esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work and Contributions
### Visual Task Analysis  
Existing methods for medical image analysis apply SISR models to construct HR images followed by using the constructed HR images as input to individual models for individual tasks. For example, various methods applied deep learning-based SISR models to LR images and used the constructed HR images as input to separate segmentation (e.g., U-Net network [23; 28; 29]) and classification (e.g., VGG network [23; 28] and DenseNet network [30]) models. This traditional approach for analysis is not efficient because it involves unnecessary repetitions of learning (end-to-end) multiple task-specific models in isolation.  
Contrary to previous works that separate image enhancement from other visual tasks, we propose to use our customized SISR model as a shared representation to simultaneously learn multiple subsequent visual tasks. Specifically, the weights of our SISR model, which learns the mapping from LR to HR, are directly used to simultaneously learn tasks such as image segmentation and classification. Using the proposed SISR model as a shared backbone improves generalization and prevents unnecessary repetitions of learning visual task models in isolation. This can lead to a decrease in resource utilization and training time, and boosts the use of deep learning models in limited-resource settings.
# Progress of Process Monitoring for the Multi-Mode Process: A Review
## 4 Steady-State Mode Process Monitoring Methods
### Supervised Process Monitoring Method  
A supervised process is where the mode type and process are known, or can be simply divided by a clustering algorithm. Existing data-based monitoring methods for supervised processes include the expert system method, the overall modeling method, the adaptive modeling method, the multiple-model method and the K-nearest neighbor method.
## 2 Case Study  
In the framework of the IMPReSA Project, funded by the Emilia Romagna Region, an innovative cementitious material that uses synthetic fibers as reinforcement has been developed; non-recyclable plastic particles are retrieved from artificial turf carpets from soccer pitches at the end-of-life stage. From artificial turf carpets at the end-of-life stage, it is possible to recover additional raw materials in different percentages to use as by-products, stimulating the emergence of a supply chain network between industries based on the exchange of materials and by-products, according to the industrial symbiosis approach. Table 1 lists the recoverable materials.  
According to IS taxonomy [19], the network, built up around an anchor company and involving at least three actors, can be considered and treated as an evolving self-organized IS system. Figure 1 shows the network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**By-Products** & **Percentage [\%]** & **Total Amount per Year [tons]** \\ \hline PE-PP fibers & 12.5 & 173.15 \\ Rubber & 34.5 & 699.53 \\ Silica sand & 50.5 & 477.90 \\ Bituminous membrane & 2.5 & 34.63 \\ \hline \hline \end{tabular}
\end{table}
Table 1: By-products recoverable from artificial turf carpets at the end-of-life stage.  
Figure 1: Schematic diagram showing the industrial network and circularity interactions among companies.  
Company 1 is an industrial actor that collects and treats waste, and can exchange both by-products and raw materials with other actors. Company 2 collects and treats artificial turf carpets by mechanical sorting to wash and separate all the materials. Company 3 could adopt the bituminous membrane scraps for road asphalt mixtures while Company 4 could recycle the rubber to produce playground surfaces. Company 5 uses silica sand in general construction activities. Company 6 aims to use PE-PP fibers as dispersed reinforcement in a cementitious matrix to innovate and make their flagship products greener. Closing the loop, Company 1 produces artificial turf carpets, using recovered sand and rubber.
## 2 Materials and Methods
### Battery Swapping Station (BSS) Configuration Optimization Model
#### 2.5.2 Objective Function  
The objective function of our optimization model is to minimize the BSS annual costs \(F\); thus, it can be formulated as follows:  
\[\text{min}F=F_{P}+F_{O} \tag{16}\]  
where the first component \(F_{P}\) is the cost at the planning stage, and the second component \(F_{O}\) is the cost at the operating stage.  
1. **Costs at the planning stage** At this stage, the decision variable \(N_{B}\) is the total number of stock battery blocks in BSS. The costs include the ET battery blocks' equivalent annual costs of initial investment \(F_{Inv}\) and the annual operation and maintenance costs \(F_{OM}\). \[F_{P}=F_{Inv}+F_{OM}\] (17) \[F_{Inv}=c_{B}N_{B}E_{B}\frac{r(1+r)^{L_{B}}}{(1+r)^{L_{B}}-1}\] (18) \[F_{OM}=c_{Bm}N_{B}E_{B}\] (19)
2. **Costs at the operating stage** At the operating stage, the charging scheduling of the BSS should consider the characteristics of ToU price \(c_{t}\) and time-dependent \(BSD\). In Equation (20), the only term is the electricity purchase cost of BSS. The decision variable \(N_{C,t}\) is the total number of battery blocks in the charging state at time slot \(t\). \[F_{O}=\sum_{t=1}^{T}c_{t}N_{C,t}P_{c}\] (20)
## 4 Discussion
### Conclusions  
The objective of the presented research was to analyze the potential use of intelligence in the service-layer architecture of new-generation communication networks for optimizing quality on multimedia services. The work presented in this paper demonstrates the possibility of developing a DRL model through which we can guarantee an adequate QoE. Having a series of previously configured profiles and managing the bitrate adaptation only as an action are enough to create a control pattern in content transmission systems. This is demonstrated from an uncontrolled setup with no previously available dataset for training.  
The developed reward functions were sufficient to represent an optimization in the system. The initial training phases returned negative rewards due to their exploratory nature. However, the last moments of training offered high reward values. This demonstrated the optimizer ability to adapt to the various impairments situations induced, offering the best possible action under every condition. Finally, an improvement over the system itself is observed in the quality of the received image, that is, the MOS. During the training phase, there were 25% values for a bad MOS range. However, this percentage was improved using the optimizer, reducing up to 10% of the values. Furthermore, when the optimizer was enabled and trained 90% of the values were in the high-quality range of MOS.  
However, this is still a simulated scenario prior to real deployment in a real 5G environment. Commercial ideas cannot be approached due to initial research. Furthermore, we demonstrate the optimization task with one single network link and one single optimized transcoder. Despite these drawbacks, we can confirm the overall good performance of  
Figure 5: MOS distribution: (**left figure**) Values obtained for the application without optimizer. (**right figure**) Optimizer applied to the service.  
combined RL and DL, making it possible to automate the optimization of multimedia content delivery over virtualized networks.
# ERMOCTAVE: A Risk Management Framework for IT Systems Which Adopt Cloud Computing
## 3 EMNOCTAVE Method
### EMNOCTAVE Phase 1  
In the phase, threat profiles for critical assets are reported as a result. The profiles are used to identify vulnerabilities and risks. The phase proceeds in the following 8 processes.  
**P1.1**: Objective setting: this process defines core objectives of the organization who uses cloud computing services. From the objectives, the reason to use the cloud computing services is derived.
**P1.2**: Internal environment: the main roles in the organization are described in detail to identify assets and vulnerabilities in the following processes.  
* Identify assets: this process creates a list of assets. The following question should be answered.
* what are important assets?
* Identify current security practices: this process creates a list of security practices in use. The following question should be answered:
* Which cloud functionalities are used to protect the important assets?
* Identify critical assets: this process selects important assets critical to the objectives. The following questions should be answered.
* Which assets largely impact on the objectives if they are disclosed to unauthorized people?
* Which assets largely impact on the objectives if they are modified without authorization?
* Which assets largely impact on the objectives if they are lost or unavailable?
* Describe security requirements for critical assets: this process clarifies security properties of critical assets. The following questions should be answered.
* Is the critical asset proprietary or sensitive?
* What is the security requirement for the critical assets? Are confidentiality, integrity, or availability important for them?
* Identify current vulnerability of the organization: this process creates a list of vulnerabilities using the following question.
* Which damage on the assets injures the objectives?
* Create threat profiles for critical assets: the goal of this process is to identify threats that affect critical assets through the vulnerabilities; the following question should be answered.
* Which potential threats have a non-negligible possibility?
## 3 Simulation and Results
### Comparison of PSNR and SSIM Results  
The performance of the proposed algorithm was quantitatively evaluated using the peak signal-to-noise ratio (PSNR) and structure similarity index (SSIM). Table 2 shows the result of the proposed algorithm and other existing methods on distorted images with AWGN values of 5-30. Figure 10 shows a graphical representation of Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Image**} & \multirow{2}{*}{**AWGN**} & \multicolumn{2}{c}{**WF**} & \multicolumn{2}{c}{**2 DACSWF**} & \multicolumn{2}{c}{**SBITV**} & \multicolumn{2}{c}{**FMGF**} & \multicolumn{2}{c}{**PFA**} \\ \cline{3-11}  & & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** \\  &
## 3 Results and Discussion
### Fuel Consumption and Electric Driving Shares
During the summer tests, the average real-world fuel consumption is about 4.6 L/100 km and in autumn it is 5.3 L/100 km, which corresponds to an increase of 15%. Concerning the fuel consumption for urban vehicle speeds, the average value of the summer tests is 4.2 L/100 km and for the autumn tests the average fuel consumption value is 5.1 L/100 km. For rural vehicle speeds, the average fuel consumption of the summer tests is 4.2 L/100 km and 4.6 L/100 km for the autumn tests. Average motorway fuel consumptions are 6.5 L/100 km (summer tests) and 6.7 L/100 km (autumn tests). The higher variance of the motorway fuel consumption in the summer tests can be explained by the greater fluctuations of the electric driving shares for motorway driving (Table 4). However, the average values are nearly identical. The fuel consumption increases for driving the PHEV in autumn compared to summer are approx. 10% for rural vehicle speeds and 3% for motorway vehicle speeds. Regarding urban vehicle speeds, the average fuel consumption of the autumn tests is about 21% higher than in the summer tests. As the winter tires increase the fuel consumption independently from the vehicle speed, the authors assume that the cold start and warming-up phase of the ICE and the energy demand of auxiliary consumers are the main influencing factors for the significant fuel consumption increase at urban vehicle speeds.
## 3 Methodology and Data Sources  
### Modeling Framework  
The basic framework and procedures for the GIS-based integrated method are illustrated in Figure 2. First, the frequency and characteristics of urban pollution episodes in Changchun during the SOB season of 2020-2021 were determined based on hourly air quality and meteorological data. Then, hourly transport pathways and wind-field grids of the external pollution sources during pollution episodes were reconstructed using the regional gridded meteorological data archives from the National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS). Third, the preprocessed satellite wildfire data on the spatial distribution of farmlands were screened by hourly wind-field grids to identify external SOB pollution sources. Fourth, a two-stage spatiotemporal multi-box based on hourly wind-field grids was developed to estimate the contribution of external SOB pollution sources to urban pollution episodes. Finally, we analyzed the impact of individual factors, including the burning area of SOB sources, their transport distance, the average PBLH along the transport pathway, urban PBLH, and urban wind speed.
## 2 Materials and Methods
### Methods
#### 2.3.4 Bayes-Copula Conditional Probability Model  
In order to quantitatively assess the impact of CDHEs on rubber plantations GPP and to reveal its potential loss, we constructed a Bayes-Copula conditional probability model between the CDHI and GPP loss in rubber plantation ecosystems. The probability of GPP loss refers to the possibility that, under specific compound drought and heatwave scenarios, the SAGPP of rubber plantation ecosystems is less than or equal to the loss threshold _sagpp_. Firstly, we used Pearson correlation analysis to determine the correlations between the optimal index combinations and selected appropriate Copula functions from Table 1. Secondly, we applied the kernel density estimation method to determine the marginal distributions of the CDHI and SAGPP. Finally, we constructed a conditional probability model between the CDHI and SAGPP and evaluated the goodness of fit for each Copula function using the squared Euclidean distance. Taking the mild compound drought and heatwave scenario as an example, the specific calculation formula is shown in Equation (6):  
\[\begin{array}{l}P(\text{SAGPP}\leq sagpp|-0.5\leq\text{CDHI}\leq-1)\\ =\frac{C(F_{\text{CDHI}}(-0.5),F_{\text{SAGPP}}(0))}{F_{\text{CDHI}}(-0.5)}- \frac{C(F_{\text{CDHI}}(-1.0),F_{\text{SAGPP}}(0))}{F_{\text{CDHI}}(-1.0)}\end{array} \tag{6}\]  
where \(F\) is the marginal distribution function, and \(C\) is the Copula function.
# Determination and Risk Assessment of Flavor Components in Flavored Milk
## 3 Results and Discussion
### The Screening of Flavor Concerned Component  
The screening process related to the acute toxicity dosage grading standard in GB 15193.3-2014, and took LD 50 \(<\) 3000 as the basic screening criterion under the following two conditions: (I) detection rate greater than 10% (frequency of detection in all flavor samples) and LD 50 \(<\) 3000. (II) The detection rate was lower than 10%, the components of seven favorite flavor samples accounted for more than half of the total, and LD 50 \(<\) 3000. The fifteen flavor ingredients of concern were 2-methylpropanal, ethyl 3-methylbutyrate, 1-hexanol, allyl hexanoate, 2,3,5-trimethylpyrazine, furfural, benzaldehyde, linalool, 5-methylfurfural, benzyl acetate, methyl salicate, benzenemethanol, maltol, methyleugenol, and phenol 2-methoxy-4-(2-propenyl)-, as shown in Table 3. The linear equation, R2, and detection limit of flavor components of concern are shown in Tables S4 and S5.  
\begin{table}
\begin{tabular}{r r r r r r r r r r} \hline \hline
**Number** & **RT/min** & **CAS Number** & **Name** &
\begin{tabular}{c} **Molecular** \\ **Formula** \\ \end{tabular} & **Calculated RI** & **Library RI** & **LDS0/mg/k** & **ADI \({}^{*}\) (ug kg kg\({}^{-1}\), bw d \({}^{-1}\))** & **TTC (ug kg\({}^{-1}\), bw d \({}^{-1}\))** \\ \hline
1 & 6.599 & 78-84.2 & 2-methylpropanol & C\({}_{4}\)H\({}_{0}\)O & 812 & 820 & 960 & 500 & 1800 \\
2
###### Abstract  
A finite element model, which includes reservoir rock, cement stone, casing, and perforation channels, was developed. The purpose of the study is to create a geomechanical model of the zone around the well, which includes support elements and perforation channels. This model will help predict changes in the productivity coefficient of a terrigenous reservoir and determine the most efficient mode of operation of a producing well. In order to exclude the stress concentration within the casing-cement stone and cement stone-rock, the numerical model applies contact elements. As a result, structural elements slip, while the stresses are redistributed accurately. The numerical simulation of a stress state in the near-well zone was carried out by using the developed model with differential pressure drawdown on the terrigenous reservoir, one of the oil fields in the Perm region. It is shown that the safety factor of the casing reaches roughly 3-4 units. The only exceptions are the upper and lower parts of the perforations, where this parameter is close to one unit. The safety factor of cement stone accounts for 2-3 units. However, parts with its lowest value (1.35) are also concentrated near the perforation channels. In order to analyze the change in permeability, the dependence of the safety factor on effective stresses was taken into account. Therefore, it was found that, in the upper and lower parts of perforations, the stresses decreased, while permeability rose by up to 20% of the initial value. An increase in differential pressure drawdown, on the contrary, can lead to a permeability reduction of 25%, especially in the lateral parts of the perforations. Areas of rock destruction under tensile and compressive forces were identified by using the Mohr-Coulomb criterion. It is estimated that with an increase in pressure drawdown, the areas of rock destruction under tensile force disappear, while the areas of rock destruction under compression increase. After further analysis, it was found that, with the maximum pressure drawdown of 12 MPa, the well productivity index can decrease by 15% due to the reservoir rock compaction.  
stress-strain state; permeability; numerical finite element model; cement stone; near-well zone; differential pressure drawdown +
Footnote †: journal: Applied Sciences  
1
# Real-Time Monitoring of Indoor Air Quality with Internet of Things-Based E-Nose
## 1 Introduction
The rapidly emerging IoT concept supports many different areas and applications including health, education, agriculture, industry and environmental monitoring. Real-time environmental monitoring and analysis is an important area of research for IoT [16]. IoT provides remarkable features to smart cities which improve environmental quality control, innovative real-life solutions and services. The smart home is an indispensable element of smart cities. In the future, smart homes will be fully managed through smartphone applications and will include IoT wearable devices [17] that are supported by microsensors. To date, several have been conducted in the literature with the aim of establishing real-time monitoring solutions for air quality analysis. Numerous IoT-based air quality monitoring systems using micro-sensors for data collection have been proposed, including open source technologies for data processing and transmission [18]. According to an IAQ analysis [19], it has been reported that indoor PM\({}_{2.5}\) pollution mainly comes from outdoor sources. In another study, three typical activities such as a having fireplace in the house, cooking with kitchen appliances and toasting were investigated, and indoor pollutant levels such as PM\({}_{2.5}\), PM\({}_{10}\) and VOC were found to be above the limit values [20]. Monitoring of the PM concentration for 63 participants between the ages of 18-65 years in the Perth metropolitan area indicates that the PM concentration increases the number of heartbeats by 4-6 beats per minute (ppm) and has a significant effect on systolic blood pressure (SBP) [21]. An IoT-based IAQ measurement system was created using the WEMOS D1 mini microcontroller and the PMS5003 PM sensor. This system allows the household individual to intervene for ambient assisted living (AAL) [22]. In this study, a low-cost, portable, IoT-based and real-time monitoring system that can measure ambient air quality with a range of sensors is proposed. For the proposed system, an Android interface has been designed primarily by the Blynk platform. Then, with the embedded architecture ESP32 module, the controller unit of the system is created. Using this IoT controller with an internal Wi-Fi module, all measured air quality data are displayed in the mobile app and these data are stored on a cloud server. The mobile interface provides users with numerical and graphical data on contaminating gas concentrations, temperature and humidity. The proposed e-nose measurement system sends a notification to the users via the mobile application if any gas concentration levels reach health-threatening values. In this way, households can take measures to reduce gas concentrations when necessary. The data can be viewed graphically through the mobile interface which allows users to observe the effects of activities such as sleep, cleaning and cooking on the gas concentration. In addition, the IoT-based e-nose air quality measurement system has a low-cost (about $100), easy-to-install and open-source feature produced by a DIY approach.
# Efficient Design of Reversible Adder and Multiplier Using Peres Gates
## 5 Simulation and Analysis  
As a one-step forward in this experimentation, different 4-bit adder configurations were simulated and verified, as shown in Figures 20-23, using ripple carry, carry look ahead, carry save, and carry skip approaches, respectively. Similarly, the designed circuits are simulated to validate their functionality and to analyze the performance in terms of speed and quantum cost. The simulation results demonstrate the successful addition of binary numbers with reversible characteristics.  
Figure 21: Four-bit carry look-ahead adder using Peres gates.  
Figure 20: Four-bit ripple carry adder using Peres gates.  
After simulating all the sub-circuits like reversible AND gate, OR gate, half adder, and full adder, various adders, as mentioned above, were simulated and verified for their functional validity.  
The next step is to analyze the 4-bit multiplier circuits using the ripple carry and carry save approach, as shown in Figures 24 and 25, also validated successfully.  
Figure 23: Four-bit carry skip or carry bypass adder using Peres gates.  
Figure 22: Four-bit carry-save adder using Peres gates.  
Figure 24: Four-bit multiplier using ripple carry adder.  
Figure 25: Four-bit multiplier using carry-save adder.  
The analysis encompasses key aspects such as speed, quantum cost, and adhering to the principles of reversible logic.  
The simulation of the proposed ripple carry adder (RCA) design using Peres gates was conducted with meticulous attention to detail, employing a comprehensive simulation setup. The objective was to validate the functionality and assess the performance of the reversible circuit under various input conditions.  
The designs were simulated using RCViewer+ and ModeSim- Intel FPGA starter Edition 10.5b, incorporating models of Peres gates and relevant circuit elements for 1,4,8,16-bit adders, 1,2,4-bit multipliers, and 2,4-bit Vedic multipliers. Simulations were executed with various input scenarios covering a range of binary input cases, including cases with and without carry-in adders, as shown in Figures 26-32.