esa-sceva/satcom-chunk-collection huggingface dataset:
# Pointer Meter Recognition Method Based on Yolov7 and Hough Transform
###### Abstract  
The current manual reading of substation pointer meters wastes human resources, and existing algorithms have limitations in accuracy and robustness for detecting various pointer meters. This paper proposes a method for recognizing pointer meters based on Yolov7 and Hough transform to improve their automatic readability. The proposed method consists of three main contributions: (1) Using Yolov7 object detection technology, which is the latest Yolo technology, to enhance instrument recognition accuracy. (2) Providing a formula for calculating the angle of a square pointer meter after Hough transformation. (3) Applying OCR recognition to the instrument dial to obtain the model and scale value. This information helps differentiate between meter models and determine the measuring range. Test results demonstrate that the proposed algorithm achieves high accuracy and robustness in detecting different types and ranges of instruments. The map of the Yolov7 model on the instrument dataset is as high as 99.8%. Additionally, the accuracy of pointer readings obtained using this method exceeds 95%, indicating promising applications for a wide range of scenarios.  
p ointer meter recognition; Yolov7; Deeplabv3+; PGNet; Hough transform Article
## 3 Results and Discussion
### Characteristics of the A-CF/EP/PEF Composites
#### 3.1.2 Morphology of the A-CF/EP Composites  
The cross-section morphology and microstructure of the A-CF/EP/PEF composites are shown in Figure 4. Clearly, the hierarchical structure constructed jointly by the EP/FEP composite, the filler A-CF, and the low-surface-energy FEP creates a hydrophobic surface. A good interpenetrating structure is formed between the resin and the filler, and the matrix is uniformly dispersed in A-CF/EP/PEF (Figure 4a). On the one hand, carbon fibers just act as \"grasses\" planted firmly in the \"soil\" of the resin matrix (Figure 4b), and the two are closely combined. On the other hand, the EP with good adhesion and the FEP with low surface energy are well mixed (Figure 4c). Areas of different colors in Figure 4c are interspersed with each other, and the interface between the two is well combined.
# A Systematic Control Design Method with Active Damping Control in Voltage Source Converters
## 3 Design of the Control Gain Matrix  
In the existing literature [12; 13; 14; 15; 16; 17; 18; 19], it is evident that using more states in the control structure is better for the dynamic performance and stability margin under a weak grid. However, how to design the control gain matrix is an unsolved question. This section presents a linear-quadratic regulator (LQR) design framework to achieve an optimal, robust, and multivariable control of grid-connected VSCs.  
Given the system dynamics in (6), the static full-state-feedback law is as follows:  
\[u=-K_{f}x \tag{8}\]  
The optimal gain matrix, \(K_{f}\), is calculated to minimize the following infinite-horizon quadratic cost function:  
\[(u)=\int_{0}^{\infty}\Big{(}x^{T}Qx+u^{T}Ru\Big{)}dt,Q=Q^{T}\geq 0,R=R^{T}\geq 0 \tag{9}\]  
where \(Q\) and \(R\) are constant weighting matrixes that set relative weights of state deviation and input usage; the optimal gain matrix is given by the following:  
\[K_{f}=R^{-1}B^{T}P \tag{10}\]  
where the positive definite matrix, \(P\), is a solution of the following algebraic Riccati equation:  
\[A^{T}P+PA-PBR^{-1}B^{T}P+Q=0 \tag{11}\]  
The solution leads to a stable closed-loop transfer function \(\Big{(}A_{cl}=A-BK_{f}\Big{)}\) and minimizes the defined performance quadratic cost function (\(J\)). Furthermore, it is well-known that the equation has a single positive-definite solution if, and only if, the pairs of \((A,B)\) and \(\big{(}\sqrt{Q},A\big{)}\) are stabilizable and detectable, respectively. Moreover, there are suitable numerical methods to find the solution, even for MIMO systems or high -dimensional problems.
# SINDVis: User-Centered Dynamic Interactive Visualization System for Space Information Networks
## 3 Theory and Methods
### SINDVis Description of Functions
#### 3.2.3 Description of the Fusion Usage of Views  
As mentioned above, for multi-class users, the GeoView and TopoView complement each other. The principles of specific supplemental use are summarized as follows.  
Firstly, The GeoView is the entity domain view, and when the scale of SIN is small, it is simple to show its composition structure and operation laws. However, the scale of SIN is constantly increasing, and the structure of SIN is becoming more complex. When the scale of SIN develops to a certain degree, the GeoView will only be able to display its constituent structure but will not analyze its operation laws.  
Secondly, The TopoView is the topology domain view, and it is an abstract form with the space entities of SIN mapped into network nodes, and relations between entities are abstracted as the network links of SIN. The TopoView demonstrates the dynamic evolution process of SIN from a network perspective. Meanwhile, the dynamic evolution process is demonstrated without considering the scale of SIN. In addition, TopoView also can support the visual analysis of the changing relationships between the key nodes and core links of SIN.  
To sum up, TopoView can be used to complement an insufficient GeoView with limited presentation. The GeoView can complement the abstract features of TopoView by providing an intuitive display. Therefore, only by using these two complementary views in combination can the SINDVis can play its role to the fullest.
# Convolutional Two-Stream Network Using Multi-Facial Feature Fusion for Driver Fatigue Detection
## 3 Experimental Results
### National Tsing Hua University-Driver Drowsiness Detection (NTHU-DDD) Dataset  
The NTHU-DDD dataset [25] was a dataset developed by National Tsing Hua University, which was used at the Asian Conference on Computer Vision Workshop on Driver Drowsiness Detection. The entire dataset contained 36 subjects of different ethnicities, which were recorded with and without wearing glasses/sunglasses under a variety of daytime and nighttime simulated driving conditions. All movements of the driver were captured, including normal driving, yawning, slow blink rate, falling asleep, laughing, etc. The training set contained 360 video clips of 18 subjects, while the evaluation set consisted of 20 video clips of four subjects. The dataset contained a lot of normal, drowsy, talking, and yawn face data in various scenarios. Thus, the algorithm should consider robustness in all circumstances. Some screenshots from the dataset are shown in Figure 9.
# An Energy-Efficient and Secure Routing Protocol for Intrusion Avoidance in IoT-Based WSN
## 2 Related Work and Research Formulation
However, the proposed solutions exchange a lot of messages in the network field that dissipate additional energy and lead to routing overhead. In the energy efficient balanced cluster-based data aggregation (EEBCDA) algorithm [48], the network field is structured into unequal sized rectangular grids with one cluster head. The cluster head's position is shifted among member nodes within each grid. By exploiting the information of residual energy, the set of few nodes are selected as cluster heads within each grid. However, the operation of generating a grid is more complex and required more overheads. Furthermore, the remaining structure of EEBCDA is comparable to LEACH. Moreover, reliable and energy-efficient data collection for large scale WSNs [49] aims to improve the energy resource of nodes with consistent routing. In the hotspot region, a fewer number of nodes are selected for capturing information while more monitor nodes are selected in a non-hotspot region. Moreover, based on converging the multi-path route for event monitoring nodes, the aggregated data packets are sent towards the sink node. However, data security is overlooked in the proposed solution and networks may compromise information disclosure in a harsh and complex environment. In LEACH-ensuring reliable data delivery (LEACH-ER) [50], by exploiting energy and data reliability factor the selection process for the cluster head, is initiated. The main idea behind LEACH-ER was to reduce the rate of packets reception inside a cluster among cluster heads and member nodes, which results in improving network lifetime and energy efficiency. However, routes are non-optimized, which causes retransmission and route breakages. Energy efficient in [51], a multi-hop communication routing (MCR) protocol is proposed and aims to improve network lifetime and load balancing. In MCR, based on weighted metrics, the cluster head election is made. Furthermore, multi-hop criteria adopted for data forwarding forwards BS. However, the constructed routings are not optimum and within the cluster, the data transmissions occur based on single-hop.
# Experimental Study on Grouting Visualization of Cover Layer Based on Transparent Soil
## 4 Result Analysis
### Analysis of Transparent Soil Velocity Field  
Particle Image Velocimetry (PIV) technology offers the advantages of being transient, multi-point, and non-contact. This study utilizes PIV to analyze the instantaneous velocity field characteristics of transparent soil during the grouting process. Representative results are presented in Figure 7: (1) Grouting time \(t\) = 10 s: As the grouting pressure gradually increases to 50 kPa, the pump transmits pressure to the slurry. Under the combined influence of pressure and formation resistance, the slurry exhibits upward splitting and spreading behavior. The maximum velocity of the transparent soil occurs at the leading edge of the slurry front, reaching 0.80 mm/s. Densification of the surrounding soil is observed along the expansion direction. (2) Grouting time \(t\) = 130 s: With the continuous expansion of the main slurry vein, the transparent soil around the slurry front presents a state of compaction to the outside. The highest velocity remains at the top of the grout front, reaching 1.70 mm/s. A rightward expansion trend is also observed; the observed asymmetric diffusion results are more likely attributable to microstructural self-organization effects or local pathway variations inherent to the random medium. (3) Grouting time \(t\) = 240 s: The slurry continues to expand horizontally toward the right and forms a fracture channel through the top of the transparent soil at the right side of the model box, resulting in slurry overflow. The velocity of the transparent soil near the outlet reaches 0.85 mm/s. Notably,  
Figure 6: Schematic diagram of the diffusion transport of the slurry front.  
PIV technology provides intuitive advantages for observing the forms of slurry splitting and compaction-induced diffusion, as well as for predicting the resulting movement trends of the soil strata. In this experiment, the splitting behavior observed in transparent soil grouting can be summarized as follows: Under continuous grouting pressure, the slurry initially compacts the surrounding transparent soil and forms a splitting channel. The slurry then occupies the channel and continues to propagate outward along the grout vein. Therefore, the diffusion mode of the slurry in this experiment is primarily governed by splitting, with supplementary effects from permeation-induced compaction.
# Blind Deconvolution with Scale Ambiguity
## 3 The Proposed Approach
### Joint Prior
#### 3.1.1 Transformed Deconvolution Formulation  
In order to demonstrate how the scale factor affects the optimization process, we introduce a scale factor \(s\) in model (2) and analyze that which part would be changed. Suppose that \((u,k)\) and \((z,m)\) are two solutions with the relationship \(u=sz\) and \(k=m/s\), where \(s>0\) and \(s\
eq 1\). Then, the data term \(\|u*k-f\|_{2}^{2}\) is the same as \(\|z*m-f\|_{2}^{2}\). In contrast, the regularization term is rescaled by some amount. Moreover, as \(\|k\|_{1}=1\), we have \(\|m\|_{1}=s\), which is not equal to one. This indicates that the \(L^{1}\) normalization constraint can not be satisfied any more.  
The above analysis also fits the facts that the \(L^{1}\) normalization constraint is just an arbitrary choice. We argue that it is necessary to impose more general normalization constraint on the blur kernel. In this paper, we consider the following optimization model with the \(L^{p}\)-constraint:  
\[\min_{m,z}\|z*m-f\|_{2}^{2}+\lambda\|\
abla z\|_{0.5}\qquad\text{ subject \ to }\|m\|_{p}=1,\;m\geq 0, \tag{3}\]  
where \(z\) and \(m\) denote the latent image and the blur kernel, respectively. \(\|m\|_{p}=(\sum_{i}m_{i}^{p})^{1/p}\) denotes \(L^{p}\) norm of \(m\), where \(i\) denotes the index of kernel elements.  
Optimizing (3) directly may be impracticable due to the complex \(L^{p}\) constraint (\(p\) is unknown). However, the aforementioned analysis inspired us to transform (3) to the following expression if we relate \((u,k)\) to \((z,m)\) by \(k=m/\|m\|_{1}\) and \(u=z\|m\|_{1}\):  
\[\min_{k,u}\|u*k-f\|_{2}^{2}+\lambda\|k\|_{p}^{0.5}\|\
abla u\|_{0.5}\qquad \text{ subject \ to }\|k\|_{1}=1,\;k\geq 0, \tag{4}\]  
where \(\|k\|_{p}^{0.5}=(\sum_{i}k_{i}^{p})^{0.5/p}\). There are differences between model (2) and model (4), even though they have similar formulations. The regularization term in model (4) is a rescaled version of the regularization term in model (2). The penalty coefficient \(\lambda\|k\|_{p}^{0.5}\) varies during iterations due to it being related to the previous iterated \(k\), it's not a fixed value. Different from this, the penalty coefficient \(\lambda\) in model (2) is a fixed scalar. In addition, model (2) is usually used for non-blind deblurring. In blind deblurring case, additional kernel priors (e.g., \(\|k\|_{2}^{2}\) or \(\|k\|_{1}\)) usually employed to solve blur kernel \(k\). In comparison, model (4) can be adopted to both kernel estimation and intermediate latent image reconstruction. \(\lambda\|k\|_{p}^{0.5}\|\
abla u\|_{0.5}\) plays a role as a joint prior in this work. We solve (4) in Section 3.2.  
Note that the constraint on the blur kernel in (4) is still \(L^{1}\) constraint. This is also consistent with the commonly physical understanding of the PSF.
# Current Status and Future Directions for Combined Tillage and Sowing Technology and Equipment
## 4 Research on Precision Sowing Technology
### Seed Guiding Technology and Device
Increased seed movement constraints, the optimization of the structure of the seed guide components, and other means are used to reduce the seed displacement collision in the seed guide tube, or to avoid the seeds falling into the seed groove by bouncing and rolling, in order to improve the quality of the seeding. Seed guiding technology plays an important role in realizing orderly single-seed sowing. As the final stage of the sowing process, differences in its relevant performance will directly affect the uniform distribution of seeds on the seedbed, thus influencing the sowing quality. It is necessary to further clarify the movement rules of seeds in the seed guiding components, and to explore the optimal structures and working parameters under different working environments, in order to ensure the smooth movement and stable seed dropping of seeds during the seed guiding process. By combining it with information-based means, we can monitor the movement intervals between adjacent seeds, their migration positions, seed dropping speeds and so on, thus achieving the monitoring of the whole sowing process. In the subsequent research on combined tillage and sowing technology, it is required to combine agronomic requirements, crop characteristics, and environmental  
Figure 15: [PERSON] 2015 series seeding monomer. features; further explore the optimal structural seed guiding devices for different crops and different environments as well as different combinations of seed metering devices and seed guiding tubes; and construct optimal operation schemes to achieve the best operation results.
# Effects of Blade Extension on Power Production and Ultimate Loads of Wind Turbines
###### Abstract  
Blade extension is an important type of technical transformation to improve the energy production of turbines for early-built wind farms. To evaluate the effects of blade extension on wind turbines, a 1.5 MW commercial wind turbine with three 37.5 m long blades is taken as the research object; the power enhancement and the load variations are systemically evaluated for three different blade extension lengths (1 m, 1.5 m and 2 m) resorting to the software GH-Bladed. The load cases cover all the requirements of the IEC-61400-1 standard. It is found that the optimum tip-speed ratio \(\lambda_{opt}\) and the corresponding power coefficient \(C_{pmax}\) increase with the blade extension length. The annual energy power production is enhanced by about 3%, 4% and 6% for the extension length of 1 m, 1.5 m and 2 m, respectively. The steady loads and dynamic loads, especially the thrust force of the rotor, the flapwise moment of the blade root and the overturning moment at the tower bottom, are significantly enhanced as the improvement of the power. In particular, the percentage increase of these quantities are over 10% when the extension achieves 2 m. It is also shown that blade extension can produce good economic benefit and the benefit improves with the extension length within the safety margin. The paper provides an important reference for this type of technical transformation.  
wind turbine; blade extension; power performance; dynamic load Article
## 2 Background
### Internet of Underwater Things (IoUT)
Another way to support data gathering and overcome bit-rate issues is the creation of distinct communication channels in underwater sensor networks, such as UWSN [16]. Similarly, implementing an information-centric model facilitates the solution to this challenge. Therefore, in [21] a depth-based caching mechanism is recommended, in order to balance latency issues and exchange of unnecessary information, indicating that the creation of hybrid communication models is very important to overcome the physical phenomena of the water and further develop IoUT. Fifth generation (5G) and the upcoming sixth generation (6G) connectivity networks are making essential improvements already to the interconnection of IoUT assets, facilitating their communication and exchange of data with tremendous speeds (>1 Gbps) on a large number of devices. In related work [15], the optical wireless communication (OWC) is proposed with the aim to improve underwater wireless communication, concluding that OWC with RF technology can solve big issues in the underwater domain, such as the efficient management of big data, with high bandwidth, low latency, high protection, and low fuel usage.
# A Sub-6 GHz MIMO Antenna Array for 5G Wireless Terminals
###### Abstract  
This paper presents a novel antenna with its array and MIMO configuration for the 5G sub-6 GHz applications. The proposed antenna element operates at the central frequency of 5.57 GHz dedicated for Sub-6 GHz 5G communication applications. The antenna element holds a circular-shaped radiating portion with an inner-circular slot, plus a rectangular slot at its right edge to make the proposed design resonate at the desired frequency band. The RT5880 substrate is used with a thickness of 0.787 mm, and the low-loss tangent of 0.0009. To achieve a desired gain of 12 dB, a four-element array configuration is adopted, which improved a bore side gain to 12.4 dB from 6.66 dB. Then, the two-port configuration is adopted such that the isolation achieved between them is more than \(-\)30 dB. The total efficiency of the proposed antenna array is observed to be more than 80% within the operating bandwidth. Moreover, the Specific Absorption Rate (SAR) analysis is also presented for the proposed MIMO configuration, obeying the standard value (i.e., \(<\)2 W/kg for any 10 g of tissue). The measured results are in good agreement with the simulated results. All the simulations of the proposed design are performed in the CST MWS software.  
5G; antenna array; CST; sub-6 GHz; MIMO; SAR +
Footnote †: journal: Electronics
## 5 Network Function Virtualization (NFV)
\begin{table}
\begin{tabular}{c p{142.3 pt}} \hline \hline
**Value Added** & **NFV for Smart Cities** \\ \hline \hline \multirow{2}{*}{Ease of maintenance/migration} & NFV facilitates the live migration of VMs. This is particularly valuable for maintaining continuous service availability and performing maintenance tasks. \\ \hline \multirow{2}{*}{Ease of recovery} & NFV simplifies disaster recovery processes. VMs and their configurations can be easily backed up, replicated, and restored in case of hardware failures or other disruptions, ensuring business continuity for critical smart city services. \\ \hline \multirow{2}{*}{Efficient use of hardware} & Multiple services and applications can share the same physical hardware without conflicts. This resource sharing increases the utilization of hardware components and reduces the need for dedicated resources for each service, leading to cost savings. \\ \hline \multirow{2}{*}{Resources optimization} & NFV efficiently allocates computing resources between various smart city applications. This leads to reduced hardware requirements, energy consumption, and cost. Moreover, it achieves higher computing efficiency in terms of overhead. \\ \hline \multirow{2}{*}{Ease in integrating new services} & NFV accelerates the deployment of new services and applications. VMs can be quickly provisioned, configured, and deployed, allowing smart cities to roll out innovative services faster and respond to emerging needs promptly. \\ \hline \multirow{2}{*}{Ease of testing new services} & NFV offers a controlled environment for testing and development. New services or updates can be tested in isolated VMs, reducing the risk of disrupting the production environment. \\ \hline \multirow{2}{*}{Flexibility} & NFV environments are highly adaptable. Smart cities can reconfigure and repurpose virtual resources as needed, enabling them to respond quickly to changing requirements and technological advancements. \\ \hline \multirow{2}{*}{Resource scaling} & NFV enables the dynamic scaling of resources based on demand. As the usage of smart city services fluctuates, virtualized environments can scale up or down to accommodate changing workloads, ensuring optimal performance and responsiveness. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Benefits of implementing NFV for smart cities.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 3 Evaluation Metrics
### Gray-Level Co-Occurrence Matrix (GLCM) Analysis
#### 3.4.1 Homogeneity  
In image encryption, homogeneity analysis is used to assess the uniformity of texture in a cipher image. It measures the closeness of the elements in the GLCM distribution to the GLCM diagonal [54]. Homogeneity values are in the range of (0, 1), where a cipher image with high homogeneity indicates that similar pixels are close to each other, which may reveal information about the plaintext image. On the other hand, lower homogeneity values indicate better encryption quality; a cipher image produced by an image encryption algorithm with low homogeneity has a more randomized pixel distribution. The homogeneity formula is as follows:  
\[Homogeneity=\sum_{i,j}\frac{p(i,j)}{1+|i-j|}, \tag{20}\]  
where \(i\) and \(j\) represent two gray-level values in the GLCM, and \(p(i,j)\) denotes the probability of \(i\) and \(j\) occurring horizontally adjacent to each other in the image, given by the value of the element at position \((i,j)\) in the normalized GLCM.