esa-sceva/satcom-chunk-collection huggingface dataset:
# Variation in Mechanical Properties of Ti-13 Nb-13 Zr Depending on Annealing Temperature
## 1 Introduction  
Titanium alloy is a promising candidate among various metallic materials for biomedical applications, particularly as an orthopedic implant material. This arises from its high resistance to fatigue fracture and corrosion, relatively low Young's modulus, and biocompatibility [1]. Materials scientists and biomedical engineers first paid attention to the two most frequently used titanium alloys for biomedical applications: commercially pure titanium (CP-Ti) and Ti-6 Al-4V alloy. However, CP-Ti has a disadvantage of low mechanical strength [2], while Ti-6 Al-4V has an issue of toxicity owing to Al and V alloying elements [3]. In addition, both alloys possess an excessive Young's modulus (>100 GPa), and thus are vulnerable to the stress-shielding effect. These drawbacks of CP-Ti and Ti-6 Al-4V gave rise to the development of various alternative titanium alloys with high strength, low Young's modulus, and superior biocompatibility.  
Ti-Nb-Zr ternary system has attracted great attention for this purpose due to their less toxic alloying elements as well as enhanced mechanical properties [4]. Furthermore, Ti-13 Nb-13 Zr alloy, one of the earlier developed materials, has already resolved the biocompatibility issue by being listed in the ASTM international standard for implant applications [5]. The standard introduces two methods of heat treatment, namely solution treatment (ST) and capability aging (CA). The ST process is composed of a heat treatment above the beta-transus temperature (i.e., 1008 K, [6]) and subsequent rapid cooling to room temperature. The CA process refers to a subsequent annealing of ST Ti-13 Nb-13 Zr performed at 768 K for 6 h.  
In addition to the ST and CA processes, previous studies have suggested a variety of alternative thermomechanical processes to increase a biomaterial performance of Ti-13 Nb-13 Zr alloy [7; 8; 9; 10; 11; 12]. For example, [PERSON] et al. [8] tried several combinations of hot-working, ST, and cooling conditions to optimize mechanical performance. [PERSON] et al. [9] introduced a warm cross-rolling to cause an ultrafine-grained structure in Ti-13 Nb-13 Zr. [PERSON] et al. [10; 11] suggested improved mechanical propertiesof this alloy through a multipass caliber rolling for the first time. He recently succeeded in enhancing the process to confer the lowest Young's modulus reported ever for Ti-13 Nb-13 Zr [12]. However, these studies paid less attention to the simple annealing of Ti-13 Nb-13 Zr alloys, except for the aforementioned CA process. Gathering such data becomes more important nowadays due to the recent emergence of machine learning [13]. Hence, this study investigated the variation in mechanical properties (i.e., strength, hardness, and Young's modulus) of Ti-13 Nb-13 Zr alloys depending on the annealing temperature in the (\(\alpha+\beta\)) domain. The mechanisms under these variations are interpreted on the basis of their microstructural characteristics.
## 3 Video Monitoring & Internet of Things
### Video Monitoring
#### 3.1.6 RFID Devices  
Electro-magnetic fields are used by radio-frequency identification (RFID) to automatically identify and track tags affixed to items. These tags include data that are electronically stored. The passive tags acquire energy from an RFID reader placed close to the radio signals being questioned. The active tags have a local energy source (e.g., a battery) [57].
# A Deep Learning Approach to Teeth Segmentation and Orientation from Panoramic X-Rays
## 3 Materials and Methods
### Model Architecture  
Figure 3 demonstrates our deep learning model. Unlike the original FUSegNet designed for wound segmentation, we introduce grid-based attention gates into the skip connections to enhance the model's focus on spatially relevant features, which is particularly beneficial for fine-grained dental structures. It has four major parts: encoder, decoder, grid-based attention gates (AGs), and parallel spatial and channel squeeze-and-excitation (P-scSE) module. Furthermore, to go beyond horizontal bounding boxes, which do not account for angular orientation, we implement a PCA-based oriented-bounding-box (OBB) technique, as detailed in Section 3.4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attribute** & **Description** \\ \hline Dataset Name & DNS Dataset \\ Access & Publicly available upon request to the authors of [5] \\ Total Images & 543 panoramic X-ray images \\ Image Resolution & 1991 \(\times\) 1127 pixels \\ Fold Distribution & 5 folds: 4 for training and validation, 1 for testing \\ Images per Fold & Test: 111 images; Other folds: 108 images each \\ Patch Size & 512 \(\times\) 512 pixels \\ Patch Overlap & 10 \(\times\) 10 pixels \\ Normalization & Pixel intensities scaled to [0, 1] \\ Segmentation Labels & 32 unique tooth labels \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the DNS Dataset and Preprocessing Steps.  
Figure 3: Overview of the proposed deep-learning model for teeth instance segmentation. It is a modified FUSegNet architecture with four key components: an EfficientNet-based encoder, a decoder with spatial and channel integration, grid-based attention gates for refined skip connections, and a P-scSE module for enhanced feature representation.  
Figure 2: Tooth labeling used in this study. Each of the 32 teeth is assigned a unique label (1–32) and categorized into four anatomical groups: incisors, canines, premolars, and molars. Each group is further subdivided into upper and lower teeth, resulting in eight total categories. These categories are later used for categorical performance analysis.
# Security Scheme Based on Parameter Hiding Technic for Mobile Communication in a Secure Cyber World
## 4 Security Analysis
### Key Definition, Encryption, and Decryption  
In the proposed security scheme, key values used to encrypt identification parameters are generated through the challenge response process. A key value used for IMSI and GUTI encryption is defined as the value that is not used in the challenge response process among the generated sequences through hash functions, which are secretly shared between UE and MME. Relevant key values are defined only within UE and MME and are not transmitted outside through communication processes. Therefore, in order to detect the encrypted IMSI and GUTI, attackers can detect identification parameters only through the attacks to encrypted algorithms such as AES-256.  
\begin{table}
\begin{tabular}{c c c} \hline
**Vurnerable Information and Layer** & **Original Initial Attach** & **Proposed Initial Attach** \\ \hline Mutual Authentication between UE and eNB & Not Supported & Supported \\ \hline Mutual Authentication between UE and MME & Not Supported & Supported \\ \hline Mutual Authentication between eNB and MME & Not Supported & Supported \\ \hline IMSI & Plain Text Transmission & Cipher Text Transmission \\ \hline RNTI & Plain Text Transmission & Cipher Text Transmission \\ \hline GTUI & Plain Text Transmission & Cipher Text Transmission \\ \hline \end{tabular}
\end{table}
Table 2: Security analysis.
# Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines
## 4 Edge Node Deployment Algorithm Design
### Improved Dual Population Genetic Algorithm
#### 4.1.3 Fitness Function  
In this scenario, the objective function aims to minimize both delay and cost, denoted as the combined objective function W:  
\[W=\beta_{1}T_{max}+\beta_{2}C^{all} \tag{15}\]  
\[\beta_{1}+\beta_{2}=1 \tag{16}\]  
where the weight coefficients \(\beta_{1}\) and \(\beta_{2}\) are used to balance the considerations of delay and cost. In order to maximize the fitness function, an adjustment of the objective function is necessary to ensure that the function value becomes more prominent as the total overhead decreases. The fitness function can be expressed as:  
\[fitness=1/(W+1) \tag{17}\]  
To avoid the situation where the denominator is 0, add 1 to the denominator.
## 3 Link Model
### Link Budget  
Given the satellite positions \(\mathbf{r}_{km}\) at any time \(t\), we can use a link budget model to dynamically estimate the minimum transmitted power required to maintain link connectivity over an entire orbital period \(T\). Assuming any two satellites described by the pairs of integers \((k,m)\) and \((p,q)\), their distance is simply determined as  
\[R=|\mathbf{r}_{km}(t)-\mathbf{r}_{pq}(t)|. \tag{10}\]  
Figure 4: The satellite connectivity requirements considered in this work. Each satellite C must, at all times, communicate which its two closest neighbors in the same orbit (S\({}_{1}\) and S\({}_{2}\)) as well as its three nearest satellites L\({}_{i}\) in the lower orbit and U\({}_{i}\) upper orbit, respectively.  
Figure 5: Block diagram of the OIL model, consisting of the data source, the modulator (MOD), the laser transmitter, the transmitter optics, the optical channel, the receiver optics and photodiode (pin), and the demodulator (DEMOD).  
In free space, the link budget equation relating the received power \(P_{\text{R}}\) and the transmitted power \(P_{\text{T}}\) can be written as [5]  
\[P_{\text{R}}=L_{\text{ch}}P_{\text{T}}, \tag{11}\]  
where  
\[L_{\text{ch}}=\left(\frac{\lambda}{4\pi R}\right)^{2}n_{\text{T}}n_{\text{R}}G_ {\text{T}}G_{\text{R}}\text{e}^{-G_{\text{T}}\sigma_{\text{T}}^{2}}\text{e}^{- G_{\text{R}}\sigma_{\text{R}}^{2}}, \tag{12}\]  
is the channel gain, \(\sigma_{\text{T}}\) and \(\sigma_{\text{R}}\) are the standard deviations of the pointing errors at the transmitter and receiver sides, respectively; \(n_{\text{T}}\) and \(n_{\text{R}}\) are the optical efficiency of the transmitter and receiver, respectively; while \(G_{\text{T}}\) and \(G_{\text{R}}\) are the transmitter and receiver gains determined by  
\[G_{\text{T}}=\frac{16}{\Theta_{\text{T}}^{2}}, \tag{13}\]  
\[G_{\text{R}}=\left(\frac{D_{\text{R}}\pi}{\lambda}\right)^{2}, \tag{14}\]  
where \(\Theta_{\text{T}}\) is the divergence of the transmitting beam, \(D_{R}\) is the receiver's telescope diameter, and \(\lambda\) is the wavelength. The required received power is determined by the receiver sensitivity \(P_{\text{sens}}\) and the required link margin \(L_{\text{LM}}\) as follows [5]:  
\[L_{\text{LM}}=\frac{P_{\text{R}}}{P_{\text{sens}}}. \tag{15}\]  
We discuss the estimation of the receiver sensitivity in the next subsections. There is an optimal value for \(\Theta_{\text{T}}\) lying in between that can be found by maximizing (11). Taking the first derivative of \(L_{\text{ch}}\) with respect to \(\Theta_{\text{T}}\), we readily see that the optimal beam divergence is  
\[\Theta_{\text{T}}=4\sigma_{\text{T}}. \tag{16}\]  
We, therefore, consider this beam divergence value in our calculations.
# An Image Processing Method for Extraction of the Stress Wave Reflection Period
## 2 Algorithm for the Automatic Extraction of the Reflection Period Based on Image Processing  
The algorithm is divided into two parts: signal preprocessing, which generates the time-frequency spectrogram of the stress wave, and extraction of the reflection period. The flow diagram is shown in Figure 1. Signal preprocessing to generate the time-frequency spectrogram includes wavelet denoising, quadratic self-correlation, and STFT. The extraction of the reflection period in the time-frequency spectrogram uses image processing algorithms, i.e., edge detection, grayscale, and binarization. Then, the averages of the column pixels of the binary image is counted to obtain a pixel distribution curve. Self-correlation is performed on the distribution curve to improve the SNR. Finally, the fast Fourier transform (FFT) is used for spectrum analysis to find the frequency corresponding to the maximum amplitude (except for the zero frequency); that is, the primary frequency of the image pixel distribution. The steps are outlined below.
# Advanced Medical Image Segmentation Enhancement:
## 3 Materials and Methods
### Integrating PSO and HE for Image Segmentation
#### 3.4.1 Algorithmic Integration and Parameter Dynamics  
The main goal of the integration is to ensure algorithmic harmony between PSO and HE. PSO, a robust optimization technique, is harnessed to adjust finely crucial HE parameters such as bin width and intensity distribution. The defining feature is the formulation of the objective function within PSO, which is meticulously designed to encapsulate a quantitative measure of image quality or segmentation performance. This strategic design guides the optimization process toward discovering HE parameter settings that maximize the effectiveness of subsequent segmentation steps. The dynamics of PSO parameters, including the inertia weight (\(w\)), cognitive component (\(c_{1}\)), social component (\(c_{2}\)), and maximum iterations, are delicately balanced to orchestrate a seamless dance between exploration and exploitation.
# Quality of Service Based NOMA Group D2D Communications
## 1 Introduction
### Contributions  
The main contributions of this work are as follows:  
* We propose Q-NOMA group D2D communications in which D2D users are randomly distributed over the entire two-dimensional plane. Unlike the existing proposals, we order the DRs according to their QoS requirements, which is more appropriate for the D2D communications scenario. Furthermore, in contrast to PPP, which is most commonly used to model D2D users (both DTs and DRs), we model the spatial topology of DTs by GPP and DRs are considered to be randomly clustered around DTs. These spatial distributions of DTs and DRs are suitable to analyse the proposed network with any number of D2D users. In addition, based on the QoS ordering, we propose two policies to compute power allocation coefficients that could lead to two implementations of the proposed Q-NOMA group D2D communications.
* We derive the interference distribution at the probe DR by utilizing the results from stochastic geometry. The Laplace transform of interference over GPP is derived in [21], which involves complex double integrals. In order to obtain useful insights, a major step in characterizing the interference is the approximation of integrals in the interference Laplace transform by applying Gaussian-Chebyshev and Gauss-Laguerre quadratures. This approximation results in an interference Laplace transform expression, which is easy to implement.
* Based on the interference approximation results, we further derive the closed-form expression for outage probability of the DRs in the proposed Q-NOMA group D2D communications.
* We present numerical results to validate the accuracy of the derived outage results and compare the performance of the proposed Q-NOMA group D2D with conventional paired D2D communications using OMA.
## 4 Proposed Control Method
### Incremental Nonlinear Dynamics Inversion Control with Nonlinear Disturbance Observer  
The proposed controller combines incremental nonlinear dynamic inversion and nonlinear disturbance observer. The INDI-NDO control input \(u\) can be selected as follows:  
\[u=u_{0}+\hat{b}^{-1}\Big{(}\
u-\dot{\hat{x}}_{0}-\hat{\varepsilon}\Big{)} \tag{15}\]  
where \(\
u\) is the pseudo control input. Then, Equation (6) becomes  
\[\dot{x}=\
u+\Big{(}\dot{x}_{0}-\dot{\hat{x}}_{0}\Big{)}+(\varepsilon-\hat{ \varepsilon}) \tag{16}\]  
The state derivative \(\dot{x}_{0}\) has to be estimated to apply the control input in Equation (15). The estimated state derivative is \(\dot{\hat{x}}_{0}\) using the first-order low-pass filter as follows:  
\[\dot{\hat{x}}=\frac{1}{\tau_{\text{est}}}(x-x_{0}) \tag{17}\]  
The \(\hat{\varepsilon}\) is estimated using Equations (11)-(13). Selecting the system dynamics as follows:  
\[\Delta\dot{x}=\hat{B}\Delta u+\varepsilon \tag{18}\]  
we then transform Equation (13) to  
\[\dot{z}_{aux}=-Lz_{aux}-L\big{(}\hat{B}\Delta u+L\Delta x\big{)} \tag{19}\]  
After integrating the above equation, \(\hat{\varepsilon}\) can be calculated as follows:  
\[\hat{\varepsilon}=z_{aux}+L\Delta x \tag{20}\]  
The control and measured variables are \(\omega=[p\ q\ r]^{T}\), meaning the NDO input is selected as \(\Delta x\equiv\Delta\omega=[p-p_{0}\ q-q_{0}\ r-r_{0}]^{T}\). Hence, the disturbance \(\varepsilon\) is observable, since the control and measured variables are identical, as in  
\[\begin{array}{ll}y&=Hx+\eta\\ &=\omega+\eta\end{array} \tag{21}\]  
where \(y\) is the measurement of \(\omega\), the observation matrix \(H\in\mathbb{R}^{3\times 3}\) is an identity matrix \(I_{3\times 3}\), and \(\eta\) (random variable vector) has a normal distribution \(N\big{(}0_{3\times 1},\sigma^{2}I_{3\times 3}\big{)}\) with zero mean vector \(0_{3\times 1}\in\mathbb{R}^{3\times 1}\) and variance \(\sigma^{2}\).
## 3 Results and Discussion
### CVT-Applied Analysis Model Result
#### 3.2.2 Comparison of Electric Economy According to Response of CVT  
One of the main performance criteria of the transmission is the time required for transmission. To maintain the shift pattern at the optimum efficiency point, the transmission should be implemented as quickly as possible, depending on the driver's needs and the vehicle's driving state. Figure 13 shows the change in the electronic fuel economy depending on the response speed of the transmission. It was confirmed that, as the response time increased, the electronic fuel economy decreased.
# Improved Charge Injection and Transport of Light-Emitting Diodes Based on Two-Dimensional Materials
## 1 Introduction
The further reduction of GO can increase the electrical conductivity of GO, leading to the reduced GO (rGO) with improved electrical conductivity. However, because most of the oxygen functional groups are removed in the reduction process, rGO is more difficult to disperse in water because it tends to aggregate. Transition metal dichalcogenides (TMDs), a class of 2D materials with the similar characteristics to graphene, have the chemical formula MX\({}_{2}\), in which M is a transition metal such as molybdenum (Mo) and X is a chalcogen such as sulfur (S), selenium (Se) and tellurium (Te). TMDs possess layered structure, which is composed of one layer of metal atoms between two layers composed of chalcogen atoms. Metal/chalcogen atoms in each layer are bonded strongly, while the bonding of atoms from adjacent layers is weak. Therefore, TMDs can be easily exfoliated into atomically thin layers through different  
Figure 1: Schematic structures of (**a**) graphene, (**b**) graphene oxide, (**c**) transition metal dichalcogenides (TMDs), reprinted with permission from [23], [PERSON], 2017.  
technologies and exhibit optical and electrical properties which are dependent on the layer number. These unique properties determine their wide application in optoelectronics and nanoelectronics. Here, we summarize the recent advances on the application of 2D materials for improving the performance of LEDs, including OLEDs, QLEDs and PeLEDs, using organic films, quantum dots and perovskite films as emission layers (EMLs), respectively. Several conventional 2D materials such as graphene and its derivatives and TMDs are employed as interlayers and dopant in functional layers such as hole transport layer (HTL)/hole injection layer (HIL) to enhance the hole injection and transport, reduce the exciton quenching existing at HTL/EML interfaces, improve the surface roughness of substrate. The recent progress of LEDs base on 2D materials is reviewed and the inherent physical mechanism is discussed. The potential application of 2D materials in PeLEDs is also presented and analyzed.
## 5 Trace Sampling Based on Type, Duration, and Errors
### Sampling by a Trace Duration
Proper histogram construction is a crucial milestone for frequency-based sampling. In general, the problem is the impact of outliers on the bin construction process. Standard procedures use an equidistant split of the data range, and data outliers can enlarge it, resulting in big bins with resolution distortion. A more accurate procedure should involve outlier detection to preserve the data in separate bins with further application of the classical procedure for the main part of the data. We consider outlier detection via a modified MAD (median absolute deviation) algorithm [74] with a slight modification. We define upper and lower baselines as 0.9 and 0.1 quantiles of data:  
\[M(up)=q_{0.9}(data),\ M(low)=q_{0.1}(data), \tag{13}\]  
where \(q_{s}(data)\) is the \(s\)-th quantile of the data with \(0\leq s\leq 1\). We calculate the upper and lower distances:  
\[dist(up)=|data(up)-M(up)|,\ dist(low)=|data(low)-M(low)|, \tag{14}\]  
where \(data(up)\) are data points bigger than or equal to \(M(up)\) and \(data(low)\) are data points smaller than or equal to \(M(low)\). We set upper and lower MADs as  
\[MAD(up)=q_{0.8}(dist(up)),\ MAD(low)=q_{0.8}(dist(low)). \tag{15}\]  
Based on the MADs, upper and lower thresholds are defined as  
\[upper=min(M(up)+2.5 MAD(up),max(data)), \tag{16}\]  
Figure 6: The sampling of traces of Figure 5 for different values of the parameter \(\beta\). Figure 7: The values of \(G_{dur}(\beta)\) show the sampling rates for the different parameter values \(\beta\). The red cross corresponds to \(\beta=0.083\) with a total sampling rate of 0.0996 (around 10%). and  
\[lower=max(M(low)-2.5 MAD(low),min(data)). \tag{17}\]  
All data points lying above or below than the corresponding thresholds are assumed to be upper and lower outliers, respectively. We cut outliers from the data and construct the classical histogram for the remaining data with some predefined numbers of bins (say bins = 5). Then, we calculate the number of lower and upper outliers and append them to the main histogram from left and right, respectively. Hence, if data have small-value outliers, then the first bin of the histogram contains the number of data points within the interval \([data(min),upper)\). If data have big-value outliers, the last bin contains the number of data points within the interval \((upper,max(data)]\). The main part of the histogram relates to data points within the interval \(([lower,upper])\). This procedure allows the proper construction of the corresponding histograms with a small number of bins. It is worth noting that the main part of the histogram consists of uniform intervals, while the widths of the first and last bins could be different from those bins. Finally, if data have outliers from both sides, the corresponding histogram has \(bins+2\) final bins. If data have outliers from only one side, then the final number of bins is \(bins+1\). No other bins are added to the classical histogram if data come without outliers.