esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Detection Method for Wind Turbine Generators Based on Attention-Based Modeling
## 2 Attention-Weighted Long Short-Term Memory Network (AW-LSTM)
### Attention-Weighted LSTM Fault Detection Model
This is mainly achieved through the following three steps. Step 1: For the \(d\)-th IMF component, assuming the current output vector of the current step is \(\mathbf{IMF}_{d,t}\) and the previous hidden state \(\mathbf{h}_{t-1}\), calculate the current hidden state. For simplicity, the update of the long short-term memory network in Formula (4) is simplified as  
\[\mathbf{h}_{t}=LSTM(\mathbf{IMF}_{d,t},\mathbf{h}_{t-1}), \tag{6}\]  
where the data dimension of the hidden unit is 256, and where \(\mathbf{h}_{t}\) and \(\mathbf{h}_{t-1}\) are the hidden states of the previous and current steps. Step 2: Allocate attention weights \(\alpha_{i}\) to the LSTM hidden state according to the attention operation, and calculate the new hidden layer state vector \(\mathbf{s}_{t}\), as shown in Equation (5). It is worth noting that the dimension of the obtained attention hidden feature \(\mathbf{s}_{t}\in R^{m\times 1}\)  
Figure 3: Empirical mode decomposition (EMD). We show that six sub-sequences \(\{\mathbf{IMF}_{i}\}_{i=1}^{6}\) and one residual component \(\mathbf{r}\) are obtained by the EMD decomposition. is consistent with the initial hidden state. Since the attention weights at each time point are different, the initial hidden state at each time point also plays a different role in fault prediction. Moments that are helpful for fault prediction are assigned larger weights, while those that are not important are assigned correspondingly smaller weights. Step 3: Stack \(L=4\) layers of attention-weighted long short-term memory networks (AW-LSTM), to increase the model's robustness and expressive power, and use different IMF components as inputs to the neural network in parallel. Data output layer. The function softmax is used to calculate the label distribution probability of different IMF components on annotated data at each time point, and the outputs corresponding to \(D\) IMF components are averaged to obtain the final result \(\hat{y}\):  
\[\hat{y}=\text{softmax}\left(\sum_{d=1}^{D}\tanh(\mathbf{W}_{d}s_{d}+\mathbf{b}_{d}) \right), \tag{7}\]  
where \(\mathbf{W}_{d}\in R^{1\times m}\) is the weight matrix between the attention hidden layer and the output layer, \(\mathbf{b}_{d}\) is the bias term, and \(\hat{y}\in[0,1]\) represents the probability of a fault occurring in the current bearing signal. If \(y>0.5\), a fault is considered to exist; otherwise, the gearbox is fault-free. The structure diagram of the proposed AW-LSTM gearbox fault detection model is shown in Figure 3.
## 2 Experimental Program for Field Tests  
### Experimental Procedure  
A cast in situ reinforced concrete pile with a partial steel casing was load tested at a bridge construction site located in the coastal area of Zhejiang, China. The cast in situ pile had a designed diameter of 1.8 m and an embedment length of 87.9 m. In the pile body, reinforcement bars with a nominal yield strength of 300 MPa and an ultimate tensile strength of 350 MPa were used. The casing had an outer diameter of 2 m, a thickness of 0.075 m, and an embedment depth of 33 m (Figure 2). The casing had a nominal yield strength of 300 MPa and an ultimate tensile strength of 350 MPa. Therefore, the test pile had an outer diameter of 2.0 m at depths from 0 m to \(-\)33.0 m and an inner diameter of 1.8 m from \(-\)33.0 m to \(-\)87.9 m.  
Figure 1: Monopile with casing and without casing.  
Sliding microtiters (Solexperts AG, Monchaltorf, Switzerland), vibrating wire strain gauges, and inclinometers (CX901F, China) were used to measure the axial strain of the casing and the concrete (Figure 2). Seven steel-angled channels with a length of 22 m were welded onto the outer surface of the casing, five of which were used for inclinometers, and another two were used for sliding microtiters. The bottoms of these channels were covered by tapered steel bases to prevent soil intrusion during the casing driving process (the driving was accomplished with a hammer). Grouting was performed in channels to keep these instruments and the casing in cooperative deformation conditions. The rebar cage was also installed with inclinometer tubes and strain gauges to measure the deflection of the concrete. Strain gauges were attached evenly around the pile at 9 different depths along the pile length. For each depth, 4 gauges were attached evenly around the cross-section. The layout of the strain gauges is shown in Figure 3.  
Adjacent to the test pile, there was another pile, and the loads were applied through a jack placed between the two piles as a counter force system. A jack with a 60 MPa capacity and precision of 0.4 MPa was used to provide lateral load to the test pile. This gave a comfortable margin of capacity over the designed ultimate lateral force of the pile of 1000 kN. Four dial gauges were used on the opposite side of the loading point to survey the lateral deflection of the pile top under different loading conditions (Figure 3).  
The construction of the pile generally contains the following four procedures: (1) drive a casing, (2) drill a hole, (3) deploy a rebar cage, and (4) fill the hole with concrete.  
Figure 2: Angle steel outside the steel casing.
# Crop Land Change Detection with MC&N-PSPNet
## 3 Results  
### Experimental Result  
The experimental results based on the proposed method are shown in Figure 7 below. The overall edge extraction effect has better performance with a small amount of mis-extraction, the overall accuracy and Kappa value reached 95.03 and 0.8815, respectively, and the training time was 2.109 h.  
Figure 6: Impact of backbone network structure, down-sampling multiplier, optimizer type, and dataset ratio on information detection accuracy and efficiency: (**a**) effect of different parameter settings on OA, Kappa, and training time; (**b**) different parameter settings’ effect on Loss; (**c**) different parameter settings’ effect on Val_Loss. (Note: in the figure, M stands for MobileNetV2 as the backbone feature extraction network, R stands for ResNet50 as the backbone feature extraction network, 8 and 16 stand for different down-sampling multiplicities, S stands for the selection of SGD optimizer, A stands for the selection of Adam as the optimizer, and (1) (2) stands for the ratios of the training set to the validation set of 1:9 and 2:8, respectively).
###### Abstract  
To resolve the issues of the high porous medium flow resistance, low oil production rate, high oil decline rate, and low oil recovery factor for the cyclic steam stimulation (CSS) of horizontal wells in heavy oil reservoirs, the CSS method assisted by the electric heating (E-CSS) of horizontal wells was proposed in this study. Combining the heat from electric heating and steam during E-CSS, the analytical model of formation temperature rise was established for the three phases of electric-assisted CSS (i.e., injection, soaking, production), and physical experiments were carried out to compare the performance of conventional CSS and E-CSS. The experimental results were used to validate the analytical model and reveal the impact of the key electric heating mechanism on the horizontal CSS performance. Meanwhile, the typical well model was used to forecast the E-CSS potential. The results indicate that electric heating can achieve uniform heating in the steam injection phase, maintain heating around the wellbore in the soak phase, and reduce flow resistance and enhance oil output in the production phase. Forecasts of the typical well model indicate that electric heating can enhance the oil recovery factor by 9.4% and the oil-steam ratio from 0.14 to 0.23, implying a significant application potential in heavy oil reservoirs developed by horizontal CSS.  
horizontal well; cyclic steam stimulation; electric heating; flow resistance; oil recovery factor Article
## 3 Results and Discussion
### Real Wastewater
#### 3.3.2 Rapid Small-Scale Columns  
As for the photo-Fenton process, the higher complexity of real wastewater hinders the GAC treatment performance since more species are competing for adsorption. The breakthrough curves for CarboTech DGF 8x30 GL and NRS Carbon 0.5-2.5 are plotted in Figure 7. The adsorption capacity was slightly reduced; therefore, the breakthrough was achieved slightly earlier than expected. For example, for NRS Carbon 0.5-2.5, a 50% breakthrough was achieved after 180 BV. In the case of the CarboTech DGF 8x30 GL, the 50% breakthrough was achieved after 280 BV. The 20% breakthrough, that means the maximum time in which the 80% removal is achieved, is 40 and 30 BV for CarboTech DGF 8x30 GL and NRS Carbon 0.5 - 2.5, respectively.  
The first-order kinetic was also applied to model the adsorption. The model fitted for both GAC being the R\({}^{2}\) 0.994 for both. With real wastewater, the CarboTech DGF 8x30 GL was able to adsorb approximately 70 mg g\({}^{-1}\); for the NRS Carbon 0.5-2.5, it was almost 60 mg g\({}^{-1}\). The effect of a more complex matrix was similar for both GAC types, which slightly reduced the adsorption capacity by 7 and 12%.  
The combination of technologies was implemented to reduce treatment time and reagent dosage in the photo-Fenton process and to extend the GAC's lifetime. The photo-Fenton process was applied as the first process for 5 min (6 s irradiation time). This  
Figure 7: Modelled adsorption of indigo carmine in the RSSC under the first-order kinetics hypothesis in real wastewater,3.3.3. Photo-Fenton and GAC Combined Treatment on Real Wastewater.  
treatment time was selected to prioritise energy reduction, resulting in 75% energy savings. The conditions of the photo-Fenton were the optimal ones, 5 mg L\({}^{-1}\) of iron chelated with EDDS in a molar ratio of 1:1 and 40 mg L\({}^{-1}\) of H\({}_{2}\)O\({}_{2}\) irradiated by a 500 W MP lamp.  
The removal of indigo carmine achieved was 28%, resulting in a final concentration of 19 mg L\({}^{-1}\), and this solution fed the RSSC. The 50% breakthrough of CarboTech DGF 8x30 GL was reached after 1500 BV (Figure S4), and the 20% breakthrough was after 320 BV. For the NRS Carbon 0.5-2.5, the 50% breakthrough was 800 BV, and the 20% breakthrough was after 110 BV.  
Compared with each process alone, photo-Fenton was reduced from 20 min to 5 min, meaning a nominal savings of 125 Wh in terms of irradiating energy. The GAC lifetime was extended almost 8 times for CarboTech DGF 8x30 GL and 6 times for NRS Carbon 0.5-2.5, defining a 50% breakthrough (Figure 8) as the breakthrough criterion. If the BV of 20% breakthrough is compared for both configurations, the GAC lifetime was extended almost 5 times and 7 times for CarboTech DGF 8x30 GL and NRS Carbon 0.5-2.5, respectively.
# Preparation of Piezo-Resistive Materials by Combination of PP, SEBS and Graphene
###### Abstract  
The use of polyolefins in structural components requires the simultaneous improvement of stiffness and toughness of the matrix, whilst in the case of sensing components during operation, additional functions are needed such as electrical conductivity. However, providing various desired properties without impairing those intrinsic to the materials can be somewhat challenging. In this study we report the preparation of an isotactic polypropylene (iPP)/styrene-ethylene-butylene-styrene triblock copolymer (SEBS)/graphene system that combines enhanced mechanical properties with electrical conductivity. Blends were prepared by solution mixing (SoM) and solution/solid state mixing (SoM/SSM) formulation routes prior to melt processing. The nanocomposites were characterized by scanning electron microscopy (SEM) and thermogravimetric analysis (TGA) and the electrical and mechanical properties were evaluated. The materials prepared via the SoM/SSM route displayed good electrical conductivity while retaining the mechanical properties of iPP, making them attractive materials for low cost and high throughput structural components with sensing capacity.  
nanocomposites; polyolefin; elastomer; young's modulus; toughness; electrical conductivity +
Footnote †: journal: Composites Science
# A Deep Belief Network Classification Approach for Automatic Diacritization of Arabic Text
## 5 Methodology
### Evaluation Metrics  
Throughout our experiments, we evaluate the accuracy of the DBN using the Diacritization Error Rate (DER). DER is the percentage of characters with incorrectly predicted diacritics. We also report the Word Error Rate (WER) of our best performing model. WER is the percentage of incorrectly diacritized words. A word is considered incorrectly diacritized if it has at least one incorrectly diacritized letter.  
* Diacritization Error Rate (DER): which is the ration of characters with incorrectly restored diacritics. DER can be calculated as follows: \[DER=(1-T_{S}/T_{G})\times 100\%\] (10) where \(T_{S}\) is the number of letters assigned correctly by the system, and \(T_{G}\) is the number of diacritized letters in the gold standard text.
* Word Error Rate (WER): the percentage of incorrectly diacritized white-space delimited words. At least one letter in the word must have a diacritization error so that it can be counted as incorrect. \[WER=(1-T_{W}/T_{G})\times 100\%\] (11) where \(T_{W}\) is the number of words fully and correctly diacritized by the system, and \(T_{G}\) is the number of diacritized words in the gold standard text.
## 4 Discussion
### Limitations  
As climatic factors like precipitation and temperature typically exhibited delayed impacts on vegetation growth and as a result, drought also shows lagging effects on ecosystem WUE. In the future, we should explore the drought's delayed impact on WUE with multiple methods, together with a different drought index.  
Previous studies reported, despite the biophysical variables, the timing and duration of drought also influenced the increase or decrease in WUE [17, 27, 66]. Summer drought usually caused the decrease in WUE, especially for theiferous and broadleaf forest. One contributing factor is the increased vapor pressure deficit during drought conditions, which causes stomatal closure due to excessive water through transpiration, subsequently reducing canopy photosynthetic capacity [67, 68]. However, the effects of spring drought or autumn drought on WUE are less clear. The duration of drought also impacts an ecosystem's WUE. Prolonged droughts lasting longer than one month result in decreased seasonal and annual GPP and WUE.  
Additionally, our study did not analyze the influencing of CO\({}_{2}\) on WUE and Rd. Previous study has indicated that CO\({}_{2}\) fertilization can enhance ecosystem resilience, particularly on the Tibetan plateau. The study also showed the CO\({}_{2}\) can positively impact WUE over the long term [9]. Although our study observed a decreasing trend in WUE, this may suggest that CO\({}_{2}\) had minimal influence. However, a more detailed analysis about the impact of CO\({}_{2}\) on WUE is warranted in future investigations. Furthermore, human activity impact can also be explored in the near future.
# Nanotechnology Solutions for Controlled Cytokine Delivery: An Applied Perspective
## 3 Nanoparticles as Cytokine Delivery Systems
### Types of Nanoparticles for Cytokine Delivery
#### 3.1.3 Gold Nanoparticles  
The use of gold nanoparticles (AuNPs) in biological applications is increasing, partly due to their chemical inertness and unique optical and physical properties, suitable for use as imaging agents [57]. These nanoparticles are easy to functionalize and to synthetize with controlled dispersion, and are characterized by a high surface area-to-volume ratio. AuNPs proved to be great therapeutic agents as they can easily travel to target cells and support high drug load [58]. [PERSON] and coworkers demonstrated that gold nanoparticles can be used as nanocarriers for cytokine delivery to tumors in mice models with fibrosarcoma [34]. The researchers proved that TNF-loaded gold nanoparticles could deliver active cytokine to tumors without associated toxicity (as judged by animal weight loss), when compared to the administration of the free cytokine that showed no activity for the same dosage. The nanocarriers were functionalized with a novel tumor-homing peptide, Asn-Gly-Arg (NGR), which is a CD13 ligand expressed in the tumor neovascularization [34]. This example demonstrates that AuNPs can be used as a platform for the delivery of cytokines for cancer therapy. Although generally described as biocompatible, gold nanoparticles can, in some cases, elicit an immune reaction and it is thus essential to evaluate cellular responses such as cytokine or reactive oxygen species (ROS) production, an overlooked aspect in many reports. For instance, [PERSON] and co-workers showed that the surface charge of gold nanoparticles determined whether pro-inflammatory cytokine production is increased or decreased [59; 60]. However, other physical and chemical properties of the nanoformulations, such as size and hydrophobicity of the particle coating, may also be responsible for causing cytotoxicity, influencing compatibility with cellular immune responses such as transport processes or cellular damage [60]. In this way, the design of nanoparticles for biomedical applications should respect these key aspects as well as the final objective and type of application. The modification and functionalization of the surface remains one of the most relevant parameters when the main objective is to escape interactions with cells of the immune system.
# Image Super-Resolution Based on CNN Using Multilabel Gene Expression Programming
## 1 Introduction  
Aiming at addressing image degradation during digital image acquisition and processing, Single-Image Super-Resolution (SISR) technology [1; 2] enables high-resolution (HR) images to be recovered from low-resolution (LR) ones with high-frequency texture details and edge structure information observations of images to meet people's image quality needs.  
Early reconstruction-based SR methods mainly modeled the acquisition process of low-resolution observation images, used the regularization method to construct the prior constraints of high-resolution images, estimated the HR images from the LR observation images, and finally restored the super-resolution image. The problem turns into an optimization problem of a cost function under a constraint [3]. Learning-based super-resolution restoration technology was first proposed by [PERSON] [4]. This type of algorithm uses the similarity of different images in high-frequency details to obtain the relationship between the HR and LR images through the algorithm to guide the reconstruction of the output image. Prior knowledge can be obtained through learning, instead of defining the prior knowledge in the model-based reconstruction method.  
In recent years, deep learning has been a hot topic in the field of machine learning, and its related theories have attracted widespread attention from researchers. It has been gradually used in the field of image super-resolution reconstruction and has better effects than shallow learning [5; 6; 7; 8; 9; 10; 11; 12; 13]. The most classic of them is an end-to-end deep learning model, the super-resolution convolution neural network (SRCNN), constructed by a three-layer convolutional neural network proposed by [PERSON] et al. [5]. The image is input after Bicubic preprocessing, and it is trained on LR and HR images in pairs.  
The gradient descent method continuously adjusts the weights to obtain a mapping from LR to HR. Its simple network structure design and excellent image restoration results created a precedent for deep learning in super-resolution image reconstruction. However, the SRCNN method does not obtain better results when the number of layers in the network is increased. The training convergence rate is slow and it is not suitable for multiscale amplification.  
Based on the above problems, we propose an improved deep learning algorithm for Gene Expression Programming (GEP) preclassification. This method uses the GEP multilabeling algorithm to classify the trained image set and select a subset of samples that are related in color and texture feature categories, thereby reducing the complexity of the convolutional neural network parameters. We compare the performance of our approach to that of other state-of-the-art methods and obtain improved objective image quality.
# A Performance Evaluation for Software Defined Networks with P4
## 4 Experimental Design
### Tier 1--Single Type of Traffic Run
#### Case Study 2--TCP Traffic  
In this case study, to establish a connection with the server and the client and to transmit data between the client and the server, iPerf [64] v2.0 was used. iPerf's capabilities and functions were thoroughly examined and presented by [PERSON] et al. [59]. Given the stability of v2.0 over v3.0 in iPerf, the former was selected for the experiments. For multi-path and grid topologies, data with a file size limitation of 1 GB was encapsulated, as TCP was chosen. For the Internet topology, a data burst for 1200 s was chosen to identify which network abstraction was able to process the largest amount of data. For ease of emulation and calculation, a download with a file size of 1 GB was chosen for multi-path and grid  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multirow{2}{*}{**Traffic**} & **Multi-Path Topology and** & **Internet Topology** \\  & **Grid Topology** & \\ \cline{2-3}  & **Client \(\rightarrow\) Server** & **Client \(\rightarrow\) Server** \\ \hline \multirow{2}{*}{ICMP} & H1 \(\rightarrow\) H5, H2 \(\rightarrow\) H6, H3 \(\rightarrow\) H7, & H1 \(\rightarrow\) H21, H5 \(\rightarrow\) H22, H9 \(\rightarrow\) H23, \\  & H4 \(\rightarrow\) H8 & H13 \(\rightarrow\) H24, H17 \(\rightarrow\) H25 \\ \hline \multirow{2}{*}{TCP} & H1 \(\rightarrow\) H5, H2 \(\rightarrow\) H6, H3 \(\rightarrow\) H7, & H2 \(\rightarrow\) H26, H6 \(\rightarrow\) H27, H10 \(\rightarrow\) H28, \\  & H4 \(\rightarrow\) H8 & H14 \(\rightarrow\) H29, H18 \(\rightarrow\) H30 \\ \hline \multirow{2}{*}{UDP} & H1 \(\rightarrow\) H5, H2 \(\rightarrow\) H6, H3 \(\rightarrow\) H7, & H3 \(\rightarrow\) H31, H7 \(\rightarrow\) H32, H11 \(\rightarrow\) H33, \\  & H4 \(\rightarrow\) H8 & H15 \(\rightarrow\) H34, H19 \(\rightarrow\) H35 \\ \hline \multirow{2}{*}{CDN} & H1 \(\rightarrow\) H8, H2 \(\rightarrow\) H8, H3 \(\rightarrow\) H8, & H4 \(\rightarrow\) H36, H8 \(\rightarrow\) H36, H12 \(\rightarrow\) H36, \\  & H4 \(\rightarrow\) H8 & H16 \(\rightarrow\) H36, H20 \(\rightarrow\) H36 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration of traffic. Hosts are shown in Figure 2.  
topologies. The 1 GB file size served as an example to a scenario where a user intends to download a file of a fixed size from a File Transfer Protocol (FTP) server. For a scenario where the user is engaged in a TCP transmission with a time constraint (i.e., Secure Shell (SSH) or Telnet), a time limitation of 1200 s was employed for the Internet topology. This was also applied with respect to the size of the network and for extending the download time, and the amount of data.