esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
In this study, to develop superabsorbent medical materials, a scaffold with a hybrid double-layer structure was manufactured using natural polymers, and the physical properties and cell viability were evaluated. Alginate and CMC were mixed and reacted with a cross-linking agent, and then a sponge with a single structure (one-layer) was prepared through a freeze-drying method, which was reacted with a BDDE cross-linking agent, and then a double-structure (two-layer) sponge was prepared through the same process. In Alg-CMC with a single structure (one-layer), the lower the CMC content, the higher the number of holes, and the uniform porosity and slightly increased tensile strength confirm the stability of the material. In all the groups, the tensile strength in the dry state was measured and found to be higher than that in the wet state. The sponge hybrid with dextran (a dual structure) indirectly shows that the tensile strength in a dry state is approximately 40 to 44 N and the tensile strength in a wet state is approximately 30 to 32 N, which can thus absorb a large amount of moisture and blood and may be used as a material with stable mechanical strength. In addition, it was confirmed that the survival rate of human keratinocytes was improved, and the stability of the material was confirmed through cell compatibility. In conclusion, it was confirmed that the developed hybrid double superabsorbent sponge material can contain and maintain mechanical properties and a large amount of moisture. This material is functional, biocompatible, and cell effective. In the future, it is expected to be used in a variety of ultra-absorbent medical materials, such as for wound treatment (bandages, wound bands) and drug delivery systems with intelligent hydration gel, tissue engineering scaffolds, and moisture sensors.  
Figure 8: Cell viability was tested after obtaining the medium eluted from the (human keratinocyte cell line, HaCaT cells); only cells, control sponge, (**a**) Alg-CMC ratio (5:5); (**b**) Alg-CMC ratio (5:5) with dextran; (**c**) Alg-CMC ratio (7:3); (**d**) Alg-CMC ratio (7:3) with dextran (MTS assay, \(n=3\), mean \(\pm\) SD).  
**Author Contributions:** Conceived and designed the experiments, [PERSON] and H.J.; performed the experiments, [PERSON]; analyzed the data, [PERSON] and [PERSON]; contributed reagents/materials/analysis tools, [PERSON]; wrote the paper, [PERSON] All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Korea Institute of Industrial Technology (JA210007, development of the green-hydrogen production system by alkaline-electrolysis/desalination and core parts).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** This study was supported by the Korea Institute of Industrial Technology (JA210007, development of the green-hydrogen production system by alkaline-electrolysis/desalination and core parts).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Offloaded Computation for QoS Routing in Wireless Sensor Networks
###### Abstract  
In Wireless Sensor Networks (WSNs) used for real-time applications, ensuring Quality of Service (QoS) is essential for maintaining end-to-end performance guarantees. QoS requirements are typically defined by a set of end-to-end constraints, including delay, jitter, and packet loss. In multi-hop scenarios, this requires multi-constrained path computation. This research examines the standard Routing Protocol for Low-Power and Lossy Networks (RPL), which employs a Destination-Oriented Directed Acyclic Graph (DODAG) for data transmission. Nonetheless, there are several challenges related to multi-constrained route computation in the RPL: (1) The DODAG originates from an objective function that cannot manage multiple constraints. (2) The process of computing multi-constrained routes is resource-intensive, even for a single path. (3) The collection of QoS-compatible paths does not necessarily form a DODAG. To address these challenges, this paper suggests modifications to the existing protocols that shift computationally demanding tasks to edge servers. Such a strategic adjustment allows for the implementation of QoS-compatible route computation in WSNs using the RPL. It enhances their ability to meet increasingly stringent demands for QoS in numerous application environments.  
wireless sensor network; routing; quality of service; multi-constrained optimization; offloading; MEC architecture 202516464  
1 Operations Research Department, Faculty of Computers & Artificial Intelligence, Cairo University, Cairo 12613, Egypt [EMAIL_ADDRESS]  
2 Faculty of Artificial Intelligence, Horus University, New Damietta 8027201, Egypt  
3 Laboratoire d'Informatique, de Robotique et de Microeelectronique de Montpellier (LIRMM), University of Montpellier, Centre National de la Recherche Scientifique (CNRS), 34095 Montpellier, France 3*Correspondence: [EMAIL_ADDRESS]
###### Abstract  
Smart agriculture development mainly depends on the intelligence and reliability of autonomous agricultural machinery. Automatic navigation systems (ANSs) play a key role in intelligent agricultural machinery design, as they not only reduce farmers' workloads but also improve their land utilization rates. In this paper, a tractor ANS based on dynamic path search and a fuzzy Stanley model (FSM) was designed, and its capability for whole-field path tracking was tested. First, the tracking performance of the steering control module was validated after the automatic reconstruction of the tractor platform. Then, a navigation decision system was established based on a unified reference waypoint search framework, where the path generation for whole-field coverage was presented. Finally, the gain coefficient of the Stanley model (SM) was adjusted adaptively according to the tracking error by utilizing the fuzzy logic controller. Subsequently, the developed tractor ANS was tested in the field. The experiment's results indicate that the FSM outperformed the SM in straight path tracking and whole-field path tracking. When the tractor traveled at a speed of \(1\,\mathrm{m}\mathrm{/}\mathrm{s}\), the maximum lateral tracking error for the straight path was \(10\,\mathrm{cm}\), and the average lateral tracking error was \(5.2\,\mathrm{cm}\), showing improvements of \(16.7\%\) and \(10.3\%\) compared to the SM. Whole-field autonomous navigation showed that the maximum lateral tracking error was improved from \(34\,\mathrm{cm}\) for the SM to \(27\,\mathrm{cm}\) for the FSM, a reduction of approximately \(20.6\%\), illustrating the superiority of the FSM in the application of whole-field path tracking. As the maximum tracking error of whole-field autonomous navigation appears in the turning stage, where tractors often stop working, the designed ANS satisfies the requirements of a self-driving system for unmanned tractors.  
autonomous tractor; automatic navigation; dynamic path search; fuzzy logic control; Stanley model +
Footnote †: journal:
## 2 Materials and Methods
### Localization of the Human Operator
#### 2.2.2 Using a Wireless Network for Localization  
Each autonomous tractor in the RHEA scenario is equipped with an RTK positioning system that provides an accurate position for the wireless node installed on the vehicle (Figure 1). Therefore, we propose a hybrid positioning approach that runs on the UPD and combines the output of an RSSI-based localization algorithm using signals from the wireless routers mounted on the autonomous tractors and a procedure for the acquisition of the tractor coordinates when a user carrying the UPD is near the tractor. This hybrid approach is conceived as a complementary positioning mechanism to improve the accuracy of the UPD position provided by the low-cost GPS receiver integrated into the UPD. The first step to realizing the hybrid solution was the implementation of the RSSI-based localization algorithm. This technique provided insight into the performance of the radio-based positioning solution. In the second step, the implemented solution was integrated with a positioning procedure that will be activated when a user is near the autonomous tractor. Due to limited time, the development of this procedure is planned as future work. The subsystems necessary for the realization of the network-based localization algorithm are channel characterization and localization algorithms based on radio channel data.
## 3 Pilot Experiment and Results
### Data Acquisition and Gait Analysis of Pilot Experiment  
Patient data acquisition of three lower limb postoperative patients was done in the Beijing Aerospace General Hospital, as shown in Figure 11. The basic information of the three patients is shown in Table 2.  
Figure 11: Photographs of the clinical trial.  
Figure 10: Pressure curves of healthy person gait.  
Figure 9: Diagram of gait conversion process. (**A**) Rear foot touch; (**B**) Entire foot touch; (**C**) Rear foot leave and forefoot touch; (**D**) Entire foot leave; (**E**) Foot strike.  
In Figure 12, the typical gait of healthy person is used as the comparison with the gait of the patients. It is easy to find that the gait of the three patients is significantly different from that of the normal persons. The gait of the same disease in different rehabilitation stages is also different to each other. Preliminary testing and pilot experiment show that the app has achieved basic functions, which can obtain accurate and reliable plantar pressure for quantitative gait analysis in the future.
## 1 Introduction
### Research Gap  
Although machine learning models have demonstrated effectiveness in PQD classification, there are several challenges to consider:  
Firstly, the classification of PQDs is primarily treated as a signal processing problem. In this regard, machine learning methods process time series signals as the input and require feature engineering to extract domain-specific information. Exclusion of frequency domain information when processing time domain signals can limit classification accuracy because important features may be missed. Moreover, training a classification model from scratch requires a large dataset and computational resources, resulting in a longer training time. Additionally, tuning hyperparameters is a challenging task, especially to mitigate overfitting.  
Secondly, the classification performance of a single model often faces generalization problems across different datasets, which can be improved by integrating several deep learning models.  
These challenges can be addressed by transforming the time domain signals into time-frequency images utilizing continuous wavelet transform (CWT). The proposed approach instigates an ensemble technique employing DCNNs with transfer learning. The pre-trained DCNN architecture offers significant advantages in image classification, showing robustness in capturing significant features that are usually ignored in conventional techniques. Moreover, the need for feature engineering is avoided by simultaneously performing feature learning and classification. Hence, converting POD signals to images allows the model to visually classify disturbances and consider complex characteristics that are not present in time series data. Additionally, the generalization capability of individual learners can be improved by utilizing an ensemble of various DCNNs.  
Table 1 presents a summary of the reported work of the most recent studies in the literature. It is evident from Table 1 that there are only a few studies that have used both time and frequency features for PQD classification, i.e., that consider the PQD classification problem as an image processing problem. Moreover, there is a notable gap in leveraging ensemble DCNN architectures to classify PQDs using both time and frequency features for improved accuracy.
## 2 Glycerol-Based Biomanufacturing Using _Yarrowia_
### Bioconversion of Glycerol to Lipids
#### 2.1.2 Pure and Crude Glycerol as Carbon Source for Lipid Production by _Yarrowia lipolytica_  
The exploitation of the natural ability of oleaginous microorganisms to intracellularly store considerable amounts of lipids is a vigorous biotechnological research field. _Y. lipolytica_ is one of the most valuable and frequently employed oleaginous platforms, which was, however, previously used as a protein rather than single cell oil (SCO) producing yeast (Table 1). In addition, the high _ex novo_ lipid productivity and accumulation when FA rich substrates were used brings into question its true oleaginous character. Nonetheless, due to the presence of ACL activity, _Y. lipolytica_ is considered an oleaginous yeast possessing specific lipid metabolism divergent from other oleaginous yeasts. The fact that it is one of the few yeasts with a sequenced whole genome offers a very convenient platform for developing novel genetic engineering tools and redesigning the lipid biosynthetic pathway [52]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Strain** & \begin{tabular}{c} **Carbon** \\ **Source 1** \\ \end{tabular} & **Product** & **Engineering 2** & **Strategy** & **Set-Up 3** & \begin{tabular}{c} **Data from** \\ **[Ref.]** \\ \end{tabular} \\ \hline \begin{tabular}{c} ACA-DC \\ 50109 \\ \end{tabular} & CG & 41\% \(w/w\) lipids & ME & \begin{tabular}{c} Mixtures of saturated free \\ fatty acids, CG, and glucose \\ \end{tabular} & SF & [20] \\ \hline \begin{tabular}{c} A101 \\ \end{tabular} & CG & 40\% \(w/w\) lipids & SE; PE & \begin{tabular}{c} Co-overexpression of \(GUT1\) \\ and \(SCT1\); pH 3.0 \\ \end{tabular} & SF & [36] \\ \hline \begin{tabular}{c} A101 \\ \end{tabular} & CG & \begin{tabular}{c} 12.5\% \(w/w\) lipids \\ \end{tabular} & SE; PE & \begin{tabular}{c} Co-overexpression of \(GUT1\) \\ and \(SCT2\); pH 3.0 \\ \end{tabular} & B & [36] \\ \hline \begin{tabular}{c} SM7 \\ \end{tabular} & CG & \begin{tabular}{c} 35.80\% \(w/w\) lipids, lipase \\ activity \\ 38 U/mL \\ \end{tabular} & ME & \begin{tabular}{c} Addition of surfactants and \\ oils in the medium; pH 6.5 \\ \end{tabular} & SF & [50] \\ \hline \
\begin{tabular}{c} LGAM 5(7)1 \\ \end{tabular} & CG & 43\% \(w/w\) lipids & PE &
\begin{tabular}{c} Highly aerated and agitated \\ continuous nitrogen-limited \\ cultures with glycerol \\ concentration in the feed at \\ different dilution rates; \\ pH 6.0 \\ \end{tabular} & B & [53] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Engineering strategies (strain, medium, process) and cultivation systems using pure or crude glycerol as carbon source for the production of lipids by _Y. lipolytica_. Biosynthesis of TAG by _Y. lipolytica_ is well studied, but most processes utilize glucose, with a pH around 6.0. Glycerol tolerance for oleaginous microorganisms is strain-dependent, and the concentration of carbon source should be fine-tuned to achieve a high
## 4 Discussion
Velocity bunching modulation and ocean scene coherence time are the essential causes of the azimuth  
Figure 8: Data observed by SAR-1 as follows: (**a**) ocean surface backscattering coefficient and (**b**) ocean SAR image. Figure 10: Data observed by SAR-3 as follows: (**a**) ocean surface backscattering coefficient and (**b**) ocean SAR image. Figure 9: Data observed by SAR-2 as follows: (**a**) ocean surface backscattering coefficient and (**b**) ocean SAR image. cutoff. The angle between the direction of wave propagation and the direction of SAR flight is a key factor affecting the azimuth cutoff. When the direction of wave propagation is fixed, the different SAR flight directions have different effects on the azimuth cutoff wavelength. The manifestation in the SAR images is that the images become blurred, and the wave information whose wavelength is smaller than the azimuth cutoff wavelength is lost. However, the degree of azimuth cutoff cannot be intuitively judged from the SAR images. Currently, there is no clear equation for calculating the azimuth cutoff wavelength, which can only be estimated by parameters such as the SWH, wind speed and range-to-velocity ratio (R/V). In future research, wave spectrum inversion can be realized based on simulated SAR data and compared with the buoy omnidirectional spectrum or wave spectrum data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) to study the energy loss under different degrees of azimuth cutoff. The compensation of the azimuth cutoff wavelength can be realized based on multiview SAR wave synchronization data. In Section 3.3, we analyzed the accuracy of the SAR wave synchronization data obtained by networked SAR satellites. The results showed that the variation of the backscattering coefficients with wind speed is consistent with the theory. In addition, the results reflected in Figures 11-13 verify the accuracy of the simulated SAR ocean wave data. In particular, the shape of the fit SAR spectrum is compressed upward in the azimuth direction. Waves with wavelengths less than a certain value cannot be imaged, which also reflects the effect of azimuth cutoff on SAR imaging. There is another thing that needs to be noted. The results of observed SAR spectrum were obtained through flip horizontal. The reason is derived from the analysis of Figures 8-10. Figure 11: SAR-1: Max Plank Institute (MPI) algorithm inversion results as follows: (**a**) Observed SAR spectrum (after horizontal flip), (**b**) first guess PM wave spectrum, (**c**) fit PM wave spectrum, and (**d**) fit SAR spectrum. Figure 12: SAR-2: MPI algorithm inversion results as follows: (**a**) Observed SAR spectrum (after horizontal flip), (**b**) first guess PM wave spectrum, (**c**) fit PM wave spectrum, and (**d**) fit SAR spectrum. Figure 13: SAR-3: MPI algorithm inversion results as follows: (**a**) Observed SAR spectrum (after horizontal flip), (**b**) first guess PM wave spectrum, (**c**) fit PM wave spectrum, and (**d**) fit SAR spectrum.
# Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDN
## 4 Improved Saturation Attack Method  
The root cause of the failure of the typical saturation attack is that it assumes fixed and identical timeouts for all flow rules. An improved saturation attack method is introduced here to overflow the flow table regardless of its timeout strategy.
## 2 Basic Equations  
We considered the flow of an incompressible nanofluid in the region \(y>0\) driven by a stretching/shrinking surface located at \(y=0\) with a fixed stagnation point at \(x=0\) in the presence of Soret and Dufour effects as well as of a second-order slip effect at the boundary, as illustrated in Figure 1. It was assumed that the free stream and stretching/shrinking velocity \(\frac{U_{w}}{U_{\infty}}\) were in the linear form, with \(U_{\infty}=cx\) and \(U_{w}=bx\), respectively, where \(c\) and \(b\) were constant with \(c>0\). It should be noted that \(b>0\) and \(b<0\) correspond to the stretching and shrinking sheet, respectively. Under these conditions, the boundary layer equations are  
\[\frac{\partial u}{\partial x}+\frac{\partial v}{\partial y}=0 \tag{1}\]  
\[\frac{\partial u}{\partial t}+u\frac{\partial u}{\partial x}+v\frac{\partial u }{\partial y}=U_{\infty}\frac{dU_{\infty}}{dx}+\
u\frac{\partial^{2}u}{ \partial y^{2}} \tag{2}\]  
\[\frac{\partial T}{\partial t}+u\frac{\partial T}{\partial x}+v\frac{\partial T }{\partial y}=\alpha\frac{\partial^{2}T}{\partial y^{2}}+\Omega\Bigg{(}D_{B} \frac{\partial C}{\partial y}\frac{\partial T}{\partial y}+\frac{D_{T}}{T_{ \infty}}\bigg{(}\frac{\partial T}{\partial y}\bigg{)}^{2}\Bigg{)}+\frac{D_{B} k_{T}}{c_{s}c_{p}}\frac{\partial^{2}C}{\partial y^{2}} \tag{3}\]  
\[\frac{\partial C}{\partial t}+u\frac{\partial C}{\partial x}+v\frac{\partial C }{\partial y}=D_{B}\frac{\partial^{2}C}{\partial y^{2}}+\frac{D_{T}}{T_{ \infty}}\frac{\partial^{2}T}{\partial y^{2}}+\frac{D_{B}k_{T}}{T_{m}}\frac{ \partial^{2}T}{\partial y^{2}} \tag{4}\]  
along with the initial and boundary conditions  
\[\begin{array}{llll}t<0:&u=v=0,\;\;T=T_{\infty},\;\;C=C_{\infty}&\mbox{for any $x,y$}\\ t\geq 0:&u=U_{w}+A\Big{(}\frac{\partial u}{\partial y}\Big{)}_{y=0}+B\Big{(} \frac{\partial^{2}u}{\partial y^{2}}\Big{)}_{y=0},&v=0,\\ &T=T_{w},D_{B}\frac{\partial C}{\partial y}+\frac{D_{T}}{T_{\infty}}\frac{ \partial T}{\partial y}=0&\mbox{at $y=0$}\\ &u\to U_{\infty},\;\;T\to T_{\infty},\;\;C\to C_{\infty}&\mbox{as $y\to\infty$}\end{array} \tag{5}\]  
where \(u\) and \(v\) are the velocity components along \(x-\) and \(y-\) axes, respectively, \(T\) is the nanofluid temperature. \(U_{slip}\) is the slip velocity at the wall. The [PERSON]'s slip velocity model (valid for arbitrary Knudsen numbers, \(Kn\)) is used in this paper and is given as follows [23]  
\[U_{slip}=\;A\frac{\partial u}{\partial y}+B\frac{\partial^{2}u}{\partial y^{ 2}} \tag{6}\]  
where \(A\) and \(B\) are constant.
## 2 Materials and Methods  
### Vineyard and Treatments  
The study was carried out for four years (2013-2016) in a 6-year-old commercial vineyard located in the Prosecco DOC area, in Northeastern Italy (lat. 45\({}^{\circ}\)7\({}^{\prime}\)55\({}^{\prime\prime}\) N; long. 11\({}^{\circ}\)26\({}^{\prime}\)47\({}^{\prime\prime}\) E). The study site is situated in an alluvial plain area, formed on the ancient fine deposits of the rivers Adige and Brenta. The total surface of the vineyard is 1.46 ha, and the surface where the treatments were applied was 380 m\({}^{2}\) (Figure 1).  
The vineyard was planted in 2007 with Glera variety grafted onto Kober 5 BB rootstock and trained to a double Guyot system, with 8 buds per cane and with a vine spacing of 2.65 m \(\times\) 1.0 m. During the vegetative season, the growing shoots were positioned vertically using a trellis system based on two pairs of horizontal galvanized steel catch wires located at 100 and 140 cm above ground. Shoots were mechanically trimmed twice in the season, between flowering and veraison, at 180 cm above the ground (leaving around 13 nodes per shoot). As typical of commercial practice in this area, herbicide was applied in the under-row while the inter-row was covered by natural grass. No irrigation was applied during the experiment. All cultural practices in this experiment were similar to those applied in commercial vineyards in the local area.  
Since the beginning of the trial, four fertilization treatments corresponding to four levels of mineral K were imposed each year (0, 15, 30, 60 kg K\({}_{2}\)O/ha). The amounts of K were selected to be equal or below the conventional K fertilization practice in the Prosecco DOC production area, where amounts between 40 and 80 kg K\({}_{2}\)O/ha are applied annually, depending on the soil characteristics and climate conditions [30,31].  
Each treatment was imposed to 6 plots (replicates) consisting of 6 contiguous vines, with sampling and measurements being carried out on the 4 middle plants. Plots were arranged in a completely randomized block design. Mineral K was supplied once before flowering, as potassium sulphate (K\({}_{2}\)SO\({}_{4}\), 50% K\({}_{2}\)O). The fertilizer was applied manually on an approximately 60 cm wide line in the under-row. In all the treatments, nitrogen and phosphorous fertilization was performed two times, before budding and before flowering, with 70 kg N/ha and 30 kg P/ha as ammonium nitrate (40% N) and ammonium phosphate (12% N and 23% P), respectively, according to the vine management in the area.