esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Conclusions
It is able to account for sharp shading artifacts by applying the inhomogeneous diffusion to the spatial nonuniformity restoration field to estimate it. The proposed methodology is also shown to be robust to the lower tissue quality of cancer patients data and the high-intensity distortions that are present in most images. The method also restores images effectively despite the more extensive and elongated whole-body anatomy compared to that of more traditional images of individual organs such as the brain which are typically processed for intensity nonuniformity restoration. To the authors' knowledge, the proposed method can restore, for the first time, for intensity nonuniformities suffering from discontinuities in the context of MRI image processing. This method contributes to the restoration of datasets from whole-body imaging, which is an important and developing topic for several clinical applications. Conceptualization, S.H. and I.E.P. ; Data curation, L.D.R. and I.E.P. ; Methodology, S.H. and L.F.; Project administration, A.M.; Resources, L.D.R. and I.E.P. ; Supervision, A.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data were analyzed retrospectively and fully anonymized in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its amendments, the European Regulation 536/2014 and its latest addendum ICH GCP E6(R2)/2017, as well as with the guidelines of the Institutional Review Board (IRB) for clinical studies of the University of Jena (IRB number 2019-1452-Daten). The data presented in this study are available on request from the corresponding author and the authors that were responsible for data curation. The data are not publicly available due to privacy and ethical reasons. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} PSF & Point Spread Function \\ RF & Radio-Frequency \\ IRB & Institutional Review Board \\ \(T_{1}\)w & \(T_{1}\)-weighted \\ \(T_{2}\)w & \(T_{2}\)-weighted \\ WB-MRI & Whole-Body Magnetic Resonance Imaging \\ RHS & Right Hand Side \\ ICH GCP & International Conference on Harmonization--Good Clinical Practice \\ TSE & Turbo Spin Echo \\ FFE & Fast Field Echo \\ STIR & Short Tau Inversion Recovery \\ yo. & Years old \\ MDL & Minimum Description Length \\ MAP & Maximum A Posteriori \\ \end{tabular}
These studies show that HOC is a suitable material for SSF applications and the type of the microorganism in SSF is one of the important factors on the characteristics of the fermented product. When the fermentation studies on HOC were reviewed, no study was found to increase the bioactive component content of HOC using _Aberggillus oryzae_. _A. oryzae_ is a mold that has produced fermented products such as koji, sake, shoyu, and miso in the Far East since ancient times [49; 50]. It is in GRAS (generally accepted as safe) practices and is in the MFC (microbial food cultures) class. Therefore, there is no deficiency in its consumption with the fermented product after fermentation. This also eliminates the decomposition of microorganisms in the downstream processes [51]. As a non-pathogenic microorganism, it does not produce aflatoxin [49]. Many hydrolytic enzymes, such as \(\alpha\)-amylase, \(\beta\)-glucosidase, protease, and lipase, are secreted by _A. oryzae_ during fermentation [52; 53; 54]. Studies have shown that peptides with various bioactive properties can be produced from different protein sources after hydrolysis by the proteases of _A. oryzae_[55; 56; 57; 26; 27; 28]. These studies clearly demonstrate that _A. oryzae_ hydrolyzes proteins with high proteolytic activity analysis, which was one of the motivations for the current study. Functional properties provide insight into the usability of cereal-based flows, protein extracts, and similar carbohydrate-protein-based powder blends in industrial applications. For this reason, many studies on plant proteins or oilseed cakes have investigated properties such as oil and water absorption capacity, emulsifying activity, emulsion stability, foaming activity, and foam stability [58; 59; 60; 61; 2]. Water absorption/retention influences the texture, juiciness, and taste of food formulations, especially impacting the shelf-life of bakery products [59]. A high water absorption capacity (WAC) allows bakers to incorporate more water into doughs, enhancing handling characteristics and maintaining bread freshness. The improved ability of the meal to absorb and retain water and oil can enhance structural binding, flavor retention, mouthfeel, and reduce moisture and fat losses in food products [62]. The oil absorption capacity (OAC) relies on the physical entrapment of the oil molecules by a capillary-attraction process of the hydrophobic molecules such as proteins [2; 62]. Therefore, in the current study, we aimed to produce fermented hazelnut oil cake with increased bioactivity so that the optimization of the SSF process conditions by _Aberggillus oryzae_ using Box-Behnken design and response surface methodology was done and the FHOC was characterized to reveal its potential to be used as bioactive and functional food ingredient.
## 1 Introduction  
Since their discovery in 2004 [1], small carbon-based nanoparticles (NPs) or as-called carbon dots (CDs) have attracted significant interest because of their bright fluorescence [2, 3] and possible applications in photonics [2, 4, 5, 6, 7], sensing [8, 9, 10, 11, 12], photocatalysis [13, 14, 15, 16], and biomedical treatments [7, 17]. While CDs are usually associated with very small NPs with sizes from 1 to 10 nm [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], the mean sizes of larger carbon NPs (CNPs) can be above 10 nm [10, 11, 12]. While such carbon-based NPs can be prepared by using different bottom-up and top-downapproaches [1; 6; 7], the bottom-up method of the synthesis via thermal decomposition of carbon-containing precursors in microwave [1; 9] and hydrothermal [10; 11; 12] reactors are most convenient, flexible, and scalable.  
Both CDs and CNPs exhibit many advantageous properties such as high photoluminescence (PL) quantum yield, photostability, and spectral tunability [5; 6; 7; 10]. These features, together with low toxicity, make CDs and CNPs highly attractive for both the bioimaging application [4; 6; 7; 10] and therapies, e.g., photo- and radiofrequency hyperthermia [7; 17].  
Among many potential bioimaging applications of CNPs, their use as multimodal fluorescence imaging probes and simultaneously as contrast agents in magnetic resonance imaging (MRI) has gained considerable attention [18; 19; 20; 21; 22; 23; 24]. It was found that gadolinium-doped CDs (Gd-CDs) with efficient PL could be simultaneously used as contrast agents (CAs) for MRI [18; 19]. Dual modality imaging by using Gd-CDs with efficient PL and MRI contrasting was demonstrated in vitro and in vivo [20; 21; 22]. Gadolinium and bismuth co-doped CDs were explored for multimodal imaging in fluorescence, computer X-ray tomography, and MRI modes [23]. Moreover, Gd-CDs were demonstrated to be a promising theranostic agent for fluorescence and MRI-guided cancer phototherapy [24].  
While facile syntheses of fluorescent Gd-doped CDs and CNPs by both MWS and HTS are successfully demonstrated, each technique will offer distinct advantages and challenges for further applications in optical bioimaging and MRI. Here, we present a comparative analysis of Gd-CNPs prepared by MWS and HTS methods to evaluate their performance as red fluorescent labels and efficient CAs in MRI. By elucidating the similarities and differences between the two synthesis methods, this study aims to provide valuable insights into the optimal fabrication approach for Gd-doped CDs and CNPs with enhanced imaging capabilities.
## 3 Sources of Stochastic Noises
### Artificial Neural Network (ANN)  
An ANN-NLE based on the multilayer perceptron (MLP) has been implemented in [21], MLP-ANNs form a complex map with non-linear decision boundaries between input and output spaces, helping in inverting the effects of non-linear distortion. ANN is an emerging technology applied in contemporary wireless communications for the reduction of OFDM-based non-linear distortion in power amplifiers. ANN schematic diagram for m-QAM CO-OFDM is shown in Figure 3b, which is placed directly after the fast Fourier transform (FFT) at the digital part of the CO-OFDM receiver (see Figure 3a). In summary, it comprises of p sub-neural networks considering a single hidden-layer. Each sub-network is being associated to each subcarrier \(k\), and where \(s(k)\) is the training vector. The received symbols, \(i\), for each subcarrier \(x(k)\) are processed by the ANN neurons which are subsequently multiplied with a weight value, \(w_{k,i}\), for each subcarrier where the outputs of all subcarriers are summed. In the training stage, the minimum mean-square error (MMSE) algorithm determines the error signal and updates the weights, which are iteratively updated until the desired error value is reached, thus indicating the optimal match between the sub-network output and the transmitted CO-OFDM symbols. The error signal is given as \(E(k)=s(k)-\delta(k)\), where \(\delta(k)\) is calculated in terms of a non-linear activation function (NAF), \(\varphi_{k,i}\), that is given by \(\hat{s}(k)=\sum\limits_{i=1}^{M}w_{k,i}\varphi_{k,i}(s(k))\). The chosen NAF is a differential sigmoid function and is a \"split\" complex NAF, where two conventional real-valued functions process the I-Q components in contrast to our proposed approach which processes the complex data simultaneously, thus accounting for real and imaginary data cross-information. The number of ANN neurons in every sub-neural network is equal to the number of points of the constellation. The 2D ANN is based on the [PERSON]'s resilient-back propagation (RR-BP) algorithm and performs an approximation to the global minimization achieved by the steepest descent [20]. The training function updates the weights and bias values according to RR-BP, which minimizes the difference between the ANN output and the desired output by splitting the complex OFDM data in two real-valued data collections. The transfer functions for the hidden layer of the ANN are differentiable and similar to the hyperbolic tangent function. For the output layer, the linear function \"purelin\" was employed [20]. The MMSE in Figure 3a represents the subsystem that implements RR-BP to find the weights that minimize the error vector \(E(n)=\parallel S(n)-\hat{S}(n)\parallel^{2}\), where \(S(n)\) and \(\hat{S}(n)\) are the desired and calculated output vectors, respectively. The weights are updated according to the steps described in Figure 4 by applying the gradient descent on the cost function \(E(n)\) to reach a minimum. Finally, at the end-output of Figure 3b we introduced slack variables that allow some misclassified symbols but penalizes them.
## 2 System Model  
Figure 1 shows an exemplary scenario for the single-cell multicast transmission scheme with a supplementary multicast channel, where six users are placed randomly in a circular cell with a radius of \(R\). One BS under consideration, indexed 0, is located at the origin of the \(\mathbb{R}^{2}\) domain and it services the users in a multicast group, \(\Omega\), with a fixed target multicast rate, \(R_{T}\). Other-cell interference is accounted for by applying a fluid model where neighboring base stations (BSs) are distributed uniformly with a density of \(\lambda\) [\(\frac{\text{BSs}}{\text{km}^{2}}\)] in a region distant from BS 0 by at least \(D_{min}\)[15]. We also assume that all the BSs use constant transmission power over the entire bandwidth and we let \(p_{b}\) be the transmission power density of BS \(b\). We also consider a single-input single-output system. We assume all the wireless links experience path loss, Rayleigh fading and additive white Gaussian noise (AWGN). Then, the signal-to-interference-plus-noise ratio (SINR) per hertz of user \(j\), \(\gamma_{j}\) is given as:  
\[\gamma_{j}=\frac{p_{0}g_{j,0}}{\sum_{b\
eq 0}p_{b}g_{j,b}+\sigma_{0}^{2}}, \tag{1}\]  
where \(g_{j,b}\) is the channel gain between user \(j\) and BS \(b\), which accounts for path loss and fading and \(\sigma_{0}^{2}\) is the noise power density for the users of BS 0.
# Using Feature Selection with Machine Learning for Generation of Insurance Insights
## 2 Related Works
### Machine Learning Applications in Insurance Sector
As discussed by [PERSON] et al. [6], most industry approaches to this problem understand the problem as a classification problem andapply supervised learning models in an attempt to classify claims as fraudulent or not. [PERSON] et al. [28] provide good examples of this approach. These approaches face two related problems; first, there is no natural fraud label in insurance claims data, so data must be labelled by hand and this process is subject to error and inconsistency. Second, fraud is a major problem for insurance companies, but the vast majority of insurance claims are not fraudulent, so datasets suffer from a class imbalance. [PERSON] et al. [29] provide one approach to the problem of class imbalance. An alternative approach is not to aim to improve the existing machine learning approach incrementally but to look to text mining, deep learning, and unsupervised learning. [PERSON] et al. [30] combine text mining with deep learning to propose a framework for the detection of fraudulent motor insurance claims. They first use Latent Dirichlet Allocation on the description of accidents contained in claims document to extract features and then combine these with traditional features in a deep neural network model. They report that their results outperform traditional supervised learning approaches. [PERSON] et al. [6] utilise a deep neural network in combination with unsupervised techniques. We note, however, the need for very large datasets and the production of opaque black-box models with the use of deep learning. Another area where there is scope for machine learning is churn prediction or the propensity to buy an insurance product. Again the techniques utilised to tackle this issue in insurance can readily draw from other industries, including financial services. The literature in the field specifically related to insurance, especially if we exclude medical insurance in the USA, is relatively sparse. An overview of how traditional machine learning techniques can be applied in this area is provided in [7,31]. How neural networks can supplement a tree model is shown in [8]. Although they are working with supermarket data, we would expect similar outcomes with insurance data.
# Light-Induced Advanced Oxidation Processes as PFAS Remediation Methods: A Review
## 4 Direct Photolysis  
Given that PFAS does not absorb visible light and absorption is generally limited to the UVC region [130], the use of solar irradiation is not possible and artificial sources are required. In fact, the dissociation energy for C-F bonds can be up to 544 kJ/mol [102], corresponding to photons of 220 nm. UV light sources of wavelength < 220 nm and high power are somewhat less common than their UV-A or B counterparts. They have generally lower lifetime, require more specialized equipment and safety precautions, and as such the associated costs are higher. This is reflected by the scarcity of reports focusing on this approach present in literature. Nevertheless, important results can be found either in dedicated reports or extracted from photocatalysis papers where they appear as baseline or control experiments. Some results are collected in the Table 2 below:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Irradiation** & **Power** & **Substrates** & **Reaction Time** & **Degradation/Defluorination** & **Ref.** \\
**Wavelength (nm)** & **(Electrical, W)** & **Substrates** & **Reaction Time** & **Yield (\%)** & **Ref.** \\ \hline
254 & 200 & PFOA 1.35 mM & 72 h & 89/33 & [116] \\
254 & 15 & PFOA 0.025 mM & 6 h & 13/2 & [131] \\ \hline
185 (prevalent) & 20 & PFOA 0.12–2.42 \(\mu\)M & 3 h & 87/21 & [132] \\
254 (prevalent) & 20 & PFOA 0.12–2.42 \(\mu\)M & 3 h & 31/0.5 & [132] \\ \hline
185 & 15 & PFOA 60 \(\mu\)M & 2 h & 62/17 & [133] \\  & & perfluoropropionic acid, & & & \\
220–460 & 200 & perfluorobutyric acid, & 24 h & 16–24/9–12 & [134] \\  & & perfluoropentanoic acid & & & \\
254 & 36 & PFOA 0.024 mM & 8 h & 20/9 & [135] \\
254 & 24 & PFOA 1 mg/L & 24 h & 21/9 & [136] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Direct photolysis of PFAS compounds in water.  
Although direct photolysis seems to be moderately effective for initiating degradation, the reported defluorination yields are quite low, indicating that complex mechanisms are taking place rather than simple photoinduced bond dissociation. Along with the problems related to equipment, the difficulties presented by the direct photolysis approach provide the motivation to investigate other methodologies, for example where light absorption is performed by a photocatalyst (as described in the previous section).
## 2 Related Works
### Feature Matching  
In visual localization algorithms, image feature matching plays a crucial role in enhancing localization accuracy. Feature matching algorithms are generally classified into two categories: detector-based and detector-free methods. Detector-based algorithms are traditional image matching techniques, with many studies showing good performance using hand-crafted local features. SIFT [30] and ORB [31] are the most successful examples of such hand-crafted features. These methods excel at establishing correspondences in complex environments, including uneven lighting and cluttered backgrounds, and typically offer fast processing speeds. However, they tend to produce sparse matches, which can negatively affect subsequent pose estimation. SuperPoint [32] was the first model trained on synthetic datasets to extract corner points, which was then generalized to real-world images. DISK [33] treats feature point extraction as a probability distribution problem and trains the network via reinforcement learning. D2-Net [34] employs a convolutional network that performs both feature extraction and description, yielding more robust descriptors from higher-level feature maps. Today, detector-based methods primarily rely on pre-trained detectors for feature extraction, followed by matching networks to establish correspondences. SuperGlue [35], a groundbreaking approach in this field, uses SuperPoint for feature extraction and integrates global information from within and across images through self-attention and cross-attention mechanisms in Transformers. This enhances the feature descriptors, which are then used to compute a score matrix. SuperGlue further applies the Optimal Transport (OT) algorithm to process the score matrix and generate a soft assignment matrix for matching. Despite their successes, detector-based methods have two key limitations. First, during the training of the matching network, the feature detector's parameters must be fixed, preventing the integration of feature detection, description, matching, and outlier rejection into a unified end-to-end trainable network. Second, significant viewpoint changes can lead to feature points not being consistently observed across images, resulting in performance degradation and matching failures.  
Detector-free methods match images directly without relying on detected keypoints, resulting in semi-dense or dense correspondences. LoFTR [36] was the first to use Transformer models for modeling long-range dependencies in detector-free matching. While these methods offer advanced matching capabilities, the dense transformation of the entire coarse feature map remains computationally expensive and inefficient. In contrast to other algorithms, we propose a feature matching method based on an integrated model, which integrates the strengths of both matching techniques while reducing computational overhead. This approach has demonstrated effective matching performance across various application scenarios.
# Sustainability in Bio-Based Edible Films, Coatings, and Packaging for Small Fruits
## 5 Edible Film and Coating Functionalities Important for the Small Fruit Industry
### Innovations in the Edible Film, Coating and Packaging Industry  
Due to innovation in this field, edible films and coating have gained new functionalities, like pathogen inhibition or food preservation characteristics, allowing the food industry to provide fresher, pleasanter, healthier, and high-quality products to consumers [2]. Nanotechnology can help improve the aspects of materials [43] and reduce the problems of degradation, low solubility or bioavailability, and undesirable taste of specific compounds. Indeed, nanostructuring of plant-based materials, a process involving incorporating nanoparticles or nanoscale structures into plant-based materials to improve mechanical, barrier, and antimicrobial properties, can be a significantly important line of research [224]. Another application of nanotechnology is the creation of nanocomposite edible films, where nanofillers, such as nanoparticles or nanoclays, are incorporated to modify mechanical strength, gas permeability, or functional characteristics [225].  
Nanoencapsulation can still be further developed to enhance compounds' stability, release, and delivery and to better link bioactive compounds with shell materials used in encapsulation. Indeed, research continues to evaluate different biopolymers to encapsulate ingredients, including polysaccharides, fats waxes, or proteins [226]. A novel development uses aerogels, a light, and highly porous material with a large surface area, mechanical rigidity, and low thermal conductivity, made of polysaccharides [227]. The importance of these aerogels arises from the fact that they can be easily incorporated with other components and are biodegradable, biocompatible, and edible. Another exciting research field is the 3D printing of plant-based edible films. It allows a customized design with exact control over the deposition of materials, enabling the creation of complex geometries and shapes [228]. However, it still presents several challenges, namely adjusting rheological properties and ensuring food safety and functionality, which are key to enhancing this technique.