esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Conclusions  
The two sections of metal layers coated on the same Bragg grating caused two resonance peaks with different temperature sensitivities.  
The experimental results show that \(\Delta K^{\prime\prime}{}_{T}\) < \(\Delta K_{T}\) < \(\Delta K^{\prime}{}_{T}\), which is in agreement with the modeling analysis. From the experimental results and the analysis, it can be concluded that Type B SMC-FBG can result in the most obvious difference in the temperature sensitivity between the two resonance peaks among these three type. In Type B, when the outer stepped-metal coating can be successfully plated, with a thinner thickness and a smaller thermal expansion coefficient of the inner coating, and with a larger thermal expansion coefficient of the outer coating, a more obvious difference of the temperature sensitivity is caused. We can choose another metal material for Type B SMC-FBG to restructure the dual-peak resonance with a much bigger difference between the two peaks, e.g., aluminum/nickel and silver/nickel. This kind of SMC-FBG can be used as a dual-parameter sensor which can measure two physical parameters at one location simultaneously.  
On the other hand, the spectrum deformation of Type A is much less than Type B and Type C from the experimental results. We consider that the spectrum deformation is related to the stresses caused by the plating process. We will study this problem in future.  
Data curation, Y.F.; Methodology, Y.F.; Project administration, H.Z.; Software, Z.-D.Z.; Validation, E.T.; Writing--original draft, Y.F.; Writing--review & editing, E.T.  
This research was funded by the National Natural Science Foundation of China [grant number 51665039].  
The authors declare no conflict of interest.
## 4 Construction and Optimization for the Deepfake Detection Model
### Construction of the FG-TEFusionnet Model for Deepfake Detection
#### 4.3.3 Multi-Feature Decision Fusion Module  
The multi-feature extraction and decision fusion framework in this study incorporates three distinct network branches. In the network branch dedicated to inter-frame information extraction, the RNN _g1_ leverages the SENet attention mechanism to extract geometric features from the video input, yielding the branch prediction _pred\({}_{1}\)_. Concurrently, RNN _g2_ extracts velocity features of the optimal facial landmarks, resulting in the branch prediction _pred\({}_{2}\)_. Within the network branch focused on intra-frame information extraction, EfficientNet integrates the WSDAN network and an image texture enhancement module to extract detailed texture information from facial regions within the input video frames. This process effectively highlights the extracted features, with the output prediction result labeled as _pred\({}_{3}\)_. The final multi-feature extraction and decision fusion network predict the overall outcome pred for detecting deepfake videos. Consequently, depending on the type of prediction results, the voting method is employed to fuse and finalize the outcomes from each branch, as depicted in Equation (17).  
\[y_{pred}=f(pred_{1}+pred_{2}+pred_{3}) \tag{17}\]  
In Equation (17), f denotes the voting method commonly employed in machine learning classification tasks. Furthermore, the decision logic of base classifiers in the voting method adheres to a hard decision rule based on majority consensus. Specifically, if two of the three independent branches reach consensus on a sample's class prediction, the consensus prediction is adopted as the ensemble result. Conversely, when branch predictions diverge (two votes are false, one vote is true), the system defaults to a false classification.
## 3 Results and Discussion
### XRD and Morphology Analysis of Composites  
Figure 5a,b shows the XRD patterns of the pure PP and composite PP-20%LDH with 20% Ni\({}_{0.2}\)Mg\({}_{2.8}\)Al-LDH, respectively. The XRD pattern of PP-20%LDH shows the characteristic reflections of Ni\({}_{0.2}\)Mg\({}_{2.8}\)Al-LDH and PP. The XRD analysis confirmed that the Ni\({}_{0.2}\)Mg\({}_{2.8}\)Al-LDH is dispersed in the PP matrix. Furthermore, the (003) diffraction peak of Ni\({}_{0.2}\)Mg\({}_{2.8}\)Al-LDH retains its original position in the PP-20%LDH. This result indicates that the intercalation structures of Ni\({}_{0.2}\)Mg\({}_{2.8}\)Al-LDH do not exist in the PP-20%LDH.  
It is well known that good dispersion of LDH in the polymer matrix can effectively improve the thermal and mechanical performances of the composites [48]. In order to observe the dispersion of the Ni\({}_{0.2}\)Mg\({}_{2.8}\)Al-LDH platelets in the PP matrix, the TEM images at 150,000 times magnification for composite PP-5%LDH and PP-20%LDH were shown in Figure 6a,b, respectively. It can be seen from Figure 6a that a small amount of the intact nano-sized flake Ni\({}_{0.2}\)Mg\({}_{2.8}\)Al-LDH particles are presented in the PP matrix. Therefore, the XRD pattern of PP-20%LDH shows the characteristic reflections of Ni\({}_{0.2}\)Mg\({}_{2.8}\)Al-LDH. At the same time, a large number of small parts with lateral size of 10-40 nm appear in the PP-5%LDH, which are caused by the break of Ni\({}_{0.2}\)Mg\({}_{2.8}\)Al-LDH layers. For PP-20%LDH, the many exfoliated Ni\({}_{0.2}\)Mg\({}_{2.8}\)Al-LDH layers are dispersed disorderly in the PP matrix. The above morphologies observed by TEM can be confirmed that Ni\({}_{0.2}\)Mg\({}_{2.8}\)Al-LDH showed the homogeneous distribution in the PP matrix. Furthermore, the PP-5%LDH and PP-20%LDH composites with the nano-sized filler are the mixed intact flake and exfoliated structures [49].  
Figure 4: Transmission electron microscope (TEM) image (100,000\(\times\)) of Ni\({}_{0.2}\)Mg\({}_{2.8}\)Al–LDH.  
Figure 5: XRD patterns of the pure polypropylene (PP) and PP–20%LDH; (**a**) pure PP; and (**b**) PP–20%LDH. The asterisk represents the diffraction peak of PP.
# Experimental Study on the Straw Flexible Space Assembly Flood Control System
## 3 Proposal and Validation of the Straw Flexible Space Theory
### Straw Flexible Space Theory Validation Test  
A straw flexible water stop was prepared and the straw device was made as dense as possible. The straw device was placed in the water stop tester as shown in Figure 3, without squeezing the straw device excessively. The distance between the water baffles and the backing board was measured as the initial condition for the straw space test. Once the assembly was complete, the tester system was filled with water to 750 mm and left to stand for a while. A certain amount of time elapsed before the straw absorbed water and expanded, and the distance to which the water-bearing board moved was measured at regular intervals.  
The test was carried out on an hourly basis, recording the water absorption after 1, 3, and 6 h, and analyzing the water absorption and swelling of the straw by the size of the flexible space at different times. As shown in Figure 4, water was added to the test machine  
Figure 3: Flood control system test rig. (**a**) Water-retaining test machine. (**b**) Straw flexible space test.  
reservoir to 750 mm and test data was obtained after 1, 3, and 6 h. The swelling of the straw in the test space was analyzed. The test results show that the straw swelled after a certain water absorption time. The width of the straw space in the initial state was 165 mm; after 1 h it was 174 mm; after 3 h it was 183 mm; and after 6 h it was 186 mm. The distance that the swelling pushed the water baffles showed that it pushed 9 mm at the beginning of 1 h, 9 mm from 1-3 h, and only 3 mm from 3-6 h. The swelling started with a rapid expansion, and for the later expansion rate, it slowed down. After 6 h, the straw expansion was 11%, and at this time, the straw water absorption and expansion of the reaction force was seen, as straw water absorption and expansion of the water baffle has a supporting role, and straw as a flood control system filler can enhance the stability of the flood control system. The theoretical assumption of a flexible spatial flood control system with straw is satisfied.
###### Abstract  
Drought causes significant losses in vegetation net primary productivity (NPP). However, the lack of real-time, large-scale NPP data poses challenges in analyzing the relationship between drought and NPP. Solar-induced chlorophyll fluorescence (SIF) offers a real-time approach to monitoring drought-induced NPP dynamics. Using two drought events in the Huang-Huai-Hai Plain from 2010 to 2020 as examples, we propose a new SIF/NPP ratio index to quantify and evaluate SIF's capability in monitoring drought-induced NPP dynamics. The findings reveal distinct seasonal changes in the SIF/NPP ratio across different drought events, intensities, and time scales. SIF demonstrates high sensitivity to commonly used vegetation greenness parameters for NPP estimation (R\({}^{2}\) > 0.8, \(p\) < 0.01 for SIF vs NDVI and SIF vs LAI), as well as moderate sensitivity to land surface temperature (LST) and a fraction of absorbed photosynthetically active radiation (FAPAR) (R\({}^{2}\) > 0.5, \(p\) < 0.01 for SIF vs FAPAR and R\({}^{2}\) > 0.6, \(p\) < 0.01 for SIF vs LST). However, SIF shows limited sensitivity to precipitation (PRE). Our study suggests that SIF has potential for monitoring drought-induced NPP dynamics, offering a new approach for real-time monitoring and enhancing understanding of the drought-vegetation productivity relationship.  
solar-induced chlorophyll fluorescence (SIF): drought monitoring; net primary productivity (NPP); SIF/NPP ratio index; Huang-Huai-Hai (HHH) Plain; normalized difference vegetation index (NDVI) +
Footnote †: journal: remote sensing
###### Abstract  
The demand for fresh water is on the increase, and the irrigation industry in Australia is looking to a future with less water. Irrigation consumes the bulk of the water extracted from various sources, and hence the efficiency of its use is of outmost importance. This paper reviewed the advancements made towards improving irrigation water use efficiency (WUE), with a focus on irrigation in Australia but with some examples from other countries. The challenges encountered, as well as the opportunities available, are also discussed. The review showed that improvements in irrigation infrastructure through modernisation and automation have led to water savings. The concept of real-time control and optimisation in irrigation is in its developmental stages but has already demonstrated potential for water savings. The future is likely to see increased use of remote sensing techniques as well as wireless communication systems and more versatile sensors to improve WUE. In many cases, water saved as a result of using efficient technologies ends up being reused to expand the area of land under irrigation, sometimes resulting in a net increase in the total water consumption at the basin scale. Hence, to achieve net water savings, water-efficient technologies and practices need to be used in combination with other measures such as incentives for conservation and appropriate regulations that limit water allocation and use. Factors that affect the trends in the irrigation WUE include engineering and technological innovations, advancements in plant and pasture science, environmental factors, and socio-economic considerations. Challenges that might be encountered include lack of public support, especially when the methods used are not cost-effective, and reluctance of irrigations to adopt new technologies.  
water use; irrigation efficiency; remote sensing; emerging technologies; real-time control; surface irrigation +
Footnote †: journal: Water
## 3 Results and Discussion
### Ablation Study  
To ascertain the contribution of individual elements within the NHLBVNet architecture introduced herein, the following ablation experiments were designed:  
* NHLBVNet--ResNet In the NHLBVNet model, the ResNet structure is eliminated, which means that in the combustion feature extraction module, the output of the linear layer is directly used as the output of the combustion feature extraction module.
* NHLBVNet--FCL  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & \(\mathbf{R}^{2}\) & **MAE** & **RMSE** \\ \hline MLP & 0.766 & 19.326 & 7.934 \\ LR & 0.783 & 18.151 & 7.011 \\ XGBoost & 0.931 & 6.013 & 2.812 \\ XGBoost + MLP & 0.956 & 3.838 & 1.959 \\
**NHLBVNet** & **0.993** & **1.892** & **1.225** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The best performance comparison on dataset.  
Figure 6: (a) XGBoost and (b) NHLBVNet prediction results.  
The feature combination layer in the NHLBVNet model is eliminated, no longer performing the collection and combination of features, and instead, the output from the feature extractor layer serves directly as the output for the combustion feature module.
* NHLBVNet--Attention Layer (Att) The attention structure in the NHLBVNet model is eliminated, and attention weights are no longer assigned and calculated for features; all features are considered equally important for model learning.  
The results of the ablation experiments for the corresponding modules are listed in Table 5. When the respective structures are removed, the model performance is affected to varying degrees, which validates the necessity of each module for NHLBVNet.
# Relationship between Fault Level and System Strength in Future Renewable-Rich Power Grids
## 4 Fault Level Contribution of PE-Based RESs According to IEC 6090 Standards  
The SC current contributions of PE-based RESs are limited and of different characteristics compared with those that can be observed from SGs. Due to the fast control response of the PE interfaces utilized in such RESs, they are modeled as current sources with fixed contribution according to the latest version of the IEC 60909 standards [10]. At first, the traditional Thevenin equivalent method is used in the fault calculation to determine the SC current without taking RESs into account. Then, the SC current contribution of the RESs is added to those calculated values using superposition. In other words, the SC contribution of PE-based RESs is not accounted for with the impedance model utilized for the SC current calculations. To account for their SC currents, PE-based RESs are modelled as current sources in the positive sequence with infinite impedances. It should be noted that the positive-sequence infinite impedance \(Z_{RES}\) is presumed to be shuntly connected; as a result, it is ignored and has no impact on the RESs fault contribution to the faulty bus. The value of the current source is typically inductive and it is usually provided by the manufacturer. Note that this value depends on several parameters, such as the converter overrating capability during the fault, the control strategy, and the FRT requirement. It is common to presume that the highest current injected by the PE-based RES, \(IRES_{max}\), is limited to approximately 1-1.4 p.u. of the converter's rated current [9]. However, the SC contribution of the RESs at the faulty bus depends on both the transfer impedance between the RES source and the faulty bus, and the Thevenin equivalent impedance at the faulty bus, as expressed in Equation (7):  
\[IRES_{SC}\ =\ \frac{1}{Z_{k}}\sum_{j\ =\ 1}^{j\ =\ M}Z_{ij}\times IRES_{max} \tag{7}\]  
where,  
* \(M\) is the number of connected PV systems.
* \(IRES_{max}\) is the maximum current source of the RES system connected to the \(j\)th bus.
* \(IRES_{SC}\) is the total SC current contribution of all connected RESs.  
Figure 3: Thevenin equivalent of the system seen at the capacitor bank.  
\(Z_{ij}\) is the transfer impedance between the bus \(j\) and the faulty bus \(i\).
* \(Z_{k}\) is the equivalent impedance at the faulty bus \(k\).
## 5 Use Cases
### Air Quality Monitoring
For instance, by changing the sensing requirements in the specifications of the nodes we obtained different node software suitably configured for programming (e.g., with the node IDs from the planning) and also hardware specifications that make sure that the node is capable to support the operation and sensing functions at run-time in each of those configurations. For each of these, the DT generated also the models for network simulation. From these simulations we discovered that the packet transmission rate achieved with the selected schedule was not suitable. Thus, we iteratively refined the planning and the requirements until the network operation proved satisfactory. In the end, the sensor node that we selected was composed of:  
* A Nucleo Development Board, NUCLEO-F401 RE, which enables to build and evaluate the prototype with the STM32 F4 family of high performance microcontrollers in an embedded application;
* An Xbee Zigbee Shield that allows an Arduino board to communicate wirelessly using Zigbee;
* A Digi International XBP24 BZ/SIT-004, which is an embedded RF module that provides low-cost, low-power, wireless connectivity using the ZigBee PRO Feature;
* A MOSFET Power Controller to switch a battery supply on and off;
* A Grove-Gas Sensor (MQ9-MQ5) that is useful for gas leakage detecting since it can detect Carbon Monoxide, Coal Gas, Liquefied Gas, LPG, Natural Gas, Town Gas;
* A Grove-Temp & Humi Sensor, which is a temperature and humidity sensor that provides a pre-calibrated measurement. For proper operation, both node types need batteries, solar panels, a communication module, and antennas for in-field and long-range communications. Figure 29 shows one sensor, with its solar panel. Network planning and application development took about two days of work. After this, the physical deployment of ten nodes in the field took two more days of work, in which two engineers went by car, sometimes in poor weather conditions and, for some nodes, on rough terrain. We can estimate that a trial-and-error approach, in which we would have to physically install more than ten configurations and topologies, would have probably required more than one week just for the physical deployment of the network, and an additional week to perform in-field tests and optimization. Thus, the use of the WSN-DPCM platform saved us significant time and costs. Using the middleware-like services embedded in the node software by DT synthesis according to node specifications, the CMT was able to connect at run-time to node software. The CMT enabled us to perform monitoring operations directly from our desk, with no need of in-field tests. This way we were able to continuously monitor the operation of the WSN deployed in the field through CMT interfaces to check that our WSN was properly operating.
###### Abstract  
In emergency rescue scenarios, drones can be equipped with different payloads as needed to aid in tasks such as disaster reconnaissance, situational awareness, communication support, and material assistance. However, rescue missions typically face challenges such as limited reconnaissance boundaries, heterogeneous communication networks, complex data fusion, high task latency, and limited equipment endurance. To address these issues, an unmanned emergency support system tailored for emergency rescue scenarios is designed. This system leverages 5G edge computing technology to provide high-speed and flexible network access along with elastic computing power support, reducing the complexity of data fusion across heterogeneous networks. It supports the control and data transmission of drones through the separation of the control plane and the data plane. Furthermore, by applying the Tammer decomposition method to break down the system optimization problem, the Global Learning Seagull Algorithm for Gaussian Mapping (GLSOAG) is proposed to jointly optimize the system's energy consumption and latency. Through simulation experiments, the GLSOAG demonstrates significant advantages over the Seagull Optimization Algorithm (SOA), Particle Swarm Optimization (PSO), and Beetle Antennae Search Algorithm (BAS) in terms of convergence speed, optimization accuracy, and stability. The system optimization approach effectively reduces the system's energy consumption and latency costs. Overall, our work alleviates the pain points faced in rescue scenarios to some extent.  
Gaussian global seagull algorithm; resource optimization method; UAV; 5G; mobile edge computing; emergency support +
Footnote †: journal:  
0000-0001-4500-3227-3227][PERSON]  
1 Air Defense and Antimissile School, Air Force Engineering University, Xi'an 71000, China; [EMAIL_ADDRESS] (S.H.); [EMAIL_ADDRESS] (M.W.)
# A Bird Vocalization Classification Method Based on Bidirectional FBank with Enhanced Robustness
## 5 Experiment and Result Analysis
### Result Analysis
To further verify the effectiveness performance of the proposed function, the denoising effects of each function on real signals are compared in Figure 11.  
In addition to the valuable signals, the original audio also contains noise with significant amplitude. All three threshold functions can effectively suppress noise and reduce the noise amplitude. The improved threshold function better retains the characteristics of the original waveform and reduces the noise intensity of the waveform. The hard threshold causes the loss of continuity and waveform distortion. The soft threshold introduces permanent errors to the signal. Compared with the other two threshold functions, the waveform amplitude is obviously amplified or contracted. The proposed method enhances the traditional threshold function, establishing a foundation for subsequent classification tasks. Group II: comparison of different features in classification performance  
The classification performances of five different features were compared. The evaluation metrics included Accuracy, F1 score, Kappa, Precision, and Recall. MFCC, FBank, iWAVE-FBank, iWAVE-iFBank, and iWAVE-BiFBank were compared under the same conditions. The classification experiments were repeated ten times, and the average of the results was taken as the final outcome, as shown in Table 6 and Figure 12. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Threshold Function**} & \multicolumn{4}{c}{**Blocks**} & \multicolumn{4}{c}{**Bumps**} \\ \cline{2-9}  & **0.01** & **0.5** & **3** & **10** & **0.01** & **0.5** & **3** & **10** \\ \hline Hard-Threshold [19] & **35.76** & 24.30 & 19.98 & **17.66** & 32.71 & 24.26 & 19.87 & 15.70 \\ Soft-Threshold [20] & 28.43 & 21.87 & 18.21 & 16.26 & 27.52 & 20.50 & 16.21 & 13.48 \\ Proposed Method & 35.70 & **24.50** & **20.45** & 17.05 & **33.54** & **24.62** & **20.36** & **16.58** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: SNR values (dB) of the proposed function and traditional functions. Figure 11: Denoising performance comparison of proposed function and traditional functions. As shown in Table 6 and Figure 12, the performance of FBank is significantly higher than that of MFCC. For FBank and iWAVE-FBank, denoising the audio data can enhance the performance on the classification task. Moreover, iFBank emphasizes high-frequency information, and its performance is superior to that of FBank, which proves its reliability and effectiveness. iWAVE-BiFBank has the best classification performance, achieving an accuracy 16.02% and 10.96% higher than that of MFCC and FBank, respectively. Figure 13 shows the confusion matrices of FBank and iWAVE-BiFBank, highlighting the number of errors for each species. The error rate for _Numenius phaeopus_ and _Accipiter nisus_ is particularly high with MFCC and FBank. The results indicate that the proposed method increases the accuracy of _Numenius phaeopus_ recognition from 64.14% to 87.24% and of _Accipiter nisus_ from 76% to 94.5%.