esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The existing FFT-aided NMF-based SRM, which cannot simulate the vertical turbulent wind field, has been updated to simulate a non-stationary turbulent wind field in both horizontal and vertical directions. NMF can be utilized to decouple the EPSD matrix, the FFT technique can be then utilized to improve the simulation efficiency of the non-stationary SRM. The turbulent wind fields of the RSB are generated based on the field-measured EPSDs during typhoon Wipha. The non-stationary turbulent wind fields for towers are generated considering the coherence function with the phase angles. Relative comparisons are conducted to validate the reliability and accuracy of the generated wind fields. The following conclusions can be drawn accordingly:  
* The existed NMF-based SRM for horizontal non-stationary turbulent wind field simulation is updated to also generate the vertical non-stationary turbulent wind field. With the aid of the FFT, the simulation efficiency is enhanced.
* In the NMF-based SRM, the phase angles can be considered in the coherence functions. The complex EPSD matrix can be transformed into the real modulus matrix after the phase was separated. With this treatment, the efficiency of decomposition can be improved.
* The estimated auto-/cross-correlation functions generally fit well with the target ones, validating the effectiveness and reliability of the non-stationary turbulent wind fields generated. The presented  
Figure 11: Comparison of estimated correlation functions and target ones (5000 samples).  
wind field simulation method can provide research basis for non-stationary buffeting analysis of the RSB.  
[PERSON] performed the numerical simulation; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the manuscript; all authors reviewed the manuscript. Correspondence should be addressed to [PERSON].  
The research was supported by the National Natural Science Foundation of China (No. 51722804), the National Ten Thousand Talent Program for Young Top-notch Talents (No. W03070080), the Key R & D Plan of Jiangsu Province (BE2018120) and the China Scholarship Council (No. 201906090074).  
The authors declared no potential conflicts of interest.
# Instant Center Identification of Single-Loop Multi-DOF Planar Linkage Using Virtual Link
## 5 Instant Center Identification of the Single-Loop Planar Linkages
### Single-Loop Five-Bar Planar Linkage
#### 5.1.2 Single-Loop Five-Bar Planar Linkage with Prismatic Joints
The single-loop six-bar planar linkage with only rotation joints becomes the virtual seven-bar linkage by adding the virtual links 2', 5' and 7' in Figure 15, which contains two virtual five-bar loops using Criterion 1. The instant centers are listed in Table 6 after this change. In Figure 15, the coupled loop chain 2'-7'-5' is the common part of two virtual loop 1-2'-7'-5'-6 and 2'-3-4'-5'-7', namely, the instant center \(I_{25}\) formed by the common part of two virtual loop, is the common instant center. Using Criterion 2, the wanted instant centers occur if the common instant centers of the two loops coincide. It is clear that the single loop 1-2'-7'-5'-6 is just the single-loop five-bar planar linkage discussed in Section 5.1 in which the instant centers had been recognized in Figure 8. Therefore, the instant center I\({}_{25}\)' is known and can be used for the identification of the instant centers of the virtual loop 3-4-2'-7'-5' when the loop is taken as a single loop. In this way, the instant centers I\({}_{15}\), I\({}_{17}\), I\({}_{25}\), I\({}_{26}\), I\({}_{67}\), I\({}_{35}\), I\({}_{37}\), I\({}_{24}\), and I\({}_{47}\) are immediately obtained, and the instant center I\({}_{13}\) is figured out according to the instant centers I\({}_{12}\), I\({}_{23}\), I\({}_{17}\), and I\({}_{37}\) using Aronhold-Kennedy theorem. As the same to the instant center I\({}_{13}\), the instant centers I\({}_{14}\), I\({}_{36}\), and I\({}_{46}\) are solved
## 5 Method Design
### Trajectory Similarity Search
#### 5.3.2 Scheduling Algorithm  
Given a target trajectory, firstly, PMMTss extracts candidate points from the multiple-layer index for each point in the target trajectory. The process of extracting candidate points is shown in Figure 4. Three points, including \(P_{i}=(t_{1},s_{1},O)\), \(P_{j}=(t_{2},s_{1},O)\) and \(P_{k}=(t_{3},s_{1},O)\), are items in SPCL, and all of them have the same value of the \(s\) and \(\sigma\) attribute. According to the definition of point similarity in Definition 3, we extract a set of trajectory points from the index that is similar to each target point. The extraction is divided into two stages, the filtering and refinement stages. In the filtering stage, a time range is calculated by the target point and the predefined interval, through which the interval offset in the index is located. Time range can fall in one or multiple time intervals, with the time range of \(P_{i}\) falling in one time interval and the time range of \(P_{j}\) and \(P_{k}\) falling in two time intervals. In the refinement stage, the candidate points in the range \([t_{i}-\Delta t,t_{i}+\Delta t]\) are extracted from the located time interval.  
The workflow of the scheduler is illustrated in Figure 5, which shows an iterative search process. The input trajectory is SPCL, it has \(n\) points. The number of search rounds is \(k\), in particular, \(k=\lceil\log_{2}n\rceil\). A group of tasks are being executed in a parallel way. Each task is represented by three attributes: an original point, an arrow and a destination point. The purpose of each task is to merge different candidate trajectory segments according to smart card identities. In the first round, each task extracts two candidate point sets from the index and produces a set of candidate trajectory segments. In the intermediate rounds, candidate trajectory segments are merged and filtered, generating new candidate trajectory segments for the next round. In the last round, candidate trajectory segments with the same identity are combined, forming the final trajectory set.  
The details of the scheduler are described in Algorithm 1.  
Figure 4: Extracting candidate points from index.  
Figure 5: The parallel multi-way merging-based trajectory similar search algorithm.  
```
Input:CSet contains n sets of candidate trajectory points. Output: resList is a list of generated trajectories.
1 procedureMulti-wayMerging (CSet)
2 taskQueue\(\leftarrow\varnothing\)
3 resList\(\leftarrow\varnothing\)
4 forpointsSet\(\in\)CSetdo
5 initialize a task by\(pointSet\to taskQueue\)
6 endfor
7 whilesize\((taskQueue)\geq 2\)do
8\(loopCount\leftarrow\)size\((taskQueue)/2\)
9 for\(i=1\to i=loopCount\)do
10\((task1,task2)\gets poll(taskQueue)\)
11 new MergeThread\((task1,\)\(task2,\)\(taskQueue)\)
12 endfor
13 synchronize the threads;
14 endwhile
15 resList\(\leftarrow\)getTr\((poll(taskQueue))\)
16 endprocedure
```  
**Algorithm 2** Merging and Filtering for Candidate Trajectory Segments.
# Real-Time Conveyor Belt Deviation Detection Algorithm Based on Multi-Scale Feature Fusion Network
## 4 Discussion
### Subjective Evaluation Results  
As seen from Figure 8, it is obvious that the conveyor edge detected by FCN-8s is a series of points, which can roughly describe the conveyor edge. However, there are many breakpoints and discontinuity, and the detection edge is blurred, which cannot meet the requirements of the task  
Figure 8: Comparison of experimental results in three methods. From the third to the sixth column: FCNs, proposed deep network, UNet and Deeplab v3 with post-processing. The proposed method shows superior performance, particularly with its ability to correctly segment the conveyor edge with almost no breakpoint as compared to FCNs and Deeplab v3. Besides, the proposed work gets finer results when compared to UNet.  
Figure 9: The performance on the test dataset with different weight coefficients. (**a**,**b**) represent the influence of weight coefficients on pixel accuracy and testing accuracy, respectively.  
of conveyor belt edge detection. Besides, the original UNet result is rough with one end thick and the other thin, and the problem still exists after image erosion, which is that we cannot achieve the detection effect. In addition, the structure of Deeplab v3 is complex with a long training time, while there are many breakpoints and rough edges in the result before post-processing. After linear fitting, the breakpoint problem is solved, but the rough edges still exist. We can see intuitively from the figure that in our method result, the conveyor edge is finer, the breakpoints are fewer and the boundary is clearer, which can reflect the conveyor belt edge well.  
Although UNet is helpful for precise localization in image segmentation after many times of up-sampling by bilinear interpolation and successive symmetric fusion, it cannot fully learn edge features because the positive and negative samples are imbalanced in the edge detection task in this paper, and the high-dimensional features are eliminated in the process of upward fusion layer by layer, which leads to rough detection results. Deeplab v3 is generally used in complex image segmentation tasks with a pre-training model based on the ResNet network and various scales of atrous convolution getting different sizes of field. The fusion of the multi-scale atrous convolution and the output after global average pooling is helpful for solving the problem of missing image edge details. However, for the simple edge detection dataset in this task, Deeplab v3 is too complex, and atrous convolution cannot effectively extracts features for the conveyor belt occlusion part. As a result, no good experimental results can be obtained.
## 4 Analysis of the Simulation and Experimental Results
### Compound Jamming Recognition Performance Based on MAML and Time-Frequency Analysis  
In this section, we will demonstrate the results of compound jamming recognition based on MAML. The network used in this article is Resnet18. First, we trained the MAML network using eight kinds of single jamming signals and set up 8000 epochs to complete the training process. Then, on the basis of four kinds of compound jamming, we completed the fine-tuning of the MAML network. Finally, we tested the recognition accuracy of the MAML network on four kinds of complex jamming.  
As shown in Figure 11, by observing the overall recognition accuracy curve of the compound jamming recognition algorithm based on MAML in this article, we cannot but notice that compared with the model using a single jamming signal and the Omniglot dataset, the model using only eight single jamming signals does not have an ideal recognition effect on the compound jamming signal. This is because there are too few types of tasks in the meta-training stage. The lack of task diversity in the meta-training results in the poor generalization ability of the model on new tasks. With the increase in JNR, the overall recognition accuracy of the compound jamming presented an increasing trend. Under the conditions of high JNR, the overall recognition accuracy was close to 100%, which guaranteed the effectiveness of the proposed algorithm. At the same time, the recognition accuracy of four kinds of compound jamming signals is shown in the figure. With the improvement of JNR, the recognition accuracy of each type of compound jamming signal shows an increasing trend. When JNR = \(-8\) dB, the recognition accuracy of each type of compound jamming reached 90%. After JNR = 0 dB, the recognition accuracy of the four compound jamming signals was close to 100%, which further ensured the performance of the compound jamming recognition algorithm based on MAML. In this article, we counted the number of parameters and Flops of the network, in which Params is 11.7 M and Flops is 1.82 G.
# Origin and Composition of Exosomes as Crucial Factors in Designing Drug Delivery Systems
## 3 Methods for Exosomes Characterization
The main difference is the inside surface of the chip, which can be functionalized in various ways, e.g., by coating, multi-layer depositing, electrodeposition, and etching [30; 31; 32; 33]. For different microfluidic characterization methods, different types of microchips are fabricated, including immunochips, magnetic, and electrochemical chips [34; 35; 31]. Thus far, for exosomes research, fluorescence correlation microscopy (FCM), surface plasmon resonance (SPR), nuclear magnetic resonance (NMR and microNMR) methods, and atomic force microscopy (AFM) were used [6; 29; 36; 37]. These methods are based on antibody-mediated detection and further quantification of exosomes and exosomal proteins either stained by fluorescence dyes (FCM), chip-membrane-bound and SPR imaged, using the NMR, or scanning mica-adhered exosomes (AFM) [6; 37; 38]. These methods are attracting increasing attention but still require further development and validation. Other authors report the use of liquid chromatography tandem mass spectrometry (LC-MS/MS) and flow cytometry for further proteins identification and exosomes characterization [26]. Exosomes and their proteins can also be detected with colorimetry (labeled antibodies/ELISA), direct fluorescence staining (DiO dye), changes in electrochemical properties, and optical special effects methods [29; 35; 38; 39]. For results assessment, there is also a need for additional equipment, such as a plate-reader or fluorescence microscope [30]. Flow field fractionation (FFF) is both a characterization (in conjunction with detectors, e.g., UV) and isolation technique based on differences in flow speed of different size particles on microchips. Smaller particles will move faster, while larger ones will be more restrained and travel slower [40; 41]. This method is used for assessing amount and size in a superior manner to NTA in the case of distinguishing subtle populations of exosomes [42].
## 3 Imaging in the Follow-Up of Treated HCC
### Post-Treatment Imaging Features of Tumor Viability  
According to LI-RADS, tumor viability is defined as a nodular or thick concentric lesion that either presents contrast wash-in, wash-out, or contrast dynamics similar to pretherapeutic imaging examinations [12] However, viable tumor tissue after treatment can take on different forms (Figure 10).  
Figure 9: MRI scan: (**a**)—T2 weighted with fat suppression; (**b**)—subtraction; (**c**)—T1 weighted with fat suppression after contrast injection, in the portal venous phase. Transient hyperemia (dotted arrow in (**a**) and (**b**)) surrounding an HCC tumor treated using DEB-TACE. The hyperemia appears as a halo with hazy contour and discrete high signal intensity on T2 WI, arterial phase wash-in, but without portal venous phase wash-out (differentiating it from tumor tissue).  
However, there are several distinctive features and recurrence profiles of interest to radiologists and clinicians alike. Hypovascular recurrences can be identified after treatment and their imaging features may be different than the original more vascularized primary tumor [156]. While it is commonly accepted that hypervascularization of a recurrence is an additional risk factor and that hypervascular primary nodules are associated with a poorer  
Figure 10: MRI axial images obtained by subtracting a T1 fat saturated image without contrast enhancement from a T1 fat saturated image with contrast enhancement in the arterial phase (**a–g**). Persistent/recurrent tumor tissue is hypervascular and can have different shapes: It can be similar to pretreatment (failure of treatment, arrow in (**a**)); Peripheral, inside the treated nodule (incomplete treatment, arrow in (**b**)); Crescent-shaped (arrow in (**c**)); Asymmetric ring (arrow in (**d**)); Nodular-shaped (arrow in (**e**)); Multinodular-shaped (arrows in (**f**)); or wedge-shaped (arrow in (**g**)) with vascular invasion (chevron in (**g**)).  
prognosis, it is uncertain how to interpret and manage hypovascular recurrences, especially due to their rarity [157; 158]. Another notable HCC subtype is nodule-in-nodule, which is regarded as a morphological marker of early HCC dedifferentiation [159; 160]. While, apparently, the prognosis of patients with this unique subtype is better than the \"conventional\" appearance, knowledge of the imaging features and aspects at post-therapeutic follow-up is essential for correctly identifying tumor recurrence [5; 161].
## 4 Experiments and Analysis of Results  
For the assessment of the algorithm's efficacy and to highlight its advancements over current methodologies, a total of 21,338 8-bit grayscale images were selected from three image datasets: BOSSBase [28], BOWS-2 [29], and UCID [30], for testing and comparison in experiments. The primary set of six test images utilized in these experiments are presented in Figure 5. The evaluation of the algorithm's performance in this research focuses on four crucial aspects: reversibility, data embedding efficiency, data expansion, and security.  
Figure 5: Six commonly used images in experiments. (**a**) Airplane. (**b**) Baboon. (**c**) Jetplane. (**d**) Lena. (**e**) Man. (**f**) Tiffany.  
The experimental setup utilized an Intel(r) Core(tm) i5-9300H CPU (Lenovo, Beijing, China) operating at 2.40 GHz, equipped with 8 GB of memory, running on the Windows 11 platform, and utilizing the MATLAB R2021b simulation environment.
## 3 Results and Discussion  
### Geometry Optimization  
The geometrical parameters of AFB1 are described in Table 1. These parameters were calculated using two methodologies: B3 LYP/6-311G(d,p) and M06-2X/6-311G(d,p) (see the Supplementary Information). Afterwards, the resulting bond distances and angles were used to evaluate which of the two approximations adjusted the best to the experimental data. Table 1 shows correlations up to 0.99 for bond distances in the gas and solvent phases. For bond angles, the correlations were better in the gas phase than in the solvent phase, with values up to 0.97 in the gas phase. Based on these results, it was decided to work with the two functionals in combination with the same basis set, as both methodologies presented a correct description of the molecular structure of aflat. The absence of imaginary frequencies confirmed the global minima in the potential energy surface. The optimized structures of the aflat were used for the remaining calculations. Figure 2 shows the optimized structures of aflat calculated with B3 LYP/6-311G(d,p) in the gas phase.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{3}{c}{**DFT Functional/Basis set 6-311G(d,p)**} & \multirow{2}{*}{**Experimental**} \\ \cline{2-2} \cline{4-5}  & \multicolumn{2}{c}{**Gas**} & \multicolumn{2}{c}{**Solvent (water)**} & \multicolumn{1}{c}{**Reference (a)**} \\ \cline{2-5}  & **B3 LYP** & **M06-2X** & **B3 LYP** & **M06-2X** & \\ \hline Bond Distance & & & & & \\ (Å) & & & & & \\ 20C-22C & 1.32 & 1.32 & 1.32 & 1.32 & 1.30 \\ 20C-12C & 1.51 & 1.51 & 1.51 & 1.51 & 1.50 \\ 12C-15C & 1.56 & 1.55 & 1.55 & 1.55 & 1.55 \\ 15C-30 & 1.41 & 1.40 & 1.41 & 1.40 & 1.41 \\ 30-22C & 1.38 & 1.37 & 1.38 & 1.37 & 1.38 \\ 22C-31H & 1.07 & 1.07 & 1.07 & 1.07 & 1.04 \\ 20C-28H & 1.07 & 1.07 & 1.07 & 1.07 & 1.06 \\ 12C-23H & 1.09 & 1.09 & 1.09 & 1.09 & 1.01 \\ Correlation (r) & 0.9917 & 0.9907 & 0.9926 & 0.9918 & \\ \hline Bond Angles & & & & & \\ (°) & & & & & \\ 22C-3O-15C & 107.5 & 107.7 & 107.5 & 107.7 & 107.2 \\ 3O-15C-12C & 107.5 & 107.4 & 107.5 & 107.4 & 107.5 \\ 15C-12C-20C & 101.0 & 101.0 & 101.0 & 101.0 & 101.0 \\ 31H-22C-20C & 130.8 & 130.6 & 130.9 & 130.7 & 132.0 \\ 28H-20C-22C & 126.9 & 127.1 & 124.7 & 126.6 & 129.0 \\ 31H-22C-30 & 114.0 & 114.2 & 114.2 & 114.4 & 113.0 \\ 28H-20C-12C & 124.5 & 124.5 & 124.7 & 124.8 & 122.0 \\ 23H-12C-15C & 113.1 & 113.5 & 112.7 & 113.0 & 114.0 \\ 23H-12C-20C & 112.5 & 112.9 & 112.7 & 113.1 & 113.0 \\ Correlation (r) & 0.9708 & 0.9751 & 0.9525 & 0.9688 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometrical parameters for AFB1 calculated in the gas and solvent phases using the IEFPCM solvation model and the X-ray diffraction data reported for the same compound.  
Figure 2: Optimized molecular structure of aflat calculated with B3 LYP/6-311G(d,p) in the gas phase.
# CubeSat-Based Observations of Lunar Ice Water Using a 183 GHz Horn Antenna: Design and Optimization
###### Abstract  
Technological advancements have revolutionized the space industry, facilitating deep space exploration using CubeSats. One objective is to locate potential life-support elements, such as water, on extraterrestrial planets. Water possesses a distinct spectral signature at 183 GHz, useful in remote sensing and environmental monitoring applications. Detecting this signature provides crucial information about water and ice presence and distribution on celestial bodies, aiding future exploration and colonization efforts. Mostly in space remote sensing uses corrugated horn antennae due to high gain and radiation patterns but fabrication of corrugated antenna is very challenging or even impossible in some cases. To ease this challenge, in our research we propose ideas to transform a corrugated horn antenna into a smooth-walled design by using MATLAB Cubic smoothing Splines algorithms. We compare simulation results between smooth-walled and corrugated antennas, and we can see some improvements in insertion losses, Voltage Standing Wave ratio (VSWR), and gain. We also manufactured this 183 GHz antenna using a commercially available 3D printer by utilizing Acrylonitrile Butadiene Styrene (ABS) material. The antenna surface was then coated with a thin layer of copper using conductive paint. In the end, we practically evaluate smooth-walled antenna functionality and compare it with the theriacal results. Validating the antenna's functionality proposes a cost-effective and accessible production method to be used in a CubeSat engineering model or university students' project.  
corrugated horn antenna; smooth-walled horn antenna; CubeSat THz remote sensing; outer space exploration; THz antenna optimization; smooth spline curve fitting; spectral signature; lunar ice water +
Footnote †: journal: Applied  
cseiences