esa-sceva/satcom-chunk-collection huggingface dataset:
# Single-Shot Object Detection with Split and Combine Blocks
###### Abstract  
Feature fusion is widely used in various neural network-based visual recognition tasks, such as object detection, to enhance the quality of feature representation. It is common practice for both the one-stage object detectors and the two-stage object detectors to implement feature fusion in feature pyramid networks (FPN) to enhance the capacity to detect objects of different scales. In this work, we propose a novel and efficient feature fusion unit, which is referred to as the Split and Combine (SC) Block, that splits the input feature maps into several parts, then processes these sub-feature maps with different emphasis, and finally gradually concatenates the outputs one-by-one. The SC block implicitly encourages the network to focus on features that are more important to the task, thus improving network efficiency and reducing inference computations. In order to prove our analysis and conclusions, a backbone network and an FPN employing this technique are assembled into a one-stage detector and evaluated on the MS COCO dataset. With the newly introduced SC block and other novel training tricks, our detector achieves a good speed-accuracy trade-off on COCO test-dev set, with 37.1% AP (average precision) at 51 FPS and 38.9% AP at 40 FPS.  
deep learning; object detection; feature fusion +
Footnote †: journal: Applied Sciences
# Mechanical Properties of Disconnectable Coupling Joints for Steel Bracing under Eccentric Load
## 5 Flexural Performance under Eccentric Load
### Parameters Analysis on Flexural Performance
#### 5.2.7 Influence of Middle-Rib Plate Height  
According to Figure 14(a), the flexural performance of DC joints decreases with the height of the middle-rib plate. Due to the restraint effect of the channel steel on both sides of the middle-rib plate, the height of the middle-rib plate weakly impacts flexural performance. Figure 14(b) shows the influence of middle-rib plate height on initial rotational stiffness and moment resistance. The initial rotational stiffness and moment resistance decrease linearly with the height of the middle-rib plate when the height is less than 430 mm. When the middle-rib plate height increases by 50 mm, the initial rotational stiffness and moment resistance decrease linearly by 7.8 MN\(\cdot\)m\(\cdot\)rad\({}^{-1}\) and 3.8 kN\(\cdot\)m, respectively, and their  
Figure 24: Impact of the channel steel thickness on flexural performance.  
reduction rates are 9.3% and 1.3%, respectively. When the middle-rib plate height exceeds 430 mm, the reduction rate of initial rotational stiffness and moment resistance slightly increase to 10.2% and 1.5%, respectively.
# Improving DFT-Based Image Watermarking Using Particle Swarm Optimization Algorithm
## 3 Proposed Method
### Embedding Process  
According to [31; 32], the red-green-blue (RGB) color model has the most correlated components, the YCbCr has the least correlated and the forward and backward transformations between both color models are linear; in this way, considering a color image \(I\):  
1. Perform RGB-YCbCr color model transformation and isolates the luminance component \(Y(x,y)\) from YCbCr transformation;
2. Supported by a secret key \(k_{1}\), generate a watermark which is a 1-D binary \(\{1,\,0\}\) pseudo-random pattern with zero mean, \(W=\{w_{b}\mid b=1,\,\ldots,\,L\}\), where \(L\) is the length of the watermark;
3. Apply the 2D DFT transform \(F(u,v)\) defined by (1) to the original luminance component \(Y(x,y)\) and obtain the magnitude \(M(u,v)\) and phase \(P(u,v)\) information;
4. Select a pair of radiuses \(r_{1}\) and \(r_{2}\) in \(M(u,v)\) and the annular area \(A=\pi(r_{2}{}^{2}-r_{1}{}^{2})\) between \(r_{1}\) and \(r_{2}\) should cover the middle frequency in \(M(u,v)\) around the zero frequency coefficient \(M(0,0)\);
5. To guarantee the security of \(W\), scramble its data bits using a secret key \(k_{2}\);
6. Encode \(W\) using the spread spectrum modulation DS-CDMA: with a predefined secret key \(k_{3}\), assign to each watermark data bit \(w_{b}\) a pseudorandom binary \(\{-1,\,1\}\) sequence \(g_{b}\) with length \(A/2\). Each \(g_{b}\) sequence is dependent on \(w_{b}\) in the following way: \[\begin{array}{l}if\ w_{b}=0\rightarrow+g_{b}\\ if\ w_{b}=1\rightarrow-g_{b}\end{array}\] (5) The encoded watermark \(W_{DS-CDMA}\) is obtained using (6):\[W_{DS-CDMA}=\sum_{b=1}^{L}\pm g_{b}; \tag{6}\]  
1. Embed the encoded watermark \(W_{DS-CDMA}\) in the coefficients of the upper half of \(M\)(\(u\),\(v\)) in the annular area \(A/2\) that cover the middle frequency, in an additive manner: \[M^{\prime}(u,v)=M(u,v)+\alpha W_{DS-CDMA},\] (7) where \(\alpha\) is the watermark strength factor and \(M\), \(M^{\prime}\), are the original and the watermarked DFT magnitude, respectively. According to DFT symmetrical properties, to produce real values after the watermark embedding, the lower half part of the middle frequency band of \(M\)(\(u\),\(v\)) should be modified symmetrically.
2. Return the watermarked luminance component \(Y^{\prime}\)(\(x\),\(y\)) to spatial domain using the inverse DFT (IDFT) employing \(M^{\prime}\)(\(u\),\(v\)) and the corresponding initial phase \(P\)(\(u\),\(v\)) as shown in (8): \[\begin{array}{l}Y^{\prime}(x,y)=IDFT(F^{\prime}(u,v)),\ where:\\ F^{\prime}(u,v)=M^{\prime}(u,v)\cdot\cos(P(u,v))+M^{\prime}(u,v)\cdot(j\cdot \sin(P(u,v)));\end{array}\] (8)
3. Finally, using the watermarked luminance \(Y^{\prime}\)(\(x\),\(y\)) and the original chrominance information, perform YCbCr to RGB color model transformation and obtain the watermarked image \(I_{\rm w}\).
# A Novel Optimization Design Method of Form Ginding Wheel for Screw Rotor
## 4 Experiment and Results  
### Experimental Setup  
In order to verify the above effects of the installation parameters of the grinding wheel on the rotor profile, a set of screw rotor grinding experiments were conducted on an SU G500H profile grinding machine (Star SU, Farmington Hills, MI, USA) using an alumina grinding wheel (NORTON-3 NQ60-H12 VSP) (NORTON, Shanghai, China). The screw rotor used was a QT500 (HB 170-210). A Variocut G600 HC (Castrol, Taicang, China) was used as the cutting fluid in the grinding process. In current production practice, the installation angle is usually equivalent to the lead angle \(\lambda\). The geometric parameters of the screw rotor were shown earlier, in Table 1; Table 3 shows the experimental installation parameters. The female rotor was ground as shown in Figure 10. Figure 11 shows the machined profile measurement setup with a fully automatic CNC-controlled P26 precision measuring center.  
Figure 9: Profile features of wheel grinding under different installation parameters. (**a**) Center distance, 129.45 mm. (**b**) Center distance, 149.45 mm. (**c**) Center distance, 169.45 mm. (**d**) Center distance, 189.45 mm. (**e**) Center distance, 209.45 mm.  
The distribution of abrasive grains on the surface of the grinding wheel is too complicated to analyze directly. Here, in order to facilitate the research, the following assumptions are made to simplify the surface of the grinding wheel:  
1. The grinding wheel was dressed well before every grinding pass by the diamond wheel.
2. The abrasive grains have the same protrusion height and are evenly distributed on the grinding wheel surface.  
Figure 11: Fully automatic computer numerical control (CNC)-controlled P26 precision measuring center. 1. Printing mechanism 2. Clamping device 3. Screw rotor. 4. Measuring head 5. Display instrument.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Center Distance (mm)** & **Installation Angle (\({}^{\circ}\))** \\ \hline
1 & 129.45 & 44.09 \\
2 & 129.45 & 45.09 \\
3 & 129.45 & 46.09 \\
4 & 129.45 & 47.09 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Installation parameters.  
Figure 10: Grinding experimental setup. 1. Worktable, 2. Diamond dressing wheel, 3. Grinding wheel, 4. Screw rotor, 5. Location sensor, 6. Auxiliary support.  
A set of screw rotor grinding experiments was conducted to verify the proposed method. The installation parameters for the screw rotor in the form grinding process, including the installation angle and the center distance, are listed in Table 3. Based on the screw rotor data (\(x_{t},y_{t}\)), the screw surface \((X(t,\theta),Y(t,\theta),Z(t,\theta))\) can be determined; furthermore, the form grinding wheel can be dressed under the conditions of the given installation parameters. After grinding experiments were carried out under the given conditions, the profile error could be calculated according to the method mentioned in Section 2.
# Design of a Baseband Signal for the 406 MHz Satellite Emergency Radio Transmitter Based on STM32
## 3 Baseband Signal Generation METHOD
### Data Bits Design
#### 3.3.2 Design of the First Protected Data Field  
Bits 25-85 of the information bits, which are also bits 0 to 60 of the first protected data field, is illustrated in Table 2 in detail.  
Variables are set up according to the data bits and are used to calculate the BCH code. The latitude and longitude information is obtained from the GPS receiver; the other data is set up according to the registration information of the transmitter; and the sequence is obtained as \(m_{1}(x)\)
## 5 Analysis and Results
### Limitations  
In this study, the proposed algorithm can be used in different scenarios; as discussed earlier, its performance is enough to fulfill the BCIoT requirement used in our described scenario (discussed in the dataset section of the previous section). However, it is simulated and has some limitations:  
* Its working is fine for block size \(<\)500 KB, and performance could be degraded a little bit for larger block size as discussed in the results section.
* Difficulty level directly affects it because the number of hashes per block is increased, and it is tested on blockchain 2.0.
* Selection of master node cannot be performed twice consecutively for a single node if its block is not broadcasted.
* Validator node cannot become a Master in the next round if it is a validator in the previous block and master does not broadcast this block in the IoT blockchain.
* This algorithm is working in public blockchain and not tested for private blockchain.
* Security is kept in mind during its construction due to vulnerable IoT systems, but it has not been not tested or tried to compromise it.
* It is suitable and working fine for IoT scenarios where a little delay is present or can be tolerated.
## 4 Discussion
### The Potential of Sentinel-2 Optical Data to Analyze Ecosystem BPRs at the Global Scale  
The most striking result of our study is that, when the optical diversity (CV) was calculated from S2 simulated data (thus both spectral and spatial resolutions corresponded to the S2 sensor),across-scale patterns of diversity and productivity still could be observed, with only slightly lower R\({}^{2}\) values compared to the CV calculated from full-range hyperspectral data (Figure 3).  
Such results are opening exciting perspectives on the possible use of S2 to monitor grassland BPRs spatial patterns starting from within-site observations (focusing on the links between spectra and biodiversity indices) and scaling up to the landscape, regional, and global levels of observation. S2 is expected to help ecologists to extensively explore BPRs patterns linked to higher levels of diversity (such as \(\beta\)-diversity) for grassland ecosystem types where single plant size is small. Using remote sensing imagery, selected optical-based proxies of plant traits are expected to significantly improve the combined analysis of taxonomic and functional diversity [31]. Also, further studies are needed to assess the suitability of coarser resolution data to monitor diversity and productivity patterns in other ecosystem and vegetation types (e.g., forests, shrublands) where the higher single plant size is expected to facilitate the detection of lower-level diversity.
# Novel MIMO Antenna System for Ultra Wideband Applications
## 4 Diversity Performance Parameters
### Mean Effective Gain (MEG)  
In comparison to an isotropic antenna, MEG is a measurement of how much power the antenna elements receive. The S-parameters of the MIMO antenna system can be used to calculate the MEG as [22; 28]:  
\[MEG_{i}=0.5\Big{[}1-|S_{m}|^{2}-|S_{nm}|^{2}\Big{]} \tag{3}\]  
and  
\[MEG_{j}=0.5\Big{[}1-|S_{nm}|^{2}-|S_{mm}|^{2}\Big{]} \tag{4}\]  
For the proposed MIMO antenna, the MEG is predicted between port-1 & port-2 and port-1 & port-3 (see Figure 16). It can be noted that the MEG of the ports is \(\leq\)\(-\)3 dB, while the ratios of MEG1/MEG2 and MEG1/MEG3 are less than 3 dB.  
A comparison among proposed and previously presented four-port UWB MIMO antenna designs is presented in Table 1. The proposed design offers high impedance bandwidth compared to the designs presented in [8; 12; 13; 19]. Although the dimensions of the proposed MIMO antenna are large, it provides comparable isolation and ECC characteristics without the utilization of any isolation enhancement network compared to the designs listed in Table 1. Moreover, the design of [18] did not utilize any isolation enhancement network, so it offers a high value of ECC compared to the proposed design. In addition, the authors in [8; 13; 14; 16; 19] calculated ECC using S-parameters, which is not recommended for practical applications.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & **Array Size** & **Isolation Enhancement** & **Frequency Band** & **Bandwidth** & **Isolation** & **ECC** & **DG** \\  & **(mm\({}^{2}\))** & **Network** & **(GHz)** & **(GHz)** & **(dB)** & **(dB)** \\ \hline
[8] & 32 \(\times\) 36 & FSS & 3–10 & 7 & \(>\)16 & \(<\)0.0025 & – \\
[12] & 45 \(\times\) 45 & FSS + Decoupling Structure & 3.1-10.6 & 7.5 & \(>\)10 & \(<\)0.17 & \(>\)9.92 \\
[13] & 43 \(\times\) 40 & Decoupling Structure & 3–11 & 8 & \(>\)15 & \(<\)0.2 & \(>\)9.75 \\
[14] & 72 \(\times\) 72 & Neutralization Lines & 2.8–13.3 & 10.5 & \(>\)18 & \(<\) 0.06 & – \\
[16] & 58 \(\times\) 58 & Decoupling Structure & 3–16 & 13 & \(>\)18 & \(<\)0.07 & – \\
[18] & 40 \(\times\) 40 & Not utilized & 3–13.5 & 10.5 & \(>\)15 & \(<\)0.4 & \(>\)9.9 \\
[19] & 92 \(\times\) 92 & Neutralization Lines & 2.84–11 & 8.16 & \(>\)15 & \(<\)0.02 & – \\ This work & 90 \(\times\) 90 & Not utilized & 2.77–12 & 9.23 & \(>\)15 & \(<\)0.1 & \(>\)9.97 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison among proposed and previously published four-port UWB MIMO antennas.  
Figure 16: MEG between (**a**) port-1 & port-2 and (**b**) port-1 & port-3.
# Frequency-Domain Lifting-Line Aerodynamic Modelling for Wing Aeroelasticity
## 2 Frequency-Domain LLT-like Modelling
### Relation between Bound Circulation and Circulatory Lift
In order to overcome this difficulty, it is convenient that the following rational matrix approximation form of the aerodynamic matrix along the imaginary axis of the complex plane is  
\[\mathbf{E}(k)\cong(ik)^{2}\mathbf{A}_{2}+ik\mathbf{A}_{1}+\mathbf{A}_{0}+ \mathbf{Q}(ik\mathbf{I}-\mathbf{A})^{-1}\mathbf{R} \tag{27}\]  
where \(\mathbf{A}_{2}\), \(\mathbf{A}_{1}\) and \(\mathbf{A}_{0}\) are \([2\times 2]\) matrices, \(\mathbf{A}\) is a diagonal \([N\times N]\) matrix, where \(N\) is the number of aerodynamic poles that can be arbitrarily increased to better approximate the aerodynamic transfer functions, and \(\mathbf{Q}\) is a \([2\times N]\) matrix and \(\mathbf{R}\) is a \([N\times 2]\) matrix. All these are constant-coefficient matrices which can be determined by a least-square approximation technique [26]. Indeed, the combination of Equation (27) with Equation (26) followed by the transformation into time domain yields, for \(\tau=t\,V/b\),  
\[ \begin{cases}\mathbf{f}(\tau)=\mathbf{A}_{2}\,\ddot{\mathbf{q}}+\mathbf{A}_{ 1}\,\dot{\mathbf{q}}+\mathbf{A}_{0}\,\mathbf{q}+\mathbf{Q}\,\mathbf{r}\\ \dot{\mathbf{r}}=\mathbf{A}\,\mathbf{r}+\mathbf{R}\,\mathbf{q}\end{cases}  \tag{28}\]  
which represents the state-space model of the wing generalised forces derived from the frequency-domain LLT solver presented in this work, perfectly suitable for aeroelastic stability analysis and control purposes. In Equation (28), \(\mathbf{r}\) denotes the vector of \(N\) additional aerodynamic states introduced by the rational contribution in Equation (27).
## 5 Optimization of LSTM for Fall Detection System
### Sampling and Regularization Rate  
In this section, the impact of different samplings of input parameter and different regularization rates in the STM-LSTM-based fall detection system is examined. Figure 11 shows validation data accuracy vs. regularization rate in the STM-LSTM-based fall detection system with respect to the different sampling intervals of input parameter. The regularization rates are 0.00015, 0.00065, 0.0015, 0.0065, 0.015, 0.065, and 0.15. The black squares, red circles, blue up-triangles, green down-triangles, and purple diamonds denote different sampling intervals of 1, 3, 5, 7, and 9, respectively. Peak values of accuracy of sampling intervals 1, 3, 5, 7, and 9 are 96.42% at \(\lambda\) = 0.00065, 96.42% at \(\lambda\) = 0.0015, 95.53% at \(\lambda\) = 0.0065, 94.64% at \(\lambda\) = 0.0065, and 94.64% at \(\lambda\) = 0.0065, respectively. As the sampling interval increases to 5, the regularization rate at the peak of accuracy increases, but when it is 5 or more, the regularization rate at the peak of accuracy is the same. When the sampling interval increases, the peak values of accuracy decrease. As the sampling interval increases, the length of input data decreases, and thus the noise can be slightly reduced, and the complexity of the input data can be reduced. Therefore, overfitting can occur due to a small number of samples [47]. An excessive increase of sampling interval is more dependent on the simplification of data than the noise reduction, resulting in a decrease in accuracy than an increase in overfitting.
## 6 Discussion
### Geological Significance and Application to Mineral Exploration  
The above spectra and EPMA analysis show that the chemical composition of chlorite around BIFs and high-grade iron ore is obviously different. This is probably due to their different formation conditions. Previous studies on the Qidashan Iron Deposit showed that the BIFs in this area were deposited in an oxygen-deficient environment on the seafloor and, finally, formed through low-to-medium-grade metamorphism. The high-grade iron ores were formed from BIFs protore through iron reactivation-reprecipitation process by neutral-acidic hydrothermal solution. Correspondingly, the chlorites related to BIFs were formed in the low-to-medium-grade regional metamorphism, whereas the chlorites associated with high-grade iron ores were formed by neutral-acidic Fe-rich hydrothermal alteration, and the protoliths is argillaceous siltstone [14; 44; 45; 46]. The difference in the chemical composition of chlorite caused by the forming conditions is well recorded in the wavelength position of chlorite. The Gongchangling Iron Deposit is the largest high-grade iron ore deposit in Anshan-Benxi area, where, similarly, the wall rocks around the high-grade iron ore suffered strong chloritization and the chlorite in alteration wall rocks is Fe-rich [13; 14; 47]; this is also the case for other high-grade iron ore sites such as Nanfen, Wangjiapuzi, etc. [14]. In this means, the wavelength of chlorite can be used as a favorable tool for the high-grade iron ore exploration in the entire Anshan-Benxi area. Since the bandpass of hyperspectral imaging systems is less than 10 nm, these results may also provide a basis for the aerial or satellite hyperspectral remote sensing mineral exploration in the near future.
## 3 Design of Array Antenna  
### Single Element  
Figure 4a shows the configuration of the microstrip CP patch antenna. The antenna is printed on the top layer of the substrate (Rogers RT6006 of thickness = 1.27 mm, \(\epsilon_{r}=6.15\), and tand = 0.0027) while the ground is printed on the bottom layer of the substrate. The corners of the patch antenna are truncated at 45\({}^{\circ}\) to split the field in two orthogonal modes with equal magnitude and 90\({}^{\circ}\) phase shift to generate the CP. The design is optimized to achieve the low AR required to achieve circular polarization at the desired frequency.  
Figure 4: Configuration of **(a)** Single element. **(b)** 2 \(\times\) 2 SR arrays using the presented SP feed: (\(w_{f}=1.7\), \(l_{1}=4.495\), \(l_{stub}=1.6\), \(l_{2}=0.945\), \(w_{c}=1.485\), \(w_{p}=6.74\), \(w_{1}=0.186\) mm).  
Figure 3: Performance of SR phase feed: **(a)** S-parameters of SR feed with open stub. **(b)** Phase difference of the signals between the consecutive output ports with open stub. **(c)** S-parameters of SR feed without open stub. **(d)** Phase difference of the signals between the consecutive output ports without open stub.
## 4 Conclusions  
In this paper, sandstone treated by a coupled effect of F-T weathering and acid erosion were tested by NMR, then microscopic characteristics, including porosity, pore size distribution, permeability, fractal dimensions and their correlation, were acquired and analyzed. The conclusions are summarized as follows.  
1. The porosity of sandstone increases with F-T cycle increase, while the porosity increase grows with pH decrease during 10 F-T cycles. F-T weathering contributes more to porosity growth than acid erosion in this experiment.
2. In the experiment, macro porosity, meso porosity and micro porosity account for the largest, second and least ratio of porosity growth. Meso porosity, micro porosity and macro porosity account for the largest, second and least ratio of total porosity after 40 F-T cycles. Acid erosion plays a major role in micro pores' development. F-T weathering plays major role in meso pores' and macro pores' development.
3. Permeability obviously increases with F-T cycle increase, while acid erosion exerts little influence on permeability increase overall. The permeability of sandstone immersed in acid solution with pH equal to 2.8 reached 1720.76 \(\times\) 10\({}^{-4}\) mD from the original 79.46 \(\times\) 10\({}^{-4}\) mD after 40 F-T cycles.
4. The fractal dimensions of meso pores (D\({}_{2}\)) range from 2.639 to 2.738, and the fractal dimensions of macro pores (D\({}_{3}\)) range from 2.841 to 2.979. D\({}_{2}\) and D\({}_{3}\) increase with F-T cycle increase overall, and they increase with pH decrease overall.
5. Porosity has an exponentially strong correlation with permeability. D\({}_{2}\) and D\({}_{3}\) have good linear correlation with permeability, while the correlations between porosity and fractal dimensions are not that obvious.  
Conceptualization, S.Y. and H.D.; methodology, S.Y. and H.D.; software, G.T.; validation, S.Y., J.D. and G.T.; formal analysis, S.Y.; investigation, J.D.; resources, J.D.; data curation, G.T.; writing--original draft preparation, S.Y.; writing--review and editing, S.Y.; visualization, G.T.; supervision, H.D.; project administration, H.D.; funding acquisition, H.D. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China under Grant number 51874352.  
The authors declare no conflict of interest.
# Research on Trajectory Planning and Tracking Methods for Coal Mine Mobile Robots
## 2 Trajectory Planning Based on MINCO Trajectory Classes
### Trajectory Planning Based on MINCO Trajectories and Safe Corridors
#### 2.2.2 Safe Corridor Generation
If there are a total of \(\mathbf{M}\) paths from the initial point to the goal point, then the passable region represented by the safety corridor can be expressed as:  
\[\widetilde{\mathcal{F}}=\underset{i}{\cup}\mathcal{P}_{i}^{\mathcal{H}} \tag{29}\]  
where \(\mathcal{P}_{i}^{\mathcal{H}}=\{\mathbf{x}\in R^{m}|\,\mathbf{A}_{i}\mathbf{x}\mathbf{\preceq} \mathbf{b}_{i}\}\) denotes the convex polyhedron obtained after the shrinkage process corresponding to the \(i\)th path.