esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances and Applications of Semiconductor Photocatalytic Technology
## 3 Common Classification of Photocatalysts  
### Oxide Photocatalyst  
#### 3.1.1 TiO\({}_{2}\)-Based Photocatalyst  
In 1976, [PERSON] et al. discovered that nano-TiO\({}_{2}\) could dechlorinate polychlorinated biphenyls, a hard-degradable organic compound, under ultraviolet irradiation [56; 57; 58; 59; 60; 61], and opened a new chapter in photocatalytic degradation of semiconductor nanomaterials. In 1980, [PERSON] et al. proposed a photocatalytic mechanism using TiO\({}_{2}\) as a catalyst [62], which promoted the development of TiO\({}_{2}\) in the field of photocatalysis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Influencing Factors** & & **Effect on Photocatalysis** \\ \hline
**Catalyst concentration** & & - The reaction rate increases with the increase of the catalyst concentration. - Above a certain dose, the reaction rate decreases as the catalyst \\  & & - Provide light of different wavelengths. - Improve light intensity and promote photocatalytic reaction. \\
**Light source and light intensity** & **Light intensity** & - Improve light intensity and promote photocatalytic reaction. \\ \hline
**PH value** & & - Related to target degradation products. \\ \hline
**Plus oxidants** & & - Reducing the recombination of photogenerated electrons and holes to improve photocatalytic efficiency. \\ \hline
**Inorganic ion** & **Anion** & - Improve the separation speed of photogenerated electrons and holes and promote photocatalytic reaction. - Becomes a scavenger of hydroxyl radicals, forming anion radicals. - The competitive adsorption of active sites on the surface of the catalyst \\  & & may affect the photocatalytic degradation of organics. \\ \hline
**Temperature** & & - Has little effect. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Effects of influencing factors on photocatalytic reaction.  
TiO\({}_{2}\) has three crystal forms--rutile, brookite, and anatase. Among these three crystal structures, anatase and rutile-type TiO\({}_{2}\) are often used as photocatalysts [63]. TiO\({}_{2}\)-based photocatalysts have been widely used in air purification, water pollutants degradation, antibacterial disinfection, deodorant, and antifog, among other applications, due to their high activity, stable properties, low cost, and non-toxic environment.  
TiO\({}_{2}\) has a wide band gap (about 3.2 eV). Therefore, TiO\({}_{2}\) can only absorb ultraviolet light when used as a photocatalyst. However, ultraviolet radiation occupies only 5% of sunlight, which greatly limits the application of TiO\({}_{2}\) in the visible range. A series of modifications to TiO\({}_{2}\) is usually carried out, thereby improving the utilization of visible light by TiO\({}_{2}\) and reducing the photogenerated electron-hole recombination rate on the surface of TiO\({}_{2}\). In the end, its photocatalytic efficiency can be improved. As shown in Figure 2, common methods used to improve photocatalytic efficiency include semiconductor surface photosensitization, semiconductor surface noble metal deposition, metal/nonmetal doping, and complex semiconductor modification.
## 3 Methodology
### Generalized Additive Model  
To analyze the impacts of spatial heterogeneity on fitting accuracy, the contributions to the correlation coefficient R from \(CV_{LST}\), \(CV_{LAI}\), \(CV_{TVDI}\), and \(CV_{SEE}\) were explored based on a generalized additive model (GAM). Here, the correlation R substituted into GAM was calculated between aggregated GM-/OWCM-derived SM and RFSM in coarse 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) spatial resolution throughout the study period. GAMs are useful when developing and evaluating the non-monotonic relationship between independent and dependent variables [88; 89]. To better describe the contributions of the four CV variables in GAM, Tci and sample size were added as explanatory variables to the model. The expression of GAM was therefore constructed as follows:  
\[Score_{pixel}=0.25\times n(Score_{LAI})+0.75\times n(Score_{SM}) \tag{8}\]where \(c_{0}\) is an unknown coefficient; \(x_{i}\) is the CV value of the \(i\)th continuous nonlinear explanatory variable; \(f_{i,\ i=1,\ldots,6}\) are nonparametric (unspecified) smooth functions between the correlation coefficient R and the nonlinear heterogeneity variables (\(CV_{LAI}\), \(CV_{LST}\), \(CV_{TVDI}\), \(CV_{SEE}\)), Tci, and sample size; and \(\epsilon\) is the corresponding error. Explained deviance (\(D_{m}\)) is used to confirm the contribution of various explanatory variables to the total R, computed as follows:  
\[D_{m}=1-D_{r}/D_{n} \tag{9}\]  
where \(D_{r}\) is the residual model deviance, and \(D_{n}\) is the null model deviance. GAM is finally applied based on the six sequences described above (i.e., \(CV_{LAI}\), \(CV_{LST}\), \(CV_{TVDI}\), \(CV_{SEE}\), Tci, and sample size) using the R language package _mgcv_ ([[https://www.rdocumentation.org/packages/mgcv/](https://www.rdocumentation.org/packages/mgcv/)]([https://www.rdocumentation.org/packages/mgcv/](https://www.rdocumentation.org/packages/mgcv/)), accessed on 21 June 2022).
# Systematic Analysis of the Hydrogen Value Chain from Production to Utilization
## 2 Hydrogen Production Technologies
### Thermochemical Processes  
In thermochemical processes, syngas is produced, using heat and chemical reactions, from a variety of substances, such as coal, oil, natural gas, and biomass. The syngas is then upgraded, through a water-gas shift reaction and/or through separation techniques, to obtain a high yield of hydrogen. These processes have the advantage of being able to produce hydrogen at an industrial level, although they are characterized by being highly energy intensive. Steam methane reforming, partial oxidation, autothermal reforming, gasification, and pyrolysis are the main thermochemical technologies, and these are explored in more detail below.
# Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges
## 2 Traffic Signs and Research Trends  
Aiming at standardizing traffic signs across different countries, an international treaty, commonly known as the Vienna Convention on Road Signs and Signals [8], was agreed upon in 1968. To date, 52 countries have signed this treaty, among which 31 are in Europe. The Vienna convention classified the traffic signs into eight categories, designated with letters A-H: Danger/warning signs (A), priority signs (B), prohibitory or restrictive signs (C), mandatory signs (D), special regulation signs (E), information, facilities or service signs (F), direction, position or indication signs (G), and additional panels (H). Examples of traffic signs in the United Kingdom for each of the categories are shown in Figure 2.  
Despite the well-defined laws in the Vienna Treaty, variations in traffic sign designs still exist among the countries' signatories to the treaty, and in some cases considerable variation within traffic sign designs can exist within the nation itself. These variations are easier to be detected by humans,  
Figure 1: Non-identical traffic signs: (**a**) Partially occluded traffic sign, (**b**) faded traffic sign, (**c**) damaged traffic sign, (**d**) multiple traffic signs appearing at a time.  
Figure 2: Examples of traffic signs: (**a**) A danger warning sign, (**b**) a priority sign, (**c**) a prohibitory sign, (**d**) a mandatory sign, (**e**) a special regulation sign, (**f**) an information sign, (**g**) a direction sign and (**h**) an additional panel.  
research groups have worked on creating traffic sign datasets for the task of detection, recognition and tracking. Some of these datasets are publicly available for use by the research community. The detailed information regarding the publicly available databases are summarized Table 2. According to [1; 9], the first and most widely used dataset is the German traffic sign dataset, which has two datasets: The German Traffic Signs Detection Benchmark (GTSDB) [10] and German Traffic Signs Recognition Benchmark (GTSRB) [11]. This dataset collects three important categories of road signs (prohibitory, danger and mandatory) from various traffic scenes. All traffic signs have been fully annotated with the rectangular regions of interest (ROIs). Examples of traffic scenes in the GTSDB database are shown in Figure 5[12].
## 5 Simulation Setup and Result Analysis
### Throughput of MCMP, MMSPEED, SPIN, EAP and PT  
We evaluate the throughput efficiency of each routing protocol in Figure 8. PT appears to be compatible with O-MAC. In Figure 8, we present the results of simulations of mobile and static scenarios using MCMP, MMSPEED, SPIN, EAP, and PT with O-MAC. To examine the robustness of these five routing protocols, we simulate a combined mobile and static scenario. The static sensor nodes are fixed in the three monitoring regions of the network, whereas the mobile nodes are attached to people (e.g., rescue team members, doctors, nurses, and other staff) and the moving vehicles being used in the affected area to rescue the injured and victims. We analyze the flow of communication and delivered successful data (throughput) when performing the different activities in the three regions.  
Figure 8: **(a)** Throughput when the nodes are all static and **(b)** Throughput when 50% of the nodes are mobile.  
Figure 7: Predicted pheromone on multiple links.
# The Modeling of Time Series Based on Least Square Fuzzy Cognitive Map
## 4 Modeling Time Series Using LSFCM  
This section covers how to use the LSFCM to model time series. The entire process of the modeling is outlined in Figure 2. First, the data are normalized and divided into the training set and test set in chronological order. Second, fuzzy time series are constructed from numerical data based on a fuzzy c-means clustering algorithm. Third, the FCM is learned using the least square method to efficiently obtain the weight matrix of the FCM according to the historical data. After the FCM model is established, we can compute the forecasted values using the LSFCM model.  
In order to improve the prediction accuracy of the developed LSFCM model, the model is further optimized by refinement of the concepts. The evolutionary optimization algorithm serves as the optimization vehicle to refine the concepts which have been developed by the fuzzy c-means clustering. The degree of fuzzy membership of each datum to the clusters will be altered and the ensuing LSFCM process is carried out again. The optimization process is driven by the minimization of the reconstruction error. Through these measures, the quality of the FCM model improves. The optimization procedure which is called LSFCM-ref is an iterative process and is repeated until the desired number of iterations is reached. In what follows, the proposed model of forecasting time series will be presented in detail.
## 2 Mechanism Introduction and Modeling Analysis
### Seedling Dropping Process Segmentation  
Taking the pot seedling at the No. 1 position as the research object, its dropping process could be divided into the following four stages: (1) after dropping off from the seedling trap, it falls freely from position A to position B, and a collision occurs at position B (Figure 9a); (2) after the collision, the pot seedling is toppled and slid to position C along position B on the inner wall of the seedling cylinder (Figure 9b); (3) after the pot seedling reaches position C, it loses the support of the inner wall of the seedling separating cylinder and carries out an oblique launching motion and collides with point D of the inner wall of the seedling separating cylinder on the opposite side (Figure 9c); (4) the pot seedling continues to fall after the collision at position D. During the dropping process, the leaves of the pot seedling are constrained by the position of the dropping mouth and the seedlings are adjusted to fall vertically until reaching the lower planting tank.  
Figure 8: Drive cam and seedling dropping process.  
During the dropping process, the seedling dropping cylinder should have the following effective constraints on the pot seedlings: (1) During the dumping process, after the collision between the potted seedlings and the inner wall of the dropping seedling tube, it should be ensured that the dumping direction of the seedlings is the direction of the inner wall on the same side, not the opposite side, otherwise, the seedlings will be planted backward or will cross the dropping mouth, causing a blockage; (2) The oblique pushing motion of potted seedlings starting from point C should collide with the opposite wall of the seedling tube and change the direction of the seedling movement speed by collision. If there is no collision, the seedling will fall out of the seedling cylinder at a large oblique speed, deviating from the direction of the planting ditch below, thereby resulting in a decrease in the planting accuracy; (3) The dropping seedling mouth should form an effective constraint on the seedling leaves during the dropping process after the second collision of the pot seedlings. By this constraint, the seedlings are adjusted to the vertical dropping state. Without such a constraint, the seedlings would maintain an inclined rate of descent after the collision, which would also deviate from the direction of the planting trench and reduce the planting accuracy.
# Transforming Group Codes in Mealy Finite State Machines with Composite State Codes
## 5 Example of Synthesis of Mealy FSM \(U_{3}\)  
Let us discuss an example of synthesis based on the proposed method. We applied this method to synthesize an LUT-based circuit for FSM A1. To obtain the circuit, we used LUTs with six inputs. The behavior of FSM A1 is represented by a state transition graph, shown in Figure 4.  
**Step 1. ** An analysis of Figure 4 shows that FSM \(A1\) has the following characteristics: \(M=12\), \(L=7\), \(N=8\) and \(H=26\). This determines the sets \(A=\{a_{1},\ldots,a_{12}\}\), \(X=\{x_{1},\ldots,x_{7}\}\) and \(Y=\{y_{1},\ldots,y_{8}\}\). Using approach [18], we can transform the STG (Figure 4) into the equivalent STT with \(H=26\) rows (Table 1). Each arc of the STG represents the pair of vertices \(\langle a_{m},a_{S}\rangle\). The \(h\)th row of the STT corresponds to the \(h\)th arc of the STG. The columns of the STT include the following variables: \(a_{m}\) is the current state (the arc's beginning); \(a_{S}\) is a state of transition (the arc's end); \(X_{h}\) is an input signal written above the \(h\)th arc (it determines the transition from \(a_{m}\) into \(a_{S}\)); \(Y_{h}\) is a collection of outputs written above the \(h\)th arc \((Y_{h}\subseteq Y)\); \(h\) is the number of transitions \((h\in\{1,\ldots,H\})\). The transition from an STG to the equivalent STT is transparent and straightforward [18]. **Step 2. ** This step is executed using the greedy algorithm proposed in [34]. We have \(S_{L}=6\). Applying the method from [34] gives us the partition \(\Pi_{AC}=\{G^{1},G^{2},G^{3}\}\) with \(KC=3\). The states \(a_{m}\in A\) are distributed among the groups in the following way: \(G^{1}=\{a_{1},a_{2},a_{3},a_{5}\}\), \(G^{2}=\{a_{4},a_{6},a_{7},a_{11}\}\) and \(G^{3}=\{a_{8},a_{9},a_{10},a_{12}\}\). So, for each group, we have \(NS_{k}=4\). The group \(G^{1}\) determines the partial sets \(X^{1}=\{x_{1},x_{6},x_{7}\}\) and \(Y^{1}=\{y_{1},\ldots,y_{7}\}\). The group \(G^{2}\) determines the partial sets \(X^{2}=\{x_{2},x_{3},x_{4}\}\) and \(Y^{2}=\{y_{1},y_{2},y_{3},y_{5},y_{6},y_{7}\}\). The group \(G^{3}\) determines the partial sets \(X^{3}=\{x_{2},x_{4},x_{7}\}\) and \(Y^{3}=\{y_{1},\ldots,y_{5},y_{7},y_{8}\}\). Using (6), we can obtain \(R_{k}=2(k\in\{1,2,3\})\). Using (7), we can obtain the value \(R_{S}=2\). We have \(L_{k}=3\) for each group, \(G^{k}\subset A\). Taking into account variables \(g_{k}\), we can find that a single LUT with six inputs is enough to generate any partial function. **Step 3. ** We have \(KC=3\). Using (5), we can obtain \(R_{G}=2\). This determines the set \(\tau_{G}=\{\tau_{1},\tau_{2}\}\). Now, we can form the set \(\tau_{S}=\{\tau_{3},\tau_{4}\}\). There is no influence of the group codes on the number of LUTs in the blocks \(LUTer1\)-\(LUTerKC\). Due to this, we can encode the groups in the following way: \(CG(G^{1})=00\), \(CG(G^{2})=01\) and \(CG(g^{2})=10\).
## 1 Introduction
Direct targeting treatment and reduced systemic side effects are important but are not the only reasons for vitamin D3 nebulization being considered in the treatment of the above-indicated disorders. Among the advantages of inhalation, the following should be mentioned: (1) Direct delivery to the effector site allows to the use of lower doses of drug to obtain the therapeutic effects, compared to other drug delivery systems; (2) The inhaled drug could be absorbed quickly into the bloodstream through the highly-vascularized lung tissue, which can lead to faster therapeutic effects compared to other routes of administration; (3) Due to the almost 100% bioavailability and limited impact of the biotransformation processes, it is easier to determine the therapeutic dose and select it for the specific needs of a patient; (4) Inhalation is a convenient and non-invasive method of drug delivery, particularly for patients with chronic respiratory conditions who need regular medication; (5) It could be used simultaneously with oxygen therapy, which is very often required by patients with chronic respiratory conditions; (6) Inhalation is often more acceptable to patients than injections or oral administration, leading to better compliance with prescribed treatments. Despite the many reasons for using inhalation to administer vitamin D3 metabolites in lung diseases, the occurrence and development of which have been associated with disturbances in the level of this hormone, no researcher has attempted to restore the physiological concentration of calcitriol in lung tissue via nebulization with this compound and/or its precursor. The presented study is the first attempt to explore this topic. The aim of the study was to determine the influence of calcidiol and calcitriol nebulization on the 1,25(OH)\({}_{2}\)-vitamin D3 level in lung tissue compartments in mice with vitamin D3 deficiencies induced by dietary cholecaliferol restriction.
## 2 Study Area, Data, and Methodology  
### Study Area  
Nepal is a landlocked country located in the central part of the Himalayan region, bounded by China in the North and India in the remaining direction (Figure 1). An abrupt change in elevation gradient (e.g., less than 100 to over 8000 m masal, including MountSagarmatha: the world's highest peak of 8848.86 masl) is observed in the relatively smaller proportion of extent in latitude [78; 79]. A typical north-to-south (N-S) cross-section consists of the high Himalayas in the North, a series of mountains and hills, including river valleys, and low-elevated hills and flat lands in the south [78; 79]. A number of microclimates [80] and climate classes [81] exist across the country ranging from tropical to polar, because of diverse topography, altitudinal contrasts, orographic effects, and climatic systems.  
The summer monsoon (southeast to northwest) is the main large-scale wind circulation modulating the spatial feature of rainfall ([PERSON], 2000). However, due to topographical features, orographic effects, sharp altitudinal variation along short latitudinal distances, and local wind circulation, the spatial characteristics of precipitation vary a lot. In addition, the western disturbance (northwest to southeast) dominates during the winter season [80]. As a result, westerly derived precipitation is greater in the high northwest mountains in the form of rain and snow [82]. The influence of two large systems (summer monsoon and westerly-derived winter precipitation) is not evenly distributed across the country. Generally, about 80% of the country's precipitation occurs during the summer monsoon [80; 82]. In particular, July and August contribute to almost half of annual precipitation [83]. Based on precipitation and other meteorological characteristics throughout the country, there are four broad seasons: (1) pre-monsoon (March-May), (2) monsoon (June-September), (3) post-monsoon (October-November), and (4) winter (December-February). The monsoon is the most important season for agriculture, and the post-monsoon is the harvesting season of main crops. Although low temperatures limit plant/crop growth during winter, cultivations are equally feasible in mid-hills and lowlands [83].  
Figure 1: Map of Nepal superimposed with the network of selected ground-based precipitation stations [N = 25] and IMERG grids [N = 1538]. The background is the elevation. Three high-precipitation areas are shown in a triangle, i.e., Lumle, Gumthang, and Num. (**a**) The inset at the top right shows the neighboring countries of Nepal.
## 2 Materials and Methods  
### Reagents  
The following products were purchased from Sigma-Aldrich, S.p.A. (Milan, Italy): Dimethyl sulfoxide (DMSO, CAS: 67-68-5), methanol (CAS: 67-56-1), isopropanol (CAS: 67-63-0), Triton X-100 (CAS: 9002-93-1), Doxorubicin hydrochloride (CAS: 25316-40-9), MTT reagent (3-(4,5-dimethylthiazol-2 yl)-2,5-diphenyltetrazolzolium bromide, CAS: 298-93-1), DCFH-DA (2,7'-dichlorohydroofluorescein diacetate, CAS: 4091-99-0), SDS (CAS: 151-21-3), NP-40 (CAS: 9016-45-9), sodium deoxycholate (CAS: 302-95-4), Tris-HCl (CAS: 1185-53-1), glycerol (CAS: 56-81-5), \(\beta\)-mercaptoethanol(CAS: 60-24-2), bromophenol blue (CAS: 115-39-9), Protease Inhibitor Cocktail (CAS 30827-99-7), radioimmunoprecipitation assay (RIPA) buffer (CAS number 127087-87-0) (0.1% sodium dodecyl sulfate, 1% NP-40, 0.5% sodium deoxycholate in PBS, pH 7.4), Bradford solution (CAS number 67-56-1). The following reagents were purchased from Euroclone (Milan, Italy): fetal bovine serum (FBS, CAS number 9014-81-7), trypsin-ethylenediaminetetraacetic acid solution (CAS number 9002-07-7), glutamine (CAS number 56-85-9), penicillin-streptomycin and phosphate-buffered solution (PBS). Reagents used for qRT-PCR were purchased: Quick-RNA MiniPrep Kit from Zymo Research, Irvine, CA, USA; the High-Capacity cDNA Reverse Transcription Kit from Applied Biosystems, Foster City, CA, USA; iTaq(tm) Universal SYBR Green Supermix from Bio-Rad; primers were purchased from Eurofins Genomics Europe Shared Services GmbH, Ebersberg, Germany. The reagent used for Western blot: nitrocellulose membranes from Amersham Bioscience (Buckinghamshire, UK), ECL(tm) Western blot Detection Reagents from GE Healthcare (Chicago, IL, USA) and the SuperSignal(tm) West Pico PLUS Chemiluminescencet Substrate from Thermo Scientific (Waltham, MA, USA).
# Blast Loading Prediction of Complex Structures Based on Bayesian Deep Active Learning
## 1 Introduction
This approach allows for the selection of higher-quality samples for training, enabling the model to achieve similar predictive performance as passive learning while requiring fewer training samples. This approach has garnered significant attention in fields such as text classification [27; 28] and image recognition [29; 30]; however, relevant research in blast loading prediction remains limited. To solve the above problems, a Bayesian deep active learning (BDAL) method for predicting blast loading in complex structures was developed. This approach combines Bayesian theory with deep learning models to provide a quantitative measure of uncertainty alongside rapid predictions of blast loading. By doing so, the reliability of the predicted results can be expressed through the quantified uncertainty. To address the challenging requirement for large amounts of training data, which are often costly to acquire, a selective strategy is implemented in the BDAL model. This strategy emphasizes the active selection of more valuable training data, reducing the computational demands of numerical simulations while building a high-precision blast loading prediction model with fewer training data. Compared to existing studies, this method provides more reliable prediction results and significantly reduces the construction costs of deep models for blast loading prediction.