esa-sceva/satcom-chunk-collection huggingface dataset:
# Continuous-Variable Quantum Key Distribution Robust Against Polarization-Dependent Loss
## Appendix B The Calculation of SNR  
In order to obtain \(e_{ab}\), we have to calculate the practical SNR of Bob, and the final result is combined with Appendix A. When the quantum channel is introduced with some excess thermal noise, [PERSON]'s noise is actually made up of three different parts. The first part is the vacuum noise \(V_{S}\), whose variance is always \(\frac{1}{4}\). The second part is the electronic noise, whose variance is \(V_{el}\). The third part is the thermal noise. The variance of the thermal noise depends on \(\tau\), which is the squeezing factor of [PERSON]'s EPR source, and \(\eta\), which is the quantum efficiency of the channel. The signal-to-noise ratio then reads [37]  
\[SNR=\frac{u_{i}^{2}}{V_{B}}, \tag{10}\]  
where \(u_{i}=\Re\{\sqrt{\eta\eta_{m}\alpha_{i}}\}\) is [PERSON]'s average value of \(S\) quadrature when [PERSON] sent \(\alpha_{i}\). \(\eta_{m}\) is the detection efficiency of the homodyne detector. When we get the analytical expression for the secret key rate between [PERSON] and [PERSON] for the case where there is no excess noise, we have  
\[SNR=\frac{\Re\{\sqrt{\eta\eta_{m}\alpha_{i}}\}}{V_{S}+V_{el}}. \tag{11}\]  
Now, we consider the excess noise. Let the average thermal photon number be \(\left\langle n_{th}\right\rangle\). We have  
\[\left\langle n_{th}\right\rangle=(1-\tau^{2})\sum_{n=0}^{\infty}n\tau^{2n}= \frac{\tau^{2}}{1-\tau^{2}}. \tag{12}\]  
Then, [PERSON]'s noise variance reads  
\[V_{B}=V_{S}+\frac{1}{2}(1-\eta)\eta_{m}\left\langle n_{th}\right\rangle+V_{el}. \tag{13}\]  
Combing Equation (13) with Equation (10), we can get the expression for signal-to-noise ratio for the case with excess noise, which is  
\[SNR=\frac{\Re\{\sqrt{\eta\eta_{m}\alpha_{i}}\}}{V_{S}+\frac{1}{2}(1-\eta)\eta _{m}\frac{\tau^{2}}{1-\tau^{2}}+V_{el}}. \tag{14}\]
## 4 Test Result  
### Test Environment  
The FPGA Fault Injection Tool environment consists of a host PC and a target board (Zybo Z7-20). The specifications of the host PC and the Zybo Z7-20 board used in this study are presented in Table 2. The host PC and the Zybo Z7-20 board are connected via micro-USB, and the data are transmitted and received through UART serial communication.  
In this study, four security circuits (DES, AES, RSA, and SHA) were selected as the target circuits for the DUT (device under test), and fault injection tests were conducted. DES and AES are both symmetric key cryptographic algorithms used to protect data confidentiality, while RSA is an asymmetric key algorithm designed to support secure data transmission and digital signatures. SHA plays a crucial role in verifying data integrity. These circuits represent essential cryptographic technologies and are widely used cryptographic modules that establish benchmark standards in real systems. Evaluating the sensitivity of systems equipped with these modules to SER (soft error rate) is a critical task for enhancing overall system security.
# An Empirical Evaluation of Ensemble Models for Python Code Smell Detection
## 4 Result Analysis
### Effect of FST on Outcomes  
In this study, accuracies were calculated for the LC and LM datasets. Table 7 compares the accuracy score with and without the applied FST for the LC and LM. Gradient Boost, Max voting, AdaBoost, and XGBoost obtained better accuracy with FST than without FST for the LC datasets. Similarly, all models (except Max Voting) obtained better accuracy scores for the LM dataset. This indicates that chi-square FST helps these models by selecting the most relevant features, thereby improving their performance. Feature selection is essential for metric-based datasets, especially in dynamically typed languages like Python.  
**RQ.2 Answer**: To answer RQ.2, it was observed that applying FST significantly improved accuracy performance across most Ensemble methods for both the LC and LM datasets. This underscores the crucial role of FST in improving detection performance by filtering irrelevant metrics. The results confirm that effective feature selection is critical in Python's metric-rich and less standardized coding environment.
## 2 Materials and Methods  
### Study Sites  
The study was conducted in the central and north-eastern part of Latvia, in eight deciduous forest stands with nutrient-rich organic (peat) soil--six silver birch (_Betula pendula_ [PERSON]) stands with drained and wet peat soil and three black alder (_Alnus glutinosa_ ([PERSON] [PERSON]) stands with wet peat soil (Figure 1).  
Each tree species was represented by at least one mature stand, one middle-aged stand and one young stand. In each sample plot three trees were selected, representing the largest, middle-sized and the smallest trees of the stand. Characteristics of the forest stands are shown in Table 1 and characteristics of individual trees are shown in Table 2.  
The average annual air temperature in Latvia from 1991 to 2020 was +6.8 \({}^{\circ}\)C. In Latvia, in general, the warmest month of the year is July with an average temperature of +17.8 \({}^{\circ}\)C, while the coldest is February with an average temperature of \(-\)3.1 \({}^{\circ}\)C. The average amount of precipitation in Latvia is 678.6 mm. The wettest months are July (75.7 mm) and August (76.8 mm), while the driest is April (35.8 mm). The average relative air humidity is 81% [34]. In Latvia, the vegetation period begins mid to late April and lasts until October to November, depending on the part of Latvia (eastern or western) and the distance from the Baltic Sea [35]. At various points in winter, there was a snow cover on the ground in the study area, although it was not persistent.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No. of Sample**} & \multirow{2}{*}{**Tree Species**} & **Soil Moisture** & \multicolumn{2}{c}{**Location, WGS84**} & **Stand Age,** & **Diameter at Breast** & **The Average** \\  & & **Condition** & **X** & **Y** & **Years** & **(1.3 m) Height, cm** & **Tree Height, m** \\ \hline
1 & Silver birch & Drained & 57.3218 & 26.0641 & 20 & 15 & 18 \\
2 & Silver birch & Drained & 56.6873 & 25.0482 & 30 & 12 & 17 \\
3 & Silver birch & Drained & 56.6942 & 24.5836 & 59 & 27 & 21 \\
4 & Silver birch & Wet & 57.2906 & 25.9987 & 60 & 19 & 18 \\
5 & Silver birch & Wet & 56.9289 & 24.9666 & 70 & 22 & 22 \\
6 & Black alder & Wet & 56.6596 & 24.1421 & 23 & 9 & 11 \\
7 & Black alder & Wet & 56.9280 & 56.9280 & 53 & 22 & 24 \\
8 & Black alder & Wet & 56.5737 & 56.5737 & 72 & 29 & 29 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the studied forest stands.  
Figure 1: Location of forest stands and sample plots. Forest stand identifiers are shown as numbers. Two sample plots are located in the same stand (Ma—black alder and B—silver birch).
## 3 Experiments and Discussion
### Signal Transmission of BSN
#### 3.2.4 Influence of Signal Transmission Path  
In the signal transmission system based on the human body medium used in a BSN for healthcare, generally, biomedical sensors and the corresponding transmitters are located at different positions on the human body, and send their data to a sink node located somewhere on the human body (such as the wrist). Therefore, the influence of the signal transmission path within the human body should be considered. As shown in Figure 10, the receiving electrode was fixed on the position \(R\) of the human body, while the transmitting electrode was fixed on the positions \(T_{i}\), \(T_{2}\), \(T_{3}\) and \(T_{4}\), respectively, thereby resulting in signal transmission paths with different lengths, which include the arm path (\(T_{I}\)-\(R\), 20 cm), torso-arm (\(T_{2}\)-\(R\), 70 cm), leg-arm (\(T_{3}\)-\(R\), 180 cm) and left arm-right arm (\(T_{4}\)-\(R\), 120 cm) in our experiment.  
Figure 9: The modulated signal received by the Mach-Zehnder EO sensor on the conditions that CF = 8 MHz and BF is **(a)** 100 kHz, **(b)** 1 MHz, **(c)** 4 MHz and **(d)** 8 MHz, respectively. Figure **(e,f,g,h)** are the corresponding demodulated signals of the modulated signal shown in Figure **(a,b,c,d)**, respectively.  
transmission path will result in a smaller signal attenuation [14,15]. Moreover, the comparatively high quality of the demodulated signal can also be achieved in this case, as shown in Figure 11(e). As a result, the measured BER is only 0.32% in this case. On the other hand, it can be found from Figure 11(b,c,d) that the modulated signals corresponding to the paths of torso-arm, leg-arm and left arm-right arm have similar waveforms and amplitudes. Meanwhile, according to the corresponding demodulated signals shown in Figure 11(f,g,h), all the signals shown in Figure 11(b,c,d) can be demodulated correctly, and the measured BER's corresponding to Figure 11(f,g,h) are 0.47%, 0.62% and 0.49%, respectively. Therefore, it can be deduced that signal transmission path generally has less influence on the quality of the signal transmission based on the human body medium.
## 2 Proposed Methods for Speaker Recognition
#### 2.3.1 AE Model  
The AE architecture for the third step of our proposed BLSTM-MFCCAE-TL method is shown in Figure 12. An AE is composed of a neural network, and the architecture is divided into an encoder and a decoder. The encoder encodes the input data into a set of vectors. The decoder uses the output of the encoder as the input and decodes it. According to [16], additional features obtained from the pretrained AE can improve recognition accuracy. In the AE of the third step, the encoder's input is the \((N,\,39)\) MFCC feature, after a layer of 2048 neuron-size neural network, and then outputs 1024 neurons. The decoder then decodes 1024 neurons and outputs the decoding result of \((N,\,39)\). In the pretraining stage, the input and output layers of the AE are two audio features of the same speaker. The AE is trained using the sound features of unrelated text to find the feature vector that represents the speaker. After the pretraining of the AE, we take the first 1014 feature vectors of the encoder output layer and reshape them into two-dimensional feature vectors of \((26,\,39)\), which we call the AE feature. The MFCC features of \((N,\,39)\) were then combined with the AE features of \((26,\,39)\). In this study, \(N\) was set as 512 in the experiment. The combined feature vector of size \((512+26,\,39)\) is used as the input of the BLSTM model in the next step.  
Figure 11: BLSTM-MFCCAE-TL method architecture diagram.  
Figure 12: Autoencoder model architecture diagram.
## 6 Applications and Simulations
### Peer-to-Peer Greedy Routing  
Various 3D sensor networks of different sizes (ranging from 1,000 to 2,500) and shapes are simulated in this work. In addition to the network model shown in Figure 1 (Model 1) and Figure 8 (Model 7 and Model 8 with two and three internal holes), Figure 6 illustrates several other examples, where the first row shows original networks, the second row illustrates the established UTC mesh structures and the third row depicts the results after volumetric harmonic mapping. In all the models, sensor nodes are randomly distributed. The radio transmission range is around 0.11, resulting in an average nodal degree between 16 to 30. Note that such a nodal degree is moderate in 3D, although it appears high for 2D networks. The boundaries are detected as discussed in the previous section. For example, the inner boundary is highlighted in magenta in Figure 6i,j.
## 3 Results and Discussion
### OAM Detection Under Different Turbulence Intensities  
Figure 6 shows the confusion matrix detected by the Transfer OAM classifier for 15 modalities (each containing 500 data points) under different AT intensities. The diagonal elements in the confusion matrix represent the number of correctly detected elements, and the number of diagonal elements is directly proportional to the accuracy of model detection. It can be clearly seen from the figure that each mode tends to disperse to adjacent modes under the action of turbulence, mainly due to the high similarity between the intensity images of adjacent OAM modes. Under weak turbulence, the accuracy of detecting 15 OAM modes is as high as 100%; 99.81% accuracy can also be achieved under moderate turbulence. As the turbulence strengths increase to strong turbulence, the intensity distribution of the vortex beam is severely disrupted, resulting in a decrease in detection accuracy. However, even so, under our designed Transfer OAM model, the accuracy can still reach 90.73%. The analysis of the confusion matrix confirms that the Transfer OAM model can still effectively identify complex OAM patterns under different turbulence intensities.  
Figure 5: Shows the influence of data volume on detection accuracy under different turbulence intensities before and after using the Transfer OAM mode classifier, with a transmission distance of z = 1000 m.
# Fourier Ptychographic Reconstruction Method of Self-Training Physical Model
## 4 Experimental Results and Analysis
### Construction of Data Sets
#### 4.1.3 Experimental Data Preparation  
The method based on deep learning needs to build data sets for network training and testing. In this paper, 3.1 data sets are used to build the network. The low-resolution images collected by FPM are still preprocessed and synthesized into dual-channel images, which are sent to the network as intensity and phase, respectively. In the experiment, the FPM system has an objective lens with a numerical aperture (NA) of 0.13, and a 2560 \(\times\) 2560 pixel (6.5 um pixel size) CMOS camera is used to collect and record light intensity images. The planar array is used as a 13 \(\times\) 13 programmable control light source element LED, and the illumination wavelength is 505 nm to provide illumination at 100 mm below the sample.  
In this paper, the data sets are divided into a training set and a test set. The training set is used to train the neural network. The network calculates the error between the truth value and the network output by calculating the value of the loss function, and updates the hyperparameters of the network by back propagation and gradient descent. The test set is used to quantitatively evaluate the effect of network training during the training process. In order to prevent overfitting, the process of back propagation is turned off when using the test set.  
In addition, in order to enhance the ability of the reconstructed network model to process real collected data, a data set with a mixture of real collected data and simulated data sets is built. In the mixed data set, the original low-resolution images of 50 groups of samples were first collected by a Fourier ptychographic microscopy, and the results reconstructed by the traditional phase recovery method were used as the true values. For speeding up the training of the network and achieving better training results, the images in the mixed data set are cut into fixed-size image blocks before network training, and more training data samples are obtained by reducing the cutting step size. After the above operation, 500 sets of low-resolution complex amplitude images and the corresponding truth values were finally obtained, in which the ratio of training set to test set was 9:1. The physical model training flow chart is shown in Figure 10.
## 6 Conclusions  
In this study, a SWB BPF with dual stopband features at 4.4 GHz and 7.5 GHz has been designed using a UTL loaded with three-\(\lambda\)/4 stubs and a coupled T-shaped resonator. The operating principle of such a filter was explained in detail, and its controllable broadband response with notch band features was successfully achieved to show some attractive features, such as a wide bandwidth of about 14.3 GHz, a simple topology, a flat group delay, a low insertion loss of 0.4 dB, a high return loss of greater than 10 dB, and sharp rejection at the lower and upper cut-off frequencies, with good out-of-band performance up to  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref. No.** & **Passband (GHz)** & **FBW (\%)** & **IL/RL (dB)** & **C.F/BW (GHz)** \\ \hline
[11] & 1.8–3.1 & 62.5 & 0.5/20 & 2.45/1.3 \\ \hline
[12] & 2.7–8.23 & 132\% & 2.5/10 & 5.46/5.53 \\ \hline
[14] & 2.92–10.95 & 107 & 0.49/\textgreater{}12 & 6.93/8.03 \\ \hline
[49] & 3.21–10.77 & 109.4 & 0.8/15 & 8.6/7 \\ \hline
[50] & 1.44–2.66 & 60 & 0.6/20 & 2.05/1.22 \\ \hline
[51] & 1.64–2.47 & 40 & 0.8/20 & 2.05/0.83 \\ \hline
[52] & 2.3–4.08 & 50.3 & 1.2/12 & 6.38/1.78 \\ \hline
[53] & 2.4–7.2 & 83 & 0.5/14 & 6/4.8 \\ \hline
[54] & 2.94–10.39 & 111.6 & 0.5 & 6.66/7.45 \\ \hline
[55] & 3.1–10.6 & 109 & \textless{}0.5 & 6.85/7.5 \\ \hline
[56] & 3.1–10.6 & 119 & 0.35 & 6.85/7.5 \\ \hline
[57] & 3.7–9.6 & 106.2 & \textless{}1 & 6.65/3.2 \\ \hline
[58] & 3.6–10.4 & 103.9 & \textgreater{}0.5 & 7/6.8 \\ \hline
[59] & 2.2–2.53 & 4 & 2.5/\textgreater{}20 & 2.3/0.33 \\ \hline
[60] & 1–3 & 135 & 0.1/\textgreater{}20 & 2/2 \\ \hline
[61] & 2.95–10.75 & 113.9 & 0.6/14 & 6.85/7.8 \\ \hline
[62] & 3.05–10.62 & 100.9 & 1.5/13 & 6.83/7.57 \\ \hline
[63] & 9.5–10.5 & 8.5 & 1.8/\textgreater{}10 & 10/1 \\ \hline
**This work** & **2.5–16.8** & **148.18** & **\textgreater{}0.4/\textgreater{}10** & **9.65/13.9** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons with other reported work.  
Figure 14: Simulated and measured S11 and S21 frequency plots. (**a**) The proposed SWB BPF. (**b**) The proposed SWB BPF with dual stopbands.  
25 GHz, respectively. These characteristics enable the integration of the proposed filter into upcoming wireless communication systems.  
Methodology, A.B.; Software HFSS-15, A.D.; Validation, A.D.; Formal analysis, A.B. and G.Z.; Investigation, A.D.; Writing--original draft, A.B. and G.Z.; Writing--review & editing, A.B. and G.Z.; Visualization, A.D.; Supervision, G.Z.; Project administration, G.Z.; Funding acquisition, G.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the \"Young Talent Sub-project of Ningbo Yongjiang Talent Introduction Programme under grant no 20100859001\".  
Not applicable.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Rapid-Detection Sensor for Rice Grain Moisture Based on NIR Spectroscopy
## 2 Materials and Methods
### Chemometric Analysis
#### 2.3.1 Partial Least Squares  
Partial least squares (PLS) is the most widely used regression modeling method in spectral data analysis due to its flexibility and reliability in dealing with redundant spectral data [22]. It takes advantage of the variables containing the most comprehensive information, as well as identifying noise by decomposing and filtering the spectral data, and is then able to explore the linear combination of spectral data and the chemical compositions [23]. When PLS is applied to dealing with the spectral data, the spectral matrix is first decomposed, and the main principal component variables are obtained; then, the contribution rate of each principal component is calculated. The flexibility of PLS makes it possible to establish a regression model in cases where the number of samples is lower than the number of variables [24]. In this study, the PLS model was established with the spectral data as \(X\) (the number of the spectra original variables was 350) and the measured moisture content of rice grain as \(Y\), whose best principal factor was determined by root mean square error of cross validation (RMSECV).
# Algorithms Utilized for Creep Analysis in Torque Transducers for Wind Turbines
## 7 Straight Line Fit Results (Trend Line)  
A straight line adjustment \(y=ax+b\) is implemented, taking into account only constant uncertainty values. In the first approach in Equation (5), signal uncertainty constants have been assumed to be \(u(y_{i})=0.0001\) mV/V, and analytical solutions have been used (\(6,7\)). In the second approach, an approximate solution for Equation (4) has been used with constant uncertainties \(u(t_{i})=0.3\) s, \(u(y_{i})=0.0001\) mV/V using the algorithm of method WTLS for straight line, which is described in Section 5.1.  
The following time dependencies were obtained for matching using the same method as OLSs--the first case:  
* For adjustments of the low level of signal: \(y=-9.0\times 10^{-6}\)\(t+0.001024\) with the value of the criterion function \(\phi_{yt\text{min}}=3.36\);
* For adjustments high level of signal: \(y=1.006\)\(27\times 10^{-5}\)\(t+1.046325\) with the value of the criterion function \(\phi_{yt\text{min}}=141.78\).  
For the second more general case of fit with constant uncertainties \(u(t_{i})=0.3\) s and \(u(y_{i})=0.0001\) mV/V, method WTLS for straight line has been applied. Almost identical solutions were obtained, as in the first case:  
* For adjustments of the low level of signal, diagram of a straight line in Figure 6a: \(y=-8.09\times 10^{-6}\)\(t+0.00106\)with the value of the criterion function \(\phi_{yt\text{min}}=3.36\);
* For adjustments of the low level of signal, diagram of a straight line in Figure 6b: \(y=1.0062\times 10^{-5}\)\(t+1.046325\) with the value of the criterion function \(\phi_{yt\text{min}}=141.66\).  
Figure 5: The quasi-parabolic characteristics: (**a**) \(\phi_{xy}(\theta_{1})\) and (**b**) \(\phi_{xy}(\theta_{0})\) for global minimum (the red dashed line)—example for third degree polynomial \(y=1.79\times 10^{-7}t^{3}-2.23\times 10^{-5}t^{2}+8.89\times 10^{-4}t\) + 1.035436 with the value of the criterion function \(\phi_{xy}\approx 26.13\).  
Figure 6: Adjust straight lines (purple) to the measurement points (blue): (**a**) low level and (**b**) high level.  
* For adjustments of the low level of signal \(y=-9.22\times 10^{-10}\)\(t^{3}+5.20\times 10^{-7}\)\(t^{2}-7.88\)\(\times\)\(10^{-5}t+0.003742\) with the value of the criterion function \(\phi_{\mathrm{\textit{\tiny{gymin}}}}=1.55\);
* For adjustments of the high level of signal \(y=1.79\times 10^{-7}\)\(t^{3}-2.23\times 10^{-5}\)\(t^{2}+8.89\times 10^{-4}\)\(t+1.035436\) with the value of the criterion function \(\phi_{\mathrm{\textit{\tiny{gymin}}}}\approx 26.13\). The relationships in both the first case (the dashed curves of the third degree) and in the second case (the purple continuous curve) are illustrated (Figure 8).