esa-sceva/satcom-chunk-collection huggingface dataset:
Specifically, we have the following: (1) The backbone of existing lightweight network architectures, due to the use of grouped convolutions, which divide the feature map into multiple groups, causes an insufficient exchange of feature information between each group. This overlooks some useful redundant features, leading to a weakened ability to represent features. (2) Existing lightweight networks often employ larger strides or larger pooling windows to reduce the dimensions of the feature map, resulting in the loss of spatial feature information. Due to the use of smaller receptive fields, the network struggles to integrate enough neighboring pixels to enhance the classification ability of individual pixels, thereby causing inefficiency in the network's convolutional structure. Through the analysis above, it is evident that previous designs of lightweight convolutional structures struggle to balance maintaining lightness with enhancing segmentation accuracy. Therefore, in this paper, we introduce RRMSA-Net, consisting of the Rapid Redundant Feature-Generation (RRG) and multi-level spatial aggregation (MSAS) modules. Specifically, we first prioritize spatial feature optimization as the network backbone, replacing standard convolution operations with a module that generates feature maps while reducing computational burden. Secondly, to enhance the feature representation capability of feature maps after linear transformations, we designed a set of dual-attention mechanisms. By weighting the attention of these feature maps in both the spatial and channel dimensions, we establish contextual dependencies across different dimensions on local features, improving the feature-extraction capability. Lastly, to fully utilize neighboring pixels for accurate category discrimination, we designed a multi-scale context-aggregation module aimed at enhancing the model's understanding of context through effective aggregation of neighboring spatial features, thereby achieving improvements in RS segmentation accuracy. Our contributions are as follows:  
1. We conducted a systematic analysis of the contribution of redundant features to segmentation and the impact of spatial contextual information on segmentation performance. 2. To address the issue of the loss of redundant features, we introduced the \"Rapid Redundant Feature-Generation\" module. This module achieves higher segmentation accuracy with significantly fewer computational resources than conventional backbone architectures, and notably enhances inference speed. 3. To tackle the problem of missing semantic information in individual pixels, we propose the \"multi-level spatial feature aggregation\" module. This module effectively aggregates neighboring spatial features to enhance the model's understanding of context, thereby improving segmentation accuracy.
## 5 Conclusions  
This study was conducted to develop a tensile constitutive model by following an inverse nonlinear analysis framework of flexural concrete beams reinforced with steel bars. An incremental-iterative analysis scheme was followed to study the nonlinear flexural beam behavior and generate the analytical graphs. The proposed tensile constitutive model has a sudden drop at cracking strain, followed by a descending curve down to zero at \(\left(1.4\varepsilon_{y}\right)\). Series of comparisons were performed to validate the accuracy of the adopted model against the global experimental behavior for a large pool of beams. Two extra comparisons were conducted to check the applicability of the model against the sectional response of different beams. As a result of these comparisons, the model showed an excellent agreement with the validating experiments for the sectional and the global responses. The second main goal of this study was to confirm the trilinear-based behavior for moment versus curvature and moment versus maximum compressive strain graphs. Two additional beams were used for that purpose. Through all the comparisons in this study, and by using the proposed model, the trilinear behavior was verified in all the sectional and the global responses. Even though the literature has several tension stiffening models, the authors believe that this new model is more objective since:  
1. The final degradation of cracked concrete is related to the yielding strain in steel rather than multiples of the cracking strain of the concrete.
2. The loss of energy due to cracking fracture is captured in a more pronounced way than earlier models.
3. The current model furnishes a single mathematical expression making it easier to implement in analytical formulations.  
By minimizing the error between the analytical and experimental load-deflection responses in terms of the tensile constitutive model parameters, it was concluded that the fracture energy level released upon cracking and the vanishing of the tension stiffening contribution as it relates to the yielding strain in steel represent the more rational findings.  
Conceptualization, H.A.R. and A.A.; methodology, A.A. and H.A.R.; validation, A.A. and H.A.R.; formal analysis, A.A.; writing--original draft preparation, A.A.; writing--review and editing, H.A.R.; visualization, A.A.; supervision, H.A.R.; project administration, H.A.R.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 25: [PERSON] (1997) [19] moment vs. maximum compressive strain analytical graphs.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to inclusion in a follow up study under investigation.  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 4 Communication Networks and Technologies in NMGs  
Communication networks in NMG are categorised into three main categories based on coverage distance and data rate [29]: Wide Area Networks (WANs), Field Area Networks (FANs) or Industrial Area Networks (IANs), and Home Area Networks (HANs) or building area networks (BANs), as illustrated in Figure 5. A relation between coverage distance and data rate requirements in NMG communication networks is presented in Figure 6.  
We summarise the communication technologies used in different network types in Table 2. Both wired and wireless communication technologies are utilised in NMGs, offering distinct capabilities in terms of coverage, data rates, and applicability. Wired technologies, such as Ethernet cables, offer high data rates with a limited coverage distance, while 4G cellular networks provide extended coverage at lower data rates. Various communication technologies are used in each type of communication network. Real-life NMGs, including communication technologies used, scale, and generation components, are tabulated in Table 3. Practical deployments demonstrate that multiple communication technologies \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Reference** & **Real Power** & \begin{tabular}{c} **Real Commun-** \\ **Network** \\ \end{tabular} & \begin{tabular}{c} **Congested** \\ **Network** \\ \end{tabular} & \
\begin{tabular}{c} **Real-Life** \\ **Background** \\ **Traffic** \\ \end{tabular} &
\begin{tabular}{c} **RTT Measure-** \\ **ment** \\ \end{tabular} \\ \hline
[13] & ✓ & & & & \\
[21] & ✓ & & ✓ & & \\
[22] & ✓ & & ✓ & & ✓ \\
[23] & & & ✓ & & \\
[24] & ✓ & ✓ & ✓ & & \\
[25] & & ✓ & & & ✓ \\
[28] & ✓ & ✓ & ✓ & ✓ & ✓ \\ This paper & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of QoD Measurements in Current Papers. Figure 5: Communication network types in NMGs are illustrated. Various types of power generation components, such as PVs, wind farms, and hydroelectric systems, can be used to generate electric power in WANs. FANs represent slightly smaller NMGs, such as industrial area NMG deployments. HANs are the smallest communication network type in NMGs. They consists of a small numbers of DERs such as home-based PVs [30]. are employed in NMGs to manage diverse generation components, with the generation capacity reaching up to 16 MW.
## 4 Theoretical Simulation and Experimental Verification
### Multi-Probe Array Field Experiment  
Figure 12 shows the experimental platform set up to simulate the coal mine site. The belt width of the belt conveyor is 400 mm, and the belt running speed is the same as that at the coal mine site, which is 0.3 m/s. Four multi-probe arrays are set one meter below the exit of the belt conveyor. These four multi-probe arrays are evenly spaced, with a detection range of 300 mm. Data are collected at a horizontal distance of 5 mm from the falling metallic foreign object. The position and horizontal width of the metallic foreign object are determined based on the range of signals received by the multi-probe array. The vertical width of the metallic foreign object is then calculated by the time range of the effective detection signals from the multi-probe array.  
A 300 \(\times\) 300 \(\times\) 10 mm alloy steel Mn13 metal plate was selected for the drop test experiment. The signal generator with a power amplifier emitted the same signal as in the single-probe test experiment. After the probe coil temperature stabilized, the experiment was started. The data acquisition card began collecting data when the metal foreign object was about to leave the conveyor belt.  
Figure 13 shows the valid data collection interval of PEC differential signals. The positions marked by circles in the figure represent the start and end points of the effective receiving signal range. All four multi-probe arrays detected effective differential signals, and it was determined that the horizontal width of the metal foreign object was 300 mm. By averaging the four differential signals, the effective detection data for the differential signals was from 424.4 ms to 485.4 ms. Based on the initial falling velocity of the metallic foreign object being the same as the operating speed of the belt conveyor (0.3 m/s), the calculated vertical width of the metallic foreign object is 290 mm. The relative error compared to the actual vertical width of 300 mm is 3.33%, which is less than 5% and meets the field testing requirements. Further analysis of the single-period waveform characteristics in the effective detection interval of the differential signals _Uref(t)_ and _Bref(t)_ confirmed the presence of non-ferromagnetic metal. The average peak value of the extracted signals was 45.8 mV, 1.1 mT, and the corresponding average peak times were 0.1 ms and 0.19 ms. According to Figures 10 and 11, it was confirmed that the dropped metal was a 10 mm thick alloy steel Mn13.  
Figure 12: Field experiment platform with the multi-probe array.
# Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review
## 3 Neural Network-Based Forecasting Approaches
### ANN-Based Forecasting Approaches
The authors of [168] presented the LSTM neural network model to forecast the delay time in a chaotic optical system and compared the model with the delayed mutual information method and autocorrelation function method. It is worth noting that the proposed model can also enhance the security and maturity of optical chaos secure communications. In [183], the authors presented a LSTM-based forecasting model by integrating ensemble and reinforcement learning techniques. Further, an adaptive gradient algorithm was used to train the network and validated on the Lorenz, Duffing, and Rossler systems. The authors of [184] developed a FFNN-based prediction model to estimate the change in future state values of a Rossler system. In [169], the authors presented a gate recurrent unit-based Deep RNN model to forecast time series of three chaotic systems, (i) Lorenz, (ii) Rabinovich-Fabrikant, and (iii) Rossler, which showed better performance than the LSTM-based Deep RNN model. This model can also be used for real-time applications to predict the hyper-turbulent frameworks to control the turbulence or synchronize the framework model.
# Comparative Study of Movie Shot Classification Based on Semantic Segmentation
###### Abstract  
The shot-type decision is a very important pre-task in movie analysis due to the vast information, such as the emotion, psychology of the characters, and space information, from the shot type chosen. In order to analyze a variety of movies, a technique that automatically classifies shot types is required. Previous shot type classification studies have classified shot types by the proportion of the face on-screen or using a convolutional neural network (CNN). Studies that have classified shot types by the proportion of the face on-screen have not classified the shot if a person is not on the screen. A CNN classifies shot types even in the absence of a person on the screen, but there are certain shots that cannot be classified because instead of semantically analyzing the image, the method classifies them only by the characteristics and patterns of the image. Therefore, additional information is needed to access the image semantically, which can be done through semantic segmentation. Consequently, in the present study, the performance of shot type classification was improved by preprocessing the semantic segmentation of the frame extracted from the movie. Semantic segmentation approaches the images semantically and distinguishes the boundary relationships among objects. The representative technologies of semantic segmentation include Mask R-CNN and Yolact. A study was conducted to compare and evaluate performance using these as pretreatments for shot type classification. As a result, the average accuracy of shot type classification using a frame preprocessed with semantic segmentation increased by 1.9%, from 93% to 94.9%, when compared with shot type classification using the frame without such preprocessing. In particular, when using ResNet-50 and Yolact, the classification of shot type showed a 3% performance improvement (to 96% accuracy from 93%).  
Keywords:shot type classification; semantic segmentation; CNN; Mask R-CNN; Yolact +
Footnote †: journal: Medical Image Processing
## Materials and Methods
### Temporal Change Detection  
To derive spatial and temporal patterns and to set up reference state for further comparisons, we conducted a multi-temporal analysis for selection of threshold values for the indicators used. We used  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vegetation Index** & **Equation** & **Author** \\ \hline Raw bands & G, R, NIR, MIR, SWIR & [50] \\ Normalized Difference Vegetation Index (NDVI) & \(\frac{\text{NIR}-\text{R}}{\text{NIR}+\text{R}}\) & [51] \\ Soil-Adjusted Vegetation Index (SAVI) & \(\frac{(1+\text{L})(\text{NIR}-\text{R})}{\text{NIR}+\text{R}+\text{L}}\) & [52] \\ Normalized Difference Wetness Index (NDWI) & \(\frac{\text{NIR}-\text{MIR}}{\text{NIR}+\text{MIR}}\) & [53] \\ Enhanced Vegetation Index (EVI) & \(2.5\frac{(\text{NIR}-\text{R})}{((\text{NIR})+6\text{R}-7.5\text{B}+1)}\) & [54] \\ Green Vegetation Index (GVI) & \(\frac{\text{IR}+\text{SWIR}}{\text{R}+\text{MIR}}\) & [55] \\ NDVIc & NDVI\(\cdot(1-\frac{(\text{MIR}-\text{MIRmin})}{(\text{MIRmax}-\text{MIRmin})})\) & [56] \\ Photosynthetic Vegetation (\%) Layer (PV) & N/A & [57] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Vegetation indices used in this study.  
the BFAST and BFAST monitor algorithms [59; 60] as the tools to perform a trajectory analysis of canopy cover derived from Landsat imagery (74) from the period 1987 to 2014. The Breaks For Additive Seasonal and Trend (BFAST) method is an iterative algorithm that combines the decomposition of time series into seasonal, trend, and remainder components as methods for detecting changes. BFAST integrates the iterative decomposition of time series into trend, seasonal and noise components with methods for detecting and characterizing changes within the time series [61].  
The workflow to implement the BfastSpatial algorithm required the following steps: (a) pre-processing of surface reflectance data; (b) inventorying and preparing data for analysis and (c) analysis and formatting of change detection results. The algorithms were implemented using the BfastSpatial package for R software available at [[http://github.com/dutri001/bfastSpatial](http://github.com/dutri001/bfastSpatial)]([http://github.com/dutri001/bfastSpatial](http://github.com/dutri001/bfastSpatial)).  
Change detection based on bi-temporal images [62] was implemented to detect Biomass and NPP change layers. The choice of this approach considered the fact that biomass/carbon data were available only in two different periods (2004-2007 and 2009-2011). Even though NPP data were available from the period 2007-2013, for the sake of completion, the bi-temporal change detection of this indicator was also computed.
# A Novel Nonlinear Magnetic Equivalent Circuit Model for Magnetic Flux Leakage System
## 5 Testing the MEC Model with FEA
### Elapsed Times  
MEC and FEA studies were conducted using a computer equipped with an Intel Core i9-12900H processor running at 2.9 GHz, 32 GB of RAM, and a 64-bit Windows 11 operating  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{\(w_{e}\) (mm)} & \multicolumn{2}{c}{**MRE\(\_w_{e}\) (\%)**} & \multirow{2}{*}{\(w_{s}\) (mm)} & **MRE\(\_w_{s}\) (\%)** \\  & **(For \(w_{s}\) 30 to 70)** & & **(For \(w_{e}\) 5 to 25)** \\ \hline
5 & 8.7 & 30 & 3.5 \\
10 & 6.0 & 40 & 4.5 \\
15 & 4.6 & 50 & 5.4 \\
20 & 3.7 & 60 & 6.2 \\
25 & 3.1 & 70 & 6.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The MRE values according to change in the \(w_{e}\) and \(w_{s}\) parameters.  
Figure 13: The MEC model’s relative errors for different \(w_{e}\) and \(w_{s}\) values.  
system. While the average time for FEA studies was 21.5 min, this duration was 5.2 s for MEC model analyses. Additionally, it was observed that consecutive FEA studies involve extra time-consuming processes such as geometry modifications, system reinstallations, and simulation restarts.
# Interference Mitigation for Metro Train Location Based on Karhunen-Loeve Decomposition
## 3 Interference Mitigation Algorithm Based on KL Decomposition
\[\hat{r}(t)=r(t)-\hat{J}(t). \tag{13}\]  
In the first interference mode mentioned in the second part, the power of the interference signal is relatively constant, which means that the eigenvalue distribution of the interfered signal is constant after KL expansion, and the interference signal can be reconstructed by selecting fixed eigenvalues. Compared with the chirp signal, the eigenvalue distribution of the single-tone interference signal is more concentrated, and the calculation amount of the reconstructed interference signal is smaller.
## 4 Discussion
### Lipophilicity  
Lipophilicity is a factor that significantly affects the biological activity of drugs. It plays a very important role in the interaction of the drug with the receptor, determining the toxicity and solubility of the substance. Lipophilicity influences pharmacokinetic processes in living organisms, especially the distribution process, i.e., the transportation of a drug across biological membranes [47]. The knowledge of lipophilicity parameters is therefore very important, both for the research devoted to searching for new drug structures with the required biological activity and for understanding the correct mechanisms of pharmacokinetics and distributing drugs already present on the market.  
In the case of methanol-water solvent systems (Tables 2 and 3), the highest logP\({}_{\text{EXP}}\) values of the tested hydrazide-hydrazones of phenylacetic acid **2-16** were calculated, with the exception of substances (**4-7**, and **13**). The lower logP\({}_{\text{EXP}}\) values were found for acetonitrile (except for acylhydrazones **1** and **16**).  
For all solvent systems used in our experiments, the highest logP\({}_{\text{EXP}}\) values were calculated for compounds **3**, **5**, **6**, **7** and **8** containing two halogen substituents (2-hydroxyphenyl) in _ortho_- and _para_-positions. Due to this fact, it can be stated that the position types of the halogen atoms in a 2-hydroxyphenyl ring did not affect the lipophilicity of these five acylhydrazones (**3**, and **5-8**).  
In the case of 2-hydroxynitrophenyl-substituted acylhydrazones (compounds **12**, **13** and **14**), differences were clearly seen. Hydrazide-hydrazone **13**, which was substituted with a 2-hydroxy-5-nitrophenyl, displayed the highest logP values for all solvent systems used in our experiments, which illustrates the importance of positional isomerism in the lipophilicity of these compounds. The acylhydrazone **2** substituted with 5-bromo-2-hydroxyphenyl showed similar logP values to compound **4** with 5-chloro-2-hydroxymethyl substituent (Tables 2 and 3).  
When compounds **9**, **10** and **11** are compared, the 2-hydroxy-3-ethoxyphenyl-substituted hydrazide-hydrazone of phenylacetic acid (**11**) was found to be lipophilic, just like compound **10**, which possessed a 3-methoxy-4-hydroxy-5-iodophenyl or acylhydrazone **9** substituted with a 2-bromo-3-hydroxy-5-methoxyphenyl. Such a situation was registered in the case of all solvent systems used. Bearing in mind all the tested acylhydrazones of phenylacetic acid **2-16**, the lowest lipophilicity was shown for compounds **15** and **16** substituted with the 2-hydroxy-3,5-dinitrophenyl and 2,3,4-trihydroxyphenyl, respectively (Tables 2 and 3).
###### Abstract  
Although there is an important lack of commercial thermoelectric applications mainly due to their low efficiency, there exist some cases in which thermoelectric generators are the best option thanks to their well-known advantages, such as reliability, lack of maintenance and scalability. In this sense, the present paper develops a novel thermoelectric application in order to supply power to volcanic monitoring stations, making them completely autonomous. These stations become indispensable in any volcano since they are able to predict eruptions. Nevertheless, they present energy supply difficulties due to the absence of power grid, the remote access, and the climatology. As a solution, this work has designed a new integral system composed of thermoelectric generators with high efficiency heat exchangers, and its associated electronics, developed thanks to Internet of Things (IoT) technologies. Thus, the heat emitted from volcanic fumaroles is transformed directly into electricity with thermoelectric generators with passive heat exchangers based on phase change, leading to a continuous generation without moving parts that powers different sensors, the information of which is emitted via LoRa. The viability of the solution has been demonstrated both at the laboratory and at a real volcano, Teide (Canary Islands, Spain), where a compact prototype has been installed in an 82 \({}^{\circ}\)C fumarole. The results obtained during more than eight months of operation prove the robustness and durability of the developed generator, which has been in operation without maintenance and under several kinds of meteorological conditions, leading to an average generation of 0.49 W and a continuous emission over more than 14 km.  
Keywords:thermoelectric generator; volcano surveillance; power supply; geothermal; LoRa; autonomous; heat pipe +
Footnote †: journal: Sensors
# Make Patient Consultation Warmer: A Clinical Application for Speech Emotion Recognition
## 2 Materials and Methods
### Experimental Design and Procedures
#### 2.5.3 Comparing and Analyzing the Recognition Results  
Previous studies have employed emotional expression by professional actors or self-recorded and labeled emotional speech files. In this study, emotion recognition was conducted with a speech from actual clinical practice, and the participants were not professional actors; therefore, the results were consistent with actual interpersonal communications. The recognition results of the clinical speech files using the trained SER model were compared with the manually labeled emotional data to calculate the SER accuracy.