esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Modeling and Validation of Peak Ability of Biomass Units
## 2 Biomass Power Generation Process  
This paper studies the biomass unit based on the grate combustion furnace. Its structure is shown in Figure 1. The grate combustion furnace is composed of reciprocating parts with a certain inclination angle, which is divided into three areas. Among them, the first part is the dry area. The biomass fuel gradually heats up through the heat conduction and radiation of the top heat source, and then releases water. The second part is the combustion area, where volatile gas and fixed carbon begin to burn and the temperature in the furnace rises rapidly. With the continuous increase in temperature, the combustible gas in the fuel gradually precipitates due to the thermal effect and diffuses upward with the primary air blowing in. The third part is the burnout area, where the fixed carbon is completely burned and finally turned into ash and slag, which is discharged from the slag discharge port [14; 15; 16].  
The core hardware of a biomass power plant boiler includes a furnace, superheater, flue, reheater, air preheater and other auxiliary equipment. The production process of biomass grate furnaces is shown in Figure 2, which is mainly divided into three key stages.  
First, biomass fuel needs to be pretreated [17]. This link involves the crushing and drying of collected straw, peanut shell, sawdust and other materials to ensure that the fuel meets the particle size and moisture standards suitable for combustion, so as to improve its combustion efficiency. Pretreatment cannot only improve the combustion performance of fuel but also directly affect the overall operation efficiency and economic benefits of biomass power plants. The treated fuel is then stored and transported to the boiler. In the warehouse, the fuel is properly stored for daily production. During production, the staff accurately grab an appropriate amount of fuel with a professional large grab and place it on the feeding conveyor belt. With the operation of the belt, the fuel is delivered to the furnace. Then, it enters the combustion control phase. To ensure the best combustion effect, it is necessary to accurately control the key parameters such as the combustion temperature, air supply volume and water supply flow. Finally, it goes through the heat collection and tail gas treatment. The heat energy generated by combustion is converted into steam, which drives the steam turbine to generate electricity, realizing the energy-efficient conversion of biomass units.  
Figure 1: Structure diagram of grate furnace.
## 5 Conclusions  
This study investigates the differences in DC electrical treeing and partial discharge characteristics of XLPE samples produced under two manufacturing processes. Experimental tests on electrical treeing under DC voltage and partial discharge were conducted, and the samples were also subjected to comprehensive physicochemical characterization. Since the two manufacturing processes correspond to laboratory manufacturing and actual factory cable manufacturing, the research in this paper can provide useful insights for manufacturing DC cables with longer lifetimes and greater reliability. The following conclusions were drawn:  
1. Although both the laboratory-prepared and industrial cable samples exhibited similar evolution in DC electrical treeing, the laboratory specimens demonstrated significantly superior resistance to electrical treeing and partial discharge, as evidenced by longer breakdown times and more favorable partial discharge parameters. This suggests that the laboratory manufacturing process provides a distinct advantage in terms of insulation performance.  
2. The superior performance of the laboratory samples can likely be attributed to the more precise control over crosslinking conditions, such as optimized crosslinking temperature and more uniform temperature distribution. These controlled conditions resulted in higher molecular weight and narrower molecular weight distribution in the laboratory samples, contributing to denser crystalline regions, lower conduction currents, and enhanced dielectric strength.  
3. It is important to recognize that applying experimental parameters and trends derived from laboratory samples directly to real-world XLPE cables may not always be appropriate. In fact, when transitioning these findings to industrial cable manufacturing, it is essential to account for the differences in the manufacturing process and material properties by adding an appropriate safety margin in the design to ensure long-term reliability and performance.  
Writing--original draft: Z.Y. and Z.Z.; investigation: W.Y., L.Z. and Y.L.; methodology: B.Q.; writing--review and editing: Z.Z. All authors have read and agreed to the published version of the manuscript.  
This work is sponsored by the Science and Technology Project of the State Grid Corporation of China (No. 5500-202158298A-0-0-00).  
Not applicable.  
The data are available and explained in this article, and readers can access the data supporting the conclusions of this study.  
The authors declare no conflicts of interest.
## 3 Methodology
### System-Level Health Condition Prediction
#### 3.4.3 Metrics for RUL Prediction  
Root mean square error (\(RMSE\)) measures the accuracy of the predictions and is the most commonly used regression metric [32]. \(RMSE\) is defined as the root mean square of the RUL prediction errors during the time interval from \(T_{END}\) to \(T_{TTS}\) and is expressed as:  
\[RMSE=\sqrt{\frac{1}{T_{END}-T_{TTS}}\sum_{t=T_{TTS}}^{T_{END}}\left(L_{t}-{L^{ *}}_{t}\right)^{2}}, \tag{16}\]  
where \(T_{END}\) and \(T_{TTS}\) are the time indexes corresponding to the prediction start point and end point, and \(L_{t}\) and \(L^{*}{}_{t}\) are the predicted RUL and the ground truth RUL at time \(T\). The larger the value of \(RMSE\), the greater the average prediction error.
# Two-Stage Single-Channel Speech Enhancement with Multi-Frame Filtering
## 5 Results and Discussion  
### Effect of the Padding Mode on Multi-Frame Filters  
The enhancement performance of the multi-frame filters produced by the Filter Module has an impact on the proposed system. To improve the performance of the whole system, we need to set appropriate hyperparameters for the filters. Since the MFMCWF filter achieves better performance than the MVDR filter in [9], in this study, in addition to the MFMVDR filter, we also tried to let the Filter Module output the multi-frame Wiener filter (MFWF) filter, hoping to explore the performance gap between the two under single-channel conditions.  
There are two hyperparameters for the filters: one is the padding mode and the other is the total number of frames. Here, the padding mode means the number of frames on the left of the current frame when the total number of frames is determined.  
In order to find the appropriate padding mode for multi-frame filters, we set the total number of frames to 17 and feed the enhanced results from stage 1 and the noisy speeches to the Filter Module. The calculated filter is used to enhance the noisy speeches.  
Figure 3: The diagrams of baseline models. (**a**) The diagram of LSTM model. (**b**) The diagram of the Conv-TasNet. (**c**) The diagram of the DPRNN.  
Table 1 shows the effect of the padding mode on MFMVDR, the impact on MFWF has the same trend, for the sake of brevity, the corresponding table is not given here. In the first row, all the frames are padded on the right side of the target frame, which means the system only uses future information, similarly, the last row means the system only uses history information. All the other rows mean the system uses both history and feature information. From Table 1, we can tell that using both history and future information can make the filter have better enhancement performance. Since the results in Table 1 are obtained when the total number of frames is set to 17, without loss of generality, we decided to pad the same number of frames on both sides of the target frames in subsequent experiments.
## 5 RC Frame Model Used for Model Uncertainty Quantification  
### Description of the Structural Model  
A 5-storey and 4-bay RC frame is designed according to the Eurocodes [64; 65]. The frame presented in Figure 8a is assumed to be a part of an office building. The height of the first storey is 4.5 m, while the height of the other four storeys is 3.6 m each. The span for each bay is 6.0 m and the distance between two adjacent frames is also 6.0 m. The cross-sections and reinforcement layout of beams and columns are shown in Figure 8b. The dimensions of the beams and columns are 250 mm \(\times\) 500 mm and 500 mm \(\times\) 500 mm, respectively. The concrete cover is 30 mm in all elements. Dead loads of both floors and roof are assumed to be DL = 5.0 kN/m\({}^{2}\) (characteristic value), while the live loads on both the floors and roof are LL = 3.0 kN/m\({}^{2}\) (characteristic value) [65]. The columns on the ground floor are labeled as A, B, C, D, and E from left to right (Figure 8a).  
Concrete of type C20/25 [64] is used, i.e., the characteristic cylinder compressive strength is \(f_{\rm ck}\) = 20 MPa. According to _fib_ Model Code 2010 [61], the mean compressive strength of concrete is assumed to be \(f_{\rm c}\) = \(f_{\rm ck}\) + 8 = 28 MPa, while the mean tensile strength is \(f_{\rm ct}\) = 0.3 \((f_{\rm ck})^{2/3}\) = 2.2 MPa. The mechanical properties of the concrete are summarized in Table 1.  
The characteristic yield stress and tensile strength of the reinforcing steel are \(f_{\rm yk}\) = 500 MPa and \(f_{\rm uk}\) = 575 MPa (ductility class C), respectively [64]. The mean yield stress of steel is assumed to be \(f_{\rm y}\) = \(f_{\rm yk}\) + 2\(\sigma_{1}\) = 560 MPa, where \(\sigma_{1}\) = 30 MPa is the standard deviation [66]. The mean tensile strength is assumed to be \(f_{\rm u}\) = \(f_{\rm uk}\) + 2\(\sigma_{2}\) = 655 MPa, where \(\sigma_{2}\) = 40 MPa is the standard deviation [66]. The ultimate strain of the reinforcement is  
Figure 8: Layout of structural model: (**a**) RC frame and (**b**) both column and beam cross–sections.  
Figure 7: Comparison of the experimental and numerical results: (**a**) load–displacement relationship and (**b**) horizontal vs. vertical displacements.  
assumed to be 12% [44]. The [PERSON]'s modulus \(E_{\rm s}\) = 205 GPa is adopted. The mechanical properties of the reinforcement are summarized in Table 1.  
A 2D finite element (FE) model of the RC frame is built in OpenSees using the previously introduced modelling techniques (Section 4). On the basis of the FE model and the mean values of the previously mentioned material properties, deterministic analyses of both incremental dynamic analyses (IDA) and static pushdown analyses are carried out. Three cases of column removal scenarios are investigated:  
Case A: loss of the column A;  
Case B: loss of the column B; and  
Case C: loss of the column C.
## 3 Experiments and Analysis
### Experimental Results
#### 3.3.2 Comparing Experiment Results
In addition, our model achieved the fusion of the semantics of adjacent feature scales (no more than two upsampling and downsampling) by regularly adding the fusion module for both scales through its U-shaped structure. As a result, the new semantic features after the fusion module were retained as the feature parts of the adjacent scales (after up- and downsampling), which effectively alleviated the semantic gap problem caused by directly fusing two semantic features with a large-scale gap. From the comparison experiments, the classification model modeled by our method had a significantly higher accuracy. From the actual extraction results in Figure 12, the sea ice types of the seven sea areas involved in the experiment were extracted. Compared with networks such as the U-Net, the DF-UHRNet is more accurate and detailed for the boundary delineation of sea ice types. Its boundary extraction for multi-year ice, thin ice, and open sea ice is more consistent with the actual sea ice category distribution obtained from the visual interpretation compared with the U-Net, HRNet, and UHRNet networks. The recognition results achieved following the introduction of the post-processing method were closer to those within the original ice map, and the evaluation results improved by 4.64%, 3.54%, 1.57%, and 7.28%, respectively, with an overall recognition accuracy of 90.5% and a kappa coefficient of 81.78%. By comparing the identification results of different regions, we can conclude that our model reveals clear sea ice boundaries, offers more accurate extraction results, and requires fewer network parameters in its deployment compared with the other models. The proposed post-processing method can thus effectively fit the identification results to the original ice chart data. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Params 1** & **Model Size (MB) 2** & **MIoU (\%)** & **Accuracy (\%)** & **F1 (\%)** & **Kappa (\%)** \\ \hline HRNet-W18 & 9,671,835 & 118.212 & 67.60 & 77.02 & 76.95 & 55.12 \\ U-Net & 10,158,707 & 119.571 & 74.14 & 83.18 & 83.43 & 67.18 \\ U-HRNet (Small) & 6,107,107 & 72.966 & 75.60 & 83.54 & 82.99 & 68.54 \\ DF-UHRNet (Ours) & 5,079,175 & 61.248 & 78.50 & 86.96 & 86.55 & 74.50 \\ Our Post-processing & 5,079,175 & 61.248 & 83.14 & 90.50 & 88.12 & 81.78 \\ \hline \hline \end{tabular}  
* \({}^{1}\) \"Params\" is the total number of parameters to be trained during model training. 2 \"Model Size\" is the storage space occupied by the model.  
\end{table} 
Table 4: Results of the evaluation of four networks and post-processing in five regions after introducing high-resolution sea ice concentration features. Figure 12: Comparison of sea ice classification results of the U-Net, HRNet, UHRNet, and DF-UHRNet in five different regions.
# Image Transmission over Cognitive Radio Networks for Smart Grid Applications
## 3 Proposed MAC Layer for Image Streaming over CRSNs
### The Structure of the Proposed Model for the Delay-Sensitive Data Transmission  
In delay-sensitive data transmission applications such as image and multimedia communications, time constraints are the most challenging issue. In this work similar to [72; 73; 83], we have defined \(D_{m}\) as the maximum allowable time to send an input image. Therefore, a successful transmission is one that an image packet including \(I_{b}\) bits and transmission rate \(R_{b}\) are fully transmitted within \([0,D_{m}]\).  
In our proposed JOPSS method, the goal is to minimize the average time spent on retransmission and spectrum sensing. Given the Equation (3), time overhead \(\tau_{OH}\) is a function of spectral sensing and retransmission events. Any increase in the packet length may increase the probability of collision in the current channel, so retransmission can occur with a higher probability. Since in the retransmission, the re-larger packets should be transmitted again, the delay may significantly increase. But if the interference does not occur, the longer packet means that the time overhead due to spectrum sensing will be fallen.  
On the other hand, a packet length reduction means reducing the possibility of interference. Also in the event of interf erence, smaller packets should be resent, which means that the effect of retransmission in the overhead delay is reduced. But unfortunately, in this case, the time overhead due to spectrum sensing is much greater than before because of calling the process in shorter times. Therefore, if similar to the existing work in this field, only the packet length is defined as the optimization variable, it will not satisfy the high performance needed. For this purpose, we jointly extract the optimal packet length (\((\tau_{P}^{i})^{*}\)) and the number of spectral sensing iterations (\(X^{*}orNSSI\)) in our proposed model. Finally, we use the peak signal-to-noise ratio (PSNR) criterion in the receiver to evaluate the quality of receiving images. Table 3 lists all parameters of the proposed MAC layer model with their descriptions. In our model, \(\tau_{P}^{i}\) is the packet length in \(i\)'th channel, and taking into account the time \(T_{s}\) as a SS time, the time required to send each packet will be equal: \(t_{p}^{i}=\tau_{p}^{i}+T_{s}\)
# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 3 Research Methodology
### Research Questions  
This paper seeks to address the following research questions:  
* RQ1: What is the current landscape of state-of-the-art video deepfake detection studies that utilise CNN with LSTM?
* RQ2: What are the most common feature extraction techniques used in video deepfake detection tools that utilise CNN with LSTM?
* RQ3: What are the most common datasets used in the implementation of video deepfake detection tools that utilise CNN with LSTM?
* RQ4: What are the factors that have the strongest influence on detection accuracy for video deepfake detection when implementing CNN with LSTM?
* RQ5: Is using CNN with LSTM more effective for video deepfake detection compared to models that do not utilise LSTM?  
Figure 4: The five stages of the systematic literature review.  
RQ6: What are the challenges involved in implementing video deepfake detection using CNN with LSTM?
# SSTrack: An Object Tracking Algorithm Based on Spatial Scale Attention
## 4 The Experiments  
### Implementation Details  
The training environment of SSTrack is Ubuntu 18.04, Python version 3.8, Pytorch version 1.11.0, CUDA version 11.3, and employs an RTX 3090 GPU. The backbone is loaded with a pre-trained model of Swin V2 [37] on ImageNet-1k, and joint training is conducted using the GOT-10k, TrackingNet, and LaSOT training sets.  
The algorithm's tracking capabilities and reliability are validated to comprehensively simulate tracking performance in natural and complex environments. The article conducts quantitative and qualitative comparisons with existing mainstream Siamese tracking algorithms on GOT-10k, TrackingNet, and LaSOT test sets. The test sets encompass various target categories, such as pedestrians, vehicles, and animals, and include diverse scenarios like indoor, road, and outdoor settings, making the test results more convincing.
# The Carbon Dynamics of Dry Tropical Afromontane Forest Ecosystems in the Amhara Region of Ethiopia
## 3 Methods  
### The Ecosystem Model  
For the simulation of forest carbon storage (objectives 1 and 3), we employed the biogeochemical-mechanistic ecosystem model Biome-BGC [55; 56], which simulates flux dynamics of the water, carbon, and nitrogen cycle for a terrestrial ecosystem on a daily time step. For this study, we used Biome-BGC version 4.1.2 [29; 51] with an improved self-initialization algorithm to mimic undisturbed ecosystems [57; 58] and forest management routines e.g., historic land use changes, thinning, clear-cut and planting [30]. Biome-BGC is fully prognostic and does not require detailed forest data for model initialization. It is initialized by a so called \"spin-up\" run [27; 29; 57; 59], which mimics the accumulation of soil and vegetation carbon and nitrogen under constant conditions (available climate data recycled, constant preindustrial CO\({}_{2}\) and nitrogen deposition) until a \"steady-state\" is reached (typically after few thousands of years). Since CO\({}_{2}\) concentration and nitrogen deposition have increased and most forests have experienced some form of forest management, which may have led to forest degradation, the next step is typically to perform \"modern-forest-simulations\" addressing these changes to realistically mimic the current ecosystem fluxes for a given forest [27; 30].  
Key processes within Biome-BGC are the photosynthetic carbon assimilation, autotrophic respiration (maintenance respiration plus growth respiration), allocation among different plant compartments, mortality, litterfall, mineralization processes, nutrient and soil water uptake, rainfall interception, soil water infiltration and storage, runoff and evapotranspiration. Leaf area determines potential forest productivity as well as rainfall partitioning (interception, through fall) and evaporation from soil. Carbon assimilation is calculated with the Farquhar photosynthesis routine [60].  
Growth respiration is a fixed fraction of the amount of carbon allocated to different plant structural compartments [61], whereas maintenance respiration depends on the nitrogen content of the tissue [62]. CO\({}_{2}\) availability for photosynthesis is regulated by the stomata, where the CO\({}_{2}\) influx depends on maximum conductivity, and stomatal closure is limited by radiation, moisture of the atmosphere and water content in the leaves, which is controlled by the soil water potential. Soil water potential is calculated from the soil water and texture following [63; 64]. Key model outputs are Gross Primary Productivity (GPP), Net Primary Productivity (NPP; GPP minus autotrophic respiration), and carbon storage in the living biomass, the litter, coarse woody debris and the soil.
## 1 Introduction
However, these articles still have room for improvement, as follows: (1) In terms of month selection, most of these articles chose remote sensing images close to the soil sample collection time without discussing which month is most suitable for inversion. (2) The impact of image levels has not been considered; most articles used Landsat 8 OLI Collection 2 Level-1 data as the data source while neglecting Level-2 data and have not compared the performance of the two types of data in soil salinity inversion. (3) Most authors chose either spectral indicators or mathematically transformed variables, without combining both data types, and the most suitable spectral variables are still not scientifically determined. Therefore, this study employed two levels of Landsat 8 OLI Collection 2 images: Level-1 and Level-2, 12 months; then, the images whose cloud cover was less than 10% were selected. Utilizingseven original bands, 29 different optical indicators, and derived 39 variables using six mathematical transformation methods (reciprocal (\(\mathbf{1/R}\)), logarithm \(\boldsymbol{InR}\), exponential \(\boldsymbol{e^{R}}\), square root \(\boldsymbol{R^{0.5}}\), first derivative \(\boldsymbol{R^{\prime}}\), and second derivative \(\boldsymbol{R^{\prime}}\)) the three models of partial least squares regression (PLSR), random forest (RF), and backpropagation neural network (BPNN) were established. Finally, the following conclusions can be drawn: (1) which month is most suitable for inversion; (2) which level of Landsat 8 OLI Collection 2 performs better in model construction; (3) whether the combination of spectral indicators and mathematically transformed variables can improve model performance, and which variables are the most suitable for soil salinity identification.
# A Novel Monocular Vision Technique for the Detection of Electric Transmission Tower Tilting Trend
## 2 Contour Extraction of Transmission Tower  
In order to analyze the posture of the tower using the monocular camera, the first step is to identify and extract the tower's profile, which is divided into two stages. The first stage involves identifying the pole and tower in the image based on the characteristics of monocular vision and extracting the corresponding coordinate information of the tower's height and the camera's distance. The RGB images obtained by the camera are converted into grayscale images through image processing and denoised to facilitate accurate contour extraction. The first image obtained after the camera installation is a sample reference image, and subsequent images are used to compare the distance between the monitoring camera and the tower and the changes in the tower's attitude. In the second stage, the distance between the camera and the tower is calculated using the camera imaging model and the tower imaging model to determine the three-dimensional position of the tower. The analysis process of tower tilt is shown in Figure 1.