esa-sceva/satcom-chunk-collection huggingface dataset:
# Privacy-Preserving Byzantine-Resilient Swarm Learning for E-Healthcare
## 6 Security Analysis of PBSL
### Privacy and Confidentiality  
**Theorem 1**.: _PBSL can achieve the privacy of local model gradient \(\mathbf{g}_{t}^{k}\) and the confidentiality of global model \(\mathbf{w}_{t}\)._  
Proof.: We show that \(\mathbf{g}_{t}^{k}\) and \(\mathbf{w}_{t}\) are able to be guaranteed at different stages of swarm learning.  
* In the stage of local computation, \(\mathbf{g}_{t}^{k}\) is encrypted with \(pk_{FHE}\), i.e., \[\llbracket\mathbf{g}_{t}^{k}\rrbracket_{pk_{FHE}}=Enc(\mathbf{g}_{t}^{k},pk_{ FHE})\] (15) where \(t=\{1,2,\ldots,T\}\) and \(k=\{1,2,\ldots,K\}\), and \(TA\) splits \(sk_{FHE}\) into \(K\) distributed secret keys \(sk_{FHE}^{k},sk_{FHE}^{2},\ldots,sk_{FHE}^{k}\) for \(\mathbf{MCs}\). Because any \(\mathbf{MCs}_{k}\) with only a single \(sk_{FHE}^{k}\) cannot decrypt \(\llbracket\mathbf{g}_{t}^{k}\rrbracket_{pk_{FHE}}\), PBSL can protect the privacy of \(\mathbf{g}_{t}^{k}\) at this stage.
* At the stage of global model aggregation, the winning \(\mathbf{MCs}_{k}\) homomorphically aggregates \(\{\llbracket\mathbf{g}_{t}^{k}\rrbracket_{pk_{FHE}}\}_{k=1,2,\ldots,K}\) to generate \(\llbracket\mathbf{g}_{t}\rrbracket_{pk_{FHE}}\), then homomorphically calculates \(\llbracket\llbracket S_{t}^{k}\rrbracket_{pk_{FHE}}\) by \(\llbracket S_{t}^{k}\rrbracket_{pk_{FHE}}=2\cdot ReLU(\llbracket\mathbf{g}_{t }\rrbracket_{pk_{FHE}}\odot\llbracket\mathbf{g}_{t}^{k}\rrbracket_{pk_{FHE}} )^{{}^{\prime}}-\llbracket\mathbbm{1}\rrbracket_{pk_{FHE}}\), and updates \[\llbracket\mathbf{w}_{t}\rrbracket_{pk_{FHE}}=\llbracket\mathbf{w}_{t-1} \rrbracket_{pk_{FHE}}-\gamma\frac{\sum_{k=1}^{K}\llbracket S_{t}^{k} \rrbracket_{pk_{FHE}}\cdot\llbracket\mathbf{\widehat{g}}_{t}^{k}\rrbracket_{ pk_{FHE}}}{\sum_{k=1}^{K}\llbracket S_{t}^{k}\rrbracket_{pk_{FHE}}}\] (16) The whole process is performed on cipher-text. And \(\mathbf{MCs}_{k}\) with only a single \(sk_{FHE}^{k}\) cannot decrypt \(\llbracket\mathbf{w}_{t}\rrbracket_{pk_{FHE}}\). Therefore, PBSL can protect the confidentiality of \(\mathbf{w}_{t}\) in the stage.
* At the stage of collaborative global model decryption, all \(\mathbf{MCs}\) decrypt the \(\llbracket\mathbf{w}_{t}\rrbracket_{pk_{FHE}}\) distributedly, and each \(\mathbf{MCs}_{k}\) shares its partially decryption result \(\mathbf{w}_{t}^{k}\). Because any \(\mathbf{MCs}_{k}\) with only a single \(sk_{FHE}^{k}\) cannot decrypt \(\llbracket\mathbf{w}_{t}\rrbracket_{pk_{FHE}}\), PBSL can protect the privacy of \(\mathbf{w}_{t}\) in the stage.
## 4 Results and Discussion
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Classifiers** & **Dataset** & **\# Features** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline \multirow{3}{*}{SVM} & [29] & 48 & 0.91 & 0.90 & 0.91 & 0.91 \\  & [46] & 30 & 0.87 & 0.87 & 0.87 & 0.87 \\  & Our dataset & 25 & 0.98 & 0.97 & 0.99 & 0.98 \\ \hline \multirow{3}{*}{SGD} & [29] & 48 & 0.89 & 0.90 & 0.88 & 0.89 \\  & [46] & 30 & 0.84 & 0.90 & 0.75 & 0.82 \\  & Our dataset & 25 & 0.98 & 0.98 & 0.97 & 0.98 \\ \hline \multirow{3}{*}{NB} & [29] & 48 & 0.81 & 0.99 & 0.62 & 0.76 \\  & [46] & 30 & 0.68 & 0.97 & 0.38 & 0.55 \\  & Our dataset & 25 & 0.68 & 0.99 & 0.38 & 0.54 \\ \hline \multirow{3}{*}{MLP} & [29] & 48 & 0.86 & 0.86 & 0.86 & 0.86 \\  & [46] & 30 & 0.84 & 0.84 & 0.83 & 0.84 \\  & Our dataset & 25 & 0.96 & 0.97 & 0.95 & 0.96 \\ \hline \multirow{3}{*}{k-NN} & [29] & 48 & 0.85 & 0.85 & 0.86 & 0.85 \\  & [46] & 30 & 0.88 & 0.88 & 0.89 & 0.88 \\  & Our dataset & 25 & 0.97 & 0.99 & 0.98 & 0.97 \\ \hline \multirow{3}{*}{DT} & [29] & 48 & 0.93 & 0.93 & 0.93 & 0.93 \\  & [46] & 30 & 0.90 & 0.95 & 0.84 & 0.89 \\ \cline{1-1}  & Our dataset & 25 & 0.96 & 0.98 & 0.94 & 0.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of our dataset with benchmark datasets. 0.99. The proposed work is evidently effective in distinguishing between legitimate and phishing instances within the provided dataset. The competitive accuracy score positions the proposed work at a level equivalent to or even surpassing other studies. The robust performance of the proposed work suggests the efficacy of the appropriate combination of the features and classifiers in the context of phishing detection.
## 3 The Establishment of the Photovoltaic Array Fault Diagnosis Model
### The Posteriori Probability of the Optimal LSSVM Multi-Classifiers  
Based on the posteriori distribution, which synthesized the sample information and the a priori information, the Bayesian inference has good statistical inference results. In this paper, Bayesian theory is used to optimize the parameters of the LSSVM classifier, regularization parameter \(\theta\), and kernel parameter \(\sigma\); it then obtains the optimal classifier.  
The LSSVM achieves pattern recognition by hard decision, that is, it classifies the sample by outputting \(1\) or \(-1\) directly; however, pattern recognition is an uncertain problem, so this paper diagnoses the fault by the output probability of LSSVM.  
For a given sample set \(T=\{(x_{1},\,y_{1}),\,\ldots,\,(x_{g},\,y_{g})\}\in(R^{\text{m}}\times y)^{g}\), in this paper, \(x_{i}\) presents the parameters \(U_{\text{OC}}\), \(I_{\text{SC}}\), \(U_{\text{m}}\), \(I_{\text{m}}\), \(I_{\text{ph}}\)', \(R\)', \(R_{\text{sh}}\)' of photovoltaic array, \(y_{i}\) presents its real states of short circuit, open circuit, abnormal aging, and normal.  
The posterior probability of the given sample set can be described as:  
\[P(y|x)=[P(y)P(x|y)]/P(x) \tag{11}\]  
In the Formula (11), \(P(y)\) is the a priori probability of the given sample set, which analyzes the probability of a sample according to the a priori knowledge; \(P(y|x)\) is the posteriori probability of the given sample set according to the training set, and the posteriori probability reflects the influence of the training sample data on the test sample.  
In this paper, the posteriori probability of the given sample set is:  
\[P(y|x,D_{1},\theta_{MP},H)=\frac{P(y)P(x|y,D_{1},\theta_{MP},H)}{P(x|D_{1}, \theta_{MP},H)} \tag{12}\]  
In the Formula (12), \(D_{1}\) is the given sample data space, and \(H\) is the fault diagnosis model space.  
A posteriori probability is derived from the two-classifiers. According to the above analysis, we can know that the photovoltaic array fault diagnosis model constructs six two-classifiers, so six posteriori probabilities can be generated from the sample data. Then, by combining the posteriori probabilities by the Formula (13), we can get four combination probabilities.  
The final posteriori probability of determining the test sample \(x\) belonging to the \(i\)-class is:  
\[P(i|x)=\frac{2}{L(L-1)}\sum_{j=1,j\
eq i}^{L}P_{ij}(i,j|x) \tag{13}\]In Formula (13), \(P_{ij}(i,\,j\,|\,x)\) is the posteriori probability of \(x\) that belongs to the \(i\)-class in two-classifiers that consist of class \(i\) and \(j\) class, and \(L\) is the sort of photovoltaic array it is in. In this paper, \(L\) is 4; they are the states of short circuit, open circuit, abnormal aging, and normal.  
When comparing the size of the four combination probabilities, we can judge the fault type of the testing set.
# Comprehensive Study of IoT Vulnerabilities and Countermeasures
## 2 Preliminary Materials
### Commonly Used Protocols
#### 2.7.5 6 LoWPAN  
6 LoWPAN is a networking technology that allows IPv6 packets to be efficiently transmitted over low-power wireless networks, such as those based on the IEEE 802.15.4 standard. It supports various mesh network topologies and can fragment and reassemble packets as needed. 6 LoWPAN implementations are small enough to fit 32 K flash memory parts. 6 LoWPAN enables low-power mesh and sensor networks to take advantage of the benefits of IP networking [32; 33]. It has frequency bands of 2.4 GHz, 868 MHz, and 915 MHz (the same as ZigBee) and data rates between 50 and 250 kbit/s [33].
## 3 Hybrid Network Implementation Using LoRaWAN and LoRaMESH
### LoRa Modulation  
A proprietary spread-spectrum modulation technique derived from existing Chirp Spread Spectrum (CSS) technology, LoRa offers a conflict of choice between sensitivity and data rate, while operating on a fixed bandwidth channel of 125 KHz or 500 KHz (for uplink channels), and 500 KHz (for downlink channels). In addition, LoRa uses orthogonal propagation factors, this allows the network to preserve the battery life of the connected end nodes by making adaptive optimizations of the power levels and data rates of an individual end node. For example, an end device located near a gateway must transmit data with a low spread factor (Spreading Factor--SF), since very little connection budget is required. However, an end device located several kilometers from a gateway will need to transmit with a much higher SF. Theoretical transmission rate values as a function of the SF [120], where SF7 is 21,875 bits/s, SFR is 12,500, SF9 is 7031, SF10 is 3906, SF11 is 2148, SF12 and SF13 is 1172. LoRa is purely a physical layer (PHY), or bit, implementation, as defined by the OSI Seven Layer Network Model. Instead of cabling, air is used as a means to transport LoRa radio waves from an RF transmitter in an IoT device to an RF receiver in a gateway in the case of the LoRaWAN protocol or coordinator in the LoRaMESH protocol, and vice versa.
## Results
### Implementation of the Machine Learning Model Developed to Map Aroma Compounds from Aerial Imagery  
Aerial imagery allowed the extraction of canopy architecture parameters from sub-images per plant that are effectively comparable to upward-looking images from the VitiCanopy App. This was validated using a simple linear regression between the averaged LAI\({}_{\text{e}}\) parameters from the 24 sampled  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stage** & **Samples** & **Observations (Samples \(\times\) 6 Targets)** & **R** & **MSE** \\ \hline Training & 147 & 882 & 0.82 & 0.09 \\ Testing & 26 & 156 & 0.81 & 0.11 \\ Overall & 173 & 1038 & 0.82 & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results from the artificial neural network model showing the numbers of samples and observations, correlation coefficient (R), and performance based on mean squared error (MSE) for each stage.  
Figure 6: Artificial neural network overall model showing the observed responses (targets) in the x-axis and the predicted values (outputs) in the y-axis of the six volatile compounds. R = correlation coefficient.  
trees in the field using VitiCanopy (observed) and those obtained from the same plants extracted from the aerial image (correlation coefficient (R) = 0.91; data not shown).  
The machine learning model developed and described in Figure 6 and Table 3 was implemented using the canopy architecture parameters obtained from the aerial imagery. Figure 7 shows the LAI\({}_{\text{e}}\) and aroma profile data from each of the three blocks studied, using a kriging interpolation technique. The denormalized GC area values obtained from the machine learning model implementation were from 0 to \(60\times 10^{3}\), which was used as a scale for all the aroma profiles. Since LAI\({}_{\text{e}}\) is one of the most important canopy architectural parameters, it was individually mapped with values ranging from 0 (missing plants) to 6. Figure 8 shows the scattered values, which represent the LAI\({}_{\text{e}}\) and aroma profile for individual plants from the field studied.  
Figure 7: Original aerial image obtained from Google Maps Pro (**A**), and mapping of effective leaf area indexes (LAI\({}_{\text{e}}\)) (**B**) and aroma profiles for phenethyl acetate (honey/floral/yeasty/cocoa) (**C**); 2-phenyl-2-butenal (sweet/cocoa/nutty/beany) (**D**); isoamyl acetate (banana/fruity) (**E**); pyrazine tetramethyl (roasted/cocoa/coffee/nutty/earthy) (**F**); phenylacetaldehyde (honey/rose/cocoa) (**G**); and 2,3-butanediol (cocoa butter/creamy) (**H**).
## 3 Model
### LFC Throughput  
The LFC throughput \(\Omega\) is defined as the ratio of the product \(uH\) to the average power of the laser beam in the material \(P\), i.e., \(\Omega=uH/P\). The higher the \(uH\) at the same \(P\), the higher the throughput \(\Omega\). Substituting the expressions \(P=E_{l}f\) and \(u=s_{l}f\) from (90) gives  
\[\Omega=s_{1}H/E_{1}. \tag{93}\]  
Thus, \(\Omega\) can be equivalently defined as the ratio of the cut area \(s_{1}H\) per pulse to the pulse energy. Using here the expression (17) for \(E_{1}\), as well as choosing the step of filaments following as \(s_{1}=0.9s_{1 max}\), where \(s_{1 max}\) is given by the expressions (22,23), leads to the relation:  
\[\Omega=\ \frac{1.8}{\pi}\frac{(w-w_{dis})^{1/2}}{w}\left(\frac{1}{r_{f}} \right)\left(\frac{1}{w_{mod}^{1/2}}\right)\ (\gamma KA_{P-H})^{1/2}A_{L-P}. \tag{94}\]  
It shows that \(\Omega\) does not depend on the filament length \(H\) and increases when:  
1. The ratio \((w-w_{dis})^{1/2}/w\) is maximal. This takes place, as can be easily shown, at \(w=2w_{dis}\);
2. The filament radius \(r_{f}\) is minimal, which is the case, as follows from the discussion of the expression (19) above, when using a shorter wavelength of laser radiation \(\lambda\);
3. The modification threshold \(w_{mod}\) is minimised. When the energy transfer mechanism by heat conduction rather than by the shock wave dominates in the formation of the impact zone around the filament, it is preferable for this, as we have seen above, to use repetition frequencies in the high-frequency range (54) \(f\geq f_{1}\), but not in the low-frequency range (55) \(f\leq f_{2}\). In this case, the increase of the heat preservation factor \(\eta\) in the range \(f\geq f_{1}\) following the estimates (63) and (64) leads according to the equations (66)-(68) to a decrease in the \(w_{mod}\) by almost two times compared to the low-frequency range \(f\leq f_{2}\);
4. The energy conversion coefficients \(A_{L-P}\) and \(A_{P-H}\) are maximal, which emphasizes the importance of using filaments with the VED \(w\) that satisfies the conditions (12) for eliminating significant losses of filament energy to thermal radiation.
# A Review of Knowledge Graph-Based Reasoning Technology in the Operation of Power Systems
## 2 Knowledge Graph Key Technologies
### Knowledge Mining
#### 2.3.1 Entity Linking  
Entity linking is the process of linking entity names of text to target entities in the knowledge base. First, the entity names in the text are identified by named entity recognition techniques of knowledge extraction. Then possible candidate entities are identified by employing search engines, etc. Finally, the entity referred to by the entity name is identified from the candidate entities, and entity disambiguation is completed.  
Candidate entity disambiguation [54] can be considered a ranking problem, where a given entity refers to the corresponding candidate entities in the order of connection probability. It can be implemented in the form of graphs and deep learning. Among them, graph-based approaches [55] represent entity mentions, entities, and the relationships among them in the form of graphs, and then perform collaborative reasoning on the relationships on the graphs. The deep learning-based approach [56] computes the semantic relevance of entity mentions and entities through neural networks to achieve link matching.
## 2 Materials and Methods
### MD Simulations  
#### 2.3.1 Screening  
MD simulations of the top 15 selected protein-ligand complexes were performed using GROMACS software version 2019.3 [58] to assess the suitability and stability of the protein-ligand interactions. The topology files of ligands were created using the CgenFF server [59]. Complexes were subjected to the force field of CHARMM36 [60]. The protonation states for titratable residues in YTHDC1 were determined with H++ [61] and DelPhiPKa [62] servers to be in a default protonation state (Figure S9). The system was situated 10 A from the box edge in a cubic box. A TIP3P water model [63] was applied for each complex. To neutralize the system, Cl\(-\)ions were added as counterions. Typically, physiological conditions involve a salt concentration of 150 mM NaCl; this was not included in the molecular dynamic simulations. The reason to omit additional salt was to focus on the specific interactions between the proteins and ligands without the potential interference of ionic strength. Such a setup simplifies the system and can gain preliminary insights into the binding mode under neutral conditions. The steepest descent algorithm was subsequently used to minimize the system energy. Next, the NPT, or the constant number of particles (N), constant pressure (P), and constant temperature (T), as well as the NVT, or the constant number of particles (N), constant volume (V), and constant temperature (T), equilibration phases of the 100-ps simulations were applied to energy-minimized systems. Using the Parrinello-Rahman method [64], isotropic pressure coupling for 2 ps with an isothermal compressibility of \(4.5\times 10^{-5}\) bar\({}^{-1}\) was carried out to maintain a pressure of 1 bar \({}^{-1}\). A velocity-rescaling thermostat [65] was applied to keep a constant temperature of 300 K. The particle mesh Ewald (PME) method was used to treat electrostatic interactions [66]. The van der Waals with the force-switch modifier [67] and Coulomb interactions were truncated at 1.2 nm.  
Systems were subjected to a production run of 10 ns each, during which conformations were stored every 10 ps. For the complexes of the protein with 6 MZ and the 15 selected compounds, 100-ns simulations were performed, and the frames were saved every 100 ps. The GROMACS RMS utilities were used for root mean square deviation (RMSD) analysis. The MM/PBSA method was utilized in MD simulations to determine the binding energies of the 15 complexes.
## 5 Conclusions  
We have presented a new global 1 km fAPAR and LAI dataset, covering the years 2002-2011, derived from the ESA GlobAlbedo product. It is derived using a 1D radiative transfer (RT) model solution to the problem of RT in an inherently 3D canopy medium. The RT parameter retrieval scheme used to derive fAPAR and LAI is intended to be applied globally with no requirement for an underlying biome map and provides a consistent estimate of the uncertainty in derived parameters. We have compared the resulting GlobAlbedo-derived fAPAR and LAI estimates with the widely-used MODIS estimates of the same properties, over different years, at site, region and hemisphere scale. We show that the products agree closely in most cases in terms of timing, but not in terms of peak values, with the GlobAlbedo-derived values generally lower due to the underlying RT model assumptions, particularly LAI. These differences are strongly dependent on biome type (in the sense of the MODIS scheme) and season. The GlobAlbedo-derived values are consistent with the RT schemes of large-scale Earth system models, but less so with finer-scale ground-based (indirect) estimates of fAPAR and LAI. We provide an estimate of the coefficients required to calibrate the GlobAlbedo-derived to the MODIS LAI and fAPAR data, should that be required. We also suggest that users can easily generate their own transformation coefficients at the region and site level should this be required, through the GlobAlbedo online tools, although care should be taken in particular biome types and regions, particularly sparse forest/savanna, tropical regions and high latitudes.  
The authors would like to thank ESA for their support through Estellus (WACMOS, ESA contract #4000106711/12/I-NB) and the GlobAlbedo project (ESA contract# 22390/09/I-OL). FastOpt did the processing of all TIP products on a pro bono basis from specially processed BRDF datasets processed by SK at CEMS. The fAPAR/ LAI GUI and subsetting tool was developed with partial NCEO funding and EU-FP7 QA4 ECV funding (Project No 607405) at UCL-MSSL by Dr [PERSON]. [PERSON]'s work was supported in part via NERC National Centre for Earth Observation (NCEO), the NERC GREENHOUSE project (NE/K002554/1) the EU H20:20 BACI project (Project No. 640176). [PERSON] and [PERSON] carried out the initial part of this work when at FastOpt, Lerchenstrasse, Hamburg D-22767, Germany. MD collected and analyzed data for the comparisons and wrote the manuscript; PL and JPM developed the GlobAlbedo BRDF/albedo algorithm; JPM and SK developed tools and methods for GlobAlbedo data processing and analysis; BP, TK and MV developed the JRC-TIP tools and methods for the biophysical parameter retrievals. All authors commented on the manuscript.  
The authors declare no conflict of interest.
## 3 Optimization-Based Strategies
### Global Information-Driven Optimization
#### 3.2.3 Stochastic Dynamic Programming (SDP)
Higher values of \(\lambda\) tend to consider the energy balance costs to make the vehicle operate in a more charge sustaining fashion; however, this requires more iterations to ensure the convergence of the solution and more computational times. Based on the above issues, a compromise between guaranteeing charge sustaining and achieving lower computation time is discussed in [87]. In the end, the value of \(\lambda\) was in the range 0.95-0.995, and the realization of SDP took about 20 iterations, with each policy evaluation step taking about 30 s.  
The SDP-based strategy solves the problem that the global driving information of the vehicle needs to be known in advance in the DP-based strategy; however, it needs to predict vehicle speed, and the prediction accuracy would affect the global optimal solution, and the \"curse of dimensionality\" would occur when the policy-based iteration is used to solve SDP. In other words, the implementation of SDP is not straightforward, and embedded real-time operation may be memory intensive, despite not being computationally intensive. Moreover, the driver demand model was established by using the existing standard driving cycles or historical driving data as the training samples, and it cannot guarantee robustness or accuracy in cases where the actual driving cycle is quite different from the training driving cycle.