esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
We have shown a bolometric readout mechanism to extract the signal from a refractometric sensor based on SPR excitation caused by a nanostructured rectangular grating. Our approach exploits the metal, where the surface plasmon is originated, as part of an electric read-out circuit to measure a change in the index of refraction of the analyte. Its response is based on the narrow spectral absorption due to the excitation of plasmon resonances in the dielectric/metal interface. The absorbed power heats the metal and increases its temperature. This variation in temperature also modifies the resistance of the metal layer, and this change in resistance can be read-out by an external circuit. The main advantage of this structure compared to conventional refractometric sensors is its simplicity and signal acquisition robustness. The electric signal delivered by the sensor provides information about the index of refraction of the analyte. This allows for a compactness and integration, useful for applications where spectral or angular interrogation techniques are impossible. Also, the system can work as a refractometer for aqueous media. Both the sensitivity and the FOM can be adapted to the optoelectronic interrogation method, and the numerical results in the FOM are competitive with existing technologies. Although the values are not extremely high (FOM is in the few hundreds), the main advantage of this design is its capability to deliver the signal as an electric voltage, and that it works in normal incidence conditions, with a compact illumination source. Due to the micrometer size, it can measure tiny volumes of analyte, as happens in the analysis of human tears.  
All the authors contributed the same in conceptualization, for mal analysis, methodology, validation, visualization, and writing. [PERSON] and A.C. contributed to software analysis. [PERSON]-P. contributed in funding acquisition. [PERSON] contributed to supervision. All authors have read and agreed to the published version of the manuscript.  
This contribution has been possible thanks to the Ministry of Higher Education of Egypt for funding the PhD stay of [PERSON]. The participation of AC and JAGP has been supported by the Ministerio de Economia y Competitividad of Spain through project DPI2016-75272-R.  
The authors acknowledge [PERSON] for the edition of the English style and grammar of this manuscript, and for the fruitful discussions along the writing of this paper.  
The authors declare no conflict of interest.
## 4 System Model
### MNs to GNs  
This type of delay is the time it takes for data packet delivery from its source to its destination (GNs). In a network, nodes are placed in high density areas. This means that there are a lot of links between the MNs and the GNs, which adds distance and steps to communications and causes MNs-to-GNs delays. MNs-to-GNs delay happens when the time it takes for MNs to connect with GNs and the distance between them are both high. In UWSN, more than one path exists between the sender and receiver node. With the help of forwarder selection, paths create in a specific direction, reducing the paths to GNs. Our proposed protocol is elliptical in shape, creating GNs along its trajectory. The nodes make paths towards the trajectory GNs possible. As a result, communication delays between MNs and GNs decrease. Many paths exist between the MNs and GNs, so the SPT has been applied to find the shortest one. MNs calculate the distance toward the GNs, and GNs calculate by using the SPT algorithm as shown in Figure 4. P represents all paths toward the GNs in terms of SP (shortest path).  
\[\text{Num}=\text{P} \tag{1}\]  
The C-SPT is used to choose the shortest path from all existing paths by using the SPT algorithm.  
\[\text{C-SPT (num)} \tag{2}\]  
Suppose that G is a graph, E is both edges, and V is a distance. Set the source node as the current node and mark the rest of the nodes as unvisited nodes. Assess the unvisited nodes for the current node and estimate their distances. Compare the current distances to the calculated distances and assign each V the value corresponding to the shortest distance. When the current node considers all its neighbors, mark them as visited. If the destination source is not available, consider it unvisited. If highlighted, keep it as visited.
## 2 Materials and Methods
### Sampling and Measurements  
Soil sampling was performed three times during the experiment to minimize soil disturbance: on the first day (initial conditions), third day (after oxidation), and 31 st day (after biodegradation). Soil was sampled by using a graduated core sampler at depths of 5, 15 and 25 cm, and composite samples were prepared from each depth profile measuring 10 g for TPH extraction. After sampling, the holes created by the core sampler were re-filled with soil from the tanks. TPH analysis was performed by the Korean Soil Analysis Method, ES 07552.1a [28; 33; 34]. TPH was then extracted with dichloromethane (Samchun) by using a sonicator (Sonics Vibra Cell, CT, USA) and analyzed by GC-FID with a capillary column (Agilent, HP 6890, CA, USA) [28; 35]. The hydrocarbons (n-alkanes) within the range C\({}_{8}\)-C\({}_{40}\) (i.e., n-octane to n-tetracontane) were quantified as TPHs [28] using an eight-point calibration curve obtained from the injection of standard TPH solution of n-alkanes (Florida TRPM Standard 500 ug/mL, Restek, PA, USA).  
The pH (soil: deionized water = 1:5) and electric conductivity (EC) were measured by using a pH meter (MultiMeter K5000-CP, NeoMet, iSTEK, Seoul, Korea). Similarly, the oxidation-reduction potential (ORP) of soil was measured using an ORP electrode (iSTEK). Gravimetric soil water content (_w/w_) was determined by placing 10 g of soil samples in a drying oven (JEO TECH, FO-600M) at 105 \({}^{\circ}\)C for 24 h and water content was measured using a digital balance after drying [36]. The volumetric soil water content (VWC) was monitored for the experimental period by using soil-moisture sensors and recorded using software (ECH\({}_{2}\)O Utility). Soil organic matter (SOM) was determined by the loss on ignition (LOI) method after oven drying at 105 \({}^{\circ}\)C using a furnace (WiseTherm, Wisd.B). The leachate volume was measured by using a measuring cylinder the day after each foam spraying/solution pouring. The bacterial population (CFU/g of dry soil) was monitored throughout the experimental period by the dilution and plating method [24]. Briefly, a 1 g soil sample was serially diluted in water, and 100 \(\upmu\)L of each diluted soil suspensions was sprayed over the surface of solid agar in petri plates (Agar A + nutrient, R\({}_{2}\)A). Then, the petri plates were incubated in an incubator at 28 \({}^{\circ}\)C for 48-72 h. All the measurements were performed in duplicate.  
Figure 1: Schematic diagram of the top view of the soil tank indicating position of holes (only 3% of soil surface area) and soil sampling locations for total petroleum hydrocarbon (TPH) concentration and other parameters (70 cm long column containing 30 cm soil height).
# Nicotinamide Adenine Dinucleotide (NAD) Metabolism as a Relevant Target in Cancer
## 3 NAD+ as a Substrate in Nonredox Reactions  
NAD+ and NADH can be reduced/oxidised in an almost unlimited manner [5]. However, the oxidised form, NAD+, is much more abundant than NADH, especially in the cytoplasm and nucleus (100-fold higher in those compartments, only 7 to 8-fold higher in the mitochondrial matrix) [19], highlighting the tight regulation of the process. High NAD+ levels are required since they participate in many other nonredox molecular processes, such as DNA repair, posttranslational modifications, cell signalling, senescence, inflammatory responses and apoptosis [1; 2; 4; 6; 17; 20; 21] In these nonredox reactions, NAD+ is a substrate for different types of enzyme families, mainly sitruns (SIRTs), poly-(ADP-ribose) polymerases (PARPs) and cyclic ADP-ribose (cADPRSs). These enzymes use NAD+ as an ADP-ribose donor, releasing the catabolic product nicotinamide (NAM) [7; 22] (Figure 2). The SIRT family consists of NAD+-dependent deacetylase enzymes that remove acetyl groups from lysine residues from proteins, such as histones, generating NAM and o-acetyl-ADP-ribose [22; 23; 24]. PARP family members transfer ADP-ribose long chain polymers from NAD+ to their target proteins involved in many different biological processes, such as metabolism, transcription or DNA repair [20]. The ectoenzymes CD38, CD157, CD39 and CD73 belong to the ADP-ribose synthases (cADPRS) family that consume NAD+ and NADP+, generating cyclic ADP-ribose (cADPR) or nicotinic acid adenine dinucleotide phosphate (NAADP), which act as secondary messengers of calcium signalling [19; 23; 25]. SIRTs, PARPs and cADPRSs are capable of coregulating each other, competing for the same subcellular NAD+ pool. SIRT1 can deacetylate PARP1 to inhibit its activity, thereby increasing the levels of NAD+, while PARP2 activity can inhibit the transcription of SIRTs [19; 22; 23]. Therefore, the activation of all these enzymes strongly depends on the availability of free NAD+. The importance of the role of NAD+ in these nonredox reactions has been well established, and recently, it has been suggested that NAD+ is the link connecting signal transduction and metabolism in physiological processes and many diseases [9].
# Low-Cost Solution for Kinematic Mapping Using Spherical Camera and GNSS
## 4 Discussion and Conclusions
Significant errors were observed when the operator walked too close to the facade or when objects were partly occluded by the GNSS antenna. With the knowledge of these results, it can be said that the device is suitable for mapping objects where the required accuracy is between 5 and 10 cm. The major advantage of this device is its very low price, which is around EUR 1000. The price of the Faro Orbis mobile scanner is around EUR 60,000. The usefulness of this equipment is therefore especially beneficial for quick and cheap surveying of smaller sites, especially in smaller villages, and for teaching, where this equipment can be easily and cheaply implemented into basic teaching of surveying and mapping. Major limitations of the current workflow include the need for manual time synchronization between the GNSS track and video frames, which may introduce errors if not performed precisely. In environments with degraded GNSS signals, such as narrow streets or under vegetation, the absence of additional sensors can lead to unstable or inaccurate trajectories. The final disadvantage of this methodology is the difficulty of data processing. A user educated in photogrammetry is needed to evaluate the images. Future work could focus on improving the quality of the measured data by adding additional equipment to take photos of better quality than images from videos or small laser scanners. It would also be useful to create a device with professional GNSS equipment or to improve the position using data from the camera gyroscope. Conceptualization, L.B. ; methodology, L.B. ; software, L.B. ; validation, L.B. ; formal analysis, L.B. ; investigation, L.B. ; resources, L.B. ; data curation, L.B. ; writing--original draft preparation, L.B. ; writing--review and editing, L.B. ; visualization, L.B. ; supervision, K.P. ; project administration, K.P. ; funding acquisition, K.P. All authors have read and agreed to the published version of the manuscript. This work was supported by the Grant Agency of the Czech Technical University in Prague (grants SGS25/046/OHKI/1T/11). Not applicable. The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy concerns. Many thanks to the reviewers and the editor for their useful comments and suggestions. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} AI & Artificial Intelligence \\ DCNN & Deep Convolutional Neural Network \\ GCP & Ground Control Point \\ GNSS & Global Navigation Satellite System \\ INS & Inertial Navigation System \\ INSV & Insta360 Video (proprietary format) \\ MP4 & MPEG-4 Part 14 (video format) \\ MVS & Multi-View Stereo \\ PPK & Post-Processed Kinematics \\ RAM & Random Access Memory \\ RAW & Raw (unprocessed data format) \\ RGB & Red, Green, Blue (color model) \\ RTK & Real-Time Kinematics \\ SfM & Structure from Motion \\ SLAM & Simultaneous Localization and Mapping \\ UTC & Coordinated Universal Time \\ \end{tabular}
## 2 Overview of the Study Area and Data Validity
### Zone Classification Based on Water Systems and Geological Characteristics  
To investigate the influence of geotechnical and hydrogeological factors on the prediction results of the ML model, the Saemangeum area was divided into four zones based on water systems and geological characteristics. Zone 1 is influenced by the Mangyeong River, while zone 4 is influenced by the Dongjin River. Zone 3 is relatively close to both rivers and is subject to their mutual influence, whereas zone 4 is farther from both rivers but still remains under their mutual impact (Figure 3).
# Stress Behaviors at Rib-to-Floorbeam Weld and Cutout Details under Controlled Truck Loading
## 3 FEA of OSD Panel Model under Wheel Loading
### FEA Model  
As shown in Figure 13, the OSD panel model consisted of five longitudinal ribs, three floor beams and four span decks with the ribs numbered from the west to the east and the floor beams numbered as F1 to F5 from north to south. The structure layout of the OSD was represented in the panel model based on the design drawing of this bridge shown in Figure 2c. All welds were modeled, but the residual stress was not considered.  
The model measured 12 m long in the bridge longitudinal direction (\(Z\)), 3 m wide in the bridge transverse direction (\(X\)) and 0.616 m high from the deck plate (\(Y\)) with the origin defined at the intersection of F2 and the center line of R14 at deck plate level. The limited model width in the transverse direction was based on the consensus that the RF and Cutout details present significant local stress effects to wheel loading, as was also demonstrated in the controlled truck tests in this paper. Meanwhile, since solid-web diaphragms were used in this box girder, only the floor beam web above the horizontal stiffeners was modeled, which can help to avoid a very large model scale, while guaranteeing acceptable accuracy [31].  
Figure 13: Finite element model of the OSD panel.  
The Solid45 element, which is a three-dimensional brick element with eight nodes and each node has three translational degrees of freedom, was employed to discrete the panel model. To exercise a good control of the mesh quality, such as aspect ratio, skewness, and orthogonal quality, structured meshes were used in the model, as shown in Figure 14. Since the fatigue details always present stress concentration at weld toes, very fine elements were provided at the area close to the weld toes. At the RD detail and perpendicularly to the weld toe, the first element measured 2 mm, as shown in Figure 14b. At the RF and Cutout details, the minimum mesh size was 3 mm, as shown in Figure 14c. Consequently, there were about 2.05 \(\times\) 10\({}^{6}\) elements in the OSD panel model.  
For boundary conditions applied on the panel model, three degrees of freedom of all nodes at these boundaries were constrained, including its easternmost and westernmost longitudinal boundaries (in the \(Z\)-direction) on the deck plate, the northern and southern ends of the ribs, as well as the bottom and eastern and western ends of F2, F3 and F4. The applied boundary conditions only approximate the real constraint of the panel model from the whole structure. However, since the details of interest are located far away from the model boundaries, according to the Saint-Venant's principle [32], the present boundary conditions would not present significant errors to the stress responses at those details.
## 2 Materials and Methods
### Datasets
#### 2.2.1 LULC Data  
LULC data were obtained from Landsat satellite images for four periods (i.e., 1979, 1990, 2000 and 2010) from the USGS ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/))). Table 2 shows the image details for the four  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data Type** & **Period** & \begin{tabular}{c} **Temporal** \\ **Resolution** \\ \end{tabular} & **Spatial Scale** & **Purpose** & **Source** \\ \hline LULC data & 1979, 1990, 2000, 2010 & Yearly & Areas & Built-up area detection & \begin{tabular}{c} United States Geological Survey \\ (USGS) \\ Shanghai \\ Meteorological (SMB) \\ \end{tabular} \\ Daily precipitation data & 1979–2010 & Daily & Point & \
\begin{tabular}{c} Precipitation \\ trend analysis \\ \end{tabular} &
\begin{tabular}{c} Shanghai Meteorological \\ Bureau (SMB) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Datasets used in this study.  
Figure 1: LULC types of Shanghai in 2010.  
periods. After atmospheric and geometric correction, these images were further classified into built-up and non-built-up areas based on a supervised classification method. The non-built-up area included four land use types: farmland, green land, bare land and water (Table 3). The classification results show that the total accuracy is higher than 85% and the values of the Kappa coefficient (a measure of agreement between the two individuals, see [PERSON] et al. [59]) are between 0.79 and 0.89. Figure 2 shows the spatiotemporal distribution of the built-up and non-built-up areas. In this period (1979-2010), Shanghai experienced rapid urbanization and the extent of built-up area in 2010 reached 2350.05 km\({}^{2}\), which is nearly 10 times than that in 1979.
## 2 Materials and Methods
### Materials
The primary crystalline phase in TS2 (which is the most crystalline slag among the three studied slags) is fayalite comprising 17.3% of the total mass. Minor  
Figure 1: XRD patterns and phase quantification results of the treated slags: (**a**) TS1, (**b**) TS2 and (**c**) TS3. amounts of magnetite/franklinite, wuestite, monticellite and metallic iron are also detected in TS2, while 74.5% of the slag is composed of amorphous phases. TS3 contains a higher amorphous content (95.3%; comparable to that of TS1) with minor amounts of crystalline phases such as magnesium aluminium iron oxide, wuestite and metallic iron. The treated slags were milled using a planetary ball mill at 400 rpm for 10 min. The particle size distributions of the slags were measured using a Horiba laser diffraction particle size analyser in isopropyl alcohol. Representative samples were dispersed in isopropyl alcohol by stirring and ultrasound prior to testing (the refractive index of the slag is assumed to be 2.3-0.05 \(\times\) i, which is suitable for high-ferrous cementitious materials). The characteristic particle sizes of the slags (i.e., mean, mode, D\({}_{(v,10)}\), D\({}_{(v,50)}\) and D\({}_{(v,90)}\)) are presented in Table 2. It is observed that TS2, which contains the highest crystal content, is milled finer than TS1 and TS3 and that TS3 has the coarsest particle size distribution. Nonetheless, the milled slags are used without further mechanical manipulations as it is reported in the literature that the fineness of such slags has slight effects on their reactivity and performance as SCMs [19; 20]. Moreover, the milling energy is kept constant to promote comparability for practical purposes. The density of the slags was measured via helium pycnometry (AccuPyc II 1340 V1.09 Helium pycnometer), and the results are provided in Table 2. The accurate density values were later used in hydration studies and SEM image analysis for determination of slag degree of reaction (DoR) over time, which is explained in more detail in Section 2.2. The slags were found to have rather different density values. TS1 as the most amorphous sample with the lowest iron oxide content and the highest silica content has a considerably lower density compared to other slags. In addition, TS2 shows the highest density due most likely to its significantly higher crystalline content compared with TS1 and TS3.
# Construction Cost Prediction Using Deep Learning with BIM Properties in the Schematic Design Phase
## 5 Machine Learning Analysis for Construction Cost Prediction with Properties
### Error Rates Analysis of Test Cases
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Case** & \begin{tabular}{c} **Training** \\ **Error Rate** \\ \end{tabular} & \
\begin{tabular}{c} **Test** \\ **Error Rate** \\ \end{tabular} &
\begin{tabular}{c} **Relative Error** \\ **Rate** \\ \end{tabular} \\ \hline \multirow{6}{*}{BIM-1} & Case A & 25.66\% & 59.89\% & 133.41\% \\  & Case B & 35.47\% & 39.19\% & 10.48\% \\  & Case C & 28.64\% & 65.84\% & 129.88\% \\  & Case D & 38.90\% & 91.40\% & 134.95\% \\  & Case E & 48.34\% & 21.83\% & 54.84\% \\  & Case F & 23.90\% & 64.03\% & 167.92\% \\ \hline \multirow{6}{*}{BIM-2} & Case A & 23.39\% & 58.86\% & 151.61\% \\  & Case B & 34.45\% & 27.34\% & 20.64\% \\ \cline{1-1}  & Case C & 24.33\% & 63.22\% & 159.82\% \\ \cline{1-1}  & Case D & 29.89\% & 70.97\% & 137.46\% \\ \cline{1-1}  & Case E & 40.26\% & 14.39\% & 64.25\% \\ \cline{1-1}  & Case F & 23.07\% & 63.54\% & 175.42\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Error rates from BIM properties. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Case** & \begin{tabular}{c} **Training** \\ **Error Rate** \\ \end{tabular} & \
\begin{tabular}{c} **Test** \\ **Error Rate** \\ \end{tabular} &
\begin{tabular}{c} **Relative** \\ **Error Rate** \\ \end{tabular} \\ \hline \multirow{6}{*}{Com-1} & Case A & 19.73\% & 34.24\% & 73.58\% \\  & Case B & 22.27\% & 11.27\% & 49.38\% \\  & Case C & 20.94\% & 20.24\% & 3.33\% \\  & Case D & 28.56\% & 55.88\% & 95.69\% \\  & Case E & 22.85\% & 4.99\% & 78.16\% \\  & Case F & 22.24\% & 39.33\% & 76.80\% \\ \hline \multirow{6}{*}{Com-2} & Case A & 18.04\% & 36.14\% & 100.32\% \\  & Case B & 23.40\% & 9.11\% & 61.09\% \\ \cline{1-1}  & Case C & 20.54\% & 21.10\% & 2.73\% \\ \cline{1-1}  & Case D & 22.46\% & 41.61\% & 85.26\% \\ \cline{1-1}  & Case E & 26.69\% & 5.72\% & 78.59\% \\ \cline{1-1}  & Case F & 17.55\% & 39.04\% & 122.48\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Error rates from ‘building general + BIM properties dataset’.