esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Statistical Analysis  
#### 3.3.1 Fuzzy Comprehensive Evaluation of Water Damage Resistance  
Using the fuzzy comprehensive evaluation analysis approach, the results of the research on macroscopic adhesion and the susceptibility of asphalt mixes to moisture were pooled and evaluated. Prior to separating the findings of the positive and negative correlation data, the macro index data were first standardized. The weight coefficients for each index were then determined using the entropy weight method's calculation procedures (see Table 3). The fuzzy evaluation matrix R was established based on the results of macroscopic adhesion tests and moisture susceptibility tests of the five aggregates, and the matrix R was standardized to eliminate the effects of magnitude and quantitative extremes to finally obtain the affiliation matrix \(R_{p}\). Finally, the fuzzy mathematical model was used for comprehensive evaluation, and the evaluation results are shown in Table 4.  
The results of the fuzzy comprehensive evaluation analysis of the macroscopic adhesion index and the moisture susceptibility index show that the type B limestone, basalt, type A limestone, diabase, and granite had the highest scores among the five aggregates. Notably, granite received the lowest rating, while basalt and limestone received the highest ratings based on the results of each sub-index. This general consistency between the rankings based on li and the results of each sub-index is remarkable.
## 1 Introduction
In a complex 3D structure, FEA has made it possible to calculate the stress distribution within the structure by dividing the structure into tiny shapes called elements, and their ends meet to form nodes [22]. FEA, being a numerical approach to analyze stress distribution, is the most repeatedly used method for in vitro studies [23]. To obtain an accurate 3D model, a micro-computed tomography (micro-CT) scan can be used to generate the data [24]. This is helpful and is used because it saves time and reduces technical complexities. [PERSON] et al. [18] evaluated the biomechanical and thermal behavior of a preparation design as a conservative treatment option that aims to preserve both gingival and tooth health structures using a finite element analysis with non-preparation and conventional designs. They found that the preparation design geometry affects the long-term success of laminate restoration and the proposed design yields more uniform and appropriate stress distributions than other techniques. Similarly, another study studied the stress distributions in a finger bone using a dental implant and the loading force was applied along the long axis of the implant using finite element analysis [17]. They found that the maximum stress was located at the head of an abutment screw. The minimum stress was in the apical third of the implant fixture. The weakest point was calculated by the safety factor which was in the spongy bone at the apical third of the fixtures. Hence, to achieve long-term success, the designers of implant systems must confront biomaterial and biomechanical problems including in vivo forces on implants, load transmission to the interface, and interfacial tissue response. The distribution of load and stresses generated within the crown-tooth complex is very different from to intact tooth compound. Therefore, this study aimed to investigate and evaluate the effect of stresses generated in a hybrid dental ceramic crown in the posterior region of the mouth, fabricated with CAD-CAM technology for a conventional tooth preparation design with the help of the FEA method.
## 2 Materials and Methods
### Study Site and Sampling
#### 2.1.1 CO\({}_{2}\) and CH\({}_{4}\) Fluxes  
One branch per tree from the lower third of the crown but fully exposed to the sun for most of the day was sampled. The branches sampled were selected to fit inside the chamber in which gas exchange was measured. The static Plexiglas Chamber (100 mm inner diameter, 300 mm length) with a stopcock on top was used and connected to a soil respiration chamber (100 mm inner diameter, 200 mm length), Model SRC-1, PP Systems, Amesbury, MA, USA, with a total volume of 3.527 cm\({}^{3}\) linked to an infrared gas analyzer Model EGM-4, PP Systems, Amesbury, MA, USA. Air temperature, relative humidity (PASS VP-3, Decagon Devices, Pullman, WA, USA) and solar radiation (PYR, Decagon Devices, 380-1120 nm) were continuously measured and recorded using a data logger (Decagon Devices, Model Em50). Plant CO\({}_{2}\) and CH\({}_{4}\) fluxes were measured in the light (photosynthesis) (Figure 1a). Each measurement was taken over 90 s. For plant CH\({}_{4}\) fluxes, the closed chamber (volume 2.356 cm\({}^{3}\)) was sealed for 300 s before extracting a gas sample using polyethylene syringes fitted with nylon stopcocks (25 mL) and injected into pre-evacuated 12 mL vials (Exetainers, Labco Ltd., Lampeter, Wales, UK). Figure 2 shows the measurements procedure (circles in blue). Methane concentrations were determined by gas chromatography (Perkin Elmer model Clarus 600 with FID detector) at the Soils Laboratory of Universidad de Concepcion, Chillan, Chile (details in SM).
## 3 Results
### Real Sample Analysis  
The applicability of the SPME Arrow-PMS method was evaluated using milk and chicken samples. Since no target analytes were detected in these samples, spiking was performed at three concentration levels. According to Chinese National Standard GB 31650, the maximum residual limits for sulfadimidine and thiabendazole in meat and milk are 25 ug kg\({}^{-1}\) and 100 ug kg\({}^{-1}\), respectively. Additionally, amantadine, clenbuterol, and ratcopamine are prohibited in animal-derived food products. Given the LOQ for thiabendazole (25-28 ug kg\({}^{-1}\), Table 2), spiking levels for amantadine, clenbuterol, sulfadimidine, and ratcopamine were set at 25, 100, and 200 ug kg\({}^{-1}\), while spiking levels for thiabendazole were set at 30, 100, and 300 ug kg\({}^{-1}\). As shown in Table 3, recoveries ranged from 87% to 107% for milk samples and from 85% to 116% for chicken samples, with RSDs between 2.1%  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Sample**} & \multirow{2}{*}{\begin{tabular}{c} **LODs** \\ **(ug kg\({}^{-1}\))** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **LOQs** \\ **(ug kg\({}^{-1}\))** \\ \end{tabular} } & \multirow{2}{*}{**Linear**} & \multirow{2}{*}{**Analytes**} & \multirow{2}{*}{**Recovery**} & \multirow{2}{*}{**Detection**} & \multirow{2}{*}{**References**} \\  & & & & & & & & \\ \hline \hline \multicolumn{8}{l}{
\begin{tabular}{c} a UA-LIME- \\ DES-HPLC-UV \\ \({}^{\text{b}}\) RP-HPLC-UV- \\ LAC \\ \({}^{\text{c}}\) LC-MS/MS \\ \({}^{\text{d}}\) AE-LC- \\ MS/MS \\ \({}^{\text{  
and 14.7%. The developed SPME Arrow-PMS method exhibited excellent accuracy and precision for the quantitative and qualitative determination of the five analytes in complex food matrices.  
Despite the acceptable accuracy and precision shown in the spiking experiments, the current method's sensitivity for detecting amantadine, clenbuterol, and ratcopamine at trace levels remains limited. This could be a reason why no positive residues were found in the unspiked milk and chicken samples. Further optimization may be required to meet the stricter regulatory limits in certain regions.
## 7 Open Issues and Research Challenges
### Privacy  
Maintaining the privacy of user data is also of utmost importance. It can be achieved using cryptographic algorithms using digital signatures, SHA-256, and message digest algorithms. All these algorithms require high computation power to encode and decode the information.
# Blockchain-Enabled Privacy-Preserving Ecosystem for DNA Sequence Sharing
## 3 System Design and Implementation
### System Architecture
#### 3.1.2 DNA Sharing Chain  
The DNA Sharing Chain implements the access control mechanism for DNA data sharing. Patients and research institutes, following decentralized identity registration, upload relevant materials for validation by the Ministry of Health and Welfare, which creates access control objects upon certification. These objects maintain the access levels for DNA segments and allow patients to manage permissions. Requests for data access trigger smart contracts that query these objects to generate lists of available data, enabling seamless database interactions.  
The DNA sharing chain utilizes Hyperledger Fabric to implement efficient and secure access control. Key-value structures store access control data, enabling streamlined querying and management. We choose Hyperledger Fabric (a private consortium blockchain) for this component because it provides a permissioned environment with modular consensus and no cryptocurrency requirement, making it well-suited for sensitive biomedical data sharing [23]. Hyperledger Fabric's design allows for high transaction throughput and fine-grained identity management through its Membership Service, aligning with our trust model [18].  
* Patient Access Control Contract: This contract manages patient-specific access permissions. Patients can assign access levels to DNA segments using an updateAccessLevel-List() function. Table 1 describes access levels ranging from public (accessible to all research institutes) to private (not accessible).  
Figure 1: System architecture.
## 4 Results and Discussion
### Most Suitable Classification Method
Its high spatial and temporal resolution supports extensive, long-term ecological studies across large geographical areas [80; 81]. Such phenological databases can reconcile the scale discrepancy between satellite phenology and ground observations and contribute to a more profound comprehension of forest ecosystem dynamics and future climate change modeling [82; 83]. In the present study, we tapped into the potential of multitemporal PS satellite imagery in ecological research. This approach addresses a gap in the usage of remote sensing with very high spatial resolution (e.g., WorldView) and moderate spatial resolution (e.g., Landsat) [84]. In doing so, our case study concerning allergenic tree species classification significantly advances our understanding of urban vegetation specifics, thereby enhancing our comprehension of urban ecosystem services [85]. In this context, the visualization of each classification scenario with two example subsets, which show the improvement in allergenic tree species detection, is shown in Figure 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Classified**} & \multirow{2}{*}{**Ash**} & \multicolumn{4}{c}{**Reference**} & \multirow{2}{*}{**UA [\%]**} \\  & & & **Blirch** & & **Plane** & **Other** & **UA [\%]** \\ \hline Ash & 366 & 50 & 90 & 189 & 52.66 \\ Birch & 99 & 552 & 118 & 244 & 54.49 \\ Plane & 184 & 103 & 498 & 161 & 52.64 \\ Other & 163 & 137 & 101 & 372 & 48.12 \\ PA [\%] & 45.07 & 65.56 & 61.71 & 38.51 & CS1 \\ \hline \multirow{2}{*}{Classified} & \multicolumn{4}{c}{Reference} & \multirow{2}{*}{**UA [\%]**} \\  & Ash & Birch & Plane & Other & UA [\%] \\ \hline Ash & 501 & 43 & 44 & 144 & 68.44 \\ Birch & 21 & 454 & 10 & 128 & 74.06 \\ Plane & 41 & 26 & 535 & 63 & 80.45 \\ Other & 87 & 151 & 57 & 438 & 59.75 \\ PA [\%] & 77.08 & 67.36 & 82.82 & 56.66 & CS2 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Confusion matrix of the best classification scenario (CS1–3; bottom right corner), i.e., single-date PS imagery using RF classifier, multitemporal PS imagery using MLP, and multitemporal PS imagery with ancillary data using XGB classifier. Figure 3: Number of input features from the VSURF feature selection method where PS indicates PlanetScope spectral band, VI vegetation indices, and GLCM texture features. Figure 2: Comparison of classification maps of the entire study area (first column) generated using single-date PS imagery (CS1), multitemporal PS imagery (CS2), and multitemporal PS imagery with ancillary data (CS3), and example subset 1 (second column) and subset 2 (third column) which show improvement in allergenic tree species detection.
# Finite Element Analysis of Thermal Stress and Thermal Deformation in Typical Part during SLM
## 3 Simulation Process and Operation  
### Simulation Flow  
The simulation methodology is presented in Figure 2. This simulation included two aspects. On one hand, the heat source definition, the heat transfer mode, and the thermal-mechanical coupling calculations were carried out, based on the finite element theory. On this basis, the simulation model was utilized for a layered slice, whereas the layer thickness data were added to the processing technology file. In addition to the scanning path development, a common path striped pattern was input to the processing technology file. On the other hand, process parameters such as the material properties, printing support, process equipment, and laser parameter settings could be set within the corresponding parameter window. Moreover, the reasonable value could be modified on the basis of previous simulation results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Temperature T(K)** & **273** & **1563** & **1713** & **2793** & **3153** \\ \hline \multirow{3}{*}{**316L Stainless Steel**} & Thermal conductivity, K (W/m K) & 0 & 0 & 32 & 40 & 45 \\  & Specific heat capacity, C (J/kg K) & 0 & 110 & 180 & 290 & 800 \\  & Density, \(\rho\) (kg/m\({}^{3}\)) & 4750 & 4450 & 7000 & 6000 & 5750 \\ \hline \multirow{5}{*}{Solid 316L Stainless Steel} & Thermal conductivity, K (W/m K) & 12 & 30 & 32 & 40 & 45 \\  & Specific heat capacity, C (J/kg K) & 470 & 700 & 820 & 820 & 820 \\ \cline{1-1}  & Density, \(\rho\) (kg/m\({}^{3}\)) & 8000 & 7250 & 7000 & 6000 & 5750 \\ \cline{1-1} \cline{2-6}  & Thermal expansion, \(\alpha\) (I/K) & \(12\times 10^{-6}\) & \(14\times 10^{-6}\) & \(14.2\times 10^{-6}\) & \(14.5\times 10^{-6}\) & \(15\times 10^{-6}\) \\ \cline{1-1}  & Young’s modulus, E (GPa) & 206 & 20 & 13.5 & 0.2 & 0.02 \\ \cline{1-1}  & Poisson’s ratio, \(\mu\) & 0.3 & 0.4 & 0.41 & 0.43 & 0.45 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thermal physical parameters of 316L stainless steel (Date from literature [10]).
## 3 Results  
### Sequence and Structure Comparisons  
Figure 1 shows the significant similarity (39%, sequence identity of 23%) shared between the _A. thaliana_ phosphosugar phosphatase AtSgpp [19] and DL-glycerol-3-phosphatase AtGpp [18]. The expected structure classified both proteins as members of the HAD wide family of magnesium-dependent acid phosphatases subfamily I with the C1-type cap domain [19; 35]. The greatest similarity occurs at the four motifs forming the catalytic scaffold of the active site platform framed by the core domain [36], which contains the highly conserved sequence motifs (selfsame in both proteins) by which family members are recognized (Figure 1, motifs 1, 2, 3, and 4). These motifs position substrate-cofactor-binding and catalytic residues that are involved in the core chemistry [16]. Conversely, apart from the stringently conserved loop marker Gly (G) flanked by Lys/Arg (K/R), limited sequence homology is shared at the predicted cap domain substrate recognition motif 5, which is responsible for the chemical diversification within the family. Motif-5 positions residues whose side chains contribute to the catalytic site, probably operating in domain-domain binding, active-site desolvation, and/or catalysis [20] (Figure 1). Apart from sequence homology, AtSgpp and AtGpp show overlapping structures. The more shocking difference is observed in the loop preceding the motif 5 of the cap domain, which is predicted to be extended by three additional residues in AtSgpp (Figure 1). To verify whether these differences within the cap domain could be related to the different substrate affinity shown by both enzymes, we investigated the activity of the AtSgpp mutant AtSgpp3\(\Delta\) (\(\Delta\)F53, \(\Delta\) N54, \(\Delta\)N55), which mimics the length of the counterpart loop in AtGpp. The structural model of the resulting mutant AtSgpp3\(\Delta\) did not distort either the proper predicted folding of the rest of the protein structure (Figure S1), or the pK\({}_{\text{a}}\) values of the side chains of catalytic residues in the active site.
## 10 Challenges and Open Research Problems
### Security Challenges
Thus, the adversary node not only gains access to packets of nodes within its radio range but also blocks these nodes from transmitting to the final receiver or genuine network sink. Again, other UAVs, relay nodes, or edge devices can be used to perpetrate this attack in UAV-assisted networks. A potential solution for the communication security challenges in UAV-based networks is to use blockchain technology [314]. Proper authentication will also ensure that only vetted elements are admitted into the network. Designing a robust intrusion and detection system will also protect UAV-aided networks from malicious attacks [319]. In addition, innovativecommunication protocol design such as use of spread spectrum technologies, MIMO techniques, or cooperative UAV features can also help to protect UAV communication from eavesdroppers. Due to their directionality, mm-wave communication can also reduce the threat of jamming and eavesdropping since the electromagnetic signal beams are focused on the intended receiver(s) [324]. Data encryption is very helpful in dealing with information and data security, as is the proper design of the configuration files through a process known as _system hardening_[319]. Wormhole attacks can be detected and defended using packet leashes [321], where some information is added to a packet to restrict the maximum transmission distance per link. In addition, ANN have been proposed to create threat maps in the operating environment of UAVs, with recurrent neural networks used to predict the normal trajectory motion of the UAV and hence detect any deviations in real-time as suspected cyber attack [317].
# MFMNet: Multi-Scale Feature Attention Mixture Network for Short-Term Load Forecasting
## 3 Results
### Evaluating on AEP Dataset
The existing improved methods CNN-Fuzzy and MIFnet have significantly improved compared with LSTM and CNN. Figure 6 shows the comparison of ablation experiments. From the results of the ablation experiments, it can be seen that the prediction accuracy of the two-branch attention mechanism model at the time series mutation is better than that of the network without the attention mechanism. As can be seen from Figure 6, when there is no attention mechanism, the prediction results fluctuate greatly and the prediction inertia is large, resulting in the prediction results at the wave peak often being larger than the actual values. In addition, the multi-scale convolution module can effectively improve the accuracy of model prediction. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **MSE** & **MAE** & **RMSE** & **MAPE** \\ \hline LR & 0.62 & 0.56 & 0.79 & 0.18 \\ ARMA & 0.57 & 0.62 & 0.76 & 0.17 \\ BP & 0.54 & 0.46 & 0.73 & 0.21 \\ SVR & 0.59 & 0.51 & 0.77 & 0.25 \\ DT & 0.60 & 0.55 & 0.77 & 0.26 \\ CNN & 0.38 & 0.47 & 0.62 & 0.19 \\ LSTM & 0.43 & 0.42 & 0.66 & 0.18 \\ STLF [12] & 0.29 & 0.34 & 0.54 & 0.18 \\ ELM [14] & 0.53 & 0.48 & 0.73 & 0.21 \\ Resnet & 0.33 & 0.36 & 0.57 & 0.12 \\ CNN-Fuzzy [22] & 0.31 & 0.35 & 0.56 & 0.14 \\ MB-LSTM [28] & 0.28 & 0.31 & 0.53 & 0.12 \\ MIFnet [30] & 0.28 & 0.34 & 0.53 & 0.11 \\ Transformer [37] & 0.26 & 0.34 & 0.51 & 0.13 \\ \hline Without MCM & 0.25 & 0.34 & 0.50 & 0.12 \\ Without DAM & 0.24 & 0.35 & 0.49 & 0.11 \\ MFAMNet & 0.20 & 0.27 & 0.45 & 0.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of different methods on the AEP dataset.
## 5 Maintenance
### Fault Detection  
Each area of maintenance requires a huge amount of data; Fault detection (FD) and anomaly detection are not exceptions. This task shares many solutions with the aforementioned anomaly detection method in quality control, due to their common nature. Since the main goal is to observe and identify failures, classification, clustering, regression and anomaly detection algorithms are best suited for this use-case [143].  
In [144], a clustering-based solution is discussed for the fault detection of a Power Distribution Network, where the decision tree algorithm outperformed KNN and SVM in terms of accuracy. In [145], the authors proposed a clustering approach for detecting multi-component degradation in aircraft fuel systems. Decision tree-based solutions, such as the one detailed in [146; 147], are also capable of providing an accurate detection rate, while maintaining a low computational complexity.  
Nonetheless, using neural networks for this purpose is also a common solution, such as in [148], where a completed maintenance framework is built upon it. Artificial Neural Network-based solutions can be effectively used for feature extraction and for analysis time-series data, as is shown in [149; 150]. Moreover, fault detection is the one of the main tasks of predictive maintenance that strongly relies on real-time computation. Ref. [151] presents a Convolutional Neural Network (CNN)-based solution for motor fault detection that can provide accurate estimations in real time.
## 5 Experimental Results
### IDS Discussion  
The loss scores for training and testing were 0.11 and 0.10, respectively. The SVM-based model gave scores of 95.30% for training and 95.81% for testing, respectively. The values for precision, recall and F1-Score were 94.00%, 98.00% and 96.00%, respectively, for a normal class. On the other hand, the values for precision, recall and F1-score were 98.00%, 94.00%, and 96.00%, respectively, in the case of the anomaly class. FPR was 00.05 and TPR was 97.84%. From Table 3, it can be seen that the proposed model achieved a high TPR, and also achieved a superior FPR in comparison to other existing studies.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Training Accuracy\%** & **Training Loss** & **Testing Accuracy\%** & **Testing Loss** \\ \hline
95.30 & 0.11 & 95.81 & 0.10 \\ \hline \multicolumn{4}{c}{**Confusion Metric for GRU**} \\ \hline
**TP** & **FP** & **FN** & **TN** \\
20098 & 1354 & 443 & 22661 \\ \hline \multicolumn{4}{c}{**Classification Report**} \\ \hline
**Class Labels** & **Precision** & **Recall** & **F1 Score** \\ Normal Class & 94.00 & 98.00 & 96.00 \\ Anomaly Class & 98.00 & 94.00 & 96.00 \\ \hline \multicolumn{4}{c}{**FPR**} & **TPR \%** \\ \hline
0.05 & & 96.15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results for our proposed model.  
Figure 16: ROC curve for our proposed model.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **TPR\%** & **FPR\%** \\ \hline DAR Ensemble [38] & 78.88 & N/A \\ Naive Bayes [39] & 82.00 & 5.43 \\ GAR Forest [40] & 85.00 & 15.00 \\
**Proposed Model** & **96.15** & **5.11** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the proposed model and other approaches.