esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Deep Learning Approach for Machinery Prognostics Based on Time Windows
## 2 The Proposed Deep CNN Architecture  
As presented in Figure 2, the deep learning architecture proposed consisted of the convolution layers, pooling layers and the fully-connected layer. Firstly, the normalized sensor data with the time window was directly used as input to the CNN, and then the feature maps were increased. Secondly, the convolution layer which was characterized by the kernel module was utilized for feature extraction and the down sampling scheme was operated by the pooling layer. Thirdly, the dimension reduction was performed before the fully connected layer, and then the feature mapping was implemented at the fully connected layer.
## 1 Introduction
Furthermore, a study investigating the impact of CA vertical height--ranging from \(-4\) mm to \(+4\) mm--in the extraction space region found that canine tipping could be effectively reduced depending on the vertical design of the aligner in this area [17; 18]. Although previous studies have investigated the influence of the design type and vertical height of CAs in the extraction space on tooth movement patterns, the specific impact of the buccolingual width of the extraction space on biomechanical characteristics of tooth movement remains insufficiently explored. From a biomechanical perspective, the buccolingual dimension of structural elements within extraction spaces directly influences force distribution and moment-to-force ratios applied to adjacent teeth. Insufficient width may result in inadequate force transmission, while excessive width could generate undesirable force magnitudes exceeding optimal biological limits. Buccolingual beam width variations become particularly relevant in patients where inadequate space closure frequently occurs due to insufficient aligner rigidity in the extraction region. Therefore, this study aimed to evaluate the biomechanical effects of CAs with varying buccolingual beam widths on the posterior movement of the canine in the maxillary first premolar extraction site. Specifically, CAs were designed with beam widths of 0 (edentulous), 1, 2, and 3 mm within the extraction space. Using the finite element method (FEM),this study comprehensively assessed the displacement patterns of the canine, degree of tipping and rotation, deformation patterns of the aligner, and magnitudes of the generated forces and moments.
# The Effect of Silica-Filler on Polyurethane Adhesives Based on Renewable Resource for Wood Bonding
## 3 Results and Discussion
### Bondline Shear Strength
Strengthening effect of small additions of silica on the mechanical properties of polyurethanes remains in accordance with the results reported in recent literature [50; 51].
## 3 System Control Structure
### Control Strategy of the GSC  
Since the DC-link voltage, \(\overline{u}_{dc}\), of the GSC can be compared to the magnetic linkage, \(\overline{\psi}\), of the SG, the authors establish the relationship between the angular frequency, \(\overline{\omega}_{GSC}\), of the GSC's output voltage and the DC-link voltage, \(\overline{u}_{dc}\), i.e., making \(\overline{\omega}_{GSC}\) equal to \(\overline{u}_{dc}\). In the control loop of the GSC in Figure 3, the DC-link voltage, \(\overline{u}_{dc}\), passes through an integrator, and the output is the phase \(\theta\) of the GSC's output voltage. The output reactive power can be controlled by adjusting the amplitude, U\({}_{t}\), of the GSC's output AC voltage. The modulation voltage signal, u\({}_{\text{tabc}}\), of the GSC is generated according to U\({}_{t}\) and \(\theta\).  
The fault detector estimates the grid state and generates a fault flag according to the AC side's voltage and current of the wind turbine. When the short-circuit fault occurs, the fault flag is 1, otherwise the flag is 0. In order to suppress the overcurrent during the grid failure, the virtual resistor, R\({}_{v}\), is added to the GSC's control loop. The virtual resistor, R\({}_{v}\), is cut out from the control loop after the fault is cleared. Corresponding to Figure 3, the switch SG is closed when the fault flag is 1. The switch SG is open and the virtual resistor, R\({}_{v}\), is cut out when the fault flag is 0.
# Understanding and Classifying Permanent Denial-of-Service Attacks
## 3 Case Studies of PDoS Attacks
### NotPetya  
NotPetya [15], first detected in 2017, similarly diverges from typical ransomware. Technically, NotPetya combines aspects of ransomware with wiper malware, primarily targeting Microsoft Windows systems, leveraging \"EternalBlue\" for entry, and exploits the SMBv1 protocol vulnerability, as well as \"Mimikatz\", to extract credentials.  
Once executed, NotPetya propagates within networks, overwrites the MBR, preventing normal boot processes, and then encrypts the Master File Table (MFT), rendering the file system unreadable. This dual-level encryption approach--targeting both the MBR and the MFT--is highly effective in denying access to the system without the decryption key. Despite presenting a ransom demand, NotPetya's design includes flaws in its payment and decryption system, leading researchers to classify it more as a wiper disguised as ransomware.
# A Vision-Based Two-Stage Framework for Inferring Physical Properties of the Terrain
## 4 Conclusions  
In many mobile robot tasks, inferring the friction and stiffness properties in advance is very beneficial. In this article, we proposed a vision-based two-stage framework for the physical property estimation of terrain. Considering the lack of a relevant dataset, an image dataset FITI for terrain segmentation was established. In the first stage of the framework, we designed a corresponding terrain segmentation network named TerrainNet to infer the types of different terrains of the image densely. TerrainNet has three novel components. We carried out experiments and the results show that the \(mIoU\) of the proposed network is 10.68% higher than that of the baseline ResNet-101, and has advantages over FCN, SegNet and DeepLabv3. In the second stage, we realize the mapping from the terrain type to physical properties. Our two-stage framework can infer the physical properties of the same terrain in different states. Finally, a complete experiment proved the validity of the vision-based two-stage framework. We evaluated our framework with quantitative metrics \(mIoU\) and \(PA\). The results show that our framework has high accuracy.  
In future work, we will consider the combination of physical properties and three-dimensional information, and realize the navigation of the mobile robot.  
Conceptualization, Y.D. and F.Z.; methodology, Y.D.; software, C.C.; validation, Y.D. and Y.L.; formal analysis, W.G.; investigation, L.S.; resources, F.Z.; data curation, F.Z.; writing-original draft preparation, Y.D.; writing-review and editing, Y.D.; visualization, Y.L.; supervision, W.G.; project administration, W.G.; funding acquisition, F.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of China (grant number 61773139). The Foundation for Innovative Research Groups of the National Natural Science Foundation of China (grant number 51521003), Shenzhen Science and Technology Research and Development Foundation (grant number JCYJ20190813171009236) and Shenzhen Science and Technology Program (grant number KQTD2016112515134654).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & \(PA\) & \(mIoU\) \\ \hline friction property & 75.85\% & 60.18\% \\ stiffness property & 76.63\% & 61.21\% \\ \hline \hline \end{tabular}
\end{table}
Table 11: Accuracy of the vision-based two-stage framework.
# Learning Deep CNN Denoiser Priors for Depth Image Inpainting
## 1 Introduction
The most commonly used variable splitting techniques are the iterative shrinkage/thresholding (IST) algorithm [10] and the split Bregman iteration (SBI) algorithm [11]. First, we adopt the SBI algorithm for the separation of the terms in Equation (1), and then, we train a set of effective CNN denoisers and integrate the results into the framework of model-based optimization methods to complete the task of depth image inpainting. The structure of the CNN includes rectified exponential linear units (RELUs) [12], batch normalization (BN) [13] and residual learning [14]. The proposed method can be used to more effectively complete the task of depth image inpainting. We chose CNN denoisers for the following reasons:  
1. The CNN inference process is very efficient due to the parallel computation capability of Graphics Processing Units (GPUs). 2. A CNN with a deep architecture exhibits a powerful prior modeling capacity. 3. The deep architecture of the CNN facilitates discrimination learning. Our contributions are summarized as follows: (1). The SBI algorithm is used to separate the data-fidelity term and the regularization term. Therefore, the depth image inpainting problem is transformed into the corresponding denoising subproblem. (2). We trained a set of effective CNN denoisers that are used as important components of a model-based optimization method to solve the denoising subproblem. The remainder of our paper is organized as follows: Section 2 describes the related work on variable splitting techniques and the application of denoiser priors in image reconstruction. Section 3 introduces the method of training deep CNN denoisers for depth image inpainting. In Section 3, we also present important details of the CNN structure. In Section 4, we report experiments performed on our dataset and compare the results of various algorithms. In Section 5, we discuss future research directions.
## 5 Plasma Precursors
### Hydrogen
#### 5.1.2 Vibrational Excitation  
Vibrational excitation is an important process in the plasma physics of non-equilibrium plasmas involving molecular gases such as hydrogen. H\({}_{2}\) is capable of maintaining vibrational energy for an extensive period and can use such energy in ensuing chemical reactions. The lifetime of vibrationally excited molecules is in the region of 10\({}^{-3}\) to 10\({}^{-2}\) s, which is long when compared with chemical reaction time periods and the exchange of vibrational energy. This excitation method is a dominant form of energy transfer in the case of molecular hydrogen. Most of the discharge energy, which can be up to 95% in some cases, can be vibrational excitation produced through the impact of electrons. One of the reasons for this effectiveness is because the energy level for vibrational excitation is low when compared with the energy levels involved in other plasma reactions [18]. The three reactions involving vibrational excitation for hydrogen have threshold voltage levels (E\({}_{\text{th}}\)) of 0.52 eV, 1.0 eV, and 1.5 eV, and the reaction is denoted by (9) [87].  
\[\text{H}_{2}+\text{e}\rightarrow\text{H}_{2}^{*}+\text{e} \tag{9}\]  
where  
\[\text{H}_{2}^{*}=\text{H}_{2}\text{ (v}=1)\text{, H}_{2}\text{ (v}=2)\text{ or H}_{2}\text{ (v}=3).\]  
The vibration excitation process of hydrogen presented in (9) usually results in the formation of an intermediate having a short lifetime. The reason for this is that vibrational excitation is not usually the result of a direct elastic impact between an electron and the hydrogen molecule itself, but an ongoing resonant process. A direct consequence of this is that vibrational excitation cross sections can be much larger than one would expect from electron to molecular collisions, given the extreme size disparity between the two particles. The maximum cross section for the vibrational excitation of H\({}_{2}\) by electron impact is \(4\times 10^{-17}\) cm\({}^{2}\)[87].
## 1 Introduction
Catalysts in high-temperature regions tend to deactivate faster, thereby reducing the hydrogen production efficiency of the ATMSR microreactor and the performance of the reformer system [19; 20]. We have previously designed a 10 W-class reformer system [21]. The reformer and burner are made of copper and have good thermal conductivity. The flow channel of the reformer is a Swiss-roll channel with a length of 354 mm and a depth of 2 mm. The flow channel of the combustion chamber has a length of 220 mm and a depth of 2 mm, as shown in Figures S1 and S2 [21]. By adjusting the air flow during hydrogen combustion, the burner can heat up to 300 \({}^{\circ}\)C in 10 min and maintain it at 300 \({}^{\circ}\)C for 20 h. When using methanol as fuel, the burner can reach up to 280 \({}^{\circ}\)C in 5 min. Additionally, after a start-up situation, hydrogen from the fuel cell exhaust can be supplied to the combustor to maintain the reformer temperature. Through this mixed combustion strategy, we can not only provide a self-starting system but also eliminate the waste of unreacted hydrogen in the fuel cell [22]. Based on this 10 W system, we expanded the gas production capacity of the reformer system to 100 times to the KW level by adopting a parallel clover-type microchannel design for the reformer as shown in Figure S1. To better serve the enlarged reformer, we redesigned the burner channel into a cloverleaf channel, which maintains its previous catalytic length and flow resistance due to similar fluid channel lengths. This burner, using an evenly distributed catalyst, can be integrated directly into the reformer using vertical stacking and providing direct heat, reaching a start-up temperature of 250 \({}^{\circ}\)C in 40 min. After 18 h of continuous operation, with a methanol feed amount of 1.8 mL/min, the fuel conversion rate, system degradation rate, and energy efficiency can approach 95%, 0.17%/h, and 72%, respectively. However, uneven reaction rates between the burner inlet and outlet resulted in a maximum temperature difference of 49 \({}^{\circ}\)C, resulting in poor start-up time, CO selection, and long-term operational performance of the reformer system. Therefore, in this research, the optimization of a clover type combustor as a heat supply for the 1 kw-reformer system is realized by re-designing the density and arrangement of the catalyst: the number of catalysts at the fuel inlet is reduced and gradually increased towards the outlet with three different gradient ratios. This approach mitigates the reaction non-uniformity caused by pressure drop reduction, enhances overall heat supply uniformity, and achieves a more uniform overall temperature resulting in highly efficient reactions.
## 4 Proposed System
### Advantages and Disadvantages  
There are major benefits of the proposed inertial system, as discussed hereafter. (1) Safety: during column failure or if the collapse performance level must be reached, large displacements may occur. In this case, the safety mechanism limits the displacement of the mass (Figure 7). Then, the safety of laboratory personnel, equipment, and specimen instrumentation is also improved. On the other hand, if the inertial masses are attached directly on top of the specimen's head, when the column fails or the concrete blocks fall, the instrumentation and shake table components could sustain permanent damage. (2) Preparation time: since the connection between the masses and specimen is simple, the time to assemble and disassemble the test setup decreases. Prior to testing, the IMS rests on the safety catch using hydraulic jacks as supports. Once the specimen is mounted on the shake table, the masses are lifted by applying pressure to the jacks and then connecting the rigid link to the specimen's head. Therefore, when a test matrix considers several tests, the research time reduces substantially. (3) Small out-of-plane displacement: The use of the pinned-end stiff link with only free in-plane rotation allowed (transverse rotation restrained) diminishing or nearly removing the out-of-plane displacements. However, to ensure pure in-plane behavior, one caster was attached to each longitudinal beam of the safety catch frame to restrain the out-of-plane displacement of the IMS. The steel column is provided with two steel plates (one on each side) where the casters slide if out-of-plane displacements occur. It is worth noting that the gap between the plates and the caster was only 4.8 mm (3/16 inches) on each side.  
The key downside of the IMS is that the full capabilities of the shake table may not be used if the extra mass is relatively large compared to the one used in the IMS. This condition arises because the efficiency of the shake table is correlated to the weight acting on the simulator [11]. Additionally, by putting the inertial mass on the simulator, substantial overturning moments are incorporated into the system and may thus pose a significant challenge for proper control in the closed-loop system. Another downside of the IMS is that axial forces required to simulate the weight of the superstructure in the case of bridge columns are not added. Therefore, another system is required to resolve this problem. For the proposed IMS, two hydraulic jacks apply the axial loads on the specimen through high-strength rods.  
Figure 6: IMS assembly process.
# Analysis of Vibration Characteristics of the Grading Belt in Wolfberry Sorting Machines
## 2 Materials and Methods  
### Structural Optimization Scheme Design  
The development of fresh wolfberry sorting machines represents a critical step in enhancing both the processing efficiency and quality of wolfberries. As a core component of screening equipment, the vibration characteristics of the grading device play a decisive role in determining sorting accuracy and equipment stability. This paper investigates the issues of intensified vibration, belt wear, and mechanical damage to fresh wolfberries caused by drive wheel misalignment in traditional sorting machines. By leveraging the theory of flexible multi-body dynamics and dynamic simulation technology, a dynamic model of the belt drive system is developed, followed by comprehensive simulation analysis and rigorous experimental validation. The underlying mechanism of vibration generation is elucidated, and a targeted structural optimization scheme is proposed. The ultimate objective is to achieve high sorting accuracy and enhanced belt stability for effective classification. The main research contents are as follows:  
By establishing a three-dimensional model of the fresh wolfberry sorting machine and analyzing its working principle, the issues inherent in the transmission structure are identified and discussed. This paper presents a theoretical analysis of the vibration characteristics of the belt drive model. Subsequently, relevant models were developed and theoretical formulas derived for the transverse vibration theory applied in this study. The results indicate that the primary factors influencing transverse vibration include initial tension, rotational speed, and center distance. The theoretical foundation established herein provides critical support for the simulation of the flexible belt drive system discussed in subsequent sections.  
In Recur Dyn software, a virtual prototype model was developed to simulate the operational behavior of the prototype under various influencing factors (initial tension, rotational speed, and deflection angle). Additionally, the analysis focused on evaluating its dynamic characteristics, such as lateral vibration displacement and frequency. The feasibility of the model was verified by combining it with on-site experiments. Subsequently, single-factor experiments were conducted to systematically analyze and discuss the influence patterns of different factors on lateral vibration. Finally, the parameter optimization of the virtual prototype was achieved based on response surface analysis, thereby identifying the optimal working conditions.  
ANSYS Workbench 2024 R1 Workbench finite element analysis software was employed to perform modal analysis on the belt drive system of the sorter. This analysis aimed to investigate the natural frequencies and mode shapes of the system, thereby facilitating the identification of potential resonance issues. Based on the optimal working conditions derived from the simulation, the transmission structure was systematically optimized with respect to the pulley structure design and the length of the fixing device.
# Photocatalytic Properties of Immobilised Graphitic Carbon Nitride on the Alumina Substrate
## 3 Results and Discussion
### Photocatalytic Activity of Immobilised \(g\)-C\({}_{3}\)N\({}_{4}\) and Its Reusability
The obtained results indicate the high photocatalytic stability of the immobilised \(g\)-C3N4 on alumina substrate and point towards significant reusability of such setup, which is highly adoptable for various device architectures. Figure 8: (**A**) Decolourisation (664 nm) and mineralisation (292 nm) of MB as a function of irradiation time under simulated solar irradiation and (**B**) plotted first-order rate constant (\(k\)). The possible mechanism involved in the degradation of MB dye of the immobilised \(g\)-C\({}_{3}\)N\({}_{4}\) is given schematically in Figure 10. Proposed mechanism presents the explanation of separation of photo-induced electron-hole pairs. When the energy photon (\(h\
u\)) is absorbed on the \(g\)-C\({}_{3}\)N\({}_{4}\) surface, the electrons (e\({}^{-}\)) in valence band (VB) absorb photons to get excited and move towards conductive band (CB) leaving positive holes (h\({}^{+}\)) in VB. Furthermore, electron-hole pairs are produced which play a key role in oxidation and reduction processes. The photocatalytic efficiency of a photocatalyst depends on several crucial factors such as adsorption ability, morphological structure, and charge separation of photo-induced electron-hole pairs [42]. From the oxidation and reduction process hydroxyl (\({}^{\bullet}\)OH )and superoxide radical (O\({}_{2}\)\({}^{\bullet-}\)) are obtained, respectively, which react with MB dye pollutant to transform MB into non-hazardous compounds such as water and carbon dioxide [43]. A lot of research efforts were put into narrowing the energy bandgap while reducing electron-hole pair's recombination by creating heterojunctions or doping metals into \(g\)-C\({}_{3}\)N\({}_{4}\) as electron entrapment sites, generating highly oxidized holes to efficiently separate holes and electrons; therefore, photocatalytic performance improves [41]. Figure 10: The possible mechanism of the photocatalytic degradation of MB at immobilised \(g\)-C\({}_{3}\)N\({}_{4}\) catalyst on the alumina substrate when exposed under simulated solar irradiation. Figure 9: Effect of recycling test of immobilised \(g\)-C\({}_{3}\)N\({}_{4}\) on alumina substrate for photocatalytic degradation of MB.
## 3 Results
### Observed Correlation of Rainfall, GNSS-IWV, and CAPE at the GNSS Station Locations
#### 3.1.3 Relation between Rainfall and GNSS-IWV  
In the next step, we analyse the relation of rainfall with GNSS-IWV. We show that, for wet days with rainfall above 0.1 mm/day, there exists an exponential relationship between the TRMM rainfall data and GNSS-IWV. The Q-Q (Quantile-Quantile) plot also shows an identical distribution within the assumed log-linear relation (after linearisation) between rainfall and GNSS-IWV for wet days and above the 10 th percentile (Figure 6A,B). We observe that, below the 10 th percentile, rainfall and GNSS-IWV do not follow an identical distribution (Figure 6A,B).  
We tested our data for other possible model fits such as power, Gaussian, and polynomial, but we found that the exponential model fit the data better compared to other models based on \(p\)-value \(<\) 0.001 and the statistics of the fit (Table 1).  
Figure 5: Squared wavelet coherence between the CAPE and rainfall (2008–2013) for TUCU (**A**) and CATA (**B**) stations and between the GNSS-IWV and rainfall (2008–2013) for TUCU (**C**) and CATA (**D**) stations. The arrows indicate the lag phase relation between rainfall and CAPE and rainfall and GNSS-IWV.