esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementation of the HHL Algorithm for Solving the Poisson Equation on Quantum Simulators
## 4 Discussion  
This paper presented a study on solving the Poisson equation on a two-dimensional grid using a quantum algorithm. The results of the study have the potential to be generalized to other problems where efficient computation of the solution to this equation is required. The modified HHL algorithm using CRZ rotations is a promising approach to solve such problems.  
Comparison of this study with previous works revealed the advantages of the modified HHL algorithm. It has quantum parallelization, which can lead to faster computations compared with iterative classical methods. In addition, this algorithm reduces the amount of memory required and is independent of the mesh dimension, making it more universal.  
Despite its advantages, it is worth noting some limitations of quantum computing, such as the need for a large number of qubits to solve large problems and the dependence on certain assumptions about the matrix \(A\). These limitations can affect the performance of the algorithm in some cases.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Quantum Simulators** & **Type** & **Time (s)** \\ \hline qasm\_simulator & General & 51.353 \\ \hline ibmq\_qasm\_simulator & General, context aware & 24.219 \\ \hline statevector\_simulator & Schrödinger wavefunction & 5.745 \\ \hline matrix\_product\_state & Extended Clifford & 0.6654 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computation time of the Poisson equation in different simulators.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Quantum Resources** & **Number of Qubits** & **Time (s)** \\ \hline simulator\_extended\_stabilizer & 63 & 0.7246 \\ \hline ibmq\_quito & 5 & 4.7431 \\ \hline ibmq\_belem & 5 & 5.0169 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Execution time in quantum simulators and various quantum computers.  
Practical applications of this research include solving problems in physics, engineering, and computer modeling where an efficient solution of the Poisson equation is required. Improved efficiency and memory savings make the quantum solution more attractive for such applications.  
For further practical implementation, it is recommended to continue research in the field of quantum algorithms for solving the Poisson equation. It is important to explore the possibilities of optimizing and improving the modified HHL algorithm, and also to consider applying this algorithm on powerful quantum systems, such as Osprey and Eagle from IBM Quantum, to solve real-world problems with large volumes of data.
# An Improved Few-Shot Object Detection via Feature Reweighting Method for Insulator Identification
## 4 Experiment and Analysis
### Evaluation Index  
Precision refers to the proportion of real cases in all predicted positive cases, and recall refers to the proportion of real cases in all positive cases [26]. The calculation method is shown in Equations (2) and (3):  
\[\text{Precision}= \frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
\[\text{Recall}= \frac{\text{TP}}{\text{TP}+\text{FN}} \tag{3}\]  
where TP represents a positive sample detected and is in fact a positive sample; FP represents a positive sample when in fact it is a negative sample; and FN means it is tested as a negative sample but is actually a positive sample.  
Furthermore, the average accuracy index of mainstream evaluation indicators of object detection is introduced, as shown in Equation (4):  
\[\text{mAP}= \frac{\sum\limits_{i=1}^{n}\text{AP}_{n}}{n} \tag{4}\]  
To comprehensively evaluate the effectiveness of the improved method, the harmonic average evaluation indexes F1-score and F2-score of precision ratio and recall ratio are introduced, as shown in Equations (5) and (6):  
\[\text{F}_{1}=\frac{\text{TP}}{\text{TP}+0.5\text{FP}+0.5\text{FN}} \tag{5}\]  
\[\text{F}_{2}=\frac{\text{TP}}{\text{TP}+0.8\text{FP}+0.2\text{FN}} \tag{6}\]  
Given the unique characteristics of insulator images, the method aims to highlight the detection capability of insulators with varying scales. To achieve this, weights are adjusted based on different AP values, and a multi-scale accuracy metric, \(\text{AP}_{\text{ms}}\), is defined, as shown in Equation (7):  
\[\text{AP}_{\text{ms}}=0.5\times\text{AP}_{\text{S}}+0.15\times\text{AP}_{ \text{M}}+0.35\times\text{AP}_{\text{L}} \tag{7}\]  
where the \(\text{AP}_{\text{S}}\) boundary frame area is less than 32 \(\times\) 32, the \(\text{AP}_{\text{M}}\) boundary frame area is between 32 \(\times\) 32 and 96 \(\times\) 96, and the \(\text{AP}_{\text{L}}\) boundary frame area is greater than 96 \(\times\) 96.
## 4 Experimental Results  
In this section, the experimental results of the proposed model are discussed. This chapter is mainly divided into three topics, and these topics are the following three research questions. We evaluated the results in relation to these research questions using the MLP model shown Figure 14. Since this model has a variable number of input data, and outputs 3 classes (ADHD, ADHD at risk, normal group) according to the input, it can be equally applied to the following three research questions. In addition, in the following experiments 1-3, the performance was verified using the leave-one-person-out cross-validation method. This method uses one person's data as test dataset and the remaining data as training datasets. This process is repeated for all subjects, and the average value of the results is taken as the performance.  
* Research Question 1: Does video-labeling feedback improve the performance of the proposed ADHD screening diagnostic tool?
* Research Question 2: Is the performance of ADHD screening diagnostic tools using robots consistent?
* Research Question 3: Is the performance of the ADHD screening diagnostic tool using the developed robot superior to that of the existing ADHD screening diagnostic tool?  
A total of 925 children participated in the experiment, and among them, the data of children with many missing values or omissions from expert diagnoses were deleted. The verification was performed using data from 828 children. Consequentially, the participants consisted of 66 children with ADHD, 181 children with the risk of ADHD, and 581 normal children.  
Figure 14: MLP model used in the experiment.
## 2 Methodology
### Numerical Model  
#### 2.2.1 Governing Equations  
The mathematical model used in this study is based on the three-dimensional Reynolds-averaged Navier-Stokes (RANS) equations for the turbulent flow of incompressible Newtonian fluid with constant thermophysical properties. The differential form of these equations in the Cartesian coordinate system of an inertial reference frame at steady-state condition can be written as follows [16]:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial}{\partial x_{j}}\big{(}\rho U_ {j}\big{)}=0 \tag{1}\]  
\[\frac{\partial\rho U_{i}}{\partial t}+\frac{\partial}{\partial x_{j}}\big{(} \rho U_{i}U_{j}\big{)}=-\frac{\partial p}{\partial x_{i}}+\frac{\partial}{ \partial x_{j}}\Bigg{\{}\bigg{[}\mu\bigg{(}\frac{\partial U_{i}}{\partial x_{ j}}+\frac{\partial U_{j}}{\partial x_{i}}\bigg{)}\bigg{]}-\rho\overline{u_{i}u_{j}} \bigg{\}}+\rho\mathbb{g}_{i} \tag{2}\]  
where \(U\) is the averaged component of the velocity vector, \(\rho\) is the density of air, \(\mu\) is the viscosity of air, \(\rho\mathbb{g}_{i}\) is the gravitational body force, \(p\) is the static pressure, and \(\rho\overline{u_{i}u_{j}}\) is the Reynolds stress terms, which will be modelled by an appropriate turbulence model.
## 2 Analytical Strategies for Authentication and Quality Control
### Spectroscopic Methods
#### 2.1.2 Raman Spectroscopy  
Raman spectroscopy is an optical measurement technique that provides information on molecular composition based on the inelastic scattered light from a sample when excited by a laser beam. The photons of a laser beam interact with the molecules of the matrix: a part of the radiation diffuses elastically in all directions without loss of energy, that is, at the same frequency as the incident radiation (elastic or Rayleigh scattering), while a smaller part is diffused inelastically yielding (Raman scattering Stokes) or gaining (anti-Stokes Raman scattering) energy [29]. It is possible to obtain information about the vibrational modes of molecules from the energy shift obtained. Raman spectroscopy is widely used to identify molecules and detect functional groups. [29]. Its main advantages are the high analytic selectivity and the possibility of carrying out rapid \"in situ\" analysis through plastic or transparent glass containers, thus limiting contact with the sample. Recently [30], Raman spectroscopy (performed at an excitation wavelength of 532 nm) combined with chemometric algorithms and machine learning was applied to classify 42 brandy and cognac samples with various geographical origins and aging times. To compute the chemometric models, the range of Raman shifts between 800 and 300 cm\({}^{-1}\) was selected because it was considered the most informative. The chemometric classification models were processed by an extreme gradient boosting (EGB) algorithm [31], reaching an accuracy of 100% for both geographical origin and aging period. With the aim of differentiating wine spirits based on aging time, wood species and aging technology, other authors [32] applied the FT-Raman methodology to 60 samples of differently aged wine spirits. The Raman spectra processed by PCA confirmed the good capabilities of FT Raman to discriminate the differently aged samples. The most relevant regions to observe differences were in the range from 3000 to 2600 cm\({}^{-1}\) and from 1570 to 790 cm\({}^{-1}\). Despite the rather limited number of applications in the field of distillates, Raman spectroscopy combined with multivariate analysis or machine learning techniques appears to be a promising technique for the authentication of brandy spirits especially because it allows, in many cases, obtaining very specific chemical information without sample manipulation.
# Adaptive Leakage Protection for Low-Voltage Distribution Systems Based on SSA-BP Neural Network
## 5 Experimental Verification
### Compared with Other Methods  
A comparison between the method proposed in this paper and the traditional leakage protection methods is shown in Table 2. The adaptive leakage current detection method based on VMD-DFNN can cope with the change in normal leakage current and realize the full adjustment of the protection action threshold. The adaptive leakage protection based on fuzzy logic can realize the adjustment of normal leakage value in the range of \(-\)30% to 20%. These two methods have neither an action-threshold grading setting and nor do they involve the threshold adjustment in advance. In contrast, the method in this paper acts according to the normal leakage current, and there is no action dead zone. Therefore, the proposed method has higher reliability and protection effects.
## 3 Results and Discussion
### SEM Analysis  
Figure 6 shows the SEM images of the SAR specimens of each mixture at 8 days. The observed hydration products of the SAR paste were mainly ettrinigite and C-S-H gel, and no portlandite was observed. The gaps between the ettrinigite were large, reflecting the hydration process of the C-S-H gel. As shown in Figure 7, the ettrinigite was interlaced with C-S-H gel to form a compact structure at 28 days, meaning that the compressive strength of the SAR specimens had increased. The SEM results show that the ettrinigite content of the SAR specimens decreased as the proportion of seabed silt increased, consistent with the analysis of compressive strength.  
Figure 5: XRD patterns of SAR paste at 1, 8, and 28 days.  
Figure 6: SEM images of the SAR specimens at 8 days.  
In Figure 8a, the ettringite needles had a diameter of about 60-90 nm. The spot under the red block of the ettringite needle in the M4 specimen at 28 days (Figure 8a) was tested by EDS, as shown in Figure 8b. The ettringite formed in the SAR paste contained Si, Mg, Ca, Al, S, and O (Figure 8b and Table 3), supporting the compositional analysis in the XRD test.
# Driver Clustering Based on Individual Curve Path Selection Preference
## 2 Methods and Materials
### Parameter Fitting  
The LDM structure proposed in (9) is a linear model, whose parameters can be calculated by fitting the model to measured data. For this, the naturalistic driving of 30 drivers has been recorded [19]. This yields the following dataset, considering (11):  
\[\mathcal{D}^{u}=\{\{u_{k}^{\text{left}},u_{k}^{\text{right}}\}\}_{k=1}^{N} \quad\mathcal{D}^{\delta}=\{\delta_{k}\}_{k=1}^{N} \tag{15}\]  
where \(N\) is the number of samples in the given measurement. When processing the data (15) the samples are ordered in matrix form (16).  
\[D^{u}=\text{col}(\mathcal{D}^{u})\in\mathbb{R}^{N\times 6}\quad D^{\delta}= \text{col}(\mathcal{D}^{\delta})\in\mathbb{R}^{N\times 3} \tag{16}\]  
Equation (9) can be rewritten in a compact form, using (16) and considering \(\delta_{0}\equiv 0\) (i.e., the data is centralized):  
\[D^{\delta}=\hat{f}(u)+e=D^{u}P+e \tag{17}\]  
where \(P=\left[P^{\text{left}}\quad P^{\text{right}}\right]^{\top}\in\mathbb{R}^{6 \times 3}\) is the model parameter matrix, and \(e\in\mathbb{R}^{N\times 3}\) is the fit error. The model parameters can be calculated by minimizing a pre-selected objective function, considering \(e_{i}=e[:,i]\), i.e., the \(i^{\text{th}}\) column of \(e\):  
\[\arg\min\{e_{i}^{\top}e_{i}|D^{u},D_{i}^{\delta}\}=\arg\min\{V(\hat{f}_{i},D^{u },D_{i}^{\delta})\} \tag{18}\]  
The objective function \(V(\cdot)\) is formulated in a way to minimize the least squares error between the function estimate and the training data:  
\[V(\hat{f}_{i},D^{u},D_{i}^{\delta})=(\delta_{i}-D^{u}P_{i})^{\top}(\delta_{i}- D^{u}P_{i}) \tag{19}\]  
where \(P_{i}=P[:,i]\), i.e., the \(i^{\text{th}}\) column of the parameter matrix. Equation (19) can be solved in closed form to obtain the optimal value of \(P_{i}\), using the pseudo inverse formulation of \(D^{u}\):  
\[P_{i}^{*}=\delta_{i}(D^{u})^{\dagger} \tag{20}\]  
Figure 2: Example to show the planning concept with LDM.  
From this, the optimal parameter matrix is formulated simply:  
\[P^{*}=\left[P_{1}^{*}\quad P_{2}^{*}\quad P_{3}^{*}\right] \tag{21}\]  
It can be shown that for all drivers, the parameter values reach their steady state well within the length of the data sequence. To prove that the regression is continuously executed on a gradually increasing subset of the data sequence (22).  
\[\tilde{P}^{*}=\delta_{i}(\tilde{D}^{u})^{\dagger} \tag{22}\]  
where \(\delta_{i}\) and \(\tilde{D}^{u}\) are the subsets of \(\delta_{i}\) and \(D^{u}\), in the \(k^{\text{th}}\) calculation cycle \(\delta_{i,k}=\delta_{i}[1:k]\) and \(\tilde{D}_{k}^{u}=D^{u}[1:k,:]\). The convergence to steady state is calculated based on the error between the estimated parameters \(\tilde{P}^{*}\) and the reference parameters \(P^{*}\). Then, the normalized RMS value of the estimation error is calculated (23).  
\[\text{NRMS}_{\tilde{P}^{*}}=\frac{\sqrt{\|\tilde{P}^{*}-P^{*}\|_{2}}}{\text{ max}(P^{*})-\text{min}(\tilde{P}^{*})} \tag{23}\]  
where \(\|\tilde{P}^{*}-P^{*}\|_{2}\) is the \(\ell_{2}\) norm of the parameter estimation error.
## 2 Results and Discussion
### Volatile Compounds in the Wines
#### 2.2.2 Medium-Chain Fatty Acids  
Fatty acids impart the wine with fruity, cheese, fatty, and rancid notes. Volatile fatty acids can improve the complexity of wine at sub-sensory threshold levels, but can cause negative effect on wine aroma when above their thresholds [8]. Four medium-chain fatty acids (MCFAs) were detected in this study. Hexanoic acid, octanoic acid, and decanoic acid exceeded their individual threshold (Table S1, Figure 2). Low concentrations of UFAs (12 and 60 mg/L) increased MCFAs productions, including octanoic acid, decanoic acid, and total contents when compared to the control, and their contents in LA_12 wine were 132.7%, 123.0%, and 167.2% higher than that of the control, respectively, and the values were also 43.7%, 46.8%, 72.7% higher than those of ALA_12, respectively. However, with the increment of UFAs concentrations (120 and 240 mg/L), this promotion disappeared and it  
Figure 3: Total concentrations of C6 alcohols, higher alcohols, acetate esters, acids, ethyl esters, terpenes, and norisoprenoids in the final wines with different treatments. Values of the same compound marked with the same lowercase letters indicates no significant difference (\(p<0.05\), Duncan’s test).  
conversely became the inhibition, regardless of UFA types. Exogenous UFAs (such as palmitoleic acid, linoleinic acid) can suppress the biosynthesis of fatty acids and MCFAs [9; 21]. Our data showed that the inhibition only occurred at a high concentration of UFAs (above 120 mg/L in this study). Slight increment of UFAs content promoted MCFA formations. Among three UFAs, LA presented the highest promotion in the same concentration.
# Ultrasonic Power and Data Transfer through Multiple Curved Layers Applied to Pipe Instrumentation
## 2 Power and Data Acoustic Channel System Overview  
The acoustic channel is composed of three major components, namely the outside block, inside block and acoustic channel, as Figure 1 illustrates. The outside block is composed of a signal generator and a radio frequency (RF) amplifier, both are responsible for sending a carrier signal to an ultrasonic transducer (made of lead zirconate titanate (PZT) ceramic), named the outside transducer, and a circuit to demodulate the received signal, based on an envelope detector. The inside block is composed of a circuit that rectifies and stores the received energy, a piezoelectric transducer (named the inside transducer), and a circuit to modulate the signal. The acoustic channel block is the medium connecting both sides by means of an ultrasonic wave propagating across it. Unlike the conventional application [14; 18], in this paper the propagating medium is not composed of a single metallic flat layer. Instead, it presents a multilayer curved structure formed by two concentric tubes separated by water.
# A Hands-On Laboratory for Intelligent Control Courses
## 4 Practical Sequence
### DAQ Construction  
The objective of the activity is the construction of the data acquisition card to read the position.  
The element in charge of reading the position that is delivered from the encoder is the data acquisition card, which sends the current position as feedback to the controller loop. In this loop, the controller performs the necessary operations and generates the \(U_{c}\) signal that is sent back to the data acquisition card. Once back, the signal is converted to a PWM (\(U_{PWM}\)) value. Then, the power stage, where the H-bridge is located, modifies the voltage values (\(U_{M}\)) for the movement of the motor shaft.  
The student will work with the micro-controller dsPIC33 FJ12 MC in the DAQ, since, being a dsPIC (Digital Signal Peripheral Interface Controller, it) has a higher processing speed than a conventional PIC (Peripheral Interface Controller), to avoid any loss in encoder accounts. Figure 2 presents the circuit necessary for the operation of the PIC. As can be seen, two capacitors, \(C_{1}\) and \(C_{2}\), with a value of 100 pF are required; these capacitors are connected to pins 9 and 10 of the dsPIC and in parallel with the crystal \(X1\) with a value of 20 MHz. To restart the dsPIC a push button is employed, which requires a resistance \(R\_MRCL\) value of 10 k\(\Omega\). For protection against currents and voltages, 4\(n\)25 optocouplers are added, one per output. The encoder is connected to terminals \(QE_{A}\) and \(QE_{B}\) that correspond to pins 24 and 26. To facilitate the work of the student, both the design of the Printed Circuit Board (PCB) and the 3D design of the card are given in [47].  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Practice** & **Descriptions** & **Time (h)** \\ \hline
1. DAQ construction. & The student develops the acquisition card using a PIC and its physical circuit necessary to read encoder and implement PWM, in addition to performing serial communication. & 2 \\ \hline
2. Library creation for membership functions in C language. & The student develops the library needed to mathematically represent the membership functions and facilitate their application in the main code. & 2 \\ \hline
3. Control loop construction. & The student creates the control loop by selecting the required power stage and using the previously developed DAQ. & 4 \\ \hline
4. Design of the fuzzy controller. & The student designs the controller defining the input and output variables, as well as the required membership functions and finally the defuzzification process. & 8 \\ \hline
5. Implementation of the controller. & The student develops the main code for the implementation of the previously designed controller. & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Practices required to develop the project.  
Figure 2: Minimum circuit required for dsPIC.
## 2 Defect Detection Technique Using Multi-Channel Eddy Current System
### Calibration Data
#### 2.4.2 Signal Processing (Denoising and Detrending)  
Figure 6 shows the raw data of the 16 Ch eddy current sensor passing the calibration test piece in Figure 4. Since only one defect existed in the calibration test piece, the amplitude change occurred once for each sensor. It can be seen that there was a difference in amplitude depending on the characteristics of each sensor and that sensors 4 and 8 generated a great deal of noise.  
Figure 4: Calibration specimen.  
Figure 5: Calibration: (**a**) before and (**b**) after.  
Since the noise of the signal prevented the identification of the location of the defect or acted as an error in analyzing the size of the defect, noise removal was performed through denoising. The denoising technique used in this study was \"DC Offect\", and signal processing was performed by applying data between the initially generated low frequency and a specific high frequency. Figure 7 shows the denoising process, and it was confirmed that the noise disappeared as a result of applying \"DC Offect\".  
As can be seen in Figure 7, the denoising data had a certain tendency. Certain trends could also be viewed as noise, and detrending removed certain trends in the data. In this study, the \"Polynomial Fitting--Bisquar and Givens\" technique was used for detrending. Figure 8 shows the process of detrending the data of Ch4 after the denoising process.  
Figure 6: Reference data.  
Figure 7: Denoising process.