esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Behavior of Twin-Spool Rotor-Bearing System with Pedestal Looseness and Rub Impact
## 3 Analysis
### Dynamic Analysis of the System with Rub Impact Failure  
The vibration behavior of the rub impact nodes will also impact the overall vibration behavior of the entire machine. Figure 16a,b show the shaft centerline orbit of the LP rotor before and after the occurrence of a rub impact. When the gap between the rotor and stator is wide, the system rotates at a speed of 1034 rad/s without experiencing any rub impact faults. The orbit of the shaft's centerline in the entire LP rotor forms a perfect circle. During this time, the system exhibits simple harmonic motion, and the vibration of the fan, intermediary bearing, and the No.1 LP turbine remains low. Occasionally, the rotor-stator clearance is decreased during the design process of an aero-engine to enhance its performance. However, this reduction in clearance might result in rub impact faults. Figure 16b displays the centerline orbit of the entire LP rotor following the occurrence of rub impact failure in the No.3 LP turbine. The No.1 LP compressor is enclosed by a bearing, which reduces its susceptibility to failure. However, the vibration amplitude at the other nodes experiences a substantial rise and no longer exhibits simple harmonic motion. The rub impact failure primarily impacts the inter-shaft bearing and the No.1 LP turbine, causing the vibration amplitude at these two locations to increase by almost twofold and enter a condition of intricate motion. The HP rotor experiences rub impact failure like the LP rotor. This results in an almost doubling of the overall amplitude with the inter-shaft bearing being more significantly affected as it is located closer.  
Figure 14: Shaft centerline orbit of HP rotor at 1428 rad/s. (**a**) Faultless HP rotor. (**b**) HP rotor with slight loose fault.  
frequency component is caused by the local rub impact; the rotor intermittently collides with the case to change its trajectory, resulting in regular fluctuations in the vibration signal.  
To investigate the impact of changes in the rub impact initial gap on the vibration characteristics of the system, this study examines the variation in the shaft centerline orbit at different rub impact gap values (\(\delta=2.17,2.15,2.00,1.73\) mm), as depicted in Figure 18. When the clearance between the components is almost equal to the amplitude of vibration, Figure 18a displays the path of the shaft's centerline during the rub impact. At this point, the amplitude of vibration of the disk is slightly greater than the gap between the components, indicating that the disk and the case remain in contact, resulting in a complete rub impact throughout the entire rotation of the disk. As the rub impact gap decreases, the shaft centerline orbit gradually transitions to Figure 18d. The amplitude fluctuations of the system's vibration response resemble those shown in Figure 17c, indicating that the system is experiencing local rub impact.
## 3 Experimental Setup and Methodology  
### Experimental Setup and Measurement Execution  
The aim was to enable a complete reconstruction of the local sound pressure under a limited incomplete angle. For this purpose, a full angle projection measurement of the sound field was performed with the CLIV system proposed in [9] as a reference. The sound field to be investigated was limited by a translucent PMMA cylinder of 100 mm diameter to allow a angular scan range of 180\({}^{\circ}\) and shield the reference beam of the interferometer from the propagating sound wave in the measurement object. An approximately plane sound wave was generated by a speaker mounted to the top end of the tube. Thus, the sound wave propagates perpendicular to a hexagonal array of 169 Helmholtz resonators at the bottom of the cylinder (see Figure 5). Each Helmholtz resonator is cylindrical, has a resonator volume of 1.41 cm\({}^{3}\) and a hole aperture of 2 mm diameter (see Figure 6). This results in a resonator frequency of 1479 Hz. The measurement of the local sound field was performed at the resonator frequency. The maximum effective lateral range of the CLIV system is 25 mm with a spatial resolution of 31.5 \(\upmu\)m. In order to perform a measurement of the entire cylinder, the measurement was divided into seven, 2 cm wide lateral areas, which were stitched in post-processing.  
Figure 5: Acoustic measurement setup for generating a local sound field with 180\({}^{\circ}\) optical access.  
Figure 6: Liner model used for measurements, providing 169 Helmholtz resonators.  
The measurement procedure starts with setting the lateral position. After that follows the measurement of each scan angle with a resolution of 0.1\({}^{\circ}\) in a angular scan range of 180\({}^{\circ}\). When a scan is completed, the next lateral position is scanned.  
The measuring time per single angle scan is 1s. In addition, there is a camera hardware-related memory cycle of approx. 2 min, which is decisive for the total measurement time per scan point. The measurement volume is located 31.5 um above the resonator surface. This allows for the largest possible local sound pressure changes and avoids reflections from the resonator surface that affect the measurement result. The measuring system is capable of recording a pixel area of 64 pixels in height with the necessary frequency. However, only a reduced height of 16 px was used in order to speed up the measuring process.
# Massive MIMO Indoor Transmissions at 38 and 65 GHz Applying Novel HBF Techniques for 5G
## 3 Methodology
### Implemented Algorithms
#### 3.1.2 Massive MIMO Hybrid Beamforming Applying SFBC (SFBC-HBF)  
The conventional SFBC-OFDM technique is a space-frequency coding; in other words, assuming N\(s\) = 2, if \(\mathbf{s}\in\mathbb{C}^{\text{N}sx1}\) is the vector of input symbols whose value is [s1 2], then the output of the SFBC mapper will be \(c(k)=[s1\;s2]\) and \(c(k+1)=[s2^{*}-s1^{*}]\) for each pair of subcarriers \(k\) and \(k+1\), respectively. Note that the spectral efficiency is the same as in the SISO case because in each carrier pair, 2 symbols were transmitted between the 2 streams. However, the spectral efficiency applying the HBF algorithm was N\(s\) times higher than the SISO case, as can be seen in the results.  
Applying the SFBC technique to the input signal, the block diagram would be as follows the Figure 4:The input signal passed through the SFBC mapper and was then sent to the \(V_{D}\). The received symbols \(\mathbf{y}(k)\ \in\mathbb{C}^{N_{x}\times 1}\) for the \(k\)th subcarrier can be represented by:  
\[\mathbf{y}(k)=\mathbf{H}_{new}(k)\mathbf{c}(k)+\mathbf{n}(k), \tag{9}\]  
where \(\mathbf{n}(k)\ \sim\ \mathcal{CN}\big{(}0,\sigma^{2}\mathbf{I}_{Nr}\big{)}\) denotes the additive noise vector at the \(k\)th subcarrier.  
In practice, in order to simplify the decoding and to be able to apply linear decoding, this SFBC coding was translated into the matrix by which the transmitted signal was multiplied, as was indicated using space-time coding [26]. In this case, the received signal \(\mathbf{y}(k^{\prime})\in C^{NrNsx1}\) is as follows:  
\[\mathbf{y}\big{(}k^{\prime}\big{)}=\mathbf{H}_{new}^{c}\big{(}k^{\prime}\big{)}\mathbf{s} \big{(}k^{\prime}\big{)}+\mathbf{n}\big{(}k^{\prime}\big{)}, \tag{10}\]  
where \(k^{\prime}=1\), \(2\), \(\ldots\), \(N\)/\(N\)s, and \(\mathbf{H}_{new}^{c}(k^{\prime})\in\mathbb{C}^{NrNsxNs}\) is the space-frequency code matrix, which, if \(N\)s = 2, is defined as follows:  
\[\mathbf{H}_{new}^{c}(k^{\prime})=\left[\begin{array}{ccc}h_{new11}^{k^{\prime}}& h_{new21}^{k^{\prime}}\\ h_{new21}^{*(K^{\prime}+1)}&-h_{new11}^{*(K^{\prime}+1)}\\ &\vdots&\vdots\\ h_{new1N_{r}}^{k^{\prime}}&h_{new2N_{r}}^{k^{\prime}}\\ h_{new2N_{r}}^{k^{\prime}}&-h_{new1N_{r}}^{*(K^{\prime}+1)}\\ \end{array}\right], \tag{11}\]  
where \(h_{new2N_{r}}^{k^{\prime}}\) is the coefficient (2, Nr) of the matrix \(\mathbf{H}_{new}(k)\) at the \(k^{\prime}th\) subcarrier.  
The estimation of the transmitted symbols is as follows:  
\[\overset{\wedge}{\hat{\mathbf{s}}}\big{(}k^{\prime}\big{)}=\mathbf{H}_{new}^{c}\big{(} k^{\prime}\big{)}^{+}\mathbf{H}_{new}^{c}\big{(}k^{\prime}\big{)}\mathbf{s}\big{(}k^{ \prime}\big{)}+\mathbf{H}_{new}^{c}\big{(}k^{\prime}\big{)}^{+}\mathbf{n}\big{(}k^{ \prime}\big{)} \tag{12}\]
# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 5 Performance Evaluation
### Dissemination Protocols  
We consider two representative protocols from the literature for performance comparison. Each protocol is simulated in VANET environment and then in MEC for comprehensive analysis. The following list describes these protocols:  
* Enhanced Slotted 1-Persistance (ES1P): which is an enhanced version of the well-known Slotted 1-Persistance [40]. The enhancement is achieved by integrating the same suppression mechanism utilized in our methodology to accommodate urban vehicular environment. ES1P does not utilize traffic condition information to control the broadcast. Instead, it considers a predefined number of timeslots for setting the broadcasting delays of the receiving vehicles.
* TURBO: which is a representative example of the recent beacon-based protocols for VANETs [15]. In contrast, our methodology and ES1P utilize beacon-free data dissemination.
* ES1P-MEC: which presents the performance of ES1P with the aid of MEC.
* TURBO-MEC: which presents the performance of TURBO with the aid of MEC.
# LCNS Positioning of a Lunar Surface Rover Using a DEM-Based Altitude Constraint
## 3 Moonlight and LCNS
### LCNS Constellation  
The LCNS constellation considered in this paper is comprised of four satellites in elliptical lunar frozen orbits (ELFO). These orbits are highly eccentric and have their argument of perilune (i.e., the point at which the spacecraft in lunar orbit is closest to the Moon) close to the Moon's North Pole to ensure maximum coverage above the Moon's South Pole. The four satellites are located in two different planes, wherein the satellites in the first plane are separated by 123.4\({}^{\circ}\), and the satellites in the second plane are separated by 180\({}^{\circ}\). The orbital parameters are provided in Table 1.  
The constellation parameters in this publication have been derived by ESA based on internal studies, but these may be further optimised. Better performances are indeed expected with the constellation provided in the Phase A/B1 studies currently ongoing.
## 3 System Overview
### Basic Notations  
#### 3.2.1 Coordinate Transformation  
The forklift pose can be represented by a transform matrix \(\mathbf{T}\):  
\[\mathbf{T}=\begin{bmatrix}\mathbf{R}&t\\ \mathbf{0}^{\top}&1\end{bmatrix} \tag{1}\]  
where \(\mathbf{T}\in SE(3)\) is a transformation matrix from the current forklift coordinate system to the world coordinate system, with the relative rotation matrix \(\mathbf{R}\in SO(3)\) and the translation vector \(\mathbf{t}\in\mathbb{R}^{3}\). In particular, \(SE(3)\) represents the special Euclidean group [33], defined as follows:  
\[SE(3)=\left\{\mathbf{T}=\begin{bmatrix}\mathbf{R}&t\\ \mathbf{0}^{\top}&1\end{bmatrix}\in\mathbb{R}^{4\times 4}|\mathbf{R}\in SO(3), \mathbf{t}\in\mathbb{R}^{3}\right\} \tag{2}\]  
The 3D rotation matrix \(\mathbf{R}\) constitutes a special orthogonal group:  
\[SO(3)=\left\{\mathbf{R}\in\mathbb{R}^{3\times 3}|\mathbf{R}\mathbf{R}^{\top}= \mathbf{I},\det(\mathbf{R})=1\right\} \tag{3}\]  
During the estimation of the relative transformation using optimization tools, the optimized relative pose can be estimated by minimizing the energy function. For that purpose, the Lie algebra [33] of \(\mathfrak{se}(3)\) corresponding to the tangent space of \(SE(3)\) was used:  
\[\mathfrak{se}(3)=\left\{\boldsymbol{\xi}=\begin{bmatrix}\boldsymbol{\rho}\\ \boldsymbol{\phi}\end{bmatrix}\in\mathbb{R}^{6},\boldsymbol{\rho}\in\mathbb{R }^{3},\boldsymbol{\phi}\in\mathfrak{so}(3),\boldsymbol{\xi}^{\wedge}= \begin{bmatrix}\boldsymbol{\phi}^{\wedge}&\boldsymbol{\rho}\\ \mathbf{0}^{\top}&0\end{bmatrix}\in\mathbb{R}^{4\times 4}\right\} \tag{4}\]  
We denote the Lie algebra elements as \(\boldsymbol{\xi}=[\boldsymbol{\rho},\boldsymbol{\phi}]^{\top}\in\mathbb{R}^{6}\), in which \(\boldsymbol{\rho}\) is translation and \(\boldsymbol{\phi}\) is 3D rotation. The meaning of \(\wedge\) is extended: \(\wedge\) acts on \(\boldsymbol{\xi}\) to convert a six-dimensional vector into a four-dimensional matrix. Each transformation matrix \(\mathbf{T}\in SE(3)\) can be computed from \(\boldsymbol{\xi}\in\mathfrak{se}(3)\) using the exponential function according to Lie algebra:  
\[\mathbf{T}=\exp(\boldsymbol{\xi}^{\wedge}) \tag{5}\]  
For a point \(\mathbf{P}=[X,Y,Z]^{\top}\) in the world coordinate system, we used \(\widetilde{\mathbf{P}}=[X,Y,Z,1]^{\top}\) to represent its homogeneous coordinate formation, so we could apply left multiplication of the transformation matrix \(\mathbf{T}\) to transform \(\widetilde{\mathbf{P}}\) into \(\widetilde{\mathbf{P}}^{\prime}\) in the current camera coordinate system:  
\[\widetilde{\mathbf{P}}^{\prime}=\mathbf{T}\widetilde{\mathbf{P}} \tag{6}\]  
For cases without ambiguity, \(\mathbf{P}\) could represent both the homogeneous and non-homogeneous coordinates.
## 2 Methods
### Stratification Strategy  
The traditional method based on data stratification selects only a single confounding factor, such as age or gender. The reason gender can become a confounding factor is that men and women have many differences in physiological organs and body structures, such as height and weight, hormone secretion, fat distribution, etc., which can change the efficacy of drugs and affect the adverse reactions to drugs. The same is true for age. Due to the large proportion of antibiotic-related reports in CSRD and the complex relationship between age, gender and antibiotics (for example, metronidazoleole is mainly used for female gynecological diseases, and quinolone is mainly used for the elderly), this paper proposes a stratification method based on a decision tree by integrating the relevant factors of patients and drugs. The specific steps include:  
1. Determining the age division intervals by using the cumulative distribution of antibiotic-related reports based on the patient's age;
2. \(\chi^{2}\) was used to analyze the correlation between age and drug category (\"Antibiotics\" or \"Non-antibiotics\"), as well as gender and drug category;
3. Data stratification was conducted by a classification algorithm, based on a decision tree by using drug category as the class label, and the two confounding factors of \"gender\" and \"age\" as the stratification conditions;
4. DPA was performed on the multiple datasets generated by the decision tree;
5. The performance of this method is compared with that of non-stratification, gender-stratification and age-stratification. Classification performances were assessed by means of precision, recall and F-measure.  
The overall research framework is shown in Figure 1.  
Figure 1: Overall research framework.
# Occlusion Avoidance in a Simulated Environment Using Reinforcement Learning
## 6 Experimental Results
### Occlusion Avoidance
#### 6.2.2 Comparative Analysis  
To compare the newly developed reinforcement learning agent with our prior solutions, we performed experiments using five different agents:  
1. a random agent moving in random directions;
2. an environment agent that stepped in the environment gradient direction;
3. a pre-trained agent that moved towards the predicted gradient direction--as introduced in Section 5.2;
4. two reinforcement learning agents--as introduced in Section 5.3.  
The agents were only able to perform normalized length (\(l=1\)) steps. For these experiments, we set up the environment to contain three random objects, but the initial azimuth values were now always set to \(\varphi=0\). We ran each setting of the environment with the five aforementioned agents for 50 randomly initialized environments.  
Figure 5: Two chairs and two tables test cases: the number of steps taken by the agents to reach an unoccluded state from various initial azimuth (\(\varphi\)) values.  
The outcomes of the experiments are displayed in Table 4. From the shown results, it can be observed that the reinforcement learning agents were, in fact, capable of arriving at an unoccluded state in a reliably lower amount of steps than all the other agents used. The superiority of the robustness of this latter method could be observed in both the mean value and the variance of the number of steps taken by the agents. The intersection normalized RL agent outperformed all the other methods in our test scenario; however, the reasons contributing to this result are subject to further tests and examination.  
We also performed a paired sample Bayesian \(t\)-test [39] using the BEST library to provide statistical proof of our method's efficacy. The test used 50 paired samples performed with the aforementioned four methods. For simplicity's sake, we only used the Intersection-normalized version of the RL agent, as the two versions were very close in performance. The summarized results of the tests are presented in Table 5, while the full results are shown in Figure A1 in the Appendix A.  
The results demonstrated that all three proposed methods credibly surpassed the random policy with significant effect sizes. In practice, this meant that our methods reached the unoccluded state in significantly fewer steps. It was also apparent that the two neural network-based methods achieved higher results than the method using the environment gradients. However, the predicted-gradient method only surpassed the former with a confidence of 94.6%, barely falling short of the commonly accepted 95% threshold. The RL agent-based solution, however, credibly outperformed all other solutions, albeit the average difference between the two best performing methods was less than a single step. Still, this difference resulted in a confidence of 98.47%, which, we argue, was possible due to the significantly lower variance of the RL agent's performance.
# Analysing Near-Miss Incidents in Construction: A Systematic Literature Review
## 5 Discussion
### Key Aspects of Near-Miss Investigations in the Construction Industry (Question 4)  
A review of near-miss incidents in the construction industry indicates a notable shift in the methods employed to address safety and risk management. A review of the literature reveals a tendency towards the adoption of more integrated and dynamic research methods. These combine quantitative risk assessment with causal analysis of hazards, with the objective of identifying the principal factors affecting worker safety.  
The initial area that requires attention is the development of risk assessment systems that are based on data obtained from actual workplaces. The advent of modern technology, such as real-time tracking of worker locations, has rendered it a pivotal instrument in the identification and prediction of potential collisions and other hazardous interactions. This approach not only enhances the precision of risk assessments, but also facilitates expeditious response to evolving site circumstances.  
In the context of near-accident root cause analysis, the development of management processes such as [PERSON] et al.'s eight-step process illustrates the importance of a systematic approach to each incident. The proactive utilisation of near-accident data to enhance design practices can markedly diminish the probability of accidents. The PaICF model elucidateshow profound causal analysis can facilitate more efficacious risk factor management at disparate organisational levels.  
A pivotal instrument for comprehending the dynamics and evolution of safety scenarios over time is the examination of time series of near misses. The insights gained can serve as the foundation for anticipating the timing of subsequent occurrences that may, under specific circumstances, culminate in workplace accidents.  
It is becoming increasingly evident from research into the physical factors involved in construction processes that both the people involved and the tools and machinery they utilise are integral components of the safety ecosystem. It is crucial to comprehend the interactions between humans and machines, as well as the influence of safety culture on worker conduct, for the effective management of risks.  
In conclusion, these four pivotal research areas illuminate novel strategies for enhancing safety in the construction industry. They propose a paradigm shift from a reactive to a proactive approach to safety management, wherein nearly every incident is regarded as an opportunity for learning and improvement.
## 4 Semantic Representation: EUREKA Schema Proposal
### Design Goals  
The EUREKA schema was designed with four primary goals in mind. First, _interpretability_ was prioritized to ensure that the structured outputs are human-readable, allowing clinicians to verify and act upon them easily. Second, the schema emphasizes _expressiveness_, enabling it to capture nuanced clinical meanings such as temporal qualifiers (e.g., \"since last night\"), compound symptoms (e.g., \"chest pain with shortness of breath\"), and context-specific urgency markers. Third, _multilingual compatibility_ was considered essential to abstract language-specific phrasing into consistent conceptual slots, ensuring robustness across diverse linguistic inputs. Lastly, the schema supports _intervention alignment_, allowing the parsed structures to be directly mapped to clinical decision trees and actionable protocols.
# An On-Demand Charging for Connected Target Coverage in WSRNs Using Fuzzy Logic and Q-Learning +
## 1 Introduction
Fuzzy Q-charging combines two techniques: Fuzzy logic and Q-learning, each of which is designed to accomplish a given task with a certain goal. First, we exploit Fuzzy logic in an optimization algorithm that determines the optimal charging time at each charging location. The Fuzzy logic-based algorithm aims at maximizing the number of alive sensors. Fuzzy logic is used to cope with network dynamics by taking various network parameters into account during the determination process of the optimal charging time. Second, given the optimal charging time at every charging location, we leverage the Q-learning technique to select the next charging location to maximize the network lifetime. The MC maintains a Q-table containing the charging locations' Q-values representing the charging locations' goodness. The Q-values will be updated in a real-time manner whenever there is a new charging request from a sensor. We design the Q-value to prioritize charging locations at which the MC can charge a node depending on its critical role. After finishing tasks in one place, the MC chooses the next one, which has the highest Q-value, and determines an optimal charging time. The main contributions of the paper are as follows. * We propose a Fuzzy logic-based algorithm that determines the energy level to be charged to the sensors. The energy level is adjusted dynamically following the network condition. Based on the above algorithm, we introduce a new method that optimizes the optimal charging time at each charging location. It considers several parameters (i.e., remaining energy, energy consumption rate, sensor-to-charging location's distance) to maximize the number of alive sensors. * We propose _Fuzzy Q-charging_, which uses Q-learning in its charging scheme to guarantee the target coverage and connectivity. _Fuzzy Q-charging_'s reward function is designed to maximize the charged amount to essential sensors and the number of monitored targets. The remainder of the paper is constructed as follows. Section 3 describes the network model and Q-learning. We briefly review the related work in Section 2. Section 4 introduces our proposed algorithm. Section 5 includes the performance evaluation. Finally, Section 6 concludes the paper and shows our future work.