esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Impact of external errors
### Impact of environmental effects
#### 3.2.2 Wet troposphere  
The case is similar for the tropospheric correction, which is derived from measurements of a three-frequency nadir radiometer (18.7, 23.8 and 34.0 GHz) that is a standard part of an optimal altimetry payload. The amplitude of the variations of the water vapor content of the troposphere have been looked at through the computation of SSMI (Special Sensor Microwave Imager) data (see [23]), the worst cases being analyzed during periods of Hurricane Juan in fall 2003. First of all, the overall mean error (difference between the nadir and the off-nadir wet tropospheric delays) in the FR has been analyzed versus latitude. Stronger signals in the tropics result in an error that may be 50 % larger than the global average (see Figure 13 (a)). Maximum errors can reach 3 cm in the Middle Range (MR) and beyond (see Figure 13 (b)). Occurrence of the Hurricane Juan on the 30 th of September is clearly visible in the tropospheric error plots (see arrows).  
The statistical distribution of the errors when going through a typical large atmospheric feature (a few hundred of kilometers wide) has been computed (see Figure 14 (a) and (b)). For distances within the swath larger than 50 km, almost 50 % of the errors go above 1 cm.
## 2 Previous Studies on Teachers' Views about AR in Education  
Although there are many studies that investigate AR educational applications as well as the students' views, experiences, and results of using ARinE [3; 4; 5; 9; 11; 12; 13; 14; 15], few studies analyze the teachers' points of view. This is an important factor to be considered in order to maximize the capacity of this technology in education.  
In general, teachers show a positive attitude towards using ARinE [16; 17]. They think that AR increases quality of teaching, autonomous and collaborative learning [17] and promotes students' exploratory behavior [8]. It can also promote student interactions and the interactions between students and learning material [17]. They believe that AR enables visualization of abstract concepts making them more understandable, facilitates students to understand learning content better, increases knowledge retention and thus improves learning [9; 17; 18; 19]. Additionally, they recognize that AR increases students' positive attitude, enjoyment, interest, motivation, curiosity, and knowledge durability [8; 9; 17; 19]. Teachers reported that AR can increase learning interest, it has the potential to enhance learning motivation [8; 9], promotes a positive attitude, enhances satisfaction, and can raise engagement [17; 19].  
However, most teachers do not have any experience on AR [10; 20]. More specifically, they do not have appropriate technological and pedagogical skills [16; 21] such as programming skills and using 3D design software [22] as well as practical knowledge on AR content creation and integration in education [17]. Furthermore, they believe that a number of constraints prevent the wide adoption of ARinE. Such constraints include the AR cost [10; 17; 19], the lack of quality AR resources [9; 10; 21], the lack of time [10; 18; 19; 22], the lack of digital infrastructure [8; 16; 18], and the lack of institutional support [21].  
Regarding students' attention there are contradictory results: some teachers state that students lose attention to the AR app because they use their devices for other reasons [8], while other teachers argue that AR captures students' attention and can increase students' cognitive load [9]. In addition, teachers believe their students lack appropriate skills and they require long time to become able to run AR apps [8], therefore the overhead required to overcome these usability issues is not usually desirable. Finally, several previous studies urge for teachers' training on using ARinE [10; 17; 18; 21; 22].
# A Review of the Artificial Neural Network Models for Water Quality Prediction
## 3 Three Basic Model Structures in Water Quality Prediction
### Feedforward Architectures
GRNNs is a modified form of the RBFNNs model, but it differs from RBFNNs in structure. Patten and summation layers are located between the input and output layers [27]. The training between the input and pattern layer of GRNNs is equivalent to the research on the input and hidden layer of the RBFNNs. WNNs have made some changes based on the traditional MLPs, in which the non-linear sigmoid activation functions is replaced by the Morlet wavelet function commonly used in the WNNs hidden layer. Therefore, WNNs are suitable for solving non-stationary time series problems [64]. The biggest innovation of ELMs is the random selection of hidden nodes and the use of a least squares method to determine the output layer weight. CCNN is different from the above feedforward networks because it constructs the neural network without a hidden layer at first and automatically adds hidden units instead of fixing the network architectures and then training the weights and thresholds. The first step of MNN, a special feedforward network, is data clustering using the fuzzy c-means method [65]. The second step is updating the clusters by adding the new datasets. To achieve better prediction accuracy, a neural network with the maximum similarity between the inputs and centroids of the cluster is chosen.
## 2 Dataset, Models and Method
### Method  
1. As shown in Figure 1, the land-atmosphere mesoscale model (ARPS-SiB2) was established using initial and boundary conditions from NCEP-FNL data.
2. The ARPS model was integrated for a predefined period (10 min) and the calculated atmospheric forcing data transferred to the SiB2 model.
3. At the beginning of the SiB2 integration, the ensemble (50 members) of soil moisture profiles was generated. SiB2 was executed independently for each ensemble member of the soil moisture profile, retaining the same model parameters and atmospheric forcing. At the end of the SiB2 calculation, the mean values of the updated soil state and fluxes were computed and fed back to the ARPS model as the lower boundary conditions of the atmospheric model. Then, the ARPS-SiB2 model was integrated forward in time.
4. At times, when AMSR-E observations were available, the brightness temperatures at 6.9 and 10.65 GHz were perturbed to produce an ensemble of observations with prescribed statistics. The SiB2-driven ensemble of soil moisture profiles, surface temperature, and canopy temperature were used to obtain the simulated brightness temperatures using the forward microwave radiative transfer model. The EnKF calculated the assimilated soil moisture profiles using simulated and observed brightness temperatures, as shown in Equation (4). In the case of soil moisture assimilation (no cloud assimilation), the updated soil state and fluxes were fed back to the ARPS model and the ARPS-SiB2 model was integrated forward in time.
5. In the case of cloud data assimilation, CMDAS was activated as soon as the LDAS completed the soil moisture assimilations. The control variables (profiles of temperature, specific humidity, pressure, air density, mixing ratio of cloud water, rain water, hail, snow, and cloud ice) were obtained from ARPS as an initial state to run the model operator ([PERSON]'s ice microphysics [24]). The 4-stream fast model calculated the modeled brightness temperatures for 89 GHz at the satellite level by considering the land surface as the lowest boundary. Land surface emissivity was calculated using assimilated soil moisture content. The Shuffle Complex Evolution scheme was used to estimate the assimilated cloud parameters (_i.e._, cloud liquid water, snow, and rain) by minimizing the cost function calculated between the modeled and observed brightness temperatures. Then, the updated soil state and fluxes were fed back to the ARPS-SiB2 model.
6. Finally, with the reinitialized land surface and atmospheric conditions, the ARPS-SiB2 model was integrated forward in time to predict the land and atmospheric evolution until the next AMSR-E observations were available. The results from the ARPS-Sib2 model were recorded at 30-min intervals.
# Dual-Branch Multi-Scale Relation Networks with Tutorial Learning for Few-Shot Learning
## 5 Discussion
### The Effect of the Dual-Branch Structure  
The classification effect of each branch in few-shot scenarios is investigated in experiments, and the results are presented in Table 5. From the table, it can be observed that the combined model of both branches exhibits improved performance compared to either individual Branch alone in few-shot classification tasks. In the 5-way 1-shot setting, there is a 0.37% improvement over Branch 1 and a 1.3% improvement over Branch 2. Similarly, in the 5-way 5-shot setting, there is a respective improvement of 0.61% and 3.4% compared to Branch 1 and Branch 2. The experiments validate that deep features (Branch 1) containing richer semantic information contribute significantly to final classification outcomes in multi-scale settings while incorporating shallow information enhances overall performance. Notably, our approach employs a tutoring learning module \(T\) where we utilize Branch 1 to tutor the learning process of Branch 2 by capturing the similarity between support and query image pairs, resulting in remarkable improvements in classification  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **5-Way 1-Shot** & **5-Way 5-Shot** \\ \hline Matching Nets [8] & \(34.80\pm 0.98\) & \(44.70\pm 1.03\) \\ Prototypical Nets [7] & \(40.90\pm 1.01\) & \(52.93\pm 1.03\) \\ Relation Nets [9] & \(47.67\pm 0.47\) & \(60.59\pm 0.40\) \\ GNN [53] & \(55.85\pm 0.97\) & \(71.25\pm 0.89\) \\ DN4 [38] & \(61.51\pm 0.44\) & \(89.60\pm 0.44\) \\ MsKPRN [11] & \(76.64\pm 0.84\) & \(89.88\pm 0.46\) \\ DbMRNT(Ours) & \(\mathbf{78.30\pm 0.83}\) & \(\mathbf{90.70\pm 0.44}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Few-shot classification results on the Stanford Cars.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Type** & **5-Way 1-Shot** & **5-Way 5-Shot** \\ \hline MAML [22] & Meta & \(51.67\pm 1.81\) & \(70.30\pm 1.75\) \\ Prototypical Network [7] & Metric & \(53.31\pm 0.89\) & \(72.69\pm 0.74\) \\ Relation Nets [9] & Metric & \(54.48\pm 0.93\) & \(71.32\pm 0.78\) \\ CCRN [52] & Metric & \(55.07\pm 0.20\) & \(71.34\pm 0.30\) \\ HMRN [51] & Metric & \(57.98\pm 0.26\) & \(74.70\pm 0.24\) \\ MSLPN [20] & Metric & \(58.69\pm 0.96\) & \(74.12\pm 0.73\) \\ DCN [14] & Metric & \(60.58\pm 0.72\) & \(72.42\pm 0.69\) \\ ABNet [49] & Metric & \(62.10\pm 0.96\) & \(75.11\pm 0.78\) \\ MRN [54] & Metric & \(62.65\pm 0.84\) & \(74.20\pm 0.64\) \\ DbMRNT(Ours) & Metric & \(\mathbf{62.95\pm 0.92}\) & \(\mathbf{79.00\pm 0.70}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Few-shot classification results on the tieredImageNet.  
accuracy by approximately +0.4% for the 1-shot setting and +0.3% for the 5-shot setting. Our experiments demonstrate the efficacy of our dual-branch architecture combined with a tutoring learning module, which also benefits from the adaptable learning of similarity metrics facilitated by relation networks. Our method harnesses the potential inherent in this type of measurement.
# Incorporating Setup Effects into the Reliability Analysis of Driven Piles
## 4 Reliability Analysis
### The Effect of \(\rho_{\text{R0,Restup}}\) on the Reliability Index  
In this subsection, the effect of \(\rho_{\text{R0,Restup}}\) on the reliability index of driven piles considering setup effects is studied. The \(\lambda\) and COV of loads and resistances for driven piles in Table 2, FOS = 3.0, \(M_{\text{setup}}\) = 1.0, and \(\rho\) = 3.69 were used. The reliability index of driven piles was computed using Equation (9) for \(\rho_{\text{R0,Restup}}\), ranging from \(-1.0\) to 1.0. Figure 4 depicts the reliability indices corresponding to \(\rho_{\text{R0,Restup}}\) of driven piles.  
The results show that in clay and sand, the reliability indices of driven piles decrease with rising \(\rho_{\text{R0,Restup}}\); however, the decrease rate of reliability indices with \(\rho_{\text{R0,Restup}}\) between \(-1.0\) and \(0\) is significantly greater than those with \(\rho_{\text{R0,Restup}}\) between \(0\) and \(1.0\) for clay and sand.
## 2 Background and Basic Principle
### Analysis of Target-Complex Background Coupling
Note: From left to right are the original image, the 3D grid map of the whole image, and the grid map within the local area of the target. features in the image, forming a basis for evaluating potentially similar elements. The standard 2D Gabor function is described as follows:  
\[g_{\sigma,\omega_{0}}(x,y)=\frac{1}{2\pi\sigma_{x}\sigma_{y}}e^{\frac{1}{2}( \frac{2}{\sigma_{x}^{2}}+\frac{y^{2}}{\sigma_{y}^{2}})}e^{i\omega_{0}(x+y)} \tag{19}\]  
where \(\sigma_{x}\) and \(\sigma_{y}\) are the standard deviations of the Gaussian window function in the horizontal and vertical directions, respectively, and \(\omega_{0}\) is the spatial frequency of the plane wave. Considering the size of the weak target, in order to enhance the adaptability to different sizes of targets as small as possible, the filter kernel parameters \(\lambda=2\) and \(\theta=0,45^{\circ},90^{\circ}\), and \(135^{\circ}\) are chosen in this paper. The design of the filter kernel is shown in Figure 3.  
The original image is convolved using a filter kernel to obtain a resultant map of the features of the image in the specified direction:  
\[I_{gabor}^{i}=I\times gabor(\theta_{i}) \tag{20}\]  
\[I_{Gabor}=\sum_{i=1}^{N}\frac{I_{gabor}^{i}}{N} \tag{21}\]  
where \(N=4\) is the filtering result of different angles and \(I_{Gabor}\) is the resultant map of image features fused with filtering features of different directions based on \(I_{Gabor}\) to calculate the evaluation index of the region similar to the target based on the potential similarity point elements. Firstly, the concept of a High Pixel Rate (HPR) is defined here to estimate the potential target confusion that may be caused by the highlighted elements in the whole image by counting the number of pixels in the image that are higher than the target signal strength:  
\[\text{HPR}=\frac{\#(I_{Gabor}>\mu_{T_{gabor}})}{\#I_{Gabor}} \tag{22}\]  
where \(\#(I_{Gabor}>\mu_{T_{Gabor}})\) denotes the number of pixels in the optical data with an intensity greater than the target signal and \(\#I_{Gabor}\) denotes the total number of pixels in the image. Further, R, E, U, ASM, CON, COR, and IDM are computed from \(I_{Gabor}\) considering texture features retained after filtering. \[\text{LSCR}=\frac{\mu_{T}-\mu_{LB}}{\sigma_{LB}} \tag{16}\]0 Four-directional Gabor filter kernel. In summary, for the image data of any scene, eight point-similarity-based parameters for evaluating target confusion elements are extracted from the point of view of confusion-prone elements and combined into the following confusion feature vectors:  
\[\text{LSCR}=\frac{\mu_{T}-\mu_{LB}}{\sigma_{LB}} \tag{16}\]1  
Typical scenes were analyzed using a measure of the confounding factors contained in the background of the image, and the results are shown in \[\text{LSCR}=\frac{\mu_{T}-\mu_{LB}}{\sigma_{LB}} \tag{16}\]2
# Field Measurement Study on Dynamic Characteristics of the Shanghai World Financial Center
###### Abstract  
It is of great practical importance to study the vibration response characteristics of super high-rise buildings under an earthquake action to provide a basis for seismic design and later maintenance of structures in coastal areas. During this study, the Shanghai World Financial Center (SWFC)'s health monitoring system was utilized to monitor earthquakes of magnitude 6.4 in Taiwan, 6.0 in Japan, 7.2 in the East China Sea, and 4.4 in Jiangsu, in real-time. Through the improved Envelope Random Decrement Technique (E-RDT), the dynamic properties of super high-rise buildings were examined under different earthquake effects in terms of the acceleration power spectrum, natural frequency, damping ratio, and mode shape. The results demonstrated that (1) the vibration responses of the structure in X (East-West) and Y (North-South) directions under four earthquakes were consistent, and with increasing floor height, the discreteness of the amplitude and acceleration signals of vibration responses increased. (2) The first two natural frequencies of the structure in X and Y directions decreased with the increase in amplitude, but the damping ratio increased with the increase in amplitude. The minimum values of the first two natural frequencies are 0.1498 Hz and 0.4312 Hz, respectively, and the maximum values of the first two damping ratios are 0.0086 and 0.0068, respectively. (3) Under different earthquake excitations, the SWFC's mode shape's estimates were similar, and their change trends in the X and Y directions were nonlinear as the number of floors increased. The structure was not seriously damaged by the four earthquakes. This study can provide helpful information for the seismic design of super high-rise buildings based on its findings.  
field measurement; seismic response; dynamic characteristics; super high-rise building +
Footnote †: journal: Applied Sciences
# MASA: Multi-Application Scheduling Algorithm for Heterogeneous Resource Platform
## 3 A Multi-Application Scheduling Formal Definition
### Scheduling Scenario Description  
RF signal-processing applications are mostly computationally intensive tasks, and assigning the task to a suitable PE is an essential step, e.g., the FFT can be processed 10 times faster on a hardware accelerator than on a GPP. If directly using the neural network to process both job DAGs and PE information and generate executable tasks and available PE pairs, the complexity of the neural network's decision space is exponentially increased, preventing the neural network from converging to a steady state. Therefore, in this paper, we design a two-phase scheduling algorithm MASA to reduce the complexity of the decision space, i.e., task prioritization and task-mapping phases, as shown in Figure 1. Firstly, in the task prioritization phase, the NN-Scheduler mainly processes the information of job DAG without considering the information of PE. Second, in the task-mapping phase, the Heuristic Scheduler makes decisions based only on the identified high-priority tasks and PE information.  
The NN-Scheduler sorts the tasks in the multi-DAG, and the high priority tasks are moved to the ready queue to wait for execution, while the low priority tasks waiting for pretasks to be completed are left in the outstanding queue. The hardware database includes static profiles (PE type, estimated computational execution time for each task, maximum number of tasks to be processed by the PE) and dynamic profiles (PE utilization, status  
Figure 1: Multi-application scheduling scenario.  
of the PE as busy or idle) of the PE. In each mapping phase, ready tasks are deployed to available PEs according to the Heuristic Scheduler and assigned to executable tasks after waiting for the PEs to become idle. After PEs have processed the executable tasks, the SCHEDULER removes the dependencies of the tasks and updates the status information of the tasks and the PEs. After the above is completed, a new scheduling decision phase is triggered and the NN-Scheduler moves the outstanding tasks to the ready queue if all of their predecessors have been deleted. After each scheduling decision is completed, SCHEDULER updates the environment state to continue in subsequent decision phases until all waiting tasks in the outstanding queue are removed.
# Research on the Coupled Bending-Torsional Flutter Mechanism for Ideal Plate
## 2 Flutter \"Incentive-Feedback\" Mechanism Theory
### Vertical Modal Damping and Stiffness
The aerodynamic derivatives \(H_{i}\) and \(A_{i}\) in the damping of the vertical vibration system are expressed by the dimensionless flutter derivatives \(H_{i}^{*}\) and \(A_{i}^{*}\), and the damping of the vertical vibration system is as follows:  
\[C_{h\kappa,1}=C_{h0} \tag{48}\]  
\[C_{h\kappa,2}=-\rho B^{2}\omega_{sh}H_{1}^{*}=\rho^{2}B^{6}\frac{\omega_{sh}^{3} }{\sqrt{\gamma_{sh}^{2}+\Gamma_{sh}^{2}}}H_{1}^{*}\left(-\frac{1}{\rho B^{4}} \frac{\sqrt{\gamma_{sh}^{2}+\Gamma_{sh}^{2}}}{\omega_{sh}^{2}}\right) \tag{49}\]\[C_{ha,3}=\rho^{2}B^{6}\frac{\omega_{sh}^{3}}{\sqrt{\gamma_{ah}^{2}+\Gamma_{ah}^{2}}}H _{2}^{*}A_{1}^{*}\Big{(}-\sin\theta_{2}+2\xi_{sh}\cos\theta_{2}+\xi_{sh}^{2}\sin \theta_{2}\Big{)} \tag{50}\]  
\[C_{ha,4}=\rho^{2}B^{6}\frac{\omega_{sh}^{3}}{\sqrt{\gamma_{ah}^{2}+\Gamma_{ah}^{ 2}}}H_{3}^{*}A_{1}^{*}(-\cos\theta_{2}-\xi_{sh}\sin\theta_{2}) \tag{51}\]  
\[C_{ha,5}=\rho^{2}B^{6}\frac{\omega_{sh}^{3}}{\sqrt{\gamma_{ah}^{2}+\Gamma_{ah}^ {2}}}H_{2}^{*}A_{4}^{*}(-\cos\theta_{2}-\xi_{sh}\sin\theta_{2}) \tag{52}\]  
\[C_{ha,6}=\rho^{2}B^{6}\frac{\omega_{sh}^{3}}{\sqrt{\gamma_{ah}^{2}+\Gamma_{ah}^ {2}}}H_{3}^{*}A_{4}^{*}\sin\theta_{2} \tag{53}\]  
The aerodynamic damping of the vertical vibration system is uniformly written in the following form:  
\[ \begin{array}{l}C_{ha,i}=\rho^{2}B^{6}\frac{\omega_{sh}^{3}}{\sqrt{\gamma_{ ab}^{2}+\Gamma_{ah}^{2}}}\psi_{ha}\Big{(}H_{j}^{*},A_{k}^{*}\Big{)}\phi_{ha}( \theta_{2},\xi_{sh})\\ (i=2,\cdots,6)\qquad(j=1,2,3)\qquad(k=1,4)\end{array}  \tag{54}\]  
where the function \(\psi_{ha}\Big{(}H_{j}^{*},A_{k}^{*}\Big{)}\) is the flutter derivative correlation term; and \(\phi_{ha}(\theta_{2},\xi_{sh})\) is a function of the phase difference \(\theta_{2}\) and the vertical modal damping ratio \(\xi_{sh}\), where the aerodynamic damping of \(C_{ha,2}\) is \(-\frac{1}{\rho B^{6}}\frac{\sqrt{\gamma_{ah}^{2}+\Gamma_{ah}^{2}}}{\omega_{sh} ^{2}}\). The aerodynamic derivatives \(H_{i}\) and \(A_{i}\) in the stiffness of the vertical vibration system are expressed by the dimensionless flutter derivatives \(H_{i}^{*}\) and \(A_{i}^{*}\), and the stiffness of the torsional vibration system is as follows:  
\[K_{ha,1}=K_{h0} \tag{55}\]  
\[K_{ha,2}=-\rho B^{2}\omega_{sh}^{2}H_{4}^{*} \tag{56}\]  
\[K_{ha,3}=\rho^{2}B^{6}\frac{\omega_{sh}^{4}}{\sqrt{\gamma_{ah}^{2}+\Gamma_{ah} ^{2}}}H_{2}^{*}A_{1}^{*}\Big{(}\cos\theta_{2}+\xi_{sh}^{2}\sin\theta_{2}\Big{)} \Big{(}1+\xi_{sh}^{2}\Big{)} \tag{57}\]  
\[K_{ha,4}=-\rho^{2}B^{6}\frac{\omega_{sh}^{4}}{\sqrt{\gamma_{ah}^{2}+\Gamma_{ah }^{2}}}H_{3}^{*}A_{1}^{*}\Big{(}1+\xi_{sh}^{2}\Big{)}\sin\theta_{2} \tag{58}\]  
\[K_{ha,5}=-\rho^{2}B^{6}\frac{\omega_{sh}^{4}}{\sqrt{\gamma_{ah}^{2}+\Gamma_{ah }^{2}}}H_{2}^{*}A_{4}^{*}\Big{(}1+\xi_{sh}^{2}\Big{)}\sin\theta_{2} \tag{59}\]  
\[K_{ha,6}=-\rho^{2}B^{6}\frac{\omega_{sh}^{4}}{\sqrt{\gamma_{ah}^{2}+\Gamma_{ah }^{2}}}H_{3}^{*}A_{4}^{*}(\cos\theta_{2}-\xi_{sh}\sin\theta_{2}) \tag{60}\]