esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Principle
### Sector Scan Imaging Based on Vector Coherence Factor  
The processing object of sector scan imaging coherent weighting is an A\(-\)scan signal at each emission angle. For the sector scan emission angle \(\theta_{k}\), the flow chart of the coherent weighting of the A\(-\)scan signal is shown in Figure 3.  
At the \(k\)th emission angle, aperture data \(\{S_{1},\ldots,S_{N}\}\) represents the RF signals received by the \(n\)th array element. According to circular statistics, the instantaneous phase angle of \(\{S_{1},\ldots,S_{N}\}\) can be regarded as the angles sample \(\{j_{1},\ldots,j_{N}\}\). Then the unit circle is composed of cosine and sine values of the instantaneous phase. Under this condition, as shown in Figure 3, the process of the VCF weighting for sector scan images is as follows:  
Step I: By recession delay processing for the aperture data \(\{S_{1},\ldots,S_{N}\}\), the signal amplitude slice set \(\{S_{\tau}1,\ldots,S_{\tau}N\}\) of delayed data is summed and forms an A\(-\)Scan signal for imaging in \(k\)th emission angle;  
Step II: Before recession delay, extract the \(\{\cos\!\!\!\!\!\!\!\!  
The instantaneous phase \(\varphi_{n}\) can be expressed as:  
\[\varphi_{n}=\arctan\frac{imag}{real}=\arctan\frac{h(S_{n})}{S_{n}} \tag{5}\]  
According to Equations (3) and (4), another expression of \(\mid\!R\!\mid\) of the mean resultant vector length in sector scan imaging, i.e., the VCF of the A\(-\)Scan signal for \(\theta_{k_{s}}\) is as follows:  
\[VCF_{\theta_{k}}=\sqrt{\left(\frac{1}{N}\sum_{n=1}^{N}\cos\varphi_{\tau n} \right)^{2}+\left(\frac{1}{N}\sum_{n=1}^{N}\sin\varphi_{\tau n}\right)^{2}} \tag{6}\]  
Then, the A\(-\)scan signal with emission angle \(\theta_{k}\) is weighted by VCF, and the expression of the sector scan image is:  
\[I_{\theta_{k}-VCF}=I_{\theta_{k}}\cdot VCF_{\theta_{k}} \tag{7}\]  
According to Equations (3)-(6), it is not difficult to find that VCF is a method to reflect the degree of phase coherence through the mean resultant vector length, and its value range is [0, 1]. At a certain emission angle, when the phase of the delayed signals of all array elements is highly consistent, the VCF tends to 1; on the contrary, when the phase of the delayed signal is randomly distributed, the VCF tends to 0.  
Furthermore, VCF can be used as a weighting factor for beam\(-\)forming image reconstruction, and the factor itself can be directly used for imaging. At the same time, the adaptive beam\(-\)forming process can also be performed by multiplying the conventional image and the coherent image.
## Materials and Methods
### Model Development  
#### 2.2.1 Model Structure  
TRIPLEX 1.0 integrates three well-established models: 3-PG [40], TREEDYN3.0 [41], and CENTURY4.0 [42]. TRIPLEX 1.0 can simulate forest growth and key C and N cycling processes which mainly include forest production, particularly GPP, net primary productivity (NPP), and NEP (see Figure 1) [28; 30; 31]. Model simulations were conducted using a monthly time step, and monthly GPP was calculated from photosynthetically active radiation (PAR), leaf area index (LAI), forest age, mean air temperature, soil water, and the percentage of frost days per month. Boreal forest NPP was estimated using Equation (1):  
\[\mathrm{NPP}=\mathrm{GPP-Ra} \tag{1}\]  
where Ra is autotrophic respiration, which is estimated using N, air temperature, and component C pools (e.g., wood, branches, foliage, coarse roots, and fine roots) [30]. NEP is calculated as GPP subtracted by ecosystem respiration (ER), which is comprised of Ra and heterotrophic respiration (Rh). Rh is calculated by subtracting root respiration from soil respiration, expressed as the exponential function of temperature and Q\({}_{10}\)[30]. Annual values are estimated by summing monthly results. In TRIPLEX 1.0, soil C and N are based on the CENTURY soil decomposition sub-model [42]. This sub-model provides realistic estimates of both C and N mineralization rates for Canadian boreal forest ecosystems [43]. More detailed information on TRIPLEX 1.0 is described in [PERSON] et al. [28] and [PERSON] et al. [29]. Although the insect sub-model is designed to resolve how C dynamics respond to SBW outbreaks, many potentially confounding factors, such as climate change, forest fires, and land-use changes, are not directly considered.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Plot ID** & **QC\_1** & **QC\_2** & **QC\_3** \\ \hline Longitude & 67.82\({}^{\circ}\) W & 68.12\({}^{\circ}\) W & 67.66\({}^{\circ}\) W \\ Latitude & 49.58\({}^{\circ}\) N & 49.71\({}^{\circ}\) N & 49.48\({}^{\circ}\) N \\ Plot size & 4000 m\({}^{2}\) & 4000 m\({}^{2}\) & 4000 m\({}^{2}\) \\ Soil type & Thick glacial till & Thick glacial till & Thick glacial till \\ Primary Species & 61\% balsam fir & 72\% balsam fir & 67\% balsam fir \\ Observed defoliation (year) & 2006-2016 & 2006-2016 & 2006-2016 \\ Stand origin (year) & 1805 & 1805 & 1930 \\ Average annual temperature (1981–2010) & 1.7 \({}^{\circ}\)C & 1.7 \({}^{\circ}\)C & 1.7 \({}^{\circ}\)C \\ Average annual precipitation (1981–2010) & 1001 mm & 1001 mm & 1001 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Stand location, soil type, and observed defoliation of the balsam fir study sites in the North Shore region of Quebec, Canada.
## 3 Validation  
### Mesh Independence Verification  
The calculations are complicated by the presence of outdoor wind fields and solar chimneys in the calculation area. Structured and unstructured grids are used to divide the model. To ensure the accuracy of the calculation results a grid encryption is used near the heat source and the chimney channel. The local encryption calculation grid is shown in Figure 3. The grid independence is assessed during the calculation and simulation to generate six sets of grids, the grid numbers are 842,790, 963,847, 1,056,646, 1,188,238, 1,318,472, and 1,408,494, and the values of natural ventilation under different grids are shown in Figure 4. The deviation between the ventilation values of the 4 th and 5 th grids is only 1%, and the 4 th grid is chosen for the numerical simulation due to the accuracy of the results and the speed of calculation.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Thickness** & **Density** & \begin{tabular}{c} **Heat Conductivity** \\ **Coefficient** \\ \end{tabular} & \
\begin{tabular}{c} **Specific Heat** \\ **Capacity** \\ \end{tabular} &
\begin{tabular}{c} **Solar Radiation** \\ **Reslectance** \\ \end{tabular} \\ \cline{2-7}  & **mm** & **kg/m\({}^{3}\)** & **W/(m-k)** & **J/(kg-k)** & **Transmittance** & **Reflectance** & **Absorption Rate** \\ \hline Glass cover plate & 3 & 2500 & 0.75 & 837.4 & 0.84 & 0.1 & 0.06 \\ Polyvinyl chloride & 50 & 100 & 0.047 & 1380 & 0 & 0.05 & 0.95 \\ Air & — & 1.205 & 0.0259 & 1005.43 & 1 & 0 & 0 \\ Insulated walls & 200 & 1800 & 0.814 & 879 & 0 & 1 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical parameters of the main materials and air.  
Figure 3: Grid independence assessment.
# Analysis of Cavity Corner Geometry Effect on Recirculation Zone Structure
## 3 Results and Discussion
### Investigation of Flow Topology in the x-y Plane
The profiles are measured along the cavity in the middle plane at equal distances of \(0.5h_{1}\). The velocity profiles of Ch4 are not presented since they are identical to Ch1. As can be seen, velocity profiles in Ch1 and Ch2 coincide in most cases. Slight differences are observed only at the beginning of the cavity and near the trailing wall due to different corner rounding radii. In Ch2 at Re = 100 (Figure 8), the recirculation zone is affected by a gradually decreasing channel cross-section. Therefore, the interaction between the main flow and recirculation zone is strengthened, causing a stronger recirculation zone at \(0.3<y/h_{1}<0.75\). Higher \(u\) velocities along the cavity in Ch3 at Re = 100 are determined by a shorter and weaker recirculation zone. As Re increases, velocity profiles coincide well because flow structure in the cavity is stable and small changes in cavity geometry have a diminishing influence on flow structure. The most significant geometry impact is observed in Ch3 at Re = 100, where the recirculation zone is weaker and a thinner reverse flow layer is observed compared to other cases. The highest velocity values are observed when Re \(\geq\) 100 due to the lowest pressure losses. Figure 10 gives pressure loss dependency on Re. Pressure loss is calculated as the difference between inlet and outlet pressure. As can be seen, pressure losses in all cases are similar at low Re. As Re increases, pressure losses remain similar in Ch1 and Ch2; however, decreased pressure losses are observed in Ch3, as could have been foreseen from the velocity fields and profiles presented above. The reduction of pressure losses due to cavity corner rounding could serve as a roadmap in designing specific applications. Figure 8: Velocity \(u\) profiles in Ch1, Ch2 and Ch3 at Re = 100. Figure 7: Wall shear distribution on the bottom of Ch1 **(a)** and \(x_{\rm C}\) dependency on Re **(b)**. Figure 9: Velocity \(u\) profiles in Ch1, Ch2 and Ch3 at Re = 1000.
## 4 Conclusions  
This study presents a framework that converts CAD data into OBJ files and calculates the characteristics of incident light intensity using only essential surface element information. This adaptable method can be applied to various vehicle types, provided that CAD data are available. This approach enables a focused analysis of potential VIPV installation sites, optimizing both resource usage and computation time without forgoing accuracy in the estimation of incident light intensity.  
The annual comparison of incident light intensity showed that, despite self-shadowing effects, the light intake of the roof was approximately 8% greater than that of the hood. This slight but consistent advantage highlights the potential benefits of roof placement for sustained energy capture under various daily and seasonal conditions. Our findings also suggest that positioning panel-bearing surfaces as close to the vertical as possible is crucial for optimizing incident light intensity capture. This underscores the importance of aligning the unit normal vector in predictive models for curved surfaces.  
This study establishes a foundational methodology for integrating VIPVs and provides valuable insights into effective design choices and more efficient computational techniques. By improving the calculations of incident light intensity based on a vehicle's specific geometry, this approach facilitates more customized, energy-efficient design innovations within the automotive industry. This contributes to advancements in sustainable transportation technologies. Future studies could expand on these findings by incorporating the dynamic positioning of VIPV surfaces and examining the impact of real-world environmental factors.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**GHI** & \(\mathbf{I_{\text{total,roof}}}\) & \(\mathbf{I_{\text{total,hood}}}\) \\ \hline
1480 \(\text{kWh/m}^{2}\) & 1460 \(\text{kWh/m}^{2}\) & 1340 \(\text{kWh/m}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Estimated incident light intensity on the roof and hood of the vehicle.  
This would enhance the precision and applicability of predictions related to solar energy capture across various automotive contexts.  
Conceptualization, K.A. and Y.O.; Methodology, S.M. and K.A.; Validation, S.M.; Formal analysis, S.M.; Data curation, S.M.; Writing--original draft preparation, S.M. and K.A.; Writing--review and editing, S.M., K.A., and Y.O.; Supervision, K.N. All authors have read and agreed to the published version of the manuscript.  
This study was partially supported by the New Energy and Industrial Technology Development Organization under the Ministry of Economy, Trade and Industry (Japan) [Grant Number JPNP20015].  
Not applicable.  
Data Availability Statement: Data will be made available on request.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# A Review of Image Inpainting Methods Based on Deep Learning
## 8 Outlook and Challenges
The underlying premise is that when a significant image portion is obscured, the image inpainting challenge virtually mirrors unconditional image generation. This insinuates that robust image generation capabilities can substantially bolster the inpainting of vast areas. However, studies have shown that even after obscuring 75% of the image data, restoration remains possible, underscoring the redundancy inherent in images. This raises a pertinent question: Do images genuinely possess such redundancy, enabling restoration even after losing a significant chunk of their data? In this context, the role of generative models might be to diversify inpainting. Striking a balance between precise reconstruction and diverse generation is poised to be a future challenge in image inpainting research. Additionally, there is a discernible gap in techniques focusing on high-resolution image inpainting, although high-resolution imaging remains a cornerstone in contemporary image processing tasks. Consequently, forthcoming research in image inpainting should pivot towards high-resolution and extensive damage areas. Z.X. : conceptualization and methodology; X.Z. : methodology and software; W.C.: conceptualization and supervision; M.Y. : validation and data curation; J.L. : investigation and writing--review; T.X. : methodology, software, and validation; Z.W. : conceptualization and supervision. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the National Natural Science Foundation of China under Grants 52274160 and 51874300, the National Natural Science Foundation of China and Shanxi Provincial People's Government Jointly Funded Project of China for Coal Base and Low Carbon under Grant U1510115, the Fundamental Research Funds for the Central Universities under Grant 2023 QN1079, the National Natural Science Foundation under Grant 62273235, and the Joint Fund of the Ministry of Education under Grant 8091B022101. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. This study does not involve the generation or analysis of new datasets, and therefore, data availability is not applicable. **Conflicts of Interest:** The authors declare no conflict of interest.
# IoTDS: A One-Class Classification Approach to Detect Botnets in Internet of Things Devices
## 3 One-Class Classifiers  
In traditional supervised classification techniques, data collected previously has to be associated with labels representing all the classes of interest when inducing a model capable of detecting each class. This requirement for labelled data from each possible class can be problematic in some cases, e.g., when the labelling cost is too high, or some class occurs much less frequently [24]. When considering botnet detection, labelled data may be collected by a specially designed testbed or honeypot. This requires manual work to set up vulnerable devices and some kind of access to botnets. Additionally, there is an inherited class unbalance problem when dealing with malware detection, where legitimate data is easily available and in a far greater scale.  
An emerging alternative is the use of one-class classification techniques, which do not model all classes. Instead, data from a class of interest is used to create a model capable of yielding if a given new instance of data belongs to this class or not [24]. In the context of botnet detection, after collecting legitimate behaviour data (which is more straightforward to obtain than malicious data), one-class classification algorithms induce a model that discerns if a given new instance is legitimate or not. Here, it is considered that when an instance does not fit within the model boundaries, it is associated with malicious behaviour. In this sense, one-class classifiers can be used as an alternative to traditional supervised classification algorithms to reduce the labelling cost and the need for malicious data in the training phase significantly. Likewise, they are easier to adapt, given that periodical model updates using new legitimate data demand less effort than updates with labelled legitimate and malicious data. In this work, we compare the performance of 4 one-class classification algorithms: EE [25], IF [26], LOF [27], and OSVM [28].  
These four algorithms were selected as they represent different approaches in one-class classification. The first algorithm, EE, was chosen due to its simplicity in detecting outliers.  
This algorithm computes an ellipse that separates the outliers from the inliers. Next, representing ensemble methods, we picked IF. Ensemble methods are reportedly robust and have been used in many network security problems [29], being a possible solution for this work due to its highly accurate predictive performance. We also wanted to have a clustering-based approach that could analyse the data using the similarity between the normal instances, thus we chose LOF. Lastly, we searched for an algorithm that has good results in building reliable models for detecting behaviour changes, and we found OSVM, that was successful in detecting machine faults [30].
## 2 Related Works
### MCDM-Based Systems  
[PERSON] et al. (2021) integrated multiple decision-making techniques to rank hotels based on past clients. In this method, the value of users was determined based on a \"recency, frequency, and helpfulness\" model. User opinions on the social network were analyzed via the fuzzy linguistic approach involving a multigranular two-tuples model. This methodology was evaluated using data from Tripadvisor (15). [PERSON] et al. (2021) applied the MCDM technique to model user preferences in a tourist recommendation system (16). This technique, which is based on the VIKOR method, was applied to a web-based system that enabled tourists to enter certain criteria, and subsequently combined them to generate the highest-ranking advisor in a recommendation system. [PERSON] et al. (2021) proposed a novel method for hotel recommendation based on a metaheuristic technique and MCDM (17). This method was based on the artificial bee colony algorithm and the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) model to recommend a hotel based on user interests. [PERSON] et al. (2016) employed a methodology based on MCDM methods such as the analytical hierarchical process (AHP) and the TOPSIS method for hotel selection (18). In this method, AHP was used to determine the weights for the hotel criteria; subsequently, TOPSIS was applied to select the best hotel from among the candidate hotels based on these weights. In tourist scenarios involving large amounts of data, conventional MCDM models need to be improved using techniques such as clustering. In this regard, a methodology based on hierarchical clustering and TOPSIS was proposed by [PERSON] et al. (2015) for selecting suppliers under limited-supply conditions (19).  
Although MCDM-based systems have demonstrated their significance in achieving objectives such as recommending or selecting hotels, the models constructed by these systems are static, and cannot adapt to the real-time conditions of the user. Therefore, in the field of tourism, MCDM-based systems that accept contextual information as inputs need to be developed. Contextual data enable the system to model real-time user status. Based on the mentioned studies, there is a lack of consideration of MCDM-based systems that accept context. These capabilities enable the system to change the model based on user context--such as preference and position--for tourist applications.
## 5 Results and Discussion
### Effect of Medial Compression
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \(\mathbf{E_{\text{bdy}}}\) & \(\mathbf{E_{\text{cvr}}}\) & \(\mathbf{E_{\text{lig}}}\) & \(\mathbf{p_{\text{sub}}}\) & \(\mathbf{\rho}\) & \(\mathbf{\eta}\) & \(\mathbf{x_{0}}\) \\
**Data Set** & **(kPa)** & **(Pa)** & **(kPa)** & **(Pa)** & **(kg/m\({}^{3}\))** & **(Poise)** & **(mm)** \\ \hline Experimental Value & 11.8 & 600 & 2 & 1,000 & 1049 & - & - \\ \hline Without Medial Compression & 12.32 & 632.1 & 1.807 & 987.7 & 1051 & 2.981 & 8.39 \\  & \((3.96\%)\) & \((3.53\%)\) & \((9.74\%)\) & \((3.98\%)\) & \((0.25\%)\) & \((4.66\%)\) & \((0.38\%)\) \\ \hline With Medial Compression & 12.28 & 629.8 & 1.842 & 991.9 & 1051 & 3.11 & 8.27 \\  & \((4.30\%)\) & \((4.1\%)\) & \((12.16\%)\) & \((4.536\%)\) & \((0.26\%)\) & \((4.66\%)\) & \((0.30\%)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Estimates and the associated uncertainties as a percentage of the estimate values (in brackets) for material properties of the silicone vocal folds and initial position for cases with and without medial compression (\(p_{\text{sub}}=1\,\mathrm{kPa}\)). with compression is 18% higher, on average, due to the pre-stress of the system (\(825.7\pm 58.6\) Pa versus \(665.0\pm 53.1\) Pa).
## 3 Findings and Discussion  
### Identification of Criteria for Airport Transfer Center Selection  
In this section of the study, we first outline the criteria employed in evaluating alternatives for airport hub selection using the AHP method. Subsequently, the priority values of these criteria are calculated. Seven criteria are identified based on the existing literature [24]. The determined criteria are evaluated by the insights of three experts boasting over fifteen years of professional experience. One of the designated experts is the operational team leader, one is the refueling expert and the other evaluator is the support services expert. The selected experts are selected from airports other than the airports evaluated in the study. Evaluations obtained from experts are used in the analysis by taking the arithmetic average. The identified criteria are shown in Table 4.  
The first criterion pertains to cost considerations, while the subsequent criteria encompass factors aimed at enhancing airline operations. Facilities such as the quantity of aprons and runways within the airport terminal are instrumental in streamlining flight planning activities for airline companies.  
Similarly, passenger transportation services and aircraft maintenance facilities play a pivotal role in the planning activities of airline companies. Airport operating capacity stands as another crucial criterion favored by airline companies. Airports with limited capacity are likely to receive lower preference rates. The availability of both in-city and out-of-city transportation and transfer options enhances the attractiveness of airports. The ease with which passengers can reach their ultimate destinations post-flight serves as a significant factor influencing their preference for this mode of transportation in the future. The strategically advantageous location of the terminal for cultural or leisure trips was deemed as another criterion with potential benefits. Finally, social facilities within the terminal are expected to impact the flight plans of airlines, particularly benefiting connecting passengers [24].