esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in Fiber-Shaped Electronic Devices for Wearable Applications
## 3 Device Applications
### Electronic Devices
#### 3.1.2 Memory Devices
These resistor-type memory devices have high integration density and a simple crossbar geometry structure, which is an advantage for production, but they can suffer from undesired cross-talk in ICs [35]. Although that problem can be solved by incorporating supplementary switching elements, this type of complex structure requires additional processes. Compared with resistor-type fiber memory devices, fibriform transistor-type memory has reliable switching, compatibility with ICs consisting of MOS FETs, and a non-destructive read-out without cross-talk interference between memory units [36]. [PERSON] et al. demonstrated fiber-shaped organic transistor memory devices (FOMs) based on a ferroelectric copolymer of vinylidene fluoride and trifluoroethylene [P(VDF-TrFE)] [3]. The FOMs were fabricated onto a silver (Ag) microwire using a capillary tube-assisted coating method (Figure 3e) [3]. The simple solution process formed smooth and compact nanograined P(VDF-TrFE) films on thin fiber substrates [3]. The resulting FOMs exhibited excellent memory performance with high flexibility, reliable endurance for 100 cycles, and a long-term retention time of \(\sim\)5 \(\times\) 10\({}^{4}\) s at voltages below 5 V (Figure 3f) [3]. The FOMs, sewn into a stretchable fabric, operated stably when the fabric was subjected to uniaxial and diagonal strain and crumpling [3]. Fiber-shaped transistors and memory devices are far from practical implementation. The contacts in the warps and wefts of electronic fibers in a textile structure are loosened by mechanical stress, which can easily cause the electronic fibers to lose their function. Therefore, it is important to form robust electrical connections between the memory units, and to maintain performance as the electronic fibers are detached and reattached. For further improvement, integration with other functional fibers and new device architectures will be required.
# Chitosan-Coated Nanoparticles: Effect of Chitosan Molecular Weight on Nasal Transmucosal Delivery
## 3 Results
### Ex Vivo Transport Experiments across Rabbit Nasal Mucosa  
Figure 6A shows the SVT permeation across the excised rabbit nasal mucosa using Franz type vertical diffusion cells. The results of the ex vivo permeation studies on the rabbit nasal mucosa evidenced that the formulations significantly increased the permeation of SVT compared to the control. \(\text{LNC}_{\text{SVT-LMWchit}}\) showed the highest percentage of SVT permeation after 4 h (19.2 \(\pm\) 0.5%-cm\({}^{-2}\)), followed by \(\text{LNC}_{\text{SVT-HMWchit}}\) (11.0 \(\pm\) 0.5%-cm\({}^{-2}\)), while the control (SVT suspension) showed an extremely low degree of permeation (2.6 \(\pm\) 0.2%-cm\({}^{-2}\)). The results indicate that SVT permeation from \(\text{LNC}_{\text{SVT-LMWchit}}\) was 1.74 and 7.4 times greater than \(\text{LNC}_{\text{SVT-HMWchit}}\) and the SVT control, respectively.  
The results of SVT retained in the mucosa tissue after 4 h experimental time are shown in Figure 6B. It can be seen that there was better retention of both formulations with respect to the control. The SVT fraction found in the tissue for the control was 4.2 \(\pm\) 0.9%-cm\({}^{-2}\), while in the case of \(\text{LNC}_{\text{SVT-LMWchit}}\) and \(\text{LNC}_{\text{SVT-HMWchit}}\), the fraction of simvastatin found in the tissue was more than  
Figure 5: Amount (\(\upmu\)g) of SVT transported across RPMI 2650 cells grown under air–liquid interface conditions (\(n\) = 4, \(\pm\) SD). Significant difference (\(p\) < 0.05) is expressed considering the following comparisons: * SVT versus \(\text{LNC}_{\text{SVT-LMWchit}}\), ** SVT versus \(\text{LNC}_{\text{SVT-HMWchit}}\), *** LNC\({}_{\text{SVT-LMWchit}}\) versus \(\text{LNC}_{\text{SVT-HMWchit}}\).  
Figure 6: (**A**) Ex vivo SVT permeation across rabbit nasal mucosa up to 4 h in simulated nasal electrolytic solution (SNES) containing 0.5% of polysorbate 80 at 37 \({}^{\circ}\)C (\(n\) = 3, \(\pm\) SD). (**B**) Percentage of SVT retained in nasal mucosa after 4 h of the permeation test in Franz-type diffusion cells (\(n\) = 3, \(\pm\) SD). Asterisk (*) indicates significant differences between SVT versus \(\text{LNC}_{\text{SVT-LMWchit}}\) and \(\text{LNC}_{\text{SVT-HMWchit}}\).  
doubled, i.e., \(9.8\pm 1.3\) and \(9.9\pm 2.5\%\cdot\mathrm{cm}^{-2}\), respectively. In this case, the difference between the amount of retained SVT from \(\mathrm{LNC}_{\mathrm{SVT-LMWchit}}\) and \(\mathrm{LNC}_{\mathrm{SVT-HMWchit}}\) was not statistically significant.
## 2 Materials and Methods
### Coupling of MIKE 11 and MIKE SHE  
MIKE 11 uses complete dynamic wave formulation of the Saint-Venant equation and is able to simulate the wide range of hydraulic structures such as weirs, culverts, gates, and bridges, etc. The river network in the defined catchment containing the main Tollense River, its tributaries, and control hydraulic structures are shown in Figure 7 (right). A fully-dynamic 1D-Saint-Venant equation was used to describe channel flow in the Tollense river and its tributaries. Two weirs, named as Osten and Tuckhude, equipped with adjustable gates and variable operational strategy, were modelled in MIKE 11, as shown in Figure 7 (left). Channel cross-sections were based on ADCP surveys conducted at 29 locations along the Tollense river, in the river section starting from Demmin (downstream) to Klempenow (upstream). The river bed was considered fully connected with the SZ, as a result the exchange between the river and the GW aquifer was defined by the hydraulic conductivity of the aquifer instead of the river bed material. In case of the Tollense River, time series of discharge at daily temporal resolution was inserted as an upstream BC at Klempenow and average water levels were used as a downstream BC at Demmin. The data used in the present study in the MIKE11 model includes river network, control strategy of hydraulic structures, river cross sections, hydraulic structures and their geometry, seasonal Manning's roughness coefficients, river boundary conditions, and hydrodynamic parameters.  
A dynamic coupling of MIKE SHE and MIKE 11 considers the exchange of data among the two models after every computational time step and their coupling is done by means of line segments, serving as river links in MIKE 11 to the adjacent MIKE SHE grids. River links are created for coupled reaches and their locations are defined automatically from MIKE 11 river point coordinates that define the river branches of a hydrodynamic model. During simulation, the water levels within the coupled reaches are transported from MIKE 11 H-points to the adjacent MIKE SHE river links, as shown in Figure 8. In return, MIKE SHE estimates the OL-flow to each river link as an exchange between the neighbouring grid squares and the river aquifer, and finally these terms are sent back to the corresponding MIKE 11 H-points as an outflow or inflow for the following computational time step. If the water level in a grid square gets higher than its topographic level, it is considered as flooded, and as soon as a grid square gets flooded MIKE SHE computes infiltration, seepage, OL-flow, and ET  
Figure 6: Boundary conditions used in the SZ, where the black line is zero flux BC, the yellow line is positive gradient BC, and the red line is negative gradient BC.  
in the similar manner as for a grid square with SW ponding resulting from precipitation and surface runoff, or the water table intercepting the ground surface [54].
# Partial Substitution of Wheat Flour with Palm Flour in Pasta Preparation
## 1 Introduction  
The process of globalization has significantly influenced the dietary habits of the world population. Dry pasta is widely recognized as one of the food staples consumed worldwide, thanks to its versatility, ease of preparation and storage, good nutritional quality, and affordable cost [1]. Furthermore, dry pasta, as an affordable and long-lasting product, serves as an excellent matrix for nutritional enrichment, given that traditional wheat pasta lacks certain nutrients [2]. Therefore, recent studies have shown various initiatives aimed at enhancing the nutritional quality of this product [1; 2; 3; 4]. A highly promising alternative involves the partial incorporation or complete substitution of the wheat ingredient with agorathostry byproducts or underutilized plants in society, which, nonetheless, possess high nutritional value and can contribute to the improvement of the health and well-being of the population.  
The prickly pear cactus (_Opuntia focus-indica_), also known as the nopal cactus, likely originated in Mexico but is cultivated in various regions around the world due to its excellent adaptability [5]. The plant belongs to the _Cactaceae_ family and consists of four main parts: the cladodes, flowers, fruit, and seeds. Cladodes are edible and traditionally consumed as fresh or cooked vegetables in Mexico. However, in some countries, like Brazil, their use is more related to animal feed and less explored for human consumption. Given the technological potential of this plant and its rich composition, which includes phytochemicals such as phenolic acids and flavonoids, citric and malic acids, and smallamounts of carbohydrates [6], the composition of this plant has sparked interest in current research, which has focused on innovative applications of cladodes in the food industry.  
The concept of utilizing byproducts or underutilized plants rich in bioactive compounds aligns with the growing consumer demand for natural molecules that are proven to promote well-being [7]. Specifically, the cladodes of _Opuntia focus-indica_ in the food industry can become new resources for fortifying staple foods. Their potential nutraceuticaltical effects include possible anticancer effects, antioxidant properties, anti-inflammatory benefits, antidiabetic (Type II) effects, as well as anti-hyperlipidemia and hypercholesterolemia [8]. The application of _Opuntia focus-indica_ cladodes in food products is still limited but emerging and entirely feasible.  
Based on scientific studies found in the literature, the development of products such as bread [9], cookies [10], and bioactive films [6] enriched with Opuntia focus-indica products demonstrates this trend toward transforming conventional foods into functional foods, highlighting the potential of cacti for the food industry. However, the development of enriched pasta using _Opuntia focus-indica_ flour has not been explored. In light of the above, this study aimed to evaluate different formulations for the preparation of tagliatelle-type pasta to create a product enriched with _Opuntia focus-indica_ flour.
# Fatigue Characteristics Analysis of Carbon Fiber Laminates with Multiple Initial Cracks
## 5 Fatigue Reliability Modeling of Multi-Cracked Laminates  
### Uncertainty Analysis  
During the analysis of multi-crack laminates, factors such as incomplete data and measurement inaccuracies can greatly affect the outcome. Introducing uncertainty analysis becomes essential to assess the cracks' impact on service life, and this paper introduces a fatigue assessment methodology, aiming to establish a theoretical and methodological foundation for the investigation of fatigue characteristics in laminates.  
The focus of this paper is on examining how physical uncertainties affect the fatigue life of laminates with multiple pre-set cracks. The uncertainties primarily arise from the material properties of the carbon fiber plate and the dimensions of the multiple cracks. Variations in the mechanical properties of carbon fiber plates during production and processing can cause fluctuations within an expected range. As such, we consider the mechanical parameters of the carbon fiber plate along three orthogonal axes--elastic modulus E, Poisson's ratio \(\
u\), and shear modulus G--as sources of uncertainty. The precise measurement of initial crack dimensions, including length, angle, and the distance between cracks, is often challenging with conventional methods, leading to uncertainties that affect the progression of cracks and the endurance of the plate under fatigue conditions.  
This study evaluates the combined effects of uncertain factors like crack dimensions, angles, and spacing on the projected fatigue life of carbon fiber plates containing multiple  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specimen Number** & **Simulation Results** & **Experimental Mean** & **Relative Error** \\ \hline A1F & 35,895 & 35,359 & 1.52\% \\ A2F & 40,214 & 38,856 & 3.49\% \\ A3F & 54,015 & 52,032 & 3.81\% \\ A4F & 41,156 & 39,383 & 4.5\% \\ A5F & 32,187 & 33,482 & 3.86\% \\ A6F & 44,895 & 42,690 & 5.17\% \\ A7F & 21,247 & 19,937 & 6.57\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of lifetime prediction results.  
Figure 12: Comparison of fatigue life results.  
cracks. It presumes that the distribution of uncertain parameters adheres to a Gaussian normal distribution. As detailed in Table 7, these random variables are not only normally distributed but also independent of each other, which is crucial for quantifying the potential influence of uncertainty analysis on fatigue life.
# PDTR: Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks
## 4 Proposed Method PDTR
### The Probabilistic Routing Table Set-Up Phase
#### 4.1.1 Hop-count Distribution  
The source node selects the neighbor node with a lower hop-count as its forwarder to relay the sensed data. This distribution has the aim to give priority to nodes close to the sink; the closer the forwarder node is to the sink, the less energy is needed for data transmission, and the priority is higher for the forwarders that are closer to the sink. We define \(h(n_{i})\) by the hop-count of the node \(n_{i}\) as the minimum number of hops to reach the sink. In \(\mathbb{N}\) sensor nodes network, the maximum hop-count can be \(\mathbb{N}\)-1, because at the worst case, each node has only two neighbor nodes, a back-warder node and forwarder node, except for the sink node, which does not have a forwarder (parent), and source nodes do not have a back-warder. Here, the hop-count of the node \(n_{i}\) neighboring nodes is defined as a random variable: \(h(n_{i})=\big{(}h(n_{1}),\ldots,h\big{(}n_{m_{i}}\big{)}\big{)}\), \(m_{i}\)_neighbors_.  
Let \(\mathbb{N}_{i}\) be the neighboring nodes set of \(n_{i}\) such that\(|\mathbb{N}_{i}|=m_{i}\), as defined in Table 1.  
The hop-count function is normalized by the following Equation (3).  
\[\overline{h}\Big{(}n_{j}\Big{)}=1+\left(\frac{h\Big{(}n_{j}\Big{)}}{\mathbb{N}-1 }\right)\forall n_{j}\in\mathbb{N}_{i} \tag{3}\]  
The forwarding probability function of the hop-count distribution from the sender \(n_{i}\) to its neighboring nodes is calculated by Equation (3) and given by the following Equation (4).  
\[\tilde{h}\Big{(}n_{i,j}\Big{)}=\frac{\overline{h}\Big{(}n_{j}\Big{)}}{\sum_{l= 1}^{m_{i}}\overline{h}(n_{l})},\ \forall n_{j}\in\mathbb{N}_{i},\ |\mathbb{N}_{i}|=m_{i} \tag{4}\]  
From Equation (4), we can see that further nodes have bigger probabilities, but we want to give a higher probability to the closer nodes. Let \(\overline{h}\Big{(}n_{j}\Big{)}\) be the mass probability of the hop-count distribution, such that the higher probability is given to the nodes with lower hop-count, as expressed in Equation (5).  
\[\overline{h}\Big{(}n_{i,j}\Big{)}=\frac{\Big{(}1-\tilde{h}\Big{(}n_{i,j}\Big{)} \Big{)}^{\alpha}}{\sum_{l=1}^{m_{i}}\Big{(}1-\tilde{h}\Big{(}n_{i,j}\Big{)}^{ \alpha}},\ \forall n_{j}\in\mathbb{N}_{i} \tag{5}\]  
To determine the impact of the hop-count distribution on the other two distributions defined in the proposed PDTR, we defined in Table 1, a hop-count control parameter denoted by \(\alpha\) such that: \(\alpha\geq 0\), as shown in Equation (5).
# A Study of Emergency Aircraft Control During Landing
## 7 SIL Simulations
### Tracking of Flight Trajectory Changes
The landing gear extension and associated increase in required thrust \(\delta\text{T}_{0}\) significantly improve the control performance in both channels. Figure 6: Control parameters during the final simulator trials with controller settings from Table A4a,c,d. (**a**) Flight path angle tracking; (**b**) heading tracking; (**c**) spiral trajectory tracking.
## 2 Materials and Methods
### Lipid Extraction and Analysis  
\"Harsh\" surface lipid extraction was performed as previously described [27]. Briefly, pellicle biofilms were harvested 5 days after inoculation via centrifugation at 3000\(\times\)\(g\) for 10 min, resuspended in 5 mL hexanes and sonicated at 55 \({}^{\circ}\)C for 15 min. The extracts were then clarified via centrifugation at 1000\(\times\)\(g\) for 10 min and dried under nitrogen gas at 30 \({}^{\circ}\)C (Biotage TurboVap LV). For \"gentle\" surface lipid extraction, pellicle biofilms were harvested 5 days after inoculation via centrifugation at 3000\(\times\)\(g\) for 10 min, resuspended in 5 mL hexanes and vortexed for 30 s. The extracts were then clarified via centrifugation at 1000\(\times\)\(g\) for 10 min and the supernatants were dried under nitrogen gas at 30 \({}^{\circ}\)C (Biotage TurboVap LV). To extract total lipids, the same procedure as gentle hexane extraction was used, except that the harvested biofilms were resuspended in 5 mL of chloroform/methanol (2:1, \(v/v\)).  
For analysis via thin-layer chromatography, surface and total lipid extracts were resuspended in chloroform/methanol (2:1, \(v/v\)) and spotted onto silica plates (Millipore-Sigma, Chicago, IL, USA 1.05553.0001). Loads were normalized according to dry weight. Free myclates (FM) were resolved with chloroform/methanol (96:4, \(v/v\)) [11]. Mycolate wax ester (MWE) and monomeromycoldiy alacylglycerol (MMDAG) were resolved with toluene/acetone (99:1, \(v/v\)) [7]. Thehalose dimycolate (TDM), trehalose monomycolate (TMM), phosphatidylcholine/ethanolamine (PE), phosphatidylcholine/ethanolosaides (PIMs) were resolved with chloroform/methanol/water (30:8:1 \(v/v\)) [43]. Lipids were visualized via immersion in 10% phosphomolybc acid in ethanol and charred via heating. The amounts of FM, MWE, MMDAG, TDM, TMM, PE, PI, CL, and PIMs, as a percentage of the total signal for each sample, were calculated via densitometry using ImageJ [44], and the mean \(\pm\) standard deviation (S.D.) from three independent experiments were analyzed. Statistical analysis was performed using GraphPad Prism version 10.
## 2 Numerical Methods
### Vehicle Dynamics
#### 2.1.1 Driver Model  
The driver model utilized in the current investigation was derived from the research conducted by [PERSON] et al. [21], in which the crosswind response of a bus was investigated. They modified the constants for proportional gains provided by [PERSON] et al. [17] to suit the geometry of a bus based on vehicle dynamics data for the bus using the wheel angle, \(\delta\), computed via the understeer gradient, \(K_{us}\), the curvature radius, \(R\), and the length of the wheelbase, \(L_{ub}\), with the assumptions of linear tyre behaviour and no-load transfer. Equation (7) provides the driver model, including proportional gain parameters for preview lateral distance, \(k_{l}\), yaw angle, \(k_{\Psi}\), and lateral displacement, \(k_{y}\).  
\[\delta=k_{l}\Delta y_{2}+k_{\Psi}\Delta\psi+k_{y}\Delta y_{1} \tag{7}\]  
The driver's steering input is denoted by \(\delta\) in Equation (7). Additionally, \(\Delta y_{2}\), \(\Delta\psi\), \(\Delta y_{1}\) are the preview lateral distance, yaw angle, and change in lateral displacement of the vehicle, respectively. Computations of \(\Delta y_{1}\) and \(\Delta\psi\) were determined during the solution of vehicle dynamics equations. Additionally, the calculation of the preview lateral distance, \(\Delta y_{2}\), was based on the assumption of a one-second preview time, resulting in a length of 25 m, considering a longitudinal velocity of \(v_{x}=25\), as illustrated in Figure 1. Thus, \(\Delta y_{2}=\Delta y_{1}+v_{x}\times t\times\sin(\Delta\psi)\) was utilized to calculate the preview lateral distance.  
Sets of constants for proportional gains, which were employed to represent three different drivers' attitudes, are given in Table 2. The first set of constants, denoted as Driver 1, were the proportional gains in the driver model utilized in the study of [PERSON] et al. [21]. The values of the constants in other sets, i.e., Driver 2 and Driver 3, were proportionally increased relative to the constants in their preceding sets. Therefore, the driver became more skilled as the number of drivers increased. In other words, Driver 1 represented the least skilled driver, Driver 2 represented the more skilled driver than Driver 1, and Driver 3 represented the most skilled driver in the study.
# Evaluation of VoIP QoS Performance in Wireless Mesh Networks
## 2 VoIP Application and Scenarios
### Military Operations Deployment  
Military operations need a stable, highly available, dynamic, and isolated communication system to connect military personnel. Although a military typically has its own means of communication, mesh networks could still be used for military units as a primary or backup means of communication [52]. In cases where there are high chances of military radio system failure or interference to the main communication channel on small- or large-scale military operations, then Wi-Fi mesh could be a suitable choice. The use of Wi-Fi and wireless mesh for military purpose is presented in [53] for Wi-Fi and in [54] for wireless mesh, but there are still some security concerns about the use of Wi-Fi for the military. An example of a squad and a platoon formation where WMN could be used to facilitate military personnel communication is shown in Figure 4a,b.