esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Scenarios and Setup
### Simulation Scenario and Setup in VLC Bands
\begin{table}
\begin{tabular}{c c c} \hline
**Item** & **Parameter** & **Value** \\ \hline Room & Size & \(10.97\times 6.62\times\) 2.40 m\({}^{3}\) \\ \hline \multirow{3}{*}{Reflections specifications} & Number of reflections & 4 \\  & Material reflectance & Wavelength-dependent \\  & Type of reflections & Specular and diffuse reflections \\ \hline \multirow{6}{*}{Coating material} & Walls & Plaster \\  & Ceiling & Ceiling \\  & Floor & Floor \\  & Table, desks, and doors & Pine Wood \\  & TV screen & Plate Window Glass \\  & TV shelf and electric box & Galvanized Steel Metal \\ \hline \multirow{3}{*}{Scatter fraction} & TV screen & 0.2 \\  & Desks and doors & 0.5 \\  & Other objects & 1 \\ \hline \multirow{6}{*}{TX} & Model of LED & PAR20 \\  & Number of LED & 9 \\ \cline{1-1}  & Optical power of each LED & 2 W \\ \cline{1-1}  & Analysis rays & 10\({}^{7}\) \\ \cline{1-1}  & Minimum relative ray intensity & 10\({}^{-3}\) \\ \hline \multirow{2}{*}{Channel} & Length \(d_{LOS}\) & 3-10.51 m \\  & Delay resolution & 0.2 ns \\ \hline \multirow{2}{*}{RX} & Type of receiver & Detector Polar \\  & Radial size & 10 mm \\ \hline \end{tabular}
\end{table} 
Table 2: Parameters of simulation setup. The non-sequential ray tracing features of Zemax(r) can be used to calculate the received power and path lengths from the light source to detector for each ray, which can be further used to obtain the CIRs between the light source and the detector point. In our simulation, the detector is placed at the 16 positions, respectively. Here, Monte Carlo analysis and Sobol sampling are adopted as the random ray tracing methods. The number of reflections was determined based on the simulation of particular ray propagation, where the normalized intensity dropped to \(10^{-3}\), i.e., the value of parameter \"minimum relative ray intensity\" in Zemax(r). All other key parameters can be found in Table 2.
## 2 Materials and Methods
### Overlay Networks Based on Multiple Different Time Intervals
#### 2.2.2 Construction of Shortcut Links  
Figure 4 shows an example of the constructed shortcut links described in Section 2.1.1. In Figure 4, the minimum unit is a day from 1 January 2001, and the length of the key space is 64 days (from 1 January to 5 March). The numbers within the circles show the nodes with their keys (days), and the arrows show the shortcut links of the node assigned to key 1. The upper right table shows a part of the linked nodes for each key. In the example, in Figure 4, the node assigned to key 1 constructs the shortcut links which have the following intervals:  
* Month: 1 (31 days)
* Day: 15, 7 (one week)
* Neighbor: successor, predecessor  
Compared to the finger table in Chord, our proposed method constructs the shortcut links to the nodes assigned to the keys for the next month and week. Therefore, our proposed method has few messages and hops to forward the queries such as \"I want to know the data for each one month  
Figure 4: An example of the constructed shortcut links in our proposed method.  
Figure 3: The assumed forwarding of interval queries.  
from the specific day\", \"I want to show the data in the same day of week\". In addition, there are four shortcut links in Figure 4, and it is not a significant difference compared to Chord, which constructs \(m-1\) shortcut links as the maximum case.
# A General Solution for the Errors in Variables (EIV) Model with Equality and Inequality Constraints
###### Abstract  
Targeting the adjustment of the errors-in-variables (EIV) model with equality and inequality constraints, a general solution that is similar to the classical least square adjustment is proposed based on the penalty function and the weight in measurement. Firstly, we take the equality constraints as inequality constraints that do not satisfy the constraint conditions and construct the penalty functions of equality and inequality constraints, respectively. Thus,  
there are a number of studies involving the extended form of the EIV model and the extended algorithm of TLS, such as structured TLS [11], outliers processing in TLS [12], multivariate EIV model [2], variance component estimation algorithm for EIV model [13; 14], TLS prediction [15], equality and inequality constrained TLS [11; 16; 17; 18; 19; 20; 21] and so on.  
In the field of surveying data processing, the adjustment models with equality or inequality constraints need to be constructed when some prior information is available. The algorithm of the EIV model with equality constraints is generally elucidated based on the principle of Lagrange [17; 19]. [PERSON], [PERSON] and [PERSON] [20], [PERSON], [PERSON] and [PERSON] [21] and [PERSON], [PERSON] and [PERSON] [18] investigated the EIV model with inequality constraints. Although their methods are available to solve this inequality constraints problem, the optimization theory involved in the derivation of these algorithms is far from the traditional measurement adjustment theory. In addition, EIV models with equality and inequality constraints are discussed separately in most studies, while their combination is less investigated [19].  
In fact, both equality and inequality constraints may exist in surveying data processing. Thus, it is necessary to figure out simpler and more easily implemented algorithms. Three EIV models with constraints, namely the EIV model with equality constraints (EC-EIV), EIV model with inequality constraints (IC-EIV) and EIV model with equality and inequality constraints (EIC-EIV), shall be included when equality and inequality constraints are available. These three EIV models with constraints are degenerated into G-M models with constraints if the coefficient matrix error is excluded. Thus, a general solution for the EIV model with equality and inequality constraints is in high demand.  
In this paper, a general solution that is similar to classical least square adjustment is proposed based on the penalty function and the weight in measurement. In Section 2, the EIV model with equality and inequality constraints is introduced. In Section 3, the detailed calculation formula and approximate accuracy evaluation formula of the general solution are presented. In Section 4, three examples, including the equality constraint, inequality constraint, and equality and inequality, are presented to illustrate the validity of the proposed general solution. The results show that the general solution is effective, stated in Section 5.
## 4 Simulation Results
### Star Topology WSN  
Figures 5 and 6 show the PDRs of the TMP method [18] and the proposed method in the star topology WSN, respectively. The data rate of each node is shown in Table 2. In the TMP method, the PDR of end devices decrease as the number of nodes increases. This is because the TMP method does not adequately control DC for high data rate nodes. On the other hand, in the proposed method, the PDR of end devices do not decrease so much. It is seen from this result that the proposed method can cope with the increase in the data rate.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Frequency band & 2.4 (GHz) \\ Packet length & 100 (byte) \\ Simulation time & 10–1000 (s) \\ Tx energy & 31.25 (mW) \\ Rx energy & 35.28 (mW) \\ Idle energy & 0.712 (mW) \\ Sleep energy & 0.144 (mW) \\ macMaxBE & 5 \\ macMinBE & 3 \\ macCSMABackoff & 4 \\ macMaxFrameRetries & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 5: Packet Delivery Ratio (PDR) of each node in Tele-Medicine Protocol (TMP) method.
# Raster Angle Prediction of Additive Manufacturing Process Using Machine Learning Algorithm
## 3 Machine Learning Algorithms
### Gaussian Process Regression  
Gaussian process regression (GPR) is a successful and flexible ML method generally employed to solve nonlinear multivariate regression and classification problems [37]. The most important advantage of GPR is that uncertainty measurements can be made on its results; thus, it can produce successful predictions even in small non-parametric datasets [38]. It assumes that the underlying function generating the results is a Gaussian process, which is a completely stochastic process. The mean function of the GPR can be any function that maps input variables to output variables, while the covariance function encodes the smoothness and similarity of the function [39].  
Given a set of input-output pairs (\(x_{1}\),\(y_{1}\),), (\(x_{2}\),\(y_{2}\)), \(\cdots\), (\(x_{n}\),\(y_{n}\)), the probability of the data is:  
\[p(YIX,\theta)=N\Big{(}yI0,\;K+\sigma^{2n}\Big{)} \tag{4}\]  
The purpose of GPR is to find the function \(f(x)\) given below. This function is the distribution of y over x. Here, x refers to the input parameters in the Gaussian process. The y represents a finite number of outputs connected to x [40].  
\[p(fIX,y,\theta)=N(fIm(x),\;K) \tag{5}\]  
By choosing different mean and covariance functions, different types of datasets can be modeled. The exponential kernel function, a commonly used covariance functions, was used for the GPR model developed in this study. The main disadvantage of GPR is that it can be computationally slow and costly, especially for large datasets. However, several approximation methods can be used to speed up the calculation [41].
# Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data
## 4 Discussion
In high-yielding lupin crops, denser foliage absorbs also more red light for photosynthesis [80], reducing reflectance in this band that is strongly associated with chlorophyll absorption. A negative correlation between \(B12\) (SWIR band) reflectance and crop yield was also observed. This can be attributed to its sensitivity to plant water content. Healthy, well-watered lupin crops absorb more SWIR light, resulting in lower \(B12\) reflectance [81], suggesting higher yield potential. In this fashion, the limitations of the present study should be mentioned. First, the performance of the ML models was assessed using a dataset within a specific geographical and climatic context, namely the Mediterranean. This may limit the generalizability of the outcomes to other regions that have different environmental conditions. Moreover, the Spearman correlation analysis, utilized to remove redundant features, although a common data preprocessing step [82; 83], it may come with potential disadvantages. Indicatively, there is a risk that aspects that may otherwise provide complementing information in non-linear models will be lost. Despite its disadvantages, the use of this analysis offers notable advantages, particularly in reducing multicollinearity and dimensionality, which can enhance both model interpretability and computational efficiency. This is of central importance as it supports the practical usability and scalability of ML-based solutions in real-world agricultural settings. Meanwhile, faster model deployment and training are made possible by increased computational efficiency, particularly when working with large remote sensing datasets or incorporating the model into time-sensitive decision-making pipelines [84]. Besides, although remote sensing-based features such as spectral bands and VIs are considered a valid input [40; 85], the study did not include other important agronomic variables like soil properties, which could influence yield outcomes. Based on the findings of this study and towards addressing the aforementioned limitations, improving model generalizability through its validation across several geographic locations and climatic conditions should be the prioritized in future research. Additionally, incorporating multi-temporal satellite imagery could enhance the model's ability to capture dynamic crop growth patterns and improve the robustness of yield predictions. In order to reduce the need for large amounts of labeled data, future research could also examine active learning strategies, where the model selectively requests the most informative data points to be labeled [86; 87]. Furthermore, incorporating a wider range of agronomic variablescould improve the model's ability to capture critical interactions that affect yield, like soil fertility, irrigation practices, and pest management.
## 5 Discussion of the Results
### Optical Analysis  
During the tests and observations using the microscope described above, it was not possible to observe particles that were smaller than 10 um, excluding those with strong fluorescence, which does not mean that no such particles were accumulated in the tissues. Moreover, the authors are convinced that these plastics were contained, but the detection of such small particles is extremely difficult, and at any stage of processing, it is possible to miss them. Optical analysis works very well for identifying larger particles and tissue mapping, which makes it possible to mark all visible fragments for further analysis. And while Nile Red staining is a very promising method, there is still uncertainty about the level of precision of the described results below 10 um. Similar limitations in the detection of small microplastic particles using optical microscopy and Nile Red staining have been reported in the literature. Research indicates that particle properties, such as morphology, color, and degree of degradation, may affect the detection efficiency after Nile Red staining. For example, black microplastics exhibit weak fluorescence, making them difficult to detect, and fibers are more difficult to identify compared to regularly shaped particles [48]. Therefore, although Nile Red staining is a useful tool for the rapid identification of microplastics, its limitations in the detection of small particles and the possibility of obtaining false positive results suggest the need to use complementary analytical methods, such as FTIR or Raman spectroscopy, to obtain more precise and reliable results.
## 3 Experiments
### Implementation Details  
In order to improve the quality of the dataset, we pre-processed the text data by removing stopwords (e.g., \"in\", \"of\", \"from\") and punctuation. Then, all word embeddings from the text data were initialized by 200-dimensional GloVe vectors pre-trained by [PERSON] et al. (2018). For the out-of-vocabulary words, we randomly sampled their embeddings from a uniform distribution \(U\) (\(-0.1\), \(0.1\)). Some works adopted the fine-tuned training strategy for word vectors to improve the performance for text sentiment classification tasks ([PERSON] et al., 2018). In contrast, with the purpose of better reflecting the generalization ability of the model, we preferred to use the general embeddings for all datasets. What is more, we treated all the context words as sentiment resource words to implement the self-attention mechanism as if there was no sentiment resource word in the sentence.  
For the deep neural network, the hidden states of the GRU unit in each layer were set to 200. In the convolution layer, we employed 1D convolutional filter windows of 3, 4, and 5 with 100 feature maps each, and a 1D pooling size of 4.  
During the training process, we optimized the proposed model with the AdaDelta algorithm ([PERSON] et al., 2015) by following the learning rate of \(10^{-2}\) and the mini-batch size of 32. To alleviate the overfitting problem, we employed the dropout strategy ([PERSON] et al., 2015), with a dropout rate of 0.5 for the Bi-GRU layer, 0.2 for the penultimate layer, and \(10^{-5}\) for the coefficient \(\lambda_{r}\) of \(L_{2}\) regularization. To evaluate the performance of the sentiment classification task, we used Accuracy and F1 as the metrics.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **c** & **1** & **m** & **train** & **dev** & **test** & **[V]** & **IV\({}_{\text{pre}}\)** \\ \hline Movie review (MR) & 2 & 21 & 59 & 10,662 & - & CV & 20,191 & 16,746 \\ Stanford Sentiment Treebank (SST) & 5 & 18 & 51 & 8544 & 1101 & 2210 & 17,836 & 12,745 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The summary statistics of the two datasets. c: number of target classes, l: average sentence length, m: maximum sentence length, train/dev/test: train/development/test set size, \(|\text{V}|\): vocabulary size, \(|\text{V}_{\text{pre}}|\): number of words present in the set of pre-trained word embeddings, CV: 10-fold cross validation.
# Intelligent Air Cutting Monitoring System for Milling Process
## 5 Results and Discussion
### Vibration Signal Diagnosis Method  
For the monitoring experiment, based on cutting vibration signals as the judgment basis, the experiment parameters are shown in Table 2. The accelerometer is installed on the rise, and the cutting path is the same as in the previous experiment. The experiment result data are shown in Table 3. It can be seen that the monitoring results of the three cuttings are similar. The mean, standard deviation, and uncertainty of cutting time, air cutting time, and machining time were 10.443 s, 0.048 s, 0.27%; 11.941 s, 0.046 s, 0.22%; 22.384 s, 0.016 s, 0.04%, respectively. The total machining time is about 22.4 s, and the cutting time is about 10.4 s. Since the feed rate is 600 mm/min and the cutting length is 100 mm, the theoretical cutting time should be 10 s. The difference of 0.4 s is perhaps because of signal processing time and judgment calculation time; however, this deviation value is still in the acceptable range.  
Figure 5: Monitoring result of G00 air cutting time.  
The original vibration signal during machining is shown in Figure 7. The change in vibration amplitude among air cutting, cutting, and table movement can be seen. The 1024 data is collected every 0.1 s, then its root mean square (rms) is calculated, as shown in Figure 8. As seen, there is a significant difference in vibration value for cutting and no cutting. When there is no cutting and the machine is idle, the vibration is around 0.02 g. Meanwhile, the vibration is 0.05 g when the table moves. The vibration value during actual cutting is about 0.2 g, which is nearly ten-times the value of the non-cutting vibration. Therefore, this method is suitable for monitoring purposes.
# A Novel Computational Imaging Algorithm for Electrical Capacitance Tomography
## 7 Conclusions  
To alleviate the ill-conditioned characteristics of the ECT inverse problem, an image reconstruction algorithm based on the RELM is proposed. The main conclusions of this paper are summarized as follows:  
1. [label=(0)]
2. Combined with the prior information of the imaging targets, the RELM algorithm improves the imaging quality. The implementation of the algorithm mainly includes two stages: at the first stage, according to a large number of training samples, the RELM model solved by the ISB algorithm between the capacitance correlation coefficient and the imaging target is extracted; at the second stage, the correlation coefficient of  
Figure 15: Comparison of image errors (**(a**–**c**) correspond to the image errors for the phantom objects in Figure 14a–c).  
the measured capacitance of ECT system is calculated, which is then used as input for the RELM model to predict the final imaging.
2. The solution method of capacitance correlation coefficient is introduced. The newly designed objective function based on L1-norm and STV norm can better model the inverse problem of the ECT, which alleviates the adverse effects caused by the inaccuracy of capacitance data and enhances the sparse characteristics of imaging targets. The ISB method greatly improves the solution efficiency for the objective function.
3. Numerical simulation results verify the effectiveness of the RELM algorithm. For the seven kinds of complex flow patterns shown in Figure 7, the RELM algorithm achieves the best imaging quality and the smallest image errors compared to the other above-mentioned algorithms. Under different noise levels, the RELM algorithm has strong robustness and noise resistance. When the number of hidden layer nodes of the RELM is greater than 700, the image error remains at a low level and the attenuation degree is small.
4. In this study, a set of the ECT system is built to realize on-line imaging. The experimental results proved that the RELM algorithm has feasibility and effectiveness in the actual ECT image reconstruction process. This paper proposes a new ECT imaging algorithm. Its advantages are verified by numerical simulations and experiments. The high-precision imaging quality of the RELM algorithm provides a reliable basis for studying the internal mechanism of the imaging target, which should be further studied in the future.  
Conceptualization, Q.Z. and S.L.; methodology, Q.Z.; software, Q.Z.; validation, Q.Z. and S.L.; resources, S.L.; writing--original draft preparation, Q.Z.; writing--review and editing, Q.Z. and S.L.; visualization, W.C.; supervision, W.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China under Grant No. 61871181.  
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.
## 2 Materials and Methods
### Model Parameter Validation Index  
The parameters are determined based on measured data. The Nash efficiency coefficient, total runoff balance error coefficient, and relative error of peak flow are selected as the objective function indicators for the parameter determination of the hydrological and hydraulic modules [55]. These indicators reflect the relationship between the simulation results and the law of hydrological and hydraulic change process in the study area. The Nash efficiency coefficient ENS, total runoff balance error coefficient EQW, and relative error of peak flow EPR are calculated using the following formulas:  
\[E_{\text{NS}}=1-\frac{\sum_{t=1}^{n}(q_{t,obs}-q_{t,sim})^{2}}{\sum_{t=1}^{n} \left(q_{t,obs}-\overline{q}_{t,sim}\right)^{2}} \tag{6}\]  
\[E_{\text{QW}}=\frac{\sum_{t=1}^{n}(q_{t,obs}-q_{t,sim})}{\sum_{t=1}^{n}q_{t, obs}} \tag{7}\]  
Figure 3: SWMM generation.  
\[E_{PR}=\frac{\left|q_{p,obs}-q_{p,sim}\right|}{q_{p,obs}}\times 100\% \tag{8}\]  
where  
\(q_{t,obs}\)--Measured flow at time t (m\({}^{3}\)/s)  
\(q_{t,sim}\)--Simulated flow at time t (m\({}^{3}\)/s)  
\(q_{obs}\)--Average measured discharge (m\({}^{3}\)/s)  
\(q_{p,obs}\)--Measured peak flow (m\({}^{3}\)/s)  
\(q_{p,sim}\)--Simulated peak flow (m\({}^{3}\)/s)  
\(N\)--Number of flow measurements
## 3 Constitutive Model of Rock Mass Under THMC Coupling
### THM Coupling Models
#### 3.2.3 Influence of Multi-Field Coupling on Rock Damage
Some researchers also use equivalent continuum models to study the rheological effects of fluid-structure interactions in rocks, deriving rheological models that are analyzed with finite element programs. Research into the THMC coupling of rocks has advanced considerably, yet challenges remain due to the complexity of interactions between rock mechanical properties, permeability, and thermal-chemical processes, both at micro and macro scales [67]. Progress has relied on integrating microscopic research, physical experimentation, mathematical modeling, and numerical simulation to better understand and quantify these interactions. Mathematical models for multi-field coupling, including thermodynamic, fluid flow, mechanical, and chemical transport equations, have been developed, accompanied by sophisticated numerical simulation techniques to simulate THMC processes. Numerical Simulations and Constitutive Models  
Studies suggest that effective THMC research requires a multiscale approach, incorporating models of microscopic and mesoscopic pore structures to accurately capture coupling dynamics across scales. [PERSON] et al. [114] developed a constitutive model for THMC coupling in porous media, providing a basis for numerical simulation in rock mechanics. Similarly, a model addressing the interactions among temperature, pore pressure, mechanics, and water chemistry within shale formations enhances the framework for understanding shale's response to multi-field influences [118].