esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
There is growing interest in multi-nozzle array printing, as it has the potential to increase productivity and produce more intricate products. However, a key challenge is ensuring consistent flow across each outlet. In the heat exchangers, achieving uniform distribution of flow in parallel channels is a classic goal. To address this issue in multi-nozzle array direct printing technology, high-viscosity slurry fluids can be utilized in place of water, and the structure of compact parallel channels can be employed. This study experimentally and numerically investigated the flow distribution law of Herschel-Bulkley fluids (high-viscosity slurry fluids) entering each manifold of the compact parallel channels, which contained a single circular inlet and multiple outlets. The research identified two types of factors that impact the non-uniformity flow coefficient (\(\Phi\)), which reflects the uniformity of flow distribution in each channel of the structure: entrance and exit conditions (\(V\), \(P_{1}\), \(P_{2}\)) that have a negligible effect on \(\Phi\), and structural dimensions (\(D\), \(S\), \(L\), \(N\), \(A\), \(d\)) that are the primary influence factors. By analyzing the experimental results, a prediction model was derived that could accurately calculate \(\Phi\) (error \(<0.05\)) based on three structural dimensions: \(A\), \(S\), and \(L\). Through proper design of these structural dimensions, a consistent flow rate of each channel of the parallel channels can be ensured.  
Herschel-Bulkley fluids; parallel channel; flow distribution; structural dimensions; prediction model +
Footnote †: journal: Applied Sciences
## Data and Methods
### Wetland Probability Mapping Algorithm
#### 2.3.5 Calculating Wetland Fractions  
In order to compute wetland fractions at a given spatial scale, a threshold was determined to transform wetland probabilities into a binary signal for wetland presence. An area-threshold analysis was used to determine a cut-off probability above which we assume the 1 km pixel is completely covered by wetlands (Figure 4). Different probability thresholds were used to determine wetland presence at 1 km. We then calculated the area of the wetland extent over the entire UZRB basin at each threshold during the period of maximum wetland extent for a given year (around 9 April). The respective areas were compared to the wetland area determined by the stationary land cover map (Figure 3). Through this analysis, a threshold of 70% was determined to best represent the presence of wetlands; that is pixels with wetland probabilities \(\geq\)70% were assigned the value 1 and pixels with wetland probability \(<\)70% were assigned the value 0. Maps of wetland fractions were then created by averaging over 5 \(\times\) 5 pixel windows. The resulting wetland maps are described in Section 3.1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Land Cover** & **MODIS** & **Number of Pixels in** & **Number of** \\  & **Code (IGBP)** & **Stationary Map 1** & **Training Pixels** \\ \hline Water & 0 & 480 & 0 \\ Evergreen needle-leaf forest & 1 & 0 & 0 \\ Evergreen broadleaf forest & 2 & 6732 & 2000 \\ Deciduous needle-leaf forest & 3 & 0 & 0 \\ Deciduous broadleaf forest & 4 & 77 & 0 \\ Mixed forest & 5 & 173 & 0 \\ Closed shrublands & 6 & 251 & 0 \\ Open shrublands & 7 & 1067 & 0 \\ Woody savannas & 8 & 355,893 & 4000 \\ Savannas & 9 & 432,824 & 4000 \\ Grasslands & 10 & 17,067 & 4000 \\ Permanent wetlands & 11 & 14,135 & 14,000 \\ Croplands & 12 & 879 & 0 \\ Urban and built-up & 13 & 251 & 0 \\ Cropland/Natural vegetation mosaic & 14 & 1343 & 0 \\ Snow and ice & 15 & 0 & 0 \\ Barren or sparsely vegetated & 16 & 3 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of training pixels (28,000 total) for each MODIS land cover type.
## 2 Results
### Distinct RHR2 Requirements for Biofilm Formation and Invasion  
Glycerol accumulation makes a significant contribution to turgor in many fungi [16,33]. The hypothesis that hyphal turgor is required for invasion under our conditions predicts that a mutant defective in glycerol accumulation will be defective in invasion. We tested that prediction with an _rhr2\(\lambda\)/\(\lambda\)_ mutant, which is defective in the final step in glycerol synthesis. Previous studies have shown that glycerol accumulation is reduced two-fold during biofilm growth of this mutant [21,22]. We observed that the _rhr2\(\lambda\)/\(\lambda\)_ mutant was severely defective in substrate invasion, and that integration of a wild-type copy of _RHR2_ into the mutant genome restored invasion (Figure 1D). These results indicate that _RHR2_ is required for invasion.  
Our prior studies revealed that expression of adhesin genes depends upon _RHR2_[21]. As a result, the _rhr2\(\lambda\)/\(\lambda\)_ mutant is unable to form a biofilm, and overexpression of _ALS1_ or other adhesin genes restores biofilm formation ability of the mutant. As expected, then, the _rhr2\(\lambda\)/\(\lambda\)_ mutant was unable to form a biofilm in our invasion assay. The invasion defect of the _rhr2\(\lambda\)/\(\lambda\)_ mutant might be a consequence of its biofilm defect. To test this hypothesis, we assayed invasion ability of an _rhr2\(\lambda\)/\(\lambda\)_ mutant that overexpresses _ALS1_. We observed that _ALS1_ overexpression restored biofilm formation under these conditions, but caused only a minor increase in invasion (Figure 2A,C). We further tested this hypothesis through overexpression of three transcriptional regulators of adhesin expression and biofilm formation in the _rhr2\(\lambda\)/\(\lambda\)_ mutant background. Overexpression of two of these transcription factors, Brg1 and Ume6, caused increased expression of the adhesin genes _ALS3_ and _HWP1_; the third transcription factor, Brc1, had no such effect (Figure 2B). In addition, overexpression of Brg1 and Ume6, but not Brc1, restored biofilm formation of the _rhr2\(\lambda\)/\(\lambda\)_ mutant (Figure 2A). However, overexpression of any of these transcription factors had only a minor effect on invasion (Figure 2C). We conclude that _RHR2_ is required for substrate invasion in some capacity beyond the expression of adhesin genes. We believe that this requirement reflects the contribution of glycerol accumulation to hyphal turgor, though our results do not rigorously exclude other possible roles for glycerol in this process.  
Figure 2: _Cont._
## 4 Results and Discussion  
### Result of the UWB Positioning Error  
Figure 8 shows the trajectories of the matched UWB and RTS observations. The trajectory of UWB experienced the most severe drifts at the top left edge areas, where the NLOS error would be greater than elsewhere.  
We computed the statistical result in Table 5. It is evident that the order of errors in the \(z\) component is much larger than that of the other two components, mainly due to the geometric distribution of the UWB base stations in the \(z\) component being worse. The bias of the UWB positioning errors in the \(x\) and \(y\) components are about 10 cm, while \(-52.5\) cm in the \(z\) component. The RMSE values are 21.1 cm, 21.7 cm, and 89.2 cm in the \(x\), \(y\), and \(z\) components, respectively, which means that the UWB system's total three-dimensional positioning error reaches up to 94.2 cm, more than three times the nominal value in Table 1. Furthermore, the maximum error in the \(x\) and \(y\) components is nearly 50 cm for 95% of samples and \(\sim\)1 m for 100%. In contrast, the 2D location accuracy of the DW1000 chip in the LOS condition reported by [PERSON] et al. is 25 cm for 95% and 32 cm for 100% [32]. It is evident that NLOS conditions have severely degraded the accuracy of UWB positioning.  
Figure 9 shows the arrow diagram of the UWB positioning error versus the reference value (RTS coordinate). Each arrow in a figure shows both the direction and value of a UWB positioning error. There is a common tendency among the three subgraphs that each component exhibits a significant nonlinear distribution with solid relevance to location. However, the features of the \(x\), \(y\), and \(z\) components are different.  
For the \(z\) component, the distribution pattern of errors is much more complicated than that of the \(x\) and \(y\) components. In the top left corner of Figure 8, the errors in the three components are significantly larger than elsewhere, mainly because the LOS signal between this area and the UWB station C was entirely blocked by the corridor wall, which led to a significant NLOS effect. Furthermore, the UWB base C was installed relatively lower compared with the other three base stations (less than 30 cm above the ground). Such geometric distribution of base stations plays a vital role in the UWB positioning accuracy on the \(z\) component.
# Data-Driven Computational Methods in Fuel Combustion: A Review of Applications
## 3 Analysis of the State of the Art
### Challenges Related to Data Availability, Quality, and Generalization  
Based on the analysis of the publications discussed in Sections 3.1 and 3.2, one of the key challenges can be identified, which is the quality and consistency of data used in AI and GA models (for example, Refs. [6; 19; 23]) and hybrid configurations (e.g., CFD + GA in Refs. [3; 10; 11]), which require large datasets with high accuracy and representativeness. However, this data is often obtained from different sources--experiments, simulations, or previous publications, leading to inconsistencies. The lack of uniform measurement protocols, differences in operating conditions, and a limited number of experimental cases make these models often difficult to generalize.  
In Ref. [19], literature data with over 2200 points was used to train the GA-ANN model, but despite its high efficiency (\(\mathbb{R}^{2}=0.991\)), the model was limited to the conditions of isooctane and alternative fuel blends. In Ref. [76], data from CFD simulations (576 cases) were used to train ANN to optimize fuel injection. The authors noted that changing the geometry of the combustion chamber or other boundary parameters significantly affects the performance of the model. In Ref. [78], operational data from a coal-fired power plant made it possible to create a predictive model for \(\text{NO}_{\text{x}}\) emissions and ammonia consumption, but its effectiveness was limited to a specific system, which again emphasizes the problem of model transferability.  
An additional challenge is the lack of sufficient data from real industrial systems, which is due to both technological limitations (lack of real-time measurement systems) and legal barriers (trade secrets). In Ref. [70], despite the use of advanced RF and AdaBoost algorithms, the authors emphasized the limitations associated with the small number of observations and indicated the need to develop more comprehensive datasets.  
It is also worth noting that many models perform well solely within the parameters on which they were trained. Beyond this range--especially with variable fuel ratios, different combustion conditions, or modified geometries--their predictive accuracy decreases significantly. Therefore, there is a need to use techniques such as active learning, transfer learning, feature analysis using PCA, and automatic anomaly detection. Unfortunately, as the analysis of Refs. [10,21,104] shows, these techniques are still rarely implemented, which limits the practical application of AI/GA models on an industrial scale.
## 2 Three-Dimensional Dubins Path
### Two-Dimensional Dubins Path  
This subsection describes the 2D Dubins path. Much of this material is found elsewhere but is re-represented using unified vector notation in any plane in 3D space to account for extensions to a 3D path.  
For each \(plane_{i}\), four configurations of the conventional 2D Dubins path can be designed with two points \(\mathbf{p}_{i},\mathbf{p}_{i+1}\) and two directions \(\mathbf{e}_{i},\mathbf{e}_{i+1}\). The four configurations are RSR, RSL, LSR, and LSL, where R is a right turn, L is a left turn, and S is a straight line. The shortest path is chosen by comparing the distance along the Dubins path between two points.  
We define a left rotation as a rotation about the unit vector \(\mathbf{e}_{n,i}\), which is normal to \(plane_{i}\) and points the upward direction in a reference frame, as shown in Figure 2.  
\[\mathbf{e}_{t}=\frac{\mathbf{e}_{i}\times\mathbf{e}_{i+1}}{\| \mathbf{e}_{i}\times\mathbf{e}_{i+1}\|_{2}} \tag{2}\] \[\mathbf{e}_{n,i}=\begin{cases}\mathbf{e}_{t},&\text{if }\mathbf{e}_{t} \cdot\mathbf{k}\geq 0\\ -\mathbf{e}_{t},&\text{else}\end{cases}\]  
If \(\mathbf{e}_{i}\) and \(\mathbf{e}_{i+1}\) are parallel, the normal vector to \(plane_{i}\) is set to the previous one as \(\mathbf{e}_{n,i}=\mathbf{e}_{n,i-1}\).  
Figure 1: Waypoints and planes.  
Figure 2: Normal vector to a plane.  
For a given direction vector and the minimum radius of rotation \(R\) at waypoint \(\mathbf{p}_{i}\), there are two tangent circular arcs: left and right, as shown in Figure 3. A vehicle can either turn left or right, traversing these circular arcs with the circle's rotation direction. The centers of both circular arcs are chosen such that the direction vector at waypoint \(\mathbf{p}_{i}\) is tangential to both circular arcs and computed as follows:  
\[\mathbf{c}_{l,i} =\mathbf{p}_{i}+R\mathbf{e}_{n,i}\times\mathbf{e}_{i} \tag{3}\] \[\mathbf{c}_{r,i} =\mathbf{p}_{i}-R\mathbf{e}_{n,i}\times\mathbf{e}_{i}\]
# Automatic Composition System Based on Transformer-XL
## 3 Model
### Backbone Framework  
The main framework of our model was based on Transformer-XL [15]. We added the improved Mask mechanism to make the model have the ability to see bidirectional information. In the training process, the music was divided into several specific segments. The calculation process for the N-layer Transformer-XL predicting the \(\tau\)-th segment was as follows:  
\[\tilde{h}_{\tau}^{n-1}=\left[SG\left(h_{\tau-1}^{n-1}\right)\circ h_{\tau}^{n -1}\right] \tag{1}\]  
where \(h_{\tau}^{n-1}\) denotes the hidden features in the (N\(-1\)\(N-1\))-layer of the \(\tau\)-th segment, \(\text{Stop\_Gradient}\Big{(}h_{\tau-1}^{n-1}\Big{)}\) means that \(h_{\tau-1}^{n-1}\) is not involved in the BP calculation, and \([A\circ B]\) indicates that A and B are spliced in the length dimension.  
Each segment of the N-layer is represented by the query vector, the key vector, and the value vector. They are calculated as follows:  
\[q_{\tau}^{n},k_{\tau}^{n},v_{\tau}^{n}=h_{\tau}^{n-1}W_{q}^{T},\tilde{h}_{\tau }^{n-1}W_{k}^{T},\tilde{h}_{\tau}^{n-1}W_{v}^{T} \tag{2}\]  
where \(q\), \(k\), \(v\) denotes the query, key, and value in the self-attentive. \(W\) is the parameter to be learned by the model.  
The improved Transformer-XL model also uses the relative position encoding as follows:  
\[A_{i,j}^{\text{rel}}=E_{x_{i}}^{\text{T}}W_{q}^{\text{T}}W_{k,E}E_{x_{j}}+E_{ x_{i}}^{\text{T}}W_{q}^{\text{T}}W_{k,R}R_{i-j}+u^{\text{T}}W_{k,E}E_{x_{j}}+v^{ \text{T}}W_{k,R}R_{i-j} \tag{3}\]  
where \(E_{x_{i}}=h_{\tau,j}\). \(R_{i-j}\) indicates the relative position code of Transformer-XL. \(u^{\text{T}}\) and \(v^{\text{T}}\) are learnable parameters.  
Figure 5: Relative position coding.  
Equation (2) allows us to simplify Equation (3). Then, the hidden features in the N-layer of the \(\tau\)-th segment are calculated using the model.  
\[A^{n}_{\tau,i,j}=q^{n}_{\tau,i}T^{k}_{\tau,j}+q^{n}_{\tau,j}{}^{T}W^{n}_{k,R}R_{ i-j}+u^{T}k_{\tau,j}+v^{T}W^{n}_{k,R}R_{i-j} \tag{4}\]  
\[\alpha^{n}_{\tau}=Mask-Soft\text{max}(k^{n}_{\tau})v^{n}_{\tau} \tag{5}\]  
\[z^{n}_{\tau}=LayerNorm(Linear(\alpha^{n}_{\tau})+h^{n-1}_{\tau}) \tag{6}\]  
\[h^{n}_{\tau}=Positionwise-Feed-Forward(z^{n}_{\tau}) \tag{7}\]  
Through the formulas above, it was observed that the improved Transformer-XL was able to identify long-distance dependence. Combined with the improved Mask mechanism, it may have given the generated music more integrity.
# Dynamic Computation Offloading with Deep Reinforcement Learning in Edge Network
## 3 Modeling And Problem Formulation
### Delay Model
#### 3.2.1 Task-Offloading Delay  
The task-offloading delay is defined as the time taken for data transmission between the local edge server and target edge server. The time needed for task offloading is related to the data capacity and the transmission speed. Therefore, the data transmission delay from user \(C_{i}\) to the edge server \(F_{j}\) at time slot \(T_{t}\) can be expressed as follows:  
\[d_{to}^{j,t}=\frac{C_{i}.bytes}{V_{tr}^{i,j,t}} \tag{1}\]  
where \(C_{i}.bytes\) is the capacity of the task data; \(V_{tr}^{i,j,t}\) is the data transmission speed and can be defined as:  
\[V_{tr}^{i,j,t}=Blog_{2}(1+\frac{P_{tx}^{i}G^{i,j,t}}{N_{noise}}) \tag{2}\]  
where \(B\) represents the channel bandwidth; \(P_{tx}^{i}\) represents the transmission power of user device \(C_{i}\); \(G^{i,j,t}\) represents the channel gain between the user device \(C_{i}\) and edge server \(F_{j}\); \(N_{noise}\) is the background noise.
# Bridge Displacements Monitoring Method Based on Pixel Sequence
## 5 Conclusions  
The DoG algorithm proposed in this paper utilizes the structural characteristics of the bridge to extract signals for bridge deformation. It achieves good accuracy under various loading conditions and demonstrates higher data stability compared to existing mature algorithms. Additionally, the following aspects require in-depth research in the context of this study:  
1. The unavoidable vibrations during the measurement process. When using the camera, consideration must be given to the potential translational movements of the camera. Monitoring the angular acceleration or velocity of the camera is necessary to reduce environmental noise and improve robustness.  
Figure 25: Comparison between DoG algorithm and LVDT displacement under dynamic load ((a) Description of 10 kg; (b) Description of 20 kg; (c) Description of 30 kg; (d) Description of 40 kg).  
* The transition from laboratory validation to field validation needs to be smooth and well-validated.
* In practical applications, environmental complexities may arise. It is important to intelligently and accurately select the Gaussian kernel scale during the algorithm process to ensure that when elements in the image respond to the Gaussian function, the background can be more precisely filtered.  
At the same time, the research in this paper is fundamental research, focusing only on monitoring deformation under structural loading conditions. Therefore, the experiments were conducted at a speed of 0.15 m/s. When extracting the mechanical characteristics of the structure based on bridge deformation, larger dynamic responses need to be used, such as increasing the vehicle speed or applying impact to the structure. However, from the perspective of deformation monitoring alone, the algorithm proposed in this paper performs well. In addition, applying the fundamental algorithm developed in this paper to practical monitoring requires further research, such as how to improve the stability of the DoG method in complex backgrounds and enhance the efficiency of the algorithm. Additionally, applying the fundamental algorithm to the overall system in the later stages is another challenge that needs to be addressed.  
Methodology, T.W.; Validation, Z.S.; Formal analysis, X.L.; Data curation, Z.Z.; Project administration, W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Shenzhen Science and Technology Program [CY20220818095608018], Chongqing Natural Science Foundation General Project [CSTB2022 NSCQ-MSX1409], and Chongqing Natural Science Foundation Doctoral Direct Project [CSTB2023 NSCQ-BSX0030]. And The APC was funded by [CY20220818095608018].  
Not applicable.  
Data will be made available on request.  
The authors would like to thank the department of the State Key Laboratory of Mountain Bridge and Tunnel Engineering at Chongqing Jiaotong University for their help in this investigation.  
The authors declare no conflict of interest.
## 3 Field Test
### Analysis of the Results
The periodic pressure step distance ranges from 14 to 19 m, and the average step distance is 16.8 m.  
Based on the mine pressure curve plotted during the excavation of the working face by 100 to 200 m, the average pressure of the supporting structure is 9725.3 kN. The average pressure of the 33#, 110#, and 154# hydraulic supports is 7995.6 kN during the excavation of the working face from 200 to 300 m, a decrease of 17.8% compared with the pressure from 100 to 200 m. At the same time, the average step distance has decreased by 18% compared to the 100-200 m excavation. In summary, the pressure of the supporting structure in the working face direction and the periodic average step distance of the pressure both decrease after roof cutting. Roof cutting promotes staged fracturing of the roof, preventing a large-scale simultaneous collapse, thereby reducing the intensity of each pressure. Roof cutting creates pre-fracturing surfaces artificially, cutting the roof into discrete short cantilever units. The vertical cracks formed facilitate the redistribution of horizontal stress, with part of the pressure converting  
Figure 15: The resistance of the supporting structure after each of the excavation cycles: (**a**) the supporting structure after each of the excavation cycles at hydraulic support 33#; (**b**) the supporting structure after each of the excavation cycles at hydraulic support 110#; (**c**) the supporting structure after each of the excavation cycles at hydraulic support 154#. into lateral stress parallel to the working face, reducing the driving effect of the stress gradient in the working face direction on periodic roof fracturing. The data from monitoring points 1#-10# correspond to the unfractured section, while the data from monitoring points 11#-20# correspond to the fractured section. The data from 10 m to 30 m ahead of the working face from each monitoring point were analyzed to obtain the data curve (Figure 16). The maximum roof to floor convergence increase in the non-fractured section is 137 mm, while in the fractured section, the convergence is 50 mm. The maximum amount of roadway between two sides in the non-fractured section increases by 158.7 mm, while in the fractured section, the amount increases by 47 mm. Compared to the non-fractured section, the average roof to floor convergence rate (the daily roof and floor convergence of the working face, when the daily excavation distance of the working face is 2.4 m) decreases from 10.10 mm/d to 3.65 mm/d, and the amount of roadway between the two sides' convergence rate (the daily convergence of the amount of roadway between two sides at the working face) decreases from 10.80 mm/d to 2.98 mm/d.