esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms
## 6 Application of Multi-Agent Deep Reinforcement Learning: State of the Art
### [PERSON]'s SADRL Approach for a Hierarchical Multi-Agent Environment
#### 6.4.2. [PERSON]'s SADRL Approach with Actors and Critics  
In [78], [PERSON] et al. embedded this approach in distributed agents (i.e., D2D links) that select their resources (i.e., channels with transmission opportunities) (A.1) based on channel quality and interference levels in order to meet the co-channel interference constraint. The proposed approach addresses the challenges of ultra-densification (C.1)caused by a large number of UEs, and high dynamicity (C.2) due to the dynamic condition of the operating channels.  
Each D2D link \(i\) represents state \(s_{t}^{i}\) with historical knowledge, particularly selected actions in the past. The action \(a_{t}^{i}\) represents the selected time frames for transmission. The reward \(r_{t+1}^{i}(s_{t+1}^{i})\) represents the number of sub-time frames that can be used by the agent \(i\), and the penalty when a collision occurs. Each D2D link selects action independently without knowing the traffic load condition of the network, and so it is partially observable in nature. The SADRL with actor and critic networks approach has been shown to increase throughput (P.3), while ensuring a fair coexistence with various networks in unlicensed channels.
# Research on CNN-BiLSTM Fall Detection Algorithm Based on Improved Attention Mechanism
## 2 Related Work
[31] proposed the CBAM-CNN-LSTM model, and experiments proved that the collaboration between the LSTM, CNN and CBAM can enhance the modeling ability and improve the prediction accuracy. The CBAM-CNN-LSTM combines the advantages of the CNN and LSTM: the CNN is used to extract deep features of data, the LSTM is used to analyze temporal dependencies between data, and the CBAM can extract meaningful content and important information from data. Compared with the traditional CNN and LSTM models, this model is more reliable. Therefore, in view of the problems of the CNN and LSTM deep learning networks mentioned above, a CBAM-IAM-CNN-BiLSTM fall detection algorithm, based on IMU combined with accelerometer and gyroscope to collect human motion data, is proposed in this paper in order to make full use of the data and features of effective fall detection. In this algorithm, the spatial features of the data are extracted by the CNN, the temporal features are extracted by the LSTM, and the features are fused. The CBAM is added to the CNN as an attention mechanism, and one-dimensional convolution is introduced instead of the dense layer in the CBAM's channel attention module to aggregate the information between channels, which can reduce redundant calculations and better extract features so as to improve the robustness and stability of the fall detection algorithm in complex environments.
# BEAC-Net: Boundary-Enhanced Adaptive Context Network for Optic Disk and Optic Cup Segmentation
## 5 Experiments
### Datasets  
RIM-ONE-v3 dataset: The RIM-ONE dataset consists of three versions with image numbers 169, 455, and 159. In this paper, we use the dataset with five ophthalmologist annotations in the dataset. We used the division method of [PERSON] et al. [31] to obtain the training and test images in the dataset.  
DRISHTI-GS dataset: The Drishti-GS dataset contains 101 retinal images and mask annotations for the optical discs and optical cups used to detect glaucoma. The dataset has been divided into 50 training and 51 testing images. All the images have been annotated by four ophthalmologists.  
66 Vision-Tech Dataset:The 66 Vision-Tech dataset contains a total of 160 fundus images, 140 for training and 20 for testing. The camera captured the original image size of \(2000\times 1600\) pixels, and due to the strong light setting and eyeless design, the overall color of the image was yellowish and slightly foggy. Therefore, the original image needed to be pre-processed, as shown in Figure 7.  
1. Data annotation. Two ophthalmologists used Labelme to annotate 160 fundus images of the optic disk (OD) and optic cup (OC). In this paper, 140 were randomly selected as the training set and 20 were randomly selected as the test set using the division method following [31].
2. Data Augmentation. The fundus images are pre-processed for image defogging and contrast enhancement using the Automatic Color Enhancement (ACE) [32] algorithm, which corrects the final pixel values by calculating the degree of lightness and darkness of the target pixels and the surrounding pixels and their relationship to achieve contrast adjustment of the image. Due to the small number of images, in the experiment, we use random vertical flip, random horizontal flip, and random diagonal flip to every image [33], and one image is expanded to \(2\times 2\times 2=8\) images by expanding the dataset.
3. Cropping ROI [34]. A small percentage of the OD area in the fundus images is used to obtain more accurate boundary segmentation of the optic disk and reduce the influence of background noise regions on the segmentation results. The center of the OD is detected by the brightest point in the fundus image as the center of the OD. Then, it performs an external expansion twice the radius of the cropped OD region  
Figure 6: Fundus images taken by different fundus cameras.  
to obtain the full area of the OD and OC, which is sent to the BEAC-Net model for training and testing. In particular, the cropping images reduce the computing load on the computer and improve the computing efficiency. The labels are cropped into the corresponding ROI simultaneously.
## 2 Theoretical Model and Methods  
The permittivity of graphene can be obtained by using \(\varepsilon_{\text{g}}=1+i\sigma_{\text{g}}/(\varepsilon_{0}\omega\Delta)\)[25; 45], where \(\Delta=0.33\) nm is the thickness of the graphene layer, \(\omega\) is the frequency, \(\varepsilon_{0}\) is the permittivity in free space, and \(\sigma_{\text{g}}\) is the surface conductivity of graphene. Under the random-phase approximation, the dynamic optical response of graphene can be obtained from [PERSON]'s formula [45; 46; 47]. In the terahertz range, the intraband transition of electrons dominates [48; 49], and then \(\sigma_{\text{g}}\) could be approximated as  
\[\sigma_{g}=\frac{2 ie^{2}k_{B}T}{\pi\hbar^{2}(\omega+i/\tau)}\ln[2\cosh(\frac{u _{c}}{2k_{B}T})] \tag{1}\]  
where \(\tau=0.5\) ps, \(T=300\) K, \(u_{c}\) is the chemical potential, \(\hbar\) is the reduced [PERSON]'s constant, \(k_{B}\) is the Boltzmann constant, and \(e=1.6\times 10^{-19}\) C, and \(\varepsilon_{2}=1\).  
For the proposed scheme shown in Figure 1a, we assume that the lowest order graphene plasmon (GP) mode transmits along \(z\)-direction with a time dependence of \(e^{-|z|t}\). Then the transversal mode fields (E and H) satisfy a two-dimensional wave equation [20],  
\[\left[\
abla_{t}^{2}+k_{0}^{2}(\varepsilon\mu-n_{eff}^{2})\right]\left\{ \begin{array}{c}E(x,y)\\ H(x,y)\end{array}\right\}=0 \tag{2}\]  
where \(\varepsilon\) and \(\mu\) are the permittivity and permeability. Since the eigenvalue equation here is very complicated, we adopt the finite element method software COMSOL Multiphysics to calculate the complex effective mode index, \(n_{\text{eff}}\). \(\beta=k_{0}n_{\text{eff}}\) is the complex propagation constant with \(k_{0}=2\pi/\lambda_{0}\), where \(\lambda_{0}\) is the wavelength in the free space. The propagation length is defined as \(L_{\text{P}}=1/\text{Im}(\beta)\). The normalized mode area is defined as \(A_{\text{eff}}/A_{0}\), where \(A_{0}=\lambda^{2}{}_{0}/4\) is the diffraction-limited mode area, and the effective mode area \(A_{\text{eff}}\) is obtained by [50; 51; 52],\[A_{\text{eff}}=\frac{\iint W(\text{r})d^{2}\text{r}}{\text{max}\{W(\text{r})\}}\, \tag{3}\]  
where \(W(\text{r})\) is the electromagnetic energy density and is as follows:  
\[W(\text{r})=\frac{1}{2}\bigg{\{}\frac{d[\varepsilon(\text{r})\omega]}{d\omega} |\text{E}(\text{r})|^{2}+\mu_{0}|\text{H}(\text{r})|^{2}\bigg{\}}. \tag{4}\]  
We also adopted a figure of merit (_FoM_) [39; 53] to assess the propagation length and effective mode area, which is defined as _FoM_ = Re(\(\beta\))/Im(\(\beta\)).
## 4 Materials and Methods
### Vector Construction  
We used SnapGene 4.3.6 for vector design. The backbone of the vector is PBI221, purchased from Wuhan Miaolino Biotechnology Co., Ltd., Wuhan, China ([[http://www.miaolingbio.com/](http://www.miaolingbio.com/)]([http://www.miaolingbio.com/](http://www.miaolingbio.com/)), accessed on 1 June 2023).  
The process for obtaining the _BKT_ gene fragment is described below. _Chlamydomonas reinhardtii_\(\beta\)-Carotenoid ketogenese (_CrBKT_) gene information was obtained from both the NCBI (National Center for Biotechnology Information, accessed on 16 January 2021.) database and the JGI (Joint Genome Institute) database. The JGI gene ID of _CrBKT_ is cre04.g215000, and the NCBI gene ID of _CrBKT_ is CHLRE_04g215000v5. Additionally, the _CrBKT_ gene fragment added into PBI221 was derived from the CDS of _CrBKT_ after codon optimization. Then, 'Run blast (standard nucleotide blast)' was performed in NCBI  
Figure 15: Colors of dried cells of the WT, C18 and P1. For each picture, 5 mL of _Chlamydomonas reinhardtii_ cells were filtered through 0.22 \(\upmu\)m membrane and dried.  
using _CrBKT_ gene mRNA (NCBI reference sequence: xm_001698647.1) with the program for highly simple sequences ('Megablast'), clicking on 'Blast', to start the alignment. The _PrBKT_ gene of the yeast _Phaffia rhodozyma_ with the highest homology (NCBI reference sequence: LN483254.1) was selected for the _PrBKT_ gene fragment after codon optimization. Codon optimization was performed using the website [[https://www.novoprolabs.com/tools/codon-optimization](https://www.novoprolabs.com/tools/codon-optimization)]([https://www.novoprolabs.com/tools/codon-optimization](https://www.novoprolabs.com/tools/codon-optimization)), accessed on 18 January 2021. The sequence type was set to DNA and the optimization of species was set to _Chlamydomonas reinhardtii_.  
The paromomycin resistance gene (PARA) sequence was maintained in _E. coli_ by our group. The PARA fragment was added at the region between the EGFP (enhanced green fluorescent protein) and terminator, and a single cutting enzyme in the backbone was selected as the junction point. After codon optimization, the _CrBKT_ and _PrBKT_ gene fragments were added at the region between the promoter and EGFP in the PBI221, respectively. Then, we obtained two designed vectors, PBI221-_CrBKT_ and PBI221-_PrBKT_. Information of the two vectors was sent to Nanjing Genscript Bio Technology Co., Ltd., Nanjing, China, where the PARA, _CrBKT_ and _PrBKT_ gene fragments were directly synthesized, enzymatically linked and verified via sequencing based on the designed digestion sites in the sent PBI221. Sequences of Vectors PBI221-_CrBKT_ and PBI221-_PrBKT_ are shown in Supplemental Material S1. Plasmid extraction involved the use of Tangen ([[https://www.tiangen.com/](https://www.tiangen.com/)]([https://www.tiangen.com/](https://www.tiangen.com/))) high-purity plasmid Mini Extraction Kit (DP104).
## 4 Validation of New AI-FEM Multiple Cracks Coalescence Criteria
### Results
#### 4.4.2 Comparison of FCG Results from AI-FEM, Experiment, and Codes
Figure 9c compares the normalized crack area (\(A_{c}/A_{plate}\)) as a function of cycles using the FCG results from AI-FEM and the ASME Section XI methodology. The AI-FEM results are represented by red open circles, and the ASME Section XI results by blue open circles. As shown in Figure 9c, applying the coalescence criteria presented in ASME Section XI idealizes the crack, leading to the assumption of non-crack areas as crack. Therefore, a difference in crack area occurs from the coalescence point, and the area difference increases with cycles. This trend is also observed in Figure 9d, which compares the crack shapes over cycles. In the figure, the AI-FEM results are depicted with dashed lines, and the ASME Section XI results with solid lines. For the same cycle (color), the ASME Section XI results show earlier crack coalescence (195,000 cycles) and a larger crack at the same cycle. The same results were observed in Table 6, where a quantitative comparison was made between natural crack growth simulated by AI-FEM and FCG results using the ASME Section XI \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Coalesced** & **Normalized** & **Normalized** \\  & **Cycles** & **Crack Area** & **Crack Depth** \\  & **(Cycles)** & **(\(A_{c}/A_{plate}\))** & **(\(a/t\))** \\ \hline AI-FEM & 220,000 & 0.23 & 0.50 \\ ASME Section XI & 195,000 & 0.30 & 0.64 \\ Maximum difference & 16\% & 30\% & 22\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Quantitative comparison of AI-FEM and ASME Section XI FCG results in a flat plate with multiple cracks. coalescence criteria. As shown in Table 6, applying the coalescence criteria of ASME Section XI resulted in more conservative outcomes compared to those obtained using the newly proposed coalescence criteria in all aspects. The coalescence cycles differed by 16%, the normalized crack area by 30%, and the maximum crack depth by approximately 22%. These results indicate that applying the ASME Section XI crack coalescence criteria may result in a conservative crack evaluation. Investigation into Conservatism of Codes Using Multiple Crack Coalescence Criteria Based on Plastic Zone Size
# Simple HPLC-PDA Analysis to Determine Illegal Synthetic Dyes in Herbal Medicines
## 2 Materials and Methods  
### Selection of Herbal Medicines and Synthetic Dyes  
The Chinese government suggests a list of synthetic pigments and herbal medicines to investigate color adulteration [6]. This study chose three herbal medicines with the color red for developing the HPLC method because of their potential to be adulterated with synthetic dyes due to their color. Even though Cornus fruit and Hawthorn fruit are not listed as target herbal medicines by the Chinese government, a large amount of Cornus fruit and Hawthorn fruit consumed as dietary supplements in our country have been mainly imported from China. This study included them as target herbal medicines for developing an HPLC analysis. The Chinese government has monitored several herbal medicines to discern the adulteration of synthetic pigments such as Tartrazine, Sunset yellow, Azorubin, Metanil yellow, Acid red 73, New Coccine, Erythrosine, Auramine O, and Orange II using HPLC-UV analysis. The target synthetic colorants depend on which herbal medicines are examined. The HPLC-UV analysis can quantify six synthetic colorants at once. However, this study selected ten synthetic colorants (Table 1) for establishing an HPLC-PDA analysis to determine illegal synthetic colorants in three herbal medicines by adding Amaranth, which is known as a hazardous material.
## 3 Workflow Description
### Automatic Detection of the Radiator Channels and Volumetric Flow Rate Monitoring  
After generating the computational grid, the radiator ducts are automatically identified to monitor the volumetric flow rate in each of them during the simulation. This operation is performed by using the OpenFOAM command _topoSet_, through which T-WorkFlow defines a control surface for each radiant channel. In the corresponding dictionary, it is necessary to specify the coordinates of a point belonging to each control surface and its normal. The latter is known due to information obtained via the FreeCAD macro, cf. Section 3.1. To identify each channel, the software first creates a vector connecting the barycenter of the two outermost ducts. Subsequently, along this vector, a number of evenly spaced points  
Figure 10: Surface mesh at the radiator inlet and cross-sectional detail of the volume mesh of the flow domain near the tanks.  
equal to 10 times the (known) number of radiant channels are defined. For each of these points, a control surface faceZoneSet of OpenFOAM is created using the _topoSet_ command. If the point used to define the control surface is actually inside a channel, the corresponding surface file consisting of adjacent mesh faces will be written. However, if the point provided to faceZoneSet is in the region between two channels, the nearest enclosed surface to that point will be considered to create the control surface. As this procedure will produce more than a control surface for each radiant duct, the software compares the files related to these surfaces in the constant/polyMesh/sets and retains only one copy for each group. This results in a single file for each channel, ensuring that the corresponding control surfaces can be effectively used to monitor the volumetric flow rate in the channels.  
After defining the surfaces through which the flow rate of each channel will be evaluated, the method used by OpenFOAM to compute this quantity during the simulation needs to be set. For this purpose, the _surfaceFieldValue_ function from the OpenFOAM library _lightedFunctionObjects_ is used. This function calculates the sum of the volume fluxes across the faces of a specified mesh region. In particular, T-WorkFlow automatically appends the following block of instructions at the end of the controlDict file for each captured control surface, as well as for the inlet and outlet surfaces  
flux_pipe_{i + 1} { type surfaceFieldValue;  libs (\"/opt/openfoam10 /platforms/linux64 GccDPInt32 Opt/lib/libfieldFunctionObjects.so\");  log  true;  writeControl runTime;  writeInterval {delta};  writeFields true;  surfaceFormat none;  regionType faceZone;  name controlZone{faces[i]};  operation orientedSum;  fields (phi); } where flux_pipe_{i + 1} is the volumetric flow rate through the single duct; delta is the interval, measured in solver iterations, between successive outputs of the flow rate, as specified by the user (cf. Section 3.1); and controlZone{faces[i]} is the control surface related to that specific channel.
# Horizontal Polarized DC Grounded Omnidirectional Antenna for UAV Ground Control Station
## 4 Simulation Verification
### Field Verification  
The field simulations were performed with the help of CST Microwave Studio software. The electric and magnetic fields' cross-sectional views through the SMA connector are shown Figure 5,c. The cross-sectional views of the electric and magnetic fields through  
Figure 4: Effect on S\({}_{11}\) (**a**) by changing the feed pin diameter and (**b**) by changing the slot length.  
Figure 3: Azimuthal gain vs. the number of slots along the antenna’s circumferential axis.  
the pins that connect the inner coaxial cylinder to the slot apertures in the outer coaxial cylinder are shown in Figure 5b,d. At the input SMA connector of the antenna, the electric field is spread radially outward (TEM mode). At the edge of the oversized coaxial antenna assembly near the SMA connector, the electric field is again radially outward as that of the TEM mode. This demonstrates that the SMA connector's inner pin's adjusted length effectively converted the connector TEM mode into the oversized coaxial assembly TEM mode, as shown in Figure 5a. As this mode travels toward the pin connected to the slot, the field circulates the slot area. All four slots have the same circulation pattern, which indicates that they are all in phase, as seen in Figure 5b. The electric field steadily travels from the slot middle toward the slot end and thereby emits a horizontally polarized field, as seen in Figure 5a,b. The directional radiation slot patterns are large enough to converge to generate an omnidirectional, horizontally polarized outward wave. Correspondingly, the magnetic fields form closed loops (TEM mode) at the SMA feed and eventually transform to perpendicular loops corresponding to the E field outside the antenna, as clearly visible in the simulated field trajectories in Figure 5b,d.  
Figure 5: The cross-sectional views of the fields at the SMA connector: (**a**) the electric field, (**b**) the magnetic field cross-sectional views of the fields at the slot-feeding pins, (**c**) the electric field (**d**) the magnetic field.
## 3 Concept, Classification, and Data Sources of Digital Service Trade
### Analysis of Small-World Characteristics
#### 4.2.1 Analysis of Network Clustering Coefficient  
The network clustering coefficient is an important indicator for evaluating the degree of node clustering in a network, with values ranging from 0 to 1. The closer it is to 1, the tighter the connections between nodes and the higher the degree of clustering.  
From the trend shown in Figure 3, the clustering coefficient of the overall digital services trade network remains around 0.9, close to 1, which is already at a high level. This indicates that the clustering phenomenon is more obvious in the overall digital services trade network, and it is not difficult to explain this phenomenon: when conducting digital service trade, due to the lack of uniformity in the foreign policies of RCEP member countries, under the RCEP framework, the ten ASEAN countries, as a regional economic cooperation organization, may 'unite to warm up' and formulate common digital service trade policies to enhance their internal trade development. Although China, Japan, and South Korea have geopolitical conflicts, their economies have been severely impacted by the global economic downturn. As the three major economies in Asia, they must remain united and seize the opportunity to improve the trade environment.  
In various segmented industries, except for the fluctuation of clustering coefficients in the personal entertainment service trade, the network clustering coefficients of other industries are steadily increasing, and the final value remains stable at around 0.9. These indices reflect a high degree of clustering and close connections among RCEP member countries in the overall digital services trade network and various sub-industry trade networks, and there is also a clear clustering phenomenon in each sub-industry.  
Figure 3: Clustering coefficient of overall digital services trade and sub-industry trade network among RCEP member countries from 2006 to 2021. Data source: compiled by UCINET 6.0 software.
## 2 Related Work  
Spatial crowdsourcing is becoming increasing popular, whereas its location privacy is causing concern. K-anonymity [2; 3; 4] has been widely used for privacy protection in location-based systems. The main idea of k-anonymity is that the location of a user is hidden among k other users [5; 6; 7]. The other component of k-anonymity involves generating k-1 properly selected dummy points [8; 9], and performing k queries to the service provider, using the real and dummy locations. However, the security of k-anonymity is poor when the attacker has background knowledge about the users.  
Differential privacy [10] provides a strong privacy guarantee when attackers have background knowledge. [PERSON] et al. [11] proposed a framework for protecting the privacy of worker locations in SC based on differential privacy. A trusted third party cell service provider (CSP) is needed to clean and noise the worker locations for the SC-server's queries. However, the trusted third party, such as a CSP, is not available sometimes, and users' privacy is not guaranteed even if CSP claim to keep sensitive information safe. On the other hand, the privacy requirements of some workers cannot be satisfied because the privacy budgets of all workers are uniformly distributed by CSP.  
In this paper, we propose a framework based on local differential privacy [12; 13] to protect workers' locations. The framework can defend against attackers with background knowledges because it achieves differentially-private protection guarantees. Furthermore, the framework does not require a trusted third party such as CSP which used in [11] and workers can customize their own privacy requirements because workers can add noises to their locations locally.