esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Trust Model for Node-Centric Misbehavior Detection in Dynamic Behavior-Homogeneous Clusters
## 7 Collaborative Node-Centric Misbehavior Detection Based on Hybrid Trust Values
### Cloud Layer Misbehavior Detection and Periodic Checks  
Due to the high dynamics and limited resources of vehicles and cluster head vehicles, the results of malicious vehicle determination based on mixed trust values by vehicles and cluster heads need to be preemptively warned by RSUs and then aggregated to the cloud layer's Misbehavior Management Component (MMC). The MMC processes and manages malicious vehicles based on further determination results.  
RSUs collect information about malicious nodes reported by vehicles and cluster heads and add potential malicious nodes to a grey-list. They conduct periodic checks by collecting their average trust values \(T_{\mathit{totm}}(v_{i})\). Assume that the average trust value of \(v_{i}\) at time \(k\) is \(T_{\mathit{totm}}(v_{i})_{k}\) and at time \(k+1\) is \(T_{\mathit{totm}}(v_{i})_{k+1}\). When \(T_{\mathit{totm}}(v_{i})_{k}\approx T_{\mathit{totm}}(v_{i})_{k+1}\), the RSU forwards \(T_{\mathit{totm}}(v_{i})_{k}\) to the MMC. When \(\left|T_{\mathit{totm}}(v_{i})_{k+1}-T_{\mathit{totm}}(v_{i})_{k}\right|>\Delta T\), the RSU updates the trust value and forwards \(T_{\mathit{totm}}(v_{i})_{k+1}\) to the MMC.  
Upon receiving \(T_{\mathit{totm}}(v_{i})\), the MMC compares it with \(T_{\mathit{totm}}(v_{i})_{k+1}\). If \(T_{\mathit{totm}}(v_{i})_{k+1}>T_{\mathit{thresh}}^{C_{i}}(v_{i})\), it indicates that the recent trust value of \(v_{i}\) is higher and the node behavior is more reliable, which may lead to a reduction in the duration of periodic checks \(t\), and vice versa. Assuming \(t\in\left[0.3\;\mathrm{s},300\;\mathrm{s}\right]\), \(T_{\mathit{totm}}(v_{i})_{k+1}-T_{\mathit{thresh}}^{C_{i}}(v_{i})=\Delta T\) the calculation method for the duration of periodic \(t(v_{i})\) checks for the vehicle \(v_{i}\) is as follows:  
\[\begin{cases}T_{\mathit{totm}}(v_{i})_{k+1}-T_{\mathit{thresh}}^{C_{i}}(v_{i} )>0,\;\;t(v_{i})=\left[0.3\;\mathrm{s},150.15\;\mathrm{s}\right]\\ T_{\mathit{totm}}(v_{i})_{k+1}-T_{\mathit{thresh}}^{C_{i}}(v_{i})<0,\;\;t(v_{i}) =\left[150.15\;\mathrm{s},300\;\mathrm{s}\right]\end{cases} \tag{29}\]  
\[\begin{cases}t(v_{i})=150.15\;s-\frac{T_{\mathit{totm}}(v_{i})_{k+1}-T_{ \mathit{thresh}}^{C_{i}}(v_{i})}{T_{\mathit{totm}}(v_{i})_{k+1}}\cdot\omega_{t},\;\Delta T>0\\ t(v_{i})=150.15\;s+\frac{T_{\mathit{thresh}}^{C_{i}}(v_{i})-T_{\mathit{totm}}(v _{i})_{k+1}}{T_{\mathit{totm}}(v_{i})_{k+1}}\cdot\omega_{t},\;\Delta T<0\end{cases} \tag{30}\]  
where \(\omega_{t}\) is periodic check parameter.
## 2 Preliminaries
### Information Geometry
#### 2.2.1 Concept of Statistical Manifold  
A manifold, an important concept in differential geometry, is an extension of Euclidean space. It can be visualized as being pieced together from Euclidean spaces, with each local part possessing the properties of Euclidean space [21]. A family of parameterized probability distributions, under certain regularity conditions, can be regarded as a manifold, known as a statistical manifold. The n-dimensional vector serves as the coordinate of the manifold. Each point on the statistical manifold represents a probability distribution function. The geometric structure of the statistical manifold reflects the intrinsic properties of the family of probability distribution functions, which are the foundation of information theory research. Therefore, the statistical manifold provides an important carrier for the geometric study of information theory.  
Figure 3: Flow diagram of polar code BPL algorithm using traditional path selection criteria.
## 4 Relay Based QoS-Aware Routing Protocol for I2O WBAN
### Proposed Protocols
#### 4.4.2 Routing Phase  
If the in-body sensor node residual energy is greater than (14), then the coordinator will check the distance between a relay and the node. The coordinator assigns TDMA time slots to the in-body sensor node and a nearby relay, which is selected based on the cost function similarly to [36]:  
\[C\left(S\right)=d\left(S\right)/R\left(S\right) \tag{33}\]  
Relay nodes are available for all in-body sensor nodes. For the two-relay based protocol, when two possible relays have the same cost function values, the one with a smaller value of (15) is selected.
## 3 Materials and Methods
### TinyML Platform  
#### 3.4.1 Picking the Platform  
An important factor impacting the whole system design is the choice of the TinyML platform. Among the major features required for consideration are price, accessibility, speed, power consumption and memory size. Three candidates were examined in order to choose the best-fitting TinyML platform:  
* STM32F103 CB, representing the STM32 F1 xx family,
* STM32F411 CE, representing the STM32 F4 xx family,
* STM32H743 VI, representing the STM32 H7 xx family.  
First two microcontrollers are widely available in the form of small evaluation boards, popular among tinkerers, called Bluepill for STM32F103 and Blackpill for STM32F411. The boards are shown in Figures 5 and 6. The third microcontroller is not as popular as the two previous ones, due to the lack of availability of small form-factor boards incorporating the STM32H743 chip, although it can be found in expensive, full-sized development boards.  
As seen in the Table 3, the microcontrollers vastly differ from each other. The STM32F103 chip is the cheapest, but it also incorporates the worst power-consumption-to-speed ratio of 0.44 mA/MHz, compared to 0.1 mA/MHz for STM32F411 and 0.275 mA/MHz for STM32H743. It is also the only candidating microcontroller without FPU and with only 20 KB of RAM, what renders the chip unsuitable for this task.  
The STM32H743 chip is the most expensive, being over double the STM32F103 price. It contains more than enough memory and is also the fastest of the candidating microcontrollers, containing the ARM Cortex M7 core capable of reaching a speed of 480 MHz. However, in terms of TinyML, it is not suitable due to a worse power-consumption-to-speed ratio than STM32F411, and not being as widely available as the STM32F103 and STM32F411 chips.
## 4 Results
### Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Proxy Usage Type** & **MAE (\(\mu\)g/m\({}^{3}\))** & **RMSE (\(\mu\)g/m\({}^{3}\))** & **R\({}^{2}\)** \\ \hline Low-cost sensor use (one input) & 2.328 & 2.950 & 0.54 \\ \hline “Real” instrument use (two inputs) & 1.945 & 2.414 & 0.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance metrics of the Bayesian WB proxy using one and two types of inputs. Figure 6: Bar chart of the percentage of measured BC data points within three levels of the confidence interval (\(\sigma\)) of the estimated BC. The combination of air pollutant proxies with low-cost sensors and \"real\" instruments is promising because proxies are not affected directly by physical sensor failures and environmental conditions. However, the proxy accuracy might drift over time because there may be new measured data that has not been incorporated in the process of proxy development, which is known as extrapolation, or the low-cost sensors/instruments might physically degrade. This phenomenon can be detected through uncertainty quantification from the proxy-based Bayesian predictive distribution and the proxy can then be re-trained and updated accordingly. In term of the choice of proxy approach, consideration of WB or BB models also needs to take into account the relative predictive performance in terms of bias or variability (bias-variance tradeoff) [76]. An estimator can be biased if the estimate is consistently higher or lower than the true estimate. Although WB models are easier to understand and fast to train, they can (in general) be less flexible, which may result in estimates far away from the true values (potential for high bias). On the other hand, although BB models have the ability to capture unexplained complexity and provide accurate estimates (low bias), the additional complexity may result in high variance [77], that is they can be very sensitive to small fluctuations in the training set. This phenomena is often referred to as over-fitting. In this situation, estimates can also change considerably if different training data is used (leading to a lack of reproducibility). The main objective of any supervised machine learning algorithm is to obtain low bias and low variance, although there is often a trade off involved. In our approaches, the variance associated with the estimates and predictions is minimised in the Bayesian models using (informative) prior information which is often referred to as a form of parameter regularisation. In complex settings and in particular with noisy data there is an increasing need to impose some form of regularisation in the model. As we have seen in the results, the inclusion of this information does not come at a significant cost in terms of the parameter estimates as the results for the complex BB provides similar performance to the WB model. Therefore, the Bayesian WB models are able to provide good performance across these two known issues (bias and variance).
# Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA
## 2 Model and Data  
### The ISBA LSM  
The ISBA LSM is used to simulate the interactions between land surfaces and the lower atmosphere by resolving the energy and water balances at the surface level within the SURFEX (SURface Externalisee) modeling platform developed by the Centre National de Recherches Meteorologiques (CNRM) ([[http://www.umr-cnrm.fr/surfex](http://www.umr-cnrm.fr/surfex)]([http://www.umr-cnrm.fr/surfex](http://www.umr-cnrm.fr/surfex)), accessed on 30 August 2023). Within SURFEX, ISBA generates a set of simulated variables, including heat, carbon, water, and energy surface fluxes, either coupled to an atmospheric model or \"offline\", i.e., without coupling to the atmosphere. In this study, version 8.1 of SURFEX was used in the offline mode. The atmospheric forcing came from the AROME-France (Application of Research to Operations at Mesoscale) numerical weather prediction model of Meteo-France [35], which is run operationally at a spatial resolution of 1.3 km \(\times\) 1.3 km over France [36].  
Vegetation growth is represented in this study, using the ISBA-A-gs configuration [17; 37]. This version of ISBA simulates the net CO\({}_{2}\) assimilation rate (A) and stomatal conductance (\(g_{\mathrm{s}}\)) of vegetation at the leaf and canopy level, which allows the simulation of LAI, respiration, and energy and water fluxes. ISBA-A-gs is able to represent the feedbacks between LAI and soil RZSM: increasing LAI values tend to increase plant transpiration and to reduce RZSM through root water extraction, while decreasing RZSM values reduce photosynthesis, \(g_{\mathrm{s}}\), and LAI at some stage. To better represent RZSM, the ISBA diffusion multilayer representation of the soil [38; 39] was used. Soil moisture and soil temperature were calculated for 14 layers down to 12 m for soil temperature and 8 to 10 layers down to 1 m and 2 m for soil moisture, depending on vegetation characteristics. In this study, the WG2 and WG5 modeled soil moisture values were considered. They correspond to the ISBA soil layers 2 and 5, 0.01-0.04 m and 0.2-0.4 m, respectively. They can be considered as proxies for SSM and RZSM, respectively. The ISBA model version we used does not include irrigation.
## 3 Materials and Methods
### Methods of Improvement
#### 3.1.1 Bagging or Bootstrap Aggregation  
Bootstrap aggregation, commonly known as bagging, is an ensemble learning method aiming to promote diversity amongst ensemble members by manipulating the training data. This method uses a statistical approach to estimate a population derived by averaging results from numerous small data samples. These samples are created by selecting observations from a larger data set and then returning them, a procedure termed sampling with replacement.  
As depicted in Figure 1, bootstrap aggregation is represented graphically. Several subsets, identical in size and selected with replacement, are extracted from the primary data. A CNN of consistent architecture is applied to each subset. The results from these individual models are then collated and voted upon to produce a singular prediction. The Alexnet experiment kept the hyperparameters as specified in its original publication, while for VGG-16 and Inception, we employed pretrained weights from IMAGENET. It is important to note that all experiments were conducted in a manner that did not elevate the computational demands, keeping up with the simplicity of this ensemble approach.  
The essence of the bootstrap method is estimation. It samples small portions, computes statistics for each, and then averages them. It is imperative that data preparation occurs within the sample data's loop, especially before model fitting or hyperparameter tuning. Such a step prevents data leakage, a scenario where the model, having complete access to the entire data set, inadvertently optimizes itself and causes itself to overfit.  
In the case of bagging ensemble learning, averaging the predictions across the models typically results in better predictions than a single model fit on the training data set directly.
# Experimental Study on the Features of Infrasonic Waves of Sandstone under Shear Load
## 2 Experiment to Acquire Infrasonic Signals during Sandstone Shear Destruction
### Experimental System
#### 2.1.2 Infrasound Signal Acquisition System  
The infrasound signal acquisition system includes an infrasound sensor and an infrasound data collector. The infrasound sensor is an IDS2016 infrasound sensor developed by the Institute of Acoustics, Chinese Academy of Sciences, with a sensibility more than 50 mV/Pa, coincidence error less than +/-1 dB, and a frequency band from 0.5 Hz to 300 Hz@3 dB, which covers the whole band range of infrasounds produced by the shear failure of rock. The infrasound data collector employs the nSYS2008 digital network transport protocol, which can pick up the signals received by the infrasound sensors synchronically at high speed through a local area network and transform the soundwaves into digital signals for later analysis. The sampling frequency of the experiments in this study was 100 Hz.
## 3 Mathematical Model  
### Particle Suspension Velocity  
When the airflow speed reaches a certain level, the particles will be transported by the airflow, that is, the suspension speed [28]. There are many formulas for calculating the suspension speed [29; 30; 31] of particles in pneumatic conveying. The regional suspension velocity formula and its applicable particle size method are the most extensive, the specific formula is as follows:  
(1) Viscous resistance zone  
\[u_{b}=\frac{d_{b}^{2}(\rho_{b}-\rho_{a})g}{18\mu}\quad\text{and }d_{b}\leq 1.225\bigg{[}\frac{\mu^{2}}{\rho_{a}(\rho_{b}-\rho_{a})} \bigg{]}^{\frac{1}{3}} \tag{2}\]  
(3) Pressure difference resistance zone  
\[u_{b}=5.45\sqrt{\frac{d_{b}(\rho_{b}-\rho_{a})}{\rho_{a}}}\quad\text{and }20.4\bigg{[}\frac{\mu^{2}}{\rho_{a}(\rho_{b}-\rho_{a})}\bigg{]}^{\frac{1}{3} }\leq d_{b}\leq 1100\bigg{[}\frac{\mu^{2}}{\rho_{a}(\rho_{b}-\rho_{a})} \bigg{]}^{\frac{1}{3}} \tag{3}\]  
\(u_{b}\) is the particle suspension speed, \(\rho_{a}\) and \(\rho_{b}\) are air and particle density, respectively, \(d_{b}\) is particle size, \(\mu\) is aerodynamic viscosity (1.81 \(\times\) 10\({}^{-5}\) Pa\(\cdot\)s). The corresponding particle size ranges are respectively the viscous resistance regions: \(d_{b}\)\(\leq\) 0.065, transition region: 0.065 \(<\)\(d_{b}\)\(\leq\) 0.60, pressure difference resistance zone: 0.60 \(<\)\(d_{b}\)\(\leq\) 32.56, unit: mm.  
The solid feed used in this paper is located in the pressure difference resistance area and, accordingly, the suspension speed can be obtained by taking 10.3 m/s in the corresponding formula.
## 2 Related Works
### Dynamic Layer Management PBFT  
The Dynamic Layer Management PBFT (DLM-PBFT) is proposed to address the scalability challenges of traditional PBFT, where the scalability decreases as the number of nodes increases [20]. The authors propose a model to estimate consensus time, taking into account the hierarchical structure and the overall number of nodes in the blockchain network. Based on the consensus time estimation model, DLM-PBFT dynamically adjusts the network's hierarchical structure to enhance consensus performance. This allows for rapid consensus regardless of fluctuations in the number of nodes in the network. Experimental results demonstrate that DLM-PBFT offers enhanced processing speed compared to both traditional PBFT and SDMA-PBFT. However, DLM-PBFT did not consider the latency between each node, which is an important factor when forming groups and hierarchical structure.  
The following Table 1 summarizes the differences between the existing methods and the proposed method. As summarized in Table 1, the proposed method has a lower time complexity in terms of message exchange frequency compared to existing methods. Additionally, it supports clustering, grouping, and dynamic hierarchy management to enhance the scalability of PBFT by reducing the consensus time.  
As shown in Table 1, the communication complexity of the typical PBFT is \(O(N^{2})\). As explained earlier, G-PBFT divides all nodes in the network into multiple groups based on their location information, and each group performs PBFT-based consensus. Therefore, if we represent the number of nodes within a single group as \(N_{g_{node}}\) and the total number of groups as \(N_{group}\), then the communication complexity is \(O(N_{group}\times N_{g_{node}}^{2})\). The communication complexity of SDMA-PBFT can be described as follows. If we define the size of each area within SDMA-PBFT as \(K\) and the total number of nodes participating in the network as \(N\), the number of areas is given by \(N/K\) and the number of hierarchical layers is represented by \(\log_{K}N\). Consequently, the overall communication complexity becomes \(O(N\times K\times\log_{K}N)\). In DLM-PBFT, given that the total number of nodes is denoted by \(N\) and the maximum number of nodes within a group is \(K\), the number of groups can be represented as \(N/K\). As a result, the communication complexity can be expressed as \(O(N\times K^{2})\). The communication complexity of the proposed PBFT method is \(O(N_{cluster}\times N_{node}\times N_{g\_node}^{2})\), which is further detailed in Section 3.3.3.
## 2 Results and Discussion
### Adsorption Experiment
#### 2.9.3 Adsorption Thermodynamics  
The thermodynamic parameters of CFA adsorption were studied for the most effective adsorbent (NR/WMS\(-\)0.05) at different temperatures (298-318 K). The standard enthalpy change (\(\Delta S^{\circ}\)), standard entropy change (\(\Delta H^{\circ}\)), and Gibbs free energy (\(\Delta G^{\circ}\)) of adsorption were determined using the thermodynamic equation [42] presented in Equation (1):  
\[\ln\left(\frac{q_{e}}{C_{e}}\right)=-\frac{\Delta G^{\circ}}{RT}=\frac{\Delta S ^{\circ}}{R}-\frac{\Delta H^{\circ}}{RT} \tag{1}\]  
where \(R\) is the gas constant (8.314 J mol\({}^{-1}\) K\({}^{-1}\)) and T is the absolute temperature (K). The value of \(\Delta\)H\({}^{\circ}\) and \(\Delta\)S\({}^{\circ}\) were, respectively, calculated from the slope and intercept of \(\ln\left(\frac{q_{e}}{C_{e}}\right)\)versus \(1/T\). The obtained thermodynamics parameters (\(\Delta G^{\circ}\), \(\Delta\)S\({}^{\circ}\) and \(\Delta\)H\({}^{\circ}\)) are summarized in Table 5. The value of \(\Delta\)G\({}^{\circ}\) was negative, indicating the spontaneous nature of the CFA adsorption on the NR/WMS-NH\({}_{2}\) materials. Moreover, the \(\Delta\)G\({}^{\circ}\) values of the adsorption were increased with increasing temperature from 298 to 318 K, which suggests that the adsorption is more favorable at high temperatures [3]. The positive values of \(\Delta\)H\({}^{\circ}\) and \(\Delta\)S\({}^{\circ}\) suggest an endothermic process in which the randomness at the solid-solution interface increased during the adsorption due to adsorption and subsequent desorption [42; 43].
## 4 Test Results
### Principal Components Analysis
The second variable (Z2) is strongly correlated with air void content (V\({}_{\rm m}\)) and absorption (N\({}_{\rm w}\)), and moderately correlated with indirect tensile strength (ITS) and viscous behavior of specimens at high temperature as shown by (\(\delta\)) measurement. This variable explains about 21% of the variation in the population. The third variable (Z3) is correlated with the parameters responsible for moisture resistance. The portion of variation it explains is only about 6%, which can probably be attributed to the low sensitivity to the presence of water of the set of MCAS mixtures with waste materials. The last variable (Z4) explains only 4.6% (Table 3) of data variation in the dataset, and is correlated mainly with the density of recycled mixtures in conjunction with moisture resistance. The low proportion of variation in (Z4) is probably caused by the presence of \"heavy-weight\" steel slag aggregate. The new variables providing a global view of the set of effects were used in the process of grouping objects in the dataset of the recycled mix specimens. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Value No.**} & \multicolumn{4}{c}{**Eigenvalues (Correlations) and Related Statistics**} \\ \cline{2-5}  & **Eigenvalue** & **\% of Variance** & **Cumulative Eigenvalue** & **Cumulative \%** \\ \hline
1 & 20.51652 & 56.99035 & 20.51652 & 56.9903 \\
2 & 7.57836 & 21.05101 & 28.09489 & 78.0414 \\
3 & 2.26342 & 6.28727 & 30.35830 & 84.3286 \\
4 & 1.63930 & 4.55360 & 31.99760 & 88.8822 \\
5 & 0.79279 & 2.20219 & 32.79039 & 91.0844 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Eigenvalues of new variables. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{6}{c}{**Factors-Variables Correlation (Factor Loadings)**} & \multicolumn{6}{c}{**Eigenvector (Based on Factor Loadings)**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**Standard**} \\ \cline{2-2} \cline{4-11}  & **Z1** & **Z2** & **Z3** & **Z4** & **Z1** & **Z2** & **Z3** & **Z4** \\ \hline \(\rho_{\text{MCAS}}\) (Mg/m\({}^{2}\)) & \(-0.399209\) & \(-0.365731\) & \(0.173339\) & \(\mathbf{-0.569877}\) & \(-0.088135\) & \(-0.132854\) & \(0.115217\) & \(-0.445095\) & \(2.24\) & \(0.060\) \\ \hline N\({}_{w}\) (\%) & \(0.004669\) & \(\mathbf{0.762452}\) & \(-0.499738\) & \(0.347742\) & \(0.001031\) & \(0.276965\) & \(-0.332170\) & \(0.271599\) & \(2.75\) & \(1.042\) \\ \hline V\({}_{m}\) (\%) & \(0.201374\) & \(\mathbf{0.701808}\) & \(-0.28358\) & \(0.052567\) & \(0.044458\) & \(0.258231\) & \(-0.191668\) & \(0.041057\) & \(11.26\) & \(2.171\) \\ \hline ITS\({}_{\text{DKY}}\) (GPa) & \(-0.327910\) & \(\mathbf{-0.516954}\) & \(\mathbf{0.470119}\) & \(-0.429201\) & \(-0.072394\) & \(-0.187786\) & \(0.312482\) & \(-0.335222\) & \(609.63\) & \(191.513\) \\ \hline TSR (\%) & \(0.100090\) & \(-0.267748\) & \(\mathbf{0.505318}\) & \(\mathbf{0.656611}\) & \(0.022097\) & \(-0.097261\) & \(0.3358
# Preparation of Few-Layer Graphene Dispersions from Hydrothermally Expanded Graphite
###### Abstract  
In this study, we propose a novel approach to prepare few-layer graphene (FLG) dispersions, which is realized by exfoliating natural graphite flakes in a surfactant aqueous solution under hydrothermal treatment and liquid-phase exfoliation. In order to obtain stable and well-dispersed FLG dispersions, pristine graphite is hydrothermally expanded in a hexadecyltrimethylammonium bromide (CTAB) aqueous solution at 180 \({}^{\circ}\)C for 15 h, followed by sonication up to 3 h. In comparison to long-time sonication methods, the present method is significantly efficient, and most importantly, does not involve the use of an oxidizing agent and hazardous media, which will make it more competent in the scalable production of graphene.  
few-layer graphene; CTAB; hydrothermal treatment; sonication +
Footnote †: journal: Applied Sciences