esa-sceva/satcom-chunk-collection huggingface dataset:
# Wind Tunnel Measurements of Surface Shear Stress on an Isolated Dune Downwind a Bridge
## 2 Methods
### Surface Shear Stress Sensor  
Irwin sensor is a simple omnidirectional pressure meter that determines the friction velocity without the requirement of alignment by measuring the near-surface vertical pressure differential, which can be used to estimate the surface shear stress in the complex flow [24; 25]. It has been used successfully in aeolian sand researches in both lab [13; 21; 22; 26] and field [27; 28] to explore the relationship between shear stress and sand transport potential in complex, nonuniform airflow. Many studies have confirmed that the sensor can be simply calibrated [24; 29] and is applicable to measure the surface shear stress on a sand dune downwind a bridge in the wind tunnel experiment.  
Based on the Irwin empirical function, the friction velocity prepared to calibrate the 34 Irwin sensors was determined using the velocity profile technique. As shown in Equation (1), the calibration of one sensor is [29]:  
\[\frac{u_{*}h}{v}\ =\ a+b\left(\frac{\Delta p\mu^{2}}{\rho\sigma^{2}}\right)^{n} \tag{1}\]  
where \(h\) is the height of the obstacle, \(v\) stands for kinematic viscosity. The calibration coefficients we used is an averaged result of 34 Irwin sensors. The parameter \"\(a\)\" equals 3.86, \"\(b\)\" equals 0.11 and \"\(n\)\" equals 0.52. The calibration result is accurate enough for the wind velocity within 2 m s\({}^{-1}\) and 18 m s\({}^{-1}\)[29]. The friction velocity \(u_{*}\) can be converted to shear stress using \(\tau\ =\ \rho u_{*}^{2}\).  
Figure 3 is the schematic of installation of sensors over the sand dune. In brief, 34 Irwin sensors were used stream-wise. Among them, 8 were set on the windward slope, 5 were set on the leeward slope, 3 were mounted upwind the dune, and 18 were mounted downwind the dune. The measuring position changed three times along the \(y\)-axis at \(y=0\) cm, 6 cm (L/6) and 18 cm (L/2), respectively. One sensor was set at 17.5 H upwind the model to detect the shear stress on a bare surface (\(\tau_{0}\)) for a reference value to normalize surface shear stress (\(\tau^{\prime}\ =\ (\tau_{x}-\tau_{0})/\tau_{0}\)). The bridge model, sand dune model and the stream-wise wind velocity were symmetric to the centerline of the wind tunnel. Therefore, the distribution of shear stress on the sand dune was also symmetrically distributed with the centerline.  
Figure 2: Schematic diagram of wind tunnel experiment.
## 4 Results
### Experiment 3: Cross-Lingual Text Rendering Quality  
This experiment aims to evaluate the ability of visual text generation models to produce accurate, legible, and stylistically consistent text across a diverse range of languages and scripts. This experiment tests the models' adaptability to various linguistic systems, including alphabetic (English, Russian, Hebrew), logographic (Chinese, Japanese), abjad (Arabic), abugida (Hindi, Bengali, Thai), and syllabic (Korean Hangul). In doing so, it provides a comprehensive assessment of how well these models handle the unique characteristics of each writing system. The prompts used in this experiment are designed to explore a wide array of text contexts and layouts. These include simple single-line phrases, multi-line text structures, and mixed-language combinations, testing the models' ability to maintain clarity and coherence across diverse linguistic inputs.  
However, the results revealed that none of the evaluated models--Ideogram, DALL-E 3, and Stable Diffusion--were able to generate text correctly in languages other than English. The models struggled with maintaining the structural and stylistic integrity of non-Latin scripts, leading to illegible characters, distorted symbols, and incorrect text outputs. For scripts such as logographic and abjad, where text complexity is higher, the inaccuracies were even more pronounced. These findings underscore a significant limitation in current text-to-image models, highlighting their lack of cross-lingual rendering capabilities and their inability to handle the unique typographic and spatial requirements of non-Latin languages.  
This experiment emphasizes the urgent need for advancements in multilingual text generation, ensuring that future models are equipped to handle linguistic diversity with the same level of accuracy, clarity, and visual consistency as seen in English. Table 8 shows examples of images generated in different languages: English, Chinese, Arabic, and Hindi.
# Energy-Efficient Channel Coding Strategy for Underwater Acoustic Networks
## 6 Conclusions  
In this paper, we addressed the problem of energy-efficient communication in UAN through the use of an optimized mixed FEC/fountain channel coding scheme. Numerical results demonstrate the potential advantages of the proposed scheme in terms of energy consumption when compared to uncoded transmission and to the case of non-optimized (fixed) parameters. The proposed strategy leads to a graceful degradation in terms of energy efficiency with the increase of link distance, leading to an enlarged range for communication among nodes in UAN, while also increasing the network lifetime.  
**Acknowledgments:** This work has been partially supported by Conselho Nacional de Desenvolvimento Cientifico e Tecnologico - CNPq (Brasilia, Brazil).  
**Author Contributions:** [PERSON] and [PERSON] conceived the coding strategy; [PERSON] and [PERSON] conceived the theoretical energy model; [PERSON] and [PERSON] designed the experiments; [PERSON] and [PERSON] implemented and simulated the proposed model.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Case Studies and Analysis
### Test Results and Analysis for PFG at SCP1
#### 3.2.1 Test Results and Analysis in Two-Dimensional Planes  
All the test results of the PFG to each CIV at SCP1 are listed in Figure 2. As shown in (a) of Figure 2, the APFG and RPFG to CIV1 at SCP1 both present sinusoidal curves, the fluctuation ranges  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Conditions** & **Voltage \(V\)r** & **Phase Difference \(\delta\)** & **Magnitude of \(v_{12}\)** & **Phase Angle \(\theta\)** \\ \hline Cond.1 & 1 & \(-\pi\)/8 & & \\ Cond.2 & 1 & \(-\pi\)/24 & 0 p.u. to 4.0 p.u. & \(-\pi\) to \(\pi\) \\ Cond.3 & 0.9 & \(-\pi\)/8 & & \\ Cond.4 & 0.82 & \(-\pi\)/2 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Typical system operation conditions in case study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenarios** & **phase Angle \(\theta\)** & **Magnitude of \(v_{12}\)** & **Phase Difference \(\delta\)** \\ \hline \hline \(\mathrm{Sen.1}\) & \(-\pi\)/3 & & \\ \(\mathrm{Sen.2}\) & \(-\pi\)/8 & 0 p.u. to 4.0 p.u. & \(-\pi\) to \(\pi\) \\ \(\mathrm{Sen.3}\) & \(\pi\)/8 & \\ \(\mathrm{Sen.4}\) & \(\pi\)/3 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Typical regulation scenarios for CIV3 in case study.  
of the two PFG are the same except the extreme values are different. In (b) of Figure 2, the PFG to CIV2 present more complicated sinusoids, the two curves of APFG to CIV2 by Reg.1 and Reg.2 modes are symmetrical about the y-axis through the origin, while those of RPFG to CIV2 show central or rotational symmetry features, both the APFG and RPFG at the positive and negative half regions have one larger amplitudes and one smaller amplitudes, indicating the PFG to CIV2 can be regulated to larger or smaller required amplitudes in different regions by different regulation modes. In (c) of Figure 2, all the curves of APFG and RPFG to CIV3 show sinusoidal features, the distinctions only lie in the magnitudes of the curves dependent on \(v_{t}\) of different operation conditions. The above features are accordant with the theoretical analysis of the Equations (1)-(3).  
Furthermore, the curves of (d)-(f) in Figure 2 depict the principles of PFG to each CIV from another perspective. In (d) of Figure 2, the curve of APFG to CIV1 against RPFG to CIV1 form a closed circular loop. In (e) of Figure 2, the two curves of APFG to CIV2 against RPFG to CIV2 by Reg.1 and Reg.2 modes also form a type of axial symmetrical closed shapes, manifesting that the PFG to CIV2 can be regulated throughout an overall closed region by the two regulation modes of CIV1 and CIV2, and the regulation modes are more efficient and practical. As for (f) of Figure 2, the APFG to CIV3 against RPFG to CIV3 still form closed circular loops in all the operation conditions. The above features of PFG to each CIV at SCP1 are also accordant with the theoretical analysis and Equations (1)-(3).
## 3 Experiments and Analyses
### Result Analyses
#### 3.3.2 Results of Comparison Experiments  
In order to further verify the effectiveness of the proposed algorithm, several single-stage mainstream models such as SSD, YOLOv5 and RetinaNet [28] are compared to BDE_RCNN, as shown in Figure 6. Among them, SSD network is based on a convolutional network of forward propagation to generate the boundary frame with fixed size and target scores of the sample. Its final detection result is obtained by non-maximum suppression. YOLOv5 is one of the typical single-stage algorithms. By dividing the feature graph into multiple lattices and detecting the targets in each lattice, the position and category of targets within the lattices can be predicted at once. Using the FPN structure and focal loss function, the RetinaNet network solves the imbalance of positive and negative samples in the target detection network. Figure 6 shows that the three single-stage models fail to detect the whole cells and miss some cells for the same dataset, as shown by the red boxes marked in Figure 6a-c, respectively. Meanwhile, BDE_RCNN can correctly recognize the single cells and adherent cells, as shown in Figure 6d. This is attributed to lacking the step of candidate region extraction in the single-stage network, and only the first-stage network completes the two tasks of classification and regression.  
Figure 5: Loss/lr curves of four models. (**a**) Original model; (**b**) model 1; (**c**) model 2; (**d**) BDE_RCNN.  
Table 2 shows the comparison results of SSD, YOLOv5, RetinaNet and BDE_RCNN in the evaluation indexes. It is seen that the overall detection accuracy of the improved BDE_RCNN is better than that of single-stage networks. For mAP and F1 indexes, BDE_RCNN is 46.5% and 44% better than SSD, respectively. Compared to YOLOv5, they increase by 17.1% and 17.8%, respectively. Similarly, the two indexes increase by 6.9% and 7.5% compared to RetinaNet, respectively. These results indicate that BDE_RCNN has stronger feature extraction capability compared to single-stage mainstream networks in the actual scene, thus effectively improving the automatic recognition effect for cells.
# CSRLoan: Cold Start Loan Recommendation with Semantic-Enhanced Neural Matrix Factorization
## 5 Experiment
### Experimental Settings
#### 5.1.3 Compared Baselines  
In this study, we compare CSRLoan with seven representative baselines to evaluate the performance. Because of the particular data, i.e., statement text, used in our problem, we only chose recommendation methods that can be easily extended to the text information as the baselines. Notice that many graph neural-network-based methods [16; 17] are proposed recently, and we do not compare CSRLoan with them due to the cold start setting. We roughly divide compared baselines into three groups: general recommendation methods, loan recommendation methods, and ablations of CSRLoan.  
**General recommendation methods**.  
* **MF-BPR**[37]: A Bayesian personalized ranking optimized MF model with a pairwise ranking loss. It is tailored toward recommendations with implicit feedback data.
* **CML**[2]: A recently proposed algorithm that minimizes the distance between each user-loan interactions in Euclidean space.  
**Loan recommendation methods**.
* **MBR**[14]: A motivation-based recommendation method to utilize the unstructured data. This is the state-of-the-art method for loan recommendations.
* **FAR**[3]: A fairness-aware recommendation method based on one-class collaborative-filtering techniques.  
**Ablations of CSRLoan**.
* **NMF**: Traditional NMF that removes the pre-training and default risk estimation module.
* **CSRLoan \({}_{pre-}\)**: Directly train CSRLoan with randomly initialized statement encoder.
* **CSRLoan \({}_{risk-}\)**: A variant of CSRLoan which removes the default risk estimation module.  
Note that some of these compared methods are not for loan recommendations, and we utilize historical data to train these models and use the same testing data to evaluate the performances.
## 5 Conclusions  
In this study, we established a thermodynamically consistent TM-PFM within COMSOL Multiphysics to simulate two distinct thermal fracturing modes in reservoir rocks under high-temperature conditions. Key findings include:  
1. The developed coupled thermo-mechanical phase-field model (TM-PFM) demonstrates a pronounced capability for accurately capturing the complete evolution of thermal fractures in reservoir rocks.
2. During initial thermal shock, the substantial temperature difference induces a significant temperature gradient at the contact boundary within an extremely short duration. This rapid thermal loading generates immense thermal stresses. To relieve these stresses and minimize the system's total energy, numerous short cracks initiate along the convective boundary. As the cryogenic region propagates inwards, these cracks undergo competition, evolving into cracks with diverse lengths and morphologies.
3. Under thermal shock conditions, crack density progressively increases with higher heat transfer coefficients, while crack spacing decreases correspondingly. This phenomenon stems from the fact that a larger heat transfer coefficient accelerates the boundary cooling rate, exacerbating the temperature gradient and consequently the thermal stress magnitude.
4. Elevated temperatures cause reservoir sandstone to exhibit differential thermal expansion due to mineral heterogeneity, inducing significant thermal stresses. Thermal cracks initiate once these stresses exceed the mineral strength. For the reservoir sandstone studied, 400 \({}^{\circ}\)C represents the threshold temperature for thermal crack initiation. Below this temperature, cracking is minimal, however, exceeding 400 \({}^{\circ}\)C triggers a marked increase in crack density.  
Conceptualization, G.T. and D.G.; formal analysis, W.Z. and L.D.; data curation, X.M.; writing--original draft preparation, G.T.; writing--review and editing, G.T. and M.L.; supervision, D.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Deep Earth Probe and Mineral Resources Exploration --National Science and Technology Major Project of China (No. 2024 ZD1003600).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Figure 23: Variation of elastic modulus and damage element with temperature.  
**Conflicts of Interest**: Authors [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] were employed by SINOPEC Star Petroleum Co., Ltd. (Sinopec Star). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Recycling 3D Printed Concrete Waste for Normal Strength Concrete Production
## 1 Introduction
Adding to this, BV-MI 01: 2005(H) [15] introduces recommendations that involve varying levels of RCA replacement, ranging from 30% to 100%, based on class strength and the geometrical properties of aggregates. However, it advises not to use RCA replacement for C50/60 and above class strength. It is noteworthy that different countries have adopted varying approaches to regulate the percentage of RCA replacement. For example, Brazil, China, Spain, and the UK have set a lower limit, restricting RCA replacement to 20% for structural concrete [16]. The diversification in these guidelines reflects regional considerations, experiences, and research findings, emphasizing the need for a nuanced approach in addressing the challenges associated with incorporating recycled materials in concrete production. In conventional concrete, the use of aggregates with a diverse range of particle sizes is a common practice aimed at enhancing packing density and overall strength. However, in the realm of 3D-printed concrete, a departure from this conventional approach is observed. Instead, a more deliberate emphasis is placed on achieving a controlled and uniform particle size distribution of the fine aggregate [17]. This strategic adjustment is designed to facilitate a consistent flow through the printer nozzle, thereby ensuring the smooth extrusion of the material during the 3D printing process. Hence, it is essential to determine the optimal replacement level for the recycled aggregate (RA) sourced from 3D-printed concrete waste to uphold the mechanical properties of the concrete. Additionally, a mechanism needs to be developed for incorporating the fine particle size RA into the mix. The decision to include fine recycled aggregate is influenced by the fact that the parental material (i.e., 3D-printed concrete) consists solely of fine aggregate, introducing an additional aspect for investigation. This study aims to investigate the feasibility of recycling 3D-printed concrete waste as an aggregate in normal strength concrete, focusing on compressive strengths for the C30/37 and C40/50 MPa classes, covering both fine and coarse aggregates in particle size distribution. Despite the increasing interest in 3D-printed concrete, there is a notable gap in comprehensive research on utilizing recycled aggregate (RA) from this material in conventional concrete production. This study seeks to bridge the divide between cutting-edge construction techniques and sustainable resource management. By repurposing waste materials from 3D-printed concrete processes, the objective is to showcase the potential for establishing a closed-loop system within the construction industry.
## 2 Methods
### Convolutional Neural Network  
A convolutional neural network (CNN) is an effective feed-forward neural network with complex structure and convolutional computation. [PERSON] et al. [18] invented the CNN deep learning algorithm in 1998, and the gradient algorithm was used to optimize the model. There are many typical network models, such as LeNet, AlexNet, VGGNet [19], ResNet [20], GoogleNet [21], etc. The development history of network models is shown in Figure 5. In the areas of target recognition, semantic segmentation, and image classification, CNN has so far produced several ground-breaking research findings.  
Shared weights, pooling, and sparse concatenation are three ways that CNN differs from other deep learning algorithms (also known as local acceptance domain). It lessens dimensional sampling, lessens the dimensionality of the data in time and space, uses fewer training parameters, simplifies the network, and successfully prevents algorithm overfitting [22]. The classification recognition model of CNN mainly consists of feature extraction of input data through convolutional and pooling layers, classification recognition by combining features and classifiers through fully connected layers. The topology of the traditional convolutional neural network model is represented by the input layer, convolution layer, pooling layer, fully connected layer, and output layer, as shown in Figure 6. Convolutional layer, activation layer, and pooling layer repetition increase the network depth, which is the fundamental element of CNN. The convolutional layer uses multiple convolutional kernels to achieve enhancement, noise reduction, and feature extraction of images using a variety of approaches. The pooling layer, also known as downsampling, speeds up algorithm computation by extracting feature values, reducing the complexity of the computational network, and processing fewer data overall [23]. The basic modules that implement CNN's feature extraction function are the convolutional and pooling layers [24].  
Figure 5: Network model development history.
# Yield Prediction of Winter Wheat at Different Growth Stages Based on Machine Learning
## 2 Materials and Methods
### Accuracy Assessment  
This study constructed winter wheat yield prediction models using historical yield data and remote sensing data, followed by accuracy evaluation. All constructed yield prediction models underwent validation using K-fold cross-validation, with root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R\({}^{2}\)) employed as evaluation metrics.  
\[MAE=\frac{\sum_{i=1}^{n}\left|y_{i}-y_{p}\right|}{n}, \tag{1}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}\left(y_{i}-y_{p}\right)^{2}}{n}}, \tag{2}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}\left(y_{i}-y_{p}\right)^{2}}{\sum_{i=1}^{n}\left( y_{i}-\overline{y}\right)^{2}}, \tag{3}\]  
where \(n\) represents the number of samples, \(y_{i}\) denotes the actual observed value for the \(i\)th sample, \(y_{p}\) is the predicted value for the \(i\)th sample, and \(\overline{y}\) is the mean of all the observed values.
# An Efficient GPS Algorithm for Maximizing Electric Vehicle Range
## 2 Literature Review
Additionally, the methodology will be validated using a real electric vehicle on roads to ensure the accuracy of our results. This paper is organized as follows. in Section 2, the data present detailed information on how the conventional GPS applications work on suggesting routes. Then, the next section shows the mathematical model developed with the equations used to calculate the total energy consumption. After that comes the results carried out for the case studies to show how some factors, when they change, affect the range of the vehicle per charge, some of these factors include the car model, wind speed, and road inclinations. Moreover, a validation for the simulated results is carried out in real life, and then the discussion of these data is followed by the conclusion. Most of the geographical positioning systems (GPSs), such as Google Maps which is the most used application, utilize distance between two locations as a criterion for selecting optimal routes [21]. Other factors considered include current traffic flow, speed restrictions, availability of alternative routes, and user preferences such as avoiding tolls or congested highways. Machine learning techniques are employed by Google Maps to enhance the accuracy of its routing algorithms, ensuring that proposed routes are regularly updated to reflect the latest traffic conditions and other variables [22]. When calculating routes between unique locations, Google Maps takes the following criteria into consideration:  
* Shortest path: Google Maps frequently seeks the path that connects two places in the least period, but this is not always the greatest option because it does not account for considerations such as traffic. * Fastest way: Google Maps may also attempt to find the shortest path between two places, even if it means taking a longer, less-traveled route. * Least crowded path: Google Maps may prioritize the least congested way between two places, even if it requires a longer or more difficult route. * Toll-free path: Google Maps may attempt to locate a route that avoids tolls, even if it means taking a longer route. * EV-friendly path: Google Maps may search for an EV-friendly route, considering possibilities that pass by recharge stations. Users have the option to set personal preferences, such as avoiding toll roads, which Google Maps will then take into account when generating the route [20]. Several factors will be considered based on the user's selections and the prevailing traffic conditions. The current issue lies in the fact that conventional GPSs fail to account for the recharging time discussed earlier. This means that they may suggest a route based solely on the shortest time or distance, disregarding the energy consumption involved. Conversely, there may be another route that proves more economical, minimizing the need for recharging [23].