esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual-Window Superpixel Data Augmentation for Hyperspectral Image Classification
## 3 Results
### Datasets
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline \multicolumn{6}{c}{**Oitaven**} \\ \hline \multicolumn{6}{c}{**Total**} \\ \hline \# & **Classes** & **Spp** & **Pixels** & **Train** & **Test** & **Classes** & **Spp** & **Pixels** & **Train** & **Test** \\ \hline \(1\) & \multicolumn{1}{c}{Water} & \(611\) & \(309\),\(248\) & \(367\) & \(120\) & Water & \(322\) & \(163\),\(930\) & \(183\) & \(70\) \\ \(2\) & \multicolumn{1}{c}{Oak} & \(3710\) & \(1\),\(374\),\(889\) & \(2227\) & \(739\) & Oak & \(1992\) & \(804\),\(040\) & \(1201\) & \(390\) \\ \(3\) & \multicolumn{1}{c}{Tiles} & \(192\) & \(78\),\(785\) & \(112\) & \(48\) & Tiles & \(224\) & \(138\),\(678\) & \(137\) & \(33\) \\ \(4\) & \multicolumn{1}{c}{Meadows} & \(4464\) & \(2\),\(440\),\(331\) & \(2709\) & \(863\) & Meadows & \(5901\) & \(3\),\(423\),\(506\) & \(3550\) & \(1142\) \\ \(5\) & \multicolumn{1}{c}{Asphalt} & \(91\) & \(43\),\(861\) & \(52\) & \(14\) & Asphalt & \(1632\) & \(737\),\(409\) & \(976\) & \(331\) \\ \(6\) & \multicolumn{1}{c}{Bare Soil} & \(434\) & \(113\),\(329\) & \(256\) & \(92\) & Bare Soil & \(388\) & \(123\),\(416\) & \(253\) & \(83\) \\ \(7\) & \multicolumn{1}{c}{Rock} & \(336\) & \(79\),\(152\) & \(192\) & \(72\) & Rock & \(859\) & \(174\),\(088\) & \(522\) & \(172\) \\ \(8\) & \multicolumn{1}{c}{Concrete} & \(358\) & \(128\),\(022\) & \(206\) & \(79\) & Concrete & \(64 Scenes 1 to 3 were segmented using SLIC with a superpixel size of 50 and a compactness parameter of 20, whereas datasets 4, 5, 6 and 7 used a superpixel size of 800 and a compactness parameter of 40. The compactness determines the balance of space and spectral proximity, with higher values favoring space proximity and causing segments to take on a more square shape. Two augmentation factors of 4\(\times\) and 16\(\times\) were considered, and are displayed in the tables next to the name of each data augmentation technique. For every experiment, the following three accuracy measures [30] are reported: overall accuracy (OA), representing the number of overall pixels correctly predicted; average accuracy (AA), the mean of correctly classified pixels per class; and Kappa (\(\kappa\)), which measures the agreement between pixel predictions across all classes, also taking into account the occurrences attributed to chance [31]. The values shown are the results of 20 Monte Carlo runs for each scenario. All the values were obtained under identical experimental conditions.
## 3 Results and Discussion
### MATLAB Treatment
Figure 5: Oxidation pattern corresponding to active samples A1, A2 and A3 by means of principal component analysis (PCA). one, A2, was a free radical scavenger that should also have a strong effect. This theory has been successfully confirmed by the proposed method. Finally, the behaviour of antioxidant A3 was studied. Almost all A3 samples were similar to samples B after 8 days (10% of the samples were found in the 4-day group, and 10% of the samples were probably out of any group), which means that this antioxidant did not work. It can also be concluded that the active agent A3 did not have a negative effect on the samples in terms of accelerating the deterioration of mushrooms, since antioxidant A3 did not give worse results than the samples B. The presented method can undoubtedly evaluate, in an objective manner, the colour distribution in the surface of both fresh white and brown oxidized mushrooms. Consequently, the investigation of antioxidants' influence on food surface colour could be carried out. The literature describes results of recent developments and applications of novel methods for the assessment of quality deterioration of fresh mushrooms. [PERSON] et al. [24] have demonstrated that hyperspectral imaging (HSI) can be used as storage-time quality assessment tool. Analysis was conducted on a macro-hyperspectral imaging platform. The data for HSI indicators needed to be fitted with rate-order equations. [PERSON] et al. [25] developed an image measurement algorithm for common mushroom caps based on a convolutional neural network. The internet protocol cameras of the image measurement system for mushrooms were used. The proposed algorithm used YOLOv3 for image analysis, with an efficiency of 82.7%, and might facilitate future research in a large amount of data analysis. Finally, Internet of Things (IoT) was applied for classification of mushroom toxicity by [PERSON] et al. [26]. It is based on a system of interconnected computing devices that can transfer data over the Internet without interaction between human and machine. IoT is currently used on an industrial scale to connect farmers to their farms using sensors, allowing them to monitor farm conditions in real time from anywhere.
## 3 Methodology  
The overall flow of our method is a typical supervised learning flow. Namely, we generate a training data set using CFD simulation with various parameters and then train a model as a mapping function from the condition of the air in a room to the parameters that generate the corresponding condition.  
The key issue in this approach is input data. The target environment in the simulation is 3D, which is generally quantized by elements such as voxels or meshes, and each element has some parameters including temperature, airflow volume, airflow direction and so on. Thus, the entire parameterized 3D space that is a result of CFD simulation has extremely high dimensional information. We therefore introduce a compression of the 3D parameterized air condition in a room into a 2D multi-channel image.  
The input data format we designed is a two channel image that shows a 2D room map, and each channel contains the distribution of temperature and airflow volume. We assume the distributions are at a 1 m height from the floor as a representative distribution in the room because the aim of our method is users' thermal comfort and we considered the temperature at the height of user's head as the most effective for thermal comfort. For the purposes of this paper, we defined 1 m as the representative height by considering the head height of a seated user. Moreover we ignore the direction of airflow because the definitions of thermal comfort according to ANSI/ASHRAE Standard 55 [2] considers Operative temperature, Air speed, Relative humidity, Metabolic rate, and Clothing level but it does not consider the direction of airflow. For example, the comfort zone is defined by following equations  
\[t_{min,Icl}=\left[(I_{cl}-0.5 clo)t_{min,1.0 clo}+(1.0 clo-I_{cl})t_{min,0.5 clo} \right]/0.5 clo, \tag{1}\]  
\[t_{max,Icl}=\left[(I_{cl}-0.5 clo)t_{max,1.0 clo}+(1.0 clo-I_{cl})t_{max,0.5 clo} \right]/0.5 clo, \tag{2}\]  
where \(t_{min,Icl}\) is lower operative temperature \(t_{o}\) limit for closing insulation \(I_{cl}\), \(t_{min,Icl}\) is upper operative temperature \(t_{o}\) limit for closing insulation \(I_{cl}\), \(I_{cl}\) is terminal insulation of the clothing i question, and \"clo\" is a unit used to express the thermal insulation provided by garments and clothing ensembles. Average air speed \(V_{a}\) greater than 0.2 m/s increase the lower and upper operative temperature \(t_{o}\) limit for the comfort zone.  
To apply the air temperature \(T\) and flow volume \(F\) to an image, we normalize them by following equations,  
\[T_{N}=\frac{T-T_{min}}{T_{max}-T_{min}}, \tag{3}\]  
\[F_{N}=\frac{F}{F_{max}}. \tag{4}\]  
Figure 1 shows the examples of input image which represents airflow and temperature distributions.  
Figure 1: Examples of input images.  
We employed ResNet-50 [17] as a model for inferring air conditioners' control parameters. We then add full connection layer for the regression and classification of air conditioners' parameters. Figure 2 shows the overall pipeline of our method.
## 3 Results and Discussion
### CH/CNC and CH/CD Edible Films
#### 3.1.4 Mechanical Properties  
Mechanical properties are very important as they provide a direct indication of the strength and cohesion of the films or coatings. They are related to the structural coherence and the mechanical resistance against the destruction of the food product during its transport, storage or preservation. Commonly, the mechanical resistance of films is studied according to (i) the breaking stress (\(\sigma\)), which shows the maximum traction force per film's cross section required to break the film, and (ii) the elongation at break (\(\varepsilon\)), which provides the degree to which a film can be stretched before it breaks [55; 57; 58]. The mechanical properties of the films measured in this study were the maximum breaking force (F), the breaking factor (Puncture Strength), the deformation at break (D), the percent of elongation at break (\(\varepsilon\)) and the elastic modulus and the breaking stress (\(\sigma\)). Mechanical properties concern not only the hardness of the films but also their stability and homogeneity. The target was to produce edible films with the ideal combination of these characteristics. Table 4 shows the effect of CNC and CD addition on the mechanical properties of the formed edible films. Results show that the films with only CH were flexible and presented high mechanical strength. In fact, they presented high values in all the properties examined (Table 4). This is in agreement with similar studies, according to which chitosan is a material that creates edible films and coatings with good mechanical properties [6; 59]. This is also confirmed by comparison with other popular film forming materials, such as gelatin, which exhibit significantly lower values in their mechanical properties [34; 35].
## 4 Discussion
### Climatic Factors Drive Monthly ET Influenced by Elevation
Based on the monthly scale (Figure 19d), the area of the regions with significant negative correlation between the two first fluctuated upward from January to December and then fluctuated downward, achieving a maximum value of about 45.2% in July, and the percentage of area was significantly lower than that of the regions where ET was significantly positively correlated with T or SR, which was consistent with the results in Section 3.2.3. From the perspective of spatial distribution (Figure 19c), the regions with significant negative correlation between ET and P mainly existed in the regions with high elevation (within elevation classes X to XII), and the area proportion was more than 10%. The area proportion of this area fluctuated and increased in the range of elevation classes I to XI and then decreased in the region of elevation classes XI to XII. This indicated that the negative correlation between ET and P increased with increasing altitude, which could explain the spatial variation in the region of significant negative correlation between ET and P in Section 3.2.2, mainly related to altitude. In this study, the correlation between ET and climatic factors in the YRB showed a complex pattern at the monthly scale, so it was necessary to explore the impact of climate on ET at different altitudes. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Level** & **Elevation Interval/m** & **Area/km\({}^{2}\)** & **Percentage/\%** \\ \hline I & \(-\)143–500 & 69.90 & 38.83\% \\ II & 500–1000 & 28.02 & 15.56\% \\ III & 1000–1500 & 16.08 & 8.94\% \\ IV & 1500–2000 & 9.28 & 5.15\% \\ V & 2000–2500 & 8.20 & 4.55\% \\ VI & 2500–3000 & 5.16 & 2.86\% \\ VII & 3000–3500 & 4.88 & 2.71\% \\ VIII & 3500–4000 & 7.07 & 3.93\% \\ IX & 4000–4500 & 13.05 & 7.25\% \\ X & 4500–5000 & 15.94 & 8.86\% \\ XI & 5000–5500 & 2.23 & 1.24\% \\ XII & \textgreater{}5500 & 0.19 & 0.11\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Elevation classification in the YRB.
## 3 Results and Discussion
### Experiment Verification
#### 3.2.4 Uncertainty Evaluation by GUM Method  
Based on the ISO GUM (guide to the expression of uncertainty in measurement) [39] uncertainty analysis system of international standards and national measurement standards, the material mass measurement inside the equipment with a connected piping uncertainty analysis method is studied. The sources of measurement uncertainty were analyzed according to the GUM method, which mainly included the standard uncertainty introduced by measurement repeatability \(\mu_{A}(y)\). The calculations of these uncertainties are shown below.  
According to [PERSON]'s formula, the experimental standard deviation of a single measurement is:  
\[s(\alpha)=\sqrt{\frac{\sum_{i=1}^{15}{(x_{i}-\overline{x})}^{2}}{n-1}}\approx 2 \times 10^{-5} \tag{18}\]  
Therefore, the standard uncertainty component introduced by measurement repeatability is:  
\[\mu_{A}(\alpha)=\frac{s(\alpha_{i})}{\sqrt{5}}=8.94\times 10^{-6} \tag{19}\]  
The maximum permissible error of the weighing sensor is \(\pm\)0.2 g, which can be converted into standard uncertainty according to uniform distribution:  
\[\mu_{B1}(m)=\frac{\sqrt{5\times(\frac{0.2}{\sqrt{3}})^{5}}}{999.98}=1.01\times 1 0^{-5} \tag{20}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Repeatability\%** & **Hysteresis\%** & **Linearity\%** \\ \hline Vacuum/kPa & & & \\
76 & 0.191 & 0.169 & 0.141 \\
81 & 0.292 & 0.203 & 0.199 \\
85 & 0.357 & 0.208 & 0.231 \\
91 & 0.357 & 0.159 & 0.153 \\
93 & 0.201 & 0.126 & 0.115 \\ Steam flow rate/L\(\cdot\)h\({}^{-1}\) & & & \\
0 & 0.353 & 0.270 & 0.286 \\
0.3 & 0.410 & 0.269 & 0.284 \\ Droplet velocity/g\(\cdot\)min\({}^{-1}\) & & & \\
10 & 0.327 & - & 0.301 \\
20 & 0.441 & - & 0.272 \\
30 & 0.471 & - & 0.289 \\
40 & 0.398 & - & 0.334 \\
50 & 0.498 & - & 0.311 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Repeatability, hysteresis, and linearity under each experimental condition.  
The relative standard uncertainty of synthesis is:  
\[\mu_{c}=\sqrt{\mu_{A}\left(\alpha\right)^{2}+\mu_{B1}\left(m\right)^{2}}=\sqrt{ \left(8.94\times 10^{-6}\right)^{2}+\left(1.01\times 10^{-5}\right)^{2}}=1.35 \times 10^{-5} \tag{21}\]  
When \(k=2\), the extended uncertainty is:  
\[U=k\cdot\mu_{c}=2.70\times 10^{-5} \tag{22}\]
## 3 Mathematical Formulation
### Optimization System
#### 3.2.1 Evo  
The EVO solves an optimization problem that aims to define the charging schedule for each EV. It uses the current state of each EV (SOC and SOH) and the forecasts of local PV generation, the SESS condition, and the local industry power demand. Hence, this optimizer relies on the results of DT-EV, DT-PV, and DT-IAO. The main inputs for the EVO are the following:  
* Scheduled trips data (departure and arrival time, distance (km) or energy intensity (kWh)). * Forecast of the PV energy production, industry demand, and grid energy price, coming from DT-PV, DT-IAS, and day-ahead prices, respectively. * SOC, SOH, and FEC since EV fleet commissioning, coming from DT-EV. * SOC of the SESS, coming from DT-SESS. With this information, the optimizer minimizes the cost of charging the EV fleet, while the technical constraints of the microgrid remain unaffected. Therefore, the algorithm should consider the microgrid even if it is not in charge of managing it. Hence, the main EVO outputs are as follows:  
* A charging profile for each EV. * Forecast of the aggregated charging power demand for the next 24 h.  
The MILP model that defines the EVO aims to minimize the cost associated with the charge of EVs, considering the grid energy cost and other factors such as the SESS and vehicle battery degradation. The objective function is depicted in (6)-(9). \[\min J_{EVO}=Grid_{\$}+SESS_{\$}+EV_{\$} \tag{6}\]  
\[Grid_{\$}=\sum_{k=1}^{h}C_{k}^{\$}P_{g_{k}}\delta_{t} \tag{7}\]\[SESS_{\$}=C_{SESS}\sum_{k=1}^{h}P_{SESS_{k}}\delta_{t} \tag{8}\]  
\[EV_{\$}=C_{cyc}\sum_{v=1}^{m}\sum_{k=1}^{h}P_{EV_{k}}^{ev}\delta_{t}+C_{cal} \sum_{c=1}^{m}\sum_{k=1}^{h}f(E_{EV_{k}}^{ev}) \tag{9}\]  
where \(Grid_{\$}\), \(SESS_{\$}\), and \(EV_{\$}\) represent the cost of grid-supplied energy and the degradation costs associated with the SESS and EV systems, respectively. Furthermore, k is the time step and h is the time horizon considered to solve the optimization, with \(k\in H=\{1,2,\ldots,h\}\). Moreover, \(C_{k}^{g}\) is the energy price per kWh, \(P_{\$}\) is the purchased power from the grid, \(\delta_{t}\) is hours per time step, and \(C_{SESS}\) and \(P_{SESS_{k}}^{s}\) are the degradation cost per kWh drawn from the SESS and the power demand of the SESS, respectively. Furthermore, \(C_{cyc}\) and \(C_{cal}\) are the cycling and calendar degradation cost of the EV batteries, and \(P_{EV_{k}}^{evs}\) and \(E_{EV_{k}}^{ev}\) are the charging power and energy available at each EV, respectively, with \(ev\in V=\{1,2,\ldots,m\}\). The cycling and calendar degradation costs are estimated using (10) and (11), while (12) describes a linear approximation of calendar aging in terms of the energy available during storage.
## 2 Materials and Methods
### Eugenol Detection  
To detect eugenol in the fish fillet, the method previously developed by us was used [25]. Briefly, 2 g of fillet was weighted into a 50 mL plastic tube, extracted using ultrasonication with 5 mL of hexane over 10 min, and followed by centrifugation at 5000 rpm for 5 min. The supernatant was applied to a phenyl solid phase extraction (SPE) column. Then, the target was eluted with 2 mL of ethyl acetate for quantification after the column rinsed with 5 mL of hexane. Eugenol measurement was conducted on an Agilent 7890N gas chromatograph (GC) coupled to a 7000B tandem mass spectrometer (GC-MS/MS) with a triple quadrupole analyzer (QqQ) that was also equipped with an Agilent J&W Scientific DB-17 MS capillary column (30 m \(\times\) 0.25 mm, ID 0.25 \(\upmu\)m). The method limits of quantification (LOQs) was 1.2 \(\upmu\)g/kg for eugenol. Concentrations in the range of 5-500 \(\upmu\)g/L performed good linearity with a coefficient of determination (r\({}^{2}\)) greater than 0.999. The recoveries for the target ranged from 76.4% to 99.9% with relative standard deviations (RSD) ranging from 2.18% to 3.60%.
## 4 Workload and Data Center Modeling
### Analytical Modeling Approach
#### 4.1.1 Workload Modeling
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Definition** \\ \hline \multicolumn{3}{c}{Workload and Data center Modeling Section Symbols} \\ \hline \(W\) & total workload demands \\ \(J_{s}\) & submitted jobs during time window \(T_{w}\) \\ \(T_{s}\) & submitted tasks during time window \(T_{w}\) \\ \(J_{e}\) & total ended jobs during time window \(T_{w}\) \\ \(T_{e}\) & total ended tasks during time window \(T_{w}\) \\ \(T_{j}\) & tasks submitted in job \(j\) \\ \(R\) & overall resource vector (RAM, CPU, IO, Storage and Network) demand units \\ \(R_{ij}\) & requested resources vector of task \(i\) in job \(j\) \\ \(RE_{i}\) & release resources vector of index \(i\) \\ \(t\) & absolute running time \\ \(R_{t}\) & requested resources vector at time \(t\) during time window \(T_{w}\) \\ \(J_{st}\) & number of submitted jobs at \(t\) time during time window \(T_{w}\) \\ \(T_{st}\) & number of submitted tasks at \(t\) time during \(T_{w}\) \\ \(T_{it}\) & number of tasks submitted in job \(j\) at time \(t\) \\ \(RE_{t}\) & total released resources at time \(t\) during time window \(T_{w}\) \\ \(T_{et}\) & number of ended tasks during time window \(T_{w}\) \\ \(T_{net}\) & number of tasks that are not finished normally and will be submitted again \\ \(T_{cft}\) & number of ended tasks successfully completed during time window \(T_{w}\) \\ \(T_{net}\) & number of tasks that are not finished normally and will be considered to be resubmitted in the next time window \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Key Symbols Used in the Analysis and their Definitions.
## 3 Proposed Hybrid Framework
### Data Stored Set  
In addition to maintaining UTXO locally, nodes added to blockchain also need to maintain a data store set DSS, as shown in Figure 6. The purpose of our data store set is to speed up the query of the amount of data on the node chain. Instead of traversing the entire blockchain and spending significant time, users can just look up information on the data store set.  
The red transaction in the picture represents a storage transaction. Each storage transaction indicates that a certain address has declared 100 MB of data in the blockchain. The green transaction in the figure represents a contract transaction, which is the public information after the user deploys the smart contract. Other users can obtain the information of calling the smart contract by querying the contract transaction, which can better improve the efficiency of the transaction.  
DSS is a collection of hash tables that can be looked up by setting address-value pairs. When a user successfully publishes a data store transaction, each node in the blockchain needs to add the user's address to the data store and add related records. Values are chained structures, where each block record stores information, time, data hash, and contract address. We stipulate that each stored transaction of the user can only declare 100 MB of data at a time, and 100 MB of data will generate a random number of 32 B after  
Figure 6: The structure of DSS, including hash-head node and two types of transaction. The two types of transaction after the hash header node have different meanings. Generally speaking, storage transactions are the majority, while contract transactions are the minority. The data declared by users through the blockchain is represented by storage transactions, while only one contract transaction can handle multiple data.  
the data summarization algorithm. A block of the blockchain is 1 MB in size, with 80 B block headers and variable block bodies. The transaction information is stored in the block body, which means that a block can contain up to 2.5 GB of data storage information, thus preventing the blockchain from being too long. If a user successfully publishes a storage message, the user will locate the message through the address first in DSS. If there is no record of the address, a new record will be added. If it exists, the chain structure is traversed in the value corresponding to the address. Each block represents 100 MB of data for the address to be linked. The process of querying user data volume is to traverse the chain structure of an address.
## 3 Problem Definition and Formulations
### Mathematical Formulation
#### 3.3.1 Notations and Parameters  
\(N_{c}\): Set of customer request vertices for IF, OF, ER, and ED  
\(N_{s}\): Set of shipping company request vertices for IE and OE. Although the location of these vertices is the terminal, we still assign a unique number to them to ensure that each request is executed and the direct transfer from IE to OE is forbidden.  
\(N_{r}\): Set of all request vertices, \(N_{r}=N_{c}\cup N_{s}\), including \(N_{DEC}\), \(N_{PFC}\), \(N_{PEC}\), and \(N_{PFC}\)  
\(N_{d}\): Set of depot vertices  
\(N\): Set of all network vertices, \(N=N_{r}\cup N_{d}\)  
\(K\): Set of all container trucks, \(k\in K\)  
\(C_{1}\): The total cost per unit of traveling time associated with fuel consumption and CO\({}_{2}\) emissions  
\(C_{2}\): Penalty costs per unit time incurred by trucks that surpass the designated working hours  
\(C_{3}\): Total cost per unit dwelling time of trucks  
\(M\): An extremely large positive integer  
\(T_{\text{max}}\): Maximum truck working hours  
\([TS_{i},TE_{i}]\): Time window of request vertex \(i\)  
\(n_{i}\): Maximum number of trucks allowed to depart from and return to depot \(i\)  
\(m_{i}\): Inventory of empty containers at depot \(i\) at the beginning of planning horizon  
\(\tau_{ij}\): Transfer time on arc \((i,j)\)  
\(t_{i}\): Time taken to load/unload a container onto/from a truck at vertex \(i\)  
\(\varphi_{i}\): Preceding vertex of the vertex \(i,i\in N_{r}\)