esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Bioactive Compounds Commonly Found in Green-Colored Fruits and Vegetables
### Potential Health Benefits
#### 2.1.2 Flavan-3-ols: Epigallocatechin Gallate; EGCG  
General Characteristics and Food Sources  
Green tea is one of the most popular natural beverages that is produced from the leaves of _Camellia sinensis_ L. through a series of processing steps. In recent years, green tea has received much extensive attention for its putative bioactivities in preventing metabolic diseases and improving human health. The chemical composition of green tea includes chlorophyll, polyphenols, flavonoid, gallic acid, catechins, and caffeine, with chlorophyll and catechins being the primary contributors for its green appearance (Table 1). The catechins are a kind of polyphenol compounds and consist of (\(-\))-epicatechin-3-gallate (ECG), (\(-\))-epicatechin (EC), (\(-\))-epigallocatechin (EGC), and (\(-\))-epigallocatechin-3-gallate (EGCG) [60]. Of these, EGCG is the most abundant catechin present in green tea and accounts for approximately 30%-50% of the catechin content. Its chemical formula is C\({}_{22}\)H\({}_{18}\)O\({}_{11}\), and its molar mass is 458.37 g/mol (Table 2).  
Bioavailability  
In spite of its numerous health benefits, the green tea catechchin, EGCG, has poor bioavailability mainly because of the intensive deterioration during digestion, especially in the small intestine [60; 61]. In addition, low transcellular transport and rapid biliary excretion of EGCG partially contributes to its poor bioavailability [62]. For example, maximum plasma EGCG concentrations of up to 1-2 mol/L were achieved within 2 h after consumption, followed by rapid clearance, restoring plasma concentrations to the baseline levels within 24 h of initial consumption in humans [61]. Additionally, it has previously been reported that long-term treatment of rats and mice with dietary green tea resulted in an initial increase in plasma EGCG levels over the first four days of the treatment [63]. Over the subsequent 10 days of treatment, the plasma levels of EGCG decreased and eventually restored to the baseline levels. Thus, these results indicate that EGCG had low absorption and rapid clearance rates in blood, resulting in its poor bioavailability.  
Therefore, it is important to determine a proper strategy to improve the bioavailability of EGCG, and thus better develop it as a therapeutic agent or functional food in improving human health and preventing metabolic diseases. Alteration of the delivery system for EGCG by oral administration was believed to be a feasible strategy to improve its bioavailability. Recently, EGCG was reported to display increased stability, and thus improved bioavailability and therapeutic efficacy, when formulated as dual drug-loaded PEGylated poly(lactic-co-glycolic acid) nanoparticles [64]. Likewise, [PERSON] et al. found that the complexation of royal jelly proteins with the green tea extract enriched with EGCG would enhance its bioavailability [65].
# Review of Remote Sensing Methods to Map Coffee Production Systems
## 1 Introduction
### Using Remote Sensing to Map Coffee
Moderate resolution satellite constellations, such as the integration of 30 m Landsat with 10 m, \"Landsat-like\" Sentinel-2A and 2B instruments from the Copernicus Programme provide global, multi-spectral imagery every 2.9 days [52]. With these recent advances in remote sensing technologies and data availability, combined with the increased demand for sustainable coffee production, we see the opportunity to capitalize on these technological advances and address sector-wide demands to improve remote sensing methods for more accurate, reliable, and scalable approaches to coffee mapping.
# Covert Channel Based on Quasi-Orthogonal Coding
## 4 Simulation Tests
### Simulation No. 6  
Sorting was carried out for the parameters as for case no. 3 (sorting a block of data equal to 64, and a covert symbol with a length of 16 samples). The modulation for covert data is 4 PSK. The results are presented in Figure 11. The benefits of the sorting are noticeable, although the transmission rate compared with 8 FSK is twice smaller.
# Two-Stage Robust Lossless DWI Watermarking Based on Transformer Networks in the Wavelet Domain
## 3 Proposed Method
### Reversible Information Embedding
#### 3.3.2 Integrity Information  
In order to verify whether the marked image \(I_{m}\) has been attacked during transmission, we need to ensure the integrity of the image \(I_{e}^{\prime}\) before reversible embedding. We use the hash value \(H_{o}\) of \(I_{e}^{\prime}\) as the integrity information and embed it into \(I_{e}^{\prime}\) together with \(D_{c}\) using the reversible watermarking method, as shown in Figure 8. When verifying the integrity of \(I_{m}\) after transmission, we first use the same reversible watermarking method to extract \(D_{c}\) and \(H_{o}\). Then, we restore the image \(\overline{I_{e}^{\prime}}\) and calculate its hash value \(H_{d}\). If \(H_{o}\) and \(H_{d}\) are equal, we consider that \(I_{m}\) has not been attacked during transmission, and the cover image can be restored losslessly. Otherwise, it indicates that \(I_{m}\) has been attacked during transmission, resulting in a permanent loss of restoration information. In this case, we can directly extract the watermark information from the distorted image \(I_{a}^{\prime}\) for copyright verification.  
We adopted a generic reversible algorithm framework and traditional reversible watermarking methods such as histogram modification (HM) [24], pixel value ordering (PVO) [25], difference expansion (DE) [26], and prediction error expansion (PEE) [27] techniques. In this paper, we specifically used difference expansion. The details of directly extracting a robust watermark or restor ing the cover image are presented in Sections 3.5 and 3.6, respectively.
# Performance Evaluation of Adaptive Tracking Techniques with Direct-State Kalman Filter +
## 4 Experimental Setup
### Performance Metric
The average of \(\mathrm{P}_{\mathrm{System}}\) with respect to the \(C/N_{0}\) levels leads to a comprised metric \(\overline{\mathrm{P}}_{\mathrm{System}}\) that evaluates the overall system performance of an adaptive tracking technique:  
\[\overline{\mathrm{P}}_{\mathrm{System}}=\frac{1}{N_{C/N_{0}}}\sum_{n=1}^{N_{ C/N_{0}}}\mathrm{P}_{\mathrm{System}}^{n} \tag{58}\]  
where \(N_{C/N_{0}}\) is the number of \(C/N_{0}\) levels.
_Remote Sens._**2013**, \(5\), 4045-4066; doi:10.3390/rs5084045  
_Remote Sensing_  
**ISSN 2072-4292**  
www.mdpi.com/journal/remotesensing  
_Article_  
**NASA Goddard's LiDAR, Hyperspectral and Thermal (G-LiHT) Airborne Imager**  
**[PERSON] 1,*, Lawrence A. Corp 2, [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1 and [PERSON] 2**  
NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA;  
E-Mails: [EMAIL_ADDRESS] (R.F.N.); [EMAIL_ADDRESS] (E.M.M);  
[EMAIL_ADDRESS] (D.C.M.); [EMAIL_ADDRESS] (J.T.M.);  
[EMAIL_ADDRESS] (J.G.M.); [EMAIL_ADDRESS] (K.J.R.);  
[EMAIL_ADDRESS] (V.L.)  
Sigma Space Corporation, Lanham, MD 20706, USA;  
E-Mails: [EMAIL_ADDRESS] (L.A.C.); [EMAIL_ADDRESS] (P.M.M.)  
Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +1-301-614-6689; Fax: +1-301-614-6695.  
_Received: 20 June 2013; in revised form: 6 August 2013 / Accepted: 8 August 2013 / Published: 13 August 2013_  
**Abstract:** The combination of LiDAR and optical remotely sensed data provides unique information about ecosystem structure and function. Here, we describe the development, validation and application of a new airborne system that integrates commercial off the shelf LiDAR hyperspectral and thermal components in a compact, lightweight and portable system. Goddard's LiDAR, Hyperspectral and Thermal (G-LiHT) airborne imager is a unique system that permits simultaneous measurements of vegetation structure, foliar spectra and surface temperatures at very high spatial resolution (\(\sim\)1 m) on a wide range of airborne platforms. The complementary nature of LiDAR, optical and thermal data provide an analytical framework for the development of new algorithms to map plant species composition, plant functional types, biodiversity, biomass and carbon stocks, and plant growth. In addition, G-LiHT data enhance our ability to validate data from existing satellite missions and support NASA Earth Science research. G-LiHT's data processing and distribution system is designed to give scientists open access to both low- and high-level data products ([[http://gliht.gsfc.nasa.gov](http://gliht.gsfc.nasa.gov)]([http://gliht.gsfc.nasa.gov](http://gliht.gsfc.nasa.gov))), which will stimulate the community development of synergistic data fusion algorithms. G-LiHT has been used to collect more than 6,500 km\({}^{2}\) of data for NASA-sponsored studies across a broad range of ecoregions in the USA and Mexico. In this paper, we document G-LiHT design considerations, physical specifications, instrument performance and calibration and acquisition parameters. In addition, we describe the data processing system and higher-level data products that are freely distributed under NASA's Data and Information policy.  
remote sensing; airborne scanning LiDAR; imaging spectroscopy; surface temperature; sensor fusion; data fusion; ecosystem structure; forest disturbance; forest health; primary production
## 2 Materials and Methods
### Data Acquisition and Preprocessing  
The EEG signals were recorded using the Emotiv EPOC+ EEG neuroheadset system ([[https://www.emotiv.com](https://www.emotiv.com)]([https://www.emotiv.com](https://www.emotiv.com))). The Emotiv system consists of 14 EEG electrodes that are arranged on the scalp according to the 10-20 international electrode placement system at the following locations; \(F_{7}\), \(T_{7}\), \(AF_{3}\), \(O_{1}\), \(F_{4}\), \(P_{7}\), \(AF_{4}\), \(F_{8}\), \(F_{3}\), \(P_{8}\), \(O_{2}\), \(FC_{5}\), \(T_{8}\), and \(FC_{6}\). These electrodes are referenced to the common mode sense (CMS)/ driven right leg (DRL) at the \(P_{3}\) and \(P_{4}\) locations. Figure 2 shows the positions of the EEG electrodes on the scalp. The acquired EEG signals are internally sampled at a frequency of 2048 Hz. Moreover, the Emotiv system is configured to filter the recorded EEG signals using a hardware band-pass filter with a bandwidth of 0.2 to 43 Hz and downsamples the EEG signals to 128 samples per second. Finally, the muscle and electrooculography artifacts within the EEG signals are reduced using the automatic artifact removal (AAR) toolbox [21; 22].  
Figure 1: Timing diagram of the experimental procedure during each trial. \(T\) represents the length of the trial.
## 8 Discussion
### Motivations
#### 8.3.3 Energy Efficiency  
Widespread adoption of cloud computing and network functions virtualization (NFV), combined with a smart, programmable management system, will result in a significant reduction in service deployment time, significant savings in energy consumption, and improved network management in general [400]. Cloud computing and NFV, combined with a smart, programmable management system, will enable a significant reduction in service deployment time, significant savings in energy consumption, and improved network management in general [401]. In this scenario, ML can be utilised to improve performance by making greater use of available network and virtual machine resources while also reducing total energy requirements and expenses. The nature of NFV opens the door to more flexible management allowing for fast, dynamic deployment to match current demand in real-time, as well as the migration of services to locations with reduced energy costs and/or footprints [402, 401], When used in conjunction with a smart management system, this can result in downscaling tactics that can result in significant savings in both energy consumption and operating expenses[403, 404, 405].
# A Novel Methodology for Planning Urban Road Safety Interventions
## 4 Validation of the Proposed Methodology via Case Study Application
### Identification of High-Risk Sites  
Pinpointing high-risk sites is essential in road safety management to prioritize interventions and ensure effective corrective actions. This process relies on analyzing historical accident data to identify critical points (reactive) and assessing potential risk conditions of road infrastructure regardless of past incidents (proactive).  
As mentioned earlier, the European Commission's 2023 NWA guidelines [26] introduced a unified methodology for classifying roads into five risk levels. This system combines both approaches, prioritizing intervention for areas flagged as \"High Risk\" (see Figure 4) by reactive analysis, even if proactive evaluations suggest lower risk.  
For the selected case study, with comprehensive accident data available, the reactive approach was prioritized to identify problematic areas and plan immediate interventions. A key tool in this process is concentration maps, which use GIS technology to identify clusters of incidents visually.  
Concentration maps, or heatmaps, are GIS tools that visually represent accident density within a specific area. These maps generate a density raster, highlighting zones with frequent incidents using a graduated color scale. Critical areas, or black spots, are visually intensified, guiding planners to high-priority intervention zones.  
As a result, four separate concentration maps were created, focusing on incidents involving pedestrians (Figure 5a), cyclists (Figure 5b), motorcyclists (Figure 5c), and vehicles (Figure 5d).
## 3 Dynamical Model for Motion Planning
### Longitudinal and Lateral Coupling Model  
From the conclusions in Section 2, it is known that in the normal operating range the lateral force of the tire has a linear relationship with the slip angle due to the small lateral acceleration, and the effects of lateral and vertical load transfer can be ignored; thus the vehicle can be considered a rigid body moving on the two-dimensional road surface. The road friction coefficient is an important parameter for dynamic modeling, especially when the longitudinal and lateral acceleration is large. In our statistical results of normal driving conditions, when the vehicle speed was greater than 10 m/s, the longitudinal and lateral acceleration was relatively small (less than 2.2 m/s\({}^{2}\)), so we did not consider the impact of road friction coefficient on vehicle dynamics.  
Figure 2: Statistical results of natural driving data. (**a**) Longitudinal acceleration distribution. (**b**) Lateral acceleration distribution. (**c**) Yaw rate distribution. (**d**) Steering angle distribution of front tire.  
As depicted in Figure 3, \(\phi\) denotes the heading angle, \(\omega\) is the yaw rate, \(v_{x}\) and \(v_{y}\) denote the longitudinal and lateral velocities in the vehicle body coordinate \(x-y\) respectively. The symbols \(\delta_{f}\) and \(F_{T}\) denote the steering angle of front tires and longitudinal driving force, \(\alpha_{f}\) and \(\alpha_{r}\) denote the slip angle of front and rear tires, \(F_{yf}\) and \(F_{yr}\) denote the lateral force of front and rear tires, respectively.  
According to Newton-Euler equations, the following inertial force balance equations are established [28]:  
\[\begin{array}{l}\dot{v}_{x}=\frac{F_{T}\cos(\delta_{f})}{m}+\frac{2C_{yf}}{m} \frac{v_{y}+l_{r}\omega}{v_{x}}\sin(\delta_{f})+v_{y}\omega-\frac{2C_{yf}}{m} \delta_{f}\sin(\delta_{f}),\\ \dot{v}_{y}=\frac{F_{T}\sin(\delta_{f})}{m}-\frac{2C_{yf}}{m}\frac{v_{y}+l_{r} \omega}{v_{x}}\cos(\delta_{f})-v_{x}\omega+\frac{2C_{yf}}{m}\delta_{f}\cos( \delta_{f})-\frac{2C_{yr}}{m}\frac{v_{y}-l_{r}\omega}{v_{x}},\\ \dot{\omega}=\frac{F_{T}\sin(\delta_{f})}{l_{z}}f-\frac{2C_{yr}}{l_{z}}\frac{ v_{y}+l_{r}\omega}{v_{x}}\cos(\delta_{f})f_{f}+\frac{2C_{yr}}{l_{z}}\delta_{f}\cos( \delta_{f})f_{f}+\frac{2C_{yr}}{l_{z}}\frac{v_{y}-l_{r}\omega}{v_{x}}l_{r}, \end{array} \tag{1}\]  
where \(m\) and \(I_{z}\) are the vehicle mass and inertia, \(l_{f}\) and \(l_{r}\) are the distances from vehicle's center of gravity to the front and rear axles, \(C_{yf}\) and \(C_{yr}\) are the cornering stiffness of front and rear tires, respectively. Equation (1) describes both vehicle longitudinal and lateral dynamics, and their coupling effects finely, but it is still complex for application in a model-based motion planner.
## 3 Results and Discussion
### Proposed Identification of Chemicals in Avocado (P. americana Mill.) Semil 34 Seed
 & C\({}_{18}\)H\({}_{32}\)O\({}_{2}\) & [M+H-H\({}_{2}\)O]\({}^{*}\) & 263.2367 & 263.2369 & \(-\)0.7 & [57] \\ \hline
20 & 18.62 & Avocadyne & C\({}_{17}\)H\({}_{32}\)O\({}_{3}\) & [M+H-2H\({}_{2}\)O]\({}^{*}\) & 249.2210 & 249.2213 & \(-\)1.2 & [58] \\ \hline
21 & 18.63 & Methylenehexadecanoic acid & C\({}_{17}\)H\({}_{32}\)O\({}_{2}\) & [M+H-2H\({}_{2}\)O]\({}^{*}\) & 233.2262 & 233.2272 & \(-\)4.2 & \\ \hline
22 & 9.96 & Nonadecaniodic acid & C\({}_{19}\)H\({}_{36}\)O\({}_{4}\) & [M+H-H\({}_{2}\)O]\({}^{*}\) & 311.2575 & 311.2581 & \(-\)1.9 & \\ \hline
23 & 23.66 & Oleoyl ethylamide & C\({}_{20}\)H\({}_{39}\)NO & [M+H]\({}^{*}\) & 310.3101 & 310.3103 & \(-\)0.6 & \\ \hline
24 & 25.47 & cis-13-Docosenoic acid & C\({}_{22}\)H\({}_{42}\)O\({}_{2}\) & [M+H-H\({}_{2}\)O]\({}^{*}\) & 321.3149 & 321.3152 & \(-\)0.9 & [59] \\ \hline
25 & 10.62 & \begin{tabular}{c} (92)-5,8,11- \\ Hydrooxyoctadec-9-enoic acid \\ \end{tabular}  & C\({}_{18}\)H\({}_{34}\)O\({}_{5}\) & [M-H]\({}^{-}\) & 329.2329 & 329.2333 & \(-\)1.2 & \\ \hline
26 & 14.84 & \begin{tabular}{c} (122)-9,10,11- \\ Tribhydroxyocadec-12- \\ enoc acid \\ \end{tabular}  & C\({}_{18}\)H\({}_{34}\)O\({}_{5}\) & [M-H-H\({}_{2}\)O]\({}^{-}\) & 311.2221 & 311.2228 & \(-\)2.2 & \\ \hline
27 & 25.47 & Erucamide & C\({}_{22}\)H\({}_{43}\)NO & [M+H]\({}^{*}\) & 338.3435 & 338.3417 & 5.3 & \\ \hline
28 & 23.0805 & 1,2,4-nonadecenriol & C\({}_{19}\)H\({}_{40}\)O\({}_{5}\) & [M+H]\({}^{*}\) & 299.2945 & 299.2945 & 0.0 & \\ \hline  & & \multicolumn{4}{c}{Flavonoids} & & & \\ \hline
29 & 2.41 & Procyanidin B2 & C\({}_{30}\)H\({}_{26}\)O\({}_{12}\) & [M+H]\({}^{*}\) & 579.1493 & 579.1497 & \(-\)0.6 & [48] \\ \hline
30 & 3.82 & Procyanidin B1 & C\({}_{20}\)H\({}_{26}\)O\({}_{12}\) & [M+H]\({}^{*}\) & 579.1490 & 579.1497 & \(-\)1.2 & [48] \\ \hline
31 & 4.04 & Procyanidin A1 & C\({}_{30}\)H\({}_{24}\)O\({}_{12}\) & [M+H]\({}^{*}\) & 577.1338 & 577.1341 & \(-\)0.5 & [58] \\ \hline
32 & 3.54 & Pavetannin B6 & C\({}_{45}\)H\({}_{36}\)O\({}_{18}\) & [M+H]\({}^{*}\) & 865.1968 & 865.1974 & 1.3 & \\ \hline  & & \multicolumn{4}{c}{Furanoid lignas} & & \\ \hline
33 & 8.16 & Syringaresinol & C\({}_{22}\)H\({}_{26}\)O\({}_{8}\) & [M+H-H\({}_{2}\)O]\({}^{*}\) & 401.1592 & 401.1595 & 0.3 & [20] \\ \hline  & & \multicolumn{4}{c}{Glycerolipids} & & \\ \hline
34 & 18.57 & Lichesterinic acid & C\({}_{19}\)H\({}_{32}\)O\({}_{4}\) & [M-H]\({}^{-}\) & 325.2377 & 325.238 & \(-\)0.9 & \\ \hline  & & \multicolumn{4}{c}{Glycerolphospholipids} & & \\ \hline
35 & 18.11 & \begin{tabular}{c} 1-Oleoyl-sn-glycero-3- \\ phosphocholine \\ \end{tabular}  & C\({}_{26}\)H\({}_{32}\)NO\(
## 3 Results and Discussion
### UV-Visible Analysis  
Surface plasmon resonance (SPR) is an analytical technique used to describe changes in electronic configurations of NPs because of surface effects. Measurements of the SPR NPs band offered an indirect piece of evidence to substantiate the creation of AuNPs. The SPR was determined within 400-700 nm under ambient conditions using distilled water as the reference sample. The peaks of AuNP (SPR) can be adjusted from visible to infrared in the electromagnetic spectrum. The main characteristics of the SPR is a sharp decline with decreasing core size for AuNPs due to the onset of quantum size effects that become significant for particles with core sizes less than 3 nm in diameter. Thus, the SPR is absent for AuNPs with a core diameter of less than 2 nm, as well as for bulk gold [54]. AuNPs display the SPR band at about 520 nm in the visible region and the SPR band peaks and the wavelength is usually influenced by the particle size [55].  
In this study, the suspensions of Au ions and AuNPs are shown in Figure 3. The final color of the suspension is shiny red, as can be seen in Figure 3c. The aqueous colloidal solution of Au was transformed from dark red to red at the start of ultrasonic treatment. After 5 min of sonication, the red shine color remained unchanged. The UV-vis spectral analysis of the AuNPs reveals a SPR absorption band at 520 nm (Figure 3a), which is consistent with the previous study [51; 55].
## 3 Incremental Redundancy  
This paper uses the term \"Incremental Redundancy\" (IR) to denote all the bits transmitted with the objective of aiding in the recovery of one or more codewords whose decoding had previously failed. Figure 2 shows three different types of IR:  
1. Chase Combining [46]: the sequence of IR bits is identical to a subset of the bits previously sent. It is simple and computationally efficient, since the transmitter does not need to generate new bits and the decoder can just refine the previous LLRs using maximal ratio combining. However, some of the information transmitted might be redundant to the receiver, so it is a suboptimal approach.
2. Bundle parity bits: the sequence of IR bits consists of a bit-wise erasure code over the previously transmitted codewords [47]. This paper uses the XOR of the codewords in a bundle, unless stated otherwise.
3. Codeword parity (or extension) bits: the sequence of IR bits extends each of the previously transmitted codewords with either previously punctured bits or with completely new parity found by adding new rows and columns to the parity check matrix \(H\).  
We assume that the decoder can handle the decoding of a (possibly extended) codeword, but does not have enough memory to jointly decode all the codewords in a bundle. Each codeword is therefore decoded independently, although Chase Combining and bundle parity bits can be used to refine its LLR values.  
We now study the effect that each of these types of IR bits has on the codewords. In a nutshell, Chase Combining and bundle parity bits increase the SNR for some bits in the failed codewords, and extension bits reduce the rate of the codeword. These improvements in SNR and rate can be translated into a lower probability of error using Equation (1).
## 3 Results and Discussion
### Economic Application of Optimization of Concrete Dosages to a Real Building
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multirow{2}{*}{**Concrete**} & **Vol** & **Current Costs** & \multicolumn{2}{c}{**Option A**} & \multicolumn{2}{c}{**Option B**} & \multicolumn{2}{c}{**Option C**} \\  & & **(m\({}^{3}\))** & **(EUR/m\({}^{3}\))** & **(EUR)** & **(EUR/m\({}^{3}\))** & **(EUR)** & **(EUR/m\({}^{3}\))** & **(EUR)** & **(EUR/m\({}^{3}\))** & **(EUR)** \\ \hline Supports & RC\(-\)30 & 33.9 & 53.3 & 1808.6 & 51.8 & 1757.7 & 43.1 & 1462.5 & 49.8 & 1689.9 \\ Beam tubular TT & PC\(-\)45 & 38.4 & 56.7 & 2179.9 & 48.0 & 1845.4 & 45.7 & 1757.0 & 51.5 & 1980.0 \\ Beam delta type & PC\(-\)40 & 18.6 & 55.4 & 1029.5 & 48.0 & 892.8 & 45.7 & 850.0 & 51.5 & 957.9 \\ Joist tubular & PC\(-\)40 & 176.9 & 55.4 & 9793.0 & 48.0 & 8492.5 & 45.7 & 8085.6 & 51.5 & 9111.8 \\ Joist ceiling & PC\(-\)40 & 4.3 & 55.4 & 239.6 & 48.0 & 207.8 & 45.7 & 197.9 & 51.5 & 223.0 \\ Wall panel & PC\(-\)40 & 85.4 & 54.2 & 4627.0 & 48.0 & 4097.7 & 45.7 & 3901.4 & 51.5 & 4396.5 \\ Ceiling panel & PC\(-\)40 & 27.9 & 54.2 & 1510.4 & 48.0 & 1337.7 & 45.7 & 1273.6 & 51.5 & 1435.2 \\ Reinforced panel & RC\(-\)25 & 22.5 & 49.8 & 1121.5 & 49.4 & 1112.0 & 43.1 & 970.2 & 49.8 & 1121.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Cost analysis for a real industrial building (890 m\({}^{2}\) surface) with three dosage strategies (A, B, C).