esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Uniform Clustering Algorithm for UWSNs Based on Energy Equalization
## 4 Algorithm Description
### Routing
#### 4.2.1 Division of the Forwarding Area  
To save energy, the routing process employs a multi-hop transmission method. Each time a packet is forwarded, consideration should be given to the distance from the next hop relay node to the sink to make sure that they are closer. Therefore, this algorithm takes the sink as the center and the distance from the sink to the current forwarding node as the radius to simulate a circular arc area. Moreover, the overlapping part of the area covered by this arc and the communication coverage area of the current forwarding node is used as the candidate area of the next hop relay node. For a more visual illustration, a cross-section of a part of the network area is taken as a schematic diagram, as shown in Figure 3. In this figure, \(z_{3}\) is the source node; \(z_{2}\) and \(z_{4}\) are candidate relay nodes in its forwarding area.  
Figure 2: Flow chart of the clustering algorithm.  
Its forwarding area is expressed as follows:  
\[\left\{\begin{array}{l}\left(x-x_{0}\right)^{2}+\left(y-y_{0}\right)^{2}+\left( z-z_{0}\right)^{2}\leq d^{2}\\ \left(x-x_{1}\right)^{2}+\left(y-y_{1}\right)^{2}+\left(z-z_{1}\right)^{2}\leq r ^{2}\end{array}\right. \tag{15}\]  
where \((x,y,z)\) represent the coordinates of candidate next hop nodes in the forwarding area; \((x_{0},y_{0},z_{0})\) stand for sink coordinates; \((x_{1},y_{1},z_{1})\) denote the coordinates of the current relay node.
## 4 Experimental Results
### Results for the Synthetic Dataset  
The synthetic dataset was simulated by randomly selecting five reference endmembers from the United States Geological Survey (USGS) spectral library. The complete hyperspectral dataset contained a total of 200 \(\times\) 200 pixels, where each pixel was recorded on 224 spectral bands from 0.4 \(\upmu\)m to 2.5 \(\upmu\)m. The simulated dataset contained non-Gaussian SV noise and other complex SVs caused by causes. Please refer to [55] for detailed information on the synthetic dataset.  
The resulting abundance maps, endmembers and quantitative measurements obtained during unmixing are given in Figures 3 and 4 and Table 3, respectively. Thanks to the different SVs in the composite dataset, it can be clearly seen that the FCLSU algorithm performed well on the composite dataset without taking the endmember variation into consideration and that the results for the GLNMF algorithm in terms of abundance estimation and endmember extraction were poor, which may have been due to the fact that when SVs are present, the GLNMF algorithm could not be well constructed, and its abundance map has serious noise. The gtvMBO algorithm's unmixing was biased due to its non-negative endmember constraint. Compared with the traditional unmixing method, the DL-based model estimated the endmembers well, further indicating its potential. Although the results of the two endmembers extracted by the EACNN were a little different from those extracted by the EGU-pw, its performance indicators revealed that EACNN performed very well. Its stability and effectiveness were demonstrated, and it produced more accurate unmixing results in blind HU tasks.  
Figure 4: _Cont._  
Figure 3: The endmember extraction results for the synthetic data.
## 4 Performance Evaluation of GASM Algorithms over Cloud/MEC RAN Architecture
### Planning with GASM in a Non-Reconfigurable Scenario
Therefore, the number of users in an AO that the planning algorithm employs to design the VNF mapping is \(k\Lambda_{i}\) where \(\Lambda_{i}\) is the average number of users in each AO and \(k\) is a scaling factor. The problem with the static planning is that GASM designs a provisioning which is not totally adapted to a scenario with time-varying traffic. Consequently, the provided VNF provisioning may lead to over use of computing resources and, consequently, to an increase of both the energy consumption and OPEX, as well as to a possibly higher service blocking rate. Figure 4a shows the service blocking ratio with three different scaling factors: \(k=1,\ k=1.5\) and \(k=2\). For each number of users we repeated the simulation 300 times (i.e., 300 values of \(\Lambda_{i}\) in Equation (1) for the number of users connected to each AO). Then, at the beginning of each simulation, we assume that the average number of users in each AO, \(\Lambda_{i}\), has been estimated, and the VNF provisioning is done at the start of the simulation and not reconfigured during the three-day simulation. The corresponding percentages of CPU cores in use are shown in Figure 4b. Figure 4a shows that the best results in terms of service blocking ratio in the simulated scenario are obtained when \(k=1.5\). With \(k=1\), the number of users is under-estimated and, therefore, the network and the VNF provisioning configuration are not prepared to deal with that number of users. Consequently, the service blocking ratio is very high, so that it cannot be used in a real network. Contrarily, if \(k=2\), the number of users in the network is over d  
Figure 4: Network planning results using GASM with \(\mathrm{k}=1,\ \mathrm{k}=1.5\) and \(\mathrm{k}=2\): (**a**) Service blocking rate, (**b**) percentage of CPU cores in use. unnecessary increment in the number of CPU cores in use, and even the service blocking ratio is higher than that obtained with \(k=1.5\). Hence, it is possible to conclude that making a static planning is very complex as it is necessary to scale the estimated traffic in a proper manner, which is a difficult task. Using a low scaling factor would lead to an increment of the service blocking ratio, whereas an over-dimensioning of the network would lead to an unnecessary increment of the number of the CPU cores in use. Finally, the execution time that GASM required to create the VNF mapping is shown in Figure 5. The results show that GASM requires more time to find a solution as the number of average users increases but it is always less than 240 s in a machine equipped with AMD Opteron 6128 and 64 GB RAM. Previous results show the advantage of using GASM for solving the VNF provisioning, but they also show how inefficient it is to plan the VNF provisioning configuration in a static way. Consequently, we propose VNF reconfiguration using GASM with the expectation of improving both the service blocking probability and CPU usage.
# Internet of Things (IoT) Cybersecurity: Literature Review and IoT Cyber Risk Management
## 3 The Four-Layer IoT Cyber Risk Management Framework
### IoT Cyber Ecosystem Layer
#### 3.1.5 Standardization Organizations  
Despite a significant amount of investment made on information assurance, there were no standards to assist cybersecurity managers in choosing the most appropriate cybersecurity technologies [68]. Recently, the need for the effective development of secured IoT products and services has led to standardization efforts for IoT security. Three standardization organizations' efforts for IoT cybersecurity standardization are discussed below.  
The Study Groups of ITU's Telecommunication Standardization Sector (ITU-T) developed ITU-T Recommendations that define elements in the global infrastructure of ICTs [69]. The Global Standards Initiative on Internet of Things (IoT-GSI) of ITU-T promoted a unified approach for the development of technical standards to enable the global operations of the IoT and wrapped up its activities in July 2015 with the establishment of the Study Group 20 on \"IoT and its applications including smart cities and communities.\" Their IoT-related series of recommendations were made from Y.4000 to Y.4999.  
The ISO/IEC JTC 1 Special Working Group (SWG) 5 Internet of Things (IoT) was established in 2012 to develop standards relevant to the IoT and encourage them to be recognized and utilized by industry and other standards-setting organizations. Their preliminary report was made in 2014 [70]. Relevant to IoT cybersecurity standards is ISO/IEC TR 29181-5:2014 Information technology-Future Network-Problem statement and requirements-Part 5: Security ([[https://www.iso.org/standard/57487.html](https://www.iso.org/standard/57487.html)]([https://www.iso.org/standard/57487.html](https://www.iso.org/standard/57487.html))).  
The European Telecommunications Standards Institute (ETSI) released its first globally applicable standard for consumer IoT security, TS 103 645, to specify high-level provisions for the security of Internet-connected consumer devices and their associated services [71]. TS 103 645 requires implementers not to use universal default passwords and to include a vulnerability disclosure policy.
# Lightweight Human Ear Recognition Based on Attention Mechanism and Feature Fusion
###### Abstract  
With the development of deep learning technology, more and more researchers are interested in ear recognition. Human ear recognition is a biometric identification technology based on human ear feature information and it is often used for authentication and intelligent monitoring field, etc. In order to make ear recognition better applied to practical application, real time and accuracy have always been very important and challenging topics. Therefore, focusing on the problem that the [EMAIL_ADDRESS] value of the YOLOv5s-MG method is lower than that of the YOLOv5s method on the EarVNI.0 human ear dataset with low resolution, small target, rotation, brightness change, earrings, glasses and other occlusion, a lightweight ear recognition method is proposed based on an attention mechanism and feature fusion. This method mainly includes the following several steps: First, the CBAM attention mechanism is added to the connection between the backbone network and the neck network of the lightweight human ear recognition method YOLOv5s-MG, and the YOLOv5s-MG-CBAM human ear recognition network is constructed, which can improve the accuracy of the method. Second, the SPPF layer and cross-regional feature fusion are added to construct the YOLOv5s-MG-CBAM-F human ear recognition method, which further improves the accuracy. Three distinctive human ear datasets, namely, CCU-DE, USTB and EarVNI.0, are used to evaluate the proposed method. Through the experimental comparison of seven methods including YOLOv5s-MG-CBAM-F, YOLOv5s-MG-SE-F, YOLOv5s-MG-CA-F, YOLOv5s-MG-ECA-F, YOLOv5s, YOLOv7 and YOLOv5s-MG on the EarVNI.0 human ear dataset, it is found that the human ear recognition rate of YOLOv5s-MG-CBAM-F method is the highest. The [EMAIL_ADDRESS] value of the proposed YOLOv5s-MG-CBAM-F method on the EarVNI.0 ear dataset is 91.9%, which is 6.4% higher than that of the YOLOv5s-MG method and 3.7% higher than that of the YOLOv5s method. The params, GFLOPS, model size and the inference time per image of YOLOv5s-MG-CBAM-F method on the EarVNI.0 human ear dataset are 5.2 M, 8.3 G, 10.9 MB and 16.4 ms, respectively, which are higher than the same parameters of the YOLOv5s-MG method, but less than the same parameters of YOLOv5s method. The quantitative results show that the proposed method can improve the ear recognition rate while satisfying the real-time performance and it is especially suitable for applications where high ear recognition rates are required.  
YOLOv5s-MG; ear recognition; accuracy; attention; feature fusion +
Footnote †: journal: Applied Sciences
# Passive Harmonic RFID System for Buried Assets Localization
## Harmonic Tag
### Tag Characterization
Therefore, this modulation technique increases the tag read range because measurements are performed at \(2f_{0}+f_{m}\), and thus are not affected by the coupling limitations. Furthermore, different oscillation frequencies can be chosen, as shown in Figure 10, by changing the quartz crystal depending on the target to be identified (e.g., water, gas, or electricity installations). These features highlight the potential of applying the proposed design in this work. The received level clearly complies with the regulations. Figure 8: Measured conversion loss as a function of input power when the frequency doubler is modulated with a 32 KHz 0.45 V square wave. Figure 9: Measured spectrum at \(2f_{0}=1732\) MHz (\(\Delta f=0\)) for two scenarios: without the tag and with the tag in the air at 0.7 m from the TX/RX antennas.
# On the Influence of Data Imbalance on Supervised Gaussian Mixture Models
## 2 Materials and Methods  
### Probabilistic Generative Models for Supervised Learning  
In probabilistic classification, a statistical model is employed to predict the class \(C_{k}\) (where \(k=1,\ldots,K\)) for a given observation characterized by a feature vector \(\mathbf{x}_{i}\). This model provides a _posterior class probability_, denoted as \(\Pr(C_{k}\mid\mathbf{x}_{i})\), for each class, which is subsequently utilized to determine class membership for novel observations.  
Certain modeling techniques directly estimate posterior probabilities by constructing a discriminant function \(\eta_{k}(\mathbf{x}_{i})\), that directly maps features \(\mathbf{x}_{i}\) to each class \(C_{k}\). These are known as _discriminative models_, with a prominent example being the logistic regression model for binary-class problems.  
Alternatively, other approaches aim to model the distribution of both features and classes, either explicitly or implicitly. Posterior probabilities are then derived using [PERSON] theorem. Consequently, through the learning of class-specific densities \(f(\mathbf{x}_{i}\mid C_{k})\) and the prior class probabilities \(\Pr(C_{k})\) for each class, the posterior class probabilities can be calculated as:  
\[\Pr(C_{k}\mid\mathbf{x}_{i})=\frac{f(\mathbf{x}_{i}\mid C_{k})\Pr(C_{k})}{\sum_{g=1}^ {K}f(\mathbf{x}_{i}\mid C_{g})\Pr(C_{g})}.\]  
Approaches following this methodology, such as those rooted in finite mixture modeling, are referred to as _generative models_.  
Assume a training dataset \(\mathcal{D}_{\text{train}}=\{(\mathbf{x}_{1},y_{1}),\ldots,(\mathbf{x}_{n},y_{n})\}\) is available, for which both the feature vectors \(\mathbf{x}_{i}\) and the true classes \(y_{i}=\{C_{1},\ldots,C_{K}\}\) for each observation are known. Classification models are fitted using data from such a training set, ultimately yielding parameter estimates. However, if this same dataset is also employed for tasks like model fine-tuning (such as hyperparameter estimation) and classifier evaluation, it tends to yield an overly optimistic evaluation of performance. This phenomenon is termed _overfitting_, signifying the potential hazard of tailoring a model too closely to a specific set of data, which may consequently struggle to generalize well to additional data or predict future observations accurately. Given these considerations, it is recommended to carry out model fine-tuning using a _validation set_, a dedicated dataset typically reserved separately from the original dataset. Alternatively, one can adopt resampling techniques, such as _cross-validation_, which involve repeatedly partitioning the data into training and validation sets. If enough data are available, a separate subset of the initial dataset could be set aside in advance as a _test set_ for the final evaluation of the classifier.
## 2 Numerical Model  
### Theoretical Equations  
The Euler two-fluid model was used to describe the wind-blown sand movement near the ground. It regards air and sand as the two phases of a continuous medium. The  
Figure 1: Sand sedimentation around railway culverts, (**a**) at entrance of a culvert on the Qinghai-Tibet Railway, and (**b**) inside a culvert on the Golmud-Korla Railway [18]. continuity equations and momentum equations of the air phase and sand phase can be expressed as [24]:  
\[\frac{\partial}{\partial t}\big{(}\alpha_{g}\rho_{g}\big{)}+\
abla\big{(}\alpha_{ g}\rho_{g}v_{g}\big{)}=0, \tag{1}\]  
\[\frac{\partial}{\partial t}\big{(}\alpha_{g}\rho_{g}u_{g}\big{)}+\
abla\big{(} \alpha_{g}\rho_{g}u_{g}u_{g}\big{)}=-\alpha_{g}\
abla p+\
abla\tau_{g}+\alpha_ {g}\rho_{g}\mathrm{g}+f_{sg}, \tag{2}\]  
\[\frac{\partial}{\partial t}(\alpha_{s}\rho_{s})+\
abla(\alpha_{s}\rho_{s}v_{s}) =0, \tag{3}\]  
\[\frac{\partial}{\partial t}(\alpha_{s}\rho_{s}u_{s})+\
abla(\alpha_{s}\rho_{s} u_{s}u_{s})=-\alpha_{s}\
abla p-\
abla p_{s}+\
abla\tau_{s}+\alpha_{s}\rho_{s} \mathrm{g}+f_{sg}, \tag{4}\]  
where \(t\) is the time, \(\alpha_{g}\) and \(\alpha_{s}\) are the volume fractions of the air and sand phases, respectively, where \(\alpha_{g}+\alpha_{s}=1\), and \(\alpha_{s}\) is 1.5%; \(\rho_{g}\) and \(\rho_{s}\) are the density of the air and sand phases, respectively, \(\rho_{g}\) = 0.752 kg/m\({}^{3}\), and \(\rho_{s}\) = 2650 kg/m\({}^{3}\); \(v_{g}\) and \(v_{s}\) are the velocity vectors of the air and sand phases; \(u_{g}\) and \(u_{s}\) are the velocities of the air and sand phases; \(\tau_{g}\) and \(\tau_{s}\) are the surface stress tensors of the air and sand phases; \(p\) is the pressure of an ideal fluid; \(p_{s}\) is the solid pressure of the sand phase; g is the acceleration of gravity (taken as \(-\)9.81 m/s\({}^{2}\)). \(f_{\mathrm{sg}}\) is the interaction force between the air and sand phases:  
\[f_{sg}=\frac{3C_{D}\alpha_{s}\alpha_{g}\rho_{g}}{4d}|u_{r}\alpha_{g}^{-2.65} \tag{5}\]  
where, \(C_{D}\) is the drag coefficient; \(d\) = 0.15 mm is the equivalent diameter of the sand particles; \(u_{r}\) is the relative velocity between the air and sand phases. A fluid flowing near the ground or over irregular terrain produces complex eddy currents, and multiple eddy currents form turbulence. Reynolds Average Navier-Stokes method (RANS), the most widely used in engineering, is adopted in the turbulence model in this paper.
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 6 Simulation Results
### Performance Comparison
#### 6.3.7 Packet Drop Ratio  
Figure 13 illustrates the number of packets dropped in the network. RE-PBR has a greater packet drop ratio when the node number is 100 because it explicitly chooses three metrics (energy, depth and link quality) to ensure a high quality signal reaches the destination. Although it ensures reliable data delivery, if the link quality is not good, it drops the data packet. This is the reason it has a high drop ratio.  
WDFAD-DBR has a lower drop ratio as compared to RE-PBR because it looks up two hops and assigns priority to each neighbor node. Although it has a lower drop ratio, it is higher than all proposed schemes.  
The proposed schemes show variation in the results with respect to each other and have better performance than RE-PBR and WDFAD-DBR. The CA-DBR has the minimum drop rate compared to the baseline and other proposed schemes. The reason is the consideration of collision on the wireless channel. It chooses the link that has the minimum number of nodes surrounding the source node. This parameter helps with delivering the data successfully at the destination. Whereas C-DBR and A-DBR have a similar pattern of drop ratio, A-DBR adjusts the transmission range to bypass the hot spot problem; although, sometimes, it depletes energy suddenly in transmission adjustment; while C-DBR is a cluster based scheme and transmits the composite data packet, which means, if a composite  
Figure 12: Comparison of network lifetime.  
packet of 10 packets is dropped, it would be considered as one. The B-DBR uses a backtrack process to find an alternate path instead of dropping the data packet; therefore, it has a lower drop ratio than RE-PBR and WDFAD-DBR.
# A Review of Experimental Techniques for Measuring Micro- to Nano-Particle-Laden Gas Flows
## 2 Classification of Experimental Methods
### Parameters Used to Characterize Flows  
Characterizing the dynamic behavior of the carrier and the particle phases are the two most important aspects of particle-laden gas flows. [PERSON] (2007) pointed out that experimental investigation of dispersed multiphase flows have mainly focused on measuring the average velocity and Reynolds stress of the carrier phase or the concentration and root mean square velocity of the particles. In addition, high-order quantities, such as Lagrangian particle velocity correlations, turbulent dissipation rates of the carrier phase, the relationship between two particles and particle-fluid velocity also have received some attention, as [PERSON] indicated [PERSON] (2007). An investigation of the carrier phase velocity is often carried out to determine the effect of the dispersed particles on the gas flow. [PERSON] et al. [PERSON] et al. (2010) summarized the parameters of the dispersed phase, which have received most attention in studies of particle-laden gas flow systems, as follows:  
* particle size (such as particle diameter or equivalent diameter of non-spherical particles)
* particle shape and surface area
* particle concentration or mass flow rate
* particle migration velocity and rotational velocity
* the relationship between particle size and particle velocity  
Many of these parameters are described by distribution functions, rather than single values. In this case, means and standard deviations of higher-order moments of their distribution functions are the quantities determined. With advances in optical and laser technology, it is even possible to measure particle temperature and chemical components [PERSON] (2007). Recently, nano-particle-laden gas flows have become a topic of great concern, linked to global atmospheric and environmental deterioration. In characterizing these flows, the focus has been on human health and medical treatments, for example, determining particle behavior within the human respiratory tract, particle toxicity, therapeutic effects or the thermal conductivity of nanofluids.