esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges
## 3 Changes and Trends
### Northern High Latitudes
Based on recent satellite optical and microwave observations for years 2003 through 2017 over the high northern latitudes, the mean summer (JJA) MODIS NDVI record (MYD13A1.006; [244]) showed similar spatial patterns with the AMSR-E/2 VOD record [216], despite the different spatial resolutions and underlying physics of the observations (Figure 5a,b). Major greening and biomass growth trends are found in northern taiq and tundra regions, where both NDVI and VOD show significant increases (\(p\)-value \(<\) 0.05) (Figure 5c,d). However, declining NDVI and VOD trends are also widespread, indicating decreasing productivity. The declining trend areas largely occur in boreal forest but are generally less significant than positive trend areas. Figure 5: Vegetation conditions and growth trend over the high northern latitudes from 2003 through 2017. (**a**) Mean summer MODIS NDVI; (**b**) Mean summer AMSR-E/2 VOD; (**c**) Trends in mean summer NDVI; (**d**) Trends in mean summer VOD. In c and d, positive values indicate an increasing trend and negative values indicate a decreasing trend; pixels with significant trend (\(p\)-value \(<\) 0.05) are shown with crosshatch.
# Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio +
## 4 Protocol Analysis and Covert Channel Design
### Analysis of Covert Storage Patterns
#### 4.2.3 Add Redundancy (PS3) Pattern  
The hidden information is embedded into freed space of a header element which was created by the covert sender. In PDCP, creating new space in a header element is only possible by modifying the sequence number, for example by using only 12-bit numbers instead of 18-bit values. The remaining 6 bits could probably be used to embed the hidden data. However, manipulating the sequence number may affect the correct processing by the legitimate receiver and would raise suspicion if applied too often. Consequently, only a small portion of the PDUs can be used for the covert channel. Overall, adding redundancy in the PDCP header is less suitable for the covert transmission.
# Performance Analysis of Cache Based on Popularity and Class in Named Data Network
## 5 Simulation Models and Results
### Simulation Models  
To evaluate the performance of Static-CAPIC and Dynamic-CAPIC compared to the LCD+sharing scheme, a test with three content classes that need to be cached was carried out, according to Table 1. The detail of simulation parameters as shown in Table 2. For the Static-CAPIC, a pre-determined cache proportion for the first, second, and third class are 15, 42, and 92 packets, respectively. These values are based on the percentage of content population number for each class, which are 5%, 10%, and 15% of the first, second, and third class content population, normalized to the total content store size, which is 150. The parameters analyzed for these tests are cache hit ratio and average path stretch. The cache hit ratio illustrates the ratio between the number of hit requests and the total of consumer requests. The average path stretch indicates the number of hops from the consumer to the node with the requested data. The bigger cache hit ratio is better, while the more significant path stretch is worse.  
Figure 7: Number of top content for 1 st class with various Frequency Limit Factors.  
Consumer's requests are modeled as described in Section 2.3. For Static-CAPIC and Dynamic-CAPIC, each content class gets a different treatment and different cache portion in the content store. This differs from the LCD+sharing scheme, where there is no separation in the content store, and every content class is treated in the same manner.  
The greater the Frequency Limit Factor, the more limited the amount of top content considered in determining the proportion of cache in the content store. Setting a too small value of Frequency Limit Factor causes all content in the class to be evaluated to determine the proportion of cache.
# Low Thermal Expansion Machine Frame Designs Using Lattice Structures
## 1 Introduction
In this paper, we proposed a new and practicable design method for structures which provide low planar CTE, with an emphasis on meeting the performance requirements of a precision instrument support frame. We described the low planar CTE bi-material structural design concept, based on a repeating AM unit cell and high-CTE inserts. Our numerical modelling showed that planar tailored thermal expansion can be achieved by controlling the internal AM lattice geometry and through the suitable selection of the fabrication materials. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper Reference** & **Structure Type** & **Material(s)** & **Effective CTE (10\({}^{-6}\) K\({}^{-1}\))** \\ \hline
[8] & Lightweight cellular metal composites & Aluminium and Invar & \(-\)14 to 17.1 \\ \hline
[11] & Composites with extremal CTEs using topology optimisation & Invar and nickel & \(-\)4.97 to 35.0 \\ \hline
[12] & A porous material with planar negative CTE & VeroWhitePlus and TangoBlack Plus & \(-\)434 to 396 \\ \hline
[13] & Porous composites with tunable CTE & VeroWhitePlus and TangoBlack Plus & \(-\)300 to 1000 \\ \hline
[14] & A repeating hexagonal lattice with bi-material ribs & Two materials with CTE & Large positive, zero, and \\ \hline
[24] & A honeycomb lattice with bi-material ribs & difference of \(10^{-5}\) K\({}^{-1}\) & large negative \\ \hline
[15] & 2D and 3D lattices with bi-material elements & Aluminium and copper & \(-\)68.1 \\ \hline
[25] & Planar chiral lattices and cylindrical shells & Stainless steel 431 or Al7075, and Invar & \(-\)65.77 to 91.64 \\ \hline
[16] & A continuous honeycomb structure with inserts & Two different CTE materials & Near-zero \\ \hline
[17] & An Octet bi-materials & Al6061 and Ti–6 Al–4V & 0.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of current tailored thermal expansion lattice structures.
# Influence of Oak Chips and Oak Barrel Ageing on Volatile Profile in Chardonnay Wine of Romania
## 4 Discussion
### Volatile Compounds in Chardonnay Aged Wine
In the present study, there were significant differences in the fatty acid content between control wine and the wine samples aged by different methods. Decreased acid concentrations could be caused by the loss of volatile acids and esterification reactions between alcohols and acids during the ageing process [53]. Also, [PERSON] et al. [54] explained that the increase in volatile acidity in wine in contact with oak wood is mainly due to two reasons: firstly, the acidity may increase or decrease as a result of the chip roasting process, and secondly, may be due to acetic metabolism (i.e., acidic bacteria in wine). As expected, the lactones showed the highest concentration in wine samples treated with non-toasted wood (non-toasted chips and barrel). This could be explained by the fact that some lactones are found in non-toasted wood, and thermal treatment degraded those compounds. Lactones add caramel and sweet flavor to wines [38]. Unlike the expected behavior, for ageing methods in/with non-toasted wood, total phenol volatiles content was similar for both methods, for the three ageing durations. It could be stated that, at least the first three months of ageing in non-toasted barrels are similar with ageing with non-toasted chips, regarding this chemical family. Regardless of the ageing method, wines which underwent the wood ageing presented greater vanillin concentrations, than the corresponding control wine. In our experiments, no control of MLF was conducted (no culture of bacteria and no inhibitors were added), so the increasing of vanilla volatile compounds in wood aged wine could under gone as the capacity of lactic bacteria (spontaneous MLF) to convert vanillin glycoside pre-cursors present in oak wood [55; 56]. [PERSON] et al. [21] states that \(p\)-vinylguaiacol is a fermentation product rather than a compound derived from oak contact, formed through decarboxylation of natural grape components ferulic and \(p\)-coumaric acids by _S. cerevisiae_ enzymatic activity. The concentration of 4-vinyl-guaiacol in our Chardonnay wine samples could indicate that this compound formed mainly during the fermentation period, but was also extracted from toasted wood products. Overall woody aroma of barrel-aged wines results from different chemical compounds naturally present in oak wood, originated in the toasting process and/or formed as a consequence of the wood-wine interaction. [PERSON] et al. [3] described furfurals, 5-methylfurfural, cis- and trans-whiskey lactones, guaiacol, eugenol and vanillin as odorants derived from oak wood contact, or formed during ageing. The statistical test of the experimental data disclosed that the time and the ageing method significantly impact the oak wood volatiles concentration of the studied wines.
# A Semi-Supervised Deep Learning Framework for Change Detection in Open-Pit Mines Using SAR Imagery
## 4 Results
### Results on the Test Site  
We selected Site C as the test site to evaluate our change detection model's performance. Site C remained entirely unseen by the model during the training and validation stages, thus providing a proper assessment of its generalization capabilities. This test site possesses distinct characteristics compared to Site A and Site B, introducing new challenges that the model had to effectively address to be considered a reliable change detection tool. To evaluate the model's performance on Site C, we employed both two-fold cross-validation configurations. In particular, we conducted a comparative analysis between models trained with and without the inclusion of pseudo-labeling, utilizing the data augmentation strategies in both cases. The results of the model's performance on our test site are presented in Table 6. This evaluation allowed us to demonstrate the model's ability to detect changes accurately and reliably in a previously unseen environment, providing valuable insights into its real-world applicability and generalization capabilities.  
The effectiveness of our proposed deep learning methodology for change detection in the mining sector using SAR images is demonstrated through the visual examples depicted in Figures 9-12.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & \multicolumn{2}{c}{**F1-Score**} \\ \hline  & \(\mathbf{A^{Train}-B^{Val}}\) & \(\mathbf{B^{Train}-A^{Val}}\) \\ \hline
**Pseudo-Label Set** & \(\mathbf{B}\) & \(\mathbf{A}\) \\ \hline + Augmentations & \(0.7549\pm 0.0086\) & \(0.7652\pm 0.0077\) \\ + Pseudo Labeling & \(\mathbf{0.7908}\pm 0.0118\) & \(\mathbf{0.7742}\pm 0.0106\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Test results. We report the mean \(\pm\) std over three runs.  
Figure 8: Examples of pseudo-labeled patches computed by the pseudo-labeling algorithm, to be used as additional training data.  
Figure 11: Comparison of the methods on a test patch.  
Figure 10: Comparison of the methods on a test patch.  
Figure 9: Comparison of the methods on a test patch.  
The retrained model showcased its capability to accurately detect changes of various scales, encompassing both large-scale transformations and slight alterations within the mining sites. These visual examples prove the model's ability to accurately discern changes. It identified and highlighted significant changes, suppressing false positives and ensuring that stable areas were appropriately classified as unchanged. In summary, our results demonstrate the efficacy of our proposed deep learning methodology for change detection in the mining sector using SAR images. The model consistently achieved high F1-score values, indicating its robust performance in accurately identifying changes within the mining sites. These findings emphasize the potential of deep learning approaches to deliver reliable and precise change detection outcomes in the mining sector, leveraging the power of SAR data for enhanced monitoring and decision-making processes.
# TransECA-Net: A Transformer-Based Model for Encrypted Traffic Classification
## 5 Discussion  
The TransECA-Net model proposed in this study demonstrates high performance in encrypted traffic classification tasks, but its practical deployment faces several challenges. Currently, the model has limitations in adapting to zero-day traffic patterns, struggling to effectively identify traffic types not encountered during training, which may compromise classification stability in dynamic network environments. Additionally, the model's sensitivity to adversarial attacks cannot be overlooked--attackers can disrupt traffic features through carefully crafted perturbations, leading to degraded classification performance and posing potential threats to network security reliability.  
Figure 8: Linear graph of training loss values for TransECA-Net model.  
Figure 6: Linear graph of training loss values for SAE + 1D-CNN model.  
Figure 7: Linear graph of training loss values for CNN + LSTM model.  
Future research should prioritize enhancing the model's dynamic awareness of unknown traffic patterns to achieve real-time detection and adaptive updates for zero-day traffic. Simultaneously, efforts should focus on improving the model's robustness at the algorithmic level by exploring adversarial training, feature perturbation defense mechanisms, or model fusion strategies to counter increasingly sophisticated adversarial attacks. Future work could also investigate integrating multiple data sources for encrypted traffic classification to further boost accuracy and robustness.  
Moreover, building a real-time network monitoring system that integrates TransECA-Net into cybersecurity infrastructure is essential for rapid response to emerging threats. Concurrently, research should evaluate the model's applicability across diverse network environments, such as IoT or 5G networks, to ensure effectiveness in varied application scenarios. Through these improvements, TransECA-Net is expected to achieve more secure and sustainable encrypted traffic analysis capabilities in open threat environments.
# Spatiotemporal Analysis of Urban Expansion in Beijing, China
## 2 Research Area and Research Methods  
### Study Areas  
Beijing is located in the northern North China Plain in the middle latitudinal zone (115 deg25' E-117 deg30' E, 39 deg28' N-41 deg05' N). The terrain is high in the northwest and low in the southeast. The region has a typical warm temperate semi humid continental monsoon climate, with an average annual temperature of 12.3 degC. The zonal vegetation type is warm temperate deciduous broad-leaved forest. Beijing is the political and cultural center of China. Beijing has 11 districts and counties under its jurisdiction, with a total area of approximately 16,400 km2. The circular pattern is orderly, and the radial development axis is distinct. The upward expansion of cities has a typical directionality, and a gradient change occurs along the line, changing outward from the north-south central axis centered on the Forbidden City in a radial direction to form its important development axis [11].  
From 1995 to 2020, the nonagricultural population of Beijing increased from 6.969 million to 19.166 million, the total population increased from 12.511 million to 21.893 million, the built-up area increased from 477 km2 to 1485 km2, an increase of 3.11 times, the GDP increased from 0.15 trillion yuan to 3.59 trillion yuan, accounting for approximately 3.54% of the national GDP, and the total investment in fixed assets increased from 5.705 billion yuan to 96.538 billion yuan, indicating that the urbanization process of Beijing has been accelerating.  
The urban development of Beijing has always shown circular and pancake-shaped development, with the boundary of the second green isolation area located within 1 km of the city center to the outside of the Sixth Ring Road. Currently, the boundaries of urban planning areas are also within this range. This study takes the area from the center of Beijing to the boundary of the second green isolation zone (1 km outside the Sixth Ring Road) as the research area (Figure 1), which has a concentric circular layer structure.
## 3 Experimental Results  
### Stress-Fatigue Life Curve  
Generally, the fatigue behavior of a material can be described by the relationship between applied stress level and fatigue life [24; 25; 26]. According to \"Design of Steel Structures-Part 1-9: Fatigue\" [20] and \"Standard for Design of Steel Structures\" [21], the S-N curve of the bolts could be expressed as:  
\[(\Delta\sigma)^{m}\cdot N=C \tag{1}\]  
Figure 6: The fatigue testing machine and the loading device.  
where \(\Delta\sigma\) indicates the stress range, \(N\) represents the fatigue life, and coefficients \(m\) and \(C\) are connected with the bolt type, the stress ratio, and the loading mode. The logarithmic form of the S-N curve can be expressed by:  
\[\lg(\Delta\sigma)=A\cdot\lg\text{N}+\text{B} \tag{2}\]  
where \(A\) and \(B\) are constants determined according to the results of fatigue tests. We calculated the stress range using a 75% confidence level of 95% survival probability for fatigue life, considering the standard deviation. Hence, Equation (3) describes the formula for the S-N curve in both standards:  
\[\lg(\Delta\sigma)=A\cdot\lg\text{N}+\text{B}\pm 2\text{s} \tag{3}\]  
The fatigue testing of the M60 high-strength bolts under constant amplitude provided 27 data points, among which the fatigue life of bolts M60-29 and M60-30 exceeded 2 million cycles. Table 3 lists the test results. The statistical regression analysis of the data presented in Table 3 determined the M60 high-strength bolt's S-N curves, as delineated in Figures 7 and 8. Equation (4) expresses the formula for the stress-fatigue life curve:  
\[\Delta\sigma=336.9\cdot\text{N}^{-0.413} \tag{4}\]  
where the coefficient of determination (\(R^{2}\)) of Equation (4) equals 0.9166, indicating a high fitting degree. Equation (5) defines the log-log form of the stress-fatigue life curve with a prediction level of 95%:  
\[\lg(\Delta\sigma)=-0.365\cdot\lg\text{N}+4.099\pm 0.067 \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & **Stress Ratio** & **Maximum** & **Minimum** & **Stress Range,** & **Fatigue Life,** \\  & **(R)** & **Stress, \(\sigma_{\text{max}}\)** & **Stress, \(\sigma_{\text{min}}\)** & **\(\Delta\sigma\) (MPa)** & **\(N\) (\(\times 10^{4}\))** \\ \hline M60-01 & 0.3 & 315.7 & 98.6 & 217.1 & 8.06 \\ M60-02 & 0.3 & 315.7 & 98.6 & 217.1 & 11.44 \\ M60-03 & 0.3 & 315.7 & 98.6 & 217.1 & 7.42 \\ M60-04 & 0.3 & 315.7 & 98.6 & 217.1 & 8.12 \\ M60-0 Figure 8: The stress–fatigue life relationship: the M60 high-strength bolt’s S–N curve.  
Figure 7: The variation of log (\(\Delta\sigma\)) with log (\(N\)): The log–log plot of the M60 high-strength bolt’s S–N curve.
## 4 Performance Validation
### Performance Metrics of Scheduling Algorithm
#### 4.1.1 Turnaround Time  
Turnaround time refers to the duration taken for a process to finish, starting from the moment it was submitted to the point of completion. It measures how long it takes for a method to complete and can determine the efficiency of a scheduling algorithm. Turnaround time is calculated by subtracting the submission time from the completion time of a process.
## 5 Conclusions  
The MOCT lasercom system aims to provide power efficient, pulsed optical communication to nanosatellite platforms at lunar distances. The amplifier design is expected to be able to close optical links from LEO to Earth, LEO to LEO, and Moon to Earth based upon the simulation used to model the amplifier design. This simulation predicts that the amplifier design, utilizing 4 W of pump power, will produce optical pulses with a peak power of \(\sim\)1 kW. The amplifier verification test showed that hardware in the MOCT design configuration, utilizing 500 mW of pump power, is able to produce amplified pulses with a peak power of 1.37 W. This demonstrated peak power of 1.37 W is 20% higher than the simulated results, which had different seed laser parameters and a marginally higher pulse repetition rate. The BER measurement demonstrated core functionality of the majority of the MOCT components and gave insight on the expected communication performance, where the measured BER was 0.776% for 102 nW of peak power and 0.257% for 176 nW of peak power.  
Conceptualization, J.W.C. and P.S.; Methodology, N.B., M.C., and P.S.; Software, N.B., M.C., T.R., and P.S.; Validation, N.B.; Formal Analysis, P.S.; Investigation, N.B., M.C., T.R., and P.S.; Resources, N.B., J.W.C., M.C., S.P., T.R., and P.S.; Data Curation, N.B. and P.S.; Writing--Original Draft Preparation, N.B., M.C., S.P., T.R., and P.S.; Writing--Review and Editing, N.B., J.W.C., M.C., S.P., T.R., and P.S.; Visualization, N.B., M.C., T.R., S.P. and P.S.; Supervision, N.B. and J.W.C.; Project Administration, N.B. and J.W.C.; Funding Acquisition, J.W.C.  
This research was funded by the National Aeronautics and Space Administration Early Career Faculty Program Grant No. NNX14 AO53G and the National Aeronautics and Space Administration Cooperative Agreement No. NNX16 AT13A.  
The authors would like to acknowledge [PERSON] from NASA Glen Research Center for his support in the initial stages of development of the Miniature Optical Communication Transceiver.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ASE & Amplified Spontaneous Emission \\ APD & Avalanche Photodetector \\ BER & Bit Error Ratio \\ CSAC & Chip Scale Atomic Clock \\ CW & Continuous Wave \\ DLL & Delay Locked Loop \\ \end{tabular}  
\[T_{point} =2\cdot e^{-2\cdot U_{tx}^{2}/\phi^{2}} \tag{10}\] \[R_{point} =\cos U_{rx}\] (11) \[G_{[dB]} =10\cdot log_{10}(G) \tag{12}\]
# Antibacterial Activity of Green Synthesised Silver Nanoparticles on _Saccharomyces cerevisiae_
## 2 Materials and Methods
### Bactericidal Activity  
The cytotoxicity of samples to bacterial cells was determined using the MTT assay (3-[4,5-Dimethylthiazol-2-yl]-2,5-diphenyltetrazololium bromide; Thiazoyl blue, \"Sigma-Aldrich\", St. Louis, MO, USA), which is based on the ability of dehydrogenases of living cells to reduce unstained forms of MTT to blue crystalline formazan soluble in DMSO (dimethyl sulfoxide). The assay was performed on a 96-well plate. A (Sarstedt, Numbrecht, Germany): each sample was added in triplicate, then a test or reference bacterial culture was added and incubated for 18 h. The solution of MTT at a concentration of 0.1 mg/mL was added to the cells and incubated at a temperature of 37 \({}^{\circ}\)C for 1.5 h. After incubation, the unreacted reagent was removed and DMSO was added to the cells, before measuring the optical density of the samples on a plate-spectrophotometer with a vertical ray (Thermo Labsystems, Vantaa, Finland) at a wavelength of 490 nm [9].  
The bactericidal activity of the samples was evaluated in relation to the index of cell survival. Furthermore, the cells grown in the M9 medium were taken as 100% cell survival in the control sample. Cell survival in the samples was calculated relative to the control.