esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Control System
### Improved CVB Method
Thanks to its high adaptability, which allows model parameters to be modified without hardware intervention, this approach not only improves efficiency and simplifies control but also reduces system costs while guaranteeing robust performance under a range of operating conditions. It is therefore a promising solution for improving the reliability and efficiency of MMCs. This control method relies on controlling the charging and discharging of the SM capacitors based on the direction of the arm current and the instantaneous capacitor voltage using a logic function-based algorithm [21; 22; 27], as shown in Figure 4.  
The proposed CVB method offers several advantages, such as scalability and straightforward programming. It eliminates the need for a _PI_ controller to set capacitor voltages and instead utilizes an algorithm based on simple logic comparisons. In order to implement this CBV method, the capacitor voltages of the SMs in each arm are estimated using the dynamic equation of the SM capacitor voltage:  
\[i_{csyn}=C\frac{dV_{csyn}}{dt} \tag{11}\]  
where \(i_{csyn}\) and \(V_{csyn}\) represent the SM current and SM capacitor voltage, respectively, with \(x\) representing the phase (x = a, b, c) and y representing the arm (y = p, n). Using _Euler_ approximation and assuming a sampling period of \(T_{s}\), the predicted SM capacitor voltages at discrete time are derived from (12):  
\[V_{csyn}(t+T_{s})=V_{csyn}(t)+i_{xy}\frac{T_{s}}{C} \tag{12}\]  
Figure 4: Flowchart of the CVB algorithm. where \(i_{xy}\) is the upper or lower arm current. The estimated SM capacitor voltages are introduced into the comparison logic, which compares each capacitor voltage to obtain an index number _Ih_. The highest index number corresponds to the lowest capacitor voltage and vice versa. The arm current direction d is then determined. The SMs are then sorted in ascending or descending order of SM index numbers according to the direction of the arm current using (13), which presents the actual index number _Ah_:  
\[Ah=Ih\times d+(N-1-Ih)\times(1-d) \tag{13}\]  
The SMs with the lowest capacitor voltage are inserted for the positive current direction and are charged. Similarly, the SMs with the highest capacitor voltage are inserted for the negative current direction and are discharged. The required number of inserted SMs \(G_{xy}\) is obtained from the output vector generation command VSG. The insertion and bypass (short-circuit) states Mxyh for each SM are generated by comparing the actual index number Ah with the reference index number (_N_- \(G_{xy}\)). Finally, the calculation optimization is performed by comparing the inserted SMs \(G_{xy}\) current number with the inserted SMs \(G^{\;p}_{xy}\) previous number. If the voltage levels are different, the algorithm applies the new control signals (i.e., \(S_{xyh}=M_{xyh}\)). Otherwise, the algorithm maintains the previous switching state (i.e., \(S_{xyh}=M^{\;p}_{xyh}\)).
## 5 Different Activity Coefficient Equations in Geochemical Models
### Geochemical Equilibrium Models  
Modeling the chemical equilibrium of target compounds is relevant during the control of pH, alkalinity, temperature and other factors. As part of this approach, one must determine the rates of the contribution of various components with specific chemical characteristics to mixtures. A model established using thermodynamic equilibrium theory and experimental data is suitable for the probing of the reaction of geochemical equilibrium; furthermore, the model can present the reaction process in a natural environment in numerical form. With extensive theoretical research and experimental data analyses on geochemical reactions, geochemical equilibrium models have become increasingly reliable for the simulation of in situ subsurface water-rock interactions and the prediction of solution concentrations through mineral ingredient analysis. Thermodynamic chemical equilibrium typically aims at the determination of spontaneous changes to an equilibrium state. Any system that is not at equilibrium would change spontaneously with the release of energy. In general, simulation is more straightforward for a closed system than an open system, in which the equilibrium condition is simplified with mass balance and thermodynamic equilibrium. This study compared the performance of four chemical equilibrium models, namely PHREEQC, MINEQL+, MINTEQA2, and EQ3/6, in terms of the prediction of activity coefficients.
# Characterization of CYGNSS Ocean Surface Wind Speed Products
## 2 Materials and Methods
### CYGNSS Wind Speed Data Products
#### 2.1.1 Project v3.2 L2 Fully Developed Seas and L3 Merged Storm Wind Speed
The merging is accomplished using gridded (i.e., Level 3) versions of each to simplify the merging procedure, as described in [13]. A flowchart describing the generation of MRG from FDS and YSLF is shown in Figure 1b. There have been a number of versions of these products produced over the 7+ years that CYGNSS has been in orbit. The latest version, v3.2, of the FDS and MRG wind speeds is evaluated in this work [14; 15]. Figure 1: (**a**) Level 2 wind speed processing flow: FDS (maroon) and YSLF (green). (**b**) Level 3 FDS and MRG processing flow.
# Balance of Antioxidants vs. Oxidants in Perinatal Asphyxia
## 7 Therapeutic Strategies  
### Hypothermia  
The effectiveness of hypothermia in reducing brain damage [112] and lessening severe neurological effects [113] in newborns with moderate to severe NE has been well-documented and is now the standard treatment for term and early preterm neonates with NE [50]. The main advantages of hypothermia arise from decreasing brain metabolism, which lessens the damage from secondary brain energy failure [114]. Furthermore, hypothermia prevents brain cell death, reduces harmful effects from overstimulation, controls the activity of glial cells [115], and stimulates certain RNA molecules [116]. Additionally, hypothermia has been shown to directly decrease the levels of oxygen-based free radicals after hypoxia/ischemia and reperfusion, as seen in both in vivo [42] and in vitro studies [117] (Table 3).  
\begin{table}
\begin{tabular}{p{128.0 pt} p{128.0 pt} p{128.0 pt}} \hline \hline
**Option** & **Mechanism of Action** & **Phase of Hypoxemia/Ischemia** \\ \hline \multirow{4}{*}{Hypothermia} & Decreases ROS & Within six hours of \\  & Decreases brain metabolism & Hypoxia/ischemia \\  & Controls activity of glial cells & Reverfusion phase \\  & Pevents cell death & \\ \cline{1-1}  & Mitochondrial therapy: & Suppresses mitochondrial complex I in the respiratory chain & Hypoxia/ischemia phase \\  & 1. Metformin. & Affects inflammatory T cells & reperfusion phase \\  & 2. Coenzyme Q10. & Decreases the production of ROS & Hypoxia/ischemia phase \\  & 3. Mitoquinone. & Reduces superoxide production, lipid peroxidation, and ROS generation & Hypoxia/ischemia phase \\  & Antioxidants & \\  & 1. Free radical initiators and scavengers: & \\  & & Inhibits xanthine oxidase & \\  & 1. Allopurinol. & Chelator for non-protein-bound iron & Reverfusion phase \\  & & Reduces inflammation & Hypoxia/ischemia phase \\  & & Decreases ROS & Hypoxia/ischemia phase \\  & & Decreases caspase activation & Reverfusion phase \\  & & Pevents cell death \\ \hline \hline \end{tabular}
\end{table}
Table 3: Treatment options for neonatal encephalopathy.
# Analysis on Response of a Single Pile Subjected to Tension Load Considering Excavation Effects
## 5 Validation of the Proposed Approach
### Case Two  
[PERSON] [35] performed centrifugal tests to investigate the uplift pile response in dry FF sand with a centrifugal acceleration of 80g. The model pile made of aluminum had a length of 32 cm and a diameter of 3.2 cm. The elastic modulus of the model pile was 70 GPa. For the dry FF sand, the relative density \(D_{r}\) was 85%, with the original porosity \(e_{0}\) was 0.804. The unit weight \(\gamma\) was 14.87 \(\mathrm{kN/m^{3}}\). According to the back-calculation [15], the interface mobilized friction angle \(\delta\) was 39\({}^{\circ}\). The ratio of lateral earth pressure coefficient \(K\) to lateral earth pressure coefficient at rest \(K_{0}\) was determined by [36]:  
\[\frac{K}{K_{0}}=\frac{C\exp\left\{\frac{D_{r}}{100\%}\left[1.3-0.2\ln\left( \frac{e_{r0}^{\prime}}{P_{a}}\right)\right]\right\}}{F(K_{0})} \tag{30}\]  
where \(D_{r}\) is the relative density; \(\sigma_{v0}^{\prime}\) is the effective overburden stress; \(p_{a}\) is the atmospheric pressure; \(F(K_{0})\) is the function related to the lateral earth pressure coefficient at rest \(K_{0}\), \(F(K_{0})=\exp(0.2\sqrt{K_{0}-0.4})\); \(C\) is an empirical coefficient and can be adopted as 0.7 following the suggestion of [PERSON] and [PERSON] [36]. The ratio of pile-soil interface friction angle \(\delta\) to effective friction angle of soil was set as 1.  
Figure 7 shows a comparison between the calculated load-displacement curve and the measured load-displacement curve. The calculated results from [PERSON] [22] and [PERSON] et al. [37] are also presented in Figure 7. In Figure 7, it can be seen that the calculated load-displacement curve is generally consistent with the measurements and the calculated results of [PERSON] [22] and [PERSON] et al. [37]. When the load is less than 2000 N, the proposed approach underestimates the load, while the approaches of [PERSON] [22] and [PERSON] et al. [37] overestimate the load. When the load is larger than 2000 N, the proposed approach and approaches of [PERSON] [22] and [PERSON] et al. [37] overestimate the loads.  
Figure 6: Measured and calculated load-displacement curves of a single uplift pile ([30; 33; 34]).  
Figure 9 shows a comparison between the tested and calculated pile load-displacement curve. When the load is less than 6000 kN, the proposed approach underestimates the load, while the proposed approach performed well in predicting pile load-displacement curves when the load is larger than 6000 kN. In the original paper, the soil shear modulus was not presented, and the constrained modulus was used in the proposed approach. Using the constrained modulus underestimates the initial stiffness of the pile-soil interface. Hence, the proposed approach underestimates the load when the load is less than 6000 kN. The ultimate bearing capacity of the uplift piles before and after excavation is 6210 kN and 5750 kN, respectively. Hence, the ultimate bearing capacity of the uplift piles was reduced by 7.4%, with an excavation depth of 13 m.
# Biodegradation of Picolinic Acid by _Rhodococcus_ sp. PA18
## 3 Results
### Growth of Strain PA18 and PA Biodegradation  
The relationship between the growth of strain PA18 and PA degradation in MSM is indicated by Figure 2. The concentration of PA remained unchanged in the control samples, which did not contain strain PA18. This confirmed that abiotic losses, such as the volatilization and adsorption of PA, were negligible in this study. PA18 degraded 100 mg/L PA at a relatively higher rate within the first 24 h (Figure 2A). The degradation rate decreased substantially from 24 to 28 h, possibly as a result of a lack of substrate, with complete degradation within 28 h. Furthermore, the PA degradation was associated with a concomitant increase in the bacterial cell density. The OD\({}_{600}\) value reached 0.31 within 16 h. Interestingly, after a four-day lag phase, the strain PA18 cultured in a LB medium also showed a high-efficiency degradation of PA (Figure 2B). In contrast, the negative control with no inoculated PA18 showed no discernable degradation of PA over the same 120 h. At the same time, the cells grew rapidly with the degradation of PA.  
Figure 1: Neighbor-joining phylogenetic tree shows the position of strain picolinic acid 18 (PA18) among some members of the genus _Rbloodococcus_, based on the 16S rDNA sequences.
# Precoder and Decoder Co-Designs for Radar and Communication Spectrum Sharing +
## 2 Generalized Multicarrier Radar-Communication Coexistence Model
### Multiuser Radar-Communication Spectrum Sharing Scenario
Moreover, channel reciprocity may be exploited for interference channels. * Synchronization: Both radar and communication systems are assumed to be synchronized. If they are colocated, then they may share the same clock, in which case, synchronization is not an issue. The other subsystems need to be synchronized in a similar manner to any multiuser communication system; the clock synchronization may be easier in communication systems but still feasible for radar. Existing radar clock synchronization technology may be employed, such as using a Global Navigation Satellite System (GNSS) [58; 59], using a pilot signal [60] or using an OFDM frame [61] to achieve time and frequency synchronization [28]. * Shared Information: As shown above, the following information is shared among all users: selection matrix \(\mathbf{\Omega}^{(l)}\) and data covariance \(\sigma_{S}\). This shared information is needed to calculate the transmitted signal power and consequently to solve the following optimization problem at the \(l\)th time slot. Alternatively, the transmitted signal power can be constrained by the power limitation. * Doppler: In this paper, radar is detecting a far-field point target, and all communication channels are experiencing block fading. The Doppler shift is assumed to be constant during a coherent interval for the \(L\) pulse. * Schedule: The feedback of the channel state matrix, transmission of clock synchronization and shared information call for schedules between the communication and radar systems. Providing channel feedback is common in most modern communication systems and part of the standards. A radar system can also take advantage of feedback and estimate channels. One feasible approach is to transmit information across the radar-coherent interval based on a time division multiplex [7].
## 5 Experimental Results
It implies that the joint-PPE scheme with \(L_{\mathrm{p}}=2\) should be preferred to limit the complexity of the system as it provides similar performance benefits as the joint-PPE scheme with \(L_{\mathrm{p}}=4\). In summary, the experimental results confirm the hypothesis, which states the usefulness of the joint-PPE over the PE-only. The results prove that the interferences such as ISI and ICI are completely compensated with a minimal noise amplification, which are highlighted by the improvements in the BER performance. The eye diagrams of the received electrical signals \(y_{v}(t)\) with PAM-4 and PAM-2 transmissions are represented in Figure 9 and Figure 10, respectively. It is clear from the wider eye openings that the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**PRBS Length** & **Symbol Rate (in GHz)** & **Delay Time (in ns)** & **Delay Length (in m)** \\ \hline
512 & 2.5 & 204.8 & 41.82 \\
1024 & 2.5 & 409.6 & 83.64 \\
2048 & 2.5 & 819.2 & 167.27 \\
4096 & 2.5 & 1600 & 334.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Analysis of the required length of the delay lines for the PFS configuration. transmission quality is enhanced when the proposed joint-PPE scheme with \(L_{\rm p}=4\) is utilized over MMSE PE-only. Figure 8: BER in dependence on the average optical received power while using a 100 m long-MMF channel and MMSE equalizer, where PAM-2 and PAM-4 are depicted with blue and red color, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{
\begin{tabular}{c} **Modulation** \\ **Format** \\ \end{tabular} } & \multirow{2}{*}{**Equalization**} & \multirow{2}{*}{\(L_{\rm p}\)} & \multirow{2}{*}{\(L_{\rm f}\)} & \multirow{2}{*}{\(P_{\rm r}\) **(in dBm) at**} & \multirow{2}{*}{\(P_{\rm r}\) **Gain**} \\  & & & & & BER of \(10^{-4}\) & **(in dB)** \\ \hline First & PAM-2 & PE-only & - & 25 & \(-14\) & - \\ scenario & & Joint-PPE & 2 & 23 & \(-15.13\) & \(1.13\) \\  & & & 4 & 21 & \(-15.93\) & \(1.93\) \\ Second & PAM-4 & PE-only & - & 25 & \(-9.95\) & - \\ scenario & & Joint-PPE & 2 & 23 & \(-10.91\) & \(0.96\) \\  & & & 4 & 21 & \(-11.79\) & \(1.84\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Measured optical received power \(P_{\rm r}\) gain of the \((2\times 2)\) optical MIMO system at \(10^{-4}\) BER using a 100 m long MMF channel at \(f_{\rm T}=2.5\) GHz.
## 5 Simulations
It is impossible to track the expected value accurately, and the control performance is relatively poor. The overshoot and the droop under the control of HGWO-NMPC are smaller, the maximum overshoot value is 0.012, and the maximum droop value is almost 0. HGWO-NMPC has little oscillation in the initial stage of the accelerating response. Figure 11: The simulation results of NMPC based on HGWO. (**a**) Fuel flow curve; (**b**) relative speed curve. Therefore, the presented HGWO-NMPC method in this paper shows that the considered aircraft engine control system has fast convergence, high precision, good dynamic performance, and an ability to handle constraints well. It can thus handle the requirement of control constraints and the performance optimization requirements of aircraft engines.
# Effect of Plant Biostimulants on Nutritional and Chemical Profiles of Almond and Hazelnut
## 2 Materials and Methods  
### Samples  
Hazelnut (_Corylus avelana_ [PERSON]) and almond (_Prunus dulcis_ (Mill.) [PERSON]. [PERSON]) samples were obtained from experimental fields in the Northeast of Portugal. Almond samples belong to _Vairo_ cultivar and were obtained from trees submitted to five treatments: (i) Control (without treatment/biostimulant); (ii) Phytoalgae (Fitcalgas Green(r), a seaweed extract of _Ascophyllum nodosum_); (iii) Foliar B (Tradebor(r), boron as ethanolamine, 15.4% _w/v_); (iv) Amino acids (Sprint Plus(r), free amino acids, 28.8% _w/v_); (v) Soil B (Neobor(r), sodium tetraborate pentahydrate, 14.85% B); All products were compatible with organic farming. Hazelnut samples belong to the Ennis cultivar and were obtained from trees submitted to six different treatments resulting from the combination of the plant biostimulants Fitcalgas Green(r) and Sprint Plus(r) with or without a basal soil NPK fertilization program. The treatments were: (i) Control (without treatment/biostimulant); (ii) NPK (60 kg ha\({}^{-1}\) of N, P2O5, and K2O); (iii) NPK + phytoalgae; (iv) Sprint Plus; (v) NPK + Sprint Plus; (vi) Phytoalgae; In all six cases, a basal treatment with boron was made. All treatments were made in the early spring of 2019.  
Both experiments were arranged as completely randomized designs with three replicates. Once in the lab, each sample was analyzed in triplicate. After collection, samples were immediately frozen and lyophilized (Telstar LyoQuest Lyophilizer), to prevent the occurrence of any degradation. Then, samples were ground in a chopper (model A327R1, Moulinex, Barcelona, Spain), homogenized, and stored away from light and moisture.
## 3 Methodology
### Input Saturation Processing  
In practice, input saturation is one of the constraints which may degrade system performance or even affect system stability. Input saturation is described by  
\[u(\zeta(t))=\text{sat}(\zeta(t))=\begin{cases}\text{sgn}(\zeta(t))M,\quad| \zeta(t)|\geq M\\ \zeta(t),\quad|\zeta(t)|\leq M\end{cases}, \tag{19}\]  
where \(M\) is the saturation amplitude.  
In this paper, the hyperbolic tangent function is used to approximate the saturation function:  
\[H_{M}(\zeta)=M*\tanh(\zeta/M)=M*\frac{e^{\zeta/M}-e^{-\zeta/M}}{e^{\zeta/M}+e^ {-\zeta/M}}. \tag{20}\]  
Then, \(\text{sat}(\zeta(t))\) can be expressed as:  
\[\text{sat}(\zeta)=H_{M}(\zeta)+\rho(\zeta)=M*\tanh(\zeta/M)+\rho(\zeta), \tag{21}\]  
where \(\rho(\zeta)=\text{sat}(\zeta)-H_{M}(\zeta)\) is a bound function in time, and its bound can be obtained:  
\[|\rho(\zeta)|=|\text{sat}(\zeta)-H_{M}(\zeta)|\leq M*(1-\tanh(1))=D. \tag{22}\]  
Note that in the section \(0\leq|\xi|\leq M\), the bound of \(\rho(\zeta)\) is between \(0\) and \(D\).  
Considering the input saturation and state limitation of the system, the command integral filter is designed as follows:  
\[\begin{bmatrix}\dot{\zeta}_{1}\\ \dot{\zeta}_{2}\end{bmatrix}=\begin{bmatrix}\zeta_{2}\\ 2\zeta\omega_{n}[H_{R}(\frac{\omega_{n}^{2}}{2\zeta\omega_{n}}[H_{M}(\alpha_{i })-\zeta_{1}])-\zeta_{2}]\end{bmatrix}, \tag{23}\]  
where the input is the virtual control quantity \(\alpha_{i}\), and the output is \(\overline{x}_{i}\) and its first derivative. \(\tau_{1}\) and \(\tau_{2}\) are the filter states, \(\zeta\) is the damping ratio, \(\omega_{n}\) is the natural frequency, and \(H_{M}\) and \(H_{R}\) are the amplitude and rate saturation function, respectively. Using (23) with proper designed parameters, the following inequalities hold:  
\[|H_{M}(\alpha_{i})-\zeta_{1}|\leq l_{1}\vartheta, \tag{24}\]where \(l_{1}>0\), \(\theta=1/\omega_{n}>0\).
# 108 m Underwater Wireless Optical Communication Using a 490 nm Blue VECSEL and an AOM
###### Abstract
As the distance of communication increases, the signal waveform starts to distort, necessitating the use of increasingly advanced coding and shaping techniques at either the transmitter or the receiver. Compared to on-off keying (OOK), pulse position modulation (PPM) signal formats offer higher power efficiency and better noise immunity for a given bit error rate (BER); therefore, they are widely used in UWOC systems. In 2018, [PERSON] et al. reported a UWOC system using 256 PPM with a transmission rate of 1.7 MHz and a transmission distance of 120 m in Jerlov type II water [19]. In 2021, [PERSON] et al. demonstrated an 84 m/1 Mbps UWOC system using 256 PPM [20]. In 2023, [PERSON] et al. demonstrated an underwater wireless optical communication system based on 32 PPM modulation, achieving a communication distance of 35.64 m and a communication rate of 1.9 Mbps [21]. As such, PPM has become the modulation format selected by most researchers. In this paper, the outer cavity structure of the VECSEL light source is constructed based on the designed gain chip, followed by the measurement of the basic performance parameters of the VECSEL. Then, a 490 nm VECSEL-based UWOC system was designed. The system employed an AOM combined with the PPM to maximize the system's communication performance. The underwater channel was tested in a 6 m water tank in the laboratory, which was expanded to 108 m by a light reflector. A signal BER of 2.7 \(\times\) 10\({}^{-5}\) at the receiver side was obtained for a signal time-slot frequency of 50 MHz, and the baseband signal rate at this time was 4.69 Mbps. This result demonstrates that VECSEL-based and AOM-based systems have good transmission performance in long-distance underwater communication.