esa-sceva/satcom-chunk-collection huggingface dataset:
# Electrospun Nanomaterials for Energy Applications: Recent Advances
## 8 Hydrogen Production by Photo-Assisted Electro-Chemical Water Splitting
[237] have greatly enhanced the H\({}_{2}\) production rate using electrospun TiO\({}_{2}\)/WO\({}_{3}\)/Au nanofibers. The enhanced PEC activity of TiO\({}_{2}\)/WO\({}_{3}\)/Au with respect to TiO\({}_{2}\) and TiO\({}_{2}\)/WO\({}_{3}\) nanofibers has been attributed to the synergistic effect of Schottky and surface plasmon resonance (SPR) due to Au NPs decorating the surface of the composite fibers. In the Z-scheme photosynthetic heterojunction system, WO\({}_{3}\) and Au respectively act as \(h^{+}\)-collector and \(e^{-}\)-collector, promoting effective charge separation, whereas the SPR effect favors the absorption of visible light. Recently, [PERSON] et al. [238] have demonstrated the possibility of fabricating efficient photo-anodes by direct deposition of electrospun hematite fibrous mats over FTO/glass plates. The physicochemical properties of the oxide fibers can be effectively modified by the insertion of elemental impurities in their lattice [51; 239; 240]. A preliminary study has shown that the doping electrospun of Fe\({}_{2}\)O\({}_{3}\) fibers with aliovalent impurities is a viable route to improve their conductive properties and, hence, their PEC performance [241]. The photocurrents measured in these preliminary experiments (0.94 uA/cm\({}^{2}\) and 1.42 uA/cm\({}^{2}\) for pure and Si-doped Fe\({}_{2}\)O\({}_{3}\) fibers)--comparable to those reported for hematite films produced by different technique and precursors [242]--are by far below the benchmark for the optimized electrospun hematite photo-anodes (0.5 mA/cm\({}^{2}\)[238]). Nonetheless, the nearly threefold increase in the donor concentration (from 1.51\(\cdot\)10\({}^{19}\) to 4.35\(\cdot\)10\({}^{19}\) cm\({}^{-3}\)) brought about by doping is responsible for the remarkable enhancement (53%) of photocurrent [241]. In the presence of hole scavengers, such as H\({}_{2}\)O\({}_{2}\), the surface recombination almost disappears, and photocurrent density increases by one order of magnitude (from 1.42 to 15 uA/cm\({}^{2}\) at 1.23 V [241]). Aiming at outlining the larger number of energy-related applications as possible, only the photo-assisted approach to WS has been reviewed here. Of course, also electrospun catalysts able to catalyze the hydrogen and oxygen evolution reactions without the aid of light deserve to be mentioned, as well as ENMs utilized in other technologies for clean energy production, such as fuel cells (see f.i. [243; 244; 245]) and solar cells (see f.i. [247; 248; 249; 250]).
## 3 Investigations Focusing on Flow Regime Characteristics
### Investigations with Single-Sized Spherical Particles
#### 3.1.3 Modeling Studies  
Considering the mutual competition between the impacts of pool convection and particle inertia on changing the flow regime characteristics as discussed above, a judge index was proposed for predicting the flow regimes and bed shapes in the DBF process [67]:  
\[I=K_{\text{B}}\frac{I_{\text{convection}}}{I_{\text{inertia}}}=K_{\text{B}}\times \frac{\left(\frac{\
u_{\text{P}}}{\
u_{\text{c}}}\right)^{a}\left(\frac{H_{ \text{W}}^{2}}{LW}\right)^{b}}{\left(\frac{\rho_{\text{P}}-\rho_{\text{I}}}{ \rho_{\text{I}}}\right)^{c}\left(\frac{H_{\text{W}}\
u_{\text{I}}}{\rho_{ \text{I}}}\right)^{d}} \tag{5}\]  
where \(I\) is the judge index; \(I_{\text{convection}}\) and \(I_{\text{inertia}}\) are two dimensionless quantities that represent the prominences of pool convection and particle inertia, respectively; \(\
u_{\text{pr}}\) stands for the average particle releasing rate estimated by \(\
u_{\text{pr}}=\frac{4V_{\text{P}}}{\
u t\lambda t_{\text{B}}^{2}}\), with the total particle volume \(V_{\text{P}}\), as well as the releasing time \(t\), measured in accordance with a duration of releasing the first particle to the last one from the nozzle; \(\
u_{\text{c}}=\frac{H_{\text{I}}}{\rho H_{\text{n}}}\) is the critical velocity for discharged particles to trigger the pool convection; \(a\), \(b\), \(c\), \(d\), and \(K_{\text{B}}\) are empirical constants (see Table 3).  
Figure 14: Illustrations for the base angle, vertex angle, and center angle. (**a**) Pool-convection dominant regime (Regime II), and (**b**) particle-inertia dominant regime (Regime IV) [24].  
Equation (5) is the so-called base model. Based on Equation (5), as shown in Figure 15a, the regime map, where different flow regimes are located in their individual regions, was proposed to predict flow regime transitions [67]. To clarify the regime boundaries between two neighboring regimes, three theoretical boundary values of \(I\) were suggested [67]. For example, Regime I is obtained when \(I\geq 9.5\); Regime II is attained when \(1.5\leq I<9.5\); when \(0.5\leq I<1.5\), Regime III appears; and when \(I<0.5\), Regime IV emerges. Moreover, in addition to the regime map, the rationality and reliability of this model have been further confirmed and ensured through the parametric analyses on the values of \(I\) and experimental flow regime transitions [67]. On the whole, compared to the empirical models of Equations (1), (3) and (4) and focusing on the accumulated bed heights, the model of Equation (5) not only predicts the final bed shape but is also applicable in revealing flow regime transitions during DBF processes.
# Memory-Efficient Batching for Time Series Transformer Training: A Systematic Evaluation
## 2 Background
### Time Series Sample Preparation  
While data preparation shares some common principles across time series foundation models, large language models (LLMs), and vision transformers, there are notable differences in the preprocessing steps, data representations, and batch formation strategies tailored to the characteristics of each domain. Understanding these differences is essential for designing efficient training pipelines and optimizing memory usage in deep learning applications.  
Data preparation for training time series models diverges significantly from the processes employed for LLMs and vision transformers, while all three leverage large-scale pre-training strategies, the inherent nature of the data they handle necessitates distinct approaches. LLMs primarily operate on tokenized text data, where sentences or documents are converted into sequences of tokens. The emphasis lies on preparing sequential text data while preserving its inherent order. Vision transformers' data preparation, on the other hand, involves partitioning images into non-overlapping patches and flattening them into sequences of vectors. In contrast, time series data preparation primarily focuses on segmenting sequential data into fixed-length subsequences using techniques like sliding windows to capture temporal dependencies. This involves storing overlapping segments, leading to increased memory usage. The issue intensifies when dealing with high-resolution time series data, small window sizes for capturing fine-grained temporal patterns, or small incremental steps to increase sample density. These notable differences reflect the unique characteristics of each domain.  
Figure 1 shows the sliding-window operation performed on univariate time series data. We assume that these time series data are already sorted based on the timestamps in increasing order. The left side of Figure 1 displays an example of applying a sliding window of size six, and a stride of size one to the time series data containing one variable. The sliding window is moved over one step at a time to create the next sequence. The step value is often referred to as \"stride\". Usually, the stride value is less than the window size, to allow for overlapping values across sequences. Due to the current implementation of sample preparation, each sequence of time series data is converted to tensors on CPU before transferring to the GPU memory resulting in data duplication across all sequences. The data duplication can become increasingly apparent when dealing with multivariate time series data as demonstrated on the right side of Figure 1. It is important to note that both the window size and stride are hyperparameters that need to be configured. Domain-specific data characteristics (e.g., sampling frequency, seasonal characteristics, patterns, etc.) are often taken into account when selecting the best optimal values for these parameters. Hyperparameter optimization is not the focus area of this paper as it directly affects the performance of the model.
### Characterization
#### 2.3.2 Electrical and Electrothermal Characterization  
The electrical conductivity tests were carried out using a Keithley 2410 Source-Meter by obtaining the electrical resistance, R, from the V-I (Voltage-Intensity) slope in the range of 0 to 50 V. Then, the electrical conductivity, K, was obtained from the expression (1), with L being the distance between electrodes and A being the cross-sectional area of the specimen.  
\[\text{K}=\text{L}/(\text{A}\cdot\text{R}) \tag{1}\]  
On the other hand, the self-heating tests were performed by using the same source-meter unit, Keithley 2410, by applying 1000 V. The self-heating tests were performed in two stages of 5 min each: first, a heating stage was conducted by applying the voltage, and then, a second stage involved cooling by turning off the power source. Here, the average and maximum temperature increments with regard to room temperature, \(\Delta\text{T}_{\text{av}}\) and \(\Delta\text{T}_{\text{max}}\), respectively, were recorded using a FLIR E50 thermal camera.
## 5 Proposed Predictive Maintenance (PdM) Structure Using Internet of Things (IoT)
### Proposed PdM Structure Using IoT
#### 5.2.2 Results Transmission  
The analysed data results will be transmitted to a remote database and a user in the case of alerting results through a communication module that uses internet connection for real time monitoring. In order to avoid an addition cost for new network setup, the existing infrastructures, such as GSM, shall be used.
## 4 Results
### Back-Calculated Results of Elastic Modulus
#### 4.2.5 Comparison and Discussion  
A comparative analysis of the seven cross-sections, as shown in Figure 12, reveals the following ranking of average asphalt elastic modulus values for the asphalt pavement treated with different materials: high-strength geopolymer (without waterproofing agents) > polymer > high-strength geopolymer (with waterproofing agents) > low-strength geopolymer (without waterproofing agents) > low-strength geopolymer (with waterproofing agents) > intact pavement > untreated pavement. The results demonstrate that high-strength geopolymer (without waterproofing agents) and high-polymer materials exhibit optimal performance, with their post-grouting average elastic moduli significantly exceeding those of other sections. These materials are well-suited for grouting-based crack rehabilitation in this roadway, offering superior mechanical properties and structural reinforcement under operational traffic loads.  
High-strength geopolymer and high-polymer materials are widely regarded as effective solutions for enhancing pavement performance due to their exceptional mechanical properties, a perspective further substantiated by the quantitative data provided in this study. While the current research offers valuable short-term performance insights, comprehensive long-term performance evaluations under operational conditions remain critical for future investigations. Therefore, subsequent studies should prioritize the following:  
1. Long-term durability assessment of grouting materials under diverse environmental conditions.  
Figure 11: Elastic modulus of asphalt in crack-free intact section.  
Figure 12: Comparison of elastic modulus for seven sections.  
2. Sensor deployment optimization to quantify the influence of sensor configurations on data integrity and strain measurement accuracy.
3. Algorithmic refinement of modulus back-calculation methodologies by integrating multi-axial stress coupling models.  
These advancements will enhance the accuracy and applicability of research findings, enabling predictive maintenance frameworks for sustainable pavement management.
# Mechanisms of Fluoride Toxicity: From Enzymes to Underlying Integrative Networks
## 4 The Implication of Fluoride Toxicity on Human Health
### Disturbances of the Thyroid Gland  
Investigations from endemic areas demonstrate that the thyroid gland is sensitive concerning fluoride burden [108; 109]. Thyroid-stimulating hormone (TSH) is a pituitary hormone that stimulates the thyroid gland to produce thyroid hormones thyroxin (T4) and triiodothyronine (T3). A high level of TSH indicates an underactive thyroid gland or hypothyroidism. The receptor for TSH belongs to the category of GPCRs. AlFx is able to mimic TSH by switching on its associated G protein. It is suggested that the consequent overproduction of cAMP leads to a feedback mechanism resulting in a desensitization of the TSH receptor and ultimately to a reduced activity of the thyroid gland.  
The deficient thyroid function with elevated TSH at high fluoride exposure was reported in both children and adults in fluoride endemic areas in India since 2005 [110; 111; 112; 113]. According to [PERSON] et al., 47% of children living in a New Delhi neighborhood with an average water fluoride level of 4.37 mg F/L have evidence of clinical hypothyroidism attributable to fluoride [111].  
[PERSON] et al. recruited 571 resident children aged 7-13 years from endemic and non-endemic fluorosis areas in Tianjin, China. They found that every 1 mg/L increment of water fluoride was associated with a 0.13 \(\mu\)IU/mL increase in TSH. [PERSON] et al. found that teenagers drinking water with one ppm of fluoride in Northern Mexico have reduced T3 levels [115].  
Every 1 mg/L increment of urinary fluoride was associated with 0.09 \(\mu\)g/dL decrease in T4 [115].  
[PERSON] et al. [108] compared the average amount of T3, T4, and TSH in people with hypothyroidism and people without thyroid disease, concerning fluoride concentrations 0-0.29 and 0.3-0.5 mg/L in drinking water in Iran. The major conclusion of this study was that fluoride impacts TSH and T3 even in concentrations lesser than 0.5 mg/L.  
A recent study from an endemic fluorosis village, Talab Sarai in Pakistan, with a high content of fluoride in the drinking water (6.23 mg F/L) found teeth fluorosis in 93% of 130 investigated childrenwith the age of 12 \(\pm\) 3.3 years. Eighty percent of the children displayed clear thyroid hormonal derangements, with 37% having high TSH and 43% with T3 and T4 disorders [109].  
The diagnosis of hypothyroidism was nearly twice as frequent in fully fluoridated areas in England, as compared to non-fluoridated areas [116]. The population-based study of the weighted sample of 6,914,124 adults aged 18-79 living in Canada who have higher levels of urinary fluoride also shows increased risk for underactive thyroid gland activity [117].  
Such epidemiological studies bring strong evidence that thyroid dysfunction appears in both endemic fluorosis areas and in areas with CWF [94; 118].
## 3 System Architecting (Architectures Synthesis)
### Black Box Modeling and Analysis  
The starting point is a set of requirements describing the system mission and some potential additional requirements and/or constraints. These requirements are captured in the system model to ensure their traceability with other model elements throughout the whole process. The initial requirements for the system of interest are illustrated within a SysML requirements diagram given in Figure 4.  
The following step is to identify the system context describing the environment in which the system operates as well as the different entities (users, operators, other systems,...) with which the system interacts. By identifying the different interactions of the system, the context diagram will help identifying the input/output flows that the system exchanges and also the interfaces required to achieve these interactions. As the system may have different contexts during different phases of its life, we may represent different context diagrams. For this paper however, we only focused on the usage phase. A SysML Block Definition Diagram (BDD) in Figure 5 gives the context of the EMA assembly workplace. In this diagram, we can see that the workplace is part of an _Aircraft Assembly Line_ and hosted in a workshop. The system interacts with an _Aircraft Integration Service_ that will order and supervise the operation as well as a _Maintenance Operator_. The workplace also interacts with the EMA (initially unmounted) and different parts of the aircraft to which it will be connected.  
The assembly scenario will be as follows: First, the system shall automatically (without the help of the operator) bring the EMA from the storing area to the assembly area. This phase corresponds to the \"Displacement to the assembly area\" (c.f. Figure 6. Then, the system shall position and orient the EMA to make it ready for being assembled by the operator. Finally, in the \"Assembling EMA\" phase, the operator assembled the EMA while the system to be designed maintains it in position. Finally, once the EMA is assembled, the system steps back either to the other wing zone if another EMA shall be assembled, or to another zone if the mission is completed. The state machine in Figure 6 describes the different operating phases of the system.  
Figure 4: Initial requirements.  
At the end of the black box phase, a set of requirements is defined through the analysis of the different previous steps. These requirements will be the basis for identifying the candidate solutions in the white box phase.
## 3 Discussion
### Theoretical Prediction of Solubility of Wild Thyme Compounds in Green Solvents
#### 3.1.2 Wild Thyme Compounds Solubility in Green Solvents Assessed by COSMO-RS Software  
The COSMO-RS software was also used for evaluation of solubility of wild thyme compounds in selected green solvents. Results are presented in probability of solubility (%) where low probability is marked with red color, medium probability with yellow color, and high probability of solubility with green color. Similar trend was shown as with HSPs, but higher potential for solution of solutes was predicted with COSMO-RS software. Results showed that MeTHF has the highest probability of solubility of wild thyme compounds, and that all solvents, except water, have higher potential for solution during extractions with wild thyme, compared to \(n\)-hexane. Solvents are ranked as follows: MeTHF \(>\) CPME \(>\) ethyl acetate \(>\) methyl acetate \(>\) 1-butanol \(>\) ethyl oleate \(>\) isopropanol \(>\) ethanol \(>\) methanol \(>\) DMC \(>\) limonene \(>\) cymene \(>\)\(\beta\)-myrcene \(>\)\(\alpha\)-pinene \(>\)\(n\)-hexane \(>\) water.  
These results show the solubility of each individual compound in the solvent, but the extraction does not depend only on solubility, but also on the quantity of each compound in the plant. Therefore, experimental analysis was performed. Taking into consideration results obtained by two theoretical prediction methods, it was decided to perform extraction using green solvents with potential to replace \(n\)-hexane: water, ethanol, limonene, \(\alpha\)-pinene, DMC and ethyl acetate. Since no electrical discharge was achieved with limonene, \(\alpha\)-pinene, DMC and ethylacetate, for further analysis, only water and aqueous ethanol 25% and 50% (\(\
icefrac{{v}}{{v}}\)) were taken.
## 3 Results  
### Electrical Characterisation of CAP  
As shown in Figure 2, after the ignition of CAP producing device, a typical current-voltage waveform is observed. From an electrical point of view, the discharges in the dielectric barrier are presented in the form of packages with a frequency of 100 Hz. Each package can be described as having an initial zone (I) in which the voltage begins to rise, a median zone (II) in which the voltage reaches its maximum value and remains constant, a zone of progressive decrease in voltage (III) and a zone final where the voltage drops to the minimum value (IV) (Figure 3). The frequency of the electrical voltage identified in the packages was 23 kHz.  
By examining the shape of the voltage waveform in each package, it was determined that regions II and III are the determining elements for the plasma discharge. To gain a clearer understanding of the discharge properties, we analyzed the number and amplitude of the currents in these two areas across 10 packages. The currents were found to have intensities ranging from 0.06 A to 0.72 A (Table S1), with the most common intensity being 0.06 A, averaging around 977 currents on the positive half-cycle and 632 currents on the negative half-cycle (Figure 4).
# Advanced Label-Free Laser Scanning Microscopy and Its Biological Imaging Application
## 2 Laser Scanning Coherent Raman Scattering Microscopy (CRSM)
### Laser Scanning Coherent Anti-Stokes Raman Scattering Microscopy (CARSM)
Resolution approaching 130 nm was achieved by narrowing the microscope's excitation volume in the focal plane through the combined use of a Toraldo-style pupil phase filter with the multiplicative nature of four-wave mixing [27]. A supercritical focusing CARSM has been developed by using two optimized phase patterns with combination of concentric rings with alternant 0 and \(\pi\) phase, which is generated by a spatial light modulator and applied to the pump beam for minimizing the focal spot size [28]. The theoretical research has indicated that diffraction limit can be also broken by controlling the relative phase around the center of the pump and Stokes pulses [29]. Recently, a novel higher-order CARSM was investigated by using the higher-order nonlinear optical processes and detect the CARS signal at new wavelength to achieve high-contrast, super-resolution vibrational imaging [30]. Several distinct characteristics, including high specificity, high spatial resolution, high sensitivity, and label-free, make CARSM potentially ideal for noninvasively imaging complex biological and chemical samples. In living cells imaging, various molecule vibrational modes can be detected by CARS, such as phosphate stretch vibration, amide I vibration [14; 31], OH stretching vibration [32; 33] and CH stretch vibration [34; 35], which can be applied for visualizing DNA, protein, water and lipids, respectively. Meanwhile, high imaging speed and signal level make CARS more suitable for observing biological dynamic process in vivo [15; 36]. The synchronous change of the cell voltage and distribution of chemical states of water inside a nation membrane was monitored with CARSM. For example, the transient values of the number of water molecules per sulfonic acid group increased rapidly from 7.4 at 0 s to 11.7 at 4.5 s under current density jump from 0.1 to 1.0 A-cm\({}^{-2}\)[37]. In addition, CARSM also found applications in gas phase analysis [38] and material characterization [39]. Multiplex CARSM imaging was demonstrated and used to visualize the thermodynamic state, including the liquid crystalline and gel phase of lipid membranes [19]. In addition, CARSM has also been a powerful characterization tool for Hexagonal Boron Nitride [40], porous carbon [41] and graphene [42].