esa-sceva/satcom-chunk-collection huggingface dataset:
# Comprehensive Review of RF MEMS Switches in Satellite Communications
## 2 Advanced Technologies and Mechanisms in RF MEMS Switches
### Actuation Mechanism
#### 2.1.2 Piezoelectrically Actuated RF MEMS Switches  
Figure 1b shows the structure of a typical piezoelectrically driven RF MEMS switch. In these switches, piezoelectric materials are applied to the top of the beam. Under voltage, these materials produce elongation and strain along the entire piezoelectric layer, thereby driving the beam membrane. The magnitude of the actuating force depends on the piezoelectric coefficient, and low actuation voltages can be achieved by choosing materials with high piezoelectric coefficients [26].  
Common piezoelectric materials in MEMS devices are AlN [32] and PZT [33; 34]. PZT, a ferroelectric material, has a spontaneous polarization that can be permanently altered by an electric field, i.e., by changing the orientation of dipoles to produce a hysteresis loop to the electric field (or voltage) [35]. Compared to PZT, AlN has the advantage of compatibility with current CMOS processes and lower leakage current at the same film thickness [32]. Additionally, AlN has a lower dielectric constant, meaning lower energy consumption for a given actuator size [26]. Although PZT has a higher piezoelectric coefficient, high-quality, reproducible thin films have always been a technical challenge. [PERSON] et al. [33] designed and integrated a PZT-based RF MEMS switch into CMOS technology, demonstrating the BEOL (Back End of Line) CMOS compatibility of this integration technique. The switch's actuation voltage is 15 V, with an insertion loss of less than 0.5 dB and isolation better than 30 dB in the frequency range of 0.4 to 6 GHz.
## 2 Results
### Association of PRS with Amyloid-\(\beta\) and Tau Proteins in Cerebrospinal Fluid  
We examined the impact of genetic factors on the levels of A\(\beta\)40, A\(\beta\)42, A\(\beta\)42/A\(\beta\)40, tTau, and pTau181 in CSF obtained from 57 dementia patients. Linear regression was used to determine the association, where PRS and the presence of _APOE-e4_ were considered as predictors (Table 3). Dependent variables were logarithmically transformed (In) for this analysis, allowing the coefficients to represent the percentage change in the concentration of the studied CSF markers (when multiplied by 100). The level of A\(\beta\)40 was not affected by the studied genetic factors, while the concentration of A\(\beta\)42 was lower in the carriers of _APOE-e4_. The carriers of _APOE-e4_ and those with higher PRS had a lower A\(\beta\)42/A\(\beta\)40 ratio. The levels of both tTau and pTau181 exhibited a positive correlation with the presence of _APOE-e4_ and PRS. Figure 3 depicts the relationship between CSF biomarkers and the presence of _APOE-e4_ and PRS.  
It should be noted that individuals carrying the \(\varepsilon 2\) allele exhibited a 35.3% higher A$42/A$40 ratio compared to those without the allele (\(p=0.006\)). The presence of this allele did not result in statistically significant differences in other CSF markers.
# Application of Augmented Reality in Waterway Traffic Management Using Sparse Spatiotemporal Data
###### Abstract  
The development of China's digital waterways has led to the extensive deployment of cameras along inland waterways. However, the limited processing and utilization of digital resources hinder the ability to provide waterway services. To address this issue, this paper introduces a novel waterway perception approach based on an intelligent navigation marker system. By integrating multiple sensors into navigation markers, the fusion of camera video data and automatic identification system (AIS) data is achieved. The proposed method of an enhanced one-stage object detection algorithm improves detection accuracy for small vessels in complex inland waterway environments, while an object-tracking algorithm ensures the stable monitoring of vessel trajectories. To mitigate AIS data latency, a trajectory prediction algorithm is employed through region-based matching methods for the precise alignment of AIS data with pixel coordinates detected in video feeds. Furthermore, an augmented reality (AR)-based traffic situational awareness framework is developed to dynamically visualize key information. Experimental results demonstrate that the proposed model significantly outperforms mainstream algorithms. It achieves exceptional robustness in detecting small targets and managing complex backgrounds, with data fusion accuracy ranging from 84.29% to 94.32% across multiple tests, thereby substantially enhancing the spatiotemporal alignment between AIS and video data.  
object detection; time-delay fusion; maritime safety; augmented reality; high-density waterway; traffic monitoring; ship-shore coordination Article
# Effects of Long-Term Fertilization on Phosphorus Form and Availability in Black Soil
###### Abstract  
This study explored the effect of the combined application of chemical and organic fertilizers on phosphorus morphology and its conversion to an active state. A long-term field positioning experiment comprising five treatments was conducted in black soil. The results concluded that the soil total phosphorus (TP), available phosphorus (AP), inorganic phosphorus, and organic phosphorus contents of all treatments ranked as follows: 1.5M1 NPK \(>\) M2 NPC \(>\) M1 NPK \(>\) NPK \(>\) CK. The long-term application of chemical and organic fertilizers increased the proportion of soil reactive phosphorus and moderately reactive phosphorus but decreased the proportion of mildly active phosphorus and residual phosphorus. A phosphorus-31 nuclear magnetic resonance (\({}^{31}\)P NMR) spectral analysis showed that the contents of orthophosphate, pyrophosphate, phosphoric acid diesters, and orthophosphate acid monoesters increased with the application of chemical and organic fertilizers, of which 1.5M1 NPK usually resulted in the highest increases. In conclusion, the long-term application of chemical fertilizers could promote the conversion of soil phosphorus into active phosphorus and improve the effectiveness of soil phosphorus, and the long-term use of organic and chemical fertilizers was more effective than the use of chemical fertilizers only, with 1.5M1 NPK providing the best effects.  
long-term fertilization; black soil; phosphorus form; \({}^{31}\)P NMR +
Footnote †: journal: applied sciences
# Embedded Based Real-Time Monitoring in the High-Pressure Resin Transfer Molding Process for CFRP
## 2 High-Pressure RTM
### Monitoring System Architecture  
Figure 5 shows the hardware schematic representation of the embedded system for real-time condition monitoring of the HP-RTM. The embedded monitoring system consists of a single board signal processor (sbRIO-9637, National Instruments, Austin, TX, USA), an embedded computer (MIO-5272U, Advantech, Taiwan), amplifiers, and filters. The single-board signal processor is equipped with a Zynq-7020 FPGA and acquires signals with 16 16-bit analog inputs and 28 digital channels. The embedded computer with an Intel Core i7 2.6 GHz processor and 4 GB DDR3 SDRAM was adopted to configure the Human-Machine Interface (HMI). The sensors used in the HP-RTM process are acquired on 16 analog channels in total with a maximum 200 kHz sampling rate, and the operation signals (Range selection and Start operation) of the cavity pressure sensors are controlled using two digital channels. Analog channels are used to monitor the pressure and temperature at the injection of the resin and hardener, the cavity pressure and temperature inside the mold, and the end-gap of the mold. The cavity pressure and temperature sensors installed in the mold use a separate amplifier in order to amplify the signal and the other sensors use built-in amplifiers.  
Each signal was transmitted using an analog-to-digital converter via a filter such as high-pass filter (HPF) or low-pass filter (LPF) for noise reduction. The collected signals were used to monitor the states of the HP-RTM process by FPGA-based real-time embedded monitoring system.  
Figure 4: Sensor layout: (**a**) The HP-RTM equipment; (**b**) Mold.
## 4 Conclusions  
In summary, we prepared the carbon quantum dots-MSN with different sizes (15, 100, 200 nm) to deliver pesticide in plant. It was found that the loading content increased as the particle size increased, which was related to the specific surface area, pore volume, and pore diameter of the nanoparticles. The carbon quantum dots-MSN as carriers increased the solubility of insoluble pesticide, and the nanoparticle size had no clear difference impact on the release rate. Meanwhile, uptake is particle-size-dependent and the smaller the Pyr@M size, the easier it is to be absorbed and transmitted by the roots and to be quickly transferred to other parts of the cucumber plant. This information guides the design of delivery systems based on MSN in target crops. In addition, the pathway and mechanism of nano-pestico vary according to plant species, pesticide category, and application method.  
It is worth discussing that nanomaterials might be potentially toxic [30; 44; 45]. There was a correlation between toxicity and particle size, however, the smaller particle size of MSN would make it unlikely to enter the cells and cause more toxic effects. In a future study, it is more important to figure out the human and animal toxicity of these kinds of products.  
The following are available online at [[https://www.mdpi.com/2076-341](https://www.mdpi.com/2076-341)]([https://www.mdpi.com/2076-341](https://www.mdpi.com/2076-341)) 7/11/2/575/s1. Figure S1: SEM of Pyr TC.  
Conceptualization, P.Z. and M.J.; methodology, Y.X. and C.X.; Synthesis, C.X. and Y.X.; investigation, Y.X. and F.T.; data curation, Y.X.; writing--original draft preparation, Y.X.; writing--review and editing, P.Z., Q.H. and M.J.; supervision, P.Z.; project administration, Q.H.; funding acquisition, L.C. and Q.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research Development Program of China (2017 YFD0200300), National Natural Science Foundation of China (NSFC) (No. 31701828), Agricultural Science and Technology Innovation Program (CAAS-ZDRW202008), and Program of New Type Institution of Scientific Research (12 ZCZDSY13900).  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.  
Figure 8: Translocation rate of Pyr from cucumber root to stem and leaf in certain intervals. \(p<0.05\) compared between different treatments.
## 2 Isolation Structure and Electromagnetic Force  
The structure of the electromagnetic vibration isolation system is shown in Figure 1, including a spring, two displacement sensors and two electromagnets locate in the spring in order to save space of the isolation system. Some precision instruments might be affected by the motor inside and the disturbance from the external environment. When two electromagnets are passed currents with the identical size but opposite direction, the electromagnetic repulsion force will be generated to attenuate vibration and keep the stability for the isolated object. Due to the fact that two electromagnets connect with spring in parallel, the electromagnetic repulsion force and the elastic force of the spring attenuate vibration together. The changes of displacement produced from the vibration are recorded by two displacement sensors. With the values of displacement, the electromagnetic repulsion force will be transferred to the plate and apply to the vibration source by equivalent stiffness and damping coefficient to control currents.  
Due to the fact that the relationship between electromagnetic force and the gap and coil current with nonlinear characteristics, the electromagnetic vibration isolation system is complex to control. For acquiring the nonlinear relationship between electromagnetic force and the gap and coil current, two electromagnets are simulated by COMOSOL Multiphysics when they passed currents with the identical size but opposite direction. There are two cylindrical electromagnets with identical size and parameters in COMSOL Multiphysics. Both two electromagnets are 65 mm in diameter and 30 mm in height. The iron core of the electromagnetism's 36 mm in diameter and 30 mm in height. The electromagnet has 2700 rings and the diameter of the coil is 0.53 mm.  
Two electromagnets with a 2D axisymmetric model are established by COMOSOL Multiphysics. The COMOSOL Multiphysics model of two electromagnets is meshed based on free triangular. With the established COMSOL model of two electromagnets, the electromagnetic force can be calculated at any gap and electric current in COMSOL simulation. Based on the COMSOL simulation results, the nonlinear relationship between the electromagnetic force \(f_{e}\) and the gap \(y\) and coil current \(i\) can be fitted as shown in Equation (1). Equation (1) is obtained by data fitting from COMSOL simulation and verified in experiments which can be referred to [26]. Due to the simulation results in size and varied trend are mainly consistent with the experimental results, the fitting result in Equation (1) is utilized based on the simulation results in this paper:  
\[\begin{split} f_{e}(y,i)&=8259.7863*y^{2}+2.4055*i^{ 2}-61.9546*y\\ &+3.1128*i-311.2811*y*i-0.5093\end{split} \tag{1}\]
## 2 Methodology
### Drug Release and Kinetics  
The in vitro drug release study was performed using a customized horizontal glass diffusion tube and cellulose dialysis membrane (MWCO 12-14 kDa, Spectra/por(r) Spectrum Laboratories, Inc., Rancho Dominguez, Berkeley, CA, USA). The tubes with 0.5 g of gel were attached to the dissolution apparatus and was allowed to stir at 100 rpm in 250 mL of phosphate buffer (pH 7.4) maintained at 37 \(\pm\) 0.5 \({}^{\circ}\)C. Samples were collected at predetermined time intervals (5, 15, 30, 45, 60, 120, 180, 240, 300 and 360 min) and replaced with equal volumes of fresh buffer solution. Samples were filtered using syringe membrane filter (0.2 \(\upmu\)m, Millipore Corporation, Bedford, MA, USA) and readily analyzed by UV-Vis spectrophotometer ([PERSON], 6205, spectrophotometer, UK) at 214 nm. A blank experiment was carried out under similar experimental condition without the drug [25]. A control experiment was carried out with insulin solution.  
The in vitro release percentages were examined using different kinetic models in order to explain the mechanism of drug release from prepared formulations. The following equations for Zero order, First order and Higuchi diffusion model were applied to find out the mechanism of insulin release [27]. If R\({}^{2}\) is the greatest for a certain model, then release kinetics is best fitting to this model.  
Zero order \"A plot of drug concentration% against time\".  
\[\text{C}=\text{K}_{0}\text{t} \tag{2}\]  
where C is the insulin concentration released at time t and K\({}_{0}\) is the zero order release constant.  
First-order \"A plot of log drug concentration% against time\".  
\[\text{Log}\text{C}=\text{Log}\text{C}_{0}-\text{k}_{1}\text{t}/2.303 \tag{3}\]  
where, C\({}_{0}\) is the initial insulin concentration and K\({}_{1}\) is the first-order release rate constant.  
Higuchi diffusion model \"A plot of drug concentration against square root of time\".  
\[\text{Q}=\text{K}_{\text{H}}\text{t}^{\circ.5} \tag{4}\]  
where, Q is the cumulative amount of the drug, K\({}_{\text{H}}\) is the Higuchi dissolution constant and t\({}^{\circ.5}\) is the square root of the time.
# Improvements in Rock Mass Description for Stope Design by Geophysical and Geochemical Methods
## 5 Ground-Penetrating Radar (GPR) for Representation of Fracture Surface Geometry
### Results and Discussion  
The photogrammetrically obtained fracture surface geometry was compared to the results from the GPR measurements. Both datasets were downsampled to 10 mm in the X and Y directions, and the Z values were compared point by point. Initial mean differences in the Z values were between 6.10 and 10.55 mm, with standard deviations of between 2.84 and 3.55 mm (Figure 9, left). This level of accuracy is representative of the general performance of the method using locally sourced relative dielectric permittivity values for  
Figure 8: An example of the photogrammetric model of the mapped fracture surface (grey) and fracture locations as interpreted from the GPR survey, downsampled to 10 mm (red). Close up of the bottom half of slab RAKKA-1.  
Figure 7: Example of the obtained photogrammetric model of the bottom half of slab RAKKA-1. The opened fracture surface is facing up. The origin of the coordinate system is at the top side corner at the bottom of the image. Distance markers can be seen in the corners.  
calibration. To account for uncertainty in the relative dielectric permittivity values, the GPR measurements were then calibrated with a constant shift using the median difference values rounded to 1 mm. This resulted in mean differences in the Z values of between 2.33 and 2.70 mm, with standard deviations of between 1.70 and 2.34 mm (Figure 9, right). This in turn is representative of the performance of the model with respect to small-scale variations. It was thus shown that, with proper calibration, GPR can accurately map the geometry of the fracture surface to the millimeter level.
# Design of Novel Laser Crosslink Systems Using Nanosatellites in Formation Flying: The VISION
## 3 Concept of the Laser Crosslink Systems
### Optical Link Budget  
There are several power penalty sources: pointing loss, optics loss, fiber coupling loss, electronics degradation, and sensor noise. The SNR margin should be higher than 10 dB to guarantee the optical link performance of the VISION systems. The optical link budget analysis process refers to the case studies of CLICK-B/C [14].
# Sustaining Rice Production through Biofertilization with N\({}_{2}\)-Fixing Cyanobacteria
## 3 Results and Discussion
### Plant Growth Promotion in Field Trials
To address this, further studies will be carried out in the future. From a commercial point of view, there is little information regarding commercial byproducts or biofertilizers based on cyanobacterial formulations. Biofertilizers are cost-effective, non-toxic, and easy to apply [36]. If that were the case for cyanobacterial biofertilizers, their use would not only reduce production costs, but would also provide ecosystem goods and services and ensure a more sustainable agricultural system. **Author Contributions:** Conceptualization, V.M. and F.P.M.-H.; methodology, M.I.-P., C.A., F.M.G.-C., C.R.-M., and P.A.-M.; data curation, M.I.-P. and C.A. ; writing--original draft preparation, V.M. ; writing--review and editing, all authors; funding acquisition, V.M. and F.P.M.-H. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Corporation Tecnologica de Andalucia (grant BFE14300) and by the Fundacion de Investigacion de la Universidad de Sevilla (Spain) (grant FIUS05710000). [PERSON] is a recipient of a predoctoral contract from the University of Seville (VI PPIT-US). Figure 5: Effect of biofertilization with the cyanobacterial consortia in rice field trials. (**A**) Length of aerial part of rice plants measured at different plant developmental stages. T-Student test revealed a significant difference between treated and non-treated plants at 4 months (\(p\) = 1.22 \(\times\) 10\({}^{-26}\)). (**B**) Plants collected at 4 months after planting were photographed. The same number of plants are shown in each treatment. (**C**) Effect of the cyanobacterial biofertilizer on the number of grains per panicle. T-Student reveals a significant difference between treated and non-treated plants (\(p\) = 3.79 \(\times\) 10\({}^{-6}\)). (**D**) Effect of the cyanobacterial biofertilizer on the weight of the panicle. T-Student reveals a significant difference between treated and non-treated plants (\(p\) = 4.93 \(\times\) 10\({}^{-13}\)). **Institutional Review Board Statement:** The study was conducted according to the guidelines of the Nagoya protocol for the access to Spanish genetic resources for research use under royal decree 124/2017 of 24 February. Reference: ESNC26. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available upon reasonable request from the corresponding author. **Acknowledgments:** We thank [PERSON], [PERSON], [PERSON], and [PERSON] for their collaboration in the preparation of the bioinoculant and sampling. **Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Linear and Nonlinear Methods
### Artificial Neural Network Approach
#### 2.2.2 LSTM  
The long short-term memory (LSTM) is an artificial recurrent neural network structure [37]. Different from feedforward neural networks, it has feedback connections that can consider the memory effect of time series. Cyclic connections are used to acquire short-term memory and capture information from sequences of data. Figure 2 shows the structure of a memory cell of an LSTM network.  
Apart from the cell, the LSTM network has three gates, namely, an input gate, an output gate, and a forget gate. The cell remembers values over abitrary intervals and the three gates regulate the flow of information into and out of the cell. More specifically, the transfer of input and output activations is controlled by the input gate and output gate, respectively. The forget gate enables the LSTM to update the interior state adaptively. The processes of updating the cell state and calculating the output of LSTM can be given as  
\[f_{t} =\sigma_{g}(W\mathbf{\rho}x_{t}+U\mathbf{\rho}h_{t-1}+b_{t}), \tag{5}\] \[i_{t} =\sigma_{g}(W_{i}x_{t}+U\mathbf{i}h_{t-1}+b_{i}),\] (6) \[o_{t} =\sigma_{g}(W_{o}x_{t}+U_{o}h_{t-1}+b_{o}),\] (7) \[c_{t}^{\prime} =\sigma_{h}(W_{c}x_{t}+U_{c}h_{t-1}+b_{c}),\] (8) \[c_{t} =f_{t}\odot c_{t-1}+i_{t}\odot c_{t}^{\prime},\] (9) \[h_{t} =o_{t}\odot\sigma_{h}(c_{t}), \tag{10}\]  
Figure 1: The basic architecture of the BPNN.  
Figure 2: Structure of an LSTM memory cell.  
where \(\mathbf{x_{t}}\) refers to input vector to the LSTM cell; \(\mathbf{i_{t}},\mathbf{f_{t}}\), and \(\mathbf{o_{t}}\) represent activation vectors of the input gate, forget gate, and output gate, respectively; \(\mathbf{h_{t}}\) denotes the hidden state vector also known as the output vector of the LSTM unit; and \(\mathbf{c^{\prime}_{t}}\) and \(\mathbf{c_{t}}\) represent the cell input activation vector and cell state vector, respectively. Additionally, \(\mathbf{W}\) and \(\mathbf{U}\) stand for the weight matrices; \(\mathbf{b}\) is the corresponding bias vector parameter, \(\mathbf{\sigma}_{g}\) is the logistic sigmoid function, \(\mathbf{\sigma}_{h}\) is the hyperbolic tangent function, and \(\odot\) denotes the Hadamard product (element-wise product).
## 4 System Model
### Successful Transmission  
In the current scenario, successful transmission occurs when a packet which is generated from the source is successfully received at the sink node. In this case, if a packet is transmitted from the initial node, it follows different paths to reach a destination. During transmission process, packet moves from one depth to other and one layer to other. However, the direction of the packets is the sink node.  
Figure 1: Proposed system model.