esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Set with Motion Parameters  
The samples in the OTMP dataset were classified into two types: rectangular frame-shaped objects and circular frame-shaped objects. All samples were taken by a monocular fixed-focus pinhole camera. The trajectory information of multiple sets of sample targets in space and the pose information of the camera itself were simultaneously recorded usingan indoor motion-capture system. Besides, the camera internal parameter matrix \(CM\), the sample calibration set \(S\), the sample-space motion-trajectory parameters \(T_{s}\), and the camera's pose \([R_{c}|T_{c}]\) after calibration using the checkerboard are provided. Notably, this study focused on the spatial trajectory \(T_{s}\) of the target and offered the rotation matrix \(R_{s}\) of the rigid target for part of the data. Finally, \(\left[T_{s},R_{s},R_{c},T_{c}\right]\) corresponding to the synchronization is presented for each frame of data. The storage capacity of this dataset was 2.67 GB; the total number of frames was 10432; and the resolution was 1280 \(\times\) 720. This dataset was uploaded to GitHub: [[https://github.com/6](https://github.com/6) wa-car/OTMP-DataSet.git]([https://github.com/6](https://github.com/6) wa-car/OTMP-DataSet.git) (accessed on 30 December 2021).  
Figure 1 illustrates the indoor collection environment of this dataset. Figure 2 exhibits some typical sample types in the data set. Figure 3 shows the spatial motion trajectory of the monocular camera and the target measured by the motion-capture system.  
Our dataset can be used to study the problems of target tracking in 3D space, target classification, and visual SLAM in a motion-interference environment. In this study, a part of this dataset was selected as the training samples to investigate the training method of the FDA-SSD network. In the experimental part, the reliability of the 3D-space target-tracking algorithm based on FDA-SSD and geometric constraint methods was verified with other non-overlapping data as the experimental test data.
# Virtual Reality and Digital Twins for Catastrophic Failure Prevention in Industry 4.0
## 5 Discussion
Future iterations of the system may incorporate edge computing to decentralize data processing closer to the sensor source, and implement industrial communication protocols such as OPC UA or MQTT with quality-of-service control. These enhancements would improve system robustness, reduce response time and facilitate deployment in time-sensitive or remote industrial contexts. Future enhancements to the system should be aimed at increasing realism, usability and scalability. One key direction involves the integration of advanced haptic devices and 360\({}^{\circ}\) immersive video, which would provide more authentic tactile feedback and contextual visualization. These features were not implemented in the current version due to hardware constraints and cost considerations but are considered high-impact improvements for simulation-based training in high-risk scenarios. Another relevant challenge is latency and data synchronization in remote or mission-critical industrial environments. Although the current implementation was tested under stable conditions, future versions may incorporate edge computing to offload processing closer to the data source, as well as 5G or industrial-grade communication protocols with QoS to support real-time responsiveness in complex deployments. Additionally, AI integration is proposed as a future development path, using machine learning models such as LSTM or rule-based systems to support predictive maintenance and personalized training. This would require a structured data infrastructure and greater computational capacity but would significantly enhance system intelligence and adaptability. Finally, recognizing that system interfaces may pose a barrier for non-specialized operators, future versions will focus on user interface simplification, adding guided workflows, voice-assisted cues and customizable interaction layers to improve accessibility and ease of use across different user profiles.
# Effect to the Surface Composition in Ultrasonic Vibration-Assisted Grinding of BK7 Optical Glass
## 3 Results and Discussions
### Effect of Grinding and Ultrasonic Parameters on Surface Composition  
The machined surface of ultrasonic vibration assisted grinding of BK7 optical glass consists of different proportions of ductile, brittle, and powdering areas. Understanding the increase in proportion of ductile removal, reduce the proportion of powder removal and brittle removal would help to improve the machined surface quality and clarify the direction of process optimization. Therefore, based on the above analysis, the effect of process parameters on the surface topography composition during ultrasonic vibration-assisted grinding of BK7 optical glass was summarized, as shown in the Figure 9.  
Figure 9 briefly summarizes the effect of ultrasonic vibration parameters and grinding parameters on the proportion of ductile removal, powdering removal, and brittle fracture. It is believed that brittle fracture always occupies the biggest proportion on the machined surfaces, while that of powdering removal is the smallest.  
Figure 8: Machined surface morphologies of optical glass material at different ultrasonic vibration status.  
Figure 9: Effect of processing parameters on surface composition in ultrasonic vibration assisted grinding of optical glass BK7.  
When the ultrasonic vibration amplitude and the spindle rotation speed are high the feed rate and the grinding depth are small, the ductile removal in the machined surface occupies a higher proportion. However, the changing of grinding wheel diameter did not show any obvious effect on the proportion of ductile removal and brittle fracture. However, the bigger the ultrasonic vibration amplitude is, the more obvious the repeated impact and ironing pressing effects are. Because of the high brittleness and low fracture toughness, excessive impact and force would lead to the rapid growth in sizes of surface and subsurface cracks. These disadvantages may cause the surface and subsurface qualities to decrease, thus, the limiting value of ultrasonic vibration amplitude in the effect to surface composition is 7 \(\upmu\)m. The feed rate has the dominant effect to powdering removal, and the grinding depth has no obvious effect on the powdering removal. The lower feed rate would increase the proportion of powdering removal to a certain degree; changing of grinding depth has little effect on the proportion of powdering removal.
## 2 Analysis of the Reverse Torque and Vibration in the Mud Pump
### Working Principle and Structure of the Mud Pump Module
#### 2.2.2 Error Analysis of Torque Simulation Results for a Single-Stage Mud Pump  
Fluent is a computational fluid dynamics software that utilizes numerical methods to solve fluid mechanics problems. The numerical methods employed by Fluent are based on finite volume and finite element methods, enabling a numerical simulation and analysis of various fluid flow problems. In this analysis, Fluent was used to analyze the torque of a single-stage mud pump. Based on the analysis in Section 2.2.1, it was straightforward to obtain the single-stage pump's reverse torque at 1500 rpm. However, for other rotational speeds, it was sufficient to set different rotational speeds for the analysis. Based on the fluid simulation results, the relationship between the simulated reverse torque and rotational speed can be obtained. Figure 5 illustrates the variation curve of the calculated reverse torque and simulated reverse torque concerning the rotational speed, while Figure 6 shows the variation curve of the relative error concerning the rotational speed. It can be observed that the average relative error of the simulation is 3.41%, which demonstrates the correctness of the simulation results.
## 6 Performance Evaluation
### Performance Evaluation for Data Generation  
For data generation, training was performed with 20,000 epochs, and this study was repeated four times. Figure 17 shows the progression of fake images generated from the generative network during the learning process. Figure 17a represents the real image of the Abhores type among malware. Subsequent images, Figure 17b-d, depict the results at 1000, 10,000, and 20,000 epochs, respectively.  
At 1000 epochs, the generated fake images lack proper features and exhibit a noticeable presence of different noise patterns compared to the real images. As training progresses to 10,000 epochs, the ambient noise in the generated images starts to somewhat resemble the real images. Finally, at 20,000 epochs, we can observe a significant improvement, with the generated images exhibiting a closer resemblance to the real images. This iterative learning process demonstrates that with sufficient training, the generative model can achieve images that are more similar to the real ones.  
Figure 18 depicts the loss values of the generator and discriminator during the data generation process. In both the first and second experiments, we observe that the generator loss value stabilizes around 3000 epochs. This indicates that the model is progressing without experiencing mode collapse during the generation of data. Mode collapse occurs when the generator fails to produce diverse samples and is stuck generating only a limited set of outputs. The stable loss values suggest that the training process is robust, ensuring  
Figure 16: Sample image from VXHeavens dataset. (**a**) C2 Lop.A, (**b**) Helpud.A, (**c**) Treemz.gentA, (**d**) Seimon.D, and (**e**) Storark.A.  
Figure 17: Fake image according to epochs.  
Figure 15: Sample image from Malimg dataset. (**a**) Adialer.C, (**b**) Agent.FYI, (**c**) Allaple.A, (**d**) Allaple.L, and (**e**) Alueron.genIJ.  
that the generator continues to generate diverse and meaningful data throughout the epochs.  
Table 5 presents the FID and cross-correlation values for each count. A smaller FID value indicates that the model generates data more similar to the original data. The average FID index obtained is 5.4603, which suggests that the generated data is relatively similar to the original data. Additionally, the average correlation coefficient is 0.8898, indicating a high degree of similarity between the original data and the generated data. These results, along with the FID, demonstrate that the generative model has successfully produced data that closely resembles the characteristics of the original data.
# Radio Frequency Fingerprinting for Frequency Hopping Emitter Identification
## 3 Proposed RF Fingerprinting-Based Emitter Identification Method
### User Emitter Classification
#### 3.3.1 Base Classifier: Deep Inception Network Classifier
In this probability, the estimated emitter ID \(\widetilde{k}\) is defined as the maximum probability that input samples will be included in a particular emitter ID \(c_{j}\) (see the Equation (18)).
# Optimized Gateway Placement for Interference Cancellation in Transmit-Only LPWA Networks
## 3 Single GW Placement for Two SNs  
We first consider the simplest scenario, where only two SNs are deployed. When the packets transmitted by the two SNs collide, one GW in the optimum location should decode both of the packets successfully. In this section, the theoretical model where the GW should be optimally placed is deduced by IC after capturing.
## 3 Design Methodology Based on the Common Framework
### Identifications and FE Model Calibration
The frequency response functions (FRFs) between excitation (vertical forces), proportional to the acceleration measured in the actuator moving mass, and the structural accelerations were acquired by applying a band-limited (0.3-50 Hz) white noise signal with a duration of 600 s and utilizing a sampling frequency of 1000 Hz. Employing a subspace-based system-identification technique, an accurate model of the experimental system was obtained [43]. Consequently, the first vibration modes were identified with sufficient precision to devise a multi-input multi-output (MIMO) integral resonant control (IRC) scheme and evaluate the achievable damping in simulations. The FRFs of the measured and modeled system are presented in Figure 7, demonstrating the effectiveness of the system-identification process in capturing the dynamic behavior of the structure. These FRFs serve as crucial benchmarks for validating the proposed MIMO IRC control scheme and assessing its performance in mitigating vibrations across the targeted frequency range. The algorithm identifies six vibration modes, whose parameters are as follows (see Equations (1)-(3)):  
* Natural frequencies (\(\mathbf{\Omega}\)): \(\omega_{1}=2\pi 5.0\) rad/s, \(\omega_{2}=2\pi 13.7\) rad/s, \(\omega_{3}=2\pi 21.0\) rad/s, \(\omega_{4}=2\pi 22.8\) rad/s, \(\omega_{5}=2\pi 25.6\), and \(\omega_{6}=2\pi 27.9\) rad/s. * Damping ratios (\(\mathbf{Z}\)): \(\xi_{1}=0.009\), \(\xi_{2}=0.015\), \(\xi_{3}=0.020\), \(\xi_{4}=0.020\), \(\xi_{5}=0.010\), and \(\xi_{6}=0.010\). \end{tabular}  
Figure 6: Inertial mass actuator working in voltage mode.
## 3 Retrieval
### Setup
#### 3.1.1 Measurement Vector \(y\)  
The measurement vector contains that data measured by the instrument from which we want to gain knowledge about the atmosphere (e.g., the CO\({}_{2}\) concentration). Each of OCO-2's bands consists of 1016 spectral pixels which we group into four fit windows: SIF (\(\sim\)758.26-759.24 nm), O\({}_{2}\) (\(\sim\)757.65-772.56 nm), wCO\({}_{2}\) (\(\sim\)1595.0-1620.6 nm), and sCO\({}_{2}\) (\(\sim\)2047.3-2080.9 nm). The center wavelengths of the individual spectral pixels have been obtained from an example OCO-2 L1b file (oco2_L1 bScGL_04243a_150419_B7000r_150608142047.h5, _[[https://daac.gsfc.nasa.gov_](https://daac.gsfc.nasa.gov_)]([https://daac.gsfc.nasa.gov_](https://daac.gsfc.nasa.gov_))). The separate SIF fit window ensures that the SIF information solely comes from free Fraunhofer lines rather than from O\({}_{2}\) absorption features which makes it much easier to avoid misinterpretations with scattering properties [29]. The measurement vector is of dimension \(m\times 1\) (\(m\approx 2600\)) and an example is illustrated in Figure 3 (top). Note that within this publication, the measurement vector consists of simulated observations for which the true atmospheric state is known.
# Hybrid Energy Routing Approach for Energy Internet
## 7 Simulation and Results
### Results of the Proposed Energy Routing Approach
#### Case 3 analysis: congestion management  
Energy transmission congestion can occur in energy systems when the transmitted power exceeds the capacity of the transmission paths, known as the overflow problem. This could have serious consequences in the energy system. As illustrated in Table 10, in this simulation case, it is assumed that there is an overlapping transmission time such as in case 2, and the capacity of power lines \(L_{3-7}\) and \(L_{8-13}\) are restricted to 12 kw and 6 kw, respectively.  
As mentioned before in Section 5, the IACO-based energy routing protocol eliminates all the ERs and lines that cannot support the transmitted power from the network before finding the efficient path to avoid the overflow problem, and to ensure the selection of a non-congestion minimum loss path. Table 13 and Figure 10 summarize the results for this case. By comparison to case 2, we can state that:  
* Consumers \(D3\) and \(D7\) have the same transmission power.
* For consumer \(D7\), the efficient path from \(D7\) to \(D2\) is the same as in case 2. While, the path toward \(D4\) (\(13\to 8\to 9\to 1\to 17\)), cannot be used in this transaction since the transmission power (12 kw) exceeds the capacity of line \(L_{8-13}\) (6 kw). Therefore, it has been replaced by another path with higher power transmission loss, which causes an increase in the energy transmission cost.
* For consumer \(D3\), as shown in Figure 10a, \(D7\) selected the producer \(D4\) with the efficient path \(13\to 6\to 7\to 3\to 1\to 17\). In this situation, the power line \(L_{3-7}\) reaches its maximum capacity, so it is not available to be shared. Therefore, \(ER_{10}\) turns from the path in case 2 (\(13\to 6\to 7\to 3\to 2\to 10\)) to a new path (Table 13) with a higher loss resulting in a higher fitness value. Since consumer \(D7\) changed the selected path compared with case 2, this creates the opportunity to find a new path with minimal loss between \(D3\) and \(D2\) (see Table 13 and Figure 10b). As a result, \(D3\) turns to consumer \(D2\) with a minimum fitness value.
## 5 Discussion  
Existing models presented by several research groups, including the author of this paper, demonstrate two approaches to the accurate simulation of SLM or similar processes. The first approach uses homogenization of the properties of the material in the powder, liquid, and solid states. This allows multilayer simulations on the mesoscale and is mainly used for simulations of the microstructure evolution. Another approach applied at a particle scale is limited to one cycle of the processes, sometimes with more than one track. This  
Figure 16: Simulation of the fourth cycle of the laser treatment: 3D view (**a**), cross-section immediately after the treatment (**b**), and another variant of the fourth cycle (**c**).  
Figure 15: Simulation of the powder deposition process before the fourth cycle of laser treatment: before deposition and after the removal of the untreated powder (**a**), the process of filling with the powder (**b**), and the final state after deposition (**c**).  
approach, which is applied in this work, is more complex and allows the study of finer effects with high accuracy but requires more computational effort.  
The next step in the development of the model presented in the previous sections is related to the extension of the simulation possibilities. The possibility of simulating several cycles of the process opens new perspectives for simulating, studying, and analyzing the process. To perform simulations of several cycles, it was necessary to add feedback from one module, which simulated a cycle, to the model responsible for powder deposition, that is, to close the algorithm circuit (see Figure 2). Such simulations are presented in previous sections.  
Almost all publications present simulations of metallic powder with atomized particles of different sizes. Papers with deposition of particles of another shape (arbitrary shape) can be found but they are not dedicated to the simulation of PBF processes. The author implemented a powder with particles of arbitrary shape, which is characteristic of bioactive glass powder particles, in previous papers and this implementation is also shown here.  
The ability to simulate several cycles also creates another possibility: the simulation of the process with different materials with very different properties and with different sizes and shapes of powder particles. This possibility is demonstrated in the previous section, where after the cycles with titanium alloy, the bioactive glass powder was deposited. However, this last cycle, should be treated as demonstrative and qualitative only because the properties of the glass are very simplified.  
The optical, thermal, viscosity, and other properties of the bioactive glass vary in a wide range and strongly depend on the temperature. The development of a model of the properties of bioactive glass and its verification, validation, and implementation are the objectives of further research.
## 3 Results
### Cytokine Results  
#### 3.7.1 Bradford Assay  
The residual protein content in the bioprocessed supernatants of RAW 264.7 cells treated with collagen and LPS was evaluated using the Bradford assay (Figure 7). To precisely measure the residual protein content in the cell culture supernatants, cells were cultivated in serum-free media when collagen and LPS were supplied. The concentration of protein in the vehicle was 2.22 \(\pm\) 0.41 \(\upmu\)g/mL. Treatments in the 10 and 30 \(\upmu\)g/mL collagen-only groups, as well as the 1 and 3 \(\upmu\)g/mL LPS-only groups, showed no significant differences. However, both 100 \(\upmu\)g/mL of collagen and 10 \(\upmu\)g/mL of LPS significantly increased the protein concentration, 2.42 \(\pm\) 0.62 \(\upmu\)g/mL and 2.42 \(\pm\) 0.35 \(\upmu\)g/mL, respectively (\(p<\) 0.05). In addition, synergle effects of collagen with LPS were observed, where the protein concentrations treated with the combined samples of collagen with LPS were significantly increased (\(p<\) 0.05). Groups with 100 \(\upmu\)g/mL of collagen, regardless of LPS concentrations, were significantly different (\(p<\) 0.05); notably, the protein concentration of 100 \(\upmu\)g/mL of collagen with 10 \(\upmu\)g/mL of LPS was the highest at 2.55 \(\pm\) 0.28 \(\upmu\)g/mL.  
Figure 6: 3D images of wrinkles on the cheek before (0 weeks) and after (2 and 4 weeks) using the collagen peptide buccal delivery film.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sensory Question (1)**} & \multicolumn{2}{c}{**Control Group**} & \multicolumn{2}{c}{**Test Group**} \\ \cline{2-5}  & **2 w** & **4 w** & **2 w** & **4 w** \\ \hline The degree of skin hydration & 3.95 \(\pm\) 0.74 & 4.38 \(\pm\) 0.74 & 4.14 \(\pm\) 0.64 * & 4.73 \(\pm\) 0.83 * \\ The degree of reduction in sagging skin & 3.62 \(\pm\) 0.67 * & 4.14 \(\pm\) 0.91 * & 3.86 \(\pm\) 0.83 * & 4.45 \(\pm\) 0.91 * \\ The degree of elasticity of the skin & 3.81 \(\pm\) 0.81 & 4.33 \(\pm\) 0.91 & 4.18 \(\pm\) 0.85 * & 4.68 \(\pm\) 0.78 * \\ The degree of smoothness of the skin & 4.14 \(\pm\) 0.57 & 4.43 \(\pm\) 0.87 & 4.27 \(\pm\) 0.70 & 4.73 \(\pm\) 0.83 \\ \hline \hline \end{tabular}  
* Evaluated on a 6-point hedonic sale, from 1 = no effect to 6 = extremely good effect. The changes were significantly different between intakes after 2 and 4 weeks with \(p<\) 0.05 (*).  
\end{table}
Table 3: The questionnaire scores after 2 and 4 weeks of using the collagen peptide buccal delivery film.
## 5 Achieved Results and Performance Comparison
### Hardware and Software in Experimental Setup  
The designs were implemented on various FPGA platforms, specifically Virtex-4, Virtex-5, Virtex-6, and Virtex-7. The synthesis process was carried out using the Xilinx ISEdesign suite, with a specific focus on version 14.7, chosen for its compatibility and efficiency in realizing our objectives.