esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Spacecraft for Multi-Planetary Defense and Prosperity
## 3 Space Threats and Planetary Defense History
### Planetary Defense: When and How It Started  
#### 3.2.1 Timeline  
As early as in a book published in 1964 [87], authors [PERSON] and [PERSON] mentioned the potential risks and dangers of planetoid impacts, both naturally and those that might be intentionally brought about with hostility. Moreover, they argued for categorizing the minor planets and developing the technologies to land on, deflect, or even capture planetoids. In 1967, students at MIT completed a design study called Project Icarus to prevent a hypothetical impact on Earth by asteroid 1566 Icarus [88] that received considerable publicity. Furthermore, a series of studies on historical impact events on Earth such as [79, 89] in the 1980s led to a later program to map objects in the solar system which cross Earth's orbit and are large enough to cause serious damage if they collide with Earth.  
In January 1992, NASA sponsored a near-earth-object interception workshop hosted by Los Alamos National Laboratory where the issues and challenges to cope with intercepting celestial bodies that could impact Earth were discussed [90]. Furthermore, a 1992 US congressional study produced a Spaceguard Survey Report, and it resulted in a 1994 mandate that NASA locate 90% of near-Earth asteroids larger than 1 km within 10 years [91]. The impact of a celestial body much larger than 1 km in diameter could lead to worldwide damage potentially including the extinction of the human species. The initial Spaceguard goal was achieved with a period slightly longer than 10 years. An extension to the project  
Figure 10: Comparisons of approximate sizes of three notable impactors with New York Manhattan as the background.  
required NASA to reduce the minimum diameter of a near-Earth object to 140 m and locate more than 90% of them by 2020 [92, 93]. However, the new goal was not met even with a 10-fold increase in a NEO program budget [94]. As of April 2018, more than 8000 near-Earth asteroids of 140 m and larger had been spotted, while 17,000 such near-Earth asteroids were estimated to remain undetected. By 2019, the total number of discovered near-Earth asteroids of all sizes was more than 19,000 [95].  
In particular, the NASA-funded Catalina Sky Survey (CSS) discovered over 1150 NEOs between 2005 and 2007 [96]. In 2005, the Catalina Station (near Tucson, Arizona) based CSS became the most prolific NEO survey and surpassed Lincoln Near-Earth Asteroid Research (LINEAR) in the total number of NEOs and potentially hazardous asteroids discovered. As of 2020, the Catalina Sky Survey (CSS) has discovered 47% of the total recorded NEOs, and the annual number has seen a steady growth [97].
## 3 Major Bioactive Compounds and Potential Applications
### Vitamins
For each compound class, the table highlights representative bioactive molecules, their known microalgal sources, and documented biological activities, including antioxidant, anti-inflammatory, immunomodulatory, antimic-crobial, anticancer, and metabolic effects. It also outlines the relevance of these compounds in the context of human health, cosmetics, and functional foods, offering a consolidated view of microalgae's potential as a sustainable and multifunctional platform for bioactive production. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Compound Class** & **Major Compounds** & **Microalga Sources** & **Biological Activities and Potential Applications** & **References** \\ \hline \multirow{4}{*}{Alkaloids} & Saxitoxin, Neosavitoxin, & Dinoflagellates & Neuroprotection via Nav channel \\  & Saxitoxin, Neosavitoxin, & _(Alexandrium, Gymondinium,_ & blockade (analogesia); anticancer \\  & Dolastatin-10, Curacin & _Pyrodinium)_, & through tubulin disruption \\  & A, Lyngbyatoxin A & cyanobacteria (_Lynghya,_ & and apoptosis; antioxidant \\  & & Spirulina, & cosmetics; possible immune \\  & & _Trichodesmium_) & modulation in functional foods \\ \hline \multirow{4}{*}{Mycosporine-like Amino Acids} & Shinorine, Porphyra-334, & & Photoprotective via UV absorption; strong antioxidant and anti-inflammatory effects; supports skin repair and collagen maintenance; inhibits NF-kB and COX-2; promising for anti-aging cosmetics, oral photoprotection, and inflammation-related food supplements \\  & & & \\  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Compound Class** & **Major Compounds** & **Microalga Sources** & \multicolumn{2}{c}{**Biological Activities**} & \multicolumn{1}{c}{**References**} \\ \hline \hline \multirow{9}{*}{Phycobiliproteins} & C-Phycocyanin, & & & Antioxidant (ROS scavenging), & \\  & & & anti-inflammatory (COX-2, iNOS & \\  & & & inhibition), immunosuppression & \\  & C-Phycocyanin, & & (NK cell activity, cytokine regulation), & \\  & AlOphycocyanin, & & anti-apoptotic (Bcl-2/
# First Results from Sentinel-1A InSAR over Australia: Application to the Perth Basin
## 3 Study Area
Measurements are made on either a quasi-monthly basis or as daily averages derived from continuous loggers and are accessible via the Department of Water Information Reporting Service ([[http://wir.water.wa.gov.au/Pages/Water-Information-Reporting.aspx](http://wir.water.wa.gov.au/Pages/Water-Information-Reporting.aspx)]([http://wir.water.wa.gov.au/Pages/Water-Information-Reporting.aspx](http://wir.water.wa.gov.au/Pages/Water-Information-Reporting.aspx))).
# Fine Recognition of MEO SAR Ship Targets Based on a Multi-Level Focusing-Classification Strategy
## 5 Dataset Construction and Experimental Results
### Coarse Focusing-Classification Experiment
The well-trained coarse-classification network is directly used to classify the coarsely focused simulated MEO SAR data. The precision of the coarse-classification network is shown in Table 5. The experimental results show that the proposed first-level focusing-classification network can effectively achieve classification of three types of ship target.
# Listeners' Spectral Reallocation Preferences for Speech in Noise
## 4 Expt. 2: Spectral Reallocation Preferences for Band-Limited Speech
### Stimuli  
As in Expt. 1, sentences came from the Sharvard Corpus [70]. For the adjustment phase only, the same sentence subset (1-380) as in Expt. 1 was employed. This decision was driven by the limited availability of utterances in the corpus, given that listeners required a potentially unlimited amount of speech material during this phase. While during adjustment listeners may have recognised elements from sentences used in the earlier experiment, which in turn may have led to changes in preferences, we believe that any such changes due to repeated exposure would be modest, and that our main research question involves comparisons of conditions within each experiment rather than across experiments. Note that a different set of utterances (sentences 541-700) from those used in Expt. 1 was used during the test phase.  
Features being modified (LP, HP, BW, CF) all involved spectral filtering implemented with frequency-domain FIR filters, designed using a Chebyshev window with 100 dB of attenuation from the pass to stop bands, via the fir1 and chebwin functions in Matlab 2016b. For each of the four conditions, the parameter adjustment range of interest was divided into 25 steps, a number chosen to ensure that the transitions between steps were smooth and to avoid redundant steps. After filtering, every sentence in every condition was normalised to have the same RMS energy.  
For the LP and HP conditions, the frequency cut-off varied from 0.45 to 5.4 kHz in equal steps on a logarithmic scale. For the BW condition, all bands were centred on 1470 Hz, and the bandwidth varied from 1.14 to 4.58 octaves in equal steps of 0.143 octaves, corresponding to a bandwidth of 1.2 kHz at the narrowest end of the continuum and 6.9 kHz at the widest. The CF condition consisted of octave bands of speech with centre frequencies equally spaced on a log scale from 0.42 kHz to 5.09 kHz. Parameter ranges for the LP, HP and BW conditions were designed to permit choices that would lead to intelligibility scores close to ceiling in the quiet condition. No such choice was possible in the CF condition, but the bandwidth was restricted to a single octave for compatibility with earlier studies that examined the intelligibility of octave bands of speech (e.g., [79]).
# Transformer Fault Diagnosis Method Based on Incomplete Data and TPE-XGBoost
###### Abstract  
Dissolved gas analysis is an important method for diagnosing the operating condition of power transformers. Traditional methods such as IEC Ratios and Duval Triangles and Pentagon methods are not applicable in the case of abnormal or missing values of DGA data. A novel transformer fault diagnosis method based on an extreme gradient boosting algorithm is proposed in this paper. First, the traditional statistical method is replaced by the random forest regression algorithm for filling in missing values of dissolved gas data. Normalization and feature derivation of the outlier data is adopted based on the gas content. Then, hyperparameter optimization of the transformer fault diagnosis model based on an extreme gradient boosting algorithm is carried out using the tree-structured probability density estimator algorithm. Finally, the influence of missing data and optimization algorithms on transformer fault diagnosis models is analyzed. The effects of different algorithms based on incomplete datasets are also discussed. The results show that the performance of the random forest regression algorithm on missing data filling is better than classification and regression trees and traditional statistical methods. The average accuracy of the fault diagnosis method proposed in the paper is 89.5%, even when the missing data rate reaches 20%. The accuracy and robustness of the TPE-XGBoost model are superior to other machine learning algorithms described in this paper, such as k-nearest neighbor, deep neural networks, random forest, etc.  
power transformer; fault diagnosis; dissolved gas analysis; data filling; random forest regression; TPE-XGBoost algorithm
## 3 Materials and Methods
### Losses  
To minimize the detection confidence, we defined a loss function that combined class confidence, Intersection over Union (IoU), and smoothness terms.  
The class confidence term, \(\mathcal{L}_{\text{cls}}\), was designed to reduce the confidence scores of bounding boxes overlapping with the truck. For this, we computed the Intersection over Prediction (IoP) between each predicted bounding box \(B_{\text{pred}}^{i}\) and the ground truth box \(B_{\text{gt}}\)  
Figure 1: The single-model texture optimization pipeline.  
\[\text{IoP}(B^{i}_{\text{pred}},B_{\text{gt}})=\frac{\text{Area}(B^{i}_{\text{pred}} \cap B_{\text{gt}})}{\text{Area}(B^{i}_{\text{pred}})} \tag{4}\]  
and selected the set \(\Omega_{\text{IoP}}\) of boxes where \(\text{IoP}>\tau_{\text{IoP}}\). The class loss is then  
\[\mathcal{L}_{\text{cls}}=-\sum_{i\in\Omega_{\text{IoP}}}\sum_{c=1}^{C}\log(1- b^{i,c}_{\text{cls}}) \tag{5}\]  
where \(b^{i,c}_{\text{cls}}\) is the confidence score for class \(c\) in box \(i\), and \(C\) is the number of classes.  
The IoU term, \(\mathcal{L}_{\text{IoU}}\), penalizes a high overlap between predicted and ground truth boxes in the set \(\Omega_{\text{IoU}}\), defined for boxes where \(\text{IoU}>\tau_{\text{IoU}}\):  
\[\mathcal{L}_{\text{IoU}}=\sum_{i\in\Omega_{\text{IoU}}}\text{IoU}(B^{i}_{ \text{pred}},B_{\text{gt}}). \tag{6}\]  
To ensure the adversarial texture was smooth and physically realizable, we utilized the convolutional smooth loss [25], termed \(\mathcal{L}_{\text{smooth}}\). For each pixel \(T_{i,j}\) in the texture, we calculated a variance:  
\[D_{i,j}=\sum_{n=-\lfloor k/2\rfloor}^{\lfloor k/2\rfloor}\sum_{m=-\lfloor k/2 \rfloor}^{\lfloor k/2\rfloor}(T_{i,j}-T_{i+n,j+m})^{2} \tag{7}\]  
and the smoothness loss is then  
\[\mathcal{L}_{\text{smooth}}=\frac{1}{W\cdot H}\sum_{ij}\sqrt{D_{i,j}} \tag{8}\]  
where \(W\) and \(H\) are the texture's width and height. The total attack loss combines these terms, weighted by factors \(\beta\) and \(\gamma\):  
\[\mathcal{L}_{\text{atk}}=\mathcal{L}_{\text{cls}}+\beta\mathcal{L}_{\text{IoU }}+\gamma\mathcal{L}_{\text{smooth}} \tag{9}\]
## 3 Materials and Method
### Proposed Neural Network Models
#### 3.2.2 Transformer Model  
The model is compiled using the Adam optimizer and MSE loss function. The original ECG signal is reshaped for the model input for training purposes. After training, an initial set of time points is generated uniformly over the signal length. The ECG readings at these points are computed using the modified signal and interpolated with Cubic Splines interpolation to match the duration of the original signal. MSE is used to calculate the initial distance between the signals. Instead of processing the full input sequence evenly, the transformer model uses an attention mechanism to selectively focus on key portions of the sequence [26].  
The self-attention mechanism is the mechanism that allows the model to weigh different parts of the input sequence differently based on their relevance to one another [27]. The multi-head attention mechanism is followed by residual connection and layer normalization in the proposed model. Finally, the attention mechanism is added after the convolutional layers but before the final output layers. This setup allows the model to use convolutions in capturing local patterns and, at the same time, leverage global dependencies through self-attention. The core of the transformer model is the self-attention mechanism, which computes the attention scores as follows:  
\[Attention(Q,K,V)=softmax\left(\frac{QK^{T}}{\sqrt{d_{k}}}\right)V \tag{3}\]  
where \(Q\) (queries), \(K\) (keys), and \(V\) (values) are projections of the input sequence, and \(d_{k}\) is the dimension of the keys.  
The position-wise feed-forward networks add positional encoding to the input sequence \(x\). The aim is to ensure that each data point in the sequence is not characterized by itself but includes its position within a given sequence. Adding positional encoding to the model allows it to identify where elements are situated in a sequence, which is relevant for time series analysis, where the order of data points matters. Each position in the sequence passes through a feed-forward neural network:  
\[\text{FFN}(x)= \text{max}(0,\ xW_{1}+b_{1})W_{2}+b_{2} \tag{4}\]  
where \(W_{1}\), \(W_{2}\), \(b_{1}\), and \(b_{2}\) are learnable parameters. Figure 4 provides a detailed representation of the flow of elements within the transformer model.  
Figure 4: Architectural diagram of transformer model.  
Figure 3: Architectural diagram of TCN model.
# A Primer on CFD-DEM for Polymer-Filled Suspensions
## 3 Particle-Laden Flows  
A coupled CFD-DEM approach was used for modeling particle-laden flows [13]. The so-called _unresolved_ approach [13; 39; 40] was employed in this study because only the bulk behavior of the suspension is of interest. In this formulation, the particle size is smaller than the computational grid size. Thus, it is assumed that the particles do not completely fill a computational cell. Consequently, the particles are not resolved in the CFD simulation, as in the case of the immersed boundary method [41], but only their interaction with the fluid phase is considered in terms of momentum transfer. Hereafter, the equations for the dispersed phase (DEM) and continuum phase (CFD) are described.
## 3 Optimization Scheme  
### Flowchart for the Optimization Scheme  
The flowchart of a hierarchical optimization scheme for large-scale truss sizing and topology optimization is shown in Figure 2. Before optimizing the mixed variables by GA, a first-level approximation problem is established by the BMA method. As the state of the model, the current optimal variable contains a size vector \(X_{p}^{*}\) and topology vector \(\mathbf{\alpha}_{p}^{*}\) coded in the GA chromosome. The current optimal \(\mathbf{\alpha}_{p}^{*}\) chromosome is then labeled and clipped. To solve for different types of design variables, GA is nested with an internal optimization, as shown in the dotted box. In an external GA, topology variable vectors are binary-coded to form individuals, and a certain number of individuals make up a population. The initial population is generated based on the current best individual clipped, while all the individuals are only spliced at the validity check and in the fitness calculation. Based on the given value of the topology variable vector, truss cross-sectional sizes for each member in the \(k\)-th population are determined through internal optimization. To determine the optimal size of this topology design, the Taylor expansion approximation is conducted based on the first-level approximate functions, and then the dual method and BFGS method are used to solve it. On the basis of the optimal results of internal optimization, the fitness value of each individual can be obtained. After the end of the GA iteration, the current optimal design variable \(\left[\mathbf{X}_{p}^{*},\mathbf{\alpha}_{p}^{*}\right]\) is obtained through elite selection based on structural analysis. It updates the first-level approximation problem as a new point is added, and the whole algorithm goes to the next \(p+1\) iteration. Structural analyses and sensitivity analyses are implemented in the general finite element analysis software MSC_PATRAN/NASTRAN, while the two-level approximation and the GA and size optimization are implemented by the FORTRAN program. The Patran Command Language (PCL) is utilized for software calculation and programming optimization to realize the automation of a series of complex processes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Design Domain** & **Cross-Sectional Type** & **Topology Variables** & **Size Variables** \\ \hline Truss & BAR & \(\alpha_{i}\) (\(i=1,\,2,\,3,\,4,\,5,\,6,\,7\)) & \(x_{i}\) (\(i=1,\,2,\,3,\,4,\,5,\,6,\,7\)) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definition of design variables.  
Figure 2: Optimization flowchart.
## 4 Experimental Validation  
The proposed method utilizes the bona fide recursive algorithm with fixed input and output data size to extract the dynamic behaviors. The effectiveness has been demonstrated through a numerically simulated 8-story frame under seismic excitation. Meanwhile, the criterion for selecting the row number of Hankel matrices, the measure for re-moving the spurious modes contaminated by noise, and the down-sampling processing for a real-time or online SHM have been considered and studied with plain suggestions. To further verify the reliability of the proposed method for seismically excited structures, two full-scale specimens are experimentally tested with shaking tables. One is a 3-story steel frame subjected to the El Centro Earthquake (18 May 1940) in the north-south direction. A brace with a special mechanism is designed so that the inter-story stiffness can be reduced during seismic excitations. The other one is a 10-story concrete frame subjected to the Kobe Earthquake (17 January 1995) in three directions. The intensity of excitations is escalated until the peak ground acceleration (PGA) of the original time history. In each experiment, the ground shaking and the structural responses were measured from the accelerometers. Unfortunately, in both experiments RSI was not implemented in real-time because of some  
Figure 7: The averaged computation time and percentage spent on computation in each step. (**a**) Averaged computation time; (**b**) ratio in each step.  
technical problems. Instead, the measurement was first recorded and then processed with the bona fide recursive algorithm.
## 7 Conclusions and Future Recommendations  
This paper is concerned with the presentation of the acoustic results obtained from a survey carried out at the Monte Castello di Vibio theatre and analyses the main acoustic parameters compared with the Teatro 1763 in Bologna with the same volume. The measurements were carried out using an omnidirectional sound source and four types of microphones.  
The evaluation of the acoustic measurements carried out inside the Monte Castello di Vibio theatre shows that it responds well to speech because of its smaller volume compared to other traditional theatres. It is a more suitable venue for lecture events, as evidenced by the analysis of the main acoustic parameters: a shorter Reverberation Time and the value of the Early Decay Time (EDT) is lower than the ideal value that an opera house should have. For the Clarity index, the C\({}_{80}\) is far above the ideal level for musical performances, while the C\({}_{50}\) has a good performance. The Lateral Fraction (LF) and the Interaural Cross-Correlation (IACC) also confirm this statement. However, as an Italian theatre also with a small volume, the 1763 theatre shows a difference in the main acoustic parameters. This leads to the conclusion that a theatre's plan layout and interior decoration determine its acoustic characteristics more than its size.  
Based on this acoustic investigation, future research may focus on two areas: firstly, the exploration of acoustic simulations of some theatrical performances to find out which ones are better suited to this unique historic theatre. Secondly, based on the results obtained in comparison with the 1763 theatre, the Monte Castello theatre could be modified, for example, by choosing fabrics with different absorption coefficients for the interior and by adding components to improve the acoustic properties.  
Conceptualization, L.T., R.Y. and A.B.; methodology, L.T. and A.B.; software, R.Y.; validation, L.T.; formal analysis, R.Y.; investigation, R.Y.; resources, L.T.; data curation, R.Y.; writing--original draft preparation, R.Y.; writing--review and editing, A.B. and R.Y.; visualization, R.Y.; supervision, L.T. and A.B.; project administration, L.T.; funding acquisition, L.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to acknowledge [PERSON], from ENEA, for their collaboration during the measurements in the theatre. Furthermore, the authors would like to acknowledge [PERSON] for her help during the measurement conducted into the Teatro 1763 in Bologna.  
**Conflicts of Interest:** The authors declare no conflict of interest.