esa-sceva/satcom-chunk-collection huggingface dataset:
## Experimental Results and Discussion
### Mercury Intrusion Porosimetry (MIP)  
Figure 9 shows the cumulative intrusion for the specimens according to CO\({}_{2}\) concentration. For non-CO\({}_{2}\) curing, the cumulative intrusion increased as the replacement level of AOD slag increased. The cumulative intrusion was approximately 0.15, 0.27, and 0.3 mL/g for STP0, STP30, and STP60, respectively. This indicates that the cumulative intrusion was not proportional to the amount of AOD slag. For CO\({}_{2}\) curing, the cumulative intrusion decreased as the CO\({}_{2}\) concentration increased. These results were similar to the results of chloride diffusivity according to the CO\({}_{2}\) curing concentration (see Figure 7). This appears to be because \(\gamma\)-C\({}_{2}\)S in the AOD slag generated silica gel and calcite through the CO\({}_{2}\) uptake, filling the large pores [20; 21]. [PERSON] et al. [33] examined the microstructure of a steel slag paste under CO\({}_{2}\) curing and reported that the pores were filled due to the generation of calcite.  
Figure 8: Chloride diffusivity according to curing temperature (5% CO\({}_{2}\)).  
For STP0, the cumulative intrusion decreased slightly until the CO\({}_{2}\) concentration reached 10%, after which it increased when the CO\({}_{2}\) concentration was 15%. This appeared to be because CO\({}_{2}\) was excessively supplied in the curing process, and tricalcium silicate (C\({}_{3}\)S) and C\({}_{2}\)S in the OPC generated calcite instead of silicate gel through the hydration reaction, thereby increasing the volume of pores [34]. For STP30 and STP60, the cumulative intrusion decreased significantly as the CO\({}_{2}\) concentration increased. When the CO\({}_{2}\) concentration was 15%, STP0, STP30, and STP60 exhibited similar values. In particular, the reduction of porosity was largest when CO\({}_{2}\) concentration increased from 0 to 5%.  
The pore volume fractions according to pore size were also analyzed. For STP0, no noticeable change was observed in the pore volume fraction according to the CO\({}_{2}\) concentration. For specimens with AOD slag, the fraction of pores smaller than 100 nm increased and that of the pores larger than 1000 nm decreased as the CO\({}_{2}\) concentration increased. Pores smaller than 100 nm correspond to the inter-layer or inter-granular pores in C-S-H gels, and their fractions were increased by the carbonation of the AOD slag [35,36].  
Figure 10 shows the cumulative intrusion of specimens according to curing temperature. In the pore structure of STP0, no significant difference was observed according to the CO\({}_{2}\) curing concentration and temperature compared to the specimen incorporating the AOD slag. For STP30 and STP60, however, the cumulative intrusion decreased as the CO\({}_{2}\) curing temperature increased.  
As the CO\({}_{2}\) curing temperature increased, the amount of silicate gel and calcite increased, filling the capillary pores. Similar results were also reported by other studies [35,36].  
Figure 9: Cumulative intrusion for specimens according to CO\({}_{2}\) concentration.
## 3 Experimental Results and Discussion
### Evaluation on the Single Unobserved Video Subsequence with Random Length Scenarios
#### 3.2.3 Evaluation Results When the Temporal Gap Is at the Middle of the Video Sequences  
In this evaluation scenario, we aim at evaluating the performance of the proposed approach in recognizing the four fall-related activities when the unobserved video subsequence is happening at the middle of the video sequence. In particular, we have investigated the scenario in which the  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Activity** & **Precision** & **Recall** & **F1-Measure** & **Average Gap’s Length (Frames)** \\ \hline Walking & 79.7\% & 81.2\% & 80.4\% & 29 \\ Sitting & 69.3\% & 80.0\% & 74.3\% & 35 \\ Falling from sitting & 71.5\% & 74.0\% & 72.7\% & 32 \\ Falling from standing & 61.8\% & 68.0\% & 64.7\% & 36 \\ \hline Overall average & 70.6\% & 75.8\% & 73.0\% & 33 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The recognition results of the four fall-related activities from partially-observed videos when the missing subsequence of frames is at the end of the testing video sequences.  
temporal gap is mainly affecting the video frames belonging to the second sub-activity, along with a subset of the frames that belong to the first and last sub-activities in the four fall-related activities. Hence, our assumption is that the majority of the observed video frames are belonging to the first and last sub-activities. To achieve this goal, we have set the temporal gap interval to \([\beta_{1},\beta_{2}]\), where \(\beta_{1}\) and \(\beta_{2}\) are random integers that satisfy \(\frac{2T}{10}<\beta_{1}<\beta_{2}<\frac{8T}{10}\). The scenario in this section can be viewed as a simplified version of the scenario described in Figure 1c, as it consists of a single unobserved subsequence of frames. Table 6 presents the recognition results of the four fall-related activities expressed in terms of the precision, recall and F1-measure as an evaluation metric. The last column in Table 4 provides the average length of the temporal gaps introduced in the testing video sequences for each activity over the 10 repetitions of the LOVSO-CV procedure. The proposed approach achieved an average recognition accuracy of 78.1% in recognizing the four fall-related activities from partially-observed video sequences.  
The results in Table 6 show that the proposed approach achieved a better recognition accuracy in comparison with the results obtained when the unobserved video subsequences were at the end of the video sequences, as described in Section 3.2.2. This can be attributed to the fact that, in this scenario, we observe two video subsequences from each testing video. These observed video subsequences comprise the starting and ending sub-activities of each activity. Furthermore, the observed video subsequences might contain subsets of the video frames that belong to the intermediate sub-activities in each activity. This in turn can enhance the recognition accuracy as depicted in Table 6.
# Structure, Biology, and Therapeutic Application of Toxin-Antitoxin Systems in Pathogenic Bacteria
## 4 Structural Characteristics of TA Proteins from Pathogenic Bacteria
### VapBC
gonorrhoeae_, there are the known structures of a heterotetramer of the FitAB complex and a relatively loose octamer of the FitAB complex bound to a 36-bp DNA fragment from the fitAB promoter (Figure 5A) [162]. The higher-order structure of the DNA-bound form explains why the TA complex enhances the binding affinity of the antitoxin for the DNA. The FitA antitoxin contains the \(N\)-terminal ribbon-helix-helix (RHH) motif responsible for sequence-dependent DNA binding, and the C-terminal toxin-binding region. The FitB toxin shows a high degree of sequential and structural similarity to well-known PIN domains. The conserved residues of the toxin are Asp5, Glu42, Asp104, and Asp122 that are clustered to form an acidic pocket (Figure 5A). The structure of the VapBC complex from _R. felis_ was determined in a complex form with a 27-bp double-stranded DNA molecule derived from the promoter was determined [163]. Four toxins and four antioxins form an octameric structure with 4:4 stoichiometry upon DNA binding (Figure 5B), as in the case of FitAB. However, in the absence of DNA, four toxins and two antioxins form a hexameric structure with 4:2 stoichiometry. A single antitoxin is sufficient to simultaneously block two putative active sites of homodimeric toxins, as is also found in the MazEF complex [122]. These findings show how the binding stoichiometry can control the balance between the neutralization of toxicity and the transcriptional regulation of the TA operon. Similar to the FitB toxin, the VapC toxin forms a homodimer that contains a PIN-domain. The \(N\)-terminal DNA-binding domain of the VapB antitoxin forms a swapped-hairpin \(\beta\)-barrel fold that is characteristic of the AbrB/MazE family. This is a good example of the interplay between toxin and antitoxin families. The VapC toxin from _S. flexneri_ has a typical PIN domain structure that contains five-stranded \(\beta\)-sheet surrounded by seven \(\alpha\)-helices [164]. The VapB antitoxin has the \(N\)-terminal DNA-binding domain of SpoVT/AbrB type that consists of four-stranded \(\beta\)-sheet, and an extended \(C\)-terminal tail that wraps around the toxin. These molecules are assembled into a compact heterooctamer in a stoichiometry of 4:4 (VapB\({}_{4}\)C\({}_{4}\)) (Figure 5C). The higher-order structure is also present in solution state, indicative of the strong TA interaction, which is demonstrated by size-exclusion chromatography and chemical cross-linking experiments. The VapBC complex specifically binds to the promoter region including the \(-10\) Pribnow box and the \(-35\) sequences.
# Vehicle Politeness in Driving Situations
## 4 Results
### Hypothesis Testing
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Speech Strategy in**} & \multicolumn{3}{c}{**Speech Strategy in**} \\  & \multicolumn{3}{c}{**Normal Events**} & \multicolumn{3}{c}{**Failure Events**} & \multicolumn{3}{c}{**Interaction**} \\ \cline{2-7}
**DVs** & \multicolumn{1}{c}{**Plain**} & \multicolumn{1}{c}{**Polite**} & \multicolumn{3}{c}{**Plain**} & \multicolumn{1}{c}{**Polite**} & \\ \cline{2-7}  & **Mean (SD)** & **Mean (SD)** & **\(F\)-Ratio (\({\eta_{p}}^{2}\))** & **Mean (SD)** & **Mean (SD)** & **\(F\)-Ratio (\({\eta_{p}}^{2}\))** & **\(F\)-Ratio (\({\eta_{p}}^{2}\))** \\ \hline Openness & 6.50 (1.77) & 6.90 (1.62) & 0.85 (0.02) & 7.32 (1.87) & 6.08 (1.25) & 8.32 \({}^{\star\star}\) (0.14) & 0.02 (0.00) \\ Social Presence & 6.96 (1.51) & 8.06 (1.08) & 9.65 (+0.16) & 7.37 (1.47) & 7.65 (1.37) & 0.63 (0.01) & 0.01 (0.00) \\ Politeness & 7.81 (1.74) & 8.64 (1.22) & 4.42 (0.08) & 8.16 (1.77) & 8.29 (1.32) & 0.11 (0.00) & 3.33 (0.06) \\ Attractiveness & 7.42 (1.57) & 7.76 (1.62) & 0.69 (0.01) & 7.95 (1.61) & 7.23 (1.51) & 3.18 (0.06) & 4.88 \({}^{\star}\) (0.09) \\ Satisfaction & 6.96 (1.47) & 7.90 (1.60) & 5.42 (0.09) & 7.70 (1.54) & 7.15 (1.63) & 1.84 (0.03) & 1.96 (0.04) \\ Ease of Use & 7.93 (0.97) & 7.66 (1.77) & 0.63 (0.01) & 8.16 (1.09) & 7.43 (1.63) & 4.25 \({}^{\star}\) (0.08) & 7.56 \({}^{\star}\) (0.13) \\ Use Intention & 7.46 (1.60) & 8.27 (1.11) & 4.66 (0.08) & 7.86 (1.56) & 7.87 (1.30) & 0.00 (0.00) & 0.63 (0.01) \\ \hline \hline \end{tabular} Note.\({}^{\star}\)\(p<0.05\), \({}^{\star\star}\)\(p<0.01\).  
\end{table} 
Table 1: Results of MANOVA Testing. Figure 4: Interaction Effects between Politeness Strategies in Different Situations on Experience Components, (**a**) attractiveness and (**b**) ease of use. A vehicle that is polite all the time does not provide a superior interaction experience. The results show that the best interaction strategy for a vehicle is to communicate with drivers politely in normal events, yet interact with them plainly in failure events. Drivers evaluate a vehicle more favourably when the vehicle uses the politeness strategy _only_ in normal working situations. Thus, the answer to the research question is that vehicles adopting the selective politeness strategy are perceived better than vehicles using the permanent polite strategy regardless of situations. In order to control possible effects of participants' gender, age and possession of driving license, we also conducted two-way multivariate analyses of covariance (MANCOVAs). The results showed no significant covariate effects of gender, \(F(7,A3)\) = 0.26, age, \(F(7,A3)\) = 1.62 and driving license, \(F(7,A3)\) = 0.31.
# The Potential of Aquafaba as a Structure-Shaping Additive in Plant-Derived Food Technology
## 5 Functional Properties of Aquafaba in Food Technology
### Emulsifying Properties  
The emulsion that is created using aquafaba is an 'oil-in-water' (O/W) emulsion. Using mechanical energy (e.g., a hand blender), oil molecules disperse in the aquafaba. The emulsifier is responsible for creating a layer separating oil from water [28]. It is thanks to the emulsifier that there is no stratification of the two fractions. Proteins that have amphiphilic properties are the emulsifier in aquafaba. They expose their hydrophilic part to the water contained in the aquafaba and their hydrophobic part to the oil. Saponins are considered surfactants that reduce the interfacial tension between water and oil. Polysaccharides, contributes to increasing the stability of the emulsion by increasing the viscosity of the aqueous phase [16]. Aquafaba is characterized by a low content of dry matter (5-8%) and protein (0.85-1.5% in the wet state), which is another factor increasing the stability of the emulsion. Overall, more stable emulsions, protein-oil-water, contain low protein concentrations (0.2-1%) [16]. Aquafaba's emulsifying ability can be used in, e.g., vegan mayonnaise [45].  
Foaming and emulsification depend on many different factors including diversity and seed species and aquafaba production conditions. One publication noted that the best emulsion was formed when the seeds were soaked at a refrigeration temperature for 16 h [20]. [PERSON] et al. [44] have shown that the best properties for forming and stabilizing emulsions occur at pH 7. In other studies, it was found that the best emulsion was obtained by using chickpeas and water in a 2:3 ratio and boiling for 60 min. [37]. Subjecting aquafaba to high pressure also positively affects its emulsifying ability [30].
# Design of Novel Laser Crosslink Systems Using Nanosatellites in Formation Flying: The VISION
## 3 Concept of the Laser Crosslink Systems
### Optical Link Budget
#### 3.2.1 Pointing Error Budget  
Pointing loss due to pointing errors is dominant in the optical link performance degradation of laser crosslink missions. The pointing error budget is classified into point-ahead and tracking terms, as shown in Figure 7. The point-ahead errors contain body pointing and mechanical misalignment, which can be corrected using CPS. Tracking errors are related to the signal noise of the detectors and jitter residuals, which are reduced over the FPS.  
Figure 7: Diagram of the pointing error budget structure.  
The pointing errors for each PAT sequence at inter-satellite distances of 50 km and 1000 km are summarized in Table 3. For PAT#1 and PAT#2, only attitude maneuvers were conducted to roughly correct the AOA. During PAT#3, the bias of point-ahead errors was mostly eliminated. Finally, at PAT#4 and COM#1, the systems can achieve a total pointing error smaller than 1 \(\upmu\)rad with the FSM control.
###### Abstract  
Accurate characterization of evapotranspiration (ET) is imperative in water-limited cropping systems such as California vineyards and almond orchards. Satellite-based ET modeling techniques, including the atmosphere-land exchange inverse model (ALEXI) and associated flux disaggregation technique (DisALEXI), have proven reliable in determining field scale ET. However, validation efforts typically focus on ET and omit an evaluation of partitioned evaporation (E) and transpiration (T). ALEXI/DisALEXI is based on the two-source energy balance (TSEB) model, making it uniquely qualified to derive E and T individually. The current study evaluated E and T estimates derived using two formulations of DisALEXI; one based on Priestley-Taylor (DisALEXI-PT) and the other on Pennam-Monteith (DisALEXI-PM). The modeled values were validated against partitioned fluxes derived from the conditional eddy covariance (CEC) approach using EC flux towers in three wine grape vineyards and three almond orchards for the year 2021. Modeled estimates were derived using Landsat 8 Collection 2 thermal infrared and surface reflectance imagery as well as Harmonized Landsat and Sentinel-2 surface reflectance datasets as input into DisALEXI. The results indicated that the modeled total ET fluxes were similar between the two methods, but the partitioned values diverged, with DisALEXI-PT overestimating E and slightly underestimating T when compared to CEC estimates. Conversely, DisALEXI-PM agreed better with CEC-derived E and overestimated T estimates under non-advective conditions. Compared to one another, DisALEXI-PM estimated canopy temperatures -5\({}^{\circ}\)C cooler and soil temperatures -5\({}^{\circ}\)C warmer than DisALEXI-PT, causing differences in E and T of -2.6 mm day\({}^{-1}\) and +2.6 mm day\({}^{-1}\), respectively. The evaluation of the iterative process required for DisALEXI indicates DisALEXI-PM ET values converge on ALEXI ET with proportionate adjustments to E and T, while DisALEXI-PT convergence is driven by adjustments to E. The analysis presented here can potentially drive improvements in the modeling framework to provide specific soil and canopy consumptive water use information in unique canopy structures, allowing for improved irrigation and water use efficiencies in these water-limited systems.  
evaportanspiration; flux partitioning; ALEXI/DisALEXI; satellite remote sensing 20231568. [[https://doi.org/10.3390/rs1501006817](https://doi.org/10.3390/rs1501006817)]([https://doi.org/10.3390/rs1501006817](https://doi.org/10.3390/rs1501006817)) December 202231568. [[https://doi.org/10.3390/rs1501006817](https://doi.org/10.3390/rs1501006817)]([https://doi.org/10.3390/rs1501006817](https://doi.org/10.3390/rs1501006817)) December 202231568.
# Experimental Study on Mechanical Properties of Mask-Improved Calcareous Sand
## 2 Laboratory Testing Program
### Triaxial Test  
Mask-calcareous sand mixtures, known for their excellent drainage properties, were tested using a consolidation drainage triaxial test. The testing procedure strictly followed ASTM D7181 [23] and was divided into four stages: sample preparation, saturation, consolidation, and shear. During the sample preparation stage, the specimen was compacted in  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mask Mass Content (\%)** & **0** & **0.3** & **0.6** & **0.9** \\ \hline
**Max. density (g/cm\({}^{3}\))** & 1.56 & 1.50 & 1.44 & 1.41 \\
**Min. density (g/cm\({}^{3}\))** & 1.35 & 1.27 & 1.19 & 1.13 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Density test data summary.  
Figure 1: Tested materials.  
Figure 2: Grading Curve.  
three layers to achieve a relative compactness of 0.7 to obtain an equivalent initial state [24]. In the saturation stage, the pore pressure coefficient was monitored, and saturation was considered complete once it reached 0.9. In the consolidation drainage triaxial test, it is essential to maintain a uniform shear rate to facilitate uniform water discharge. The shear rate is set at 1.5 mm/min, based on the material characteristics, until an axial strain of 20% is reached [18]. Additionally, the crushing of calcareous sand increases significantly when the confining pressure exceeds 200 kPa [25]. Therefore, the confining pressures chosen for this test were 80 kPa, 200 kPa, and 500 kPa. Mixtures of mask-calcareous sand with particle sizes of 5 mm \(\times\) 5 mm and 10 mm \(\times\) 20 mm were subjected to testing under a confining pressure of 200 kPa. After the test, the particles were collected, dried, and sieved to determine the breaking rate. Additionally, to account for the variability inherent in a single test, the mask-calcareous sand mixtures with different mask contents were tested under three confining pressures, with each test conducted three times [26]. This experimental design allowed for a quantitative assessment of measurement uncertainty and verification of result reproducibility.  
All specimens were assigned unique identifiers, as summarized in Table 2. The specimen names include \"C\", \"P\", and \"S\", which denote mask mass content, applied pressure, and mask size, respectively. For instance, \"C03P80\" refers to a specimen with a mask content of 0.3% and an applied pressure of 80 kPa and \"C03555\" refers to a specimen with a mask content of 0.3% and a mask size of 5 mm \(\times\) 5 mm. Additionally, the rubber film thickness was 0.3 mm, which introduced an acceptable deviation in the confining pressure, as determined by the calculation method outlined in ASTM D7181 [23]. Consequently, no correction was applied to the stress-strain curve. The pre-shear pore ratio increases as the mask content rises. This is because the flexible mask can absorb compaction efforts, thereby reducing overall density and leading to a higher pre-shear pore ratio.
## 3 Proposed Fault Diagnosis Scheme
### Adaptive Boosting Algorithm  
AdaBoost (adaptive boosting) is an ensemble learning algorithm that can be used for classification or regression. It is called adaptive because it creates a strong learner (a classifier that is well-correlated to the true classifier) by iteratively adding weak learners (a classifier that is only slightly correlated to the true classifier). Figure 2 illustrates the working principle of AdaBoost classifier.  
AdaBoost.M2 [37] is an extension of AdaBoost. It introduces pseudo-loss, which is a more sophisticated method to estimate error and update the instance weight in each iteration compared with AdaBoost. Given a set of samples S {(x_1, y_1),..., (x_m, y_m)} x_i \(\epsilon\) X with labels y_i \(\epsilon\) Y = {1,..., C}, the distribution of samples is initialized as \(D_{1}(i)\) = 1/m. The AdaBoost.M2 algorithm proceeds in a series of \(T\) rounds. In the \(t^{\text{th}}\) round, a weak learner \(L_{t}\) is trained using distribution \(D_{t}\), and the pseudo-loss of weak hypothesis \(h_{t}\): X\(\times\)Y\(\rightarrow\)[0,1] is computed as  
\[\varepsilon_{t}=\frac{1}{2}\sum_{(i,y\in B)}D_{t}(i,y)(1-h_{t}(x_{i},y_{i})+h_ {t}(x_{i},y)) \tag{8}\]  
where B = {(i,y): \(i=1,\ldots,\) m, \(y\
eq y_{i}\)}. Donate  
\[\beta_{t}=\varepsilon_{t}/(1-\varepsilon_{t}), \tag{9}\]  
\[w_{t}=(1/2)\times(1-h_{t}(x_{i},y_{i})+h_{t}(x_{i},y)) \tag{10}\]  
Then, the distribution \(D_{t}\) is updated according to  
\[D_{t+1}(i,y)=(D_{t}(i,y)/\ Z_{t})\beta_{t}^{w_{t}}, \tag{11}\]  
where \(Z_{t}\) is a normalization constant chosen such that \(D_{t+1}\) is a distribution. After all \(T\) rounds are finished, the final hypothesis is given as  
\[h_{fn}=\text{arg}\max_{y\in Y}\sum_{t=1}^{T}{(\log\frac{1}{\beta_{t}})\cdot h_ {t}(x,y)}, \tag{12}\]  
Figure 2: The working principle of AdaBoost classifier.  
The AdaBoost.M2 is used to construct the multiclass classifier for bearing fault diagnosis. The weak learners can be chosen diversely such as Support Vector Machine (SVM) [38], neural networks [39], and decision tree [40]. Among these learners, the decision tree is generally faster to train and does not need parameter tuning, except for specifying the number of trees. Moreover, it avoids the problem of overfitting that has to be considered for neural networks. Therefore, the decision tree is finally chosen as the weak classifier based on which the AdaBoost tree-based ensemble classifier is constructed.