esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Statement
### Node Performance Evaluation Strategy  
In DHT clusters, to achieve efficient querying of transaction data in the sharding state, each cluster needs to elect a head node for a query, i.e., the master node. Therefore, it is  
Figure 1: Fabric ledger storage.  
necessary to evaluate the performance of each node and then compare the stability values of all nodes in the cluster, before selecting the node with the largest stability value as the query master node. The calculation formula of the stability value \(T(i)\) is:  
\[T(i)=\left\{\begin{array}{ll}\frac{p}{1-p\cdot(m\%_{p}^{\frac{1}{p}})}\cdot W,&\quad i\in\mathrm{n}\\ 0,&\quad otherwise\end{array}\right. \tag{3}\]  
The ratio of master nodes to total nodes in the network is designated by \(p\). Because \(1/p\) may appear as a non-integer, \(1/p\) is rounded up and expressed as \(1/p=\lceil 1/p\rceil\). The cluster label is denoted by \(m\), while \(n\) is the number of nodes in a cluster. Using Equation (3), we can calculate the performance of each node in the network. This equation provides the foundation for analysis of the query mechanism performance.  
To analyze comprehensively the performance of the elected master nodes, we introduced control parameter \(W\). The primary function of the master node is to achieve fast querying of historical data, so it is necessary to give priority to the node bandwidth parameter. The node with large bandwidth is preferentially elected as the master node. Equation (4) is used for calculating the parameter \(W\):  
\[W=\alpha\cdot\frac{B_{i}(m)}{B_{avg}(m)}+\beta\cdot\frac{S_{i}(m)}{S_{avg}(m)} \tag{4}\]  
\(\alpha\) and \(\beta\) are weights where the relationship is expressed as: \(\alpha+\beta=1\). In specific applications, control of the weights of bandwidth and storage in the process of calculating stability values can be achieved by varying the values of the two weight factors \(\alpha\) and \(\beta\).  
\(B_{i}(m)\) denotes the bandwidth of the \(i\)th node in the \(m\)th cluster. \(B_{avg}(m)\) denotes the average of the bandwidth values of all nodes in the \(m\)th cluster, calculated by Equation (5):  
\[B_{avg}(m)=\frac{\sum\limits_{i\in n}B_{i}(m)}{n} \tag{5}\]  
\(S_{i}(m)\) denotes the storage capacity available at the \(i\)th node in the \(m\)th cluster. \(S_{avg}(m)\) denotes the average of the storage capacity of all nodes in the \(m\)th cluster and \(n\) denotes the total number of nodes in the cluster, calculated by Equation (6):  
\[S_{avg}(m)=\frac{\sum\limits_{i\in n}S_{i}(m)}{n} \tag{6}\]
## 8 Conclusions  
This paper proposes a calculation method for calculating the core loss of permanent magnet synchronous motor in the full working range, considering the motor control strategy. Research shows that when the PMSM runs at a low-speed and high-torque region, MTPA control is adopted. At this time, the magnetic saturation inside the stator is relatively high, and the magnetic saturation effect has a greater impact on the phase current, magnetic field, and stator core loss. As the speed increases, the PMSM runs in the high-speed and field-weakening region, and the larger magnetic field weakening current reduces the degree of magnetic saturation in the stator, and the magnetic saturation effect reduces the influence of the motor phase current, magnetic field, and stator core loss. Therefore, considering the magnetic saturation effect under low-speed and high-torque conditions is very important for the accurate calculation of stator core loss.  
The research results of this paper provide ideas for other researchers to accurately predict motor core loss when designing motors and also provide methods for analyzing core loss of existing motors.  
[PERSON] conceived the proposed method for calculating stator core losses in the IPMSM under SVPWM excitation, analyzed the data, and drafted the manuscript; [PERSON] and [PERSON] contributed analysis tools. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was financially supported by the National Natural Science Foundation of China (Grant No. 51677005), the National Key Research and Development Program (No. 2017 YFB0102402), and the China Scholarship Council (No. 201906030192).  
**Acknowledgments:** We authors acknowledge the Beijing Institute of Technology and Newcastle University for sharing technical experts.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World
## 4 Conclusions  
The world is quickly changing with land surface conditions changing dramatically due to anthropogenic impacts over the last two decades. Correspondingly, geophysical datasets, particularly, remote sensing datasets, are created at fast increasing rates. It is challenging to efficiently and innovatively use these datasets for understanding hydrological processes in various climatic and vegetation regimes under anthropogenic impacts, which also offer a wide range of research opportunities. To address these challenges, efforts need to be undertaken to use various remote sensing techniques to improve hydrological simulations and predictions in a changing world. Ten peer-reviewed papers were published in this Special Issue, and can be summarized into the following four categories:  
* Estimating evapotranspiration;
* Rainfall monitoring and prediction;
* Flood simulations and predictions; and
* Monitoring coohydrological processes.  
Figure 1: Conceptualized summary of model and data fusion techniques for improving hydrological predictions.  
The ten papers presented in this Special Issue reflect the efforts for improving hydrological simulations and predictions using various remote sensing techniques. The papers published in this issue advance the remote sensing of hydrology by applying a new measurement approach, such as UAV or model-data fusions. Though the published ten papers in this Special Issue only cover parts of the summarized categories, we believe that continuous efforts in using remote sensing techniques in hydrology definitely promote hydrology. Furthermore, the authors more broadly discuss how to smartly use the state-of-the-art remote sensing techniques to improve hydrological model simulations and predictions to tackle a quickly changing world.  
[PERSON] conceived and led the development of the Special Issue and this paper; [PERSON] and [PERSON] each contributed to the writing of this paper. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the CAS Pioneer Talent Program and the National Natural Science Foundation of China (grant no. 41971032).  
Not applicable.  
As guest editors of this Special Issue, we thank the journal editors and all the authors submitting manuscripts to this Special Issue. Our thanks are extended to the referees who put great efforts in reviewing the submissions, which is the cornerstone for the high-quality publications of Remote Sensing.  
The authors declare no conflict of interest.
## 4 Analysis of Old Hanger Test Detection
### PE Sheath Detection  
#### 4.5.1 Tensile Performance  
The tensile properties of the PE sheath of the hanger directly reflect the degree of protection the PE sheath confers to the internal steel wire [21]. The PE sheath, after being immersed in water at the lower anchor head for a long time, was selected as the test material, hot-melted and poured, and finally, three sets of tensile specimens were made. The appearance and size of the sample are shown in Figure 17a,b.  
An electronic tensile testing machine carried out a tensile test of the specimen to test, the tensile fracture stress. The tensile performance test results are shown in Table 3. As can be seen from the table, the hanger PE shows a certain degree of the aging phenomenon, the tensile properties are severely attenuated and can no longer meet the specifications requirements [18].
# A High-Dynamic-Range Optical Remote Sensing Imaging Method for Digital TDI CMOS
## 2 The Digital TDI Imaging Model and Analysis of Its Features
In order to develop the adaptive enhancement and fusion algorithm according to the specific characteristics of the source images, it is an essential condition to have an objective and accurate image quality evaluation method [20]. Non-reference evaluation indicators usually include contrast, entropy, average gradient, and others [21,22]. Entropy characterizes the gray scale distribution of image from the perspective of statistics. The larger the entropy is, the more abundant information will be contained in the image, and the better the visual effect will be [21]. Assuming that the probability of gray values \(x_{i}\) is \(P(x_{i})\), the entropy of the image is defined as follows:  
\[H(x)=-\sum_{i=1}^{N}p(x_{i})\log_{2}^{p(x_{i})} \tag{1}\]  
The average gradient is the rate at which the image gray value changes in both horizontal and vertical directions. It represents the relative clarity of image, and the ability to express the details [22]. The average gradient of is defined as follows:  
\[\overline{G}=\frac{1}{m\times n}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}\sqrt{\frac{ \left(\frac{\partial f(i,j)}{\partial x}\right)^{2}+\left(\frac{\partial f(i, j)}{\partial y}\right)^{2}}{2}} \tag{2}\]  
where \(m\) and \(n\) are the numbers of rows and columns of image, and \(f(i,j)\) is the gray value of the pixel in row \(i\) and column \(j\). Figure 3: Comparison of the acquired images between digital time delay integration (TDI) complementary metal-oxide-semiconductor (CMOS) and TDI charge coupled device (CCD). (**a**) Image of digital TDI CMOS; (**b**) Image of TDI CCD; (**c**) Histogram of the image of digital TDI CMOS; and, (**d**) Histogram of the image of TDI CCD. For the processing of image enhancement and fusion, ensuring the source images have balanced gray scale distribution and clear texture details is very important. Therefore, this paper takes the entropy and the average gradient as the main indices of evaluation. Figure 4 shows the changing trend of entropy and average gradient as the TDI stage increases from 1 to \(N\)  
It can be seen that as the TDI stage increases, the entropy and the average gradient increase to a certain value at first and then decrease. When the TDI stage is at 5 in the case of entropy and 13 for the average gradient, the measures reach their peaks. That is, the two images represent the highest level of detailed information and clarity, respectively. Selecting them as the processing materials and taking the maximum entropy and average gradient as the goal can maintain the valuable characteristic of the multi-stage images of digital TDI CMOS, and provide a powerful guarantee for the optimization of imaging effects.
## 3 Results and Discussion
### Phase II: Greenhouse Panel Study
#### 3.3.1 Total Suspended Solids (TSS) and Turbidity in Surface Runoff  
Total suspended solids (TSS; g/L) were significantly higher in the three surface runoff samples collected at two-month intervals in the control panel with no plants as compared to the panel with camelina (Figure 3A). Turbidity in the surface runoff samples also followed the same trend as in case of TSS with turbidity values at least four times higher in control than camelina panel (Figure 3B). This indicates the effectiveness of a sustained plant cover in reducing stamp sand erosion.  
Figure 3: Top panel: Total Suspended Solids (TSS) (**A**) and turbidity (NTU) (**B**) in surface runoff in control and camelina panels. Bottom panel: Total Cu (mg/L) in runoff (**C**) and leachate (**D**) in control (no plant) and camelina panels during the study period. Data are reported as mean (\(n=3\)) \(\pm\) S.D. Means are compared by Tukey Kramer-HSD test (\(\alpha=0.05\)). The treatment levels that are not connected by the same letter are significantly different from each other.
## 3 Payload Design
### Suppayload 1: Data Acquisition System  
Subpayload 1 included components that were utilized in the payload's previous iteration. Subpayload 1 consisted of an independent sensor and OBC and is the only subpayload that provided downlink communications. Data from other subpayloads was not downlinked.
## 3 Experimental Results and Analysis
### Ablation Study
All the models were tested using the same dataset and hyperparameters from 22 May 2019 12:00:00 to 11 June 2019 15:00:00. Each network was trained 10 times, and the average results were used for calibration, as summarized in Table 5.  
As shown in Table 5, Model 8 shows an increase of 13.75%, 14.79%, 29.41%, and 12.00% in RMSE, MAE, MSE, and MAPE, respectively, compared to the MCRTF-CM model. Model 5 also shows a significant performance drop. The use of residual connections in this model retains original input information, enriching the output representation by \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **MAE** & **RMSE** & **MSE** & **MAPE (\%)** \\ \hline MCRTF-CM(AF) & 6.128 & 8.009 & 64.137 & 22.435 \\ MCRTF-CM(WF) & 8.402 & 10.774 & 116.072 & 30.854 \\ MCRTF-CM & 5.450 & 7.206 & 51.926 & 19.216 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation study of different factors. adding this information to the model's outputs. Residual connections facilitate gradient flow, alleviating the vanishing gradient problem and accelerating model convergence, making the network easier to train. However, a multi-scale convolutional network alone is insufficient to fully capture temporal features in sequences. Model 6, which removes the FECAM, shows a performance decrease with the RMSE, MAE, MSE, and MAPE increasing by 6.52%, 7.05%, 13.48%, and 2.85%, respectively, compared to MCRTF-CM, indicating that introducing Discrete Cosine Transforms in the frequency domain extracts features not easily detectable in the time domain. Models 1-4, retaining most of the MCRTF-CM structure, exhibit relatively smaller performance drops, further validating the effectiveness of combining convolutional networks with feature extraction and learning in both time and frequency domains. The MCRTF-CM model excels by thoroughly exploiting various sequence features, learning long-term dependencies among features, and suppressing irrelevant features in the frequency domain, resulting in the best performance.
# Linewidth Measurement of a Narrow-Linewidth Laser: Principles, Methods, and Systems
## 3 Research Progress
### Linewidth Testing Method Based on Noise Analysis
#### 3.5.2 Optical Coherent Reception Method Based on Delayed Self-Homodyne/Self-Heterodyne  
In 2012, [PERSON] et al. proposed a delayed self-heterodyne method based on phase modulation (PM) and a delayed self-homodyne difference method based on a coherent receiver [76] to demonstrate that the in-phase and orthogonal components of the self-heterodyne signal can be demodulated at the first and second harmonics of the phase modulation carrier, while the in-phase and orthogonal components of the self-homodyne difference signal can be demodulated at the orthogonal position of the carrier. The experimental and simulation results were consistent, verifying the feasibility of this technology.  
In 2016, [PERSON] et al. used a heterodyne-based offline signal processing method to estimate the laser linewidth and phase noise [82]. The method introduces a heterodyne scheme with data acquisition and offline signal processing, uses time-domain-stored signal analysis data, evaluates coherence time, phase noise, and laser linewidth as functions of bias current, and identifies points where coherent loss occurs to obtain phase noise spectral density and linewidth. The results are provided for lasers with linewidth ranging from 20 kHz to 2 MHz.  
In 2017, [PERSON] et al. proposed a linewidth measurement scheme based on three-wave interference and digital coherent reception [107]. A three-wave interferometer was used to mix the continuous wave generated by the target laser with the surface acoustic waves of two local oscillators. Complex amplitudes, including three-beat frequencies, are obtained through a dual-channel photodetector, and the data are transmitted to a digital signal processing unit using a digital analog-to-digital converter. The linewidth of each laser can be obtained by solving three simultaneous equations.  
In 2018, the team designed a coherent reception method to measure FM noise spectra by mixing three wavelengths with each other [108]. Because the three wavelengths are in pairs without interference, this scheme does not require the use of long fiber delay lines or complex polarization controllers. It can also simultaneously measure the FM noise spectra of the three lasers. The linewidth of the three lasers can be obtained separately based on the three-wave beat frequency signal spectra.  
In 2022, [PERSON] et al. reported a correlated self-heterodyne (COSH) method for measuring ultra-low-noise laser linewidth [109] with an experimental setup, as shown in Figure 10b. The three-port AOM combines both frequency shift and beam splitting functions [110; 111; 112] and can use an AOM with a larger frequency shift range to suppress the RIN. This method can achieve ultra-low-noise laser measurements under a high-frequency offset, with the advantages of low optical power requirements, fast acquisition time, and high-intensity noise suppression.
## 4 Results and Discussion  
Weighting Coefficients of Influencing Factors in the Wet/Dry Season for Different Hydrological Years  
The [\(\Delta\)h] matrix in Equation (4) can be composed of groundwater level fluctuation data in space. The GWLF data from the available groundwater monitoring wells in a period can be used to construct the [\(\Delta\)h] matrix. By using the [\(\Delta\)h] matrix and the given [P] matrix, as shown in Figure 12, the weighting coefficients of the influencing factors  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline  & \multicolumn{2}{c}{**November**} & \multicolumn{2  
through Equation (4) can be obtained. In this way, the abilities of various factors to control groundwater level fluctuations in a period can be investigated.  
Figure 13 shows the estimated weighting coefficients for eight influencing factors in different years' wet and dry seasons. Regardless of the season or year, the composition of weighting coefficients is quite different. The weighting coefficients among the eight factors for most years are relatively different in the wet season. The factors that have a more significant influence on GWLF include hydraulic conductivity (K), depth to the water table (GW), lithology (LT), drainage density (D), and regolith thickness (RT). In the dry season, the weighting coefficients among the eight factors for most years are relatively indistinguishable. Lithology (LT), slope (S), and porosity (P) are the factors that contribute more to the potential for groundwater level changes compared to other factors. In addition, the weighting coefficients of each factor in the dry season and wet season of the same year are also different. The primary factors affected by the season include hydraulic conductivity (K), depth to the water table (GW), and drainage density (D).  
The above results show that the weighting coefficients of the factors are variable. Unlike the previous studies [9; 10; 11; 12; 13; 14; 15; 16; 17; 18], the weighting coefficients for the selected factors may keep constants. By inspecting the possible reasons for the difference, the weighting coefficients for the selected factors may vary depending on rainfall patterns or groundwater recharge conditions. For example, there is a big difference between the accumulated rainfall in the wet and dry seasons in Taiwan; thus, rainfall replenishment into the formations may have a significant discrepancy. Furthermore, the annual accumulated rainfall in Taiwan is diverse due to climate change; thus, rainfall replenishment into the formations may also vary. Therefore, in this study, the weighting contribution of the influencing factors was obtained through the actual groundwater level change data, and then the calculated weighting coefficients were used to estimate the spatial groundwater level potential change. This newly proposed method can better capture the actual dynamic behavior of groundwater.
###### Abstract  
The heat dissipated from high geo-temperature underground surrounding rocks is the main heat source of working faces, while thermal water upwelling and trickling into the roadway will notably deteriorate the mine's climate and thermal comfort. Predicting airflow temperature and relative humidity (RH) is conductive to intelligent control of air conditioning cooling and ventilation regulation. To accommodate this issue, an intelligent technique was proposed, integrating a genetic algorithm (GA) and long short-term memory (LSTM) based on rock temperature, inlet air temperature, water temperature, water flow rate, RH, and ventilation time. A total of 21 input features including over 200 pieces of data were collected from an independently developed modeling roadway to construct a dataset. Principal component analysis (PCA) was conducted to reduce features, and GA was used to tune the LSTM and PCA-LSTM architectures for best performance. The following research results were yielded. The proposed PCA-LSTM-GA model is more reliable and efficient than the single LSTM model or hybrid LSTM-GA model in predicting the air temperature \(T_{f_{out}}\) and humidity \(RH_{out}\) at the end of the water trickling roadway. The importance scores (ISs) indicate that \(T_{f_{out}}\) is mainly influenced by the surrounding rock temperature (IS 0.661) and the inlet air temperature (IS 0.264). While \(RH_{out}\) is primarily influenced by the rock temperature in the water trickling section (IS 0.577), the inlet air temperature (IS 0.187), and the trickling water temperature and flow rate (total IS 0.136), and it has an evident time effect. In addition, we developed relevant equipment and provided engineering practice methods to use the machine learning model. The proposed model, which can predict the mine microclimate, serves to facilitate coal and geothermal resource co-mining as well as thermal hazard control.  
Keywords:thermal hazards; machine learning; microclimate conditions; principal component analysis; genetic algorithm; long short-term memory +
Footnote †: journal: Applied Sciences