esa-sceva/satcom-chunk-collection huggingface dataset:
# Ab Initio Study of Martensitic Transformation in NiTiPt High Temperature Shape Memory Alloys
## 3 Results and Discussion
### Energy Differences between the Austenite and Martensite
First and foremost, it can be found from Figure 7 that the overall trends of the \(\Delta E\) and \(M_{\rm s}\) of Ti\({}_{50}\)Ni\({}_{50-x}\)Pt\({}_{x}\) alloys with the increasing of the Pt concentration are remarkably similar to each other, which confirms that the present calculated results are fully in line with the experimental observations. For Pt \(\leq\) 8.33 at.%, martensite start temperatures and \(\Delta E\) are less sensitive to composition, and decrease slightly with a minimum observed at 6.25 at.% Pt; for 8.33 at.% \(<\) Pt \(<\) 12.5 at.%, \(\Delta E\) and \(M_{\rm s}\) increase slightly. However, at higher levels of Pt, especially exceeding about 15 at.%, \(\Delta E\) and \(M_{\rm s}\) increase sharply with Pt content. This can be explained as follows, with the dramatically increasing of \(M_{\rm s}\), correspondingly, more energy is needed for the martensitic transformation. Taking into account good coincidences with the results of experimental studies, the resulting model can be a good tool for simplifying and rationalizing experimental studies of the NiTiPt system.
# Zn Doping Improves the Anticancer Efficacy of SnO2 Nanoparticles
## 3 Results and Discussions
### UV-Vis Analysis  
The absorption spectra and bandgap energy (hv) of synthesized NPs are shown in Figure 5A,B. However, Figure 5A shows that the observed change in the absorption edge of the Zn-doped SnO\({}_{2}\) NPs (1% and 5%) was linked to a higher wavelength compared to the pure SnO\({}_{2}\) NPs. This phenomenon led to a decrease in the bandgap energy with the addition of Zn ions. Figure 5B displays the Tauc graphs from the plots of (\(\alpha\)hv)\({}^{2}\) versus the photon energy (hv) of the prepared NPs. Using [PERSON]'s plot formula [45] (\(\alpha\)hv = A (hv\(-\)Eg)\({}^{n}\), where \(\alpha\), A, n, and Eg are the absorption coefficient, the energy-independent constant, the transition type, and the optical band gap (Eg)), the bandgap energies (hv) of synthesized NPs were calculated. Table 1 shows the band gap energies of SnO\({}_{2}\) NPs (3.50 eV) and Zn-doped SnO\({}_{2}\) NPs (1% and 5%) (3.42 eV and 3.34 eV). These band gap energies of theses NPs exhibited direct electronic transitions [46]. UV results showed that the Zn doping effects on the optical properties of SnO\({}_{2}\) NPs due to the presence of stacking faults. Stacking faults are crystallographic defects or imperfections in the arrangement of atoms within a crystal lattice, as reported in a previous study [47].  
Figure 4: FTIR spectra of pure SnO\({}_{2}\) NPs and Zn-doped SnO\({}_{2}\) NPs.
## 2 Communication System Model and Theory
### Correlation Cost Factor-Based Updates to Matched Filters Reception Method for M-SS (CCF-MSS)
#### 2.3.2 Correlation Cost Factors  
The correlation cost factor is defined as the ratio of the second-largest correlation peak to the maximum correlation peak at each frequency shift search position within the two-dimensional correlation matrix formed by the carrier frequency offset (CFO) and the spreading code index. During the despreading process, the greater the difference between the maximum correlation peak and the other correlation peaks, the more accurate the despreading result. Based on the correlation cost factor, we can assess the accuracy of the despreading process, and subsequently adjust the direction of Doppler compensation for the following symbols. The correlation cost factor is expressed as follows:  
\[G_{i}=\frac{R_{sec_{i<n}}}{R_{max_{k>n}}},sebject\quad to\quad k=0\pm 1\pm 2,\ldots \tag{12}\]  
where \(G_{i}\) is the correlation cost factor, \(R_{sec}\) is the second largest correlation peak, \(R_{max}\) is the largest correlation peak, k is the number of search steps, and n is the number of PN codes.
## 3 Results and Discussion
### Design of Experiment (DOE)
#### 3.2.2 Main Effect Factors  
Figure 8 presents the main effects of the different factors studied in the source phase. It shows that the concentration of Cr(VI) and the concentration of Aliquat-336 had positive effects on the extraction efficiency of Cr(VI) in the source phase, while temperature had a negative effect on the extraction efficiency. In fact, by increasing the Cr(VI) content, the driving force increased with a consequent improvement of the extraction efficiency. Higher Aliquat-336 concentrations improved the Cr(VI) transport across the membrane, whilst higher source temperatures did not favor formation of the Cr(VI)-Aliquat-336 complex, although diffusion of Cr(VI) ions to the membrane surface increased. Higher temperatures also led to a decrease of the viscosity of Aliquat-336, which can favor the release of Aliquat-336, with a consequent loss of efficiency.  
Figure 9 shows the individual effects of the different factors selected for the receiving phase. It can be seen that the investigated factors ([Aliquat-336], [NaCl] and T) all had a positive effect on the back-extraction performance of Cr(IV) in the receiving phase.  
Figure 8: Factors of main effects plot for source phase.
## 3 Feature-Engineering
### Feature Extraction
#### 3.2.1 Statistical Features  
The first category of features to be extracted is the set of statistical features which represent the basic characteristics of each time-series within segmented, non-overlapping time windows: _minimum, maximum, mean, median, standard deviation, variance_, and _energy_.  
Statistical features are extracted within windows whose sizes are defined by the number of observations and not by a specific time period due to the nonuniform sampling rate (hourly to bi-hourly) as mentioned before. Extraction is based on the raw measurements of the vital signs and their first derivatives as well as the calculated standard early warning scores (EWS) of these measurements based on ZOL hospital standards. A weak point about statistical features is the static nature of these features as they do not reveal the dynamic behaviour of the time-series. Therefore, another category of features is required to be explored, namely dynamic features.
## 2 Pharmaceuticals and Personal Care Products (PPCPs) in the Ecosystems
### Microbial Remediation in Culture
#### 2.1.2 Mycoremediation
Genetically modified _Aspergillus_ increases lactose production, which can be used for biotechnological purposes [195, 196, 163]. The other study shows the optimization and scale-up of the degradation process regulated by _Penicillium oxalium_ in a batch reactor and compared the degradation between free and immobilized biomass [197]. Some species of _Penicillium_ can be used effectively in TCS, NP and hormones degradation [198, 199, 200, 201, 202]. The yeast _Yarrowia lipolytica_ has been studied for the expression of cytochrome P450 and it seems capable of degrading diclofenac and NPX [191, 203]. _Trichoderma harzianum_ is able to degrade 17 \(\beta\)-estradiol [204] and CBZ with a performance similar to that of _Pleurotus ostreatus_[205]. Free lacase from _Myceliophthora thermophile_ and _Lentinula edodes_ can be used to remove estrogens (e.g., [206, 101]), diclofenac, NPX [207] and antibiotics [208]. A recent study has underlined the potential of ascomycetes in purification mycotreatments both for their degradative capacity and for their possibilities in bioadsorption [209]. Regarding Mucoromycota, _Cunningham elegans_ can transform ATN [201], GFZ [211], NPX [212] and FLU, in two compounds with less antimicrobial activity [213]. Native lipases from _Mucor jacunicus_ were tested for the degradation of IBU [214] without very good results. _Mucor hiematis_ associated with commercial fungi can establish synergistic relationships for water purification [215]. _Fusarium_ species degrade estrogens and NP [216, 217]. _Umbellopsis isabellina_ is able to degrade and reduce the toxicity of hormones and NP [218, 219]. Some Ascomycota and Mucoromycota could be better PPCPs degraders than basidiomycetes, not only for their high tolerance to extreme conditions, but also by the high presence of cytochrome P450 family (CYP) epoxidases and transferases involved in the metabolism of xenobiotics in both groups [164]. However, the improvement of these filamentous fungi through the development of commercial products, biotechnological advances or applications on an industrial scale are in their infancy (Figure 3B).
# Recurrence Quantification Analysis as a Form of Postural Control Assessment: A Systematic Review
## 4 Discussion
### RQA, Linear Parameters, and Future Research  
This section includes papers in which the authors combined RQA results with traditional linear measures such as CoP path length, ellipse area, and mean sway velocity. Within the first group, linear and RQA measures were used together in five [37; 44; 50; 51; 53] of the eight studies. In the second group, such a connection was noted in four manuscripts [45; 55; 57; 58]. In the last group, all 12 studies included both sets of parameters. It is worth highlighting that in all the mentioned studies, linear and non-linear methods were described separately as they evaluate different features of postural control.  
[PERSON] et al. [50] showed that removing vision during quiet standing results in a significant increase in CoP path length and surface area in a group of young and elderly subjects. Such findings were in line with results from the papers [18; 43; 44; 51; 61; 63] included in this review. Moreover, [PERSON] et al. [50] observed three typical postural behaviors associated with a lack of visual control. The first was characterized by a rigid posture associated with a reduction in the magnitude of postural oscillations. The second behavior was characterized by a functional exploratory strategy [75] associated with increased CoP magnitude. The third behavior was characterized by increased postural fluctuations and an inability to adapt to the conditions. As mentioned in the introduction, such behaviors should be described using both linear and nonlinear measures [3]. Linear measures provide crucial information about the magnitude and direction of postural sway. On the other hand, nonlinear measures, and RQA in particular, are relatively new and provide a different perspective on postural control. They consider the complexity and dynamics of the human movement system, which cannot be easily captured by linear measures. Overall, as discussed in seven papers of this review [17; 20; 44; 53; 61; 65], the use of both linear and nonlinear measures provides a comprehensive understanding of postural control and helps researchers and clinicians identify the underlying mechanisms of postural instability and develop appropriate interventions.  
According to some authors [37; 51; 53], future studies should include additional EMG-type equipment, functional tests, or more challenging sensory conditions to facilitate data interpretation. [PERSON] et al. [44] recommended future studies for preschool and primary school-aged children to assess the developmental trend of standing balance in children. It also seems that the inclusion of RQA analysis for patients with various disabilities or diseases is an open space, given that there were only eight papers in this section.
# The Link Between Physical Function, \(\beta\)-Amyloid, and Cognitive Aging in Women
###### Abstract  
This study aimed to examine associations between functional capacity (FC), brain \(\beta\)-amyloid (A\(\beta\)) burden, and longitudinal cognitive performance. Data from 89 cognitively normal women (70.0 \(\pm\) 2.7 years) in the Women's Healthy Ageing Project cohort were analyzed. FC was assessed using the timed up and go (TUG) test and the A\(\beta\) burden was quantified via a F-18 Florbetaben PET scan with Standardized Uptake Value Ratio (SUVR). Cognition was evaluated longitudinally using the Preclinical Alzheimer Cognitive Composite (PACC) over 3.9 \(\pm\) 2.6 years. Multiple linear regression, mediation analysis, and linear mixed-effects models were applied. Baseline A\(\beta\) burden and years of education were associated with cognitive performance two to six years later, while the TUG performance was associated with cognitive outcomes at two years. A\(\beta\) burden was found to mediate the relationship between FC and cognition over time. A significant three-way interaction (TUG \(\times\) SUVR \(\times\) time) was observed, indicating that declines in the TUG performance over time were exclusively associated with steeper cognitive decline among women with elevated A\(\beta\) burden (SUVR \(\geq\) 1.42). These findings suggest that maintaining functional mobility may be particularly relevant for women with increased A\(\beta\) burden and support future research targeting early motor-cognitive markers.  
Alzheimer's disease; amyloid; PET; timed up and go; cognition; successful aging 2020
# Causality in Control Systems Based on Data-Driven Oscillation Identification
## 2 Proposed Methodology
### Transfer Entropy Approach  
Transfer Entropy (TE) is an information-theoretic interpretation of [PERSON]'s causality definition. In practice, this is a measure of information transfer from one variable to another by measuring the reduction of uncertainty while assuming predictability. TE is given by the Equation (1).  
\[T_{x\to y}=\sum_{y_{i+h},Y_{i},X_{j}}p(y_{i+h},Y_{i},X_{j})\cdot log\frac{p( y_{i+h}|Y_{i},X_{j})}{p(y_{i+h}|Y_{i})}, \tag{1}\]  
where \(p\) means the complete or conditional Probability Density Function (PDF), variables \(Y_{i}\) and \(X_{j}\) respectively as \(Y_{i}=[y_{i},y_{i-\tau},\ldots,y_{i-(l-1)\tau}]\), \(X_{j}=[x_{j},x_{j-\tau},\ldots,x_{j-(k-1)\tau}]\), \(\tau\) is a sampling, \(h\) is a prediction horizon. Concluding, it is the difference between an information about a future observation of \(x\) obtained from the simultaneous observation of past values of both \(x\) and \(y\), and the information about the future of \(x\) obtained from the past values of \(x\) alone. Phenomenon of entropy in both directions is highly probable that is why a measure described as \(T_{x\to y}=T_{x|y}-T_{y|x}\) is decisive due to quantity and direction, what is causality.  
The practical implementation of the Transfer Entropy approach between pair of variables according to Equation (1) requires its simplification to the form presented in Equation (2):  
\[T_{x\to y}=\sum_{y_{i},y_{i-\tau}x_{i-\tau}}p(y_{i},y_{i-t},x_{i-\tau})\cdot log \frac{p(y_{i},y_{i-t},x_{i-\tau})p(y_{i-t})}{p(y_{i-t},x_{i-\tau})p(y_{i},y_{ i-t})}, \tag{2}\]  
where \(p\) means the conditional (PDF), \(\tau\) and \(t\) are the time lags in \(x\) and \(y\), respectively. If the time-series length is short, the \(t\) is set to 1 under the assumption that the maximum auto-transfer of information occurs from the data point immediately before the target value in \(y\).
# A Hierarchical Fusion SAR Image Change-Detection Method Based on HF-CRF Model
## 2 Materials and Methods
### D-DRUNet Fusion Semantic Segmentation Network
#### 2.1.3 Dynamic Convolution Kernel with Multi-Scale Guide Mask  
Traditional UNet networks use the CNN structure, and the convolution kernel size needs to be determined in advance, which is difficult to adapt to changes in dynamic remote sensing image content. The D-DRUNet network introduces a dynamic region sensing method [57] and adopts the feature pyramid network (FPN) structure [58] to improve the dynamic convolutional guide mask generation method to dynamically divide the spatial dimensional distribution, according to the input features.  
Dynamic region-aware convolution (DRConvs) consists of a learnable guide mask module and a filter generation module that automatically generates region-sharing patterns of filters, based on each input image's features. The guide mask module divides the features with similar semantic information into the same region, which determines the distribution of filters in the spatial dimension; the filter module generates the corresponding filters that would be assigned to different regions, and different filters extract the information at different abstraction levels.  
In particular, the D-DRUNet network is designed with the FPN structure to improve the guide mask generation of DRConvs by fusing features among three scales to increase the content localization capability of the guide mask, and the principle of the method is shown in Figure 4. Figure 5 shows the visualization result graph of the improved guide mask region segmentation on the Berne dataset. Figure 5a shows the Berne log and mean-difference maps, and Figure 5b shows the results of the guide mask delineation on the corresponding difference maps with higher false alarms; Figure 5c shows the region delineation results of the FPN structure guide mask, and the accuracy improvement is obviously significant.  
Figure 4: Improved dynamic convolution structure diagram.  
To verify the effect of the D-DRUNet network, we conducted local validation experiments on the Ottawa dataset, and the results are shown in Figure 6 and Table 1. The D-DRUNet-S method is a semantic segmentation model with a decoder structure that includes a single skip connection. It shows significant improvement in detection performance, compared to the UNet network. However, the detection results exhibit a high false alarm rate, as shown in the dashed box in Figure 6c. This is likely caused by severe reconstruction distortion due to the lack of encoding information from another modality, resulting in an imbalanced performance between false alarms and missed alarms, and poor Kappa performance. The D-DRUNet achieves a favorable trade-off between false positives and false negatives by adopting a double-skip-connection fusion method, resulting in a significant improvement in performance.
# Pressure Drop and Particle Settlement of Gas-Solid Two-Phase Flow in a Pipe
## 4 Results and Discussion
### Relationship between the Pressure Drop and Mixture Inlet Velocity  
#### 4.5.1 Effect of Particle Volume Concentration  
The relationship between the pressure drop per unit length \(p/L\) and mixture inlet velocity \(V\) for different particle volume concentrations is shown in Figure 10, where the values of \(p/L\) increase as the \(V\) increases for different volume concentrations, which is in accordance with the law that the pressure drop is directly proportional to the velocity in the Hagen-Poiseuille flow. In the figure, the values of \(p/L\) also increase as the particle volume concentration increases for the different inlet velocities. Since the particle density is larger than that of the gas density, the high particle concentration per unit volume means that a larger pressure drop is required to transport the mixture over the same distance.
# The Need for Ecosystem 4.0 to Support Maintenance 4.0: An Aviation Assembly Line Case
## 3 Case Study
### Assembly Line Description  
The assembly line mentioned in de Mello [3] is considered and shown in Figure 2, capable of assembling a three-piece wing box as an example. This assembly operation is divided into four steps, as demonstrated in Figure 2:  
1. First assembly level: parts like skins, stringers, stiffeners, and doublers are joined to form upper and lower panels, as well as spar, ribs, and bulkhead subassemblies;
2. Second assembly level: these subassemblies are joined to form left, right and centre rib/spar grid structures;
3. Third assembly level: upper and lower panel subassemblies are joined with the rib/spar to form left, right and centre boxes;
4. Fourth assembly level: three wing boxes are joined to form the final wing.  
As shown in Table A1 of Appendix A, 57% of all fasteners and rivets needed to assemble the wing are used at the first level, making it easier to automatize. Joining skin panels to the grid in the third level requires 34% of all rivets and fasteners, and even though automatization here is more challenging than the first level, it is still worth it. Although the final level only requires 3% of the fasteners, the time needed to install these larger fasteners without the aid of robots would be a magnitude higher than the time required to install fasteners in first level assembly; thus, automatization is highly recommended in this level, and it was adopted for the case study analysed in this paper. The second level only requires 6% of the total rivets/fasteners. Finally, manual installation is easy and acceptable in this step, thus turning the use of robots uneconomical. As a result, the second level's assembly tasks were assumed to be manually executed in the model proposed. The line was modelled according to the parameters listed in Table A2 of Appendix A. The parameters were acquired from the semi-structured interviews during the field exploration and confirmed through the literature review, as previously presented in Table 1.
## 4 Experimental Research
### Experimental Results and Analysis
#### 4.5.2 Fine-Grained Experiment  
Previous studies failed to detect specific types of offensive content in detail, so we conducted further fine-grained detection of Chinese offensive language, focusing on specific categories of offensive language, including race, gender, region, etc. We also performed offensive language detection for each of the three types of social text. Through this research, we aim to more accurately identify and understand offensive language and provide more effective content filtering and monitoring mechanisms for social media.  
Figure 5 shows the results of the specific classification of offensive language by race, gender, and region. Among these performance indicators, the highest was recall (81.3%), and the lowest is precision (80.3%), while accuracy and F1-score were around 80.8%. The experimental results of offensive language detection for different text types showed that the gender category performed best in offensive language detection, despite the small number of data. This phenomenon may be attributed to the inclusion of specific words (such as \"male\" and \"female\") in the offensive language of the gender category, which may be more easily captured by the model, thus improving the accuracy of the detection. In contrast, the offensive language in the race category presents a more complex text structure, which makes it difficult for the model to accurately capture hidden features, resulting in lower detection performance.  
This phenomenon can also be explained by the fact that in the gender category, the explicit gender-related words lead to clearer feature performance, which is conducive to effective classification. In the race category, the offending language may be more subtle and varied, with greater changes in semantics and context, which increases the complexity of  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Params** & **Runtime(s)** \\ \hline RoBERTa & 102,269,186 & 590.35 \\ RoBERTa + TKE & 102,864,386 & 615.98 \\ RB\_BG\_MHA & 131,995,747 & 863.23 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental comparison of the number of parameters and running time in RoBERTa-based models.  
Figure 5: Offensive language detection: category prediction and analysis of race, gender, and region topics.  
model identification. Therefore, differences in data characteristics and language complexity may be one of the key factors leading to performance differences.