esa-sceva/satcom-chunk-collection huggingface dataset:
# Focus on Point: Parallel Multiscale Feature Aggregation for Lane Key Points Detection
## 4 Experiments
### Implementation Details  
In the experiments, all input images were uniformly resized to 256 \(\times\) 512 before being fed to the network to reduce computational load, and RGB values of images were normalized to between 0 and 1. For optimization, we used Adam optimizer and poly learning rate decay policy to train model, and the learning rate was set 0.0001 for TuSimple and 0.001 for CULane. The batch size was set to 8, the total number of training epoch was, respectively, set to 500 and 250 for TuSimple and CULane. In addition, we also applied various data augmentation methods, such as Gaussian noise, shadow, translation, random rotation and intensity changing.  
The model training and testing were conducted on a computer equipped with the GeForce GTX 1080 Ti 12 GB. To save memory and boost computation, most network components were composed of self-written bottleneck blocks in Pytorch. For real-time detection tasks, the average rate of the model can be maintained at approximately 36 frames. The hyperparameters of the above loss functions were determined experimentally, and Table 2 shows the setting values of the hyperparameters. \(\alpha\) was set to 0.70, \(\gamma\) to 2 and \(\Delta\) to 1.5. In the training phase, the confidence threshold of key points \(Pt_{i}\) was 0.81, and the distance threshold \(\delta_{i}\) was 0.15.
## 3 Proposed Cross-Working Condition RUL Prediction Method
### Implementation Procedures
#### 3.3.3 RUL Prediction  
1. Set cross-domain prediction tasks according to operating conditions, build the source domain with the labeled training set and the target domain with the unlabeled training set and the target domain test set, and carry out RUL labeling processing on the source domain with the labeled training set.
2. Build the JTMN model and initialize its parameters. The model hyperparameters were determined by grid search and cross-validation.
3. Use the labeled source domain and unlabeled target domain as training sets to train the JTMN model until it converges, so as to minimize regression errors and distribution differences in the source domain.
4. The trained JTMN is directly used for RUL prediction of the test set. The testing process can simulate an online scenario where samples taken chronologically are predicted using JTMN.
5. The superior performance of the proposed method is demonstrated using evaluation indicators and analysis of the execution results.
## Appendix D
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|} \hline \multirow{2}{*}{**References**} & \multirow{2}{*}{**PC type**} & \multicolumn{2}{c|}{**Highest MV on MAT (...) in Comparison with**} & \multirow{2}{*}{**MAT (\%)**} & \multirow{2}{*}{**Authors Findings**} \\  & & & (...) \(\boldsymbol{J}\)**Lengths/Contents (wt \%)** & & & \\ \hline \multirow{4}{*}{[100]} & \multirow{4}{*}{RO} &... & UPCUBF (PP+BF(10 vol \% of BF))... pristine PC & \multirow{4}{*}{+1.35} & MAT decreased with the addition of PP\_MA in comparison with the PC & \\  & & &... & without MA. MAT for (PP + PP\_MA) + UBF (PP-MA + UBF) mixtures & \\ \cline{3-5}  & &... & MTCUBF(PP + & the lowest. This result agrees with the verified increase on MP of the same PC and proved the efficiency of the selected sequence of steps for the mixing process. & \\ \cline{3-5}  & &... & MTCUBF (PP + (PP-MA + BF) 10 vol \% of BF)... pristine PC & \multirow{4}{*}{+0.3} & MAT decreased with the addition of PP\_MA in comparison with the PC & \\ \cline{3-5}  & &... & MTCUBF (PP + (PP-MA + BF) 10 vol \% of BF)... pristine PC & & & \\ \hline \multirow{4}{*}{[156]} & \multirow{4}{*}{SPINM} & SPINM with G 25-50 wt \%, 50 wt \% of BF and & \multirow{4}{*}{+1.35} & MAT decreased with the addition of PP\_MA in comparison with the PC & \\  & & & crosslinker of 0.8\% (saturation time = 26 h) & & & \\ \cline{3-5}  & & & & & 0.8\% to 2.6\% & The highest observed MAT of the SPINM may be due to \\ \cline{3-5}  & & & & the hybridization nature of both the fibers (288\% \(\pm\) 5\%) and matrix (78\% \(\pm\) 3\%). & & \\ \hline \multirow{4}{*}{[32]} & \multirow{4}{*}{L} &... & Pristine PLC... & \multirow{4}{*}{30 wt \%} & \multirow{4}{*}{0.8} & The MAT for neat polyester matrix was negligibly small after a period of 35 days. However, the MAT for samples with 10, 20 and 30 wt \% were found to increase regularly and leveled off at longer periods, which was an indication of saturation. The maximum MAT was found for PCBF with 30 wt \% of BF. & \\ \hline \end{tabular}
\end{table} 
Table 10: Cont. \begin{table}
\begin{tabular}{|c|c|c|c|c|c|} \hline \multirow{2}{*}{**References**} & \multirow{2}{*}{**PC type**} & \multicolumn{2}{c|}{**Highest MV on MAT (...) in Comparison with**} & \multirow{2}{*}{**MAT (\%)**} & \multirow{2}{*}{**Authors Findings**} \\  & & (...) \(\prime\)Lengths/Contents (wt \%) & & & \\ \hline \multirow{11}{*}{[137]} & \multirow{8}{*}{W} &... PCVWBF... pristine PC & 10 vol \% & \multirow{8}{*}{+1394} & PCWBF absorbed water very rapidly at the initial stage, and later, & \\  & & & & & \\ \cline{4-5}  & &... & PCVWBF... & & & \\ \cline{4-5}  & & (saturation time = 143
## 4 Discussion
At the same time, the influence of earthquakes, rain, and vegetation on the COR value should also be considered to obtain a more comprehensive COR value evaluation system. Lastly, while actual control measures are being developed, the results of field tests and numerical simulations should be comprehensively considered. The results of the proposed approach can provide a reference for the preliminary formulation of disaster prevention measures. However, a more accurate disaster range may require further field tests and historical disaster ranges of slopes with similar geological conditions as a reference.
## 3 Data Compression Technology-Based Intelligent Data Acquisition (IDAQ) System
### Embedding of the CAFB  
Finally, Figure 7e shows the logic for embedding the optimally-designed CAFB from the previous study. The optimally-designed CAFB in Figure 7e was programmed with software-based Matlab (r20158) from The Mathworks, Inc. (Natick, MA, USA) code for efficient systemization. Furthermore, for the purposes of realizing and evaluating the CAFB in this paper, the test was performed by configuring the IDAQ system with the multi-input module for the acceleration channel embedded with the optimally designed CAFB and with a one-way wireless communication method activated. In addition, the operating software of the IDAQ system was comprised of data measurement, channel setup, RF communication, and multi-channel logic. The logics were programmed with LabVIEW (2012) from National Instruments (Austin, TX, USA) and utilized as the operating software of the IDAQ system.
# A Novel RBFNN-CNN Model for Speaker Identification in Stressful Talking Environments
## 4 Model Architecture
### Deep Learning Experiments
#### 4.3.6 Bi-GRU  
There are three GRU layers present within the proposed BI-GRU model. Each of these three GRU layers has 32 filters. We added two dropout layers, which help in avoiding overfitting; we set a dropout ratio of 20%. Finally, we added a fully connected layer and two dense layers that have activation functions Softmax and RELU, respectively, with 16 nodes.
# Autonomic Network Management and Cross-Layer Optimization in Software Defined Radio Environments
## 5 Experimental Results
Furthermore, the majority of the collisions were found in 2 and 3-hop transmissions, whereas the 1-hop transmissions were routinely completed unobstructed. It is worth mentioning that during scenarios 5 and 6, it was observed that there were zero collisions between the PU and the SUs, hence the mechanism's claim of transparent SU operation is validated, accomplishing another key performance objective of the proposed approach, as the PUs were able to access the spectrum unobstructed. Whenever the PU started transmitting in a channel, the SUs evacuated it, displaying their adaptive capabilities. Figures 6 and 7 illustrate the achieved spectrum utilization addressing the primary goal of CRNs (i.e., increase of spectrum utilization without interfering PUs). Moreover, by accounting the number of the assigned channels (\(L_{1}\)-norm of vector \(\vec{u}_{s}\), as defined in Section 4.1) and not just the channels utilized in transmissions, it can be deduced that the approach promoted the fair allocation of resources, since no particular node is constantly assigned more channels in comparison to the others (see some representative cases depicted in Figure 8). Thus, the proposed framework has been proven to consist of an autonomic, cross-layer, low-overhead sub-optimal approach, with extremely good performance for CRN networks, accomplishing all performance objectives.
## 5 Experimental Validation (Simulated)
### Discussion  
The simulation outcomes substantiate the proposed framework's viability, demonstrating that IASF-IoT provides a highly efficient, effective, and lightweight security solution suitable for IoT environments. The minimal resource consumption across computational, memory, and energy parameters strongly suggests that the framework can be feasibly implemented even on resource-limited IoT devices without hindering their primary functionalities.  
While the quantum-resistant cryptographic elements and blockchain transactions were represented conceptually, the assumed overheads are conservative and informed by existing literature, supporting the validity of simulation outcomes. The excellent detection accuracy and rapid response times validate the AI-driven orchestration mechanism's potential effectiveness in real-world scenarios.  
The proposed IASF-IoT framework is particularly suitable for critical infrastructure scenarios such as smart grid SCADA systems, where the integration of blockchain and quantum-safe cryptography ensures both tamper-proof logging and long-term data security. Similarly, in healthcare IoT environments, the privacy-preserving differential mechanisms combined with federated learning offer a viable path for decentralized anomaly detection. These use cases highlight the framework's applicability in domains where data integrity, confidentiality, and real-time responsiveness are paramount.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Simulated Range** \\ \hline Processing Impact & \textless{}0.05\% \\ Memory Usage & \textless{}0.1\% \\ Energy Consumption & 0.3–1.5 mAh/day \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of IoT device performance metrics.  
Figure 5: Attack response time vs. detection accuracy.  
Future experimental validations involving actual IoT hardware, practical blockchain deployments, and implementation of quantum-resistant cryptographic protocols are necessary to complement these simulation findings and further validate the IASF-IoT framework comprehensively.
# Classification of Parkinson's Disease Using Machine Learning with MoCA Response Dynamics
## 2 Methods
### Machine Learning Approach  
To challenge the Rough Set Exploration System results, we implemented three classic machine learning models in Python using the scikit-learn (sklearn) library, which facilitates a range of machine learning tools. Additionally, we utilized complementary Python libraries for data manipulation and visualization, including pandas, seaborn, and matplotlib [33; 34; 35; 36].  
Here, we employed a train_test_split approach, allocating 30% of the data for testing before modelling. This split was performed randomly to ensure that 21 subjects were used for training and 10 independent subjects for testing.  
We note that a small dataset generates a higher risk of overfitting, where the model learns the noise and specific details of the training data too well and performs poorly on new, unseen data. Therefore, to mitigate the risk of overfitting, we also implemented cross-validation techniques during the model training phase. This approach allowed us to assess the model's performance more accurately and ensure its generalizability to new, unseen data.  
Moreover, due to the risk of overfitting, we avoided more complex solutions like deep neural networks. Thus, we considered simpler models with regularization (e.g., L2). Specifically, we selected Logistic Regression, Support Vector Machine (SVM), and Random Forest models. The application of L2 regularization indirectly influences feature selection by shrinking the less important feature's coefficients closer to zero, which helps in identifying more significant predictors. However, L2 regularization does not perform explicit feature elimination but rather adjusts the scale of contribution of each feature.  
Each of these models has its strengths and weaknesses, and their performance can vary based on the specific characteristics of the dataset. Therefore, for a better overview of the performance, we analyzed and compared them side by side.  
To address the consideration of hyperparameter tuning, we used GridSearchCV with cross-validation, and we limited the range and number of hyperparameters. We used a Stratified K-Fold cross-validation method that ensured the same proportion of classes in each fold. The dataset was balanced, meaning there was no need for synthetic oversampling techniques like SMOTE.
## 2 Mathematical Model of Multi-Satellite Scheduling
### Constraints
#### 2.3.2 Data Storage Constraints  
Due to the limited storage space on the satellite, the data size between the two missions of the satellite cannot exceed the capacity of the storage device. The mathematical expression is shown in Equation (3).  
\[\text{If}\ \forall t_{r}\in\text{T},\ \text{If}\ DS_{rm}>DS_{mfree},\ \text{then}\ x_{rm}=0 \tag{3}\]  
In Equation (3), \(\text{x}_{rm}=1\) indicates that the satellite \(S_{m}\) observes the target \(t_{r}\), and \(x_{rm}=0\) indicates that the satellite \(S_{m}\) does not observe the target \(t_{r}\).
## 6 Analysis of the Influence of the Reflector Material on the Neutron Flux
### 3D Monte Carlo Study  
The numerical study of the effect of different reflector materials was closed with a 3D Monte Carlo evaluation of the required volume for a critical core, depending on the chosen reflector material. The massive reduction in the required core volume for the different chosen reflectors, compared with the NaCl reflector used in previous studies [5; 6], is given in Figure 16. While a core with a 30 cm reflector made from NaCl requires almost 2 m\({}^{3}\) of fuel to achieve a critical configuration, in this study, the chosen metallic reflectors can ensure core criticality with less than 1 m\({}^{3}\) of fuel. The exact result is a matter of optimization, with the SS304 reflector and the copper reflector requiring \(\sim\)0.85 m\({}^{3}\). However, the study has shown that the copper reflector could most probably be reduced in thickness to achieve the best result, while the lead reflector could deliver better performance with a higher thickness. For confirmation of the results, an additional continuous energy Monte Carlo calculation was performed (see the right-hand bar in Figure 16). This test indicated a difference of about 5% between the multi-group approach and the continuous energy approach, which seems to be acceptable for this kind of broad optimization study. However, for a more detailed analysis, the use of a continuous energy Monte Carlo code seems to be more appropriate.  
An initial cost analysis of different reflector approaches identified the SS304 reflector as the cheapest solution based on the pure reflector cost; see Table 2 with the data source in the footnote. In general, there is still some optimization potential considering the cost of the fuel, the cost of the reflector, and, potentially, the cost of the required core and reflector support, especially in the case of applying the approach of using a moving reflector for reactivity control. In addition, the effect of the reflector on the quality of the experiments should be added to the consideration.  
An initial step to obtain a deeper understanding of the potential savings in core volume and the role of the changes in the axial as well as in the radial reflector, some cases have been investigated for the copper- and the lead-reflected core. From Table 3, it is obvious that increasing the size of the radial reflector was much more efficient than increasing the size of the axial reflector. The effect can be observed with both materials independently.  
Figure 16: Evaluation of the required core volume to achieve a critical core, depending on the chosen reflector material.
# Dynamic Line Scan Thermography Parameter Design via Gaussian Process Emulation
## 4 Discussion
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Parameter** & **Example 1** & **Example 2** & **Example 3** & **Example 4** \\ \hline Ambient Temperature [°C] & 20 & 20 & 20 & 20 \\ Velocity [mm/s] & 10 & 10 & 10 & 10 \\ Camera Height [mm] & 450 & 450 & 450 & 450 \\ Diameter Hole [mm] & 14 & 12–24 & 22 & 12 \\ Startdepth Hole [mm] & 6 & 2–9.8 & 2–9.8 & 2–9.8 \\ Heating Power [W] & 50 & 500 & 200–400 & 600–800 \\ Distance cam.–heat [mm] & 335–420 & 100 & 500–600 & 50–200 \\ \hline \hline \end{tabular} Example 1 handles the question ’What parameter combination should be used to be able to detect a predefined defect with a certain start depth and diameter, with a minimal of amount of heating energy needed?’. Example 2 searches for the best parameter combination to detect as many holes as possible with only adjusting the heating power. Example 3 predicts the regions in the input space that result in undesirable temperatures for the sample. Consequently, the interesting regions can be found as the remaining parameter combinations. Example 4 visualizes the regions of the input space that correspond to appropriate temperature differences. Depending on the colors, one can find a suitable region.  
\end{table} 
Table 2: Optimal parameter sets found for the examples explained above. is due to the fact that the density of data points is lower in those regions and thus the uncertainty is higher (there are no data points beyond the boundary). Integrated Variance Reduction takes this drawback into account and calculates the total amount of uncertainty reduction a new data point yields. It does so for each point in the test set. This reduces the score of points in the vicinity of the boundary. It is to be expected that Integrated Variance Reduction would reduce the number of time consuming sampled input points, but at a higher computational cost. This is also stated in [18]. The effect of this remains an open question. Third, as also stated above, the Gaussian process used in this study can be further developed to incorporate prior knowledge in the form of constraints.
###### Abstract  
This study evaluates the biomechanical efficacy of clear aligners in closing anterior maxillary diastemas using enhanced force systems. While clear aligners offer esthetic and functional benefits, their predictability in controlling bodily movement and torque remains limited. This research investigates the effects of structural modifications, such as the addition of flowable resin to interdental embrasures and intrusive force application, on the force and moment characteristics during mesial bodily movement of maxillary central incisors. Experiments were conducted using 3D-printed maxillary models with a 6-axis force/torque sensor under controlled conditions. Four experimental groups were tested: Group 1 (0.3 mm mesial bodily shift), Group 2 (0.3 mm mesial shift + 0.1 mm intrusion), Group 3 (0.3 mm mesial shift with resin reinforcement), and Group 4 (0.3 mm mesial shift + 0.1 mm intrusion with resin reinforcement). The results showed that Groups 1 and 3 exhibited extrusive forces, while Groups 2 and 4 exhibited intrusive movement with minimized extrusion. Resin reinforcement significantly increased mesiodistal force application and reduced unwanted tipping and rotational moments, improving bodily movement efficiency. The addition of intrusive movement minimized extrusive forces but introduced a minor lingual inclination. The combination of both modifications provided the most controlled and efficient tooth movement. These results suggest that modifying clear aligners with localized structural enhancements can improve treatment predictability and efficiency. Clinically, the application of flowable resin enhancements offers a simple and effective approach to optimizing clear aligner therapy.  
c 2025 15 3672
## 2 Materials and Methods
### Concepts for Discriminating the Oil-Slick Category
#### 2.3.3 Concept 3: Data Transformations  
The initial exploratory analysis explored a single non-linear normalization (log\({}_{10}\)) and one linear standardization (Ranging). This contrasts with the first refined study in which the impact of eight non-linear transformations were tested: no transformation (x), reciprocal (1/x), logarithm base 10 (log\({}_{10}\)(x)), nigerian logarithm (ln(x)), square root (x\({}^{1/2}\)), square power (x\({}^{2}\)), cube root (x\({}^{1/3}\)), and third power (x\({}^{3}\)). This comparison revealed that cube root and log\({}_{10}\) performed superiorly than the other transformations, having similar seep-spill discrimination accuracies: overall accuracies of \(\sim\)70%.  
Notwithstanding the fact that the data were transformed in multiple ways to seek improvements in the discrimination process, to obtain the calibrated product in \(dB\), log\({}_{10}\) was applied at the pixel level, whereas the data transformations, which include log\({}_{10}\), were applied to the attributes representing the entire oil slick's surface (e.g., size information and SAR basic statistics).
# Blockchain-Based Distributed Patient-Centric Image Management System
## 4 Overview of the PCIM System
### Medical Image Access Sharing  
Medical image access sharing between a patient and an image requestor is based on PCAC-SC protocol. For example, consider a new image requestor interested in accessing the patient medical images for research purposes.  
The protocol for gaining an access based on PCIM system is as follows:  
* Step 1: Requestor shares \(K_{IR}^{+}\) a public key using _requesting_access()_ a SC function.
* Step 2: Patient downloads the encrypted image from the IPFS network using the IPFS hash value.
* Step 3: Patient decrypts the encrypted image with patient's own private key \(K_{p}^{-}\).
* Step 4: Patient obtains the requestor's public key by providing the requestor's blockchain address.
* Step 5: Patient encrypts the original image with the requestor's public key \(K_{IR}^{+}\) and uploads the encrypted image to the IPFS network.
* Step 6: Patient signs a transaction on the blockchain along with the requestor's public key, the patient's public key and the IPFS hash value using _approve_IRs()_ function.
* Step 7: The image requestor is able to retrieve the medical image using the IPFS hash value and decrypts with his/her own private key \(K_{IR}^{-}\). In this way, medical images are shared between the patient and the requester.  
In this protocol, if the same medical image has been requested by multiple requesters, then patients need to encrypt the image with requester's public key, and upload the same image to the IPFS network. This process may result in a waste of storage resources. To overcome this issue, the participant of the network follows a strict regulation for limiting the number of approvals, such that only a certain number of image requests are approved by the patient within a particular period of time. Placing such limitation can help reduce the waste of storage space in the IPFS network.