esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
As was previously stated, the standard PTP can work with various protocols, such as RSTP, which constructs the synchronization tree instead of PTP and blocks the redundant paths in order to construct a loop-free synchronization topology. Moreover, RSTP detects the failure occurrence in the network after a specific duration, ranging between hundreds of milliseconds and a few seconds [14], and subsequently, RSTP recovers the failure in the network and starts the synchronization mechanism through the standard PTP. No studies have yet reported on fault recovery in PTP systems. In this paper, we propose a novel fault recovery-based approach, called timing fault recovery (TFR), to solve the fault occurrence issue in PTP systems. TFR detects and recovers the fault occurrence in the network with a short duration using a synchronization message detection mechanism. Subsequently, TFR has a recovery mechanism to rebalance the synchronization in the network. Accordingly, TFR significantly reduces the local clock drifting of the nodes in PTP networks. Additionally, it increases clock constancy and network reliability and availability. The fault recovery feature of the TFR approach enables the standard PTP to be suitable for all types of network topologies with mission-critical conditions. The rest of the paper is organized as follows. Section 2 briefly introduces the standard PTP. Section 3 describes the proposed TFR approach. In Section 4, the performance of TFR is analyzed and compared to that of the standard PTP with RSTP. Section 5 describes the simulation results to validate the analysis that is described in Section 4. Finally, we provide our conclusions in Section 6.
## 2 Materials and Methods
### CAPS Assay  
Total DNA was extracted from the leaves using Isoplant II (Nippon Gene, Tokyo, Japan) according to the manufacturer's instructions. The following restriction enzymes were used: _HindIII_, _PvuI_, _Hul_, _Hurl_, _Dral_, _Rsal_, _HacIII_, _HincII_ and _Eco_RV (Takara Bio, Shiga, Japan). Citrus-specific genetic markers (19 types, Table 1) constructed by [PERSON] et al. [12] were used for CAPS analysis. The PCR reaction mixture was prepared to 12.5 \(\mu\)L and was composed of 10 ng template DNA, 10 pmol primer, 1\(\times\) volume reaction buffer, 1.5 mM MgCl\({}_{2}\), 0.2 mM dNTPs, and 0.5-unit Prime Taq DNA polymerase (GeNet Bio, Daejeon, Republic of Korea). PCR reactions were performed in an Applied Biosystems Veriti 200 (Thermo Fisher Scientific, Waltham, MA, USA) thermal cycler with initial heating at 94 \({}^{\circ}\)C for 3 min and held for 30 s, followed by a four-step heating process at 62, 60, 58, and 56 \({}^{\circ}\)C (30 s each). Afterwards, the thermal process was held at 72 \({}^{\circ}\)C for 1 min (2 cycles), followed by repeated heating at 94 \({}^{\circ}\)C for 30 s, 54 \({}^{\circ}\)C for 30 s, and 72 \({}^{\circ}\)C for 2 min for 35 cycles. The heating was then extended at 72 \({}^{\circ}\)C for 7 min. The PCR products were digested with restriction enzymes as follows: PCR products (4 \(\mu\)L) were mixed with reaction buffer (1 \(\mu\)L) and 2-3 units of restriction enzyme, and then diluted to a final volume of 10 \(\mu\)L with sterilized water. After digestion at 37 \({}^{\circ}\)C for 4 h, the digested genetic mixture was separated by gel electrophoresis on a 1.5% agarose (Takara Bio) and stained with GelRed(Biotium, Hayward, CA, USA). The resulting bandswere then detected under UV light, and the band patterns were labeled as \"aa\", \"bb\" and \"ab\" according to their fragment size [10; 12]. The genetic polymorphism distance was calculated, and the genetic distance between _C. depressa_ accessions was presented in a phylogenetic cluster tree using the unweighted pair group method with arithmetic mean (UPGMA) (MEGA Version 4.1, Tempe, AZ, USA).
# Fixed Wireless Access in Flexible Environment: Problem Definition and Feasibility Check
## 3 Assumptions and Inputs
### Parameters of the Existing Network  
The parameters of the existing network infrastructure are:  
1. the set of the potential connection points (CP): \[pCP=\{pCP_{1},pCP_{2},\ldots,pCP_{\rm p}\}.\] (2)  
These points are locations (sites) around the PL suitable for a new radio link, with either a direct wired or radio access to the existing network. Unused transmission capacity is assumed available at each point.
2. For each link in the existing network with available throughput (to be assigned to traffic from PL), the following parameters are required: available throughput in each timeslot, link availability, and link latency. Note that these are the parameters used by the SDN controller of the existing network as well.
# Lifting Activity Assessment Using Kinematic Features and Neural Networks
## 2 Materials and Methods
### Definition of Lifting Cycle  
After acquisition, Smart Analyzer (BTS) and Matlab (version 8.0.0.783; MathWorks, Natick, MA, USA) were used to process the data. The vertical displacement and velocity of one of the four markers placed over the vertexes of the crate (right side in anterior position) were evaluated. By differentiating the marker's position after a 4-Hz low-pass filter (Butterworth 3 rd order), the velocities were obtained. The onset and the end point of the lifting task were defined, respectively, as the time point at which the velocity of the crate marker on the vertical axis exceeded the value of 0.025 m/s [30] and as the time point when the crate marker velocity fell below the same velocity threshold. Kinematic and kinetic data underwent a time normalization procedure so as to be reduced to 101 samples using a polynomial procedure.
## 3 Proposed Methods
### Maximum Soft Information (MSI) Finding  
For the MSI finding technique, we start by considering mutual data in the form of three soft-information values or LLRs produced by 1D SOVA detectors in a 3 \(\times\) 1 column vector, i.e., \(\left[\lambda_{k-1},\lambda_{k,0},\lambda_{k,1}\right]^{\mathbf{T}}\), similar to HIF. Here, \(\left[\cdot\right]^{\mathbf{T}}\) is a transpose operator. Since \(\boldsymbol{\lambda}\) is a real number vector, when we examine \(\max(\text{abs}(\boldsymbol{\lambda}))\) in Figure 7, we can determine the highest LLR among the three considered LLRs, where the \(\max(\mathbf{x})\) function returns the element with the highest value from the vector \(\mathbf{x}\). Consequently, the sign of the highest LLR will be defined to be an estimated user bit, \(\hat{u}_{k,0}\). To better understand the MSI process, we can refer to the flowchart displayed in Figure 7, which can be described as follows.  
Figure 6: Example of HIF technique operation (**a**) hard information produced with SOVA detectors through a threshold detector and (**b**) the estimated user bits that produced from HIF technique.  
Figure 7: A flowchart explaining the MSI technique.  
**Step 1:** Given a \(3\times 1\) vector \(\mathbf{\lambda}=[\lambda_{k,-1},\lambda_{k,0},\lambda_{k,1}]^{\mathbf{T}}\), we check if the signs are either \([+,+,+]^{\mathbf{T}}\) or \([-,-,-]^{\mathbf{T}}\); the estimated user bit, \(\hat{u}_{k,0}\), will be determined based on that particular symbol.  
**Step 2:** If the signs of all elements in \(\mathbf{\lambda}\) are not identical, we choose the element that has a maximum absolute value. Then, the sign of the chosen element will determine the estimated user bit.  
Figure 8 illustrates an example of how the MSI technique operates. The LLRs associated with the three tracks are produced from the 1D SOVA detectors at SNR = 8 dB as shown in Figure 8a. For instance, all elements in the first and second columns have the same sign, and the estimated user bit is determined accordingly. However, the signs of the elements in the fourth column are not identical, with a maximum absolute value of \(-30.47\). In this case, the estimated user bit of the fourth column will be \"\(-1\)\", as shown in Figure 8b. Similarly, in the fifth column, the highest absolute value is 13.72, and the estimated user bit will be \"\(+1\)\".
## 3 Results and Discussion
### Impact of Fermentation Parameters on Population Dynamics
In addition, the consumption kinetics of amino acids can vary according to the amino acid and differ from those observed in \(Sc\), leading to possible competition in \(Sc/Lt\)culture. Nevertheless, [PERSON] et al. (2015) noticed that \(Lt\) could grow and maintain itself even if no nitrogen was present in the synthetic medium, making use of autolysis phenomena to obtain nitrogen compounds [21]. In addition, the amount of nitrogen, even at the lowest value, is usually sufficient to allow for yeast growth and complete fermentation, as a minimum of 140 mg/L is usually recommended [22]. For all these reasons, the result observed here could be explained by the fact that the experimental conditions chosen did not aim to emulate complete nitrogen scarcity, or by a strain-specific tolerance to these lower levels of nitrogen, or both. By studying the different fermentation factors, we expected to see an effect of all these factors on the population dynamics, as observed by other authors. Nevertheless, when all these factors were combined, their relative importance could be seen. It seemed in the conditions studied that overall, temperature was the most important factor in optimizing \(Lt\) presence over time during fermentation (\(\text{AUC}_{Lt}/\text{AUC}_{Sc}\) ratio). In addition to temperature, oxygenation and the inoculum ratio also appeared to be significant, but were still secondary factors in obtaining the persistence or predominance of \(Lt\) over \(Sc\) throughout the fermentation process. To our knowledge, this is the first time information has been obtained regarding the combined effect of different fermentation parameters on the population dynamics. Such information constitutes a tool to control proper \(Lt\) implantation, and also to help in determining which yeast (\(Sc\) or \(Lt\)) will dominate the other during a mixed culture with given or adjustable parameters. This ensures both that \(Lt\) will have enough overall  
Figure 1: Impact of fermentation parameters on the \(\text{AUC}_{Lt}/\text{AUC}_{Sc}\) ratio: standardized effect (**left**) and Pareto chart of contributions to variance (**right**). Data are noted as statistically not significant (\(p\)-value > 0.1, white) or very significant (\(p\)-value < 0.01, dark grey). The black curve on the Pareto chart is the cumulated contribution to variance. persistence to potentially influence final wine quality and that \(Sc\) will still persist to ensure fermentation does not go on for too long or become stuck.
# Drought Propagation in Brazilian Biomes Revealed by Remote Sensing
## 2 Materials and Methods
### Datasets
#### 2.2.5 Terrestrial Water Storage  
TWS anomalies (_TWSA_) were analyzed with the Gravity Recovery and Climate Experiment--GRACE dataset, developed by NASA and the German Aerospace Center, which measures changes in the Earth's gravitational field to detect regional changes in Earth's water bodies [67]. Water storage is assessed in the form of _TWSA_. GRACE data are processed by different processing centers: CSR (University of Texas at the Center for Space Research), GFZ (GeoForschungsZentrum Potsdam), and JPL (NASA Jet Propulsion Laboratory). Here, the GRACE datasets from JPL were used, as also adopted in previous studies [72, 73]. Monthly _TWSA_ has a spatial resolution of approximately 111 km.
## 4 Results and Discussion
### Simulation Result
#### 4.2.2 Advanced Responses in Simulations  
After further simulation of the generated scenarios, it was observed that the MSV, which was not constrained by specific trajectories, tended to exhibit trajectories even more dangerous than those generated originally. Certain conditions occasionally led to triggered collision warnings in SUMO or lateral collisions. Building on this observation within CARLA's intelligent agent model, the \"CAV\" would further demonstrate a more advanced response, directly engaging in emergency braking to halt the vehicle, thus averting potential accidents more effectively. After statistical analysis, it was found that 35% of the samples experienced emergency braking scenarios in the CARLA simulation. Furthermore, even when the MSV followed a predefined trajectory and the NRV was controlled by an autonomous driving model, similar situations could still occur. This further illustrates the inherent danger of these generated scenarios. The advanced responses demonstrated by the \"CAV\" in the CARLA simulation, such as engaging in emergency braking to prevent accidents, underscore the critical nature of these scenarios in real-world driving conditions. To better compare with the original data, simulations were re-run using conventional manually driven vehicle models that do not possess pre-emptive autonomous braking capabilities, and new trajectory information was obtained.
# Shear Mechanical Properties of Rock Joints Under Non-Uniform Load Based on DEM
## 3 Numerical Modeling
### Boundary Conditions and Loading Scheme  
In this study, the top loading plate was divided into 10 smaller sections (\(N=10\)), and distinct loads were applied to achieve a non-uniform distribution of overburden loads; as illustrated in Figure 6, there are different differences in the initial stage of the shear stress-shear displacement curve, that is, in the micro-crack compaction stage, which were caused by the difference in normal stress and the dispersion of the sample itself. For the FFC and NFC conditions, \(G_{d}\) was set at 1.1% \(G_{T}\) and \(-1.1\%\)\(G_{T}\), respectively. \(G_{I}\) was set at 15% \(G_{T}\) for the FFC condition and 5% \(G_{T}\) for the NFC condition. According to Equation (1), the calculation formula for the load division of each strip is shown as follows:  
\[G_{n}=\left\{\begin{array}{ll}15\%\cdot G_{T}-1.1\%\cdot(n-1)G_{T}&(NFC)\\ 5\%\cdot G_{T}+1.1\%\cdot(n-1)\times G_{T}&(NFC)\end{array}\right. \tag{3}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter Type** & **Microscopic Parameters** & **Value** \\ \hline \multirow{3}{*}{Particle} & Density (kg/m\({}^{3}\)) & 2500 \\  & Radius (mm) & 0.6–0.9 \\  & Porosity & 0.1 \\ \hline \multirow{6}{*}{Flat-joint contact model} & Contact modulus, \(\overline{E_{c}}\) (GPa) & 1.8 \\  & Contact stiffness ratio, & 1.0 \\ \cline{1-1}  & Tensile strength (MPa) & 13.8 \\ \cline{1-1}  & Cohesion (MPa) & 28.8 \\ \cline{1-1}  & Internal friction angle (\({}^{\circ}\)) & 32.5 \\ \cline{1-1}  & Friction coefficient,\(f\) & 1.5 \\ \hline \multirow{3}{*}{Smoot-joint contact model} & Normal stiffness, kn (GPa) & 100 \\ \cline{1-1}  & Shear stiffness, ks (GPa) & 0.62 \\ \cline{1-1}  & Friction coefficient,\(f\) & 0.92 \\ \hline \end{tabular}
\end{table}
Table 1: Calibration results of microscopic parameters.  
Figure 5: Laboratory shear stress–shear displacement curves of jointed rock mass under different normal stress conditions.  
For FOC, the strip load had a centrally symmetric isometric distribution with \(d\) set at 2.5% \(G_{T}\), while \(G_{5}\) and \(G_{6}\) were both set at 15% \(G_{T}\). According to Equation (2), the calculation formula for the strip load is as follows:  
\[G_{n}=\left\{\begin{array}{ll}5\%G_{T}+2.5\%(n-1)G_{T}&n\leq 5\\ 27.5\%G_{T}-2.5\%(n-1)G_{T}&n>5\end{array}\right. \tag{4}\]  
As part of simulating direct shear tests, four different forms of load distribution under various overburden loads (\(G_{T}\) = 10 kN, 15 kN, 20 kN, 25 kN, 30 kN) were designed, as shown in Table 2.
## 2 Materials and Methods
### Materials Characterization  
The crystallinity of the prepared photocatalysts was measured using X-ray diffraction (XRD) spectroscopy, using a PANalytical X-pert Powder diffractometer (model: Pro DY38059) and employing Cu K\(\alpha\) radiations of 0.15406 nm. The scan range was 20 = 10\({}^{\circ}\)-80\({}^{\circ}\). The surface morphology of pure Zn-Fe LDH was investigated by scanning electron microscopy (SEM, TESCAN Vega LMU), and images were taken at 15 kV. The functional groups analysis for Zn-Fe LDH were identified by Fourier-transform infrared (FTIR) spectrometry using a PerkinElmer spectrometer in ATR mode within the range from 4000 cm\({}^{-1}\) to 700 cm\({}^{-1}\). The absorption of the prepared Zn-Fe LDH was investigated by UV-visible diffuse reflectance spectroscopy (DRS, Jasco V-770), employing an integrated sphere kit. Rice husk extract, before and after the photocatalytic reaction, was analyzed by gas chromatography-mass spectrometer (GC-MS) using an Agilent Technologies/7890 GC system. The injection of the sample was done manually using a glass syringe, and the injection volume was 1 \(\upmu\)L in the presence of helium (He, 99.999% pure) as a carrier gas. The flow rate of He was 1.2 mL/min with a split ratio of 1:20. An HP-5 MS capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) was used, and the column oven was held at 40 \({}^{\circ}\)C initially for 3 min. The oven temperature was then raised to 190 \({}^{\circ}\)C (ramp 5 \({}^{\circ}\)C/min) and held for 20 min. The injection temperature was 270 \({}^{\circ}\)C.  
A three-electrode arrangement was employed to measure the photocurrent density. In brief, an FTO substrate coated with Zn-Fe LDH (exposed are 1.0 cm\({}^{2}\)) acted as the working electrode, a platinum (Pt) wire as the counter electrode, and Ag/AgCl as the reference electrode. All electrodes were immersed in a beaker containing 0.1 M Na\({}_{2}\)SO\({}_{4}\) solution, and the working electrode was illuminated with a 75 W LED light during photocurrent measurements. The lamp was switched on and off for 20 s.
## 4 Conclusions  
This study provides a comparison between battery- and solar panel-powered sensor nodes. The sensor nodes were used for soil water content monitoring. The results showed that to keep a rechargeable battery life of several months, the number of activations per day had to be lower than 10 data transmissions. The power level of the energy storage device of the solar panel powered sensor node remained constantly close to 90% through all days. Furthermore, the energy storage device of this node was not affected by the increased wireless communication (24 activations in 24 h) during the last phase of the experimental procedure. Thus, the solar panel permits us to reach a potentially infinite sensor node lifetime. The energy requirements of the nodes will decrease as the integration of the node circuits increases. The use of low-power, low-size wireless sensors is the most appropriate way to acquire soil water content data at different soil depths, close to plant roots, and across large geographical areas.  
WSN are used to monitor soil water content in annual crops such as hemp (_Cannabis sativa_ L.), which are mainly cultivated in the summer months. Therefore, the interest regarding the energy power of the sensor nodes is mainly focused on the 3 months of summer, from the beginning of June to the beginning of September. In future work, for perennial crops such as orchards, additional tests will be carried out in different seasons such as spring and fall.  
**Author Contributions:** Conceptualization, Z.T. and I.G.; investigation, I.G., Z.T. and E.S.; methodology, I.G. and E.S.; supervision, I.G. and M.L.; validation, Z.T. and V.P.; visualization, E.S. and T.P.; writing--original draft, Z.T. and I.G.; writing--review and editing, M.L., T.P. and V.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH-CREATE-INNOVATE research and innovation program \"Development of an energyindependent system for precision irrigation and application of Regulated Deficit Irrigation by using Rain Water-ReDIrri\" (project code: T2 EDK-03987).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Acknowledgments:** This research was supported by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH-CREATE-INNOVATE research and innovation program \"Development of an energyindependent system for precision irrigation and application of Regulated Deficit Irrigation by using Rain Water-ReDIrri\" (project code: T2 EAK-03987).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Computational Fluid Dynamics (CFD) Simulations and Flow Model Test (FMT) Experiments
### Flow Model Test (FMT) Experiments  
The flow model test is performed as a validation, focusing on the dispersal effect of the optimized triangular deflector for the accumulated particles with relatively large sizes. The optimal installing position of the triangular deflector is verified. The schematic view of the FMT platform is shown in Figure 5.  
Figure 4: Three different installing positions for the triangular deflector (Z-positive).  
Figure 3: The schematic view of the triangular deflector (Z-positive).  
The SCR-deNO\({}_{x}\) facility with a scale of 1:12 is made from plexiglas while the baffles geometry is made of aluminum slices. Perforated plates are substituted for the catalyst layers to provide the pressure drop. Several small holes are processed in advance on the plexiglas as follows: six of them are used to set the triangular deflectors, as shown in Figure 6a. Meanwhile, the other nine of them are located above the perforated plates and are used for the measurement, which is shown in Figure 6b. As can be seen, they are sealed with plastic tapes if no measurement is conducted.  
Since chemical reaction is not considered, air is substituted for the flue gas. The air velocity at the inlet is modified to be close to 4.50 m/s, which is in accordance with the 100% BMCR condition. To avoid environmental pollution, crushed millet is used to substitute for the ash particles with relatively large sizes and is sprinkled from the facility's inlet. The diameter of the crushed millet ranges from  
Figure 5: The platform of flow model test (FMT) experiments.  
Figure 6: The small holes processed on the Plexiglas: (**a**) holes for setting the deflector, (**b**) holes for measurement (labelled as A-I).  
0.22 mm to 0.45 mm. A Testo-445 multi-function measuring instrument is used to measure the air velocity and the pressure loss with a vane probe and a pressure probe. The measuring range of the vane probe is 0.6 to 40 m/s and the corresponding accuracy is \(\pm\)(0.2 m/s \(\pm\) 1.5% of measurement value) (+0.6 to +40 m/s). The measuring range of the pressure probe is 0 to 10,000 Pa and the corresponding accuracy is \(\pm\)10 Pa (0 to +2000 Pa) and \(\pm\)0.5% of measurement value (+2000 to +10,000 Pa).  
Meanwhile, a semicircular steel tube is used to collect the crushed millet. The tube is inserted into the nine small holes one by one, representing the distribution of the ash particles. The weights of millet for the nine collections are measured by a METTLER-TOLEDO analytical balance. Figure 7 illustrates the 72 detection spots over the perforated plate. They are arranged in an 8 x 9 matrix and the characters A-I refer to the nine small holes used for the measurement.  
To avoid measurement error, variables are measured when the equipment runs to a relatively steady state. The experiments are repeated with deflector at different installing positions. Values of air velocity, pressure loss and weights of the crushed millet are recorded and analyzed. Comparisons are made between the results of the FMT experiments and CFD simulations.