esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Derivation of the CMAC Equation to Reverse Atm-I  
CMAC development began after observation of the response of reflectance data between clean and hazy images collected across short time spans for the same AOI. The same pattern of reflectance alteration was observed in every band for the images affected by haze: in the continuum of dark to bright responses, low reflectance was increased due to aerosol backscatter, while high reflectance was attenuated. These dark-to-bright responses form a continuum; between these opposing end member responses, there exists a reflectance level called the \"axis point\", where backscatter and attenuation cancel, and the reflectance value does not change between TOAR and SR. These observations were translated into a graphic conceptual model (Figure 2) that inverts and adjusts the well-known empirical line method [13] into a linear representation of the atmospheric effect at the time of image capture. The resulting TOAR deviation line encodes the reflectance response for any pixel, dark to bright, affected by a single level of atmospheric effect. TOAR deviation lines are uniquely defined by their slope and offset that constitute the two parameters CMAC requires to reverse the atmospheric effects to retrieve SR. In agreement with the original observation, as Atm-I increases, backscatter, defined by offset, increases dark reflectance, and attenuation, defined by both slope and offset, decreases the bright reflectance.  
Precedence for the linear conceptual model can be found in the literature (Figure 3).  
Figure 1: A TOAR image of Lake Tahoe in smoke from a regional wildfire on a portion of the 6 September 2021 L8 image tile (**a**). The Atm-I grayscale developed from the 300 \(\times\) 300 m grid cells of the Atm-I model captures in gross detail the haze pattern visible in the TOAR image (**b**). Bright ground features in the grayscale partially result from forward scatter of the greater energy reflected by bright targets as described in paper 1 [7]. The Atm-I of this moderate haze ranged from about 970 to 1240.  
The conceptual model of Figure 2 was translated into the CMAC equation for a reversal of the Atm-I effect for each pixel across the scene. The closed form of this solution allows rapid bandwise AC of each image pixel guided only by the brightness of the Atm-I grayscale raster. The CMAC equation reverses the atmospheric effect and can be applied once calibration has established the mathematical relationships translating the Atm-I grayscale brightness into the TOAR deviation line slopes (m) and offsets (b). This translation occurs through equations that express two functions derived by pairing the slope and offset with the driving parameter, Atm-I, observed from many QIA images obtained under sequentially increasing Atm-I.  
CMAC Equation: SR = (TOAR - b)/(m + 1)
# The Recycling of Carbon Components and the Reuse of Carbon Fibers for Concrete Reinforcements
## 1 Introduction  
Carbon fiber reinforced polymers (CFRP) are being used in an increasing number of areas in recent years. Examples include the automotive and aircraft industries, the manufacture of wind turbines and space exploration [1]. The global demand for carbon fibers in 2022 was around 117 kt [1].  
In addition to the use of carbon in the automotive and aerospace industries, carbon fibers are increasingly being used in the construction sector in the form of reinforcements. In the construction sector, concrete or reinforced concrete is the most widely used building material. According to [2], due to ongoing population growth, the construction industry is responsible for up to 70% of all land alterations (such as land sealing), up to 50% of resource consumption, and up to 40% of energy consumption. In 2018, 4.11 billion tons of cement were produced worldwide, resulting in 2.25 billion tons of CO\({}_{2}\) emissions. According to [3], a thick concrete cover layer is typically required to protect the steel beneath it against corrosion, depending on the component and other factors. In contrast, carbon fibers are corrosion-resistant. As a result, a large amount of concrete can be saved by replacing the reinforcement steel with carbon rods or carbon mats, since the concrete cover that protects the reinforcement steel from corrosion can be reduced. As the density of carbon fibers is four times lower compared to reinforced concrete and the tensile strength up to six times higher, up to 80% of the concrete can be saved compared to reinforced concrete while still having the same load-bearing capacity [4]. For example, the production of a reinforcement mat made out of virgin carbon fibers requires 12.8 t CO\({}_{2}\)/t [4]. Global carbon fiber waste is projected to increase by 20 kt per year by 2025 [1]. Additionally, a large number of carbon components reach their end of life and must be recycled or disposed of. Therefore, a sustainable reuse of carbon fibers is essential. The use of recycled carbon fibers in construction represents a great potential area of application. However, the development of components from recycled fibers is still an incompletely explored research field.  
This article provides an overview of the different methods of recycling carbon fibers and presents the production of carbon reinforcements from recycled carbon fibers. Moreover, initial tactile tests (single-fiber tensile tests and yarn tensile tests) with recycled carbon fibers are presented, and the resulting challenges for reinforcement development are outlined. The novel production of non-metallic reinforcements based on recycled carbon fibers aims to close the carbon cycle. This article provides an overview of the initial considerations and preliminary investigations regarding the production of reinforcements from recycled carbon fibers.
## 3 Results and Discussion  
Using SAP2000 finite element (FE) software, three types of models (uniform stiffness model, beam-spring model, and stiffness reduction model) were established for a double-cell PUTCGE with the dimensions outlined in Section 2.1. In the beam-spring model, beam elements were used to simulate the sidewalls and the top and bottom slabs, and rotating springs were used to simulate seam joints, as shown in Figure 4a. A schematic diagram of the bending moments for the beam-spring model is given in Figure 4b, where the shaded part with colors represents the tension side. Two other types of models were also established but not shown here. Among the three types of models, only the stiffness reduction model required the use of reduction of the moment of inertia. A comparison and an analysis are both conducted in the section that follows, mainly considering the effects on the stiffness reduction and bending moment adjustment coefficients caused by the influences of the joint stiffness ratio, the aspect ratio of width to height, the resistance coefficient of the subgrade reaction, and the embedment depth.  
Figure 4: Finite element analysis model and results schematic: (**a**) analysis model and (**b**) bending moment diagrams.
## 4 The Development of 3 SMVI Software and Integration
### The Error Algorithm for Roundness Holes  
The roundness error algorithm, which is explained in Section 2.1, uses the minimum zone circles technique and includes the following steps:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Image No.** & \
\begin{tabular}{c} **Angle in** \\ **Degree** \\ \end{tabular} &
\begin{tabular}{c} **Mild Delrin Dia. 30 mm** \\ \end{tabular} \\ \hline
1 & \(0^{\circ}\) & 29.9383 \\
2 & \(31^{\circ}\) & 29.9397 \\
3 & \(62^{\circ}\) & 29.9378 \\
4 & \(93^{\circ}\) & 29.9292 \\
5 & \(124^{\circ}\) & 29.9364 \\
6 & \(155^{\circ}\) & 29.9281 \\
7 & \(186^{\circ}\) & 29.9368 \\
8 & \(217^{\circ}\) & 29.9311 \\
9 & \(248^{\circ}\) & 29.9315 \\
10 & \(279^{\circ}\) & 29.9299 \\
11 & \(310^{\circ}\) & 29.9324 \\
12 & \(360^{\circ}\) & 29.9308 \\ \multicolumn{2}{c}{_Error in \(\mathbf{\mu m}\)_} \\ \multicolumn{2}{c}{_Roundness Circle Hole_} & \(=R\mathbf{c}_{max}\) & \multicolumn{1}{c}{**2.9 \(\mathbf{\mu m}\)**} \\ \multicolumn{2}{c}{\(-R\mathbf{c}_{min}\)} \\ \hline \hline \end{tabular}
\end{table}
Table 2: The diameter values from the vision system for mild Delrin workpiece.  
1. The labeled image is scanned from left to right and top to bottom. During this process, the \((x,\ y)\) coordinates of the edge pixels of a selected part (color) are extracted and stored in an array known as Edge Pixels;
2. The minimum zone method is then applied to the pixels in the Edge Pixels array to determine the center and radius of the minimum zone circle;
3. The center of the minimum zone circle is calculated, and the distances between this center and all pixels in the Edge Pixels array are computed. The minimum and maximum distances are identified as \(R_{min}\) and \(R_{max}\).
# An Efficient SS-MAC Protocol for IEEE 802.15.4-Based WSNs of Cluster Tree Topology
## 3 The Proposed SS-MAC Protocol
### Steady State Phase (SSP)
#### 3.3.3 Transmission Mechanism  
In a sharable slot, each tree node in the same level performs the CSMA/CA operation to compete with the public channel for sending data packets to its parent. This competition only occurs between tree nodes of the same parent. Since the number of child nodes of a parent is usually small, this competition is local and controllable. Due to the different transmission channels and the non-overlapping transmission time, there is generally no traffic collision between member nodes and other CHs in the network. SS-MAC specifies that the maximum size and payload of packets transmitted between tree nodes are 127 and 121 bytes, respectively. Using large packets can reduce the number of packets as well as the number of nodes competing for public channels. When a node receives a correct packet, it responds with an ACK; otherwise, it uses a NACK to notify the sender that the received packet is incorrect or lost.  
The number of packets that a tree node needs to send varies. When the number of packets in the nodes' buffer increases due to retransmission caused by packet errors or other cause, some tree nodes may not complete the transmission of all packets within their sharable slots. Thus, the SS-MAC protocol designs a field named number of remaining packets indicator (RDI) in each packet to indicate the number of remaining packets in the nodes' buffer. The information will eventually be forwarded to the sink, which then updates \(W_{s}\) and \(\alpha\) of the next Session_ST according to RDI and the levels in which the remaining packets are located. If the number of packets in the nodes' buffer exceeds a certain threshold, there is an overflow risk. To reduce this risk, the network can increase the length of their sharable slots by resetting \(W_{s}\) and \(\alpha\) to ensure that the packets in their buffers have enough time to be sent.  
Tree nodes begin to collect data from member nodes after \(SS(l)\). The communication between tree nodes is performed on the public channel, while the communication between tree nodes and member nodes is performed on private channels. This frequency division multiplexing (FDM) mechanism effectively avoids inter-node interference.
# Flexibility Based Day-Ahead Generation-Reserve Bilevel Decision Model
## 3 Flexibility Based Reserve Decision Method
### Universal Generating Function Model for Load  
The load forecasting error of the power system obeys a normal distribution [27] so the actual load at time \(t\)\(P^{\text{D}}(t)\) conforms to normal distribution \(N(\overline{P}_{\text{d}}(t),\sigma_{\text{d}}(t))\), where \(\overline{P}_{\text{d}}(t)\) is the predicted load value at time \(t\) and \(\sigma_{\text{d}}(t)\) is the load forecasting standard deviation. \(\sigma_{\text{d}}(t)\) can be obtained through statistics of historical data, or comes from empirical value.  
Step \(P_{\text{intv}}\) is set for discretization of probability load model. According to probability theory, the study is limited in the range of \([\overline{P}_{\text{d}}(t)\)-\(3\sigma_{\text{d}}(t),\overline{P}_{\text{d}}(t)\)+\(3\sigma_{\text{d}}(t)]\) for efficiency. The load is divided into \(N_{\text{d}}\) states. In a certain load state \(k\), the load value is \(P^{\text{d}}_{k}(t)\) and the error is \(\Delta P^{\text{d}}_{k}(t)\), then the corresponding probability \(p^{\text{d}}_{k}(t)\) is:  
\[\begin{array}{ll}p^{\text{d}}_{k}(t)&=\Pr\left[p^{\text{d}}_{k}(t)-\frac{P_{ \text{intv}}}{2}<P^{\text{D}}(t)<P^{\text{d}}_{k}(t)+\frac{P_{\text{intv}}}{2 }\right\}\\ &=\Phi\left(\frac{2\Delta P^{\text{d}}_{k}(t)+P_{\text{intv}}}{2\sigma_{\text{ d}}(t)}\right)-\Phi\left(\frac{2\Delta P^{\text{d}}_{k}(t)-P_{\text{intv}}}{2\sigma_{ \text{d}}(t)}\right)\end{array} \tag{17}\]  
where \(\Pr\{\cdot\}\) is the probability of event \(\{\cdot\}\), \(\Delta P^{\text{d}}_{k}\) is the error of load state \(k\) and \(\Phi(\cdot)\) is the standard normal distribution function. Then the UGF model of load can be obtained:  
\[u_{\text{d}}(z,t)=\sum_{k=1}^{N_{\text{d}}}p^{\text{d}}_{k}(t)\cdot z^{P^{ \text{d}}_{k}(t)} \tag{18}\]  
Similar to the ramping of generator, the load change at time \(t\) is \(R^{\text{d}}(t)=P^{\text{D}}(t+1)-P^{\text{D}}(t)\), the prediction errors of load at adjacent time are independent so the UGF model of load change is:  
\[u_{\text{D}}(z,t)=u_{d}(z,t+1)\otimes_{-}u_{d}(z,t)=\sum_{k=1}^{S_{\text{D}}} p^{\text{d}}_{k}(t)\cdot z^{R^{\text{d}}_{k}(t)} \tag{19}\]  
where \(\otimes_{-}\) is the difference operator of UGF method, \(S_{\text{D}}\) is the total number of load change states, \(R^{\text{d}}_{k}(t)\) refers to the load change value of state \(k\) and \(p^{\text{D}}_{k}(t)\) is the corresponding probability.
# An Efficient Carrier Synchronization Scheme for Demodulation Systems
## 1 Introduction
The proposed carrier synchronization scheme is simulated under the condition that the symbol rate is 25 Mbaud and the CFO is 20% symbol rate, and its performance almost coincides with the theoretical curve results. In addition, its operating frequency can be up to 200 MHz, with less resource consumption and less complexity of implementation.
# PolySorbate-Based Drug Formulations for Brain-Targeted Drug Delivery and Anticancer Therapy
## 3 PS-Based Formulations for Various Routes of Drug Administration
### PS-Based Formulations for Transdermal Delivery  
Transdermal drug delivery involves the topical administration of drugs across the skin barrier. The topical delivery of active compounds is hampered by the limited skin permeability [74]. Surfactants, such as PSs, enhance the permeation of drugs through the skin [75]. Microemulsions composed of eucalypt and PS 80 were fabricated as transdermal delivery vehicles for curcumin [76]. The curcumin permeation rate of the microemulsion was 15.7-fold higher than that of the control (eucalypt only). The microemulsions containing 25% water, 25% ethanol, 37.5% ethanol, and 12.5% PS 80 exhibited the highest curcumin permeation rate [77]. Microemulsions composed of PS 80, medium-chain glycerides, and propylene glycol were developed for the efficient transdermal delivery of hydrophilic (i.e., adenosine) and hydrophobic (i.e., progesterone) drugs [78]. Microemulsions with 47% (\(w/v\)) PS and 63% (\(w/v\)) PS were prepared. Microemulsions with 63% (\(w/v\)) PS exhibited higher efficacy in transdermal delivery of hydrophilic agents than those with 47% (\(w/v\)) PS. In addition, various PS-based microemulsions have been developed for the transdermal delivery of testosterone [79], meloxicam [80], capsaicin [81], fluorouracil [82], and ketoprofen [83].
## Conventional GTR and GBR
### Membranes with a Functional Layer
#### 2.2.2 Membranes Releasing Growth Factors  
The essential factors for periodontal regeneration depend on the interactions among the scaffold material and growth factors, cells, and blood supply [29]. In particular, growth factors have an essential role in the healing process. They influence tissue repair including angiogenesis, chemotaxis, and cell proliferation, and they modulate the synthesis and degradation of extracellular matrix proteins. Several bioactive molecules such as platelet-derived growth factor (PDGF), basic fibroblast growth factor (b-FGF), tumor growth factor (TGF), bone morphogenetic protein (BMP), and enamel matrix derivatives (EMD) showed positive outcomes in stimulating periodontal regeneration [32]. In several studies, PDGF-BB-loaded PLLA membranes showed an enhanced regeneration procedure and proliferation of PDL than the control group [32; 33]. Other studies on b-FGF with collagen sponge, TGF-beta with PLLA membrane, and recombinant BMP-2 with hybrid alginate/nanofiber mesh showed effective regeneration in defects [34]. EMD is assumed to elicit the new attachment formation upon inserting collagen fibers between new alveolar bone and new cementum [34]. The use of EMD affects the gene expression, protein synthesis, and stimulation of osteoblast and PDL cells [34].
## 5 Experimental Tests
### Hydrogen Consumption Estimation  
In order to implement a real FC on the test bench and to size the hydrogen storage system, an estimation of the \(H_{2}\) consumption must be performed. Since the proposed layout is designed considering the fuel cell FCS-C300 manufactured by Horizon Fuel Cell Technologies as a reference, its consumption curve was used for the fuel consumption estimation. This curve was obtained from the datasheet of the fuel cell stack and is shown in Figure 19. Considering the power delivered by the emulated FC during the considered tests, the instantaneous and cumulative hydrogen consumptions were derived. Table 6 shows the results in terms of max and total consumption of hydrogen for the performed tests. Figures 20 and 21 show the results for the real work scenarios in terms of hydrogen consumption. Assuming that the efficiency of the FC system adopted in the test bench is similar to the efficiency of the real FC system for the tractor, the hydrogen consumption can be scaled up to the consumption of the real FC tractor by multiplying it by the scale factor. According to this assumption and considering a medium work condition such as the use of a shredder, to guarantee 4 h of endurance of the real hydrogen tractor, approximately 7 kg of hydrogen should be stocked on-board. Considering stocking the hydrogen in gaseous form inside cylinder tanks at 700 bar, an estimated volume of about 200 L is required. In accordance with the actual state-of-art of hydrogen storage systems, a mass of around 120 kg is estimated for the tanks.
# Trepenoid Metabolic Engineering in Photosynthetic Microorganisms
## 2 Pathway Modules in Terpenoid Biosynthesis  
Metabolic engineering is the application of systems and synthetic biology approaches to engineering cells for the production of industrially useful biochemicals. These factories can be designed and engineered for optimal resource allocation towards the desired pathway. Terpenoid precursor and biosynthetic pathways are well characterized in different organisms and have been manipulated in a multitude of studies, which renders them model pathways for metabolic engineering. The biosynthesis of terpenoids has a distinctive modular structure, which makes their pathways particularly suited to synthetic biology-based engineering approaches: a natural grouping of biochemical reaction types creates four distinct modules that can be independently manipulated (Figure 1). Pathway modularization is useful in combinatorial engineering approaches [11; 21; 22] and is useful as a concept guiding engineering studies.  
Module I: Redirection of Resources towards isopentenyl pyrophosphate and dimethylallyl pyrophosphate  
Production hosts commonly have insufficient availability of the C5 prenyl pyrophosphate precursors IPP and DMAPP to provide an adequate flux to the heterologous pathways. Successful examples of terpenoid production hosts include maximization of the metabolic flux towards the formation of IPP and DMAPP through the overexpression or manipulation of genes of the endogenous MVA and MEP pathways as well as the insertion of the MEP pathway in organisms with only an MVA pathway to supplement flux [23; 24; 25; 26; 27; 28]. The latter strategy has the additional objective of overcoming endogenous flux-constraining mechanisms as well as introducing emergent synergistic effects. One interesting study aimed at the production of isoprene pinpointed that, when both the MVA and MEP pathways are co-expressed in _E. coli_, they work in synergy and both have a higher flux when compared to having only one pathway present [27; 29].  
In the MEP pathway, the first two steps catalyzed by deoxy-xylulose 5-phosphate synthase and deoxy-xylulose 5-phosphate reductoisomerase (DXS and DXR, respectively) as well as the isomerization of C5 prenyl phosphates by the isopentenyl-diphosphate delta-isomerase (IDI) (Figure 1) are rate limiting and must be overexpressed for a high terpenoid yield [15; 30; 31]. The MVA pathway is limited by the first catalytic steps--from acetyl-CoA to mevalonate--and in particular by the 3-hydroxy-3-methyl-glutaryl-coenzyme A reductase (HMGR) [32; 33].  
In the presence of the MVA/MEP pathway overexpression/augmentation, over-accumulation of prenyl phosphates is problematic and causes cellular toxicity [34]. For this reason, a balance between flux through the DMAPP/IPP synthetic pathways and consumption of prenyl phosphates is required. A strong sink \"pull\" approach is commonly required to compensate for the upstream pathway flux augmentation.
## Data and Methods
### Data
#### 3.1.4 PERSIANN-CDR  
PERSIANN-CDR is a long-term gridded precipitation product developed for hydro-climatic studies. PESIANN-CDR is developed from infrared and passive microwave observations that are processed with PERSIANN algorithm for generating the rain rates. Rain rate information are retrieved from low earth and geostationary satellites, however the passive microwave observations used are only available from 1997 and prior period. To overcome the limitation of passive microwave observations, National Centers for Environmental Prediction (NCEP) stage IV radar data is used to train the nonlinear regression parameters of the artificial neural network model. The artificial neural network estimates precipitation from cold cloud pixel information and nearby features, using the gridded satellite brightness temperature information of international satellite cold climatology project. Finally, PERSIANN-CDR is checked and corrected for bias correction against GPCP monthly product. The final PERSIANN-CDR product provide daily scale precipitation which covers the period from 1983 to the present day, with a grid size of 0.25\({}^{\circ}\) between 60\({}^{\circ}\)S and 60\({}^{\circ}\)N. Further details are provided in References [2; 21].