esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Context of the Solution: The Isolated Environment  
About two hundred thousand people are living in isolated regions in northern Canada. The Nunavut, located in the north of Quebec, is a good example of what is an isolated place. The Quebec government has invested a lot of money with a grant called \"Ie plan nord\", but nevertheless, the telecommunication network stays almost non-existent (Figure 3): the optical fiber and telecommunication towers have been deployed in some places, but it is very far from the totality of the territory. The only solution in some places is the satellite but at a very high cost. The railway security system that has been developed is dedicated to trains transporting iron ore from mines located in northern Quebec to the port of Sept-Iles where it is shipped by boats to other countries for processing. Most of the railways in this region do not have any electrical network or any optical fiber network. In the same way, there is no LTE network. Those considerations lead to eliminating technical solutions such as LoRa or Sigfox used in the internet of things systems, which need an internet connection, but also LTE solutions. Note that the trains are freight trains often bought in the USA and adapted for very hard climate conditions for the north of Quebec. Those trains are very poor technologically, and so, the electronic solution of the system developed must be adapted to those trains. Therefore, it has been a very high challenge level to develop the solution presented further in this paper, which is based on ZigBee protocol.
# Decentralized Multi-Agent Control of a Manipulator in Continuous Task Learning
## 5 Results
### Comparison of Centralized and Decentralized Approach  
The training comparison between the centralized and decentralized approach for PPO and SAC agents is shown in Figures 14 and 15. The decentralized PPO algorithm accumulates higher average reward than the centralized PPO; however, it also shows a non-monotonic behavior. Whereas, the learning curve of the centralized PPO agent is smooth, as demonstrated in Figure 14.  
Figure 12: Progression of the mean accumulated reward for the decentralized single-agent PPO. Training results are reported for three experiments with different random seeds. The standard deviation of three runs is shown in the plot.  
Figure 13: Comparison of accumulated reward for the decentralized single-agent SAC and PPO.  
On the other hand, the decentralized single-agent SAC in Figure 15 demonstrates a much faster and efficient learning process as it accumulates a very high average reward and shows the task success in just 600K timesteps while the centralized SAC agent needs at least 2M steps to reach success. The multi-agent SAC curve instead is relatively flat and does not reach any task success in the first 2M timesteps.  
Table 1 summarizes the results of all analyzed approaches and shows the comparison of maximum accumulated reward achieved in an episode considering the first 2M steps of training. The decentralized single-agent SAC is the most efficient approach of all, since the agent accumulates the highest reward, while the combination of multi-agent SAC and PPO achieves the lowest reward. This might be due to the lack of coordination among agents in the multi-agent configuration as there are no explicit terms in the reward to encourage such coordination. Therefore, an interesting direction for the future work is to shape the reward function that encourages coordination among agents in a multi-agent setting.
## 4 Experimental Results
### Quantitative Results  
To obtain a quantitative measure of the segmentation performance achieved by the four deep networks, we used the Mean Intersection-Over-Union (mIOU) metric. MIOU is one of the most common metrics for evaluating semantic image segmentation tasks. In particular, we first computed the IOU for each semantic class, namely carcinoma, tissue non-carcinoma, and non-tissue, and then computed the average over all classes. The \(mIOU\) is defined as follows:  
\[mIOU=\frac{1}{C}\sum_{i=1}^{C}\frac{TP_{j}}{TP_{j}+FP_{j}+FN_{j}}, \tag{2}\]  
where \(TP\) is true-positive, \(FP\) is false-positive, \(FN\) is false-negative, and \(C\) is the total number of classes.  
Table 1 shows the quantitative results of the semantic segmentation on three different inputs: RGB, (Red + Hue), and (Red + Value). The results show that the use of the combination (Red + Value) generates better results than (Red + Hue) input. Moreover, a deeper network, such as U-Net modified with ResNet50 as encoder, performs better than the original U-Net (having a more shallow encoder).
## 3 Controller Parameters Tuning
### Particle Swarm Optimization Algorithm Based on GOBL  
The PSO algorithm was proposed by [PERSON] and [PERSON] in 1995 [27]. In the PSO algorithm, the particle changes its velocity \(v\) and position \(x\) in accordance with Equation (6).  
\[\left\{\begin{array}{l}v_{id}^{t+1}=\omega\cdot p_{id}^{t}+c_{p}\cdot r_{1} \cdot(pbest_{id}-x_{id}^{t})+c_{g}\cdot r_{2}\cdot(gbest-x_{id}^{t})\\ x_{id}^{t+1}=x_{id}^{t}+v_{id}^{t+1}\end{array}\right. \tag{6}\]  
where \(i\) is the particle index; \(d\) is the search dimension; \(t\) is the number of iterations; \(\omega\) is the inertia weight; \(c_{p}\) and \(c_{g}\) are the cognitive and social learning coefficients, respectively; \(r_{1}\) and \(r_{2}\) are two random numbers belonging to [0, 1]; \(pbest_{id}\) is the current historical optimal position of the \(i\)-th particle in the \(d\)th search dimension; and \(gbest\) is the current population optimal position.  
The particle in the PSO algorithm changes its position \(x\) primarily by modifying the speed \(v\). Hence, the particle position change is constrained by change in speed. The particles are directly transferred to new positions based on GOBL, and their fitness compared before and after movement, which broadens the search range for particles and speeds up convergence.  
Let \(X=(x_{1},x_{2},\cdots,x_{N})\) be \(N\) particles in \(d\)-dimensional space where each particle satisfies \(x_{i}\in[a_{i},b_{i}]\) (\(a\), \(b\), are \(d\)-dimensional search space boundary), then the opposite particles \(\widetilde{x}_{i}\) are obtained by Equation (7).  
\[\widetilde{x}_{i}=a_{i}+b_{i}-x_{i} \tag{7}\]  
As an improvement of OBL, GOBL can further enhance the search potential of particles, thereby enhancing the global optimization ability of the algorithm. The GOBL equation is shown in Equation (8).  
\[\widetilde{x}_{i}=k\cdot(a_{i}+b_{i})-x_{i} \tag{8}\]  
where \([a_{i},b_{i}]\) is the boundaries of the \(i\)-th particle and \(k\) is a random number belonging to [0, 1].  
\(a_{i}\) and \(b_{i}\) dynamically changes in iteration to avoid unnecessary opposite search following Equation (9) [28].  
\[a_{i}=\min(x_{i}),b_{i}=\max(x_{i}) \tag{9}\]  
The particle is modified according to Equation (10) when it surpasses the search space.  
\[\widetilde{x}_{i}=\left\{\begin{array}{l}rand(a_{i},b_{i}),\widetilde{x}_{i} <a_{i}\,or\,\widetilde{x}_{i}>b_{i}\\ \widetilde{x}_{i},a_{i}<\widetilde{x}_{i}<b_{i}\end{array}\right. \tag{10}\]  
The opposite population is combined with the original population during particle initialization and each iteration, and the first \(N\) particles with the best fitness are chosen to establish a new population. This method enhances the global search ability of the particles.
## 4 Experiments
### Analysis of Experimental Results  
#### 4.3.1 Fingerprint Verification  
Although the fingermark liveness detection method is proposed for signed fingermarks, it still needs to have the basic ability to detect the liveness of unsigned fingerprints. In this paper, the SFNet model was tested on the public fingerprint dataset LivDet2017 to verify the effectiveness of the proposed network structure, and the experimental results are shown in Table 3, and the other four sets of models were from the results of the LivDet2017 competition [23]. It can be seen that compared with the four best performing algorithms in the LivDet2017 competition, SFNet achieves the optimal results in terms of detection accuracy on all sub-datasets, which indicates that SFNet has a better fingerprint liveness detection capability.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Green Bit** & **Digital Persona** & **Orcanthus** & **Overall** \\ \hline JLW\_A & 95.08 & 94.09 & 93.52 & 94.23 \\ JLW\_B & 96.44 & 95.59 & 93.71 & 95.25 \\ ganfp & 95.67 & 93.66 & 94.16 & 94.50 \\ ZYL\_2 & 96.26 & 94.73 & 93.17 & 94.72 \\ SFNet & 96.82 & 96.64 & 95.27 & 96.16 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison results of detection accuracy of different models based on the LivDet2017 dataset. Unit: %.  
Figure 6: Fake signed fingermarks formed from different materials. Which shows a fingermark with Chinese signature.
## 2 Materials and Methods
### Data Analysis  
A Monte Carlo analysis was performed to take into account uncertainties introduced by muscle volume segmentation [59; 60], which directly impact the MIF values assigned to the modeled muscles and, in turn, affect the model predictions. Each muscle volume was assumed to be normally distributed, with distinct means and standard deviation. These distributions were calculated to reflect a 5% variability from the reference value determined by the expert operator. An inverse cumulative distribution function was utilized to sample the variables from their respective distributions. Overall, 9000 models (one thousand per subject) were generated, varying the maximal isometric force of each muscle, and employed to perform the in silico MVIC test. The maximal torque obtained simulating the MVIC test with all the models was then compared with the experimental data, to quantify the effect of the uncertainties of the input on the predictions.  
As statistical analysis, Pearson correlation and Friedman tests have been performed. The non-parametric statistical Friedman test [61; 62] was conducted despite the limited sample size to determine whether the differences in results across various models were statistically significant.
###### Abstract  
Driven by the urgent need for sustainable alternatives to fossil fuels, the focus on the exploration of lignocellulosic biomass, particularly lignin, as a promising renewable feedstock for biofuels and high-value chemicals has intensified. This study investigated the depolymerization of KL using a DES comprising ChCl and ZnCl\({}_{2}\). Our analysis systematically focused on the effects of reaction temperature, time, and the DES-to-lignin ratio on the yields and characteristics of the products. Optimal KL depolymerization was observed at a temperature of 190 \({}^{\circ}\)C and a duration of 8 h, yielding a maximum liquid product yield of 54.44% and RL yield of 45.56%. The results revealed that increasing the reaction temperature enhanced the depolymerization process owing to a reduction in the viscosity of the DES, which improved mass transfer and interactions with lignin. Under these optimal conditions, the molecular weight of the bio-oil was considerably lower (M\({}_{\mathrm{w}}\) = 1498 g/mol and M\({}_{\mathrm{n}}\) = 1061 g/mol) than that of the bio-oil obtained without DES treatment (M\({}_{\mathrm{w}}\) = 1872 g/mol and M\({}_{\mathrm{n}}\) = 1259 g/mol), indicating a more favorable molecular weight distribution with DES treatment. Furthermore, elemental analysis revealed a reduction in the O, N, and S contents of the RL following DES treatment, increasing the high heating value from 24.82 MJ kg\({}^{-1}\) for the non-DES-treated RL to 26.44 MJ kg\({}^{-1}\) for the DES-treated RL. These findings underscore the potential of the (ChCl:ZnCl\({}_{2}\)) DES as a sustainable and effective medium for lignin valorization, paving the way for the synthesis of high-quality biofuels and chemicals from lignocellulosic biomass.  
kraft lignin; deep eutectic solvent; phenolic compounds; recovered lignin; bio-oil +
Footnote †: journal: Applied sciences
# Molecular Mechanisms of Biofilm Formation in Helicobacter pylori
## 4 Mechanism of Biofilm Formation
### Initiation of Adherence
#### 4.1.3 Role of Chemotaxis  
During the adherence process, chemotaxis is the first stage of biofilm formation but does not affect the production of mature biofilms. A study examining various chemotaxis signaling mutants found that chemotaxis proteins are involved in biofilm initiation by affecting flagellar rotation [33]. Moreover, mutants that preferred counterclockwise movement encouraged the formation of biofilms, such as \(\Delta cheA\), \(\Delta cheW\), or \(\Delta cheV\). On the other hand, mutants that preferred clockwise movement hindered the formation of biofilms, such as \(\Delta cheZ\), \(\Delta chePep\), or \(\Delta cheV3\). These counterclockwise (CCW) flagella were inadequate in stimulating the production of biofilms, indicating the presence of other factors responsible for this process [33].
# Impact of Pore Geometry and Water Saturation on Gas Effective Diffusion Coefficient in Soil
## 3 Results and Discussions
### Effective Knudsen Diffusion Coefficient  
The effective diffusion coefficient for gas transport in soil can be calculated by the dusty gas formula when the accuracy requirement is not too strict [15]. However, the dusty gas model requires the effective bulk and effective Knudsen diffusion coefficients, which cannot be directly measured and should be estimated indirectly. Different empirical formulas are available to estimate the effective bulk diffusion coefficient [18; 21; 53] by porosity and tortuosity. However, study on the effective Knudsen diffusion coefficient is limited. As the effective Knudsen diffusion coefficient mainly depends on pore diameter and the molecular mass of gas, we calculated the effective Knudsen diffusion coefficient of oxygen and hydrogen in the two soil samples using the pore-scale method. Figure 10 shows the changes of normalized effective Knudsen diffusion coefficient (\(D_{kn}/d\)) with a mean pore diameter. The simulated effective Knudsen diffusion coefficients of oxygen and hydrogen in soil sample 1 were very close, and this further proves that the normalized effective Knudsen diffusion coefficient depends only on the pore geometry, and not on the molecular mass of gas. At pore scale, the Knudsen diffusion coefficient increased linearly with pore diameter as Equation (2); but after a volumetric average, this linear dependence is broken and becomes a power law. The increase of the normalized effective Knudsen diffusion coefficient with the mean pore diameter can be described by the following equation:  
\[D_{kn}/d\ =\ \eta\delta^{\prime\gamma}, \tag{7}\]  
where \(\eta\) is a parameter related to pore geometry, and \(\gamma\) is a constant with the value equal to 0.99. The comparison between the normalized effective Knudsen diffusion coefficients is estimated by Equation (7), with that calculated from pore-scale simulations showing a good agreement as shown in Figure 10, with \(\eta\) = 0.0027 for soil sample 1 and \(\eta\) = 0.001 for soil sample 2.  
Figure 9: (**a**) Oxygen diffusion in sample 1. (**b**) Oxygen diffusion in sample 2. (**c**) Methane diffusion in sample 1. (**d**) Hydrogen diffusion in sample 1.  
As mentioned before, the mean pore diameter increases with gas saturation following a power law \(\delta^{\prime}=\alpha(1-S)^{\beta}\), and the increase of the effective Knudsen diffusion coefficient with mean pore diameter can be described by the power law \(D_{kn}/d=\eta\delta^{\prime\gamma}\); we can easily draw out the effective Knudsen diffusion coefficient with gas saturation following a power law \(D_{kn}/d=m(1-S)^{n}\). To prove the validity of this equation, we simulated the effective Knudsen diffusion coefficient of different gases in the two soil samples under various saturations and the results show that the effective Knudsen diffusion coefficient increased with gas saturation following a power law as shown in Figure 11.
## Materials and Testing  
### Composition of Mineral-Cement Mixtures with Foamed Bitumen  
Six types of mineral-cement mixtures with foamed bitumen (MCAS) were used. The design of the recycled mix composition was modified by varying bitumen amount and, above all, quantity and type of waste materials added. The following waste materials were used in the experiment:  
* dolomite dust (D),
* basalt rock dust (B),
* steel slag (SG),
* virgin dolomite aggregate 0/31.5 (VA),
* reclaimed asphalt pavement (RAP),
* recycled aggregate (RA)--generated from the road base course by milling,
* recycled cement concrete (RC).  
The principal aim when designing MCAS was to choose additional waste materials with similar granulometric compositions. The basic virgin aggregate (VA) had a continuous gradation curve with a maximum \"D\" grain size of 31.5 mm and complied with Polish requirements WT-2010 [15] based on EN 13242 [16]. In order to provide the required size aggregate to be used in a Marshall sample, constituents of the recycled waste materials were screened through a #22.4 mm sieve. The mineral mix was designed following the Wirtgen guidelines [7]. The target range defined by the limiting curves [7] allowed adding more dust, thus creating a possibility of considering a variant of recyclable mixture containing basalt and dolomite dust. The grading curves of the materials used are shown in Figure 1.  
The experiment was designed to assign the samples of MCAS recycled mixtures to appropriate mix types in a series of tests using a single plot of the grading curve. Consequently, the basic mineral  
Figure 1: Grading curves of the waste materials.  
skeleton of the recycled mixture remained the same. Variables were the combinations of waste material proportions or mineral dust quantities. Note that the gradation of the reclaimed cement concrete (RC) and the reclaimed aggregate (RA) components was almost identical, which greatly facilitated interpretation of the test results. It should be added that the marginal material denoted as a mineral dust (derived from dedusting system by extraction of aggregates in the mine) in accordance to Polish standards and regulations was a waste material. The same inactive dusts were used and analyzed in paper [17].  
Mixture types and assumptions in the form of waste material quantities are compiled in Table 1.  
Cement content (Table 1) was established at the average value from the recommended range [18]. In recycled mixtures, the amount of foamed bitumen was also established at the average level of 2.5%. The mixtures containing mineral dust were an exception. The material derived from recycled pavement (RAP) was tested for quality in compliance with WT-2/2010 [15].
# An Adaptive Multi-Fidelity Surrogate Model for Uncertainty Propagation Analysis
###### Abstract
In the context of multi-fidelity modeling for multi-dimensional correlated responses, the Gappy-POD approach [22; 23] has been recognized as a promising strategy. This approach combines high- and low-fidelity responses from training samples to generate multi-fidelity responses. It identifies a set of key orthogonal basis functions and then predicts the high-fidelity responses of the new sample from its corresponding low-fidelity responses, thereby eliminating the need for extensive high-fidelity simulations. However, a common challenge encountered in the Gappy-POD approach is the selection of high-fidelity training samples, namely, where to conduct high-fidelity simulations within the parameter space. Existing literature on the Gappy-POD model has not specifically addressed this issue. [PERSON] et al. [23] employed Latinized centroidal Voronoi tessellation to acquire multi-fidelity samples for constructing a Gappy-POD surrogate model predicting RAE2822 airfoil flow fields. This sampling method is fundamentally a random one. [PERSON] et al. [24] implemented Latin hypercube sampling to obtain high-/low-fidelity samples to construct the Gappy-POD model during gas turbine second-stage vane optimization. [PERSON] [25] proposed boundary-intermediate sampling for NACA0012 airfoil optimization. However, this strategy only applies to low-dimensional problems, as the required number of samples increases exponentially for high-dimensional problems. Basically, these methods are all a priori sampling methods, meaning they do not utilize feedback information from prediction results. From the experience of many single-output modeling efforts, designing sampling strategies based on model prediction results can significantly enhance modeling efficiency [14; 15; 16]. This is exactly the motivation for this paper. To address the need to quantify uncertainty in multi-dimensional correlated flow field responses, we propose an adaptive multi-fidelity modeling approach that uses the Gappy-POD algorithm. The primary focus of the paper is on the design of experiments, specifically exploring how to select samples for initial high-fidelity CFD simulations from numerous low-fidelity samples, as well as how to incrementally incorporate high-fidelity sample data based on model prediction feedback. The second section of this paper introduces the Gappy-POD method, which is the framework of the multi-fidelity model. The third section delves deeper into how to select high-fidelity samples, including the method for selecting the initial samples and the adaptive sampling criterion. The fourth section presents the implementation process of the adaptive multi-fidelity model and uncertainty propagation model. The fifth and sixth sections present the two test cases and the analysis results, highlighting the prediction error reduction achieved through the application of the experimental design methods compared to the traditional random sampling algorithm. Finally, conclusions are drawn, and a discussion of future research directions concludes the paper.
## 3 Results and Discussion
### Hardness  
The results from Rockwell C tests performed using a test force of 150 kgf are illustrated in Figure 12, the treated specimens are on average 21% harder than the untreated specimens. However, it is important to note that the hardness is highly variable in both samples. The lowest hardness values from the treated sample are roughly equivalent to the hardest values from the untreated sample. This variability is likely due to the very rough surfaces of both samples. As a result, no statistical conclusion can be drawn from the hardness data about which sample is harder overall. It can be said that the hardness of the treated specimen is more consistent than that of the untreated specimen, and that there are points on the treated specimen that are harder than points on the untreated specimens. This possible increase in hardness at certain points could be attributed to the work hardening done by the UIT, a result of the increase in dislocations density at the surface which will resist further deformation. The increase in hardening could lead to a resistance to crack initiation sites in those locations, which corresponds to an increase in fatigue life [65].