esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Building Height Estimation: Machine Learning Models for Urban Image Analysis
## 4 Machine Learning Methods
### Convolutional Neural Networks  
The convolutional neural network is a type of neural network developed specifically for image processing [30]. Each layer in a convolutional network consists of applying an image convolution using various kernels and obtaining several new images. All images  
Figure 1: Figure showing the effects of a convolution on an image. (**a**) Original image from the Cityscapes test dataset. (**b**) Result of applying a \(\left(\begin{smallmatrix}1&0&-1\\ 1&0&-1\end{smallmatrix}\right)\) kernel to the grey-scaled original image. This kernel highlights edges on the image as it computes the local difference around every pixel.  
Figure 2: A (3 \(\times\) 3) kernel with dilation rate \(d=2\) has a field of view of (5 \(\times\) 5). Blue are the elements for the kernel and white are the skipped elements  
are concatenated over the feature dimension. This means that, given an input image with three features, red, green and blue channels and size (width \(\times\) height \(\times\) 3), applying 32 different kernels would result in a matrix with 32 features and size (width \(\times\) height \(\times\) 32). The parameters that will be optimised are those of all kernels from all image convolutions in the network.  
After a convolution is applied, a non-linear activation function is applied to each pixel of the output image. This makes the neural network a nonlinear function, achieving complex behaviour. ReLu is the most widely used activation function, as it is simple and fast to compute. On most layers, we use a variation of ReLu called GeLu, as shown in Figure 3, which performs better for classification tasks [23]. A depthwise separable convolution [28] is a type of convolution that uses even fewer parameters, achieving the same result as a normal convolution. Therefore, in our models, we almost exclusively use this type of convolution. Convolutional networks usually extract local information from an image, although it is not suitable for extracting global information, as two distant pixels in the image will not be processed by the same or by nearby kernels. To avoid this problem, models usually include a bottleneck. This consists of gradually reducing the resolution of the image and increasing the feature dimension. This brings all the pixels closer together. After running through the bottleneck for semantic segmentation tasks, the image enters the decoder, which gradually upsamples the image to its original resolution. This approach presents the disadvantage that detail is lost in the output image. The atrous spatial pyramid layer [29] is a convolutional layer that is also aimed at solving the global information problem by using dilated kernels, that is, kernels that skip proximate pixels and use those which are further away.
# Comparative Analysis of Current Control Techniques to Support Virtual Inertia Applications +
## 3 Design of Current Controllers
### PR (Proportional-Resonant) Controller  
For the design of the PR controller, it is important to ensure that the controller can show zero steady-state error to signals at the resonant frequency, which in this case would be the grid frequency of 60 Hz. As such, the \(\omega\) parameter in this design was taken to be 376.9 rad/s (60 Hz), and the ratio of the parameters, \(\frac{K_{I}}{K_{P}}\), was taken to be 300, which ensures that the resonant constant, \(K_{I}\), has a sufficiently  
Figure 7: Frequency response of the plant, the designed controller, and the loop transfer function (LTF) of the overall system. (**a**) PI Type-2 controller. (**b**) PR controller.  
high value. A high value of \(K_{I}\) around the resonant frequency ensures the controller can exhibit near to zero steady-state error and track the 60-Hz sinusoidal signal accurately [22]. The constant \(K_{P}\) was then calculated such that the resulting loop transfer function had a damping ratio of 0.42, which corresponds to a phase margin of 45\({}^{\circ}\), as in the case of the PI controller designed previously. The calculated value of \(K_{P}\) was 13.20. Figure 7 shows the frequency response of the plant, the designed PR controller, and the loop transfer function of the overall system. It can be observed that the controller has a high gain around the grid frequency of 60 Hz, as desired. Using these calculated values in (4), the transfer function of the designed PR controller was:  
\[C_{PR}(s)=\frac{13.2s^{2}+3960s+1,876,010}{s^{2}+14,122} \tag{10}\]
## 4 Method
### The EMD-LSTM Model  
Another forecasting model used in this study is the EMD-LSTM model, which is a model that combines the Empirical Mode Decomposition (EMD) method with LSTM. The EMD was proposed by [PERSON] et al. [26] to decompose signals into eigenmodes. The basic principle of the EMD algorithm is firstly find the maximum and minimum points of the original signal \(X(t)\) and then fit these extreme points using curve interpolation to obtain the upper envelope \(X_{max}(t)\) and lower envelope \(X_{min}(t)\) and finally determine the average of upper and lower values:  
\[m_{1}(t)=\frac{X_{\text{max}}(t)+X_{\text{min}}(t)}{2} \tag{13}\]  
The first intrinsic mode function (IMF) can be obtained by subtracting the original signal \(X(t)\) from the \(m_{1}(t)\), and the other IMFs can be obtained in the same way. Finally, the residual component (res) with only one extreme point that cannot be further decomposed can be acquired, which means the algorithm is finished. The core of the EMD algorithm is to decompose the original signal into several IMF components and one res component, which are of decreasing complexity. The sum of all these components is the original signal.  
Compared with the LSTM algorithm, the EMD-LSTM is based on the idea that the EMD algorithm decomposes the training data and then puts the decomposed data into the LSTM model for training. Since most of the components derived from EMD decomposition have lower complexity, and LSTM can obtain better fitting results for them. So, the error of the final prediction result will be reduced to some extent. The working flow chart is shown in Figure 5.  
In Figure 5, the training data are first decomposed using the EMD algorithm, then each component is trained using the LSTM model, merging all predictions to obtain the final prediction.
# Time Series Forecasting Method Based on Multi-Scale Feature Fusion and Autoformer
## 2 Materials and Methods
### Proposed Method  
#### 2.3.1 Multi-Scale Feature Fusion Network  
Time series data exhibits multi-scale characteristics encompassing long-term trends, short-term fluctuations, periodic patterns, and transient anomalies. Traditional single-scale convolutional operations struggle to capture these diverse temporal dependencies simultaneously. To address this limitation, we propose a multi-scale feature fusion network that systematically integrates hierarchical temporal features through parallel convolutional branches. As illustrated in Figure 2, the architecture comprises two principal components:* The \(1\times 1\) convolutional branch specializes in inter-channel interactions and cross-scale feature recalibration. Through adaptive average pooling along the temporal dimension, this branch extracts compressed global contextual information while preserving channel interdependencies.
* The \(3\times 3\) convolutional branch extends the receptive field along the temporal axis, enabling the model to capture both localized temporal patterns and extended dependencies. This configuration facilitates the identification of seasonal patterns and macro-trends while discriminating between high-frequency noise and low-frequency trends through differentiated frequency responses of convolutional kernels [29].  
The feature fusion mechanism employs grouped processing of feature maps with subsequent pooling, convolution, and normalization operations. This architecture maintains channel interdependencies while enhancing multi-scale feature extraction capabilities, which proves essential for modeling complex temporal dynamics where cross-scale information interactions occur.  
The multi-scale feature fusion network effectively enhances the model's ability to extract multi-scale features from time series data by integrating features from different convolutional kernels. It provides more comprehensive and accurate feature representations for time series forecasting tasks. This allows the model to better learn both short-term and long-term dependencies in time series data and extract temporal features at different scales.
## 4 Energy Consumption Model
### Gps  
Typical GPS receivers are capable of a low-power sleep where ephemeris data is held in memory for a hot start, which in some situations (short durations between fix requests), may provide position data faster with less energy required than a cold start. A GPS receiver was modeled using three states of operation: (1) _Active_, where the receiver was fully powered and seeking or tracking a position; (2) _Sleep_, where the receiver was holding information in memory and checking its position periodically as configured by the manufacturer; and (3) _Off_, where no power was being used by the receiver. The active mode was modeled with a current draw of 27.5 mA while a fix was being acquired, which takes 45 s from power on (cold start) or 5 s coming out of sleep (hot start). _Sleep_ mode was measured to be 441 \(\upmu\)A and occurred between requested fixes. _Off_ mode corresponded to 0 mA.  
Time to First Fix (TTFF) was modeled by relating to the GPS receiver's operational state (_Active_, _Sleep_, _or_ _Off_) and the required minimum position acquisition time. While _Active_, the acquisition time  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Component** & **Mode** & **Current** & **Units** \\ \hline Microcontroller—MSP430F5 Series & Active & 2.32 & mA \\ Microcontroller—MSP430F5 Series & Sleep & 2.1 & \(\upmu\)A \\ GPS Receiver—Origin 447\(\times\) Series & Active & 27.5 & mA \\ GPS Receiver—Origin 447\(\times\) Series & Sleep & 441 & \(\upmu\)A \\ GSM Modem & Active & 825 & mA \\ Photovoltaic Cell & Active & 150 (max) & mA \\ \hline \hline \end{tabular}
\end{table}
Table 2: Table of current consumption for components of a behavior monitor.  
was considered to be 1 s. During _Sleep_, the acquisition time was 10 s. Transition from the _Off_ state to acquire a position required 45 s.  
Selection of which GPS operational state to use for minimal power usage required a simple analysis of the states. Fix intervals less than or equal to 10 s utilized the _Active_ state. In between 10 s and 15 min fix intervals, the GPS was placed into _Sleep_ state in between acquisitions to time and the power required over a full startup from an _Off_ state. Any requested fix interval greater than 15 min places the GPS receiver in an _Off_ state in between location acquisitions.
# An Efficient Probe-Based Routing for Content-Centric Networking
## 3 Design and Description of Our Proposal
### Data Structure
#### 3.1.2 UP-To Date FIB Mechanism  
Compared to other content explorations that periodically and completely send designated packets to explore the distribution of cached content, our approach improves the diversity and miss rate of FIB with minimal network overhead by appending fields to  
Figure 1: FIB structure.  
Figure 2: Shortest path table structure.  
conventional interests and data packets. Two fields, _probe_ and _probe-response_, are added to both the interest packet and the data packet, as shown in Figure 2(a,b).  
* _Probe_ is a content name. Depending on the performance measurement, the _probe_ can be arbitrarily assigned. In this paper, according to the QoS, the _probe_ is chosen from either PIT or FIB. _PIT probe_ selects the most popular entry in PIT to satisfy as many pending interests as feasible and further directly reduce interest waiting time. _FIB probe_ selects the entry with maximum cost from FIB to increase routing accuracy and prevent transmission failure. Section 4.4 simulates and analyzes the two selections on different performance measurements.
* _Probe-response_ is a list of the _probe_'s content providers. The router identification (ID) is added to the list when the _probe_ is matched during the exploration phase. Routers update their FIB using _probe-response_ data.
# An Analysis of Sound Event Detection under Acoustic Degradation Using Multi-Resolution Systems
## 2 Materials and Methods
### Experiments under Acoustic Degradation Scenarios
#### 2.4.1 Frequency Filtering  
We apply high- and low-pass filtering separately. In both cases, aiming to cover different degrees of acoustic degradation, the cutoff frequencies are 500 Hz, 1000 Hz, and 2000 Hz, leading to a total of six copies of the DESED Public evaluation set. The filters used are FIR (Finite Impulse Response), with a steepness factor of 0.85.
# Satellite Image Super-Resolution by 2D RRDB and Edge-Enhanced Generative Adversarial Network +
## 2 Related Works
### Edge Detection and Extraction  
Edge detection is a very important image feature extraction method in the area of computer vision, and it is also relatively easy to use. We use edge detection to find a set of pixels in the image that significantly change the brightness of the pixels. The edge extraction itself is a filtering process, using different operation elements to extract different features. Each type of operand has its own characteristics. There are generally three traditional methods: Sobel operator [27,28], Laplacian operator [27,28] and Canny operator [8].  
1. Sobel Operator In target detection, Sobel operator has a better effect on image processing with more gray gradient and noise, but it is not very accurate in edge positioning (i.e., the edge of the image is more than one pixel). Therefore, the accuracy of Sobel operator is not very high.
2. Laplacian Operator Since the Laplacian method is more sensitive to noise, it is rarely used to detect edges. However, it is used to determine whether edge pixels are regarded as bright or dark areas of the image. The Laplacian is the second derivative operand, which will produce steep zero crossings at the edges. Laplacian operands are isotropic and can sharpen borders and lines in any direction.
3. Canny Operator The best things about Canny edge detection algorithms are: (1) Detecting edges with a low error rate, which means capturing as many edges as possible in the image and as accurately as possible. (2) The detected edge should be accurately positioned at the center of the true edge. (3) A given edge in the image should be marked only once, and the noise of the image should not produce false edges wherever possible.
# Securing the Future of Web-Enabled IoT: A Critical Analysis of Web of Things Security
## 5 Security Analysis of WoT
### WoT Threats
#### 5.2.2 T2: Data Breaches  
Data leakage or breaches occur when sensitive data are exposed to unauthorized entities. This threat affects things, nodes, APIs, and thing descriptions. Vulnerabilities such as inadequate data protection and poor access control are often exploited through attacks like data exfiltration and interception. The consequences of data breaches include identity theft, financial losses, and significant reputational damage [3; 4].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ID** & **Threat** & **Description** \\ \hline T1 & Confidentiality Threats & An attacker gains access to WoT devices or services without proper authorization. \\ \hline T2 & Data Breaches & An attacker compromises the system and expose sensitive data of IoT devices. \\ \hline T3 & Service Unavailability & An attacker consumes system resources, making the system unable to satisfy other legitimate requests. \\ \hline T4 & Misuse of Resources & An attacker frees up some resources and uses them for their own benefit. \\ \hline T5 & Firmware Tampering & An attacker modifies the firmware of WoT devices to introduce malicious functionality. \\ \hline T6 & Privilege Escalation & An attacker maliciously modifies the privileges information in unauthorized manner. \\ \hline T7 & Change in WoT Function Definition & An attacker modifies the operational parameters or functionality of the WoT environment. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the common threats in the WoT environment.
# Research on Hard Rock Pillar Stability Prediction Based on SABO-LSSVM Model
## 1 Introduction
However, there are currently few application cases that optimize classical models using intelligent algorithms for mine pillar stability prediction. In this study, based on [PERSON]'s mine pillar dataset, the SABO intelligent optimization algorithm is used for parameter optimization of the LSSVM model, and the optimized model is applied to predict the stability grade of mine pillars. First, by analyzing the redundancy of features in the mine pillar dataset and conducting feature selection, five parameter combinations were constructed to examine their effects on the performance of different models. Second, the performance of the SABO-LSSVM model was compared with that of other prediction models. Finally, various data sampling methods were used to correct the bias of the optimized model for different categories of mine pillars.
## 1 Introduction
Scientists compare prediction performance during training to different feature pairs to determine the optimum features using training methods. Forward, backward, and recursive deletion are illustrations of mathematical algorithms that can pick the best characteristics by expanding or changing the candidate solution, despite the high computational overhead [13]. The embedded method combines both filter and wrapper feature selection methods. It keeps a balance between computation speed and precision. To reduce complexity and delete features with fewer than minimal transactions, the penal technology is used in ElasticNet and other embedded methods [14].
## 3 Results and Discussion
### Influence of Various Parameters on the Yield of Biodiesel
The influence of catalyst amount on the conversion is illustrated in Figure 7. The yield of FAME was greatly dependent on the amount of catalyst applied. The yield increased rapidly from 62.0% to 89.9% with the increase of the catalyst bed height from 150 to 300 mm. It demonstrates that a high catalyst bed height provides a longer reaction time and more active sites to promote the reaction between waste oil and methanol at a given flow rate [27]. However, when the catalyst bed height exceeded 250 mm, the rate of reaction increased slightly. Therefore, the optimum catalyst bed height for biodiesel production in this research is found to be 250 mm. Reusability is very important for the economical application of KOH/JS as a heterogeneous catalyst for biodiesel synthesis. It is important to ensure that the active species are not leached from the solid support of the catalyst. If the leaching of the active species is too high, the active species could act as a homogeneous catalyst, and thus, the process advantages of the heterogeneous catalyst could be lost [13]. The reusability was checked by using the catalyst without any further purification and activation. KOH/JS catalyst was collected after adding the reaction and fresh reactants. The yield of FAME, determined after each catalyst collection, was employed to evaluate the reusability. The optimal operating conditions: a residence time of 2 h, reaction temperature of 60 \({}^{\circ}\)C, methanol/oil molar ratio of 16 and catalyst bed height of 250 mm, were determined by the operational parameters employed in the PBR. Under these conditions, a high yield of biodiesel was obtained (Figure 8). WFO conversions exhibited no apparent decrease and exceeded 80% in each of 5 consecutive runs. The contents of KOH on JS after the reaction of biodiesel production were 29.2 wt%. This heterogeneous  
Figure 7: Effect of catalyst bed height on the yield of biodiesel in PBR. catalyst showed high stability and reusability for transesterification without loss of its catalytic activity during reaction. Small loss of the catalyst is an important factor for good catalyst reusability [24].
## 1 Introduction  
Recently, driven by the rapid development of high-power density integrated circuits for the high-frequency communication system and the Low Earth Orbit (LEO) satellite communication, there is an urgent demand for advanced high-frequency dielectric substrates with outstanding dielectric properties and high thermal conductivity (TC) [1, 2, 3]. Polymer composite substrates, which exhibit excellent dielectric properties and facile processability, have received much attention [4, 5]. Among various polymers, PTFE has been regarded as a promising candidate due to its low dielectric constant, low dielectric loss, excellent chemical stability, and thermal stability [6, 7, 8]. However, its low intrinsic TC of 0.2 W\(\cdot\)m\({}^{-1}\)\(\cdot\)K\({}^{-1}\) prevents its application in highly integrated electronic devices with high-power density [9].  
Enhancing the thermal conductivity (TC) while maintaining the dielectric constant remains a critical challenge. Introducing high-TC fillers, such as graphene [10], hexagonal boron nitride (hBN) [11], glass fiber (GF) [12], aluminum oxide (Al\({}_{2}\)O\({}_{3}\)) [13], and aluminum nitride (AIN) [14], into the PTFE matrix is a popular approach to improve the TC of PTFE-based substrates. Unfortunately, the large amount of filler, which is required to achieve a high TC, leads to the deterioration of mechanical and dielectric properties in composites [15]. To avoid the adverse effect, optimizing the orientation of PTFE molecular chains is an alternative strategy to enhance its TC. The low intrinsic TC of polymers originates from phonon scattering on disordered molecular chains. To overcome it, some strategies have been used to optimize the arrangement of molecular chains [16; 17; 18; 19]. For instance, stretching or electrospinning methods are utilized to achieve single-crystal polymer nanofibers with high along-axial TC [20; 21]. However, these molecular chain arrangement methods are inapplicable to PTFE composite substrates due to the high melt viscosity, insolubility, and creep characteristics of PTFE. Our previous work demonstrates that the biphasic interface, determined by the shapes of the filler and PTFE crystal nucleus, can induce the crystal growth direction [22]. Thus, it is suggested that introducing fillers with lamellar structure can induce the interface-parallel orientation of the PTFE molecules at the interface, which is expected to enhance the TC of the polymer matrix.  
In this work, we propose a strategy to induce the molecular chain orientation by the introduction of fluorinated graphite (FGi), whose lamellar structure and PTFE-like chemical composition render it an ideal filler for regulating PTFE molecular orientation [23; 24; 25]. The FGi fillers provide nucleation sites for PTFE on their surface, influence the packing of PTFE molecular chains through the steric hindrance effect of surface functional groups, and induce molecular growth along the interface. As an expected result, the PTFE composite substrates show the improved TC due to the enhanced molecular orientation of the PTFE matrix with their maintained outstanding dielectric properties.