esa-sceva/satcom-chunk-collection huggingface dataset:
# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 3 UWB Positioning System  
Ultra-wideband systems have emerged as a promising technology in the field of position monitoring, especially for moving objects within a closed indoor environment. The quality of the pulses issued by ultra-wideband transmitters is characterized by high temporal accuracy, which makes it possible to measure the transmission and reception time (time of flight) easily, enabling high precision in determining the location. The accuracy can vary from a few decimeters to centimeters in optimal conditions [9]. Most position detection systems usually consist of two main components, which may be identical in terms of design and physical components but different in terms of the method of use within the system: the anchor, and the tag.  
In most of the current systems, anchors are used as receivers for the signal issued by the tag, and to determine the distance between the anchor and tags and then forward the measurement to the IPS controller. Numerous anchors are used, depending on the area of the place to be covered. Currently, the coverage of a single anchor operating with ultra-wideband technology has reached approximately 100 m in line-of-sight conditions (LoS: the state of having a direct connection between the sender and the receiver without any obstacles). This contributes to reducing the cost of the infrastructure used, which represents one of the factors that limit the use of these systems. Anchors are installed in a specific way and with dimensions that depend on the type of method or algorithm used to determine the position.
# Design and Mechanical Behavior Research of Highway Guardrail Patrol Robot
## 3 Analysis of Robot Obstacle-Crossing Behavior  
### Description of Guardrail Obstacles  
The guardrail inspection robot proposed in this study operates on a double-waveform beam guardrail, assuming the guardrail is in good working condition. Guardrails with significant deformations caused by collisions, corrosion, or fractures due to natural factors are not considered in this analysis. To ensure the robot can smoothly complete its tasks while minimizing the damage to the guardrail during operation, its obstacle-crossing performance is analyzed under the condition of no initial speed.  
Many factors influence the actual working conditions of the robot's obstacle-crossing performance. To simplify the analysis, the following assumptions are made:  
(1) The weight of the non-structural components, such as motors and controllers, is neglected, and the overall mass of the robot is simplified as a concentrated mass. Assuming the robot is symmetrical along its vertical axis, the three-dimensional spatial problem is reduced to a two-dimensional planar problem;  
(2) The robot's frame, obstacle-crossing wheels, auxiliary wheels, and other components are treated as rigid bodies, with deformation factors disregarded in the analysis.  
(3) The damping factor of the spring is neglected, and it is assumed that the support reaction force acting on each wheel is linearly proportional to the spring deformation, with the proportionality constant \(k\).  
Based on the above assumptions, the obstacle crossing of the robots can be divided into the following four situations: front wheel obstacle crossing of auxiliary wheels (FAOC), rear wheel obstacle crossing of auxiliary wheels (RAOC), obstacle crossing of non-driven rubber-coated wheels (NDOC), and obstacle crossing of driven rubber-coated wheels (DOC). As shown in Figure 2, the red wheel represents the driving wheel, the arrow indicates the direction of travel, and the yellow fill represents the wheel that is crossing obstacles.
## 5 Ablation Study and Discussions
### Impact of Filter Selection Scheme and Different CNN Models  
AlexNet was used in the proposed method as a CNN backbone for feature extraction. To justify the filter selection scheme of the current method, more experiments were conducted on the RPD dataset, using all the filters of AlexNet (denoted as \"AlexNet\"), namely without filter selection. Moreover, experiments were also conducted using two other CNN models commonly used for feature extraction, including VGG16 [44] and ResNet50 [43]. The VGG16 network has five convolutional blocks, each containing several convolutional layers. The filter from the last convolutional layer of each convolutional block was selected. Five filters were selected for subsequent use. The ResNet50 has five convolutional stages, and each contains one or several convolutional layers. Similarly, when the ResNet50 network was used, only one filter from the last layer of each convolutional stage was selected. Table 5 presents the number of filters selected in the above experiments and their corresponding feature map sizes.  
Table 6 shows the quantitative results of adopting different CNN models. AlexNet\({}^{\star}\) used more filters than AlexNet. Surprisely, AlexNet\({}^{\star}\) had almost no improvement in the average detection accuracy, but its average detection time was nine times that of current method with an AlexNet backbone with edge-based filter selection. It is believed that the number of filters used may not be the key reason for accuracy improvement. It can be seen from Table 6, although it is commonly understood that deeper CNN models perform better in feature extraction, the accuracy of repeated pattern detection is not significantly improved using deeper CNN features. Considering the balance between accuracy and computation cost, a pre-trained AlexNet could provide rich enough features for repeated pattern detection. In addition, the time cost of VGG16 was longer than that of ResNet50. As the corresponding selected feature maps had larger sizes, more activation peaks were extracted, taking longer processing time. It is therefore believed that the key factor determining the efficiency of a method is the number of applied activation peaks rather than the number of filters.
## 2 Literature Review
### Technology Acceptance Model  
Before integrating VR technology devices into sports training, understanding user characteristics and analyzing their behavioral patterns regarding product adoption is crucial. The Theory of Reasoned Action (TRA), proposed by [PERSON] and [PERSON], is a sociopsychological framework used for understanding and predicting individual behaviors. According to the TRA, behavioral intentions are influenced by an individual's attitude toward the behavior and subjective norms [27]. Expanding on the TRA, [PERSON] developed the Technology Acceptance Model (TAM) to elucidate and predict individuals' levels of acceptance of information technology [28].  
The TAM is widely employed owing to its simplicity and clarity, which enable direct predictions of behavioral intentions based on users' perceptions. Based on prior research, this study categorized the primary variables of TAM into five domains [29; 30] and adapted them into a scale for the VR experiment by using a five-point Likert scale. The variables are the following:  
1. Perceived usefulness: This refers to the extent to which a user subjectively believes that employing a particular system enhances job performance. When users perceive that the system is easy to use, the users may be inclined to accomplish more tasks with the same level of effort. Thus, this belief is simultaneously influenced by perceived ease of use and external variables;
2. Perceived ease of use: This indicates the degree to which a user believes that using a particular system would require minimal effort;
3. Perceived enjoyment: This refers to the extent to which the use of information technology is perceived as enjoyable and interesting;
4. Attitude toward use: User attitude toward the use of information technology is influenced by both perceived usefulness and perceived ease of use;
5. Behavioral intention to use: The decision to use an information system is determined by the behavioral intention, which is influenced by the user's attitude toward technology use and perceived usefulness.
# A Fault-Signal-Based Generalizing Remaining Useful Life Prognostics Method for Wheel Hub Bearings
## 1 Introduction
With this new method, the long term RUL of wheel hub bearings can be dynamically prognosticated in real time in a complex and changing working environment. The main novelties and contributions of this paper are as follows:  
* A fault signal model is designed with the signal in a complex and mutative environment to describe the fault damage severity in the inaccurate fault signal data with different fatigue damage types. * A generalizing RUL prognostics method is designed based on the fault signal model, and the simplified solution of the generalizing RUL prognostics method is deduced. * The experimental results show that this method gained good accuracies of RUL prognostics for all the amplitude, energy, and kurtosis features with fatigue damage types. This method can process inaccurate fault signals with different kinds of noises in the actual working environment. Additionally, the RUL prognostics method can be conducted in the long term, so the RUL prognostics method has a good generalization. The structure of this article is as follows. In the second part, the fault factors and indicators of wheel hub bearings are discussed. In the third part, a fault-signal-based generalizing RUL prognostics method is proposed for wheel hub bearings. The proposed RUL prognostics method is validated in the fourth part, and we draw a conclusion in the last part.
## 2 Materials and Methods
### Optical Sensing Application  
As an application, different concentrations of iron tiny particles were added within the solution with the best fluorescence intensity. The fluorescence intensity quenching was measured using the fluorescence intensity setup. Also, fluorescence lifetime was detected for in-situ gold-ceria NPs at 530 nm for different iron particle concentrations. The used setup was a modified version of the previously mentioned fluorescence intensity setup with an added optical chopper step (MC2000B, Thorlab) and a chopping frequency of up to 10 kHz, to make a discrete excitation source. The total integration time of the PMT was 22 ns, which was much smaller than the expected milliseconds lifetime region, and consequently showed a negligible impact of PMT in the measured milliseconds fluorescence lifetime of gold-ceria- NPs. In addition, both the rise and fall times of the optical pulses generated from the optical chopper at 10 were detected and found to be around 0.5 ms.
# Effective 5G Wireless Downlink Scheduling and Resource Allocation in Cyber-Physical Systems +
## 5 Performance Evaluation
### Goodput Measurements
#### 5.1.1 Average Goodput for Different Doppler Frequencies  
In a dense environment, signals may be reflected or the mobility of users may cause the Doppler effect in signals. The Doppler effect may reduce the overall performance of the network. Hence, in this paper, we have tested the proposed SRA and the baseline [13] against the standard Doppler frequencies for 5, 50 and 300 Hz, as suggested in [83]. Figure 3 shows the average goodput for different Doppler frequencies suggested for the EPA, EVA and ETU channel models. The performance is evaluated for the LTE, URLLC and eMTC use-cases.  
The overall trend shows that the goodput increases as the number of users in a cell increases. It also shows that dynamic-FBMC outperforms dynamic-OFDM, greedy-FBMC and greedy-OFDM. For three users in a cell, dynamic-FBMC outperforms dynamic-OFDM, greedy-FBMC and greedy-OFDM by approximately 6 Mbps (\(\approx\)11.7%), 13 Mbps (\(\approx\)29.5%) and 17 Mbps (\(\approx\)42.5%), as shown in Figure 3a-c for the LTE use case. For URLLC and eMTC, its goodput is higher than the others' by up to approximately 1-3 Mbps, as shown in Figure 3d-f for URLLC and Figure 3g-i for eMTC. In the case of more than six users in a cell, dynamic-FBMC outperforms greedy-FBMC by up to 1-3 Mbps (\(\approx\)1.3-5.4%) and dynamic-OFDM by up to 10 Mbps (\(\approx\)17.24%) with different Doppler frequencies, as shown in Figure 3d-f for URLLC and Figure 3g-i for eMTC. Similarly, dynamic-OFDM outperforms greedy-OFDM by 2-3 Mbps (\(\approx\)5.4%) for the EPA, EVA and ETU channels with different Doppler frequencies, as shown in Figure 3. Overall, dynamic-FBMC outperforms dynamic-OFDM by up to 8-10 Mbps (\(\approx\)12.12-17.24%) for different Doppler frequencies, as shown in Figure 3a-c for LTE, Figure 3d-f for URLLC and Figure 3g-i for eMTC when more than six users are present in the cell.
## 7 Conclusions  
This paper presents a novel overlap-optimized decoder for high-speed communications, aimed at fulfilling the high data rate requirements of this domain. A QC-LDPC code is constructed, which allows for decoding time overlap of up to one third, thereby enhancing the decoder's throughput. To strike a balance between decoding performance and implementation complexity, a Modified 2-bit MSA is used. In addition, a Boolean operation-based CNU and VNU is proposed to facilitate c2v/v2c message calculation by logic circuits, thereby reducing resource overhead. To address the quasi-cyclic characteristic and reduce read/write latency, a shift-register-based memory strategy is employed. The proposed decoder achieves a coding gain of 5 dB at a BER of \(10^{-6}\) and attains a throughput of 7.76 Gbps at the working frequency of 156.25 MHz. Implementation results on the Xilinx Virtex UltraScale + FPGA VCU118 Evaluation Board reveal that the proposed decoder uses only 10% of the total resources of the board, attesting to its efficiency and practicality. In summary, the proposed overlap-optimized decoder provides a promising solution to meet the high data rate requirements of sensor networks.  
**Author Contributions:** Requirement analyses, Y.S., Z.Z. and J.L.; decoder design, Y.S., L.Z. and Z.Z.; writing--original draft, Y.S.; writing--review and editing, Y.S., X.Y., X.B. and J.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This paper was supported by the the National Key Research and Development Program of China under No. 2019 YFB1803200.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Proposed Decoder** & **[36]** & **[16]** & **[41]** & **[42]** \\ \hline Standard & Proposed LDPC & 5G NR & 5G NR & CCSDS & CCSDS \\ \hline Code Rate & 7/8 & 22/27 & 1/3 & 7/8 & 7/8 \\ \hline Code Length & 8176 & 10368 & 6528 & 8176 & 8176 \\ \hline Max. Iter. & 8 & 5 & 10 & 10 & 10 \\ \hline Algorithm & Modified 2-bit MSA & Hybrid & \multirow{2}{*}{OMS} & \multirow{2}{*}{F-NMS} & \multirow{2}{*}{F-AMSA} \\ \cline{1-1}
## 5 Simulation Setup
Therefore, in order to realize the effect of preemption overhead in the EFBNs, the overhead per preemption is estimated as 124 ns, which is equivalent to the processing time of a 155-byte packet [39]. In order to obtain the average of the results, we repeated the simulations 10 times for different topologies and traffic loads. Furthermore, the proposed work has been compared with the LLR scheme [18]. This comparison is reasonable, because the LLR scheme is the most recent development on the given topic, and is considered to be one of the effective solutions to select the low-load paths in fronthaul bridged networks.
# Hash Based DNA Computing Algorithm for Image Encryption
## 3 Methods
### Content-Aware Encryption Module
_IEEE Access_**2020**, \(8\), 88093-88107. [CrossRef]
* _[PERSON] et al. (2020)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] DNA and Plaintext Dependent Chaotic Visual Selective Image Encryption. _IEEE Access_**2020**, \(8\), 159732-159744. [CrossRef]
* _[PERSON] et al. (2021)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] An efficient medical image encryption using hybrid DNA computing and chaos in transform domain. _Med. Biol. Eng. Comput._**2021**, _59_, 589-605. [CrossRef] [PubMed]
* _[PERSON] et al. (2021)_ [PERSON]; [PERSON]; [PERSON] Multi-image encryption algorithm based on image hash, bit-plane decomposition and dynamic DNA coding. _IET Image Process._**2021**, _15_, 885-896. [CrossRef]
* _[PERSON] et al. (2023)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Medical Image Encryption by Content-Aware DNA Computing for Secure Healthcare. _IEEE Trans. Ind. Inform._**2023**, _19_, 2089-2098. [CrossRef]
* _[PERSON] et al. (2018)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Exploiting self-adaptive permutation-diffusion and DNA random encoding for secure and efficient image encryption. _Signal Process._**2018**, _142_, 340-353. [CrossRef]
* _[PERSON] et al. (2011)_ [PERSON]; [PERSON]; [PERSON] NPCR and UACI randomness tests for image encryption. _Cyber J. Multidiscip. J. Sci. Technol. J. Sel. Areas Telecommun. (JSAT)_**2011**, \(1\), 31-38. * _[PERSON] et al. (2014)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Design of image cipher using latin squares. _Inf. Sci._**2014**, _264_, 317-339. [CrossRef]
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* _[PERSON] et al. (2021)_ [PERSON]; [PERSON]; [PERSON] Chaotic image encryption algorithm based on arithmetic sequence scrambling model and DNA encoding operation. _Multim. Tools Appl._**2021**, _80_, 10949-10983. [CrossRef]

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###### Abstract  
Wake-up radio is a promising approach to mitigate the problem of idle listening, which incurs additional power consumption for the Internet of Things (IoT) wireless transmission. Radio frequency (RF) energy harvesting technique allows the wake-up radio to remain in a deep sleep and only become active after receiving an external RF signal to 'wake-up' the radio, thus eliminating necessary hardware and signal processing to perform idle listening, resulting in higher energy efficiency. This review paper focuses on cross-layer; physical and media access control (PHY and MAC) approaches on passive wake-up radio based on the previous works from the literature. First, an explanation of the circuit design and system architecture of the passive wake-up radios is presented. Afterward, the previous works on RF energy harvesting techniques and the existing passive wake-up radio hardware architectures available in the literature are surveyed and classified. An evaluation of the various MAC protocols utilized for the novel passive wake-up radio technologies is presented. Finally, the paper highlights the potential research opportunities and practical challenges related to the practical implementation of wake-up technology for future IoT applications.  
Energy efficiency; energy harvesting; green communication; Internet of Things; MAC protocols; wake-up radio; wireless sensor network +
Footnote †: journal: Sensors
## 5 Case Study  
### Dam Section and Finite Element Model  
The reservoir dam of a hydropower station is an earth core rockfill dam. The top elevation of the dam is 2510.00 m, and the bottom elevation of the core wall at the riverbed is 2198.00 m. A 2 m thick concrete base is set at the bottom, with the maximum dam height being 314 m. The dam crest is 16.00 m wide, the upstream slope is 1:2.0, and at an elevation of 2430.00 m, a 5 m wide service road is provided. The downstream slope is 1:1.9, and an access road is set along the dam slope.  
A two-dimensional finite element model of the typical cross-section of the earth core rockfill dam is established as follows: along the river direction, extending approximately one times the dam height upwards and downstream from the dam base; vertically, it extends about 1.5 times the dam height downward from the core wall base. In the structural discretization, the dam body, dam foundation cover layer, and rock mass are all modeled using quadrilateral isoparametric elements (with a few degraded elements), and the entire calculation domain is divided into 1630 elements and 1626 nodes. The dam body is divided into 451 elements and 546 nodes.  
Displacement boundary conditions for the finite element model: the upstream and downstream sides have normal constraints, the bottom boundary has a fixed boundary condition, and the top boundary is free. The typical cross-section of the dam and the finite element model mesh are shown in Figure 1.
## 3 Architectural Overview
### Machine Learning-Based Prediction Unit in MMS-DSR  
The Machine Learning-Based Prediction Unit in MMS-DSR utilizes advanced CNN-LSTM models to predict future network states, enabling proactive adjustments to routing and beamforming parameters. This predictive capability aims to maintain high performance in the dynamic environments typical of 6G networks and smart cities.  
* Predictive Modeling: the unit employs CNN-LSTM models to analyze historical and current network data to forecast future conditions such as node mobility, channel quality, and potential interference sources. This analysis helps predict the stability and performance of each link in a route, which is crucial for smart cities where conditions change rapidly.
* Integration with Beamforming: the predictions include recommended adjustments to beamforming vectors to optimize communication links for future network states. By predicting how channel conditions will evolve, the unit guides the Beamforming Optimization Unit to adjust angles and power levels proactively, ensuring optimal performance in the varied urban topography and infrastructure.
* Feedback Loop: predictive insights are fed back into both the Route Discovery and Multi-metric Scoring Engine, allowing these modules to prioritize routes that are expected to offer the best performance in the near future. This feedback loop ensures that routing decisions are made with an eye towards future conditions, not just current metrics, enhancing the adaptability and foresight of the routing protocol in smart cities.
* Model Training and Updating: the CNN-LSTM models are continuously trained and updated with new data to improve their accuracy. This ongoing training process allows the models to adapt to changes in network behavior and topology, ensuring that the predictions remain relevant and accurate over time. This continual learning is essential in smart cities, where urban conditions and patterns can evolve unpredictably.
# Magnonic Metamaterials for Spin-Wave Control with Inhomogeneous Dzyaloshinskii-Moriya Interactions
## 1 Introduction
We begin our work by rapidly deriving the spin-wave dispersion relation with spatially modulated DMI. Then, we further study spin-wave refraction and reflection at the interface between two magnetic media with different DMI and build a generalized [PERSON]'s law of spin waves, similar to [PERSON]'s law in optics. According to the magnonic Snell's law, spin-wave can also experience total internal reflection (TIR) at the DMI step interface when their incident angle is larger than a critical value (i.e., the critical angle). Moreover, magnonic Snell's law and TIR are observed and confirmed by micromagnetic simulations. Utilizing the artificial magnonic metamaterials based on spatially modulated DMI, a spin-wave fiber owing to TIR (which can transmit spin waves over a long distance) and a spin-wave lens holding tremendous possibility to build spin-wave circuits are proposed as proofs of concept. The paper is organized as follows. In Section 2, we introduce our theoretical model and method. Detailed results of micromagnetic simulations are presented in Section 3. Then, we discuss the realization of spin-wave fibers and lenses in Section 4. Finally, we end the paper with a summary in Section 5.