esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Design
#### 2.1.2 Software  
The Cryologger platform is programmed in C++ and is extensible, meaning that modifications to system functionality can be easily made by enabling or disabling specific blocks of code. The code is written in and uploaded using the Arduino Integrated Development Environment (IDE; v1.8.19), which benefits from numerous software libraries available from the open-source community. These libraries reduce complexity by helping to interface with components and include examples of real-world applications that can guide userswho are new to programming. Two key libraries used to develop the software for the Cryologger ITB are the TinyGPS and Iridium SBD libraries, written by [PERSON] [38, 39]. These libraries enabled the functionality required to communicate with the GNSS receiver and RockBLOCK transceiver, respectively.  
The programming logic optimizes sleep and wake cycles to minimize overall power consumption. When initially powered on, the system attempts to acquire a signal from the GNSS receiver and synchronizes the RTC with the current date and time. It then sets an alarm for the initial sampling interval and enters a low-power, deep-sleep mode. When the alarm triggers, the system wakes, records the time, and acquires a GNSS position. Next, measurements of all onboard sensors are collected, including temperature, pressure, battery voltage, and orientation of the beacon (i.e., pitch, roll, and tilt-compensated heading) from the accelerometer and magnetometer. These data are stored in memory, and when the software determines the appropriate transmission interval has been met, it attempts to transmit the data using the RockBLOCK satellite transceiver. The software then disables power to all components, sets the next alarm, and returns to sleep.  
Several software design techniques are intentionally utilized to ensure the long-term reliability of the Cryologger ITB. First, the Watchdog Timer module (WDT) of the Arduino microcontroller is used to ensure a system reset is triggered in the case of a software malfunction that prevents normal operation. The WDT is a timer with a fixed period, which counts the ticks of its clock until it is periodically reset every 8 s, or the timeout period of 16 s is reached. In the event of a timeout, the WDT will trigger a system reset. This functionality is intended to allow the program to gracefully recover from malfunctions that could otherwise cause it to become inoperable. Additionally, the software provides redundancy in the event of a sensor or component failure. For example, if a sensor fails to initialize, future attempts to obtain a measurement with the concerned instrument will be skipped. Finally, when transmitting data via the RockBLOCK transceiver, the software checks for the presence of incoming messages sent by the end-user, which can be used to issue a command to force the system to reset manually.
## 3 Results
### Simulation of ET with RZWQM2 Model  
The RZWQM2 model has a good effect in regard to simulating the daily \(ET\) of cabbages. The measured evapotranspiration (\(ET_{m}\)) and simulated evapotranspiration (\(ET_{s}\)) values during the three rotations have the same trend. The \(ET_{m}\) during the whole growth period was 275.6 mm, 232.2 mm, and 272.5 mm, respectively. Based on the model simulation, the \(ET_{s}\) values were 256.9 mm, 215.1 mm, and 251.6 mm, respectively, 6.8%, 7.4%, and 7.7% lower than those of the \(ET_{m}\), respectively, with the simulation accuracy being close to the research results of [PERSON] [45] and [PERSON] [46]. Figure 7 shows the scatter plot of  
Figure 5: Correlation between ET and its main driving factors. * Indicating statistical significance (\(p<0.05\)), ** Indicating statistical significance (\(p<0.01\)).  
Figure 6: The biplot of principal components analysis (PCA) between \(ET\) and its drivers for the cabbages.  
all \(ET_{m}\) and \(ET_{s}\) data, with an \(R^{2}\) of 0.73. The fitting relationship between the two is \(ET_{s}\) = 0.77 \(ET_{m}\) + 0.61.
## 3 Results and Discussion
### Estimation of Volatiles over Ripeting
The majority of literature reports have focused on the aroma of avocado fruit; however, a limited number of studies have investigated the aroma of avocado oil [25, 33, 34, 35, 36, 37]. Nonetheless, similar results can be drawn from the study of avocado fruit volatiles. [PERSON] [42] reported that the sesquiterpenes of avocado fruit decreased during ripening. Moreover, similar results have been reported in other climacter fruits, in which series of changes in metabolic biosynthesis occur during storage ripening [43]. In particular, the monoterpenes in mango fruits (_Mangifera indica_ L. \"Kensington Pride\") have increased on the fourth day of ripening and decreased afterwards [43]. Furthermore, in the same fruit, the monoterpenes, sesquiterpenes, and aromatics were determined at a higher total amount in ripened mango compared with the unripe and over-ripe stages [44]. In another study by [PERSON] on figs (_Ficus carica_ L.) [45], \(\beta\)-caryoophyllene and D-limonene increased significantly from the unripe to the ripe stage and were suppressed in the fully ripe stage. Ethylene is well-known to control the storage duration and rate of ripening of climacter fruits. A potential hypothesis is that the rise could be linearly correlated with ethylene synthesis. Several studies reported that climacter fruits including apple (_Malus domestica_ Borkh.) [46], tomato (_Solanum lucopersicum_ L.) [47], and mango [48] undergo a rapid production of terpenes which depends on the response of ethylene. Thus, ethylene plays a key role in the metabolic events of volatiles during ripening [49]. Nevertheless, numerous underlying processes are yet to be delineated and merit additional study [50]. Another possible interpretation of the suppression of terpenoids over the later ripening stages (over-ripe) could be correlated with the presence of terpenoid hydroperoxides. The mesocarp of avocado fruit contains idioblastic cells that contain oil sacs and sesquiterpene hydroperoxides [7, 51, 52]. During maturation or enzymatic reaction, a degradation of a primary wall of the parenchyma cells occurs, which releases the oil from the idioblastic cells, and then the released hydroperoxides act on the terpenoids.
###### Abstract  
In this work, carbon dot-decorated graphite carbon nitride composites (CDs/g-C\({}_{3}\)N\({}_{4}\)) were synthesized and innovatively used as a SPME coating for the sensitive determination of chlorobenzenes (CBs) from water samples, coupled with gas chromatography-mass spectrometry. The CDs/g-C\({}_{3}\)N\({}_{4}\) coating presented superior extraction performance in comparison to pristine g-C\({}_{3}\)N\({}_{4}\), owing to the enhancement of active groups by CDs. The extraction capacities of as-prepared SPME coatings are higher than those of commercial coatings due to the functions of nitrogen-containing and oxygen-containing group binding, \(\pi\)-\(\pi\) stacking, and hydrophobic interactions. Under optimized conditions, the proposed method exhibits a wide linearity range (0.25-2500 ng L\({}^{-1}\)), extremely low detection of limits (0.002-0.086 ng L\({}^{-1}\)), and excellent precision, with relative standard deviations of 5.3-9.7% for a single fiber and 7.5-12.6% for five fibers. Finally, the proposed method was successfully applied for the analysis of CBs from real river water samples, with spiked recoveries ranging from 73.4 to 109.1%. This study developed a novel and efficient SPME coating material for extracting organic pollutants from environmental samples.  
carbon dots; graphite carbon nitride; sorbent; solid-phase microextraction; chlorobenzenes +
Footnote †: journal: Nanomaterials
# The Role of Earth Observation, with a Focus on SAR Interferometry, for Sinkhole Hazard Assessment
## 3 Statistical Modelling and Probabilistic Sinkhole Hazard Assessment
It was concluded that an increase in water ingress from ponding rainwater in areas not serviced by storm water drains led to an increase in sinkhole events [13]. The information derived from sinkhole inventories have led to the development of Dolomite Risk Management Strategies (DRMS) for the region. It was determined that, after the development of the DRMS in 2004, the occurrence of sinkhole and subsidence events in the period reduced from 50 events per year in the early 2000's to 5 events per year, a reduction of 90% [13]. It can be seen that effective sinkhole and subsidence hazard management strategies depend on a comprehensive understanding of the geological mechanisms of sinkhole formation as well as the local sinkhole characteristics, triggering and controlling factors. Section 4 will discuss the contribution from earth observation towards collecting reliable and appropriate data for sinkhole hazard assessment, with a focus on the compilation of sinkhole inventories and monitoring ground deformation associated with sinkhole development.
## 2 Materials and Methods
### Methylene Blue Removal  
Initial experiments aimed to assess the impact of particle size on dye adsorption in order to determine the mass limitations of the process for each biomass. A batch system using 20 mg of each biomass feedstock in the 5 particle size ranges (<106 \(\upmu\)m, 106-212 \(\upmu\)m, 212-300 \(\upmu\)m, 300-425 \(\upmu\)m, 425-600 \(\upmu\)m) was mixed with 10 mL of a 200 ppm MB solution (Sigma-Aldrich, St. Louis, MO, USA) in a glass cylindrical cell and then placed in an incubator for 24 h at 30 \({}^{\circ}\)C.  
The next set of experiments explored the influence of pH on dye adsorption. Several batches of biosorbent at different pHs were left in an incubator at 30 \({}^{\circ}\)C for 24 h. Each glass cylindrical cell had 10 mg of biosorbent mixed with 10 mL of 200 ppm initial MB concentration at different pHs (2.0, 4.0, 6.0, 8.0, and 10.0). The pH was adjusted by adding 0.1 M HCl or 0.1 M NaOH to the solutions.  
The influence of the initial MB concentration on dye adsorption was explored using \(\sim\)30 mg of biosorbent mixed with 30 mL of MB solution (all at a set pH of 6) at concentrations of 200 ppm, 100 ppm, and 50 ppm. To assess the impact of temperature, \(\sim\)30 mg of biosorbent mixed with 30 mL of MB solution (pH of 6) at a 200 ppm solution was mixed with a glass beaker at 160 rpm and stored in an incubator at 20, 30, and 40 \({}^{\circ}\)C for 24 h. To obtain the adsorption curves and calculate the adsorption kinetics, \(\sim\)140 \(\mu\)L (0.46% of total solution) of the solution was removed at specific time intervals using automatic pipets.  
After completion of each run, a syringe filter (20 \(\mu\)m pore size) was used to filter the solution, and a ultraviolet visible (UV/Vis, Shimadzu UV mini-1240) spectrophotometer was used to measure the MB concentration. For each experiment group, one sample was conducted in triplicate to assess the experimental error. The dye concentration at equilibrium was calculated from the calibration curve. The wavelength of maximum absorbance (664 nm) was used to obtain the calibration curve. The dye removal efficiency (Equation (1)) (wt.%) [7; 62] and adsorption capacity (mg/g) (Equation (2)) [7; 62] were determined as follows:  
\[\text{MB Removal Efficiency}\ \eta=(\text{C}_{0}-\text{C})/\text{C}_{\text{c}} \times 100 \tag{1}\]  
\[\text{q}=(\text{C}_{0}-\text{C})\times\text{V}_{\text{sol}}/\text{m}_{\text{ ads}} \tag{2}\]  
where C\({}_{0}\) is initial MB concentration (mg/L) and C is the residual MB concentration (mg/L), V\({}_{\text{sol}}\) (L) is the MB adsorption volume, m\({}_{\text{ads}}\) (g) is the mass of biosorbent, and q (mg/g) is the biosorbent adsorption capacity.
## 2 Materials and Methods
### Proximate and Ultimate Analyses  
Proximate analysis was conducted using thermogravimetric analysis (TGA) on a thermal analyzer (DTG-60A, Shimadzu, Kyoto, Japan). lyophilized samples weighing 5-10 mg were placed in a platinum pan and subjected to TGA under an atmosphere of N at a flow rate of 25 mL min\({}^{-1}\). The heating rate was set to 10 \({}^{\circ}\)C min\({}^{-1}\) until a final temperature of 900 \({}^{\circ}\)C was reached. The moisture content was determined as the mass loss occurring prior to reaching 105 \({}^{\circ}\)C. Volatile matter was defined as the mass loss observed between 105 \({}^{\circ}\)C and 900 \({}^{\circ}\)C, resulting from thermal decomposition. The residue remaining after reaching 900 \({}^{\circ}\)C contained fixed carbon and ash.  
Ultimate analysis for elemental composition was performed using a Flash 2000 elemental analyzer (Thermo Fisher Scientific, Milan, Italy). This analysis determined the content of carbon (C), hydrogen (H), oxygen (O), nitrogen (N), and sulfur (S). The calorific value (CV) of the samples was calculated using the equation provided by [34].  
\[\text{CV (MJ kg}^{-1})=0.3278\text{C}+1.419\text{H}+0.09257\text{S}-0.1379 \text{O}+0.637 \tag{3}\]
# Deep-Learning Based Positron Range Correction of PET Images
## 1 Introduction
### Proposed Method  
In this work, we propose a deep-learning based PRC method (Deep-PRC) applied as a postprocessing step to the reconstructed PET images. Our goal was to develop a fast PRC method for 3D PET imaging that is able to provide PET images for medium- and large-range radionuclides, rivaling in spatial resolution the ones reconstructed with the standard short-range \({}^{18}\)F radionuclide. The NN was trained with realistic simulated cases of preclinical studies of reconstructed images of \({}^{68}\)Ga and \({}^{18}\)F corresponding to the same activity distribution, and it was able to produce accurate and precise \({}^{68}\)Ga PR-corrected images similar to the \({}^{18}\)F ones.  
We assumed that the image reconstruction method used to obtain the images had already incorporated the PRC for \({}^{18}\)F, and, therefore, the purpose of this work was to obtain a PRC for \({}^{68}\)Ga that made it look similar to the corresponding \({}^{18}\)F counterpart, without the risk of double correcting this effect. The source code is available in Github [36].
## Abbreviations  
ACAT Acetyl-CoA C-acetyltransferase ACOC Acetyl-CoA carboxylase APX Ascorbate peroxidase CDPK Calcium-dependent protein kinase CPT Cis-prenyl transferase DAPs Differentially accumulated proteins DIGE Differential in-gel electrophoresis DMAPP Dimethylallyl diphosphate EDCP EH domain-containing protein EIP Ethylene-inducible protein FADS Farnesyl pyrophosphate/diphosphate synthase FPP Farnesyl diphosphate GGPP Geranylgeranyl diphosphate GGPS Geranylgeranyl pyrophosphate synthase GGPS Geranylgeranyl pyrophosphate synthase GLR Glutathione reductase GO Gene ontology GPP Geranyl diphosphate GPS Geranyl pyrophosphate synthase HMGR Hydroxymethylglutaryl coenzyme A reductase HMGS Hydroxyethylglutaryl coenzyme A synthase HRBP HRT1-REF bridging protein, a Nogo-B receptor HRT _Hevera_ rubber cis-prenyltransferase, also named CPT IPP Isopentenyl pyrophosphate iTRAQ Isobaric tags for relative and absolute quantification LRPs Large rubber particles MEVD Mevalonate disphosphate decarboxylase MEVK Mevalonate kinase MVA Menvalonate
## 1 Introduction
However, contemporary industry often requires measurements to be as short as possible (especially in the case of in-process measurements). Nevertheless, a too-high tracing speed can lead to significant measurement errors. This is due to the risk of losing contact between the tip and the surface to be measured (a so-called \"tip flight\" phenomenon). Taking into account this problem, the authors conducted research on the influence of the tracing speed of the measuring tip on the measurement results of the selected surface roughness parameters. [PERSON] et al., in their paper [27], dealt with this problem, but the research was very limited. Firstly, the measurements were carried out on only one sample, which was the UKAS Reference Specimen 112/1534. Secondly, only a small range of parameters was observed, including Ra, Rq, and Rz for 2D measurements and their equivalents Sa, Sq, and Sz for 3D measurements. This was the reason why the authors of this article conducted studies to assess the impact of the tracing speed for a large number of samples with different types of irregularities. In this work, 11 roughness parameters were observed describing various features. The main aim of this study was to prove that the effect of tracing speed is different for different surface roughness parameters. The detailed objective of this research was to determine which of the analyzed parameters are less sensitive and which are more sensitive to changes in the tip travel speed. Compared to other papers examining the influence of tracing speed on roughness measurement results, this article is distinguished primarily by a large number of tested surfaces and a large number of analyzed roughness parameters. The research methodology is presented in the second section. The third section contains the results of the research. The fourth section presents a discussion of the results. The last section of this work contains a summary and final conclusions.
# Prediction of Ground Surface Settlements Induced by EPB Shield Tunneling in Water-Rich Soft Strata
## 4 Prediction of Short-Term Ground Deformation  
Using the empirical method described in Section 2, a reliable prediction for the long-term settlement can be carried out. However, the ground deformation that occurred during the shield tunneling (i.e., short-term) is also the main concern in practice, especially in  
Figure 5: Statistical result of the trough width parameters \(K\). (**a**) trough width parameter \(K\) vs. buried depth \(H\), (**b**) probability distribution of \(K\).  
the urban water-rich soft strata such as Changzhou. Many accidents, such as structural damage or ground collapse, could occur because of a lack of well-controlled TBM drive. In this section, an accurate prediction method for three-dimensional ground deformation during shield driving is established.
## 1 Introduction  
Electrical power systems (EPSs) are among the largest and most complex systems that engineers deal with at the present time. EPSs contain many components of various types, scales, and complexity from low-power devices to power turbogenerators or high-voltage transmission lines. The management and operation of such complex systems is a truly complicated task.  
The amount of renewable energy sources and their variable generation schedule require more complex solutions that help manage and balance rapidly changing electricity flows. The rapidly growing number of generating consumers is changing the settled/traditional stepped-down flow of electricity. Due to the large number of renewable energy sources (RES) in the distribution network, in some cases the power flows between transmission and distribution systems have become two-way/bidirectional, which raises additional management challenges for transmission system operators.  
The primary and most important function of electrical system operators is system management (operational management). This concept is usually considered in the field of electric power systems as system management/control in real time; it includes system balancing, correction actions, disconnections/faults. For longer-term usage, the term planning is used, which describes actions that will be performed in the future (mid-term/long-term). The term planning is used to refer to the actions required to manage (operate) the system under certain conditions (scenarios) that may occur in the mid or long term. More about the planning of electrical systems can be found in references [1; 2].  
However, the fast EPS transformation is blurring the boundaries between real-time management and long-term planning. Today, renewable energy sources that are not easily predictable (for example, solar, wind, and tidal energy) [3; 4] combined with highly unpredictable load patterns (electric car charging, climate change, etc.) [5] make both management and planning more demanding. Electric networks operate/work in their limit modes more and more often, whereby such variability/fluctuations shorten the resource of various elements of the electric system, and as a result, the reliability of the system suffers. It is becoming more and more important to predict possible power flows and their distributions in the power system and thus identify the most severe operating modes of the power system. Since our proposed ranking method is associated with severe/restricted operating modes, during which the safe limits of the thermal conductivity of the electrical network are exceeded, the safety assessment of the electrical system will be examined in more detail.
## 3 Data Collection, Experimental Setup and NN Model Selection
### Dataset
#### 3.1.2 Neural Network Comparative Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **Number of** \\ **Weighted** \\ **Connections** \\ \end{tabular} } & \multicolumn{4}{c}{**Quick Changes**} & \multirow{2}{*}{**Hard Brake**} & \multirow{2}{*}{
\begin{tabular}{c} **Sharp** \\ **Cornering** \\ **(Rad/s)** \\ \end{tabular} } \\  & **Motorway (m)** & & & **Roundabout (m)** & & **Acceleration (m)** \\ \hline \multirow{2}{*}{2} & 0.05 & 0.41 & 0.38 & 0.28 & 0.52 \\ \hline \multirow{2}{*}{3} & 0.06 & 0.62 & 0.33 & 0.33 & 0.56 \\ \hline \multirow{2}{*}{4} & 0.06 & 0.59 & 0.34 & 0.35 & 0.51 \\ \hline \multirow{2}{*}{5} & 0.06 & 0.60 & 0.38 & 0.34 & 0.41 \\ \hline \multirow{2}{*}{6} & 0.05 & 0.61 & 0.39 & 0.35 & 0.43 \\ \hline \multirow{2}{*}{7} & 0.05 & 0.63 & 0.37 & 0.32 & 0.47 \\ \hline \multirow{2}{*}{8} & 0.05 & 0.60 & 0.37 & 0.32 & 0.46 \\ \hline \multirow{2}{*}{9} & 0.05 & 0.60 & 0.35 & 0.34 & 0.45 \\ \hline \multirow{2}{*}{10} & 0.06 & 0.61 & 0.35 & 0.28 & 0.51 \\ \hline \multirow{2}{*}{11} & 0.06 & 0.58 & 0.36 & 0.25 & 0.50 \\ \hline \multirow{2}{*}{12} & 0.05 & 0.38 & 0.38 & 0.28 & 0.51 \\ \hline \multirow{2}{*}{13} & 0.06 & 0.62 & 0.33 & 0.32 & 0.51 \\ \hline \multirow{2}{*}{14} & 0.06 & 0.59 & 0.34 & 0.35 & 0.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The performance evaluation based on the Cumulative Root Squared Error (CRSE) metric of the Input Delay Neural Network (IDNN) in each investigated scenario across several time steps on the orientation rate estimation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Number of** \\ **Weighted** \\ **Connections** \\ \end{tabular} } & \multicolumn{4}{c}{**Number of Trainable Parameters**} \\  & \multicolumn{2}{c}{**MLNN**} & \multicolumn{2}{c}{**vRNN**} & \multicolumn{2}{c}{**GRU**} & \multicolumn{2}{c}{**LSTM**} & \multicolumn{2}{c}{**IDNN**} \\  & **(2-Layer)** & **(2-Layer)** & **(2-Layer)** & **(2-Layer)** & **(2-Layer)** \\ \hline \multirow{2}{*}{8} & 33 & 65 & 185 & 245 & 65 \\ \hline \multirow{2}{*}{16} & 97 & 225 & 657 & 873 & 161 \\ \hline \multirow{2}{*}{32} & 321 & 833 & 2465 & 3281 & 449 \\ \hline \multirow{2}{*}{64} & 1153 & 3201 & 9537 & 2705 & 1409 \\ \hline \multirow{2}{*}{128} & 4353 & 12,545 & 37,505 & 49,985 & 4865 \\ \hline \multirow{2}{*}{192} & 9601 & 28,033 & 83,905 & 111,841 & 10,369 \\ \hline \multirow{2}{*}{256} & 16,897 & 49,665 & 148,737 & 198,273 & 17,921 \\ \hline \multirow{2}{*}{320} & 26,241 & 77,441 & 232,001 & 309,281 & 27,521 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Number of trainable parameters in each neural network (NN) across various weighted connections.