esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The main purpose of this research was to introduce a classification method, which combines a rule induction procedure with the Takagi-Sugeno inference model. This proposal is a continuation of our previous research, in which a classification process based on interval type-2 fuzzy rule induction was introduced. The research goal was to verify if the Mamdani fuzzy inference used in our previous research could be replaced with the first-order Takagi-Sugeno inference system. In the both cases to induce fuzzy rules, a new concept of a fuzzy information system was defined in order to deal with interval type-2 fuzzy sets. Additionally, the introduced rule induction assumes an optimization procedure concerning the footprint of uncertainty of the considered type-2 fuzzy sets. A key point in the concept proposed is the generalization of the fuzzy information systems' attribute information to handle uncertainty, which occurs in real data. For experimental purposes, the classification method was tested on different classification benchmark data and very promising results were achieved. For the data sets: Breast Cancer Data, Breast Cancer Wisconsin, Data Banknote Authentication, HTRU 2 and Ionosphere, the following F-scores were achieved, respectively: 97.6%, 96%, 100%, 87.8%, and 89.4%. The results proved the possibility of applying the Takagi-Sugeno model in the classification concept. The model parameters were optimized using an evolutionary strategy.  
fuzzy sets; interval type-2 fuzzy sets; Takagi-Sugeno model; fuzzy information systems; classification; fuzzification optimization
## 4 Discussion  
In this study, we introduced a novel deep learning model, MixerNet-SAGA, tailored explicitly for road extraction tasks from remote sensing images. Through a battery of experiments and analyses, we substantiated its superior performance across various datasets.  
To elucidate these findings, we delve deeper in this section. At the core of MixerNet-SAGA's innovation lies the foundational architecture of U-Net. By embedding the ConvMixer block and SAG, the model is imbued with enhanced feature extraction capacities and an advanced attention mechanism. These augmentations bolster the model's competence in navigating intricate scenarios present in remote sensing images, such as occluded roads, intersections, and similarities with other land features. Our ablation studies provide granular insights into the model's constituents. Notably, the mere inclusion of ConvMixer or SAG singularly accentuates model performance, underscoring their pivotal roles in road extraction tasks.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Network** & **Parameters (M)** & **FLOPS (GLOPS)** \\ \hline U-Net & 29.95 & 5.64 \\ \hline HRNet & 25.56 & 5.4 \\ \hline ResNet & 5.87 & 6.61 \\ \hline ResUnet & 38.52 & 8.64 \\ \hline DeepLabv3 & 28.53 & 4.66 \\ \hline RADANet & 73.85 & 2.12 \\ \hline SDUNet & 80.24 & 3.53 \\ \hline MixerNet-SAGA & 50.08 & 1.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter calculation results of the eight network models used in this paper.  
Yet, while MixerNet-SAGA has exhibited commendable performance, it is not without limitations:  
The model may grapple with scenarios involving extreme intra-class spectral variability or inter-class spectral similarity.  
Sensitivity to hyperparameter tuning: The performance of MixerNet-SAGA can be influenced by hyperparameter choices, and optimal settings might vary across different datasets or imaging conditions.  
Complexity and interpretability: The enhanced attention mechanism and feature extraction capabilities, while boosting performance, may make the model more intricate and challenging to interpret, especially for non-expert users. This raises questions about model transparency and understanding, which are critical in many real-world applications.  
Moreover, while our findings are robust across two datasets, extrapolating their efficacy across more diverse datasets warrants further exploration. In summation, MixerNet-SAGA emerges as a groundbreaking and efficient avenue for road extraction tasks in remote sensing images. Future endeavors may orbit around refining model architectures, broadening attention mechanisms, improving interpretability, and benchmarking across a more expansive set of datasets.
# NOMA or OMA in Delay-QoS Limited Satellite Communications: Effective Capacity Analysis
## 3 Effective Capacity
### Effective Capacity Expressions for Some Extreme and Special Cases  
Firstly, the capacity achieved by Users \(c\) and \(t\) in some extreme and special cases, such as \(\rho\to 0\) and \(\rho\rightarrow\infty\), are studied. Based on (11)-(14) and along some simple manipulations, for User \(c\), we have  
\[\lim_{\rho\to 0}E_{c}^{T}(\theta_{c})\ \approx 0,\lim_{\rho\to 0}E_{c}^{N}( \theta_{c})\ \approx 0,\lim_{\rho\rightarrow\infty}E_{c}^{T}(\theta_{c})\approx \infty,\lim_{\rho\rightarrow\infty}E_{c}^{N}(\theta_{c})\ \approx-\ln\left(\alpha\right)^{\frac{\varphi_{c}}{\ln 2}}/ \varphi_{c}. \tag{15}\]  
While for User \(t\), we have  
\[\lim_{\rho\to 0}E_{t}^{T}(\theta_{t})\ \approx 0,\lim_{\rho\to 0}E_{t}^{N}( \theta_{t})\ \approx 0,\lim_{\rho\rightarrow\infty}E_{t}^{T}(\theta_{t})\approx \infty,\lim_{\rho\rightarrow\infty}E_{t}^{N}(\theta_{t})\approx\infty. \tag{16}\]  
It is worth noting that the effective capacities of both access schemes start at zero for both users when \(\rho\) is small enough. Moreover, when \(\rho\rightarrow\infty\), capacities achieved with both access schemes approach infinity except the rate of User \(c\), who experiences a worse channel quality, is limited by \(-\ln\left(\alpha\right)^{\frac{\varphi_{c}}{\ln 2}}/\varphi_{c}\). This phenomenon means that even for a very large value of \(\rho\), the achievable rate of User \(c\) still tends to a limited value, which is closely related to the power allocation coefficient \(\alpha\) and delay-QoS exponent \(\theta_{c}\).
# Unmanned Aerial Vehicle Landing on Rugged Terrain by On-Board LIDAR-Camera Positioning System
## 4 Landing Site Detection
### Place Recognition
#### 4.2.3 Neural Network Architecture  
This study proposes a high-altitude scene recognition method that combines a deep residual network with an attention mechanism, as illustrated in Figure 6. The aim of this approach is to optimize the UAV landing site selection process.  
The convolutional block attention module (CBAM), developed by [PERSON] et al. [45], enhances the model's handling of complex aerial views [46], making it suitable for UAV aerial scene classification. This module comprises two key submodules, namely, the Channel Attention Module (CAM) and the Spatial Attention Module (SAM), which focus on extracting different aspects of features.  
The CAM, shown in Figure 6a, changes the features of different channels by applying globally pooled weights learned from the input features. Subsequently, the SAM, depicted in Figure 6b, takes these weighted channel features as input and further magnifies the model's spatial recognition by assessing the importance of features at each location. This dual attention mechanism of adaptive filtering enables CBAM to capture key scene information with greater precision.  
ResNet50 [47] served as the neural network backbone, as illustrated in the lower half of Figure 6. It was selected as the core architecture for this research, which aimed to construct an efficient model for scene recognition. The design philosophy of ResNet50 revolves around using residual learning to address the degradation problem encountered during the training of deep neural networks [48]. By incorporating skip connections, ResNet50 helps the network learn residual mappings for additional layers, rather than directly fitting the desired map. This feature enhances training efficiency and reduces the risk of over-fitting.  
In UAV aerial scene recognition tasks, ResNet50 has demonstrated powerful feature learning capabilities owing to its deep structure and the stability gained through its residual learning mechanism [49]. Moreover, the efficiency of ResNet50 makes it particularly suitable for use in UAV systems with limited computational resources. Despite being a deep network, its residual connections enable it to efficiently perform forward and backward propagation without significantly increasing the computational costs. This means that UAVs can process and analyze aerial images quickly without reducing their accuracy, thereby supporting real-time decision-making.  
The feature identification capabilities of ResNet50 are further enhanced when combined with the attention mechanism of CBAM. This enables the network to learn complex feature representations, thereby improving the model's robustness and generalizability in classifying and recognizing aerial scenes.  
Figure 6: ResNet50 neural network residual function with convolutional block attention module network architecture, (**a**) Internal structure diagram of CAM module, (**b**) Internal structure diagram of SAM module.
## 4 Research Methodology
### Selection of Simulation Parameters
#### 4.2.4 Laminar Flow Coefficient, Yield Stress, and Viscosity Coefficient  
Based on the FLO-2D manual recommendations and the characteristics of the Dashiling Gully mudslide, the laminar flow damping coefficient K was determined to be 2300. Yield stress \(\tau_{y}\) and the viscous coefficient \(\eta\) are two critical parameters in the rheological model used for numerical simulation calculations, as they influence the flow and deposition processes of the mudslide.  
The relationship between yield stress \(\tau_{y}\) and volume concentration \(C_{v}\) can be expressed as the following:  
\[\tau_{y}=\partial_{2}e^{\beta_{2}C_{v}} \tag{8}\]  
Similarly, the relationship between the viscosity coefficient \(\eta\) and volume concentration \(C_{v}\) is given by the following:  
\[\eta=\partial_{1}e^{\beta_{1}C_{v}} \tag{9}\]  
For this study, the values of these parameters were selected based on the FLO-2D manual and the specific conditions of Dashiling Gully, resulting in the following: \(\partial_{1}=0.811\), \(\partial_{2}=0.0046\), \(\beta_{1}=13.72\), and \(\beta_{2}=11.24\).
# Mapping Surface Flow Velocity of Glaciers at Regional Scale Using a Multiple Sensors Approach
## 3 Results
### Uncertainties Analysis
#### 3.3.3 Comparison between Sentinel-2 and Venus in the Southern Alps of New Zealand  
For glaciers in the Mount Cook area in the Southern Alps of New Zealand, we can compare the performances of Sentinel-2 (Figure 3A) with the 5-m spatial resolution Venus satellite (Figure 6A). To do this, we formed a time-averaged ice velocity map from the Venus images. The resulting map is shown in Figure 6A. Note that we observe a clear step in background velocity from Venus due to the change of the acquisition footprint that was enlarged after November 2018 (before, the footprint was more focused on Tasman Glacier), hence fewer data are available in these newly covered zones. Similarly to Sentinel-2 (Figure 4C), we estimate the Venus nominal precision for each cycle from the distributions of the weighted standard deviation in ice-free areas (Figure 6B). The evolution of the precision with the repetition cycle obtained for Venus is similar to that of Sentinel-2 (Figure 6B), while we expected that the accuracy of Venus would be better due to the increased spatial resolution (5 m versus 10 m for Sentinel-2). We attribute the lower performance of Venus to the quality of the image ortho-rectification. This is however largely compensated by the number of acquisitions (every 2 days) that can be stacked and helps to capture finer temporal details (Figure 6A).  
Figure 6: **(A)** Glacier surface velocity mosaic from Venus images acquired in years 2018 and 2019 over the Tasman Glacier region (Southern Alps of New Zealand); **(B)** Evolution of precision on stable ground for Venus (black cross) and Sentinel-2 (green diamond) with the length of the repeat cycle.
# Smart Disaster Response in Vehicular Tunnels: Technologies for Search and Rescue Applications
## 6 Conclusions  
Since a tunnel is a type of enclosed space, when a fire occurs it is very difficult to perform search and rescue operations, due to the rapid spread of fire and thick smoke. In particular, a lot of time is required for searching evacuation, because of its long and wide space. To effectively cope with such a difficult environment, it is important to promptly detect and recognize the location, causes, and type of accident, and to identify the positions of victims.  
In this paper, we proposed a fire and accident detection system based on IoT technology, and also proposed a remote monitoring system in which it is applied. However, since it is still difficult to predict the spread pattern of a fire or disaster, it is necessary to equip firefighters with specialized equipment to directly check the internal situation. For this purpose, we proposed a system for adopting and operating ground reconnaissance robots and aerial reconnaissance robots. These sensors, robot equipment, and fire fighters should be able to communicate with each other, and their positions should also be grasped in real time. For this, UWB- and Bluetooth-based location tracking technology that can be operated in tunnels were analyzed, and a navigation technique using these devices was presented. The detection sensors, reconnaissance robots, and positioning technology proposed in this paper can be used alone, but the efficiency is expected to be maximized if the three technologies are complementarily used.  
In future work, we plan to study technologies that more actively recognize accidents and respond to them immediately. We will also pursue the development of technologies to ensure the quick and efficient evacuation and safety of casualties and survivors.  
[PERSON] carried out the main conceptual design of the proposed system and writing of the manuscript. [PERSON]-[PERSON] mainly dealt with data analysis and interpretation. [PERSON] and [PERSON] focused on the experimental activity (i.e., configuration and realization of proposed system). [PERSON] had contributed to provide the fire fighting operations and scenarios for field deployments.  
This work was supported by Fire Fighting Safety & 119 Rescue Technology Research and Development Program funded by the Ministry of Public Safety and Security of Korea (MPSSFireSafety-2015-78).  
The authors declare no conflict of interest.
# Power System Voltage Correction Scheme Based on Adaptive Horizon Model Predictive Control
## 1 Introduction
References [3; 4; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 18; 19] apply constant horizon parameters, but how those parameters are determined is not discussed. The horizon parameters in [5] is chosen to be the time in which the predicted voltage drops by a predefined percentage at the initial control instant, and the horizon parameters are kept constant during the entire receding-horizon optimization process. In [16; 17], the controller uses constant horizon parameters unless the prediction horizon is increased to anticipate load tap changer actions. The control methods discussed so far determines the control horizon parameters off-line based on experience or enumeration without quantitative analysis. In this paper, a coordinated voltage correction scheme based on adaptive horizon model predictive control (AH-MPC) is proposed to maintain a satisfactory voltage profile while minimizing the control cost. Generator voltage references, shunt capacitors and on-load tap changers are considered as candidate control actions. The proposed scheme is based on the principles of MPC. However, it differs from the standard MPC-based method by way the horizon parameters are chosen and embedded in the optimization procedure. The main contribution of this paper is developing a principle of determine the horizon parameters, which is a key feature missing in previous control schemes. An index is formulated to evaluate and ensure the applicability of the horizon parameter configurations, and barely consume extra computation time. Within each sampling interval, the length of the horizon parameters are updated according to the proposed evaluation index and real-time measurements, aiming at better control performances compared with the traditional MPC-based methods. This paper is organized as follows: Section 2 introduces the standard MPC-based voltage control model. Section 3 presents the determination of the adaptive horizon parameters. The voltage correction scheme based on AH-MPC is presented in Section 4. The simulation results and conclusions are presented in Sections 5 and 6 respectively.
## 5 Conclusions  
The GPVS system for monitoring gravel movement has been developed in this research. The system is validated by conducting laboratory flume experiments using natural gravel. The experimental results confirm the feasibility and efficacy of the GPVS in determining gravel movement and transport. Furthermore, the acoustic approach has been shown to predict the gravel transport rate rather accurately. This study leads to the following conclusions:  
The GPVS is very sensitive to the sound generated by passing gravel and is not sensitive to ambient noise. This is especially advantageous because it eliminates the problem of ambient noise interference that most current acoustic systems have. Meanwhile, a developed GPVS can appropriately monitor the real-time gravel movement. Furthermore, using the collected acoustic signals, the GPVS can produce a relatively accurate quantitative estimation of the gravel transport rate.  
The GPVS can reliably monitor total pressure regardless of where it is installed or what the GPVS panel position is, and wherever gravel is piled up. The results reveal that the GPVS measurements are independent of water depth, indicating that GPVS pressure data are primarily caused by gravel movement.  
The acoustic approach can properly determine the gravel transport rate for weak gravel transport. For heavy gravel transport, the accuracy of this method decreases, and the pressure method will be used in this situation.  
Due to the experimental flume's restrictions, only a single gravel size is tested under steady and uniform flow conditions to assess the accuracy of the acoustic signal collectedby the GPVS. Further study is required for cases with strong gravel transport and graded sediments in order to improve the system.
## 2 Materials and Methods
### Image Processing for Extracting Twelve Vegetation Indices
#### 2.3.2 Canopy Height Model from Digital Surface Model  
The estimation of crop height within each subplot relies on the utilization of a Canopy Height Model (CHM) [34]. To create a Digital Terrain Model (DTM) representing the field's topography, the _QGIS_ software is employed. The CHM is obtained by subtracting the Digital Surface Model (DSM) data from the DTM. To ensure heightened accuracy, a filtering process is applied to the CHM image layer, eliminating pixels with a height below a predefined threshold of 0.05 m. Subsequently, the CHM image layer is segmented using a plot segmentation shapefile, enabling the extraction of statistical data for each individual ROI to acquire canopy height (CH) information. The calculation of canopy volume (CV) is performed by multiplying the data related to canopy density (CD) with the corresponding CH data for each specific ROI. This sequential process is carried out for all the ROIs investigated in the study, thereby obtaining the CV values.
# Predicting Employee Absence from Historical Absence Profiles with Machine Learning
## 4 Experimental Design  
### Research Objectives and Questions  
This study uses demographic profiles and historical attendance profiles to predict future absenteeism using various predictive modelling techniques, including single-target versus multi-target and single model versus ensemble of models. This section outlines the research questions that guide our investigation of the effectiveness of different predictive models over different time periods.  
The research questions formulated for this study (see Table 6) **aim to explore the capabilities of predictive models in accurately predicting employee absences over short, medium, and long terms**. These questions are designed to test the relative performance of different modeling approaches and their practical implications for HR analytics. By addressing these questions, we aim to uncover the most efficient and accurate predictive strategies, thus assisting HR professionals in strategic decision-making processes.
# Forecasting Seasonal Sales with Many Drivers: Shrinkage or Dimensionality Reduction?
## 3 Methods
### Exponential Smoothing Models with Explanatory Variables
#### 3.2.1 Univariate Exponential Smoothing Models  
The exponential smoothing family of models is one of the most widely used forecasting approaches in retailing, and in business forecasting in general [57]. With ES the time series is modelled as a combination of three components, the local level, the local slope, and the seasonal pattern, that together interact with the error term. The combination of these components can be additive or multiplicative, permitting the capture of an extensive variety of time series patterns. A useful interpretation of exponential smoothing is to see each of the aforementioned components modelled as an appropriate weighted moving average, the weights of which decay exponentially. This requires a single parameter per component, making the models relatively easy to understand, parameterise, and implement. Ref. [60] embedded ES in a state-space model formulation, providing the statistical rationale for maximum likelihood estimation of model parameters, selection of the appropriate model form using information criteria, and the generation of prediction intervals, greatly simplifying and automating the modelling process. Here, we follow the recommendations of [61] and select between the alternative models using the (AICc). In our case, as we model retail sales, due to the presence of multiplicative promotional effects we first logarithmically transform the data. Therefore, we can also restrict the ES models to be strictly additive, substantially simplifying and speeding up the modelling process (from 30 alternative models to only 6, see Table 1). Given the prevalence of exponential smoothing we refer the reader to [PERSON] et al. [57] (Chapters 4 & 5) and [61] for the model details.
## 1 Introduction
\begin{table}
\begin{tabular}{c c} \hline
**Topic of the Article** & **Contributions Given in the Article** \\ \hline Intelligent Transportation Systems and & Vehicle detection and collision avoidance technologies [1], data-driven intelligent transportation systems with learning aspects [2], communications solutions for automated driving and connected vehicles [3] \\ \hline Unmanned Aerial Vehicles and Their Connectivity & Communication solutions for UAVs, covering characterization of networks, routing, seamless handover and energy efficiency [10]; Latest 5G and Beyond technologies for UAVs in [11] \\ \hline Coverage Path Planning for Robotics and Ships & A survey on determining a path that passes over all points of an area or volume of interest while avoiding obstacles [12]. Covers autonomous harvesters, vacuum cleaners, maritime applications, etc. \\ \hline Underwater Vehicle Communications & Comprehensive survey on underwater robotics until 2000 in [13]. Recent survey looking at RF, optical and acoustic communications [14] \\ \hline Autonomous Ship Technologies & Classification of existing autonomous surface vessel prototypes [15]. Application of surface vehicles and development on guidance, navigation and control in [16]. Assessment of safety issues in remote-controlled vessels [17] \\ \hline Maritime Communications Including & Survey covering underwater, UAV and surface networks [18]. Hybrid architecture and identification of the data transmission needs of an autonomous ship [5], intelligent network management for hybrid network [8], maritime cybersecurity aspects covered in [19] \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison to current state of the art and novelty of this article.