esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In conclusion, we presented a TOPCon optimization by applying the numerical tool, AFORS-HET, as well as an experimental method. On the base of the model, we examined the carrier selectivity towards passivated electron contacts overall. Our work furnished a comprehensive understanding of tunnel oxide, rear-SRV, doping density, as well as of the design of TOPCon solar cells. Our experimental results show that the higher J\({}_{\rm o}\) and iV\({}_{\rm oc}\) reached up to \(\sim\)1.1 fA/cm\({}^{2}\) and 738 mV, respectively. As far as the simulation is concerned, the high-quality tunnel oxide with a work function of 4.0 eV as well as a thickness of 1.2 nm is necessary for sensible carrier transport and field passivation. The champion results of a V\({}_{\rm oc}\) of 761.5 mV and J\({}_{\rm sc}\) of 43.5 mA/cm\({}^{2}\) were achieved with the novel TOPCon solar cell simulation. Representing a superior oxide layer with a low D\({}_{\rm it}\) range of 1 \(\times\) 10\({}^{8}\) cm\({}^{-2}\) eV\({}^{-1}\), as well as a low D\({}_{\rm ph}\) below 1 \(\times\) 10\({}^{-8}\), was essential for attaining the maximum simulated values. The higher concentration of doping in the nc-SiO\({}_{\rm x}\) layer was beneficial for ameliorating carrier-collection, which is useful as it enables a manufacture to be more tolerant of a thicker oxide. Furthermore, this is also promising for mass production. In a nutshell, it is possible to monitor the above-mentioned parameters,  
Figure 12: Effect of the oxide thickness (0.6–1.8 nm) and oxide work function (3.6–4.4 eV) on (**a**) V\({}_{\rm oc}\), (**b**) J\({}_{\rm sc}\), (**c**) FF, and (**d**) efficiency on the TOPCon solar cell.  
for instance, oxide thickness, doping concentration, rear-SRV, D\({}_{\text{ht}}\), and D\({}_{\text{ph}}\), which can then be controlled, therefore, we consider that a high-efficiency output is achievable. With will-designed manufacturing, TOPCon solar cells are moving closer to achieving their theoretically predicted efficiency.  
Conceptualization, M.Q.K. and E.-C.C.; formal analysis, S.Q.H. and M.A.Z.; investigation, M.Q.K. and D.P.P.; writing--original draft preparation, M.Q.K.; writing--review and editing, E.-C.C. and S.Q.H.; supervision, E.-C.C. and J.Y.; project administration, E.-C.C. and J.Y.; funding acquisition, J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by grants from the New and Renewable Energy Technology Development Program of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) funded by the Korean Ministry of Trade, Industry and Energy (MOTIE) (Project No. 20203030010310, 20213030010240).  
Not applicable.  
All the data available in the manuscript.  
The authors declare no conflict of interest.
# Wireless Channel Models for Over-the-Sea Communication: A Comparative Study
## 2 Key Concepts in Modeling Over-the-Sea Channels  
To design a channel model, a calculation of the propagation loss (PL) is highly important. It is also necessary to derive the correct fading margin. A radio channel designed for over-the-sea wireless communication may experience frequency selective fading, slow fading, and/or fast fading. Small-scale fading is due to the random phases and amplitudes of various multipath components. In addition, inter-symbol interference (ISI) causes signal blurring. Mathematically, it is possible to model multipath propagation using the channel impulse response [5]. Power delay profile (PDP) is also another important term to take care of. It is the intensity of a signal as a function of time delay in the multipath channel. Channel delay spread parameters, such as mean excess delay, maximum excess delay, and root mean-squared delay that can be extracted from PDP, are important features for designing a channel for a wireless transmission system. Moreover, atmospheric ducts play a prime role in influencing existing channel models. Before delving into the channel models, some important terms will be briefly discussed for the convenience of readers.
# Adaptive Energy Management in 5G Network Slicing: Requirements, Architecture, and Strategies
## 3 Energy Management Proposal for 5G Network Slicing
### Energy Management Strategies for Adaptive Consumption
#### 3.2.3 Degradation in Service Quality  
If the available power level is not sufficient to satisfy the power demanded by a certain service (or services), the MNO may choose to apply a _quality degradation_ to that service, i.e., allocate a lower amount of energy than requested, as observed for the service \(S_{4}\) in Figure 1b, in which the assigned power level is half of the demanded. This strategy allows reducing consumption to a level tolerable by the service, which allows maintaining its functionality. A descriptive example of the application of this strategy is the decrease in the brightness level of a screen of a mobile terminal or computer, which allows a lower consumption at the cost of a minimum decrease in performance. Another possible action that the MNO can execute on the services consists in the implementation of a combined strategy, in which _time shifting_ and _quality degradation_ are applied to the service simultaneously, as shown in the example in Figure 1b for service \(S_{2}\). On the other hand, considering that generation and consumption fluctuate constantly, during periods of energy surplus (due to high generation or low demand), one strategy that could be adopted is the increase in quality (i.e., deliver more energy of the demanded). Thus, the implementation of this strategy on services that tolerate the increase in energy supply would prevent the energy produced from being wasted if it is not consumed.
# Assisting Drivers at Stop Signs in a Connected Vehicle Environment
## 3 Types of Road Intersection Controlled by Stop Signs
### Three-Leg Road Intersections  
On downtown and rural road networks, these intersections are commonly seen. On these road networks, three-leg road intersections are created in one of two main architectural designs: T-junctions and Y-junctions. Stop signs can be installed on one or more input traffic flows to control safe traffic sharing of road intersections. In the rest of this section, we investigate all possible scenarios of installing stop signs at three-leg road intersections.
## 2 Background and Related Work
Several obstacles in mm Wave networks, such as user and vehicle mobility, beam reselections, mismatched beamforming weights from DL-based methods, and beam conflicts in co-located user devices, motivate further research. By integrating supervised learning with super-resolution technology, the authors accomplished beam and power allocation with minimal overhead. The DL-based approach analyzes the correlation between high-resolution and low-resolution beam images to forecast beam quality. Following this, a strategy for beam and power allocation based on DL is formulated, which permits precise allocation by utilizing a subset of the time-sequential low-resolution images that are currently accessible. The paper focused on theoretical and numerical evidence supporting the efficacy of this approach; however, further emphasis can be placed on generating precise quantitative outcomes or the practical implications for real world scenarios. Additional investigation would help to authenticate the feasibility and enhancements in the performance of this DL-guided allocation technique within mm Wave networks.
## 4 Conclusions  
This work investigated, for the first time, the possibility of exploiting both hazelnut pruning and shelling wastes as innovative sources to obtain cellulose nanocrystals and lignin nanoparticles, for reuse as sustainable tool for the control of hazelnut bacterial blight. The proposed lignocellulosic starting materials and the extraction protocols turned out to be a very promising starting point to increase the value of the whole hazelnut supply chain, in a context of circular economy. Cellulose nanocrystals were successfully synthesized by an acid hydrolysis after isolating the cellulosic component from hazelnut pruning shoots and shells through a chemical bleaching, while lignin nanoparticles were obtained thanks to a solvent-antisolvent method after a fractionation of the lignin component. In this work, the antimicrobial properties of the proposed lignocellulosic nanocarriers were investigated too. The conducted assays proved the ability of both cellulose nanocrystals and lignin nanoparticles of in vitro inhibiting the bacterial growth of _Xanthomonas arboricola_ pv. _corylina_ and, moreover, when in vivo applied, they were able to diminish the disease incidence in a comparable way to copper oxychloride. These results, together with the confirmed phytobiological compatibility, suggest the innovative use of the tested nanomaterials as sustainable nanospesticides development. Furthermore, other studies are already underway to better understand the biological mechanisms behind the promising bacterial inhibition activity shown in this work, giving to farmers of different chains sustainable alternatives to traditional agrochemicals.
## 4 Limit Performance Index
### Determined Limit Performance Index  
Wind load pushover analyses of 30 uncertain models (U1-U30) and 1 certain model (C1), each with varying ice thicknesses, are performed, and the displacement pushover curves of the tower top are obtained, as shown in Figure 7. Due to the uncertainty of material parameters and geometric dimensions, the maximum tower top displacement and ultimate wind speed of each model under different ice thicknesses are different, and with the increase in ice thickness, the ultimate wind speed gradually decreases. The pushover curve of the C1 model is basically located in the middle of the pushover curves of the U1-U30 models. Then, the limit performance index calculation formulas of different models are determined by linear regression fitting on maximum tower top displacement, as shown in Table 4. It can be seen that the fitting formulas have a strong correlation with the simulation results by calculating R\({}^{2}\). These formulas can be used to calculate the limit displacement of the tower top under different ice thicknesses. Once the displacement of the tower top exceeds this limit value, it indicates that the transmission tower fails. It should be noted that there is a large gap between the tower top displacement curves in Figure 7b, which occurs at a wind speed of 20 m/s. This is because, according to the Chinese code [43], the wind pressure non-uniformity factor and wind load adjustment factor used for calculating static wind loads differ significantly between wind speeds above and below 20 m/s. In contrast, due to the small ice thickness, this phenomenon did not occur in Figure 7a.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Case No.** & **The Calculation Formula** & **R\({}^{2}\)** & **Case No.** & **The Calculation Formula** & **R\({}^{2}\)** \\ \hline U1 & \(LS=-0.0056x+0.4167\) & 0.9923 & U16 & \(LS=-0.00596x+0.4256\) & 0.9400 \\ U2 & \(LS=-0.0051x+0.3919\) & 0.9499 & U17 & \
## 3 Motion Parameter Analysis of Propulsion Mechanism
\[2y_{o}=r(-2s\alpha c\beta c\gamma-2c\alpha s\gamma), \tag{7}\]  
Substitution of Equation (3) into Equation (7) results in  
\[c\beta(3c\alpha s\gamma+s\alpha c\gamma)+3s\alpha c\gamma=c\beta(-2s\alpha c \gamma)-3c\alpha s\gamma\]  
Then it can be deduced that for any nutation angle \(\beta\), the following relationships need to be satisfied. \[\left\{ \begin{array}{c}3\cos\alpha\sin\gamma=-3\sin\alpha\cos\gamma\\ 3\sin\alpha\cos\gamma=-3\cos\alpha\sin\gamma\end{array} \right.\]  
That is,  
\[\gamma=-\alpha, \tag{8}\]  
From Equation (8), the rotation angle of the installation platform around the \(z\)-axis is  
\[\varphi=\alpha+\gamma=0\]  
\[x_{o}=(r/2)\cos(2\alpha)(\cos\beta-1), \tag{9}\]  
\[y_{o}=(r/2)\sin(2\alpha)(1-\cos\beta), \tag{10}\]Because the length \(l_{1}\) between spherical joints \(S_{1}\) and revolution joints \(R_{1}\) is a fixed value, the following geometric relation need to be satisfied. \[\|R_{1}S_{1}\|=l_{1}, \tag{11}\]  
Let,  
\[A=r\Big{(}\cos^{2}\alpha\cos\beta+\sin^{2}\alpha\Big{)}+x_{o}-R\]  
\[B=r\sin\alpha\cos\alpha(\cos\beta-1)+y_{o}\]  
Then simultaneous Equation (11) and \(\mathbf{P}_{S1}^{S}\), one obtains  
\[z_{o}=r\cos\alpha\sin\beta-\sqrt{\left(l_{1}\right)^{2}-A^{2}-B^{2}}, \tag{12}\]  
From Equations (8), (9), (10), and (12), in the spatial attitude parameters of the propeller installation platform, \(\alpha\) and \(\beta\) are independent variables, the rest are all nonlinear functions with \(\alpha\) and \(\beta\).
# Research on Fault Detection and Automatic Diagnosis Technology of Water Hammer in Centrifugal Pump
###### Abstract
The collaborative work of cloud computing and edge computing not only improves diagnostic efficiency but also promotes the intelligent development of fault diagnosis. The main contributions of this article can be summarized as follows:  
1. A centrifugal pump water hammer monitoring method is proposed, which can effectively capture and record water hammer events, providing new ideas for practical engineering applications. 2. The introduction of edge layer and cloud collaborative data processing and transmission methods improves the efficiency of data processing and real-time transmission, providing reliable technical support for timely prevention and handling of water hammer events. 3. An intelligent water hammer monitoring and diagnosis system is designed, which has self-learning alarm thresholds and can automatically send alarm messages, achieving automatic fault diagnosis and reducing reliance on manual labor. The remaining sections of this paper are organized as follows: In Section 2, the mechanism and characteristics of water hammer faults are analyzed. In Section 3, the proposed monitoring method for water hammer faults is introduced in detail. In Section 4, the water hammer fault monitoring and diagnosis system is designed, including data acquisition and scheduling methods. Section 5 is about experimental verification. Section 6 is the conclusion of this study.
## 4 Discussion
### Impact Analysis of Drivers  
LAI was the dominant factor and exhibited the strongest explanatory power for the spatial differentiation of vegetation GPP on the Qinghai-Tibet Plateau in 2001, 2006, 2011,2022, and overall period from 2001 to 2022. In contrast, ET was the dominant factor in 2016. LAI provided the strongest explanation for the spatial variability of vegetation GPP on the Qinghai-Tibet Plateau. The spatial variability of annual vegetation GPP on the Qinghai-Tibet Plateau was not governed by a single, absolute factor, primarily because the factors influencing the spatial and temporal characteristics of vegetation GPP varied regionally and evolved over time. The explanatory power of temperature and the saturated water vapor pressure difference was weaker than that of LAI, exhibiting a slow, gradual increase over the years. Although these factors had an increasing influence on GPP, their explanatory power for the spatial distribution of GPP was generally weaker than that of LAI. In the GPP model, LAI was the most critical driver input [49; 50].  
The dominant interaction factors were ranked, with the most dominant being the combination of the dominant single factor and another key factor influencing GPP in each corresponding year. The distribution of the strongest dominant interaction factors on the Qinghai-Tibet Plateau for the years 2001, 2006, 2011, 2016, 2022, and the entire period from 2001 to 2022 were as follows: LAI \(\cap\) VPD, LAI \(\cap\) Temperature, LAI \(\cap\) Precipitation, ET \(\cap\) LAI, ET \(\cap\) LAI, and LAI \(\cap\) VPD. The explanatory power of these dominant interaction factors in each of those years was 0.9032, 0.7806, 0.90.71, 0.9801, 0.9152, and 0.9051, respectively, with the explanatory power exceeding 0.50, indicating a significant synergistic effect between them. The dominant interaction factors for the six representative years exhibited some variation. For the years 2001, 2006, 2011, and the full period 2001-2022, the combinations of LAI and other factors were dominant, indicating that LAI played a key role in interactions with other factors such as VPD, air temperature, and precipitation. Following 2011, the two-way synergy between ET and LAI primarily affected the spatial variations of GPP [51].
## 4 Modeling the SMA-LRB Isolator
### The Hysteresis Model of SMA Wires  
[PERSON] and [PERSON] [7] calculated the axial force of the wires at different levels of shear strain instead of modeling the connection in detail. By specifying the geometrical properties of the isolator and wires, the strain was found for each time step. With the help of the constitutive model of SMA, this strain was converted into the axial stress of the wire. Finally, the axial force was expressed in terms of time and applied to the LRB isolator as an external force. In [7], this process is presented in detail. To find the hysteresis curves of the SMA wire, a MATLAB function was developed by the authors based on the presented algorithm of [PERSON] and [PERSON]. The results obtained from the written code (PCODE) are illustrated in Figure 6 and compared with the source data (MCODE) for various loadings (E5, E6, E7, and E8). It should be noted that as [PERSON] and [PERSON] [7] pointed out, the shear hysteresis of the SMA wires is not the same as the flag-shaped hysteresis of the SMA, due to the effect of the wire's configuration on the transferred force to the LRBs. Moreover, the developed hysteresis model by [PERSON] and [PERSON] [7] is independent of the strain rate effect, and further research needs to be conducted to investigate the influence of strain rate on the hysteresis of SMA-LRBs.
###### Abstract  
Plasmid DNA (pVAX1-NH36) was encapsulated in nanoparticles of poly-n-l-lactic-co-glycolic (PLGA) functionalized with polyethylene glycol (PEG) and folic acid (PLGA-PEG-FA) without losing integrity. PLGA-PEG-FA nanoparticles loaded with pVAX1-NH36 (pDNA-NPs) were prepared by using a double emulsification-solvent evaporation technique. PLGA-PEG-FA synthesis was verified by FT-IR and spectrophotometry methods. pVAX1-NH36 was replicated in _Escherichia coli_ (_E. coli_) cell cultures. Atomic force microscopy (AFM) analysis confirmed pDNA-NPs size with an average diameter of 177-229 nm, depending on pVAX1-NH36 loading and zeta potentials were below \(-24\) mV for all preparations. In vitro release studies confirmed a multiphase release profile for the duration of more than 30-days. Plasmid release kinetics were analyzed with a release model that considered simultaneous contributions of initial burst and degradation-relaxation of nanoparticles. Fitting of release model against experimental data presented excellent correlation. This mathematical analysis presents a novel approach to describe and predict the release of plasmid DNA from biodegradable nanoparticles.  
drug delivery; pVAX1-NH36; mathematical analysis; copolymer synthesis; leishmaniasisiasis; folate nanoparticles +
Footnote †: journal: Applied Physics Letters  
Article
## 3 Data Routing Approaches in WSNs
### Energy-Efficient Approaches
#### 3.1.2 Energy Consumption Reduction
GAF is a location-based approach but since the nodes are organized in several partitions (like clusters) and there are some leaders in each partition to transmit data to other nodes (like cluster heads), it can be considered as a hierarchical approach, too. Figure 4: Recursive Geographic Forwarding Algorithm of GEAR (adapted from [1]). In the routing process of GAF, the cost of the nodes with the same points considered equal and they have assumed equivalent. Hence, several equivalent nodes can sleep during the routing process which can conserve considerable energy. Figure 5 depicts an example of this model. Any of nodes 2, 3, 4 and 5 can be the next hop for nodes 1. Thus, nodes 2, 3, 4 and 5 are equivalent and the non-selected nodes can sleep. GAF defines tree states in its algorithm: _discovery_, _active_, _sleep_. In the _discover_ state, the nodes determine their neighbors in the grid, the _active_ state illustrates that the node is involved the routing process and the _sleep_ state means that the radio of the nodes is turned off. In this approach, the reliable mobility of the nodes is also supported. Each active node calculates the time in which it leaves the grid and sends a notification to its neighbors to inform them about the time. Thus, before the active node leaves, one of the sleeping nodes goes into active state to be replaced with the previous active node. For example, in Figure 5, if we consider node 2 as the active node which is going to leave its related area, before it leaves the area, any of nodes 3, 4 or 5 can replace it.
## 6 Comparison with the Classical PD and PID Controllers
### Comparison with the Classical PID Controller
\begin{array}{l}\
u_{s}\ddot{\phi}=\Delta u-a_{*}\sin(\theta_{d}) \phi-f_{v}^{*}\dot{\phi}-f_{s}^{*}\text{sgn}(\phi)-a_{*}(-\cos(\theta_{d})+ \sin(\theta_{d})\phi+\cos(\phi+\theta_{d}))+\Delta_{t}\\ \dot{\phi}_{\chi}=\phi\end{array} \right. \tag{112}\]  
The nonlinear dynamic system (112) can be reformulated under the dynamical model (22) where in the present case, the matrices \(\mathcal{A}\), \(\mathcal{B}\), \(\mathcal{D}_{1}\), and \(\mathcal{D}_{2}\) are defined like so:  
\[\mathcal{A}=\left[ \begin{array}{ccc}0&1&0\\ -\frac{a_{*}}{\
u_{*}}\sin(\theta_{d})&-\frac{f_{*}^{*}}{\
u_{*}}&0\\ 1&0&0\end{array} \right],\quad\mathcal{B}=\left[ \begin{array}{c}0\\ \frac{1}{\
u_{*}}\\ 0\end{array} \right],\quad\mathcal{D}_{1}=\left[ \begin{array}{c}0\\ -\frac{f_{*}^{*}}{\
u_{*}}\\ 0\end{array} \right],\quad\mathcal{D}_{2}=\left[ \begin{array}{c}0\\ \frac{a_{*}}{\
u_{*}}\\ 0\end{array} \right] \tag{113}\]  
and where the two quantities \(\Phi_{1}(\mathcal{X})\) and \(\Phi_{2}(\mathcal{X})\) are defined by expressions (23a) and (23b), respectively. Thus, by employing the affine state-feedback controller, we can obtain a controller dynamic model structure similar to that of the previous work. The distinction is found in the dimensions of the gain \(\mathcal{K}\) and the many matrices that define the two nonlinear models. As a result, we will use the same LMI design methodology and, as a result, derive the identical LMIs as those found in Theorems 1-4. We will only take into consideration the outcomes generated in Section 5.2.4 in the sequel, and we will compare them with the PID controller and the LMI-based minimization issue produced in Theorem 4. **Remark 4**: _The affine state-feedback controller (viewed as an affine PD controller) offers advantages over the PID controller in specific scenarios, including better adaptation to nonlinear systems, improved static error suppression, faster disturbance response, and reduced reliance on integration. For example, [68] demonstrated the superiority of a PD controller over a PID controller in neural network tracking control for a single flexible link. Similarly, [69] showed that PD control outperforms PID in set-point tracking. However, the choice between the two depends on system characteristics and performance requirements. In this study, the advantages of the affine state-feedback (PD) controller over the PID controller are discussed further in the next section._