esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Acquisition, Processing, and Analysis of Magnetotelluric Sounding
### Data Processing  
SSMT200 software, provided by Phoenix, was used for data processing. First, the original time series was transformed from the time domain to the frequency domain by Fourier transform. Then, robust estimation was used to obtain apparent resistivity, phase, and impedance [30]. After processing, the apparent resistivity and phase curves of all measuring points were obtained, which showed the underground electrical characteristics. Figure 3 shows the apparent resistivity and phase curves of all measuring points in the study area. Overall, the study area has low resistivity, with typical basin resistivity characteristics. In terms of the shape of the curve, the resistivity is lower in the frequency range higher than 1 Hz, which may be related to the sedimentary caprock of the basin. In the 1-0.01 Hz range, it has relatively high resistivity, showing that the crust of the study area has high resistance, which is related to the crystalline basement of the basin. It has a low resistivity in the frequency range below 0.01 Hz, showing that the deep part of the basin has lower resistivity.  
Figure 2: Geological profile of the aquifer (modified from [8]).
# Scene Text Detection Based on Multi-Headed Self-Attention Using Shifted Windows
## 3 Method  
The general framework of the scene text detection based on multi-headed self-attention proposed in this paper is shown in Figure 1. First, the features at five scales were extracted from the input image using the CNN-based residual neural network ResNet50 [22] noted as \(C_{1}\), \(C_{2}\), \(C_{3}\), \(C_{4}\), and \(C_{5}\), which were \(1/2\), \(1/4\), \(1/8\), \(1/16\), and \(1/32\) the size of the original image, respectively. Then, the four scales of \(C_{2}\), \(C_{3}\), \(C_{4}\), and \(C_{5}\) were fed into the FPN [9]; multi-scale feature fusion was performed by up-sampling the higher-level features and connecting the low-level features top-down; four features of \(1/4\) the size of the original image were obtained, noted as \(P_{2}\), \(P_{3}\), \(P_{4}\), and \(P_{5}\). The detailed process is described in Section 3.1. After that, \(P_{2}\), \(P_{3}\), \(P_{4}\), and \(P_{5}\) were fed into the feature enhancement module for multi-headed self-attention calculation based on shifting windows, and finally an enhanced feature \(E\) was obtained. The detailed process is described in Sections 3.2 and 3.3. Then, a probability map and a threshold map of the same size as the original map were generated by a series of convolution operations on a feature \(E\) of \(1/4\) size during training. The binary map was also obtained using a differentiable binarization. Then, the detection results were obtained by post-processing. The detailed procedure is described in Section 3.4. In addition, label generation is introduced in Section 3.5; the loss function is introduced in Section 3.6; the evaluation metrics are introduced in Section 3.7.
# Detection of Threats to Farm Animals Using Deep Learning Models: A Comparative Study
## 3 Materials and Methods
### Fast R-CNN  
Microsoft proposed FastRNN in 2018 [32]. The model is based on the residual network (ResNet) [33]. If we look at the structure of ResNet, in this model, every network layer is not stacked over each other but forms a residual mapping, which is always easier to optimize. The model is based on the VGG model and modified by adding residual layers. ResNet is trained on 25.5 million parameters. This makes ResNet a very heavy network as well. FastRNN exploits the learned residual connections of ResNet and provides stable training along with comparable training accuracies at a much lower computational cost. The prediction accuracies of FastRNN are 19% higher than regular RNN structures and a little less than the gated RNN [32; 34].  
Let \(X=[x_{1},\dots,x_{T}]\) be the input vector, where \(x_{T}\in R^{D}\) denotes the t-th step feature vector. So, for a multi-class RNN, we aim to learn the function \(F\): \(R^{D\times T}1,\dots,L\), which will predict one of these L classes for a given data point \(X\). The standard RNN architecture hidden state vectors are represented as below:  
\[h_{t}=\tanh(Wx_{t}+Uh_{t-1}+b) \tag{1}\]  
Learning \(U\), \(W\) in the above architecture is difficult as the gradient can have an exponentially large (in \(T\)) condition number. Unitary network-based methods can solve this problem by expanding the network size, and, in this process, they become significantly large, and the model accuracy may not remain the same.  
However, FastRNN uses a simple weighted residual connection to stabilize the training by generating well-conditioned gradients. In particular, FastRNN updates the hidden state \(h_{t}\) as follows:  
\[{h_{t}}^{\prime}=\sigma(Wx_{t}+Uh_{t-1}+b) \tag{2}\]  
\[h_{t}=\alpha.{h_{t}}^{\prime}+\beta.h_{t-1} \tag{3}\]  
Here, \(\alpha\) and \(\beta\) are trainable weights that are parameterized by the sigmoid function and limited in the range \(0<\alpha\), \(\beta<1\). \(\sigma\) is a nonlinear function such as tanh, sigmoid, or ReLU, and can vary across datasets.
## 4 Limitations of the Study
Due to the limited resolution of micro-CT (11.92 \(\upmu\)m), the CT image analysis only focused on through-going microchannels. Since no through-going microchannels were observed in the CT images, the real microstructure revealed in the binary images was very limited. While the actual microstructure might contain some defects (with a size smaller than 11.92 \(\upmu\)m), such as local fractures and voids inside the sample, these defects cannot be observed by the micro-CT used in this study. Simulations used in this study did not consider those local fractures and voids. Finally, average porosity and permeability properties were assumed for each section. In reality, porosity and permeability values can be distributed heterogeneously within the rock and the cement matrix.
# Light Propagation and Multi-Scale Enhanced DeepLabV3+ for Underwater Crack Detection
## 2 Methods
### Innovation 3: Curvature Flow-Based Guidance  
Cracks in underwater structures often have distinct geometric properties, such as sharp edges and sudden changes in curvature [35]. Preserving these properties is essential for accurate crack detection. To enhance the model's ability to detect cracks, the curvature flow-based guidance was introduced, which prioritizes regions in the image with significant changes in curvature--characteristic of crack boundaries. This guidance mechanism is integrated into the loss function of the DeepLabV3+ model, providing additional supervision during training to help the model focus on high-curvature regions. By emphasizing these areas, the model is better able to identify and delineate cracks, improving segmentation accuracy.
# Seismic Performance Evaluation of Highway Bridges under Scour and Chloride Ion Corrosion
## 2 Earthquake and Scour Combination in the Failure Probability of Bridge
### Calculation of Seismic Failure Probability of Bridges at Different Scour Depths
#### 2.1.1 Scour Depth Distribution  
The calculation formula of scour depth in HEC NO.18 [31] of the Federal Highway Administration is adopted taking the correction of [PERSON] et al. [32] into consideration, which is as follows.  
\[H=2\lambda_{\mathrm{s}}y_{0}K_{1}K_{2}K_{3}K_{4}\left(\frac{D_{\mathrm{P}}}{ y_{0}}\right)^{0.65}F_{\mathrm{r}}^{0.43} \tag{1}\]  
where \(H\) is scour depth; \(\lambda_{\mathrm{s}}\) is the correction factor for scour depth; \(K_{1}\)-\(K_{4}\) are correction coefficients considering the shape of pile foundation, the direction of water flow, the condition of the river bed, and the particle size of material in the river bed, respectively;\(D_{\text{P}}\) is the width of the pile foundation perpendicular to the direction of water flow; \(y_{0}\) is the water depth; and \(F_{\text{r}}\) is the Froude number, which is determined as follows.  
\[F_{\text{r}}=\frac{\
u}{\left(\text{g}y_{0}\right)^{0.5}} \tag{2}\]  
where \(\
u\) is the flow velocity, which can be obtained from the size and flow of the river section; and g is gravitational acceleration.  
[PERSON] et al. interpreted the determination of scour depth distribution and reached the conclusion that the correction coefficient \(K_{1}\) for the shape of the pile foundation and \(K_{4}\) for the river bed material both are equal to one. Other parameter models can be achieved for determining the probability distribution of scour depth parameters by referring to the classification and summary of prior art from [PERSON] et al. (2017), as shown in Table 1.
# uDMA: An Efficient User-Level DMA for NVMe SSDs +
## 5 Experiments and Results
### Real-World Benchmark Test  
We ran the db_bench implemented in RocksDB to test the effectiveness of uDMA. To get closer to the natural environment, we used the ReadRandomWriteRandom mode to test the ops and micros of RocksDB under uDMA and SPDK. We ran RocksDB for 5 min per test, and the block size gradually increased from 4 KB to 128 KB.  
Figure 8 shows the average I/O latency of RocksDB under uDMA and SPDK. Compared with SPDK, uDMA reduced the I/O latency by an average of 11.13%. According to the results shown in the figure, we can see that when the data size was 4 KB, the performance of uDMA was similar to that of SPDK. However, with the gradual increase of the data size, the reduced I/O latency of uDMA increased gradually, with a maximum of 11.39%. The main reason was that uDMA used fixed memory pools to reduce user-level DMA requests and fixed memory operations. By contrast, SPDK required frequent allocations and pinned memory blocks.  
The IOPS results under SPDK and uDMA are shown in Figure 9. The IOPS of both SPDK and uDMA decreased as the data size increased. In the initial phase, when the data size was 4 KB, the IOPS of SPDK and uDMA were similar. However, with the gradual increase in data size, the decrease of the IOPS of SPDK was significantly higher than that of uDMA. By contrast, uDMA's IOPS was up about 9.5%. Because SPDK needed to allocate and pin memory blocks frequently for I/O requests, its IOPS degradation was relatively large.
# A New Improved Multi-Sequence Frequency-Hopping Communication Anti-Jamming System
## 4 Jamming Utilization Method Based on IMSFH
### Frequency-Detection Network
#### 4.3.2 Training and Communication Process  
Due to the dynamic changes in complex jamming communication environments, as shown in Figure 5, this paper adopts an online learning strategy to train the network in real-time during each communication cycle. This approach enables the network to adapt promptly to environmental changes, thereby learning the rule of reactive jamming more effectively. Similarly, according to our analysis, the weights of the convolutional layer remain relatively stable, as it primarily extracts the fundamental time-frequency characteristics between user signals and reactive jamming. As a result, we adopted the idea of transfer learning as referenced in [33], updating only the weights of the fully connected layer. This approach not only handles the dynamic characteristics of reactive jamming in complex jamming environments but also reduces the computational complexity of updating parameters. The GPU used in the experimental environment is the NVIDIA GeForce RTX 2080 Ti \(\times\) 2, with a floating-point computing performance of \(26.9\times 10^{12}\) operations per second, while the FLOP of the model proposed in this paper is \(63.3\times 10^{6}\). Consequently, it is capable of meeting the real-time requirements of online learning during the communication process.  
As outlined in the communication framework, the process begins with the generation of information bits and the corresponding frequency-hopping signals. Since the training signals and training hops are known, the training signals can be received. The signals are then processed into a spectrum waterfall through data preprocessing, which is subsequently divided into a test set and training set for network training. Through training, the dynamic rule of reactive jamming can be effectively learned. Given that the training duration is predetermined, the reception timing of the communication signal can be accurately inferred. Similarly, the trained network is employed to classify the spectrum waterfall of the communication signal, facilitating frequency detection and subsequently extracting bit information based on the identified frequency characteristics. In the end, to further analyze and validate the reactive jamming rule, the bit information is subjected to a Cyclic Redundancy Check (CRC). Upon successful CRC, the frequency detection result is confirmed as accurate and is subsequently employed as a training sample for future communication iterations. The specifics of the proposed algorithm are outlined in Algorithm 1.  
Figure 5: Leaning Strategy.
## 6 Simulation Results and Discussion
### Sensitivity Analysis
#### 6.2.2. Diesel Price  
Considering the local market conditions, we set the fuel price to vary from USD 0.62-1/L in steps of 15% change from the minimum value (USD 0.62/L). Keeping the minimum load (76.94 kWh/day) and minimum GHI (5.05 kWh/m\({}^{2}\)/day), when the diesel price varied from USD 0.62 to 1/L the LCOE increased from USD 0.182 to 0.198/kWh (Figure 15) and NPC also increased to USD 134,656. Similarly, the line plot of diesel price variation effect on the LCOE and NPC was presented in Figure 18 above. Maintaining the average GHI of 5.94 kWh/m\({}^{2}\)/day and load of 76.94 kWh/day, when the diesel price varied from USD 0.62 to 1/L, relative increments of LCOE and NPC from the optimal value were observed in Figure 18.
# Accelerated Correction of Reflection Artifacts by Deep Neural Networks in Photo-Acoustic Tomography
## 4 Results
### Experimental Setup
#### 4.1.3 Parameter Setting  
For our proposed network, the initial learning rate \(\lambda\) was set as \(1.0\times 10^{-3}\) and was adjusted by every epoch, namely \(\lambda_{t}=\frac{\lambda}{\sqrt{t}}\) at the \(t\)-th epoch. For the Adam optimization, the coefficients used for computing running averages of gradients and its square were set as 0.9 and 0.999, respectively. The network was implemented with PyTorch DL library [55] and trained within 60 epochs using four NVIDIA GeForce GTX 1080 Ti GPUs. The batch size was set as 512 during the training.  
For these two iterative algorithms, the number of iterations for ATR and Landweber is empirically set to be 10. The regularization parameter in Landweber is empirically chosen as 0.03.
# Numerical Study on the Mechanical Behavior of Sand-Rubber Mixtures under True Triaxial Tests
## 4 Conclusions  
1. The intermediate principal stress ratio \(b\) has a significant impact on the strength of the RSM. As the intermediate principal stress coefficient \(b\) increases, the shear strength \(q\) gradually increases and the peak internal friction angle of the shear strength index first increases and then decreases. The addition of large rubber particles makes the strength greater than RC = 10% when RC = 30%, which is different from other rubber particles of similar particle size.
2. The failure behavior of RSM materials was compared with four widely used existing failure criteria. The results show that the relationship between the peak internal friction angle and the \(b\) value is basically consistent with the modified Lade-Duncan criterion. All the existing criteria cannot represent the failure behavior of RSM materials, and further research needs to be conducted to develop new failure criteria for mixture materials with components that possess very different properties.
3. When 0% < RC < 30%, the normal contact force plays a major role in the bearing of external loads, and it is consistent with the change law of the peak deviatoric stress.
4. The intermediate principal stress has a significant impact on the anisotropy of the system. However, despite variations in particle size distribution and particle stiffness, RSM materials have an inherent linear relationship between the stress ratio and the partial fabric.  
Figure 13: The relationship between the strong deviatoric fabric and the stress ratio of samples with (**a**) RC = 0%; (**b**) RC = 10%; and (**c**) RC = 30%.  
**Author Contributions:** Conceptualization, Y.L. and X.G.; methodology, Y.L.; data curation, X.G. and L.Y.; formal analysis, X.G. and H.D.; validation, Z.C.; writing--original draft, X.G.; writing--review and editing, H.D., L.Y. and Z.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Nomenclature**  
DEM  Discrete element method  
RSM  Rubber-sand mixture  
RC  Rubber content  
\(b\)  Intermediate principal stress ratio  
\(q_{peak}\)  Peak deviator stress  
\(\phi_{\rm p}\)  Peak internal friction angle  
\(\sigma_{\rm c}\)  Confining pressure  
\(\sigma_{\rm 3}\)  Small principal stress  
\(\varepsilon_{1}\)  Major principal strain  
\(f_{n}\)  Normal contact force  
\(<f_{n}>\)  Average normal contact force
## 3 Weather Component Analysis during the \"Oriental Star\" Accident
### Investigate Weather Components in Deterministic Product  
According to the nearby local meteorological observatory record, the on-site hourly rainfall reached 64.9 mm during UTC 1300-1400 Z on 1 June 2015, which was at a torrential rain level typically observed within 24-hour period. Figure 3 presents rainfall images from various sources that were close to the \"Oriental Star\" accident. In the Doppler Radar image (Figure 3a), a squall line with multi-convective cells just passed the \"Oriental Star\" location. In the satellite infrared image from CMORPH project (Figure 3b), the accumulated 3 h precipitation map shows that overwhelming precipitation occurs at the time and place of the tragedy.  
Figure 3: (a) Doppler radar rain rate snapshot at the “Oriental Star” shipw moment; (b) CMORPH 3 h precipitation during UTC1200–1500 Z on 1 June 2015. The red box represents the area of (a), and the purple thick line represents the last track of the “Oriental Star”. The star marks the ship’s final wreck location.  
The next step was to investigate the quantitatively-predicted weather components. In the ECMWF deterministic products, the forecasted CP distribution (Figure 4a) is very similar to the observation field (Figure 3b), with the maximum value exactly located at the shipw position. However, this numerical forecast was initialized at UTC 1200 Z on 1 June 2015, just 90 min ahead of the accident. Due to the dynamic computation and data transition time, the analyzer was able to obtain the prediction data as early as 8 h after the model initialization time. So it was necessary to go backwards for an additional 24 h, i.e., the forecasts was initialized on 31 May UTC 1200 Z. It turned out that an area with a high CP value was observed to be adjacent to the last route of \"Oriental Star\" ship (Figure 4b), giving hope to the early-warning signal. In contrast, the LSP variable had a much higher value but was far away from the ship position (Figure 4c), with a minimum distance that exceeds 250 km.  
Besides wind and precipitation variables, this study checked all 33 variables in the ECMWF deterministic product, but no other weather components showed high spatial-temporal connectivity with the \"Oriental Star\" accident (Table 1). Only air temperatures at a high level (Figure 5) are in the middle or end of an increasing trend. From a meteorological view, this trend indicates that the local air mass was uprising during that day, however  
Figure 4: (**a**) Cumulus precipitation (CP) prediction over UTC 12–18 Z initialized at 12 Z 1 st June 2015; (**b**) same as (**a**) but initialized at UTC 12 Z 31 st May; (**c**) same as (**b**) but for large-scale precipitation (LSP) prediction (Reprinted with permission from Ref. [PERSON] et al. 2020 [16]). The black thick line represents the last route of the “Oriental Star”.  
this provides very limited information. Due to the superiority of the CP variable, in the remaining sections, it will be referred to as a predictor.
## 2 Materials and Methods  
### Ferritic-Martensitic 9 Cr and Ferritic, Stainless 22 Cr Steel  
Grade 91 is a standard, so-called 9 wt% Cradourced ferritic-martensitic (AFM) steel, widely applied for structural purposes in power engineering. It features tempered martensite structure in the as-delivered state and is strengthened by solid solution and precipitation of MX (M: V, Nb; X: N), mainly located at lath and M\({}_{23}\)C\({}_{6}\) (M: Mo, Cr) particles, preferentially located at prior austenite grain boundaries (PAGBs) [17; 18; 19]. The grade 91 material applied in this study was taken from a pipe section (\(\mathcal{O}_{\text{outer}}\) = 160 mm, t\({}_{\text{tw}}\) = 20 mm), manufactured by Vitkovice Steel, CZ according to ASTM A335 specifications. Austenitizing and tempering were executed at 1050 \({}^{\circ}\)C/0.5 h/air-cooling and 750 \({}^{\circ}\)C/1 h/air-cooling. The resulting as-received microstructure is depicted in Figure 1.  
Crofer(r)22 H (X1 CrWNbTiLa22-2, W.-Nr. 1.4,755 [20]) is a 22 wt% Cr, stainless, fully ferritic steel, originally developed as a low-cost, metallic interconnect material for light-weight design [21; 22] solid oxide fuel cell stacks by Forschungszentrum Juelich GmbH, Germany, in cooperation with VDM Metals GmbH, Germany. It is produced by VDM Metals GmbH by vacuum induction melting (VIM) and delivered in the solution in an annealed (1050 \({}^{\circ}\)C/5 + minutes, depending on section thickness/rapid air-cooling) state. It precipitates small (Fe, Cr, Si)\({}_{2}\) (Nb, W)-Laves phase particles (Figure 2) [23; 24], which effectively strengthen the material [20; 25; 26; 27], during application in the temperature range from 600-900 \({}^{\circ}\)C. Thermomechanically induced precipitation of the Laves phase particles [28; 29; 30; 31; 32] plays a significant role in the fatigue performance of this type of steel, too. Furthermore, Crofer(r)22 H offers excellent resistance to corrosion in steam and CO/CO\({}_{2}\)-containing atmospheres, based on the formation of a protective, duplex Cr\({}_{2}\)O\({}_{3}\)/(Mn, Cr)\({}_{3}\)O\({}_{4}\) layer at the component surface [23]. For these reasons, advanced Laves phase strengthened steels are considered candidate alloys for future power conversion equipment of higher efficiency [32] and increased operational flexibility [33]. A typical microstructure is displayed in Figure 2. Besides precipitation of small Laves phase particles, the formation of  
Figure 1: Scanning electron microscopy (SEM) micrograph of ferritic-martensitic grade P91 steel (1050 \({}^{\circ}\)C/0.5 h/AC + 750 \({}^{\circ}\)C/1 h/AC).  
particle-free zones (PFZs), preferentially along high angle grain boundaries (HAGB) [33; 34], is a typical microstructural feature of this steel type.  
From a metallurgical point of view, the main difference between the two materials besides microstructure and strengthening precipitate species are the differing solid solution contents of chromium, tungsten and molybdenum. The chemical compositions of the ferritic-martensitic grade 91 and the fully ferritic, stainless Crofer(r)22 H steels are summarized in Table 1.