esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Ground-Based Measurements
#### 2.7.3 Spectral Response of Vegetation and Bare Soil  
Spectral reflectance measurements of vegetation, calibration targets and bare soils were collected using an ASD FieldSpec 4 Wide-Resolution spectrometer. Since the distance between fiber optics and canopy was approximately 0.8 m, the collected spectra represented an average reflectance from a circle of 0.36 m in diameter. For compensation of slight movements of the fiber optics introduced by the operator while collecting the spectra, a number of 10 repetitions were set to default.  
The sampling of vegetation spectra was performed between 11:00 a.m. and 1:00 p.m. local time, \(\pm\)1 h before and after image acquisition. Forty-four spectra were taken at 22 permanent observation sites representing the natural variability of site properties. Each is represented by two spectral measurements for compensation of slight variations of standing fresh phytomass in its surrounding. The mean spectra of the two repetitions were then averaged to a single spectral response curve that represents the characteristics of the fresh phytomass at the site. The spectral measurements of the ten calibration panels (five white and five black panels) were taken during the same period (11:00 a.m. and 1:00 p.m. local time) as the vegetation spectra. The panels were placed in pairs (a white and a black) close to the four corners and the center of the study area.  
The spectral response of bare soil was collected at 18 September between 11:00 a.m. and 1:00 p.m. local time under clear sky conditions. Sample locations were identical to those selected for the collection of vegetation spectra. One day after the seeding of winter wheat, the topsoil showed a gentle surface roughness. From this, little influence on light scattering can be assumed. Topsoil conditions varied in moisture (7% to 12%) according to differences in terrain position and soil properties. The spectra were then used to calculate slope and intercept of the soil-line required for the calculation of the TSAVI. The best correlation (\(R^{2}\) = 0.99) between the b\({}_{658}\) and one of the six NIR bands was found for b\({}_{756}\) (Figure 11). The resulting parameters of the regression (\(a\) = 1.07; \(b\) = 0.02) were then used for the calculation of TSAVI.
## 2 Related Work
### GARCH Model  
The GARCH model is a standard statistical model used to model changes in volatility in time-series data, which can be used in finance to quantify risk and predict price fluctuations [10]. The GARCH model models current volatility by introducing information about past volatility and considering the autocorrelation and heteroskedasticity of volatility. The model is an extension of the ARCH model [11], where the ARCH stands for autoregressive conditional heteroskedasticity model. In finance, volatility is a fundamental concept because it can be used to quantify the risk of asset prices. The GARCH model is more suitable than the ARCH model to describe volatility in financial markets by establishing the relationship between current and past volatility to predict future volatility and by taking into account the autocorrelation and heteroskedasticity of volatility. However, it also has some limitations, such as being based on basic assumptions and assuming both a standard distribution and a linear relationship [12].
# Development of a Multi-Objective Optimal Design Approach for Combined Water Systems
## 2 Optimal Design of Combined Water Systems
### Design Optimization  
In this study, we propose an optimal design method for a water system that combines a WDS, a UDS, and a WRS. To perform the optimal design of the combined water system, the Harmony search (HS) algorithm, a well-known metaheuristic optimization algorithm, was used [17]. Since WDSs and WRSs are based on pressure water supply, EPANET was used, and the UDS used EPASWMM. For the design of the combined water system, these two hydraulic solvers were linked with HS through MATLAB, and two design approaches based on single-objective and multi-objective optimization were proposed according to the decision variables and objective functions.
## 3 Automated Pipe Jacking Friction Resistance Identification and Warning System
### Construction Methods with a Resistance Identification and Warning System  
The construction processes for the automated friction resistance identification and warning system and for pipe jacking are listed in steps, as follows:  
1. The installation of resistance sensors on the outer wall of the jacking pipe according to the designated positions, and their connection, with data cables, power cables, a monitoring host, and their display.
2. Near the tunnel entrance, removal of the steel sheet piles when the jacking machine is 200 mm away from the tunnel entrance wall, and quick advance of the tunneling machine into the working face.
3. Completion of the following steps: installation of guide rails--installation of infrared alignment devices--installation of reinforced backrest--fixing of jack feet and installation of jacks--connection of hydraulic cylinders--hoisting of the tunneling machine as well as pipe sections--hoisting of the crown plate--pipe jacking and other related procedures.
4. Performance of actions to ensure pressure grouting and displacement of the variable slurry. Determination of the slurry ratio based on the initial plan and determination of the grouting pressure based on the depth of the pipeline and the groundwater level.
5. The total grouting volume should not be less than 2% of the volume of the annular space outside the pipe. To consider slurry losses, continuous grouting must be performed regularly to ensure the integrity of the slurry jacket.
6. Monitor the jacking process, including preliminary, jacking, and completion measurements. Measurements must be taken every 50 cm of jacking, with frequent adjustments. The correction angle should be kept within 10' to 20', not exceeding 1'. Measurements should be promptly recorded and submitted for review at the end of each pipe jacking section.
7. Identification and control of jacking resistance. Sensors in the jacking process automatically identify the jacking resistance. The pressure sensor automatically detects pressure during pipe jacking construction. According to formula (4), the frictional resistance during the jacking process is calculated, resulting in the actual resistance  
Figure 7: Diagram of the resistance threshold pre-alarm control process.  
value. When the resistance value exceeds the pre-alarm threshold, the system automatically triggers an alarm, reminding construction personnel to adjust the resistance by improving the variable slurry. At this point, soil condition analysis at the construction site is required to optimize the slurry ratio and modify it, accordingly, reducing frictional resistance to achieve smooth jacking.
## 4 Training of MIMO-NARX Meta-Models
### Nine-DOF Shear Structure  
A nine-DOF shear structure is used as the MDOF nonlinear dynamic system in this study, which is simplified from a nine-story benchmark frame [34] as shown in Figure 1a. The first three natural frequencies and mode shapes of this nine-DOF structure are shown in Figure 1b.  
Figure 1: Nine-DOF shear structure simplified from nine-story frame. (**a**) Target structure; (**b**) Natural frequencies and modes.  
The equation of motion of the nine-DOF under seismic excitation can be expressed by Equation (1) and the nonlinear restoring vector \(f_{r}\) is emulated using the Bouc-Wen model [35; 36], which can be formulated as:  
\[f_{r}=k_{bw}[\alpha_{bw}x+(1-\alpha_{bw})z] \tag{29}\]  
\[\dot{z}=A_{bw}\dot{x}-\gamma_{bw}\dot{x}|z|^{n_{bw}}-\beta_{bw}\big{|}\dot{x} \big{|}|z|^{n_{bw}-1}z \tag{30}\]  
where \(f_{r}\) is the hysteresis force for each story; \(x\) is the inter-story displacement between two neighboring floors; and the Bouc-Wen model parameters (\(\alpha_{bw}\), \(A_{bw}\), \(\gamma_{bw}\), \(\beta_{bw}\), and \(n_{bw}\)) are listed in Table 1. Under a typically selected ground motion excitation, moderate nonlinearity can be observed in Figure 2 for the Bouc-Wen models of each story.
## 3 Results
### Ways and Levels of Inducing Mediolateral Perturbations  
Lateral perturbations can be induced in two ways: by pulling or pushing, and at two levels: pelvic and shoulder. The first method (pulling) was the most common, as it was opted for in five publications [5; 19; 20; 23; 25]. The pulling mechanism usually consisted of an electronically controlled motor with cables. The cables were connected with a harness worn by subjects. Activation of the machine resulted in the generation of perturbing force. [PERSON] et al. [20] used their design of a motor-based pulling device. [PERSON] et al. [25], [PERSON] et al. [5] and [PERSON] et al. [23] used a pulling system designed by [PERSON] and [PERSON] [22], that involved a closed-loop stepper motor which induces perturbations. [PERSON] et al. [19] utilized a cable system connected to a certain weight. The weight release was operated electrically.  
Two authors, [PERSON] et al. [15] and [PERSON] et al. [24] decided to generate perturbations by pushes. Since [PERSON] et al. [15] preferred unpredictable perturbations, they chose the most simple method, i.e., pushing the participants with a stick. They believed that inducing perturbations with any mechanical device would make them easier for subjects to predict. In opposition were [PERSON] et al. [24] who used a pendulum of a certain mass and a flag. The authors wanted the perturbations to be easily predictable.  
[PERSON] et al. [21] used a multi-purpose rehabilitation frame (MRF). The MRF was designed by [PERSON] et al. [26]. It consisted of a frame with two two-degrees-of-freedom joints at the level of the subjects' ankles, and two one-degree-of-freedom joints at the subjects' hips level. The MRF was attached via a bracing system on the cross-bar. The movement was possible due to servo-controlled actuators.  
The vast majority of authors, i.e., six [5; 19; 20; 21; 23; 25], took the lower limb girdle as the place of perturbation application. [PERSON] et al. [24] used a pendulum with a load that knocked the study group off balance by pushing at the shoulder rim, while [PERSON] et al. [15] studied pushes through both the upper and lower limb rims.  
The method of determining the force applied to throw off balance was not always clearly defined. In three publications, it was 20% [19], 8% [20], or 5% [24] of the body weight. Additionally, [PERSON] et al. [20] applied forces of three magnitudes. The maximum force was 8% of the body mass, medium force 5.28% (0.66*8%) of the body mass, and minimal force 2.64% (0.33*8%) of the body mass. [PERSON] et al. [21] conducted a pilot study. On this basis, they determined that suitable perturbation magnitude for the conducted study would be such when it was easy for the subjects to maintain balance after a backward pull. Similarly, [PERSON] et al. [19] selected the strength of the perturbation so that it was necessary for the subjects to take a step to regain balance. In three publications [5; 15; 23], explanation of chosen parameters was not given.
# Spectral and Energy Efficiency Trade-Off in UAV-Based Olive Irrigation Systems
## 2 Method  
To the best of the authors' knowledge, and in light of the previous description of related works, researchers have not considered the combination of unmanned aerial vehicles and massive MIMO technology in smart irrigation systems. Therefore, we consider in this section the problem of water scarcity for olive orchards. We propose a smart irrigation system where multiple drones (equipped with onboard sensors) are deployed to gather information (on humidity, temperature, and soil moisture) through remote sensing techniques. As aforementioned, UAVs are used in this work to leverage their advantages in flexible deployment and low cost. However, in such a considered system, the olive farm spans a large area, and thus, providing power to UAVs and gathering data from them may be an issue, especially since these vehicles are limited in energy. In addition, the enormous number of connected devices increases the demand for spectral efficiency. To address these challenges, we adopt massive MIMO (multiple input, multiple output) technology in the UAV-based irrigation system. Indeed, this technology will play a significant role in future sixth-generation (6G) wireless mobile networks and can potentially enhance the energy efficiency and spectral efficiency [13].  
In the following, we will describe the architecture of our proposed UAV-based olive irrigation system.
## 3 Results
### Comparative Analysis  
A comparative study was conducted for this study. First, the photocatalyst was compared with its virgin version of NPs. The versions of NPs here are ZrO\({}_{2}\) and CdWO\({}_{4}\). The photocatalytic activity of both these NPs for water splitting was observed and it was found that both these versions of the nano-catalysts showed lower H\({}_{2}\) production rates as compared to the heterojunction photocatalyst (ZrO\({}_{2}\)-CdWO\({}_{4}\)). The low efficiency of these virgin nano-photocatalysts may be due to the low charge separation and fast electron-hole recombination between VB and CB, whereas a heterojunction photocatalyst is a junction that is formed when zirconium oxide and cadmium tungstate are mixed and it can prevent electron-hole recombination; therefore, this photocatalyst is responsible for the increased H\({}_{2}\) production rate (Figure 8a).  
The present photocatalyst was also compared with several previous photocatalysts and it can be seen from Table 4 that the present photocatalyst has shown comparable performance to the previous photocatalyst and has also shown improved results. This suggests that the existing photocatalyst is a better catalyst for H\({}_{2}\) production.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Order** & **Material** & **Sacrificial Agent** & **Light Sources** &
\begin{tabular}{c} **H\({}_{2}\) Production** \\ **(a mol/g)** \\ \end{tabular} & **Ref.** \\ \hline
1. & Cu0-CdWO\({}_{4}\) & MeOH & Sunlight & -1600 & [16] \\ \hline
2. & TiO\({}_{2}\)/ACS & Glycerine & Sunlight & 203 & [34] \\ \hline
3. & Pr-Cu-TiO\({}_{2}\) & MeOH & LED irradiation & –2384 & [45] \\ \hline
4. & Pr-TiO\({}_{2}\) & MeOH & Sunlight & 195.5 & [46] \\ \hline
5. & ZrO\({}_{2}\)-CdWO\({}_{4}\) & MeOH & Sunlight & 1574 & This study \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative table of different nanomaterials for H\({}_{2}\) production.  
Figure 9: Reutilization of ZrO\({}_{2}\)-CdWO\({}_{4}\) for H\({}_{2}\) production.  
However, comparing photocatalysts in terms of their H\({}_{2}\) production capacity would be a biased analysis as the performance parameters and conditions differed for each reported catalyst. Many photocatalysts are given in Table 4; most of these catalysts were made through the chemical method; in comparison, the present catalyst has been made through the precipitation method, which makes the present catalyst cheap, low cost, and environmentally friendly. Additionally, the current photocatalysts is a type I heterojunction, which performs the water-splitting process more smoothly than other catalysts for seven reused cycles. This gives it an advantage over the other reported catalyst. Therefore, the present catalyst is better than other reported photocatalysts. Although, to validate these inferences, more concrete fundamental mechanistic studies are required.
# Nanocarriers-Assisted Nose-to-Brain Delivery of Levodopa: Current Progress and Prospects
## 2 Levodopa (L-DOPA)
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Product Name** & **Dosage Form** & & **Active Ingredients** \\ \hline Sinemet® and generics & Immediate-release tablets & • Levodopa 250 mg + Carbodopa 25 mg \\ \hline Sinemet® CR and generics & Sustained-release tablets & • Levodopa 200 mg + Carbodopa 50 mg \\ \hline Rytary® & Extended-release capsules & • Levodopa 195 mg + Carbodopa 48.75 mg \\ \hline Stalevo® and generics & Immediate-release tablets & • Levodopa 50 mg + Carbodopa 12.5 mg + Entacapone 200 mg \\  & & • Levodopa 75 mg + Carbodopa 18.75 mg + Entacapone 200 mg \\  & & • Levodopa 100 mg + Carbodopa 25 mg + Entacapone 200 mg \\  & & • Levodopa 125 mg + Carbodopa 31.5 mg + Entacapone 200 mg \\  & & • Levodopa 150 mg + Carbodopa 37.5 mg + Entacapone 200 mg \\  & & • Levodopa 200 mg + Carbodopa 50 mg + Entacapone 200 mg \\ \hline Madopar® and generics & Tablets and capsules & • Levodopa 100 mg + Benserazide 25 mg \\  & & • Levodopa 200 mg + Benserazide 50 mg \\ \hline Madopar® Dispersible tablets and generics & Dispersible tablets & • Levodopa 50 mg + Benserazide 12.5 mg \\  & & • Levodopa 100 mg + Benserazide 50 mg \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Available Levodopa combined products for oral use. Figure 1: Biosynthetic pathway of catecholamines and the place of L-DOPA (*-note). based on lowering the dose and increasing L-DOPA's in vivo stability. For this purpose, several approaches are being addressed, among which are predominantly investigations on prodrugs, alternative drug administration sites, and nanoparticle-assisted delivery. At present, besides the oral dosage forms, approved for use are the intrajeunal L-DOPA infusion (Duodopa(r) 20 mg/mL sustained-release intestinal gel-infusion) and the Inhrija(r) levodopa 42 mg inhalation powder in a capsule. Not yet established in practice but under extensive research and clinical trials remain the intranasal, transdermal, and rectal route [40; 41; 42; 43; 44].
# Roles for Exosomes in the Pathogenesis, Drug Delivery and Therapy of Psoriasis
## 6 Clinical Application of Exosomes for Treating Poriasis  
Currently, there are very few clinical studies on exosomes for psoriasis treatment. Most of the studies are still in the preclinical stage. Recently, [PERSON] et al. have reported the safety and efficacy of MSC-derived exosomes from adipose tissue of healthy donors for treating mild to moderate plaque psoriasis [102]. In this clinical study, twelve patients with plaque psoriasis were divided into three groups and given a single dose of exosomes (50, 100 and 200 ug, respectively). The severity of skin lesions was evaluated, and skin samples were collected pre- and post-treatment for H&E staining and analysis of anti-inflammatory factors. It was found that there were no significant adverse effects seen in patients after the treatment with exosomes. Only two patients experienced minor discomfort. The results showed that the thickness of the lesional skin was significantly decreased in patients receiving 100 and 200 ug doses of exosomes, while erythema and induration were significantly alleviated only in patients receiving a dose of 200 ug. Additionally, the immunohistochemistry results demonstrated that patients receiving 100 and 200 ug of exosomes exhibited a decrease in expression of IL-17 and CD3 and a dramatic increase in Foxp3 in skin lesions. Moreover, the expression of IL-23 and TNF-\(\alpha\) in the skin lesions of the patients were significantly decreased, while the expression of IL-10 was increased after treatment with 200 ug of exosomes. These results suggest that 200 ug of exosomes is safe and effective for psoriatic patients in the early phase. These can significantly improve the clinical symptoms and reduce proinflammatory factors in patients with plaque psoriasis. However, the number of patients included in this study is small, and a larger sample size is needed to further verify the effectiveness of MSC-exosomes in psoriasis treatment. In addition, a higher dose or multiple doses may also be considered in future studies.
# Compensation of Measurement Uncertainty in a Remote Fetal Monitor
## 3 Uncertainty Estimation Approach
### System Model
#### 3.1.2 Perturbation Model  
The probabilistic part is modeled by a stochastic process \(\{\xi_{k}\}\), which must be strongly stationary, uncorrelated, with a cumulative distribution function (CDF) \(F_{\Xi_{k}}\). These hypotheses are experimentally validated. The probabilistic model is used to determine the properties of the estimators used to determine the heart rate; considering that the noise \(\xi_{k}\) is naturally present and that it is transformed into \(r_{k}\) by the system during the route that the signal travels from the fetus heart through amniotic fluid, body fat, etc., to the fetal monitor output. Figure 3 shows a block diagram of the process for estimating the error distribution function \(F_{\Xi_{k}}\).  
Figure 2: Scheme for linear system modeling based on auto-regressive and exogenous (ARX) structure.
## 4 Discussion
The ethanol titer produced by _N. intermedia_ during the first fermentation stage (32.2 g/L) is probably one the highest reported by this species. In the previous studies with starchy residuals, the reported ethanol concentrations ranged approx. 5 g/L (wheat-based thin stillage) [26], approx. 8 g/L (liquid obtained after hydrothermal pretreatment of brewer's spent grains) [27] and approx. 18 g/L (acid-pretreated wheat bran) [28]. This proves that bread waste is a more efficient residue that does not require harsh conditioned pretreatment. It could also be speculated that longer first stage fermentation would result in even higher ethanol production because no production rate decline was observed on the ethanol concentration curve (Figure 1b). Additionally, previous attempts at producing ethanol on bread waste by filamentous fungi were less efficient than the one reported here. [PERSON] et al. [29] used _R. oryzae_ to produce ethanol and nutritional biomass from bread residue without the application of any material pretreatment except sterilization. They obtained a maximal ethanol concentration of roughly 17 g/L at 15% bread concentration in the medium. In this study, _R. oryzae_ was able to produce 21.3 g/L ethanol at the same bread content, which shows that the enzymatic liquefaction of the substrate improves the fermentation efficiency. Moreover, bread residues could also be used for production of foodstuffs using FF growing in solid state fermentation. For example, [PERSON] et al. [30] analyzed bread waste fermentation by _N. intermedia_ under solid state conditions, which resulted in obtaining a 'fungal burger' with acceptable sensory features and enhanced nutritional properties, e.g., three-fold protein content increase, nine-fold dietary fiber content increase and substantial production of vitamins (D2, E and C). [PERSON] et al. [31] used bread waste to produce protease and glucoamylase by A. awamori also under solid state conditions. The fermented product exhibited 114 U/g glucoamylase and 83.2 U/g protease activities, which shows the potential of bread residues for fungal enzyme production as well. It is known that the by-products play a crucial role in the economical balance of ethanol production [22]. The major distillery by-product is the stillage, which, after drying, is sold as a high-protein feed additive known as dried distiller's grains with solubles (DDGS). However, little is known about the protein content of bread waste DDGS. Wheat (major substrate for bread baking) based DDGS contains as much as 38% protein [32]. In this study, fungal biomass with the protein content exceeding 45% was obtained as a major by-product. Therefore, edible filamentous fungi might be a more efficient option for mitigating protein deficiency than plant-based protein isolates. Moreover, fungal fermentations result in the production of other valuable products (such as ethanol, which was presented in this study), while isolation of plant protein generates by-products in the form of non-protein fractions, which could also be utilized by fungi [33].