esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Result Analysis
### Deformation Characteristics
#### 3.3.2 Peak Strain  
The peak strain versus temperature curves of the specimens under different loading and unloading methods were derived from the strain data of the cyclic loading and unloading curves at the peak stress point (Figure 8). As shown in the figure, with increasing temperature, the peak strains of the uniaxial compression test, constant lower limit, and variable lower limit cyclic loading and unloading tests all exhibit an increasing trend. Notably, after reaching 400 \({}^{\circ}\)C, the magnitude of this upward trend increases substantially: from 7.55% to 11.52% under uniaxial compression (a 52.58% increase), from 9.33% to 14.14% under constant lower limit (a 51.55% increase), and from 10.74% to 13.16% under variable lower limit (a 22.53% increase). At 573 \({}^{\circ}\)C, the quartz crystals within the granite undergo a phase change from \(\alpha\)-phase to \(\beta\)-phase, resulting in the formation of numerous crystal-penetrating cracks and a sharp increase in the rate of thermal crack generation [40]. Additionally, the peak strains under both constant lower limit and variable lower limit cycles were significantly higher than those observed in uniaxial compression tests at all temperatures, indicating that cyclic loading led to increased internal deformation of the granite specimens.  
Observing the peak strain curves under the two loading cycles, it is evident that, as temperature increases, the peak strain of specimens in the variable lower limit cycle follows a steady, linear upward trend. In contrast, the peak strain of specimens under the constant lower limit cycle decreases slightly (by 10.37%) at 200 \({}^{\circ}\)C, forming a \"U-shaped\" trend. This occurs due to thermal expansion at low temperatures, which causes internal cracks in the specimen to close, allowing sufficient time for deformation recovery under the constant lower limit cycle. Under the variable lower limit cycle loading and unloading method,once high temperature and fatigue damage reach a critical level, the specimen at each temperature rapidly exhibits damage responses. The peak strain continues to increase with temperature because the variable lower limit cycle does not allow sufficient elastic recovery time before the next cycle, leading to greater residual deformation.
## 2 Materials and Methods
### Data Sources and Processing
#### 2.2.3 Data of Agricultural Status in Response to Drought  
Vegetation greenness reflects vegetation growth response to drought, decreasing as drought intensifies [53]. It is closely correlated with gross primary productivity (GPP), though this relationship weakens in less arid conditions [54]. GPP, representing carbon fixed by crop photosynthesis, is also sensitive to drought. Water use efficiency (WUE), which measures dry matter per unit water consumed through evapotranspiration [55], is an important index of regional agricultural water management and crop growth under drought. The risk of crop yield loss increases nonlinearly with drought severity [56]. In this study, vegetation greenness, GPP, WUE, and crop yield were used to assess crop responsesto drought. The normalized difference vegetation index (NDVI) and leaf area index (LAI) were used to reflect crop greenness and crops yields, respectively.  
Data on land cover, NDVI, GPP, and the LAI from 2003 to 2020 were analyzed with the Google Earth Engine based on MODIS products from NASA. Cropland was extracted from the MCD12Q1 LC_Type1 product (500 m resolution). Monthly GPP was calculated using the MOD17A2H product (8-day cumulative composite) [57]. The LAI was obtained from the MODIS MOD15A2H product (8-day, 500 m resolution). Finally, these data were upscaled to 1 km to match VMFDI resolution. Maize and wheat data from 2000 to 2019 were sourced from the ChinaCropArea1 km, with 2020 data from ChinaWheat10 and 10 m maize cropland data, both resampled to 1 km.
## 5 Optimization of SP-PMVM
### Optimize \(\beta_{mcc}\)  
Since the angle \(\beta_{mcc}\) has a great influence on \(T\_\)_avg, the power factor and the \(T\_\)_ripple_ of the SP-PMVM, the mechanical angle \(\beta_{mcc}\) is optimized in the range of \(-2.5^{\circ}\)-\(5^{\circ}\) in the FEM software, while other parameters are kept constant, as listed in Table 1.  
Figure 7 shows the results of the average torque, cogging torque and torque ripple at different \(\beta_{mcc}\). It can be found that the maximum torque is obtained at \(0^{\circ}\). The \(T\_\_\)_cog_ and \(T\_\)_ripple_ almost reach the minimum values at this point as well. Hence, this point, namely \(\beta_{mcc}\) = 0, \(k_{st}\) = 0.46, and \(k_{rt}\) = 0.3, is better than initial values which are given in Table 2. Additionally, this set of parameters will be used for the following analysis and optimization.  
Figure 6: Average torque, cogging torque, and torque ripple of SP-PMVM at different \(k_{st}\).
### Key Contributions  
We present the BERT-Stacked BiLSTM-Multiple-Attention (BLA) model, which aims to track the dialogue over a fixed vocabulary. The model combines BERT [37], Stacked BiLSTM (SBL) [38], along with multiple-attention. In addition, we have implemented a pre-trained language model for word embedding in our framework. With the success of pre-trained language models such as ELMO [39] and BERT [37] over the last few years, only a few researchers had used these models in the domain of DST upon the datasets WoZ-2.0 and DSTC-2 to tackle the classification problem. As stated earlier, the classification problem lies within a special category where distinguished frameworks apply sentence classification to themselves. To this point, we extended the BERT network by incorporating SBL and multi-attention to learn the contextualized word representation, which increased the robustness of the model in terms of predicting the slots and their corresponding values. Our contributions are summarized below:* We present BLA to tackle the sentence classification problem in DST by taking a dialogue context and candidate slot value pair to accurately track slots and their corresponding values.
* We propose a model that exploits the sequential and overall features encoded in BERT to improve the performance of neural networks.
* We extract the contextual word features from a dialogue context using the BERT pre-training language model along with Stacked BiLSTM, and multi-attentions.
* Upon evaluating our model on two real-world datasets, we achieved state-of-the-art performance over the current baseline models regarding turn request and joint goal accuracies.
* With detailed experiments in the ablation study on distinguishing variants of neural networks, we have confirmed the importance of feature extraction from Stacked BiLSTM and Multiple Attention Mechanism for the proposed model in terms of the improvement over turn goal, turn request, and joint goal accuracies.
## 3 Results and Discussion  
### Raman Spectra and Image Processing  
Initially, the Raman spectra of both adulterants were visually evaluated to find informative wavenumber ranges (360-1800 cm\({}^{-1}\) in both cases) and thus reduce the hypercube dimension in the spectral domain region. Figure 2 shows the fluorescence-corrected Raman signature of paprika powder and both adulterants in the selected spectral range, revealing that the adulterant spectra featured numerous peaks, with most of them overlapping with signals of other adulterants or paprika powder.  
Fluorescence correction and de-noising of Raman signals were performed using adaptive iteratively reweighted penalized least squares (air PLS) and median filter methods, with the band images for Sudan-I and Congo Red shown in Figure 3. Based on the original images, one can easily observe that the paprika powder background was very intense, with the occasionally occurring spikes (cosmic ray effect) suppressing relevant information and making both band images representing two different adulterants look identical. However, hypercube preprocessing nullified the background effect and high-frequency noise, enhancing the Raman images, with visual inspection of the corrected images facilitating the visualization of adulterant particle distribution.
## 3 Proposed Method  
The partial occlusion and scale variation in object tracking comprise a crucial problem. Many researchers have tried to solve this problem, but it is still known to be a difficult problem. In addition, object tracking in real-time is also significant because its target is videos. The real-time object tracking that these problems have solved can be applied to many vision applications. Therefore, our goal is to develop real-time object tracking with the consideration of partial occlusion and scale variation.  
The proposed work is based on KCF, which consists of (i) the detection part for describing objects and (ii) the training part for learning. The updated model is used in the detection part of the next frame, and the entire process repeats to track objects continuously.  
In this paper, we propose object tracking using the partial block scheme and the adaptive update model. The partial block scheme is proposed to solve the partial occlusion problem, and the adaptive update model employs the weighted learning rate. The weighting is calculated from the reliability of the response of each block with a sigmoid function. If the reliability of the response is high, we use a higher learning rate. On the other hand, if the reliability of the response is low, we use a lower learning rate. Furthermore, a sparse update is performed to reduce the increased computation due to multiple partial blocks. Figure 2 shows the block diagram of the proposed method.  
Figure 2: The block diagram of the proposed method. We separate the partial blocks from the object in the frame and perform the translation estimation and scale estimation. Finally, we perform the model update with the weighting function or skip the update. PSR, peak-to-sidelobe ratio.  
The proposed methods can be divided into four steps as follows:  
1. Partial block separation: separating the partial blocks from the whole block of an object. Partial blocks can be adjusted in size and position according to the parameter.
2. Translation estimation: calculating the responses using a kernelized correlation filter of all blocks and then selecting the translation response map.
3. Scale estimation: estimating the object scale with the scale space and calculating the scale factor.
4. Adaptive model update: model updating with the adaptive learning rate considering the reliability of responses.
# Climate-Smart Agro-Hydroological Model for a Large Scale Rice Irrigation Scheme in Malaysia
## 4 Conclusions  
A user-friendly agro-hydrological model was developed and successfully deployed for adaptive irrigation and wise water resource management towards water security in Tanjung Karang Rice Irrigation Scheme in Malaysia under new climate change realities. The model outputs reveal that future effective rainfall will decrease from the baseline period in the dry season and increase in the wet season. The decrease is more pronounced in March, April, May, and June. However, the season becomes wetter in April, May, and June during the later future periods (2050s and 2080s) compared to the 2020s, which indicates that future dry seasons might be wetter than the current dry season. This predicted rainfall might militate against the conditions of drought that currently impact the paddy scheme. Based on these findings, flooding is likely to increase during the wet (main rice growing) season, as well as intermittent dry spells during the dry season. This difference in rainfall in both seasons needs to be taken into account in the scheme's water resource planning. The projected available water for supply to the scheme, which largely depends on the streamflow from the Bernam basin, would decrease in both dry and wet seasons, with a notable decrease during the dry season period. Simulated flows from the developed model could be integrated with adaptive cropping patterns to enhance future water allocation. Since the project is a run-of-the-river project, a recommended supply could be adopted that enables proportionate distribution of water among the tertiary canals through proper control of the off-take structure gate. The water demand in the scheme will differ greatly during the months of the future dry seasons, with the increase in effective rainfall during the wet season compensating for the high dry season water demand. No irrigation will therefore be needed in the months of May and June. The main challenge, however, will be to properly manage the monthly variation of irrigation water demand in the command area. Failure to effectively manage the irrigation system will lead to much greater uncertainty as the project is a run-of-the-river irrigation scheme with limited storage facility. The model can be extended by taking into account the historical data of local stations, the GCMs, and features of the irrigation scheme, even though it is customized for local application to the Tanjung Karang Rice Irrigation Scheme. Based on the study findings, the following potential implications can be drawn from the model outputs with suggested options:  
1. Occurrence of floods and droughts: Due to increased rainfall events, the future of paddy farming is likely to face floods during the wet season and droughts during the dry season. Such changes
## 2 HSI: Basic Concepts
### Image Sensing and Acquisition Modes  
Before reaching the detector, the light radiation interacts with the chemical (activating vibrational effects on molecule functional groups) and physical (light-scattering effects) nature of the sample, and a part can be absorbed (and so turned into heat energy that will never reach the detector), reflected, or transmitted. These interaction processes enable HSI to be implemented in different sensing (Table 1 and Figure 2) and acquisition modes (Figure 3) depending on the sample size and the acquisition time available to perform the analysis, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sensing Mode** & **Description** \\ \hline \multirow{4}{*}{**Diffuse reflectance** (Figure 2a)} & The detector and the light source are generally positioned above the sample, and the reflected light from the illuminated area is acquired by the detector. This mode is applied to evaluate external quality properties, such as size, shape, color, surface texture, and physical defects. \\ \hline \multirow{4}{*}{**Transmittance** (Figure 2b)} & The sample is placed between the detector and light source, and the radiation which crosses over it is measured. This method is generally used to evaluate internal quality features such as internal defects or chemical compound concentration. However, the sample thickness has an impact on the signal, which decreases as thickness increases. \\ \hline \multirow{4}{*}{**Interactance** (Figure 2c)} & The imaging area is isolated from the light source by a predetermined angle or distance. The measured light passes through a little layer of tissue beneath the surface. Thus, it is possible to obtain more information from the inside of the sample than with the reflectance mode. \\ \hline \multirow{4}{*}{**Fluorescence** (Figure 2d)} & It measures the radiation emitted by the sample after excitation by absorbing light radiation at a high energy level. The emission in its two main spectral ranges, from blue to green (450-550 nm) and from red to far red (690–740 nm), and it is characterized by three peaks in the blue, red, and far-red bands. In fluorescence imaging, the light source and the detector are positioned at the same side of the sample. Generally, the light excitation sources used are Xenon arc lamps, lasers, or LEDs. \\ \hline \multirow{4}{*}{**Raman scattering** (Figure 2e)} & It requires a block of the excitation light from the detection end and an intense excitation light source coupled with a high-performance detector to ensure adequate signal quality. The excitation is typically performed with diode lasers at 785 or 830 nm. As the Raman signals are very weak, commercial Raman imaging systems typically have small imaging areas at the millimeter scales or less (for microscopic applications). \\ \hline \hline \end{tabular}
\end{table}
Table 1: Imaging sensing mode summary.
## 3 Analysis of Alignment Tolerance on the Double-Diffracted Grating Interferometer  
When a relative movement occurred between the fixed grating and moving interferometer, rotation angles of the interferometer would not be constant, but would fluctuate in a small range.  
Figure 1: Optical configuration and mechanical fixture of the proposed double-diffracted spatially separated heterodyne grating interferometer. FC1 and FC2, optical fiber collimators; BS1 and BS2, non-polarized beam splitters; PBS, polarized beam splitter; M1, M2, and M3, mirrors; RR1 and RR2, retroreflectors; G, linear grating; FCr and FCm, optical fiber couplers; KM1 and KM2, kinematic mounts; MF, mechanical fixture. The colored markers\(f_{1}\) and \(f_{2}\) represent different modulated frequencies.  
The angles, distinguished by roll, yaw and pitch from the axes they rotate about, are illustrated in Figure 2. Besides, the slight translation in y and z directions, are defined as offset and stand-off errors respectively [14].  
Also indicated by Figure 2, it is easy to visualize that the offset error is uninfluential as long as the spots remain in the ruling area. Actually, in a linear or planar grating interferometer, the translational movements along x and y directions do not cause errors. However, errors in the other four DOFs will worsen the interference signal as their values increase.
# Robust Estimators in Geodetic Networks Based on a New Metaheuristic: Independent Vortices Search
## 2 Classical and Robust Approaches
### Sign-Constrained Robust Least Squares  
Unlike other REs presented so far, the Sign-Constrained Robust Least Squares (SRLS) has multiple optimization functions [35]:  
\[\min\left(\mathbf{v}^{T}\mathbf{W}_{R}\mathbf{v}\right)/r_{W_{R}}, \tag{9}\]  
where the diagonal elements of \(\mathbf{W}_{R}\) are defined as  
\[w_{i}^{R}=\begin{cases}w_{i},&\text{if }\left|v
## 3 Code Synchronization Algorithm Based on the Segment Correlation
### Code Tracking  
The block diagram of code tracking loop module used in this paper, which is based on a delay-locked loop, is shown in Figure 11. Its main components are the code tracking loop discriminator and loop filter.  
The algorithm of the discriminator can be expressed through Equation 10:  
\[\frac{1}{2}\frac{E-L}{E+L} \tag{10}\]  
where, \(E=\sqrt{I_{ES}^{2}+Q_{ES}^{2}}\) and \(L=\sqrt{I_{LS}^{2}+Q_{LS}^{2}}\).  
Figure 11: Block diagram of code tracking loop module.  
Figure 10: Block diagram of the proposed serial acquisition algorithm.  
The loop filter plays an important role in the code tracking loop. Compared with a first-order or a third-order loop filter, a second-order loop filter has many advantages, such as: the frequency deviation is constant; the zero-frequency gain is infinite; its stability is very good it is also easy to implement. This paper adopts a second-order loop filter solution, the frequency domain model of which is shown in Figure 12. \(z^{-1}\) denotes the unit delay of the integrator.
## 3 Results and Analysis
### Fatigue Test
#### 3.3.2 Strain Ratcheting and Cyclic Stress-Strain Curves  
Figure 6 shows the maximum strain measured over the number of cycles. It is evident that for a low stress ratio (\(\sigma_{\max}\)/\(\sigma_{\text{ut}}\)) below 0.6, the strain stabilization reaches around 2000 cycles for all laminates. On the other hand, laminates with off-axis reinforcement to the load direction show earlier failure for 0.7 and 0.8 stress ratios. Moreover, higher strain is observed for [+45\({}^{\circ}\)/\(-\)45\({}^{\circ}\)]\({}_{8}\), basket weave warp, and basket weave weft, which is reasonable due to the stress level and failure mode.  
Stabilization of strain is directly related to the second stage of the damage, where the crack development occurs; therefore, considering the maximum strain (maximum strain from the hysteresis loop) and the applied stress at \(\sim\)2000 cycles, an approximate cyclic stress-strain plot can be built.  
Figure 7 shows the cyclic stress-strain curves for the studied laminates. Analogous to the results from static tests, the curves are nonlinear, and due to the similarity of the trend of the data observed to the one found in metals, a nonlinear stage power law (\(\epsilon_{p}=\frac{\sigma}{E}+\left(\frac{\sigma}{K}\right)^{1/n^{\prime}}\)) model (Ramberg-Osgood) can be adapted, where E is the modulus and \(K^{\prime}\) and \(n^{\prime}\) are the coefficient and exponent of the power law. Moreover, fitted curves were also included in the plot (Figure 7). The fitted coefficient and exponent are detailed in the Table 3, demonstrating a good correlation coefficient for [0\({}^{\circ}\)]\({}_{8}\), [0\({}^{\circ}\)/90\({}^{\circ}\)]\({}_{8}\), basket weave warp, and basket weave weave weft (R\({}^{2}\) > 0.87). However, [+45\({}^{\circ}\)/\(-\)45\({}^{\circ}\)]\({}_{8}\) shows poor agreement with R\({}^{2}\) = 0.61. Although for [90\({}^{\circ}\)]\({}_{4}\), the R\({}^{2}\) = 0.79, due to the dispersed strain and stress values, the obtained parameters should be taken with caution. These cyclic stress-strain curves allow the building of hysteresis loop curves for damage analysis in composites.  
Figure 5: S-N curves fitting (**a**) Basquin model and (**b**) Weibull model.  
Figure 6: Maximum strain over number of cycles, (**a**) [0\({}^{\circ}\)]s, (**b**) [0\({}^{\circ}\)/90\({}^{\circ}\)]s, (**c**) basket weave warp, (**d**) basket weave werf, (**e**) [+45\({}^{\circ}\)/ \(-\)45\({}^{\circ}\)]s, and (**f**) [90\({}^{\circ}\)]s.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Laminate** & \(\mathbf{K^{\prime}}\) & \(\mathbf{n^{\prime}}\) & \(\mathbf{R^{2}}\) \\ \hline \([0^{\circ}]\)s & 796.79 & 0.3703 & 0.92 \\ \([0^{\circ}\)/90\({}^{\circ}]\)s & 644.38 & 0.4266 & 0.91 \\ Basket weave warp & 259.90 & 0.3603 & 0.87 \\ Basket weave werft & 338.93 & 0.3759 & 0.88 \\ \([+45^{\circ}\)/\(-45^{\circ}]\)s & 259.82 & 0.3850 & 0.61 \\ \([90^{\circ}]\)s & 425.08 & 0.5466 & 0.79 \\ \hline \end{tabular}
\end{table}
Table 3: Cyclic stress–strain power law model parameters for the studied laminates.