esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Wavelength Selection  
The wavelength selections for various numbers of selected wavelengths, \(k\), for both the ideal case and the convolved case, are shown in Figure 4. Notice that while the selections for the ideal case include both lobes of the oxygenated hemoglobin extinction coefficient curve, the selections for the convolved case tend to favor the left lobe only. This can be explained by observing the effect that the convolution has on the spectra, as shown in Figure 5. Selected wavelengths for \(k\) = 5, 6, and 7 are given in Table 3.  
Figure 4: _Cont._  
Figure 5 shows examples of simulated spectra for low (<0.10), medium (0.10-0.30), and high (>0.30) SO\({}_{2}\) values for both the ideal and the convolved cases. The spectra in Figure 5a represent skin with low melanin fractions (<0.10) and those in Figure 5b represent skin with high melanin fractions (>0.30). For both melanin fractions, there is a noticeable separation between the curves near the higher wavelength lobe for the ideal case. However, this separation is greatly diminished for the convolved case. Thus, the ability to distinguish between different SO\({}_{2}\) values using wavelengths in this region is similarly diminished. Interestingly, wavelength selection does favor wavelengths near the lower wavelength lobe even though there is little separation between the curves in any of the plots of Figure 5.  
Figure 4: Wavelength selections for various values of \(k\). The left column shows selections for the ideal case and the right column shows selections for the convolved case. (**a,f**) \(k=5\), (**b,g**) \(k=11\), (**c,h**) \(k=20\), (**d,i**) \(k=40\), (**e,j**) \(k=60\). The selections are superimposed on plots of the absorption coefficients for oxygenated (red) and deoxygenated (blue) hemoglobin.  
The selection of wavelengths in this region is likely needed more for slope estimation (third term in Equations (1) and (2)) than for SO\({}_{2}\) estimation (second and fourth terms).
## 2 Methodology
### Block Diagram of a Single-Axis Tracker  
The prototype of the tracker that we have realized consists of an electromechanical structure to support the solar panel and direct it in the direction of the perpendicular radiation of the sun on an axis of two East-West directions.  
The tracking system essentially consists of two parts: The control or command part (the physical quantities and the solar radiation follow the sun and position the turret optimally) and the operative part (elevation). The control or programming units are used to process the position information of the sun to transmit it to the actuators to track the sun, as well as to detect operating anomalies and perform equipment safety operations. It also performs other functions, such as the control of the system's mechanical movements by limiting the rotation angles from (0\({}^{\circ}\) to 180\({}^{\circ}\) on the horizontal) and the panel's orientation at the end of the day towards the starting position of the system.  
The role of the operating or electronic block is to ensure that the solar radiation is always perpendicular to the panel. It, therefore, relies on systems allowing the reception of orders sent by the calculator, their conversion into electrical signals, their transmission to the articulated mechanical system, and the activation of the motor. As shown in the block diagram in Figure 1, when the tracking system steers the solar panels by rotating them in the direction of the sun and maintaining an ideal angle based on two light-dependent resistors (LDR) and a motor, the electrical power per square meter is maximized [20].  
The two LDRs are used in our work to capture the light in East-West directions. Each LDR will supply the controller an analog signal depending on the lighting through the voltage divider \(V_{out}\), given by Equation (1), with an \(R_{1}\) resistance LDR and \(R_{2}\) resistance standard. The controller changes the motor direction [21; 22; 23; 24; 25].  
\[V_{out}=\frac{R_{2}}{R_{1}+R_{2}}*Vcc \tag{1}\]  
The principle is to notice that the light received by the two LDR photoresistors is identical as soon as the sensor is oriented in the axis of the light. Therefore, we measured the light received by each photoresistor by measuring the voltage at their terminals. This measurement is carried out by connecting the ports pin1 to 2 of the card. The program then compares these voltages ordering the servo motor to rotate in the direction of the photoresistor(s) that receives the least light. We used a resistor of 1 k\(\Omega\) to measure the voltage change between the photoresistor and a resistor of 1 k\(\Omega\) (voltage divider) placed on the control unit (Arduino card).  
Our MATLAB modeling requires describing the models of the different design blocks [18]: The model of the D.C. motor (servo motor) with a P.I.D. controller (proportional integral derivative), the model of the chopper to vary the voltage, the model of the radiation quantity falling on the solar panel, the model of the sun and the model of the photovoltaic panel.
## 1 Introduction
### Contributions and Organization  
Compared to existing works about dynamic pricing strategies of EV charging management discussed, EV users' economic and convenience preferences are considered, to determine charging strategies for different users in this paper. It is notable that dealing with the topic from the perspective of the user separates this study from the other studies in the literature. Based on the driving characteristics and charging preferences of users, this paper designs sub-item evaluation indicators, to represent the comprehensive satisfaction of EV users. Furthermore, a dynamic time-of-use pricing strategy for EV charging considering user satisfaction degree, which is implemented in the day-ahead, is proposed to achieve the goal of friendly charging for the micro-grid. Thus, the charging behavior of the EVs will not only achieve the goal of peak load shifting, but also stabilize the load fluctuation, and improve the utilization rate of renewable energy in the micro-grid. The main contributions in this paper are summarized as follows:  
* The travel patterns of users are researched to establish the grid connection scenes of individual EVs, and a criterion for judging whether an EV is in controllable state is proposed to forecast the controllable capacity of large-scale EVs;
* The charging preferences of different types of EV users are studied, and user economic satisfaction degree and electricity convenience satisfaction degree are defined, to establish the comprehensive satisfaction degree model for obtaining different users' charging strategies;
* A dynamic time-of-use pricing strategy for EV charging, taking account of the choice of EV users based on the dispatching requirements of the micro-grid, is raised, which considers the demand response and realizes the effective dispatch of EV charging load based on price signals.  
The remainder of this paper is organized as follows. In Section 2, several problem formulations to analyze the controllability of EVs are established. In Section 3, the calculation model of user satisfaction degree is presented. A pricing strategy for EV charging considering the preferences of EV users is proposed in Section 4. In Section 5, the method is verified to be effective, and the proposed conclusions are drawn in Section 6.
###### Abstract  
This paper deals with the imaging problem for sliding spotlight (SS) and terrain observation by progressive scan (TOPS) modes in bistatic configuration of the geostationary (GEO) transmitter with a low earth orbit satellite (LEO) receiver, named GTLR-BiSAR system. A unified imaging algorithm is proposed to process the GTLR-BiSAR data acquired in SS or TOPS modes. Our main contributions include four aspects. Firstly, the imaging geometry of this novel configuration is described in detail. Furthermore, the GTLR-BiSAR signal expressions were deduced in both time and frequency domains. These signal expressions provide great support for the design of processing the algorithm theoretically. Secondly, we present a unified deramping-based technique according to the special geometry of GTLR-BiSAR to overcome the azimuth spectrum aliasing phenomenon, which typically affects SS and TOPS data. Thirdly, the spatial variance of GTLR-BiSAR data were thoroughly analyzed based on the range-Doppler (RD) geolocation functions. On the basis of a former analysis, we put forward the azimuth variance correction strategy and modified the conventional chirp scaling function to solve the range variance problem. Finally, we completed the derivation of the two-dimensional spectrum after the range chirp scaling. On the basis of spectrum expressions, we compensated for the quadratic and residue phase, and the azimuth compression was completed by SPECAN operation. In addition, we provide a flow diagram to visually exhibit the processing procedures. At the end of this paper, the simulation and real data experiment results are presented to validate the effectiveness of the proposed algorithm.  
bistatic SAR configuration; GEO transmitter; LEO receiver; sliding spotlight (SS); terrain observation by progressive scan (TOPS); deramping operation; SPECAN +
Footnote †: journal: Remote Sensing  
Article  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] Unified Algorithm for the Sliding Spotlight and TOPS Modes Data Processing in Bistatic Configuration of the Geostationary Transmitter with LEO Receivers
###### Abstract  
The purpose of the investigation was to determine the influence of a four-week unilateral (UNI) and bilateral (BIL) resistance training program on peak torque and peak power of the lower limbs in soccer players. Background: We evaluated the effects of a 3:1 step load training program using UNI and BIL forms of exercises on the level of peak torque and peak power of the knee joint extensors and flexors. Methods: The study included 16 division I soccer players having the highest number of matches played in the first round of the season. The motor tests included isokinetic evaluation of peak torque and peak power of the extensors and flexors of the knee joint. Results: The results showed that both types of training sessions were equally effective. Only in terms of power during knee flexion, unilateral training contributed to improvement, whereas bilateral training did not. Conclusions: The use of periodization using a step load progression based on an extended eccentric phase of the movement during the preseason period in combination with UNI training may increase peak torque and peak power of knee flexors and extensors in soccer players.  
periodization; injury prevention; peak torque; stability; velocity-based training Article  
A Comparison of a Step Load Unilateral and Bilateral Resistance Training Program on the Strength and Power of the Lower Limbs in Soccer Players.
# A Scheduling Scheme for Improving the Performance and Security of MU-MIMO Systems
## 2 System Model
### Imperfect Channel Estimation  
The ZF detector requires knowledge of \(\mathbf{H}\), defined in (1), to recover the transmitted symbol vector. In practice, channel gains are estimated by transmitting pilot symbols that are known at the receiver [40]. However, the pilots can be affected by noise, delay, or synchronization problems. Therefore, it is necessary to employ channel estimation techniques in practical systems [41]. One of the most used estimation techniques due to the trade-off between complexity and performance is based on the MMSE criterion [40], that is based on linear operators that minimize the mean squared error (MSE) between the real channel gains and the estimated ones. In particular, the MMSE estimator is an adequate estimator for jointly Gaussian distributed random variables. However, it is often used in non-Gaussian scenarios and performs well for different distributions that are not too different from a Gaussian distribution [42]. In this sense, and considering that the channel gains, i.e., the entries of \(\mathbf{H}\), can be modeled as Gaussian random variables, some works have considered this estimator for MIMO systems [41; 42; 43; 44; 45]. In practice, the MMSE estimation is not perfect and, therefore, there is residual interference within the process. In this case, ref. [43] (Equation (2)) shows that the estimated channel matrix can be written as  
\[\mathbf{\hat{H}}=\sqrt{1-e^{2}}\,\mathbf{H}+e\mathbf{\Omega}, \tag{8}\]  
where \(\mathbf{\Omega}\) is an \(A\times U\) matrix whose elements are complex Gaussian random variables, independent and identically distributed with zero mean and unitary variance [43]. Furthermore, the elements of \(\mathbf{\Omega}\) are independent of the elements of \(\mathbf{H}\). Thus, the parameter \(e\), which assumes values in the interval \([0,1]\), represents the level of error in the channel estimation process. Therefore, \(e=0\) implies perfect channel estimation and \(e=1\) implies that there is a complete error in the channel estimation.  
By the above, from (7) and (8), the vector of samples at the output of the ZF detector can be written as  
\[\mathbf{z}=\mathbf{\hat{H}}^{\dagger}\mathbf{y}=\mathbf{s}+\mathbf{\hat{H}}^ {\dagger}\mathbf{n}. \tag{9}\]
# PLORC: A Pipelined Lossless Reference-Free Compression Architecture for FASTQ Files
## 4 Results and Discussion
### Throughput Rates  
Our PLORC compressor and decompressor achieve a better TPR than Gzip, LZ4, and Zstd on FPGA, enabling the online compression of FASTQ files. Moreover, our decompressor achieves a TPR matching that of the compressor, as shown in Table 9. This means that our compressor and decompressor can work at nearly identical speeds. The development of automated high-speed data processing accelerators calls for fast data recovery [54; 55]. A decompressor with a high throughput rate is helpful to address the issue [56].  
Our compressor and decompressor are fully pipelined, with no dependencies between the input and output of the same submodule. The PLORC compressor and decompressor can process input streams continuously, significantly improving their TPRs. Our architecture requires no data buffering or string matching. Therefore, there is no unpredictable latency. In addition, the latency between the input and output is small considering the large input data scale and the streaming design.  
Gzip, LZ4, and Zstd achieve compression by searching the dictionary for a string that matches the input data. Each search contains an uncertain amount of access to BRAM. Multiple BRAM access before finding a matching string or traversing the dictionary without finding a matching string introduces unpredictable latency, interrupting the input data stream. This interruption forces the following parts of the algorithm to wait.  
Large memory must be allocated for the reference-based designs to store the reference sequence. For example, the reference sequence for the human genome is 17 GB according to [16]. In addition, frequent memory access is also required to find the matched string. The memory access process introduces unpredictable latency, hampering continuous data processing. Moreover, to increase the TPR of the design, a clock domain crossing design between memory devices and FPGA logic is introduced in [16]. The memory devices in REMA work at a faster clock than the FPGA logic. The clock domain crossing design makes the implementation more complex and consumes more hardware resources. Additionally, it prevents the FPGA logic from achieving a better TPR by increasing the clock frequency.
## 3 Results and Discussion
### Central Rotating Mechanism  
The design of the rotation mechanism was mainly by six universal wheels, pulley, and belt, as shown in Figure 12. The supporting weight of this subsystem was \(\sim\)250 kg, and the required torque was calculated by Equation (2). The result showed the required torque was 188 \(N\cdot m\), and the rated torque of the motor was about 2.39 \(N\cdot m\). The reducer reduction ratio was 1:40. Finally, the drive was combined with the belt, and the reduction ratio was 1:120. Then, we found that the final torque was about 286.8 \(N\cdot m\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Item** & **Specification** \\ \hline The length of the upper plate & 0.7 m \\ The drive motor output shaft & 1 m \\ The length of the crank & 0.21 m \\ The length of the linkage & 0.25 m \\ The initial height & 0.3 m \\ Heave stroke & \(\pm\)20 cm \\ Roll angle & \(\pm\)20\({}^{\circ}\) \\ Pitch angle & \(\pm\)20\({}^{\circ}\) \\ Yaw angle & \(\pm\)180\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The multi-axis motion platform specifications.  
Figure 11: 3D design drawing of multi-axis motion platform.
## 4 Experiments
### Implementation Details  
Skeleton data can be obtained from motion capture systems (such as the Kinect v2 camera, etc.) or through pose estimation algorithms. For the NTU 2D and 3D skeleton data that we used, there are some similarities and differences worth noting:* The NTU dataset has a maximum of 300 frames for each video clip. To unify the dimension of input frames, skeleton sequences with fewer than 300 frames were padded with zeros. At most, the skeleton data of two individuals were selected for each action sequence; if there were fewer than two individuals, they were padded with zeros.
* The NTU RGB+3D skeleton data were obtained from the Kinect v2 camera, which can sense the 3D positions of 25 body joints. Therefore, each skeleton sequence contains the coordinates of 25 body joints for 300 frames, with 3 input channels consisting of 2D pixel coordinates (x, y) and depth coordinate z.
* The NTU RGB+2D skeleton data were obtained using the HRNet-w32 [37] pose estimation algorithm, which provides 2D coordinates for 17 body joints. Thus, each skeleton sequence contains the coordinates of 17 body joints for 300 frames. Following reference [11], we set three input channels consisting of 2D pixel coordinates (x, y) and confidence score.
* The different methods of obtaining skeleton data resulted in variations in the number of body joints and their adjacency relationships. When using GCN operations to process skeleton data, it is necessary to customize the adjacency matrix between joints (as shown in Figure 3c). Therefore, when dealing with skeleton data from different sources, it is necessary to reset the adjacency matrix to reflect the current body joint connectivity.  
The experiment was conducted using 2 RTX 2080 Ti (11 GB) GPUs, with an Intel(R) Xeon(R) Platinum 8255C [EMAIL_ADDRESS] GHz CPU. The model was trained for a total of 70 epochs. The starting learning rate was initialized to 0.1, with a warm-up strategy applied during the first 10 epochs [39], and decayed using a cosine schedule after the 10 th epoch. The model training utilized the SGD optimizer with a momentum of 0.9 and a weight decay of 0.0001.
## 4 Discussion
### Velocities  
We can observe that the longitudinal velocity of the DED sample is lower than conventional samples. We can hypothesize that one of the reasons for the difference in velocity between conventional and DED samples could be due to the lack of \(\gamma^{\prime\prime}\) phase in the DED sample. This could also be partly attributed to the wide distribution of carbide phases, which are known to have a lower modulus [49; 50]. As noted in Section 4.1, previous destructive analysis had shown that the DED IN718 sample had a higher volume fraction of Laves phases and a lower volume fraction of \(\gamma^{\prime\prime}\) phases [40]. Previous work on aging of IN718 [45] shows that IN718 samples without the \(\gamma^{\prime\prime}\) phase exhibited lower longitudinal and shear velocities compared to the sample with the \(\gamma^{\prime\prime}\) phase. The hypothesis that the lack of \(\gamma^{\prime\prime}\) in the DED-IN718 sample will decrease the velocity agrees well with the present observations for the longitudinal velocities. Metallography of the present samples [40] also showed a large distribution of carbide phases, which once again agrees with the hypothesis of reduction in velocity for DED samples.  
The shear velocity in the transverse direction D1-2 is also lower than the conventional sample, which once again is in good agreement with previous observations [45]. However, the shear velocities of the DED sample are higher than the conventional sample along the build direction (D1-3 and D2-3), which is contrary to the hypothesis. The velocity shows some degree of anisotropy, which points towards texture. However, to fully understand it, an electron backscatter diffraction (EBSD) analysis is needed, which is beyond this article. Other articles have carried out similar analyses for IN625 but using powder bed fusion rather than DED [51].
# M-Emu: A Platform for Multicast Emulation
## 3 Implementation
### Service-Forwarding Architecture
#### 3.1.1 Control Plane  
The control plane is divided into a basic function module and algorithm module. In the basic function module, the topology analysis module analyzes the centricity, connectivity and clustering attributes of a topology. The data statistics module calculates the load of all nodes in the network in a certain period and records it in a text file in a certain format. The source and receiver generation module provides an interface that can determine the request number that the Designated Router (DR) receives from sources and receivers. Users can flexibly modify the distribution of the request number according to the experimental requirements.  
The algorithm module of the control plane mainly provides the interface of functional node deployment and some multicast algorithm interfaces. The functional node deployment interface can determine the number and location of some functional nodes, such as the RP and name resolution system nodes (NRS). The GP selection module, RP selection module and other algorithm interfaces can use global information to run in a centralized paradigm.
## 3 Results and Discussion
### Flow Stress Behavior Modeling Experiments
#### 3.3.3 Verification of the Proposed Models  
The prediction quality of the proposed models was quantified by the correlation coefficient (R), the average absolute relative error (AARE), and the root means square error (RMSE). Figure 11 displays the correlation between the experimental and the modeled flow stress via both ACE (Figure 11a) and ANN (Figure 11b) models. The values of the performance metrics R, AARE (%), and RMSE for both models are presented in Table 4. Both ACE and ANN models demonstrated strong workability for the fitting of the flow stress of the investigated alloy; however, the ANN exhibited smaller errors than the ACE.
# A Single RF Emitter-Based Indoor Navigation Method for Autonomous Service Robots
## 4 RF-Based Target Localisation
### Enhancing Angle and Distance Estimation
The number of particles to use is based on a trade-off between accuracy and computational load, with O(\(N\)) linear complexity for \(N\) particles. An increase in particles reduces error and provides better spread. Better spread reduces the chance of missing the true point and converging on an incorrect local maximum. In our case, we used 1000 particles. The target point provided by the particle filter and used in path planning is a weighted sum of all the particles, as shown in Equations (10) and (11). When comparing the error in the instantaneous position value calculated directly from the antennae, and the error in the filtered position estimate, we found that filtering reduced the average error by up to 50%. An example is illustrated in Figure 8, where the root mean squared error of the position has been plotted over the course of a benchmark test.