esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Physical Properties
#### 3.2.5 Drying Shrinkage (S)  
Drying shrinkage occurs during drying. This is when an increase in the force of surface tension occurs, and, as a result, the structure of the material collapses and shrinks. Shrinkage is characterized by a hardened structure occurring both on the surface and in the interior of the dried material [50].  
The study showed that both the drying method and the type of vegetable had a significant effect on the value of the parameter studied (\(p\)-value \(<\) 0.0000). Lower values of drying shrinkage were shown for freeze-dried materials due to slow sublimation. [PERSON] et al. (2020) reported similar results, with the highest drying shrinkage observed with VD and the lowest with FD [30]. Obtaining higher values during VD may be due to the pressure difference between the inner and outer parts of the samples, which can also cause compressive stresses due to shrinkage. The application of the vacuum impregnation process reduced the values of the tested parameter in all the samples, but VI did not significantly affect the tested parameter (\(p\)-value = 0.2698). Lower values of the parameter were observed for broccoli and occurred at 22.17-38.55% depending on the drying and pretreatment method used (Figure 6). Significantly higher shrinkage values were observed for courgette without VI, which may be related to the considerably higher amount of water contained in courgette. Drying shrinkage hinders the re-hydration of the material and negatively affects the quality of the dried material. Therefore, it is important to keep its values as low as possible [49]. The highest values of drying shrinkage were obtained for untreated vacuum-dried courgette (88.89%). At the same time, the use of vacuum impregnation reduced this index from 59.71% to 57.18%, depending on the drying method used. In the case of the convection drying method, the shrinkage rate is 90%, and in the case of microwave-vacuum drying, it is 70% [50]. Sublimation drying is the method by which the lowest values of the studied parameter were obtained. [PERSON] et al. proved that after microwave-vacuum drying of parsley root, the drying shrinkage was at the level of 72-78% and depended on the microwave power and particle size of the material [50]. Other authors observed a drying shrinkage of 70% when drying kohlrabi using the microwave-vacuum method [35]. [PERSON] and [PERSON] proved that after vacuum drying (5 kPa) at 60 \({}^{\circ}\)C, the drying shrinkage of strawberries was 22.84% [51].  
Figure 5: Volumetric gel index of freeze-dried (FD) and vacuum-dried (VD) broccoli (B) and courgette (C) before and after the vacuum impregnation process with beetroot juice. Values followed by the same letter (a–e), were not significantly different (\(p>\) 0.05), according to [PERSON]’s HSD test.
## 3 Results and Discussion
### Color Measurement of Edible Fixed Oils  
The determination of color is an important measurement to predict the quality and consumer acceptability of edible oils. Table 3 presents the color parameters (CIE L\({}^{*}\), a\({}^{*}\), b\({}^{*}\), and hue), as well as the R (red), Y (yellow), B (blue), N (neutral), chlorophyll, and b-carotene values in the Lovibond color scale for all the examined edible fixed oils. The lightness (L\({}^{*}\)) values (0 = black to 100 = white) differed significantly among the examined oils, ranging from 26.03 in virgin coconut oil to 67.07 in pine cone oil. Linseed oil (60.04), black cumin oil (60.05), coffee bean oil (59.89) as well as plum oil (62.71), macadamia oil (63.30), and canola oil (63.17) showed comparable lightness values. Moreover, the lightness values of poppy seed, chia seed, and grape seed oils did not differ significantly. For the majority of oils, the a\({}^{*}\) values were negative (green), ranging from \(-\)0.63 in peanut oil to \(-\)5.92 in linseed oil, whereas sea buckthorn oil had the highest positive (red) a\({}^{*}\) value (+8.17), followed by pumpkin seed oil (+3.26) and avocado oil (+0.01). All the fixed oils examined had positive b\({}^{*}\) values (yellow), varying from +3.83 (peanut oil) to +43.69 (canola oil), indicating an increase in the yellow proportion in color. On the other hand, the negative a\({}^{*}\) (\(-\)0.14) and b\({}^{*}\) (\(-\)0.55) values for virgin coconut oil pointed out colorless or very pale color. The hue angle parameter describes the dominant spectral color component (such as red, green, or blue), whose value ranges from 0\({}^{\circ}\) to 360\({}^{\circ}\). Each angle corresponds to one color: red-purple color at an angle of 0\({}^{\circ}\), yellow color at 90\({}^{\circ}\), bluish-green color at 180\({}^{\circ}\), and blue color at 270\({}^{\circ}\)[35]. Hue values measured for sea buckthorn and pumpkin seed oils were 31.55\({}^{\circ}\) and 55.43\({}^{\circ}\), respectively, thus imparting a reddish color, whereas, for the rest of the fixed oils, the predominant color was yellowish to slightly green due to hue angle values (89.97\({}^{\circ}\) in avocado oil to 108.68\({}^{\circ}\) in pomergnate oil). The grading of oil based on the Lovibond color scale is mainly related to yellow and red.
# Vibration Energy Harvester Based on Bilateral Periodic One-Dimensional Acoustic Black Hole
## 5 Conclusions  
A piezoelectric VEH with a bilateral periodic one-dimensional ABH is proposed in this paper. First, the energy focus effect of an ABH in the VEH is simulated by COMSOL under a Gaussian pulse excitation. The simulation results show that the beam with a bilateral ABH is easier to generate more energy with than the traditional one. The performance of the VEHs with an ABH is tested in an experiment. The experimental results show that the output voltage and power of the VEH with a bilateral periodic 1D ABH is higher compared with the VEH with a traditional one-dimensional ABH and increased with the number of ABH arrays. The main research results are as follows:  
1. After the best impedance matching is completed: the output power of the traditional VEH reaches 33.63 mW, that of VEH Model 1 can reach 35.62 mW, that of VEH Model 2 can reach 75.01 mW and that of VEH Model 3 can reach 91.52 mW.  
Figure 21: Transient output voltage of VEHs in optimal impedance.  
Under the same external conditions: the output voltage of the traditional VEH reaches 84.78 V, that of VEH Model 1 can reach 88.36 V, that of VEH Model 2 can reach 137.58 V and that of VEH Model 3 can reach 169.16 V.  
The energy harvesting performance of VEH Model 3 is the best. The results show that the energy collection efficiency will increase with the increase in the ABH arrays. It is expected that the proposed VEH with an ABH may provide an effective energy supply option for WSN sensor nodes, help to monitor the life of some equipment and, finally, promote green and sustainable development.  
Conceptualization, Z.Z. and H.W.; methodology, Z.Z. and C.Y.; software, Z.Z.; validation, H.W. and H.S.; formal analysis, Z.Z. and C.Y.; investigation, H.W. and H.S.; resources, C.Y.; data curation, H.W.; writing--original draft preparation, Z.Z. and Y.Y.; writing--review and editing, H.W. and Y.Y.; visualization, Z.Z. and H.W.; supervision, C.Y.; project administration, H.W. and C.Y.; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.  
Support was received from the overseas study visit and training program for outstanding young backbone talents of Anhui Province (gxgwfx2021035); the innovation team of Anhui Polytechnic University; the Special Display and Imaging Technology Anhui Technology Innovation Center open project support (2020 AJ06001), Anhui Polytechnic University--Jiujiang District Industrial Collaborative Innovation Special Fund Project (2022 cyxtb4); the Anhui Provincial Natural Science Foundation (No. 1908085 QA13); the National Natural Science Foundation of China (No. 11902001) and the Natural Science Research Project of Institutions of Higher Education in Anhui Province (No. KJ2017A114).  
Not applicable.  
The authors declare no conflict of interest.
Figure A2: Kinetic energy density and strain of ABH model 2: (**a**) ABH in 0.35 ms, (**b**) ABH in 0.5 ms, (**c**) ABH in 0.7 ms, (**d**) ABH in 0.9 ms.
## 2 Related Work  
Building upon existing research in the topology recovery of underwater sensor networks and drone cluster networks, the air-sea cross-domain communication network is regarded as an extension of underwater networks to water-based networks. For multi-node failure network damage, the network generally becomes multiple disconnected subsets; topology reconstruction and swarm intelligence are often used to restore the network.
# Adversarial Hierarchical-Aware Edge Attention Learning Method for Network Intrusion Detection
## 3 Problem Formulation and Preliminaries
### Edge-Based Intrusion Detection  
While existing GNNs excel at node-level tasks like social network analysis, they face two key limitations in edge-based intrusion detection:  
* Edge Feature Neglect: Most GNNs (e.g., GCN [11]) treat edge features as static topological structures, failing to dynamically fuse them with node representations during message passing.
* Adversarial Vulnerability: Traditional GNNs are brittle against topology-aware attacks where adversaries perturb edge features (e.g., packet size, protocol flags) to evade detection.  
GNNs extend deep learning to non-Euclidean graph data by propagating messages along topological structures. Formally, given a graph \(G=(V,E)\) with node features \(X_{v}\in R_{dn}\) and edge features \(X_{e}\in R_{de}\), the ll-th layer of a GNN can be defined as\[\mathbf{h}_{v}^{(l)}=\phi^{(l)}\left(\mathbf{h}_{v}^{(l-1)},\bigoplus_{u\in\mathcal{ N}(v)}\psi^{(l)}(\mathbf{h}_{u}^{(l-1)},\mathbf{h}e_{uv}^{(l-1)})\right)\]  
where \(\bigoplus\) is a permutation-invariant aggregation operator (e.g., mean, sum), and \(\phi\) and \(\psi\) are learnable functions.  
Meanwhile, network intrusion detection systems (NIDSs) can be naturally formulated as an edge classification problem on network flow graphs. Nodes represent IP addresses with features \(X_{v}\). Edges are network flows \(e_{uv}\in E\), with features \(X_{e}=[packetount,duration,TLSversion\dots\dots]\). The objective is learning a function \(f:E\to\mathcal{Y}_{e}\) that maps edge to labels \(\mathcal{Y}_{e}\in Benign,DDoS,Malware,\dots\)  
Existing GNN-based NIDSs [13] primarily focus on binary edge classification (malicious vs. benign) or multi-class edge classification (different types of attacks) separately, by training two models. Therefore, we aim at hierarchical detection, which means to classify coarse-grained and fine-grained models with one model at the same time.
## 2 Materials and Methods
### Statistical Analysis  
For determination of high and low SRSF1 and MDV expression values, the better discriminant value was determined by the Youden index, assessed by ROC curve. The rates of high and low levels of SRSF1 and MDV expression in melanoma of patients with and without metastasis were non parametrically compared by chi-square tests. Agreement among observers was tested by Cohen's K. Moreover, we performed univariate and multivariate analyses, based on a Cox proportional hazards regression model (metastasis-free time as the outcome); this model included: (i) age, (ii) gender, (iii) tumor location (choroid vs ciliary body), (iv) temporal or nasal anatomic site, (v) cell subtype (epithelial vs spindle cell vs mixed), (vi) echography parameters (height and largest diameter), (vii) immunoexpression (low and high) of SRSF1 and MVD. If a predictor had a \(p\) value < 0.15 (cut off) in the univariate analysis, it was included in the multivariate one. Survival analysis according to SRSF1 and MVD expression levels (high and low) was performed by Kaplan-Meyer test; survival rates were compared by log-rank (Mantel-Cox) test. \(p\) values < 0.05 were considered as statistically significant.
## 4 The Proposed Development Model: Holistic Security and Accessibility Layered Architecture (HSALA)
### Novelty and Scope of HSALA  
HSALA restructures layered design by systematically weaving accessibility and security into every stage. This results in a noticeably more comprehensive development architecture--one that surpasses traditional models in several important aspects:  
1. Dedicated Security Layers Aligned with Functional Layers: Instead of having an overarching or optional security module, HSALA integrates security checks, mechanisms, and best practices at each layer (Data, Persistence, Application, Content Presentation and Accessibility Enhancement). This modular approach ensures that no layer is overlooked--a critical departure from traditional architectures.
2. Explicit Accessibility Layer: Beyond a simple \"Presentation\" tier, HSALA adds a dedicated Accessibility Enhancement Layer, which is not typically found in three-tier models. This layer ensures that advanced accessibility measures (e.g., ARIA attributes and multi-factor authentication adaptations for users with disabilities) are consistently designed, tested, and maintained as a core requirement.
3. Concurrent Accessibility-Security Integration: A hallmark of HSALA is the recognition that certain accessibility features (e.g., ARIA roles and accessible authentication flows) can introduce new security challenges, and vice versa. By pairing each functional layer with a corresponding security layer that also accounts for accessibility changes, HSALA moves beyond a mere \"granular breakdown\" and presents a new dimension: a \"dual-track\" model in which accessibility and security co-evolve.
4. Holistic Lifecycle Emphasis: Traditional three-layer models typically address separation of concerns between data, logic, and presentation. HSALA, in contrast, addresses the entire lifecycle--from data storage and secure access to user interface and post-deployment analytics--unifying accessibility and security concerns at each phase. This is not merely a more granular breakdown, but an entirely different vantage point that requires changes in how teams plan, design, and implement each layer.  
Consequently, the proposed theoretical architecture is not just an incremental improvement but a significant realignment of layered web application design, ensuring that accessibility and security are prioritized at every stage.  
Figure 6: Accessibility enhancement layer and accessibility security layer.
# The Interaction between the LEO Satellite Constellation and the Space Debris Environment
## 7 Conclusions  
Launching large constellations into LEO to promote the integration of communication Internet is a development direction that many companies are presently focusing on. In order to ensure the safety and order of the space environment, the relevant responsible personnel need to understand the possible lasting impacts of such missions on the space environment and design them reasonably in the early planning, operation and testing stages. The fewer the number of satellites in the constellation and the smaller the orbital inclination, the less harm to the space debris environment. To realize the function, we can comprehensively consider the relationship between the number of satellites in the constellation and the orbital parameters, and select the scheme that has the least impact on the environment, and achieves the highest reliability of satellite post-mission disposal. It is crucial to strictly comply with the space debris mitigation guidelines proposed by the Inter-Agency Space Debris Coordination Committee (IADC) [22], and create a good space environment order.  
Conceptualization, R.W., X.Y. and S.R.; methodology, S.R., R.W.; software, S.L., R.W. and S.R.; validation, S.R., R.W.; formal analysis, S.R., R.W.; investigation, S.L., X.Y. and X.S.; data curation, S.R.; writing--original draft preparation, S.R.; writing--review and editing, all authors; visualization, S.R., X.S. and S.L. All authors have read and agreed to the published version of the manuscript.
## 3 Results and Discussion
### Initial Screening for Transglycosylation Ability Using pNP-Gal as Donor  
Initial screening of the transglycosylation capacity of Aga27A and _Bo_Gal36A was carried out with pNP-Gal as the donor substrate. For this, reactions were set up with 40 mM pNP-Gal as the donor with methanol, propanol, glycerol, allyl alcohol or propargyl alcohol as potential acceptors. In addition, reactions with pNP-Gal as the sole reactant were set up in order to assess if self-condensation [28] occurred, i.e., a transglycosylation event where pNP-Gal functions as both the donor and acceptor. pNP-Gal was chosen as a screening substrate due to the liberation of pNP during catalysis (for both hydrolysis and transglycosylation), which produced a direct colorimetric indication as to whether the enzymes were active or not under the tested conditions. All reactions liberated pNP.  
MALDI-TOF MS analysis of reactions with pNP-Gal as the sole substrate showed that Aga27A catalyzed self-condensation, generating both pNP-galactosidase and pNP-galactotrioside (Figure 1), while _Bo_Gal36A appeared to lack this ability. Both enzymes catalyzed transglycosylation using pNP-Gal as the donor and propanol as the acceptor (Figure 1). For Aga27A, self-condensation products were observed in these reactions as well. Similar data were obtained for the two enzymes when incubated with pNP-Gal and the other tested acceptors (Figures S3 and S4). Thus, both enzymes used all acceptors with pNP-Gal as glycosyl donor, summarized in Table 1.  
As both enzymes displayed a capacity to utilize a wide range of acceptors, further evaluations of their properties were carried out to determine their potential as catalysts in transglycosylation reactions.
## 4 Experimental Results
### Minimum Detectable MMW Signal Source Measurements  
The minimum detectable MMW signal was measured by attenuating the output power of the MMW source until the point where there was no effect of the MMW power on the difference in the output frequency generated from the LTF. The minimum detectable MMW power incident on the N527 GDD's effective cross-section was measured for \(T_{Gate}\) = 100 ms, where \(T_{Gate}\) is defined as the LTF counting time for the case where the MMW radiation is in the ON state and also in the OFF state. The results of the minimum detectable signal (_MDS_) for these cases are presented in Table 1.  
The deviation in the output frequency was measured in a dark room such that the light incident on the LTF was only the light incident from the GDD. Twenty LTF output  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **with 550 nm Filter** & **without Filter** \\ \hline _MDS_ at (\(T_{gate}\)=100 ms) & 0.02 mW & 0.1 mW \\ \hline \hline \end{tabular}
\end{table}
Table 1: Minimum detectable MMW source signal (MDS) measurement results with and without 550 nm long pass filter at \(T_{gate}\)=100 ms.  
Figure 8: Experiment setup of the noise measurements.  
Figure 9: The influence of the 550 nm long pass filter on the noise levels of the GDD + LTF pixel.  
frequency measurements were taken using a single pixel in a consecutive manner of one with MMW radiation and one without it. Every sample was taken in \(T_{gate}\) = 100 ms. The subtraction between two consecutive samples gives the MMW influence on the output frequency. The measurement results are summarized in Table 1 for GDD N527.  
The effect of the 550 nm long-pass filter on the GDD was measured, with the setup shown in Figure 4, at full MMW source output power of 600 mW, transmitted toward the GDD and LTF pixel. Results are shown in Figure 10. In this measurement, \(T_{gate}\) was chosen to be 1 sec. The filter actually increases the LTF square wave frequency, thus increasing SNR beyond the improvement generated by the reduction in noise.
## 3 Results
### Comparison of Prediction and Actual Time Data  
The validity of the ML algorithms was tested by comparing the estimation data of the ML algorithms developed to evaluate the time factor in mail delivery of the e-scooter tool and the actual data. The comparison of prediction data of the time-based output variable with 1253 real data, which are also the test data for the ML algorithms, is shown in Figure 6a-d.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Output Features** & **Algorithms** & **MSE** & **RMSE** & **MAE** & \(\mathbf{R^{2}}\) & **StDev** \\ \hline \multirow{4}{*}{Delivery time (min)} & RF & 177.10 & 13.31 & 6.85 & 0.82 & 27.98 \\  & GB & 149.32 & 12.22 & 6.08 & 0.85 & 27.76 \\  & \(k\)NN & 205.59 & 14.34 & 7.74 & 0.79 & 27.79 \\  & NN & 172.42 & 13.13 & 7.16 & 0.82 & 27.09 \\ \hline \multirow{4}{*}{Energy cost} & RF & 0.00 & 0.03 & 0.02 & 0.92 & 0.09 \\  & GB & 0.00 & 0.02 & 0.01 & 0.96 & 0.08 \\ \cline{1-1}  & \(k\)NN & 0.01 & 0.08 & 0.05 & 0.37 & 0.08 \\ \cline{1-1}  & NN & 0.01 & 0.08 & 0.05 & 0.40 & 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The results of performance evaluation metrics result based on prediction stages.  
Figure 6: _Cont._  
Considering the input parameters, one thousand two hundred fifty-three data were tested to estimate the time parameter in the packet or mail delivery of e-scooter drivers. As a result of utilizing the GB algorithm based on Figure 6b, it has been observed that the difference between the actual and forecast data is minimal. The fact that the correlation coefficient value of the GB algorithm is higher than the other algorithms and the error margin values are lower had a significant effect on the estimation data. As a result, knowing the input parameters and using the GB algorithm, the estimated value of package or mail distribution with the e-scooter vehicle is closer to the actual data. Thus, the value of the output parameter, which is be calculated with the changes that occur in the input parameters, is vital for the future.
## 4 Method Validation
### Field Applications in Real Mines
#### 4.2.2 Results of Meter Collection and Reading Efficiency  
We conducted a comparative experiment to evaluate efficiency and accuracy between manual inspection and the MAMRS. Firstly, 100 images of meter readings were manually collected and recorded, along with the time taken and accuracy achieved. Then, under the same conditions, MAMRS was used to capture and read 100 images, with the time taken and accuracy similarly recorded. Finally, we compared the efficiency of manual inspection with that of MAMRS to demonstrate MAMRS's enhanced efficiency and its potential for achieving fully automated, unmanned inspections. The results of meter collection and reading efficiency are shown in Table 11.  
Figure 21: Mine application examples.  
Figure 20: Pointer meter reading error chart.