esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Conditional Diffusion Model for OFDM-Based Image Reconstruction  
In this section, the proposed CDM for the reconstruction of distorted images in OFDM-based wireless communications is introduced. As the modulation schemes increase in complexity, the received signals become more sensitive to channel conditions and noise. The proposed CDM was designed to integrate factors that contribute to the distortion of the received signals, based on the ideas presented with regard to the conditional diffusion model [21]. These factors include the modulation schemes, the SNR conditions, and multipath fading. Furthermore, both the diffusion process and the reverse process were designed to find the optimal variance for each transmission environment. OFDM-based image reconstruction using the proposed CDM is depicted in Figure 4. The CDM utilizes an interpolation parameter, \(v_{t}\), to design a diffusion process to learn the distortions that occur in wireless communications.  
The CDM is trained using pairs of \(x_{0}\) and \(y\) from a training dataset. This training step enables the CDM to learn the relationship between the original and the distorted images. After training, the CDM is used for reconstruction. A distorted image \(y\), which could be any arbitrarily distorted image received through OFDM transmission, is input into the trained CDM.
# Machine Learning Applications for Short Reach Optical Communication
## 4 Machine Learning Applications for Indoor Optical Wireless Communication and Positioning  
Indoor OWC technology emerges as a complementary method to RF based wireless communication systems with a number of attractive features, such as extremely broad bandwidth, license-free spectrum, high communication security and anti-electromagnetic interference [124]. Among indoor OWC technologies, both the visible and near-infrared spectra have been investigated [125], corresponding to the VLC system and the infrared system, respectively. The infrared system typically adopts the near-infrared LD as the light source and PD as the detector, which have broad modulation bandwidth. However, the deployment of additional infrared transceivers increases the cost, and the maximum power of LD is limited by the laser eye and skin safety regulations [31]. Furthermore, strict alignment between the infrared transmitter and the receiver is typically needed, due to the limited coverage area of light beam and the relatively small reception angular range of the receiver. In contrast, in VLC systems, LED is adopted as the transmitter, and hence, the existing infrastructures can be used to realize illumination and communication simultaneously. Among different types of VLC technologies, optical camera communication (OCC) has recently attractive a lot of interests since it harnesses camera as the receiver with unique features, such as the larger receiver field of view (FOV), spatial and wavelength separation ability and low cost [126; 127].  
Figure 12: Soft DNN model training process.
## 2 Working Principle  
The schematic diagram of the proposed system is shown in Figure 1. The steering system uses a permanent magnet synchronous motor to drive the pump of the closed hydraulic circuit so that the steering-by-wire and flow matching of the loader is realized. By collecting the tilt angle of the electronic control handle, the controller converts it into the target steering angle of the loader, which is regarded as the target value of closed-loop control \(\theta^{*}\). The displacement of the steering cylinder is converted into the current steering angle of the loader by the controller as the feedback of the closed-loop control \(\theta\). The difference between them is the control deviation \(\theta_{e}\), which is the input of the controller. After adjusting, the motor controller outputs the motor speed signal \(n\) to control the flow from the motor pump to the steering cylinder and to control the output displacement of the steering cylinder, that is, to control the actual steering angle of the loader, so that the steering angle of the loader follows the tilt angle of the electric control handle.  
Figure 1: Schematic diagram of closed pump flow matching steering system based on variable speed.  
Compared to the traditional steering system, the proposed system has the following characteristics:  
1. The closed-circuit pump-control system is adopted and the throttling loss and overflow loss of the traditional steering system can be avoided.
2. Permanent magnet synchronous motor is used as the driving motor, which has good speed regulation characteristics and dynamic response characteristics. According to the requirements of working conditions, variable speed flow matching is satisfied, and flow pressure high-frequency response is realized.
3. The electronic control handle is used as the steering control lever, which has good response characteristics and steering accuracy and requires a small range of movement, greatly reducing the labor intensity of the driver.
## 2 Materials and Methods
### Data Availability  
#### 2.2.1 Downscaled GOME-2 SIF  
[PERSON] et al. (2020) employed a semi-empirical approach based on GOME-2 to generate a new SIF product whose resolution is 0.05\({}^{\circ}\) and 8 days [41]. The explanatory variables for SIF were divided into three categories: vegetation parameters (including NDVI and EVI), moisture parameters (evapotranspiration and NDWI), and temperature parameters. Two sets of datasets (JJ and PK) were produced using the SIF inversions [42; 43]. Both sets of data were calibrated using daily correction factors to account for diurnal variations in GOME-2 instantaneous SIF. This study utilized the PK dataset.  
To eliminate the effect of APAR on SIF, the SIF\({}_{\text{yield}}\) was further calculated on the basis of fPAR and PAR data. The GLASS PAR product was obtained from the global shortwave radiation product produced by the GLASS albedo product and the conversion factor production. Eight days of the MOD15A2H product with a 500 m resolution were used for fPAR. SIF\({}_{\text{yield}}\) was calculated as follows [44]:  
\[\text{SIF}_{\text{yield}}=\text{SIF}/(\text{fPAR}\times\text{PAR}), \tag{1}\]  
where PAR is photosynthetically active radiation and fPAR is a fraction of PAR.
###### Abstract  
During the spraying operation, it is important to consider the environmental conditions, particularly the wind velocity. Droplets carried by the wind out of the spray zone may be carried onto nearby plants, soil, water bodies, residential areas, etc. Various measures have been developed and used to reduce droplet drift to address this problem. Robotic spraying systems, such as unmanned aerial vehicles and spraying robots, are now increasingly being used. The influence of lateral winds on the spraying processes of these systems has not yet been extensively investigated. In this study, spray coverage and spray drift of manufactured artificial plants were investigated. Spraying was carried out with an XAG R150 spraying robot and a lateral wind from 2 m s\({}^{-1}\) to 8 m s\({}^{-1}\) was generated with an air flow generation stand by varying the air flow velocity every 2 m s\({}^{-1}\). Spray coverage on the artificial plants was measured at two heights (0.5 and 1 m). The droplet coverage measurements were significantly influenced by the lateral wind velocity and the height of the plant coverage measurement site. The results showed that even in the presence of a high lateral wind velocity (\(v\) = 6-8 m s\({}^{-1}\)), the droplet spray had better coverage of the middle part of the artificial plant (0.5 m from the ground) than the upper part (1 m from the ground). For the spray drift studies, three solutions with low concentrations (0.1%) of chemical drift reduction agents (DRAs) were sprayed, with water as control. It was found that the proportion of drifting droplets also increased with increasing lateral wind velocity. The spray coverage at 3 m from the spray zone (spray drift) was 1.6% at a lateral wind velocity of \(v\) = 2 m s\({}^{-1}\), 4.2% at \(v\) = 4 m s\({}^{-1}\), 5.3% at \(v\) = 6 m s\({}^{-1}\), and 8.1% at \(v\) = 8 m s\({}^{-1}\). The use of DRAs was able to reduce spray drift in strong (\(v\) = 8 m s\({}^{-1}\)) lateral winds. It was found that at 3 m from the spray zone, at a spray height of 1 m, the spray coverage was about 40.7% lower than that of water for DRA1, 44.4% for DRA2, and 43.2% for DRA3.  
s  
spraying robot; field spray test; lateral wind velocity; spray coverage; artificial plant; drift reduction agents
## 2 Related Work
### Combining Knowledge Graphs and Pre-Trained Language Models  
Knowledge graph embeddings excel in structured knowledge representation, while pre-trained language models are powerful for understanding and generating natural language text in various contexts. Extensive research has been conducted to harness the advantages of both structured knowledge and unstructured text comprehension. Below, we summarize strategies for combining knowledge graphs with pre-trained language models into three categories:  
* **Traditional ensemble methods**. These encompass the most commonly used ensemble methods. The ensemble can be conducted directly with the embeddings, i.e., feature-level fusion, which is also called early fusion. A straightforward way to combine embedding vectors is simply to \"stack\" them into a higher-dimension embedding vector [PERSON] et al. (2017). An alternative is to add up the embedding vectors in a linear way, for example, through a linear interpolation, where the weights can be either preset or trained [PERSON] et al. (2016); [PERSON] et al. (2017); [PERSON] et al. (2017). Learning a projection function between embeddings is another solution. For linear projection, [PERSON] et al. [PERSON] et al. (2018) conducted linear regression in two ways, i.e., ordinary least squares and orthogonal procrustes to learn the transformation matrix. [PERSON] et al. (2018) instead used ridge regression and generalized procrustes analysis. The projection function can also be non-linear, for example, using a neural network to learn a multilayer perceptron (MLP) ([PERSON] et al., 2018). An alternative to early fusion ensembles is to perform the combination in the decision stage, i.e., late fusion. Each individual model makes their own independent decisions, which are then combined or fused to produce the final result ([PERSON] et al., 2018).
* **Retrofitting**. Originally proposed in ([PERSON] et al., 2018), this process uses external lexical relational resources from knowledge graphs to refine an existing matrix of word embeddings to obtain higher-quality semantic vectors. [PERSON] et al. (2018) used a generalization of the \"retrofitting\" method to create ensembles that learned from Word2 Vec, GloVe, and ConceptNet. [PERSON] et al. (2018), [PERSON] et al. (2018, 2019) further leveraged the information in words, in relations embedded in edges and multisense knowledge, and developed extensions of the retrofit ensemble models.
* **Injecting external structured knowledge in the transformer architecture**. One method involves incorporating entity embeddings using a layerwise local/global fusion strategy ([PERSON] et al., 2018, 2020). Alternatively, [PERSON] et al. (2020) trained an entity-aware BERT model using the self-attention mechanism and masked language modeling objective. Another approach involves a deep integration through knowledge attention and a recontextualization mechanism ([PERSON] et al., 2018). Injecting external knowledge in the transformer architecture requires further training or fine-tuning of the language models, which means they are relatively computationally expensive methods.
## 2 Numerical Methodology
### CFD Simulation and Parameter Settings  
In this work, the incompressible flow of helium (He) at 300 \({}^{\circ}\)C (helium density is 0.2518 kg/m\({}^{3}\), dynamic viscosity is 3.0705 \(\times\) 10\({}^{-5}\) kg/m\(\cdot\)s) was used as the fluid medium. The energy equation was not considered in our CFD model. Thus, the governing equations of the continuity equation and the momentum equation is as follows:  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot(\rho U)=0 \tag{6}\]  
\[\frac{\partial(\rho U)}{\partial t}+\
abla(\rho U\otimes U)=-\
abla p+\
abla \cdot\tau, \tag{7}\]  
where \(\rho\) is the density of fluid. \(U\) is the velocity of fluid. The stress tensor, \(\tau\), is related to the strain rate by  
\[\tau=\mu\bigg{(}\
abla U+(\
abla U)^{T}-\frac{2}{3}\delta\
abla\cdot U\bigg{)}. \tag{8}\]  
where \(\mu\) is the dynamic viscosity of the fluid.  
Based on the pebble bed configurations gained in DEM simulation, a CFD model of the bed is reconstructed as shown in Figure 7. In the CFD model, the ceramic pebbles are assumed to be fixed, owing to the one-way coupling framework used in this work. By referring to another study [18], the laminar flow of the purge gas helium in the pebble bed was considered in the simulation. In addition, inlet velocity and outlet pressure conditions are adopted in this study. The inlet flow velocity, \(v_{i}\), is 0.1 m/s. The outlet relative pressure, \(P_{o}\), is 0 Pa. The ambient base pressure, \(P_{a}\), is set to be 0.3 MPa. In addition, the slip boundaries are adopted in the periodic boundaries of the pebble bed. The fixed walls and pebble surfaces are set as the non-slip wall. In order to achieve a stable flow field and avoid the occurrence of reflux and end-wall effects, the inlet and outlet of the pebble bed are extended for 5 d and 10 d, respectively, as shown in Figure 7. Then, the numerical solution is carried out to obtain the flow characteristics of the pebble bed. Finally, the post-processing of the simulation results is carried out.  
In this study, the CFD simulations were performed in the commercial software ANSYS CFX 2019R2. The pressure loss along the flow direction of the pebble beds was obtained by the averaged values of the cross-sectional plane perpendicular to the flow direction. The detailed theoretical equations can be referred to from the literature [38]. To obtain the converged solutions at steady state, all CFD simulations were conducted until the residuals of the continuity and momentum equations became less than 10\({}^{-5}\).  
Figure 7: Reconstructed pebble bed geometric model.
## 3 Optimum Parameters Selection
### Decomposition Level  
Figure 5 shows the performance of the proposed RFI mitigation wavelet technique using the Haar wavelet, with \(2^{16}\) sequence length, as a function of the decomposition level. As intuitively expected, the larger the number of decomposition levels, the better the reconstruction (slope \(\sim\)-\(1/3\) decade per unit level), the smoother the function, and the smaller the number of terms in the decomposition that have to be used to reconstruct the signal properly. The quality of the reconstruction of the sinusoidal signal (and later cancellation) saturates above five levels, the Doppler signal saturates above six levels, the chirp signal saturates above seven levels, and only the PRN signals starts saturating at 11 levels. In all cases the heuristic SURE thresholding method is used, which is the one that achieves the best performance, only matched by the fixed thresholding for PRN signal.
# Field Navigation Using Fuzzy Elevation Maps Built with Local 3D Laser Scans
## 7 Field Navigation
### Computing Tread Speeds  
Once the motion direction \(d_{j}\) is determined, the commanded angular velocity \(\omega\) for Andabata is computed as: \(\omega=G\,\delta_{j}\), where \(G=1\) is a proportional gain that regulates heading changes of the vehicle [21]. The longitudinal velocity during navigation \(v\) is a constant value that depends on the difficulty of the terrain.  
The Dir_selection node sends the desired longitudinal \(v\) and angular \(\omega\) velocities to the Kinematic_model node, which are converted into left and right tread speeds by using a symmetric inverse kinematic model:  
\[v_{l}=v-y_{lCR}\,\omega,\quad v_{r}=v+y_{lCR}\,\omega, \tag{17}\]  
where \(y_{lCR}=0.45\,\mathrm{m}\) is the mean value of tread instantaneous centers of rotation (ICRs) of Andabata on horizontal plane [32]. When any of the tread speeds exceeds their maximum speed \(v_{max}\), the commanded speeds (Equation (17)) are divided by:  
\[e=\frac{|v|+y_{lCR}\,\,|\omega|}{v_{max}}. \tag{18}\]  
In this way, the commanded curvature \(\gamma\) of the vehicle (and its turning radius) is kept unaltered:  
\[\gamma=\frac{\omega/e}{v/e}=\frac{w}{v}. \tag{19}\]
## 2 Forward Model
### Surrogate Model
Each FE magneto-thermal analysis takes about 147 s to be solved on a computer equipped with an Intel(R) Core (TM) i7-10700 KF CPU @ 3.80 GHz, 128 GB of RAM, and an NVIDIA Quadro RTX-4000 graphics card. Building the whole DB1 database takes about 266 h.  
The database is divided into two subsets, following the rule of thumb 70-15-15 i.e., 70% (\(N_{t}=4082\) couples of vectors) are utilized for training, 15% (\(N_{v}=875\) couples of vectors) are utilized for validation, and 15% (\(N_{e}=875\) couples of vectors) are utilized for testing. In order to evaluate the quality of the prediction, the Mean Absolute Percentage Error MAPE (%) was calculated considering the \(N=N_{v}+N_{e}\) vectors of temperature values of both validation and test set:  
\[\text{MAPE}=100\frac{1}{N}{\sum_{j=1}^{N}}\frac{1}{N_{r}}{\sum_{i=1}^{N_{p}} }\frac{\left|\hat{Y}_{j,i}-Y_{j,i}\right|}{\left|\hat{Y}_{j,i}\right|} \tag{14}\]  
where \(Y\) is the vector of \(N_{r}\) true values calculated with the FE model, and \(\hat{Y}\) is the vector of \(N_{r}\) values predicted by the relevant surrogate model. The Root Mean Square Error (RMSE) and R-squared are also evaluated:  
\[\text{RMSE}=\sqrt{{\sum_{j=1}^{N}}{\sum_{i=1}^{N_{p}}}\frac{\left(\hat{Y}_{j, i}-Y_{j,i}\right)^{2}}{NN_{p}}} \tag{15}\]  
\[R^{2}=1-\frac{{\sum_{j=1}^{N}}{\sum_{i=1}^{N_{p}}}{\left(\hat{Y}_{j,i}-Y_{j,i} \right)^{2}}}{{\sum_{j
## 4 Experiment and Results
### Data Preprocessing
The learning rate was set to 0.0001, and the training process was conducted over a maximum of 10,000 iterations. The model consisted of a transformer architecture with six layers, incorporating a dropout rate of 0.1 to prevent overfitting. The resolution of the data was represented by r_size values of 256, 128, and 64 across three hierarchical levels (r_level). For optimization, Binary Cross Entropy (BCE) was employed as the loss function. The hyperparameters can be found in Table 5.
## 4 Results and Discussion
### Tensile Tests
This technique was able to check the absence of unexpected rotations during testing. Additionally, other white dots were painted at the upper and lower ends of the specimen and on the metal caps. The absence of relative displacements between the points of the specimen and the caps allowed to ensure the correct adhesion between them and confirmed that the displacement was concentrated on the ligament of the specimen. Figure 6: (**a**) Stress-displacement curves of tensile tests. (**b**) Stress-displacement detail. (**c**) Orientation factor-stress curves of tensile test. The high-definition camera, placed recording the back of the specimens, monitored the pattern of cracks that appeared in the ligament section. The video-extensometry technique allowed to record the strains during testing [33], to observe the nucleation and growth of the cracks when they imperceptible to the naked eye. Figure 8 shows the development of cracks in the ligament during testing. Two cracks appeared in the external part of the ligament and then both grew symmetrically towards the central area, until the complete fracture of the concrete ligament was achieved.
# Enhanced Effect of Phytoextraction on Arsenic-Contaminated Soil by Microbial Reduction
## 3 Results and Discussion
### Effects of Iron-Sulfur-Reducing Flora on Soil Microbial Community  
The bacterial composition of the raw soil and rhizosphere soil consisted primarily of 15 phyla, including Proteobacteria, Actinobacteria, Gemmatimonadetes, Deinococcus-Thermus, Chloroflexi, Acidobacteria, Euryarchaeota, Bacteroidetes, Planctomycetes and others. Proteobacteria and Actinobacteria were the dominant phyla, accounting for a cumulative abundance ranging from 65.28% to 75.86% (Figure 9a). In terms of bacterial genus, Pseudomonas and Burkholderia were the most prevalent in the community (Figure 9b). Notably, the relative abundance of Desulfovich in the rhizosphere soil of _Pteris vittata_ increased significantly with the application of iron-sulfur-reducing bacteria and plant organic matter, compared to the raw soil. This suggests that Desulfovichrio is well adapted to the soil environment and exhibits steady proliferation.  
Figure 8: Full spectrum of XPS scans of soils (**a**), XPS spectra of As3d (**b**), S 2p (**c**), Fe 2p (**d**) of raw arsenic-contaminated soil and soil treated after phytoremediation (T5 group).
## 3 Finite Element Modeling  
In this section, a numerical simulation based on thermo-mechanical phase-field coupling modeling will be conducted to investigate the influence of flame thermal shock on the behavior of pre-existing cracks in ceramic plates (Figure 2). The proposed model is implemented in the software COMSOL Multiphysics 5.5. The implementations in COMSOL can much more easily to be extended to problems with more fields than those in other software [47], because only the governing equations in strong form are needed in COMSOL. Regarding thermo-mechanical problems, three basic modules, i.e., the Solid Mechanics Module, Poisson's Equation Module, and Heat Transfer in Solid Module, can be adopted to solve the displacement \(\mathbf{u}\), the crack phase field \(d\), and the temperature \(T\). We employ a staggered scheme to solve the coupled system of equations. The difference between the staggered scheme and monolithic scheme is discussed in reference [48].  
We propose two numerical simulation schemes to validate the predictive capability of the model. First, we assess the influence of initial crack angle on crack propagation. We can simulate the crack propagation under flame thermal shock by setting different angles. We can study the effect of angle variation on crack propagation behavior by observing the propagation path and rate of the crack. Second, we evaluate the influence of previous crack length on crack propagation. We may examine the response of crack propagation by varying the length of the pre-existing crack. We can evaluate the impact of the pre-existing crack length on crack propagation behavior by comparing the characteristics of crack propagation at different lengths.  
The loading conditions, sample dimensions, and boundary conditions for the ceramic plate are depicted in 0. The ceramic specimen is composed of high-purity ceramic (99.5% Al\({}_{2}\)O\({}_{3}\)) and has a rectangular shape with a side length of 10 mm. Horizontal and vertical displacements are restricted by the lower boundary. An inward heat flux \(q_{0}=2,000,000\left(W/m^{2}\right)\) is applied to the upper boundary. The temperature \(T_{0}=20\)\({}^{\circ}\)C is kept constant over the entire ceramic (including the crack surface). The mechanical parameters of the ceramic plate are displayed in Table 1. The triangular element is used to mesh the geometry model, and the size of the mesh of the crack is \(b_{0}=0.0001\) mm. The initial crack width is \(W=0.05\) mm and the length is \(L=1\) mm. We investigate the influence of different initial crack angles on crack propagation by changing the initial crack angle (\(0^{\circ}\), \(26.6^{\circ}\), \(45^{\circ}\), \(63.4^{\circ}\), and \(90^{\circ}\)). In the simulation, we apply free triangular mesh elements with a minimum element size of h = 0.005 mm. We discovered that local refinement of the mesh elements in the crack propagation region cloud produced more accurate results during the study.