esa-sceva/satcom-chunk-collection huggingface dataset:
# FEM Modelling Approaches of Bolt Connections for the Dynamic Analyses of an Automotive Engine
## 5 Simplified Modelling Approaches  
The following simplified modelling approaches for the bolt connections were benchmarked against the validated \"full 3D bolt model\":  
1. A simplified 1D modelling (Figure 6a) based on combinations of rigid and 1D beam elements (hereinafter \"1D model\"). Such an approach was based on the use of beam elements (CBAR) to model the pin and rigid elements (RBE2) to connect it to the components: stars of rigid elements connecting independent central nodes on bolt axis to the surrounding dependent nodes (in this way such nodes are constrained to undergo the same displacements); an intermediate 1D rigid element was added so as to allow for slight misalignments between components (rather typical for models with many components).  
Figure 4: Experimental and numerical (“full 3D”) inertance vs. frequency measured at engine bracket, with excitation in the same point (highlighted in Figure 3a).  
Figure 5: Eigenmode at nearly 2700 Hz (Figure 4) with highlight of engine bracket response.  
A simplified \"Glue surfaces\" condition where master and slave surfaces are glued together with a \"freeze\" contact condition (Figure 6b), namely, no relative motion is allowed between such interacting surfaces. This modelling can be considered as the simplest modelling approach among the three proposed here because the bolt is not modelled at all (consequently discarding its mass contribution). In particular, just the stiffening effect of bolts is modelled, using stars of rigid elements connecting master nodes, selected along the bolt axis, with slave nodes, selected along the internal radius of the cylindrical surface (Figure 6b).
# Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review
## 4 Findings and Discussion
### Obtained Results Discussion
#### 4.4.5 Control Plane Offloading  
According to the research in [96], throughput can improve by up to 102 times and latency can decrease by 98% when switch hardware stores state data. In [57] authors show that offloading 35.79% of PFCPS messages to programmable devices results in a 57% increase in throughput compared to solutions without this feature. Additionally, the study conducted in [67] reports that network latency can be reduced by up to 40% compared to traditional multi-switch topology implementations that do not process NGAP packages.
## 3 The Fault Propagation and Instances of Cascading Failure Models
studies the resilience of the air traffic control network under network attacks on route facility network nodes by attackers. In the following scenario, the attacker simulates an attack on the route nodes in the route facility network, causing the route nodes to fail, and resulting in the failure of control coordination network nodes that rely on the service data provided by the node, as shown in Figure 6. The process of cascading failure is as follows:  
1. Node \(T3\) fails;
2. Due to service dependency, \(T3\) causes \(C2\) and \(M3\) to fail;
3. Due to control dependency, \(C2\) causes \(I2\), \(I3\), and \(I5\) to fail;
4. Due to internal links, \(I2\) causes \(I4\) to fail. When the network reaches a steady-state and the cascade failure ends, a total of 7 nodes have failed, while 7 nodes are still functioning normally.
# A Multi-Frame PCA-Based Stereo Audio Coding Method
## 2 Multi-Frame PCA-Based Coding Method
### Multi-Frame Joint PCA  
In the experiment, a phenomenon was observed that the rotation matrices of adjacent frames are greatly similar. Therefore, it is possible to do joint PCA with multiple frames to generate one rotation matrix, that is, multiple frames use the same rotation matrix. Therefore, the codec can transmit fewer rotation matrices, and bitrate can be reduced.  
Below is one way to do joint PCA with least error. First, frequency domain coefficients of n sub-frames are constructed as \(n\) original matrices \(X_{[\begin{array}{ccc}16&15\end{array}]^{\prime}}\)\(X_{2[\begin{array}{ccc}16&15\end{array}]^{\prime}}\)\(\cdots X_{n[\begin{array}{ccc}16&15\end{array}]^{\prime}}\) respectively; then, the original matrices of each sub-frame are used to form one original matrix \(X_{[\begin{array}{ccc}16n&15\end{array}]^{\prime}}\). This matrix is used to obtain one rotation matrix and \(n\) reduced-dimensional matrices.  
If too many matrices are analyzed at the same time, the codec delay will be high, which is unbearable for real-time communication. Besides, the average quality of restored audio signal decreases with the increase in the number of frames. Therefore, the need to reduce bitrate and real-time communication should be comprehensively considered. A subjective listening test was designed  
Figure 5: Example for matrix construction (“value” means the value of cells in original matrix, “column” means the column of original matrix, and “row” means the row of original matrix).  
to find the relationship between the number of frames and the quality of restored signal. 10 audio materials from European Broadcasting Union (EBU) test materials were coded with multi-frame PCA with different numbers of frames. The Mean Opinion Score (MOS) [22] of the restored music was recorded by 10 listeners. The statistical results are shown in Figure 6.  
As is shown in Figure 6, when the number of frames is less than 6 or 8, the decrease of audio quality is not obvious. A suitable number of frames is then subjected to joint PCA. Taken together, when 8 sub-frames are analyzed at the same time, the bitrate and the delay of encoder is acceptable, that is, for every 40 ms signal, 8 sub-frame reduced-dimensional (Rd) matrices and one rotation matrix are transferred. Main functions of the mono encoder and decoder combined with multi-frame joint PCA are shown in Figures 7 and 8. In encoder, 40 ms signal is used to produce 8 Rd matrices and 1 rotation matrix. In decoder, after receiving 8 Rd matrices and 1 rotation matrix, 8 frames are restored to generate 40 ms signal.  
Figure 6: Subjective test results for different number of frames.  
Figure 7: Multi-frame in encoder. (PCA, Principle Component Analysis; Rd, reduced-dimensional).
## 1 Introduction
### Novelty and Contributions  
In this paper, we propose an Energy-Efficient UAV-assisted Clustering Hierarchical (EEUCH) routing protocol which ensures reliable data collection from ground SNs with the assistance of UAVs. Furthermore, we consider wake-up radio (WuR)-enabled SNs to minimize the energy consumption by diminishing idle listening and overhearing, when there is no UAV for data collection and when multiple wake-up calls (WuCs) are receiving from multiple UAVs, respectively. WuR-enabled SNs are equipped with two radios: (i) main radio (MR) and (ii) wake-up receiver (WuRx). The MR consumes 1000 times more energy, due to reliable data packets transmission, than that of the WuRx [30], which listens to the channel continuously for incoming WuC [31; 32]. Thus, WuR works in an on-demand manner [33]. In the proposed model, all the SNs are in sleep mode, in which they sense the surroundings and wait for the WuC from the UAV. As the WuC is detected by the SN's WuRx, the SN turns on the MR for data packets transmission to the UAV, following the proposed data flow model. When the data packets are received by the UAV, it sends an acknowledgment (AcK) to the SN. The SN receives the Ack, turns off the MR, and goes back into sleep mode. The major contributions of this paper are listed as follows.  
Figure 1: A typical WSN depicting a cluster-based hierarchical routing protocol.  
* We design and develop a novel cluster-based routing protocol, EEUCH, for WSN-based applications of IoT in which there is no CH election phase. UAVs act as CHs by default and are responsible for the collection of data packets from the FoI.
* We create a novel data flow model in which UAVs act as ABSs. This model uses a combination of clear channel assignment (CCA), carrier sense multiple access/collision avoidance (CSMA-CA), and time division multiple access (TDMA) slots to ensure energy-efficient and reliable data collection.
* We extend the concept of WuR to the network layer in EEUCH for two main reasons: to enhance the network lifetime, stability time, energy efficiency, and the overall data collection from the FoI, and to address the challenge of cluster overlapping.
* A novel UAV-assisted clustering hierarchical model for remote data collection that ensures nonoverlapping and independent clusters and offers a high degree of flexibility in network size is presented.
* The results of our proposed protocol outperform the benchmark cluster-based routing protocols (i.e., homogeneous, two-tier, and three-tier heterogeneous) with significant gains in nine key network performance parameters.
# Using AI to Improve MIMO Antennas with SRR for 26 GHz by Analyzing Data
## 1 Introduction
### Related Work
The MIMO performance is excellent, with a diversity gain (DG) above 9.99 and an extremely low envelope correlation coefficient (ECC < 0.00005). To predict parameters such as directivity, five supervised ML models are evaluated, and random forest regression again proves the most effective, achieving the lowest prediction errors across MSE, MAE, and RMSE metrics. However, all performance assessments are based on simulations only, with no experimental fabrication or testing conducted, potentially limiting real-world validation. The structure of this document is arranged as follows. This study starts by creating a singular rectangular microstrip patch antenna, refined for effective operation at 28 GHz, utilizing analytical techniques and machine learning to determine the ideal geometric parameters for improving impedance matching and gain. This basic antenna acts as the basis for a more intricate MIMO setup. In the second stage, a two-element MIMO system is created, incorporating split ring resonator (SRR) structures between the antenna elements to minimize mutual coupling. A specialized dataset is created to investigate changes in SRR dimensions, unit counts, and placements. Machine learning models, especially random forest, are developed to forecast ideal SRR setups that reduce S21 while ensuring acceptable S11 levels. A third dataset is utilized to examine how the positioning of SRR and the number of cells affect the balance between isolation and reflection. Ultimately, the suggested design is assessed based on crucial MIMO performance metrics, such as ECC, TARC, CCL, DG, gain, and efficiency. The complete procedure is encapsulated in a comparative table that outlines all datasets, optimization phases, and ultimate antenna performance metrics, showcasing the efficacy of this ML-supported multi-stage design approach. Thus, we adopt a systematic approach that merges theory with practice, incorporating AI to enhance the accuracy of the design process. This is a groundbreaking initiative that could be significantly pertinent to the existing issues in the area of MIMO antennas for 5G/6G.
# Quantum Key Distribution in Kubernetes Clusters
## 6 Test and Validation
### Key Exchange Time and Throughput
Maintaining this exchange rate and changing the low-level exchange rate to 1 kbit/s already leads to a success rate lower than 100%. Indeed, this happens with a certain probability because of some delay in producing the required keys. From an operator standpoint, this event results in a failure in retrieving the QKD secret, which could be solved by essentially forwarding the same request again. With the same method, we increased the requests per second, and according to what we expected, the success rate decreased accordingly. The bottom line is that when we vary the exchange and request rate, the system is resilient even if it cannot serve all requests. Besides, it notifies the upper level of the failure, letting the high-level application take consequent actions. When normal conditions are established again, the success rate returns to 100%. Figure 8: Execution time to handle a KeyRequest resource type. As we did for the PTP exchange in the first test, in the third one, we collected data on the execution time for an MH exchange. In Figure 10, the results of this test are reported and they show how the getKey and getKeyWithJ execution time varies depending on the number of hops. We performed these tests in a configuration with three Kubernetes clusters. As we also expected, the results show that the operator time does not change during the various experiments. Even the getKeyWithId time does not change because this operation is the same performed by the sSAE, and it does not depend on the number of hop between source and destination. The only time that change is the getKey time which increases linearly according to the number of hops. This is due to the chain of key reservations that must be built along with each link of the path.
## 4 Results
### Verification  
In this subsection, the results of the verification test are analysed. Table 5 displays the results in terms of AE and RE for all the samples measured in the verification test, with all the prototypes and configurations. The AE and RE are calculated using absolute values (being all obtained results positive).  
The average AEs, considering all samples for each prototype and each configuration are lower than 2.3 g/L in all the cases. The highest average AE is detected for the P2 and the fourth configuration. In general terms, we can affirm that P2 is the ones with higher AEs with average AE of 1.18 g/L for configuration 1, as the lowest AE, and 2.26 g/L for configuration 4, as the highest AE. According to results of P1, its AEs are lower than the ones of P4, being the configuration 4 the one with lowest AE (1.01 g/L) and configuration 3 the one with the highest AE (1.49 g/L). Finally, data from P3 is characterised by low AE; the minimum and maximum average AEs are 0.15 g/L for configuration 1 and  
Figure 12: Adjusted models for P3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Concentration of OF (g/L)**} & \multirow{2}{*}{**Conf.**} & \multicolumn{2}{c}{**P1**} & \multicolumn{2}{c}{**P2**} & \multicolumn{2}{c}{**P3**} \\  & & **AE (g/L)** & **RE (\%)** & **AE (g/L)** & **RE (\%)** & **  
1.27 g/L for configurations 2 and 3. Thus, in general terms, P3, and particularly the first configuration, is the combinations which offer the lowest average AE in the verification tests.  
According to the AE in the less concentrated sample, which was one of the requirements for our sensor, the lowest AE is obtained for P3 and first configuration (AE of 0.02 g/L). The data of P2 in verification tests is again the one with highest AE. There are only five combinations of prototype and configurations which ensure an AE in the less concentrated sample lower than 1 g/L: P1 configuration 2, P2 configuration 1, and P3 configurations 1, 3, and 4. We have not detected that the AE increases or decreases with the concentration of the verified sample.  
The RE is highest in the verification sample with less concentration of OF in most of the cases. Attending to the differences between the prototypes and their configurations, we can see in Table 5 that the P1 is the one with more stable RE, as happened in AE. P2 is the one with the highest RE, and P3 is characterised by the smallest average RE.
## 2 Materials and Methods  
A systematic literature search was conducted from three biomedical literature databases, namely PubMed, ScienceDirect and Google Scholar, according to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) protocol [15]. In addition, we performed a manual search using all references of the selected articles in order to detect any additional relevant literature. No chronological restriction was applied to our search strategy. PubMed search was performed using the following terms: (sleep disorders OR restless leg syndrome OR hypersomnia OR insomnia OR parasomnias OR circadian dysfunction) and (schizophrenia OR psychotic disorder OR psychosis OR neuroleptic side-effects). GoogleScholar search was conducted using the following terms: \"sleep disorders\" or \"insomnia\" or \"circadian function\" and \"schizophrenia\" or \"psychosis\" or \"psychotic disorders\", while ScienceDirect search was performed with the terms \"schizophrenia and sleep disorders and genetic variations\". Our search focused exclusively on full papers that encompassed a detailed description of their methodology and findings. Two reviewers (K.K. and E.E.T.) independently reviewed all titles and abstracts retrieved from the search according to specific predefined inclusion criteria. At the next step of the search, both reviewers met to discuss each article's eligibility and in 85% of cases they managed to reach a consensus. In the remaining cases of unresolved disagreement between the two reviewers, a third reviewer (G.P.P.), blind to the other reviewers' suggestions, made the final decision.
# Compensation of Frequency Drift in Frequency-Sweep Polarization-Modulation Ranging System
## 4 Conclusions  
We systematically discussed the principle of the FSPM based ranging method and the impact of thermally induced phase delay on \"in phase\" frequency and wavelength multiple. To eliminate the frequency drift caused by the phase delay, a measurement method based on a reciprocating sweep was proposed. To obtain an accurate wavelength multiple, a multi-frequency interval approach was proposed according to the equal interval distribution of \"in phase\" frequencies. The results of our experiment proved the efficiency and applicability of the methods. The maximum deviation of the measured results between the FSPM-based system and the interferometer was 54.4 \(\upmu\)m.  
W.Z.; Conceptualization, S.G., Y.L. and R.J.; methodology, C.L.; validation, S.G., Y.L. and Y.P.; formal analysis, R.J.; resources, S.G.; writing--original draft preparation, Y.L., J.S., Y.P.; writing--review and editing, W.Z.; supervision.  
This research was funded by Civil Aerospace Pre-research Project, grant number D020214.  
The authors declare no conflict of interest.
# Development of Stochastic Fatigue Model of Reinforcement for Reliability of Concrete Structures
## 4 Case Study: Cret De I'Anneau Viaduct
### Reliability Analysis  
The First Order Reliability Method (FORM) is used for reliability analysis [2,46]. An open-source MATLAB-based toolbox, namely the FERUM (Finite Element Reliability Using MATLAB), is used for performing all FORM calculations [47]. The cumulative (accumulated) probability of failure in time interval [0, t] is obtained by Equation (16):  
\[P_{F}(t)=P(g(t)\leq 0), \tag{16}\]  
The probability of failure is estimated by FORM [47]. The corresponding reliability index \(\beta(t)\) is obtained by Equation (17):  
\[\beta(t)=-\phi^{-1}(P_{F}(t)), \tag{17}\]  
where, \(\phi()\) is standardized normal distribution function.  
The annual probability of failure is obtained by:  
\[\Delta P_{F}(t)=P_{F}(t)-P_{F}(t-\Delta t),t>1 year, \tag{18}\]  
where \(\Delta t\ =\) one year. The corresponding annual reliability index is denoted \(\Delta\beta\).
# A Review of Recent Research into the Causes and Control of Noise during High-Speed Train Movement
## 2 High-Speed Train Noise Composition
### High-Speed Train Mechanical Vibration Noise
#### 2.2.2 Wheel-Rail Noise  
Wheel-rail noise is mainly divided into three types: impact noise, rolling noise, and spike noise. When the train speed is less than 300 km/h, rolling noise is the main noise source [30]. Wheel-rail noise is generated by the relative motion between the wheels and the track due to the roughness of the rail [31]. The degree of wheel and rail roughness also determines the amplitude of vibration and other dynamic characteristics of each component during train travel. There is a mutual force between the wheels and the rails during motion of the train, and this force causes the rails and wheels to vibrate, thus radiating noise into the surrounding air. In 1976, to find the sound radiation between the wheel and rail, [PERSON] et al. [32] proposed a simplified engineering method to predict wheel-rail noise by simplifying the wheel and rail as an infinitely long Eulerian beam and an Eulerian beam under continuous elastic support, respectively. [PERSON] considered the symmetry of the wheel based on [PERSON]'s work and calculated several typical wheels of that time by the finite element calculation method [33]. For the rail model, [PERSON] considered the influences of the rail sleeper [34] and wheel rotation [35], used the two-dimensional boundary element method to calculate the vibration sound radiation efficiency, and developed the wheel-rail noise prediction software \"TWINS\" according to the research results.  
With the continuous improvement of the wheel-rail noise prediction model, the technique to eliminate wheel-rail noise is becoming increasingly mature. Reducing wheel and track surface roughness, improving the wheel-rail design, and using local shielding can better solve the train wheel-rail noise problem. [PERSON] et al. [36] added shields to the bogies; [PERSON] et al. [37] eliminated the noise from the train wheels by changing the train track and sleepers; [PERSON] et al. [38] and [PERSON] et al. [39] proposed improvements to the wheels to suppress wheel-rail noise by increasing the elasticity of the wheels; and [PERSON] et al. [40] changed the modalities of the wheels using sandwich-type dampers, thus reducing vibration and noise. For train wheels, [PERSON] et al. [41] improved wheel wear by optimizing the curvature of the wheel webs so that the wheels maintain a stable noise level after long-term use. In terms of train rails, [PERSON] et al. [42] believed that replacing rails on viaduct lines with damped rails can reduce noise while changing the peak frequency band of the noise.