esa-sceva/satcom-chunk-collection huggingface dataset:
# Automotive Seat Comfort and Vibration Performance Evaluation in Dynamic Settings
## 2 Materials and Methods
### Laboratory Evaluation
#### 2.2.4 Measures  
WBV  
While the participants were being exposed to the actual field-measured and sinusoidal vibration on the motion platform, raw unweighted vibration data were collected at 1280 Hz using a data recorder (DA-40; Rion Co. LTD; Tokyo, Japan) with a tri-axial seat-pad accelerometer (Model 356B40; PCB Piezotronics; Depew, NY, USA) mounted on the testing seats and an additional tri-axial accelerometer (352C33; PCB Piezotronics; Depew, NY, USA)  
Figure 3: Lab test protocols. * Trial-axial field vibration was randomized between Sonata and Yukon. ** X-Y-Z Sinusoidal vibration was randomized among the axes.  
magnetically mounted to the floor of the motion platform according to the ISO 2631-1 WBV standard [17]. The raw unweighted acceleration data were filtered, weighted, and analyzed to calculate root-mean-square (RMS) weighted average vibration (Aw), as shown below. Aw was then normalized to daily equivalent vibration exposure measures [A(8)]:  
\[A_{w}=\left[\frac{1}{T}\int\limits_{0}^{T}a_{w}^{2}(t)dt\right]^{\frac{1}{2}} \tag{1}\]  
where \(A_{w}\)(t): instantaneous frequency-weighted acceleration at time, \(t\); \(T\): the duration of the measurement, in seconds.  
Power spectral density (PSD) analyses were also performed to determine how the foam characteristics of the three seats impacted the vibration energy as a function of frequency (m\({}^{2}\)/s\({}^{4}\)/Hz). A custom-built LabVIEW program (Version 2018; National Instruments, Austin, TX, USA) was used to extract and summarize the vibration energy content over the frequency range from 0.5 to 30 Hz with a frequency resolution of 0.5 Hz.  
Self-Reported Measures  
Pre-/post- rankings were collected based on participants' preferences. The Borg CR-100 scale was used to evaluate self-reported localized body discomfort in the upper and low back, shoulders, neck, ankles/feet, knees, thighs, and tailbone (0 being no discomfort at all; 100 being maximal discomfort). Perceived seating comfort was measured using a 7-point Likert scale (1 being least comfortable; 7 being most comfortable).
## 4 The Experiments
### Model Sensitivity Analysis  
The objective of the model sensitivity study was to analyze the impact of measurement disturbances on the prediction accuracy of the classifier. In this area of research, the analysis was conducted based on the model with the highest prediction accuracy. The obtained model, trained on noise-free data representing Failure 1, was verified on the Failure 2 and Failure 3 test data, to which Gaussian noise with a specified variance was added. Since the input data for the predictive model underwent a standardization process, each variable had a mean equal to 0 and a standard deviation equal to 1. This data processing allows us to define a single universal noise level (\(\sigma\)) for all attributes and add it to each variable. In the analysis, it was assumed that subsequent cases would be tested with additive Gaussian noise by generating a noise vector of size 8 and adding it to each testing sample. The experiments were repeated 20 times for given \(\sigma\), and 6 \(\sigma\) values were evaluated: \(\sigma=[0.01,\ 0.05,\ 0.1,\ 0.15,\ 0.2,\ 0.25]\), each time measuring the prediction performance. Finally, the mean value and standard deviation were measured for each value of _sigma_.
# Spatiotemporal Contextual 3D Semantic Segmentation for Intelligent Outdoor Mining
## 4 Discussion and Insights
### Comparison with Multimodal Fusion Methods  
Another important insight stems from comparing our method with other multimodal fusion approaches such as Two-streamMOS and LiDAR-IMU-GNSS. While these methods combine point cloud and image data, our method, which focuses on spatiotemporal feature aggregation, achieves better performance by effectively modeling the temporal dependencies of point cloud data. This result suggests that the challenge of aligning multimodal data, particularly the dimensional inconsistency between point clouds and images, leads to the loss of critical feature information during fusion. Our approach, on the other hand, avoids this issue by relying solely on point cloud data, showing that a more focused and effective exploitation of spatiotemporal features can outperform complex multimodal fusion techniques.
# MPResiSDN: Multipath Resilient Routing Scheme for SDN-Enabled Smart Cities Networks
## 5 Evaluation Methodology
### Experimental Setup  
All experiments were carried out using Ubuntu MATE 18.04, with 2 GB of RAM and a 1.90 GHz processor. The networks were emulated using Mininet 2.2.2 interfaces with Python 2.7.17. The challenges were applied by configuring links losses. The RYU controller was used to implement the multipath algorithm. Data traffic was generated using iPerf3 and captured using tcpdump. The results were analyzed using Wireshark and Python Pandas. The emulation parameters are listed in Table 1.
# Electromagnetic Soil Characterization for Undergrounded RFID System Implementation
###### Abstract  
This paper can be divided into two main parts. In the first part, an extensive experimental study for electromagnetic characteristic assessment of different soil samples is presented. In the second part of the paper, a practical verification of the obtained link budget model is performed using a buried metal-backed RFID tag antenna under a 40 cm sand layer. This antenna is designed to operate at 915 MHz with MONZA3 chip. Using Impinj Speedway system the tag antenna could be detected, and its information could be read from different distances of up to more than 2.5 m.  
Keywords:soil EM characterization; electrical permittivity; underground RFID; oil mining; RFID measurement +
Footnote †: journal: Electronics
# The Development of an Advanced Air Mobility Flight Testing and Simulation Infrastructure
## 4 Use Cases and Results
### The Flight Plan Update Test  
For this test, 12 flight plans are created based on direct routes by connecting the origin and destination points and given as an input to the pre-flight services part of the co-simulation environment. The original flight plans undergo risk assessment, dynamic capacity balancing, and strategic conflict resolution modules, respectively. First, the shortest paths are created for each individual flight plan based on obstacles and are updated locally considering the risk levels. Eight flights are rerouted after the risk assessment process. For the dynamic capacity management, flights are checked in case they are passing through a saturated sector. Therefore, three flight plans are updated with its alternative trajectory. Finally, conflicts at strategic phase are checked, and two of the flight plans are updated via departure delays. Figure 7 shows the initial direct flight plans and their updated versions, and Table 3 depicts the number of changed flights after risk assessment, dynamic capacity balancing, and strategic conflict resolution services.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Risk Assessment** & \
\begin{tabular}{c} **Dynamic** \\ **Capacity** \\ **Management** \\ \end{tabular} &
\begin{tabular}{c} **Strategic** \\ **Conflict** \\ **Resolution** \\ \end{tabular} & **Total** \\ \hline Affected & \multirow{2}{*}{8/12} & \multirow{2}{*}{3/12} & \multirow{2}{*}{2/12} & \multirow{2}{*}{10/12} \\ Flights/Total Flights & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of updated flights after each pre-flight service.  
Figure 6: The AAM simulator framework.
# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 7 Preliminary Analysis A: Performance Comparison of ICIC Algorithms  
The proposed solution of this paper is fully described in Section 10. However, before presenting the final results, we present three preliminary analyses that were important to guide some decisions regarding the Q-Learning algorithm. The first analysis is presented in this section, and it is a performance comparison between the ICIC techniques presented in Section 3.1. This study evaluates the impact of FFR on cell-edge UEs in a simple scenario. The algorithms were executed under the same conditions and their parameters assume the default values defined either on ns-3 or in the literature.
## 2 An Isolated, High-Power, Photovoltaic Pumping System (Monitored System)
### Variable-Frequency Drive  
The AC induction motor is driven by a 45 kW variable-frequency drive (VFD) [19] that works at a voltage of 400 V with a maximum continuous current of 94 A. The variable-frequency drive has 2 analog inputs, 2 analog outputs, 3 digital inputs, 3 configurable digital outputs, 2 relay outputs, one safe torque off input, 2 Modbus RS485 communication ports and an on-board PLC (Programmable Logic Controller). The on-board PLC includes, in addition to programming functions, two PID controllers with anti-windup and user scaling to provide flow-specific functionality, a water hammer control with S-ramp decelerations, low load condition monitoring, and hysteresis for dry pump detection [19].  
Table 11 of Appendix A presents the main characteristics and parameters of the VFD [19].
###### Abstract  
This study aimed to determine the most effective wavelength for the proliferation of Human Buccal Fat Pad Mesenchymal Stem Cells (BFPMSCs) in cell culture. These cells can be used for different purposes such as regenerative periodontal procedures. Materials and Methods: The wells containing BFPMSCs were subjected to laser irradiation at 635, 660, 808, and 980 nm wavelengths with 1, 15, 2.5, and 4 J/cm\({}^{2}\) energy densities. Cell proliferation and viability were evaluated after 1, 3, and 5 days with the methyl thiazolyl tetrazolium (MTT) assay. Result: The proliferation rate of human Buccal Fat Pad Mesenchymal Cells (BFPMSCs) was increased on the first and third days at a wavelength of 808 nm and day five at a wavelength of 980 nm in comparison to the control group. Our findings distinguished that PBMT with 635, 660, 808, and 980 nm wavelengths increased the proliferation of BFPMSCs. Conclusion: The best laser radiation setting, which led to the highest proliferation rate of the cells, included a wavelength of 808 nm with 2.5 J/cm\({}^{2}\) energy density.  
mesenchymal stem cells; MTT; photobiomodulation therapy; cell proliferation; buccal fat pad
# Structural Behaviour of Aluminium-Timber Composite Beams with Partial Shear Connections
## 2 Materials and Methods
### The Experimental Programme of the Study  
Four-point bending tests were conducted on three aluminium-timber composite beam specimens with different screw spacing (Figure 1).  
The beams were tested using an Instron 8505 Plus machine (Instron, High Wycombe, Buckinghamshire, UK). Each composite beam consisted of an LVL slab and an extruded beam made of an AW-6060 T6 aluminium alloy. The dimensions of the analysed beams were identical. However, the number of shear connectors and their spacing were different (Figure 2). Nine linear variable differential transformers (LVDTs) were used to measure the deflection and the slip between the LVL panel and the aluminium beam. A spreader beam was used to obtain pure bending of the composite beams between the loading points (when the shear from the dead load was not taken into account). The composite beams were located on roller supports. Steel plates were used in each support to prevent the local yielding of the girder flange. The 10 mm clearance holes were drilled through the top flanges of the aluminium beams. The diameter of the pre-drilled holes in the LVL slabs was 7 mm. The pre-drilled holes made it easier to install the screws. The transverse screw spacing was 50 mm. The longitudinal spacing was different for each beam (i.e., 120 mm, 160 mm, and 200 mm). Based on the connector spacing, the tested beams were designated as B120, B160, and B200. The authors of this paper used a torque wrench (Sandvik Belzer, IZO-I-100, 10-100 N\(\cdot\)m, Sandvik, Portlaoise, Ireland) and a torque moment of 40.0 N\(\cdot\)m to install the screws. Due to the fact that the torque wrench was used, all screws were tightened with the same torque moment and LVL was not crushed during screw installation. The loading procedure consisted of two steps. In step one, the composite beams were loaded to ca. 20 kN and then unloaded to ca. 5 kN. In step two, the beams were loaded up to the failure point. In step one, the clearances in the beam connections were reduced. In each step, a displacement control regime and a piston velocity of 1.0 mm/min were used to evaluate the behaviour of the analysed beams after the failure load had been achieved.  
Figure 1: Four-point bending test set-up.  
Figure 2: The analysed beams subjected to four-point bending (1–9—linear variable differential transformers).
# Residual Vibration Reduction in Flexible Systems Based on Trapezoidal Velocity Profiles
## 4 Sensitivity Analysis  
As stated above, the vibration suppression conditions C1 and C2 provide two options for parameter tuning, offering flexibility in implementation. If either of these conditions is satisfied, complete residual vibration elimination can be achieved for an undamped system. The two conditions depend on the system parameter \(\omega_{n}\). Since modeling errors are inevitable in practice, these conditions may not achieve zero vibration. In this case, satisfying both C1 and C2 would improve robustness against parameter uncertainties. This characteristic can be better understood through sensitivity curves [43], which plot the dimensionless residual vibration percentage against variations in natural frequency and damping ratio (see Figure 5). For a clearer visualization, Figure 6 gives an enlarged view of Figure 5.  
The reference maximum vibration for trapezoidal profiles represents the full-scale value. The reduction trend of the vibration level with increasing actual natural frequency is induced by the relative decrease in the peak acceleration value given by Equation (18).  
Figure 5: Sensitivity curves of the profiles with respect to variations in the actual natural frequency \(\omega\) and damping value \(\zeta\): (**a**) optimized trajectory with only one condition C1 or C2; (**b**) optimized trajectory with both conditions C1 and C2.  
Figure 6: Enlarged view of sensitivity curves in Figure 5: (**a**) optimized trajectory with only one condition C1 or C2; (**b**) optimized trajectory with both conditions C1 and C2.  
While residual vibration can be eliminated for lightly damped modes, this capability deteriorates as the mismatch between estimated and actual natural frequencies grows. However, the optimized profile with double conditions maintains significantly lower residual vibration than the optimized profile with a single condition within a certain range of modeling error. For quantitative comparison, we define the insensitivity as the range of normalized natural frequency where the dimensionless residual vibration percentage remains below 10%, as indicated by the horizontal dashed red line in Figure 6. Observe that the double conditions produce an optimized profile that exhibits superior robustness compared to the single condition. Particularly, for lightly damped systems, the optimized profile with a single condition provides an insensitivity value of 0.206, while the optimized profile with double conditions provides an infinite insensitivity value as the tolerance is satisfied over the entire frequency band of \(\omega/\omega_{n}>0.74\). Given that practical modeling errors typically fall within this range, the latter is preferable under parameter uncertainties. Additionally, damping values below 0.1 have a minimal impact on residual vibration levels. This observation supports the common assumption of lightly damped modes for dominant deformations in industrial machines.
# Human-AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM Era +
## 6 Emerging Human-AI Collaboration Problems in RSA
### Emerging Problem 8: Reconstructing High-Resolution Live Video Feeds  
As discussed in Problem 7, the quality of the live camera feeds can significantly affect the RSA performance. Besides the steadiness, the clearness is also an important aspect of the quality of the live camera feed, especially for recognition tasks. Limited by the configuration of the user's smartphone camera and the transmission bandwidth [43], the resolution of the live camera feed showing to the agent is not always high enough to support recognizing small objects. The enhancement of the low-quality video feed is a major problem in RSA services, as listed in problem G4.(2) in Table 1. Therefore, we identify an emerging research problem of reconstructing high-resolution live video feeds that can be addressed by human-AI collaboration.  
If only considering AI techniques, we can adopt video super-resolution methods [106]. Video super-resolution aims to reconstruct a high-resolution video from the corresponding low-resolution input. It is a classic CV problem but also challenging. The state-of-the-art video super-resolution methods [152; 153; 154] delicately design deep neural networks and train the models with large-scale labeled datasets. When applying these models to RSA services, a major bottleneck is the high complexity of the models and the limited computational resources for processing the full-size video.  
To overcome this limitation, we can resort to a solution based on human-AI collaboration. In RSA services, the agent is usually interested in viewing the details of a certain part of the live video feed. For example, in the task of reading signages and texts, the agent only cares about the area with signages and texts on the video. Therefore, the video super-resolution model can be run only on the portion of the video specified by the agent. In this way, we can have enough computational resources to only process the relevant part. Similar to Problem 7, the study of Problem 8 (reconstructing high-resolution live video) can directly benefit Problem 3 (recognizing digital content) and Problem 4 (recognizing texts on irregular surfaces).
## 4 Demodulation Method
### Low Complexity Demodulation Methods
#### 4.2.1 SET Method  
The SET method used only high-energy sub-carriers among the sub-carriers of received data to demodulation. First, we observed the energy \(\left(\left|d_{j,m}(c)\right|^{2}\right)\) of the sub-carriers in the CSI of received data and defined the thresholds optimized for each SNR (signal to noise ratio) through simulation. Only subcarriers whose energy exceeded the thresholds was used for Euclidean distance calculation as in the following Equations (13) and (14).  
\[e_{j,l,m}(\text{c})=\left\{\begin{array}{cc}\left|p_{j,l}(c)-d_{j,m}(c) \right|^{2}&\text{if }\left|d_{j,m}(c)\right|^{2}>\text{{threshold}}\\ 0&\text{Otherwise}\end{array}\right. \tag{13}\]  
\[\text{q}_{l,m}=\sum\limits_{j=1}^{N}\sum\limits_{c=0}^{C-1}e_{j,l,m}(\text{c}) \tag{14}\]  
Thereafter, as in the ED method, we demodulated the data with smallest value of the Euclidean distance between received preamble and data symbol using Equation (12).
## 6 Simulations Results
### Effect of Snapshot Number on Success Rate and RMSEs  
The simulation conditions were the same as in Section 6.1. Among them, the only snapshot numbers were 64, 128, 256, 512, and 1024. The relationship curve between success rate and snapshot number is shown in Figure 10, and the relationship curve between RMSEs and snapshot number is shown in Figure 11. With the increase in the snapshot number, the success rate increased and the RMSEs decreased. The success rates of A8 and A9 consistently outperformed the others. The RMSEs of A7 and A9 were always lower than the others, especially when the snapshot number was larger. A9 was overall optimal, with a high success rate and small RMSEs.  
Figure 8: The relationship curve between success rate and JSR.  
Figure 9: The relationship curve between RMSEs and JSR.
###### Abstract  
Dual-functional radar-communication systems are extensively employed for the detection and control of unmanned aerial vehicle groups and play crucial roles in scenario monitoring. In this study, we address the downlink precoding problem in large-scale multi-user multiple-input multiple-output dual-function radar-communication systems equipped with low-resolution quantized digital-to-analog converters. To tackle this issue, we develop a weighted optimization framework that minimizes the mean squared error between the transmitted symbols and their estimates while satisfying specific radar performance requirements. Due to the complexity introduced by discrete constraints, we decompose the original problem into three sub-problems to reduce computational burden. Furthermore, we propose a dynamic projection refinement algorithm within the alternating direction method of multiplier framework to efficiently solve these sub-problems. Numerical experiments demonstrate that our proposed method outperforms existing state-of-the-art techniques, particularly in terms of bit error rate in low signal-to-noise ratio scenarios.  
Keywords:dual-functional radar-communication; large-scale multiple-input multiple-output; low-resolution quantized precoding; discrete optimization +
Footnote †: journal:  
_Article_
# AI-For-Mobility--A New Research Platform for AI-Based Control Methods
## 5 AFM--Holistic Application Test Vehicle for AI-Based Control Methods  
With its holistic sensor and actuator architecture, the AFM is a test platform with universal application possibilities regarding AI-based vehicle control methods. As already emphasized in Section 1.1, all application layers for vehicle control functions can be addressed by the AFM (cf. Figure 2).  
First projects and research activities have already been started, where the AFM has proved its strong capabilities.  
For the development and testing of novel state estimation methods, the AFM is an excellently suited test platform due to its extensive sensor equipment. These methods can be implemented and evaluated in the vehicle both in a model- and AI-based way. The AFM is used as a test vehicle for an ongoing project to investigate AI-based state estimation methods (so-called virtual sensors) using cloud resources. The sensor data synchronously recorded via the RCP platform forms the basis for computationally expensive data processing, such as training of virtual sensors in the cloud. Moreover, approaches for both detection and compensation of sensor failures can be tested on the AFM.  
Investigations of AI-based vertical dynamics control approaches can be tested on the AFM using the semi-active suspension technology [29]. For example, reinforcement learning methods can be used to determine a control law that addresses both ride comfort and road holding properties. Using simplified models identified on the basis of four-post test rig measurement (see Section 2.1) or derived from the high-fidelity model (see Section 4), a good trade-off between the computational performance and the model accuracy for the learning process can be found.  
In addition, numerous other projects are already planned. Novel methods for implementing path-following control algorithms (cf. [30]) can be realized on the AFM thanks to the DBW kit enabling planar motion control. Due to a large number of perception sensors, both static and dynamic obstacles can be included in the path-following control, thus allowing cooperative control algorithms to be investigated on the AFM such as single- and/or multi-agent reinforcement learning (cf. [31]). By coupling a path-following controller with the AI-based path detection via camera images (see Section 2.3.2), the AFM demonstrates that multiple application layers of the control pyramid (Application Layer & Motion Execution Layer, see Figure 2) can be addressed.  
The high demand from industry partners and research institutions to use the AFM as a test platform confirms its attractiveness in the context of testing AI-based control algorithms.