esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Learning Curve Dynamics and Model Generalization  
While strong performance metrics can sometimes create a false impression of clinical utility, verifying the dynamics of learning curves is important for establishing the trustworthiness of a model. A well-trained model will show validation and training curves that progressively align, maintaining a consistent generalization gap as it converges. In this analysis, both the SMOTE-optimized and non-SMOTE approaches demonstrated this behavior, with a smaller gap between training and validation trajectories in the SMOTE method, indicating improved generalization. Healthy learning patterns are characterized by validation accuracy that steadily approaches training accuracy, avoiding erratic divergence or complete overlap. A smaller gap is preferable to a larger one; however, a complete overlap can obscure overfitting to both datasets, while an expanding gap suggests poor generalization. Importantly, inverted curves, where validation accuracy exceeds training accuracy, may signal issues such as contamination of the validation set or biased sampling, which can compromise reliability. For OCT classification, these dynamics help confirm whether the features learned truly capture pathological signatures rather than artifacts from the dataset. The smaller gaps observed with SMOTE indicate a better handling of class imbalance, while unstable patterns could render even high numerical metrics clinically questionable. Therefore, validating learning curves should always be a must for distinguishing robust biomarkers from misleading correlations.  
Figure 5: Confusion matrix evaluating the ML model’s performance on cross-pathology testing for retinal pathologies excluded from training (e.g., AMD, CSR, MH). The matrix demonstrates robust generalization with 95.09% sensitivity (1997/2100 abnormal cases correctly identified) and 95.05% specificity (2206/2321 normal scans accurately classified) while maintaining balanced error rates of 4.91% false-normal (103 misclassified pathologies) and 4.95% false-abnormal (115 normal cases flagged as abnormal). This validates clinically reliable detection of unseen retinal pathologies.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Metric** & **Accuracy** & **Recall** & **Prec.** & **F1** & **Kappa** & **MCC** & **Specificity** & **Score** & **Clinical Utility** \\ \hline ML optimized & 0.9420 & 0.9410 & 0.9730 & 0.9560 & 0.8660 & 0.8674 & 0.9460 & 0.9610 & 0.9163 \\ \hline Cross-pathology testing & 0.9507 & 0.9505 & 0.9554 & 0.9529 & 0.8700 & 0.8720 & 0.9519 & 0.9656 & 0.9488 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of metrics for trained model with optimization and cross-pathology testing. The table evaluates performance across multiple metrics, including accuracy, AUC, recall, precision, F1 score, Kappa coefficient, MCC, specificity, and a composite score calculated via weighted combinations of AUC, sensitivity, and specificity.
## 5 Experimental Results
### Performance of MILP and CP Models
#### 5.1.3 Discussion  
According to the results of the experiments, the CP algorithm is more efficient than the MILP model when it comes to solving our scheduling problem for both _way_ (without ressource calendar constraints) and \(RC\) formulations (with resource calendar constraints). It can perform well in handling any size of problem and proves the optimality of a large number of instances. Even with high-availability periods, the CP model can still find optimal solutions. It also proves the optimality of several instances and outperforms the MILP model when it comes to finding feasible solutions.  
Summing up, we can clearly see that the computational effort required to solve our scheduling problem depends on the size of instances and the number of unavailability periods. The difference between CP and MILP increases as the number of jobs and the number of unavailability periods increase. CP can provide significant savings in computational effort compared to MILP formulation and finds better solutions and is the best overall in all instances.
## 3 Results  
The scheme of the experiment is shown in Figure 6.  
The technological stage was the first step towards experimenting with the possible interactions of new materials (NFC-tags; Aerogel) not yet used in dental practice, with conventional acrylic denture materials.  
The sample dentures were macroscopically examined after the completion of the technological process; no changes were found in the structure of the acrylic material. The intelligent materials used did not interact with the physical characteristics of the acrylic resin used for denture manufacturing.  
A positioning error was observed in three of the sample dentures; the round NFC-tags slid from the initial position towards the edge of the sample by 1-3 mm, although not outside the denture, and could still be read (Figure 7a,b).  
The information introduced in the NFC-tags before embedding them in the dentures was retained after the completion of the technological steps. The check-up of the stored information was done by re-reading it after the completion of the manufacturing process. In the monitoring stage, no changes were observed during the first two months of immersion.  
In the third month of monitoring, four sample dentures (two containing NFC-tags alone and two containing NFC-tag + Aerogel) placed in a 0.2% HCl solution were no longer readable. All remaining sample dentures were readable and showed no macroscopic structural changes (Table 2).  
Figure 6: Flow work diagram.  
The experiment was extended by replacing the four sample dentures that became unreadable in the third month of exposure to the chlorohydric acid solution with new ones of the same types, and immersing them in 0.2% HCl solution also for 3 months. These new samples were submitted to the same monitoring protocol as the others. All four new samples were still readable after 3 months. Therefore, it was assumed that, in the case of the four sample dentures that failed, most likely there was an error in the technological stage of manufacturing.  
The comparative macroscopic analysis of the 32 sample dentures showed that, over longer periods of time, the environments in which they were immersed acted on the acrylic materials by changing certain properties. Starting at the end of the first year of monitoring, the acrylic resin showed imbibition processes, becoming porous and pigmented, in some places opaque (the transparent ones) or rough (the pink ones).  
At each monitoring stage, all the sample dentures labeled with a NFC-tag alone and with NFC-tag + Aerogel, embedded in both types of acrylic resins (pink and transparent), showed unaltered readable information.
## 2 Lamb Wave Propagation in a Plate Strip with Finite Width  
### Semi-Analytical Finite Element(SAFE) Method  
To study the leaky Lamb wave radiation from a plate strip with finite width, it is necessary to sufficiently understand the propagation characteristics of the Lamb wave in the plate strip. Figure 1 illustrates the coordinate system of a plate strip with thickness \(h\) and width \(W\); the plate is infinitely long to \(\pm x\) direction and the plate material is assumed to be a homogeneous, isotropic, and lossless one. Wave propagation in the plate strip has been analytically studied by many researchers [28; 29; 30; 31; 32; 33; 34; 35; 36], but most of their analytical studies show limitations of effective frequency and thickness-width ratio by assumptions used in the solution derivation. Fortunately, the semi-analytical finite element (SAFE) method, a numerical method with no limitations of the analytical solutions, has been developed [35; 36; 37]. Since the SAFE method can calculate dispersion curves for arbitrary cross-section waveguide geometries, such as rails as well as plate strips [35; 36; 37], it is now prevalent in NDT and SHM fields.  
The SAFE method only constructs the two dimensional finite element (FE) model for the cross-section of the analysis object and the analytical solution of the wave propagation is then applied to the constructed FE model; the cross-section parallel to the \(yz\) plane is only modeled using FE and the equation of a traveling wave along the \(+x\) direction is applied. The governing equation of the constructed FE system without the external load can be written as follows:  
\[\left\{\gamma^{2}\mathbf{K}_{2}+i\gamma\mathbf{K}_{1}+\mathbf{K}_{0}+\omega^{ 2}\mathbf{M}\right\}u=0 \tag{1}\]  
where \(\mathbf{K}_{2}\), \(\mathbf{K}_{1}\) and \(\mathbf{K}_{0}\) are stiffness matrices, \(\mathbf{M}\) is the mass matrix of the cross section and the displacement vector \(u\) is given by:  
\[u=\mathbf{U}(y,z)e^{i(yx-\omega t)} \tag{2}\]  
where \(\mathbf{U}\) denotes displacement functions of the cross section. From this eigenvalue equation, the wavenumber \(\gamma\) can be solved at each frequency \(\omega\) and the dispersion curves can be drawn within the interested frequency range. Recently, SAFE methods using commercial Finite Element Method (FEM) software have been introduced, and thereby SAFE modeling has become convenient [38; 39]. In this study, to analyze the wave propagation in the plate strip, a modal analysis method under periodic boundary conditions [39] was employed in the commercial FEM software ANSYS (release 2017, ANSYS Inc., Canonsburg, PA, USA).
# Non-Mypopic Beam Scheduling for Multiple Smart-Target Tracking in Phased Array Radar Networks
## 1 Introduction
* The beam scheduling problem is formulated as an infinite-horizon discounted RMABP. The smart targets are characterized by tracking-action-dependent dynamic models with high maneuvering, which will hide themselves when being tracked. Each target is associated with a restless project, whose TEC states and dynamic states evolve through a non-linear filter based on the dynamic model probability vector. * In the practical case with multi-dimensional TECs, the trace of the TEC state is used as a threshold. Due to the non-linear measurement function, the TEC states under actions can be only updated based on target measurements. We predict TEC states and dynamic states based on the US method when calculating MP indices. Then, the US-MP index policy is proposed. * For the scalar TEV states, in cases with realistic parameters, we numerically verify PCL-indexability, which leads to Whittle indexability, of the target-tracking problem. The effectiveness of the proposed US-MP index policy for the multi-dimensional TEC state case is also assessed by a simulation study, where it is shown to outperform myopic baseline policies. The remainder of this paper is organized as follows. In Section 2, the target dynamic models and measurement model are defined. Then, the TEC state update under different actions is introduced and the scheduling problem is formulated. In Section 3, the application of the Whittle index policy is discussed for scalar state cases. The beam selection scheme is developed in the RMABP model based on US-MP indices for practical tracking cases. The computational method of the lower bound of optimization functions and the computational complexity of policies are presented. In Section 4, scalar and multi-dimensional state cases are considered, and reckless and cautious targets with different dynamic model probabilities are also considered. The simulation results demonstrate the superiority of the proposed MP index policies. Section 5 concludes the paper.
# An Improved Empirical Wavelet Transform and Its Applications in Rolling Bearing Fault Diagnosis
## 4 Simulated Signal Verification
### Case Study 2
The success rate of EWT decomposition is 16.67% and of FEWT is 28.58%. The FEWT decomposition efficiency and success rate are higher. The impact component (EMI) and modulation component (EM2) are compared and the results are shown in Figure 13b. It is known that the components decomposed by FEWT are more consistent with the original components. Therefore, the boundary of the proposed method is more reasonable. Figure 12: The modal components and details obtained by two methods. Pulse component decomposed by (**a**) the EWT method and (**c**) the FEWT method. Modulating component decomposed by (**b**) the EWT method and (**d**) the FEWT method. (**e**) Details of (**b**); (**f**) details of (**d**). Figure 13: (**a**) The success rate of separation; (**b**) error rate.
# Improving Streaming Video with Deep Learning-Based Network Throughput Prediction
## 4 Prediction of Network Throughput  
### The Prediction Problem  
We have measurements of the past throughput \(\{r_{t},r_{t-1},\dots,r_{t-n}\}\), and we are to predict the value of \(r_{t+m}\), \(m>0\). Hence, we use a predictor \(\hat{r}_{t+m}\), which takes the past throughput  
\[\hat{r}_{t+m}=\phi(r_{t},r_{t-1},\dots,r_{t-n}). \tag{5}\]  
The problem is to choose \(\phi\), which minimizes the difference between \(\hat{r}_{t+m}\) and \(r_{t+m}\).  
Typically, a DASH algorithm predicts network throughput for the nearest future \(r_{i+1}\), using an average of past throughput measurements [27]  
\[\hat{r}_{t+1}=\frac{1}{n}\sum_{k=1}^{n}r_{t+1-k}. \tag{6}\]  
According to [28], DASH players apply the estimator (6) or its variation based on weighted, exponential or harmonic moving average. In our work, we improve the estimation by replacing the simple computation (6) with an LSTM ANN.  
To measure the accuracy of the prediction algorithm, we chose the normalized root mean square error (NRMSE)  
\[NRMSE=\frac{1}{\overline{r}}\sqrt{\frac{\sum_{i=1}^{N}(r_{t}-\hat{r}_{t})^{2}} {N}}, \tag{7}\]  
The NRMSE represents the squared difference between predictions \(\hat{r}_{t}\) and observed values \(r_{t}\) divided by the number of observations \(N\). An average of observed values \(\overline{r}\) normalizes the calculation result. The lower the NRMSE value, the better the model describes empirical data. The normalization allows us to compare predictions among traces with different throughput levels.
## 3 Influence Factors of the UAV-LDV System
### Noise Level
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Conditions**} & \multirow{2}{*}{**Noise Level (dB)**} & \multicolumn{5}{c}{**First-Order Natural Frequency (Hz)**} \\ \cline{3-6}  & & **Numerical Simulation** & **Fixed Test** & **Error** & **Mobile Test** & **Error** \\ \hline \multirow{8}{*}{Condition 1} & 20 & & 101.73 & 0.72\% & 105.97 & 4.17\% \\  & 30 & & 101.92 & 0.91\% & 105.92 & 3.92\% \\  & 40 & & 102.1 & 1.09\% & 92.48 & \(-\)9.42\% \\  & 50 & & 103.21 & 2.19\% & 93.28 & \(-\)9.62\% \\  & 70 & & 98.43 & \(-\)2.54\% & 92.65 & \(-\)5.87\% \\  & 100 & & 96.62 & \(-\)4.34\% & 86.26 & \(-\)10.72\% \\  & 20 & & 91.61 & 0.57\% & 96.37 & 5.80\% \\  & 30 & & 90.63 & \(-\)0.50\% & 88.67 & \(-\)2.66\% \\  & 40 & & 92.83 & 1.91\% & 92.48 & 1.53\% \\  & 50 & & 91.25 & 0.18\% & 94.34 & 3.57\% \\  & 70 & & 94.12 & 3.33\% & 84.05 & \(-\)7.73\% \\  & 100 & & 88.24 & \(-\)3.13\% & 101.04 & 10.92\% \\  & 20 & & 88.81 & 0.33\% & 80.32 & \(-\)9.26\% \\  & 30 & & 88.1 & \(-\)0.47\% & 86.58 & \(-\)2.19\% \\  & 40 & & 89.29 & 0.87\% & 92.72 & 4.74\% \\  & 50 & & 90.79 & 2.56\% & 95.31 & 7.67\% \\  & 70 & & 86.02 & \(-\)2.82\% & 94.68 & 6.96\% \\  & 100 & & 85.31 & \(-\)3.63\% & 96.95 & 9.52\% \\  & 20 & & 66.28 & 0.26\% & 64.1 & \(-\)3.04\% \\  & 30 & & 66.9 & 1.19\% & 64.47 & \(-\)2.48\% \\  & 40 & & 66.1 & \(-\)0.02\% & 61.94 & \(-\)6.31\% \\ Condition 4 & 50 & & 63.04 & \(-\)4.64\% & 62.88 & \(-\)4.89\% \\  & 70 & & 68.77 & 4.02\% & 71.94 & 8.82\% \\  & 100 & & 70.12 & 6.07\% & 73.67 & 11.44\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of first-order natural frequency results. background noise level continues to rise, it eventually reaches the threshold for disturbing the curtain wall vibration. At this stage, the SSD index is more sensitive to the vibration characteristics of the structure, and the system's recognition ability is gradually recovering. However, if the noise level is smaller than 20 dB or larger than 40 dB, either excitation or noise will dominate while others attenuate this tendency. As a result, the absolute value of the energy level will be smaller, leading to a smaller range of overall energy variations. Therefore, it is advisable to perform HFSGCW damage detection in a quiet environment. Figure 12: Comparison diagrams of SSD. Note: The results of each test under the same condition are listed in the same column.
# Machine Learning-Based Adaptive Synthetic Sampling Technique for Intrusion Detection
## 3 Material & Methods
### Data Pre-Processing
#### 3.2.1 Data Cleaning  
The NSL-KDD dataset is notorious for containing a large number of redundant records, which can be detrimental to the learning algorithms. As a result, it is critical to remove duplicates and irrelevant records from the dataset. Also, any missing values in the dataset must be identified and handled appropriately, either by filling them with appropriate values or by removing the missing value records.
# Single Trace Side Channel Analysis on NTRU Implementation
## 1 Introduction
### Our Contribution  
In this paper, we propose the first single trace side channel analysis (STA) against on both NTRU Open Source and NTRUEncrypt with experimental results, and propose a countermeasure. Previous SCA on NTRU [7; 8; 9] targeted the polynomial multiplication between the cipher-text and the secret key. However, since NTRU was patented until 2017, existing SCAs on NTRU are based on the assumption that publicized polynomial multiplications are used in the decryption process. In this paper, we performed STA on the decryption algorithm used in [10] and on the version submitted in NIST standardization project [11].  
Based on the proposed analysis, every NTRU based cryptosystem can be vulnerable to our attack. Moreover, as the PKC is mostly used to exchange the session key between two parties, there might be the case where power consumption trace can only be obtained by one execution of the algorithm. Since we recover with a single power consumption trace, our attack is indeed a threat to these implementations whereas existing DPA cannot be applied in this circumstances. We implement the algorithm on the ATmega128 8-bit processor of the KLA-SCARF AVR [12] and applied the proposed attack. The details of our attack are presented in Section 3.  
We propose two versions of countermeasure against the proposed analysis. Although the previous DPA target the different implementation, their method can still be applied on NTRU Open Source and NTRUEncrypt. In this paper, we propose a countermeasure that prevents not only our proposed attack but also the DPA. The proposed countermeasure on NTRU Open Source does not increase the computational cost. Furthermore, the proposed countermeasure on NTRUEncrypt reduces the computational cost approximately by half. The description of our countermeasure is presented in Section 4.
# Bangla Character Detection Using Enhanced YOLOv11 Models: A Deep Learning Approach
## 2 Related Work
### Character Recognition in Different Characters Using YOLO
#### 2.1.4 YOLO for Kuzushiji and Old Japanese Script Recognition  
This research applies cutting-edge machine learning methods to recognize Kuzushiji, an archaic Japanese cursive script used in historical texts. Due to limited annotated data, the study leverages Generative Adversarial Networks (GANs) to augment the dataset with synthetic samples, significantly enhancing model performance. A decoupled YOLOv4 architecture is employed for detection, with non-maximum suppression (NMS) used to improve prediction accuracy. Training strategies are optimized to ensure class balance and effective learning. The study also compares different GAN variants DCGAN, LSGAN, and WGAN for their effectiveness in data augmentation. Results indicate that GAN-based augmentation substantially improves detection and classification outcomes in the context of complex scripts. This work builds on earlier applications of YOLO for historical text analysis and underscores the synergy of GANs and YOLO in tackling low-resource and high-complexity character recognition challenges [10].  
Although YOLO-based methods have been successfully applied to character recognition tasks across a variety of languages, including Chinese, Bangla, Greek, and Japanese, many of these approaches face similar challenges. These include difficulties in handling the complex shapes and variations of different scripts, limited performance on noisy or low-quality images, and a strong reliance on large amounts of annotated training data. In many cases, the models used are off-the-shelf YOLO variants with minimal adjustments for the unique traits of non-Latin scripts. For Bangla, in particular, the presence of visually similar letters and compound characters adds an extra layer of difficulty. To address these issues, we propose a refined YOLOv11 architecture incorporating compound scaling, C3k2 and C2 PSA modules, and a training pipeline tailored to the intricacies of Bangla script. These improvements significantly boost recognition accuracy, especially for complex and visually similar characters, across a range of testing scenarios.
# A High-Resolution, Wide-Swath SAR Imaging System Based on Tandem SAR Satellites
## 4 Verification and Analysis
### Analysis of the Subsauth Number  
According to the introduction above, Sat-2 randomly assigns the transmitted pulse to multiple subswatths. Therefore, the received echo of each subswath is undersampled, and a greater number of subswatths corresponds to fewer sampling points and worse reconstruction performance. A detailed analysis is listed in this part. The reconstruction  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Reference** & \begin{tabular}{c} **Coarse** \\ **Observation** \\ \end{tabular} & \
\begin{tabular}{c} **L1 Algorithm** \\ **Algorithm** \\ \end{tabular} &
\begin{tabular}{c} **Proposed** \\ **Algorithm** \\ \end{tabular} \\ \hline Azimuth & \(0.6\) & \(3.6\) & \(0.6\) & \(0.6\) \\ PSLR (dB) & \(-12.27\) & \(-11.77\) & \(-10.47\) & \(-17.84\) \\ ISLR (dB) & \(-7.37\) & \(-9.35\) & \(-7.97\) & \(-17.13\) \\ SSIM & \(1\) & \(0.89\) & \(0.87\) & \(0.93\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluation results.  
Figure 14: Imaging results of real airborne SAR data. (**a**) Coarse observation of Sat-1. (**b**) Reconstruction result of the L1 algorithm. (**c**) Reconstruction result of the proposed algorithm.  
error is regarded as noise. The signal-to-reconstruction-noise ratio is defined to analyze the reconstruction performance, that is  
\[SNR_{ME}=\frac{\left\|\sigma_{i}\right\|_{\text{new}}^{2}}{\left\|\sigma_{i}- \sigma_{i}\right\|_{\text{new}}^{2}} \tag{24}\]  
where \(\sigma_{i}\) and \(\sigma_{i}\) are the ideal result and the estimated result of the \(i\)th range gate, respectively. \(\left\|\cdot\right\|_{\text{new}}\) is the new norm defined in Section 3.2.5. The \(\left\|\sigma_{i}-\hat{\sigma}_{i}\right\|_{\text{new}}^{2}\) in Equation (24) can be expanded as  
\[\left\|\sigma_{i}-\hat{\sigma}_{i}\right\|_{\text{new}}^{2}=\left\langle(\sigma _{i}-\hat{\sigma}_{i}),\mathbf{Q}^{-1}(\sigma_{i}-\hat{\sigma}_{i})\right\rangle \leq\left\|\sigma_{i}\right\|_{\text{new}}^{2}-\sigma_{i}^{H}\mathbf{A}^{H} \Big{(}\mathbf{AQA}^{H}\Big{)}^{-1}\mathbf{A}\sigma_{i} \tag{25}\]  
Then, we can obtain  
\[SNR_{ME}=\frac{\left\|\sigma_{i}\right\|_{\text{new}}^{2}}{\left\|\sigma_{i}- \hat{\sigma}_{i}\right\|_{\text{new}}^{2}}\geq\frac{1}{1-\sigma_{i}^{H} \mathbf{A}^{H}\Big{(}\mathbf{AQA}^{H}\Big{)}^{-1}\mathbf{A}\sigma_{i}/\left\| \sigma_{i}\right\|_{\text{new}}^{2}} \tag{26}\]  
Figure 15 shows the variation curves of \(SNR_{ME}\) on the subswath number, where the lowest \(SNR_{ME}\) and the highest lower bound, calculated by Equation (26), among all range gates are selected.  
According to Figure 15, when the subswath number approaches 20, \(SNR_{ME}\) is close to the theoretical lower bound, and the imaging result may be completely submerged by the reconstruction error.