esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Fermentation Color Spectral Dynamic Changes  
#### 3.2.1 Lightness Changes during Fermentation  
In general, the lightness of the apple cider increased throughout the fermentation process (Figure 2). EC1118 cider showed the largest lightness change from day 1 to day 3, but after day 3, lightness began to slow for one day, and after day 4, it started to increase again. This might indicate that the yeast strain grew rapidly, which influenced the lightness of 'Wodarz' cider during the fermentation process. Quite similarly, after day 9, all strains worked slowly and smoothly, increasing the lightness until the end of the fermentation process on day 30. At the end of fermentation, 71B cider showed the highest lightness, while EC1118 cider was the least clear. Further, WY4766, WLP77F, and Maurivin B ciders had medium lightness: from 65 to 80 lightness. Lightness variations are often related to the differences in thickness between various zones in the fermentation products and are important to the acceptability of final cider in fermented and unfermented cider products; as such, lightness shifts and the relative clarity of ciders are frequently monitored throughout production processes and at completion due to their importance as part of CIELAB and role in visual differentiation [42; 43; 44; 45].  
\begin{table}
\begin{tabular}{c c c} \hline
**pH** & **Total Acidity (g L\({}^{-1}\)) 1** & **Malic Acid (g L\({}^{-1}\))** \\ \hline
3.74 \(\pm\) 0.07 & 2.7 \(\pm\) 0.1 & 3.35 \(\pm\) 0.91 \\ \hline \end{tabular} \({}^{\dagger}\) Total acidity expressed as malic acid equivalents.  
\end{table}
Table 2: Initial acid characteristics of ’Wodarz’ apple cider musts.  
\begin{table}
\begin{tabular}{c c c} \hline
**PAN (mg of N L\({}^{-1}\))** & **Ammonia (mg L\({}^{-1}\))** & **Total Phenolics (AU)** & **Single Fruit Mass (g)** \\ \hline
19.50 \(\pm\) 2.19 & 1.40 \(\pm\) 0.29 & 14.53 \(\pm\) 1.00 & 121.50 \(\pm\) 6.38 \\ \hline \end{tabular}
\end{table}
Table 3: Initial nitrogen, phenolic, and fruit mass characteristics of ’Wodarz’ apples and apple cider musts.  
Figure 2: Evolution of L\({}^{*}\) (lightness) of ’Wodarz’ apple cider during fermentation with five different yeast strains. DAI = days after inoculation with yeast strain. Lightness (L\({}^{*}\)) represents the lightness changes during fermentation process. Bars indicate the standard error of the mean for individual treatment with three replicates.
# ARMONIA: A Unified Access and Metro Network Architecture
## 3 ARMONIA Algorithmic Mechanisms
### Resource Allocation
For each slice demand the additional cost of each solution is considered, taking thus into account the slice demands served up to that point (the current state of the network). The objective function is a weighted combination of (i) the incremental spectrum utilized, (ii) the number of transponders, and (iii) the cost of processing nodes. ```
1:Inputs: Network Topology,
2:Transponder configurations T
3:Objective weights: w\({}_{1}\)w\({}_{2}\)
4:New slice demand = (s, d, bandwidth, processing)
5:
6:calculate k paths
7:For each path with av_processing > slice required_processing
8:Find spectrum voids
9:For each transmission configuration combination T
10:Use the QoT estimator to calculate the QoT
11:Calculate number of connections, spectrum slots needed
12:endfor
13:endfor
14:
15:For each non-dominated path-tuple-processing cost configuration
16:For each connection
17:if size(void) > required_spectrum
18:Select best fit void
19:endif
20:Select best fit processing
21:endfor
22:w\({}_{2}\)-spectrum \(+(1-\text{w}_{1}-\text{w}_{2})\)-pr_cost
23:if cost < min cost
24:update best solution
25:endif
26:endfor
27:if size(best_solution) > 1
28:establish the slice demand
29:elseif
30:block slice demand
31:endif
```  
**Algorithm 1** Pseudocode of the resource allocation heuristic algorithm.
# Development of a Social DTN for Message Communication between SNS Group Members
## 3 How to Build a Social DTN
### DTN Routing Protocol
#### 3.4.2 Algorithm for Sending Messages  
This subsection describes the algorithm used by the Advertiser when the user requests the application to send a message. The Advertiser uses the Algorithm 1 to select the node to which it will forward the message.  
```
Step 1: The Advertiser starts Nearby Connections API [13] and begins to search for a Discoverer. Step 2: If the Advertiser finds a Discoverer, it compares the Service ID of its own terminal and that of the Discoverer. Case 2-1: If the two Service IDs are not identical, the Advertiser discards the communication with the Discoverer. Go to Step 4. Case 2-2: If the two Service IDs are identical, the Advertiser collects the MAC addresses of all Discoverers. Go to Step 3. Step 3: The Advertiser decides as follows depending on the type of message it will send: Case 3-1: If the message is rreQ_m, the Advertiser sends the message. Go to Step 4. Case 3-2: If the message is rreP_m or dsnD_m, the Advertiser checks the following: If 3-2: the DA in each message exists in the TNA in its routing table, THEN 3-2: IF 3-2-1: the RNA associated with that TNA exists in the group of MAC addresses collected, THEN 3-2-1: the Advertiser sends the message. Go to Step 4. ELSE 3-2-1: the Advertiser discards the message. Go to Step 4. Else 3-2: the Advertiser discards the message. Go to Step 4. Step 4: The Advertiser makes a transition to the halt state.
```  
**Algorithm 1** Algorithm for Sending Messages
# Potential and Limitations of Open Satellite Data for Flood Mapping
## 4 Results
### Comparing Flood-Mapping Approaches: Automatic Emergency, Traditional and Combined Approach  
For the case of the 2015 Ebro and 2016 NW Italy floods, Figure 14 shows a comparison between the flooded areas mapped by EMS-Copernicus [52; 58], the flooded area made with manual interpretation for this work and the official flood maps produced by river authorities.  
Figure 14: Comparison between flooded area mapped by EMS-Copernicus, the remote sensing flood maps made for this work and the official flood maps. The 2016 Po basin flood: (**A**) Area near Alessandria, (**B**) area south of Turin. The 2015 Ebro flood around Zaragoza (**C**).  
The EMS-Copernicus mapping is automatic mapping in quasi-real time that identifies critical areas during the emergency but does not represent the real flood extent for further post-emergency analyses. For instance, the EMS flood mapping in the case of the Alessandria area (Figure 14A) is based on Radarsat-2 data (acquired on 27 November 2016), and the map shows only the remaining flooded areas (dark blue). The EMS underestimates the flooded area with respect to the results of this work and official maps.  
The area at the south of Turin (Figure 14B) was partially mapped by EMS only from Carignano downstream to Turin. The flood map for the area near Moncalieri, investigated with Pleiades satellite at 26 November 2016, detected most of the flooded areas and fitted well with our map. The sector near Carignano town, mapped with an image of 27 November 2016, shows only residual flooded areas.  
In the case of Ebro valley, the EMS flood map was made with a Radarsat-2 image acquired on 2 March 2015 (Figure 14C). The map shows an almost complete fit with our flood mapping and the official flood mapping of CHE.  
The comparison with official mapping showed good results. The flood maps made for this study based on remote sensing data match well with the official maps of the Ebro basin authority (CHE) with a 95% overlap (Figure 14C). The area inundated by Tanaro (Figure 14A) proposed in this study also shows a good match, approximately 90%, with ARPA Piemonte flood maps. There are more differences in the area at the south of Turin (approximately 85% matching, Figure 14B); this sector is mapped mostly with post-flood data, and the areas affected by shallow water depth have fewer signs of the flood. Flood mapping based on satellite data presented more challenge in the urban areas, in which a more detailed survey is often required, as shown in a previous work [47].  
In the case of the Arahal flood, it is not possible to estimate the accuracy of flood mapping because official maps are not available. However, the combination of Sentinel-1, Landsat-8, and Sentinel-2 data implemented with a water depth model allowed creating a reliable map that was locally validated.
## 2 Hybrid System and Techno-Economic Analysis Method  
### Topology and Design Principle of Grid-Connected PV and Fuel Cell (FC) Hybrid System  
This section mainly discusses the design and operation principle of the system considered, Figure 1 In addition, the technical and economic characteristics of the components are identified. The maximum capacity shortage is considered as 0%, and 50% minimum fraction of renewable energy. The system is designed to meet the load power requirement of 2262.4 kWh/day and 230 kW peak load occurring in June.  
The grid, PV, and FC are the major power supply contributed as follows: the grid is main power source at once solar radiation is not available specifically at night or early morning. FC is back up power to assist the grid when the load power requirement is not satisfied by power from the grid. PV is a daylight power supply to load and provide excess power to the electrolyzer for hydrogen production, even sell back to the grid if it is more than the demand of hydrogen production.  
The hydrogen released by the electrolyzer is used to produce power in FC. A direct current to alternating current (DC-AC) converter is the interconnection between hybrid direct current (DC) system (PV-FC) and grid-connected system. It is used to convert the DC power produced by PV, FC section into alternative current (AC) and accommodate it in the AC bus bar where the AC load is connected.  
PV tracking techniques and their influence on techno-economic factors in the proposed system are examined. We use Homer to undertake simulation, calculation, and optimization of the designed system for different PV tracking techniques separately. In addition, this paper also seeks for optimizing the system sizing. The optimal design grid-connected (PV-FC) which reflected optimal techno-economic characteristic is recommended. The technical and economic characteristics of the system design components are applied in the below section.
## 1 Introduction
They reported that the cooling capacity and coefficient of performance (COP) of the designed system in FGB mode were improved 13%-18% and 4%-7%, respectively, compared with that of the DX mode [10]. [PERSON] et al. (2010) investigated experimentally on performance enhancement of an automotive air conditioning system using newly developed micro-channel heat exchangers with lower weight and volume compared with conventional and currently used heat exchangers. They reported that the COP of the designed system under all the other test conditions except the idle condition was higher than that of the conventional system. In addition, the cooling capacity of the designed system increased by 5% and the COP improved by 7.9% under high vehicle speed [11]. [PERSON] and [PERSON] (2012) studied the performances of R1234 yf and R134a in a heat pump bench tester under the conditions for automotive air-conditioners. They performed the 'drop-in' tests under summer and winter conditions with R1234 yf and R-134a in a bench tester equipped with an open type compressor for automotive air conditioning systems. They reported that the COP and the cooling capacity of R1234 yf are 2.7%-4.0% lower than those of R-134a, respectively [12]. Also, [PERSON] et al. [13] conducted a weathering test on interior automotive components under high temperature condition. Previous studies on automotive air conditioning systems for cabin cooling in the vehicles have focused on the conventional passenger vehicles, including small- and medium-size or light- and heavy-duty vehicles. However, the research on the performance evaluation of the air conditioning system for special purpose vehicles has been limited in the open literature. Therefore, the objective of this study is to design and experimentally investigate the cooling performance characteristics of the air conditioning system for a special purpose vehicle under extremely hot weather conditions and higher thermal load conditions caused by equipped passengers. In addition, the special air conditioning system with two parallel refrigeration cycles for a special purpose vehicle was built to ensure a sufficient cooling performance for equipped passengers. The paper is divided into four sections: introduction of the problem statement, experimental setup and configuration, results and discussion, and conclusion. The experimental setup section discusses the system configuration, data reduction, thermal load and air flow rate evaluation. Refrigerant enthalpy method has been used for calculations and the results for cooling performance of air conditioning are discussed using COP (for steady state performance) and cooling speed (for transient state performance). This paper is an extended version with updated results of the conference paper presented at 6 th International Conference on Experiments/Process/System Modeling/Simulation/Optimization (IC-EpsMsO, Athens, Greece) in 2015.
# Structural Tunable Plasma Photonic Crystals in Dielectric Barrier Discharge
## 4 Conclusions  
In summary, we demonstrate a rich variety of structural tunable plasma photonic crystals by self-organization of the filaments in DBD. By changing the applied voltage, the plasma structures can be tuned flexibly in three ways, including changing the spatial symmetry, the lattice constant and the crystal orientation. The plasma structures changes from square lattice to triangular lattice, the lattice constant is reduced, and the crystal orientation varies \(\frac{\pi}{6}\) when the applied voltage is increased. The band diagrams of the plasma photonic crystals under a TM wave are studied by using the finite element method, which shows that the positions and sizes of the band gaps change significantly for different  
Figure 6: Reorientation of the triangular lattices when increasing of the applied voltage. (**a,b**): snapshots of the plasma structures. (**a**) \(U=7.4\) kV, \(p=52\) W (**b**) \(U=7.6\) kV, \(p=56\) W. The other discharge parameters are: \(f=53\) kHz, \(p=440\) torr, \(d=2.0\) mm, \(t=40\) ms. The working gas is 65% Ar and 35% air. The white dotted hexagons indicate the angular reorientation of the plasma photonic crystals. (**c,d**): the corresponding voltage-current waveforms associate with the triangular lattices of different orientations. The blue lines on the voltage waveforms indicate the ranges of the voltages that the discharges occur.  
plasma structures. A complete band gap is produced for the square plasma lattice, while a number of unidirectional band gaps are generated when the square lattice is switched to the triangular lattice. The photonic bands move towards higher frequencies by decreasing the lattice constant and the widths of the band gaps are increased. A flexible way to fabricate reconfigurable plasma photonic crystals is suggested, which may find wide application in the manipulation of microwaves or terahertz waves.  
The author [PERSON] performed the experiment measurements and wrote the paper. [PERSON] and [PERSON] performed the simulations. [PERSON], [PERSON] and [PERSON] discussed the results and revised the paper. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant Nos. 11875014, 11721091 and 11655002), the Natural Science Foundation of Hebei Province (Grant Nos. A2016201066 and A2017201099).  
The authors declare no conflict of interest.
# Design of Broadband High-Frequency Multi-Throw RF-MEMS Switches
## 3 Results and Discussion
### Low Voltage and Rapid Response
Such design advantages facilitate faster activation of RF MEMS switches at reduced voltage conditions. The comparison between Figure 4b and Figure 4c underscores that the perforation method for the upper electrode not only decreases the damping coefficient and augments switching speed but also alleviates post-deposition residual stress and reduces Young's modulus. These enhancements further amplify the switch speed and diminish the requisite driving voltage, leading to optimized performance of the RF MEMS switch. Due to these technical refinements, the beam's peak stress has been curtailed to 26 MPa, thereby ensuring heightened reliability and stability of the switch [29]. The simulated pull-down state of the cantilever beam in COMSOL is showcased in Figure 4d, validating the switch's performance under operational conditions, indicating its capability to pull down by 0.5 \(\upmu\)m. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(K\) & \(K1\) & \(K2\) & \(K3\) & \(K4\) & \(K5\) & \(K6\) & \(K7\) \\ \hline \(L\) (\(\upmu\)m) & 1 & 6 & 3 & 12 & 3 & 6 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dimensions of different parts of the proposed folded beam. Figure 4: COMSOL stress simulation. (**a**) Regular perforated beam; (**b**) Improved unperforated beam; (**c**) Improved perforated beam; (**d**) COMSOL simulation in pull-down state.
# Genetic Algorithms to Maximize the Relevant Mutual Information in Communication Receivers +
## 4 Results and Discussion
### Quantization of the Channel Output with Minimum Loss of Relevant Information
#### 4.1.3 Genetic Algorithm Quantizer Design with Approximate Nearest Neighbor Search
The rest of the entry nodes is chosen, such that all resulting entry nodes have possibly large distances among each other. The shown results indicate that the proposed compression mappings based on the approximate nearest neighbor search algorithm from Section 3.1.3 with parameters \(\boldsymbol{\theta}\) optimized using genetic algorithms can deal with very huge cardinalities \(|\mathcal{T}|\). Such large cardinalities \(|\mathcal{T}|\) are required to minimize the remaining quantization loss, such that \(\mathrm{I}(\mathrm{X};\mathrm{T})\approx\mathrm{I}(\mathrm{X};\mathrm{Y})\), as it can clearly be seen in Figure 8. Moreover, the performance is virtually identical to the KL-means quantizers. Figure 8: Comparison of KL-means and genetic algorithm optimized quantization using approximate nearest neighbor search from Section 3.1.3 for \(|\mathcal{T}|=256\) in terms of preserved \(\mathrm{I}(\mathrm{X};\mathrm{T})\). \(\mathrm{I}(\mathrm{X};\mathrm{Y})\) serves as an ultimate upper bound.
## 2 Overview of the IEEE 802.15.4e Protocol
### Channel Access Mechanism  
The devices in the network follow CSMA/CA mechanism to access the channel that is the key component in IEEE 802.15.4e MAC to communicate with the PAN coordinator. A device having data initializes the variables \(BE\) and \(NB\), where \(BE\) is the backoff exponent and \(NB\) represents the number of times the CSMA/CA mechanism is required to be postponed before attempting the transmission. Before every transmission, the value of \(NB\) is initialized to zero. The MAC layer waits for a random backoff period in the range of \([0,2^{BE-1}]\) units as the backoff period before accessing the channel. After the backoff duration expires, the tagged device goes for two times clear channel assessment (CCA) to know the channel condition and allows transmitting data provided the channel is found idle during both CCAs. After transmitting the data, the tagged device waits for the acknowledgment, which is interpreted as the successful packet transmission. However, if the channel is found busy during any CCA, the tagged device increases the value of \(BE\) and \(NB\) by one for each channel access failure and goes for the random backoff before performing the next CCA. The maximum values of \(BE\) and \(NB\) are \(BE_{max}\) and \(\text{maxMacBackoff}(NB_{max})\), respectively. The values of \(BE\) and \(NB\) are incremented for each channel access failure, and the packet is dropped by the device if the value of NB exceeds the value of \(NB_{max}\). The flow chart of the IEEE 802.15.4e CSMA/CA mechanism is depicted in Figure 2.
## 3 Results and Discussion
### Physical Properties
#### 3.1.2 Texture
The values of cohesiveness for KM, MM, and BM were significantly higher (\(p<0.05\)) than those for their analogue counterparts KA, MA, and BA, respectively, while no statistically significant difference was observed between SuM and SuA. The physicochemical properties of vegetable oils differ considerably from those of animal fats, which can impact the juiciness and texture parameters of the products [30; 31]. Fat plays a crucial role in determining the tenderness and juiciness of both meat and meat analogue products. However, it is well recognised that meat analogues differ from meat in terms of texture, mouthfeel, flavour, and taste [10; 23]. Oils (sunflower, coconut, palm, etc.) as well as various plant-based ingredients (particularly structural) are used to ensure that the texture (connection and shape stability) and properties are similar to those of meat [32]. The Warner-Bratzler test, which measures the maximum shear force of meat products through the application of a knife-cutting motion, is commonly used to analyse the texture of various types of meat products, particularly whole-muscle products and sausages [7; 28]. In this study, the shear force and toughness values of StM were significantly higher (\(p<0.05\)) than those of StA. However, the texture of salami was not measured because it was not possible to prepare the samples in the required shape and size due to the thin slices of this product. The texture and structure of meat analogue products depend on a combination of three factors, including of ingredient selection, processing methods, and formulation techniques. Such factors play an important role in creating a product with textural and structural characteristics that closely mimic those of conventional meat [33; 34]. \begin{table}
\begin{tabular}{l c c} \hline \hline  & **Sample Type** & **Hardness** & **Cohesiveness** \\ \hline SuA & 8.70 \(\pm\) 0.59 b  & 1.19 \(\pm\) 0.01 \\ SuM & 13.89 \(\pm\) 1.92 a  & 1.19 \(\pm\) 0.03 \\ KA & 7.36 \(\pm\) 1.07 b  & 1.05 \(\pm\) 0.01 b  \\ KM & 25.22 \(\pm\) 3.70 a  & 1.28 \(\pm\) 0.02 a  \\ MA & 2.05 \(\pm\) 0.18 & 1.00 \(\pm\) 0.04 b  \\ MM & 2.04 \(\pm\) 0.22 & 1.11 \(\pm\) 0.04 a  \\ BA & 1.25 \(\pm\) 0.15 b  & 0.96 \(\pm\) 0.06 b  \\ BM & 2.98 \(\pm\) 0.33 a  & 1.12 \(\pm\) 0.02 a  \\  & **Shear force** & **Toughness** \\ StA & 13.12 \(\pm\) 2.35 b  & 61.79 \(\pm\) 13.33 b  \\ StM & 21.09 \(\pm\) 2.70 a  & 72.55 \(\pm\) 6.57 a  \\ Sample types & Hardness & Cohesiveness \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Texture parameters (hardness, cohesiveness, shear force in Newtons, toughness in KPa) of analogue meat and conventional meat products.