esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Preprocessing  
The routine BOLD-fMRI preprocessing procedure were conducted as follows (using GRETNA toolbox) [35]: (1) removal of the first 10 volumes of each scan for magnetization equilibrium; (2) slice timing correction to eliminate temporal offsets between slices; (3) realigning images for head motion to reduce head movement effects on the spatial correspondence of the brain across volumes; (4) normalizing images to the EPI template in the standard MNI atlas space (resampling to 3 mm isotropic voxels) to facilitate group average and group comparison; (5) spatially smoothing using a 6 mm (usually twice the voxel size) full-width half-maximum (FWHM) Gaussian kernel to improve the signal-to-noise ratio; (6) linear detrending to reduce the effects of linear drift in the signal; (7) regressing out nuisance signals including signals averaged from whole brain, white matter, CSF and head motion (Friston 24 parameters) to reduce the effects of non-neuronal fluctuations; (8) temporal filtering with a high-pass filter of 1/128 Hz to reduce the low-frequency drift. No subject in this study was excluded by significant head motions (more than 2 mm of translation and 2\({}^{\circ}\) of rotation).
## 4 Parametric Analysis
### Parameter Analysis Under Monotonic Load  
#### 4.1.1 The Investigation on the Influence of the Shape of the Steel Plate in the ED Zone  
The monotonic behavior of four specimens with different shape types for steel plated in the ED zone including the rectangular shape (HES1), the X-type (HES2), the rectangular shape with one diamond-shaped hole (HES6) and the rectangular shape with one circular hole (HES7) are simulated. Their load-displacement curves, the stress distribution of the steel plates in the ED zone and typical analyzed results are depicted in Figure 11 and Table 3.  
Figure 11: The influence of the shape of the steel plate in the ED zone. (**a**) Load-displacement curves, (**b**) the steel plates in ED zone (HES1), (**c**) the steel plates in ED zone (HES2), (**d**) the steel plates in ED zone (HES6), (**e**) the steel plates in ED zone (HES7).  
It is observed in Figure 11a that the bearing capacity the HES with rectangular steel plates in the ED zone is the largest. Among the four shape-types of the steel plates in the ED zone, the bearing capacity of the rectangular typed specimen is larger than those others but this type of shape failed to fully develop the plastic deformation. Among the three optimized shapes, the X-shaped specimen HES2 with the smallest surface area exhibits the best performance including the initial stiffness and bearing capacity as shown in Table 3.
## 3 Spectral Scattering Imaging
### Spectral Scattering Imaging (SSI) Configurations
On the other hand, the filter wheel and LCTF based imaging configurations (Figure 6c,d) acquire scattering images for the chosen wavelengths in sequence with the camera synchronized with the filtering device. The two configurations are relatively slow in imaging speed, but they are able to obtain high quality scattering images. In addition to broadband illumination, a laser module that is composed of multiple lasers at different wavelengths can also be used as a light source for multispectral scattering measurement [49]. Compared to the broadband light source, the laser source has a higher power and efficiency of light utilization, and thus is more suitable for fast image acquisition. Figure 7 shows a laser-based multispectral scattering imaging prototype built for measurement of fruit firmness. The prototype uses a specially designed laser lighting module, which emits four laser beams that are coupled to a single optic fiber, and its imaging unit uses a common-aperture imaging spectrograph with four bandpass filters matching the four lasers, to acquire four scattering images all at once. Figure 6: Four sensing configurations for scattering measurement: (**a**) line-scan spectrograph based, (**b**) common-aperture spectrograph based, (**c**) liquid-crystal tunable filter (LCTF) based and (**d**) filter wheel based.
# Open-Set Recognition of Pansori Rhythm Patterns Based on Audio Segmentation
## 5 Experiment Setup
### Dataset Acquisition
#### 5.2.4 Sound-Event Detection (DCASE2019 Task 4)  
The objective of sound-event detection is to identify specific events within a continuous audio signal, pinpointing both the type of event and the precise timing of its occurrence. For our experiments, we utilized strongly annotated synthetic data encompassing ten types of sound events, namely \"Dog\", \"Cat\", \"Alarm_bell_ringing\", \"Dishes\", \"Frying\", \"Blender\", \"Running_water\", \"Vacuum_cleaner\", and \"Electric_shaver_toothbrush\". The dataset comprised 2045 training samples, each recorded at a 44.1 kHz sampling rate with a duration of 10 s. Each event within these samples was annotated with start and end times, with the shortest event marked at 250 milliseconds. Additionally, intervals of silence between consecutive events were noted, with the shortest silence span recorded at 150 milliseconds. To ensure consistent annotation, a \"pause\" class was designated to label these silent intervals. However, events sometimes overlapped, allowing multiple events to co-occur. To address this challenge of multi-label segmentation, we employed a method of identifying the \"dominant\" class during overlapping intervals, thereby assuming that events occur sequentially rather than concurrently. The sound of \"Electric_shaver_toothbrush\" was designated as the unknown class.  
Following the dataset distribution guidelines provided by the DCASE Community Challenge 2019 ([[https://dcase.community/challenge2019/task-sound-event-detection-in-domestic-environments](https://dcase.community/challenge2019/task-sound-event-detection-in-domestic-environments)]([https://dcase.community/challenge2019/task-sound-event-detection-in-domestic-environments](https://dcase.community/challenge2019/task-sound-event-detection-in-domestic-environments)), accessed on 28 June 2024), the data were segmented into training, validation, and testing sets. During preprocessing, the raw audio was transformed into log-Mel spectrograms with a window size of 2048 and a hop length of 500, using 32 Mel-filter bands. The model underwent training for 2000 epochs with a batch size of 12. The loss function incorporated cross-entropy (\(\alpha\)), center (\(\beta\)), and contrastive (\(\gamma\)), with importance ratios set to 1, 0.1, and 0.1, respectively, as defined in Equation (1). Additionally, the learning rates for the model and center loss were set at 0.001 and 0.00005, respectively.
## 5 Performance Comparison of the WSN with Different Spatial Node Distributions
### Uniform Random Distribution  
Table 7 reveals the results for 49 nodes under random positions along a 150 m \(\times\) 150 m area. All nodes generate and send traffic to the coordinator. The coverage radio is 50 m. The results confirm that this is the worst case among the three presented scenarios. Nevertheless, the MPH protocol has a better performance in all metrics with respect to the other three protocols. Regarding the retransmission metric, MPH is 24% less than AODV, 18% less than DSR and 19% less than ZTR. With regard to the CSMA retry metric, MPH shows an average reduction of 27% against AODV and DSR and 25% againstZTR. Concerning the overhead metric, MPH exhibits 33% less than AODV, 31% less that DSR, and now, MPH is better, at 4% with respect to ZTR. About the discovered routes metric, MPH has a greater average discovered routes, such as 48% more than AODV, 40% more than DSR and 5% more than ZTR. Regarding the recovery time metric, MPH is faster in the recovery of the topology, being 27% over AODV, 26% over DSR and 9% relative to ZTR. Finally, with regard to energy, MPH has an energy savings of 16% compared with AODV, 14% against DSR and 5% in relation to ZTR, showing a similar behavior as in the first scenario.
# Ellipsoidal K-Means: An Automatic Clustering Approach for Non-Uniform Data Distributions
## 2 Adaptive Ellipsoidal Clustering Technique  
Clustering involves identifying natural groups within multidimensional datasets based on similarity measures [24]. In general, distance measures are used to evaluate similarities between models. The clustering problem is formulated with the aim of minimizing a specific criterion, as outlined below [25]:  
\[J(x,c)=\sum_{i=1}^{m}\sum_{k=1}^{K}\omega_{ik}\|x_{i}-c_{k}\|^{2}, \tag{1}\]  
where \(\|\cdot\|\) represents the Euclidean norm, \(K\) denotes the number of clusters, \(i=1,\ldots,m\) designates the object indices (or patterns), \(x_{i}\) is the \(i\)th data point, and \(c_{k}\), (\(k=1,\ldots,K\)) represents the center of the \(k\)th cluster, which is defined as  
\[c_{k}=\frac{1}{|N_{k}|}\sum_{i=1}^{m}\omega_{ik}x_{i}, \tag{2}\]  
where \(N_{k}\subseteq\{1,\ldots,m\}\) represents the set of indices of patterns in the \(k\)th cluster, \(|N_{k}|\) is the cardinality of \(N_{k}\), and \(\omega_{ik}\) is the association weight of pattern \(x_{i}\) with the \(k\)th cluster, which takes a value of 1 or 0, indicating whether the pattern \(i\) is assigned to the \(k\)th cluster. Specifically, \(\omega_{ik}=1\) if the pattern is allocated to the cluster, and 0 otherwise.  
In this paper, the proposed approach is adapted by minimizing the sum of Euclidean distances in an \(n\)-dimensional space between each data point and its corresponding cluster center across all data instances. The strategy involves adjusting the positions of certain instances, either by dispersing those outside the cluster boundaries or by bringing them closer to instances in other clusters, thus enhancing their similarity. This method allows the objective function to yield results that are closer to the optimal clustering configuration.
# Decision Support Models for Operations and Maintenance for Offshore Wind Farms: A Review
## 4 Modeling Techniques
### Types of Models
#### 4.2.2 Markov Models  
Discrete Time Markov chains (DTMCs) are stochastic processes fulfilling the Markov property as explained by e.g., [PERSON] [79]. Stochastic processes consist of states and the probabilities to get from one state to another, called the transition probability. The Markov property assures that the next state of the chain only depends on the current state and the process is thus memoryless. DTMCs can be displayed as a state diagram for simple chains, displaying the different states the model can be in and the transition probabilities, an example is shown in Figure 2. A form that can be implemented and used in simulation software is the use of a transition matrix, including all the transition probabilities from each state to another, after sorting the states. DTMCs are already used in some of the existing models to model e.g., the significant wave height over time. A model combining DES with the DTMC state change mechanism is the most used modeling technique today. This provides simple state changes with transition probabilities and jumps from event to event, so times with no state changes do not have to be simulated. This again saves computational time and effort. The main problem with discrete models is that the turbine itself operates continuously. It experiences continuous loads from wind and wave that cause the failure events. In addition, the power output changes with the wind speed and is thus not constant between state changes. Depending on the wind speed and market price for electricity, the downtime losses are also continuous. Therefore, using a discrete model will always be a simplification of what is happening in reality. For use in the wind industry, discrete models can be a resource effective solution for modeling maintenance. However, they should be benchmarked with models that are more complex to provide confidence in the simplification.  
Continuous Time Markov Chains (CTMCs) are analogue to the DTMC, continuous stochastic processes fulfilling the Markov property. The main difference to the DTMCs is that instead of the transition probabilities, CTMCs use transition rates - the derivatives with respect to time of the transition probabilities between different states. While DTMCs can only model time steps of a fixed size, CTMCs offer the possibility to also describe phenomena that benefit from a continuous representation, such as the production, loads and weather. Additionally, CTMCs might be described with fewer parameters than DTMCs. While the implementation of a DTMC is very straight forward and much like a DES, CTMCs require more advanced methods. An example of a discrete Markov process is a random walk with an integer step-size, its scaling limit in dimension 1 (step-size converging to zero) is the continuous process called Wiener process, describing Brownian motion.  
Figure 2: An example of a Markov chain, displayed as both a state diagram (**left**) and a matrix with transition probabilities (**right**).
# Comparative Study on Load Monitoring Approaches
## 2 Load Monitoring
### Intrusive Load Monitoring (ILM)
[22] showed that in its implementation, ILM can be configured in two ways. That is, manually and automatically, depending on the identification to be done. Thus, we distinguish:  
* Manually configured intrusive load monitoring: Refers to training the models on load data acquired in the learning environment. In this situation, manual user intervention is required to \"in-place\" label the data based on the load. The load recognition performance is good because the signals are emitted by the same load that was used for model learning. In this case, only temporal variability impacts model performance. * Intrusive monitoring of the automatically configured load: It does not require \"on the spot\" labelling of the data, which will later constitute the database. The system is trained a priori and can receive signals from \"untrained\" devices. However, temporal variability, as well as intra-class variability, could have an impact on the recognition performance. Furthermore, the observed intra-class variability is caused by the differences in the make or model of the devices in the same class. They add that, while manually configured, intrusive load monitoring requires more time than automatically configured intrusive load monitoring to implement and operate, it is still much more accurate than automatic configuration. In summary, while ILM provides accurate and satisfactory results on the power status of each device, implementing it as part of an infrastructure deployment can be costly and complex. The use of smart outlets or sensors is essential, and a sub-metering system is required for its operation. In addition, careful planning and management of the sub-metering system is required to ensure proper operation of the ILM.
# Current Uncertainties and Future Challenges Regarding NAD+ Boosting Strategies
## 13 Role of Intestinal Microbiota in the Bioavailability of NAD+ Precursors  
The gut microbiota produces hundreds of bioactive compounds, including B vitamins. Human digestion and the microbiome play a role in the provision of B3 vitamin forms, although their interaction has not yet been fully characterized [188]. Through the small intestine, both nicotinamide and nicotinic acid are directly absorbed where nicotinic acid is converted to nicotinamide [189]. Intestinal bacteria play an important role in NAD+ synthesis from tryptophan [188; 190]. Different microorganisms that synthesize NAD have been identified [188]. Gut microbiota are involved in de novo biosynthesis pathway generating NAD+ from L-aspartate [191]. Certain gut microorganisms increase the biosynthesis of NAM in the presence of L-aspartate and L-tryptophan [192]. Gram-negative bacteria release circulating lipopolysaccharide (LPS), and the possible link between infection, inflammation, and CD38 was reported [193]. An important role of the gut microbiome in the assimilation of orally delivered NMN and evidence for indirect upregulation of the NAD+ metabolome was recently reported as a pre-print [194]. Studies on the relationship between gut flora and NAD+ boosters are sparse. Future studies should examine how specific NAD+ boosters affect the microbiota, how they regulate immune cell activity, how the gut microflora affects the uptake of NAD+ precursors, and how the gut environment affects the potential degradation of NAD+ precursors, as it has been suggested, for example, that NR may be degraded in the gut to NAM [195].
## 3 Results
### Rainfall's Impact on Influent and Effluent Wastewater Parameters
The effluent nitrogen levels were consistently low, ranging from 6.61 mg/L in July (8.00 mm rainfall) to 8.32 mg/L in November (3.67 mm rainfall), reflecting the system's efficiency in nitrogen removal irrespective of the rainfall variability. The ammonia concentrations in the influent varied slightly, peaking at 33.51 mg/L in December (1.60 mm rainfall) and declining to 21.10 mg/L in June (4.12 mm rainfall), corresponding to the dilution effect of rainfall. The effluent ammonia, however, remained undetectable across all seasons, highlighting the treatment process's robust capacity for complete ammonia removal regardless of rainfall variations. In summary, while the seasonal rainfall significantly influenced the influent concentrations of parameters such as the phosphorus, COD, BOD\({}_{5}\), TSS, nitrogen, and ammonia levels, the treatment system effectively mitigated these fluctuations. This ensured that the effluent quality consistently met regulatory standards, demonstrating the system's resilience and efficiency under variable rainfall conditions.
# Vegetation Loss Measurements for Single Alley Trees in Millimeter-Wave Bands
## 2 Additional Attenuation of Vegetation
### Radiative Transfer Equation Perspective
The amplitude factors \(A_{k}\) are determined by following system of linear equations:  
\[\sum_{k=\frac{N+1}{2}}^{N}\frac{A_{k}}{1-\frac{\mu_{n}}{5k}}=\frac{\delta_{n}} {P_{n}}, \tag{14}\]  
where  
\[\mu_{n}=-\cos\Bigl{(}\frac{n\pi}{N}\Bigr{)}, \tag{15}\]  
\[\delta_{n}= \begin{cases}\delta_{n}=0,&\text{for $n\
eq N$}\\ \delta_{n}=1,&\text{for $n=N$}\end{cases}  \tag{16}\]  
Here, we define all the parameters used in Equation (10):  
* \(\alpha\) (the ratio of the forward scattered power to the total scattered power),
* \(\Delta\gamma_{R}\) (the beamwidth of the receiving antenna (degrees)),
* \(\Delta\gamma_{R}=0.6\cdot\Delta\gamma_{3 dB}\) (the introduced normalization for the beamwidth of the receiving antenna),
* \(\gamma_{RP}\) (the angle between the receiving antenna point and \(z\) axis. One can assume 0 degree for perfect alignment with the \(z\) axis),
* \(\Delta\gamma_{s}\) (3 dB width of the forward scatter lobe of the phase function),
* \(\tau=(\sigma_{a}+\sigma_{s})z\) (it represents the medium's optical density),
* \(\text{W}=\frac{\sigma_{s}}{\sigma_{a}+\sigma_{s}}\) (albedo, which describes the likelihood of a scattering event in the homogeneous medium),
* \(\hat{W}=\frac{(1-\alpha)W}{1-\alpha W}\) (it specifies that only the fraction \(1-\alpha\) of the total power scattered in any scattering event is transferred into the isotropic background),
* \(\hat{\tau}=\tau\)(1-\(\alpha\)W) (it shows that for incoherent propagation, the contribution of the scattering term is reduced. The incoherent component is mainly affected by attenuation),
* \(\mu=\cos(\theta)\) (It specifies the theta angle distance to normal \(z\)),
* \(\mu_{p}=\cos(\theta_{p})\) (it specifies the theta angle distance to normal \(z\) for incident intensity),
* \(P_{N}=\sin^{2}(\frac{\pi}{2N})\) (it is the normalization for piece-wise linear approximation for \(I_{2}\)),
* \(m\) order of the first term \(I_{1}\) will not change significantly for \(m>10\) (for most cases, M = 10),
* \(G_{R}\) receiving antenna gain (for the specific angle).
# Detection of Exceptional Malware Variants Using Deep Boosted Feature Spaces and Machine Learning
## 5 Results and Discussion
### Performance Analysis of Proposed Frameworks on Exceptional Malware Classes  
Exceptional classes in the dataset are those classes for which the discrimination is difficult due to smaller interclass differences. The Softmax-based implementation of customized CNN is performing well for a set of malware classes while missing exceptional malware. Therefore, in the proposed DFS-MC malware analysis approach, we emphasised reducing misclassification for exceptional malware. The customized ResNet-18 and DenseNet-201 correctly classified Autorun.K/Swizzor.genII and C2 Lop.P/WintrimBX, respectively.  
Both the architectures improved the precision and detection rate compared to the existing CNN, as shown in Figures 8 and 9. However, the customized ResNet-18 and DenseNet-201 misclassified some exceptional classes (C2 Lop.P/WintrimBX and Autorun.K/Swizzor.genII, respectively) when employed individually. Hence, as an outcome of the experiment, we selected two models instead of one to tackle the risk of misclassification.  
Therefore, we developed a novel DBFS-MC framework, which significantly reduced the misclassification of exceptional malware classes. The proposed DBFS-MC efficiently learned most of the exceptional malware classes by concatenating the decision of both the networks, as shown in Figure 10. The deep feature boosting helps merge the potentials of customized ResNet-18 and DenseNet-201 architectures while improving the deficiency of the single model. Precision and recall analysis for the exceptional malware classes are 0.94 and 0.95, as shown in the Figure 10.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**Training Scheme**} \\ \cline{2-10}  & \multicolumn{4}{c}{**Training from Scratch**} & \multicolumn{4}{c}{**Transfer Learning Based**} \\ \cline{2-10}  & **Acc \%** & **Recall** & **Precision** & **F1-Score** & **Acc \%** & **Recall** & **Precision** & **F1-Score** \\ \hline DenseNet & 97.70 & 0.9286 & 0.9286 & 0.9286 & 98.39 & 0.9483 & 0.9452 & 0.9468 \\ ResNet18 & 98.07 & 0.9387 & 0.9368 & 0.9377 & 98.37 & 0.9463 & 0.9427 & 0.9445 \\ GoogleNet & 97.54 & 0.9284 & 0.9232 & 0.9258 & 97.60 & 0.9270 & 0.9250 & 0.9259 \\ Inception & 97.54 & 0.9262 & 0.9268 & 0.9265 & 96.95 & 0.8965 & 0.9860 & 0.9391 \\ Xception & 97.21 & 0.9170 & 0.9141 & 0.9156 & 98.31 & 0.9421 & 0.9467 & 0.9444 \\ ResNet50 & 97.59 & 0.9289 & 0.9258 & 0.9273 & 97.72 & 0.9301 & 0.9284 & 0.9292 \\ AlexNet & 97.59 & 0.9279 & 0.9286 & 0.9282 & 98.15 & 0.9417 & 0.9378 & 0.9397 \\ VGG16 & 97.43 & 0.9246 & 0.9221 & 0.9234 & 97.91 & 0.9355 & 0.9304 & 0.9329 \\ VGG19 & 97.64 & 0.9281 & 0.9284 & 0.9283 & 98.23 & 0.9436 & 0.9420 & 0.9428 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Deep feature extracted from customized CNN models and SVM classification (DFS-MC).  
Figure 8: Performance (precision) comparison of implemented CNN models.  
Figure 10: Performance of DBFS-MC for exceptional malware.  
Figure 9: Performance (recall) comparison of implemented CNN models.