esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Magnetic Measurements  
To determine the hysteresis loop, remanence \(B_{r}\), coercivity \(H_{c}\), saturation values \(H_{s}\), \(B_{s}\), and magnetic losses, the AMH-1M-S Permeameter (Laboratorio Elettrofisico, Nerviano, Italy) was used. It is an automatic DC and AC measuring system suitable for the evaluation of soft magnetic strips and metal rings with high resolution. Before the test, a primary set of \(N_{H}\) turns (drive) must be wound around the sample, and a secondary set of \(N_{B}\) turns (sense) must be applied to detect the magnetic flux. The arrangement of turns on the toroidal cores were similar to that presented in [23]. The field \(H(t)\) is calculated after measuring the current in the primary winding \(I(t)\):  
\[H(t)=\frac{N_{H}\cdot I(t)}{l_{m}} \tag{2}\]  
where:  
\(H(t)\)--magnetizing force [A/m],  
\(N_{H}\)--number of turns,  
Figure 1: Toroidal magnetic cores: (**a**) bonded with sols, and (**b**) reference.  
\(I(t)\)--current [A],  
\(l_{m}\)--mean magnetic path length [m].  
If the outer diameter of the core \(D_{e}\) is not greater than 10% of the inner diameter \(D_{i}\), the value of \(l_{m}\) can be calculated using the following equation:  
\[l_{m}=\pi\frac{(D_{e}+D_{i})}{2} \tag{3}\]  
In other case, \(l_{m}\) can be calculated as follows:  
\[l_{m}=\pi\frac{D_{e}-D_{i}}{\ln{(D_{e}/D_{i})}} \tag{4}\]  
To measure the current, the voltage across a low-inductance resistance is determined, and then the magnetic flux \(\Phi\) is calculated after notification level of the voltage \(V(t)\) in the secondary winding:  
\[\Phi=-\int V(t)dt \tag{5}\]  
At the end, magnetic induction \(B\) is received by the following relationship:  
\[B=\frac{\Phi}{N_{B}\cdot\text{A}} \tag{6}\]  
where \(A\) is a cross-section of the core.
# A Novel Technique for Semantic Segmentation of Hyperspectral Images Using Multi-View Features
### Contribution  
The following are the main contributions of the paper to the field of HSI analysis:  
* A novel multi-dimensional perspective and feature extraction approach to HSI analysis is introduced, treating each hyperspectral image as multi-view/multi-modal dataset. Each spectral band of the image provides a distinct \"view\" of the scene, facilitating innovative techniques for simultaneous feature extraction from multiple views. For this, CCA is applied for the first time due to its ability to capture correlated information across all the views simultaneously.
* The extracted multi-view features using CCA are used to predict per pixel class and performed semantic segmentation using SVM.
* A rigorous experimental investigation is conducted to evaluate the performance metrics associated with semantic segmentation of HSIs using multi-view features obtained through CCA. The approach is meticulously compared with conventional feature extraction technique PCA and other machine learning algorithms for segmentation. This comprehensive analysis not only validates the effectiveness of the Proposed Technique but also provides insights into its superiority over existing techniques.
# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 4 Interference in B5G Networks
### Ultra-Dense Networks (UDNs)
#### 4.3.2 Interference in UDNs
Interference can be controlled by the coordination of the network or through advanced receiver design [132].
# Research on the Face Forgery Detection Model Based on Adversarial Training and Disentanglement
## 4 Experiments
### General Comparisons
#### 4.3.1 In-Dataset Evaluation  
Initially, we compare our method with six approaches on in-dataset evaluations: Xception [26], SLADD [23], CORE [16], Capsule [7], SPSL [35], and RECCE [36]. For these comparisons, we train models using the code provided by the original authors and strictly follow the parameters specified in their papers. To maintain evaluation fairness, all models underwent an identical data-preprocessing procedure. Their performances are assessed on the four sub-datasets of FF++.  
Table 3 shows the in-dataset evaluation results. The results show that our method demonstrates superior performance compared to the other six methods in most cases. Specifically, in the FF-DF subset, our method surpasses Xception [26]. Moreover, the average performance (AVG) of our method across all four subsets achieves a remarkable 96.75%.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Method** & **FF-DF** & **FF-FS** & **FF-F2F** & **FF-NT** \\ \hline Baseline & 92.58 & 92.42 & 92.86 & 84.60 \\ Baseline+dis & 94.52 & 90.29 & 94.28 & 85.32 \\ Baseline+dis+mi & 96.80 & 94.58 & 96.28 & 89.80 \\ Baseline+dis+mi+advmi & 97.10 & 97.62 & 96.83 & 91.63 \\ \hline \hline \end{tabular}
\end{table}
Table 1: In-dataset ablation study. ’Baseline’ denotes the basic adversarial training framework; ’dis’ refers to the addition of the feature disentanglement component; ’mi’ indicates the inclusion of the distribution classifier component; ’advmi’ represents the adversarial distribution classification loss.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **FF-DF** & **FF-FS** & **FF-F2F** & **FF-NT** & **AVG** \\ \hline Xception [26] & 97.30 & 97.01 & 98.20 & 94.00 & 96.63 \\ SLADD [23] & 94.03 & 94.38 & 95.31 & 89.30 & 93.26 \\ CORE [16] & 96.27 & **97.61** & **98.61** & 93.06 & 96.39 \\ Capsule [7] & 93.18 & 93.22 & 95.30 & 88.77 & 92.62 \\ SPSL [35] & 95.21 & 95.56 & 97.60 & 89.14 & 94.38 \\ RECCE [36] & 98.14 & 95.96 & 97.67 & **94.52** & 96.57 \\ \hline Ours & **98.53** & 97.32 & 98.21 & 92.93 & **96.75** \\ \hline \hline \end{tabular}
\end{table}
Table 3: In-dataset evaluation performance. We assess performances using four sub-datasets (AUC (%)), with ’AVG’ denoting the average performance across the four sub-datasets. The bold number represents the highest AUC value, and the underlined number denotes the second highest AUC value.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **FF-DF** & **FF-FS** & **FF-F2F** & **FF-NT** \\ \hline Baseline & 92.58 & 92.42 & 92.86 & 84.60 \\ Baseline+dis & 94.52 & 90.29 & 94.28 & 85.32 \\ Baseline+dis+mi & 96.80 & 94.58 & 96.28 & 89.80 \\ Baseline+dis+mi+advmi & 97.10 & 97.62 & 96.83 & 91.63 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cross-dataset ablation study.
###### Abstract  
The simultaneous wireless information and power transfer (SWIPT) technique has been considered as a promising approach to prolong the lifetime of energy-constraint wireless sensor networks (WSNs). In this paper, a multiple-input multiple-output (MIMO) full-duplex (FD) bidirectional wireless sensor network (BWSN) with SWIPT is investigated. Based on minimum total mean-square-error (total-MSE) criterion, a joint optimization problem for source and relay beamforming and source receiving subject to transmitting power and harvesting energy constraints is established. Since this problem is non-convex, an iterative algorithm based on feasible point pursuit-successive convex approximation (FPP-SCA) is derived to obtain a local optimum. Moreover, considering the scenarios in which source and relay nodes equipped with the same and different numbers of antennas, a low-complexity diagonalizing design-based scheme is employed to simplify each non-convex subproblem into convex problems and to reduce the computational complexity. Numerical results of the total-MSE and bit error rate (BER) are implemented to demonstrate the performance of the two different schemes.  
beamforming; bidirectional wireless sensor network (BWSN); full duplex (FD); multiple-input multiple-output (MIMO); simultaneous wireless information and power transfer (SWIPT) +
Footnote †: journal: Journal of the Acoustical Society of America
## 6 Conclusions  
A loss detection sensor with a double-layer vibration damping structure has been devised to decrease mechanical vibration interference and enhance sensor detection precision. To test and verify the vibration-damping efficacy of sensors using various damping materials, a dynamic analysis model of the sensor damping structure was formulated, and a simulation test bench was constructed. The results reveal that by selecting sample materials for vibration damping, properly augmenting the mass of the sensor impact plate and counterweight mechanism, and reducing the resonance frequency of the vibration  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Damping** & **Vibration Source Frequency** & **Vibration Frequency of** & **Impact Plate Amplitude** & **Charge Voltage Value** \\
**Material** & **(HZ)** & **the Impact Plate (HZ)** & **(mm)** & **(V)** \\ \hline \multirow{3}{*}{No material} & 4 & 3.90 & 1.86 & 2.91 \\  & 5 & 5.06 & 2.01 & 3.01 \\  & 6 & 5.96 & 1.86 & 2.83 \\ \hline \multirow{3}{*}{rubber} & 4 & 3.70 & 1.34 & 2.31 \\  & 5 & 4.84 & 1.56 & 2.59 \\  & 6 & 5.78 & 1.32 & 2.22 \\ \hline \multirow{3}{*}{EVA sponge} & 4 & 3.54 & 0.47 & 0.95 \\  & 5 & 4.49 & 0.68 & 1.18 \\ \cline{1-1}  & 6 & 5.58 & 0.54 & 1.06 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Amplitude and charge voltage records at different source frequencies.  
damping structure, the impact of mechanical vibration on the sensor can be efficiently suppressed. This helps in elevating the sensor detection accuracy.  
In the research of this article. Conceptualization, J.W. and F.W.; methodology, Y.L.; software, W.Z.; validation, B.Z., X.F. and J.W.; formal analysis, W.Z.; investigation, Y.L.; resources, B.Z.; data curation, J.W.; writing--original draft preparation, J.W.; writing--review and editing, F.W.; visualization, Y.L.; supervision, X.F.; project administration, J.W.; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program Project of China (2021 YFD2000601).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Determination of Melamine in Milk Using Fluorescence Assays  
Based on its good selectivity and purification ability, an MIP-SPE cartridge was used for solid-phase extraction before the competitive fluorescence assays. Figure 5A shows the competitive binding reaction between FITC-Mel and melamine at various melamine concentrations. Lower melamine adsorption on the MDMIPs resulted in greater FITC-Mel binding to the MDMIPs and a decrease in the fluorescence intensity of the supernatant. Figure 5B shows that the fluorescent intensity of the supernatant increased linearly with the logarithm of the melamine concentration in the range 0.1-20 mg/L. The linear regression equation for the curve was \(y=0.6386x+0.7098\) and the correlation coefficient (\(R^{2}\)) was 0.9345. The limit of detection was 0.05 mg/L in negative milk samples (signal/noise = 3).  
Figure 5: (**A**) Fluorescence spectra of FITC-Mel with different concentrations of melamine and (**B**) Standard calibration plots of \((l-I_{0})/I_{0}\) against the logarithm of the melamine concentration from 0.1 to 20 mg/L in milk samples.  
To investigate method selectivity, some common potential interfering substances such as dicyandiamide, trazine, cyanuric acid and chloramphenicol noted were tested by conducting experiments at the same concentration (1.0 mg/L). As shown in Figure 6, only melamine could induce a considerable change of the fluorescence signal at 526 nm. No dramatic changes at 526 nm were observed in the presence of dicyandiamide, trazine, cyanuric acid and chloramphenicol.  
Milk samples spiked with different concentrations of melamine (0.25, 0.5, 1.0, and 2.5 mg/L) were pretreated using liquid-liquid extraction and MIP-SPE. The melamine in the spiked milk samples was determined using the developed competitive fluorescence assay. HPLC/MS/MS was also used to determine the melamine recoveries and RSDs for the spiked milk samples. The results are shown in Table 1; the recovery means for melamine in the spiked milk samples were between 70.2% and 92.7%, and the RSD values were in the range 5.4-12.4% (\(n=3\)). The successful use of the competitive fluorescence assay to determine melamine in spiked milk samples suggests that the developed method would be useful for monitoring melamine in milk and formula powdered milk.
## 4 Flight Load Prediction Case Study
### Investigation of Different Activation Functions  
It is well-known that the performance of the artificial neural network varies with different activation function \(\varphi(\cdot)\). Therefore, it is important to choose the proper activation function for more accurate prediction of flight loads. In this section, 5 different activation functions are investigated, namely the sigmoid function, sine function, hard-limit function, hyperbolic tangent function, and Morlet wavelet function (see Figure 7).  
Based on the traditional extreme learning machine with 13 input flight parameters and 100 hidden layer neurons, different activation functions were investigated, and the accuracy of the wing bending moment prediction using the testing data is shown in Table 2.  
From Table 2, it can be seen that the ELM achieves high prediction accuracy of the wing bending moment when the activation function is sigmoid and hyperbolic tangent function. In addition, the predicted wing bending moments based on the sigmoid activation function are presented in Figure 8 to compare with the actual measu  
Figure 6: Demonstration of three flight parameters and wing bending moment of typical loop maneuver: (**a**) altitude, (**b**) Mach number, (**c**) vertical load factor, and (**d**) wing bending moment.  
Figure 8b shows that the correlation coefficient between the predicted and actual measured wing bending moments is 0.9991, indicating high prediction accuracy.
## 2 Methods  
The core of this research is the Electronic Health Record dataset provided by the National Center of Familial Hypercholesterolememia in Gdansk, Poland. The initial dataset has 1198 records with 101 parameters corresponding to the data gathered over 2 visits each enrolled patient had. Every record corresponds to one patient. The average age of a patient was 58 years old (youngest was 21, oldest was 88), the sex distribution was 58% female and 42% male, and all patients had a genetic test performed to confirm or deny FH variants. A total of 41% of patients had positive DNA testing towards pathological or potentially pathological variants of FH. The data covered the laboratory results, the nurse investigation, the medical investigation, as well as physical examination and DNA testing towards specific genetic variants. This study aims to provide a reliable method of assessing familial hypercholesterolemiaemia based on the measurements that can be ordered and gathered in regular outpatient clinics. Therefore, the number of parameters was limited to 48. The new dataset contained the information on laboratory measurements of: total cholesterol (TC), LDL-C calculated and direct, high density lipoprotein (HDL), triglycerides (TG), glucose, creatinine level, EGFR, alanine transaminase (ALT), aspartate aminotransferase (AST), creatine kinase (CK), C reactive protein (CRP), thyroid stimulating hormone (TSH), urine test, glycated hemoglobin (HbA1c), lipoprotein a (Lp(a)), apolipoprotein B (APOB), the results of electrocardiogram (ECG) examination (heart rate, the presence of sinus rhythm or atrial fibrillation, ST deviation, T wave deviation, presence of LBBB/RBBB, pathological q, stimulation), information of physical examination (lungs and heart oscillation, examination of abdomen, presence of oedema of lower extremities, tendon xanthomas, xanthelasmas, corencal arcus), the information of patient sex, age, waist and hip circumference, body mass index (BMI), waist-hip ratio (WHR), heart rate (HR), systolic and diastolic blood pressure, highest reported LDL-C, highest reported TC, and LDL-C after recalculation in patients treated with lipid lowering therapy without known lipid results before treatment. The data were updated with results of DNA tests confirming or ruling out the presence of pathological or potentially pathological variants in LDL, APOB, or PCSK9 genes as well as with DLCN score. The overall study design is presented in Figure 1.  
Figure 1: The overall study design presents consecutive steps as well as the selected parameters of utilized methods.
## 3 Results and Discussion
### Wind Pressure Coefficients
For instance, at the wind direction angle of 90\({}^{\circ}\), the RMS wind pressure coefficient of the lateral separation point 3 in CQU is 0.25, while it is 0.31 in HIT, and the error reaches 16%. Similar results were made previously for different structures [28; 29]. This phenomenon may be linked to the enhanced spanwise coherence of vortices and a reduction in perturbation frequency, which is caused by the increase in turbulence scale resulting in a higher level of pressure fluctuations within the separation bubble [30]. As a consequence, employing a smaller turbulence integral scale during testing will lead to a lower measured RMS wind pressure coefficient on the structure, potentially impacting the accurate estimating of wind load. The difference in turbulence integral scale may arise from the variations in the test section dimensions of the wind tunnel laboratories. Besides, as [PERSON] [11] mentioned, the distributions of the RMS pressure coefficient depended on the ratio (i.e., \(\rm L_{u}^{x}/D\)) of the turbulent integral length scale to the width of the windward face. As the value of D decreases in the \(\rm\beta=90^{\circ}\) direction, the differences between CQU and HIT increase, as shown in Table 1, which leads to larger discrepancies in the fluctuating pressure values at the 90\({}^{\circ}\) direction. \[\rm C_{p,mean}=\frac{P_{i}-P_{\infty}}{P_{0}-P_{\infty}}=\frac{P_{i}-P_{\infty }}{0.5\rho U_{H}^{2}} \tag{3}\]  
\[\rm C_{p,rms}=\frac{\sigma_{pi}}{P_{0}-P_{\infty}}=\frac{\sigma_{pi}}{0.5\rho U _{H}^{2}} \tag{4}\]  
where \(\rm P_{i}\) is the mean wind pressure value at each pressure measurement point, \(\rm P_{0}\) is the total pressure at the reference height, \(\rm P_{\infty}\) is the static pressure at the reference height, \(\rm\rho\) is the air density, and the value is usually 1.225 (kg/m\({}^{3}\)). \(\rm U_{H}\) is the wind velocity at  
Figure 4: Schematic diagram of the pressure measurement experiment in the CQU wind tunnel laboratory. the reference height, and \(\sigma_{\text{Pl}}\) is the root-mean-square value of the wind pressure at each measurement point.
## 5 Discussion
### Merging Behavior in Different Traffic States  
The analysis reveals that traffic density plays a crucial role in determining merging behavior. In light traffic conditions, as seen at Sites A1, B1, B3, and B4, drivers tend to merge earlier--about 300 m from the taper--where larger gaps are available. This behavior aligns with lane-changing theories, suggesting that in low-density environments, drivers have more opportunities to merge smoothly, minimizing disruptions. Early merging in light traffic helps drivers maintain higher speeds and reduces the need for sudden lane changes near the work zone.  
In contrast, under heavy traffic conditions, particularly at Site C1, merging occurs much closer to the taper, typically within 50 m. This is consistent with gap acceptance theory, where limited gap availability forces last-minute merges, increasing bottleneck risks. Vehicles in congested traffic also experience significant speed reductions, merging at speeds below 30 km/h. This behavior reflects the difficulty of finding acceptable gaps in dense conditions, leading to delayed merges and higher chances of congestion. These findings highlight the importance of proactive traffic management strategies, such as dynamic merge control, to alleviate congestion in high-density work zones.
## 3 Analysis and Validation of the Physical Model
### Parameter Identification and Regression Analysis
The counting of measuring points occurs at position \(n\), with \(i\) values ranging from 1 to 5. The first set comprises measuring points at positions \(n=2i\), while the second set encompasses all measuring points at positions \(n=i+(i-1)\). For the two sets with five data points, the polynomials and tuning parameters of the physical model are identified in each case. The cross-validated values for \(\|z(t)\|^{2}\) are shown in Figure 4d for PO001, in Figure 4e for PO002, and in Figure 4f for PO003. Even with a limited number of measurement points, the physical model can significantly improve the mapping of data points between measured values compared to polynomial approximations. Demonstrably, the physical model can be effectively parameterized even when utilizing a limited quantity of measuring points. The increase in the number of measuring points directly contributes to the improvement of the recirculation performance characteristic. The representation accuracy of the physical model corresponds to the precision of a fourth-degree polynomial.
# Research on the Inertial Migration Characteristics of Bi-Disperse Particles in Channel Flow
## 4 Results and Discussion
### A Pair of Single-Scale Particles
#### 4.1.2 Effect of Blocking Ratio  
Figure 7 shows the change in the distance between a pair of single-scale particles with different blocking ratios calculated by the 2000 \(\times\) 150\(\Delta x\) flow domain during the migration process. The initial distance is \(l\) = 2D and Re = 20. It can be seen that, the smaller the blocking rate, the larger the horizontal spacing between particles, which is consistent with the numerical results of [PERSON] et al. (2010); when the blockage ratio is small, the restriction of the pipeline to the particles is weaker, and the spacing of the particles cannot be kept stable.
# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 3 Methodology and Application of Indoor Localization in Various Fields
### Transforming Industrial Operations with Indoor Localization Technologies
#### 3.2.4 Fast and Robust Wi-Fi Localization Methodology for Dynamic Industrial Environments
The combination of RSSI-based distance estimation, dynamic resolution control, and iterative model refinement allows the system to deliver reliable and scalable indoor positioning in dynamic and large-scale environments.
# A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods
## 3 Theoretical Background of Deep Learning Methods Used for Self-Driving Cars
### Auto-Encoder (AE)  
Large amounts of images obtained during the driving process lead to the explosive growth of dimensionalities, which will reduce the effectiveness of the calculation. To deal with this problem, Auto-Encoder (AE) is often applied, which is mainly used for dimensionality reduction and feature learning. AE is mainly composed of an encoder network, and a decoder network (see Figure 7).  
As shown in Figure 7, the encoder is used to convert the high-dimensional original data into a low-dimensional vector representation, so that the compressed low-dimensional vector can retain the typical characteristics of the input data. The decoder is used to restore the original data. The training objective of AE is to minimize the error between the input and the output [35].  
On the basis of the traditional AE, many improved methods are proposed to add some constraints on the hidden layer, forcing the hidden layer to express difference from the input. For example, the Convolutional Auto-Encoder (CAE) uses the convolution layer and pooling layer to replace the full connection layer of traditional AE, which can retain the spatial information of the two-dimensional signal. In the decoding process of CAE, deconvolution is used, which can be viewed as the inverse operation of convolution. By building a deconvolution network, the low resolution of feature representations can be mapped to the input resolution and the network generates accurate boundary localization with pixel-wise supervision [36].