esa-sceva/satcom-chunk-collection huggingface dataset:
# Semi-Supervised Learning Matting Algorithm Based on Semantic Consistency of Trimaps
## 2 Related Works
### Traditional Matting Methods  
Traditional matting methods usually rely on low-level features such as color, texture, and brightness to separate foreground objects. Depending on the specific separation strategy, these traditional methods can be divided into two categories: sampling-based methods and propagation-based methods.  
Sampling-based methods sample pixels in the known region and select the best foreground and background color pairs to estimate the alpha value of each pixel in the unknown region. There are various ways to sample pixel pairs and select appropriate color pairs. For example, Bayesian matting [4] uses the Gaussian Mixture Model (GMM) to learn the local color distribution and iteratively estimate alpha, foreground, and background using maximum likelihood criteria. Robust matting [5] samples a set of foreground-background pairs for each unknown pixel based on color similarity. Global matting [6] selects all pixels on the boundary of the known foreground and background regions to construct candidate pairs and finds the best color pairs.  
Propagation-based methods establish connections between adjacent pixels, then use different optimization strategies to propagate alpha values from the known region to the unknown region. Non-local propagation-based methods establish connections between non-adjacent pixels. Closed-form matting [7] is a classic propagation-based method, which derives a cost function based on the local smoothness assumption and estimates the global optimal alpha value by solving a sparse linear equation system. Poisson matting [8] assumes the local smoothness of the input image, then utilizes the gradient field and Dirichlet boundary conditions to solve the Poisson equation and estimates the alpha value.
## 2 Materials and Methods
### Analysis of Volatile Compounds by GC-MS  
Headspace solid-phase micro-extraction (HS-SPME) was used to extract the volatile profiles from the samples [3], which were then identified using gas chromatography (Agilent 7890B) coupled to a mass spectrometer (Agilent MSD 5977A), with an autosampler (Gerstel MPS 2 XL Twister, Linthicum Heights, MD, USA). A 15 mL SPME glass vial containing 1 g of sodium chloride and 3-octanol as internal standard (i.s.) (20 \(\mathrm{\SIUnitSymbolMicro g}\)/mL in sample, Sigma-Aldrich, St. Louis, MO, USA) was filled with 7.5 mL of sample supernatant. The sample was incubated at 40 \({}^{\circ}\)C for 10 min to equilibrate before volatile chemicals were extracted using HS-SPME with a DVB/CAR/PDMS fiber (50/30 \(\mathrm{\SIUnitSymbolMicro m}\), Supelco, Bellefonte, PA, USA) inserted in the vial's headspace for 45 min (40 \({}^{\circ}\)C). The conditions detected by the GC-MS were based on [PERSON] et al. [3]. Compounds were separated using a VF-WAXms (30 m \(\times\) 0.25 mm, i.d. 0.25 \(\mathrm{\SIUnitSymbolMicro m}\)), carrier gas (e.g., He) velocity at 1 mL/min, splitless mode, and 240 \({}^{\circ}\)C injection mode. The oven temperature program was initially set at 40 \({}^{\circ}\)C to 100 \({}^{\circ}\)C by 2 \({}^{\circ}\)C/min, then raised to 250 \({}^{\circ}\)C by 10 \({}^{\circ}\)C/min and held for 10 min. For MS conditions, the electron impact (EI) was set at 70 eF, the source temperature at 230 \({}^{\circ}\)C, and the scan time segments ranged from 35 to 550 m/z.  
The NIST database was used to identify volatile chemicals by comparing the mass spectrum and retention index (RI) by the Kovats Index (KI). RI values were calculated using n-alkane external standard solution C8-C20 (Sigma-Aldrich, St. Louis, MO, USA) with the same GC-MS conditions. The ratio of the GC peak area of each volatile in the total ion chromatogram to the peak area of the internal standard was used to determine the concentration of the analyses as a semi-quantification, as per the following equation [3; 17], where I.S. is the internal standard.  
\[\textit{Volatile compound}\ (\mu g/L)=\frac{\textit{Peak area of unknown}\cdot\textit{Concentration I.S.}\ (\mu g/L)}{\textit{ Peak area of I.S.}}\]
# Creating Automated Microsoft Bicep Application Infrastructure from GitHub in the Azure Cloud
## 4 Solution Architecture
### Azure Resource Provision
#### 4.2.1 Virtual Network  
The Azure Virtual Network (VNet) forms the backbone of the infrastructure, providing logical isolation and secure communication between resources. Networking is a foundational module in this design. It defines the Azure Virtual Network (VNet), subnets, and delegations required for the application. By parameterizing address spaces and subnet definitions, the networking module can adapt to the IP requirements of different environments while enforcing best practices such as subnet delegation for Azure Container Apps environments. This approach ensures that services can communicate securely within private networks, avoiding exposure to public internet endpoints where not required. It includes the following:  
* Definition of address space to segment IP ranges appropriately.
* Multiple subnets, with dedicated subnets for application containers; database services; Azure AI Search; and private endpoints.
* Delegated subnets where needed (for Azure Container Apps), enabling Azure to manage necessary network configurations while maintaining isolation.
* Integration with Azure Private DNS Zones for private name resolution of resources.  
Bicep modules define the VNet structure declaratively, including subnet configurations and delegations. This modular approach enables easy adjustment of address ranges or subnet assignments for different environments. The full source code is provided in Appendix A.
## 2 Power Scalable Digital Baseband  
### Optimizing Power  
Figure 1 shows a typical receiver. SNR\({}_{FE}\) is the SNR seen at the input of the ADC. It is the ratio of total signal power to the total noise power. It should not be confused with Eb/No typically used in communication theory literature. Input of the ADC, consists of the signal and the noise. We have assumed a 2 nd order Butterworth bandpass filter preceding the ADC. The noise present at the input of ADC also has out of desired signal band components. This makes SNR\({}_{FE}\) negative when noise is high. The packet error rate (PER) requirement translates to BER of \(6.25\times 10^{-5}\)[11]. \(f_{s}\) and \(Q_{dig}\) are chosen to minimize power while achieving target BER. More formally:  
\[\underset{Q_{dig},\ f_{s}}{\text{minimize}} \text{Power} = f(Q_{dig},f_{s})\] (1) subject to BER \[= h(Q_{dig},f_{s},\text{SNR}_{FE},\text{interference}) \tag{2}\] \[\leq 6.25\times 10^{-5}\]  
BER is independent of \(Q_{dig}\) and \(f_{s}\), if these parameters are chosen very high. In such a case the implementation of digital portion does not alter the SNR calculation of the receiver, _i.e._, SNR seen at the input of the ADC is almost the same as SNR seen at the input of the demodulator. But in doing so the digital portion is over-designed and hence wastes power. In order to achieve a given BER, there can be different combinations of \(Q_{dig}\) and \(f_{s}\) for a given SNR\({}_{FE}\) and interference levels, each with its own power cost. Values of \(Q_{dig}\) and \(f_{s}\) that minimize power as given in Equation (1) will be used. Furthermore, with varying values of SNR\({}_{FE}\) and interference, the optimal choices for \(Q_{dig}\) and \(f_{s}\) can vary, necessitating  
Figure 1: Cartoon of a typical receiver with variable \(f_{s}\) and \(Q_{dig}\) of the digital section.  
an adaptive resolution based digital section. For different levels of SNR\({}_{FE}\) and interference, the optimal design parameters (\(Q_{dig},f_{s}\)) will be stored in the LUT and used to configure the receiver. Finding a closed form expression for the function \"\(h\)\" in Equation (2) is hard due to the non-linear relationships. Coarser the ADC quantization \((Q_{dig})\), harder it becomes to analyze the signal. Hence BER is found through MATLAB simulations, for different (\(Q_{dig},f_{s}\)) values. The power function in Equation (1) is obtained by Synopsys Prime Power for different \(Q_{dig}\) and \(f_{s}\) values. Finally, the optimum \(Q_{dig}\) and \(f_{s}\) values are obtained by a simple search over design space.
## 3 Results
### Cell Viability  
The absorbance of 2D-cultured sPDLSCs increased significantly over time, from 0.26 to 2.60 (Figure 3). In particular, it increased exponentially on day 10 (\(p<0.05\)). By contrast, the absorbance of 3D-cultured sPDLSCs decreased over time, from 0.31 to 0.25. This decrease was significant from days 3 to 5 (\(p<0.05\)), but not between days for the first 3 days (\(p>0.05\)). At day 10, the absorbance increased slightly, but not significantly (\(p>0.05\)).  
The 2D-cultured sPDLSCs did not show a significant decrease in viability; however, the cell viability decreased significantly in the center of the spheroids in the 3D culture (Figure 4).  
Figure 4: Live/dead assays. The 2D-cultured sPDLSCs did not show a significant decrease in viability; however, the cell viability decreased significantly in the center of the spheroids in the 3D culture. (**A–F**) Live/dead assay results for 2D-cultured sPDLSCs at days 3, 5, and 7 (magnification, \(\times 100\)). The number of cells increased, and no dead cells were observed. (**G–L**) Live/dead assay results for 3D-cultured sPDLSCs at days 3, 5, and 7 (magnification, \(\times 100\)). The number of dead cells in the center of spheroids increased with decreasing spheroid diameter.  
Figure 3: Results of CCK-8 assays. The 2D-cultured sPDLSCs showed an increase in absorbance and 3D-cultured sPDLSCs showed a significant decrease in absorbance from day 1 to day 5. The absorbance of 3D-cultured sPDLSCs increased non-significantly on day 10. * \(p<0.05\); Scheffé post hoc test following one-way ANOVA.
## 3 Results and Discussion
### Abrasion Resistance
#### 3.4.1 Surface Layer  
Figure 7 displays the abrasion loss per unit area on the surface layer of IRPC with five replacement rates under sulfate attack. It is clearly reflected in Figure 7 that the abrasion loss per unit area of the specimens decreased after 30 sulfate wetting-drying cycles but increased significantly after 60 wetting-drying cycles, except for the specimens with 75% and 100% RCA\({}_{2}\) replacement rates. This is because the sulfate erosion products produced in the early stage of sulfate attack had the filling effect on the pores and cracks of the specimen [42]. In addition, the secondary hydration reaction occurred and improved the strength of the specimen [43]. Therefore, the compactness and abrasion resistance of the surface layer were enhanced. After 60 wetting-drying cycles, the volume of sulfate erosion products accumulated inside the specimens expanded, resulting in micro-cracks in the specimens. The expansion stress made the surface mortar become brittle, resulting in a decline in abrasion resistance.  
Figure 6: Mass-loss rate of IRPC with variable replacement rates after different sulfate wetting-drying cycles.
# Simulation of AEBS Applicability by Changing Radar Detection Angle
## 5 Conclusions  
In this study, traffic accidents occurring on real roads were reinterpreted using the traffic accident analysis simulation program PC-Crash. In this process, we examined the changes that occur when the AEBS is applied while changing the radar detection angle to a traffic accident that has already occurred.  
1. First, to evaluate the significance of PC-Crash simulation, pre-simulation was performed based on the Euro NCAP AEBS test standard. As a result, in the case of \"City\" and \"Inter-urban\" simulation scenarios, collision avoidance or collision mitigation was shown regardless of the radar angle and the speed of the test vehicle. In the case of the pedestrian scenario (AEB VRU-Pe), different results were obtained depending on the radar angle, and in the case of the radar detection angle of 4\({}^{\circ}\), the pedestrian dummy could not be detected.
2. After confirming the validity of the PC-Crash program during pre-simulation, individual accidents were reinterpreted by applying AEBS using actual traffic accident data (ACCC). As a result, pedestrian collision and rear collision cases showed 100% collision avoidance, but in the instance of intersection and lane change accidents, collision avoidance was not satisfactory.
3. In addition, further simulations focusing on intersection accidents showed the highest rate of collision avoidance when the radar detection angle was increased from 20\({}^{\circ}\) to 30\({}^{\circ}\). Through this study, we showed that application of the AEBS can reduce the possibility of traffic accidents by using the simulation tool PC-Crash. In the future, the AEBS evaluation items used in this study and the Euro NCAP need to be reviewed to see if they meet the national traffic conditions and safety evaluation standards. Additionally, it is necessary to improve the reliability of potentially reducing traffic accidents with the AEBS by applying the actual traffic accident database (ACCC) to various applications of AEBS and sensor types, as well as radar detection angles.  
[PERSON], [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] contributed reagents/materials/analysis tools; [PERSON], [PERSON], and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 18: Collision avoidance rates from additional simulations.
# Sensorless Air Flow Control in an HVAC System through Deep Learning
## 1 Introduction
Through this method, we can operate the HVAC system without a static pressure sensor utilizing other available sensors such as temperature, fan speed, and airflow sensors, hence reducing costs for HVAC system operation. In addition, we have also demonstrated an innovative technique to replace the physical sensor value with the logical Deep Neural Network (DNN) method through the proposed method. In terms of the immediate value and the future applicability of the model, the contributions of this paper are as follows:  
1. This paper demonstrates that a Long Short-Term Memory (LSTM)-based predictor can replace a static pressure sensor component using a real AHU. This approach can reduce costs in many ways: the hardware cost ($500-600 per sensor) for each AHU, the installation and operational cost, and the maintenance cost. 2. This paper explores the impacts of various input parameters and processing steps on the performance of the LSTM-based multivariate time-series prediction. It provides insight into optimization that can be applied to similar systems. 3. This paper shows that the developed prediction model can be applied to HVAC systems with different capacities and in different seasonal conditions. The remainder of this paper is organized as follows: Section 2 summarizes the application of artificial intelligence to HVAC systems and the research on how to replace sensors. Section 3 describes the deep learning model for static pressure prediction. The model configuration and selected input and output elements are explained. Section 4 describes the data used in the experiment and the hyperparameters applied to the proposed model. In this section, the results of the experiment are analyzed and discussed. Finally, the conclusions of this paper are given in Section 5.
# A Vision Detection Scheme Based on Deep Learning in a Waste Plastics Sorting System
## 3 Materials and Methods
### Workflow of Plastic Dynamic Detection
#### 3.4.4 Calculation of Plastic Velocity  
The configuration between the camera and the robotic arm in this paper is eye-to-hand. After the plastic leaves the camera's field of view, it still needs to be transferred a certain distance to reach the grasping baseline. Therefore, it is necessary to calculate the relative motion velocity of the plastic to ensure accurate grasping. In this paper, the velocity is solved with the help of the displacement of the plastic between the two aforementioned virtual detection lines. Assuming that the transmission direction and vertical direction of the conveyor belt are the x-axis and y-axis, respectively, theoretically, the plastic will not be displaced along the y-axis. During the time \(t_{1}\) to \(t_{n}\), we save the data in the format \([(x_{1},t_{1}),(x_{2},t_{2}),...,(x_{n},t_{n})]\). Since we only consider a small distance directly below the camera, the velocity can be solved based on the first and last points:  
\[v=\frac{x_{n}-x_{1}}{t_{n}-t_{1}}. \tag{16}\]  
When the measurement error is large, the error will be further amplified after some time, leading to the failure of the grasp. The data measurement noise of our system mainly comes from the prediction error of object detection. We adopt Kalman filtering to filter the measured velocity. It outputs a more reasonable predicted value based on the system variables, and this value is used as the final relative motion velocity of the plastic.
## 4 Conclusions  
This study investigated the zinc chelation capacity of collagen peptide fractions from tilapia skin with different molecular weights and the bioaccessibility of their zinc complexes. The results showed that the low molecular weight peptide fraction P1 with diverse metal-chelating amino acid residues participated in the chelation, exhibited stronger zinc chelation capacity than P3. The FTIR spectra of the three peptide-zinc complexes with different characteristics showed the amino group was the main metal-chelating ligands. Compared to the other two complexes, the P1-zinc complex possessed higher zinc solubility under neutral pH conditions, stronger gastric stability and zinc dialyzability during gastrointestinal digestion. High molecular weight peptide fraction can limit zinc dialyzability from complexes, which can influence zinc bioaccessibility, and this effect was more pronounced in the P3-zinc complex. Therefore, peptide fractions with low molecular weight can improve gastric stability and zinc absorption during intestinal digestion. In _vivo_ studies focusing on determining their bioaccessibility of zinc in complexes with low molecular weight peptide fractions and their practical ability to enhance zinc absorption will be further carried out.  
Conceptualization, L.C.; Data curation, L.C.; Formal analysis, L.C.; Funding acquisition, X.S.; Investigation, L.C.; Methodology, L.C.; Project administration, X.S.; Software, L.C.; Supervision, X.S. and G.X.; Validation, L.C.; Visualization, L.C.; Writing--original draft, L.C.; Writing--review & editing, L.C. and G.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China, grant number 2018 YFD0901103; Marine and Fisheries Bureau of Haikou, grant number HHCL201804; Scientific Research Foundation of Hainan University, grant number KYQD1662.  
This work was supported by the funding of the National Key R&D Program of China [No. 2018 YFD0901103]; the Marine and Fisheries Bureau of Haikou [No. HHCL201804]; and the Scientific Research Foundation of Hainan University [No. KYQD1662].  
We declare that there are no conflicts of interest.
# Infrared Image Superpixel Segmentation Based on Seed Strategy of Contour Encoding
## 3 Methodology  
Superpixel segmentation is a process of pixel labeling, where each pixel has a unique label. In dense regions of the image, more superpixels are required to adhere to the object's boundaries. In contrast, fewer compact superpixels should be distributed in sparse regions. In order to adapt the superpixels to the image content while increasing boundary adherence, we propose an IR image superpixel segmentation method based on a seed strategy of contour encoding. Our approach begins by encoding the contour map of the image, which is introduced in Section 3.1. Based on the coded result, a rough homogenous map \(\psi\) is obtained. Then, we present a seed sampling strategy extracted from \(\psi\) in Section 3.2, which initializes our cluster and makes the segmentation insensitive to disconnected contours. In Section 3.3, clustering is performed based on the initial seed set and weighted similarity evaluation. The optimal interval is applied to update the seed position, instead of the entire superpixel region. Finally, in Section 3.4, high-quality superpixels that can adapt to different shapes are obtained by refining the initial superpixel map based on a graph method. The block diagram of the proposed methodology is shown in Figure 1.
## 1 Introduction
On the other hand, EMD can filter the signal adaptively [22]. By this method, different features in the original sequence can be filtered out step by step, and the corresponding subseries can be regarded as intrinsic mode functions (IMFs). Unfortunately, its decomposition process may suffer from the problems of model mixing and end effect. By adding many Gaussian white noise samples in EMD, EEMD has been developed [23]. However, it has the problem in the determination of noise amplitude and ensemble number. Nevertheless, these decomposition algorithms still have been successfully applied in the traffic flow prediction. For example, [PERSON] et al. [24] proposed a hybrid forecasting method of short-term traffic volume based on EMD and the improved SVM. The forecasting results verified that EMD could improve accuracy significantly. [PERSON] et al. [25] adopted a new hybrid model for traffic volume prediction by using the combination of EEMD and SVM. The results showed this model had superior performance over the single SVM. [PERSON] et al. [26] presented a hybrid prediction model based on the improved complete EEMD (ICEEMDAN) algorithm, the kernel online sequential extreme learning machine (KOSELM), and the ARIMA model. The forecasting accuracy had been improved significantly. Despite these applications, the decomposition-based methods still go through various challenges. To further enhance the accuracy of the traffic volume prediction, it is necessary to find new methods to deal with short-term traffic volume data. This paper proposes a novel time varying filtering based empirical mode decomposition (TVF-EMD) algorithm, which vividly describes the time-varying characteristics of data and overcomes the occurrence of mode mixing [27]. Specifically, TVF-EMD is firstly adopted to decompose the short-term traffic volume data and obtain multiple subsequences. Secondly, the LSSVM model is adopted for each subsequence to perform the final prediction. On this basis, five evaluation indexes including the mean absolute error, mean relative percentage error, root mean square error, root mean square relative error and equal coefficient are used to systematically evaluate the forecasting results. Meanwhile, the comparison of the proposed method with other forecasting models including EMD-LSSVM, LSSVM, and ARIMA is conducted. Finally, some conclusions are provided. The rest of this paper is organized as follows: In Section 2, TVF-EMD and LSSVM are briefly discussed. Simultaneously, the structure and procedure of the proposed method are described in detail; In Section 3, two case studies are performed and the effectiveness of the proposed method is analyzed and discussed; In Section 4, some conclusions are summarized.