esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Halophytes have gradually been introduced in marine integrated multi-trophic aquaculture (IMTA) systems due to their capacity to bioremediate nutrient-rich marine effluents and their potential use for human consumption due to their content of omega-3 and omega-6 fatty acids (FA). To foster the valorization of halophytes produced using an IMTA framework for human consumption, it is important that culture conditions keep or enhance their FA profile, when compared to that displayed by conspecifics in the wild. The main objective of the present study was to compare the FA profiles of three halophyte species (_Halimione portulacoides_, _Salicornia ramossissima_ and _Sarocornia perennis_) cultured in aquaponics coupled to an IMTA system with that of wild conspecifics retrieved from donor sites. The FA profiles were compared considering different plant organs (edible parts and roots) and sampling dates (spring, summer and autumn). Results show that the FA profiles of specimens cultured in aquaponics were significantly different from that of wild conspecifics, displaying a high content of omega-3 FAs in edible parts, particularly during summer, and mostly in the form of a-lionic acid (ALA, 18.3\(\pi\)-3). In more detail, for the specimens cultured in aquaponics, ALA concentration in the edible parts of each species ranged from 5.10 to 7.11 \(\mathrm{\SIUnitSymbolMicro g}\mathrm{m}^{-1}\) DW in \(H\). _portulacoides_, from 5.66 to 9.19 \(\mathrm{\SIUnitSymbolMicro g}\mathrm{m}^{-1}\) DW in \(S\). _ramossissima_ and from 5.49 to 7.20 \(\mathrm{\SIUnitSymbolMicro g}\mathrm{m}^{-1}\) DW in \(S\). _perennis_. Concerning the omega-6 linoleic acid (LA, 18:2\(\pi\)-6) identified in edible parts, the concentrations ranged from 2.25 to 2.46 \(\mathrm{\SIUnitSymbolMicro g}\mathrm{m}^{-1}\) DW in \(H\). _portulacoides_, from 3.26 to 4.84 \(\mathrm{\SIUnitSymbolMicro g}\mathrm{m}^{-1}\) DW in \(S\). _ramossissima_, and from 2.17 to 3.06 \(\mathrm{\SIUnitSymbolMicro g}\mathrm{m}^{-1}\) DW in \(S\). _perennis_. The nutritional quality was assessed through the ratio of PUFA/SFA, for both wild and cultured plants, and revealed values well above the threshold (0.45), the threshold value indicative of good nutritional quality. Overall, the culture conditions tested in the present work reinforce the potential of aquaponics coupled to marine IMTA to produce high-quality halophytes suitable for human consumption.  
Keywords:\(\alpha\)-linolenic acid; fatty acids profile; cultured halophytes; aquaponics +
Footnote †: journal: _Sci. 2021_, 11, 11586. [[https://doi.org/10.3390/app112411586](https://doi.org/10.3390/app112411586)]([https://doi.org/10.3390/app112411586](https://doi.org/10.3390/app112411586))
## 6 Efficiency and Security Analysis
### Comparative Assessment  
In this section, we present an informative comparative assessment of the GHZ\({}_{3}\)MQPEC protocol with respect to some notable previous protocols designed to address the private equality comparison problem for an arbitrary number \(n\) of millionaires, and also for the special case of two millionaires. We gathered in Table 2 the most important design choices for multiparty quantum private comparison protocols, such as the choice of entanglement, the use or not of unitary operations, and the reliance on QKD schemes, the latter adding extra complexity and overhead to the protocol itself. Last, but not least, we included the important efficiency metric. The results contained in Table 2 highlight the novelties incorporated in the GHZ\({}_{3}\)MQPEC protocol and corroborate its most important qualitative and quantitative traits.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**\# of Millionaires**} & **Quantum State** & **Unitary Operations** & **Use of QKD** & **Qubit Efficiency** \\ \hline
[20] & 2 & EPR & & 25.00\% \\ \hline
[24] & 2 & GHZ\({}_{3}\) & & & 33.33\% \\ \hline
[44] & \(n\) & GHZ\({}_{n}\) & & & \(\frac{m}{nm}\) \\ \hline
[45] & \(n\) & \(d\)-level basis states & & & \(\frac{m}{nm}\) \\ \hline
[49] & \(n\) & \(d\)-level states & & & \(\frac{m}{nm+1}\) \\ \hline
[50] & \(n\) & \(d\)-level Bell states & & & \(\frac{m}{nm}\) \\ \hline  & \multicolumn{4}{c}{Quantum} & & & \\
[48] & \(n\) & Homomorphic & & & \\  & \multicolumn{4}{c}{Encryption} & & & \\ \hline
[36] & 2 & Single Photons & & & \(\geq\)50.00\% \\ \hline GHZ\({}_{3}\)MQPEC & \(n\) & GHZ\({}_{3}\) & & & \(33.33\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison among GHZ\({}_{3}\)MQPEC and previous multiparty quantum private comparison protocols.
# Estimation of Wind Turbine Blade Icing Volume Based on Binocular Vision
## 2 Principle and Method
### Volume Calculation
The trapezoidal integration method can capture the variations in the surface in all directions more accurately, significantly improving the precision of volume estimation. In this study, we adopted both volume calculation methods: the Sum of Heights and Unit Area Method and the Trapezoidal Integration Method. Through comparison, we found that the Sum of Heights and Unit Area Method is computationally simple and suitable for cases with high grid density and smooth surface variations. However, it has larger errors when the surface varies greatly or the grid density is low. The Trapezoidal Integration Method has higher computational complexity but can capture subtle variations in the surface more precisely, making it suitable for scenarios requiring high-precision volume estimation. Considering computational efficiency and accuracy, we prioritized the Trapezoidal Integration Method for volume calculation in this paper to enhance the accuracy of the results.
## 4 Methodology
### Cooperative Resource Allocation Method Based on Nash Bargaining Game and Lagrangian Optimization
#### 4.1.1 Definitions  
First, we define the relevant concepts in the game model to mathematically model the resource allocation process:  
Figure 1: Schematic diagram of main conception of CMPT.  
1. Network topology: Let the satellite network consist of \(N\) satellite nodes and \(M\) ground station nodes. Each node \(n\in\{1,2,\ldots,N+M\}\) has certain resource demands and transmission capabilities.
2. Utility function: For each node \(n\), its utility function \(U_{n}\) reflects the relationship between the resources allocated to the node and its performance. The utility function maximizes the node's throughput, minimizes latency and energy consumption, and considers various resource constraints.
3. Resource allocation: In the process of resource allocation, the power allocation \(P_{n}\) and bandwidth allocation \(W_{n}\) are distributed among the nodes based on the strategies defined in the game model.
4. Cooperative game: Each node engages in cooperative behavior by sharing information and coordinating strategies to optimize the resource allocation of the overall system. This cooperative game facilitates information sharing and collaboration among nodes, aiming to enhance the overall utility.
# Satellite Image Super-Resolution by 2D RRDB and Edge-Enhanced Generative Adversarial Network +
## 4 Results and Discussions  
We compare the proposed method with several satellite image SR methods based on the GAN, including SRGAN [3], ESRGAN [4], EEGAN [5], and EESRGAN [6], respectively. The test was performed on a number of popular and publicly available satellite image databases. We adopted three popular and well-known full-reference objective evaluation metrics [35; 36] (i.e., peak signal-to-noise ratio (PSNR), structural similarity (SSIM) [37] and feature similarity (FSIM) [38]) to test the performance of all compared models. We also use no-reference evaluation indicators, including average gradient (AG) [39] and naturalness image quality evaluator (NIQE) [40] to evaluate those reconstructed images without high-resolution images as reference.
# Performance Evaluation of a Long-Span Cable-Slayed Bridge Using Non-Destructive Field Loading Tests
## 2 Non-Destructive Field Loading Test for Pingnan Xiangszhou Bridge
### Field Loading Test Conditions and Loading Arrangement  
Pingnan Xiangszhou Bridge is a longitudinal symmetrical structure. Therefore, spans 1-3 shown in Figure 5a were selected for further analysis. Measurement data from a total of five sections of the main girder--numbered from position A to position E, as shown in Figure 5a--were selected for strain and deflection analysis. A total of 10 loading cases were designed, including centric loading and eccentric loading, where vehicles are deviated to the left side of the bridge in eccentric loading cases. The layout of a standard cross-section of the main beam is shown in Figure 5b. The detailed loading test setup and sensor instrumentation layout of all loading cases are shown in Figure 6 and Table 1, respectively.  
In Figure 5a, 1 represents the position of the moment control section, while \"\(\blacktriangledown\)\" represents the position of the vertical deflection control section of the main girder.  
According to the Chinese standard \"Load test methods for highway bridges\" (JTG/T J21-01-2015), the load efficiency of the static load test should be 0.85-1.05 for completion acceptance load tests; otherwise, it should be between 0.95 and 1.05. The load efficiency (\(\eta_{q}\)) of the static load test can be calculated according to Equation (7):  
\[\eta_{q}\ =\ \frac{S_{s}}{S\cdot(1+\mu)}, \tag{7}\]  
where \(S\) is the calculated value of the most unfavorable effect generated by the control load, \(S(1+\mu)\) is the calculated effect value of the control load, and \(S_{S}\) is the calculated effect value of the test load.  
Figure 4: Finite element models of Pingnan Xiangszhou Bridge: (**a**) single-beam model and (**b**) plate element model.  
Figure 5: Strain measuring point arrangement of Pingnan Xiangszhou Bridge: (**a**) layout elevation and (**b**) layout of a standard cross-section of the main beam (unit: cm).  
Figure 6: The on-site loading test conditions in this study.  
In the table above, the bending moment unit is kN-m, and the lower edge of the section is positive in tension and negative in compression. The displacement unit is mm, which is positive upward and negative downward. The unit of cable force increment is kN, which increases to be positive and decreases to be negative. The stress unit is MPa, and the tensile stress is positive while the compressive stress is negative. In Table 1, \(S(1+\mu)\) is the calculated effect value of the control load, \(S_{S}\) is the calculated effect value of the test load, and \(\eta_{q}\) is the load efficiency.  
A total of 24 three-axle trucks were used in the field loading test, and the technical parameters of the used vehicles are shown in Table 2.
## 4 Datasets and Experimental Settings
### Experimental Platform and Evaluation Metrics  
Sometimes, the performance of the model can be influenced to varying degrees by different experimental configurations. Table 1 summarizes the configuration used in our experiments.  
In our experiments, we employed two datasets with distinct hyperparameter configurations. The detailed settings are presented in Table 2.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Item** & **Description** \\ \hline Operating System & Linux (Ubuntu 16.04) \\ GPU & NVIDIA GTX 3080 Ti \\ Deep learning environment & PyTorch 1.10.0 + CUDA 11.3 \\ Framework & MMRotate [35] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experiment environment for model training.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & **Epochs** & **Learning Rate** & **Momentum** & **Weight Decay** \\ \hline DIOR-R & 12 & & & \\ HRSC2016 & 36 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter settings for two datasets.  
During DIOR-R training, the learning rate was reduced ten-fold in epochs 8 and 11, while for HRSC2016, the same reduction occurred in epochs 24 and 33.  
For performance evaluation, we employed three standard object-detection metrics: frame per second (\(FPS\)) to gauge inference speed, average precision (\(AP\)) to quantify detection accuracy for each category, and mean average precision (\(mAP\))--the average of all APs--to provide an overall measure of the multiclass performance of our model.  
The calculation equations of \(FPS\), \(AP\), and \(mAP\) are defined as follows:  
\[FPS=\frac{FrameCount}{ElapseTime}, \tag{20}\]  
\[AP=\int_{0}^{1}P(R)dR,\qquad mAP=\frac{\sum_{i=1}^{N}\mathrm{AP}_{i}}{N}, \tag{21}\]  
where \(FrameCount\) represents the total number of frames, \(ElapseTime\) represents the total time, and \(N\) represents the total number of categories; precision (\(P\)) and recall (\(R\)) are calculated as:  
\[P=\frac{TP}{TP+FP},\qquad R=\frac{TP}{TP+FN}, \tag{22}\]  
where \(TP\) is the count of true positives, \(FP\) signifies false positives, and \(FN\) denotes false negatives, referring to detections the model fails to identify.
###### Abstract  
This paper presents a cross-correlation function-based method applied to a spatially shifted differential strain readout vectors using distributed sensors under backscattering random noise and impact excitations. Structural damage is generated by low/medium energy impact on two aeronautical 24-ply CFRP (carbon fiber reinforced plastic) stiffened panels. Two different drop impact locations, two different sensor layouts and two different post-impact solicitations are provided for a skin-stringer debonding detection and length estimation. The differential signal with respect to an arbitrarily selected grounding is used. Then the effects of noise filtering are evaluated post-processing the differential signal by cross-correlating two strain vectors having one sensor gauge position lag. A Rayleigh backscattering sensing technology, with 5 mm of spatial resolution, is used to log the strain map. The results show a good coherence with respect to the NDI (nondestructive inspection) performed by ultrasonic C-scan (an ultrasonic imaging system) flaw detector.  
structural damage; non-model-based method; distributed fiber optic sensors; cross-correlation +
Footnote †: journal: Applied Sciences
# A Survey on Blockchain-Based Federated Learning
## 3 Blockchain-Based Federated Learning
### Functions of BFL
#### 3.2.3 Attack Resistance  
To address the problem of poisoning attacks, BFL typically employs consensus mechanisms deployed in the blockchain to verify model updates, thus effectively preventing poisoning attacks [106; 107; 108]. [PERSON] et al. [82] proposed replacing the central server with a blockchain system to utilize the blockchain's immutable nature to eliminate poisoning attacks. [PERSON] et al. [88] introduced a proof-of-validation (PoV) consensus mechanism used to collaboratively verify the update quality of local models among predefined miners. In this scheme, only validated model updates can be stored in a block, thereby preventing poisoning attacks. To reduce malicious poisoning model updates, [PERSON] et al. [109] proposed a reputation-based crowdsourcing incentive mechanism. Under this mechanism, if a user is detected to be making malicious updates, their update model will be rejected. They will not only miss out on rewards for that update round, but will also have their reputation reduced, affecting future profits and leading to penalties. [PERSON] et al. [110] introduced a scoring mechanism to determine whether a device is malicious and might launch poisoning attacks, thereby selecting trainers to participate in the model update to resist such attacks. [PERSON] et al. [111] proposed a multi-Krum consensus mechanism, which rejects model updates that go against the direction of most model updates. In each update round, a validation peer committee is elected by majority vote. This committee uses multi-Krum to reject malicious model updates, thus preventing poisoning attacks. [PERSON] et al. [112] introduced a decentralized validation mechanism to verify local model updates. This mechanism votes on the validity of each model, then uses the voting results to eliminate potential malicious devices.
## 2 Model Test of Heap Leaching under Lateral Liquid Injection  
### Test Device  
A self-designed and manufactured device was utilized to carry out the model test of the rare earth heap leaching under the lateral liquid injection conditions. The test device consisted of a model tank, a liquid injection device and a data acquisition system, as shown in Figure 3. The model tank was made of transparent acrylic sheets, and it was divided into three zones by using porous plates with a pore diameter of 0.4 cm. The left zone is a buffer zone, in which glass marbles were placed to provide a buffering effect for the leaching liquid to flow into the rare earth pile. A liquid inlet with a diameter of 0.8 cm was set at the left end of the buffer zone. At its top, an exhaust valve was installed to discharge the air within the buffer tank when the buffer tank was not fully filled with the leaching liquid. Once the buffer tank was completely filled, the exhaust valve was closed. The middle zone of the model tank was used for stacking the rare earth samples. This zone has a length of 25 cm, a width of 10 cm, and a height of 15 cm. In the real heap leaching of rare earths, the bottom of the pile would be inclined with a slope of 1-3% so that the leaching liquid can be drained freely from the bottom. In order to reproduce the reality, the bottom of the middle zone was set to be sloping with a gradient of 2%. On the side of the middle zone, four circular holes with intervals of 5 cm were reserved for connecting the sensors that are placed within the rare earth pile and the data acquisition instrument outside the model tank. The right zone of the model tank is a collection tank that was used to collect the exuded leaching liquid and mineral particles. The liquid injection device was composed of a support frame and a liquid supply box with a valve. The liquid supply box was connected with the model tank through a rubber tube. The data acquisition system contained pore water pressure sensors, a data acquisition instrument, and a computer. In the tests, the ammonium sulfate solution with a concentration of 2% was used as the leaching liquid. The distance from the model tank bottom to the free surface of the leaching liquid in the liquid supply box is defined as the hydraulic head of the leaching liquid, which is denoted by the symbol \(H\).
# Research on a Noise Reduction Method Based on Multi-Resolution Singular Value Decomposition
###### Abstract  
Reducing noise pollution in signals is of great significance in the field of signal detection. In order to reduce the noise in the signal and improve the signal-to-noise ratio (SNR), this paper takes the singular value decomposition theory as the starting point, and constructs various singular value decomposition denoising models with multiple multi-division structures based on the two-division recursion singular value decomposition, and conducts a noise reduction analysis on two experimental signals containing noise of different power. Finally, the SNR and mean square error (MSE) are used as indicators to evaluate the noise reduction effect, it is verified that the two-division recursion singular value decomposition is the optimal noise reduction model. This noise reduction model is then applied to the diagnosis of faulty bearings. By this method, the fault signal is decomposed to reduce noise and the detail signal with maximum kurtosis is extracted for envelope spectrum analysis. Comparison of several traditional signal processing methods such as empirical modal decomposition (EMD), ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), wavelet decomposition, etc. The results show that multi-resolution singular value decomposition (MRSVD) has better noise reduction effect and can effectively diagnose faulty bearings. This method is promising and has a good application prospect.  
Keywords:signal noise reduction; multi-resolution singular value decomposition; signal to noise ratio; mean square error +
Footnote †: journal: Applied Sciences
# Estimation of Compressible Channel Impulse Response for OFDM Modulated Transmissions
## 5 Computational Complexity Issues  
In order to evaluate the numerical complexity of the proposed method, a relative analysis to references methods is presented. A justification of this methodology is that all the methods (beside **LS**) have the same preliminary processing as **C** method. These common processing steps are as follows:  
* **LS estimate** of the frequency characteristics. It requires \(N\) complex multiplications;
* **CIR calculation** using \(N\)-point IFFT. The complexity is proportional to \(\mathcal{O}(N\log N)\);
* **Initial selection** of \(M\) consecutive CIR coefficients according to energy maximization rule. \(2N\) real number multiplications and \(N\) additions for the coefficients energy calculation, and \(2(N-1)\) additions and \(N+M\) comparisons for the final coefficients selection are required;
* **Noise power estimation** according to (14) (\(N-M-1\) real number additions);  
The proposed CIR selection method adds the following processing steps which all are performed for real numbers:  
* **Coefficients sorting.** The coefficients of \(\hat{h}_{C}\) are sorted in terms of their energy. The maximum time complexity depends on implemented sorting methods and varies from \(\mathcal{O}(M\log M)\) to \(\mathcal{O}(M^{2})\)[25];
* **Length \(U\) of the moving average filter.** This step is performed iteratively until inequality (27) is met. Every iteration consumes a single addition and comparison. The number of iterations is proportional to SNR and corresponds to the length \(U\).
* **Fine coefficients selection** out of \(M\)-element set. This processing step implements (21). \(2(M-1)\) additions, and the \(M+U\) comparisons are required.  
It is worth noting that in the proposed **G** solution, no multiplications are required (besides simple thresholds determination in Equations (22) and (27).This is a clear complexity reduction compared to the quality-related **R** method, where two residual errors along with their energies (Equations (17) and (18)) are iteratively calculated. In addition, the processing step S1 has to be implemented there.  
The efficiency of the sorting operation is crucial in both discussed approaches which rely on greedy CS methods and the MSE components analysis. This step needs some sort of additional computational resources (e.g., time and memory space) that depend on an implementation of a target sorting algorithm in the system. However, further implementations studies are not considered here, because this problem is strongly related to a hardware and software platform design of the receiver (the detailed design issues are beyond the scope of this paper).