esa-sceva/satcom-chunk-collection huggingface dataset:
# Parallel Extreme Learning Machines Based on Frequency Multiplexing
## 3 Principle of Operation
### Readout  
The readout procedure consists in measuring the optical intensity of each comb line, which corresponds to the values of hidden neuron states. The programmable filter \(\text{SF}_{2}\) is set in such a way as to implement notch filters, which select only one comb line per time. Since \(\text{SF}_{2}\) provides two different output ports, the readout speed is doubled by reading two different comb lines per time via the two photodiodes \(\text{PD}_{1}\) and \(\text{PD}_{2}\). Once the vector of hidden layer measurements \(\mathbf{I}^{\text{out}}\) is recorded on the computer memory, the optimal weights \(\mathbf{W}\) are evaluated and the output \(\mathbf{y}^{\text{out}}=\mathbf{W}\cdot\mathbf{I}^{\text{out}}\) is calculated on the computer. Figure 2b shows one example measurement of the hidden layer intensities \(\mathbf{I}^{\text{out}}\) recorded during the experiment.  
In the case of the parallel operations mode, the readout is still executed by selecting one comb line per time through notch filters; however, now, lines of different combs represent neurons of different parallel ELMs. Thus, photodiode measurements of the \(j\)-th comb represent the hidden layer of the \(j\)-th ELM and are recorded in the vector \(\mathbf{I}^{\text{out}~{}j}\) (\(j\in[1,~{}N_{S}]\)); for each of the ELMs executed in parallel, the computer evaluates the set of optimal weights \(\mathbf{W}^{j}\) and then calculates the output \(\mathbf{y}^{\text{out},~{}j}=\mathbf{W}^{j}\cdot\mathbf{I}^{\text{out},~{}j}\).  
The output weights \(\mathbf{W}\) are estimated using the ridge regression algorithm. The optimal regularization constant is selected for each task.  
In the case of classification tasks, which are the only ones tested in the present work, the ELM is required to assign a class to each set of input features. If the task provides only two possible output classes, the ELM output \(y^{\text{out}}\) is a scalar value, and the prediction of the system is assumed to be one class or the other depending on whether \(y^{\text{out}}<0.5\) or \(y^{\text{out}}>0.5\). If the task provides more than two output classes, the ELM output \(\mathbf{y}^{\text{out}}\) is a vector that has as many elements as the number of classes, and the prediction of the system is the class corresponding to the index of the maximal element in \(\mathbf{y}^{\text{out}}\).  
Once the optimal weights are known, they could be applied optically, setting the proper optical attenuations on \(\text{SF}_{2}\) in such a way that the photodiode readings represent the network output \(\mathbf{y}^{\text{out}}=\mathbf{W}\cdot\mathbf{I}^{\text{out}}\). This scheme for optical weighting has already been demonstrated in [19] but is not tested in this work.
# Adaptive Cooperative Control of Multiple Urban Rail Trains with Position Output Constraints
###### Abstract  
This paper studies the distributed adaptive cooperative control of multiple urban rail trains with position output constraints and uncertain parameters. Based on an ordered set of trains running on the route, a dynamic multiple trains movement model is constructed to capture the dynamic evolution of the  
controller, allowing for more rational regulation of multiple trains movements, rather than relying on a centralized control mechanism that requires to observe and operate all trains. Therefore, distributed control is better for multiple trains cooperation system. In addition, since the actual train operation process is subject to various constraints, some researchers have studied the problem of distributed cooperative control with constraints. The authors of [18; 19; 20] investigated a multiple trains cooperative control method with saturation constraints. The authors of [21] proposed a multi-agent system with speed limit to improve the tracking accuracy of train operation control. The authors of [22] comprehensively studied distributed multi-agent systems with input control and non-convex velocity constraints. Previous literature on MTCC considers speed constraints and saturation constraints, but not position constraints. However, during the actual train operation and station stopping, the control system has strict requirements on the position output of the trains.  
Inspired by these discussions, this paper focuses on the multiple trains distributed adaptive cooperative control scheme with position output constraints (POC) to track the desired train speed and position. Specifically, the main contributions of this paper are as follows.  
* Considering the safety of multiple trains operation and station fixing stops, the distributed cooperative control law based on potential functions and POC is designed to ensure that each train tracks the desired train position and speed, at the same time, ensuring that the distance between each train and adjacent trains is within the predefined safety range.
* The adaptive laws automatically estimate the drag coefficient online, and proposes a single-value learning adaptive train cooperative control method to further simplify the structure of the controller.
* Different distributed control designs are applied to the various trains, not only for a single train, but also for a collection of \(n\) trains (\(n>1\) is a natural number). When the number of train groups varies, there exists no requirement to adjust the control structure or redesign the adaptive laws.  
The rest of this paper is organized as follows. Section 2 introduces the multiple trains dynamic system model. Section 3 gives the detailed control schemes. Section 4 discusses the simulation results. Section 5 provides the concluding comments.
## 4 Discussion
### Comparison and Analysis of the Joule Integral  
The IEC 61660-2 (1997) standard [32] adopts the product of the square of the thermal equivalent of the short-term current (\(\mathrm{I_{th}}\)) and short-circuit duration (\(\mathrm{T_{k}}\)), and it assumes  
\begin{table}
\begin{tabular}{c c c} \hline
** IEC 61660-1 (1997)** & **According to Formulas (2)** & **Simulation** \\  & **and (3)** & **steady-state short-circuit** \\  & current, \(\mathrm{I_{k}}=1540\) A & \(\mathrm{i_{p}}=0.226\) ms; \(\mathrm{i_{p}}=4329\) A & \(\mathrm{t_{p}}=0.23\) ms; \(\mathrm{i_{p}}=4345\) A \\ \hline \end{tabular}
\end{table}
Table 2: Short-circuit currents obtained with the three methods.  
Figure 8: Equivalent circuit of the rectifier in the IEC 61660-1 (1997) standard.  
Figure 9: The approximated waveform of the short-circuit current in the IEC 61660-1 (1997) standard.  
this to be approximately equivalent to the Joule integral. The calculation process is approximate, and the energy of the capacitor discharge on DC-link is not taken into account. Formulas (24) and (32) in this standard are used to calculate the thermal effect of the short-circuit current, and a \"substitute rectangular function\" (A\({}_{\rm i}\)) is used to approximately replace the Joule integral, as shown in Figure 10.  
Assuming a short-circuit duration of 0.1s, Table 3 shows the results for the Joule integral according to the IEC 61660-2 (1997) standard [32], Equation (13) in this research, and the simulation, respectively.  
The calculation results of the first method differed significantly from those of the latter two. Analyzing Formula (24) for A\({}_{\rm i}\) and its coefficients in the IEC 61660-2 (1997) standard, it can be seen that A\({}_{\rm i}\) is only related to \({}_{\rm i}\)p, but not to the steady-state current. Equation (13) in this research calculates the thermal effects of the capacitor discharge and steady-state current at the same time, so it was more accurate and consistent with the results of the simulation. In addition, the t\({}_{\rm p}\) value calculated with Formula (15) in the IEC 61660-1 (1997) standard [31] was much larger than that calculated with Formula (3) in this research. Therefore, the \"equivalent rectangular function\" (A\({}_{\rm i}\)) was much larger than the Joule integral obtained with the second and methods.  
According to Figure 6, the calculation of the Joule integral is related to the short-circuit duration T\({}_{\rm k}\). When T\({}_{\rm k}\) < T\({}_{\rm 1}\), it is calculated according to Formula (11); when T\({}_{\rm k}\) > T\({}_{\rm 1}\), it is calculated according to Formula (13). In engineering practice, the case is almost always T\({}_{\rm k}\) > T\({}_{\rm 1}\).
## 5 Model Evaluation
### Evaluation of Our Models  
We use a nested cross-validation approach to select the best fitted models (inner 4-fold cross-validation) and measure the models' generalization to new and unknown patients (5-fold outer cross-validation). We split patients randomly into 60% training, 20% validation, and 20% test sets while maintaining a similar ratio of TLF patients across folds. During model selection, we also compare combinations of different pre-processing steps using different statistical tests: a Kruskal-Wallis test as an omnibus test and a Wilcoxon sign-ranked test as a paired test.  
The performance of each technique is reported using the confusion matrix: true positive (TP), true negative (TN), false positive (FP), and false negative (FN) rates. In addition, we measure precision, recall (TP rate, sensitivity), specificity (TN rate), FP rate and FN rate, and F1 scores. For each of the \(F\) cross-validation folds, a confusion matrix \(C_{f}\) (\(f=\{1,2,\dots,F\}\)) of size \(2\times 2\) is computed. Given \(F\) confusion matrices, we calculate the mean and standard deviation of the following measures of performance:  
\[\text{Precision}=\sum_{f=1}^{F}\frac{\text{TP}_{f}}{\text{TP}_{f}+\text{FP}_{ f}},\text{ std}=\frac{1}{F-1}\sqrt{\sum_{i=1}^{F}\left(\frac{\text{TP}_{f}}{\text{TP}_{f}+ \text{FP}_{f}}-\text{Precision}\right)^{2}} \tag{3}\]  
\[\text{Recall}=\sum_{f=1}^{F}\frac{\text{TP}_{f}}{\text{TP}_{f}+\text{FN}_{f}},\text{ std}=\frac{1}{F-1}\sqrt{\sum_{i=1}^{F}\left(\frac{\text{TP}_{f}}{\text{TP} _{f}+\text{FN}_{f}}-\text{Recall}\right)^{2}} \tag{4}\]  
Similarly, we calculate the mean and standard deviation of the following performance metrics:  
\[\text{F1}=\frac{\text{TP}}{\text{TP}+\frac{1}{2}(\text{FP}+\text{FN})}\]  
\[\text{Specificity}=\text{True Negative Rate}=\frac{\text{TN}}{\text{TN}+ \text{FP}}\]  
\[\text{False Negative Rate}=\frac{\text{FN}}{\text{TP}+\text{FN}},\text{ False Positive Rate}=\frac{\text{FP}}{\text{TN}+\text{FP}}\]  
In addition, we also report the receiver operating characteristic (ROC) and precision-recall (PR) curves to compare models. The ROC curve shows the trade-off between the true positive rate and the false positive rate. The PR curve shows the trade-off between precisionand recall for different thresholds and is better suited for imbalanced class distributions. As a baseline, a random classifier, the so-called no-skill classifier, is expected to predict a random class or the same class in all cases.
## 4 Results
### Experimental Results and Analysis
#### 4.3.3 Validation in Real Road Environments  
To verify the applicability of the proposed system in real road environments, a real-time license plate recognition system was built using Raspberry Pi 4, Raspberry Pi Camera Module v2, and a 10,000 mAh battery pack. The experimental procedure is shown in Figure 9. This system transmits images captured by the camera to a cloud server, where they undergo license plate detection, corner detection, license plate correction, and character recognition processes. The models used in each system include a YOLOv8-based vehicle and license plate detection model and a CSPNext + CEM + Ensemble-based Corner Detection model.  
These real-time systems were installed in various environments such as parking lots and roads to evaluate their performance under actual conditions. License plate recognition performance was verified under various lighting conditions, weather conditions, and shooting angles, and it was confirmed that the proposed parallel ensemble processing technique operates effectively in real environments. The operation process of the system can be found in Figures 10 and 11.  
Homography verification results confirmed that the proposed CEM and parallel ensemble processing technique effectively detect the corners of various distorted license plates occurring in real environments, such as road environments and parking lots, and operate effectively in real environments.  
Figure 10: Validation results in real road environments: (**a**) Vehicle license plate detection in real road environments; (**b**) Corner Detection; (**c**) Homography-Based Distortion Correction; (**d**) License plate recognition based on OCR.  
Figure 9: Validation in actual road and parking lot environments.
# Effects of Bilberry Extract on Hepatic Cholesterol Metabolism in HepGG2 Cells
## 4 Discussion
The results from the present study support that the protective effects of BE on hypercholesterolemia can be attributed to the stimulation of genes for biliary cholesterol excretion in the liver. Increased hepatic de novo lipogenesis is known to contribute to hyperlipidemia. The transcription factor SREBPP1c regulates ACC, FAS and SCD-1, the enzymes for fatty acid biosynthesis [47]. The expressions of hepatic SREBPP1c and its downstream lipogenic genes ACC and FAS were significantly decreased by BE treatment. CPT-1 and ACOX are the key enzymes responsible for mitochondrial and peroxisomal fatty acid oxidation, respectively [79; 80]. BE significantly upregulated both CPT-1 and ACOX expression in HepG2 cells. Consistent with these results, polyphenols increased CPT-1 in HepG2 cells. Epigenetic modulation of cholesterol metabolism-related genes such as HMG, LDLR and NPC1L1 are reported. BE significantly altered genes involved in cholesterol metabolism, i.e., biosynthesis, transport, bile acid conversion and secretion in the liver. Therefore, we investigated whether BE can alter sirtuin expression in HepG2 cells. The expression of SIRT1, SRIT2, SIRT5, SIRT6 and SIRT7 was downregulated, whereas SIRT3 and SIRT4 expressions were upregulated by BE treatment. Further study is needed to investigate the mechanistic insight of BE on the alteration of SIRTs.
## 5 Parameters Influencing Impedance Frequency Response
### DC Voltage  
Finally, we have investigated the impact of the DC bus voltage on the impedance frequency response of the machine. For this purpose, we performed two characterizations with \(U_{DC}=300\) V and \(U_{DC}=600\) V. The results presented in Figure 28 show that the amplitude of the DC bus voltage has little effect on the accuracy of the impedance frequency response method. The reason for this lies in the very definition of the proposed method, since the adaptive threshold used to identify the various dominant frequency components that are used to derive the frequency characterization is defined using this parameter. The approach is therefore inherently independent of the DC bus voltage amplitude.  
Figure 27: Comparison of the impact of the switching frequency on the cleaned impedance characteristic (**a**) and its interpolation (**b**).
# Performance and Evaluation of GNSS Receiver Vector Tracking Loop Based on Adaptive Cascade Filter
## 4 Discussion and Conclusions  
This paper designs an adaptive cascaded vector tracking loop to improve the stability and accuracy of the GNSS receiver in case of partial outage and limited satellites' availability. First, in the signal tracking module, the KF is used to estimate the code phase error, carrier phase error, and carrier frequency error. Then, in the navigation solution module, the filtered output code phase error and carrier frequency error are converted into pseudo-range error and pseudo-range rate error, as the measurement signal of the EKF, and the feedback correction loop NCO is estimated through the navigation filter. Further, online adjustment of the measurement noise covariance matrix and the process noise covariance matrix are performed through the signal C/N\({}_{0}\) and the innovation sequence, respectively. The vehicle test results show that, when the satellite is in good condition, the positioning result of the proposed method is slightly superior to the traditional method in terms of stability and accuracy. When the satellite signal is blocked or insufficient, the proposed method can still maintain high-quality positioning results, but the traditional method has failed. The proposed method in this paper provides a new idea for the practical application of the GNSS receiver signal tracking method. The next key task is to consider the multi-sensor fusion technology, and further improve the positioning accuracy and robustness of GNSS satellite signals in complex environments when assisted by external information, such as INS, Visual Odometer, 3D-Map, etc.  
Conceptualization, B.Z. and Q.W.; methodology R.Z.; software, L.W.; validation, B.Z., Q.W. and H.Y.; formal analysis, F.J.; investigation, B.Z.; resources, R.Z; data curation, Q.W.; writing--original draft preparation, H.Y.; writing--review and editing, B.Z.; visualization, Q.W.; supervision, L.W.; project administration, R.Z.; funding acquisition, Q.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, grant number 2018 YFB1702500.  
Restrictions apply to the availability of these data. Data were obtained from the Department of Precision Instrument and are available from the authors with the permission of B.Z. and Q.W.  
I would like to express my gratitude to all those who helped me during the writing of this thesis. A special acknowledgement should be shown to [PERSON] and [PERSON], from whose valuable suggestions I benefited greatly. I am particularly indebted to [PERSON] and [PERSON], and who gave me kind encouragement and supervised the work all through my writing. I wish to extend my thanks to [PERSON] who helped design the experiments finally.  
The authors declare no conflict of interest.
# An Efficient Relayed Broadcasting Based on the Duplication Estimation Model for IoT Applications
## 4 Simulation Study
### The Performance Evaluation of the Flooding Mechanism Reliability  
In the last simulation, we investigated the reliability of several flooding mechanisms in depth. So far, we have evaluated their reliability with the performance index \(\overline{F}_{val}\). Since it is an average value when considering all of the nodes, \(\overline{F}_{val}\) is not effective for observing how many nodes receive how many valid frames. By considering this point, we introduced another performance index \(R_{val}(p\%)\), defined as the ratio of the nodes that received the valid frames more than \(p\%\) of the total nodes. For example, if 80 nodes among 100 nodes received valid frames (that are neither duplicated nor corrupted) more than 95%, \(R_{val}(95\%)\) becomes 0.8. Please note that \(R_{val}\) is analogous to the complementary cumulative distribution of the number of valid frames.  
Figure 17a shows \(R_{val}(p\%)\) for various values of \(p\) ranging from 80 to 99% when \(N_{node}\) was 100 and the network radius was \(2D_{tx}\). The \(R_{val}\) of BASE was smaller than 0.04 when \(p\geq 85\%\), whereas that of PBF was 1 even when \(p=99\%\), i.e., PBF could ensure the reliability strictly in all of the nodes but nearly no nodes could receive more than 85% of the frames in BASE. The \(R_{val}\) of DBF sharply decreased from 0.94 to 0.2 when \(p\) was increased from 88 to 91%, and it gradually decreased when \(p>91\%\). In the case of CBF, \(R_{val}\) suddenly decreased from 0.95 to 0.37 when \(p\) was increased from 97 to 99%. However, due to the re-queuing scheme, the \(R_{val}(99\%)\) of DRBF\({}_{\text{RQ}}\) and ADBBF\({}_{\text{RQ}}\) maintained 0.99 and 0.94, which were greater than that of CBF by 2.7 and 2.5 times, respectively.  
We repeated the simulation by increasing \(N_{node}\) to 200 and the network radius to \(3D_{tx}\) and obtained \(R_{val}\), as shown in Figure 17b. When compared to Figure 17a, the performance of BASE and PBF were not much changed. The outstanding performance of \(R_{val}\) was still maintained in ADBBF\({}_{\text{RQ}}\); i.e., even when \(p=98\%\) and 99%, the \(R_{val}(p\%)\) of ADBBF\({}_{\text{RQ}}\) were 0.94 and 0.90, whereas those of CBF were only 0.6 and 0.1, respectively. These results in Figure 17 reconfirm that the re-queuing scheme is essential for ensuring the strict reliability and that the approximation error in the duplication ratio does not lead to a considerable degradation in performance if the re-queuing scheme is applied.
# Tip Estimation Method in Phantoms for Curved Needle Using 2D Transverse Ultrasound Images
## 1 Introduction
This is because the sagittal method has a strong relationship to the movement of the US probe when the needle is out of the view-field of the US images, and this movement maybe deforms the prostate as well as affects the needle target. For a deflected needle, the transverse US image is a better choice for its detection. Compared to the sagittal images, the transverse US images are easy to be acquired when the US probe scanning along the needle, no matter how much the needle is curved. Figure 1: Two methods by using 2D ultrasound for detection. In this paper, we present a method to track a long-curved needle from the 2D transverse US images and estimate its tip for the guidance of needle insertion. Ultrasound is first used to detect the cross-sections of the long-flexible needle (shown in Figure 2 STEP 1). The needle shape tracking method combined needle detection with Kalman filter develops an accurate location and a robust tracking procedure with scan intervals from 1 mm to 8 mm (shown in Figure 2 STEP 2). Unlike the previous study [26], the 3D needle positions obtained from 2D US images and optical tracking systems have been used in KF for the precise location. The curve fitting method is then used to achieve the shape reconstruction and the needle tip position is estimated based on its length and the curve fitting result (shown in Figure 2 STEP 3). The advantage of the proposed method is that the shape and tip position can be estimated through scanning the needle's cross-sections at intervals along the direction of needle insertion without detecting the tip. Besides, a novel histogram method is introduced to detect the needle in image processing, which can improve the needle localization under the effect of needle comet tail and the poor reflection, despite of the abrupt intensity changes. In addition, a robotic ultrasound system (RUS) [28] is built to evaluate the proposed needle tip estimation method. Results showed that the estimation of the tip position is less than 1 mm with 4 mm scan intervals. The rest of this paper is organized as follows. The proposed methods will be introduced in detail in Section 2. Section 3 intends to represent the experimental setup and the results. Finally, the discussion and conclusions are detailed in Section 4.