esa-sceva/satcom-chunk-collection huggingface dataset:
# A Decision Support System for Melanoma Diagnosis from Dermoscopic Images
## 4 Implemented CAD System
### Feature Extraction  
#### 4.2.1 Geometry Block  
This block considers measures that give information about the lesion shape, which is the size of the lesion area and the asymmetry index.  
Figure 1: Block diagram of the conceived CAD system.  
A normalized lesion area size is evaluated as the ratio between the sum of the number of pixels composing the segmented lesion and the number of pixels belonging to the ROI.  
For the quantification of the asymmetry degree, the segmented lesion area is compared with a circle equivalent to the lesion (same area size), which is centered at the lesion centroid. The circular shape was selected for the intrinsic symmetry of this geometric figure and the invariance with image rotation.  
The procedure for the asymmetry index evaluation involves the following steps:  
* Computation of the centroid of the segmented skin lesion area;
* Evaluation of the circular area equivalent to the segmented lesion;
* The logical EXOR operation between the equivalent circle and the segmented lesion area (Figure 3).  
Figure 2: Results of the various steps implemented in the segmentation block. (**a**) Image of the database being tested, (**b**) ROI localization and detected borders of the coherent color areas, (**c**) segmentation into coherent color areas, (**d**) ground-truth provided by the dataset, (**e**) comparison by overlapping between the ground-true and the obtained segmentation (green: the segmentation performed by the procedure, red: ground-truth, yellow: overlapping area).  
The result of this Boolean operation provides information about the skewness of the lesion. The asymmetry degree is evaluated as the ratio of the number of pixels resulting from the EXOR operation to the number of pixels composing the segmented lesion.
## 2 The Modified Empirical Wavelet Transform Method
### The EWT Method  
As shown in Figure 1, data processing is an important procedure in the AE system. The aim of EWT method was to extract the signal modes, therefore it could be applied to process the AE signals. The EWT process contains three important aspects: (1) in segmenting the spectrum of the signal, the first step was to detect all the local maxima of the spectrum, then get the boundaries which were the midpoint of two consecutive maxima, using the algorithm given in Equation (1); (2) constructing the empirical wavelets by defining a bank of wavelet filters composed of one low-pass filter and \(N-1\) band filters based on the boundaries, which is given in Equations (2)-(5); (3) extracting the empirical modes by applying the wavelet filter banks to divide the signal into frequency sub-bands. More detailed information can be found in reference [28].  
\[w_{n}=\left\{\begin{array}{ll}0,&n=0\\ \frac{\Omega_{n}+\Omega_{n+1}}{2},&n=1,2,\ldots,N-1\\ \pi,&n=N\end{array}\right., \tag{1}\]  
where \(\omega_{n}\) is the segmentation boundaries; \(\Omega_{n}\) is the corresponding angular frequency of the local maxima.  
\[\hat{\psi}_{n}(\omega)=\left\{\begin{array}{ll}1,&(1+\gamma)\omega_{n}\leq| \omega|\leq(1-\gamma)\omega_{n+1}\\ \cos\left[\frac{\pi}{2}\beta\left(\frac{1}{2\gamma\omega_{n+1}}(|\omega|-(1- \gamma)\omega_{n+1})\right)\right],&(1-\gamma)\omega_{n+1}\leq|\omega|\leq(1+ \gamma)\omega_{n+1}\\ \sin\left[\frac{\pi}{2}\beta\left(\frac{1}{2\gamma\omega_{n}}(|\omega|-(1- \gamma)\omega_{n})\right)\right],&(1-\gamma)\omega_{n}\leq|\omega|\leq(1+ \gamma)\omega_{n}\\ 0,&\text{otherwise}\end{array}\right., \tag{2}\]  
Figure 1: The structure diagram of AE detection theory.  
\[\hat{\phi}_{n}(\omega)=\left\{\begin{array}{ll}1,&|\omega|\leq(1-\gamma)\omega_{n }\\ \cos\left[\frac{\pi}{2}\beta\left(\frac{1}{2\gamma\omega_{n}}(|\omega|-(1-\gamma) \omega_{n})\right)\right],&(1-\gamma)\omega_{n}\leq|\omega|\leq(1+\gamma) \omega_{n}\\ 0,&\text{otherwise}\end{array}\right., \tag{3}\]  
where \(\hat{\psi}_{n}(\omega)\) is the empirical wavelets, \(\hat{\phi}_{n}(\omega)\) is the empirical scaling function; \(\gamma\) ensures no overlap between two consecutive segments, \(\beta(x)\) is an arbitrary function, they are given as follows:  
\[\gamma<\min_{n}\left(\frac{\omega_{n+1}-\omega_{n}}{\omega_{n+1}+\omega_{n}}\right) \tag{4}\]  
\[\beta(x)=\left\{\begin{array}{ll}x^{4}(35-84x+70x^{2}-20x^{3}),&0<x<1\\ 0,&x\leq 0\\ 1,&x\geq 1\end{array}\right. \tag{5}\]
# Digital Twins in Solar Farms: An Approach through Time Series and Deep Learning
## 2 Background
### Time Series
#### 2.1.2 Frequency Domain Analysis  
Time series are usually signals with a high presence of noise, which are not easy to treat using time-domain techniques. For this reason, an analysis in the frequency domain is recommended. The Fourier transform allows us to detect noise by decomposing the signal and observe the resulting frequency spectrum.  
Some of the techniques that allow analysis in the frequency domain are:  
* Amplitude vs. frequency: allows observing the frequency response of a signal, which helps to detect the presence of noise at low or high frequencies.
* Spectrogram: shows the strength of a signal over time of the frequencies in the signal. It allows detecting the presence of harmonics in the signal.  
In the frequency domain, the application of a filtering process is widely used to eliminate the presence of noise. Among the most used filters are the Butterworth and Chebyshev [5; 6].
# Nonlinear Integral Type Observer Design for State Estimation and Unknown Input Reconstruction
## 4 Numerical Example
Figures 8-11 are used to demonstrate the main difference between the SMO and the PIO for discontinuous unknown input reconstruction. Since the SMO is a class of discontinuous control strategy, it is able to reconstruct the non-smooth unknown input rapidly. Figures 8 and 9 clearly show the fast recovery property of the SMO. Figures 10 and 11 illustrate that the control signals generated by PIO deviate from the true value of the unknown input at 2 s, 4 s, 6 s and 8 s. This because that the magnitude at these points changes with infinite fast speed, that is \(\left\|\mathbf{D}_{22}\hat{\mathbf{f}  
To make quantitative comparison, the simulation results are summarized in Table 2. Table 2 contains two categories: the first one is the estimation precision comparison for system subject to continuous unknown input and the second one is for system suffering the discontinuous one. For system with continuous disturbance \(d_{1}(t)\), simulation results show that the estimation conducted by applying the PIO is better than those obtained by using the SMO. In contrast, when system is subject to discontinuous disturbance \(d_{2}(t)\), SMO can still reach fast estimation even though the disturbance is not differentiable and the \(L_{2}\) condition for the signal time derivative is not satisfied [22]. Therefore, the input reconstruction performance by SMO is better than those by PIO in the sense of maximum estimate errors. However, it is argued that most of disturbances in the real world are continuous and no continuous control effort is capable of precisely eliminating discontinuous disturbances. As a result, under this circumstance the resulting estimation performances conducted by the PIO will be better than those conducted by the SMO. Additionally, since no discontinuous control component is directly applied in the PIO, the robust integral type observer will be an adequate choice for bandwidth limited hardware realizations.
# RS-YOLOX: A High-Precision Detector for Object Detection in Satellite Remote Sensing Images
## 2 Related Work
### Object Detection in Remote Sensing Image  
In recent years, many researchers have used the latest two-stage algorithm or one-stage algorithm in satellite remote sensing images and made corresponding improvements to the algorithm.  
[PERSON] et al. [21] proposed an improved Faster R-CNN model to detect tailing ponds in remote sensing images. They used an attention mechanism in the Faster R-CNN, and the average precision (AP) of detecting tailing ponds reached 85.7%. However, the single dataset they used cannot reflect the generalization of the model.  
[PERSON] [23] proposed the YOLT model, an improved YOLOv2 model for satellite remote sensing image detection. This model uses a ResNet-like residual structure to connect the features of multiple layers to obtain a more fine-grained feature representation, improving the detection of small objects, but it does not solve the problem of the imbalanced number of positive and negative samples in remote sensing images.  
[PERSON] et al. [24] proposed applying the YOLOv5 algorithm to detect remote sensing images, but did not optimize the YOLOv5 algorithm for the purpose.
# Medical Applications of the GEMPix
###### Abstract
Its use as a tracking detector operated as a highly granular and compact Time Projection Chamber (TPC) has been shown [16]: the device can be operated in a hybrid mode with some pixels measuring deposited charge and others measuring the drift time of the charge in order to obtain a complete 3D reconstruction of the track. It has been introduced as a novel method to detect the very weak (5.9 keV) X-ray emission of \({}^{55}\)Fe in radioactive waste produced by the operation of particle accelerators and experimental facilities, to determine its specific activity (Bq/g) in metallic waste characterization [17]. Furthermore, the GEMPix has been used as a soft X-rays diagnostic tool for laser produced plasmas [18; 19] and for studies on a dark matter detector with a negative ion TPC read by a GEMPix [20]. The present paper will review the medical applications of the GEMPix. It is mostly a review paper, but the last sections describe on-going work and anticipate the most recent results that will be the subject of two dedicated publications currently under preparation. After illustrating the design and operating principle of the detector (Section 2), Sections 3 and 4 describe its imaging application in photon and electron radiation therapy (that we call 'conventional radiation therapy' in the following) and particle therapy. The results presented in these two sections have been published in [21] and in [22; 23; 24], respectively. A proof-of-principle of two GEMPix detectors as TPCs for proton tomography [25] is discussed in Section 5. Section 6 illustrates a very recent version of the detector for microdosimetry called GEMTEQ, whereas Section 7 discusses the latest development for a larger area version of the GEMPix (LaGEMPix) for use in QA in hadrontherapy. Finally, future developments are outlined in Section 8.
# Ground Stability Analysis in Non-Open-Cut Tunneling Method Using Small-Diameter Steel Pipe Piles
## 3 Concept of Non-Open-Cut Method Using Small-Diameter Steel Pipe Piles  
The non-open-cut method is used for constructing underground crossings at low depths under existing roads and rail tracks. Most non-open-cut methods use a pipe roof type that inserts medium- and large-diameter steel pipe piles (800-2800 mm) to construct the roof and excavate inside the piles. As discussed in Section 2, however, settlement or heave of the ground is possible when medium- and large-diameter steel pipe piles are used. Therefore, small-diameter steel pipe piles (Figure 1a, o 114 mm, 6 T) are used for forepoling to minimize the settlement or heave of the ground. In such piles, low-pressure grouting is used to minimize ground disturbance [8]. The front pressure panel (Figure 1b) is developed to prevent the tunnel face from collapsing and the accompanying risks during the use of the non-open-cut method. The front pressure panel consists of several pushing plates and face jacks. The shoe structure, which is made of steel plates, was attached around the front face of the precast structure, and it prevented the collapse of the ground and falling rock (Figure 1c).  
Figure 1: Actual composition of equipment: (**a**) small-diameter steel pipe piles. (**b**) Front pressure panel and precast structure. (**c**) Shoe structure (left, upper, and right).  
The non-open-cut method used in this study generally follows several construction steps as shown in Figure 2. First, the forepoling is installed using a small steel pipe, and the pressure panel is placed at the tunnel face. Thereafter, the precast structure is propelled forward (Stage 2) and the pressure panel is pulled back (Stage 1). Subsequently, the tunnel face is excavated using an excavator. The tunnel is constructed by repeating these steps.
# A New String Edit Distance and Applications
## 2 Restricted Forensic Levenshtein Distance
### Details of the RFL Algorithm  
The foundation of the RFL algorithm is built on the Levenshtein distance. In this section, a perspective on the standard dynamic programming algorithm for classical Levenshtein is discussed, then Weighted Levenshtein is discussed, and lastly the novel modifications are presented. Zero-based indexing for strings will be used, with \(s[i:j]\) to represent the substring of \(s\) from the \(i\)-th to the \((j-1)\)th character, inclusive. Similarly, \(s[i:]\) will represent the substring composed of the \(i\)th character up to and including the last character, and \(s[:j]\) will represent the first \(j\) characters, up to index \(j-1\), inclusive.
## 2 Composite Signal's Distribution and the Dataset
### Distribution Fitting
the PDF and the cumulative distribution function (CDF) of \(v\) are described in (5) and (6), respectively, as follows:  
\[f(v;s,a,b)=\frac{\left(\frac{b}{g^{a}}\right)v^{a-1}e^{-\left(\frac{a}{b}\right) ^{b}}}{\Gamma\left(\frac{a}{b}\right)} \tag{5}\]  
\[F(v;s,a,b)=\frac{\gamma\left(\frac{a}{b},\left(\frac{v}{g}\right)^{b}\right)}{ \Gamma\left(\frac{a}{b}\right)} \tag{6}\]  
where \(s\), \(a\), and \(b\) are the parameters of the GGD, \(\Gamma\left(\frac{a}{b}\right)\) is the gamma function, and \(\gamma\left(\frac{a}{b},\left(\frac{v}{g}\right)^{b}\right)\) is the lower incomplete gamma function [3]. To link the DAC's gain, \(G\), with the PDF of the transmitted signal, we consider the DAC's clipping level, \(\alpha\), and aim to scale the signal's amplitude to match \(\alpha\) based on a specific percentile of the GGD. The optimal DAC's gain, \(G\), can be defined as:  
\[G=\frac{\alpha}{F^{-1}(\theta;s,a,b)} \tag{7}\]  
where \(F^{-1}(\theta;s,a,b)\) represents the inverse CDF (quantile function) of the GGD for a given probability \(\theta\) (e.g., the 95 th percentile), indicating the voltage level below which a specified percentage of the signal's amplitude distribution lies. This calculation ensures the adjusted signal amplitude optimally utilizes the DAC's dynamic range, minimizing the risk of clipping and quantization noise.
# Cooperative Downlink Listening for Low-Power Long-Range Wide-Area Network
## 2 Overview of LoRaWAN  
Long-range wide-area networks (LoRaWANs) specify a physical (PHY) and medium access control (MAC) layer protocol for an extremely long-range and low-power network, which is standardized by LoRa alliance. The major advantage of LoRa is in the long-range capability. According to LoRa Whitepaper (2017), entire cities or hundreds kilometers in radius can be covered by a single LoRa base station or LoRa gateway. Using chirp spread spectrum (CSS), LoRa can significantly increase communication range while maintaining robustness to interference. These characteristics are attractive for many IoT applications, and many regions and countries are trying to accept the LoRa technology on their regional ISM band. Figure 1 shows the LoRa regional spectrum usage.  
One of the outstanding features of LoRaWAN is based on a star topology connected to the network server in which gateways play a role in relaying messages between end devices and the network server as shown in Figure 2. Gateways have two independent communication links: LoRa communication and IP backhaul. The supported data rates are 0.3-50 kbps. In addition, LoRa provides an adaptive data rate (ADR) function.  
Figure 1: Long-range (LoRa) regional spectrum usage (Adapted from the specification Ver.1.02 regional Parameters, LoRa Alliance, 2016).  
Figure 2: Long-range wide-area network (LoRaWAN) architecture (Adapted from LoRa Whitepaper, LoRa Alliance, 2015 [16]).  
Figure 3 shows LoRa protocol stack (PHY and MAC). Based on regional ISM band, physical layer includes LoRa modulation, and according to application characteristics, MAC has three different classes: Class A (baseline), Class B and C (optional). Class A is used for the low-energy end devices and allows bi-directional communication between end devices and network server. However, downlink communication from network server to each end device is only allowed shortly after the end of uplink transmission of an end device.  
As shown in Figure 4, shortly after uplink transmission, the end device allows two RX windows 1 and 2 during RECEIVE_DELAY1 and 2, respectively. For RX1 window, the same channel and data rate as those used in uplink transmission are used, but RX2 window is allowed to use a different channel and data rate from the uplink transmission.  
LoRaWAN has two different types of join methods: over-the-air activation (OTAA) and activation by personalization (ABP). OTAA is an automatic join process performed during deployment of nodes and ABP is a manual join process.  
The DevAddr is composed of a 32-bit identifier in which seven bits are used as the network identifier (NwkID) and 25 bits are used as the network address (NwkAddr) which is assigned to each end device by a network manager.
## 6 Conclusions  
* Based on the generalized longitudinal equivalent continuous model and the elastic foundation beam theory, the horizontal axis deviation of a small radius TBM tunnel is simplified as the deflection of the circular curved beam on the Winkler foundation.
* The model has been solved by the transfer-matrix method, which can solve the deformation and internal force with high accuracy at any cross-section conveniently and fast. When the foundation bedding coefficient and bending stiffness stepped change, the exact solutions of the model can be achieved. When the foundation bedding coefficient and bending stiffness change continuously, the numerical solutions meeting accuracy requirements can be obtained by increasing the number of the order.
* In a setting of Shenzhen rail transit line 6 phase II, the calculation model of segment axial horizontal deviation for the small radius TBM tunnel construction is established. The applicability of this model is verified by the field data.
* The effect of the jack thrust on the lateral deviation of the tunnel segments decreases with the increase of the distance between the shield tail and the ring. The influence range depends on the total thrust and increases with the total thrust.
* When the lateral thrust in the external side of the curved line is larger than that in the internal side of the curved line, the horizontal axis of the segments moves to the external side, and the offset has a positive relationship with the thrust difference (or path difference). When the lateral thrust in the external side of the curved line is smaller than that in the internal side of the curved line, the horizontal axis of the segments still moves to the external side. However, the offset decreases with the increase of thrust difference (or path difference).  
Figure 11: Lateral accumulated displacement curves of the first 20 assembled rings under different thrust deviation angles.  
**Author Contributions:** Conceptualization, S.Q.; methodology, S.Q. and R.L.; data curation, R.L. and G.W.; formal analysis, R.L. and G.W.; validation, R.L.; investigation, P.X. and G.W.; writing--original draft preparation, G.W. and P.X.; resources, S.Q. and P.X.; writing--review and editing, G.W. and P.X.; supervision, S.Q. and G.W.; funding acquisition, S.Q. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by China Railway No.5 Engineering Group Co. Ltd.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Dual-Band Wearable Antenna Integrated with Glasses for 5G and Wi-Fi Systems
###### Abstract  
This paper presents a dual-band antenna designed for integration into eyewear. The antenna is intended for a system supporting visually impaired individuals, where a wearable camera integrated into glasses transmits data to a remote receiver. To enhance system reliability within indoor environments, the proposed design supports both fifth-generation (5G) wireless communication and Wi-Fi networks. The compact antenna is specifically dimensioned for integration within eyeglass temples and operates in the 3.5 GHz and 5.8 GHz frequency bands. Prototype measurements, conducted using a human head phantom, validate the antenna's performance. The results demonstrate good impedance matching across the desired frequency bands and a maximum gain of at least 4 dBi in both bands.  
wearable antennas; dual-band antennas; 5G; FDTD 2025158018
## 3 Proposed Framework
### Integrated Prioritization Module (IPM)  
The Integrated Prioritization Module (IPM) is a pivotal component of our proposed framework, playing a crucial role in the overall prioritization process. This module is strategically designed to optimize requirement prioritization through a structured approach which is segmented into three distinct subunits: the Grouping Processing Unit (GPU), the Review Processing Unit (RPU), and the Analytical Hierarchical Process (AHP) Processing Unit (APU). Each of these subunits addresses specific aspects of the prioritization process:
###### Abstract  
In the present work, we employed artificial neural network (ANN) that is optimized with two hybrid models, namely imperialist competition algorithm (ICA) as well as particle swarm optimization (PSO) in the case of the problem of bearing capacity of shallow circular footing systems. Many types of research have shown that ANNs are valuable techniques for estimating the bearing capacity of the soils. However, most ANN training models have some drawbacks. This study aimed to focus on the application of two well-known hybrid ICA-ANN and PSO-ANN models to the estimation of bearing capacity of the circular footing lied in layered soils. In order to provide the training and testing datasets for the predictive network models, extensive finite element (FE) modelling (a database includes 2810 training datasets and 703 testing datasets) are performed on 16 soil layer sets (weaker soil rested on stronger soil and vice versa). Note that all the independent variables of ICA and PSO algorithms are optimized utilizing a trial and error method. The input includes upper layer thickness/foundation width (h/B) ratio, footing width (B), top and bottom soil layer properties (e.g., six of the most critical soil characteristics), vertical settlement of circular footing (s), where the output was taken ultimate bearing capacity of the circular footing (\(F_{tilt}\)). Based on coefficient of determination (R\({}^{2}\)) and Root Mean Square Error (RMSE), amounts of (0.979, 0.076) and (0.984, 0.066) predicted for training dataset and amounts of (0.978, 0.075) and (0.983, 0.066) indicated in the case of the testing dataset of proposed PSO-ANN and ICA-ANN models of prediction network, respectively. It demonstrates a higher reliability of the presented PSO-ANN model for predicting ultimate bearing capacity of circular footing located on double sandy layer soils.  
ICA-ANN; PSO-ANN; ANN; optimization; circular footing 2019 9 4594; doi:10.3390/appr9214594