esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Change of Seasons  
The temperature characteristics of Zhengzhou City across different seasons are illustrated in Figure 7. Our analysis indicates that summer is the season with the highest average, maximum, and minimum temperatures, whereas spring and autumn temperatures are relatively similar. Interestingly, we found that the heat island effect has a more significant impact during autumn when temperatures are at their lowest. These findings underscore the need for developing appropriate strategies to mitigate the effects of heat islands on public health and the environment, particularly during the autumn season.  
We investigated the daytime and nighttime variations in the heat island effect across different seasons in Zhengzhou City, and the results are presented in Figure 8. Figure 8 illustrates how the heat island effect changes over a day and throughout various seasons in the city. Based on our findings, the impact of the heat island effect in Zhengzhou City during the daytime tends to be most significant during autumn and least significant during winter. This highlights the seasonality of heat island effects and has important implications for developing effective mitigation strategies. Our analysis reveals no significant difference in the heat island effect between spring and summer (0.69 \({}^{\circ}\)C and 0.62 \({}^{\circ}\)C, respectively). However, a considerable difference is observed between autumn and winter (2.21 \({}^{\circ}\)C and 0.02 \({}^{\circ}\)C, respectively). These findings suggest that the heat island effect is most pronounced during autumn, and effective mitigation strategies should focus on this season in particular. Our analysis of Zhengzhou City's heat island effect reveals interesting seasonal differences. During the nighttime, the heat island effect is most potent in the summer (2.4 \({}^{\circ}\)C) and comparatively weaker in the autumn (2.06 \({}^{\circ}\)C) and winter (1.7 \({}^{\circ}\)C), with the most negligible impact in the spring (0.74 \({}^{\circ}\)C).  
Interestingly, during the day, the heat island effect is stronger than the nighttime effect in autumn (2.21 \({}^{\circ}\)C). During the spring, summer, and winter, the nighttime heat island effect is stronger than the daytime effect. These findings emphasize the importance of season-specific mitigation strategies to address the heat island effect in urban environments.  
Figure 7: Seasonal variations in LST and UHI intensity in Zhengzhou City. The graph shows that summer experiences the highest average, maximum, and minimum temperatures, whereas spring and autumn temperatures are comparable. Notably, the heat island effect has a more significant impact during autumn when temperatures are at their lowest.
# A Novel CA-RegNet Model for Macau Wetlands Auto Segmentation Based on GF-2 Remote Sensing Images
## 3 Data and Methodology
### Deep Learning Models
#### 3.2.2 The Res2 Net Model  
This study employs the Res2 Net model, known for its superior performance in vision tasks and its unique hierarchical connections within residual blocks [26]. Unlike traditional models like ResNet, Res2 Net excels in extracting multi-scale features and assimilating extensive contextual data. This research highlights the proficiency of the Res2 Net model in parsing the multifaceted characteristics of wetlands, encompassing components like water and vegetation. The model's adeptness at assimilating extensive contextual nuances furnishes a more discerning array of features, advantageous for ensuing classification tasks.  
This study employs the Res2 Net model, starting with a 2D convolutional layer for spectral-spatial feature extraction, followed by a max pooling layer to reduce spatial complexity. Key to the model is sequential Res2 Net blocks, specialized for efficient, multi-scale feature extraction [27]. These blocks contain parallel convolutional towers that capture complex spectral-spatial relationships. A shortcut connection merges the resulting feature maps, aiding efficient backpropagation and countering the challenge of gradient vanishing. Notable parameters include a primary convolutional layer with 64 filters and a 7 \(\times\) 7 kernel, succeeded by a max pooling layer with a 3 \(\times\) 3 kernel and a 2 \(\times\) 2 stride. In this study, the model underwent 150 epochs, a parameter established through cross-validation outcomes to assess aspects such as model complexity and learning rate, which are used to avoid the overfitting. A strategic application of a hybrid loss function, integrating both CE_Loss and FocalLoss, was employed to mitigate the further risk of overfitting [28,29]. Figure 4 depicts the model's architecture.
# Variable Selection in Time Series Forecasting Using Random Forests
## 1 Introduction
### Aim of the Study  
The primary aim of our study is to investigate how the performance of RF is related to the variable selection in one-step forecasting of short time series. The proposed framework in Section 1.2 can help in providing an empirical answer to the problem of variable selection. In conclusion, it is shown that a low number of recent lagged variables performs better, highlighting the importance of the training set's length. The RF algorithm is proven to be competent in time series forecasting, even though it has not exhibited the best performance in most of the examined cases. This does not imply that the RF are of little practical value. Instead, as has been shown in [12], there is not a universally best forecasting method, therefore, in practical applications we should use multiple methods for obtaining a good forecast. In such cases, we emphasize that appropriate methodologies for the optimization of the performance of each method should be used. Our contribution regards the optimization of the forecasting performance of the RF.
# Improved Subsidence Assessment for More Reliable Excavation Activity in Tehran
## 2 Numerical Modeling  
For this study, PLAXIS 3D Tunnel (version 1.2) was utilized. PLAXIS 3D Tunnel is a specialized three-dimensional finite element computer program that is utilized to assess the stability and deformation for different kinds of tunnels in rock and soil. Although the program can be used for various geotechnical structures, it has features specifically designed for NATM and shield tunnels. Additionally, because soil is a multi-phase material, handling the hydrostatic and non-hydrostatic pore pressure in the soil requires specialized techniques. Many tunnel projects include the modeling of structures and the interaction between the structures and the soil, even though the modeling of the soil itself is a significant topic. Special characteristics of PLAXIS 3D Tunnel are designed to address the various facets of intricate geotechnical structures.  
A practical way to construct both circular and non-circular tunnels out of arcs and lines is to use the PLAXIS 3D Tunnel program. To simulate the tunnel lining and its interaction with the surrounding soil, plates and interfaces can be incorporated. Curved boundaries within the mesh are modeled by fully isoperimetric elements. A range of pragmatic techniques have been employed to examine the distortion resulting from the tunnel's construction. Additionally, by turning on and off groups of pieces, the staged construction function makes it possible to simulate the construction and excavation processes in a realistic manner. Furthermore, the application of loads enables a practical evaluation of the stresses and displacements brought by, for instance, tunnel excavation or subterranean building [23].
## 1 Introduction
In order to collect the necessary data for 6-DOF tracking of catheters, two options are available. The first option involves the integration of two receiving coil probes of electromagnetic (EM) sensors into the tip of the catheter. This method offers portability, but has a low accuracy of up to \(\sim\)5 mm and may result in out-of-field data [13]. Additionally, the hardware required for this method is not readily available in catheterization labs, and manual integration of the probes into the catheter tip can introduce additional errors or the need for specialized equipment that is not scalable, given the FDA requirements for such procedures [19]. Furthermore, this method is limited to specific types of catheters and is not a general solution for 6-DOF tracking. The second option for 6-DOF tracking involves the use of real-time bi-plane fluoroscopy imaging. This method offers a general tool that can be used for all types of catheters, and X-ray fluoroscopy machines are widely available in catheterization labs, although bi-plane c-arms are less common. However, this method only offers 5-DOF tracking and does not directly provide roll angle information, which is crucial for catheterization procedures. Roll angle is an essential parameter of 6-DOF tracking in catheterization, particularly when using Intracardiac Echocardiography (ICE) catheters. It provides precise navigation of the catheter by rotating it to access different parts of the heart and avoid obstacles, better visualization of the heart and surrounding structures by changing the view direction of the ultrasound transducer, increased flexibility in catheter positioning, and reduction in procedure time by easily obtaining the best imaging view. To rectify the lack of roll angle sensing capabilities and to acquire complete 6-DOF tracking data for AR/MR systems, this paper has proposed a machine learning-based model to predict roll angles of an ICE catheter based on the positional tracking of the ICE catheter using bi-plane fluoroscopy imaging. The proposed model is a Multi-Input and Single Output (MISO) machine learning-based universal approximator that aims to accurately predict the roll angle of a catheter using inputs derived from the coordination of two intrinsic markers extracted from the synchronous frames of videos obtainedfrom Antero Posterior (AP) and Left Anterior Oblique at 90 degrees (LAO90) planes of fluoroscopy imaging. This paper is organized into three additional sections. Section 2 provides a definition of the ICE catheter and elaborates on the importance of roll angle prediction for its accurate tracking during cardiac catheterization; additionally, the proposed machine learning-based model for roll angle prediction is described. In Section 3, we present the results of our study, showcasing the accuracy of the proposed model through various error criteria. Finally, the key findings of this research are summarized and conclusions are drawn in Section 4.
## 4 Experiments and Analysis
### Dataset Description  
Through the MGM module in Section Meta-Feature Generation (MGM), meta-features were generated by calculating the features of the measurement data. This subsection demonstrates how the meta-features were generated with real sensor measurements.  
The dataset used in this study consists of real-world LPG usage data collected from HD Energy and KT (Korea Telecom), covering a period of 370 days from 1 September 2023 to 5 September 2024. A total of 887 sensors were installed across diverse environments, including residential buildings, commercial offices, factories, and restaurants throughout South Korea. In the preprocessing stage, sensors containing all-zero values or severe outliers were removed, resulting in a refined dataset of 566 valid sensors. Each sensor's time series was converted into a set of meta-features representing statistical and dynamic characteristics of the signal. The meta-features include statistical indicators such as the mean, variance, standard deviation, kurtosis, skewness, and entropy, as well as time-series-specific attributes such as linearity and stationarity, as detailed in Table 3.  
These meta-features were used as inputs to the meta-learning framework proposed in this study. The meta-learning model was trained to predict the most suitable forecasting model for each sensor by analyzing its meta-features. The forecasting models were trained on the same preprocessed dataset using an 80-10-10 split for training, validation, and testing, respectively. The goal of the dataset preparation and meta-feature extraction process was to enable fast and adaptive model selection under computational constraints, thereby supporting efficient time-series forecasting across a large number of sensors.
## 2 Multi-Beam Massive MIMO Configuration Using CMA in the Uplink
### Principle of CMA  
The principle of CMA is explained in this section. The CMA is categorized as a blind adaptive array algorithm, because it requires information of received signals only. Because CSI estimation is not needed when using CMA, the communication efficiency is improved greatly. In addition, the CMA is not affected by the carrier or timing offsets. The cost function Q of CMA is expressed as  
\[\begin{array}{rcl}Q(\mathbf{W})&=&E\left[||y(t)|^{p}-\sigma^{p}|^{q}\right]\\ &=&E\left[\left||\mathbf{W}^{H}\mathbf{X}(t)|^{p}-\sigma^{p}\right|^{q}\right]\end{array} \tag{1}\]  
where \(y\) is the output voltage of the array and \(\sigma(>0)\) is a constant value, which is usually set to be the power of the desired signal. Symbols \(p\) and \(q\) are positive integers with values of one or two. The weight of each element is controlled so that (1) is minimized. Although the weight of the CMA adaptive array is updated by using (1), it is not possible to express the optimum weight directly in the closed form, because (1) is nonlinear with respect to the weight. Therefore, the weight of CMA is controlled by an asymptotic technique such as SD-CMA so that (1) is minimized.  
If the weight and the cost function after \(m\) iterations are \(\mathbf{W(m)}\) and \(Q(m)\), respectively, the weight updating equation using SD-CMA can be expressed as  
\[\mathbf{W}(m+1) = \mathbf{W}(m)-\mu\
abla_{W}Q(m)\]  
Figure 1: Configuration of the multi-beam massive multiple-input–multiple-output (MIMO) in uplink.  
where \(m\) is the number of repetitions (iterations) of the weight update, and \(\mu\) is the step size. In addition, \(\
abla_{W}Q(m)\) is the slope vector of \(Q\) with respect to \(W\). In the SD-CMA, the step size determines the convergence characteristics of the weight in the CMA adaptive array. In order to improve the performance of the CMA adaptive array, LS-CMA is proposed [24]. Because the convergence of weight in LS-CMA is more rapid than that in SD-CMA, LS-CMA is used for the uplink. The weight updating equation by LS-CMA is denoted by  
\[\begin{split}\mathbf{W}(m+1)=&\ \mathbf{W}(m)-\left[\sum_{j=1}^{n}\mathbf{X}(i )\mathbf{X}^{H}(i)\right]^{-1}\sum_{i=1}^{n}\mathbf{X}(i)\left[y^{*}(i)-\delta^{*}(i) \right]\\ =&\ \left[\sum_{i=1}^{n}\mathbf{X}(i)\mathbf{X}^{H}(i) \right]^{-1}\left[\sum_{i=1}^{n}\mathbf{X}(i)\delta^{*}(i)\right]^{-1}\\ &\delta^{*}(i)=\frac{\sigma}{|y(i)|}y(i)\end{split} \tag{3}\]  
where \(\mathbf{X}(i)(i=1,\cdot\cdot\cdot,n)\) and \(y(i)(i=1,\cdot\cdot\cdot,n)\) are the input vector and output signal, respectively, at the \(i\)-th sample.
## 3 Analysis of Engineering Example  
### Engineering Overview  
The core of a modern incremental launching construction is to achieve the longitudinal movement of the beam along the design direction through the \"jacking-pushing-lowering-retracting\" cycle of the walking incremental launching equipment. The incremental launching equipment is composed of a vertical jack, horizontal jack, and horizontal correction module [25]. In the incremental launching construction, the beam body is first lifted by the vertical jack. When the set piston stroke of the jack is reached, the beam body is pushed forward by the horizontal jack to set the stroke of the single incremental launching cycle. Then, the vertical jack piston retracts, and the beam body falls on the temporary cushion beam. Finally, the horizontal jack retracts to complete the single incremental launching cycle [26], as shown in Figure 4.  
The main bridge of the Huanghe Grand Bridge has a total length of 3650 m, with a span layout of 6 \(\times\) (6 \(\times\) 100 m) + 1 \(\times\) 50 m, comprising a total of 37 spans, and is divided into seven sections. The superstructure adopts a steel-concrete composite girder, while the substructure consists of vase-shaped piers with pile foundation caps. Both the left and right spans of the bridge have a 2% unidirectional cross slope. The height of the main girder cross-section is 5.0 m (including 0.28 m of concrete slab thickness), with a height-to-span ratio of approximately 1/20. The transverse width of the concrete bridge deck is 16.55 m, and the distance between the center points of the webs on both sides is 10.1 m. The cantilever length on the outside of the web is 3.175 m. The cross-section of the main girder is shown in Figure 5.  
Figure 4: Single launching cycle. (**a**) Placing incremental launching equipment. (**b**) Jacking-up the beam body. (**c**) The horizontal jacks push the beam forward. (**d**) Lower the beam, and retract the jacks.  
Two bridge incremental launching platforms were set up. Platform 1 is located between Pier 143 and Pier 147, and it is responsible for the creation of 24 spans of the main girder. Platform 2 is located between Pier 169 and Pier 170, and it is responsible for the incremental launching of 12 spans of the steel-box girder. The elevation layout of the third and fourth sections is shown in Figure 6.
## 3 Dynamic Fog Storage Services  
Typical IoT environments are usually composed of many different components, such as: sensors, gateways, computing and storage nodes. Essentially, such environments generate large quantities of unstructured data. However, the type and amount of data differ between use cases. For instance, a smart and safe construction site that facilitates smart, automated, secure and sustainable building process might operate with more than 500,000 data operations per minute [1]; whereas, a scenario for a smart city crossing environment might operate with significantly lower amounts of data [36]. Due to such significant differences between IoT scenarios, the QoS requirements for data storage and processing differ as well, which represents a challenging task for SLA management.  
In the investigated case (see Figure 1), a user, who is located in Slovenia, may require a smart application that will operate with data from IoT devices that frequently change their geolocation (e.g., smart vehicles, smartphones or wearable devices). To operationally achieve and maintain high QoS, the data are required to be dynamically stored in close proximity to the data sources. The complete workflow is composed of six consecutive steps, which are as follows: (1) the user defines QoS requirements for the preferred storage, (2) the Kubernetes-based orchestrator allocates available resources (i.e., storage infrastructures), (3) the decision-making mechanism derives a set of Pareto non-dominated storage infrastructures, (4) the user manually selects a Pareto-optimal Fog storage node from the set of non-dominated storage nodes by choosing the preferred trade-off between the desired NFRs and signs the SLA agreement, (5) deploy containerised database and commence persisting data, (6) SLA monitoring and reporting.
# Multi-Layered Satellite Communications Systems for Ultra-High Availability and Resilience
## 4 System Model: MLSS Concept
### High-Level Architecture (HLA)
It provides external APIs and product catalogs, enables order management, and is responsible for the billing of users. Related functionalities need to be implemented in the system.
# Analysis of the Learning Process through Eye Tracking Technology and Feature Selection Techniques
## 3 Results  
### Statistical Study  
#### 3.1.1 Previous Analyses  
Before starting the testing of the hypotheses, it was checked whether the sample followed a normal distribution, for which a study was conducted on the values of skewness (values below \(\mid\) 2.00 \(\mid\) are considered accepted values, and a value of skewness = \(-\)0.22 was found) and kurtosis (values below \(\mid\) 8.00 \(\mid\) are considered accepted values, and a value of kurtosis = \(-\)2.06 was found). Therefore, the results indicate that the distribution follows the assumptions of normality, which is why parametric statistics were used to test the hypotheses.
## 2 Materials and Methods
### Nanoparticle Characterization  
Nanoparticle size distribution and zeta potentials were measured using a zetasizer Nano ZS equipment (Malvern Instruments Ltd., Worcestershire, UK). Measurements of pDNA-NPs sizes were performed by dynamic light scattering. Each sample was measured three times with 10 runs respectively. Additionally, each sample for zeta potential was measured by duplication with at least 10 runs at constant temperature (25 \({}^{\circ}\)C) by laser doppler electrophoresis. Z-averages and zeta potentials were obtained from three independent experiments.  
Surface morphology of pDNA-NPs was analyzed by scanning electron microscopy (SEM) through a field emission scanning electron microscope (JSM-7800F, JEOL, Pleasanton, CA, USA). Nanoparticle samples were prepared using formvar carbon film and air-dried for several minutes. Surface morphology was confirmed by atomic force microscopy (AFM JSP-4210, JEOL, Tokyo, Japan) in noncontact mode by using an NSC15 silicon cantilever (MikroMasch, Santa Clara, CA, USA). For surface analysis, a drop of pDNA-NPs solution was deposited onto freshly cleaved mica and air-dried for several minutes. AFM images were analyzed with WSxM software [48].  
pVAX1-NH36 loading in pDNA-NPs (DL) was determined by subtracting the amount of pVAX1-NH36 recovered in wash solutions from the initial amount of pDNA added [35; 49; 50]. Free pVAX1-NH36 concentration in all solutions was measured by HPLC-HIC [51] with a source 15 PHE (Phenil-Polyestriene) column of 0.46 cm of diameter per 10 cm of length on an Akta purifier 10 UPC (GE Healthcare, Chicago, IL, USA). Briefly, the column was equilibrated with 1.5 M ammonium sulphate in Tris-HCl 10 mM with pH of 8.0 at a flow rate of 1 mL/min and a sample (30 \(\mu\)L) was injected at the same flow rate. The column was eluted for 0.8 min with the same buffer used for equilibration and an isocratic elution was performed using Tris hydrochloride buffer (Tris-HCl) for 0.7 min. After this period, ammonium sulphate concentration was increased to its initial value. This condition was maintained during the next 5.5 min to re-equilibrate column. Absorbance at 260 nm and conductivity of eluate were recorded continuously. Plasmid amount was quantified using a calibration curve obtained with a standard model plasmid prepared in a 0-60 \(\mu\)g/mL concentration range. Encapsulation efficiency (EE) was defined as a percentage of pVAX1-NH36 encapsulated in pDNA-NPs with respect to the initially added amount of pVAX1-NH36.