esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Edge-Cloud-Based Smart System for Chatter Suppression in Train Wheel Repair
## 5 Hybrid Edge-Cloud-Based Smart System for Chatter Detection and Suppression
### Edge-Computing-Based Smart System for Chatter Detection and Suppression  
The applied expert algorithm first processes the time domain signal captured by the accelerometer and converts it to the frequency domain through a fast Fourier transform (FFT). A frequency domain integration is then performed based on the acceleration spectrum, and a vibration severity is calculated. The severity calculation is a simple quadratic sum of the calculated vibration velocity values over the selected range, in accordance with the ISO-10816 standard [46], as in Equation (2):  
\[v_{rms}=\sqrt{v_{1}^{2}+v_{2}^{2}+\ldots+v_{n}^{2}} \tag{2}\]  
It was determined from the experimental testing carried out in Section 2 that if the cutting process was stable, the severity in this range would lie below 0.5 mm/s rms. When light chatter appeared, this value rapidly rose over 1 mm/s rms, up to around 2.4 mm/s rms. Strong chatter cases clearly exceeded 4 mm/s rms. With this experimental learning in mind, the implemented algorithm is roughly described in Table 3.  
Figure 11: Data visualization for wheel cutting process fingerprint of the workpiece: (**a**) machined wheel list; (**b**) tool trajectory and reset points; (**c**) vibration severity graphs.  
Depending on the severity value detected, different actions were taken. First, a decrease in cutting speed was programmed, which was observed to be the most effective solution. If the severity value did not go below 4 mm/s rms, CSSV was added to the cutting speed decrease. In case that both solutions applied simultaneously were not capable of reducing the vibration values after 10 s, the system prompted an alarm and moved the tool out of the workpiece to a safety position, preserving the machine mechanics and avoiding having to scrap a wheelset. The system then proposed to finish the cutting operation by doing an additional roughing pass at half the original depth of cut.  
If the chatter is suppressed, various resetting points were programmed all over the wheel profile, in order not to carry out the machining entirely either at low spindle speed or with SSV active, since this could have side effects in terms of productivity decrease or early tool wear. Based on the experience obtained in the cutting tests carried out in Section 2, the process was divided in 10 zones connected by 9 resetting points (see Figure 11).
# Model-Robust Estimation of Multiple-Group Structural Equation Models
## 5 Simulation Study 2: Noninvariant Item Intercepts (DIF)
### Results  
In Table 3, bias and relative RMSE for the factor means of the second and the third group (i.e., \(\alpha_{2}\) and \(\alpha_{3}\)) are presented. In the condition of no DIF effects (i.e., \(\delta=0\)), all different estimators were unbiased. There were minor efficiency losses for robust moment estimation with \(p=0.25\) and \(p=0.5\) for the sample size \(N=500\). However, in larger samples, there were almost no differences among the different estimators. In the simulation conditions with small or moderate DIF effects, ML and ULS were substantially biased. Notably, RME with \(p=1\) also produced a nonnegligible bias. RME with powers \(p=0.25\) or \(p=0.5\) performed similarly to RegML concerning bias. RME had only slightly increased efficiency losses. Interestingly, RME \(p=0.25\) performed better than \(p=0.5\) in terms of RMSE. Given the fact that there were only small efficiency losses with RME, the computationally more expensive RegML could perhaps be avoided in practice if the goal is estimating factor group means.
###### Abstract  
Based on the classical theory of nonlinear Thomson scattering and the single electron model, we performed extensive numerical simulations in MATLAB R2022b to comprehensively investigate how the initial phase of a tightly focused, circularly polarized laser pulse affects the radiation characteristics of high-energy electrons at different energy levels. Our findings indicate that the polar angle corresponding to the maximum radiation energy remains constant as the initial phase of the laser changes from 0 to \(2\pi\), while the azimuth angle correspondingly moves from 0 to \(2\pi\). Moreover, as the initial phase changes, the pulse width of the electron radiation peak displays a quasi-periodic oscillation with a period of \(\pi\). Notably, an increase in the initial energy of the electrons results in a significant enhancement in both the peak radiation value and the collimation of the radiation. These results demonstrate that manipulating the initial phase of the driving laser pulse enables effective control over the spatial distribution of radiation light.  
laser physics; initial phase; numerical simulation; circular polarization; tightly focused laser
## Appendix A
### Original Test Set Results for Individual Classes  
Table A3 reports the results of the original test set referring to the WPS holdout participants. Table A4 reports the same results but referring to the SHS participants.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Mdl**} & \multirow{2}{*}{**Metric**} & \multicolumn{2}{c}{**Stairs**} & \multicolumn{2}{c}{**Stairs**} & \multirow{2}{*}{**Static**} & \multirow{2}{*}{**Walking**} & \multirow{2}{*}{**Wheelchair**} \\  & & **Ascent** & & & & & \\ \hline \multirow{4}{*}{**Base.**} & **precision** & 0.89 \(\pm\) 0.03 & 0.99 \(\pm\) 0.01 & 0.98 \(\pm\) 0.01 & 0.97 \(\pm\) 0.01 & 0.70 \(\pm\) 0.13 \\  & **recall** & 0.88 \(\pm\) 0.02 & 0.86 \(\pm\) 0.06 & 0.96 \(\pm\) 0.03 & 0.97 \(\pm\) 0.02 & 0.93 \(\pm\) 0.06 \\  & **f1-score** & 0.89 \(\pm\) 0.02 & 0.92 \(\pm\) 0.04 & 0.97 \(\pm\) 0.01 & 0.97 \(\pm\) 0.01 & 0.79 \(\pm\) 0.1 \\  & **specificity** & 0.99 \(\pm\) 0.0 & 1.00 \(\pm\) 0.0 & 0.99 \(\pm\) 0.0 & 0.95 \(\pm\) 0.02 & 0.99 \(\pm\) 0.01 \\ \hline \multirow{4}{*}{**Aug.**} & **precision** & 0.88 \(\pm\) 0.09 & 0.94 \(\pm\) 0.05 & 0.9 \(\pm\) 0.07 & 0.97 \(\pm\) 0.01 & 0.41 \(\pm\) 0.23 \\  & **recall** & 0.89 \(\pm\) 0.09 & 0.88 \(\pm\) 0.12 & 0.94 \(\pm\) 0.04 & 0.89 \(\pm\) 0.09 & 0.92 \(\pm\) 0.12 \\  & **f1-score** & 0.88 \(\pm\) 0.06 & 0.90 \(\pm\) 0.08 & 0.92 \(\pm\) 0.03 & 0.92 \(\pm\) 0.05 & 0.53 \(\pm\) 0.23 \\  & **specificity** & 1.00 \(\pm\) 0.0 & 1.00 \(\pm\) 0.0 & 0.99 \(\pm\) 0.0 & 0.93 \(\pm\) 0.02 & 0.98 \(\pm\) 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table A3: Single class results of baseline and augmented model for the original test set. Considered data: WPS holdout participants.
## 3 WAAS System Upgrade
Figure 3 shows the VPL contour of the upgraded WAAS with 38 reference stations, two GEOs and an IGP mask with expansion around Alaska and Canada. As can be seen in Figure 3, users in all of CONUS, in most of Alaska and Canada, will have LPV service with 99.9% or greater availability (VPL \(<\) 50 m (LPV VAL)). Figure 4 shows another VPL contour of the same upgraded WAAS, but a  
Figure 2: The 99.9% VPL contour of IOC WAAS. The VPL values in CONUS are greater than 20 m, and some places are higher than 50 m (LPV VAL). different IGP mask is used in this simulation. This IGP mask has more IGP in the northeast region of Canada and in the northwest region of Alaska than the expanded IGP mask used in Figure 3. As can be seen in Figure 4, the 99.9% \(\mathrm{LPV}\) service coverage is further extended to the northwest region of Alaska. However, the LPV service coverage is not significantly improved over that of Figure 3. That is because we do not increase the number of WAAS reference station in the same region. In other words, there are not enough ionosphere observations to support the additional IGP. Therefore, one could expect more improvement in the LPV service coverage in the northeast region of Canada and in the northwest region of Alaska provided that there are new additional reference stations. Figure 4 however shows that we could gain some improvement in the LPV service coverage by modifying the Grid Ionospheric Vertical Error (GIVE) algorithms [7] with more IGP. The FAA also plans to add a new geostationary satellite (PRN-133) in 2010, which will be at 98\({}^{\circ}\)W. The new additional GEO is to ensure that all users in WAAS service volume will have at least two GEOs in view, and the new additional GEO could improve the satellite geometry for better positioning and continuity. Figure 5 shows another VPL contour of the same upgraded WAAS, but three GEOs are used in this simulation. As can be seen in Figure 5, the 99.9% LPV service coverage is further extended to the north region of Canada. The additional GEO improves the geometry for the position estimation. Figure 4: The 99.9% VPL contour of the upgraded WAAS with more IGP in Alaska. In comparison with Figure 3, the LPV service coverage in Alaska is improved. Figure 5: The 99.9% VPL contour of the upgraded WAAS with extended IGP and three GEOs. In comparison with Figure 4, the LPV service coverage in the north region of Canada is slightly improved.
# Biology and Regulation of Staphylococcusococcal Biofilm
## 4 Activity of the _ica_ Operon Encoded Enzymes
### 6 Slipped-Strand Mispairing or \"Streisinger Slippage\"
A 5 bp TATTT deletion in IGR affects binding of the repressor proteins (IcaR, TcaR, and Rob) causing overexpression of _icaADBC_. The same motive in _icaB_ and _icaC_ causes frameshift mutations by small deletions or insertions, leading to gene inactivation. (3) In _S. epidermidis_, there is downstream of the _icaR_ gene, _lacZ_, that encodes a non-coding RNA which silences icaR expression, causing _icaADBC_ activation. (4) Finally, insertion sequences (IS) can integrate in _ica_ genes, causing inactivation of the corresponding gene. More recently, a long, non-coding (nc) RNA named _icaZ_ was found to exclusively exist in _ica_-positive _S. epidermidis_ strains such as O47 or RP62A, but not in _S. aureus_ or other staphylococci [102]. _icaZ_ blocks _icaR_ mRNA translation, causing the derepression of the _icaADBC_ operon, thus causing increased PIA/dPNAG production (Figure 3). For _S. epidermidis_, PIA/dPNAG appears to play an even greater role in colonization and biofilm-associated infections than for _S. aureus_, which has an additional arsenal of surface proteins that support colonization and biofilm formation. In _S. aureus_ and most likely other staphylococcal species, the overexpression of PIA/dPNAG appears to be beneficial only under certain infectious conditions, such as in biofilm-associated infections in which it is crucial that the pathogen adheres to the tissue or implant material, or in CF in the lung, where PIA/dPNAG can protect bacteria from phagocytosis and certain antibiotics. _S. aureus_ is also a leading cause of prosthetic joint infections (PJI) in which biofilm-associated organisms demonstrated recalcitrance to immune-mediated clearance and antibiotic susceptibility [103]. The authors of the latter study discovered no polymorphisms in the IGR or _icaBC_ genes but found an SNP within the _icaR_ coding region. This resulted in a V176E change in IcaR that affected its binding activity, resulting in increased _icaADBC_ operon transcription and PIA/dPNAG production. During infection, bacteria are exposed to different stress situations. Thus, a hyperbiofilm former may have an advantage in implant-associated infections but probably not in sepsis. However, since the different types of infection usually occur simultaneously or sequentially during an infection, we usually isolate a mixture of _S. aureus_ strains that range from non-biofilm to hyper-biofilm strains when taking samples in the clinic. Most likely, a moderate mucoid state is the normal state in _S. aureus_ and _S. epidermidis._ However, a hyper-mucoid phenotype is advantageous only under specific infectious conditions, particularly colonization and survival. Perhaps this is why so many mechanisms for controlling and fine-tuning _ica_ expression have evolved. However, under normal growth conditions, the hyperproduction of PIA/dPNAG causes a fitness burden, and PIA/dPNAG-negative strains have a growth advantage and overgrow PIA/dPNAG-positive strains [99].
# Temperature Sensing with Nd\({}^{3+}\) Doped YAS Laser Microresonators
## 4 Results and Discussion  
### Emission Spectrum of Nd\({}^{3+}\)  
When the Nd\({}^{3+}\) doped YAS microsphere was excited under 532 nm laser action, the spectrum plotted in Figure 3 was obtained. The excitation was carried out in the center of the microsphere and the detection was located near its surface; hence, sharp peaks appear superimposed on the typical Nd\({}^{3+}\) emission spectrum due to the WGM resonances. In this spectrum, the \({}^{4}\)F\({}_{5/2}\)\(\rightarrow\)\({}^{4}\)I\({}_{9/2}\) (810 m) and \({}^{4}\)F\({}_{3/2}\)\(\rightarrow\)\(
## 3 HDELC Algorithm  
The high-dimensional ensemble learning classification algorithm (HDELC algorithm) can improve the model's performance by using both global and local structural information of high-dimensional data. Specifically, it is divided into two significant aspects. Firstly, the data feature space is processed. The feature space is divided into multiple local feature regions by constructing a threshold and feature reconstruction matrix. Second, the model is trained using several single classifiers and subspace ensemble methods within different local feature regions. The prediction results between different feature space regions use ensemble to obtain more accurate prediction results. Figure 1 shows the flow chart of the HDELC algorithm based on the feature space reconstruction matrix.  
Given a training set, \(M=\{(x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{L},y_{L})\}\) with \(L\) samples, where \(x_{i}(i\in\{1,2,\ldots,\ L\})\) is the training sample, \(y_{i}(i\in\{1,2,\ldots,k\})\) is the label of the \(i\)th training sample, \(k\) is the number of classes, and each sample \(x_{i}\in\{x_{i1},x_{i2},\ldots,x_{in}\}\) consists of \(n\) features. \(\phi=\left\{\varphi^{1},\varphi^{2},\ldots,\varphi^{H}\right\}\) denotes all classifiers in the ensemble model. (See Algorithm 1).  
Figure 1: Structure of the HDELC approach.
# Experimental Study of 3D Concrete Printing Configurations Based on the Buildability Evaluation
###### Abstract  
Different formulations for 3D printable cementitious composites have been developed for extrusion-based printing. However, there is a lack of configuration guides for actual printing operations, which integrate one printable material and one printing system closely. Three testing methods for configuration determination were proposed and tested with three material proportions, with initial setting times of 2, 8, and 13 min, respectively. The building index (_BI_) measures the layer stacking stability based on the material, scale, and device. The height reduction test (HRT) quantifies the shortening in the height of the printed filaments. The leaning angle (LA) refers to the maximum slope of the stacked layers. In this study, results showed the critical values were (a) 0.167 for the height reduction ratio (_HRR_), (b) 40\({}^{\circ}\) for LA, and (c) 0-19.1, 0-61.1, and 0-99.4 for _BI_ of the three mixtures. They were the meta parameters used to guide the CAD sketching, material development, and printing configurations, including the printing speed and layer height.  
3D concrete printing; buildability; printing configuration; height reduction test; building index; leaning angle +
Footnote †: journal: Applied Sciences
# AI-Driven Pilot Overhead Reduction in 5G mmWaveMassive MIMO Systems
## 2 System Model
### AI-Driven Pilot Placement
#### 2.2.2 Supervised ML
In the next section, we present results of applying the above-mentioned AI methods (linear regression, random forest regression, and neural networks SVD enhancement) to reduce the pilot overhead size while aiming to maintain the BER and SE at acceptable levels.
## 2 Materials and Methods
### Navigation Decision Module Design
#### 2.3.3. Deviation Calculation Based on Dynamic Path Search
In order to handle the large deviation in the calculation error of the curved segment, we introduced a preview window to help solve the problem of untimely reference waypoint  
Figure 8: Framework of unified reference path waypoint updates. (**a**) Schematic diagram of path search; (**b**). Flowchart of dynamic waypoint updates. updates. It can be seen that, for the waypoint update of the curved segment, the \(\overline{R}_{k}\) will continue to increase until \(|P_{k}R_{i+1}|>|P_{k}R_{i}|\) or until the preview window waypoints are rechecked.
## 4 Experiments
### Experiments on Real Data  
After measuring the performance of our method on GPD-Net and CMD-Net datasets, we perforedm experiments on real data. The NOCS dataset [14] was presented for the category-level 6D object pose and size estimation problem, and the Mask RCNN [53] was used in [14] to hypothesise initial 2D bounding boxes of the interested objects. Regarding  
Figure 8: Sample results from experiments on CMD-Net dataset. Instances in the 1 first row shows rotated noise-corrupted point clouds in the **testing stage** (\(\sigma=0.02\)). In the second and the third rows, CMD-Net canonicalizes test inputs, and the last row depicts denoised instances in the canonical space. Please compare this figure with Figure 5.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & & \(\sigma=\textbf{0.01}\) & & \(\sigma=\textbf{0.015}\) & & & \(\sigma=\textbf{0.02}\) & \\ \hline
**Method** & **Bench** & **Car** & **Rife** & **Bench** & **Car** & **Rife** & **Bench** & **Car** & **Rife** \\ \hline Noisy & 0.59 & 0.81 & 0.44 & 1.06 & 1.18 & 0.90 & 1.71 & 1.64 & 1.60 \\ CMD-Net & 0.54 & **0.77** & **0.77** & 0.58 & **0.83** & **0.52** & 0.80 & **0.96** & **0.64** \\ PNLK-D Net & **0.52** & 0.78 & 0.90 & **0.50** & 0.90 & 0.63 & **0.71** & 1.02 & 0.81 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Influence of PointNetLK on the denoising performance.  
the object viewpoints, true positives of Mask-RCNN involve data from background clutter. In addition, the camera viewpoint and the imperfections on the acquisition sensor give rise to missing points on the object surface in the depth channel. We tested our method on the _laptop_ category of this dataset and demonstrated the superiority of CMD-Net across background clutter and missing pixels (points) through several qualitative examples. In Figure 9, each triplet contains arbitrarily rotated test instances. As depicted, they are cluttered, and the objects' surfaces are corrupted by missing points. Despite this fact, our method can successfully canonicalizes and denoises test images. The resilience of CMD-Net emanates from: (i) the training strategy we employ. Since our complete algorithm is capable of learning the convex hull of the shapes, it learns the deteriorating effects and eliminates those in the test process. We achieve this elimination as follows: During the self-supervised training phase, we simulated the clutter coming from both background and foreground. Our method extends the convex hull of the shapes injecting the shapes 3D outlier points. Optimizing the loss between the outlier-corrupted and noise-free shapes, the method learns to remove the cluttered data. (ii) the design of CMD-Net. Throughout our research, we initially started engineering our method with PointNet; however, it was not effective across missing points, thus being relatively unable to correctly canonicalize and denoise the input. Therefore, we re-engineered our model using PointNet++-based modules, hierarchically learning given clouds.
## 3 Methodology
### Spaces Division and Extraction
#### 3.3.2 Generation of Wall Candidates
Figure 15b list the reconstruction results of each functional space. As shown in Table 1, only one of 23 doors is not successfully recognized and it achieves 95 % recognizing accuracy. Table 1 lists time consumption and the measurement accuracy of reconstructed components. For the time consuming, the proposed method costs 23.2 s, 31.8 s, 84.8 s, 78.3 s for components reconstruction in case (1), case (2), case (3) and case (4), respectively. The processing time increases with the complexity of the scenes. In case (3) and case (4), the algorithm achieve the accuracy ranging from 97% to 100% in all recognized component categories. The door and window components achieve the best results, and this finding is consistent with the conclusions of case study (1) and case study (2). To validate the effectiveness of the proposed, the reconstruction results are compared with the state-of-art method proposed by [PERSON] et al. [1], which was used for BIM extraction with laser point

Figure 13: Reconstruction results for case study (2). cloud and mainly concentrated on the building with single functional space. As demonstrated in their experimental results, the proposed method by [PERSON] et al. is able to achieve an average measurement error with 89.094, 95.25% and 92.376 in three different kinds of building respectively. In horizontal comparison, the proposed method is used for reconstruction in single functional space, and achieves 97.23%, 98.68% measurement accuracy respectively shown in Table 2. It indicates that the proposed method provided better reconstruction accuracy than [PERSON]'s method. Besides, for case (3) and case (4), the proposed method achieve about 98.21% and 97.06% measurement accuracy, which are also better than the reconstruction accuracy presented by [PERSON] et al. [1]. In addition, we plot the average dimension error of each components in four study cases in Figure 16. The average error for each components is calculated by dividing whole error by measured area dimension. As shown in Figure 16, opening components achieve higher accuracy than wall, ceiling and floor in all study cases. The possible cause of this is that point clouds are usually difficult to comprehensively collect from large spaces due to the limitations of mapping range or occlusion.
## 4 Multiple Services Set Identifier (MSSIDs) Technique
### Path Loss of MSSIDs RSS Signals  
The RSS signal value decreases with distance, \(d\), according to the function of the logarithm-distance path loss model [38]. Figure 5 shows the behavior of MSSIDs' signals and the mean of these signals versus distance d. The behavior of the mean of MSSIDs is close to the path loss model with path loss  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SSID** & **SSID1** & **SSID2** & **SSID3** & **SSID4** \\ \hline
**SSID2** & 0.021 & - & - & - \\
**SSID3** & 0.000 & 0.013 & - & - \\
**SSID4** & 0.012 & -0.01 & 0.045 & - \\
**SSID5** & -0.012 & 0.007 & 0.029 & 0.012 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The time correlation coefficient between MSSIDs’ signals at a certain point.  
Figure 4: Spatial power distribution of two APs with three SSIDs. (**a**) SSID\({}_{1}\) of AP\({}_{1}\); (**b**) SSID\({}_{2}\) of AP\({}_{1}\); (**c**) SSID\({}_{3}\) of AP\({}_{1}\); (**d**) SSID\({}_{1}\) of AP\({}_{2}\); (**e**) SSID\({}_{2}\) of AP\({}_{2}\); (**f**) SSID\({}_{3}\) of AP\({}_{2}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SSID** & **SSID1** & **SSID2** & **SSID3** & **SSID4** \\ \hline
**SSID2** & 0.021 & - & - & - \\
**SSID3** & 0.000 & 0.013 & - & - \\
**SSID4** & 0.012 & -0.01 & 0.045 & - \\
**SSID5** & -0.012 & 0.007 & 0.029 & 0.012 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The spatial Correlation coefficient between MSSIDs’ signals over AOI.  
exponent (\(n=3.5\)). The fusion of MSSIDs' signals by averaging them contributes to mitigating the effect of the multipath issue. Table 4 illustrates that the standard deviation of the RSS signal decreases with distance \(d\). In addition, the standard deviation of the mean of MSSIDs is less than individual signals of MSSIDs.  
Most researchers consider each RSS signal to be transmitted from a certain single AP with a unique Media Access Control (MAC) address. MSSID signals have a unique MAC address for each one but they are transmitted from the same AP. This article fills in this gap by investigating the behavior of MSSID signals and designing a classifier based on these signals to improve the performance of IPS.