esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Adam Optimization Algorithm  
The Adam algorithm [23] is a stochastic optimization algorithm designed to accelerate the optimization process and improve algorithm performance. It adjusts the learning rate adaptively to optimize the parameters of neural networks, demonstrating excellent performance in large-scale data and noisy environments.  
Let the parameters at the current step \(t\) be \(\theta_{t}\), the gradient of \(\theta_{t}\) at the current step be \(g_{t}\), the first moment estimate be \(m_{t}\), and the second moment estimate be \(v_{t}\). Their respective formulas are as follows [24]:  
\[m_{t}=\beta_{1}m_{t-1}+(1-\beta_{1})g_{1} \tag{6}\]  
\[v_{t}=\beta_{2}v_{t-1}+(1-\beta_{2})g_{t}^{2} \tag{7}\]  
In the equation, \(\beta_{1}\) and \(\beta_{2}\) represent adjustable exponential decay rates.  
\[\hat{m}_{t}=\frac{m_{t}}{1-\beta_{1}^{t}} \tag{8}\]  
\[\hat{\sigma}_{t}=\frac{v_{t}}{1-\beta_{2}^{t}} \tag{9}\]  
We update the model parameters \(\theta_{t}\):  
\[\theta_{t}=\theta_{t-1}-\alpha\frac{\hat{m}_{t}}{\sqrt{\hat{\sigma}_{t}}+\varepsilon} \tag{10}\]  
In the equation, \(\alpha\) denotes the learning rate; \(\varepsilon\) is a very small numerical value.
## 4 Conclusions  
It is well established that intermetallic compound layer thickness, phase transformations and area of bonding have an overriding influence on the mechanical and metallurgical properties of welding using stainless steel and aluminum alloys. In this investigation, a high entropy alloys powder was chosen as a filler for laser welding. The conclusions derived from this research are as given below:  
In this laser welding experiment for joining UNS S33207 to aluminum alloy 6061, Fe\({}_{5}\)Co\({}_{20}\)Ni\({}_{20}\)Mn\({}_{35}\)Cu\({}_{20}\) HEAs powder was chosen as the lap welding filler.  
The Fe and Al alloy's mutual mixing and reaction were prevented by monitoring the melting quantity of the parent metal in the hyper-duplex stainless-steel side. Al/Fe intermetallic compounds were not identified in the joints weld zone.  
The metallographic phases identified for the HEAs in the weld region were DSS and V solid solutions.  
The large entropy property of HEAs favored the production of arbitrary solid solutions over IMCs. As a result, the HEAs' after-melting phase was comprised of DSS and V solid solutions.  
Fractography revealed mostly fine dimples, with a few coarse dimples also visible.  
The weld had a tensile strength of 237 MPa, and the tensile fracture was prolonged at the joint's HEAs/Al metal interface.  
The highest microhardness achieved in the weld zone was at the HEAs/Fe interaction area, which was 703 HV, with the HEAs/ Al weld interface having a microhardness of 207 HV.  
Conceptualization, D.G.M., J.T. and S.S.K.; methodology, D.G.M. and S.S.K.; formal analysis, D.G.M., J.T. and S.S.K.; investigation, D.G.M. and S.S.K.; writing--original draft preparation, D.G.M.; writing--review and editing, D.G.M., J.T. and S.S.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant No. 51842507.  
Figure 13: Microhardness profile from the welded joint cross-section.
## 6 Parameter Analysis  
### Effect of Loading Mode on Test Beam Damage  
Both the P2 and P3 beams are prestressed reinforced concrete beams. The P2 beam is subjected to cyclic loading, while the P3 beam is subjected to hierarchical loading.  
Figure 13: P3 beam load–prestress curve.  
Comparing the experimental results and acoustic emission (AE) results of the P2 and P3 beams, it was observed that both beams exhibited no significant phenomena during the initial loading stage. When the load reached 40 kN, cracks started to appear in both the P2 and P3 beams, accompanied by detectable stable AE signals. At a load of 60 kN, multiple cracks emerged at the bottom of the P2 beam, accompanied by subtle splitting sounds. The strain readings from strain gauges 3, 4, and 5 vanished, indicating the formation of through-cracks. In contrast, when the load reached 70 kN for the P3 beam, the strain gauge readings vanished, and cracks formed at the bottom of the beam. Both the P2 and P3 beams demonstrated an increase in AE signals. With further load increase, the P2 beam underwent rapid crack propagation and brittle failure at 70-75 kN, whereas the P3 beam underwent the crushing of the top concrete and brittle failure between 80 and 85 kN.  
The above analysis indicates that the use of hierarchical loading in pre-stressed reinforced concrete beams leads to an approximately 16.7% increase in the load required for the formation of through-cracks at the bottom of the beam. Additionally, there is an approximately 20% increase in the ultimate load resulting in complete failure, as shown in Figure 14a. This suggests that the cyclic loading method is more detrimental to prestressed reinforced concrete beams.
# Performance of Eco-Friendly Zero-Cement Particle Board under Harsh Environment
## 3 Results
### Mass Loss  
Figures 8 and 9 illustrate the effects of freezing-thawing and wetting-drying cycles on mass loss. Generally, the particle board specimens under freezing-thawing or wetting-drying conditions exhibited mass loss. A consistent increase in mass loss was observed as the number of freezing-thawing and wetting-drying cycles increased. For instance, the FT300 specimen exhibited a more substantial mass loss after 25 cycles (i.e., 5.31%) than after exposure to 10 cycles (i.e., 3.94%), as shown in Figure 8.  
Figure 8: Effect of freezing-thawing and wetting-drying cycles on mass loss of agro-waste alkali-activated particle board.  
This observation can be attributed to the following rationales: In the initial phase, the expansion stress induced by freezing triggered specimen cracking. Consequently, a transfer of external water permeated internal voids and cracks, contributing to an augmented particle board specimen mass. However, as the freezing-thawing progressed, escalating freeze-thawing damage resulted in more degradation fragments from the specimen. This led to a reduction in the specimens' mass. Subsequently, when the mass loss attributed to spalling surpassed the mass gain stemming from absorbed water, an overall decrease in the particle board specimen mass ensued, accentuating the magnitude of mass loss.  
On the other hand, the 25 WD200 specimen displayed the highest change in mass variations, amounting to approximately 12.11%. Additionally, the higher amount of slag in the specimen led to a lower percentage of mass loss. For example, 25 WD200 had more mass loss than 25 WD300 and 25 WD400, which had 12.11%, 8.74%, and 4.68% mass losses, respectively. Moreover, it is notable that wetting-drying cycles exhibit a more significant degradation potential when compared to freezing-thawing curing conditions, even when considering an equivalent number of cycles, even at higher binder mixtures. The mass loss was 4.68% for 25 WD400, while it was 2.86% for the 25 FT400 specimen.
## 2 Materials and Methods
### Extraction Process  
Extraction runs were performed on ground TP using ground TS and WGF as sources of oily co-solvents. Three different preparations of the extraction bed were arranged:  
(1) TP without any co-matrix; (2) by blending TP with TS or WGF, named BPS (Blended Peels and Seeds) and BPWGF, respectively; (3) by placing a layer of TS or WGF in the upper part of the ground TP bed, to have a percolation first through the co-matrix and then through the TP layer. In the following, these beds are indicated as LSP and LWGFP.  
Dynamic extraction trials were performed to determine  
1. Total extraction yields of lycopene and \(\beta\)-carotene after 2 h runs carried out on P;
2. Total extraction yields of lycopene and \(\beta\)-carotene after 2 h runs carried out on BPS;
3. Total extraction yields of lycopene and \(\beta\)-carotene after 2 h runs carried out on BPWGF;
4. Total extraction yields of lycopene and \(\beta\)-carotene after 2 h runs carried out on LSP;
5. Total extraction yields of lycopene and \(\beta\)-carotene after 2 h runs carried out on LWGFP;
6. Cumulative extraction yields of lycopene and \(\beta\)-carotene determined during 2 h of extraction process on LSP bed.
7. Cumulative extraction yields of lycopene and \(\beta\)-carotene determined during 2 h of process on LWGFP bed.  
Figure 1: A schematic representation of the extraction plant using Norflurane as solvent [32,35].  
Oil, lycopene, and \(\beta\)-carotene yields (\(\eta_{\rm o}\), \(\eta_{\rm LL}\), and \(\eta_{\rm\beta C}\)) were evaluated with reference to the total oil (OC, \(\rm g_{\rm oil}/g_{\rm dry\ solid}\)), lycopene (LC, \(\rm mg_{\rm lycopene}/g_{\rm dry\ solid}\)), and \(\beta\)-carotene (\(\rm\beta C\), \(\rm mg_{\beta\text{-carotene}}/g_{\rm dry\ solid}\)) contents, respectively, according to Equations (1)-(3):  
\[\eta_{\rm o}=\frac{\rm m_{\rm o}}{\rm m_{\rm s}\times(\rm OC)} \tag{1}\]  
\[\eta_{\rm LL}=\frac{\rm m_{\rm L}}{\rm m_{\rm s}\times(\rm LC)} \tag{2}\]  
\[\eta_{\rm\beta C}=\frac{\rm m_{\rm\beta C}}{\rm m_{\rm s}\times(\rm BC)} \tag{3}\]  
where \(\rm m_{\rm o}\), \(\rm m_{\rm L}\), and \(\rm m_{\rm\beta C}\) are the amounts by weight of oil, lycopene, and \(\beta\)-carotene extracted, respectively, and \(\rm m_{\rm s}\) is the mass of solid matrix treated.  
In Table 1, the main process conditions used for the extraction trials are summarized.  
The TP-to-TS mass ratio (\(\rm M_{TP}/M_{TS}\)) used in this work for trials #2/(BPS), #4/(LSP), and #6/(LSP) was suggested by [PERSON] et al. [29] as the optimal choice to maximize the lycopene concentration in tomato seed oil during sc-CO\({}_{2}\) extraction. A higher amount of WGF used in #3/(BPWGF), #5/(LWGFP), and #7/(LWGFP) was chosen to compensate the lower oil content in WGF than that measured in TS.
## 4 Radial-Shaped Geo Clustering and Angular Routing
### Routing Model  
In RSC, intracluster communication is established with single-hop routing. For intercluster communication, multihop or hierarchical routing is necessary, because the data from a cluster aggregated by the CH are transmitted to the sink node via other CHs in the structure deployed nearby. The sink node can be located anywhere within the network deployment, and the routing path is established based on the location of the sink node. Data transmission is a suboptimal process using traditional routing models based on the shortest path distance. We adopted chain-based routing to achieve optimal data transmission. Therefore, we propose a routing algorithm based on the angular inclination technique used in [PERSON] et al. [17] mainly to transmit data toward the sink node. The primary objective is to identify an optimal routing path from every CH to the sink node, as implemented in [23; 27; 28; 29].  
To find the optimal path, a threshold angle is taken and the angle between the CH and the sink is calculated by finding the angular difference from lines Nos. 3 to 6 in Algorithm 3. Once the angle is measured, the distance between inter-CHs towards the sink is calculated from lines 7 to 11. Initially, neighbor CH is zero. After the distance and the angular measurement, the CH with minimum distance and maximum energy is selected as a next hop CH. This process is depicted in the algorithm from lines 17 to 23.  
In this angular routing, an angular structure is formed between two CHs, and the data are forwarded from one angular aligned CH to the other. It reduces the hop count, unlike the heuristic approach of finding the shortest path. Data forwarded from CH to CH are random in the LEACH protocol, and the chain model-based forwarding of data from one cluster member to another is implemented in PEGASIS. There is no assurance of reaching the sink node in the former technique and finding alternate routes is complex in the case of node failure in the latter. These problems are alleviated in the current routing technique that is applied in RSC.  
```
1:Input:\(\alpha\)- Threshold value, CH\({}_{\text{n}}\) -number of CHs
2:Output: Optimal routing path to the Sink
3: Find the angle amongst CH and the Sink
4:for\(Ch_{(i)}\)=1 to CH\({}_{\text{n}}\)
5:\(\theta=\tan^{-1}\Big{(}\frac{y}{x}\Big{)}\)
6:\(\beta 1=\ \theta+\ \alpha\)\(\beta 2=\ \theta-\ \alpha\)
7:for\(Ch_{(i)}\)=1 to 1
8:if\(Ch_{(i)}\)! = \(Ch_{(l)}\)
9:if\(Ch_{(l)}\).degree \(\geq\ \beta 2\) &&\(Ch_{(l)}\).degree \(<\ \beta 1\)
10:\(path\Big{(}\ Ch_{(i)}\Big{)}=l\)
11:\(distance\Big{(}ch_{(i)}\Big{)}=Dist(ch_{(i)},ch_{(l)})\)
12:endif
13:endif
14:endfor
15:endfor
16: Neighbor( \(Ch_{(i)}\))=0
17:forj=1 to \(path\Big{(}Ch_{(i)}\Big{)}\)
18:if\(path\Big{(}ch_{(i)},\ ch_{(j)}\Big{)}!=0\)
19:\((Min_{i}(j)==\ Min_{i}(j-1))\)
20: Find the node which has min. distance
21:\(Min_{dst}(node.id)=\ Ch_{i}(j)\)
22:endif
23:endfor
24:\(next_{hop(ch_{(i)})}=Min_{dst}(node.id)\)
```  
**Algorithm 3**Angular Routing in RSC.
## 3 Numerical Simulation
### Numerical Model  
The calculation domain of the numerical model is shown in Figure 2. The speed is used as the inlet boundary condition of the model, and the pressure is used as the outlet boundary condition of the model. The non-slip wall surface is used as the boundary condition between the surface of the temporary pier and the bottom wall and the top, the left and the right surfaces are set as symmetric boundary conditions. The spacing between the centers of adjacent temporary piers is 3D; D is taken as 1.2 m; the distance between the center of the temporary pier and the symmetrical boundary is 10D; the distance between the center of the upstream/downstream temporary pier and the entrance/exit boundary is 10D/25D, respectively. The flow rate considers a total of five flow rates: 2, 2.5, 3, 3.5, 4 m/s; three typical depths of 10, 15 and 20 m were selected for the submerged depth; in total, there were 15 working conditions.  
The calculation model takes the center of the upstream pier as the coordinate origin, the downstream direction is the \(x\)-axis direction, the \(y\)-axis direction is perpendicular to the flow direction and the flow depth direction is the \(z\)-axis direction. A hexahedral-structured grid with a size of 0.2 mm was used to mesh the model. The grid around the cylinder and the wake area are refined to improve the accuracy of the numerical calculation. For example, the grid of 0.5 times the spacing between columns around the cylinder is refined, the gradient rate of the grid in the refined area is 1.07 and the first layer of the grid perpendicular to the wall is \(y^{+}=1\).
## 3 Results and Discussion
### Binding Modes of Small Molecular Inhibitors on HSP47  
To predict how these inhibitors would bind on HSP47, we docked these molecules on HSP47 using AutoDock4 and AutoDock Vina software. The docking grid box was defined to include the whole collagen binding region on the HSP47 surface. Apart from Com II, Hs1, and Hs55, a recently identified HSP47 inhibitor Col003 and its analogues were also selected for docking [20; 36]. The docking results indicated that Com II and Col003 bind to the surface area of HSP47 involved in binding the GXR motif of collagen (colored yellow in Figure 5, subpocket1). For Com II, the nitrogen atom of the aniline group forms a hydrogen bond with Asp385, while the NO\({}_{2}\) group forms a hydrogen bond with Gln254 and the main chain of Tyr383, which might play an essential role in the compound binding. The fluorophenyl group of Com II stacks on the relatively hydrophobic area of HSP47 formed by Tyr245, Met271, Leu381, and Phe382. Col003 binds to a similar position, forming hydrogen bonds with His274 and the main chain of His273 of HSP47 through its hydroxy group, also forming a hydrogen bond with the main chain of Val275 through the carbonyl group (Figure 5a and Figure S5). Its benzyl moiety binds the same hydrophobic patch of HSP47 as Com II (Figure 5b and Figure S5). In contrast, the binding of both Hs1 and Hs55 seems to involve both subpocket1 (Figure 5a) and the neighboring surface area for the docking of collagen residues at positions -3 and -4 (pale blue, subpocket2, Figure 5a), mimicking a collagen peptide chain binding both these surface pockets simultaneously. The carboxyl group of Hs1 forms hydrogen bonds with Arg222 of HSP47, while the hydroxyl group on 9,10-dihydroanthracene forms a hydrogen bond with Asp385. At the same time, the 9,10-dihydroanthracene ring forms hydrophobic interactions with Met271, Leu381, Phe382, and Tyr383. Hs55 has a longer extended conformation and docks in a similarly orientation as Hs1, bridging both collagen binding pockets of HSP47 simultaneously. Although the binding mode of these compounds on HSP47 needs to be verified by structural studies, these docking results indicate that it is feasible to target two subpockets on the HSP47 surface at the same time. Subsequent chemical structure modification may improve the activities and specificity of these compounds for efficient HSP47 activity inhibition.
# Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms
## 6 Privacy-Preserving Architectures for Scalable IoT-Based Healthcare Systems
### Zero-Trust Architectures for Enhanced Security  
Zero-trust architectures require continuous verification before granting access as they treat all devices and users as potential threats. Such an approach limits unauthorized access to sensitive healthcare data, which reduces the attack surface. Implementing adaptive access controls, robust authentication mechanisms, and continuous monitoring are pivotal to the success of zero-trust architectures [47].  
The privacy-preserving architectures discussed in this section align closely with the privacy-preserving framework outlined in Section 3. The centralized, decentralized, and hybrid models contribute to Data Protection Mechanisms by enforcing encryption and secure access control measures. Edge computing solutions also promote User Awareness and Empowerment by enabling localized processing while reducing exposure to external security threats. Furthermore, the use of blockchains and federated learning in decentralized architectures directly supports Privacy-Preserving Data Sharing, ensuring that patient data remain protected while facilitating collaborative analytics.
# Study and Simulation of an Under-Actuated Smart Surface for Material Flow Handling
###### Abstract  
Smart surfaces are becoming more and more popular in the field of intralogistics, as they combine great flexibility with easy reprogrammability. Pursuing this trend, the following article proposes a modular surface to perform handling tasks, such as sorting, stopping, or slowing down material flows. Differently from the current technology, the surface used is under-actuated, thus, it exploits the speed, already possessed by the object, or the gravity to perform, with a simplified hardware, for the aforementioned tasks. In practice, these handling actions are completed using an array of rotors, of which only the direction of the rotation axis is controlled. Moreover, the axis can only assume certain discrete orientations in the plane, further simplifying the design. Thus, what is created is a controllable and under-actuated friction field, which, in contrast with similar existing systems, does not require active driving forces to manipulate the material flow. In the article, the analytic model of the surface is described, and a software simulation environment is introduced to demonstrate its functioning. In addition, examples of sorting, slowing down, and stopping operations and a validation of the simulation itself are presented.  
smart surface; friction force field; under-actuation; feeding; simulation; material flow handling +
Footnote †: journal: Applied Sciences
# Morphological Study of _Bacillus thuringiensis_ Crystals and Spores
## 2 Materials and Methods  
### Bacterial Strains and Spore/Crystal Preparation  
Seven _B. thuringiensis_ strains were used in this study, whose characteristics and crystal components are summarized in Table 1. Three _B. thuringiensis_ var. _kurstaki_ strains are considered: HD-1, the reference anti-lepidopteran strain used in the commercial biopesticide Thuricide(r) [28]; the LIPMKA strain, isolated from Lebanese soil [9]; and the BLB1 strain, isolated from Tunisian soil [29]. All three are active against lepidopteran larvae and produce bipyramidal (B) and cubic (C) \(\delta\)-endotoxin crystals. In addition, three strains of _B. thuringiensis_ var. _israelensis_ were also used: AM65-52, kindly provided by Pr. [PERSON] from the GME laboratory of the National Institute for Agronomical Research (INRA-Jouy-en-Josas), as the anti-dipteran reference strain used in the commercial product Vectobac(r) [30]; the AR23 strain isolated from Lebanese soil [15]; and the BUPM98 strain isolated from Tunisian soil [31]. All three are active against dipteran larvae--particularly mosquitoes--and produce spherical (S) crystals. Finally, a non-cytolytic novel anti-dipteran strain H3, synthesizing spores and spherical crystals, was also considered in this study [32].  
The seven strains were kept as 30% glycerol stock solutions at \(-\)80 \({}^{\circ}\)C. They were defrosted on LB (Lysogenic broth) agar medium at 30 \({}^{\circ}\)C, from which a colony was isolated then plated on a T3 agar medium to simulate the sporulation [33]. Cultures were grown in the following optimized conditions: pH 6.8 and 30 \({}^{\circ}\)C for 48 h. At the beginning of our study, the culture medium was inoculated with a number of _B. thuringiensis_ cells corresponding to an optical density of 0.15, which was subsequently spread on the petri dishes containing T3 agar medium.  
After 48 h, the crystals and spores of the different strains of _B. thuringiensis_ were separated according to a protocol developed by [PERSON] et al. [34].