esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Applications of RIM
### Geotechnical Engineering: Solid Soil Particle-Groundwater Interaction
The integration of transparent soil technology with particle image velocimetry has successfully demonstrated the displacement vectorsof the soil surrounding a laterally loaded pile, offering valuable insights into the soil's displacement behavior. Moreover, the application of transparent visualization technology has the potential to be extended to the monitoring of soil deformation processes and displacement patterns across a range of fields, including but not limited to retaining walls, roadbeds, and agricultural soils. This underscores the versatility and broad applicability of this innovative approach. [PERSON] et al. [55] prepared a transparent cemented soil (TCS) using fused quartz as the skeleton, hydrophobic timed silica powder as the cement, and a mixed mineral oil as the pore fluid. The TCS exhibited geotechnical properties similar to those of natural soil and soft rock, making it suitable for physical modeling tests. The study presents empirical formulas for the change in shear strength parameters under varying conditions. [PERSON] et al. [56] expanded upon this research by preparing TCS with different shear strengths and conducting unconsolidated-undrained triaxial compression tests. They analyzed the mechanical characteristics of TCS and revealed the mesoscopic mechanism of changes in its mechanical parameters. Although transparent geotechnical model experiments provide relatively convenient experimental conditions and intuitive visualization compared to in-situ testing, they tend to simplify some of the influencing factors in real engineering, for instance, approximation of geotechnical materials and simplification of boundary conditions, etc. This will also be discussed in a future in-depth study in Section 3.3. Validation in combination with traditional observation can better help the study to be more accurate.
# Self-Reporting Technique-Based Clinical-Trial Service Platform for Real-Time Arrhythmia Detection
## 3 Results  
### Wearable ECG Device Selection  
The VP-100 (TriBell Labs, Gyeongsan, Korea), a wearable ECG device, was adopted for this clinical-trial service platform [24]. This device, approved as a Class 2 medical device by the Korea's MFDS, measures a single-channel ECG using disposable, lubricated electrodes attached to the chest. The measured signals were digitized and bandpass filtered. R-peaks were then detected to compute the R-R intervals and HRs, which were expressed in units of beats per minute (BPM). Based on the results, three types of arrhythmias were identified: bradycardia (criteria: BPM < 60), tachycardia (criteria: BPM > 100), and premature ventricular contraction (PVC, criteria: QRS intervals > 120 ms). The raw ECG, HRs, and arrhythmia-detection outcomes were saved in the internal storage medium, and 256 ms long packet data of 512 bytes were transmitted to the mobile app via Bluetooth connection. The amount of ECG data transmitted to the mobile app for an hour is approximately 6.9 megabytes (MB).  
VP-100 weighs 50 g, and its diameter and thickness are 66 mm and 11 mm, respectively; these enable participants to wear the device for several hours without discomfort. The technical specifications of the device are listed in Table 1. Figure 1 shows the placement of VP-100 on a participant's chest.  
\begin{table}
\begin{tabular}{c c} \hline Sampling rate & 250 Hz \\ Bandwidth & 1–30 Hz \\ Resolution & 24-bit \\ Common mode rejection ratio & 105 dB \\ Mode of operation & Continuous \\ Interface type & Wireless (Bluetooth V2.1) \\ Data-transmission distance & Up to 10 m \\ Power source & Li/Po rechargeable battery 3.7 V/800 mAh \\ Operating time & Up to 16 h with a fully charged battery \\ Internal storage & 4 GB secure digital card \\ \hline \end{tabular}
\end{table}
Table 1: Technical specifications of the VP-100.
## 3 Proposed Handover Scheme
### Handover Decision  
Based on the position prediction, handover initiation time is determined when the unnecessary handover rate is estimated to meet the requirement and the outage probability is minimized.
# Adaptive Satellite Navigation Anti-Interference Algorithm Based on Inverse Cosine Function
## 3 Improved Variable Step Size LMS Algorithm
### Improved Inverse Cosine Variable Step Size LMS Algorithm
Therefore, by employing Formula (3), we can establish the relationship between step size and error in the following manner:  
\[\mu(n)=\left|\arccos(e(n))-\frac{\pi}{2}\right| \tag{13}\]  
According to Expression (13), and also for better control adjustment of the function shape, the parameters \(\alpha\), \(\beta\), \(\gamma\), and \(m\) are introduced to obtain the improved variable step size function, as follows:  
\[\mu(n)=\frac{-2\arccos[\alpha|e(n)^{\gamma}|]+\pi}{m\arccos[\alpha|e(n)^{ \beta}|]+2} \tag{14}\]  
The normalization algorithm is used in the iterative formula for the weighting coefficients, and the parameter \(\xi\) is introduced to avoid a too small value of \(\mathbf{X}^{T}(n)\mathbf{X}(n)\), which is the minimum value to be neglected. In summary, the step size formula of the ICVS-LMS algorithm is enhanced by incorporating the inverse cosine function and introducing a parametric control function shape, aimed at better adhering to the algorithm's application rules [41]. The golden key steps of the ICVS-LMS algorithm, which utilizes the inverse cosine function, can be summarized as follows:  
\[e(n)=d(n)-\mathbf{W}^{T}(n)\mathbf{X}(n) \tag{15}\]  
\[\mu(n)=a[1-\frac{1}{be^{2}(n)+1}] \tag{9}\]0  
\[\mu(n)=a[1-\frac{1}{be^{2}(n)+1}] \tag{9}\]1  
\[\mu(n)=a[1-\frac{1}{be^{2}(n)+1}] \tag{9}\]2 Curve before and after adjusting the inverse cosine function.
## 3 Topology Optimization Design of U-Frame of Space Electro-Optical Tracking and Pointing Turntable
### Subsection Conceptual and Mathematical Models of Topology Optimization  
The U-frame structure illustrated in Figure 1 within the turntable system results from empirical design based on weight constraints, with a lack of exploration into the optimal static and dynamic stiffness in the design process. To address this, a conceptual model for the topology optimization of the U-frame is established, taking into account both performance design requirements and lightweight design constraints. Specifically, the optimization objective function aims to minimize the structural compliance of the U-frame (thereby maximizing the overall structural stiffness). Additionally, constraints for multi-constraint topology optimization encompass the volume percentage of the optimized model compared to the initial model, the first natural frequency value of the structure, and the maximum stress value. The mathematical model for topology optimization design is outlined as follows.  
The objective function for the topology optimization is to minimize the static strain energy of the U-frame, aiming to maximize the structural stiffness. Multiple constraints are considered in the optimization process, including the percentage of optimized model volume compared to the initial model volume, the first-order natural frequency value of the structure, and the maximum stress value. These constraints enable a  
based optimization solution method, one can derive the objective function in relation to the design variables as detailed below.  
\[\frac{\partial C}{\partial x_{e}}=-px_{e}^{p-1}\mathbf{u}_{e}^{T}\mathbf{K}_{e} \mathbf{u}_{e}=-\frac{p}{\chi_{e}}x_{e}^{p}\mathbf{u}_{e}^{T}\mathbf{K}_{e} \mathbf{u}_{e}=-p\frac{E_{e}}{\chi_{e}} \tag{5}\]  
where \(p\) is the penalty index [21], \(p=3\) in this study, \(\mathbf{u}_{\text{e}}\) is the element displacement vector, \(\mathbf{K}_{\text{e}}\) is the element matrix, and \(\mathbf{E}_{\text{e}}\) is the element strain energy. It can be seen from Formula (5) that the derivative of the objective function is only related to the element, and since the formula is negative, when the material properties of the element improve (the element density becomes larger), the structural compliance decreases, that is, the stiffness of the structure increases, which is also consistent with the actual physical model.
# Granular Flow Impact on Shed Tunnels and the Buffering Effect of Cushion Layers
###### Abstract  
Granular flow is one of the most destructive geological hazards in mountainous areas, posing a severe threat to the economy and personnel safety in the region. Shed tunnels are widely used for the prevention and mitigation of granular flow hazards. Thus, comprehensively studying the impact mechanisms of granular flows on shed tunnels is significant for disaster prevention and mitigation. This study adopts a combined approach of a physical model experiment and Particle Flow Code (PFC) simulation to investigate the impact force of granular flow on shed tunnels and the buffering effect of cushion layers. The influences of slope angle, cushion layer thickness, and cushion layer particle size are discussed. It is revealed that as the slope angle increases, the velocity of the granular flow and the impact force on the shed rise significantly. When the slope angle increases from 40\({}^{\circ}\) to 60\({}^{\circ}\), the peak velocity surges by 25%, while the impact force intensifies by 2-3 times. Moreover, increasing the thickness of the cushion layer can mitigate the interaction between the granular flow and the shed tunnel, thereby enhancing structural safety. With an increase in cushion layer thickness from 0 to 200 mm, the impact force is reduced by approximately 50%. Meanwhile, reducing the particle size of the cushion layer effectively decreases the impact force, resulting in less kinetic energy and providing a stronger cushioning effect.  
granular flow; shed tunnel; impact force; cushion layer; PFC +
Footnote †: journal: Applied Sciences
# A CMOS Multiplied Input Differential Difference Amplifier: A New Active Device and Its Applications
## 4 Discussion  
Several advanced active devices with a various number of terminals and various principles of controllability have been developed in recent years [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. Recently proposed active devices are compared in Table 2. However, only one of these exhibits the ability to perform multiplication between two input voltages, so acts as our MIDDA concept. MMCC device [5; 6] has this operation implemented to voltage transfer between \(Y\) and \(X\) terminals of current conveyor of second generation (product of voltages from \(Y_{1}\) and \(Y_{2}\) terminals is available at X terminal). It may serve for the electronic control of voltage gain (\(A\)) between the \(Y\) and \(X\) terminals. However, as we stated in the introductory part of this paper, the MMCC does not simultaneously provide the results of multiplication in the form of current, and the summation/subtraction operations. Therefore, these limitations further complicate the implementation of the device in building blocks and applications discussed in this paper (for example in the case of the AM-DSB modulator).  
Figure 10: Example of measured output response of the AM-DSB modulator with 5 MHz carrier frequency and modulation depth \(m=25\%\): (**a**) time domain; (**b**) frequency domain (output spectrum).
# Adapting Satellite Soundings for Operational Forecasting within the Hazardous Weather Testbed
## 5 Conclusions  
The HWT and other operational forecasting testbeds provide a forum for researchers and forecasters to exchange ideas which can set the agenda for future research. However, for useful information to be exchanged, researchers need to develop a clear understanding of the role their work plays in operations. NWP models have been refined for 30 years to address forecaster needs. In most cases, satellite data will not have the same spatio-temporal resolution as a model. However, satellite datasets are valuable as a supporting data source to corroborate model output. In places where in-situ observations are sparse and models are not available or unreliable, satellite data will have a more primary role.  
Regardless of where one works in the research product development, it is critical to clearly understand the needs of the forecasters. This requires direct communication and collaboration with forecasters and a flexible research strategy to address their feedback. NOAA testbeds are a useful means for evaluations in an operational setting, showing researchers the daily realities of operational weather forecasting. For products outside of operations, this may mean informally surveying forecasters, providing online tutorials and training, and initiating direct communication. Another informal means of bridging the research-user gap is making sure data are accessible and providing tools to visualize them. This can lead to the rapid development of a product beyond continued algorithm refinement.  
Conceptualization, M.G. and C.D.B.; software, E.B.B., J.F.D., and B.H.K.; investigation, R.B.E. and N.S.; resources, E.B.B.; writing, R.B.E. and N.S.; visualization, R.B.E.; supervision, M.G., C.D.B., W.S., and K.W.; project administration, W.S.; funding acquisition, M.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Most aspects of this research were supported by the JPSS PGRR program. The portion of the research carried out at Marshall Space Flight Center was also supported by [PERSON] of the Earth Science Division at NASA HQ as part of the NASA Short-term Prediction Research and Transition Center. The portion of the research carried out at Jet Propulsion Laboratory (JPL), California Institute of Technology, was also supported under a contract with the National Aeronautics and Space Administration.  
The authors declare no conflict of interest.
# Quantum Multi-User Broadcast Protocol for the \"Platform as a Service\" Model
## 4 Analysis
### Analysis of Security under Typical Attack
#### 4.4.1 Attack via Direct Measurement  
One can analyze the security of multicast data packet communication via direct measurement. \(F_{i}(i=1,2,\cdots,M)\) receives the random \((4n+\delta)\) qubits, who measures each qubits in the basis \(\sigma_{x}\) or \(\sigma_{z}\) at random. \(S_{i}(i=1,2,\cdots,N)\) receives \(\varepsilon(|\Psi_{0}\rangle\langle\Psi_{0}|)\), where \(\varepsilon\) describes the quantum operation due to the combined effect of the channel and eavesdropper's ([PERSON]) actions. \(S_{i}(i=1,2,\cdots,N)\) then publicly announces this fact. For now, each of the \(N+1\) nodes has its own states described by separate density matrices. Note that, at this point, since \(S_{0}\) did not reveal \(b\), [PERSON] has no knowledge of what basis she should have measured to eavesdrop in the communication. At best, she can only guess. If her guess were wrong, then she would have disturbed the state received by \(S_{i}(i=1,2,\cdots,N)\). Moreover, whereas in reality the noise \(\varepsilon\) may be partially due to the environment in addition to [PERSON]'s eavesdropping, it does not help [PERSON] to have complete control over the channel. Thus, [PERSON] is entirely responsible for \(\varepsilon\).  
When the quantum multicast router first receives a multicast packet from \(S_{1}\), the packet check is performed, using the routing table to verify that the packet arrived on the right interface for the packet's source. If the packet arrived on any other interface, drop it.
## 6 Multi-Objective Optimization
### Analysis of Results  
The optimization results of the NSGA-II algorithm are given below. A comparison of the primary suspension parameters before optimization and after optimization are shown in Table 6, while a comparison of the simulation results before optimization of the algorithm and after optimization are shown in Table 7 [35].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Suspension Parameter** & **Pre-Optimization** & **Post-Optimization** & **Rate of Change/\%** \\ \hline longitudinal stiffness & & & \\ \(x_{2}\)/(N\(\cdot\)m\({}^{-1}\)) & 500,000 & 565,667 & 13.133 \\ lateral damping & & & \\ \(x_{3}\)/(N\(\cdot\)s\(\cdot\)m\({}^{-1}\)) & 10,000 & 10,220 & 2.200 \\ vertical damping & & & \\ \(x_{5}\)/(N\(\cdot\)s\(\cdot\)m\({}^{-1}\)) & 25,000 & 28,245 & 12.980 \\ vertical stiffness & & & \\ \(x_{6}\)/(N\(\cdot\)s\(\cdot\)m\({}^{-1}\)) & 100,000 & 90,867 & \(-\)9.133 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of various suspension parameters before and after optimization using the NSGA-II algorithm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dynamics Index** & **Disengage Rail** & **Wheel Load** & **Wheel Axle Lateral** \\  & **Factor** & **Reduction Rate** & **Force (N)** \\ \hline evaluation criteria & 0.8 (second limit) & 0.6 (second limit) & \\  & 1.2 (first limit) & 0.65 (first limit) & 62,780 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation criteria for the dynamic performance indexes.  
The optimization results of the AMGA algorithm are given below. A comparison of the suspension parameters before and after optimization are shown in Table 8, while a comparison of the simulation results before and after optimization are shown in Table 9.  
The dynamic simulation results reveal that, after primary suspension parameters were optimized by the two algorithms, the dynamic performance indexes of the bogie still conformed to the \"Dynamic Performance Evaluation and Test Method for Particular Class Vehicles and Tracked Machine\" (GB/T17426-1998) [25]. In particular, the optimization by the AMGA algorithm improved the vertical acceleration by 3.497% and the lateral acceleration by 23.333%, while that by the NSGA-II algorithm enhanced the vertical acceleration by 4.895% and the lateral acceleration by 11.667%.  
In summary, the AMGA algorithm demonstrated a more salient effect on improving both the vertical and lateral accelerations. These results suggest that the AMGA algorithm is more adaptable when a battery track engineering vehicle with an inner axle-box bogie navigates a minimum radius curve of 20 m.
## 4 Vehicular Ad Hoc Networks  
Vehicular ad hoc networks (VANETs) include moving entities (vehicles) and fixed entities (roadside units). These entities work together to share important traffic information about roads [14; 24]. The emergence of new technologies such as 5G mobile networks has provided new services for VANET to connect vehicles with everything (V2X). In this regard, the 3 rd Generation Partnership Project (3 GPP) has proposed the new radio V2X standard (NR-2X) for SideLink (SL) communication to directly communicate different items such as vehicles and personal devices without connecting to RSUs [25]. 3 GPP mainly emphasizes high reliability, maximum coverage, low delay, and energy-saving, especially for battery-based equipment. According to Figure 1, 3 GPP supports four modes of V2X connections: vehicle-to-vehicle (V2V), vehicle-to-pedestrian (V2P), vehicle-to-infrastructure (V2I), and vehicle-to-network (V2N).  
* V2V and V2P provide the direct connection between vehicles-user equipment (UE) and as well as between vehicles and vulnerable road users (VRU) such as cyclists, bikers, and wheelchairs.
* V2I focuses on the connection between vehicles and road infrastructure.
* V2N allows UEs to communicate with a server.
## 4 Innovation of the TPM Methodology Regarding the Division of Preventive Maintenance
### Standardisation of Processes  
All processes in individual AM blocks of the preventive maintenance are systematically standardised and made more effective based on failures and abnormalities. All failures in reactive maintenance on production equipment are registered in a machine component tree--equipment internal components breakdown. Then, individual components are divided according to the failure frequency in reactive maintenance (Figure 6), and the frequency number places them into the standard of the AM block execution. Thus, individual processes of the AM block of preventive maintenance are controlled in real time according to the failure occurrence of a given component. Maintenance can then effectively react to the current situation in reaction maintenance and adjust or modify the AM standards according to the given situation.  
Figure 6: Division of production equipment according to the internal construction.  
This method also enables analyses based on incidents found during the AM block and their division according to their occurrence. Each AM block is then automatically standardised as individual steps of preventive maintenance stem from reactive maintenance. They are standardised according to the internal construction of the equipment, see the table above. All AM prevention points are stored in a database under a unique ID so that the data are historically traceable, such as:  
* Number of findings in individual points;
* Historical points;
* Others.  
Each point is assigned specific equipment where it will be performed, its order within the prevention sequence, a recommended time of performance, description, and type.  
As mentioned above, equipment is divided into areas according to its position in the production hall, where each sector is planned on one day, Monday--Sunday in 7/24 production. Regarding priority, the equipment is planned each even or odd week. Such a planning model ensures that the planning process is as effective as possible. Apart from preventive maintenance, an important factor is that the planning takes place automatically based on equipment priority, mentioned later in this text, and based on the division of the production hall into individual sectors. Sectors are areas controlled by the same group of operators. In the case of equipment downtime, operators are effectively reorganised to other workplaces. Another significant benefit is that the maintenance team working in one sector does not have to move from place to place, which saves time for transporting.
## 2 Materials and Methods
### Design of the High Capacitance Ratio RF MEMS Switch and Simulation  
The proposed high capacitance ratio MEMS switch is shown in Figure 10a,b. It includes a serpentine flexure MEMS metallic beam, metal-insulator-metal (MIM) floating metallic membrane, dielectric layer and a CPW transmission line.  
Table 1 shows the material of each part of the MEMS switch.  
The physical dimensions of the suggested RF MEMS switch are shown in Figure 11. Four serpentine springs are coupled to the RF MEMS switch beam.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name of Each Part** & **Material** \\ \hline MEMS beam & Au \\ MIM floating metallic membrane & Au \\ Dielectric layer & Si\({}_{3}\)N\({}_{4}\) \\ CPW transmission line & Au \\ Substrate & High resistance silicon \\ \hline \hline \end{tabular}
\end{table}
Table 1: The material of each part of the MEMS switch.  
Figure 10: (**a**) The top view of the proposed MEMS switch; (**b**) The dismantled figure of the proposed MEMS switch.  
Then we use the HFSS software to model and simulate the switch through the above structure. Figure 12 shows the simulated S parameters and current distribution diagrams. From Figure 12a,c, we can draw a conclusion that the insertion loss is better than 0.5 dB at 30 GHz when the switch is in the up state and the isolation is more than 42 dB at the resonant frequency in the down-state. Current distribution diagrams shown in Figure 12b,d demonstrate the remarkable signal isolation capacity of the MEMS switch.  
Figure 11: Physical dimensions of the suggested RF MEMS switch.  
Figure 12: Simulated S parameters and current distribution diagrams: (**a,b**) up-state; (**c,d**) down-state.