esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### EM Properties of PP/GNP Nanocomposite  
The variation of real (\(\epsilon_{r}\)) and imaginary (\(\epsilon_{r}\)\({}^{\prime\prime}\)) parts of relative permittivity for different weight percentages of PP/epoxy, PP/GNP/epoxy in the X-band are presented in Figure 7a and b, respectively. The real permittivity of the graphene/epoxy and PU/graphene/epoxy composites remains almost constant with the increasing frequency in the X-band represented in Figure 7a. At very high frequencies, this value becomes constant, which means it is almost independent of frequency. The real permittivity of the GNP-PP-epoxy composite varies from 3.9 to 6.2 in the X-band region. Maximum permittivity values are obtained for 2.7% of the PP/GNP/epoxy composite among all graphene composites in the 8.2-12.4 GHz range. This is due to increased electrical conductivity compared to the PP/epoxy composite because of GNPs and electronic and atomic polarization in the composite material. Figure 7b shows the variations of imaginary parts of permittivity (\(\epsilon_{r}\)\({}^{\prime\prime}\)) as a function of frequency at room temperature. The imaginary parts of all graphene composites are 0.6 to 1.2 in the X-band. There is a nearly constant value of \(\epsilon_{r}\)\({}^{\prime\prime}\) in the PP/graphene/epoxy composites with an increase in frequency, and 2.7% of PP/graphene and epoxy composites exhibit approximately 1.2 in the entire frequency range. The decrease in the value of \(\epsilon_{r}\)\({}^{\prime\prime}\) with frequency occurs when the jumping frequency of electric charge carriers cannot follow the alteration of the electric field applied beyond a particular frequency.  
The permeability of the PP/epoxy and PP/GNP/epoxy is shown in Figure 7c and d, respectively. Microwave absorption can be altered using magnetic permeability, which  
Figure 6: TEM analysis of (**a**) GNP and (**b**) GNP/PP composite.  
stems from natural resonance, exchange resonance, magnetic hysteresis, domain wall displacement, and eddy currents. The complex permeability of graphene/epoxy composites is very low (of the order of 0.3-1.0) because of the diamagnetic nature of graphene.
## 4 Computer Simulation Experiments
### Algorithm Precision Analysis
#### 4.2.2 Weak Signal Environment Evaluation  
The simulation signal C/N\({}_{0}\) was set to 18 dB-Hz for the simulation of a weak signal environment with a simulation time of 10 min. Figure 8 presents the simulation results of the ASTKF C/N\({}_{0}\) estimation method in this weak signal environment. Table 4 presents a statistical comparison of the C/N\({}_{0}\) estimation results.  
Figure 8: Simulation weak signal C/N\({}_{0}\) estimation results: (**a**) 0.5 s averaging time; (**b**) 1 s averaging time; (**c**) 3 s averaging time; and (**d**) 5 s averaging time.  
\begin{table}
\begin{tabular}{c} **Std** \\ **(dB-Hz)** \\ \end{tabular} \\ \hline
**NWPR** & 52.47 & 1.22 & 52.42 & 1.05 & 52.35 & 0.71 & 52.32 & 0.52 \\
**VSM** & 52.54 & 1.17 & 52.49 & 1.00 & 52.42 & 0.67 & 52.40 & 0.50 \\
**ASTKF** & 52.58 & 0.15 & 52.58 & 0.15 & 52.57 & 0.14 & 52.57 & 0.14 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Strong signal C/N\({}_{0}\) estimation statistics.  
According to Figure 8 and Table 4, in the weak signal environment, the C/N\({}_{0}\) estimation error is larger than that in the strong signal environment. However, compared with the NWPR and the VSM, the estimation results of the ASTKF estimation method are more stable, which is consistent with the simulation results in the strong signal environment. Similarly, the estimated standard deviation of ASTKF using an averaging time of 0.5 s is smaller than that of NWPR with an averaging time of 5 s and that of VSM with an averaging time of 3 s. Therefore, regardless of the strength of the received satellite signal, the ASTKF estimation method can use a shorter averaging time than the NWPR and the VSM.  
In addition, comparing Tables 3 and 4, in the weak signal environment, increasing the averaging time can significantly improve C/N\({}_{0}\) estimation accuracy. Therefore, for receivers that are operating in special environments (such as indoor environments), the averaging time should be extended suitably.
## 3 Results
### Joint Angle Analysis
#### 3.2.1 Push Task  
The joint angular displacements were analyzed at the pose where the object began moving forward, and participants were subjected to the maximum compressive force. Except for the Elbow_F/E (\(p\) = 0.49, F = 0.47) in Figure 5, all other joint displacements were significantly different between the Real_HM and Predicted_HM (\(p\)! 0.0001 for the four joints: Knee_F/E (F = 21.23), Hip_F/E (F = 118.09), Sh_F/E (F = 36.04), and Sh_Abd/Add (F = 87.0); \(p\) = 0.012, F = 7.21 for the Trunk_F/E). It should be noted that, although the comparison of Knee_F/E was significantly different, the average difference was only 3.6\({}^{\circ}\), which represents 2.6% of the maximum joint excursion. For all the other joints with significant difference displacement, the average values in Predicted_HM were greater than the Real_HM. Interestingly, we observed that for the Sh_Abd/Add joint, while we recorded an abduction movement in the Real_HM, the Predicted_HM movement was an adduction.  
When comparing genders, only the Sh_F/E was significantly different (\(p\) = 0.04) in the Real_HM. However, in the Predicted_HM, there were four joints with significantly different displacements: the Knee_F/E (\(p\) = 0.01), Trunk_F/E (\(p\) = 0.049), Hip_F/E (\(p\) = 0.01), and Sh_F/E (\(p\) = 0.02). Although the comparison of the Knee_F/E was significantly different, the average difference was only 1.0\({}^{\circ}\), which represents 0.7% of the maximum joint excursion. Female subjects had significantly greater shoulder flexion than males in both Real_HM and Predicted_HM and significantly less trunk and hip flexion than males in the Predicted_HM.  
For the cross-correlation analysis, there was a weak positive correlation between the Trunk_F/E and Hip_F/E for male participants with R = 0.3, but a moderate negative correlation between the trunk and hip for females with R = \(-\)0.64 in the Real_HM. In the Predicted_HM, there was a strong relationship between these two aforementioned variables for males and females with Rs of 0.97 and 0.92.
# Efficient Position Change Algorithms for Prolonging Driving Range of a Truck Platoon
## 4 Performance Analysis and Simulation Results
We run the STS algorithm ten times and calculate the averages of total distance and position change count over \(n=2\) to 6. Figure 4(a) shows the average increased driving distance in percentage compared to the fixed order truck platooning with unequal initial fuel of 10-time runs on simulation per each \(\delta\). Similar to Figure 1, the increased driving distance reaches up to 4.5%. Figure 4(b) presents the average position change count, where it can be seen that STS also successfully reduces position counts in the unequal initial amount of fuel. It should be noted that the distance enhancements are not as high as the case of equal initial fuel amount. This is because since the initial fuel amounts are unequal, during a number of initial time slots, the positions of the trucks may not change much. Then, the fixed order case may show similar sequences  
Figure 4: Performance of STS over Various Switching Period (\(\delta\)). to the case of applying our algorithms. Nonetheless, we can conclude that STS successfully reduces position counts while achieving the higher driving range, regardless of the amount of initial fuel.
# Frequency Response Extension Method of MET Vector Hydrophone Based on Dynamic Feedback Network
## 5 Discussion  
Electrochemical vector hydrophones have an important application value in the detection of low-frequency radiation noise from underwater targets. Although traditional electrochemical vector hydrophones based on the principle of molecular electron transfer (MET) have high sensitivity, their working bandwidth in the low-frequency band is limited, which restricts the detection capability of low-frequency radiation signals from underwater targets. To solve this problem, this paper proposes a method to extend the frequency response of MET electrochemical vector hydrophones based on a dynamic feedback network.  
By introducing a dynamic force balance negative feedback system based on a digital signal processor (DSP), the working bandwidth of the electrochemical vector hydrophone was successfully extended, and the detection capability of low-frequency signals was enhanced. The system has field adjustability and can adaptively compensate for a long-term frequency characteristic drift of the system. Experimental results show that the proposed method effectively improves the frequency response performance of the electrochemical vector hydrophone, providing a new technical solution for its application in the detection of low-frequency radiation noise from underwater targets.
## 3 Current WSNs Designed for Do nana
### ICARO 1 Network  
Based on our previous experience in WSNs, in 2010, we deployed a WSN to solve a specific problem that the biologist had in Do nana (Figure 6): monitoring the flood level in marsh areas. This network was called ICARO 1. The ICARO 1 network is composed of ten wireless sensors that are deployed between the zone of \"El Ojillo\" and \"El Zacallon\" [15]. This network works on the 2.4 GHz ISM band using the IEEE 802.15.4 Protocol. Figure 6 shows a map with the current deployment of the system. The maximum distance between the nodes is 200 m.  
The ICARO 1 nodes (Figure 7) are powered by small solar panels, and they are designed to allow the execution of distributed and collaborative algorithms. Data fusion and data aggregation are used to reduce bandwidth usage and power consumption. The nodes of the ICARO 1 network integrate the following sensors:  
Figure 5: Node of the ARTICA 2 network.  
Figure 6: Localization of the ICARO 1 network. (**a**) satellite image, (**b**) topographical map.  
* Temperature, which is calibrated to obtain an accuracy of 0.1 \({}^{\circ}\)C in the \(-\)40-65 \({}^{\circ}\)C temperature range.
* Relative humidity, which is in the 0-100% RH range.
* Solar radiation (in the visible light range of 320-1,100 nm and the NIR 320-730 nm range, using photodiodes).
* Rainfall level, using a tipping spoon with a resolution of 0.2 mm.
* Wind direction, with one degree of accuracy.
* Wind speed, with an anemometer that works in the 3-240 km/h range.  
In this network, the flood level estimation is obtained by using a novel computational intelligence algorithm [15], which is based on the use of SOM maps [14]. This algorithm is designed to optimize the energy consumption and to obtain the flow level information using environmental variables, such as the rain level or average temperature. These variables are acquired every 5 min, but a message is sent only if a meaningful flood level variation is detected. The training of the algorithm is obtained from historical environmental information obtained from the DBS and is evaluated via simulations.  
After its deployment in 2010, the experimental results obtained under various scenarios have demonstrated good performance of the system and have validated the results that were obtained by simulation. This arrangement is depicted in Figure 8, where F\({}_{\text{Real}}\) represents the real flood level, F\({}_{\text{Avg}}\) represents the flood average estimation and F\({}_{\text{Mod}}\) represents the flood mode estimation.  
Figure 7: Node of the ICARO 1 network.
# Super-Wideband Monopole Printed Antenna with Half-Elliptical-Shaped Patch
## 4 Comparison of Proposed SWB Antenna with Reported Works  
Table 3 shows a comparison of the performance of the proposed design with recently published SWB antenna works. As is well known, achieving a high bandwidth ratio requires minimizing the lowest operating frequency and maximizing the highest operating frequency. In this regard, the proposed antenna exhibits a combined performance of the high bandwidth ratio (1:47) and the lowest minimum resonant frequency (0.5 GHz) amongst the compared designs.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Dimension (\(\lambda_{0}\))** & **Substrate Material** & \
\begin{tabular}{c} **Operating** \\ **Frequency (GHz)** \\ \end{tabular} &
\begin{tabular}{c} **Bandwidth** \\ **Ratio** \\ \end{tabular} & **Result Type** \\ \hline  
3 & Measurement \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the reported SWB antennas.  
Figure 12: Surface current comparison at 29 GHz in 90\({}^{\circ}\) phase between (**a**) simulation without connector model and (**b**) with connector model.  
In references [12, 16, 19] and [27], the bandwidth ratio exceeds 1:30, but the lowest frequency is limited to 1.25 GHz. Some other works also demonstrate the capability to extend the maximum frequency up to 71 GHz, as seen in reference [13]. Additionally, references [17, 29] and [30] show maximum frequencies exceeding 40 GHz. However, while extending the maximum frequency, the lowest frequency also increases and never drops below 2 GHz.  
References [10, 14, 33] and [36] demonstrate the lowest frequencies of 1 GHz, 0.95 GHz, 1.3 GHz, and 1.05 GHz, respectively. In none of them did the frequency go lower than 0.95 GHz, although there were bandwidth ratios exceeding 1:10. Reference [34] demonstrated a lower minimum resonant frequency (0.3 GHz) when compared to our proposed antenna, but the operational bandwidth was limited, only reaching a maximum frequency of 20 GHz.
Sensors_**2012**, _12_, 10001-10013; doi:10.3390/s120810001  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Research on a Novel Low Modulus OFBG Strain Sensor for**  
**Pavement Monitoring**  
**[PERSON] *, [PERSON] and [PERSON]**  
School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China;  
E-Mails: [EMAIL_ADDRESS] (Q.H.); [EMAIL_ADDRESS] (Q.L.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-137-9666-4010; Fax: +86-451-8628-2704.  
_Received: 25 June 2012; in revised form: 16 July 2012 / Accepted: 17 July 2012 / Published: 25 July 2012_  
**Abstract:** Because of the fatigue and deflection damage of asphalt pavement, it is very important for researchers to monitor the strain response of asphalt layers in service under vehicle loads, so in this paper a novel polypropylene based OFBG (Optical Fiber Bragg Gratings) strain sensor with low modulus and large strain sensing scale was designed and fabricated. PP with MA-G-PP is used to package OFBG. The fabrication techniques, the physical properties and the sensing properties were tested. The experimental results show that this kind of new OFBG strain sensor is a wonderful sensor with low modulus (about 1 GPa) and good sensitivity, which would meet the needs for monitoring some low modulus materials or structures.  
**Keywords:** OFBG; strain measuring; low modulus; asphalt concrete
# A Two-Stage Semi-Supervised High Maneuvering Target Trajectory Data Classification Algorithm
## 3 Algorithms
### Classification Feature Enhancement Based on Siamese Network  
The second stage was to use the pre-trained autoencoder model to build a Siamese neural network and use the labeled data to train the model's classification ability for labeled categories. In addition, this fine-tuning could also guide and improve the feature extraction ability of the model.  
The model design of the second stage is shown in Figure 6. The main body was the Siamese neural network. The two branches of the Siamese neural network were composed of the encoding network in the pre-trained autoencoder, and the two networks shared weights. The loss in the model consisted of the reconstruction loss of the autoencoder and the output loss of the Siamese network. This was calculated as follows:  
\[\text{Loss}=\text{L}(W,Y,X_{1},X_{2})+\text{Loss1}+\text{Loss2} \tag{5}\]  
Among them, the calculation formula of \(\text{L}(W,Y,X_{1},X_{2})\) is shown in Equation (3), and the formulas of Loss1 and Loss2 are shown in Equation (4).  
Figure 5: Sequence autoencoder network structure.  
Figure 6: The model of stage two.  
Calculating the two losses at the same time ensured that the network parameters did not have a negative impact on the restoration effect of the autoencoder while using the label data for adjustment, thereby retaining the clustering ability of the first-stage model.  
Conventional Siamese neural networks perform classification by calculating similarity and setting thresholds. In this study, the data were processed in a clustering manner, so the Siamese network was used to calculate the similarity of the trajectory data set. Then the similarity matrix was used for clustering. Compared with direct classification, this method better preserved the model's ability to deal with unlabeled categories.
## 3 Results and Discussion
### The Influence of Vibration Amplitude on the Size of the Chips Generated  
This section proposes a series of numerical simulations aimed at analyzing the effect of machining techniques on the size of the chips generated. To do this, two techniques were examined: conventional machining without vibrations (amplitude of 0 \(\upmu\)m) and machining assisted by ultrasonic vibrations (amplitude of 15 \(\upmu\)m). It is important to note that the numerical simulations are carried out under the same cutting parameters, namely a feed of 3000 mm/min and a spindle speed of 5000 rpm. The results of this study are presented in Figure 12.  
The machining of NHC structures involves a complex two-stage process. Initially, the milling cutter engages with the thin walls of the structure, directing them towards the upper part of the tool where the high spindle speed shreds and pushes the material back, forming chips. This dynamic interaction between the material's structural geometry and the cutting tool is significantly influenced by the rapid spindle rotation, showcasing the intricate responsiveness of machining operations to tool movement and speed. The obtained results demonstrate a clear correlation between chip size and vibration amplitude. Increasing the vibration amplitude leads to a noticeable reduction in chip size, attributed to the combined effect of high-intensity vibrations and rapid spindle speed, which minimizes contact between the milling cutter and the NHC core walls. In this regard, the reduced contact facilitated by high-intensity vibrations and rapid spindle speed allows for easier tool penetration, promoting crack propagation and small chip formation. This reduction in chip size not only lowers cutting forces but also improves overall milling quality and efficiency, extending the cutting tool lifespan.  
Figure 11: Displacement distribution at different vibration amplitudes.
# Response to Variations in River Flowrate by a Spaceborne GNSS-R River Width Estimator
## 3 Results  
The fitted power series for each of the overpasses is shown in Figure 5, where the observed signal at each time step for each track is plotted in blue, and the red line shows the power series fitted to the signal within 22% of the peak at river center. Figure 6 shows the results of the linear regression between the AGW and the observed Pascagoula flowrates. The R\({}^{2}\) coefficient of the regression is 0.97.
# Effect of Deacidification Treatment on the Flavor Quality of Zaosu Pear-Kiwifruit Wine
## 1 Introduction  
Pear (_Pyrus communis_ L) is an economically important fruit crop, which is widely distributed worldwide because of its adaptability to different climatic conditions. In 2019, China's pear output reached 17 million tons, of which the Zaosu pear (_Pyrus bretschneideri Rehder_) output was close to 1 million tons [1]. However, the fresh fruit market has reached its saturation point, significantly reducing the price of Zaosu pears; as a consequence, orchadists are increasingly processing pears into value-added products, among which Perry, or pear wine, appears to have promising market prospects. However, the commercial development of Zaosu pear wine is hindered by its bland flavor and aroma, which are not appealing to consumers [2]. The green kiwifruit (_Actinidia chinensis_) is extensively planted in China, with an output of nearly 2.2 million tons in 2019 [3]. The addition of kiwifruit juice to pear wine fermentation is a possible solution to improve the flavor and consumer appeal, because of its richness in flavor precursors such as amino acids, sugars, and proteins [4, 5].  
In a previous study, however, we found that wine prepared from a mixture of pear and kiwifruit juice was unacceptably sour and astingent, because of its high content of malic, quinic, and tartaric acids, derived from the kiwifruit juice [2]. The usual method to alleviate excessive acidity is deacidification by chemical or biological processing. Unfortunately, chemical acid reduction is not effective in all cases and may reduce the stability of the wine. A biological deacidification method, however, such as malolactic fermentation (MLF), can significantly reduce the concentration of malic acid and total acidity in wines, without negatively impacting the flavor quality [6, 7, 8, 9]. Although the application of MLF in grapewine production has been thoroughly studied, its effect on the flavor profile and acidity of pear-kiwifruit wine is largely unknown.  
In addition to acid reduction, MLF can improve the aroma and taste of fermented beverages, such as Negroamaro wine [6], durian wine [10], Pinot nori wine [11], and Shiraz wine [12]. Recently, we employed an autochthonous _Oenococcus oeni_ (_O. oeni_) strain GF-2, with high \(\beta\)-D-glucosidase and diglycosidase activity, to obtain an aromatic Chardonnay wine via MLF, after a normal alcoholic fermentation [13]; however, there has been little research on modification of fruit wine flavor by MLF with this strain.  
The aim of this study was to compare the effects of chemical deacidification and MLF by _O. oeni_ strain GF-2 on the flavor enhancement and organoleptic properties of Zaosu pear-kiwifruit wine. Through a combination of chemical compositional and sensory analysis, the regulatory mechanism of the formation and evolution of organic acids, polyphenols, and aromatic compounds on the flavor of pear-kiwifruit wine was identified. These findings established a foundation for the production of pear-kiwifruit wine with strong consumer appeal.
# Development of Active Microvibration Isolation System for Precision Space Payload
## 4 Experimental Validation  
### Set Up of Experimental System  
To verify the effectiveness of the above mentioned vibration isolation control scheme, a prototype of the vibration isolation system was established, as shown in Figure 8. The structure of the system is shown in Figure 1. The acceleration signal of the load platform was measured using the acceleration sensors in the vibration isolation module. The signal is processed according to the designed active vibration isolation control algorithm using the real-time controller, then it is collected and output through AD/DA. Finally, to suppress the disturbance of the platform, the voice coil motor was driven by a linear power amplifier. Moreover, to observe variances, the monitoring signal of the load platform in three directions was measured by accelerometer 2. The host computer can obtain the signal in the real-time controller and modify the control parameters. The overall control of the system was realized using LabVIEW 2020 programming. The physical and controller parameters of the system are listed in Tables 2 and 3.  
Figure 7: Simulation curves of active control vibration isolation using different control approaches. (**a**) Vibration signal in time domain; (**b**) PSD.