esa-sceva/satcom-chunk-collection huggingface dataset:
# DTN and Non-DTN Routing Protocols for Inter-CubeSat Communications: A comprehensive survey
## 2 Challenges in CubeSat Communications
To date, many energy-efficient routing protocol have been proposed to tackle the said issue, which will be discussed in the next chapter. Where LEO satellites are usually deployed on orbits of 200-1200 km, they are additionally associated with a natural drag that increases when the satellites are orbiting closer to the Earth due to atmospheric drag forces, such as thermospheric heating and cooling, as well as the gravitational forces. This will force CubeSats to change their trajectory over time, such that the meeting time with other CubeSats will also change, which will require an accurate Attitude Control Subsystem (ACS) to maintain both the orbit and the direction of the antenna on the CubeSat body. Even though different ACS techniques were proposed for CubeSat missions, such as low control accuracy active and passive magnetic ACSs, as well as reaction wheels and torque coils [19], the limited resource capability of the CubeSats restrict the application of advanced stability control devices, especially on 1U-3U CubeSats that are commonly used in the large CubeSat constellations, for correcting the orbital drag and stabilizing the rotation of CubeSat. Thus, communication might be impossible because of the trajectory change and the rotation of CubeSats can change the antenna pointing direction, leading to link disruption and data loss, even when two CubeSats are in close proximity. Beyond the entire abovementioned reasons making routing in CubeSat networks extremely challenging, yet another issue that might cause link disruption in CubeSat networks is the failure of the CubeSat itself. CubeSats are characterized by high failure rates, as most of them are made from COTS components with the impossibility of recovery in the case of failure. The failure of one CubeSat might result in the failure of multiple routing paths, which leads to data dropping and the necessity for routing table recalculations. Table 1 summarizes the routing challenges and implications that CubeSat networks may encounter in LEO, including space communication challenges and the other constraints, due to the CubeSat structure limitations. _Electronics_**2020**, \(9\), 482
# Enhancing the Privacy of Network Services through Trusted Computing
## 2 Backgrounds and Related Work
### Intel SGX
To mitigate the side-channel attack and system call snooping against the SGX technology, the authors of [39] adapt the ORAM protocol to present a data-oblivious filesystem. To further address notorious performance issues of ORAM, the authors of [40] leverage an external device, FPGA, to implement a trusted storage service within a completely isolated environment secure from side-channel attacks.
# Bar-Wave Calibration of Acoustic Emission Sensors
## 3 Results and Discussion
### AE Sensor Response
#### 3.2.14. Olympus V101 Transducer  
This transducer is a nominally 25.4 mm diameter, 0.5 MHz design for ultrasonic applications. This was used in our calibration studies and is unlikely to be used as an AE sensor. However, its larger size offers a comparison to smaller sensor sizes in considering wave cancellation effects. Bar-wave responses in displacement (solid blue) and velocity (dotted blue) are shown in Figure 22. Both of the blue curves peaked at 63 kHz, giving 5.1 dB (displacement) and 73 dB (velocity). The bar-wave displacement response drops off sharply at 200 kHz and this sensor is useful only for the low frequency (large wavelength) region. The dashed green curve gives the displacement sensitivity to normally incident waves with a broad maximum of 5.3 dB at 550 kHz and a dip at 1.15 MHz, giving a useful range from 150 kHz to 1 MHz.  
For the bar-wave curve (blue), a dip appears at 380 kHz. This corresponds to the second null for wave velocity of 4.3 mm/\(\upmu\)s or for the third null for wave propagating at 3.0 mm/\(\upmu\)s. The dispersion curves (Figure 5c) show a mode at 3.4 mm/\(\upmu\)s and a few modes at 2.7 mm/\(\upmu\)s, so the third null appears to produce the observed dip. A minor dip exists at 270 kHz, corresponding to the second null for the velocity of 3.0 mm/\(\upmu\)s. A dip for the first null is absent, however. Thus, the aperture effects again caused the dips in V101.  
Figure 21. Receiving sensitivity of DECI SH225 (104). The displacement receiving sensitivities are shown in blue curves and velocity sensitivities are in red. Solid lines are for the bar-wave data, while dashed curves are for the sensitivities to normally incident waves.
# Using Financial Instruments to Transfer the Information Security Risks
## 7 Demonstration: An Example Application
### Information Security Vanilla Options
#### 7.2.2 Contract Specifications  
The specifications of an ISVO contract to address the risk in the given scenario is shown in Table 12.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Fixed Specifications** & **Variable Specifications** \\ \hline Issuer & Information Security Prediction Market \\ \hline Information Security Derivative Type & Information Security Vanilla Option \\ \hline Contract ID & ISVO 123 \\ \hline Underlying Event or Condition & The company “C” suffers a (pre-defined type of) cyber-attack \\  & during the trading period of the contract and its cloud \\  & services are unavailable to its clients. \\ \hline Trading Start Date and Time & 1 October 2015 00-00-01 CET \\ \hline Trading End Date and Time & 31 December 2015 23-59-59 CET \\ \hline Minimum Investment Required & Amount : 1000 (One Thousand) \\  & Currency : USD \\ \hline Maximum Investment Permitted & Amount : 500,000 (Five Hundred Thousand) \\  & Currency : USD \\ \hline Contract Trading Unit (Lot Size) & One (01) \\ \hline Transaction Fee & 0\% (Zero for the sake of easy calculation and demonstration) \\ \hline Payout Trigger Criteria & Cloud computing services of “C” are unavailable for at least \\  & continues 60 min in one day. \\ \hline Decision Criteria & Press release by the company “C” or a regulatory filing by the \\  & company “C”, such as to the stocks trading regulator. \\ \hline Payout (Return) Structure & Scaling-Increasing; Payout of $100 per contract for per hour \\  & of service unavailability, $0 otherwise. \\ \hline Pay-Off Horizon & On the day of settlement \\ \hline Settlement Date & If there is no news/report of any cyber-attack on the company \\  & ’C’ then the settlement will be on the fourth business day \\  & from the last day of trading. However, if there is any news/report of any cyber-attack during the trading period \\  & then the settlement will be on the fourth business day after the incident’s report is released or the fourth business day \\  & from the last trading date, whichever is earlier. \\ \hline Independent Third & Party & No \\ Verification Required & & \\ \hline Eligible Investors/Traders & Only verified \\ \hline \hline \end{tabular}
\end{table}
Table 12: Information Security Vanilla Options (ISVO) Specifications.
## 5 Results
### Classification Algorithms for DUI
#### 5.3.3 XGBoost  
The results of the XGBoost model are presented in Table 4. The notably high training accuracies suggest significant overfitting, leading to relatively poor performance on the test data. In urban scenarios, the model performs reasonably well, with an accuracy  
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Data** & **Scenario** & **Accuracy\_Train** & **Accuracy\_Test** & **F1\_Value** & **Precision** & **Recall** \\ \hline Vehicle & City & 99.76 & 58.33 & 59.02 & 58.06 & 60.00 \\ \hline Camera & City & 99.88 & 58.33 & 57.63 & 58.62 & 56.67 \\ \hline Fusion\_Data & City & 100.00 & 63.33 & 59.26 & 66.67 & 53.33 \\ \hline Fusion\_Interpretation & City & 100.00 & 70.00 & 68.97 & 71.43 & 66.67 \\ \hline Vehicle & Rural & 99.52 & 58.33 & 57.63 & 58.62 & 56.67 \\ \hline Camera & Rural & 99.52 & 58.33 & 52.83 & 60.87 & 46.67 \\ \hline Fusion\_Data & Rural & 99.88 & 73.33 & 72.41 & 75.00 & 70.00 \\ \hline Fusion\_Interpretation & Rural & 100.00 & 60.00 & 53.85 & 63.64 & 46.67 \\ \hline Vehicle & Highway & 99.52 & 48.33 & 52.31 & 48.57 & 56.67 \\ \hline Camera & Highway & 99.76 & 51.67 & 52.46 & 51.61 & 53.33 \\ \hline Fusion\_Data & Highway & 99.88 & 45.00 & 44.07 & 44.83 & 43.33 \\ \hline Fusion\_Interpretation & Highway & 100.00 & 48.33 & 53.73 & 48.65 & 60.00 \\ \hline Vehicle & Complete Track & 99.76 & 65.00 & 64.41 & 65.52 & 63.33 \\ \hline Camera & Complete Track & 99.64 & 56.67 & 58.06 & 56.25 & 60.00 \\ \hline Fusion\_Data & Complete Track & 99.76 & 58.33 & 56.14 & 59.26 & 53.33 \\ \hline Fusion\_Interpretation & Complete Track & 100.00 & 58.33 & 56.14 & 59.26 & 53.33 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model performance of random forest.  
Figure 6: Confusion matrix of random forest: rural road with fusion at data level.  
ranging from 56% to 63%. On rural roads, the model shows moderate performance, achieving between 51% and 63% accuracy. However, on the highway, prediction accuracy is notably low, even falling below the threshold of random guessing (50%). Overall, XGBoost performs better in urban scenarios, particularly when data are fused at the interpretation level (see Figure 7).
## 2 Antenna Design
### Step-by-Step Design Process  
To elucidate the operating principle of the proposed filtering CP ME dipole antenna, Figure 2 presents four reference antennas along with their corresponding results. As shown in Figure 2a, Ant.I employs a rectangular slot to excite the orthogonally placed ME dipoles through capacitive coupling, while a \(\lambda/4\) phase delay line connected to the \(-45^{\circ}\) oriented ME dipole establishes the quadrature phase relationship necessary for LHCP operation at 3.9 GHz. The antenna inherently generates two radiation nulls: a lower stopband null at 2.6 GHz resulting from the crossed E dipoles' fundamental mode cancellation and an upper stopband null at 7.5 GHz created by the rectangular slot's half-wavelength resonance, though with limited suppression due to the single-null mechanism. Ant.II in Figure 2b replaces the rectangular slot with grid slots that function as an electromagnetic bandgap structure, shifting the upper stopband null to 6.7 GHz through their periodic unit cell resonance (p \(\approx\lambda/4\)) while significantly improving the suppression and roll-off rate viathe distributed filtering effect. Ant.III in Figure 2c introduces inverted-L-shaped parasitic elements (L \(\approx\lambda/4\) at 4.45 GHz) along \(-45^{\circ}\) to create an additional degenerate resonance mode through coupled oscillation with the main dipoles, effectively broadening the CP bandwidth via dual-resonance excitation at 3.9 GHz and 4.45 GHz. The proposed antenna in Figure 2d further incorporates \(\lambda/4\) open-circuited stubs on the feed line to introduce a third radiation null at 7.25 GHz through transmission line cancellation, completing the triple-null filtering mechanism that achieves good upper stopband suppression (>25 dB) while maintaining broad CP bandwidth performance.  
Figure 2: Structure and results of four antennas (solid lines: Realized Gain; dashed lines: Axial Ratio).
## 3 Results
### Spatial and Temporal Variation of NDVI, EVI, and SIF
The spatial significance difference between the NDVI and EVI is in the northwest, whereas the distribution of the NDVI and SIF is similar, and the main difference is in the northwest. Figure 4 presents the spatial distribution, spatial trend distribution, and significance ratio of the three vegetation remote sensing products. More than 60% of the NDVI annual average is below 0.454, with the higher (0.454-0.601) and highest (0.601-0.865) values accounting for 19.3% and 14.4%, respectively. For the EVI annual values, 48.4% are below 0.125, with the higher (0.125-0.191) and highest (0.191-0.417) values accounting for 18.7% and 32.9%, respectively. More than 70% of the SIF annual values are below 0.231, with the higher (0.157-0.231) and highest (0.231-0.456) values accounting for 18.2% and 8.9%, respectively. Figure 3: Spatial distribution of interannual NDVI trends, EVI trends, and SIF trends and significance. (**A**): Spatial variation of NDVI values; (**B**): Spatial variation of EVI values; (**C**): Spatial variation of SIF values; (**D**): Trend changes in NDVI; (**E**): Trend changes in EVI; (**F**): Trend changes in SIF; (**G**): NDVI significance test; (**H**): EVI significance test; (**I**): SIF significance test. The spatial trend distribution ratios of the NDVI and EVI (Figure 4D,F) are similar, with significant degradation types accounting for 2% and 2.1%. Consequential degradation types comprise 4.4% of the spatial trend distribution ratio for the SIF (Figure 4F). As seen when combined with Figure 3A-C, the SIF is more sensitive to vegetation degradation than the NDVI and EVI. In contrast, its sensitivity to vegetation improvements is considerably weaker than the NDVI and EVI. Fortunately, the analysis of the three planting areas reveals that the proportion of sites with significant vegetation degradation in China is less than 3%, while the proportion of areas with substantial improvements is more than 5%. The significance ratios of the NDVI and EVI spatial trends (Figure 4G,H) are primarily evident in two extreme situations, accounting for 53.3% and 74.1%. The significance ratio of the SIF spatial trend (Figure 4I) is 52% in extreme cases, which is less than those of the NDVI  
Figure 4: Pie chart of NDVI value, EVI value, and SIF value and its change trend, the proportion of significant spatial distribution change. (**A**): Pie chart of NDVI value; (**B**): Pie chart of EVI value; (**C**): Pie chart of SIF value; (**D**): Pie chart of NDVI trend; (**E**): Pie chart of EVI trend; (**F**): Pie chart of SIFT trend; (**G**): Pie chart of NDVI significance test; (**H**): Pie chart of EVI significance test; (**I**): Pie chart of SIF significance test. and EVI. This difference demonstrates the inaccuracy of the NDVI and EVI concerning vegetation trend changes in extreme situations from the perspective of specific values.
# Research on 3D Geological Modeling Method Based on Deep Neural Networks for Drilling Data
## 5 Experiments and Results
### Experiment Parameters
#### 5.1.4 Batch Size  
Batch size plays a critical role in determining how many samples are used in each step of updating the network's weights and biases. Larger batch sizes can accelerate the training process but consume more memory, while smaller batch sizes can provide more stable gradient estimates but may introduce increased noise during training. Selecting the appropriate batch size requires balancing the computational resources available with the stability of the training process. In this paper, a systematic approach is taken to identify the optimal network structure for each subnetwork and to evaluate the training loss, test accuracy, and total training time for different batch size configurations. The objective is to determine the batch size that strikes a balance between maintaining network performance and minimizing the overall training time. This optimization process is achieved through experiments and comparisons, leading to the selection of the most suitable batch size configuration for a specific task and dataset. It allows for the fine-tuning of deep neural network training and ensures efficient model optimization. Figure 4 demonstrates a comparison of the optimal network training loss, test accuracy, and total training time for different batch size cases. It can be seen that despite the differences in the training data and training tasks of each subnetwork, the overall trend shows that as the batch size increases, the training loss gradually decreases, the test accuracy continuously improves, and the total training time gradually shortens. Specifically, at batch sizes of 512 and 1024, the network expressiveness reaches its optimal state, as evidenced by lower loss, higher accuracy, and faster training speed. However, as the batch size continues to increase, the training loss begins to increase, the test accuracy decreases, and the total training time increases. Geological data are characterized by a high degree of discreteness due to the sampling process and sampling difficulty. During the training process, the training data used in each round of iteration are randomly selected, and it is difficult to ensure that each training can contain multiple feature data points. Therefore, using a large training batch can cover more multi-feature data in each iteration calculation, reduce the impact of the discrete nature of the geologic data on the training results, and improve the accuracy of the calculation. Considering the network performance and training efficiency, a batch size of 1024 was selected for this work.  
Figure 4: Training loss, accuracy, and training duration variation for different batch sizes.  
Figure 3: Loss and accuracy change curves for different numbers of hidden layers and hidden layer dimension conditions (batch size is 256).
## 4 Results and Discussion
### Research Limitations
#### 4.3.3 Multi-Objective Optimization Algorithms  
* Selection and comparison of algorithms: This research primarily compared two multi-objective optimization algorithms--MOEA (multi-objective evolutionary algorithm) and NSGA-II (non-dominated sorting genetic algorithm II)--yet many advanced  
Figure 11: Boundary algorithm performance comparison between Chaoyang District and Tongzhou District.  
alternatives (such as SPEA2 or MOEA/D) remain unexamined. Future investigations should compare a broader range of algorithms to identify those best suited for specific problems.
* Diversity and flexibility of solutions: While NSGA-II excels in convergence ability along with solution quality, there are instances where the diversified solutions provided by the MOEAs may offer advantages. Further studies could explore how to combine the strengths of both algorithms into new hybrid approaches that simultaneously enhance convergence capabilities while maintaining solution diversity.
# GLClick: Interactive Segmentation Combining Global and Local Features
## 4 Experiment
### Datasets  
We conduct experiments on five commonly used image segmentation datasets: GrabCut [34], Berkeley [35], SBD [36], DAVIS [37], and Pascal VOC [38]. Additionally, we evaluate our method on three medical image datasets.  
GrabCut [34]: This dataset consists of 50 images, each featuring a single target object with easily distinguishable foreground and background, serving as a standard benchmark for evaluating interactive segmentation models.  
Berkeley [35]: This dataset comprises 96 images with 100 test instance masks, presenting more challenging scenarios due to less distinct foreground-background separation.  
SBD [36]: This dataset includes 8498 training images and 2857 test images, with 20,172 instances in the training set and 2857 in the test set. Our model is trained on the training set and evaluated on the test set.  
DAVIS [37]: This dataset is designed for video object segmentation tasks and consists of 50 videos containing numerous images and frames, with each video featuring multiple objects annotated as distinct entities.  
Pascal VOC [38]: The validation set comprises 1449 images and 3427 instances, and we conduct our evaluation solely on this validation set.  
For the medical image datasets, we utilize the following:  
ssTEM [33]: This dataset comprises two stacks of 20 medical images each, and we utilize the same stacks as those used in the baseline for comparison;  
BraTS [39]: This dataset includes 369 MRI volumes, and we evaluate our method on the same 369 slices utilized in the baseline;  
OAIZIB [40]: This dataset consists of 507 MRI volumes, and we test on the same 150 slices used in the baseline, which encompass 300 instances.  
These evaluations encompass a diverse array of segmentation tasks, including natural images with varying degrees of difficulty in distinguishing foreground from background, as well as medical images requiring precise delineation of anatomical structures.
# Comparison of \({}^{210}\)Pb Age Models of Peat Cores Derived from the Arkhangelsk Region
## 3 Results and Discussion
### Chronology \({}^{210}\)Pb and Rate of Peat Accumulation
#### 3.3.1 Application of the CA Model
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline z(d), & \(t\)(\(t\))(\(t\)) & \(u\)(\(t\)(t\)) & Year & \(u\)(\(t\)) & Year & \(u\)(\(t\)) & \(u\)(\(t\)) & \(u\)(\(t\)) & \(\Delta t\) & \(s\) & \(r\) & \(u\)(\(t\)) & \(g\) cm – Year– 1 & \(u\)(\(t\)) & \(r\) & \(u\)(\(t\)) \\
**cm** & **In(C)** & **Year** & **Year** & **(A.D)** & **ur(\(t\))** & **ur(C)** & **ur(C)** & **(Year)** & **(Year)** & **(Year)** & **ur(\(\Delta t\))** & **(cm– Year– 1)** & \(u\)(\(t\)) & **(g cm – Year– 1)** & \(u\)(\(t\)) \\ \hline  & & & & & & & 0.00 & & 0.00 & & & & & \\
1.5 & 5.65 & –4.1 & 3.8 & 2025 & \(-\)0.0007 & 0.005 & 0.12 & & & 2.81 & 4.79 & 1.07 & 1.82 & 0.05 & 0.08 \\  & & & & & & & 2.81 & 4.79 & & & & & & \\ \hline
4.0 & 5.22 & 9.7 & 8.8 & 2011 & 0.0016 & 0.005 & 0.27 & & & & 11.12 & 6.95 & 0.18 & 0.11 & 0.01 & 0.00 \\  & & & & & & & 13.93 & 5.04 & & & & & & \\ \hline
6.0 & 4.96 & 18.1 & 4.9 & 2002 & 0.0031 & 0.005 & 0.15 & & & 5.76 & 7.15 & 0.35 & 0.43 & 0.02 & 0.02 \\  & & & & & & & 19.70 & 5.08 & & & & & & \\ \hline
8.0 & 4.86 & 21.2 & 8.9 & 1999 & 0.0036 & 0.005 & 0.28 & & & \(-\)0.01 & 7.18 & \(-\)177.38 & 112.976.25 & \(-\)6.85 & 4364.19 \\  & & & & & & & 19.68 & 5.08 & & & & & \\ \hline
10.0 & 4.96 & 18.1 & 4.9 & 2002 & 0.0031 & 0.005 & 0.15 & & & \(-\)0.33 & 6.17 & \(-\)6.02 & 112.03 & \(-\)0.36 & 6.65 \\  & & & & & & & 19.35 & 3.51 & & & & & & \\ \hline
12.0 & 4.88 & 20.6 & 5.0 & 2000 & 0.0035 & 0.005 & 0.16 & & & 1.60 & 5.41 & 1.25 & 4.24 & 0.05 & 0.17 \\  & & & & & & & 20.95 & 4.12 & & & & & \\ \hline
14.0 & 4.86 & 21.3 & 6.6 & 1999 & 0.0036 & 0.005 & 0.20 & & & 3.68 & 6.16 & 0.54 & 0.91 & 0.03 & 0.04 \\  & & & & & & & 24.63 & 4.58 & & & & & \\ \hline
16.0 & 4.65 & 27.9 & 6.4 & 1993 & 0.0047 & 0.005 & 0.20 & & & \(-\)2.93 & 5.97 & \(-\)0.68 & 1.39 & \(-\)0.04 & 0.07 \\  & & & & & & & 21.70 & 3.83 & & & & & \\ \hline
18.0 & 5.04 & 15.5 & 4.2 & 2005 & 0.0026 & 0.005 & 0.13 & & & \(-\)11.73 & 6.94 & \(-\)0.17 & 0.10 & \(-\)0.01 & 0.00 \\  & & & & & & & 9.97 & 5.78 & & & & & \\ \hline
20.0 & 5.38 & 4.5 & 10.8 & 2016 & 0.0008 & 0.005 & 0.34 & & 19.27 & 12.70 & 0.10 & 0.07 & 0.00 & 0.00 \\  & & & & & & 29.25 & 11.31 & & & & & & \\ \hline
22.0 & 3.84 & 54.0 & 19.9 & 1967 & 0.0091 & 0.005 & 0.62 & & & 23.06 & 19.30 & 0.09 & 0.07 & 0.00 & 0.00 \\  & & & & & & & & 52.31 & 15.64 & & & & & \\ \hline
24.0 & 3.94 & 50.6 & 24.1 & 1970 & 0.0085 & 0.005 & 0.75 & & 15.06 & 30.42 & 0.13 & 0.27 & 0.00 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The CA model calculation data for the studied peat profile.