esa-sceva/satcom-chunk-collection huggingface dataset:
# Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms
## 5 Results and Discussion
### Phase I: Maze Exploration
#### 5.1.2 Cumulative Effort (Steps)  
Figure 16 shows a comparison between the performances of the five models (when simulated with 100 ants each on the three mazes, with 30 repetitions each). The pure AA-based model did not introduce much of an improvement compared to a purely random solution. This lack of improvement can be attributed to the clustering effect of pheromone in pure AA, which is likely limiting the exploration of the maze. iAA is the best-performing algorithm among the five, closely followed by the performance of iAA-R.  
Contrary to expectations, iAA-B did not positively add to the performance of the iAA algorithm with its beacon initialization that was supposed to better guide the ants to the target. This is due to a trapping effect noticed in the simulation, as illustrated in Figure 17, where ants get trapped in nooks of the maze while being pulled towards the target.  
As shown in Figure 18a, the growth in maze complexity is weakly supra-linear with the increase in maze size (cf. Table 3). The performance of the iAA strategy, measured by the number of necessary steps, in Figure 18b, with a fixed search group size, initially grows very fast with the increase in maze area, but appears to approach a saturation-like behavior above a certain maze size. Based on this and insights from related numerical experiments,  
Figure 16: Comparing the performance of the 4 AA-based models and a random movement solution in solving the 3 of the sample mazes (M1, M3 and M5), which are of different sizes and complexities, with 100 ants each.  
Figure 17: Inverted AA with beacon initialization for pheromone intensities can lead agents into the traps, marked as green-dashed U-shaped walls in the figure, as they tend to blindly follow a positive pheromone gradient and are unable to go around these obstacles.  
this point of near saturation determines the optimal maze size and complexity problem that can be solved by a search group of 100 agents.  
The weakly supra-linear dependence of the maze complexity on the maze size is to be expected, given that the number of \"topological\" choices (e.g., how to get around the maze), grows faster than the size itself. This can be checked through simple counting, by developing versions of Figure 11f while allowing the maze size to increase. The suggestion of the existence of the optimal size of the search agent group (Figure 18b) for a fixed maze size (while averaged overall a number of configurations at a fixed size), implies the presence of the non-linear dependence of the _Number of Steps_ on the _Number of Agents_.  
Based on our numerical experiments, it appears that the Search Task (Phase I) could have \"too few\" or \"too many\" agents, which is understandable in the context of the ant agent repellent action.
## 2 Study Area and Experimental Data
### Data
#### 2.2.4 DEM (Digital Elevation Model)  
Multi-Error-Removed Improved-Terrain (MERIT) DEM is an improvement of SRTM3 (Shuttle Radar Topography Mission v.3) DEM, with a spatial resolution of 3 arc-second (\(\sim\)90 m). It removes multiple error components from the SRTM3 DEM, including stripe noise caused by the sensor error, speckle noise of surface reflectance, absolute bias derived from the limited control points of the ground, and tree height bias where the canopies were incorrectly classified as the land surface [34; 35]. MERIT was chosen because its accuracy is higher than that of SRTM and NASADEM (NASA Digital Elevation Model) [36] and because of the data availability in the Arctic. In order to match the resolution of MOD17A2H, the DEM dataset was resampled to 500 m using the bilinear method.
## 4 Results and Discussion
### Performance Comparison between DPCT and WBCT  
First, analysis is focused on the performance comparison between wet-bulb and dew-point cooling towers in Polish climate conditions in terms of the obtainable water temperature. The results of the comparison are presented in Figure 8. It can be seen that the outlet water temperatures obtainable by DPCT vary from 4.5 \({}^{\circ}\)C to 21 \({}^{\circ}\)C, while the water temperatures obtained by the wet-bulb cooling tower vary from 7 to 26 \({}^{\circ}\)C. The average difference between obtainable water temperatures is 3.9 \({}^{\circ}\)C during the entire cooling season; the maximal obtainable difference in outlet water temperature is 5 \({}^{\circ}\)C; and the minimal obtainable difference is 1.0 \({}^{\circ}\)C. The difference in obtainable temperatures is caused by the fact that DPCT allows for the pre-cooling of the airstream without changing its humidity ratio. This allows it to decrease its wet-bulb temperature. Due to the fact that the air wet-bulb temperature at the inlet to the wet channel is the theoretical limit of the cooling tower performance, the pre-cooling process in the dry channel allows to lower the limit and, in consequence, achieve higher effectiveness. For example, for inlet air parameters equal to 30 \({}^{\circ}\)C and 40% RH, the wet-bulb temperature is equal to 20.1 \({}^{\circ}\)C, and the wet-bulb temperature of the pre-cooled primary air at the outlet of the dry channel (\(1_{o}=2_{i}\)) is equal to 16.2 \({}^{\circ}\)C. This allows the water to cool to 18.3 \({}^{\circ}\)C, which is almost 2 \({}^{\circ}\)C lower than the wet-bulb temperature of the inlet air. WBCT does not have the ability to pre-cool the outdoor air; therefore, it obtains higher temperatures than DPCT. In the next two sections, the impact of the difference in obtainable water temperature on the performance of gas power plants and air conditioning systems will be investigated.  
Figure 7: Validation results: correlation between model and the experiment.
## 2 Background on the Identification Problem
### Previous Results on DKI Capacity  
[PERSON] studied RKI for DMC in ([26] Th. 1), and showed that assuming \(K=2^{n\kappa}\), the set of all attainable pairs \((R,\kappa)\), where \(R\) is the RKI coding rate and \(\kappa\) is the goal identification rate, contains  
\[\big{\{}(R,\kappa):0\leq R,\kappa~{};~{}R+2\kappa\leq\mathbb{C}_{\text{TR}} \big{\}}, \tag{4}\]  
where \(\mathbb{C}_{\text{TR}}\) is the TR capacity of the DMC. The DKI problem for the slow fading channels, denoted by \(\mathcal{G}_{\text{slow}}\), in the presence of an average power constraint and assuming a codebook size of super-exponential scale, i.e., \(K(n,\kappa)=2^{\kappa\log n}\), is studied in [51], and the subsequent bounds on the DKI capacity are established:  
\[\frac{1-\kappa}{4}\leq\mathbb{C}_{\text{DKI}}(\mathcal{G}_{\text{slow}},M,K) \leq 1+\kappa, \tag{5}\]  
for \(0\leq\kappa<1\). As far as we know, there has not yet been any research performed in the literature on the DKI capacity of the BSC with input constraint, which is pertinent to IoT systems; hence, it is the primary emphasis of this study.
## 3. Experimental Results and Discussion
### Simulation Validation Experiments
#### 3.2.2 Pressure Distribution  
The pressure distribution within the pressurized tube under different inlet airflow velocities is shown in Figure 7. In the straight section of the pressurized tube, the pressure variation is relatively small at different inlet airflow velocities. However, with an increase in inlet airflow velocity, the pressure difference in the straight section shows an initial increase followed by a decrease. Therefore, it is necessary to choose an appropriate inlet airflow velocity to reduce unnecessary energy consumption.  
In the corrugated section, the pressure is nearly uniform at all locations, without abrupt changes. However, with an increase in inlet airflow velocity, the overall pressure within the vane gradually increases, leading to a larger pressure difference and increased pressure loss. This suggests that it is essential to determine an appropriate inlet airflow velocity to reduce energy consumption while ensuring the smooth transport of seeds. An analysis of the pressure distribution contour plots within the vane indicates that the structural parameters of this pressurized tube ensure the stability of airflow velocity within the vane, thereby enhancing the seeding performance of the seeding system.
# Health Monitoring of Civil Infrastructures by Subspace System Identification Method:
## Vibration-Based Damage Detection (VDD)
### Frequency-Domain (FD) Methods
The strong demand in the field of system identification to achieve higher accuracy and extract more information from the vibration responses led to the development of TD methods.
## 2 Materials and Methods  
### Materials  
Poly(D,L-lactide-co-glycolide) (PLGA) (Resomer(r) RG504H; lactic/glycolide ratio 50:50; inherent viscosity: 0.57 dL g\({}^{-1}\) in chloroform at 25 \({}^{\circ}\)C) was purchased from Evonik (Essen, Germany). Hyaluronic acid (HA) with a molecular weight (MW) of 1600 kDa was provided by DSM (Kaiseraugst, Switzerland). In this work, poloxamers (POLOX) (PEOa-PPOb-PEOa) F127 (a = 100 and b = 65, GPC in THF Mw 13.8 kDa, Mn 10.1 kDa, intrinsic viscosity 0.19 dL g\({}^{-1}\) in THF 35 \({}^{\circ}\)C) and F68 (a = 76 and b = 29, GPC in THF Mw 9.6 kDa, Mn 8.8 kDa, intrinsic viscosity 0.15 dL g\({}^{-1}\) in THF 35 \({}^{\circ}\)C) from Lutrol (Basf, Ludwigshafen, Germany) were employed. Bovine serum albumin (BSA), dichloromethane (DCM), sodium azide, agar, dibasic sodium phosphate (Na\({}_{2}\)HPO\({}_{4}\)), potassium chloride (KCI) and sodium chloride (NaCl) were obtained from Sigma-Aldrich (Saint Louis, MO, USA). All the chemical substances were employed without any previous purification.
## 3 Results
### Volatile Organic Compound Profile

Table 1: Volatile compounds found in 80% chocolate samples, using PDMS/DVB fiber through the SPME/CG-MS technique. Through the identification attempt, it was possible to find 37 compounds analyzing their Figures S1-S4 spectra, of which 33 were identified in the negative ionization mode and four in the positive ionization mode (Table 2). Figure 2: Representation of the spectra of chocolate Sample 1 (**A1**) and Sample 2 (**A2**) for the negative ionization mode. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{N\({}^{\circ}\)} & \multicolumn{2}{c}{**Identification**} & \multirow{2}{*}{_m/z_} & \multirow{2}{*}{**MS/MS**} & \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Sample 1**} & \multirow{2}{*}{**Sample 2**} & \multirow{2}{*}{**Reference**} \\  & \multicolumn{2}{c}{**Attempt**} & & & & & & \\ \hline \hline \multicolumn{8}{c}{**Fatty acids**} \\ \hline
1 & Hydroxy octadecenoic & 327 & 171, 211, 229 & [M–H]\({}^{-}\) & ND & X & [41] \\ \hline \multicolumn{8}{c}{**Sugar**} \\ \hline
2 & Disaccharide & 341 & 341, 236, 198 & [M–H]\({}^{-}\) & ND & X & [42] \\
3 & Glucose & 179 & 161, 113, 89 & [M–H]\({}^{-}\) & ND & X & [43] \\ \hline
4 & Hexitol & 181 & 181, 113, 101 & [M–H]\({}^{-}\) & X & ND & [42] \\
5 & \(\beta\)-D-xylopyranosyl-\(\alpha\)-L- & 441 & - & [M–H]\({}^{-}\) & ND & X & [42] \\  & rhamnopyranosyl-D-tucose & & & & & & \\ \hline \multicolumn{8}{c}{**Amino acids and derivates**} \\ \hline
6 & l-tryptophan & 205 & 146, 188, 184 & [M+H]\({}^{*}\) & ND & X & [44] \\
7 & Serotonin & 149 & 132, 136, 118 & [M+H]\({}^{*}\) & X & X & [44] \\ \hline \multicolumn{8}{c}{**Benzoic acid derivatives**} \\ \hline
8 & Vanillic acid diglucoside & 461 & 353, 353, 123 & [M–H]\({}^{-}\) & ND & X & [4] \\
9 & 20-hydroxyclysoe-3-O-\(\beta\)-d- & 579 & - & [M+H]\({}^{*}\) & X & X & [45] \\ \hline \multicolumn{8}{c}{**Phenylpropanol**} \\ \hline
10 & Caffeoyl tyrosine & 354 & 342, 298, 256 & [M–H]\({}^{-}\) & ND & X & [43] \\
11 & Dideoxyclycularide & 342 & 147, 119, 120 & [M–H]\({}^{-}\) & ND & X & [43] \\
12 & Epigalalcatechnic & 305 & 289, 151, 169 & [M–H]\({}^{-}\) & ND & X & [46, 47] \\
13 & 12-hydroxy-jasmonic acid & 225 & - & [M–H]\({}^{-}\) & ND & X & [41] \\ \hline \multicolumn{8}{c}{**Flavonoids**} \\ \hline  
imethyl-O-pyrosylation B trimer & 893 & - & [M–H]\({}^{-}\) & X & ND & [4] \\
34 & Procyanidin A-type hexamer & 929 & 739, 713, 695 & [M–H]\({}^{-}\) & X & ND & [4] \\
35 & Procyanidin trimer & 944 & 695, 577, 425 & [M–H]\({}^{-}\) & X & ND & [4] \\ \hline \multicolumn{8}{c}{**Ternpenoids**} \\ \hline
36 & Soyasaponin B I & 941 & 615, 733, 879 & [M–H]\({}^{-}\) & X & X & [41] \\
37 & Soyasaponin B II & 911 & 695, 577, 425 & [M–H]\({}^{-}\) & X & ND & [41] \\ \hline \multicolumn{8}{c}{X = Compound identified; ND = Not identified.} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Proposed assignments for ions detected in chocolate samples by PS-MS.
## 1 Introduction
* MODM: MODM is a type of MCDM with a decision space that is continuous, indicating an infinite set of alternatives and attributes. MODM's purpose is to select the best option from an endless range of available alternatives that are listed below a set of limitations. As a result, an MODM problem entails the creation of alternatives that optimize or best satisfy the decision-makers' objectives. MODM issues are characterized by the fact that decision-makers must achieve many objectives that are incommensurable and that conflict with one another. A MODM model takes into account a matrix of decision-making variables, objective functions, and constraints. The most widely used MODM method is multi-objective optimization on the basis of ratio analysis (MOORA). Fuzzy set theory is applied to handle ambiguity in the subjective judgment of a decision-maker. The research presented in this study makes two contributions. First, it aids both health care practitioners and consumers in identifying the important factors/criteria that contribute to the usability aspect of mHealth applications. Second, it outlines a technique for selecting the best mHealth applications among the alternatives, using two hybrid models, CODAS-FAHP and MOORA-FAHP. The fuzzy Analytic Hierarchy Process (FAHP) is utilized to determine the weights of the criteria by removing human vagueness, while CODAS and MOORA are used for determining the different ranks associated with the alternatives. The aims of this proposed research paper are mentioned below:  
* Identification of criteria and sub-criteria associated with the usability feature of mHealth applications. * Computing criteria weight using the FAHP by removing vagueness and ambiguity in expert judgments. * Demonstrating two hybrid MCDM model for ranking the alternatives on the basis of the usability scores obtained across multiple criteria and sub-criteria. * Performing a sensitivity analysis to check the consistency of the result obtained by the proposed hybrid models. The remaining parts of this paper are organized as follows: a literature review in Section 2 focuses on the usability aspects of the MCDM approaches in the selection of the methodology; then, proposed hybrid MCDM approaches for selecting the best mHealth application, based on usability, are presented in Section 3; Section 4 discusses the methodologies adopted in this paper; the proposed models' result analyses and validations are shown in Section 5; finally, Section 6 provides the conclusion.
## 2 Material and Methods
### Experimental Design and Setup
Water reservoirs were manually emptied from any remaining water and refilled with fresh NSW every other day. A stock nutrient solution was added and gently mixed with NSW in each water reservoir during refill to achieve a final concentration of 1 \(\upmu\)M NH\({}_{4}\)NO\({}_{3}\) and 0.1 \(\upmu\)M KH\({}_{2}\)PO\({}_{4}\) (Merck, Germany) to avoid nutrient limitation during the experiment. The desired CO\({}_{2}\) concentrations (ambient or enriched) were maintained by also directly bubbling each aquarium with CO\({}_{2}\) from the gas cylinder's main tank. The loss of CO\({}_{2}\) in the aquaria was avoided by covering each ET with a transparent PVC lid that also reduced water evaporation. Water constantly overflowed from the aquaria to the water bath of the ETs, ensuring water renewal. At the same time, ETs were drained of the surplus water flowing out of the aquaria. The light was provided with two LED (light emitting diode) lamps (Hydra Fifty-two HD, Aquallumination(r), Iowa) placed at the same height at the top part of each ET, providing the aquaria with a light intensity of 200 \(\pm\) 20 \(\upmu\)mol photons m\({}^{-2}\) s\({}^{-1}\) set on a 12:12 h lightdark cycle with sunrise and sunset simulation. Seedlings were acclimated in the experimental setup at 27 \({}^{\circ}\)C and no CO\({}_{2}\) additions for five days. During this phase, all ETs were controlled by the same circulation system. Therefore, they were all connected to ensure the same initial conditions for all seedlings. Physico-chemical parameters were regularly controlled to ensure that the desired conditions were maintained. After this acclimation phase, ETs were separated according to the experimental treatments described above. The temperature of four random ETs (i.e., 12 aquaria) was increased by 1 \({}^{\circ}\)C per day from 27 to 31 \({}^{\circ}\)C, while the other four ETs were maintained at the same temperature as during the acclimation phase. Once the temperature of each aquarium was stable, CO\({}_{2}\) enrichment began and the experimental treatments lasted 35 days (20 March 2017 to 24 April 2017).
## 2 Materials and Methods
### The ZigBee-Based WSN System for Environmental Monitoring in the Pig House
#### 2.2.2 The Zigbee Wireless Communication Technology  
The ZigBee technology defines both the application and network layer protocols using low power radio-enabled devices. ZigBee supports short-distance (10-20 m) data communication over mesh, decentralized, ad-hoc, and multitier sensor nodes. The ZigBee mesh topology is used for wireless distributed networking, and the node devices realize multipoint monitoring in the form of \"one master nodes and multiple slave nodes\". The slave nodes are based on STM32 embedded control chip and equipped with various sensors such as temperature, relative humidity, concentration of ammonia and carbon dioxide. Each slave node collects real-time data and uploads it to the server through the master node, finally the remote graphical user interface (GUI) of the system displays the real-time data on the web.
# A Low Complexity Persistent Reconnaissance Algorithm for FANET
## 2 System Model and Problem Statement
### Problem Formulation
#### 2.2.1 Network Connectivity  
We examine a network connection function that only considers active links in order to more correctly evaluate the network performance of our FANET. An active link is defined as a link that is a part of any routing path that connects the executing MU and the ACP. Due to the node location \(x_{i}\)'s differentiable characteristic, we define the network connectivity \(f^{\text{C}}\) as the averaged value of all active links' quality, i.e.,  
\[f^{\text{C}}(X_{V},\rho)=\frac{1}{|\text{P}|}\sum_{(i,j)\in\text{P}}f^{\text{ C}}_{i,j}\big{(}x_{i},x_{j}\big{)}, \tag{2}\]  
where P is the set of all active links, i.e., \(\text{P}=\underset{m\in M}{\cup}\rho(m)\), and \(f^{\text{C}}_{i,j}\) represents the quality of the wireless link \((i,j)\). Accordingly, we make the assumption that the \(f^{\text{C}}_{i,j}\) can be defined as \(\|x_{i}-x_{j}\|^{p}\), where \(\|\cdot\|^{p}\) stands for the Lp-Norm.
# A Robust Image Watermarking Scheme Based on SVD in the Spatial Domain
## 5 Experimental Results and Performance Analysis
### Robustness Test  
For the purpose of copyright protection, the watermark should be extracted integrally to prove the ownership of the received image. However, in the process of image transmission and storage, digital images might be destroyed inevitably by various attacks. These attacks can be divided into two broad categories: signal processing attacks and geometric attacks. Signal processing attacks mainly include noise attacks, median filtering, image sharpening, and JPEG compression. They can reduce the energy of watermark information and degrade the visual quality of the extracted watermark. Geometric attacks can destroy the synchronization between the watermark information and the watermarked image. In other words, the watermark information still exists in the image, but their locations have been completely changed. As a result, the watermark might not be found during the watermark extraction process. The most common geometric attacks are image rescaling and image cropping. Therefore, the robustness against these attacks is another key issue in robust watermarking schemes. To test the robustness of the proposed method, these attacks mentioned above are performed on the watermarked images shown in Figure 5. In this paper, we take image Lena as an example to illustrate the robustness of the proposed method. Figure 6 gives the attacked images and the corresponding watermarks extracted from them. From Figure 6, we can see that the extracted watermarks have good visual effect under signal processing attacks and geometric attacks.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Image** & **PSNR (dB)** & **SSIM** \\ \hline Lena & 49.07 & 0.9946 \\ Barbara & 48.99 & 0.9963 \\ Boat & 49.09 & 0.9936 \\ Airplane & 49.13 & 0.9926 \\ Peppers & 49.28 & 0.9951 \\ Baboon & 49.06 & 0.9975 \\ Clock & 48.51 & 0.9890 \\ \hline
**Average** & **49.02** & **0.9941** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Peak signal-to-noise ratio (PSNR) and SSIM values of the watermarked images.  
Figure 5: Watermarked images: (**a**) Lena; (**b**) Barbara; (**c**) Boat; (**d**) Airplane; (**e**) Peppers; (**f**) Baboon; (**g**) Clock.
# On-Orbit Experimental Result of a Non-Deployable 430-MHz-Band Antenna Using a 1U CubeSat Structure
## 5 Testing on the Ground
### Radiation Pattern  
Radiation pattern tests were carried out using the anechoic chamber at the Kyushu Institute of Technology. Not only the ceiling and walls, but also the floor is covered with radio wave absorbers to simulate the space environment. Figure 9 shows the results of the radiation pattern measurement. It shows that the Loop Hentenna has a non-directional radiation pattern and a maximum gain of \(-5.7\) dBi. The radiation pattern of the dipole antenna on BIRDS-4 was measured as well, with a maximum gain of 3.2 dBi.  
Figure 8: Result of return loss measuring: (**a**) return loss, (**b**) Smith chart.  
Figure 9: Radiation pattern measurement of the Loop Hentenna mounted on Tsuru (**a**) YZ plane, (**b**) ZX plane, (**c**) XY plane.
# Application of OCT in the Gastrointestinal Tract
## 5 Conclusion/Outlook for OCT  
OCT is a rapid, volumetric, nearly microscopically resolved, subsurface endoscopic imaging modality that addresses many of the pitfalls associated with WLE and NBI. OCT has been rigorously studied in the esophagus due to its tubular anatomy and is starting to gain traction throughout the rest of the GI tract including the stomach, small intestine and colon. OCT is compatible with a fleet of accessories including laser markers, autofluorescence techniques and the traditional acoustic ultrasound. Additionally, OCT has the potential to be performed using an office-based capsule, negating the need for the burdens of sedation. Given the copious amounts of data produced by OCT, it is also a promising platform for the implementation of machine learning to achieve a level of autodetection akin to or even surpassing that of the skilled endoscopist. While machine learning in OCT still has a long way to go before it reaches such a skillful level, it can nonetheless play the role of second reader. The plethora of benefits from OCT, including accessorization, ease of delivery and machine learning, uniquely position this technology to address the disease burden of gastrointestinal maladies.