esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Organic Acids Analysis  
Samples were prepared using Oasis PRiME HLB (Waters, Milford, MA, USA) for clean-up, followed by acidification with 0.01% TFA. The concentrations of organic acids in brine samples were determined in duplicate by HPLC, following the method of [PERSON] et al. [26] with a few modifications. A Waters Alliance HPLC system (model 2695), equipped with photodiode array detector (model 2998) set at 210 nm, and an Atlantis Premier BEH C18 Ax column (4.6 \(\times\) 150 mm, 5 \(\upmu\)m bondapack) (Waters) were used. Separation was carried out at 30 \({}^{\circ}\)C for the column oven under a flow rate 1 mL/min using ultrapure water with 0.05% TFA (A) and acetonitrile (B) under linear gradients between: 0 min, 98% A; 3 min, 98% A; 12 min, 40% A; 14 min, 40% A; and 20 min, 98% A. Compound concentration was expressed in mg/L.
## 2 Problem Statement and Data Analysis
### Data Structure  
The Shanghai Futures Exchange proffers a snapshot-based order feed implemented via the CTP (Comprehensive Transaction Platform). The feed accumulates changes occurring within the preceding 500 milliseconds, encompassing multiple fields that encapsulate trade and order-book information. Specifically for our analysis, we capitalized on the price field to compute the spread between rebar and hot-rolled coil. The dataset utilized in this study was sourced from Choice Financial Software. Given the inherent noisy characteristics of financial data, we transformed the 500-millisecond spread data, ranging from 21:01 on 15 July 2020, to 10:50 on 18 May 2022, into 1 min K-line data. This transformation effectively mitigated the impact of noise, thereby enhancing our model's capacity to capture the temporal dependencies inherent in the data. Additionally, among the extensive array of contracts listed annually, we narrowed our focus to the historical data derived from January, May, and October contracts.  
Subsequently, all adjusted data points were integrated, taking into account the trading volume. This comprehensive process culminated in a comprehensive dataset consisting of 153,440 historical trading data points, spanning a period of 447 days. Each data point comprises the following features:  
1. OPEN/HIGH/LOW/CLOSE: the first/highest/lowest/last value in 1-min spread data.
2. Exponential moving average (EMA): \[EMA=Close*Weight+EMA_{pre}*(1-Weight)\]
3. Difference (DIF): \(DIF_{i}=EMA(CLOSE,12)-EMA(CLOSE,26)\)
4. Differential exponential average (DEA): \(DEA_{i}=EMA(DIF_{i},9)\)
5. Moving average convergence and divergence (MACD): \[MACD_{i}=2\times(DIF_{i}-DEA_{i})\]
6. The price spread fluctuation.
## 2 Materials and Methods
### DNMT3A R882H Mutation Correction  
The lentiviral ABE8e-GFP-Puroomycin construct was generated in our lab by replacing the cytidine base editor (CBE) in the plasmid pLenti-FNLS-P2A-GFP-PGK-Puro, a gift from [PERSON] (Addgene plasmid # 110869; [[http://n2t.net/addgene:110869](http://n2t.net/addgene:110869)]([http://n2t.net/addgene:110869](http://n2t.net/addgene:110869));RRID:Addgene_110869), with the ABE8e sequence, a gift from [PERSON] (Addgene plasmid # 138491; [[http://n2t.net/addgene:138491](http://n2t.net/addgene:138491)]([http://n2t.net/addgene:138491](http://n2t.net/addgene:138491)); RRID:Addgene_138491). The lentiviral particles were produced according to the protocol described in Section 2.9. The leukemic cell line SET2, carrying the _DNMT3A_ R882H mutation, was transduced as described in the Section 2.10. One week after transduction, GFP-positive cells were sorted and afterwards constantly maintained in 2 ug/mL Puroomycin. This stable SET2-ABE8e cell line was subjected to transduction with lentiviral particles containing pLenti-U6-gDNMT3A-EF-tdTomato-P2A-BlaSR (see Section 2.8). The correction of the _DNMT3A_ R882H mutation was verified by Sanger sequencing of the targeted _DNMT3A_ locus, obtained through PCR amplification of the region from genomic DNA, isolated from the infected cell pool 5 days post transduction. The electropherograms were submitted to the EditR software [20] to evaluate the editing efficiency. The infected cell pool was maintained in culture and the fraction of the targeted cell population was measured every 4 days by analyzing the tdTomato signal on MACSQuant(r) VYB (Miltenyi Biotec, Bergisch Gladbach, Germany).
# The Impact of Freeze Drying on Bioactivity and Physical Properties of Food Products
## 3 Parameters of Freeze Drying
### Time and Pressure  
In the research of [PERSON] et al., various ways were explained to decrease the drying time due to the high energy consumption and cost [78]. According to the article, a way among these was using pressure to decrease the drying time. As a result of this reduction, according to the article, pressure and time coordinated to improve the dry matter quality. For example, in the study by [PERSON] et al., three different pressures and drying times were selected to determine the optimum conditions for achieving maximum quality in freeze-dried mango [79]. When the results of the study were evaluated, optimal values were determined as 13 h of drying time and a pressure of 25 Pa. Additionally, in a study on orange puree, low pressure and processing time impacted the product quality positively due to preventing the degradation of vitamin C, total phenols, \(\beta\)-carotene, antioxidant activity, and changes in the original color, porosity, mechanical properties, and water content of the sample [80]. In the research of [PERSON]. [PERSON] et al., the pressure was used to determine the endpoint of the primary drying, which is an important process of FD before the secondary drying [81]. The article explained how to find the most effective primary drying time with pressure to obtain the maximum product quality. Effective control of the drying time on FD, especially, decreased the structure collapse and degradation of the bioactive properties of the sample. The pressure and time were determined for the stop system and applied to the primary drying step of FD to facilitate the manufacture of a high-quality final product. This study concluded that pressure and time, which are parameters of the FD processes, must be determined for effective usage of FD to obtain maximum product quality.
# Towards Bio-Hybrid Energy Harvesting in the Real-World:
## 3 Research Progress in Energy Harvesting from Microorganisms
### Applications of MFCs
#### 3.4.4 Solid Waste Processing  
[PERSON] et al. [242] reported a solid phase MFC system, developed to evaluate the potential of bioelectricity production by fermentation of food waste that gave promising results. The configuration utilized open air cathode sediment MFC configuration with graphite electrodes. They identified distance between the electrodes and PEM had a significant influence on power output, and the amendment of sodium carbonate improved system's power buffering capacity. [PERSON] et al. [243] reported another MFC-based system for handling solid wastes as a feedstock. The authors evaluated the system with two configurations: (1) a single chamber, combined membrane-electrode configuration; and (2) a dual chamber, proton-membrane-less configuration with brush-type anode and double air cathode. They have used cow manure for feedstock and the second configuration provided better results with higher power output. [PERSON] et al. [244] also reported a solid state MFC for processing cow manure with a single-chamber, air-cathode MFC configuration. The authors reported that a moisture content higher than 80% was suitable for current generation. Moreover, an addition of small amount of platinum catalyst improved the power density by 10-fold and output voltage by twice as much. [PERSON] et al. [245] reported their study of feasibility analysis of two MFC-based electricity generation configurations that could simultaneously treat municipal solid waste landfill leachate. They identified that for such cases, smaller configurations perform better in power generation. [PERSON] et al. [246] tested the practicality of using solid municipality waste as substrate for an MFC-based system where they categorized the organic waste components in 3 main groups including food waste, paper-cardboard waste and garden waste and concluded that the organic fraction of municipal solid waste is a promising feedstock for MFC-based waste processing. Detailed data analysis from their observations indicate that the microbial composition of the anodic biofilm became a function of the feed composition. Moreover, they also found that regulating the protein content and removing furfurals and phenolic compounds from feedstock could increase the percentage of chemical oxygen demand removal rate.
## 4 Results
### Algorithm Performance Analysis
#### 4.2.1 Descriptive Statistics
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Algorithm-CHT** & **Mean** & **Std.** & **Median** & **Min** & **Max** & **NFS** \\ \hline R1B-FR & 0.7046 & 0.05785 & 0.6858 & 0.6778 & 0.9428 & 0 \\ GA-FR & 19.72 & 34.7 & 7.387 & 2.206 & 145.5 & 0 \\ PSO-FR & 5.092 & 2.945 & 4.244 & 2.232 & 13.56 & 1 \\ MUMSA-FR & 1.641 & 0.8234 & 1.158 & 0.6581 & 3.145 & 0 \\ R1B-SR & 0.7533 & – & 0.7533 & 0.7533 & **0.7533** & 29 \\ GA-SR & 3.123 & 1.743 & 2.455 & 1.892 & 8.379 & 0 \\ PSO-SR & 3.407 & 4.932 & 2.441 & 1.334 & 28.89 & 0 \\ MUMSA-SR & 1.779 & 1.324 & 1.206 & 0.6633 & 6.056 & 0 \\ R1B-eC & 0.6886 & **0.04365** & 0.6751 & 0.6625 & 0.8178 & 0 \\ GA-eC & 5.154 & 2.55 & 3.98 & 2.339 & 11.08 & 0 \\ PSO-eC & 2.336 & 1.067 & 2.17 & 0.983 & 6.829 & 0 \\ MUMSA-eC & 1.797 & 1.694 & 0.8433 & 0.6595 & 5.484 & 0 \\ R1B-PF & 0.7961 & 0.3161 & 0.7118 & 0.6722 & 2.422 & 0 \\ GA-PF & 2.714 & 0.4742 & 2.506 & 2.077 & 3.639 & 0 \\ PSO-PF & 2.91 & 1.138 & 2.899 & 1.388 & 7.702 & 0 \\ MUMSA-PF & 2.901 & 2.317 & 2.341 & 0.7097 & 10.47 & 0 \\ R1B-NCH & **0.6823** & 0.05252 & **0.6632** & **0.658** & 0.8157 & 0 \\ \hline \end{tabular}
\end{table} 
Table 7: Descriptive statistics of the obtained results for each metaheuristic algorithm in study case 2 with the trajectory 1. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Algorithm-CHT** & **Mean** & **Std.** & **Median** & **Min** & **Max** & **NFS** \\ \hline R1B-FR & 0.7011 & 0.08787 & 0.6694 & 0.6592 & 0.9423 & 0 \\ GA-FR & 6.38 & 6.634 & 3.33 & 2.098 & 35.07 & 0 \\ PSO-FR & 3.705 & 2.125 & 3.331 & 1.706 & 13.79 & 2 \\ MUMSA-FR & 1.601 & 1.468 & 0.8563 & **0.6418** & 5.802 & 0 \\ R1B-SR & – & – & – & – & – & 30 \\ GA-SR & 3.77 & 2.143 & 2.737 & 1.889 & 7.992 & 0 \\ PSO-SR & 5.722 & 11.51 & 2.966 & 1.03 & 62.69 & 0 \\ MUMSA-SR & 1.681 & 1.233 & 1.042 & 0.649 & 4.779 & 0 \\ R1B-eC & 0.686 & 0.06462 & 0.6591 & 0.6556 & 0.8979 & 0 \\ GA-eC & 5.369 & 2.458 & 4.001 & 1.8 & 8.895 & 0 \\ PSO-eC & 2.596 & 1.027 & 2.506 & 1.394 & 7.299 & 1 \\ MUMSA-eC & 1.755 & 1.594 & 0.9007 & 0.6536 & 5.441 & 0 \\ R1B-PF & 0.7394 & 0.1097 & 0.691 & 0.6796 & 1.056 & 0 \\ GA-PF & 2.836 & 0.8672 & 2.561 & 2.065 & 6.465 & 0 \\ PSO-PF & 2.983 & 1.476 & 2.779 & 1.151 & 7.049 & 0 \\ MUMSA-PF & 3.183 & 5.02 & 2.204 & 0.6996 & 28.75 & 0 \\ R1B-NCH & **0.6588** & **0.03913** & **0.6487** & 0.6462 & **0.8027** & 0 \\ \hline \end{tabular}
\end{table} 
Table 8: Descriptive statistics of the obtained results for each metaheuristic algorithm in study case 2 with trajectory 2. It is observed in Tables 5-8 that the R1B-NCH presents an outstanding mean performance (see \"mean\" columns) in the search for solutions through study cases in spite of presenting changes in the optimization problems (rehabilitation trajectories) where the algorithm setting and the NCH technique were tuned and trained, respectively. However, descriptive statistics cannot estimate the best algorithm behaviors for future executions. Therefore, the analysis of results by 95% confidence intervals and inferential statistics [56] is carried out in the next section.
# Energy and Spectrally Efficient Modulation Scheme for IoT Applications
## 4 Simulation Results
### PSD Perfomance  
Figure 9 depicts the analytical PSD of the OSP-MSK modulation compared to the PSD of the OSP-MSK conducted by the numerical simulation. Furthermore, the PSD of the OSP-MSK is compared with the PSDs of the different constant envelope modulation schemes (i.e., GMSK and QMGMSK) in order to corroborate the bandwidth effectiveness of the OSP-MSK modulation.  
As shown in Figure 9, the spectrum of the QMGMSK [32; 33] occupies about half of the main lobe spectrum of the traditional GMSK modulation. Thus, the QMGMSK approach can be used to double the spectral efficiency of the MIMO-CEM. However, unfortunately, this occurs with deterioration in the ABER performance, as shown Figures 6 and 7. This dates back to the inapplicability of the QMGMSK with its non-coherent differential demodulation with the one-bit ADC in the MIMO-CEM system [32; 33]. However, the spectrum of the OSP-MSK modulation occupies the same main lobe spectrum as the GMSK modulation. Therefore, the proposed OSP-MSK increases the number of transmitted bits per symbol of the conventional MSK/GMSK from two bits/symbol to four bits/symbol without the need for additional bandwidth. This undoubtedly proves the effectiveness of the proposed OSP-MSK modulation in boosting the spectral efficiency over the MIMO-CEM system.  
Figure 8: The ABER performance of the proposed MIMO-OSP-MSK compared to the ABER of the conventional MIMO-CEM system with quantization errors (i.e., one-bit ADC is utilized) and without quantization errors (i.e., infinite-bit ADC is utilized) for the uncoded and coded scenarios.  
Figure 9: The power spectral density (PSD) of the proposed OSP-MSK compared to the PSDs of GMSK and QMGMSK.
# Image of a City through Big Data Analytics: Colombo from the Lens of Geo-Coded Social Media Data
## 4 Results
### Sentiment Analysis
As of Figure 5a from 2015 to 2016, VP (District), AIS (Landmark), BWP (District), BL (Edge), DP (District), and WTC (Landmark) have received 100% positive comments from analyzed social media messages. BP (District; 99.19%), LPB (Landmark; 98.33%), GFH (Landmark; 97.69%), SC (Landmark; 97.39%), BMICH (Landmark; 94.4%), KH (Landmark; 94.43%), GT (Landmark; 94.14%), and MLB (Edge; 93.98%) were the other places which received over 90% of positive perceptions from the analyzed social media messages. Nonetheless, apart from BWP and the WTC, the positive sentiments shared for other landmarks have significantly reduced from 2017 to 2018. For instance, AR, DP, and VP, which received totally positive (100%) tweets and Instagram posts between 2015 and 2016, received 83.33%, 75%, and 75% positive perceptions, respectively. Most significantly, all the other places which received over 90% of positive sentiments between the years 2015 and 2016 were either received lower, i.e., BP--95.92%, GFH--93.12%, BMICH--91.95%, GT--93.85%, and MLB--90.28%, or with a slight increment, i.e., LPB--99% and KH--94.76% from 2017 to 2018. Further, CCC and HH have moved up to the category with over 90% positive sentiments by 2017 and 2018. When compared to the 2015-2016 and 2017-2018 categories, percentages of positively classified tweets have significantly declined in 2019-2020. For instance, BP has significantly lost the positive perceptions received around 2015 (99.19%) compared to 2019-2020 (71.43%). Similarly, LPB, ARC, DP, and VP experienced the same scenario. This is mainly due to the commencement of many city-beautification projects such as the AIS renovation project and DP construction projects around 2012. However, such projects lost community attractions due to low maintenance, the existence of expensive shops, and so on. In contrast, CCC, which received comparatively low positive perceptions around 2016 (88.89%) compared to other landmarks, has increased up to 100% around 2019 and 2020. CCC is a 47-story mixed-use development that opened in 2018. According to the analyzed social media data, CCC is so far perceived positively as a landmark due to its building architecture and facilities. Exemplary tweets posted sharing positive sentiments about ARC and DP are shown in Figure 6.  
Figure 5: Sentiment analysis with distribution of positively classified tweets: (**a**) sentiment analysis 2015–2016; (**b**) sentiment analysis 2017–2018; (**c**) sentiment analysis 2019–2020.
# A Novel High-Precision Imaging Radar for Quality Inspection of Building Insulation Layers
## 2 Radar Inspection Principle and Main Technical Parameters  
### Radar Inspection Principle  
The principle of radar inspection of the external insulation system of building exterior walls is as shown in Figure 1. The radar transmitting antenna sends ultra-wideband electromagnetic waves in the form of pulses to the wall with an external insulation structure (a thin-plastered external insulation system, for example, which is usually composed of insulated panels and other attached structures), and the electromagnetic waves propagate in the wall and are reflected when they encounter a target body with electrical property  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Existing Nondestructive Testing (NDT) Methods** & **Advantages** & **Limitations** \\ \hline Optical Testing -Laser Scanning -Infrared Thermographic Testing & Capable of airborne operation and enables quantitative detection of surface defects (e.g., cracks, deformations) in insulation layers with high efficiency and accuracy & Surface-level detection only, limited to severe surface defects (e.g., cracks, cannot penetrate insulation to assess internal voids, water infiltration, bonding mortar distribution, and area coverage \\ \hline Acoustic Testing -Ultrasonic Testing -Tap Testing & Low-cost solution and requires minimal technical expertise for operators, reducing training barriers & Subjective analysis, defect localization relies heavily on empirical interpretation, introducing operator bias \\ \hline Conventional High-Frequency Ground Penetrating Radar (GPR) Testing & Deep detection capability, identifies subsurface features such as reinforcing bars (rebar) and protective layer thickness beneath the insulation. & Higher operational expenses, resolution insufficient to distinguish 1–2 cm thick bonded mortar layers \\ \hline \hline \end{tabular}
\end{table}
Table 1: The advantages and limitations of these detection methods.  
contrast, such as a bonding mortar layer, a seepage location, or a steel rein for bonding mortar, and the returned signals are received by the receiving antenna. The received electromagnetic waves are processed and analyzed. The spatial position, structure, electrical properties, and geometric shape of the target inside the wall are inferred based on the signal waveform, two-way propagation time, etc., so as to detect the target inside the wall. The high-precision imaging radar constructs a three-dimensional data volume through multiple survey line measurements to perform high-precision imaging of the insulation layer structure, which will provide data support for the quantitative inspection of the insulation layer structure.
## 4 Composite Formation Techniques
### Physical in situ methods  
Physical methods are mainly gas-phase methods. They are able to synthesize _in situ_ functionalized or encapsulated nanoparticles, appearing as hybrid core/shell nanoparticles. Their common feature is that they apply energy to transform chemical compounds (precursor and gas) into inorganic nanoparticles, and by a subsequent coating step organic compounds are grafted on the nanoparticle surfaces for coating, encapsulation or surface functionalization.  
A versatile approach for the gas-phase synthesis of hybrid core/shell nanoparticles is the application of microwaves for plasma generation. This approach was developed by [PERSON] _et al._[39-41]. The basic element of this approach is a reaction tube made of quartz glass crossing a microwave cavity. At this intersection, plasma is ignited. Volatile and water-free precursors (e.g., chlorides, carbonyls, metal-alkoxides, or metal-alkyls) are evaporated outside the reaction tube and mixed with an inert carrier gas. The components are introduced as gases into the system just in front of the plasma zone. Here. the chemical reaction in the gas-phase and the nucleation and growth of nanoparticles occurs. By using consecutive reaction zones, core/shell nanoparticles and multi-layer nanoparticles can be produced in consecutive synthesis steps. The inorganic cores are formed by homogeneous nucleation, the organic shell of hybrid nanoparticles condenses via heterogeneous nucleation and polymerizes outside of the plasma zone on the cores synthesized in the plasma (Figure 7). This approach was also used to synthesize double-coated, multifunctional core/shell nanoparticles [89,90].  
[PERSON] _et al._[50] and [PERSON] [48] applied chemical vapor synthesis (CVS) for _in situ_ polymer coating of Al\({}_{2}\)O\({}_{3}\) and SiO\({}_{2}\) nanoparticles. Instead of microwave plasma a traditional hot-wall reactor was used for inorganic nanoparticle synthesis, the coating was performed in a subsequent RF-plasma reactor. Similar setups were not only used for the synthesis of ceramic/polymer core/shell  
Figure 7: Set-up scheme for the microwave plasma synthesis of hybrid core/shell nanoparticles.  
nanoparticles, but also for the production of metal/polymer core/shell nanoparticles. [PERSON] _et al._[91] used a one step microwave plasma process to encapsulate Fe nanoparticles with polystyrene. The precursor, Fe(CO)\({}_{5}\), and the styrene monomer were added coevally; both components commonly passed the plasma. This process claims to be on an industrial level. [PERSON] and [PERSON] [92] applied a dual-plasma process for the synthesis of metal/organic core/shell nanoparticles. The metal (Cu) nanoparticles were synthesized through arc evaporation and vapor condensation, the subsequent organic coating was deposited by in-flight deposition of an organic compound through plasma polymerization.
## 1 Introduction
### A Note on Industry 5.0  
Industry 4.0 has been the dominant theme in industrial trade for the past decade. It has become a globally accepted paradigm with a profound impact on economic and social development. It is characterized by its intelligent and autonomous production systems. Such an automation-centered viewpoint has been called into question in recent years, leading to the proposal of an Industry 5.0 concept, which places the worker at the center of digital transformation [59]. Among the published literature, five major themes of Industry 5.0 were identified: supply chain evaluation and optimization, enterprise innovation and digitization, smart and sustainable manufacturing, IoT, AI, Big Data, and human-machine connectivity. Industry 5.0 as a gateway to human-machine interaction and coexistence is particularly popular among researchers [60]. It aims at redefining the relationship between humans and intelligent components toward a collaborative industrial system with comprehensive human-centered development, a long-term contribution to sustainability, and a reliable source of resilience [61].  
This paper presents the design of a smart cyber-physical system that conforms to the novel smart factory framework towards Industry 4.0 and deploys the key technologies of the smart factory, including industrial, computing, information, and communication technologies. It describes the integration of the basic layers of the smart factory for the creation of an intelligent manufacturing system. A case study of smart manufacturing, including a drilling process, is implemented as a demonstration for a simplified smart factory model.  
The remainder of this paper is organized as follows. Section 2 describes the methodology and overall architecture of the proposed smart factory. The implementation and validation of the proposed model, along with a discussion of the results, are presented in Section 3. Section 4 gives the conclusions, and finally, Section 5 presents the recommendation for future work and development of a smart factory in Industry 4.0.