esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensitive Information Detection Based on Deep Learning Models
## 3 Improved YOLOv8 Model
### Improving Model Detection Performance  
In pornographic sensitive images, targets such as body parts may vary greatly in size and shape, making it challenging for traditional convolutional networks to effectively capture both these details and large-scale features simultaneously, resulting in lower detection accuracy. To address this issue, we attempt to improve the original YOLOv8 convolutional network. Inspired by the DetectoRS architecture proposed by [PERSON] et al. for object detection [20], we incorporate the SAConv from the DetectoRS architecture into the C2F module. By integrating SAConv into the Bottleneck structure of the C2f module, we can adaptively make it more effective in handling objects of different sizes. Specifically, this improvement primarily involves replacing the Conv in the Bottleneck of the C2F module with SAConv, which we refer to as C2F_SAC. Its structural diagram is shown in Figure 5.  
SAConv utilizes different dilation rates to perform multi-scale convolution processing on input features, dynamically selecting the most suitable convolution output for each position on the feature map through a switch function. This mechanism enables SAConv to adaptively adjust the receptive field size of its convolution kernel based on changes in the size of the target object, effectively capturing feature information ranging from details to large scales. Additionally, SAConv adopts a weight sharing strategy, where convolutions with different dilation rates share base weights and are supplemented with a learnable difference. This aims to maintain the compactness of model parameters while ensuring efficiency and flexibility during the transition from pretrained models. This design, which combines multi-scale perception capabilities and parameter efficiency, significantly enhances the model's recognition performance for objects of various scales. The structure of SAConv is illustrated in Figure 6, where a switch function S is computed by using  
Figure 4: Network structure diagram of SPPF and SimCSPSPPF.  
an average pooling layer and a 1 \(\times\) 1 convolution layer before the convolutional layer. Then, S and 1-S are multiplied with the results of convolutions with different dilation rates, respectively, and finally, the results of these two parts are added together to obtain the final output.
# Investigation of Point-Contact Strategies for CFD Simulations of Pebble-Bed Reactor [PERSON]
###### Abstract  
This study numerically investigated the effects of various contact strategies on the thermal hydraulic behavior within a structured bed of 100 explicitly modeled pebbles. Four contact strategies and two thermal hydraulic conditions were considered. The strategies to avoid contact singularities include decreasing the pebble diameter, increasing the pebble diameter, bridging the pebble surfaces near the contact region, and capping the pebble surfaces near the contact region. One strategy, Strategy 3a, which involves bridging with a cylinder equal to 10% of the pebble diameter, was selected as the baseline strategy because it addressed the contact singularity while minimizing the geometric changes that affect the bed porosity. The two thermal hydraulic conditions were full-power operation (Case 1) and pressurized loss of forced cooling or PLOFC (Case 2). Simulations of the conjugate heat transfer within the structured bed were performed using the Reynolds-averaged Navier-Stokes approach with the realizable k-\(\epsilon\) turbulence model and two-layer all \(y^{+}\) wall treatment. The thermal-fluid quantities of interest were compared between the contact strategies for each case. In Case 1, the hydraulic behavior was sensitive to the contact strategy, with large differences in the pressure drop (30%) and volume-average velocity (4%). The thermal behavior was not sensitive, with less than a 0.5% difference across the strategies. To better understand the separate effects of each heat transfer mode, Case 2 was divided into the following subcases: conduction (Case 2a); conduction/radiation (Case 2b); and conduction/radiation/convection (Case 2c). Case 2a represents an early phase of the PLOFC transient. Case 2b represents an intermediate phase of the PLOFC transient, with the pebble temperatures sufficiently high for the radiative heat transfer to be non-negligible. Case 2c represents a late phase of the PLOFC transient after the establishment of the natural circulation of the heat transfer fluid. For Case 2, large differences in the contact strategy were observed only in Case 2a with only conduction. The difference in the maximum pebble temperature was 23% in Case 2a, 2% in Case 2b, and 0.3% in Case 2c.  
pebble; packed; bed; gas-cooled; salt-cooled; reactor; contact; singularity +
Footnote †: journal: Applied Sciences
# Intelligent Trajectory Prediction Algorithm for Reentry Glide Target Based on Intention Inference
## 6 Simulation
### Influence Factors of Intention and Inference Effects  
Based on the intention inference network in Section 5, the change of influence factors and threat value case 1 are as shown in Figure 14, in which the intent key point is key point 1. The relationship among the modified filtering trajectory, the key points, and the no-fly zones are shown in Figure 14a. As can be seen from Figure 14c-f, the time-to-go of the intent key point is relatively small, the cross-range is close to 0, while the longitudinal range gradually rises. The relative influence also changes as time goes on. The importance of the key point c decreases rapidly as it leaves the reachable region of the target, and its threat value also decreases rapidly, as shown in (f). As can be seen in (b), the threat value  
Figure 12: Effect of parameter modification network: (**a**) is the filtering result of the observed control parameters; (**b**) is the modification result of the filtering control parameters; (**c**) is the prediction result of the control parameters values.  
Figure 13: Effect of parameter value network: (**a**) is the lateral modified filtering trajectory; and (**b**) is the longitudinal modified filtering trajectory.  
can be calculated through the intention inference network. The threat value of key point 1 rises over time and is higher than other key points, which finally leads to the accurate prediction of intention inference.  
When evaluating the intention inference network on the testing set, the accuracy of intention inference can reach 98.7%, and it can only reach 87.3% referring to the design function in the literature [15].
## 3 Results and Analysis
### Calibration and Validation of Sweetness Estimation Models
#### 3.4.2 Sweetness Estimation Based on Hyperspectral Features from CARS  
The model accuracy under the CARS algorithm has also been improved after SNV and MSC spectral preprocessing(Tables 4 and 5; Figure 7). During the T1 period, the R\({}^{2}\) increased from 0.22 to 0.47, and the RMSE decreased from 1.55 to 1.17. In the T2 period, the R2 increased from 0.19 to 0.33, and the RMSE decreased from 1.63 to 1.43. During the T3 period, the R\({}^{2}\) increased from 0.30 to 0.55, and the RMSE decreased from 1.46 to 1.08. During the T4 period, the R\({}^{2}\) value improved from 0.62 to 0.77, while the RMSE reduced from 1.53 to 1.37.  
The accuracy of kiwifruit sweetness estimation models established by features screened by the CARS method were shown in Table 4 and Figure 7. All the models in the whole harvest period (T4) were the best. The optimal sweetness prediction model under the CARS algorithm at the T4 stage was MSC-BP. The fitting line between predicted and mea  
Figure 6: Scatter distribution between the predicted and measured sweetness in each harvest stage with features by SPA.  
Figure 7: The validation of sweetness estimation models with features by CARS algorithm.  
sured sweetness values was close to the 1:1 line, with R\({}^{2}\) being 0.77 and RMSE being 1.37 (Figure 8). Overall, the accuracy of models constructed with the features extracted from SNV and MSC spectra was improved compared to that of the model under SG spectra.  
For other harvest periods, the prediction results of the T3 period were relatively high. The best estimation model in T3 was built by combining features under SNV transformation and PLS regression method, with R\({}^{2}\) of 0.55 and RMSE of 1.08, followed by the T1 period. The best model was obtained by the MSC-CARS-PLS method in the T1 period, with R\({}^{2}\) of predictive models between 0.22 and 0.47, and RMSE between 1.17 and 1.55. The accuracy of the prediction models was lower in the T2 period, with R\({}^{2}\) ranging from 0.19 to 0.33, and RMSE from 1.44 to 1.63.
## 5 Security Analysis
### Formal Security Proof  
We start with the basic concepts needed for the security proof, then show protocol \(\mathcal{P}^{\prime}s\) security under the elliptic-curve computational Diffie-Hellman (ECCDH) assumption. The ECCDH assumption [31], a variation of the Diffie-Hellman problem, states that given a random pair \((aP,bP)\) in group \(G\), no probabilistic polynomial-time (PPT) adversary \(\mathcal{A}\) can compute \(abP\) with significant advantage.
## 2 Sensors and Test Methods
### Input Channels  
This research activity, as previously mentioned, aims to estimate the sideslip (\(\beta\)) angle through the use of neural networks (NN). The input channels for the NN, which are useful to achieve the mentioned evaluation, are presented in Table 1.  
A block diagram that summarizes the structure of the neural network inputs is depicted in Figure 1.  
These channels are essential in the field of vehicle dynamics, as they are useful to describe the vehicle's behavior during both cornering and straight-line driving. For this reason, these variables were chosen as inputs for the neural network to estimate the sideslip angle.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Input Channels** & \\ \hline
**Input Channels** & **Unit of Measure** & **Nomenclature** \\ \hline Longitudinal acceleration & m/s 2 & \(a_{x}\) \\ Lateral acceleration & m/s2 & \(a_{y}\) \\ Steering angle & deg & \(\delta\) \\ Yaw rate & deg/s & \(r\) \\ Wheels speed for each corner & rpm & \(\omega_{ij}\) \\ Vehicle longitudinal velocity & m/s & \(v_{x}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input channels for the neural network.  
Figure 1: Neural network input channels.  
In addition, to acquire data in different conditions, several analyses were performed by varying both the road and the tire conditions. Indeed, in this way, it was possible to understand the influence of these variables on the sideslip angle estimation.
# Trade-Off Analysis of Hardware Architectures for Channel-Quality Classification Models
## 5 Results  
We divide the presented results into three subsections. The first two compare our four ML hardware implementations to measure the resources employed for each algorithm in terms of the following metrics: (1) latency: number of clock cycles counted from the beginning of algorithm execution until obtaining an output (low or high channel quality); (2) Look-Up Tables (LUTs): hold a custom truth table that is loaded when the chip is powered up; (3) Flip-Flops (FFs): basic memories for sequential logic operations; (4) Digital Signal Processor (DSP): a set of instructions for applications that require numerical operations at very high speeds; (5) Minimum Period: measured in \(ns\), calculates the worst case path timing from clock edge to clock edge for flip-flops within the design; (6) Maximum Frequency: measured in \(MHz\), it is obtained by calculating the inverse of the minimum period; (7) Throughput: measured in \(bps\), it is the number of bits that can be processed per second; (8) efficiency: measured in \(bps/LUT\), it is the ratio between the throughput and the number of LUTs; (9) Balanced Accuracy: in the binary case, it is described by the mean of sensitivity and specificity or by the area under the ROC curve with binary predictions rather than scores; (10) Sensitivity (also called recall): is the true positive rate and defines how well the positive class is predicted; and (11) Specificity: is the true negative rate and defines how well the negative class is predicted. In the third one, we compare our hardware implementations with previous works in terms of the resources mentioned before. However, the comparisons are not 100% fair since the implementations aim for different objectives using different datasets with variable sizes. As far as we know, there are no hardware implementations for the channel-quality classification model.  
Figure 12: Support Vector Machine Hardware Architecture.
## 7 Numerical Homogenization Study
### Materials and Method  
For the spherical configuration, the two volume fractions \(\mathrm{P}_{1}=0.1\) and \(0.4\) are considered. In contrast, for the ellipsoidal configuration, the volume fraction is limited to \(\mathrm{P}_{1}=0.1\) and \(0.2\). This limitation to a maximum volume fraction of \(0.2\) is due to the shape of the ellipsoid, which does not allow for exceeding this fraction without the coated inclusion overlapping with the matrix.  
For a more general study, several contrasts \(C_{1}=E_{2}/E_{3}\) and \(C_{2}=E_{1}/E_{2}\), defining the different phase's materials are treated.  
In the case of numerical homogenization, specific boundary conditions are imposed. In this study, the boundary condition \"Kinematic Uniform Boundary Conditions (KUBC)\" is used for the determination of the effective bulk modulus \(k^{eff}\) and the effective shear modulus \(\mu^{eff}\) for the two considered configurations.  
This condition consists of imposing a displacement \(\underline{x}\) on the contour \(\partial V\) such that:  
\[u=D.\underline{x}\quad\forall\underline{x}\in\partial V \tag{8}\]  
with D a symmetrical second-rank tensor of imposed macroscopic strains, such as:  
\[D_{k}=\begin{bmatrix}1/3&0&0\\ 0&1/3&0\\ 0&0&1/3\end{bmatrix}\text{ and }D_{\mu}=\begin{bmatrix}0&1/2&0\\ 1/2&0&0\\ 0&0&0\end{bmatrix} \tag{9}\]  
The effective bulk modulus \(k^{eff}\) and the effective shear modulus \(\mu^{eff}\) are defined by Equations (10) and (11), respectively  
\[k^{eff}=\frac{1}{3}(\langle\sigma_{11}\rangle+\langle\sigma_{22}\rangle+ \langle\sigma_{33}\rangle) \tag{10}\]  
\[\mu^{eff}=\langle\sigma_{12}\rangle \tag{11}\]  
With  
\[\langle\sigma\rangle=\frac{1}{V}\int_{V}\sigma.dV \tag{12}\]  
The symbol \(\langle\rangle\) designates the average value.  
To mesh the different microstructures studied, a free mesh is used with an average of 50,000 quadratic 10-node finite elements as shown in Figures 12 and 13.
###### Abstract  
The satellite-to-ground optical communication system suffers from atmosphere turbulence severely. It is well-known that the coherent detection can increase the receiver sensitivity and performing aperture averaging can reduce the scintillation caused by the atmosphere turbulence. In this paper, the bit error rate, the outage probability and the average capacity of a coherent satellite-to-ground optical communication downlink with aperture averaging are analyzed. The Log-normal atmosphere turbulence model and BPSK (Binary Phase Shift Keying) modulation is employed. The analyzing focuses on the improvement of aperture averaging with different atmospheric conditions and zenith angles of the satellite. The bit error rate performance based on measuring data is given too. The results demonstrate the bit error rate and the outage probability can be reduced and the average capacity can be improved by aperture averaging efficiently. When the turbulence is stronger and the zenith angle is larger, the effect of aperture averaging is more obvious. The aperture averaging effect on BER (Bit Error Rate) is better than the effect on average capacity.  
satellite-to-ground; coherent; aperture averaging; BER; outage probability; average capacity 0000-0001-5000-00001-5000][PERSON]  
10 October 2018 27 November 2018 5 December 2018
## 6 Results and Discussion
### Performance Metrics for One Session  
The performance metrics provide a comprehensive assessment of the models' efficacy in accurately classifying gestures within the GRABMyo dataset. Key evaluation criteria such as accuracy, precision, recall, and F1-score were computed to gauge the models' performance across various classification tasks. The computed values for the model employing the PCA method for dimensionality reduction are presented in Table 3, while those for the model utilizing the LDA method are presented in Table 4. The following tables compare the performance metrics obtained for the three machine learning models: KNN, NB, and the SVM. The model utilizing PCA has an average accuracy across the five classes of 76.55% for KNN, 75.44% for NB, and 81.76% for the SVM. Meanwhile, the model utilizing LDA has an average accuracy across the five classes of 83.25% for KNN, 76.55% for NB, and 86.97% for SVM, as shown in Tables 3 and 4.  
The analysis reveals that the LDA-SVM model yields the highest accuracy for the processed signals. Based on this preliminary assessment, the subsequent Section 6.4 will present the outcomes of processing the complete GRABMyo dataset of 645 samples and its corresponding results.
# Could We Realize the Fully Flexible System by Real-Time Computing with Thin-Film Transistors? +
## 3 Architecture and System Overview  
As shown in Figure 4, we apply the AIP architecture on flexible electronic to realize energy-efficient and high-speed image processing. It is widely accepted that analog circuits are faster and more  
Figure 3: Tradition perception signal processing versus analog-to-information processing method.  
Figure 2: (**a**) Three-layer perceptron; (**b**) Curve of non-linear (NL) unit.  
energy-efficient compared with digital circuits. Therefore, we make full use of the advantages of the analog domain processing to solve the low carrier mobility problem of flexible devices. Although the unstable flexible devices and analog circuit will introduce noise, the algorithms we use have the fault-tolerant characteristics.  
The overall system architecture is illustrated in Figure 4. This architecture is aimed at realizing a fully flexible system, including sensor, memory, data converter, and the most important signal processing. The image sensor, as we mentioned before, is supposed to be the active pix sensor (APS) architecture to acquire current-mode pixel output. The outputs of the pixel are directly attached to the inputs of the Gaussian convolution unit. The input voltages are generated from on-chip memory and digital-to-analog converter (DAC), thus we can change the \(\sigma\) value according to the application requirement. The Gaussian convolution unit is organized as convolution mask, whose size is determined by the maximum possible \(\sigma\) value. The number of Gaussian convolution unit integrated into the image sensor decides the calculation parallelism, which will be analyzed later. But it also depends on the constraints of chip area, wire layout, and available power, so it requires careful planning.  
The results of Gaussian convolution are sent to the AIP feature processing unit to do features extraction and classification. As presented in Section 2.2.2, input image that has been pre-processed is directly attached to the inputs of the multiply-accumulate units. After the operations of MLP, output features could be buffered, and read out for display or other application, under the control of address and bus controller. In this architecture, the sensing and computing are both done in the analog domain. There is no clock participating in the convolution process so that the calculation time is only related to the settling time of the circuit. In this way, the image processing could implement in real time using the relatively much slower Oxide TFT devices.
# Integrated System for Official Vehicles with Online Reservation and Moving Path Monitoring
## 2 System Architecture
### Set-Top-Box (STB)
#### 2.1.1 Power Supply Module  
A lithium battery with high-temperature adaptability is used in the power supply module. The battery supplies 3.7 V and 2400 mAh and is rechargeable more than 500 times.  
Figure 1: Monitoring management for official vehicles system structure diagram.  
Figure 2: Development Architecture of the STB Monitoring Device.  
The system voltage is maintained at lower than 2.95 V. Charging the battery is stopped when the system voltage becomes higher than 4.2 V. The battery is equipped with a booster circuit with an operating frequency of 150 kHz and conversion efficiency of 85% to provide DC 5 V to the control system with a maximum output of 0.4 A.
# A Survey of NFC Sensors Based on Energy Harvesting for IoT Applications
## 3 NFC Sensors
Figure 12: Number of NFC-enabled mobile devices worldwide from 2012 to 2018 (in millions of units). One example of the above is the design of biopatches for body temperature monitoring (see Reference [60]). Here, the sensor was based on a thermistor and a Wheatstone bridge, where the internal ADC of the RF430 FRL152H NFC IC from Texas Instruments was used. The biopatch can be used as a data logger if a small 1.5-V battery (with 30 days of autonomy) is used or in passive mode for instant temperature measurement. Analog inputs are used to read the temperature sensor, and the values read by the ADC are stored in the ferroelectric random-access memory (FRAM) to be downloaded when required. The timer is responsible for managing the time intervals. Another example of a biopatch for measuring temperature or light intensity, implemented in the form of an adhesive E-tattoo, was presented in Reference [61]. An RF430 FRL152H NFC IC drives a light-emitting diode (LED) and a phototransistor that is able to detect backscattered or ambient light. Analog signals from sensors such as the thermistor and phototransistor are digitized with the ADC inside the NFC chip. These data are then transmitted by NFC. A Cu foil is laminated on thermal release tape (TRT). The circuit is made with a mechanical cutter plotter and is transferred onto water-soluble tape (WST) backed by Kapton tape. The NFC chip and the components are attached with solder paste. By dissolving the WST with water droplets, the whole circuit is transferred to the target substrate, which is water-vapor-permeable Tegaderm adhesive. Finally, another Tegaderm layer is used to provide protection from the skin.
# An Aeromagnetic Compensation Algorithm Based on a Temporal Convolutional Network
## 3 Experimental Details
### Evaluation Metrics  
The standard deviation (STD) of the residual magnetic interference after aeromagnetic compensation and the improvement ratio (IR) are usually used to evaluate compensation accuracy. The standard deviation can be expressed as follows:  
\[\text{STD}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(x_{i}-\mu)^{2}} \tag{40}\]  
where \(x\) represents the residual magnetic interference of the signal after aeromagnetic compensation, which is obtained by bandpass filtering (usually within 0.1-0.6 Hz frequency [11]); \(i\) represents the \(i\)-th sampling point; \(\mu\) represents the arithmetic mean within the bandpass; and \(n\) represents the number of sampling points, which is consistent with the length of the original signal before aeromagnetic compensation.  
The improvement rate is used to evaluate the degree of reduction in magnetic interference. It is calculated as the ratio of the standard deviation of magnetic interference in the bandpass before and after compensation and can be expressed as follows:  
\[\text{IR}=\frac{\text{STD}_{u}}{\text{STD}_{c}} \tag{41}\]  
where \(\text{STD}_{u}\) represents the standard deviation of the magnetic interference in the bandpass of the original signal; \(\text{STD}_{c}\) represents the standard deviation of the magnetic interference in the bandpass after aeromagnetic compensation. The higher the improvement ratio, the higher the compensation accuracy.