esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Input Module
The CNN has three convolutional layers (one of which is fully connected), two pooling layers (average pooling layers), one dropout layer, and a Softmax layer. The CNN-based architecture of BVS is presented in Figure 3[55]. The input data for the proposed neural network (the input layer) are boxes of \(27\times 27\) pixels on the green channel (STARE DB case). For images with higher resolution like those from MESSIDOR, larger boxes (\(55\times 55\) pixels) can be considered but in this case an adjustment of the CNN is necessary. (Note that the learning phase of the blood vessel segmentation does not have to be repeated for different resolutions of the input images. However, it is possible, on demand, with consideration of the training time.) These boxes are passed through the entire network to get their classification into two classes: vessel (V) and nonvessel (NV). The second layer with 20 neurons (local \(3\times 3\) filter functions) is a convolutional type with both stride and padding equal to 1. The third layer is an average pooling one, which performs the image subsample, based on the mean in a \(3\times 3\) sliding box. The new image (box) has a resolution of \(9\times 9\) (stride equal 3). The next layer with 50 neurons (filters) is also a convolutional type, with both stride and padding equal to 1 and, consequently, with output \(9\times 9\) pixels. The fifth layer is a new pooling layer with stride equal to 3 (padding 0) which reduces the box dimension to \(3\times 3\). It follows a dropout layer for the regularization of the network (the rate is equal to 0.5). The penultimate layer is a convolutional one of the fully connected type with 2 neurons (for classification), stride 1, and padding 0. The final layer of the CNN is called Softmax, is based on a loss function measure, and has two outputs: V and NV. The blood vessel segmentation was made by pixelwise classification. The training of the CNN used 50 epochs and 4000 small boxes of  
Figure 2: Exemplification of box division of the analyzed image: (a) sliding box algorithm; (b) nonoverlapping box algorithm. 27 \(\times\) 27 pixels (2000 with vessels and 2000 without vessels) on the green channel. The CNN for blood vessel segmentation was implemented using MatConvNet.
# Coupling and Trapping of Light in Thin-Film Solar Cells Using Modulated Interface Textures
## Results
### Light Trapping Model
Although the front contact type C shows roughness values of 70 nm and is therefore within the range of sample B the light coupling in the visible wavelength range is less effective. The larger lateral feature size exceeds the wavelength of the light, so the requirements for the effective medium theory are not fulfilled [28]. The type D surface (test series 2) shows average feature diameters beyond 1000 nm, while its roughness is similar to surface C. This leads to an even less effective light-coupling but also to a reduction in the light-trapping effect. The AID measured in reflection indicates, that the surface angles are passed to the back side almost unchanged, the scattering to large angles decreases slightly. Given the dimensions of these surface structures, we are entering a regime where the cell thickness and the lateral dimension of the structures are similar. If we consider the wavelength in silicon, geometric optics provides a reasonable picture [51]. Light, that enters the tandem solar cell with a type D front contact, is likely to be slightly tilted in its path by diffraction at the front contact. Due to the vast lateral dimensions of the craters, the reflected light beam may strike the front surface on the same facet as the incident one. Therefore, the light beam is easily released to the outside world rather than being trapped inside the solar cell by a large number of random scattering events. The model simplified the description of the optics in the thin film solar cell that can explain the observed effects. However, a detailed description of coupling to the waveguide modes and the interaction of the rough interfaces with often applied intermediate reflectors requires detailed simulations [17; 64; 65]. It was shown that the two optical effects of light-coupling and light-trapping are caused by different texture features. The results of test series 1 and 2 also demonstrate that the two different absorber materials in a tandem solar cell are impacted to a different extent by these two effects. Good light-trapping can enhance the optical performance of the ucSi:H bottom cell significantly but does not necessarily influence the a-Si:H top cell. The interplay between rough interfaces and intermediate reflector layers is discussed in literature [64; 65; 66]. A good light-coupling, on the other hand, increases the top and the bottom cell current. However, the bottom cell current is still enhanced more significantly by light-trapping structures. Therefore, it is favorable to combine these two effects with each other to generate the maximum power output in the tandem device. It was already demonstrated that two types of textures are needed to enable the two effects. Thus, a single texture cannot efficiently facilitate both effects simultaneously.
# Systemic Evaluation of PSS in the Early Concept Phase Using Graph-Based Reasoning
## 4 Solution Approach
### PSS Concept Variation Layer (CVL)  
The PSS concept variation layer (CVL) focuses on exploring different configurations of key properties of PSS elements essential for the working principles. Due to vague specification of which elements or subsystems of the PSS concept fulfill these properties, different possible configurations are considered that exhibit varying chances of successful performance and implementation. The CVL addresses the potential issue of incompatibility among logical or physical PSS elements by allowing users to anticipate and define incompatible design elements carrying these properties. This approach prevents the consideration of implausible design scenarios, ensuring that only compatible PSS concept variations are considered during the reasoning process. The assessment of combinability of manifestations of the PSS elements should not be based on an in-depth feasibility assessment, which is not yet possible in the early PSS concept phase. Instead, the aim is to identify fundamental contradictions and to map the alternative manifestations of PSS elements in a consistent form.  
As with the knowledge gaps, index nodes are used to define PSS elements in their manifestations. The index nodes, thus, activate certain combinable characteristics of the PSS elements. The evaluation logic changes depending on the activated characteristic. The change in the evaluation logic is not modeled in extreme scenarios, as in the case of knowledge gaps, but is intended to describe the relationships as realistically as possible according to the existing understanding. The various manifestations of the PSS elements, when combined, result in different PSS concept variations. Figure 11 shows the PSS concept variation layer (CVL) using an illustrative example. The characteristics of PSS elements that are relevant in an evaluation logic and have an influence on it are arranged in the bottom hierarchical position in the CVL. Depending on their characteristics, they change the evaluation logic accordingly, as shown for the _analytics for predictive maintenance_ PSS element and its influence on _node E_. The PSS elements relevant in the EVL are then summarized in the CVL as concept variations, and unfeasible combinations are deterministically recorded as such. An example of the semantic background PSS engineers could consider defining PSS concept variations is shown for node 1 on the PSS CVL. In the example, it is anticipated that a certain machine type (type C) will have no data connectivity and that the simultaneous consideration of the predictive maintenance service in the evaluation logic is therefore inconsistent. By specifying this, consistent PSS variations can be activated during reasoning, which enables a specific evaluation of the EVC and a distinguishing of the importance of knowledge gaps for each PSS concept variation.
## 1 Introduction
The utilization of this particular technique results in an improvement in the overall quality, discriminative capability, and resilience of the features, thereby providing substantial advantages for tasks related to scene identification. The last stage of recognition is performed by the SoftMax layer within our integrated model. This layer utilizes the information acquired from our innovative single-input multimodal technique to assign scene labels. The methodology employed in our study capitalizes on the advantageous effects of multimodal fusion when applied to identical input image data, resulting in significantly enhanced accuracy and resilience in scene recognition. The novel contributions of our research are outlined below:  
* Our approach differs from previous models that primarily focused on single-modal processing by introducing a multimodal fusion technique that works on the same input visual data. The present methodology introduces a novel approach that allows for the simultaneous processing of many types of information derived from a singular image. This advancement facilitates a more thorough comprehension of the scene compared to prior research that predominantly concentrated on single-modal techniques. * To enhance feature quality and robustness, we employ _MI_-based feature selection. Our approach incorporates _MI_ not only for feature selection but also within the context of multimodal fusion. We utilize _MI_ to assess the interdependence between different data modalities and scene labels, enabling effective fusion strategies that are tailored to each modality's relationship with scene categories. * The model incorporates an additional layer for integrating optimized features extracted from the input image data, representing an innovative architectural element. This feature integration step significantly enhances the quality of feature representations, further contributing to the model's superior performance compared to techniques that do not incorporate such integration. * Our method introduces the novel concept of an end-to-end model, eliminating the need for complex multistage pipelines typically employed in traditional scene recognition approaches. Our multimodal fusion scene recognition method is presented in various key sections of the paper. After the Introduction, the literature review is discussed in Section 2. The methodology describes our model's essential components in Section 3. We then give empirical results and explain their consequences in Section 4. Our succinct conclusion highlights our approach's contributions and transformative possibilities in scene recognition in Section 5.
## 3 Results  
The patients' characteristics and tumor features are listed in Table 1. Twenty-three eyes of 21 patients were enrolled in the study, including 17 females and four males. The mean age at diagnosis was 32.6 \(\pm\) 8.6 years (range 16-50 years). The right eye was involved in eight patients, and the left eye in 11 patients. Two patients were affected on both eyes. The mean baseline BCVA was 0.55 \(\pm\) 0.46 (range 0.0-1.4), and the mean greatest linear dimension of tumor was 7.03 \(\pm\) 3.87 mm (range 1.96-16.36). There were 20 eyes that had single tumors and three eyes that had multi-focal tumors, amongst which macular involvement was seen in 10 tumors, juxtapapillary superior in four tumors, juxtapapillary inferior in one tumor, juxtapapillary temporal in two tumors, juxtapapillary nasal in one tumor, and circumpapillary with macular involvement in seven tumors. Prior to enrollment in the study, 12 eyes had been treated with intravitreal injections of anti-VEGF treatment for the presence of CNV. On SS-OCT, all eyes showed unique features, as previously reported [13; 18], including horizontal lamellar lines, hyperreflective horizontal lines, horizontal tubular lamellae with an optically empty center, vertical tubular lamella, and regions of speckled reflective dots within the CO. In 17 eyes, the hyperreflective area was located above the disrupted Bruch's membrane (73.9%), and several intratumor vessels were seen in the hyperreflective area. The sclero-choroidal junction was detectable in all tumors, and a posterior ciliary vessel penetrating the sclera was also seen. Vessels between the tumor and the sclero-choroidal junction were observed as structures with low or medium internal reflectivity in seven eyes (presumed thinning of Haller's layer that was pushed toward the outside and compressed). \"Choroidal location of fluid\" was found in four eyes and FCE in 13 eyes, of which nine had a CNV lesion on the slope of, or adjacent to, the FCE (Figure 1). On SS-OCTA and en face imaging, the tumor boundary could be clearly demarcated from adjacent vessels of the choroidal Sattler's layer and Haller's layer in all eyes. By adjusting the segmentation lines on the point-to-point aligned B-scan images, we were able to identify the anterior and posterior surfaces of the tumor. The maximum area of a single captured OCTA image was 24 \(\times\) 20 mm, which enabled observation of the details of the tumor in a single image without the need to make a montage. The blood flow of the CO was detected mostly within the choriocapillaries and partly within the Sattler's layer on B-scan.
## 2 Comprehensive Evaluation Method for AC/DC Hybrid Power Network Planning
### Weight Calculation
#### 2.2.1 Analytic Hierarchy Process  
The Analytic Hierarchy Process (AHP) decomposes a complex decision-making problem into multiple objectives or criteria. Then the hierarchical single ranking and total ranking are calculated by using the qualitative index fuzzy quantization method. It can be used as a systematic method for multi-scheme optimization decision-making [6; 8; 12]. The steps of the analytic hierarchy process are as follows.  
1. Establishing hierarchical structure model.
2. Construct judgement matrices at all levels.  
\[C=\left[\begin{array}{cccc}C_{11}&C_{12}&\cdots&C_{1n}\\ C_{21}&C_{22}&\cdots&C_{2n}\\ \vdots&\vdots&\ddots&\vdots\\ C_{n1}&C_{n2}&\cdots&C_{nn}\end{array}\right]. \tag{12}\]  
In Equation (12), \(C_{ij}=1/C_{ji}\). The assignment of \(C_{ij}\) is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Power Generation** & \multicolumn{3}{c}{**Pollutant Emissions/(g/kWh)**} \\ \cline{2-4}
**Mode** & **SO2** & **NOX** & **CO2** \\ \hline Thermal power & 6.48 & 2.88 & 623 \\ Micro gas turbine & 0.000928 & 0.6188 & 184.0829 \\ Fuel cell & 0 & \textless{}0.023 & 635.04 \\ Photovoltaic power & 0 & 0 & 0 \\ Wind power & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pollutant emission data of various power generation technologies.  
Calculating subjective weights. After the judgment matrix \(C\) is established, the maximum eigenvalue \(\lambda_{max}\) of the judgment matrix \(C\) is obtained. Then the eigenvector of \(\lambda_{max}\) is obtained. After normalization, the subjective weight \(W\) = (\(w_{1}\),..., \(w_{n}\))\({}^{T}\) is obtained.
4 Consistency test. The consistency index \(CR\) of judgment matrix can be expressed as the following equation.  
\[CR=\frac{CI}{RI}, \tag{13}\]  
\[CI=\frac{\lambda_{\max}-n}{n-1}. \tag{14}\]  
In Equations (13) and (14), \(CI\) is the consistency indicator. \(n\) is the order of the judgment matrix. \(RI\) is the random consistency ratio of the judgment matrix. Its assignment is shown in Table 3. When \(CR<0.1\), the judgment matrix has satisfactory consistency. Otherwise, the judgment matrix should be readjusted until it has satisfactory consistency.  
Let \(CI\)(\(j\)) be the consistency index of the judgment matrix of the factors related to the upper level in layer B. The corresponding average random consistency index is \(RI\)(\(j\)). Then the random consistency ratio of the B-level total ranking is shown in the following Equation (15).  
\[CR=\frac{\sum\limits_{j=1}^{m}CI(j)a_{j}}{\sum\limits_{j=1}^{m}RI(j)a_{j}}. \tag{15}\]  
When \(CR<0.1\), it is considered that the results are in good agreement and the analysis results are acceptable.
## 5 Post-Processing
### Strategies to Reduce Surface Post-Processing Operations
While current hybrid additive-subtractive technologies have primarily focused on combining direct energy deposition with machining processes, the integration of PBF-LB/M with CNC milling presents a promising solution to overcoming the challenges of surface finish and precision [213]. Several companies have developed hybrid manufacturing systems that combine PBF-LB/M methods with CNC milling, enabling the production of faster and more complex parts. Matsuura has pioneered this technology with its LUMEX Avance-25, an innovative hybrid CNC machine that integrates 3D printing, metal laser sintering, and milling. This cutting-edge system enables the production of intricate metal components with superior surface finishes [214]. Since entering the market in 2017, Sodick has emerged as a leader in hybrid manufacturing. Their OPM250L and OPM350L models seamlessly integrate selective laser melting and high-speed milling, facilitating the production of complex metal molds with exceptional precision and finish. This advanced technology significantly boosts productivity, reduces lead times, and lowers costs compared to conventional manufacturing methods [215]. [PERSON] et al. developed a hybrid system integrating PBF-LB/M with a three-axis CNC milling machine [209]. PBF-LB/M with CNC milling systems are primarily employed in the mold industry, taking advantage of their ability to consolidate most, if not all, manufacturing processes within a single machine. They are particularly suited for the production of medium to large parts, such as conformal cooling channels in molds, which optimize cooling efficiency and are nearly impossible to produce as single components using conventional methods. Furthermore, these systems help reduce the likelihood of defects caused by limited access to cutting tools. In mold manufacturing, it is crucial for the cooling medium to flow with minimal resistance, necessitating exceptionally smooth surfaces in the cooling channels. This level of surface finish can only be achieved through CNC machining, rather than AM [216].
## 4 Case Studies
### Wind Power, Photovoltaic Power and Load Power Forecasting
#### 4.1.3 Model Parameter Settings  
In stacking ensemble learning, there are many options of learners. Through the experiments, we can find the best combination of learners and the optimal hyper-parameters for each forecasting problem, as shown in Tables 3-5.  
As shown from Tables 3-5, the best forecasting effects of different forecasting problems require different combinations of learners. Through experiments on combinations of learners, it is found that the base learners for wind power forecasting are LGBM, XGB, RR, SVR and KNN. The base learners for photovoltaic power forecasting are LGBM, XGB, RR, KNN and LR. The base learners for load power forecasting are LGBM, XGB and KNN. The meta-learner for all three forecasting objects is LR.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of** & **Algorithm** & **Hyper-Parameters Setting** \\ \hline \multirow{4}{*}{base learners} & LGBM & Boosting tree is GBDT, number of leaves is 100, number of trees is \\  & XGBoost & 100, tree depth is 2, learning rate is 0.1, training data ratio is 0.8, \\  & & feature column sampling rate is 0.9. \\ \cline{2-3}  & RR & The regularization term coefficient is 1. \\ \cline{2-3}  & KNN & The number of neighbors is 100. \\ \cline{2-3}  & LR & / \\ \hline meta-learner & LR & / \\ \hline \hline \end{tabular}
\end{table}
Table 4: Learners and hyper-parameters of photovoltaic power forecasting.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of** & **Algorithm** & **Hyper-Parameters Setting** \\ \hline \multirow{4}{*}{base learners} & LGBM & Boosting tree is GBDT, number of leaves is 20, number of trees is 300, tree depth is 6, learning rate is 0.1, training data ratio is 0.8, \\  & XGBoost & feature column sampling rate is 0.9. \\ \cline{2-3}  & RR & The regularization term coefficient is 1. \\ \cline{2-3}  & SVR & The distance error is 0.15. \\ \cline{2-3}  & KNN & The number of neighbors is 5. \\ \hline meta-learner & LR & / \\ \hline \hline \end{tabular}
\end{table}
Table 3: Learners and hyper-parameters of wind power forecasting.
## 4 Experiments
### Experiments about Influencing Factors
#### 4.2.4 Results regarding Data Missing and Discussion  
Data missing refers to the loss of certain data (be set to 0) or the occurrence of large or small abnormal values. Data missing is usually in the absence of prior information; it is difficult to reproduce the data, so this problem is usually more lethal than Gaussian noise. There are some preprocesses methods to try to solve it, such as deleting defective cases and interpolating. For the former method, it is possible to lose valuable information and reduce samples; the latter introduces  
Figure 10: Comparison of several improved classifiers, including the full structure BICORN-RNN, with standard RNN with different lengths of sequences.  
noise artificially. Other methods, such as robust regression, and high-dimensional mapping, are limited to complex calculations.  
This subsection aims to demonstrate the robustness of our method without complicated preprocessing algorithm facing the data missing. We simulate different data missing ratio (0% to 50%, stepped by 5%), by setting an unusually large and small value at certain time-steps randomly.  
The classification results on the dataset \"OSU Leaf\" are shown in Figure 11. As seen from Figure 11, facing the 50% data missing ratio, the accuracies of all classifiers have been significantly reduced by more than 20%. If the missing data ratio is less than 20%, the accuracy of BICORN-RNN can remain at over 80% with less fluctuation, which is the most robust one. It is reassuring that BICORN-RNN keeps a minimum accuracy line of 60% even if missing half of the data values, which may benefit from convolutional operation. By contrast, bidirectional transmission cannot be carried out smoothly whether forwards or backwards, due to the ineffective hidden values of the neurons during the processing; thus the accuracy of BI-RNN is attenuated as rapidly as that of the RNN, which is close to a random classification.  
Although the integrity and validity of data is an important factor in maintaining high accuracy, the method we proposed can be efficacious provided that less than the half data is missing.