esa-sceva/satcom-chunk-collection huggingface dataset:
# An FSFS-Net Method for Occluded and Aggregated Fish Segmentation from Fish School Feeding Images
###### Abstract  
Smart feeding is essential for maximizing resource utilization, enhancing fish growth and welfare, and reducing environmental impact in intensive aquaculture. The image segmentation technique facilitates fish feeding behavior analysis to achieve quantitative decision making in smart feeding. Existing studies have largely focused on single-category object segmentation, ignoring issues like occlusion, overlap, and aggregation amongst individual fish in the fish feeding process. To address the above challenges, this paper presents research on fish school feeding behavior quantification and analysis using a semantic segmentation algorithm. We propose the use of the fish school feeding segmentation method (FSFS-Net), together with the shuffle polarized self-attention (SPSA) and lightweight multi-scale module (LMSM), to achieve two-class pixel-wise classification in fish feeding images. Specifically, the SPSA method proposed is designed to extract long-range dependencies between features in an image. Moreover, the use of LMSM techniques is proposed in order to learn contextual semantic information by expanding the receptive field to extract multi-scale features. The extensive experimental results demonstrate that the proposed method outperforms several state-of-the-art semantic segmentation methods such as U-Net, SegNet, FCN, DeepLab v3 plus, GCN, HRNet-w48, DDRNet, LinkNet, BiSeNet v2, DANet, and CCNet, achieving competitive performance and computational efficiency without data augmentation. It has a 79.62% mIoU score on annotated fish feeding datasets. Finally, a feeding video with 3 min clip is tested, and two index parameters are extracted to analyze the feeding intensity of the fish. Therefore, our proposed method and dataset provide promising opportunities for the further analysis of fish school feeding behavior.  
Keywords:fish feeding behavior; semantic segmentation; attention mechanism; multi-scale feature; intensive aquaculture +
Footnote †: journal: _sciences_
## 3 Numerical Model
### Energy Demand Profiles  
The norm VDI 4655 [16] provides raw data about the typical electrical and thermal demand of small buildings in Germany. The daily energy consumption, expressed in kWh, is a value that depends on the building size, climate zone, week day, season, and weather. Following the norm, two types of buildings can be identified: Single-Family Houses (SFHs) and Multiple-Family Houses (MFHs). In this analysis, when referring to building size, this depends on the number of occupants in an SFH and on the number of families for an MFH. The other factors and their effects on energy demand will be explained in the following sections.  
The daily overall energy demands are a fraction of the yearly energy consumption and are expressed by the following equations:  
\[\overline{E}_{heat,d}=\overline{E}_{heat,y}\cdot F_{heat}, \tag{13}\] \[\overline{E}_{el,d}=\overline{E}_{el,y}\Big{(}1/365+N_{pp,fam} \cdot F_{el},\Big{)}\] (14) \[\overline{E}_{shw,d}=\overline{E}_{shw,y}\Big{(}1/365+N_{pp,fam} \cdot F_{shw},\Big{)} \tag{15}\]  
where the suffixes \(heat,\ el\), and \(shw\) indicate hot water for heating, electrical, and sanitary hot water, respectively; the letters \(d\) and \(y\) indicate the daily and yearly consumption, respectively, and the overbar indicates demand quantities, to avoid confusion with the produced quantities of the previous sections. \(N_{pp,fam}\) is an integer that indicates either the number of people per family or the number of families per building, depending on the building considered. The yearly values \(\overline{E}_{el,y}\) and \(\overline{E}_{shw,y}\) depend on building occupancy, while \(\overline{E}_{heat,y}\) depends on the effective house surface, expressed in square meters, as described in the norm DIN V 18599 [21]. Finally, the energy Factors \(F\) are penalty factors that depend on climate zone, week day, season, and weather. These factors are implemented in dictionary structures in the code, such that the right coefficient is used for every scenario.  
In the norm, data on energy demand are sampled and made non-dimensional with a 2 s, 1 min, and 15 min frequency. Since the minimum time to start the system is 30 min, as explained in Section 2.2, in this analysis, pairs of data with a 15 min frequency are taken and summed, such that 48 points a day, 30 min each, are obtained (data sampled with a 2 s or 1 min frequency would bring the same result, when summed to form 30-min intervals).  
The computation of the energy factors is reported in the next subsections. The energy demand of every 30 min time step \(i\) can be obtained by multiplying the non-dimensional demand shape, \(f_{i}\), by the daily energy demand:  
\[\overline{E}_{heat,i}=f_{heat,i}\cdot\overline{E}_{heat,d} \tag{16}\] \[\overline{E}_{el,i}=f_{el,i}\cdot\overline{E}_{el,d}\] (17) \[\overline{E}_{slw,i}=f_{slw,i}\cdot\overline{E}_{shw,d} \tag{18}\]
## 2 Materials and Methods
### Evaluation of Moisture Absorption  
After 60 cycles of degradation testing, the samples were last moved to a salt fog environment for 4 h. This procedure was chosen due to the melting of the ice before the determination of moisture absorption (according to EN ISO 62). Due to the different dimensions, or weight, not only the absolute but also the relative weight increase of the individual types of test samples was determined, as in the publication [21].  
The relative weight gain \(\mathrm{M_{t}}\) (%) is expressed as a proportion of the absolute weight gain, which is given by the difference between the weights of the samples (\(\mathrm{W_{t}}\) (g)--after the test and \(\mathrm{W_{0}}\) (g)--before the test) and the weight of the sample before the test, \(\mathrm{W_{0}}\) (g):  
\[\mathrm{M_{t}}=((\mathrm{W_{t}}-\mathrm{W_{0}})/\mathrm{W_{0}})\cdot 100\,(\%) \tag{1}\]  
It is important to note that the total moisture absorption of the samples is composed of the absorption of the material itself, the absorption given by the porosity of the samples, and also the absorption of any protective coating. The above-mentioned procedure is used to evaluate the overall absorption and cannot be used to determine the individual partial absorption values.  
The result is also influenced by the amount of solution adhering to the surface of the samples. For this very reason, the samples (after being removed from the salt fog environment) were suspended for 10 min at a temperature of 20 \({}^{\circ}\)C and a relative humidity of 50%. The weight determination followed immediately afterwards. This procedure made it possible to obtain more accurate data on the actual proportion of absorbed moisture in the pores of the material.  
In the case of porous-coated 3D prints, the determination of moisture absorption should also reliably detect the perforation of the protective barrier. The reason is the filling of the cavities in the sample with the salt solution, which should cause a significant increase in the weight of the given set of samples.  
However, this phenomenon can be affected by the potential filling of surface cavities (pores) with a coating or the tortuosity of the coating. The absorption of moisture into the base material can be influenced to a certain extent by optimizing the application viscosity of the coating, as a coating with a higher viscosity creates a thicker barrier but also has poor penetration into the pores in the substrate. On the contrary, too low viscosity means excellent convergence but also too thin a resulting barrier [41].
## 3 Computational Methods
### Computational Solver Setup and Fluid Properties  
The simulations were run for 5.0 s, with a fixed time step size of 0.001 s. The event scenario, as depicted in Figure 4, included an additional 1.0 s at the initial velocity and temperature values after the injection occurred. Each case was simulated without injection for the first 3.0 s. Water injection then occurred from 3.0 s to 4.0 s, followed by an additional 1.0 s of simulation. The numerical convergence criteria (residuals) were set to 10\({}^{-3}\) for  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Low-Flow-Rate Group (G1)** & **Medium-Flow-Rate Group (G2)** & **High-Flow-Rate Group (G3)** \\ \hline Flow rate (GPM) & 39.52\(\rightarrow\)93.53 & 101.95\(\sim\)133.51 & 145.83\(\sim\)178.30 \\ First cell height (m) & 14\(\times\) 10\({}^{-3}\) & 6.0 \(\times\) 10\({}^{-4}\) & 4.3 \(\times\) 10\({}^{-4}\) \\ Nodes & 406818 & 362198 & 374494 \\ Elements & 459890 & 408016 & 421176 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Three different meshing models.  
Figure 16: \(u^{+}\) vs. \(y^{+}\) relationship based on G1, G2, and G3 meshing models.  
continuity, x velocity, y velocity, z velocity, turbulence kinetic energy (k), and dissipation rate (\(\epsilon\)), and \(10^{-6}\) for energy. The maximum number of iterations for each time step was set to 10, and individual numerical convergence was monitored.  
Constant fluid properties were used for the water, with a density of 994.08 kg/m\({}^{3}\), a dynamic viscosity of 7.2147 \(\times\) 10\({}^{-4}\) kg/m-s, and a thermal conductivity of 0.6231 W/m-K.
# Surface Plasmon Resonance-Based Fiber Optic Sensors Utilizing Molecular Imprinting
## 6 Fiber Optic SPR Sensor Based on MITs
### Developments on FO-SPR-MIP-Based Sensors
#### 6.3.5 Melamine  
Melamine is a complex having excess of nitrogen and is extensively used in the manufacturing of plastics, kitchenware, etc. Due to its nitrogen excess, it works as a flame-resistant material [162] and is used to maintain the protein level in milk as an impurity which is calculated by the Kjeldahl method [163]. Its higher concentration in human body creates melamine-cyanurate complexes in the kidneys which are much less soluble in water. Thus, an excess of melamine results in various renal diseases in humans and animals. Further, its excess can cause cancer and damage the reproductive system or result in breathing problems. A fiber optic SPR and molecular imprinting-based melamine detection method was proposed by [PERSON] et al. [164]. For the synthesis of MIP/sensing medium melamine, MAA, ethylene glycol dimethacrylate (EGDMA), AIBN, benzene was used as template, functional monomer, cross-linker, initiator and solvent, respectively. The sensor's response was checked for a melamine concentration range from 10\({}^{-7}\) to 10\({}^{-1}\) M as shown in Figure 13a. The shift in resonance wavelength for this concentration change was found to be of 19.52 nm and the sensor possesses a maximum sensitivity of 10.1 nm/log M near melamine concentration of 10\({}^{-7}\) M. The melamine (template) concentration was also optimized in this study as shown in Figure 13b as it strongly affects the sensor performance as discussed in an earlier section.  
Figure 13: SPR based fiber optic melamine sensor characterized in the range 0–10\({}^{-1}\) M showing (**a**) SPR response and (**b**) optimization plot for template concentration in MIP. Reprinted with permission from [164]. Copyright 2015 Elsevier.  
The study was extended by adding a 3 nm thick high index silicon layer between Ag and MIP layer in the probe [165]. Addition of silicon layer improved the sensitivity of the sensor from 10.1 to 76.0 nm/log M; the sensor's operating range also got increased to \(10^{-8}\)-\(10^{-1}\) M. The sensing probe showed a better detection limit of \(4.3\times 10^{-11}\) M. This is because of the electric field enhancement at the silicon/sensing medium interface [8; 57; 58].
# A Cost-Efficient MCSA-Based Fault Diagnostic Framework for SCIM at Low-Load Conditions
## 7 Conclusions  
Squirrel cage induction motors are among the most popular industrial electrical motors due to their high motive power generation, durability, and low maintenance costs. The need for condition monitoring presents the opportunity for CS-based fault diagnosis; however, selecting the appropriate signals processing technique(s) for ML-based diagnostics remains open for continued research.  
This paper presented a peak detection approach for discriminative feature extraction, which concatenates FFT, PSD, and ACF peak coordinates from the current signals sourced from a three-phase SCIM. These features are received by various ML-based classifiers, whose classification results are also presented in the study. An extensive comparison of ML-based diagnostic models provides a generalization paradigm for SCIM diagnosis. Results show that RF is the most accurate, with an accuracy of 79.25%, followed by NBC and QDA, with accuracies of 73% and 71.4%, respectively. Furthermore, computational cost assessment of the ML-based diagnostic models is conducted for improved diagnostic assessment of the models. Results show that RF's computational cost of 3.66 s is in an acceptable range, while NBC has the lowest at 0.003 seconds.  
The confusion matrix of the best-performing models revealed that the turn-to-turn fault was imprecisely predicted, providing an avenue for future research. Amongst the paper's limitations, it is believed that the developed easy, nonintrusive, low-cost FDI framework offers a reliable direction for motivating future work.  
Conceptualization, C.N.O. and U.E.A.; methodology, C.N.O. and U.E.A.; software, U.E.A.; formal analysis, U.E.A.; investigation, C.N.O. and U.E.A.; resources, C.N.O., U.E.A., and J.-W.H.; data curation, C.N.O. and U.E.A.; writing--original draft, C.N.O. and U.E.A.; writing--review and editing, C.N.O. and U.E.A.; visualization, U.E.A.; supervision, J.-W.H.; project administration, J.-W.H.; funding acquisition, J.-W.H. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Ministry of Science and ICT (MSIT), Korea, under the Grand Information Technology Research Center support program (IITP-2020-2020-0-01612) supervised by the Institute for Information & Communications Technology Planning & Evaluation (IITP).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to laboratory regulations.  
The authors declare no conflict of interest.
# Comprehensive Analysis of PPP-B2b Service and Its Impact on BDS-3/GPS Real-Time PPP Time Transfer
## 3 Results  
### Experimental Data and Processing Strategies  
The Multi-GNSS Experiment (MGEX) stations distributed across China and its surrounding areas provide real-time observations of navigation satellite systems. To assess the B2b-RTPPP time transfer performance with different constellations, we collected PPP-B2b messages using the SINO K803 kit, over a seven-day period from 2 April to 8 April, 2022 (DOY 92-98, 2022). Software developed based on the RTKLIB software package was used to decode the PPP-B2b products according to the ICD [22] and test PPP method. The GBM final products were downloaded from the GFZ. Figure 2 presents the location of five selected stations. Note that USUD station was taken as the central node, and four time links (MIZU-USUD, TLM2-USUD, JFNG-USUD, and LCK3-USUD) were designed.  
Figure 1: Principle of B2b-RTPPP.  
Figure 2: The location of selected stations used for B2b-RTPPP time transfer.  
The stations were selected based on the following criteria: (1) they can simultaneously track B1I, B3I, L1, and L2 signals; (2) they are located in China and its surrounding areas; and (3) the receivers, if possible, are connected to high-performance on-board clocks. Table 2 provides information on the five stations. The B2b-RTPPP processing strategies are presented in Table 3.
# State Estimation for DC Microgrids using Modified Long Short-Term Memory Networks
## 1 Introduction
The same simulation schemes are applied to the other methods of SE (e.g., conventional WLS, MLP-based, and normal LSTM-based SE) as a comparison to validate the performance of the proposed modified LSTM-based SE. The contributions of this paper in comparison with the existing literature can be summarized as follows:  
* The paper investigated a deep-learning-based state estimator for DC microgrids. * The state estimator adopted the LSTM network method, which until recently has only been applied in forecasting studies. * The LSTM for the state estimation of DC microgrids was modified to obtain better performance, using WLS-based loss function for training. The rest of this paper is structured as follows. In Section 2, the background of the conventional SE method for electrical power systems is explained. Section 3 covers the basics of deep learning methods, such as multilayer perceptron (MLP) and LSTM networks. Further discussion about SE for DC systems, the system configuration used for simulation, and the types of measurements for SE are presented in Section 4. Section 5 gives an overview of the proposed state estimator. The simulation outcomes of the previously studied and the proposed state estimators are then demonstrated and discussed in Sections 6 and 7, respectively. Finally, the conclusion is presented in Section 8.
## 4 Materials and Methods  
### Available Datasets  
An extensive examination of the datasets suitable for DIC analyses was conducted in the first step of the study, via an examination of the International Space Agency's satellite image databases and national/regional databases of DTM and aerial images. Among the remotely sensed datasets, all images with a suitable geometric resolution and pre-post event images were chosen:  
* COSMO-SkyMed SAR Images (in both ascending and descending geometry);
* LANDSAT 8 OLI-TIRS Images;
* DTM; and,
* High-resolution Aerial Optical Images.  
The complete COSMO-SkyMed (CSK) dataset includes 60 SAR amplitude images in ascending geometry that were acquired between May 2011 and May 2015, and 28 SAR amplitude images in descending geometry that were collected between August 2009 and May 2015 (Figure 4). All images were collected in StripMap HIMAGE acquisition mode, and their ground-range and azimuth resolutions are approximately 3 m. According to Reference [8], because of its side-looking acquisition mode, SAR images are subjected to geometrical distortions. The types of areas that are affected by overlays or shadows cannot be imaged by the sensor [57]. In fact, the topography and, more precisely, the local slope along the line of sight (LOS) of the satellite can generate distortions. Hence, a method of overcoming this limitation is to combine ascending and descending orbits over the same area.  
The LANDSAT 8 OLI-TIRS dataset includes two panchromatic images (with 15 m nominal resolution) acquired on 26 October 2013 (LC81880322013299 LGN00) and 15 February 2014 (LC81880322014046 LGN00).  
The high-resolution (HR) aerial optical image dataset includes two orthophotos collected in July and December 2013 (i.e., before and after the landslide failure) respectively, with similar geometric resolutions; i.e., 0.3 m/pixel and 0.2 m/pixel for the pre-failure and the post-failure images, respectively.  
Figure 4: Complete COSMO-SkyMed (CSK) Synthetic Aperture Radar (SAR) amplitude imagery dataset used in the analyses (in both ascending and descending geometries). The red line represents the landslide failure occurrence. In this diagram, all the available CSK scenes have been reported by highlighting the scenes used for the temporal average filtering process (in yellow and red) and those used directly in the DIC analyses without applying any type of temporal or spatial filter (in blue and green). The number of CSK SAR absolute amplitude images (in both acquisition geometries) is part of the whole CSK dataset, which covers a wider temporal window.  
Furthermore, through two airborne LiDAR surveys performed by local authorities before and after the failure, two high-resolution DTMs (\(1\times 1\) m cell size) were collected in July and December 2013, respectively.  
A synopsis of the available datasets is shown in Table 1, which provides a description of the platforms used, the types of sensors, the nominal geometric resolutions and the dates of image collection.
## 5 Experiments and Evaluation
### Dataset  
To test the proposed CM-DANA method create a synthetic dataset that simulates various types of abnormal behavior:  
1. Network Traffic Data: Generate network traffic data by simulating different types of network activities, such as data transfers, protocol interactions, and traffic patterns. Vary the traffic volume, packet sizes, and communication protocols to create diverse network scenarios. Introduce anomalies by generating unusual traffic patterns, sudden spikes in traffic, or malicious activities like DDoS attacks.
2. Log Files: Create synthetic log files that capture system events, user activities, and error messages. Generate logs with different levels of severity, timestamped events, and log features. Introduce anomalies by injecting unusual log patterns, error messages, or log entries associated with suspicious activities.
3. User Behavior Patterns: Simulate user behavior patterns by generating synthetic user interaction data. Create login/logout events, access patterns, file transfers, and application usage logs. Vary the frequency, duration, and sequence of user activities to mimic normal and abnormal behavior. Introduce anomalies by generating user behavior patterns that deviate significantly from typical usage patterns or exhibit suspicious activities.
4. Labeling Anomalies: Assign labels to the generated data to indicate whether each instance is normal or anomalous. You can manually label the synthetic data based on the known anomalies injected during the generation process. Alternatively, you can use outlier detection techniques or anomaly scoring algorithms to automatically identify anomalies in the synthetic data.
5. Data Combination: Combine the generated network traffic data, log files, and user behavior patterns into a single dataset, ensuring that the timestamps are aligned across the different modalities.  
Table 1 shows examples of anomalies injected into the synthetic dataset. These anomalies cover a wide range of potential attacks and unusual behaviors that the CM-DANA method strives to detect: