esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, a real-time communication subsurface mooring system was developed to address the challenge of obtaining marine data in real-time. The system collects underwater sensor data via an underwater inductive modem module and then transmits the data to the station receiver via satellite communication. The hydrodynamic simulation and sea trail of the SCB were carried out in this paper. The findings indicate that even in a severe sea state, the float can maintain a relatively stable attitude with an inclination angle of less than 30\({}^{\circ}\), enabling the maintenance of normal satellite communication. An efficient data recovery strategy was also designed in this study, which ensure high data recovery efficiency and low power consumption when the system is working in a complex and changeable marine environment for a long time. The system has undergone sea trials in the South China Sea for more than 3 months and transmitted more than 2034 sets of underwater sensors data, with a success rate of over 91%. The CTD data returned in real-time from our system are consistent with the data of the HYCOM and World Ocean Atlas (WOA), and a cyclonic mesoscale eddy was detected in the operation area. This system provides a good method for the long-term real-time monitoring of marine physical phenomena and marine environments changes.  
Conceptualization, X.X., C.T. and C.S.; formal analysis, C.T.; investigation, S.Z.; methodology, P.L. and Y.S. ([PERSON]); project administration, C.T. and C.S.; resources, C.S.; software, P.L. and C.W.; supervision, X.X. and C.S.; validation, Y.S. ([PERSON]) and Y.M.; visualization, Y.S. ([PERSON]) and Y.M.; writing original draft preparation, P.L. and Y.S. ([PERSON]); writing--review and editing, C.T. and S.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Major Scientific and Technological Projects of Hainan Province, grant number ZDKJ202016.  
Not applicable.  
Figure 15: The temperature versus salinity (T-S) diagram for the western South China Sea at the mooring area. The color of the T-S diagram shows the density of the water. The red and black lines represent the T-S curves of the western South China Sea and Luzon strait masses from WOA.
# Multi-Technology Multi-Operator Site Sharing: Compliance Distance Analysis for EMF Exposure
## 4 Results and Discussion
### Shared Macro Site
Similarly, at the R\({}_{\rm CD}\) based on FCC limits, the highest contributor was N3500 (39%), followed by L800 (21%), L1800 and L2100 (11% each), and G900 and U900 (9% each). Figure 8 summarizes the exposure contributions of each technology at the R\({}_{\rm CD}\), and these were in line with the results found in [36; 37] for the power density. Figure 8: The EMF radiation contribution at the R\({}_{\rm CD}\) in the horizontal direction for the macro site shared by 4 MNOs. Figure 7: The exposure ratio in the main direction for multi-technology macro site with reference to ICNIRP limits for GP. Equation (6) gives shorter compliance distances compared with the calculations performed using Equation (4) because it is based on realistic rated power for the mMIMO rather than the conservative power levels used in Equation (4). The R\({}_{\text{CD}}\) increased with the increase in the number of MNOs sharing the same location; Table 3 summarizes the percentage of the distance increase compared with a single MNO with reference to the ICNIRP and FCC limits. It shows a +41% increase for 2 MNOs for all standard limits, +73% for 3 MNOs, and +100 for 4 MNOs. The results in Table 4 can be scaled up to any number N of MNO operators sharing the same site, assuming all operators use the same configuration and technologies. In this case, Equation (6) and can be written for one MNO as:  
\[CD_{1}=\left( \begin{array}{cc}\dfrac{1}{4\pi}\sum\limits_{f>300\ MHz}^{300 \ GHz}\dfrac{\rho_{r,f}.a_{b,f}.P_{T,f}.G_{A,f}}{S_{inc,RL,f}}\end{array}  \right)^{1/2} \tag{7}\]  
For N number of MNOs, Equation (7) becomes Equation (8):  
\[CD_{N}=\left( \begin{array}{cc}\dfrac{1}{4\pi}\sum\limits_{f>300\ MHz}^{300 \ GHz}N.\dfrac{\rho_{r,f}.a_{b,f}.P_{T,f}.G_{A,f}}{S_{inc,RL,f}}\end{array}  \right)^{1/2} \tag{8}\]  
\[CD_{N}=N^{1/2}.CD_{1} \tag{9}\]  
Equation (9) represents a simplified formula that gives the compliance distance based on the EMF Site-Sharing Scale (EMF-SSS formula). In reality, it is very hard to find a group of MNO operators that use the same configuration for multi-technology, including the power rating and system load. However, Equation (9) is useful for predicting the CD and indicating the expected increase in the CD when accommodating more than one MNO at the same site, and Figure 9 plots this relationship using Table 2 as input data for Equation (8).
# Solar Sail-Based Mars-Synchronous Displaced Orbits for Remote Sensing Applications
## 2 Mathematical Preliminaries
### Sail Performance Required to Maintain a Circular DNKO
The green disk indicates the plane of the DNKO, which is covered by the spacecraft with a constant angular velocity \(\omega\) at a distance \(d\) from Mars. As better detailed in Section 3.1, the solar sail-based spacecraft is outside the sphere of influence (SOI) of Mars and, therefore, the perturbative effect of the planet can be neglected [43]. Following the method detailed in [16] and looking at the scheme in Figure 3, the balance between propulsive, gravitational, and centrifugal accelerations acting on the spacecraft results in the following two scalar equations  
\[\beta\,\frac{\mu_{\odot}}{r^{2}}\,\cos^{2}\alpha\,\sin\alpha=\rho\,\omega^{2}\, \sin\gamma \tag{6}\]  
\[\beta\,\frac{\mu_{\odot}}{r^{2}}\,\cos^{3}\alpha+\rho\,\omega^{2}\,\cos\gamma= \frac{\mu_{\odot}}{r^{2}} \tag{7}\]  
which describe the spacecraft equilibrium in a rotating reference frame along \(\hat{p}_{r}\) and \(\hat{p}_{\gamma}\), respectively. Now, by substituting Equation (6) into Equation (7) and using Equations (3) and (4), one obtains the following analytical result, which expresses the sail cone angle \(\alpha\) as a function of the triplet \(\{\rho,\,H,\,r\}\), viz. \[\alpha=\arctan(\frac{\rho\,H\,r}{a^{3}-r\,\rho^{2}}) \tag{8}\]  
Figure 3: Sketch of the gravitational (yellow vector), propulsive (red vector), and centrifugal (green vector) accelerations acting on the spacecraft during the flight along the DNKO. Figure 1.0 Solar sail-based spacecraft in a spherical reference frame \(\mathcal{T}_{S}\) and definition of angles \(\theta\) and \(\gamma\). The gray surface indicates the orbital plane of Mars. while the required lightness number \(\beta\) is obtained from Equation (6) by using Equations (3) and (4). In that case, the result is  
Figure 1.1  
where \(\alpha\) is given by Equation (8). Note that changing the sign of \(H\) (or, equivalently, of \(\gamma\)) does not produce any change in the value of the required lightness number \(\beta\), while it causes the cone angle to change its sign. Therefore, by virtue of this symmetry, only the results relating to positive \(\gamma\) values are shown in the rest of this paper. The previous analytical results can be considered as a specific application to a Mars-based mission scenario of the general mathematical model discussed in [16].
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 4 Security and Resilient Architecture
### Smart Grid Testbed  
#### 4.5.1 Power System Simulators/Tools  
Real Time Digital Simulator (RTDS) allows physical hardware-integrated and real-time power grid simulation. It can accurately simulate the physical response of the devices facing various attack scenarios [92].  
DIgSILENT PowerFactory, on the contrary, is a software platform that only provides non-real-time simulation. It integrates different algorithms for state estimation and contingency response [93].  
Power System Analysis Toolbox (PSAT) and YALMIP toolbox in Matlab can be used to analyse networks in small- and medium-scale power systems [55; 94].
## 2 Results
### Influence of Climatic Factors on Grassland AGB Dynamics
#### 2.3.2 Correlation between Climatic Factors and Grassland AGB  
The correlation between grassland AGB and temperature from 2000 to 2022 is shown in Figure 7. There was a positive correlation between Xinjiang grassland AGB and tem  
Figure 6: Magnitude and trend in mean annual temperature and mean annual precipitation in Xinjiang over the period 2000–2022. (**a**) Average annual temperature (\({}^{\circ}\)C); (**b**) average annual precipitation (mm); (**c**) the trend of temperature (\({}^{\circ}\)C-a\({}^{-1}\)); (**d**) the trend of precipitation (\({}^{\circ}\)C-a\({}^{-1}\)).  
perature, accounting for 52.33% of the total correlation. A significant positive correlation accounted for 3.09% and was mainly distributed in the Altay region in northern Xinjiang and the Hami region in eastern Xinjiang. Furthermore, different grassland types exhibited varying proportions of correlation (Figure 8a), with temperate steppe deserts having the highest positive correlation (62.25%) and highest significant positive correlation (5.76%) and alpine deserts having the lowest proportion (36.96%). The study also showed that the alpine desert had the highest proportion of negative correlations (63.04%), whereas the temperate steppe desert had the lowest (37.75%). In conclusion, there were spatial differences in the correlations between different grassland types and temperature, and the AGB demonstrated a positive correlation with temperature.  
There was a positive correlation between grassland AGB and precipitation (Figure 7b,d), accounting for 54.35% of the total area, with a significant positive correlation accounting for 8.38%, mainly found in the Yili River Valley region in western Xinjiang and the northern and eastern parts of Xinjiang. On the other hand, negative correlation accounted for 45.65% of the area, with only 4.96% being significantly negatively correlated, particularly in the northern part of Xinjiang. Significance testing for different grassland types revealed that the alpine meadows had the highest proportion of positive correlation (69.61%), whereas lowland meadows had the highest proportion of negative correlations (61.72%). Addition  
Figure 7: Spatial distribution of correlation and significance test between AGB and meteorological factors in Xinjiang from 2000 to 2022: (**a**,**c**) temperature and (**b**,**d**) precipitation.  
ally, temperate desert grasslands (17.36%) and lowland meadows (3.85%) had the highest and lowest proportions of significant positive correlations with precipitation, respectively (Figure 8b). Overall, the different types of grasslands in Xinjiang were positively correlated with precipitation. From 2000 to 2022, the correlation between grassland AGB and precipitation was greater than that with temperature.
## 3 Results and Discussion
### Effect of Parameters and Optimization of Cell Lysis
The combination that showed the highest D value was considered the optimal one (Figure 3). The results obtained showed that a yeast suspension concentration of 5%, a yeast/glass bead ratio of 1:2, and three vortexing cycles of 10 min each would be the optimal parameters for spent brewer's yeast cell lysis that produce a disruption efficiency of 99.8% (Figure 3). Different results in the rate of cell lysis were found both for spent brewer's yeast and fresh yeast in the \(\beta\)-glucan isolation protocol. Thus, [PERSON] et al. (2008), after autolysis of a 15% (\(w/w\)) spent brewer's yeast followed by an autoclavation of a 10% (\(w/w\)) cell suspension with glass beads in a ratio of 5:1 yeast/glass beads (bead diameters 0.3-0.4 cm), managed to release about 48% (\(w/w\)) cellular substances [19]. [PERSON] et al. (2009),under the same conditions but this time using a 30% (\(w/w\)) cell suspension, reported an autolysis rate of 53%, which is also lower than our results, probably because of the different processing conditions (glass bead diameter of 0.4 mm, temperature, slurry concentration, etc.). The experiment used fresh yeast as raw material and in the context of cell lysis demonstrated that a concentrated mixture of 30% fresh yeast requires special cell lysis conditions (autolysis followed by heating with glass beads in an autoclave at 121 \({}^{\circ}\)C) [28]. [PERSON] and [PERSON] (2018) managed to disrupt 79.7% of total cells in similar conditions of cell lysis by using a 15% spent brewer's yeast suspension, with a 1:1 yeast/glass bead ratio and five vortexing cycles of 30 min each [26].Their findings suggest that an efficient cell rupture under a 1:1 yeast/glass bead ratio requires a higher number of vortexing cycles and implicitly a much longer time (about 150 min)at highly concentrated yeast cell suspensions.
# A Multiarmed Bandit Approach for LTE-U/Wi-Fi Coexistence in a Multicell Scenario
## 6 Performance Evaluation of MAB-ADC
Figure 6 sketches an overview of the scenario using a Cumulative Distribution Function (CDF) that compares the discussed solutions. The CDF of the user's throughput is useful to visualize the performance of different groups of users and to assess how each solution impacts each group. Since in Figure 4 the value 0.6 is the fixed DC value that results in higher throughput, we compare its results with the coordinated MAB and the independent MAB for both LTE-U and Wi-Fi users. Figure 5: Relative gain of 10 th percentile user’s throughput. Figure 6: CDF of the user throughput for the MAB solutions and a fixed DC value of 0.6. According to the CDF, the independent MAB shows a throughput gain for more than 50% of the worst LTE-U users, while Wi-Fi raises almost no loss compared with the reference curve of a fixed 0.6 DC value. The coordinated MAB, on the other hand, shows a significant gain for all Wi-Fi users, but that gain comes at the expense of the LTE-U users, particularly the worst-performing ones in the coordinated MAB approach. These results are in accordance with the discussions from Figures 2 and 4. The independent MAB solution is better at providing similar throughput for both operators.
## 2 Materials & Methods
### Gait Mode Estimation and Finite State Machine Design  
In general, walking can be divided into stance and swing sections. In a normal person, walking is clinically divided into several stages, as needed [12]. In this study, to develop a simple and predictable gait control protocol, as shown in Figure 6, the gait mode was classified into four functional modes (SWF: swing flexion, SWE: swing extension, STF: stance flexion, and STE: stance extension).  
The patient wearing the EGO first moves the center of gravity to the opposite leg for walking and then uses the trunk muscles to create a hip movement to move the leg. Therefore, the patient's walking intention can be predicted by the movement of the center of gravity and the hip's motion.  
As shown in Equation (1), the differential values (\(GRF_{diff}\)) of the left and right GRF sensors were extracted to detect the movement of the center of gravity, and the average values (\(GRF_{MH}\)) of the mid-sensor (\(GRF_{mid}\)) and heel-sensor (\(GRF_{het}\)) were applied as differential data.  
\[GRF_{diff}=\ GRF_{MH}^{L}-GRF_{MH}^{R} \tag{1}\]  
Figure 7 shows the KAEGO's finite state machine for gait control developed in this study. The initial state was set to the STE mode, and the knee actuator was driven with high impedance to keep the patient in a standing position.  
SWF (mode 1) is the first step in a gait cycle, and when the gait controller detects the patient's gait intention, it generates a patient-specific knee flexion torque, and a buzzer notifies the patient that the gait mode is switched to swing mode. In addition, the knee angle (\(\theta_{knee}\)) is controlled in real-time so as to not exceed the reference value, and when the  
Figure 6: Classification of exoskeleton orthosis gait into four functional modes for level walking.  
Figure 7: The finite state machine for the developed exoskeletal orthosis gait control. The gait transition starts from the STE (Stance Extension) mode, where \(\theta_{Hip}^{*}\) is the opposite side hip angle.  
change in knee angular velocity (d\(\theta_{Rnee}\)) decreases below the reference value, it is switched to the SWE (mode 2). In this mode, a patient-specific extension torque is applied to the knee actuator to extend the flexed knee.  
STF (mode 3) is a preparatory step for the impact when the foot touches the ground and is switched when the knee is fully extended or heel contact (\(GRF_{het}\)) is detected. At this time, the knee actuator acts as a mechanical spring or damper and is driven to minimize the impact applied to the knee joint when contacting the heel.  
STE (mode 4) is a mid-stance section, as shown in Figure 5b, where the knee is fully extended, and the sole is parallel to the ground. In this mode, one gait cycle is completed, and if the controller detects the patient's intention to walk again, the state is switched to SWF, and a new gait cycle is started.
## 1 Introduction
Additionally, various physiological changes induced by diosogenin in bacteria suggest a potential impact on intracellular ATP levels; however, no direct evidence has been presented to substantiate this. If diosogenin indeed reduces intracellular ATP levels, considering the established relationship between ATP depletion, decreased metabolic activity, and the nature of the dormancy associated with persister cell formation [15], it can be hypothesized that diosogenin treatment may affect persister cell formation. The dual-mechanism therapeutic strategy of targeting both (p)ppGpp synthesis and membrane potential presents diosogenin as a compelling compound for combating bacterial persistence. By simultaneously modulating intracellular metabolic pathways and membrane-associated signaling mechanisms, diosogenin may effectively prevent the formation of persister cells while preserving non-target bacterial viability. Additionally, this dual-action approach may offer a synergistic effect when combined with conventional antibiotics, potentially enhancing antibiotic efficacy and reducing the likelihood of infection relapse. Therefore, this study investigated the potential of diosogenin as a dual-action inhibitor of persister cell formation in _S. aureus_. The primary objective is to evaluate whether diosogenin can downregulate the expression of _relP_ and _relQ_, thereby inhibiting (p)ppGppsynthesis and preventing the induction of the stringent response. Additionally, the study tested diosogenin's effects on membrane potential and intracellular ATP levels, assessing whether the metabolic disruption it causes contributes to persister cell suppression under ciprofloxacin, gentamicin, and oxacillin exposure. The significance of this study lies in its focus on preventing persister cell formation through metabolic modulation rather than merely eradicating persister cells after they have already formed. If successful, our findings may establish diosogenin as a promising adjunctive therapy for chronic bacterial infections in which persister cells contribute to prolonged infection duration and treatment failure, particularly those involving _S. aureus_. Moreover, the identification of a naturally derived, low-toxicity compound capable of dual mechanism action could serve as a foundation for the development of novel anti-persister therapeutics aimed at targeting both metabolic and signaling pathways.
## 2 Methods  
### Phase Plane  
The phase plane gives the position and momentum of a particle in a reference system, and its construction represents the transformation of the particle trajectory into a two-dimensional energy field [6]. The phase plane can be defined mechanically using the [PERSON]'s principle:  
\[H(p,q)=\frac{p^{2}}{2m}+V(q) \tag{1}\]  
where \(p\), \(q\), and \(V(q)\) are the linear momentum, the position, and the potential energy of the particle, respectively. The system equilibrium is given when: \(\dot{q}=\frac{\partial H}{\partial p}\) and \(\dot{p}=-\frac{\partial H}{\partial q}\). Then, for a function \(\phi(p,q)\), the evolution of the phase plane as a function of time is:  
\[\frac{d\phi}{dt}=\frac{\partial\phi}{\partial q}\frac{\partial H}{\partial p} -\frac{\partial\phi}{\partial p}\frac{\partial H}{\partial q} \tag{2}\]  
The dynamic stability of the system according to [PERSON]'s theorem is obtained when:  
\[\frac{dH}{dt}=\frac{\partial H}{\partial q}\dot{q}+\frac{\partial H}{\partial p }\dot{p}=0 \tag{3}\]  
Equation (3) means that the volume of the phase plane is preserved in time. Assuming that the linear momentum is only function of the velocity, and that the mass is constant, the phase plane describes the relation between velocity and position of the particle, as presented in Figure 1.  
Figure 1a represents the phase plane evolution of a particle with harmonic motion, where the displacement is \(x=sin(wt+\phi)\) and the velocity is \(x=cos(wt+\phi)\); the pitch of the spiral is the period \(\tau\) of the function. Figure 1b is the typical phase plot of a dynamic system; in this case, the perfect circle represents the same sinusoidal function as Figure 1a, in a two-dimensional space. Figure 1c is the RP of the same function. One of its main characteristics is that the period given by the spiral pitch in Figure 1a is obtained from the separation of the diagonal lines of Figure 1c. Ultimately, Figure 1 can be used to describe an unbalanced disk without friction.  
Figure 1: (**a**) Evolution of state variables; (**b**) phase plane; (**c**) recurrence plot (\(e=0.1\)) for the harmonic function: \(x=sin(2t+\phi)\).
## 3 Results and Discussion
For SBP, this could be a solution; but for GS, the gap is too large. Furthermore, in both cases, the equipment purchase costs, building and construction costs, and utility costs are the highest expenses within the total capital investment. By removing the pretreatment section (units V-101, PM-101, PM-102, and the sulfuric acid (Figure 2)) from the pretreatment pathway several aspects change. The efficiency of the enzymatic hydrolysis of non-pretreated GS will be lower. In the fermentation, less ethanol will be produced and therefore the revenue becomes smaller. Furthermore, the total capital investment decreases since the equipment purchase costs are lower, which also lowers the direct and indirect plant costs. In addition, since the sulfuric acid is no longer used, the volume of the process decreases; and therefore, the equipment costs and the utility costs decrease. Additionally, the raw material costs and waste handling cost decreases since sulfuric acid is no longer needed or disposed of. Compared with corn stover, SBP and GS contain a lower amount of cellulose and hemicellulose, and therefore the revenue from the ethanol production is not high enough to cover the total capital investment and operating costs which leads to a non-economical viable plant. Since V-101 required heating to 121/180 \({}^{\circ}\)C, a large amount of steam was needed to obtain these temperatures. After that, a large amount of cooling water was required to obtain the right temperature for fermentation. Since this is no longer part of the process, the operation costs are significantly lower. In Table 4, it can be found that the production of bioethanol from SBP and GS without pretreatment is viable. SBP gives a payback time of 5.09 years, an IRR of 23.05%, and an NPV of 3,367,000 \(\upepsilon\). GS provides a payback time of 5.85 years, an IRR of 19.30 and an NPV of 2,763,000 \(\upepsilon\). Comparing SBP with GS, investing in SBP would be advised since the NPV and IRR are higher. The costs that are saved on the equipment and utilities do not weigh up against the loss in revenue. To break even, the selling price of the ethanol for SBP and GS must be 0.68 \(\upepsilon\)/kg and 0.96 \(\upepsilon\)/kg, respectively, which are too high because the ethanol still must be purified.