esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Guided Box Filter Based on Hybrid Optimization for Medical Image Denoising
## 4 Results and Discussion  
This section discusses the validation of simulation experiments for a proposed model. The experiments were conducted using MATLAB (R2020a) on an Intel(R) Core (TM) i5 with CPU 1.60 and 2.11 GHz with 2 GB graphics. Input images of size 192 \(\times\) 192 were degraded with Gaussian noise of varying variance \(\sigma^{2}\in[0.01\)-0.09], Poisson with a noise density of 0.05, salt and pepper with a noise density of 0.05, and speckle noise with a noise variance of 0.05, and then the proposed hybrid-optimization-based decimation box filters were used to remove the noise. The filters were trained for 100 epochs and performance metrics were used to evaluate the results. PSO, FF, CS, and proposed HCPSO optimization methods were compared in the experiments. For result analysis, medical image datasets are used that are described in the below sub-section.
## 6 Conclusions  
This research employs the LightGBM-SHAP regression model to conduct a comprehensive examination of the differences between SIF remote sensing data, NDVI, and kNDVI in monitoring vegetation water stress and quantifying the variability of GPP due to drought during the period 2001-2020. The findings reveal that SIF exhibits a higher degree of sensitivity and accuracy in characterizing the anomalous dynamics of GPP during drought periods in comparison to NDVI and kNDVI.  
A notable aspect of SIF is its capacity to reflect vegetation response to drought conditions earlier than the other indices, with a time lag of only 0-1 month. This contrasts with NDVI and kNDVI, which demonstrate time lags of 0-3 months and 0-4 months, respectively. This difference is likely attributable to direct correlation of SIF with plant photosynthesis, whereas NDVI primarily indicates overall vegetation greenness and biomass. Moreover, the study underscores that VPD is the predominant climatic factor affecting vegetation changes in regions south of 30\({}^{\circ}\)N in China. In terms of kNDVI performance, the research highlights the significant influence of the choice of kernel parameter \(\sigma\) values. Appropriately estimating this parameter can substantially improve the effectiveness of kNDVI in monitoring vegetation drought conditions. Therefore, we suggest that the use of kNDVI should be adapted to the vegetation coverage and growth cycle of the study area to achieve the best results.  
Overall, our study validates the advantages of using satellite-based SIF data for monitoring changes in vegetation affected by water resources, particularly in capturing subtropical vegetation GPP changes during droughts. These findings offer valuable guidance in selecting remote sensing indices for timely monitoring and precise assessment of the impacts of hydrological processes on vegetation growth. Furthermore, they pave the way for future research directions, such as exploring the combined impact of additional climatic factors and further refining algorithms and parameter optimization for more accurate monitoring of vegetation status.  
Conceptualization, C.W. and J.W.; methodology, L.L. and Y.Z.; software, C.X.; validation, Y.Z., C.W., L.L., X.L. and X.X.; formal analysis, X.X.; investigation, X.X.; resources, L.L.; data curation, X.X.; writing--original draft preparation, C.W., J.W. and X.X.; writing--review andediting, Y.Z., L.L. and X.L.; visualization, W.T.; supervision, X.X. and Y.Z.; project administration, X.X.; funding acquisition, C.W. and X.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Hainan Province Science and Technology Special Fund (Grant No. ZDYF2022 GXJS228), Haikou Science and Technology Plan Project (Grant No. 2022-015) and Key Laboratory of Ocean Geomatics, Ministry of Natural Resources China (Grant No. 2021A02).  
Data availability Statement: Data are available within the article.  
The authors declare no conflicts of interest.
# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 2 Infrared and Visible Image Fusion Methods
### Methods Based on Deep Learning
#### 2.6.1 Methods Based on Convolutional Neural Network
First, depth features from the source photos are extracted using ResNet. After obtaining the initial weight map using the local average L1 norm, bicubic interpolation is used to adjust its size to match that of the source image. The weight mapping is then combined with the source picture, and the weighted average technique is utilized to rebuild the fusion image. A new unsupervised and unified dense connected network (FusionDN) is proposed by [PERSON] et al. [52] that is trained to produce fused images with modifications to the source images. At the same time, two data-driven weights obtained by one weight block will retain the feature information of various source images. In addition, rather than creating separate models for every fusion task or roughly combining training tasks, [PERSON] et al. achieved a single model applicable to multiple fusion tasks by using elastic weight merging to prevent forgetting what was learned from earlier tasks when training multiple tasks sequentially. A broad image fusion framework based on a convolutional neural network (IFCNN) was proposed by [PERSON] et al. [53]. First, features from several input photos were extracted using two convolutional layers. The convolution features are then fused using a suitable fusion rule based on the type of input image. The fused image is then obtained by reconstructing the fused features using two convolution layers.
# Detection of Precipitation and Fog Using Machine Learning on Backscatter Data from Lidar Ceilometer
## 4 Weather Detection  
In this section, we use the denoised backscatter data, cloud volume, and cloud height as training data, and describe how to detect weather phenomena using three machine learning (ML) models: random forest, SVM, and ANN.
## 2 Methodology
### Overview of the Proposed Method  
The flowchart of the proposed method is shown in Figure 1. Firstly, four essential features are extracted from GNSS raw observation data, namely, satellite elevation angle, carrier-to-noise ratio, pseudorange residual, and pseudorange rate consistency, which are comprehensively used to enhance the classification accuracy. Secondly, satellite signals received by GNSS receivers in complex scenarios are generally divided into two categories: direct and indirect signals, the latter including multipath and NLOS signals, and each type of signal has a certain internal relationship with the above four main features. According to this characteristic, this paper adopts the k-means clustering algorithm to cluster the signals and selects the DBI as the internal evaluation index of the clustering effect. Finally, both the raw INS measurements and the GNSS double-differenced pseudorange observations are tightly integrated with EKF filtering, resulting in reliable and high-precision positioning results.  
It should be pointed out that the measured circumstance of the GNSS receiver of the base station is open and superior with no obstacle occluding all around as shown in Figure 2. Additionally, the Trimble GNSS-Ti earth-type check-ring antenna is installed. This paper purports that there is no NLOS signal and the multipath is well suppressed, which is small relative to the rover and does not affect the subsequent positioning solution. Therefore, multipath/NLOS signal detection for base station GNSS observations is not required.
# Mutual Authentication Protocol for D2D Communications in a Cloud-Based E-Health System
## 5 Security Analysis
### Patient's Mobile Device Loss/Stealing  
The security objective is accomplished through the access control of the patient's mobile device using a login and password. The system is only accessible if a valid login and password pair is inserted. If the mobile device is stolen or lost, no unauthorized person can access the patient's system, because it would not have a valid login and password pair.
# Privacy-Preserving Solution for European Union Digital Vaccine Certificates
## 8 Experimental Results
### Performance on Smart Cards  
To evaluate the performance of our privacy-preserving digital vaccine certificate on the smart card platform, we used the MULTOS-based smart card implementation from our previous work in [21; 27]. The Show-Verify algorithms were implemented using Barreto-Naehrig (BN) BN254 elliptic curve from the MCL library [25]. Only standard Multos API and free public development environment (Eclipse IDE for C/C++ Developers, SmartDeck 3.0.1, MUtil 2.8) were used. In the case of using smart cards for storing and presenting certificates, the verification time (in the worst case, i.e., 20 stored attributes, all hidden) can be expected to be up to around 3 s as shown in Figure 12. Since smart cards do not support BLE communication, it would be necessary to use Personal Computer/Smart Card (PC/SC) and contact/contactless communication interface.  
\begin{table}
\begin{tabular}{l l l r} \hline \hline
**Certificate Field** & **Field Key** & **Sample Value** & **Size [B]** \\ \hline First name & firstname & [PERSON] & 17 \\ Surname & surname & [PERSON] & 14 \\ Day of birth & birthdate\_day & 25 & 19 \\ Month of birth & birthdate\_month & 8 & 20 \\ Year of birth & birthdate\_year & 1990 & 22 \\ Photo & photo & (Base64 data) & \(12,600\) \\ Unique issuer ID & unique\_id & 12345678 & 21 \\ Vaccination day & vaccination\_day & 10 & 21 \\ Vaccination month & vaccination\_month & 10 & 20 \\ Vaccation year & vaccination\_year & 2022 & 24 \\ Order of dose & dose & 2 & 9 \\ Total doses & total\_dose & 2 & 15 \\ Completed vaccination & completed\_vaccination & True & 29 \\ Vaccine & vaccine & SARS CoV-2 Sample Vaccine & 36 \\ Product & product & Product A & 20 \\ Manufacturer & manufacturer & Manufacturer A & 30 \\ Issuer & issuer & BUT Brno, Czech Republic & 34 \\ State of EU & state\_eu & CZ & 14 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data fields and sample values within the test certificate. The field size includes keys and JSON delimiters.  
Figure 11: Application performance depends on the number of attributes. The size of all attributes was 32 B. Subfigure (**a**) shows needed time for detecting the BLE verifier terminal, subfigure (**b**) shows needed time for computing cryptographic proof on the user’s side, and subfigure (**c**) shows needed time for verification of the cryptographic proof on the verifier’ side.
# Assessment of Laser Galvanometer Scanning System Accuracy Using Ball-Bar Standard
## 2 Materials and Methods  
### Specification of the Tested Device  
Virtual templates are created by laser spot projection on the object. The laser beam is focused and hits a pair of mirrors located on precise galvanometers positioned in X and Z axes (Figure 1). Then, the beam leaves the housing through the protective glass and is projected onto the object's surface. Due to the rapid movement of galvanometers, the human eye perceives a laser spot as a complex projection of predetermined figures.  
The system of laser projectors (Figure 1) includes the following,  
1. Laser
2. Focusing optic
3. Galvanometer X
4. Galvanometer Z
5. Mirrors
6. Protective glass
7. Laser output beam  
In addition to performing projections, the system can also perform scans of a selected area of the object and compare the position of recognized features against the CAD model. It gives the basis for conducting the accuracy tests described below.  
The tested device was a FARO Laser Projector TracerSI with BuildIT 2019 software. Table 1 specifies the technical specification of the tested device. Figure 2 shows the tested system and experimental setup.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Projection Distance Range & 1.8–15.25 m \\ Projection Angular Range & 60\({}^{\circ}\) Az \(\times\) 60\({}^{\circ}\) El \\ Focused Line Width & 0.5 mm \\ Positional Accuracy & \(\pm\)0.25 mm at 4.6 m \\ Operating Temperature Range & 10–35 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical specification of the tested device [13].  
Figure 1: Principle of operation of the laser scanning system.  
Figure 2: The tested system: (**a**) galvanometer Scanning System and (**b**) experimental setup.
# FFNN-TabNet: An Enhanced Stellar Age Determination Method Based on TabNet
## 6 Discussion  
The FFNN-TabNet model proposed in this study provides a new approach for the accurate determination of stellar ages. Previous research methods, such as traditional stellar age determination techniques and those based on machine learning, often faced challenges in accuracy and effectively capturing complex nonlinear relationships in the data. The main feature of the FFNN-TabNet model lies in its efficient processing of complex data structures and deep understanding of nonlinear relationships. This not only ensures high accuracy in determining stellar ages but also demonstrates its powerful capability in analyzing and interpreting complex astronomical data. The use of the Bayesian optimization algorithm played a key role in hyperparameter tuning, enhancing the overall performance of the model. This approach provides new insights into optimizing deep-learning models, especially when dealing with high-dimensional and complex data like astronomical observations. An analysis of the feature contribution comparison experiment reveals that the inclusion of the FFNN module significantly enhances the model's capability to capture nonlinear relationships among features. Furthermore, this analysis identifies the features most closely related to stellar age. These pivotal features can guide future observational and data-collection strategies, allowing researchers to focus on attributes with the most significant impact on determining stellar ages. FFNN-TabNet breaks through the limitations of traditional methods, offering a new tool for future research in stars and galaxies and paving new paths for the application of deep-learning technologies in astronomy.  
Future research will focus on three main areas: (1) applying the FFNN-TabNet model to different types of stars to enhance its generalization ability; (2) further refining feature  
Figure 12: Comparison of the actual ages of red giants and the determination results by the FFNN-TabNet model. The red solid line denotes the optimal linear fit for the data points. This experiment utilized the test dataset with the features C_H, K_H, Co_H, Ti_H, Al_H, and Mn_H removed.  
engineering to improve the computational efficiency of the model; (3) integrating with astronomical visualization technologies to help researchers more intuitively understand the data and model results.
## 3 The Main Parameters and Indices of MOT
### Application Performance Indices
#### 3.4.1 Volume  
A typical structural diagram of an atomic gravimeter, which includes a measuring probe and a vibration isolation platform, is shown in Figure 5[73]. The measuring probe is divided into three regions: the preparation region, the interference region, and the detection region. In the preparation region, six beams of a cooling laser are used to trap the atoms. The interference region requires a Raman laser and its reflected laser to change the atomic state.  
Figure 5 clearly shows that trapping atomic groups in the vacuum region via an MOT requires numerous beams and a complex spatial structure. These factors are crucial when considering the miniaturization of such devices. Therefore, the volume of the MOT serves as the primary performance index for devices.  
Efforts have been made to reduce the size of the MOT and enhance device integration. Research on pyramids, grating MOTs, and chip MOTs has been conducted [74; 75; 76], and these methods have gradually been implemented in practical devices [77; 78].
## 4 Results
### Potential Reasons for Misclassifications  
Physiological Signal Overlap: The primary challenge in classifying emotions arises from the physiological similarity between certain emotional states. For example, high-arousal emotions like anxiety and excitement typically increase both HRV and GSR. Without additional discriminative features, such physiological overlaps lead to misclassifications.  
Limited Feature Diversity: The features currently extracted from HRV and morphological ECG aspects, while effective, may not fully capture the complexity of emotional experiences in dynamic VR environments. For emotions with similar arousal levels, the differences in physiological responses may be too subtle for the current feature set to detect.
# Method for Wind-Solar-Load Extreme Scenario Generation Based on an Improved InfoGAN
## 4 Case Study  
In this section, a case study is performed to verify the correctness and validity of the model proposed in this paper. The relevant parameter settings are as follows: the wind, solar, and load historical datasets are taken from the publicly available measured data of the Belgian grid, and the meteorological data are provided by the Wunderground website. The dataset includes all historical data from 2018 to 2022, and the time step is set to 15 min; therefore, there are 96 data points in the 24 h daily output/load curve. Eighty percent of the dataset is used as a training set, and the remaining 20% is used as a test set. All the simulations are performed on the Python-based TensorFlow 2.3.0 platform. The computer configuration is an Intel i7-8700 3.20 GHz CPU, 16.0 GB of RAM, and an NVIDIA GTX 1660 GPU.  
Case 1: Traditional probability sampling models [11; 12];  
Case 2: Traditional generative adversarial network models [15];  
Case 3: The proposed improved information maximizing generative adversarial network model.  
In Case 1, empirical probability distributions are used for sampling, with wind speed following a Weibull distribution, solar intensity following a Beta distribution, and load following a normal distribution [11; 12]. The relevant parameters for each distribution are provided in Table 3.
## 5 Numerical Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Ring and**} & \multicolumn{1}{c}{**TD**} & \multicolumn{3}{c}{**TI**} \\
**Stringer Number** & \(\wedge\)**-Profile** & \(X\)**-Profile** & \(\mathbf{O}\)**-Profile** & \(\mathbf{U}\)**-Profile** & **Sigmoidal-Profile** & \(\wedge\)**-Profile** & \(\mathbf{X}\)**-Profile** & \(\mathbf{O}\)**-Profile** & \(\mathbf{U}\)**-Profile** & \(\mathbf{Sigmoidal-Profile}\) \\ \hline Unstiffered & 411.2273 & 408.5345 & 405.5307 & 408.2149 & 392.371 & 421.7055 & 419.1132 & 415.1666 & 418.5061 & 400.4704 \\ \(N_{t}=5\) & 459.2519 & 454.296 & 463.9345 & 460.9213 & 439.7066 & 472.9684 & 468.3331 & 476.1383 & 474.1699 & 450.2883 \\ \(N_{t}=5\) & 432.5377 & 430.5091 & 424.9031 & 429.7281 & 409.3133 & 443.834 & 442.0279 & 435.547 & 440.66 & 418.4353 \\ \(N_{t}=N_{t}=5\) & 508.5485 & 504.2132 & 515.1755 & 513.6448 & 409.9117 & 515.2078 & 512.2288 & 519.7585 & 519.4056 & 487.7804 \\ \(N_{t}=7\) & 448.2045 & 463.5372 & 477.4953 & 471.558 & 451.0799 & 482.7139 & 478.9199 & 490.3426 & 487.1171 & 462.3776 \\ \(N_{t}=7\) & 441.8178 & 439.6434 & 433.33342 & 438.8732 & 416.2962 & 453.5255 & 451.6761 & 444.2205 & 450.2502 & 425.8656 \\ \(N_{t}=N_{t}=7\) & 555.4144 & 553.2414 & 569.8029 & 568.5566 & 520.784 & 550.762 & 548.6321 & 557.7685 & 557.7418 & 519.8421 \\ \(N_{t}=10\) & 480.1307 & 475.8978 & 491.597 & 464.6137 & 464.2851 & 494.8228 & 491.5553 & 506.0405 & 500.4459 & 476.2802 \\ \(N_{t}=10\) & 456.9784 & 454.8882 & 446.7671 & 453.9572 & 427.7454 & 468.3527 & 466.4062 & 457.7995 & 465.3265 & 437.6988 \\ \(N_{t}=N_{t}=10\) & 595.4563 & 586.3257 & 605.6545 & 604.3793 & 563.6043 & 606.5443 & 606.4783 & 618.9873 & 617.3305 & 571.0981 \\ \hline \end{tabular}
\end{table} 
Table 5: Effect of the stiffness on the critical thermal buckling load of the cylindrical shell reinforced by wavy graded CNTs for different profiles considering \(L/R=2\), \(R/h=200\), \(w=0.425\), \(s=1\), \(A_{R}=1000\), \(V_{CNT}^{c}=0.17\).
## 3 CPN Models
### Function Models  
Prior to present what is a Function models, it is worth remembering that the implementation of a neSC application consists of a set of functions, named task, command and events (see Section 2.2.1), that are very similar to C functions. On the other hand, a function itself is a block of code containing a set of operators/statements such as assignments, loops and _if-then-else_. As presented in the previous section, these elements are modelled by the Basic models. Therefore, a Function model consists of a set of Basic models that are put together according to a proposed rule. In order to better illustrate this composition, consider the following neSC function (command):  
command void Module.read({  
SLEEP_TIME = 100; //assign  
call Temp.read(); //invocation  
This function has two elements, assignment (=) and invocation (call), that are executed sequentially. Hence, its respective Function model is simply the composition of the Basic models of these two operators as shown in Figure 10.  
The assignment operator is modelled through the place _assign_ and the transition \(a\)1_, whilst the invocation operator is modelled by the place _call_ and the transition \(c\)1_ (see Section 3.1.1). These Basic models are merged according to the following rule: the transition from the first model (_a_1_) is connected (connector) to the place in the second model (_call_). This rule is used to compose all Basic models.  
Figure 9: CPN model of the event _receive_.
# A Co-Design-Based Reliable Low-Latency and Energy-Efficient Transmission Protocol for UWSNs
In such cases, control packets previously used in routing can be replaced by a small number of neighbor information update packets through prediction. Under the condition that the neighbor table is available, a sender selects several nodes from its neighbors as the next-hop. In this process it guarantees that selected receivers are adjacent to each other. Before forwarding the data packet, all selected receivers exploit the holding time mechanism in the MAC process to choose one node to forward the packet. By using routing information, CDTP only needs to ensure that at least one node from the chosen neighbors can be received successfully. In summary, this paper makes two main contributions. 1. _Achieve lower end-to-end latency and energy consumption with fewer control packets while alleviating the multi-copy problem:_ CDTP requires less control packet exchange between senders and receivers during routing and MAC, but can also alleviate the multi-copy problem. Using the neighbor information stored in each node, senders select the relay candidate subset of neighbors and coordinate the forwards hop-by-hop. The special case we mentioned before may render the holding time mechanism invalid. Thus, CDTP provides a novel holding time mechanism to alleviate the multi-copy problem and reduce delay. CDTP combines the advantages of both: low latency of the receiver-based protocol and fewer flying packets in the sender-based protocol. 2. _Improve the reliability and efficiency of acoustic communication by using a co-design mechanism:_ These two features are often contradictory in the underwater environment. Independent working routing and MAC protocols cannot achieve these features. In routing protocols, however, the conflicts from the MAC process are not considered. This problem is handed over to the MAC protocol. In fact, routing information can help improve the probability of handshake success in MAC. Analogously, it utilizes the information in the MAC process to estimate the relative speed between nodes and to predict when the node is out of range. With the help of updated neighbor information, the communication between nodes will be more reliable. The remainder of this paper is organized as follows. Section 2 presents design challenges. Section 3 describes the model and problem of the proposed protocol. The specific design of the protocol is discussed in Section 4. Simulation results are provided in Section 5. Related work is reviewed in Section 6, followed by conclusions in Section 7.