esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis
### Modeling and Validating Based on Optimal Spectral Index  
The spectral indices were modeled with the plant nitrogen nutrient index to determine the ideal spectral index model for estimating the plant nitrogen nutrient index, based on the optimal band combinations of the screened individual spectral indices. Table 2 displays the model and accuracy of estimating the nutritional index of winter wheat using both the original ideal spectral indices and the first-order derivative spectral indices of the canopy.  
The coefficient of determination \(R^{2}\) for the inversion model using the original spectral index ranged from 0.79 to 0.84, while the accuracy of the optimal model based on the first-order derivative spectra was consistently higher, with \(R^{2}\) values above 0.83 in all cases. Using the first-order derivative spectra can enhance the precision of the inversion process.  
Figure 6 displays the comparison of the winter wheat nitrogen nutrition index, as predicted by the regression model, along with the actual observed values. Upon validation of the established model, the model's \(R^{2}\) value ranged from 0.72 to 0.77. Additionally, the validation accuracies of the first-order derivative spectral indices were higher than those of the original spectral indices, with the exception of the difference spectral index. The results from both modeling and validation demonstrate that the RSI (FD\({}_{666}\), FD\({}_{826}\)) built using the original spectra of the UHD185 performs relatively better. The modeling set achieves an \(R^{2}\) of 0.85, an RMSE of 0.15, and an RE of 12%. The validation set achieves an \(R^{2}\) of 0.75, an RMSE of 0.18, and an RE of 10%. This corresponds to a model of \(y=-2.0655x+0.156\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Index** & **Model** & **Equation** & \(R^{2}\) & **RMSE** & **RE** \\ \hline NDSI (R\({}_{578}\), \(R_{682}\)) & Q & \(y=-44.477x^{2}+26.433x-2.0741\) & 0.79 & 0.03 & 0.14 \\ NDSI (FD\({}_{690}\), FD\({}_{802}\)) & Q & \(y=-0.6492x^{2}+3.4831x-2.8449\) & 0.83 & 0.07 & 0.13 \\ RSI (R\({}_{578}\), \(R_{682}\)) & Q & \(y=-7.4935x^{2}+25.069x^{2}-19.311\) & 0.79 & 0.03 & 0.14 \\ RSI (FD\({}_{666}\), FD\({}_{826}\)) & L & \(y=-2.0655x+0.156\) & 0.85 & 0.15 & 0.12 \\ DSI (R\({}_{826}\), R\({}_{830}\)) & Q & \(y=-116238x^{2}+924.08x+0.031\) & 0.84 & 0.06 & 0.12 \\ DSI (FD\({}_{690}\), FD\({}_{810}\)) & Q & \(y=3650.4x^{2}+93.263x-0.4386\) & 0.84 & 0.06 & 0.12 \\ \hline \end{tabular} Note: Q stands for quadratic polynomial, L stands for linear function, and FD stands for derivative spectrum.  
\end{table}
Table 2: The results of modeling NNI of winter wheat based on the optimal spectral index.  
Figure 5: Contour map of the coefficients of determination between spectral indices composed of any two bands of spectra and the nitrogen nutrient index of the plant. (**a**) NDSI; (**b**) RSI; (**c**) DSI; (**d**) NDSI_FD; (**e**) RSI_FD; (**f**) DSI_FD.
# Closed-Loop Current Stimulation Feedback Control of a Neural Mass Model Using Reservoir Computing
## 2 Methods
### ESN Performance Metrics  
The mean-squared error (MSE) for the train and test sets was used to evaluate the performance of the ESN. Only the last time point predicted by the ESN, averaged over both PCs, was used when calculating the MSE. We used this approach because the overlap of the input and output signal otherwise could have caused confounds in training performance. The MSE of the PC output was measured to determine how well the ESN closed-loop feedback controller suppressed the energy of the PC across both columns. This MSE was compared to the model without any feedback, as well as a random control: injecting the current stimulation \(\mathbf{I}(n)\) selected from an iteration with a different noise input. We also compared the power spectral density of the PC output from these three conditions.
# An Experimental Study on Repairing of Reinforced Concrete Beams Having Damaged Longitudinal Bars
## 2 Materials and Methods
### Concrete and Steel  
All the tested beams and cubes were poured with a ready-mixed concrete in a single batch. Cube tests of size 150 mm were performed (as per the procedure given in BS EN 12390 [42]) after 28 days of casting leading to an average compressive strength of 43 MPa. The elastic modulus of concrete was calculated to be 33.8 GPa, based on the formulations outlined in EN 1992-EC2 [43], which incorporate the characteristic compressive strength of the concrete.  
The deformed steel reinforcing bars with a diameter of 12 mm were utilized for the longitudinal reinforcement, while stirrup reinforcing bars with a diameter of 6 mm were employed. The yield and ultimate strengths experimentally determined for the longitudinal and stirrup rebars are given in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Specimen**} & \multirow{2}{*}{**Group**} & \multicolumn{4}{c}{**Repair Details**} \\ \cline{3-6}  & & **Type** & **Amount (mm)** & **Placement** & **Groove Size (mm)** \\ \hline RefB & & - & - & - & - \\ RefB-D & Group A & - & - & - & - \\ \hline RB1-SNSM-S & & Steel Rebar & \(2\mathcal{O}12\) (L = 2000) & Side & \(25\times 25\) \\ RB2-SNSM-G & & GFRP Rebar & \(2\mathcal{O}10\) (L = 2000) & Side & \(25\times 25\) \\ \hline RB3-NSM-G & & GFRP Rebar & \(2\mathcal{O}10\) (L = 2000) & Below & \(25\times 25\) \\ RB4-NSM-G-U & & GFRP Rebar & \(2\mathcal{O}10\) (L = 2000) & Below & \(25\times 25\) \\ \hline RB5-EBR-CS & & - & GFRP Sheet & \(100\times 2400\) & Underside & - \\ RB6-EBR-CS-U & & GFRP Sheet & \(100\times 2400\) & Underside & - \\ \hline RB7-EBR-S & & Steel Rebar & \(2\mathcal{O}12\) (L = 2000) & Below & - \\ RB8-EBR-G & & GFRP Rebar & \(2\mathcal{O}10\) (L = 2000) & Below & - \\ \hline \end{tabular} RefB. Reference Beam, D: Damaged, RB: Repaired Beam, SNSM: Side-Near-Surface Mounted, NSM: Near-Surface Mounted, S: Steel bar, G: GFRP bar, EBR: External Bonding Reinforcement, CS: CFRP Sheet, U: U-shape boundary sheet, L: Length.  
\end{table}
Table 1: Details of each tested beam.  
Figure 3: The minimum dimensions of the grooves [40].
# Leveraging Hybrid RF-VLP for High-Accuracy Indoor Localization with Sparse Anchors
## 5 Conclusions  
The paper presents a hybrid RF-VLP positioning system that integrates an RF-assisted VLP algorithm, enabling each receiver to accurately determine its location using the signals from a single LED anchor without relying on additional fixed RF anchors. A prototype system is developed and its positioning performance is analyzed across various environments. The experimental results reveal that the system achieves a positioning RMSE of approximately 26.1 cm, outperforming traditional Bluetooth-based positioning solutions under comparable conditions. Furthermore, we analyze the positioning performance under different receiver configurations, and the results indicate that the system consistently achieves decimeter-level accuracy across various configurations, demonstrating its versatility. The proposed RF-VLP hybrid positioning approach effectively addresses the limitations of conventional methods by enhancing the low accuracy of RF-based systems and extending the limited range of traditional VLP solutions. In summary, the system proposed in this paper offers a scalable and promising solution for future indoor positioning applications, such as intelligent logistics. In the future, we will also discuss and conduct further research on more complex positioning scenarios.  
Figure 10: Localization result under different receiver spacing.  
**Author Contributions:** The contributions of the authors in this paper are as follows: Conceptualization, B.L. and Y.L.; methodology, B.L. and Y.L.; software, B.L.; validation, B.L. and Y.L.; formal analysis, B.L. and Y.L.; investigation, B.L. and Y.L.; resources, B.L. and Y.L.; writing--original draft preparation, B.L.; writing--review and editing, B.L. and Y.L.; supervision, Y.S. and Y.Y.; project administration, Y.S. and Y.Y.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China grant number 62272329.  
Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Animal Experiment  
A total of 50 Kunming mice (seven weeks old, 25 male and 25 female) were kept at 25 \(\pm\) 2 \({}^{\circ}\)C, 50 \(\pm\) 5% in a feeding environment with relative humidity and with light and shade indoors adjusted alternately every 12 h. A second set of 50 mice with similar body weights were randomly divided into five groups: normal group, control group, SDC-L group (mice were fed with low-concentration (2 g/kg) Shuidouchi extract by gavage), SDC-H group (mice were fed via gavage with high-concentration (4 g/kg) Shuidouchi extract), and sulfasalazine group (positive control group). After the onset of the experiment, mice in each group were freely fed with the respective diet. Except for mice in the normal group, all other mice received 2% (w/v) DSS solution as drinking water during the third week. After feeding distilled water for one week, all other mice except for the normal group received 4% (w/v) DSS solution as drinking water during the fifth week. Except for the third and fifth weeks, all mice received distilled water as drinking water for the rest of the time [16; 17]. During the entire experimental duration of five weeks, the SDC-L and SDC-H groups received 2 and 4 g/kg of extract of Shuidouchi daily after the optimization of crafts. The mice in the sulfasalazine group were fed with 20 mg/kg sulfasalazine daily. All mice fasted for 24 h on Day 35 and were killed on Day 36. The colon length and weight of each mouse were measured and the colon weight/colon length index was then calculated. The disease activity index (DAI) of each mouse was simultaneously calculated, as follows: DAI = (fraction of weight loss + fraction of stool shape + fraction of stool occult blood)/3 (Table 4). The blood and colon tissues were also collected for later use. The protocol for these experiments was approved by the Animal Ethics Committee of the Chongqing Medical University under the animal permit number SYXK (Yu) 2018-0003.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Weight Loss (\%)** & **Shape of Stool** & **Occult Blood/Bloody Stool** & **Score** \\ \hline
0 & Normal & Negative & 0 \\
1–5 & Soft stool & Negative & 1 \\
6–10 & Soft stool & Occult blood & 2 \\
11–15 & Diarrhea & Occult blood & 3 \\ \textgreater{}15 & Diarrhea & Blood stool & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Disease activity index (DAI) scoring standard.  
Figure 2: Standard curve of soybean isoflav content (genistein).  
Determination of Endothelin (Et), Somatostatin (Ss), Substance P (Sp), and Vasoactive Intestinal Peptide (Vip) and Levels of Serum in Mice  
Serum was obtained from mice by centrifugation (4500 r/min, 15 min). The endothelin (ET-1), somatostatin (SS), substance P (SP), and vasoactive intestinal peptide (VIP) levels in serum were determined using the respective kits (Abcam, Cambridge, MA, USA) [18].
# Characterization and Water Content Estimation Method of Living Plant Leaves Using Terahertz Waves
## 3 Results and Discussion  
In this paper, we aimed to determine the electromagnetic properties of leaves including permittivity, and physiological features such as weight and thickness that can affect the WC of leaves. In addition, a strong correlation between the determined properties and WC of leaves was observed. Furthermore, the transmission response of all eight leaves were investigated for four consecutive days.
# Self-Interaction Attention Mechanism Based Text Representation for Document Classification
## 5 Results and Analysis
### Performance Comparison
#### 5.1.1 Holdout Method  
In the holdout method, for each dataset, we randomly divide the data into 10 equally sized subsamples. Then we select 8 subsamples for training, a single subsample for validation and a single subsample for testing. In Table 3, we present the experimental results of all discussed models in this paper for document classification on Yelp 2016 and Amazon Reviews (Electronics), respectively.  
Regarding the baselines, TextHAN outperforms TextRNN, showing an improvement of 25.76% and 7.14% in terms of accuracy in Yelp 2016 and Amazon Reviews(Electronics), respectively. This means that hierarchical architecture does indeed represent the document well for classification. In addition, our proposal with the self-interaction mechanism, i.e., TextSAM\({}_{AVE}\), TextSAM\({}_{MAX}\) and TextSAM\({}_{ATT}\), generally outperform the baseline model (except that TextSAM\({}_{AVE}\) loses the competition with TextHAN on Yelp 2016.). This proves that our proposed self-interaction attention mechanism can significantly promote the performance of document classification.  
In particular, TextSAM\({}_{MAX}\) is the best performing model among our proposals. On the Yelp 2016 dataset, TextSAM\({}_{MAX}\) shows an obvious improvement of 5.97% against the best baseline, TextHAN, and achieves an improvement of 7.28% against TextSAM\({}_{AVE}\) and of 5.75% against TextSAM\({}_{ATT}\). Similarly, on Amazon Reviews (Electronics), compared with TextHAN, TextSAM\({}_{AVE}\) and TextSAM\({}_{ATT}\)  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **Yelp 2016** & **Amazon Reviews (Electronics)** \\ \hline TextRNN [11] & 0.4433 & 0.5127 \\ TextHAN [14] & 0.5575 & 0.5493 \\ TextSAM\({}_{AVE}\) & 0.5507 & 0.5636 \\ TextSAM\({}_{MAX}\) & **0.5908** & **0.6265** \\ TextSAM\({}_{ATT}\) & 0.5587 & 0.5709 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy on the document classification task in the holdout methods. The results of the best baseline and the best performer in each column are underlined and boldfaced, respectively.  
TextSAM\({}_{MAX}\) achieves improvements of 14.05%, 11.56% and 9.74%. By applying the max pooling operation on each dimension of interaction representation, TextSAM\({}_{MAX}\) can extract the most representative feature to better represent a document.  
TextSAM\({}_{ATT}\), like TextSAM\({}_{MAX}\), outperforms TextHAN by 0.22% in terms of accuracy on Yelp 2016 and 3.93% on Amazon Reviews (Electronics). TextSAM\({}_{AVE}\) performs worse than TextSAM\({}_{ATT}\) but still improves the accuracy of 2.60% over TextHAN on Amazon Reviews (Electronics). The difference in performance between TextSAM\({}_{ATT}\) and TextSAM\({}_{AVE}\) may be explained by the fact that averaging the interaction will ignore the fact that each document has its own emphasis and specific topic.
## 4 Non-Ischemic Heart Diseases
### Mitral Valve Prolapse and Mitral-Amulus Disjunction  
Malignant arrhythmias can arise from myocardial fibrosis secondary to various conditions, including valvular anomalies. Among these, mitral-annulus disjunction (MAD) stands out, characterized by the atrial displacement of the posterior mitral valve leaflet hinge point [42]. This anomaly may present independently or, more commonly, in conjunction with mitral valve prolapse. There is speculation that MAD might precede mitral valve prolapse, yet the precise relationship between the two remains poorly understood. MAD, therefore, may be linked to ventricular arrhythmias, ranging from frequent premature ventricular contractions to cardiac arrest. One plausible physiological mechanism hypothesized is that the anatomical anomaly induces mechanical stress and myocardial stretching on the infero-basal wall and papillary muscle fibers, potentially leading to myocardial hypertrophy and scarring, ultimately serving as an arrhythmogenic focus [43]. In a study by [PERSON] et al., myocardial fibrosis of the lateral wall, identified through DECT, was reported in a patient with mitral valve prolapse and MAD [44]. In this case report, the patient presented to the emergency department, where magnetic resonance imaging was not available. Therefore, a CCT scan was performed, consisting of two acquisitions with retrospective electrocardiogram gating. The first acquisition was performed in the arterial phase (with no evidence of coronary stenosis and plaques), and the second, for LIE assessment, was conducted 10 min after contrast medium administration. The myocardium of the left ventricle displayed a uniform thickness, except for the mid-ventricular lateral wall, which exhibited increased LIE compared to the rest of the myocardium. Additionally, the prolapsed appearance of the mitral valve was observed, along with a significant space between the mitral valve annulus and the myocardium of the left ventricle. In this case, LIE proved to be crucial in assessing the presence of fibrosis in the papillary muscles and lateral wall.  
Although CT could be considered a potentially valuable imaging modality for the assessment of the mitral valve and valvular apparatus in any plane and in three dimensions, careful consideration of the risks and benefits is necessary for each patient. This is because obtaining such imaging requires a retrospectively electrocardiogram-gated acquisition (with its associated radiation dose) and contrast media administration.
# Quaternion Codes in MIMO System of Dual-Polarized Antennas +
## 6 Simulation and Results
The freedom of the number of receive antennas that can be utilized by the QQDs during their transmission from a quaternionic channel-based system has been further emphasized in the Figure 5. The code \(\mathbf{Q}_{4}\) shows increasing diversity gains as the number of receive dual-polarized antennas are increased at the receiving end. The computational complexity of the proposed decoder used for the codes in the Figures 3-5 promises linear and decoupled decoding at the receiver. The decoder remains independent of the number of unique transmitted symbols, that is, \(\zeta\). This has a huge impact in simplifying the complexity of the receiver in terms of the calculations to be performed. In case of the coupled decoder, the receiver complexity remains dependent  
Figure 4: BER vs. SNR performance of \(\mathbf{Q}_{1},\mathbf{Q}_{2},\mathbf{Q}_{3},\mathbf{Q}_{4},\mathbf{Q}_{5}\) & \(\mathbf{Q}_{6}\) for two receive dual-polarized antenna. Figure 5: BER vs. SNR performance of \(\mathbf{Q}_{4}\) for one, two and three receive dual-polarized antenna. on the number of unique transmitted symbols and has an exponential relationship with it. Considering N transmit antennas and T time slots used to transmit a single block of code, the computational complexity of the coupled decoder is \(O(4^{\zeta}(N)(T)(2))\). However, this reduces significantly in the case of the proposed decoder design, where the computation complexity reduces to \(O(4(N)(T)(2))\). The decoupled decoding of the quasi-orthogonal codes has been a research problem as this compromises the coding rate for increased number of transmit antennas. A unique construction technique has been presented to form quasi QODs with higher code rates. For the antenna configuration of four transmit single receive dual-polarized antennas, the quasi QOD presented in (25) has a compromised receiver complexity but promises higher gains in comparison to the coupled traditional ML-based decoder designs as is clear from the Figure 6.
# New Optimal Design of Multimode Shunt-Damping Circuits for Enhanced Vibration Control
## 2 Methodology
### Design Optimization
#### 2.4.2 Objective Function  
Consider the transfer function matrix \(\mathbf{H}^{\textit{th}}(s)\in\mathbb{C}^{N\times N}\) as representing the piezoelectric composite shunt system (48). It is well known that the frequency response \(\mathbf{H}^{\textit{sh}}(j\omega)\) of a system is a function that relates the output response to a sinusoidal input at a frequency \(\omega\). In fact, the frequency response (FR) of a system at frequency \(\omega\) is simply its transfer function evaluated by substituting \(s=j\omega\), i.e.,  
\[\mathbf{H}^{\textit{sh}}(j\omega)=\mathbf{C}^{\textit{sh}}\Big{(}j\omega\mathbf{I}-\mathbf{A} ^{\textit{sh}}\Big{)}^{-1}\mathbf{B}^{\textit{sh}} \tag{52}\]  
Recall that the elements \(H^{\textit{sh}}_{ij}(\omega)\) of the matrix \(\mathbf{H}^{\textit{sh}}(j\omega)\) provide the system frequency response of the \(i\) DoF to a force at the \(j\) DoF for each forcing frequency \(\omega\).  
Once the frequency range of interest and the DoF at which vibrations are to be damped are defined, the FRF can be used to define performance indexes to be optimized for the selection of optimal shunt parameter values. In the case of multiple-mode shunt damping, the range of interest contains multiple peaks in the region of modes chosen to be damped. Therefore, in this work, the whole frequency range of interest is divided into regionscontaining the frequencies of interest, and the objective function \(f\) is chosen to be the weighted sum of the maximum amplitudes of the FRF over those regions, i.e.,  
\[f(\mathbf{x})=\sum_{k}a_{k}\max_{\underline{\omega_{k}}\leq\omega_{k}\leq\overline{ \omega_{k}}}\left|H_{ii}^{sh}(\omega_{k})\right| \tag{53}\]  
where \(x\) denotes the set of design variables, \(H_{ii}^{sh}(\omega)\) denotes the response of the beam at the tip (\(i\) node) for an excitation at the same point (\(i\) node), \([\underline{\omega_{k}},\overline{\omega_{k}}]\) denotes a region close to the frequency \(\omega_{k}\) and the summation index \(k\) takes values over the set of frequencies of interest. In Equation (53), \(a_{k}\) is the weighting factors satisfying \(a_{k}\geq 0\), \(\sum_{k}a_{k}=1\). By varying the values of \(a_{k}\) we can give emphasis to a particular resonant frequency of interest. In this work, the second and third mode of a cantilever piezoelectric beam are considered for damping without giving emphasis in a particular mode, so \(k=2,3\), and \(a_{2}=a_{3}=0.5\).
# Evaluation of the Robustness for Integrated Production Scheduling and Maintenance Planning Problem
## 2 Modeling and Assumptions
### Machine Breakdown Modeling  
We further assume that machines are not continuously available due to the occurrence of machine breakdowns. Since the machines are assumed to wear out with ongoing production process, the time to failure of a new machine is modeled in an Increasing Failure Rate (IFR) mode. Random machine breakdowns (RMBs) can be described by two parameters: the machine breakdown probability and its downtime.  
In this paper, the following assumptions are made regarding the RMBs:  
1. Machine breakdowns occur only during machine processing;
2. Once the failure is fixed, the interrupted operation will continue to be processed from the point where it was interrupted;
3. The downtime of the machine is constant.  
It is generally believed that machine failure is subject to a certain probability distribution. In this paper, suppose the failure time of the machine is run-based and governed by Weibull probability distributions. \(\left(\beta_{j},\ \theta_{j}\right)\) is the set of parameters of the Weibull function for the machine \(M_{j}\). Furthermore, we assume \(\beta_{j}>1\), which means the machine degrades over time. The reliability of the machine \(M_{j}\) at time \(t\) is \(R(t)=\exp\left(\int_{0}^{t}\lambda(t)dt\ \right)=e^{-(t/\theta_{j})^{\beta_{j}}}\).
# HCTC: Hybrid Convolutional Transformer Classifier for Automatic Modulation Recognition
## 7 Potential Real-World Applications
### Satellite Communications  
The proposed model can be applied in satellite systems to improve signal detection and decoding, leading to more robust communication links in challenging environments [67; 68].