esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Dynamic Modeling and Vibration Characterization of Integrated Bearings
## 2 Integrated Bearing Dynamic Performance Analysis
### Results of the Integrated Bearing Dynamics Model  
The bearing operates at ambient temperature, with all contact surfaces of the components assumed to be ideal circles. The spindle rotates at a speed of 50,000 r/min. The radial force in the Y-direction within the radial plane is 200 N, while the radial force in the Z-direction is 0 N, and the axial force (in the X-direction) is also 0 N. The cage pocket clearance is 0.051 mm, and the curvature radius coefficients for both the inner and outer raceways are 0.529. After solving the model, the vibration velocity of the spindle's center of mass in the radial direction over time is obtained, as shown in Figures 5 and 6.  
As shown in Figures 7 and 8, the variation of the radial vibration acceleration at the center of mass of the spindle over time is obtained after solving the model. The effective value is calculated, and using Equation (15), the vibration acceleration level is determined.  
\[L=20\text{lg}\frac{a_{i}}{a_{0}} \tag{15}\]  
In the equation, (\(a_{0}\)) represents the standard reference value for vibration, which is typically taken as \(9.81\times 10^{-3}\) m/s\({}^{2}\).  
The frequency domain spectrum is shown in Figure 9. The main frequency components are 833 Hz(fr), 1666 Hz(2 fr), 2333 Hz(fr + 0.5 fi), and 3800 Hz(fi). Among them, fr represents the rotational frequency, and fi represents the bearing passing frequency. In general, the main frequency components in the spectrum are the frequency conversion, bearing passing frequency and their combined frequency. The bearing passing frequency calculation formula is shown in Equation (16).  
\[fi=zf(1+d/D) \tag{16}\]  
Here, (z) represents the number of rolling elements, (f) is the bearing rotational frequency, (d) is the diameter of the bearing balls, and (D) is the pitch diameter of the bearing.  
Figure 8: Vibration acceleration of the spindle’s center of mass in the Z-direction.  
Figure 7: Vibration acceleration of the spindle’s center of mass in the Y-direction.
## 5 Evaluations
### Experiments on Layout Approach
#### 5.1.1 Experiment Settings
Additionally, the layout results for each method are provided directly to assist in the evaluation process. For layout quality, this paper employs the following three metrics [10] for a multidimensional assessment, including edge crossing metric, shape similarity metric, and edge angle of incidence metric. **Edge crossing metric \(M_{c}\):** This metric is used to quantify the number of edge crossings in a layout, with a larger value indicating fewer edge crossings in the layout. The definition of \(M_{c}\) is as shown in Formula (8). \[M_{c}=1- \begin{cases}\frac{c}{c_{mx}}&c_{mx}>0\\ 0&otherwise\end{cases}  \tag{8}\]  
where \(c\) represents the number of edge intersections present in the network layout, and \(c_{mx}\) is an approximate upper limit of edge crossings in the network, considering that a network with \(m\) edges can have up to \(m(m-1)/2\) crossings without considering their degrees. Therefore, \(c_{mx}\) is defined as the difference between \(m(m-1)/2\) and impossible crossings, as shown in Formula (9):  
\[c_{mx}=\frac{m(m-1)}{2}-\frac{1}{2}\sum_{i=1}^{n}d(v_{i})(d(v_{i})-1) \tag{9}\]  
**Shape similarity metric \(M_{s}\):** This metric is used to assess whether the layout accurately reflects the network's shape. A higher value indicates that the layout more faithfully represents the network's shape. It is defined as the similarity between the network and the shape network generated by its layout, as shown in Formula (10). \[M_{s}=\eta(G,\mu(P)) \tag{10}\]  
where \(\eta\) represents the similarity function between two networks, \(\mu\) is the shape network function, and \(P\) represents the set of node positions in the layout \(L\)corresponding to the network \(G\). **Edge angle of incidence metric \(M_{a}\):** This metric's quantification criterion is to maximize the minimum angle of incidence for node edges, with a larger value indicating more equal angles of incidence for nodes. It is defined as Formula (11):  
\[M_{a}=1-\frac{1}{|V|}\sum_{v\in V}|\frac{\theta(v)-\theta_{min}(v)}{\theta(v)}| \tag{11}\]  
where \(\theta_{min}(v)\) represents the minimum angle of incidence for the edges of node \(v\), \(\theta(v)\) represents the ideal angle of incidence for node \(v\), and it is defined as \(360^{\circ}/d(v)\).
## 2 Materials and Methods
### Space and Channel Reconstruction Convolution (SCConv)  
Convolutional neural networks have demonstrated excellent performance with large amounts of computer vision tasks; however, this performance improvement comes at the cost of significant computational resource consumption, partly due to the redundant features extracted by the convolutional layers. In SC-FMNet, U2 Net [19] is used as the baseline, which is a two-layer nested U-Net structure. Due to the complex network structure of U2 Net, the number of model parameters required is large, especially when training on large-scale datasets; the training time is long, and the training efficiency is low.  
In this paper, we improved the MSNet network by introducing a spatial and channel reconstruction convolution module [20] (SCConv) in the coding part of the two branches, as shown in Figure 1. In each feature extraction, it is obtained by summing up the last extracted features and the previous features; therefore, the SCConv module is added to the previous features, and by introducing two modules, the Spatial Reconstruction Unit (SRU) and the Channel Reconstruction Unit (CRU), the model performance is reduced by utilizingthese two modules to learn the spatial correlation and channel correlation of the feature map, respectively, thus reducing the spatial redundancy and channel redundancy of the feature map while decreasing the computational complexity. By introducing two modules, the Spatial Reconstruction Unit (SRU) and the Channel Reconstruction Unit (CRU), the spatial correlation and channel correlation of the feature map are learned, respectively, so as to reduce the spatial redundancy and channel redundancy of the feature map, to improve the performance of the model and at the same time to reduce the computational complexity, and in this way, to realize a more fine-grained feature sextraction method. The structure of SCConv is shown in Figure 2.  
The SCConv module consists of two parts: the SRU optimizes spatial features, and the CRU optimizes channel features. First, the input feature \(X\) passes through the \(1\times 1\) convolution process from the previous convolution block (ConvBlock) and enters the SCConv module. The spatially optimized feature \(X^{\text{m}}\) is then generated after SRU. SRU reduces spatial redundancy in features and enhances useful spatial information through separation and reconstruction operations. The spatially optimized feature \(X^{\text{m}}\) is further passed into the CRU to generate the channel-optimized feature \(Y\). The CRU reduces channel redundancy through segmentation, transformation, and fusion operations while preserving the expressive power of the features. Finally, the channel-optimized feature \(Y\) is again processed by \(1\times 1\) convolution and passed to the next convolution block for further processing.  
Figure 1: SC-FMNet network structure diagram.  
Figure 2: SCConv structure diagram.
# High-Performance Garbage Collection Scheme with Low Data Transfer Overhead for NoC-Based SSDC
## 2 Background
### NoC-Based SSD Controller  
As SSD capacities and performance demands continue to grow, traditional bus-based SSD architectures face scalability and bandwidth limitations. To address these challenges, modern SSD controllers increasingly adopt NoC architectures. NoC-based SSDCs consist of a CPU core, SRAM, DRAM, and multiple NAND flash memories connected by an interconnect as shown in Figure 3. This interconnect functions as a communication backbone within the chip, providing scalability and enabling parallelism across multiple NAND flash memory channels.  
Figure 3: NoC-style SSD controller architecture.  
Figure 2: Illustration of garbage collection in SSD.  
The NoC interconnect is composed of routing slices, each of which serves as a buffer with routing capabilities. Data communication occurs as packets traverse these slices, and the total latency depends on the number of slices along the path. For GC operations, this means valid data from a NAND flash block must travel through multiple slices to reach the host-side CPU, where error correction is performed. Once processed, the data are routed back through the interconnect to its destination NAND flash memory. This round-trip significantly increases latency due to the cumulative delays introduced by slice traversal.
## 2 Overview of InSAR Methodology
### Noise Signals Corrupting SAR Interferograms
It can be modelled as [6]:  
\[I=\hat{\gamma}_{1}\cdot\hat{\gamma}_{2}^{*}=|\hat{\gamma}_{1}\cdot\hat{\gamma}_ {2}^{*}|\exp[j\varphi] \tag{17}\]6  
where \(b_{\perp}\) is the interferometric perpendicular baseline, \(\lambda\) is the operational wavelength, \(\vartheta\) is the side-looking angle, \(b_{\perp}\) is the slant-range distance from the sensor to target, \(r\) is the range resolution and \(\Delta r\) is the (average) terrain slope angle. From Equation (30), we can easily note how complete decorrelation occurs when the perpendicular baseline is larger than the critical value, defined as: \(b_{\perp c}=(\lambda/2)\cdot\frac{r\tan(\vartheta-\Omega)}{\Delta r}\). (iii) The Doppler Centroid (DC) decorrelation accounts for the loss of coherence due to a non-perfect overlap of the azimuth spectra for the master and slave SAR acquisitions. The loss of coherence is modelled as follows:  
\[I=\hat{\gamma}_{1}\cdot\hat{\gamma}_{2}^{*}=|\hat{\gamma}_{1}\cdot\hat{\gamma}_ {2}^{*}|\exp[j\varphi] \tag{17}\]7  
where \(f_{DC_{Master}}\) and \(f_{DC_{Slave}}\) are the DC frequencies of the master and slave SAR images, \(L\) is the real antenna aperture and \(v\) is the velocity of the platform. (iv) Mis-registration decorrelation is due to co-registration mistakes [41]. (v) Temporal decorrelation occurs in repeat-pass interferometry. It is very difficult to be modelled from a statistical point of view. Nonetheless, a quantification of temporal decorrelation is available [6]. The volumetric change of a resolution element is measured by considering the influence of horizontal \(\sigma_{x}\) and height \(\sigma_{z}\) random changes, respectively. Then, by assuming those changes are unrelated to the initial position and are characterized by independent Gaussian probability distributions, the temporal decorrelation is given by:  
\[I=\hat{\gamma}_{1}\cdot\hat{\gamma}_{2}^{*}=|\hat{\gamma}_{1}\cdot\hat{\gamma}_ {2}^{*}|\exp[j\varphi] \tag{17}\]8  
Temporal decorrelation is one of the main limitations of InSAR technology and particularly over vegetated areas, where decorrelation increases with the amount of vegetation cover because the scatterers of the plants sensibly change over time. From Equation (32), it is clear that volumetric decorrelation is more significant at the lower frequencies (C-band) than at the higher frequencies (L-band). For this reason, use of L-band SAR data, provided (for instance) by the ALOS-1/2 Japanese RADAR instrument, is advantageous for the monitoring of surface height and the changes over time of terrain covered by vegetation [61]. To reduce the decorrelation noise in single InSAR interferograms, several noise-filtering methods have been developed over the last three decades [12; 57; 62; 63; 64]. More recently, some new hybrid multi-temporal InSAR noise-filtering approaches, based on the joint exploitation of sequences of InSAR interferograms, have also been proposed [65; 66; 67; 68].
###### Abstract  
Due to the complexity of the missile air data system (ADS) and the harshness of the environment in which its sensors operate, the effectiveness of traditional fault diagnosis methods is significantly reduced. To this end, this paper proposes a method fusing the model and neural network based on unscented Kalman filter (UKF) and Inception V3 to enhance fault diagnosis performance. Initially, the unscented Kalman filter model is established based on an atmospheric system model to accurately estimate normal states. Subsequently, in order to solve the difficulties such as threshold setting in existing fault diagnosis methods based on residual observers, the UKF model is combined with a neural network, where innovation and residual sequences of the UKF model are extracted as inputs for the neural network model to amplify fault characteristics. Then, multi-scale features are extracted by the Inception V3 network, combined with the efficient channel attention (ECA) mechanism to improve diagnostic results. Finally, the proposed algorithm is validated on a missile simulation platform. The results show that, compared to traditional methods, the proposed method achieves higher accuracy and maintains its lightweight nature simultaneously, which demonstrates its efficiency and potential of fault diagnosis in missile air data systems.  
fault diagnosis; air data system; unscented Kalman filter; Inception V3; attention mechanism +
Footnote †: journal: Applied sciences
# Unsupervised Learning-Based Spectrum Sensing Algorithm with Defending Adversarial Attacks
## 5 Experimental Results
### Algorithm Performance Analysis  
#### 5.2.1 Influence of the Perturbation Attacks on the Target Model \(F_{\beta}\)  
The perturbation value of FGSM and PGD white box attacks is set per \(\in[0,0.1]\) with an interval of 0.02, and the number of iterations of PGD attacks is 10. When the false alarm probability is 0.05 and 0.1, the relationship curves between the perturbation values and the detection probability of the target model \(F_{\beta}\) trained only with clean samples for the two adversarial samples with the \(SNR\) of \(-12\) dB, \(-10\) dB and \(-8\) dB are shown in Figure 3 and Figure 4, respectively.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Values** \\ \hline batch size & 64 \\ optimizer & Adam \\ number of adversarial pre-training round & 30 \\ fine-tuning round & 10 \\ learning rate & 0.0001 \\ per & 0.06–0.1 \\ PGD iteration attack times & 10 \\ \hline \end{tabular}
\end{table}
Table 2: The setting of the hyperparameters.  
\begin{table}
\begin{tabular}{c c} \hline
**Experimental Environment** & **Environment Configuration** \\ \hline Operating System & Win 11 \\ CPU & AMD Ryzen 7 5800 H with Radeon Graphics \\ GPU & NVIDIA GeForce RTX 3060 \\ Memory & 32 GB \\ Programming Languages & Python3.9 \\ Deep Learning Framework & PyTorch1.12.0 \\ \hline \end{tabular}
\end{table}
Table 3: Experimental environment and computer configuration.  
The following can be seen from Figures 3 and 4:  
1. When the attack perturbations are 0, that is, the spectrum sensing network is not attacked, the detection probability of the target model \(F_{\beta}\) for clean samples can reach more than 93%. With the increase in the attack perturbations, the detection probability of the target model \(F_{\beta}\) for adversarial samples gradually decreases.
2. The reduction in \(SNR\) and false alarm probability decreases the detection probability of target model \(F_{\beta}\) for adversarial samples, and PGD attack has a greater impact on target model \(F_{\beta}\) than the FGSM attack. When the \(SNR\) is \(-12\) dB, \(-10\) dB and \(-8\) dB, per is 0.06 and 0.1, and the false alarm probability is 0.05 and 0.1, respectively, the detection probabilities of the target model for FGSM and PGD adversarial samples are shown in Table 4.
3. When the per increases to 0.1, the detection performance of the target model deteriorates sharply, and its robustness is poor. To make the defense model have certain robustness to the adversarial samples with different attack perturbations, three defense models arec trained by adversarial samples with the perturbation value ranging from 0.06 to 0.1 in the following experiments.  
Figure 4: \(P_{f}\) is 0.1, the detection probability of the target model.  
Figure 3: \(P_{f}\) is 0.05, the detection probability of the target model.
# Control Strategies for Gait Tele-Rehabilitation System Based on Parallel Robotics
## 2 Materials and Methods
### Control Strategies  
The gait simulator described in Section 2.1 enables independent 6-DOF movement of each foot. Based on this system, feet trajectories that promote rehabilitation may be created. This basic control strategy is referred to as Fixed Gait Trajectory Mode (FGTM) (Figure 3a). In this work, novel strategies that enable modulating platform movement in real time are proposed.  
Regarding the FGTM, emulating standard gait trajectories may be one of the first approaches applied in lower limb robotic rehabilitation systems [2]. For that purpose, feet pose during gait was recorded using optic motion capture [20] to serve as a high-level reference to the CU. The reference trajectory for each foot was measured with respect to the standard double support position. This is the same starting position used in the gait simulator, defined by the rotation matrix \({}^{b}\mathbf{R}_{p0}\) and position \({}^{b}\mathbf{t}_{p0}\), both known constants in our setup. Note that, in the convention used here, while \({}^{b}\mathbf{t}_{p0}\) represents the position of the coordinate system \(G_{p0}\) in terms of \(G_{p}\), \({}^{b}\mathbf{R}_{p0}\) is used to compute rotations from \(G_{p0}\) to \(G_{p}\). For each data point measured by the motion capture system, corresponding high-level commands to the robotic system were computed using Equation (1).  
\[{}^{b}\mathbf{R}_{p} ={}^{b}\mathbf{R}_{p0}{}^{p0}\mathbf{R}_{p} \tag{1}\] \[{}^{b}\mathbf{t}_{p} ={}^{b}\mathbf{t}_{p0}+{}^{b}\mathbf{R}_{p0}{}^{p0}\mathbf{t}_{p}.\]
# Flavonoids as Promising Multitarget Agents in Alzheimer's Disease Therapy
## 2 Activities of Flavonoids from Natural Sources
### Inhibition of A\(\beta\) Production
#### 2.1.2. Chalcones and Dihydrochalcones  
_Boesenbergia rotunda_ (L.[PERSON], a plant commonly used as a food ingredient, as well as in traditional medicine, also known as fingerroot and Chinese ginger, contains cardamonin, the 2\({}^{\prime}\)_A_\({}^{\prime}\)-dihydroxy-6\({}^{\prime}\)-methoxychalcone (**23**), which presents a BACE1 IC\({}_{50}\) of 4.35 uM [61]. 2,2\({}^{\prime}\)_A_\({}^{\prime}\)-Trihydroxychalcone (**24**), isolated from the plant _Glycyrrhiza glabra_ L. (licorice), exhibits a BACE1 IC\({}_{50}\) of 2.50 uM [51]. Xanthohumol, the 2\({}^{\prime}\)_A_\({}^{\prime}\)-trihydroxy-6\({}^{\prime}\)-methoxy-3\({}^{\prime}\)-(3-methylbut-2-en-1-yl)chalcone (**25**), isolated from the dichloromethane fraction of an extract of _Sophora flavescens_, is another chalcone with activity against BACE1 (IC\({}_{50}\) = 7.19 uM) [68]. From the same plant, _S. flavescens_, kurariidin, the 2,2\({}^{\prime}\)_A_\({}^{\prime}\)-tetrahydroxy-3\({}^{\prime}\)-[5-methyl-2-(prop-1-en-2-yl)hex-4-en-1-yl]-6\({}^{\prime}\)-methoxychalcone (**26**), and Kurariidinol, the 2,2\({}^{\prime}\)_A_\({}^{\prime}\)-tetrahydroxy-3\({}^{\prime}\)-[5-hydroxy-5-methyl-2-(prop-1-en-2-yl)hexyll]-6\({}^{\prime}\)-methoxychalcone (**27**), were isolated from the ethyl acetate fraction, exhibiting BACE1 IC\({}_{50}\) values of 6.03 and 7.10 uM, respectively [68]. Phlorizin, the 4,4\({}^{\prime}\)_/6\({}^{\prime}\)-trihydroxy-2\({}^{\prime}\)-(\(\beta\)-D-glucopyranoside) dihydrochalcone (**28**), isolated from _Malus domestica_ Borkh. (_cv._ Anna), is a dihydrochalcone that inhibits BACE1 with an IC\({}_{50}\) of 2.70 uM [69]. Thus, chalcones and dihydrochalcones are very active in their inhibition of BACE1 activity, regardless of the presenting preryl groups, lavandulyl groups, hydrated or not, and glycosyl groups.  
Figure 6 shows the chemical structure of chalcones and dihydrochalcones (compounds **23-28**) that inhibit BACE1 with IC\({}_{50}\) range of 2.50 to 7.19 uM.  
Figure 5: Structure of catechins and flavanones that inhibit \(\beta\)-secretase.
# A Realistic Approach of the Maximum Work Extraction from Solar Thermal Collectors
## 4 Results--Discussion
### Discussion  
The developed model is a new way to estimate the optimum operating temperature of a solar collector and, consequently, the maximum work extraction, based on Carnot cycle. A literature review showed that many researchers have investigated solar exergy or maximum work produced by solar energy theoretically ([PERSON], [PERSON], [PERSON], [PERSON], [PERSON]). Other researchers, such as [PERSON], performed a more realistic study that includes more parameters of the solar collectors.  
The suggested model of this study takes into account the concentration ratio, the optical efficiency, the receiver emittance, and the operation conditions (ambient temperature and solar irradiation). The heat convection losses are negligible in this model, because the receiver is assumed to be inside a huge dome. However, a small increase in the emittance can be applied in order to take into consideration the convection losses, something that leads to accurate results. Moreover, in concentrating collector the convection losses have a minor influence of the thermal losses, something that enforces the main assumption of this study. Furthermore, this model takes into account the exact ambient temperature and the exact solar irradiation available in any case. A simple validation of the model and a comparison with [PERSON] model [17] is given in Section 4.3. The proposed increase in the emittance leads to correct optimum operating temperature determination. Moreover, the developed model is compared with the results about the IST PTC from SANDIA Reports [18; 20], and the obtained results prove that the developed model is valid.  
Furthermore, it can be said that the present model gives results close to the real performance of the system, while the traditional equations of (Petela, Jeters, Spanner, etc.) give extremely high values that do not correspond to the use of solar collectors. The results of Table 1 proved maximum performance up to 50% with solar dishes, while the Petela model gives 93.11%, for example. So, it is proved that the use of a model that takes into consideration the collector characteristic is important in order to perform accurate calculations.
# Design and Experiments of Autonomous Path Tracking Based on Dead Reckoning
## 2 Autonomous Driving Based on Dead Reckoning
### Error Measurement Based on Image Processing
#### 2.3.2 Image Processing and Lane Detection  
Following each test, the distance between the vehicle and the lane line in each frame was calculated by analyzing the saved video, frame by frame. In this paper, a simple and effective image processing algorithm was used for the automatic detection of the lane lines, and the distance was then calculated. The lane line detection and calculation method can be seen in Figure 7.  
Figure 5: Error measurement based on the distance between the vehicle and side lane.  
The image was first converted to HSV color space for each input frame before the white area was segmented out into HSV space using the color extraction method. Morphological operations such as erosion and dilation were then performed on the segmentation results as a means of filtering out the noise, and candidate targets were ultimately extracted at intervals of 20 pixels on the left and right with the horizontal center of the image as the central axis before that with the largest area was chosen as the final lane line position.