esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Harvest Time of Apple Trees: An RNN-Based Approach
## 4 Results
It was observed in the tests with this scenario that the answers obtained corroborate the initial hypothesis that the apple harvest date can be estimated through ML methods using a multivariate approach based on historical data of phenology and climatic parameters related to temperature. Figure 8: Dynamics of the predictions obtained with the PredHarv model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Real** & **Predict** & **RMSE** & **MAE** \\ \hline
1 & 128 & 127.4 & 0.45 & 0.43 \\
2 & 124 & 123.3 & 0.50 & 0.39 \\
3 & 135 & 135.8 & 0.41 & 0.33 \\
4 & 132 & 132.7 & 1.32 & 1.08 \\
5 & 134 & 133.3 & 1.31 & 1.02 \\ \hline Average & & & **0.79** & **0.65** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Prediction results with the second evaluation scenario—dataset DS2. * Results of the Fourth Evaluation Scenario: In the fourth evaluation scenario, we used AutoML methods to select the best algorithm and tune its hyperparameters applied to the DS1 dataset. We use samples according to the K-fold cross-validation scheme, with k = 10, as described in Section 3.3.4. We divided the solutions into two categories. In the GML category, we use Auto-Sklearn. The best result in the ranking of the models tested by AutoSklearn was with Multiple Linear Regression (MR). Table 6 presents the results obtained from the model. In the DP category, we use AutoKeras. The library is focused on automatic searching of DL neural architecture (NAS), automatically adjusting the number of dense layers, units, activation functions used, dropout values, and other DL hyperparameters. This task is often called architecture research, and it is often called neural architecture research. The MLP neural network structure returned by Autokeras is composed of a sequential model composed of a Dense layer with 128 neurons and ReLU activation function. Then, there is another layer of Dense type with 1024 neurons and ReLU activation function. In the output step, there is a Dense layer with only one neuron, and 134 parameters were tested. The network training was performed with 300 epochs. Table 7 presents the results of the metrics obtained from samples submitted to the model.
## 3 Results and Discussion
### TE Transfer in the Aerial Plant Parts of M. sylvestris
These low BCF tot suggested an excluder phenotype of _M. sylvestris_ and confirmed the interest of _M. sylvestris_ in a phytostabilization strategy on a highly TE-contaminated site. To our knowledge, no study has reported the concentrations in different plant compartments of mallow as carried out in the present study. The concentrations in the mallow flowers from the control (Table 4), which are reported here for the first time, were the lowest compared to those measured in the malva flowers collected on the contaminated site. Furthermore, the concentrations in the plant shoots were compared to the expected concentrations in uncontaminated and contaminated conditions. Turkish studies have reported TE concentrations in mallow obtained at a local market (uncontaminated plant). For these four TE, the concentrations ranged between (ug g\({}^{-1}\) of DW) Cd: 0.07, Cu: 7-10, Pb: 0.69-3.5, and Zn: 25.9-49.4 [46; 47; 48; 49]. Using XRF, [PERSON] et al. [50] measured samples in Italy containing Cd, Cu, Pb, and Zn concentrations of 0.6, 16.7, 1.5 and 40.8 ug g\({}^{-1}\) of DW, respectively. Compared to the TE control concentration measured in the shoots on uncontaminated soil (Table 4), only Zn was slightly above the concentration reported in [46; 47; 48; 49; 50]. These different studies showed a slight variation of the TE concentration in uncontaminated conditions. In our study on a TE-contaminated site, only Cu showed concentrations that were similar to or below the ones reported in the control (Table 4). For the other TE, the concentrations were 6, 12, and 4.5 times higher than the values reported in the control plants for Cd, Pb and Zn, respectively (Table 4). The TE concentrations reported in contaminated sites or in the artificially contaminated soil varied considerably [30; 49; 51; 52]. The Cd concentrations ranged between 0.24-3.5 ug g\({}^{-1}\) and 33.5 ug g\({}^{-1}\) when the soil was artificially contaminated [30; 49; 51; 52]. For the Cu, Pb, and Zn, the literature concentrations were of 4.3-20 ug g\({}^{-1}\), 8-9 ug g\({}^{-1}\), and 75-400 ug g\({}^{-1}\), respectively [30; 49; 51]. In our study, just the Zn concentrations values that were were above or similar to the highest value retrieved in these other experiments in contaminated conditions.
## 7 Conclusions  
This paper presents a novel high-gain, wideband MTS antenna with CP radiation. The antenna consists of a centrally symmetric MTS structure and a slot-coupled feeding network. Through CMA, parasitic patches and mode-suppressing patches are added around the MTS to enhance the desired radiation modes and suppress the unwanted ones. Subsequently, a feeding network combining a ring slot with an L-shaped microstrip line is designed to excite two orthogonal modes with a 90\({}^{\circ}\) phase difference, enabling wideband CP and high-gain radiation. The experimental results demonstrate that the proposed antenna achieves an IBW of 39.5% (4.92-7.37 GHz), a 3 dB ARBW of 33.1% (5.25-7.33 GHz), and a peak gain of 9.4 dBic at 6.9 GHz. In terms of practical applications, given its low-profile, high-gain, and wide bandwidth characteristics, the proposed antenna holds significant potential. In satellite communications, the low-profile design allows for seamless integration on satellites with limited space, and the high gain and wide bandwidth ensure reliable and high-speed data transmission over long distances. For radar systems, the wideband CP radiation enhances target detection and tracking capabilities, especially in complex electromagnetic environments. Moreover, in unmanned aerial vehicles (UAVs), the compact size and high-performance radiation of the antenna make it well-suited for on-board communication and sensing tasks, thereby improving the overall functionality and performance of UAVs.  
Conceptualization, Z.L., Y.L. and S.H.; Methodology, Z.L. and S.H.; Software, Z.L., Y.L. and M.Z.; Validation, S.H.; Formal analysis, Z.L.; Resources, Z.L.; Data curation, M.Z. and S.H.; Writing--original draft, Z.L. and Y.L.; Writing--review & editing, Z.L., Y.L., M.Z. and S.H.; Supervision, W.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# Research on the Efficient Space Debris Observation Method Based on Optical Satellite Constellations
## 5 Numerical Simulation Experiment
### Object Detectable Analysis
#### 5.2.2 Analysis of the Observation Arc Length  
Figure 10 shows the relationship between the number of observable space objects in the different constellations and the length of the arcs. It can be seen that there were various arc lengths in the space objects observed by the different constellation configurations. For the same range of the detection arc length, the overall trend was that the more satellites the constellation has, the more space objects it can observe. However, for constellations with the same number of satellites, the monitoring efficiency was also slightly different due to the different constellation configurations. According to the post-processing requirements of the monitoring data, if the arc length was long, it was conducive to the determination of the space object orbit and the improvement in the accuracy.  
Figure 11 shows the distribution of the number of arcs and arcs lengths observed in the different constellation configurations. It can be seen that the number of arcs corresponding to the different arc lengths was quite different. Among them, the number of arcs with arc lengths greater than 1 min was large, which was also conducive to improving the effectiveness of the observation data. Long observation arc lengths are preferred for space objects, which is conducive to data processing and the orbit determination of space objects. When the arc length was less than 1 min, it can be seen from the above figure that the number of arcs corresponding to the arc length gradually increased from 10 s to 60 s. For the same arc length range, for example, when the arc length was approx. 40 s, the larger the constellation size, the more arc segments could be observed.  
Figure 11: Observable arc length distribution of the different constellation configurations.  
Figure 10: The distribution of observable objects with an arc length in the different constellation configurations.
## 3 Methodology and Data Collection
### Data Collection
#### 3.1.4 Meteorological Values  
Weather forecast was mapped to each _OSM segment_ (500 m in length at maximum). Forecasts are updated by the provider 4 times per day, at approximately 9.00 a.m., 1.00 p.m., 9.00 p.m., and 1.00 a.m. These forecasts were also collected 4 times per day, and forecasts for each 24 h period were collected. The aim was to ensure there was at least 1 forecast available at every hour per road segment. From time to time, it was not possible, for instance, due to the earlier issuance of a new forecast. The worst-case scenario thus involved a forecast collected 9 h prior to the required time.  
The following meteorological data were collected: Time of the forecast collection in hours, outside temperature in \({}^{\circ}\)C, wind direction (azimuth) in degrees, wind speed (m/s), air humidity (in %), atmospheric pressure (hPa), cloud amount (in %), fog (in %),  
Figure 3: Average number of measurement sessions per 1 segment and 1 h in the week from 24 February to 1 March 2020.  
low-level clouds (in %), mid-level clouds (in %), high-level clouds (%), dew point in \({}^{\circ}\)C, and precipitation amount in the past hour (mm).
## 1 Introduction  
Owing to the demand for high-speed data transfer, 5G communication based on the millimeter wave has been actively developed. The global spectrum allocated for the 5G millimeter wave is mainly distributed in the Ka-band (26.5-40 GHz) [1, 2]. Therefore, a wideband flat-gain amplifier operating in the full Ka-band is needed for implementing multistandard or ultrawideband radio systems supporting the global spectrum.  
Conventionally, wideband amplifiers operating at several gigahertz have been implemented using a resistive feedback technique [3, 4, 5]. The resistive feedback makes the input impedance more resistive, thereby allowing wideband input matching and enhancing the linearity [6]. However, a disadvantage of the resistive feedback is that the gain decreases in the upper end of the bandwidth [7]. Therefore, multiple amplifying stages must be cascaded to compensate for the gain roll-off, which increases the overall DC power consumption. Recently, a feedback technique based on a transformer balun was proposed to achieve a wideband gain response with a compact size [8, 9]. However, the complicated structure of the transformer balun makes it challenging to optimize the transformer parameters such as the primary and secondary inductances and the coupling coefficient to achieve both the wideband input matching and flat-gain response at the same time.  
This paper presents a full Ka-band amplifier with a low gain variation. In the first gain stage, the inductive neutralization is adopted to compensate for the gain roll-off in the upper end of the Ka-band, while the frequency staggering is employed in the subsequent gain stages for wideband and flat-gain responses. In Section 2, the analysis and design of the amplifier are presented. The experimental results are presented in Section 3. Finally, the conclusions are drawn in Section 4.
## 2 Materials and Methods
### Experimental Methods
#### 2.2.2 Test Methods  
1. Density and specific surface area test: The raw material density test method was based on GB/T 208-2014 [25] \"Cement Density Test Method\", and the specific surface area was determined in accordance with GB/T 8074-2008 [26] \"Cement Specific Surface Area Test Method\".
2. Particle size distribution test method: The particle size distribution of the prepared powder was assessed using the Morphologi 4-ID particle size and shape analyzer (Malvern Panalytical, Malvern, UK).
3. Mechanical property test: The bending and compressive strength of the mortar at 3 d, 7 d, and 28 d were tested using a mortar press in accordance with the GB/T 17671-1999 [27] \"Cement Mortar Strength Inspection Method (ISO Method)\" standard.
4. Hydration heat test: The hydration heat release process of the slurry after 72 h was examined at 25 \({}^{\circ}\)C using an isothermal calorimeter (I-CAL4000/8000) manufactured by Calmetrix, Boston, MA, USA.
5. X-ray diffraction test: The mineral composition of the sample powder was analyzed using an X-ray diffractometer (X'PERT PRO) (Malvern Panalytical, Malvern, UK). The diffraction angle ranged from 5\({}^{\circ}\) to 80\({}^{\circ}\), and the rate was set at 4\({}^{\circ}\)/min.
6. Scanning electron microscopy test: A scanning electron microscope (JSM-6380 LV, JEOL Ltd., Tokyo, Japan) was employed to observe the micro-morphology of the sample under the following conditions: voltage 5 kV, current 5 \(\upmu\)A, magnification 5000\(\times\)_g_, scale 10 \(\upmu\)m, and glided sample surface.
7. Thermogravimetric differential thermogravimetry test: Approximately 3 mg of the sample was weighed and placed into the crucible. The thermogravimetric analyzer (Q500 IR) (Newcastle, DE, USA) was utilized to conduct tests in a nitrogen atmosphere, with a heating rate of 10 \({}^{\circ}\)C/min.
## 3 Problem Ingredients and Approach of the Proposed MILP Models  
In order to state the problem more formally and clearly, we present our ideas which are based on a real-life context. We have taken into account a set of assumptions, which are as follows:  
* **Time window**: Includes the service time at each customer, loading time for each vehicle, travel time between each couple of customers, time necessary to park the vehicle, and the workday duration.
* **Combined capacity; volume and weight**: The majority of VRP formulations consider the physical capacity of vehicles, which is generally expressed in terms of weight. In our problem, goods are delivered using boxes. Therefore, beside the capacity in terms of weight, we have to make sure that each vehicle has the capacity in terms of number of boxes to be fitted into the vehicle. Thus, we defined a new volume capacity that includes both loaded weight and box numbers. We denote that we have considered the volume occupation in each vehicle to be discrete. Motivated by this assumption, which complicates the use of vehicle space, we assume that the combined capacity has not been addressed in the literature.
* **Customer balance (stock) in terms of boxes**: We suppose, initially, that each customer has boxes ready for pickup. These boxes are considered by our models before performing any delivery.
* **Optimization choice**: Three different assumptions are considered. The first one guarantees that the pickup and delivery are performed simultaneously, without leaving any empty boxes for any customer. The second assumption is more flexible. It provides two choices of pickup and delivery operation, separately and/or simultaneously. The third one ensures that the pickup and delivery operation are performed simultaneously. However, it provides a flexibility to drop and leave some boxes, which will be consequently penalized.
* **Heterogeneity of the fleet** Multiple vehicle types exist, having different sizes.
# An Improved Rectifier Circuit for Piezoelectric Energy Harvesting from Human Motion
## 3 Experimental Design
### Experimental Setup and Procedures
#### 3.2.1 Shaker Test  
The experimental setup of the shaker test is shown in Figure 10. A function generator (Agilent 33210A) was used to deliver a sinusoidal signal to a power amplifier (Z706, B & K Agilent), which then amplified the signal before activating the mechanical shaker (APS-113). The shaker generated vibrations according to the input frequency and amplitude to excite the cantilever beam with attached PD.  
To simulate common human activities, low excitation frequency [41] (i.e., 1 Hz and 2 Hz) was applied. For each frequency, the amplitude of input voltage (voltage produced by PD) was adjusted to obtain the desired peak OC voltage, from 1 to 5 V. All the input voltages were sequentially applied to both rectifier circuits. The rectified voltage from each circuit was stored in two storage capacitor, namely, C1 and C2. A range of resistances R1, R2, R3, and R4, were also studied for each case. The output power of each circuit was calculated using Equation (8). The ratings of various parameters are summarised in Table 2.
# Trajectory Tracking Control of a Manipulator Based on an Adaptive Neuro-Fuzzy Inference System
## 5 Simulation Results and Analysis  
In this section, the trajectory tracking performance of the proposed controller is discussed. The controller is applied to the hydraulic manipulator shown in Figure 1; each link adopts a separate controller. Taking the working condition of trimming the top as an example, the trajectory tracking control is studied, and the expected trajectory is obtained from Equation (6). The simulation is implemented by MATLAB/Simulink (Ver. R2018a) on a personal computer, and the Simulink model is shown in Figure 9. The sampling time and simulation time are set at 1 ms and 10 s, respectively.  
In order to prove the superiority of the ANFIS-PID controller, its performance is compared with FPID and PID controllers. Through the simulation experiment, the trajectory tracking curve of each link controlled by the above three controllers is obtained, as shown in Figure 10. Figure 11 is the error graph between the actual trajectory and the expected trajectory. As we can see from the figures, all three control methods can make the system reach the final stable state. When controlled by PID controller, the overshoot of the system is relatively large, which leads to the poor stability of the system. Each link tracks the expected trajectory at about 2.1, 2.5, 2.2, 2.0 \(\mathbf{s}\), respectively. The tracking speed is slow and the tracking error is large. Although the FPID controller can reduce the overshoot of the system, its tracking error is large, and the tracking speed is still not fast enough. Compared with other controllers, the ANFIS-PID controller has a smaller angular displacement tracking error and a smaller overshoot, and can quickly track the desired trajectory. In addition, the system is more stable. Therefore, the ANFIS-PID controller can better accomplish the angular displacement tracking task of the manipulator.  
Figure 10: (**a**) Trajectory tracking for joint 1; (**b**) trajectory tracking for joint 2; (**c**) trajectory tracking for joint 3; (**d**) trajectory tracking for joint 4.  
Figure 9: Simulation model of the ANFIS-PID control system.  
In order to reflect the trajectory tracking effect more clearly and accurately, the trajectory tracking error of Figure 11 is quantified, and the mean absolute error and root mean square error of the actual trajectory of each joint are calculated separately, as shown in Table 1. The quantitative analysis shows that the mean absolute errors of the ANFIS-PID controller proposed in this paper are 0.36%, 0.88%, 0.63% and 0.86% of the PID controller, and 1.82%, 2.96%, 1.99% and 1.67% of the fuzzy PID controller, respectively. Similarly, the root mean square error of the ANFIS-PID controller is 0.56%, 0.90%, 0.69% and 1.10% of the PID controller and 2.81%, 2.99%, 2.16% and 2.11% of the fuzzy PID controller, respectively. The above analysis also proves that the control method proposed in this paper can obtain higher control accuracy compared with PID and fuzzy PID methods.
# Polymeric and Crystalline Materials for Effective and Sustainable CO\({}_{2}\) Capture
## 3 Petroleum-Based Polymers
### Ionic Polymers  
Imidazolium salt-modified porous hyper-crosslinked polymers were introduced as a suitable CO\({}_{2}\) adsorbent by [PERSON] et al. [170]. More specifically, the benzyl halide derivatives were directly self-polymerized by the Friedel-Craft reaction. Then, the unreacted benzyl halide groups were further reacted with \(N\)-methylimidazole to furnish the POM-IM structure (see Figure 10). The evaluation of the performances of the POM-IM material showed a CO\({}_{2}\) uptake of 3.68 mmol g\({}^{-1}\) at 273 K and 1 bar. In addition, POM-IM can act as a catalyst for the conversion of epoxide to cyclic carbonate in yields ranging from 68 to 93% depending on the substrate. The same group also reported the preparation of phosphonium salt incorporated into a hyper-crosslink porous polymer [171]. In this case, a phosphonium salt was reacted with benzene and formaldehyde dimethyl acetal to give a series of polymers. A CO\({}_{2}\) capture capacity of up to 1.68 mmol g\({}^{-1}\) was obtained at 298 K and 1 bar. These phosphonium-based polymers were also able to convert epoxide functions into cyclic carbonates.  
Another example of imidazolium-based hypercrosslinked ionic polymer was reported by [PERSON] et al. [172]. In their study, they synthesized a 2-phenylimidazolium HIP by a Friedel-Craft reaction between 2-phenylimidazoline and dichloro-\(p\)-xylene in the presence of FeCl\({}_{3}\) at 80 \({}^{\circ}\)C (Figure 10). The CO\({}_{2}\) uptake for this material was found to reach 2.1 mmol g\({}^{-1}\) at 298 K and 1 bar. Furthermore, these HIP derivatives showed catalytic activity towards the conversion of epoxide to cyclic carbonate with high selectivity and yields.  
Porous cationic polymers were reported by [PERSON] et al. They used a Pd-catalyzed Sonogashira-Hagihara approach for their preparation [173]. Essentially, tetrakis(4-ethylnylphenyl)methane was reacted with 1,10-bis(4-iodophenyl)-[4,40-bipyridine]-1,10-diium salts (dichloride, ditetrafluoroborate, or dihexafluorophosphate) in presence of a Pd source at 100 \({}^{\circ}\)C. The obtained PCP-Cl polymer exhibited a CO\({}_{2}\) capture capacity of up to 1.4 mmol g\({}^{-1}\) at 298 K and 1 bar (Figure 10). In addition, the PCP derivatives also showed a catalytic activity for the conversion of epoxides to cyclic carbonates. [PERSON] et al. also reported the synthesis of porous cationic polymers [174]. Starting from tetrakis(4-aminophenyl)methane, formaldehyde, and methyl glyoxal were added, and the mixture was reacted immediately according to a Debus-Radziszewski imidazolium synthesis. Anion exchange was then carried for different salts such as sodium tetrafluoroborate, bis(trifluoromethane)sulfonimide  
Figure 9: Examples of \(N\)-heterocyclic carbene and \(N\)-doped polymers [168,169].  
lithium salt, potassium hexafluorophosphate, and sodium trifluormethanesulfonate leading to several derivatives. Click-based porous cationic polymer CB-PCP-1 exhibited a CO2 adsorption capacity of 2.05 mmol g\({}^{-1}\) at 273 K and 1 bar (Figure 10).