esa-sceva/satcom-chunk-collection huggingface dataset:
# Perspectives of Hydrogen Generation in Cavitation-Jet Hydrodynamic Reactor
## 1 Introduction
Furthermore, several alternative technologies have emerged for industrial-scale hydrogen production without carbon-containing minerals, thus achieving zero greenhouse gas emissions [21; 22]. An area of interest is the study of cavitation processes in high-speed fluid flows through specific tubes or the creation of gas-steam bubbles via ultrasonic irradiation in a liquid medium. Following cavitation treatment, water molecules can be used as a source of \"green\" hydrogen [23; 24; 25]. Cavitation involves the formation, growth, and implosion of microbubbles due to ultrasonic irradiation or hydrodynamic local acceleration flow, leading to sono-chemical reactions. These microbubbles serve as potent microreactors where pyrolytic reactions produce free radicals and oxidants (such as HO\({}^{\bullet}\), H\({}^{\bullet}\), HO\({}_{2}^{\bullet}\), and H\({}_{2}\)O\({}_{2}\)) as well as molecular hydrogen (H\({}_{2}\)). As these microbubbles collapse rapidly, they become highly pressurized and reach temperatures of several thousand Kelvins, thus creating a high-powered microreactor environment [26; 27]. The utilization of aluminum and its alloys offers a promising avenue for producing hydrogen from water, which could address the environmental issues related to the recycling of aluminum waste [28; 29; 30]. The process of generating hydrogen (H\({}_{2}\)) through the reaction of aluminum (Al) with water in neutral solutions is recognized for its simplicity, safety, efficiency, and eco-friendliness [31]. However, the formation of a dense passive oxide layer on Al and its alloys significantly reduces the H\({}_{2}\) production efficiency. Efforts have been made to remove this oxide layer to improve the hydrogen generation efficiency in the aluminum-water reaction process. The detailed analysis indicates that hydrogen can be obtained by various methods, including coal and biomass gasification, water electrolysis, methane vapor reforming, plasma chemistry, and other alternative methods not covered in this article. Coal and biomass gasification necessitate high operating temperatures, leading to significant energy expenditures for producing modest hydrogen quantities. Water electrolysis, which employs electricity from renewable sources like wind and solar power, offers environmental advantages. However, the substantial initial investment for infrastructure leads to elevated hydrogen production costs and limited capacity relative to conventional industrial hydrogen production [32]. This work discusses a combined technology for generating hydrogen from water and aqueous alkali solutions within a cavitation-jet reactor (CJR) along with the chemical activation of aluminum, which is used in fabricating the reactor construction components. This technology aims to address not only a significant technological challenge but also issues associated with the decarbonization and recycling of aluminum-containing waste from both industrial and household appliances.
## 4 Discussion
### Interactions Between Fungus and Bacteria  
The in-house study showed that bacteria of the species _B. thuringiensis_ and _B. subtilis_ had a slightly lower inhibitory effect on the growth of the fungus _F. oxysporum_ than that of _B. amyloliquefaciens_ (about 45%). _B. thuringiensis_ bacteria were able to inhibit mycelial growth by about 40%. [PERSON] et al. [41] presented slightly different results on the effect of _B. thuringiensis_ against _F. oxysporum_. The study of the cited authors showed that _B. thuringiensis_ strain BCN10 had no antifungal properties against _F. oxysporum_. The differences could be due to the metabolic capacity of the strain or to the too short duration of the analysis, as they found that the inhibitory effect against the mycelium of _F. oxysporum_ increased with the duration of the experiment [41].  
On the other hand, [PERSON] et al. [42] and [PERSON] et al. [43] confirmed that the volatile organic compounds produced by _B. subtilis_ could be used as biological control agents to protect against plant pathogenic microorganisms, including _F. oxysporum_. On the other hand, _B. subtilis_ C9 showed potent antifungal activity in inhibiting the mycelium of _F. oxysporum_, which was 93.61% (for one of the compounds produced, the DG4 isomer of acetylbutanediol) [42]. In contrast, [PERSON] et al. [44] tested the fungistatic activity of a _B. subtilis_ suspension against _F. oxysporum_ in in vitro and in vivo tests on maize. The VOCs were then purified and analysed for their antifungal properties in in vitro tests. The tests showed that _B. subtilis_ bacteria exhibited high antifungal activity against _F. oxysporum_, and such a result gives hope for the use of the secondary metabolites of _B. subtilis_ as biocontrol agents with high versatility without harming the environment [44].  
The results of [PERSON] et al. [45] suggest that the VOCs produced by strain _B. velezensis_ CT32 have broad-spectrum antifungal activity against _F. oxysporum_. The authors suggest that the volatile compounds produced by the tested _Bacillus_ strain could potentially be used as an agent against the fungus _F. oxysporum_.  
Many scientific reports indicate a strong inhibitory effect of the volatile organic compounds (VOCs) produced by _Bacillus_ bacteria on the fungus _F. oxysporum_[23; 25; 46]. In contrast, the literature on the effects of VOCs is much sparser. Therefore, the analyses presented here are novel in terms of understanding the antagonistic properties of bacteria of the genus _Bacillus_ against the fungus _F. oxysporum_ and especially regarding the antagonistic properties of the VOCs they produce.
### Limitations of RISs and CRL  
However, there are limitations of RIS technology as well as causal reinforcement learning. Firstly, regarding RIS technology, its practical application may encounter some limitations. For example, the deployment of RISs may require a substantial amount of hardware equipment and a complex installation process, which could increase system costs and deployment difficulties. Additionally, the performance of RISs may be influenced by environmental conditions, such as building structures or weather conditions, which could affect the effectiveness of the RIS and consequently degrade the communication quality and reliability.  
As for causal reinforcement learning, its limitations primarily manifest in the model complexity and training time. Causal reinforcement learning may necessitate a large amount of data for training, and in complex environments, it may require significant time to converge to optimal solutions. Furthermore, the design and optimization of causal reinforcement learning algorithms may require specialized knowledge and expertise, potentially limiting their application in practical systems.  
Moreover, challenges may arise in the interaction and integration of both technologies in practical applications. For instance, effectively integrating RIS technology and causal reinforcement learning algorithms into smart electric vehicle charging communication networks to achieve synergistic effects would require further research and optimization.  
In summary, despite the potential advantages of RIS technology and causal reinforcement learning in smart electric vehicle charging communication networks, they also face practical limitations that need to be considered and addressed in real-world applications.
## 5 Discussion
Moreover, it can be noticed the input voltages of the piezo actuators (see Figures 20 and 21) are biased by a constant value of 50 V, which brings the positive/negative signal from the sensing system in the correct range [0, 100] V. When using piezoelectric stack actuators in vibration control, the devices are required to generate a displacement in a two-sided fashion, i.e., to extend and contract. However, most of such devices, due to piezoelectric properties, show a unipolar behavior and cannot be fed with negative voltage. Aside from using asymmetrically bipolar devices, some design alternatives exist to avoid imposing a constant input signal, such as modifying the device spring preload, using two piezo-stack in series, or an advanced solution such as the one proposed by [PERSON] et al. [19] for a large space structure. However, it should be reminded that piezoelectrics generally show very limited current needs and low power consumption in low/medium frequency range [42], and the chosen configuration will also depend on the passive structure design and available space. The architecture proposed here has a very limited impact on the composite passive structure, a minimum number of actuators and sensors being used, with limited mass in the order of a few tens of grams (as reported in Tables 4 and 5), even if considering the stack L-shaped supporting structure. An additional advantage is the scalability of the system: the vibration suppression performance can be further improved if more than two actuators for each solar panel are considered. In this case, a trade-off between desired efficacy and mass/cost impact has to be considered according to the selected application.
# Encoder-Decoder Structure Fusing Depth Information for Outdoor Semantic Segmentation
## 4 Frequency weighted intersection over union(FWIoU):
#### 3.4.2 mIoU Results for the Cityscapes Dataset  
Table 2 summarizes the comparison of our results with results obtained using other semantic segmentation methods. Compared with the segmentation algorithms without fusion depth information, our method had a higher mIoU, more accurate segmentation results and a good overall performance. At the same time, we also concluded that semantic segmentation results with estimated depth information could achieve comparable perfor  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **SwiftNet [38]** & **RFNet [16]** & **Our Method** \\ \hline Road & 94.9 & 96.1 & 94.8 \\ Sidewalk & 54.9 & 61.6 & 82.3 \\ Wall & 47.3 & 56.9 & 74.1 \\ Fence & 57.7 & 60.4 & 75.3 \\ Vegetation & 90.8 & 91.1 & 89.7 \\ Terrain & 57.1 & 57.1 & 69.1 \\ Sky & 91.9 & 91.8 & 91.7 \\ Bus & 80.6 & 83.4 & 82.4 \\ Train & 60.4 & 73.9 & 82.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the proposed method with other semantic segmentation methods using different objects.  
Figure 8: The IoU results for different object classifications with the Cityscapes dataset.  
mance for the ground truth depth map and even outperformed some competitive methods. In addition, the PA and FWIoU of our method were 93.1% and 87.9%, respectively.  
Although the fusion of RGB images and depth maps with the self-calibrating fusion architecture and feature pyramid attention mechanism raised the computational cost, our semantic segmentation model ran at an average speed of 10.43 FPS (frames per second) on a single Nvidia Titan Xp Gpu for input images with a quantity of 1525 and size of 768 \(\times\) 768. Training approximately 200 epochs on 2975 training sets, the trainable parameters were about 444.9 M. Overall, our multimodal semantic segmentation method incorporating predicted depth information achieved the best results while maintaining real-time performance, which also demonstrated that utilizing predicted depth information could improve the efficiency of semantic segmentation.
# Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories
## 5 Adversarial Ant Colony Optimization Algorithm for Solving Non-TT Routing Problem
### Definition of the Optimization Problem in Non-TT Routing
#### 5.3.2 State Transition Probability  
We then calculated the state transition probability of the ants and selected the next node to visit by roulette, based on the state transition probability. The state transition probability was calculated as follows:  
\[P_{ij}^{kl}(t)=\begin{cases}\frac{|\tau_{ij}^{k}(t)|^{2}[\eta_{ij}(t)]^{\beta} }{\sum\limits_{k\in\textit{allocalloc}_{l}}|\tau_{ij}^{k}(t)|^{2}[\eta_{ij}(t)] ^{\beta}},&j\in allowed_{l}\\ 0,&\text{otherwise}.\end{cases}, \tag{14}\]  
Figure 9: A sample procedure of the AACO algorithm solving the non-TT route assignment problem.  
where \(P_{ij}^{k}(t)\) is the probability that the \(l\)th ant in the \(k\)th ant colony chooses to visit \(v_{j}\) from the current node \(v_{i}\) at the time \(t\), \(allowed_{l}\) is the set of nodes that are directly accessible and have not been visited yet, \(\tau_{ij}^{k}(t)\) is the pheromone concentration left on the edge \(e_{ij}\) by the \(k\)th ant colony, and \(\alpha\) is the pheromone influence factor and \(\beta\) is the cost function influence factor, both of which are preset constants. The cost function was defined as:  
\[\eta_{ij}(t)=\frac{1}{c_{ij}+\delta h_{i}}, \tag{15}\]  
where \(h_{i}\) is SW hops between \(v_{i}\) and \(v_{j}\).
###### Abstract  
Magnetism in Croatian caves has been significantly underexplored, despite the well-documented preservation of cave sediments. This study investigates whether magnetic susceptibility can be effectively recorded in Croatian caves and, if so, whether there are differences between in situ and laboratory measurements, as well as the extent of these differences. The focus is on two prominent sites within the Velebit Mountain range of the Dinaric Karst: the deep Slovacka Cave and the extensive Crompac Cave System (ex. Kita Gacesina Cave). Magnetic susceptibility was measured in soft sediments using the Kappameter KM-7 for in situ assessments and the Bartington MS2B System for laboratory analyses. The comparison of the two devices aimed to evaluate the feasibility of detecting magnetic susceptibility variations within carbonate sediments. Sampling locations were selected based on preliminary assessments of magnetic susceptibility variations and accessibility for sampling. Characteristic samples from each profile underwent X-ray powder diffraction analysis. Results revealed significant variations in magnetic susceptibility across each profile under both in situ and laboratory conditions. In situ measurements ranged from \(-45\times 10^{-5}\) SI to \(120\times 10^{-5}\) SI, while laboratory measurements ranged from \(4.00\times 10^{-5}\) SI to \(99.40\times 10^{-5}\) SI. The portable Kappameter KM-7 provided satisfactory results for preliminary investigations, while the Bartington MS2B System demonstrated high precision in analyzing these variations in detail. These findings support the potential for further investigations into magnetic susceptibility in karst cave environments, with an emphasis on understanding depositional history and the timeline of climate and tectonic changes.  
magnetic susceptibility; Dinaric Karst; caves; sediment analysis; Bartington MS2B
# A Systematic Review of Defensive and Offensive Cybersecurity with Machine Learning
## 3 Results
### Defensive Machine Learning Techniques
This classification algorithm is suitable for solving complicated data problems by extraction of sophisticated patterns from features with limited prior knowledge. Backpropagation is a common method used in training a neural network, and has been considered to result in local solutions or cause low training speed so a single-hidden layer feed-forward neural network, called extreme learning machines (ELMs) was proposed. The ELMs use bias and random weights for the connection of hidden neurons and input. Also, the ELMs use only a one step calculation of least squares approximation to determine the weights, thereby speeding up the learning process [18]. Finally, many current IDS studies have proposed hybrid detection techniques, combining both signature-based detection techniques and anomaly-based detection techniques. Several papers integrated classification algorithms to create effective IDS. One of the papers of interest proposed a Signature-based Anomaly Detection System (SADS) to overcome some of the drawbacks of the conventional IDS, such as false alarms, by integrating naive Bayes and random forest classifiers [16]. It was also discovered that when random forest and NBTree algorithms, which combine naive Bayes and decision tree classifiers, were used cooperatively based on the sum rule scheme, the detection accuracy was greater than the singular random tree algorithm's detection accuracy [19]. Malicious web sessions have also been automatically classified to multiple vulnerability scan classes and attack classes using various multiclass supervised ML methods such as SVM, J48, and PART [20].
## 4 Discussion
### Iranian Genotypes Are Not Generally More Tolerant to Severe Drought Stress Than German Genotypes  
This field study was conducted based on the hypothesis that wheat genotypes developed in very dry climates (e.g., Iran) have an intrinsically higher drought tolerance compared to those from moderate climates (e.g., Germany), and may represent candidates for use in central Europe in the future when late season drought spells are likely to increase significantly.  
Different wheat genotypes were selected representing drought tolerant and drought sensitive cultivars, based on preceding studies [18]. It is interesting that this classification was only confirmed for the two Iranian genotypes, while drought-induced yield reductions were similar for the four German genotypes, irrespective of their reported drought tolerance or sensitivity. This is relevant, since it indicates that reported tolerance to drought may not be consistently observed when plants are grown in a different environment, where other factors such as soil type or light intensity may play a role. It highlights the need for testing/selection of genotypes under different environmental conditions in order to choose the optimum adapted genotype under changing climatic conditions. With respect to yield stability, only the tolerant Iranian genotype Alvand performed better, while the yield reduction under stress was even stronger in Mihan compared to all German genotypes. The Iranian genotypes originate from a different gene pool than the German ones, and it is known that the composition of wheat proteins depends on the genetic background [34]. However, it seems that neither the genetic background alone, nor the climatic conditions under which a genotype was developed, automatically provide a significant advantage under drought conditions.  
Drought Tolerant Iranian Genotypes Have a High Baking Quality and Might Be Suitable Candidates for Drought-Prone Areas in Germany  
Unexpectedly, the Iranian genotypes both exhibited a very good bread baking quality, despite the fact that these genotypes were not selected for this trait and overall had a rather low concentration of glutenins. It seems that these genotypes compensate for the low amount of HMW glutenins by high levels of \(\omega\)- and \(\gamma\)-gliadins, which are strongly increased under severe drought. With respect to baking quality, at least the tolerant genotype Alvand may thus be considered a possible candidate for cultivation in highly drought-prone areas of central Europe. However, its total grain yield was the lowest of all tested genotypes under well-watered and drought conditions, raising the question of whether it would be competitive enough economically for the European market.
# Study on Preparation and Rheological Properties of 3D Printed Pre-Foaming Concrete
## 3 Results and Discussion
### Static Yield Stress  
Three-dimensional printing is a layer-by-layer superposition process. Each layer needs to have sufficient yield stress to resist the upper pressure without deformation. At a constant shear rate, the yield stress of the mixtures varies with time as shown in Figure 6. It can be seen from Figure 6 that the static yield stress of the blank is 183.543 Pa. However, under the same surfactant content, the static yield stress of 3 DFC0.5 mixed with HPMC increases to 1189.998 Pa, which is about six times that of the blank. The addition of HPMC can effectively improve the static yield stress of foamed mortar. The sample 3 DFC0 without surfactant shows the highest static yield stress of 1735.109 Pa, which is much higher than that of the sample with surfactant. When the content of surfactant is from 0.5% to 2.0%, the static yield stress decreases from 1189 to 687 Pa. Some studies have interpreted this change as the result of the competition between the skeleton yield stress of 3 DFCs and bubble surface tension. To better evaluate this phenomenon, a plastic capillary number was introduced to quantify the balance between capillary forces and fluid stresses Equation (1) [33]:  
\[\text{Ca}_{\tau}=\frac{\tau}{2\text{T}/\text{R}} \tag{1}\]  
where \(\tau\) is the paste's yield stress that tends to deform the bubbles ;T is the capillary stress that tends to minimize to bubbles' surface area; R is the average pore size of bubble. When the surface tension largely dominates the yield stress of the foam suspension, the capillary number is low, and the bubbles appear as rigid inclusions in the mortar. Therefore, the yield stress of 3 DFCs is not affected by the bubbles for gas volume fraction \(\O\) (Table 3) less than 50. However, when the importance of surface tension decreases, \(\text{Ca}_{\tau}\) increases, and bubbles appear to be soft and easily deformed. A significant reduction of yield stress with \(\O\) is due to mutual exclusion between the gelling attraction of mortar and the inflated surfactant molecule, with a general increasing trend as a function of foam content.  
Figure 5: The (**a**) fluidity loss and (**b**) Mini-slump gain of blank and 3 DFCs over time.  
To go one step further, we list a dimensionless yield stress \(\tau_{p}(\varnothing)/\tau_{p}(0)\) as a function of \(\varnothing\) through Equation (2) [34]:  
\[\frac{\tau_{p}(\varnothing)}{\tau_{p}(0)}=\sqrt{\frac{(1-\varnothing)(5+3 \varnothing)}{5-2\varnothing}} \tag{2}\]  
Therefore, we calculate the dimensionless yield stress through the above formula in Table 3. It is worthwhile to note all the five values of \(\tau_{p}(\varnothing)/\tau_{p}(0)\)-1, meaning that the yield stress of a bubble suspension is comparable to the one of the suspending emulsion for the range of parameters investigated [34].
## 3 EKF-GAPF-Based Method for Multipath Parameter Estimation  
As shown in Figure 3, the implementation process of EKF-GAPF is as follows: First, model and sample the signal received by the receiver, and initialize the parameters required  
Figure 2: Code phase tracking error curves due to multipath interference.  
by the algorithm. Second, utilize EKF to preliminarily identify signal parameters and provide reasonable prior probability distribution suggestions for PF, thereby improving the state prediction of particles, making it more in line with actual system dynamics, reducing randomness and error accumulation. Third, use PF to sample and update the state of the particle set, and calculate the normalized weights of particles. Fourth, introduce GA to optimize the resampling process of PF [27], which gradually evolves particles to high-probability regions of posterior probability distribution through elite preservation, adaptive crossover, and mutation operations. Fifth, after multiple iterations, the parameter estimation results are output, achieving high-precision optimization of state estimation.