esa-sceva/satcom-chunk-collection huggingface dataset:
# On Training Data Selection in Condition Monitoring Applications--Case Azimuth Thrusters
## 3 Results
### Proportion of Acceptable Training Subsets  
Figure 8 shows the percentage value of acceptable training subsets relative to the sample size of different datasets considered. The acceptable training subset means that the residual sets of all the considered features within the considered speed range were normally distributed within the subset. The quantity of the final selected training subsets was, however, lower than shown in Figure 8 when only the widest rotational speed ranges without overlap became selected for monitoring, as shown in Section 3.2.  
Tests were conducted by using all eleven features together (all models) and separately in groups by using the time-domain model (features 1-3), the bearing model (features 4-8) and the gear model (features 9-11). With 'basic training subsets', the total number of subset candidates was 55 (see Figure 2), while the number varied with the 'adaptive training subsets' approach based on the dataset. The number of candidate subsets was then (3160, 3495, 3890, 4036, 4198, 4310, 4432, 4407, 4355, 4378, 4277, 4298, 4326, 4396, 4459) for the datasets with sizes from 200 to 3000 samples with the steps of 200 samples, respectively.  
Figure 8: Percentage of acceptable training subsets using different combinations of features. Dataset segmentation approach based on ‘basic training subsets’ on the left and ‘adaptive training subsets’ on the right.  
In general, the increasing size of the dataset resulted in the decreased percentage value of acceptable subsets. A large feature set, such as the case with 'all models', resulted in a lower number of acceptable subsets than the smaller feature sets. Both approaches to dataset segmentation resulted in a relatively similar proportion of acceptable subsets. However, the absolute number of acceptable subsets was much higher in the 'adaptive training subsets' approach. Moreover, the case of 'all models' and N = 3000 on the left had 0% acceptable subsets, whereas on the right, the corresponding result was 3.2%, which implies that the approach with thousands of training subset candidates is more useful with large datasets. Please refer to Appendix B for a similar analysis focusing on each feature separately.
## 3 Results and Discussion
### Effects of STL Addition on BT VMs  
#### 3.2.1 Identification of VMs  
A total of 141 VMs were detected among the tea samples (Table S1), including 28 alcohols, 23 esters, 12 aldehydes, 11 ketones, 16 aromatic hydrocarbons, 14 terpenes and alkynes, 32 alkanes, and 5 others. These results differed slightly when compared with those of previous studies [7; 13]. This might be because of the different sample pretreatments and volatile compound detection technologies used. Using the novel approach of IRAE-HS-SPME combined with GC-MS/MS, the present study obtained more types and quantities of VMs, representing an improvement over previous studies. Among them, the most abundant compounds were (E)-2-hexenal, 1-methyl-1H-1,2,4-triazole, (E)-3-hexen-1-ol, benzaldehyde, 1-octen-3-ol, 1-phenyl-1,2-propanediol, linalool, isophorone, benzeneacetaldehyde, phenylethyl alcohol, methyl salicate, nerol, and _trans_-\(\beta\)-ionone. The total VMs content and the content and proportion of the volatile compound types were significantly (\(p<0.05\)) different among the BT, BoS, and STL samples (Table S2), with the highest total volatile compound content being in the STL samples (\(p<0.05\)), followed by those in the BT and then the BoS samples, although the difference between the last two sample types was not significant. The ester, alcohol, ketone, enyne, alkyne, and alkane contents were the highest in the STL (\(p<0.05\)), which is consistent with the findings of [PERSON] [24]. However, the aldehyde content was the highest in the BT samples (\(p<0.05\)). In
# ConceptVAE: Self-Supervised Fine-Grained Concept Disentanglement from 2D Echocardiographies
## 5 Quantitative Model Analysis
### Aortic Valve Detection  
To further evaluate the generalization capability of ConceptVAE, we aim to detect latent space grid locations corresponding to the aortic valve (AV) region in views not used during pre-training (i.e., PLAX). Similarly to Section 5.2, for this task we train a linear  
Figure 6: The ROC curves comparison between ConceptVAE and the Vicreg baseline model, for distinguishing in-distribution echocardiographic views from OOD PLAX ones. ConceptVAE has an AuROC score of 0.753, while the Vicreg baseline has an AuROC of 0.655.  
convolutional layer on top of frozen embeddings to perform a proxy object detection task. Each testing sample has a bounding box annotation around the AV along with a label indicating if it's open or closed (depending on the cardiac phase depicted in the test image). We downsized the bounding boxes to the output stride of the latent space and used an overlap threshold \(t\) to determine the _objectness_[48] of each latent space grid location, i.e., if the down-sampled bounding box overlaps a grid location with a ratio larger than \(t\), then that grid location obiteness is set as 1, otherwise 0. Moreover, for each object grid location the newly added convolutional layer also predicts the AV state (open or closed).  
For ConceptVAE, the input to the linear layer is a \(5\times 5\) window of both concept probabilities and associated styles for the concepts having the highest probability. The output consists of 3 channels, one for classifying objectness and the other two for classifying the AV state. For the baseline Vicreg model, the setup is similar, but the input is the feature vector of a \(1\times 1\) latent space grid location (see Section 4 for details). Balanced binary cross-entropy losses are employed to train both objectives (i.e., detection and labeling).  
The results are illustrated in Table 3. The mAP scores are close (with the baseline slightly better by 1.6% mAP), while the objectiveness AP is much larger for our proposed model (+12%). This is because our model does a better job in locating Aortic Valve grid positions, but somewhat lags in correctly classifying the AV state for the detected AV locations. We hypothesise that locating the AV can be done by analyzing concepts (e.g., exploiting a linear separability of concept probabilities w.r.t. AV presence) while the AV state can be inferred from the style component of the latent space. To test this, we trained a new linear layer only on the concept components of the latent space and observe a severe degradation in label classification performance while retaining the objectness classification performance. The previous section revealed that the detected concepts on the _near-OoD_ PLAX views are still descriptive of the image's semantics; however, the style component may not fully capture all relevant fine details, since the proposed model was not trained on PLAX views as opposed to the baseline model.
## 4 Control Strategies
### PV Converter Control
#### 4.1.2 PV Converter Control for Sharing the Microgrid Load Power  
As has already been mentioned, if the microgrid is islanded, the battery is fully charged, and the total DG power exceeds the microgrid AC + DC load power, then each DG source must reduce the generated power until the load power demand is shared among the DG units. Ideally, this sharing should be proportional to the maximum power delivered by each DG unit.  
If the DG power is greater than the load power demand, considering that no power can be transferred to the grid or the battery, the excess power flows to the DC bus capacitors bank, increasing the DC bus voltage. Thus, based on the information that the microgrid is islanded and that the battery state-of-charge is complete, the DC bus voltage increase indicates that the power generated by each DG unit must be reduced.  
A typical voltage-power curve for a PV system is shown in Figure 4. However, it can be observed that, below the MPP voltage, the voltage-power relation can be considered approximately linear.  
The strategy proposed here to make the DG units share the total power required by the microgrid loads is based on the DC bus voltage increase per unit of the maximum allowed voltage increase, expressed as:  
\[\Delta V_{DC,pu}=\frac{\Delta V_{DC}}{\Delta V_{DC}^{MAX}}=\frac{V_{DC}-V_{DC }^{rated}}{V_{DC}^{MAX}-V_{DC}^{rated}}. \tag{13}\]  
Assuming that on the left side of the P-V characteristic, the PV-generated power is approximately proportional to the PV string output voltage, this voltage can be reduced linearly with the DC bus voltage increase. Since the DC bus voltage becomes constant only when the generated and load powers are equal, the steady-state condition ensures that, for some DC bus acceptable overvoltage, the PV output voltage ensures DG and load powers to match. The PV output voltage command can then be obtained, as shown in Figure 5.  
If other PV units were connected to the microgrid, their output voltages would be calculated using the scheme presented in Figure 5. Thus, the generated powers would be approximately proportional to each PV unit's maximum power available.  
Figure 4: Typical P–V characteristic of a PV string.
# An Analysis of Rock Bolt Dynamic Responses to Evaluate the Anchoring Degree of Fixation
## 3 Results  
The data processing involved the detection of the main peaks of the amplitude spectrum for each experiment as a final step. We obtained the peaks of each shot for the observed rock bolts; in a few cases, the poor quality of the record did not allow for a suitable analysis of the spectrum or good detection of the peaks. The data are reported in Table 5. The analysis was limited to the detection of the main frequency; occasionally, we detected a second frequency peak.  
Figure 14: Amplitude spectrum for good grouting conditions (see Table 4).  
Figure 13: Amplitude spectrum for intermediate grouting conditions (see Table 4).  
This preliminary evaluation allowed us to check the quality of the grouting. If the axial vibration was dominant with respect to the flexural modes, the grouting and rock bolt were deemed to be working properly. In low-quality grouting, the flexural modes of vibration tended to be predominant with respect to the axial vibration. We noticed the following observations:  
* A few rock bolts had a resonance frequency with a value of up to around 1600 Hz. These were mainly associated with the main axial vibration and were considered to potentially signal good coupling.
* Resonance frequencies lower than 100 Hz were mainly associated with flexural modes of vibration in the rock bolts and indicated poor-quality grouting.  
The FEM modal analysis assumed the same excitation source in terms of the frequency content and intensity. We noted the following main considerations:  
* In the case of poor-quality grouting, the spectrum displayed two main peaks. One was at a frequency lower than 20 Hz; the other, which was more \"energetic\", was at a frequency of around 320 Hz.
* In the case of intermediate-quality grouting, the amplitude spectrum first showed a peak, also more energetic, at a frequency of around 40 Hz and then a second, less relevant, peak at a frequency of around 337 Hz.
* In the case of good-quality grouting, only one peak was detected at a frequency of 126 Hz.  
The simulation revealed significant differences in terms of the amplitude and frequency content of the recorded vibration signal, with a good correlation between the behavior of the resonance frequencies with respect to the quality of the grouting.
## 1 Introduction
Phylogenetic trees inferred from independent gene sequence analyses (16S rRNA, _recA_, _gyrB_, _rpoB_, _acdS_), indicate that there are numerous divisions inside the _Burkholderia_ genus with significant (\(>\)90%) bootstrap values [19, 20, 21, 22, 23]. Altogether, these analyses suggest that three major clades may exist inside the _Burkholderia_ cluster. These include the _B. cepacia_ complex (BCC), the \"_pseudomallei_\" group and a recently described clade that contains non-pathogenic plant-beneficial _Burkholderia_ species. Despite the apparent genetic diversity within the _Burkholderia_ genus, a common characteristic is that they may utilize QS as part of their colonization and invasion strategies. QS is a process in which bacteria regulate gene expression according to their local population density. In gram-negative bacteria, the most common QS system involves the production and response to _N_-acylated homoserine lactones (AHLs) [24]. AHL production is catalyzed by an AHL synthase belonging to the LuxI-family of proteins; this enzyme requires _S_-adenosylmethionine (SAM) and an acylated acyl carrier protein (ACP) from the fatty acid biosynthesis pathway, as substrates. Upon reaching a minimal threshold level, AHL binds to a sensor/regulator protein that typically belongs to the LuxR-family of proteins. The _N_-terminal region of the LuxR protein has an AHL-binding domain which is reported to facilitate the formation of functional homodimers that allow it to bind to various DNA sequences and regulate the transcription of target genes [25]. Most commonly, one of the targets of the LuxR/AHL complex is the _luxI_ gene thus creating a positive feedback loop which increases the production of AHL. This loop is important for the timing of the QS response in accordance with the AHL concentration and hence, population density [26]. Previous studies have shown that LuxR regulators are sometimes present in excess in comparison to AHL synthases. These have been termed as orphan or solo LuxR as they are not associated with cognate AHL synthases [27]. In some of the cases, these orphan or solo LuxR have been found to respond to endogenously/exogenously produced AHLs or to molecules produced by eukaryotes. These solo LuxR therefore, might help in the expansion of QS network and be involved in interspecies and inter-kingdom signaling [28,29]. According to an excellent review published in 2006, AHL QS circuitry is widely present in the genus _Burkholderia_[30]. In the present work we update this catalogue and provide new details on local gene arrangements, chromosomal locations, regulatory patterns and the chemical nature of QS signals distributed among the known and emerging sub-groups of the genus.
# Innovative Strategies for Combining Solar and Wind Energy with Green Hydrogen Systems
## 3 Innovative Strategies
### Optimized Energy Conversion and Storage
#### 3.1.2 Advanced Storage Solutions for Hydrogen
The reaction mechanisms are as expressed in Equations (1) and (2):  
\[\text{Direct Hydrogen Reaction}\qquad\qquad\text{M}+\text{x}/2\text{H}_{2} \leftrightarrow\text{M}\text{H}\text{x} \tag{1}\]  
\[\text{Electrochemical Dissociation of Water}\qquad\qquad\text{M}+\text{x}/2\text{H}_{2} \text{O}+\text{x}/2\text{ e}^{-}\leftrightarrow\text{M}\text{H}\text{x}+\text{x}/2 \text{ OH}^{-} \tag{2}\]  
where M is a metal or alloy. The formation of metal hydrides releases heat due to chemisorption, while desorption--where hydrogen is released for use--requires an equivalent amount of energy to be supplied externally. This reversible reaction can be triggered by reducing pressure or increasing temperature. Understanding the thermodynamics of these reactions is crucial for optimizing metal hydride storage systems. Metal hydrides provide higher hydrogen storage capacity compared to compressed and liquefied hydrogen and operate at moderate temperatures and pressures, making them a safer alternative to the more extreme conditions required for gas compression and liquefaction. They can also undergo multiple cycles of hydrogen loading and release if impurities do not interfere. Generally, many metal hydrides require moderate energy inputs ranging from 20 to 55 kJ/mol H\({}_{2}\) for hydrogen release [43]. However, metal hydrides present challenges such as slower sorption and desorption kinetics, higher temperatures required for hydrogen release, and potential formation of undesirable gases during discharge. Table 4 lists various metal hydrides and their hydrogen storage capacities.
## 2 Results
### Strain HF1 Identification  
Based on the biochemical and physiological characteristics observed for strain HF1 (Supplementary Table S1), this strain was identified as a member of the genus Bacillus. Phylogenetic trees generated based on the 16S rDNA (accession number: OR673900), gyrA (accession number: OR687234) or groE (accession number: OR687235) sequences from strain HF1 and closely related species indicated that this strain was assigned to the same branch as other _B. atrophaeus_ strains (Figure 2A-C). Based on these results, strain HF1 was thus identified as _B. atrophaeus_.  
Figure 1: Strain HF1 inhibits _V. pyri_ mycelial growth. **(A)** The morphology of _V. pyri_ colonies at 6 dpi on PDA medium following exposure to strain HF1. **(B) _V. pyri_ hyphal morphology at 2 days following culture in the presence of strain HF1. Control (CK) _V. pyri_ is shown on the left. Strain HF1 treated-_V. pyri_ is shown on the right. Scale bar: 20 \(\upmu\)m.  
Figure 2: Phylogenetic trees for strain HF1 and related bacterial species. Phylogenetic trees were constructed using 165 rDNA **(A)**, gyrA **(B)**, and groE **(C)** sequences.  
Analyses of the Antifungal Activity of B. atrophaeus Strain HF1 against V. pyri-Induced Pear Valsa Canker  
To assess the ability of strain HF1 to inhibit _V. pyri_ growth in vivo, strain HF1 cell suspension was next used to treat detached pear twigs that were then inoculated with _V. pyri_. This strain HF1 cell suspension was able to effectively suppress _V. pyri_-induced pear Valsa canker development (Figure 3A). While the average lesion size on CK twigs was 3.93 cm, on strain HF1-treated twigs, this size was reduced to just 2.12 cm (Figure 3B). These findings thus demonstrated the ability of strain HF1 cell suspension to suppress _V. pyri_-induced pear Valsa canker.  
The Impact of B. atrophaeus Strain HF1 Culture Filtrate on V. pyri Mycelial Growth and Membrane Permeability  
Next, the antifungal activity of culture filtrate prepared from strain HF1 was assessed based on their impact on _V. pyri_ mycelial growth. As shown in Figure 4A-C, strain HF1 culture filtrate significantly suppressed _V. pyri_ mycelial growth, with inhibition rates of 14.80%, 50.94%, and 64.54% for 2%, 5%, and 10% concentrations of strain HF1 culture filtrate, respectively. Fluorescence observation showed that strain HF1 culture-filtrate-treated hyphae emitted a strong GFP fluorescent signal after SYTOX green staining, while no GFP fluorescent signal was detected in CK hyphae (Figure 5), indicating _V. pyri_ hyphae cell membrane permeabilization was affected by strain HF1 culture filtrate. These results thus confirmed that strain HF1 culture filtrate was able to antagonize _V. pyri_ growth.
## 5 Conclusions  
In conclusion, we demonstrated mid-IR SC generation from 1.5 to 4.5 \(\upmu\)m with sub-nanojoule femtosecond pump pulses by using a short 12-cm-long segment of dispersion-engineered step-index tellurite fiber with very low OH content. Our simple tellurite fiber configuration also allowed us to explore various SC regimes beyond 2 \(\upmu\)m. By combining a fine control of core diameter in such tellurite fibers with the recent development of 2-\(\upmu\)m fiber lasers, we present an easy way to tailor mid-IR SC generation and related coherence properties for the next generation of compact and low-power photonic devices (in particular in the essential 2-6 \(\upmu\)m spectral range).  
**Author Contributions:** [PERSON], [PERSON], [PERSON], [PERSON] and T.S. were involved in the experiments of supercontinuum generation; [PERSON], [PERSON], [PERSON], P.M., G.G., J.-C.J. and F.S. fabricated and took part in the characterization of both the tellurite glasses and fibers. B.K. performed the numerical analysis of fiber properties and the modelling of supercontinuum generation. All the authors contributed to the analysis of the results and to the writing of the manuscript. [PERSON] and [PERSON] supervised the project.  
**Funding:** This research was funded by CNRS and JSPS grant number 1726 PRC \"TIBERIS\" bilateral joint research program, by the French Investissements d'Avenir ISITE-BFC program, grant number ANR-15-IDEX-0003 Projects SCUVIRA and BRIGHT, by the ANR Agence Nationale de la Recherche Labex ACTION program grant number ANR-11-LABX-01-01, by the Council of the French Region Bourgogne Franche-Comte PARI program grant number 2016-9205 AAO033503073 and the European Regional Development Fund (FEDER) grant number BG0005961.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 13: (Top panels) Optimized SC spectra recorded with 12-cm-long segments of tellurite step-index fiber with a distinct core diameter, namely (**a**) 3 \(\upmu\)m, (**b**) 3.5 \(\upmu\)m, and (**c**) 4 \(\upmu\)m. The pump wavelength was fixed to 2300 nm and the input peak power is \(\sim\)3 kW. (Bottom panels) Corresponding numerical simulations are shown with dashed blue lines.
## 6 Data Collection and Analysis
### Subpayload 1  
A total of 65,951 images were captured and stored over 6.5 h, resulting in 170 gigabytes of data. These images comprised of 6600 at a resolution of \(2048\times 2048\) pixels, 60 at a resolution of \(512\times 512\) pixels, and 59,291 at a resolution of \(1024\times 1024\) pixels. The hourly image capture rate remained consistent with the previous year, but there were significant delays expected and observed during the imaging period (refer to Section 7.1 for further delay analysis).  
Data Telemetry  
The payload effectively transmitted images and telemetry health files throughout the entire mission duration. A total of 160 files were downlinked over the course of the mission. Figure 7 shows a sample image that was downlinked.  
Figure 6: Software block diagram of the STARDUST payload.
## 3 Materials and Methods
### Methods
#### 3.2.9 Calculation of T-Invariants  
Step 1: Incidence matrix (**C**)  
The T-Invariants are non-negative vectors that satisfy Equation (4), obtained through linear algebra. Given a system with 11 places \((P1,P2,\ldots,P11)\) and 9 transitions \((T1,T2,\ldots,T9)\), the incidence matrix **C** describes how transitions affect places. Each row corresponds to a place, and each column corresponds to a transition.  
The values in the matrix are as follows:  
* 1: A token is added to the place when the transition occurs.
* \(-1\): A token is removed from the place when the transition occurs.
* 0: The transition does not affect the place.  
Step 2: Define the linear system  
From the incidence matrix **C**, the following system of homogeneous linear equations is derived, defined by Equation (4):  
\[\textbf{C}\cdot\textbf{x}=\textbf{0}. \tag{4}\]  
To obtain the T-invariants, it is necessary to solve Equation (4).  
Here, **x** is a column vector of size nine (corresponding to the number of transitions). The solution **x** describes the combinations of transitions that, when activated, do not alter the total number of tokens in the system.  
Step 3: Solve the system  
This is a homogeneous system of equations. When solving, we find that all transitions \(x_{1},x_{2},\ldots,x_{9}\) are related, as shown in Equation (5):  
\[x_{1}=x_{2}=x_{3}=\cdots=x_{9} \tag{5}\]  
In other words, \(x_{1},x_{2},\ldots,x_{9}\) are proportional to each other. This indicates that all transitions can be activated in sequence without altering the total distribution of tokens in the system.  
T-Invariants  
The resulting T-invariant can be represented as a vector where all elements are equal to the same scalar value k. If a vector **x** is a solution, any scalar multiple \(k\cdot\textbf{x}\), with \(k>0\), is also a valid solution. This implies that k serves as a scaling factor for the T-invariant, allowing  
Figure 4: Graphical representation of the proposed Petri net.  
its magnitude to be adjusted proportionally. Specifically, the vector can be expressed as in Equation (6):  
\[\mathbf{x}=k\cdot[1,1,1,1,1,1,1,1]^{T}. \tag{6}\]  
where the invariant property of the system is reflected.  
This T-invariant indicates that all transitions \(T_{1},T_{2},\ldots,T_{9}\) can fire together repeatedly without changing the global state of the Petri net.