esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Dimensionality Reduction
#### 3.3.2 Feature Extraction  
Features extraction [62] involves creating new features from existing ones, offering a valuable method to reduce resource requirements without sacrificing initial feature information. Convolutional Neural Networks (CNNs) are widely used in the field of deep learning for feature extraction. However, they often face challenges such as vanishing and exploding gradients, which the Residual Network (ResNet) architecture addresses [21]. ResNet introduces skip connections, allowing the direct addition of the input from one layer to the output of another. This approach enables the network to learn residual information, significantly enhancing performance and training efficiency. Implementing ResNet architecture not only improves model efficacy but also mitigates issues related to gradient vanishing in Deep Neural Networks.  
In this paper, we employed ResNet to extract features selected by the LogitBoost algorithm from \(D1\) and \(D2\). Our ResNet architecture comprises a convolution block, six residual blocks, and global average pooling (GAP), illustrated in Figure 4. The convolution block consists of a convolutional layer, batch normalization (BN), swish activation, and a max pooling layer. The convolutional layer uses kernels to capture diverse features, BN regularizes and accelerates training, and swish is an activation function introduced by [PERSON] et al. [63]. The structure of the residual block includes a convolution layer, batch normalization, swish activation, and a shortcut connection, as depicted in Figure 5. Each unit in the residual block and convolutional block functions identically, except for the shortcut connection, which adds the input of the block to its output.  
As illustrated in Figure 5, \(x\) stands for the input to the residual block; \(F(x)\) represent the output prior to the second activation function; and \(F(x)+x\) denotes the result produced by the residual block with the shortcut connection.  
Figure 4: Structure of ResNet.
# Photodynamic Properties of CdSe/CdS Quantum Dots in Intracellular Media
## 3 Results and Discussion
### Fluorescence Lifetime  
Figure 4 shows the fluorescence lifetime of the CdSe/CdS QDs in different intracellular media, namely low cell concentration treated with PEG (C\({}_{\mathrm{L}}\)-P), low cell concentration without PEG (C\({}_{\mathrm{L}}\)), and high cell concentration treated with PEG (C\({}_{\mathrm{H}}\)-P). Fluorescence from the high cell concentration sample without PEG treatment is too weak to yield any fluorescence lifetime measurement. For comparison, the fluorescence lifetime of the QDs in just the solvent is also obtained. It can be observed that the fluorescence decay curves have a fast and slow component as it initially decreases quickly and then slowly. Therefore, the fluorescence decay curves were fitted to a biexponential function to have two lifetime values. A least squares fit was used to ensure that the correct function was utilized. The fit has an R2 value of 0.994. This value suggests a good agreement between the experimental curve and the data fitting curve. The first lifetime is less than 1.2 ns and varies with the samples, while the second lifetime is 2 ns and does not change with the samples. The resolution of the TCSPC set-up used to obtain the fluorescence lifetime is approximately 35 ps and the pulse duration of the laser used is approximately 50 ps. On the other hand, the fast fluoresce decay time is 1.2 ns; therefore, the measured fluorescence lifetimes are not biased by limitations to the set-up. These fluorescence lifetimes can be attributed to surface recombination and deep trap recombination, respectively. Fluorescence lifetimes due to surface recombination are summarized in Table 2. The results show that the fluorescence lifetime is faster, almost decreasing to half when the QDs were placed in the intracellular media  
Figure 3: Fluorescence spectra of the CdSe/CdS QDs seeded in the Jurkat cells 3 h (**a**), 6 h (**b**), and 9 h (**c**) after treatment. C\({}_{\mathrm{L}}\): low cell concentration of \(1\times 10^{4}\) cells mL\({}^{-1}\); C\({}_{\mathrm{H}}\): high cells concentration of \(1\times 10^{6}\) cells mL\({}^{-1}\); C\({}_{\mathrm{L}}\)-P; CH-P: polyethylene glycol (PEG)-treated cells. The fluorescence spectra of the Jurkat cells without CdSe/CdS QDs are also presented in (**a**) for comparison.  
compared to when they were in the water. The fluorescence lifetime when the QDs are in the solvent is 1.92 ns, whereas the lifetime in the intracellular media ranged from 0.93 to 1.21 ns, depending on the cell concentration. The decrease in fluorescence lifetime can be attributed to the removal of the QDs' surfactant ligands, which are then replaced with small molecules such as amines that are present in the intracellular media [34; 35; 36; 37]. They may also be caused by fluorescence resonance energy transfer [38; 39; 40]. In this case, the binding protein serves as the acceptors, while the QDs are the donor. The QDs acceptors reabsorb the resonance energy from the protein donor, thereby resulting in the direct change in the fluorescence lifetime of the donor moiety.
## 3 Results and Discussions
### Evaluations of Cutting Curve Segmentation  
DenseNet [57] is applied as the baseline model for the cutting curve. Considering the predictable relationship between waves at different pixels, the \(l_{1}\) and \(l_{2}\) error of the cutting curve with the segmentation mask along with the peak signal-to-noise ratio (PSNR) are taken to evaluate the cutting indication performance. Ablation studies are conducted to further illustrate the improvement produced by channel 2# and the leapfrog algorithm, as given in Table 3. It is concluded that the proposed TNH module with the optimization process performs the best on all the metrics. The addition of the TNH and leapfrog algorithm increases the segmentation PSNR and reduces the mis-division error, and more target pixels are included in the mask, which is depicted by the higher segmentation metrics. The proposed channel R-CNN outperforms the Mask R-CNN method with improvements of 1.9%, 0.6%, 0.5%, and 1.1% for the _mIOU_, _OPA_, _SMA_, and _DC_ metrics, respectively. For the U-Net model, its segmentation performance is close to the proposed network for the metrics of _OPA_ and _SMA_, while its efficiency (24.7 ms) is relatively poor in comparison to all models, which may be due to the deep architecture and extensive skip connections. The numerous layers and complex connections in U-Net lead to a higher computational load during both training and inference. As for the comparison of efficiency, the baseline model DenseNet obtains the fastest processing time of 20.4 ms, the baseline model combining with the TNH and leapfrog algorithm exhibit a decrease in processing efficiency (0.8 ms and 1.4 ms, respectively), but considering the improvement in detection accuracy, the processing time of 21.8 ms is acceptable on the premise of meeting clinical real-time requirements. The mask segmentation performance of the proposed network structure is proved by the model performance comparison.  
Figure 8: Visual segmentation results of the DNH. (**a**) Original image. (**b**) Labeled image. (**c**) Image mask.  
Figure 9: Performance comparison of thoracic tissue contour segmentation on specific categories. (**a**) FCN-8s. (**b**) U-Net. (**c**) Channel R-CNN.
# A CSI-Based Multi-Environment Human Activity Recognition Framework
## 3 Results
### Classification Results
#### 3.3.1 Results of the First Evaluation Scenario  
The dataset of the office environment was recorded from 10 individuals. Hence, the LOSO-CV procedure is applied by using the data of nine individuals to train the SVM classifier and the data of the remaining individual are used to test the SVM classifier. This process is repeated 10 times, such that each time the data for a different individual are used to test the SVM classifier and the data for the remaining individual are used to train the SVM classifier. The final results are obtained by averaging the results gathered from the 10 repetitions of the LOSO-CV process. The dataset size used for this experiment is equal to 3000 observations acquired from all 10 subjects. Each time we train the SVM classifier, we used 2700 observations, and the remaining 300 observations were left to test the developed SVM classifier.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Equation** \\ \hline True-Positive Rate (TPR) & \(\frac{TP}{TP+FN}\) \\ False-Positive Rate (FPR) & \(\frac{FP}{TN+FP}\) \\ Precision & \(\frac{TP}{TP+FP}\) \\ F1 Score & \((1+\beta^{2})\times\frac{Precision\times TPR}{(\beta^{2}\times Precision)+ TPR}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: The employed performance evaluation metrics. TP, TN, FP, and FN represent the true-positive, true-negative, false-positive, and false-negative values, respectively.  
The confusion matrix generated based on using the dataset of the office environment is shown in Table 6, which shows that a 91.27% overall activity recognition accuracy can be achieved. The statistics for each of the activities can be found in Table 7. The results show that, to detect a falling activity, a true-positive rate of 88% and a false-positive rate of 2.73% were achieved.
# Building Realistic Mobility Models for Mobile Ad Hoc Networks
## 3 Realistic Scenarios and Modelling Issues  
The extensive use of simulations in MANET research leads directly to the development of scenarios and models for those simulations (e.g., BonnMotion for mobility scenario generation by [PERSON] et al. (2010) [50]. The need to map simulation models to appropriate MANET applications in order to gain meaningful insights into protocol performance is obvious and also well-documented, with recent papers including [PERSON] et al. (2011) [63], [PERSON] et al. (2011) [98], and [PERSON] et al. (2012). [PERSON] et al. (2013) specifically state that mobility management is important for protocol design and their performance evaluation, particularly realistic movement models ([PERSON] et al., 2014, [99]). However, most mobility models used to evaluate wireless communication systems have limited resemblance to reality ([PERSON] et al., 2010, [100]). Undeniably, realistic mobility modelling affords high-fidelity modelling to facilitate better understanding and interpretation of a system's performance ([PERSON] and [PERSON], 2010, [101]). Examples of realistic mobility modelling are as follows: modelling mobility in disaster area scenarios ([PERSON] et al., 2009, [102]; [PERSON] et al., 2014, [99]) integrate with event driven (e.g., environmental events), role-based (e.g., police, civilians), and gravitational mobility model (e.g., attraction to events or otherwise; [PERSON] et al., 2007, [103]); post-disaster mobility (PDM) model to model civilian and rescue activities in a disaster-struck area; environmental-aware mobility (EAM) model ([PERSON] et al., 2006, [104]); MObility model generator for VEhicular networks (MOVE) for generation of realistic vehicular mobility models ([PERSON] et al., 2007, [105]); and working day movement model that intuitively depicts movement patterns of people in their everyday life ([PERSON] et al., 2008, [106]). Similarly, [PERSON] and colleagues (2013) [107] identify key factors for mobility models in VANETs, while [PERSON] et al. (2010) highlight the importance of understanding input parameters for mobility modelling and their associated, required level of accuracy in order to yield reliable results. To reiterate, the focus of this paper is three-fold: the size and shape of the world being used in the model, the elapsed time being simulated by the model, and the number and movement of the nodes within the model.
## 3 Machine Learning Models
### Deep Learning Models
#### 3.3.3 Long-Short Term Memory Networks (LSTM)  
LSTM is a special kind of RNN with additional features for memorizing the sequence of data, namely, remembering the trend of the data until some point in time is rendered possible through some gates along with a memory line. Each LSTM is a set of cells, or system units, where data streams are recorded and stored. Cells simulate a transmission line that connects one unit to another, carrying data from the past and gathering it for the present. The LSTM cell architecture is depicted in Figure 5. Three types of gates are involved in every LSTM to control the state of each cell:  
* The forget gate outputs a number between 0 and 1, where 1 indicates full retention of the content, while 0 indicates full discarding of it. * The memory gate chooses which of the new data will be stored in each cell. * The output gate decides the output of the cell. The value of the output is based on the current state of the cell, along with the filtered new data. LSTM networks have the ability to \"learn\" long-term dependencies in the data stream and are widely used in tasks that work with sequential data, for example, time series. LSTMs suffer from the vanishing gradient problem [42], whereby when the time step is large enough, the value of the gradient can become very small. This problem occurs as the network feeds the outputs back to the input and runs the algorithm while the weights hardly change at all [43]. The majority of recent works (2019-) comparing the ARIMA method with deep learning techniques in time series forecasting choose LSTM networks or their variants, due to the memory they introduce in the forecasting process and to deal with the vanishing gradient problem. Figure 5: Building block of a Long-short term memory network. The variables c and h represent the current state and the hidden state of the LSTM cell, while the input is represented by the x variable. The sigmoid gates inside the block constitute the forget, input and output gate respectively. The current state is updated with respect to the input, according to the sum of the current state and a combination of the input and the hidden state of the block.
# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 3 Materials and Methods
### Machine Learning Models
#### 3.4.4 XGBoost  
This model is known for its high-performance gradient boosting. XGBoost is highly efficient, flexible in general, and similarly to RF, is a decision-tree-based ensemble machine learning model.  
Two different XGBoost models were built, one for the first scenario with varying inputs of feature and one for the second scenario. To fit our non-linear data, the study utilized an XGBoost with a regression squared error loss function. Separate models were created for each scenario. Each utilized a grid search for a range of values to optimize hyperparameters for each experiment's scenarios and set of features. Because of XGBoost's well-known high-performance gradient boosting, this model line fit was able to capture the data's extreme values.
# Exploring the Effect of Virtual Environments on Passive
## 6 Discussion and Limitations  
Our results showed that test conditions for passive haptic perception had a major effect on the participant's transformation perception of the virtual object compared to the real one. First, participants tended to perceive a greater difference when the virtual object felt physically close to the body. It can be assumed that this result is related to proprioceptive cues depending on the distance. Note that proprioception is the sense of self-movement and body position used to detect kinematic parameters such as joint position [24]. Second, we found that people typically noticed a difference more clearly in the rotation case. According to the results in Figures 7 and 8, the number of people and the degree of difference in the various orientation steps were significantly large. Furthermore, depending on the angle, when rotational gain was used, we found that we could properly adjust the angle of the virtual object to the user [25,26]. In the scale factor, participants in the experiment tended  
Figure 8: The survey results of the participant’s degree of perceived difference for each step of the transformation factors; a score of 0 indicates perfect matching, while a score of 100 indicates complete inconsistency.  
Figure 7: Number of participants perceiving a difference in transformation factors: five steps in position (**left**), five steps in rotation (**middle**), and five steps in scale factor (**right**). In this figure, for example, 22 participants at the 0 cm step of the position factor considered the two passive haptic environments (real or virtual) to be equal.  
to underestimate the size of the virtual object when it was smaller than the actual object. This was also found in previous similar studies [3]. Our experiments also showed the effect of \"minimum cue\" in terms of position and scale factors to create VR content. Thus, in certain situations, we found that people did not notice if the virtual object was slightly different from the real object. This outcome may facilitate development of immersive VR environments with haptic interaction in a more economical way.  
Figure 9 shows an example of the limitations in our experiments for passive haptic perception. Our current results need to be updated to provide more useful haptic experiences including complex situations such as interactions with both hands and manipulation of the virtual object in the air. Also, with the sensing capability of the physical prop, we will improve the system to more complex interactions; for example, participants with respect to a feeling for different-shaped virtual objects will need to analyze how they feel differently. Additionally, passive haptic should be provided anytime and anywhere in a virtual environment, and it is necessary to handle a navigation situation as well as a fixed location. Furthermore, we plan to extend the evaluation to support cases where the virtual object is a deformable soft body in passive haptic situations.
# Characterization and Detection Classification of Moldy Corn Kernels Based on X-CT and Deep Learning
## 3 Results
### Maize Kernel Mold Identification and Degree Classification
#### 3.3.2 ResNet50 Improved Model Training Results  
The enhanced ResNet50 model was utilized to detect and identify 1000 images without mildew features and 1000 images with mildew features selected from each set of maize kernel slice images from 0 to 15 d, with 93% accuracy on the test set. The ratio of images with mildew features to the entire set of sliced images of maize kernels was utilized as a parameter to distinguish the degree of mildew. Mild mildew was designated as 0-30%, moderate mildew as 30-50%, and severe mildew as greater than 50%. Five groups of corn kernels were sampled from the 5-d, 10-d, and 15-d sections of the image set. A total of 100 to 1300 images were taken for mildew detection, and the results are shown in Table 2 below. The percentage of mold images detected in each group was calculated and plotted in Figure 11. The comparison of group 3 with the other groups in Table 2 indicates that the degree of mold in different maize kernels was not dependent on the incubation time but was closely associated with the amount of bacteria present on the maize seeds themselves. Figure 11 shows that 4-5 d and 4-10 d were mild mold, 4-15 d and 1-5 d were moderate mold, and the remaining groups were heavy mold.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Image Quality** & **Incubation Phase** & **Group 1** & **Group 2** & **Group 3** & **Group 4** & **Group 5** \\ \hline \multirow{3}{*}{Non-moldy} & 5 d & 614 & 461 & 42 & 1052 & 503 \\  & 10 d & 436 & 442 & 33 & 843 & 423 \\  & 15 d & 356 & 345 & 23 & 681 & 353 \\ \cline{1-1}  & 5 d & 587 & 740 & 1159 & 149 & 698 \\ \cline{1-1} \cline{2-6} \multirow{2}{*}{Moldy} & 10 d & 765 & 759 & 1168 & 358 & 778 \\ \cline{1-1}  & 15 d & 845 & 856 & 1178 & 520 & 848 \\ \hline \hline \end{tabular}
\end{table}
Table 2: ResNet50 improved model test mildew feature classification table.  
Figure 10: ResNet50 improved model training accuracy and classification loss chart: (**a**) model training accuracy change chart, (**b**) classification loss chart.
# Wintertime Local Wind Dynamics from Scanning Doppler Lidar and Air Quality in the Arve River Valley
## 3 Material: WLS200S Lidar
### Inter-Comparison  
In this section, \(V_{los}\), measured from the WLS200S, and the same quantity derived from zonal and meridional velocities measured by the WLS8-5, are compared. DWL profilers have been used increasingly for atmospheric and wind turbine studies in the last decade. Comparison studies have shown very good agreement with readings from mast-mounted anemometers, with a correlation coefficient \(R^{2}=0.99\)[42], or with radiosonde wind measurements [43]. The WLS8-5 is thus considered as a reference in the current study.  
Figure 4: Scanning strategy of the WLS200S during the Passy-2015 field experiment. A horizontal PPI scan is represented in red, a slanted PPI scan at elevation 5\({}^{\circ}\) in black and the three vertical scans at azimuth 295\({}^{\circ}\), 350\({}^{\circ}\), 28\({}^{\circ}\) in green.  
The comparison was made using the first gate of the WLS8-5, 40 mV AVL at Site 1, and the closest WLS200S measurement extracted at azimuth 287\({}^{\circ}\), 2600 m in the LOS direction. One-minute-averaged zonal and meridional velocities from the WLS8-5 were computed and the corresponding LOS velocity was derived by applying Relation (2):  
\[V_{los}=-u*sin(\gamma)-v*cos(\gamma), \tag{2}\]  
where u and v represent zonal and meridional components, respectively, measured by the WLS8-5 and \(\gamma\) the azimuth angle of the WLS200S. The result of the comparison is displayed in Figure 5 and reveals a very good correlation between the two lidars with a correlation coefficient of \(R=0.95\). The linear regression slope (\(s=0.93\)) indicates that the \(V_{los}\) derived from the WLS8-5 are slightly under-estimated in comparison to those measured by the WLS200S. An explanation for this under-estimation may be the difference in observation volumes of the two instruments. The WLS200S performs an integration over a very thin horizontal cylinder 100 m long, whereas the WLS8-5 performs an integration over a 3D cone of approximately 21 m mean diameter and 20 m height.  
To evaluate the consequence of these differences in the volume of integration, the same comparison was made by considering only the cases in the time range 10:00 a.m.-4:59 p.m. UTC that were likely to be more homogeneous along the vertical because of convective mixing over the first 500 m (see Figure 3). This subset gave a similar correlation coefficient and a linear regression slope much closer to 1 (\(s=0.98\)), suggesting that the more homogeneous the wind is in the vertical direction, the better is the comparison. This suggests that at least a part of the slight underestimation of the WLS8-5 measurements relative to the WLS200S ones was due to the differences of the volume of integration in the vertical direction.  
This comparison finally allows a validation of the WLS200S database. This was confirmed by a determination of the error on \(V_{los}\) measurements, which showed that 80% of the database has a mean error of 0.12 m\(\cdot\)s\({}^{-1}\) (Appendix A).