esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods for Manufacturing and Testing Samples
#### 2.2.2 ZT-CFRP-Prepreg Fabrication  
For fabricating ZT-CFRP prepreg and laminates, the EPON 862 and Araldite LY 3031 blend was the same as it was for the control CFRP prepreg; next, the surfactants, S-191 and S-192, were added with 1-wt% CNF to the blend. The mixture was subjected to a high shear mixing (HSM) period of one hour at 300 RPM, alternating clockwise and counterclockwise every 30 min at a temperature of 90 \({}^{\circ}\)C. Sonication (by a QSONICA Q700 sonicator) was carried out at a 10-amplitude value and at a 90 \({}^{\circ}\)C shutdown temperature for an hour. After the sonication, a quality-control sample from the mixture was evaluated under an optical microscope (Nikon Eclipse LY150 (Digital Sight DS-Fi1) equipped with an extended depth of focus (EDF) module to ensure there were no agglomerates of CNFs; otherwise, resonication was applied. After the CNF-dispersion quality check, Aradur 3032 curing agent was added into the solution to obtain the well-dispersed, fast-curing CNF/epoxy blend. Immediately after adding the curing agent into the CNF/resin blend, the matrix beaker was placed into a 10 \({}^{\circ}\)C water bath to extend the pot life and allow sufficient time to perform the subsequent radial flow alignment (RFA) step in ZT-CFRP-prepreg manufacturing process while maintaining the optimal matrix viscosity.  
For producing ZT-CFRP prepregs, the most important step was aligning and threading the CNFs into the carbon fabric in the \(z\)-axis direction to form the ZT-CFRP prepreg. In thisstudy, the patented radial flow alignment (RFA) technique [30], shown in Figure 2, was used to quickly align and thread CNFs radially through the unidirectional carbon fabric wrapped outside the perforated hollow roller. As described in [23], converging radial flow rheology causes the CNFs to be effectively aligned and carried by the resin flow to thread through carbon fabric (see Figure 3 for microscope pictures) during the resin-flow process, producing the desired ZT-CFRP prepreg. In this paper, this RFA process was performed twice to produce the ZT-CFRP prepregs to be used for two separate types of laminate, i.e., ZT-CFRP/OOA-VBO and ZT-CFRP/MCFA-AM.  
Figure 3: Scanning-electron-microscope images of the delaminated surface of 1 wt% CNF z-threaded CFRP laminate produced by RFA technique: (**a**) 100\(\times\) magnification; (**b**) 1000\(\times\) magnification; (**c**) 5000\(\times\) magnification (obtained from [23], with permission).  
Figure 2: Radial flow alignment (RFA) process used to manufacture the ZT-CFRP-prepreg tape.
## 3 Results
### Correlations between Observations Collected under Different Conditions for Photosynthesis
#### 3.3.1 Low PAR Conditions Including the Morning, Midday, and Afternoon Time Periods
Considering only the midday timeframe, at low PAR, strong correlations were  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Variables**} & \multicolumn{3}{c}{**Morning**} & \multicolumn{6}{c}{**Midday**} & \multicolumn{3}{c}{**Afternoon**} \\  & & \multicolumn{3}{c}{**Low PAR**} & \multicolumn{3}{c}{**High PAR 1**} & \multicolumn{3}{c}{**Low PAR 2**} & \multicolumn{3}{c}{**Low PAR 2**} \\ \hline
**Independent** & **Dependent** & **R2** & **\(n^{3}\)** & \(p\) & **R2** & \(n\) & \(p\) & **R2** & \(n\) & \(p\) & **R2** & \(n\) & \(p\) \\ \hline \multicolumn{10}{c}{**Photosynthetically active radiation (PAR) and absorbed PAR (APAR)**} \\ \cline{2-13}  & PAR & 0.60 & _48_ & **\(\ast\) & 0.11 & _28_ & ns & 0.16 & _19_ & ns & 0.43 & _48_ & \(\ast\) \\  & APAR & **0.79** & _48_ & **\(\ast\) & 0.08 & _28_ & ns & **0.75** & _18_ & **\(\ast\) & 0.37 & _48_ & \(\ast\) \\ \cline{2-13}  & \multicolumn{1}{c}{_Leaf chlorophyll fluorescence metrics (Pulse amplitude modulated F, MoniPAM)_} & & & & & & & & & & & \\ \cline{2-13}  & YII & 0.15 & _48_ & ns & **0.81** & _27_ & **\(\ast\) & 0.63 & _18_ & \(\ast\) & 0.17 & _48_ & ns \\  & ETR & **0.75** & _48_ & **\(\ast\) & 0.67 & _27_ & \(\ast\) & **0.70** & _18_ & **\(\ast\) & 0.66 & _48_ & **\(\ast\) \\ \cline{2-13}  & \multicolumn{1}{c}{_Canopy reflectance VIs_} & & & & & & & & & & & & \\ \cline{2-13}  & NDVI & 0.22 & _48_ & ns & 0.23 & _28_ & ns & 0.65 & _19_ & \(\ast\) & 0.22 & _48_ & ns \\  & Clred & 0.09 & _48_ & ns & 0.36 & _28_ & ns & 0.60 & _19_ & \(\ast\) & 0.09 & _48_ & ns \\  & PRI & 0.48 & _48_ & ns & **0.78** & _28_ & **\(\ast\) & 0.73 & _19_ & **\(\ast\) & 0.45 & _48_ & \(\ast\) \\ \cline{2-13}  & \multicolumn{1}{c}{_Canopy solar induced fluorescence (SIF)_} & & & & & & & & & & & & \\ \cline{2-13}  & SIF\({}_{A}\) & **0.86** & _48_ & **\(\ast\) & 0.65 & _28_ & **\(\ast\) & 0.80 & _19_ & **\(\ast\) & 0.65 & _48_ & \(\ast\) \\  & SIF\({}_{B}\) & **0.68** & _47_ & **\(\ast\) & 0.68 & _28_ & **\(\ast\) & 0.68 & _19_ & **\(\ast\) & **0.73** & _48_ & \(\ast\) \\  & SIF\({}_{A+B}\) & **0.88** & _48_ & **\(\ast\) & **0.71** & _28_ & **\(\ast\) & **0.83** & _19_ & **\(\observed between GPP and \(\text{SIF}_{\text{A+B}}\) (R\({}^{2}\) = 0.83), followed by \(\text{SIF}_{\text{A}}\) (R\({}^{2}\) = 0.80), PRI (R\({}^{2}\) = 0.73), and \(\text{SIF}_{\text{Ay}}\) (R\({}^{2}\) = 0.74). Correlations to LUE  
Under low PAR, there was no significant association between LUE and APAR. Leaf-level ETR and YII were correlated with LUE only weakly during the morning, and YII moderately during midday and the afternoon (Table 4). PRI was strongly correlated to LUE (R\({}^{2}\) = 0.71), using only the afternoon observations. At midday, under low PAR, both \(\text{SIF}_{\text{Ay}}\) and NDVI were similarly correlated with LUE (R\({}^{2}\) = 0.71 for both).
## 3 Proposed Method
### Process Design
#### 3.4.4 Verification Stage of Transaction Legitimacy
Alice calculates the hash value \({h_{4}}^{\prime}=Hash(note_{4})\) of the payment note \(note_{4}\) and publishes \({h_{4}}^{\prime}\) to the verification network;
4. The verification nodes verify the correctness of the commit value of the note, i.e., verifies that \(h_{4}\) is equal to \({h_{4}}^{\prime}\). 5. Update the world state of the note  
Each verification node updates the Note Commitments table to record the binding relationship between the hash values \(h_{\text{out}}=[h_{4},h_{5}]\) of output notes (\(note_{4}\), \(note_{5}\)) and the corresponding stealth addresses \(T_{4}\) and \(T_{5}\), indicating the output of this transaction, and updates the Nullifier table to record the invalid identifiers \(T_{1}\) and \(T_{2}\) of input notes (\(note_{1}\), \(note_{2}\)), indicating the input of this transaction. The entire process of this transaction is now complete. The world state of the note after the transaction is completed is shown in Table 3.
# Corrosion and Scaling in Geothermal Heat Exchangers
## 4 Corrosion in Geothermal Environments
### Uniform Corrosion
composition, temperature, pH, pressure, metal material, and fluid flow characteristics. Uniform corrosion is commonly associated with carbon steels in geothermal plants, and some CS corrosion rates are detailed in Table 2.  
The influence of temperature on the corrosion rate of CS in geothermal environments is not straightforward and can vary based on specific conditions. [PERSON] et al. [28] and [PERSON] et al. [29] observed that the corrosion rate tends to increase with rising temperature and decreasing pH. However, the presence of protective corrosion product layers can mitigate corrosion if they are sufficiently dense and adherent. For instance, [PERSON] et al. [29] noted a reduction in the corrosion rate at 160 \({}^{\circ}\)C compared to 80 \({}^{\circ}\)C and attributed this to the formation of a denser carbonate corrosion product layer [29]. Similar observations of lower corrosion rates at higher temperatures were reported for API L80 steel [26; 27], although explanations were not provided. [PERSON] et al. [30] identified different corrosion mechanisms at various temperatures (80, 120, and 160 \({}^{\circ}\)C) for API P110 steel. Corrosion was uniform at 80 and 160 \({}^{\circ}\)C, with a corrosion rate an order of magnitude lower at 160 \({}^{\circ}\)C. At 120 \({}^{\circ}\)C, corrosion was more localized. The corrosion products identified at 80 \({}^{\circ}\)C consisted of a Fe\({}_{3}\)C network, retained from the original metal microstructure, and impregnated with porous FeCO\({}_{3}\), offering limited corrosion protection. Above 160 \({}^{\circ}\)C, a more protective crystalline Fe-oxide film formed, further lowering the corrosion rate [32]. [PERSON] et al. [31] also observed a reduction in the corrosion rate at 150 \({}^{\circ}\)C compared to 70 \({}^{\circ}\)C, attributed to the formation of a dense crystalline FeCO\({}_{3}\) layer. At 70 \({}^{\circ}\)C, the corrosion product formation included chukanovite (Fe\({}_{2}\)(OH)\({}_{2}\)CO\({}_{3}\)) and siderite (FeCO\({}_{3}\)), which are less dense and, therefore, less protective. In the liquid phase, the flow rate of the fluid significantly affects the corrosion rate, with increasing flow rate correlating with higher corrosion rates [28]. However, in the vapor phase, temperature is the dominant factor and the impact of flow rate is negligible [29]. In the vapor phase, corrosion is primarily induced by the formation of carbonic acid in condensed water in the presence of CO\({}_{2}\). The presence of oxygen can accelerate corrosion, particularly when introduced during maintenance operations [26].
# Biosynthesis of Rubbellins in _Ramularia collo-cysni_--Genetic Basis and Pathway Proposition
## 1 Introduction
Only two derivatives named rubellin A **1** and B **2** were originally reported [20]. Four more derivatives named rubellins C **3**, D **4**, E **5**, F **6**, and 14-dehydro rubellin D **9** have since been identified in _R. collo-cygni_[21]; however, rubellins B **2** and D **4** were the most abundant in infected barley leaves [22] suggesting that  
Figure 1: Structures of the rubellins A–F (**1–6**), chrysophanol **7**, the possible precursor of the rubellins and emodin **8**, its analogue, as well as the 14-dehydro rubellin D derivative **9**. these two metabolites might be the end product of the rubellin biosynthetic pathway. Using labelled acetate, [PERSON] et al. showed that similar to most anthraquinones, rubellins are synthesised through the polyketide pathway [21]. Furthermore, rubellins are structurally related to other anthraquinone-derived metabolites such as cladofulvin and dothistromin that are synthesised by nonreducing polyketide synthases (NR-PKSs) in the dothidomycte fungi _Cladosporium fulvum_ and _Dothistroma setposorum_, respectively [23, 24]. Therefore, the biosynthetic gene cluster leading to rubellin production is expected to be based on an NR-PKS core gene and to contain homologues of genes involved in the biosynthesis of other anthraquinone metabolites such as emodin, monodictyphenone or chrysophanol. These metabolites are intermediate in the biosynthesis of known anthraquinone-derived SMs such as cladofulvin, sterigmatocystin, and agnestin [23, 25]. [PERSON] et al. identified several gene clusters located around polyketide synthases (PKSs) in the genome of _R. collo-cygni_[26]. Here we investigated the cluster located around the _Pks1_ gene in the context of rubellin biosynthesis. The aim of this study was to identify a candidate rubellin biosynthetic gene cluster in _R. collo-cygni_. Understanding the biosynthesis of these SMs could contribute to a better understanding of their role in _R. collo-cygni_ biology and disease symptoms development which could help identify breeding targets to improve host resistance to RLS. Furthermore, in the light of the renewed interest in the antibiotic and antitumor activity of the rubellins and the potential of these SMs as therapeutics to treat Alzheimer's disease through the inhibition of tau aggregates formation [27, 28, 29], understanding the biosynthesis of these SMs could provide an efficient and inexpensive source of rubellin. Currently, rubellin biosynthesis relies on synthetic chemistry [30]; therefore, unravelling the biosynthetic pathway in _R. collo-cygni_ could pave the way for pathway engineering and ectopic expression of the cluster.
## 3 Results and Discussion
### Analysis of the Laser Driven Proton Tracks in CR-39 Detectors by Visible Light Microscopy
Similar results were obtained after the interaction of the high-power laser pulse with the Al and Ag structured targets of 5 and 10 \(\upmu\)m thicknesses. Recently, in [29] by particle-in-cell simulations of target normal sheath acceleration of protons was obtained a proton spectrum (Figure 2e from reference) similar with that one from Figure 10.  
Optical images of the proton tracks identified on both front sides of the post-etched CR-39 detectors can be observed in Figure 8.  
These pictures illustrate traces with irregular, circular or elliptic shapes of different sizes that make difficult the proton track analysis. The spots with irregular shapes can be attributed to unwashed contaminants of the etching solution that remain on the detector surface, or to the material etched by the chemical solution. The irregular shapes with dimensions smaller than 0.88 \(\upmu\)m (the minimum number of tracks in CR-39 detectors that might be attributed to protons, Figure 4b) or higher than 8 \(\upmu\)m (the maximum number of tracks in CR-39 detectors that might be attributed to protons, Figure 4b) renders difficult the identification of proton tracks. Only the circular and elliptic shapes observed on the surface of post etched CR-39 detectors with very well-defined edges could be assigned to proton tracks. This is an indication of a good signal to noise ratio and proof of protons. It may be concluded that for a proper identification of proton track diameters in CR-39 detectors by optical microscopy, it is important to establish, beside a clear signal of proton pits, the theoretically values for the minimum and maximum diameters of the proton tracks. The non-uniform distribution of the proton tracks on the surface of post-etched CR-39 detectors due to the surface morphology of Al and Ag targets was also observed. Similar proton track distributions were observed for structured dynamic plasma targets [18]. Figure 10: Dependence of number of proton tracks on the proton energy obtained for (**a**) first and (**b**) second CR-39 detector in the case of the interaction of the high-power laser pulse with the Al target of 5 \(\upmu\)m thickness.
## 1 Introduction
Previous reports [17; 20; 23; 28] have investigated La-Co co-substituted SrM samples without using sintering additives. Polycrystalline samples were prepared through solid-state reaction [17], powder samples were prepared through the sol-gel process and subsequent heat treatment [20; 23], and single crystals were grown through the flux growth method [28]. However, their results are not in good agreement. [PERSON] et al. [20] reported that it is possible to synthesize M-type single-phase powder with the same amount of La and Co (i.e., the molar ratio of Co/La = 1.0) up to \(x=0.4\) by using the sol-gel process and subsequent heat treatment at 900 \({}^{\circ}\)C for 24 h in air. On the other hand, [PERSON] et al. [17] reported that the Co/La molar ratios of samples sintered at 1300-1350 \({}^{\circ}\)C for 24 h in air decreased to less than 1. For \(x\) values greater than 0.1, the deviation increased. This trend of La-Co co-substitution with a relatively larger amount of La relative to Co has also been observed for flux-grown single crystals in air [28]. To achieve a single-phase M-type structure in La-Co-substituted SrM through solid-state reactions without the use of additives is a difficult challenge. However, it is essential to determine the crystal structure and magnetic properties of La-Co-substituted SrM without sintering additives for research purposes. This is important for understanding lattice constants, \(M_{S}\), and \(H_{a}\), as reported in previous research. To clarify this discrepancy, in this study we attempted to prepare La-Co co-substituted SrM polycrystalline samples of the M-type single phase by solid-state reaction without using sintering additives. We report and discuss the structural and magnetic properties of samples fabricated up to \(x=0.3\) in Sr\({}_{1-x}\)La\({}_{x}\)Fe\({}_{12-x}\)Co\({}_{x}\)O\({}_{19}\).
## 2 IoH Architecture
### Connectivity Layer  
The IoH platform was designed as a standalone solution that does not have any dependence on other system or systems. The platform manages and takes actions without the necessary use of external services. However, this is simply not possible in this type of platforms. The connectivity layer is responsible for three things: connectivity with other IoH platforms to perform cooperative building management, connectivity with devices and resources, and connectivity with external data providers.  
The need for collaboration with other IoH platforms appears for large buildings with multiple IoH platforms managing independent zones. For instance, in a large office building with multiple independent offices, where each office has its own IoH platform, and the building itself has an IoH platform for external sensors, such as temperature and luminosity, the building's IoH platform can share the external sensor data with the offices' IoH platforms.  
The connectivity with devices and resources will be detailed in the descriptions of the IoT integration layer and the subsystems integration layer. The connectivity with external data regards public and subscription services, such as weather application programming interfaces (API) that can give important data and information to the platform in order to perform an efficient and intelligent building's management.
# Evaluation of Short-Term Aging Protocol for Asphalt Mixtures
## 1 Introduction  
The State of Qatar is investing heavily in infrastructure and construction projects and almost 95% of these investments are spent in developing road transport infrastructures [1]. About $14 billion have been announced for the development and improvement of the road network and infrastructure by the year 2019 [2]. Most roads are built using asphalt mixtures in Qatar. To ensure the construction of high-quality pavements with long service lives, proper evaluation of the materials' properties and the ability of these materials to stand up to traffic loading and weather conditions is essential.  
In a hot mix asphalt (HMA) application, an asphalt mixture is produced at an elevated temperature prescribed by the manufacturer. The asphalt mixture is then transported to the construction site, laid down, and compacted to prepare an asphalt pavement layer. Placement and compaction temperature is maintained at a minimum to produce a quality and well-performing pavement. During mixing, aggregates are coated by a very thin film of asphalt binder. These films are exposed to air at high temperature for several hours, leading to a significant change in the rheological properties of the asphalt binder. This change in the rheological properties of the asphalt mixture during production, hauling/storage, and compaction is known as short-term aging. Long-term aging occurs in the asphalt pavement during in-service periods throughout its life. Aging is an inevitable part of asphalt pavement and affects its performance in many ways.  
The ability to simulate aging in asphalt binders and mixtures produced in the laboratory has been studied extensively [3; 4; 5; 6; 7; 8]. Laboratory mixture conditioning protocols have been developed andprocedures have been adopted for use in binder specifications and mixture design. It was generally accepted earlier that laboratory aging represents field aging even though their performance results were not same [9]. Due to many changes in asphalt technology, the validity of current mix design methods has been questioned. In this study, short-term aging protocols for asphalt mixtures commonly used in the State of Qatar and the region of the Middle East have been evaluated.
## 2 Overview of AC Analysis Methods
### Cross-Correlation (X-Corr)  
Cross-correlation, abbreviated as x-corr or x-correlation, calculates the similarity between the input signal \(x\) and a reference signal \(y\), by forming a new signal \(R_{xy}\). The newly formed signal has its peak \(\left\|R_{xy}\right\|_{\infty}=R_{xy}[l]\) at the time instance \(l\), said to be the lag time at which maximum similarity between the two signals is observed. This time instance is the time shift between the two signals.  
By using a reference sinewave signal with unitary amplitude, no initial phase, and with a frequency of choice \(f\), the cross-correlation to a signal sharing the same frequency but a different amplitude and phases \(x[k]=Asin(2\pi f\delta tk+\varphi)\) returns a cross-correlation signal \(R_{xy}\) signal which can be used to determine the phase and the amplitude of the signal. In this case, \(\left\|R_{xy}\right\|_{\infty}\) at time instance \(l\) is evaluated as (12).  
\[R_{xy}[l] = A\frac{N}{2}cos(\varphi+2\pi fl\delta t)\] \[- \underbrace{\frac{A}{2}\frac{sin(2\pi fN\delta t)}{sin(2\pi f \delta t)}cos(2\pi f\delta t(N-1-l)+\varphi)}_{\approx 0}\]  
where \(\delta t\) is the sampling time. Next, \(R_{xy}[l]\) is normalized by \(\frac{N}{2}\) and an integer number of periods of the signal are used to nullify the second expression. It is important to have a good phase precision when using cross-correlation to cancel \(\varphi+2\pi fl\delta t\). This corresponds to the existence of an integer \(l\) that verifies \(-\varphi\frac{F\delta}{2\pi f}\). More details are given in Section 3.3. Finally, the phase can be extracted from (13).  
\[\varphi=2\pi\text{lags}[l]\delta tf(\text{mod }2\pi) \tag{13}\]  
where \(lags=\{-N\delta t,(-N+1)\delta t,\ldots,(N-1)\delta t\}\). Like DTFT, cross-correlation can only work with one frequency, and multi-frequency analysis requires a multiplication of the algorithm for each desired frequency.