esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Background: The literature on the use of static magnetic field (SMF), particularly in orthodontics, has certain gaps. Furthermore, the mechanism by which SMF affects orthodontic tooth movement (OTM) is still unclear and quite contradictory. Thus, the goal of this systematic review and meta-analysis was to assess SMF's effect on OTM. This study also sought to analyse the variability of the studies included in the analysis and the size of the impact of SMF on OTM. Methods: Using the PRISMA guidelines, reviewers implemented a search strategy across several online databases, filtering out the initial articles that were obtained by applying relevant inclusion and exclusion criteria. Results: The overall effect size for the odds ratio was found to be 0.58, with a 95% confidence interval ranging from 0.40 to 0.86. This indicates that exposure to static magnetic fields is associated with a significantly reduced likelihood of orthodontic tooth movement in the animal studies analysed. Similarly, the overall effect size for the risk ratio was calculated to be 0.71, with a 95% confidence interval ranging from 0.55 to 0.91. The risk ratio also suggests a significant impact of SMF on OTM, with animals exposed to magnetic fields being at a lower risk of experiencing substantial tooth movement compared to those not exposed. However, it is important to note that moderate heterogeneity was observed among the included studies. Conclusion: The findings of this systematic review and meta-analysis indicate that there may be a causal relationship between OTM and SMF. However, the small number of studies included in this review and their poor methodological quality limit the available data, highlighting the need for further well-designed research to support these conclusions.  
Orthodontic tooth movement; static magnetic fields; orthodontic treatment; magnets in orthodontics; magnetic fields +
Footnote †: Copyright** © 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4](https://creativecommons.org/licenses/by/4)]([https://creativecommons.org/licenses/by/4](https://creativecommons.org/licenses/by/4)).
## 3 Conclusions  
A laser-accelerated proton beamline delivering 3-MeV particle energy has been conceived, designed, realized, and tested, aimed at practical applications, like PIXE measurements. The proton source used is based on the TNSA process and implemented using a 14 TW laser system. Quadrupole permanent magnets are used to transport the protons from the TNSA source to the sample site in air through a thin Kapton window. The magnetic beamline design is compact and cost-effective and has been defined using Monte Carlo simulations in order to achieve a collimated proton beam over several cm in length, as well as to remove unwanted fast electrons from the beam path.  
From dosimetry measurements, the number of few MeV protons after propagating 4 cm in air is estimated to be \(2\times 10^{9}\) in 100 shots, which is on the same order as the number of protons impinging on the PIXE sample used in the simulation experiments reported in [18; 19]. This finding indicates that PIXE measurements are feasible with the presented laser-plasma accelerated proton beamline within tens of seconds assuming a 10-Hz repetition rate operation. Finally, it is noted that the energy spectrum of the laser-accelerated proton beam can be easily tuned (by moving the TNSA target with respect to the laser focus) in order to have different cut-off energies, therefore allowing to implement differential PIXE measurements of in-homogeneous samples in depth [18].
# Aero-Engine Remaining Useful Life Estimation Based on CAE-TCN Neural Networks
## 3 Experiments
### Data Pre-Processing  
Consider the parameters in the CMAPSS datasets have different units and orders of magnitude. Data normalization was used to scale the data into a same specific interval. In the experiment, the sensor data was normalized by standardization (Formula (7)).  
\[X^{\prime}=\frac{X-\mu}{\sigma} \tag{7}\]  
where \(X\) is the original data, \(X^{\prime}\) is the normalized result and \(\mu\) and \(\sigma\) are mean and variance of \(X\), respectively.  
Figure 6 is the data visualization for engine 1 in FD001 and FD002. There are three trends as shown, constant, ascending and descending trend, such as s1, s2, s7. All these features of FD001 are summarized in Table 2. The same characteristic was also found in FD003. However, no discernible trends of sensor readings could be found in FD002 and FD004. This may be caused by the multiple operation conditions. Hence, K-means++ was used to cluster the different operation conditions. The clustering results are displayed in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multirow{2}{*}{**Dataset**} & \multicolumn{5}{c}{**CMAPSS**} \\ \cline{3-6}  & & **FD001** & **FD002** & **FD003** & **FD004** \\ \hline \multirow{4}{*}{Training dataset} & Engine units & 100 & 260 & 100 & 249 \\  & Total samples & 20,631 & 53,759 & 24,720 & 61,249 \\  & Maximum life cycles & 362 & 378 & 525 & 543 \\  & Minimum life cycles & 128 & 128 & 145 & 128 \\ \hline \multirow{4}{*}{Testing dataset} & Engine units & 100 & 259 & 100 & 248 \\  & Maximum cycles & 303 & 367 & 475 & 486 \\  & Minimum cycles & 31 & 21 & 38 & 19 \\  & Total samples & 13,096 & 33,991 & 16,596 & 41,214 \\ \hline \multicolumn{6}{c}{Operating conditions} & 1 & 6 & 1 & 6 \\ \multicolumn{6}{c}{Fault modes} & 1 & 1 & 2 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the CMAPSS datasets.  
Figure 6: (**a**) Normalized sensor data of engine 1 in FD001; (**b**) Normalized s1, s2 and s7 of engine 1 in FD001; (**c**) Normalized sensor data of engine 1 in FD002; (**d**) Normalized s1, s2 and s7 of engine 1 in FD002. (Different colors represent different sensor data in subfigures **a** and **c**.)As shown in Figure 7, through the K-means++ algorithm, the multiple operating conditions were clustered into six clusters. The parameters varied greatly under different operation modes. It was necessary to encode these operating conditions into the input features for prediction. Hence, one hot encoding was applied to label the operation condition of the current cycle. In addition, the operating mode of each engine will change throughout its life. An extra cumulating state was also added since the start of the records for each engine. The final feature used for FD002 and FD004 is described in Figure 8; there are 36 columns of input features.
## 5 Solving Algorithms
### Uncertainty Analyses of Harmonic and Negative Sequence Components Based on the 2m+1 PEM  
The load-shedding optimization model and network reconfiguration optimization model make strategies according to the current electrical quantity and network topology collected by the distribution network automation system. According to the description in chapter 2 of the original manuscript, this information is deterministic; thus, the optimization models also adopt deterministic models and power-flow algorithm for the fundamental component.  
In the network reconfiguration model, power-quality indexes are required to meet the constraints not only at the current moment but also in the following period of time. This is also one of the main contributions of this paper. In the following period of time, the power-quality index is uncertain, so the harmonic power flow and the unbalanced power flow of the fundamental component are obtained based on the 2m+1 PEM method, in which the estimation accuracy is similar to the classical Monte Carlo method but computational burden is much less to accommodate the need for online application.  
In this paper, uncertainty analyses of harmonic and negative sequence components are independent of each other. The harmonic power-flow calculation adopts the method used in References [10; 28; 29]. The negative sequence component calculation method used in References [30; 31; 32] is introduced to analyze the ranges of system's negative sequence currents.  
Due to the similarity of the two analyses, harmonic uncertainty analysis is chosen as the example to give detailed descriptions. Specific steps are as follows.  
(1) The sequences of the measured harmonic current values of harmonic sources are presented to calculate the mean and variance of each variable.  
(2) Calculate the 2m+1 estimation points and corresponding weighting factors via Equations (21)-(23).  
(3) Carry out harmonic power-flow calculation 2m+1 times to obtain all the results of \(f(\mu_{1},\mu_{2},\ldots,\mu_{i})\) and \(f(x_{\mu})\).  
(4) Multiply the results of step 3 and their corresponding weighting factors together to get the expectation \(\mu(\text{Y}_{k})\) and standard deviation \(\sigma(\text{Y}_{k})\) of each output variable \(\text{Y}_{k}\) according to Equations (24) and (25).  
(5) Obtain the distribution range of Y\({}_{k}\) as \([\mu(Y_{k})-2\sigma(Y_{k}),\ \mu(Y_{k})+2\sigma(Y_{k})]\).  
The reasons to select \([\mu(Y_{k})-2\sigma(Y_{k}),\ \mu(Y_{k})+2\sigma(Y_{k})]\) as the distribution range of \(Y_{k}\) are as follows.  
* For normal distributions, the probability of the observed value within \([\mu(Y_{k})-2\sigma(Y_{k}),\ \mu(Y_{k})+2\sigma(Y_{k})]\) is about 95.45%.
* The event with probability less than 5% generally can be treated as a small probability event.  
If Y\({}_{k}\) is node voltage total harmonic distortion, negative sequence current of synchronous generator-based DG, and negative sequence voltage of IIDG, the upper bound \(\mu(Y_{k})+2\sigma(Y_{k})\) will be _THD-ul_, _Cune-ul_, and _Vone-ul_, respectively.
## 2 System Model
### Performance Metrics  
We take the throughput \(R_{m,n}^{d}\) achieved by UE \(n\) of slice \(m\) in direction \(d\) in the period \(T\) as the object of QoS evaluation. Then, the QoS satisfaction rate \(q_{m,n}^{d}\) of a UE \(n\) can be expressed as  
\[q_{m,n}^{d}=min(1,\frac{R_{m,n}^{d}}{\bar{R}_{m}^{d}}),\forall m \in\mathcal{M},d\in\mathcal{D},n\in\mathcal{N}_{m}, \tag{5}\]  
where \(\bar{R}_{m}^{d}\) is the QoS requirement of UE of slice \(m\) in direction \(d\). Based on the UE-level QoS satisfaction rate \(q_{m,n}^{d}\), the slice-level QoS satisfaction rate of slice \(m\) is expressed as  
\[q_{m}^{d}=\frac{1}{|\mathcal{N}_{m}|}\sum\limits_{n=1}^{|\mathcal{N}_{m}|}q_{ m,n}^{d}. \tag{6}\]  
Based on the slice-level QoS satisfaction rate \(q_{m}^{d}\), the QoS satisfaction rate at the BS-level is defined as the normalized multi-slice QoS satisfaction rate, which is expressed as  
\[q=\sum\limits_{m\in\mathcal{M}}\sum\limits_{d\in\mathcal{D}}\alpha_{m}^{d}\cdot q _{m}^{d} \tag{7}\]  
Figure 2: The joint resource allocation of NR band and SUL band. Each TDD pattern has only one downlink-to-uplink switch point. Hard slicing is operated in the NR band, and soft slicing is operated in the SUL band.  
where \(\alpha_{m}^{d}\) is the weight of slice \(m\) in direction \(d\). The larger \(\alpha_{m}^{d}\) is, the more important the corresponding slice \(m\) in direction \(d\) is. \(\alpha_{m}^{d}\) is constrained by  
\[\sum_{m\in\mathcal{M}}\sum_{d\in\mathcal{D}}\alpha_{m}^{d}=1. \tag{8}\]  
Let \(\eta_{m}^{d,nr}\) and \(\eta^{sul}\) denote the slice loads using the NR and SUL bands, respectively. The load-balancing metric of slices is defined as  
\[\varsigma=var(\{\bigcup_{m\in\mathcal{M},d\in\mathcal{D}}\eta_{m}^{d,nr}\} \cup\{\eta^{sul}\}), \tag{9}\]  
where the function \(var(\cdot)\) is to calculate the variance of slice load. The greater the load difference between slices, the greater the value of \(\varsigma\).
# Performance Evaluation of Adaptive Tracking Techniques with Direct-State Kalman Filter +
## 3 Adaptive Techniques in DSKF
### LBCA-Based DSKF  
Figure 9 shows the architecture of the adaptive DSKF using the LBCA. This technique adapts the loop bandwidth \(B\) of the DSKF and, in turn, adapts \(q\) while setting \(R\) to a constant value based on Equation (37).  
In previous studies, the LBCA has been implemented in the standard STL [28]. The LBCA-based STL presented superior tracking and system performance compared to other state-of-the-art techniques while achieving the lowest complexity [7]. This technique adapts the loop bandwidth based on a normalized bandwidth-dependent weighted difference between estimated noise and estimated signal dynamics. The normalized bandwidth \(B_{N}\) is the product between the integration time \(\tau_{\text{int}}\) and the loop bandwidth \(B\):  
\[B_{N}=B\ \tau_{\text{int}} \tag{42}\]  
Figure 8: Non-linear model of the carrier-to-noise density ratio (\(C/N_{0}\))-based DSKF.  
Figure 9: Non-linear model of loop-bandwidth control algorithm (LBCA)-based DSKF. ©IEEE. Adapted, with permission, from [20].  
First, the absolute mean \(|\mu_{e_{u}}|\) and the standard deviation \(\sigma_{e_{u}}\) of the discriminator's output are estimated. Second, the normalized dynamics \(\bar{D}\) are calculated:  
\[\bar{D}[n]=\frac{|\mu_{e_{u}}[n]|}{|\mu_{e_{u}}[n]|+\sigma_{e_{u}}[n]} \tag{43}\]  
Third, the difference between the normalized dynamics and a normalized-bandwidth-dependent weighting function \(g[n,B_{N}]\) is performed:  
\[c[n]=g_{\text{Max}}\cdot\bar{D}[n]-g[n,B_{N}] \tag{44}\]  
where \(c\) is the control value and \(g_{\text{Max}}\) indicates the maximum value of \(g[n,B_{N}]\). Finally, the control value updates the current normalized bandwidth \(B_{N}\):  
\[\hat{B}[n]=\frac{B_{N}[n]+c[n]}{\tau_{\text{int}}} \tag{45}\]  
where \(\hat{B}\) is the updated loop bandwidth. The noisy mean and standard deviation estimates can induce some noise instabilities into the updated loop bandwidth. Therefore, a Schmitt trigger is included to reduce these noise instabilities. The Schmitt trigger changes the next loop bandwidth \(B[n+1]\) by \(\Delta_{\text{B}}\) if the absolute difference between the updated loop bandwidth \(\hat{B}[n]\) and the actual \(B[n]\) exceed \(\Delta_{\text{B}}\):  
\[B[n+1]=\begin{cases}0&\text{if }n=0\\ \hat{B}[n]+\Delta_{\text{B}}&\text{if }\hat{B}[n]-B[n]\geq\Delta_{\text{B}}\\ \hat{B}[n]-\Delta_{\text{B}}&\text{if }B[n]-\hat{B}[n]\leq\Delta_{\text{B}}\\ B[n]&\text{otherwise}\end{cases} \tag{46}\]  
Based on the relationship between \(R\), \(q\), and the steady-state loop bandwidth \(B_{\text{ss}}\) from Equation (37), the updated \(q\) can be calculated:  
\[q[n+1]=2.986\,B^{6}[n+1]\,R \tag{47}\]
## 2 Materials and Methods
### MHC-PSPNet Network
#### 2.2.1 MobileNetV2 Backbone Network  
Large-scale networks and lightweight networks represent two major development trends in deep learning. However, large-scale networks require high-performance GPUs, making them unsuitable for deployment on mobile devices. Therefore, this research focuses on lightweight deep neural networks [20]. To develop a lightweight image segmentation network for personnel in search and rescue scenarios, various lightweight backbone networks were compared in the experimental section. Ultimately, MobileNetV2 was selected, as it significantly reduces computational overhead [21], making it well suited for individual image segmentation in rescue operations.  
Figure 1: PSPNet network architecture.  
MobileNetV2 achieves its lightweight nature through the use of inverted residual structures and depthwise separable convolutions. Specifically, depthwise separable convolutions consist of depthwise convolutions and pointwise convolutions, which differ significantly from standard convolutions [22]. In depthwise convolution, each input channel is convolved with its own separate kernel, where the number of kernels matches the number of channels. Pointwise convolution uses 1 \(\times\) 1 convolutional kernels to mix information across channels at each spatial location, analogous to standard convolution but without spatial extent. MobileNetV2 introduces innovations over MobileNet, such as the inverted residual structure, as depicted in Figure 3[23]. The core idea of this design is to expand dimensions before depthwise convolution and compress them afterward. This ensures that the network maintains sufficient depth and width while significantly reducing the number of parameters and computational complexity.
###### Abstract  
This paper presents a novel ultrathin frequency selective surface (FSS) bandpass filter with an extraordinary wideband tailored for operating within the S-C bands. The filter structure entails a double-layer FSS structure with mutually perpendicular unit cells etched on the top and bottom sides of a 0.003\(\lambda_{L}\) thick FR4 dielectric substrate, where \(\lambda_{L}\) is the free space wavelength at the lowest operating frequency. Thus, both TE and TM polarizations can be covered, ensuring the polarization insensitivity of the structure. The two FSS layers are loaded with resistors to implement the harmonic suppression principle. The overall periodicity is extremely compact, measuring 0.16\(\lambda_{L}\)\(\times\) 0.16\(\lambda_{L}\). An equivalent circuit analysis was conducted to comprehensively evaluate the structure and provide design guidelines. Numerical simulations and experimental measurements demonstrated that the proposed filter achieved a \(-\)3 dB transmission band spanning from 2 to 6.76 GHz (fractional bandwidth equal to 108.7%) under normal incidence. Moreover, aside from excellent wideband performance, the filter showcased a flat bandpass and stable responses up to 40\({}^{\circ}\) of incidence angle. These remarkable capabilities position the proposed filter as a valuable asset in advancing the development of radomes and applications relevant to electromagnetic interference (EMI) shielding, promising significant contributions to the field.  
frequency selective surfaces; angular stability; equivalent circuit model (ECM); polarization insensitivity; ultrathin; wide bandpass [PERSON]  
[PERSON], [PERSON], [PERSON], An Ultrathin Wideband Angularly Stable Frequency Selective Surface Bandpass Filter for S-C Band Coverage. _Appl. Sci._**2025**, _15_, 4887. [[https://doi.org/10.3390/](https://doi.org/10.3390/)]([https://doi.org/10.3390/](https://doi.org/10.3390/))  
[[http://doi.org/10.3390/](http://doi.org/10.3390/)]([http://doi.org/10.3390/](http://doi.org/10.3390/))  
[[http://ceativecommons.org/licenses/by/4.0/](http://ceativecommons.org/licenses/by/4.0/)]([http://ceativecommons.org/licenses/by/4.0/](http://ceativecommons.org/licenses/by/4.0/)).
## 4 Evaluation of the Platform and Discussion
### System Performance  
It is important to guarantee the AR application response within the specified time limits. In this case of study, the energy information generated by the appliances is continually changing, and thus the updates must be reflected in the smartphone of the end-user as soon as possible. In this test, the AR app was evaluated to analyze the time consumption of different variables (see Figure 11) of the application using the Unity Profile tool [19]:  
Figure 10: Successful image target detection depending on size and angle parameters.  
Figure 9: Augmented energy information displayed on the screen of the smartphone.  
1. AR rendering process shows the time taken by the CPU to perform rendering processes, batches, camera render, triangles, and rendered vertices.
2. The execution time of the scripts. Time used by the CPU to execute the code scripts.
3. Response time. The time required for the AR application to receive the update of data changed in the ThingSpeak cloud. This includes the following actions: (1) scan an image target, (2) recognize an IoT component, (3) contact a cloud service and (4) return the relevant data, and (5) AR app showing the energy information.  
The vertical axis of the graph shown in Figure 11 represents the time required for the mobile device processor to process a change detected by the monitoring system. As can be appreciated, this period ranges from 0 ms to 20 ms. The horizontal axis represents the frames captured by the camera. There are several graphs plot in Figure 11; the blue graph represents the time needed to run the scripts, and the green graph is the time taken to render the frame over the 4G mobile Internet. The yellow graph represents the response time needed to carry out a complete cycle.  
Let us see a specific case, concretely, the consumption times of each one of these PQ variables when frame 4 is captured and analyzed. In this case, the time taken to render the frame is 2.27 ms, and the time needed to run the scripts is 7.97 ms. It can be observed that most of the time for this frame is spent on the operations of scripts (almost three times more). It can be also observed the total execution time of the cycle is 18.83 ms. From the response time point of view, this last parameter is the most important one. The average response time is 16.45 ms. These results demonstrate that the use of Vuforia image target, with immediate decoding results, combined with an IoT device, represents an excellent choice in terms of response time. This could be characterized as very reasonable in terms of interactivity.
## 5 Results
### Zero-Shot Scenario  
In this section, the results obtained for the zero-shot approach scenario are presented. The values of the evaluation metrics are presented in Table 3. Two cases were considered. The first was a model that was trained on the SIMARGL2021 dataset and directly used on the IoT-23 dataset during the evaluation process. The second utilized the \"domain adaption\" mechanism described in the previous sections. In Table 3, significant differences in the obtained results are marked. The data demonstrate that the mechanism is useful and helps to improve the results obtained.
# Deep-Learning-Based Stream-Sensing Method for Detecting Asynchronous Multiple Signals
## 4 System Model  
In Figure 3, our system model considers a disaster situation where all cell sites are destroyed by a large-scale natural disaster, and thus all survivors are unable to use the communication infrastructure. To quickly search for survivors, a rescue UAV equipped with \(N_{\text{R}}\) antennas is deployed in the disaster area. We assume that a mobile device with a single antenna broadcasts a short rescue signal. The short signal consumes little power, and thus allows survivors to use their device batteries as long as possible. Devices _asynchronously_ transmit their rescue signals. Both the rescue UAV and the survivors' mobile devices utilize OFDM to receive and transmit signals. Each device transmits \(N_{\text{sym}}\) OFDM symbols as a rescue signal. In an OFDM symbol, the data part has \(N_{\text{FFT}}\) time-domain samples, which means that \(N_{\text{FFT}}\) subcarriers are used for transmission. As a result, since our survivor discovery system is based on OFDM and does not require any modification of the physical layer on Wi-Fi, we can maintain backward compatibility with Wi-Fi. (That is, our proposal just requires adding our survivor discovery protocol to the medium access control layer of Wi-Fi, which could be achieved with a simple update).  
Figure 4 presents our simple survivor discovery protocol. To identify the number of mobile devices around itself, the rescue UAV broadcasts a trigger message. When mobile devices receive the message, they immediately begin to transmit rescue symbols. The UAV then collects rescue signals for \(N_{\text{sym}}\cdot T_{\text{sym}}\), where \(T_{\text{sym}}\) is the duration of time of an OFDM symbol. The transmitted rescue signals sent simultaneously by mobile devices are superposed at the receiver. The rescue UAV executes our proposed asynchronous stream-sensing scheme on the received signal to determine how many mobile devices transmitted their rescue signals.  
Figure 3: Disaster scenario in which all cell sites are destroyed by a natural disaster.