esa-sceva/satcom-chunk-collection huggingface dataset:
# Status of BIPV and BAPV System for Less Energy-Hungry Building in India--A Review
## 4 Potential of BIPV/BAPV in India
India has 29 states and 7 union territory out of which Madhya Pradesh, Gujarat Ladakh, Andhra Pradesh, Maharashtra, and Rajasthan receive the maximum amount of average annual solar radiation, as compared to other states of India. Gujarat is the first Indian state which implemented a solar policy in the year 2009, well before the initiation of JNNSM. Rajasthan started its solar mission in 2011 to meet the national target. Karnataka started its solar mission for the  
Figure 6: Physical map Indian solar radiation [139]. period of 2014 to 2021. Madhya-Pradesh started its solar policy in 2012 and provided the incentives and benefits to the Private Sector to encourage the PV installation [145]. To initiate the government target, several commercial investors came in front. Cleanmax solar had invested Rs 600 cores to set up a 150 MW solar farm in Sirsa District, Haryana (near to New Delhi, 29.05\({}^{\circ}\) N, 76.08\({}^{\circ}\) E) on a stretch of 600 acres of land. Bharathi Cement had commissioned a 10 MW solar power plant in the manufacturing facility, located at Kadapa in Andhra Pradesh (14.46\({}^{\circ}\) N, 78.82\({}^{\circ}\) E). The plant is expected to generate 1.6 crore units of electric power annually, and help to reduce Bharathi's overall energy costs by reducing its dependence on thermal power.
## 3 Our Method  
### Overall Framework  
Restoring a blurred image to a clear one typically involves addressing several degradation issues, such as blurring, color bias, and uneven brightness. These degradations can result in the loss of image details and degrade both the quality and utility of the image. To  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Method** & **Key Approach** & **Color Consideration** \\ \hline \multirow{8}{*}{Prior-based methods} & DCP [11] & Dark channel assumption & ✗ \\  & CAP [32] & Blue channel attenuation & ✓ \\  & HSV-based [12] & Processes luminance in HSV space & ✓ \\  & Non-local Prior [33] & Color clustering for transmission estimation & ✗ \\  & Boundary Constraint [34] & Boundary constraints and regularization & ✗ \\  & 3C Color Channel Compensation [35] & Adjust color channels separately & ✓ \\ \hline \multirow{8}{*}{Learning-based methods} & DehazeNet [16] & CNN-based transmission prediction & ✗ \\  & MSCNN [17] & Multi-scale CNN estimation & ✗ \\  & AOD-Net [37] & Joint transmission-atmosphere model & ✗ \\ \cline{1-1}  & Enhanced pix2 pix Dehazing [38] & Image-to-image translation & ✗ \\ \cline{1-1}  & Gated Context Aggregation [39] & Gated feature aggregation & ✗ \\ \cline{1-1}  & FFA-Net [40] & Feature fusion attention & ✗ \\ \cline{1-1}  & Contrastive Learning Dehazing [41] & Contrastive feature training & ✗ \\ \hline \hline \end{tabular}
\end{table}
Table 1: The list of the surveyed methods for image dehazing.  
address these challenges, a dual-branch dehazing network, CCD-Net, has been proposed. It primarily focuses on learning image clarity and color features, effectively mitigating the aforementioned degradation issues to produce clear and naturally colored images.  
CCD-Net is composed of two main components: a dehazing branch and a color-feature learning branch, as illustrated in Figure 2. The input hazy image \(I_{\text{in}}\in R^{H\times W\times 3}\) is processed simultaneously by both branches. The dehazing branch extracts the feature map \(F_{\text{dehazing}}\), while the color-feature learning branch generates the feature map \(F_{\text{color}}\in R^{H\times W\times 3}\). These two feature maps are then combined to reconstruct a clear image.  
The first branch focuses on restoring image clarity by employing advanced dehazing techniques to enhance blurred regions and recover fine details and edges. The second branch is dedicated to learning color features in the Lab color space, aiming to correct color bias caused by atmospheric scattering and other factors, ensuring that the restored image exhibits more natural and accurate colors.  
By jointly optimizing these two objectives--clarity and color features--CCD-Net is able to effectively retain the original color and detail of the image while removing haze, avoiding the common problems of color distortion and edge blurring encountered in traditional dehazing methods. This ensures high-quality image restoration across a wide range of complex environments. Detailed descriptions of the dehazing and color-feature learning branches are provided below.
# Modernising Receiver Operating Characteristic (ROC) Curves +
## 3 Materials and Methods
### Modernising ROC
#### 3.4.2 Linearised ROC Curves Adopting the Rasch Model
We do not assume that the Rasch parameters for difficulty and ability are necessarily the same for the different classifications, such as TPR and FPR, so that for device \(i\):  
Decision scenario (I):  
\[Y_{i}=z_{TPR,i}=\theta_{TPR,i}-\delta_{TPR,i}=-\frac{SL_{tp,i}-x}{u_{tp,i}}\]  
and decision scenario (II):  
\[X_{i}=z_{FPR,i}=\theta_{FPR,i}-\delta_{FPR,i}=-\frac{SL_{fp,i}-x}{u_{fp,i}}\]  
Various coefficients and offsets will be evaluated by regression against the experimental data for each device in turn (Section 3.3). In a third step, we examined how well the experiment agreed with the theory. Appendix B contains an account of how the slope, \(M\), and intercept, \(C\), of the \(z_{ROC}\) plots for the different device types (exemplified in Section 4.4) can be expressed in terms of the various parameters occurring in the above formulae for each axis separately, with the results:  
\[M=\frac{u_{fp}}{u_{tp}}\]  
and  
\[C=-\frac{SL_{tp}-SL_{fp}}{u_{tp}}\]  
It is not unexpected that the straight-line \(z_{ROC}\) plots derived with these expressions will agree with the individual parameter values obtained for each axis separately (see Sections 4.2 and 4.3). What is important is that the expressions make explicit which factors determine the \(z_{ROC}\) plots in terms of classification \(SL\), abilities and uncertainties. For instance, the above formula for \(C\) clarifies which factors determine the \(z_{ROC}\) intercept values proposed by [PERSON] [12] as a measure of device type performance competitive with the _AUC_ metric of traditional ROC curves. Lastly, we conducted analyses comparing various measures of relative device type performance, including measurement uncertainties, as presented later in Section 4.
# WEREC: An Unsupervised Method for Educational Concept Extraction Based on Word Embedding Refinement
## 2 Related Work
### Concept Extraction in Education  
Concept extraction is a fundamental technique for knowledge mining in education (e.g., when identifying topics in students' online discussions or arranging educational knowledge graphs). Studies have demonstrated that automated domain concept extraction brings deep insights for teaching and learning [14; 15; 28; 29]. [PERSON] et al. identified e-learning domain concepts from academic articles to assemble a concept map; this helped teachers create adaptive learning materials and enabled students to better grasp the complete picture of subject knowledge [16]. [PERSON] formulated a tool to ascertain terms from electronic textbooks and assist teachers in crafting instructional materials [17]. [PERSON] et al. extracted topic concepts from students' forum posts, enabling instructors to detect and trace students' learning engagement with discourse content [30]. A set of concepts extracted from subject materials, along with a group of association rules, can be used to construct knowledge graphs and thereby promote teaching and learning. A systematic review revealed that the relationships among domain concepts are essential for estimating or predicting learners' knowledge states [15]. Together, such research has shown concept extraction to be crucial in teaching and learning practices. However, popular approaches in educational studies (e.g., TF-IDF and latent Dirichlet allocation) depend on word frequency statistics, which can easily overlook low-frequency educational concepts and struggle to capture the semantic information behind text. Therefore, it is imperative to determine how to exploit semantic information from educational concepts to facilitate concept extraction. Many strategies adopted in educational settings involve TF-IDF, C/NC values, and graph-based ranking. These statistical approaches to concept extraction (i.e., from textual data) are generally contingent on word frequency or key words. [PERSON] proposed a hybrid method based on the TextRank algorithm and TF-IDF for key concept extraction and sentiment analysis of educational texts [21]. [PERSON] improved the Chinese term extraction method by using C/NC values [20]. Although statistical methods are applicable to concept extraction, they traditionally require extensive domain knowledge and labeling to identify meaningful features. In contrast, word embedding techniques can learn directly from text corpora without manual labeling or feature engineering; that is, they can learn in an unsupervised manner. Each dimension of word embeddings can also reflect certain aspects of lexical meaning, thereby providing rich semantic information [31]. At present, pre-trained large language models can obtain word representations with more semantic information and have been employed for educational concept extraction. [PERSON] et al. extended the pre-trained embedding model by adding a graph propagation algorithm to capture relationships between words and courses, enabling domain concepts
## 3 Results
### Contact Angle  
Table 3 presents the results obtained from contact angle measurements on the surface of the test plates with and without films. The test liquids used were water and diiodomethane. Measuring the contact angle with water allows for the determination of surface hydrophilicity or hydrophobicity. For the test plates made of High Temp resin, the largest contact angle is observed on the test plate without a film. The surface of the test plate without a film and with a film based on litmus and TiO\({}_{2}\) is hydrophilic, with a larger contact angle observed on the test plate without a film. For the test plates made of Anycubic Basic resin, all surfaces are hydrophilic, with contact angle being greater for the test plate with a film based on litmus and TiO\({}_{2}\) than for the test plate without a film. The hydrophilicity of the films is important, as it facilitates the diffusion of ions from aqueous acid and base solutions, enabling contact with indicator molecules within the film. Measurements of the contact angle with diiodomethane generally yield lower values, but these values follow the same trend as the contact angles obtained with water.  
Table 3 also presents the calculated values of free surface energy according to the Owens-Wendt model. For the test plates made of High Temp resin, the free surface energy is greater for the test plates with a film based on litmus and TiO\({}_{2}\) than for the test plates without a film. The total free surface energy for the test plates made of Anycubic Basic resin is also greater for the test plates with a film based on litmus and TiO\({}_{2}\) than for the test plates without a film, with dispersive and polar component values following the same pattern.
## 3 Dynamic Model of UAV System and Tracking Differentiator
### Fractional-Order Filtered Error Surface  
In this section, a fractional-order filtered error surface is considered for designing the controller, as follows:  
\[\sigma_{j}=D^{\gamma_{1}}e_{j}+D^{\gamma_{2}-1}k_{j}^{2}(b_{j1}e_{j}+b_{j2}sig^{ 1/2}(e_{j}))-\xi_{j}, \tag{32}\]  
where \(k_{j}\), \(b_{j1}\), and \(b_{j2}\) are positive constants and \(\xi_{j}\) denotes a compensating signal defined later. For a comparison with the above fractional-order sliding mode surface, three fractional-order error surfaces based on functions used in (24), (30), and (26) are presented as follows:  
\[\sigma_{j}=D^{\gamma_{1}}e_{j}+D^{\gamma_{2}-1}k_{j}^{2}[b_{j1}\sinh(\kappa_{j 1}e_{j})+b_{j2}\sinh(\kappa_{j2}e_{j}/k_{j})]-\xi_{j}, \tag{33}\]  
\[\sigma_{j}=D^{\gamma_{1}}e_{j}+D^{\gamma_{2}-1}k_{j}^{2}[b_{j1}\left\{e_{j}+ \left|e_{j}\right|^{1/2}tansig(\kappa_{j1}(e_{j}))\right\}+b_{j2}\left\{e_{j}/ k_{j}+\left|e_{j}/k_{j}\right|^{1/2}tansig(\kappa_{j2}e_{j}/k_{j})\right\}]-\xi_{j}, \tag{34}\]  
\[\sigma_{j}=D^{\gamma_{1}}e_{j}+D^{\gamma_{2}-1}k_{j}^{2}[b_{j1}\sinh(\kappa_{ j1}\sigma_{j}(e_{j}))+b_{j2}\sinh(\kappa_{j2}sig^{1/2}(e_{j}))/k_{j}]-\xi_{j}, \tag{35}\]  
For initial conditions such that \(\gamma_{1}=0.7\), \(\gamma_{2}=0.1,k_{j}=1.2\), \(b_{j1}=2\), \(b_{j2}=2\), \(\varepsilon_{j}=0.95\), \(v_{j}=0.5\), \(\kappa_{j1}=1.5\), and \(\kappa_{j2}=1\) under the compensating signal condition of \(\xi_{j}=0\), the convergence configurations are illustrated in the following figure:  
Figure 3 shows that the convergence performance of the proposed filtered error surface is a little slower than (35) but faster than other surfaces. If the filtered error surface given in (34) based on the function given in (26) is selected instead of the function in (31), the convergence performance is a little improved as shown in Figure 2, but the designed controller may be much complex than the controller based on (32). Therefore, the fractional-order filtered error surface considered in (32) was selected under the compromise between the controller complexity and the convergence performance.
# A Seamless Image-Stitching Method Based on Human Visual Discrimination and Attention
## 3 Methods
### Visual Perception Quantification
#### 3.1.1 Visual Non-Linearity  
VN is a study on the relationship between the objective luminance and the human subjective brightness perception. According to the universally acknowledged Weber-Fechner law, brightness perception of pictures is proportional to physical stimulus on a logarithmic scale [32]. In other words, the human eyes' ability to recognize the difference of brightness declines with the increase of the objective luminance. [PERSON] demonstrated the neural basis of the Weber-Fechner law by researching monkeys' brains [31]. The Weber-Fechner law is given by Equation (1):  
\[S=K\text{-}lnL+K_{0} \tag{1}\]  
In Equation (1), \(S\) describes human visual subjective perception of brightness, and \(L\) is luminance of images. \(K\) and \(K_{0}\) are both constants associated with the average brightness of the pictures. Very bright or dark images correspond to small \(K\) values, and the value of \(K\) is taken as 1 for normal luminance range. In this paper, \(S\) is normalized to the range of (0-255) to compute the VN map.  
Figure 1: Flow of the proposed method.  
The pictures for mosaicking are always taken by several image sensors from different orientations, which may lead to inequality in data processing and illumination circumstance; therefore, there are luminance differences in input images, resulting in discontinuities at luminance of objects in stitched image. Human eyes' luminance discrimination threshold is at a low level in very bright areas, so it will improve the quality of mosaic if locating the stitching line in bright areas.
Remote Sens._**2015**, \(7\), 10626-10645; doi:10.3390/rs70810626  
_remote sensing_  
**ISSN 2072-4292**  
www.mdpi.com/journal/remotesensing  
_Article_  
New Spectral Fitting Method for Full-Spectrum Solar-Induced Chlorophyll Fluorescence Retrieval Based on Principal Components Analysis  
**[PERSON] 1,2, [PERSON] 1,*, [PERSON] 1,2 and [PERSON] 1,2**  
* Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China; E-Mails: [EMAIL_ADDRESS] (X.L.); [EMAIL_ADDRESS] (S.Z.); [EMAIL_ADDRESS] (X.Z.)
* University of Chinese Academy of Sciences, Beijing 100049, China
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-10-8217-8163; Fax: +86-10-8217-8177.  
Academic Editors: [PERSON] and [PERSON]  
_Received: 16 June 2015 / Accepted: 8 August 2015 / Published: 18 August 2015_  
the case of the field spectral data with SRs of 3 nm and 0.3 nm, the double-peak shape and the diurnal variation trend of the SIF spectra could be reasonably reconstructed by F-SFM, and the retrieved SIF values at the O\({}_{2}\)-A and O\({}_{2}\)-B bands were consistent with those retrieved by 3 FLD from data with a high SR (0.3 nm) and SNR (1000). Therefore, the F-SFM method can provide full-spectrum SIF information with high accuracy even at relatively low SRs and SNRs, and shows promise for use in applications involving the SIF shape information.  
Keywords:Solar-Induced chlorophyll Fluorescence (SIF); full-spectrum; Principal Components Analysis (PCA); Spectral Fitting Method (SFM); Fraunhofer Line Discrimination (FLD); hyperspectral remote sensing
# A Walkway from Crayfish to Oligochitosan
## 1 Introduction
While unbound edible proteins are used for human and animal food production, the presence of the remaining conjugated proteins can pose a threat to human health and may also have a bad impact on the stability of a chitin/chitosan based product when chitin and chitosan are used as food additives or as components of pharmaceutical and cosmetic compositions [24]. Demineralization is required since crustacean shells are mineralized and contain (mainly) calcium carbonate and foreign metals in the form of carbonates and phosphates [25]. Although the process of chitin separation from crustacean shell wastes includes a demineralization stage, which eliminates calcium carbonate and most of residual heavy metals from chitin [26], it has been found that commercial chitosan batches can contain an excessive concentration of heavy metals [27; 28], which can reduce the quality and shelf-life of chitosan containing products [5]. From here, the main problem arising when preparing pharmaceutical-grade oligochitosan hydrochloride is the presence of harmful residues in the chitosan used for the preparation. Unfortunately, the protocols published so far have not touched the problem of proteins and heavy metals remaining in chitosan separated from crayfish and other crustaceans, and have been focused mainly on the physicochemical properties of chitosan (solubility, solution viscosity, molecular weight, degree of deacetylation) and the presence of ash and residual moisture [13; 15; 17; 29; 30; 31; 32]. As to the hydrochloric acid salts of chitosan and its derivatives, there have been two protocols published so far on the preparation of chitosan hydrochloride [33; 34] and two protocols devoted to the hydrolysis of commercial chitosan and the preparation of oligochitosan hydrochloride with characteristics corresponding to the requirements of the _European Pharmacopoeia_ for chitosan hydrochloride [28; 35; 36]. In the present study, we describe the extraction of chitin, preparation, and analysis of chitosan and oligochitosan hydrochloride focusing mainly on the purity of chitosan and oligochitosan hydrochloride from residual heavy metals and proteins. The protocol included: (a) preparation of crude shell waste; (b) extraction of proteins from crude shell waste; (c) demineralization and decolorization of the waste and preparation of chitin; (d) deacetylation of chitin; and (e) preparation of oligochitosan hydrochloride.
# Mobile Robot Path Planning with a Non-Dominated Sorting Genetic Algorithm
## 4 NSGA-II for the Multi-Objective Path Planning Problem  
NSGA-II is one of the most popular multi-objective genetic algorithms [40]. It has the advantage of fast running speed and good convergence of solutions. Moreover, it has become the benchmark for evaluating numerous optimization algorithms. NSGA-II is the second generation non-dominated sorting genetic algorithm, and its improvements mainly include three aspects:  
* A fast non-dominated sorting algorithm is designed, which stratifies the population according to the non-domination level of the individuals. Individuals within the same layer are non-dominated.
* The crowding distance of individuals in the same layer is calculated. Then, the individuals with higher values are preferentially selected, so that the same layer individuals can be more evenly distributed in the objective space to preserve the population diversity.
* The elite strategy combines the parental population with the offspring population. First, infeasible solutions within the collection are eliminated. Secondly, the remaining individuals are ranked according to the non-dominated sorting algorithm. From the low to high levels, the individuals in each layer are placed sequentially in the new population until the number of individuals exceeds the capacity of the population. Finally, according to the crowding distance metric, the individuals in the specific layer are placed into the new population in descending order until the population is full. The elite strategy preserves all the good individuals in the parental and offspring populations, thereby improving the accuracy and robustness of the optimization results.  
Due to the above characteristics, NSGA-II has strong optimization capabilities. Therefore, the path planning is solved with NSGA-II. However, individuals formed by the traditional operators are generally not feasible paths. It is not enough to rely solely on traditional operators. This requires a larger population capacity and more evolutionary algebra. In the work, several practical evolutionary operators are introduced to solve this problem. These evolutionary operators are more purposeful to reduce the length and improve the smoothness and safety. Next, NSGA-II and all the operators are described in detail.