esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### The Correlation between Environmental Factors and Ginensosides and Gene Expression
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Temperature** & **PAR** & **Relative** & **Rain** & **Soil Water** \\  & **(\({}^{\circ}\)C)** & **(\(\mu\)E)** & **Humidity (\%)** & **(mm)** & **Potential (J/g)** \\ \hline HMGR & \(-0.24\) & \(0.44\) & \(-0.37\) & \(-0.72\) * & \(-0.69\) * \\ SS & \(-0.43\) & \(0.65\) & \(-1.00\) ** & \(-0.52\) & \(0.46\) \\ FPS & \(-0.60\) & \(0.31\) & \(-0.58\) & \(-0.94\) ** & \(-0.46\) \\ SE & \(-0.59\) & \(0.57\) & \(-0.95\) ** & \(-0.78\) * & \(0.12\) \\ DS & \(-0.38\) & \(0.71\) * & \(-0.85\) ** & \(-0.72\) * & \(-0.07\) \\ PPDS & \(-0.66\) * & \(0.43\) & \(-0.97\) ** & \(-0.69\) * & \(0.41\) \\ PPTS & \(0.77\) * & \(0.10\) & \(0.33\) & \(0.96\) ** & \(0.54\) \\ \(\beta\)-AS & \(-0.55\) & \(0.53\) & \(-0.99\) ** & \(-0.58\) & \(0.49\) \\ OAS & \(-0.54\) & \(0.34\) & \(-0.89\) ** & \(-0.38\) & \(0.74\) * \\ \hline \end{tabular}  
* indicates a significant correlation (\(p<0.05\)). ** indicates a significant correlation (\(p<0.01\)).  
\end{table} 
Table 10: Correlation analysis between environmental factors and gene expression in leaves at different growth times. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **HMGR** & **SS** & **FPS** & **SE** & **DS** & **PPDS** & **PTS** & \(\beta\)**-AS** & **OAS** \\ \hline Rg1 & \(0.25\) & \(0.87\) ** & \(0.28\) & \(0.71\) * & \(0.74\) * & \(0.70\) * & \(0.10\) & \(0.79\) * & \(0.69\) * \\ Re & \(0.98\) ** & \(0.48\) & \(0.91\) ** & \(0.72\) * & \(0.87\) ** & \(0.46\) & \(-0.71\) * & \(0.42\) & \(0.09\) \\ Rf & \(0.82\) ** & \(0.63\) & \(0.72\) * & \(0.74\) * & \(0.90\) ** & \(0.51\) & \(-0.39\) & \(0.54\) & \(0.24\) \\ Rb1 & \(0.43\) & \(-0.12\) & \(0.04\) & \(-0.09\) & \(0.19\) & \(-0.34\) & \(0.23\) & \(-0.27\) & \(-0.47\) \\ Rc & \(0.47\) & \(-0.37\) & \(0.06\) & \(-0.25\) & \(0.04\) & \(-0.53\) & \(0.09\) & \(-0.49\) & \(-0.71\) * \\ Rb2 & \(0.61\) & \(-0.08\) & \(0.25\) & \(0.03\) & \(0.32\) & \(-0.26\) & \(0.00\) & \(-0.22\) & \(-0.48\) \\ Rb3 & \(0.81\)** & \(-0.21\) & \(0.71\) * & \(0.17\) & \(0.30\) & \(-0.10\) & \(-0.80\) ** & \(-0.21\) & \(-0.47\) \\ Rd & \(0.34\) & \(-0.16\) & \(-0.05\) & \(-0.16\) & \(0.12\) & \(-0.39\) & \(0.31\) & \(-0.31\) & \(-0.49\) \\ Ro & \(0.44\) & \(-0.64\) & \(0.30\) & \(-0.31\) & \(-0.20\) & \(-0.51\) & \(-0.53\) & \(0.62\) & \(0.77\) * \\ PPT & \(0.62\) & \(0.83\) ** & \(0.60\) & \(0.83\) ** & \(0.92\) ** & \(0.69\) * & \(-0.24\) & \(0.74\) * & \(0.52\) \\ PPD & \(0.48\) & \(-0.24\) & \(0.08\) & \(-0.16\) & \(0.14\) & \(-0.43\) & \(0.13\) & \(-0.38\) & \(-0.60\) \\ Total saponins & \(0.61\) & \(0.07\) & \(0.27\) & \(0.14\) & \(0.42\) & \(-0.13\) & \(0.03\) & \(-0.07\) & \(-0.34\) \\ \hline \end{tabular}  
* indicates a significant correlation (\(p<0.05\)). ** indicates a significant correlation (\(p<0.01\)).  
\end{table} 
Table 11: Correlation analysis between gene expression and ginsenosides in roots at different growth times.
## 2 Methodology
### Determination of Reverberation Time
#### 2.4.2 The Second Averaging Method  
When many measurement points are obtained, the parameters of one averaged decay curve should be determined. It should be noted that the moment of turning off the source may vary, and it depends on the researcher, as shown in Figure 5.  
In such a case, for each of the curves, points that correspond to the same time moments should be found. Technically, it is very difficult to perform, and for that reason, a principle was adopted according to which the first three time moments were applied for further approximations, starting from the second point occurring after the moment of sound pressure level drop by 5 dB as well as the last three moments (each moment accounts for 6 measurements) that defined the moment of a drop by 30 dB, which is shown in Figure 6. As it can be observed in Figure 6, these points are approximately arranged on a straight line. Such a straight line is determined using the linear regression method. In this way, we determine the equation of a straight line averaging all decay curves.  
Figure 4: Exemplary decay curve for the frequency of 500 Hz.  
Based on the regression equation, \(\Delta\)L is determined. The linear regression function has the following form:  
\[y=\beta_{0}+\beta_{1}x+\zeta \tag{2}\]  
where \(\beta_{0}\) and \(\beta_{1}\) are structural parameters of the regression function, and \(\zeta\) is the random component. As for the random component, it is based on several assumptions. To be precise, the random component has a normal distribution, its expected value is equal to zero, its variance is constant and it is independent of the independent variable \(x\). An appropriate fit of the regression function to the data is made using the least squares method, i.e., the task is reduced to the problem where the sum of the squared deviations of the empirical values from the theoretical ones is the smallest. Having estimated the assessments of structural parameters, we obtain a regression model for the measurement consistent with the second averaging method in the following form:  
\[L_{p}=b_{0}+b_{1}x \tag{3}\]  
Figure 5: Exemplary decay curves for the frequencies. Each line represents a new measurement repetition.  
Figure 6: Linear approximation of a fragment of the decay curve for a selected frequency band.  
where \(L_{p}\) is the sound pressure level, \(b_{0}\) and \(b_{1}\) are the estimated regression coefficients, while \(x\) is the number of the point on the decay curve selected as explained in Figures 5 and 6. The statistical significance of the linear relationship should be ensured by the value of the test probability \(p\), which should be lower than the significance level of 0.05.
## 3 Optimization Design Strategy
### 3D Inverse Design Method  
IDM was employed to parameterize the shape of the impeller. The flow through the impeller is assumed to be inviscid, incompressible, and steady. The blades are described by sheets of vorticity [27], and the strength is determined by the circulation \(r\overline{V_{\theta}}\):  
\[r\overline{V_{\theta}}=\frac{B}{2\pi}\gamma_{0}^{\frac{2\pi}{B}}rV_{\theta} \mathrm{d}\theta \tag{1}\]  
where \(B\) is the blade numbers, \(\overline{V_{\theta}}\) is the circumferentially averaged tangential velocity, \(V_{\theta}\) is the tangential velocity, and \(r\) is the radial coordinate.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Design flow rate (m3/s)** & 0.427 & **Design head (m)** & 12.66 \\
**Impeller diameter (mm)** & 320 & **Impeller blade number** & 4 \\
**Rotational speed (r/min)** & 1450 & **Specific speed** & 511 \\
**Minimum hub diameter (mm)** & 60 & **Maximum hub diameter (mm)** & 210 \\
**Minimum shroud diameter (mm)** & 270 & **Maximum shroud diameter (mm)** & 368 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design parameters of the baseline model.  
Figure 2: Optimization system.  
According to the incompressible potential flow theory [12], the pressure distribution is closely related to the meridional derivative of the circulation \(\partial(r\overline{V_{\theta}})/\partial m\):  
\[\Delta p=p^{+}-p^{-}=\frac{2\pi}{B}\rho W_{m}\frac{\partial(r\overline{V_{ \theta}})}{\partial m} \tag{2}\]  
where \(p^{-}\) and \(p^{+}\) are the suction surface and working surface static pressure, respectively; \(m\) is the normalized streamline distance; \(W_{m}\) is the fluid relative velocity; \(\rho\) is the density of the fluid; and \(\partial(r\overline{V_{\theta}})/\partial m\) is the blade loading. The blade shape can be calculated by Equation (3):  
\[(\overline{V_{z}}+v_{zbl})\frac{\partial f}{\partial z}+(\overline{V_{r}}+v_ {rbl})\frac{\partial f}{\partial r}=\frac{r\overline{V_{\theta}}}{r^{2}}+ \frac{v_{obl}}{r}-\omega \tag{3}\]  
where \(v\) and \(\overline{V}\) are the periodic velocity and circumferential average velocity, respectively, and subscripts \(z\) and \(r\) represent the axial and radial components of velocity, respectively. \(f\) is the blade wrap angle that is a \(\theta\) value at the blade between the leading edge and trailing edge. To obtain the blade shape, the initial wrap angle \(f^{0}\) needs to be known. In this method, the initial wrap angle was prescribed by the stacking condition. Figure 3 shows the IDM calculation flowchart.
# State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF
## 3 Design of Vehicle State Estimator Based on IMM-MCCKF
### Derivation of the IMM-MCCKF Algorithm
#### 3.5.1 Interaction Input  
\(\hat{X}_{(i,k-1|k-1)}\) represents the state estimation results of each filter from the previous time step. Similarly, \(\mu_{k-1}^{i}\) describes the model probabilities of each filter of the prior time step. The state estimation value \(\hat{X}_{(0),k-1|k-1)}\) and covariance \(P_{(0),k-1|k-1)}\) are calculated based on \(\hat{X}_{(i,k-1|k-1)}\) and \(\mu_{k-1}^{i}\).  
The obtained estimation values are set as the initial values for the IMM algorithm. The computation method proceeds as detailed below [7]:  
\[\mu_{(ij,k-1|k-1)}=\frac{\sum\limits_{i=1}^{r}p_{ij}\mu_{(ijk-1)}}{\overline{c }_{j}} \tag{54}\]  
where \(\overline{c}_{j}=\sum\limits_{i=1}^{r}p_{ij}\mu_{(ijk-1)}\).  
\[\hat{X}_{(0),k-1|k-1)}=\sum\limits_{i=1}^{r}\hat{X}_{(i,k-1|k-1)}\mu_{(ij,k- 1|k-1)} \tag{55}\]  
\[\begin{array}{l}P_{(0),k-1|k-1)}=\sum\limits_{i=1}^{r}\mu_{(ij,k-1|k-1)} \Big{\{}P_{(i,k-1|k-1)}+\\ \Big{[}\hat{X}_{(i,k-1|k-1)}-\hat{X}_{(0),k-1|k-1)}\Big{]}\cdot\Big{[}\hat{X}_ {(i,k-1|k-1)}-\hat{X}_{(0),k-1|k-1)}\Big{]}^{\mathrm{T}}\Big{\}}\end{array} \tag{56}\]
# A Hybrid Model of Conformer and LSTM for Ocean Wave Height Prediction
## 2 Materials and Methods
### Algorithm Design Ideas  
This study proposes a Conformer-LSTM model that uses Spearman correlation analysis on common buoy data (wind speed, air temperature, atmospheric pressure, wave period) and selects features with high correlation to the target feature of wave height, such as wind speed [22], temperature [23], atmospheric pressure [24], and wave period [25], as parameters for training.  
This paper uses multi-head attention mechanisms to obtain the global dependencies of the data and combines convolutional layers to capture local features. Batch normalization [26] is used to normalize the output, reducing internal covariate shift. An adaptive feature fusion weight network is introduced, allowing for the model to dynamically adjust the fusion weights of different features according to the characteristics of the input data. Finally, a Long Short-Term Memory network is used to capture long-term dependencies in the data for prediction, reducing errors and improving prediction accuracy.
# Assessment of Water Consumption Behavior in Single Households Using Smart Water Meters
## 2 Materials and Methods  
### Study Area  
This study focused on the Chibata district in the northern part of Kossai city in Shizuka Prefecture, Japan. According to a report by the Japan Meteorological Agency, Kossai city'sclimate at the beginning of autumn in September has temperatures ranging from 22.9 to 29.8 \({}^{\circ}\)C, with an average of 25.9 \({}^{\circ}\)C, suggesting a moderately hot climate. While August is the warmest month with an average temperature of 32.3 \({}^{\circ}\)C, January is the coolest month with an average temperature of 5.6 \({}^{\circ}\)C. According to the 2019 census, Chibata district has a population of 5200 across approximately 1900 households. However, the population declined by 4% between 2010 and 2019, decreasing the demand for water consumption. The Chibata water distribution plant boasts a capacity of 2000 m\({}^{3}\)/day.  
The water supply in this area was obtained from the amount of water used in each household every two months. After the smart water meters were installed, the water supply was recorded hourly, providing data throughout the 24 h. Water meters are installed in front of each house in the Chibata district. Each water meter includes a sensor that can record hourly water consumption. The meters transmit the water consumption data to the electricity network. The meters were designed to collect and transmit data wirelessly, enabling efficient automatic monitoring in this area. The application of smart water meters aligns with IoT-based infrastructure management, enabling real-time data collection and efficient monitoring [37]. The implementation of smart water meters in all households within the water distribution area of the Chibata district represents a pioneering initiative aimed at automating meter readings and proposing helpful monitoring systems for the water consumption of each household.
# Hybrid Trust Model for Node-Centric Misbehavior Detection in Dynamic Behavior-Homogeneous Clusters
## 2 Related Work
### Trust-based detection  
The trust-based detection algorithm relies on both nodes' the historical and current trust levels. [PERSON] and [PERSON] were the first to propose a collaborative trust mechanism called CORE [14], built on the Dynamic Source Routing (DSR) routing protocol. This mechanism encourages cooperation among nodes by observing their behavior and implementing a trust evaluation system. [PERSON] and [PERSON] [15] adopted a credit-based approach to incentivize collaboration by assigning credits to nodes. Their proposed routing algorithms assess neighbor nodes based on credit values and enhanced credit mechanisms. This credit-based system rewards and penalizes nodes for transmitting and receiving packets while leveraging cooperative game theory to detect and manage selfish behavior within the network. Similarly, [PERSON] et al. [16] introduced the CORE approach, where each network participant employs a trust system to evaluate the involvement of other members. Trust scores are calculated based on data observed locally and information shared by other nodes engaged in network activities. Nodes with high trust scores gain access to network resources, whereas those with low scores are restricted.  
While trust-based detection mechanisms evaluate historical and current trust levels to identify misbehavior, they face significant scalability challenges. These methods often rely on node-to-node interactions and trust evaluations, which are difficult to scale effectively in large and highly dynamic VANETs.
# Compressive Sensing-Based Coding Iterative Channel Estimation Method for TDS-OFDM System
## 3 Cyclic Compressive Sensing Channel Estimation Algorithm Based on Channel Coding  
To solve the aliasing problem of the PN sequence and valid data in the TDS-OFDM system, we adopt the compressive sensing method [15] to accurately estimate the channel frequency response, and we improve the accuracy of received data by making use of the error correction capability of cascaded channel coding [16]. Then, the received data after error correction is used to construct a sensing matrix. Finally, the compressive sensing algorithm is applied again to estimate the channel. Figure 2 shows the system chart.  
Figure 1: Schematic diagram of data aliasing in a multipath channel.  
Figure 2: TDS-OFDM system chart based on the coding cyclic compressive sensing channel estimation algorithm.  
The system chart is composed of three sections: the yellow section represents the process of separating and reconstructing the valid data from the aliased data; the orange red section represents the iterative channel estimation process based on the CoSaMP algorithm; and the green section represents the LDPC re-encoding process.
# Mixed-Species Cover Crop Biomass Estimation Using Planet Imagery
## 2 Materials and Methods
### Biomass and Hyperspectral Data Collection and Processing  
Cover crop biomass samples were collected before CC termination in April or May of 2021 and 2022 (Table 1). Biomass was collected within each experimental unit (plot) using two randomly placed 1 m\({}^{2}\) sampling frames. Above-ground biomass within each frame was clipped, bagged together, dried in the greenhouse for over 2 months, and weighed to determine average dry biomass production per square meter area. Finally, biomass yield from each plot was expressed as Mg ha\({}^{-1}\) basis.  
Hyperspectral reflectance data were collected using FieldSpec4 standard resolution (ASD Inc., Malvern PANalytical, Westborough, MA, USA) spectroradiometer held over the cover crop canopy. This instrument measured wavebands from 350 to 2500 nm from the target area. Cover crop canopies were scanned randomly at 3 different spots within each plot, and averaged reflectance value was used for further analysis. The R [25] package 'hsdar' [26] was used to postprocess the hyperspectral data. Hyperspectra were filtered (350-400, 1330-1480, 1780-1900, 2400-2500 nm), smoothed (Savitzky-Golay noise filter), and resampled to 10 nm bands, resulting in 164 total reflectance bands. Vegetation indices for hyperspectral data were derived using _hsdar::vegindex_ command (vegindex function from hsdar package). A total of 115 VIs were retrieved and used for the random forest model-building process. A list of VIs, their derivation, and source can be found in R using '_help(vegindex)_' or at the URL: [[https://rdrr.io/cran/hsdar/man/vegindex.html](https://rdrr.io/cran/hsdar/man/vegindex.html)]([https://rdrr.io/cran/hsdar/man/vegindex.html](https://rdrr.io/cran/hsdar/man/vegindex.html)). Since hyperspectral data were collected at one time point at the end of season, no other statistics except the random forest model output for that specific time point were compared with planet imagery.
# Deep Learning Realizes Photoacoustic Imaging Artifact Removal
## 4 Discussion  
In this paper, we propose a deep learning-based photoacoustic imaging artifact-removal and segmentation algorithm to solve the problems of single means and poor adaptability in the existing technology. The proposed YOLOv8-Pix2 Pix model is able to effectively remove artifacts outside and inside the signal source, and segmentation is performed by YOLOv8 for processing and analyzing the simulated photoacoustic data of brain tumors and experimentally acquired photoacoustic data. The experimental results show that the proposed algorithm exhibits significant advantages over other methods on the brain tumor dataset, not only effectively removing the internal and external artifacts, but also better preserving the detailed structure and texture of the image. In the segmentation experiments, the segmentation labels of the images after artifact removal are highly consistent with the original image labels, and the morphological edges are clear, presenting excellent segmentation precision and accuracy. In contrast, the image segmentation labels without removing artifacts showed missing information and blurred edges, and could not accurately capture the subtle features in the image. In order to verify the effectiveness of the proposed algorithm and further explore its potential in practical applications, we designed an experimental system for photoacoustic detection. The proposed algorithm is able to maintain stable performance under complex backgrounds and artifacts, accurately identify and localize photoacoustic sources, and clearly remove complex artifacts. The deep learning-based artifact-removal and segmentation algorithm proposed in this paper has a broad application prospect in the field of photoacoustic imaging, which can provide more accurate and clearer image information for medical image processing and is expected to play an important role in clinical diagnosis and treatment. Future research will focus on optimizing the model and expanding the range of tests to verify its performance under different conditions, thereby driving its broader application in the field of medical image processing.  
Conceptualization, R.H.; methodology, R.H.; software, R.H.; validation, R.H., Y.C., Y.J., S.Y., Y.L. and J.Z.; data curation, R.H.; writing--original draft preparation, R.H.; writing--review and editing, R.H., Y.C. and Y.J.; visualization, Y.C.; supervision, H.C.; funding acquisition, H.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 (grant numbers 12374440).  
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 would like to acknowledge those who have provided sincere and selfless support in the writing of this paper, especially [PERSON] and [PERSON].  
The authors declare no conflicts of interest.
### Discussion
#### 4.6.2 Difficulty  
Path clearing is the hardest method to implement because it requires the installation of a communications device in all vehicles. Presently there are few such vehicles in Taiwan. Further, there are questions about their effectiveness in a country where drivers are not accustomed to yielding to EVs, as they are in western countries. Thus, if such hardware is installed, it must be accompanied by a strong education effort or increased enforcement of existing regulations.  
Route guidance is the easiest method to implement, since it can make use of a general road and traffic information resource such as Google Maps with a 3G or 3.5G universal mobile telecommunications system. Information from such a system can be used to calculate proper EV routes. Google has recently introduced a real-time traffic feature, but it uses colors to mark roads with different average speeds separately from its navigation system. Further, there are questions about the accuracy of Google Maps, therefore a mix of that system and data from VDs or closed-circuit cameras may make route guidance more feasible and cost-efficient. Improving the accuracy of real-time traffic information is an ongoing challenge.  
Regarding traffic light preemption--the most effective of the three methods described in this paper--the main task is to manage remote control interfaces with ITS servers and EVs. One potential problem is the fairly rare scenario of two or more EVs entering the same intersection at the same time. A solution would entail prioritizing emergency messages.
# Proactive Production Scheduling Approach for Off-Site Construction with Due Date Uncertainty
## 3 Proactive PC Production Scheduling Model with Contractor Schedule Uncertainty
### MC-Method-Based PC Production Simulation Module
```
1:job sequence, contractor's due date reliability, confidence level (\(n\)), # of simulations (\(S\)), processing time (\(pt_{tp}\))
2:tardiness at the confidence level (\(n\))
3:FOR simulation \(s\)in 1 to \(S\)DO
4:Generate the changed due date dataset (\(cd_{j\in 1..j}\)) based on the contractor's due date reliability
5:FOR process \(p\)in 1 to 6 DO
6:FOR each \(j\)in job sequence DO
7:IF\(p=4\)
8:Set the start time of job \(j\) in process \(p\) (\(st_{p}\)) as the finish time of job \(j\) in previous process \(p-1\) (\(ft_{p-1j}\))
9:ELSE
10:Set the start time of job \(j\) in process \(p\) (\(st_{p}\)) as the finish time of previous job \(j\prime\) (\(ft_{pji}\))
11:Calculate the finish time (\(ft_{pj}\)) by adding \(st_{pj}\) to the processing time of PC type t in process p (\(pt_{tp}\))
12:IF the finish time of the process (\(ft_{pj}\)) is out of the working hour,
13:IF\(p=3\)
14:Skip the next day and add it to the processing time corresponding to the job \(j\)
15:ELSEIF\(p=4\),
16:Set it to the next day
17:ELSE
18:Add non-working hours to it
19:END FOR
20:END FOR
21:Calculate tardiness
22:END FOR
23:Find tardiness at the confidence level (\(n\))
24:RETURN tardiness at the confidence level (\(n\))
```  
**Algorithm 1**MC-method-based PC production simulation module  
Then, the PC production process simulation is repeated for S simulations, and it returns the tardiness at the confidence level (\(n\)) in lines 21-22. Figure 2a shows an example of the distribution of tardiness after \(S\) (=1000) simulations under due date uncertainty; Figure 2b shows the corresponding cumulative distribution function (CDF) of tardiness, where the \(y\)-axis represents the confidence level (\(n\)). The figure highlights the position of tardiness at an 80% confidence level.