esa-sceva/satcom-chunk-collection huggingface dataset:
# Semantic-Enhanced Foundation Model for Coastal Land Use Recognition from Optical Satellite Images
## 5 Experiment
### Coastal Land Use Similarity Analysis  
This subsection measured the similarity of coastal land uses between two collected optical images. We selected 500 odd-numbered images from the 1000 images and sequentially combined each two images into a group. This way, we created 200 groups that each contained two images. Then, we computed the similarity among those two images in every group using binary land cover classification with ViTAE-RVSA, multi-class land cover classification with SAM and ViTAE-RVSA, multi-label land cover classification with SAM and ViTAE-RVSA, and the proposed methodology.  
Figure 7: Illustration of three types of selected image groups used for land use similarity evaluation, which are shown in (**A**), (**B**) and (**C**), respectively. Each image group was enclosed by a purple box.  
The images in Figure 7B explained why the foundation model was considerably competent in supporting land use recognition. In this experiment, we set 3 as the default image gridding scale. Although those two images in each group had approximately similar land covers on a global scale, they represented two different land uses. In the left image of each group, PNASNet recognized the land use category of the right image as sparse residential, which were not included in the left image. Otherwise, the integrated foundation model involving SAM and ViTAE-RVSA could correctly recognize this. Moreover, the land use in the left image is a purely natural land with no components of residential land cover. Similarly, in the middle group, a bridge in the left image was not included in the right one, meaning the land use of the left image might contain some transport functionalities. This could be recognized by semantic analysis of the proposed method.  
In Table 2, the proposed approach outperformed multi-label classification to some extent because of the quantitative weighting on each coastal land cover with the proposed VSM. In the right group of Figure 7B, multi-label classification recognized and equally weighted the impact of the sparse residential area and sand. Thus, multi-label classification identified these two images as mutually similar. The proposed method used the VSM to quantitively weight the impact of each land cover category and then differentiated these two images according to the land use.  
Moreover, the results generated by the proposed method additionally presented the advantages of foundation models. In the left images of each group shown in Figure 7C, we found that PNASNet generally could not effectively differentiate a bridge and pier within an incomplete structure. However, the integrated foundation model involving SAM and ViTAE-RVSA could differentiate between them. As depicted in the right image of each group in Figure 7C, PNASNet mistakenly recognized the abnormal water surface as sand, but the integrated foundation model involving SAM and ViTAE-RVSA correctly recognized it.
# A No Reference Image Quality Assessment Metric Based on Visual Perception +
## 7 Results and Discussion
### Experiments and Relate Analysis of Image Quality Evaluation
#### 7.2.2 Performance Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **JPEG** & **JP2K** & **BLUR** & **WN** & **FF** & **ALL** \\ \hline \multirow{2}{*}{**RMSE**} & **ROI-BRISQUE** & 2.5975 & 3.9836 & 2.2188 & 2.6453 & 3.9494 & 4.7613 \\  & **BRISQUE** & 4.0339 & 5.4350 & 4.0374 & 3.3340 & 5.6078 & 6.5468 \\ \hline \multirow{2}{*}{**KROCC**} & **ROI-BRISQUE** & 0.8071 & 0.7927 & 0.8384 & 0.8933 & 0.7398 & 0.8021 \\  & **BRISQUE** & 0.7691 & 0.7619 & 0.8286 & 0.8792 & 0.7237 & 0.7768 \\ \hline \multirow{2}{*}{**PLCC**} & **ROI-BRISQUE** & 0.9867 & 0.9693 & 0.9801 & 0.9927 & 0.9719 & 0.9465 \\  & **BRISQUE** & 0.9551 & 0.9090 & 0.9498 & 0.9903 & 0.9148 & 0.9377 \\ \hline \multirow{2}{*}{**SROCC**} & **ROI-BRISQUE** & 0.9858 & 0.9690 & 0.9865 & 0.9908 & 0.9561 & 0.9522 \\  & **BRISQUE** & 0.9647 & 0.9139 & 0.9479 & 0.9843 & 0.9053 & 0.9314 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of different distortion types. Figure 15. Obviously, the lower the confidence interval with a higher median SROCC, the better the performance is. The plots show that the ROI-BRISQUE is not statistically significantly different in performance. In addition, in order to evaluate the statistical significance of performance of each algorithm, we use one-sided t-test between those correlation values [50]. We tabulate the results of such statistical analysis in Table 6. The null hypothesis is that the mean correlation for the row is equal to the mean correlation for the column at the 95% confidence level [51]. The other hypothesis is that the mean correlation of the row is greater or lesser than the mean correlation of the column. In the Table 6, the value of \"1\" indicates that the row method is statically superior to the column method, \"\(-1\)\" indicates that the row method is inferior to the column method, and \"0\" indicates that the row and the column methods are equivalent. Figure 14. Scatter plots of JPEG, JP2k, WN, BLUR and FF. Scatter plots of predicted score versus subjective DMOS on LIVE IQA database. (**a**) JPEG database subset; (**b**) JPEG2000 database subset; (**c**) Gaussian white noise database subset; (**d**) Gaussian blur database subset; (**e**) fast-fading database subset. Figure 15. Mean SROCC value of the algorithms evaluated in Table 4, across 1000 train-test on the LIVE IQA database2. The error bars indicate the 95% confidence interval. As observed from the Table 6, we can see that the ROI-BRISQUE is statistically better than other IQA methods listed in the Table 6.
## Appendix C Relation with Lambert-Beer law  
For convenience, we recall here the connection between the transition rate \(w(\omega)\) and the Lambert-Beer law by which absorption experiments are usually interpreted (for consistency with the rest of this work, we adopt SI units, which, when needed, consistently lead to atomic units [PERSON] et al. (1997). For this reason, instead of the molecular absorbivity \(\varepsilon\), we use here the molecular cross section \(\sigma\) (whose dimension is an area):  
\[-ln\frac{I(\omega)}{I_{0}(\omega)}=A=\sigma Cl.\] (A12)  
\(A\) is the absorbance, \(I\) the sample length, and \(C\) represents the number of absorbing molecules per unit volume (number concentration). The differential form of the Lambert-Beer law, corresponding to the differential absorption \(dA\) caused by light traversing the differential path \(dl\), is the following:  
\[\sigma Cdl=dA=-d\left[ln\frac{I(\omega)}{I_{0}(\omega)}\right]=-\frac{dI}{I}.\] (A13)Hence, the intensity loss due to absorption within the path \(dl\) is:  
\[-dI=\sigma CIdl. \tag{10}\]  
We remark that \(-dI\) represents the energy loss per unit time over the cross section \(S\) of the light beam, and it is caused by the rate of energy absorbed by the molecule, \(w(\omega)\), times the number of molecules \(dN=CdV\) within the volume \(dV=Sdl\), divided by the cross section \(S\) of the light beam:  
\[-dI=\frac{w(\omega)C(Sdl)}{S}=w(\omega)Cdl. \tag{11}\]  
Therefore, by equating the two expressions of \(-dI\) given above in Equations (10) and (11), one obtains the molecular cross section \(\sigma\):  
\[\sigma=\frac{w(\omega)}{I}= \tag{12}\] \[=\frac{1}{3}\frac{\omega}{c\varepsilon_{0}}\left[\Im\left[Tr \left(\mathbf{\alpha}^{ee}(-\omega;\omega)\right)\right]\pm\frac{2}{c}\Re\left[Tr \left(\mathbf{\alpha}^{em}(-\omega;\omega)\right)\right]+\frac{1}{c^{2}}\Im\left[ Tr\left(\mathbf{\alpha}^{mm}(-\omega;\omega)\right)\right]\right]=\] \[=\frac{1}{3}\frac{2\pi}{\lambda\varepsilon_{0}}\left[\Im\left[ Tr\left(\mathbf{\alpha}^{ee}(-\omega;\omega)\right)\right]\pm\frac{2}{c}\Re\left[ Tr\left(\mathbf{\alpha}^{em}(-\omega;\omega)\right)\right]+\frac{1}{c^{2}}\Im\left[ Tr\left(\mathbf{\alpha}^{mm}(-\omega;\omega)\right)\right]\right].\]
## 3 Phase Modulation Effects of the Parameters and Parameter Selection
### Phase Modulation Effects of the Parameters
#### 3.2.1 Phase Modulation by Varying Only \(\delta_{k}\)
\(2\pi\left(f_{0}+a_{\delta 1}\right)\), which implies that \(a_{\delta 1}\) can be used to change the period of the patterned grating. This conclusion is consistent with the phenomenon found in an EP-SBIL in Ref. [21]. \(\gamma(x)\) is an average exposure contrast plus an \(F_{\mathrm{P2}}\) frequency component. With a constant step size and small \(a_{\delta 1}\), \(a_{\delta 1}\) is smaller, the average exposure contrast is higher, and the amplitude of the \(F_{\mathrm{P2}}\) component is smaller. With the general parameters for numerical calculation, the average \(\gamma(x)\) is better than 0.9 when \(\mid a_{\delta 1}\mid\)\(<\) 1.4. To maintain the exposure contrast, the adjustment range of the grating density is limited to \(\pm\)1.4 L/mm. (3) \(a_{\delta 2}\
eq\) 0, \(a_{\delta 1}\
eq\) 0 and \(a_{\delta 0}\
eq\) 0. \(\Psi_{T}(x)\) and \(\phi_{e}(x)\) are basically piecewise quadratic polynomials. \(\delta_{k}\) expressed with Equation (17) is a special case of this situation. \(\phi_{e}(x)\) in Figure 2 is the piecewise quadratic with a quadratic coefficient of zero. The results are similar to those in Figures 2 and 3. \(\phi_{e}(x)\) increases with \(x\), and \(\gamma(x)\) fluctuates between 0 and 1. The cusps of \(\phi_{e}(x)\) and the lowest \(\gamma(x)\) are located at the same \(x\) coordinates, and their spatial frequency is \(\mid a_{\delta 2}\mid/(2\pi)\). There is also the \(F_{\mathrm{P2}}\) frequency component caused by the phase inconsistency in the adjacent scans. In other words, the desired VLS grating cannot be obtained by only varying \(\delta_{k}\) in the form in Equation (18). \(\delta_{k}\) can compensate for a certain phase error or change the density of an equal-period grating in a small range.
# Several Tree-Based Solutions for Predicting Flyrock Distance Due to Mine Blasting
## 2 Model's Background
### RF Model  
RF was developed based on the CART DT algorithm by [PERSON] [66]. This algorithm combines a large number of DTs and can be used as a classification or regression method without making any prior assumptions about their association with the response variable [67]. At the beginning of the RF algorithm, samples are created using a bootstrap sample selection of the dataset, and each of these bootstrap-created samples constructs one RF tree. In addition, the unused samples from the bootstrap selection procedure, called the Out Of Bag (OOB) samples, are used in the validation process. Figure 2 shows the flowchart of the RF algorithm.  
Figure 1: A DT structure consists of root nodes, decision nodes, and terminal nodes.  
Figure 2: Flowchart of RF algorithm.  
In classification problems, the mode of voting of trees, and regression problems, the average value would be selected as the predicted value of RF [66]. The structure of each tree in RF can be controlled by (i) minimum node size, (ii) the number of trees, and (iii) the level of randomness [68]. In comparison with other machine learning techniques, RF has some advantages, e.g., (a) developed trees can be saved and used for future references, (b) is not susceptible to overfitting problems, (c) lower training time and faster prediction, and (d) embedded feature selection makes it easy for RF to rank parameters by importance [69].  
Meanwhile, one of the essential features of RF that influences its final performance is its splitting feature. In the most developed and published models using RF, the Gini-index splitting feature was used [70]; however, due to the use of the Scikit-Learn Python library [71] in this study, two splitting features were available, including Gini-index and Entropy. The GINI index uses the impurity of nodes to measure the minimum expected error rate for the set. The entropy criterion measures the information in a data group and splits the tree so that it gives more information at each split. The more extensive the entropy, the better the homogeneity of the subsets [72].
## 6 Performance Evaluations
### Simulation Model  
Our proposed algorithms were rigorously tested through a set of experiments built into the Matlab environment. All of the tests were performed on a Windows 7 machine equipped with a 2.3 GHz Intel Core i5 dual-core CPU, 4 GB of RAM and the latest updates. The simulation setup includes a mobile sink and N sensor nodes spread out across a 1000 m\({}^{2}\) region. All sensor nodes are assumed to be stationary after deployment. After being deployed into a field, all sensor nodes are supposed to remain in one place. It is also assumed that the mobile sink will begin its journey through the cluster heads from the centroid location. The mobile sink must move between the cluster nodes and then return to its original place. The simulation results for homogenous networks are presented. Moreover, all subsequent tests are carried out on the premise that the network is impacted by environmental events such as forest fires and rainstorms.  
As in [15], we used a radiation model to simulate wildfires. The data flow is generated by a Poisson process with a mean parameter, \(\lambda\). In addition, the study uses a model of WSNs with lossy links (see [34] for more information). The energy-consumption model described in [34] is used in our experiments. The simulation settings are summarised as follows.  
The uniform random deployment strategy is used for the conducted experiments. An amount of 300 sensor nodes are deployed in the monitored field with a maximum retransmission number limited to 4. Additionally, the packet size and the buffer size are set to 50 bytes and 128 bytes, respectively. Moreover, the path loss exponent is set to 3, and the frequency is set to 868 MHz. For the transmission power, the initial energy of nodes and noise floor are set to 0 dBm, 125 mJ, and \(-\)115 dBm, respectively. Regarding the maximum radio range, data rate, shadow fading variance, reference distance, event area (wildfire), event area (rainstorm), extreme range (temperature), and extreme range (relative humidity) are set to 150 m, 20 Kbps, 3, 1 m, circles with a radius from 5 m and 50 m, block-shape with coverage from 1000 m\({}^{2}\) to 40,000 m\({}^{2}\), [0, 50] \({}^{\circ}\)C, [30%, 80%], [\(-\)10, 100] \({}^{\circ}\)C, and [0%, 100%], respectively.
## 5 Conclusions  
This paper proposes using the HSCW to preprocess the pulsed CDWL's wind speed echo, aiming to solve the traditional inversion algorithm's spectral leakage problem. The first- and second-order HSCW can boost the system's incoherent accumulation SNR. The simulation experimental results demonstrate the excellent performance of the HSCW. Compared with the RW, its SNR has increased by 3.28 dB. Meanwhile, the RMSE values of the first- to third-order HSCW are as low as 164.2 kHz, 116.7 kHz, and 101.9 kHz, respectively.  
To assess the HSCW's effectiveness in wind speed inversion, four caliber inversion results were compared with a commercial pulsed CDWL. Linear fitting results at nearby points show that the second-order HSCW's measurements are closer to the commercial one than the RW and first-order HSCW. The RMSE of the second-order HSCW is 0.184 m/s, while the RW is 0.449 m/s, and the first-order HSCW is 0.266 m/s.  
It cannot be denied that compared to traditional window functions, the HSCW algorithm has a higher delay due to its self-convolution characteristics. As the order increases, the algorithm delay also increases, but it is still significantly better than the adaptive distance gate algorithm delay. In the future, the HSCW order can be adaptively selected to further enhance the real-time performance in complex environments.  
Conceptualization, S.T.; Investigation, Z.H.; Writing--original draft, C.S.; Writing--review & editing, P.L. and X.Y.; Supervision, N.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Changchun University of Science and Technology, grant number [2022 YFB2903402].  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
Figure 17: RMSE of approximate points.
# Laser Lens Size Measurement Using Swept-Source Optical Coherence Tomography
## 2 Principle and Systems
### OCT System  
The experimental setup of SS-OCT is represented schematically in Figure 1. It mainly consists of a laser swept-source, a [PERSON] interferometer, a balanced detector, two-axis Galvano mirror and a data acquisition device.  
The swept-source (SL1310V1-10048, Thorlabs) used in the system has a sweep frequency of 100 kHz, central wavelength of 1.3 \(\upmu\)m and -3 dB bandwidth of 80 nm. Its averaged output power is 25 mW. One output from the swept-source as a trigger signal is directly connected to the data acquisition card to control the data acquisition when the laser sweeps. The other output from the swept-source is lead to a 5/95 coupler 1 (1310 nm, Thorlabs), and then divided to two parts. The beam with 95% of the total power is introduced into a fiber-based [PERSON] interferometer comprising a circulator and a 50/50 coupler 2. The beam with 5% of the total power is connected to the MZI-clock (INT-MZI-1300, Thorlabs) where k-trigger is obtained for external clock generation. One output of the coupler 2 is attached to sample arm which comprises a fiber collimator lens (36 mm focal length, Thorlabs) and two-axis Galvano mirror (OCTP-1300 NR, Thorlabs). The other output of the coupler 2 is attached to a reference arm which comprises identical collimator lens and a silver-plated mirror. The lights from the sample arm and the reference arm re-coupled by coupler 2, and then interferes when the optical path difference (OPD) is smaller than coherence length. The spectral interference signal is detected by an InGaAs balanced detector (PDB47C, Thorlabs). The voltage is lead to data acquisition card (ATS9350, Alazar) with sampling rate of 500-Msamples/s. The signal processing procedure is performed by PC (T7810, Dell) with 32 GB storage memory and a LabVIEW program.
# FPGA-Based Methodology for Detecting Positional Accuracy Degradation in Industrial Robots
## 2 Materials and Methods
### Discrete Wavelet Transform
Each frequency band can be independently analyzed, compressing the signal information and reducing the computational burden for further analysis. Additionally, the DWT algorithm and its inverse can be converted to matrix operations, facilitating its implementation in FPGA-based digital systems.
## 7 Conclusions  
The study presented a practical approach to checking whether a massive structure is safe under seismic excitation. To do so, the classical reliability methodology has been used to give the safety index of an aged-CGD. To investigate the site-specific hazard effect of the aged-CGD, the PSHA is used extensively in this study, where PSHRisk-tool has been used to determine the hazard curve and deaggregation plot. IDA-based seismic performance assessment is carried out to set the limit state in two-levels. The failure probability for the structural response has been adopted by fragility analysis in each performance level. A proper 3D FEM of required CGD in Korea is reflected in its dynamic responses through the time history analysis. To excite the FEM, a set of 30 EQs was selected in that site for analyzing the maximum contribution of IM from the hazard curve. Seismic risk evaluation combining the failure probability with the integration of the slope from the hazard curve is considered here for illustrating the reliability index curve. The hygro-chemo-mechanical effect is taken for defining the aging condition. The target reliability index is expressed by two different performance levels, which encompasses damage levels of the CGD. It is found that the GM characteristics showed a great effect on the dispersion of the fragility curves. Moreover, it is notified at the 50 and 75 aged-CGD, the material property is become deteriorated due to strength reduction. The reliability index curve showed the safety pattern was for the same year, which indicates it is less safe than the other year's consideration. For the two EQs level carried from the previous study, the damage index for the corresponding EQs level has been listed to compare the reliability index with the target one (from the capacity of the structure). This safety evaluation process will give information about the safety level of a CGD through the reliability index check. The safety range will be satisfied if the reliability index for any earthquake level is greater than the target reliability index of the aged-CGD. The life safety has been taken in this study to show the maximum reliability index, where the target reliability index divides the safe and unsafe zone for the CGD for a considered earthquake. The safety measurement of the seismic site in Korea depicts the aged-CGD as safe in all taken earthquake levels but unsafe for some fractile case of the different fractile level. Lastly, it can be said that the main advantage of this proposed approach is that it provides a practical guideline to the user for investigating the safety range of potential earthquakes. The time-dependent aging consideration is a new aid to the reliability index for predicting the safety check of the deteriorated structure. This method can be implemented in another structure or can be updated suitably with another set of earthquakes.