esa-sceva/satcom-chunk-collection huggingface dataset:
# Semiconductor Laser Linewidth Theory Revisited
## 2 Prerequisites and Basic Assumptions
This results in auxiliary differential equations for polarization functions [36; 37], see Appendix A. Due to the fact, that the spectral width of the gain in semiconductor lasers is much larger than the width of the cavity resonances, \(\widehat{\mathcal{D}}(z,t)\) can be expanded again linearly in the frequency domain. In contrast to [36], we neglect here the dependence of the dispersion operator on the carrier density \(N\). Besides \(\Psi\), we consider \(N\) as the only further stochastic variable and spontaneous emission as the only source of noise, neglecting all other noise sources. The theory can be easily extended to include carrier noise as sketched in the Outlook in Section 11. We assume that \(\kappa\) does not depend on \(N\), i.e., we exclude gain-coupled lasers, and neglect fluctuations of the shape of the power profile. We neglect the impact of non-lasing side modes on the linewidth of the lasing mode and employ a single-mode approximation. Thus the linewidth rebroadening caused by mode partitioning and poor side-mode suppression, cf. [38; 39], can not be not accounted for. Regarding the properties of the noise [40], we employ the usual assumptions of ergodicity (ensemble or statistical average equals temporal average) and stationarity (i.e., \(\langle\xi(t)\xi(t^{\prime})\rangle\) depends only on the time difference \(t-t^{\prime}\)) of any stochastic variable \(\xi\). The fluctuations are assumed to have Gaussian probability distributions and delta-correlated covariance functions in time (Markov approximation) and space, i.e., \(\langle\xi(z,t)\xi(z^{\prime},t^{\prime})\rangle=2D_{\xi\xi}(z)\delta(t-t^{ \prime})\delta(z-z^{\prime})\) with \(D_{\xi\xi}\) being called diffusion coefficient. In order to obtain a Lorentzian shape of the optical spectrum around the lasing frequency, some approximations have to be employed which are described in Section 4. The full width at half maximum (FWHM) of the Lorentzian is commonly called intrinsic linewidth. As we consider only spontaneous emission noise, we calculate the quantum limit of the intrinsic linewidth. If non-Markovian noise is considered, which is beyond of the scope of the paper, the lineshape tends towards a Gaussian in the vicinity of the lasing frequency with a correspondingly larger FWHM [41; 42].
## 2 Finite Element Modeling  
### Model Size and Boundary Conditions  
This study focuses on the left side of the roadbed in sections K31+100-K31+388 of a highway treated with a PHC pipe pile composite roadbed. The slip of the roadbed is shown in Figure 2. The calculation was based on the background of these sections, and specifically on the representative section K31+295. This specific section was chosen due to its notable features, including a higher fill height and a thicker layer of soft soil. In order to simplify the calculation model and improve efficiency, a semi-symmetric finite element calculation model was established to analyze the plane strain problem. The embankment has a maximum filling height of 6.0 m, a top width of the half embankment of 24.0 m, and a slope ratio of 1:1.5. To eliminate the influence of the model boundary size, the foot of the embankment extends outward by 47.0 m, making the width of the model 80.0 m. The depth of the model was determined based on the ratio of self-gravitational stress to additional stress, which should be more than 10. Therefore, the depth of the model was set to 60.0 m, which is the maximum depth.  
The top surface of the model is a free boundary, the bottom boundary constrains the horizontal and vertical displacement, and the left and right boundaries constrain the horizontal displacement. The location of the groundwater level is determined based on the actual conditions, with the head taken from the top surface of the soft soil layer, specifically at \(-\)0.5 m as indicated in the ground investigation data. Furthermore, the seepage of groundwater is restricted by the left and right boundaries as well as the bottom boundary of the model.
## 5 Results and Discussion
### Validation of the FE Models  
Ensuring the accuracy and reliability of FE simulations is paramount. The 3D models in this study were validated against established results from [12], which employed similar modeling techniques for multi-segment cable configurations at 50 Hz. The \(\text{R}_{\text{AC}}/\text{R}_{\text{DC}}\) ratios from this study's 2D and 3D models closely align with the validated results, confirming the robustness of the Solver Machine framework implemented in ANSYS Mechanical APDL.  
Table 8 presents a direct comparison of the \(\text{R}_{\text{AC}}/\text{R}_{\text{DC}}\) ratios between this study and [12] as a benchmark for single-segment, three-segment, and five-segment models at 50 Hz. The \(\text{R}_{\text{AC}}/\text{R}_{\text{DC}}\) ratios from both 2D and 3D models are in excellent agreement with the validated results from [12], with discrepancies well within acceptable engineering margins. This close alignment reinforces the credibility of the FE models and the Solver Machine framework used in this study, ensuring that the simulations accurately capture the electromagnetic behavior of segmented cables.
# Wheat Ear Detection Algorithm Based on Improved YOLOv4
## 3 Results and Discussion  
### Dataset and Platform  
The dataset used to train, validate, and test the wheat ear detection model was derived from [19] and the Global Wheat Head Detection (GWHD) dataset [40]. The dataset in [19], named High-Resolution Ear Density (HRED) in this paper, is more ready to use on intelligent agricultural machinery in field conditions. The camera is fixed at the nadir view direction at a 2.9 m distance to the ground. There are 240 high-resolution RGB images (\(6000\times 4000\)) and 30,729 ears in total. The spatial resolution or the Ground Sampling Distance (GSD) is 0.13 mm/pixel. However, when examining the original data, we found some invalid data. Some images needed to be rotated to some degree or flipped to make the labeled bounding boxes match the wheat ears in the images. Some data are not valid at all, no matter how we rotate or flip the images. After removing the invalid data and rotating or flipping the images, there were 25,185 wheat ears in the 222 valid images left. The GWHD dataset contains large and diverse high-resolution images, which were labeled to develop benchmark wheat head detection methods. Since it was created in 2020, it has attracted attention and is very popular.  
In this work, all models were trained and tested on a workstation equipped with an Intel Core i9-9900K processor, and NVIDIA GeForce GTX 2080 Ti GPU. The operating system was Ubuntu 18.04. The model was implemented with PyTorch machine learning framework in Python programming language. CUDA version is 10.0, and CUDNN version is 7.3.1.
## 3 Methodology
### Detailed Procedure
#### 3.3.2 Phase 2: Keyword Extraction  
As the information in patents and product manuals is too extensive to allow effective analysis of the technological development trend, a set of keywords that represent the main characteristics of documents was used to address the chaos of information overload, and then an MA-based TRM was developed by considering keywords as the basic elements of nodes. Many studies have extracted keywords through text mining techniques from patent or product manuals and then utilized them to develop a morphology matrix [74], a patent map [56], or a TRM [75]. Text mining techniques were utilized to extract keywords from the above mentioned content with the aim of transforming unstructured data into valuable structural data automatically, which was suitable for constructing document vectors and morphology matrices in the subsequent phase.  
In this study, keywords are filtered through several steps. First, all the collected documents were preprocessed in Python. Word segmentation was carried out to break the patent documents into different words and prepare for the second step. For instance, the sentence \"The product consists of five parts\" was divided into \"The\", \"product\", \"consists\", \"of\", \"five\" and \"parts\". Second, the function and stop words in all the collected documents were taken out to avoid unnecessary effects. For instance, \"The\" and \"of\" were moved out to cut down the burden of specialists. Third, the keywords of each layer were extracted independently and automatically by the term frequency-inverse document frequency (TF-IDF) method, which determines the importance of a word to a document in a collection from the preprocessed documents, as shown in Table 1. Thus, a TF-IDF matrix of different types of data was established for identifying a list of keywords for each layer. Fourth, domain experts who are capable of identifying the relevance of the keywords were engaged in filtering out unnecessary and irrelevant technology or product keywords. The keyword list of each layer was generated in this step. Fifth, after deriving the keyword lists, the document vectors were constructed based on the TF-IDF values. A document vector contains the TF-IDF value of each keyword, representing how important each keyword in a layer is in a document in a quantitative way, as shown in Table 1. The TF-IDF value of keyword k in patent document n and product manual m is demonstrated in the rows of Table 1. For instance, the value of keyword 1 is 0.022, and the value of keyword 2 is 0 in patent document 1. The similarity between documents could be observed by making use of these document vectors. The similarity, which shows how closely each document is relative to other documents, was used to associate the nodes in the MA-based TRM.
## 4 Discussion
### Spatio-Temporal Distribution Characteristics of GPP over the Qinghai-Tibet Plateau
The areas with increasing trends are predominantly situated in the northeastern, western, and southwestern parts of the Qinghai-Tibet Plateau, characterized by higher altitudes. In contrast, the regions experiencing a decline in GPP are mainly concentrated in the eastern and southeastern areas of the Qinghai-Tibet Plateau. Previous studies [12] indicated that grassland degradation on the Qinghai-Tibet Plateau accounted for 38.8% of the total grassland area during the  
Figure 5: The spatio-temporal distribution of GPP on the Qinghai–Tibet Plateau. The average annual cumulative GPP (**a**) and long-term trend of monthly cumulative GPP (**b**). period 2001-2013, which mainly located in the southern, northeastern and eastern regions of the Qinghai-Tibet Plateau. In addition, human activities had a great impact on grassland restoration, which mainly occurred in the western and southwestern Qinghai-Tibet Plateau. Therefore, the degradation of grassland is closely related to the change in GPP. Figure 7b shows the percentage of GPP trends within each eco-geographic sub-zone. The regions with significant GPP increases are HIIC2, VA6, VA5, HIID3, HIC1 and IVA2, which are mainly located in the temperate zone of the Qinghai-Tibet Plateau and in the sub-cold and central subtropical regions of the plateau with better moisture conditions in the eastern region. The spatial distribution of the coefficient of GPP variation on the Qinghai-Tibet Plateau is shown in Figure 7c. Most regions had a high stability of GPP over the period 2003-2018. Meanwhile, the standard deviation of GPP is higher in the southeast and lower in the northwest, indicating that the volatility of GPP varies from southeast to northwest. Considering the land cover distribution (Figure 1A), the higher forest cover in the southeastern part of the plateau has a better carbon sequestration effect than other regions, as seen from the mean distribution of GPP on the Qinghai-Tibet Plateau. Figure 6: The spatio-temporal distributions of monthly GPP over the Qinghai–Tibet Plateau in this study.
## 4 Simulation and Evaluation
### Experimental Setup
#### 4.1.1 Parameters under the Evaluation to Validate the Effectiveness of Our Proposed Strategy  
The following are the main parameters that were evaluated and measured in these experimental evaluations. First, we describe these parameters and then we discuss our results and evaluate the comparative results.
## 3 Compensator Design
### SOC Balancing Loop Compensator Design  
According to the small-signal model shown in Figure 3, the uncompensated SOC loop gain (i.e., with unity SOC loop compensator gain) is given by Equation (23), and its bode plot is represented by the dashed curve in Figure 7.  
\[L_{\text{socr-uncomp}}(z)=Z[G_{\text{a}lN}(s)\cdot ZOH_{\text{i}cellN}(s)\cdot G_{ \text{s}acilN}(s)\cdot ZOH_{\text{s}acilN}(s)\cdot G_{\text{s}acilN}(-1)\cdot \delta_{\text{M}XN}\cdot G_{\alpha\lambda}\cdot Delay_{\text{w}}\cdot G_{\text {o}-bus}], \tag{23}\]  
where \(ZOH_{\text{i}cellN}(s)=\frac{1-e^{-\pi T_{\text{soc}}}}{s}\), \(ZOH_{\text{s}acilN}(s)=\frac{1-e^{-\pi T_{\text{soc}}}}{s}\), \(T_{\text{soc}}\) is the sampling period for the SOC value in the SOC balancing loop. Since the SOC value of a battery cell varies very slowly compared to the switching period of the power converter, the sampling rate of the SOC balancing loop does not have to be very fast. \(T_{\text{soc}}=\)1 s is found to be a good trade-off between the hardware resource consumption, system stability and SOC balancing speed.  
With a compensator given by Equation (24), the compensated SOC balancing loop gain achieves a control bandwidth of 0.057 Hz and phase margin of 59.2\({}^{\circ}\), as shown on the solid curve in Figure 7. Due to the slow sampling rate of SOC value (1 Hz), it is expected that the control bandwidth of the SOC balancing loop is much lower than that of the voltage control loop.  
\[G_{\text{SOC}}(z)=\frac{2.5\times 10^{8}}{\text{z}-1} \tag{24}\]
## 3 Results and Analysis
### Sustainability Analysis of the NDVI in the Future  
Based on the NDVI data from the Shanxi section of the Yellow River Basin for the years 2001 to 2020, Hurst Indices were calculated pixel by pixel. Referring to previous research, the Hurst Index is classified into five categories: strong anti-persistence (0 \(<\) H \(\leq\) 0.2), weak anti-persistence (0.2 \(<\) H \(\leq\) 0.4), random uncertainty (0.4 \(<\) H \(\leq\) 0.6), weak persistence (0.6 \(<\) H \(\leq\) 0.8), and strong persistence (0.8 \(<\) H \(\leq\) 1). The spatial distribution of the Hurst Index for the Shanxi section of the Yellow River Basin is illustrated in Figure 5. The results indicate that the overall change in the study area exhibits uncertainty and relatively low sustainability. Approximately 17.56% of the area is expected to show a weak anti-persistence trend, while 10.77% of the area is projected to display weak persistence. Based on the analysis of NDVI trends and the Hurst Index results, further analysis of the future NDVI changes in the Shanxi section of the Yellow River Basin was conducted. The future NDVI changes in the Shanxi section of the Yellow River Basin are generally random in spatial distribution. Areas showing continuous improvement account for 9.77% of the total area, primarily located in the northwestern part of Xinzhou City and the central-western part of Yuncheng City. Areas transitioning from improvement to degradation make up 16.67% of the total area, mainly concentrated in the western part of Lvliang City.
## 4 Conclusions  
In this paper, we constructed an analytical model to discuss the impacts of a filter on the combining efficiency and the beam quality within an FSBC scheme. Via adopting the uncorrelated surface roughness model, we made a detailed analysis of the relationship between the combining efficiency and the optical characteristics of the edge filter. To achieve an optimal combining efficiency, the surface roughness of the edge filter that is used in the FSBC system should be less than 1.2 nm, and the thickness error should be limited to within 0.25 nm. Meanwhile, the combining efficiency shows a significant negative correlation with the incident angle, for both \(s\) and \(p\) polarization cases.  
Furthermore, based on the angular spectrum theory, the effect of different surface roughness values for the filter (in terms of the combined beam quality) was briefly discussed. The simulation results show that the scattering that is induced by the surface roughness reduces the focusing power of the incident lasers, resulting in a degradation of the combined beam quality. Worse still, the propagation distance will enhance the effects of scattering, and further reduce the PIB. In this case, selecting an edge filter with a minimum surface roughness not only results in a better combined beam quality, but also enhances the effective working distance of the combining system. Even though the example considered here is an near-infrared edge filter, the analysis model is still suitable and useful for various multilayer elements with different surface roughness values.  
This work was realized by the collaboration of all authors. [PERSON] guided the research direction and proposed the revising suggestions of manuscript. [PERSON] and [PERSON] designed the algorithms, carried the simulation, performed the experiments, and analyzed the experimental data. [PERSON] offered help for the simulations. [PERSON] wrote the paper. All authors read and approved the final manuscript.  
This work was supported by the National Natural Science Foundation of China (NSFC) (No. 61575095, 61875087, and 11761131015), Sino-German Cooperation Research Group (No. GZ1391)  
The authors are very grateful to [PERSON] for his help with the experiments.  
The authors declare no conflict of interest.
## 3 Design  
This section explains the structure of the automatic recovery system, implemented by integrating Kubernetes backup and restoration tools with the Kubernetes management platform. This system automatically transfers the applications of a cluster to another cluster if the original cluster experiences a disaster and loses its connection to the Kubernetes management platform. The automatic recovery system is added to the cluster state, monitoring part of the Kubernetes management platform, and integrates by installing Kubernetes backup and restoration tools in the environment.  
The proposed automatic recovery system operates on the Kubernetes management platform and continuously monitors the state of a cluster. In the event of a disaster, the system is capable of monitoring and detecting events to identify the situation. Once a disaster is detected, the system performs resource comparison by comparing the resources of the affected cluster with those of other clusters to identify a cluster with superior resources.  
Figure 3: Flowchart of the Resource-comparison-and-alert component.  
Figure 2: Flowchart of the monitoring and event-detection component.  
* Restoration target cluster-selection component: The names of the clusters transmitted through the Resource comparison component are used to query their current CPU-utilization rates. The queried current CPU-utilization rates of these clusters are passed to a machine-learning model as the parameters to predict CPU utilization, and the predicted CPU-utilization rates are returned. The cluster with the lowest predicted CPU-utilization rate among all predicted clusters is selected. Then, the name of the selected target cluster for restoration is transmitted to the Restoration-execution component. Figure 4 shows the flowchart of the Restoration target cluster-selection component.
* Restoration-execution component: The cluster selected as the target for restoration and transmitted through the Restoration target cluster-selection component executes the restoration command of the Kubernetes backup and restoration tool.
## 3 Results
### Results for LSTM  
The parameters used for training LSTM are as follows:  
* Optimizer: adam--optimizer for training.
* ExecutionEnvironment: GPU--utilizes GPU for training.
* Shuffle: every-epoch--shuffles the data every epoch.
* MaxEpochs: 1000--sets the maximum number of training epochs to 1000.
* No of lstmLayer: 750  
The performance evaluation of the long short-term memory (LSTM) model is illustrated through the confusion matrix in Figure 6 and the detailed performance metrics in Table 4. The LSTM model was similarly assessed across three distinct classes: green, yellow, and red, representing different states of the tool used in the milling process.  
The confusion matrix reveals the model's performance and challenges in classifying the tool states. The green class shows a precision of 61.90%, with the model correctly identifying 52% of green instances but also exhibiting a relatively high misclassification rate, leading to a specificity of 84%. This indicates a difficulty in distinguishing green states from others, potentially due to overlapping characteristics with yellow and red states.  
For the yellow class, the LSTM model achieves a precision of 34.48% and a sensitivity of 40%, suggesting a significant challenge in accurately identifying this intermediate tool state. The model's specificity of 62% for the yellow class further underscores the difficulty in classifying instances that may not exhibit clear-cut features of wear or optimal condition.  
The red class results indicate a precision and sensitivity of 44%, with a specificity of 72%. These figures suggest that while the LSTM can recognize severely worn tools at  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **Precision** & **Sensitivity** & **Specificity** & **F1 Score** & **Accuracy** \\ \hline Green & 61.90\% & 52.00\% & 84.00\% & 56.52\% & 73.33\% \\ Yellow & 34.48\% & 40\% & 62.00\% & 37.03\% & 54.66\% \\ Red & 44.00\% & 44.00\% & 72.00\% & 44.00\% & 62.66\% \\ \hline Overall & & & & & 45.33\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance metrics of LSTM denoting the key parameters used for model evaluation for each of the recognized classes.  
Figure 6: Confusion matrix for LSTM. Numbers off the diagonal denote cases that were not correctly classified. The colors used represent the proportions of the examples and visualize the number of cases for each case, with lighter values denoting lower counts.  
a moderate rate, there is still substantial room for improvement in distinguishing these instances from less worn states.  
Table 4 provides a comprehensive breakdown of the LSTM's performance, quantifying its accuracy, precision, sensitivity, and F1 scores across all classes. Notably, the overall accuracy of the LSTM model stands at 45.33%, highlighting the challenges faced by the model in this multi-class classification task.