esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[22] conducted horizontal bidirectional loading tests for real-sized high-damping rubber bearings and compared the hysteresis loops of these bearings under bidirectional horizontal loadings with those under unidirectional loadings. [PERSON] et al. [23] investigated the dynamic responses of two buildings connected by viscoelastic dampers under bidirectional excitations. [PERSON] [24] also studied two horizontal ground-motion components in terms of response spectral matching. All the aforementioned studies were conducted by numerical simulation. Moreover, some researchers have investigated the torsional effect of a plan-irregular structural model through shaking table tests [25; 26]. However, all the aforementioned studies have mainly focused on the seismic performance of buildings in one principal earthquake (i.e., the mainshock) and have investigated the torsional response of buildings during the mainshock. There are only a few studies that have focused on the effect of aftershocks on the structural seismic response. To the best of the authors' knowledge, the only studies aimed at MSAS sequences are summarized as follows: [PERSON] and [PERSON] [27] thought it was important to evaluate the effect of the direction of earthquakes' sequences, in particular, when considering the seismic behavior of irregular RC frames. [PERSON] et al. [28] investigated the performance of three asymmetric-plan RC structures during the Tohoku earthquake sequences and found there was an increase in the residual drift demands for irregular RC buildings compared with regular RC structures. Nevertheless, there is still a lack of research that has systematically evaluated the seismic performance of eccentric structures or explored the influence of different eccentricities. Based on the aforementioned discussions, this study systematically investigates the seismic behavior of typically eccentric RC structural models under MSAS sequences and discusses the influence of different eccentricities and unidirectional/bidirectional earthquake loadings on unidirectional/bidirectional eccentric structures. In this study, three-dimensional numerical models with eccentricities of 5%, 10%, 15%, 20%, 25%, and 30%, and an eccentricity of 0 (symmetric structural model) are developed by changing the positions of the structural mass centers, and they are divided into unidirectional and bidirectional structural models. For the input earthquake record, a representative mainshock-aftershock sequence is selected and scaled to different amplitudes according to the IDR, which is then applied under unidirectional and bidirectional loading. A static pushover analysis and a nonlinear time history analysis are conducted on the structural numerical models. Based on the aforementioned analysis, the seismic responses in six types of case are discussed in this study.
## 2 Variable Selection and Research Hypotheses
### Traffic Accessibility  
Good spatial planning and accessibility can significantly enhance the utilization of medical facilities, as the accessibility of healthcare services is primarily determined by factors such as transportation distance and road network connectivity [29]. The London Plan proposed that the location of medical institutions should be determined based on population needs, with priority given to areas that are easily accessible by public transportation or within walking distance. This viewpoint was validated by the research of [PERSON] et al. [35], who found that well-developed transportation networks improve the efficiency of medical facility utilization, especially when paired with appropriately planned parking facilities, which play a crucial role in promoting the effective use of healthcare resources. The number of parking spaces is one of the core indicators of hospital infrastructure planning, directly influencing the hospital's capacity and service quality. While international studies have rarely focused directly on parking spaces, they have confirmed the significant impact of end-of-traffic facilities on healthcare service utilization rates [17]. For example, the Trip Generation Manual by the Institute of Transportation Engineers (ITE) in the United States clearly states that parking capacity is a key parameter in predicting hospital traffic flow [36]. Furthermore, a study by [PERSON] and [PERSON] found that in European cities with highly developed public transportation systems, the contribution of parking spaces to hospital accessibility is minimal. However, in suburban hospitals in the United States, where private car use is more dominant, parking convenience has a significant positive correlation with patient satisfaction [15]. This study seeks to further test the effectiveness of planning indicators such as parking space quantity. Additionally, the study by [PERSON] et al. [37] revealed that the number and distribution of bus stops have a significant impact on residents' choice of healthcare services. Based on these findings, this study selects three indicators to measure hospital traffic accessibility: the number of bus routes (\(\tau_{1}\)), the number of parking spaces (\(\tau_{2}\)), and the number of subway lines (\(\tau_{3}\)) within a 500-m radius of the hospital. The selection of these three variables is primarily based on their direct impact on transportation channels for medical visits and their significance in explaining patient travel behavior.  
\[H_{3}=o_{1}\tau_{1}+o_{2}\tau_{2}+o_{3}\tau_{3}+\vartheta \tag{3}\]  
In the formula, \(H_{3}\) represents the traffic accessibility level function; \(\tau_{i}\) denotes the observed variables of traffic accessibility; \(o_{i}\) refers to the factor loading of \(H_{3}\) on \(\tau_{i}\); \(\vartheta\) represents the error term.  
Based on the above theoretical analysis, three hypotheses are proposed: hospital size, medical level, and traffic accessibility all have a positive impact on outpatient volume (Figure 1).
# Design and Experimental Evaluation of an Electrorheological
## 3 Evaluation of the Haptic Module
### Dynamic Testing: Experimental Results
As expected, the response is greatest with greater voltage. To best examine the electrorheological effect, Figure 11c isolates the force due to the fluid's viscosity change from the total force by subtracting out the actuator's passive resistance, i.e., the actuator's force-displacement curve when no voltage is applied. As shown, the force due to the ER effect rises rapidly at 0.6 mm when the voltage pulse is applied. For the 2 kV applied voltage, the pulse triggered about 0.6 N due to the ER effect, whereas for 4 kV, about 0.8 N was generated. By closing the loop, the module can determine when to provide a pulse, for example, to emulate the clicking sensation of a tactile button [53]. Again, to find whether the change in stiffness resulting from the pulse excitation is noticeable can be determined using the JND. Figure 11d shows the force rates across the actuator's indentation for each pulsed voltage. As shown, when the voltage pulse triggers at 0.6 mm, the force rate increases for all voltages, where the 4 kV excitation resulted in the highest peak force rate of about 31%. All curves peak above the 7-10% threshold, indicating that the stiffness change because of the voltage being stepped on is noticeable to end users. Figure 10: Results of the haptic step response testing: **(a)** programmed voltage step response with respect to indentation depth; **(b)** force–depth curve for 2, 3, and 4 kV pulses; **(c)** isolated force due to the ER effect; **(d)** force rate with respect to indentation depth focused on the effect of the step excitation. Finally, to demonstrate controlling the actuator's output over the entire indentation, we map the depth to the actuation frequency of an applied 4 kV square wave, as shown in Figure 12. At 0 mm depth, the frequency is set to 5 Hz. Over the 1 mm stroke, the frequency increases linearly to 10 Hz, as shown in Figure 12a. The actuator's response is presented in Figure 12b. As shown, the force-depth curve contains oscillations that increase in frequency with respect to depth, as programmed. To best see this effect, we again isolate the force due to the ER effect from the total force by removing the off-state response from the signal, shown in Figure 12c. As seen in the plot, the output frequency was effectively mapped to depth using the proposed sensor and control method. This result demonstrates the module's potential to take in an arbitrary control signal with respect to depth and render it as combined kinesthetic and tactile sensation when pressed by a user. Figure 11: Results of the haptic pulse testing: (**a**) programmed voltage pulse with respect to indentation depth; (**b**) force–depth curve for 2, 3, and 4 kV pulses; (**c**) isolated force due to the ER effect; (**d**) force rate with respect to indentation depth focused on the effect of the pulse.
# On the Impact of Mobility on Battery-Less RF Energy Harvesting System Performance
## 4 Simulations
### Available Energy  
Table 2 shows the peak usable energy for hybrid mode within a high energy zone. Combined with the results in Sections 4.1-4.3, it demonstrates that the sensor node can achieve efficient energy harvesting with high coverage and activations while supporting a large energy storage, which allows a sensor to execute an energy-intensive task whenever required.
## 2 Description of the Multi-Sensor Monitoring System
### Integration of Radar and Wireless Mote  
The SBT80 sensor boards require no modification to work with the TelosB mote [5] as they directly plug into the TelosB platform. However, the miniature Doppler radar was not built to such specifications. In order to retrieve data from the radar, we designed an additional electrical circuitry to integrate the radar and the TelosB mote platform. As a result, we successfully integrated the two independently manufactured devices.  
Figure 3 shows the hardware setup of our integrated radar-mote system. A power supply provided the required 5 volts to the radar and to the amplifier between the radar and mote. The mote is powered by two AA batteries. The signal generator supplies a 1 kHz ramp wave ranging from 5 V to 10 V to tune the output frequencies [f\({}_{0}\),f\({}_{1}\)] = [24.0,24.3] GHz of the Doppler radar transceiver.  
When we physically connected our components together, we found that the radar outputs a negative voltage, but the mote only accepts positive voltage input. Thus we introduced one inverting one non-inverting amplifier circuits between the output of the Doppler radar and the mote's input port, one for the I channel and the other for the Q channel. The inverting amplifier circuits invert the input signal with a gain of 5 and they were built using off-the-shelf components. This solution worked well in the present experiments.  
The integrated system must provide reliable and accurate reading of the actual signal from the radar using the mote. The mote ADC has a 12-bit resolution. A series of reference experiments using a standard lab oscilloscope has confirmed that the accuracy was indeed sufficient for our goals. We managed to achieve the sampling frequency of 200 kHz by tuning the programmable parameters of the mote ADC using standard TinyOS data structures and interfaces. For our experimentation we sample the Doppler radar at 200 kHz and take 2,000 samples, illuminated with a train of 10 chirp pulses. We sample the vibration and acoustic sensors at 8 kHz and also take 2,000 samples from each sensor. In  
Figure 3: Diagram of radar integrated with the wireless mote.  
the above setup, the radar receives its tuning voltage input from a stand-alone signal generator and power from a separate power source. However, on-board chips can serve these purposes and we are testing such a design in our on-going experiments.
## 2 Materials and Methods  
The location of Fukuoka is shown in Figure 1. Fukuoka is a large city with a population of 1.5 million. It is located in the west of Japan and is subject to transboundary air pollution during the winter and spring seasons [5; 6; 7; 8; 9; 15].  
The observation period was from February to April 2020. PM was collected by a high-volume air sampler (HV-RW, Sibata Scientific Technology Ltd., Tokyo, Japan) with a slit-type PM\({}_{2.5}\) separator installed on the roof of the National Hospital Organization Fukuoka National Hospital (Minami-ku, Fukuoka, Japan) at a flow rate of 1000 L/min on a fluoroplastic binder glass fiber filter (TX40 HI20 WW, Pall Corporation, Port Washington, WI, USA) for approximately 24 h. The filter that collected PM\({}_{2.5}\) was cut for PAHs and ion analysis. PAHs were analyzed by Kanazawa University [15; 16; 17], and The National Institute for Environmental Studies analyzed ionic components. Filters were soaked in ultrapure water, irradiated with ultrasonic waves, filtered through disk filters (pore size 0.45 \(\upmu\)m), and the ionic components were analyzed by ion chromatography (Prominence, Shimadzu Corporation, Kyoto, Japan). Ions analyzed were sulfate, nitrate, chloride, sodium, ammonium, magnesium, calcium, and potassium ions.  
For PM\({}_{2.5}\) mass concentration, trace metals including metalloids (Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Pb, Al, Si, S, K, and Ca), elemental carbon (EC), and organic carbon (OC) were measured at City Hall (Chuo-ku, Fukuoka, Japan, for PM\({}_{2.5}\)), Fukuoka University (Jonan  
Figure 1: Location of Fukuoka City.  
ku, Fukuoka, Japan, for metals, EC, and OC); we used data measured by the Ministry of the Environment, Japan, which are publicly available [18]. Daily averages of trace metals and OC/EC data with 4 h and 1 h time resolutions were calculated. Ozone was measured by ourselves at Fukuoka University by using ultraviolet absorption spectrometry in accordance with the ozone standard measurement method established by the Japanese Ministry of the Environment (Model 49i, Thermo Fisher Scientific Inc., Waltham, MA, USA). Ozone was measured with a time resolution of 1 min and daily averages were also calculated. For meteorological data (temperature, relative humidity, solar radiation, wind speed and wind direction), we used data from the Fukuoka Meteorological Observatory, which is publicly available [19]. The NOAA's HYSPLIT (The Hybrid Single-Particle Lagrangian Integrated Trajectory model) system was used to estimate air mass transport pathways [20; 21].
## 2 Experimental Procedure and Acoustic Emission Signal Analysis
### Signal Analysis  
In conventional acoustic emission, different features or descriptors are extracted from the signals [44; 45]. For each signal, prior to the extraction of descriptors, the waveform pre-processing consists in the three following steps [44]: pre-trigger removing, tail cutting, and Shape Preserving Interpolation (SPI) resampling.  
The interest in the use of this specific resampling technique is that, after tail cutting, each waveform has a different number of points, thus it is not possible to numerically store them all in a matrix for further processing. With the SPI resampling, the original sampling frequency can be recovered and the full pre-processed waveforms can be stored. The beginning of the signal is determined when a fixed threshold is exceeded (32 dB). For the tail cutting, to determine the end of the signal, we employ a floating threshold (10 % of the peak amplitude). We calculated more than 15 parameters on each AE signal (Table 2).  
Temporal features, such as amplitude, rise time, duration, can be extracted from the temporal evolution of the signal. In this work, the main features investigated are amplitude, energy, frequency centroid (FC) and peak frequency (PF). The opening frequency (top) or the roll-on frequency is such that the interval [0; top] contains 5% of the energy of the complete spectrum. Thus, it gives information on the presence of components at low frequencies. The cut-off frequency (fcut) or the roll-off frequency is such that the interval [0; fcut] contains 95% of the energy of the complete spectrum. Additionally, the spectral contents of the waveforms are analysed in frequency domain by a Fast Fourier transform (FFT) and with the Smoothed Pseudo Wigner-Ville Distribution (SPWVD).
## 4 Case Study
### Experimentation and Testing Process
#### 4.1.2 Location of Facial Emotion Description Points and Emotional Feature Extraction  
The 900 video clips were decomposed, with each video sample decomposed into 25-100 frames. An AAM was built and the emotional description points were located using the AAM.  
The feature extraction method based on geodesic distances and sample entropy was used to extract the spatial and temporal features of emotions, and 900 cognitive emotion feature vectors were obtained.
# Software-Defined NB-IoT Uplink Framework--The Design, Implementation and Use Cases
## 5 Uplink Implementation
### Transform Precoding  
In order to mitigate the problem of the high peak-to-average power ratio (PAPR), the classical OFDM has been replaced by the SC-FDMA technique within the NB-IoT uplink transmission. The main difference between those two methods is the additional discrete Fourier transform (DFT) block that is implemented as a part of the transform precoding module. The detailed transformation scheme is described in [2], and it should be noted that the whole procedure encourages students to consideration combining both frequency and time domains.
# PointSwin: Modeling Self-Attention with Shifted Window on Point Cloud
## 4 Experiments
### Classification on ShapeNetPart Dataset  
The ShapeNetPart dataset is a subset of the ShapeNetCore Dataset [58; 59], which is mainly used for training and testing in the part segmentation task of point clouds. It contains about 16,000 models of 16 categories of objects. The number of objects in a class varies from 2 to 6, and the total number of objects in all classes is 50. Although this dataset was originally designed to serve part segmentation, a relatively small number of publicly available datasets in the point cloud domain were available in previous years, and this dataset was also used to help validate model performance in the shape classification domain. Therefore, in order to more fully compare the performance with the baseline method PointNet and control the influencing variables as much as possible at the same time, we also performed the accuracy test of the PointSwin model on this dataset.  
As shown in Table 2, the efficient model can achieve better accuracy performance than PointNet. Compared with the original model PointSwin, the accuracy performance and computational efficiency of PointSwin-E are superior.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Input** & **mACC** & **OA** & **Params** & **Time-Consuming * \\ \hline VoxNet [2] & voxel & 83.0 & 85.9 & - & - \\ Subvolume [54] & voxel & 86.0 & 89.2 & - & - \\ PointNet (vanilla) & point & - & 87.2 & - & - \\ PointNet & point & 86.2 & 89.99 & 3.71 M & 9 s \\ A-SCN [55] & point & 87.6 & 90.0 & - & - \\ MVCNN [56] & image & - & 90.1 & - & - \\ DeepPets [57] & point & - & 90.3 & - & - \\ PointNet++ (W/O norml) & point & - & 90.7 & - & - \\ \hline PointSwin & point & 86.9 & 90.88 & 3.85 M & 120 s \\ PointSwin-E & point & 86.39 & 90.8 & 4.21 M & 21 s \\ \hline \hline \end{tabular}  
* The data are the average time spent training each epoch under the NVIDIA TITAN RTX graphics card environment.  
\end{table}
Table 1: Results of ModelNet40 shape classification.  
On this dataset, we observed that PointSwin showed lower prediction accuracy results than PointNet. We believe that this is due to the imbalance of the samples in the dataset, which causes the PointSwin model to rely too much on the feature opinions represented by local features when making decisions, so that the model falls into the predicament of local overfitting. This also inspired us to think about the future research direction. We believe that when applying the PointSwin model to other point cloud processing tasks in the future, it is necessary to consider the impact of sampling density and sample imbalance on the local Self-Attention mechanism to improve the generalization performance of the model.  
Currently, we verify the improved classification performance of our proposed model on this dataset. In future work, we will generalize the model to solve segmentation problems and will continue to test and verify segmentation effects on the ShapeNetPart and ShapeNetCore datasets.
# Dynamic UAV Inspection Boosted by Vehicle Collaboration Under Harsh Conditions in the IoT Realm
## 3 Problem Formulation
### Initial Static Task Model
#### 3.1.2 UAV Flight Route Planning Model
The total route length \(l_{a_{q}}^{u_{k}}\) of UAV \(u_{k}\) at anchor point \(a_{q}\) can be calculated by Equation (7). Assuming that UAV \(u_{k}\) has a total of \(x\) flights at anchor point \(a_{q}\), and since the charging time of the UAV is neglected according to the model assumptions, the total flight time \(t_{a_{q}}^{u_{k}}\) of UAV \(u_{k}\) at anchor point \(a_{q}\) is calculated as shown in Equation (8). \[l_{a_{q}}^{u_{k}}=\sum_{r_{i}\in R}z(u_{k},r_{i})x(a_{q},r_{i})l_{r_{i}} \tag{7}\]  
\[t_{a_{q}}^{u_{k}}=\sum_{i=1}^{x}t_{a_{q}}^{u_{k}}=\sum_{i=1}^{x}\frac{l_{r_{i} }}{v_{nav}}=\frac{l_{a_{q}}^{u_{k}}}{v_{nav}}=\frac{\sum_{r_{i}\in R}z(u_{k}, r_{i})x(a_{q},r_{i})l_{r_{i}}}{v_{nav}} \tag{8}\]  
When the vehicle is parked at the anchor point, a total of \(M_{uav}\) UAVs take off simultaneously to perform the mission in parallel. Some UAVs may need to perform the mission repeatedly over multiple trips. The flight time of each UAV at anchor point \(a_{q}\) can be calculated according to the relevant equations. The total mission execution time at a single stopping anchor point is determined by the longest delivery time of the UAV. Thus, after comparison, the total flight time of the longest UAV is taken as the mission delivery time for all UAVs at anchor point \(a_{q}\), which is denoted as \(t_{a_{q}}^{u_{max}}\). Let there be a total of K docking anchor points. The total flight time at each docking anchor point is summed to obtain the global flight duration \(T_{uav}\), which is calculated by Equation (9). \[T_{uav}=\sum_{i=1}^{K}t_{a_{i}}^{u_{max}}=\sum_{i=1}^{K}\frac{l_{a_{i}}^{u_{ max}}}{v_{nav}}=\sum_{i=1}^{K}\frac{\sum_{r_{i}\in R}z(u_{k},r_{i})x(a_{i},r_{ j})l_{r_{j}}}{v_{nav}} \tag{9}\]