esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Endogenous Phytodromones  
Hormones have an important function in plants that grow under abiotic stress by triggering responses that favor plants to subsist in stressful conditions, which may result in reduced growth as the plant redirects its resources to tolerate stress [22]. The levels of endogenous phytohormones in plants grown under the different treatments were affected. Inoculated plants with regular irrigation increased by 50 and 70% the ABA levels for GB03 and WCS417r, respectively, compared to non-inoculated control plants (\(p<0.05\)) (Figure 3A). When plants were grown under water stress, they showed a 2.5-fold increase for both MS and SS compared to control plants (regular irrigation) (\(p<0.05\)). In contrast, the plants under stress and inoculated did not reveal any changes in the ABA content in relation to controls (MS and SS) (\(p>0.05\)), with the only exception being inoculation with GB03 in cultivated plants under MS, which resulted in an increase of 50% in the ABA level (\(p<0.05\)) (Figure 3A). In relation to the SA levels, its content in inoculated plants increased 4 times with GB03 and was 10 times higher with WCS417r, in relation to the control of regularly watered plants (\(p<0.05\)). In contrast, there was no variation in the SA concentration in plants subjected to water stress (\(p>0.05\)) (Figure 3B). However, SA levels in plants inoculated with GB03 or WCS417r and subjected to MS raised by 13.5 and 15 times, respectively, in relation to the MS control (\(p<0.05\)). This same trend was registered in inoculated plants subjected to SS, with an increase of approximately 10 times being recorded in relation to the SS control (\(p<0.05\)) (Figure 3B).  
Figure 3: Endogenous plant hormone level (**A**) abscisic acid (ABA) (**B**) salicylic acid (SA) (**C**) jasmonic acid (JA) in peppermint plants inoculated with PGPR strains subjected to moderate (MS) or severe drought stress (SS). Different letters indicate significant differences between treatments based on [PERSON]’s LSD test (\(n=15\)–\(20\), \(p<0.05\)).  
The JA endogenous concentration showed a 50% increase for both strains compared to non-inoculated plants (\(p<0.05\)) (Figure 3C). Nevertheless, when plants were grown under different stress levels (MS or SS), no variation were observed in the JA level in relation to control plants irrigated regularly (\(p>0.05\)). Inoculated plants cultivated under stress did not show any variation in comparison with their respective MS or SS controls, with the exception being MS plants inoculated with GB03, which demonstrated an increase approximately of 35% in relation to the MS control (\(p<0.05\)) and with the WCS417r strain significantly increasing the JA level by 50% in relation to the SS control (\(p<0.05\)).
## 3 Results
### Stream Sector and Data Filtering  
In accordance with IEC 61400-12-1 Annex A, major buildings and wind turbines affecting the vicinity of meteorological masts and wind turbines were selected as major obstacles. As explained in Section 2.1, the analysis was performed on only the 5 wind turbines located on the windward side of the 111 wind turbines of the Anholt offshore wind farm (A01, A02, A03, B01, and C01 wind turbines). To set the effective measurement azimuth of the wind direction, the wind direction affected by the nearby wind turbines was determined by the location (separation distance, angle) between the turbines. Accordingly, the distortion azimuth that should be excluded from the effective measurement azimuth due to the wake effect from the adjacent wind turbine was calculated using Equation (14). However, to check the sensitivity of the analysis result to the wake effect, the wake effect  
Figure 7: WindSim simulation results for the Anholt offshore wind farm: (**a**) wind speed at 105 m; (**b**) Weibull shape factor; (**c**) turbulence intensity; and (**d**) wake dissipation rate.  
cannot be completely excluded, so the main wind direction of the offshore LiDAR was commonly applied, despite the calculation of the effective azimuth for each turbine. In this case, only bins corresponding to 3.5% or more of the frequency of wind speed occurrence at 1-degree intervals were extracted, and wind directions from (94 to 288) degrees were selected (Figure 8).  
\[\alpha=1.3\;Arctan\left(\frac{2.5D_{n}}{L_{n}}+0.15\right)+10 \tag{14}\]  
\(\alpha\) : Disturbed sector  
\(D_{n}\) : Rotor diameter at neighborhood wind turbine  
\(L_{n}\) : Distance between neighborhood wind turbine and target wind turbine  
Data filtering consisted of a total of 4 steps. First-order filtering was performed for the effective azimuth introduced above. Second-order filtering was performed on the section containing missing values. The third and fourth orders are filtering for wind speed and power generation, respectively. In the third phase, the section with 0 power generation was filtered, despite the inflow of wind corresponding to the operating wind speed of the wind turbine (>3.5 m/s). Lastly, in the fourth step, filtering was performed for the section less than the cut in wind speed of the wind turbine. The analysis was performed by selecting the time when the data of all turbines are valid at the same time. Finally, about 41% of the total raw data was used for analysis. Table 5 shows the data filtering rate and filtering items.  
Figure 8: Free stream sector angle (yellow) for each turbine to be analyzed within the Anholt offshore wind farm and the main wind direction of the entire offshore wind farm (gray hatched line). The free stream sector refers to the wind direction free from wake effects by the surrounding turbines.
# A Cox Model-Based Workflow for Increased Accuracy in Activity-Travel Patterns Generation
## 4 Results  
Before presenting the results, it is essential to describe the convergence safeguards and training configurations employed in each model. For the simple Cox model, no specific measures were taken, and convergence was straightforward to achieve. For the Cox model with frailty, Hamiltonian Monte Carlo sampling was carried out using four chains with 2000 warm-up and 3000 sampling iterations each. Initial values for regression coefficients, latent frailties, and class probabilities were drawn from weakly informative priors, while a high target acceptance probability and tree depth limit of 20 were used to avoid divergent transitions. These settings ensured convergence and robust posterior estimates, as verified by diagnostic statistics. In the Cox-based machine learning model, a fully connected neural network with a single hidden layer of 8 units, ReLU activation, and dropout (0.4) was trained over 100 epochs using the Adam optimizer (learning rate = 0.01, batch size = 4). Toprevent overfitting and ensure generalizability, both an 80/20 train-test split and 5-fold cross-validation were implemented. These procedures, together with consistent random seed initialization, provided a stable and reproducible framework for model training and evaluation.  
It is also important to mention that the input dataset consists of 513 individual observations, including 311 work-related, 43 educational, 27 market, and 132 trips categorized as \"other.\" The models are trained on these data but are also used to repair this initial dataset, as shown in the workflow in Figure 3, which ultimately results in a synthetic population of approximately 13,500 observations.
## 4 Classification of MAC Protocols Based on Channel
### Centralized Multi-Channel MAC Protocols  
Control is centralized by a central coordinator, but the channel is partitioned to deal with safety and non-safety messages by prioritizing it to minimize latency for safety messages.
## 3 Results and Discussion
### Mechanism Analysis of Temperature Effect on Shock Sensitivity of HMX-Based Booster
It is known that the \(\beta\)\(\rightarrow\)\(\delta\) phase transition of HMX crystalsleads to a reduction in their partial load-bearing capacity, which has a greater effect on the shock sensitivity of HMX-based explosives themselves [45]. The crystal transformation of HMX explosives at high temperatures causes a significant increase in their blast sensitivity [5; 46; 47; 48]. In addition, due to the \(\beta\)\(\rightarrow\)\(\delta\) phase transition in HMX explosives, there is a 6% volume expansion [11], which reduces the density of HMX explosives and increases the number of hotspots formed in shock compression. There is also a theory that, in a high-temperature environment, as the HMX cell volume increases compared to room temperature, the relative amplitude of its internal intermolecular vibration increases, and the vibration frequency also accelerates, resulting in a reduction in the strength of the valence bond of the atoms in the HMX particles, which are more prone to conformational changes, leading to an increase in the shock wave susceptibility of the HMX explosives [48; 49]. Therefore, in a high-temperature environment of HMX-based booster, the explosive charge will be subjected to thermal expansion, and the internal porosity of the explosive and crystal cavities will increase, resulting in a decrease in its own density and a corresponding increase in shock sensitivity. In addition, as the ambient temperature rises to the HMX phase transition temperature, the changes in the charge structure of HMX explosives, such as crystalline phase transition, crystal cracking or fragmentation and the coexistence of \(\beta\) and \(\delta\) HMX crystals, may not only lead to the generation of more hot spots within the pyrotechnic charge but also promote a faster growth rate of the internal hot spot reaction so that the hot spot grows more rapidly into the surrounding preheated particles [17; 18; 47; 50], leading to an increase in the shock sensitivity of the explosive.
## 3 Experimental Setup  
Figure 2 depicts the schematic diagram of the experimental setup of the 10 GBaud (40 Gb/s) 16 QAM coherent signal. In the transmitter-digital signal processing (DSP), look-up table-based pre-distortion was used to mitigate the opto-electronic component impairments, similarly to [19]. A narrow linewidth (<100 kHz) external cavity laser (ECL) was tuned to 1549.5 nm and, using an arbitrary waveform generator (AWG) operating at 20 GS/s, two uncorrelated pseudo-random level  
Figure 1: Density-Based Spatial Clustering of Applications with Noise (DBSCAN) example for Min. Points = 4.  
signals (\(2^{15}\)-1) were applied to the in-phase/quadrature (I/Q) modulator to generate the 16 QAM signal. After IQ modulation, the optical signal was transmitted over 50 km of standard single-mode fiber (SSMF). At the receiver, noise loading was added using an optical amplifier to set different optical signal-to-noise ratio (OSNR) values and subsequently the optical signal was converted to an electrical one using a homodyne coherent receiver. Afterwards, the signal was captured by a real-time oscilloscope sampled at 50 GS/s for offline receiver-DSP, in which the data was first resampled to two samples/points using prior knowledge of the clock frequency. Then the constant modulus algorithm (CMA) combined with the multi-modulus algorithm (MMA) was utilised for signal equalisation. An \(\mathrm{M^{th}}\) power frequency drifting compensation method was employed to compensate the frequency offset between the signal and the local oscillator in the coherent receiver. The decision-directed phase-locked loop (DDPLL) method was employed for the carrier phase recovery. Finally, the machine learning algorithm was processed before the hard decision and the bit error rate (BER)/Q-factor (=\(20\mathrm{log}_{10}\)\(\sqrt{2}\)\(\mathrm{erfc}^{-1}\)\((2 BER)\)) calculation, similarly to other reported work with machine learning signal processing [20; 21; 22; 23; 24; 25].
# Ribosomal Hibernation-Associated Factors in _Escherichia coli_
## 1 Introduction  
Bacteria have developed sophisticated adaptive systems to survive during various environmental changes, including nutrient starvation, temperature shock, osmolarity changes, and rapid pH changes. These systems include inhibition of cell growth, reduction of cell volume, changes in cell shape, compression of nucleoids, changes in the cell wall composition, and changes in cytoplasmic components [1]. These environmental adaptations may result in the formation of biofilms encased in extracellular polymers [2] or the conversion of bacteria into persister cells with reduced vitality [3], making them resistant to antibiotics and responsible for chronic infections [4; 5; 6; 7]. To further combat these stress conditions, bacteria utilize a system to dimerize and inactivate ribosomes [8]. In some gammaproteobacteria, such as _Escherichia coli_ and _Vibrio cholerae_, two ribosomal protein factors, ribosome modulation factor (RMF) and hibernation-promoting factor (HPF), bind to the 70S ribosome to produce its dimeric form, the 100S ribosome. In most other bacteria, including _Staphylococcus aureus_, _Lactobacillus paracasei_, and _Thermus thermophilus_, the 100S ribosome is formed through binding of a long-form of HPF to the 70S ribosome [9; 10; 11]. A ribosomal hibernation model has been proposed in which the 100S ribosome loses its translational activity to reduce cellular energy consumption and functions as a reservoir for protecting ribosomes from degradation by RNases [12; 13; 14; 15]. Recently, high-resolution structures of 100S ribosomes from various bacteria have been reported, improving the understanding of ribosome hibernation [16; 17; 18; 19; 20].  
In this review, we describe previous findings on the 100S ribosome and related protein factors in _E. coli_, as shown in Table 1. We attempted to integrate the hibernation stage into the ribosome cycle and discuss the elaborate stress response pathway involved in bacterial translational control.
## 3 Results and Discussion
### Material Characterization
#### 3.1.2 Optical Properties of N/S-Carbon QDs
The presence of heteroatoms such as nitrogen and sulfur on the material surface seems to play an important role in its photoluminescence properties, as shown in similar studies [40]. Specifically, the functional groups that contain nitrogen are known to be capable of passivating the active sites on the material surface by stabilizing the excitons and improving its overall photoluminescence capabilities [40]. Therefore, the existence of nitrogen- and sulfur-containing functional groups on the N/S-Carbon QDs surface might explain why the measured QY was higher than that of other nitrogen-doped CQDs containing lower quantities of heteroatoms [40].
## 2 Life-Cycle Assessment  
LCA is a methodology to evaluate the material flows and environmental impacts associated with the production of goods and provision of services over its full life-cycle from extraction and processing of raw materials through manufacture, operation and, finally, disposal [9,10]. The LCA is divided into four main phases:  
* **goal and scope**--including the definition of the _functional unit_, which quantifies the service delivered by the product system, definition of system boundaries, clarification of assumptions and limitations, allocations methods, e.g., between co-products, and impact categories;* **life-cycle inventory** (LCI)--tracking material and energy flows from and to the environment, often involving either the creation of a \"bottom-up\" model of the production process, the use of input-output (I-O) tables to convert between financial and physical data, or some hybrid of the two;
* **life-cycle impact assessment** (LCIA)--evaluating the environmental impacts of flows associated with the LCI, including selecting appropriate impact categories, indicators and environmental impact models, classification and measurement of impacts using a common metric to place different categories on an equivalent basis and;
* **interpretation**--including identification of significant issues arising from the LCI and LCIA stages, evaluation of completeness, sensitivity and consistency, and conclusions, limitations, and recommendations.  
For the purposes of the current analysis, data from the LCI stage has been used. The goal of the majority of the studies used is to determine the CED of the three renewable energy technologies under analysis. The functional unit is normally one kWh of electricity generation in order to generate an energy intensity metric [kWh\({}_{p}\)/kWh\({}_{e}\)]. Most of the studies within the meta-analysis are based on bottom-up models, though some of the data from [7] comes from hybrid models.
## 3 Asymmetric Failure Characteristics of Roadway Under Dynamic Pressure in the Floor
### Distribution Characteristics of Mining Stress in Roadway Surrounding Rock  
During the mining process of the working face, the direction of the mining-induced abutment pressure formed in the roof rock strata of the roadway is not absolutely vertical but inclines to a certain extent, which makes the internal stress distribution of the surrounding rock of the mining roadway present an asymmetric form and further causes the asymmetric failure of the surrounding rock of the roadway. Moreover, as the mining distance of the working face increases, the failure mode of the surrounding rock of the roadway tends to be complex [30; 31]. Taking the circular roadway as the research object, let the radial stress, tangential stress, and shearing stress of the surrounding rock of the roadway be \(\sigma_{r}\), \(\sigma_{\theta}\) and \(\tau_{r\theta}\), respectively. The expression of the stress components of the surrounding rock of the roadway is as in Equation (6):  
\[\left\{\begin{array}{l}\sigma_{r}=\frac{1}{2}(\lambda+1)P_{1}\Big{(}1-\frac {r^{2}}{R^{2}}\Big{)}+(\lambda-1)\Big{[}\frac{3^{4}\cos 2(\theta-\alpha)}{R^{4}}- \frac{4r^{2}}{R^{2}}+1\Big{]}\\ \sigma_{\theta}=\frac{1}{2}(\lambda+1)P_{1}\Big{(}\frac{r^{2}}{R^{2}}+1\Big{)} -(\lambda-1)\Big{[}\frac{3^{4}\cos 2(\theta-\alpha)}{R^{4}}+1\Big{]}\\ \tau_{r\theta}=\frac{1}{2}(\lambda-1)P_{1}\Big{(}-\frac{3r^{4}}{R^{4}}+\frac{ 2r^{2}}{R^{2}}+1\Big{)}\sin 2(\theta-\alpha)\end{array}\right. \tag{6}\]  
In Equation (6), \(\theta\) is the angle between any point of the surrounding rock of the roadway and the horizontal line, \({}^{\circ}\).  
According to rock mechanics, the expressions of the maximum principal stress \(\sigma_{1}\) and the minimum principal stress \(\sigma_{3}\) of the surrounding rock of the roadway are as follows:  
\[\left.\begin{array}{l}\sigma_{1}\\ \sigma_{3}\end{array}\right\}=\frac{\sigma_{\theta}+\sigma_{r}}{2}\pm\frac{1}{ 2}\sqrt{\frac{1}{4}(\sigma_{r}-\sigma_{\theta})^{2}+4\tau_{r\theta}^{2}} \tag{7}\]The Mohr-Coulomb strength criterion can be described as follows:  
\[f=(\sigma_{1}-\sigma_{3})[\sin(2\theta)-\tan\varphi\cos(2\theta)]-\tan\varphi( \sigma_{1}-\sigma_{3}) \tag{8}\]  
In Equation (8), \(\varphi\) is the internal friction angle of the rock, \({}^{\circ}\).  
Combining Equations (7) and (8) to calculate the Mohr-Coulomb strength criterion \(f\) of the surrounding rock of the underlying roadway is shown in Equation (9), and the relevant parameters of Equation (9) are shown in Table 2.  
\[f=\sqrt{\tan\phi+1}\sqrt{A_{15}A_{16}{}^{2}C_{3}{}^{2}\bigg{(}-\frac{3r^{4}}{ R^{4}}+\frac{2r^{2}}{R^{2}}+1\bigg{)}^{2}+\frac{[A_{16}C_{1}r^{2}-2C_{2}(C_{4}+1)R^{2 }+4C_{2}r^{2}]^{2}}{4R^{4}}}-A_{16}C_{1}\tan\phi+4C_{2}\frac{r^{2}}{R^{2}}\tan\phi \tag{9}\]  
Equation (7) is the expression of the ultimate strength of the surrounding rock of the roadway based on the Mohr-Coulomb criterion. When the criterion \(\left\lvert f\right\rvert\geq 2c\) (2 times the cohesion), the surrounding rock of the roadway changes from the elastic state to the plastic state [32].
## 2 Experimental Program
### Accelerated Corrosion Test
Total charges according to current density and duration of the test (from 3.5 to 78 days) are reported in Table 2. The purposes of these parameters were  
* To obtain external cracks with visible widths within a period that was compatible with the duration of the project (3 years);
* To determine whether the internal (corrosion product layer distribution and concrete cracking) and external (rust stains and concrete cracking) degradation states of specimens exposed to the same total charge are similar at different current densities and accelerated corrosion exposure times. During the accelerated corrosion test, the temperature, impressed current (constant) and voltage of each prism were recorded every two hours with a data acquisition unit. Different durations of the corrosion tests were defined according to the impressed current density (Table 2). Each duration was associated with an impressed current density to achieve a targeted value of the total charges. To illustrate, 14, 7 and 3.5 days were associated with 50, 100 and 200 \(\upmu\)A/cm\({}^{2}\), respectively, to give a total charge of 168 A.h/m\({}^{2}\). At the end of each duration, two prisms were disconnected (Table 2). The top (T) and front (F) faces of these two prisms were immediately photographed in order to keep a record of the crack pattern. Widths of external cracks were recorded after different exposure times using a crack-measuring magnifier (resolution 0.05 mm). One prism was used for non-destructive electrochemical characterizations and the other one for physical characterizations (indicated with an * in Table 2). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **RC Prism** & **Impressed Current Density** & **Duration of the Accelerated** & **Total Charge** \\  & **Label** & **(\(\upmu\)A/cm\({}^{2}\) of Steel Surface)** & **Corrosion Test (Days)** & **(A.h/m\({}^{2}\))** \\ \hline Reference & P01, P02 & 0 & 0 & 0 \\ \hline  & P21, P22 & 50 & 5 & 60 \\  & P23*, P24 & 50 & 14 & **168** \\ Series 1 & P25*, P26 & 50 & 28 & **336** \\  & P27*, P28 & 50 & 70 & **840** \\  & P29, P30 & 50 & 78 & 936 \\ \hline  & P31*, P32 & 100 & 7 & **168** \\  & P05*, P06 & 100 & 14 & **336** \\ Series 2 & P07, P08* & 100 & 21 & 504 \\  & P09*, P10 & 100 & 28 & 672 \\  & P11*, P12 & 100 & 35 & **840** \\ \hline  & P13, P14* & 200 & 3.5 & **168** \\ Series 3 & P15, P16* & 200 & 7 & **336** \\  & P17*, P18 & 200 & 17.5 & **840** \\  & P19, P20 & 200 & 19.5 & 936 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of the samples and parameters for the accelerated corrosion test.