esa-sceva/satcom-chunk-collection huggingface dataset:
## Results  
### Date Seeds Chemical Composition  
The chemical composition of three selected date seed varieties (moisture, fat, crude protein, ash, and carbohydrate content) is shown in Table 1. There were significant differences (\(p\leq\) 0.05) in moisture and fat content among the three date varieties of seeds. The highest values of moisture content (12.4%) and fat content (8.7) were observed in the Barhi seeds, while the Ruthana seeds showed the lowest values of moisture content and fat content, 6.6 and 6.3%, respectively. In contrast, the protein and ash contents showed no significant differences among the seed varieties, and ranged from 7.88 to 9.94% for protein and from 0.94 to 1.04% for ash. Similar to moisture and fat, carbohydrates showed significant (\(p\leq\) 0.05) differences among the date seed varieties, ranging from 70.0 to 76.44%. Ruthana seed showed the highest content whereas Barhi seed had the lowest carbohydrate content.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Samples** & **Moisture \%** & **Fat \%** & **Protein \%** & **Ash \%** & **Carbohydrates \%** \\ \hline Barhi & 12.4 \(\pm\) 0.05 a & 8.7 \(\pm\) 0.146 a & 7.9 \(\pm\) 0.785 a & 1.04 \(\pm\) 0.115 a & 70.0 \(\pm\) 0.624 c \\ Ruthana & 6.6 \(\pm\) 0.064 c & 6.3 \(\pm\) 0.155 c & 9.7 \(\pm\) 0.673 a & 0.97 \(\pm\) 0.040 a & 76.4 \(\pm\) 0.422 a \\ Qatarah & 7.6 \(\pm\) 0.075 b & 7.9 \(\pm\) 0.155 b & 9.9 \(\pm\) 1.258 a & 0.94 \(\pm\) 0.006 a & 73.6 \(\pm\) 1.181 b \\ \hline \end{tabular} Values are means (\(\pm\)SD) of triplicate samples. Values with the same letter are not significantly different (\(p\leq\) 0.05) as assessed by [PERSON]. Lower caps letters indicate significant differences among cultivars.  
\end{table}
Table 1: Chemical composition of date cultivars seeds.
# A Spectral Clustering Algorithm for Non-Linear Graph Embedding in Information Networks
## 4 Experiment
### Algorithm Comparison  
Table 4 presents the average running time of the \(SEG\_SC\) algorithm in comparison with similar deep neural network models such as Graphencoder, indicating that their running times are approximately equal. This result meets the purpose of sparse embedding representation time optimization.  
In terms of cluster performance, Table 5 summarizes the clustering results of seven datasets, indicating that \(SEG\_SC\) offers relatively higher ACC. Table 6 displays the comparison values of various algorithms on large-scale data, demonstrating stronger NMI performance advantages as the data scale increases.  
From the results shown in Tables 5 and 6, it can be seen that the model proposed in this paper exhibits superior performance compared to the other models. As shown in Table 5, \(SEG\_SC\) achieved higher ACC. This improvement was significant compared to the other algorithms, attributed to the optimization of graph similarity. In addition, as the graph uses a non-linear projection, it can be observed from Table 6 that in the various types of large-scale datasets, the NMI values tend to be optimal.  
The advantage of this model lies in using sparse similarity graphs to replace the original graph's similarity matrix and adopting non-linear encoding schemes instead of feature decomposition. During the final step in particular, the low-dimensional features inputted into K-means are complemented by spectral clustering. Furthermore, the non-linear encoding scheme reduces the dimensionality of the data.
# Identification of Epileptic EEG Signals Using Convolutional Neural Networks
## 1 Introduction
In this study, these two methodologies are combined in the body of CNN to identify seizure from EEG signals. CNN is a machine-learning method that is based on learning representation where the framework naturally learns and finds the features required for detection from the numerous layers processing input datasets [29]. Deep learning has officially demonstrated its capacity and has outperformed human reasoning in image and audio recognition problems [29; 30]. It has been utilized in numerous complex applications of machine learning, such as the diagnosis of Alzheimer's at an early stage [31], detection of chest diseases [32], concrete comprehensive strength estimation [33]. Additionally, numerous expansive technological companies, for example, Apple (Cupertino, CA, USA), Google (Mountain View, CA, USA), Baidu (Beijing, China), IBM (Armonk, NY, USA), Microsoft (Redmond, WA, USA), Netflix (Los Gatos, CA, USA) and Facebook (Menlo Park, CA, USA) have grasped and used deep learning in their studies [34; 35; 36]. The use of artificial intelligence elements in the development of computer models for the identification of diseases has attracted the interest of numerous researchers [37; 38; 39; 40; 41; 42]. During the design phase of these retrospective models, functional connectivity derived from EEG signal recordings and dynamical simulations was combined. The goal of our research is to predict surgical outcomes by utilizing a convolutional neural network learning capability to detect epileptic seizure. Recently, a number of papers have been published for the identification of epileptic seizures [43; 44; 45; 46; 47] using machine-learning techniques. Different approaches including GAs [43], Fourier-Bassel series expansion of EEG signals [44], particle swarm optimisation (PSO)-based neural networks [45], Hilbert transform and vector quantization based classifier [46] and wavelet filters [47] have been applied to solve epileptic seizure identification problems and have recorded high accuracy. The basic contributions of our study include but are not limited to the following: we present a productive technique based on convolutional neural network for the pre-processing of a raw EEG medically related dataset and improve the performance of the system. Here, we formulate a rule to enable the CNNs to work well in the detection of epileptic seizures with least feature control. We also make available a framework that functions admirably on different domains. Another contribution of our paper is that it reveals factors that determine accurate detection of a seizure in other related datasets. Furthermore, we utilize the framework as a network measure to propose another resection method to diagnose epilepsy seizures. The remaining part of the paper is organised as follows. Section two demonstrates CNNs utilized for epileptic EEG signals' identification. Section three presents the simulation results. Finally, section four summarizes the entire content.
###### Abstract  
This study analyzed the volatile organic compounds (VOCs) of three mango varieties (Harumanis, Tong Dam and Susu) for the discrimination of authentic Harumanis from other mangoes. The VOCs of these mangoes were extracted and analysed nondestructively using Head Space-Solid Phase Micro Extraction (HS-SPME) coupled to Gas Chromatography-Mass Spectrometry (GC-MS). Prior to the analytical method, two simple sensory analyses were carried out to assess the ability of the consumers to differentiate between the Harumanis and Tong Dam mangoes as well as their preferences towards these mangoes. On the other hand, chemometrics techniques, such as principal components analysis (PCA), hierarchical clustering analysis (HCA), and discriminant analysis (DA), were used to visualise grouping tendencies of the volatile compounds detected. These techniques were successful in identifying the grouping tendencies of the mango samples according to the presence of their respective volatile compounds, thus enabling the identification of the groups of substances responsible for the discrimination between the authentic and unauthentic Harumanis mangoes. In addition, three ocimene compounds, namely _beta_-ocimene, _trans beta_-ocimene, and _allo_-ocimene, can be considered as chemical markers of the Harumanis mango, as these compounds exist in all Harumanis mango, regardless the different sources of the mangoes obtained.  
Keywords:Harumanis; _Mangifera indica_ L.; VOCs; sensory analysis; chromatographic; chemometrics; authentication +
Footnote †: journal: molecules
# Statistical Model for Mobile User Positioning Based on Social Information
## 5 Conclusions  
Knowledge of the spatial distribution of user equipment in the scenario is key when performing management of cellular networks. In this work, the distribution of connections is achieved by means of an algorithm that, relying on user context information and knowledge of the agglomeration of social network posts, determines in which tiles of the scenario a connection is most likely to occur.  
The results obtained indicate that the spatial distribution of social network posts provides highly relevant information of the user agglomerations, bringing a granularity to the connection positioning process that is not seen with a classic E-CID method. In addition, the context information of the users, although to a lesser extent, also increases the accuracy in the positioning, being able to segregate connections in buildings or open spaces. This makes the inclusion of both data sources interesting when it comes to understanding the distribution of users in the network.  
The analysis of publications in the scenario reflects that, as expected, the spatial information provided by each social network is highly dependent on its popularity. In the case studied, the distribution of publications is almost entirely determined by Twitter. It remains to be tested whether, by adding social networks of similar popularity (e.g., Instagram or Facebook), granularity is improved.  
The model presented in this work serves as a support in network management tasks where the position of users is a relevant factor, such as the planning of new cells or the management of areas with capacity problems. The construction of land use and publications maps are the most computationally intensive tasks, but, once performed, the results obtained can be stored for future applications. On the other hand, the low computational complexity of the method allows easy integration into radio planning tools. By combining different data sources, the method can take full advantage of the latest network management systems based on big data.  
Conceptualization, J.L.B.-L., M.T. and M.F.-N.; methodology, J.L.B.-L., M.T. and M.F.-N.; software, J.L.B.-L. and L.R.J.; validation, J.L.B.-L., M.T. and M.F.-N.; formal analysis, J.L.B.-L., M.T. and M.F.-N.; investigation, J.L.B.-L., M.T. and M.F.-N.; resources, [PERSON]-L., M.T. and S.L.-R.; data curation, J.L.B.-L.; writing--original draft preparation, J.L.B.-L.; writing--review and editing, M.T., M.F.-N., L.R.J. and S.L.-R.; visualization, J.L.B.-L.; supervision, M.T. and M.F.-N.; project administration, J.L.B.-L., M.T. and M.F.-N.; funding acquisition, M.T. and S.L.-R. All authors have read and agreed to the published version of the manuscript.  
This work has been funded by the Spanish Ministry of Science, Innovation and Universities (RTI2018-099148-B-I00) and Junta de Andalucia (UMA18-FEDERJA-256).
## 3 Controlled Antibiotic Delivery
### Endogenous Stimuli-Responsive Antibiotic Delivery
The response of the drug delivery systems to these internal stimuli allows specific release of the antibiotic payload in the infected site. In this sense, antibiotics are able to reach bacterial cells without drug leakage in normal tissues, thus decreasing the development of resistance. However, the success of these systems is still limited under optimal laboratory conditions where pathological parameters are often not correlated with those found in humans (high variation in environmental factors). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Stimulus** & **Nanocarrier** & **Stimuli-Respusive Compound** & **Ref. ** \\ \hline \multirow{6}{*}{\ \begin{tabular}{c} Acid pH in infection \\ site \\ \end{tabular}  } & \multirow{2}{*}{Nanocomplex} & 2,3-dimethyl maleic anhydride & \multirow{2}{*}{[48]} \\  & & & grafted dithiolissocachamide & \\ \cline{2-5}  & & Polymeric NPs & Poly(\(\beta\)-amino ester) & [62] \\ \cline{2-5}  & & & 2,3-dimethyl maleic anhydride & \multirow{2}{*}{[63]} \\  & & & modified PEG & \\ \cline{2-5}  & & Micelle & Poly(2-dimethylamino)ethyl & \\  & & & methacrylate & \\ \cline{2-5}  & & & & \\ \cline{2-5}  & & Acid intracellular & \multirow{2}{*}{Polymeric NPs} & Poly(diacetone & \multirow{2}{*}{[51]} \\  & pH of the host cell & & acrylamide-hydrazone-isoniazid) & \\ \cline{2-5}  & & Neutral pH in & \multirow{2}{*}{Nanocomplex} & Chitosan & [49] \\  & _H. Pylori_ site & & & \\ \hline \multirow{6}{*}{ \begin{tabular}{c} Lipase: Phosphatase \\ and phospholipase \\ (_S. aureus, E. coli,_ \\ _P. aeruginosa_) \\ \end{tabular}  } & \multirow{2}{*}{Micelle} & Poly(\(\beta\)-amino ester) (ester bonds) & \multirow{2}{*}{[55]} \\  & & & PEG-DSPE (phosphoester bonds) & \\ \cline{2-5}  & & Nanogel & Polyphosphoester & [57] \\ \cline{2-5}  & & Nanogel & PCL and polyphosphoester & [58] \\ \cline{2-5}  & & & & \\ \cline{2-5}  &
# Identification of Hybrid Okra Seeds Based on Near-Infrared Hyperspectral Imaging Technology
## 1 Introduction
To our knowledge, there is no research on the classification of hybrid okra seeds with the help of near-infrared hyperspectral imaging. Because of its small size, we can obtain a large amount of okra seed information at the same time, which is convenient for analysis and processing. In this study, a total of 1740 okra seed samples of three different related varieties were collected. The purpose of this study was to investigate four goals: (1) to examine the feasibility of using near infrared range (NIR) hyperspectral imaging techniques to identify the related hybrid okra seeds; (2) to select optimal characteristic wavelengths that identify the differences among hybrid okra seeds and their parent; (3) to build optimal discrimination models based on characteristic wavelengths, thus simplifying the prediction model and speeding up the operation; and, (4) to visualize the classification results of okra seeds in the form of a pseudo-color image by developing image processing algorithms.
# Automatic Point-Cloud Registration for Quality Control in Building Works
## 2 Materials and Methods
### Method
#### 2.2.2 Pre-Processing  
Commonly, the point clouds acquired by 3D laser scans contain noise. The noise is composed by points measured with positional errors. These errors are due to vibrations, reflections, far points or moving elements during the scan works, so it is important to filter them to prevent their influence in the rest of the calculus.  
The first operation is an automatic noise elimination using statistical outlier removal (SOR) filter [50]. The SOR filter automatically eliminates the noise present in the point clouds by performing a proximity analysis for each point with the neighbour points (Figure 4). For this case, the fast statistical outlier removal (FSOR) proposed by [PERSON] et al. [51] was used to minimize the processing time and optimize resources.  
After cleaning the point cloud, a passthrough filter [52; 53] based on the coordinate histogram of the point clouds is applied. Because the Faro Focus captures data in the range 0.60-330 m, the system measures points that are too far from the scan station and consequently useless for the application (Figure 5a,c). The first automatic filter applied removes the farthest points computing the coordinates distribution along the different axis (\(x\), \(y\), \(z\)). For each axis, the coordinates are converted to scalar field, and its histogram of distribution is filtered according to the position with highest accumulation of points in  
Figure 4: Fast statistical outlier removal (FSOR) filter applied over the point cloud. (**a**) Detail of raw point cloud with noise; (**b**) result after applying FSOR filter.  
each case. This procedure was applied in the three directions obtaining a first filtered point cloud (Figure 5b,d).  
Finally, a homogenization of the point cloud is performed. It is well known that 3D point clouds acquired with a laser scanner present heterogeneous point density. The further away from the station point, the lower point density. The voxel-grid filter [53; 54] allows the generation of a uniform dataset by dividing the space in cubes of custom size and replacing the points on each cube by the centroid of that set of points. The voxelization [45] is performed in an automatic way: the size of the voxel depends on the density of the point cloud, which is calculated through the study of the mean distance of each point to its closest neighbours. In this way, the process is completely automated, as well as adaptative to the data of each scenario.
# A Broadband Active Microwave Monolithically Integrated Circuit Balun in Graphene Technology
## 4 Discussion  
### Comparison with State of the Art  
Finally, the balun circuit is compared to the state of the art of active baluns made with different technologies such as silicon [27], SiGe [28], GaAs [29], and GaN [30] (Table 1). These publications are chosen to have a similar technology gate length than the graphene technology used in this paper ranging from 0.18 to 0.5 \(\upmu\)m. The transit frequencies of these technologies are also in the same range spanning from about 20 and up to 40 GHz. Moreover, the baluns are designed for broadband applications below 16 GHz. The graphene balun presented in this work have comparable performance in terms of phase difference and amplitude balance compared to the industrial technology despites its low maturity. As mentioned early, the major drawback is due to the losses in the graphene balun which is intrinsically correlated to the transistor performances and not to the circuit topology itself.  
Figure 4: Three port S parameters measurement up to 20 GHz using a PNAX: (**a**) phase difference between the two outputs; (**b**) amplitude balance between the two outputs.  
Figure 3: (**a**) Photography of the balun under measurement condition. (**b**) Oscilloscope measurement when applying a sinusoidal input voltage with an amplitude of 32 mV at the input (channel D) and sensing the two outputs (channel B and C).
# Real-Time Maintenance Optimization with Industrial Internet of Things
## 3 Models of Conventional and Real-Time Optimization  
When modeling the maintenance processes of production systems, a wide range of factors must be considered that significantly influence both the cost of maintenance operations and the state of the production system. This state (or reliability) is determined by the condition of the technological and logistics resources of the production system. In systems with high maintenance demands (paper industry, food and beverage production, compressor systems), determining the optimal maintenance strategy is especially critical, as frequent maintenance can result not only in substantial maintenance costs but also considerable production downtime (idle time). In the case of production systems (particularly production lines composed of a limited number of resources), it is important to perform maintenance operations simultaneously across the entire system. This approach ensures that the entire production line is not idle due to the maintenance of a single machine; instead, all system resources can be maintained during a comprehensive maintenance period. Therefore, in a conventional production environment, the goal is to determine a maintenance period that ensures an adequate system state and, consequently, a predefined level of reliability, while also minimizing maintenance costs. Based on these facts, this section introduces a mathematical model suitable for determining the optimal maintenance period in a conventional production environment, where no real-time state information is available. Following this, a novel approach to the model is presented, which, by utilizing a properly applied IIoT solution, enables the real-time determination of the optimal maintenance schedule based on up-to-date (real-time) state information of the system's resources. This allows for achieving maximum productivity at minimal maintenance cost. Accordingly, the section is structured into three main parts:  
* The first part proposes a mathematical model for optimizing maintenance processes, focusing on determining the optimal maintenance periods;
* The second part discusses an IIoT-based approach that enables the collection of real-time state information from the production system;
* The third part proposes a novel, enhanced version of the original mathematical model discussed in Section 3.1, adapted for real-time optimization in IIoT technologies-supported production systems.
# Unsupervised Image Segmentation on 2D Echocardiogram
## 4 Results
### Quantitative Analysis
#### 4.1.1 Segmentation Accuracy  
The segmentation accuracy was evaluated using the Dice coefficient and IoU. These metrics were applied to compare our model's segmentation against the ground truth as well as the EchoNet Dynamic model. As previously discussed, the Dice coefficient measures the overlap between predicted and ground truth segmentations, while IoU offers a stricter comparison by assessing the intersection relative to the union of the predicted and true segmentation.  
The results from the segmentation metrics, shown in Table 1, indicate that our method achieved a Dice coefficient of 0.8225 (95% CI: [0.8098, 0.8353]) and an IoU score of 0.7071 (95% CI: [0.6928, 0.7213]) when compared to the ground truth. In comparison, the EchoNet Dynamic model reported a higher Dice coefficient of 0.9241 (95% CI: [0.9207, 0.9275]) and an IoU of 0.8600 (95% CI: [0.8542, 0.8657]). EchoNet's higher scores can likely be attributed to the fact that it leverages extensive (segmentation masks created by expert sonographers) and more complex architecture. However, this small discrepancy emphasizes the potential of an unsupervised approach, especially when fine-tuned or combined with more sophisticated post-processing techniques or data acquisition methods. The confidence intervals demonstrate the stability of our model's performance across multiple tests, further confirming the robustness of our unsupervised segmentation approach.
# Pressure-Reducing Design of 3D-Printed Diabetic Shoe Midsole Utilizing Auxetic Lattice Structure
## 1 Introduction
By constructing finite element models (FEMs), this research simulated the load distribution and deformation at the footwear-foot interface. Existing studies have shown that aerobic exercises such as walking and running can significantly benefit the health and physical conditions of diabetic patients [22]. Aerobic exercise helps improve a patient's body weight, body mass index, and fasting plasma glucose levels [23]. In addition, aerobic exercise can also alleviate vascular endothelium-dependent dysfunction in pre-diabetic individuals [24]. Under moderate and safe plantar loading conditions, aerobic exercise (running) positively impacts key parameters indicative of the progression of type 1 and type 2 diabetes, including lowering glucose and glycated hemoglobin levels, improving lipid metabolism, reducing inflammation, increasing insulin sensitivity, and enhancing pancreatic \(\beta\)-cell function [25]. Therefore, exercise prescriptions that include aerobic exercises such as walking and running are recommended for patients with diabetes [26]. In light of the health benefits of aerobic activities for patients with diabetes, this study explored the pressure-relieving capabilities of designed shoe midsoles under two different states of aerobic activity. Additionally, to validate the results of the FEA simulation, this research employed an in-shoe pressure measurement system for actual experiments under the same conditions. This study proposes the development of new footwear offloading technologies based on the application of auxetic designs in shoe midsoles, offering a novel and feasible solution to modify the plantar pressure distribution and prevent plantar ulcers in the early stages of diabetes.