esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of the Applications of Text Mining for the Food Domain
## 6 Text Mining in Recipe Recommendation and Personalized Nutrition  
### Analysis of Recipe Data  
The importance of recipe data lies in the use of text mining methods, which help to analyze huge food datasets and extract structured insights from them to support personalized recipe suggestions and nutritional strategies. Using NLP, essential details such as ingredients, measurements, and procedures are extracted from unstructured recipe narratives and transformed into organized datasets. These foundational data facilitate the exploration of ingredient relationships, assessment of substitutes, and understanding the synergistic properties of ingredient combinations [86]. This analytical capability enables the customization of recipe recommendations based on users' gastronomic preferences and dietary restrictions, allowing for transformations like turning a calorie-dense Western dish into a nutrition-conscious, Asian-inspired variant [87].  
Additionally, the analysis of recipe content provides insights into nutritional profiles, allowing assessments of caloric content, macronutrient distributions, and other nutritional parameters [88]. Machine learning applications, such as predicting nutritional values based on textual descriptors, are invaluable for individuals managing specific health conditions, optimizing athletic performance, or pursuing wellness and fitness goals [89].  
On the other hand, the meticulous examination of consumer feedback on recipes aids in creating precise models of user food preferences. Applying association rule mining algorithms to historical food interactions helps deduce individualized taste preferences, while scrutinizing online food platforms provides a macroscopic view of dietary preferences and emerging food trends [90; 91; 92].  
As the global demand for animal-based proteins puts a strain on the environment, the transition towards plant-centric diets becomes crucial for sustainability. Researchers have explored online recipe databases to understand dietary customs, offering insights into regional gastronomic heritage [93]. Additionally, methodologies like block-based linked data generation facilitate the communal exchange and discovery of recipe information, transcending the food domain for broader applicability [94].  
To sum up, text mining is a revolutionary method of evaluating large recipe collections and is essential to customized nutrition services and intelligent meal recommendation systems. The field of food and nutritional services is projected to witness a substantial growth in the use of text mining due to the increased emphasis on health consciousness and the digitization of the food industry.
## 4 Results and Discussion
Therefore, the variation in parameter \(a\) only enables hiding the PBGs and controlling the intensity of the resonance peaks. Figure 4: Reflectance spectra of quasi-periodic Fibonacci multilayered stacks as function of frequency and the incidence angle (\(\theta\)) and for different values of parameter \(m\) of the plasma refractive index (\(m\) set to be 10, 20 and 50; \(l=10\); and \(dp=10\)). As in other previously published scientific works concerning the management of the position, number and width of PBGs using the Fibonacci photonic structure, this current work adds another property with the use of the plasma material. In Figures 4 and 5, and in comparison with previously published papers, the advantage of using the plasma material in a Fibonacci structure instead of ordinary semiconductor [32] and superconductor [33] materials is clear, enabling the possibility of tuning the intensity of the resonance peaks between PBGs, as well as the possibility of hiding (or showing) the PBGs. These electromagnetic behaviors of our structures have the potential to withstand high-power microwaves and pave the way for their use in filtering applications for communication and as micro-cavities to localize waves and to develop intense LASER equipment [34].
## 3 Method
### SC Attention Mechanism  
The Spatial and Channel (SC) module, as shown in Figure 5, is applied within the SCC2f module, composed sequentially of the SAU and the CAU. In the bottleneck residual block, the SAU operation begins by extracting spatially refined features from the intermediate input, and then the CAU operation refines the channel-wise features. This process  
Figure 4: (a) YOLOv8 network structure. (b) RPS-YOLO network structure.  
utilizes both spatial- and channel-wise detailed information to recover feature information and enhance the network's expressive capacity.
# Explainable Machine Learning for Mapping Rainfall-Induced Landslide Thresholds in Italy
## 2 Study Area, Data, and Methodology
### Methods
#### 2.3.5 Sharp  
Shapley Additive Explanations (SHAP) is a widely adopted technique for interpreting machine learning models, derived from Shapley values in game theory [52]. Shapley values quantify the contribution of each feature to model predictions by assessing its marginal impact across all possible subsets of features. SHAP provides a unified framework for measuring feature importance by calculating the weighted average of each feature's marginal contributions to the model output across all feature subsets, thereby ensuring both consistency and fairness in model interpretation [53].  
For a given model output \(f(x)\), the SHAP value is calculated as follows:  
\[\phi_{i}(f)=\sum_{S\subseteq N\smallsetminus\{i\}}\frac{|S|!(|N|-|S|-1)!}{|N|!}[f( S\cup\{i\})-f(S)] \tag{2}\]  
where \(\phi_{i}(f)\) represents the shapley value of feature \(x_{i}\), \(f(S)\) is the model output for the feature subset \(S\), \(S\) is a subset of the feature set, and \(|S|\) is the size of subset \(S\). \(|N|\) denotes the total number of features.  
Using Formula (1), SHAP can accurately quantify the independent contribution of each feature to the model prediction, thereby ensuring both consistency and fairness in model interpretability.  
In this study, SHAP was explicitly applied to the best-performing trained model to interpret the contribution of each predictor variable to landslide prediction. The SHAP  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Macro-** \\ **Region** \\ \end{tabular} } & \multirow{2}{*}{**Region**} & \multicolumn{2}{c}{**Training and Validation Set**} & \multicolumn{2}{c}{**Test Set**} \\ \cline{3-6}  & & **Positive** & **Negative** & **Positive** & **Negative** \\ \hline \multirow{2}{*}{Northwest} & Aosta Valley, Piedmont, & & & & \\  & Liguria, Lombardy & 1501 & 15,010 & 375 & 3750 \\ \hline Center & Tuscany, Umbria, Marche, Lazio & 1406 & 14,060 & 352 & 3520 \\ \hline \multirow{2}{*}{South} & Abruzzo, Molise, Campania, & & & & \\  & Apulia (Puglia), Basilicata, Calabria & 694 & 6940 & 173 & 1730 \\ \hline Islands & Sicily, Sardinia & 471 & 4710 & 117 & 1170 \\ \hline \multirow{2}{*}{Northeast} & Trentino-Alto Adige/South Tyrol, & & & & \\  & Veneto, Friuli Venezia Giulia, & 340 & 3400 & 85 & 850 \\ \multicolumn{2}{c}{Emilia-Romagna} & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Distribution of positive and negative samples across macro-regions in Italy.  
analysis was implemented using the Python's SHAP library and applied to the independent test set comprising 20% of the total dataset. This approach ensures unbiased interpretation by evaluating feature contributions on data that were not used during model training or validation.
## 3 Literature Review and Related Work
### Positioning Implementing Time-Series of Spatially Distributed Fingerprints
As discussed in the previous subsection, these time-series of fingerprints are interpreted as occurring along one dimension, even when the path described can be in two dimensions. As far as we observe in the works surveyed, if the path is described in 2D, the information about the displacements in 2D does not remain associated with the fingerprints collected, and it is not passed along or preserved to further contribute as side information in 2D. Thus, the time-series only relates the side information from contiguous samples, which is interpreted as side information in one dimension. The last column in Table 4 indicates the number of side information dimensions considered in the works surveyed. We found that in all of the works surveyed, the number of side information dimensions was one; thus, all are labeled as one-dimensional (1D). A methodology for generating a time-series of fingerprints with spatial side information in more than one dimension is possible, although one was not found in our literature review for this category. One option would be to combine multiple scans along multiple paths to associate adjacent positions in 2D with the target position to be estimated. Relating multiple paths would require the input of additional information from another source, such as from an inertial sensor, to be able to relate the relative displacement between the paths. An example of such an approach is discussed in [199] (details given in Section 3.3). Another option would be to use two or more sensors or receivers such that these were not aligned with the direction of movement along the path. Such arrangements were not found in the literature surveyed. The proposal to use an arrangement of sensing elements as we propose with the 2D sensor array was not found in the literature either.
# PET/CT Radiomics in Lung Cancer: An Overview
## 2 Methodology
### Segmentation  
Segmentation (delineation) is the process whereby the part of the scan that is relevant for the analysis (Region of Interest--ROI) is separated from the background. Note that sometimes the literature makes a distinction between a two-dimensional ROI and a three-dimensional one, the latter being  
Figure 1: The standard workflow in radiomics.  
in some cases indicated as Volume of Interest (VOI). For the sake of simplicity, we use the acronym ROI to indicate both a two- and a three-dimensional region. The output of this step is a binary (boolean) matrix the same size of the raw data, where 'true' (1) indicates that the voxel belongs to the ROI, 'false' (0), otherwise. Segmentation is a time-consuming and anything but easy step, for many lesions will show unclear and ill-defined borders. The process is also complicated by the presence of areas such as necrosis, atelectasis, and/or inflammation, whose role in the radiomics work-flow is not fully understood yet. Although a number of automated (e.g., adaptive thresholding [25], convolutional networks) and semi-automated (e.g., level-set [26], region growing [27]) methods have been proposed, manual delineation is still regarded by many as the ground truth [6].
# A3C-R: A QoS-Oriented Energy-Saving Routing Algorithm for Software-Defined Networks
## 1 Introduction
* A routing optimization goal that takes into account both QoS and energy saving is designed, and the A3C-R intelligent routing algorithm training framework is built based on A3C's asynchronous training and advantage functions. And we design the state, action, and reward of the intelligent routing and network environment interaction process, carry out multi-agent model training and global parameter sharing, and improve algorithm efficiency and convergence. * A simulation experiment environment was built, and we verified the convergence of the A3C-R routing algorithm, as well as the QoS and energy-saving optimization effects of flows under different network traffic loads.
# Anomaly Detection in Network Traffic via Cross-Domain Federated Graph Representation Learning
## 5 Experiments and Results
### Experimental Set-Up
#### 5.1.1 Dataset and Parameter Settings
Specifically, we searched over the following ranges:  
* Dimension of degree-based structure embedding \(k_{1}\): [8; 16; 32; 64; 128];
* Dimension of random walk-based structure embedding \(k_{2}\): [8; 16; 32; 64; 128];
* Learning rate: \([5\times 10^{-3},1\times 10^{-3},5\times 10^{-4},1\times 10^{-4}]\);
* Weight decay: \([7\times 10^{-4},5\times 10^{-4},3\times 10^{-4},1\times 10^{-4}]\). The final selections (\(k_{1}=k_{2}=32\); learning rate = \(1\times 10^{-4}\); weight decay = \(5\times 10^{-4}\)) were based on the best validation performance in terms of accuracy and stability. All methods were implemented using PyTorch 2.0.1, and all experiments were performed on a single computer server with NVIDIA GeForce RTX 4090 (24 GB of memory) GPUs, Intel(R) Xeon(R) W-2133 CPUs @ 3.60 GHz, and 32 GB of RAM.
### Validation of Health Data  
It has been observed that it is not standard practice to validate data following a systematic process prior to using electronic health record (EHR) data for research purposes [17]. Given the potential of research results to influence clinical practice, having the means of validating data or at least of gauging their quality is a critical prerequisite, especially where data has to be linked or compared across multiple sources. Formal validation approaches based on shapes constraint language (SHACL), description logic, or predicate logic with machine learning have been proposed for validation purposes relating to EHRs, knowledge-acquisition systems, and clinical-support systems. [PERSON] and [PERSON] [18] used SHACL ontology design patterns to validate EHR information models and through this approach were able to trap modelling and terminology-binding errors in CIMI (Clinical Information Modelling Initiative) models. [PERSON] et al. [19] derived a predictive model using automated knowledge-acquisition techniques from patient records and used it to validate existing guidelines and create a refined clinical knowledge model from which they generated rules for treatment plans taking the oral cavity as an example. [PERSON] et al. [20] built on the earlier work of [19] and incorporated Z notation to provide inferenceable mathematical models and formally verify the knowledge-acquisition model for consistency. [PERSON] et al. [21] applied machine-learning techniques for correcting staging values in EHRs and deriving rules for treatment plans based on decision paths drawn from the acquired knowledge base. [PERSON] et al. [22] used DL for the basis of a decision-aid tool for staging of mammary gland and colorectal tumours (serving as a blueprint for to other types of tumour) to help overcome the high deviation rates found in tumour-staging practice.  
The validation of CR data sets contributing to the European CR common data set is in contrast a more well-defined task due firstly to the structured nature of the data; and secondly, to the already harmonised nature of the variables comprising the data set. The validation then proceeds against a set of rules that have previously been agreed either at international level (such as the rules for multiple primary tumours) or at pan-European level. The work cited in the previous paragraph would be particularly pertinent to the up-stream processes relating to the primary data providers and to the derivation and verification of the rule base itself. At the level of European harmonisation, the rules are the touchstone against which all the data sets are validated. The task is therefore one of modelling the rules reliably in software and verifying that the software applies the rules correctly using a comprehensive set of pre-defined test cases.
# Design and Assessment of an Austenitic Stainless Alloy for Laser Powder Bed Additive Manufacturing
## 2 Materials and Methods
### Criteria for Alloy Design
#### 2.1.1 Phase Stability: Targeting an Austenitic Structure
Furthermore, to encourage the search for alloys that preferably reside within the austenitic region, the distances of the alloy composition from both the ferrite and martensite boundaries (illustrated as d\({}_{\text{m}}\) and d\({}_{\text{f}}\) from a random alloy X in Figure 1) is defined. An objective for alloy design is then set to maximize the smallest of the two distances. In conclusion, this criterion regarding phase stability combines a constraint on composition to ensure the presence of austenite and an objective to maximize its stability. To further validate the austenitic nature of the alloy, the output prediction of the Scheil model is also considered. The simulation is conducted using Thermo-Calc 2021b with the TCFE10 database and TC-Python API. Although this model is primarily used to predict hot cracking, as discussed in the following section, it also provides insight into the phase fractions after solidification. To address the risk of the excessive formation of undesirable brittle phases and intermetallics, which could potentially affect mechanical properties, a second constraint has been implemented. This constraint ensures that \(\delta\)-ferrite and austenite together constitute at least 99% of the phases after solidification, effectively rejecting alloy compositions containing more than 1% of other phases.
###### Abstract  
In this article we present the main results obtained in the ARTEMIS-JU WSN-DPCM project between October 2011 and September 2015. The first objective of the project was the development of an integrated toolset for Wireless sensor networks (WSN) application planning, development, commissioning and maintenance, which aims to support application domain experts, with limited WSN expertise, to efficiently develop WSN applications from planning to lifetime maintenance. The toolset is made of three main tools: one for planning, one for application development and simulation (which can include hardware nodes), and one for network commissioning and lifetime maintenance. The tools are integrated in a single platform which promotes software reuse by automatically selecting suitable library components for application synthesis and the abstraction of the underlying architecture through the use of a middleware layer. The second objective of the project was to test the effectiveness of the toolset for the development of two case studies in different domains, one for detecting the occupancy state of parking lots and one for monitoring air concentration of harmful gasses near an industrial site.  
keywords: wireless sensor networks; simulation; Internet of Things; design flow; assisted deployment
## 4 Proposed Resource Allocation Scheme
### Optimal Problem Formulation
#### 4.2.2 Computational Complexity of Resource Allocation Scheme  
The Quality-of-Experience (QoE) and Profit-aware Resource Allocation or assignment of requests to the BBUs as formulated in Equation (6) is a multi-objective non-linear programming (MONLP) problem. Here the size of service request or data to be processed, \(d_{r}\), as well as the processing capacity of BBU, \(\eta_{b}\), vary from one to the other. Any BBU can be allocated to perform any request and processing time, and profit gain may vary depending on the request-BBU assignment. It is required to process all the selected requests by assigning exactly one request to each available BBU in such a way that the total profit of the assignment is maximized and the time delay to get first response is minimized. There exists a nonlinear relationship among the considered variables in Equation (6) and its constraints. Therefore, the resource allocation problem can be identified as a combinatorial optimization problem [21] and modeled as a generalized assignment problem (GAP) [22], which is a proved NP-Hard problem [23]. Thus, our optimal solution is proven to be an NP-Hard one.
## 5 Simulation Result and Discussion
### Single Classification Results  
Figures 16 and 17 show the specificity and sensitivity of the CNN benchmark architectures, respectively. Based on Figure 16, ShuffleNet (with all learning rate values) and DenseNet (with a learning rate of \(5\times 10^{-4}\)) achieved a specificity of 1.0. ShuffleNet showed remarkable performance in classifying the positive images with specificity of 1.0 at all the learning rate values, whereas DenseNet reached specificity of 1.0 only at the learning rate of \(5\times 10^{-4}\). ResNet and EfficientNet showed lower specificity values compared to those of ShuffleNet and DenseNet. XceptionNet and MobileNet resulted in the poorest performance at all learning rates. The DFU image classification depends on thermal color distribution from the thermal images. As ShuffleNet works by shuffling the channel information of each colored layer, it keeps the information flow during the training process in the network. Other CNN models might lose information during the training and weighting process due to separable and unrelated channels from each layer.
# RIS-Assisted Cooperative Time-Division Multiple Access
## 1 Introduction
### Notations  
For the notations, we use uppercase boldface for matrices and lowercase boldface for vectors. Symbols \(\mathbf{A}^{H}\) indicates the conjugate transpose of matrix \(\mathbf{A}\). Similarly, \(\mathbf{A}^{T}\) and \(\mathbf{A}^{-1}\) indicate the transpose and inverse (or pseudoinverse) of matrix \(\mathbf{A}\), respectively. Symbol \(|a|\) denotes the absolute value of a complex number \(a\), and \(\|\mathbf{a}\|\) indicates \(\ell_{2}\)- norms of vector \(\mathbf{a}\).
## 2 Materials and Methods  
### Sample Collection and DNA Isolation  
Seven (07) autosomal recessive KC families (Figure 1) and one hundred (100) sporadic KC cases were recruited locally from hospitals in Islamabad, Pakistan. Blood sampling of the Keratoconus patients was performed irrespective of age, sex or occupation (males = 64, females = 36). The average age of onset of KC in the current cohort was 20 years. Patients with a positive family history of KC and those who experienced eye rubbing due to allergies, together with a family history of KC, were recruited into the study. Exclusion criteria from the study consisted of eye trauma and other corneal pathologies in patients where keratoconus was secondary to another primary disease, infection or those who had excessive eye rubbing.  
All the recruited KC patients underwent corneal examination. This comprised an examination to determine the average Orb scan measurements, which evaluates the curvature of both anterior and posterior corneal layer. In the KC patients, it was determined to be 438.6 \(\pm\) 47.7 \(\upmu\)m (normal range-550 \(\upmu\)m), which suggested a thinning of the cornea. Based on the measurement of corneal thinning, corneal pachymetry showed that the patients had an average thickness of 462.5 \(\upmu\)m \(\pm\) 8.0 (normal range-554.9 \(\upmu\)m \(\pm\) 7.4). The curvature of the anterior surface was measured using Keratometry, and the patients had a curvature of 52.5 \(\pm\) 8.8 D (normal range 7.2 \(\pm\) 0.8 D). Age, sex and occupation matched 100 healthy control individuals (males = 62, females = 38) who were also randomly recruited into the study as control subjects for the KC cohort, and these controls were naive for any eye disease or inherited disease and had no major health issues. Once all the legal requirements and written consent had been obtained, blood samples were taken from all KC patients and control individuals. The present study complied with the Helsinki declaration, while ethical and scientific approval was obtained from the Ethics Committee/Institutional Review Board Department of Biosciences, COMSATS University Islamabad, Pakistan.  
Genomic DNA was extracted from the plasma lymphocytes of the patient using the salting-out method as described previously [20]. Briefly, DNA extraction consisted of lysis of red blood cells (RBC) with erythrocyte lysis buffer (ELB) followed by the lysis of white blood cells (WBC) with Tris-Natrium chloride-ethylenediamine tetra acetate buffer (TNE), SDS and Pronase E. These samples were incubated overnight at room temperature, and DNA was salted out using NaCl salt and ethanol solution, which later then precipitated and re-suspended in TE buffer. This DNA was stored at \(-20\)\({}^{\circ}\)C for further study.