esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods and Data
### Model Evaluation Criteria  
Classification accuracy (ACC) is the commonly used indicator for evaluating the performance of classifiers. For a given test dataset, ACC is the ratio of the number of correctly classified samples to the total number of samples, defined by Equation 4. It is based on a matrix of actual data and predicted data, called confusion matrix, as shown in Table 2.  
\[\text{ACC }=\frac{\text{TP }+\text{ TN}}{\text{TP }+\text{ FP }+\text{ TN }+\text{FN}} \tag{4}\]  
However, when the imbalance of data is high, which means the number of positive data and the number of negative data have a large deviation, the ACC indicator does not  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Actual Data**} & \multicolumn{2}{c}{**Prediction**} \\ \cline{2-3}  & **0** & **1** \\ \hline
**0** & TP & FN \\
**1** & FP & TN \\ \hline \hline \end{tabular}  
* TN: true negative; FP: false positive; FN: false negative; TP: true positive.  
\end{table}
Table 2: Confusion matrix.  
Figure 3: Schematic of random forest.  
work well [32]. Moreover, the area under the curve (AUC) indicator can deal with the problem of dataset imbalance [33].  
AUC refers to the area under the receiver operating characteristic (ROC) curve. The horizontal axis of the ROC curve is false positive rate (FPR), and the vertical axis is the true positive rate (TPR). The value of the area (i.e., AUC) is between 0 and 1. The closer the value of AUC is to 1, the better the performance of the model is.  
\[\text{TPR}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{5}\]  
\[\text{FPR}\ =\frac{\text{TN}}{\text{FP}+\text{FN}} \tag{6}\]
## 3 Results and discussion  
### Self-Snow-Melting Test  
#### 3.1.1 Effect of Snowfall Intensity  
With the asphalt mixture containing 5% of V-260, the effect of different snowfall intensity on the melting quality of snow at \(-\)15, \(-\)10, \(-\)5, 0, and 5 \({}^{\circ}\)C is illustrated in Figure 8.  
The following conclusions are drawn:  
1. At the five different temperatures, the anti-icing asphalt mixture exhibited a higher snow melting efficiency compared with the conventional asphalt mixture. The reason is that the snow increased the air humidity, which promoted the precipitation of anti-icing filler and formed a salt solution. The salt could be transferred from inside the high-concentration zone to the low-concentration surface, further promoting the precipitation of anti-icing filler and thereby accelerating the snow melting.  
Figure 8: The effect of different snowfall intensity on the melting quality of snow at different temperatures.  
Under the condition of moderate or light snow, the appropriate application temperature of the anti-icing asphalt mixture is not less than \(-10\)\({}^{\circ}\)C. Under the condition of heavy snow or blizzard, the appropriate application temperature is not less than \(0\)\({}^{\circ}\)C, because snow was evidently melted at these temperature ranges. At below \(0\) and \(-10\)\({}^{\circ}\)C, respectively, in both cases, snow was rarely melted. However, the brine solution can lower the adhesion between pavement and ice-snow layer, and the snow-removing efficiency can be improved when other snow-removing methods are applied.
# Queue Spillover Management in a Connected Vehicle Environment
## 3 Intersection Signal Control Modeling
### Intersection Queue Overflow Judgment
If 70% of the road length \(L\) is the control limit \((0.7L)\), when the differential is reached \(0.7L-L_{c}\), we need to take control measures. * When the control threshold of the intersection overflow is obtained, the overflow of the intersection can be judged according to the detection of the queue length. To eliminate the contingency of queuing, it is necessary to wait for at least one cycle, when the vehicle is detected at the overflow control limit. Then check whether the vehicle queuing trend continues to rise, if it continues, the judgment will be determined to overflow. Then the controller optimizes current phase and continues to detect the difference until it approaches zero.
# 1-Bit Reconfigurable Transmitarray Antenna with Out-of-Band RCS Reduction +
## 3 Element Design
### AFST Element
The height \(h\) between the GND and the upper layer is 8 mm to ensure a good impedance match. From the simulated results shown in Figure 6, we can see that a good out-of-band dual-polarized absorption performance is achieved between 3.6\(\sim\)5.9 GHz and 8.9\(\sim\)13.7 GHz, with the  
Figure 3: Simulated S parameters of the AFST. absorptivity greater than 90%, which is calculated using \(1-|S_{11}|^{2}-|S_{21}|^{2}\). Moreover, the insertion loss of the in-band transmission is about 0.5 dB when the GND is removed. Figure 4: Simulated current distributions and their corresponding equivalent circuit models at different resonant frequencies: (**a**) \(f_{L}\), (**b**) \(f_{M}\), and (**c**) \(f_{H}\). Figure 5: Element of the proposed dual-polarized AFST in (**a**) perspective view and (**b**) top view.
# Quantum Circuit-Width Reduction through Parameterisation and Specialisation
## 3 Quantum Circuit Simulation  
In this section, the full-state vector simulation of quantum circuits is considered in more detail. As mentioned in Section 1.1, the quantum state of \(n\) qubits in a qubit register is defined by \(2^{n}\) complex amplitudes defining the quantum wave function. In a **full-state vector** quantum circuit simulator, the \(2^{n}\) complex numbers are all stored in memory irrespective of the amplitude. Specifically, in cases where many of the amplitudes are zero, the potential savings offered by only storing non-zero amplitudes are not employed. During the evolution of the quantum state in the considered quantum computation, some or even all amplitudes will change in time, complicating approaches where only non-zero amplitudes are stored.  
In general, to simulate the operation of a quantum gate on a full state vector, the entire memory space needs to be accessed. The access pattern depends on the index of the gate's target qubit in the quantum register. This index \(t\) is \(0\) for the least significant qubit in the indexing used (at the bottom of quantum circuit diagrams), while \(t=n-1\) for the most significant qubit (at the top of quantum circuit diagrams). The memory space is accessed in pairs, where the stride between the pair elements is \(2^{t}\), for target \(t\). Each pair is then updated by direct matrix-vector multiplication by the gate's \(2\times 2\) matrix. As an example of the computational work involved in quantum circuit simulation, consider the application of a general gate, \(G=\begin{bmatrix}a&b\\ c&d\end{bmatrix}\), where \(a,b,c,d\in\mathbb{C}\), on the most-significant qubit. If the initial quantum state is defined by \(\left|\Psi\right\rangle_{0}\) and the state after applying the gate by \(\left|\Psi\right\rangle_{1}\) then  
\[\left|\Psi\right\rangle_{1}=\sum_{k=0}^{2^{n}}C_{k}^{(1)}|k\rangle \tag{4}\]  
where complex amplitudes \(C_{k}^{(1)}\) are related to previous amplitudes \(C_{k}^{(0)}\) as  
\[C_{k}^{(1)} = aC_{k}^{(0)}+bC_{k+2^{t}}^{(0)} \tag{5}\] \[C_{k+2^{t}}^{(1)} = cC_{k}^{(0)}+dC_{k+2^{t}}^{(0)}\]  
for \(k\in[0,2^{n-1}-1]\). The indexing of the amplitudes in Equation (5) shows that the simulation of gate operations in the quantum circuit model typically involves large memory strides. For the application of a gate on the qubit that represents the most-significant bit in terms of indexing, this stride equals half the state-vector length, i.e., \(2^{n-1}\). In an application of a gate on the qubit representing the least-significant bit in the indexing, the stride in memory reduces to 1.
# A Protocol Layer Trust-Based Intrusion Detection Scheme for Wireless Sensor Networks
## 4 Performance Analysis
### Statistical Analysis  
We utilize t-distribution to analyze the performance of our trust-based intrusion detection scheme because it is suitable to detect the difference between two means in the circumstance of limited samples, which is similar to [34]. \(\text{T}_{\text{dj}}(\text{t})\) is a random variable with normal distribution and the standard deviation of \(\text{T}_{\text{dj}}(\text{t})\) is unknown. In order to calculate the false positive and false negative probabilities, we then transform \(\text{T}_{\text{dj}}(\text{t})\) into a random variable \(\text{X}_{\text{j}}(\text{t})\) following t-distribution with n-1 degrees of freedom, which is denoted by:  
\[\text{X}_{\text{j}}(\text{t})=\frac{\text{T}_{\text{dj}}(\text{t})-\text{ \mu}_{\text{j}}(\text{t})}{\text{S}_{\text{j}}(\text{t})/\sqrt{n}}, \tag{20}\]where \(\mathrm{T_{q}(t)}=\frac{1}{n}\sum_{i=1}^{n}\mathrm{T_{ij}(t)}\) is the sample mean, \(\mathrm{\mu_{j}(t)}\) is the population mean of the trust value of node j, \(\mathrm{S_{j}(t)}=\sqrt{\frac{1}{n-1}\sum_{i=1}^{n}\bigl{(}\mathrm{T_{ij}(t)-T_ {q}(t)}\bigr{)}^{2}}\) is the standard deviation of the trust value that node i evaluated with respect to node j, and n is the number of neighboring nodes of node j. We can obtain \(\mathrm{\mu_{j}(t)}\) by running simulations for many times. Thus, according to Equation (20), the probability that node j is evaluated as a compromised node is given by:  
\[\mathrm{P(\mathrm{\mu_{j}(t)}<Th_{trust})}=\mathrm{P}\Biggl{(}\mathrm{X_{j}(t )}>\frac{\mathrm{T_{q}(t)-Th_{trust}}}{\mathrm{S_{j}(t)/\sqrt{n}}}\Biggr{)}. \tag{21}\]  
The false positive probability can be calculated by:  
\[\mathrm{\rho^{fp}(t)}=\mathrm{P}(\mathrm{X_{j}(t)}>\frac{\mathrm{T_{q}^{N}(t)- Th_{trust}}}{\mathrm{S_{j}^{N}(t)/\sqrt{n}}}=\gamma)=\int_{\gamma}^{\infty} \frac{\Gamma(\frac{n+1}{2})}{\sqrt{n\pi\Gamma(\frac{n}{2})}}(1+\frac{\mathrm{ x}^{2}-\frac{n+1}{2}}{\mathrm{d}x}, \tag{22}\]  
where \(\mathrm{T_{q}^{N}(t)}\) (\(\mathrm{S_{j}^{N}(t)}\)) is the mean value (standard deviation) under the condition that node j is not compromised, superscript N denotes Not compromised and \(\Gamma(\mathrm{x})=\int_{0}^{\infty}\mathrm{t}^{x-1}\mathrm{e}^{-t}\) dt is the gamma function.  
The false negative probability is expressed as:  
\[\mathrm{\rho^{fn}(t)}=\mathrm{P}(\mathrm{X_{j}(t)}\leq\frac{\mathrm{T_{q}^{C}( t)-Th_{trust}}}{\mathrm{S_{j}^{C}(t)/\sqrt{n}}}=\delta)=\int_{-\infty}^{\delta} \frac{\Gamma(\frac{n+1}{2})}{\sqrt{n\pi\Gamma(\frac{n}{2})}}(1+\frac{\mathrm{ x}^{2}}{n})^{-\frac{n+1}{2}}{\mathrm{d}x} \tag{23}\]  
where \(\mathrm{T_{q}^{C}(t)}\) (\(\mathrm{S_{j}^{C}(t)}\)) is the mean value (standard deviation) under the condition that node j is compromised, superscript C denotes Compromised.
# An Intelligent Optimized Route-Discovery Model for IoT-Based VANETs
## 6 Experimentation and Result Analysis  
In this section, we present our works in two different subsections. Initially, we present our results regarding the parameter tuning of our proposed JMA algorithm. In the second subsection, we analyze the proposed algorithm with varying deployed vehicle densities, such as 10, 30, and 50 vehicle-density test cases. The obtained outcomes are evaluated using 1000 various Monto Carlo trails to ensure the efficacy of the algorithm. With the aid of 1000 Monto Carlo trails, average outcomes are measured and noted as mean results, whereas the worst and best results are taken out from one of the 1000 trails. In this work, the free space model is studied to simulate the obtained SNR \(\psi\) in dB towards each incoming vehicle \(j\). For ease of simplicity, the free space model is formulated as follows:  
\[\psi_{(i,j)}=\kappa_{T}^{i}-20 log_{10}(d_{i,j})+20 log_{10}(\Omega)-147.5-\gamma _{j} \tag{5}\]  
where \(\kappa_{T}^{i}\) is transmission energy of an individual \(i^{th}\) communicating vehicle, \(\omega\) is the processing frequency of the VANET, \(\gamma_{j}\) is specified as noise power at each \(i^{th}\) communicating vehicle determined using the mathematical model namely Gaussian noise \(\gamma_{n}\sim\aleph(0,\sigma_{\rho}^{2}),\sigma_{\rho}^{2}=1\) is noise adjustment, and \(d_{(i,j)}\) is the Euclidean distance between i,n paired transmitting vehicles mathematically represented as  
\[d_{(i,j)}=\sqrt[n]{(x_{i}-x_{j})^{2}+(y_{i}-y_{j})^{2}} \tag{6}\]  
where \((x_{i},y_{i})\) and \((x_{j},y_{j})\) are coordinates of vehicles scattered in the deployment area R. The experimentation of the proposed model is carried out in MATLAB R2019a. The parameter setting of VANET is shown in Table 1.  
Figure 3: Methodology Diagram.
# The Odyssey of Entropy: Cryptography
## 4 Entropy and Other Cryptographic Areas
### Cryptography-Based Privacy
#### 4.3.1 Steganography and Steganalysis  
Perfectly secure steganography takes great advantage of entropy analysis [220]. In this kind of steganography, the covertext keeps its statistical distribution despite the added hidden text. This makes the stegotext statistically indistinguishable from the covertext. The entropy of image blocks was used by [PERSON] et al. [221] to define a novel distortion measure for steganographic schemes based on frequency domain transformations such as DCT (discrete cosine transform). A framework for blind decoding of image steganography was presented by [PERSON] et al. [222] that is based on measuring the local entropy distributions of decoded images. It was shown in this report that the local entropy distributions of incorrectly decoded images are different from those of normal ones, and the reason is the abnormal structures in erroneously decoded images. [PERSON] and [PERSON] [223] argued that the existence of a correlation between the cover image and the payload reduces the number ofbits needed to hide a given message. They reasoned that this reduction is due to the fact that the correlation decreases the conditional entropy of the message given the cover. A variable-rate image steganography method was proposed by [PERSON] and [PERSON] [224] that tunes the data embedding rate in each image block based on the block entropy. In addition to steganography, some researchers investigated the use of entropy in steganalysis [225].
# Dynamic Decoupling and Trajectory Tracking for Automated Vehicles Based on the Inverse System
## 3 Methodology  
Once an inverse system whose inputs and outputs are strictly opposed to the origin system exists, the decoupling method based on the inverse system will be valid, as shown in Figure 2; that is, the inverse system and the control object are connected in series to form an equivalent multiple single-input single-output combined system without interactions.
## 2 Materials and Methods
### Suppressor Mutant Characterization  
Suppressor mutant colonies were isolated and genomic DNA was collected using the Wizard Genomic DNA Purification Kit (Promega, Madison, WI, USA) following manufacturer recommendations. For whole-genome sequencing analysis, 50 ng of genomic DNA was sheared using a Covaris E220 ultrasonicator (Woburn, MA, USA), and sequencing libraries were subsequently prepared using the KAPA Hyper Prep Kit (KAPA Biosystems, Basel, Switzerland). Sequencing was performed on an Illumina HiSeq4000 sequencer (Illumina Inc., San Diego, CA, USA) following the PE150 protocol. Sequencing data were uploaded to the public server at usegalaxy.org and sequencing fragments ranging between 35-151 bp were analyzed using the Galaxy web platform [14]. AlaRS C666A suppressor mutations were also identified using Sanger sequencing of PCR-amplified fragments of the AlaRS editing domain.  
AlaRS protein sequences were aligned using the EMBL-EBI Clustal Omega multiple sequence alignment tool [15] and visualized using Jalview [16]. Positions of the suppressor-substituted amino acids were modeled using Phyre2 [17] and visualized using PyMOL software.
## 2 Materials and Methods
### Methodology
#### 2.1.2 Data Understanding  
Anthropometric measurements and the utilization of the smart band followed the recommendations outlined by the local ethics committee (UCSM-096-2022) and adhered to the principles of the Declaration of Helsinki (World Medical Association) concerning ethical standards for human research.  
1. Collection of initial data: Anthropometric measurements were conducted on-site at each school. The evaluation team comprised professional physical education teachers and research assistants. Weight and height were measured using [PERSON]'s standardized method. To determine body weight (kg), a BC 730 (Tanita Corporation) electronic scale was used, with a scale from 0 to 150 kg. Standing height was measured using a portable stationeter (Seca 216, Seca Gmbh and Co., Hamburg, Germany), accurate within 0.1 mm. Waist circumference (WC) was measured using a tape measure (Seca) to the nearest 1 mm. The body mass index (BMI) was calculated by dividing the kilograms of weight by the square of the height in meters: BMI = weight (kg)/height\({}^{2}\) (m). According to the BMI Z-score, patients were classified as underweight/normal weight with Z-scores between \(-2\) and +0.99, overweight from 1 to 1.99, obese from 2 to 2.99, and very obese \(\geq\)3 [20]. To categorize abdominal adiposity (WC) by age and sex, the suggestions described by [PERSON] et al. [21] were used. It was categorized into two groups (without risk < p75 and with risk > p75). The motor competence tests that were evaluated were the quantification of the number of steps during school recess, and the \(G\)-minute walk test was performed using a smart band (Huawei band 7) with an AMOLED screen of 194 \(\times\) 368 and with 1.47 inches. This smart band has been used in other similar research [22]. The smart band was placed on the wrist of each student's hand, and its use was explained.
2. Describe and explore the data: The Kolmogorov-Smirnov test verified the dataset's normality. Descriptive statistics (mean, standard deviation, min, and max) were calculated. Table 1 shows the description of the data of the schoolchildren.
## 7 Numerical Properties of CVM
### Spectral Radius  
The spectral radius is used to reveal the stability of an integration method. The variation of the spectral radius with \(\Delta t/T_{0}\) is shown in Figure 3, for different \(\delta\) and \(\sigma\). In general, the spectral radius is very close to 1 for the small value of \(\Delta t/T_{0}\), while it decreases, step by step, and finally tends to a certain constant. In Figure 3a, \(\sigma~{}=~{}1\) is chosen, which means that there is no inclusion of a stability amplification factor. It shows that the spectral radii will become larger than 1 and, finally, approach a certain constant as \(\delta>1\), after a particular value of \(\Delta t/T_{0}\). Figure 3b shows that the spectral radius is always less than or equal to 1 with the entire value of \(\Delta t/T_{0}\) for \(\delta\leq\sigma~{}=~{}2\). These results suggest that \(\sigma\) can extend the unconditional stability range of CVM from \(\delta<1\) to \(\delta<\sigma\).
## 5 MPA-Optimized MVMD and Improved Wavelet Threshold Noise Reduction Method
### MPA for Parameter Optimization of MVMD
The two parameters of the individual learning factor and the social learning factor of the PSO algorithm are set to 1.5; the warning value of SSA is ST = 0.7, and the number of discoverers and alerters is 30% and 50% of the population size, respectively. The effect coefficient of the fish aggregation device in MPA is set to 0.2. From Figure 6, the MAEE of MPA is 3.1369, which appears in the sixth generation. After searching, the parameter \(k\) is 6, and the parameter \(\alpha\) is 6981. Therefore, \(k\) in MVMD is set to 6, while \(\alpha\) is set to 981. The input parameters are used to perform MVMD decomposition on the PD signal that filters out narrowband interference signals. In addition, the SSA algorithm converges faster than the PSO algorithm, but the SSA algorithm is falls easily into the local optimum. The PSO algorithm has strong global search ability, but the convergence speed is slow. The MPA algorithm has a small number of iterations, a fast convergence speed and strong global search ability, and its optimization results are closer to the global optimal value. In summary, the optimization ability of the MPA algorithm is better than that of the PSO algorithm and SSA algorithm. Using MPA to optimize the core parameters of MVMD can significantly reduce the subjectivity of the decomposition structure and improve the decomposition effect.
## 2 Related Work
Utilizing autocorrelation functions, Lomb-Scargle periodograms, and modified Allan deviation metrics, the study showed that GPS C5Q and Galileo C7Q/C8Q code signals exhibited the lowest noise levels (\(\leq\)10 cm standard deviation), whereas GLONASS C1C and C2C signals demonstrated the highest (up to 90 cm). Carrier phase observations were notably consistent across all systems, with noise levels ranging from 1.5 to 3.5 mm. Temporal correlation analysis indicated that 1 Hz carrier phase data could be considered temporally uncorrelated, unlike code observations, which required intervals exceeding 20 s to achieve temporal independence. Furthermore, noise types were classified as flicker or white phase modulation (PM) for code signals, while GPS and Galileo carrier phases were exclusively characterized by white PM.
# Separable Gaussian Neural Networks: Structure, Analysis, and Function Approximations
## 3 SGNN vs. GRBFNN  
Without loss of generality, the analysis below will assume that each hidden layer has \(N\) neurons. To understand how the weights of SGNN relate to those of GRBFNN, we equate Equations (1) and (9), which yields a nonlinear map  
\[\mathcal{W}_{j}=g_{j}\Big{(}W_{i_{d}i_{d-1}}^{(d-1)},W_{i_{d}-i_{d-2}}^{(d-2)},\ldots,W_{i_{2}i_{1}}^{(1)}\Big{)}, \tag{12}\]  
whose explicit form is  
\[\mathcal{W}_{j}=W_{i_{d}i_{d-1}}^{(d-1)}W_{i_{d-1}i_{d-2}}^{(d-2)}\ldots W_{i_ {2}i_{1}}^{(1)}, \tag{13}\]  
with  
\[j=i_{1}+(i_{2}-1)N+\cdots+(i_{d}-1)N^{d-1}. \tag{14}\]  
It is evident that SGNN can be transformed into GRBFNN. However, GRBFNN can be converted into SGNN if and only if the mapping of Equation (13) is invertible.  
In general, SGNN would have much fewer parameters than GRBFNN, which means, for most possible GRBFNN networks, there is no equivalent SGNN network with an identical set of centers and widths. It is unclear at this time whether SGNNs can form a dense sub-set of GRBFNNs. The aim of this paper is to show that SGNN can cost substantially lesscomputational effort than GRBFNN but provide comparable (occasionally even greater) accuracy through extensive numerical experiments in modeling ten very different functions. In this regard, even if SGNNs cannot lead to an arbitrarily close approximation of GRBFFs, there is still value in using them for high-dimensional problems due to their computational efficiency. In addition, SGNNs can have superior performances in approximating complex functions rather than deep neural networks with activation functions such as ReLU and Sigmoid, as shown in Section 7.  
In the following, we demonstrate the computational efficiency of SGNN over GRBFNN in terms of trainable variables and the number of floating-point operations of forward and backward propagation.