esa-sceva/satcom-chunk-collection huggingface dataset:
# Visual SLAM Based Spatial Recognition and Visualization Method for Mobile AR Systems
## 2 Related Works
### Orb-Slam3  
Visual SLAM can be implemented using images acquired by either a camera or other image sensor. It can be implemented at low cost using a relatively inexpensive camera and support various cameras of Mono/Stereo/RGB-D method. Visual SLAM can be classified into sparse type SLAM with a low map density and dense type SLAM with a high map  
Figure 1: Direct SLAM and indirect (feature-based) SLAM.  
density. Additionally, Visual SLAM is ideal for sophisticated AR projects because maps can be saved and later reloaded [9; 10; 11].  
In this paper, the ORB-SLAM3 system proposed in 2020 was used, improving the existing ORB-SLAM and ORB-SLAM2 among various visual SLAMs as shown in Figure 2. It supports VIO (Visual Inertial Odometry) and multi-map [12; 13].  
ORB (Oriented FAST and Rotated BRIEF) feature is a representative real-time image feature that improves the problem of the undirected FAST detector and greatly accelerates the overall image feature extraction by using a very fast binary descriptor, BRIEF. ORB-SLAM3 enables fast Edge extraction, and the main direction of the feature point is calculated by the ORB, and the rotation invariant property is added by the BRIEF descriptor. In addition, a simple descriptor that well expresses the surrounding image area is generated at the location where the feature point is extracted, and most of the feature extraction has excellent parallelism. Accordingly, it is suitable for the SLAM system that requires a lot of real-time demand because it maintains the data characteristics and the scale invariance and improves the speed.  
Compared to ORB-SLAM2 proposed in 2017, ORB-SLAM3 as shown in Figure 3, the most advanced library among current ORB-SLAMs, adds a model that uses IMU values and a fisheye camera model to Mono/Stereo cameras. In addition, a multi-map management function has been added, and by creating a new map when the tracker is lost, the possibility of tracking failure is reduced, enabling continuous tracking, improving the loop detection algorithm, and detecting previously visited locations with higher probability.  
Figure 2: Classification of the Visual SLAM Technologies.
## 3 Synthesis of Crown Ethers and Their Derivatives
### Antibacterial Potential  
The Schiff bases containing bis-benzo-15-crown-5 and their sodium derivatives were examined using a well diffusion method against bacterial strains; _Staphylococcus aureus_, _Shigella dysenteria_ type 2, _Listeria monocytogenes_, _Escherichia coli_, _Salmonella typhi_ H, _Staphylococcus epidermidis_, _Brucella abortus_, _Microococcus luteus_, _Bacillus cereus_, and _Pseudomonas putida_ while choosing DMF as a control. The Schiff bases with 5-methoxy groups have shown maximum activity against strains with a concentration of \(10^{3}\) uM [54]. Interestingly, the chelates (**12**, **13** and **14**) containing Na are found more potent against bacterial strains than bis-benzo-15-crown-5 compounds as shown in Figure 4. The bactericidal activity was compared with commercial drugs; kanamycin K30, sulfamethoxazole SXT25, Amoxycillin AMP10, sublactam SCF, and nystatin NYS100 (Table 1) to describe the potency of compounds [54].  
Figure 4: Structure of Schiff bases containing bis-benzo-15-crown-5 ether **12**–**14**.  
The novel chromene crown ethers and their respective sodium and potassium complexes were tested against strains; _S. dysenteria_ type 2, _S. epidermidis, P. vulgaris, K. pneumoniae_ sp., _Shigella dysenteria_, and _S. marcescens_ sp. Most of the derivatives were found to be moderately active against these pathogens. The compound **15** given in Figure 5. showed 15 mm zone of inhibition against _Shigella dysenteria_[62].
# A New Flexible Method for Solving Multi-Expert Multi-Criterion Decision-Making Problems
## 4 Numerical Verification and Comparison  
### Overview  
An illustrative example of CNC machine tool selection (adapted from Reference [50]) was used in this section to demonstrate the merit of the proposed method. This study also compared results generated using the LOWGA operator and ILOWGA operator methods. CNC is a computer-aided design protocol that uses computers to control machine tools. Selecting the most appropriate CNC machine tool for the Pakistan Machine Tool Factory Pte Ltd can improve product reliability and increase overall productivity [50]. This company started production in 1971, and company management hoped to choose the optimal CNC machine tool to pursue the largest output with the least cost. The hierarchical structure for selecting the optimal CNC machine tool is shown in Figure 2. As shown in Figure 2, the CNC machine tool selection included six criteria and five alternatives. The six criteria used to evaluate the alternatives were capital cost (C1), spindle speed (C2), bar capacity (C3), horsepower (C4), agility (C5), and turning meter (C6). The five alternatives were Mazak (A1), [PERSON] (A2), [PERSON] (A3), [PERSON] (A4), and [PERSON] (A5).  
In this case, the decision-making committee for selecting the optimal CNC machine tool included four experts \(Dk\) (\(k=1,2,\ldots,4\)), chosen based on their backgrounds and experience, each providing different assessment criterion weights and criterion rating values for the five alternatives, as shown in Tables 1 and 2. The four experts include a manager, one outside expert, and two engineers from the factory.  
Appropriate semantic variables of the criterion weights scale were as follows.  
\[L_{1}=\{s_{1}:VU(\mathit{very\;unimportant}),s_{2}:U(\mathit{unimportant}),s_{3}:M( \mathit{medium}),s_{4}:I(\mathit{important}),s_{5}:V\mathit{l}(\mathit{very\; important})\}.\]  
The appropriate semantic variables regarding the scales of the criterion rating values of the five alternatives were as follows.  
\[L_{2}=\{s_{1}:VB(\mathit{very\;bad}),s_{2}:B(\mathit{bad}),s_{3}:MB( \mathit{medium\;bad}),s_{4}:M(\mathit{medium})\] \[s_{5}:MG(\mathit{medium\;good}),s_{6}:G(\mathit{good}),s_{7}:GV( \mathit{very\;good})\}.\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \(\boldsymbol{C1}\) & \(\boldsymbol{C2}\) & \(\boldsymbol{C3}\) & \(\boldsymbol{C4}\) & \(\boldsymbol{C5}\) & \(\boldsymbol{C6}\) \\ \hline \(D1\) & VI & I & M & VI & M & VI \\ \(D2\) & VI & M & * & VI & * & [I, VI] \\ \(D3\) & VI & I & VI & M & I & I \\ \(D4\) & [I, VI] & M & VI & [I, VI] & [M, I] & I \\ \hline \end{tabular}
\end{table}
Table 1: Semantic variables of the criterion weights for the five alternatives.  
Figure 2: Hierarchical structure for selecting the optimal computer numerical control (CNC) machine tool.
## 3 Experimental Design and Procedures
### Experimental Procedure and Calculation  
This experiment used an electronic balance (GX-6100, A&D, Japan; accuracy of \(\pm\)0.1 g, resolution of \(\pm\)0.01 g) to weigh 480 g of pure water (keep the water temperature at 25 \({}^{\circ}\)C with an isothermal unit in advance), placed it in the hot water heater, and then, the upper tube cap was covered and heated with constant power (40 W and 80 W). The \(T_{Thr}\) was set to 43, 53, and 63 \({}^{\circ}\)C, respectively, and when the water temperature reached \(T_{Thr}\), the temperature controller and circuit control unit automatically powered off to stop heating. At this time, the recorder continued to record temperature changes until the end of the test time (the total experiment time was 90 min). The temperature and humidity of the experimental environment were controlled by a temperature/humidity control system at 25 \({}^{\circ}\)C \(\pm\)0.5 \({}^{\circ}\)C and 60% RH \(\pm\)5%, respectively. Each experimental parameter (four test samples: Blank, Base, TiO\({}_{2}\)-FIRC, MWCNTs-FIRC; three \(T_{Thr}\): 43, 53, and 63 \({}^{\circ}\)C; two heating powers: 40 W and 80 W, for a combination of 24 test parameters in total) were performed four times (96 tests were performed in total, and each experiment time was 90 min). Because the heating load used by the hot water heater in this study is only 480 g of pure water, the experimental results are easily affected by environmental conditions and operating errors. Therefore, it is necessary to control the experimental environmental conditions and repeatedly measure to reduce the deviation of the experiment.  
Figure 4: The equipment diagram of the hot water heater performance experiments: (**a**) detailed structure and (**b**) actual photograph.  
The heating rate (\(S_{tr}\), \({}^{\circ}\)C/sec) was calculated from the recorded data in each experiment, and this study took the time and water temperature (\(T_{1}\)) differences corresponding to 10% to 90% of the \(T_{thr}\), where a high \(S_{tr}\) meant that the hot water heater had high heating efficiency (a greater temperature rise at the same time). In addition, this study recorded the average water temperature (\(T_{1}\)) within 10 min before the end of the experiment (\(T_{lat10}\), \({}^{\circ}\)C), where the higher \(T_{lat10}\) represents the better thermal insulation performance of the hot water heater. Furthermore, regarding the standard deviations (\(STD_{4 Thr}\)) of the temperature difference between the upper (\(T_{2}\)) and center (\(T_{1}\)) water temperatures during the experiment, the lower \(STD_{4 Thr}\) means the water temperature between \(T_{2}\) and \(T_{1}\) is closer, and the water temperature is better distributed in the hot water heater. The \(S_{tr}\), \(T_{lat10}\), and \(STD_{4 Thr}\) were taken as the performance indicators to evaluate the influence of FIRC on the performance of the hot water heater. Finally, the calculation results of the four repeated experiments with the same experimental parameters were averaged to obtain the final experimental results.
## 6 Baseline Statement and AAMO Approach Results
### Simulation Setup
#### 6.1.1 Scenarios  
Simulations are conducted over synthetic scenarios (See Figure 10) where long distances and obstacle presence may offer similar challenging conditions that would be expected in the real world. The _Loop_ and _Cross_ scenarios (see Figure 10a,b) were mainly used to confirm the correctness of the implemented solutions and to show the advantages of using a multi-robot approach over a single one. Unfortunately, and caused by the shape and size of the free zones, on those scenarios there are nearly no possibilities to demonstrate any advantage of the proposed approaches over the others. Finally, the _Maze_ scenario (Figure 10c), that represents the most challenging environment, was used to establish comparative results among the approaches. These results are further analysed below in Sections 6.3 and 6.4, respectively. Due to the big amount of collected data, only the values related to _Maze_ runnings are summarised and discussed here. Even so, all charts and screen-shots generated from data concerning all of the three environments are available online: www.fing.edu.uy/~fbenavid/projects/MuRE/mure.html.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Robot Features \& Capabilities** \\ \hline Model & _ATRV_ \\ Maximum speed (\(s_{i}\)) & 1 (m/s) \\ Laser scan window & 360 (\({}^{\circ}\)) \\ Laser range & 6 (m) \\ Laser resolution & 3 (\({}^{\circ}\)) \\ Communication parameters. \\ \hline Range (\(c_{i}\)) & 30 (m) \\ Wall attenuation factor (\(W_{af}\)) & 3.1 (dBm) \\ Distance attenuation factor (\(D_{af}\)) & 1.523 \\ C factor & 4 (walls) \\ Fleet features. \\ \hline Heterogeneity & Homogeneous \\ Initial positions & Left Bottom corner \\ Environment features. \\ \hline Terrain & 80 \(\times\) 80 (m\({}^{2}\)) \\ Wall height & 2 (m) \\ Wall thickness & 0.2 (m) \\ Corridor width & 8 (m) \\ Grid Map features \& parameters. \\ \hline Mesh & Cartesian grid \\ Cell side & 2\(r_{i}\) \\ AAMO parameters. \\ \hline \(\gamma\) & 3 \\ \(\rho\) & \(2\cdot(|R|-1)\) \\ HO-Threshold & 20, 15, 10 (m). \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation setup.
## 2 Experimental Methodology  
### High-Rise Building Models  
A 1:300 scale model of a high-rise building, square in plan, has been used in this investigation. Two roof shapes are considered: a flat roof and a deck-type roof as shown in Figure 1a,b, respectively. As shown in Figure 1a, the height of the building is denoted by \(H\) (400 mm) and the width by \(D\) (133.3 mm).  
In the context of the atmospheric boundary layer winds, low-rise buildings are defined as those having two heights smaller than the width (\(2H<D\)) and \(H<30\) m [46]. This matches the definition of squat buildings given by [PERSON] [47], whereas the building is considered tall when \(2H>D\). This distinction is made based on the behaviour of the wind and its path to overcome the building as an obstacle. When the building is tall, the wind finds it easier to flow around the sides than over the top of the building, except for an area very close to the top, as opposed to squat buildings [47]. In recent literature, related to the flow around buildings, the term high-rise building is used for buildings having a height-to-width ratio of \(H\)/\(D\)\(\geq\) 2:1, as in [48,49]. Thus, the ratio of \(H\)/\(D\) = 3:1 is chosen as an example for the geometry of a high-rise building.  
The inclination of the deck roof in Figure 1b is 30\({}^{\circ}\). The dimensions of the deck roof are the same as those of the flat-roof building with the added cap to the flat roof. The hypothesis here is that the deck-type roof helps to reduce both the separated flow and its turbulence intensity in the above roof flow and hence enhance the quality and mean speed of the wind above the roof for wind energy harvesting. The flow pattern around the two buildings was investigated under four different wind angles, namely 0\({}^{\circ}\), 15\({}^{\circ}\), 30\({}^{\circ}\) and 45\({}^{\circ}\).
## 4 Factors Affecting the Successful Introduction of New-Age Nanotechnologies in Pavement Engineering
### Nano-Silane Technology as a Binder  
Alkoxy-silanes have been shown to be effective as stone preservatives following a process of hydrolysis and condensation [1; 6]. These functional molecules are typically as small as a few nano-meters in size. This logically limits its ability to effectively bridge gaps and bind together stone/aggregate/soil particles with gaps exceeding that of a three-dimensional gel formed during the consolidation phase of the alkoxy-silane treatment. This bridging distance is usually limited to a maximum distance of between 10 and 60 \(\upmu\)m [1].  
In terms of practical road pavement engineering, granular materials are graded in terms of aggregate sizes varying from 63 mm to less than 0.075 mm fractions. It follows that applicable nano-silane technologies have considerable limitations in being used as a soil/material stabilising agent on its own. However, at these fractions the non-reactive Si-CX or Si-(R) bond (similar to that of an emulsifying agent) of the alkoxy-silane (which is added to the stabilising agent such as bitumen emulsion or equivalent), embeds itself firmly into the bitumen molecule [6]). When the modified bitumen emulsion is added to the aggregate the reactive bonds of the silicon molecule, attaches the bitumen firmly to the minerals in the aggregate (Figure 1).  
In practice, the alkoxy-silane modification to traditional stabilising agents such as bitumen emulsions, acts as an effective aggregate adhesive. In addition, it also changes the surface of the stone/aggregate/soil to be hydrophobic. Water is actively being pushed out of the pavement layer and the minerals in the layer are denied access to water molecules, with significant benefits in terms of the future prevention of weathering due to chemical decomposition (the presence of water is a pre-requisite for chemical decomposition of naturally available materials [5]).
## 3 Results  
### Selection of Gas Sensors  
To provide an accurate, real-time estimation of the amount of acetic acid accumulating during cellulose acetate films degradation, it was necessary firstly to identify the most common species released by the film during the degradation process. From literature sources [22; 23; 24; 25; 26], it was found that the average cellulose film degradation releases short organic acids (used for cellulose esterification, mainly acetic acid), various solvents using during the film manufacture, and residues of products used in conservation treatments (mainly butanol, used as antifungal treatment).  
To define which vapors were most prominently emitted, cellulose acetate cinematographic films were analyzed by means of SPME-GC/MS. The results obtained are reported in Section 2.7. From the reported data it emerged that, as expected, the main marker of cellulose acetate film degradation is acetic acid, but other volatiles are also released, such as phenols, short chain alcohols, and naphthalene.  
To the best of our knowledge, there are no commercial sensors currently available which are, at the same time, capable of quantifying acetic acid down to ppb level (as required to intervene before autocatalytic phenomena begin), do not respond to other volatiles released during film storage, and have extremely low space and power consumption requirements to make them suitable for long-term monitoring inside the boxes of historical cinematographic films To solve this problem, two sensors were selected, based on literature [27; 28; 29]: one showing higher response when exposed to acetic acid compared to other similar volatiles (such as trans-2-nonenal, pelargonic acid, diacetyl, butyl acetate, and n-decane), and a second one reported to be more selective towards short chain alcohols (such as ethanol, propanol, butanol). The first sensor will hereafter be identified as BIOS1 while the second will be identified as BIOS2.
# Feasibility Test and Analysis for Heavy Haul Freight Using WDPS Based on MIMO-OFDM
## 2 System Configuration
### Safety Circuit  
All trains operating in Korea must be configured with a safety circuit to stop the train without intervention by a driver when an important device malfunction occurs. In the case of a heavy haul freight with WDPS, a safety circuit must be configured so that the emergency braking signal can simultaneously generate when (1) there is a communication failure and (2) if the emergency braking condition of locomotive ID 8503 or ID 8506 occurs.  
The conditions for safety circuit operation are as follows:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Interface** & **Description** \\ \hline WDPS \(\leftrightarrow\) braking unit & braking control command \\ \hline WDPS \(\leftrightarrow\) CCU & speed data, braking data, pantograph data, MCB data, moving distance, master or slave data, etc. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Control data for heavy haul freight.  
Figure 3: WDPS configuration and antenna installation.  
1. The communication failure: when the wireless communication is cut off for more than 2 s, the safety circuit operates to generate and emergency braking command, and that the cause of failure is indicated on the DU.
2. The EB condition of ID 8503 or ID 8506 locomotive: Since the freights are non-powered, the emergency brake contact (EMBR) of the GDB in locomotives is connected to the WDPS to configure the safety circuit as follows, if any of the following configurations are interrupted, the emergency braking command generated. _[GDB EMBR signal detection of master locomotive] \(\rightarrow\) [WDPS] \(\rightarrow\) [slave locomotive EB loop open] \(\rightarrow\) [master locomotive EB command] \(\rightarrow\) [EMBR signal of slave locomotive detection] \(\rightarrow\) [WDPS] \(\rightarrow\) [master locomotive EB loop open]_
## 5 Results and Analysis
### GPS-Only Constellation Scenarios  
Examining Table 3, the case of narrowband interference, the receiver was able to keep lock-onto at least 5 satellites at jamming power of -75.6 dBm, but lost lock to all satellites at jamming power of -75.2 dBm. On the other hand, in the case of wideband interference, the receiver maintained lock onto 5 satellites at jamming power of -82 dBm and lost track of all satellites at jamming power of -80 dBm. Therefore, the wideband interference resulted in a 6% lower solution availability compared to the narrowband interference, as can be seen in Table 4.  
Figure 6 shows the average carrier-to-noise ratio for all available GPS satellites in each scenario. In this figure and all subsequent sections, the red-dotted vertical lines denote the period at which the  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Jamming Signal** & **Narrowband Interference** & **Wideband Interference** \\ \hline Target band & GPS & GLONASS & GPS & GLONASS \\ Type & CW & CW & SCW & SCW \\ Frequency (MHz) & 1575.42 & 1602 & 1576.42–1574.42 & 1605.38–1598.06 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Jamming signal specifications.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenario \#** & **Jamming Signal** & **Constellation** \\ \hline
1 & Narrowband–GPS band & GPS \\
2 & Narrowband–GPS band & GPS/GLONASS \\
3 & Narrowband–GLONASS band & GPS/GLONASS \\
4 & Wideband–GPS band & GPS \\
5 & Wideband–GPS band & GPS/GLONASS \\
6 & Wideband–GLONASS band & GPS/GLONASS \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulated scenarios description.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Jamming Power (dBm)** & **–90** & **–88** & **–86** & **–84** & **–82** & **–80** & **–78** & **–76** & **–75.6** & **–75.2** & **–74.8** \\ \hline
**Narrowband** & 10 & 10 & 10 & 9 & 9 & 9 & 9 & 7 & 5 & 0 & 0 \\
**Wideband** & 9 & 8 & 8 & 9 & 5 & 0 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Minimum number of available GPS satellites.  
jamming signal was applied. In both narrowband and wideband interference scenarios, the signal was totally lost when the carrier-to-noise ratio dropped below 27 dB-Hz. The figure clearly shows the outage the receiver experienced in both scenarios. It can also be noted that the receiver went into an outage in the case of wideband interference before the narrowband scenario. Figure 7 shows the DOP values for both scenarios. The more satellites lost due to the presence of the jamming signal, the higher the value of DOP is, indicating higher uncertainty in the position solution provided by the receiver based on the available satellite geometry.
# Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks
## 6 Network Parameters Adjustment and Performance Enhancement
### Study of the Number of Clusters  
The number of clusters was another important parameter of the network. Too few CHs may cause long-distance communication between cluster members and CHs. However, excessive CHs caused oversized chains to be generated during intracluster communication. Plenty of energy will be wasted to forward the data packages and the network latency will greatly increase. We set different cluster numbers in our schema when the radius of the mobile sink was 0.25R, and the simulation results are shown in Figure 13. When the cluster number was 5, the network had a better performance, especially with regard to the time when the first node died.