reliability and performance make it an ideal choice for demanding environments where precise control, data integrity, and uninterrupted communication are paramount.
# Differential Evolution in Waveform Design for Wireless Power Transfer
## 2 Wireless Power Transfer System Model
### Propagation Channels  
A realistic testbed of the propagation channel was fabricated in a laboratory. Since the use of CSI is more practical in a multipath propagation environment, we developed complex channels by placing a rectangular box of highly reflective surfaces and various metal obstacles between the transmitter and the receiver. A set of VNA measurements were conducted to characterize four different propagation channels, based on the four different sites of the receiver antenna, respectively (Figure 2). The obtained data by the VNA measurements were utilized to compute \(\mathbf{b}\) and \(\mathbf{\psi}\) depending on the waveform characteristics.  
Figure 2: Top view (schematic representation) of the fabricated realistic testbed for conducting VNA measurements in a complex multipath propagation channel. Details of the schematic view include the three-dimensional box interposed between the transmitter and the receiver and the highly reflective metal objects placed randomly inside the box (tifoil has adhered to its inner walls). The sites of the receiver antenna that correspond to the propagation channels are also marked (in red color) and numbered.