esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 The State of Phenological Monitoring in India
They effectively tracked the phases of greenup, leaf maturity, senescence, and dormancy and reported that greenup occurred four days earlier in 2018 than in 2017, whereas dormancy occurred one day later. [PERSON] et al. (2013) used digital cameras to monitor forest phenology in Madhav National Park in Madhya Pradesh and compared their findings to satellite data [80]. However, the use of digital cameras for phenology monitoring in India is limited, with studies often focusing on a restricted range of plant species and short monitoring periods. Figure 5: The distribution of ground phenology monitoring sites in India from published journals since 1975. It is crucial to establish a long-term phenological monitoring network to better understand the drivers and responses to climate change. Short-term studies of 3-5 years (the average period of most financed research projects or a university doctorate thesis) are profoundly inadequate to capture the intrinsic unpredictability of natural systems caused by environmental stochasticity, as evidenced by numerous recent studies conducted worldwide. Indeed, the results of such short-term research can be quite deceiving. Given the absence of a comprehensive long-term ground-based phenological monitoring system across India's diverse forest types and the limitations of satellite-based monitoring, there is a pressing need for a new monitoring network. Daily phenological observations using time-lapse digital cameras, such as PhenoCam, offer a solution to address these challenges. This approach provides high temporal and spatial resolutions, allowing for cost-effective, labor-efficient data collection, long-term monitoring, and reliable canopy phenological monitoring. Adopting digital repeat photography techniques can facilitate the establishment of an extensive and reliable phenological monitoring network in India [54; 81; 82]. Figure 6: Ground-based forest phenology monitoring sites in India. (Based on the past literature since 1975. We identified 37 ground observation sites as follows: 31 short-term and six long-term).
# Development and Optimization of a SPME-GC-FID Method for Ethanol Detection
## 3 Results
### Salt Concentration  
The effect of salt on the efficacy of SPME headspace extraction of the ethanol was investigated by adding varying amounts of salt to the analyte solution to induce the salting-out effect. The salting-out effect occurs when salt is added to a solution containing analytes (molecules of interest), causing the analytes to become less soluble in the solution and precipitate out of the solution. Sodium chloride can improve the salting-out effect by raising the ionic strength of the solution, resulting in greater recovery rates for the analytes of interest. NaCl concentrations between 0 to 40% (\(w/v\)) were investigated and the results are shown in Figure 3.  
Overall, five concentrations of NaCl (0%, 10%, 20%, 30%, and 40% \(w/v\)) were tested, and it was found that the highest peak areas of ethanol quantification were obtained at a sodium chloride concentration of 30% \(w/v\), with a statistical significance of * \(p<0.05\). Increasing the salt concentration beyond 30% resulted in a decrease in the obtained signals. At lower salt concentrations, the presence of some ions in the sam  
Figure 3: The effect of salt concentration (NaCl—0%, 10%, 20%, 30%, and 40%) on ethanol extraction (ethanol concentration: 10 mg/L; extraction temperature: 50 \({}^{\circ}\)C; extraction time: 30 min; SPME fiber: CAR/PDMS). Results are shown as the mean \(\pm\) SD (\(n=3\)). * \(p<0.05\) for 30% and 40% NaCl when compared with 0% NaCl, using the ANOVA test, followed by [PERSON]’s multiple comparisons tests.  
the extraction of ethanol by promoting favorable interactions with the SPME fiber coating and the analyte molecules. However, when the salt concentration becomes too high, the excess ions can compete with the analyte for adsorption sites on the SPME fiber or disrupt the solubility of ethanol in the sample matrix. The solubility of NaCl in water is influenced by temperature, and even at 50 \({}^{\circ}\)C, the maximum solubility is approximately 35.7 g/100 mL. When attempting to create a 40% NaCl solution, the saturation point is exceeded, leading to the formation of a saturated solution where excess salt does not dissolve. Consequently, this results in an impractical concentration that lacks reproducibility in experimental settings. This information is reinforced through the chromatograms presented in Figure S6 of the Supplementary Material section. The excess undissolved salt can interfere with the extraction process, making it challenging to achieve consistent and reliable results. Therefore, our experimental findings indicate that a 40% NaCl concentration is not conducive to effective ethanol extraction due to its impracticality and lack of reproducibility. Therefore, the optimal salt concentration for this study is 30%.
## 6 Results
### Simulation Results  
The time-response simulations are performed in MATLAB. Figure 8 presents the simulation results in the \(dq\) reference frame, with the hierarchical control response based on PIs shown on the left and the response of the proposed MPC strategy displayed on the right. Initially, the VSI supplies a resistive load of 47 \(\Omega\) with a reference voltage of 150 V. Both control strategies achieve offset-free behavior due to the integral action. However, the MPC approach achieves steady-state operation faster than the PI-based approach, demonstrating improved settling time. At \(t=0.07\) s, the resistive load is increased to 100 \(\Omega\) to evaluate the system's response to load changes when no constraints are active. The MPC response is almost instantaneous, attributed to the feed-forward compensation of the measured disturbance, \(\mathbf{i_{0}}\).  
At \(t=0.12\) s, the load resistance is changed to 11 \(\Omega\) to evaluate compliance with the current limitation depicted in Figure 5. The MPC controller effectively enforces the steady-state current limit of 8 A by reducing the modulated voltage (\(\mathbf{v_{\text{sdq}}}\)). Additionally, the MPC demonstrates improved settling time compared to the PI controllers. While transient responses briefly exceed the limits, the PI controllers exhibit significantly higher overshoot and are unable to restrict the load current effectively.  
Figure 8: Comparison of the simulated responses of hierarchical PI control and the proposed MPC strategy to output current disturbances caused by abrupt resistive load changes.
## 3 Experiments
### Calibration Results  
The total field intensity of experimental site measured by GSM-19 Overhauser magnetometer (GEM Systems, Markham, ON, Canada) is 55,046.65 nT, which is used as a reference for calibrating the magnetometer error. During the process of data collection, the magnetometer is rotated to capture samples with different attitudes. From Figure 5a, it can be seen that interfered by the magnetometer error, the measurement locus appears as an ellipsoid. With the fitted ellipsoid, we are capable of obtaining the magnetometer error calibration parameters \(\mathbf{k}\) and \(\mathbf{h}_{b}\). Figure 5b illustrates the calibration result, from which we can see that the maximum residual of total field is reduced from 105 nT to less than 3 nT, indicating a significant decrease in the measurement error of the magnetometer.  
In the misalignment error calibration experiment, rotations of the magnetometer around \(y_{t}\)-axis and \(z_{t}\)-axis are performed separately to obtain experimental data. As mentioned above, the pitch angle of the magnetometer ought to avoid being changed after calibrating misalignment error. Hence, the rotation sequence is first around \(y_{t}\)-axis and then \(z_{t}\)-axis. Figure 6 illustrates the misalignment error calibration result. It can be seen that, interfered by the misalignment error during rotation, the fluctuation of \(y\)-component exceeds 981.1 nT while that of \(z\)-component exceeds 269.2 nT. Meanwhile, we can see that the fluctuations have been reduced to less than 7.4 nT and 5.9 nT respectively after calibration, which indicates the misalignment error is decreased to an acceptable range.  
Figure 5: Calibration result of magnetometer error: (**a**) The measurement locus and the fitted ellipsoid; (**b**) The residual of total field after calibration.  
Prior to aligning the axis of the magnetometer to the true north direction, the theodolite is required to be leveled. With the help of a high-precision plate level, the horizontal error can be restricted within 20\({}^{\circ}\) after leveling. In the procedure of aligning to the north, the latitude and longitude of the selected sites A and B are measured as (44.08735182\({}^{\circ}\) N, 124.90305218\({}^{\circ}\) E) and (44.0875215302\({}^{\circ}\) N, 124.90305218\({}^{\circ}\) E), as illustrated in Figure 7. The angle \(\theta\) is calculated to be 83.49442\({}^{\circ}\) using the above positional parameters. Meanwhile, the relative position of the two sites is in SW-quadrant, which means the theodolite requires to be rotated 96.50558\({}^{\circ}\) in the north direction to align the \(x_{t}\)-axis of magnetometer to the true north.  
After the above steps of calibration, it can be assumed that the magnetometer error and misalignment error have been eliminated and the \(x_{t}\)-axis of the magnetometer is aligned to the true north direction.  
Figure 6: Calibration result of misalignment error: (a) calibration result by rotating around \(y_{t}\)-axis; (b) calibration result by rotating around \(z_{t}\) -axis.  
Figure 7: Diurnal variation observation system.
## 4 Propagation Model Evaluation
### Scenario Deployment
The smaller circle represents the nodes that always receive node \(\#1\) transmissions; meanwhile the rest of nodes included in the bigger circle (those that are out of the smaller one) may receive the transmissions with a certain probability defined by the propagation model.
## 2 The Method for Self-Healing Efficiency Estimation
### Adaptive Filter Preparation
#### 2.1.1 Transformation of Full Wavefield Images from Space to the Wavenumber Domain  
Two-dimensional Fourier transform (2D FT) is used to convert full wavefield reference data \(w(x,y,t_{i})\) from the space-space-time domain into the wavenumber-wavenumber-time domain \(W\big{(}k_{x},k_{y},t_{i}\big{)}\). The discrete form of 2D FT may be written as:  
\[W\big{(}k_{x},k_{y},t_{i}\big{)}=\sum_{x=0}^{X-1}\sum_{y=0}^{Y-1}w(x,y,t_{i})e ^{-\beta\pi(\frac{k_{x}\cdot x}{X}+\frac{k_{y}\cdot y}{Y})}i=1,2,\ldots,\text{ number of time frames}, \tag{2}\]  
where \(X\) and \(Y\) are the numbers of data points along the \(x\) and \(y\) directions, respectively. Exemplary full wavefield images at one arbitrarily chosen time frame (\(t_{1}\) = 108 \(\upmu\)s) in both the space-space and the wavenumber-wavenumber domains are presented in Figure 2.
###### Abstract  
This paper proposes a novel particle image velocimetry (PIV) technique to generate an instantaneous two-dimensional velocity field for sediment-laden fluid based on the optical flow algorithm of ultrasound imaging. In this paper, an ultrasonic PIV (UIV) system is constructed by integrating a medical ultrasound instrument and an ultrasonic particle image velocimetry algorithm. The medical ultrasound instrument with a phased sensor array is used to acquire acoustic echo signals of particles in water and generate two-dimensional underwater ultrasound images. Based on the optical flow field, the instantaneous velocity of the particles in water corresponding to the pixels in the ultrasonic particle images is derived from the grayscale change between adjacent images under the L-K local constraint based on the optical flow field, and finally, the two-dimensional flow field is obtained. Through multiple sets of experiments, the proposed algorithm is verified. The experimental results are compared with those of the conventional cross-correlation algorithms. The results show that the L-K optical flow method can not only obtain the underwater velocity field accurately, but also has the advantages of good smoothness and extensive suitability, especially for the flow field measurement in sediment-laden fluid than conventional algorithms.  
PIV; UIV; ultrasound images; optical flow field; L-K local constraint Article
## 2 Previous Studies  
MA and NER are considered to be sequence-labeling problems, where POS and NE tags are annotated to a word sequence. For sequence labeling, most previous studies have used statistical-based machine learning (ML) methods, such as structural support vector machine (SVM) [3] and conditional random fields (CRFs) [4]. A method for unknown morpheme estimation using SVM and CRF has been proposed [5]. However, ML models depend on the training corpus size and manually designed features. To resolve these problems, studies based on deep learning have been conducted. Many MA and NER studies have used recurrent neural network (RNN) [6; 7]. NER was performed using bidirectional long short-term memory (Bi-LSTM) and CRFs [1]. In another study, an attention mechanism and a gated recurrent unit (GRU) were used, which reduced the number of gates and time complexity of LSTM [8]. An effective method for reflecting external knowledge (i.e., NE dictionary) into Bi-GRU-CRFs was proposed [9]. Additionally, RNNs and CRFs have been used in MA studies [10; 11]. To alleviate MA error propagation, an integrated model that simultaneously performs MA and NER has also been studied, which used two layers of Bi-GRU-CRFs [12]. [PERSON] et al. [13] proposed a model which alleviates morphological ambiguity by jointly learning NER and morphological disambiguation taggers using Bi-LSTM-CRFs for Turkish. As mentioned above, many ML models have used CRFs to obtain optimal paths among all possible label sequences. However, these models did not always yield good performances. Bi-LSTM-Softmax [14] demonstrated better performance than Bi-LSTM-CRFs for POS tagging. To obtain optimal label paths better than those obtained with CRFs, a label attention network (LAN) was proposed, which captured the potential long-term label dependency by providing incrementally refined label distributions with hierarchical attention to each word. Therefore, we adopted this LAN in our integrated model.
# Band Gap Properties in Metamaterial Beam with Spatially Varying Interval Uncertainties
## 3 Results and Discussion
### Influences of Interval Field Parameters on the Safe Band Gap  
In this subsection, we further study the influences of interval field parameters \(C_{B}\) and \(I_{B}\) on the safe band gap, and the results are presented in Figure 7. It was observed that the calculation results of the MCM and VM were in perfect agreement. In Figure 7a, as \(C_{B}\) increased, the ending frequency of the safe band gap gradually decreased, which led to the gradual narrowing of the safe band gap. Obviously, the greater the deviation amplitude of the interval field, the greater the influences of spatial uncertainties on the band gap. In Figure 7b, as \(I_{B}\) increased, the ending frequencies obtained by the MCM and VM increased significantly, while the ending frequency obtained by the IPFEM increased slightly. In other words, the stronger the spatial dependency of the interval field, the weaker the influences of spatial uncertainties on the band gap. In addition, as shown in Figure 7, the ending frequency obtained by the IPFEM was always higher than the reference solutions (MCM and VM). To validate the accuracy of the IPFEM, Figure 8 shows the relative errors of the IPFEM with respect to the MCM. As can be seen from Figure 8, an increase in \(C_{B}\) or a decrease in \(I_{B}\) led to an increase in relative errors. However, the relative errors presented in Figure 8 were basically controlled within 6%.  
Figure 6: Influences of the number of terms in KL expansion \(N\) on the ending frequency of the safe band gap. (**a**) \(C_{B}=0.05,I_{B}=0.8I_{\text{beam}}\). (**b**) \(C_{B}=0.02,I_{B}=0.5L_{\text{beam}}\). (**c**) \(C_{B}=0.06,I_{B}=0.3L_{\text{beam}}\).  
Figure 7: Influences of interval field parameters on the ending frequency of the safe band gap. (**a**) Influences of \(C_{B}\). (**b**) Influences of \(I_{B}\).
## 3 Experimental Results and Discussion
### Experimental Settings  
To understand the effectiveness, we considered two groups of Hurst exponents or two kinds of classes--one has 11 Hurst exponents (11 classes) and the other 21 Hurst exponents (21 classes). The 11 Hurst exponents are composed of \(H=1/22\), \(3/22\),..., and \(21/22\); the  
Figure 1: The two adopted procedures for classifying 2D FBM images based on the same 2D FBM images.  
Figure 2: The procedure for transfer learning of pretrained models.  
21 Hurst exponents are composed of \(H=1/42\), \(3/42\),..., and \(41/42\). Hence, the resolution of 11 classes is \(1/11\), and the resolution of 21 classes is \(1/21\).  
Similar to [PERSON] et al. [30], as our dataset, we generated 1000 realizations of 2D FBM of size 32 \(\times\) 32 \(\times\) 1 for each Hurst exponent or class and saved them as numerical data (called FBSs) as well as images (called 2D FBM images or FBIs). The procedure for each 2D FBM was as follows: First, we calculated the covariance matrix according to (4), which is related to (2). Second, we used the Cholesky factorization to decompose the covariance matrix in order to the lower triangular. Third, we generated a realization of white Gaussian noise with zero mean and unit variance. Finally, we generated a realization of 2D FBM by multiplying the lower triangular and the white noise. Hence, we generated 11,000 surfaces and images for 11 Hurst exponents and 21,000 surfaces and images for 21 Hurst exponents. Note that FBIs are approximate to FBSs with some details or information lost.  
To understand some possible appearances of different Hurst exponents, Figure 3 shows three images, each with 16 patches of  
From Figures 3-6 (the images based on 11 Hurst exponents with resolution 1/11), we can obviously find that images of two neighboring Hurst exponents or classes are not easy to differentiate from each other by human beings, even for images of three neighboring Hurst exponents. Therefore, it can be easily understood that the images based on 21 Hurst exponents with resolution 1/21 are more difficult to classify from two to four neighboring Hurst exponents, especially for higher Hurst exponents.
## 3 The LBP-FFT-CNNs Model
### Data Preprocessing  
#### 3.3.1 A Binarization Process Based on the Adaptive Threshold  
Image binarization is the process of converting a grayscale image into a black-and-white representation by setting pixel values to either 0 or 255. This transformation is based on an adaptive threshold \(T\), as shown in Equation (1). If a pixel's value exceeds the threshold \(T\), it is set to white (255); otherwise, it is set to black (0). Here, \(g\) represents the input image, and \(G\) represents the resulting binarized image.  
Figure 5: Effect of neuron count on distance estimation error.  
In Equations (2) and (3), \(T(x,y)\) denotes the threshold value at pixel location \((x,y)\) in the image, while \(I(x,y)\) represents the grayscale value of images at that pixel. The parameter block size, denoted as \(bs\), specifies the size of local region used for threshold determination, and \(C\) is a constant that adjusts computed threshold.  
Adaptive thresholding [17; 18] determines threshold value based on the statistical characteristics of local image regions. This method offers superior adaptability to variations in lighting conditions and noise distributions across different areas of images. Moreover, it helps preserve image details while minimizing information loss during the binarization process.  
\[G(x,y)=\begin{cases}0,\text{ {otherwise}}\\ 255,\text{ {if}}\text{ }g(x,y)\geq T\end{cases} \tag{1}\]  
\[mean(x,y)=\frac{1}{bs^{2}}\sum_{i=x-\frac{bs-1}{2}}^{x+\frac{bs-1}{2}}\sum_{j= y-\frac{bs-1}{2}}^{y+\frac{bs-1}{2}}I(i,j) \tag{2}\]  
\[T(x,y)=mean(x,y)-C \tag{3}\]
## 3 Case Study Area  
Figure 1 shows the HSR routes of both existing and newly opened Honam HSR lines (bold) in April 2015. Several cities had an HSR line that directly connected to the capital city, Seoul. We focus on three cities--Iksan, Jeongeup, and Mokpo--for evaluating the impact of the HSR since these cities are expected to directly benefit from the HSR. Among the three cities, Mokpo is expected to see regional economic growth as the long travel time to Seoul will decrease because it is the last train station in the HSR line (330 km in length). The dot size represents the population of each city--Iksan (320,000), Jeongeup (130,000), and Mokpo (250,000). Since there were no special changes on transport systems and events that cause irregular travel demand within a one-year analysis period, we would like to see empirical evidence of the impact of the HSR via mobile phone data, one year before and after the opening of the HSR.  
Table 1 presents before and after conditions of the Honam HSR. Before the opening of the Honam HSR line, there were two segments: a high-speed segment (300 km/h between Seoul and Daejeon) and a regular-speed segment (150 km/h between Daejeon to Mokpo) since there was no infrastructure in place for the Honam line. According to the statistics from the Korea Railroad Corporation in 2006, the New Honam line decreased the travel time by 53 min from Seoul to Mokpo and increased the passenger volume by 281(million/year), a 42% increase, although the fare was increased by approximately 30%.