esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Augmented reality medical training simulators can provide a realistic and immersive experience by overlapping the virtual scene on to the real world. Latency in augmented reality (AR) medical training simulators is an important issue as it can lead to motion sickness for users. This paper proposes a framework that can achieve real-time rendering of the 3D scene aligned to the real world using a head-mounted display (HMD). Model deformation in the 3D scene is categorised into local deformation derived from user interaction and global deformation determined by the simulation scenario. Target shapes are predefined by a simulation scenario, and control points are placed to embed the predefined shapes. Free-form deformation (FFD) is applied to multiple shapes to efficiently transfer the simulated model to the HMD. Global deformation is computed by blending a mapping matrix of each FFD with an assigned weighting value. The local and global deformation are then transferred through the control points updated from a deformed surface mesh and its corresponding weighting value. The proposed framework is verified in terms of latency caused by data transmission and the accuracy of a transmitted surface mesh in a vaginal examination (VE) training simulation. The average latency is reduced to 7 ms, less than the latency causing motion sickness in virtual reality simulations. The maximum relative error is less than 3%. Our framework allows seamless rendering of a virtual scene to the real world with substantially reduced latency and without the need for an external tracking system.  
augmented reality; latency; head-mounted display; medical simulation; free-form deformation +
Footnote †: 2021: 11, 9925. [[https://doi.org/10.3390/app11219925](https://doi.org/10.3390/app11219925)]([https://doi.org/10.3390/app11219925](https://doi.org/10.3390/app11219925))
# Accuracy Analysis of GNSS Hourly Ultra-Rapid Orbit and Clock Products from SHAO AC of iGMAS
## 1 Introduction
Moreover, the latency of ultra-rapid products is usually 3 h, meaning that the user can only exploit the predicted orbit and clock from the first to the ninth hour in the prediction session. With notable developments of PPP theory, the real-time PPP has attracted more interest and will be widely applied in navigation, automatic piloting, atmospheric sounding, natural disasters, hazard monitoring, and rescue, etc. [12]. However, the time consumption, timeliness, and accuracy of the ultra-rapid orbit and clock products are the main issues for real-time studies or applications. More global distribution stations and constellations should be processed to generate the products if we want to pursue better accuracy and precision. However, it will cause more time consumption and increase latency so as to hardly satisfy the real-time or near-real-time users. Thus, the methods for increasing the updated rate of ultra-rapid orbit and clock products have been studied by many researchers. [PERSON] et al. (2012) have studied the accuracy of ultra-rapid products from the ACs of IGS and combined IGU products, assessed by rapid products. They found that the 40-45 h observation arc length can generate better predicted orbit than a 1 day case [13]. interval in order to achieve the predicted orbit and clock products, with high accuracy and low latency. This article presents a new strategy and method to generate the hourly ultra-rapid orbit and clock products, which may be different from previous studies. Then, the accuracy of the products are analyzed.
# Confidential Smart Contracts and Blockchain to Implement a Watermarking Protocol
## 3 Design Choices  
The limitations reported in the previous section have pushed researchers to identify possible solutions for designing watermarking protocols that ensure adequate performance, scalability, confidentiality, and low execution costs of smart contracts on blockchains. In particular, the solution used to implement the watermarking protocol proposed in this article is based on the \"layer-2\" execution scheme, in which smart contracts are executed outside the blockchain [8,19]. In this scheme, the execution system is organized into two independent layers. The first is made up of an environment that is responsible for the execution of smart contracts only, while the second is made up of the blockchain that validates the results produced by the execution of smart contracts by reaching consensus on the generated transactions (see Figure 2). In this way, it is possible to use an execution environment that is specific for smart contracts and efficient and scalable, able to avoid both cost limits and the replication of the execution of smart contracts on all blockchain nodes. In addition, the fact that the execution of smart contracts is separated from the consensus reached by the blockchain nodes on the generated transactions allows the implementation of an execution environment capable of guaranteeing confidentiality for smart contracts. For this purpose, the proposed solution is based on Trusted Execution Environments (TEE)s [19]. A TEE can create a completely isolated execution environment on a processing node in which the operating system software or applications cannot interfere, corrupt, or even know the information related to the execution of a smart contract (see Figure 3). It thus represents a secure and private \"enclave\" in which the code execution is confined. In addition, a TEE can prove the correct execution of the code through specific attestations and without performance degradation compared to an execution outside the TEE.  
Figure 2: Layer-2 execution model.
# Analysis of Damage and Permeability Evolution of Sandstone under Compression Deformation
## 2 Coupling Analysis of Rock Damage and Permeability
### Permeability and Deformation
Substituting Equations (8) and (9) into Equation (7), the relationship between porosity and permeability can be established for fracture flow. \[k=k_{0}(\phi/\phi_{0})^{2\chi+s} \tag{10}\]  
The coefficient \(2\chi+s\) is used to describe the degree of influence on permeability, and its value varies from one to five. Although the forms of Equations (6) and (10) are the same, they still cannot be unified due to the difference in the equation exponents. To unify the relationship between porosity and permeability in porous media flow and fracture flow, we tested the porosity and permeability of 42 cores and cited 39 sets of literature data, a total of 81 cores. The cores investigated were both intact and fractured rocks, including sandstone, mudstone, shale, granite, and rock salt. The porosity and permeability varied from 0.12% to 59.44% and \(10^{-20}\) m\({}^{2}\) to \(10^{-12}\) m\({}^{2}\), respectively. In order to make the theoretical model and experimental data comparable, these test data were normalized here. Taking the rock with the minimum permeability and porosity as a reference, all porosity ratios (\(\phi/\phi_{r\!f}\)) were set as X-coordinate data, and all permeability ratios (\(k/k_{r\!f}\)) were set as Y-coordinate data. The analysis results indicate that the theoretical model agreed best with the experimental data when the exponent of the relationship function between porosity and permeability was around 3.0, as shown in Figure 3:0 This value was the same as with the classic Carman-Kozeny model [31; 32]. Based on the above comparison results, the relationship between porosity and permeability in porous media flow and fracture flow can be unified. Substituting Equation (5) into Equation (10), the evolution function of rock permeability can be obtained. Figure 3:1
## 4 Experimental Results and Analysis
### Diagnostic Results of Model  
In the 1D convolutional neural network used in this paper, batch processing and the number of iterations of the model training set are set to 32 and 50 by grid search; learning  
Figure 13: Envelope spectrum analysis of reconstructed signals by different methods: (**a**) EMD; (**b**) traditional VMD; (**c**) improved VMD.  
rates are set to 0.001. The cross-entropy loss function and Adam optimization algorithm are used. In bearing fault diagnosis, the accuracy rate and loss rate are the most important indices to evaluate the model, so this paper uses these two indices to make a comprehensive evaluation of the model diagnosis effect. The results are shown in Figure 14. Figure 14a,b show that the accuracy rate and loss rate of the model fluctuated significantly in the first six iterations because the model learned fault features quickly. After that, the accuracy rate and loss rate of the model tended to be flat and finally reached a stable state. The accuracy of the validation set is higher than that of the training set, so the results show that the proposed model has not been fitted.  
To further analyze the proposed method, three evaluation metrics were applied: _precision_, _recall_, and _F1-score_. _Precision_ is the probability of actual positive samples from all predicted positive samples. _Recall_ is the probability of being predicted as a positive sample from the actual positive samples, and the _F1-score_ is the weighted average of precision and recall [40]. The precision, recall, and _F1-score_ for each bearing category are defined as follows [40]:  
\[\begin{array}{c}precision=\frac{TP}{(TP+FP)}\\ recall=\frac{TP}{(TP+FN)}\\ F1\_score=2\frac{precision\_recall}{(precision+recall)}\end{array} \tag{27}\]  
In Equation (27), _TP_ represents the number of positive samples predicted correctly, FP represents the number of negative samples predicted incorrectly, _TN_ represents the number of negative samples predicted correctly, and _FN_ represents the number of positive samples predicted incorrectly.  
The calculation results of the _precision_, _recall_, and _F1-score_ of the method in this paper are shown in Table 4. From Table 4, it can be seen that except for a few samples in fault category 2, fault category 5, and fault category 8 that are not identified, all other types of samples are effectively identified, and the experiment proves that the proposed method in this paper has a strong fault diagnosis capability.  
In addition, the impact of emd, traditional vmd, and improved vmd on the detection accuracy was evaluated. As shown in Table 5, the results of the study clearly show that the improved VMD method outperforms the other two methods in terms of accuracy for the same dataset with a signal-to-noise ratio of \(-6\) dB under 0 HP functioning modes.  
Figure 14: Experimental results. (**a**) The change in accuracy. (**b**) The variation in loss rate.
## 5 Conclusions  
In the optimization scheduling of the DES, the SSO method has been proposed. By transforming a BiOP into a CSiOP, the pareto front of the BiOP is accurately depicted, and, according to the changing characteristics of the pareto front, the stepper search method is introduced to solve the problem of the difficulty of choosing among many feasible solutions in the MOP, obtaining a more reasonable and unique recommended solution and effectively balancing the contradiction between two optimization objectives. Furthermore, the SSO method has fewer parameters, and the parameters have less impact on the accuracy of the optimization results. The method is also highly stable. Additionally, when facing different bi-objective optimization problems, the SSO method can search for excellent recommended solutions and show good adaptability to bi-objective optimization problems.  
At present, the limitation of this study is that the proposed method can only be applied to bi-objective optimization problems. Therefore, expanding the application of the SSO method to more optimization objective problems is the focus of future research. In addition, the diversity of pareto frontier morphology changes will be the core link of subsequent research.  
Conceptualization, S.M.; Methodology, S.M.; Validation, Z.M. and G.S.; Formal analysis, Z.M. and Y.C.; Writing--original draft, Z.M.; Writing--review & editing, S.M., Y.C. and G.S.; Visualization, Y.C.; Funding acquisition, G.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Beijing Nova Program (Z211100002121081).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
Author [PERSON] was employed by the company Shandong Electric Power Company Haiyang Power Supply Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
### Sigfox  
Sigfox deploys proprietary base stations under unlicensed sub-GHz ISM bands with the help of its patented UNB (Ultra Narrow Band) technology. Sigfox can reach a maximum data rate of 100 bps by using BPSK modulation in an ultra-narrow band of 100 Hz. This novel method maximizes the use of the frequency spectrum, which leads to lower power consumption, significant receiver sensitivity, and cost-efficient antenna design [9].  
In the initial days of its release, Sigfox was limited to only uplink transmission. Later, it was able to provide bidirectional communication, where downlink transmission occurs after an uplink broadcast. With a payload length of 12 bytes, the maximum number of uplink messages that may be sent in a day is 140. All uplink communications cannot be acknowledged by downlink messages. The payload was restricted to four per day. Sigfox uses transmission duplication and time/frequency diversity to ensure the reliability of uplink transmission without acknowledgment. Three separate frequency channels are used by each end-device to transmit messages, which reduces costs and simplifies the complexity of the end-device [8].  
Figure 1: Network Architecture for LoRaWAN.
## 5 Multi-Dimensional RWTA in QKD Optical Networks  
Compared with traditional optical networks, routing and resource allocation in QKD optical networks includes RWTA conducted on QKCh and MBCh in addition to RWA conducted on TDCh. In this paper, we design a multi-dimensional RWTA algorithm to dynamically allocate resources for different channels, i.e., wavelength assignment for TDCh, and wavelength and time-slot assignment for QKCh and MBCh due to the wavelength consistency and time continuity the QKCh needs to meet. Suppose the service request in QKD optical networks is denoted as \(r(s,d,t_{a},TW_{i},t,l)\). When \(l\) equals to 0, it means that there is no security requirement for the request; otherwise, it needs to configure key-updating period \(T\) according to the value of \(l\) until the data transmission is finished. The proposed multi-dimensional RWTA in QKD optical networks is shown in Algorithm 3.  
```
1 Request \(r(s,d,t_{a},TW_{i},TW_{u},t,l)\) arrives;
2 Generate key initialization request \(r(s,d,t_{a},TW_{i},t,l)\);
3 Search the wavelength-timeslots for \(r(s,d,t_{a},TW_{i},t,l)\) according to ReLoss-TCC-based RWTA Strategy;
4 Search the available wavelengths for TDChs on path \(p\);
5 store the wavelengths into set \(W\);
6 If\(W\
eq\varnothing\)
7 Allocate the wavelength for TDChs by \(FF\) strategy;
8 Start the data transmission in TDChs;
9\(k=l\);
10\(While\) data transmission is not finished
11 Select the value of \(T\) according to \(l\);
12 generate key updating request \(r(s,d,t_{a}+k*T,TW_{u},t,l)\);
13 insert into key-updating request queue \(Q\) by TW-SO Strategy;
14 Search the wavelength-time-slots for requests in queue \(Q\) according to ReLoss-TCC-based RWTA Strategy;
15\(k=k+l\);
16 EndWhile
17 Else
18 Block the request;
19 EndIf
```  
**Algorithm 3** Multi-dimensional RWTA in QKD optical networks.
# A Multi-Degree of Freedom Tuned Mass Damper Design for Vibration Mitigation of a Suspension Bridge
## 3 Numerical Optimization and Simulation
### Optimization Criteria  
Traditionally, based on the [PERSON] method, the optimized frequency ratio and TMD damping ratio are aimed to minimize the structural response by the minimization of the structural dynamic magnification function. This classic procedure consists of two separate steps: tuning of the frequency  
Figure 2: Block diagram of the bridge—2 DOFs TMDs system with decentralized control.  
of the damper, and selection of the optimal level of the TMD damping ratio. This classic procedure has been used in our previous research [27]. The goal of this study is to design the parameters (\(k_{i}\) and \(c_{i}\) for \(i=1,2,\ldots,6\)) in order to determine the optimum frequency ratio \(\
u_{j}\) (\(j=1,\ldots,4\)) and the optimum TMD damping ratio \(\xi_{j}\) for minimizing the response of the bridge system.  
Due to the fact that the maximum amplitude of the bridge system should be controlled in a reasonable range, herein the damping performance of TMDs is evaluated by the \(H_{\infty}\) criterion. \(H\) is the selected FRF value of the bridge system under the excitation \(f_{d}\). The value range of \(H_{j}\) is constrained by the value range of \(\omega_{j}\) (\(\omega_{j}\) \(\in\) [0.7\(\omega_{\text{sj}}\),1.3\(\omega_{\text{sj}}\)]). The goal of the optimization is to minimize the maximum value of each value range of \(H_{j}\). \(\mathbf{\chi}\) = [\(k_{1}\), \(c_{2}\),..., \(k_{6}\), \(c_{6}\)]T is selected as the design parameter vector of the TMDs. The optimization problem can be written as:  
\[\begin{array}{ll}\text{Find}:&\mathbf{\chi}=\big{[}k_{1},c_{1},k_{2},c_{2},k_{3 },c_{3},k_{4},c_{4},k_{5},c_{5},k_{6},c_{6}\big{]}^{\text{T}}\\ \text{Minimize}:&\sum\limits_{j=1}^{4}W_{j}\Big{(}\max\Big{|}H_{j}\Big{(}\mathbf{ \chi},\omega_{j}\Big{)}\Big{|}\Big{)}\\ \text{Subject to}:&I=\Big{\{}j\big{|}0.7\omega_{\text{sj}}\leq\omega_{j}\leq 1.3\omega_{\text{sj}},\,\omega_{j}=\big{|}eig(A+B_{12}FC_{2})\big{|}\Big{\}} \end{array} \tag{22}\]  
where \(j\) (\(j=1,\ldots,4\)) is the mode number considered and \(\omega_{\text{sj}}\) is the \(j\)-th natural frequency of the primary system. \(\omega_{j}\) is the evaluation of the eigenvalues, which corresponding to the modal frequencies of the entire system inside the specified frequency band. \(W_{j}=0.25\), which is the weight coefficient. For each TMD, 2% of the total mass of the structure.
# A Message Passing Approach to Biomedical Relation Classification for Drug-Drug Interactions
## 2 Materials and Methods  
In this section, we introduce the dataset and the architecture of our proposed graph neural network-based classification model for drug-drug interaction extraction, where, given a sentence containing drug mentions, each drug pair is classified into one of the five possible interaction categories. It consists of three main parts, which are the DDI Knowledge Graph, the BERT-based language model and the GNN-based classification module as shown in Figure 1. Specifically, a knowledge graph based on our proposed DDI task related schema is created where a domain-adapted BERT-based language model is then applied to generate meaningful word representations. This knowledge is then integrated into a selected part of the graph, where a GNN is trained to classify the drug pair relationship.
## 1 Introduction  
Ground-penetrating radar (GPR) technology is a non-destructive testing method that has been widely applied in subgrade safety monitoring. Due to its non-destructive, rapid, and efficient nature, GPR has emerged as a crucial tool for the monitoring of subgrade safety [1, 2]. It is primarily utilized in detecting subgrade structures, identifying defects, and assessing stability, enabling the discovery of anomalies such as voids, loose areas, and cracks within the subgrade [3, 4].  
GPR technology can be used to effectively detect voids and cracks in the subgrade, providing valuable insights for subgrade reinforcement and repair efforts. Furthermore, it can be used to detect defects in urban roads, particularly cracks and loose areas [5, 6]. However, the interpretation of GPR data poses challenges due to the varying electromagnetic properties of different media and the complexity of underground structures. Additionally, the detection efficacy is notably influenced by environmental factors, such as groundwater and salinity, which can lead to suboptimal performance in complex environments [7].  
Deep learning technology is renowned for its exceptional performance in image and pattern recognition and has progressively been integrated into the intelligent identification of subgrade defects [8]. One of its primary applications in this domain is object detection. By leveraging convolutional neural networks (CNNs), subgrade cracks can be accurately detected through the use of extensive training samples. Moreover, the Faster R-CNN model has been successfully employed to identify diverse types of defects, including potholes and cracks [9; 10; 11].  
The current research on subgrade defect detection using radar and deep learning primarily focuses on enhancing image recognition accuracy and extracting meaningful image feature data. Acquiring and annotating a substantial volume of subgrade defect images is crucial for intelligent defect recognition. Nevertheless, the lack of precisely labeled datasets represents a significant bottleneck in improving the model performance [12; 13]. Given the diverse range of subgrade defects and their distinct characteristics, further research is necessary to develop generalized models that are capable of detecting various types of defects [14; 15].  
This study utilizes GprMax3.0 software to conduct forward simulations of loose, void, and cavity defects in subgrades. By removing direct waves and enhancing the signal gain, we analyze the characteristics of the defect images. Complex signal analysis techniques are applied to extract the instantaneous amplitude, phase, and frequency features of different defects, thereby improving the accuracy of defect image recognition. Additionally, we employ the PyTorch2.0 deep learning framework along with YOLO and SSD models for the intelligent identification of defect images. The recognition results of these two algorithms are compared and evaluated based on metrics such as accuracy and the recall rate. The findings are then applied to test sections to validate the accuracy and rationality of the research outcomes.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 5 Fibers with Anti-Resonant Guidance
### Absorption and Scattering by Filler Gas and Contaminants
Notes: (1) absorption bands are usually composed of numerous narrow absorption lines; (2) limits of bands correspond to approx. 10% of peak absorbance. The measurement of fiber loss spectrum enables the detection of contaminants and estimation of their average concentration, as the attenuation they produce is proportional to partial pressure. For example, the spectral loss graph of NANF published by [PERSON] et al. [55] reveals absorption bands of water vapor, ammonia, methane, propane and possibly one or two more compounds the authors failed to identify.