esa-sceva/satcom-chunk-collection huggingface dataset:
# Time Domain Room Acoustic Solver with Fourth-Order Explicit FEM Using Modified Time Integration
###### Abstract  
This paper presents a proposal of a time domain room acoustic solver using novel fourth-order accurate explicit time domain finite element method (TD-FEM), with demonstration of its applicability for practical room acoustic problems. Although time domain wave acoustic methods have been extremely attractive in recent years as room acoustic design tools, a computationally efficient solver is demanded to reduce their overly large computational costs for practical applications. Earlier, the authors proposed an efficient room acoustic solver using explicit TD-FEM having fourth-order accuracy in both space and time using low-order discretization techniques. Nevertheless, this conventional method only achieves fourth-order accuracy in time when using only square or cubic elements. That achievement markedly impairs the benefits of FEM with geometrical flexibility. As described herein, that difficulty is solved by construction of a specially designed time-integration method for time discretization. The proposed method can use irregularly shaped elements while maintaining fourth-order accuracy in time without additional computational complexity compared to the conventional method. The dispersion and dissipation characteristics of the proposed method are examined respectively both theoretically and numerically. Moreover, the practicality of the method for solving room acoustic problems at kilohertz frequencies is presented via two numerical examples of acoustic simulations in a rectangular sound field including complex sound diffusers and in a complexly shaped concert hall.  
discretization error; explicit method; finite element method; high order scheme; room acoustic simulations; time domain +
Footnote †: journal: Applied Mechanics and Applications
## 4 Implementation
### Proposed Scheme  
Figure 12 displays the spatial frame format of our proposed scheme in an \(8\times 8\) LED matrix. The four outermost corners of the LED array were used as anchors for corner detection. From the coordinates of these corners, all the LED coordinates can be easily calculated on the basis of perspective transformation. We used 40 LEDs for data transmission in this LED matrix, as shown in Figure 12. In this scheme, we proposed four-position anchor parts in four corners (Figure 12) with a particular positioning of anchor 3, which supports rotation on the receiver side. With the Zeros Crossing technique, 16 LEDs used as position anchors served as the training signal part, which helped the camera to determine the threshold between the \"ON\" and \"OFF\" statuses of all LEDs. The preamble, which helps the receiver side to detect the start of frame easily, was inserted into each frame. Matched filter technique is applied with the line coding to optimize the SNR value. Thus, the communication distance is improved. The artificial intelligence (AI) algorithms were applied to help detecting multiple LED array with mobility effects to increase system performance. The SN was proposed to reduce the effect of frame rate variation in the camera, and the serial number of the packet was used as an SN. In the  
Figure 11: Bit error rate curve for optical OOK modulation schemes.  
implementation, based on the relationship between the packet rate on the transmitter side and the camera frame rate on the receiver side, the SN length was accordingly considered and controlled. If the camera's frame rate on the receiver side is smaller than the packet rate on the transmitter side, undersampling occurs in the OCC system. In contrast, if the camera frame rate on the receiver side is higher than the packet rate on the transmitter side, oversampling occurs. The packet rate on the transmitter side is defined as the number of packets transmitted in a single period (e.g., 30 packets per second). The packets can be identified by their SNs, which contain the serial information of each packet. Because of the effect of camera frame rate variation, the SN proved to be useful for decoding data because it enabled the camera to verify the newly arrived packet in the oversampling case and detect the hosted packet in the undersampling case. [PERSON] et al. [20] used 1-bit Ab for the oversampling case and 2-bits Ab for the undersampling case. This created confusion in the decoding process on the receiver side, which limited the number of missing payloads it was able to detect. Our scheme proposed using n bits of SN instead of Ab bits. The length of the SN can be modified depending on the implementation and the camera parameters. If conditions are not ideal or the camera is not of high quality, the number of missing packets is high, and the SN length is larger. However, the length of SN reduces with the quality of the camera and thus increases system performance.
# TrendFlow: A Machine Learning Framework for Research Trend Analysis
## 3 Framework Design
### Clustering Module
#### 3.3.1 Clustering Algorithm  
In this work, we employ and compare five clustering algorithms: K-Means, DBSCAN, agglomerative clustering, the Gaussian mixture model (GMM), and spectral clustering.  
Figure 1: The framework structure of TrendFlow. The \(\ominus\) symbol represents dimensionality reduction.  
These algorithms were selected because they can either inherently determine the number of clusters or utilize additional techniques to estimate the optimal number of clusters without prior specification. More specifically:  
1. For K-means, we apply the elbow method [41] to automatically determine the optimal number of clusters. This technique involves plotting the explained variation as a function of the number of clusters and identifying the point where adding more clusters does not significantly improve the explained variation. This point, referred to as the \"elbow,\" indicates the optimal number of clusters.
2. For GMM, we use the Bayesian information criterion (BIC) [42] to estimate the optimal number of clusters. BIC evaluates the trade-off between model complexity and goodness-of-fit, providing a measure that helps select the best model from a set of candidate models with different numbers of clusters.
3. DBSCAN inherently determines the number of clusters based on the density of the data points in the feature space. The algorithm identifies dense regions in the data and groups them into clusters without requiring a predetermined number of clusters.
4. Agglomerative clustering is a hierarchical clustering algorithm that automatically determines the number of clusters through the construction of a hierarchical tree. By analyzing the tree structure, an optimal number of clusters can be determined by selecting a suitable cut-off point that effectively represents the underlying data.
5. Spectral clustering inherently determines the number of clusters by leveraging the eigenvectors of the graph Laplacian matrix. The eigengap heuristic [43] can be used to automatically estimate the optimal number of clusters. This method involves computing the eigenvalues of the Laplacian matrix and selecting the number of clusters corresponding to the largest gap between consecutive eigenvalues.
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 8 Results
### Adult Dataset Results
For example, Fairlearn-GB attained a strong accuracy (0.8662 \(\pm\) 0.0021) alongside relatively low fairness metric disparities, including a statistical parity difference of 0.1174 \(\pm\) 0.0047 and modest TPR and FPR differences.
# Evaluating GNSS Receiver Resilience: A Study on Simulation Environment Repeatability
## 4 Automated Testbed
The simulation concluded, data recording ceased, and the receiver was reset to its initial state, clearing its memory before the next repetition. This cycle was repeated 50 times for each interference type and power level. The evaluation of receiver response involved three distinct types of interference: a continuous wave (CW) jammer precisely at the GPS L1 center frequency of 1575.42 MHz; a three-frequency jammer at 1575.42 MHz with offsets of \(\pm\) 735 kHz; and frequency-modulated (FM) interference centered at 1575.42 MHz, featuring an 8 MHz deviation and a 1 kHz modulation rate. Each of these interference types was tested across six power levels, spanning \(-\)70 dBm to \(-\)45 dBm. To establish a baseline, 50 measurements were also conducted without any interference, ensuring that any residual signal from the RF jammer remained below \(-\)155 dBm. The signal power level from the GNSS simulator was set to \(-\)130 dBm (representing the first lobe power of the BPSK modulation spectrum) and validated with a spectrum analyzer before any measurements were taken.
## 3 Method of Prediction and Optimization  
### Long Short-Term Memory (LSTM)  
Uncertain variables such as power output and load play a significant role during the operation of ME-VPP, and they can affect the accuracy and efficiency of power management directly. Thus, powerful prediction method and appropriate prediction time horizon are necessary for the operation of ME-VPP. A plenty of prediction methods were proposed to predict the uncertain variables, such as traditional method (Grey model, Kalman, etc.) and novel method (RNN, LSTM, etc.). The prediction method should meet the accuracy and efficiency of operation work, meanwhile, a proper prediction time horizon should be selected. If too short, system operator may fail in sufficient preparation for power management in few hours later; if too long, computation resources will be wasted and the accuracy of predicted variables cannot be guaranteed.  
In this paper, we choose the LSTM to predict the uncertain variables. Corresponding experiments are set in Section 4 to choose the proper time horizon of prediction.
## 4 Discussion
### Snow Drought and Snowmelt Flood
#### 4.3.3 Snow Droughts  
Snowfall influences both flooding and drought due to its role in regulating the hydrological processes in snow-fed watersheds, with lower than normal winter snow accumulation resulting in a hydrological drought by reducing the water availability later in the hydrological year [80; 81]. April-June water resources, which are supplied mainly by snowmelt, and annual water availability, are both positively correlated with the maximum snow thickness in the headwaters of the Irtysh River [33]. Interannual fluctuations in the maximum snow thickness were amplified after the early 1990s (Figure 3a), which led to frequent shifts in drought and flooding in the SAM.
## 2 Literature Review
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Material** & **Input Variables** & **Output Variables** \\ \hline [PERSON], et al. (2011) & ABS & \begin{tabular}{c} Air gap, raster orientation, bead \\ width, color, model temperature \\ \end{tabular} & Tensile and compressive strength \\ \hline [PERSON], et al. (2012) & ABS & \begin{tabular}{c} Air gap, raster angle/width, layer \\ thickness \\ \end{tabular} & Elasticity, flexibility \\ \hline [PERSON], et al. (2013) & ABS & \
\begin{tabular}{c} Raster orientation, air gap, bead \\ width, color, model temperature \\ \end{tabular} & Compressive strength \\ \hline [PERSON], [PERSON] and [PERSON] (2018) & ABS &
\begin{tabular}{c} Print orientation, road width, layer \\ thickness, air gap, raster angle \\ \end{tabular} & Dimensional accuracy \\ \hline [PERSON], et al. (2018) & PC & Air gap, raster angle/width & Tensile strength \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of additive manufacturing studies for fused filament fabrication (FFF) based on the design of experiment (DOE). Furthermore, most materials for FFF examined in the existing studies are low-performance polymers such as ABS, PLA, and PC. There are a few studies relevant to FFF using high-performance polymers such as PEEK and CFR-PEEK, but the process parameters for the materials and their operational aspects have not been sufficiently discussed [37; 42]. In particular, CFR-PEEK has been pointed out as a very promising material for 3D printing, since it can be used not only for various engineering applications, but also for medical applications due to its study mechanical properties and low biological toxicity [15; 17; 42; 43]. While the preliminary studies on CFR-PEEK are available in the literature, the breadth and depth of the relevant studies are less comprehensive than that of other common polymer materials. In response to the above stated shortcomings, this study focuses on the FFF process of CFR-PEEK to identify the relationships between the FFF process parameters and manufacturing performance measures through a full factorial DOE, in which the information loss from the experiments is minimized. For this, the effects of important FFF process parameters (i.e., layer thickness, build orientation, and printing speed) [20] on the printing time, dimensional accuracy, and material cost for the experiments are investigated, respectively. Moreover, three different designs are considered to confirm whether identified relationships vary depending on design characteristics. Based on the DOE results, the optimal parameter settings considering all the manufacturing performance measures as well as the individual optimal parameter settings for each performance measure are suggested through the methodology proposed in the next section.
###### Abstract  
This work presents an innovative approach for tuning the Kalman filter in INS/GNSS integration, combining states from the inertial navigation system (INS) and data from the Global Navigation Satellite System (GNSS) to enhance navigation accuracy. The INS uses measurements from accelerometers and gyroscopes, which are subject to uncertainties in scale factor, misalignment, non-orthogonality, and bias, as well as temporal, thermal, and vibration variations. The GNSS receiver faces challenges such as multipath, temporary signal loss, and susceptibility to high-frequency noise. The novel approach for Kalman filter tuning involves previously performing Monte Carlo simulations using ideal data from a predetermined trajectory, applying the inertial sensor error model. For the indirect filter, errors from inertial sensors are used, while, for the direct filter, navigation errors in position, velocity, and attitude are also considered to obtain the process noise covariance matrix \(\mathbf{Q}\). This methodology is tested and validated with real data from Castro Leite Consultora's commercial platforms, PINA-F and PINA-M. The results demonstrate the efficiency and consistency of the estimation technique, highlighting its applicability in real scenarios.  
Kalman filter tuning; INS/GNSS integration; inertial sensor error model +
Footnote †: journal: Sensors  
0000-0001-4227-331  
0000-0002-4227-331
## 6 Conclusions  
In this paper, to analyze the effects on the fatigue life of bearings and the shaft system resulting from the combined preload and angular misalignment induced by inaccurate mounting, a shaft-bearing system with bearing preload and angular misalignment was investigated. By improving the computational efficiency of ball element force analysis, the system model was solved fast and reliably. Comparisons were also made to verify the correctness of the system model and calculation program. Based on the contact force distribution of ball bearings, the fatigue life of each ball bearing was obtained. The results showed that both the preload and angular misalignment had significant effects on the fatigue life of ball bearings and the shaft-bearing system.  
An optimal angular misalignment existed for the shaft-bearing system and could prolong the system life. At the low preload value, the system life at the optimal angular misalignment was much higher than that at 0 mrad, and such a difference decreased with the increasing preload value. The optimal angular misalignment of the shaft system was not always the same as that of the misaligned bearing, which depended on the preload value and the fatigue life of each bearing. The two preload methods, constant-displacement preload and constant-force preload, had significant effects on the variation of the preloaded bearing life with the angular misalignment, but weak effects on the highest system life. At high constant-force preload, the system life varied very slowly with angular misalignment, and an optimal angular misalignment range existed. The angular misalignment occurring on different bearings could lead to a significant difference of the highest system life when the preload value was low, but the difference could be ignored when the preload value was high.  
Conceptualization, Y.Z. and L.X.; Methodology, Y.Z. and M.Z.; Software, M.Z.; Validation, Y.Z. and M.Z.; Analysis, Y.Z. and Y.W.; Writing--Original Draft Preparation Y.Z., M.Z. and Y.W.; Writing--Review and Editing, Y.Z. and M.Z.; Supervision, Y.Z. and L.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This work was supported by the Liaoning Provincial Natural Science Foundation of China (Grant Number 20170540739), the Open Fund of Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education, Northeastern University (Grant Number VCAME201802), and the Natural Science Foundation of China (Grant Number U1708255).  
The authors declared no potential conflict of interest with respect to the publication of this article.
# An Overview of Material Extrusion Troubleshooting
## 3 Troubleshooting of ME Parts
### Printer-Associated Problems
#### 3.1.6 Grinding Filament  
Grinding or stripping of filament refers to excessive shaving of plastics, causing the gear teeth to have little material to grab onto (Figure 7). The contributing factors include if the retraction setting is set too fast, the printing temperature is too low, the printing speed is too high, overtightened extruder, dirty extruder gears, jammed filament tubes, and/or a clogged nozzle. Solutions include the following:  
1. Reducing the retraction speed by 50%. Excessive stress will be applied on the extruder if the retraction speed or the distance is set too high.
2. Increase the nozzle temperature by 5 to 10 \({}^{\circ}\)C to reduce the viscosity and better flow of the material.  
Use a slower printing speed or decrease the default printing speed by 50%. This reduces the rotation of the extruder motor which may be the cause of the grinding issue.
4. Make sure that the tension of the extruder idler wheel is correct as an over-tightened idler cannot push the filament towards the nozzle [72].
5. Remove any plastics residue with pliers or using a pin.
6. Open the extruder idler wheel to inspect if there is any debris in the tube that may inhibit the flow of filament.
7. Unclog the nozzle (refer to 3.1.4: Clogged nozzle).
## 2 Materials and Methods
### Image Quality-Quantitative Analysis  
For quantitative analysis, the two radiologists performed the region of interest (ROI) measurement in consensus. First, two ROIs were drawn on the DWI images with b = 0 s/mm\({}^{2}\) to include the largest possible areas of rectal cancer and normal rectal wall of the three groups, respectively. Then, the ROIs were copied to the DWI images with b = 1000 s/mm\({}^{2}\) for rs-EPI and SMS rs-EPI to obtain the mean signal intensity and standard deviation (Figure 1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **rs-EPI** & **2 \(\times\) SMS rs-EPI** & **3 \(\times\) SMS rs-EPI** \\ \hline b value (s/mm\({}^{2}\)) & 0.1000 & 0.1000 & 0.1000 \\ \hline Number of Readout & & & \\ segments & 5 & 5 & 5 \\ Fat press method & SPAIR & SPAIR & SPAIR \\ TR (ms) & 5000 & 2270 & 1800 \\ TE (ms) & 51 & 52 & 54 \\ Reversal time (s) & 210 & 210 & 210 \\ FOV (mm) & 216 \(\times\) 216 & 216 \(\times\) 216 & 216 \(\times\) 216 \\ Matrix & 128 \(\times\) 128 & 128 \(\times\) 128 & 128 \(\times\) 128 \\ Voxel size (mm\({}^{3}\)) & 1.7 \(\times\) 1.7 \(\times\) 4.5 & 1.7 \(\times\) 1.7 \(\times\) 4.5 & 1.7 \(\times\) 1.7 \(\times\) 4.5 \\ thickness (mm) & 4.5 & 4.5 & 4.5 \\ Distance Factor (mm) & 0.45 & 0.45 & 0.45 \\ Number of slices & 24 & 24 & 24 \\ iPAT & GRAPPA 2 & GRAPPA 2 & GRAPPA 2 \\ Acceleration factor & & 2 & 3 \\ Acquisition time (s) & 137 & 60 & 51 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of three DWI sequences.  
Signal-to-noise ratio (SNR) and contrast-to-noise (CNR) were calculated according to the following equations [19]:  
\[\text{SNR}\ =\ \frac{S_{lesion}}{SD_{background}}\]  
\[\text{CNR}\ =\ \frac{|S_{lesion}-S_{normal\ tissue}|}{\sqrt{S\mathit{D}_{lesion }2+S\mathit{D}_{normal\ tissue}2}}\,\]  
where \(S_{lesion}\) represents the signal intensity of the rectal mass, \(S_{normal\ tissue}\) represents signal intensity of the normal rectal wall, and \(S\mathit{D}_{background}\), \(S\mathit{D}_{lesion}\), \(S\mathit{D}_{normal\ tissue}\) represent the standard deviation of signal intensity of the background area, rectal mass, and normal rectal wall, respectively.  
The above quantitative parameters were measured three times to obtain the average values of the two radiologists.  
The above ROI was copied onto the ADC image to measure ADC values of rectal masses. In addition, the ROI also selected the non-mass region of the rectum, and the ADC values of normal tissue were measured. The thickest level of the intestinal wall in T2 WI was selected for measurement. Geometric distortion was evaluated by comparing lesion lengths in the largest areas of rectal cancer between axial T\({}_{2}\)WI images and the b = 0 s/mm\({}^{2}\) and b = 1000 s/mm\({}^{2}\) of three rs-EPI sequences. Anterior-posterior (AP) length and left-right (LR) width of the lesion were measured, and the distortion rate was calculated (rs \(-\) EPI distance \(-\) T\({}_{2}\)WI distance)/T\({}_{2}\)WI distance \(\times\) 100 [20].
# Inertial Sensors-Based Assessment of Human Breathing Pattern: A Systematic Literature Review
## 2 Methodology
### Data Extraction and Categorization
#### 2.4.3 Measured BP Metrics  
A wide variety of different types of devices led to different combinations of sensors used. A total of 13 studies used measurements from accelerometers only [48; 50; 51; 52; 54; 56; 57; 58; 59; 61; 63; 67; 69]. Five studies used measurements from accelerometers and gyroscopes [59; 62; 64; 65]. Four studies used measurements from accelerometers, gyroscopes, and magnetometers [53; 60; 66; 68].  
Figure 2: Body locations of sensor attachment and number of articles referring to each location. The number in each circle is the total number of articles that mention placing a sensor in that location. Referring to the body chart in the figure: Anterior view, from top to bottom, right to left of the body chart: right 2 nd–3 rd rib, manubrium, left 2 nd–3 rd rib, right 4 th–5 th rib, body of sternum, left 4 th–5 th rib, right 7 th rib, xyphoid, left 7 th rib, right 10 th rib, bellow xyphoid, left 10 th rib, bellow right ribs, abdominal region, bellow left ribs, and anterior superior iliac spine; Posterior view, from top to bottom, left to right of the body chart: left 2 nd–3 rd rib, thoracic intervertebral (T4–T5), right 2 nd–3 rd rib, left 10 th rib, right 10 th rib, and sacrum. The only circle outside the body chart is referred to as the body region not related to respiratory movements or a stationary object, as are the sensors found in the anterior superior iliac spine and the sacrum. This figure has been designed using assets from www.freepik.com (accessed on 30 April 2024).  
In 19 studies, a reference device was used for data comparison [48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 65; 66; 67; 68; 69]. Among the reference devices, the spirometer was the most frequently employed, appearing in nine studies [48; 49; 50; 51; 52; 53; 54; 56; 67; 68]. Other reported reference devices encompassed stretch sensors like piezo-electric bands [60; 62; 63], optoelectronic plethysmography [59; 66], polysomography [57; 63; 64], pressure sensors and strain gauge respirometry [49; 61], pneumotachograph [58], air flow pressure transducer [65], and ergospirometry [48].  
In three studies, the use of reference devices was not clearly indicated [55; 56; 64], and one study utilized signal bank data [69].