esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, a wideband stacked patch antenna consisting of three HRSi substrates was proposed. The design approach was demonstrated step by step with the guidance of CMA. The antenna unit and \(2\times 2\) subarray operating at Ka-band were fabricated through bulk silicon micromachining and wafer bonding processes for validation. The measured results showed a bandwidth of 13.8% and 11.4% for the antenna unit and the \(2\times 2\) array, with a boresight gain of 6.1 dBi and 9.3 dBi, respectively. With this enhanced performance, a cost-effective and practical AiP design on silicon, which has the advantages of high integration and mechanical reliability, can be used for highly integrated millimeter-wave phased arrays.  
**Author Contributions:** Model design and fabrication design, Y.Z.; simulation, validation, and model analysis, L.L.; simulation and fabrication, Y.H.; validation, supervision, Y.X.; supervision, H.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China grant number 62001522.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** Author [PERSON] and [PERSON] are employed by the company The 13 th Research Institute of China Electronics Technology Group Corporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Materials and Methods  
The present study was carried out based on clinical examination, pre-treatment photographs, panoramic and periapical radiographs and study models of 3000 consecutively treated orthodontic individuals from the department of orthodontics at Tel Aviv University. Out of the 3000 orthodontic individuals, the sample consisted of 1220 (41%) males and 1780 (59%) females, ranging from 10-25 years old (mean age 15 years), all were of Israeli ethnical origin. Their records were examined to detect permanent teeth agenesis in both dental arches, except for third molars. The data were recorded according to gender, age, number of missing teeth, location within each jaw and sidedness. Tooth agenesis was diagnosed clinically and defined as a lack of crypt formation or crown mineralization detection in the panoramic radiographs.  
Inclusion criteria included complete records with good quality photographs and diagnostic radiographs. Exclusion criteria included history of traumatic dental avulsion or extraction of permanent teeth, orthodontic or surgical treatment in the dental arches, and congenital craniofacial anomalies or syndromes. To avoid any misdiagnosis of missing teeth, two examiners ([PERSON] and [PERSON]) examined all the records twice separately and 100% reproducibility was achieved in the diagnosis of hypodontia.  
Informed consent was obtained from the parent/guardian of each individual prior to inclusion in the study, which was approved by the Institutional Review Board Ethics Committee of Tel Aviv University. Descriptive and comparative statistical analyses were conducted using the SPSS software (Statistical Package for Social Sciences, version 20.0, SPSS Inc., Chicago, IL, USA). The significance of the findings was tested using \(t\)-test or Chi-Square tests, with \(p\) < 0.05 designated as statistically significant. A sample calculation was established using Win Pepi version 11.18, and Fisher's Exact Test.
## 5 Discussion
### Practicality and Necessity of Proposed Circuit  
According to the study in this section, the proposed FCL circuit has better performance in limiting fault current under DC pole-to-pole fault, compared with previous FCL method under same parameter setting. In the viewpoint of reality, the power loss during normal state is considered acceptable as analyzed in Section 5.3. In order to cooperate with protection relaying, the proposed FCL circuit is installed at the ends of transmission line so that communication delay among DCCB, measure point and protection relay could be reduced. Moreover, big smoothing reactor (200 mH) in [42; 43; 44] would influence the dynamic response speed and proposed FCL circuit could help reduce value of smoothing reactor for better performance of dynamic response. However, it requires further investigation on economic efficiency of proposed method, considering previous FCL methods, such as superconductor based fault current limiter, FCL circuit and converter topology based approach.  
Another point should be noted that the proposed EFCLC would make more contribution to fault interruption and clearance, compared with traditional FCL behavior with DC terminal reactor. The basic advantage of EFCLC over DC terminal reactor could be summarized as following aspects. Firstly, EFCLC is only triggered as fault detected to limit fault current, thereby having no influence on normal state and system dynamic response. By contrast, to achieve the same level of FCL performance of EFCLC, larger DC terminal reactor is required even under normal state, which does affect system dynamic response. Seconded, compared with DC terminal reactor, EFCLC could accelerate fault clearing speed due to its energy dissipation resistor. The last but not the least, the FCL inductor in EFCLC would be bypassed after IGBT blocks. Hence, the fault isolation time would be reduced to some degree since remained inductor would delay the fault current clearance.
# Research on High-Efficiency Routing Protocols for HWSNs Based on Deep Reinforcement Learning
## 1 Introduction
### Contribution  
In this study, an energy-efficient routing strategy for HWSNs based on the Deep Q-Network (DQN) is proposed. The strategy is validated for its effectiveness in optimizing energy efficiency and extending the network lifetime through system modeling, algorithm design, simulation experiments, and performance analysis. The specific contributions are as follows:  
Firstly, an intelligent routing strategy based on the DQN is introduced to dynamically optimize energy efficiency in HWSNs.  
Secondly, a MATLAB simulation environment is established to verify the performance of the algorithm under different application scenarios.  
Finally, through detailed experiments and simulation analyses, the advantages of the DQN-based routing strategy in reducing energy consumption, extending network lifetime, and improving data transmission performance are demonstrated.
## 3 Results and Discussion
### Quality Evaluation
#### 3.1.1 MI  
Mutual information (MI) [22] shows the correlation between two events, which can be used for evaluating image fusion performance. The higher score of MI is, the richer the information is obtained from the source images. The MI of two images is defined as follows:  
\[MI=\frac{I_{FA}(f,a)}{H(A)+H(F)}+\frac{I_{FB}(f,b)}{H(B)+H(F)} \tag{14}\]  
where \(F\) is the fused image and \(A\),\(B\) are the source images. \(H(A)\), \(H(B)\) and \(H(F)\) are the entropies of images A, B, and F, respectively.
# Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans
## 2 Historical Evolution
### Deep Learning Revolution (2018-2024)
#### 2.3.11 S-Net  
In 2021, [PERSON] et al. introduced the S-Net, a neural network that integrates attention mechanisms and long-hop connections for liver tumor segmentation from CT images. Their method, featuring a classic encoder-decoder structure with added attention, enhanced the semantic information processing across the network. The effectiveness of S-Net was validated on several datasets including MICCAI 2017 LiTS, where it achieved a Dice global score for tumor segmentation of 0.7555 and a Dice per case score of 0.613, demonstrating its potential for improving radiotherapy planning [22].
## 2 Evaluation Study and Design
### Fluid-Dynamic Methods and Simulations  
The methodology involves the use of CFD software (STAR-CCM+ software v.2410) to simulate the distribution of viral load emitted by an infected individual within a confined environment. This simulation specifically calculates the viral dose potentially absorbed by a reference susceptible individual over a defined time interval. Following the determination of the viral dose, this data is applied to the Airborne Risk Indoor Assessment (ARIA) calculation model, which has been developed and published by the World Health Organization (WHO) [28]. The ARIA model enables the association of an average probability of infection with the potentially absorbed viral dose (Figure 6).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Frequency [GHz]** & **Conductivity [S/m]** & \
\begin{tabular}{c} **Relative** \\ **Permitivity** \\ \end{tabular} &
\begin{tabular}{c} **Loss** \\ **Tangent** \\ \end{tabular} \\ \hline
8 & 7.7978 & 45.497 & 0.38511 \\ \hline
9 & 9.1923 & 44.126 & 0.41607 \\ \hline
10 & 10.626 & 42.764 & 0.44666 \\ \hline
11 & 12.083 & 41.419 & 0.47672 \\ \hline
12 & 13.55 & 40.101 & 0.50616 \\ \hline \end{tabular}
\end{table}
Table 1: Dielectric properties of human muscle at different frequencies (from IFAC-CNR).  
Figure 6: Probability of infection as a function of viral load provided by the ARIA model.  
CFD simulations were conducted using the Star-CCM+ software v.2410 developed by Siemens and licensed to the Department of Energy of the Polytechnic University of Turin. These simulations employed Reynolds-Averaged Navier-Stokes (RANS) equations in a three-dimensional (3D) framework. The study focused on modeling aerosol transport and diffusion within a confined environment to understand the spread of airborne viruses from an infected individual. The simulations were categorized into two main groups based on the modeling approach: the first group utilized a Lagrangian method, while the second group employed an Eulerian method. Simulations employing both methodologies were conducted. The primary objective was to enhance security levels, while the secondary aim was to validate the reliability of the Eulerian approach. This approach is notably faster and demands less computational power, has already been proposed and is a promising candidate for future research extensions [29].  
Based on the described geometry, an analysis was conducted for a summer scenario utilizing a mixed radiant cooling and primary air system (mixing ventilation). The boundary conditions used for the simulations are expressed in Table 2.  
The emission from the infected individual was modeled in constant and sinusoidal mode according to the schedule shown in Table 3.
# MPC and PSO Based Control Methodology for Path Tracking of 4 WS4 WD Vehicles
An sequential quadratic programming (SQP) based control allocation  
Figure 1: The 4 WS4 WD (four wheel steering and four wheel drive) vehicle. is developed to control the 4 WS4 WD vehicle in the lower controller. The inclusion of full independent force control and steering control on all four wheels enable the maximization of performance. In this work, comparison of MPC and PSO on the same vehicle model is provided, in which the proposed PSO control methodology is a further refinement of the PSO methodology presented previously in [18]. The PSO control methodology in this paper simplifies the derivation and gives an algorithm in a more general form, which facilitates the comparison with other control methods. In addition, both methodologies are compared with the kinematic model based method proposed in [19]. The paper is organized as follows: Section 2 describes the 4 WS4 WD vehicle modelling. The MPC and PSO control methodologies are presented in Section 3. In Section 4, simulation setup and the reference path are presented. The result of the two controllers are compared. In Section 5, the discussion about two methodologies are provided based on their theoretical analysis and simulation results. Finally, conclusions are provided in Section 6.
## 3 Results and Discussion  
### Toxicants in Oily Sludge Heat Treatment Residue  
#### 3.1.1 Organic Compounds in Heat Treatment Residues  
The main toxicants in the residue were heavy metals and some organics. The hazardous effects of heat treatment residues on the environment and plants mainly depend on the organic composition and the heavy metal content in the residue. Therefore, we sought to investigate the organic composition of the heat treatment residues using GC-MS analysis.  
Our findings indicated that the main components of the three heat treatment residues were straight-chain alkanes, of which hexadecane and octadecane had growth-promoting effects [27; 28], whereas hexadecane had an inhibitory effect on Cd toxicity [29]. Additionally, these alkanes were also able to promote the immobilization of free metal ions in live tissues [20; 30]. The main compounds in the pyrolytic residue were straight-chain alkanes with carbon numbers between 16 and 25 as well as a few compounds with more than 30 carbon atoms. Furthermore, the main compounds in the thermal-washing residue were straight-chain alkanes with carbon numbers between 15 and 30 as well as 2,6,10,14-tetramethylpentadecane, which were likely produced via the isomerization of lefins [31]. No compounds with more than 30 carbon atoms were detected in the high-temperature oxidation residue. However, this residue did contain 2,4-di-tert-butylphenol and dioctyl adipate. The compound 2,4-di-tert-butylphenol is toxic to aquatic organisms and may have long-term adverse effects on aquatic ecosystems.
# A Privacy-Preserving Record Linkage Method Based on Secret Sharing and Blockchain
## 4 Methods
### Data Preparation and Generation Module
#### 4.2.5 Data Distribution
Using its own vector \(E_{k}\) and the \(t-1\) downloaded vectors, the node computes the complete masked difference vector \(E_{\text{full}}=(e_{1},e_{2},\ldots,e_{m})\), where

\[e_{l}=e_{l,1}\oplus e_{l,2}\oplus\cdots\oplus e_{l,t} \tag{9}\]

Based on \(\pi_{\text{gid}}\), the node ensures that \(e_{l,k}\) corresponds to the correct index. The XOR property guarantees the following:

\[e_{l}=\bigoplus_{k=1}^{t}e_{l,k}=d_{i,j,l}\oplus r_{\text{gid},l} \tag{10}\]

Here, the randomness of \(r_{\text{gid},l}\) protects \(d_{i,j,l}\) from being exposed. Next, node \(N_{k}\) uses \(e_{l}\), the arithmetic mask share \([r_{\text{gid},l}]^{\pi_{\text{gid}}(k)}_{A}\), and the arithmetic share of the constant \(1\), \([1]^{\pi_{\text{gid}}(k)}_{A}\), to compute the arithmetic share for each bit \(l\). The results are as follows:

If \(e_{l}=0\) (i.e., \(d_{i,j,l}=r_{\text{gid},l}\))

\[[d_{i,j,l}]^{k}_{A}=[r_{\text{gid}}]^{\pi_{\text{gid}}(k)}_{A}\text{mod}\ p \tag{11}\]

If \(e_{l}=1\) (i.e., \(d_{i,j,l}=1-r_{\text{gid},l}\))

Figure 3:0

The permutation mechanism ensures consistency, and the shares from all nodes satisfy

Figure 3:1

The algorithm for batch interaction and masked difference reconstruction is described in pseudocode as shown in Algorithm 2. ``` Input: masked_diff: Masked difference vector; n: Number of computation nodes; perm: Permutation function for data reordering Output: full_masked_diff: Fully reconstructed masked difference
1: cid_masked_diff \(\leftarrow\) UploadToIPFS(masked_diff)
2: SubmitToSmartContract(task_id, node_id, cid_masked_diff)
3: WaitForAllNodes(task_id, n)
4: other_cids \(\leftarrow\) GetOtherNodesCIDs(task_id, n)
5: other_masked_diffs \(\leftarrow\) [DownloadFromIPFS(cid) for cid in other_cids]
6: Reorder(other_masked_diffs, perm)
7: full_masked_diff \(\leftarrow\) masked_diff
8:for j \(\leftarrow\) 1 to n \(-\) 1 do
9: for i \(\leftarrow\) 1 to m do
10: full_masked_diff[i] \(\leftarrow\) full_masked_diff[i] XOR other_masked_diffs[j][i]
11: endfor
12:endfor
13:return full_masked_diffIn Algorithm 2, lines 1-6 handle uploading the local masked difference to IPFS and submitting it to the smart contract, then wait for all nodes to submit, download other nodes' masked differences, and reorder them using the permutation function. Lines 7-13 aggregate the local and other nodes' masked differences through bitwise XOR operations, generating and returning the complete masked difference vector for subsequent processing.
## 6 Results and Discussion
### Analysis of \(k\), \(\varepsilon\) and \(v_{T}\)  
The predicted \(k\) profiles are shown in Figure 4a for cases 16 and 19 in Table 1, and Figure 4b for low and high Reynolds number cases (7 and 27). There is reasonable improvement of the profile away from the wall as a result of the new \(f_{v}\) closure for increasing drag reduction and for various Reynolds numbers.  
In Figure 5, the prediction of the dissipation rate are compared with the DNS data of both LDR (case 16) and very HDR (case 22), along with predictions for the \(v^{2}-f\) model of [PERSON] et al. (2018). The predictions for LDR are captured well with the DNS for both near and far from the wall. For HDR, there is a significant improvement near the wall compared with the \(v^{2}-f\) model. This is a result of the \(E_{\tau_{p}}\) closure formulated (See Equation (33)) which decreases \(\varepsilon\) as flow viscoelasticity increases. The model of [PERSON] et al. (2018) shows similar results to the current model and is not plotted so that the figure is clearer. However, the complexity of the present \(E_{\tau_{p}}\) closure model is reduced substantially and removes all friction velocity dependence, but can still predict all the main flow features with good performance.  
The local eddy viscosity is plotted in Figure 6a for all ranges of DR. The combined performance of \(f_{v}\), \(k\) and \(\varepsilon\) gives rise to the predictions shown. We observe a reduction in the eddy structures within the buffer-layer and log-layer for increasing DR, as the DNS suggests. The damping function predicts well this behavior with the near-wall polymer extension via \(C_{kk}\) and the global reduction via \((1-A)\)  
Figure 4: Comparison of turbulent kinetic energy between DNS data (\(\times\) crosses) and predictions with the new model (continuum lines), and previous model (dash lines): (**a**) \(Re_{\tau_{0}}=395\)—red (LDR case 16)—and blue (IDR case 19); (**b**) \(Re_{\tau_{0}}=125\)—green (HDR, case 7) and \(Re_{\tau_{0}}=1000\)—blue (IDR, case 27).
# Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau
## 4 Discussion  
### Accuracy Evaluation and Uncertainty Analysis of Inversion Models  
We verified the model with reference data, and the accuracy of the model is high. However, we found that with the increase in forest stand age, different vegetation indices showed a saturation effect. This study established a remote sensing inversion model of forest stand age in the Loess Plateau and realized the mapping of forest stand age used in the remote sensing process considering the physiological function and canopy structure of the forest ecosystem. The accuracy of the model is reliable, but there are some defects. Although clouds and shadows are removed from the input data of the inversion model, there are some differences in the seasons, and the error transmission of the data will aggravate the uncertainty of the remote sensing dataset of forest stand age information in the Loess Plateau.  
Cloud cover in optical remote sensing images will obscure the ground information to different degrees, which causes the blurring and missing of the surface observation information and greatly affects the imaging quality of remote sensing images, significantly reducing the accuracy of a quantitative remote sensing model [41]. To avoid the impact of cloud coverage on the accuracy of the model, Sentinel-2's new cloud removal method (S2 cloudless algorithm) was used. The S2 cloudless algorithm is a machine learning algorithm through which cloud mask files can be calculated for Sentinel-2 images [42]. [PERSON] compared the cloud removal accuracy of the traditional Fransk and Sen2 Cor algorithms and the machine-learning-based S2 cloudless algorithm and found that S2 cloudless showed the best result (average accuracy 83%), and the lowest accuracy was obtained with Fransk (70%). The seasonal variability in cloud masking accuracy did not exceed 6% [43]. Therefore, we reduced the impact of cloud inversion on the accuracy of the model and used the S2 cloudless algorithm to improve the accuracy of the model.  
Spatial Inconsistency between the Observed Sample and Remote Sensing Data Significantly Affects the Accuracy of the Model  
The scale inconsistencies of the observed plots also significantly affect the accuracy of the model; observational data are mainly from China's Ninth National Forest Inventory (NFI), which includes various forest quadrats from 2014 to 2018, but these data have been updated to 2019 by rasterization into a 30 m resolution map using the nearest neighbor resampling method [19; 20]. Although the average age and height of the dominant tree species within a forest polygon were used as the final age and height of the forest, the scale  
Figure 9: Spatial distribution of forest stand age in the Loess Plateau (**(a)** spatial distribution of forest stand age; (**b**) frequency of forest area at different forest stand ages).  
of the observed quadrangle would cause errors. At the same time, the spatial matching between satellite image pixel size and observation quadrats also significantly affects the accuracy of the model, but these errors are unavoidable.
## 4 Simulation Results
Meanwhile, reliance on the received power from the information signal transmitted by the BS became more dominant, leading to a reduction in the impact of the PB deployment. Moreover, as shown in the previous figures, the RSMA method consistently achieved a considerable decrease in the total transmission power in comparison to the NOMA and SDMA methods. In the case of the DNN-based method, the training dataset consisted solely of samples representing a scenario with \(d_{x}=13\) m, as detailed at the beginning of Section 4. In contrast, the testing data in Figure 10 contained samples encompassing scenarios with varying values of \(d_{x}\). The proposed DNN-based scheme exhibited high generalization performance, achieving comparable transmission power to the PSO-based method. Furthermore, this study analyzed the effect of slightly changing the number of PCs in the PCA module. Similar to the results shown in Figure 6, the cases of PC = 2 and PC = 3 achieved similar results, with PC = 2 having lower transmission power and demonstrating the best generalization performance.
## 3 Methodology
### Feature Extraction Methods  
#### 3.4.1 Machine Learning Models  
* Term Frequency-Inverse Document Frequency (TF-IDF): TF-IDF is employed for text reorganization and feature extraction. Term frequency (TF) highlights the frequency of a word within a specific document. In contrast, inverse document frequency (IDF) measures the rarity or commonness of the word across the entire document corpus. This combination helps in identifying important words in a document while reducing the impact of commonly used words.
* n-gram: The n-gram method aids in understanding context by examining word sequences. n-grams are sequences of 'N' words, where 'N' can be any integer. For example, unigrams are single words, bigrams are pairs of words, and trigrams are sequences of three words. This method is beneficial for spellchecking, text prediction, and contextual analysis. In this research, we use n-grams with a range of 1 to 3 (unigrams, bigrams, and trigrams) to capture a broader context and improve text analysis.