esa-sceva/satcom-chunk-collection huggingface dataset:
# Single Image Super-Resolution Method Based on an Improved Adversarial Generation Network
## 4 Experiments
### Comparisons with the State-of-the-Art Algorithms  
We compare the proposed EGAN with four state-of-the-art SR algorithms: SRCNN, VDSR, DRRN, and SRGAN. Visual examples are shown from Figures 7-10, and the quantitative results are provided in Tables 3 and 4. These experimental results shown that the EGAN outperforms the other methods in terms of average PSNR, SSIM, and IFC metrics. Figure 6: Convergence analysis on the loss functions between SRGAN and EGAN. Our loss function with energy-based regularization shows powerful performance, as well as a relatively fast convergence. There is a trade-off between the speed (time) and the performance (PSNR) by a factor of 4 on the datasets, including Set5, Set14, BSD100, and Urban100. All algorithms are trained on the ImageNet dataset for a fair comparison. In Table 3, we find that our network solves the real-time SR task while displaying good performance. For example, our algorithm achieves a 0.6 dB higher average PSNR than the SRGAN, with the same SR time. Meanwhile, EGAN is also a fast method that is twice as fast as the DRRN, while reaching a stable point. As shown in Table 3, the proposed method has better experimental results compared with other algorithms under different scales. It shows that the convolutional skip-connections in the network structure of this paper are practical for dealing with super-resolution reconstruction problems. Our framework is beneficial for providing richer details for the pre-trained module compared to a single input method, and it shows better performance in real time. As reported in the visual examples, the generative HR images not only express more pleasing effects, but they also have much sharper and more vivid contours under the deep supervision of the discriminator. In Table 4, our algorithm achieves a greater score when relying on the novel network structure. It is crucial for the plausible results, based on four benchmark datasets, to use the Laplacian pyramid and the convolutional skip-connections. In addition, the energy-based regularization strategy, which can capture high-frequency details, has been adopted in our loss function.
# A Predictive Method for Temperature Based on Ensemble EMD with Linear Regression
## 2 Ensemble EMD with LR Related Works  
To enhance the accuracy of temperature forecasting, a novel approach for temperature prediction that combines ensemble empirical mode decomposition (EEMD) with linear regression (LR) has been introduced as LR-EEMD. Drawing inspiration from the divide-and-conquer strategy for tackling complex issues and the iterative optimization approach of machine learning, this hybrid method aims to improve on the temperature prediction performance of existing models. This paper begins by introducing the fundamental concepts and terminologies, which is followed by a detailed discussion on the application of EEMD and LR. Additionally, empirical mode decomposition (EMD) will be introduced in relation to this study.
###### Abstract  
Measurement of the elemental composition of shells is increasingly emerging as an avenue for obtaining high-resolution insights into paleoclimate and past seasonality. Several studies have shown significant correlations between Mg/Ca ratios measured on shell carbonate and the sea surface temperature (SST) within which this carbonate was precipitated. However, other investigations have reported large variability in this relationship between species. Therefore, further studies, including taxa previously not considered are still required in order to validate these new species as suitable climate proxies. Here, we measured Mg/Ca ratios for limpet _Patella depressa_ Pennant, 1777 samples live-collected in northern Spain for the first time. The elemental ratio was measured using laser-induced breakdown spectroscopy (LIBS), a technique that significantly decreases the time required for sample preparation and increases the number of shells that can be analyzed. In this study, calibration-free LIBS (CF-LIBS) methods were applied to estimate molar concentrations of chemical elements on biogenic calcium carbonate. The Mg/Ca ratio evolution along the shell growth axis was compared with stable oxygen isotope (\(\delta^{18}\)O) profiles obtained from these same limpets and the SST at the place where the mollusk grew to determine if the sequences obtained correctly reflected environmental conditions during the life-span of the mollusk. The results showed a significant correlation between Mg/Ca ratio series and both \(\delta^{18}\)O profiles and SST, highlighting the paleoenvironmental and archaeological potential of LIBS analyses on this mollusk species that is frequently found in archaeological contexts in the western Europe.  
archaeology; mesolithic; N Spain; mollusk shells; marine environment; spectroscopy +
Footnote †: journal: Applied Sciences  
0021
# Moving Towards Body-to-Body Sensor Networks for Ubiquitous Applications: A Survey
## 4 BBN Design Challenges and Open Issues
### Heterogeneous Devices and Traffic  
In future BBNs, the WBAN coordinators could be any type of portable devices and should communicate and forward heterogeneous traffic; plain text data as well as video or audio contents. Furthermore, the entry of a new device or the disconnection of a WBAN due to roaming, should be systematically handled, without altering the neighboring WBANs operating.  
Yet, in BBNs, a service-oriented architecture (SoA) is recommended for the service providers and users in order to ensure the interoperability among the heterogeneous devices, that are interconnected within the BBN. The SoA is a kind of middleware that provides access to heterogeneous resources in a deployment independent manner [103; 106]. The key role of such middleware is to allow the interconnection between the things and the application layer through the abstraction of the heterogeneous functionalities of the devices into a common set of services [107; 108]. Thus, the BBN middleware can be divided into a set of layers: object abstraction, service management, service composition, and application [109]. Due to the advantages of such layered architecture, SoA could provide extensible, scalable and modular BBN services.
## 2 Preliminaries
### Network Model  
This paper considers a network architecture that includes multiple sink nodes [18], as depicted in Figure 2. The utilization of this architecture simplifies the practical deployment of the UASN. The network comprises a collection of sensor nodes, denoted as \(\mathcal{SN}\), and sink nodes, denoted as \(\mathcal{SK}\). The maximum transmission range of the sensor nodes is \(R\). Thus, the network can be represented \(\mathcal{N}=\mathcal{SN}\cup\mathcal{SK}\).  
In Figure 2, the 3D underwater area is randomly populated with sensor nodes that are equipped with sensing devices and acoustic modems. These nodes are responsible for performing observation and exploration tasks. Meanwhile, the sink nodes deployed on the water surface are equipped with both acoustic and radio frequency (RF) modems. The acoustic modem is used for the underwater communication, which includes the communication between the underwater sensor nodes and the communication between the underwater sensor nodes and the sink nodes. The RF modem is used for the surface communication, which includes the communication between the sink nodes and the communication between the sink nodes and the satellites. The underwater sensor nodes are responsible for gathering data from the monitoring areas and forwarding it to the sink nodes, which serve as the designated destinations for the underwater data packets. The sink nodes aggregate the sensory data and transmit them to the seashore control center for further processing and analysis by satellites.  
In our assumption, we consider a successful delivery of a data packet to be when it reaches any sink node in the network.
## 5 Results and Discussion
### Analysis of the Thermal Performance of the Storage Tank during Discharge with a c-h Ratio of 0  
#### 5.2.1 Time-Dependent Temperature Profile  
Figure 13 shows the axial temperature distribution inside the tanks for E4, E5, and E6 during the discharge process at dimensionless times of 0.01, 0.5, and 0.8.  
When the obstacle is near the bottom, less water participates in the mixing process. Under different flow rates, the thermocline temperature gradient in Type c is the smallest and occupies the largest range. When the obstacle is near the bottom, the thermocline temperature gradient region in the storage tank is smaller, and more high-temperature hot water can be obtained in the end. This difference is further amplified at higher flow rates. At a flow rate of 0.3 m/s, the difference in the temperature gradient region range between the middle obstacle storage tank and the bottom obstacle storage tank is the greatest.  
Installing a porous obstacle near both the top and bottom outlets of the storage tank, compared to installing a porous obstacle only at the bottom outlet, further improves the stratification performance. In all simulations, Type f exhibits the greatest thermocline gradient and occupies a smaller water layer thickness.  
Figure 12: Internal streamline diagram at a flow velocity of 0.3 m/s for c–h ratio 0.38: (**a**) conventional tank; (**b**) orifice plate obstacle tank; (**c**) porous obstacle tank.
###### Abstract
The findings reported are related to the effect of alcohol fumigation on engine characteristics, such as brake specific fuel consumption or thermal efficiency, and gaseous and PM emissions. In general, the reported results show that efficiency decreases at low loads; small increases can be observed for medium and high loads. Regarding NOx emissions, their reduction is proportional to the alcohol fraction, and they are significantly affected by engine loads. A reduction of PM emissions is also reported due to the low PM tendency of alcohol fuel due to the oxygen content. In this way, the traditional NOx-PM trade-off is circumvented. In all findings reported in [16], CO and HC emissions increase due to lower mean in-cylinder temperature related to the \"cooling\" effect of the alcohol fuel. The increase of HC emissions is also related to the quenching phenomenon and mixture trapped into the crevices. Many other authors study the effect of ethanol in the DF mode adopting EGR, e.g., [PERSON] et al. [21] observed that high fraction of ethanol leads to a reduction of NOx and employing EGR further improvements are observed with no adverse effect on engine efficiency. [PERSON] et al. [22] found that the use of ethanol in the DF mode with a high rate of EGR and a retarded single injection strategy lead to ultra-low NOx and PM emissions at the cost of elevated HC and CO emissions. Among the extensive literature on ethanol dual-fuel engines, this study provides new findings to the technical literature on the maximum ethanol fraction as a function of the nozzle hole numbers and its influence on the combustion behavior in the DF mode and emissions, particularly greenhouse gas (GHG). The analysis was conducted because of the combustion process in the DF mode starts from high reactivity zones in the combustion chamber, and the nozzle hole number can influence the number of these zones. To achieve this goal, the maximum fuel substitution ratio (FSR) is evaluated in a dual-fuel (DF) combustion mode by means a one-factor-at-a-time parametric analysis to discriminate the effects of the calibration parameters (combustion phasing CA50, rail pressure, injection strategy, and EGR). With the appropriate calibration, the influence of injector nozzle holes number on maximum FSR, engine combustion indicators and emissions are assessed. The experimental investigations were conducted on an Light-Duty (LD) single-cylinder diesel engine in two steady-state test points, 1500 and 2000 rpm related to low and medium engine load conditions. After the experimental setup and procedure description, in the last part of the manuscript, first is discussed how the maximum fuel substitution ratio (FSR) is influenced by the combustion phasing, rail pressure, injection pattern and exhaust gas recirculation (EGR). Then the influence of the nozzle hole number in the dual-fuel operating mode is discussed.
## 2 Effect of the Average Particle Size on the Model's Macroscopic Mechanical Behavior
### Effect of the Average Particle Size on the Model's Mechanical Behavior  
Uniaxial compression tests are carried out on models with different average particle size and analyze the effect of the particle size on the uniaxial compressive strength and elastic modulus. Keeping the particle size ratio unchanged at 1.5, five groups of particle size ranges and corresponding \(L/d_{\mathrm{ave}}\) are set. The five particle size ranges and corresponding \(L/d_{\mathrm{ave}}\) are shown in Table 2. Considering the dispersion of rock materials, carrying out repeated tests is necessary. By changing the random factor, four numerical models with the same particles and different spatial arrangements are generated for each group. This means that the four models have the same macroscopic rock properties but different microscopic distributions of particles and are numbered 01, 02, 03, and 04.  
The generated models of different particle sizes are shown in Figure 2. A change in the particle size will directly cause a change in the particle number. The relationship between the particle number and \(L/d_{\mathrm{ave}}\) is shown in Figure 3. It can be seen from Figure 3 that the larger the \(L/d_{\mathrm{ave}}\), the smaller the particle size and the denser the particles in the model. The number of particles and the increase rate of particle numbers increase with the increase in \(L/d_{\mathrm{ave}}\).  
The quadratic function is used for fitting and the equation is as follows:  
\[y=5.08+0.78x+2.25x^{2} \tag{2}\]  
where \(y\) is the number of particles and \(x\) is the corresponding \(L/d_{\mathrm{ave}}\).  
The fitting coefficient R\({}^{2}\) is one, indicating that the growth of particle number with \(L/d_{\mathrm{ave}}\) is fully consistent with quadratic polynomial fitting, which can be used to predict the numerical particle number. The effect of the average particle size on the uniaxial compressive strength and elastic modulus are studied by uniaxial compression tests on the four groups of specimens. The stress-strain curves of one group (specimen 03) are shown in Figure 4. The larger the \(L/d_{\mathrm{ave}}\), the larger the uniaxial compressive strength and the slope of linear part of stress-strain curves before the peak. However, the peak displacement corresponding to different \(L/d_{\mathrm{ave}}\) fluctuates between 6 \(\times\) 10\({}^{-3}\) and 8 \(\times\) 10\({}^{-3}\). This shows that the smaller and denser the particles, the higher the compressive strength and elastic modulus.  
\begin{table}
\begin{tabular}{c c c} \hline
**Specimen ID** & **Particle Size Range (\(R_{min}\)\(\sim\)\(R_{max}\)) (mm)** & \(L/d_{ave}\) \\ \hline  & 2.0–3.0 & 10 \\  & 0.6–0.9 & 33 \\
01, 02, 03, 04 & 0.4–0.6 & 50 \\  & 0.267–0.4 & 75 \\  & 0.2–0.3 & 100 \\ \hline \end{tabular}
\end{table}
Table 2: Particle size ranges and corresponding \(L/d_{\mathrm{ave}}\).  
Figure 3: Relationship between the particle number and \(L/d_{\text{ave}}\).  
Figure 2: Schematic diagram of the generated uniaxial compression model.
# A Hierarchical Security Event Correlation Model for Real-Time Threat Detection and Response
## 1 Introduction
### Related Works
#### 1.2.2 Real-Time Correlation  
Firstly, it is necessary to define real-time data. One such definition is given by [10], where real-time data are defined as \"...information that is delivered immediately after collection. There is no delay in the timeliness of the information provided...\". Others have defined real-time data as data that are available when needed. Approaches that have been seen in research to optimize data collection for real-time performance include direct acquisition [11], event streaming [12; 13; 14], and the use of in-memory storage [15]. The key issue is to reduce the latency in the delivery of the event to the application that needs it. The latency is often caused by architectures that introduce too many hops such as aggregation points for the data, as well as access times due to the data being stored on disk or databases. In terms of real-time correlation and detection, the most common approach is to correlate all events that can fit into some defined time window. The challenge is to decide the optimal duration for the time window that will make the data available to the consumer application when needed. This question usually depends on some expert knowledge of the system and the type of data. If the time window is large, the time to analyze batched events and eventually react if there is an attack might be too long. This technique is seen in [16, 17]. In [17], they follow the real-time algorithm from [17] for real-time processing of alerts. Incoming alerts are first grouped into batches that are sorted according to their creation time. Within the batch, the alerts are correlated according to a configurable time window, and each window is passed on to the analytics component as soon as it has been processed.  
[PERSON] et al. [18] designed a detection system that operated in two phases. Initially, they mined frequent item sets for both normal and attack classes using nested sliding windows. Then, frequent item sets of incoming real-time network traffic flows were compared with these two classes. If the incoming flow was detected as anomalous, it was then passed on to the classification component, which used a combination of deep belief networks. Real-time processing was achieved by having the data processed from memory rather than from discs or databases. This significantly reduced the latency that is usually incurred due to disk access. The data are typically loaded into data structures in memory from where the correlation and analysis are run. Other researchers have used in-memory databases such as Oracle in-memory [19].
## 2 Preliminary  
### 5G SA Structure  
The 3 rd Generation Partnership Project (3 gpp) is a de facto standard for 5G, which defines the architecture of a 5G network and its components [18]. The 3 GPP defines the 5G standard as having two different operating modes: non-standalone (NSA) and standalone (SA). In addition, the architecture is separated into user plane and control plane, each functioning independently. The user plane, also referred to as the data plane, enables data transfer to and from users when using services in the network. The control plane securely opens a communication channel to provide services, performs functions such as user authentication, and conveys \"signal messages between network components including user equipment (UE)\" to enable communication service.  
Network components encompass UE, base station, and CN functions including the Access and Mobility Management Function (AMF). The air (wireless) interface between the UE and base station is called the radio access network (RAN) in which the CN includes the base station, the AMF through the wireless link, the User Plane Function (UPF), the Session Management Function (SMF), and so forth.  
As a base station is an intermediary node serving as an only-one gateway between wireless and wired domains in the 5G network, it uses RRC and Next Generation Application Protocol (NGAP) for signaling messages in the control plane. The RRC protocol is used to control radio bearers between terminals and a base station, and NGAP is used to carry non-access stratum (NAS) between a base station and the AMF. Moreover, next-generation NodeB (gNB) is a 5G base station that has a radio coverage that geographically reaches 20 km\({}^{2}\) or below [19]. Therefore, a few tens of gNBs are required to cover the service area of one metropolitan city such as Seoul.  
As far as those defined by 3 GPP are concerned, there are at least nine CN functions such as AMF, as shown in Table 1[18].  
They use service-based architecture (SBA)-based HTTP/2 protocol with JSON, and use Packet Forwarding Control Protocol (PFCP) only between the Session Management Function (SMF) and the User Plane Function (UPF) to connect the user plane and the control plane. Unlike the base stations that must be installed in the vicinity of their neighboring base stations, with a specific distance between them observed, to form the wireless cells that cover a region of interest, CN functions, located in the internal network constructed by a mobile network operator, provide services from centralized servers and operate using this small number of high-performance systems.  
Whereas many base stations (although varied in number depending on the size of the service area) must be densely placed in the cell to provide 5G services, CN functions require a small number of servers, the fact of which conveys multifaceted implications from the security perspective. One of them, inter alia, is that, although it is far harder for attackers to gain access or attack CN functions, the consequential impact from any CN function, once assaulted or abused, is often much severe, incomparably so, than as in the case of a base station.
# Improvements in Rock Mass Description for Stope Design by Geophysical and Geochemical Methods
## 4 Laser-Induced Breakdown Spectrometry (LIBS) for Geochemical and Geometallurgical Characterization
### LIBS Methodology  
In our research, LIBS was used to assess rock composition and mineral content, as well as to evaluate geotechnical and geometallurgical properties. To study this, scanning LIBS measurements were taken both at a tunnel face and from drill cores (Figures 2 and 3, respectively) of an active mine with prototype equipment, and methods were developed to estimate mineralogy and other geometallurgical properties from high-resolution LIBS images. Our investigations to calibrate LIBS data for rapid elemental analysis of pellet data and drill core samples are reported in [29]. A prototype stand-off LIBS device was developed for mine wall imaging along with the experimental results [30].  
Figure 2: LIBS scanning from the tunnel face carried out for this study. Yellow tape indicates no entrance due to unsupported tunnel section (**left**). (**Upper middle**): LIBS signal allocated to arsenopyrite on scan area located on the left side of the tunnel. (**Upper right**): Laser ablation acoustic energy signal intensity changes revealing an ore vein as the highest intensity yellow and larger lithological changes across the tunnel wall. (**Lower right**): Prototype device for LIBS scanning of drill cores.
# Analyzing Brain Waves of Table Tennis Players with Machine Learning for Stress Classification
## 2 Materials and Methods
### Data Preprocessing
#### 2.2.2 Feature Extraction  
Signal processing and analysis methods are divided into two types: time domain and frequency domain. The time domain analysis is time-based and observes signal variations on the time axis. In this study, the time domain signals are converted by FFT into amplitudes of multiple bands to form PSD in order to capture the PSDs of different brain wave bands. FFT is a fast algorithm for discrete Fourier transform (DFT). The calculation of DFT is defined as Formula (2):  
\[X_{k}=\sum_{j=0}^{n-1}x_{j}e^{-2\pi i\mathrm{j}/n}\,\ k=0,1,\ldots n-1 \tag{2}\]  
where \(X\) is the discrete frequency signal, \(x\) is the discrete time signal, and \(n\) is the number of samples.  
During the stress test, the time domain signal is 30 s for each test or 60 s of rest before the test is obtained. The conversion of the time domain signals by FFT organizes and obtains 8 bands per second, namely, Delta (0\(\sim\)4 Hz), Theta (4\(\sim\)8 Hz), Low Alpha (8\(\sim\)10 Hz), High Alpha (10\(\sim\)13 Hz), Low Beta (13\(\sim\)20 Hz), High Beta (20\(\sim\)30 Hz), Low Gamma (30\(\sim\)46 Hz), and High Gamma (46-70 Hz). Every one of the 8 electrode positions corresponds to these 8 bands (such as Fp1_HighAlpha, Fp1_LowAlpha, etc.), resulting in a total of 64 features, and the unit is \(\upmu\mathrm{V}/\sqrt{\mathrm{Hz}}\).