esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
The fatty acid and \(\alpha\)-tocopherol content as well as the individual carotenoid content of the encapsulated SBO using three different carriers were determined by HPLC-DAD, GC-MS, and HPLC-FID, respectively. The mean values of determined BAMs in encapsulated and non-encapsulated SBOs were compared, and the results are shown in Tables 1 and 2 and Figure 1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fatty Acids** & **SBO** & **SD-GA** & **SD-\(\beta\)-CD** & **SD GA:\(\beta\)-CD (1:1)** \\ \hline \multicolumn{6}{c}{Saturated (\%)} \\ \hline Myristic & \(p\) = 0.07 & 0.15 \(\pm\) 0.01 a & 0.18 \(\pm\) 0.00 a & 0.17 \(\pm\) 0.00 a & 0.16 \(\pm\) 0.00 a \\ Pentadecanoic & \(p\) = 0.94 & 0.07 \(\pm\) 0.01 a & 0.07 \(\pm\) 0.00 a & 0.07 \(\pm\) 0.00 a & 0.07 \(\pm\) 0.00 a \\ Palmitic & \(p\) = 0.36 & 25.44 \(\pm\) 1.05 a & 25.20 \(\pm\) 0.00 a & 25.49 \(\pm\) 0.00 a & 25.38 \(\pm\) 0.01 a \\ Heptadecanoic & \(p\) = 0.27 & 0.11 \(\pm\) 0.01 a & 0.14 \(\pm\) 0.00 a & 0.15 \(\pm\) 0.00 a & 0.14 \(\pm\) 0.00 a \\ cicaric & \(p\) = 0.83 & 1.59 \(\pm\) 0.02 a & 1.59 \(\pm\) 0.00 a & 1.63 \(\pm\) 0.00 a & 1.66 \(\pm\) 0.00 a \\ Arachidic acid & \(p\) = 0.08 & 0.28 \(\pm\) 0.02 a & 0.24 \(\pm\) 0.00 a & 0.28 \(\pm\) 0.00 a & 0.29 \(\pm\) 0.00 a \\ Docosanoic acid & \(p\) = 0.65 & 0.12 \(\pm\) 0.01 a & 0.13 \(\pm\) 0.00 a & 0.13 \(\pm\) 0.00 a & 0.12 \(\pm\) 0.01 a \\ SUM: & \(p\) = 0.20 & 27.76 \(\pm\) 0.06 a & 27.55 \(\pm\) 0.00 a & 27.92 \(\pm\) 0.01 a & 27.81 \(\pm\) 0.01 a \\ \hline \multicolumn{6}{c}{Unsaturated (\%)} \\ \hline Palmitoleic acid & \(p\) = 0.27 & 31.85 \(\pm\) 1.15 a & 31.67 \(\pm\) 0.01 a & 31.26 \(\pm\) 0.00 a & 30.94 \(\pm\) 0.01 a \\ Oleic acid & \(p\) = 0.84 & 16.38 \(\pm\) 1.04 a & 16.36 \(\pm\) 0.02 a & 16.57 \(\pm\) 0.01 a & 16.53 \(\pm\) 0.01 a \\ Vaccenic acid & \(p\) = 0.74 & 7.43 \(\pm\) 1.03 a & 7.40 \(\pm\) 0.01 a & 7.28 \(\pm\) 0.00 a & 7.24 \(\pm\) 0.00 a \\ SUM: & \(p\) \textless{} 0.01 & 55.66 \(\pm\) 0.09 b & 55.44 \(\pm\) 0.02 b & 55.11 \(\pm\) 0.00 a & 54.71 \(\pm\) 0.00 a \\ \hline \multicolumn{6}{c}{Polyunsaturated (\%)} \\ \hline Linoleic acid & \(p\) = 0.35 & 11.20 \(\pm\) 0.45 a & 11.32 \(\pm\) 0.01 a & 11.75 \(\pm\) 0.00 a & 12.01 \(\pm\) 0.00 a \\ \(\alpha\)-Linoleic acid & \(p\) = 0.43 & 5.24 \(\pm\) 0.14 a & 5.35 \(\pm\) 0.00 a & 5.17 \(\pm\) 0.00 a & 5.14 \(\pm\) 0.00 a \\ SUM: & \(p\) = 0.28 & 16.44 \(\pm\) 0.45 a & 16.67 \(\pm\) 0.02 a & 16.93 \(\pm\) 0.00 a & 17.15 \(\pm\) 0.00 a \\ \hline \end{tabular}
\end{table} 
Table 1: Fatty acid composition (weight percentage of total fatty acids) of non-encapsulated SBO and encapsulated SBO produced by spray drying using gum arabic, \(\beta\)-cyclodextrintrim, and their mixture. The sea buckthorn berries contained 14.47% oil and spray-dried powders obtained with GA, \(\beta\)-CD, and GA:\(\beta\)-CD (1:1) contained 10.30%, 25.57%, and 13.00% oil, respectively. The fatty acid composition of the encapsulated oil was the same as that of the non-encapsulated oil, with unsaturated fatty acids predominating, including monounsaturated fatty acids, such as palmitoleic acid (30.94-31.67%), oleic acid (16.36-16.57%),
## 3 Results  
### ANOVA Analysis  
In the present work, the software Design Expert 8.0.6 was applied to analyze the experimental results and the ANOVA analysis was employed to analyze the significance of the \(Mc\), \(v_{air}\), and \(L\) on the \(\Delta p\). The results are tabulated in Table 5. It can be seen from the table that the sum of squares, mean square, \(F\)-value, and \(p\)-value for each of the terms of the model were calculated, of which the \(F\)-value and \(p\)-value are the criteria for evaluating the fitting performance of the specific term [36]. Specifically, the larger the \(F\)-value is, the more significant the term is and the better the fitting performance is; on the other hand, the \(p\)-value with the range of (0.01 \(<p<\) 0.05) indicates the term is significant (characterized by  
Figure 2: The variations of the \(d_{g}\) and \(\epsilon\) with \(Mc\).  
the symbol \"**\") while a \(p\)-value less than 0.01 indicates that the term is extremely significant (characterized by the symbol \"**\").  
As can be seen from Table 5, the \(\Delta p\) model \(F\) value of 148.56 corresponds to a low \(p\)-value (\(p<0.0001\)), which implies that the model was significant as the test failed to accept the null hypothesis that variations in the response could not be explained by the model. This indicates that the \(\Delta p\) model developed by the RSM is significant, and the experimental \(\Delta p\) can be explained by the obtained model. Moreover, the values of \(R^{2}\), Adj \(R^{2}\), and Pred \(R^{2}\) are similar and all close to 1 [37], which also indicates that the \(\Delta p\) model is in a well-fitting performance and can be used to predicted the \(\Delta p\). The value of the C.V.% term is 10.98%, indicating that there are only 10.98% of the experimental data that cannot be explained by the \(\Delta p\) model developed by the RSM, in statistics, if the value of the C.V.% is greater than 15%, the data may be abnormal and should be eliminated [19]. On the other hand, the predicted residual sum of squares (PRESS) is a measure of how well the \(\Delta p\) model fits each point in the design. The high PRESS value (35358.41) in the present work may be caused by the abnormal 10.98% data. The influences of \(Mc\), \(v_{air}\), and \(L\) on the \(\Delta p\) model can be determined by the \(F\)-value as well as the \(p\)-value. It can be obviously summarized that the terms of \(Mc\), \(v_{air}\), \(L\), \(Mc\)-\(v_{air}\), \(Mc\)-\(L\), \(v_{air}\)-\(L\), and \(v_{air}\)\({}^{2}\) have significant influences on the \(\Delta p\) while the \(Mc^{2}\) and \(L^{2}\) have no influence on the \(\Delta p\). For the significant terms, by estimating the corresponding \(F\)-value, the contributors to the \(\Delta p\), in ascending order of significance, are as follows: \(v_{air}\), \(Mc\)-\(v_{air}\), \(Mc\)-\(L\), \(Mc\), \(v_{air}\)-\(L\), \(L\), and \(v_{air}\).
# Event-Based Emergency Detection for Safe Drone
## 2 Background  
Many studies argue that the dominating drone-control method, PID (Proportional Integral Derivative) control, offers the simplest and yet most efficient solution to many real-world control problems [6; 7; 8]. However, some reviews point out the limitations of the current dominating technology. [PERSON] [9] mentioned that using PID regulators for the following platform (AR Drone) was successful only with a movement speed of less than 5 km/h. However, to achieve satisfactory results with higher speed, other types of regulators should be tested [9]. We considered this issue was due to the feedback architecture and the low-level approach it takes. Instability is the disadvantage of feedback. When using feedback there is always a risk that the closed-loop system will become unstable [10]. And moreover, control methods that rely on GPS (global positioning system) data [11; 12; 13] cannot avoid the sensor data error problem [3].  
Recent research has claimed new methods to handle the instability of drone control with various approaches [14]. Some of them improved the existing algorithm, whereas others took a higher-level approach by implementing mission-control layers on top of PID controllers. Numerous studies introduced fault-tolerant control mechanisms (e.g., [15; 16; 17; 18; 19; 20; 21]). [PERSON] [17] and [PERSON] [19] introduced sensor-failure detection methods. These approaches focus on providing a fault-tolerant sensor data filter and reducing sensor errors during computation. However, it was not possible to overcome all types and degrees of sensor failures in these studies. Furthermore, they only apply to internal fails. Our approach, on the other hand, is capable of detecting both external and internal fails and emergencies, and is independent of the type and degree of failure. Previous approaches did not take situation awareness into account and lacked the ability to perceive and respond to situations outside of normal conditions.  
Our novel method takes a strategic-level approach to meet the requirements of modern drone applications. Unlike PID-based control methods, our methodology does not involve the low-level control loop. However, it takes place in the detection and diagnosis of abnormal situations and operates as a second-hand safety insurance. This is achieved by implementing temporal scoping, checking the interval of time spent maneuvering from one location to another. By operating in conjunction with PID-based control algorithms, we should be able to provide a much wider range of flight-safety insurance coverage and maintain the preservation of cutting-edge robust control technology at the same time.
# The Synergy of Renewable Energy and Desalination:
## Abbreviations  
AC Alternative Current  
ANN Artificial Neural Networks  
BESS Battery Energy Storage System  
BHS Battery Hydrogen Storage  
BW Brackish Water  
CAESS Compressed Energy Storage System  
CFD Computational Fluid Dynamics  
CNN Convolutional Neural Networks  
CPP Centrifugal Pumping Plant  
CPV-T Concentrator Photovoltaic Thermal  
CSP Concentrated Solar Power  
DC Direct Current  
DoD Depth of Discharge  
DSWM Demand Side Water Management  
DRL Deep Reinforcement Learning  
DTC Direct Torque Control  
ERD Energy Recovery Device  
ESS Energy storage Systems  
FESS Flywheel Energy Storage System  
FO Forward Osmosis  
GA Genetic Algorithms  
IFOC Indirect Field-Oriented Control  
IM Induction Motor  
INC Incremental Conductance  
LHS Latin Hypercube Sampling  
LHESS Latent Heat Energy Storage System  
LLC Life-Cycle Cost  
LPSP Loss of Power Supply Probability  
LSTM Long Short-Term Memory
# Fuzzy Logic-Based Performance Enhancement of FSO Systems Under Adverse Weather Conditions
## 1 Introduction
### Existing Works
Finally, fuzzy logic is employed in routing optimization within multi-path optical networks, where a fuzzy logic diagnostic and control subsystem helps in evaluating transmission threats and making dynamic decisions for data routing. Fuzzy logic (FL) provides an efficient method to improve the performance of the SAC-OCDMA code over FSO links. In this method, fuzzy logic enables dynamic adaptation to channel conditions by optimizing power and code allocation, while also providing a mechanism for error detection and correction, ultimately enhancing network reliability [18].
# Research on Unsupervised Classification Algorithm Based on SSVEP
## 3 Results and Analysis
### Parameter Setting and Evaluation Standard
#### 3.2.2 Harmonic Number  
SSVEP's main components are stimulation frequency and harmonics. Harmonic may give helpful information for identifying the frequency. After determining the number of subbands, we need to determine the number of harmonics. As the number of harmonics grows, the signal strength may weaken, increasing computation time while not improving the accuracy.  
As a result, we compared harmonic numbers to find the best suitable value. The number of harmonics is set to 3\(\sim\)7, and all other variables are strictly controlled. The time duration is 1.5 s with five subbands. The accuracy is shown in Figure 8.  
Figure 8: The accuracy of different harmonic numbers.  
Figure 7: Time–frequency diagram and subband decomposition.  
As shown in Figure 8, the number of four harmonics obtained is the maximum accuracy of 85.9%. After greater than four harmonics, the data accuracy will be reduced to a certain extent, and thus the number of harmonics should be 4.
## 4 Results
With BPO-ES, the maximum, minimum, and mean SINR of multi-user are 11.04 dB, 8.06 dB, and 10.03 dB. With BPO-PSO, the maximum, minimum, and mean SINR of multi-user are 10.99 dB, 8.06 dB, and 9.98 dB. The average SINR of BPO-PSO is enhanced by 27.1% compared to BPO-WIA. Moreover, compared to BPO-ES, the relative error of mean SINR of multi-user obtained by BPO-PSO is 0.51%. Furthermore, the equivalent power flux density (EPFD) proposed in ITU Radio Regulations is used to evaluate the interference mitigation performance [40]; the higher EPFD means a higher interference. The cumulative distribution function (CDF) curves of EPFD are given in Figure 13. It can be seen that the CDF curve of EPFD of BPO-PSO basically overlaps with that of BPO-ES. Furthermore, the EPFD performance of BPO-ES and BPO-PSO is better than that of BPO-WIA. For example, the probability of BPO-PSO is improved by 12.86% compared to BPO-WIA for EPFD = 103 dBW/m\({}^{2}\). Figure 11: The CCDF of SINR. Figure 12: The average SINR comparison of BPO-WIA, BPO-ES, and BPO-PSO.
## 4 Proposed SiM-RPO Algorithm
### MS Prioritization and Nodes' Coverage  
For more information and data collection, the MSs are not bounded to collect data only from their own regions' nodes. Rather, they can collect data from any region of the network. The paths determination is done for coverage of all the network. If a node finds multiple MSs in its transmission range, as shown in Figure 3, it communicates with the MS having high priority. High priority is assigned to MS\({}_{1}\) and then to MS\({}_{2}\), MS\({}_{3}\), and MS\({}_{4}\), respectively.  
Figure 2: Info packet format.  
If a node lies at a corner of the square opposite the triangular path, then it cannot communicate with the MS in the same square region. This is because the node having a 130-m transmission range does not hear the info packet for data forwarding from that MS. However, it communicates with other MSs in the adjacent square region. Because it hears the info packet for data forwarding from that MS, and all of the network is covered by the MSs for the collection of the data.
# A Novel Binary Competitive Swarm Optimizer for Power System Unit Commitment
## 3 Proposed Binary Competitive Swarm Optimizer  
The algorithm is inspired by particle swarm optimization whereas adopts a brand-new particle evolutionary scheme. The particles of PSO update their speed and locations mainly based on the \(P_{best}\) and \(g_{best}\), where \(P_{best}\) is the best position of each particle among the evolutionary process and \(g_{best}\) is the global best position along the whole iterations. However, the CSO maintains a completely different evolutionary strategy compared with PSO. Its update method is mainly realized by the mutual competition learning mechanism between particles, solving the large-scale function with good optimization performance. Additionally, this method has many applications for complex economic dispatch and unit commitment optimization problems. Inspired by the original CSO, a novel binary CSO method is proposed in this paper for solving the UC problem.
# Machine Learning-Based Garbage Detection and 3D Spatial Localization for Intelligent Robotic Grasp
## 3 Methodology  
Using deep-learning-based instance segmentation and point cloud processing, this study proposed an architecture for garbage segmentation and 3D spatial localization. As shown in Figure 2, the input data consists of a pair of registered color and depth images captured by Intel's RealSenseD415 camera. Firstly, the color image is fed into Segmentation Model \(N\), which predicts the class and mask of each garbage instance in the image. This process can be expressed as \(\left\{l_{mask}^{c}|c\in[0,C)\right\}=N(l_{color})\), where \(N\), \(l_{color}\), and \(\left\{l_{mask}^{c}|c\in[0,C)\right\}\) respectively represent the DL model, RGB color image, and predicted  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Year** & **Classes** & **Images** & **Annotated** & **Back** \\  & & & & **Instances** & **Ground** \\ \hline Wade-AI [24] & 2016 & 1 (rubbish) & 1396 & 2247 & Wild \\ UAVVaste [6] & 2021 & 1 (rubbish) & 772 & 3718 & Wild aerial \\ MJU-Waste [25] & 2020 & 1 (trash) & 2475 & 2532 & Indoor \\ Cigarette butt & 2018 & 1 (cigarette) & 2200 & 2200 & Synthetic \\ TrashCan [26] & 2020 & 8 (trash\_name) & 7212 & 6214 & Underwater \\ ZeroWaste-f [27] & 2021 & 4 & 1874 & 9463 & Conveyor \\ TACO [8] & 2020 & 60 (28 top class) & 1500 & 4783 & Diverse \\ \hline \hline \end{tabular}
\end{table}
Table 1: Publicly available garbage dataset for instance segmentation purpose.  
masks. Simultaneously, combine the camera intrinsic parameters (\(f_{x},f_{y}\), \(u_{0}\), \(v_{0}\)) and depth image to create the entire scene point cloud \(\{P_{i}|i\in[0,WH)\}\) by referring the Formula (1).  
\[\left\{\begin{array}{l}X_{i}=I_{depth}(v,u)(u-u_{0})/f_{x}\\ Y_{i}=I_{depth}(v,u)(v-v_{0})/f_{y}\quad,(u\in[0,W),v\in[0,H),i=u+vW)\\ Z_{i}=I_{depth}(v,u)\end{array}\right. \tag{1}\]  
where \(P_{i}=(X_{i},Y_{i},Z_{i})\) is one of the point in the point cloud \(\{P_{i}|i\in[0,WH)\}\), \(I_{depth}\) is depth image, \(f_{x},f_{y}\), \(u_{0}\), \(v_{0}\) is camera intrinsic parameters and \(W\), \(H\) is the width and height of RGB image respectively. In addition, the color of \(P_{i}\) is the RGB value at the \((u+v\times W)\) position in \(I_{color}\).  
Secondly, once we have achieved the \(\big{\{}I_{mask}^{c}|c\in[0,C)\big{\}}\), we can utilize it to extract the garbage point cloud from the entire scene point cloud using the Formula (2).  
\[P_{k}^{c}=P_{i},\ if(I_{mask}^{c}(v,u)=c),\ (u\in[0,W),v\in[0,H),c\in[0,C),i=u+vW) \tag{2}\]  
where \(P_{k}^{c}\) is one of the \(C\) extracted garbage point cloud \(\big{\{}P_{k}^{c}|c\in[0,C)\big{\}}\).  
Finally, a point cloud clustering algorithm, based on DBSCAN [30], was adopted to fine-tune the splited \(\big{\{}P_{k}^{c}|c\in[0,C)\big{\}}\). The main reason of this step is to remove outliers and calculate the best-fitting 3D OBB for \(\big{\{}P_{k}^{c}|c\in[0,C)\big{\}}\).
# Tracking System for a Coal Mine Drilling Robot for Low-Illumination Environments
## 3 The Proposed Method
### Visual Tracking System Framework for Drilling Robot  
The hardware and software framework of the visual tracking system for the coal mine drilling robot is shown in Figure 4. In this system, the sensor layer consists of monocular color cameras, 3D Lidar, and other sensors. We used only the camera for the acquisition of raw images in this work. The captured images can be manually framed for target selection via the operator interface or automatically framed for target selection using the target recognition algorithm. After confirming the target to be tracked, the LLCT module performs the target tracking and feeds the position back to the on-board computer and controller. This is used for subsequent robot formation tracking and other functions.  
The software system uses ROS [48] for interaction, i.e., a communication node approach. The sensor drive module, the target tracking module, the SLAM module, and the path planning modules operate independently in the lower computer system. The host system runs the visualization interface and the remote control command-sending module. Each system and module interacts by subscribing to the appropriate topic or requesting the appropriate service. The target tracking nodes can, therefore, be switched on independently when there is a demand for them.
# Fault Feature Extraction Using L-Kurtosis and Minimum Entropy-Based Signal Demodulation
## 4 Results and Discussion
Crucially, the spectrum now exhibits not only the previously observed characteristic frequency of the motor shaft and its harmonics but also the three gear meshing frequencies (GMF1, GMF2, and GMF3) corresponding to the three stages of the gearbox. Most importantly, the fault frequency of 1.6 Hz associated with the seeded wear tooth defect is now clearly visible in the spectrum. This successful identification of the fault frequency through the proposed scheme demonstrates its efficacy in extracting valuable fault information from complex gearbox vibration data. The combination of segmentation based on statistical features (L-kurtosis and entropy), passband filtering, and the Hilbert transform envelope analysis proves to be highly effective in isolating and highlighting subtle fault signatures that would otherwise be masked by noise and complex gear meshing dynamics. Figure 7: Processing of signal of helical gearbox. (**a**) Raw signal. (**b**) Envelope spectrum of raw signal. (**c**) Segmentation of the signal. (**d**) Reconstructed signal using band pass filter. (**e**) Envelope spectrum of the reconstructed signal.
# An NDN Cache Management for MEC
## 5 Performance Evaluation  
To evaluate the performance using a simulation (an ns-3 based ndnSIM), we configure a discrete event-driven simulation and utilize a name domain topology with five domains as follows:  
* A domain consists of a hierarchical name domain topology with a depth of 5.
* A root node has 5 child nodes. Except for leaf nodes, each internal node has 4 child nodes, respectively.
* A root node is a custodian node of the domain.
* Except for a root node and leaf nodes, each node is the custodian node of a hierarchical subdomain.
* A leaf node is a user's device.  
Additionally, the configuration for this simulation is as follows.  
* 16,000 objects are available, and these objects have the same popularity.
* Each node can cache up to 1000 objects.
* A node has a cache life of 50 s. Therefore, if no cache hit occurs for 50 s, the cache is deleted from the node.
* We randomly selected users' devices to generate the _Interests_ for objects that are also randomly selected.  
Figure 6 shows the results of the performance evaluation of the four schemes: [NDN], [LCD], [WAVE], and [LdC]. \"The number of requests\" means the number of _Interests_ generated by users. The average response rate is 96.7%. Figure 5(a),b shows the total number amount of _Interests_ and Data respectively which are transmitted between nodes. It shows that, in the three cases of the [LCD], [WAVE] and [LdC], the number of transmitted _Interests_ increases a small amount. However, this difference is very slight. It means that the hop counts of the transmitted Interests/Data are similar when applying these four schemes. Additionally, it means that almost the same nodes respond to the _Interests_ regardless of the applied scheme.  
Figure 6: Performance simulation result.  
Nevertheless, as shown in Figure 6c, there are huge differences among the storage overheads of these four schemes. It is expected that [LdC] improves the storage overheads for caching Data by approximately 65% compared with [LCD]/[WAVE] and by approximately 95% compared with [NDN]. Hence, Figure 6a-c shows that LdC can improve the storage overheads of nodes without the degradation of transmission performance.  
Furthermore, Figure 6d shows the comparison result of the cache utilization of these four schemes. When randomly generating 9996 _Interests_, we analyze the total amount of cached Data in the network (represented by lines) and count the number of cache hits (represented by bars). Although [LdC] caches much less Data than other schemes, [LdC] cache utilization is at least 10% to at most 50% higher.