esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
As described in the paper [25], a hybrid converter was presented that was capable of providing both AC and DC loads with one DC input where a voltage source inverter replaced the control switch of the boost converter. An optimized filter design for a boost converter was created using GA in [26]. By using GA, it was possible to reduce the lower-order harmonics of an inverter considerably. Due to the nonlinear characteristics and time-varying features of power converters, GA was applied to optimize PI parameters [27]. The bee colony optimization algorithm generated duty cycles for the Sheppard-Taylor and interleaved converter in [28; 29]. In [30], authors constructed and developed a hybrid converter that was intended for marine applications. An EA based on pulse-width modulation was proposed in [31] for a hybrid interleaved boost-Cuk converter. This paper introduced the pulse-width-modulation technique and described the formula to analyze the influence of independent duty cycles and an input-current ripple; however, differential evolution was employed to minimize the input-current ripple. The approach proposed therein had certain limitations. First, the zero-ripple duty cycle (\(D_{Z}\)) was maintained at a constant. Maintaining \(D_{Z}\) at a constant affected the performance because the input-current ripple estimation depended on \(D_{Z}\). Second, the approach used a basic DE algorithm and failed to utilize the new advances proposed in the DE literature. Motivated by these, the main contributions of this paper are listed as follows:  
1. Introduce a modified hybrid converter that simultaneously optimizes three variables (duty cycle \(D\), scale factor \(k\), and \(D_{Z}\));
2. A new framework to be designed for the hybrid power converter;
3. We proposed a unified EA to minimize the input-current ripple;
4. A hybrid algorithm that combines DE and GA is suggested to enhance the current model's performance. The remainder of this paper is organized as follows: In Section 2, we provide an overview of the existing hybrid power converters, DE, and GA. Section 3 provides further details on the proposed method. The results are presented in Section 4, and the conclusions are presented in Section 5.
# Low-Cost, Distributed Environmental Monitors for Factory Worker Health
## 2 Materials and Methods
### Monitor Design Requirements  
In integrating the sensors into the monitors, it was important to consider a variety of factors that would affect their long-term use. For example, the monitors needed to be inconspicuous and positioned to avoid being inadvertently hit or damaged on the busy factory floor. After working through several options with factory personnel, we designed the monitors to fit between the webs of the I-beam columns that supported the factory roof. Another factor affecting the monitor design was that the circuitry in the monitors needed to be protected from dust and debris. To some extent, this requirement conflicted with the need for the dust sensors to have direct access to the environmental air, which meant including an inlet and outlet to channel air through the device. The gas sensors also needed to be exposed to the environment, but in a manner that avoided allowing large particles to accumulate on their filters. The monitor's protected position inside the I-beam webbing potentially baffled sounds from the environment, so the microphone needed to extend far enough away from the I-beam that the column would not muffle sound from sources on either side. Finally, the monitors required the ability to communicate with the wireless network to store all their data in a central server as well as the capacity to support an on-board flash drive backup.
## 2 Study Area, Data and Methods
### Extracting Phenological Metrics  
The Double Logistic Function (D-L) curve method was used to reconstruct the NDVI time series data in Northeast China (NEC) from 1982 to 2015, and the vegetation phenological parameters were extracted using the curvature method [20]. These parameters included the start of growing season (SOS), the end of growing season (EOS), maximum growth activity date (PEAK position), length of growing season (LOS), green up rate (RSP) and senescence rate (RAU) (Figure 2). RSP and RAU are crucial phenological metrics for depicting growth rate, which have not received adequate attention in previous studies of plant phenology. Spatiotemporal patterns of RSP and RAU, as well as their  
Figure 1: Topographic map (**a**) and vegetation types (**b**) in Northeast China.  
implications for vegetation activity during growth transition periods remain poorly understood. The growth curve fitting and phenological parameter extraction formulas are as follows:  
\[\text{NDVI(t)}=(\text{mNDVI}-\text{wNDVI})(\frac{1}{1+\text{e}^{-\text{mS(t-S)}}} +\frac{1}{1+\text{e}^{-\text{mA(t-A)}}}-1)+\text{wNDVI} \tag{1}\]  
where NDVI(t) represents the NDVI value at the \(t\)th day of year (DOY), wNDVI and mNDVI are the NDVI in winter and the maximum NDVI in peak position during plant growth process, respectively. S and A stand for the inflection points on the curve rising and falling process, and mS and mA are the rate of curve increasing or decreasing at the inflection point (RSP and RAU). The NDVI value in winter, termed as wNDVI, was determined using the following equation:  
\[\text{wNDVI}=\sqrt{\text{max}(\text{NDVI}_{10})\times\text{max}(\text{NDVI}_{11})} \tag{2}\]  
where NDVI\({}_{10}\) and NDVI\({}_{11}\) represent the NDVI values in October and November, respectively. Except for wNDVI, parameters were calculated using the iterative nonlinear least squares method. The curvature was obtained using the NDVI fitting curve, and the curvature formula was derived from this. When the derivative is zero, the curve is the greatest. This point was defined as a phenological transition period (SOS and EOS) during vegetation growth [20]. The R package 'greenbrown' was used to obtain these phenological metrics [34].
## 6 Conclusions  
The combination of FCM and QA is employed in this paper to optimize a network's energy consumption and prolong the network's lifetime. Initially, candidate CHs are selected based on a predefined energy threshold, prioritizing nodes with higher residual energy. Then, the proposed FQA considers effective parameters, including residual energy, the number of neighboring nodes, distance to the BS, and node centrality as descriptors of FIS to achieve the best decision, and the remaining nodes are assigned to particular CHs with higher potential according to energy and distance. Subsequently, an objective function is formulated based on potential energy derived from the energy matrix, distance matrix, and constraint terms. Then, we combine it with the kinetic energy generated by the interaction between n sets to obtain the Hamiltonian. The QA algorithm calculates the Hamiltonian to obtain the routing scheme from CHs to the BS with the minimum energy consumption. Finally, we utilized an on-demand re-clustering mechanism to decrease energy consumption further. Simulation results showed that FQA performs better than the existing protocols OAFS-IMFO, FC-RBAT, FRNSEER, and BOA-ACO in all scenarios regarding energy consumption, network lifetime, the number of surviving nodes, and throughput. Significantly, the network lifetime of FQA in scenario 1 has increased by 81.23%, 46.36%, 29.83%, and 14.95%, compared to OAFS-IMFO, FC-RBAT, FRNSEER, and BOA-ACO, respectively.  
Figure 10: Network throughput comparison in different scenarios: (**a**) area 100 m \(\times\) 100 m, 15%CH; (**b**) area 200 m \(\times\) 200 m, 10%CH.  
However, the proposed FQA is implemented only for a homogeneous and stationary network. In our future work, we will consider the incorporation of heterogeneous networks and mobile sinks into our research.  
Conceptualization, H.W. and H.H.; methodology, K.L. and H.H.; software, K.L.; validation, H.W. and K.L.; formal analysis, K.L. and H.H.H.; investigation, H.H. and C.W.; resources, H.W. and C.W.; data curation, K.L. and C.W.; writing--original draft preparation, K.L.; writing--review and editing, H.W. and H.H.; visualization, H.W. and C.W.; supervision, H.H.; project administration, H.W. and H.H.; funding acquisition, H.W. and C.W. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Science and Technology Development Project of Jilin Province [grant number: 20210201113 GX; 20210201051 GX; 20240302054 GX; 20210203161 SF].  
Not applicable.  
Data Availability StatementData are contained within the article.  
The authors declare no conflicts of interest.
## 3 Proposed Methodology
### Hybrid Feature Selection (Step 3)  
After obtaining normalized data, we carried out feature selection to obtain the optimal feature subset for our experimentation. Hybrid methods offer a good way of combining weak feature selection methods to obtain more robust and powerful ways to select variables. Rather than using a single approach to select feature subsets as the previous methods do, hybrid methods combine the different approaches to obtain the best possible feature subset. The big advantage that hybrid methods offer is high performance and accuracy [14], better computational complexity than with wrapper methods, and models that are more flexible and robust against high dimensional data.  
There are two types of hybrid feature selection, i.e., 1. Filter & Wrapper methods, and 2. Embedded and Wrapper Methods. We chose embedded and wrapper methods, which can be used to select top features, and then performed a wrapper method to rank the selected features which contribute more to the results. We used the recursive feature elimination technique to obtain our optimal feature subset. Recursive feature elimination works as follows:  
* Train a model on all the data features. This model can be tree-based, lasso, or others that can offer feature importance. Evaluate its performance on a suitable metric of your choice.
* Derive the feature importance to rank features accordingly.
* Delete the least important feature and re-train the model on the remaining ones.
* Use the previous evaluation metric to calculate the performance of the resulting model.
* Now, test whether the evaluation metric decreases arbitrarily. If it does, that means this feature is important. Otherwise, you can remove it.
* Repeat steps 3-5 until all features are removed (i.e., evaluated).
* This method removes the feature only once rather than removing all the features at each step. This is why this approach is faster than pure wrapper methods and better than pure embedded methods.
* We used random forests to select the best features. We also used recursive feature elimination and cross-validation (RFECV) to rank the optimal features selected. RFECV uses different parameters for feature selection details, which are as follows:
* min_feature_to_select: as its name suggests, this parameter sets the minimum number of features to be selected.
* Step: how many features do we remove at each step?
* CV: an integer, generator, or iterable that describes the cross-validation splitting strategy.
* Scoring: the evaluation metric we use.  
The dataset has a total of 20 features, as discussed in the above section, but after carrying out a hybrid feature selection, the optimal feature set came out as 14. The features 'WMC', 'DIT', 'NOC', 'CBO', 'RFC', 'ICOM', 'CA', 'CE', 'NPM', 'ICOM3', 'LOC', 'DAM', 'MOA', 'MFA', 'CAM', 'IC', 'CBM', 'AMC', 'MAX_CC', 'AVG_CC' are the 14 features selected using hybrid methods, and these features contribute the most to the results.
# Super-Resolution Lensless Imaging of Cells Using Brownian Motion
## 2 Materials and Methods
### Super-Resolution Reconstruction Algorithm for Cell Image in a Lensless System
#### 2.3.2 Normalized Convolution Super-Resolution Algorithm  
The displacement parameters of the cells are obtained using motion estimation. In this study, a normalized convolution-based (NC) method based on [PERSON] et al. [28] was used to reconstruct low-resolution cell images. In this algorithm, we used the Taylor series expansion to obtain the intensity at the point \(s_{0}\),  
\[\hat{f}(s,s_{0})=p(x,y)=p_{0}(s_{0})+p_{1}(s_{0})x+p_{2}(s_{0})y+p_{3}(s_{0})x^{ 2}+p_{4}(s_{0})xy+p_{5}(s_{0})y^{2}+\ldots, \tag{18}\]  
where \(p_{i}\) is the projection coefficient, which is the partial derivative of \(p_{i}(s_{0})\) for the basis function at point \(s_{0}\).  
\[\varepsilon(s_{0})=\int\bigl{(}f(s)-\hat{f}(s,s_{0})\bigr{)}^{2}c(s)a(s-s_{0})ds, \tag{19}\]  
where \(c\) is the deterministic function of the signal, whose range is 0 to 1. The 0 represents unreliable and the 1 represents reliable. \(a(s-s_{0})\) is a window function, which is the fitness function. According to the Equation (19), the \(p\) can be written in the form of a matrix,  
\[\mathbf{p}=\bigl{(}\mathbf{B}^{T}\mathbf{W}\mathbf{B}\bigr{)}^{-1}\mathbf{B}^ {T}\mathbf{W}\mathbf{f}, \tag{20}\]  
where \(\mathbf{f}\) is an \(N\times 1\) matrix of input intensity \(f\) (\(s\)), \(\mathbf{B}\) is an \(N\times m\) matrix of basis functions, and \(\mathbf{W}=\text{diag}(\mathbf{c})\ldots\text{diag}(\mathbf{a})\) is an \(N\times N\) diagonal matrix.  
The least-squares solution in Equation (20) can be written in the form of convolution operation:  
\[\hat{f}_{0}=\frac{a\otimes(c\cdot f)}{a\otimes c}, \tag{21}\]  
where \(\hat{f}_{0}\) is the interpolated image, \(\otimes\) is the convolution operation, and \(c\cdot f\) is the pixel-wise multiplication of the certainty.
## 4 Results and Discussion
### Dimensional Accuracy  
Cubic Specimens  
Dimensional accuracy was assessed using 50 mm cubes. Table 3 summarizes the deviations relative to nominal dimensions:  
SLA consistently maintained tighter tolerances (\(\pm 0.05\) mm) compared to FDM (\(\pm 0.15\)-\(0.20\) mm). The higher deviations in FDM are attributed to factors such as nozzle diameter variability, thermal contraction, and warping, particularly in ABS. These results highlight SLA's precision advantage for applications requiring high dimensional accuracy, such as interlocking parts or assemblies.
# Road Crack Detection by Combining Dynamic Snake Convolution and Attention Mechanism
## 2 Base Model and Proposed Model  
The excellent performance of U-Net in the medical field made it widely used in semantic segmentation. In addition, U-Net still performs well in small datasets. Therefore, we selected U-Net as the basic model in this study. The model mainly includes three parts: downsampling, upsampling, and jump connection. The main focus of this paper's algorithmic improvements lies in downsampling and upsampling. During the upsampling and downsampling processes, a DSC module was integrated to accommodate the slender and winding pipe pile structure. Additionally, CBAM was embedded into the convolution process to enhance the detection module for small cracks. In the feature fusion stage, the downsampling was in contact with the upsampling feature map of the same size. The improved algorithm network Attention-Dynamic Snake Convolution U-Net (ADSC-U-Net) is illustrated in Figure 1. The network incorporates the DSC module from layer one to layer five, and the attention mechanism module is added in from layer two to layer four. In each downsampled layer on the left part of Figure 1, its feature map is concatenated with the corresponding upsampled feature map on the right, ensuring that the number of channels of these two feature maps is aligned. To provide a detailed explanation of the downsampling process, the third downsampling layer is used as an example here. As illustrated in Figure 2, the encoding process is comprised of three distinct phases: convolution, group convolution, and ReLU activation function. Subsequently, the encoded features are conveyed through the DSC module, which primarily introduces biases in the X and Y axes, and the CBAM module, which transforms them into three distinct components.  
These three components are then merged into a unified output through concatenation and additional encoding. Therefore, this whole process results in a transformation from an original grayscale representation on the left to an output image on the right. It can be observed that, following downsampling, the semantic information of the original grayscale input becomes more abstract due to the DSC and CBAM modules, while still retaining crack details.  
Table 1 presents the modules and channel numbers used in each layer of the ADSC-U-Net network. Due to the inclusion of the DSC and CBAM modules, there has been an increase in the number of channels.
## 5 Conclusions  
A mathematical model of the impact with three degrees of freedom was developed. The models developed by [PERSON] [2] and [PERSON] [3] were extended to study wedge sections considering vertical \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{C_{v}}\) & \(\mathbf{\theta}\) (\({}^{\circ}\)) & \(\mathbf{\theta}\) (\({}^{\circ}\)) & \(\mathbf{\Phi}\) (\({}^{\circ}\)) & \(\mathbf{\overline{\tau}}\) (\({}^{\circ}\)) & \(\mathbf{2L_{k}/B}\) \\ \hline
3 & 20 & 6 & 0 & 0 & 0.4788 \\ \hline
3 & 20 & 6 & 0 & 5 & 0.4958 \\ \hline
3 & 20 & 6 & 0 & 10 & 0.5199 \\ \hline
3 & 20 & 6 & 0 & 15 & 0.5662 \\ \hline
3 & 20 & 6 & \(-\)10 & 0 & 0.4925 \\ \hline
3 & 20 & 6 & \(-\)10 & 5 & 0.4318 \\ \hline
3 & 20 & 6 & \(-\)10 & 10 & 0.3858 \\ \hline
3 & 20 & 6 & \(-\)10 & 15 & 0.3663 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental conditions by [PERSON] and [PERSON] [28]. motion, horizontal motion, and rotation, while the flow separation from the knuckles was evaluated. The pressure distribution and forces were computed during the impact. The model was compared with CFD simulations, experiments, and numerical solutions reported by other authors in the scientific literature, and the results show good agreement. Different impact conditions and degrees of freedom were evaluated, considering forced and free motions. The CFD simulations over predict the forces after the flow separation from the knuckle. The solution has a low computational cost when compared to commercial CFD software. The model was extended to three dimensions by the application of slender body theory in order to study planing boats in steady conditions. According to the results obtained, and the comparison carried out with different authors and the different conditions considered, this model could be used to study the non-linear dynamics of a planing boat with six degrees of freedom and variable sections along the keel with a low computational cost in comparison with commercial CFD software. The formulation can be used in future works to analyze the seakeeping with cross flow, dynamic stability, maneuverability, pressures, and impact loads over the structure and the crew. Conceptualization, R.A. and A.B. ; methodology, R.A.; software, A.B. ; writing--original draft preparation, R.A. and A.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare
###### Abstract  
This study selected the Yangtze River Delta urban agglomeration as the research area, combining it with the current situation of the transportation development of the Yangtze River Delta urban agglomeration to construct the urban agglomeration transportation integration index system and evaluate the development status of the Yangtze River Delta urban agglomeration transportation integration. The study examined the influence mechanism of transportation infrastructure on industrial agglomeration. The results are as follows: (1) From 2011-2020, the Yangtze River Delta urban agglomeration's transportation integration index showed a clear upward trend. (2) The integration level of local transportation played an important role in promoting local industrial agglomeration. Promoting industrial agglomeration in neighboring areas had a negative spillover effect on industrial agglomeration in this region. Developing transportation integration in other regions had an insignificant positive effect on the development of local industrial agglomeration. (3) Urban agglomeration transportation integration impacted regional industrial agglomeration, mainly through the \"cost effect.\" Thus, cities in the Yangtze River Delta in 2020 need to accelerate the construction of relevant transportation infrastructure so as to promote the integrated development of higher-quality transportation in the Yangtze River Delta urban agglomeration.  
industrial agglomeration; transportation integration; urban agglomeration; Yangtze River Delta urban agglomeration Article
# Digitization of Medical Device Displays Using Deep Learning Models: A Comparative Study
## 2 Methodology  
### Models Description  
Object detection architectures can be broadly classified into single-stage and two-stage detectors, each presenting specific trade-offs regarding accuracy, speed, and computational efficiency. The selection of the appropriate architecture is highly dependent on the application requirements. Given the potential future deployment of this system on mobile devices or devices with limited resources, the comparative analysis focused on single-stage detectors, due to their lower computational requirements and ability to achieve real-time performance without significantly compromising detection accuracy.  
Single-stage detectors such as YOLO [16] and the SSD [17] employ an end-to-end approach that directly predicts bounding boxes and class probabilities in a single network pass. These models are optimized for speed and are particularly suitable for real-time applications. However, due to the lack of a separate region refinement stage, they may exhibit a slight decrease in accuracy when detecting small or overlapping objects.  
For this study, the comparative analysis included the following models: YOLOv3u, YOLOv5 mu, YOLOv8n, YOLOv8m, and YOLOv8l from the YOLO family (Ultralytics repository) and the SSD MobileNet V2 Feature Pyramid Network Lite (FPNLite) from the TensorFlow Model Garden API. The models were evaluated at two distinct input image resolutions: 320 \(\times\) 320 and 640 \(\times\) 640.  
In addition to the aforementioned models, a Convolutional Neural Network (CNN) was developed for the classification component. The architecture of this network was based on the work carried out by [PERSON] et al. [1], and was designed to classify the objects extracted from the bounding boxes predicted by the detection models.