esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Surrounding Rock Excavatability Identification Based on Deep Forest
#### 3.2.2 Model Evaluation Indexes and Hyperparameter Optimization  
This study is characterized as a classification problem. Therefore, accuracy and \(F1\) score were used to evaluate the model's performance [28]:  
\[Accuracy= \frac{TP+TN}{TP+TN+FP+FN} \tag{19}\] \[F1= \frac{2 PR}{P+R}\] (20) \[P= \frac{TP}{TP+FP}\] (21) \[R= \frac{TP}{TP+FN} \tag{22}\]  
where \(TP\) and \(TN\) indicate true positive and true negative, respectively; \(FP\) and \(FN\) indicate false positive and false negative, respectively; \(P\) is precision; \(R\) is recall.  
In order to improve model performance, hyperparameter optimization is a key step for obtaining the optimal combination of model parameters. Grid search is a common hyperparameter optimization method. However, this method requires an exhaustive search of a predefined hyperparameter grid and, thus, is time-consuming to run. To address this shortcoming, this paper adopted the method of selecting hyperparameter ranges with prior knowledge to reduce the computational overhead. The specific steps of hyperparameter optimization using grid search are shown in Figure 24.  
Each set of hyperparameters was subjected to a 5-fold cross-validation as a judging criterion for the grid search. The 5-fold cross-validation process (Figure 25) is described as follows: (1) The training set is divided into five subsets; (2) four subsets are used as new training sets, one is used as the validation set, and each subset needs to be used as a validation set once; (3) the accuracy scores of each validation set are summed and averaged in order to obtain the final model's accuracy score.  
In this paper, only the hyperparameters involved in the cascade forest were searched for optimization. This is because the number of input features, i.e., ten, used in this study  
Figure 24: Hyperparameter optimization process.  
Figure 25: Five-fold cross-validation structure.  
was small; thus, multi-grained scanning was not required. The performance of the cascade forest is powerful enough. After the optimization, the hyperparameter optimization of the cascade forest experienced six iterations. Figure 26 shows the optimization process of each iteration. Figure 27 shows the optimal accuracy of each iteration by 5-fold cross-validation. According to Figures 26 and 27, as the search step for the 1 st-6 th iterations decreased, the optimal score gradually increased from 0.9598 to 0.9608. The optimal model hyperparameters are shown in Table 7. It should be noted that the cascade layer is adaptive according to the early stopping strategy. Thus, the value of \"max_layer\" has a slight effect on the model.
# A Non-Smooth Numerical Optimization Approach to the Three-Point Dubins Problem (3 PDP)
## 6 Algorithms
### Discretization-Based Method (DBM [29])  
This algorithm is the baseline used in the literature to compare new methods. It is based on the observation that once the angle \(\theta_{m}\) is fixed, the corresponding length of the 3 PDP is readily obtained by applying the classic Dubins solution two times, first from \(\mathbf{P}_{\
u}\), \(\theta_{i}\) to \(\mathbf{P}_{\mathbf{m}}\), \(\theta_{m}\) and a second time from \(\mathbf{P}_{\mathbf{m}}\), \(\theta_{m}\) to \(\mathbf{P}_{t}\), \(\theta_{f}\). The method discretizes the round angle with \(N\) points and selects as the solution of the 3 PDP the angle that returns the minimum length. This is not very accurate and not very fast. Nevertheless, it is a straightforward method used as a baseline with standard value \(N=360\) and a discretization of one degree. It is described in Algorithm 2.  
```
1:functionDubins3 pDBM(\(\mathbf{P}_{\mathbf{i}}\), \(\theta_{i}\), \(\mathbf{P}_{\mathbf{f}}\), \(\theta_{f}\), \(\mathbf{P}_{\mathbf{m}}\), \(\kappa_{max}\), \(N\))
2:\(\theta_{m}\gets 0\); \(\Delta\phi\gets 2\pi/N\); \(\ell_{min}\leftarrow\infty\);
3:while\(\theta_{m}<2\pi\)do
4:\(S\leftarrow\textsc{TwoDubins}(\mathbf{P}_{\
u},\theta_{i},\mathbf{P}_{\mathbf{m }},\theta_{m},\mathbf{P}_{t},\theta_{f},\kappa_{max})\);
5:if\(S.\ell<\ell_{min}\)then
6:\(\ell_{min}\gets S.\ell\); \(C\gets S\); (* select this solution *)
7:endif
8:\(\theta_{m}\leftarrow\theta_{m}+\Delta\theta\);
9:endwhile
10:return\(C\); (* the structure of the selected solution *)
11:endfunction
1:functionTwoDubins(\(\mathbf{P}_{\mathbf{i}}\), \(\theta_{i}\), \(\mathbf{P}_{\mathbf{m}}\), \(\theta_{m}\), \(\mathbf{P}_{t}\), \(\theta_{f}\), \(\kappa_{max}\))
2:\(S.A\leftarrow\textsc{Dubins}(\mathbf{P}_{\mathbf{i}},\theta_{i},\mathbf{P}_{ \mathbf{m}},\phi_{m},\kappa_{max})\);
3:\(S.B\leftarrow\textsc{Dubins}(\mathbf{P}_{\mathbf{m}},\theta_{m},\mathbf{P}_{t},\theta_{f},\kappa_{max})\);
4:\(S.\ell\gets S.A.\ell+S.B.\ell\); (* store total length *)
5:\(S.\theta_{i}\leftarrow\phi_{i}\); \(S.\theta_{m}\leftarrow\phi_{m}\); \(S.\theta_{f}\leftarrow\phi_{f}\);
6:return\(S\); (* return a structure with the two curves *)
7:endfunction
```  
**Algorithm 2** Discretization-Based Method (DBM [29])
# Effects of Drying Methods and Temperatures on the Quality of Chestnut Flows
## 3 Results and Discussion
### Analyses of Sugars  
The drying temperature affects the quantity of sugars [13]. The variation of sugars (sucrose, glucose, fructose, galactose, and mannitol) is due to the hydrolysis of starch, the decomposition of sucrose into glucose and fructose, and the caramelization and degradation of sugars [41]. The results obtained and depicted in Figure 4 reveal a slight increase in sucrose (Figure 4A) for the flours dried at 70 \({}^{\circ}\)C compared to those at 40 \({}^{\circ}\)C, and it is higher in flours dried with the traditional method, where the temperature was unstable, in agreement with [PERSON] et al. [42]. This behavior is also in line with the results by [PERSON] et al. [1], who concluded that the higher the drying temperature is, the higher the sugar content, and in particular, sucrose. The latter indeed increases with temperature because of the thermal and enzymatic degradation. The reason for glucose and fructose rising is yet unknown. In any case, the behavior of glucose, fructose, and galactose is different, as can be seen in Figure 4B-D. The highest content appears for the dried flour at 40 \({}^{\circ}\)C. For traditionally dried flours, there is just no trace of galactose.  
Figure 4: _Cont._  
Figure 3: Polyphenol content (mg(GAE)/g \(\pm\) standard deviation) of chestnut flours. The columns are grouped by drying method, drying temperature (\({}^{\circ}\)C), health condition of fruits, and year. Each different letter corresponds to a significant difference with a \(p\)-value \(\leq 0.01\).  
Figure 4: Sugar content (mg/g) in chestnut flours is grouped on the basis of drying method, chestnut condition (non-defective and defective), temperature (\({}^{\circ}\)C), and year. (**A**) sucrose; (**B**) glucose; (**C**) fructose; (**D**) galactose; (**E**) mannitol. Each different letter corresponds to a significant difference with a \(p\)-value \(\leq\) 0.01.  
What is also noted, however, is an increase in sugars (glucose, fructose, galactose, and mannitol) in the defective flours. In fact, sugar content typically increases when the plant or fruit is under stress [43]. The stressful conditions seem to favor the accumulation of fructose and glucose that results from the degradation of sucrose as a response to stress [44]. Mannitol contributes to the osmotic potential and plays a major role in the abiotic stress response mechanism [45; 46]. Therefore, the increase resulting in the present work (Figure 4E) is likely due to a response to oxidative stress and an accumulation of ROS. In fact, sugars are, in general, involved in the defense response, as they serve as precursors of various secondary metabolites [47; 48; 49].
# A Novel Proprietary Internet Video Traffic Dataset Generation Algorithm
## 3 Methodology
### NFStream Preprocessing
```
1:Input: Each flow sample from segmented CSV files
2:Output: nILAD, nTLAD, nPAD, nLLAD, nILPD, nTLPD, nLLPD, nPPD dataset
3:for each CSV file in segmented CSV files do
4:for each flow sample in CSV file do
5:if Flow sample application name = \"Unknown\" then
6: Delete the flow sample
7:endif
8:if Flow sample application category = \"Unspecified\" then
9: Delete the flow sample
10:endif
11:endfor
12: Save all remaining samples
13:for each flow sample in remaining samples do
14: Read each sample application name major protocol
15: Save read results into a new column 'protocol category'
16:endfor
17: Count flow samples \(N\) for each protocol category class
18:if\(N\geq 6\)then
19: Keep all flow samples for the specific class
20:else
21: Delete all flow samples for the specific class
22:endif
23: Keep all remaining flow samples in the CSV file
24:endfor
25:Merge different CSV files' samples into each dataset
26:Output nILAD, nTLAD, nPAD, nLLAD, nILPD, nTLPD, nLLPD, nPPD dataset
```  
**Algorithm 1** Pseudo-code for nDPI-Detected Application Category datasets and Protocol Category dataset generation (nDACPC)  
For example, in the case of DNS.Microsoft, application categories are divided into four main classes, Web, Cloud, System, and ConnCheck, based primarily on the request server names decoded by the nDPI library. One instance is the domain name www.msftconnecttest.com, used by Microsoft systems to check network connectivity, while the domain for Microsoft diagnostic data management services is events.data.microsoft.com. Similarly, the Microsoft account login domain includes login.live.com, and the Microsoft search engine domain is www.bing.com. Each application category can contain multiple domain names due to the diverse nature of the decoded domain names. Despite this variability, the application category can often be determined based on keywords within the domain name. If no domain names are decoded in the flow, the application category is determined by the minor protocol or a single protocol. The major protocol, minor protocol, and application category serve as ground-truth features used as labels for supervised training and interact with and reinforce each other. From this information, it is apparent that some service application traffic samples are mixed with video traffic samples, and there are still low-quality, irrelevant samples in the dataset. By integrating the characteristics of streamed videos and other prior knowledge, the existing proprietary datasets can be further improved and refined.
## 3 Materials and Methods
### Data Acquisition  
The first dataset in our work, High-Resolution Fundus (HRF) [34], comprises 45 RGB images of three different classes, DR, glaucoma, and normal conditions, with 15 images per class. The HRF dataset is provided with ma annotation by three expert ophthalmologists, specifying the OD center pixel location and the diameter of the OD. The second dataset, Dataset_2, consists of 288 RGB fundus images assembled from datasets. First, 192 images are from the Kaggle DR dataset [35] (96 images for DR class and 96 for the normal class). The remaining 96 images for glaucoma are collected from the BinRashed Eyes Glaucoma dataset [36]. Thus, the classes of Dataset_2 are balanced in that each class contains 96 images. Since Dataset_2 was acquired from two different sources, unlike HRF, it needs to be manually annotated to locate the OD center pixels. For this task, we made use of the expertise of three skilled ophthalmologists from King Fahd Central Hospital (KFCH), Saudi Arabia, who ably specified the center pixel and the diameter of the OD. The two  
Figure 2: Block diagram of the proposed retinal computer-aided diagnosis system.  
datasets used in the present work thus have three classes, DR, glaucoma, and normal (free of disease). The DR and normal images are macula centered, while the glaucoma images are OD centered. Table 2 provides general information related to the two image datasets.  
Figure 3 shows the different pathological patterns for DR and glaucoma compared to the normal cases.
# Cross-Correlation and Fractal Analysis in the Images Diatoms Symmetry
###### Abstract  
Diatoms are one of the main species of interest due to their possible impact on the maintenance of ecosystems, as well as their photosynthetic capacity and biotechnological applications. Reliable, easy, rapid identification of diatoms remains a highly desirable goal to avoid the difficulty for experts in the visual analysis of frustules; a method commonly used to compare the distinctive traits of diatoms, such as ornaments and the marks from images taken by optical and electronic microscopy for identification. This study proposed three simple morphometric methods based on fractal dimension measurements in conjunction with 2D cross-correlation parameters to analyze diatom images. This novel approach demonstrates the differentiation feasibility between a wide variety of diatom species with heterogeneity in their structures and shapes, which could possibly expand the application of this preprocessing process for the identification of new types of images or other biological species.  
diatoms; cross-correlation; fractal dimension; power spectra Article
## 3 Model Description
### Detailed Structure of TCReC
#### 3.2.4 Encoder and Decoder  
The encoder of TCReC maps the input data to a low dimension in order to extract characteristics with high density, and the decoder can remap the characteristics to high-dimensional data to realize data reconstruction. The encoder is mainly based on the encoder module of Transformer, and its structure is shown in Figure 4. In order to simplify the model, the decoder used in this paper has the same structure as the encoder.  
An encoder module is mainly composed of a multi-head self-attention layer and a feedforward neural network layer. Self-attention mainly focuses on the relevance of the internal parts of the input, which enables the model to better understand the various parts of the characteristics in the network traffic and extract the valuable information. However, an attention matrix can only obtain one set of correlation. The multi-head attention initialize multiple matrices for training to obtain multiple groups of outputs, and can obtain the correlation between data blocks from multiple expression spaces.  
Residual connection is used in both encoder and decoder layers, the essence of which is to express the output as the superposition of a nonlinear transformation of input and output, enhance gradient flow through layer hopping connection, break the symmetry of the network, and solve the problem of gradient disappearance and degradation in a deep neural network [41].  
In addition, the data will undergo layer normalization before being input into each layer. This mode of layer normalization before input is called the pre-norm mode. Different from the post-norm mode adopted in the original Transformer encoder module, this method does not rely on the pre-heating stage of learning rate, which can reduce the amount of introduced super parameters, and make the training process more efficient [42].
# Effects of Anthropomorphic Driving Vehicles on Traffic Flow
## 4 Discussion  
Comparing Scenario 1 and Scenario 2, the travel time and time loss of the autonomous vehicle were lower (Figure 7). Under congested road conditions, the traffic efficiency of the autonomous vehicle was higher, which was reflected in there being more vehicles passing through the intersection (more noticeable at 17-18 o'clock in Figure 7a and 7-9 o'clock in Figure 7b) and a higher average speed (Figure 9 17-18 o'clock). Under non-congested road conditions, the average speed of human drivers was higher. Comparing Scenario 1 and Scenario 3, anthropomorphic driving was superior to non-automated vehicles in terms of number of vehicles per unit time, time loss, travel time, and average speed. Comparing Scenario 2 and Scenario 3, the number of vehicles per unit time and time loss of anthropomorphic driving were similar to those of autonomous driving, but anthropomorphic driving vehicles could reduce the travel time and increase the average speed. The advantage of anthropomorphic driving vehicles over non-automated vehicles comes from the former's shorter reaction time and fixed action points, while their advantage over autonomous vehicles comes from their relaxed compliance with speed limit regulations. These advantages can increase the average speed of anthropomorphic vehicles in congested traffic conditions by 21.29% compared to non-automated vehicles and 19.05% compared to autonomous vehicles.  
From the results, we can see that, in smooth traffic conditions, anthropomorphic driving vehicles have a higher average speed than autonomous vehicles. This may be due to human drivers' incomplete compliance with the speed limit on the road (strictly complying with the speed limit in places with speed limit cameras and exceeding the speed limit by less than 20% in places without speed limit cameras). However, in congested traffic conditions, autonomous vehicles have more advantages. From the perspective of solving traffic congestion, autonomous vehicles are more beneficial. Anthropomorphic autonomous vehicles combine the advantages of human drivers and autonomous vehicles. In all indicators measured in this study (number of vehicles passing a point per unit time, travel time, time loss, and average speed), anthropomorphic autonomous vehicles were not inferior to autonomous vehicles and human driving. In particular, in terms of average speed in congested roads, they could be 21.29% faster than human drivers and 19.05% faster than autonomous cars.  
There are many limitations to this study. First, the road network in this study was based on part of a city, and simulations of traffic in other countries and cities may produce different results. Second, the road network did not consider the impact of road slope, other road users (bicycles, motorcycles, pedestrians, buses), and other road types (railways, light rail, highways) on the road network. Finally, the vehicle model and driver model used in this paper only considered the parameters with the greatest impact on traffic, and other influences (such as driver type, vehicle type, etc.) were not considered.
## 4 Conclusions  
In this paper, the impact of side effects on the pull-out bearing performance of deep-sea floating foundation helical anchors is examined through laboratory model tests. The main conclusions are as follows:  
1. The uplift bearing capacity of helical anchors decreases as the shaft-to-plate diameter ratio (\(d_{m}\)/\(D_{H}\)) increases.
2. The uplift bearing capacity of a helical anchor increases with the depth-to-diameter ratio (\(H\)/\(D_{H}\)) but decreases with increasing diameter ratio across different diameter ratios.
3. The pullout load factor, \(N_{c}\), decreases as the shaft-to-plate diameter ratio (\(d_{m}\)/\(D_{H}\)) increases. However, for different diameter ratios, \(N_{c}\) decreases with decreasing diameter ratio, but the rate of the decrease gradually slows and stabilizes.
4. The pull-out bearing capacity, \(N_{c}\), increases with the depth-to-diameter ratio (\(H\)/\(D_{H}\)), but upon reaching the critical depth-to-diameter ratio (\(H\)/\(D_{H}\geq 4\)), \(N_{c}\) stabilizes and no longer increases with further increases in depth-to-diameter ratio or diameter ratio. However, within the variation interval \(0.5\leq H\)/\(D_{H}\leq 4\), the initial value of \(N_{c}\) gradually decreases as the diameter ratio increases. This paper derives its conclusions primarily from field tests and outlines the following directions for future research:  
1. This study focuses solely on the static-loaded vertical pullout resistance of spiral anchors. However, in complex and dynamic marine environments, anchorage foundations may be influenced by wave-induced cyclic loading under lateral forces. Therefore, further research is necessary to understand the impact of cyclic loading on the lateral behavior of spiral anchors.  
2. This study examines the vertical pullout bearing performance of spiral anchors in controlled indoor soft soil conditions. Given the unique characteristics of oceanic soft soil, a suction force is likely to develop beneath the anchor plate of spiral anchors during pullout. Advancing the understanding of vertical pullout bearing performance through finite element analysis is crucial to optimizing suction force effects.  
Methodology, P.J.; validation, P.J.; formal analysis, Z.Z. and B.C.; resources, Y.H.; data curation, P.J., Z.Z., and B.C.; writing--original draft, B.C.; writing--review and editing, Y.H.; visualization, Y.H.; supervision, Y.H.; funding acquisition, Y.H. All authors have read and agreed to the published version of the manuscript.  
This research is funded by Jiangsu Coastal Power Infrastructure Intelligence Engineering Research Center through grant No. JC2024081.  
Not applicable.  
All data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.  
Authors [PERSON] and [PERSON] were employed by the company Lianyungang Zhiyuan Electric Power Design Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 5 Conclusions  
In summary, we analyze the influence of the exposure level of the power-spectrum pattern on the coherent power-spectrum scattering imaging method and propose a method of adjusting the feedback coefficient \(\beta\) adaptively. As a negative feedback coefficient for the reconstruction algorithm, \(\beta\) is used to reduce the error between the iteration output and the expected image, and finally, make the process work stably. The simulation results and the experimental results show that, for different exposures cases of captured power-spectrum patterns, the \(\beta\) values for the reconstruction algorithm should be changed, accordingly so as to achieve the optimum reconstructed output. For the three exposure cases of power-spectrum pattern capture, i.e., underexposure, moderate exposure, and overexposure, the \(\beta\) value should be selected in the range of 0.3-0.8, 0.6-1.1, and 1.0-1.25, respectively. Anyway, the \(\beta\) value selection guides will be helpful for the coherent diffraction imaging (CDI) with the hybrid input and output (HIO) algorithm, which could avoid the inappropriate \(\beta\) value for achieving successful object image reconstructions, and enhance real-time performance of coherent diffraction imaging. This method promotes the real-time imaging of single-shot coherent scattering imaging, and could also be used in fast variable-aperture Fourier ptychography scattering imaging [19].  
Conceptualization: W.W.; Methodology: Y.G. and W.T.; Software, W.Y.; Formal analysis: M.L.; Investigation, M.Z. and J.Q.; Writing--original draft preparation: Y.G.; writing--review and editing: X.L.; Funding acquisition: J.Z.  
National Science Foundation of China (NSFC) (61070040, 61108089, 61205087, 61107005), 973 Program (613192), Advance Research Project (7301506), Hunan Provincial Natural Science Foundation of China (805297133191), Education Department of Hunan Province (17C0043), Hunan Provincial Natural Science Fund (2019)J80105).  
The authors acknowledge assistance from [PERSON] and [PERSON].  
The authors declare no conflict of interest.