esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Key 6G Technologies
### Improved Wireless Interface
#### 5.2.5 Coordinated Multipoint and Cell-Free (CoMP)  
CoMP technology represents a category of solutions enabling multiple access points to collectively serve several mobile stations, thereby realizing network-layer Multiple Input-Multiple Output (MIMO) and enhancing spatial diversity beyond traditional physical layer MIMO methods. CoMP, sometimes called cooperative MIMO or network MIMO, was first included in 3 GPP Release 11 for LTE advanced systems. CoMP is positioned as a critical role in 5G due to recent findings demonstrating its ability to mitigate downlink inter-cell interference and facilitate joint user identification in the uplink [143]. With the advent of 6G and new spectrum bands above 10 GHz, CoMP technologies--which use base station-level diversity--become essential to supplement spatial diversity achieved at the antenna level.  
This is especially true in light of the difficulties presented by high blockage problems in high-frequency bands.  
Furthermore, the concept of CoMP involves user equipment (UE), establishing several simultaneous connections with different base stations within the same radio access technology (RAT), thus paving the way for a groundbreaking 'cell-free' radio access network (RAN) structure. This architecture cooperatively serves all UEs via coherent transmission in a CoMP approach [144] by connecting a central processing unit to many single-antenna access points dispersed across the coverage region. According to recent research, such cell-free massive MIMO systems may reduce fronthaul signaling [145] and perform better than conventional cellular massive MIMO configurations.  
However, despite the performance gains from cooperative decoding, CoMP faces certain technical challenges. Firstly, the effectiveness of CoMP depends heavily on the clustering of collaborating base stations, necessitating the development of suitable clustering schemes, a subject of extensive research in recent years [146]. Secondly, achieving synchronization among cooperating base stations is crucial and must be done without causing inter-carrier and inter-symbol interference [147]. Additionally, channel equalization and estimation must be conducted in an inter-base-station coherent mode, significantly increasing computational complexity.
## 1 Introduction
For instance, secrecy outage probability (SOP) and average secrecy capacity (ASC) expressions of FSO links over \(\mathcal{F}\) turbulence channels are provided in [30]. In [31], the SOP expression of an FSO system over a correlated Malaga turbulence channel is derived. Also, SOP and strictly positive secrecy capacity (SPSC) expressions of a mixed FSO/RF system are presented in [32]. Further, the secrecy performance of a parallel FSO/mm-wave system over an \(\mathcal{F}\) turbulence channel is studied in [33]. It can be observed from the aforementioned literature work that the studies regarding SLIPT-based FSO communication systems are still limited and, to the best of the authors' knowledge, SLIPT-based DH FSO/FSO communication systems are not studied in the open literature. Thus, in this work, we propose a DH DF-based FSO/FSO communication system where the relay harvests energy from the received optical signal through the SLIPT technique. We consider time-splitting [12]-based SLIPT where a portion of the transmission time is dedicated for energy harvesting. We also consider an eavesdropper at the relay to evaluate the physical layer security of the proposed communication system model. The main contributions of this study can be summarized as follows:  
* We derive the probability distribution function (PDF) and the cumulative distribution function (CDF) expressions of the end-to-end (e2e) instantaneous signal-to-noise ratio (SNR) for the proposed SLIPT-based DH FSO communication system. * By utilizing the derived CDF and PDF expressions, we obtain the OP, ergodic capacity, average BER, and throughput expressions of the proposed communication system. * To analyze the physical layer security of the communication system, SOP and SPSC expressions are also presented. Finally, Monte Carlo simulations are carried out to validate the derived analytical expressions and to analyze different system parameters of the proposed communication system. The rest of this paper is organized as follows: In Section 2, the system model, statistical characterization of the turbulence channels, and EH at the relay descriptions are provided. In Section 3, expressions such as OP, ergodic capacity, average BER, throughput, SOP, and SPSC are derived. Section 4 presents the simulations, and Section 5 concludes the study.
## 6 Implementation and Discussion of Results
### Stock Prices Prediction Phase  
The next phase is the price prediction phase, and this is done by decomposing the signal into five sub-signals and passing the output to the LSTM to make the prediction. The decomposing step is implemented using Python's \"vmdpy\" library with the hyperparameters in Table 2.  
The price prediction is conducted via a recurrent neural network (LSTM) with the configurations defined in Table 3.  
It is important to note that the accurate prediction from this phase leads to accurate decisions from the DRL phase. The efficiency of this prediction is evaluated, and the results are shown in Figure 9, comparing the actual and predicted prices. The figure shows that our prediction module works very well, as there is a significant correlation between the actual and the predicted prices.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline \(\alpha\) & Moderate bandwidth constraint & 5000 \\ \(\tau\) & Noise tolerance with no strict fidelity enforced & 0 \\ k & modes & 5 \\ DC & DC part is not imposed & 0 \\ init & Will initialize all omegas uniformly & 1 \\ tol & — & \(1\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: VMD hyperparameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Learning rate & 0.001 \\ Input size & 5 \\ Hidden size & 200 \\ Number of epochs & 2000 \\ Number of layers & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: LSTM Parameters.  
Figure 8: Plot for accuracy rate from models.
# The Impact of Noise on Learning in Children and Adolescents: A Meta-Analysis
## 3 Results  
### Qualitative Synthesis  
A summary of the studies and their characteristics and findings is provided in Table S1 (the effect sizes, sample sizes, and age ranges covered, etc.), which serves as a reference for the analyses that follow.  
This meta-analysis included a diverse range of studies examining the influence of noise on students. Most of the studies focused on elementary school students, like those in [10,11], which examined students aged 7-11 and 10-12, respectively. Secondary school students were also well represented, as seen in studies [12,13], which focused on childrenaged 11-16 and 14-18. This broad age range and the different stages of learning provide a comprehensive view of how noise impacts children differently.  
There is considerable variability in the sample sizes of the studies. The smallest samples consist of 17 participants [14] and 20 participants [15]. Intermediate sample sizes were common, as seen in [10; 16], which featured samples ranging from 142 to 186 participants. Large-scale studies such as those in [12; 17] included extensive sample sizes of 976 and 989 participants, respectively. Additionally, some studies report data based on the number of schools rather than individual participants, indicating potentially large sample sizes, such as 142 primary schools in central London [10] and 96 secondary schools in Greater London [18].  
The studies were conducted in various countries, including Australia, Italy, the United Kingdom, Korea, the United States, Brazil, China, Germany, and Sweden. However, this reveals that certain regions, such as Africa, South America (excluding Brazil), and large parts of Asia, are underrepresented in this body of research.  
In the analysis of the studies included, various effects of noise on different cognitive and academic performance were identified.
# Surface Forming Criteria of Ti-6 AL-4V Titanium Alloy under Laser Loading
## 3 Results and Discussion
### Influence of Shock Times on Forming Effect  
The laser shock experiments on TC4 titanium alloy target with thickness of 0.08 mm were carried out under the following conditions: the laser energy was 6], the spot diameter was 7 mm, and the impact time ranged from 1-7. The outline and morphologies of targets with different impact times are shown in Figure 5. Table 4 shows the changes in the target spalling deformation degree from 1 shock time to 7 shock times.  
Figure 5 provides some interesting data regarding the maximum deformation of the target, and the graph also describes the trend of spallation degree. The nanosecond laser impacted a TC4 titanium alloy target with a thickness of 0.08 mm; under the conditions of 6 J laser pulse energy and a laser spot diameter of 7 mm, the plastic deformation degree of the target gradually increased with the upward trend in the number of impacts. This occurred until cracks were generated to finally penetrate the target material, forming holes with varying degrees of tearing. This experiment presents a complete fracture process of the titanium alloy target: during the first to the third impact, the target undergoes plastic deformation under laser loading, and micro-cracks and micro-holes are generated internally, and gradually expand and merge into larger cracks and holes. After the fourth laser impact, the combined cracks and holes form a continuous fracture surface and transition the entire  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Thickness (mm)** & **Energy (J)** & **Aperture (mm)** & **Morphology** & **Fracture Width** & **Maximum Deformation (mm)** \\ \hline
0.08 & 1 & & Smooth without & & 1.5548 \\  & & & crack & & 2.1628 \\
0.08 & 2 & & Smooth without & & 3.0661 \\
0.08 & 3 & & crack & & \\
0.08 & 4 & \(<\)7 & Small hole & 2.8 & \\
0.08 & 5 & \(<\)7 & Triangle hole & 11.2 & \\
0.08 & 6 & 7 & Petals shape & 9 & \\
0.08 & 7 & 7.3 & Petals shape & 7.9 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Measurement of experimental data of different impact times.  
Figure 5: (a) The outline of targets with different impact times. (b) The morphologies of target material with different impact times.  
material, after which the fracture surface continues to develop under constant laser loading, gradually forming petal-shaped holes. Therefore, in the laser shock processing of TC4 titanium alloy of this size, the appropriate number of laser pulse shocks should be selected according to the demand.
# A Review on Internet of Things for Defense and Public Safety
## 5 Building IoT for Tactical and Emergency Environments
### Computation  
#### 5.4.1 Hardware and Software Platforms  
The growth of smart phone use in the last years has provided the basis for IoT hardware platforms. This tendency derives into new products being presented to the market at a fast pace. SoCs with very low power consumption, small form factor and oriented at supporting wireless communication technologies such as Wi-Fi and BLE, are being developed and enhanced. Arduino, Raspberry Pi, UDOO, FriendlyARM, Intel Galileo, Gadgetter, ESP8266, BeagleBone, Cubieboard, Zolertia Z1, WiSense, Mulle, and T-Mote Sky are just some examples of popular hardware platforms. Most of such devices are built on top of hardware solutions based on ARM Cortex M microcontrollers or ARM Cortex A microprocessors, but some use their own SoCs.  
All these hardware platforms can be divided into two groups. On the one hand, there are SBCs (Single-Board Computers) like Raspberry Pi and Intel Galileo, which are powerful, and usually run some kind of modified Linux distribution. They support a vast set of security and communication alternatives, but their power consumption is high. On the other hand, the second type of platforms includes the notes. The ESP8266 or T-Mote Sky are good examples. They are much less power-hungry, being able to run on standard batteries for extended periods of time. However, they lack the processing capabilities of SBCs, and run on proprietary or ad-hoc software. In addition, one of the main problems of the currently available commercial notes is their lack of support for secure communication protocols and encryption. Nonetheless, motes recently presented address such an issue: for instance, the Arduino MKR1000 includes hardware acceleration for Elliptic Curve Cryptography (ECC), and the ESP32 has support for AES-256, SHA2, ECC and RSA-4096.  
Regarding software platforms, examples of Real-Time Operating Systems (RTOS) are Android, Contiki, TinyOS, LiteOS or Riot OS. The most common advanced programming environments and open standards are ARINC 653, Carrier Grade Linux, Eclipse, FACE, and POSIX. It must be also noted that Google and other important technological companies partnered with the auto industry to establishthe Open Auto Alliance (OAA) [132] to bring additional features to the Android platform to advance in the Internet of Vehicles paradigm.
# Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering
## 3 Methodology  
A comprehensive methodological framework was adopted to enhance the accuracy and robustness of the landslide susceptibility modeling (Figure 4). The process began with the meticulous collection and processing of data from various sources, including topographic, geological, hydrological, soil, and human activity-related factors. Next, both homogeneous ensemble models (random forest, XGBoost, LightGBM, and CatBoost) and heterogeneous ensemble models (bagging and stacking) were employed to evaluate and rank the importance of the influencing factors. This analysis involved the in-depth optimization of model performance, with critical influencing factors identified through the use of targeted feature engineering techniques. The predictive performance of the models was validated using Receiver Operating Characteristic (ROC) analysis, followed by  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Factor** & **Source** & **Format** \\ \hline Elevation, Slope, Plan Curvature, & Digital Elevation Model from ALOS, provided by & Raster grid (.tif) \\ Profile Curvature & Alaska Satellite Facility, 12.5 m resolution & Raster grid (.tif) \\ TWI, and SPI & Derived using ArcGIS 10.6 from the DEM & Raster grid (.tif) \\ Lithology & Geological map from the local Department of Natural & Vector data (.shp) \\  & Resources, scale 1:50,000 & Vector data (.shp) \\ Soil Type & 1:1,000,000 Soil Map of China, compiled by Nanjing & Vector data (.shp) \\ Distance to the Road and River & Calculated using ArcGIS Euclidean distance tool & Raster grid (.tif) \\ NDVI & Landsat 8 TM imagery, processed via ENVI 5.3 & Raster grid (.tif) \\ Land Use & 2020 land use classification from Landsat 8 by & Raster grid (.tif) \\  & Resource and Environment Science Data Center, CAS & Raster grid (.tif) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of influencing factors.  
iterative optimization through the removal of less significant factors. Finally, the optimized models were validated using ROC curves and statistical analysis, resulting in the creation of a detailed landslide susceptibility distribution map. This map delineates areas of high, medium, low, and extremely low susceptibility, providing a solid scientific foundation for developing landslide risk management and prevention strategies.
# Lamb Wave-Based FDM-PPM Method Data Transmission Scheme in Plate Structures
## 2 Lamb Wave Communication Method Based on FDM-PPM
### Experimental Setup  
The Lamb wave FDM-PPM acoustic communication experimental platform is illustrated in Figure 7. The experiment was conducted on a Q235 steel plate measuring 1.5 m in length and 1.8 mm in thickness. The material properties of the steel plate include Young's modulus of 210 GPa, a Poisson's ratio of 0.3, and a density of 7890 kg/m\({}^{3}\). Two circular ceramic piezoelectric patches, made of PZT-5 material, were affixed to the front side of the steel plate, designated for the excitation and reception of Lamb wave signals, respectively. The patches have a diameter of 10 mm and a thickness of 1 mm. A signal generator (Tektronix, Beaverton, OR, USA) and a single-channel power amplifier (TEGAM, Cleveland, OH, USA) were employed to drive the piezoelectric disks. A four-channel oscilloscope (Tektronix, Beaverton, OR, USA) with a maximum sampling rate of 2 GHz was used for signal acquisition at the receiving end.
## 4 Conclusions  
Remote sensing plays an increasing role in EBC research, especially regarding large spatial and/or long-term temporal scales. Moreover, the use of remote sensing deepens with the support of state-of-the-art remote sensing products and technology. Certainly, it is impossible to make progress without the assistance of GIS and GPS. It is believed that remote sensing will develop in a path similar to that of computer science, which has penetrated all aspects of human life. EBC performs as a propeller to push up the naissance of advanced remote sensing instruments and techniques. For example, the object-based image analysis (OBIA) is maturing in hopes to answer the question \"why are remote sensing and digital image processing still so focused on the statistical analysis of single pixels rather than on the spatial patterns they build up\" raised by [114]. [PERSON] summarized the status of OBIA for remote sensing through a comprehensive review several thousand abstracts [115]. However, with the popularity of remotely sensed data and commercial remote sensing packages, it is easy to obtain processed remote sensing products based on certain algorithms or modules. These products can be applied to answer questions in the field of EBC. But, it is noteworthy that these products may not be suitable or accurate enough to use. Therefore, it is still urgent to make EBC practitioners and remote sensing specialists communicate efficiently.
## 4 Experimental Results
### Methods Evaluation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Embeding Devices** & **Nvidia Jetson Nano** & **Rockchip RK3566** & **AllWinner H618** \\ \hline Architecture & cortex-A57 * 4 & cortex-A55 * 4 & cortex-A53 * 4 \\ Operation performance (GFLOPS) & 43.449 & 57.396 & 47.691 \\ Cache bandwidth (MB/s) & 3706.2 & 3080.8 & 1323.3 \\ \hline YOLOv8s (single-core) & 506.74 ms & 353.46 ms & 832.76 ms \\ YOLOv8s (dual-core) & 283.40 ms & 203.13 ms & 486.68 ms \\ YOLOv8s (quad-core) & 174.82 ms & 140.72 ms & 356.32 ms \\ \hline FastestDet (single-core) & 34.16 ms & 30.06 ms & 58.00 ms \\ FastestDet (dual-core) & 22.32 ms & 23.36 ms & 40.30 ms \\ FastestDet (quad-core) & 15.93 ms & 20.47 ms & 37.63 ms \\ \hline
**Proposed model (320 \(\times\) 320) (single-core)** & **30.46 ms** & **21.09 ms** & **47.74 ms** \\
**Proposed model (320 \(\times\) 320) (dual-core)** & **16.13 ms** & **14.66 ms** & **29.60 ms** \\
**Proposed model (320 \(\times\) 320) (quad-core)** & **13.86 ms** & **12.13 ms** & **24.66 ms** \\ \hline
**Proposed model (352 \(\times\) 352) (single-core)** & **36.52 ms** & **24.78 ms** & **56.32 ms** \\
**Proposed model (352 \(\times\) 352) (dual-core)** & **22.18 ms** & **16.88 ms** & **37.50 ms** \\
**Proposed model (352 \(\times\) 352) (quad-core)** & **17.23 ms** & **15.27 ms** & **30.41 ms** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the different model inference speeds on embedded devices. To further validate the model, the evaluation is expanded using the COCO dataset, focusing on three representative scenarios: large-scale traffic flow on highways, mixed pedestrian and vehicle environments, and single-vehicle settings, as shown in Figure 11. The COCO dataset, with its closer object perspectives and generally larger targets compared to the VisDrone dataset, allowed the proposed model to demonstrate enhanced detection performance, particularly in identifying the majority of objects within traffic scenes. However, the model still shows room for improvement in detecting smaller, distant objects and in scenarios where objects are heavily occluded. Figure 11: Visualization samples of the proposed model (\(352\times 352\)) on the COCO dataset. Figure 9: Comparison of frames per second on embedded devices (quad-core). Figure 10: Visualization samples of the proposed model (\(352\times 352\)) on the VisDrone dataset.
###### Abstract  
This study aimed to investigate differences in the three-dimensional (3D) upper airway dimensions in Vietnamese participants. **Methods:** This study included 341 Vietnamese participants grouped based on the vertical growth pattern (ANB angle) (skeletal Class I, 123; Class II, 124; Class III, 94). The patients were categorized into subgroups based on the horizontal growth pattern according to the Frankfort mandibular angle (hypodivergent, 35; normodivergent, 175; hyperdivergent, 131) to compare the frequency distribution of the three growth patterns in each skeletal class. The airway dimensions of the three skeletal classes were divided into four volumes using 3D virtual software (In VivoDental Software 6.0). The height, width, and cross-sectional area (CSA) of each part, as well as the total volume and minimum CSA, were measured and analyzed. **Results:** The airway space was reduced in hyperdivergent Class II individuals, underscoring an important connection between upper airway dimensions and vertical skeletal patterns, which suggests that vertical growth patterns contribute to pharyngeal narrowing and subsequent upper airway obstruction. Significant differences (_p_ < 0.001) in the minimum CSAs and volumes of the middle and inferior pharyngeal airways were observed based on [PERSON]'s skeletal classification. **Conclusions:** Our insights are valuable for orthodontics, especially in diverse populations, such as the Vietnamese, due to differences in the influence of genetic and environmental factors on skeletal and airway characteristics.  
upper airway dimensions; vertical skeletal class; facial types; cross-sectional area +
Footnote †: journal: Applied sciences  
**Clication:** [PERSON]; [PERSON]; [PERSON]; [PERSON]. Three-Dimensional Upper Airway Analysis of Different Craniofacial Skeletal Patterns in Vietnamese Adults. _Appl. Sci._**2024**, 14, 10477. [[https://doi.org/10.3390/app142210477](https://doi.org/10.3390/app142210477)]([https://doi.org/10.3390/app142210477](https://doi.org/10.3390/app142210477))