esa-sceva/satcom-chunk-collection huggingface dataset:
# AA-HMM: An Anti-Adversarial Hidden Markov Model for Network-Based Intrusion Detection
## 4 Methodology
### Architecture  
The architecture of the AA-HMM is shown in Figure 3, which was constructed upon five basic five: PE, PV, WW, LOWW, and DW. Given the study of [7], the detection ability or security level of an anomaly-based NIDS can be divided into three levels (from lowest to highest): (1) strong knowledge acquisition and decent generalization abilities, (2) strong generalization and decent adaptive abilities, and (3) strong adaptive and anti-adversarial abilities. The AA-HMM consists of three layers corresponding to these three security levels:  
1. In order to achieve the lowest security level, the regular HMM was adopted as the base algorithm to learn the traffic pattern and make predictions.
2. To attain the second security level, a pair of feedback variables, called Model Difference (MD) and Difference Trend (DT), were designed to improve the adaptability of base HMM.
3. To achieve the top security level, the variable pair called Threshold (TH) and Threshold Controller (THC) were integrated to realize the required anti-adversary ability.  
Figure 3: Anti-Adversarial Hidden Markov Model’s (AA-HMM) overview architecture.  
Figure 2: Correlation between accuracies of Dynamic Window (DW) with a fixed Window Width (WW = 125) and window indices.
## 3 Field Measurements and Model Verification
### Experimental Results  
The time history and Fourier spectrum of the hammer force are illustrated in Figure 13. The peak value of the hammer force is about 40 kN. It can be observed from the spectrum that a large amount of energy can be aroused in the range of 0-100 Hz. The vertical acceleration time history and spectrum of the sensors between 0 and 80 m on the ground are shown in Figure 14.  
Figure 14: _Cont._  
Figure 12: Layout of hammer point and sensors’ locations for underground impact test.  
Figure 13: Time courses and frequency spectrum of hammer force.  
Figure 14: _Cont_.  
Figure 14 indicates the following:  
1. Under the exciting force of the automatic drop hammer, clear acceleration signals can be collected in the range of 0-80 m on the surface. With the increase in the horizontal distance on the surface, the surface acceleration vibration value also gradually decreases. The peak vertical acceleration on the surface at 0 m is about 0.035 m/s\({}^{2}\), that at 30 m is 0.01 m/s\({}^{2}\), and that at 80 m is reduced to 0.001 m/s\({}^{2}\).
2. It can be seen from the time history diagram that the maximum vibration acceleration occurs at different locations on the surface at different times. The maximum vibration occurs at about 0.04 s at 0 m on the surface, at about 0.2 s at 30 m on the surface, and at 0.4 s at 80 m on the surface, which also reflects the time difference of vibration propagation in the soil layer.
3. It can be seen from the spectrum diagram that under the action of the pulse excitation force, a certain amount of energy can be excited at different horizontal distances from the surface between 0 and 100 Hz, of which the energy above 80 Hz is slightly attenuated.
# Heterogeneous Graph Purification Network: Purifying Noisy Heterogeneity without Metapaths
## 4 Proposed Method  
This section first presents the framework of the proposed model and then introduces each component in detail step by step.  
Figure 1 illustrates the architecture of HGPN, which consists of several key components: a subgraph decomposition method, a pre-processing and embedding layer, an inter-subgraph aggregation layer, a type-specific update layer, and a cluster-aware loss. The original heterogeneous graph is first decomposed into multiple type-specific subgraphs based on edge types. The pre-processing and embedding layer is then used to process or initialize node features and map them into a uniform shape for further use.  
Next, the inter-subgraph aggregation layer utilizes heterogeneous graph attention layers to learn type-specific messages from each subgraph individually while purifying noisy edge structures. The inter-subgraph aggregation aims to fuse these multi-subgraph views into a compact one and improve the interactions of message representations across different types of relations. The type-specific update method uses the comprehensive neighbor-passed messages obtained earlier to update the representation of the target nodes. Type-specific batch normalization and neighborhood smoothness related dynamic residual connections are adopted to purify node features and maintain their original distribution.  
Additionally, a cluster-aware loss is designed to ensure feature distinguishability and prevent feature over-mixing, which also contributes to feature purification. Finally, the learned node representations can be used to facilitate various downstream tasks, such as node classification, node clustering, and link prediction.
# Low Power Blockchained E-Vote Platform for University Environment
## 1 Introduction  
Blockchain technology has become secure, irreversible, anonymous and impossible to hack. Blockchain is based on decentralized registers. It allows encrypting data by transforming them into encrypted transactions and save them in blocks. After being mined, these blocks are added to the Blockchain and by then they become irreversible and impossible to modify by anyone. Blockchain is used for several types of applications in different fields such as e-health [1; 2; 3], industry [4; 5; 6], agriculture [7; 8] as well as the academic field [9; 10]. In this paper, blockchain will serve as an information backup system. In fact, the voting operation is a data-saving operation. Each voter chooses the person for whom they will cast their vote. Thus, each voting operation could be represented by a transaction recorded in the blockchain. This operation enables securing the vote and to maintain traceability while recording the attributed vote. Therefore, it could be said that the Blockchain is certainly a guarantor of the security of the system as well as a system of safeguarding information in our use case  
The application straddles the social and academic domains. It is about implementing a system that enables remote voting. This voting operation will be performed in a universitysetting. As a case study, the remote voting of the student representative within the university will be selected. This system is made to allow the security and the digitalization of the university operations in Tunisia. Indeed, among the national objectives we can cite the security of citizens in this period of the COVID-19 pandemic. This securitization requires the distancing and the valorization of remote work. To face this challenge, digitization turned out to be the best way to be implemented in the different ministries and particularly in the university education. The main elements of originality may be summarized as follows: A platform based on two Blockchains, namely a private Blockchain (Ethereum) and a public Blockchain (Quorum), is suggested.  
* This system will allow students to vote remotely in order to elect their representative.
* It is a secure system by design (Blockchain) and by mobility (remote voting).
* The result which minimizes the risk of error or fraud is generated automatically.  
Apart from the introduction, the remaining of this paper is structured as follows. In Section 2, we present a state of the art on the Blockchain part applied to different domains and particularly to e-voting in developing countries. Section 3 presents the proposed approach. It is the generic platform that has been chosen to link the ministry to different universities and by analogy the university to different institutions. In Section 4, we describe the implementation result of the application and its implementation on a Raspberry PI platform. Section 5 discusses the low power application. Finally, Section 6 presents the conclusions and future work.
## 3 Results and Discussion
### Dynamics of Monomeric Anthocyanin in Fermented Wort and Beer  
Figure 4 illustrates monomeric anthocyanin kinetics during fermentation for each treatment. Before fermentation, monomeric anthocyanin was present in words containing Riceberry rice malt, except for B100. The monomeric anthocyanin declined as fermentation progressed and experienced a further reduction during the yeast flocculation period in both \(\mathrm{POF}^{-}\) and \(\mathrm{POF}^{+}\) fermented word (Figure 4) samples. The reduction in monomeric anthocyanin was because of the precipitation of anthocyanin due to the decline in pH and the yeast cell membrane adsorption of anthocyanin during fermentation [26]. Furthermore, anthocyanin can precipitate via anthocyanin and hydroxycinnamic acid interactions, but this requires a prolonged period [27], such as during beer maturation. According to [PERSON] et al. [27], declines in anthocyanin during fermentation are also contributed to by yeast metabolites such as acetaldehyde, which reacts with anthocyanin to form pyranoanthocyanins or polymeric anthocyanin. The transformed compound reacts with proteins and precipitates at low temperatures during yeast flocculation [28]. Hence, there is a low anthocyanin content in composite beer containing high barley malt proportions (60-100% (\(w/w\))) because barley is a rich source of protein. These factors, in addition to the instability of monomeric anthocyanin, collectively led to the decline of the EBC colour value of beer after 4 weeks of maturation in R80B20 and R100 significantly (Table 1).  
Figure 4: Monomeric anthocyanin kinetics in fermented composite wort (B100, R40B60, R60B40, R80B20, and R100) by the two yeast strains BRY 97 (_S. cerevisiae_), \(\mathrm{POF}^{-}\); and Belle Saison (_S. cerevisiae var. diastaticus_), \(\mathrm{POF}^{+}\).
# Conceptual Design and Preliminary Verification of Distributed Wireless System of Weigh-in-Motion
## 3 Design of Distributed WIM System  
Due to the vertical vibration of the vehicle itself, as well as the deformation, unevenness, or defects of the road surface, the vertical vibration of the measured vehicle is caused. In this case, the wheel load of the vehicle (the pressure of the vehicle tires on the road surface) is dynamically changing with a certain periodicity. Obviously, the error between the dynamic wheel load and the vehicle weight is closely related to the vibration characteristics of the running vehicle. At present, most of the sensors arranged in the WIM system only install strip sensors in a narrow range of the vehicle's traveling direction. The time for the tires of the moving vehicle passing above the sensor is only about 0.01-0.1 s. The dynamic wheel load throughout the cycle of the change process cannot be detected. Furthermore, the results in the weighing system have to be calibrated on site. To make up for this shortcoming, we need to arrange multiple WIM sensors in the driving direction of the vehicle, but the existing WIM sensors are difficult to realize without breaking the integrity of the outer road surface. However, this arrangement can be conveniently implemented using the previously proposed self-powered wireless sensors for WIM. These kinds of sensors do not need to be slotted and wired under the pavement surface because of the self-powered energy and wireless signal transmission. Hence, it can be designed as spatially independent distributed sensors, arranged in a certain form on the lane (Figure 5). Through layout optimization, the best distribution form that meets the actual requirements can be obtained. Then, measuring the change of dynamic wheel load in the whole cycle would provide more reliable data for the calculation of static wheel load (vehicle weight) and more accurate identification of the vehicle wheelbase, axle number, and travel speed.  
The schematic diagram of a distributed WIM system is shown in Figure 5. The wireless self-powered weighing sensors after optimization are distributed in the lane. When a sensor  
Figure 4: A prototype of piezoelectric sensing unit.  
Figure 5: The schematic diagram of a distributed WIM system.  
detects the wheel load of a vehicle passing by, it records the change of the wheel load and transmits the data to the WIM station set at the roadside in real-time. After data processing, the WIM station obtains the vehicle speed, number of axles, axle load, and wheel load, and then uploads the information to the cloud database.
## 3 Results
### IV Model for LSP  
The IV model is an indirect conventional statistical model that is often used in LSP [5; 65]. According to these researches, the information value \(V_{i}\) of the predisposing factor can be formulated as:  
\[V_{i}=\log\frac{L_{i}/T_{i}}{L\ /\ T} \tag{5}\]  
where \(L_{i}\) indicates the number of landslide grid cells with the presence of predisposing factor, \(T_{i}\) indicates the grid cells number with predisposing factor, \(L\) indicates the sum number of landslide grid cells, \(T\) indicates the total number of grid cells in Shicheng County. In principle, the presence of predisposing factor does not contribute to landslide evolution when the value of \(V_{i}\) is negative; and the presence of predisposing factor contributes to landslide evolution when the value of \(V_{i}\) is positive. A higher value of \(V_{i}\) suggests a greater correlations between predisposing factor and landslide occurrence. Hence, the total information value \(V\), which reflects the landslide susceptible index of each grid cell, can be calculated as:  
\[V=\sum_{i=1}^{n}\log\frac{L_{i}/T_{i}}{L\ /\ T} \tag{6}\]  
In this study, the landslide susceptibility map generated by the IV model is shown in Figure 6d, for which the landslide susceptibility levels were divided into very low (23.7%), low (22.8%), moderate (26.7%), high (17.2%), and very high (9.2%) levels (Table 2). The raw distribution of landslide susceptibility indices in Shicheng County was very similar to that generated by the PSO-MLP, MLP-only, and BPNN models.
# Collision Avoidance from Multiple Passive Agents with Partially Predictable Behavior
## 1 Introduction
### Contribution  
This paper addresses the problem of navigating a kinodynamically constrainted robot, among multiple passive agents with partially predictable behavior. In order to solve the problem, this paper develops the following two contributions.  
* First, it generalizes the concept of Nonlinear velocity obstacle (NLVO) [27] to develop a new approach to identify the set of control inputs to robot that will lead the robot towards collision, named control obstacle. It seeks to address the issue of navigating a robot with kinodynamic constraints while considering that a passive agent is moving along an arbitrary path, probably non-linear. The original approach of NLVO does not consider the robot model, and thus is limited to the agent with dynamics as of single integrator.
* Secondly, a novel collision avoidance strategy is proposed, that allows the robot to make a safe decision for avoiding a collision with the passive agents. The safe navigation decision is based on the concept of minimum safety margin which is the measure of how safe the path is in the presence of various sources of unmodeled uncertainties.  
This paper presents the implementation of the proposed approach for a car-like agent and a double integrator. The performance of the proposed algorithm is evaluated for dynamic environment considered in [26], where the predicted trajectory of passive agents change frequently. The simulated experiments show better performance of the proposed approach compared to current VO based approaches, in terms of computation time and success rate.
# A Two-Step High-Order Compact Corrected WENO Scheme
## 2 Numerical Methods  
This section primarily outlines concepts of high-order conservative difference schemes and the WENO scheme, as well as high-order correction methods based on the compact scheme. Without loss of generality, we begin with the scalar conservation equation in the one-dimensional case.  
\[\frac{\partial}{\partial t}u(x,t)=\frac{\partial}{\partial x}f(u(x,t)) \tag{1}\]  
The high-order numerical schemes focus on the spatial discretization on the right-hand side of Equation (1), utilizing the discrete grid points (evenly distributed) in Figure 1. The normal grid points are marked as circles with integer indices (\(j-2,j-1,j,j+1,j+2\)), while the indices for the midpoints are \(j-3/2,j-1/2,j+1/2\), and \(j+3/2\), respectively.  
Figure 1: Illustration of the discrete grid points. The normal grid points are marked as circles with integer indices (\(j-2,j-1,j,j+1,j+2\)). The midpoints are marked as squares with indices (\(j-3/2,j-1/2,j+1/2,j+3/2\)). The yellow, green, and red rectangles represent three substencils (See Section 2.2).
## 2 Data and Theory Description
### The CPRAP Usage Data Description
#### 2.1.3 FY-3A/MERSI and Sentinel-3A/OLCI PWV Data  
The FY series of satellites has formed an all-weather, three-dimensional and continuous satellite observation network of the Earth's atmosphere, ocean and surface environment in response to climate change. Its designed purpose is used for disaster monitoring research. The MERSI onboard FY-3A can provide 5-min, 10-day and monthly PWV products with spatial resolutions of 1 km, 5 km and 5 km, respectively [28; 29]. The statistical result shows that the accuracy of MERSI-derived water vapor products is relatively lower than that derived from GNSS, RS or ERA5 [30]. In this study, the PWV data derived from FY-3A was selected over the period of 2012 to 2013 in China.  
The Copernicus project was launched in 2003 to provide global satellite remote sensing data services mainly through the coordinated management and integration of existing and future launches of satellite data and field observation data in European and non-European countries [31]. One of the main objectives of Sentinel-3A mission was to measure the Earth's weather and climate on land and sea [32]. Three levels of processed products, including Level 0 (L0), Level 1 (L1) and Level 2 (L2), has been provided by OLCI onboard Sentinel-3A. In addition, the L2 PWV product provided by OLCI has a spatial resolution of 300 m. However, the validation of this product was merely performed before. Therefore, the data of Sentinel-3A L2 were selected and validated in this paper over the period from 1 March 2019 to 4 March 2020 in China.
## 3 Results
### Selection of the Best Inversion Strategy
#### 3.3.2 LAI Retrieval Performance Comparison  
Table 8 shows LAI retrieval performances when using the mean of the \(q=100\) best solutions. Inversions based on NDVI and MSAVI2 differences performed equivalently in terms of RMSE (less than 0.25 m\({}^{2}\)/m\({}^{2}\)), bias, STDB, and \(R^{2}\) (above 0.80). RMSE INT LAI performed the worse for all validation metrics, followed by SAM INT LAI.  
Figure 9 shows that the highest LAI are slightly underestimated and vice versa, and that estimation uncertainties increase with LAI. Concerning low LAI points, only the fall 2013 points (blue) can be overestimated. No clear distinction in terms of performances is observed between other seasonal measurements. The global performance shows a \(R^{2}\) of 0.8 and a total RMSE of 0.22 m\({}^{2}\)/m\({}^{2}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Fall** & **Summer** & **Summer** & **All Dates** \\  & & **2013** & **2014** & **2016** & **All Dates** \\ \hline  & \(q\) & 100 & 100 & 100 & 100 \\ \hline  & RMSE & & & & \\ LAI & INT LAI & 0.61 & 0.61 & 0.63 & 0.62 \\ \([\)m\({}^{2}\)/m\({}^{2}]\) & SAM & & & & \\  & INT LAI & & & & \\  & NDVI & 0.17 & 0.23 & 0.24 & 0.22 \\  & MSAVI2 & 0.18 & 0.24 & 0.29 & 0.25 \\ \hline  & & **Summer** & **Fall** & **Summer** & **All Dates** \\  & & **2013** & **2013** & **2014** & **All Dates** \\ \hline  & \(q\) & 300 & 300 & 300 & 300 \\ \hline  & RMSE & & & & \\  & INT CAB & & & & \\  & SAM & & & & \\ C\({}_{ab}\) & INT CAB & & & & \\ \([\)m\({}^{2}]\) & MCARI2 & 10.44 & 14.38 & 10.57 & 12.15 \\  & TCARI/OSAVI & 5.86 & 8.09 & 4.31 & 6.34 \\  & Maccioni & 8.38 & 9.34 & 6.12 & 8.02 \\  & gNDVI & 9.09 & 4.22 & 2.89 & 5.39 \\  & GM\_94b & 8.62 & 3.86 & 3.39 & 5.21 \\ \hline  & \(q\) & 400 & 400 & 400 & 400 \\ \hline  & RMSE & & & & \\  & INT CAR & & & & \\  & SAM & & & & \\  & INT CAR & & & & \\  & R515/R570 & 5.74 & 4.32 & 2.74 & 4.01 \\  & CRI & 2.87 & 3.83 & 1.91 & 2.89 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Root mean square error (RMSE) of the various criteria at the retained \(q\) value for LAI, C\({}_{ab}\) and Car estimations of each date.
## 3 Numerical Results
### Multiprecision Emulation  
In this section we analyse the stability and accuracy of the low-precision version of the code using RPE [34]. We assumed that the emulator assigned the same number of significant bits for all variables of merit; otherwise, one would have to develop a floating-point arithmetic unit for varied precision, which can be unstable and too complex. The multiprecision analysis provided by CADNA showed that a 17-bit precision arithmetic was required to guarantee that the accuracy of the LU algorithm was enough to propagate the input uncertainty. Firstly, we tested the code for this guaranteed precision; however, the actual precision requirements might have been even lower due to the inaccuracy of the DSA estimation method, and also because the precision estimate varied in time and depended on the index of the matrix entry. Therefore, it was useful to run the code several times while making the precision lower and lower until either the rounding error became too large, or simulation became unstable. In the next paragraph, we show how these criteria were met for the reactor core's transient model.  
The RPE emulation aimed to confirm that the rounding error of the low-precision simulations did not exceed the uncertainty originated from the model's input. Hence, we calculated and compared two data sets referring to (a) the full-precision simulations produced by the random sampling and (b) the full-to-reduced precision simulation discrepancy produced for the same random samples. Figure 4 shows the full-to-reduced discrepancy in the peaking power \(P_{max}\), the total energy release \(P_{tot}\) and the fuel and coolant temperatures at specific time moments. The rounding error computed for the 15-bit precision was comparable to the variation of the parameters of merit due to the uncertainty of the input data. A further drop in precision led to a much higher scope of the rounding errors, and at some point, it resulted in an unstable simulation resulting in a split of the cloud of points.  
Figure 3: Mean number of significant digits over the simulation.  
Figure 2: Number of significant digits estimated by CADNA for 9 \(\times\) 9 matrix \(A\) and vector \(b\) and the initial structure of the input matrix; empty cells indicate zero elements.