esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Compression with a Time Limit
## 3 Lossless Compression
### Lossless Compression of Two-Dimensional Data with a Time Limit
#### 3.2.4 Compression of a Set of Non-Animated Images with Different Scenes  
The dataset \(D\) to be compressed consisted again of 1000 images, for a total size of 306 MB (320,864,256 bytes). Single runs of the compression tools yielded the following pairs (\(b_{i},t_{i}\)).  
Table 6 shows us that the fastest compressor was BMP, while the slowest was JPEG2000.  
From the point of view of the compression obtained, the best compressor was JPEG2000, while the worst was BMP. The _useful-setup_ is therefore composed of the compressors BMP, FELICS, JPEG-LS, and JPEG2000.  
Again, two time budgets were set to experiment the algorithm.  
With an assigned time budget, \(t_{1}\) = 61,000 ms then the optimal mix is given by the mix of the BMP and FELICS setups, and it is very close to the FELICS setup. All this is shown in Figure 7:  
The optimum is on the convex hull. The number of images to compress with BMP was 224, while for FELICS it was 776. The compression time was 61,215 ms., while the output size was 366 MB (383,557,600 bytes).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Tool** & **Size of Output in Bytes** & **Compression Time in ms.** \\ \hline PNG & 326,568,532 & 97,109 \\ TIFF & 311,831,649 & 100,836 \\ JPEG-LS & 226,142,667 & 130,656 \\ JPEG2000 & 182,808,175 & 569,792 \\ BMP & 691,254,002 & 36,708 \\ FELICS & 289,826,020 & 67,992 \\ \hline \hline \end{tabular}
\end{table}
Table 6: **Non-Animated images with different scenes.**  
Figure 7: Optimal mix, t = 61,000 ms.  
If we increase the time budget to \(t_{2}\) = 132,000 ms then the optimal mix is given by the mix between the JPEG-LS and FELICS configurations, and it is very close to the JPEG-LS configuration. Figure 8 shows it:  
The optimum is on the convex hull. The number of images to compress with JPEG-LS was 997, while for JPEG2000 it was 3.  
The compression time was 132,764 ms, while the output size was 216 MB (226,036,083 bytes).
## 2 Materials and Methods
### Transition Transport Conditions
#### 3.2.2 Snake-Shaped Driving Scenarios  
The sprayer was set to perform a serpentine maneuver on a high-adhesion road with a coefficient of 0.8 at a speed of 15 km/h. The simulation time was set to 10 s with a step size of 0.001 s. Steering angle control signals, as shown in Figure 13a, were applied to the sprayer's front wheels. Comparative analysis was conducted for three scenarios: no stability control, PID + average distribution control, and SMC + optimal distribution control. Figure 13b-d display the state parameters of the sprayer under different control methods, and Table 6 shows a comparison of the evaluation metrics of the sprayer.  
The analysis of Figure 13 and Table 6 reveals that both the \"PID + average distribution\" and \"SMC + optimal distribution\" control methods effectively suppress sprayer instability. However, the \"SMC + optimal distribution\" control method improves the control of the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Evaluation Indicators** & **No Control** & **PID + Average Distribution** & **SMC + Optimal Distribution** \\ \hline Maximum yaw rate & & & \\ deviation/rad\(\cdot\)s\({}^{-1}\) & 0.049 & 0.037 & 0.006 \\ Maximum deviation of the & & & \\ centroid sideslip angle/rad & 0.058 & 0.055 & 0.048 \\ Maximum longitudinal & & & \\ coordinate offset/m & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of evaluation indexes under transfer field transportation–snake driving.  
Figure 13: Transition transport–snake-shaped driving condition down of the sprayer status parameters: (**a**) the front-wheel angle signal input; (**b**) the change in the lateral swing angle; (**c**) the change in the center of gravity offset angle; (**d**) the driving path.  
yaw rate and centroid sideslip angle by 83.7% and 12.7%, respectively, compared to the \"PID + average distribution\" method. Additionally, this control method more effectively follows the reference path, reducing the maximum longitudinal coordinate offset by 54.8% compared to the \"PID + average distribution\" method, with only a 2.03% deviation from the reference value, thus ensuring excellent trajectory-following performance during transit.
# Acoustic Emission for Structural Monitoring of Historical Masonry: An In-Field Application
## 5 Results  
### Load Test Results: Displacement Analysis  
Table 2 presents the results obtained in terms of displacements recorded by the comparators, excluding the values registered by rod No. 6 (\"inactive\").  
The following figure (Figure 7) shows the load-displacement curves for each active comparator, with displacement values corrected by subtracting the readings from the inactive comparator. For clarity, the loading and unloading phases are distinguished within each graph.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Cycle**} & \multirow{2}{*}{\begin{tabular}{c} **Load** \\ **[kN]** \\ \end{tabular} } & \multicolumn{2}{c}{**Displacement Readings (Net of the Values Recorded by the ‘Inactive’ Reference Sensor)**} & \multicolumn{1}{c}{\multirow{2}{*}{\begin{tabular}{c} **[mm]** \\ \end{tabular} }} & \multirow{2}{*}{\begin{tabular}{c} **D1** \\ **D2** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **D2** \\ **D3** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **D4** \\ **D5** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **D5** \\ \end{tabular} } \\ \hline \multirow{9}{*}{CYCLE 1} & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\  & 1.25 & 0.07 & 0.09 & 0.06 & 0.06 & 0.05 \\  & 2.50 & 0.16 & 0.17 & 0.14 & 0.13 & 0.11 \\  & 3.75 & 0.19 & 0.22 & 0.15 & 0.18 & 0.13 \\  & 5.00 & 0.21 & 0.27 & 0.17 The results from the first cycle show a residual displacement at the end of unloading, albeit minimal. This behaviour is likely attributable to a settling phenomenon within the structure, which had not previously been subjected to the imposed external loads. The second cycle indicates partial recovery of the residual displacement observed after the first cycle. This suggests a redistribution of internal stresses and further accommodation of the masonry under load.  
In contrast, the third loading cycle reveals a more pronounced recovery of the residual displacements accumulated during the previous cycles. This behaviour was consistently recorded by all comparators and may be interpreted as the result of internal reorganisation within the arch, potentially indicating a stabilisation of the structural system.  
Overall, the displacement results confirm that, despite the inherent irregularities of the masonry and the presence of reinforcement, the arch demonstrated a predominantly elastic response up to the maximum applied load of 19.62 kN. These findings are in agreement with those obtained from the numerical model and the Acoustic Emission (AE) analysis, which will be discussed in the following sections.  
Figure 7: Load–displacement graphs for each comparator net of Comparator 6 (‘inactive’). (**a**) D1; (**b**) D2; (**c**) D3; (**d**) D4; (**e**) D5.
# Roles of Modeling and Artificial Intelligence in LPBF Metal Print Defect Detection: Critical Review
## 5 Conclusions  
The integrative potential of LPBF-printed parts in various innovative applications depends on its ability to compete with traditionally manufactured parts regarding the produced parts' infallibility and robustness in terms of the quality of parts. Despite the high accuracy of LPBF printing, defect formations are inevitable. Reducing or eliminating factors that lead to final part defects is crucial to producing parts of satisfactory quality. Sensing technologies, numerical methods, and artificial intelligence (AI) algorithms are utilized to predict and identify LPBF defects. This review explains the common LPBF-related defects (geometrical/dimensional, surface quality, microstructure, and mechanical) and the causes generally associated with them. Melt pool defect modeling has experienced advances, such as the incorporation of the finite element, finite volume, and analytical methods. Combinations of sensing technologies and statistical modeling with numerical methods (FEM) for the mechanical modeling of lattice structures (reconstructed CT scans, statistically driven defects, and embedded defects) are elaborated. The applicable ML algorithms typically found in LPBF defect detection efforts and standard sensing techniques are summarized. The integration of ML techniques with optical, thermal, and acoustic/ultrasonic techniques is explained.  
Integrating AI techniques with sensing technologies and numerical modeling has allowed accelerated optimization and the in situ auto-tuning of process parameters. AI integration has helped to improve our understanding of certain defect formations, assisting sensing technologies in predicting and detecting defects based on the trained algorithm's criteria. With the continuing push for rapid innovation in many industries, LPBF-printed parts are expected to advance significantly with the use of many technologies. In the foreseeable future, AI-integrated sensing technologies will continue to play a more significant role in LPBF defect detection methodologies, providing enhanced design freedom and reliability for many industries.  
Conceptualization, S.W. and A.A.; investigation, S.W., original draft preparation, S.W. and A.A.; writing--review and editing, S.W. and A.A. All authors have read and agreed to the published version of the manuscript.  
This work was funded by internal funding from Idaho State University (ISU) and the Center for Advanced Energy Studies (CAES), 2023-CAEA01.  
Not applicable.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# JLMS25 and Jiao-Liao Mandarin Speech Recognition Based on Multi-Dialect Knowledge Transfer
## 5 Experiment
### Ablation Study  
To elucidate the contributions of individual components, we conducted ablation studies on MDKT. We evaluated the performance of several configurations: the original wav2 vec 2.0 base model, the wav2 vec 2.0 model augmented with three WFA adapter layers (wav2 vec 2.0 + WFA), the base model enhanced with a single AttAdapter layer (wav2 vec 2.0 + AttA), MDKT with three training phases (MDKT-tri), and MDKT with only the first and third training phases, excluding the second training stage (MDKT-bi). The findings are presented in Table 3.  
As shown in Table 3, integrating WFA adapter into the wav2 vec 2.0 model resulted in a 3.2% and 4.2% reduction in CER and a 4.2% and 7.1% reduction in WER on the KeSpeech Jiao-Liao Mandarin and JLMS25 datasets, respectively, compared to full fine-tuning. Additionally, incorporating AttAdapter into the wav2 vec 2.0 model led to a 2.7% and 5.4% decrease in CER and a 4.6% and 7.5% decrease in WER on the KeSpeech Jiao-Liao Mandarin and JLMS25 datasets, respectively. These findings indicate that integrating dialectal and sequence features through AttAdapter is beneficial, while conveying multi-dialect knowledge via WFA adapter enhances model performance. A comparison between MDKT-tri and MDKT-bi demonstrated that the three-stage training strategy effectively improves model performance in the target dialect.
## 4 Discussion
### Time Stability of the Radiation Induced Free Radicals Studied by EPR Spectroscopy
For the samples irradiated by lower doses (at this case 1 kGy), identification of previous irradiation is not possible for a time period more than 45 days.
# Polar Region Integrated Navigation Method Based on Covariance Transformation
## 3 Design of an INS/GNSS Integrated Navigation Filter Model with Covariance Transformation  
When an aircraft flies in the polar region, it is necessary to change navigation frames from the n-frame to G-frame, and vice versa. In addition to the transformation of navigation parameters, the integrated navigation filter also needs to transform. The Kalman filter includes the state equation and the observation equation, and its update process includes a prediction update and measure update, respectively. The Kalman filter acts to update the error state and its covariance. Different Kalman filters, designed on different navigation frames, have different filter states \(\mathbf{x}\) and covariance matrices \(\mathbf{P}\), which need to be transformed.  
The filtering state at low and middle latitudes is usually expressed by:  
\[\mathbf{x}^{n}(t)=\left[\phi_{E}^{n},\phi_{N}^{n},\phi_{U}^{n},\phi_{E}^{n},\delta v _{N}^{n},\delta v_{U}^{n},\delta L,\delta\lambda,\delta h,\varepsilon_{x}^{b},\varepsilon_{y}^{b},\varepsilon_{z}^{b},\
abla_{x}^{b},\
abla_{y}^{b},\
abla _{z}^{b}\right]^{T} \tag{24}\]  
At high latitudes, the integrated filter is designed in the grid frame. The filtering state is usually expressed by:  
\[\mathbf{x}^{G}(t)=\left[\phi_{E}^{G},\phi_{N}^{G},\phi_{U}^{G},\phi_{E}^{G},\delta v _{E}^{G},\delta v_{U}^{G},\delta x,\delta y,\delta z,\varepsilon_{x}^{b}, \varepsilon_{y}^{b},\varepsilon_{z}^{b},\
abla_{x}^{b},\
abla_{y}^{b},\
abla _{z}^{b}\right]^{T} \tag{25}\]  
The transformation relation of the covariance matrix is as follows:  
\[\begin{split} P^{G}(t)&=E\bigg{\{}\bigg{(}\widetilde{ \boldsymbol{x}}^{G}(t)-\boldsymbol{x}^{G}(t)\bigg{)}\Big{(}\widetilde{ \boldsymbol{x}}^{G}(t)-\boldsymbol{x}^{G}(t)\Big{)}^{T}\bigg{\}}\\ &=E\Big{\{}\boldsymbol{\Phi}(\widetilde{\boldsymbol{x}}^{n}(t)- \boldsymbol{x}^{n}(t))(\widetilde{\boldsymbol{x}}^{n}(t)-\boldsymbol{x}^{n}( t))^{T}\boldsymbol{\Phi}^{T}\Big{\}}\\ &=\boldsymbol{\Phi}E\Big{\{}(\widetilde{\boldsymbol{x}}^{n}(t)- \boldsymbol{x}^{n}(t))(\widetilde{\boldsymbol{x}}^{n}(t)-\boldsymbol{x}^{n}( t))^{T}\Big{\}}\boldsymbol{\Phi}^{T}\\ &=\boldsymbol{\Phi}P^{n}(t)\boldsymbol{\Phi}^{T}\end{split} \tag{34}\]  
Once the aircraft flies out of the polar region, \(\boldsymbol{x}^{G}\) and \(\boldsymbol{P}^{G}\) should be converted to \(\boldsymbol{x}^{n}\) and \(\boldsymbol{P}^{n}\), which can be described as:  
\[\begin{split}\boldsymbol{x}^{n}(t)&=\boldsymbol{ \Phi}^{-1}\boldsymbol{x}^{G}(t)\\ \boldsymbol{P}^{n}(t)&=\boldsymbol{\Phi}^{-1}P^{G}( t)\boldsymbol{\Phi}^{-T}\end{split} \tag{35}\]  
The process of the covariance transformation method is shown in Figure 2. At middle and low latitudes, the system accomplishes the inertial navigation mechanization in the n-frame. When the aircraft enters the polar regions, the system accomplishes the inertial navigation mechanization in the G-frame. Correspondingly, the navigation parameters are output in the G-frame. During the navigation parameter conversion, the navigation results and Kalman filter parameter can be established according to the proposed method.
# Fast Normalization for Bilinear Pooling via Eigenvalue Regularization
###### Abstract  
Bilinear pooling, as an aggregation approach that outputs second-order statistics of deep learning features, has demonstrated effectiveness in a wide range of visual recognition tasks. Among major improvements on the bilinear pooling, matrix square root normalization--applied to the bilinear representation matrix--is regarded as a crucial step for further boosting performance. However, most existing works leverage [PERSON]'s iteration to perform normalization, which becomes computationally inefficient when dealing with high-dimensional features. To address this limitation, through a comprehensive analysis, we reveal that both the distribution and magnitude of eigenvalues in the bilinear representation matrix play an important role in the network performance. Building upon this insight, we propose a novel approach, namely RegCov, which regularizes the eigenvalues when the normalization is absent. Specifically, RegCov incorporates two regularization terms that encourage the network to align the current eigenvalues with the target ones in terms of their distribution and magnitude. We implement RegCov across different network architectures and run extensive experiments on the ImageNet1K and fine-grained image classification benchmarks. The results demonstrate that RegCov maintains robust recognition to diverse datasets and network architectures while achieving superior inference speed compared to previous works.  
bilinear pooling; matrix normalization; deep learning; image classification +
Footnote †: journal: Applied sciences
## 3 Methods  
### Transfer Learning  
SSL does not require a human-annotated dataset in the training process but is based on the fact that unlabeled data are rich in themselves [2]. Furthermore, ViT has a lower locality inductive bias than CNNs; therefore, it is difficult to train with a small dataset [38]. Training a self-supervised model based on ViT from scratch is challenging because FGVC datasets are generally small in scale.  
In this study, we address this issue by transfer learning from ImageNet pretrained models. Specifically, the initial parameters of ViT are set to the pretrained weights with DINO and then optimized in a self-supervised manner on FGVC datasets. Meanwhile, FGVC datasets are typically small scale, where SSL is known to benefit from training on large-scale unlabeled data. In this situation, updating all parameters during transfer learning may lead to suboptimal performance. Previous literature has shown that updating only the normalization layer when transferring a self-supervised pretrained model with SSL can be an effective strategy [13]. However, these results are mainly obtained with CNN models, and it remains unclear for ViT due to the architectural difference. Hence, we conducted experiments by hierarchically setting the learnable parameters to explore effective transfer learning strategies. We evaluated the scenarios of learning all parameters of ViT, just the normalization layer following [13], or the last block.
# A Real-Time Simulator for Navigation in GNSS-Denied Environments of UAV Swarms
## 3 Configuration of Simulator
### Hardware Configuration of Simulation Platform  
A Dell laptop with an Intel i7 9750H-2.6 Ghz CPU is used as a simulation device. The field of view of the camera is 120\({}^{\circ}\) and the image size is 1920 \(\times\) 1080 pixels. The gyroscope measurement range is 450\({}^{\circ}\)/s, and the zero bias stability of the gyroscope is set to 7\({}^{\circ}\)/h. The measurement range of the accelerometer is 20 g and the zero bias stability of the accelerometer is 14 ug. The measurement range of the UWB is 200 m, and the measurement error is 10 cm. We use the OpenCV 4.7 library for image processing, and Python3.7 is used for programming.
## 4 Panel Data Analysis-Based Surface Texture Prediction Model
### Influence of Panel Data Time Scale on Model Regression Results  
Table 10 shows the regression results (R\({}^{2}\), adjusted R\({}^{2}\), and \(p\) value) of the random-effects model based on different time scales of SMTD data. R\({}^{2}\) is a biased estimator of the multiple correlation coefficient, and the adjusted R\({}^{2}\) can take into account the number of independent variables used in the model. It is seen from Table 10 that the adjusted R\({}^{2}\) of the model using two years SMTD data is the largest, and the adjusted R\({}^{2}\) of the model using four years SMTD data is the smallest. The reason may lie in that the measured SMTD shows a gradual decreasing trend in the first two years, and after that an unusual increase of the measured SMTD is observed. The cause of the SMTD increasing in the third year is unclear [15].  
Figure 7 shows the linear fitting relationship between the model regression results based on different time scales of SMTD panel data and the measured data. The panel data analysis-based model shows good regression effects when it is applied on the pavement constructed with the dense-graded asphalt concrete as the asphalt surface layer. For example, when the panel data analysis model is developed using three years panel data, the obtained regression R\({}^{2}\) is 0.739 for SAC13-65%, 0.631 for SAC13-70%, and 0.725 for SMA13-75%, respectively. As for porous asphalt concrete PAC13-80%, the obtained R\({}^{2}\) is only 0.2. The reason is mainly due to the large dispersion of the field data obtained from the SRT-9 section (the only section paved with PAC13-80%), which leads to a large individual difference of the random-effects panel data model for PAC13-80%. It can be seen from  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Regression** & **One Year Data** & **Two Years Data** & **Three Years Data** & **Four Years Data** \\
**Results** & & & & \\ \hline R\({}^{2}\) & 0.689190 & 0.731946 & 0.582507 & 0.469469 \\ Adjusted R\({}^{2}\) & 0.679301 & 0.727758 & 0.578184 & 0.465361 \\ \(p\) value & 0.000 & 0.000 & 0.000 & 0.000 \\ \hline \hline \end{tabular} Note: Adjusted R\({}^{2}=1-\left[\left(1-\text{R}^{2}\right)\left(n-1\right)/\left(n-k-1 \right)\right]\), where \(n\) is the sample size of panel data, and \(k\) is the number of independent variables.  
\end{table}
Table 10: Regression results of random-effects model using different time scales of panel data.  
Figure 6: Random error component (\(u_{i}\)) of surface texture prediction model based on different time scales of SMTD data.
# Airborne Quantum Key Distribution Performance Analysis under Supersonic Boundary Layer
## 4 Conclusions  
Airborne quantum key distribution (QKD) that can synergize with terrestrial networks and quantum satellite nodes is expected to provide flexible and relay links for the large-scale integrated communication network. However, the photon transmission rate would be randomly reduced, owing to the random distributed boundary layer that surrounding to the surface of the aircraft when the flight speed larger than Mach 0.3, which would change the local refractive index and energy flux density drastically. Different from satellite based implementations, airborne platforms need to consider the influence of boundary layer due to the atmosphere. In this article, an airborne QKD performance evaluation scheme with supersonic BL effect is proposed. Through modeling and analysis, owing to the complex small-scale turbulence structures in the supersonic boundary layer, the deflection angle and correspondingly drifted offset of the beam varied obviously and randomly, and the distribution probability of photons are redistributed. The result shows that the subsonic and supersonic boundary layer would decrease \(\sim\)35.8% and \(\sim\)62.5% of the secure key rate respectively. Our work provides a theoretical guidance towards a possible realization of high-speed airborne QKD.  
The model design and simulations were done by [PERSON] and [PERSON]. The experimental data of supersonic boundary layer was obtained by [PERSON] The background was analysed by [PERSON], [PERSON] and [PERSON] are responsible for the review. The effort was conceived and supervised by [PERSON] and co-supervised by [PERSON], [PERSON] and [PERSON] wrote the draft, and all the authors reviewed the script. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China under Grant No. 61971436, No. 61972410 and No. 61803382, the Natural Science Basic Research Plan in Shaanxi Province of China (No. 2018020 JQ6020).  
Not applicable.  
The authors declare no conflict of interest.