esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Activation Function for Parameter Reduction in CNNs on CIFAR-10 and CINIC-10
## 4 Training and Results
### Results  
The model achieved a training accuracy of 77.2% and a test accuracy of 81.6%, demonstrating its ability to learn relevant features effectively from the CIFAR-10 dataset. Both the training and testing losses continued to decrease steadily, indicating that the model was effectively optimizing during the training phase. Similarly, the test loss followed a downward trend, suggesting good generalization to unseen data.  
Execution time refers to the duration a program or algorithm takes to complete a task on a given hardware system. It is a crucial factor in evaluating performance, efficiency, and scalability.  
In our case, the preprocessing of a single image takes **0.43 milliseconds (ms)**. This measurement serves as a baseline for performance evaluation and allows us to compare different implementations or optimizations.  
As shown in Figure 8, the accuracy continues to improve as the epochs increase. In that case, we refer to the accuracy as Top-1 accuracy. Top-K accuracy is an evaluation metric in machine learning that indicates whether the correct class is present within the top K predictions. The most commonly used Top-K accuracies are Top-1, Top-3, and Top-5, with their corresponding results presented in Table 2. These Top-K accuracy metrics provide a more comprehensive understanding of a model's performance, especially in scenarios where the correct class might not always be the top prediction. Top-1 accuracy focuses on the exact match, while Top-3 and Top-5 accuracies allow for a more lenient evaluation by considering the presence of the correct class among the top three or top five predictions, respectively. In our case both Top-3 and Top-5 accuracies have nearly perfect predictions, because of the small dataset. Even though the dataset is small, the high Top-3 and Top-5 accuracies are still a positive outcome, indicating that the model is performing well in predicting the correct classes within its top predictions.
## 3 Two-Dimensional Frequency Domain
### LFM-CW SAR Raw Signal Spectrum  
The LFM-CW SAR raw signal in the time domain was obtained by the linear frequency modulated continuous wave (LFM-CW) synthetic aperture radar (SAR) system; then, we converted said signal to the two-dimensional frequency domain. Therefore, the range Fourier transform was first applied to Equation (21), as shown in Equation (31).  
\[S_{DC}(f_{t})=\int_{-\infty}^{\infty}s_{dc}(t)exp(-j2\pi f_{t}t)dt \tag{31}\]  
The range Fourier transform was applied, making \(f_{0}\gets f_{0}+f_{t}\)[16; 17]; thus, we obtained Equation (32), which is in the range-Doppler domain.  
\[S_{DC}(f_{t})=I(f_{t})exp\left(j\left(2\pi(f_{0}+f_{t})\Delta t-\pi k_{r} \Delta t^{2}\right)\right) \tag{32}\]  
where \(I(f_{r})\) is the \(sinc\) amplitude of the signal; however, the processing was conducted in phase, so this amplitude was not taken into account. The residual video phase was removed; that is, the \(\pi k_{r}\Delta t^{2}\) phase was removed, which is known as the residual phase removal.  
\[S_{DC}(f_{t})=exp(j2\pi(f_{0}+f_{t})\Delta t) \tag{33}\]The azimuth Fourier transform was applied to convert the signal in Equation (33), which was in the range-Doppler domain to the two-dimensional frequency domain.  
\[S_{DC}(f_{t},f_{n})=\int_{-\infty}^{\infty}S_{DC}(f_{t})exp(-j2\pi f_{n}n)dn \tag{34}\]  
Then, the integrand phase of Equation (34) is shown in Equation (35), and the derivative was applied and set equal to zero because it was a stationary signal. __ newpage  
\[\omega(f_{t},n)=2\pi(f_{0}+f_{t})\Delta t-2\pi f_{n}n;\quad\frac{\text{d} \omega(f_{t},n)}{\text{d}n}=0 \tag{35}\]  
After applying the derivative, we had \(n\) and substituted it into phase \(\theta(f_{t},n)\), obtaining the phase shown in expression (36).  
\[n=\frac{R_{0}\Big{(}\frac{f_{t}c}{2\text{d}V_{s}(f_{0}+f_{t})}-\frac{V_{s}}{c} \Big{)}}{V_{s}\sqrt{1-\Big{(}\frac{f_{t}c}{2\text{d}V_{s}(f_{0}+f_{t})}-\frac{ V_{s}}{c}\Big{)}^{2}}} \tag{36}\]  
Finally, for SAR raw signals collected by linear frequency modulation continuous wave radars, we use Equation (37), which is in the two-dimensional frequency domain.  
\[S_{DC}(f_{t},f_{n})=exp(j\omega(f_{n},f_{t})) \tag{37}\]  
where  
\[\omega(f_{n},f_{t})=\frac{4\pi\alpha R_{0}}{c}\sqrt{(f_{0}+f_{t})^{2}-\Big{(} \frac{cf_{n}}{2\alpha V_{s}}-\frac{V_{s}}{c}(f_{0}+f_{t})\Big{)}^{2}} \tag{38}\]
# Quality Assurance of Chest X-ray Images with a Combination of Deep Learning Methods
## 4 Discussion
This study involves several limitations. First, this study was performed at a single institution. Further investigation is desired to evaluate the generalization performance of the QA system. The larger datasets, including another institution's clinical images, should be applied for model training. Moreover, we excluded images of children under 15 from the datasets. This processing may lead to a statistical bias compared with the actual clinical scene. Although this processing is essential to achieving a high-accuracy model for CXR in adult patients [12]. Second, all clinical images were retrospectively prepared. Therefore, a black mask is present in the upper-right corner of the image to hide a previous marker corresponding to the patient's position. This mask might work as a landmark to correct the orientation, angle, and left-right reversal. Further investigation using plain CXR imaging is warranted. Although, the correction model is created from Chest14 data that does not have a mask. Third, all evaluations were performed using a GPU machine. The portable X-ray console PC without a GPU may delay the processing time. The clinical trial should be attempted using a daily-use X-ray console system. Third, this study aimed to construct a specific AI for the QA of CXR. Hence, this system cannot make fully alternative human work from the perspective of the difference between the AI model and human cognition [13]. Still, furthermore, double-checking should be enforced by both the QA system and technologists.
# SatGuard: Satellite Networks Penetration Testing and Vulnerability Risk Assessment Methods
## 5 Discussions and Future Directions
### Future Directions  
To address these limitations, future work will prioritize three avenues. First, we plan to integrate hardware-in-the-loop simulations using satellite-grade components (e.g., LEON processors, CCSDS-compliant transceivers) to evaluate firmware-level and radiation-induced vulnerabilities. Second, continuous retraining of LLMs with real-time threat intelligence--leveraging platforms like MITRE ATT&CK--will enhance adaptability to emerging attack vectors. Finally, experimental validation will expand to include optical communication scenarios, employing software-defined radios (SDRs) and optical link emulators to probe vulnerabilities in laser-based protocols. These steps aim to bridge the gap between controlled laboratory environments and the dynamic challenges of next-generation satellite networks.
# Pose Estimation of Primitive-Shaped Objects from a Depth Image Using Superquadric Representation
## 4 Experiments
### The Comparison with Other Methods and the Baseline Method  
In order to evaluate the effectiveness of our pose estimation, we compared the pose estimation accuracy with four methods. First, we compare with Iterative Closest Point (ICP) algorithm with point-to-plane metric [7], which achieves faster convergence than the point-to-point metric. Unlike the point-to-point metric, which has a closed-form solution, the point-to-plane metric is usually solved using the standard nonlinear least squares method, we employ the Levenberg-Marquardt algorithm. Second, we compare with feature-based RANSAC method, proposed by [PERSON] et al. [37]. We employ Fast Point Feature Histograms (FPFH) feature extractor to extract the point features and match the features using RANSAC with pre-rejection step pose in the estimation loop in order to avoid verification of pose hypotheses that are likely to be wrong. Thirdly, we compare with Normal Distributions Transform (NDT) algorithm [38]. At last, we compare with our baseline method, which does not employ coefficient \(\beta\) at Equation (9), to evaluate the effectiveness of excluding the outlier points implicitly.  
Note that the baseline and our method estimates the shape, scale, and the pose of each object at the initial frame, and only the pose parameter is updated for the successive frames. For the othermethods (ICP [7], NDT [38], RANSAC [37]), we estimate the pose between two successive frames using objects' point cloud.  
[PERSON] et al. [16] also estimate the poses of a cube from a depth image, for instance. However, as the conventional methods for primitive shape pose estimation have to know the type of primitive shaped objects (e.g., cuboid, cylinder and sphere) before estimating the pose. Instead, we employ superquadrics to represent the primitive shapes so that the representative primitive shapes can be represented using a single equation. Moreover, the method proposed by [PERSON] et al. [16] has an assumption that the two faces of the cuboid can be seen from the camera. As the fair comparison against these methods is difficult, we did not conduct the comparison against them.
###### Abstract  
This paper proposes a fault identification method based on an improved stochastic subspace modal identification algorithm to achieve high-performance fault identification of dump truck suspension. The sensitivity of modal parameters to suspension faults is evaluated, and a fault diagnosis method based on modal energy difference is established. The feasibility of the proposed method is validated by numerical simulation and full-scale vehicle tests. The result shows that the proposed average correlation signal based stochastic subspace identification (ACS-SSI) method can identify the fluctuation of vehicle modal parameters effectively with respect to different spring stiffness and damping ratio conditions, and then fault identification of the suspension system can be realized by the variation of the modal energy difference (MED).  
Keywords:modal parameter identification; dump truck suspension; stochastic subspace identification; fault identification +
Footnote †: journal: Applied sciences
## 4 Wind Loading and Vibration Mitigation
### Considered Load Cases
#### 4.2.3 Simulated Conditions  
Wind flow conditions are characterized with typical metrics of the streamwise velocity component, presented in Figure 7. The turbulent flow approaches the building at a \(0^{\circ}\) angle of attack (streamwise perpendicular to the wide face). This is defined by a lower mean magnitude at a higher turbulence intensity \(TI\). The energy spectrum of this velocity contains higher energy distributed over multiple frequencies. Smooth flow (i.e., minimal to no oncoming fluctuations) is seen in the case of the \(90^{\circ}\) angle of attack (streamwise parallel to the wide face). For this latter scenario, the along-wind velocity component has lower energy, as observed in the spectrum.  
The energy content of the flow is inherent to the natural conditions of the approaching wind. The shape of the building determines the characteristics of the aerodynamic forces that arise under such conditions. These are presented in Figure 8. Along-wind, the vertical lines (dashed) represent the weak and strong bending frequencies, with \(0.2\,\mathrm{Hz}\) and \(0.23\,\mathrm{Hz}\). We can see that there is no peak in the energy content at these values. Conversely, for the cross-wind component, the smooth flow peaks at approximately \(0.2\,\mathrm{Hz}\) (dashed), with it matching the weak bending mode. In the case of approaching turbulence, we can also observe a cross-wind peak at \(0.08\,\mathrm{Hz}\) (marked as dash-dotted). This peak is expected as, also in turbulent conditions, vortex shedding will exhibit itself. However, the mechanism overlaps with the fluctuations already present in the oncoming flow field.
# Investigation on Hydroplaning Behaviors of a Patterned Tire on a Steel Bridge Deck Pavement
## 1 Introduction
It was found that the hydroplaning speed decreased with the increase in water film thickness, and tended to stabilize when the water film thickness reached 2 mm, and the improvement of pavement microtexture in the 0.2 to 0.5 mm range can delay hydroplaning. However, the tire in the model was smooth tire, and the modeling of the pavement surface topography was rough. With the application of X-ray CT technology to the numerical modeling of pavement meso-structure [9; 10; 11], the model accuracy of pavement surface topography improves. [PERSON] et al. [12; 13; 14] developed a 3D FE model of pavement surface topography based on the X-ray CT technology, which can reconstruct the structure details of pavement surface texture well. On this basis, they established a hydroplaning model for patterned tire, and verified the effectiveness of hydroplaning model by the experimental results. [PERSON] et al. [15; 16] developed a tire-fluid-pavement model based on the CEL algorithm, and it was found that higher tire inflation pressure, thinner water film, and more abundant macrotexture can enhance the hydroplaning speed through numerical analysis. The hydroplaning analysis is an essential element of driving safety evaluation on asphalt pavement, but the study of hydroplaning behaviors on the steel bridge deck pavement (SBDP) has never been involved. The hydroplaning propensity on SBDP is generally higher than road pavement due to the small pavement macrotexture depth [17; 18]. The SBDP is a special pavement structure type, which is a thin-layer structure paved on the orthotropic steel bridge deck [17; 18; 19]. Due to the special operating conditions and unique structure features for bridges, the structure design and material application for SBDP are different from conventional road pavement. The previous research [20] showed that the tire deformation and grounding characteristics when the vehicle is running on the SBDP differ from that on road pavement. Accordingly, it is necessary to develop a hydroplaningmodel for SBDP. This study aims to investigate the hydroplaning behaviors on the SBDP through establishing a tire-fluid-SBDP interaction model by _ABAQUS_ software, in which the patterned tire model and SBDP model with complex macrotexture are involved.
# Non-Contact Measurement of Small-Module Gears Using Optical Coherence Tomography
## 2 Methods and Experiments
### Whole Teeth Profile Imaging  
The profile image and its data matrix at different positions could be obtained by rotating the gear. The location characteristics of the tooth tip or the tooth root were used as the basis to conduct the combination of data matrix; the contour map of the entire tooth profile could be achieved intuitively.  
The tested gear was a small copper gear with a modulus of 0.2 mm. Whole tooth profile imaging by OCT is shown in Figure 9a. Due to the small gear tooth profile, we also used a metallographic microscope for tooth profile detection. The measurement was under five times magnification for the objective lens (minimum magnification). The field number of the eyepiece of the metallographic microscope that we used was 18 mm, according to Equation (9):  
\[F=FN/eta \tag{9}\]  
here, \(FN\) is the field number of the eyepiece, \(eta\) is the magnification of objective of the lens, and \(F\) is the field of view of the objective lens. After calculation, with the objective lens with the lowest magnification of five times, the field of view that the objects could be observed at was 3.6 mm, indicating the limited measuring range of metallographic microscopy [20]. As shown in Figure 9b, it only could obtain profiles of 2-3 teeth in the gear images. At the same time, a poor continuity of the tooth profile curve was achieved. We could not calculate the gear parameters because at least three teeth were needed to be obtained for the calculation. At the other hand, the metallographic microscope (if mounted on a stage) can acquire several images and reconstruct the full gear. Therefore, OCT here is much faster, for the industrial applications required. In the OCT experiment, the measurement range was 10 mm \(\times\) 10 mm; therefore, using the OCT method to measure the tooth profile of a small-module gear allows for a higher flexibility and a larger application range than the metallographic microscope.  
In addition, the metallographic microscope could only measure the tooth-end face of the gears, while OCT could select the tooth profile of any section in the direction of the tooth width. As shown in Figure 9c, the scan line can be anywhere from 1 to 1\({}^{\prime}\) without being limited to the end face, then the tooth profile condition in any direction of tooth width can be judged. Therefore, OCT has specific and unique advantages.
# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 5 Publicly Available Datasets
### Netflix Dataset  
The Netflix dataset is a collection of user ratings of movies in the Netflix library. It can be used for creating an FRS, which allows users to make recommendations to one another without revealing their individual preferences. The dataset comprises over 20 million ratings from over 480,000 users for over 17,000 movies. The data are provided in a matrix containing users, movies, and their ratings. This allows for creating a collaborative filtering system, where users can make recommendations to one another based on the ratingsthey have given to movies. Moreover, this dataset provides additional information, such as release year, genre, and user age, which can be used to build a more personalized recommendation system.
## 2 General Principles of Augmented Reality  
The most common type of AR technology is based on the superimposition of a computer-generated image on the real world that is captured with a camera and then projected through screens [8; 11; 12; 13; 14; 15; 16; 17]. Special head-mounted displays (HMD) or \"smart glasses\" have been successfully used to obtain a view of the surgical site without the need for any screens [8; 18] that force the surgeon to look away from the surgical site, as usually happens with standard 2D intraoperative imaging.  
A position tracking system, a display device, and a system control software are the basic elements required [9]. The first step is the registration phase that can be marker-based [19], marker-less (surface registration) [10], or through an augmented C-arm device [20]. The surface registration was introduced by [PERSON] et al. [10] as a radiation-free approach with intraoperative surface digitization and navigation. Following this, the tracking phase allows the object to remain in the correct position during the surgical procedure, despite eventual motion, and adapt in a 3D space to the user and the instruments' position. Therefore, a correct registration phase is essential for the success of the following phases [10]. After the registration and tracking are performed, the elaborated images are shown through three different approaches: HMD, monitors, and projectors [7]. Among those, video see-through headsets (HMD) have been introduced to avoid the necessity for a display; however, they are not free from complications including headaches and nausea being reported by users [21; 22]. Alternatively, the virtual information can be directly projected onto the real environment, overlaying the information on a semi-transparent mirror [23].
# A Novel Mobile Communications Authentication Scheme with Roaming Service and User Anonymity
## 5 Security Analysis  
In the authentication phase of our proposed scheme, we improved the session key \(\text{ID}_{\text{HA}}\) by using symmetric encryption computation, compared with [PERSON]'s scheme that does not perform any encryption protection. Therefore, the anonymity level of the proposed scheme is even stronger than that of [PERSON]'s scheme. In other words, the security level increased from C2 to C3 (in [13], the C2 level means that the FA does not know the identity of the anonymous user, and the C3 level means that the attacker does not know the relationship among the MU, FA and HA) Hence, the attacker cannot intercept \(\text{ID}_{\text{HA}}\) and cannot use replay attacks to paralyze the HA. In addition, for the problem of generating the session key in the roaming phase, our approach lets the MU encrypt the calculated session key by using the shared key EC and transmits it to the FA. Therefore, no problems are encountered during transmission. The FA does not need to recalculate the session key, and therefore, the amount of computation is reduced because there is no additional matching of session keys to individual MUs. Finally, the computation of the session key is balanced between the sender and the receiver in the authentication phase. In this section, we show that the proposed scheme can withstand some possible attacks and affords several good security properties.
## 3 Results  
### Parameters of the Numerical Simulations  
In all the numerical simulations, unless explicitly stated otherwise, the length of the SASs was set to \(L\ =\ 25\,\mathrm{cm}\), and the height of the waveguide \(2A\ =\ 15\,\mathrm{cm}\). The fluid filling the waveguide and SASs was assumed to be air at normal conditions with the following material parameters: the sound speed \(c_{0}\ =\ 343.2\,\mathrm{m}\,\mathrm{s}^{-1}\), the ambient density \(\rho_{0}\ =\ 1.204\,\mathrm{kg}\,\mathrm{m}^{-3}\), the adiabatic exponent \(\gamma\ =\ 1.4\), the dynamic viscosity \(\mu\ =\ 1.83\times 10^{-5}\,\mathrm{Pa}\,\mathrm{s}\), the specific heat capacity at constant pressure \(c_{\mathrm{P}}\ =\ 1004\,\mathrm{J}\,\mathrm{kg}^{-1}\,\mathrm{K}^{-1}\), and the thermal conductivity \(\kappa\ =\ 2.59\,\mathrm{W}\,\mathrm{m}^{-1}\,\mathrm{K}^{-1}\). For these conditions, the cut-on frequency \(f_{\mathrm{cut-on}}\ =\ 1144\,\mathrm{Hz}\).
## 5 MPPT Algorithms
### Artificial Neural Network (ANN)  
The Artificial Neural Network (ANN) approach leverages its ability to model non-linear systems and recognize patterns to predict the maximum power point (MPP) under varying environmental conditions. The ANN used in this study was trained with irradiance and temperature as input features, while the output was the predicted maximum power point voltage (\(V_{mp}\)). This prediction served as the reference for a PI controller, which adjusted the duty cycle of the buck converter to drive the PV panel towards the MPP.  
The main characteristics of the implemented ANN are summarized in Table 7.
## Challenges and Potential Solutions
### Spectral Efficiency versus Energy Efficiency  
Several studies in wireless communication have all attempted to ascertain an acceptable point between spectral efficiency and energy efficiency, and one which is not detrimental to both features. As we witnessed in the literature, this optimization topic has dominated the various wireless generations and shall continue to attract considerable research attention in 6G communications [79]. Unlike the 1-5G communications, the 6G counterpart can present an innovative route-breaking, energy-harvesting technology to significantly ease this tradeoff. By energy harvesting, consumer nodes will have the capacity to harvest radio, vibratory, and solar energy from the ambient environment, thereby addressing the complications of energy utilization. The environmental advantages obtained via ubiquitous intelligent surfaces would be crucial in reducing the spectrum-energy tradeoff by responding to the ever-changing radio propagation conditions.