esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Signal Model
Firstly, we construct virtual array snapshots from the first two sub-training sequences. Then, with the root-MUSIC algorithm, we obtain the coarse estimations of the \"interference frequencies + CFO\" and the \"expected signal frequency + CFO\". Next, we construct new virtual array snapshots from the third sub-training sequence and perform beamforming on them according to the previous frequency estimations, i.e., form virtual beam at the \"expected signal frequency + CFO\" and form beam nulls at \"interference frequencies + CFO\" to suppress the in-band interferences and protrude the expected signal. Finally, we reuse the root-MUSIC algorithm to process the beamformed data. From the obtained roots, we pick the one that is nearest to the unit circle and take its amplitude as the metric to determine whether the signal is arriving or not. If so, we extract its phase to calculate the CFO and take the left endpoint of the current time window as the coarse estimation of the starting position of the burst frame.
## 4 Discussion
Both of them, however, reported up to a 20 dB lower \(SNR_{out}\) and \(SNR_{imp}\). The majority of the other studies, regardless of the complexity of their algorithms, also reported a lower \(SNR_{imp}\) and/or higher errors compared to this study. We could distinguish the normalized LMS adaptive filters with RMSE equal to 1.5 \(\upmu\)V [56] and 5.6 \(\upmu\)V [58] which are closest to the RMSE range in this study (0.8-1.5 \(\upmu\)V). The studies with the best \(SNR_{imp}\) are the moving average PLL [12] (42.2-58.5 dB) and the synchrosqueezing transform with an adaptive filter [60] (47-52 dB), which are, however, slightly lower than this study (57-60 dB) for constant amplitude PLI. Surprisingly, the deep artificial networks and autoencoders [63; 64; 65] present limited \(SNR_{out}\) (23-36 dB) and \(SNR_{imp}\) (22-36 dB). In conclusion, the SF filter demonstrates excellent performance among state-of-the-art studies, which was confirmed by the statistical evaluation of each of the 12 ECG leads.
## 5 Case Study
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**T** [\({}^{\circ}\)F] & **P [psi]** & \(\boldsymbol{V_{\mathrm{H_{2w}}}}(\mathbf{MBE})\) [SCF] & \(\boldsymbol{V_{\mathrm{H_{2w}}}}(\mathbf{simulation})\) & **\% Error** \\ \hline
180 & 3345 & \(1.65\times 10^{11}\) & \(1.66\times 10^{11}\) & 0.55\% \\
180 & 3000 & \(1.47\times 10^{11}\) & \(1.49\times 10^{11}\) & 1.00\% \\
180 & 2500 & \(1.20\times 10^{11}\) & \(1.21\times 10^{11}\) & 1.03\% \\
180 & 2000 & \(9.15\times 10^{10}\) & \(9.25\times 10^{10}\) & 1.08\% \\
180 & 1500 & \(6.21\times 10^{10}\) & \(6.28\times 10^{10}\) & 1.14\% \\
180 & 1000 & \(3.16\times 10^{10}\) & \(3.19\times 10^{10}\) & 0.94\% \\
180 & 500 & 0.00 & 0.00 & 0.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of hydrogen capacity estimates in a depleted gas reservoir using analytical MBE approach and numerical simulation.
## 3 An Architecture for Self-Organization
### Self-Cure and Self-Protection  
The self-cure module is responsible for detecting, localizing, and repairing faults in the routing protocols OLSR and AODV: when a router halts or stops working properly, the remaining router nodes should detect the fault with the help of the monitoring agent (MN) and create alternative routes for the connections that have been compromised by the faulty router. On the other hand, the self-protection modules should enforce security within the network, possibly using techniques based on immunological systems [30, 31, 32] in an effort to detect intrusion in complex WMN.  
A feature of the architecture proposed in this paper is the potential to use existing routing protocols for wireless networks, such as OLSR and AODV. Further the architecture can be extended to operate with other routing protocols, which would require adjustments in the self-x modules to support a given protocol. With all its functionalities implemented, the architecture would suppress all the limitations of existing routing protocols for WMN with respect to performance, fault tolerance, and security.  
As a first experience, we proposed modules for self-optimization and self-configuration with focus on the WMN performance of the network. Several simulation experiments were performed to assess the effectiveness of the proposed architecture and the self-x modules that were implemented. The conceptual design of the architecture was proposed in [33], while the implementation of the self-configuration module for OLSR was presented in [34].  
All in all, the proposed architecture can be thought of as an extension of existing routing protocols. The motivation to this research was not the development of a self-organizable routing protocol from scratch, but rather the extension of the concepts of self-organization to existing routing protocols for wireless networks.
## 2 Requirements Specifications and Measurements Gaps
### Sea Ice Thickness  
In the time frame from 2020-2030, the sea ice thickness variable presents a gap in the revisit time (24 h) and in the vertical spatial resolution of 1 cm with 0.1-cm accuracy [2]. Ice thickness maps with a resolution of 50 km and up to 50-60 cm in thickness are produced with the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), L-band microwave radiometer onboard the Soil Moisture and Ocean Salinity (SMOS) mission of the ESA, with an accuracy mission of 0.5-6 cm [45]. Another technology is the LiDAR altimeter, but its narrow swath and high cost do not compensate its benefits in terms of the variables with a gap. GNSS-R [39] and SAR [46] technologies also are capable of measuring this variable.
## 3 Data Collections
### Satellite Measurements
#### 3.1.1 Background
As a result, Earth observing satellites have yielded a long-term record of changes in vegetation photosynthetic activity, while ground-based eddy covariance measurements are providing automated, continuous and spatially averaged measures of carbon exchanges between the surface and the atmosphere. A global increase in 'greening' has been evidenced through remotely sensed indices, such as the Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI), suggesting an increase in the seasonally-integrated LAI of up to 25-50%, while less than 4% of the terrestrial areas exhibit a decrease of LAI [30]. Key drivers of phenology include temperature and photoperiod in the mid- and high-latitudes [2; 31; 32; 33], while precipitation is regarded as the dominant driver in the tropics [34; 35]. Satellite-based observations have been widely used to characterize the evolution of vegetation in the Northern Hemisphere, but other regions have received far less attention: A systematic review of phenology in Africa over the last decade reveals a recent increase in the number of phenological studies based on remote sensing but suggests that further investigations are needed across this continent, especially to focus on the relationship between climate and vegetation phenological changes [36]. Remote sensing research in the Lowveld has tended to focus on the use of mono-directional spectral measurements (from airborne and/or spaceborne sensors) which typically inform on the chemical composition of the targets (in [37; 38; 39; 40]). More recently, sporadic airborne campaigns collecting LightDetection And Ranging (LiDAR) measurements have been used to document the structural properties of the vegetation canopies, e.g., to map the differences in canopy and sub-canopy cover height and distribution [26; 41; 42]. One of the main advantages of using LiDAR observations is the ability to capture sub-canopy structural changes specifically to examine bush encroachment, otherwise unattainable in mono-directional spectral measurements which only account for the aerial extent of the vegetation canopy [43]. Hybrid techniques combining structural observations from LiDAR and spectral measurements from hyperspectral and multispectral data sets demonstrate the ability to classify individual tree species [25; 44]. To a lesser extent, Advanced Synthetic Aperture Radar (ASAR) measurements, which can observe the surface irrespective of the cloud cover, have been used to create regional scale baseline maps of savanna woody cover in the Lowveld [9]. Cutting-edge approaches utilizing LiDAR, ASAR, and hybrid-techniques are very interesting but infrequent and limited in geographical scope, constrained in part by the limited spatial coverage and the cost of obtaining the data sets.
# High-Accuracy Insulator Defect Detection for Overhead Transmission Lines Based on Improved YOLOv5
## 4 Discussion
### Comparison of Different Receptive Field Modules  
At the end of the model backbone network, the receptive field module is added to fuse the feature information of each scale. The CRF receptive field module designed in this paper is compared with SPP and the receptive field block (RFB) [32] to verify the effectiveness of the CRF module. The results are shown in Table 1:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Receptive Field** & \multicolumn{3}{c}{**N-Insulator**} & \multicolumn{3}{c}{**D-Insulator**} & \multirow{2}{*}{**mAP@50**} \\ \cline{2-2}
**Module** & & & & & \\ \hline SPP & 0.877 & 0.739 & 0.937 & 0.956 & 0.913 \\ RBF & 0.874 & 0.919 & 1 & 0.967 & 0.966 \\ CRF & 0.915 & 0.908 & 0.988 & 1 & 0.974 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different receptive field modules.  
Three kinds of receptive field modules were compared by detecting the accuracy rate, recall rate of normal and defective insulators, and the average accuracy of the two categories when the confidence level was 50. As can be seen from the data in the table, for the CRF module of normal insulators, the accuracy rate is 0.915, while the RBF module shows a better recall rate. In the detection of defective insulator areas, the detection accuracy of the RBF module is the highest, the recall rate of the CRF module is \(1\), and the overall detection accuracy of the CRF module is the highest. In summary, the designed CRF module compares the first two receptive field modules, combines the proportion information of the target in the graph, sets convolution kernels of different sizes, better obtains global and local feature information, and enhances the network's fusion of semantic and texture information.
# Quantum Computing Approaches for Mission Covering Optimization
## 2 Formalism for Mission Covering Optimization  
The objective of an MCO problem is to allocate a set of resources to missions such that each resource is assigned to at most one mission, and each mission's requirements are satisfied.  
An MCO problem is described by a 7-tuple:  
\[(\mathbf{M},\mathbf{R},\mathbf{Q},\mathsf{C},\mathsf{REQ}_{M},\mathsf{REQ}_{R},f _{obj})\]  
1. \(\mathbf{M}\) is the set of missions,
2. \(\mathbf{R}\) is the set of resources,
3. \(\mathbf{Q}\) is the set of qualifications,
4. \(\mathsf{C}\) is the capability function,
5. \(\mathsf{REQ}_{M}\) is the mission's requirements function,
6. \(\mathsf{REQ}_{R}\) is the resource's requirements function,
7. \(f_{obj}\) is the objective function that scores problem solutions.  
In the rest of this section, these terms will be explained in detail.
# Lightweight Multiscale CNN Model for Wheat Disease Detection
## 3 Results
### Effect of Attentional Mechanisms on the Model  
In the experiment, we investigated how CBAM, NAM, SE, CA, and ECA affected the performance of a residual structure model, and designed nine sets of experiments to compare their effects. Table 5 shows the results of adding different attention mechanisms to the model. The table indicates that adding an attention mechanism has little impact on the number of model parameters and computations. Adding only one attention mechanism improves the accuracy, precision, recall, and F1-score of the model for all mechanisms except CA; ECA is the most helpful to the model, as evidenced by the increase in its accuracy, precision, recall, and F1-score by 0.59%, 0.51%, 0.62%, and 0.57%, respectively. Adding two attention mechanisms increases the detection accuracy of the model by 1.18-2.71%, with CBAM + ECA working best. To further explore the role of the attention mechanism, we used Grad-CAM to draw a heat map of the model with the attention module and visualize the network's focus area on wheat diseases. Figure 10 shows that each row corresponds to a residual layer and each column corresponds to the added attention mechanism. The model with an added attention mechanism can focus on the diseased area of wheat leaves, effectively obtaining the relevant information in the pictures, which is very helpful for disease identification.  
Figure 9: Visualization result of feature map output by layer. The first row of the feature maps does not have any Inception module, the second row contains the Inception-1 module, the third row contains both the Inception-1 and Inception-2 modules, and the fourth row contains all three Inception modules. (**a**) Feature map of layer 1; (**b**) feature map of layer 2; (**c**) feature map of layer 3.  
The heat map shows that in the first residual layer, SE performs the best in detection, but it also misclassifies some healthy areas as diseased ones; CBAM + CA detects edge wheat leaves and basically avoids diseased areas; and CBAM + SE pays little attention to disease characteristics. For the second residual layer, except for CBAM + ECA and SE, all the other attention mechanisms greatly improve the model's focus on the disease, but they over-attend to the image features, resulting in some background features also being considered as characteristics of the disease. For the third residual layer, all models except CBAM + CA focus on the diseased areas, but only CBAM + ECA identifies the disease features most clearly and detects almost all of them. ECA, CA, CBAM, and NAM perform similarly in detection, but they only focus on the main regions of the disease and ignore the features that exist at the margins. After comparing nine sets of experiments, we chose CBAM + ECA as the best-performing method.
# A Review of Pool-Boiling Processes Based on Bubble-Dynamics Parameters
## 4 Bubble Growth and Detachment Model
### Bubble Growth in Non-Uniform Boiling
#### 4.2.2 Empirical Modeling of Bubble Growth  
Many models of bubble growth in non-uniform boiling have been proposed based on experimental results. [PERSON] and [PERSON] (1977) observed bubble growth in the saturated boiling of various liquids on a smooth metallic zirconium belt at different wall-superheat levels. They evaluated a number of homogeneous growth models based on their data and proposed that the empirical growth coefficient \(\beta\), which is usually a function about \(Ia\), would be smaller than the homogeneous model due to non-uniform superheating in non-uniform boiling. These data were correlated in the form given by Equation (27), and the empirical coefficient 2.5 and the exponent 0.75 to the Jacob number were taken (mostly one in uniform boiling).  
\[R=2.5 Ja^{0.75}\sqrt{\alpha_{L}t} \tag{27}\]  
[PERSON] et al. [78] synthesized experimental data from various studies on saturation boiling with water on different metal surfaces at different pressures and wall superheat, and proposed fitting these data with the formula by Equation (28), where the exponent of time, \(n\), depends on the ambient pressure in Mpa. Factor \(n\) varies equally as the main factor controlling the growth rate varies. This is because growth is much more influenced by the inertial growth stage at low pressures, whereas growth is mainly controlled by heat transfer under high-pressure ambient conditions.  
\[R=\beta\alpha_{L}^{1/2}t^{n},\beta=2.1077 Ja^{0.7902} \tag{28}\]  
\[n=1.0012e^{-p/0.3257}-0.9624e^{-p/0.6161}+0.5 \tag{29}\]  
[PERSON] and [PERSON] [79] collected saturated pool boiling data for a variety of liquids (including water, CCl\({}_{4}\), etc.) from the literature, on metal surfaces in the horizontal direction, and they proposed Equation (30) to fit these data based on their analysis, in which the correction factor \(B=1.55\) for water, CCl\({}_{4}\), and n-hexane, and \(B=0.645\) for n-pentane and acetone.  
\[R=\frac{1}{2}BA\rho^{0.135}Ja^{0.5}\sqrt{\alpha_{i}t} \tag{30}\]  
The accuracy of the above relation was evaluated by [PERSON] et al. [66] based on experimental data from non-uniform boiling and it was found that the [PERSON] et al. [78] model has better accuracy at atmospheric pressure, while [PERSON] et al.'s [79] model showed better accuracy at low and medium pressures. This phenomenon may be attributed to the incorporation of the Archimedes number, which gravity into account, which is neglected in other studies.
# Correct Pronunciation Detection of the Arabic Alphabet Using Deep Learning
## 4 Results and Discussion
### Arabic Alphabet Pronunciation Classification
#### 4.4.1 Without Data Augmentation  
The confusion matrix of the Arabic alphabet pronunciation classification shows the number of correctly classified and misclassified samples. The accuracy of these samples can be seen in Table 10, it shows the confusion matrix of DCNN, AlexNet, and BLSTM models for the dataset without data augmentation. AlexNet has a lesser error rate than DCNN and BLSTM.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Without Data Augmentation**} & \multicolumn{3}{c}{**With Data Augmentation**} \\ \cline{2-7}  & **Random Split** & **Hold-Out** & **5-Fold CV** & **Random Split** & **Hold-Out** & **5-Fold CV** \\ \hline DCNN & 96.41\% & 95.46\% & 96.37\% & 97.88\% & 95.28\% & 96.24\% \\ Alex Net(TL) & 97.12\% & 96.89\% & 96.55\% & 99.14\% & 97.42\% & 98.43\% \\ BLSTM & 72.41\% & 73.54\% & 74.35\% & 77.71\% & 76.12\% & 78.17\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Arabic alphabet pronunciation classification with and without data augmentation.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Models** & **Mean** & **SD** & **Margin of Error** \\ \hline DCNN & 95.03\% & \(\pm\)2.57 & \(\pm\)0.90 \\ Alex Net(TL) & 97.36\% & \(\pm\)1.64 & \(\pm\)0.58 \\ BLSTM & 73.21\% & \(\pm\)2.40 & \(\pm\)0.80 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Alphabet classification values of mean, standard deviation, and error.
# Rock Fragmentation Characteristics by TBM Cutting and Efficiency under Bi-Lateral Confinement
## 2 Test Methodology
### Specimen Preparation  
To investigate the breaking characteristics of rock and the efficiency of TBM disc cutters subjected to various biaxial confining pressures, a number of granite samples were prepared with high uniaxial compressive strength and brittleness. The specimens were prepared from the same rock to ensure that the mechanical properties of each specimen were consistent. The surface of each specimen was polished to eliminate negative effects. According to the standard code recommended by the International Society of Rock Mechanics (ISRM), uniaxial compression, Brazilian cleavage, and direct shear tests were undertaken on the specimens. The values of mechanical parameters were obtained as follows: uniaxial compressive strength \(\sigma_{c}\) = 94.8 MPa, tensile strength \(\sigma_{t}\) = 7.8 MPa, elastic modulus \(E\) = 22.3 MPa, Poisson's ratio \(v\) = 0.23, internal friction angle \(\varphi\) = 49.2\({}^{\circ}\), and cohesion \(c\) = 23 MPa.  
A previous study performed by [PERSON] et al. [29] reported that when the ratio of the radius of the plastic deformation zone to the minimum width of the specimen is less than 1/6, the effect of size is negligible. Therefore, according to the bilt cutting model, the relevant parameters are expressed as follows:  
\[(1+\mu)\xi^{(1+K_{d})/K_{d}}-\mu\xi^{(K_{p}-1)/K_{p}}=\gamma \tag{1}\]  
\[K_{d}=\frac{1+\sin\varphi}{1-\sin\varphi};K_{p}=\frac{1+\sin\psi}{1-\sin\psi} \tag{2}\]  
\[\mu=\frac{\lambda K_{p}}{K_{d}+K_{p}} \tag{3}\]  
\[\lambda=\frac{(K_{p}-1)(K_{d}-1)+(1-2v)(K_{p}+1)(K_{d}+1)}{2K_{p}} \tag{4}\]  
\[\xi=\frac{r_{*}}{t} \tag{5}\]  
Figure 3: The fixed-base surface profilometer (ST150) used in this study (Nanovea Company, Irvine, USA).  
where \(\xi\) denotes the ratio of plastic deformation zone to penetration depth, \(K_{p}\) represents an expansion coefficient, \(K_{d}\) is a passive coefficient, \(\mu\) is a friction coefficient for the crack surface, \(\lambda\) denotes a lateral pressure coefficient, \(r^{\star}\) is the radius of plastic deformation zone, \(t\) represents the penetration depth, and \(\gamma\) is a constant for characterization of the size and shape of disc cutters.  
Taking into account the loading capacity of the profilometer, the size of a granite sample was 250 mm \(\times\) 200 mm \(\times\) 100 mm. Moreover, in the experiment the penetration depth was 6 mm. Substituting the mechanical parameters of rock in Equation (1) into Equation (5), the radius of the plastic deformation zone was found to be 1.9 mm, and its ratio to the minimum size of the specimen was far less than 1/6. According to the results achieved by [PERSON] et al. [29], the size of the sample can be determined accordingly.
# Security Issues in IoT-Based Wireless Sensor Networks: Classifications and Solutions
## 3 Security Protection Schemes
### Key Management Protocol Schemes
The advantages of the proposed method allow the storage capacity to be reduced by using one-way functions to generate secret keys; in addition, the pre-key distribution scheme has high resilience, resistant to spoofing or denial of service attacks. To support further in WSNs, several pre-key distribution methods [70; 71] were studied to enhance the security of the network against various malicious attacks. Based on the probabilistic key mechanism, [PERSON] [72] proposed a mechanism based on random key pre-distribution. In the mechanism, each sensor randomly selects a set of keys that recognize their identity from the key pool before deployment. After determining the public key, two neighboring nodes exchange and compare the identification list of keys in each node's key chain. Simulation results show that to establish a shared key connection for a network of 10,000 nodes, it is only necessary to distribute a key ring of 250 keys to each sensor node (selected from a pool of 100,000 keys). This is suitable for network scalability. In addition, the security capability of the proposed method based on random graph theory allows for efficient key management, including selective revocation of node keys, management, or node addition/deleting. Key management methods play an important role in establishing encryption, authentication, and access control of WSN systems. This is to ensure the security of all communication channels in the network. The advantage of key management schemes is their flexibility according to the network structure (LKHW techniques are suitable for small-scale networks, while larger networks with randomly deployed network nodes can deploy techniques such as LEAP, BROSK, and CDTKeying). The choice of key management scheme can be implemented based on the requirements of each specific application including factors such as network size, network node resources, security requirements and scalability. For example, deterministic key schemes are suitable for small-scale networks, randomly deployed, and do not require strict security, while probabilistic key schemes are suitable for larger-scale networks, with better performance, but require more resources. In addition, some key management protocols show effective resistance to specific attacks. For example, the LEAP protocol can prevent wormhole attacks, HELLO flooding, and node replication; on the other hand, the BROSK protocol shows effectiveness against denial of service attacks, replay attacks, and spoofing attacks. Some key management solutions such as IOS/DMBS and CDTKeying are able to maintain security when the network is expanded. However, the high communication cost and key storage requirements of some protocols are not suitable for nodes with small resources (LEAP with four types of keys requires more memory and energy). In addition, some key management protocolsare limited when the network has changes in the number of nodes (additional nodes, dead nodes) or mobile nodes.