esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Recent efforts have been made to monitor the seasonal metrics of plant canopy variations globally from space, using optical remote sensing. However, phenological estimations based on vegetation indices (VIs) in high-latitude regions such as the pan-Arctic remain challenging and are rarely validated. Nevertheless, pan-Arctic ecosystems are vulnerable and also crucial in the context of climate change. We reported the limitations and challenges of using MODerate-resolution Imaging Spectroradiometer (MODIS) measurements, a widely exploited set of satellite measurements, to estimate phenological transition dates in pan-Arctic regions. Four indices including normalized vegetation difference index (NDVI), enhanced vegetation index (EVI), phenology index (PI), plant phenological index (PPI) and a MODIS Land Cover Dynamics Product MCD12Q2, were evaluated and compared against eddy covariance (EC) estimates at 11 flux sites of 102 site-years during the period from 2000 to 2014. All the indices were influenced by snow cover and soil moisture during the transition dates. While relationships existed between VI-based and EC-estimated phenological transition dates, the R\({}^{2}\) values were generally low (0.01-0.68). Among the VIs, PPI-estimated metrics showed an inter-annual pattern that was mostly closely related to the EC-based estimations. Thus, further studies are needed to develop region-specific indices to provide more reliable estimates of phenological transition dates.  
Keywords:land surface phenology; remote sensing; pan-Arctic; vegetation index; solar-induced fluorescence +
Footnote †: journal:
# Detecting Malware C&C Communication Traffic Using Artificial Intelligence Techniques
## 1 Introduction
### Overview of the Ramnit Banking Malware  
Ramnit is a versatile and persistent malware that evolved from a worm in 2010 into a sophisticated banking Trojan targeting financial institutions and sensitive data. It spreads through phishing emails, malicious attachments, exploit kits, and infected files, using techniques like man-in-the-browser (MITB) attacks and web injections to steal online banking credentials and other personal information. Ramnit communicates with Command and Control (C&C) servers via encrypted channels, enabling data exfiltration and command execution. Its modular architecture allows for adaptability, while advanced evasion techniques, such as polymorphism and rootkits, make it difficult to detect. Despite law enforcement disruptions, Ramnit has resurfaced over the years with enhanced capabilities, posing a significant threat to global cybersecurity.  
Ramnit is an enhanced version of the Zeus malware and incorporates code from the Zeus banking malware [24]. The C&C communication channel is encrypted, and this is usually achieved using custom encryption techniques [25]. Ramnit can also use HTTPS to obfuscate the communication channel and hide any data that are transmitted between systems. Ramnit is sophisticated enough to detect and evade security tools, and once it infects a device, it can persist on the device for a long time [25]. Ramnit can also use evasion techniques to avoid detection, and some of these include the use of anti-debugging techniques, polymorphism, and encryption [26].
###### Abstract  
In order to study the influence of workpiece surface topography on deformation and residual stress during shot peening, the paper built a rough surface single-shot peening simulation model and designed a rough surface workpieces shot peening experiment to verify the correctness of the built simulation model, which differed from the smooth surface simulation model and was closer to the actual processing situation. Based on the W-M fractal dimension theory, the shot peening model of the target with a rough surface is established, and the simulation analysis is carried out by finite element software. The effect of different impact positions of the pellet on surface residual stress and surface displacement were also simulated. The simulation found that with the increase of surface complexity, the surface deformation and residual stress would both increase after shot peening. Through shot peening tests on workpieces with different surface roughness, it is found that the error between simulation and experiment results on surface deformation and residual stress are both less than 20%, which verifies the validity of the rough surface single-shot peening model and that the established model could provide a new method on shot peening simulation analysis.  
residual stress; rough surface; shot peening; surface deformation; W-M fractal dimension
## 2 Numerical Simulation Methodology
### Numerical Methods  
#### 2.3.1 Calculation Process  
In order to evaluate the operating performance of the CCKEF system, the air velocity, temperature and pollutant concentration distribution in the kitchen and the particle purification efficiency of the electrical purifier should all be calculated. Therefore, the turbulence model, species transport model, MHD model and DPM model were combined in this paper. As the fluid in the kitchen ventilation system was in a turbulent state and contained curved near-wall flows and swirl flow at the elbow and electrostatic purifier, the flow field was computed with a three-dimensional, steady-state RNG k-\(\epsilon\) model combined with buoyancy effect and standard wall function. The temperature distribution was determined by solving the energy equations. The concentration of gas pollutants was predicted by solving the species transport equation. After the flow field calculation converged, the MHD model was opened. A generalized convection diffusion equation was employed to describe the  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Position**} & \multirow{2}{*}{**Type**} & \multicolumn{5}{c}{**Parameters**} \\ \cline{3-6}  & & **Velocity** & **Temperature** & **Mole Fraction** & **Particle** & \begin{tabular}{c} **Electric** \\ **Potential** \\ \end{tabular} \\ \hline Envelop/counter & Wall & No slip & Adiabatic & - & Reflect & Insulation \\ \hline Lamps & Wall & No slip & \(130.6\,\mathrm{W/m^{2}}\) & - & Reflect & Insulation \\ \hline Personnel & Wall & No slip & \(115\,\mathrm{W/m^{2}}\) & - & Reflect & Insulation \\ \hline Pot(top) & Wall & No slip & Measured value & \begin{tabular}{c} 0.2\% C\({}_{6}\)H\({}_{6}\), \\ 99.8\% H\({}_{2}\)O \\ \end{tabular} & Escape & Insulation \\ \hline Pot(exterior) & Wall & No slip & & - & Reflect & Insulation \\ \hline Smoke outlet & Mass flow inlet & \(0.012\) kg/s & Measured value & \
\begin{tabular}{c} 9.2\% CO\({}_{2}\), 18.3\% \\ H\({}_{2}\)O, 72.5\% N\({}_{2}\) \\ \end{tabular} & Escape & \(\frac{\partial\mathrm{V}}{\partial\mathrm{m}}=0\) \\ \hline Hood/duct & Wall & No slip & Adiabatic & - & Reflect & Insulation \\ \hline Wire plate & & & - & Reflect & 25 kV \\ \hline Collecting plate & Wall & No slip & Adiabatic & - & Trap & 5 kV \\ \hline Other plates & & & - & Reflect & 0 kV \\ \hline Outlet &
\begin{tabular}{c} Reverse \\ velocity-inlet \\ \end{tabular} & Measured value & - & - & Escape & \(\frac{\partial\mathrm{V}}{\partial\mathrm{m}}=0\) \\ \hline Door 1/door & Pressure-inlet & 0 & Measured value & \(23.1\%\) O\({}_{2}\), 76.9\% N\({}_{2}\) & Escape & \(\frac{\partial\mathrm{V}}{\partial\mathrm{m}}=0\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Boundary conditions for case CCKEF system.  
electric potential distribution and the ion migration induced by corona discharge in the purifier. After obtaining the convergence solution of the steady flow field and electric field, the DPM model in the Lagrange frame was opened to track the movement trajectory of particles and calculate the number of particles captured by the purifier.
## 2 Methodology  
### Overview of Strategy and Materials Database  
In this study, the advanced computational protocol for reliably predicting the dielectric strengths of organic compounds (PFC and non-PFC compounds) comprises the following stages (Figure 1): (i) introduction of the computational protocol previously developed for predicting the dielectric strengths of PFC materials [19; 21], (ii) reparameterization of the previous protocol to cover all PFC and non-PFC compounds, and (iii) full upgrade of the computational protocol through the introduction of new influencing factors. Each of these stages is described in detail in the next section. It should be noted that the previous computational protocol has been confirmed to predict the dielectric strengths of PFC compounds, but its reliability for predicting the dielectric strengths of non-PFC compounds has not yet been validated.  
The materials database utilized in this study contains both PFC and non-PFC compounds (Figures 2, S1 and S2). A total of 150 PFC compounds, including 13 compounds that were not included in our previous database of 137 compounds, were designed to obtain a comprehensive PFC-based database with a broad array of fragments, such as chalcogen, halogen, and pnictogen [20]. The newly added PFC compounds are represented by chlorotrifluoromethane (CCIF\({}_{3}\)), hydrotrifluoromethane (CHF\({}_{3}\)), and tetrafluoromethane(CF\({}_{4}\)) [5; 19; 22]. In addition, 18 non-PFC compounds, including N\({}_{2}\)O, CO\({}_{2}\), SO\({}_{2}\), CCl\({}_{4}\), and CH\({}_{4}\), were added to the PFC-based database with the aim of finalizing the database design process. The newly added non-PFC compounds also contained a broad array of fragments [5; 9; 19; 22]. Notably, to provide guidance on design direction, information on compounds that exhibit relatively high dielectric strengths within the 168 PFC/non-PFC compounds analyzed are listed as follows: C\({}_{4}\)HCII\({}_{6}\)O, C\({}_{4}\)HF\({}_{9}\)O, C\({}_{4}\)H\({}_{2}\)F\({}_{8}\)O, and C\({}_{5}\)F\({}_{10}\) with dielectric strengths higher than 3.0, exhibiting the dependence of the dielectric strength on the number of fluorine atoms. Furthermore, note that the primary objective of this study is to develop models capable of accurately predicting the dielectric strengths of both PFC and non-PFC organic compounds. Therefore, while acknowledging the significance of other properties, such as boiling point and greenhouse warming potential, in the development of insulating gas alternatives to SF\({}_{6}\), our primary focus remains on reliably predicting the dielectric strengths of the compounds.
# Multi-Device Security Application for Unmanned Surface and Aerial Systems
### Contributions  
This work contributes to the state of the art in unmanned system communication and command and control, demonstrating the viability of protocol use for improved security and efficient functionality. Specifically, we provide:  
* test implementation and APO for integration of the Messaging Layer Security onto the Robotic Operating System.
* simulation benchmarking for overhead and setup time per ciphersuite.
* encryption key update interval frequency comparison testing.
* ciphersuite selection recommendations.
* the first cross-domain testing of MLS on unmanned systems, utilizing a ScanEagle UAS VM and CASSMIR USV physical device.
## 3 Analysis of Rock Damage Characteristics
### Macroscopic Analysis of Rock Damage Characteristics  
The CT scan images were imported into ImageJ software (1.54-win-java8) and cropped to the appropriate size. The following steps were then applied. First, Image \(\rightarrow\) Type \(\rightarrow\) 8-bit was used to convert the image to 8-bit format. Then, Image \(\rightarrow\) Adjust \(\rightarrow\) Threshold was applied, with the threshold set to a range from 0 to 160, resulting in the rock portion appearing white and the cracks appearing black. Next, Process \(\rightarrow\) Math \(\rightarrow\) Subtract was used with a threshold value of 100, followed by Edit \(\rightarrow\) Invert to reverse the colors. Finally, any noise in the image was manually erased to obtain the image shown in Figure 6, where the white portion represents the cracks generated by the blasting and the gray portion represents the rock mass. These images were used for the three-dimensional reconstruction.  
The processed segmentation images were reconstructed into three-dimensional images using Mimics software (19.0), converting the two-dimensional images into three-dimensional models. This resulted in the three-dimensional reconstruction of the crack distribution within the specimen after blasting, as shown in Figure 7. It can be observed from the figure that, under 0 Mpa in situ stress conditions, the specimen exhibited full-penetration fractures and severe damage. Under 0.5 Mpa, 1 Mpa, and 2 Mpa in situ stress conditions, the upper part of the specimen (the stemming and charge segments) showed full-penetration fracture characteristics, while the bottom segment was not penetrated and exhibited minimal damage. Under 3 Mpa in situ stress conditions, although the upper part of the specimen fractured, no full-penetration cracks formed, and the bottom segment showed no significant damage. Overall, as in situ stress increased, the propagation and distribution of the blast-induced cracks were suppressed, and the extent of the damage and fracturing gradually decreased.  
Figure 6: _Cont._  
Figure 6: The processed segmentation images of the post-blast sandstone from different sections under varying in situ stress conditions (The figures in each row from top to bottom represent in situ stresses of 0 MPa, 0.5 MPa, 1 MPa, 2 MPa, and 3 MPa, respectively). (**a**) Central slice of the stemming segment (\(h\) = 97.5 mm); (**b**) Central slice of the charge segment (\(h\) = 62.5 mm); (**c**) Central slice of the bottom segment (\(h\) = 20 mm).  
Figure 7: Three-dimensional reconstruction diagram of the internal fracture distribution of the specimens after blasting.
# Developing Productivity-Safety Effectiveness Index Using Data Envelopment Analysis (DEA)
## 3 Proposed Approach  
### Research Framework  
The research procedure consists of four steps to extract relative efficiency values of productivity over safety performance, as shown in Figure 3. First, the data comprise variables that include the production index and incident index, published by the Korean government organization of statistics, the Korean Statistical Information Services (KOSIS) through the Statistics Korea (KOSTAT), and the Korea Occupational Safety and Health Agency (KOSHA). The variables for production and incident indexes of manufacturing industries are selected. Second, the DEA is designed with an appropriate model, and we determine the use of input-oriented CCR/BCC models, for considering the importance of safety performance more than that of productivity performance. Using DEA, the relative efficiency is measured by developing _productivity and safety performance efficiency indexes_ (PSEI), focusing on the safety performance. Also, the weights of input and output variables to the PSEI score are derived to understand the critical variables that can increase the PSEI score.
# Model Updating Using Frequency Response Functions Based on Sherman-Morrison Formula
## 4 Case Study
### Experimental Verification
#### 4.3.2 Parameter Updating
Theoretically, if the two models are the same physical modal, the MAC value is 1, if the two models are not the same physical modal or if the modal vectors are independent of each other, the MAC value is 0. The MAC value will be affected due to the nonlinearity of the structure, external noise interference of the measured data and improper data processing. It is generally believed that if MAC  
Figure 16: Comparison of FRFs h\({}_{13}\). Figure 17: Measurement of modal shapes: (**a**) Experimental model; (**b**) FE model. value is greater than 0.9, the two models are related, and if MAC value is less than 0.05, the two models are unrelated. Table 9 shows the comparison of modal shapes between the first 4 order of the FE model before and after updating and the corresponding experimental model. The first modal of the solar panel frame is first-order bending around \(X\) axis; the second modal is first-order torsion around \(Y\) axis; the third modal is second-order bending around \(X\) axis; the fourth modal is second-order torsion around \(Y\) axis. The blue line in the modal shape diagram represents the fixed side. It can be seen from Figure 18 that, after several iterative computations, the MAC value of the first 4 orders after updating are all improved, indicating that the FE modal shapes after updating are well matching with the experimental ones, which further verifies the accuracy requirement of the proposed method.
# Feature Fusion Graph Consecutive-Attention Network for Skeleton-Based Tennis Action Recognition
## 3 Problem Definition  
Tennis stroke classification involves examining continuous time series recordings that illustrate a tennis player's body movements, in order to classify them into one of the defined stroke categories. The 2D poses of a person with joints over frames can be expressed mathematically as in Equation (1) [28]:  
\[P_{obs}^{2D}=[P_{1},P_{2},\ldots,P_{i},\ldots,P_{T}]\in\mathbb{R}^{T\times J \times 2} \tag{1}\]  
Here, each \(P_{i}\in\mathbb{R}^{J\times 2}\) corresponds to the 2D coordinates of all \(J\) joints at frame \(i\). Given these observed sequences of 2D player poses, the primary goal in tennis motion classification is to develop a parametric model capable of accurately assigning each sequence to one of several predefined action or stroke classes. Formally, this can be represented by (2):  
\[\hat{y}=\text{Model}(P_{obs}^{2D})\quad\text{where}\quad\hat{y}\in\{c_{1},c_{2 },\ldots,c_{M}\} \tag{2}\]  
where \(\hat{y}\) is the predicted stroke class, and \(c_{1},c_{2},\ldots,c_{M}\) represents the set of all possible stroke classes. We use spatio-temporal graphs to represent the observed skeleton poses at \(J\) joints for one person in \(T\) frames. At each frame \(t\), we characterize a spatio-temporal graph \(G_{t}=(V_{t},E_{t})\). Here, the set of vertices \(V_{t}\) indicates the skeletal joints, and the set of edges \(E_{t}\) shows the connections between these joints. Positional information for each node at time \(t\) is stored. The edges are defined using an adjacency matrix \(A_{t}\), where its entries \(a_{i,j}=1\) denote connected joints and \(a_{i,j}=0\) denote disconnected ones. Thus, the input to the classification model is then represented as \(\{G_{t}^{2D}\}_{t=1}^{T}\).
## 6 Effects of Roasting on Polyphenol Content
### Hazelnuts  
The polyphenol content in hazelnuts is primarily influenced by variety, harvest conditions, and geographical origin. [PERSON] et al. (2012) studied seven different Turkish hazelnut varieties and reported that total polyphenols can be between 178 and 727 mg acid gallic equivalent (GAE)/100 g. After the roasting process, achieved at 140 \({}^{\circ}\)C for 30 min, the same authors observed a decrease in phenolics, ranging from 50 to 195 mg GAE/100 g. Furthermore, the scientists measured the loss of antioxidant capacity before and after the roasting process, finding a decrease ranging from 25.3% to 71.7% depending on the cultivar. Similarly, the percentage of tannins decreased significantly, with losses reaching approximately 75.2% [77]. The decrease in phenolic compounds after roasting can be attributed
## 3 Results  
### NSCs in Space Environment (SPC) Preserve Their \"Stemness\" and Proliferate  
This experiment was designed to ascertain the proliferation of NSCs solely while in microgravity. In order to elucidate if NSCs in \"space microgravity\" proliferate, we used the Type IV automated experiment insert from Kiwi (Airbus, Friedrichshafen, DE), allowing for a fresh medium change twice during space travel. The medium within which the cells traveled was replaced with fresh medium containing bromodeoxyuridine (BrdU) two days after the cargo berth to the ISS. Therefore, BrdU incorporation took place during three days. NSC cultures were then arrested with the second stem cell medium and placed at 4 \({}^{\circ}\)C for further examination upon return to the laboratory (Scheme 1).  
NSCs were seeded onto a mesh-cell carrier because cells adhere firmly to it and have a better resistance to vibrations and g-forces during take-off and re-entry to the atmosphere. This protocol was tested and verified during the science definition phase of the investigation following NASA's requirements (Figure 1A).  
Upon return, the NSCs were recovered and post-fixed to perform triple immunofluorescence against two NSC markers as previously described [6]--polysialic acid (PSA) cell adhesion molecule (NCAM), known as PSA-NCAM, and the intermediate filament protein nestin, also known as a neural stem/progenitor cell marker--as well as BrdU for the detection of new DNA as a marker for cell proliferation (Figure 2).  
Figure 1: (**A**) View of an automated Type IV Unit Kiwi (Airbus). (**B**) Tank 1 (green) contained NSCs, medium, and BrdU. At time (T) + 2 this medium was delivered to NSCs, at T + 5 medium from the second tank (blue) was delivered to arrest the cells and the units were placed at 4 \({}^{\circ}\)C. Two units containing 12 pieces of mesh carrier with cells were used for the “in space” proliferation study. (**C**) Representative view of the mesh carrier on which the NSCs were seeded. (**D**) Mesh with NSCs recovered from the devices post-flight. (**E,F**) Views of StaARS-1, which offers real-time command capability without the need for astronaut time.