esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Network Architecture
#### 3.3.1 Data-Centric  
The use of random distributions by several sensor applications makes it impossible to use a global identification system to locate nodes, and consequently, the traditional address-based routing becomes non-applicable. Data-centric routing is a query-based strategy that overcomes these limitations and can adapt to this environment by dint of its naming mechanism base and the description of data in the queries [53]. The representative routing protocols in this category are:  
\(\bullet\) **ACtiveQUery forwarding In sensoR nEntworks (ACQUIRE)**  
As a data-centric routing protocol, ACQUIRE uses an active querying mechanism that visualizes WSNs as a distributed database. It forwards the query at each node to be resolved partially in each hop. Hence, the active query becomes smaller and smaller until being resolved completely. ACQUIRE has the ability to manage complex queries, but it remains ineffective in terms of energy consumption [54].  
\(\bullet\) **RoUting on finGerprint Gradient in sEnsor networks (RUGGED)**  
RUGGED is designed as a data-centric routing protocol. It routes the query to the event through a random node based on natural information gradient repository, probabilistic functions and the fingerprint of the event, which is an information gradient resulting from the occurring of a physical event in the environment. RUGGED provides reliable and efficient routing, but it is energy-intensive [55].
## 2 Materials and Methods
### Genome Completeness and Quality Assessment  
The paired-end Illumina reads and unpaired nanopore reads were assembled using hybridSPAdes [34] with k-mer sizes of 33, 55, 77 and 127. To examine the assembly for potential non-target contigs, de novo assembly was subjected to BLASTX using DIAMOND [35] against a custom database comprising the protein sets of the NCBI nr database (downloaded in July 2018). The results of this DIAMOND search were used as input for blobtools v1.1.1 [36]. The final results showed the absence of foreign contigs; therefore, further filtering was unnecessary. The quality of the assembly and genome statistics were assessed using QUAST v5.0.2 [37]. The completeness of the genomes was assessed using all single-copy BUSCO genes of the ascomycota_odb9 set (part of the phylciraptor pipeline; git commit 4 cfd3c4; accessed on 2 July 2021; see below).
## 2 Materials and Methods
### Design of ADRC and ST-SMC Hybrid Control  
The UAV is a highly nonlinear and complex coupled system, and when operating in dynamic environments such as random airflow disturbances, it demands higher standards for control strategies and algorithms. Traditional control algorithms, such as ADRC, significantly enhance the system's disturbance rejection performance and dynamic response speed due to its built-in ESO, which estimates and compensates for external disturbances and system modeling errors in real time. However, ADRC has limitations when dealing with complex nonlinearities and strong uncertainties. In contrast, the ST-SMC algorithm, by improving the design of traditional sliding mode control, offers stronger robustness and disturbance rejection capabilities. It effectively reduces the high-frequency oscillations caused by the switching function in conventional sliding mode control algorithms. The ST-SMC not only tracks the system state more smoothly but also avoids energy consumption and instability issues resulting from multiple sliding surface switches during fast response processes. Therefore, this section integrates the advantages of ADRC and ST-SMC, enabling the controller to achieve complementary performance. Under random airflow disturbances, the combined algorithm leverages the dynamic compensation properties of ADRC to stabilize external disturbances, while the robustness of ST-SMC suppresses the impact of system nonlinearities and uncertainties on control accuracy. This results in excellent dynamic and static control performance under random disturbances while maintaining high real-time performance and control efficiency [25].
## 1 Introduction
Others monitored the occurrence of EPB activity by means of Global Positioning System (GPS) S4 scintillation receivers by using results from the Thermosphere Ionosphere Electrodynamic General Circulation Model (TIEGCM) [51]. Others implemented a study of the ionospheric response to the geomagnetic storm that occurred on 22-23 June 2015 using measurements of the vertical total electron content (VTEC) and \(n_{e}\) over Swarm satellites [36; 52]. They observed that during the start of the main phase of the storm, ionospheric effects were driven by the Prompt Penetration Electric Field (PPEF). All these studies investigated the effect of a single storm on the ionosphere. Also, the authors of [53] found that the PPEF is the main driver of post-sunset EPBs during the storm onset, while afterward, the DDEF is the main driver of enhanced predawn EPBs and the suppression post-sunset and at midnight. To the best of the authors' knowledge, the general characteristic of the EPBs during different types of geomagnetic storms have not yet been investigated. The aim of this paper is to investigate the features and morphology of the EPBs under different types of geomagnetic storms (super, intense, and moderate) in the period of seven years (2014-2020) using IBI data from the Swarm satellite constellation and COSMIC satellites. The following sections of the current study have been organized as follows: In Section 2, we describe the data used and the selection criterion for both the type of storm and the IBI events. Section 3 presents the results and observations. Section 4 discusses our results and explains the features of the EPBs observed during different types of geomagnetic storms. Finally, Section 5 presents a conclusion of our findings.
## 4 Experiments
### Experiment Settings  
Our model was trained on a Windows computer with NVIDIA GTX1660 ti 6G GPU. The language utilized for the proposed method was Python 3.7. The CNN network was trained in the framework of Keras 2.0.6 and Tensorflow 1.3.0.  
According to the work of [PERSON] et al. [41], the Kaiming Initialization method is used in this model, which means the weight parameters are initialized from a Gaussian distribution with mean 0 and variance, and here N is the number of connections between two layers. As for the activation function, the ReLU function is used in each layer except the last layer. The last layer uses the Softmax function as the activation function.  
As for the loss function, we chose the cross-entropy function to evaluate the difference between the predicted probability and the actual probability. In the gradient descent algorithm, the widely used Adam optimization algorithm was selected. The initial learning rate was set to 0.000001, and in order to reduce the training time of the model and prevent the oscillation of loss, the learning rate decay method was also applied.  
This method was verified on the DAGM2007 dataset, the micro-surface defect database, and KolektorSDD dataset. We trained two CNN networks for each class of image with the same parameters, where one uses the original image, and the other uses the high-frequency sub-image after one-level wavelet decomposition of the original image.
# Teleportation-Based Error Correction Protocol of Time-Frequency Qubit States
## 3 Spatial Separation of the Two Logical Time-Frequency Qubit States
### Spatial Separation of the Two Logical Time-Frequency GKP States
We represent in Figures 4d,e and 5d,e, the output probability distribution in the spatial ports \(a\) and \(b\) when the initial state is \(\ket{+_{\omega}}\). We observe a peculiar interference effect due to the increasing of the overlap between the zero and one logical state. In practice, for instance, we have to filter out the single-photon state that enters the wrong spatial port by placing appropriate frequency filters. In Appendix A, we also investigate the separation of the odd and even components of the comb when the two-photon state is an input of the frequency qubit beam splitter. This is relevant to the teleportation-based error correction protocol because the state to be teleported is combined with a single photon from an EPR pair during the Bell measurement. The spatial separation is, in that case, also completely effective when each Gaussian distribution approaches a Dirac distribution, and not otherwise. Explicitly, for the two-photon state, we obtain \(\ket{0\bar{1}}_{aa}\rightarrow\ket{0_{\bar{C}}}_{a}\ket{\bar{1}_{\bar{C}}}_{a ^{\prime}}+\ket{0_{\bar{C}}}_{a^{\prime}}\ket{\bar{1}_{\bar{C}}}_{a^{\prime}}\), whose corresponding expressions are given in Appendix A. The case to combine two different single-photon states also plays a  
Figure 4: (**a**) Spectral probability density distribution of the input state \(\ket{+_{\omega}}\) of the Mach–Zehnder interferometer for \(0.1\overline{\omega}=\sigma\) and \(\kappa=0.1\). (**b**,**c**) Probability density distribution in the spatial port _b_\(P_{b}(\omega)\) (resp. spatial port _a_) if we start with either the zero (dark blue) or one (red) logical state. (**d**,**e**) Probability density distribution in the spatial port _b_\(P_{b}(\omega)\) (resp. spatial port _a_) if we start with the equal superposition of the zero and one logical state (light blue). The overlap of the zero and one logical state is not important enough to observe an interference effect (by comparison with Figure 5d,e. Figure 5: (**a**) Spectral probability density distribution of the input state \(\ket{+_{\omega}}\) of the Mach–Zehnder interferometer for \(0.2\overline{\omega}=\sigma\) and \(\kappa=0.1\). (**b**,**c**) Probability density distribution in the spatial port _b_\(P_{b}(\omega)\) (resp. spatial port _a_) if we start with the equal superposition of the zero and one logical state (light blue). Their overlap is important enough to observe an interference effect (by comparison with Figure 4d,e) that distorts the spectral distribution of the single-photon state, which leaks to the wrong spatial port. role in quantum communication scenarios, where the second qubit is from an attacker who tries to collect the information about the qubit carrying the information of interest.
## 5 The Potential of Computer Vision Research in UAV Datasets
### Leveraging UAV Datasets for Computer Vision Applications
#### 5.1.5 Nighttime Tracking and Low-Light Conditions  
Including 110 annotated sequences with over 100,000 frames, DarkTrack2021 is crucial for improving UAV tracking at night. By employing Spatial-Channel Transformers (SCT) and non-linear curve projection models, it improves the quality of low-light images and offers a thorough assessment framework. The UAVDark135 dataset and the ADTrack algorithm are designed for all-day aerial tracking. ADTrack performs well in low light and adjusts to various lighting conditions thanks to its discriminative correlation filter foundation. More than 125,000 frames, specially annotated for low-light tracking scenarios, are included in the UAVDark135 dataset.
# Shaking Table Testing of Liquefied Soil Layer Located in the Bottom Slab of a Subway Station
## 3 Analysis of Test Results of Foundation Soil
### Model Box Boundary Effect Test  
The input seismic waves propagate through the model and are reflected at the boundaries of the model box, thus affecting the test results. The shear model box is used in this test in order to reduce the reflection of seismic waves at the boundary, thus ensuring the correctness of the test results. Therefore, free-field vibration tests were used to test the boundary conditions of the model box. The boundary effects of the model box are evaluated by analyzing acceleration test data at different locations at the same depth. The accelerometer arrangement in the free-field test is shown in Figure 7.  
Figure 6: Macroscopic changes in foundation soil before and after testing. (**a**) Surface of model soil before test; (**b**) surface of model soil after test; (**c**) damage to modeled soil surface; (**d**) subsidence of soil; (**e**) surface cracking; (**f**) crack width; (**g**) scope of damage.  
To perform testing, we input 0.1 g of white noise, input 0.1 g and 0.5 g of the Ming Shan wave, and input 0.1 g and 0.5 g of the Beijing Hotel wave. The acceleration amplification factors of points A2, A4, and A11 at 60 mm below the surface of the model soil are shown in Figure 8a. The acceleration amplification factors for points A3, A5, and A12 at 337 mm below the modeled soil surface are shown in Figure 8b.  
Points A2 and A3 are 0.24 m from the model box boundary; points A4 and A5 are 0.49 m from the model box boundary; and points A11 and A12 are 1.17 m from the model box boundary. When the peak acceleration of the input seismic wave is 0.1 g or 0.5 g, the acceleration amplification factors of A2, A4, and A11 do not differ considerably, as is the case with the acceleration amplification factors of A3, A5, and A12. This indicates that the boundaries of the shear model box have less influence on the acceleration response of the  
Figure 8: Acceleration amplification factor. (**a**) 60 mm below soil surface (**b**) 337 mm below soil surface.  
Figure 7: Arrangement of accelerometers in a free field.  
modeled soil, which in turn indicates that the model box boundaries have less influence on the test results.  
Since the acceleration amplification factor is related to the peak acceleration at each measurement point, evaluating the boundary effect of the model box using only the acceleration amplification factor is not sufficiently comprehensive. Here, the boundary effect index is used to evaluate the boundary effect of the model box from the whole process of vibration [25]. The boundary effect indices of the model box under seismic wave action are shown in Table 5. It can be seen that the boundary effect indices of the model box under seismic waves are small, indicating that the shear model box used in this test can effectively reduce the influence of boundary effects on the test results.
# A Watermarking Method for 3D Printing Based on Menger Curvature and K-Mean Clustering
## 2 Related Works
### 3D Printing Watermarking  
Until now, there have been two methods proposed and related to 3D printing watermarking. [PERSON] et al. [13] proposed a method of extracting watermark from physical 3D-printed objects that is created from 3D mesh data. The watermark is embedded into the spread spectrum of 3D meshes and then extracted from physical 3D-printed objects via the scanned 3D triangle meshes of 3D-printed objects. This method has low accuracy because the scanned 3D triangle meshes have many errors in the 3D scanning process, and after transformation to the frequency domain, the spread spectrum of 3D meshes was changed greatly. Moreover, the length of the embedded watermark bits is fixed at 256 bits. Therefore, this method is not flexible. [PERSON] et al. [14] proposed a technique to protect the copyright of the digital content for 3D printers. This method considers the copyright information as a watermark and inserts the copyright information into solid objects in the 3D printing process. This technique requires a complex hardware system of halogen and laser lights for the watermark embedding process. This technique is also not flexible and is expensive. Moreover, this method did not show how to extract the embedded watermark from 3D-printed objects. Previously, we ([PERSON] et al. [15]) introduced a simple idea for 3D printing watermarking. However, in that paper, we just mentioned the main concept of the idea for 3D printing watermarking based on Menger curvature and experimented on our idea based on a virtual environment. We only tested the watermark embedding ability into Menger curvature and evaluated the invisibility of the proposed method in that paper. We did not experiment with a 3D printer or 3D scanner and evaluated the robustness and performance of the proposed method. In this paper, we improved our idea by experimenting with a 3D printer and 3D scanners, applying a correction method to construct the scanned 3D models from 3D-printed objects. Finally, we analyze, evaluate and compare the robustness and performance of the proposed method to the conventional works and two previous methods of 3D printing watermarking.
# Impact of Intersection Left Turn Guide Lines Configuration on Novice Drivers' Behavior
## 4 Discussion
### Discussion of Steering Angle Characteristic Results  
In small intersections, steering angle fluctuation ranges during left turns are similar, indicating comparable turn stability. However, the full guide line scenario (1-C) exhibits relatively better turn stability. In these intersections, with their small left-turn radii and simple layouts, novice drivers can easily observe the road and maintain stability even without explicit guide lines. The presence of guide lines encourages a smoother turning path, leading to relatively stable changes in steering angles. However, the impact is not significantly pronounced compared to scenarios without guide lines.  
In Scenario 2, steering angles during left turns show general similarity across different guide line settings. However, with one guide line and full guide lines, the fluctuation range is relatively small, indicating that guide lines enhance left-turn stability. In Scenario 3, the fluctuation range of steering angles is notably larger, and the maximum fluctuation point varies with guide line settings, suggesting an influence on steering control during left turns. However, no apparent positive impact on turn smoothness is observed. In medium intersections with larger radii and higher traffic flows, guide lines provide clearer guidance, standardizing behavior and reducing steering angle fluctuation at mid-intersection. In scenarios with two left-turn lanes, lane changes during entry and turning phases may lead to larger fluctuations even with guide lines, especially for novice drivers with varying degrees of experience.  
In Scenario 4, guide line settings exhibit distinct effects on mean steering angles. Full guide lines lead to lower mean angles and smoother transitions, while one guide line and no guide line scenarios show higher mean angles and less smooth transitions, indicating a significant impact on novice drivers in large intersections with a single left-turn lane. In Scenario 5, the mean steering angle is smaller in the mid-curve. In Scenarios 5-B and 5-C, there is significant steering angle variation. In Scenario 6, full guide lines result in the smallest fluctuation range of steering angles, indicating better turning stability. One guide line shows a slightly larger fluctuation, while no guide line exhibits a more gradual fluctuation. These findings suggest that in large intersections, guide lines provide clear guidance, indicating the correct turning path. Full guide lines may eliminate hesitation and confusion, leading to lower mean steering angles. Guide line settings can influence drivers' behavior, with different settings affecting their decisions and driving manner before and after the turn. Full guide lines may guide vehicles to make left turns more standardized, reducing drastic changes in steering angles, providing clear instructions and boosting driver confidence, and resulting in smoother turns. The results show similar findings with the research work of [27; 30; 31].
## 2 Understanding WebAssembly
### Non-Web Embeddings  
Although Wasm is primarily designed for web usage, it is important for it to be compatible with other environments beyond the web. These environments can vary from simple shells used for testing purposes to fully-fledged application environments found in data centers, IoT devices, and desktop or mobile apps. There may be a need to incorporate Wasm within larger software programs as well. Non-web environments may provide different sets of APIs compared to web environments, which can be easily discovered and utilized through features such as testing and dynamic linking. While JavaScript virtual machines such as node.js can support Wasm, the design of Wasm aims to ensure that it can function independently without relying on a JavaScript VM. The Wasm specification itself does not attempt to define a comprehensive portable library similar to libc. However, certain core features in Wasm semantics that resemble functions found in native libc are included as primitive operators in the core Wasm specification. For example, the \"grow_memory\" operator, similar to the \"sbrk\" function, is part of the core specification. In the future, additional operators similar to \"dlopen\" may be included. In cases where there is overlap between web and non-web environments, shared specifications can be proposed; however, these would be separate from the Wasm specification. An example of this is the Loader specification, which is being developed for both web and node.js environments and is distinct from the JavaScript specification. To achieve source code-level portability, it is expected that communities will create libraries that map source-level interfaces to the capabilities provided by the host environment. These libraries can be developed during either the build-time or runtime process. Wasm provides essential components such as feature testing, built-in modules, and dynamic loading to facilitate the creation of these libraries. Examples of anticipated libraries include Portable Operating System Interface (POSIX) and Specification and Description Language (SDL). Overall, by ensuring that non-web implementations do not rely on web APIs, Wasm can be used as a portable binary format across various platforms. This offers advantages in terms of portability, tooling, and language compatibility, as Wasm supports semantics at the level of languages such as C and C++.
###### Abstract  
This paper designs a new composite transmission device for improving the transmission torque by squeezing magnetorheological fluid (MRF) with an electrothermal shape memory alloy (SMA) spring. Based on the finite element method, a numerical analysis of the magnetic circuit and magnetic field distribution of the magnetorheological (MR) transmission is presented, as well as a theoretical derivation and calculation of the squeezing force output by the electrothermal SMA spring and the transfer torque of the variable thickness MR transmission. In addition, the output characteristics of the electrothermal SMA spring at different temperatures are analyzed, as are the torque characteristics of the variable thickness MR transmission. The research shows that the electrothermal SMA springs exhibit a highly non-linear squeezing force output during the temperature rise, and the increase in current affects the martensite phase transition quality, and thus the phase transition temperature. The squeezing force generated by the springs increases significantly when the temperature is within the martensitic phase transformation interval, with a maximum squeezing force of 318.43 N when the SMA temperature reaches 100 \({}^{\circ}\)C. The proposed variable thickness MR transmission can increase the maximum torque by 4.88 times under SMA spring squeezing force, and its maximum transmitted torque is increased from 15.08 to 73.56 N.m. By squeezing the MRF with an electrothermal SMA spring, the torque of the variable thickness MR transmission can be increased quickly and effectively.  
electromechanical shape memory alloys, magnetorheological fluid; squeeze; variable thickness; torque 10.390/app12094297