esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Hygroscopic Coating on Aluminum Fin Surface of Air Conditioning Heat Exchanger
## 3 Hydrophobic/Hydrophilic Coated Heat Exchangers  
During the operation of the fin-tube heat exchanger, when the surface temperature of the fin is lower than the dew point temperature of the inlet air, condensation phenomena will appear on the surface of the fin [59]. For conventional fin-tube heat exchangers, condensed water adheres to the surface of the fin in the form of water droplets, increasing the pressure drop of the air-side core, provides a site for biological activity, creates the possibility of corrosion, and reduces the air-side heat transfer rate. Furthermore, they have low dehumidification capacity, the evaporator temperature will be reduced by increasing the energy consumption of the air conditioner to meet the comfort requirements of the indoor humidity [60]. To solve the above problems, the condensing surface with both a high nucleation rate and efficient droplet departure has been proposed [61]. At present, the most attractive feature is that hydrophilicity enhances the nucleation process, which requires a large non-condensable gas content and a small degree of subcooling. However, the condensation water film on the hydrophilic surface will impede the heat exchange between the fins and the air and reduce the heat exchange efficiency of the air conditioner. It was reported that the hydrophobic surface could promote the droplets to roll away easily on the condensation surface, and the improved mobility of droplets was demonstrated to successfully enhance the heat exchange characteristics of the air conditioning heat exchanger [62; 63; 64]. Therefore, various methods of fabricating hybrid hydrophobic-hydrophilic surfaces have been proposed, which regions are having hydrophilic behavior  
Figure 4: Schematic diagram explaining DCHE air conditioners in winter [58].  
Figure 3: Schematic of the solar-driven desiccant-coated heat exchanger cooling system [55].  
(contact angle \(<90^{\circ}\)) and regions having hydrophobic behavior (contact angle \(>90^{\circ}\)). Water is harvested on hydrophilic regions; after reaching a critical value, the water droplets will fall off the surface. And hydrophobic regions are used to transport the tiny water droplets and exchange heat between the fins and the air. It could promote the condensed droplets removed from the surface before coalescing into a film, and the nucleation rate and heat exchange efficiency are successfully improved [65,66].
## 6 Biotechnological Uses and Production Potentiality of Carotenoids from Haloarchaea  
### Biotechnological Uses of Carotenoids from Haloarchaea  
Carotenoids have numerous applications as colorants (in food products and cosmetics), feed additives for poultry, livestock, fish, and crustaceans (Patent ES2324077 A1. See Table 2), antioxidants, antitumor and heart disease prevention agents, precursors of vitamin A and enhancers of _in vitro_ antibody production. Hence, they are widely applied in the food, medical, pharmaceutical, and cosmetic industries as dyes and functional ingredients [3; 6; 58]. More than 600 carotenoids are known to occur naturally and many of them are still being identified. Although the carotenoids' market is highly segmented it has grown significantly in the last few years and this growth is projected to continue. \(\beta\)-Carotene and the xanthophylls astaxanthin, cantaxanthin, and lutein are the major carotenoids with commercial interest, and Europe is currently the largest market for this kind of compounds with nearly 45% of worldwide sales [136]. There are several advantages and disadvantages of chemical synthesis for carotenoids production. Chemical synthesis technology has been developed so far for many carotenoids (mainly for all of those most demanded by the market). This synthesis procedures produce carotenoids of exceptional purity and consistency at high concentration, and usually the overall cost of the production is relatively low. However, the chemical synthesis of certain carotenoids is very complex, and as a consequence of that, it is slow and expensive. Besides, the chemical synthesis of a new carotenoid usually requires the development of a new chemical route _in vitro_. Finally, some stereoisomers may not be active as the naturally occurring carotenoids isomers, or may have undesired side effects. As a consequence of all those aspects, the production of carotenoids from biological sources has been an area of intensive investigation. Also the consumer preference for natural products, as well as high costs, presence of by-products and damaging effects on the environment have together intensified efforts to identify alternative sources for chemical method. The production of natural colorants through fermentation has a number of advantages, such as cheaper production, higher yields, possibly easier extraction, less batch-to-batch variations and no seasonal variations. The production is flexible and can easily be
# Applications of GNSS-RO to Numerical Weather Prediction and Tropical Cyclone Forecast
## 4 Outlook
### Trends Toward Increasing the Amount of RO Soundings
#### 4.1.3 Airborne RO Technique  
To some extent, most of studies we reviewed were the Spaceborne RO (SRO) technique, because the receivers are onboard LEO satellites. The GNSS also has a ground-based observation, which provides its signal delays as a source of the water vapor contents for NWP, such as zenith total delay (ZTD) and integrated water vapor (IWV) [81]. However, in the case of receivers onboard aircrafts, the Airborne RO [82] technique is employed. The ARO is a solution to the shortcomings of the SRO, i.e., the insufficient observations and random temporal and spatial distributions. For instance, in the face of a target location where a TC is generated, the obtained profiles from the SRO are sparse. The ARO addresses this issue by maneuvering the aircraft to the target location and by collecting the samples several times. The drawbacks of the ARO are the less stable receiver platform and the low SNR of the current antennas.  
The authors of [83] used the ARO to study Hurricane Karl in 2010 and the heavy rainfall over Colorado in September 2013. The results showed improvement in the initial moisture and pressure field measurements, resulting in a better 36 h rainfall forecast. That dissertation reported that if all U.S. continental flights were to set up this receiver, the estimated RO soundings could reach 3000 every 12 h. If the drawbacks of the ARO can be solved, it will be a step forward in the application of RO in NWP and TC monitoring.
# A Case Study Comparing Methods for Coal Thickness Identification in Complex Geological Conditions
## 3 Methods
### Seismic Multi-Attribute Method
#### 3.2.2 BP Neural Network Prediction Model  
A BP (Backpropagation) neural network consists of an input layer, an output layer, and one or more hidden layers [38], as shown in Figure 6. During implementation, initial data normalization is performed to control the scale of each feature within the same range. Then, the network model and structure are established. Next, training samples are selected as needed, and the network is trained by incorporating the input and expected output data. Through forward propagation and backpropagation processes, weight and threshold adjustments are made, and output errors are calculated until the learning error requirements are met, achieving function approximation [39]. After training, the model is evaluated using actual data to make the final result determination.  
The thickness of coal seams exhibits a linear relationship with certain seismic reflection wave attributes within a defined range. However, the correlation between seismic reflection wave attributes and coal seam thickness typically demonstrates non-linear characteristics. BP neural networks can adjust parameters via error feedback, thereby ensuring the network's capacity for non-linear mapping.  
Figure 6: Neural network model.
# Escaping Local Minima in Path Planning Using a Robust Bacterial Foraging Algorithm
## 4 Results and Discussion
### Scenario 1: Cases without Local Minima Problem
#### 4.1.1 Case 1: Static and Moving Obstacle  
The target is defined at (\(-20\), \(90\)) and the robot starts to travel from (\(50\), \(-90\)), as shown in Figure 2. The robot continues its travel on a pre-determined path till it senses the presence of an obstacle on its path. The task is to approach the target location without any collisions. In Figure 2, the robot successfully reaches the target by rerouting in order to avoid a circular static obstacle. It is worth mentioning that the location of the obstacle was not known. The repulsive Gaussian cost function forces the robot to pick the farthest bacterium from the obstacle that lies at the boundary of the sensing range. Due to this constraint, the robot keeps on moving alongside the boundary till it re-approaches the defined path.  
A dynamic obstacle is also considered, which moves on a horizontal plane at a constant speed of 5.217 cm/s. The situation is depicted in Figure 3, where the robot's movement is registered at four different time instances. It can be observed easily that until the robot reaches the vicinity of the obstacle and senses its presence, its motion remains unaffected. The robot resumes its travel on the same course when the obstacle passes away and cannot be sensed any further. During this process, the RBF keeps on generating new particles around the robot to search for a better solution. The robot's movement is confined due to the obstacle; therefore, generation of the new particles is limited to a very small region. The elapsed time required to reach the target is about 11.86 s. The total cost function in each step of the robot is the sum of \(J_{o}\), \(J_{g}\), and \(J_{add}\) of all the particles around the robot. The next candidate particle is the one having the lowest cost function error. The repellent Gaussian cost function is maximal only if the robot is near the obstacle, and zero if it cannot sense the influence of the obstacle.  
Figure 3: Trajectory of the robot when a moving obstacle is encountered.  
Figure 2: Trajectory of the robot when a static obstacle is encountered.
## 4 Case Study
### Deployment of the CVROSS  
In the design of the CVROSS, the deployment of the proposed system consists of four major components: (i) noise reduction, (ii) vehicle recognition, (iii) calculation of parking gaps and (iv) calculation of available parking spaces. The proposed system was deployed using Simulink and LabVIEW for algorithm modelling and real-world prototyping respectively, as shown in Figure 11. The models and algorithms for parking-gap calculation and parking-gap assignment and the fuzzy logic for vehicle parking reservation, were developed in the Simulink environment, while the user  
Figure 10: Selected traffic lanes for site visits.  
interface and the system prototyping and data acquisition elements, were controlled and constructed in the LabVIEW environment.
## 5 Performance Evaluation
### Registration and Verification of Contracts  
Considering that each registered license plate corresponds to a contract, we evaluated the registration time of contracts in our blockchain through the gateway. For this experiment, we hosted our blockchain on a machine running Ubuntu 18.04 LTS with 2 GB of RAM and Intel Core i7 3.8 GHz processor. We then used the web3.js library to register and verify the contracts by establishing a connection with the blockchain. Following, we measured the execution time for the registration of 1, 10, and 100 contracts. Table 5 presents the results obtained. From these results, we observe that the time necessary to register transactions in blockchain varies linearly with the number of contracts being registered concurrently.  
Next, we used the address of each contract generated to perform its search in the blockchain and evaluate the connection time of the gateway with the blockchain. We dismissed the time the gateway takes to obtain the contract address from the database. As the results presented in Table 6 show, the increase in the number of blocks has minimal impact on the time for obtaining a contract stored on the blockchain.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Network** & **Contracts Stored** \\ \hline Default Ganache & 22 \\ Ethereum Main-net & 26 \\ Our Network & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Contracts stored by block in different networks.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Addresses** & **Gas Cost** \\ \hline
1 & 173,833 gas \\
10 & 300,290 gas \\
100 & 1,324,958 gas \\ \hline \hline \end{tabular}
\end{table}
Table 3: Gas cost varying the address quantity.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Contracts** & **Execution Time** \\ \hline
1 & 0.52 s \\
10 & 4.20 s \\
100 & 38.35 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Execution time for contract registry in blockchain.  
Based on the results obtained from the experiments presented above, we consider that our architecture can be employed in real-world systems. However, it is worth noting that we did not perform database scalability tests on a real system, and several aspects still need to be analyzed. For instance, registering the same vehicle as it travels through the city requires a strategy to reduce storage costs. Furthermore, the database scalability depends on the number of capture points, the resolution of the images, and the additional information to be stored. The system's scalability also depends on its ability to meet the time requirements for registering multiple images captured by the multiple cameras of one or more cities served by the system. These aspects will be investigated in future works that involve applying the proposed solution in a real system.
## 4 Fault Diagnosis Method
### Block Diagram of Fault Diagnosis Process  
The block diagram of the fault diagnosis process is shown in Figure 2. First, the battery pack identifies the second-order equivalent circuit model parameters of the battery monomer by SSA every 5% SOC, and the data with smaller cumulative errors are screened for analysis according to the fitness function; second, the parameter identification results are fed into the EKF algorithm to estimate the SOC; and lastly, the remaining discharge capacity is computed based on the estimated SOC, and the remaining discharge capacity is calculated with the medium to long term time scales by using the box plots to compare the inconsistency of the battery cells.  
Figure 2: Block diagram of troubleshooting process.
## 4 Energy and Power Optimization Allocation
### Step-by-Step Optimization Scheme  
In the step-by-step optimization scheme, each optimization step only focuses on a single parameter vector, and the optimization results of the previous step can serve as prior knowledge for subsequent steps. Here, we only give the power optimization allocation strategy for the single-slot synchronous and asynchronous ISAL networks. According to the optimization allocation method in the single-slot networks, the power optimization allocation in the dual-slot networks is easy to obtain.
# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 2 Stakeholder and System Interoperability  
### Ecosystems of Industrial Production  
Industry 4.0 is often envisioned so that the methods and tools that are associated with IT (Information Technology) and OT (Operational Technology) are converging [6]--although this is a simplification. Information sharing across all levels is a critical key principle--hence ICT is a better term to cite--as well as real-time feedback, security, and high reliability (which are related requirements).  
The importance of information sharing throughout the ecosystem cannot be over-estimated--neither the challenges it implies for Industry 4.0. One way to demonstrate stakeholder heterogeneity is through the logistics and supply chain approach of Figure 2. Data on individual assets, their feature description, status, location or even lifecycle history are available to get shared securely throughout the value chain. The same applies to the data related to the production or logistics processes as well.  
Figure 1: Structure of the current paper.  
Figure 2: Main stakeholders and processes of a generic supply chain [7].  
When narrowing down the ecosystem to the production itself--which happens in the manufacturing plant--the stakeholders are traditionally the production equipment vendors, the providers of \"raw\" or pre-processed materials, the production owner itself, and the distributors. In production with advanced Industry 4.0 capabilities, however, the communications (equipment or service) provider comes to play as well. This is because the deployment and operation of flexible but reliable and secure communication systems require a special skill set and experience that is found best in the resume of ICT engineers.  
The Industry 4.0 ecosystem aims to provide more flexible, cheaper, environment-friendly, and sustainable factories. To investigate and evaluate this ecosystem only from the Industrial Production perspective the focus shall be on production floor elements. The key members' responsibility in this environment is presented--as shown in Figure 3--and briefly introduced the connections between them.
## 2 Signal Model and Conventional Algorithm Analysis
### Influence Factors Analysis  
As analyzed above, the maximum sum of the absolute values of cross-correlation can be expressed as:  
\[\begin{array}{rcl}S(\hat{l}_{b})&=&\sum\limits_{n=0}^{N-1}\lvert C(n,\hat{l}_ {b})\rvert=\sum\limits_{n=0}^{N-1}\lvert\sum\limits_{k=1}^{20}Y(20n+\hat{l}_{b} +k)\rvert\\ &=&\sum\limits_{n=0}^{N-1}\lvert 0.5 AD_{k}R(\delta\tau)\mathrm{sinc}[\delta f _{d}(k)T_{coh}]\exp[j\cdot\phi_{k}+j\pi\delta f_{d}(k)T_{coh}]\rvert\end{array}, \tag{9}\]  
where the coherent integration time \(T_{coh}\) is 20 ms.  
In the tracking stage, the code phase offset is small and \(R(\cdot)\) is close to the peak value, so \(\mathrm{sinc}[\delta f_{d}(k)T_{coh}]\) represents the amplitude attenuation of coherent integration due to the frequency deviation. Because the coherent integration time of tracking is 1 ms, the frequency deviation is limited within [\(-\)250 Hz, 250 Hz], but the coherent integration time \(T_{coh}\) of the ML method is 20 ms. As shown in Figure 3, the amplitude shall be attenuated to zero with a frequency deviation of an integral multiple of 50 Hz. If the frequency deviation is 25 Hz, the amplitude attenuation 10 lgsinc[\(\delta f_{d}(k)T_{coh}\)] shall be \(-\)1.9 dB. The results in [16] and [18] show that if the frequency deviation is larger than 25 Hz, the ML method is nullified.  
Figure 2: Diagram of the ML bit synchronization method.
## 2 Results and Discussion
### Volatile Compounds Analysis
 \\ \hline Esters & & & & & & \\ Ethyl acetate & 883 & 4.498 \(\pm\) 0.065 \({}^{\mathrm{a}}\) & 3.453 \(\pm\) 0.144 \({}^{\mathrm{c}}\) & 4.465 \(\pm\) 0.115 \({}^{\mathrm{a}}\) & 4.052 \(\pm\) 0.116 \({}^{\mathrm{b}}\) & 3.141 \(\pm\) 0.064 \({}^{\mathrm{d}}\) \\ Isobutyl acetate & 1006 & 0.002 \(\pm\) 0.000 \({}^{\mathrm{a}}\) & 0.001 \(\pm\) 0.000 \({}^{\mathrm{d}}\) & 0.002 \(\pm\) 0.000 \({}^{\mathrm{b}}\) & 0.001 \(\pm\) 0.000 \({}^{\mathrm{c}}\) & ND \\ Ethyl butyrate & 1044 & 0.049 \(\pm\) 0.002 \({}^{\mathrm{a}}\) & 0.027 \(\pm\) 0.001 \({}^{\mathrm{c}}\) & 0.042 \(\pm\) 0.001 \({}^{\mathrm{c}}\) & 0.046 \(\pm\) 0.001 \({}^{\mathrm{b}}\) & 0.034 \(\pm\) 0.002 \({}^{\mathrm{d}}\) \\ Isoamyl acetate & 1120 & 0.493 \(\pm\) 0.012 \({}^{\mathrm{a}}\) & 0.353 \(\pm\) 0.006 \({}^{\mathrm{c}}\) & 0.463 \(\pm\) 0.007 \({}^{\mathrm{b}}\) & 0.449 \(\pm\) 0.028 \({}^{\mathrm{b}}\) & 0.330 \(\pm\) 0.007 \({}^{\mathrm{c}}\) \\ Ethyl hexanoate & 1232 & 2.697 \(\pm\) 0.085 \({}^{\mathrm{a}}\) & 1.356 \(\pm\) 0.074 \({}^{\mathrm{c}}\) & 2.142 \(\pm\) 0.120 \({}^{\mathrm{b}}\) & 2.158 \(\pm\) 0.061 \({}^{\mathrm{b}}\) & 0.568 \(\pm\) 0.023 \({}^{\mathrm{d}}\) \\ Ethyl (\(E\))-hex-2-enoate & 1245 & 0.040 \(\pm\) 0.001 \({}^{\mathrm{a}}\) & 0.021 \(\pm\) 0.001 \({}^{\mathrm{c}}\) & 0.038 \(\pm\) 0.001 \({}^{\mathrm{b}}\) & 0.036 \(\pm\) 0.004 \({}^{\mathrm{b}}\) & ND \\ Ethyl heptanoate & 1332 & 0.045 \(\pm\) 0.001 \({}^{\mathrm{a}}\) & 0.016 \(\pm\) 0.001 \({}^{\mathrm{d}}\) & 0.034 \(\pm\) 0.001 \({}^{\mathrm{b}}\) & 0.029 \(\pm\) 0.002 \({}^{\mathrm{c}}\) & 0.003 \(\pm\) 0.001 \({}^{\mathrm{e}}\) \\ Ethyl octanoate & 1434 & 2.870 \(\pm\) 0.080 \({}^{\mathrm{a}}\) & 0.768 \(\pm\) 0.022 \({}^{\mathrm{c}}\) & 1.706 \(\pm\) 0.086 \({}^{\mathrm{b}}\) & 1.790 \(\pm\) 0.140 \({}^{\mathrm{b}}\) & 0.574 \(\pm\) 0.027 \({}^{\mathrm{d}}\) \\ Ethyl nonanoate & 1530 & 0.005 \(\pm\) 0.000 \({}^{\mathrm{a}}\) & ND & 0.002 \(\pm\) 0.000 \({}^{\mathrm{b}}\) & ND & ND \\ Ethyl decanoate & 1638 & 1.737 \(\pm\) 0.026 \({}^{\mathrm{a}}\) & 0.758 \(\pm\) 0.013 \({}^{\mathrm{d}}\) & 1.249 \(\pm\) 0.039 \({}^{\mathrm{b}}\) & 1.023 \(\pm\) 0.05 \({}^{\mathrm{c}}\) & 0.27 \(\pm\) 0.034 \({}^{\mathrm{a}}\) \\ Ethyl benzoate & 1644 & 0.028 \(\pm\) 0.001 \({}^{\mathrm{a}}\) & 0.021 \(\pm\) 0.001 \({}^{\mathrm{c}}\) & 0.028 \(\pm\) 0.002 \({}^{\mathrm{a}}\) & 0.027 \(\pm\) 0.001 \({}^{\mathrm{b}}\) & ND \\ Ditely succinate & 1687 & 0.773 \(\pm\) 0.009 \({}^{\mathrm{a}}\) & 0.653 \(\pm\) 0.023 \({}^{\mathrm{a}}\) & 0.726 \(\pm\) 0.017 \({}^{\mathrm{b}}\) & 0.717 \(\pm\) 0.030 \({}^{\mathrm{b}}\) & 0.695 \(\pm\) 0.036 \({}^{\mathrm{b}}\) \\ Ethyl phenylacetate & 1776 & 0.019 \(\pm\) 0.002 \({}^{\mathrm{a}}\) & 0.014 \(\pm\) 0.001 \({}^{\mathrm{b}}\) & 0.015 \(\pm\) 0.001 \({}^{\mathrm{b}}\) & 0.018 \(\pm\) 0.001 \({}^{\mathrm{a}}\) & ND \\ Phenethyl acetate & 1825 & 0.092 \(\pm\) 0.001 \({}^{\mathrm{a}}\) & 0.090 \(\pm\) 0.003 \({}^{
# Physical-Layer Security in Power-Domain NOMA Based on Different Chaotic Maps
## 4 Simulation Results
### Security Analysis  
Regarding encryption, simply encrypting data is insufficient. Encrypted data should be extremely reliable. Some security analyses, such as statistical analyses represented by histogram and correlation analyses (Figure 8) and information entropy analysis (Table 2), must be performed to demonstrate high reliability.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Monte Carlo Simulation** & **MATLAB Programming** \\ \hline User Number & Three Users (\(U_{1}\), \(U_{2}\), \(U_{3}\)) \\ \hline Data Type/Size & Image Message/(128 \(\times\) 128) \\ \hline Bandwidth & 1 MHZ \\ \hline Transmit SNR & 0 to 40 dB \\ \hline Thermal Noise Density & Log\({}_{10}\)(kT) = \(-\)174 dBm/Hz \\ \hline Path Loss Exponent & 4 \\ \hline Modulation Scheme & QPSK \\ \hline Channel Type & AWGN and Rayleigh-Fading Channel \\ \hline Number of Antennas & Tx = 1, Rx = 1 \\ \hline Power Allocation Factors & \(a_{1}=0.62\), \(a_{2}=0.3\), \(a_{3}=0.08\) \\ \hline Distances (m) & \(d_{1}=186\), \(d_{2}=90\), \(d_{3}=24\) \\ \hline Cryptographic Algorithm & Chaotic Map-Based Cryptography \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 8: Histogram and correlation analyses.