esa-sceva/satcom-chunk-collection huggingface dataset:
# A GPS Phase-Locked Loop Performance Metric Based on the Phase Discriminator Output
## 3 Augmented PLL Linear Model
#### Noise-Equivalent Bandwidth  
The noise-equivalent bandwidth is the \"bandwidth of the ideal filter that would pass the same noise power as the filter under study\". The noise-equivalent bandwidth expressed in units of Hertz is [13],  
\[B_{n}=\frac{1}{2|H(0)|^{2}}\int_{-\infty}^{\infty}|H(j2\pi f)|^{2}\,df \tag{36}\]where \(B_{n}\) denotes a one-sided bandwidth, and subscript \(n\) means that it is the noise-equivalent bandwidth.  
Equation (36) specifies the relationship between the bandwidth and loop filter coefficients. For example, a first-order loop has a single loop filter coefficient. Satisfying a bandwidth requirement would allow that single coefficient to be computed using Equation (36), thereby completing the loop filter design. Second and third order loop filters offer additional design freedom. For a third-order loop, two loop filter coefficients in addition to the bandwidth must be specified. The third-order loop filter coefficients used in this work are, \(a_{3}=1.1\), \(b_{3}=2.4\), and \(w_{0,3}=0.7845B_{n}\), where, \(a_{3}\) and \(b_{3}\) are chosen to be the typical third-order loop filter coefficients specified in ([12], p. 180; Table 6.5). The only terms left to define in Equations (26) and (29) are the oscillator phase-noise PSDs.
###### Abstract  
This study comparatively analyzed the behavior of elastic properties by aspect ratio of the ultra-high molecular weight polyethylene (UHMWPE) fibers that are added when creating a composite material of polypropylene and UHMWPE. The volume fraction (VF) of UHMWPE fibers added to polypropylene was fixed at 5%. The elastic properties were lumped for analysis according to the aspect ratio of the UHMWPE fibers oriented on the polypropylene matrix; they were analyzed using the Halpin-Tsai model, which involves a theoretical approach and finite element analysis based on the homogenization method. Finite element analysis was performed for fiber aspect ratios of 0.2 to 30 UHMWPE via the homogenization technique using the ANSYS Material Designer. For theoretical comparison, UHMWPE fiber aspect ratios of 0.2 to 100 were comparatively analyzed using the Halpin-Tsai model. When the aspect ratio of UHMWPE fiber was 0.2, it was calculated as 1518 MPa, and when the aspect ratio was 30, it was 2365 MPa, and it increased by 55.8%. As the aspect ratio increased, E22 and G12 converged to a constant value (1550 MPa). In the future, when the volume fraction of UHMWPE changes from 0 to 50%, a study must be conducted to analyze the predicted behavior of the elastic properties when the aspect ratio of the UHMWPE fiber changes.  
ultra-high molecular weight polyethylene (UHMWPE); polypropylene; aspect ratio Article
###### Abstract  
The encapsulation efficiency (EE%) and structural changes within the Anthraquinones-encapsulated casein micelles (CM) powders were evaluated in this study. For this purpose, the anthraquinone powder extracted from Aloevera, its freeze-dried powder (FDP) and whole leaf Aloe vera gel (WLAG) has been encapsulated in CM through ultrasonication prior to spray dying to produce nanocapsules: CM encapsulated anthraquinone powder (CMAQP), CM encapsulated freeze-dried powder (CMFDP) and CM encapsulated Whole leaf aloe vera gel (CMWLAG). Based on the pH of the solution before drying, CMAQP had the highest EE% following spray drying. However, due to air-interface-related dehydration stresses, SD resulted in a slight decrease in the EE% of anthraquinone (aloin, alo-emodin, and rhein) in CMAQP. Meanwhile, a significant increase in EE% of CMFDP was observed compared to the aqueous state. According to SEM findings, the particle size of CMAQP was 2.39 \(\upmu\)m and \(\xi\)-potential of \(\sim\)\(-\)17 mV. The CMFDP had a rough fractal surface with large particle sizes and potential of 3.49 \(\upmu\)m and \(\sim\)\(-\)11 mV respectively. CM deformed, having the least EE% and lowest \(\xi\)-potential (\(-\)4.5 mV). Spray drying enhances melanoidin formation in CMWLAG, as evidenced by the highest chroma values. The results suggested that EE%, stability, and degree of Maillard reaction are closely linked to the type of anthraquinone encapsulated, the pH of the solution, and the nanostructure of casein micelles during spray drying.  
casin micelles; spray-drying; anthraquinones; encapsulation efficiency; _Aloe vera_; SEM; FTIR 2022
## 4 Simulation and Analysis
### Variable Speed Analysis  
According to the foregoing constant speed analysis, the linear-motor single-drive system has a critical creeping velocity of 2 mm/s, and its feed speed without over modulation is 3.7 mm/s. The dual linear-motor differential-drive system does not produce over-modulation and has a feed rate of 2.3 mm/s. Further, the output velocity difference between the linear-motor single-drive system and the dual linear-motor differential-drive system is explored under variable velocity conditions.  
In the case of the linear-motor single-drive system, sinusoidal velocity signal with amplitude of 2 mm/s, the result is shown in Figure 24. For the dual linear-motor differential  
Figure 21: Linear-motor single-drive feed speed (2.3 mm/s).  
Figure 22: Linear-motor single-drive feed speed (3.7 mm/s).  
Comparing Figures 24 and 25, Figures 26 and 27, and Figures 28 and 29, the fluctuation of the dual linear-motor differential-drive system is found to be smaller than the linear-motor single-drive system when the sinusoidal velocities are low for both, indicating that the former outperforms the latter regarding variable-speed micro feed. As is also clear from Figures 24-29, when the velocity crosses zero point, evident velocity fluctuation takes place  
Figure 28: Single-drive sinusoidal feed (Am 3.7 mm/s).  
Figure 27: Differential-drive sinusoidal feed (Am 2.3 mm/s).  
Figure 29: Differential-drive sinusoidal feed (Am 3.7 mm/s).  
in both the dual linear-motor differential- and linear-motor single-drive systems, with the former exhibiting markedly weaker fluctuation than the latter.  
Comparing Figures 24 and 25, Figures 26 and 27, and Figures 28 and 29, it can be concluded that the maximum error in the output speed of the dual linear-motor differential-drive system is reduced by 75%, 84.62%, and 25%, respectively, compared to the maximum error in the output speed of the linear-motor single-drive system. Meanwhile, when both the dual linear-motor differential-drive system and the linear-motor single-drive system do not produce over modulation, the maximum speed error of the two systems is not significantly affected by the input alternating speed.
## 2 Materials and Methods
### Sample Preparation  
For this study, samples of raw cow's milk were commercially purchased from a local dairy supplier. Both unadulterated and starch-adulterated milk samples were prepared and analyzed using MSANN technology. The unadulterated raw cow's milk serves as a control group. Adulterated milk samples were prepared by adding starch to the raw milk at various concentrations of 0.01%, 0.02%, 0.04%, 0.06%, and 0.08% by volume. The selection  
Figure 3: Portable MultiSpectroANN (MSANN) Device Mod Algorithm.  
of these specific concentrations was based on prevalent levels of adulteration observed in the dairy industry, aiming to reflect realistic scenarios for testing the sensitivity and accuracy of our detection methods [25]. To ensure robust model training and validation, a large dataset consisting of X samples for each concentration was prepared, resulting in a total of 5X samples. This extensive dataset allows for effective training and testing of the ANN, enhancing the model's ability to generalize across different levels of adulteration. These concentration levels were chosen to evaluate the detectability of adulteration and the detection capacity of MSANN technology. The milk containing 0% starch by volume represents completely unadulterated raw milk and serves as a control sample. The concentration levels used in this study represent those commonly encountered in practical cases of starch adulteration [26]. Each milk sample was carefully prepared and homogenized to ensure the reliability and repeatability of the data obtained during analysis. The prepared samples were stored and processed for analysis using MSANN technology. Visually, the samples showed no discernible signs of adulteration; they appeared identical to pure milk samples. This lack of visual differentiation highlights the necessity of employing advanced spectroscopic techniques, as the subtle nature of starch adulteration cannot be detected through standard visual inspection alone.
## 6 Discussion
### Collaboration between TSOs, DSOs and Other Government Agencies  
An important issue during the restoration process is the TSO collaboration especially among the neighboring ones. Usually, when a blackout occurs in a country during the top-down restoration procedure, the neighboring TSOs need to energize the interconnection power lines. The European Awareness System (EAS) is a useful tool between the TSOs, and it proved its value during the great disturbance on 8 January 2021. Certainly, organizing meetings between dispatchers of different TSOs will help in exchanging views and improving know-how, making it more effective in such unexpected events.  
Before a major blackout and during the TSO's effort to avoid it, DSOs are usually urged to cut down loads. If a blackout occurs, the DSO's \"strategy\" until now was to wait until the restoration from the TSO and to add loads during the restoration process. However, nowadays, the electricity system has continued to develop its black-start procedures and grid-control methods as the Distributed Energy Resources (DER) [98; 99; 100]. DER units make up a larger portion of the system and they can aid in the restoration of the distribution island and the transmission system [102; 103]. Having quick control over inverter-based generation for supporting frequency and voltage as well as balancing active and reactive power are the most crucial features to support a stable restoration procedure.  
For minimizing the restoration time in a blackout, and especially if it has been caused by a natural disaster such as a fire, earthquake, flood, etc., cooperation between TSO, DSO, and government agencies such as the Army, the Fire Brigade, and the Police should be ensured. The development of an information system that will integrate information from meteorological data and areas controlled by the Police, Army, and Fire Brigade will give an optimal result. For example, there is often no coordination between the Fire Brigade and TSOs during forest fires, with the result that while the TSO is trying to electrify lines that are cut, water is thrown from airplanes and the lines' circuit breakers trip again.
# Refinement and Computation Method for Line/Body Topological Relationships
## 3 Derivation and Computation of Basic Intersection Types
### Refinement Relationship Computation
In theory, the Euler number of a three-dimensional manifold with a boundary can be calculated according to Equation (3):  
\[Eul(X)=b_{0}-b_{1}+b_{2} \tag{3}\]  
where \(b_{0}\) can represent the number of connected components of the body object, \(b_{1}\) represents the number of channels in the body object, and \(b_{2}\) represents the number of voids in the body object [34]. The value of the Euler number\(f_{E}\) in this study can be simplified based on the positions of the two endpoints of the intersection lines--for example, for intersection lines in \(F\) where \(b_{0}=1\), \(b_{1}=0\), and \(b_{2}=0\), \(f_{E}=b_{0}-b_{1}+b_{2}=1\). For unit intersections in \(V\), if both endpoints are on the surface of the body (as in Figure 4n-q, etc. ), \(b_{0}=1\), \(b_{1}=1\), \(b_{2}=0\), then \(f_{E}=b_{0}-b_{1}+b_{2}=0\). If both endpoints are inside the body (as in Figure 4c), \(b_{0}=1\), \(b_{1}=0\), \(b_{2}=1\), then \(f_{E}=b_{0}-b_{1}+b_{2}=2\); otherwise, if one endpoint is inside the body and the other is on the surface of the body (as shown in Figure 4f,g, etc. ), \(b_{0}=1\), \(b_{1}=0\), \(b_{2}=0\), then\(f_{E}=b_{0}-b_{1}+b_{2}=1\). Thus, we can determine the basic type of each intersection line between the line and the body. Then, by sorting the unit intersections in the order from the starting point to the endpoint of the line object, we can obtain the expression of the refinement relationship between the involved line and the body.
## 3 Related Work
### Autonomous Driving Simulators--An Overview
They are the best simulators for testing perception, mapping, vehicle control, and localization systems for autonomous vehicles. The majority of their characteristics are similar, e.g., open-source, portability, 2D/3D ground truth, flexible API, etc., however, the LGSVL does not offer camera calibration to conduct multi-view geometry or Simultaneous Localization and Mapping. In that sense, we decided to choose the CARLA simulator for this research. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Features**} & \multicolumn{1}{c}{**CARLA**} & \multicolumn{2}{c}{**AirSim**} & \multicolumn{2}{c}{**DeepDrive**} & \multicolumn{2}{c}{**LCSVL**} & \multicolumn{2}{c}{**NVIDIA Drive**} & \multicolumn{2}{c}{**\#Pro**} & \multicolumn{2}{c}{**MATLAB**} & \multicolumn{2}{c}{**Gazebo**} \\ \hline \multirow{4}{*}{**General**} & \multirow{4}{*}{Protubability} & Licence & Open-Source & Open-Source & Open Source & Open-Source & Commercial & Commercial & Commercial & Commercial & Open-Source \\  & & Portability & Windows and & Windows and & Windows and & Windows and & Windows and & Windows and & Windows and & Windows and & Windows and & Windows and & Windows and \\  & & Linux & Linux & Linux & Linux & Linux & Linux & Linux & Linux & Linux & Linux & Linux \\  & & Physics Engine & Unreal Engine & Unreal Engine & Unreal Engine & Unreal Engine & Unreal Engine & Unreal Engine & U & Unreal Engine & DART \\ \cline{2-11}  & \multirow{2}{*}{Scorping} & \multirow{2}{*}{Python} & \multirow{2}{*}{C++, Python, Java} & \multirow{2}{*}{C++, Python} & \multirow{2}{*}{Python} & \multirow{2}{*}{Python} & \multirow{2}{*}{Python} & \multirow{2}{*}{U} & \multirow{2}{*}{MATLAB} & \multirow{2}{*}{C++, Python} \\  & & Languages & & & & & & & & & \\ \hline \multirow{4}{*}{**Environmental**} & Urban Driving & Town & Town, City & Road Track & City & City, Harbor & Town, City, Road & N & Road Track \\  & Off-Read & N & Forest, Mountain & N & N & N & N & N & N & N \\  & Actor-Human & Y & N & N & Y & N/A & Y & Y & Y \\  & Actor-Cars & Y & Y & Y & Y & N/A & Y & Y & Y \\  & Weather & Y & Y & Y & Y & Y & Y & Y & N & N \\  & Conditions & Y & Y & Y & Y & Y & Y & N & N \\ \hline \multirow{4}{*}{**Sensors**} & RGB & Y & Y & Y & Y & Y & Y & Y & Y \\  & Depth & Y & Y & Y & Y & N/A & Y & N & N \\  & Thermal & N & Y & N & N & N/A & N/A & N & Y \\  & LIDAR & Y & Y & N & Y & Y & Y & Y & Y \\  & RADAR & Y & N & N & Y & Y & Y & Y & Y \\ \hline \multirow{4}{*}{**Output Training Labels**} & Semantic & Y & Y & N & Y & Y & Y & Y & Y \\  & Segmentation & & & & & & & & & \\  & 2D Rounding Box & Y & N & N & Y & Y & N/A & Y & Y \\  & 3D Rounding Box & Y & N & Y & Y & N/A & N/A & Y & Y \\ \hline \multicolumn{1}{c}{Legend: U: Unknown, Y: Yes, N: No.} & & & & & & & & & \\ \end{tabular}
\end{table} 
Table 2: Comparison of Autonomous DrivingSimulators.
# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
## 3 Routing Protocols for UWSNs
### EP-VIR-3 & BF-SPR-3
#### 3.2.2 Localization-free Routing Schemes without mobility consideration
Nodes with better quality link have short holding time, as compared to longer holding time in the case of nodes with bad link quality. This results in reduction in overhead. The drawback of this protocol is that it does not take into account mobility and connectivity voids, which result in decrease of packet delivery ratio, and increase in the end-to-end delay. JARDM  
Reference [64] proposes a symbiotic framework of duty cycling and any path routing to address the challenges of high energy requirement and resultant short life span of underwater sensor networks. The model is aimed at improving the end-to-end packet delivery ratio (PDR) and energy efficiency, while reducing end-to-end delay. To achieve the objective, the authors propose two novel techniques to augment asynchronous duty cycling protocols: LPL (or low power listening), and LPP (low power probing) (Figure 18), which enables sensor nodes to listen and send probes with less power, and thus save energy. Moreover, an analytical frame work is presented that can evaluate performance of the symbiotic designs, such as proposed in this work. The two duty cycling methods proposed in this paper i.e., LPL and LPP, help in improving the network lifetime by conserving energy. Moreover, the symbiotic framework of LPL and any path routing is shown to be most suitable for short-term real time applications, as this design achieves acceptable packet delivery ratio, while reducing energy consumption. HYDRO  
HYDRO  
[65] is an energy harvesting aware routing protocol. It makes forwarding decisions and relay node selection based on four criteria, which are: the current energy level of a node, predicted amount of energy to be obtained through energy harvesting, channel conditions, and macro level energy available along the whole path from source to destination. The protocol smartly combines knowledge about available energy resource and the amount of energy that can be harvested in the near future, to make more flexible routing decisions, thus improving the packet delivery ratio and energy consumption. A multi model approach is proposed for energy harvesting that enables the  
Figure 17: Base angle adjustment to alter flood area. Figure 18: (a) LPP duty cycling, (b) Strobed LPL duty cycling. shallower nodes to harvest solar energy, whereas deeper nodes capitalize on underwater currents to harvest energy. HyDRO achieves a high level of fairness by delivering data from all nodes. However, it suggests that nodes that are low on charge will go to all-off stage, and when they have harvested enough energy, turn on again. This will create voids when a node is in all-off stage. The authors do not explain how these voids will be handled.
# Analysis of Earth Pressure Variation for Partial Displacement of Retaining Wall
## 2 Materials and Testing Procedures  
### Experimental Setup  
To determine the distribution of earth pressure behind the moveable wall and fixed wall, a series of trap-door tests were conducted on a model test apparatus, as shown in Figure 2. The test box was made of steel, with dimensions of 2.5 \(\times\) 0.5 \(\times\) 0.5 m (length, width, and height, respectively). The purpose of this design was to eliminate'size effect' and cover the influence range of mobile plates. The trap-door apparatus was located at the middle of one side of the box wall and was composed of four movable plates with a 10 cm width. The upper and lower ends of each movable plate were respectively jointed with one lever. These levers could be fixed or rotated to induce different displacement modes and distance of movable doors. To detect the horizontal earth pressure, stress detectors with an accuracy of 0.01 kPa were arranged at different heights on the movable plates and fixed wall, as shown in Figure 3. The type of stress detectors used in the experiment was a strain-acquisition instrument with a diameter of 20 mm produced by Donghua Company, as shown in Figure 3. To limit the detector connectors, the stress detectors were arranged on the left side. According to the symmetry criteria and study of Cui [6], the earth pressure on the right side could be recorded symmetrically.
# Two-Phase Deep Learning-Based EDoS Detection System
## 4 System Design
### Internal Modules
#### 4.3.1 Collector  
The collector module will capture all network packets going through a virtual switch installed on openVswitch and collect the system resources of the victim server. The collector uses the Wireshark tool to achieve this mission. The Wireshark tool is the world's most popular network protocol analyzer [24]. Wireshark has tools for capturing, viewing, and analysis of data packets. Every 5 s, the collector runs Wireshark tool to capture all of the network packets and saves these network packets into a pcap file. The pcap files are placed into storage in the collector machine.
## 4 Results and Discussion
### Heat Resistivity
#### 4.2.2 Dependencies of Currencies, Electrical Power, and Resistance on Voltage
This is probably caused by defects in the filament strand due to the pressure in the feed mechanism (see Figure 10). Due to the filler-related low resistance of the FFE material, the maximum voltage is limited to 12 V for a raster angle of 0\({}^{\circ}\) and \(\pm\)45\({}^{\circ}\) and to 20 V for the 90\({}^{\circ}\) infill pattern. Compared to PPC, the electrical power of the FFE at 12 V is about factor 8 (0\({}^{\circ}\)), 6 (\(\pm\)45\({}^{\circ}\)), or 3 (90\({}^{\circ}\)) higher (see Figure 19). The power-voltage characteristics have an almost exponential increase in contrast to PPC and 3 dk. The dependence of the raster angle orientation is obvious for current, electrical power and resistance. Especially for a raster angle of 90\({}^{\circ}\), the resulting resistivity is higher than in the experimental investigations presented in Section 4.1. This probably results from the larger material output and higher energy input at the turnaround points that affects resistivity positively as the width of the specimen is low and, thus, is the time span between two adjacent strands to cool down. Resistance  
Figure 17: Representation of current, electrical power and resistance depending on voltage of PPC. does not depend on the temperature in contrast to PPC and 3 dk. However, it slightly increases due to a softening of the specimen if the glass transition temperature of the matrix polymer (PLA) is exceeded. In Figure 20, the experimental results of M3D are represented. The current-voltage curves of the 0\({}^{\circ}\) and the \(\pm\)45\({}^{\circ}\) raster angle have a high gradient and sharply decrease shortly above 3 V compared to the other materials. Consequently, the electrical power is limited to about 0.4 W as the resistance steeply increases due to softening of the PCL. The resulting damage is clearly visible in Figure 15. Due to this damage, the standard deviation of current, electrical power and resistance before softening is comparatively high. After softening the standard deviation is significant lower. Although the resistance after the damage increased, it is compared to the other materials still low. Moreover, the maximum current of the 90\({}^{\circ}\) oriented specimen is significant lower, most likely by the fact that the softening between the processed strands earlier influences resistance. However, it is not clear why resistance briefly stagnates between 2.5 V and 3.5 V before rising again. Figure 19: Representation of current, electrical power and resistance depending on voltage of FFE. Figure 18: Representation of current, electrical power and resistance depending on voltage of 3 dk.