esa-sceva/satcom-chunk-collection huggingface dataset:
# An Interactive System Based on the IASP91 Earth Model for Earthquake Data Processing
###### Abstract  
System software for interactive human-computer data processing based on the IASP91 Earth model was designed. An interactive data processing system for visualizing earthquake data was designed and implemented via the Intel Fortran platform. The system reads and processes broadband seismic data acquired by field stations, mainly including the reading and import of raw data, pre-processing, identification of seismic phases and inter-correlation traveltimes picking. In the data processing step, shortcomings have been improved and functions have been gradually refined and enhanced, making it easier and faster to process data. It has already processed more than 1000 large seismic events received by the station from 2013 to 2018. The practical application shows that the human-computer interaction system is easy to operate, accurate, fast and flexible, and is an effective tool for processing seismic data.  
seismic data; IASP91 earth model; data processing; Traveltimes; system software +
Footnote †: journal: Applied Sciences  
1
Footnote †: journal: Applied Sciences  
1
Footnote 1: College of Earth Sciences, Guilin University of Technology, Guilin 541004, China 1  
programming skills are required to use the functions of the software and no additional files need to be copied. The names of the functions are simple and can be easily found and used by the user.  
Based on a preliminary Earth reference model, the International Association of Seismology and Physics of the Earth's Interior developed a model of the Earth that reflects global average properties, called the IASP91 earth model, in 1991. Australian seismologist [PERSON] calculated the travel schedules of seismic waves of different phases propagating through the Earth's interior based on this model [16]. Focusing on the kinematic information of seismic wave propagation reduces the theory of seismic wave fluctuations to ray theory, which facilitates the study of seismic wave propagation [17]. The continuous research and study of ray tracing methods has led to a variety of ray tracing methods being constantly updated and improved. With this research, the kinematic and dynamical characteristics of seismic waves propagating in the Earth's internal medium are gradually becoming clearer. Ray tracing has become an indispensable and important tool for studying the physical properties and velocity structure of the Earth's inner medium [18; 19; 20; 21; 22; 23; 24].  
Accurate seismic phase picking is a prerequisite for seismic data processing methods such as seismic wave velocity imaging. This work has included building a Windows-based graphic user interface to visualize the natural seismic data in a simple and efficient way, and to make the human-computer interaction more intuitive and smooth [25]. Similarly, the human-computer interface enables the accurate identification and picking of seismic phases and other functions.
# A Review of Acoustic Emission Source Localization Techniques in Different Dimensions
## 4 Acoustic Emission Source Localization Techniques for Three-Dimensional (3D) Structures
### Point Location Based on Arrival Time Approach
#### 4.5.1 The USBM Method  
This method was developed by researchers from the United States Bureau of Mines (USBM) in the early 1970s. The development of this method was part of the mining bureau's efforts to make acoustic emission/microseismic (AE/MS) technology an effective engineering tool for determining the stability of rock structures. This method was first published in 1970 and further revised in 1972 [97; 98]. Since then, it has become the primary AE/MS source localization method used in North America for mines.  
The algorithm is as follows:  
Let \(D_{i}\) represent the distance from the source to the ith transducer,  
\[D_{i}=\sqrt{\left(x_{i}-x\right)^{2}+\left(y_{i}-y\right)^{2}+\left(z_{i}-z \right)^{2}}=v(t_{i}-t)\]  
\[D_{i}-D_{1}=v(t_{i}-t_{1})\]  
where i = 2, 3, \(\ldots\), m. Squaring and simplifying the above equation, this yields  
\[d_{i}+\frac{e_{i}}{d_{i}}-\frac{2(a_{i}x+b_{i}y+c_{i}z)}{d_{i}}=-2D_{i} \tag{2}\]  
\[\begin{array}{c}a_{i}=x_{1}-x_{i}\\ b_{i}=y_{1}-y_{i}\\ c_{i}=z_{1}-z_{i}\\ d_{i}=v(t_{i}-t_{1})\\ e_{i}={x_{1}}^{2}+{y_{1}}^{2}+{z_{1}}^{2}-\left({x_{i}}^{2}+{y_{i}}^{2}+{z_{i} }^{2}\right)\end{array}\]  
The non-linear system defined by (2) can be linearized by subtracting the second equation from the rest of them. This process yields  
\[f_{i,1}x+f_{i,2}y+f_{i,3}z=h_{i}+g_{i}v^{2} \tag{3}\]  
where  
\[\begin{array}{c}f_{i,1}=2(\frac{a_{i}}{t_{2}}-\frac{a_{i}}{t_{i}})\\ f_{i,2}=2(\frac{b_{i}}{t_{2}}-\frac{b_{i}}{t_{i}})\\ f_{i,3}=2(\frac{c_{i}}{t_{2}}-\frac{c_{i}}{t_{i}})\\ g_{i}=t_{2}-t_{i}\\ h_{i}=\frac{c_{i}}{t_{2}}-\frac{c_{i}}{t_{i}}\\ i=3,\ 4,\ldots,m.\end{array}\]  
Equation (3) defines a linear system.  
In matrix notation, the linear system defined by Equation (3) can be written as  
\[Ax=b\]  
where  
\[A=\begin{bmatrix}f_{3,1}&f_{3,2}&f_{3,3}\\ \vdots&&\vdots\\ f_{m,1}&f_{m,2}&f_{m,3}\end{bmatrix},\;x=\begin{bmatrix}x\\ y\\ z\end{bmatrix},\;b=\begin{bmatrix}h_{3}+g_{3}v^{2}\\ \vdots\\ h_{m}+g_{m}v^{2}\end{bmatrix}\]
# Reliability of NAND Flash Memories: Planar Cells and Emerging Issues in 3D Devices  
[PERSON]  
1 Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy; [EMAIL_ADDRESS] (C.M.C.); [EMAIL_ADDRESS] (A.L.L.)  
1 Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy; christian.  
channel boosting during program). Finally, there are issues that are specific to memories, such as cell-to-cell interference and program noise, as discussed in detail in [15]. On top of all this, reliability issues, to be discussed in the following, played a key role in challenging further scaling.  
This increasingly dramatic scenario prompted all major NAND Flash manufacturers to seek an alternative way to continue to deliver increasing storage capacity, that has resulted in the demise of the planar technology and the pursuit of three-dimensional (3D) memory arrays. In this field, a pioneering proposal was put forward in 2001 [16], evolving into early proposals based on layer stacking [17; 18; 19] and reaching the first cost-effective solution of a 3D-integrated array in 2007 [20]. From there on, 3D NAND based on vertical channels have taken the lead in the quest for higher density, resulting in the first commercial product in 2013 [21; 22] and reaching a capacity of 786 Gb with a density of 4.29 Gb/mm\({}^{2}\)[23] while stacking up to 64 layers [23; 24; 25]. Chip capacity was boosted not only by 3D integration, but also by multi-bit storage: while two bit-per-cell (i.e., multi-level cells, MLCs) were used in the first prototype [21], today's technology relies on storing three bit-per-cell (triple-level cells or TLCs) and quadruple-level cells (QLCs) are under active development. Recent reviews of the different 3D NAND technologies and architectures can be found in [26; 27; 28].  
The revolutionary move to 3D memories was not without problems, as discussed in [29]. In fact, while traditional device reliability issues must still be accounted for, new phenomena came to prominence, which are not merely an extension of what has already been observed on planar devices, but rather a consequence of the different device and array fabrication. The aim of this paper is then to review the main reliability issues that play a key role in planar NAND Flash devices and discuss the main experimental data and current understanding. Then, 3D NAND reliability is addressed, highlighting the role of such \"traditional\" mechanisms in the new cell size and geometry and introducing issues that are specific to the new memory cell. The analysis of the literature--though selected--reveals on the one hand the complexity of Flash reliability and, on the other hand, the huge efforts undertaken by the scientific community to understand the root causes of the experimental observations and develop effective predictive models that allowed a successful product design. Three-dimensional NAND bring further challenges to the table, with tighter requirements in an increasingly complex device, meaning that Flash reliability will offer an exciting field for scientific investigation for many years to come.
## 4 Discussion  
These case series demonstrate that the soft tissues could be successfully augmented using the roll-flap technique. To achieve an increase in soft tissue volume, the thickness of the pedicle graft should be considered. In this case series, as the thickness of the pedicle graft increased, a greater gain in soft tissue volume was achieved.  
The importance of mucosal thickness around implants has been demonstrated in many studies [11,12]. It is suggested that once the implant is exposed to the oral environment and function, a mucosal attachment of a certain minimum dimension (2 mm) is required to protect osseointegration [11]. According to [PERSON] et al., initially thin mucosal tissues can cause crestal bone loss after implant placement and 1 year in situ. If the initial tissue thickness is less than 2.5 mm, bone loss up to 1.45 mm can be expected within the first year of function [12]. Therefore, a soft tissue thickness of 2 mm or more prevents marginal bone loss around implants.  
The role of peri-implant mucosal thickness is also important to prevent the exposure of the metal surface of prosthetics or abutments, leading to better aesthetic outcomes [13, 14, 15]. Differences in light reflection between ZrO2 and Ti implant abutments become imperceptible to the human eye when mucosal thickness exceeds 2 mm [13]. According to [PERSON] et al., the thickness of the peri-implant soft tissue seemed to be a crucial factor for the impact of the abutment on the color of soft tissues with a thickness of \(\leq\)2 mm [14]. A mucosal thickness of 2 mm or more benefits implants, making the roll technique clinically significant. However, sufficient palatal flap thickness should be provided to avoid palatal flap thinning after the surgery.  
The proper assessment of the soft tissue over time is important to evaluate the outcome of the roll flap technique. There are several methods to assess soft tissue and monitor changes over time. These include direct measurements, e.g., through a probe or a measurement on physical or digital dental models. Each method has its pros and cons. For example, it is crucial to avoid subjecting patients to unnecessary risks. Intraoral dental scans offer a convenient, risk-free imaging tool that offers detailed 3D information. According to [PERSON] et al., digital measurements of the soft tissue using 3D scanned images can replace conventional clinical measurements using a periodontal probe since they are more accurate and reliable [16]. However, the intraoral scan has its limitations. There is difficulty scanning highly reflective or moist surfaces. Also, there is a learning curve, and there are purchasing and managing costs [17].
# Role of Functional Monomers upon the Properties of Bisphenol A Molecularly Imprinted Silica Films
## 2 Experimental Section
### Batch Binding Measurements and Regeneration of BPA Films  
Binding experiments were performed to investigate the specificity of MIP films by measuring the adsorbed amount of BPA onto MIPs and NIPs. The binding tests referred to contacting the MIP and NIP films with 35 mL BPA solution of a known concentration (experiments conducted in batch conditions in duplicate) and measuring the initial and the final concentration of BPA. Due to BPA poor solubility in water, organic solvent-based assays were employed. The UV-visible absorption spectrum of bisphenol A in ethanol is given in Figure S2. The specific wavelengths of bisphenol A were registered at 229 nm and 278 nm. Since the value at 229 nm is susceptible to the influence of the solvent, the quantification of BPA was performed against the value at 278 nm as characteristic wavelength for BPA adsorption [35]. Thus, the calibration of BPA at \(\lambda\) = 278 nm was obtained in ethanol using a series of standard solutions containing BPA in the 0.2-0.008 g/L concentration range. The concentration of BPA was found to have a good linear relationship with \(r^{2}\) = 0.997 (according to Figure S3). Herein, the concentration limits chosen for the study, meaning 0.008 and 0.2 g/L, represented the lowest and the highest quantity of BPA that can be detected by UV.  
The kinetic of BPA rebinding was studied in the first 120 min of contact with 0.2 g/L BPA solution in ethanol. Afterward, using various concentrations of BPA (i.e., 0.2, 0.15, 0.1, and 0.08 g BPA/L in ethanol), the adsorbed amount of BPA was measured after a period of only 5 min, considered proper for specificity.  
The reusability of the BPA-MIPs and NIPs deposited on glass slides was also investigated by using the same batch of films through the adsorption-desorption studies in five consecutive cycles.  
The re-binding capacities, Q (mg BPA g\({}^{-1}\) polymer film), of BPA-MIP and NIP films were calculated using Equation (1); where C\({}_{N,i}\) (g/L) and C\({}_{N,f}\) (g/L) are the initial and final concentration of BPA in the feed solution, m\({}_{\text{p}}\) (mg) the films weight (m\({}_{\text{p}}\) = 40 \(\pm\) 1 mg) and V\({}_{\text{S}}\) (L) is the volume of the feed solution (i.e., 35 mL).  
\[Q=\frac{\left(C_{N,i}-C_{N,f}\right)\cdot V_{s}}{m_{p}} \tag{1}\]  
Imprinting factors, F, expressed by Equation (2), quantified the specificity with which BPA-MIP films re-bind BPA, compared to their corresponding NIPs, where QMIP and QNIP are the re-binding capacities of BPA-MIP and NIP, respectively.  
\[F=\frac{Q_{MIP}}{Q_{NIP}} \tag{2}\]
## 4 Results of Numerical Simulation
### Anchorage with Supplementary Reinforcement
The strength of the concrete has a more significant effect on anchoring without supplementary reinforcement than on anchoring with supplementary reinforcement, e.g., a 150% increase in concrete strength will increase the resistance by 52% for anchorage without supplementary reinforcement and only by 38% for anchorage with supplementary reinforcement. Figure 15:3 Ultimate load increase: (**a**) influence of the concrete compressive strength; (**b**) influence of the supplementary reinforcement. Figure 15:4 Comparison of parametric study results: (**a**) anchorage without supplementary reinforcement—compressive concrete strength effect; (**b**) anchorage with supplementary reinforcement—compressive concrete strength effect; (**c**) anchorage with supplementary reinforcement—reinforcement diameter effect. The effect of increasing the diameter and cross-section area of the supplementary reinforcement on the anchorage efficiency is documented in Figure 23b. The use of supplementary reinforcement with a rebar diameter of \(\mathcal{O}\) 6 mm leads to a significant increase in the anchorage resistance by up to 55%. However, further increasing the reinforcement cross-section area is not so excessively significant. For example, increasing the supplementary reinforcement cross-section area by 300% (\(\mathcal{O}\) 6-\(\mathcal{O}\) 12) further increases resistance by only 65%.
## 4 The Como Test
### Analysis of the Time Series between Como EPN and Rover  
The Milano test confirmed the complete consistency of BSW5.2 and LGO results: because of this we adopted LGO to process the Como test. The hourly results were converted to east, north, and up displacements with respect to the zero position: they can be compared with the imposed displacements (Figure 5) and the residuals (differences between estimated and imposed displacements) can be computed. One session provided a float solution with residuals of 7 cm in east, 4.4 cm in north, and 9.1 cm in up directions: thus, this solution was removed from the list of results before proceeding to the next step of the analysis.  
Figure 5: Baseline between the primary reference and rover. Hourly sessions: comparison between imposed and estimated displacements. (**a**) East; (**b**) north; (**c**) horizontal; and (**d**) up.  
The estimated horizontal displacements seem quite consistent with the imposed ones, while the vertical results are significantly worse and just follow the general trend. North results are slightly worse than east results. The statistics of the residuals are presented in Table 7: means range between 1 mm (east) and 3 mm (up), while RMSEs range between 4 mm (east) and 13 mm (up); they are satisfactory but worse than those of the Milano test: this is due to worse observation conditions.  
The following tests involve significance and congruence analyses. To perform them, displacements are estimated by differencing couples of estimated coordinates of the rover:  
\[\delta\mathbf{x}_{ij}=\mathbf{x}_{j}-\mathbf{x}_{i} \tag{3}\]
# Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism
## 4 Case Study
### Experimental Data and Evaluation Metrics
Despite the small amount of missing data, all timestamps are fully recorded, ensuring high data quality. This dataset provides rich information for fine-grained analysis of electricity consumption,making it suitable for machine learning tasks such as regression and clustering. The forecasting time span is from December 2006 to November 2010, totaling 47 months. The load data are recorded per minute, with each data point representing electricity consumption for one minute. This high-precision data granularity makes the dataset highly suitable for short-term power load forecasting and detailed energy consumption analysis. In this paper, four error metrics are used to evaluate the effectiveness of the forecasting model, including Maximum Error (Max Error), Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The formulas for each evaluation metric are as follows:  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}|, \tag{17}\]  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}{(y_{i}-\hat{y}_{i})^{2}}, \tag{18}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}{(y_{i}-\hat{y}_{i})^{2}}}, \tag{19}\]  
\[Max\ Error=max(|y_{i}-\hat{y}_{i}|) \tag{20}\]  
where \(y_{i}\) is the true value and \(\hat{y}_{i}\) is the predicted value.
# Adaptive Modulation Scheme for Soft-Switching Hybrid FSO/RF Links Based on Machine Learning
## 3 Threshold Estimation and Data Set Generation by Machine Learning
### Adaptive Modulation  
The spectral efficiency of M-PSK is defined as the data rate transmitted within a given bandwidth, and can be given by [18]  
\[\eta_{MPSK}=\frac{C}{W}=\frac{\sum_{j=1}^{N}b_{j}\log_{2}M_{j}}{2}, \tag{19}\]  
where \(C\) represents the data rate used for transmission, \(W\) shows the channel bandwidth, the modulation order is represented as \(M_{j},M_{j}=2^{j},j=1,2\cdots N\), and \(b_{j}\) is the probability of receiving SNR in the interval \([\gamma_{j},\gamma_{j+1}]\), which can be expressed as  
\[b_{j}=Pr\{\gamma_{j}\leq\gamma_{j+1}\}=F_{\gamma}(\gamma_{j+1})-F_{\gamma}( \gamma_{j}), \tag{20}\]  
where \(F_{\gamma}(.)\) is the cumulative distribution function (CDF) of the turbulence-induced fading. Given \(F_{\gamma}(\gamma_{j+1})\to 1\), (18) can be simplified to  
\[\eta_{MPSK}=\frac{N-\sum_{j=1}^{N}F_{\gamma}(\gamma_{j})}{2}. \tag{21}\]  
And the spectral efficiency of the M-QAM is as follows [19]:  
\[\eta_{MQAM}=\sum_{j=1}^{N}ja_{j}=N-\sum_{j=1}^{N}F(\gamma_{j}), \tag{22}\]  
The spectral efficiency of the L-PPM is as follows [20], there is only one optical pulse among the N times slots:  
\[\eta_{LPPM}=\frac{\log_{2}L}{L}. \tag{23}\]  
where have only one optical pulse among the \(L\) times slots.
## 3 Performance Evaluation
### Computer Simulations  
Real-time implementation of the aforementioned procedure required the use of specific values of the protocol parameter \(M\). A simulation environment would be useful to efficiently evaluate the effects of such parameters because many simulation runs can be performed in a fraction of the time it takes to perform a real-time procedure. Measurements of real channel data were acquired by running the same real-time procedure described in Section 3.2 without routing and power control. Each ED continuously sampled RSSI data from all other devices and transmitted them to the host. A set of captured RSSI tables (as seen in Table 1) were fed to the simulation, which proceeded to run the routing algorithm. The simulation produced accumulated energy use data, along with power settings, and routes taken for all devices, while sweeping over many values of \(M\).  
Figure 4: Experimental setup environment for network architecture with access point off the body. AP1, AP2, AP3 and AP4 mark the position of the four antennas of the AP.
# Marine Internet for Internetworking in Oceans: A Tutorial
## 3 Environments for Communication in Oceans
### Coastline and Island  
Along coastlines or on islands, network infrastructures such as base stations and access points (BSs/APs) can be deployed. Coastline infrastructures can be linked directly by optical fiber to terrestrial Internet to extend Internet services to oceans. Similarly, infrastructures deployed on islands near coastlines can also be linked to the terrestrial Internet, and the link can be wireless or wired. In these cases, users can connect terrestrial Internet via a deployed BS/AP with their devices such as mobile phones and notebooks. Furthermore, power supply here is not a big issue since a BS/AP can be supported directly by electricity grids or generators. On remote islands, wind and solar energy stations can be also installed.