esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Evolution of the Crack Network under an SSR of 0.2  
Figure 6 shows the initiation and propagation process of the crack network during crater blasting under an SSR of 0.2. Under the coupling of dynamic and static loads at 80 us, a radial crack parallel to the static stress was observed first at the centre of the blast hole, while the radial cracks perpendicular to the static stress were not observed, as shown in Figure 6b. At 110 us, the initial circumferential crack started to appear on the left of the blast hole (see Figure 6c). At 130 us, the initial circumferential crack was completely formed. At the same time, two second radial cracks parallel to static stress direction were observed above and below the initial circumferential crack, as shown in Figure 6d. However, there were no second radial cracks perpendicular to the static stress outside the initial circumferential crack. At 160 us, the second circumferential cracks, which tended to propagate along the static stress, started to form on both sides of the blast hole in the direction of the perpendicular static stress (see Figure 6e). At 200 us, the second circumferential crack was closed, and the second radial cracks were randomly distributed between the initial and the second circumferential crack (see Figure 6f). At 300 us, the main radial cracks and circumferential cracks were fully formed, when a small amount of blasting dusting started to leak out and the blasting crater morphology was determined (see Figure 6g). After 300 us, the blasting fragments began to fly outward increasingly as time passed.  
Figure 6: The crack network at different times during crater blasting under an SSR of 0.2. **(a–i)** The crack network after 0, 80, 110, 130, 160, 200, 300, 500, and 890 μs of explosion, respectively.  
The morphological characteristics of the crack network under SSR of 0.2 were basically similar to those under an SSR of 0.15, both of which were elliptical with a long axis that was parallel to the static stress, as shown in Figures 5 and 6. However, when the SSR was 0.2, the radial cracks in the centre of the blast hole that were perpendicular to the static stress were completely restrained and did not appear. Moreover, when compared with the SSR of 0.15, the ellipse phenomenon of the crack network under the SSR of 0.2 was more obvious.
# First Results from Sentinel-1A InSAR over Australia: Application to the Perth Basin
## 5 Results
### Multi-Temporal Analysis
#### 5.2.1 Broad-Scale, Slow Subsidence  
Broad (\(>\)5 km wide) subsidence relative to the reference region that is common to both sets of satellite images is observed in three locations: proximal to Hillarys, through the Swan Valley and south of Fremantle (labelled (a) in Figure 4A,B). Whilst subsidence proximal to Hillarys has been identified in past deformation studies in the Perth Basin [1], displacements in the Swan Valley and south of Fremantle have not previously been detected due to the limited spatial coverage of historical datasets [8].  
The signal south of Fremantle is offset in latitude between datasets: the maximum displacements for Sentinel-1A are beyond the extent of the TerraSAR-X scene, and it is therefore difficult to draw a profile through the signal in both datasets. Broad subsidence through Hillarys and the Swan Valley is shown by displacement profile x (Figures 4 and 5A,B). Displacement rates proximal to Hillarys reach 7 mm/y to 10 mm/y, with the maximum magnitude of deformation \(\sim\)3 km east of the coast in the TerraSAR-X dataset. The profile shows that subsidence through the Swan Valley occurs over a wider area with displacements rates up to \(\sim\)15 mm/y over the observation period. At the eastern end of the profile, both datasets show correlation with the location of the Darling Scarp, likely due to residual stratified atmospheric noise. Plotting the values of profile x for Sentinel-1A against those for TerraSAR-X demonstrates general agreement between the two datasets with a correlation coefficient of 0.67 (Figure 5C). Scatter may be partly attributed to differences in the satellite look angle, the different times of the satellite acquisitions and associated variations in uncorrected atmospheric delay.
## 3 Data Collection and Exploratory Data Analysis
### Description of Footfall Data
To ensure that statistical assumptions for modeling footfall are met, a Box-Cox power transformation with a \(\lambda\) value of 0.22 is applied in Figure 4c. This transformation reduces skewness and brings the distribution closer to normal, which makes the footfall data more suitable for regression. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**Across all Metro Stations**} & \multicolumn{2}{c}{**Across all Metro Stations**} \\ \cline{2-5}  & **Mean** & \begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **Variable** & **Count** & **Share** \\ \hline Hourly footfall (in passengers) & 321 & 323 & Stations connecting airport, bus terminus, or intercity train stations & 4 & 12.5\% \\ \hline Daily ridership (in passengers) & 2754 & 1878 & \begin{tabular}{c} Stations that are interchange or have \\ ability to transfer to other \\ metro stations \\ \end{tabular} & 2 & 6.25\% \\ \hline Peak hour bus frequency nearby \\ metro stations (in minutes) & 13.48 & 4.90 & Stations with cycles for hire/rent & 11 & 34.38\% \\ \hline Monthly household income near \\ metro stations (in INR) & 26,709 & 10,762 & \
\begin{tabular}{c} Metro stations with two-wheeler \\ parking inside the station premise \\ \end{tabular} & 29 & 90.65\% \\ \hline Population near metro station \\ (in persons) & 43,631 & 13,309 &
\begin{tabular}{c} Metro stations with four-wheeler \\ parking inside the station premise \\ \end{tabular} & 23 & 71.88\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the descriptive statistics across the metro stations. Figure 5 shows the variation in hourly metro footfall across three different factors. The first plot, Figure 4(a), categorizes metro stations into four types: airport, terminal, interchange, and other stations. It highlights that airport stations and interchange stations typically experience higher hourly footfall, followed by terminal stations, but other stations exhibit lower and more dispersed footfall patterns. The second plot, Figure 4(b), illustrates the station-level hourly footfall variation across five time periods: before the AM peak, AM peak, afternoon off-peak, PM peak, and after the PM peak. It reveals that the AM peak and PM peak periods have significantly higher footfall than off-peak periods. Figure 4(c) shows the footfall variation with a change in headway (frequency of metro services). It highlights that shorter headways (\(\leq\)10 min) correspond to higher footfall, whereas longer headways result in lower footfall. These figures provide insights into how station type, time of day, and service frequency influence passenger footfall. The model specification discussion in the next section attempts to capture the trends observed in the exploratory analysis. Figure 4: Distribution of footfall at Chennai metro stations.
# Design and Fabrication Technology of Metal Mirrors Based on Additive Manufacturing: A Review
## 3 Design Technology of Additively Manufactured Metal Mirrors
### Lattice
#### 3.3.1 Uniform Lattice  
As mentioned above, the uniform lattice periodically arranges lattice cells with the same type and density into a uniform lattice structure. [PERSON] team at the UK Centre for Astronomical Technology applied the lattice to the internal structure of a metal mirror for the first time, with the support of the CCAM CubeSat project. The product is a concave spherical mirror with a radius curvature of 350 mm. Its mechanical diameter and thickness are 84 mm and 17.3 mm, respectively. Researchers used Autodesk Netfabb software to design and optimize the lattice structure of the mirror. Then they tested and summarized the structure of numerous lattice cells. The final choice is BCCz lattice, whose  
Figure 23: (**a**) Rhombic dodecahedron (top) and truncated octahedron (bottom) with the density grading (100%, 60%, 30%, and 10%). In both subplots, the image on the far right demonstrates how the polyhedrons perfectly tessellate [18]; (**b**) The optimized hammer handle that was constituted of 3 different density grading cells [63].  
Figure 22: Several standard lattices.  
performance and weight are optimal, and the resulting mirror's lightweight rate reached 31% (Figure 24) [64].  
The team performed a set of comparative experiments with six technical routes, as shown in Figure 25. The results in Table 6 show that, except for the directly polishing AlSi\({}_{10}\)Mg mirror blank (Route 5), all other mirrors reached the visible or infrared levels. The reason for that may be that the polishing process touched a fractured layer through the analysis of X-ray tomography and microscope magnification. The pores are large, so the sample's surface roughness did not meet the requirements. This experiment demonstrated the feasibility and practicality of the lattice structures in metal mirrors and laid the foundation for future iterative experiments [65; 66].
# Camera Calibration in High-Speed Robotic Assembly Operations
## 4 Results and Discussion
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline \multirow{2}{*}{**Data Set**} & \multicolumn{2}{c}{**P9**} & \multicolumn{2}{c}{**P8**} & \multicolumn{2}{c}{**P7**} \\  & \multicolumn{2}{c}{**P6**} & \multicolumn{2}{c}{**P5**} & \multicolumn{2}{c}{**P4**} \\  & \multicolumn{2}{c}{**P3**} & \multicolumn{2}{c}{**P2**} & \multicolumn{2}{c}{**P1**} \\ \hline \multirow{3}{*}{H1 20 px} & \(-49\) & \(-50\) & \(450\) & \(0\) & \(-40\) & \(452\) & \(49\) & \(-50\) & \(447\) \\  & \(-51\) & \(-1\) & \(463\) & \(0\) & \(-1\) & \(456\) & \(51\) & \(-1\) & \(457\) \\  & \(-51\) & \(51\) & \(467\) & \(0\) & \(41\) & \(465\) & \(51\) & \(50\) & \(463\) \\ \hline \multirow{3}{*}{H1 30 px} & \(-73\) & \(-74\) & \(446\) & \(0\) & \(-74\) & \(445\) & \(73\) & \(-74\) & \(444\) \\  & \(-75\) & \(-1\) & \(456\) & \(0\) & \(-1\) & \(457\) & \(74\) & \(-1\) & \(450\) \\  & \(-77\) & \(77\) & \(469\) & \(0\) & \(77\) & \(471\) & \(77\) & \(76\) & \(467\) \\ \hline \multirow{3}{*}{H1 40 px} & \(-96\) & \(-97\) & \(438\) & \(0\) & \(-98\) & \(441\) & \(96\) & \(-96\) & \(435\) \\  & \(-101\) & \(-1\) & \(458\) & \(0\) & \(-1\) & \(456\) & \(99\) & \(-1\) & \(451\) \\  & \(-102\) & \(102\) & \(468\) & \(0\) & \(104\) & \(475\) & \(103\) & \(102\) & \(469\) \\ \hline \multirow{3}{*}{H1 50 px} & \(-119\) & \(-120\) & \(436\) & \(0\) & \(-120\) & \(435\) & \(118\) & \(-119\) & \(432\) \\  & \(-126\) & \(-1\) & \(458\) & \(0\) & \(-1\) & \(456\) & \(125\) & \(-1\) & \(453\) \\  & \(-129\) & \(128\) & \(471\) & \(0\) & \(130\) & \(474\) & \(128\) & \(127\) & \(468\) \\ \hline \multirow{3}{*}{H2 20 px} & \(-63\) & \(-64\) & \(575\) & \(0\) & \(-60\) & \(574\) & \(63\) & \(-64\) & \(572\) \\  & \(-64\) & \(1\) & \(586\) & \(0\) & \(1\) & \(587\) & \(64\) & \(-1\) & \(583\) \\  & \(-66\) & \(65\) & \(598\) & \(0\) & \(61\) & \(596\) & \(66\) & \(65\) & \(594\) \\ \hline \multirow{3}{*}{H2 30 px} & \(-94\) & \(-95\) & \(569\) & \(0\) & \(-95\) & \(568\) & \(94\) & \(-94\) & \(566\) \\  & \(-96\) & \(-1\) & \(585\) & \(0\) & \(-1\) & \(587\) & \(96\) & \(-1\) & \(578\) \\  & \(-99\) & \(98\) & \(599\) & \(0\) & \(99\) & \(601\) & \(99\) & \(98\) & \(597\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Raw data (mm). Figure 13: Part of the Python test program. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Height** & **Px** & **Angle [\({}^{\circ}\)]** & **Error** \\ \hline  & 20 & 79.3466 & 0.82\% \\
1 & 30 & 79.2076 & 0.99\% \\
1 & 40 & 79.0834 & 1.15\% \\  & 50 & 80.2278 & 0.28\% \\  & 20 & 78.2116 & 2.24\% \\
2 & 30 & 79.0063 & 1.24\% \\
2 & 40 & 79.1527 & 1.06\% \\  & 50 & 79.4779 & 0.65\% \\  & 20 & 77.9434 & 2.57\% \\
3 & 30 & 79.438 & 0.32\% \\  & 40 & 79.4375 & 0.70\% \\  & 50 & 80.1961 & 0.25\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Second approach results—planar interpolation.
## 2 Materials and Methods
### Measurement of Equivalent Forces Exerted on Corn Stalks  
A schematic diagram of equivalent force is depicted in Figure 1; the Slaver unit was fixed on the corn stalk with an inelastic belt, which is also connected to the strain sensor by a hook during the field measurement. After the Slaver unit and the Master unit were fixed on, a well-distributed force was exerted on the stalk by the operator to avoid the high dynamic tension in the measurement process. The pull force vector \(F\) was measured by the Master unit along with the Slaver. A downward vector along with the corn stalk, and a rightward vector perpendicular to the corn stalk, form the pull force vector \(F\). Both downward and rightward vectors are independent of each other and have an influence upon the corn stalk in the bending process when the pull force is exerted on the corn stalk.  
In order to evaluate the stalk lodging properties, an equivalent pull force was calculated (Equation (1)) to eliminate the influence caused by the direction of pull force. For normalization, the pull force was applied at 440 mm up the corn stalk from the ground. In this experiment, the stalk lodging angles were fluctuated in the range of 0\({}^{\circ}\) to 45\({}^{\circ}\) in the vertical plane. The angle \(\beta\) and stalk lodging angle \(\alpha\) were monitored by the home-made device at the same time. The stalk lodging angle \(\alpha\), monitored by the single axis angle sensor 2 (SCA60C-N1000060), was transmitted by RF transceiver 2 (NRF24L01), which was mounted inside the Slaver unit, to RF transceiver 1 (NRF24L01) in the Master unit. The original pull force \(F\), measured by the strain sensor (JLBS-5 Kg), and angle \(\beta\), obtained by the single axis angle sensor 1 (SCA60C-N1000060) mounted inside the Master unit, were processed by using the microcontroller (PIC24 FV16 KA304). The measurement results and equivalent forces were finally displayed on the liquid crystal display (LCD) screen (BJ12864F) in real-time. For the case depicted in Figure 1, the formula for calculating the equivalent force can be written as  
\[F\prime=F\cdot\cos(\alpha+\beta) \tag{1}\]  
where \(F\) represents the pull force, \(\alpha\) is a positive value measured by the Slaver unit. The angle \(\beta\) is a negative value under the direction of pull force \(F\). Consequently, the influence caused by the directions of pull force is eliminated when the equivalent force is consistently perpendicular to the corn stalk.  
Figure 1: Schematic diagram for calculating the equivalent force under the pull force exerted upon the stalk.
# An Energy Efficient Modulation Scheme for Body-Centric Terahertz (THz) Nanonetworks +
## 1 Introduction
### Structure  
The rest of the paper is organized as follows: In Section 2, we define the concept of the novel modulation scheme, while in Section 3, a comparison, regarding energy consumption and data rate, is drawn between the new scheme and TS-OOK. Finally, some concluding remarks are presented in Section 4.
# APT Attack Detection Scheme Based on CK Sketch and DNS Traffic
## 4 Experiments
### Metrics  
True Positive (_TP_), indicating samples that are malicious traffic and are correctly detected as attack traffic.  
True Negative (_TN_), indicating samples that are actually normal traffic and are correctly detected as normal traffic.  
False Positive (_FP_), indicating samples that are normal traffic but are incorrectly detected as attack traffic.  
False Negative (_FN_), indicating samples that are malicious traffic but are incorrectly detected as normal traffic.  
1. TPR True Positive Rate (_TPR_) means the proportion of true positive samples to all positive samples in the current positive sample, which is the accuracy of the measurement. The accuracy of the measurement is also known as sensitivity. It is expressed by Equation (7).  
\[TPR=\frac{TP}{TP+FN}, \tag{7}\]  
\begin{table}
\begin{tabular}{l c c} \hline
**Dataset** & **File Name** & **File Size** \\ \hline contagio & Sality & 38.2 MB \\  & XTremeRAT & 3.50 MB \\  & BIN\_TrojanCookies & 905 KB \\  & BIN\_Sanny-Daws & 868 KB \\  & BIN\_Lagulon & 626 KB \\  & PDF\_CVE-2011-2462 & 341 KB \\  & 8202\_tbd & 323 KB \\  & BIN\_XtremeRat & 308 KB \\  & BIN\_LetsGo\_yahoosb & 306 KB \\  & BIN\_8202 & 305 KB \\  & BIN\_Taidoor & 196 KB \\ \hline ICEDID & 2020-07-14-IcedID & 3.72 MB \\  & 2020-05-19-IcedID & 3.60 MB \\ \hline FORMBOOK & 2020-07-09-Formbook & 1.55 MB \\ \hline Zloader & 2020-06-09-ZLoader & 6.18 MB \\ \hline \end{tabular}
\end{table}
Table 2: Malicious dataset attributes.  
Precision Precision means the proportion of samples that are positive and correctly classified as positive to all samples classified as positive. It is expressed by Equation (8). \[\text{Precision}=\frac{TP}{TP+FP},\] (8) 3. Accuracy Accuracy means the percentage of all samples that are correctly detected. It is expressed by Equation (9). \[\text{Accuracy}=\frac{TP+TN}{TP+FP+TN+FN},\] (9) 4. F-measure F-measure, also known as F1-Score, combines the precision and recall considerations of detection accuracy and is the summed average of the two, calculated using Equation (10). \[\text{F-measure}=\frac{2\times Precision\times Recall}{Precision+Recall},\] (10) 5. Throughput It is the number of successfully processed data per unit of time. The experimental part uses the number of packets successfully deposited into the sketch structure per second as the throughput. Higher throughput means faster and shorter processing time for network traffic and better performance. 6. Processing time Our scheme first performs feature extraction and summary statistics through CK sketch, and then includes machine learning model training and prediction after preprocessing. The data preprocessing and resampling parts contain a lot of manual offline operations, and the running time is difficult to count, so we only focus on the time consumed by the construction of the machine learning part of the classification model and test set prediction.
# Relationship between CO2 Fertilization Effects, and Stand Age, Stand Type, and Site Conditions
## 6 Conclusions  
We used emerging remote sensing products to explore the relationship between CFE and stand age changes and compared the changes in CFE among different stand types and site conditions. Our study found that: (a) In both coniferous forests and broadleaf forests, with age, CFE exhibits a trend of an initial rapid increase to a maximum followed by a decline. During the decline period, the rate of decline gradually decreases until CFE stabilizes. (b) Between the two forest types, CFE in coniferous forests had a more prolonged period of rapid increase and a higher maximum. (c) The CFE was significantly higher under high-quality site conditions than under medium- and low-quality site conditions at all stand ages, except 80-100 year group, where no significant difference was observed between high-quality site conditions and medium-quality site conditions. Medium-quality site conditions exhibited a significantly higher CFE than low-quality site conditions across all age groups. Moreover, the differences between the different site quality levels gradually decreased with the forest age. The above results indicate that the CFE has a significant nonlinear relationship with stand age, and high-quality site conditions can further enhance the CFE. These results provide insights to explore the factors impacting the CFE and provide theoretical support for the formulation of future carbon neutrality policies.  
Conceptualization, methodology, writing--original draft, formal analysis, investigation, and visualization, S.B.; conceptualization, validation, resources, writing--review and editing, supervision, and project administration, B.W.; conceptualization, resources, supervision, and project administration, M.L.; formal analysis and investigation, X.K.; formal analysis, J.S.; investigation, Y.C.; resources and supervision, W.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (2020 YFC1511603-1), the Fundamental Research Funds for the Central Universities (2572022 DT03), and Carbon neutrality special scientific Foundation project HFW220100054.  
Data will be made available on request.  
The authors declare no conflict of interest.
## 5 Implementation and Evaluation
### Results
#### 5.2.2 Communication Efficiency  
Figure 3 illustrates the communication cost (in MB) for FedProx and FedAvg over 50 training rounds, comparing scenarios with and without gradient compression. FedProx with compression achieves the lowest communication cost, reducing bandwidth usage by approximately 30% compared to its uncompressed counterpart. FedAvg shows similar communication costs to FedProx without compression, but lacks the benefits of gradient optimization. The steady decline in communication cost across rounds reflects the diminishing size of updates as the models converge. Overall, the results demonstrate that gradient compression is critical for reducing communication overhead in large-scale decentralized systems, making FedProx more scalable in bandwidth-constrained environments.  
Table 2 summarizes the total communication cost for FedProx and FedAvg over 50 training rounds, with and without gradient compression. FedProx with gradient compression achieves the lowest communication cost, reducing bandwidth usage by 30% compared to FedProx without compression and 27.6% compared to FedAvg. This reduction highlights the effectiveness of gradient compression in minimizing data exchange while  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Edge Server** & **FedProx Accuracy (\%)** & **FedAvg Accuracy (\%)** \\ \hline
1 & 85.3 & 80.1 \\
5 & 87.5 & 81.2 \\
10 & 83.9 & 79.8 \\
15 & 82.5 & 78.7 \\
20 & 81.2 & 75.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Local accuracy per edge server for FedProx and FedAvg.  
Figure 2: Global accuracy over training rounds.  
maintaining model performance. FedAvg, lacking compression, incurs higher communication costs similar to unoptimized FedProx, emphasizing the need for communication-efficient techniques in large-scale federated learning systems. Overall, FedProx with compression is the most efficient method in bandwidth-constrained environments.