esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Role of Lipids for Membrane Function and Intracellular Transport
### Lipid rafts  
Lipid rafts are usually described to be enriched in sphingomyelin, cholesterol, and PLs with saturated fatty acyl groups, resulting in most models for lipid rafts containing only PLs with two saturated fatty acyl groups in the inner leaflet. We challenge such a view because many cells have been described to contain a large amount of lipid rafts. At the same time, quantitative lipidomic studies performed during recent years have demonstrated that cells contain very few PLs with two saturated fatty acyl groups [82]. The fact that areas with a large fraction of sphingolipids and cholesterol are less fluid than other parts of cellular membranes may partly be because sphingolipids often contain very few N-amidated fatty acyl groups with double bonds and that PLs with polyunsaturated fatty acyl groups mainly are found in other membrane areas. We also wonder if the view that lipid rafts contain a large fraction of PLs with two saturated fatty acyl groups comes from an early study with detailed quantitative lipidomic analyses of detergent resistant membranes (DRMs). In that study, the DRMs were reported to be highly enriched in saturated PLs, but readers might have overlooked that the authors defined that \"a phospholipid was deemed saturated if containing no more than one double bond\" [149].  
Gb3 is reported to be present in both DRMs and non-DRM domains of several cell lines [150]. There has been much discussion about DRMs as a tool to study lipid rafts [151], but the lipid composition of DRMs may, at least to some extent, reflect the composition of such rafts. Various studies, including some using DRMs, have indicated that Gb3 species present in high lipid order membrane fractions are involved in retrograde transport and toxicity of Shiga toxin [152; 153]. Nanodomains containing cholesterol have been reported to be important for Shiga toxin-induced intracellular signaling since the addition of the cholesterol binding substance filipin had an inhibitory effect [77], and extraction of cholesterol using methyl-\(\beta\)-cyclodextrintrin reduced transport of Shiga toxin to the Golgi apparatus [154].
# Developing a Cybersecurity Training Environment through the Integration of OpenAI and AWS
## 2 Materials and Methods
### Description of the Problem
Developing practical skills is encouraged so that users of the training environment develop practical skills in identifying, preventing, and responding to cybersecurity incidents, improving their ability to act effectively in real situations. By simulating different types of cyberattacks, the environment allows organizations to evaluate and improve their incident response strategies and protocols [16]. To provide a more concrete and applied view of how OpenAI tools are used in the proposed cybersecurity environment, the following examples and case studies are presented:  
* Phishing Attack Simulation with GPT-3: Using GPT-3, a detailed scenario of a phishing attack was developed. In this scenario, GPT-3 generated a series of email communications that closely mimicked the tactics used by real attackers. This includes creating compelling content and simulating attackers' responses to user interactions. This simulation allowed trainees to experience and respond to a phishing attack in a safe environment, developing critical skills to recognize and mitigate such threats. * Security Breach Visualization with DALL-E: In another case, DALL-E was used to visualize the impact of a security breach on a corporate network. From a textual description of the breach, DALL-E generated images depicting the progression of the attack, including initial access, network propagation, and data extraction. These visualizations helped participants better understand the multifaceted nature of security breaches and the importance of quick and effective responses. These examples present the practical application of GPT-3 and DALL-E in cybersecurity. By providing realistic scenarios and detailed visualizations, these tools significantly improve trainees' ability to understand and respond to cyber threats, preparing them for real-world challenges.
## 3 Technical Background  
### Overview of the Four Vector Intelligent Metaheuristic (FVIM)  
The Four Vector Intelligent Metaheuristic (FVIM) [28] is an innovative optimization algorithm designed to overcome the challenges associated with traditional swarm-based algorithms, such as Particle Swarm Optimization (PSO). At its core, FVIM is structured to strike a balance between exploration and exploitation within the search space, thereby enhancing the probability of converging to a global optimum solution. This algorithm builds upon a mathematical framework that integrates multiple agent behaviors and dynamically adjusts their movements to avoid premature convergence, which is considered a common issue in many optimization algorithms.
## 2 Materials and Methods
### Flow Cytometry Analysis  
MACSQuant Analyzer 10 (Miltenyi Biotec, Bergisch Gladbach, Germany) was used for flow cytometry analysis. To measure cell surface markers, Tregs or PBMCs were stained with CD4-PerCp, CD25-PE, CD127-Vio770, CD152-APC, CD39-FITC and CD223-APC antibodies (all from Miltenyi Biotec, Bergisch Gladbach, Germany), according to the manufacturer's protocol. To measure FoxP3 and Helios protein content, PBMCs or eTregs were treated with CD4-FITC, CD25-APC (all from Miltenyi Biotec, Bergisch Gladbach, Germany), FoxP3-PE or Helios-PE antibodies (BD Pharmingen, San Diego, CA, USA) followed by flow cytometry analysis according to the manufacturer's protocol. Mean fluorescence intensity (MFI) was used to determine intracellular protein expression of FoxP3 and Helios. To measure cell proliferation, isolated CD4\({}^{+}\) cells were labeled with the vital dye CFSE (5-(and 6-)carboxyfluorescein diacetate succininidyl ester, eBioscience Inc., San Diego, CA, USA) and analyzed by flow cytometry every 24 h during expansion. The proportion of proliferated cells was determined as the proportion of cells with reduced CFSE signal. Alternatively, a CD4\({}^{+}\)CD25\({}^{+}\) regulatory T cell isolation kit, human (Miltenyi Biotec), was used to obtain pTregs as a comparison group for functional tests (FOXP3 promoter methylation, gene expression and in vitro immunosuppression assays).
# Online Signature Verification Systems on a Low-Cost FPGA
## 5 Experimental Results
### Speed Processing
We compare the execution time of different DTW implementations since the result depends on several parameters. The dimension of samples affects the calculation of the distance, the sample width and region \(R\) determine the number of points to compute \(g(i,j)\), and the data format affects the latency of the arithmetic circuits. Therefore, the cycle time of calculating \(g(i,j)\) is a better comparative measurement. The core shown in [3] presents important differences when compared with the DTW algorithm implemented in this work. First, it does not compute the optimal warping path since it only requires the minimum \(g(i,j)\) value at the last row, which scores the similarity of the aligned samples. The algorithm is repeated in 12 different templates in order to classify the captured action. Moreover, each distance is calculated from the addition of 3 square root operations. Additionally, it does not use any specific region, calculating \(g(i,j)\) at all possible points. Finally, to improve the execution time, the DTW stops if the minimum value of \(g(i,j)\) at any point in a row is greater than a threshold condition. The execution time for 576-width floating-point samples ranges from \(8.7\,\times\,10^{6}\) to \(11.6\,\times\,10^{6}\) clock cycles at 100 MHz. The higher time should be considered since it calculates \(g(i,j)\) on all the points (331,776 points), providing a cycle time of 349 ns (34.9 clock cycles). The DTW accelerator [26] is much closer to the proposed version used in this paper since it implements the Euclidean distance for 2D samples. However, the resolution of samples is limited to 8-bit, and it uses the Sakoe-Chiba region. The execution time for 250-width samples is 0.12 ms (7200 clock cycles) by computing only the 5% of the \(G\) matrix (3125 points) due to the parameters of the region. Therefore, the cycle time when calculating \(g(i,j)\) is about 38.4 ns (2.3 clock cycles). The custom computing system works with 28-bit samples using a Q1.27 fixed-point format. The DTW execution time, for 256-width samples on an Iakura region that computes 33.3% of the \(G\) matrix (21,845 points), is 0.437 ms (21,870 clock cycles). Therefore, the cycle time of \(g(i,j)\) achieves 20.01 ns, which is very close to the theoretical one (1 clock cycle). The FPBA is not specifically designed to calculate the DTW but rather to speed up several computing stages of biometric algorithms. Therefore, the performance is not as competitive as in the DTW accelerators. The 256-width samples are in 32-bit floating-point format, taking 15.8 ms to calculate the DTW in the same region as in the custom system. The cycle time is 723.28 ns (29.0 clock cycles).
# Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
## 5 Tests on New Methods for Gamma/Hadron Separation
### Receiver Operating Characteristic (ROC) Curve  
While the Q factor is considered a more important parameter, the receiver operating characteristic (ROC) curve is also commonly used to evaluate the performance of gamma/hadron discrimination methods. The ROC curve (see Figure 9) shows the trade-off between correctly identifying gamma rays (true positives) and misidentifying hadrons as gamma rays (false positives), and is a useful tool for comparing different methods. However, the Q factor is preferred because it is a proxy for the significance of the detection of gamma-ray signal over the overwhelming hadronic background in [PERSON] observations. Therefore, in gamma/hadron discrimination for IACTs, the Q factor is a crucial parameter that must be carefully considered in the development and evaluation of Machine Learning methods. AUC values are reported in Table 3.  
Figure 8: Bar plots showing the performance of five models in terms of true identification rate (\(\gamma\) acceptance rate, in dodgerblue) and rejection rate of impostor matches (Hadron rejection rate, in blue), along with the quality factor (in red) for each model for four different gammaness thresholds (0.5, 0.75, 0.8 and 0.9).
## 3 Results
### The Results of Ground Drift
The above analysis was conducted for the area of 2-6 m, and it is also applicable to most of the analysis of the 20-30 m area. It is worth noting that flow rates of the nozzle at the pressures of 2, 3, 4, and 5 bar were 486.94, 606.87, 696.78, and 822.64 mL/min, respectively. After adjusting for liquid pressure, the flow rates of 3, 4, and 5 bar were 1.25, 1.43, and 1.69 times that of the reference pressure of 2 bar (Figure 6), respectively. However, the deposition ratios at the pressures of 3, 4, and 5 bar for the different wind speeds were higher than the corresponding flow ratios. This phenomenon resulted from the fact that adjusting the pressure changes the droplet size distribution, and small droplets are more prone to drift. The deposition ratio was elevated with an increase in wind speed, except for 6 m/s. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Treatment** & **Wind** & **Liquid Pres-** & **Total** & **2-6 m** & **6-10 m** & **10-20 m** & **20-30 m** \\  & **Speed/m/s** & **sure/Bar** & **Deposition/mL** & **Deposition** & **Drift** & **Deposition** & **Drift** & **Deposition** & **Drift** \\  & & & & **Amount/uL** & **Ratio/\%** & **Amount/uL** & **Ratio/\%** & **Amount/uL** & **Ratio/\%** \\ \hline T1 & & 2 & 2475.724 & 1222.371 & 49.37 & 621.894 & 25.12 & 587.386 & 23.73 & 44.07295 & 1.78 \\ T2 & & 3 & 4167.46 & 2060.974 & 49.45 & 1198.685 & 28.76 & 717.4525 & 17.22 & 190.3495 & 4.57 \\ T3 & & 4 & 4304.962 & 2449.905 & 56.91 & 977.9673 & 27.27 & 646.0866 & 15.01 & 231.00 & 5.37 \\ T4 & & 5 & 5852.122 & 3209.959 & 54.85 & 1212.077 & 20.71 & 965.1313 & 16.49 & 465.0456 & 7.95 \\ T5 & & 2 & 4842.014 & 2006.572 & 41.44 & 1154.344 & 23.84 & 1427.488 & 29.48 & 253.6094 & 5.24 \\ T6 & & 3 & 889.367 & 3957.871 & 46.457 & 2466.842 & 27.84 & 1922.877 & 21.70 & 511.7781 & 5.78 \\ T7 & 4 & 4 & 10,432.54 & 5127.935 & 49.15 & 2974.449 & 28.06 & 1563.328 & 14.99 & 813.8298 & 7.80 \\ T8 & & 5 & 17,488.26 & 7399.918 & 42.20 & 4069.525 & 23.27 & 4351.35 & 24.88 & 1687.472 & 9.65 \\ T9 & & 2 & 864.327 & 2283.841 & 34.48 & 2385.356 & 27.92 & 2732.136 & 32.66 & 412.9939 & 4.94 \\ T10 & 6 & 3 & 14,538.85 & 5030.711 & 34.60 & 394.958 & 26.93 & 484.639 & 33.32 & 749.2401 & 5.15 \\ T11 & & 4 & 17,334.73 & 7632.688 & 44.03 & 4455.789 & 25.70 & 4100.936 & 23.66 & 1145.319 & 6.61 \\ T12 & & 5 & 18,382.46 & 8698.682 & 47.32 & 5106.224 & 27.78 & 3795.882 & 20.65 & 781.6683 & 4.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Drift levels at various downwind areas for different conditions. Figure 6: The deposition ratio and flow rate ratio of each adjusted liquid pressure.
# Three Steps towards Better Forecasting for Streamflow Deep Learning
## 2 Methodology
### Study Area and Data Description  
Malaysia's climate is hot and with high humidity all year round. The country is exposed to two major monsoon seasons, mainly the north-east monsoon from November to February and the south-west monsoon from May to August. During the north-east monsoon, a significant increase in rainfall occurrence can be detected in the eastern and southern regions of the country. Moreover, the south-west monsoon and inter-monsoon seasons of March to April and September to October can cause intense convective rainfall on the country's west coast.  
Kuala Lumpur is Malaysia's capital city, as shown in Figure 5. The city is highly urbanized and covers an area of 243 km\({}^{2}\) with an estimated population density of 6696 residents per square kilometer [52]. Changes in land use and land cover have been intense since the 1980s due to the economic boom. The city receives an average annual rainfall of 2600 mm and is subjected to flash floods. It is situated in the middle of the Klang River basin with a watershed area of 1288 km\({}^{2}\). The Klang River flows through a 120 km distance [53], with 11 major tributaries flowing across Selangor state and Kuala Lumpur [54]. Batu, Gombak, Ampang and upper Klang River at the upper catchment of Kuala Lumpur are the main tributaries of Klang River that contribute significantly to the flow at the downstream point of Masjid Jamek, which is a famous historical site and a tourist attraction.  
Figure 5: Map of study location at the Klang River Catchment.  
The flash flood occurrence in 1971 that lasted for five days with massive damage of RM36 million prompted the government to develop a comprehensive Kuala Lumpur Flood Mitigation Plan (KLFM) [55]. The Stormwater Management and Road Tunnel (SMART) project built in 2007 is part of the early plan to divert flow from the upper catchment of the Klang River and Ampang River to the Kerayong River downstream [56].  
SMART is a mega project to construct a 9.7 km tunnel that combines wet and dry systems [52]. During a major storm, mode 2 is activated when the flow reaches more than 70 m\({}^{3}\)/s at the confluence of the Klang and Ampang rivers [57]. Moreover, Mode 3 is activated when the flow at the confluence reaches 150 m\({}^{3}\)/s. Total storage of 3 million\({}^{3}\) infrastructure is available to cater for the excess stormwater. During regular days, a total length of 3 km is available for dual-deck motorway use [57].
# Internet of Drones: Routing Algorithms, Techniques and Challenges
## 4 Routing Algorithms
### UAV-Uav (U2U) Communication  
As a part of routing, UAVs must also be able to communicate with each other in order to establish effective communication. These arrangements are called U2U (UAV to UAV communication) and U2I (UAV to infrastructure communication). For this purpose, UAVs come in different shapes and sizes in order to meet their demand. The new framework is UAVs collecting data from WSNs. The structure and services of a UAV-WSN need to be specified.  
Networking, data traffic requirements, protocols, and network topologies make up IoDs. Linear sensor networks (LSN) is a network topology being considered for these jobs, in addition to geometric and clustered WSNs, and is a very efficient one. The advantages include a decrease in energy consumption, low interference, and greater flexibility for the sensor nodes. As a result, UAVs can provide connectivity to the WSN.  
Figure 8: The chicken swarm optimisation algorithm.  
In areas where accessibility is difficult, for example, disaster-stricken locations and remote areas, UAVs can play their part in gathering data. Cellular (3G, 4G, 5G), Internet (WiFi 802.11 n/g), and even satellite localisation is possible through UAVs in these areas. UAVs can be efficiently deployed to perform and monitor tasks in a dynamic, cost-effective manner using sensors like cameras, heat sensors, radiation readers, and gas monitors. Efficient and reliable data transfer is possible through UAVs. Storage of collected data and performing tasks on the stored data is considered as a pioneer task of UAVs.  
For a given application, UAVs can be homogenous or inhomogenous depending upon their capability and storage capacity. If the UAVs collect unequal amounts of data, they are equipped with heterogenous capabilities. In this case, a collaborative storage of data is desired among UAVs to conveniently store collected data.
# On the Detection and Removal of Oxides from Laser-Treated Surfaces
## 3 Results  
### General Approach  
It should be noted that the results presented below are semi-quantitative only. The samples have significant surface roughness (which is the main purpose of laser texturing), the content of oxides on the surface is fairly small, and their thicknesses may vary between the valleys and ridges of the patterns. Precise EDS analysis relies on the assumption of a smooth surface and a homogeneous distribution of elements within the gauge volume. Moreover, oxygen was detected on practically every sample that was exposed to air. Also, the X-ray intensity of lighter elements was lower than that of the heavier ones. The roughness and spatial distribution presented a similar complication for the XRD analysis. Thus, the samples are far from ideal [25; 26; 27]. Therefore, the general approach was to determine the oxides semi-quantitatively through conducting measurements on (a) a pristine sample (i.e., freshly ground metal) and (b) sample(s) oxidized by a known process. This way, a practical boundary between a 'clean' and 'oxidized' surface can be determined, which is specific for each material. For materials with the same type of morphology, at the least, a qualitative comparison of the degree of oxidation among treatments with different parameters can be obtained.
# Influence of Fiber Mixing Process on the Cracking Resistance of Cold Recycled Asphal Mixture
###### Abstract  
Fiber reinforcement is often used to improve the road performance of cold recycled asphalt mixture (CRAM). The purpose of this research is to evaluate the impact of fiber mixing process on the cracking resistance of CRAM from multiple perspectives. Four kinds of fiber mixing processes, named A, B, C, and D, were designed by changing the order of fiber addition during the mixing process. Then, semicircle bending tests and indirect tensile tests were conducted to characterize the low-temperature cracking behavior of fiber CRAM. Freeze-thaw cycle tests under both dry and water-saturated conditions were performed to investigate the freeze-thaw damage behavior of fiber CRAM. Furthermore, the fiber dispersion in CRAM was observed using scanning electron microscopy (SEM). The results show that the fiber mixing process has a significant effect on the cracking resistance of CRAM. The CRAM specimens prepared by process C have the largest fracture energy, splitting strength, and fracture work, while the specimens made by process D have the smallest value. Specially, the fracture energy of the specimens prepared by process C is 77.23% larger than that of the specimens prepared by process A, while the fracture energy of the specimens prepared by process D is 5.6% smaller than that of the specimens prepared by process A. The reason for this phenomenon is that the fiber is well dispersed in the specimens prepared by process C, which contributes to obtain a better crack resistance. For all CRAM specimens, with the increase of freeze-thaw cycles, splitting strength and fracture work of fiber CRAM decrease. However, there is an obvious difference in the reduction rate of splitting strength and fracture work, especially for the specimens under the water saturation condition. The specimens made by process C have the smallest reduction rate, which indicates a better resistance to freezing and thawing damage. According to the analysis of fiber macro-distribution state in loose CRAM, the fiber dispersion is affected by the humidity conditions in the mixing environment. The best humidity conditions are obtained for fiber dispersion in process C. Based on the SEM observation, the overlapping bridging network structure can be observed in the microstructure of the specimens prepared by process C, allowing the mixture to better transfer and disperse stress.  
cold recycled asphalt mixture; fiber mixing process; resistance to cracking; freeze-thaw damage; fiber dispersion state +
Footnote †: journal: Applied Sciences