esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Data-Driven Model Results
This, however, only improved R\({}^{2}\) by 0.5%, MAE by 8.6 W/m\({}^{2}\), and RMSE by 1.6 W/m\({}^{2}\). These improvements were low, compared with the addition of features. For the long-term prediction, the best \(C\) was 0.5. The improvements for this were even smaller. The results of the data-driven model can be seen in Figure 8, displaying the same trend as described for the initial model (untuned, without added features). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Data-Driven**} & \multicolumn{3}{c}{**Contextual**} \\ \hline
**Algorithm** & **Initial Features** & **Additional Features** & **Tuned** & **Negative Elimination** & **Negative Elimination and Night Hours** & **Seasonal Adaptation** & **Overall Improvement** \\ \hline \multicolumn{1}{c}{} & \multicolumn{3}{c}{**R\({}^{2}\)**} \\ \hline SVM & 68.72\% & 80.32\% & 80.78\% & 80.78\% & 80.78\% & 82.56\% & 20.13\% \\ FBP & 77.83\% & 83.11\% & 83.11\% & 83.19\% & 83.22\% & 83.97\% & 7.89\% \\ \hline \multicolumn{1}{c}{} & \multicolumn{3}{c}{**MAE**} \\ \hline SVM & 72.67 & 54.02 & 55.61 & 55.57 & 55.56 & 56.67 & 22.01\% \\ FBP & 99.57 & 67.07 & 67.07 & 63.96 & 62.42 & 57.81 & 41.94\% \\ \hline \multicolumn{1}{c}{} & \multicolumn{3}{c}{**RMSE**} \\ \hline SVM & 160.33 & 127.19 & 125.67 & 125.67 & 125.67 & 119.74 & 25.32\% \\ FBP & 135.02 & 117.85 & 117.85 & 117.59 & 117.46 & 114.83 & 14.95\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Long-term results using data-driven and contextual optimisation. Figure 8: Error metrics comparing SVM and FBP for data-driven short- and long-term forecasts in Denver. (**a**) Coefficient of determination (R\({}^{2}\)) (**b**) Mean absolute error (**c**) Root-mean-square error.
# Lithium-Ion Battery State of Health Estimation Based on CNN-LSTM-Attention-FVIM Health Features
## 3 Algorithm Principle Explanation
### Long Short-Term Memory Network
This is the output stage. LSTM's excellent ability to capture and process long-term dependencies allows it to handle complex types of sequence data, and LSTM has high flexibility. When dealing with different problems and data, it can achieve the purpose of accurate prediction by adjusting the network structure and hyperparameters.
# Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer's Disease Detection
## 3 Methodology
### Model Evaluation  
To comprehensively evaluate the performance of the models, several key metrics were employed. First, a full _classification report_ includes metrics such as accuracy, precision, recall, and F1-score, providing a holistic view of the model's performance for each category. Particular emphasis was placed on the F1-score, as it effectively balances precision and recall, making it especially valuable for assessing model performance on imbalanced datasets [28]. By combining these metrics, the classification report ensures a nuanced understanding of the model's strengths and weaknesses in handling different classes. Secondly, the _confusion matrix_ breaks down the predictions into true positives, true negatives, false positives, and false negatives, enabling the identification of specific areas where the model may misclassify. This granular view is essential for diagnosing issues such as class imbalance or systematic errors in predictions [29]. For example, high false positives in certain categories might indicate over-sensitivity, while low true positives might suggest insufficient sensitivity to certain features. The metrics used are defined as follows:  
* **True Positive (TP)**: Cases correctly identified as positive (correctly diagnosed with Alzheimer's);
* **True Negative (TN)**: Cases correctly identified as negative (correctly diagnosed as healthy);
* **False Positive (FP)**: Cases incorrectly identified as positive (healthy subjects misdiagnosed with Alzheimer's);
* **False Negative (FN)**: Cases incorrectly identified as negative (Alzheimer's subjects misdiagnosed as healthy).  
From these basic quantities, the following performance metrics were calculated:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{3}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{4}\]  
\[\text{Recall (Sensitivity or True Positive Rate)}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{5}\]  
\[\text{Specificity (True Negative Rate)}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{6}\]  
\[\text{F1}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{7}\]  
Finally, the _Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC)_ were used exclusively for evaluating the ensemble techniques, specifically hard voting and stacking. The ROC curve plots the True Positive Rate (Sensitivity) against the False Positive Rate (1-Specificity) across various threshold settings. An ideal classifier achieves a curve that closely approaches the top-left corner of the plot, corresponding to a True Positive Rate of 1.0 and a False Positive Rate of 0.0. The AUC serves as a single-value metric summarizing the overall performance of the ensemble models [30]. AUC values range from 0.5, indicating no discrimination (equivalent to random guessing), to 1.0, representing perfect classification. High AUC values indicate strong class separation capabilities, reflecting the robustness of the ensemble methods compared to individual classifiers.
## 4 Discussion
This can be achieved empirically by employing local stimulations with TMS and estimating, with PCI, the information content of the causally generated interactions characterizing the integrated EEG response [17]. Unlike LZc, PCI is expected to decrease in systems with a loss of network interactions, given the restricted integrated response. Furthermore, PCI would be equally low in systems where the integrated response is redundant (i.e., undifferentiated), thus capturing an effective balance between functional integration and functional differentiation. By the same token, in addition to the spontaneous complexity metric discussed above, we simulated this empirical approach by employing local perturbations (i.e., brief local pulses), extensively varying the stimulation site across different conditions (i.e., silencing different nodes). Each local manipulation had a detrimental effect on PCI, albeit with a heterogeneity influenced by the specific stimulus-silencing pair (Figures 3a and S2). An even more substantial impact of local manipulations on PCI was found in Hconn (Figure S4A-C). A marked difference between spontaneous and perturbational estimates of complexity was further confirmed by the lack of a correlation between LZc and PCI across conditions (Table S4). Such divergence, already observed in experimental studies [65; 66], was particularly evident upon local manipulations leading to segregated network dynamics (reduced CC\({}_{\text{IFR}}\)). This resulted in a less complex evoked response (as quantified by PCI; Figure 3b,c) despite increased LZc (Figure 2b). This result is also in line with several empirical findings across a variety of conditions where a disruption of effective connectivity across widespread brain networks results in a spatially constrained, short-lived EEG response to direct cortical stimulations with TMS and, in turn, in a significant reduction of PCI [17; 18; 25; 67; 68]. Overall, the comparison between LZc from spontaneous activity and PCI confirms that the latter more coherently hinges on an actual balance between functional integration and functional differentiation. This intuition is further suggested by the 3D model relating MFR and CC\({}_{\text{IFR}}\) applied to PCI. In this case, compared to LZc, the 3D model does not describe PCI values across conditions resulting in a slope coefficient for CC\({}_{\text{IFR}}\) not significantly different from 0 for most stimulations (8 out of 12; Supplementary Results, Section S3).
# A Hybrid Deep Learning Approach for Cotton Plant Disease Detection Using BERT-ResNet-PSO
## 3 The Proposed Hybrid Deep Learning Approach
### BERT-Based Encoder for Image Segmentation Dataset
#### 3.2.3 Output Feature Representation  
Global Average Pooling (GAP) is used to generate a robust feature representation that can be fed to a classification layer to identify an unpaired disease. Multiple residual blocks process the input, and the global average pooling layer downsamples spatial dimensions of the feature map to a compact feature vector, which encapsulates the important features of the image. This operation is expressed as:  
\[F_{\text{GAP}}=\frac{1}{H\times W}{\sum}_{i=1}^{H}{\sum}_{j=1}^{W}F_{\text{ ResNet}}(i,j) \tag{8}\]  
where \(F_{\text{GAP}}\) is the globally pooled feature vector, and \(H\) and \(W\) are the height and width of the feature map.
# Reader-Tag Commands via Modulation Cutoff Intervals in RFID Systems +
## 4 Modulation Cutoff Intervals Process  
Since the envelope detector is used by the DBMT circuit to promptly follow the signal level at the antenna frontend, counting the clock cycles of which the CW is cut off becomes feasible. In other words, commands as a cutoff intervals can be applied in many protocols  
Figure 6: Timing diagram example of the DBMT outputs for a tag sending PIE encoded stream of bits starting by 01100\(\cdots\) and CW cutoff by the reader at the 4 th bit.  
to indicate a multitude of actions required by the tag. The modulation cutoff intervals process is proposed to be applied in different RFID protocols. For example, in anti-collision protocols, a cutoff interval of a length of X can be designed to specify collision, while a cutoff interval of a length of Y can indicate a positive acknowledgment (ACK). Through the MCI process, the transmission of specific commands will be eliminated and replaced by a lookup table that matches cutoff intervals to their corresponding meaning/commands. In the following, we utilize the MCI process in anti-collision and counting protocols.
## 2 Materials and Methods
### Related Works
#### 2.2.3 Health Monitoring of Aircraft Engines  
Extensive investigations have been carried out on monitoring the health of gas paths in aeroengines, broadly dividing the approaches into model-based and data-driven methods. Model-based strategies rely on creating an accurate physical-mathematical model to depict the engine's operational characteristics [32; 33]. However, the complexity of and variability in aeroengines, compounded by environmental and operational conditions, often hinder the accuracy of these models. In contrast, data-driven methods leverage actual operational data to uncover insights about the engine's health, demonstrating superior adaptability and generalization capabilities. This approach has gained significant attention, with researchers like [PERSON] et al. [34] and [PERSON] et al. [35] developing classification and regression models for anomaly detection in engines. Yet, despite their advantages, data-driven methods face challenges such as the requirement for extensive training data, the complexity of the models, and the handling of high-dimensional, intricately correlated data.  
Recent advancements have seen the emergence of information fusion-based health monitoring as a promising direction within data-driven strategies. This approach is categorized into data-level, feature-level, and decision-level fusion, with feature-level fusion particularly noted for its ability to compress information and accurately reflect engine health through the integration of relevant features. Researchers like [PERSON]. [36] and [PERSON]. [37] have demonstrated the successful application of feature fusion techniques in diagnosing and monitoring engine health.  
The challenge with utilizing gas-path data directly lies in their high-dimensionality and noise, necessitating dimension reduction or feature extraction for effective monitoring. Methods such as iterative reduced-dimension kernel principal component analysis proposed by [PERSON] et al. [38] help in distilling fault features from complex data. While deep learning offers powerful feature extraction capabilities, it suffers from limitations like black-box models and extensive training requirements. Slow feature analysis emerges as a viable alternative, focusing on extracting features that vary slowly over time to distinguish normal operational changes from actual anomalies, as shown in applications by [PERSON]. [39] and [PERSON] et al. [40].  
Given the non-linear and dynamic nature of aeroengine operation, timely and accurate anomaly detection is crucial, requiring methods that minimize false alarms without compromising detection accuracy. The variability in engine states challenges the effectiveness of static thresholds, prompting the need for dynamic threshold adjustment strategies. Research in this area has proposed sliding window models with real-time adaptability, improving the precision of threshold setting and thus enhancing the health monitoring system's overall performance [41; 42; 43].
## 2 Numerical Methods and Target Model
### Target Model and Details of LES Simulation  
The entire computational domain and the target vehicle model are shown in Figure 3. The applied boundary conditions are summarized in Table 2. The dimensions of the entire computation domain were set to be large enough to allow any flow and acoustic disturbances generated around a car body to leave without any reflection from the boundary plane, which is of critical importance for accurate aeroacoustic simulation. The target model was the HSM, which is a simplified vehicle model designed to reproduce complex vortex structures due to the main structure of an actual automobile, such as an A-pillar.  
[PERSON] et al. [7] measured the exterior and interior noise due to the external flow of the HSM at the full-scale Hyundai Aero-Acoustic Wind Tunnel (HAWT) of Nam-Yang Technical Center of the Hyundai Motor Company [26]. Additionally, the modal test and the transfer path analysis were performed to measure the frequency response of window vibration and cabin acoustics of HSM. Noise transmission through the body was assumed to occur only through two left- and right-side windows and the front windshield. Figure 4 shows the grid distribution for the entire computational domain at the central vertical cross-sectional plane. The maximum grid size around the HSM surface did not exceed 5 mm, which allowed for resolving an acoustic wave of the maximum target frequency of 5000 Hz with a minimum of 13 points per one wavelength. The tetrahedral cells were used together with 13 layersof prismatic elements near the wall surface, as shown in the zoomed plot in the right-top corner of Figure 3. The time interval \(\Delta\)t was set to 1.25 \(\times\) 10\({}^{-5}\) s, producing the unsteady time-history data with a sampling rate of 80,000 Hz. For this case, the car cruising wind was set to be 110 km/h with a yaw angle of 0\({}^{\circ}\). The whole computational domain consisted of about 250 million cells, which were constructed using Fluent Meshing (version: 19.1).
# A Learned Reduced-Rank Sharpening Method for Multiresolution Satellite Imagery
## 3 Results
### Ablation Study
#### 3.2.1 Regularization
Performance degrades notably at lower \(r\) values (\(r<7\)), likely due to insufficient model capacity, and shows slight deterioration at very low (\(\lambda<0.001\)) regularization strengths. These findings support our choice of default parameters (\(\lambda=0.0025\), \(r\) determined through SVD) while demonstrating that the method maintains robust performance across a reasonable range of parameter values.
## 5 Performance Assessment
### Statistical Indicators
M1 experiences the minimum bias at the continental station in Budapest, Hungary (\(nMBE=-0.18\%\)), while the worst result is obtained at the arid station in Alice Springs, Australia (\(nMBE=26.79\%\)). M2 achieves the best \(nRMSE\) at Toravere (\(nRMSE=25.4\%\)), while the worst is obtained at Tamanrasset (\(nRMSE=52.9\%\)). The lowest bias is achieved by M2 at Toravere (\(nMBE=0.25\%\)), while the maximum is obtained at Tamanrasset (\(nMBE=29.2\%\)). As this last value notes a very large bias, we underline that only at six stations, out of thirty-six \(nMBE\) for M2 exceeds 10%. Overall, the performance of the reference models in estimating DHI is modest, with \(nRMSE\) falling between 23.7% (achieved by S1) and 82.5% (achieved by E2). At some stations, DHI estimates are significantly biased, with \(nMBE\) falling between \(-26.0\%\) (achieved by PB) and \(+54.5\%\) (achieved by E2). At most stations, the reference models experience a reasonable bias, with the 1 st and 3 rd quartiles of \(nMBE\) taking the following values, respectively: \(-5.8\%\) and \(+3.8\%\) for PB, \(-10.7\%\) and \(+0.7\%\) for E2, \(-5.2\%\) and \(+4.6\%\) for S1, \(-3.7\%\) and \(4.7\%\) for S3, and \(-8.9\%\) and \(0.8\%\) for Y4.
# Mobile User Connectivity in Relay-Assisted Visible Light Communications
## 3 System Model
### Noise  
At the Rx, there are three dominant noise sources: shot noise, thermal noise and intersymbol interference caused by an optical paths difference. The total noise variance is calculated as:  
\[\sigma_{total}^{2}=\sigma_{shot}^{2}+\sigma_{thermal}^{2}+\gamma^{2}P_{rISI}^{2} \tag{4}\]  
where \(\gamma\) is the photodiode responsivity (A/W) and \(P_{rISI}\) is the received power by intersymbol interference (ISI) given by:  
\[P_{rISI}=\int_{T}^{\infty}(h(t)\otimes s(t))dt \tag{5}\]  
where \(h(t)\) is the impulse response, \(s(t)\) represents the transmitted optical pulse and the symbol \(\otimes\) denotes convolution. The shot noise is defined in terms of its variance as [21]:  
\[\sigma_{shot}^{2}=2q\gamma(P_{r}+P_{rISI})B+2 qI_{bg}I_{2}B \tag{6}\]  
where \(q\) is the electric charge, \(P_{r}\) is the received optical power, \(B\) is the equivalent noise bandwidth, \(I_{bg}\) is the background dark current and \(I_{2}\) is the bandwidth noise factor. The thermal noise variance is independent of the incident power and is given by [21]:  
\[\sigma_{thermal}^{2}=\frac{8\pi kT_{k}}{G}\eta A_{r}I_{2}B^{2}+\frac{16\pi^{2 }kT_{k}\Gamma}{g_{m}}\eta^{2}A_{r}^{2}I_{3}B^{3} \tag{7}\]  
where the two terms represent feedback-resistor noise and field effect transistor (FET) channel noise, respectively. Here, \(k\) is the Boltzmann's constant, \(T_{K}\) is the absolute temperature, \(I_{3}\) is the noise  
Figure 3: Impulse response of the link with MP acting as a Rx when using: (**a**) front camera; and (**b**) rear camera.  
bandwidth factor, \(G\) is the open-loop voltage gain, \(\eta\) is the fixed capacitance of a PD per unit area, \(\Gamma\) is the FET channel noise factor and \(g_{m}\) is the FET transconductance.
# DPMF: Decentralized Probabilistic Matrix Factorization for Privacy-Preserving Recommendation
## 4 Evaluation
### Setting
#### 4.1.4 Metric  
For rating prediction task, we adopt root mean square error (RMSE) to measure the distance between the predicted result and the test data. With \(Y\) denotes all the user-item pairs in the test set of rating prediction task, we define RMSE as follows:  
\[RMSE=\sqrt{\frac{1}{Y}\sum_{(i,j)\in Y}(\hat{R}_{ij}-R_{ij})^{2}} \tag{24}\]  
Moreover, we follow the work of [24] to use \(P@k\) and \(R@k\) as two metrics to evaluate the item recommendation task. For user \(i\), they are defined as \(P@k=\frac{|S_{i}^{T}capS_{i}^{R}|}{k}\), \(R@k=\frac{|S_{i}^{T}capS_{i}^{R}|}{S_{i}^{T}}\), where \(S_{i}^{T}\) denotes the items that user \(i\) chooses in the test set and \(S_{i}^{R}\) denotes the recommended items of user \(i\) which contains \(k\) results.
## 2 Results and Discussion
### PathVisio Pathway Analysis
Protocatechuric acid was not detected in RR in one study [26]; however, here, we have detected this metabolite in RR (Figure 5A). In HCA, cluster 3 revealed a positive correlation of vanillic acid, protocatechuric acid, and anthocyanins, and these metabolites were more abundant in BR than in RR (Figures 3 and 5A). In addition, these metabolites significantly contributed to the separation of BR from others in PLS-DA and OPLS-DA (Figures 1 and 2). These observations supported the notion that vanillic acid and protocatechuric acid are the major precursors or accelerants of the anthocyanin biosynthesis pathway [26]. Most volatiles, except for 1-butanol, were more abundant in BR than in RR. Specifically, fatty acid-derived volatiles, such as 1-hexanol, 2-heptanone, hexanal, 3-octen-2-one, 1-octen-3-ol, and nonanal, were notably more abundant in BR than in RR (Figure 5B and Table S5). These observations were consistent with previous studies, which showed that fatty acid-derived volatiles are abundant in BR [9; 16]. Considering the terpenoid biosynthesis pathway, carotenoid,tocopherol, phytosterol, and monoterpene levels were higher in BR than in RR. Furthermore, policosanols were more abundant in BR than in RR. In multivariate statistical analysis, these metabolites significantly contributed to the separation of BR from other rice samples, and were positively correlated. To sum up, BR has active secondary metabolism responsible for the black color, such as terpenoid (carotenoid,tocopherol, phytosterol, and monoterpene), anthocyanin, policosanol, and fatty acid-derived volatile metabolism. By contrast, from the secondary metabolic pathways, RR only exhibited active phenylpropanol metabolism for the synthesis of red color compounds. Therefore, BR contained high amounts of carbon-based secondary metabolites, with relatively lower levels of primary metabolites, such as amino acids and organic acids, than those in other rice samples. The primary metabolites are the building blocks for the synthesis of secondary metabolites. Hence, BR contained only low levels of most of the primary metabolites. In addition, BR contained high levels of sugars, except for fructose, because they are important carbon sources for secondary metabolism starting from glycolysis. Specifically, sucrose and genes associated with sucrose metabolism modulate phenylpropanol metabolism and induce flavonoid production and accumulation in various plants [39; 40].