esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Data Description
\[x_{i}^{*}=2 arctanx_{i}/\pi \tag{3}\]  
Target Labels and Feature Variables  
By organizing the logging and recording data in the study area, the target label was the porosity and permeability data tested in the laboratory, and the characteristic variables were derived from the average of the logging series data at the same depth location of the target label. By applying max-min normalization to feature variables and target labels, we found that among them, the medians of the normalized data for acoustic time difference logging (AC), compensated neutron logging (CNL), natural gamma logging (GR), resistivity shallow logging (RLLS), resistivity deep logging (RLLD), resistivity logging (RT), thorium logging (Th), uranium logging (U), permeability logging (PERM_L), anyway tangent preprocessing (Artcan(PERM)), and measured permeability (PERM) are distributed between 0 and 0.2; the median distribution of normalized data for caliper logging (CAL), potassium logging (K), porosity logging (POR_L), measured porosity (POR), and logarithmic preprocessing (LOG(PERM)) ranged from 0.2 to 0.4; and the median distribution of normalized data for photoelectric factor logging (PE), density logging (DEN), spontaneous potential logging (SP), and density logging (DEN) ranged from 0.4 to 0.8, as shown in Figure 3. An amount of 80% (2711) of the feature variable and target label dataset was used as the training set and 20% (678) as the test set for training the predictive model. The Pearson correlation coefficients between the analyzed feature variables and the target labels are shown in Figure 4. It can be observed that the CNL, DEN, GR, PE, RLLS, RLLD, RT, SP, K, Th, and U logging curves are negatively correlated with the target labels POR and PERM, whereas the AC, CAL, POR_L, and PERM_L logging curves are positively correlated with the target labels POR and PERM. When there is a strong correlation between feature variables, such as feature variables RLLS, RLLD, and RT, considering multicollinearity, only one of them can be selected as the feature variable. When the feature variables are strongly correlated with the target label and can better represent the target label, they are selected as feature variables. The most suitable combination of XGBoost algorithm feature variables is selected using feature variable combination.
## 7 Real Data Applications
### Analysis of First Dataset  
**First**, in order to compare the fit models, we employed the aforementioned criterion measurements included in Table 6. Figure 5 displays the dataset's P-P plots, the fit UEHLD PDF plots with their empirical CDF, and the relative histogram with the fit UEHLD. These graphical goodness-of-fit methods in Figure 5 also corroborate the results in Table 6.  
Figure 4: Data description. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Min** & **Q(0.25)** & **Q(0.5)** & **Mean** & **Q(0.75)** & **Max** \\ \hline I & 0.0110 & 0.1410 & 0.1510 & 0.2434 & 0.326 & 0.9490 \\ \hline II & 0.0230 & 0.1323 & 0.3360 & 0.3659 & 0.5265 & 0.9260 \\ \hline III & 0.0023 & 0.01432 & 0.0273 & 0.03571 & 0.04632 & 0.1781 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Descriptive summary datasets. **Second**, from Table 5 and Figure 4, we cannot use the MPS method to estimate the parameters of Dataset I because this dataset has more equal values, then \(F(t_{i},\varphi,\delta)-F(t_{i-1},\varphi,\delta)\) is equal to zero at most observations. Consequently, Table 7 only contains the ML and Bayesian estimates with the SELF of the UEHLD's parameters. 10,000 MCMC samples were generated using the MCMC method. To apply the MCMC sampler process, the starting values of the unknown parameters were assumed to represent their MLEs.
# 3D Convolution Recurrent Neural Networks for Multi-Label Earthquake Magnitude Classification +
###### Abstract
For this purpose, recurrent layers are employed on each kernel of the third CNN layer to leverage additional spectral information for the earthquake recognition task. In this study, CNN is used for local feature extraction and recurrent layers to model the state-to-state and input-to-state transformations, capturing information in the spatiotemporal domain. In addition, considering that there is no dataset for the multi-label earthquake recognition task, we propose constructing a new dataset in this study. The data catalog comprises 93 K seismic waveforms, out of which six new classes are generated from two categories of an existing earthquake dataset, STEAD (STanford EArthquake Dataset) [29]. In summary, we claim three main contributions of this work:  
1. We examine earthquake magnitude categorization as a multi-label classification task by evaluating the features extracted through log-Mel spectrograms and analyzing the relationships among different earthquake classes. 2. We present a 3D-CNN-RNN based architecture to evaluate the multi-label earthquake classification task. It encapsulates a 3D-CNN to extract features from an input spectrogram, and recurrent layers are employed on each kernel of the final CNN layer to model the similarities among different earthquake signals. 3. We develop a new multi-label earthquake dataset and reorganize an existing dataset [29] for the earthquake categorization task. This paper is organized as follows: Section 2 discusses our approach to categorize the earthquake magnitudes and comprehensively explains the use of 3D convolutional and recurrent neural networks and their adaptation to multi-label earthquake magnitudes classification tasks. Section 3 describes the dataset properties, extraction, and transformation methods to evaluate the proposed framework. Section 4 describes the feature extraction methods, and Section 5 explains our experiments on the transformed dataset acquired from STEAD. Section 6 concludes this work and discusses possible future research directions motivated by the proposed method.
## 7 Clinical Implications in OSA Treatment  
### Considerations in Treatment with Mandibular Advancement Device  
Using mandibular advancement devices (MADs) for the treatment of OSA presents specific challenges for managing periodontal health, especially in patients with pre-existing gingivitis or periodontitis. A longitudinal study evaluating the periodontal outcomes of OSA patients treated with MADs reported no significant periodontal changes after approximately eight years of follow-up; however, this study included only patients with healthy periodontal status at baseline, limiting its applicability to individuals with compromised periodontal health [72]. Furthermore, most randomized clinical trials investigating MADs in OSA specifically exclude patients with a history of periodontal disease, leaving the effects of MADs on those with pre-existing periodontal issues largely unexplored [73].  
Severe, untreated PD also represents a contraindication to MAD use due to factors such as gingival inflammation, tooth mobility, and reduced periodontal support [15]. For such individuals, a comprehensive assessment of periodontal stability is critical prior to initiating MAD therapy. Regular periodontal monitoring and maintenance should also be integrated into the treatment plan to mitigate potential risks and preserve periodontal health [16; 74; 75].
## 2 Analysis
### Boundary Conditions  
For X = 0 (channel entry)  
\[u=u_{0};\ T=T_{0},c=c_{0};\ T_{nf}=T_{nf0};\ \delta(0)=\delta_{0};\ u_{\rm nf}=u_{ \rm nf0} \tag{12}\]  
For Y = 1 (dry plate)  
\[\left[-\lambda\frac{1}{d-\delta}\frac{\partial T}{\partial Y}\right]_{Y=1}=h( T(X,1)-T_{0})\ \mbox{and}\ \frac{\partial c}{\partial Y}\right)_{Y=1}=0 \tag{13}\]  
For Y = 0 (interface between the two phases) (in the interface y = \(\delta\) then returning to the Equation (1), y = \(\delta\) give Y = 0)  
For the case where we neglect the normal stress forces  
\[\frac{1}{d-\delta}\mu\!\left(\frac{\partial u}{\partial Y}\right)_{Y=0}=\frac {1}{\delta}\mu_{nf}\!\left(\frac{\partial u}{\partial Y_{nf}}\right)_{Y_{nf} =1} \tag{14}\]  
The equality of the velocity for the two phases  
\[u(X,Y=0)=u_{nf}\!\left(X,Y_{nf}=1\right) \tag{15}\]  
We consider that the film thickness variation is negligible, and the rate of phase change is small: (interface between the two phases) (in the interface y = 0 then returning to the Equation (1), y = \(\delta\) give Y = -1)  
\[v(X,Y=0)=\left(\frac{D}{1-c(X,0)}\frac{1}{(d-\delta)}\frac{\partial c}{ \partial Y}\right)_{(Y=0)} \tag{16}\]  
\[T(X,Y=0)=T_{nf}\!\left(X,Y_{nf}=1\right)\!,\ c(X,Y=0)=c_{sat}(T(X,Y=0) \tag{17}\]  
\[-\frac{1}{\delta}\lambda_{nf}\!\left(\frac{\partial T_{\rm sw}}{\partial Y_{nf }}\right)_{Y_{nf}=1}=-\lambda\frac{1}{d-\delta}\!\left(\frac{\partial T}{ \partial Y}\right)_{Y=0}-\frac{1}{d-\delta}\frac{\rho L_{v}\!\left(D+\overline {D}\right)}{(1-c(X,1))}\!\left(\frac{\partial c}{\partial Y}\right)_{Y=0} \tag{18}\]  
For Ynf = -1 (heated plate), regarding the Equation (2)  
\[u_{nf}=0,-\lambda_{nf}\!\left(\frac{\partial T_{nf}}{\partial Y}\right)_{Y_{nf }=0}=q_{w} \tag{19}\]  
where \(c_{sat}(T(x,Y=0))=\frac{M_{\rm x}P_{\rm ss}}{M_{\rm x}P_{\rm ss}-M_{\rm x}(P-P _{\rm ss})}\) is a vapor concentration at saturation. P\({}_{\rm vs}\) is a vapor pressure at saturation (Vachon [16]).  
In addition, we define the local evaporation rate m(X) and the total evaporation rate at the interface m\({}_{\rm t}\) at the axial location X as:  
\[m(X)=\frac{\rho D}{1-c(X,0)}\frac{1}{d-\delta}\!\left(\frac{\partial c}{ \partial Y}\right)_{Y=0} \tag{20}\]  
\[m_{t}(X)=\int_{0}^{X}m(X)dX \tag{21}\]  
The global mass balance at each section and the boundary conditions closes the mathematical system. The present problem defined by the system of equations with the boundary conditions is solved numerically using a finite difference marching procedure in the downstream direction. A fully implicit scheme where the axial convection terms are approximated by the upstream difference, and the transverse convection and diffusion terms by the central difference is employed. The simulations are to describe the effect of alumina volume rate on the transfers. Particular attention is given to show the efficiency of the alumina content on the phase change temperature.
## 1 Introduction
### Study Area  
The study area encompassed part of the Greater Houston metropolitan area (surface area: 10,374 km\({}^{2}\); Figure 1). The area has experienced recurring and intense flooding events in the past two decades related to tropical cyclones and heavy inland rainfall. Some of the major events include Tropical Storm Allison (2001), the April 2016 North American storm complex sometimes referred to as the \"Tax Day storm,\" Hurricane Harvey (2017), and Tropical Storm Imelda (2019). Hurricane Harvey was one of the wettest rainfall events in the history of the United States, producing an unprecedented amount of rainfall, estimated to be between 36 and 48 inches, in Houston and the surrounding areas from 25 to 31 August (2017). It resulted in severe flooding and associated damage amounting to USD 125 billion [15; 16]. The second and less intensive recent flood event that affected the study area was the Tax Day storm (17-18 April 2016) that dumped more than 17 inches of rainfall over a short period of time and caused billions of dollars of flood damage [17; 18]. Tropical Storm Imelda (18-21 September 2019) poured as much as 43 inches of rainfall (NOAA NCEI, 2020) into parts of southeast Texas, although the severity of the flooding event and the spatial/aerial coverage over the study area were not as significant and intense as the previous two flood events [19; 20].  
In addition to flooding resulting from extreme precipitation from hurricanes and cyclones over the study area, intense rainfall in the upstream watersheds of the San Jacinto and Brazos rivers that pass through the study area (Figure 1) further complicate the flooding problem [21]. Over time, urbanization and other intense anthropogenic activities across the study area have led to changes in the land cover and land surface elevation, which are believed to have aggravated the impacts of flooding [14; 22; 23]. [22] estimated that over the past two decades (1997-2017), nearly 2040 km\({}^{2}\) (\(\pm\)400 km\({}^{2}\)) of land in the Greater Houston area has been changed to less permeable developed land cover, out of which 14% were wetland areas.
## 3 Background Work
### ECDSA Signature Scheme
#### 3.3.2 Sign Algorithm  
The message (\(Me\)), private key \(PrivK\), and a signature \((q,t)\) are the input and output, respectively, of the ECDSA Sign algorithm. The ECDSA Sign algorithm employs the EIGamal signature technique, which functions as follows:  
* \(h=Ha(me)\)--Using a cryptographic hash algorithm, determine the message's hash value.
* Securely produce the random integer \({}^{\prime}l^{\prime}\in[1\ldots n-1]\). \(Ra=l*G\), \(q=Ra.x\)--Determine the x-coordinate of the random point.  
* \(t=l-1*(h+q*PrivK)(modn)\)--the signature proof calculation.
* Output \(q,t\).  
Here, \([1\ldots n-1]\) is the range for the integers \(q,t\). The random point is encoded using the Sign technique. \(Ra=l*G\), along with a proof t, demonstrates that the signature is knowledgeable of the message and the private Key _PrivK_. The Public Key, \(PubK\), may be used to validate the proof \(t^{\prime}\).
# Attention-Guided Network Model for Image-Based Emotion Recognition
## 2 Materials and Methods  
### Base Network Architecture  
The VGG16 was selected as the base network model, as a deep representation depth is considered important in classification results [2]. It is composed of five convolutional blocks and three fully connected (FC) layers. The first and second convolution blocks contain two convolution layers with rectified linear unit (ReLU) activation functions and a pooling layer. Convolution blocks three, four, and five have three convolution layers with ReLU activation and a pooling layer at the end. The architecture takes 224 \(\times\) 224-pixel RGB images as input and is composed of 41 layers with a total of 138 million parameters. For this study, the model was trained from scratch without pre-trained weights.  
To boost robustness and guide the models' decision making during training, attention modules were added throughout the architecture. In this study, the integration strategy of [5] was adopted for all the proposed attention modules of AGFER, SEFER, and CBAM-FER. They are placed after the second, third, and fourth convolutional blocks prior to the last pooling operation of each of the blocks. Following the work of [36], the last pooling layer (\"pool5\") was also removed from the architecture to increase the spatial dimension for better classification results and assessment of the decision visualization. This impact can be seen in the ablation study performed in Section 5.1, which assesses change in performance when specific parts of the CNN are removed. Figure 1 represents the network architecture and the implementation of the attention block strategy.  
Figure 1: Schematic of the base network architecture and the integration of attention modules.
# Global Power Dynamics in the Contemporary Space System
## 5 System Dynamics--Global Power Dynamics
### Decentralized and Multilevel Interactions  
A notable global power dynamic in the contemporary space system is the increasing decentralization of space interactions across multiple levels of action. The centralized global governance of outer space that emerged at the inception of the space age--which encompasses a global binding legal framework (Outer Space Treaty (1967)) and intergovernmental space institutions under the United Nations (COPUOS/UNOOSA (1958-1959))--has undergone a transition to a more decentralized and fragmented distribution of power.  
This transition has been referred to by some experts as \"the big bang of space governance\", which describes the evolution of the space system's initial monocentric and hierarchical structure to a decentralized distribution of power ([PERSON], 2018). In essence, states are shifting away from binding, mandatory, and supranational commitments, opting instead for more informal, non-binding, and multilateral agreements. [PERSON] and [PERSON] define this phenomenon as \"polycentric governance\", a term denoting a system in which multiple independent decision-making centers exist, with at least partial overlap in their respective jurisdictions ([PERSON], 2018; [PERSON], 2018).  
In addition, this suggests the presence of a multitude of actors, engaging in a variety of interactions across multiple levels or arenas of action (local, national, regional, and international). This phenomenon serves to reinforce the ongoing process of fragmentation and decentralization, as well as the distribution of power among multiple centers ([PERSON], 2018).
# The Evaluation of Software Security through Quantum Computing Techniques: A Durability Perspective
## 2 Materials and Methods
### Quantum Algorithm
#### 2.3.2 Lattice-Based Cryptography Algorithm [A2]  
In the environment of post quantum-based security procedures, this propositioned technique assures to protect the information counter to the quantum computing [24; 25]. [PERSON], [PERSON], and [PERSON] brought together the lattice-based encryption, which is steady or unbreakable nowadays. The lattice-based erections are an n-dimensional intermittent gap, in which the n-dimensional vector \(c1\cdots\cdots cn\in R^{n}\) lattice-based generated set of vectors are shows in Equation (1).  
\[\mathcal{L}(c1,\ \cdots,\ \cdots,\ \cdots,cn)=\left\{\sum_{i=1}^{n}x_{i}c_{i}:\ x _{i}e\mathbb{Z}\right\}. \tag{1}\]  
The vector \(c1\), \(c2\), \(\cdots\), \(cn\) are recognized as elementary lattices [25]. Here, \(\mathbb{Z}\) is the arbitrary session of lattice, R is the set of real numbers, and L is the dimension of lattice [26]. The problems of lattice-centered security procedures are the short vector problem (SVP) and Lenstra-Lenstra-Lovasz (LLL) algorithm. In SVP, participation lattice is indiscriminate, and its approximation is short. In 1982, the researcher gave the LLL algorithm. This algorithm has the approximation of \(2^{O(n)}\), where n is the magnitude of lattice.
# Prediction of Air Quality Combining Wavelet Transform, DCCA Correlation Analysis and LSTM Model
## 3 Results  
### Meteorological Series Sym8 Wavelet Four Layer Transform Noise Reduction  
To improve the quality of meteorological data, wavelet transform noise reduction is applied to four time series of temperature, humidity, barometric pressure, and wind speed.  
Figure 2 depicts the visualization comparison graph of time series data with noise reduction using the wavelet transform. (a) demonstrates the processing effect of wavelet transform noise reduction for the temperature data, (b) demonstrates the processing effect for the humidity data, (c) demonstrates the processing effect for the pressure data, and (d) demonstrates the processing effect for the wind speed data.  
As demonstrated in Figure 2a, the temperature profile reflects a seasonal cycle, and as our data range from April 2019 to July 2021, the change in temperature reflects the seasonal change in the region precisely.  
As indicated in Table 1, we utilized the signal-to-noise ratio (SNR) and root mean square error (RMSE) in this research to quantify the fluctuation in data quality, where a larger SNR value and a smaller RMSE indicate a more effective noise reduction.  
Figure 2: Temperature Feature Reconstruction (**a**). Humidity Feature Reconstruction (**b**). Pressure Feature Reconstruction (**c**). Wind Feature Reconstruction (**d**).
## 3 Design of the Wirelessly Activated Node  
In order to face the hostile electromagnetic environment where the battery-less sensor nodes will operate, a dedicated design strategy and a novel layout have been conceived to provide a reliable architecture, whose characteristics must include: (i) compactness, to enable node deployment even on small engine parts; (ii) insensitivity to materials located in close proximity; (iii) energy autonomy, to eliminate batteries and related maintenance; (iv) ultra-low-power sensing and communication operations. To comply with these multiple requirements, a multi-layer architecture has been implemented with two co-located antennas, one for the RF-WPT and one for the communication operations.  
A simplified diagram of the wireless sensor node is depicted in Figure 3a and consists of the following sub-systems:  
* Two co-located antennas, operating in the same frequency band and adopting frequency diverse technique discussed above, one for receiving the energy to be harvested from the WPT, and one for the communication.
* A rectifier for converting the RF signal into a DC signal to be fed to the buck-boost DC/DC converter in the power management section.
* A power management section that efficiently harvests energy at the rectifier's output to the storage elements and manages the power supply to the sensor node.
* A low power microcontroller unit (MCU, STM32L476 RE, STMicroelectronics, Geneva, Switzerland) to control the node peripherals.
* A 2.4 GHz SX1280 transceiver (Semtech Corporation, Camarillo, CA, USA) supporting LoRa communication protocol, feeding a 2450 AT42E0100 (Johanson Technology,  
Figure 2: (a) Picture of the illuminator feeding circuitry working in the 2.4 GHz band [21] © 2020 IEEE; (b) front view of one of the three coaxial-fed circularly polarized patch antennas.  
Camarillo, CA, USA) 2.4 GHz surface mount technology (SMT) above-metal mini chip antenna.
* An ultra-low-power three-axis accelerometer (IIS2 DLPC, STMicroelectronics, Geneva, Switzerland) for inertial sensing.
* An ultra-low-power real-time clock (AB1805, Abracon, Spicewood, TX, USA) for duty-cycling the node operation, by turning on the node at specific time intervals only.  
It is also noteworthy to observe that using two different antennas for WPT and communication is strategic to minimize energy losses, to avoid RF switches in the EH path. Whereas the performance of the WPT heavily depends on the radiation efficiency of the adopted antenna (which has been therefore custom designed), the communication based on LoRa has been proved to be robust to input signal levels as low as \(-130\) dBm, largely achievable with the compact chip antenna which is integrated on the node.