esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The most inner loop is the fastest dynamics corresponding to the angular rate states \(p\), \(q\), and \(r\), which are controlled by three inputs: aileron, elevator, and rudder deflections. The consequent outer states \(\alpha\), \(\beta\), and \(\mu\) are slower than the previous loop, which are controlled by angular rate states \(p\), \(q\), and \(r\). The designed control law guarantees asymptotic stability of the error between the desired states and the current one. The premise of this approach is that there is a significant execution time between the inner fast states and the outer slow states in the open-loop plant. 2. The most critical task for the flight control system is to maintain flight stability under uncertainties and perturbed conditions. In fixed-wing UAV system, altitude and attitude angles \(\phi\), \(\theta\), and \(\psi\) are playing a vital role for flight stabilization. However, NLDI is a model-dependent flight controller, which means it is prone to any small system variations. In this paper, a new modification on NLDI incorporates all uncertainties that increases the system robustness against the model mismatch. 3. The study presented in this research is to compare and analyze two common paths following algorithms for UAV. The concept of path following based on carrot chasing is to track a virtual target point sliding along the line of sight between two consequent waypoints. After each time updating, the UAV gets closer to the path and asymptotically follows the path. On the other hand, the concept of Pure pursuit and line-of-sight (PLOS) path following algorithm vanishes the cross-track error distance using LOS control law, in addition to eliminating the LOS angle error using pure pursuit guidance law. The paper is structured as follows. Section 2 presents the path following formulation. The UAV is directed to its heading by altering its attitudes. Section 3 discusses the derivation of the control law using NLDI. The modified NLDI (MNLDI) control law is derived in Section 4. Simulation results are presented in Section 5. Finally, the conclusions of this paper are given in Section 6.
###### Abstract  
Limited power point tracking (LPPT) is emerging as a new technology for power management controllers for small-scale wind turbines (SSWTs) thanks to its advantages in terms of operation flexibility, economy and system security. LPPT operates in such a way that power requested by the user can be extracted from the wind turbine while respecting constraints. However, operating in LPPT mode still requires a deep understanding to obtain a compromise between minimizing power oscillations and transient response. For that, three LPPT power control strategies for an SSWT intended to be integrated in a direct current (DC) urban microgrid are investigated. These methods concern perturb and observe (P&O) with fixed step size, P&O based on [PERSON]'s method and P&O based on the fuzzy logic (FL) technique. The experimental results highlight that all methods function correctly and reach the limited power point (LPP). The FL method improves dynamic performances with more steady oscillations around LPP compared to fixed step size and [PERSON]'s methods. The sudden variation of wind velocity and power lead us to conclude that the FL method ensures a good balance between reducing oscillation of wind turbine (WT) output power around the operating point and convergence of rising time toward LPP.  
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Footnote †: journal: Applied Sciences  
_Article_  
Limited Power Point Tracking for a Small-Scale Wind Turbine Intended to Be Integrated in a DC Microgrid
## 4 Model Optimization Strategies and Result Analysis
### Result Analysis  
#### 4.2.1 Impact of Transfer Learning on the Model  
To explore the impact of transfer learning on model training, several comparison experiments were designed under the same learning rate and attention mechanism. Specifically, Experiments 1, 2, 3, and 4 were compared with Experiments 5, 6, 7, and 8. The comparative results of Experiment 1 and 5 and Experiment 4 and 8 are shown in Figure 8. As shown, the accuracy rates of Experiment 1 and 5 are 75.87% and 90.27%, respectively, and the F1 scores are 0.7581 and 0.8535. The accuracy rates of Experiment 4 and 8 are 87.00% and 94.67%, respectively, with F1 scores of 0.8536 and 0.9438. The accuracy and F1 scores of models using transfer learning techniques both improved. This indicates that the following: 1 fine-tuning all layers performs better; 2 compared to fine-tuning only the last layer, models that fine-tune all layers achieve a significant increase in accuracy. This suggests that the dataset used in this study differs significantly from the ImageNet dataset in terms of feature characteristics. Fine-tuning all layers can integrate feature learning, share knowledge, and provide a larger network capacity, adapting better to the target task's requirements.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameters** & \begin{tabular}{c} **Transfer** \\ **Learning** \\ \end{tabular} & \
\begin{tabular}{c} **Gradient** \\ **Learning Rate** \\ \end{tabular} &
\begin{tabular}{c} **Attention** \\ **Mechanism** \\ \end{tabular} & **Accuracy** & **Loss Value** & **F1 Score** \\ \hline Experiment 1 & \(\times\) & \(\times\) & \(\times\) & 75.87\% & 2.3360 & 0.7581 \\ Experiment 2 & \(\times\) & \(\times\) & \(\surd\) & 79.87\% & 2.2569 & 0.7894 \\ Experiment 3 & \(\times\) & \(\surd\) & \(\times\) & 82.33\% & 0.9301 & 0.8110 \\ Experiment 4 & \(\times\) & \(\surd\) & \(\surd\) & 87.00\% & 0.8766 & 0.8536 \\ Experiment 5 & \(\surd\) & \(\times\) & \(\times\) & 90.27\% & 0.7059 & 0.9015 \\ Experiment 6 & \(\surd\) & \(\times\) & \(\surd\) & 91.20\% & 0.9521 & 0.9087 \\ Experiment 7 & \(\surd\) & \(\surd\) & \(\times\) & 93.87\% & 0.9274 & 0.9383 \\ Experiment 8 & \(\surd\) & \(\surd\) & \(\surd\) & 94.67\% & 0.8794 & 0.9438 \\ \hline \hline \end{tabular}
\end{table}
Table 8: DenseNet-121 network model training results.
## 4 Conclusions and Future Work  
The combination of FT-NIR spectroscopy and discriminant analysis was successfully utilized to classify two cultivars of sweet corn seeds. Two cultivars were correctly classified at an accuracy of 99.59%, based on the full-range wavelengths. This proved that the FT-NIR spectroscopy collected from a single-kernel of sweet corn seed could be used to classify cultivars, even though these cultivars had similar internal composition contents resulting from the same breeding parent. The GA method selected 126 feature wavelengths, and the models built on the feature wavelengths data could also achieve a high accuracy of 99.19%. This indicated that selecting feature wavelengths could be an effective way to achieve simpler models than models based on full-range wavelengths. This procedure could be more valuable and necessary before developing a spectral system, which could increase the speed of acquiring a spectrum of seed samples and improve applicability for industrial-scale detection purposes. The proper classification models which were built in this research could be integrated into the control system of an on-line seed sorting machine, which can be dedicated to improving the seed purity efficiently for the seed industry.  
Future research may focus on exploring the feasibilities of discriminating more species of sweet corn seeds to classify the seed cultivars more reliably and efficiently. Furthermore, the planting environment factors, including climate, soil, and atmosphere, can also affect the phenotypic traits of cultivars; therefore, future studies should also consider these factors when selecting samples.  
[PERSON] designed the entire core architecture. [PERSON] analyzed the data and wrote the paper. [PERSON] reviewed and edited the manuscript. [PERSON] performed the experiments and acquired the data. [PERSON] analyzed the data. [PERSON] supervised the research. All authors read and approved the manuscript.  
This research was supported, in part, by the Sub-task of National Key Research and Development Plan of China (Project No. 2018 YFD0701002), (Basic Research and Application Research) Major Projects of Guangdong Province (Project No. 2016 KZDXM028), the Sub-task of National Science and Technology Support Plan of China (Project No. 2015 BAD18B0301), the Science and Technology Program of Guangdong Province (Project No. 2017B020206005), the Science and Technology Program of Guangzhou (Project No. 201704020067), and the South China Agricultural University Doctoral Students Overseas Joint Education Programs (Project No. 2018 LHPY023).  
We are thankful to the Guangdong South China Agricultural University Seed Industry Company Limited for providing seed sample materials. The authors also thank the anonymous reviewers for their critical comments and suggestions to improve the manuscript.  
The authors declare that there are no conflicts of interest regarding the publication of this paper.
## 2 Architecture and Protocol
### Algorithm  
The VRCL in a VR AP could perform DL VR video frame rate control and UL VR sensing frame rate control, depending on its wireless connection status. Figure 5 shows how the VRCL controls its DL VR video frame rate. The manufacturer of a VR system could set its packet loss rate thresholds and corresponding VR video frame rate. A video refresh rate of 90 Hz is usually used in current-generation VR systems, but next-generation VR systems may support a refresh rate of at least 120 Hz. This means that \"refresh_rate_1\" in Figure 5 needs to be set to its native refresh rate of the VR display. Refresh rate parameters with larger index numbers should be set to a largervalue than refresh rate parameters with smaller index numbers. For \"DL_PLR_threshold\" parameters, as listed in Figure 5, the same rules should be applied, and a parameter with a larger index needs to be set to a larger value. The proposed algorithm in Figure 5 aims to solve the connection problem of wireless multi-user VR systems, by controlling the required channel throughput. If the channel condition is worse than \"DL_PLR_threshold_n\" (see Figure 5), the VR AP should perform a recovery procedure. The recovery procedure can be modified by the manufacturer, but the use of alternating channels and 5-GHz off-loading is at least recommended. Figure 6 shows an algorithm that controls the trigger frame transmission rate. A high value for \"Trigger_rate_VR\" (see Figure 6) indicates a small trigger frame interval, based on the PLR history of the UL sensing data frame. Because associated VR devices never perform EDCA procedures, they are disabled by the VR AP, and only overlapped BSS (OBSS) stations could cause channel collisions and channel interference. Similar to DL VR video frame rate control, the VRCL controls the frame rate and performs recovery procedure in poor channel conditions. In this paper, channel alternation and bandwidth modifications are recommended for the recovery procedures.  
The VRCL in a VR headset could receive the DL VR video frame rate and UL VR sensing frame rate information from the VR AP. Based on each set of information, the VR application could generate an interpolation frame that is utilized during the DL VR video frame interval. Each interpolation frame should utilize motion-sensing information, even though the information was not delivered to the VR AP. From the motion-sensing information, the VR application layer should generate a motion vector, and the reverse vector of the generated motion vector should be applied to the interpolation frame.  
Figure 5: Proposed algorithm to adjust downlink (DL) VR video frame rate, and examine its feasibility of VR capacity for a given user profile.
# Monitoring System Enhancing the Potential of Urban Bekeeping
## 3 Results
### Sensors
#### 3.1.3 Percentage of Outliers in the Measurement Data  
The measurement data were examined for outliers. It is known that the identification of outliers is crucial for building a reliable statistical model of the data and obtaining a valid statistical characterization of the measured object. Based on outliers, abnormal conditions can be examined regarding the circumstances and frequency of their occurrence.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Multi-Sensor Device Location**} & \multicolumn{3}{c}{**Daily Data Collection Efficiency [\%]**} \\ \cline{2-4}  & **5 th Percentile** & **50 th Percentile** & **95 th Percentile** \\ \hline Hive 1 & 99.53 & 99.99 & 100.00 \\ \hline Hive 2 & 99.81 & 100.00 & 100.00 \\ \hline Hive 3 & 99.80 & 99.99 & 100.00 \\ \hline Apiary & 99.84 & 99.99 & 100.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Daily data collection efficiency for the individual multi-sensor devices of the monitoring system.  
Figure 6: The results of beehive air (black) and apiary air monitoring (red) using multi-sensor devices. TVOC(1) and TVOC(2) refer to measurements performed using SGP41 and PID AH2, respectively.  
In general, the statistical distribution of the measurement data may be different from normal. We chose the criterion for outlier detection, which is not sensitive to data distribution. Based on this criterion, an outlier is the data scoring more than 1.5 interquartile range above the upper quartile or below the lower quartile. Figure 7 presents the percentage of outliers for the measurement data collected by our monitoring system. As shown, for a particular air parameter, a similar percentage of outliers was noted across all measurement points. Different parameters even measured at the same location had a different percentage of outliers. It may be concluded that the percentage of outliers depends on the measured parameter. In other words, a particular feature of the measured object, represented by this parameter is likely to have a contribution of abnormal states as calculated, or the sensor of a particular quantity is likely to give that contribution of abnormal readouts. In general, the percentages of outliers in our monitoring system were higher than expected in the case of normally distributed data, which is 0.03%. Less than 2% of outliers were found in the case of relative humidity, CO\({}_{2}\) concentration, and temperature. A higher percentage of outliers, yet still below 6%, was associated with the measurements of TVOCs (both using semiconductor gas sensors and PID) and particulate matter. The measurement of NOx concentration was featured by a very high number of outliers 6-18%. As mentioned, for building the statistical model of the data outliers shall be rejected, if no explanation is found for their existence. Based on our analysis, the associated data loss would be substantial only in the case of NOx.
# EDM of D2 Steel: Performance Comparison of EDM Die Sinking Electrode Designs
## 2 Materials and Methods
### Machining Responses and Measurements  
Four important machining responses are taken into consideration to evaluate the machining performance of different electrode designs. These responses are machining time (MT), hole taper angle (\(\upomega\)), radial overcut (ROC), and surface roughness (SR) of the inner walls of the machined holes. Each of these performance measures against each of the 10 improved designed electrodes were compared with the performance of conventional tool design (cylindrical tool). Machining time was measured with the help of a stopwatch.  
The measurements of the hole dimensions were performed with coordinate measurement machining (CMM) having a least count of 1 \(\upmu\)m. Hole diameters at the entry and exit planes were measured by six-point measurements and average diameters were recorded. Hole taper angle was calculated by the use of entry and exit diameters. Since during EDM, the machined feature is always larger than the tool size due to erosion and crater formation, radial overcut (ROC) is taken as one of the response measures in this work. It is calculated from the diameter of the machined hole and diameter of the tool electrode [32]. The difference of these two diameters is termed as radial overcut. Tool diameter was measured for three times through screw gauge and average value was taken for the ROC calculations. The roughness of the machined surfaces was measured by the Surtronic surface roughness meter using 3 mm evaluation length. Three readings for surface roughness were taken at different regions and average values are reported. All the roughness values are presented in terms of R\({}_{\upomega}\).
# In Silico Investigation of the RBC Velocity Fluctuations in Ex Vivo Capillaries
## 2 Physical and Mathematical Description of the Problem  
In a healthy person, blood consists of 55% plasma. We can reasonably assume that plasma behaves similarly to water, as only 8% of its composition comprises microscaleproteins and solid particles, with the remainder being water [19]. The remaining 45% of the blood is occupied by RBCs, with less than 1% being other blood cells.  
RBCs are biconcave, cytoplasm-filled blood cells that are primarily responsible for the transport of oxygen and nutrients to tissues. Although the cytoplasm viscosity is five times greater than that of plasma, for computational simplification we assume that it has the same viscosity as plasma.  
The ex vivo capillary segments used in this study are third-order capillaries, meaning that they are located after the third bifurcation following the arteriole, as illustrated in Figure 1a. These specific capillary geometries were sourced from an external work (see the Acknowledgment Section), ensuring that no animals were sacrificed exclusively for this study. Although the exact method to obtain the STL files is beyond the scope of the current study, the following explanation is quoted from the experimentalist regarding geometry preparation as it was asked by the reviewer: \"Immediately prior to animal termination, retinas were extracted and fixed with methanol, and capillaries were perfused with gelatin, with no other tissue processing performed. This approach ensured that the three-dimensional models, acquired with confocal microscopy, are representative of their actual in vivo conditions, a comparison previously validated [20]\". The average hydraulic diameter of these capillaries and their deviation are shown in Figure 1b. These measured values align with prior ex vivo and in vivo measurements from similarly ordered capillaries in the mouse retina [21; 22].  
To facilitate the comparison, equivalent straight tubes were generated using the average hydraulic diameter of the corresponding capillaries. Since our focus is solely on the transport of RBCs within single channels, both plasma and RBCs enter the channels from an inlet and exit from an outlet. The flow rates for the different capillaries were determined on the basis of their average hydraulic diameter, while the flow rates of the equivalent tubes were matched to their corresponding capillaries. Throughout these simulations, the flow is consistently assumed to be isothermal.
# A Novel Computational Imaging Algorithm for Electrical Capacitance Tomography
## 4 Regularized Extreme Learning Machine
### Regularized Extreme Learning Machine
As a result of submitting Equations (11) and (12) into Expression (10), a new objective function can be obtained:  
\[\min_{V}\left\{\left\|HV-Y\right\|_{1}+\lambda_{1}\left\|V\right\|_{1}+\lambda _{2}\binom{\left\|D_{xx}V\right\|}{+\left\|D_{xy}V\right\|+\left\|D_{yx}V \right\|+\left\|D_{yy}V\right\|}\right\} \tag{13}\]Suppose that \(\left\|WV\right\|_{1}=\left\|D_{xx}V\right\|+\left\|D_{xy}V\right\|+\left\|D_{yx}V \right\|+\left\|D_{yy}V\right\|\), then Expression (13) can be rewritten as:  
\[\min_{V}\{\left\|HV-Y\right\|_{1}+\lambda_{1}\left\|V\right\|_{1}+\lambda_{2} \left\|WV\right\|_{1}\} \tag{14}\]  
where \(W=\left[D_{xx},D_{xy},D_{yx},D_{yy}\right]^{T}\). To solve Expression (14), the ISB algorithm is preferred. Expression (14) can be converted to the following form [23]:  
\[\left\{ \begin{matrix}\min_{V}\{\left\|d_{1}\right\|_{1}+\lambda_{1}\left\|V \right\|_{1}+\lambda_{2}\left\|d_{2}\right\|_{1}\}\\ s.\ t.\ d_{1}=HV-Y,d_{2}=WV\end{matrix} \right. \tag{15}\]  
According to the ISB algorithm theory, Expression (15) can be rewritten as follows:  
\[\left(V_{k+1},d_{1,k+1},d_{2,k+1}\right)=\min_{V,d_{1},d_{2}}\left\{\begin{matrix} \left\|d_{1}\right\|_{1}+\lambda_{1}\left\|V\right\|_{1}+\frac{\lambda_{3}} {2}\left\|d_{1}-HV+Y-b_{1,
# Improved MUSIC Method against Range Dimension Deceptive Jamming Based on FDA-MIMO
## 2 The FDA-MIMO Signal Model
In Equation (12), \(\otimes\) is the Kronecker product, \(\mathbf{a}(r,\theta)\) and \(\mathbf{b}(\theta)\) are the transmitting and receiving steering vectors, respectively, and we have  
\[\mathbf{a}(r,\theta)= \begin{bmatrix}1\\ e^{j2\pi[-\Delta f_{1}2r/c+f_{0}d_{T}\sin\theta/c]}\\ \vdots\\ e^{j2\pi[-\Delta f_{M-1}2r/c+f_{0}(M-1)d_{T}\sin\theta/c]}\end{bmatrix}  \tag{13}\]  
\[\mathbf{b}(\theta)= \begin{bmatrix}1\\ e^{j2\pi f_{0}d_{R}\sin\theta/c}\\ \vdots\\ e^{j2\pi f_{0}(N-1)d_{R}\sin\theta/c}\end{bmatrix}  \tag{14}\]  
Its transmit-receive antenna pattern can be written as  
\[B=\left|\mathbf{w}_{D}{}^{\mathrm{H}}\cdot\mathbf{S}\right|^{2}=\left|\sum_{n= 0}^{N-1}e^{j2\pi f_{0}\frac{n\theta_{R}(\sin\theta-\sin\theta_{R})}{c}}\right| ^{2}\times\left|\sum_{m=0}^{M-1}e^{j2\pi[-\Delta f_{m}\frac{2(r-r_{0})}{c}+f_ {0}\frac{n\pi f_{T}(\sin\theta-\sin\theta_{R})}{c}]}\right|^{2} \tag{15}\]  
At this time, the transmit-receive antenna pattern can be decomposed into the transmit antenna pattern and the receive antenna pattern, and the FDA-MIMO radar scheme using multi-matching filters can generate an antenna pattern related to the range dimension.