esa-sceva/satcom-chunk-collection huggingface dataset:
# Beyond Classical AI: Detecting Fake News with Hybrid Quantum Neural Networks
## 2 Materials and Methods
### Proposed Model: HQDNN
#### 2.3.1 Model Architecture  
The HQDNN architecture integrates classical and quantum computing components within a unified framework tailored for binary fake news classification. The model accepts inputs with two different representations depending on the experimental setting:  
TF-IDF-based input: The textual statements are transformed into 1000-dimensional TF-IDF vectors. These vectors are then passed through a classical dense layer that reduces dimensionality from 1000 to 2.  
DistilBERT-based input: Alternatively, contextualized sentence embeddings with 768 dimensions are extracted using a pre-trained DistilBERT model. A dense layer is then used to reduce this to a 2-dimensional vector.  
Regardless of the input type, the 2-dimensional output is encoded into a parameterized quantum circuit with two qubits. The circuit applies RX rotations parameterized by the input, entangles the qubits via a CNOT gate, and applies additional RY and RZ gates. Pauli-Z measurement is performed on each qubit, producing a quantum output vector of size 2.  
This vector is then passed through a final sigmoid-activated linear layer, which outputs the probability that the statement is fake or real. The overall HQDNN model architecture is illustrated in Figure 1.
## 2 Materials and Methods
### Physicochemical Analyses  
pH and tittatable acidity (TA) were determined by titration aliquots (Schott Titroline 7500 KF Volumetric KF Titrator; Mainz, Germany) of 10 mL of must or wine, by 0.1 N NaOH to an end point of pH 7.0, using an automatic pH titration system and expressed as g of tartaric acid/L. Alcohol content in wine was determined by using an oscillating densimeter DMA 4500M ([PERSON], Graz, Austria), with the result as expressed as the volume percent (% vol.). Sugar content was measured by HPLC, described previously by [PERSON] et al. [13]. Results are expressed in grams per 100 mL. The color of must and wine was determined using an A5 Chroma-Meter (Minolta CR300, Osaka, Japan), referring to color space measured by Comission Internationale de l'Eclairage system as CIE L*a*b* where L*--brightness, a*--color from green (-a*) to red (+a*), and b*--color from blue (-b*) to yellow (+b*).  
Analysis of Phenolic Compounds by Ultra Performance Liquid Chromatography-Photodiode Array (UPLC-PDA)  
The samples were prepared as described by [PERSON] et al. [5]. Prior to analysis, must and wine samples were filtrated through a 0.22 \(\upmu\)m membrane filter. The samples were analyzed by using an Acquity UPLC system (Waters, Milford, MA, USA) with a PDA detector (Waters, Manchester, UK). Chromatographic separations were performed on a UPLC BEH C18 column (1.7 \(\upmu\)m, 2.1 mm \(\times\) 100 mm; Waters Corporation, Milford, CT, USA) maintained at 30 \({}^{\circ}\)C. Elution was performed at a flow rate of 0.42 mL/min. The gradient started with 99% of 1% aqueous formic acid, isocratic conditions for 1 min, followed by a 11 min linear gradient of 1% to 40% acetonitrile with 1% formic acid applied. From 12 min, the acidified acetonitrile was increased to 100%, followed to 2 min, and then returned to initial conditions at 2 min. The volume injected was 5 \(\upmu\)L. Polyphenolic compounds were subsequently tentatively identified by liquid Chromatography-Electrospray Ionization-Mass Spectrofotometer/Mass Spectrofotometer Quadrupole Time-of-Flight (LC-ESI-MS/MS QTof) as described previously by [PERSON] et al. [5] by comparing the areas and retention times with standards and considering the generated fragments and data from the literature. Calibration curves were made from selected standards: (-)-epicatechin, (+)-catechin, procyanidin B2 for flavan-3-ols evaluated at 280 nm, catfaric acid for phenolic acids at 320 nm, quercetin-3-\(O\)-glucoside, and myricetin-3-\(O\)-glucose for flavonoids at 360 nm. The sum of phenolic acids, flavan-3-ols and flavonols was estimated by summing the content of each compound identified by chromatography analysis, respectively. Quantification was achieved by injection of solutions of known concentrations ranging from 0.05 to 0.5 mg/mL (\(R^{2}\leq\) 0.998). The results were expressed as mg/L for must and wine.
# Lenke Classification of Scoliosis Based on Segmentation Network and Adaptive Shape Descriptor
## 2 Methods
### Lenke Classification of scoliosis  
#### 2.5.1 Lenke Classification of Sociosis Based on Cobb Angle  
In clinical practice, the Lenke classification method [4] is currently recognized as one of the authoritative classification systems in spinal surgery, which divides scoliosis into six types according to the Cobb angle in radiographs.  
Figure 10: Example of symmetric matching.  
In the Lenke classification system, a complete spine can be divided into three areas [4]: the proximal thoracic (PT), main thoracic (MT), and thoracolumbar/lumbar (TL/L) areas. Three key characteristics need to be identified when performing Lenke classification. The first is to determine the position (PT, MT, or TL/L) of the major curve, i.e., the curve segment with the largest Cobb angle. If MT and TL/L have the same Cobb angle, the MT will be considered as the position of the major curve. Second, it needs to be determined whether the curve is structural. A curve is defined as structural if the Cobb angle is \(\geq\)25\({}^{\circ}\) in the coronal plane or the angle is \(>\)20\({}^{\circ}\) in the sagittal plane. Finally, the structural curves for each PT, MT, and TL/L need to be calculated, and then, the Lenke type of scoliosis can be determined according to the criteria shown in Table 1. More specifically, the major curve is structural and occurs at the MT for the Lenke types from 1 to 4, while the major curve is situated at the TL/L for Lenke type 5 and type 6. Furthermore, if the major curve occurs at the MT and both PT and TL/L curves are nonstructural, then the scoliosis is Lenke type 1. For Lenke type 2, the main difference from type 1 is that the PT curve is also structural, i.e., the double thoracic curves are structural. Using the same method to check the minor curves, the Lenke types from 3 to 6 can also be distinguished, and more details are presented in Table 1. According to the above criteria, an automatic Lenke classification algorithm based on Cobb angle measurement for scoliosis was designed as shown in Figure 11. This algorithm can help radiologists directly obtain the Lenke type from segmented X-ray spine images with an automatic measurement algorithm of the Cobb angle (See Algorithm 1). Both the coronal and sagittal planes of the Cobb angle are considered for determining the Lenke classification [30], but we only employ coronal X-ray spine images, i.e., the Cobb angle is \(\geq\)25\({}^{\circ}\) in the coronal plane, in this paper.
# Evaluation of Coastal Sea Level Offshore Hong Kong from Jason-2 Altimetry
## 5 Results
### Coastal Seasonal Signal along the Jason-2 Pass  
Using Equation (5) we also computed the amplitude and phase of the seasonal signal for all sea level time series. The resulting values are shown in Table 4 (for the annual signal) and Table 5 (for the semi-annual signal). All three cases give rather similar results that agree well with X-TRACK. The annual phases lie between 300\({}^{\circ}\)-360\({}^{\circ}\) and are significantly larger than the tide gauge-based phase. Amplitudes are also slightly larger. The semiannual phases lie between 220\({}^{\circ}\)-270\({}^{\circ}\) and are close to the tide gauge-based phase. Amplitudes are also slightly larger. We cannot exclude that some local seasonal signal at the tide gauge site is responsible for the difference observed between altimetry and tide gauge data.
# Measurement of Lower Extremity Alignment Using a Smartphone Application
## 1 Introduction
### Statistical Analyses  
Intraclass correlation coefficients (ICCs) were used to analyze interrater reliability for the PLEAA measured using the smartphone application. In terms of the HKAA, a standard lower extremity alignment angle measured in the PACS image program [24]. To evaluate the possibility of using the application as a diagnostic aid, Pearson's correlation coefficients were analyzed in a validity test to determine the correlation between the PLEAA measured using the smartphone application and the actual HKAA measured using radiographs [25]. All statistical analyses were performed using SPSS version 22.0 (IBM Corp., Armonk, NY, USA), and a \(p\)-value of <0.05 was considered statistically significant. In addition, 95% confidence intervals were reported.
# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 3 Potential Applications
### Traffic Management  
Traffic management systems can play a key role in the context of smart cities for safer and more convenient roads. Providing traffic management solutions to decrease traffic congestion level is crucial for rescue systems efficiency. The authors in [1] proposed to provide a rescue system to decrease traffic congestion and improve the ability to manage urban traffic. They described a case study of rapid road accident rescue. The verification of the proposed system was simplified by selecting 10 accidents randomly from an accidents dataset in a month, and then using the data to simulate the practical traffic flow. Two assumptions were made: 60% of drivers keep clear of an ambulance without an electronic police monitor, and 95% of them keep clear of an ambulance with a police monitor. They show a preliminary validation of the system in normal times and in rush hour. Firstly, they compare three solutions in normal times without congestion. Secondly, they compare them during rush hour. Their solution saved 9.9 min without a police monitor and 12.5 min with a police monitor in the first-arrival time of ambulance prospect, and saved 15.5 and 19.5 min respectively in traffic recovery time.
###### Abstract
The proposed P-CRLH phase shifter design has the following advantages over the phase shifters reported in the literature: (1) in contrast to commercially available phase shifters, the proposed P-CRLH phase shifter does not require external dc biasing control circuitry, (2) a 60% size reduction as compared to similarly published work in [29], (3) no need of interconnects and bonding wires for the proposed P-CRLH phase shifter as required in conventional MEMS/Reed switches and (4) no need to break the printed transmission line for the installation of the proposed P-CRLH phase shifter as otherwise required for PIN/varactor diodes installation in the circuit [30; 31]. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Research Work**} & **Type of Phase** & \multirow{2}{*}{**Advantages**} & \multirow{2}{*}{**Disadvantages**} \\  & **Shifter/RF Switch** & & & \\ \hline \multirow{2}{*}{[19]} & \multirow{2}{*}{PIN diodes tunable} & Radiation-pattern & \multirow{2}{*}{\(\bullet\)} & The parasitic influence of the p-i-n \\  & & reconfigurable phased array & & \\ \hline \multirow{2}{*}{[20]} & \multirow{2}{*}{Varactor diodes tunable} & Desired phase shift & \multirow{2}{*}{\(\bullet\)} & Lumped components (capacitor and \\  & LH-TL phase shifter & is achieved & & inductor) are required \\ \hline \multirow{2}{*}{[21]} & \multirow{2}{*}{Magnetic field controlled seed switches} & Does not affect the radiation & \multirow{2}{*}{\(\bullet\)} & Complex feeding circuitry is required to \\  & & performance of the antenna & & operate the switches \\ \hline \multirow{2}{*}{[22]} & \multirow{2}{*}{MEMS-tunable CRLH phase shifter} & Limited phase shifts can be achieved & \multirow{2}{*}{\(\bullet\)} & Complex mechanical feed network \\  & & phase shifter & & is required \\ \hline \multirow{2}{*}{[25]} & \multirow{2}{*}{Magnetic field controlled} & Have been explored for & \multirow{2}{*}{\(\bullet\)} & Have not been studied for phased \\  & & reconfigurable antennas & & array applications \\ \hline \multirow{2}{*}{[29]} & \multirow{2}{*}{Magnetic field controlled CRLH phase shifter} & \multirow{2}{*}{Provides desired phase shift} & \multirow{2}{*}{\(\bullet\)} & Large size \\  & & controlled CRLH phase shifter & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Qualitative comparison of the proposed work with selected existing techniques.
# Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network
## 2 Proposed Resilience Measurement Methodology
### Bayesian Network Theory
#### 2.2.2 Conditional Probability Table Calculation with the Noisy-OR Function  
The calculation of the CPT is crucial for reliable model inference. However, the CPT is generally regarded as complicated, especially for the case of a large BN with many nodes. Therefore, many studies have focused on CPT calculations, of which expert elicitation associated with fuzzy theory has been widely employed [34; 53; 54]. Based on expert elicitation, the Noisy-OR model [55] is utilized to calculate the CPT with the following assumptions (suppose a child node \(Y\) has \(n\) parents, \(X_{1},\ X_{2},\ \ldots,\ X_{i},\ \ldots,X_{n}\), as shown in Figure 4a):  
1. All the nodes in the proposed Bayesian network can be regarded as Boolean variables; that is, the nodes have binary states, true or false, representing positive or negative outcomes, respectively;
2. The causes (parent nodes) of \(X_{1},\ X_{2},\ X_{3},\ \ldots X_{n}\) are mutually independent;
3. The probability that \(Y\) is true when only one causal factor, \(X_{i}\), is true while all other factors except \(X_{i}\) are false can be expressed as: \[P_{i}\ =\ P(Y|\overline{X_{1}},\overline{X_{2}},\ldots,X_{i},\ldots,\ \overline{X_{n}})\] (23) where \(P_{i}\) represents the connection probability between the parent node, \(X_{i}\), and the corresponding child node, \(Y\). The connection probability can be derived by: \[p_{i}\ =\ \frac{p(Y|X_{i})-p(Y|\overline{X_{i}})}{1-p(Y|\overline{X_{i}})}\] (24)  
Suppose every causal factor, \(X_{i}\), in Figure 4a is a member of the set \(\big{\{}X_{p}\big{\}}\) and all the causal factors with a \"true\" state label belong to \(\big{\{}X_{T}\big{\}}\). Additionally, the causal factors with a \"false\" label are members of \(\big{\{}X_{F}\big{\}}\). Then, the CPT of \(Y\) can be obtained by  
\[p\big{(}Y|X_{p}\big{)}\ =\ 1-\prod_{X_{i}\in X_{T}}(1-p_{i}) \tag{25}\]  
For the limit case in which \(\big{\{}X_{T}\big{\}}\) is a null set, that is, the states of all the causal factors are \"false\", then \(p\big{(}Y|X_{p}\big{)}\ =\ 0\). However, it is almost impossible for the probability, \(p\big{(}Y|X_{p}\big{)}\), to equal zero. In practice, the subsequent event, \(Y\), may occur even if all the causal factors are \"false\" because some unknown causal factors may still exist beyond the identified causal factors. Therefore, the leaky Noisy-OR model (illustrated in Figure 4b) is introduced to handle the unknown causal factors; in this case, the conditional probability of Y can be calculated by:  
\[p\big{(}Y|X_{p}\big{)}\ =\ 1-\prod_{X_{i}\in X_{T}}(1-p_{i})(1-p_{L}) \tag{26}\]  
where \(P_{L}\) represents the connection probability between \(Y\) and the leaky causal factors \(X_{L}\).
## 4 Results and Discussions
### Reputation-Based Security vs. Reputation-Based Functionality  
Figure 4 matches the reputation based on functionality with the reputation based on security. Apart from two applications Express Union and Age Scanner Finger, applications are always more  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Applications** & **Number of Reviews** & **Positive Semitents (\%)** & **Negative Semitents (\%)** & _E (pa, f) Functionality_ \\ \hline Instagram & 2,288,402 & 75\% & 25\% & 0.75 \\ Facebook & 1,479,779 & 63\% & 37\% & 0.63 \\ CIC & 1715 & 76\% & 24\% & 0.76 \\ Messenger & 1,010,765 & 65\% & 38\% & 0.65 \\ SOC Connect & 10 & 50\% & 50\% & 0.50 \\ Whatapp & 2,824,936 & 70\% & 30\% & 0.70 \\ QR scanner & 11,026 & 82\% & 18\% & 0.82 \\ Ecobank & 643 & 43\% & 57\% & 0.43 \\ Clean Master & 139,070 & 38\% & 17\% & 0.83 \\ Séurity Master & 78,840 & 86\% & 14\% & 0.86 \\ Age Scanner Finger & 118 & 5\% & 95\% & 0.05 \\ Express Union & 13 & 15\% & 85\% & 0.15 \\ BICEC Mobile & 5 & 60\% & 40\% & 0.60 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Reputation in terms of functionality.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Application** & **Number Security Related Reviews** & **Application Polarity** & _E (pa, f) Security_ & \multicolumn{3}{c}{_E (pa, f) by Security Service_} \\  & & & & _Con._ & _Int._ & _Aut._ & _Ava._ \\ \hline Instagram & 100 & – & 0.40 & 0.36 & 0.45 & 0.32 & 0.47 \\ Facebook & 150 & – & 0.31 & 0.42 & 0.36 & 0.11 & 0.33 \\ CIC & 90 & – & 0.23 & 0.12 & 0.34 & 0.18 & 0.26 \\ Messenger & 150 & + & 0.51 & 0.57 & 0.43 & 0.37 & 0.66 \\ SGC Connect & 6 & – & 0.18 & 0.13 & 0.25 & 0.15 & 0.17 \\ Whatapp & 150 & + & 0.58 & 0.38 & 0.58 & 0.68 & 0.67 \\ QR scanner & 80 & + & 0.51 & 0.46 & 0.57 & 0.34 & 0.68 \\ Ecobank & 150 & – & 0.30 & 0.52 & 0.33 & 0.11 & 0.22 \\ Clean Master & 98 & – & 0.39 & 0.43 & 0.38 & 0.19 & 0.43 \\ Security Master & 103 & – & 0.42 & 0.21 & 0.35 & 0.67 & 0.43 \\ Age Scanner Finger & 105 & – & 0.19 & 0.01 & 0.08 & 0.1 & 0.56 \\ Express Union & 13 & – & 0.41 & 0.43 & 0.33 & 0.64 & 0.23 \\ BICEC Mobile & 2 & – & 0.41 & 0.34 & 0.47 & 0.45 & 0.38 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Reputation in terms of security; \(\div\): negative, \(\div\): positive.  
reputed in terms of functionality than in security. This fact can be explained by two situations. The first situation is that people feel more comfortable in terms of service than in terms of security. The second situation is that developers use vulnerable APIs which generate security flaws. Another remark is that developers of messaging applications tend to put more accents in security service than the others. This is the case of Messenger and Whatsapp. The correlation coefficient between reputation based security and reputation based functionality is 0.44 indicating that there is no relation between reputation based security and reputation based functionality. This result confirms that it is not possible to estimate security enforcement based on functionalities implementation. In other words, consumer reviews reveal that developers really lack to look into security aspects.
# MT-FBERT: Malicious Traffic Detection Based on Efficient Federated Learning of BERT
## 5 Discussion  
In this section, we discuss the limitations of this work. (i) Single-Flow Detection Capability. MT-FBERT operates as a flow-by-flow detection scheme, where the input is exclusively composed of information from a single network flow. In multiflow scenarios (e.g., accessing a web page often generates multiple concurrent flows), the detection performance of MT-FBERT may exhibit degradation. To mitigate this limitation, the input architecture could be extended to incorporate multidimensional data, enabling the model to leverage multidimensional attention mechanisms for multi-flow analysis. (ii) Pre-training Data Security. Although MT-FBERT demonstrates strong robustness and generalization across various malicious traffic detection datasets, its effectiveness hinges on the availability of clean pre-training data. When an attacker deliberately inserts low-frequency subwords as toxic embeddings, a poisoned pre-trained model with hidden backdoors can be induced, compelling the model to mispredict target classes and subsequently compromise the normally fine-tuned model in specific threat detection tasks [14,15]. Notably, the problem of constructing toxic tokens within network traffic remains unexplored in existing literature.
## 2 Data Schema
### Common Data Environment and Metadata Management
If they do not interface with one another perfectly, the automated transfer of metadata is impossible. In this regard, a standard exchange protocol needs to be considered. Moreover, the format of information exchange and data-drop delivery needs to be considered to ensure that the metadata are computer-readable and easy to manage from any organization. Therefore, the metadata should be stored in primitive file formats such as.doc/.pdf/.txt for the text data,.xls/.cvs for the spreadsheet data, and.dxf/.fbx for the 3D model data. Figure 3 shows an example of a transition of metadata when more than one CDE solution is applied in a collaborative project.
## 2 Multi-Physics Coupling Mathematical Model
### Boundary Conditions and Methods  
In the solution of the coupled multi-physics mathematical model of DC GIL, the GIL is configured horizontally, and the heat exchange with the external environment conforms to the characteristics of a long, straight horizontal cylinder under natural convection, which is implemented by specifying the heat transfer coefficients. The ambient temperature is maintained at 20 \({}^{\circ}\)C, while the effects of solar radiation and wind speed are excluded from the analysis. The \(\mathrm{SF}_{6}\) gas pressure is maintained at 0.5 MPa. The central conductor, the insert, and the enclosure are assigned Dirichlet boundary conditions.  
\[\left\{\begin{array}{ll}\varphi_{HV}=U\\ \varphi_{GND}=0\end{array}\right. \tag{18}\]Here, \(U=+200\) kV is the voltage applied by the central conductor. The boundary conditions for the fluid are defined in Equations (9) and (10) based on the direction of the current. At the boundary of current outflow, the positive ion concentration is zero and the negative ion concentration gradient is also zero. Conversely, at the boundary of the current entry, the negative ion concentration is zero and the positive ion concentration gradient is zero.  
In the convective diffusion Equations (9) and (10), the source terms are non-constant, and significant concentration gradients exist at the boundaries. When the Peclet number Pe exceeds 1, indicating relatively small diffusion terms, numerical oscillations are likely to occur, leading to convergence difficulties in the computation. Therefore, in solving the convective diffusion Equations (9) and (10), isotropic diffusion is introduced by adding artificial diffusion terms in the flow direction, while simultaneously increasing element density in the corresponding solution domain to avoid numerical oscillations and non-convergence during the solution process, thereby enhancing numerical stability. Streamline diffusion is considered an effective approach for resolving convective diffusion problems, yielding relatively accurate numerical solutions with high computational efficiency [31; 32].  
\[\text{Pe}= \frac{|b^{\pm}E|h}{2D^{\pm}} \tag{19}\]  
Here, \(h\) is the grid cell size.
## 4 Proposed Hybrid Model of NLMPC and Data-Driven Approach
### Data-Driven Correction  
The error term \(\varepsilon_{i}\) (\(X_{i}\),\(u_{i}\),\(t\)) can be approximated using data-driven methods, such as machine learning models. The objective is to learn the mapping from states and inputs to the error term:  
\[\varepsilon_{i}(x_{i},u_{i},t)\approx\hat{\varepsilon}_{i}(x_{i},u_{i}) \tag{19}\]  
This learned model \(\hat{\varepsilon}_{i}\) is then integrated into the NMPC framework to improve the accuracy of predictions.