esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Calorimeter Measurement  
The temperature rise caused by Joule's heat can be measured under certain conditions. The generated heat should not be affected by the environment. Therefore, an adiabatic measurement system is required, for which a calorimeter is used.  
A calorimeter consists of a vacuum flask and a plug. A calibration heater and a thermometer were placed inside the flask in which a liquid surrounds both the devices and the measurement probe. The liquid serves as a medium to absorb the heat. The magnetic stirrer within the flasks ensures an even and fast temperature distribution within the liquid. The connections of the cell, the calibration heater, and the thermometer are led through a plug as shown in Figure 2, i.e., the structure of the calorimeter.  
Chemical effects within the cell, like the reversible heat effect, need to be considered within this method as it influences the heat release.  
To determine the energy loss, firstly, the heat capacity of the system must be determined, for which a calibration procedure is performed. This procedure uses a heating resistor or so-called calibration heater to supply a known amount of energy to the system. The amount of heat \(Q_{calibration}\) supplied can be calculated from Equation (5),  
\[Q_{calibration}=Pt=UIt \tag{5}\]\(P\) is the electrical power of the resistor and \(t\) in \(s\) is the duration of the power supply where current flows through the resistor, i.e., the value of the resistor is known. Together with the set voltage and [PERSON]'s law, the amount of heat can be calculated based on Equation (6),  
\[Q_{calibration}=\frac{U^{2}}{R_{ resistor}}t \tag{6}\]  
The supplied energy leads to a temperature increase within the calorimeter. This temperature-difference \(\Delta T\) calibration is measured with a thermometer. Based on this temperature difference and the amount of heat \(Q_{calibration}\), the heat capacity \(C_{system}\) of the system can be determined according to Equation (7),  
\[C_{system}=\frac{Q_{calibration}}{\Delta T_{calibration}} \tag{7}\]  
After the calibration, the cell is supplied with a current profile. Due to the cell's internal resistance, heat is generated, which is absorbed by the calorimeter liquid. The resulting temperature rise \(\Delta T_{measurement}\) is measured with the thermometer. With the heat capacity \(C_{system}\) and the temperature difference \(\Delta T_{measurement}\), the energy loss \(Q_{loss}\) can be calculated (Equation (8)),  
\[Q_{loss}=C_{system}\Delta T_{measurement} \tag{8}\]  
Then, the cell's internal resistance can be determined with Equation (4).
## 5 Optical Experiment
The original images, encrypted images and keys were averaged over sixteen images to suppress noise. The images encrypted using the key with NAE 0.0005, and corresponding decrypted images are shown in Figure 10.  
Decryption was carried out by inverse filtering with Tikhonov regularization [50]. After that, the decrypted images were quantized. Due to the nonuniformity of image histogram, nonuniform quantization was used [51]. Image histogram was divided into zones with equal pixel quantity. The quantity of quantization levels is the number of these zones. The quantized decrypted images and error distribution maps are shown in Figure 11. The dark points in error distribution map correspond to the pixels with gray level lower than correct ones, bright point--pixels with gray level above correct one. Figure 10: Encrypted (**a**,**c**) and decrypted images (**b**,**d**) corresponding to MCDDC with 128 \(\times\) 128 (**a**,**b**) and 254 \(\times\) 254 (**c**,**d**) elements. Figure 9: The encryption keys with NAE 0.0005 (**a**) and 0.001 (**c**) and corresponding DOEs (**b**,**d**). In the experiments, the information was successfully extracted from all decoded MCDDCs presented in Table 2. The minimum achieved error rate was 13.8%. It is necessary to set the input error correction level at least 1.5 times greater than the actual error rate to avoid the probability of data decoding failure. In our case, it is appropriate to use the error correction level of 20%, which corresponds to the density of data encoding of 0.24.
# Investigation of Crack Propagation and Healing of Asphalt Concrete Using Digital Image Correlation
## 4 Conclusions  
Healing is an important parameter of asphalt pavements for mixture design and service life prediction. However, there is still a lack of a proper method to observe and quantify this phenomenon for asphalt mixtures. In this study, the DIC method has been used to firstly predict the crack locations and secondly explore the possibility of observing and measuring the healing ability of asphalt mixtures from the strain map on the surface of the samples. According to the results,  
* This method is able to predict the location of the cracks and observe the healing phenomenon happening in the microcracks.
* It is possible to observe the amount of healing of different microcracks on the surface of the specimens and it was shown that, as predicted in the healing models, the tip of the microcracks's heals more than their widest part.
* The amount of time it takes for the specimens to heal around the microcracks (HT) can be calculated. This value can be used to plan the minimum time that is required for an asphalt mixture to have its maximum healing at a certain temperature.
* Finally, two parameters of strain recovery (H%) and strain recovery speed (SR), which can be used qualitatively to compare the healing ability and healing speed of different mixtures, were proposed and calculated for a mixture.  
Future research will focus on using this method to investigate the healing of different asphalt mixtures at different temperatures and compare the proposed DIC method with other existing healing identification methods.  
Conceptualization, N.H. and A.M.; methodology, N.H.; software, N.H. and B.R. and J.D.; formal analysis, N.H.; investigation, N.H.; resources, A.M. and G.J. and J.B.; writing--original draft preparation, N.H.; writing--review and editing, N.H. and C.V. and S.V. and A.M. and G.J.; supervision, C.V. and S.V.; project administration, W.V.d.b.  
Figure 9: P/Pottal calculated by digital image correlation (DIC) for three identical asphalt concrete specimens (S1–S3).  
**Funding:** The authors would like to thank the research council of the Faculty of Applied Engineering of University of Antwerp for granting this project funded by the Everdepoel legacy.  
**Conflicts of Interest:** The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Moreover, the authors declare no conflict of interest.
After testing several potential candidates, the azeotrope chosen was 19% v/v isopropanol (strong solvent) in n-hexane (weak solvent) with a boiling point of 62.7 \({}^{\circ}\)C. In his application of cleaning shellac varnish from a painted 16 th century sleight in the collection of the Coach Museum at Versailles, [PERSON] noted that in addition to offering evaporation at constant composition, the azeotrope has a higher vapor pressure than either pure solvent (a 'positive azeotrope'), providing yet another tool for safeguarding the artifact by limiting contact time with the oil paint surface or by reducing absorption into cracks and fissures in the paint [8]. The same azeotrope proved useful in other treatments of sleighs from the Coach Museum [11; 12]. [PERSON] [9] and [PERSON] [10] have further described the use of azeotropes in paintings conservation. [PERSON] utilized the azeotrope of 38% v/v 2-butanone in cyclohexane, which boils at 71.8 \({}^{\circ}\)C, to reform and thin a synthetic resin varnish on an oil painting and to remove rubber cement from a 17 th C Peruvian devotional painting [12]. While these conservation treatments reported in the literature were undoubtedly highly successful, doubts arise over the actual evaporation behavior of these boiling point azeotrope compositions when utilized at the room temperature conditions typical of conservation treatments. Azeotropes are both temperature and pressure sensitive [14; 15], with the azeotropic composition changing across wide ranges in either parameter. The present study employs various instrumental methodologies to experimentally determine the room temperature azeotropic composition of the isopropanol:n-hexane and 2-butanone:cyclohexane systems. The techniques used encompass vapor pressure determinations, gravimetric analyses, refractive index measurements, and directly evaluating the composition of the liquid phase in evaporating mixtures through gas chromatography. The investigation hopes to provide more clarity to the conservation community on the possible role of azeotropes in conservation cleaning while highlighting the potential for false expectations of solvent mixtures that are not azeotropes at room temperature conditions.
## 2 Experimental Section
The pulse duration (open state time of the Ga shutter), monitored with an optical pyrometer, was 8 s, while the pulse repetition period was 22 s. The GaN growth consisted of 300 periods. The nitrogen consumption in both cases (AlN and GaN growth) was 1.6 sccm at an RF source power of 350 W. The power of the substrate heater was maintained at a constant level throughout the growth process. The experimental samples were also investigated using an in situ reflection high-energy electron diffraction technique (RHEED). RHEED patterns at all growth stages were monitored using a standard 15 kV RHEED (STAIB Instruments) system equipped with a 6-inch phosphor screen flange and a high-resolution CCD camera. The electron beam in RHEED hits the wafer surface at an angle of inclination (\(\sim\)1\({}^{\circ}\)), which makes this method extremely valuable for studying the crystal integrity of graphene samples and can be a valuable tool for checking the structure and quality of graphene on the surface of amorphous substrates before manufacturing various devices. It should be noted that RHEED patterns can be obtained in the entire range of azimuthal angles \(\varphi\) from 0\({}^{\circ}\) to 360\({}^{\circ}\) due to the possibility of wafer holder rotation controlled by a stepper motor. The beam reflection profiles were obtained at different sample rotation angles in increments of 1\({}^{\circ}\). To supplement the obtained experimental results, we carried out modelling of the AlN-graphene interaction. Calculations were carried out within the density functional theory with dispersion Grimme corrections [29], which ensure correct accounting for non-covalent interlayer interactions, as provided in the Quantum Espresso package [30, 31]. Ultrasoft pseudopotentials with the GGA-PBE functional were used for all elements [32, 33]. The cutoff energy of the plane wave basis was chosen to be equal to 45 Ry. The number of points of the Monkhorst-Pack grid [34] in \(k\)-space was 4 \(\times\) 4 \(\times\) 1. The first order Metfessel-Paxton scheme with 0.01 Ry smearing was used [35]. Structural optimization continued until the residual forces became less than 0.1 meV/A.
# Prototyping of Environmental Kit for Georeferenced Transient Outdoor Comfort Assessment
## 4 Data Acquisition and Handling
### GPS Data  
The global positioning system, used for high precision positioning and navigation, has three progression sectors: the satellite constellation (24 satellites orbiting Earth), ground control network (master control station of the satellites), and user equipment (GPS receivers). The GPS receiver simultaneously calculates its two-dimensional position, velocity, and time by the signal evaluation from a minimum of three pseudo-ranges (satellite data), while altitude can be calculated from a fourth pseudo-range. The quality of fix improves with the higher number of pseudo-ranges used for triangulation [29].  
The National Marine Electronics Association (NMEA) defines the communication interface between marine electronic equipment as the standard for GPS communications. The prototype's GPS module also uses the NMEA format as preferred by most computer programs. The communication data includes real-time positional information.
## 4 Materials and Methods
### Gene Cloning and Construction of Plasmid and Strain  
PCR fragments of putative _Aurantiochytrium IDI_ (771 bp), _GGPPS_ (1254 bp) and _Escherichia coli IDI_ (549 bp) were amplified with primer pairs P4/P5, P9/P10 and P7/P8 (Table 2) using Phanta Max Super-Fidelity DNA Polymerase (Vazyme Biotechnology, Nanjing, China). In detailed, a 50 \(\upmu\)L reaction consisting of 1 \(\upmu\)L cDNA/genomic DNA, 1 \(\upmu\)L DNA Polymerase, 1 \(\upmu\)L dNTP Mix (10 mM each), 25 \(\upmu\)L 2\(\times\) Phanta Max Buffer, 2 \(\upmu\)L primer pairs (10 \(\upmu\)M) and optimal amount of ddH\({}_{2}\)O was incubated with the PCR amplification procedure.  
Plasmid constructions were subsequently conducted. Specifically, the frameshift mutated zeaxanthin \(\beta\)-glucosidase gene \(CrtX\) in the plasmid pACCAR16\(\upmu\)crtX was removed, generating a concise and compact plasmid. The resulting vector, bearing \(\beta\)-carotene biosynthesis gene cluster \(CrtE\)-\(CrtB\)-\(CrtI\)-\(CrtY\) from _Pantoea ananatis_, was designated as pEBY. Then, the deficient plasmid pBIY was constructed by delating \(CrtE\) in the plasmid pEBY. Subsequently, the amplified _GGPPS_ candidate was introduced into the deficient plasmid pBIY to construct pGBIY vector harboring _GGPPS_-\(CrtB\)-\(CrtI\)-\(CrtY\) gene cluster. In other words, the excavated _GGPPS_ in situ replaced the original \(CrtE\) by homologous recombination for functional identification. Next, the endogenous _E. coli IDI_ gene (_EclDI_) and _Aurantiochytrium IDI_ gene (_AIIDI_) were allocated downstream of the _GGPPS_-\(CrtB\)-\(CrtI\)-\(CrtY\) cluster, constructing plasmid pEBIY-EclDI and pEBIY-AIIDI, respectively (Figure 4). Moreover, the four recombinant plasmids were transformed into _E. coli_ BL21 to generate corresponding stains, referred to as EBIY, BIY, GBIY, GBIY-EclDI, GBIY-AIIDI in Table 1.
# A Wireless Covert Channel Based on Dirty Constellation with Phase Drift
###### Abstract  
Modern telecommunications systems require the use of various transmission techniques, which are either open or hidden. The open transmission system uses various security techniques against its unauthorized reception, and cryptographic solutions ensure the highest security. In the case of hidden transmissions, steganographic techniques are used, which are based on the so-called covert channels. In this case, the transparency and stealth of the transmission ensure its security against being picked up by an unauthorized user. These covert channels can be implemented in multimedia content, network protocols, or physical layer transmissions. This paper focuses on wireless covert channels. We present a novel method of steganographic transmission which is based on phase drift in phase-shift keying or quadrature amplitude modulation (QAM) and is included in the so-called dirty constellation techniques. The proposed approach is based on the drift correction modulation method, which was previously used in the watermarking of audio-signals. The developed solution is characterized by a variable bit rate, which can be adapted to the used modulation type and transmission conditions occurring in radio channels. In the paper, we present the method of generating and receiving hidden information, simulation research, and practical implementation of the proposed solution using the software-defined radio platform for selected QAM.  
Keywords:wireless communications; covert channel; steganography; steganalysis; dirty constellation; wireless postmodulation steganography; phase drift; drift correction modulation; undetectability; security; quadrature amplitude modulation +
Footnote †: journal: _Electronics_  
_Article_  
A Wireless Covert Channel Based on Dirty Constellation with Phase Drift
## 3 The Proposed Model
### System Model
#### 3.1.2 Energy-Dissipation Model  
Reception and data transmission are two fundamental functions in WSNs. Usually, the data-communication method expends lots of energy when compared to data reception. Now, energy cost during \(k\)-bit data transmission over distance \(d\) is formulated as follows.  
\[E_{Tx}(k,\ d)\triangleq\begin{cases}k\Big{(}E_{elec}+\varepsilon_{f}d^{2} \Big{)},&if\ d\leq d_{0}\\ k\big{(}E_{elec}+\varepsilon_{mp}d^{4}\Big{)},&if\ d>d_{0}\end{cases} \tag{4}\]  
In Equation (4), \(d_{0}\) indicates the reference distance and \(d_{0}\triangleq\sqrt{\varepsilon_{fs}/\varepsilon_{mp}}\). Elec shows the per bit energy cost for running the receiver or transmitter circuits, and \(\varepsilon_{fs}\) and \(\varepsilon_{mp}\) indicate amplification parameters of the transmitter for free space and multipath fading, correspondingly.  
Furthermore, energy costs during \(k\)-bit data reception are formulated as follows:  
\[E_{Rx}(k)\triangleq kE_{elec} \tag{5}\]
## 2 Wireless Multimedia Sensor Networks
### Conclusion  
We have presented in detail the characteristics and requirements of WMSNs. With each requirement, we also discuss its existing solutions. To conclude our introduction to WMSNs, we show in Figure 7 as to how its characteristics and requirements are related. We believe such an illustration will act as bridge among researchers in different fields and provide them with the big picture needed for such an interdisciplinary subject. As shown in the figure, the core of WMSNs are the battery powered camera sensors. These camera sensors possess unique directional sensor coverage and generate large volume of multimedia sensory data. First, the existence of directional sensor coverage requires camera sensors to be accurately calibrated so as to make the captured imagery data geographically meaningful. At the same time, in order to successfully accomplish the monitoring tasks, coverage optimization and camera sensor collaboration are also needed. Second, as it is impractical to send all multimedia data to the sink, redundancy within the data should be minimized. The common approaches to achieve this is to use local processing, distributed source coding, multimedia in-network processing, and in-network data storage and query processing techniques. However, even after redundancy is reduced, the amount of data to be transmitted is still at large compared with traditional WSNs. Therefore much higher bandwidth is required. Moreover, within the multimedia data, there includes a big portion of real-time video data that calls for stringent QoS guarantees. On the other hand, due to the battery powered nature, the lifetime of camera sensors directly limits the lifetime of WMSNs. As a result, energy efficiency is required for all computation and communication done by the camera sensors for they accounts for the majority of energy consumptions. For example, computation intensive tasks such as local processing and distributed source coding needs to be done energy efficiently; communication protocols  
Figure 6: Camera Sensor Collaboration Schemes in WMSNs.  
designed to achieve higher bandwidth and/or to provide QoS guarantees should be energy-aware; and protocols such as camera sensor collaboration, multimedia in-network processing and etc., that require both computation and communication, need to have energy efficiency designed in mind as well. Last but not the least, dynamic power management can also be adopted to manage the internal operations of a camera sensor in order to further conserve energy.
# Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review
## 4 Classification of ML Models Applied in the Optimization of Wastewater Treatment Processes  
As was presented in Sections 2.2 and 2.4, AI techniques such as ML and DL (supervised learning--classification, regression; unsupervised learning--clustering, dimensionality reduction) and hybrid models (such as ANNs, ANFIS, FL, CNNs, LSTM, genetic algorithms (GAs), etc.) have been effectively applied in the wastewater treatment processes optimization; in modeling the complex, dynamic, and high-nonlinear behavior of these processes; in facilitating the analysis of complex datasets; in a quality prediction of the relationships between different pollutants and key effluent parameters (such as wastewater pH); with real benefits on the plant operating process efficiency; on the environmental monitoring systems accuracy; in the detection and monitoring of environmental pollutants; in improving the SCADA system real-time monitoring capabilities; in enhancing the accuracy of climate models and predictions (their capability to handle large datasets and to complex patters identification); and so on [7; 31; 49; 65].  
Another important aspect is that the usage of these AI techniques (advanced techniques for data analysis and predictive modelling) not only come with real benefits for WWTP optimal operating (minimization of resources and energy consumption, efficient pollutants removing, etc.), but also it aligns them with the global standards for environmental protection through the classification of the treated wastewater usability, this being a continuously growing trend [7; 27; 65]. The continuous development of ML and DL models provides, besides the optimizations of wastewater treatment processes, valuable insights for the decision-making process and regulation development, addressing at the same time critical problems such as pollution control and resource management [28; 31; 41]. The usage of ML and DL methods in the optimization of wastewater treatment processes not only helps solve stringent environmental problems but also opens the way for the identification of innovative solutions. Next, a short description of the ML models applied in wastewater treatment process optimization is provided.  
A hierarchical grid displaying the structure of supervised learning and unsupervised learning techniques for wastewater treatment applications is depicted in Figure 8. In this chapter, the supervised learning methods are classified into two main groups: Traditional ML and Deep Learning techniques.