esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
_Lactobacillus_ strains are known to produce exopolysaccharideaccharides (EPS) with recognized health benefits (i.e. prebiotic and immunomodulation) but production is limited by low yields. Co-culture has been shown to improve metabolite productivity, particularly bacterios and EPS. Although lactic acid bacteria (LAB) and yeasts are found in several fermented products, the molecular mechanisms linked to the microbial interactions and their influences on EPS biosynthesis are unclear. The aim of the present study was to investigate the effect of co-culture on EPS production by three _Lactobacillus rhamnosus_ strains (ATCC 9595, R0011, and RW-9595M) in association with _Saccharomyces cerevisiae_. Fermentation, in both mono and co-culture, was carried out and the expression of key LAB genes was monitored. After 48 h, results revealed that EPS production was enhanced by 39%, 49%, and 42% in co-culture for R0011, ATCC 9595, and RW-9595M, respectively. Each strain showed distinctive gene expression profiles. For a higher EPS production, higher EPS operon expression levels were observed for RW-9595M in co-culture. The construction of gene co-expression networks revealed common correlations between the expression of genes related to the EPS operons, sugar metabolism, and stress during EPS production and microbial growth for the three strains. Our findings provide insight into the positive influence of inter-kingdom interactions in stimulating EPS biosynthesis, representing progress toward the development of a bio-ingredient with broad industrial applications.  
exopolysaccharideaccharides; co-culture; whey permeate; yeast; lactic acid bacteria +
Footnote †: journal: Applied Sciences
# Out-of-Plane Experimental Study of Strengthening Slender Non-Structural Masonry Walls
## 2 Experimental Program
### Non-Structural Wall Test Specimens  
As part of the main experimental investigations of the slender non-structural partition walls under out-of-plane seismic loading, 11 full-scale test specimens were built with solid regular solid bricks (250/120/65 mm) laid in cement-time mortar type and using a half-bond pattern. The thickness of the NF non-structural wall specimens was 12 cm, and their slenderness was 26. The constructed specimens were 194 cm long, 316 cm high, and with a brick width of 12 cm, as shown in the Figure 1. Prior to the construction of NF specimens, bricks were properly prepared by prewetting them to prevent the brick capillary suction of water from fresh mortar during construction. The average thickness of horizontal joints was 12 mm, and the average thickness of vertical joints was 10 mm. Three out of the eleven specimens (labeled NF-1, NF-2, and NF-3) were tested as-built.  
Figure 1: Geometry of a non-strengthened NF specimen.  
Compressive strength tests of 10 masonry units were conducted according to the standard EN 772-1 [58]. It was found that an average compression strength of brick reached 36.3 MPa with a coefficient of variation of 15.5%. Additionally, flexural and compressive strength tests of 66 mortar prisms (40 mm \(\times\) 40 mm \(\times\) 160 mm), collected during the construction of non-structural wall specimens, were conducted according to the standard EN 1015-11 [59]. The mixing ratio of cement-lime mortar was made at ratios 1:3:9 (cement/lime/sand) by volume, which is adequate to the mortar traditionally used in masonry structures from the late 19 th century onwards. The average flexural strength was equal to 1.08 MPa, with a coefficient of variation of 23.3%. The average compressive strength was 5.02 MPa, with a coefficient of variation of 9.6%. Moreover, the bond wrench tests were conducted according to EN 1052-5 [60] to evaluate an average bond strength of 61 specimens. An average flexural bond strength of 0.22 MPa with a coefficient of variation of 36% was observed.
## 2 Materials and Methods
### MFCS Energy and GHG Impact Boundaries  
First, to set operational boundaries and inventory the MFCS activities with respect to direct and indirect emissions (Scope 1, Scope 2, and Scope 3, see Figure 6 for emission targets), the General Reporting Protocol (GRP) [42] was used, which follows both the GHG Protocol Corporate standard [50; 56], and the International Standard Organization 14064 [57; 58]. Figure 6 provides an overview of the relationship between the three scopes and the activities that generate direct and indirect emissions along the value chain of the MFCS [42].  
The MFCS indirect emissions expressed in tons of carbon dioxide equivalent (tCO\({}_{2e}\)) are accounted in scope two with respect to purchased electricity occurring from upstream activities such as the electrical consumption by onsite pumps and water treatment systems [42; 57; 58; 59]. Scope 1 is excluded because there are no onsite fossil fuels used for the operation of the MFCS water system. In addition, Scope 3 includes the other non-scope two indirect anthropogenic activities [50]. The GRP [42] does not include the calculation methodologies for Scope 3 emissions, and instead refers to the World Resource Institute (WRI) World Business Council for Sustainable Development (WBCSD) GHG Protocol Corporate standard [50]. However, onsite non-potable water supply and water recycling are not clearly defined in the latter protocol. Thus, considering the above findings, in relation to Section 1.4 including Table 3, a methodology is proposed, which first includes the energy needed by the onsite MFCS non-potable water system to power each step of the water need for outdoor use, adapted from The Climate Registry (TCR) [35]. Second, it includes the end-use of non-potable water and thus the onsite recycled water treatment and distribution needed. Third, the energy use against water consumption is addressed including the consideration of volume reducing factors such as the LI soil enhancement intervention outcome [6] and the WFs' water demand calculation [51].  
Figure 6: Overview of the GHG protocol scopes and emissions across the value chain [50].
## 3 Results and Discussion  
### Analysis of Three Different Feature Models Based on Non-Stacking Algorithms  
To explore the biological features that can be used to identify IL-10-induced peptides, we first applied two deep representation learning methods for feature extraction, namely AAindex and UniRep. For each type of feature, we employed three different ML methods (LR, SVM, and LGBM) to develop models and perform initial optimization. Next, we combined these two features to generate fused features, namely AAindex + UniRep, and the fused feature combination was used as input for LR, SVM, and LGBM to construct prediction models and refine their performance. The model evaluation results of AAindex and UniRep in the non-stacking LR, SVM, and LGBM models are visualized in the form of Figure 2. Overall, the LR model performed worst, while the LGBM model had moderate performance, and the SVM model performed well. A comparison of the single features of AAindex (red) and UniRep (blue) with the fused feature (green) demonstrated that the SVM model using the fused feature exhibited the best performance in the non-stacking models; it had 2466 feature dimensions, with an ACC of 0.896 (Figure 2(B\({}_{1}\))) and an MCC of 0.792 (Figure 2(B\({}_{2}\))) in independent testing. Although independent testing showed that AAindex + UniRep had the highest ACC in the SVM algorithm, the UniRep feature demonstrated the highest ACC in LR and LGBM (Figure 2(B\({}_{1}\))). Further, five-fold cross-validation results indicated that the UniRep feature achieved the highest ACC in LR and SVM (Figure 2(A\({}_{1}\))). Hence, direct fusing of the two features can improve the performance of certain models, but it is not always effective.
## 3 Results
### The Effect of Different Hormones on the Color and Quality Traits of Grape Berry
#### 3.3.2 Long-Term Response of VvmiR156b/c/d and VvSPL9 to Exogenous Hormones in 'Wink' Berry Skin  
To further identify the long-term expression pattern of VvmiR156b/c/d and _VvSPL9_ during grape color conversion, qRT-PCR was used for relative expression levels. As Figure 5 shows, VvmiR156b/c/d had a similar expression pattern, in which VvmiR156b had the highest expression level, followed by VvmiR156d, and VvmiR156c had the lowest expression levels. With the ripening and color transformation of grape berries, VvmiR156b/c/d showed a gradually rising trend, and the highest expression level was detected at 28 DAT, while the expression level of _VvSPL9_ showed the opposite trend as a whole, which further verified that VvmiR156b/c/d could negatively regulate the expression of _VvSPL9_.  
Figure 4: Differential expression of VvmiR156b/c/d and their _VvSPL_ target genes in response to different hormone treatments at different time points [1 h after treatment (1 HAT), 3 HAT, 5 HAT, 7 HAT] of leaf development. Asterisks indicated statistically significant differences at (* \(p<0.05\); ** \(p<0.01\)) as determined.  
Interestingly, different hormone treatments exhibited three regulatory modes for VvmiR156b/c/d-_VvSPL9_. GA treatment downregulated the expression level of VvmiR156b/c/d in the early color-change period (0 DAT-14 DAT), upregulated the expression level of VvmiR156b/c/d in the middle color-change period (21 DAT), and then downregulated the expression level of VvmiR156b/c/d in the berry mature period (28 DAT-35 DAT); in contrast, GA treatment increased the expression of _VvSPL9_ in the early color-change period (0 DAT-14 DAT) and berry mature period (28 DAT), suggesting that GA treatment significantly enhanced the negative correlation of vmiR156b/c/d-_VvSPL9_, and mainly regulated berry coloring in the middle color-change period. ABA and MeJA treatments significantly increased the expression of VvmiR156b/c/d in the early stage of color conversion (7 DAT) and downregulated the expression of VvmiR156b/c/d in the late stage of color conversion (21 DAT-35 DAT). Furthermore, _VvSPL9_ had a stronger regulatory effect in the early stage of color conversion (7 DAT), which indicated that ABA and MeJA treatments promoted berry coloring in the early color-change period. Moreover, NAA was a negative regulator of VvmiR156b/c/d. NAA significantly downregulated the expression of VvmiR156b/c/d in berry mature period (28 DAT), and NAA showed a gentle upregulated effect on _VvSPL9_. In conclusion, we preliminarily concluded that VvmiR156b/c/d had the earliest response to ABA and MeJA, followed by GA, while NAA had the latest response to VvmiR156b/c/d.
## 5 Conclusions  
The vigorous development of the commercial aerospace industry and the interconnection of everything in the intelligent world has become an irreversible trend. Satellite remote sensing has entered the era of a big data link with IoT. Compared with traditional remote sensing data interpretation calculations on the ground that consume electricity resources on the earth, it is more environmentally friendly to run high-performance computing with solar power from satellites in orbit. Although the energy savings of a satellite may be negligible. However, with the construction of giant constellations, if the satellites are fully utilized for high-performance computing, the savings in power resources on Earth will be considerable.  
On-orbit interpretation gives remote sensing images an expanse of application space. The experiment performance shows that our method is effective. YOLO-ViTSS has anti-noise ability, lightweight characteristics, and energy-saving. The model size of 1.31 MB and computational complexity is 1.1 GFLOPs can be easily integrated into the satellite computing platform. The average training power consumption of 151 W can be powered by a 0.7-square-meter satellite solar array, which can meet the microsatellites' power supply. YOLO-ViTSS has great potential deployed to the onboard computer on SAR satellite constellations to build a novel real-time, online training supportable, online IoT accessible, eco-friendly, and sustainable SAR image interpretation mode.  
Conceptualization, F.X.; Data curation, F.X. and S.L.; Formal analysis, F.X.; Funding acquisition, F.X., Y.L. and B.L.; Investigation, F.X. and B.L.; Methodology, F.X.; Project administration, B.L.; Resources, F.X.; Software, F.X.; Supervision, H.L. and Y.L.; Validation, F.X.; Visualization, F.X.; Writing--original draft, F.X.; Writing--review & editing F.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 13: An offshore visualization scenario. Each subgraph represents the Bounding-box generated before NMS in the different scale models.
# Non-Invasive Testing of Physical Systems Using Topological Sensitivity
## 3 Application to SHM Using Guided Lamb Waves
As in the cases considered in Figure 1, the emitting/receiving devices are located very close to a lateral boundary of the plate planform, and the method identifies well the small defect using 80 and 30 forcing frequencies in the cases considered in the left and right plots, respectively. Note that, in both cases, the emitting/receiving devices are at a different side of the through slit or plate thicker portion than that where the defect is. Although, for simplicity in the presentation, all examples above are concerned with the simplest case in which a single defect is present, and the method can also cope with multiple defects of different sizes/shapes. Figure 1: Color maps of the topological sensitivity along a rectangular interrogation window in a rectangular Aluminium plate containing a through slit (indicated with a horizontal black line in the left plot) and a through inclusion of Titanium (indicated with a horizontal brown line in the middle plot); both plots share the color scale for the topological derivative, given in the right plot. In both cases, four dual emitters/receivers are considered, in which their positions are indicated with black x symbols, and the actual position of the defect indicated by a white + symbol. As can be seen, the method identifies well the position of the defect at the blue (negative) peak of the topological sensitivity. Courtesy of Dr. [PERSON]. Figure 2: Counterpart of Figure 1, considering a plate with complex planform (**left**) and variable thickness (**right**). In the latter case, that portion of the plate exhibiting larger thickness is indicated as that between the two dashed horizontal lines. Courtesy of Dr. [PERSON]. Thus, we can conclude that the topological sensitivity-based measurement post-processing method is a very promising tool to increase the defect detection threshold. The method is just an alternative way of analyzing data and very few modifications of existing experimental setups are needed, just to change the excitation signal from a chirp to several harmonic signals with different forcing frequencies. Additional details can be obtained from the authors.
# Crossband Filtering for Weighted Prediction Error-Based Speech Dereverberation
## 3 Proposed WPE with Crossband Filtering
### Conventional WPE
#### 3.1.1 Filter Estimation  
The filter \(\mathbf{g}_{f}\) is estimated in a frequency-wise manner based on the maximum likelihood (ML) criterion, assuming that the signal \(\mathbf{z}_{f}:=\left\{z_{f,t}\right\}_{t}\) follows a complex Gaussian distribution with zero mean and time-dependent variances \(\boldsymbol{\lambda}_{f}:=\left\{\lambda_{f,t}\right\}_{t}\). The filter coefficients \(\mathbf{g}_{f}\) and the variances \(\boldsymbol{\lambda}_{f}\) are alternately estimated to minimize the following objective:  
\[\mathcal{L}(\mathbf{g}_{f},\boldsymbol{\lambda}_{f})=\sum_{t}\left[\frac{|y_{ f,t}-\mathbf{g}_{f}^{H}\mathbf{y}_{f,t;L}|^{2}}{\lambda_{f,t}}+\log\lambda_{f,t} \right]. \tag{11}\]  
The entire estimation process for the first channel (i.e., \(m=1\)) is described in Algorithm 1. The extension to other channels is straightforward.  
```
Input:  Observed multichannel signal in STFT domain \(\left\{y_{f,t}^{(m)}\right\}_{f,t,m}\)  Small constant \(\epsilon>0\), filter length \(L\), number of iterations \(N\) for\(f=0,\dots,F-1\)do
1. Initialize \(\lambda_{f,t}=\max\left\{\frac{1}{L}\sum_{t^{\prime}=-L/2+1}^{t+L/2}\left|y_{ f,t}^{(1)}\right|^{2},\epsilon\right\}\)
2. for\(n=1,\dots,N\)do  Compute: \(\Phi_{f}=\sum_{t}\frac{\mathbf{y}_{f,t;L}\mathbf{y}_{f,t;L}^{H}}{\lambda_{f,t }^{2}}\) \(\phi_{f}^{(1)}=\sum_{t}\frac{\mathbf{y}_{f,t;L}y_{f,t}^{(1)}}{\lambda_{f,t}^{ 2}}\)  Update:  Filter: \(\mathbf{g}_{f}^{(1)}=\left(\Phi_{f}^{H}\Phi_{f}\right)^{-1}\Phi_{f}^{H}\phi_ {f}^{(1)}\)  Enhanced signal: \(z_{f,t}^{(1)}=y_{f,t}^{(1)}-\mathbf{g}_{f}^{(1)H}\mathbf{y}_{f,t;L}\)  Variances: \(\lambda_{f,t}=\max\left\{\frac{1}{L}\sum_{t^{\prime}=-L/2+1}^{t+L/2}\left|z_{ f,t}^{(1)}\right|^{2},\epsilon\right\}\)
3. endfor
```  
**Algorithm 1** Conventional WPE Filter Estimation for First Channel
## 3 Results and Discussion
### Thermodynamic Properties of PLA  
The thermodynamic properties calculated for each heating rate were determined using the kinetic parameters obtained above and the equations presented in Section 2.3. The change in enthalpy, Gibbs free energy and entropy are shown in Table 2.  
Figure 5: Comparison of the FR, KAS and FWO models at 10 \({}^{\circ}\)C min\({}^{-1}\).  
Figure 6: Comparison of the FR, KAS and FWO models at 15 \({}^{\circ}\)C min\({}^{-1}\).  
In Table 2, it can be observed that there is just a little change (5-6 kJ mol\({}^{-1}\)) between enthalpy and activation energy at the same heating rate, showing quite similar values. A lower activation energy helps the formation of a general activated complex because the TG curve shows a single degradation peak, which indicates that the process is carried out in a single step, that is, a single general reaction is assumed despite the fact that there are a number of reactions; hence, a single activated complex exists. The difference between the values of \(\Delta\)H and E confirm that the formation of the products can be carried out by providing 5 kJ mol\({}^{-1}\) of additional energy [29]. The entropies present negative values, indicating that the degree of disorder of the products formed through dissociation of bonds is lower than that of the reactants. The low \(\Delta\)S at the rate of 15 \({}^{\circ}\)C min\({}^{-1}\) means that the material only undergoes some physical and chemical changes, until it reaches a state close to its thermodynamic equilibrium. PLA has little reactivity and it takes a long time to form an activated complex. On the other hand, a high \(\Delta\)S means that the reactivity will be high, and it will take less time to form the activated complex; this happens when the heating rate is lower. On the contrary, the positive values of the Gibbs free energies indicate that the process requires a contribution of energy for it to occur (non-spontaneous process), taking into account that \(\Delta\)G means a total increase in the energy of the system for the formation of the activated complex [30]. The sample at the rate of 15 \({}^{\circ}\)C min\({}^{-1}\) presents an activation energy and an enthalpy slightly lower than the other heating rates. However, the difference in the Gibbs free energies was not significant.
## 3 Results and Discussion
### MMP-2 and MMP-8 Facilitate MSC IL-6-Induced Migration in 3D Spheroid Culture  
In order for MSCs to migrate, they must change from an adhesive phenotype to a migratory phenotype. The migratory behaviour of MSCS involves MMP activity, with different MMP profiles depending on the migratory reason [29]. Within our system, focusing on MSC migration in response to IL-6, MSC spheroids cultured in standard DMEM indicated an overall decrease in both MMP and TIMP secretion from 1 to 3 days (Figure 7). This correlates with previous evidence that MSCs become quiescent in collagen gels with time and thus do not migrate [18]. However, the MMP secretion profile for MSC spheroids challenged with 1 ng/mL IL-6 is notably different; there is a significant increase in MMP-8 a collagenase specific to types I-III [30]. Interestingly, other collagenases specific to collagen type I, MMP-1 and MMP-13, show no significant change following IL-6 treatment. The only significant  
Figure 6: MSC spheroids (10,000 cells/mL) cultured in (**A**) standard media at 3 h; (**B**) 1 ng/mL IL-6 at 3 h; (**C**) standard media at 24 h; (**D**) 1 ng/mL IL-6 at 24 h. Arrows indicate initial cellular migration at 3 h. Green indicates presence of mNPs. Images taken with Zeiss Axio Vert A1 fluorescent microscope (excitation = 365 nm and emission = 420 nm). Scale bar = 10 μm.  
change in TIMP secretion was through an increase in TIMP-4. TIMP-4 is known to interact with MMP-2 [40], a gelatinase, which indicates a significant decrease following IL-6 treatment (Figure 7). The MMP profiles seen in 2D and 3D differ notably in the types of MMP upregulated and the levels with which they change. In monolayer culture, MSCs are only surrounded by ECM laid down themselves and any proteins that may be present in the foetal calf serum within the culture medium. However, in 3D culture, cells are surrounded by type I collagen, making migration much more difficult and thus taxing on the cell, hence why the secretion of some MMPs are downregulated.  
MSCs respond to IL-6 in their environment, often secreted by invading cancer cells. IL-6 activates JAK/STAT and MAPK pathways to permit survival and proliferation of MSCs [41]. This leads to the formation of actin stress fibres to facilitate migration [42]. In this way, local MSCs act as a source of fibroblasts to permit tumourigenesis of the neoplastic cells [43]. Therefore, the response of MSCs to paracrine IL-6 is of importance in understanding the growth patterns of disseminated cancer cells.