esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Nucleation Activity  
[PERSON] and [PERSON] [75; 76] introduced a semi empirical model for the determination of the nucleation activity, \(\varphi\), of foreign substrates in polymer melts from DSC measurements. \(\varphi\) is a factor by which the word of 3D nucleation decreases with the addition of a foreign substance. When \(\varphi\approx 0\) the substance is extremely active, and when \(\varphi\approx 1\) then the filler is considered inert. Nucleation activity, \(\varphi\), can be calculated as follows:  
\[\varphi=\frac{B^{\ast}}{B} \tag{9}\]  
where \(B\) is a parameter that can be experimentally determined by the slope of Equation (9) after plotting ln\(\alpha\) vs. \(1/\Delta T_{\text{p}}^{2}\), where \(T_{\text{p}}=T_{\text{cc}}-T_{\text{g}}\) because, in cold crystallization, the \(T_{\text{cc}}-T_{\text{g}}\) difference is the determining parameter.  
\[ln\alpha=Const-\frac{B}{\Delta T_{\text{p}}^{2}} \tag{10}\]  
In the presence of a nucleating agent, \(B\) is replaced by \(B^{\ast}\) in Equation (10). The Dobreva plots of PLA and its nanocomposites are presented in Figure 5a and the calculated \(\varphi\) values in Figure 5b. All nanocomposites have reduced nucleation activity factor \(\varphi\) in comparison with neat PLA, which means they enhanced 3D nucleation and are active as heterogenous nucleating agents. The trend observed for \(\varphi\) is consistent with the trend of activation energy, implying that the activity of the fillers as nucleating agents for the non-isothermal cold crystallization of PLA follows the order ZnO > TiO2 > Ag.
# An Audio-Based Method for Assessing Proper Usage of Dry Powder Inhalers
## 2 Methodology
### Silence Removal  
In the process of extracting segments that contain respiratory sounds from the acoustic signal, the algorithm that is presented takes the input signal and returns the endpoints of the segments corresponding to the individual sound events. This process belongs to the broader field of voice activity detection, a technique used in speech processing, in which the presence or absence of human speech is detected. The algorithm removes all \"silent\" areas of the signal and returns those containing sound to classify each of them into the final classes of _Actuation_, _Inhale_, _Exhale_, _Button Press_. For this purpose pyAudioAnalysis [30] library was used.  
The first step in the activity detection process is the extraction of 34 short-term features (Table 1). Then, a binary Support Vector Machine (SVM) model is trained to distinguish high-energy from low-energy short-term frames using 50% of the components with the highest energy concentration along with the 50% of the lower ones. The short-term features use, for this purpose, 50 ms window size and 40 ms frame step, resulting in a sequence of feature vectors with 34 elements each. In the next step, the SVM model is trained to distinguish the high-energy short-term windows from the low-energy ones and applies them throughout the recording, using a median threshold value to detect the segments that contain sound. A dynamic threshold is used to detect active segments. Finally, the \"non-silent\" sections are grouped and the \"silent\" areas are removed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No** & **Feature** & **Description** \\ \hline
1 & Zero Crossing Rate & The rate of sign-changes of the signal during the duration of a particular frame. \\
2 & Energy & The sum of the squares of the signal, normalized by the length of the frame. \\
3 & Entropy of Energy & The entropy of the normalized sub-frames. \\
4 & Spectral Centroid & The center of gravity of the spectrum. \\
5 & Spectral Spread & The second central moment of the spectrum. \\
6 & Spectral Entropy & The entropy of normalized spectral energies for a set of sub-frames. \\
7 & Spectral Flux & The square difference between the normalized magnitudes of the \\  & & spectrum of two consecutive frames. \\
8 & Spectral Roll-off & The frequency below which 90\% of the magnitude distribution of the spectrum is concentrated. \\
9-21 & MFCCs & MFCCs form a cepstral representation where the frequency bands \\  & & are not linear but distributed on a mel scale. \\
22-33 & Chroma Vector & A 12 element representation of spectral energy. \\
34 & Chroma Deviation & The standard deviation of the 12 chroma coefficients. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Audio features extracted in silence removal [30].  
Figure 2: Waveforms of audio signals corresponding to the four classes, i.e., _Actuation_, _Inhale_, _Exhale_, and _Button Press_.
## 3 Experiment and Results
### Data Acquisition  
In this paper, the original wind speed and wind power time series used for forecasting were acquired in 2018 from four observation sites in Shandong province in China. These were used to establish and test the forecasting performance of each model.  
Statistical descriptions for the datasets obtained for the two sites (mean, standard deviation, skewness, kurtosis, minimum, maximum, and median) are provided in Table 1. The mean values characterize the central tendency of the historical observations. The standard deviation values (the wind speed hovered around 2 m/s, and the wind power was around 200 kw) clearly reflect the fluctuations in wind speed and wind power. The skewness values of the wind speed and wind time series were greater than zero, which indicates that both types of time series had right-skewed distributions. Most wind time series had peak values greater than 3, indicating a fat tail distribution. Most kurtosis values of the wind power time series were greater than 3, indicating a fat tail distribution. The kurtosis values of the wind speed time series were less than 3, indicating a slight tail distribution.
# Data Analytics for Profiling Low-Voltage Customers with Smart Meter Readings
## 2 Load Profiling
### Clustering for Assessing Load Profiles  
Although each clustering algorithm has its peculiarity, a general approach for the clustering analysis of load profiles can be defined. As proposed in [28], load consumption patterns can be classified by applying four main steps:  
1. _Consumption pattern acquisition._ The consumption pattern of each end-user has to be firstly gathered using the metering infrastructure. Two issues arise from this step that can alter the entire process of classification--the duration of the measurement campaign (it should last at least one year to capture the seasonality of the variations in the end-users' consumption) and the representativeness of the sample (that should be constituted by many customers spread in the area that the study aims to represent to avoid the risk of having no statistical value). At the end of the measurement campaign, the initial database is constituted. Still, many spurious measures or loss of communications may occur during the measurement campaign. Thus, the initially formed database must be processed to detect and eliminate bad data and create a more reliable and robust database.
2. _Representative features selection_. In this phase, the selection of the representative features of the dataset individuals is performed. Features can be time-domain data, load shape factors, frequency-domain coefficients, projection analysis method coefficients, etc. [10; 29]. In the paper, this step has been applied to characterize the behavior of a single customer (i.e., a given customer can exhibit load profiles very different one day from each other day, even during the same type of day) and to discover the similarity of the consumption shapes of different end-users. The number of identified features can grow in number, and thus techniques such as principal component analysis (PCA) may be conveniently used for reducing the number of features [30].
3. _Clustering application_. Suitable clustering algorithms must be applied to the dataset of features selected in the previous step. With this step, in the proposed approach, it is possible to identify the single customer's most frequent behavior by grouping similar load shapes within the dataset of the given end-user or to group the customers of the database into classes. Thus, each cluster's identified centroid may alternatively be used for selecting the LP that characterizes the single customer or may become the LP associated with the customers belonging to the cluster.
4. _Validation_. The last step is devoted to validating the clustering process by demonstrating its effectiveness with ad hoc tests.  
Data pre-processing can be performed before the application of a clustering algorithm to improve the success of clustering. Pre-processing means, for instance, choosing a specific load condition (i.e., selecting a specific seasonal period or a type of day) and defining an initial partition in macro-classes according to external features (nominal power, climatic zone, contract, etc.) [28; 29].
# Management of Acute Life-Threatening Asthma Exacerbations in the Intensive Care Unit
## 4 Acute Life-Threatening Asthma Advanced Management in the ICU
### Anaesthetic Agents
#### 4.5.2 Inhalational Anaesthetic Agents  
Inhalational anaesthetic agents such as isoflurane, sevoflurane, enflurane, and halothane are known to induce bronchodilation through the blockage of voltage-gated calcium channels, depletion of sarcoplasmic reticulum calcium stores, and possibly through GABAergic mechanisms; however, this effect is not seen with desflurane [91, 92, 93]. There was early evidence in the 1980s and 1990s of a clinical benefit in treating severe refractory asthma with halothane or isoflurane in both adults and children; however, these studies were small, involving only a few patients [94, 95]. Isoflurane improves arterial pH and reduces partial pressure of arterial carbon dioxide in mechanically ventilated children, and sevoflurane may provide clinical improvement in mechanically ventilated children with life-threatening asthma [96, 97]. Further smaller studies and case reports have continued to show clinically beneficial bronchodilation from volatile agent administration, but to date, no randomised, placebo-controlled trials exist to explore these benefits conclusively [98, 99, 100].  
There is sufficient theoretical and clinical evidence to suggest that a trial of volatile anaesthetics may be beneficial in severe refractory asthma exacerbations. Indeed, BTS guidelines suggest that a trial of sevoflurane could be beneficial where appropriate gas scavenging facilities exist [10]. It must be noted that use of inhalational anaesthetic agents in the ICU may only be performed in the presence of appropriate gas-scavenging equipment along with specialised delivery devices which are designed to conserve inhalational anaesthetics. One such device is the AnaConDa(tm), which enables the administration of volatile anaesthetics (isoflurane or sevoflurane) and is inserted between the Y-piece and ET tube. While its use as an additional rescue therapy continues, further studies are needed to evaluate the clinical impact of such devices on robust patient outcomes [101].
## 5 Conclusions  
Using flux measurements and time-series MODIS EVI products at three boreal forests in Canada, we analyzed and compared the potential of three spring phenological indicators of annual C fluxes. The C flux phenology, CUP onset, was an indicator of annual GPP at all sites, but was significantly correlated with annual NEP only at the SK-OBS site. Spring temperature showed limited potential as a proxy for C flux at the deciduous site, but represented a consistent measure of annual GPP and NEP at the two evergreen sites. While MODIS-derived spring EVI showed a weak correlation with GPP at the SK-OBS site, it exhibited a strong relationship with annual NEP at all sites, irrespective of differences in plant functional type. This finding is especially important as it provides a good proxy for net C uptake across species, and so will facilitate scaling up for regional and global analysis of the effects of climate change on C balance.  
A potential limitation of our analysis is that we focused primarily on spring phenology, and autumn phenology might also have important effects on the annual C balance. This is in accordance with recent reports that autumn phenology regulates interannual NEP variability [1, 5]. A combination of both would increase the power of ecosystem models to predict changes in C sequestration with future climate change. NEP also depends on stand age (_i.e._, different amplitudes in \(R_{e}\)), and given the mature and old forest sites examined in this study, our findings may not be applicable to young forests and other ecosystems (e.g., crops, grasslands). Because the components of soil respiration differ among forests of various ages [42, 43], caution should be used when applying our results to young forests. Therefore, further analysis and evaluation would facilitate extension of our results to more ecosystems in diverse ecoregions.
## 4 Experiments
### Ablation Study
#### 4.5.3 Independent Effects of Noise Variants  
To isolate individual noise impacts, we performed component-wise ablations:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **RMSE** & **REL** & \(\delta_{\mathbf{1.25}}\) & \(\delta_{\mathbf{1.25^{2}}}\) & \(\delta_{\mathbf{1.25^{3}}}\) \\ \hline NLSPN & 0.240 & 0.048 & 97.1 & 99.5 & 99.8 \\ +Self-Training & 0.184 & 0.032 & 99.2 & 99.9 & 100.0 \\ +Self-Training + Noise & 0.090 & 0.011 & 99.7 & 99.9 & 100.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The ablation experiment results for self-training and noise enhancement.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **RMSE** & **REL** & \(\delta_{\mathbf{1.25}}\) & \(\delta_{\mathbf{1.25^{2}}}\) & \(\delta_{\mathbf{1.25^{3}}}\) \\ \hline Full Framework & 0.090 & 0.011 & 99.7 & 99.9 & 100.0 \\ KD Ablation & 0.163 & 0.024 & 99.5 & 99.9 & 100.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The ablation experiment results for pixel-wise knowledge distillation.  
RGB Gaussian blur only: Gaussian blur (\(\sigma=1.5\)) was applied exclusively to RGB inputs with unmodified depth data.
2. Depth perturbation only: random displacement (\(\varepsilon=3\)) was introduced solely to sparse depth measurements.
3. Dual noise: the concurrent implementation of both modalities.  
Table 6 demonstrates that individual noise strategies--RGB Gaussian blur or sparse depth perturbation--each enhance performance, while their combined implementation achieves the peak efficacy (RMSE 0.090). This dual-modality approach leverages complementary perturbations across visual and geometric data domains, synergistically improving the robustness against heterogeneous input variations.
## 4 Materials and Methods
### Proposed DDPG Control Methodology for Attack Mitigation  
In this paper, a multi-agent DDPG algorithm is leveraged to predict the stator and rotor current reference values, \(\left\{i^{*}_{ds},\ i^{*}_{qs},\ i^{*}_{dr},\ i^{*}_{qr}\right\}\) for the gride-side and rotor-side converters. These values are then be used to predict the \(d\)-\(q\)\(\left\{v^{*}_{dr},\ v^{*}_{qr}\right\}\) reference rotor voltages that are then passed to the pulse width modulation (PWM) controller to determine the proper duty cycle for the rotor-side converter. If an FDIA/BDIA is launched against the reference values for the controllers, the effect will be seen on the DC-link voltage, and the DDPG controller will then replace the missing reference values to prevent the system from reacting to the attack. If a DoS attack occurs and communication between the SCADA and the converter system is blocked or jammed, the packet sampling time \(\mathfrak{t}_{\text{received}}\) will violate the minimum and maximum allowed sampling times, which are based on the specific network communication structure. The affected communication line will then be disconnected from the system, and the specific DDPG agent will assume control of the affected system until a cyber team can assess and respond accordingly to the situation. In this thesis, the sampling time will be based on the simulation sampling time, which is 50 microseconds per sample.  
By integrating these algorithms into a controller that is physically attached to the converters, the PWM signals will be consistently updated in the event of a sophisticated cyber attack conducted against the communication network of a wind turbine generator or wind farm, as there will be no wireless nodes to be attacked in the DDPG network. The following Figure 5 displays the flow chart of the proposed control algorithm, with Figure 6 depicting the block diagram of the proposed multi-agent DDPG converter controllers. In Figure 5, the 'Y' means the condition was found to be true, so the controller follows this path. If the condition is found to not be true, the controller follows the 'N' path.
## 2 Related Works  
### E2E ASR Models  
There exist two popular types of E2E ASR frameworks: CTC and AED. [7] first proposes the CTC framework, which consists of an acoustic encoder and a CTC classifier. The acoustic encoder is responsible for extracting contextual information from the input speeches and generating a high-level acoustic representation \(h^{enc}\) to be fed into the CTC classifier, and the CTC classifier is a softmax network that is trained with the CTC criterion. Even though there have been many improvements on top of the CTC framework [25; 26], the conditional independent assumption [7] limits the ability of CTC to capture linguistic information, which results in an unsatisfying performance in applications.  
The AED framework is originally proposed for sequence-to-sequence modeling [27]. Considering ASR is also a sequence-to-sequence task, [28; 29] propose to apply the AED framework in E2E ASR tasks. The AED framework is composed of an acoustic encoder and an attention-based decoder, where the encoder will feed the high-level acoustic representation \(h^{enc}\) into the decoder to generate the output sequence in an auto-regressive (AR) manner. Such an AR decoding manner helps to break the conditional independent assumption in CTC and obtain a more promising performance. However, the AED framework needs more parameters and FLOPs meanwhile.  
Based on the CTC and LAS framework, there have existed various encoder and decoder architectures in existing works. Early works tend to employ RNN-based architectures for their sequence modeling capabilities [27; 30]. More recently, the Transformer network is introduced for E2E ASR [8; 9], which adopts the multi-headed attention (MHA) mechanism. The advantage of Transformer is two-fold: The MHA mechanism allows a more direct way to extract global contextual information across the whole sequence, which results in a better performance than RNN. Also, since most operations in Transformer can be calculated in parallel, Transformer provides a faster training and inference speed than RNN. Therefore, the Transformer encoder and decoder have become the most popular choices in E2E ASR.  
Existing Transformer encoders or decoders are built by stacking identical blocks, and each block is composed of the MHA module and the FFN module. Considering CNN has a strong advantage in capturing local information, there have been several works that introduce the CNN module into Transformer and achieve promising performance [10; 11; 12].
###### Abstract  
As one of the most important components in rotating machinery, it's necessary and essential to monitor the rolling bearing operating condition to prevent equipment failure or accidents. However, in vibration signal processing, the bearing initial fault detection under background noise is quite difficult. Therefore, in this paper a new feature extraction method combining sparse reconstruction and Multiscale Dispersion Entropy (MD_rms_) is proposed. Firstly, the Sliding Matrix Sequences (SMS) truncation and sparse reconstruction by Hankel-matrix are applied to the vibration signal. Then MD_rms_ is utilized as a characteristic index of vibration signal, which is suitable for a short time series. Additionally, the MD_rms_ is employed in the sparse reconstructed matrix sequences to achieve the Multiscale Fusion Entropy Value Sequence (_MFEVS_). The _MFEVS_ keeps the fault potential feature information in different scales and is superior in distinguishing fault periodic impulses from heavy background noise. Finally, the designed FIR bandpass filter based on the _MFEVS_, shows prominent features in denoising and detecting weak bearing faults, which is separately verified by simulation studies and artificial fault experiments in different cases. By comparison with traditional methods like EEMD, Wavelet Packet (WP), and fast kurtogram, it can be concluded that the proposed method has a remarkable ability in removing noise and detecting rolling bearing faint fault.  
fault diagnosis; feature extraction; rolling bearing; signal processing +
Footnote †: journal: Applied Sciences
## 7 Comparison of the Proposed and Existing Models
### Ultimate Condition  
Table 10 illustrates the comparison of \(f^{\prime}_{cc}\) prediction by the developed model and that by existing ANN models (e.g., [48; 49]). As can be seen, although a larger number of datasets was used in this study, the ANN model developed in this study offered a higher accuracy than the existing ANN models.  
A comparison between the accuracy of the existing ANN models and the ANN model developed in this study to predict \(\varepsilon_{cu}\) is presented in Table 11. Based on the table, the proposed model offered a comparable accuracy while the number of analyzed datasets was largely compared to existing ANN models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Year** & **Number of Data** &
\begin{tabular}{c} **Prediction** \\ **Parameter** \\ \end{tabular} & \(R^{2}\)**-All** \\ \hline [PERSON] et al. [25] & 2010 & 213 & \(f^{\prime}_{cc}\) & 0.89 \\ [PERSON] et al. [16] & 2020 & 169 & \(f^{\prime}_{cc}\) & 0.98 \\ [PERSON] et al. [48] & 2021 & 780 & \(f^{\prime}_{cc}\)/\(f_{co}\) & 0.88 \\ [PERSON] and [PERSON] [23] & 2008 & 110 & \(f_{cc}\) & 0.97 \\ [PERSON] [49] & 2011 & 180 & \(f^{\prime}_{cc}\)/\(f_{co}\) & 0.94 \({}^{*}\) \\ [PERSON] and [PERSON] [24] & 2012 & 128 & \(f^{\prime}_{cc}\) & 0.95 \\ \hline Proposed model & - & 836 & \(f^{\prime}_{cc}\) & 0.99 \\ \hline \hline \end{tabular}  
* The presented \(R^{2}\) is only for the test dataset.  
\end{table}
Table 10: Comparison between developed ANN models to predict\(f_{cc}\).  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multirow{2}{*}{**Threshold**} & \multicolumn{2}{c}{**Transition Zone**} & \multicolumn{2}{c}{**Ultimate Condition**} \\ \cline{3-6}  & & \(f^{\prime}_{c1}\) & \(\varepsilon_{c1}\) & \(f_{cc}\) & \(\varepsilon_{cu}\) & \(\varepsilon_{h\_{rep}}\) \\ \hline \(R=\frac{\sum_{i=1}^{n}\left(h_{i}-\overline{h}\right)\left(t_{i}-\overline{t} \right)}{\sqrt{\sum_{i=1}^{n}\left(h_{i}-\overline{h}\right)^{  
As explained previously, using a similar database offers an efficient instrument to compare the obtained accuracy of the proposed models with available best performance models. Tables 12 and 13 present the accuracy of the proposed model and best performing models for estimating \(f^{\prime}{}_{\alpha c}\) (e.g., [50; 51; 52; 53; 54; 55]) and \(\varepsilon_{ct}\) (e.g., [56; 57; 58; 59]), respectively. Based on the tables, the model by [PERSON] et al. [4] had the highest accuracy among existing models in predicting \(f^{\prime}{}_{\alpha c}\). In addition, the model by [PERSON] and [PERSON] [9] had the highest accuracy among existing models in predicting \(\varepsilon_{ct}\). However, the proposed ANN models in this study had a higher accuracy than these best performing models in predicting \(f^{\prime}{}_{\alpha c}\) and \(\varepsilon_{ct}\).