esa-sceva/satcom-chunk-collection huggingface dataset:
# ChatGPT as a Virtual Dietitian: Exploring Its Potential as a Tool for Improving Nutrition Knowledge
## 3 ChatGPT as a Nutrition Tool
### What Are the Features of Nutrition Applications That ChatGPT Can or Cannot Emulate?
#### 3.1.2 Personalized Meal Planner  
Meal planning is an important process for ensuring a healthy and well-balanced diet. More importantly, customizing meals to accommodate individual preferences, dietary restrictions, and daily caloric requirements is crucial for improving adherence to a particular meal plan [33]. When ChatGPT is presented with this array of variables (as exemplified in the sample prompt below), it is more likely to generate personalized meal plans that cater to individual needs. Like other knowledge-based systems, ChatGPT can function as a robust recommender system with its advanced algorithms and language processing capabilities. This important feature helps users diversify their diet, discover new healthy recipes, and optimize their nutrient intake. It also promotes a better understanding of how to create well-rounded meals while accommodating dietary considerations. Nonetheless, it is noteworthy that ChatGPT does not have the capability to manage calendars or schedule tasks, which are important characteristics of a meal planner application.  
**Sample prompt**: \"I am looking for a personalized meal plan that can help me manage my weight while following a vegetarian diet. To maintain my weight, I need to eat 2500 calories per day. I usually eat three times a day. I prefer Filipino cuisine, but I have a peanut allergy. Suggest a meal plan that consists of breakfast, lunch, and dinner.\"
###### Abstract  
Along with the continuous revolution of energy production and energy consumption structures, the information data of smart grids have exploded, and effective solutions are urgently needed to solve the problem of power devices resource scheduling and energy efficiency optimization. In this paper, we propose a fifth generation (5G) and satellite converged network architecture for power transmission and distribution scenarios, where power transmission and distribution devices (PDs) can choose to forward power data to a cloud server data center via ground networks or space-based networks for power grid regulation and control. We propose a Joint Device Association and Power Control Online Optimization (JDAPCOO) algorithm to maximize the long-term system energy efficiency while guaranteeing the minimum transmission rate requirement of PDs. Since the formulated issue is a mixed integer nonconvex optimization problem with high complexity, we decompose the original problem into two subproblems, i.e., device association and power control, which are solved using a genetic algorithm and improved simulated annealing algorithm, respectively. Numerical simulation results show that when the number of PDs is 50, the proposed algorithm can improve the system energy efficiency by 105%, 545.05% and 835.26%, respectively, compared with the equal power allocation algorithm, random power allocation algorithm and random device association algorithm.  
5G and satellite converged network; energy efficiency; device association; power control
# Fuzzy Logic with Deep Learning for Detection of Skin Cancer
## 4 Results and Discussion
### Performance Evaluation Metrics  
The proposed method has been evaluated in two phases; First, detection performance of the location of the lesion using sensitivity, specificity score and IoU metric are presented. The assertion of the recognised location is possible only if the score is more than 80% for the IoU. Secondly, segmentation performance is investigated based on pre-defined metrics to assess the functionality of the proposed methods. The measures used for validation can be described as follows:  
* **Sensitivity (Se)**--The measure of accurate segment lesions.
* **Specificity (Sp)**--The segmented ration for the non-lesion areas.
* **Dice Coefficient (Dc)**--This measure is used for the purpose of quantifying the segmented lesions along with explaining the ground truth connection.
* **Jaccard Index (JI)**--The Jaccard Index is a metric used to measure the overlap or similarity between the obtained segmentation results and the ground truth mask.
* **Accuracy (Ac)**--The overall performance of pixel-wise segmentation. It quantifies how accurately the segmented pixels correspond to the ground truth labels.  
The evaluation metrics mentioned above could be formulated in an equation for calculation purpose, which is as below:  
\[\text{IoU}=\frac{\text{Area}\text{ under intersection}}{\text{Area}\text{ under Union}} \tag{9}\]  
\[\text{Ac}=\frac{\text{True positive}+\text{True negative}}{\text{True positive}+\text{False negative}+\text{True negative}+\text{False positive}} \tag{10}\]  
\[\text{Se}=\frac{\text{True positive}}{\text{True positive}+\text{False negative}} \tag{11}\]  
\[\text{Sp}=\frac{\text{True negative}}{\text{True negative}+\text{False positive}} \tag{12}\]  
\[\text{Dc}=\frac{(2*\text{True positive})}{(2*\text{True positive})+\text{False positive}+\text{False negative}} \tag{13}\]  
\[\text{JI}=\frac{\text{True positive}}{\text{True positive}+\text{False negative}+\text{False positive}} \tag{14}\]  
True Positive (TP) is considered by a lesion pixel in the image if it was detected and segmented correctly. False Negative (FN) is referred when it is not detected correctly. The  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Testing**} & \multicolumn{2}{c}{**Validation**} & \multicolumn{2}{c}{**Total**} \\
**M** & **NM** & **M** & **NM** & **M** & **NM** & **M** & **NM** & **NM** \\ \hline PH2 & — & — & 40 & 160 & — & — & 40 & 160 \\ ISBI17 & 374 & 1626 & 197 & 403 & 30 & 120 & 601 & 2149 \\ ISIC18 & 779 & 7916 & 334 & 986 & — & — & 1113 & 8902 \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of Skin Lesion Datasets used in this paper.  
True Negative (TN) is referred when a non-lesion pixel is predicted as lesion one. Finally, if the non-lesion pixel is been detected correctly as non-lesion than it is regarded as False Positive (FP).
## 4 Discussion  
At present, there are some applications that obtain heart rate per minute in real time using the face (the Cardiio: Heart Rate Monitor and Philips Vital signs camera), but the technique we present uses mathematical and filtering methods as an innovative process. The current contactless technologies, which are scarce, perform processing through acquisition, storage, and subsequent processing to obtain the results, which include the heart rate, without performing real-time estimation and analysis [11; 12; 13; 14]. There were several stages of development before reaching the final product presented in this paper. For example, other platforms were used to test different algorithms, such as Raspbian (Raspberry Pi), a computer and webcam, etc. The evolution of the techniques developed using these other technologies provided us with the necessary experience to develop the present application for a mobile device.  
The sampling of 30 images per second was done to obtain the greatest amount of information from the video. In theory, with a sampling of seven images per second, one could acquire a heart rate signal because the Nyquist-Shannon sampling theorem mentions that in order to achieve an exact reconstruction of a periodic signal, the sampling rate must be more than twice the frequency of the signal to be found [15]. According to [PERSON] et al. [16], in their research on the analysis of the components in the frequency domain of the HRV measures, heart rate values can be found in the frequencies between 0.7 Hertz and 3.4 Hertz.  
A comparison was made between the different data provided by the information channels of the RGB color model and the hue, saturation, and the value channels of the HSV model with the purpose of obtaining a signal from the data that is more suitable for correctly obtaining a cardiac pulse. The red and green channel of the RGB format gives us the most suitable values for estimating heart rate, without having to perform a conversion to other models of image representation. This criterion reduces the computational time of the system by not performing a format conversion.  
The data obtained after applying the filtering step and the algorithm of the fast Fourier transform gave us inconsistent frequency values, which did not correspond to the heart rate frequency. Therefore, we decided to apply an additional process in the development of the application--a mathematical module that performs the first-order derivative of the signal acquired from the filtering process, in order to obtain the rate of change prior to the application of FFT (fast Fourier transform). The mentioned implementation gave us a more accurate, consistent, and reliable estimate throughout the sampling. Also, the Apple Accelerate framework provides exceptional performance in handling computational resources and has a better processing time compared to other libraries.
# Augmented Data-Driven Approach towards 3D Printed Concrete Mix Prediction
## 3 Results and Discussion
Consequently, the BR-augmented dataset overlaps significantly with the real dataset, leading to higher predictive scores for BR-trained models. This overlap, depicted in Figure 8 to Figure 10b,d,f, shows that BR-trained models perform well because they are evaluated on data very similar to their training data. However, these higher predictive scores can be misleading. When BR-trained models encounter unseen data points that are not present in the current dataset, their performance is likely to decline. This is because the BR-augmented data do not provide a diverse enough range of scenarios for the models to learn from, making them less robust. In contrast, DGAN-augmented data, which fills in gaps and provides a more comprehensive distribution, prepares models to handle a wider variety of scenarios, even if this is not immediately reflected in the initial performance metrics. Therefore, while BR-trained models may show better performance scores during testing on the current real dataset, they are less robust compared to DGAN-trained models. The higher performance scores for BR-trained models are due to the data overlap, but this does not guarantee sustained performance with new, unseen data. Conversely, the DGAN-trained models, despite showing lower initial performance metrics, are likely to perform more reliably with diverse and unseen data, highlighting their long-term robustness and utility. The results of the individual predicted features are discussed below:  
Figure 11: Performance test metrics for models trained on data augmented by DGAN and BR (**a**) R\({}^{2}\), (**b**) MSE, (**c**) RMSE, and (**d**) MAE.
# Detection and Classification of Bearing Surface Defects Based on Machine Vision
## 1 Introduction  
### Background  
Bearings are important components of mechanical equipment. They can convert the direct friction from parts in relative rotation into rolling friction or sliding friction of the bearing, hence reducing the friction coefficient and ensuring the long-term stable operation of the machine. The bearing surface, the critical part in direct contact with the rotating parts, has an important impact on the installation performance, usage performance, quality, and life of the bearing. If there are defects on the bearing surface, such as wear, cracks, bruises, pitting, scratches, or deformation, they can lead to machine vibration and noise, accelerate the oxidation and wear of the bearing [1], and even damage the machine. It is thus necessary to inspect the surface of the bearing to prevent defective products from entering the market. The assembly of bearings here and abroad has been fully automated for the most part, but the inspection of the bearing surface before and after assembly is still based on the naked eye of the workers. Such an inspection method is labor-intensive, low-efficiency, high-cost, and easily affected by such factors as inspector qualification and experience, visual resolution of the naked eye, and fatigue. A new detection method is, therefore, urgently needed to replace the traditional naked-eye detection method.  
Machine vision inspection technology, with its high speed, a high degree of automation, and non-destructive quality, has rapidly developed in recent years. Therefore, it is naturally advantageous to apply machine vision to the inspection of surface defects on bearings [2]. Under the backdrop of China's Industry 4.0 aim to upgrade the country's manufacturing industry, and the production needs of bearing manufacturing enterprises, now is the time to investigate bearing surface defect detection and to study inspection and classification systems based on machine vision. In-depth analysis of weak links, including the acquisition of high-definition images of the bearing surface, precision segmentation of defect areas, and selection of defect classification features, can provide efficient and automated detection methods for detecting exterior defects on bearings, which are important for improving bearing manufacturing.
# Wireless Sensor Networks for Building Information Modeling
###### Abstract
To this direction, the sensor data streams must be sourced from appropriate sensors and able to provide measurements for various parameters of interest, such as room air temperature and humidity, room occupancy, air conditioner's status (on/off), air conditioner's thermostat set-point temperature and operation mode (heating, cooling, fan, etc. ), status of doors and windows (open/closed), weather conditions (including outdoor temperature and relative humidity, and wind speed and direction) and energy consumption. The Wireless Sensor Network (WSN) is a suitable solution that is able to integrate the sensing and controlling of the environment in the context of the BIM and smart buildings. Some of the recent studies present different solutions as to how to design WSNs with regard to sensing coverage, measurements significance, network connectivity and spatial positioning of sensors. In Reference [18], the authors used genetic algorithms to improve WSNs' location accuracy; in Reference [19], the authors proposed an evolutionary algorithm to calculate the maximum sensor coverage; in Reference [20], the authors proposed an information service and live sensing in order to supervise the building energy performance; and in Reference [21], the authors investigated how BIM could support smart buildings from the architecture plan to the building management and [22] demonstrated sensor data visualization in BIM for supporting complex decisions. Applying machine-learning algorithms on the sensor-data streams provided by the WSNs that are designed for and installed in the pilot sites generates occupant-behavior profiles that mimic the inhabitants' actions and contribute to the accuracy of energy performance estimation, using ML-enabled occupant behavior methods [23, 24, 25, 26]. These profiles are subsequently used to project the building system (e.g., heating/cooling) utilization boundaries to maintain the comfort zone of the residents. This paper addresses the deployment, technology, topology and cost of robust WSNs that are able to collect and transmit real-time data required from BIM renovation toolkits. Itis based on real WSN installations, rather than simulated results, as in most cases reported in the open literature. As a consequence, it considers different field requirements, constraints and options at the BIMERR pilot sites, and, hence, it sets the lines for similar installations required by the construction industry for collecting dynamic data required for BIM and smart buildings. The paper is organized as follows: Section 2 describes the principles of the Wireless Sensor Network (WSN) deployed in the pilot sites of the BIMERR project. Section 3 presents the topology of the BIMERR WSN. Section 4 provides details about the equipment, sensors and budget. Section 5 presents measured sensor data from the BIMERR pilots, and Section 6 describes the application of WSN data in the context of BIM for training occupant behavior models.
## 4 Experimental Results
### Performance Evaluation
#### 4.2.3 Fitness Convergence Speed  
The fitness convergence comparison demonstrates the hybrid method outperforming GAs and PSO with higher maximum fitness values and a higher convergence rate. The hybrid approach reached the higher fitness values at a faster rate due to the fact that the adaptive learning property of reinforcement learning enhances the exploration process of GAs. The fitness progress graph shown in Figure 6 clearly reveals that the hybrid approach consistently improves performance without stagnation, whereas PSO suffers from premature convergence and GAs take a long time to converge to optimal solutions.
# A CNN-Based Downscaling Model for Macau Temperature Prediction Using ERA5 Reanalysis Data
## 4 Experimental Results and Discussion
### Discussions  
In an in-depth investigation of the model performance for different prediction timings, this study clearly reveals the superiority of the CNN model over the traditional bilinear interpolation and other methods. In terms of error indicators, the _RMSE_, _MAE_, and _MRE_ of the DL model are significantly lower than those of the traditional method in the instantaneous prediction (0 h), and with the extension of the prediction time to the next three hours, although the error increases, the error growth rate and the final absolute value of the error are still in the acceptable range, and the correlation coefficient is always maintained at a high level, which is better than that of the traditional bilinear interpolation method. This indicates that the CNN model has an excellent ability to capture the data fluctuation patterns and control the error accumulation.  
Given the excellent performance of the CNN model in the region around Macau, it is feasible and potentially valuable to generalize it to other regions. The error growth pattern over time is in line with the general characteristics of meteorological forecasts and maintains good correlation under long time forecasts, which provides a solid theoretical basis for its application in different geographic environments. Although there are geographical differences in different regions, the principles of spatial and temporal changes in meteorological elements are common, and the powerful learning and adaptation ability of the CNN model means it is expected to realize accurate weather prediction and geographic environment analysis in other regions as well.
# Meta-Extreme Learning Machine for Short-Term Traffic Flow Forecasting
## 3 Experiments
### Experimental Setup  
The focus of short-term traffic flow forecasting is not precisely on the ups and downs of the number of vehicles per minute [1, 27, 48], but on periods with certain time intervals. Therefore, we calculate the average value of vehicles per ten minutes based on the aggregated data of every minute in the original data, which is preprocessed and used as the sample data for the input model.  
The data includes a total of 5 weeks of data, which is divided into two parts in this paper; the data of the first 4 weeks form the training set, and the data form the last week as the test set. Since the amount of sample data is not very large, including 5040 data in total, to ensure the reliability of the prediction accuracy the sliding window fetching method is used to process the input samples. Each input vector of the ABC-ELM prediction model consists of 9 consecutive data values of the sample data, and the 10 th data value  
Figure 3: The four intersections of the Amsterdam A10 ring road that access the ring road, named A1, A2, A4, and A8. Sensors are embedded in the RSU and placed at each intersection.  
_MAPE_, and _RMSE_ as used as an auxiliary evaluation index These two evaluation criteria are expressed as Equations (9) and (10), respectively.  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}[\hat{v}(i)-v(i)]^{2}} \tag{9}\]  
\[MAPE=\frac{1}{N}\sum_{i=1}^{N}\left|\frac{\hat{v}(i)-v(i)}{v(i)}\right|\times 1 00\% \tag{10}\]  
where \(\hat{v}(i)\) and \(v(i)\) represent the forecasting and the true measurement of the \(i^{th}\) sample, and \(N\) is the total number of input samples.
# Blind Channel Estimation Method Using CNN-Based Resource Grouping
## 3 Proposed Method
### Optimal Resource Grouping Pattern Selection  
To reduce the complexity of selecting the optimal resource grouping pattern and to efficiently extract features, a two-step learning process is performed by dividing the proposed 212 resource grouping patterns into frequency and time domains. In the first step, learning is focused on selecting the optimal parameter \(f\), which is used to determine the resource grouping pattern in the frequency domain among the proposed resource grouping patterns. In the second step, the learning is focused on the parameter \(t\), which determines the resource grouping pattern in the time domain. After completing the learning process, the trained CNN combines the optimal values of \(t\) and \(f\) to output the optimal resource grouping pattern within the given channel condition.  
In the first step, since the network focuses solely on the frequency domain, learning is performed based exclusively on the number of subcarriers. First, the 212 resource grouping patterns are classified into groups that have the same number of subcarriers, \(f\). Then, for the resource grouping patterns that use the same number of subcarriers, the corresponding subcarrier count, \(f\), is defined as the new correct label for CNN training. The dataset is normalized to values between \(-1\) and \(1\) under the channel conditions corresponding to this correct value, with a size of \(1\times 1680\times 2\times N\), and the network is constructed accordingly. After completing the first-step of training, the CNN outputs the optimal \(f\). In the second step, since the network focuses solely on the time domain, learning focuses exclusively on the number of OFDM symbols in the proposed resource grouping patterns. First, the proposed patterns are grouped according to the same number of OFDM symbols, \(t\). Then, for the patterns with the same number of OFDM symbols, the corresponding number, \(t\), is defined as the new correct label for the second step of CNN training. For the channel conditions corresponding to this correct label, the dataset is normalized to values between \(-1\) and \(1\), with a size of \(1\times 1680\times 2\times N\), and the network is constructed accordingly. Uponcompleting the second stage of training, the CNN outputs the optimal \(t\), which is combined with the optimal \(f\) from the first stage to determine the optimal resource grouping pattern for the given channel environment.
## 3 Results
### Prediction of Stability  
The prediction of stability based on YTI_CR yielded an AUROC of 0.88. A selected cut-off with the highest Youden index value on the ROC curve was YTI_CR at 1.45 mm, with sensitivity of 0.888 and specificity of 0.846, suggesting good predictive accuracy at this threshold, as illustrated in Figure 4.  
Multiple logistic regression analysis was run with group as the criteria and preoperative YTI and gender as predictors. The odds ratio (OR) for YTI was 0.35 (\(p\) = 0.024, CI: 0.14, 0.87), indicating a decrease in odds with increasing YTI_CR. However, gender did not emerge as a statistically significant factor, highlighting that YTI_CR is a more pivotal determinant in the model used for predicting post-surgical stability.  
Figure 4: Receiver operating characteristic (ROC) curve.