esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical Modeling and Dynamic Characteristics of a Three-Directional Vibration Absorber
## 3 Results and Discussion
### Experimental Validation
#### 3.5.2 Harmonic Excitation Test Study  
To validate the dynamic performance of the vibration isolator, a harmonic excitation test system was established as shown in Figure 18. The data acquisition and control system utilized Econ-Tech's VT-9002 (Econ, HangZhou, China) with dedicated analysis software. The excitation system consisted of an Econ-Tech's E-JZK-100 electrodynamic shaker (maximum force: 1000 N, frequency range: 5-2000 Hz) driven by an E5874A power amplifier (Econ, HangZhou, China). Force measurements were implemented with two transducers: a KD3001T model installed beneath the isolator prototype and an ECL-YD-312 model mounted above it.  
First, sinusoidal sweep frequency tests were performed on the vibration isolator to obtain its force transmissibility curve. Subsequently, based on the nonlinear damping characteristics analysis in Section 4, the viscous damping factor \(\xi\), equivalent Coulomb friction parameters \(\xi_{c}\), the Coulomb friction coefficient for each guide rod sliding pair \(f_{c1}\)  
Figure 17: Comparison between quasi-static loading-unloading test and theoretical curves of the vibration isolator.  
Figure 18: Experimental setup for harmonic excitation tests of the vibration isolator.  
and the Coulomb friction coefficient for each spherical joint \(f_{\ell 2}\) were adjusted to fit the theoretical curve to the experimental data. Finally, it was concluded that optimal agreement was achieved with the viscous damping factor \(\xi=0.05\), Coulomb friction parameters \(\xi_{c}=0.07\) and \(f_{\ell 2}/f_{\ell 1}=2\), as demonstrated in Figure 19.
# Hybrid Electrostatic-Atomic Accelerometer for Future Space Gravity Missions
## 2 Materials and Methods
### Hybrid Instrument Simulator
#### 2.3.1 Satellite Environment  
The first block of the simulator provides a simplified model of the spacecraft motion, following a perfect Keplerian orbit around the Earth. The satellite attitude is Earth pointing. Although the attitude has been considered ideal in our simulations, it is possible to add perturbation (noise or deterministic signal) in order to take into account the imperfections of the attitude and orbit control system (AOCS). For the non-gravitational external perturbation, a simple constant model of the residual drag acceleration was applied. The simulator also takes into account the gravity gradient, which introduces acceleration in the measurements due to the offset between the center of mass of the satellite and of the electrostatic instruments, on the one hand, and of the atomic instrument, on the other hand.
# Lightweight Anomaly Detection in Digit Recognition Using Federated Learning
## 6 Resource Utilization Analysis
### Program and Memory Footprint  
The firmware occupies approximately 933 KB of Flash memory, which includes compiled program code and constant data. Static RAM usage includes about 20 KB of DRAM for global variables and roughly 114 KB of IRAM for high-speed executable code. These values confirm that the firmware, including support for training, inference, communication, and configuration, fits within the device's memory limits.  
The implementation includes dynamic runtime features, such as configurable model selection, optimizer choice, and AD thresholds. While the base AE architecture is lightweight, these added functionalities contribute to the program's overall memory usage. This additional complexity supports a flexible and reusable system that can handle a range of real-world scenarios rather than being a simple or limited demonstration.
# High-Frequency Artifacts-Resistant Image Watermarking Applicable to Image Processing Models
## 3 Experimental Results and Analysis
### Qualitative and Quantitative Results
#### 3.2.1 Spatial Invisibility  
In this subsection, we assess the performance of our proposed framework by computing the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) between the carrier image and the marked image. Higher PSNR and SSIM values indicate a closer resemblance between the carrier and marked images, indicating that our framework preserves the quality of the carrier image during watermark embedding. Visual representations of the proposed framework are presented in Figure 3. In the spatial domain, the marked images are nearly indistinguishable from the carrier images, highlighting our framework's ability to proficiently embed watermarks in the frequency domain without compromising spatial image quality. Our approach achieves commendable visual results, ensuring excellent imperceptibility. Additionally, the extraction of high-quality watermarks from marked images is important for reliable ownership verification. The extracted watermarks from the marked images generated by our framework are also showcased in Figure 3, both exhibiting favorable visual results.  
Figure 3: Visual examples of paint transfer and de-raining. The left column (**a-c**) depicts paint transfer, while the right column (**d–f**) illustrates de-raining. The top two rows display the results of embedding “Baboon,” while the bottom two rows demonstrate the results of embedding “MDPI”. (**a**) Represents the carrier images in paint transfer, (**b**) shows the marked images in paint transfer, (**c**) displays the extracted watermarks in paint transfer,(**d**) represents the carrier images in de-raining, (**e**) shows the marked images in de-raining, (**f**) displays the extracted watermarks in de-raining.  
In further quantitative evaluation, as illustrated in Tables 1 and 2, the quality of both the marked images and the extracted watermarks is assessed. Note that, for the quality of the extracted watermark, we use PSNR and BER (Bit Error Rate) to evaluate it. These metrics demonstrate the high quality of both the marked images (\(>\)50 dB) and the extracted watermarks(\(>\)40 dB or \(\leq\)0.001). And the success rate (SR) of watermark extraction is 100%. Our image watermarking framework effectively ensures imperceptibility in the spatial domain, upholds carrier image quality, and maintains watermark extraction performance.
## 4 Experimental Results
### Experimental Design  
1. To verify the effectiveness of the proposed method in this paper, a real-time boiler system was used as a test subject for comparison experiments with a non-norm-based method;
2. To verify the effectiveness of the improved PSO algorithm, comparison experiments were conducted with the basic PSO algorithm on a large-scale input system. In addition, we compared the approach in this paper with other classical swarm intelligence algorithms.
# An Effective Sensor Architecture for Full-Attitude Determination in the HERMES Nano-Satellites
## 3 Sensor Selection
### Sun Sensors
#### 3.2.1 Fine Sun Sensors
The calibration points were evenly distributed both in azimuth and elevation to cover the entire sensor's field of view. When both direction angles of the incoming light source were measured from the sensor, they were corrected in the postprocessing functions, subtracting the known error angles, which were retrieved by the linear interpolation of the nearest points in the calibration table. The effectiveness of  
Figure 7: GomSpace FSS sun sensor integrated on the solar array. Note the Kapton layer to protect the sensor’s hole. Photo courtesy of DHV Technology. the calibration procedure is evident comparing the uncalibrated data, in Figure 8a, with the calibrated ones, in Figure 8b. In particular, this operation reduced the scale factor errors and removed the geometrical distortion due to the non-orthogonality of the sensor assembly. Looking at the uncalibrated raw data, the measurement error had two distinct trends. They were due to the asymmetry of the component with respect to different directions of the incoming light. In this specific case, the positive angle direction had a lower scale factor error than the negative one. This geometrical distortion was completely removed in the calibrated measurements, where only a slight scale factor error was still present. Note that the calibration procedure also improved the measurement accuracy at zero angle, when the light source is directly above the sensor, because the calibration table accounts for the geometric biases as well. Table 4 summarizes the main performance results retrieved from the testing activities.
# Design and Simulation of Electromagnetic Linear Actuators for Jet Dispensers
###### Abstract  
Three electromagnetic-based linear actuators, namely a solenoid actuator (SA), a moving coil actuator (MCA), and a moving magnet actuator (MMA), are proposed for driving the needle in a jet dispenser. The total resistance force acting on the needle during operation, including the damping force, the friction force, the inertia force, the compression spring force, and the backpressure, are measured by an experimental model. The thrust force required to overcome this resistance force is then predicted for each actuator using finite element analysis (FEA) simulations. Simple two-dimensional models of the SA, MCA, and MMA are constructed using the same coil dimensions in every case in order to facilitate an objective comparison between them. Simulations in ANSYS Maxwell software are then performed to adjust the specific dimensions of each actuator structure in such a way as to generate the thrust force required to drive the needle in the jet dispenser with the minimum excitation current possible. The simulation results show that for a maximum needle driving frequency of 250 Hz and a stroke length of 0.5 mm, the excitation current required to generate the necessary thrust force is equal to 1.8 A and 1.9 A for the MCA and MMA models, respectively, when a return spring is not used, and 2.2 A, 3.8 A, and 4.1 A for the SA, MCA, and MMA models, respectively, when a return spring is employed. It is additionally shown that the thrust force drop of the MCA and MMA models is far less than that of the SA model, about 0.7%, 1.8%, and 61% for three models, respectively. Three preliminary designs for jet dispensers incorporating the proposed actuators are also generated for reference purposes.  
electromagnetic principle; solenoid actuator; voice coil motor; moving magnet actuator; jet dispenser +
Footnote †: journal: Applied Sciences
## 3 Results
### Compressive Strength  
Figure 4 shows the results of the compressive-strength test for the rural-road pavement concrete containing the air-cooled slag aggregates. The compressive strengths were 27.16, 26.48, 26.36, 25.60, and 25.43 MPa for the Plain, GS 50%, GG 50%, GG 100%, and GS 50%/GG 100% mixes, respectively, showing a decreasing value with an increasing substitution ratio of the coarse or fine air-cooled slag aggregates. This behavior was attributed to the higher surface porosity of the air-cooled slag aggregates compared with the conventional aggregate. All of the mixes met the design criterion of 21 MPa. The compressive strength ratios of the Plain, GS 50%, GG 50%, GG 100%, and GS 50%/GG 100% samples were 1.00, 0.97, 0.97, 0.94, and 0.94, respectively. It was evident that incorporating the air-cooled slag aggregates decreased the compressive strength relative to the Plain mix. As noted above, the GS 50%/GG 100% combination had an air content that was 6% lower than that of the Plain mix. It was established that a 1% increase in air content led to a 4-6% lower compressive strength [32]. Therefore, the trend in compressive strength was not surprising. However, the absolute reduction in compressive strength was not as large as expected; i.e., 10% or less, which was attributed to the presence of fly ash and blast-furnace slag powder that filled some pores [33]. The compressive strengths of all of the mixes exceeded the design value, so we do not expect any issues regarding this property in the field.  
Figure 2: Slump test results.  
Figure 3: Air-content test results.
## 4 Experimental Results
### Complexity Analysis  
In order to further analyze the performance of the proposed model, we conducted a complexity comparison analysis of the proposed model, including detection time for each sample and CPU occupancy rate. Comparisons of the complexity of the proposed model with other models for CIC-IDS2017, CSE-CICIDS2018, and CIC-DDoS2019 are shown in Figure 10, Figure 11, and Figure 12, respectively.  
The complexity comparison results for CIC-IDS2017, CSE-CICIDS2018, and CIC-DDoS2019 demonstrate that ROSE-BOX significantly outperforms other models in both detection speed and resource efficiency. The experimental results indicate that ROSE-BOX achieves the lowest detection time per sample across all datasets, with 284 us for CIC-IDS2017, 173 us for CSE-CICIDS2018, and 325 us for CIC-DDoS2019. These values are considerably lower than LSTM, DNN, and CNN-LSTM, which exhibit much higher detection latencies. Additionally, ROSE-BOX consistently achieves the lowest CPU occupancy rate, maintaining 15.9% for CIC-IDS2017, 13.0% for CSE-CICIDS2018, and 18.2% for CIC-DDoS2019. At the same time, deep learning models like LSTM, DNN, and CNN-LSTM consume significantly more computational resources, often exceeding 26% to 81% CPU usage. Although KNN demonstrates respectable accuracy and a low training cost, its detection effect is poor relative to ROSE-BOX due to memory-based inference and poor adaptability in high-dimensional feature spaces. Therefore, its scalability and real-time performance are limited. Furthermore, through improvement, ROSE-BOX can significantly reduce latency and CPU usage compared to XGBoost and XGB-RF. These findings highlight the effectiveness of the proposed model with other models for the CIC-IDS2019 dataset.  
Figure 11: Comparison of the complexity of the proposed model with other models for the CSE-CIC-IDS2018 dataset.  
Figure 12: Comparison of the complexity of the proposed model with other models for the CIC-DDoS2019 dataset.  
light the superior computational efficiency of ROSE-BOX in handling multi-class intrusion detection tasks in resource-constrained IIoT environments.  
The key advantage of ROSE-BOX lies in its suitability for real-time intrusion detection in resource-constrained environments. Its low detection latency ensures rapid threat response, making it ideal for real-time cybersecurity applications such as IoT security, edge computing, and cloud-based IDS solutions. Unlike deep learning models, such as LSTM, which are computationally expensive and impractical for low-power devices, ROSE-BOX achieves an optimal balance between accuracy and efficiency, making it highly deployable in environments with limited processing power. Additionally, its low CPU footprint enhances scalability, allowing deployment in large-scale security frameworks without overloading system resources. These advantages position ROSE-BOX as an effective, lightweight, and real-time detection solution that is well-suited for modern cybersecurity challenges, where efficiency and speed are critical.
## 4 Results
### Feature Resolution for Sliding Windows  
We demonstrated feature resolution using different sliding windows on the log-scaled mel spectrogram as depicted in Figure 3. From the experimental setup detailed in Section 4, the window and hop sizes were varied to understand the behavior of the network and obtain first impressions on spike activity in the hidden layer. LIF parameters and time constants were set according to Table 1.  
With w = 4096 and h = w, in the first row, the features of interest were in a lower frequency range due to higher energy concentration. With smaller window sizes and smaller overlaps (w = 2048, h = 0.5 w), the prediction strength became stronger due to the finer resolution in data points. It is interesting to note the increased spike activity in the hidden layer of SNN. For w = 1024 (h = 0.25 w), we saw a noticeable difference in feature resolution due to the increased granularity with distinguishable noise and siren sounds.  
Figure 3: **Feature resolution and their effect on performance.** Demonstration of feature resolution on mel channels for sliding window variation on the anomaly detection task (presence of siren in each panel is highlighted in red). **(a)** First two rows: w = 4096 (h = w); **(b)** Next two rows: w = 2048 (h = 0.5 w); **(c)** Last two rows: w = 1024 (h = 0.25 w). The panels in each row showcase stages of information processing in the network pipeline of the proposed method. SpikeSineNet was trained on [18] for this set of experiments.  
Overlap ratios lead to finer resolution and this was reflected in terms of spike activity which was observed to slightly increase. However, the focus of this work was not to examine the spike activity with different feature resolutions. Deduction of the spike activity for sliding windows in terms of spike sparsity could be part of the future work. This sort of analysis will be beneficial in terms of further compression of the network, without loss in performance quantifiers.
## 3 Results
### Effect of Fermentation Products on Epithelial Barrier Function  
When compared with the negative control (growth medium without FCS and fermentation products), all fermentation products including that of SC alone significantly increased TEER (123-168%, \(p\) < 0.001) and decreased the paracellular permeability (36-64%, \(p\) < 0.001) of compromised E12 monolayers (Figure 3). The effects of PDX fermentation products on TEER and permeability did not differ from that of SC. Lactitol fermentation products increased TEER to an equivalent level compared with that of SC, but those from V2 and V4 resulted in lower permeability (\(p\) < 0.01). Xylitol fermentation products from V3 and V4 significantly increased TEER (\(p\) < 0.001) and decreased permeability (\(p\) < 0.01) compared with SC, and fermentation products from V4 led to highest TEER (\(p\) < 0.001) while that from V3 resulted in the lowest permeability (\(p\) < 0.05).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Vessels**} & \multicolumn{5}{c}{**Substrates**} \\ \cline{3-6}  & \multicolumn{2}{c}{**SC**} & **PDX** & **Lacitol  
To better understand the relation between SCFAs and epithelial barrier function, correlation analysis was carried out (Table 2). Total SCFAs and butyrate concentration, as well as butyrate proportion, were positively correlated to TEER (\(p<0.05\)), and negatively correlated to permeability (\(p<0.001\)). However, acetate and propionate concentration were not correlated to TEER or permeability, but their proportion were negatively correlated to TEER (\(p<0.05\)) and positively correlated to permeability (\(p<0.05\)).
# SLMAS: A Secure and Light Weight Mutual Authentication Scheme for the Smart Wheelchair
### Key Terms Explanation
#### 1.1.1 XOR Operation  
The XOR (exclusive or) operation is a fundamental building block for encryption and decryption techniques in cryptography. A logical operation called XOR produces a binary output from two binary inputs. This is how the XOR operation is described: If both input bits are the same, the output bit is 0. The output value is 1 if the input bits are different.
# Optimal Path Routing Protocol for Warning Messages Dissemination for Highway VANET
## 4 Optimal Path Discovery Phase
### Optimal Path Discovery  
Paths for disseminating warning messages alter at each time step in bi-directional road traffic due to regular topological changes. A direction-based approach that considers the source and destination nodes' relative positions might be more effective. To achieve this, we suggest Algorithm 6, which takes \(D\), \(S\), and \(NG\) as inputs. The algorithm uses therelative locations of S and \(D\) and their moving directions in addition to a distance parameter to determine the optimum path for warning messages to reach a specific destination node. The choice of the \(O_{f}\) that is the optimal path towards the \(D\) is Algorithm 6's output. The algorithm's complexity is on average \(\Theta\)(n). The algorithm's output is \(O_{f}\) the optimal path towards the destination node each time. Five different scenarios are considered to examine the importance of the extra parameters added by OPRP for enhancing routing performance. The following subsections provide more information on these circumstances, which cover every individual scenario concerning the location of the source and destination nodes on the bi-directional highway.  
```
Input:\(D\)\(S\), and \(NG_{s}\) Output:\(O_{f}\) the optimal path towards the destination node
1. Begin:
2. for\(i\) = 0 to NG\({}_{s}\) -1 \(\Delta_{i}\)\(\leftarrow\)(\(NG_{s}\), \(D\)) \(\leftarrow\)\(\left(NG_{s}\), \(S\right)\) if\(H\)(\(S\),\(D\)) = 1 then
3. if S = Rear and \(D\) = Front node then
4. \(\delta_{i}\leftarrow\Delta_{i}/H_{i}\)
5. else
6. \(\delta_{i}\leftarrow\Delta_{i}\,H_{i}\)
7. endif
8. else
9. if\(S\) and \(D\) move toward each other then
10. \(\delta_{i}\leftarrow\Delta_{i}/H_{i}\)
11. else
12. \(\delta_{i}\leftarrow\Delta_{i}\,H_{i}\)
13. endif
14. endfor
15. \(O_{f}\leftarrow\)Min (\(\delta\))
16. return\(O_{f}\)
17. endfor
18. endfor
19. \(O_{f}\leftarrow\)Min (\(\delta\))
20. return\(O_{f}\)
21. endfor
```  
**Algorithm 6**Optimal Path Discovery.
_Appl. Sci._**2015**, \(5\), 1869-1881; doi:10.3390/app5041869  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
Kinetic Adsorption Study of Silver Nanoparticles on Natural Zeolite: Experimental and Theoretical Models  
**[PERSON] \({}^{\dagger,\star}\) and [PERSON] \({}^{\dagger}\)**  
Centro de Fisica Aplicada y Tecnologia Avanzada, Universidad Nacional Autonoma de Mexico,  
Queretaro, Qro. 76230, Mexico; E-Mail: [EMAIL_ADDRESS]  
\({}^{\dagger}\) These authors contributed equally to this work.  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +52-44-3225-6994.  
Academic Editor: [PERSON]  
_Received: 6 September 2015 / Accepted: 1 December 2015 / Published: 16 December 2015_  
**Abstract:** In this research, the adsorption capacity of Ag nanoparticles on natural zeolite from Oaxaca is presented. In order to describe the adsorption mechanism of silver nanoparticles on zeolite, experimental adsorption models for Ag ions and Ag nanoparticles were carried out. These experimental data obtained by the atomic absorption spectrophotometry technique were compared with theoretical models such as Lagergren first-order, pseudo-second-order, Elovich, and intraparticle diffusion. Correlation factors \(R^{2}\) of the order of 0.99 were observed. Analysis by transmission electron microscopy describes the distribution of the silver nanoparticles on the zeolite outer surface. Additionally, a chemical characterization of the material was carried out through a dilution process with lithium metaborate. An average value of 9.3 in the Si/Al ratio was observed. Factors such as the adsorption behavior of the silver ions and the Si/Al ratio of the zeolite are very important to support the theoretical models and establish the adsorption mechanism of Ag nanoparticles on natural zeolite.  
**Keywords:** kinetic adsorption; clinoptilolite; Ag nanoparticles; theoretical models