esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Simultaneous Symmetric and Cambered Airfoils in Novel Vertical Axis Wind Turbines
## 2 Case Study  
This study aims to use different airfoils for one turbine. In this regard, four types of VAWTs are evaluated. Their arrangements considering zero degrees of rotation are shown in Figure 1. The positive rotation of turbines (\(\Delta\theta\)) is counterclockwise with respect to the zero-degree reference shown in Figure 1. The airfoils of NACA0018 and S815 are selected since an airfoil from each of the symmetric and cambered groups are needed in order to provide the self-starting ability and high power extraction [51]. Two typical H-type Darrieus VAWTs are used, named H-Type S815 (HT-S815) (Figure 1a) and H-Type NACA0018 (HT-NACA0018) (Figure 1b) with S815 and NACA0018 airfoils, respectively. Two new VAWTs (Figure 1c,d) are named Non-Similar Airfoils 1 and 2 (NASA-1 and NSA-2). NSA-1 has two different airfoils where each opposite pair has the same airfoils. However, in NSA-2, opposing blades have different airfoils. The 2D blade cross-sections are plotted in Figure 1e. More information can be found in airfoiltools [52]. It should be mentioned that for an H-Type turbine, the type, size, and angle of blades are constant for the whole length of the blade.
## 1 Introduction
### Objectives  
Understanding the connection between electric field strength and health requires continuous research and monitoring. This ensures safe exposure limits and mitigates potential risks through informed regulations and public awareness. Many cities lack detailed maps of electric field strength, which hinders informed decision making. This research addresses this gap by pioneering a novel machine learning (ML) approach. Our method goes beyond simply predicting or estimating electromagnetic field strength; it utilizes explainable artificial intelligence (XAI) to provide valuable insights. The key lies in our comprehensive dataset. It incorporates not only field strength measurements, but also crucial factors such as population density, urbanization levels, and detailed building information. We trained various ML models, including k-nearest neighbors, neural networks, and decision trees, on this rich dataset to create highly accurate electric field strength maps. But the value extends beyond the map itself. Using XAI, we can understand how these urban elements influence electromagnetic exposure. This transparency empowers city planners, policymakers, and researchers. They gain a deeper understanding of the relationship between urban infrastructure and electromagnetic field distribution, enabling them to make informed decisions for safer and more sustainable urban environments.  
Our research on predicting electric field strength in urban areas is groundbreaking for the following reasons:  
* By leveraging explainable AI, our models do more than just estimate electric field strength; they reveal the key urban factors--such as population density, building characteristics, and urbanization levels--that most significantly influence electric field strength. This transparency fosters trust in the models and facilitates their practical application.  
Our unique dataset goes beyond traditional field strength measurements. It incorporates rich geographical features, providing a holistic view of the electric field distribution. This comprehensive approach sets a new standard for the accuracy of prediction.
* Our research generates detailed electric field strength maps for large urban areas. These maps are not only informative, they are actionable tools. They empower individuals, organizations, and policymakers to make informed decisions about urban planning, public health initiatives, and proactive risk management strategies.
* Understanding these key determinants empowers policymakers to develop targeted and effective interventions. This can include crafting regulations or urban design strategies that mitigate potential risks associated with RF radiation.
# A Real-Time Simulator for Navigation in GNSS-Denied Environments of UAV Swarms
###### Abstract
The results show that the time consumption for on-matching is 0.6 s and the position error is 4.6 m. In an SMN under GNSS-denied environments, the navigation accuracy is related to the flight altitude. Generally, a navigation error to flight altitude ratio of less than 1% is considered to reflect high precision. Due to factors such as changes in flight altitude and attitude during the flight process, the real-time aerial photos taken by drones exhibit complex geometric distortions, resulting in severe rotation, scaling and even deformation compared to the pre-stored reference images of drones. The inertial information from the IMU is not affected by external factors, which is beneficial. Inertial information can be used to solve the perspective transformation relationship between images [13]. In terms of relative navigation, the work in [14], which investigates onboard visual relative localization in GNSS-denied environments, detects the IDs through black and white colors and achieves localization through the diameter of the circles. Another approach [15] presents a novel onboard relative localization method for swarms of multirotor UAVs. The method uses a new smart sensor called UVDAR, as part of a UVDAR system, which can be used to obtain both the relative position and orientation from a modified camera. Blinking ultraviolet markers are used to identify the ID of the UAV and the orientation. In [16], the author utilizes UWB for peer-to-peer localization. In this paper, a real-time simulator for navigation in GNSS-denied environments is proposed. The demo video can be found at the following link: [[https://v.youku.com/v_show/id_XNTK5](https://v.youku.com/v_show/id_XNTK5) NDIwMzA3 Ng=.html]([https://v.youku.com/v_show/id_XNTK5](https://v.youku.com/v_show/id_XNTK5) NDIwMzA3 Ng=.html) (accessed on 1 October 2023)  
The simulator integrates the world environment, sensor physical models, dynamic models of fixed-wing drones and multi-rotor drones, controllers of the drones, a formation controller, an ISMN module, relative navigation based on UWB and a vision module. The contributions of this study are as follows:  
1. A real-time simulator for navigation in GNSS-denied environments is developed in order to improve the iteration efficiency of navigation algorithms;
2. A novel scene matching navigation algorithm called ISMN is proposed; based on the simulator, the ISMN algorithm is validated;
3. A relative navigation method that does not rely on inter-communication is proposed. The remainder of this article is organized as follows. Section 2 describes the architecture of the simulation, which focuses on scene matching navigation and relative navigation. Section 3 describes the sensor model used in the simulation. Section 4 demonstrates the simulation result. In Section 5, the conclusions are summarized.
# Identification of Mode Shapes Based on Ambient Signals and the IA-VMD Method
## 3 The Iterative Adaptation VMD Method
### Alternate Direction Method of Multipliers  
To obtain the optimal solution of the above constrained variational problem, the Lagrangian multipliers \(\lambda\) and quadratic penalty \(\alpha\) are used to render the problem of (3) unconstrained, as follows:  
\[\begin{array}{c}L(\{u_{k}\},\{\omega_{k}\},\lambda)=\alpha\sum\limits_{k} \|\partial_{t}\Big{[}\Big{(}\delta(t)+\frac{j}{\pi t}\Big{)}\otimes u_{k}(t) \Big{]}e^{-j\omega_{k}t}\|_{2}^{2}+\|S(t)-\sum\limits_{k}u_{k}(t)\|_{2}^{2}\\ +\big{\langle}\lambda(t),S(t)-\sum\limits_{k}u_{k}(t)\big{\rangle}\end{array} \tag{6}\]  
By using the alternate direction method of multipliers (ADMMs) to find the saddle point of Lagrangian \(L\), the optimal solution of the original minimization problem can be obtained. The implementation process of ADMM optimization for VMD is as follows:  
(1) Initialize, \(k\) = 0, \(n\) = 0, where \(\hat{u}_{k}^{n+1}\), \(\hat{x}(\omega)\), and \(\hat{\lambda}^{n}(\omega)\) are the Fourier transforms of \(u_{k}^{n+1}\), \(x(t)\), and \(\lambda^{n}\), respectively. \(K\) is the number of components, and \(n\) is the number of iterations.  
(2) \(n\) = \(n+1\); start the iteration cycle;  
(3) \(k\) = \(k+1\), according to the following formula update \(u_{k}\), until \(k\) = \(K\):  
\[\begin{array}{c}\hat{u}_{k}^{n+1}(\omega)\leftarrow\frac{\hat{S}(\omega)- \sum_{i<k}\hat{u}_{i}^{n+1}(\omega)-\sum_{i>k}\hat{u}_{i}^{n}(\omega)+\frac{ \lambda^{n}(\omega)}{2}}{1+2\alpha\big{(}\omega-\omega_{k}^{n}\big{)}^{2}}\end{array} \tag{7}\]  
(4) Update \(\omega_{k}\):  
\[\begin{array}{c}\omega_{k}^{n+1}\leftarrow\frac{\int_{0}^{\infty}\omega \Big{|}u_{k}^{n+1}(\omega)\Big{|}^{2}d\omega}{\int_{0}^{\infty}\Big{|}u_{k}^{n+ 1}(\omega)\Big{|}^{2}d\omega}\end{array} \tag{8}\]  
(5) Update \(\lambda^{k}\):  
\[\begin{array}{c}\hat{\lambda}^{n+1}\leftarrow\hat{\lambda}^{n}+\tau\Bigg{(} \hat{S}(\omega)-\sum_{k}\hat{u}_{k}^{n+1}(\omega)\Bigg{)}\end{array} \tag{9}\](6) Set the evaluation precision \(\varepsilon>0\). When \(\varepsilon\) satisfies \(\sum_{k}\|u_{k}^{n+1}-u_{k}^{n}\|_{2}^{2}/\|u_{k}^{n}\|_{2}^{2}<\varepsilon\), end the loop and output IMF components; otherwise, repeat steps (3)-(6).
# On the Quality of Deep Representations for Kepler Light Curves Using Variational Auto-Encoders
## 3 Experimental Setup and Results
### Model Assessment and Implementation
#### 3.2.3 Classification Validation  
Using the latent representation learned by the encoder sub-architecture, we analyzed the performance in a binary classification task (_is the object a true exoplanet_?). This is an extrinsic assessment of the quality of the representation since the model did not have learning as a specific objective (_task-free_). The classification was made using an MLP model built over the representation with 128 units and _relu_ activation, ending with a _sigmoid_ classification layer (one-unit) as output. The dataset labels were used to carry out this evaluation, with 2281 exoplanets and 3976 non-exoplanets (1797 of the KOIs are unlabeled, i.e., candidates). The network is trained in 70% of the labeled data with 30% remaining as a test set. The best parameters found over 200 epochs are stored (with 128 batch size). As the dataset contains a larger number of non-exoplanet objects (in an unbalanced scenario), an \(F_{1}\) score macro averaged (\(F_{1}\)-**Ma**) criterion was used to assess the results, where a high value means high precision and recall simultaneously on both classes.
# Continuous-Wave THz Imaging for Biomedical Samples
## 5 The Application of CW Imaging with Biological Tissues
### Tumour Tissues
#### 5.1.1 Non-M Melanoma Skin Cancers  
Non-melanoma skin cancers (NMSC) mainly include basal cell carcinoma (BCC, 74%), skin squamous cell carcinoma (CSCC, 23%), and other cancers, and their incidence has been an upward trend in recent years. These cancers mostly grow continuously from a single nidus, and complete surgical resection is the preferred treatment option [81]. Traditional surgery often removes excessive normal tissues or leaves behind residual tumour tissues, so the judgment of the precise boundary between the tumour and normal tissues is essential for surgery, and CW THz imaging can provide a valuable reference for the determination of surgical tumour margins. In 2011, [PERSON] et al. utilised two lasers at 1.39 and 1.63 THz frequencies to image ex vivo NMSC tissues by transmissive scanning imaging [82]. The NMSC tissues were soaked in pH-balanced saline in order to remain fresh for a long time. The tryptophan content increased in the cancer tissues, leading to an increase of the water content. The absorption characteristics of the THz waves by tissues at 1.4 THz depended on the water content and tryptophan, but at 1.6 THz, they related only to the water content, because the tryptophan was not significantly absorbed at 1.6 THz. The refractive index and absorption coefficient of the skin were respectively 20 mm\({}^{-1}\) and 2500 mm\({}^{-1}\) at 1.4 THz, and the spatial resolution was 0.39 mm. In contrast, the refractive index and absorption coefficient were respectively 18 mm\({}^{-1}\) and 3000 mm\({}^{-1}\) in the same condition at 1.6 THz, and the spatial resolution was 0.49 mm. In evaluating the imaging results, they found that the H&E histology results were in good agreement with the THz images, and also that the difference in transmittivity between the tumour and normal tissues was about 60%. Although CSCC only accounts for 23% in NMSC, it invades the fatty tissues under the skin and spreads to the lymph nodes or distant tissue parts. In 2014, the same research group imaged frozen CSC tissues slices based on cross-polarised THz reflective imaging system that is shown in Figure 3[40]. A good consistency was found between the THz images and H&E histology images. The intensity of the tumour regions was higher in the cross-polarised THz images, indicating the lack of collagen and partial structural damage in the cancerous areas. Further, they found that the THz images had a higher contrast between the tumour and normal tissues. In 2016, the same research group added a circular polariser to the THz imaging system that is shown in Figure 4 to image fresh human ex vivo NMSC by reflective single-point scanning method [42]. The refractive index changed little due to the structural damage of the tumour tissues.
# A Survey on Underwater Acoustic Sensor Network Routing Protocols
## 3 The Knowledge of Underwater Acoustic Sensor Networks
### The Characteristics of Underwater Acoustic Communication
#### 3.1.2 High energy consumption  
The acoustic wave attenuation in underwater environments is serious. The transmission power needed by the underwater acoustic transceivers is in order-of-magnitude higher than that of terrestrial devices [27]. In addition, due to the continuous movement of underwater nodes, the communication links are easily broken. Moreover, the bit error rate in underwater acoustic sensor networks is much higher than in terrestrial wireless sensor networks. All these issues can lead to frequent data packet retransmission, which will waste vast amounts of energy.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis was performed with IBM SPSS Statistics 19. All tests/experiments were conducted in triplicate, and the results were expressed as the means \(\pm\) standard deviation (SD). Data normality was studied using the Shapiro-Wilk test [54], and the homogeneity of variances (homoelasticity) was studied using Levene test. A one-way ANOVA test was applied for normally distributed and homogeneous data. For \(p\) < 0.05 corresponding to statistical F, calculations with the Tukey post hoc test were used to study whether the treatments had significant differences. For non-homogeneous data, the Welch test was used, followed by the Kruskal-Wallis test, in which, if \(p\) < 0.05 was obtained, the calculations were continued with the Mann-Whitney test to verify which treatments had significant differences. At the parameters where the data were not homogeneous and we used Mann-Whitney, the Bonferroni correction was applied: 0.05/6 = 0.008 and 0.01/6 = 0.001; for 3 treatments, 0.05/3 = 0.016 and 0.01/3 = 0.003.
###### Abstract  
Thirty groundwater samples were taken from Assitut's northern outskirts. The physico-chemical properties of these samples were investigated. For the evaluation of water-rock interaction, the saturation index (SI), chloro-alkaline indices (CAI1 and CAI2), Gibbs ratios for cations and anions (GC and GA), principal component analysis (PCA), and hierarchical cluster analysis (HCA) were used. (1) With the exception of five samples that were supersaturated, the rest of the groundwater samples were under-saturated with carbonate mineral dissolution (calcite, dolomite, and aragonite) and evaporite mineral dissolution (anhydrite, gypsum, halite, and sylvite). (2) The presence of ion exchange between alkali elements (Na\({}^{+}\) + K\({}^{+}\)) and earth alkaline elements (Ca\({}^{2+}\) + Mg\({}^{2+}\)) is explained by the positive values of the chloro-alkaline indices. (3) In four groundwater samples, negative chloro-alkaline indices suggest reverse ion exchange and an increasing alkali element content. (4) The Gibbs diagram demonstrates that, with the exception of four samples that reflect saline groundwater, the majority of the groundwater samples are freshwater recharging from the fresh surface water in the research area. (5) Groundwater types include Ca-HCO\({}_{3}\) (11%), Mg-HCO\({}_{3}\) (48%), Na-HCO\({}_{3}\) (23%), Mg-Cl (13%), and Na-Cl (3%). (6) The alkali, earth alkaline elements, and sulphate are the key controls on groundwater salinity, according to PCA and HCA. (7) The somewhat saline groundwater in the research area's western desert margins should not be used since it poses a health danger to people.  
groundwater; interaction; saturation index; Gibbs diagram; principal component analysis; Assit Governor +
Footnote †: journal: _Sci. 2022_, 12, 6340. [[https://doi.org/10.3390/app1236340](https://doi.org/10.3390/app1236340)]([https://doi.org/10.3390/app1236340](https://doi.org/10.3390/app1236340))
# Experimentation Environments for Data Center Routing Protocols: A Comprehensive Review
## 4 Experimentation Methodology
### Distributed Emulation  
To outperform the emulation of a single host, we can distribute it into multiple hosts. Multiple factors can limit emulating large topologies in one host for different environments. For example, in Mininet, the host resources are limited when running multiple services on each node. Secondly, in Kathara, the limitation could also be given for the number of Docker containers per host.  
After having a complete experiment built on top of Kathara, we can distribute the emulation across multiple nodes using Megalos since they share the same code base. However, they differ \"only\" in the virtualization manager (Kubernetes for Megalos).  
Nevertheless, with the addition of Kubernetes to the environment, the orchestration of the experiment becomes more complex. First, Kubernetes imposes a configuration overhead. Indeed, setting up a Kubernetes cluster from scratch could be challenging depending on the cluster network layout. Furthermore, due to some design decisions and compatibility issues, there are a few differences between Kathara and Megalos when deploying a network scenario with Kubernetes. For example, when a routing daemon such as FRR is used, the _eth0_ network interface must be disabled inside the routing daemon because Kubernetes use each device _eth0_ interface for cluster management (check its deployment status, heartbeats, etc.). Second, some networking problems related to the distribution of the emulation and the creation of virtual interfaces appear. These problems are not clearly determined, and their nature lies in unexpected behaviors of Megalos (e.g., bugs). Kathara officially supports Kubernetes as a virtualization backend system as version 3.0.0, the one used in this research.  
On the other hand, there are at least two options to distribute a Mininet emulation: Maxinet [64] and Distrinet [65]. Maxinet creates different Mininet instances in each physical node of the cluster and connects the vSwitches between different physical hosts with GRE tunnels. On the other hand, Distrinet, instead of using cgroups and network namespaces like Mininet, uses LXC containers and implements virtual links using VXLAN tunnels. Either environment could be used, but an extra configuration layer would be needed.  
Finally, it is possible to distribute the emulation with Core. While the configuration steps to enable this feature are clear using the GUI, at the time of this writing, there is no documentation available on how to accomplish the distribution using a Python script.
Analysis, Design, and Experimental Verification of a Parallel Wireless Power and Data Transmission Method for Rotary Steering Systems  
[PERSON]  
1 China Oilfield Services Limited (COSL), Tianjin 300000, China 1  
[PERSON]  
2 School of Automation, Chongqing University, Chongqing 400044, China 21 [PERSON]
and phase of the power wave [9; 10]. However, the power modulation method has the problem of power fluctuation, and the signal modulation process will lead to unstable power transmission.  
The independent modulation method uses an independent carrier wave much higher than the power resonant frequency to modulate the signal. Because of the existence of the power wave and signal modulation wave on the power coupling coil, the power channel and signal channel influence each other at two frequencies, so the key of the independent modulation method is the injection and separation of the signal modulation wave. At present, the most widely used methods are series and parallel, which connect the signal modulated wave to the power coupling coil in series or in parallel by means of a mutual inductance coupler. References [1; 11] adopt parallel signal transmission and reception, which can improve the increase in signal transmission, but the crosstalk between the power and signal is serious. The crosstalk between the two can be reduced by adding a wave-blocking network and adopting a composite resonance network. In references [8; 12], a serial injection signal bidirectional transmission WPT system is adopted, and the serial transmission gain is small. Therefore, it is necessary to optimize the parameters of the mutual inductance coupler to improve the stability of the signal transmission. In addition, reference [13] studies the parallel transmission of inductance and capacitance combination of power and data, and the data are transmitted through parasitic capacitance, but not all applications have the required aluminum plate. Compared with power modulation, the independent modulation method can achieve stable power transmission, but the signal transmission is easily disturbed by power harmonics and external noise, which can easily lead to signal transmission failure.  
Generally, the WPT system for a rotary steering system is designed to provide a constant output voltage [1]. The output voltage can be kept constant through an open loop to ensure the output voltage is insensitive to load and the coupling coefficient [14; 15; 16], or the output voltage can be adjusted through a closed loop. The closed-loop regulation can be carried out on the primary side or secondary side [17; 18]. The primary-side regulation is realized by changing the input power, and the secondary-side regulation is realized by changing the load impedance.  
In this paper, a parallel power and data transmission method of a rotary steering system with output voltage control is proposed. In order to reduce the volume of the system, power and data are transmitted through the same rotary coupling mechanism. A power resonance circuit is applied to suppress the influence of power on data transmission, and then stable and reliable data transmission is realized.
# Advancement of the DRPE Encryption Algorithm for Phase CGHs by Random Pixel Shuffling
## 4 Results Discussion and Performance Analysis
### Pixel Distribution
#### 4.6.1 Information Entropy  
The entropy of a grayscale image \(p\) for all elements of an image \(x_{i}\) can be calculated as  
\[H(X)=-\sum_{i=1}^{n}p(x_{i})\log_{2}p(x_{i}) \tag{15}\]  
The entropy values obtained for the images involved in this work can be found in Table 1. These data confirm that the entropy of the information stored in the CGH is higher than in the images and does not change during the encryption process. The difference in entropy between the original image and the recovered images is due to the limitations imposed by diffraction (speckle, pixel sizes, etc.).  
The entropy values between the CGH and the encrypted CGH are virtually the same because integer values are used to shuffle or apply phase masks to the PNG file of the CGH, preserving the same information throughout the encryption and decryption process.