esa-sceva/satcom-chunk-collection huggingface dataset:
# Preliminary Analysis of a Lightweight and Deployable Soft Robot for Space Applications
## 2 Robot Concept Design and Models
### Inflatable Link Model  
When pressurized, the link can be considered as a beam, since the pre-stress due to the pressure supply stiffens the structure. In [23] the displacement \(v(x)\) along an inflatable beam is calculated as:  
\[v(x)=\frac{F}{(E+P/S)I}\bigg{(}\frac{lx^{2}}{2}+\frac{x^{3}}{6}\bigg{)}+\frac{Fx }{P+jGS} \tag{1}\]  
where \(F\) is the external shear force acting at the distance \(l\) from the joint, \(E\) is the Young modulus, \(G\) is the shear modulus, \(P=p\pi r^{2}\) is the normal force acting on each cap that depends on the internal pressure \(p\) and the radius \(r\), \(S\) is the cross-section area that depends on the wall thickness \(t\), \(I\) is the second moment of area, and \(j\) is a correction shear coefficient, determined from the cross-section shape, usually set in literature \(j=0.5\) for circular thin tubes, e.g., in [24].  
The model can be simplified to be easily utilized for the robot control, therefore a pseudo-rigid body model [25] is used in this work, as illustrated in Figure 2. The cylindrical inflatable link, with length \(L\), is represented as two rigid bodies, having same diameter d and lengths \(l_{1}\) and \(l_{2}\), such that their sum is the link length \(L\). The two bodies are connected by a hinge with a torsional spring, whose behavior is described through the [PERSON]'s law:  
\[k\vartheta=\tau \tag{2}\]  
where \(k\) is the spring stiffness, \(\vartheta\) the angular deflection and \(\tau\) the reaction torque.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Link** & \(\mathbf{a}_{i}\) & \(\mathbf{a}_{i}\) & \(\mathbf{d}_{i}\) & \(\mathbf{\vartheta}_{i}\) \\ \hline
1 & 0 & \(\pi/2\) & 0 & \(\vartheta_{1}\) \\
2 & \(L\) & 0 & 0 & \(\vartheta_{2}\) \\
3 & \(L\) & 0 & 0 & \(\vartheta_{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rigid Robot Denavit-Hartenberg parameters.  
Figure 1: Kinematic scheme reporting Denavit-Hartenberg parameters.  
These models can be considered effective until the pressure is able to maintain the link walls stressed, otherwise the formation of wrinkles leads to the collapse of the structure. The wrinkling moment can be defined as the bending load for which the first wrinkles appear, and an increase in deflection does not produce an increase in moment. Several formulations and approaches are available in literature for the wrinkling moment estimation [26]. For the goals of this work, the following formulation by [PERSON] et al. [27] is considered to define the wrinkling moment \(M_{w}\):  
\[M_{w}=\frac{\pi}{4}\pi pr^{3} \tag{3}\]
# Vertical Force Monitoring of Racing Tires: A Novel Deep Neural Network-Based Estimation Method
## 4 Dataset Construction
### Data Collection and Labeling
#### 4.2.1 Pre-Labeling After Data Collection Phase  
The data were collected under varying conditions, including ten distinct load values ranging from 150 kgf to 420 kgf, with pressures set at two distinct values of 200 and 220 kPa, and speeds set at three distinct values of 30, 50, and 70 km/h. For each load value, acceleration data were recorded continuously for a duration of 60 s. The only real-time data acquisition involved the vertical acceleration acting on the tire. Reference values for speed, load, and pressure were measured in real-time and monitored on the high-speed machine's control and monitoring screen, as shown in Figure 5. The data acquisition for vertical acceleration was initiated only after confirming that the actual values matched the set values and that these real-time values were stable. After the data collection process, pre-labeling was performed, where the acceleration signals were tagged with the corresponding load, speed, and pressure set values. This labeling process was conducted only after it was ensured that the actual values had stabilized and matched the set values throughout the recording period.  
Figure 6 illustrates the subset of the pre-labeled acceleration data corresponding to a 60 s state. The acceleration data in voltage form were sampled from the acceleration sensor inside the tire under the conditions of 150 kg vertical force, 200 kPa internal tire pressure, and 30 km/h rotational speed. The pre-labeling acted as a preliminary tagging process to ensure the integrity of the collected signal.
## 2 Inter-Cell Scheduling Model Based on TSCS-BD
### Mathematical Model
#### 2.2.2 Optimization Objective and Constraints  
In order to explore the use of complex network features for improving algorithm performance, it is necessary to select objectives for scheduling optimization corresponding to these complex network features. In a complex inter-cell scheduling network, a node machine with a large bottleneck degree is important. The makespan of the overall scheduling plan is affected by the scheduling of operations on the node machine. The objective is to minimize the makespan \(C_{\text{time}}\).  
Following the assumptions and notations given above, the mathematical model of this problem is presented below:  
\[min\ C_{\text{time}}=min\Big{(}max\Big{\{}CT_{ij}|\ \forall\ i\in N;j\in O^{i} \Big{\}}\Big{)} \tag{1}\]  
subject to  
\[CT_{ij}=ST_{ij}+T_{ijm},i\in N;j\in O^{i};m\in M_{ij} \tag{2}\]  
\[ST_{ij+1}\geq CT_{ij}+\sum_{m=1}^{M}\sum_{m^{\prime}=1}^{M}\Big{(}TD_{mm^{ \prime}}\times\alpha_{ijm}\times\alpha_{ij+1,m^{\prime}}\times\beta_{mm^{ \prime}}\Big{)},i\in N;j\in O^{i} \tag{3}\]  
\[\Big{(}ST_{ij}\geq ST_{ij^{\prime}}+\alpha_{ij^{\prime}m}T_{ij^{\prime}m} \Big{)}\cup\Big{(}ST_{ij^{\prime}}\geq ST_{ij}+\alpha_{ijm}T_{ijm}\Big{)}, \tag{4}\]  
\[(i\
eq i^{\prime})\cup(j\
eq j^{\prime});\ \alpha_{ij^{\prime}m}\times\alpha_{ ijm}=1\]  
\[\sum_{m}^{M}\alpha_{ijm}=1,i\in N;j\in O^{i} \tag{5}\]  
In this formulation, optimization objective aims to minimize the maximum completion time, as shown in Equation (1). Equation (2) provides the definitions of processing time and completion time for operations. Equation (3) states that an operation cannot be started until the previous operation has been completed and the part has been transported to and loaded on the corresponding machine. Equation (4) states that a machine processes only one operation at a time. Equation (5) ensures that each operation is assigned to only one alternative machine.
## 4 Evaluation and Results
### Results
#### 4.2.2 Backhaul Traffic  
For the load within the core network of the MNO, we calculated the bandwidth usage at the backhaul link. Figure 4 shows massive bandwidth savings when the VNF-AS multicast transmission is exploited. The bandwidth usage at the backhaul link in the case of unicast and VNF-AS multicast modes are 2982.5 megabits/second and 460 megabits/second, respectively. Moreover, it is notable in Figure 4 that the bandwidth usage is highly dependable at the number of simultaneously active users in unicast mode. A greater number of users means higher bandwidth usage. However, in the case of multicast transmission, the number of users on a multicast channel does not affect or increase the bandwidth usage.
# Dynamic Tuning and Multi-Task Learning-Based Model for Multimodal Sentiment Analysis
## 4 Experiments
### Comparative Experiments
“T”, “I”, and “A” denote text, image, and audio modalities, respectively. “S” and “D” denote similarity features and dissimilarity features, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**MOSI**} & \multicolumn{4}{c}{**MOSEI**} \\ \cline{2-10}  & **Acc-2** & **F1** & **Acc-7** & **MAE** & **Corr** & **Acc-2** & **F1** & **Acc-7** & **MAE** & **Corr** \\ \hline LF-DNN [18] & 77.52/78.63 & 77.46/78.63 & 34.52 & 0.955 & 0.658 & 80.60/82.74 & 80.85/82.52 & 50.83 & 0.58 & 0.709 \\ MFN(A) [23] & 77.4/ - & 77.3/ - & 34.1 & 0.965 & 0.632 & 78.94/82.86 & 79.55/82.85 & 51.53 & 0.573 & 0.718 \\ LMF [40] & -/82.5 & -/82.4 & 33.2 & 0.917 & 0.695 & 80.54/83.48 & 80.94/83.36 & 51.59 & 0.576 & 0.717 \\ TFN [26] & -/80.8 & -/80.7 & 34.9 & 0.901 & 0.698 & 78.50/81.89 & 78.96/81.74 & 51.60 & 0.573 & 0.714 \\ MuIT(A) [37] & -/83.0 & -/82.8 & 40.0 & 0.871 & 0.698 & 81.15/84.63 & 81.56/84.52 & 52.84 & 0.559 & 0.733 \\ MISA(A) [38] & 81.8/83.4 & 81.7/83.6 & 42.3 & 0.783 & 0.776 & 83.6/85.5 & 83.8/85.3 & 52.2 & 0.555 & 0.756 \\ MAG-BERT [14] & 82.13/83.54 & 81.12/83.58 & 41.43 & 0.790 & 0.766 & 79.86/86.86 & 80.47/83.88 & 50.41 & 0.583 & 0.741 \\ ConFEDE [5] & 83.85/85.55 & 83.83/85.76 & 43.82 & 0.725 & 0.789 & 80.7/84.38 & 81.2/84.32 & 51.96 & 0.555 & 0.753 \\ DMMSA & 83.97/85.70 & 83.92/85.70 & 45.39 & 0.710 & 0.793 & 82.63/86.27 & 83.04/86.21 & 53.91 & 0.527 & 0.777 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of comparative experiment on the MOSI and MOSEI datasets. In Acc-2 and F1, the left side of “/” represents “negative/non-negative”, and the right side represents “negative/positive”.
# Train-Induced Unsteady Airflow in a Metro Tunnel with a Ventilation Shaft
###### Abstract
They recommend that the natural wind speed and construction cost be determined by the shaft diameter, and the shaft should be installed in the middle of the ventilation section. For an ultra-high-speed elevator hoistway with a tunnel-like structure, [PERSON] et al. [30] established a theoretical model to analyze the influence of the hoistway structure and ventilation hole on the car-induced unstable airflow. Previous scholars have conducted extensive investigations into train-induced piston wind and how the ventilation shaft affects the ventilation performance inside tunnels. However, attention has seldom been paid to the influence of the shaft dimensions on the piston wind caused by the train movement. [PERSON] et al. [28] studied the influence of ventilation shaft location on train-induced unstable airflow in subway tunnels, but they did not systematically analyze the influence of other shaft parameters. Knowledge gaps in this field and the need for practical solutions are calling for further investigations into train-induced piston wind in metro tunnels with ventilation shafts. In summary, the investigation of train-induced unsteady airflow in metro tunnels equipped with ventilation shafts is both significant and valuable. This study is organized as follows: The introductory section reviews the existing literature on unsteady airflow induced by trains and delineates the research objectives of this paper. The following section offers a comprehensive exposition of the theoretical model formulated in this study, along with its validation. Utilizing the established theoretical model, the third section investigates the formation and influencing factors of piston wind in tunnels, considering scenarios both with and without ventilation shafts. It systematically analyzes the impact of various shaft parameters on the maximum piston wind speed and ventilation efficiency. The concluding section summarizes the insights garnered from this research.
## 2 Refined Numerical Modelling Approach
### Performance Degradation Simulation Approach
#### 2.5.3 Joint Damage Behavior  
To study the joint damage behavior between segments, the shear-friction theory discussed in detail by [PERSON] and [PERSON] [28] was adopted within this study to equivalently simulate the discontinuous mechanical behavior between joints. The shear-friction theory assumes that a crack has occurred along the shear plane, and when a slip occurs along the crack the pieces of concrete on either side of the crack will be made to separate slightly due to the existence of a rough crack face. This separation will create an extra compressive stress zone near the concrete zone, which would provide more resistance to slip behavior by virtue of friction. Within this case, the below equation holds:  
\[v_{u}=\sigma_{x}\tan\phi=pf_{y}\tan\phi \tag{6}\]  
where \(v_{u}\) indicates the shear strength; \(p\) is the reinforcement parameter; \(f_{y}\) means the yield stress of reinforcement; and \(\tan\phi\) denotes the coefficient of friction.  
Shear-friction theory can be achieved in ABAQUS with penalty friction formulation, which can be defined in the tangential behavior of contact property module. The value of the friction coefficient takes 1.40 for a rough, bonded interface between cast-in-place concrete where intermediate grade reinforcement is used [29]. The limit shear stress of concrete was set as 4.2 MPa. \"Hard\" behavior was adopted to simulate the normal behavior between adjacent segments.
## 3 Block-Jacobi with Block-ISAI on Multi-GPUs
### Multi-GPU Implementation  
We implement BJPB-ISAI using open source PETSc (Portable, Extensible Toolkit for Scientific Computation) framework [29] since it offers various, efficient basic vector and matrix operations that reduce the costs of development in scientific applications. Some implementation issues are proposed and discussed in this section. Most of scientific tools express and store block matrices in scalar formats (usually in CSR formats) by ignoring zero values in blocks. To make our algorithm compatible with user provided matrices in CSR format, we develop a preprocessing procedure using PETSc APIs and cuSPARSE to transform the user provided CSR matrices into PETSc's BCSR matrices. The pseudo code of the procedure is shown in List 1. The procedure firstly exploits the subroutines in cuSPARSE [8] to transform a matrix in CSR format into the BCSR format with block size \(bs\). The subroutine (\(\mathit{cusparseXcsrNnz}\)) estimating the number of non-zero blocks based on a given block size \(bs\) is called at first, which is followed by the function \(\mathit{cusparseDcsr2 bs}\) to make the real transformation. Once the format transformation is completed, the root process partitions the block matrices by block rows (Figure 1) and sends the partitioned data to each process, while the other processes receive the partitioned data as the data source for the local BCSR matrices. After the communication between the processes is done, each process calls \(\mathit{MatCreateMP1 BAI}|\mathit{WithArrays}\) from PETSc to construct a parallel block matrix object with its local part. Each process creates a solution vector \(x\) and the right hand side \(b\) using \(\mathit{VecCreate}\) function and only stores the partitioned part locally. The essence of the Block-Jacobi preconditioner is to use the main diagonal part of the local matrix as the operating matrix for the block ILU factorization locally. Similar to scalar ILU factorizations, PETSc provides full block ILU factorization which can be extracted to form the lower and upper block triangular factors on GPU in Algorithm 1. List 2 shows how the triangular factors be accessed using the low-level data structure in PETSc. The pointer \(fact\) to the main data section of the factorization is finally obtained through successive operations from the \(subpc\) object. Note that the lower triangular factor is stored in the front part of the arrays in a forward orientation (from top to bottom) without the diagonal blocks (identity matrices), whereas the upper triangular factor is located in the back part of the arrays in a backward orientation (from bottom to top) with the inversed diagonal blocks. The storing strategy is convenient for the subsequent forward and backward substitutions in the preconditioning step if triangular solves are applied to (8). However, instead of using triangular solves in each sub-domain, we aim to employ Block-ISAI where the lower and upper factors are expressed in a general BCSR or BCSC format. Therefore, we rearrange the factors to BCSR format before Algorithm 1 is performed.
# Characterization of Waves in the Roman Wall Paintings of the Herculaneum Site (Italy)
## 3 Results and Discussion
### Analyses of the Reference Wax Samples
#### 3.1.1 FTIR and Raman Spectroscopies
Usually the CH\({}_{3}\) and CH\({}_{2}\) stretching vibrations are at higher wavenumber with respect to that found in waxes, but these vibrations decrease in frequency with increasing chain length [51]. The medium-strong band at 1461 cm\({}^{-1}\) is attributed to the bending vibrations of CH\({}_{2}\) groups of the aliphatic chain, and the strong bands at 1435 and 1417 cm\({}^{-1}\) are due to the asymmetric and symmetric bending vibrations of CH\({}_{3}\), respectively. The carbonyl bands, due to ester or free acid components, in the animal and vegetable waxes, are in the spectral region 1800-1600 cm\({}^{-1}\): a weak band at about 1730 cm\({}^{-1}\) is observable in the spectra of candelilla and spermaceti waxes. The presence of unsaturated chains in the fatty acid is highlighted by an asterisk in the beeswax Raman spectrum at 1655 cm\({}^{-1}\)[50]. Carnauba wax is a complex mixture of the esters of fatty acids with linear long-chain alcohols and unsaturated hydrocarbons. In the region of CH\({}_{x}\) stretching vibrations, we notice the vinyl vibrations at 3068 cm\({}^{-1}\). The C=C stretching mode occurs at 1631 cm\({}^{-1}\) (in cis configuration) and 1610 cm\({}^{-1}\) (isolated). The presence of the free fatty acid is confirmed by the v (C=O) band at 1714 cm\({}^{-1}\) (marked by a triangle) [47]. Figure 2: (a) Raman spectra of natural waxes: (A) beeswax; (B) spermaceti; (C) lanolin; (D) carnauba; (E) candelilla. (b) Raman spectra of artificial and mineral waxes: (F) formulated candelilla, (G) microcrystalline, (H) paraffin. The Raman spectra of mineral waves (paraffin and microcrystalline) show only the characteristic bands of the hydrocarbons. In formulated candellilla, one observes in addition the typical features of esters and of fatty acids.
# Internal Flow Prediction in Arbitrary Shaped Channel Using Stream-Wise Bidirectional LSTM
## Appendix B Comparison of Learning According to Lateral Connection Direction  
The results of an additional experiment are included to examine the impact of directionality in the latent space on learning. In this experiment, we replaced the LSTM in the SB-LSTM module with a unidirectional LSTM to investigate the difference in learning. The only difference between the two experiments was the sequential process direction in the latent space, using either forward LSTM or backward LSTM for the experimental condition. The number of parameters in both experiments was the same. The training was conducted using the same hyperparameters as in the previous experiment.  
When LSTM was added, there was a common effect that sudden discontinuous intervals of values, as shown in Figures 10 and 14, disappeared in the case of velocity prediction. However, in the case of pressure, the effect showed a significant difference depending on the direction, as shown in Figure A6. Upon analyzing the validation curve of the pressure in the training history, it was observed that even though the same number of learnable parameters was used, the forward LSTM overfitted after 90 epochs, whereas the learning of the backward LSTM was similar to that of the bidirectional LSTM. This indicates that simply adding LSTM does not guarantee improved performance. When incorporating a lateral connection, it is important to establish a pathway to receive the necessary information during supervised learning. An example result is as follows.
## 4 Experiments and Discussion
### Qualitative Evaluation
Illumination Changes Figure 7 further presents some crack region detecting results with illumination challenges. For the Canny-based model, the gaussian filtering technique can alleviate the troubles of random surrounding disturbances. However, with the global image smoothing operation, some local little cracks may be left undetected (see Figure 7(5-6)). Besides, the Canny-based model may not deal with large-area surrounding troubles (e.g., the background noises of Figure 7(4)), which cannot be deleted using the naive edge based algorithms. Due to non-uniform illuminations, it is possible that there are multiple histogram peak values for one single concrete image. In this case, the Otsu-based crack detection model was unable to determine the optimal segmentation threshold, and performed badly. Additionally, the falsely segmented local image regions were connected with the true crack damage areas, and could not be well addressed by naive post-processing methods. For addressing the concrete images from DS2, by contrast, the SVM-based crack detector can obtain preferable detection results, and could almost identify all of the crack areas through local image region binary classification. However, there were still some background false alarms (i.e., the red  
Figure 7: Representative crack damage detecting results under different background disturbances (DS1, Dataset 1), illumination changes (DS2, Dataset 2), image blurring (DS3, Dataset 3), and non-cracks (DS4, Dataset 4). DL, Deep Learning; MECD, Multilayer ELM-based crack detector. ellipse dashed boxes in Figure 7), which may have been due to its adopted simple statistical image region features. Unlike SVM based methods, the DL-based crack detection method utilized multilayer convolutional neural networks for computing the high level image region features, which could deal with the surrounding noises well. However, owing to the curse of the local minima issue during the greedy layer-wise training, it is impossible that the DL-based crack detector could recognize the total crack regions (see Figure 7(4-6)).