esa-sceva/satcom-chunk-collection huggingface dataset:
# RIS-Assisted Network Slicing Resource Optimization Algorithm for Coexistence of eMBB and URLLC
## 5 Simulation Results and Analysis
### Impact of RIS Configuration
#### 5.3.2 Traversal Achievable Rate Assisted by Different Number of RIS Reflection Elements  
Combining Equations (1)-(3), the traversal achievable rate of this wireless cellular network is as follows:  
\[\hat{R}=\log_{2}\det(I+\gamma_{eb}) \tag{66}\]  
where \(I\) denotes the unit matrix. It is assumed that the noise power is 100 dBm in this subsection, and the number of antenna configurations for the transmitter and receiver are 64 and 16, respectively. As shown in Figure 7, the RIS configuration achieves a better traversal achievable rate compared to the scenario without RIS deployment. As the number of reflective elements increases, the traversal achievable rate increases significantly, and the overall cost is lower due to the passive nature of the RIS elements. Many large-scale transmission scenarios, especially when mobile users use a large number of antennas in order to achieve a significant increase in traversal achievable rate, will increase the cost of signal processing and the required hardware. By using RIS in transmission, the cost can be reduced while ensuring the traversal achievable rate, so RIS has great potential for future wireless networks.
## 3 Proposed System
### Service Model Management Block
#### 3.6.2 Model Management Module (MMM)  
In this model management module (MMM), the base models for the model selection module are managed by getting the service results and performances from the energy control service. Since this is about time-series forecasting, the measurement data differ as  
Figure 3: The optimal model selection flowchart.  
time goes by and may contain different characteristics, such as their trend, seasonality, and stationarity. Forecasting models may not be able to represent the new patterns. Therefore, if the service performance is lower than 80%, the MMM sends a re-training request to the SMTM. In addition, for the changing characteristics of time-series data, it is important to re-train the base models after a certain period of time. For instance, the energy usage for heating may increase during the winter season in comparison with the summer season, so even though measurement data are accumulated, the patterns will be different or even unknown to the forecasting models. For this reason, re-training is essential to maintain the energy control service performance and meet user requirements.
## 3 Results and Discussion
### Flow Characteristics of the New RFB  
A numerical analysis of the new configuration (Figure 17b) showed that the flow velocity distribution at the electrode inlet was more uniform than the conventional RFB (Figure 17a). The flow distribution at the electrode inlet indicated that the flow was uniformly distributed (Figure 18). In this uniform flow, the variability range coefficient (\(R_{i}\)) significantly decreased from 0.189 to 0.030 m/s at the electrode inlet (y = -148 mm). Moreover, the symmetry coefficient (\(C_{sym}\)) at each bifurcation improved by less than 1% (Figure 19). The maximum flow rate deviation (\(D_{m}\)) was 0.66%, which was significantly lower compared with the previous value of 17.9%. This confirms that the flow distribution at the bifurcations and outlets became uniform.  
Figure 16: Comparison of chevron locations in the (**a**) conventional design and (**b**) new design.  
Figure 15: New channel design of the flow frame of RFB.  
When the pressure drop of the new design was compared with the conventional design, the pressure drop decreased by 15.3% and 11.6% at the inlet and outlet channels, respectively (Table 5). There was a difference in pressure drop, even though the inlet and outlet channels had the same geometry, because the pressure drop is not affected by the pressure drop.  
Figure 17: Velocity magnitude contour at the electrode inlet region in the (**a**) conventional design and (**b**) new design.  
Figure 18: Velocity distribution at the electrode inlet for the new RFB design.  
the Zn/Br solutions had different flow directions. Consequently, the new design exhibited an average 13.3% reduction in pressure drop for the whole flow frame geometry.
## 3 Methods
### Description of the Driving Process Based on HSMM  
The HMM has been widely used to describe the dynamic characteristics of a driver's behavior [33]. However, HMM has two significant disadvantages. One is that the number of hidden states must be set in advance. The other is that HMM does not take the state duration into consideration. To overcome the shortcomings of HMM, the hierarchical Dirichlet process-hidden semi-Markov model (HDP-HSMM) is introduced. HDP can provide prior and hidden state numbers for HMM with excellent clustering characteristics and the hierarchical sharing principle ability. The hidden semi-Markov model (HSMM) is an extension of HMM. It allows each state to have a variable duration through a semi-Markov chain. Therefore, HDP-HSMM has automatic clustering capacity and can describe stochastic characteristics in the driving process [34; 35].  
The driving process consists of two layers: a hidden state layer and observation state layer, as shown in Figure 7.  
In Figure 7, the shaded nodes represent the observable variables, \(y_{t}\), the unshaded nodes represent the driving behavior, \(z_{s}\), and \(D_{s}\) denotes the duration of a behavior. HSMM can be expressed by  
\[\pi_{0}=\frac{1}{S} \tag{7}\]  
\[\pi_{ij}=p(z_{t+1}=j|z_{t}=i,z_{t+1}\
eq i) \tag{8}\]  
\[D_{s}\sim g(\omega_{s}) \tag{9}\]  
Figure 6: The proposed framework to analyze driving styles based on the State-Action semantic plane.  
Figure 7: A graphical model of HSMM.  
\[y_{t}|z_{s},d_{s}\sim\mathrm{F}(\theta_{z_{s}},D_{s}) \tag{10}\] \[\pi_{ii}=0,\sum_{j}\pi_{ij}=1 \tag{11}\]  
where \(\pi_{0}\) is the prior probability distribution, \(\pi_{ij}\) is the transition probability representing the probability of transferring from driving behavior \(i\) to \(j\). \(y_{t}\) indicates the observation distribution of a current hidden state, and \(\theta\) represents a model parameter, \(g\) (\(\omega_{s}\)) is the state-specific distribution over the state duration, \(\omega_{s}\) is the parameter for states. \(g\) (-) is the Poisson distribution. In HSMM, the state duration is closer to the actual driving state.
# AdaptiveConv2d: A Novel Convolutional Module for Medical Image Segmentation
## 2 Materials and Methods
### Explanation of Key Functions  
**ReLU (Rectified Linear Unit):** The ReLU function is a widely used activation function in neural networks. It is defined as  
\[\text{ReLU}(x)=\text{max}(0,x)\]  
This function introduces non-linearity by setting all negative values to zero and leaving positive values unchanged. In the context of channel weight generation, ReLU is used to ensure that the output of the fully connected layer remains non-negative, enabling the channel weights to be learned in a stable and interpretable manner.  
Figure 4: Dynamic channel weight generation and optimization framework. This figure illustrates the process of dynamically generating and optimizing channel weights using both global context and energy function constraints. The channel weight \(w_{c}\) is processed through several steps, including multiplication with weight matrices, the addition of regularization terms, and the application of activation functions (ReLU and Sigmoid). This framework helps to adaptively adjust channel weights based on local features and global context, improving robustness and performance for tasks such as medical image segmentation.  
**Sigmoid Function:** The Sigmoid function is another activation function that maps input values to a range between 0 and 1, making it ideal for probabilistic outputs. It is defined as  
\[\sigma(x)=\frac{1}{1+e^{-x}}\]  
In the channel weight generation process, the Sigmoid function is used to scale the weights so that they fall within a bounded range, ensuring that the final channel weights are normalized and appropriate for further processing in the model.  
This dynamic approach to channel weight generation and optimization leverages both local and global information, improving the model's ability to capture complex patterns in the data while maintaining stability during training.
# Consumer Perceptions and Attitudes Towards Ultra-Processed Foods
## 5 Data Analysis
### Sentiment Analysis  
Following preprocessing and filtering, 113 responses remained, reflecting user perceptions about this food category. They were analysed using sentiment analysis, a machine learning technique, yielding the following results: 30 negative responses (average score: \(-0.172\)), 28 neutral responses, and 55 positive responses (average score: 0.241). Overall, respondents generally did not support the spread of UPFs despite their convenience and lower cost, particularly for busy individuals or those on a budget. The sentiment analysis was performed by computing a polarity value for each text entry and categorising it as positive, negative, or neutral, based on its numerical value, using Python 3.12 and the TextBlob 0.19.0 library.  
The detailed analysis showed that a significant portion of the group wished to reduce or avoid UPF consumption, citing factors such as newly acquired nutrition knowledge, personal health improvements, and a desire to make better dietary choices. However, convenience and lower costs remained major barriers for others, particularly those in busy or low-income households, as UPFs are often more accessible and affordable. The call for stronger regulations is evident among a noteworthy number of respondents, who suggested policies ranging from better product labelling to outright bans, emphasising the need for government intervention to protect public health. While a significant number of respondents reported a shift in their attitudes toward UPFs, induced via increased awareness and education, others remain indifferent or unchanged in their stance. Throughout, the tension between personal choice and systemic issues was evident, as many argued that increased public awareness, stricter regulation, and the availability of healthier food alternatives could help curb UPF consumption.
# Bearing Fault Diagnosis Using Grad-CAM and Acoustic Emission Signals
## 3 Experimental Setup and Data Acquisition  
To validate the proposed method, a bearing fault simulator was used for measuring healthy and faulty-state AE signals of the rolling element bearing. The established bearing fault simulator is illustrated in Figure 7. On the drive-end shaft, a three-phase induction motor was connected to a gearbox by flexible coupling. The gearbox transferred the torque of the induction motor to the non-drive-end shaft with a gear reduction ratio of 1.52:1. A tachometer was installed to measure the rotating speed of the non-drive-end shaft. A cylindrical roller bearing (FAG NJ206-E-TVP2), which was the target bearing of the experiment, was installed in the bearing housing of the non-drive-end shaft. To apply radial and axial load, a fan with adjustable blades was connected to the non-drive-end shaft via a belt. The shaft speed was 500 revolutions per minute (RPM) in this paper. An AE sensor was attached on the bearing housing of the target bearing. The measurement device for obtaining AE signals was a PCI-2-based system. A general-purpose wideband AE sensor, whose frequency response was between 100 and 1000 kHz, was used to capture resonance frequency signals containing modulated bearing signals.  
Figure 6: The flowchart of gradient-weighted class activation mapping (Grad-CAM) with an example of the CNN structure.  
Figure 8 and Table 2 shows the specification of the target bearing. The contact angle was 0 because the target bearing was a radial bearing. By using Equations (4)-(6) with the bearing parameters and shaft speed, the bearing characteristic frequencies of BPFO, BPFI, and BSF were equal to 43.68 Hz, 20.72 Hz, and 64.65 Hz, respectively.  
The seeded bearing faults, which are the outer race way with a crack on its surface (ORCS), inner race way with a crack on its surface (IRCS), and rolling element with a crack on its surface (RECS), are shown in Figure 9. The crack dimension of the bearing failures was 6 mm \(\times\) 0.5 mm \(\times\) 0.5 mm. In addition, Figure 10 illustrates an example of the AE signals for each bearing condition in the dataset. As shown in Figure 10, the healthy bearing (HB) contained less impulses than the ones in faulty conditions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Symbol in Equations** & **Value (mm)** \\ \hline Pitch diameter & \(Pd\) & 46.5 \\ The diameter of rolling element & \(Bd\) & 9 \\ Contact angle of rolling element & \(\theta\) & 0 \\ The number of rolling elements & \(Nb\) & 13 \\ Pitch diameter & \(Pd\) & 46.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The specification of the target bearing, FAG NJ206-E-TVP2.  
Figure 7: The illustration of the bearing fault simulator: (**a**) the diagram of the bearing fault simulator and (**b**) the photographs of the bearing fault simulator.  
On the contrary, the signal of bearing faults such as ORCS, IRCS, and RECS emitted more impulses created by the cyclic impacts of faults.
# AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models
## 4 AI for UAV Security
### UAV Sensing Security Technology  
Sensing security technology integrates communication and sensing functions, allowing systems to detect communication signals and continuously track UAVs while collaboratively monitoring their flight status. This approach provides an effective way to regulate UAVs. With the integration of communication and sensing functionalities, air interface resources and core network resources are shared between communication and sensing services, making it essential to enhance the security of sensing data and services alongside existing communication security measures.  
In 2016, [PERSON] et al. detailed UAV sense-and-avoid technology to enhance security by detecting other aircraft and obstacles and automatically performing evasive maneuvers [166]. In 2018, [PERSON] et al. discussed the applications of DL models in UAV visual sensing, especially in obstacle detection and monocular visual navigation in forest environments [167]. In 2020, [PERSON] et al. proposed a task-driven method of autonomous sensing and fusion based on UAV swarms that uses the distributed information fusion capability of UAV swarms to achieve autonomous sensing and state estimation of the environment [168]. In 2023, [PERSON] et al. discussed ML applications in UAV operations and communications, including key components such as sensing and feature extraction, feature inter pretation and regeneration, trajectory and mission planning, and aerodynamic control and operation [69]. In addition, UAV cooperative sensing technology enhances overall sensing capability and task execution efficiency through information sharing and collaboration among multiple UAVs.  
However, AI training approaches rely on sensing data, such as from radar, LiDAR, and camera data. However, the accuracy and reliability of these sensors directly affect the sensing results. Additionally, in UAV clustering or air-ground coordination missions, there are difficulties in fusing sensing data between different UAVs or unmanned vehicles, especially in the case of view orthogonality, communication latency, and packet data loss.  
LLMs can support the calibration of sensor data and compensate for the shortcomings of sensors in certain dimensions by learning from a large amount of data and mining the correlations of spatial dimensions. In addition, LLMs contribute to enhanced sensing accuracy and robustness through the fusion of multimodal data and simultaneously help UAVs analyze and process multimodal data. LLMs can aid in optimizing cooperative sensing strategies for UAV clusters. In particular, the multisensor cooperation strategy, driven by distributed intelligence and multimachine collaboration, improves UAV sensing security, achieving more efficient environment sensing and mission execution, as shown in Figure 13.
# Research on Reliability Assessment of Mechanical Equipment Based on the Performance-Feature Model
## 3 Method
### Pseudo Failure Characteristics Distribution and Testing Statistical Hypotheses  
Assuming the failure threshold is \(D_{f}\) and according to the obtained sample degradation model \(f(x)\), in which \(x\) is defined as an independent variable that records performance degradation data and can be time, signal characteristics, etc., the characteristic of each sample arriving at the threshold is deduced, which is called the PFC. PFC data will follow the distribution of the failure characteristic. The failure life of high reliability and long-life products generally follows Weibull distribution, logarithmic normal distribution, or Gaussian distribution.  
Primarily, we suppose that PFCs follow a distribution \(F(x)\), and \(F_{0}(x)\) is a completely determined continuous distribution function. Consider the hypothesis testing problem,  
\[\mathrm{H}_{0}:F(x)=F_{0}(x) \tag{4}\]  
For example, the PFCs follow an exponential distribution \(F_{0}(x)=1-e^{-\left(x/\theta\right)^{2}}\), and the parameter \(\theta\) can be obtained by maximum likelihood estimation, which is equal to the average of the samples. Namely, the PFC values of the products need to be proven to follow one-parameter exponential distribution parameter of \(\theta\).  
Samples \(X_{1},X_{2},\cdots,X_{n}\) are taken and the sample size is \(n\), and their sequential statistics are \(X_{1}\leq X_{2}\leq\cdots\leq X_{n}\). Arrange the samples to get their empirical distribution function \(F_{n}(x_{i})\) and calculate \(\delta_{i}\),  
\[F_{n}(x_{i})=\begin{cases}0,&x\leq X_{i}\\ i/n,&X_{i}\leq\ x<X_{i+1}\\ 1,&x\geq X_{n}\end{cases} \tag{5}\]  
\(\delta_{i}\) is calculated by Equation (6).  
\[\delta_{i}=max\{|F_{0}(x_{i})-(i-1)/n|,|F_{0}(x_{i})-i/n|\},\,i=1,2,\cdots,n \tag{6}\]  
Kolmogorov-Smirnov (KS) test parameters are listed in Table 1:  
In this table, \(x_{i}\) is the PFC of number \(i\). The biggest \(\delta_{i}\) is \(D_{n}\), and that is,  
\[D_{n}=\max_{i}\{\delta_{i}\} \tag{7}\]  
Giving the significance level \(\alpha\), the critical value \(d_{n,\alpha}\) can be obtained by looking up the critical value table of the Kolmogorov test, if \(D_{n}<d_{n,\alpha}\), it follows the hypothesis H\({}_{0}\). Otherwise, the hypothesis is rejected.
## 2 Structure Design of the Vector Propulsion Mechanism  
To implement the 2-DOF attitude adjustment (yaw angle and pitch angle) of the propeller, a 2-DOF parallel mechanism is used, as shown as Figure 1. The bulkhead of AUV connects with the propeller installation platform by the two drive branches (\(P_{2}R_{2}S_{2}\) and \(P_{3}R_{3}S_{3}\)) and one driven move branch (\(R_{1}S_{1}\)), where \(P\), \(R\), \(S\) denote prismatic joint, revolute joint, and spherical joint respectively. \(R_{1}S_{1}\), \(R_{2}S_{2}\), \(R_{3}S_{3}\) are fixed-length bars which length are \(l_{1}\), \(l_{2}\), \(l_{3}\), and, respectively, vertical to the axis of \(R_{1}\), \(R_{2}\), \(R_{3}\). For the convenience of kinematic analysis, bulkhead coordinate system \(S[O-XYZ]\) (in short, \(S\)) is established, in which, the geometric center of bulkhead is coordinate origin, then revolute joint (axis parallel to the y-axis) and prismatic joints (moving direction parallel to the z-axis) uniformly distributed at 120 degrees with zero point as the circle center. The coordinates of the joints are shown as follows:  
\[\mathbf{P}_{R1}^{S}=[R,0,0]^{\mathrm{T}},\]  
\[\mathbf{P}_{P2}^{S}=\left[-R/2,\sqrt{3}R/2,0\right]^{\mathrm{T}},\]  
\[\mathbf{P}_{P3}^{S}=\left[-R/2,-\sqrt{3}R/2,0\right]^{\mathrm{T}}\]  
where \(R\) is the distribution radius of spherical joints.  
Meanwhile, the propeller installation platform coordinate system \(M[o-xyz]\) (in short, \(M\)) is established, in which, the geometric center of platform is coordinate origin, then, the spherical joints \(S_{1}\), \(S_{2}\) and, \(S_{3}\) uniformly distributed at 120 degrees with zero point as the circle center. The coordinates of the joints are shown as follows:  
\[\mathbf{P}_{S1}^{M}=[r,0,0]^{\mathrm{T}},\]  
\[\mathbf{P}_{S2}^{M}=\left[-r/2,\sqrt{3}r/2,0\right]^{\mathrm{T}},\]  
\[\mathbf{P}_{S3}^{M}=\left[-r/2,-\sqrt{3}r/2,0\right]^{\mathrm{T}}\]  
where \(r\) is the distribution radius of spherical joints.  
As a vector propulsion mechanism, the driving joints are prismatic joints \(P_{2}\) and \(P_{3}\), which are controlled by the execution system located in the AUV bulkhead, to prevent the actuator from being exposed to harsh seawater environment.
# Power Transformer Fault Diagnosis Based on Dissolved Gas Analysis by Correlation Coefficient-DBSCAN
## 4 Example Analysis and Results  
The DGA data used in this section are from the record of fault transformer data of Huzhou Power Supply Company of Zhejiang electric power company of State Grid of China, the case data of IEC TC 10 dataset [27] and typical application cases of power grid equipment state detection technology [33]. The data cover six kinds of faults: T1, T2, T3, PD, D1 and D2. There are more than 2000 pieces of data collected. These data sources are numerous and scattered, so it is necessary to filter and sort out the data. First, initialize the data, calculate the percentage of hydrogen and various hydrocarbon gases in the gas; then, classify the data according to the fault category, calculate the mean value and standard deviation of each fault data, and eliminate the data beyond the mean value twice the standard deviation. After several rounds of screening, 60 pieces of DGA data are finally left for clustering test in this paper, of which 10 pieces are for each fault type, and each data contains five attributes: H\({}_{2}\), CH\({}_{4}\), C\({}_{2}\)H\({}_{6}\), C\({}_{2}\)H\({}_{4}\) and C\({}_{2}\)H\({}_{2}\).  
In this section, the CCDBSCAN is realized with MATLAB 2018b software system. All programs are implemented by hardware of Core i7-4710 MQ CPU, memory 8G and hard disk 1T.  
Figure 3: CCDBSCAN algorithm flow chart.
# Backstepping Based Formation Control of Quadrotors with the State Transformation Technique
## 3 Backstepping-Based Quadrotor Formation Control  
In this section, we design the leader-follower quadrotor formation controller based on the backstepping control method. We use the state transformation technique to derive the reference attitude angles for the formation control without any special assumption such as SAA.
# From Mirrors to Free-Space Optical Communication--Historical Aspects in Data Transmission
## 3 FSO Communication System Constructions
The use of these solutions allows the devices to operate in a wider temperature range, e.g., from \(-40\)\({}^{\circ}\)C to \(70\)\({}^{\circ}\)C, and prevents the frosting of the optical systems or the dewing of the lenses. In Table 1, the parameters of the FSO systems that are commercially available are presented. In the receiving circuits for some of the FSO links, special solutions may be used to regenerate the shape of the impulse waveforms. This regeneration restores the signal to its original power (Reamplifying), and restores the shape of the impulse (Reshaping) and the time relations (Retiming) by resynchronizing the local clock (resynchronization) [56].
# A Study of Stray Neutron Field Measurements for the Neutron Scattering Instruments at CSNS
## 3 Experiments and Discussion  
To verify the reliability of the adopted stray neutron measurement methods, neutron fluence spectra and H*(10) values were measured using the ERBSS and the NADED, respectively, inside the BL06 beam line at CSNS.
# Design and Validation of a U-Net-Based Algorithm for Star Sensor Image Segmentation
## 3 Results  
In this section, results of U-Net's training, validation and testing will be shown, together with a reasonable choice for \(p_{min}\) and number of initial filters. Then, a comparison test of the BOSS algorithm with traditional ones is conducted against the same set of images.