esa-sceva/satcom-chunk-collection huggingface dataset:
# AFSFusion: An Adjacent Feature Shuffle Combination Network for Infrared and Visible Image Fusion
## 3 AFSFusion Network Model
### Loss Function  
#### 3.4.1 Selection of the Loss Function  
To obtain a loss function with superior guidance capability, this study adopts three loss functions: MSE, SSIM, and TV, and sets the corresponding weights to form the total loss function. The MSE loss function restricts the level of dissimilarity between the images at the pixel level, but it does not visually match human eye perception. Therefore, SSIM, which measures the local structure of the images in terms of luminance, contrast, and structure, is selected as a loss function to better visualize the fused images. Additionally, the TV loss function is introduced to constrain the gradient information of the image, which directly reflects the contour and edge information of the object in an image. This is important for evaluating the quality of the fused image in IR and visible image fusion, as the clarity of the object contour depicted in the image is crucial. Variable weight values are set in the different loss functions for IR and visible images according to the differences in the human eye perception characteristics of IR and visible images. Different weight values are utilized in the three types of loss functions, and the specific effects can be elucidated in the analysis of the ablation experiments in Section 4.  
In particular, the MSE, SSIM, and TV functions are calculated, as shown using Equations (1)-(3), where \(\|\cdot\|_{2}\) in Equation (1) represents the two-norm operation, and \(\mu_{x}\mu_{y}\sigma_{x}^{2}\), and \(\sigma_{y}^{2}\) in Equation (2) represent the mean, variance, and covariance of \(x\) and \(y\), respectively. The parameters \(c_{1}\) and \(c_{2}\) are constants; \(\partial\) and \(\|\cdot\|_{1}\) in Equation (3) represent the gradient and one-norm operation, respectively.  
\[MSE(x,y)=\|x-y\|_{2} \tag{1}\]  
\[SSIM(x,y)=\frac{(2\mu_{x}\mu_{y}+c_{1})(2\sigma_{xy}+c_{2})}{(\sigma_{x}^{2}+ \sigma_{y}^{2}+c_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+c_{2})}, \tag{2}\]  
\[TV(x,y)=\|\partial x-\partial y\|_{1} \tag{3}\]
## 1 Introduction  
Thin sheets of bean curd are a kind of traditional bean product in Southeast Asia. They are a semi-dehydrated soybean product, produced by adding a coagulant to soy milk and then compressing it into thin sheets, and are used in a wide variety of popular dishes. They are rich in minerals, protein, and overall amino acid composition [1]. Moreover, the abundant isoflav and lecithin act as functional ingredients for preventing osteoporosis, cardiovascular disease, as well as cancer [2]. However, due to their good nutrition, thin sheets of bean curd can be a vehicle for microorganisms, which are responsible for food spoilage and may present a health hazard to humans. Thus, it is necessary to control the contamination of thin sheets of bean curd.  
Traditional technologies used for contamination control, such as additives, refrigeration, and steam sterilization, may reduce the sensorial characteristics and present some food safety issues [3]. However, nonthermal technologies, such as high-pressure, ultrasonic, and irradiation methods, have been utilized to control microbiological hazards and negative impacts on the intrinsic qualities of foods [4]. In recent decades, nonthermal plasma has been used as a new nonthermal technique that has exhibited enormous potential for applications in food industries [5]. Nonthermal plasma is produced by ionization of gas and is composed of charged particles, neutral particles, ultraviolet photons, and reactive species [6; 7]. It has gained extensive attention considering its capacity for the inactivation of a wide range of microorganisms, including bacteria, bacterial spores, biofilms,viruses, and fungi [8; 9]. Plasma-activated water (PAW), which refers to distilled water subjected to nonthermal plasma discharge [10], has been applied for the microbial decontamination of strawberries, grapes, button mushrooms, and mung bean sprouts [10; 11; 12; 13]. The reactive oxygen species (ROS), high oxidation-reduction potential (ORP), and low pH of PAW are considered to play a key role in inactivating microorganisms [14; 15]. These species in PAW are stable and can provide long-lasting effects [16]. However, there are few reports about the microbial decontamination effect of PAW for thin sheets of bean curd. Moreover, the influences of PAW on the intrinsic properties of thin sheets of bean curd also need to be investigated.  
In this work, the microbial inactivation efficacy of PAW on thin sheets of bean curd was studied. The impacts of activation time and soaking time on the microbial inactivation efficacy of PAW were evaluated. Further, the color, textural properties, total isoflav content, and sensory properties of thin sheets of bean curd treated by PAW were determined.
# Influence of Social Overhead Capital Facilities on Housing Prices Using Machine Learning
## 3 The Proposed Scheme
### Preprocessing Data
#### 3.2.6 Assessing the Importance of Subway Stations and Data Processing  
Subway station passenger traffic varies based on diverse user intentions, reflecting the significance of stations that serve various destinations, including offices, residences, hospitals, entertainment venues, and more. In this study, our objective is to gauge the importance of each subway station by analyzing passenger entry and exit volumes.  
Our primary data source was the 'Standard Station Information' from the Rail Portal, which covered the entire year of 2022 as of January 2023. During the data collection process, we encountered several challenges, notably the disparity in the operating institutions across different subway lines within the metropolitan area. Acquiring data from subway lines operated by the private sector was especially challenging, as these entities are not mandatedto disclose such information. Furthermore, several public datasets contained inaccuracies attributed to issues such as turnstile errors. To address these challenges, we compiled a comprehensive list of subway line-specific operating institutions in the metropolitan area, which isdetailed in Table 2.  
Building on this, in order to understand the usage volumes of each subway station, we collected data on the total number of boardings and alightings for 2022, as provided by a public agency portal. We initially supplemented the missing values with data from the 'Integrated Transport Card Big Data System' operated by the Ministry of Land, Infrastructure, and Transport. This system provides data based on transportation card usage during boarding and alighting. For stations with persisting missing values, we utilized 2021 data with adjustments based on the usage trend of the nearest station in 2022.  
During data preprocessing, we made transformations using the 'NEAR_SUBW_NM' (Near Subway station Name) variable to enhance its linkage with the passenger volume data. This involved reconciling station names that had changed over time and resolving duplicate station names across different lines. We standardized these names to a consistent 'Line_StationName' format. Lastly, to evaluate the significance of subway stations, the primary variable (passenger volum) was normalized using the MinMaxScaler. This technique, widely used in machine learning, scales and normalizes data to fall between 0 and 1.
## 2 Materials and Methods
### Battery Modeling
In Equations (9)-(11), the state of the charge is limited to the maximum and minimum permitted state of charge, considering the kWh capacity of BESS. Finally, Equation (12) ensures that BESS is either charging, discharging, or in idle mode. Lithium-ion batteries are considered the type of technology in this study. This is because of their maturity for grid-connected applications and high efficiency [32]. In this study, the BESS operation is modeled as an optimization problem. This research work assumes that the battery technologies are lithium-ion. Figure 2 depicts a residential home connected with a rooftop and BESS. The BESS performance specifications are listed in \[\sum\limits_{t=1}^{24}\left(\eta_{\textit{disch}}P_{t}^{B,\textit{dich}}-\eta_ {\textit{ch}}P_{t}^{B,\textit{ch}}\right)=0 \tag{5}\]0 For this purpose, BESS optimal planning and operation need to be obtained based on a cost function to obtain the desired objective from the BESS installation. Thus, the BESS operation is based on the following assumptions:  
During the day:  
* The PV system extracts the available power from the sun and produces solar energy. * See if the produced energy can be utilized to supply the demand. * Any excess energy is considered as the available energy to charge the BESS. * If the BESS is completely charged, the surplus energy is exported to the main grid. At night:
* The PV system generates little amounts of no solar energy. * The BESS initially supplies the demand. * If the load is completely supplied by BESS, the remaining energy in BESS is exported to the main grid. * If the BESS is fully discharged, the load is supplied from the main grid.
# Enabling the Orchestration of IoT Slices through Edge and Cloud Microservice Platforms
## Appendix B Mainflux IoT Server  
Mainflux [62] is an open-source IoT platform written in Go language [93]. It has a microservice-architecture where the pieces composing its business logic are contained in independent microservices. As shown in Figure 13, most of the microservices can communicate among them by the usage of the NATS message bus [91] which is an open-source messaging system for cloud native applications.  
The following are the most important Mainflux microservices used in this prototype:  
* Users microservice, which allows IoT platform to create a user account that is used to configure the connection between Edge X Foundry IoT gateway to Mainflux server. It its connected to a PostgreSQL database [94] to store all the user information.
* Things microservice, which is in charge of provisioning devices and configure communication channels used by these devices. This microservice is used to create the MQTT topic (channel) from Edge X Foundry IoT gateway to Mainflux server and provision Edge X Foundry IoT gateway as a \"Mainflux thing\" within the prototype. It is connected to a PostgreSQL database to store the provisioned device information, but not the events.
* Normalizer service, that is responsible for normalizing the events published by the connected things in the SenML format which are finally stored in the events databases.
* MQTT adapter, that includes an MQTT broker listening for events published by the provisioned devices. These devices are mainly the Edge X Foundry IoT gateway in the prototype.
* HTTP adapter, which offers an HTTP API that is used by the devices to send their measurements. There are other adapters for CoAP or WebSocket interfaces, but they are not used by this prototype yet.
* Database writers, which are in charge of storing normalized data into a database which is an InfluxDB time-series database [87] in the prototype.  
All these microservices are already containerized [95] and some unofficial guides to deploy it in a Kubernetes cluster are given. In order to facilitate the installation of Mainflux application in a Kubernetes Cluster, specific Helm Charts [68] were created for this prototype.
## 2 Data Acquisition, Preparation and Feature Selection  
### Data acquisition  
The sperm whale data were collected from an inflatable boat during four field seasons spanning four to ten weeks each (from 1997 to 1999) at Kaikoura, New Zealand [16]. Recordings were made of solitary diving male sperm whales using an omni-directional hydrophone (Sonatech 8,178; frequency response 100 Hz to 30 kHz \(\pm\) 5 dB) lowered to a depth of 20 m. This hydrophone was first connected to a fixed gain amplifier (flat response from 0 to 45 kHz) and then to one channel of a Sony TCD-D10 PROII Digital Audio Tape recorder (frequency response 20 Hz to 22 kHz \(\pm\) 1 dB with an anti-alias filter at 22 kHz). The recordings were digitized at 48 kHz and 16 bits. The use of data from solitary diving whales guarantees that no data from different animals were mixed, thus helping to obtain optimal results. The typical duration of the recorded click trains was around 2.5 mins. The 30 mins complete dive was a sequence of such click trains. The dive was divided in 10 data segments to ease data handling and manual analysis, but these did not exactly cover 10 click trains as the trains themselves are not always very well defined. A pause in the click production is sometimes too short to consider the continuation to be a new click train, but rather it can indicate that a few signals were not detected. Generally, a click train ends with a crack where it is assumed the animal is capturing a prey, but this is not always heard on a recording as the signal can be too weak.
## 4 IMOAHD Algorithm
### Individual Learning Operator (ILO)  
Individual learning is a spontaneous learning behavior of humans, which generates solutions that meet the requirements according to the existing knowledge and experience of individuals. Self-learning and self-inspection can effectively enhance problem-solving abilities. To simulate this self-learning behavior, the IMOAHD algorithm enhances its search capabilities through the individual learning operator. The individual knowledge database (IKD) is used to implement the memory function of individuals. The IKD is defined as follows:  
\[IKD=\begin{bmatrix}ikd_{1}\\ ikd_{2}\\ \vdots\\ ikd_{i}\\ \vdots\\ ikd_{\text{N}}\end{bmatrix} \tag{15}\]  
Figure 3: An example for performing RLO.  
\[ikd_{i}=\begin{bmatrix}ikd_{i1}\\ ikd_{i2}\\ \vdots\\ ikd_{ip}\\ \vdots\\ ikd_{iT}\end{bmatrix}=\begin{bmatrix}ik_{i11}&ik_{i12}&\ldots&ik_{i1j}&\ldots&ik_{i 1M}\\ ik_{i21}&ik_{i22}&\ldots&ik_{i2j}&\ldots&ik_{i2M}\\ \vdots&\vdots&&\vdots&&\vdots\\ ik_{ip1}&ik_{ip2}&\ldots&ik_{ipj}&\ldots&ik_{ipM}\\ \vdots&\vdots&&\vdots&&\vdots\\ ik_{iT1}&ik_{iT2}&\ldots&ik_{iTj}&\ldots&ik_{iTM}\end{bmatrix} \tag{16}\]  
where \(ikd_{i}\) is the IKD of the \(i\)-th individual, \(ik_{ipj}\) is the value of the \(j\)-th variable of the \(p\)-th solution in the \(i\)-th individual's IKD, and \(N\), \(T\), and \(M\) are the population size, the size of the individual knowledge database, and the solution dimension, respectively. When executing the individual learning operator, an individual generates a new solution as Equation (17):  
\[X_{i}=r_{2}*ikd_{ip}+(1-r_{2})*X_{i}^{\prime} \tag{17}\]  
where \(r_{2}\) is a stochastic number between 0 and 1, and \(ikd_{ip}\) is a solution from the IKD corresponding to the \(i\)-th individual. \(X_{i}^{\prime}\) and \(X_{i}\) are the individuals before and after the operator execution, respectively. When the algorithm performs individual learning, it explores potential optimal solutions by combining the current solution \(X_{i}\) with the solution \(ikd_{ip}\) from the knowledge database based on random numbers. Figure 4 presents an example of an individual undergoing self-learning when the random number is 0.2. Each variable in \(X_{i}^{\prime}\) updated via individual learning is recalculated using Equation (17), based on the corresponding IKD value and the random number \(r_{2}\), thereby forming the new individual \(X_{i}\).
## 2 TBM Selection Decision-Making Method
### Determination Method of Evaluation Index Weight
#### 2.3.3 Determination Method of Index Weight  
The modified expert scoring matrix \(C_{n}\) is used as the judgment matrix to calculate the weight, and the consistency test is carried out to prove whether the obtained weight is reliable.  
First, the product of each row element of the matrix is normalized (see Equation (4)), \(\alpha_{j}\) is the eigenvector.  
\[\left.\begin{array}{c}\delta_{i}=(\prod\limits_{j=1}^{n}c_{ij})^{\frac{1}{n} }\\ \alpha_{j}=\frac{\delta_{j}}{\sum_{i=1}^{n}\delta_{i}}\end{array}\right\} \tag{4}\]  
where \(n\) is the matrix order.  
The maximum eigenvalue of the judgment matrix is calculated using Equation (5); \(\lambda_{\text{max}}\) is the weight of each index.  
\[\lambda_{\text{max}}=\frac{1}{n}\sum\limits_{i=1}^{n}\frac{\sum_{j=1}^{n}a_{ji }\alpha_{j}}{\alpha_{i}} \tag{5}\]  
At this time, it is necessary to perform consistency testing on the weight obtained. First, Equation (6) is used to calculate the consistency index \(CI\).  
\[CI=\frac{\lambda_{\text{max}}-n}{n-1} \tag{6}\]  
Then, the consistency index \(RI\) is obtained by Table 2[26].  
\begin{table}
\begin{tabular}{c c} \hline
**Scale** & **Definition** \\ \hline
1 & Factor “\(i\)” is as important as factor “\(i\)”. \\
3 & Factor “\(i\)” is slightly more important than factor “\(i\)”. \\
5 & Factor “\(i\)” is more important than factor “\(i\)”. \\
7 & Factor “\(i\)” is strongly more important than the factor “\(i\)”. \\
9 & Factor “\(i\)” is definitely more important than the factor “\(i\)”. \\
2, 4, 6, 8 & Intermediate state scale value. \\ Reciprocal & Inversely proportional. \\ \hline \end{tabular}
\end{table}
Table 1: Scoring criteria of nine-scale method.  
The random consistency ratio \(CR\) is used to judge whether the judgment matrix has satisfactory consistency. If the following conditions are met (see Equation (7)), it is proved that the judgment matrix meets the consistency requirements. If it does not meet the consistency test, it needs to be rescored.  
\[CR=\frac{CI}{RI}<0.1 \tag{7}\]  
Finally, the weight vector \(Y\) is obtained.
## 3 Moments and Related Measures
### Probability-Weighted Moments  
The PWM approach is seen as a generalization of the classical probability distribution moments. The estimators of the parameters and quantiles of generalized distributions may be derived using PWMs. These moments have low variances and no significant biases, and they outperform estimators derived using the ML technique. The PWMs, denoted by \(\mathfrak{M}_{a,b}\), for \(a~{}\geq 1,~{}b\geq 0\), of a random variable \(T\), can be designed as:  
\[\begin{array}{ll}\mathfrak{M}_{a,b}&=E[T^{a}G(t)^{b}]=\int\limits_{-\infty}^{ \infty}t^{a}(G(t))^{b}g(t)dt.\\ &=\int\limits_{0}^{1}2\varphi\delta t^{a+\delta-1}\big{(}1-t^{\delta}\big{)}^{ \varphi-1}\big{(}1+t^{\delta}\big{)}^{-\varphi-1}\Big{[}1-\Big{(}\frac{1-t^{ \delta}}{1+t^{\delta}}\Big{)}^{\varphi}\Big{]}^{b}dt.\end{array} \tag{11}\]  
The PWMs (11), based on binomial expansion, can be written as:  
\[\begin{array}{ll}\mathfrak{M}_{a,b}&=\sum\limits_{u=0}^{b}\sum\limits_{d=0}^{ \infty}(-1)^{u+d}\left(\begin{array}{c}b\\ u\end{array}\right)\left(\begin{array}{c}\varphi(u+1)+d\\ d\end{array}\right)\int\limits_{0}^{1}2\varphi\delta t^{a+\delta(d+1)-1}\big{(}1 -t^{\delta}\big{)}^{\varphi(u+1)-1}dt\\ &=\text{K}_{u,d}\operatorname{B}\big{(}\frac{a}{\delta}+d+1,\varphi+\varphi u \big{)},\end{array}\]  
where \(\text{K}_{u,d}=\sum\limits_{u=0}^{b}\sum\limits_{d=0}^{\infty}(-1)^{u+d}2 \varphi\binom{b}{u}\binom{\varphi(u+1)+d}{d}\), and \(\text{B}(.,.)\) is the beta function.
## Materials and Methods
### Research Methods
#### 2.2.1 The CARS-SCA Characteristic Band Selection Algorithm
Step 3: After GF, SD, and gaussian filtering again pretreatments, the processed data and the soil samples are separately subject to SCA to obtain the correlation coefficient row vectors:  
(1) Gaussian filtering is a kind of linear smoothing filter which chooses weights according to the shape of the Gaussian function. It is very effective for suppressing noise obeying a normal distribution:  
\[g(x)=\frac{1}{\sqrt{2\pi\sigma}}\exp\left[-\left(\frac{x}{2\sigma}\right)^{2}\right] \tag{7}\]  
where \(x\) is the distance of the weight function from the maximum point, and the scale parameter \(\sigma\) represents the width of the Gaussian function, which determines the smoothness of the filtering. (2) Second derivative pretreatment can eliminate some baseline and other background interference, while improving the spectral resolution and sensitivity. It is widely used in spectral analysis. \[S(\lambda_{i})=\frac{\left[\lambda_{i+1}-2\lambda_{i}+\lambda_{i-1}\right]}{ \Delta\lambda^{2}} \tag{8}\]  
where \(\lambda_{i}\) represents the reflectance value of the ith band, and \(\Delta\lambda\) is the wavelength interval. Step 4: After each treatment in step 3, SCA between the processed spectral values and soil heavy metal As is performed. The results of each analysis are then combined to obtain a correlation coefficient matrix \(C(i\times 1)\). The bands with \(|C_{i}|\cong 0.6\) are then extracted. The data stored in the matrix \(F(i\times q)\) corresponding to the GF, SD, and GF processing are then found, and the matrix \(F(i\times q)\) is normalized to [0,1] using MMN, and the processed result is taken as the characteristic band subset. (1) MMN can reduce the difference between the magnitudes and smooth the wave function of the data sample values. \[x^{\prime}=\frac{x_{i}-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}} \tag{9}\]  
where \(x_{\text{max}}\) is the maximum of \(F_{i}\), and \(x_{\text{min}}\) is the minimum of \(F_{i}\). The entire algorithm flow is shown in Figure 2.
## 2 Simulation Principle and Model Structure  
### Model Structure  
In this study, COMSOL 6.1 software was used to model and simulate the designed multilayer structures. Figure 1a shows a schematic of a 3D unit cycle of an IDT/ZnO/SiO\({}_{2}\)/diamond structure. The terms h1, h2, and h3 denote the thicknesses of the ZnO piezoelectric film, the temperature complementary layer, and the IDT electrodes whose material is Al. The SAW device's wavelength (\(\lambda\)) is 2 \(\upmu\)m. Since the majority of the energy of the SAW device is limited to one wavelength, the diamond substrate is set to a fixed value of 4 \(\lambda\), and the thickness of the perfectly matched layer (PML) at the bottom is 1 \(\lambda\), which not only reduces the model size but also avoids the boundary reflection at the bottom [21]. This work applies a terminal rated at 1 V to one of the electrodes and grounds the other. The distance between each electrode in the model is 0.25 \(\lambda\), and the metallization rate (MR) is designed to be 50%. In this work, the model has a period of 1 \(\lambda\) along the x direction and the thickness is set to 0.2 \(\lambda\) along the y direction. For the design of the boundary conditions of the model, it is assumed that the infinite-length plate is modeled with periodic boundary conditions in both the x and y directions and that the bottom of the PML is fixed. Figure 1b shows a schematic of meshing of the model in COMSOL with a mesh size of \(\lambda\)/8.