esa-sceva/satcom-chunk-collection huggingface dataset:
# True Random Number Generator Based on Fibonacci-Galois Ring Oscillators for FPGA
## 3 True Random Number Generators Design and Evaluation on FPGA Technology
### FiGaRO RNG Design
#### 3.3.2 Overall RNG Architecture  
Figure 16 shows the architecture of the proposed RNG. FiROs and GaROs, configured as a FiGaRO, were selected as an entropy source. Our contribution to it is the inclusion of the health test and the combination of a parametric number of stages, to increase the throughput. Such architecture showed to be the most suitable for FPGA implementation due to its independence from placing and routing into FPGA. The final design has been parametrized to be customizable in several stages (i.e., the number of parallel TRNG): one, two, four and eight elements with 1-bit output can be placed in parallel. Every stage is referred to as a FiGaRO Stage, as showed in Figure 16. Please note that the number of FiRO and GaRO placed in the same FiGaRO contributes on improving entropy and reliability: indeed, if one or more oscillator gets stuck you can still have a working TRNG since the single oscillators are XORed. On the other hand, the number of FiGaRO stages placed in parallel has the only effect to increase the throughput, paying linearly in resource consumption; entropy is not affected by this parameter. Every stage has the same structure: multiple FiRO and GaRO elements can be XORed together to form a high entropy output, with the feedback polynomial reported in Equations (9)-(16). Choosing different feedback networks, the correlation between FiROs and GaROs within a FiGaRO stage is minimized. This operation also ensures a higher degree of reliability of the Entropy Source, since various contributions sum up together masking possible momentarily failures of some oscillators. Besides, the number of FiRO and GaRO XORed in every stage has been parametrized to choose between one FiRO and one GaRO (with polynomials Equations (9) and (13)),  
Figure 14: Hardware implementation of the repetition count test for a binary random source.  
Figure 15: Hardware implementation of the adaptive proportion test for a binary random source.  
two FiRO and two GaRO (with polynomials Equations (9), (10), (13) and (14)), four FiRO and four GaRO. Each FiGaRO Stage is accompanied by its health tests, implemented as reported in Section 3.3.1. The error detector circuits signal to the user failures of the health tests of each FiGaRO stage, so that the user can take countermeasures.
## 4 Discussion  
In this study, the dynamic responses of a rectangular liquid storage subjected to a three-directional earthquake ground motion were examined. The effects of nonlinearity in structural material and those of incidence angle of an input ground motion were considered in the examination. In order to extend the applicable area of the numerical approach in the present study, the approach can be improved in future studies as described below.  
In this study, the earthquake responses of a concrete rectangular liquid storage tank on the surface of rigid soil were investigated. However, steel can be used for the structural material of tank. The numerical approach in this study can be applied to a steel tank. In the case, a proper plastic model for steel should be considered. For example, a variety of plasticity models are available for metal in ABAQUS [34]. A proper plasticity model can be chosen among the models and employed for dynamic analysis of a steel tank.  
The effects of soil-structure interaction (SSI) were not considered in this study. It should be noted that the effects cannot be ignored when the system is installed and/or embedded in flexible soil [18; 39]. Therefore, the effects of SSI must be considered in future studies. The latest approaches to consider the effects of linear and nonlinear SSI can be found in the ASCE standard [40].  
In this study, only water was considered for the contained liquid. However, various fluids, such as water, oil, LNG and others, can be stored in a tank. The hydrodynamic pressure in a specific fluid can be calculated in the same way in Section 2 with a specific value for the density \(\rho\). The tank structure can show different dynamic behavior depending on the density. However, it should be noted that the density of water is usually larger than those of other fluids. Therefore, the case of water can be the conservative one. If a fluid with a larger density than water is considered, the dynamic responses of a liquid storage tank must be examined rigorously with the numerical approach in this study.  
Because building structures was damaged by a high level of vertical acceleration in the past earthquakes, the effects of combined vertical and horizontal seismic action have been studied, especially in near-fault ground motions [35; 36; 37; 38]. In this study, the effects were examined and observed insignificant for the considered rectangular liquid storage tank. However, only one far-field record of earthquake ground motion was considered in this study. Therefore, the effects of the vertical component on a rectangular liquid storage tank must be considered for a variety of far-field and near-fault ground motions in future studies.  
Figure 19: Sloshing heights at the free surface.
# Preliminary Biomechanical Evaluation of a Novel Exoskeleton Robotic System to Assist Stair Climbing
## 4 Discussion
### Human Joint Moments  
Human joint moments are influenced by the exoskeleton in various ways. On the one hand, the kinematics of the human exoskeleton system changes significantly, which is also reflected in longer stance times. On the other hand, the assistive moment of the eBUS influences at least the net knee moment of the human. Furthermore, the additional mass of the exoskeleton and the changed inertias of the lower musculoskeletal system influence the joint moments that must be generated.  
For instance, the assisting extension moment in the knee also influences the foot moment that has to be generated by the user. On one hand, this is shown by only one peak in the curves with the eBUS and, on the other hand, by the reduction of the applied foot moment itself. Here, it can already be determined that the eBUS affects the subjects in different ways or that the subjects have different movement strategies when using the eBUS.  
This assumption can be further confirmed by the curves of the applied knee moments, since the subjects with the eBUS also show different curves here. However, it can alsobe seen that the subjects have different knee moment curves even without the use of the exoskeleton, which suggests different movement strategies from the outset. Particularly noticeable is the change during the descent in all three subjects in exactly the phase in which the knee moment is assisted by the exoskeleton. In this phase, the required knee extension moment is even reduced to such an extent that the subjects generate a flexion moment to counteract the movement, as can be seen for Subject 3 especially. This could already indicate too much assistance by the system.  
The hip moments to be applied by the human show a large discrepancy between the two test scenarios during the ascent in the phase of pulling up and forward. In these phases, the maximum extension moment is 1.4 \(\pm\) 0.3 Nm/kg and could possibly be critical for the elderly. Presumably, this phenomenon has many causes. Firstly, an additional hip extension moment could be required to contribute potential energy to the additional mass of the eBUS during ascent. Secondly, the increased hip extension moment could be an indication of a redistribution of moments in favor of the hip in order to relieve the knee joint even more. Another cause for the change in hip moment could be the increased forward lean of the upper body during the trials with the eBUS. The exact causes of this phenomenon cannot be definitively answered at this point, as the changes in the kinematics due to the use of the eBUS must also be considered. For the hip moment that has to be generated in the swing phase, it can be stated that due to the additional mass of the eBUS, it is significantly higher compared to the tests without the exoskeleton, both for ascent and descent.
## 4 Simulations
### Applicability of Proposed Method in Practice  
The approach proposed in this paper can help existing fault detection systems to achieve upgrades through the transfer learning concept without reducing accuracy. This section discusses the applicability of the proposed method in practice from the perspectives of implementation complexity, computational resource requirements, and scalability.  
The implementation complexity of the proposed method is described by the algorithm flow chart presented in Figure 4. In the offline stage, the factory only needs to build two models each time to implement the algorithm. In the online stage, the factory only needs to run basic and add-on models once each is finished. This method resembles the actions required by traditional fault detection systems; that is, there is no increase in implementation complexity.  
Table 9 presents the computational resource requirements of the target method in the offline and online stages. The average values obtained after conducting the same experiment 30 times are presented. The execution environment is Windows 10, 64-bit, with an AMD Ryzen 9 5900X 12-core processor, 3.7 GHz, and 128 GB of memory. In the offline stage, the execution time of the RBF-DNN is about 30 s, and the Matlab neural kit only takes 5 s to complete. Thus, the computer environment and model running costs are acceptable  
Figure 19: The results of statistical significance experiments on four main groups.  
to most factories. In the online stage, the Matlab neural kit can be completed in about 1 s. This is less than the production time of a single product, so it can be effectively applied for instant fault detection.  
As described in Figure 3, the goal of the proposed method is to enable factories to quickly use existing fault detection models to establish extended models. Thus, the approach is inherently scalable, thereby meeting the needs of factories in practice.
# Inter-System Differencing between GPS and BDS for Medium-Baseline RTK Positioning
## 2 Combined GPS and BDS Observation Model
### Wide-Lane AR  
From Equation (4), we know that for both GPS and BDS, the estimable DD ambiguities with the ionosphere-free combination do not have integer property. In order to recover the integer property of ambiguities, WL ambiguities are usually resolved firstly using the MW model, e.g., for GPS it reads  
\[\begin{split}\Delta N^{\text{L}cs}_{WL}&=\Delta \phi^{\text{L}cs}_{WL}-\frac{1}{\lambda_{WL,G}}\cdot\frac{f_{1,G}\Delta p^{ \text{L}cs}_{1}+f_{2,G}\Delta p^{\text{L}cs}_{2}}{f_{1,G}+f_{2,G}}\end{split} \tag{14}\]  
Then with the integer WL ambiguities, the estimable DD NL ambiguities can be derived  
\[\begin{split}\Delta N^{\text{L}cs}_{1}&=\Delta N^{ \text{L}cs}_{IF}-\frac{f_{2,G}}{f_{1,G}-f_{2,G}}\Delta N^{\text{L}cs}_{WL}\end{split} \tag{15}\]  
The DD NL ambiguities of BDS can be derived similarly. Due to the larger noises of pseudorange measurements, the single-epoch estimators of WL ambiguities with Equation (14) cannot be reliably fixed to their correct integers. Therefore, the WL ambiguities are usually fixed by rounding their average values over some epochs. In real-time processing, an individual filter can be set for the WL AR, so that the WL ambiguities can be updated flexibly along with the variation of elevations and the change of the reference satellite. Besides, the precision \(\sigma_{\Delta N_{WL}}\) of each WL ambiguity can be easily obtained from the diagonal elements of the corresponding variance-covariance matrix. Denoting the distance of the float WL ambiguity \(\Delta\hat{N}_{WL}\) to the nearest integer \(\Delta\widetilde{N}_{WL}\) as \(b_{\Delta N_{WL}}=\left|\Delta\hat{N}_{WL}-\Delta\widetilde{N}_{WL}\right|\), the widelane ambiguity fixing is conducted by checking both \(b_{\Delta N_{WL}}\) and \(\sigma_{\Delta N_{WL}}\). Given the thresholds \(b_{m}\) and \(\sigma_{m}\), one can fix \(\Delta\hat{N}_{WL}\) to its nearest integer \(\Delta\widetilde{N}_{WL}\) if  
\[b_{\Delta N_{WL}}<b_{m},\text{ and }\sigma_{\Delta N_{WL}}<\sigma_{m} \tag{16}\]  
In [26], the two thresholds were recommended to be critically set as \(b_{m}=0.2\) and \(\sigma_{m}=0.1\). These values will also be adopted in this paper.
## 3 Fiber-Reinforced Concrete (FRC)
### Natural-Fiber-Reinforced Concrete (NFRC)
#### 3.1.1 Mechanical Properties of NFRC  
In terms of impact resistance, [PERSON] et al. [104] and [PERSON] et al. [105] reported the positive effects of the impact resistance of concrete reinforced by jute fiber and hybrid bamboo-steel fiber, respectively. More results on impact energy absorption of NFRC slabs were reported by [PERSON] and [PERSON] [101]. Investigations of jute FRC show that compressive strength is not considerably influenced by adding fibers; however, flexural, tensile strengths and toughness are all considerably incremented [91]. Moreover, it is stated that the modulus of elasticity, compressive strength, and repetitive impact resistance of coconut FRC (CFRC) were reduced by increasing the length of the fiber [106]. Also, a similar finding reported that the short flax fiber (12 mm) had the most effects on the flexural strength of flax FRC [107]. [PERSON] and [PERSON] tested many FRC samples by using the different FC of glass and palm trees. They stated that adding fibers, in general, improves the toughness of concrete and enhances its impact resistance; also, bio-fibers are comparable to the synthetic fibers in improving the toughness and impact resistance [83]. Moreover, from another study, hemp fibers improve the concrete fracture energy for 70% in NFRC. By bridging the cracks, fibers provide a post-cracking ductility, leading to significant improvement of toughness [102]. Furthermore, higher flexural strength is exhibited by alkaline treatment of hemp fibers compared to their non-treated equivalents [7].
# A Lightweight Attention-Based Network towards Distracted Driving Behavior Recognition
## 3 Methods
### Depthwise Separable Convolution  
Depthwise separable convolution [20] can greatly reduce the computation cost and has been widely used in lightweight CNN design. Its essence is the decomposition of standard convolution within a feature channel domain.  
Consider a feature map with size \(H\times W\times D_{in}\), where \(D_{in}\) is the number of input channels. The stride is set to be \(1\). When the feature map is convoluted by a standard convolution kernel with size \(k\times k\times D_{in}\times D_{out}\), where \(D_{out}\) is the number of output channels, the computation cost can be calculated as (2). After the convolution operation, the size of the output feature map is \((H-k+1)\times(H-k+1)\times D_{out}\). The schematic of standard convolution is shown in Figure 1.  
\[C_{standard}=H\cdot W\cdot k^{2}\cdot D_{in}\cdot D_{out} \tag{2}\]  
Depthwise separable convolution involves depthwise convolution and pointwise convolution. Depthwise convolution extracts feature on a single feature map. Pointwise convolution fuses these extracted features from different feature maps and outputs a final feature map. The feature map after the depthwise convolution is also called the intermediate feature map. The schematic of depthwise separable convolution is shown in Figure 2.  
Figure 1: Schematic of standard convolution.  
According to Figures 1 and 2, the standard convolution and the depthwise separable convolution can output the same feature map. However, their computation cost is different. For the depthwise separable convolution, it can be calculated as (3):  
\[C_{depthwise_{separable}}=C_{depthwise}+C_{pointwise}=H\cdot W\cdot k^{2}\cdot D_{ int}+H\cdot W\cdot D_{int}\cdot D_{out}. \tag{3}\]  
We can compare the computation cost of both convolution methods:  
\[\frac{C_{depthwise_{separable}}}{C_{standard}}=\frac{H\cdot W\cdot k^{2}\cdot D_{ int}+H\cdot W\cdot D_{in}\cdot D_{out}}{H\cdot W\cdot k^{2}\cdot D_{int}.D_{out}}= \frac{1}{D_{out}}+\frac{1}{k^{2}} \tag{4}\]  
Normally, the number of output channels \(D_{out}\) is much larger than the convolution kernel size \(k\). It indicates that the computation cost of depthwise separable convolution is approximately \(\frac{1}{k^{2}}\) of the standard convolution. The model size will be correspondingly reduced. However, it has been reported [20] that the usage of too much depthwise separable convolution will decrease the accuracy. Hence, we need to balance the model complexity and the model accuracy.
## 3 The Circuit-Partitioning Algorithm Based on Partition Connectivity Clustering and Tabu Search
### Circuit Modeling and Information Storage  
In digital integrated circuits, there are many types of complex connections, for example, the outputs of circuits are connected to multiple inputs, or a single output of a gate is connected to multiple inputs, etc. These connection types significantly increase the complexity of the circuit-partitioning problem. Thus, we employ graph theory to mathematically model the integrated circuits. The vertices of the graph represent the components in the circuit, while the edges represent the connections between these components. An adjacency  
Figure 1: Flowchart of the proposed circuit-partitioning algorithm, illustrating the key stages: coarsening, initial partitioning, uncoarsening, and refinement. The vertices enclosed by the red dashed frame represent a subgraph in the partition.  
matrix is used to collect the information of the graph, where an element corresponds to a connection. Usually, the adjacency matrix is relatively sparse and contains many zero elements. It is thus compressed and stored in the Compressed Sparse Row (CSR) format to reduce the storage requirements for large-scale circuits.  
In the circuit-modeling process, the adjacency matrix M is generated by traversing all components and their connection relationships. First, all components are initially treated as vertices. For example, in Figure 2, there are seven vertices to represent the corresponding components. Next, the value of the element is set to the number of connections. If there is no connection between two components, the value of element is set to 0.  
Subsequently, the adjacency matrix M is converted into the Compressed Sparse Row (CSR) storage format. The array adjncy stores the adjacent vertices for each vertex, and the related indices are stored in the array xadj, which records the starting position of each vertex's adjacency list. The array adjwgt contains the corresponding edge weights, which represent the number of connection signals. The array vwgt contains the weight of each vertex, which represents the number of vertices.  
This CSR format allows for the efficient representation and processing of the circuit's complex structure, providing a foundation for subsequent optimization and analysis.
# Quantum Stream Cipher Based on Holevo-Yuen Theory
## 5 Concrete Applications of Quantum Stream Cipher
### Optical Communication from Base on the Moon to Earth  
The Japanese government has initiated a study to increase the user transmission rate of optical space communications from 1.8 Gbps to more than 10 Gbps. Furthermore, in the future, the government aims to achieve higher transmission rates in ultra-long-distance communications required for lunar and planetary exploration. This plan is called LUCAS. We have started to design for an implementation of 1 Gbps communication system at a transmission distance of 380,000 km between the Moon and the Earth using the high-speed performance of the Y-00 quantum stream cipher.
# Atomic Receiver by Utilizing Multiple Radio-Frequency Coupling at Rydberg States of Rubidium
## 2 Methods  
### Measurement Set-Up  
The details of how to use the Rydberg atoms as a receiving antenna for communication have been discussed in some previously conducted studies [15; 16]. At both RF-resonance and RF-detuning, time-variant characteristics of complex signals could be captured by the change of the probe transmission, and modulation on a RF-carrier can cause synchronous modulation of the probe transmission. Thus, it is possible to retrieve the characteristics of the modulation signal by detecting the probe transmission in real-time.  
FDM is a multiplexing technique that modulates multiple baseband signals onto different frequency carriers and then combines them to form a composite signal. In this paper, the communication of the atom-based receiver in the case of RF-resonance and RF-detuning carriers is proposed. Figure 1 illustrates the experimental set-up. A vapor cell containing rubidium (\({}^{87}\)Rb) atoms was used as an active medium for real-time field sensing. \({}^{87}\)Rb atoms were excited to Rydberg states by counter-propagating probe laser and coupling laser via a two-photon transition. Meanwhile, through an external signal generator, two RF-carriers were amplitude or frequency modulated. Here, we use CH1 and CH2 to represent two RF-carriers, respectively. Then, they were coupled into the atomic vapor cell by the X-band horn antenna after the combiner, and different modulation parameters were selected for each modulation mode. The probe laser passing through the atomic vapor was detected by a photodiode, and recorded by a spectrum analyzer or the oscilloscope; signal components of the multiple basebands can be separated by a band-pass filter.  
Figure 2a illustrates the experiment-related Rydberg energy levels. The probe laser (\(\sim\)780 nm) generated from an external-cavity diode laser was frequency-locked to the atomic transition 5S\({}_{1/2}\) (F = 2) and 5P\({}_{3/2}\) (F = 3), and the coupling laser (\(\sim\)480 nm) generated from a high-power doubled-frequency diode laser system was frequency scanned across 5P\({}_{3/2}\) (F = 3) and Rydberg state 59D\({}_{5/2}\). By regulating and controlling the laser, the phenomenon of a ladder three-level electromagnetically induced transparency (EIT) [18] appeared. When RF E-field at a resonant frequency of 10.22 GHz between 59D\({}_{5/2}\) and 60P\({}_{3/2}\) was applied, it made EIT spectroscopy split, known as Autler-Townes (AT) effect. At the same time, another detuning RF E-field simulated by a microwave signal source was applied. When the RF E-field was frequency-detuned from the resonant transition, the observed AT-splitting spectroscopy became asymmetrical and two transmission peaks are not equal in height [16] (as shown in Figure 2b). In essence, the change of RF E-field frequency leads to the change of lineshape of AT-splitting, so both AM and FM baseband signals at multiple channels can be detected through the change of lineshape of a mixed AT-splitting [14].