esa-sceva/satcom-chunk-collection huggingface dataset:
### UAVs: Roots and Advancements
#### 1.2.1 Military Influence and Technological Advancements in UAVs  
The roots of UAV technology can be traced back to the early 20 th century, when the first attempts at unmanned flights were performed. However, it was not until the early 2000s that UAV technology started gaining popularity for civilian applications, includingagriculture and aerial photography [12]. The advancements in miniaturization, sensor technology and communication systems played a crucial role in developing UAVs. Initially, UAVs were primarily used for military purposes, providing surveillance and reconnaissance capabilities without risking human lives. Over time, the technology evolved and UAVs became more sophisticated, capable of conducting complex missions and tasks. The military has played a significant role in driving the development and advancement of UAV technology. Initially, UAVs were primarily used for military surveillance and surveys. The need for unmanned systems that could perform these tasks led to substantial investments in the research and development, resulting in technological advancements in the materials, propulsion systems, avionics, and payload capacity [13]. These technological advancements also led to the miniaturization of UAVs, making them more agile and versatile in various environments.  
One of the key advantages of UAVs in military applications is their ability to reduce risks to military personnel. By deploying UAVs for reconnaissance missions, military forces can gather critical intelligence without endangering soldiers' lives. This has led to increased accuracy in targeting activity and reduced collateral damage, which has important implications for the ethical and legal aspects of military operations. Additionally, UAVs have the potential to enhance situational awareness and support decision-making processes in complex and dynamic battlefield scenarios [13]. The ability to deploy fleets or swarms of UAVs further enhances their capabilities, enabling collaborative operations and data fusion for improved situation management.  
The military's influence on UAV technology has also significantly impacted its expansion into civilian applications. The advancements made for military purposes have paved the way for using UAVs in non-military contexts, such as logistics, environmental studies, civil protection, and disaster response [13]. The decreasing cost of components, improved battery technology, and advancements in autonomous navigation systems have made UAVs more accessible and versatile for civilian applications [14]. For example, in agriculture, UAVs are used for crop monitoring, precision spraying, and livestock management [15; 16]. In environmental monitoring, UAVs enable researchers to collect high-resolution data for studying climate change impacts, mapping urban heat islands, and monitoring air quality [17; 18]. The versatility and adaptability of UAVs have made them valuable tools in various industries, contributing to increased efficiency, reduced costs, and improved safety [19].
## 3 Biosynthesis of Aromatic and Isoprenic Precursors of Prenylquinones in Parasitic Protozoa
### Aromatic Head Group Biosynthesis
#### 3.1.2 Shikimate Pathway  
Generalities about Shikimate Pathway  
The shikimate pathway is another source of chorismate in not only photosynthetic organisms such as algae and plants but also in some bacteria and apicomplexan parasites [73; 83]. The shikimate pathway begins when the enzyme 3-deoxy-arabinoheptulosonate-7-phosphate (DAHP) synthase forms 3-deoxy-arabinoheptulosonate-7-phosphate by condensing phosphoenolpyruvate (PEP) and n-erythrose-4-P. 3-deoxy-arabinoheptulosonate-7-phosphate is reduced and dehydrated, forming shikimic acid. This molecule is phosphorylated and then reacts again with PEP to form 5-O-(1-carboxyvinyl)-3-P-shikimate by the action of the enzyme 5-enolpyruvateylshikimate-3-phosphate synthase. The herbicide glyphosate is inhibited by this enzyme [68]. Finally, chorismite is formed as a consequence of the phosphorylation of 5-O-(1-carboxyvinyl)-3-_P_-shikimate by the enzyme chorismite synthase. Chorismate can follow different pathways depending on the product to be synthesized.  
Shikimate Pathway in Parasitic Protozoa  
The shikimate pathway appears to be active in the apicomplexan parasite cytosol and is thought to be essential for the biosynthesis of folates, prehylquinones and some amino acids. It is not surprising that the herbicide glyphosate and shikimic acid analogs have previously exhibited antiparasitic activity against apicomplexan pathogens [74; 75; 94]. However, [PERSON] et al. [75] demonstrated _P. berghei_ mutants lacking the enzyme chorismate synthase were viable in the mammalian host and the mosquito vector. Therefore, [PERSON] et al. suggested the shikimate pathway not to be essential for the parasite viability and consequently not a good drug target [75].
## 7 Implementation Optimization
### Detection of Multiplexers  
Before the CSD is started, it is checked whether the upper right and lower left quarters of the matrix are already zero-matrices. If that is the case, the matrix already has the structure of a multiplexer and is directly passed to the demultiplexing step. This is signaled to the kernel by adding a specific gate angle to the list of rotation angles. This operation halves the number of resulting gates for this step of the decomposition.
# Optimizing Mobile Vision Transformers for Land Cover Classification
## 3 Materials and Methods
### Datasets
The WHU-RS19 dataset is a large-scale aerial image dataset that consists of satellite images that were collected from Google Earth. The dataset is similar to AID in that both datasets have the same image sizes (\(600\times 600\)), and they both originate from Google Earth imagery. In addition, WHU-RS19 is the smallest dataset out of the four datasets, with 1005 JPG images. Also, the images in the dataset range have a spatial resolution of up to 0.5 m as well as a diverse range of orientations and illuminations. Also, the images of the dataset belong to 19 image classes. Figure 5p-t display sample images from the WHU-RS19 dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Class \#**} & \multicolumn{4}{c}{**Labels**} \\ \cline{2-5}  & **AID** & **EuroSAT** & **UC-Merced** & **WHU RS-19** \\ \hline
0 & Airport & Annual Crop & Agricultural & Airport \\ \hline
1 & Bare Land & Forest & Airplane & Beach \\ \hline
2 & Baseball Field & Herbaceous Vegetation & Baseball Diamond & Bridge \\ \hline
3 & Beach & Highway & Beach & Commercial \\ \hline
4 & Bridge & Industrial & Buildings & Desert \\ \hline
5 & Center & Pasture & Chaparral & Farmland \\ \hline
6 & Church & Residential & Dense Residential & Forest \\ \hline
7 & Commercial & River & Forest & Industrial \\ \hline
8 & Dense Residential & Sea Lake & Freeway & Meadow \\ \hline
9 & Desert & Golf Course & Mountain \\ \hline
10 & Farmland & Harbor & Park \\ \hline
11 & Forest & Intersection & Parking \\ \hline
12 & Industrial & Medium Residential & Pond \\ \hline
13 & Meadow & Mobile Home Park & Port \\ \hline
14 & Medium Residential & Overpass & Residential \\ \hline
15 & Mountain & Parking Lot & River \\ \hline
16 & Park & River & Viaduct \\ \hline
17 & Parking & Runway & Football Field \\ \hline
18 & Playground & Sparse Residential & Railway Station \\ \hline
19 & Pond & Storage Tanks & \\ \hline
20 & Port & Tennis Court & \\ \hline
21 & Railway Station & & \\ \hline
22 & Resort & & \\ \hline
23 & River & & \\ \hline
24 & School & & \\ \hline
25 & Sparse Residential & & \\ \hline
26 & Square & & \\ \hline
27 & Stadium & & \\ \hline
28 & Storage Tanks & & \\ \hline
29 & Viaduct & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Dataset labels used in this study.
# Angular Trajectory Design for MR-SPS Using Bezier Shaping Approach
## 4 Simulation Results
### Angular Trajectory around Equinox  
Assume that the MR-SPS is on orbit at 15 September 2025 and the maximum control torque and minimum control torque are set as \(\mathbf{T}_{\rm{cmax}}=[100\quad 100\quad 100]^{T}\) N \(\cdot\) m and \(\mathbf{T}_{\rm{cmin}}=[-100\quad-100\quad-100]^{T}\) N \(\cdot\) m. The optimal angular trajectory of \(\gamma\) and \(\theta_{j}\) generated by the proposed Bezier approach and the ideal angular trajectory are shown in Figure 5. It can be obtained that the optimal angular trajectory generated by the Bezier approach is very close to one ideal trajectory at the beginning and the end but in the middle it switches to another ideal trajectory, because the rate of change of the rotation angle is quite large at about 21,600 s and 64,800 s and the control effort is not enough to track the ideal trajectory. The average energy receiving efficiency is 99.97% which is very close to 100%.  
The control torque history is shown in Figure 6. It can be observed that all the components of \(\mathbf{T}_{\rm{c}}\) satisfy the constraints of the optimal trajectory, and the peak value of \(\mathbf{T}_{\rm{c}}\) of the ideal trajectory is over \(10^{4}\) N \(\cdot\)m and is far larger than that of the optimal trajectory which is 100 N \(\cdot\)m. The value of the component \(T_{cy}\) is much less than the other components. The history of \(\tau_{\approx 1}\) and \(\tau_{\approx 2}\) is almost the same due to the symmetrical structure but the history of \(\tau_{cy}\) of the optimal trajectory and ideal trajectory is different during the period when the trajectory is switched.  
Figure 4: Control torque history around solstice. (**a**) Control torque history of the components of \(\mathbf{T}_{\rm{c}}\), (**b**) Control torque history of \(\tau_{\rm{scj}}\).  
Figure 5: Angular trajectory around equinox. (**a**) Angular trajectory of \(\gamma\); (**b**) angular trajectory of \(\theta_{j}\).
# EUV-Induced Plasma: A Peculiar Phenomenon of a Modern Lithographic Technology
## 3 Bulk Materials Facing EUV Photon Induced Plasma
### Identification of Reaction Mechanisms
#### 3.3.2 Multilayer Delamination  
Long duration exposure tests to study multilayer optics lifetime in EUV lithography are not realistically feasible, as the lifetime target is 30,000 h [93]. Therefore, adequately predicting delamination requires a thorough understanding of the interaction mechanisms between EUV-induced plasmas and those surfaces exposed. Thus far, delamination studies have been focusing on ion-surface interactions where ion fluxes were generated while using alternative sources, like thermal gas crackers [94; 95] or an ion gun [96].  
Delamination in Mo/Si multilayer samples due to ion irradiation has been experimentally confirmed using moderate energy (50-200 eV) [96; 97] and energetic (>800 eV) hydrogen ions [98; 95]. E.g., deuterium ions have been shown to be capable of having an eroding effect on both Mo and Si layer of EUV lithography relevant optical materials [94]. Multilayer delamination has been shown, even under low ion flux conditions, if the exposure time and thus the total ion fluence was sufficient [99]. Some of these studies [94; 95; 96; 99] relate their work to mirror degradation in EUV lithography by suggesting that the used ion sources serve as a proxy for the ion fluxes that are created by an EUV-induced plasma. This approach is valid for the first few microseconds after the EUV pulse, in a collisionless regime and while using biased surfaces [82]. However, this approach is not fully valid for longer time scales due to the increasing sheath thickness and the resulting transit to a collisional regime. Increased pressures (i.e., up to 100 Pa) appear to have the same effect. Furthermore, the IEDF towards grounded or floating walls consists of large fractions of low energy (<10 eV) ions [59]. Therefore, the validity of the used ion sources as a proxy for EUV-induced plasmas strongly depends on the targeted experimental conditions. For now, the investigation of the influence of ion fluxes on EUV lithography relevant optics materials has been limited to equivalence studies for collisionless sheath conditions the near biased surfaces.
# Adaptive Satellite Navigation Anti-Interference Algorithm Based on Inverse Cosine Function
## 3 Improved Variable Step Size LMS Algorithm
### Influence of Parameters on The ICVS-LMS Algorithm  
To develop a better understanding of the four additional parameters and their impact on the performance of the function, it is necessary to examine their relationship more closely, and an analysis is conducted on the varying values of these parameters.  
The curves in Figure 3a are when \(\alpha\) is taken as 0.2, 0.6, 1.0, \(\beta=6\), \(\gamma=1.5\), and \(m=1.6\), respectively. With the increase value of \(\alpha\), the convergence also accelerates, and the influence of parameter \(\alpha\) on the convergence speed diminishes as the algorithm reaches its later stages. Considering the whole convergence process of the algorithm, \(\alpha=1.0\) is chosen.  
The curves in Figure 3b are when \(\beta\) is taken as 1, 3, 6, \(\alpha=1.0\), \(\gamma=1.5\) and \(m=1.6\), respectively. With the increase value of \(\beta\), the algorithm can converge rapidly in the initial state, and the better stability; with the decrease value of \(\beta\), the algorithm exhibits slow convergence during its initial stage, and the speed is fast when it tends to be stable, and increasing the step size to a larger value, which compromises the stability of the system. When \(\beta=3\) and \(\beta=6\), it is relatively stable in the later stage of convergence. When \(\beta=6\), the speed is faster in the early stage. Based on the above factors, \(\beta=6\) is selected.  
The curves in Figure 3c are when \(\gamma\) is taken as 0.5, 1.5, 3.0, \(\alpha=1.0\), \(\beta=6\), and \(m=1.6\), respectively. With the increase in the value of \(\gamma\), the algorithm can converge quickly in the initial state. However, when the error \(e(n)\) is non-zero, the step size factor \(\mu(n)\) becomes zero, leading to the algorithm taking more time to enter the convergence state. When the  
Figure 3: Effect of various parameters on step size factor: (**a**) effect of parameter \(\alpha\) on the step size factor; (**b**) effect of parameter \(\beta\) on the step size factor; (**c**) effect of parameter \(\gamma\) on the step size factor; (**d**) effect of parameter \(m\) on the step size factor.  
value of \(\gamma\) is small, the shape of the curve deforms too fast, and it is likely to lose the best point. Based on the above factors, \(\gamma=1.5\) is selected.  
The curves in Figure 3d are when \(m\) is taken as 0.8, 1.6, 2.4, \(\alpha=1.0\), \(\beta=6\), and \(\gamma=1.5\), respectively. With the increase in the value of \(m\), the algorithm can obtain faster convergence at the beginning, but it will bring bad error values. When the value of \(m\) is small, the error value can be kept in a smaller range, but it takes a long time to converge. Through the above comparison, \(m=1.6\) is selected.
# Fog-Based CDN Framework for Minimizing Latency of Web Services Using Fog-Based HTTP Browser
## 4 Implementation and Experimental Results
### Experiment Setup and Results
#### 4.2.2 Fog Node and Hop Counts Experiment  
Our second experiment was conducted on a local network, with one Raspberry Pi 3 Model B Plus attached to the network as a fog node. A Linux based machine with a 1.8 GHz Intel Core i7 processor was attached to the network as an edge device with the Fog Browser ready for sending consumer requests. The proposed framework had the same deployment as the first experiment.  
We had deployed the fog node on different network distances from the edge device. The first run of this experiment, the fog node was one hop away from the edge device. In the second run, the fog node was two hops away from the edge device. This was achieved by attaching the fog node into another subnetwork using another network switch. In the third run, the fog node was three hops away from the consumer by attaching the fog node behind a proxy server in the subnetwork. Using the Fog Browser, we assessed each of the studied websites (Table 2) three times again in different time periods and measured the latency for both fog-based CDN and cloud-based CDN per run. As shown in the bar chart in Figure 9, our experiments show that increasing the number of hops between the consumer and the fog node increases the latency accordingly. Our proposed framework provided the offloading autonomy needed to achieve service latency reduction by 43.7% for having the fog node away from the consumers by three (3) hops. It achieved a latency reduction of 80.7% when the fog node was two (2) hops away from the consumer.  
Figure 8: Fog-based CDN latency vs. cloud-based CDN latency.
## 2 Related Work
### Pitch Angle Control (PAC)  
PAC is a useful technique for power adjustment, exceeding the rated wind speed. It is measured as the most effectual and prevalent power control technique. The conventional pitch controls the typically practiced PI controller, and the system must be acknowledged well by means of a mathematical model. In this paper, we deal through the process and the control of the DFIG power generation at different approaches. The purpose of applying PAC is expressed as follows.  
Firstly, to optimize the wind turbines' output power and lower-rated wind speed, the pitch adjustment must be at its optimal rate to provide the maximum power. Secondly, in order to reduce the load fatigue of the mechanical parts of the turbine, it has been experienced that the turbine's control system can have a major influence on the loads [10]. The controllers need to ensure that excessive loads will not affect the control action; the strategy of the controller has to take into account the effect on loads. It is possible to go further than this, and explicitly design the controller with the reduction of definite lethargy loads as an additional objective. Thirdly, the design limits for preventing the input mechanical power must be dealt with. Above rated wind speed, pitch angle control provides an operative technique of maintaining the aerodynamic command and loads produced by the rotor.  
In the case of the variable-speed wind turbine, a mechanical actuator is commonly employed to regulate the pitch angle of the blades for the sake of reducing the power coefficient (\(C_{\text{P}}\)) and sustaining the power at its rated rate. For linearization of the model, the torque is acknowledged as being proportional to the gyratory rate of the turbine [1; 35]. The control approach is implemented as follows, in Equations (3) and (4), where \(\beta\) is the rotor blades' angle and \(\omega\) is the rotor speed of the turbine:  
\[\beta_{ref}=\beta_{0}=0,\text{ for }0\;<\;\omega_{\text{t}}\;<\;\omega_{\text{tn}} \tag{3}\]  
\[\beta_{ref}=\frac{\Delta\beta}{\Delta\omega}(\omega_{t}-\omega_{tn})+\beta_{0 },\text{ for }\omega_{\text{t}}\;>\;\omega_{\text{tn}} \tag{4}\]  
It is depicted that the optimal value of the tip speed ratio (\(\lambda\)) is 10, which keeps \(C_{\text{P}}\) at its maximum value, that is, 0.5 p.u.
## 2 Materials and Methods
### Biofilm and Planktonic Analysis
#### 2.3.2 Plasmid Extraction and Quantification  
Plasmid DNA was extracted by alkaline lysis as described by [PERSON] et al. [36]. Briefly, bacterial pellets were resuspended in 100 \(\mathrm{\SIUnitSymbolMicro mL}\) of Solution I (50 mM glucose, 25 mM Tris-HCl, 10 mM EDTA, pH 8). Alkaline lysis was performed by adding 100 \(\mathrm{\SIUnitSymbolMicro mL}\) of Solution II (0.2 M NaOH, 1% (\(w/v\)) SDS) and incubating the mixture for 10 min at room temperature. Cellular debris, genomic DNA and proteins were precipitated by adding 100 \(\mathrm{\SIUnitSymbolMicro mL}\) of Solution III (5 M acetate/3 M potassium, pH 5) and incubating for 10 min in ice. The precipitate was removed by centrifuging twice at 20,000\(\times\)\(g\) for 30 min at 4 \(\mathrm{\SIUnitSymbolCelsius}\) (Centrifuge 5424, Eppendorf, Hamburg, Germany).  
Plasmid quantification in biofilm and planktonic samples was performed according to the analytical high-performance liquid chromatography (HPLC) method based on hydrophobic interaction chromatography (HIC) described by [PERSON] et al. [37]. A 4.6/100 mm HIC source 15 PHE PE column (Amersham Bioscience, Uppsala, Sweden) was connected to an HPLC system ([PERSON], Neexera-i LC-2040C 3D, Duisburg, Germany) and equilibrated with 1.5 M (NH4)\({}_{2}\)SO\({}_{4}\) in 10 mM Tris-HCl buffer, pH 8. Then, 50 \(\mathrm{\SIUnitSymbolMicro mL}\) of each sample was injected and eluted at 1 mL min\({}^{-1}\). After 1 min, the elution buffer was changed to 10 mM Tris-HCl without ammonium sulfate and maintained for 0.8 min in order to elute all bounded species. Finally, the elution buffer was changed to the equilibration buffer and maintained for 8.2 min to re-equilibrate the column for the next run. The absorbance of the eluate was recorded at 260 nm. The plasmid concentration of the samples was calculated using calibration curves constructed with serial dilutions of plasmid DNA standards (ranging from 0.04 to 20 \(\mathrm{\SIUnitSymbolMicro mL^{-1}}\)). The calibration curves were established among the peak areaappearing between 0.67 and 0.7 mL of elution volume versus the concentration of plasmid DNA standard [37]. Then, the plasmid copy number (PCN) was calculated using Equation (1) [38]:  
\[\text{PCN}=\frac{6.02\times 10^{23}\,\left(\text{copy mol}^{-1}\right)\times \text{DNA amount}\left(\text{g}\right)}{\text{DNA length}\left(\text{bp} \right)\times 660\,\left(\text{g mol}^{-1}\,\text{bp}^{-1}\right)} \tag{1}\]  
where the size of pFM23 plasmid is 6053 bp.
# Detection of Transmission Line Insulator Defects Based on an Improved Lightweight YOLOv4 Model
## 3 Detection Model of Transmission Line Insulator Defects  
Aiming at the problems that the size of insulator defects is too small in the transmission line inspection images, some insulators are not significant in the real environment, the overlapping occlusion of insulators is serious and the speed of model detection is slow. An improved YOLOv4 algorithm model was proposed in this paper to realize fast and accurate detection of insulator defects during transmission line inspection, and the process of insulator defect detection is shown in Figure 3. The basic principles of the model are introduced as follows.
# Real-Time, Deep Learning Based Wrong Direction Detection
###### Abstract  
In this paper, we develop a real-time intelligent transportation system (ITS) to detect vehicles traveling the wrong way on the road. The concept of this wrong-way system is to detect such vehicles as soon as they enter an area covered by a single closed-circuit television (CCTV) camera. After detection, the program alerts the monitoring center and triggers a warning signal to the drivers. The developed system is based on video imaging and covers three aspects: detection, tracking, and validation. To locate a car in a video frame, we use a deep learning method known as you only look once version 3 (YOLOv3). Therefore, we use a custom dataset for training to create a deep learning model. After estimating a car's position, we implement linear quadratic estimation (also known as Kalman filtering) to track the detected vehicle during a certain period. Lastly, we apply an \"entry-exit\" algorithm to identify the car's trajectory, achieving 91.98% accuracy in wrong-way driver detection.  
Keywords:convolutional neural networks (CNNs); intelligent transportation system (ITS); you only look once version 3 (YOLOv3); linear quadratic estimation (LQE) +
Footnote †: journal: Applied