esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Ligand-Assisted Growth of Nanowires
In the case of uniaxial seed shape (tetragonal, hexagonal, cylindrical), the growth of nanowires along the \(z\)-axis proceeds when the side crystal facets are covered with the compact layer of  
Figure 2: Illustration of ligand-assisted growth of a nanowire from model cuboid crystal with the anisotropy of LCN sizes \(L_{yz}=\alpha L_{xy}\) and \(L_{xz}=\beta L_{xy}\). The curves represent the boundary functions given by Equation (10): (**a**) \(\textbf{y}(x)\), (**b**) \(\textbf{y}(z)\), and (**c**) \(\textbf{z}(x)\) with the parameter values \(C=0.2\), \(\alpha=1.2,\ \beta=1\). The points \((x_{0},y_{0})\), \((z_{1},y_{0})\) and \((x_{0},z_{2})\) on the corresponding curves indicate the formation of a compact layer of ligands on the \(xy\), \(zy\) and \(xz\) facets, respectively. The compact layer of ligands is supposed to be formed first at point \((x_{0},y_{0})\); therefore, the z-edge of the crystal at this point has to be significantly lower than \(z_{\rm max}=\min\{z_{1},z_{2}\}\). Blue arrows in (**b**,**c**) indicate the growth of nanoplatelets with \(z\leq\beta=1\) similar to those in Figure 1. Red arrows in (**b**,**c**) indicate the growth of nanowires with \(\alpha>z>\beta=1\), as in this \(z\)-range, the \(x\)-edge of the crystal grows until it reaches the red line at the point \((x_{1},z)\), indicating that the crystal’s \(xz\)-facet gets covered with a compact layer of ligands thus fixing the \(y\)-edge length. (**d**) A nanowire-like shape of the crystal growing with \(\alpha>z>\beta=1\) after its \(xy\)- and \(xz\)-facets are covered with a compact layer of ligands. For \(z_{\rm max}>z>\alpha\), the growing \(x\)- and \(y\)-edges would intercept the corresponding border lines (blue in (**b**) and red in (**c**)) and form a finite-sized cuboid. ligands. This happens when the ligand critical nucleus size is small enough compared to the shortest edge of the side facet. At the same time, the ligand critical nucleus size for the facet perpendicular to the \(z\)-axis has to be large enough to prevent the formation of the compact ligand layer. In most cases, these two conditions are fulfilled when the ligand-facet interaction energies for the side facets (or side surface of a cylinder) are significantly higher than that for the facet (surface) perpendicular to the \(z\)-axis (see discussion of Figure 2).
## 2 Materials and Methods
### Media
The pretreatment of second CW involved centrifugation at 15,000\(\times\) \(g\), at \(T\) = 2 \({}^{\circ}\)C for 15 min (Sorvall LYNX 6000 Superspeed Centrifuge, Thermo Scientific, Waltham, MA, USA) and filtration of supernatant through a Whatman qualitative filter paper \"Grade 5\" with a pore size of 2.5 \(\upmu\)m. The aim of this treatment was the removal of remnant flocculated proteins. Moisture and total solids content were determined according to the oven-drying method. A sample (5 mL) of treated second CW (after centrifugation and subsequent filtration) was dried at \(T\) = 105 \({}^{\circ}\)C for 24 h. The total solids content (%) was calculated as follows: \(\frac{post/drying sample\ (g)}{pre-drying sample\ (g)}\times 100\) and the moisture content (%) as \(\frac{pre/drying sample\ (g)-post/drying sample\ (g)}{pre-drying sample\ (g)}\times 100\). Furthermore, lactose, total nitrogen, free amino nitrogen, protein, and inorganic phosphorus contents were determined, as described below (see Section 2.3). The characteristics of second cheese whey, as far as pH, total solids, lactose, free amino nitrogen (FAN), total proteins, inorganic phosphorus (IP), and total Kjeldahl nitrogen (TKN) concentrations are concerned, after centrifugation and filtration treatments, are summarized in Table 1.  
The next phase concerns the cultivation of primarily screened yeasts in pretreated second CW. The centrifugated and filtrated second CW was transferred to Erlenmeyer flasks (50 \(\pm\) 1 mL working volume) without adding extra mineral salts and was then sterilized in autoclave (\(T\) = 121 \({}^{\circ}\)C for 30 min). Thereafter, the inoculum of the pre-culture was added, and the batch-cultures were incubated, as described above, until the consumption of the carbon source. The strain with the highest production of dry biomass and cellular lipids was also cultivated in 250-mL Erlenmeyer flasks, under fed-batch conditions. The centrifuged and filtrated second CW (S\({}_{0}\)\(\approx\) 60 g/L) was used as a substrate of fed-batch culture, performed in Erlenmeyer flasks (50 \(\pm\) 1 mL working volume) without adding extra mineral salts. Intermittent feeding was conducted by a condensed cheese whey-derived lactose (purity \(\sim\) 94%, containing 3.5% moisture and 2.5% impurities such as salts) solution (330 \(\pm\) 5 g/L). When the concentration of lactose was reduced to 10-20 g/L, then appropriate volume of the lactose condensate was added (feeding pulse), so that the lactose concentration of the culture would be of c. 40-60 g/L. The inoculum preparation, inoculation, and cultivation conditions were carried out in the same way as in batch cultures.
# Energy-Efficient Hybrid Routing Protocol for IoT Communication Systems in 5G and Beyond
## 4 Proposed Methodology
### Interface Selection  
After the sub-optimal routes have been discovered between both the cluster heads and the central station and all nodes are available for communication, the sensor node should then be capable of determining suitable communication technology (interface) relying on its transfer requirements and communication capacities. In our proposed protocol, all sensor nodes, excluding the Cluster Head forward a small or large volume of data. All communication interfaces are permitted if the sensor node transfers a small volume of data. However, the communication interface with low energy loss must have the highest preference. High network bandwidth is necessitated when the sensor transfers a large volume of data. In such cases, mostly a communication interface with a data transfer rate greater than that provided by the system operating on sensor would be assigned for the \(i^{th}\) Network Access Interface (_NAI_). The ranking Equation (12) [30] is used to reach this goal.  
\[Ranking_{i}=\frac{NAI^{r}_{i}}{(NAI^{r}_{i})^{2}} \tag{12}\]  
where:  
* \(NAI^{r}_{i}\) symbolizes the data transfer rate of the \(i^{th}\)_NAI_,
* \(NAI^{c}_{i}\) symbolizes the energy depletion for \(NAI_{i}\) over a single unit communication range.  
The interface communication range must also be taken into account by the node, whether it can reach the target node or the Cluster Head.
## 5 Simulation Results
### The Base Active Algorithm
The results of this testing stage can be observed in Figure 4.  
The outcomes from the simulation include results that were expected, such as a substantial reduction in CO2 emissions (between 23% and 26%), but also some unexpected results, such as the extraordinary improvements (up to 80%) in waiting time and average speed. Another unexpected aspect of the simulation is that the effect of constant J in the CO2 emissions is almost insignificant; as a matter of fact, the CO2 emissions' reduction does decline as J increases, but the difference is very small. This means that the results depend mostly on the number of cars in a traffic flow, rather than their speed. However, the constant J does have a relevant effect on average speed and waiting time. As can be seen in Figure 4, between J = 20 and J = 100, there is a steady improvement in both indicators, about 10% in waiting time and 15% in average speed. Values of J higher than 100 do not improve waiting time or average speed, but have a small negative impact on CO2 emissions; thus, the optimal value for J is around 100.
# Real-Time Optical Flow Estimation Method Based on Cross-Stage Network
###### Abstract
The decoder estimates the flow field in a rough and precise framework. At each pyramid level, the decoder selects features of the same resolution from the feature pyramid and uses them to infer the flow field. This design structure creates a light network in the form of a CSPyramid for flow inference. The proposed network isolates the feature extraction and flow estimation processes. This helps to better understand bottlenecks in accuracy and model size. Although CAFT has a simpler architecture than FlowNet2 and RAFT, it demonstrates competitive performance in terms of accuracy and speed. CAFT is suitable for a variety of applications that require real-time optical flow estimation.
# Probiotics in the Sourdough Bread Fermentation: Current Status
## 5 Enzymes in Sourdough
### Oxidoreductases  
Oxidative enzymes are applied to treat wheat flour to restore the gluten network. Those enzymes can be divided into exogenous oxidative enzymes (laccase, glucose oxidase, hexose oxidase) and endogenous oxidative enzymes (tyrosine, peroxidase, catalase, sulfhydryl oxidase, lipoxygenase, protein disulfide isomerase, NAD(P)H-dependent dehydrogenase, thioredoxin reductase, and glutathione reductase) [165].  
Oxidoreductase (EC 1) catalyzes reactions of electron transfer. Glutathione reductase (EC 1.8.1.7) is the most known enzyme in this group related to sourdough bread properties. It acts on a sulfur group of donors, with NAD+ or NADP+ as the acceptor [166].  
Gluten protein presents intra- and intermolecular chains of disulfide bonds between amino acids [167]. The glutathione reductase activity, identified in the microbiota of natural fermentation, interferes with these thiol bonds [158]. The glutathione reductase deficient mutant strain (_Lactobacillus sanfrancisensis_) produced bread with a softer texture and higher specific volume than bread made with traditional biological yeast and non-mutant _Lactobacillus sanfrancisensis_ activity [167].  
Glucose oxidase (EC 1.1.3.4) and pyranose oxidase (EC 1.1.3.10) are oxidoreductases produced by several fungi, mostly from _Aspergillus niger_ in the case of glucose oxidase. One molecule of glucose oxidase catalyzes the conversion of \(\beta\)-D- glucose into hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) and gluconic acid [168]. According to [PERSON] et al. [169] the resulting H\({}_{2}\)O\({}_{2}\) can further oxidize the free sulfhydryl units in the gluten protein and promote the formation of disulfide bonds in the gluten network, thereby strengthening the gluten network. In addition, glucose oxidase has antimicrobial activity against fungi and foodborne bacteria [170]. These effects enhance the rheological properties, volume and height, and stability of the bread [169; 171; 172].  
Lacases (benzenediol: oxidoreductase oxygen, EC 1.10.3.2) are multi-copper enzymes and they belong to a group of so-called polyphenol oxidases. They catalyze the oxidation of a wide range of substrates while simultaneously reducing molecular oxygen to water [173; 174; 175]. Lacase activity has been found to enhance the viscoelastic dough qualities and in recent years it has been used as a dough and bread improver, primarily utilized in commercial preparations [176; 175]. Pentosans are polymerized by lacase and favor the aggregation of glutenins, which lowers the protein content of glutenin macropolymers. While not all researchers agree, some authors report that lacases are also important in baking because they can cross-link the arabinylans found in whole flour, improving the gluten index and inducing hetero-crosslinking between arabinoxylans and the gluten matrix [165].
# Multimodal Fall Detection Using Spatial-Temporal Attention and Bi-LSTM-Based Feature Fusion
## 2 Related Work
### Video-Based Fall Detection System
It also comprehensively models the changes in motion over time rather than just looking at the joint positions. An anomaly identification framework has been proposed for fall detection [44]. They incorporate ST-GCN as a feature trainer and use the reconstruction error of the encoder to recognize falls as abnormal. [PERSON] et al. treated the skeleton stream and the motion stream separately, extracted features using CNN for each, concatenated the extracted features into a single feature vector and used GCN and ST-GCN for fall detection [45], achieving an average accuracy of over 94% using the URFD and UPFD datasets. To improve the accuracy, [PERSON] et al. adopted a GCN-based spatiotemporal feature extraction and classification model for fall detection and evaluated the model using ImViA, UR-fall, and FDD datasets, achieving 99.00% accuracy [46].
# Structural Health Monitoring of Large Structures Using Acoustic Emission-Case Histories
## 1 Introduction  
Acoustic emission (AE) techniques have successfully been used for assuring the structural integrity of large rocket motorcases since 1963 [1], and their uses have been expanded to ever larger structures [2; 3], especially since the structural health monitoring (SHM) of large structures has become an urgent task for engineering communities globally. The needs for advanced methods of AE monitoring are felt most keenly by those dealing with aging infrastructure. Many publications have appeared covering various aspects of AE techniques, but documentation of actual applications of AE techniques has been limited mostly to reports of successful results without technical details that allow objective evaluation of the results, except for some exceptions in the literature where special applications were detailed [4; 5; 6]. In this Special Issue of the Acoustics section of Applied Sciences, we sought contributions, like the exceptions cited here, which describe case histories of AE being applied to large structures. That is, papers that have achieved the goals of SHM and do so by giving adequate technical information supporting the success stories. Gathered here are 14 such articles that cover structures from aerospace and geological structures, bridges, buildings, factories, nuclear facilities, etc.
# Satellite-Based Sea Ice Navigation for Prydz Bay, East Antarctica
## 5 Discussion  
The success of the proposed nautical route is assessed based on the travel time of the vessel to reach its destination. A journey is hampered when the vessel's speed is less than 1 knot. The use of remote-sensing-driven ice parameters and the meteorological data in SatSINS proved to be successful in avoiding this situation. Ice conditions, navigation parameters and deviation of the actual ship route from the planned route for the three expedition are summarized in Table 2. In all cases, the vessel travelled at an average speed of 5.7 knots or higher in pack ice. However, the SatSINS-proposed routes needed adjustments based on local observations from the ship, especially when severe atmospheric/oceanic conditions triggered variations in the ice cover at a short time scale (i.e., within a few hours). This adjustment, along with the average speed of the ship during her navigation through the ice cover, are used to assess the utility of remote sensing and meteorological information for planning an efficient route. Here we discuss a few ice parameters/processes that impede the ship navigation (represented in terms of key navigation parameters) and the degree of success of SatSINS in offering solutions. A few recommendations for generating more and better operational-oriented scientific products are also presented. Figure 9: The ice-covered Prydz Bay from MODIS (**left**) and Radarsat-2 SAR HH polarization imagery (**right**), both acquired on 29 November 2013. The waypoints of the proposed routes are shown by the blue squares, and the navigation GPS (Global Positioning System) points of RV _Xuelong_ from 30 November to 2 December 2013 are overlaid by the colored triangles, where colors are indicators of the vessel speed as displayed in the legend. The red line separates an area of 100% ice concentration (fully consolidated ice) to the north-east and another area of less than 100% with apparent leads.
## 2 Materials and Methods
### Site Description and Energy Challenges  
The site under study in this research is Aras de los Olmos, a small, rural municipality in Serranos County of the Province of Valencia, within Spain's Valencian Community. As illustrated in Figure 1, it lies in the northwestern part of the province, surrounded by a predominantly mountainous landscape. Covering roughly 76 square kilometers, the municipality has a close-knit population of around 350 to 400 residents.  
Figure 1: The geographic location of Aras de los Olmos.  
This combination of rugged terrain, modest demographic size, and a typical Mediterranean climate makes Aras de los Olmos an intriguing case for studies on rural development and renewable energy integration. Local livestock activities generate animal waste that can serve as a valuable feedstock for biomass production, making this technology a logical option for enhancing energy resilience in the region alongside other potential renewable energy sources.  
However, as Aras de los Olmos is located at the end of a power distribution line, it frequently experiences voltage drops, reduced power quality, and frequent outages, especially during peak demand periods or adverse weather conditions. These interruptions negatively affect residents, disrupt essential public services, and constrain economic activities, particularly in sectors reliant on continuous electricity, such as agriculture, small businesses, and tourism. Moreover, grid dependency increases the community's vulnerability to external disruptions and fluctuating energy prices. Consequently, there is a clear need to identify reliable, economically viable, and locally sustainable energy solutions that mitigate these issues, enhance energy security, and support long-term economic and environmental goals.
## 4 Performance Analysis
### Link Available Time
If the Lindeberg condition (16) holds, then \(S_{m}^{Y}/\sigma_{S_{m}}\rightarrow\mathcal{N}(0,1)\)._  
By Theorem 3, the distribution of the number of sensors moving out of the cluster after time \(\triangle t\) can be approximated by \(\mathcal{N}(\mu_{S_{m}},\sigma_{S_{m}}^{2})\) with \(\mu_{S_{m}}=\sum_{i=1}^{r_{m}}p_{mi}\) and \(\sigma_{S_{m}}^{2}=\sum_{i=1}^{r_{m}}p_{mi}(1-p_{mi})\). Analysis of Density Change  
To proceed the investigation, let \(f_{D_{m}}(t)\) be the node density in cluster \(m\) and \(N_{\text{in}}^{(m)}(t)\) and \(N_{\text{out}}^{(m)}(t)\) represent the number of sensors moving in and out of cluster \(m\) at time \(t\), respectively. Therefore, at time \(t+\triangle t\), in cluster \(m\) the node density change \(\triangle f_{D_{m}}(t+\triangle t)\) can be expressed by  
\[(X_{11},X_{12},\ldots,X_{1r_{1}})\]0  
where \(A_{m}\) is the area size of cluster \(m\). According to the criterions for merging and splitting clusters, the relationships between the node density change and the operation of cluster reformation are:  
\[(X_{11},X_{12},\ldots,X_{1r_{1}})\]1 \[(X_{11},X_{12},\ldots,X_{1r_{1}})\]2  
where \(N_{CM}^{(m)}(t_{0})\) is the number of cluster members in cluster \(m\) at time \(t_{0}\) and \(t_{1}\) is the time stamp to trigger the process of cluster reformation (\(t_{1}>t_{0}\)). Observe that the results presented in previous subsections show that \(p_{mi}\) is actually the link available probability \(P_{lat}(r_{i},\psi_{i};\triangle t)\) of sensor \(i\) as described in (10). Since it is complex to compute the link available distribution for each cluster member and to describe the link up/down dynamics in a cluster, the simplified averaged analysis is applied to describe the topology change. Considering the mean link available time distribution between two arbitrarily chosen sensors, (i.e the mean value of link available time over all the possible positions of the cluster member), we obtain  
\[(X_{11},X_{12},\ldots,X_{1r_{1}})\]3  
which is the averaged probability that the link is available for less than \(\triangle t\) seconds. Referring to Theorem 3, the expectation and variance of the number of link down events \(N_{\text{out}}^{(m)}(t+\triangle t)\) in cluster \(m\) after \(\triangle t\) seconds are  
\[(X_{11},X_{12},\ldots,X_{1r_{1}})\]4  
\[(X_{11},X_{12},\ldots,X_{1r_{1}})\]5  
Based on Theorem 1, after some time \(T\) the long-run sensor spatial distribution converges and the network topology is eventually stable, which suggests that for \(t>T\), given the steady-state uniform sensor distribution, the expectation of the number of link up events \(N_{\text{in}}^{(m)}(t+\triangle t)\) in cluster \(m\) at time \(t+\triangle t\) yields  
\[(X_{11},X_{12},\ldots,X_{1r_{1}})\]6  
which implies that  
\[(X_{11},X_{12},\ldots,X_{1r_{1}})\]7  
The frequency of topology change and the link up/down dynamics is further discussed and illustrated via simulation in Section 6.
## 2 Materials and Methods
### Register Control Algorithm Based on Pulse Width Subdivision  
#### 2.2.1 Pulse-Width Subdivision Color Mark Positioning Model  
The detected position of a color mark can be defined as the pulse rising-edge position \(P_{rise}\) and the pulse falling-edge position \(P_{down}\). The corresponding color mark width can be obtained by counting the number of encoder pulses from the rising edge to the falling edge \(Cnt(P_{rise},P_{down})\). However, many interference factors can affect the detection results, such as mechanical vibration, transmission error, edge jitter, color saturation characteristics, surface reflection characteristics, sensor electrical characteristics, color mark print quality, etc. In this paper, the average value of the rising and falling-edge positions is used as the detected position:  
\[P_{m}=\frac{1}{2}(P_{rise}+P_{down}). \tag{3}\]  
Figure 5 shows the proposed pulse width subdivision detection model, and the color mark position \(P_{m}\) and width \(W_{m}\) can be expressed as:  
\[\left\{\begin{array}{c}P_{m}=\frac{\tau}{2}\Big{[}Cnt(0,t_{rise})+\frac{t_{1 }-t_{r}}{t_{1}}+Cnt(0,t_{down})+\frac{t_{2}-t_{d}}{t_{2}}\Big{]}\\ W_{m}=\tau\Big{[}\frac{t_{r}}{t_{1}}+Cnt(t^{\prime}_{rise},t^{\prime}_{down}) +\frac{t_{2}-t_{d}}{t_{2}}\Big{]},\;\tau=\frac{t_{r}^{*}}{C}\end{array}\right., \tag{4}\]  
where \(\tau=L_{c}^{*}/C^{*}\) is the actual length corresponding to each coded pulse; \(Cnt(t_{rise},t_{down})\) indicates the integer number of encoder pulses contained between the rising and falling edge, excluding the two \"edge coded pulses\"; \(t_{1}\) and \(t_{2}\) represent the width time of the coded pulse corresponding to the rising and falling edge; \(t_{r}\) and \(t_{d}\) are the width time from the rising and falling edge to the next nearest coded pulse jump edge in microseconds.  
By subdividing the two \"edge coded pulses\", the positioning accuracy of color mark detection can be achieved, in theory, to the maximum extent, and the complexity of external electrical configuration can be reduced. Even if a low-resolution rotary encoder is selected, high-precision positioning and detection accuracy can be achieved. When the system detection resolution is insufficient, the proposed subdivision detection algorithm is effective in overcoming this problem.