esa-sceva/satcom-chunk-collection huggingface dataset:
# Foreground Objects Detection by U-Net with Multiple Difference Images
## 4 Experimental Results
### Generalization Ability Test Using Scenes Not Used in Training
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scene** & **Ours** & **Modified FgSegNet-v2** & **PAWCS [41]** & **SuBSENSE [2]** & **FgSegNet-v2** \\ \hline Board & **0.8114** & 0.8086 & 0.7798 & 0.5777 & 0.5816 \\ CAVIAR1 & **0.9566** & 0.9342 & 0.8589 & 0.9144 & 0.9115 \\ CAVIAR2 & 0.8094 & **0.8192** & 0.6772 & 0.8714 & 0.0306 \\ CaVignal & 0.8634 & **0.9102** & 0.3697 & 0.3980 & 0.7704 \\ Candela & 0.6402 & 0.6646 & **0.8725** & 0.5356 & 0.4144 \\ Hall\&Monitor & **0.9384** & 0.8878 & 0.7411 & 0.7758 & 0.7365 \\ Highway1 & 0.8465 & **0.8619** & 0.7015 & 0.5523 & 0.4263 \\ Highway2 & **0.9559** & 0.9537 & 0.9031 & 0.8937 & 0.2277 \\ HumanBody2 & **0.9415**  
Figure 6: Test results on SBI2015 dataset: (**a**) ground truth foreground maps, (**b**) proposed method, (**c**) SuBSENSE [2], (**d**) modified FgSegNet-v2, (**e**) FgSegNet-v2 [42]. Deep learning-based algorithms with supervised learning show the best performance in scenes that are similar to training scenes. Therefore, they require training before application to unknown scenes. However, they require a large set of training data. In particular, visual surveillance requires a ground truth segmentation map per pixel, which requires a large amount of time and is high cost. The best option would be a deep learning-based  
Figure 7: Qualitative results on real scenes that were not used in training: (**a**) proposed method trained using 49 scenes in the CDnet 2014 dataset, (**b**) proposed method trained using 24 scenes in the CDnet 2014 dataset, (**c**) SuBSENSE [2], (**d**) modified FgSegNet-v2, (**e**) FgSegNet-v2 [42]. algorithm that does not require training samples of unknown scenes. We want to have an algorithm for visual surveillance that achieves a performance comparable to that of a deep learning-based algorithm, at the same time, requires little effort in preparing samples for training. The proposed algorithm trained using many samples can achieve better performance than SuBSENSE [2] in situations where there are no training samples. We can conclude that the proposed method achieves better results on scenes that deviate from the training environment, in comparison to traditional and deep learning-based algorithms, from these experimental results. The proposed method is based on deep learning, and it does not require training samples before application to unknown scenes. Our goal is to have a foreground detection algorithm that achieves better performance than traditional unsupervised visual surveillance algorithms. The proposed algorithm meets this requirement by adjusting U-Net to use multiple difference images and training it using multiple scenes.
## 1 Introduction
A rapidly varying refractive index in the spectral domain, therefore having a high spectral dispersion of EIT media, was predicted theoretically and confirmed experimentally, and could be used to control the group velocity of light [49; 50]. In cases with slow light, the interaction between the light field and the EIT media was significantly enhanced, and giant Kerr nonlinearity was observed in the EIT media [21]. Due to the intensity dependence of the EIT effect, a spatially varying refractive index change could be induced and an EIT-induced spatial soliton was predicted theoretically and observed experimentally [51; 52]. For more detail about the enhanced optical nonlinearity in the EIT media, one can refer to review articles, for instance, by [PERSON] [10], [PERSON] et al. [9] and [PERSON] et al. [53]. Most of the early studies on the EIT effect were performed in atomic vapors due to their advantage of having a long atom decoherence time [2; 23; 39; 40; 42; 54; 55]. Compared to atomic vapors, solid state materials are robust, easy to fabricate and post-process, of high atomic density and have no atomic diffusion, although they need cryogenic temperature to suppress the dephasing effect caused by phonons. Fortunately, the decoherence time of rare earth ions is long enough for many applications, especially, the storage time of light pulses as long as minutes or even hours was achieved in Pr:YSO or Eu:YSO crystals [56; 57]. In this paper, we review the recent progress on the manipulation of light fields and its application in optical signal processing based on the EIT effect in Pr:YSO crystal.
# Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
## 2 Related Research Work  
IBM is flourishing with AI-based WATSON, Apple has showcased Siri on the market, Google has brought out Google Now, Amazon has Alexa, Facebook has virtual assistants or Chat bot, and Microsoft has hit the market with Cortana. A significant amount of research in the wireless network communication field is also focusing on the application of advanced machine learning techniques (deep learning) to embrace the intelligence and cognition within the system in an autonomous manner. A survey of the literature involving ML algorithms applied to self-organizing cellular networks is presented in [5]. This shows the great interest of researchers in applying artificial intelligence to wireless networks. An automatic and self-organized Reinforcement Learning (RL)-based approach for Cell Outage Compensation (COC) is presented in [6]. Similarly deep Q-learning has been used in network optimization [7]. The use of deep Q-learning for self-organizing network fault management and radio performance improvement is presented in [8]. The deep convolutional neural network is applied for intelligent traffic control for wireless network routing in [9]. Multiple layer perceptron (MLP) is applied for transmitting power control in device-to-device communications in [10]. A data-driven analytic framework for automating the sleeping cell detection process in an long term evolution (LTE) network using minimized drive testing functionality, as specified by 3 GPP in Release 10, is discussed in [11]. To the best of our knowledge, deep learning methods have not yet been applied to cell outage detection, mobility load balancing, or intrusion detection. Our work is the first to apply the deep learning method, namely the use of autoencoders, to solve the above-mentioned networking problems to achieve an intelligent cognitive system for 5G self organizing networks.
## 3 Results  
### Polymerization Depth  
The depth of cure (DOC) was assessed for both SDR flow+ and Vertise Flow, and the measurements are presented in Table 1. SDR flow+ demonstrated a greater average DOC of 3.5 mm (\(\pm\)0.2 mm) compared to Vertise Flow, which showed an average DOC of 2.8 mm (\(\pm\)0.3 mm). This difference was found to be statistically significant with a \(p\)-value of \(<\)0.001, as illustrated in Figure 3. These results clearly indicate that SDR flow+ allows for a deeper polymerization within a single increment. The higher DOC observed for SDR flow+ is consistent across all samples, suggesting that this material may be particularly effective for applications requiring deeper curing.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Vertise Flow** & **Meas.1** & **Meas.2** & **Meas.3** & **Mean** &
\begin{tabular}{c} **SDR Flow+** \\ \end{tabular} & **Meas.1** & **Meas.2** & **Meas.3** & **Mean** \\ \hline
1 & 2.49 & 2.45 & 2.38 & 2.44 & 1 & 3.86 & 4.02 & 3.99 & 3.96 \\
2 & 2.35 & 2.51 & 2.35 & 2.40 & 2 & 4.23 & 4.41 & 4.3 & 4.31 \\
3 & 2.3 & 2.37 & 2.49 & 2.38 & 3 & 3.53 & 3.56 & 3.61 & 3.56 \\
4 & 2.12 & 2.2 & 2.10 & 2.14 & 4 & 4.15 & 4.12 & 4.11 & 4.12 \\
5 & 2.28 & 2.37 & 2.43 & 2.36 & 5 & 3.80 & 3.79 & 3.74 & 3.78 \\
6 & 2.27 & 2.46 & 2.44 & 2.39 & 6 & 3.99 & 3.86 & 4.01 & 3.94 \\
7 & 2.65 & 2.56 & 2.22 & 2.48 & 7 & 4.18 & 4.11 & 3.98 & 4.10 \\
8 & 2.58 & 2.64 & 2.6 & 2.60 & 8 & 3.96 & 4.08 & 4.11 & 4.05 \\
9 & 2.65 & 2.58 & 2.62 & 2.61 & 9 & 4.47 & 4.49 & 4.30 & 4.42 \\
10 & 2.77 & 2.76 & 2.68 & 2.74 & 10 & 4.27 & 4.37 & 4.25 & 4.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Three measurements for each material, obtained using a digital caliper, and the average of the measurements for calculating the polymerization depth.
# Feasibility of Wave Simulation in Typhoon Using WaveWATCH-III Forced by Remote-Sensed Wind
## 2 Data and Method
### Remote-Sensed Products and Assembling Method
in which \(D_{i}\) is the distance between the individual referred wind and the initial wind vector. The temporal resolution of the assembling wind is 6 h. The above interpolation has been used for SAR wind retrieval in previous studies [4] because the spatial resolution of interpolation can be selected artificially. Note that the wind direction is directly obtained from a scatterometer because the radiometer provides no wind direction. Figure 2c shows the assembling wind map during Typhoon Mindulle at 18:00 UTC on 26 September 2021. Moreover, the gaps in the assembling wind were almost not observed due to the usage of operational products from the four satellites in this study. The cyclonic structure of the storm is well represented. The maximum wind speed \(V_{max}\) reaches 45 m/s, which is greater than that shown in Figure 3 and is close to 46.26 m/s from NHC.
###### Abstract
Nevertheless, depending solely on the information entropy between quantified data can result in substantial deviations between decision weights and actual needs [15]. In this context, the application of structural equation modeling (SEM) becomes of paramount importance. It provides a deeper level of insight, allowing researchers to better comprehend the intricate relationships among multiple factors while also considering the interactions between these elements. This heightened level of understanding is beneficial for decision makers in gaining a clearer grasp of the core issues and, in turn, provides them with more reliable support and guidance for making informed decisions. To encapsulate, the present development of design decision models based on subjective-objective weighting methods still resorts heavily to expert opinions [16]. Therefore, this paper proposes a systematic design decision approach that integrates the PFA theory as a mature paradigm collection tool and SEM as a model proposal and validation method, combining path load normalization and AHP subjective weighting as comprehensive evaluation algorithms. This approach follows a sequence of model proposal, validation, and evaluation steps and was applied to the practical context of machine tool styling decisions to confirm the scientific feasibility of the evaluation method. Within the research framework outlined above, several weighting methods were introduced, including the analytic hierarchy process (AHP), entropy weighting, and SEM path coefficient normalization weights, in order to delve into their association with actual evaluations and their applicability under different circumstances. This innovative point will contribute to a more comprehensive understanding of the accuracy and suitability of different weighting methods in the decision-making process for the industrial PF design of machine tools. Through the comprehensive study and comparison of these methods, we can offer a more scientific, objective, and reliable approach to design practices in the era of Industry 4.0. This, in turn, provides stronger support for enhancing overall product competitiveness and meeting personalized consumer demands.
## 1 Introduction
Abbreviations: 3V—third ventricle; AgRP—agouti-related peptide; Arc—arcuate hypothalamic nucleus; \(\alpha\)-MSH—alpha-melanocyte stimulating hormone; CART—cocaine and amphetamine regulated transcript; DMN—dorsomedial hypothalamic nucleus; Enk—enkephalins; GABA—gamma-aminobutyric acid; GAL—galanin; GALP—galanin-like peptide; GalR1—galanin receptor type 1; GalR2—galanin receptor type 2; GalR3—galanin receptor type 3; GHRF—growth hormone-releasing factor; Glut—glutamate; GHS-R—growth hormone secretagogue receptor; LH—lateral hypothalamus; MC3R—melanocortin receptor type 3; MC4R—melanocortin receptor type 4; MCH—melanin-concentrating hormone; NPY—neuropeptide Y; OX1R—orexin receptor 1; OX2R—orexin receptor 2; OXTR—oxytocin receptor; POMC—prooipomelanocortin; PVH—paraventricular hypothalamic nucleus; SON—supraoptic nucleus; VMN—ventromedial hypothalamic nucleus; Y1—NY receptor type 1; Y2—NY receptor type 2; Y5—NYP receptor type 5.  
consumption, including motivation [7; 8; 11]. The participation of orexins in the stimulation of excessive intake of palatable sweet and fatty foods suggests that these peptides could also promote non-homeostatic dysregulated food intake [8; 9; 11]. It was suggested that the primary role of orexins is to promote foraging behavior, with a minimal role in the reinstatement of seeking for food that may be no longer available. In fact, during food deprivation, elevated levels of orexins are detected as well as progressively shorter episodes of sleep and increased locomotor activity as a part of survival strategy [7; 8; 12]. Thus, orexins participate in the regulation of numerous functions [10; 13], being the control of food intake and associated behaviors especially relevant within this wide range of activities. Therefore, the aim of this review is to evaluate the role of orexins in this complex mechanism.
# Utilizing Energy Storage for Reliability Solutions in Active Distribution Systems
###### Abstract  
Stochastic failures in a distribution network result in different reliability events, such as voltage sags and momentary and sustained interruptions, causing significant financial losses for many customers. Strategic utilization of energy storage system (ESS) can help mitigate these reliability events. This paper investigates the role of ESS toward providing reliability solutions in the context of an active distribution system. In this regard, scenario-based probabilistic modeling of reliability event mitigation with the ESS is presented, which is integrated into the reliability evaluation framework. The proposed approach is efficient in assessing reliability solutions with ESS considering storage technology type, power/energy rating, hardware availability, presence of other distributed energy resources, etc. A range of case studies is conducted to evaluate mitigation of reliability events at a different level of the distribution system. Valuable insights into the efficient utilization of ESS are provided based on findings.  
Keywords:distributed generation; distribution system reliability; energy storage; microgrid; sustained interruption; momentary interruption; voltage sag +
Footnote †: journal: Applied Sciences
## 6 Discussion and Conclusions  
As mentioned in the Introduction, FP optical resonators are used for pre-stabilization of lasers in schemes of optical frequency standards and for registration of small opto-acoustic perturbations in gravitational antennas. The known PDH technique [4; 30] is applied, where the frequency stabilization of the laser is due to the connection with the reference optical resonator. The effectiveness of this scheme is determined by the amount of sharpness (finesse) and contrast of this resonator, which should be large. The desire to suppress electromagnetic and mechanical thermal noise forces us to consider FP resonators that are cooled to cryo-temperatures. However, the possibility of preserving their optical properties requires an experimental investigation.  
This paper presents a study of the temperature evolution of the FP cavity for three types of mirrors. The main result is a demonstration of the suitability of mirrors on calcium fluoride substrates. The flaws of stall and sapphire based mirrors were also discovered.  
The reasons for the fall in the quality of the FP of resonators with stall mirrors are not completely clear. Given that their optical nonlinearity is small, we can assume a deterioration in the optical qualities of such mirrors is due to thermo-mechanical effects. Most likely the optical glass, stall, loses its ULE properties at temperatures below \(-60\)\({}^{\circ}\)C. However, this hypothesis requires additional research.  
Optical nonlinearity (birefringence) affects sapphire mirrors. It is obvious that, with a large frequency splitting of resonances that preserves the high finesse, the use of the PDH technique may also be applied to a separate isolated peak. However, this does not prevent the loss of contrast because the optical power is spread over all of the peaks.  
The obtained results are directly important for the implementation of the cryogenic version of the OGRAN [25] gravitational detector. This setup uses LMA mirrors [31] on quartz substrates with a very high finesse value of the reference resonator \(\sim\)30,000. Quartz birefringence is close to that of sapphire. With this increased finesse, the peak splitting will be large enough to use the PDH technique for the separate one.  
**Author Contributions:** project administration, formal analysis, M.V.K.; hardware, investigation, S.I.O.; software, formal analysis, S.M.P.; conceptualization, supervision, writing--original draft preparation, V.N.R.; investigation, visualization, I.S.Y.; resources, V.V.A.; investigation, data curation, S.V.B.  
Figure 6: Evolution of the finesse and contrast for FP-cavity with mirrors on calcium fluoride during the cooling process.  
**Funding:** This research was funded by by national funding agency RSCF grant number 17-12-01095 and also by RFBR grant number 17-02-00492.  
**Acknowledgments:** The authors would like to express their gratitude to the members of OGRAN team from LPI SB RAS (Novosibirsk), particularly: [PERSON], [PERSON] and [PERSON] for their help in instrumentation and many fruitful discussions.  
**Conflicts of Interest:** The authors declare no conflict of interes.
# Inferring Urban Social Networks from Publicly Available Data
## 2 Materials and Methods  
Our urban social network is obtained as the combination of a household network with a friendship network. The construction of these networks requires to first map census data to reconstruct the population and then model interpersonal connections. Formally, the network is represented by an unweighted undirected graph \(G=(V,E)\), where \(V\) is the vertex set of size \(N=|V|\), and \(E\) is the edge set. In particular, we have \(E=E_{H}\sqcup E_{F}\), where \(E_{H}\) is the set of _household edges_, \(E_{F}\) is the set of _friendship edges_, and \(\sqcup\) denotes the disjoint union. In the following, we explain how \(V\), \(E_{H}\), and \(E_{F}\) are defined in our model. We will often use the expression _household graph_ to denote the subgraph \(G_{H}=(V,E_{H})\) and the expression _friendship graph_ to denote the subgraph \(G_{F}=(V,E_{F})\). The notation and the parameters used throughout this paper are summarized in Table 1.
## 5 Boom Configuration Optimization
### Results
#### 5.3.2 Optimization Comparison  
An overview of the results on the five test cases is given in Table 1. The reference solutions (Ref. columns in Table 1) represent real manufactured booms without using any optimization.  
Comparing the reference solution and the solution of the heuristic approach, the value of the objective function is lower in all test cases when using the heuristic optimization. These cost savings can be achieved through using the utilizations better, i.e., in most cases obtaining higher, yet still valid degrees of utilizations. An exception is Test Case 1, where the average utilization for buckling of the reference solution is very close to one, due to an unbalanced selection of plate thicknesses. By choosing better values, both costs and utilization degrees can be improved.  
The applied ES is a (\(20+100\))-ES, which was run for 100 generations and 10 repetitions were performed. Further improvements with the ES (including surrogate modeling) requires a significantly higher amount of calls to the structural analysis tool. The number of calls shown in Table 1 (see column Analysis Tool Calls) lists the calls necessary for training the surrogate models (500 samples) and the calls that were performed during the validation of the results. The number of validation-calls is varying due to caching effects since we do not evaluate the same boom twice. Note that, for both ES and heuristic, the run time is dominated by the run time of the structural analysis tool.
## 5 Results
### Hypothesis Testing
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Hypothesis** & **Path** & \(\mathbf{\beta}\) & **M** & **STDEV** & \(\mathbf{t}\)**-Value** & \(\mathbf{p}\)**-Value** & **Cl** & \(\mathbf{f}^{2}\) & **Decision** \\ \hline Model 1: \(R^{2}\) = 0.642 (strong explanatory power); SRMR = 0.06 (good fit); NFI = 0.841(acceptable fit) & & & & & & \\ H1 & SCRM-\(\beta\)SCR & 0.561 & 0.563 & 0.061 & 9.187 & 0.000 & \([0.442,0.684]\) & 0.436 & accepted \\ H3a & Moderating of DDC & \(-\)0.013 & \(-\)0.009 & 0.057 & 0.232 & 0.817 & \([-0.115,0.109]\) & 0.000 & rejected \\ H3b & Moderating of DAC & 0.200 & 0.196 & 0.052 & 3.829 & 0.000 & \([0.091,0.299]\) & 0.061 & accepted \\ H3c & Moderating of SSC & \(-\)0.024 & \(-\)0.027 & 0.045 & 0.446 & 0.655 & \([-0.131,0.081]\) & 0.001 & rejected \\ \hline Model 2: \(R^{2}\) = 0.605(strong explanatory power); SRMR = 0.07(good fit); NFI = 0.825 (acceptable fit) & & & & & & \\ H1 & SCRM-\(\beta\)SCR & 0.554 & 0.556 & 0.061 & 9.125 & 0.000 & \([0.437,0.676]\) & 0.415 & accepted \\ H2 & Moderating of DC & 0.150 & 0.147 & 0.043 & 3.479 & 0.001 & \([0.061,0.228]\) & 0.058 & accepted \\ \hline \hline \end{tabular} Note.: \(\beta\): path coefficients (original sample), M: sample mean, STDEV: standard deviation, CI: confidence interval, DAC: Digital analysis capability, DIC: Digital infrastructure capability, SCR: Supply chain resilience, SCRM: Supply chain risk management, SSC: Strategic support capability, DC: Digital capability.  
\end{table} 
Table 6: Results for hierarchical regression analysis. Figure 4: (**a**) Simple slope plot of DAC’s moderating effect, (**b**) Simple slope plot of DC’s moderating effect. Note: DAC: Digital analysis capability, DC: Digital capability.