esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The aim of this study was to determine the retention of fatty acids, \(\alpha\)-tocopherol, and carotenoids in sea buckthorn oil (SBO) encapsulated with gum arabic (GA), \(\beta\)-cyclodextrin(\(\beta\)-CD), and their mixture (1:1) under pre-optimized spray drying conditions in comparison to the bioactive molecule (BAM) content of the non-encapsulated oil. In addition, the color parameters in the spray-dried powders and the bioaccessibility of \(\beta\)-carotene, which has the highest provitamin A activity, were evaluated. The fatty acid content remained almost unchanged, while statistically significant differences in \(\alpha\)-tocopherol and carotenoid content were found between the SBO encapsulated with different carriers and the non-encapsulated oil. The retention of tocopherols and carotenoids compared to the non-encapsulated SBO ranged from 62.13 to 87.23% and from 21.17 to 97.61%, respectively. SBO encapsulated with \(\beta\)-CD showed significantly higher retention of \(\alpha\)-tocopherol (87.23%) and individual carotenoids (40.71-97.61%). In addition, the powders showed no significant differences in color parameters, and the powders encapsulated with GA and \(\beta\)-CD showed high bioaccessibility of \(\beta\)-carotene (92.50 and 90.45%, respectively). \(\beta\)-CD proved to be the most suitable carrier for the encapsulation of the carotenoids and \(\alpha\)-tocopherol of SBO, resulting in powders with high bioaccessibility of \(\beta\)-carotene.  
sea buckthorn oil; spray drying; carrier; fatty acids; \(\alpha\)-tocopherol; carotenoids; color parameters; bioaccessibility 2025 15 1194 1
# A Cross-Chain Solution to Connect Multiple DNS Blockchains in Consensus Roots System
## 7 Discussion
### Compliance Challenges of Cross-Chain DNS in Multi-Jurisdictional Environments  
Deploying cross-chain DNS systems across jurisdictions introduces major challenges related to data sovereignty and privacy compliance. Regulations differ widely in how countries control internet infrastructure and handle cross-border data flows. For example, the EU's GDPR requires lawful grounds for data processing and restricts international data transfers. In some regions, outbound DNS data are completely banned, making decentralized designs harder to implement. Privacy laws also vary, requiring flexible methods for query anonymization and secure data handling. In addition, differences in blockchain regulation may affect where nodes can be deployed and whether smart contracts are legally recognized.
## 3 Study Area and Data Processing
### Datasets  
Sentinel-1 is an environmental monitoring satellite launched by ESA in 2014 as part of the Copernicus program. Sentinel-1A SAR imagery can be used to provide monitoring data on land, sea, and atmosphere, providing Strip Map (SM), Interferometric Wide Swath (IW), Extra Wide Swath (EW), and Wave Mode (WD) imaging modes. The IW mode uses terrain observation with progressive scans SAR (TOPSAR) to acquire 3 sub-bands, control the beam range and obtain uniform quality images, achieving a wide (250 km) coverage area while maintaining a high distance and directional resolution. Sentinel_1A SAR image can be obtained from the ESA website. In this study, 25 SAR images of Yuncheng County, Heze City under Sentinel-1A satellite IW mode and VV polarization mode were collected from 20 May 2017 to 17 December 2018, and their specific parameters are shown in Table 1.
# Random Vibration Fatigue Life Analysis of Airborne Electrical Control Box
## 4 Random Response Analysis
### Mode Frequency Testing  
The experimental setup included sensors, a vibration test bench, and a control and acquisition system. The natural frequency test of the electrical control box was performed using the MPA409/M437A high-thrust high-frequency electrodynamic vibration table (ETS Solutions Ltd., Beijing, China) and the LMS Test Lab 14A software (Siemens, Munich, Germany). The details of the experimental equipment and the site are presented in Table 4 and Figure 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Model Number** & **Quantity** & **Manufacturer** \\ \hline Acceleration sensor & PCB333B32 & 2 & \
\begin{tabular}{c} PCB Piezotronics, Inc., \\ New York, USA. \\ \end{tabular} \\ Shock acceleration sensor & CA-YD-1881 & 2 &
\begin{tabular}{c} Sinocera Piezotronics, Inc., \\ Jiangsu, China. \\ \end{tabular} \\ Large-thrust high-frequency electric vibration test bench & MPA409/M437A & 1 & ETS Solutions Ltd., Beijing \\ Control and acquisition system & LMS Test Lab & 1 & Siemens Germany \\ \end{tabular}
\end{table}
Table 4: Experimental equipment.  
Figure 7: The first 10 order modes of the electrical control box. (**a**–**j**): 1 to 10 mode.  
Using the finite element calculation results, the acceleration sensors were affixed to the maximum displacement positions of each mode, and the first 10 order natural frequencies were measured and compared with the finite element results. The comparison results are shown in Figure 9.  
The first 10 natural frequencies obtained by finite element calculation exhibited good agreement with the test results (Figure 9), and the maximum relative error was 13.4%, which meets the requirements of engineering calculation, thereby indicating that the finite element model can be used for random response and fatigue life analysis.
# A Fast Identification Method of Gunshot Types Based on Knowledge Distillation
## 3 Design of the Gunshot Recognition Network
### Gunshot Preprocessing  
Gunshots are pulse signals that are transient and non-stationary. Directly inputting this one-dimensional signal into a neural network is an impractical way to extract its frequency characteristics. The logarithmic Mel spectrum, as an important characteristic spectrum for sound recognition, possesses a strong feature expression ability. The logarithmic Mel spectrum maps the sound frequencies to a Mel scale using the formula below:  
\[f_{mel}=\ 2595\times log_{10}\bigg{(}1+\frac{f}{700}\bigg{)} \tag{1}\]  
Figure 1: Schematic diagram of a muzzle sound wave.  
Figure 2: Schematic diagram of the overall scheme of gunshot classification.  
where \(f_{mel}\) is the perceptual frequency domain in Mel (Mel frequency domain for short), and \(f\) is the actual speech frequency in Hz. The actual logarithmic Mel spectrum mapping is shown in Figure 3.  
The one-dimensional gunshot signal is first transformed into a short-time Fourier transform, and a power spectrum of size (128, 256) is obtained. The power spectrum is then passed through an equal-height Mel filter bank to obtain a Mel spectrogram. Finally, the logarithm of the energy value of the obtained Mel spectrogram is converted into decibels, and generates the logarithmic Mel spectrogram. The specific transformation parameters of feature map transformation are shown in Table 1.
## 3 Results and Discussion
### Organic Acid and Sugar Profiles  
The concentrations of organic acids in the analyzed samples are shown in Table 2. The findings indicate that quinic acid and malic acid exhibited the highest concentrations, while tartaric acid displayed the lowest levels among the samples. There was no statistically significant difference in the concentrations of quinic acid between the EPF (16.0 mg/gdw) and EBF (16.4 mg/gdw) samples (_p_ > 0.05); however, the EPT (13.4 mg/gdw) had a higher concentration of quinic acid compared to the EBT (12.2 mg/gdw). Furthermore, there were no significant differences in the levels of citric acid among the EPF (8.3 mg/gdw), EPT (8.6 mg/gdw), and EBF (7.2 mg/gdw) samples; nonetheless, the EBT sample (4.7 mg/gdw) exhibited the lowest citric acid concentration (_p_ < 0.05). [PERSON] et al. [19] also reported the presence of quinic, malic, and citric acids in _E. bourgati_ extract.  
In terms of sugar composition, the EBF sample (160.9 mg/gdw) demonstrated a lower sugar content than the EPF sample (196.3 mg/gdw). Fructose was identified as the predominant sugar, with concentrations of 72.5 (EPF), 79.6 (EPT), 56.5 (EBF), and 44.8 mg/gdw (EBT). The other sugars detected in all samples included maltitol, sucrose, and glucose (Table 2).
## 4 Methodology
### Dataset Augmentation  
Data augmentation is a crucial technique in deep learning to mitigate the impact of limited dataset size and prevent overfitting. By artificially increasing the size of the dataset, data augmentation helps models to learn and generalize better, leading to improved accuracy. In the case of YOLO and R-CNN, various data augmentation techniques were used to improve the performance of the models. In YOLO, the unique technique of mosaic augmentation was employed. This involves combining multiple randomly cropped images into a grid, creating a more diverse set of data for the model to learn from. The following parameters were used for data augmentation in YOLO training: scale factor of 0.5, shear of 0.5, up and down flip of 0.2, left-right flip of 0.5, mosaic of 1, and translation of 0.1. Similarly, R-CNN performance was enhanced through the use of augmentation techniques, including flipping and translation. The parameters used were a flip factor of 0.5, a vertical flip factor of 0.008, and a horizontal flip factor of 0.5. These augmentations helped R-CNN and YOLO to avoid overfitting and generalize better to new data, leading to improved accuracy.
## 3 Results
### Point-Spread-Function  
Figure 6a,b show the PSF as an error distribution of the estimated continuous-valued annihilation position at an SPTR of 10 ps and 100 ps, respectively. The readout pitch was fixed at 3 mm. Figure 6c,d show the projection of the PSF on the \(x\)- and \(z\)-axis, respectively. Results on the \(y\)-axis were omitted because they were very similar to those on the \(x\)-axis. There were side peaks at \(\pm\)5 mm, as shown in Figure 6c. The PSF was blurred especially along the \(z\)-direction at an SPTR of 100 ps, as shown in Figure 6c.  
Figure 7a,b show the RMSE of the \(x\)-and \(z\)-position estimations, respectively, relative to the SPTR and readout pitch. The results of the \(y\)-position estimation were omitted because they were very similar to those of the \(x\)-position estimation. As shown in Figure 7a, the RMSE of the \(x\)-position estimation was almost the same for a readout pitch of 0 to 3 mm. As shown in Figure 7b, the RMSE of the \(z\)-position estimation was almost the same for all readout pitches. Table 3 shows the RMSE under representative conditions.  
Figure 6: Point spread function (PSF) as an error distribution between the true and estimated source position at an SPTR of (**a**) 10 ps and (**b**) 100 ps, respectively. (**c**) Projection of PSF on the \(x\)-and \(z\)-axes. Results on the \(y\)-axis were omitted because they were very similar to those on the \(x\)-axis. Readout pitch was fixed at 3 mm.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 3 Evaluation Metrics
### Histogram Analysis
#### 3.2.4 Deviation from Uniform Histogram  
The deviation from the uniform histogram (\(D_{uniform}\)) is a metric used to measure the deviation of a cipher image's histogram from a uniform distribution. A cipher image with a histogram distribution close to a uniform distribution indicates good quality of the employed encryption algorithm [51]. When a cipher image produced by an image encryption algorithm has a histogram distribution that is close to a uniform distribution, it implies that each pixel value occurs with equal frequency. The deviation from the uniform histogram metric can be mathematically expressed as follows:  
\[D_{uniform}=\frac{\sum_{i=0}^{255}|H_{\text{C}_{i}}-H_{\text{U}_{i}}|}{H \times W}, \tag{16}\]  
\[H_{\text{U}_{i}}=\begin{cases}\frac{H\times W}{256}&\text{if }0\leq i\leq 255 \\ 0&\text{if }0>i>255\end{cases}\]  
where \(H_{\text{C}_{i}}\) refers to the histogram value of the cipher image at intensity \(i\), and \(H_{\text{U}_{i}}\) represents the histogram value of the uniform distribution at intensity \(i\). \(H_{\text{U}_{i}}\) values are only considered when the pixel value is between 0 and 255, otherwise, \(H_{\text{U}_{i}}\) is set to 0. From Equation (16), we can calculate the upper limit of \(D_{uniform}\). If we assume that an image has extremely deviated from a uniform histogram, \(H_{\text{C}}\) will have one element with value \(H\times W\), and the rest will be zeros. By substitution \(H_{\text{C}}\) in Equation (16), the upper limit can be calculated as follows:  
\[\begin{split} D_{uniform}&=\frac{\bigg{|}H\times W -(\frac{H\times W}{256})\bigg{|}+(255\times\bigg{|}0-\frac{H\times W}{256} \bigg{|})}{H\times W}\\ &=\bigg{|}\frac{H\times W}{H\times W}-\frac{\frac{1}{256}\times H \times W}{H\times W}\bigg{|}+\frac{255\times H\times W}{256}\\ &=\bigg{|}1-\frac{1}{256}\bigg{|}+\frac{255}{256}\\ D_{uniform}&=1.9921875\end{split} \tag{17}\]  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Size** & **128\(\times\)128** & **256\(\times\)256** & **512\(\times\)512** & **1024\(\times\)1024** \\ \hline Upper limit & 32,640 & 130,560 & 522,240 & 2,088,960 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Upper limit of irregular deviation for different-sized images.  
The lower limit of [PERSON]D_{uniform}\) is 0 for any size image, and from Equation (17). It is clear that the maximum possible \(D_{uniform}\) value for any image, regardless of its size, is 1.9921875.
## 3 Results  
### Aerial Triangulation (AT)  
#### 3.1.1 Interior Orientation with Fiducial Marks  
The Wild RC10 camera was calibrated on 3 March 1988. Fiducials were manually measured on both consumer-grade and photogrammetric datasets in Inpho and Metashape and an affine transformation was computed. Table 2 reports some statistics on the quality of fiducial measurements in Inpho. Sigma0 is the root of the square sum of residuals divided by the number of observations minus the unknowns. The root mean square error (RMSE) is calculated as the root of the squared sum of residuals divided by the number of observations. The achieved values show that the measurement of fiducial markers was performed on the photogrammetric dataset with an average accuracy for all images of about 0.6 pixel whereas an accuracy of 3-4 pix was observed for the consumer-grade dataset (1 pix corresponds to 14 \(\upmu\)m).
## 2 Flow Field State Detection Technology for Multiphase Sink Vortex
### Principle of Multiphase Vortex-Induced Vibration Detection Technology  
While several detection methods have shown positive results, the chosen methods should first fulfill the fundamental characteristics of high precision, good stability, ease of maintenance, and extended equipment life. To prevent a multiphase vortex from forming, the detection technique must meet the multiphase vortex's necessary real-time detection criteria [34]. Vibration detection based on a multiphase vortex has a greater chance of being used in practice. It can meet the requirements for real-time detection in various industrial operations [35; 36]. The mixed fluid creates different frequency shock vibrations on the container or pipe due to the varying density of the fluid coming out of the container or pipe [1]. As a result, the real-time flow condition of the interior multiphase vortex may be indirectly determined based on the vibration intensity difference under fluid impact.  
The basic principle of the vibration detection method is described in Figure 2, where the vibration sensor is installed on the pipeline or operation arm end away from the container and connected with the industrial computer by a special communication wire. The vibration sensor collects the real-time fluid-induced vibration signal and transmits it to the industrial computer. The computer then uses a signal processing method to identify fluid state transitions. The industrial system's control terminal emits a pulse signal when the content of suspended solids in the fluid reaches or exceeds a predefined threshold. The sliding orifice valve closes, and the draining process comes to a halt as a result of this signal. This detection approach has the benefit of being simple to set up, disassemble,  
Figure 1: Vortex flow state detection technology. (**a**) Electromagnetic detection method. (**b**) Ultrasonic detection method. (**c**) Infrared detection method. (**d**) Vibration detection method.  
and maintain. It has no impact on the industrial flow field's flow process. Furthermore, the vibration sensor can be placed outside of the flow field. In this method, the sensor consumable problem in high-temperature environments can be effectively handled, and the detection system's operating reliability and service life may be greatly enhanced.
## 2 Results and Discussion
### Purification of Antioxidant Peptides from MGCH  
#### 2.2.1. Fractionation of MGCH Using Membrane Ultrafiltration  
Protein hydrolysate is a complex mixture of active and inactive peptides with different chain length and amino acid composition. Membrane ultrafiltration is a type of separation technique for the fractionation of protein hydrolysate on their MW and enrichment concentration of peptides with specific MW ranges [3; 24; 25]. In the experiment, MGCH was divided into two fractions including MGCH-I (MW < 10 kDa) and MGCH-II (MW > 10 kDa) by ultrafiltration with a MW Cut Off (MWCO) membrane of 10 kDa. DPPH\(\bullet\) and HO\(\bullet\) scavenging activities of MGCH-I were 81.8% \(\pm\) 2.68% and 89.8% \(\pm\) 2.98%, respectively, at the concentration of 20 mg protein/mL, which were significantly stronger than those of MGCH (DPPH\(\bullet\) 63.4% \(\pm\) 1.86%; HO\(\bullet\) 73.15% \(\pm\) 2.57%) and MGCH-II (DPPH\(\bullet\) 55.4% \(\pm\) 3.12%; HO\(\bullet\) 71.9% \(\pm\) 1.88%) (\(p\) < 0.05). [PERSON] et al. [19] and [PERSON] et al. [26] reported that MWs of hydrolysate play a crucial role in their functionality, and hydrolysate fractions with smaller MW showed stronger antioxidant activity than those of larger MW hydrolysate. In the text, MGCH-I with short chain peptides showed stronger radical scavenging activity, and these results were in line with previous reports that the antioxidant abilities of protein hydrolysates were negatively correlated with their average MW [3; 19].
# Automated Optical Inspection System for O-Ring Based on Photometric Stereo and Machine Vision
## 3 O-Ring Database
### Database for Defect Detection  
#### 3.1.1 DPS (Database of Photometric Stereo Model)  
There were 160 defects and 160 non-defects included. The defect samples included 60 samples with flow marks and 100 samples with non-fills and indentations. Each sample had six images \(I_{i}\) (\(i\) = 1, 2, 3, 4, 5, 6) to implement the photometric stereo and verify the defect detection process. A sample of this database is shown in Figure 6. There are several different dimensions and cross-section dimension O-Ring samples. The schematic diagram of those samples is shown in Figure 18.  
We put O-Rings above the button light source in the defect detection mechanism (Figure 3a). Furthermore, collecting sample images capture by trigger each light source and camera in order. Each sample was collected in the same light parameter, camera parameter. Then, we execute the defect detection we proposed in the same condition with every sample.  
In this experiment, we use a light source controller (OPT dpa2024e-8) to modify light intensity in the defect detection system (Figure 2a). Each channel of the light source set on 80. The camera exposure time set on 3000 us. By subtracting the photometric stereo model \(M_{\rm P}\) and smooth surface model \(M_{\rm S}\). The deviation model \(M_{\rm D}\) was produced. Then we used  
Figure 17: Flowchart of the contour converted the Bézier curve.  
the 50 \(\times\) 50 pixels sliding windows and set the threshold on 80 in 8-bit matrix data for the deviation model \(M_{\rm D}\) to create defect candidate area. If there are five candidate areas were connected. Then, label the sample as a defect. The threshold and the camera parameter by experiment obtain. Because the photometric stereo method in this paper was based on regression, Which type of method can't provide accurate relative height, the detection processing is shown in Figure 19.  
Figure 19: Defects detection processing with photometric stereo model \(M_{\rm P}\). **(a)** Photometric stereo model \(M_{\rm P}\) (Gray scale), those models reconstructed by corresponding images in Figure 18; **(b)** Smooth surface model \(M_{\rm S}\) (Gray scale), this model is the smooth version of Photometric stereo model \(M_{\rm P}\), create by the method in this paper Section 2.2; **(c)** the deviation model \(M_{\rm D}\), this model created by subtracting **(a,b)** (jet color map and mapped into range 0–500 \(\upmu\)m); **(d)** Candidate area (Binary images), which is produced by sliding windows with **(c)**.  
Figure 18: The schematic diagram of DPS samples. Each row is a set of sample capture with six light sources. The last row is a sample of non-defect.  
The reconstruction time consuming is about 30 s and the defect detection time consuming is about 30 s.
## 5 Performance Evaluation
### Single-Spot Deployment Scenario
#### 5.1.3 New Metric  
To evaluate both the throughput and latency gains and the coexistence performance between different schemes using a single metric, we introduce a new metric \(\gamma\). The goal of MLO is to gain as much throughput and latency performance as possible while maintaining acceptable coexistence with legacy devices/systems. Therefore, the MLD performance in an environment where legacy devices are present is compared against the performance of legacy STAs without any MLDs. The constraint itself comes from the difference in the legacy device performance before and after the addition of MLDs into the environment, and it is limited to zero (no positive values are allowed) because we only wish to leave the legacy performance unchanged rather than to boost the performance of the legacy devices. Let \(\mu_{MLD,P2}\) be the throughput of an MLD in Phase 2, and let \(\mu_{Leg,P1}\) and \(\mu_{Leg,P2}\) be the throughputs of a legacy SLD in Phases 1 and 2, respectively. Furthermore, let \(l_{MLD,P2}\), \(l_{Leg,P1}\) and \(l_{Leg,P2}\) be the corresponding packet latency values.  
The new metrics, denoted by \(\gamma_{t}\) for throughput and \(\gamma_{l}\) for latency, are given as follows:  
\[\begin{array}{l}\gamma_{t}=\frac{\mu_{MLD,P2}}{\mu_{Leg,P1}}+\alpha\times \min\{1,\frac{\mu_{Leg,P2}}{\mu_{Leg,P1}}\}\\ \gamma_{l}=\frac{l_{Leg,P1}}{l_{MLD,P2}}+\alpha\times\min\{1,\frac{l_{Leg,P1}} {l_{Leg,P2}}\}\end{array} \tag{1}\]  
where the first terms, \(\mu_{MLD}/\mu_{Leg,P1}\) and \(l_{Leg,P1}/l_{MLD}\), measure the performance gain of MLO and the second terms measure the coexistence performance. \(\alpha\) is a weighting constraint; a larger \(\alpha\) places more weight on the aspect of the coexistence of MLO with legacy systems. Since increasing the performance of legacy systems to higher than it was before is not a focus of MLO development, we upper-limit the second term by one. For each term, higher values are better. Therefore, higher \(\gamma_{t}\) and \(\gamma_{l}\) values imply better performance overall.  
The new metric of throughput performance, i.e., \(\gamma_{t}\), is shown in Figure 22 for the considered schemes with various \(\alpha\) values. (The new metric of latency performance, i.e., \(\gamma_{l}\), shows similar values and trends as \(\gamma_{t}\) and thus is omitted). When \(\alpha=0.01\), Async, Sync-FT and Sync-FT-Repick show the highest values, while Sync shows the lowest value. The other coexistence solutions show lower values than Sync-FT since they assign penalties to the MLDs. As \(\alpha\) increases, the values of \(\gamma_{t}\) for Sync, Sync-PL and the other coexistence solutions increase. When \(\alpha=1\), most schemes except Sync show similar values. This implies that the performance gain of MLO originates from a greater amount of channel access time and thus comes at the expense of performance degradation for legacy devices. Therefore, in general, the higher the performance gain of MLO is, the lower its coexistence performance.