esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Multimodal Physical and Virtual Traffic Reality Simulation System
## 4 Integration of Co-Simulation Components
### Design Challenges and Solutions of PTV Vissim--CARLA Integration
#### 4.1.1 Network Design in PTV Vissim and Map Design in CARLA  
CARLA supports custom map creation, which is essential for ensuring consistency with PTV Vissim's road network. The OpenDRIVE format is the standard used for defining road networks, including lane configurations, intersections, and traffic control elements. However, PTV Vissim does not natively support exporting networks in OpenDRIVE format.  
To resolve this, the PTV Vissim network file (.npx) is first converted to OpenDRIVE format using the NETCONVERT tool from the open-source microscopic traffic simulation software SUMO. This process removes all traffic signals and traffic signs, preserving only the road geometry. The OpenDRIVE file is then imported into RoadRunner, a 3D road network editor licensed by MATLAB, where additional elements such as terrain, sidewalks, pedestrian crossings, traffic signs, and traffic lights are incorporated.  
CARLA maps require the coordinate origin to be defined at the map center. To ensure accurate positioning, the coordinate systems of the PTV Vissim network and the CARLA map must be aligned. This requires relocating the network model in PTV Vissim and adjusting the coordinate origins in RoadRunner. Any discrepancies in coordinate alignment would result in synchronization errors between the platforms. After alignment, the OpenDRIVE and FBX files generated in RoadRunner are imported into CARLA, creating a fully integrated environment compatible with PTV Vissim. The map creation workflow is summarized in Figure 3.
# Data Privacy and Ethical Considerations in Database Management
## 4 Case Study
### Implementation of Data Privacy and Ethical Practices  
In this case study, we will apply the following data privacy and ethical practices to the Healthcare Dataset:  
1. Data Minimization: We will reduce the amount of personal information stored, focusing only on essential data for healthcare analysis.
2. Anonymization and Pseudonymization: We will apply techniques to remove or replace Personally Identifiable Information.
3. Data Encryption: Sensitive fields will be encrypted to protect against unauthorized access.
4. Data Hashing: We will implement hashing for data integrity verification.
5. Access Controls: We will establish role-based access controls to limit data access based on user roles and responsibilities.
6. Transparent Communication: We will document all data handling processes and make this information available to relevant stakeholders.  
These practices align with the ethical considerations discussed earlier and comply with regulations like GDPR and HIPAA. In the next section, we will detail the implementation of these practices and analyze their impact on data privacy, security, and usability.
# Late Reverberation Synthesis Using Filtered Velvet Noise +
## 4 Advanced FVN Algorithm
### Differential coloration Filters  
Since the cutoff frequency of the filters in each segment usually decreases towards the end of the RIR, it is possible to exploit the previous filters in the subsequent filtering stages. The basic idea is to design the first lowpass coloration filter \(H_{1}(z)\) but to construct the other filters by cascading differential filters \(\Delta H_{m}(z)\), for \(m\geq 2\). This structure is illustrated at the top of Figure 9.  
The first filter can be designed manually to imitate the spectral shape of the initial RIR segment, which has a fairly flat spectrum. Here we use a 10 th-order all-pole filter obtained with linear prediction. The magnitude response of this filter is shown in Figure 10a.  
The differential filters are second-order notch filters with the transfer function \(H(z)=1+(V_{0}-1)\)\([1-A_{2}(z)]/2\) with  
\[A_{2}(z)=\frac{-c+d(1-c)z^{-1}+z^{-2}}{1+d(1-c)z^{-1}-cz^{-2}}, \tag{6}\]  
where \(c=[\tan(\pi f_{\mathrm{b}}/f_{\mathrm{s}})-V_{0}]/[\tan(\pi f_{\mathrm{b}}/f_ {\mathrm{s}})+V_{0}]\), \(d=-\cos(2\pi f_{\mathrm{c}}/f_{\mathrm{s}})\) for \(0<V_{0}<1\) is the attenuation at the center frequency \(f_{\mathrm{c}}\) and \(f_{\mathrm{b}}\) is the bandwidth of the notch (Hz) [25]. The differential filters can be designed to match the difference between the neighboring coloration filters. Figure 10b shows responses of the notch filters designed from the family of 10 th-order coloration filters. Figure 10c shows the total effect of cascading 1 to \(M-1\) of these filters with the first filter \(H_{1}(z)\). The overall shapes and cutoff points are very similar to the responses shown in Figure 5.  
Figure 9: Advanced FVN algorithm with cascaded differential coloration filters \(\Delta H_{m}(z)\).
# Enhanced K-Nearest Neighbor for Intelligent Fault Diagnosis of Rotating Machinery
###### Abstract  
Case-based intelligent fault diagnosis methods of rotating machinery can deal with new faults effectively by adding them into the case library. However, case-based methods scarcely refer to automatic feature extraction, and k-nearest neighbor (KNN) commonly required by case-based methods is unable to determine the nearest neighbors for different testing samples adaptively. To solve these problems, a new intelligent fault diagnosis method of rotating machinery is proposed based on enhanced KNN (EKNN), which can take advantage of both parameter-based and case-based methods. First, EKNN is embedded with a dimension-reduction stage, which extracts the discriminative features of samples via sparse filtering (SF). Second, to locate the nearest neighbors for various testing samples adaptively, a case-based reconstruction algorithm is designed to obtain the correlation vectors between training samples and testing samples. Finally, according to the optimized correlation vector of each testing sample, its nearest neighbors can be adaptively selected to obtain its corresponding health condition label. Extensive experiments on vibration signal datasets of bearings are also conducted to verify the effectiveness of the proposed method.  
intelligent fault diagnosis; rotating machinery; correlation vector; k-nearest neighbor; sparse filtering; sparse coding +
Footnote †: journal: Applied Sciences
# Impact Analysis of Satellite Geometry Variation on ARAIM Integrity Risk over Exposure Interval
## 2 Problem Definition
Nonetheless, it is usually difficult to forecast the satellite geometry variation in the exposure interval for real-time aviation navigation applications. Therefore, the geometry constant assumption within the exposure interval is adopted to simplify the complexity of integrity risk evaluation. However, the satellite geometry variation due to the rising and falling of satellites undermines the widely-used geometry constant assumption. This assumption may lead to an integrity risk-evaluation error. Next, we will introduce a general integrity risk-evaluation method to investigate the influence of satellite geometry variations throughout the exposure interval on the integrity performance of the _NES_-based ARAIM.
## 4 Working Mechanism of ESRVR
### Network's Establishment  
This involves underwater deployment of nodes in three-dimensional space. All three sides of the network have the same length, which make a cube network. Nodes span the entire network and are placed with irregular positions. Therefore, the nodes' density is not uniform throughout the network. A single sink node is hoisted at the network's top, as shown in Figure 1. Nodes communicate their data to the sink node, which further communicates them to the data center on the shore. Nodes have embedded batteries, which provide electrical energy for their operation. Every node has a single unique ID to differentiate it from the rest of the nodes. Every node has an embedded pressure sensor, which is used to measure the water pressure level. This measurement determines the water depth in which a node is deployed.  
Figure 1: Sketch of the proposed network model.
## 3 The Proposed Framework
### Classification and Optimization  
After the information from both modalities is further fused by TMCMA, we simply add \(f_{v-a}\) and \(f_{a\gets v}\) together and the final prediction is made by a three-layer MLP, written as follows:  
\[\textbf{pred}=MLP(f_{v\gets a}+f_{a\gets v}) \tag{8}\]  
where \(\textbf{pred}\in\mathbb{R}^{T\times\mathbb{C}}\). Since the ground truth is denoted as \(\textbf{Y}^{gt}=\left[\textbf{y}^{0},\textbf{y}^{1},\ldots,\textbf{y}^{T-1} \right]\in\mathbb{R}^{T\times\mathbb{C}}\), we minimize the cross entropy (CE) loss between _pred_ and \(\textbf{Y}^{gt}\) to optimize our adaptive attention network during training, written as  
\[\mathcal{L}_{CE}=\frac{1}{TC}\sum_{t=1}^{T}\sum_{c=1}^{C}\textbf{Y}^{gt}_{t,c} log(\textbf{pred}_{t,c}) \tag{9}\]  
where \(t\) and \(c\) index the time dimension and category, respectively. Compared to the threshold-based attention in [19], our method does not require additional loss items to supervise the challenging _background_ segment but only uses the basic CE loss.
## 4 Applications of Intelligent Acoustic Monitoring
### Health Monitoring and Ambient Intelligence
#### 4.3.2 MEMS Microphones in Biomedical Monitoring  
In [120], [PERSON] et al. designed and tested a new MEMS microphone based on a piezoresistive cantilever, which was characterised by exceptionally high sensitivity and the ability to detect very low sound pressures, down to 0.2 mPa, in the frequency range of 0.1 to 250 Hz. The key element of that sensor was an ultra-thin cantilever, only 340 nm thick, equipped with a wide platform and narrow hinges, which allowed the authors to achieve a sensitivity about 40 times greater than in previous designs of this type. In tests, the device demonstrated the ability to measure very low-frequency sounds, such as the heart's precise sounds--S1 and S2 tones were recorded with a high signal-to-noise ratio of 58 db. The microphone could find applications in many areas, including wearable health monitoring systems, photoacoustic gas detection sensors, and monitoring natural phenomena such as earthquakes or changes in atmospheric pressure.  
We are seeing the growing importance of passive and non-invasive diagnostic methods. These systems are critical in the context of ageing societies and care for dependent people.
# Improving Response Time for Crowd Management in Hajj
## 3 Methodology
The packet en-queuing stage checks the priority of the packet received and en-queues it either to the head of the transmission queue, if it is a high-priority packet, or at the tail of the transmission queue if the received packet is of low-priority. Finally, the packet transmission stage transmits the packet from the head of the transmission queue. If a collision is detected, the next transmission is delayed based on contention window. The contention window for the high-priority packet is half the size of the contention window for the low-priority packet. In this way, in case of collision, a high-priority packet would be scheduled sooner than a low-priority packet. Figure 3: Bit-Plane Encoding.
# Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi
## 5 Results
### Steering the Physical Layer in an Ideal Scenario  
Figure 9a shows how the PHY's finite data rate affects single packet latency. The time delays are inversely proportionate to the MCS index, while the transmission time difference when selecting either the highest or the lowest MCS escalates as the payload length increases. Consequently, the most pronounced dependency of latency on the selected MCS appears during the transmission of the largest allowed payload, approx. 262 kB, where MPDU latency spans 0.28-2.71 ms. Figure 9b reveals that the decreasing cost of each additional kB of payload starts to stagnate at large payload lengths. The difference between consecutive values becomes less than 1% beyond 3 kB.  
While increasing payload length reduces the relative contribution of preamble and header overhead to PPDU length, aggregation enables several packets to share the same preamble and further reduces its share in PPDU length. However, the latency analysis results, given in Table 4, demonstrate that PPDU aggregation does not bring any significant benefits when transmitting 262 kB long payloads. Even at high MCS indexes, the limited preamble overhead is several orders of magnitude shorter than the data payload. Therefore, including PPDU aggregation doubles the incurred latency in comparison to individual PPDU transmission.  
Figure 9: From left to right: latency’s dependency on the MCS and payload length (**a**); Increase in latency per each additional kB of data, dependent on the MCS (**b**). White curves represent latency isohypses.
## 2 Materials and Methods
### DSC Analysis of the Residual PET  
The differential scanning calorimetry (DSC) measurements were performed using a TA Instruments DSC Q2000. The thermal transitions of the powders of pristine PET (untreated) and PET treated with PRS8 were studied using nitrogen with a purge rate of 50 mL min\({}^{-1}\) and a heating rate of 10 \({}^{\circ}\)C per minute. The samples were heated from room temperature to 280 \({}^{\circ}\)C. The glass transition temperature (\(T_{\rm g}\)), cold crystallization temperature (\(T_{\rm cc}\)) and melting temperature \({}^{\rm TM}\) were obtained from the DSC first heating curve. The crystallinity percentage was calculated using the following equation:  
\[Crystallinity\ \%\ (first\ heating\ cycle)=\frac{(\Delta H_{m}-\Delta H_{cc})}{( \Delta H^{\circ}{}_{m})}\times 100 \tag{1}\]  
where \(\Delta H_{cc}\) and \(\Delta H_{m}\) are the heat of the cold crystallization (area under the cold crystallization) and the heat of melting (area under the melting transition), respectively. \(\Delta H^{\circ}{}_{m}\) is the heat of melting for a 100% crystalline polymer estimated to be 140 J/g [17; 32].
## 4 NBI Suppression Algorithm  
We should notice that the reception performance of ultra-wide band wireless sensors can be easily degraded by in-band pre-existing NBIs with high level power spectrum density. In practical application situation, in-band narrow band interferences (wireless systems, such as global systems for mobile (GSM) communications systems, wideband code-division multiple-access (WCDMA) mobile communication systems, time division-synchronous code division multiple access (TD-SCDMA) mobile communication systems, long-term evolution (LTE) mobile communication systems, universal mobile telecommunications (UMTS), satellite communication systems, global navigation satellite system (GNSS), Bluetooth, ZigBee, digital video broadcasting (DVB), wireless fidelity (WiFi) and RFID networks, etc.) is ubiquitous in the ultra-wide band radio devices, which makes the inherent in-band NBI immunity not sufficient enough. The performance of practical UWB wireless sensors will deteriorate, if no proper interference suppression countermeasure is employed in front of the receiver.
## 3 Results and Discussion
### Far-Field Distance
#### 3.2.1 Results of 12 m Far-Field Distance  
Figure 23 shows the distributions of \(\rm P/P_{t}\) and \(\rm T/T_{t}\), and Table 18 provides the mean errors. The optimal total mean error ratios under different configurations are displayed in Figure 24, which are highlighted in red. For the NACA0012 model, the preferred results of the six models are 2.7%, 2.42%, 3.14%, 2.87%, 2.83%, and 4.14% in turn. For the turbulence model, SST has a minimum value of 2.42%, and that of SA is 2.83%. For the flux type, the best performance of ROE is 2.42%, and that of AUSM is 3.14%. Therefore, the configuration of SST + NACA4 combined with ROE behaves the best; the optimal value is 3.12% and the corresponding accuracy improvements are 10.08%, 29.54%, 14.3%, 22.68%, 62%, 24.99%, 15.42%, 14.2%, 46.26%, 41.38%, and 57.42%.
# Convolutional Neural Network with an Elastic Matching Mechanism for Time Series Classification
## 3 Proposed Method  
### Elastic Matching in Dynamic Time Warping  
Elastic matching in DTW is first reviewed to better demonstrate the proposed mechanism. Considering two different time series \(X=(x_{1},x_{2},...x_{i},...,x_{n})^{\text{T}}\) and \(W=(w_{1},w_{2},...w_{j},...,w_{m})^{\text{T}}\), a dynamic programming algorithm composed of Equations (1) and (2) is used to decide which points should be matched. The second point in \(X\) could match the third and fourth points in \(W\) (red rhombuses in Figure 1) using DTW. Compared with lock-step matching (blue circles in Figure 1), used in ED, the matching relationship in DTW is data-dependent and elastic:  
\[DTW(X,W)=\sqrt{c(i,j)}, \tag{1}\]  
where \(c(i,j)\) is the cumulative distance:  
\[c(i,j)=|x_{i}-w_{j}|^{2}+min\{c(i-1,j-1),c(i-1,j),c(i,j-1)\}. \tag{2}\]
## 5 Numerical Simulation
### Fatigue Life Predictions  
The predicted effective stress range and average daily number of cycles provides a reasonability basis for the fatigue reliability evaluation of the suspenders. In addition, the other random variables and constants that contribute to the fatigue damage are listed in Table 5. With the limit-state function shown in Equation (16) and the statistics of the random variables displayed in Table 6, the fatigue reliability index of each suspender of interest could be computed using Monte-Carlo simulations, as shown in Figure 11. It was observed that, among four suspenders under investigation, the short suspender S36 (mid-span suspender) and the long suspender S2 (girder end suspender) were, respectively, the most and the least prone to fatigue damage. If the service life of the suspender is designed as 100 years, the corresponding reliability indices for suspenders S1, S2, S19, and S36 are reduced to 3.28, 3.68, 2.76, and 2.18, respectively.  
Furthermore, the fatigue life can be estimated provided with a target reliability (\(\beta_{\text{target}}\)). Since, currently, no consensus has been reached in the literature regarding the target reliability for the suspenders, the value of 3.0 was adopted in the present study, which corresponds to a failure probability of 0.135%. As a result, the fatigue life of the representative suspenders S1, S2, S19, and S36 under \(\beta_{\text{target}}\) was calculated as 124 years, 167 years, 83 years, and 53 years, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Equivalent Stress Range**} & \multicolumn{6}{c}{**Numbers of Stress Cycles**} \\ \cline{3-10}
**Traffic Condition** & **Wind Speed** & \(P_{\text{Tr}\ i}P_{\text{W}\ j}\)1 & \multicolumn{6}{c}{**Values (MPa)**} & \multicolumn{6}{c}{**Numbers of Stress Cycles**} \\ \cline{4-10}  & & & \multicolumn{2}{c}{**S1**} & **S2** & **S19** & **S36** & **S1** & **S2** & **S19** & **S36** \\ \hline \multirow{6}{*}{Free-flow} & 0 \({}^{2}\) & 0.145 & 12.15 & 11.87 & 13.83 & 14.77 & 4896 & 4608 & 8352
# Acoustic Pressure Amplification through In-Duct Sonic Black Holes
## 2 Materials and Methods
### Visco-Thermal Finite Element Model  
A finite element model (FEM) of the SBHs has been implemented under Comsol Multiphysics using the Thermoviscous Acoustics-Frequency Domain Module to evaluate the validity of the TMM simulations. This model solves mass, linearized momentum, energy conservation, and linearized state equations in the viscous and thermally conducting axi-symmetric fluid domain \(\Omega_{\text{VT}}\), shown in Figure 2. An infinite length duct has been mimicked by imposing cylindrical perfectly matched layers (PML) boundary conditions at the inlet and outlet duct sections to avoid back-reflected waves. No-slip isothermal conditions are imposed on the solid duct and cavity ring boundaries \(\text{S}_{\text{VT}}\) so that viscous losses (resp. heat conduction effects) induced by gradients of the air particle velocity (resp. temperature) are accounted for near these boundaries. A key parameter for accurate simulation results is the mesh quality. In order to well resolve the visco-thermal boundary layers along \(\text{S}_{\text{VT}}\), the minimum element size was chosen to be one third of the viscous boundary layer thickness, \(\sqrt{\eta/\rho_{0}\omega_{c}}/3\), at the highest frequency of interest, e.g., at the first duct cut-on frequency \(f_{c}\approx 1.84c_{0}/(2\pi R)\) with \(\omega_{c}=2\pi f_{c}\). Also, the maximum element size was set to \(w/3\), ensuring at least ten nodal points per acoustic wavelength at \(f_{c}\). Lagrange linear (resp. quadratic) elements were used for the pressure (resp. velocity and temperature) fields, considering a 2D axi-symmetric geometry.
## 4 Results of Simulation  
### Building the Parameters of Simulation  
The dust used is fly ash, and its parameters are \(d_{g}=3.7\;\upmu\)m, \(\rho_{p}=2.20\;\frac{\mathrm{g}}{\mathrm{cm^{2}}}\), \(\sigma_{g}=1.5\), \(\kappa=1.37\). In the flow field with filtration rate \(V_{f}=0.0243\) m/s and \(V_{f}^{\prime}=0.0274\) m/s, the Reynolds number is less than 2100, which is a steady laminar flow. The characteristic value \(\lambda_{1}\) is related to the structure of the filter material, dust material, particle size, filtration speed, backwash compressed air pressure, nozzle size, and distance, and \(\lambda_{2}\) is only related to the structure of the filter material and dust material and particle size. The setting of \(\lambda_{1}\) and \(\lambda_{2}\) is according to the actual pressure drop used by the dust collector, of which the parameters are \(T_{w}=10\) s, \(T_{p}=0.1\) s, and \(C=2.0\;\times\;10^{-3}\) kg/m\({}^{3}\) with the service life of the filter for 1-2 years, as shown in Figure 7. The actual performance of the pressure drop can be divided into unstable zone (\(0\;\sim 50\) mmAq), stable zone (\(50\;\sim 100\) mmAq), and aging zone (\(100\;\sim 150\) mmAq). The unstable zone appears to rise rapidly because of a large amount of residual dust staying in the fibrous gaps of the filter, and the cleaning efficiency \(\eta^{*}\) increases rapidly at this time. When it goes to stable zone, the baseline of the pressure drop \(P_{B}\) will show a linear and slow increase. Because a certain amount of residual dust has been blocked in the filter, the subsequent dust will not easily enter the fiber gaps in the filter. This means \(\eta^{*}\) and \(P_{r}^{*}\) will slowly increase. Thus, \(100\;\sim 150\) mmAq is the aging of the filter, and at this time \(\eta^{*}\) is close to 100%. Therefore, this study selected a set of characteristic values to approximate the actual production for the pressure drop (\(\lambda_{1}=0.007\), \(\lambda_{2}=0.011\)).  
Regarding the setting of parameters during operation and production, we adopted the adhesion force between the dust and the filter and the separation force generated by the pulsed air to illustrate the effect of filter cleaning. Due to the dust concentration \(C\), the pulse interval time \(T_{w}\), and the pulse time \(T_{p}\) not affecting the adhesion and separation force, they will not affect the selection of \(\lambda_{1}\)[37]. In addition, \(\lambda_{2}\) will be affected by the structure and material of the filter, and the material and size of the dust, but not \(C\), \(T_{w}\), and \(T_{p}\). Therefore, this research focuses on the analysis of energy saving and cost reduction towards sustainability development, and takes \(C\), \(T_{w}\), and \(T_{p}\) as the main variables of the simulation test. It also puts \(P_{Equip}=1500\) mmAq, \(P_{pipe}=250\) mmAq for fixed constants, and the filter needs to be replaced when \(P_{Filter}\) reaches \(150\) mmAq. Based on the actual cost, a single filter is \(TWD\)\(5500\), the replacement cost of the filter is \(C_{F}=TWD\)\(99\),\(000\), and the per kWh of electricity is \(C_{E}=TWD\)\(3.27\).