esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Practical Model, Experimental Tests and Results
The developed wireless system was designed to operate at a low data rate. It was configured to transmit 1 power level each 100 ms (8 power levels per SMS message), but it can be configured to different rates (max. rate is 1 power level each 1 ms). In this configuration, the receiving time is on the order of 10 s, to guarantee proper management of data. This delay in consecutive transmissions is related to the implementation of the OpenBTS platform, which suffers overloads even under low transmission rates. This problem can be reduced by improving the dimensioning and processing of the OpenBTS short message output buffer (SMQueue). Considering that the wind variations are very slow, the effects of of SMS transmissions delays in the control system can be reduced by transmitting the power references in batches and changing its time base reference (e.g., from milliseconds to seconds) and the number of power levels (e.g., 8 to 140) of each SMS message. According to the GSM standard, each short message can contain a maximum of 140 bytes of data (140 power levels). For a power profile with a granularity of 100 ms, each message can cover a power program with a duration of 14 s (1 byte to represent the power level results in 255 different levels). Thus, for instance, if the granularity is changed to 2 s, instead of 100 ms, a SMS message could cover a time period of approximately 5 min (4.7 min) and the wait intervals between SMS sending would become less relevant, since, in this interval, several SMS messages will be sent for adjustments. The downside of this solution is a reduction in the temporal granularity of the transmitted control information. For applications that require a reduced latency and/or a higher transmission rate, the General Packet Radio Service (GPRS), the Enhanced Data rates for GSM (EDGE), or even the data services of the 3G and 4G networks can be considered. However, these solutions may be much more expensive (compared to the GSM's SMS), which contradicts the purpose of this study: To analyze a simple low-cost wireless remote control solution. Figure 9: Stator voltage and current when \(P\) response changes.
# X-Linked Myotubular Myopathy in a Female Patient with a Pathogenic Variant in the _Mtm1_ Gene
## 3 Results  
### Clinical Data  
The proband is a 32-year-old, non-ambulant female with complaints of weakness in her arms and legs, a nasal voice, and choking while eating. She has had symptoms since birth. She was a floppy baby with a weak cry and ptosis of the right eyelid. At the age of 6 months, muscle weakness was noted. She had a motor development delay: she started to sit at the age of 8 months and to walk at the age of 19 months. In childhood, she always walked on her toes and could never jump. She attended a special school for children with cerebral palsy. The disease progressed, and at the age of 12 years, the proband switched to home education because of prominent muscle weakness and gait disturbances. After pregnancy and delivery by cesarean section at the age of 20 years, she became non-ambulant. Since then, the condition has been slowly progressing. For several years, she has  
Figure 2: (**A**). The proband’s pedigree. (**B**). The proband’s back and lower limbs: bilateral scapula winging, kyphoscoliosis, diffuse muscle atrophy (**C**). Muscle MRI: symmetrical severe fat replacement of all pelvic girdle and leg muscles, relatively spared medial gastrocnemius muscle on the left side.  
noticed her voice changing and choking when eating. During in the last two years, after starting physical exercises, she noticed an increased improvement in muscle strength.  
The case is first in the family: she has a healthy brother and sister and a healthy daughter (Figure 2A).  
At the time of examination at the age of 32 years, her body weight was 44 kg, and her height was 146 cm. The proband has a high-arched palate, midface retrusion, moderate facial weakness, asymmetrical eyelid ptosis, horizontal and vertical right ophthalmoresis, and horizontal strabismus without diplopia. In addition, dysphonia and dysphagia are noted, but the pharyngeal reflex is normal. Muscles of the upper and lower limbs are diffusely atrophied (Figure 2B). The proband has bilateral scapula winging and kyphosoliosis, weakness of the neck flexors and extensors (2/5), shoulder girdle muscles (2/5), hand muscles (4/5), and pelvic girdle and leg muscles (0-1/5). Contractures of the knee and ankle joints are noted, and she has hypoplasia in the fourth and fifth toes. Arm tendon reflexes are reduced, patellar reflexes are absent, and ankle reflexes are normal. The proband did not show evidence of respiratory failure or skeletal asymmetry.  
The proband's creatine phosphokinase (CPK) level was normal, measured twice (50 and 70 U/L). Muscle MRI (**Magnetic resonance imaging**) of the lower limbs showed diffuse symmetrical severe fat replacement of all pelvic girdle and leg muscles without signs of inflammation (Figure 2C). The medial gastrocnemius muscle on the left side was relatively spared in comparison with other lower leg muscles.  
Considering the data obtained, a preliminary clinical diagnosis of \"congenital myopathy\" was made and later turned out to be erroneous.
## 4 Conclusions  
Hyper-sharpening has attracted numerous research in the past two decades. Apart from the classical matrix factorization-based algorithms, convolutional neural networks show considerable potential in HS and MS image processing areas due to its adaptability and robustness of image feature extraction. To minimize the spectral distortion, in this paper, we propose to integrate the convolutional neural network with a detailed injection model for HS and MS image fusion. The proposed approach employs a couple of convolutional networks for feature extraction of HS and MS images individually and predicts the missing high-level spatial details. The network is concise and efficient and is able to achieve satisfactory performance in spectral fidelity aspect. Our future work will focus on the automatic selection of network parameters in order to further promote the flexibility and adaptability of the proposed method.  
Conceptualization, X.L., F.J.; data curation, X.L.; formal analysis, X.L., D.Y. and Y.Z.; funding acquisition, X.L. and F.J.; investigation, X.L.; methodology, X.L.; Project administration, F.J.; resources, D.Y.; software, D.Y.; supervision, F.J.; validation, X.L., D.Y. and Y.Z.; visualization, X.L. and Y.Z.; writing--original draft, X.L.; writing--review & editing, D.Y. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Natural Science Foundation of Shanghai, grant number 19 ZR1453800, and the Fundamental Research Funds for the Central Universities, grant number 2232020D-46.  
Figure 9: The ERGAS and UIQI values with respect to the patch size of MS image. (**a**) University of Pavia; (**b**) Pavia City center; (**c**) Washington DC Mall; (**d**) San Francisco.  
Data Availability StatementData available in a publicly accessible repository that does not issue DOIs Publicly available datasets were analyzed in this study. Data can be found here: [[[http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes](http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes)]([http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes](http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes)); [[https://engineeringpurdue.edu/~biehl/MultiSpec/hyperspectral.html](https://engineeringpurdue.edu/~biehl/MultiSpec/hyperspectral.html)]([https://engineeringpurdue.edu/~biehl/MultiSpec/hyperspectral.html](https://engineeringpurdue.edu/~biehl/MultiSpec/hyperspectral.html)); [[https://earthexplorer.usgs.gov](https://earthexplorer.usgs.gov)]([https://earthexplorer.usgs.gov](https://earthexplorer.usgs.gov))].  
The authors would like to express their great appreciation to [PERSON] for providing the ROSIS data over Pavia, Italy, and Spectral Information Technology Application Center of Virginia for providing the Washington DC Mall data. The authors would also like to sincerely thank the United States Geological Survey (USGS) for their publicly available EO-1 Hyperion and ASTER data.  
The authors declare no conflict of interest.
# Hybrid Adaptive Bias OFDM-Based IM/DD Visible Light Communication System
## 1 Introduction
We show its performance and perform analysis by comparing the proposed scheme with HACO-OFDM and ABO-OFDM in terms of bit error rate (BER), peak-to-average-power ratio (PAPR), and power efficiency performances. In addition, the BER performance under nonlinear conditions is also demonstrated. This paper is organized as follows. Section 2 describes the transmitter and receiver in the HABO-OFDM scheme in detail. Additionally, it presents the design of the adaptive bias and the proof that the noise introduced by the adaptive bias does not affect the demodulation of the superimposed signals. Section 3 analyzes the complexity of HABO-OFDM and conventional superimposed schemes, including HACO-OFDM and PHO-OFDM. The results of the different schemes performance are shown in Section 4. We provide the BER performance of these schemes in the additive white Gaussian noise (AWGN) channel. We also provide a comparison of the performances of the PAPR and power efficiency for these schemes. In Section 5, we draw conclusions for the proposed scheme.
# Intelligent System Using Data to Support Decision-Making
## 3 Results
### CDSS-EQCM Development
#### 3.3.3 Classifier Training Details  
All five classifiers were implemented in scikit-learn v1.2.2 with the following baseline settings (and random_state = 42 for reproducibility):  
* Random Forest (RF): 100 trees (n_estimators = 100), no limit on depth (max_depth = None). * Decision Tree (DT): default Gini criterion, no pruning. * Logistic Regression (LR): solver = lbfgs, C = 1.0, max_iter = 1000. * k-Nearest Neighbors (k-NN): n_neighbors = 5, Euclidean distance. * Support Vector Machine (SVM): linear kernel, C = 1.0, probability = True. To optimize each model, we performed a grid search over the most influential hyperparameters using stratified five-fold cross-validation on the training set:  
* RF: n_estimators \(\in\) [50, 100, 200], max_depth \(\in\) [None, 10, 20]. * DT: max_depth \(\in\) [None, 10, 20], min_samples_split \(\in\) [2, 5]. * LR: C \(\in\) [0.1, 1, 10]. * k-NN: n_neighbors \(\in\) {3, 5, 7}. * SVM: C \(\in\) [0.1, 1, 10]. For each combination, we recorded the mean F1-score and selected the best setting to retrain on the full 80% training split. Final performance metrics (accuracy, precision, recall, F1) were then evaluated once on the held-out 20% test set, using the same stratified split to preserve class balance. This nested use of stratified splitting and internal cross-validation ensured both robust hyperparameter tuning and stable, reproducible model estimates. Table 1 summarizes each model's predictive performance on the held-out test set. Random Forest achieved the highest accuracy (98%), followed closely by SVM. K-NN performed more modestly (88% accuracy), reflecting its sensitivity to high-dimensional clinical features. Across models, Random Forest and SVM consistently outperformed simpler classifiers, with mean F1-scores above 0.95. Decision Tree and Logistic Regression yielded competitive results (F1 \(\approx\) 0.94), while k-NN lagged slightly (F1 \(\approx\) 0.85). These results justify our choice to include a mix of inherently interpretable and black-box models in the explainability evaluation. In Table 2 are the confusion matrices on the 20% hold-out test set (587-588 samples) for each classifier. These matrices confirm that Random Forest and SVM deliver the strongest trade-off between false positives and false negatives, justifying their top rankings in both TOPSIS and Borda evaluations. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ML Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline RF & 0.98 & 0.97 & 0.96 & 0.965 \\ \hline DT & 0.97 & 0.95 & 0.94 & 0.945 \\ \hline LR & 0.95 & 0.94 & 0.94 & 0.935 \\ \hline k-NN & 0.88 & 0.85 & 0.88 & 0.845 \\ \hline SVM & 0.95 & 0.95 & 0.96 & 0.950 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Approximate performance results of classifiers for different models.
# Analysis of Wind Speed Characteristics Along a High-Speed Railway
## 4 Predictability Analysis
### Predictable Duration of Wind Speed  
It is clear from Section 4.2 that sampling times of 1 s and 5 min lead to different predictability. The impact of various sample intervals on the predictability of historical wind speeds is covered in this section. To determine the average wind speed composition sequence for the period, the wind speed for the previous 72 h is sampled for 3 s, 6 s, 12 s, 30 s, 60 s, 120 s, 180 s, 240 s, 300 s, 600 s, 1200 s, and 1800 s. Figure 11 illustrates the continual decline in the overall [PERSON]'s test statistic as a function of sampling duration. The [PERSON]'s test statistic is lower than 1.63 and the longer sampling duration corresponds to more time periods; therefore, a large number of previous wind speed periods cannot be utilized to make predictions for the future. It is evident that the 120 s sampling interval provides the highest predictability, followed by the 300 s interval. However, in the context of emergency braking under extreme wind conditions, a high-speed train requires a braking distance of 6.5 km to decelerate from 350 km/h to 0 km/h. Using the uniform deceleration equation from physics, the minimum emergency braking time is approximately 133.7 s. In practice, the train control system accounts for various factors to determine the optimal braking strategy, often incorporating a safety margin through redundant design. Given both wind speed predictability and braking time, a wind speed prediction step of 300 s is recommended.  
Figure 10: Predictability of wind speed at RSs and WSs (sampling time = 5 min).  
Figure 9: Predictability of wind speed (sampling time = 1 s).  
Table 4 lists the historical wind speed duration for the four stations at various sample times that can be used for forecasting. Overall, as sample duration is increased, the predicted historical wind speed period becomes shorter. The wind speed parameters of the sites influence the performances that vary between different stations. The historical wind speeds of the three stations K1005, K1066, and K1245 in the last 72 h all lose predictability when the sampling time passes 600 s. To help restore its predictability at this moment, more wind speed information is required. Due to its location in a flat, open area on the shore of Yangcheng Lake, K1256 has the highest predicted historical wind speed duration at various sample times. Of the four RSs, the wind speed at this station should be the easiest to forecast theoretically.
# P4-KBR: A Key-Based Routing System for P4-Programmable Networks
## 4 P4-Kbr
### Key-Based Routing Mechanism
A range is defined by a low bound and high bound, where the low bound must be less than or equal to the high bound [6]. Thus, the implementation of P4-KBR routing proposal in P4-based networks directly solves the problem of the high occupation of devices tables that was identified in OpenFlow-based networks.
# Research on Lightweight Lithology Intelligent Recognition System Incorporating Attention Mechanism
## 4 Results  
### Training Result  
To further validate and explain the efficacy and superiority of the proposed model (CBAM+MobileNetV2), a comparison test was conducted with the representative models in the direction of lithology identification in recent years: the Resnet101 [22] model, the InceptionV3 [21] model, and the MobileNetV2 model without the fused channel spatial attention mechanism, with each model having consistent parameter settings. Figure 7a,b depict the training loss values and training accuracy curves taken from the Matlab training log. The training loss and training accuracy curves of the proposed model converge in approximately 2000 iterations, which is faster than the other three models, and their final training loss values are lower than the other three models at 0.046, and their final training accuracy reaches 98.59%, which is greater than the other three models. The final training accuracy is 98.59 percent greater than the other three models, indicating that the suggested model has a superior recognition impact on the training set photos.  
Table 4 displays the model's greatest validation accuracy, lowest validation loss, training duration, and model size following training. Table 4 shows that migration learning for the lithology classification problem directly uses the unimproved MobileNetV2 model. The verification accuracy is only 86.43% despite the model having a shorter training time and model size after training. The effect of verification accuracy and loss is also significantly decreased. The model training time and model size after training, according to this research, are just 7 min and 0.4 MB behind the MobileNetV2 network. The influence is minimal in terms of real-time model identification performance, and the suggested model's verification accuracy is excellent (95.71%). With a 9.28% MobileNetV2 model, the verification accuracy has significantly increased. The verification accuracy of the Resnet101 model and the InceptionV3 model, which are not significantly different from the model used in this work, is 94.79% and 92.55%, respectively. Table 4 shows that the Resnet101 and InceptionV3 models' training times and model sizes, respectively, were much longer than those of the model used in this study. The model has 2.56 to 3.08 and 59.83 to 78.22 multiples. It is not necessary for the model to be deployed in real time due to restrictions such limited computer resources and processing capacity. As a result, the MobileNetV2 lithology identification model integrating the channel spatial attention mechanism performs better in terms of training set accuracy, model lightweight, and speed when compared to previous transfer learning models. Note: * represents the improved model proposed in this paper.
# A Hybrid CNN-GRU-LSTM Algorithm with SHAP-Based Interpretability for EEG-Based ADHD Diagnosis
## 3 Materials and Methods  
This study aimed to develop an objective EEG-based classification model for children with ADHD using a hybrid deep learning approach. The proposed pipeline included raw EEG data collection, preprocessing, segmentation, normalization, model development, performance evaluation, and interpretability analysis. EEG was recorded in a sample of children with ADHD and TD controls matched for age during cognitive tasks relevant to attention and response inhibition. Raw EEG signals were preprocessed with a fourth-order Butterworth band-pass filter in the range 0.5-40 Hz to eliminate slow drifts and high-frequency noise. Independent Component Analysis (ICA) with the FastICA algorithm was utilized for ocular and muscular artifact separation and rejection to improve the signal-to-noise ratio. The artifact-free EEG signals were then segmented into 2 s non-overlapping epochs, time-locked to the onset of task stimuli, to preserve cognitive event-related dynamics. Each epoch was normalized using z-score transformation across channels and subjects, ensuring consistency in amplitude range and reducing inter-subject variability. The signals were acquired using a standardized EEG system with 32 channels, following the 10-20 international electrode placement system. The sampling rate was set at 512 Hz. Raw EEG data were filtered using a band-pass filter (0.5-40 Hz) to remove artifacts and environmental noise. ICA was performed to eliminate ocular and muscular artifacts. After artifact rejection, signals were segmented into epochs corresponding to cognitive task events. Each epoch was normalized using z-score transformation to standardize inter-subject variability. To capture both spatial and temporal characteristics of EEG data, a hybrid deep learning architecture was designed, combining convolutional neural layers for spatial pattern extraction with gated recurrent units and long short-term memory layers for temporal sequence modeling. Model training was conducted using the Adam optimizer with early stopping. Classification performance was evaluated using accuracy, recall, F1-score, and AUC. SHAP values were applied to interpret the model's output and identify the most influential EEG features contributing to classification decisions. All experiments were executed in Python (v3.8), using TensorFlow (v2.11) and Scikit-learn (v1.1.1.) libraries in a GPU-equipped environment. Subsets were trained (70%), validated (15%), and tested (15%) using stratified sampling.
# Product Evaluation Prediction Model Based on Multi-Level Deep Feature Fusion
## 2 Using Genetic Algorithm to Optimize Spatiotemporal Correlation Forecast Model
### Multi-Channel and Multi-Location Attention Mechanism  
In the DSCNN-BiLSTM prediction model, the information contained in each channel and spatial position is regarded as equally important due to the classical convolution operation. If the weights of important features and general features are the same, the accuracy of the model prediction will be greatly reduced [20; 21]. The attention mechanism can focus more on the information that has a greater effect on the current output results, reduce the attention to redundant information, and even filter out irrelevant information under the condition of limited computing power. This research [22] proposed an improved lightweight convolutional attention module (CBAM). It contains two independent sub-modules, namely Channel Attention Module (CAM) and Spatial Attention Module (SAM), realizes the attention mechanism separated in channel and spatial dimensions (as shown in Figure 5), and also proves that the specific combination of CAM and SAM in the form of CAM followed by serial SAM, the effect will be better. The calculation formula of CBAM can be expressed as Formula (6) and Formula (7), where the input feature map is denoted as F, and \(\otimes\) means element-wise multiplication [23; 24].  
\[\mathrm{F^{\prime}} =\mathrm{Mc}(\mathrm{F})\otimes\mathrm{F} \tag{6}\] \[\mathrm{F^{\prime}} =\mathrm{Ms}\left(\mathrm{F^{\prime}}\right)\otimes\mathrm{F^{ \prime}} \tag{7}\]
# Data Privacy and Ethical Considerations in Database Management
## 5 Results and Analysis
### Data Minimization  
To minimize the data in the dataset, we chose to reduce the information in the \"Gender\" and \"Test Results\" columns.  
In the column referring to the patient's biological gender, we replaced the entries \"Male\" and \"Female\" with \"M\" and \"F\", respectively.  
In the test results column, we replaced the categories \"Normal\", \"Abnormal\" and \"Inconclusive\" with \"N\", \"A\", and \"I\".  
By minimizing the data in the columns referring to the patient's biological sex and test results, we obtained the results in Tables 7 and 8:The results obtained by implementing data minimization practices in the dataset allowed us to reduce the amount of sensitive information stored, limiting the risk of exposure and privacy violations. Additionally, we can avoid unnecessary overhead, storage, and processing time by reducing the amount of data collected.  
Ethical Implications of Data Minimization  
While data minimization effectively reduces privacy risks, it is important to consider its ethical implications:  
* Data Utility vs. Privacy: By minimizing data, we may limit the potential for certain types of analysis or research. This presents an ethical dilemma between protecting individual privacy and potentially limiting beneficial uses of data [16].
* Potential Bias: Minimizing certain data fields (like gender) could potentially introduce or exacerbate bias in analyses if not carefully considered [70].
* Informed Consent: It is crucial to ensure that data subjects are aware of and consent to this type of data transformation, as it changes the nature of the data they initially provided [71].