esa-sceva/satcom-chunk-collection huggingface dataset:
# Heat Transfer Enhancement of Falling Film Evaporation on a Horizontal Tube by Thermal Spray Coating
## 3 Experimental Results and Discussion
### Effect of Heating Conditions on Falling Film Evaporation  
Figure 7 shows falling film evaporation heat transfer coefficients against the heat flux for the highest \(Re_{f}\) (643) in this experimental range. Because there is a hysteresis effect in the heating procedure of nucleate boiling heat transfer owing to a delay in nucleation activation, measurements were conducted while increasing and decreasing the heat flux. Open and bold symbols show experimental results in the increasing and decreasing processes of the heat flux, respectively. The dashed line shows the correlation of pool boiling heat transfer around a smooth horizontal tube suggested by [PERSON] [17].  
Figure 5: Falling film evaporation behaviors on the smooth tube. (**a**) \(Re_{f}=323\) without heating. (**b**) \(Re_{f}=430\), \(q=33.4\) kW\(\cdot\)m\({}^{-2}\). (**c**) \(Re_{f}=643\), \(q=33.4\) kW\(\cdot\)m\({}^{-2}\).  
Figure 6: Falling film evaporation behaviors on the thermal spray coated tube. (**a**) \(Re_{f}=323\) without heating. (**b**) \(Re_{f}=430\), \(q=33.4\) kW\(\cdot\)m\({}^{-2}\). (**c**) \(Re_{f}=643\), \(q=33.4\) kW\(\cdot\)m\({}^{-2}\).  
For the smooth tube, heat transfer coefficients increased with increasing the heat flux, and the values were slightly lower than the correlation. The gradient was almost the same as the correlation in the heat flux range under 40 kW\(\cdot\)m\({}^{-2}\); however, it became smaller over 40 kW\(\cdot\)m\({}^{-2}\). The agreement with the gradient indicated that nucleate boiling heat transfer was dominant in this heat flux range. Deterioration in the heat transfer coefficient in the high heat flux range was caused by partial dry out on the surface. Although the results in the decreasing process were lower than those in the increasing process, the difference was minor. This might be attributed to the increase in liquid entrainment, owing to the increase in the activated nucleation site density.  
For the thermal spray coated tube, the heat transfer coefficients were almost constant in the increasing process under 33.1 kW\(\cdot\)m\({}^{-2}\), and then gradually increased with increasing heat flux over 33.1 kW\(\cdot\)m\({}^{-2}\). This indicated that the nucleation site density on the coating must be relatively high under a low heat flux condition. In the heat flux decreasing process, heat transfer coefficients became slightly higher than those in the increasing process and gradually decreased with decreasing heat flux. The improvement in heat transfer in the decreasing process was observed only on the coating tube with the porous surface. This difference was probably caused by the hysteresis effect of the heating procedure. Here, the heat transfer enhancement by the coating could be obtained over the heat flux range and was especially large for lower heat flux values. In this experiment, a maximum enhancement factor of 5.2 was obtained at a heat flux of 10 kW\(\cdot\)m\({}^{-2}\) during the decreasing process.
## 3 Edible Coatings and Their Suitability for Fruit and Vegetables  
Although edible coatings, as well as edible films used to cover food, are considered as a modern method of extending their shelf life, the history of food coating for preservation dates back to the 12 th century. In China, waxing of fruit and vegetables and coating of meat with cellulose were used to slow down the rotting processes and preserve freshness. This method was rediscovered and used for the first time commercially in 1922 in the USA, where the waxing of fruit and vegetables began on a large scale [11; 20].  
The coating process is defined as applying to the surface or immersing plant tissues in prepared solutions with specific properties and composition selected to protect the cutting sites, along with slowing down the physiological processes of fragments or whole fruit and vegetables [22]. The coating is a barrier that makes it difficult for food to breathe, transpire and exchange fluids with the environment, resulting in better and longer storage of fresh products while maintaining microbiological cleanliness. Important for the process, as well as for the consumer, is the lack of change in the taste of the product by the applied film and its ease of removal, e.g., by washing [33; 34].  
The most sensitive to proper storage conditions are climacteric fruits, which, immediately after harvesting, if proper storage conditions are not ensured, begin an intensive process of respiration and ripening, producing ethylene. As a result of respiration, they lose the water they contain, and starch, sugars and organic acids break down into simpler molecules. This leads to the loss of many valuable ingredients, enzymes and reduced attractiveness of products [23; 24]. The International Fresh-Cut Produce Association (IFPA) defines fresh-cut products as fruit or vegetables that have been trimmed, peeled and/or shredded, fully usable and pre- or post-packaged, providing the consumer with high nutritional value, convenience of use and high taste value while maintaining freshness [1].  
Edible coatings, thanks to their ability to selectively diffuse, by slowing down the physiological processes occurring in low-processed plant products, limit the unfavourable changes in their appearance and taste [11; 21; 32].
# An Ontology-Based Expert System for Rice Disease Identification and Control Recommendation
## 4 RiceMan Application Development
### Query Construction and User Interface  
Since RiceDO and TreatO ontologies are developed by the OWL language, then the appropriate query language is thus the DL query expressed in the Manchester OWL syntax. DL query uses a class expression as an input to logically determine implicit facts inside an ontology. This research uses this competency of DL query to search for certain classes using subsumption reasoning. Note that we employ subsumption in DL query because our prime objective is to classify whether an input observation expression belongs to any disease classes or not. For this purpose, a user's input is needed to transform into a DL query before it can be used to query for a certain pest. Therefore, we develop two procedures for creating DL queries from a user's input by complying with the design of our ontologies.  
The first procedure is for converting an observation object in the database into an OWL class expression based on the following three steps: (1) we nest a set of symptoms, places, shapes, growth stages, transmission, and colors using existential quantification via properties hasSymptom, hasSymptomAt, hasShape, hasGrowthStage, hasTransmission, and hasColor, respectively; (2) we conjunct these nested expressions; and (3) we nest all using existential quantification via property abnormalityGroup. Figure 5a illustrates a constructed OWL class expression of an input observation object using this procedure.  
The second procedure is for treatment recommendation. That is, it nests a given disease name via property isBiologicalControlAgentOf (or isChemicalControlAgentOf depending upon a user's request) using existential quantification. Figure 5b,c illustrates an application of this procedure to recommend controls for a recognizable disease.  
Figure 6 shows a sequence of RiceMan's user interfaces that display the results of our queries as follows. From Steps 1-3, the user inputs the observed abnormalities as a brown oval spot on a leaf. Step 4 allows the user to define thresholds for a distance and a duration. In Step 5, RiceMan returns the identified disease(s). In Step 6, the user can click on the disease identified to get recommended treatments. Steps 7-8 show the groups of all possible abnormalities of a disease followed by the suggested controls.  
Figure 5: DL queries and results of finding possible diseases and controls for a disease: (**a**) a DL query of finding the corresponding rice diseases given an observation, (**b**) a DL query of finding biological controls for the rice brown spot fungal disease, and (**c**) a DL query of finding chemical controls for the rice brown spot fungal disease.
## 2 Experimental Program
### Bonding Test of Fe-SMA  
A bonding test was chosen to evaluate the bond behavior and strength of the Fe-SMA embedded in a concrete block with bonding lengths, pre-strained values, and recovery temperatures as experimental variables. The used concrete block size was 130 mm \(\times\) 150 mm \(\times\) 500 mm and had the modulus of elasticity and the compressive strength of 30.48 GPa and 42.4 MPa after 28 curing days, respectively. After curing for 28 days, a groove was pre-cut along the length of the concrete block on one surface using a grooving machine. The groove had depth and width of 30 mm and 1.5 mm, respectively. The Fe-SMA had a nominal thickness of 1.5 mm and width of 20.0 mm, and was elongated up to the target strain level, then was inserted in the groove with rubber pads to centrally align it. The groove of the concrete block was filled with a cement-based mortar with the compressive strength of 87.62 MPa. The recovery temperature was measured by K-type thermocouple installed at the mid-height of the Fe-SMA at the casting step. For the bonding test, the concrete block was held by a steel cage and the test setup is shown in Figure 4b.  
With the loading rate of 0.25 mm/min, the tensile load was applied under the displacement control. Bonding strength and corresponding slippage values were recorded from the load cell equipped inside the test machine and Linear Variable Differential Transformers (LVDTs).  
The experimental variables are given in Table 2. The specimen name indicates that the first initial \"L\" means the bond length, the second initials \"PS\" mean the pre-strained values, and the last initial \"T\" represents the recovery temperature.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specimen** & **Bond Length (mm)** & **Pre-Strained Value (\%)** & **Recovery Temperature (\({}^{\circ}\)C)** \\ \hline L250-PS0-T020 & 250 & 0 & 20 \\ L300-PS0-T020 & 300 & 0 & 20 \\ L350-PS0-T020 & 350 & 0 & 20 \\ L400-PS0-T020 & 400 & 0 & 20 \\ L450-PS0-T020 & 450 & 0 & 20 \\ \hline L300-PS4-T080 & 300 & 4 & 80 \\ L300-PS4-T120 & 300 & 4 & 120 \\ L300-PS4-T160 & 300 & 4 & 160 \\ L300-PS4-T200 & 300 & 4 & 200 \\ \hline L300-PS2-T160 & 300 & 2 & 160 \\ L300-PS4-T160 & 300 & 4 & 160 \\ L300-PS6-T160 & 300 & 6 & 160 \\ L300-PS8-T160 & 300 & 8 & 160 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specimens of bonding test.  
Figure 4: Bonding test setup: (**a**) Detailed of bonding test specimen; (**b**) Bonding test setup.
## 4 A Routing Protocol for LLNs (RPL)
### RPL Objective Functions for Malicious LLNs (MLLNs)
The obtained results show that the node trust metric isolates malicious nodes from routing decisions and enhances throughput compared to MRHOF [85]. [PERSON] et al. [126] improved the latter solution [125] and introduced the Secure Trust-aware RPL (SecTrust-RPL). The proposed solution computes trust based on successful packet transmission between nodes to determine their reliability in forwarding packets. SecTrust-RPL uses the following three metrics: (i) direct trust from [125], (ii) neighbour recommendation for trust computation, and (iii) trust aggregation. Direct trust measures the trustworthiness of direct neighbours, i.e., the first hop, while the neighbour recommendation measures trust towards the distant nodes of interest. The third metric, trust aggregation, measures a direct neighbour's trust value at time \(t\) to compare it against a given threshold. Therefore, SecTrust-RPL can (i) detect and isolate malicious nodes and (ii) make adaptive changes related to environmental conditions due to node mobility. Simulation results prove the efficacy of SecTrust-RPL in isolating malicious nodes during path selection, which significantly reduces PLR compared to MRHOF [85]. A similar idea as in [126] is also proposed by [PERSON] and [PERSON] [127], who suggested a lightweight trust-based mechanism that uses direct and indirect trust metrics to address the security threats in RPL. Direct trust computes based on node properties for direct neighbours, i.e., first hops, while indirect trust computes for the second hop neighbours based on the opinion of the first hop neighbours. The calculation of the direct trust metric is the same as in [125]. Furthermore, the indirect trust metric multiplies the direct trust values of the first and second hops. The total trust value for each node is then represented as the sum of direct and indirect trust values. Simulation results show that the trust-based mechanism can detect and isolate malicious nodes, thus increasing throughput compared to MRHOF [85]. The main drawback in [125; 126; 127] is the fact that these solutions are mainly based on trust metrics, which only capture the reliability issues caused by malicious attacks and do not address other reliability issues, such as link quality and remaining energy. Moreover, to the best of our knowledge, none of the existing RPL security solutions mentioned in this section address the reliability issues properly by considering the most affected factors: (i) malicious attacks, (ii) link quality, and (iii) energy. The security solutions are analysed against the reliable path requirements to identify knowledge gaps. A summary of the analysis is given in Table 6 below.
## 1 Introduction  
There has been considerable interest in the development of organic field-effect transistors (OFETs) with high electrical performance because of their potential use in various organic devices, such as flexible displays, low-cost radio frequency identification tags, and large-area sensors [1; 2; 3]. Shrinking the channel length (\(L\)) of OFETs, which is typically several tens of micrometers, is a crucial issue for enhancing the performances of electronic circuits based on OFET devices because the operational speed and output current of FET devices are, respectively, proportional to \(\mu/L^{2}\) and \(\mu/L\), where \(\mu\) is the field-effect mobility. However, reducing \(L\) to the sub-micrometer regime generally suffers from the high space-charge limited current (SCLC) associated with the application of a high source-drain electric field to the organic semiconductor; this results in parabolic drain current-drain voltage (\(I_{D}\)-\(V_{D}\)) characteristics without current saturation and an increase in the off current [4; 5; 6; 7; 8]. Such short-channel effects in OFETs have been reported to be suppressed by using ultrathin gate insulators (severalnanometers in thickness) [9] to ensure a gradual-channel approximation (the gate electric field in the channel is much larger than the source-drain electric field) [4; 10].  
We have previously studied the influence of the source electrode/organic semiconductor contact on the current-voltage characteristics of short-channel OFETs based on \(p\)-type organic semiconductors and reported that the parabolic \(I_{D}\)-\(V_{D}\) characteristics and high off current are suppressed by using organic semiconductors with high ionization potentials [11]. This behavior originates in the formation of Schottky barriers for hole injection at the source electrode/organic semiconductor contact, which reduces the SCLC and increases the effectiveness of the gate electric field in the channel because of the decrease in the net source-drain electric field. However, the formation of charge injection barriers also causes an increase in the threshold voltage of the short-channel OFETs and nonlinear \(I_{D}\) increase in the low \(V_{D}\) region. It has been reported that the threshold voltage of OFETs can be reduced by increasing the capacitance of gate insulators [12; 13] and modifying the surfaces of gate insulators (next to the organic semiconductor channels) with self-assembled monolayers (SAMs) having polar end groups [14; 15; 16; 17; 18; 19; 20]. In particular, the latter approach uses a built-in electric field produced by the permanent dipoles of SAMs, which can provide an effective route to reduce the threshold voltage of short-channel OFETs without thinning the gate insulator to several nanometers.  
In this study, we investigate the influence of the surface treatment of gate insulators with dipole SAMs on the electrical characteristics of short-channel OFETs with \(L\) of 5 um to 30 nm fabricated using \(p\)-type polymer semiconductors with different ionization potentials.
## 2 Methods
### Support Vector Machine (SVM)  
The Support Vector Machine (SVM) model is commonly used as an efficient supervised learning model to deal with linearly differentiable and nonlinear problems [34]. It transforms the initial data to a higher space by means of kernel function mapping, the structure of which is shown in Figure 2. Its kernel function expression is as follows:  
\[k(x,z)=\exp\left(-\frac{\|x-z\|^{2}}{2\sigma^{2}}\right) \tag{2}\]  
with the introduction of the kernel function, the decision rule used by SVM for classification becomes the following:  
\[g(x)=\text{sgn}(\sum_{i=1}^{n}a_{i}y_{i}(k(x_{i},x)+b) \tag{3}\]  
A slack variable \(\xi_{i}\) is introduced to improve the fitness, while a penalty term \(C\) is added to constrain the misclassification cases:  
\[\left\{\begin{array}{l}\min_{w,b}\frac{1}{2}\|w\|^{2}+C\sum_{i=1}^{n}\xi_{i }\\ y_{i}(w^{T}x_{i}+b)\geq 1-\xi_{i},\xi_{i}\geq 0,\quad i=1,2,\ldots,n\end{array}\right. \tag{4}\]  
This is then converted to a dyadic problem:  
\[\left\{\begin{array}{l}w_{\max}=\sum\limits_{i=1}^{n}a_{i}-\frac{1}{2}\sum \limits_{i=1,j=1}^{n}a_{i}a_{j}y_{i}y_{j}k(x_{i},x_{j}),\\ s.t.\sum\limits_{i=1}^{n}a_{i}y_{i}=0,0\leq a_{i}\leq C,\quad i=1,2,\ldots,n. \end{array}\right. \tag{5}\]  
where \(a_{i}\) and \(a_{j}\) denote the Lagrange multipliers; \(y_{i}\) and \(y_{j}\) denote the training sample categories, and \(y_{i},y_{j}\in(0,1)\); the penalty factor, \(C\), is employed to quantify the intricacy inherent to machine learning models. If this parameter is set too high, the model is prone to  
Figure 2: Schematic diagram of SVM structure.  
overfitting to the training data, which makes the SVM structure complex and reduces the computational efficiency; if the parameter is set too low, it will make the SVM not sufficient to capture the data features, resulting in underfitting.
## 4 Experimental Validation
### Real Scene Experimental Test
#### 4.2.1 Improved AMCL Matching Template Library Accuracy Test  
Use SLAM to generate particles on the QR code, mark the coordinate position of the QR code on the map, turn off the camera, use the particles to determine the coordinate position of the QR code, and the AGV travels to the particle aggregation area until it reaches the matching QR code marking point. Compare the position information with the actual coordinate position information of the QR code and analyze the error distance between the two.  
Figure 21: Real scene testing: (**a**) AGV front; (**b**) AGV side; (**c**) experimental site and AGVs used; (**d**) paste a column of QR code landmarks.  
Figure 22: Gmapping build synchronization action: (**a**) a half-turn from 0 to 1; (**b**) a half-turn from 1 to 0.  
Figure 23a shows that the improved AMCL particles converge on the QR1 position, at which time, Figure 23b shows that the AGV stopping position is in the area directly above the landmark. From the actual test, it is concluded that the AGV identifies the particle template on QR1 to match the exact position of the first landmark on the SLAM construction map, and then accurately travels to the first landmark area location, waiting for the next command. At this time, the industrial camera is opened to scan the code, and the real position of the AGV at this time is obtained through coordinate conversion, comparing the coordinate information and measuring the position error interval between the particle triangular matching position information using the improved AMCL algorithm and the position error interval of the QR code scanning information.  
Taking the forward direction of the AGV as the \(y\)-axis and the left-right offset distance as the \(x\)-axis, two photoelectric sensors are installed before and after the QR1 landmark position, and the coordinate information of each arrival position is re-recorded and weighted average. Table 5 shows that the combination of code-sweeping positioning and improved AMCL algorithm has the highest accuracy, with the maximum offset of coordinate information not exceeding 1 cm, and the reading distance before and after the combination not exceeding 0.5 cm, and the navigation and positioning accuracy has been improved by 64.27% on the original basis.
###### Abstract  
Renewable, reliable, and affordable future power, heat, and transportation systems require efficient and versatile energy storage and distribution systems. If solar and wind electricity are the only renewable energy sources, what role can hydrogen and fuel cell electric vehicles (FCEVs) have in providing year-round 100% renewable, reliable, and affordable energy for power, heat, and transportation for smart urban areas in European climates? The designed system for smart urban areas uses hydrogen production and FCEVs through vehicle-to-grid (FCEV2G) for balancing electricity demand and supply. A techno-economic analysis was done for two technology development scenarios and two different European climates. Electricity and hydrogen supply is fully renewable and guaranteed at all times. Combining the output of thousands of grid-connected FCEVs results in large overcapacacities being able to balance large deficits. Self-driving, connecting, and free-floating car-sharing fleets could facilitate vehicle scheduling. Extreme peaks in balancing never exceed more than 50% of the available FCEV2G capacity. A simple comparison shows that the cost of energy for an average household in the Mid Century scenario is affordable: 520-770 \(\upmu\)/year (without taxes and levies), which is 65% less compared to the present fossil situation. The system levelized costs in the Mid Century scenario are 71-104 \(\upmu\)/MWh for electricity and 2.6-3.0 \(\upmu\)/kg for hydrogen--and we expect that further cost reductions are possible.  
smart city; sector coupling; fuel cell electric vehicle; vehicle-to-grid; hydrogen storage; cost of energy; virtual power plants