esa-sceva/satcom-chunk-collection huggingface dataset:
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 2 Models  
### Artificial Neural Network  
Artificial Neural networks (ANNs) are mathematical means of computation inspired by the field of biology, especially the nervous system. They simulate the biological neuron in the human brain [1; 29]. ANNs can learn, be taught and generalise to new experiences. They are characterised as robust and self coordinating and are popularly useful in most areas. The architecture is built of many neurons organised in three major layers of input, hidden and output layers, respectively. Mathematically, ANNs can be formulated as:\[\begin{split} Y_{k}&=\varphi(\Sigma(x_{1}w_{1}+x_{2}w_{2}+x_{3 }w_{3}+\cdots+x_{k}w_{k})+\beta_{k})\\ Y_{k}&=\varphi(u_{k}-\theta_{k})\\ u_{k}&=\sum_{i=1}^{k}x_{i}w_{i}\\ \beta_{k}&=-\theta_{k}.\end{split} \tag{1}\]  
From Equation (1), a composite formulation of an ANN can be inferred. While the first two lines give the overall formulation for ANN, the last two lines explain the unit terms. The symbol \(\varphi\) denotes the activation or transfer function, which can be sigmoid, threshold, piece-wise and identity function; \(\beta_{k}\) is the bias; \(\theta_{k}\) is the threshold; \(u_{k}\) is the weighted sum; \(w_{i}\) is the weight at neuron \(i\); and \(x_{i}\) are data points.  
The training algorithm used for this research is the backpropagation algorithm (Bayesian regularised), used for training the multilayer perceptron neural networks. This algorithm is chosen because it generalises well with small and messy datasets and therefore reduces over fitting [2; 16]. In Equation (2), \(w_{ij}\) interconnects the \(i\)th output \(O_{i}\) from neuron \(i\) to its \(j\)th input neuron, if the neuron at layer \(k\) is not an input neuron then its state can be formulated mathematically as:  
\[O_{k}=f(\sum_{i}w_{ij}O_{i}), \tag{2}\]  
where \(f(x)=1/(1+e^{-x})\) is the sigmoid activation function, the summation is done in all neurons in all the layers. The backpropagation training algorithm works in the following order:  
1. Select the paired input-target vectors from the training dataset and apply to the ANN input nodes.
2. Process the network output.
3. Compute the errors between the network output and the target.
4. Minimise the errors by adjusting the neuron weights connection.
5. Repeat Steps 1-4 for all the paired input-target vectors in the training set until a reasonable error is reached for the entire set or terminating criterion is satisfied.  
The resulting mathematical formulation of the backpropagation algorithm is seen in Equation (3).  
\[\Delta W_{ij}^{k}(n+1)=e\delta_{j}O_{i}+\alpha\Delta W_{ij}^{k}(n), \tag{3}\]  
where \(n\) represents the number of iterations, \(\epsilon\) represents the learning rate, \(\alpha\) is the momentum term and \(\delta_{j}\) checks if neuron \(j\) is an output layer. In this study, ANN trained with Bayesian regularised algorithm is the main model whose accuracy was measured against SGB and GAM as bench mark models in point forecasting of wind speed.
# Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery
## Materials and Methods
### Deep Learning Models
#### 2.4.4 U-Net  
U-Net tries to improve the fine structure of segmentations and increase the amount of context used [14]. Traditionally, when a certain amount of pooling is required, if one is intending to train with large patches, it unfortunately degrades the performance such as in FCN and DeconvNet. Hence, the U-Net model utilizes skip connections to forward the unpooled features, thus the model can utilize the information of various scales. In our implementation, we abandoned the max-pooling and up-sampling operations for the same reason as in DeconvNet.
# An Investigation of the Temperature-Drift Effect on Strain Measurement of Concrete Beams
###### Abstract  
This study investigates the temperature-drift effect on strain measurement of concrete beams and proposes a method for determination of the mechanical strain of stressed concrete beams. In the study, wireless electrical resistance strain gauges were used to measure the strain of concrete beams. This study first examined how temperature changes affected the strain gauge attached to concrete beams. Subsequently, a concrete beam experiencing changes in temperature and load was monitored for six consecutive days. The test results showed that the apparent strain response of the concrete beam was significantly affected by temperature changes. After adjusting for the temperature effect, the mechanical strain generated by a load could be obtained. However, temperature-induced drift was still observed in the mechanical strain response. Based on the assumption that temperature changes are slow and gradual, and mechanical strain changes are momentary, an adjacent data subtraction method can be used to eliminate the temperature-induced drift present in the mechanical strain data. The subtraction results show that the mechanical strain generated by a load was accurately obtained. The proposed data-processing method could also be used to find the residual strain of the nonelastic response of a beam subjected to substantial short-term forces.  
strain measurement; temperature effect; mechanical strain; electrical resistance strain gauge +
Footnote †: journal: Applied Sciences
## 1 Introduction
The experimental results demonstrate that our approach, which effectively captures these spatio-temporal dependencies and regional preference variations, significantly outperforms state-of-the-art baseline methods in terms of recommendation accuracy, recall rate, and cold-start problem mitigation. The main contributions of this work can be summarized as follows:  
1. The proposed APGCN-CF framework comprehensively incorporates both seasonal and geographical characteristics into minor agricultural product recommendations. We construct a graph structure that establishes connections between users and minor agricultural products, implementing an effective framework that mitigates the negative impacts of multi-source heterogeneous data on model performance. 2. In this study, we propose an innovative approach by incorporating graph convolutional networks (GCN) to process minor agricultural product recommendation data. Through effective feature aggregation mechanisms, we establish meaningful connections between user preferences and minor agricultural product characteristics, thereby significantly enhancing the accuracy and personalization of the recommendation system. 3. The proposed framework effectively addresses conventional challenges inherent in recommender systems, particularly data sparsity and cold-start issues. Our approach serves as a versatile plugin module that can be seamlessly integrated with existing collaborative filtering methodologies to enhance their performance. Moreover, by incorporating seasonal and geographical factors into consideration, the framework demonstrates substantial improvements in recommendation accuracy and effectiveness. 4. We conduct comprehensive experiments on real-world minor agricultural product datasets to evaluate the proposed GCN-CF framework. The experimental results demonstrate that our method consistently achieves superior performance compared to state-of-the-art baseline approaches across multiple evaluation metrics, including precision, recall, and F1-score. The remainder of this paper is organized as follows. Section 2 provides a comprehensive review of related work, focusing on content-based filtering, collaborative filtering, and minor agricultural product recommendation systems. Section 3 delineates the mathematical notation and formal problem definitions. Section 4 presents a detailed description of our proposed APGCN-CF framework, encompassing the model architecture and optimization methodology. In Section 5, we evaluate the framework through extensive experiments on real-world datasets, analyzing its overall performance with particular emphasis on addressing data sparsity and cold-start challenges. Finally, Section 6 concludes the paper by summarizing our contributions and discussing directions for future research.
## 2 Current Technology  
As technology rapidly advances, it is not surprising that it has impacted nearly every aspect of our lives. In recent years, there has been a significant increase in the use of technology in healthcare and sports, leading to the emergence of new and exciting fields such as eHealth and sports technology. eHealth refers to the use of information and communication technologies in healthcare, which can potentially transform how healthcare is delivered.  
With the help of enabling technologies, such as electronic health records, telemedicine, and health information exchange, healthcare providers can offer personalized and efficient care. Similarly, in sports, technology has opened new frontiers, enabling athletes to push their limits and achieve their goals. Technology is revolutionizing how athletes train and compete, from wearable technology that tracks performance metrics to virtual reality training programs. Moreover, sports have become an area of interest for nations and states, with governments investing heavily in sports infrastructure and initiatives to boost participation in sports. This trend is fueled by the recognition of the numerous benefits of sports, including improved physical and mental health, social integration, and economic development. However, for the widespread adoption of technology in healthcare and sports to be successful, it is essential to consider user perception and acceptance of these systems. In the case of telemedicine, for example, patients' attitudes towards remote consultations and virtual care can impact the success of these programs. Modelling approaches, such as the technology acceptance model (TAM), can help identify the factors influencing users' acceptance of these systems and guide the development of effective technology solutions. In this article, we explore the enabling technologies related to eHealth, new technological frontiers in sports, and the nations' interest in sports. Additionally, we delve into the user perception of telemedicine systems, system acceptance, and modelling approaches that can guide the development of effective technology solutions in these areas. Described as a revolutionary innovation in healthcare, telemedicine has been transforming the way patients access medical care and has the potential to improve healthcare outcomes globally [10]. Telemedicine allows patients to receive medical consultations remotely using technology, such as video conferencing, mobile apps, and remote monitoring devices. This eliminates the need for in-person visits, reducing the burden on healthcare systems and improving access to care, especially for those living in rural or remote areas [11]. Numerous studies have shown the benefits of telemedicine in improving healthcare outcomes, reducing healthcare costs, and increasing patient satisfaction [12].
## 2 Materials and Methods
### Dataset  
Choosing the correct amount of data is paramount. The data employed for our analysis have been obtained from the Italian National Institute of Statistics (ISTAT) database, which was used in 2018 by the Italian Superior Institute for Environmental Protection and Research (ISPRA) to produce seismic, hydrogeological, volcanic, and social vulnerability hazard maps for the entire Italian peninsula as shown in the report by [PERSON] et al. [22]. These maps constitute a fundamental tool of support to national risk mitigation policies, allowing the identification of intervention priorities, the allocation of funds, and the planning of soil protection interventions.  
The input dataset was organized as a matrix in which the rows corresponded to each of the 331 municipalities of the Emilia-Romagna region and the columns corresponded to quantitative variables associated with different aspects of seismic and flood risk. Hence, we had 331 rows or observations and hundreds of columns or variables. For instance, we adopted as variables the number of buildings sharing certain features (such as building material, the period of construction, or the state of conservation), superficial extension, number of inhabitants, population density, seismic peak ground acceleration, etc. Overall, all the variables can be grouped into three macro-categories: variables related to vulner  
Figure 2: Ferrara territory altimetry (The map can be downloaded from [[https://www.bonificaerrara.it](https://www.bonificaerrara.it)]([https://www.bonificaerrara.it](https://www.bonificaerrara.it)) and has been released from “Consorzio di Bonifica Pianura di Ferrara”; accessed on 15 October 2021).  
ability instances, variables related to exposure instances, and variables related to hazard instances for both seismic and hydraulic risks.  
Since hydraulic risk, as a combination of hydraulic vulnerability, exposure, and hazard, has previously been evaluated for each observation by the Italian National Institute of Geophysics and Vulcanology (INGV), it was represented in the proposed analysis as a unique variable, which condensed all the variables related to hydraulic risk.  
The relative importance between some variables and the relation among them is quantified by means of the PCA method, which will be described in the next subsections.  
For instance, some of the crucial variables were identified as follows:  
* agMAX_50: maximum value of the peak ground acceleration about the grid data point;
* DENSPOP: Population density (n. of inhabitants/kmq);
* E1-E31: Type of Buildings (e.g., residential, masonry, and state of conservation);
* IDR_AreaP1/P2/P3: Hydraulic risk surface, respectively, low/medium/high;
* IDR_PopP1/P2/P3: Population living in, respectively, low/medium/high hydraulic risk surface.  
An extensive table reporting the explanations of all acronyms associated with the relevant variables is reported in Appendix A.
## 1 Introduction
### This Paper's Contribution  
The contributions of this paper are as follows: we present the state of the art in WMSN healthcare projects that have been introduced over the last decade and discuss their security flaws. We broadly explore the possible security threats that can endanger healthcare applications, including patient's privacy issues. Further this paper presents an overview of stringent government rules and regulations for healthcare organizations and their business partners. We also instigate discussion on existing security mechanisms that need to be explored, and sketch out the imperative security requirements in wireless healthcare. This paper also includes a holistic overview on recent available literature that has proposed solutions for secure healthcare application using WSNs.  
The rest of the survey is organized as follows: Section 2 gives an overview of recent WMSN healthcare projects. Sections 3 discuss the security and privacy issues for wireless healthcare applications and Section 4 discusses the stringent regulations for healthcare organizations. Section 5 discusses the issues of existing security mechanisms, and Section 6 points out the inherent security requirements to develop a secure healthcare application. Section 7 presents the related works for secure healthcare applications using WMSNs. Section 8 provides discussions on several open issues that are required for the success of future healthcare applications and in Section 9 conclusions are drawn for wireless healthcare applications.
## 1 Introduction
Based on all the above considerations, this paper proposes a novel procedure to automatically diagnose different types of bearing faults and load anomalies by means of electromagnetic signals coming from the induction motor in which the bearings are installed. The data are converted into 2D representations through the use of Continuous Wavelet Transforms and Short-Time Fourier Transforms. There are at least a two original aspects of this work: the use of data collected by performing experimental tests in the laboratory and the development of a neural metamodel based on CNN for processing electromagnetic signals from the machine being tested. To the authors' knowledge, although many papers use the current signal analysis with Wavelet Transforms for bearing diagnosis (some references are reported in Section 2), as of yet, no one has dealt with bearing fault diagnosis by means of Wavelet Transforms and ML models of machines' stray flux signals. Moreover, in this work, a double fault condition was investigated: the simultaneous presence of torque oscillation and a bearing fault. In addition, two different types of bearing fault that are difficult to treat together were considered: the generalized roughness and the single-point defect.
# Numerical Analysis on the Performance of a Radiant Cooling Panel with Serpentine-Based Design
## 3 Results and Discussions
### CFD Simulation--ANSYS Fluent
#### 3.3.1 Maximum Outlet Water Temperature and Total Heat Transfer Heat  
Based on the results obtained from the simulations, all the maximum temperatures and total heat transfer rate for each serpentine flow configuration are tabulated in Table 6 below, with the water inlet temperature, \(T_{\text{inlet}}=288\) K.  
Based on Table 7, Design 2, with the two-inlet-two-outlet serpentine flow configuration, has the highest maximum rate of heat transfer with the maximum temperature of 289.716 K at outlet 1, and 289.727 K at outlet 2. The temperature difference at outlet 1 is 1.716 K and 1.727 K at outlet 2, with a total heat transfer of 28.111 W. Based on the simulation results, heat is absorbed equally between the two pipes in Design 2, resulting in a lower temperature difference with a higher total heat transfer rate. In the case of Design 3, the middle pipe, which is outlet 2, has only 1130 mm of pipe length, resulting in a lower performance of heat absorption from the panel surface.
## 3 Practical Applications of VR in CR
### Multiplayer Semi-Inmersive Systems  
Multiplayer semi-immersive rehabilitation refers to a cognitive rehabilitation training system that utilizes virtual reality technology to provide partial immersion. It allows multiple patients to participate simultaneously but, compared to fully immersive systems, users can still perceive the existence of the real world during use [47]. Multiplayer semi-immersive rehabilitation systems can encourage cooperative and competitive activities, enhancing cognitive rehabilitation, particularly in social interaction and teamworkskills [48]. These systems are primarily applicable to the cognitive rehabilitation treatment of patients with stroke, autism, multiple sclerosis, and brain injury [49].  
Through VR technology, cognitive rehabilitation training can be more personalized, practical, and enjoyable. For example, training can simulate daily living environments, practice language communication, improve reaction speed and attention, and enhance motor coordination and balance [50; 51]. VR technology can also be combined with biofeedback technology to monitor and provide feedback on patients' physiological indicators, helping them better grasp the process and effects of rehabilitation training. Furthermore, VR technology can improve patients' emotional state and enhance their quality of life and self-satisfaction. Patients often experience emotional issues such as anxiety and depression, and VR technology can provide a comprehensive, immersive entertainment experience to help them relax, reduce tension, and alleviate stress and anxiety [52]; at the same time, the training process involves high-difficulty tasks, which can boost patients' confidence, reshape their self-esteem, and enhance their sense of self-efficacy. [PERSON] et al. (2016) [53] conducted a study on a new semi-immersive visual motor task (VMT) test in the field of upper limb motor function neurorehabilitation. The semi-immersive VR environment was simulated using the LEAP Motion Controller (a high-resolution 3D hand-tracking device that enables realistic human-computer interaction for semi-immersive VR systems) and monitored using functional near-infrared spectroscopy (fNIRS) to record VMT-related prefrontal cortex (PFC) responses. The researchers analyzed the recorded data using statistical methods and found that semi-immersive VMT is effective for neurorehabilitation.  
When using multiplayer semi-immersive rehabilitation systems for treatment, there are also some drawbacks. The presence of others may distract or intimidate some users, potentially reducing the effectiveness of the treatment.
## 2 Materials and Methods
### Data Assimilation  
Extended Kalman Filter (EKF) assimilation method was performed to analyse LAI superficial and root-zone water contents [14; 43; 44]. To determine these values, observations, and background (model forecast starting from a previous cycle) information need to be considered. In EKF, dynamically changing coefficients are used, and the analysis is obtained:  
\[x_{a}=x_{f}+\mathbf{K}\cdot\left(y_{o}-\mathrm{H}\left(x_{f}\right)\right) \tag{1}\]  
where \(x\) is the model state vector (a means analysis, \(f\) means forecast), \(\mathbf{y}\) is the observation vector, \(\mathrm{H}\) is the non-linear observation operator, \(\mathbf{K}\) is the Kalman gain matrix:  
\[\mathbf{K}=\mathbf{BH}^{T}\left(\ \mathbf{HBH}^{T}+\mathbf{R}\ \right)^{-1}, \tag{2}\]  
The \(\mathbf{K}\) gain matrix represents the relative importance of the error of the observation concerning the prior estimate. \(\mathbf{H}\) is the linearised observation operator, \(\mathbf{B}\) and \(\mathbf{R}\) are the covariance matrices of the background errors and the observation errors, respectively. They are assumed to be diagonal, and \(\mathbf{R}\) is time-invariant. In the simplified version of the EKF, namely SEKF, the background covariance matrix \(\mathbf{B}\) does not evolve with time. In some previous studies [7; 8; 45], diagonal background error covariance was used at the start of each cycle, but the implicit background covariances are derived from \(\mathbf{H}\) matrix at the analysis time.  
The analysis equation is solved at each grid point independently, as we assume there is no correlation between neighbouring grid points. On the other hand, one grid point is divided into 12 patches, but the observations contain only information from the average value of the grid. Hence in the model state vector, we have to take into account the values of all the 12 patches, i.e., it is an \(\mathrm{Nv}^{\star}\mathrm{Np}\) matrix, where \(\mathrm{Nv}\) is the number of assimilated variables and \(\mathrm{Np}\) is the number of patches. If EKF is used, patches will depend on each other and thus \(\mathbf{B}\) will not be diagonal.  
The non-linear observation operator \(\mathrm{H}\) is a key factor in the data assimilation, which projects from the model space onto the observation space.  
\[\mathrm{y}=\mathrm{H}(x), \tag{3}\]  
In SURFEX, each nature tile is divided by 12 patches; therefore, the \(\mathrm{H}\) operator is assumed as the average of the corresponding \(y_{p}\) values for each patch \(p\) with the weighted fraction of the patches (\(f_{p}\)):  
\[\overline{y}=\sum_{p=1}^{12}f_{p}H^{p}\left(x_{p}\right)=H(x) \tag{4}\]  
Figure 3: Location of the monitoring site at Hegyhatsál.  
**H** is the Jacobian matrix, and the Jacobian elements are calculated by finite differences, by perturbing each component \(x_{j}\) of the control vector \(x\), resulting from the matrix **H** for each integration \(i\):  
\[\textbf{H}_{ij}^{p}=f_{p}\frac{\partial y_{i}^{p}}{\partial x_{j}^{p}} \tag{5}\]  
The patches are independent, therefore:  
\[\frac{\partial y_{i}^{p}}{\partial x_{j}^{m}}=0,\ if\ p\
eq m \tag{6}\]