esa-sceva/satcom-chunk-collection huggingface dataset:
# Computational Fluid Dynamics-Aided Simulation of Twisted Wind Flows in Boundary Layer Wind Tunnel
## 4 Development Stage
### Optimization of Guide Vane Configuration  
#### 4.2.1 Distance from Side Wall  
To obtain the optimal configuration of the vane system in the numerical wind tunnel, this study first investigates the changes in flow pattern caused by different distances between the leading edge of the left vane (viewed along the flow direction as shown in Figure 4) and the side wall, denoted by \(d_{vane,wall}\) herein. Figure 11 illustrates the distribution of mean lateral wind speed in the wind tunnel at \(z=0.3\) m generated by the left vane positioned at \(d_{vane,wall}=450\), 600, and 750 mm, while the spacing between the vanes was kept constant at 700 mm. The mean lateral wind speed within the downstream region of the left vane in Figure 11a is considerably lower than those in Figure 11b,c, which is evidently attributed to the blockage in the area near the leading edge of the left vane, i.e., the flow has to slow down when it passes through such a narrow gap \(d_{vane,wall}=450\) mm. Such blockage is alleviated as \(d_{vane,wall}\) increases and fully disappears as \(d_{vane,wall}\) reaches 750 mm. Hence, \(d_{vane,wall}=750\) mm is selected for further numerical simulations in the following steps.
# The Law and Mechanism of the Effect of Surface Roughness on Microwave-Assisted Rock Breaking
## 1 Introduction
### Results  
#### 2.5.1 Temperature Distribution  
Figure 4 show the surface temperature distributions of a flat smooth surface and partially rough surface specimens, respectively. The high-temperature zone of the flat smooth surface was concentrated in the center of the sample, which was approximately circular, and the high-temperature distribution area was small. The peak surface temperature was 123 \({}^{\circ}\)C. The high-temperature area on the surface of the 1.5 roughness specimen was irregularly shaped and covered a large distribution area; the peak temperature was 213 \({}^{\circ}\)C. The surface profile and temperature distribution of the rock sample indicate that the shape of the temperature distribution has a certain relationship with the undulating profile of the sample, and the surface temperature peak occurred at the convex contour position.  
Figure 5 shows the curve of the peak surface temperature of the sample as a function of roughness. The peak temperature of the flat surface sample was 123 \({}^{\circ}\)C, and the peak temperature gradually increased with the increase in roughness. When the roughness reached 1.5, the growth rate decreased, and the temperature peaks of all rough surfaces were much greater than those of flat surfaces.  
Figure 6 shows the curve of the average surface temperature of the sample with roughness after MATLAB image post-processing. When the roughness was less than 1.5, the average surface temperature of the sample increased with the increase in roughness.  
Figure 3: Measurement and calculation of surface roughness. **(a)** Contour line extraction. **(b)** Roughness calculation.  
When the roughness was greater than 1.5, the average temperature decreased slowly, and a threshold between roughness values of approximately 1.4 and 1.6 promoted the maximum average temperature. The surface temperature distribution of the sample indicated that the main surface temperature distribution areas of samples with roughness values greater than 1.5 (A-7 and C-7 specimens in Figure 4) were discontinuous, showing the distribution of local high temperature. The high-temperature area decreased, and the surface area of the samples increased with the increase in roughness, resulting in decreases in the average temperature of the whole surface. The average temperature of the flat surface was approximately 74 \({}^{\circ}\)C, and the average maximum temperature of the rough surface was 112 \({}^{\circ}\)C, representing an increase of approximately 51.4%. Thus, the effect of microwave irradiation on rough surface samples was better.  
Figure 4: Temperature distribution of some rough surface samples.  
Figure 5: Peak temperature values of the samples as a function of roughness.  
Figure 6: Variation curve of the average temperature of the sample with varying roughness values.
# Designed Circularly Polarized Two-Port Microstrip MIMO Antenna for WLAN Applications
## 4 Parametric Study  
The parametric analysis of the MIMO-CP antenna is presented using different values from the geometry, which selected the best value for a better performance. The parametric study was done by the corner cut length and width (ls) and the distance (d) between the two patches. The corner cut at the patch improves the impedance matching of S11, mutual  
Figure 5: (a) Simulated surface current distribution without ground slot; (b) simulated surface current distribution with ground slot.  
Figure 6: Simulated surface current distribution to demonstrate the generation of CP currents. (a) 0\({}^{\circ}\) due to port1; (b) 90\({}^{\circ}\) due to port-1; (c) 0\({}^{\circ}\) due to port-2; (d) 90\({}^{\circ}\) due to port-2.  
coupling, and AR. The distance between the patches further improves the results within the frequency band.
# Research on Fault Diagnosis Algorithm of Ship Electric Propulsion Motor
## 2 Theoretical Background
### Principal Component Analysis  
The principle of PCA is re-linearly combining the high-dimensional variable indexes in the original fault data to construct a new variable index with a lower dimension. These low-dimensional variable indicators are orthogonal and unrelated to each other. Based on this, the coordinate projection of the data sample on the vector is determined by the difference in the independent variables between the samples. The differences reflected by other vectors are reduced in turn, and these vectors are called main components.  
The steps of PCA feature extraction are as follows:  
Step 1: Given an input signal matrix \(X=\left\{x_{ij}:x_{ij}\in R^{n\times m}\right\}\), where each row \(x_{i}\in R^{1\times m},i=1,\cdots,n\) of the matrix indicates an index and each column \(x_{j}\in R^{n\times 1},j=1,\cdots,m\) of the matrix represents a sample. The average value of the sample \(x_{j}\) can be represented as:  
\[\widetilde{x}_{j}=\frac{1}{m}\sum\
olimits_{j=1}^{m}x_{j} \tag{17}\]  
Step 2: Let \(C_{o}\) be the covariance matrix. The eigenvalues \(\lambda_{i},i=1,\cdots,n\) and eigenvectors \(d_{i},i=1,\cdots,n\) of the covariance matrix \(C_{o}\) are calculated.  
Step 3: The eigenvalues \(\lambda_{1}\geq\lambda_{2}\geq\cdots\geq\lambda_{n}\) are arranged in decreasing order, and the eigenvectors corresponding to the eigenvalues are \(d_{i},i=1,\cdots,n\). The accumulative contribution rate \(\varsigma\) of the first \(r\) principal components can be expressed as:  
\[\varsigma=\sum\
olimits_{i=1}^{r}\lambda_{i}/\sum\
olimits_{i=1}^{n}\lambda_ {i} \tag{18}\]  
Step 4: If \(\varsigma\geq 0.80\), construct a matrix \(E\in R^{n\times r}\) composed of the corresponding feature vectors. Then, the new sample matrix \(X^{\prime}\) can use \(E=(d_{1},d_{2},\cdots,d_{r})\) to map the original bearing vibration signal matrix to a new space  
\[X^{\prime}=E^{T}X \tag{19}\]  
where \(X^{\prime}\in R^{r\times m}\).
# Communication Architecture in Mixed-Reality Simulations of Unmanned Systems
## 5 The Proposed Architecture Design
### Visibility Model
#### 5.3.1 The Principle of Network Simulation Inside MR Simulations  
The principle of the deployment of network modeling features into the MR simulation is depicted in Figure 6. The figure shows the steps necessary to model the message transmission and reception in a situation when entity X sends a message to entity Y.  
Figure 5: Visibility model for SWEs within the simulation platform (GCS) showing procesing of a message from one SWE.  
The steps are as follows.  
1. The message is intercepted on the level of entity X's _Comm layer_.
2. The digest of the message (sender, receiver(s), size, and transmission power) is sent to the _Simulation server_.
3. The _Simulation server_ updates its network simulation model with this message digest.
4. The network simulation model decides (based on the information about the sender, receivers, obstacles, the actual wireless medium occupation, and the MAC protocol used) how much the message should be delayed and which receivers are reachable.
5. When the message is ready to be sent to their receivers, the network simulation model informs the _Simulation server_.
6. The _Comm layer_ of entity X is informed that the message is ready to be sent.
7. The message is passed to the _Comm layer_ of entity Y.
8. The message is received, and its content can be processed.  
Using the architecture mentioned above provides the MR simulation testbeds with the capability of network simulation and entities that can move in real time according to their mission, operator commands, or, eventually, incentives from the environment. The network simulator reacts to the entities' movement, and entities can change their plans according to the state of the network. Thus, no predefined traffic mobility models that cannot react to the changes caused by the entities' interaction or operator commands are required. It should be noted that the movement of the communication nodes and their functionality, e.g., in the process of topology management, are to be specified at higher control levels that are not addressed by the proposed architecture.
# Advanced Thermal Management for High-Power ICs: Optimizing Heatsink and Airflow Design
## 4 Results
### System in Free Convection  
The initial geometry was analyzed and simulated in free convection without the use of a fan. The temperature distribution and isothermal levels for this setup are shown in Figure 5.  
In this case, the maximum and minimum temperatures of the IC were found to be T_max = 246 \({}^{\circ}\)C and T_min = 232 \({}^{\circ}\)C, respectively. Given that the targeted operating temperature range is between 30 \({}^{\circ}\)C and 40 \({}^{\circ}\)C, additional measures--such as finning the system--must be implemented. A key observation in this solution is that heat tends to escape to the copper box due to the high thermal conductivity of copper. This reduced resistance to heat transfer is clearly illustrated in Figure 5.
## 3 Results and Discussion
### Oxide Surface Characterization
#### 3.1.4 Oxide Molecular Structure Analyses  
Representative FTIR scans for each oxide are provided in Figure 4. Both oxides revealed similar absorption peaks for phosphate (PO4\({}^{3-}\)) groups at 1050 cm\({}^{-1}\), depicting the presence of calcium phosphate compounds within the oxides. This result agreed well with the XRD analyses in Figure 1[5; 38]. Both oxides also showed carbonate (CO3\({}^{2-}\)) absorption peaks at 875, 1450, and 1570 cm\({}^{-1}\)[5; 38; 39]. A broad O-H band between 3000 and 3600 cm\({}^{-1}\), representing the bending mode of adsorbed water molecules, was observed in both oxides [5; 38; 39]. However, the hydroxyapatite OH\({}^{-}\) peak at 3570 cm\({}^{-1}\), which is characteristic of synthetic hydroxyapatite, was absent in both oxides [38; 39]. Thus, the CO3\({}^{2-}\) absorption peaks served as carbonate substitution peaks in these oxides. In the literature, two types of carbonate substitutions are observed: (a) A-type--direct substitution of OH- with CO3\({}^{2-}\) (1570 cm\({}^{-1}\)) and (b) B-type--tetrahedral group of PO4\({}^{3-}\) substituted by CO3\({}^{2-}\) (1450 cm\({}^{-1}\)) [18]. Interestingly, both the oxides showed a combination of A-type and B-type substitutions, commonly known as AB-type substitutions. The presence of the AB-type CO3\({}^{2-}\) substitution (875, 1450, and 1570 cm\({}^{-1}\)) absorption peaks instead of the characteristic hydroxyapatite absorption peak (3570 cm\({}^{-1}\)) indicated the formation of bone-like carbonated apatite in both anodized oxides in the present study [5; 38; 39]. Bone-like apatite, containing carbonate (CO3\({}^{2-}\)) substitutions, is known to increase the solubility and resorption rate, making the apatite favorable for bone remodeling [1; 18].  
\begin{table}
\begin{tabular}{c c c} \hline
**Elements** & **NOJ CP (At.\%)** & **NOJ MP (At.\%)** \\ \hline Titanium & 0.09 \(\pm\) 0.1 & 0.04 \(\pm\) 0.01 \\ Oxygen & 49.72 \(\pm\) 1.51 & 54.58 \(\pm\) 0.17 \\ Calcium & 8.37 \(\pm\) 1.49 & 10.77 \(\pm\) 0.17 \\ Phosphorus & 4.47 \(\pm\) 0.63 & 6.26 \(\pm\) 0.12 \\ Magnesium & 0.05 \(\pm\) 0.02 & 1.4 \(\pm\) 0.10 \\ \hline \end{tabular}
\end{table}
Table 2: EDS surface chemistry of each oxide group.  
Figure 3: Calculated surface Ca/P ratios for each oxide group. The EDS surface Ca/P ratio for NOJ CP was 1.90 \(\pm\) 0.10, while that for OJ MP was 1.72. The surface Ca/P ratios for both oxides were found to be statistically similar (_p_ = 0.2).
# A Multi-Node Detection Algorithm Based on Serial and Threshold in Intelligent Sensor Networks
## 9 Conclusions  
The application of 5G brings new opportunities for the development of IWSN. The IWSN composed of a large number of intelligent wireless sensor nodes has similar characteristics with the mobile communication network, and also has the problem of multi-node access and detection. Therefore, based on the research of the original MPA and its improved algorithms, the related 5G SCMA technologies are combined to carry out the research of multi-node detection in IWSN, and a novel S-T-Max-log-MPA algorithm is proposed for multi-node detection in this paper. Through the application of threshold and serial updating, the proposed algorithm reduces the iterations of sensor nodes, increases the accuracy of information transmission, and reduces the computational complexity. The simulation results show that the accuracy of information transmission of the S-T-Max-log-MPA algorithm is effectively changed, with accuracy superior to the T-MPA algorithm (up to 10.397%). The complexity of multi-node passing algorithm is effectively reduced, which is superior to the original  
Figure 7: Comparison of the Computational Complexity Reduction Ratio (CCRR) between original Message Passing Algorithm (MPA) algorithm and S-T-Max-log-MPA algorithm, where S-T-Max-log-MPA is Maximum logarithm Message Passing Algorithm based on Serial and Threshold.  
MPA algorithm (up to 60.1%). Therefore, the S-T-Max-log-MPA algorithm can not only ensure the system bit error rate, but also effectively reduce the complexity of the multi-node detection algorithm. At the same time, it can effectively solve the serious problem that the accuracy of T-MPA algorithm decreases when the threshold value is low.  
Conceptualization, G.Z. and S.H.; Data curation, Z.G. and Q.Z.; formal analysis, Z.G. and Q.Z.; investigation, J.R., S.H. and W.L.; writing--original draft, Z.G. and J.R.; writing--review & editing, G.Z. and J.R.; funding acquisition, [PERSON] and W.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Youth Science Foundation of Northeast Petroleum University under Grant No. 2019 QNL-34, the Graduate Innovative Research Work of Northeast Petroleum University and the National Natural Science Foundation of China under Grant (61871348). The authors declare no conflict of interest.
# Solar Irradiance Forecasting with Transformer Model
## 2 Dataset and Methods
### Decoder  
In the present case, the decoder block of the model works as a simulator of the future, which, by combining the historical point data, day of year, and latent space from the encoder, predicts solar irradiance 1 day ahead. This predicted day value can be brought back to the input of the model by connecting it to the original data shifted by one day to the right to obtain the next day. Figure 7 shows how the Transformer model predicts the next day. At every timestep, the decoder tries to estimate the next timestep. Future predictions are independently expressed for all of the Indian locations used.  
Figure 6: Principle of prediction for the latent space.  
Figure 7: Principle of prediction for the future solar irradiance.
## 2 Background
WSNs are also a kind of WSN that are often used to link hard-to-reach locations in countries prone to natural disasters and military applications [28]. With the aid of WSNs, traditional networks can autonomously shut down in the event of a catastrophic disaster (such as an earthquake, hurricane, storm, tsunami, and dam breakage). Cameras and other sensors prepared to save lives in emergencies can also be installed, giving a constant aerial view. Direct communication between the SNs and the BS on the ground may become impractical in large coverage regions. However, hop-to-hop communication can overcome this problem by using a routing protocol to find the optimum route/path from the source to the destination [21; 22; 23; 24; 25]. Routing protocols are responsible for discovering, establishing, and maintaining communication routes between two nodes [15; 16; 17; 18]. There are several advantages to using these protocols, including reducing overhead and bandwidth use. Many elements of WSN routing protocols make them more brutal as they are fixed network protocols but also include dynamic topology algorithm, mutual intervention, limited battery capacity, and the restricted resources accessible in SNs. Hence, it is required to use routing information to find other paths [26]. As a result, communication cannot be limited to the radius of the action of each device but rather the aggregate of the radius of the action of all devices. Furthermore, in SN, determining contact paths and their spatial organization is a crucial task. As a result, these routes are frequently restructured such that the SN may operate in unison [28; 29; 30; 31]. Therefore, it is critical that routing be dynamically performed with the WSNs autonomy being increased and the time between the source and target nodes being reduced [32; 33; 34]. With the help of various sensors, WSN are frequently employed to track areas in images or videos [32; 33; 34; 35]. This means that the Quality of Experience (QoE) measures must verify both video streaming consistency and data transfer effectiveness. Search missions, surveillance, agricultural monitoring, disaster monitoring, and environmental monitoring have all used the multi-SN scenario to identify targets (i.e., the location of a target) and to give a birds-eye perspective for surveillance (weather conditions including such wind and temperature as well as light intensity and pollution) [34; 35; 36]. Using the multi-sensor nodes scenario, traffic can also be efferent-monitored and managed [22] as a part of smart city monitoring and catastrophe management systems.
## 3 Heuristic Solution Approach
### Addressing Diversity Constraints
The complexity of the algorithm in the worst-case scenario is \(O(L*U*Ctc*Cc)\), where \(Ctc\) are the providers for which the diversity constraint does not hold and \(Ccc\) are the providers for which the diversity constraint holds. More simply, the complexity in the worst-case scenario is \(O(L*U*C^{2})\). This is because, in the worst case, all items need to be accessed for all users, and the latter have to be accessed for all changeable categories and for all categories or providers that need to change. However, the algorithm completes much earlier in most cases because once a category's diversity constraint is fulfilled it proceeds with a new category of items, without accessing all the users and items. ```
1:Xcov=calculate_solution_for_Coverage(A_grad, Kc)//as a linear programming problem through cvxpy
2:Xnew=Xcov//copy Coverage solution as a starting point for the final solution
3:foreachcategoryc:
4:Div[c]=calculate_Diversity_for_category(Xnew, c)//as described in Formula (11)
5:categories_ordered_by_Diversity=argsort(Div)
6:categories_ordered_by_Diversity_desc=reverse(categories_ordered_by_Diversity)
7:categories_to change_changeable_categories=1,categories_ordered_by_Diversity
8:foreachcategoryc in categories_ordered_by_Diversity. 9:IfDic[c] \(<\)D:
10:Categories_to_change.add(c)
11:changeable_categories.remove(c)
12:foreachcategoryc in categories_to_change:
13:Most_recommended_item[c]=find_most_recommended_item_of_category(c)
14:Users_to_recommended=argsort(A_pred[most_recommended_item[c]])
15:Users_to_recommended_except_recommended=users not recommended with the most recommended item
16:foreachuseru in users_to_recommended_recommended:
17:foreachcategoryc2 in changable_categories:
18:rec_items_of_category_to_user=find_rec_items_of_category_to_user(c2, u)
19:foreachitemitem in rec_items_of_category_to_user. 20:Xnew[u,i] =0
21:Xnew[u, most_recommended_item[c]] =1 //Calculate new Coverage for category c2 according to formula (9)
22:Cov_xnew[c2]=calculate_Coverage_for_category(Xnew, c2) //Calculate new Diversity for categories c2 and c according to Formula (11)
23:Div_xnew[c2]=calculate_Diversity_for_category(Xnew, c2)
24:Div_xnew[c]=calculate_Diversity_for_category(Xnew, c) //If Coverage or Diversity constraints for c2 are violated rollback
25:IfCov_xnew[c2]<k[c2] or Div_xnew[c2]<D:
26:Xnew[u,i] =1
27:Xnew[u, most_recommended_item[c2]] = 0
28:ElseifDiv_xnew[c] > D:
29:Break (line 12)
```  
**Algorithm 1** Heuristic algorithm for addressing diversity constraints
## 4 Results and Discussion
### Data Set 3 Benchmarking Instances  
Table 6 reports the performance comparisons against those by the MILP model and LAHC of [PERSON] and [PERSON] [18].  
The two-phase heuristic matched all the optimal solutions of all 'SFJST' instances obtained by the MILP model and LAHC, and it solved them in a much shorter time. This is attributable to the decomposition approach. In particular, the first-stage FJSP + T approximation managed to locate good machine-operation assignments.  
Out of ten 'MFJST' instances, the two-phase heuristic matched all six optimal solutions of the MILP model (MFJST01 to MFJST06), the two solutions obtained by LAHC (MFJST07 and MFJST08). Most importantly, for the two largest instances, MFJST09 and MFJST10, it generated solutions superior to those by LAHC. For MFJST07 to MFJST09, the two-phase heuristic obtains matching solutions in a much shorter time. In particular, for MFJST07 and MFJST08, the solution timing is around one-third of that of LAHC.  
The improvement in solution quality for MJSPT10 is a trade-off for increased solution timing, attributable to the direct use of solvers. The increase in problem size impacts the complexity of the instance. From Table 6, it is observed that the number of nodes within each JSPT network for MFJST09 and MFJST10 increases sharply as compared to its 'MFJST' counterparts. Additional nodes translate into extra routes for selection between nodes across jobs within the JSPT, thus increasing problem complexity.