esa-sceva/satcom-chunk-collection huggingface dataset:
# Privacy-Preserving Byzantine-Resilient Swarm Learning for E-Healthcare
## 5 Our Privacy-Preserving Swarm Learning
### System Initialization
#### 5.1.2. Cryptosystem Initialization  
Firstly, **TA** generates a key pair \(<sk_{FHE}\), \(pk_{FHE}>\leftarrow\)_KeyGen_(\(1^{\lambda}\)). **TA** sets a security parameter \(\lambda\), and chooses a power-of-two \(M=m(\lambda,q_{L})\), an integer \(h=h(\lambda,q_{L})\), an integer \(P=P(\lambda,q_{L})\), and a real value \(\sigma=\sigma(\lambda,q_{L})\). Then, **TA** samples \(s\leftarrow\mathcal{HVT}(h)\), \(a\leftarrow\mathcal{R}_{q_{L}}\), and \(e\leftarrow\mathcal{DG}(\sigma^{2})\), where \(\mathcal{HVT}(h)\) is the set of signed binary vectors whose Hamming weight is exactly \(h\) and \(\mathcal{DG}(\sigma^{2})\) samples a vector in \(\mathbb{Z}^{N}\) by drawing its coefficient independently from the discrete Gaussian distribution of variance \(\sigma^{2}\). The private key is set as \(sk_{FHE}\leftarrow(1,s)\) and the public key as \(pk_{FHE}\leftarrow(b,a)\in\mathcal{R}_{q_{L}}^{2}\) where \(b\leftarrow-as+e(mod\ q_{L})\).  
Secondly, **TA** randomly divides \(sk_{FHE}\) into \(K\) parts. **TA** computes \(\delta=\eta\cdot\lambda\), s.t., \(\eta\cdot\lambda\equiv 1\ mod\ N^{2}\), sets a threshold \(m\), s.t., \((m-1)\leq N\), and defines a polynomial function of secret sharing protocol \(f(x)=\lambda+\sum_{i=1}^{m}a_{i}x^{i}\), where \(a_{1},a_{2},\ldots,a_{m-1}\in\mathbb{R}_{\lambda}^{*}\). **TA** divides \(sk_{FHE}\) into \(K\) parts through calculating \(f(i)\). **TA** broadcasts \(<g,pk_{FHE},\eta>\) to all **MCs** through blockchain-based network.  
Finally, each \(\textbf{MC}_{k}\in\textbf{MC}\) asks **TA** for its own private key \(sk_{FHE}^{k}=f(k)\).
## 4 Discussion of the Impact of Different Wavefield Components
The results of the experiments under different wavefield conditions show that the prediction results are most accurate in the wavefields without free surface-related multiples. Training with data from a free-surface multiple-free wavefield containing direct waves and the entire wavefield yields better results than using wavefields without direct waves. The presence of multiples has little impact on the experiment, highlighting the importance of retaining direct wave components before signal reconstruction. This provides new insights for the data preprocessing of our reconstruction method. Figure 15: Comparison of BE-Transformer, CNN network prediction results, and expected data single-trace waveforms under four wave field conditions. (**a**) without free surface-related multiples wavefield; (**b**) primary scattered field; (**c**) full wavefield; (**d**) the full wavefield without direct wave.
## 2 Methods
### Simulating Instrument Noise  
Noise was superimposed over the simulated enhancements to represent random errors that would occur in true observations. The strength of the plume enhancements in comparison to the strength of the noise was the primary limitation in determining how large a power plant must be to accurately estimate its emissions.  
The noise or random error was generated following a normal distribution, and the strength of the noise was defined as its standard deviation. The default noise strength was 1 ppm for 2 \(\times\) 2 km\({}^{2}\) pixels, representing an instrument with a precision of 1 ppm, or 0.25% at 400 ppm CO\({}_{2}\) background. This was similar to the precision requirement of OCO-2 [6]. For a space-based instrument of a fixed size, higher precision is more easily achieved with larger pixels. With larger pixels the instrument collects more light such that the signal \(S\) increases proportional to the area \(A\) (\(S\propto A\)), however, noise \(N\) also increases in proportion to the square root of the signal, hence \(N\propto\sqrt{A}\). The resulting improvement to the signal-to-noise ratio (SNR) is proportional to \(\sqrt{A}\). Equivalently, for square pixels the SNR increases linearly with the pixel side length, hence we scaled the precision to be inversely proportional to the pixel side length.  
Figure 2: Simulated and noisy XCO\({}_{2}\) observations relative to a constant 400 ppm background as in Figure 1. Column 2 shows the noisy observations for square 4 \(\times\) 4 km\({}^{2}\) pixels. Columns 3 and 4 show the noisy observations for rectangular 7 \(\times\) 2.3 km\({}^{2}\) pixels with the long side oriented across the plume (column 3) and along the plume (column 4).
## 6 Results and Analysis
### Discussion  
Simulation results show that the deployed reputation model is effective at detecting and isolating misbehaving nodes. The analysis of the results of the simulated networks shows that the network with the reputation model experiences a slightly higher end to end delay than the networks without the model. However, the network with the reputation model has a much better performance in terms of the throughputs, packet delivery ratios and lower routing overheads. This suggests that the deployed reputation model is beneficial to MANETs.  
The registered network end to end delays with the deployed reputation model means that packets may take longer to get to their desired destination, which demonstrates that to accomplish a reliable MANET using the deployed reputation model, the possible increase in the average end to end of a network serves as the compromise. However, the trade-off between average end to end network delays and the other network metrics, that is better network throughput, packet delivery ratio and lower routing overhead, confirms that deployment of the proposed reputation model could lead to an improvement of the overall performance of a MANET.  
The effectiveness of the proposed dynamic reputation management system could be further established by comparing the improved network metrics recorded with the deployed reputation model with another proposed reputation model. However, the main motivation for the monitoring and detection technique proposed in this paper is to monitor, detect and curb the negative effects of selective existence attacks carried out by selfish nodes that do not participate in route discovery processes for other nodes in a MANET, as described in Subsection 3.1. This attack has not been addressed by most existing reputation managements systems.
# The Total Phenolic Content and Antioxidant Activity of Nine Monofloral Honey Types
## 2 Materials and Methods
### Determination of Total Phenol Content (TPC)  
The Folin-Ciocalteu colorimetric method was applied to determine the TPC of the samples [24]. Specifically, 5 g of each honey sample were diluted to 50 mL of distilled water and then, the solutions were filtered through a qualitative filter. In turn, 500 uL of this solution were mixed with 2.5 mL of 0.2 N Folin-Ciocalteu's phenol reagent (2 N) (Merck, Darmstadt, Germany) for 5 min and afterwards 2 mL of 75 g/L sodium carbonate (Na\({}_{2}\)CO\({}_{3}\)) (\(\geq\)99.5%, Merck, Germany) were added. After the incubation for 2 h in dark and at room temperature, the absorbance of the reaction mixture was measured at 760 nm against a methanol blank (Genesys 105 UV-Vis, Thermo Scientific, Waltham, MA, USA). The analysis was performed in duplicate. For the quantification of the phenol content, calibration curves were created using standard solutions of gallic acid (\(\geq\)98.0%, Merck, Germany) (concentrations: 10-400 mg/L). The results were expressed as mg gallic acid equivalents (GAE)/100 g honey.
# Energy-Efficient Partial LDPC Decoding for NAND Flash-Based Storage Systems
## 2 Backgrounds
### LDPC Decoding Algorithms  
The SPA and the MSA are two prominent methods used for decoding LDPC codes, which are essential for error correction in NAND flash-based storage systems. They are also known as the Belief Propagation (BP) algorithms of LDPC codes. It operates by passing probabilistic messages along the edges of a Tanner graph to estimate the likelihood of bit values. Both algorithms operate iteratively decoding the likelihood messages until they converge to a stable solution or reach a predefined number of iterations. In each iteration, the SPA combines the messages from neighboring nodes using a product operation, followed by a normalization process to update the beliefs of each bit's value. In contrast, the MSA estimates these probabilities by considering the minimum value of the incoming messages, hence the name. This method, while an approximation, significantly reduces the need for complex calculations without drastically affecting decoding accuracy. The SPA typically requires floating-point precision and involves trigonometric functions, making it computationally intensive.  
The advantage of using MSA lies in its simplicity, as it can be implemented using integer arithmetic and simple comparison operations, making it suitable for hardware with limited processing capabilities. Both algorithms benefit from the inherent error detection and correction capabilities of LDPC codes, which feature a redundant structure enabling the identification and rectification of errors in data transmission. The practical implementation of these algorithms also considers factors such as channel noise characteristics and the required level of error correction. Tailoring the algorithm to specific needs can result in various modifications and optimizations, such as the normalized MSA and the offset MSA, which aim to bridge the performance gap with the SPA.
# Damage Analysis of Reinforced Concrete Columns Based on Fiber Beam-Column Element
## 2 Analysis Model of Reinforced Concrete Columns
### Constitutive Model of the Material
#### 2.2.2 Constitutive Model for Steel Bar  
In this study, the modified Menegotto-Pinto model [26] was used to describe the stress-strain relationship of steel bars. The model can consider the isotropic strengthening effect [27] and the Bauschinger effect under cyclic loading, and has high simulation accuracy and solution efficiency.  
In order to make the modified Menegotto-Pinto constitutive model consider the damage and fracture behavior of steel bars, the Bonora damage model [28] is used to simulate the damage characteristics of steel bars [22]. The Bonora damage model, based on continuous damage mechanics, can simulate the damage performance of steel well using the nonlinear damage evolution criterion. The damage criterion of the Bonora damage model is as follows:  
\[\dot{D}=\alpha\frac{\left(D_{\text{cr}}-D_{0}\right)^{1/\alpha}}{\ln\varepsilon _{\text{cr}}-\ln\varepsilon_{\text{th}}}f(\frac{\sigma_{m}}{\sigma_{\text{eq} }})(D_{\text{cr}}-D)^{\alpha-1/\alpha}\frac{dp}{p} \tag{9}\]  
where, \(\dot{D}\) is the damage increment; \(D_{0}\) is the initial damage value; \(D_{\text{cr}}\) is the critical damage value; \(D\) is the cumulative damage value; \(\varepsilon_{\text{cr}}\) is the critical strain corresponding to the critical damage value; \(\varepsilon_{\text{th}}\) is the threshold strain for the start of damage; \(dp\) is the equivalent plastic strain increment; \(p\) is the equivalent plastic strain (plastic strain for the uniaxial constitutive model); \(\alpha\) is the damage parameter; and \(f(\frac{\sigma_{m}}{\sigma_{\text{eq}}})\) is the influence factor in the triaxial stress state, and is taken as 1 for the uniaxial constitutive model. In the analysis, the plastic strain \(p\) is obtained from the Menegotto-Pinto model. Other parameters are directly derived from Reference [28].  
The damage index \(D_{\text{I}}\) is calculated as follows:  
\[D_{\text{I}}=\frac{D_{\text{cr}}-D}{D_{\text{cr}}-D_{0}} \tag{10}\]  
[PERSON] and [PERSON] [29] modified the Bonora damage model to accommodate the cyclic loading. In the model, the steel bar is considered damaged only when it is subjected to tension. Therefore, only the tensile plastic strain is adopted when calculating the damage index. The stress-strain curve of the modified Menegotto-Pinto model considering damage is shown in Figure 5, and the corresponding damage index is shown in Figure 6.  
Figure 5: The modified Menegotto–Pinto model considering damage.
# A Multi-Dimension Spatial Method for Topology Awareness and Multipath Generating
## 5 Evaluation and Analysis
### Accuracy of Topology Awareness  
To evaluate the accuracy of topology awareness, we compare MDSM with the other three positioning methods, namely MDS-MAP [21; 23], Vivaldi [22] and GNP [24]. In MDSM, we only use MDS-MAP to generate the independent view for each autonomous system. These methods are conducted on different networks depicted in Figure 5. Then, we compare the relative errors among these positioning methods. In MDSM, the relative error is the average error of all independent views. Referring to [26], we use the relative error to indicate the accuracy and it is defined as _Relative Error_=\(\frac{|M_{ij}-C_{ij}|}{M_{ij}}(\forall i,j,i\
eq j,M_{ij}\
eq 0)\), where \(M_{ij}\) denotes the practical delay between the ith and jth nodes, and \(C_{ij}\) denotes the estimated delay calculated from their coordinates of the ith and jth nodes. The relative errors of these methods are shown in Table 3. We observe that the relative error of MDSM is the smallest and the accuracy of topology awareness is improved.  
Figure 5: The network topology of the scale-free topology feature generated with BRITE. Nodes with different colours mean they belong to different autonomous systems.  
Figure 6: The backbone topology of practically deployed bearing networks from ChinaNet, CMNet. (**a**) shows the topology of one underlay network and circles represent the backbone routers; (**b**) shows the integrated topology of underlay networks belonging to ChinaNet and CMNet; the bold lines are exchange points between them.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Node Quantity** & **MDSM** & **MDS-MAP** & **GNP** & **Vivaldi** \\ \hline
30 & 0.163 & 0.215 & 0.231 & 0.230 \\
50 & 0.175 & 0.227 & 0.245 & 0.251 \\
70 & 0.183 & 0.232 & 0.255 & 0.257 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Positioning methods.
## 4 Performance Analysis
### Theoretical Analysis
#### 4.1.2 The Delivery Probability of Inter-Cluster Routing  
By the forwarding approach, when inter-cluster messages are generated, the messages will be forwarded to the relay nodes with a higher centricity until the messages are received by the destination or the nodes in the same cluster as the destination. In the transmission process, the messages will be carried by the relay nodes from one cluster to another, and finally be delivered to a relay node in the destination's cluster. Then, the intra-cluster forwarding scheme will be used to send the messages to their destinations.  
As shown in the system model, the mobility model is a Markov process, where nodes will change from one state to another due to movement. For any node \(k\), let \(St_{n}\) is the \(nth\) state, and \(P(s_{i},s_{j},t)\) represents in the period time \(t\), node \(k\) changes from the state \(s_{i}\) to state \(s_{j}\). We can  
Since there is a limited capacity for the cache at each node, when a sufficient number of messages are generated, the cache could be overflowed, and the messages could be dropped. To compute the delivery probability, the service efficiency of the network \(\delta\) should be known. Let \(\rho\) to be the ratio of customer service per unit time, we can obtain:  
\[\rho=\frac{1}{T_{N}}\sum\
olimits_{i=1}^{T_{N}}P_{inter-cluster}(\Delta t\leq i) \tag{17}\]  
Let \(\lambda\) to be the arrival rate of the messages, \(B_{Max}\) to be the maximum cache available, we can compute the \(\delta\) as:  
\[\delta=(\lambda*T_{N}*\rho+B_{Max})/(\lambda*T_{N}) \tag{18}\]  
Since we have obtained the delivery probability without considering the cache in formula (13), and according to the service efficiency, the delivery probability with the limited cache \(P_{inter-cluster}^{\prime}(\Delta t\leq T_{N})\) can be approximated as:  
\[P_{inter-cluster}^{\prime}(\Delta t\leq T_{N})\approx\delta*P_{ inter-cluster}(\Delta t\leq T_{N}) \tag{19}\]
# Resource Optimization for 3D Video SoftCast with Joint Texture/Depth Power Allocation
###### Abstract  
During wireless video transmission, channel conditions can vary drastically. When the channel fails to support the transmission bit rate, the video quality degrades sharply. A pseudo-analog transmission system such as SoftCast relies on linear operations to achieve a linear quality transition over a wide range of channel conditions. When transmitting 3D videos over SoftCast, the following issues arise: (1) assigning the transmission power to texture and depth maps to obtain the optimal overall quality and (2) handling 3D video data traffic by dropping and re-allocating resources. This paper solves the pseudo-analog transmission resource allocation problem and improves the results by applying the optimal joint power allocation. First, the minimum and the target distortion optimization problems are formulated in terms of a power-bandwidth pair versus distortion. Then, a minimum distortion optimization algorithm iteratively computes all the possible resource allocations to find the optimal allocation based on the minimum distortion. Next, the three-dimensional target distortion problem is divided into two subproblems. In the power-distortion problem, to obtain a target distortion, the algorithm exhaustively solves the closed form of the power resource under a predefined upper-bound bandwidth. For the bandwidth-distortion problem, reaching a target distortion requires solving iteratively for the bandwidth resource closed form, given a predefined power. The proposed resource control scheme shows an improvement in transmission efficiency and resource utilization. At low power usage, the proposed method could achieve a PSNR gain of up to 1.5 dB over SoftCast and even a 1.789 dB gain over a distortion-resource algorithm, using less than 1.4% of the bandwidth.  
multi-view video plus depth; power allocation ratio; resource allocation; SoftCast +
Footnote †: journal: Applied  
_articles_
# Climate-Smart Agro-Hydroological Model for a Large Scale Rice Irrigation Scheme in Malaysia
## Materials and Methods
### Simulation of Future Impacts of Climate Change on Hydrological Processes  
HEC-HMS 4.2.1 was used for simulating precipitation-runoff processes of the UBRB. The model requires two types of data: spatial and hydro-climatic data. The spatial data include a digital elevation model (DEM) downloaded from the Geographic information system software program (DIVA-GIS) website, and soil and land use maps of the area obtained from the Department of Agriculture (DOA) Malaysia. The Arc-Hydro tool coupled with ArcGIS was used to process the spatial data. Due to missing values for a number of years, the observed hydro-climatic data in the area is not reliable. Therefore, a gridded daily dataset including rainfall, minimum and maximum temperature, relative humidity, wind speed, and solar radiation for 1976-2005 was used. The gridded data was at a spatial resolution of 5 km based on the angular distance weighting (ADW) procedure, and covered the entire Peninsular Malaysia [31]. Details of the procedure of data processing and development can be found in other studies [31; 32]. The final data used was obtained from the Water Resources and Hydrology Division,Department of Irrigation and Drainage (DID), Malaysia. Daily discharge records (m\({}^{3}\)/s) of the Bernam watershed were taken at gauging station known as SKC.  
The HEC-HMS model components include basin model, meteorological model, control specifications, and input data. The components were used to simulate the hydrologic response in the Bernam watershed. The spatial data collected was used to set up the basin model using the GIS component of the model (HEC-GeoHMS), to calculate parameters, such as sub-basin areas, times of concentration, lag time, and reach lengths based on the DEM geospatial information. Subsequently, deficit and constant loss (DCL), simple canopy, and simple surface methods were used for the computation of runoff-volume (loss), canopy, and surface methods, respectively. The Soil Conservation Service (SCS) unit hydrograph (SCS-UH) and constant monthly methods were used for transformation of precipitation excess into point runoff and simulation of time variations in baseflow, respectively. Moreover, for the channel flow, the Muskingum routing method was used as widely adopted by many studies [33, 34]. Historical streamflow data for 18 years (1981-1998) was used for model calibration and 8 years (1999-2006) for validation with 5 years of warm up period in each. Manual and automatic calibration techniques were applied to optimize model parameters. The perturbed future rainfall and climate data were used as input variables for the validated HEC-HMS model for modeling future streamflow scenarios of the basin (at the SKC outlet). The validated model was re-run with the downscaled climatic variables for the baseline and the future periods. Consequently, changes to streamflow were analyzed with respect to presently observed and projected future impacts of global climate change.
## 3 Analysis and Comparison of Machine Learning-Based Anomaly Detection
### Machine Learning Models
#### 3.1.5 Light Gradient Boosting Machine (LGBM) Classifier  
The Light Gradient Boosting Machine (LGBM) classifier is a fast, efficient, and high-performing gradient boosting system for machine learning and statistical modeling. Because of its histogram-based learning method, the LGBM is well-suited for training on huge datasets because it can more quickly calculate gradients by grouping data points into histogram bins [48]. Using a structure very similar to classic gradient boosting, it gradually adds decision trees to enhance prediction precision. The LGBM is a popular option for many machine learning tasks, including classification, regression, and ranking, as it has many features including regularization, early stopping, and the handling of missing variables.