esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Lab Experimental Validation
### Signal Analysis
#### 6.2.3 Ring Gear Fault  
Figure 11 exhibits the ring gear fault signal and its Fourier spectrum. According to the sensitive IMF selection criterion, we choose IMF2 for demodulation analysis, because it has a center frequency of 284 Hz corresponding to the gear meshing frequency.  
Figure 12 displays the amplitude and frequency demodulation analysis result of IMF2. In the envelope spectrum (Figure 12a), the ring gear fault characteristic frequency and its second harmonic \((1\)-\(2)f_{\rm r}\) are present with pronounced magnitudes. Meanwhile, prominent peaks show up at fractional harmonics of the ring gear fault characteristic frequency \((1\)-\(8)/3f_{\rm r}\). Similar to the sun gear fault case, it is the difference among the three planet gears that leads to three different impulse trains when the faulty ring gear tooth meshes with planet gears and therefore presence of the ring gear fault characteristic frequency fractional harmonics. In the Fourier spectrum of instantaneous frequency (Figure 12b), peaks also appear at the ring gear fault characteristic frequency harmonics \((1\)-\(3)f_{\rm r}\), and its fractional harmonics \((1\)-\(8)/3f_{\rm r}\). These findings confirm the ring gear fault.  
Figure 11: Ring gear fault signal: (**a**) Waveform; and (**b**) Fourier spectrum.  
Figure 12: Demodulation analysis of IMF2: (**a**) Envelope spectrum; and (**b**) Fourier spectrum of instantaneous frequency.  
Figure 10: Demodulation analysis of IMF5: (**a**) Envelope spectrum; and (**b**) Fourier spectrum of instantaneous frequency.
## 4 Internet Control Message Protocol
### Icmp Message Types  
Network errors are reported to the host using ICMP messages. Faults may be in the network, router, or any other intermediate device. The source can quickly determine the cause of the errors by observing these types of messages. Query and error reporting are two types of ICMP messages that can be used to troubleshoot network issues. When intermediate devices such as the host or router process an IP packet, these error reporting schemes can report the errors encountered. Destination inaccessibility, source-quench, time exceed, parameter problem, and redirection are some of the error reporting messages provided by the ICMP protocol to the host devices or routers [28].  
A pair of query messages will assist intermediate devices, such as hosts, routers, or network managers in obtaining error-related information from a host or router in the network [29; 30; 31; 32; 33; 34]. Devices in the network can locate any router and collect router information for further processing. Even routers can assist devices (hosts) with redirection messages using updated information about the router and routing table. Echo messages, timestamps, router advertisements, and solicitation are the message types provided by the query message of the ICMP protocol [35; 36; 37; 38]. The following are some key points to remember regarding the ICMP error messages:  
1. ICMP Messages will not be generated for the messages that contain error messages of ICMP type.
2. There is no provision for using the ICMP error messages for fragmented datagram.
3. ICMP messages will not be generated for messages that contain a multicast address.
4. ICMP error reporting messages are not generated for a datagram that contains special address ranges such as 127.0.0.0 or 0.0.0.
## 5 Result Analysis
### Comparison and Validation of the Results Obtained by CODAS-FAHP and MOORA-FAHP  
This study identified five (05) mHealth applications for evaluating usability criteria and these applications are useful for self-management of T2 DM patients. Self-management of diabetes-related chronic disease is very essential in maintaining healthy lifestyles and reduces suffering.  
In this paper, we proposed two models (MOORA and CODAS) to evaluate the usability of mHealth applications and to rank them based on ten criteria and twenty-nine sub-criteria. The FAHP was used to identify the consistent criteria and sub-criteria. The CODAS method measured the Euclidean distance (ED) and the negative ideal solution (NIS) for determining usability. It has been observed that the alternative farthest from the NIS is the best mHealth application. Ranking of the applications was carried out based on assessment scores. The MOORA method was the multi-objective optimization method which analyzed both beneficial and non-beneficial criteria for ranking the mHealth applications. That method measured normalized assessment (\(NA_{i}\)) value and showed that the application with the highest \(NA_{i}\) value was the best mHealth application.  
Comparing the results of CODAS and MOORA, among the T2 DM mHealth applications included in this study, mySugr was the best-ranked application, while the Blood Glucose Tracker was the least desired application. The rankings determined by CODAS and MOORA were slightly different. In CODAS, Glucose Buddy had Rank 3 and Diabetes: M had Rank 4, whereas in MOORA, Glucose Buddy had Rank 4 and Diabetes: M had Rank 3. The compared results of the rankings of the alternatives using these two methodologies is depicted in Figure 8.
# Discrete Environment-Driven GPU-Based Ray Launching: Validation and Applications
## 3 Validation
### Validation of the DED-RL Algorithm
#### 3.2.2 Computation Time in the San Francisco Scenario  
The average per-site computation time of DED-RL over all the 18 cell sites in San Francisco was of about 6 min, using a nVidia TESLA P100 Accelerator equipped with 3840 CUDA cores and 12 GB of GPU-RAM.  
In DED-RL simulations, a full 10 km\({}^{2}\) building map including the whole San Francisco downtown has been considered, differently from [6], where this map was simplified using an enhanced version of the simplification algorithm presented in [11], in order to achieve acceptable computation times with standard image ray tracing. For example, in [6] it is reported that for the site A000127C in San Francisco, the RT simulation time with the full 10 km\({}^{2}\) map was of about 3.5 days, which was reduced to about 10 h by applying the map simplification algorithm.  
On the other hand, the same site has been simulated using DED-RL using the full 10 km\({}^{2}\) map--with no simplification--in 212 s, so in this case, the speed-up gain of DED-RL is of 1426x with respect to RT (212 s vs. 3.5 days): in other words, with DED-RL the computation time is reduced to about 3 min despite the fact that no map simplification was applied and a higher number of bounces was considered in the simulation (max five bounces in DED-RL, vs. max three bounces in RT). By applying the map simplification algorithm to DED-RL simulation of site A000127C, the computation time is further reduced to 145 s, and the speed-up gain increases to 2085x. These huge speed-ups are in line with the analysis shown in [21] for a benchmark scenario. Results for site A000127C are summarized in Table 3. Similar results have been obtained for the other sites in San Francisco, not reported here for brevity.
# Named Entity Correction in Neural Machine Translation Using the Attention Alignment Map
## 4 Experiments
### Experimental Results  
To evaluate our NER models, we introduced two types of F1-score: entity form and surface form [53]. First, the entity form is a conventional measurement calculated from the entity level. Second, the surface form evaluates the ability of NER models to find rare entity words. In Table 8, the lexicon used in _Dictionary search_ was extracted only from the training corpus. The NER model used in the experiment was a two-stack LSTM model. Table 7 shows how the performance improved in the proposed method depending on the type of NMT model (seq2 seq or transformer), the number of trainable parameters of the model, and the output (Korean) vocabulary size. Our experiments showed that the proposed method was effective regardless of these types, and the BLEU scores improved from 2.60 to 3.70. In Table 9, experimental results show that the proposed approach successfully corrected mistranslated named entities in the output of the NMT model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Description** & **Vocab** & **Params** & **Original** & **Modified** & \(\mathbf{\Delta}\) \\ \hline Seq2 seq & (500, 512, 3, 1024, 2) & 40K & 58M & 35.75 & 39.29 & +3.54 \\ \hline Seq2 seq & (500, 512, 3, 1024, 2) & 42K & 59M & **35.83** & **39.53** & **+3.70** \\ \hline Seq2 seq & (500, 512, 3, 1024, 2) & 50K & 63M & 35.66 & 39.13 & +3.47 \\ \hline Seq2 seq & (500, 512, 3, 1024, 2) & 87K & 81M & 35.29 & 37.89 & +2.60 \\ \hline Seq2 seq-Reduced & (300, 256, 3, 512, 2) & 42K & 22M & 33.95 & 37.26 & +3.31 \\ \hline Seq2 seq-Reduced & (300, 256, 3, 512, 2) & 87K & 54M & 33.73 & 36.59 & +2.86 \\ \hline Transformer-Big & (512, 6, 8, 2048) & 42K & 65M & 33.90 & 37.07 & +3.17 \\ \hline Transformer-Big & (512, 6, 8, 2048) & 87K & 88M & 32.66 & 35.62 & +2.96 \\ \hline Transformer & (256, 3, 4, 1024) & 42K & 16M & 34.68 & 37.79 & +3.11 \\ \hline Transformer & (256, 3, 4, 1024) & 87K & 27M & 34.95 & 37.98 & +3.03 \\ \hline Transformer-Reduced & (128, 2, 2, 256) & 42K & 6M & 30.61 & 33.52 & +2.91 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance improvements in the BLEU score. _Description_: model details. _Vocab_: the number of vocabularies. _Params_: the number of model parameters. _Original_: BLEU score without the proposed method. _Modified_: BLEU score for the results corrected by the proposed method. The best scores for both “Original” and “Modified” are achieved by using the second configuration of Seq2 seq model, and those numbers are emphasized in bold face.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Entity Form** & **Surface Form** \\ \hline Dictionary search & 4.4\% & 35.0\% \\ \hline
1-layer LSTM Stack & 91.1\% & 88.0\% \\ \hline
2-layers LSTM Stack & 91.9\% & 88.5\% \\ \hline
3-layers LSTM Stack & 91.8\% & 88.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: NER accuracy in 2 types of F1-score. _Entity Form_ and _Surface Form_ mean how many entities the model finds and how many types of entities the model finds, respectively.
## 2 Materials and Methods
### Statistical Analysis  
The G*power software (version 3.1.9.4; Kiel University, Kiel, Germany) [58] was used to calculate the required sample size. We set the significance level (\(\alpha\)) at 0.05 and aimed for a statistical power of 0.80, in line with conventional standards. Based on a previous study by [PERSON] et al. (2020), which reported a [PERSON]'s effect size of 0.54 for similar outcomes [20], we estimated the effect size for our study. This effect size was deemed appropriate given the nature of our study objectives and the anticipated outcomes. With these parameters, the calculated sample size required was 10 participants per group.  
All data were processed and analyzed using STATISTICA 12 software (StatSoft, France), and data are expressed as means \(\pm\) SD.  
Shapiro-Wilk W and Mauchly tests were performed to confirm the normality and sphericity of the data distribution, respectively. Once normality and sphericity had been confirmed, parametric tests were performed.  
A two-way repeated-measure analysis of variance (ANOVA) [3 groups \(\times\) 2 sessions] was used to determine the effects of the group (GC/GZ/GP) and session (pre-/postintervention) factors on functional performance (TUG, 30S Chair Stand Test, 10 MWT, and FRT), mood (BMIS) and HRQoL (SF23). When significant differences were observed, a post hoc analysis was then performed using the Bonferroni significant difference test ([PERSON], 2011). The alpha level of statistical significance was set as \(p<0.05\).  
To calculate the effect size, the partial eta-squared \(\eta 2p\) formula was performed (small effect: 0.01 \(<\eta 2p<0.06\); medium effect: 0.06 \(<\eta 2p<0.14\); and large effect: \(\eta 2p>0.14\)).
###### Abstract  
The increase in the number of space debris is a serious threat to the safe operation of in-orbit spacecraft. The propagation law of the impact signal in the stiffened panel of the spacecraft's sealed bulkhead is very complicated, and there is less research on the impact source location in the high-stiffened panel. In this paper, an adaptive energy compensation threshold filtering (AECTF) method based on acoustic emission is proposed, which can realize large-scale, fast and accurate locating of the impact source on the stiffened panel with less resource consumption. The influence law of the stiffeners on the lamb wave is analyzed by finite element simulation, and the Lamb wave energy factor curve is obtained. The correctness of the simulation is verified by the locating experiment on the impact point. The results show that the proposed AECTF method has better adaptability and can correctly locate the impact points in complicated locations. By selecting the appropriate frequency band to filter the signal, the locating accuracy and stability can be improved. When the frequency band is 100-200 kHz, the locating result is optimal, the average absolute error is 7.0 mm, the average relative error is 0.86%, and the error standard deviation is 3.5 mm. This study will generate fresh insight into the impact location technology of high-stiffened panel and provide a reference for the in-orbit spacecraft health monitoring system.  
AECTF method; high-stiffened panel; lamb wave; energy factor; impact source location +
Footnote †: journal: Applied Sciences
# Enhanced Ant Colony Optimization for Vehicular Ad Hoc Networks Using Fittest Node Clustering
## 4 Results and Discussion
### Throughput Analysis  
This metric represents the average number of packets transmitted through the 802.11P communication modules. Table 3 and Figure 4a illustrate the throughput analysis of the DA-TRPED technique, showing that it outperforms other methods. In contrast, the conventional ACO exhibits fluctuations in throughput rates as the vehicle count increases. Interestingly, the proposed technique also shows a reduction in throughput as the number of vehicles increases from 40 to 80. This can be attributed to the estimation of DPEs, which facilitates the communication process and its efficiency.
## 3 Results
### Machine Learning Analysis for Pressure Estimation
#### 3.2.2 Analysis of Correlation Between Consolidated Temperature and Pressure Prediction
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **MAE** & **MSE** & **RMSE** & **R\({}^{2}\)** & **RMSLE** & **MAPE** \\ \hline Light Gradient Boost. Mach. & 24.3803 & 1179.303 & 29.9025 & \(-0.6947\) & 0.3968 & 0.41 \\ Dummy Regr. & 24.3803 & 1179.302 & 29.9026 & \(-0.6947\) & 0.3968 & 0.41 \\ Bayesian Ridge & 24.9298 & 1202.427 & 30.5468 & \(-0.8289\) & 0.4033 & 0.416 \\ Random Forest Regr. & 25.7203 & 1176.716 & 31.5095 & \(-1.4364\) & 0.4089 & 0.41 \\ AdaBoost Regr. & 25.1165 & 1214.541 & 31.2653 & \(-1.4662\) & 0.4057 & 0.3773 \\ Extra Trees Regr. & 26.0886 & 1220.183 & 32.2855 & \(-2.5147\) & 0.4293 & 0.4204 \\ K Neighbors Regr. & 29.2116 & 1556.675 & 36.5677 & \(-3.0332\) & 0.4713 & 0.4735 \\ Gradient Boosting Regr. & 26.5199 & 1303.739 & 33.9157 & \(-3.5019\) & 0.4332 & 0.3903 \\ Passive Aggressive Regr. & 28.393 & 1426.168 & 33.6168 & \(-4.4534\) & 0.4424 & 0.4123 \\ Extreme Gradient Boost. & 31.1872 & 1505.87 & 36.6103 & \(-4.625\) & 0.4932 & 0.4867 \\ Elastic Net & 30.5991 & 1644.279 & 37.4245 & \(-5.7394\) & 0.4984 & 0.4812 \\ Lasso Least Angle Regr. & 33.2624 & 1656.707 & 39.0917 & \(-12.0911\) & 0.6536 & 0.5131 \\ Decision Tree Regressor & 32.6131 & 1951.408 & 41.2692 & \(-12.3658\) & 0.5654 & 0.5034 \\ Lasso Regression & 33.5323 & 1677.464 & 39.3684 & \(-12.689\) & 0.6416 & 0.5169 \\ Huber Regressor & 36.7847 & 2045.709 & 42.9871 & \(-14.2362\) & 0.5908 & 0.5723 \\ Orthogonal Matching Purs. & 45.0718 & 3058.593 & 51.3642 & \(-30.2947\) & 0.8606 & 0.687 \\ Ridge Regression & 36.4573 & 2127.21 & 43.6394 & \(-32.8895\) & 0.623 & 0.5588 \\ Linear Regression & 38.0126 & 2422.576 & 45.5997 & \(-45.557\) & 0.6342 & 0.5902 \\ Least Angle Regr. & \(5.46\times 10^{35}\) & inf & inf & \(-\inf\) & 31.1333 & \(6.62\times 10^{33}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Regression model metrics: 1 st metatarsal average peak pressure and temperature consolidated. pressure values at the first metatarsus. The errors were substantial across the board, with the Least Angle Regression model showing the worst performance, recording an MAE of 5.46 \(\times\) 10\({}^{35}\) and infinite values for both MSE and RMSE, reflecting the model's complete inability to make accurate predictions. Similarly, in Table 6, which focuses on the fifth metatarsus targeting average peak plantar pressure, the results were equally discouraging. The Light Gradient Boosting Machine and Dummy Regressor again demonstrated a poor performance, with an MAE of 37.8672 and an RMSE of 46.2579, coupled with a negative R\({}^{2}\) value of \(-\)0.7488. Even the more advanced models, such as Extreme Gradient Boosting and Elastic Net, yielded
# 3D Point Cloud Generation Based on Multi-Sensor Fusion
## 3 A Method of 3D Point Cloud Generation by Multi-Sensor Fusion
### Research on Track Mobile Laser Measurement System (TMLS)
#### 3.1.1 Hardware Equipment  
The TMLS mainly included sensors such as 3D laser scanner, IMU, inclinometer and odometer. The specific models and parameters are as follows.  
(1) 3D Laser Scanner  
The 3D laser scanner is a sensor that uses the principle of laser ranging to efficiently obtain information such as the 3D coordinates and reflectivity of the measured object. We used the Z + F 5016 three-dimensional laser scanner produced by the Z + F Company in Germany, as shown in Figure 5, and the main technical parameters are shown in Table 1.  
(2) Inertial measurement unit (IMU)  
An IMU is a device used to measure the attitude angle and acceleration of an object. We used a FSINS3X optical fiber strapdown inertial navigation system produced by the Harbin Hangshi Enterprise Group, shown in Figure 6. A FSINS3X consists of fiber optic gyroscopes, quartz flexible accelerometers, navigation computers, power modules, etc. It can quickly measure the attitude, speed and position of moving carriers (cars, ships, aircraft, etc.).  
Through corresponding settings, the system can be combined with a GNSS, odometer (ODO), Doppler log (DVL) and ultra-short baseline (USBL) to obtain more accurate navigation information. Figure 6 shows the FSINS3X, and Table 2 shows its main technical parameters.  
(3) Inclination Sensor  
The inclination sensor mainly measures the track pitch angle and lateral inclination angle in the stationary phase and is used to assist in correcting the horizontal attitude angle of the IMU. This system selects a BW-VG527 dual-axis inclination sensor (as shown in Figure 7). The weighing accuracy is 0.01\({}^{\circ}\), and the RS232 communication mode was adopted. The performance indicators are shown in Table 3.
# Multi-Input Attention Network for Dehazing of Remote Sensing Images
###### Abstract  
The non-uniform haze distribution in remote sensing images, together with the complexity of the ground information, brings many difficulties to the dehazing of remote sensing images. In this paper, we propose a multi-input convolutional neural network based on an encoder-decoder structure to effectively restore remote sensing hazy images. The proposed network can directly learn the mapping between hazy images and the corresponding haze-free images. It also effectively utilizes the strong haze penetration characteristic of the Infrared band. Our proposed network also includes the attention module and the global skip connection structure, which enables the network to effectively learn the haze-relevant features and better preserve the ground information. We build a dataset for training and testing our proposed method. The dataset consists of remote sensing images with two different resolutions and nine bands, which are captured by Sentinel-2. The experimental results demonstrate that our method outperforms traditional dehazing methods and other deep learning methods in terms of the final dehazing effect, peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and feature similarity (FSIM).  
non-uniform haze; dehazing; deep learning; remote sensing images; Sentinel-2
# Curvature Sensing and Control of Soft Continuum Robots Using e-Textile Sensors
## 5 Results and Discussion
### CNN Model Performance  
The desktop TensorFlow 2.x API was used to perform the training and prediction tasks. The length of the training depends in large part on the choice of computer hardware. A CPU operating at 2.8 GHz was used in this study in order to speed up the model training procedure. The CNN model's mean squared error (MSE) losses are shown in Figure 9 across 500 epochs, showing how they gradually approach minimum loss values for both the training and validation datasets.  
Figure 7: The experimental setup includes a cable-driven continuum robot, IMU, soft e-textile sensor, and Arduino UNO for estimating the robot’s shape.  
To evaluate the models' effectiveness, we used a testing dataset of 400 samples, processed in batches of 64. Figure 10 compares the CNN-based model's predictions with the actual target values, highlighting the absolute error. Due to space constraints, only the first 100 samples are displayed. The minimal error margin of the CNN model is evidenced by the close alignment between the estimated outputs and the target signals.  
Our approach of utilizing a deep learning model with a CNN architecture aims to provide a calibration-free method for sensing the robot's shape parameters. The CNN is designed to correlate the deformation captured in the raw sensor readings, including any noise and disturbances, with the curvature measured by the IMU.  
Figure 8: (**a**) Variation in sensor readings from the 4 \(\times\) 4 matrix of e-textile sensing points labeled \(A_{0}\) to \(A_{15}\) capturing the continuum robot’s deformation in relation to changes in the robot’s curvature \(\kappa\) in (**b**).  
Figure 10: Comparison of target versus estimated values for the validation dataset of the robot’s parameters \(\kappa\) and \(\phi\) using the CNN-based shape sensing model.  
Figure 9: Graph illustrating the mean squared error (MSE) for training and validation losses during the training of the CNN-based shape sensing model for continuum robots over 500 epochs.
# Reliability Assessment of Steel-Lined and Prestressed FRC Slabs against Projectile Impact
## 2 Research Significance  
The above review shows that while some researchers have studied prestressed concrete for low-velocity impacts, there were no studies on the safety or reliability of prestressed fiber-reinforced concrete (PFRC) members and PFRCs with steel linings under impact loads. This paper proposes a simulation-based probabilistic method for assessing the reliability index of prestressed and steel-lined PFRC slabs against projectile loads, taking into account the uncertainties involved in various parameters related to geometry, material, and impact. It is expected that the study would be useful for practicing engineers and researchers in understanding the sensitivity of various parameters influencing the impact response and reliability of PFRC members with or without steel linings.