esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Feature Selection and Engineering  
A critical factor in any robust machine learning application is converting raw data into informative features. Based on domain knowledge and dataset specifics, key features included the following:  
* **Temporal Features**: Hourly/daily indicators and seasonal markers, with flags for weekends and holidays;
* **Occupancy Features**: Total occupant count, gender ratio, and occupancy change rate
* **Environmental Features**: Weather metrics (temperature and humidity) to control for external factors;
* **Building Characteristics**: Floor area, layout constraints, and total floors;
* **Event Features**: Indicators for specific events, coupled with estimates of attendee numbers.  
To avoid redundant or noisy inputs, feature importance was assessed using tree-based methods, and principal component analysis (PCA) was applied where necessary. Numerical variables were normalized to a 0-1 scale, while categorical features underwent one-hot encoding for consistency across the neural network input layers.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} PRN & Pseudo-random noise \\ BER & Bit error rate \\ PDH & Pound-Drever-Hall \\ TDI & Time-Delay Interferometry \\ DS/SS & Direct Sequence Spread Spectrum \\ BPSK & Binary Phase-Shift Keying \\ QPD & Quadrant photodetector \\ DPLL & Digital phase-locked loop \\ DLL & Delay locked loop \\ EOM & Electro-optic modulator \\ \end{tabular}
## 3 PHM System Driven by Model Data Hybrid
### System Architecture
#### 3.1.1 Basic Data Layer  
The main functions of the basic data layer are described as follows:  
1. Data injection. On the one hand, the constellation on-orbit telemetry data (including environmental parameters, orbital parameters and system parameters) are received through the telemetry data interface module and parsed into engineering values with practical physical meanings. On the other hand, command data that drive model state transitions are generated by the event schedule from mission planning. According to  
Figure 5: Comparison of MWorks Orbital Calculation Results with STK: (**a**) True Anomaly. (**b**) Inertial Position. (**c**) Subsatellite Point Track.  
Figure 6: Health monitoring system architecture for satellite constellation.  
the mapping relationship between the data and model variables, the telemetry data and command data are injected into the model to drive the simulation.
2. Model synchronization. The injection of real-time telemetry data into the digital model can ensure that the status of the constellation in the digital space is consistent with the physical entity, so as to obtain high-fidelity and more comprehensive state data and support real-time state monitoring, fault diagnosis and other services.
3. Simulation prediction. On the basis of model synchronization, another simulation thread is started to perform over-real-time simulation to obtain the subsequent state change in the constellation.
## 2 Non Destructive Sensing  
Technology measurements for quality evaluation and sorting of products such as ultrasound, X-ray, magnetic resonance, hyperspectral imaging and fluorescence based on physical properties are carried out. Research has been done on developing sensors for real time, non destructive sorting of fruits and vegetable maturity, defect and disease detection.
## 3 Stability Model Test of the Support System
### Model Components  
The materials chosen for the model sheet piles and pipe piles are nylon sheets and nylon tubes, respectively. The size of the model is determined according to similarity theory. In this test, geometric similarity determines the length similarity ratio between the prototype and the model, \(C_{l}=l_{p}/l_{m}=10\), with the subscript \(p\) denoting the prototype and the subscript \(m\) denoting the model in each symbol. From this, it can be concluded that  
\[C_{E}=E_{p}/E_{m}=C_{\gamma}C_{l} \tag{1}\]  
\[C_{l}=l_{p}/\,l_{m}=C_{l}^{4} \tag{2}\]  
where \(C_{E}\), \(C_{\gamma}\), \(C_{l}\) and \(C_{l}\) represent the similarity ratios of elastic modulus \(E\), unit weight \(\gamma\), moment of inertia \(I\), and length \(l\), respectively; \(E_{p}\), \(E_{m}\), \(l_{p}\) and \(l_{m}\) denote the elastic modulus and moment of inertia of the prototype and model.  
This model test focuses on investigating the bending deformation and displacement characteristics of pipe piles and sheet piles after being subjected to stress. Therefore, the model is designed to match the bending stiffness of the prototype, with \(E_{p}l_{p}\) and \(E_{m}l_{m}\) being approximately equal. Utilizing Equations (1) and (2), we derive the following:  
\[E_{m}l_{m}=E_{p}l_{p}/C_{\gamma}C_{l}^{5}=E_{p}l_{p}/10^{5}C_{\gamma} \tag{3}\]  
The Larsen IV steel sheet pile, as a prototype sheet pile, has a unit weight (\(\gamma_{p}\)) of 78.5 \(\times\) 10\({}^{3}\) kN/m\({}^{3}\), a moment of inertia \(l_{p}\) = 38,600 cm\({}^{4}\) per linear meter of plane (i.e., with a plane width of the steel sheet pile bp = 1 m), and a modulus of elasticity (\(E_{p}\)) of 206 GPa. The model is equivalent to a rectangular plate, which has the moment of inertia \(I=bh^{3}/12\), the capacity of nylon \(\gamma_{m}=11.5\)\(\times\) 10\({}^{3}\) kN/m\({}^{3}\), and the modulus of elasticity (\(E_{m}\)) equal to 2.83 GPa. When the plane width of the model sheet pile \(b_{m}\) = 1 m, the thickness of the model sheet pile, \(h_{m}\) \(\approx\) 17.0 mm, can be calculated according to Equation (3).  
The steel pipe pile, serving as the prototype pipe pile, has a unit weight \(\gamma_{p}=78.5\)\(\times\) 10\({}^{3}\) kN/m\({}^{3}\). The moment of inertia \(l_{p}\) = 910,540 cm\({}^{4}\) is calculated based on the formula \(I=\pi(D^{4}-d^{4})\), with an outer diameter \(D_{p}\) = 1 m, thickness \(t_{p}\) = 25 mm, and inner diameter \(d_{p}\) = 0.95 m. When the diameter of the modeled pile \(D_{m}\) = 0.1 m, the thickness of the modeled pile is approximately \(t_{m}\)\(\approx\) 2.7 mm, as calculated from Equation (3). The tie rods were made of stainless steel rods with a 4 mm diameter, bolted to nylon plates and nylon tubes at both ends.
# Dark Spot Detection in SAR Images of Oil Spill Using Segnet
###### Abstract  
Damping Bragg scattering from the ocean surface is the basic underlying principle of synthetic aperture radar (SAR) oil slick detection, and they produce dark spots on SAR images. Dark spot detection is the first step in oil spill detection, which affects the accuracy of oil spill detection. However, some natural phenomena (such as waves, ocean currents, and low wind belts, as well as human factors) may change the backscatter intensity on the surface of the sea, resulting in uneven intensity, high noise, and blurred boundaries of oil slicks or lookalikes. In this paper, Segnet is used as a semantic segmentation model to detect dark spots in oil spill areas. The proposed method is applied to a data set of 4200 from five original SAR images of an oil spill. The effectiveness of the method is demonstrated through the comparison with fully convolutional networks (FCN), an initiator of semantic segmentation models, and some other segmentation methods. It is here observed that the proposed method can not only accurately identify the dark spots in SAR images, but also show a higher robustness under high noise and fuzzy boundary conditions.  
Keywords:image segmentation; deep learning; synthetic aperture radar (SAR); oil slicks; segnet +
Footnote †: journal: Applied Sciences
## 7 Risk Analysis
### WiFi
#### 7.5.2 Denial of Service (DoS)  
1. **Communications**  
_Security risk_: If the attacker possesses knowledge about the network where the communication between the ERS and the vehicle occurs, they can execute a DoS attack by sending multiple packets to the ERS. The ERS not being able to receive information about an accident could have a severe impact on the road users' safety. Also, the operation of the entire ACN system is compromised.  
_Security measures_: To mitigate the risk of this attack, firewalls and access control lists should be implemented in order to restrict communication between devices on the network. They would only allow necessary traffic to and from the ERS system. Other possible measures are rate limiting, load balancing, and anomaly detection.
## 2 Materials and Methods
### Data Analysis  
The recorded interviews were transcribed verbatim by the authors. The thematic analysis process was carried out using Microsoft Excel [17], based on six-step inductive thematic analysis: (a) becoming familiar with the data; (b) generating initial codes; (c) searching for themes; (d) reviewing themes; (e) defining and naming the themes; and (f) producing the report [18]. To ensure reliability and validity in this qualitative research, several rigor strategies were applied, following methodological recommendations from the literature [19; 20]. These included data triangulation (interviews, observations, and field notes), the involvement of external researchers in the analysis, a rigorous thematic coding process, and detailed contextual descriptions to support transferability.  
In addition, methodological decisions were thoroughly documented to ensure confirmability, and a reflexive stance was maintained throughout the process to minimize potential bias.  
The researchers continuously read the interview transcripts to gain an overall understanding of the content. Next, the transcripts were analyzed, and the initial codes were continuously combined and compared with the data throughout the process. Then, the codes were reviewed and classified into emerging themes and subthemes. Modifications were made to the themes and subthemes to make them more inclusive through discussions among the authors. Ultimately, four main themes were developed that reflected the content of the interviews.  
Several strategies were employed to ensure the reliability of the data. Two researchers conducted daily and persistent observations to fully understand the older adults during the robottherapy sessions and their interactions with the robot cat [18; 21]. The observationshelped identify behavioral patterns that participants did not always verbalize, and allowed for a better contextualization of their verbal responses. For example, in several cases where participants expressed indifference toward the robot during interviews, the field notes revealed consistent affectionate behaviors (such as stroking the robot, smiling at it, or speaking to it), suggesting a deeper level of emotional engagement than was verbally reported. These observations contributed to the interpretation of the themes and helped reduce potential biases in the self-reported data.  
Two external researchers independently analyzed the data to enhance the credibility of the findings [22]. Additionally, an audit trail was maintained for the coding and categorization of qualitative data during the analytical process to ensure the rigor of the research [23].
# Spatial Domain-Based Nonlinear Residual Feature Extraction for Identification of Image Operations
###### Abstract  
In this paper, a novel approach that uses a deep learning technique is proposed to detect and identify a variety of image operations. First, we propose the spatial domain-based nonlinear residual (SDNR) feature extraction method by constructing residual values from locally supported filters in the spatial domain. By applying minimum and maximum operators, diversity and nonlinearity are introduced; moreover, this construction brings nonsymmetry to the distribution of SDNR samples. Then, we propose applying a deep learning technique to the extracted SDNR features to detect and classify a variety of image operations. Many experiments have been conducted to verify the performance of the proposed approach, and the results indicate that the proposed method performs well in detecting and identifying the various common image postprocessing operations. Furthermore, comparisons between the proposed approach and the existing methods show the superiority of the proposed approach.  
image postprocessing operations; spatial domain-based nonlinear residuals; deep learning technique +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods  
### Sample Preparation  
The sample preparation utilized lead (II) acetate, Pb(C\({}_{2}\)H\({}_{3}\)O\({}_{2}\))\({}_{2}\) (99.99%), to extract Pb\({}^{2+}\) as a lead precursor and sodium sulfide, Na\({}_{2}\)S.9H\({}_{2}\)O (99.99%), to take S\({}^{2-}\) as a sulfide precursor. Thioglycerol, TGL(95%), and di-thioglycerol, DTG (95%), were used as capping ligands. In this experiment, the salts were purchased from R&M Chemicals (London, UK) and the ligands from Sigma Aldrich (Irvine, UK). All chemicals were used without any further purification. The synthesis was started with the preparation of PbS QDs in aqueous solution which followed the procedure as reported by [PERSON] et al. [28]. From this procedure, the typical size of the QDs obtained was approximately 6.00 \(\pm\) 1.00 nm. The PbS solution was then aliquoted for the fabrication of PbS/MnS core shell QDs, where Mn\({}^{2+}\) precursor was added into the PbS solution with different amounts of volume in order to vary the thickness. The cation exchange method was used, in which formation of the shell proceeds through cationic exchange between Pb\({}^{2+}\) and Mn\({}^{2+}\) ions. The schematic diagram of the preparation of PbS/MnS core shell QDs is shown in Figure 1. In this procedure, the molar ratios of Pb\({}^{2+}\), S\({}^{2-}\) and Mn\({}^{2+}\) precursors were fixed to 1, 0.3 and 0.2, respectively.  
For the preparation of Mn\({}^{2+}\) precursor, 0.086 g of manganese (II) acetate, Mn(CH\({}_{3}\)CO\({}_{2}\)).4H\({}_{2}\)O (98.5%, R&M Chemicals), in solid crystal was dissolved in 3.0 mL of deionized water. For the growth of the 0.3 monolayer (ML) thick MnS shell, 0.63 mL of Mn\({}^{2+}\) precursor was injected into a three-neck flask containing PbS QDs that had been prepared earlier, and the solution was allowed to react for 30 min. Then, 7 mL of mixed solution was taken out and stored in the chemical refrigerator at a low controlled temperature of 4 \({}^{\circ}\)C to avoid oxidation and aggregation before proceeding to the characterization process. These steps were repeated by adding 0.51, 0.44, 0.30 and 0.25 mL of Mn\({}^{2+}\) precursor into the same three-neck flask to grow the 0.6, 0.9, 1.2 and 1.5 ML shells, respectively. The PbS/MnS core shell QD samples were named as CS 0.3, CS 0.6, CS 0.9, CS 1.2 and CS 1.5. All of the steps were performed in ambient nitrogen (N\({}_{2}\)).
## 11 Visual Comparison with Baselines  
In Figure 5, qualitative comparisons are presented across six prominent SRR methods: U-Net SRR, TV-SRR (total variation-based), STFNet, Diffusion-based SRR, a recent transformer GAN, and the proposed GAN architecture. The selected slices represent clinically relevant cases, including ischemic lesions, cortical thinning, and subcortical hypometabolism. Ground truth (GT) images are shown for reference.  
The proposed method was found to consistently yield sharper anatomical structures, enhanced GM-WM delineation, and improved visualization of hypometabolic PET hotspots. In comparison, U-Net produced oversmoothed reconstructions, TV-SRR exhibited edge blurring, and STFNet struggled to retain fine PET details. Although diffusion-based approaches enhanced certain structures, they often introduced non-physiological textures, see Figure 5.  
Figure 5: Visual comparison across SRR methods. (Top to bottom) MR-FLAIR, MR-T1, PET, and fused PET-MR. (Left to right) GT, U-Net, TV-SRR, STFNet, Diffusion SRR, Proposed.  
Figure 6: Effects of motion correction. Top: MR scans before and after correction; Middle: PET scans before and after correction; Bottom: Fused MR+PET scans, showing enhanced alignment and anatomical sharpness post-correction.  
Additional visual distinctions include a reduction in halo artifacts, more coherent cortical folding in MR, and improved PET contrast in deep brain regions. All reconstructions were rescaled to 256 \(\times\) 256 resolution for consistency. The final row illustrates the results of the proposed method, see Table 1.  
Figure 6: Effects of motion correction. The left column shows MR, PET, and fused MR+PET scans without motion correction, while the right column shows the scans after motion correction.  
Figure 5 presents qualitative comparisons across U-Net SRR, CS-SRR, STFNet, and the proposed method. The test images represent common clinical scenarios: mild cortical atrophy, deep gray nuclei sparseness, and hypometabolic lesions. MR reconstructions using the proposed model exhibit sharper sulcal boundaries and fewer interpolation artifacts. PET outputs demonstrate improved resolution of small hotspots, particularly near midline structures.  
Additional visual differences include reduced ring artifacts, better GM-WM contrast in MR, and smoother transitions in low-count PET regions. Figure resolution was matched across all methods (256 \(\times\) 256) to ensure fair comparison.
# Solanesol Biosynthesis in Plants
## 2 Functions of Solanesol
### Medicinal Value of Solanesol and Its Derivatives  
Solanesol possesses antimicrobial, anti-tumour, anti-inflammatory, and anti-ulcer activities, and it serves as an important pharmaceutical intermediate for the synthesis of coenzyme Q10, vitamin K2, and SDB (Figure 1) [3,6]. The physiological functions of coenzyme Q10 include anti-oxidation, anti-aging, immune-function enhancement, cardiovascular enhancement, brain-function enhancement, and the regulation of blood lipids; it may be used for treating migraines, neurodegenerative diseases, hypertension, and cardiovascular diseases [9,10], and as a dietary supplement for patients with type 2 diabetes [11]. Vitamin K2 promotes bone growth, inhibits bone resorption, stimulates bone mineralization, has preventive and therapeutic effects on osteoporosis, diminishes blood clotting, and reduces the progression of arteriosclerosis [12]. The anti-cancer agent synergizer SDB allows P-glycoprotein-mediated multidrug resistance in cancer cells to be overcome, and has synergistic effects with certain anti-tumour drugs [13,14]. Recently, it was found that solanesol induces the expression of HO-1 and Hsp70, which in turn alleviates alcohol-induced liver cell damage [15]. Additionally, it inhibits the generation of inflammatory cytokines through the p38 and Akt signalling pathways, implying an anti-inflammatory effect [16]. Therefore, solanesol and its derivatives are highly valuable from a pharmaceutical perspective.  
Figure 1: Chemical structures of solanesol and some of its derivatives. SDB: \(N\)-solanesyl-\(N\),\(N^{\prime}\)-bis (3/4-dimethoxybenzyl) ethylenediamine.
## 5 Discussion
Here, the obtained results are compared with [5; 16; 17]. For both, RA and AI configurations, it is evident that our results for HGPP and EGPP were only significantly different with respect to the DBF and ALF blocks. In the HGPP, the DBF load was approximately 9% higher than that in the EGPP, while the ALF load showed the opposite trend and an average increase of 14% in the EGPP compared to the HGPP. This indicates that the architectural resources of the EGPP mainly affected the ALF block. Therefore, acceleration efforts should be carried out in the ALF for the EGPP implementation. For the RA configuration, our HGPP results on the decoding process were similar to those reported in [5]. However, it is interesting that the DBF load was increased by approximately 10% in our HGPP analysis. Additionally, for the AI configuration, the computational load distribution of [5] was similar for the TX and SAO, roughly \(\times\)1.5 for the ALF, and approximately \(\times\)0.5 for the IP and DBF with respect to our HGPP implementation. Moreover, the results presented by [16] using VTM6.1 corresponded approximately to those obtained by our EGPP implementation, with the main difference again with the DBF, being \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Min–Max fps per Conf.**} & \multirow{2}{*}{**Hardware**} \\ \cline{3-3} \cline{5-5}  & & **AI** & **RA** & \\ \hline Our HGPP & VTM8.0 & 0.6–2.3 & 1.2–4.1 & Ryzen 3.4 GHz \\ Our EGPP & VTM8.0 & 0.3–0.8 & 0.4–1.2 & ARM v8.2 2.2 GHz \\ [17] & VTM5.0 & N/A & 1.3–4.2 & i9-9900X 3.5 GHz \\ [16] & VTM6.1 & N/A & 1.7–4.7 & i7-8700K 4.7 GHz \\ [16] GPU & VTM6.1 & N/A & 2.1–8.4 & RTX 2080 Ti 1.6 GHz \\ [5] & VTM6.0 & N/A & N/A & i7-4790k 4.0 GHz \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of the number of fps decoded by different VVC implementations. 8%. Furthermore, the computational load of all the decoding blocks presented in [17] was approximately 6% on average, varying from our HGPP implementation. These analyses imply that our HGPP architecture mainly affects DBF blocks with respect to the architecture used in [5; 16]. Additionally, the architectural influence between our HGPP and the HGPP used in [17] varies. However, the architectural resources of our HGPP processor mainly affect DBF blocks with respect to the processor used in [17]. Moreover, our EGPP processor has a greater impact on DBF blocks than the processor used in [16].
## 3 Results  
### Motion Trajectory Analysis  
The initial position of the electron is at the origin of the coordinate system. The incident circularly polarized laser chirped pulse is an electromagnetic wave, with the transverse electric field and irradiance distribution approximately following a Gaussian envelope, propagating in the +z direction with an incident angle of \(0^{\circ}\). The wavelength \(\lambda_{0}=1\)\(\upmu\)m, the intensity parameter \(a_{0}=5\) (corresponding to \(I=3.45\times 10^{19}\) W/cm\({}^{2}\)), and the chirped pulse width \(L\) is \(3\lambda_{0}\). The beam waist radius \(b_{0}\) is determined to be \(4\lambda_{0}\), and the chirp parameter \(c_{0}\) is set to \(-0.03\). The laser propagates in the positive \(z\)-axis direction, while the electron's velocity is directed along the negative \(z\)-axis. The normalized initial energy of the electron is \(\Gamma_{0}=2\). \(Tspan\) represents the duration of the interaction process, which corresponds to the time used to solve the partial differential equations, and it also indicates the measurement period of the model.  
By analyzing the electron's motion at different \(tspan\), the parameters of the electron interacting with the laser pulse up to a specific state can be obtained. The electron undergoes head-on collision with the laser pulse along the \(z\)-axis, and the influence of the laser pulse causes the electron to exhibit the motion trajectory shown in Figure 2. We define the motion along the \(z\)-axis as axial motion characteristics, while the motion in the \(xoy\) plane is described as radial motion characteristics. Notably, after interacting with the laser pulse, the electron's trajectory envelope forms an inner shape resembling an hourglass and an outer shape resembling an elliptical spindle, which is defined as the \"hourglass-spindle shape\". This paper focuses on analyzing the formation process of the hourglass-spindle-shaped trajectory and the variation of its related parameters.