esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Parameter Self-Calibration Method for Loops in Deeply Coupled Systems Based on Norm Analysis
### Norm Analysis to the IMU Error Propagation Properties
Both sides of Equation (16) could be expressed in the norm form as follows:  
\[\left\|(\delta V^{e})_{acc-bias}\right\|_{2}\approx\left\|\left[\Delta TI- \Delta T^{2}\widetilde{\Omega}^{e}+\frac{\Delta T^{3}}{2}G+\frac{\Delta T^{3} (\widetilde{\Omega}^{e})^{2}}{6}\right]C_{b}^{e}\delta A^{b}\right\|_{2}. \tag{17}\]  
According to the norm property, Equation (17) could be transformed into the following expression:  
\[\left\|(\delta V^{e})_{acc-bias}\right\|_{2}\leq\left[\Delta T+\Delta T^{2} \|\widetilde{\Omega}^{e}\|_{F}+\frac{\Delta T^{3}}{2}\|G\|_{F}+\frac{\Delta T ^{3}}{6}\|(\widetilde{\Omega}^{e})^{2}\|_{F}\right]\|C_{b}^{e}\|_{F}\|\delta A ^{b}\|_{2}, \tag{18}\]  
where \(C_{b}^{e}\) is the unitary matrix, so \(\|C_{b}^{e}\|_{F}=\sqrt{\left(tr(C_{b}^{e}C_{b}^{e})\right)}=\sqrt{3}\). Equation (18) could be transformed into the following expression:  
\[\left\|(\delta V^{e})_{acc-bias}\right\|_{2}\leq\sqrt{3}\left[\Delta T+\sqrt{ 2}\omega_{e}\Delta T^{2}+\frac{\sqrt{6}\mu\Delta T^{3}}{2r^{2}}+\frac{\mu^{2} \Delta T^{3}}{r^{4}}\right]\|\delta A^{b}\|_{2}, \tag{19}\]  
because \(\|\widetilde{\Omega}^{e}\|_{F}=\sqrt{2}\omega_{e}\) and \(\|G\|_{F}=\frac{\sqrt{6}\mu}{r^{2}}\), according to the definition of F-norm. Also according to Equation (4), the velocity error caused by the gyro error could be concluded as follows with the same analysis idea above:  
\[ \begin{split}(\delta V^{e})_{gyro-bias}&=\frac{e^{- \widetilde{F}\Delta T}\Delta T}{-\widetilde{\Omega}^{e}}\widetilde{F}^{e}C_{b }^{e}\delta\omega^{b}\\ &\approx(\Delta T^{2}-\frac{\Delta T^{3}\widetilde{\Omega}^{e}}{ 2})\widetilde{F}^{e}C_{b}^{e}\delta\omega^{b}\end{split} \, \tag{20}\]  
where both sides of Equation (20) could be expressed in the norm form as follows:  
\[ \begin{split}\left\|(\delta V^{e})_{gyro-bias}\right\|_{2}& \approx\left\|(\Delta T^{2}-\frac{\Delta T^{3}\widetilde{\Omega}^{e}}{ 2})\widetilde{F}^{e}C_{b}^{e}\delta\omega^{b}\right\|_{2}\\ &=(\Delta T^{2}+\frac{\Delta T^{3}}{2}\|\widetilde{\Omega}^{e} \|_{F})\|\widetilde{F}^{e}\|_{F}\|C_{b}^{e}\|_{F}\|\delta\omega^{b}\|_{2}\\ &\leq\sqrt{6(f_{x}^{2}+f_{y}^{2}+f_{z}^{2})}(\Delta T^{2}+\frac{ \sqrt{2}\omega_{e}\Delta T^{3}}{2})\|\delta\omega^{b}\|_{2}\end{split} . \tag{21}\]From Equations (19) and (21), the relationship between the IMU errors and the related velocity error is clear. It could be concluded that the INS velocity error mainly depends on four factors: the gyro error \(\delta\omega^{b}\), the accelerometer error \(\delta A^{b}\), the time interval \(\Delta T\), and the vehicle maneuvering which can be expressed as \(\sqrt{f_{x}^{2}+f_{y}^{2}+f_{z}^{2}}\). Through the mathematical deduction above, the quantitative velocity error can be calculated directly when the IMUs' precision and the dynamic requirement for the system are determined. This will do great favors for the stability judgement of the tracking loop in the next step.
###### Abstract
Moreover, this approach reduces the model's dependency on real-time sensor data, making it more robust in scenarios where observations are incomplete, delayed, or entirely missing. This hybrid approach combines the physical consistency of traditional models with the pattern recognition capabilities of neural networks, potentially overcoming the limitations of each method when used in isolation. Using AI-based approaches for streamflow forecasting supports both early flood warning and improved water resource management. By enabling smarter allocation and conservation of water, such methods enhance community resilience, support sustainable development, and help reduce the adverse effects of extreme weather on people and ecosystems. In semi-arid regions where irrigation infrastructure plays a central role in shaping streamflow and agricultural viability understanding and modeling, the interaction between human-managed and natural systems is critical [8]. Sustainable agricultural practices benefit significantly from advanced predictive models, which help improve irrigation efficiency, reduce water waste, and increase crop productivity [9]. The objective of this study is to develop and evaluate a hybrid framework that leverages physically based hydrological modeling, bias correction, and deep learning to improve streamflow forecasting in data-scarce environments. Specifically, the study aims to (i) simulate streamflow using HEC-HMS, (ii) apply machine learning-based bias correction to account for human-induced hydrological impacts, and (iii) forecast observed discharge using a Temporal Fusion Transformer model trained on corrected synthetic data. By doing so, the framework seeks to provide an operationally robust, interpretable, and transferable forecasting tool suitable for basins with limited or unreliable discharge observations.
## 3 Results and discussion
### Seasonal Variations in Crop WUE with Environmental and Biological Controls
As illustrated in Table 1, it again demonstrated its significant relationship with processes of \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Crop Type**} & \multirow{2}{*}{**-**} & **GPP** & **ET** & **R\({}_{\text{g}}\)** & **T\({}_{\text{a}}\)** & **VPD** & **P** & \multirow{2}{*}{**EVI**} \\  & & **(g C m\({}^{-2}\) d\({}^{-1}\))** & **(mm d\({}^{-1}\))** & **(MJ m\({}^{-2}\) d\({}^{-1}\))** & **(C)** & **(h Pa)** & **(mm d\({}^{-1}\))** & \\ \hline \multirow{2}{*}{Corn} & WUE & 0.982 \({}^{\ast}\) & 0.818 \({}^{\ast}\) & 0.740 \({}^{\ast}\) & 0.673 \({}^{\ast}\) & 0.503 \({}^{\ast}\) & 0.147 & 0.906 \({}^{\ast\ast}\) \\  & EVI & 0.906 \({}^{\ast}\) & 0.911 \({}^{\ast\ast}\) & 0.857 \({}^{\ast\ast}\) & 0.738 \({}^{\ast\ast}\) & 0.545 \({}^{\ast\ast}\) & 0.094 & – \\ \multirow{2}{*}{Soybean} & WUE & 0.978 \({}^{\ast\ast}\) & 0.732 \({}^{\ast\ast}\) & 0.533 \({}^{\ast\ast}\) & 0.577 \({}^{\ast\ast}\) & 0.248 \({}^{\ast\ast}\) & –0.029 & 0.950 \({}^{\ast\ast}\) \\ \multirow{2}{*}{Soybean} & EVI & 0.931 \({}^{\ast\ast}\) & 0.784 \({}^{\ast\ast}\) & 0.604 \({}^{\ast\ast}\) & 0.686 \({}^{\ast\ast}\) & 0.385 \({}^{\ast\ast}\) & –0.021 & – \\ \hline \hline \end{tabular} Note: \({}^{\ast\ast}\) Correlation is significant at the 0.01 level. \({}^{\ast}\) Correlation is significant at the 0.05 level. R\({}_{\text{g}}\), T\({}_{\text{w}}\), VPD and P are observed at the flux tower sites. Time series of EVI data are derived from the MODIS data.  
\end{table} 
Table 1: Correlations between 8-day tower-based WUE and satellite-driven EVI data with the controlling environmental factors at the annual corn/soybean rotation field. Figure 3: Seasonal variations of ecosystem water use efficiency from flux tower measurements (WUE\({}_{\text{EC}}\)) and estimates from MODIS products (WUE\({}_{\text{RS}}\)) at the flux tower site US-Bo1 during 2001–2006 (corn in the odd years and soybean in the even years). both GPP and ET that were consistent with previous findings in forests [44, 46], crops [6, 37], and grasslands [2, 47].
## 2 Materials and Methods
[41; 42] A preliminary treatment of GQDs with OLA was carried out by stirring the GQDs solution with an excess of neat OLA at a room temperature for 12 h and collect them by ethanol non solvent addition and redispersion in cyclohexane. Sequentially, 350 \(\mu\)L of IGEPAL CO-520, 200 \(\mu\)L of NH\({}_{4}\)OH and a varied volume of TEOS (in the range of 20-100 \(\mu\)L) were injected to 6 mL of a cyclohexane solution of GQDs, at different concentrations. The microemulsion was then kept under vigorous stirring at controlled temperature (28 \({}^{\circ}\)C) for 18 h. Functionalization with APS was carried out by adding 600 \(\mu\)L of APS to the microemulsion, stirred overnight at 28 \({}^{\circ}\)C. Amino-functionalized GQD@SiO\({}_{2}\) NPs were collected by adding methanol, which is able to disrupt the micelle, and centrifuged at 7800\(\times\)\(g\) for 20 min (Beckman J2-21). Repeated cycles of NP dispersion in ethanol and centrifugation were carried out to completely remove surfactant residuals and to finally collect NPs, finally dispersed in 2 mL of ethanol. The effective characterization and quantitative analysis of primary amino groups in a different type of systems [43], was carried out by a ninhydrin assay. A ninhydrin solution was prepared by dissolving 110 mg of ninhydrin in 16 mL of ethanol (0.68% w/v) and adding 4 mL of 2,6 lutidine. A preliminary calibration experiment was performed by adding an excess of ninhydrin solution in ethanol to APS standard solutions ranging from 50 \(\mu\)M to 15 mM (\(\epsilon\) = 2678 \(\pm\) 117 L mol\({}^{-1}\) cm\({}^{-1}\)). Of ninhydrin solution (0.03 M) 1.9 mL was added to 0.1 mL of GQD@SiO\({}_{2}\) NPs collected after the functionalization reaction. The suspension was then heated up to 90 \({}^{\circ}\)C for 5 min. The fact that the solution's color changes from the yellow (color of the free ninhydrin) to blue (color of the [PERSON]'s blue reaction by-product) accounts for the presence of amino groups and the absorbance intensity measured at 570 nm, characteristic of the [PERSON]'s by-product, was used to titrate the amount of amino groups. Characterization techniques. Transmission electron microscopy (TEM) analysis was performed by using a JEOL 100 (JEOL Italia S.P.A, Milan, Italy), operating at 100 kV and equipped with a W electron source and a CCD high resolution camera. Deposition of the NPs was carried out by dipping a carbon coated copper grid in the sample suspension and let the solvent evaporate. Size statistical analysis was carried out by measuring average NPs size and determining relative standard deviation (\(\sigma\)%) for more than 100 NPs of each sample. UV-Vis absorption spectra were recorded with a Cary 5000 (Varian) UV/Vis/NIR spectrophotometer (Agilent Technologies Italia, Milan, Italy). Fluorescentspectra were taken with a Fluorolog-3 system ([PERSON] Yvon Burlington, Canada) equipped with a THMS 600 temperature-controlled stage to monitor variation of photoluminescence (PL) spectra in the temperature range 282-313 K.
## 3 Results and Discussion
### Relative Abundance of Fungal Communities
Aspergillus had strong saccharification ability as well as generating a variety of enzymes, with a great catalytic ability for macromolecule degradation such as starch and proteins, which has positively contributed to _Baijiu_ quality. Studies have also shown that Aspergillus can also inhibit the growth of harmful microorganisms by producing acidic proteases to lower pH, which also ensures the relative safety of _Baijiu_[23]. Interestingly, the relative abundance of the Trametes genus remained zero in stages D-1 and D-2; the highest value appeared in stage D-3 (less than 1%). The relative abundances of the remaining fungal, including Monascus, Debaryomyces, Davidiella, and Mucorales_unidentified_1 did not change greatly during the fermentation process. The results indicated that the original balance of the fungal community was disturbed after _Dagu_ was added to the tank. The original main genera, such as Saccharomyces_unidentified_1, Candida, and Mucor, were not as well adapted to the environment as Saccharomyces and required a certain period of time for domestication before they could take over the dominance of the fungal community. In the _Ercha_ fermentation, the changes in the relative abundances showed good reproducibility. Notably, the addition of _Dagu_ resulted in a rapid increase in the relative abundances of Saccharomyces_unidentified_1, Candida, and Aspergillus. Although Saccharomyces also displayed the same change as in the _Dacha_ fermentation, its relative abundance was not as large as that of the _Dacha_ fermentation. In contrast, Saccharomyces_unidentified_1, Candida, and Aspergillus demonstrated a strong reproductive capacity. This indicated that these three fungal introduced by _Dagu_ could better utilize the substrate after the completion of the _Dacha_ fermentation. The addition of _Dagu_ at the beginning of _Ercha_ fermentation resulted in a significant increase in the relative abundance of the fungal community at the genus level. At last, fungal communities came to a new balance at stage E-6, where Saccharomyces, Saccharomyces_unidentified_1, Candida, Pichia, Trametes, Rhizopus, and Aspergillus also occupied larger shares, setting the stage for the production of volatile compounds.
# State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF
## 3 Design of Vehicle State Estimator Based on IMM-MCCKF  
The CKF assumes that noise follows a Gaussian distribution and demonstrates robust estimation performance in environments with Gaussian noise. However, in practical vehicular environments, noise frequently exhibits non-Gaussian characteristics, leading to diminished performance of the CKF. The MCC is a statistical analysis and signal processing strategy that is highly effective in dealing with noise that has non-Gaussian distributions. Based on the concept of correntropy, it estimates signals by optimizing a nonlinear cost function, thereby enhancing the capability to capture and process signals with heavy tails or outliers. Consequently, integrating the MCC with the CKF enhances the latter's ability to suppress non-Gaussian noise. In a single MCCKF filter, the noise matrix remains constant, hindering timely and accurate responses to changes in the system model, which can result in estimation errors. The IMM theory facilitates the parallel operation of multiple submodels, each with distinct noise characteristics, thereby mitigating the impact of statistical noise uncertainty. Therefore, this study proposes the IMM-MCCKF algorithm.
# General Network Framework for Mixture Raman Spectrum Identification Based on Deep Learning
## 4 Experiment and Results
### Choice of Fusion Strategy  
In the practical application of deep learning, feature fusion is a crucial technique aimed at effectively integrating feature information from different levels, thereby enhancing the performance of the model. For given input channels, X1, X2,..., Xn and Y1, Y2,..., Ynthere are two commonly used feature fusion strategies: Addition and Concatenation. The intuitive understanding is shown in Figure 4.  
The Addition fusion strategy adds the corresponding elements of the feature vectors one by one. This strategy has a relatively small computational load because it only performs simple addition operations. However, it may result in the loss of some features, especially when there are significant differences or conflicts between the features of different channels.  
The Concatenation fusion strategy generates the fused feature vector by sequentially connecting different feature vectors along the same dimension. This strategy can retain all the original feature information and increase the feature dimensions in the output channel, allowing the model to more comprehensively utilize these features for learning and inference. However, this strategy will correspondingly increase the computational load.  
This section conducts comparative experiments on the two fusion strategies to determine the most suitable strategy for the framework proposed in this paper. The results are shown in Table 2. Analysis of the comparative experimental results reveals that although the Addition strategy has a smaller computational load, its performance is often inferior to the Concatenation strategy due to the potential for feature loss. On the other hand, the Concatenation strategy, despite its larger computational load, performs better because it can fully retain and utilize all feature information. Therefore, after comprehensive consideration, this paper ultimately decides to adopt the Concatenation feature fusion strategy.
# A Machine Vision Development Framework for Product Appearance Quality Inspection
## 2 Structure of Machine Vision Software Framework  
### Hardware Architecture  
The hardware equipment of an appearance quality inspection system generally includes the main control computer, industrial camera, vision light source, IO communication device, and alarm [14; 15], as shown in Figure 1.  
The performance of the computer can be adjusted according to the task requirements. The computer is connected to automated machinery via IO communication devices. The alarm can be configured according to the task demands, whose alarm signal can be given by the computer or the automation equipment.  
The industrial camera is the information acquisition device of the machine vision quality inspection software. Its imaging results directly affect the design of algorithms during image processing. Most industrial cameras are based on CCD [16] or CMOS [17; 18] chips. Specific scenarios usually require the selection of the appropriate camera vendor, type, and parameters according to the inspection needs of the target product.  
Visual light sources are used to highlight the subject, weaken the background, enhance features, and make the camera obtain a higher-quality image. It is necessary to choose the proper machine vision light source solution in the design of the scheme [19; 20], such as a flat light source, hemispherical light source, ring light source, multiple light sources, etc.  
IO communication devices are usually used for communication between the vision software host computer and electromechanical devices. Common communication methods use serial ports or network interfaces [21; 22]. With the continuous development of technology, more and more protocol technologies have appeared in the field of industrial communication [23]. Considering simplicity and adequacy, serial ports and Modbus protocols are widely used for communication in industrial applications [24]. The IO modules within the software framework support the standard Modbus protocol by default.
## 1 Introduction
The sonar console operator cannot distinguish between a target and clutter from the sonar console display but it can distinguish between them from the audio signal. Therefore, this paper proposes an approach for extracting features using Power-Normalized Cepstral Coefficients (PNCC) for active sonar with real sea trial data and Mel-Frequency Cepstral Coefficients (MFCC) are used to compare the classification results for feature extraction. PNCC has been recently developed and has superior speech recognition performance compared to MFCC, which is widely used in speech recognition. PNCC is also stronger than MFCC in noisy environments [18]. PNCC is advantageous for sonar operating environments in which noise is time-varying depending on the characteristics of the underwater environment. The MFCC has been applied to active sonar target classification studies [19], but there are no studies that have applied active sonar target classification using PNCC. These feature extraction results are imaged and used as an input for a classifier. As artificial intelligence technology advances, recent identification studies show that deep learning has good performance in various fields [20; 21; 22; 23; 24; 25]. Convolutional Neural Network (CNN), a field of deep learning, is showing good performance in the field of image recognition. Recently, [PERSON] et al. [26] studied active sonar target classification using a spectrogram and CNN. In [26], the beamforming result was converted into a spectrogram, and then the spectrogram image was classified using CNN. In this paper, we propose a CNN structure suitable for active sonar data and use it as a classifier. The results of feature extraction for real sea trial data are used as the CNN input. It is difficult to collect target data from the real sea environment resulting in target data being very small compared to clutter data. Therefore, a data augmentation technique is required to increase the target data [24]. In this paper, the performance of the proposed algorithm is evaluated using the classification rate and the proposed CNN model is used to classify feature vectors. The classification results indicate that the proposed algorithm is better than the case of extracting features using MFCC. In addition, the classification result using the proposed algorithm showed better performance than the classification result using the spectrogram. Classifying targets with the proposed algorithm is a huge advantage for the sonar console operators in anti-submarine warfare because classification is possible without setting a threshold within a single ping. The paper is sectioned as follows. Section 2 introduces MFCC and PNCC. While Section 3 describes the proposed algorithm. Then, in Section 4, experiments are described, and the results are discussed. Finally, Section 5 summarizes the findings of the study.
# Multi-LEO Satellite Stereo Winds
## Appendix A
### Solution by Constrained Optimization  
Another approach to achieve the same purpose is through constrained optimization with a Lagrange multiplier (\(\Lambda\)) in either of two formulations:  
\[\chi^{2}=\sum_{n=1}^{N}\overrightarrow{e}_{n}{}^{T}W_{n}\overrightarrow{e}_ {n}+\Lambda\left(X^{T}gX-v_{c}{}^{2}\right),\text{ or} \tag{14a}\] \[\chi^{2}=\sum_{n=1}^{N}\overrightarrow{e}_{n}{}^{T}W_{n} \overrightarrow{e}_{n}+\Lambda\left(X^{T}k-h_{c}\right). \tag{14b}\]  
In the first, the constraint is \(X^{T}gX=v_{c}{}^{2}\), with a \"metric\" matrix \(g\) that can project out the velocity part of the state vector such that the solved-for speed is \(v_{c}\). The idea of constraining the wind is similar to the MINX approach to MISR retrievals [76]. In the second, the constraint is \(X^{T}k=h_{c}\), with a vector \(k\) that selects the height part of the state vector such that the solved-for height is \(h_{c}\). Following the usual procedure, Equation (14a) or (14b) is solved and the solution is a function of \(\Lambda\) that is in turn determined by solving the constraint. In the respective cases, the equivalents of Equation (10) are  
\[\left(P^{-1}+\Lambda g\right)(X-X_{0})=\sum_{n=1}^{N}B_{n}(X_{0})^{T}W_{n} \overrightarrow{b}_{n}(X_{0})-\Lambda gX_{0},\text{ or} \tag{15a}\] \[P^{-1}(X-X_{0})=\sum_{n=1}^{N}B_{n}(X_{0})^{T}W_{n}\overrightarrow{b}_{n}(X_{0} )-\frac{1}{2}\Lambda k. \tag{15b}\]  
In the first case, the solution is nonlinear in the Lagrange multiplier and will require a numerical solution. In the second case, the solution is linear in the Lagrange multiplier and the Lagrange multiplier is easily solved from the constraint equation:  
\[\Lambda=\frac{2}{k^{T}Pk}\left(X^{T}k+\left(\sum_{n=1}^{N}B_{n}(X_{0})^{T}W_{n }\overrightarrow{b}_{n}(X_{0})\right)^{T}Pk-h_{c}\right). \tag{16}\]