esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Model Deviation Evaluation Method  
Evaluation of model accuracy was carried out using the mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and variance accounted factor (VAF) of the model's predicted results. The calculation formulas are shown in Equations (3)-(7). The MAE represents the absolute deviation level of the data. The MSE and RMSE represent the level of deviation dispersion. The MAPE represents the relative size of the error with respect to the actual value. The VAF measures the degree to which the model's prediction results correlate with the actual results. Here, \(x_{\text{max}}\) represents the predicted value, \(y_{i}\) represents the actual value, and N represents the dimension of the output value.  
\[MAE =\frac{1}{N}\sum\limits_{1}^{N}\lvert y_{i}^{c}-y_{i}\rvert \tag{3}\] \[MSE =\frac{1}{N}\sum\limits_{1}^{N}\left(y_{i}^{c}-y_{i}\right)^{2}\] (4) \[RMSE =\sqrt{\frac{1}{N}\sum\limits_{1}^{N}\left(y_{i}^{c}-y_{i}\right)^ {2}}\] (5) \[MAPE =\frac{100\%}{N}\sum\limits_{1}^{N}\left\lvert\frac{y_{i}^{c}-y_{ i}}{y_{i}}\right\rvert\] (6) \[VAF =(1-\frac{\frac{\sum\limits_{1}^{N}\left(y_{i}^{c}-y_{i}\right)^ {2}}{\sum\limits_{1}^{N}y_{i}^{2}}}{)}\times 100\% \tag{7}\]
## 3 Data Prediction for Energy Saving in WSNs
### Dual Prediction Scheme
#### 3.2.2 Independent Model Generation  
In this scheme, the prediction model is generated independently in both sensors and CHs, as presented in Figure 11. There is an initialization phase where sensors transmit all the data to the CH to ensure that the CH has all the information possible from the environment before generating the model. Then, they are both capable of generating the same predictions, and sensors do not need to do an extra transmission of the model. Sensor nodes can check the accuracy of the prediction by comparing it with the measured value and transmit it to the CH if there is a need to overwrite the prediction. As a drawback, the variety of the models is restricted by the computing power limitations of sensor nodes [33].
## 2 Materials and Methods  
### Study Area and Site  
Hainan Island (18\({}^{\circ}\)10\({}^{\prime}\)-20\({}^{\circ}\)10\({}^{\prime}\) N, 108\({}^{\circ}\)37\({}^{\prime}\)-111\({}^{\circ}\)03\({}^{\prime}\) E) is located in Southern China with a total area of about 34,000 km\({}^{2}\). It has a tropical monsoon climate, warm and humid year-round, with abundant precipitation ranging from 1000 to 2600 mm yearly and an average temperature of 22-26 \({}^{\circ}\)C. Rainy and dry seasons are clearly separated, with the dry season occurring from November to April with only 10-30% of the total annual precipitation (about 140 mm), and the rainy season from May to October with precipitation of about 1500 mm. The rubber plantation area in Hainan Island was stable around 2010, with no large-scale expansion and deforestation, accounting for about 20% of the total area of Hainan Island. The plantations are found on the plateaus surrounding the central mountainous zone and are concentrated in the northern island with the total area of 7269.88 km\({}^{2}\) (Figure 1).  
The site, Danzhou Investigative and Experimental Station of Tropical Crops (19\({}^{\circ}\)23\({}^{\prime}\)47\({}^{\prime\prime}\) N, 109\({}^{\circ}\)28\({}^{\prime}\)30\({}^{\prime\prime}\) E), is in a rubber-dominated area and has an altit  
Figure 1: Study area (Study site: Danzhou Investigative and Experimental Station of Tropical Crops; The background topographic map sources: Esri, IEEE, Garmin, Intermap, increment P Corp., GEBCO, USGS, FAO, NPS, NRCAN, GeoBase, IGN, Kadaster NL, Ordnance Survey, Esri Japan, METI, Esri China (Hong Kong), OpenStreetMap contributors, and the GIS User Community).  
rain. The rubber plantation has a simple vegetation structure, with a planting density of 3 m \(\times\) 7 m. For the rubber ecosystem, the upper layer is the arbor layer of the rubber forest with a canopy height of about 16 m, and the lower layer is mostly an annual herb layer with a height of about 0.5 m. There is a 50 m tower equipped with an open-path eddy covariance (OPEC) and vertical microclimate profile system of the plant canopy, and the plantation has been continuously observed for more than 10 years [47]. The data obtained at this site were used to adjust the GPP estimation model for rubber ecosystems (Section 3.1).
# BDS-3 Triple-Frequency Timing Group Delay/Differential Code Bias and Its Effect on Positioning
## 1 Introduction
Due to the broadcast of new frequencies, triple-frequency, and even multifrequency positioning, have received wider attention [24; 25; 26; 27]. However, the TGD/DCB correction model for the triple-frequency positioning of BDS-3 has not been summarized in detail, and the effect of TGD/DCB correction on the effectiveness and positioning performance of triple-frequency positioning is unclear. This study derived the BDS-3 triple-frequency TGD/DCB correction models and aims to analyze the effect of TGD/DCB correction on BDS-3 triple-frequency SPP and PPP. Moreover, the effects of DCB on pseudorange noise, inter-frequency bias (IFB), ionospheric delay, tropospheric delay and floating ambiguity are discussed. The rest of this paper is organized as follows. After the introduction, the BDS-3 triple-frequency positioning model is first summarized in Section 2, and the triple-frequency TGD/DCB correction formulas are derived for different frequency combinations, including triple-frequency ionospheric free (IF) combination (IF123), two dual-frequency IF combinations (IF1213) and triple-frequency uncombined (UC123). Then, the stability of the DCB product released by the CAS is analyzed. Finally, the processing strategy is presented. Section 3 verifies the effectiveness of TGD/DCB correction and its effect on SPP and PPP. Discussion and conclusions are provided in Sections 4 and 5, respectively.
## 3 Comparison of the Performance of Different Iron-Based Catalysts  
Different catalysts and contaminants are described above. We select representative pollutants, 2,4-D, NOF, and BPA, as examples to illustrate the performance of various kinds of catalysts.  
As one type of auxin analogue, 2,4-D is the most applied herbicides in the world. If overused, it pollutes the water body and harms crops [102]. Figure 10 shows that when the concentration of 2,4-D was 20 mg/L, all three iron-based catalysts showed excellent degradation rate and fast degradation time. The best material is Fe@C/PB, which can degrade 99.4% of the 2,4-D in 50 min [92].  
Antibiotics are currently extensively used in human medicine, animal farming, agriculture, and aquaculture, and their residue has become a global environmental problem [103]. NOF is the third generation of quinolone antibiotic. It has certain antibacterial action [104]. Figure 12 shows that when the concentration of NOF was 15 uM, Fe\({}_{2}\)O\({}_{3}\)@CoFe\({}_{2}\)O\({}_{4}\) was 0.3 g/L. In 25 min, the degradation rate could reach 89.8% [69]. Compared with the degra  
Figure 10: Degradation of 2,4-D by three iron-based catalysts: (**a**) CuFe\({}_{2}\)O\({}_{4}\)/O\({}_{3}\)[42], (**b**) TiO\({}_{2}\)@CuFe\({}_{2}\)O\({}_{4}\)/UV [69], and (**c**) Fe@C/PB [92].  
Figure 9: Typical FESEM images of (**a**) Fe\({}_{3}\)O\({}_{4}\), (**b**) Fe\({}_{3}\)O\({}_{4}\)/GO, (**c**) Fe\({}_{3}\)O\({}_{4}\)/GO/Ag, and (**d**) Fe\({}_{3}\)O\({}_{4}\)/Ag microspheres. Inserts are magnified FESEM images of Fe\({}_{3}\)O\({}_{4}\)/GO/Ag and Fe\({}_{3}\)O\({}_{4}\)/Ag microspheres [94].  
dation rate of other pollutants, it has a greater improvement. Piezoelectric catalysis can be used to further enhance performance.  
BPA is a very common chemical product. It is widely found in plastics used in our daily life. It can lead to endocrine disorders, and cancer is also considered to be associated with BPA [105]. Figure 11 shows that when the concentration of BPA was 10 mg/L, the degradation rate of (a) the biochar loaded with CoFe\({}_{2}\)O\({}_{4}\) nanoparticles can reach 93% [39] in 8 min.  
Figure 11: Degradation of BPA by three iron-based catalysts: (**a**) biochar loaded with CoFe\({}_{2}\)O\({}_{4}\) nanoparticles [39], (**b**) Fe\({}_{3}\)O\({}_{4}\)[87], and (**c**) CuO-Fe\({}_{3}\)O\({}_{4}\)-BC [88].
## 2 Materials and Methods
### Performance measurement  
As a simple baseline for comparison, nearest neighbor classifier with Euclidean distance is used. The nearest neighbour classifier simply classifies a test instance with the class of the nearest training instance according to some distance measure. Performance of each parameter is measured by comparing the detection results with ophthalmologists' hand-drawn ground truth. Nine performance measurements, namely, true positive (TP, a number of exudates pixels correctly detected), false positive (FP, a number of non-exudate pixels which are detected wrongly as exudate pixels), false negative (FN, a number of exudate pixels that are not detected), true negative (TN, a number of non-exudates pixels which are correctly identified as non-exudate pixels), sensitivity, specificity, positive predictive value (PPV), positive likelihood ratio (PLR) and accuracy are calculated [31,32]. Equations 8, 9, 10, 11 and 12 show the computation of sensitivity, specificity, PPV, PLR and accuracy, respectively:  
\[Sensitivity=\frac{TP}{TP+FN} \tag{8}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{9}\]  
\[PPV=\frac{TP}{TP+FP} \tag{10}\]  
\[PLR=\frac{TP/(TP+FN)}{FP/(FP+TN)}=\frac{Sensitivity}{1-Specificity} \tag{11}\]  
\[Accuracy=\frac{TP+TN}{TP+FP+FN+TN} \tag{12}\]
## 3 Results
### Response Rates of SOS/EOS to Temperature and Precipitation  
During 2000-2016, we observed an advancement of 0.41 days/\({}^{\circ}\)C for SOS in meadow steppe, and a delay of 0.33 and 0.9 days/\({}^{\circ}\)C for SOS in the typical and desert steppe (Figure 6a). Meanwhile, increased spring pre-SOS precipitation caused an advance in SOS by 0.16, 0.21, and 0.66 days/mm for meadow steppe, typical steppe, and desert steppe, respectively (Figure 6b). The sensitivity of SOS to pre-SOS temperature and precipitation in desert steppe was significantly stronger than that in meadow steppe and typical steppe. In autumn, warmer temperatures delayed EOS at rates of 2.71 and 2.51 days/\({}^{\circ}\)C for meadow steppe and typical steppe, whereas they advanced EOS by 0.31 days/\({}^{\circ}\)C for desert steppe (Figure 6c). Increased precipitation caused a delay in the timing of EOS by 0.01, 0.01, and 0.03 days/mm for meadow steppe, typical steppe, and desert steppe, respectively (Figure 6d). Desert steppe showed a significantly weaker sensitivity of EOS to pre-EOS temperature, but a significantly stronger sensitivity of EOS to pre-EOS precipitation than meadow steppe and typical steppe.  
Figure 5: Spatial patterns of correlation coefficients between: (**a**) SOS and pre-SOS temperature; (**b**) SOS and pre-SOS precipitation; (**c**) EOS and pre-EOS temperature; and (**d**) EOS and pre-EOS precipitation.
# Study on Few-Shot Object Detection Approach Based on Improved RPN and Feature Aggregation
## 1 Introduction
The main innovations and contributions of this paper are as follows. 1. Few-shot object detection network based on improved RPN and feature aggregation (IFA-FSOD) was proposed using Faster R-CNN as the backbone, achieving efficient performance improvement in FSOD. 2. The original RPN has been improved by introducing a nonlinear classifier to calculate the similarity between the features extracted by the backbone network and the new class features, thereby increasing the detection precision of new class candidate boxes and screening out high IoU candidate boxes, improving the detection performance of object detection. 3. Utilizing attention mechanism to enhance feature aggregation, aggregating feature information from different levels, obtaining more comprehensive information and feature representations, solving the problem of information loss, and improving the model's generalization ability and detection performance for new classes. The organizational structure of this paper is as follows: Section 2 reviews related work. Section 3 gives our methods. Section 4 presents experimental results and analysis. Finally, we summarize the paper and provide prospects for further work in Section 5.
# Investigating and Modeling of Cooperative Vehicle-to-Vehicle Safety Stopping Distance
## 6 Simulation of Density Losses
### Safety Distance Algorithm  
With the deceleration rate model now described and in addition to the calculated Human reaction safety distance and the ADAS reaction safety distance, the algorithms previously mentioned will be briefly explained and shown;  
Mazda algorithm is a worst-case scenario to ensure collisions do not occur and is shown in Equation (12), where \(v_{\text{f}}\), \(v_{\text{f}}\), \(v_{\text{rel}}\) represent leading, following, and relative vehicle velocities, and (\(\dot{a}_{\text{f}}\))(\(\dot{a}_{\text{f}}\)) represent deceleration. This algorithm assumes that the two vehicles are at a constant velocity and that, at \(T_{2}\), the leading vehicle will begin braking at the deceleration rate specified, and that the following vehicle will begin braking after \(T_  
a part of ADAS that utilizes driver warning systems. In this algorithm, \(T_{\mathrm{R}}\) represents the reaction time of the driver. This is shown in Equation (13).  
\[D_{\mathrm{s}}(v_{\mathrm{l}},v_{\mathrm{f}})=v_{\mathrm{f}}T_{\mathrm{R}}+( \frac{v_{\mathrm{f}}{}^{2}}{\dot{a}_{\mathrm{f}}})-(\frac{v_{\mathrm{l}}{}^{2} }{\dot{a}_{\mathrm{l}}}). \tag{13}\]  
PATH is a modified version of the Berkeley algorithm and is based upon two vehicles, where the lead vehicle begins braking, and the follower reacts after \(T_{\mathrm{r}}\) and begins to brake. This algorithm also uses \(R_{\mathrm{min}}\) to represent a minimum range or a tolerance rate and is shown in Equation (14).  
\[D_{\mathrm{s}}(v_{\mathrm{l}},v_{\mathrm{f}})=\frac{1}{2}+[(\frac{v_{\mathrm{ f}}{}^{2}}{\dot{a}_{\mathrm{f}}})-(\frac{v_{\mathrm{l}}{}^{2}}{\dot{a}_{ \mathrm{l}}})]+v_{\mathrm{f}}T_{\mathrm{r}}+R_{\mathrm{min}}. \tag{14}\]  
In these algorithms, we will assume that the vehicles velocity and deceleration are the same; therefore, the relative speed will be 0. The Berkeley example will also be used with both the deceleration value calculated from our simulations and the industry standard value of 6.44 m/s\({}^{2}\)[50; 51]. The final method to be analyzed in the Highway Code guideline of a 1 m per every mile per hour [52; 53]. For instance, at 30 mph, the gap should be left is 30 m safety distance. We found that, after calculating with each algorithm, the SDA, PATH, and Mazda equate to the same safety distance as the finding we made for human-based safety distance. For this reason, we will only plot five variations of safety distance, which can be seen in Figure 12. This graph highlights how much quicker the ADAS-based braking system would react compared to a typical human reacting to a warning produced by the other algorithms or from a visual reaction from eyesight.
# Research Progress on Synergistic Technologies of Agricultural Multi-Robots
## 3 Research Progress on Synergistic Technologies of Agricultural Multi-Robots
### Formation Control
[PERSON] and [PERSON] in Korea adopted Ramadge-Wonham theory in supervisory control to solve the above deadlock problem [122]. Supervisory control is a feedback control theory for discrete-event systems, where the control goal is achieved by observing the occurrence of events or states and using allowable or prohibited controllable events. Finally, a time-driven system is combined with a low-level controller and an event-driven system with a high-level controller with the criterion of satisfying the behavior specification and maximizing the allowable events. Time-driven is used when there is no fault, and once the queue encounters a fault, the control outcome is selected based on event-driven. Simulation results demonstrate that the method can be used to control complex dynamic systems, but it has not been tested in practical applications. The characteristics and formation process of the formation control methods are shown in Table 6.  
From Table 6, it can be found that more complex or hybrid control methods are mostly used in simple or simulation environments, and the application in actual agricultural production is still dominated by the leader-follower method, and the research is also mainly focused on multiple machines traveling in a straight line in a fixed column. Further research should be conducted on how to continue driving, maintain the formation, or adjust the formation after multiple robots encounter obstacles.
## 3 Results and Case Study: UAV-Assisted Blast Design in a Basalt Quarry
### UAV Equipment and Photogrammetric Data Acquisition  
To define the design assumptions and monitor their implementation, photogrammetric measurement data were collected using a DJI Mavic 3 Enterprise (DJI Sky City, No.55 Xianyuan Road, Nanshan District, Shenzhen, China) unmanned aerial vehicle (Figure 1). This UAV is equipped with a wide-angle camera featuring a 4/3 CMOS sensor. The 20-megapixel sensor includes a mechanical shutter capable of speeds up to 1/2000 s and an image capture interval of just 0.7 s. These features prevent motion blur and enable the operator to conduct photogrammetric missions at flight speeds of up to 15 m/s.  
In addition to the wide-angle camera, the DJI Mavic 3 Enterprise is equipped with a 12 MP telephoto lens that enables up to 56\(\times\) hybrid zoom. This makes it suitable not only for standard photogrammetric missions but also for detailed inspections from considerable distances.  
The UAV can remain airborne for up to 45 min, allowing it to cover areas of up to 2 km\({}^{2}\) in a single mission. It uses the next-generation DJI O3 Enterprise (DJI Sky City, No.55 Xianyuan Road, Nanshan District, Shenzhen, China) transmission system, ensuring a stable video feed (1080 p/30 fps) even under challenging environmental conditions.  
For enhanced safety, the Mavic 3 Enterprise is equipped with the upgraded DJI APS 5.0 system, which uses six wide-angle \"fish-eye\" optical sensors to provide omnidirectional obstacle avoidance without blind spots. An advanced return-to-home (RTH) system automatically calculates the most efficient return route to the launch point, conserving time, energy, and battery life.  
Precise measurements are further supported by an integrated real-time kinematic (RTK) module, which enables centimeter-level positioning accuracy [60].  
The acquired data were processed using Strayos software [58], which is dedicated to blast design and analysis.
## 2 Experimental Program  
### Materials  
A total of three high-strength concrete mixtures were cast. They all shared the same concrete matrix, and the difference was that one of them had no fibers, while the other two had either steel or polypropylene fibers. The mixtures were identified as plain high-performance concrete (HPC), steel-fiber-reinforced high-performance concrete (SFHPC), and polypropylene-fiber-reinforced high-performance concrete (PPFHPC).  
A total of 40 test specimens of each mixture were cast in the form of cylinders with a diameter of 100 mm and a height of 200 mm. Table 1 shows the mixtures that were used.  
The SFHPC contained a volume of 78.5 kg/m\({}^{3}\) of Dramix 3D 45/50 BL hooked-ended steel fibers (BEKAERT, Kortrijk, Belgium), each of 50 mm in length, with a diameter of 1.05 mm, giving an aspect ratio of 45, a fiber tensile strength of 1115 MPa, and a Young's modulus of 200 GPa. In the case of PPFHPC, an amount of 9.1 kg/m\({}^{3}\) of monofilament polypropylene fibers Masterfiber 249 (BASF, Ludwigshafen am Rhein, Germany) was used. These fibers were 48 mm in length with a diameter of 0.85 mm, resulting in an aspect ratio of 56.5. The tensile strength was 400 MPa and the Young's modulus was 4.7 GPa. Both fibers were quite similar in terms of their geometry and the number of fibers inside the concrete mixture. The main difference was the structural behavior and overall stiffness.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dosage** & **HPC** & **SFHPC** & **PPFHPC** \\ \hline Cement (kg/m\({}^{3}\)) & 400.0 & 400.0 & 400.0 \\ Water (kg/m\({}^{3}\)) & 125.0 & 125.0 & 125.0 \\ Superplasticizer (kg/m\({}^{3}\)) & 14.0 & 14.0 & 14.0 \\ Nanosilica (kg/m\({}^{3}\)) & 6 & 6 & 6 \\ Fine aggregate (kg/m\({}^{3}\)) & 800.0 & 800.0 & 800.0 \\ Coarse aggregate (kg/m\({}^{3}\)) & 1080.0 & 1080.0 & 1080.0 \\ Fiber (\% by volume) & – & 1\% & 1\% \\ \hline \hline \end{tabular} HPC is high-performance concrete; SFHPC is steel-fiber-reinforced high-performance concrete and PPFHPC is polypropylene-fiber-reinforced high-performance concrete.  
\end{table}
Table 1: Concrete mixture.  
MasterRoc MS 685 (BASF, Ludwigshafen am Rhein, Germany) nanosilica and a Glenium 52 (BASF, Ludwigshafen am Rhein, Germany) superplasticizer were considered. Siliceous aggregate was used for both fine aggregate and coarse aggregate, with a nominal maximum aggregate size of 4 mm for fine aggregate and 12 mm for coarse aggregate.  
Mixing was done in a rotary mixer and the fibers were gradually sprinkled into the drum by hand. The specimens were cured for 180 days in a curing room at a constant relative humidity of 100% and an ambient temperature of 20 \({}^{\circ}\)C. The specimens were then removed from the curing room and held under laboratory conditions until testing. All the specimens were at least 300 days old when the test campaign began. Thus, the possible strength increase during the fatigue test was minimized.
# Grating-Assisted Fiber to Chip Coupling for SOI Photonic Circuits
## 3 Factors Affecting Grating Loss  
Any propagating mode while encountering a grating coupler exhibits four principal loss mechanisms which is illustrated in Figure 3. Accordingly the power incident \(P_{inc}\) is expressed as:  
\[P_{inc}=P_{clad}+P_{sub}+P_{refl}+P_{tr} \tag{4}\]  
where \(P_{clad}\) denotes the diffracted power to top cladding, \(P_{sub}\) the substrate leakage. \(P_{refl}\) and \(P_{tr}\) denote the back reflection and the power transmitted through grating respectively. For a uniform grating, the scattered field exhibits an exponentially decaying profile. If we assume \(\alpha\) to be the scattered field decaying coefficient, grating power leakage to cladding can be expressed as \(P_{clad}=P_{0}e^{-2\pi x}\). The coupling efficiency is expressed as \(P_{CE}=\eta P_{clad}\), where \(\eta\) is the mode field overlap between the diffracted grating field and the Gaussian fiber mode, which is theoretically limited to 80%. From above, it is clear that both \(P_{clad}\) and \(\eta\) have to be maximized in order to boost overall coupling performance.  
Figure 2: (**a**) Three dimensional (3-D) schematic of a Silicon-on-Insulator (SOI) based fiber to chip surface grating coupler with adiabatic tapers; (**b**) _k-space_ representation of the grating phase matching condition. \(k_{fr}n_{sup}\) corresponds propagation constant of superstrate and \(k_{fr}n_{sub}\) of substrate and \(G\) is the grating reciprocal lattice.