esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Evaluation and Numerical Simulation of the Thermal Performance of a Green Roof
## 3 Results and Discussion
### Equivalent Thermophysical Properties  
On-site measurement data were used for generating a model through Comsol software. Thus, different equivalent thermophysical properties were iteratively tested and the internal surface temperatures were simulated. The search for equivalent thermophysical properties aimed at finding the best reproduction of the green roof behavior, trying to get the best match between internal measured and simulated surface temperatures. The best matching is shown in Figure 9a, where the comparison between measured (green line) and simulated (black dotted line) internal surface temperatures is reported.  
Figure 8: Heat fluxes, indoor temperatures and internal surface temperatures registered during summer for the green roof (**a**) and the original one (**b**).  
During summer, the overlap between experimental and simulated data was found, setting the following equivalent parameters: thermal conductivity equal to 0.4 W/(mK), specific heat capacity equal to 840 J/kgK and mass density equal to 1100 kg/m\({}^{3}\). The equivalent thermophysical properties mentioned before were also tested during middle season (April) and winter (December), when the external climatic conditions are different (see Figure 9b,c). The EF coefficients were calculated in all the mentioned seasons: during summer, EF = 0.96 was obtained; during the middle season, EF = 0.96 was computed; and, finally, during winter, EF = 0.93 was found.  
Figure 9: Green roof equivalent model. Comparison between measured and simulated internal surface temperature: summer (**a**); middle season (**b**); winter (**c**).  
The EF values are all higher than 0.9, satisfying the desired condition reported in the methodology section (see the flow-chart in Figure 4). The equivalent thermophysical properties can therefore be used in building energy simulation tools.
# Estimation of Free Fatty Acids in Stored Paddy Rice Using Multiple-Kernel Support Vector Regression
## 3 Results
### Comparison Results  
Our experimental results from 20 experiments are shown in Table 3. The average values of MAE, MAPE, and RMSE for each algorithm were computed and are shown with the form of average \(\pm\) standard deviation (SD). For each criterion, the algorithm with the minimum average and SD is considered to be the best on this criterion.  
As can be seen in Table 3, the MKSVR model proposed in this paper achieves the best results. Compared with the MLR, BPNN, and SKSVR, the average predicting errors of MKSVR were improved. Compared with the MLR model, MAE dropped by 1.018, MAPE dropped by 5.569%, and RMSE dropped by 1.260. Compared with the BPNN model, MAE decreased by 0.090, MAPE decreased by 0.505%, and RMSE decreased by 0.132. Compared with the SKSVR model, MAE decreased by 0.134, MAPE decreased by 0.778%, and RMSE decreased by 0.132. Although the average values of each  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **MAE** & **MAPE (\%)** & **RMSE** \\ \hline MLR & 1.359 \(\pm\) 0.172 & 7.595 \(\pm\) 0.953 & 1.702 \(\pm\) 0.173 \\ BPNN & 0.431 \(\pm\) 0.093 & 2.531 \(\pm\) 0.552 & 0.574 \(\pm\) 0.132 \\ SKSVR & 0.475 \(\pm\) 0.053 & 2.804 \(\pm\) 0.310 & 0.600 \(\pm\) 0.055 \\ MKSVR & 0.341 \(\pm\) 0.038 & 2.026 \(\pm\) 0.237 & 0.442 \(\pm\) 0.037 \\ \hline \hline \end{tabular}
\end{table}
Table 3: FFA estimation (average \(\pm\) SD) results of the four algorithms.  
criterion of BPNN were slightly smaller than SVR, the SD of each criterion of BPNN was higher than that of SVR. By considering both the average and the SD of each criterion, the performance of SKSVR and BPNN are very similar, and are at an acceptable level. However, the MLR model shows extremely poor performance for each criterion.  
By analyzing Table 2, we find that the MKSVR prediction model is superior to the SKSVR and BPNN models, as well as the MLR models, and it can better predict the FFA content of grain during storage. As can be seen from the SD, the prediction model also has better accuracy and robustness.
## 2 Methods  
### Numerical Weather Prediction Model  
The Korean Integrated Model (KIM) [28] is a newly developed global weather prediction model developed at the Korea Institute of Atmospheric Prediction Systems (KIAPS); as of April 2020, it has become an operational model at the Korea Meteorological Administration (KMA). It is composed of non-hydrostatic governing equations on a cubed sphere, implemented with the state-of-the-art physics parameterization packages including radiation, gravity wave drag, vertical mixing, convection,cloud physics, and so on (see [28]). For the ensemble forecast in the local ensemble transform Kalman filter (LETKF) system, the horizontal resolution was about 50 km, but recently it has changed to 32 km. The model top is set to 0.01 hPa with 91 vertical levels in the hybrid sigma-pressure vertical coordinate. In this study, the ensemble model resolution is set to 50 km due to the limitation of computational resources. Since the model resolution is coarse, this study focuses on synoptic-scale structures rather than small-scale weather events.
## 3 Results
### Connectivity Analysis  
The scenarios with better connectivity were those in which forest areas increased (LUSES2018 + deforestation and LUSES2018 + forested). In this case, reforestation increased  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{\
\begin{tabular}{c} **Overall** \\ **Area (km\({}^{2}\))** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Number** \\ **of Patches** \\ \end{tabular} } & \multicolumn{3}{c}{**Patch Area (km\({}^{2}\))**} & \multicolumn{3}{c}{**ED = Patch Perimeter/Patch Area**} \\  & & & **Average** & **Median** & **S.D** & **Average** & **Median** & **S.D** \\ \hline LUSES2008 & 1614.54 & 1600 & 1.01 & 0.01 & 23.80 & 1809.59 & 77.78 & 56,280.65 \\ LUSES2018 & 1553.06 & 1434 & 1.08 & 0.05 & 24.21 & 215.85 & 28.21 & 1895.71 \\ LUSES2018 + for & 1623.46 & 3340 & 0.49 & 0.02 & 16.23 & 109.91 & 31.17 & 1245.31 \\ LUSES2018 + def & 1681.79 & 1581 & 1.06 & 0.02 & 24.85 & 1651.67 & 44.44 & 56,493.51 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fragmentation metrics for native forest (number of patches, patch area and total edge) in the Manabi province (Ecuador) for Land uses scenarios: 2008 (LUSES2008), 2018 (LUSES2018), 2018 avoiding deforestation (LUSES2018 + def), and 2018 considering reforestation (LUSES2018 + for).  
Figure 2: Transition in land uses between 2008 and 2018 in study area (in %).  
scenario had a number of links four times greater than that of LUSES2008, (Table 5 and Figure 4).  
When considering only the LUSES2018 + deforestation scenario, connectivity increased for the referenced vertebrate species in the west part of the Manabi province (Figure 5). On the other hand, for the reforestation scenario (LUSES2018 + forested), the number of links also increased in central and extreme northeast areas (areas where most of the reforestation was carried out) (Figure 5).  
Figure 4: Forest patches (green), links between the patches (red) and components (areas delimited by black lines) in the Manabi province (Ecuador) for Land uses scenarios: 2008 (LUSES2008), 2018 (LUSES2018), 2018 avoiding deforestation (LUSES2018 + def), and 2018 avoiding deforestation and reforestation (LUSES2018 + for).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Number of Links**} & \multicolumn{3}{c}{**Distance in Cost**} & \multicolumn{3}{c}{**Distance in Meters**} \\ \hline  & & **Average** & **Median** & **S.D** & **Average** & **Median** & **S.D** \\ \hline
2008 & 4928 & 84.10 & 86.37 & 49.27 & 1482.60 & 1235.95 & 1152.19 \\
2018 & 5301 & 86.27 & 90.08 & 50.02 & 1381.92 & 1146.40 & 1096.18 \\
2018 + for & 18430 & 96.89 & 103.34 & 46.74 & 1247.94 & 1026.40 & 918.51 \\
2018 + des & 6001 & 84.83 & 87.91 & 49.70 & 1520.34 & 1301.54 & 1170.28 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Number of links, distance in cost and meters of the links generated in the 4 scenarios.
## 2 IRS Technology Fundamentals
### IRS Reflection  
Figure 2 shows the channel model design of IRS-based wireless communication where the channels from the BS to the UE through each element of the IRS are constructed with three components. They are the channel link between BS and IRS, the reflection of IRS elements, and the channel link between the IRS and UE. Such a composite channel behaves differently from the conventional point-to-point direct channel. However, from the BS, each element of the IRS receives the superposed multi-path signals and then scatters the associated signal with an adaptable amplitude and/or phase, such as a single point source, which leads to a \"multiplicative\" channel model.  
Figure 1: The architecture of reconfigurable reflecting element based on PIN diode with different layers.  
We can express the complex reflection coefficient of the reflected signal by the \(n\)-th element of the IRS mathematically as follows:  
\[h_{n}=(\beta_{n}e^{i\theta_{n}})x_{n},n=1,\ldots N, \tag{1}\]  
where \(h_{n}\) denotes the reflected signal of the nth element, \(x_{n}\) is the incident signal to the IRS, \(N\) represents the total number of the reflecting elements at the IRS, and \(\beta_{n}\) and \(\theta_{n}\) present the Equations (1) and (2) with the reflection coefficient and control the reflected signal amplitude and phase, respectively [33]. By using the appropriate designs for the IRS phase shifts, amplitude, or both, a certain metric objective, such as the system achievable rate or the coverage, can be optimized by changing the channel environment. This is fundamentally different from conventional wireless communication research, where the design and optimization opportunities are confined to the pair of transceivers [24].  
Moreover, according to the design of the IRS-aided wireless systems, for the fundamental relationship between the reflection amplitude and phase shift, we can assume an analytical model for the phase shift. The model is generally applicable to a variety of semiconductor devices used for the application of the IRS. We consider the phase shift and the corresponding amplitude \(L_{n}=\beta_{n}(\theta_{n})e^{i\beta_{n}}\) with \(\theta_{n}\in[-\pi,\pi]\) and \(\beta_{n}(\theta_{n})\in[0,1]\), respectively. Therefore the reflection amplitude \(\beta_{n}(\theta_{n})\) can be expressed as follows:  
\[\beta_{n}(\theta_{n})=(1-\beta_{min})\bigg{(}\frac{sin(\theta_{n}-\phi)+1}{2} \bigg{)}^{\gamma}+\beta_{min}, \tag{2}\]  
where \(\beta_{min}\geq 0\), \(\phi\geq 0\), and \(\gamma\geq 0\) are the constants related to the specific circuit implementation. Figure 3 shows that the \(\beta_{min}\) is the minimum amplitude, \(\phi\) is the horizontal distance between \(-\pi/2\) and \(\beta_{min}\), and \(\gamma\) controls the steepness of the function curve. Note that, for \(\gamma=0\), (2) is equivalent to the ideal phase-shift model, i.e., \(\beta_{n}(\theta_{n})=1,\forall n\). In practice, IRS circuits are fixed once they are fabricated and these parameters can be easily found by a standard curve fitting tool [34].  
Figure 2: IRS-aided wireless communication system with BS and UE channel link.
## 2 The Proposed Method
### Die Pattern Detection and Clustering
Consequently, the number and coordinates of the columns from all detected die patterns can be obtained. Similarly, the coordinate points \(\left\{y_{k}^{\text{TL}}\mid k=1,2,\ldots N_{\text{D}}\right\}\) are clustered in the same manner. Thus, every row and its representative coordinate are obtained. Thus far, the number of columns and rows from the detected die patterns can be obtained. Assuming that the detected patterns arrange in \(\mathcal{N}\) columns and \(\mathcal{M}\) rows. Let \(\left(x_{n}^{\text{TL}},y_{m}^{\text{TL}}\right)\) be the top-left corner of an arbitrary die pattern in the original SAT image, where the subscript \(n\in\left\{1,2,\ldots,\mathcal{N}\right\}\) denotes the \(n\)-th column and subscript \(m\in\left\{1,2,\ldots,\mathcal{M}\right\}\) denotes the \(m\)-th row. Using these corners and the estimated size of the standard template, all patterns, including the die patterns and predicted pattern candidates, in the wafer image can be obtained. \[\left[x_{n}^{\text{TL}},x_{n}^{\text{TL}}+w_{\text{Tpl}}^{\ast}\right]\times \left[y_{m}^{\text{TL}},y_{m}^{\text{TL}}+h_{\text{Tpl}}^{\ast}\right] \tag{6}\]  
This indicates the two-dimensional region of the pattern located on the \(n\)-th column and \(m\)-th row. Figure 9 shows all patterns, in which the yellow and blue dots denote the locations of the detected and predicted patterns, respectively. Every pattern will be further categorized into normal, abnormal, or other predefined classes. At this point, the initial wafer map is produced; however, the patterns need to be identified later.
## 3 The Influence of Ultrasound on the Physical and Chemical Properties of Juices

Table 1: _Cont._According to the research results presented above, high-powered ultrasound treatment may be a promising alternative to traditional pasteurization or other processing methods to improve the quality of juices from various fruits and vegetables. It is worth continuing the experiments to determine the optimal process conditions that will meet both national and international requirements not only in terms of extending the shelf life, but also the pro-health value of beverages. The Influence of Ultrasononds on the Microbiological Stability of Fruit, Vegetable and Fruit-Vegetable Juices  
Due to the current trends related to a healthy lifestyle and rational nutrition, consumers, when choosing beverages, apart from taste, also take into account their pro-health value. However, in order to preserve the quality of the juice, it is necessary to preserve it before the distribution and storage process. The most popular method is still pasteurization, which involves the use of temperatures below 100 \({}^{\circ}\)C. Unfortunately, many substances that have a beneficial effect on the functioning of the human body belong to the group of thermolabile compounds. This means that they break down as a result of too-high temperatures. The pasteurization process has been modernized in recent years, so the losses of these compounds are much smaller. The main focus was on shortening the process time, in some cases even to several tens of seconds (flow pasteurization). Due to rapid technological development, there are an increasing number of alternative methods. A number of tests are carried out that allow the introduction of innovative treatments that enable the production of products with the desired characteristics and quality, including being microbiologically safe for consumers. Of particular importance are liquid food decontamination techniques that are inexpensive, simple, reliable and environmentally friendly. Ultrasonics have found application in the fruit and vegetable juice industry due to their desired multifunctional effect: reduced water and energy consumption, minimal loss of flavour and nutrients and increased product homogeneity [50]. Improvements in ultrasound generation technology in recent years have generated interest in reducing microbes. The mechanisms of ultrasonic inactivation of microorganisms are seen in a number of complex physicochemical processes, which are based on fast-changing mechanical stresses, energy dissipation, cavitation with a whole range of derivative phenomena and, in special cases, the so-called cell resonance. Studies show the destructive effect of ultrasound on colonies of microorganisms, manifested in the
## 5 Surface Engineering of SnO\({}_{2}\) ETL
### Interfacial Chemical Interaction
#### 5.2.2 Vacancy Defects
Figure 14: MPTMS connected to the perovskite layer and SnO\({}_{2}\). (**a**) MPTMS molecule’s chemical makeup and the process used to manufacture the MPTMS-modified SnO\({}_{2}\) substrate. (**b**) SnO\({}_{2}\)/MPTMS/perovskite schematic diagram. Reprinted with permission from [PERSON] et al. [153]  
Copyright 2021 Wiley-VCH GmbH. (**c**) Xanthate annealing to create a sulfur-functionalized ETL. (**d**) Current-density-voltage properties of the best-performing SnO2 device with and without 1-, 3-, and 5-times sulfur functionalization (xanthate treatment). A humidity reading from the device's manufacturing is displayed in the inset. (**e**) Comparison of the current-density-voltage on standard SnO2 and sulfur-functionalized SnO2 is shown in the inset text, which also includes the computed hysteresis index (HI). (**f**) Comparison of the best-performing PSC's current-density-voltage results with and without sulfur functionalization. The related devices' IPCE spectra are shown in the inset. (**g**) PSC's steady output power with and without sulfur functionalization. Reprinted with permission from [PERSON] et al. [155] Copyright 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim. The SnO2 ETL surface defects were passivated using (NH4)2S to increase the conductivity and electron extraction effectiveness [113]. Different (NH4)2S concentrations were combined using spin coating, and 30 min of sintering at 180 \({}^{\circ}\)C in a tube furnace was used to prepare the SnO2 ETLs, which were subsequently made from the SnO2 precursor colloids. The S atoms saturated the surface dangling bonds of SnO2 during annealing. By lowering the conductivity, electron density, and oxygen vacancy density, the S-saturated SnO2 (S-SnO2) ETL mobility was greatly increased, and carrier recombination was efficiently suppressed. Additionally, Sn-S-Pb bonds can be formed by anchoring Pb atoms  
Figure 15: (**a**) T-SnO2 nanoparticles’ schematic synthesis diagram. (**b**) Schematic representation of how TBAI functions on the T-SnO2 layer’s surface. (**c**) Steady-state PL and (**d**) TRPL spectra of the perovskite films based on various ETLs. (**e**) Champion device J–V curves as measured under AM 1.5 G illumination. (**f**) Histogram representing the PCE distribution of 22 PSCs using various ETLs. Reprinted with permission from [PERSON] et al. [157] Copyright 2021, American Chemical Society. to the surface of the S-SnO\({}_{2}\) film, thereby increasing the efficiency of electron extraction. Following the (NH\({}_{4}\))\({}_{2}\)S alteration, the S ions saturate the dangling bonds in SnO\({}_{2}\) particles, causing these oxygen vacancies to be filled with S atoms. These anchors directly link the perovskite to the ETL, thereby increasing the effectiveness of electron extraction. By lowering the density of ETL surface defect states, this method lowers the barriers to electron transport and boosts the electron mobility, conductivity, and stability.
## 1 Introduction  
### Background  
Social progress and improvements in people's quality of life profit from the rapid development of urban transportation. Unfortunately, a sharp increase in vehicle ownership poses great challenges in traffic safety, mobility, and environmental sustainability [1]. Over the decades, the development of intelligent transportation systems (ITSs) has become a significant force in dealing with these challenges. In particular, emerging connected vehicles (CVs) technologies have aroused widespread attention due to their enormous potential in improving road capacity, driving safety, and fuel economy. Employing vehicle-to-everything (V2X) communication, CVs are able to cooperate in various traffic scenarios [2].  
As bottlenecks of urban traffic, intersections have always been the focus of researchers and traffic engineers for improving traffic safety and smoothing traffic flow. Intersections can be categorized into two types, i.e., signalized and signal-free. Researchers have proposed various approaches to improve traffic efficiency at signalized intersections, e.g., fixed-time control, actuated control, adaptive control, and connected-vehicle-based methods [3, 4]. These methods can improve traffic throughput at intersections to some extent, but unnecessary stopping and idling are still inevitable. For signal-free intersections, there are no signal lights or any other display devices to prompt drivers. Traditionally, driversmust observe and estimate the adequate vehicle clearance through visual judgment for safe crossing. Currently, a real-time and reliable CVs environment can be constructed using the V2X communication technology, which provides faster response and shorter vehicular gaps for AVs to pass through intersections. Significant efforts have been made to coordinate AVs at signal-free intersections under centralized or decentralized frameworks. The centralized frameworks globally decide the movements of all vehicles using a central controller, while under the decentralized frameworks, each vehicle is treated as a single agent and collaborates independently [5]. Two kinds of strategies have generally been presented for signal-free intersection coordination under the centralized or decentralized frameworks, i.e., heuristic strategies and trajectory planning strategies.
## 3 Materials and Methods
### Topic Detection  
An additional layer of \"filtering\" is the topic detection/classification module. Topics of specific categories should never appear as breaking news, e.g., lifestyle news. For this reason, NLP and ML methods [59] have been implemented to achieve reliable topic classification and to reduce the input size that will be fed to the detector module. Apart from filtering, this makes it possible to apply weighting based on the topic, as some topics are less likely to be breaking.  
In our current approach, the vector space models of the documents are calculated as soon as the documents have been preprocessed (stop-word elimination, stemming, etc.). After this step, clustering is applied based on the cosine similarity of the vectors [60]. This approach may sound simplistic but gives good results when there are only a few topics. The involvement of field experts in the implementation procedure enables the design of a rule-based detection system [42]. Such an approach can be easily implemented from a programming point of view, but it requires deep knowledge of the domain, which makes maintainability difficult. This implementation is still in the tuning phase, as the first conducted evaluation has shown that the percentage of unclassified articles is quite high.  
Eventually, deep learning approaches [37] are going to be implemented and compared with the aforementioned one to further evaluate its performance. The main advantage of such methods is that they do not require deep domain knowledge, which leads to faster implantation and makes maintenance and further development much easier. Beyond traditional deep learning techniques, there are novel methods such as Deep Reinforced Learning [21], which can be utilized to achieve even better results.