esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Source-Based Aerosol Mass Extinction Efficiency for Polydisperse Aerosol Particles  
Based on the size-resolved \(b_{ext}\) and source-resolved mass concentrations, we could estimate the size-resolved and the source-resolved MEEs. The total aerosol \(b_{ext}\) can also be expressed as a combination of each source-based extinction coefficient. Here, we obtained the source-based MEEs from the source-based mass concentration in the SMP receptor model, and the total aerosol extinction coefficient from Mie theory. We estimated MEEs by applying the MLR method [24]:  
Figure 3: Composition-based mass extinction efficiency as a function of the geometric mean diameter for different geometric standard deviations for (**a**) elemental carbon (EC), (**b**) inorganic ions, (**c**) organic matter (OM), (**d**) NaCl, and (**e**) Crust.  
\[b_{ext}=\sum_{s}b_{ext,s}=\sum_{s}[C_{s=source}\times MEE_{s}] \tag{6}\]  
where \(MEE_{s}\) is the mass extinction efficiency of \(s\)th source (in m\({}^{2}\) g\({}^{-1}\)), and \(C_{s}\) is the mass concentration of the \(s\)th source from the SMP results (in \(\mu\)g m\({}^{-3}\)).  
Multilinear regression is a useful tool for estimating aerosol optical parameters, such as MEE [10; 11; 12]. For example, a previous study [11] investigated aerosol optical properties, mass concentrations, and chemical compositions over a one-year period and obtained the total scattering coefficient using MLR analysis. The authors calculated the total scattering coefficient as the independent variable, and the sulfate ion, nitrate ion, mineral matter, organic and elemental carbon, and residual fraction mass concentrations as the dependent variables.  
Using Equation (6), we obtained the total aerosol \(b_{ext}\) in Equation (3) based on the mass concentration of the sources. As explained, the SMP model can provide the mass concentration of each source. Subsequently, the MLR method can provide the MEE of each source. All the derived MEEs from MLR were within the 95% confidence interval and significance at the 0.05 level. It should be also noted that all \(b_{ext}\) and MEE values are size-dependent [25]. We carefully estimated the MEEs for polydisperse aerosols as a function of d\({}_{\rm g}\) and GSD. Among the nine sources based on the SMP results [14], contributions from WB were very low, except for four days when the WB contribution was high. For this reason, WB was not considered, and we excluded the optical contribution from WB when estimating \(b_{ext}\) and MEEs.
# Clustered Distributed Learning Exploiting Node Centrality and Residual Energy (CINE) in WSNs
## 3 CINE Solution
### Centrality  
As discussed in the previous section, we use _centrality_ to characterize the topological _relevance_ of a node inside a cluster and the relationship it has with neighbouring nodes. Inthe recent past, the use of centrality in WSNs has been considered as a powerful tool to perform cluster-head selection [28] in order to increase network lifetime and, in general, reduce energy consumption [29]. Additionally, centrality has been exploited to achieve efficient routing, prolong network lifetime [30; 31], and facilitate data fusion. Multiple centrality metrics have been considered in the literature for WSNs, as reported in [32]. Indeed, the most of those used are _degree centrality_, _betweenness centrality_, _eigen centrality_, and _closeness centrality_.  
The simplest centrality metric that is often used for clustering is _degree centrality_. It, in particular, evaluates the number of links owned by a given network node. _betweenness centrality_ identifies the number of times a node lies on the shortest path between all of the possible pairs of nodes. _eigen centrality_ calculates the topological relevance of a node by estimating the number of links it has with the other nodes in the network. Additionally, this metric considers the number of connections of the nodes to which a given node is connected on its own. This type of approach is, for example, used in Google Pagerank and Katz centrality [33]. Another variant of centrality is the so called _closeness centrality_, which evaluates the geodesic distance between a node and all of the other nodes in the network. Unlike betweenness centrality, closeness centrality estimates how short the shortest paths are between the node and all other nodes.  
In this paper, we design a clustering methodology that is driven by centrality awareness, energy efficiency, and the consideration of the computing and processing capabilities of nodes for performing distributed learning in WSNs. The approach, as discussed in the following, will prove to efficiently trade off contrasting features, leading to fast convergence and an increase in network lifetime as compared to static and capability-unaware distributed learning solutions.
# Microseismic Electronic Fencing for Monitoring of Transboundary Mining in Mines
## 2 Factors Affecting Localization Accuracy
### Number of Sensors  
Theoretically, the number of sensors within the measurement area should be as large as possible and the coverage as dense as possible. However, for economic and efficiency reasons, it is preferable to minimize the number of sensors while still achieving the required localization accuracy. This section investigates the impact of sensor quantity within a nested observing system on localization accuracy. As Figure 5 shows, the sensors' arrangement and localization results show that the maximum localization error of the event is 5.688 m when the number of sensors is 12 (with no station in the well). The study in Section 2.2 indicated that constraints in the depth direction are less pronounced with ground sensors alone. Therefore, three in-well sensors were added to the initial arrangement of 12 sensors (illustrated in Figure 5c). This addition significantly reduced the localization error, with the largest error decreasing to 3.583 m, which is comparable to the localization accuracy achieved with 39 sensors. The required localization accuracy for monitoring was successfully attained.
# Systematic Review: Malware Detection and Classification in Cybersecurity
## 2 Methodology
### Advanced Malware Detection Techniques
#### 3.6.5 Memory Forensics for Fileless Malware Detection  
The article \"Fileless malware threats: Recent advances, analysis approach through memory forensics and research challenges\", published in Expert Systems With Applications, examines recent advances in the detection and analysis of fileless malware, proposing an analytical approach based on memory forensics techniques. This type of malware represents an advanced threat due to its ability to operate exclusively in system memory, evading traditional file-based detection techniques.  
Unlike traditional malware, fileless malware does not require executable files on disk, but instead injects malicious code directly into system memory through vectors such as remote code execution, phishing emails, or compromised websites. It then uses operating system mechanisms, such as the Windows Registry, Task Scheduler, or WMI, to maintain its persistence.  
The article classifies malware analysis techniques into three main approaches: static analysis, dynamic analysis, and memory analysis. Static analysis is fast and resource-efficient, but vulnerable to obfuscation techniques; dynamic analysis provides visibility into malware behavior in controlled environments, although it can be detected by the malware itself; and memory analysis allows for the identification of active threats in RAM, making it particularly useful against fileless malware.  
The research proposes a memory forensics based approach to detect and study malicious behavior not visible using conventional methods. Tools such as Volatility, Process Explorer, and Wireshark are used to extract evidence from the system's volatile memory, detecting suspicious processes and connections [65].  
As a case study, the Kovter malware is analyzed, a representative example of fileless malware that resides in the Windows registry, injects itself into legitimate processes such as regsvr32.exe, and establishes connections to malicious URLs. A new dataset with 1249 samples, including Kovter variants, is also presented to support future research in this field.  
The article identifies several challenges to accurately detecting these types of threats, including the need to improve volatile data collection capabilities, adapt forensic tools, and develop models that integrate machine learning. In summary, [PERSON]'s study highlights the effectiveness of memory analysis as a key technique for detecting and understanding the persistent and evasive behavior of fileless malware.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 3 Issues Faced by RPL during Mobility
### Increase in Hand-off Delays  
Performing seamless hand-off is a major challenge in mobile IoT since performing a detailed analysis of whether a link will break or not or to select the best possible link will require many resources (e.g., bandwidth, energy) on resource-constrained mobile devices. RPL performs poorly during hand-off and results in larger hand-off delays [55].
## 2 Materials and Methods
### Resampling Techniques  
In the domain of data-driven decision making, the significance of data quality and integrity cannot be overstated. The efficacy of predictive models, irrespective of their application domain, heavily hinges on the quality of the data they are trained on. A prevalent challenge encountered in real-world datasets is data imbalance, which poses a substantial obstacle to achieving accurate predictions and robust model generalization. Data imbalance manifests when the distribution of classes within the dataset is highly skewed, with one class significantly outnumbering the others. Traditional ML models often struggle with imbalanced data, exhibiting a tendency to prioritize the majority class while potentially overlooking crucial patterns within the minority class.  
To tackle the issue of imbalanced datasets, resampling techniques have emerged as a widely adopted strategy. Resampling involves the manipulation of dataset instances, typically through under sampling of the majority class and/or oversampling of the minority class. The primary aim of this section is to offer a comprehensive insight into various resampling techniques that have proven effective in addressing the imbalance challenge.  
These techniques encompass methodologies such as the Synthetic Minority Oversampling Technique (SMOTE), SMOTE combined with edited nearest neighbors (SMOTE-ENN), SMOTE combined with Tomek links (SMOTE-Tomek), and Adaptive Synthetic Sampling (ADASYN).
## 2 Materials and Methods
### Temperature and Humidity Coupling Cycle Test  
Referring to relevant studies [20; 25], indoor-simulated temperature and humidity coupling cycle tests were conducted. For the purpose of studying the variation law of asphalt mastic performance under different conditions, the experiments set different temperature cycling ranges (\(-\)20 \({}^{\circ}\)C to 60 \({}^{\circ}\)C, \(-\)20 \({}^{\circ}\)C to 40 \({}^{\circ}\)C, 0 \({}^{\circ}\)C to 60 \({}^{\circ}\)C, 0 \({}^{\circ}\)C to 40 \({}^{\circ}\)C). Among them, the low-temperature conditions in the temperature and humidity coupling cycle are controlled by a low-temperature box, and the high-temperature and high-humidity conditions are controlled by a constant temperature water bath. The asphalt mastic samples are placed in the low-temperature box for continuous freezing for 12 h and then placed in a constant temperature water bath for 12 h, constituting one cycle. Following the above steps, different numbers of temperature and humidity coupling cycles are repeated 3 times, 6 times, 9 times, and 12 times to complete the experiment. After the cycle ends, the asphalt mastic samples are taken out and left to stand at room temperature for subsequent routine tests. The cone penetration test is shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Properties** & **Experimental Value** & **Normative Value** & **Test Method** \\ \hline Penetration (25 \({}^{\circ}\)C, 100 g, 5 s)/0.1 mm & 46.1 & 40\(\sim\)60 & T 0604 \\ Softening point/\({}^{\circ}\)C & 85.6 & \(\geq\)60 & T 0606 \\ Ductility (5 \({}^{\circ}\)C, 5 cm/min)/cm & 24.6 & \(\geq\)20 & T 0605 \\ \hline \end{tabular}
\end{table}
Table 1: Properties of SBS-modified asphalt.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Properties** & **Experimental Value** & **Normative Value** & **Test Method** \\ \hline Apparent density/(g\(\cdot\)cm\({}^{-3}\)) & 2.706 & \(\geq\)2.50 & T 0352 \\ Water content/\% & 0.4 & \(\leq\)1 & T 0332 \\ Hydrophilic coefficient & 0.4 & \(<\)1 & T 0353 \\ Mineral powder plasticity index (\%) & 2.8 & \(<\)4 & T 0354 \\ \hline \multirow{3}{*}{Particle range} & \textless{}0.6 mm passage rate/\% & 100 & 100 & T 0351 \\  & \textless{}0.15 mm passage rate/\% & 91.6 & 90\(\sim\)100 & T 0351 \\ \cline{1-1}  & \textless{}0.075 mm passage rate/\% & 81.7 & 75\(\sim\)100 & T 0351 \\ \hline \end{tabular}
\end{table}
Table 2: Properties of mineral filler.
## 3 Results
### Characterization of GA-FK Gel
#### 3.3.5 Nitrogen Adsorption Isotherms and Pore Size Distribution  
The nitrogen adsorption-desorption isothermal and pore size distribution of GA-FK gel were measured at 76 K, the results are shown in Figure 6. It can be seen from Figure 6a, the N\({}_{2}\) adsorption-desorption isotherm of GA-FK gel is type-IV B isotherm (with H2 hysteresis ring). The adsorption capacity of GA-FK gel is 22.7 cm\({}^{3}\cdot\)g\({}^{-1}\), it is bigger than that of FK (12.1 cm\({}^{3}\cdot\)g\({}^{-1}\)). The adsorption capacity rapid increases while P/P\({}_{0}\) > 0.75. This indicates that the adsorption is multilayer adsorption at this stage, and GA-FK gel has abundant mesoporous.  
The pore size distribution and specific surface area of GA-FK gel were calculated by the BJH method. As can be seen from Figure 6b, the pore size distribution of GA-FK gel is approximately symmetric and narrow. The pore size of GA-FK gel is in the range 1.8-90 nm; with an average pore size of 19.6 nm. The number of mesoporous (\(\sim\)18 nm) accounted for the most, and the pore volume is 0.035 cm\({}^{3}\cdot\)g\({}^{-1}\). The specific surface area of GA-FK gel is 7.16 m\({}^{2}\cdot\)g\({}^{-1}\), it is larger than 4.11 m\({}^{2}\cdot\)g\({}^{-1}\) of FK. This is because in modification reaction, EDC-NHS system activate the GA, and activated GA bonded to the amino of FK chain. Meanwhile, the EDC-NHS system activated the carboxyl on FK chain, and activated carboxyl bonded to the amino of FK chain, forming an amide bond between the inner FK molecular chains. This crosslinking led to the formation of a uniform three-dimensional network structure [22] that increased the strength of the material [20]. Moreover, based on this three-dimensional network, the GA-FK gel would be able to produce more mesopores during lyophilization.  
In summary, the results of the characterization of GA-FK gel shows that the mechanical properties of FK gel were improved by bonding the rigid GA molecules and crosslinking. The GA-FK gel was homogeneous and porous, and its mesoporous number, pore volume,  
Figure 5: XRD patterns of GA-FK gel.  
Figure 6: N\({}_{2}\) adsorption–desorption isotherms and pore size distribution of GA-FK gel: (**a**) N\({}_{2}\) adsorption-desorption isotherms; (**b**) pore size distribution.  
and specific surface area were significantly increased. Therefore, the preparation of GA-FK gel through this method was successful.
# Mechanical Structure Design and Experimental Study of Gamma-ray Monitor for Small Satellite Payload
## 3 Modeling
### Structural Finite Element Dynamic Analysis Equations  
#### 3.2.1 Modal Analysis Equations  
The mathematical basis of modal analysis is the eigenvalue problem of dynamic motion equations. The system is regarded as a linear element, and assuming that its structure is not affected by load and stability, that is, the stiffness and mass are constant, the dynamic equations of undamped free vibration are as follows [25]:  
\[\mathrm{M}\ddot{x}(t)+Kx(t)=0 \tag{4}\]  
\[K-\omega^{2}M\Phi=0 \tag{5}\]  
where \(M\) is the mass matrix, \(x(t)\) is the system displacement vector as a function of time, \(\ddot{x}(t)\) is the acceleration vector function, \(\omega\) is the intrinsic frequency, and \(\Phi\) is the modal vibration pattern.  
The natural frequency \(\omega_{i}\) (\(i=1,2,\ldots,n\)) of the system can be obtained by solving the above equation and the corresponding modal vibration pattern \(\Phi_{i}(i=1,2,\ldots,n)\).
# Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System +
## 5 Algorithmic Design
### Pre-Processing
#### 5.2.2 Data Pre-Processing for ResNet  
In the pre-processing stage for ResNet, only the 64 channels for Fourier coefficients are extracted from the raw IQ data obtained from the SDR. Every 5 s of these data are plotted as a spectrogram, so there is no overlap between two sequential spectrograms. These 193 \(\times\) 125 RGB images are saved as a portable network graphic (PNG) files. So the input tensor for network is 3 \(\times\) 125 \(\times\) 193 as seen in Figure 11. There is a 50% chance that they are flipped horizontally to increase variation. Figure 12 shows some examples of the PNG files for selected interference signals.  
Figure 10: The ML-based pipeline detects, classifies, and visualizes potentially interfered GNSS signals. The boxes (outlined in black) represent all relevant pipeline components, and the iterative text on the edges represents the output/input of each component. Information flows from left to right. A database in the cloud synchronizes all information and thus provides the history of all events for downstream visualization and localization.
# How Trend of Increasing Data Volume Affects the Energy Efficiency of 5G Networks
###### Abstract  
As the rapid growth of mobile users and Internet-of-Everything devices will continue in the upcoming decade, more and more network capacity will be needed to accommodate such a constant increase in data volumes (DVs). To satisfy such a vast DV increase, the implementation of the fifth-generation (5G) and future sixth-generation (6G) mobile networks will be based on heterogeneous networks (HetNets) composed of macro base stations (BSs) dedicated to ensuring basic signal coverage and capacity, and small BSs dedicated to satisfying capacity for increased DVs at locations of traffic hotspots. An approach that can accommodate constantly increasing DVs is based on adding additional capacity in the network through the deployment of new BSs as DV increases. Such an approach represents an implementation challenge to mobile network operators (MNOs), which is reflected in the increased power consumption of the radio access part of the mobile network and degradation of network energy efficiency (EE). In this study, the impact of the expected increase of DVs through the 2020s on the EE of the 5G radio access network (RAN) was analyzed by using standardized data and coverage EE metrics. An analysis was performed for five different macro and small 5G BS implementation and operation scenarios and for rural, urban, dense-urban and indoor-hotspot device density classes (areas). The results of analyses reveal a strong influence of increasing DV trends on standardized data and coverage EE metrics of 5G HetNets. For every device density class characterized with increased DVs, we here elaborate on the process of achieving the best and worse combination of data and coverage EE metrics for each of the analyzed 5G BSs deployment and operation approaches. This elaboration is further extended on the analyses of the impact of 5G RAN instant power consumption and 5G RAN yearly energy consumption on values of standardized EE metrics. The presented analyses can serve as a reference in the selection of the most appropriate 5G BS deployment and operation approach, which will simultaneously ensure the transfer of permanently increasing DVs in a specific device density class and the highest possible levels of data and coverage EE metrics.  
Keywords:green communications; green networking 5G; radio access network; base station; energy-efficiency; metric; data; coverage; power; mobile network operator; wireless +
Footnote †: journal: Sensors
# Current Status, Issues and Future Prospects of Personalized Medicine for Each Disease
## 4 Alzheimer's Disease
### Elucidation of Pathological Mechanism  
In recent years, remarkable progress has been made in understanding the pathophysiology of dementia, and the essence of the disease has been discussed. The most significant trend in AD is the amyloid hypothesis [65, 66]. The main component of senile plaque, which is one of the pathological features of AD, is a peptide of about 40 amino acids called amyloid \(\beta\) protein (A\(\beta\)). A\(\beta\) deposition is the earliest pathologically perceived lesion, A\(\beta\) aggregates and can directly exhibit neuronal toxicity, and genetic analysis of familial AD patients indicates that abnormalities in A\(\beta\) production and accumulation are deeply related to the onset of AD amyloid hypothesis.  
A further neuropathological feature of AD is the intracellular accumulation of neurofibrillary tangles consisting of hyperphosphorylated tau (p-tau) as well as A\(\beta\). This combination of amyloid (A), tau (T), and neurodegenerative (N) biomarkers is being used to classify the stage of dementia [67].
## 4 Discussion  
### Spatial and Temporal Characteristics of SWUE in the Growing Season  
The average growing season SWUE in northeast China illustrated a clear spatial heterogeneity during the past nine years. SWUE ranged between 0.341 and 6.173 gC/kg H\({}_{2}\)O. The SWUE values of cropland and grassland in the Songnen Plain, Liaohe Plain, and Hulun Buir Plateau were relatively lower compared to the regional average. According to our statistics, the GPP values of cropland and grassland were relatively low. The SM in grassland was lowest, but SM was higher in cropland. The lower SWUE values were possibly due to more consumption of soil water by soil evaporation in the ecosystems. Relatively high SWUE was observed in the Changbai Mountains, Greater Khingan Mountains, and Lesser Khingan Mountains, indicating that forests might produce greater photosynthetic productivity per unit of soil water consumption. [PERSON] et al. also found that WUE was higher in forest, and that the WUE of grassland and cropland was relatively low in China using site data [3].  
During the growing season from 2007 to 2015, SWUE fluctuated to a certain extent in northeast China. SWUE increased continuously from April to August, suggesting better efficiency of ecosystems in assimilating carbon by soil water consumption in spring and summer. The lowest SWUE mainly appeared at the end of October, showing the weak capability of photosynthetic productivity of the ecosystem generated by the use of limited soil water supply in autumn. This trend was consistent with the vegetation growth cycle. Note that SWUE variations in northeast China were more consistent with GPP than soil moisture, because the range of annual cumulative GPP (96.304-1343.26 gC/m\({}^{2}\)) was larger than that of SM (3.001-9.431 m\({}^{3}\cdot\)m\({}^{-3}\)). The interannual variation of SWUE was generally stable in northeast China from 2007 to 2015. Spatially, SWUE exhibited insignificantly change trends, possibly because our study period was relatively short. [PERSON] et al. also found that the distribution of SWUE was spatially heterogeneous at a global scale, but the interannual variations of SWUE were not illustrated in their study [9].