esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experiments and Simulation Methods  
To embody the ferroelectricity in FE-TCMOS (Figure 1a,b), a metal-ferroelectric-metal (MFM) capacitor was first fabricated using hafnium zirconium oxide (HZO) as ferroelectric material, and polarization-electric field (P-E) characteristics were obtained. Then, the simulation parameters of the ferroelectric material were achieved by fitting the simulation data to the measurement P-E data (Figure 1c) using the Sentaurus 2-D TCAD simulations where Preisach model was used for the calibration [18]. The equation for the model is as follows:  
\[P_{aux}=c\cdot P_{s}\cdot\tanh(w\cdot(E\pm E_{c}))+P_{off} \tag{1}\]  
where \(E\) is electric field, \(P_{\rm aux}\) is auxiliary polarization, and  
\[w=\frac{1}{2E_{c}}\ln\frac{P_{s}+P_{r}}{P_{s}-P_{r}} \tag{2}\]  
\[\frac{d}{dt}P(t)=\frac{P_{aux}[E(t)]-P(t)}{\tau_{p}} \tag{3}\]  
where \(P_{\rm s}\) is saturation polarization, \(P_{\rm r}\) is remanent polarization, \(E_{c}\) is coercive field, and \(\tau_{\rm p}\) is relaxation time for polarization in ferroelectric material. Figure 1c indicates that the measured and the calibrated P-E curves are well-matched [15]. Here, the relaxation time \(\tau_{\rm p}\) is set to 250 ns. These ferroelectric material parameters are reflected to the gate dielectric of FE-TCMOS for the simulations, whereas high-k dielectric (\(\varepsilon_{\rm b}\) = 25) is applied instead of the ferroelectric material for TCMOS simulations. Figure 1a shows the schematic diagram of the FE-TCMOS implemented in the simulations. A P-N junction is formed by introducing an additional doped layer between the source and the drain under the channel. When a drain voltage (V\({}_{\rm D}\)) is applied, the band-to-band tunneling (BTBT) is generated at the drain-side P-N junction of the layer, and thus the P-N junctions formed by adding the doped layer can be modeled as a drain-side tunnel junction diode. Figure 1b is the circuit schematic diagram of the FE-TCMOS, which shows that the tunnel junction diode is connected to the V\({}_{\rm out}\) node as contrast to a conventional CMOS. Specific simulation parameters are listed in Table 1.  
Figure 1: **(a)** The schematic diagram of TCMOS consisting of ferroelectric-gate field effect transistors (FE-TCMOS). The ferroelectric layer is inserted between gate and oxide interfacial layer. **(b)** The circuit schematic diagram of FE-TCMOS. Tunnel junction diode is located under the drain-side channel. **(c)** Experimental polarization-electric field (P-E) curve and calibrated P-E curve by technology computer-aided design (TCAD) simulation.
## 2 Instrumental Concept and Performance
### Gas Flow Control  
The flow of the foaming gas is controlled by a FG-201 CV mass flow controller (Bronkhorst, AK Ruurlo, The Netherlands) operating at an upstream pressure of 3 bar. The gas flow rate can be adjusted between 0-30 mL min\({}^{-1}\). The three foam cells have separate gas circuits with individual mass flow controllers. This opens the possibility to prepare foams while another is measured or to define different foam states with a fast shift between different columns. The devices are connected via RS232 connections to a serial device server (NPort 5450, Moxa, Taipei, Taiwan). Here, the RS232 signal is converted into an Ethernet signal, which is transmitted via LAN to the instrument control. Communication is realized on a dynamic data exchange (DDE) server implemented in the supplier's FlowDDE software (Version 4.81). With this connection established, the devices are controlled using the DDE client program FlowView (Version 1.23) also distributed by the supplier.  
Figure 3 shows the results of a foaming experiment in which 12 mL of tetrade-cyltrimethylammonium bromide (C\({}_{14}\)TAB) (\(c=3.5\) mM) surfactant solution were foamed with synthetic air at a flow rate of \(\dot{V}=30\) mL min\({}^{-1}\). Pictures of the foam column were taken in a time interval of 80 s and the respective foam heights extracted by image analysis. Neglecting foam decay, a theoretical foam height \(h_{th}(t)\) was calculated using Equation (2) for comparison, which is based on the volumetric gas flow rate and the cross-sectional area of the foam cell.  
Figure 3: Test of the foaming procedure and tightness of the gas flow system. (**a**) Three pictures of a foaming C\({}_{14}\)TAB (\(c=3.5\) mM) solution at different times. (**b**) Foam height as a function of time (black squares) and the theoretical height (red line) according to Equation (2).  
Figure 2: Overview of the sample environment (SE). (**a**) Backview of three foam cells on folded aluminum construction. (**b**) Technical drawing of the complete SE mounted on the breadboard. (**c**) Front view of the SE (technical drawing) without shielding.  
\[h_{th}(t)=\frac{\dot{V}\cdot t}{A} \tag{2}\]  
Here, \(\dot{V}\) is the volumetric gas flow rate, \(t\) is the foaming time, and \(A\) is the cross-sectional area of the foaming cylinder.  
The measured values are in good agreement with the theoretical foam height as shown in Figure 3b. This shows that the gas flow system works and the foam is formed as intended.
# Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs
## 3 Proposed Methodology  
While prior research has significantly advanced GAN-based data augmentation for IDS, challenges remain in stabilizing training, ensuring realistic feature representations, and improving the alignment between synthetic and real minority-class samples. Most existing approaches rely on either basic data augmentation or architectural modifications to enhance sample diversity. There is limited focus on optimizing the feature space alignment between real and synthetic data.  
To address this gap, we propose incorporating SNNL into GAN frameworks. Unlike conventional GAN-based augmentation techniques, which primarily focus on generating more data, SNNL enhances the quality of synthetic samples by enforcing intra-class compactness and inter-class separation in the feature space. This improves the classifier's ability to distinguish minority-class attack samples while maintaining stable performance on majority classes.  
In the following sections, we detail the GAN architecture we focus on, the integration of SNNL into the GAN training process, and the loss functions designed.
# Towards Improved Inertial Navigation by Reducing Errors Using Deep Learning Methodology
## 3 Results
### Train and Test Dataset Collection  
For collecting acceleration data, a total of 121 levels of accelerations (0 to \(\pm\)3 m/s\({}^{2}\)) are applied to the linear motion stage, as shown in Figure 5a. These levels formulate 121 different classes for signal classification by the CNN algorithm. The data-collection rates of both EPSON and TDK IMUs are set to 500 Hz. To collect sufficient data samples, the linear stage runs a total of 100 rounds for each level of accelerations. For each round, the collected data is divided into two sets. We divided the collected IMU data, with 70% of the sets used for CNN training, 15% for validation, and 15% of the dataset for testing. The CNN is trained such that it takes raw IMU signals and learns its error patterns with respect to the IMU signals collected from the linear stage, since it generates precise accelerations. Then, the CNN maps the IMU signals into one of the 121 classes while removing these noises from learned knowledge. For collecting angular velocity data, a total of 91 levels of angular velocities (0 to \(\pm\)45 deg/s) are applied to the rotary motor, as shown in Figure 5b. The same procedure as the acceleration data is used for the gyroscope data.
# A Terminal-Oriented Distributed Traffic Flow Splitting Strategy for Multi-Service of V2X Networks
## 4 TFSS Algorithm  
A typical V2X communication system consists of three parts: the transmitting end, the heterogeneous network, and the receiving end. The heterogeneous network integrates various types of networks, such as WLAN and cellular mobile communication networks. The development of in-vehicle communication will cause congestion in IEEE 802.11p standard communication. Therefore, it is necessary to improve communication efficiency by the traffic flow splitting of the vehicle terminal (V2T). The specific characterization of this is that the user selects different transmission networks that correspond to different bandwidths and costs.
# Gaze Estimation Based on a Multi-Stream Adaptive Feature Fusion Network
## 2 Related Work
### Model-Based Gaze Estimation  
In the field of model-based gaze estimation, researchers have developed various techniques to accurately predict visual focus, which typically rely on high-resolution images to fit and track local features used for estimating geometric parameters [24]. By analyzing corneal reflections and pupil center localization, researchers can estimate the gaze vector of the eyes [25]. [PERSON] et al. [26] proposed an innovative model-based deep gaze estimation method, which improves estimation accuracy by iteratively updating individual facial shape parameters, advancing personalized gaze estimation techniques. [PERSON] et al. [27] introduced the EasyGaze3D framework, which utilizes a single RGB camera to estimate gaze direction by detecting 2D facial landmarks and reconstructing 3D facial shapes. [PERSON] et al. [28] proposed a model-based 3D gaze estimation method that employs a Time-of-Flight (TOF) camera to capture low-resolution 3D images and computes the 3D eyeball center using single-eye corner points. Although model-based methods have achieved remarkable accuracy, their reliance on specialized hardware (e.g., structured-light scanners, depth cameras, or high-resolution infrared cameras) limits their applicability, increasing system complexity and cost while posing technical and environmental challenges for real-world deployment. Moreover, model-based methods exhibit limited generalization capability, often performing well in controlled environments or specific datasets but struggling under varying demographics, environments, or imaging conditions.
# A Thorough Investigation of the Dynamic Properties of Granite under Cyclic Loading
## 3 Experimental Setup
### Test Scheme  
Figure 5a displays the triaxial testing machine utilized for the experimental investigation. The machine has a maximum force capacity of 4000 kN in the vertical direction and 2400 kN in the horizontal direction. Positioned beneath the machine is the vertical dynamic loading device, designed with a maximum power capacity of 400 kN. The loading system in both the vertical and horizontal directions has a force resolution of 0.002 kN and a displacement resolution of 0.005 mm. The maximum loading speed in the horizontal direction was set at 400 mm/min.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Name**} & **Confining Pressure \(\sigma_{\text{c}}\)** & \multirow{2}{*}{**Frequency \(f\) (Hz)**} & **Stress Amplitude \(\Delta\sigma_{\text{d}}\)** & \multirow{2}{*}{**Number of Cycles**} \\  & **(MPa)** & & & **(MPa)** \\ \hline Y5-5 & 5 & & & \\ Y5-10 & 10 & 5 & & \\ Y5-20 & 20 & 5 & & \\ Y5-30 & 30 & & 5/10/15/20/25/27.5 & 50 \\ Y1-20 & & 1 & & \\ Y10-20 & 20 & 10 & & \\ Y20-20 & & 20 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Granite specimens.  
Figure 5: Triaxial testing machine and cyclic loading scheme: (**a**) triaxial testing machine; (**b**) schematic diagram of cyclic loading.  
Figure 5b illustrates the schematic representation of the uniaxial cyclic loading and unloading tests. The loading process was conducted in two stages. The initial stage involved linear static loading, characterized by a constant load rate while maintaining a pre-set confining pressure. Subsequently, the dynamic cyclic loading stage commenced, where cyclic sine wave loading was applied until the specimen experienced failure. During the loading process, variations were made to the loading frequency, dynamic stress amplitude, and number of cycles to acquire comprehensive test data.  
Throughout the test loading phase, the vertical displacement acquisition device is capable of capturing a maximum of 2500 data points per second. The recorded data encompass dynamic load and dynamic displacement, which are instrumental in determining the dynamic stress and dynamic strain. Subsequently, these dynamic stress and dynamic strain values were employed in the subsequent step to ascertain the dynamic elastic modulus and dynamic damping ratio of the specimen.
# Applications of Mueller Matrix Polarimetry to Biological and Agricultural Diagnostics: A Review
## 2 Principles of Mueller Matrix Analysis
### Mueller Matrix Scatterometry
This method was specially developed for use in metrology down to the scale of tens of nanometers as a fast, inexpensive and non-destructive metrology method which is very useful for sustainable nanoproduction. TMS uses a double rotating compensator configuration and illuminates the nanostructure sequentially under plane-wave test conditions with different illumination directions. The polarized scattering along each observation direction is measured so as to determine the corresponding MMs.
## 4 Test Validation
### Test for the Spoofing Signal Source in the General Environment  
This section discusses a simulation scenario wherein a spoofing signal source approximately 10 km away from the carrier is set up. The motion trajectory of the carrier remained the same trajectory as depicted in Figure 7; that is, the position of the simulated signal source is at the position \([7000,7000,-100]\) in the front-right-lower body coordinate system at 500 s in the carrier motion. Using the high-precision inertial navigation as the reference value, the carrier-phase observation from the spoofing signal source to the deceived carrier is generated, introducing the normal distribution noises with \(\sigma=0.05(\text{cycles})\) for the simulated carrier phase observations.  
Figure 11a illustrates that the error in the two-dimensional heading angle of the signal source was basically less than 0.03 rad, which aligned with the expected positioning error for the signal source. When there was a difference between the distance of the signal source distance and the motion amplitude of the carrier, both the pitch and heading angles of the signal source retained high accuracy, and the accuracy of the former was higher than that of the latter. When the system was applied to signal-source position search or fused with the zeroing antenna technology, it retained high reliability and effectiveness. This also provided high-precision angle information for multiple systems to determine the position of the spoofing signal source via multi-position orientation.  
Additionally, it can be observed from Figure 11 that the two-dimensional direction error of the signal source roughly conformed to the envelope of the \(3\sigma\) line, demonstrating the filtering stability of the proposed algorithm. Table 4 lists all the maximum errors for two-dimensional directions in corresponding tests, paving the way for the quantitative analysis of the proposed algorithm.  
As indicated in Table 4, the experimental results indicate that distance has a significant impact on the directional accuracy of the method, and when the distance is too close, the pitch angle error is greater than the yaw angle error. However, the errors are within an acceptable range at all distances, suggesting that the method has good adaptability in practical applications. Combining the attitude angle error curve of inertial navigation in  
Figure 10: MIMU attitude error curves.  
Figure 10, it can be observed that the fluctuation peaks of the orientation error curve in Figures 9 and 11 are related to the changes in inertial navigation errors.
## 5 Experiment and Performance  
In order to estimate the performances of the proposed schemes, including WSN technologies, WSN management and the interconnection between the WSN and the cable monitoring system, the corresponding tests are carried out. Firstly, experimental WSN systems are established in the laboratory environment to test the performance of the WSN technologies and WSN management schemes. Then, to test data transmission reliability of the designed WSN when it works in a real complex underground coal mine environment, tests were carried out in an actual underground mine. Finally, tests were performed using an existing CMS to estimate the interconnection scheme between the WSN and the CMS.
## 2 Model Constructions and Hypothesis
### Loss Minimization in Path Analysis  
Path analysis uses the criterion of loss minimization for the model to be accurate and robust. The central concept is to minimize how large the difference is between the actual data and the values that the model has predicted; in a formula, this is expressed as loss. The loss function also indicates how far the actual covariance matrix of parameters is from that which the model has proposed. Such input measures of performance measurement additionally include chi-square (X); root mean square error of approximation (RMSEA); and the comparative fit index (CFI). These evaluate how accurate the model is when examining the data. The optimization of the process parameter of the model is carried out in such a way as to minimize the difference. This approach guarantees that the structural paths in the model are consistent with the actual data, thus explaining the relationships and causal structures between the variables.  
In this research, the relationships identified by robot acceptance in office environments include five constructs: performance expectancy, effort expectancy, social influence, perceived sociability, and behavioral intention.  
The model is illustrated in Figure 2. It identifies effort expectancy (EE), social influence (SI), and perceived sociability (PS) as exogenous constructs. Performance expectancy (PE) and behavioral intention (BI) are endogenous constructs.  
Figure 2: Theoretical model of behavioral intention to use the robot as a co-worker.  
The mathematical equations of exogenous constructs are below.  
\[\text{BI}=\ \beta_{\text{BI,PE}}.\,\text{PE}+\ \beta_{\text{BLEE}}.\,\text{EE}+\ \beta_{\text{B,SI}}.\,\text{SI}+\beta_{\text{B,IPS}}.\,\text{PS}+\ \text{Error}_{\text{BI}} \tag{1}\]  
\[\text{PE}=\ \beta_{\text{PE,EE}}.\,\text{EE}+\ \text{Error}_{\text{PE}} \tag{2}\]
## 5 The Algorithm
### d-MP with Angle Constraint  
The (\(d\);\(\theta\))-MP algorithm, which is limited by the turning angle denoted as \(\theta\), can be found by extension of the d-MP algorithm and is shown in Theorem 2[19].  
**Theorem 2**.: _Any state vector of a system considering the angle window \(X_{\angle\theta}=(x_{1},\,x_{2},\,\ldots,\,x_{n})\) is a (d;\(\theta\))-MP candidate, i.e., the maximum flow of the system \(M(X_{\angle\theta})=d\), if and only if the flow vector (\(f_{1},f_{2},\,\ldots,f_{p}\)) satisfies the following terms, i.e., Equations (5) and (7)-(9)._  
\[f_{j}\leq F_{\angle\theta_{j}}\quad\text{for}\ j\ =\ 1,\ 2,\ \ldots,\ p \tag{8}\] \[\sum_{j=1}\left\{f_{j}\big{|}\angle\theta\in P_{j}\right\}\leq M_{\angle\theta} \tag{9}\]  
where \(\angle\theta\) is denoted as the angle \(\theta\); \(F_{\angle\theta}\) is the maximum capacity of the flow considering the angle window; \(M(e_{i})\) is the maximum flow of the edge \(e_{i}\); and \(M_{\angle\theta}\) is the maximum flow of \(e_{i}\), limited by the angle window of \(\angle\theta\).  
In Theorem 2, Equation (5) is the total flow of each MP with the angle \(\theta\), and Equations (6)-(9) are the constraints of the flow of each MP with the angle \(\theta\), each edge, and each angle.
# Distributed Task Allocation for Multiple UAVs Based on Swarm Benefit Optimization
## 4 Experiment
This strategic approach endows it with a substantial swarm benefit of 2673 units. In contrast, the CBBA method and PI method attain only 2419 units and 2393 units of swarm benefit, respectively. This advantage is attributable to the BV method's capability to dynamically update task benefits that decay over time, while simultaneously maximizing the overall swarm benefit. Figure 5 presents a statistical comparison of the swarm benefits generated by three distinct methods across various numbers of UAVs and tasks. It is evident that the proposed BV method consistently outperforms the other two methods across all tested scenarios of UAV and task quantities. Notably, as the number of UAVs and tasks increases, the disparity in the swarm benefit of task plans produced by the BV method, compared to those of the other two methods, widens significantly, further underscoring its superior performance. Table 2 provides detailed statistics on the swarm benefits generated by three distinct methods across varying values of \(N_{p}\) and \(N_{u}\). It is observed that when \(N_{p}=4,N_{u}=16\), corresponding to 16 UAVs and 64 tasks, the proposed BV method achieves an average swarm benefit of 9019, whereas the CBBA method attains an average swarm benefit of 8846. The BV method demonstrates the highest improvement over CBBA in this scenario, with an increase of 1.95%. Conversely, when \(N_{p}=3,N_{u}=10\), with 10 UAVs and 30 tasks, the BV method's average swarm benefit is 4454, and CBBA's average swarm benefit is 4427, showing the lowest improvement for the BV method over CBBA, with an increase of 0.60%. When compared to the PI method, the highest and lowest improvements in the average swarm benefit of the proposed BV method occur at \(N_{p}=5,N_{u}=16\) and \(N_{p}=3,N_{u}=16\), respectively. Specifically, at \(N_{p}=5,N_{u}=16\), the average swarm benefit for PI and BV are  
Figure 4: The task plan generated by BV with 2673 swarm benefit. Figure 5: Statistical box plots of swarm benefits generated by three distinct methods. 9978 and 10,379, with an increase of 4.02%, while at \(N_{p}=3\), \(N_{u}=16\), the average swarm benefit for PI and BV are 7029 and 7093, with an increase of 0.91%. In summary, for a swarm size of 6-16 UAVs and 18-80 tasks, the proposed method in this paper achieves an average swarm benefit improvement of 0.60-1.95% and 0.91-4.02% over the CBBA and PI methods, respectively.
## 3 Applicability and Practicability Analysis of Proposal  
In this paper, we mainly consider that there are two significant usability concerns of our proposed hierarchical underwater wireless sensor transmission (HUWST) framework: (1) the ability of the underwater node to transmit data directly to the surface base station; (2) the computing capability of the underwater node to support our proposal.  
In traditional underwater wireless sensor networks (UWSNs), the sensors are enabled to collect data and transmit the collected data to the surface base station (BS) [18]. This is due to the multi-hop forwarding mechanism between sensors [18]. The transmission distance of these sensors is, however, limited [19]. Underwater sensors in the deep are unable to communicate directly with the BS. Fortunately, the sink node (SN) with strong transmission capacity and computation capability has been deployed extensively in UWSNs [20; 21].  
Each SN is responsible for an underwater area, and sensors in this area allow transmitting data to the SN. The SN and sensors in this area form a cluster, with SN as the cluster head. SNs are not only able to transmit data to BSs via multi-hop forwarding between SNs, but also directly to BSs due to their robust transmission capability [22; 23].  
Therefore, based on real applicability and practicality, in this paper, we investigated the challenges faced by SN in UWSNs. Because the SN's energy is not infinite and difficult to charge, similar to the sensor node, it also faces the problem of large energy consumption and imbalance. As for the sensors, the same as previously mentioned, the sensors only need to transmit the data to the SNs for further processing. The sensors' energy consumption is thus assumed to be similar; we do not need to discuss the transmission problem of sensors. This paper, hence, takes SNs in the framework as the minimum analysis unit. Further, SNs have sufficient transmission capacity and computing power to support our proposal.