esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Scale Feature Fusion Hybrid Convolution Attention Model for Birdsong Recognition
## 2 Research Method
### Multi-Scale Feature Fusion Hybrid Convolutional Attention Model(MUSCA)
#### 2.2.1 Hybrid Residual Convolutional Attention Block
**Spatial Attention Mechanism:** Integrating spatial attention into the convolution block aims to enhance the model's attention to various spatial locations in the input feature map and improve its ability to capture relevant spatial features, thereby improving its overall performance and generalization ability, as shown in Figure 6. Initially, the spatial attention mechanism computes the maximum and average values of the input features along the channel dimension, resulting in a feature map with identical dimensions as the input but with a single channel. Subsequently, these maximum and average values are concatenated along the channel dimension to form a feature map with two channels. This feature map then undergoes 2D convolution to fuse the bilinear features, following which the sigmoid activation function is applied to normalize the values between 0 and 1, yielding spatial attention weights. These weights are then multiplied elementwise with the input features, and the resulting product is added to the input to mitigate potential errors in subsequent layers due to deviations in spatial attention weights. The detailed structure of the spatial attention mechanism is elaborated in the diagram.
# Modeling of Historical Marine Casualty on S-100 Electronic Navigational Charts
## 4 Case Study
### Data Collection and Preprocessing  
Marine casualty reports were collected from three major sources. These reports provide official and reliable data on maritime accidents, each with differing levels of structure and detail. In total, 21,539 records were gathered from incidents reported between 2020 and 2024--454 from GISIS, 4354 from MAIB, and 16,731 from MTIS. Table 6 provides a summary of the sources, countries of origin, record counts, and a brief description of each dataset's characteristics. These data formed the basis for developing and testing the proposed model, enabling the construction of a representative and heterogeneous casualty dataset for model evaluation.  
The structure and format of the collected data exhibited notable variation across sources. Both the MAIB and MTIS datasets are semi-structured, following predefined field formats while also containing narrative fields that require contextual interpretation. Similarly, GISIS data is provided in a semi-structured tabular form; however, access to detailed case descriptions typically requires navigating to individual report pages. For instance, as shown in Figure 5, each GISIS record includes a summary view of key attributes--such as the incident type, location, and date--alongside unstructured narrative text describing the event. This layout, while informative, necessitates manual interpretation for consistent classification and structuring.  
To ensure consistency across the heterogeneous sources, a rule-based classification procedure was applied based on the comparative taxonomy described in Table 2. This allowed all records to be categorized into seven unified incident types regardless of their original classification schemes. The results of this categorization process are presented in Table 7, which shows the number of classified incidents per category from each data source.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source Name** & **County** & **Number of Records** & **Description** \\ \hline GISIS [28] & IMO & 454 & Standardized marine casualty reports from IMO’s global reporting system. \\ \hline MAIB [29] & UK & 4354 & Official investigation reports of UK maritime accidents. \\ \hline MTIS [30] & Republic of Korea & 16,731 & Domestic maritime traffic and accident data managed by Korean authorities. \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of marine casualty data sources and record counts.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Incident Type** & **GISIS** & **MAIB** & **MTIS** \\ \hline Hull and engine accessory damage & 34 & 1097 & 6181 \\ \hline Collision and contact & 64 & 553 & 4794 \\ \hline Navigational issues & 9 & 557 & 647 \\ \hline Flooding and sinking & 84 & 193 & 1916 \\ \hline Fire and explosion & 81 & 234 & 798 \\ \hline Stranding and grounding & 37 & 377 & 884 \\ \hline Other incident & 145 & 1343 & 1511 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Categorization results of marine casualty records across data sources.
## 2 Materials and Methods
### ATR-FTIR Analysis  
Fourier-transform infrared spectroscopy was carried out in attenuated total reflectance mode (ATR-FTIR) in order to identify the main components and phases of the samples. The instrumentation used to perform the analyses was a Nicolet 380 FTIR spectrometer (Thermo Electron Corporation, Waltham, MA, USA). The detection window used was between 400 cm\({}^{-1}\) and 4000 cm\({}^{-1}\), and 64 scans with a resolution of 4 cm\({}^{-1}\) were performed, along with smoothing operations (15 points).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sampling** & **Sample ID** & **Description** & **Type of Sample** \\ \hline \multirow{6}{*}{A} & \multirow{2}{*}{SG\_6} & Original area (pictorial layer and plaster) affected by & \multirow{2}{*}{Powders and fragments} \\  & & detachments and fractures, with efflorescence. & \\ \cline{2-3}  & & Integration area affected by partial detachment of the pictorial & \\  & & film. Exfoliations can be observed due to the presence of & \\  & & saline efflorescence. & \\ \cline{2-3}  & & SG\_8 & Original area (dark yellow pictorial layer and plaster), detached, & \\  & & affected by saline efflorescence, not cohesive, and fragile. & \\ \hline \multirow{6}{*}{C} & \multirow{2}{*}{SG\_4} & Integration area with yellow-brown pictorial finishing, affected & \multirow{2}{*}{Powders and fragments} \\  & & by detachments and exfoliations—presence of salt efflorescence. & \\ \cline{2-3}  & & SG\_5 & Original area (pictorial layer and plaster) affected by & \\  & & detachments and fractures. & \\ \cline{2-3}  & & SG\_29 & Saline efflorescence selectively retrieved from the surface. & Powders and plaster residues \\ \hline \multirow{2}{*}{D} & \multirow{2}{*}{SG\_3} & Integration area without pictorial finishing and affected by & \multirow{2}{*}{Powders} \\  & & detachment and exfoliation—presence of salt efflorescence. & \\ \hline \multirow{6}{*}{E} & \multirow{2}{*}{SG\_1} & Black paint layer and plaster layer; area affected by substrate & \multirow{2}{*}{Fragments of various dimensions} \\  & & detachment and efflorescence. & \\ \cline{2-3}  & & \multirow{2}{*}{SG\_2} & Plaster layer; area affected by complete detachment of the & Powders (sampled up to 1 cm \\  & & pictorial film and efflorescence. & from the surface) \\ \hline \multirow{2}{*}{Vault, summit} & \multirow{2}{*}{SG\_37A} & Grey pictorial layer on degraded plaster, retrieved from & \multirow{2}{*}{Powders and fragments} \\  & & degraded area with detachments of the superficial layers. & \\ \hline \multirow{2}{*}{Vault, summit} & \multirow{2}{*}{SG\_41B} & Purple pictorial layer on degraded plaster, retrieved from & \multirow{2}{*}{Powders and fragments} \\  & & degraded area with detachments of the superficial layers. & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sampling details: sampling area, sample ID, description, and type of sample.
# Application Study of Adaptive Tracking Algorithm in Active Noise Control System of Transformer
## 6 Conclusions  
In this paper, a noise active control system of power transformer based on an adaptive signal tracking method is proposed and implemented. The noise reduction experiment is carried out in a 220 kV substation, and the noise reduction effect is achieved in a certain area, which provides the basis for further application research. Through the analysis of the experimental process and the results concluded, the following conclusions:  
1. Based on the signal tracking method, the active control algorithm of power transformer noise is easy and simple to calculate, and can be applied to the actual operation of the transformer noise reduction system. The power transformer active control system based on adaptive signal  
Figure 14: Spectrum analysis of ANC on and off.  
tracking method can operate in the substation, and can achieve the average noise reduction of 5 dB(A) in the fan-shaped measurement area with the angle of 33\({}^{\circ}\).
2. At present, the adaptive signal tracking method proposed in this paper only uses 100 Hz to 500 Hz noise component as the tracking target. The system has obvious effects on noise reduction of 100 Hz to 300 Hz frequency components, but it is not good for noise reduction of 500 Hz components. If the system can improve the tracking ability of the medium and high frequency noise components, the noise reduction performance can be further improved.
3. The secondary sound source array scheme proposed in this paper is only designed by empirical method. If the secondary noise source array scheme is optimized according to the radiation noise and attenuation characteristics of the transformer noise, it is expected to obtain a larger noise reduction area and better noise reduction effect.
4. Moreover, the proposed algorithm in this paper is a single channel algorithm, and the secondary sound signals emitted by nine secondary sources are the same. If radiation characteristics and spectrum energy of each component transformer noise are analyzed and a multi-channel system is used, the noise reduction capability of transformer ANC system will be further improved.  
Conceptualization, L.Y. and J.W.; data curation, J.W.; formal analysis, J.W. and Q.L.; funding acquisition, L.Y.; investigation, J.W. and D.W.; methodology, L.Y. and J.W.; project administration, L.Y.; resources, L.Y.; software, L.Y. and J.W.; supervision, L.Y.; validation, Q.L. and D.W.; writing-original draft, J.W.; writing-review and editing, Q.L.  
This research was funded by National Science Foundation of China, grant number 51377123.  
The authors declare no conflict of interest.
###### Abstract  
Accurate monitoring of forest carbon flux and its long-term response to meteorological factors is important. To accomplish this task, the model parameters need to be optimized with respect to in situ observations. In the present study, the extended Fourier amplitude sensitivity test (eFAST) method was used to optimize the sensitive ecophysiological parameters of the Biome BioGeochemical Cycles model. The model simulation was integrated from 2010 to 2020. The results showed that using the eFAST method quantitatively improved the model output. For instance, the R\({}^{2}\) increased from 0.53 to 0.72. Moreover, the root-mean-square error was reduced from 1.62 to 1.14 gC\(\cdot\)m\({}^{-2}\cdot\)d\({}^{-1}\). In addition, it was reported that the carbon flux outputs of the model were highly sensitive to various parameters, such as the canopy average specific leaf area and canopy light extinction coefficient. Moreover, long-term meteorological factor analysis showed that rainfall dominated the trend of gross primary production (GPP) of the study area, while extreme temperatures restricted the GPP. In conclusion, the eFAST method can be used in future studies. Furthermore, eFAST could be applied to other biomes in response to different climatic conditions.  
carbon flux simulation; Biome-BGC; rubber plantation ecosystem; eFAST sensitivity analysis +
Footnote †: journal: International Journal of Environmental Research and Public Health
## 3 Results and Discussion
### Enzymatic Activity
#### 3.2.1 Pineapple Shell
These treatments did not prove to be advantageous and it would be more interesting, from an economic point of view, to use pineapple residues or extracted compounds in the first 24 h after the pineapple fruit processing by the industry. The enzymatic activity of polyphenol oxidase (PPO) in pineapple shell was increased by abiotic stress at 30 \({}^{\circ}\)C (91.69-99.78 U\(\cdot\)g\({}^{-1}\) dry matter), regardless of the storage time (Figure 5c). The heat treatment at 30 \({}^{\circ}\)C without storage time (0 h-99.78 \(\pm\) 0.95 U\(\cdot\)g\({}^{-1}\) dry matter) increased the enzymatic activity of PPO 128%, compared to the raw material (43.74 \(\pm\) 0.73 U\(\cdot\)g\({}^{-1}\) dry matter). The enzymatic activity of PPO showed higher values for the heat treatment of 30 \({}^{\circ}\)C, with an increase in enzymatic activity as the storage time decreases. The storage time promoted PPO activity (Figure 5c) by 56% and 78%, respectively, for 24 h (68.09 \(\pm\) 1.37 U\(\cdot\)g\({}^{-1}\) dry matter) and 48 h (77.82 \(\pm\) 1.00 U\(\cdot\)g\({}^{-1}\) dry matter). Abiotic stress with a temperature of 30 \({}^{\circ}\)C promoted a significant increase (\(p\) < 0.05) of the enzymatic activity of PPO, while heat treatment with a temperature of 40 \({}^{\circ}\)C with storage time (0 h-29.37 \(\pm\) 1.15 U\(\cdot\)g\({}^{-1}\) dry matter and 24 h-35.03 \(\pm\) 1.33 U\(\cdot\)g\({}^{-1}\) dry matter)) significantly reduced the activity of PPO (\(p\) < 0.05), compared to the raw material (43.74 \(\pm\) 0.73 U\(\cdot\)g\({}^{-1}\) dry matter). PPO activity has also been evaluated in other fruits. The enzymatic activity of PPO in apples and pears showed greater activity between 30 and 40 \({}^{\circ}\)C [57]. Similarly, to the results obtained for apples and pears, the pineapple shell samples used in the present work showed higher values of PPO enzymatic activity in treatments at 30 \({}^{\circ}\)C (91.69-99.78 U\(\cdot\)g\({}^{-1}\) dry matter) than in treatments at 40 \({}^{\circ}\)C (29.37-53.67 U\(\cdot\)g\({}^{-1}\) dry matter). Pineapple PPO has less cresolase activity than other plants, but it catalyzes the oxidation of diphenol to \(o\)-quinone in the presence of molecular oxygen [58]. The pineapple shell samples were packed under vacuum, which limits the availability of oxygen and can reduce the occurrence of oxidation reactions.
## 4 Label Setting Based Heuristic
### Notations
The embedding is characterized by a cost \(C^{\Gamma}(S)\) given by the sum of the cloud cost \(C^{\Gamma}_{cl}(S)\) and bandwidth cost \(C^{\Gamma}_{bu}(S)\). According to the above introduced parameters, we can express \(C^{\Gamma}_{cl}(S)\) and \(C^{\Gamma}_{bw}(S)\) as follows:  
\[C^{\Gamma}_{cl}(S)=T_{s}\sum_{v\in V_{NFV1-bp}}c_{v}^{core}\sum_{w\in V_{VM}}x_ {vw}^{\Gamma}(S)\left[\frac{f_{v}(S)}{\sum_{h=1}^{K}\tau_{h,v}\frac{C_{p,h}}{ \tau_{h}^{2}}}\right] \tag{1}\]  
\[C^{\Gamma}_{bw}(S)=T_{s}c_{b}\sum_{l\in L_{v}}\sum_{p\in\Psi}\sum_{e\in L_{o} }B_{FS}S_{l,p}(S)L_{e}\delta_{e,p} \tag{2}\]  
where \(T_{s}\) is the duration of the traffic state \(S\), \(\delta_{e,p}\) is a binary parameter assuming the value \(1\) if the link \(e\in L_{o}\) belongs to the path \(p\), otherwise it is \(0\). \(S_{l,p}(S)\) is the number of FSs to be allocated on the path \(p\) to support the virtual link \(l\); \(S_{l,p}\) depends on the bandwidth \(f_{l}(S)\) requested by the link \(l\) in the traffic state \(S\) and the path length that determines the most suitable modulation system (BPSK, QPSK, 8 QAM, 16 QAM). The change of a traffic state may lead to an embedding change that needs lightpath changes and the reconfiguration of optical switches. The proposed heuristic aims at choosing embeddings that minimize the cloud and bandwidth costs and under the constraint to limit the number of optical switch reconfigurations. In particular when the traffic state changes from \(S_{i}\) to \(S_{j}\) and the embedding changes from \(\Gamma_{i}\) to \(\Gamma_{j}\), the number \(R(\Gamma_{i},S_{i},\Gamma_{j},S_{j})\) of optical switch reconfigurations can be expressed as follows:  
\[R(\Gamma_{i},S_{i},\Gamma_{j},S_{j})=\sum_{l\in V_{VM}}\sum_{e\in L_{o}}(1- \rho_{l,e}) \tag{3}\]  
where \(\rho_{l,e}\) is a variable assuming the value \(1\) if the virtual link \(l\) has changed the occupied resources in the optical link \(e\) after the embedding change. We provide an Integer Linear Programming (ILP) formulation of the cloud and bandwidth resource allocation problem in Appendix A. The problem is inherited from the one proposed in [16] with a more simple formulation and the addition of the constrain on the maximum number of switch reconfigurations.
## 4 Conclusions  
This work establishes a viable mixotrophic algal cultivation strategy using glucose-supplemented ADSW to synergistically enhance biomass production, nutrient removal, and lipid synthesis. The optimization with 20 g/L of glucose in ADSW maximized the biomass yield (271.31 mg/L/day), pigment accumulation, and nutrient removal efficiency, achieving an 88.70% TN and 82.93% TP removal. The process simultaneously generated high-value bioproducts: 45.59% lipids, 29.72% proteins, and 13.05% carbohydrates. Notably, the lipid productivity (123.86 mg/L/day) and balanced saturated (50.77%) and unsaturated (49.23%) FAME profiles validate the potential of _C. vulgaris_ FACHB-8 for both wastewater treatment and biodiesel feedstock applications.  
However, these findings are constrained by laboratory-scale conditions without the verification of industrial-scale robustness or interference from complex contaminants in raw wastewater. Future research should focus on scaling the proposed cultivation process in pilot systems to validate its technical and economic feasibility under real-world conditions. Additionally, evaluating the adaptability of this glucose-enhanced mixotrophic strategy across diverse wastewater streams (e.g., municipal and industrial) could broaden its applicability. Exploring algae-bacteria symbiotic consortia may further enhance the nutrient recovery efficiency and system stability by leveraging microbial cross-feeding mechanisms. Further investigation into the integration of carbon supplementation with other nutrient optimization strategies (e.g., nitrogen-phosphorus ratios) may unlock synergistic effects to maximize both the biomass productivity and pollutant removal efficiency.  
Conceptualization, B.Y. and J.F.; data curation, C.H., Q.C., J.W., X.C., and H.Y.; writing--original draft preparation, C.Z. and M.Y.; writing--review and editing, B.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Scientific Research Fund of Hunan Provincial Education Department (No. 22A0151), the National Natural Science Foundation of China (No. 31800076), the Scientific Research Project of Hunan Provincial Department of Science and Technology (No. 2023 NK4146), and Natural Science Foundation of Hunan province, China (No. 2019J)50245).  
Not applicable.  
The data of this study are available from the correspondence author upon reasonable request.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Thermal Analysis  
Figure 8 illustrates the thermal analysis results for samples M1, M2, M3, and M4 as TGA curves. The initial examination of these curves focused on M1 and M3, which were pure PLA-based samples containing AgNPs. M1 began to decompose, presenting an onset temperature of 312 \({}^{\circ}\)C and reaching its peak breakdown rate at 337 \({}^{\circ}\)C. In contrast, M3 began to degrade at an onset temperature of 303 \({}^{\circ}\)C, with its maximum thermal breakdown rate occurring at approximately 324 \({}^{\circ}\)C. The data suggest that the addition of the AgNPs via solution casting not only reduced the wettability properties but also reduced the thermal stability, which could be due to a correlation between the crystallinity of the PLA materials and their thermal stability, with lower crystallinity corresponding to lower thermal stability.  
In contrast, the presence of PEG-PPG-PEG in the M2 and M4 samples reduced the onset temperature of the PLA phase to 272 \({}^{\circ}\)C for M2 and to 290 \({}^{\circ}\)C for M4. These samples exhibited two decomposition peaks. The first peaks occurred at approximately 304 \({}^{\circ}\)C for M2 and at 320 \({}^{\circ}\)C for M4, signifying the thermal degradation of the PLA matrix. The second peaks indicate the thermal breakdown of PEG-PPG-PEG at 393 and that of M2 and M4 at 411 [35]. This fluctuation in the PLA peak may be attributed to the lubrication of the PLA molecules due to the PEG-PPG-PEG content. Therefore, M2 and M4 demonstrated lower thermal decomposition compared to M1 and M3, possibly due to the PEG-PPG-PEG plasticizing the PLA and reducing its crystallinity [11]. Overall, the addition of AgNPs decreased the thermal stability of the PLA-based textile compared to that of the pristine sample, as shown in Figure S3.
###### Abstract  
The carbon use efficiency (CUE) of ecosystems, expressed as the ratio of net primary production (NPP) and gross primary production (GPP), is extremely sensitive to climate change and has a great effect on the carbon cycles of terrestrial ecosystems. Climate change leads to changes in vegetation, resulting in different CUE values, especially on the Qinghai-Tibet Plateau, one of the most climate-sensitive regions in the world. However, the change trend and the intrinsic mechanism of climate effects on CUE in the future climate change scenario are not clear in this region. Based on the scheme of the coupled model intercomparison project (CMIP6), we analyze the simulation results of the five models of the scenario model intercomparison project (ScenarioMIP) under three different typical future climate scenarios, including SSP1-2,6, SSP3-7.0 and SSP5-8.5, on the Qinghai-Tibet Plateau in 2015-2100 with methods of model-averaging to average the long-term forecast of the five several well-known forecast models for three alternative climate scenarios with three radiative forcing levels to discuss the CUE changes and a structural equations modeling (SEM) approach to examine how the trends in GPP, NPP, and CUE related to different climate factors. The results show that (1) GPP and NPP demonstrated an upward trend in a long time series of 86 years, and the upward trend became increasingly substantial with the increase in radiation forcing; (2) the ecosystem CUE of the Qinghai-Tibet Plateau will decrease in the long time series in the future, and it shows a substantial decreasing trend with the increase in radiation forcing; and (3) the dominant climate factor affecting CUE is temperature of the factors included in these models, which affects CUE mainly through GPP and NPP to produce indirect effects. Temperature has a higher comprehensive effect on CUE than precipitation and CO\({}_{2}\), which are negative effects on CUE on an annual scale. Our finding that the CUE decreases in the future suggests that we must pay more attention to the vegetation and CUE changes, which will produce great effects on the regional carbon dynamics and balance.  
carbon use efficiency (CUE); gross primary production (GPP); climate change; Qinghai-Tibet Plateau; CMIP6; model-averaging +
Footnote †: journal: Physics Letters B
## 3 Experiments and Discussion
### Parameter Analysis
#### 3.3.2 Effect of Dropout Probability Values on Accuracy  
A dropout layer prevents the model from relying too much on some local features by randomly discarding the parameter values of some neurons, thereby enhancing model robustness and preventing overfitting. Table 5 shows the effects of different dropout values on accuracy.