esa-sceva/satcom-chunk-collection huggingface dataset:
# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
## 5 Towards Algorithm Development: Propagation Concepts, Path Loss and Communication Graph
### Air-to-Air Channel  
When no highly directive antennae are employed, the air-to-air channel is affected by multiple ground reflections. Additionally, due to the flying speed, UAV-UAV communication experiences considerable Doppler shifts. For our approach, though, we assumed that the UAVs were stationary at the moments when they facilitated the communication between the desired WSNs and the other nodes. In addition, the elevation angle was ignored for the air-to-air channel [62]. Consequently, we propose the employment of the free space propagation model.  
Path Loss Modelling: Free Space Model  
The simplest model consists of the free space path loss (FSPL) approximation given by Equation (6).  
\[FSPL=20\cdot\log_{10}d+20\cdot\log_{10}(f)+20\log_{10}\left(\frac{4\cdot\pi}{c }\right) \tag{6}\]  
where \(d\) is the geodesic or ground distance between the UAV and ground node expressed in km, \(f\) is the operating frequency in MHz and \(c\) is the wave propagation speed (\(c=3\times 10^{8}\) m/s). The free space path loss model can only be applied when two conditions are fulfilled:  
1. There are no obstacles in the line of sight path between the UAV and ground user;
2. There are no objects in the first Fresnel zone [72].  
In addition, the free space scenario implies the absence of any reflecting surface, and it does not consider the Earth reflections.  
The free space propagation scenario is valid until the radio signals transmitted by the UAV reach one of the following environments: rural, suburban, urban, dense urban, high-dense urban [73].
## 4 Experimental Analysis  
### Experimental Setup  
In order to verify the feasibility of the proposed method, actual experiments on vehicle platforms in urban environments were carried out. We use the open-source vehicle city dataset captured by the vehicle platform of the Hong Kong Polytechnic University, China [43]. The vehicle navigation system dataset is suitable for studying the characteristics of sensors and verifying the performance of vehicle navigation systems in urban environments. As shown in Table 1 and Figure 6a, the platform consists of IMU, GNSS, and LiDAR. Furthermore, the ground truth is collected by a high-precision SPAN-CPT system, and the  
Figure 5: Schematic diagram of sensor isolation and recovery.  
positioning error is up to 2 cm. The extrinsic parameters among sensors were calibrated in advance. The data processing part is under MATLAB platform.  
The duration of the experiment is 486 s, with a total length of 2.0 km. In the experiment, there will be multipath interference of GNSS signals and dynamic feature interference from LiDAR. The trajectory and the sections are shown in Figure 6b.  
According to environmental characteristics, three typical road sections were selected to validate the proposed algorithm. Road section (1) lasts from 110 s to 160 s, and the vehicle passes through tall buildings while the interference gradually increases. Road section (2) lasts from 250 s to 300 s and the vehicle passes through open areas while the error variation is slight. Road section (3) lasts from 350 s to 450 s, and the vehicle passes through dense areas with sudden interference. The root mean square error (RMSE) is implemented to evaluate the positioning accuracy of different algorithms [44], and we evaluate IMMFGO and MSFDIR based on the typical road sections.
# Optimization of Slotted Steel Plate Shear Walls Based on Adaptive Genetic Algorithm
## 2 Design and Implementation of Adaptive Genetic Algorithm
### Implementation of IAGA  
As specialized seismic-resistant components, SSPSWs are typically integrated within structural frames, where stringent lateral stiffness requirements are relaxed. The inherent mechanical behavior of SSPSWs--whether configured with bilateral or quadrilateral connections--prioritizes ductility over lateral stiffness enhancement. Their fundamental functionality lies in stiffness modulation through slit parameterization while maintaining an exceptional energy dissipation capacity. Consequently, SSPSWs are predominantly employed as seismic dampers rather than primary lateral load-resisting elements. The optimization framework assumes that SSPSWs contribute minimally to global lateral stiffness while maximizing ductility performance. This ductility is quantified by the coefficient \(\mu\), defined in Equation (2), where higher \(\mu\) values indicate superior deformation capacity.  
\[\begin{split}\max:\mu=\frac{u_{m}}{u_{y}}\\ \text{s.t.}:\text{stiffness}\rightarrow\text{limit}\end{split} \tag{2}\]  
In the equation, \(u_{m}\) represents the ultimate displacement at the point of maximum load, and \(u_{y}\) represents the yield displacement.  
While the ductility coefficient \(\mu\) is mathematically defined as the ratio of the ultimate displacement to yield displacement--establishing an inverse proportionality between \(\mu\) and the yield displacement--high-ductility configurations typically exhibit a proportionally scaled yield and ultimate displacement magnitudes. Accordingly, this study employs the yield displacement as the fitness metric (Equation (3)). Concurrently, a stiffness contribution constraint mandates that the slotted steel plates provide at least 10% of the total system stiffness.  
\[\begin{split}\max:\text{fitness}=u_{y}\\ \text{s.t.}:\text{stiffness}\rightarrow\text{limit}\end{split} \tag{3}\]  
Notably, imposing stiffness thresholds as lower bounds proves counterproductive for SSPSW optimization. While enhanced ductility configurations typically exhibit reduced stiffness, the stiffness-ductility relationship manifests as a nonlinear interdependence rather than as a simplistic inverse proportionality. To holistically address this coupling, a penalty function (Equation (4)) is integrated into the fitness evaluation framework, enabling the simultaneous optimization of the stiffness and ductility objectives.  
\[\text{fitness}=u_{y}-\frac{|\text{stiffness}-\text{limit}|}{\text{limit}}\times\alpha \tag{4}\]  
where \(\alpha\) is a fixed coefficient used to adjust the penalty term to match with \(u_{y}\).  
The proposed methodology integrates an ABAQUS finite element analysis with a PYTHON-implemented genetic algorithm for optimizing the SSPSW configurations. The optimization process is illustrated in Figure 2.  
Figure 2: Flow chart of ABAQUS and PYTHON interactive analysis and optimization.
# Dual-Branch Multi-Scale Relation Networks with Tutorial Learning for Few-Shot Learning
## 4 Experiments and Results
### Results
#### 4.4.1 Results of the Main Datasets
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Type** & **5-Way 1-Shot** & **5-Way 5-Shot** \\ \hline Meta-learn Nets [24] & Meta & \(43.44\pm 0.77\) & \(60.60\pm 0.71\) \\ MetaGAN [30] & Meta & \(46.13\pm 1.78\) & \(60.71\pm 0.89\) \\ MAML [22] & Meta & \(48.70\pm 1.84\) & \(63.11\pm 0.92\) \\ Meta Nets [23] & Meta & \(49.21\pm 0.96\) & - \\ Meta SSL [27] & Meta & \(50.41\pm 1.84\) & \(64.39\pm 0.92\) \\ MM-Net [26] & Meta & \(53.37\pm 0.48\) & \(66.97\pm 0.35\) \\ SNAIL [28] & Meta & \(55.71\pm 0.99\) & \(68.88\pm 0.92\) \\ Matching Nets [8] & Metric & \(43.56\pm 0.84\) & \(55.31\pm 0.73\) \\ Prototypical Nets [7] & Metric & \(49.42\pm 0.78\) & \(68.20\pm 0.66\) \\ GNN [53] & Metric & \(50.33\pm 0.36\) & \(66.41\pm 0.63\) \\ Relation Nets [9] & Metric & \(50.44\pm 0.82\) & \(65.32\pm 0.70\) \\ DN4 [38] & Metric & \(51.24\pm 0.74\) & \(71.02\pm 0.64\) \\ MSDN [16] & Metric & \(52.59\pm 0.81\) & \(68.51\pm 0.69\) \\ SoSN [40] & Metric & \(52.96\pm 0.83\) & \(68.63\pm 0.68\) \\ RPMN [41] & Metric & \(53.35\pm 0.78\) & \(69.35\pm 0.59\) \\ RCN [39] & Metric & \(53.47\pm 0.84\) & \(71.63\pm 0.70\) \\ DCN [14] & Metric & \(53.48\pm 0.78\) & \(67.63\pm 0.59\) \\ MATANet [19] & Metric & \(53.63\pm 0.83\) & \(72.67\pm 0.76\) \\ MSFN [42] & Metric & \(54.48\pm 1.23\) & \(69.06\pm 0.69\) \\ PARN [10] & Metric & \(55.22\pm 0.82\) & \(71.55\pm 0.66\) \\ MSLPN [20] & Metric & \(56.52\pm 0.92\) & \(73.45\pm 0.86\) \\ MsKPRN [11] & Metric & \(57.02\pm 0.88\) & \(72.06\pm 0.68\) \\ MRN [54] & Metric & \(57.83\pm 0.69\) & \(71.13\pm 0.50\) \\ ABNet [49] & Metric & \(58.12\pm 0.94\) & \(72.02\pm 0.75\) \\ DbMRNT(Ours) & Metric & \(\mathbf{58.82\pm 0.88}\) & \(\mathbf{74.19\pm 0.63}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Few-shot classification results on the miniImageNet.
## 4 Discussion
### Steady-State Rolling and Its Implicaiton in Computational Efficiency
During the rolling, the normal and longitudinal strains were accommodated by the transverse strain [4]. Meanwhile, the isotropic friction restricted the longitudinal and transverse deformation under the roller. The 2D model could not capture these complex 3D phenomena due to its plane-stress assumption. In fact, the out-of-plane stress played a considerable role in the final state of the longitudinal residual stress and strain. As the wall and substrate were not actually aligned within the same plane, the substrate effect could not be realistically represented in a 2D model. For a similar reason, a 2D plane strain model defined on the transverse section (e.g., y-z plane of the 3D model in Figure 2) is not suitable for accurate simulation of the rolling process. It assumes zero strain in the longitudinal/rolling direction, where large deformation was observed in the 3D models. Nevertheless, the 2D plane stress model was 86.5% more efficient than the short 3D models (Table 1). Considering the computational efficiency, the 2D model could be used for rough and fast estimation of major rolling parameters for analysis later refined using 3D models.
## 3 Results
### Classification Results in the Illinois Region
Consequently, using training patches with a low class purity value allows crops to be discriminated more accurately. Many training patches collected at the boundaries between crop parcels contributed to the correct identification of adjacent crop parcels. The significant decrease in classification accuracy when using large training patches was due to the inclusion of more subareas having non-uniform class homogeneity with a patch. Based on the results in the Illinois region, using training patches with low class purity and a smaller size is more likely to produce more accurate results in the classification of regions with heterogeneous crop distributions using mid-resolution remote sensing imagery. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**CP60**} & \multicolumn{2}{c}{**CP80**} & \multicolumn{2}{c}{**CP100**} \\ \cline{2-7}  & **PA (\%)** & **UA (\%)** & **PA (\%)** & **UA (\%)** & **PA (\%)** & **UA (\%)** \\ \hline Corn & 82.13 & 73.76 & 80.61 & 73.22 & 74.83 & 72.00 \\ Soybean & 76.87 & 86.76 & 77.42 & 86.11 & 75.96 & 82.32 \\ Winter wheat & 95.06 & 83.96 & 90.09 & 80.76 & 90.86 & 77.56 \\ OA (\%) & 81.43 & & 80.44 & & 77.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy statistics with respect to different class purity values when using a 9 by 9 patch in Illinois (PA: producer’s accuracy; UA: user’s accuracy; OA: overall accuracy). Figure 9: Classification results with respect to different class purity values when using a 9 by 9 patch in Illinois. Two subareas denoted as A and B are zoomed out for comparison purposes.
# Two-Component Bayesian Hierarchical Models for Cost-Benefit Analysis of Traffic Barrier Crash [PERSON]
## 11 Best Fit Model
It should be noted that the AADT and length of barriers were incorporated in the analysis to account for an exposure effect. As expected, for the higher barrier length and AADT, higher EPDO barrier crashes were observed. As discussed in the method section, these variables were divided by 10 before implementation. For the first layer of the models, two variables were incorporated: highway system and truck traffic. Results indicated that having a barrier on the interstate system decreased the odds of experiencing any crash compared with highway system. Although this result seems counterintuitive, the difference between experiencing a crash and experiencing a higher EPDO crash should be noted. Truck traffic similar to the second layer for the exposure effect was considered. Similarly, higher ADTT moves barriers from a no crash area to a crash area. In terms of interception, it should be noted that alpha 1 is for box beam, alpha 2 is for concrete, and the third alpha is related to W-beam. As can be seen from the magnitudes of these three intercepts, box beam has a lower EPDO crash compared with the other two barriers. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Mean** & **SD** & **2.50\%** & **97.50\%** \\ \hline \multicolumn{5}{c}{**2 nd layer, NB model**} \\ \hline alpha1 & 0.472 & 0.09 & 0.295 & 0.65 \\ \hline alpha2 & 0.564 & 0.142 & 0.307 & 0.876 \\ \hline alpha3 & 0.564 & 0.088 & 0.394 & 0.734 \\ \hline b1 & 0.001 & 0.0009 & 0.001 & 0.002 \\ \hline b10 & 2.134 & 0.152 & 1.837 & 2.436 \\ \hline b12 & 0.252 & 0.097 & 0.06 & 0.441 \\ \hline b2 & 1.867 & 0.189 & 1.501 & 2.24 \\ \hline b3 & 0.189 & 0.088 & 0.018 & 0.363 \\ \hline b4 & 0.087 & 0.083 & \(-\)0.073 & 0.249 \\ \hline b5 & \(-\)0.441 & 0.086 & \(-\)0.609 & \(-\)0.27 \\ \hline b6 & 0.501 & 0.083 & 0.344 & 0.667 \\ \hline b7 & 0.362 & 0.101 & 0.165 & 0.56 \\ \hline b8 & 0.005 & 0.001 & 0.004 & 0.005 \\ \hline b9 & \(-\)0.351 & 0.151 & \(-\)0.645 & \(-\)0.057 \\ \hline \multicolumn{5}{c}{**1 st layer, logistic regression**} \\ \hline alpha & \(-\)13.593 & 2.197 & \(-\)17.518 & \(-\)9.327 \\ \hline b17 & \(-\)35.049 & 8.392 & \(-\)47.179 & \(-\)19.414 \\ \hline b16 & 0.02 & 0.004 & 0.013 & 0.027 \\ \hline \multicolumn{5}{c}{DIC = 12,558.7} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Estimation results of two-layer model, with NB distribution for the second layer. Comparing Tables 3 and 5 in terms of confidence interval (CI) and SD, a few observations can be made. Standard errors of Naive estimates, Poisson, are smaller than NB. In addition, probabilities coverage of CI are lower compared with NB. This would result in erroneous inference due to ignoring over-dispersion, which is evidence from point estimates in terms of magnitude and signs.
## 3 Results
### Network Robustness Analysis
Analysis of the robustness coefficients for the three networks shows that network robustness is greater under random attacks, especially random edge attacks. In the early and middle attack stages, little damage occurs to any network. Under random vertex attacks, the rate at which network robustness is degraded is higher than under random edge attacks, though the rate remains relatively gradual. However, network robustness decreases rapidly under deliberate attacks, rendering networks more vulnerable than under random attacks. Deliberate attacks exhibit greater global impact, are targeted more strategically, and disrupt networks faster than random attacks, conferring stronger attack capability. Furthermore, in both random and deliberate attacks, vertex attacks have greater destructive power than edge attacks; this is because the edges removed in edge attacks often have alternative paths for replacement, while vertex attacks eliminate both connected edges and alternative paths. Figure 8: Changes in indicators under vertex betweenness centrality attack. Figure 7: Changes in indicators under random edge attack. Finally, we specifically investigated the top five genera of planktonic organisms that exerted the most significant influence on robustness within each network, as visually represented in Figure 10. The outcomes of our analysis reveal noteworthy findings, as we observed crucial genera shared among the three networks that exhibited extensive interconnections. Additionally, we identified distinct variations between the networks, highlighting unique characteristics and dynamics. The findings of this study possess direct implications for advancing sustainable technological research, and can offer substantial assistance in this critical domain.
# Distributed Consensus for Global Matrix Formation in the Principal Component Pursuit Scenario
## 1 Introduction
### Contributions  
Mainly, other algorithms communicate during a complete PCA. This is undesirable in the context of very restrictive communication due to safety reasons or very high-cost communications. We prove and show how this does not have to be the case for a distributed PCA since the agents can communicate only until some agreement is reached on the global matrix formation. Then, each agent can optimize alone, minimizing the communication as much as possible. We present proof of the proposed algorithm along with a multi-camera application.  
We also provide a simple but rigorous viewpoint to overcome the issue of overlapped regions of view or common measurement/knowledge of each agent in the network. We show how this is an issue for the classical consensus, showing over-prediction in the overlapped regions. We show this from the perspective of a distributed camera network application. We also show how to handle colored images, since most researchers use binary black and white scales for images.  
We investigate the impact on the convergence of different network sizes and densities.
# Application of Artificial Neural Network to Somatotype Determination
## 3 Results  
The ANNs were used to describe Endo, Meso and Ecto somatotypes. Hidden and output layers with a log-sigmoid (logsig), hyperbolic tangent sigmoid (tansig) and linear (linear) transfer function were used for the prediction of the somatotypes whereas and the Levenberg-Marquardt (trainlm) was the training function. The ANNs were selected to determine somatotypes. These networks had one hidden layer. Three-layer neural networks MLP 6-5-1 (multilayer perceptron) were used to determine Endo, Meso or Ecto somatotypes, while three-layer neural network MLP 6-4-3 was used to determine these three somatotypes simultaneously. It should be noted that the ectomorphy is be calculated on the basis of height and weight, therefore it does not require further simplifications and it was excluded in the calculation from MLP with one output layer. The input parameters were all parameters shown in Table 1. The transfer function for hidden layer was log-sigmoid, and for output layer tangent sigmoid and linear for MLP 6-5-1 and MLP 6-4-3, respectively. The highest values of the correlation coefficient were obtained for ANN describing Endo somatotypes (validation: 0.96). The correlation coefficient for ANN describing all somatotypes simultaneously is higher (R = 0.91 for validation set). Results of statistical analyses on the modelling of somatotypes are presented in Table 2.  
Comparing the statistics of ANNs obtained for individual somatotypes from different ANNs, it can be seen that ANN MLP 6-4-3 describes all somatotypes worse than ANNs with one output. However, the aforementioned ANN allows us to determine simultaneously (given the same weights and bias between input and hidden layer) all somatotypes and the errors are not much larger compared to the single-output ANN (RMSE greater by 0.14 and 0.08 for Endo and Meso, respectively) and so it can be considered suitable for somatotype calculation. Sensitivity analysis of ANNs showed that in the case of MLP 6-5-1 (for Endo) the TBW (the greatest R = 0.93, the lowest errors: RMSE = 0.51, \(\chi^{2}\) = 0.61) and FFM (R = 0.91, RMSE = 0.53, \(\chi^{2}\) = 0.66) have the lowest impact, whereas in sequence: Reac (smallest R = 0.78, biggest errors: RMSE = 0.84 and \(\chi^{2}\) = 1.63), BMI, Res, and RMR have the lowest impact.
## 4 Optimization Process Using NSGA-II
### TOPSIS Method  
The Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is a commonly used evaluation method in multi-objective decision-making. This method can make full use of the information in the original data and accurately reflect the closeness of each group of results to the ideal result [22]. The method includes the following steps:  
Figure 13: Flowchart of NSGA-II.  
Step 1: In order to calculate the normalized decision matrix, we need to assign weights to each objective function; \(W_{j}\) is the weight of the \(j\)-\(th\) objective function. All weights satisfy the following equation:  
\[\sum_{j=1}^{m}W_{j}=1 \tag{13}\]  
Step 2: Normalize objective functions \(F_{ij}\); \(F_{ij}\) represents the \(i\)-\(th\) optimal point of the \(j\)-\(th\) objective function.  
\[p_{ij}=\frac{F_{ij}}{\sqrt{\sum_{i=1}^{\tau}F_{ij}^{2}}}\ for\ i=1,2,\cdots, \tau;j=1,2,\cdots,m \tag{14}\]  
Step 3: Perform weighted normalization on the normalized function matrix:  
\[q_{ij}=W_{j}\times p_{ij} \tag{15}\]  
Step 4: Compute worst and best vectors:  
\[F^{-}=(\min(q_{11},q_{21},\cdots,q_{\tau 1}),\min(q_{12},q_{22},\cdots,q_{ \tau 2}),\cdots,\min(q_{1m},q_{2m},\cdots,q_{\tau m})) \tag{16}\]  
\[F^{+}=(\max(q_{11},q_{21},\cdots,q_{\tau 1}),\max(q_{12},q_{22},\cdots,q_{ \tau 2}),\cdots,\max(q_{1m},q_{2m},\cdots,q_{\tau m})) \tag{17}\]  
Step 5: Calculate the Euclidean distance between each evaluation object and the worst vector and the optimal vector:  
\[D_{i}^{-}=\sqrt{\sum_{j=1}^{m}\Big{(}q_{ij}-F_{j}^{-}\Big{)}^{2}}\ for\ i=1,2, \cdots,\tau;j=1,2,\cdots,m \tag{18}\]  
\[D_{i}^{+}=\sqrt{\sum_{j=1}^{m}\Big{(}q_{ij}-F_{j}^{+}\Big{)}^{2}}\ for\ i=1,2, \cdots,\tau;j=1,2,\cdots,m \tag{19}\]  
Step 6: Calculate the relative closeness of each target:  
\[D_{i}=\frac{D_{i}^{-}}{D_{i}^{+}+D_{i}^{-}},0\leq D_{i}\leq 1 \tag{20}\]  
Step 7: Sort \(D_{i}\) to select the optimal objective function; the maximum value of \(D_{i}\) will be chosen.
# Caroenoid Production from Microalgae: Biosynthesis, Salinity Responses and Novel Biotechnologies
## 2 Health-Promoting Carotenoids from Microalgae and Their Biofunctions
### Carotenoid Biosynthesis in microalgae  
Carotenoids in microalgae can be categorized as the primary ones related to photosynthesis, and the secondary ones accumulated under adversity [14]. In this review, we mainly focus on functional microalgal carotenoids, including lutein, \(\beta\)-carotene, astaxanthin, zeaxanthin and fucoxanthin.  
The biosynthetic pathways of carotenoids involve several intermediates and key enzymes (shown in Figure 1) [15; 16]. First, through the methylerythnitol phosphate pathway (MEP) in the chloroplast, isopentenyl pyrophosphate (IPP, C5) and its isomer dimethylallyl diphosphate (DMAPP, C5) are catalyzed at a ratio of 3:1 by geranyl diphosphate synthase (GPPS) and geranylgeranyl pyrophosphate synthase (GGPPS) to geranylgeranyl pyrophosphate (GGPP, C20). In _H. pluvialis_, this step from C5 to C20 can be catalyzed by GGPPS only [10]. Phytoene synthase (PSY) condenses two molecules of GGPPS into (15Z)-phytoene (C40), which is desaturated and isomerized into lycopene by phytoene desaturases (PDS), \(\zeta\)-carotene isomerase (Z-ISO), \(\zeta\)-carotene desaturase (ZDS) and caroten isomerase (CrtlSO).  
Lycopene may flow to two branches, \(\alpha\)-branch and \(\beta\)-branch. Lycopene \(\varepsilon\)-cyclase (LCYE) and lycopene \(\beta\)-cyclase (LCYB) catalyze lycopene to \(\alpha\)-carotene, and then cytochrome P450 beta hydroxylase (CYP97A) and cytochrome P450 epsilon hydroxylase (CYP97C) convert \(\alpha\)-carotene to lutein. As for the \(\beta\)-branch, lycopene is converted by LCYB to \(\beta\)-carotene. For the microalgae capable of astaxanthin accumulation, such as _Haematococcus pluvialis_ and _Chromochloris zofingeniss_, \(\beta\)-carotene may also flow to different branches. \(\beta\)-carotene can first be catalyzed by \(\beta\)-carotene ketolase (BKT) to canthaxanthin and then to astaxanthin by \(\beta\)-carotene hydroxylase (CHYB). \(\beta\)-carotene can also flow to zeaxanthin first by CHYB and then to astaxanthin by BKT [15]. This also reflects the diversity of microalgae metabolism. Up until now, the exact biosynthesis pathway of fucoxanthin is still unclear.