esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Study Area and In Situ Survey
#### 2.1.2 Eddy Covariance and Meteorological Data Measurement  
The open circuit eddy correlation system (OPEC) was installed on flux towers in both SHB1 and SHB2, at heights of 30 m and 15 m, respectively. This system monitored long-term, continuous half-hour averages of turbulence and energy flux, including latent heat (LE), sensible heat (H), and CO\({}_{2}\) flux. The system was equipped with a three-dimensional  
Figure 1: Study area and EC flux tower locations.  
acoustic wave anemometer (Gill WindMaster, Gill Instruments, Lymington, Hampshire, UK) and an infrared gas analyzer (Li-7500 DS, LI-COR Inc., Lincoln, NE, USA).  
Meteorological data were recorded every half hour simultaneously with the flux data. A four-component radiometer (Kipp & Zonen CNR4, Delft, The Netherlands) and a photosynthetically active radiation sensor (LI-190R, Campbell Scientific, Lincoln, NE, USA) were positioned at a height of 38 m to monitor the radiation flux. At a height of 30 m, an air temperature and humidity sensor (HMP155, Vaisala, Helsinki, Finland) and a rain barrel were installed. Sensors were placed at soil depths of 5 cm, 10 cm, and 20 cm to monitor the soil temperature (TM-L20, DYNAMAX Inc., Elkhart, IN, USA) and soil water content (EC-5, METER Inc., Pullman, WA, USA). Three heat flux panels (HFP01, HukeFlux Inc., Delft, The Netherlands) were placed at a soil depth of 5 cm to measure the soil heat flux, and the meteorological data were recorded at a frequency of 0.5 Hz. A CR1000X data logger (Campbell Scientific Inc., Logan, UT, USA) recorded 30 min averages of the above variables.  
The raw EC data were processed using EddyPro 7 (LI-COR Bioscience, Lincoln, NE, USA) to obtain half-hourly fluxes, which involved detecting spikes, correcting time lags and frequency responses, rotating coordinates along three axes, correcting for acoustic virtual temperature and density fluctuations, and calculating fluxes [31]. The EC data were calculated every 30 min with three different quality control levels (0, 1, and 2). Poor-quality flux data were excluded (marker 2). Unreasonable flux data were rejected by discarding flux data collected half an hour before and up to one hour after precipitation, excluding observations obtained during instrument failures or atmospheric instability conditions, eliminating negative CO2 flux values observed at night, and rejecting flux values that fell outside of the acceptable range. For gap filling and flux partitioning, the REddyProc online tool was utilized [32]. The daytime-based separation method was used to divide the NEE into ecosystem respiration (Re) and GPP components at 30 min intervals [33].
## 2 Materials and Methods
### Analytical Methods
#### 2.4.2 Liquid Analysis  
Liquid samples were extracted from a sampling port on each flask every 48 h using a 10 mL syringe and divided into aliquots for further analysis as described below. The TS and VS of liquid samples were measured following a standard protocol [25]. To estimate the extent of lignocellulosic hydrolysis and the amount of remaining dissolved organic matter, soluble chemical oxygen demand (COD) was determined according to the manufacturer's protocol ([PERSON] test kit HR mercury free, 20-1500 mg L\({}^{-1}\), Mississaug, ON, Canada). Samples used for COD analysis were first syringe filtered through 0.45 um nylon filter (Chromatographic Specialties Inc., Brockville, ON, Canada) and then digested using a digital reactor block (HACH DRB200, Loveland, CO, USA) at 150 \({}^{\circ}\)C for 2 h. After digestion, absorbance of the sample was measured using a spectrophotometer (DR900, HACH, Mississaug, ON, Canada).  
The pH and total bicarbonate alkalinity were measured using a BIOGAS titration Manager (R41T114, HACH, Vesenaz, Switzerland). Liquid samples were also analyzed for volatile fatty acids (VFA; acetate acid, n-butyrate, iso-butyrate, propionate, n-valerate, iso-valerate, and caproate) by GC (Agilent 6890 N, Agilent, Mississaug, ON, Canada). The samples were prepared by first filtering using 0.45 um nylon filter (Chromatographic Specialties Inc., Brockville, ON, Canada), then 25% meta phosphoric acid was added to the filtered sample in the ratio of 5:1 sample to acid. The gas chromatatograph was equipped with a flame ionization detector maintained at 250 \({}^{\circ}\)C, and a fused silica capillary column (ZB-FFAP, 30 m \(\times\) 0.32 mm \(\times\) 1.0 um: Phenomenex, Torrance, LA, USA). The equipment was set at split mode and the split injection ratio was 5:1. Helium was used as the carrier gas and the analytical steps were performed according to the procedures outlined in [PERSON] et al. [26].  
Concentration of soluble ions (NH4+, Na+, K+, Ca2+, Mg2+) were determined after filtration through 0.45 um filter paper using ion chromatography (ICS-1000 and DX-600, Dionex, Sunnyvale, CA, USA). The concentration of free (unionized) NH3 was calculated as previously reported [27]. To determine the ratio of total carbon to total nitrogen in samples, a subsample (5 mg) was freeze dried for 1 week and ground to a size \(<\) 0.15 mm using a Cyclone sample mill (UDY Corporation, Fort Collins, CO, USA), and then analyzed using a CNS analyzer (NA-1500, [PERSON], Rodano, Italy) linked via a continuous flow interface to an Optima isotope ratio mass spectrometer (Micromass, Manchester, UK).
# Non-Destructive Multi-Method Assessment of Steel Fiber Orientation in Concrete +
## 3 Results
### Ultrasound  
The greatest influence on the ultrasonic signals was expected from the specimen where the steel fibres were oriented perpendicular to the sound propagation (specimen Q40--, Figure 9 top). During the first measurement, the polarization of the ultrasonic waves is 0\({}^{\circ}\) and the fibre orientation is 90\({}^{\circ}\) (Figure 9, top left). The spectrum shows the centre frequency of the probe at about 50 kHz and strong indications at about 42 kHz and 55 kHz. In this figure, \(\mid\)S(f)\(\mid\) denotes the magnitude of the spectral density in arbitrary units (a.u.). Then, the polarization of the ultrasonic waves was rotated by 90\({}^{\circ}\) from 0\({}^{\circ}\) (i.e., probe polarization 90\({}^{\circ}\), fibre orientation 90\({}^{\circ}\), Figure 9, top right). Thus, the polarization of the ultrasonic waves and the steel fibres are oriented parallel to each other. The magnitude of the spectrum at about 50 kHz is lower and the higher frequency components are more attenuated. In the Q40 \(\mid\mid\) \(\mid\) test specimen, the steel fibers are oriented in the direction of sound propagation (Figure 9, bottom). By using a rotation of the polarization, changes in the spectrum are also visible, but the influence on the test frequency and on the higher frequency components is lower. While these measurements do suggest that fibre orientation may have an influence on polarization-induced ultrasonic signal variations, the observed correlations have
## Materials and Methods
### Data
#### 2.2.2 MODIS Nadir BRDF-Adjusted Reflectance  
In this study, MODIS 16-day nadir BRDF-Adjusted Reflectance product (MCD43A4) was used to estimate grassland AGB. MCD43A4 is computed for each of the MODIS spectral bands (1-7, see Table 1) as if they were taken from the nadir view at local solar noon of the day [31]. Both Terra and Aqua data from a 16-day period were used to provide highest quality input data. Since the view angle effects are removed from the reflectance observations, this will facilitate the subsequent applications considering the stability and consistence of the angularly normalized reflectance.  
Forty-six images with tile of h26v05 were downloaded from the Land Processes Distributed Active Archive Center (LPDAAC). These images were from DOY 1 to 361 in 2017 with an 8 days step. We did not use daily V6 MCD43A4 images directly, because the temporally adjacent images have too much temporal overlapping considering the 16-day compositing window, and this results in the temporally adjacent images revealing very slight discrepancies.  
The incidence angle of the MCD43A4 product has been normalized to local solar noon of the day, which will change across the study year (2017). Yet, the local solar noon zenith angle within the study area would not cover too wide range, considering the limited space of our study area (Figure 1). Therefore, the incidence angle of the central pixel can be safely use to represent the entire study area. Figure 2 illustrates the temporal variation of the incidence zenith angle for the central pixel computed using an astronomical model [32]. The incidence angle is very oblique during winter: the incidence zenith angle is more than 50\({}^{\circ}\) at the beginning and ending of the study year. Whilst the minimum incidence zenith angle (\(\sim\)10\({}^{\circ}\)) appeared in the summer solstice day (DOY 172).
# Thermal Energy Storage for Building Load Management: Application to Electrically Heated Floor
## 2 Materials and Methods
### EHF in TRNSYS  
#### 2.1.1 Existing Methodologies for EHF in TRNSYS  
TRNSYS is the abbreviation for TRaNsient SYStems simulation program. This software works with modules called \"_types_\". The _types_ are mathematical models, in which users can choose to link inputs and outputs of different _types_ together in order to study the performance of a system. The format of the program makes this software very flexible. _Type 56_ is used to study the thermal behavior of a multi-zone building [20]. This _type_ allows the modeling of the complex behavior of a building, taking into consideration many geometric parameters (thicknesses, surfaces, positions of zones, etc.), but also weather data and occupancy schedule.  
_Type 56_ was used to simulate a residential building. The simulation was validated with measured field data [21]. For this study, the building was modified to meet the requirements of this project: the existing heating systems (electric baseboard) were replaced by EHFs. To model the EHF in TRNSYS, two methods can be used.  
The first method is the use of the _wall gain_: the _wall gain_ is defined as \"an energy flux to the inside wall surface\". Considering that the name \"wall\" in TRNSYS is used whether it is positioned as an actual wall, floor or roof, the _wall gain_ is thus analogous to simply defining a given heat flux on a surface. However, the _wall gain_ can only be defined on the top of the surface and not within it, and consequently may change the storage system behavior.  
Another possibility is to use the _active layer_, which is designed for hydronic floor heating systems. By choosing a high water flow rate, a constant water temperature can be assumed: it can then behave as an EHF. Nevertheless, the use of the active layer requires the determination of the relationship between the parameters of the active layer (flow, pipe diameter, pipe conductivity, water temperature, etc.) and the EHF power. Moreover, to use this type of layer, some hypotheses have to be verified [22], which may limit the parametric study.  
For this study, the _wall gain_ method is selected, with some modifications to go beyond the presented limit.
# Zn Doping Improves the Anticancer Efficacy of SnO2 Nanoparticles
## 3 Results and Discussions  
### XRD Analysis  
The determination of crystalline phases of synthesized materials was achieved via the use of X-ray diffraction (XRD) techniques. Figure 1A shows the XRD spectra of pure SnO\({}_{2}\) NPs and Zn-doped SnO\({}_{2}\) NPs (1% and 5%). The XRD peaks of pure SnO\({}_{2}\) NPs (Figure 1A) were observed at 26.4\({}^{\circ}\), 33.7\({}^{\circ}\), 37.7\({}^{\circ}\), 38.8\({}^{\circ}\), 51.6\({}^{\circ}\), 54.5\({}^{\circ}\), 57.7\({}^{\circ}\), 61.7\({}^{\circ}\), 64.4\({}^{\circ}\), 65.7\({}^{\circ}\), 71.1\({}^{\circ}\), and 87.5\({}^{\circ}\). These peaks correspond with the (110), (101), (200), (111), (210), (211), (220), (002), (310), (112), (301), (202), and (321) planes, respectively. Moreover, we observed that these peaks matched with the standard SnO\({}_{2}\) (JCPDS card no: 01-0803912). It can be seen in Figure 1A that the XRD spectra of Zn-doped SnO\({}_{2}\) NPs were similar to those observed in pure SnO\({}_{2}\) without impurity peaks associated with elemental zinc or other zinc compounds. This phenomenon signals the efficient incorporation of Zn atoms in SnO\({}_{2}\) NPs [31]. Figure 1B reveals that there were slight shifts in the high angle for XRD peaks of Zn-doped SnO\({}_{2}\) NPs (1% and 5%). However, the Scherer formula [32] was used to calculate the crystallite sizes of prepared NPs for diffraction peaks. Hence, the average crystallite sizes of pure SnO\({}_{2}\) NPs and Zn-doped SnO\({}_{2}\) NPs (1% and 5%) were 21.7 nm, 24.5 nm, and 26.2 nm, respectively as shown in Table 1. We observed that the crystallite sizes of SnO\({}_{2}\) NPs increased with increasing Zn concentrations. This could be attributed to a higher ionic radii of Zn\({}^{2+}\) (r = 0.74 A) than Sn\({}^{4+}\) (r = 0.71 A), as shown in previous reports [31; 33; 34]. XRD results confirmed the successful synthesis of pure SnO\({}_{2}\) NPs and Zn-doped SnO\({}_{2}\) NPs with a tetragonal cassiterite structure. The presented results were in good agreement with previous reports [35; 36; 37].  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Synthesized NPs** & **XRD (nm)** & **SEM (nm)** & **TEM (nm)** & **Bandgap (eV)** \\ \hline Pure SnO\({}_{2}\) NPs & 21.7 & 20.18 & 15.25 & 3.50 \\ Zn(1%)-SnO\({}_{2}\) NPs & 24.5 & 26.56 & 25.92 & 3.42 \\ Zn(5%)-SnO\({}_{2}\) NPs & 26.2 & 28.15 & 26.63 & 3.34 \\ \hline \end{tabular}
\end{table}
Table 1: Structural and optical characteristics of NPs.
The development of antifungal resistance, which is a current problem, similar to antibiotics resistance, is constantly reducing the efficacy of the currently available drugs, as well as their therapeutic effects [8; 9]. It has been shown that _Pseudomonas aeruginosa_ confers protection to neighboring yeasts against azoles, increasing the risk of candidiasis during polymicrobial infections [10]. Moreover, patients with polydrug assumption, or with reduction of drug clearance, may not be able to tolerate the administration of further medicaments. Consequently, investigations into alternative methods to reduce _Candida_-biofilm infections are encouraged. The use of light devices, such as lasers or LEDs, which has shown great potential against different types of bacteria, has not always proven to be effective against _C. albicans_, which has shown defense mechanisms [11; 12; 13; 14; 15]. Indeed, the presence of enzymes, like superoxide dismutases and catalases protect against reactive oxygen species (ROS), which are responsible for bacterial deletion [16; 17; 18]. [PERSON] et al. showed the efficacy of a novel gel containing aminolevulinic acid associated with photodynamic therapy in inhibiting _C. albicans_ growth in biofilm and inoculum [19], thanks to the intrinsic acidic pH of the formulation and the induction of free radical production. However, it is important to also investigate the possible effects of other devices that could offer other therapeutic options against this commensal/serious pathogen. Another technology that showed an antibacterial activity and could have a potential efficacy against _Candida_ spp., and consequently should be further investigated, is the electromagnetic field (EF). [PERSON] et al. demonstrated that exposure of _Escherichia coli_ to 50 Hz EF for 20-120 min produces a significant change in the bacterial morphotype and aggregation, suggesting a possible role of EF as a stressing factor [20]. [PERSON] et al. showed that extremely low frequency electromagnetic fields are able to induce a change in the physicochemical properties of both Gram-positive and Gram-negative bacteria, an alteration of their respiratory activity, and a slight decrease in bacterial growth [21]. The in vitro and clinical applications of magnetic fields are very varied and include the reduction of neoplastic cells as well as reduction symptomatic of pain in patients affected by fibromyalgia, rheumatoid arthritis (RA) and symptomatic diabetic peripheral neuropathy (DPN) [22; 23; 24; 25]. Some devices emitting magnetic fields are equipped with specific programs in which different fields are emitted, in a specific order, and, consequently, are termed complex magnetic fields (CMFs). The primary objective of this study is to evaluate the effects of different timings of CMFs antibacterial protocols on planktonic _Candida albicans_. The secondary objective is to evaluate the viability of the treated yeast, cultivated on pink polyacrylic resin discs--the same material used to fabricate complete removable dentures.
# A Low Complexity Reactive Tabu Search Based Constellation Constraints in Signal Detection
###### Abstract  
For Massive multiple-input multiple output (MIMO) systems, many algorithms have been proposed for detecting spatially multiplexed signals, such as reactive tabu search (RTS), minimum mean square error (MMSE), etc. As a heuristic neighborhood search algorithm, RTS is particularly suitable for signal detection in systems with large number of antennas. In this paper, we propose a strategy to reduce the neighborhood searching space of the traditional RTS algorithms. For this, we introduce a constellation constraints (CC) structure to determine whether including a candidate vector into the RTS searching neighborhood. By setting a pre-defined threshold on the symbol constellation, the Euclidean distance between the estimated signal and its nearest constellation points are calculated, and the threshold and distance are compared to separate the reliable estimated signal from unreliable ones. With this structure, the proposed CC-RTS algorithm may ignore a significant number of unnecessary candidates in the RTS neighborhood searching space and greatly reduce the computational complexity of the traditional RTS algorithm. Simulation results show that the BER performance of the proposed CC-RTS algorithm is very close to that of the traditional RTS algorithm, and with about 50% complexity reduction with the same signal-to-noise (SNR) ratio.  
MIMO; maximum likelihood; RTS; CC; detection complexity +
Footnote †: journal:  
_Article_  
A Low Complexity Reactive Tabu Search Based Constellation Constraints in Signal Detection
## 4 Taxonomy of MAC Protocols Based on Their Use of WuR Technology
### MAC Protocols without WuR
#### 4.1.2 Carrier Sense Multiple Access (CSMA)-Based MAC Protocols for IBSN  
Authors in [40] presented B-MAC protocol which renders typical properties of a IBSN, such as simple implementation on hardware, highly predictable performance parameters, and tolerance to network changes. B-MAC implements an asynchronous access mechanism similar to X-MAC. B-MAC has a relatively high reliable data packet delivery ratio of 98.5% [41]. B-MAC could be optimized for star topology. However, B-MAC also suffers from hardware constraints such as memory and computational overhead. In [44], [PERSON] et al. present MEB-MAC, which focuses onthe channel access delay reduction for medical emergency traffic with high reliability. No energy efficiency is concerned with the implementation and analysis. It has an adverse effect on new node insertion and mobility of the network. An energy efficient MAC protocol called O-MAC is presented in [45]. O-MAC has achieved increased energy efficiency by novel receiver scheduling methods, such as Staggered On and Pseudo-randomized Staggered On. Theoretical analysis and practical implementation reveals that the protocol is 70% more energy efficient than B-MAC, S-MAC and T-MAC. The CSMA-based MAC protocol presented in [43] is called DISSense. A good analysis of MAC problems in different typologies and the benefits of cross-layer optimization are addressed in this paper. Features such as data delivery ratio, latency, duty cycling and adaptability are better than other similar protocols. DISSense can achieve good QoS in small scale networks and proportionately increase with network size. However, the performance of DISSense is traded off with energy consumption. No clear analysis of energy-efficiency is carried out though.
## 2 Establishment of the Multi-Time-Delay Control System
### Time-Delay Sources
\tag{4}\]  
where \(A_{obs}\) is the state matrix, \(B_{obs}\) is the excitation matrix, \(C_{obs}\) is the state output matrix and \(D_{obs}\) is the direct transmission matrix of an observer-based controller. \(Z_{obs}\) is the state vector and \(Y_{obs}\) is the output vector. Based on the estimated states, the output control forces can be calculated according to a specific algorithm,  
\[f(t)=-GY_{obs}(t) \tag{5}\]  
where \(G\) represents a feedback gain matrix. The time required for calculating control forces is called the time-delay \(d_{42}\). The state-space equation of a control system is thereby written as,  
\[\left\{ \begin{array}{c}\dot{Z}_{con}(t)=A_{con}Z_{con}(t)+B_{con}Y_{obs}(t )\\ f(t)=C_{con}Z_{con}(t-d_{42})+D_{con}Y_{obs}(t-d_{42})\end{array} \right. \tag{6}\]  
where \(A_{con}\) is the state matrix, \(B_{con}\) is the control matrix, \(C_{con}\) is the state output matrix and \(D_{con}\) is the direct transmission matrix. \(Z_{con}\) is the state vector. The control forces provided by a DC motor are discussed in the reference [24] as,  
\[u(t)=-\frac{L_{a}}{R_{a}}\dot{u}(t)-\frac{K_{b}K_{i}K_{b}^{2}}{R_{a}r_{m}^{2}} \big{[}\dot{x}_{a}(t)-\dot{x}_{n}(t)\big{]}+\frac{K_{i}K_{g}}{R_{a}r_{m}}v(t) \tag{7}\]  
where \(v\) is the applied voltage, \(L_{a}\) indicates the armature inductance, \(R_{a}\) indicates the armature resistance, \(K_{b}\) represents the back electromotive force constant, \(K_{i}\) is the motor torque constant, \(K_{g}\) and \(r_{m}\) are the gear ratio and the lead of the ball screw, respectively. \(\dot{x}_{a}\) and \(\dot{x}_{n}\) are the relative velocities of the auxiliary mass and its installed floor, respectively. The control-force equation and its transform are acquired from Equation (7) as,  
\[\left\{ \begin{array}{l}\dot{u}(t)=\left(-\frac{R_{a}}{L_{a}}\right)u(t)+\left[ \begin{array}{cc}\frac{K_{i}K_{g}}{L_{a}r_{m}}&-\frac{K_{p}K_{i}K_{g}^{2}}{L_{a }r_{m}^{2}}&\frac{K_{p}K_{i}K_{g}^{2}}{L_{a}r_{m}^{2}}\\ \end{array}\right]\left\{\begin{array}{l}v(t)\\ \dot{x}_{a}\\ \dot{x}_{n}\\ \end{array}\right\}\\ Y(t)=u(t-d_{6})\end{array} \right\} \tag{8}\]  
In conclusion, the time delays (\(d_{1}\), \(d_{3}\) and \(d_{5}\)) can be ignored in the paper, and the total time-delay of the control system is,  
\[\tau=d_{2}+d_{4}+d_{6} \tag{9}\]
# Digital Twin in the Maritime Domain: A Review and Emerging Trends
## 6 Scientometric Analysis of Digital Twin in the Maritime Domain
### Analysis of the Dataset  
#### 6.2.1 Publication History Analysis  
The 955 publications found in the WoS dataset were analyzed and visualized based on the published year of the article chronologically. Starting with 14 publications in the year 2002, the number of articles published in the year is 231 records in 2022 with a percentage value of 24.2% of the bulk. It is 137% more than the previous year (2021), and the trend is set towards more increments in the coming years (Figure 10).  
When we critically examine the holistic picture, we can observe the rapid increase in publications during the last five years, which indicates the blooming of DT technology within the maritime sector. From the year 2002 until the year 2017, an almost similar annual number of research articles have been published, but the same year has marked the takeoff year for the current trend. In the early years, very few publications originated, and these statistics may have been affected by the deficiencies of technology and knowledge gaps in early developments. Even the COVID-19 pandemic period has not affected the steep increment of the number of publications which have been launched from the year 2018. A similar trend line can be observed in similar studies performed by researchers on the DT applications and surrounding technologies in different disciplines such as in the manufacturing domain [125], business analytics [126], etc. Hence, the existing trend suggests more research work related to maritime DT concepts is underway and being published at present.