esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Seismic Analysis of Liquid Storage Tanks
### Hydrodynamic Analysis  
The seismic behavior of liquid storage tanks is governed by a complex interplay between the structural response of the tank shell and the dynamic response of the contained fluid. During an earthquake, seismic energy transfers from the ground to the fluid through the motion of the tank, resulting in two primary components of hydrodynamic response: the impulsive motion and the convective motion [36].  
The impulsive component represents the portion of the liquid that moves rigidly with the tank wall, behaving as though it is fully attached to the structure. This motion is characterized by shorter periods and is driven by the rapid inertial forces transmitted from the ground motion. The convective component, on the other hand, corresponds to the sloshing motion of the liquid's free surface, which occurs at longer periods due to its oscillatory nature [37]. These distinct responses can be decomposed for analytical convenience, allowing separate evaluation of their effects on the tank wall and base.
## 5 Experiments
### Implementation Details  
The proposed model in this paper is implemented using PyTorch 2.0 and trained on one NVIDIA A100 60G GPU (NVIDIA, Santa Clara, CA, USA). During training, the model undergoes 100 epochs, processing 128,000 batches per epoch. Subsequently, in the testing phase, the performance is assessed across 10,000 test instances, employing rollout, exponential, and critic strategies to derive solutions, culminating in the cost evaluation of the final batch across all instances. From Table 5, we can see there are different treatments for different sizes of data. To accommodate varying data sizes, different batch sizes and learning rates are employed: \(C=50\) for 100-node capacity vehicles with batch size \(B=512\) and learning rate \(\eta=1\times 10^{-4}\); \(C=60\) for 200 customer nodes with batch size and learning rate \(\eta=1\times 10^{-4}\); and \(C=80\) for 400-node capacity vehicles with batch size \(B=32\) and a decaying learning rate of \(\eta=1\times 10^{-3}\times 0.96^{\text{epoch}}\). Node features are embedded in 128-dimensional inputs, with the Adam optimizer employed for model training.  
The unmanned delivery vehicle is equipped with the following hardware for real-time perception and computation:  
* LiDAR: 50 m detection range for obstacle avoidance and environment mapping.
* RTK-GPS: Localization accuracy of \(\pm 2\) cm for precise navigation.
* IMU: Inertial measurement unit for real-time attitude estimation.
* Onboard Computing Unit: NVIDIA Jetson AGX Xavier for edge-based inference, achieving a latency of \(<\)50 ms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Problem Set** & **Problem Size** & **Epoch Size** & **Batch Size** &
\begin{tabular}{c} **Vehicle** \\ **Capacity** \\ \end{tabular} \\ \hline UDVRP100 & 100 & 128,000 & 512 & 50 \\ UDVRP200 & 200 & 128,000 & 512 & 60 \\ UDVRP400 & 400 & 12,800 & 32 & 80 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hyperparameters of the model.
# Hardware-Aware Mobile Building Block Evaluation for Computer Vision
## 4 Hardware-Aware Mobile Building Blocks Evaluation
### Grouped Convolutions  
In grouped convolutions, the input channels are divided into multiple groups over which a normal convolution is performed. This puts their cost in FLOPs between those of depthwise separable and standard convolutions. This is confirmed in Figure 6 (top left). Depthwise convolutions can be considered as a special case of grouped convolutions, where the number of groups equals the number of input and output channels. Similarly, standard convolutions are a special case where there is only one group.  
In theory, grouped convolutions should be able to improve hardware utilization through their improved data reuse, while still using significantly fewer FLOPs than standard convolutions. However, current implementations in most deep learning frameworks fail to leverage these advantages, which in practice makes grouped convolutions slower than their standard convolution counterpart [31]. Figure 6 shows that grouped convolutions are never the most promising solution on any of the tested hardware platforms. This means that, for most target latency ranges and platforms, using the much larger search space offered by grouped convolutions will rarely be beneficial. Instead, it is more efficient to narrow down the model design space to either depthwise separable convolutions or standard convolutions, based on their relative performance in trade-off plots like that shown in Figure 6.  
Figure 5: Depthwise separable convolutions are efficient when evaluated by FLOPS (upper left), but this does not translate to faster inference on all embedded hardware platforms.
# Concealed Conduit Routing in Building Slabs
###### Abstract  
Concealed pipes are vital facilities for transporting water, air, electricity, and natural gas in modern buildings. These pipes are constructed inside slabs of buildings, and thus conventional piping algorithms, dedicated to arranging exposed pipes in open spaces or on object surfaces, are not suitable for laying out their paths. In this article, an innovative method is presented for designing the concealed conduits of modern buildings. In the proposed piping procedure, the target building is regarded as a framework composed of beams, columns, and slabs. These substructures are encoded in a weighted graph, which serves as the top-level representation of the workspace. Then, these substructures are split into voxels and constitute the bottom-level representation of the workspace. Each concealed pipe is constructed by using a two-stage piping scheme to comply with the representation of the workspace. In the first stage, the slabs containing the terminals of the pipe are located in the top-level representation, and the shortest path connecting these slabs is calculated using [PERSON]'s algorithm. In the second stage, a feasible space is generated by collecting selective voxels in these slabs first. Then, the pipe path is routed inside the feasible space by a shortest-path-finding computation. Next, the pipe surface is generated and represented by using triangle meshes. Finally, the bottom-level representation is modified and the routing process is repeated to lay out the remaining concealed pipes. The experimental results show that the proposed piping procedure efficiently arranges concealed pipes inside buildings of various topologies and internal layouts. As it benefits from the two-level representation and the two-stage routing method, the piping process consumes reasonable computational costs. The paths of the resultant pipes are optimized, and their positions meet the geometrical constraints and safety regulations.  
concealed conduit design; pipe routing; building construction; computer-aided design Article
# Wideband Circularly Polarized Filtering Hybrid Antenna
## 5 Conclusions  
A single-fed wideband CP filtering liquid DRA has been designed based on an antenna fusion approach. The C-stub microstrip and U-slot is applied to obtain the radiation null at the lower and upper band, respectively. The inverted L-shaped strips are combined with the liquid DRA, and the extra AR minimum is introduced in interest band, thus expanding the working bandwidth. The antenna offers entirely overlapped AR and impedance bandwidths of 38.2% and the average gain within working band is 7.8 dBi. As a result, both the stable radiating and the filtering performance have been obtained without applying any specific extra circuit. The proposed wideband circularly polarized filtering hybrid antenna is promising for wireless communication systems.  
Conceptualization, Y.Q. and S.X.; data curation, Y.Q.; methodology, S.X.; software, S.X.; validation, Y.Q.; investigation, S.X.; resources, Y.Q.; writing--original draft preparation, Y.Q.; writing--review and editing, Y.Q.; visualization, S.X.; supervision, Y.Q.; project administration, Y.Q.; funding acquisition, S.X. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (61901143), the Natural Science Foundation of Zhejiang Province (LQ19F010010).  
Not applicable.  
The authors declare that they have no conflict of interest in the publication of this manuscript.
## 2 Geometrical Details and Mathematical Models
### Solver Settings  
In this paper, the simulations are made in Fluent. The second-order upwind scheme is used to discretize the convective term, and the second-order central-difference scheme is used to discretize the diffusive term. The SIMPLEC algorithm is used to handle the velocity-pressure coupling. On account of the difference with the semi-implicit method for pressure-linked equation (SIMPLE) algorithm that the velocity correction equations are settled, the omission terms in SIMPLEC algorithm are less significant than those in SIMPLE. The pseudo-transient approach is used for the under-relaxation of governing equations. The pseudo-transient approach is more suitable for high swirling flows where governing equations may cause numerical stability problems. The globally scaled residual (\(R^{\phi}\)) is taken as:  
\[R^{\phi}=\frac{\sum\limits_{cellsP}\left|\sum\limits_{nb}a_{nb}\phi_{nb}+b-a_{ p}\phi_{p}\right|}{\sum\limits_{cellsP}\left|a_{p}\phi_{p}\right|} \tag{9}\]  
where the subscript _cellsP_ is over all the computational cells \(P\). The convergence criteria for mass and momentum residuals must be less than \(10^{-6}\).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & \(C_{\mu}\) & \(C_{1}\) & \(C_{2}\) & \(\sigma_{k}\) & \(\sigma_{\varepsilon}\) & \(D\) & \(E\) \\ \hline AB & 0.09 & 1.45 & 1.83 & 1 & 1.4 & 0 & 0 \\ LB & 0.09 & 1.44 & 1.92 & 1 & 1.3 & 0 & 0 \\ LS & 0.09 & 1.44 & 1.92 & 1 & 1.3 & \(2\
u\left(\partial\sqrt{k}/\partial y\right)^{2}\) & \(2\
u_{t}\left(\partial^{2}\overline{U}/\partial y^{2}\right)^{2}\) \\ YS & 0.09 & 1.44 & 1.92 & 1 & 1.3 & 0 & \(\
u v_{t}\left(\partial^{2}\overline{U}/\partial y^{2}\right)^{2}\) \\ AKN & 0.09 & 1.44 & 1.9 & 1.4 & 1.4 & 0 & 0 \\ CHC & 0.09 & 1.44 & 1.92 & 1 & 1.3 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model constants and extra terms in governing equations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(f_{\mu}\) & \(f_{1}\) & \(f_{2}\) \\ \hline AB & \(\tanh\left(0.008 Re_{y}\right)\times\left(1+4/R_{t}^{0.75}\right)\) & 1 & \(\left[1-\exp\left(-R_{y}^{2}\right)/12\right]/9\) \\ LB & \(\left[1-\exp\left(-0.0165 Re_{y}\right)\right]^{2}\times\left(1+20.5/Re_{t}\right)\) & \(1+\left(0.05/f_{p}\right)^{3}\) & \(1-\exp\left(-R_{t}^{2}\right)\) \\ LS & \(\exp\left[-3.4/\left(1+Re_{t}/50\right)^{2}\right]\) & 1 & \(1-0.3\exp\left(-R_{t}^{2}\right)\) \\ YS & \(\left[1-\exp\left(-1.5\times 10^{-4}Re_{y}-5\times 10^{-7}Re_{y}^{3}-10^{-10} Re_{y}^{5}\right)\right]^{0.5}\) & 1 & 1 \\ AKN & \(\left\{1+\left(5/R_{t}^{0.75}\right)\exp\left[-\left(Re_{t}/200\right)^{2} \right]\right\}\times\) & 1 & \(\left\{1-0.3\exp\left[-\left(Re_{t}/6.5\right)^{2}\right]\right\}\times\) \\  & \(\left[1-\exp(-y^{*}/14)\right]^{2}\) & 1 & \(\left[1-\exp(-y^{*}/3.1)\right]^{2}\) \\ CHC & \(\left[1-\exp\left(-0.0215 Re_{y}\right)\right]^{2}\left(1+31.66/Re_{y}^{1.25} \right)\) & 1 & \(\left[1-0.01\exp\left(-R_{t}^{2}\right)\right]\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Damping function in governing equations.
# A Systematic Approach to Studying Quark Energy Loss in Nuclei Using Positive Pions +
## 2 Methodology
### Quark Energy Loss Measurements  
This analysis focuses on the energy spectrum of pions from both solid and liquid targets to extract their quark energy loss \(E\) in nuclear media. Deuterium is used as a control, representing scatterings in which quarks propagate in a \"vacuum\". A comparison of the  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Target** & \(E\) **(MeV)** \\ \hline \({}^{2}D\) & 0 \\ \hline \({}^{12}C\) & 2.9 \\ \hline \({}^{56}Fe\) & 9.4 \\ \hline \({}^{208}Pb\) & 20.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Estimation for Coulomb correction in different nuclear media.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Variables** & **Lower Value** & **Upper Value** & **Steps** & **Total Steps** \\ \hline \(Q^{2}\) & 1.0 & 4.0 & 0.5 & 6 \\ \hline \(X_{B}\) & 0.12 & 0.57 & 0.9 & 5 \\ \hline \(\Phi_{PQ}\) & \(-\)180 & 180 & 30 & 12 \\ \hline \(P_{T}^{2}\) & 0.0 & 1.0 & 0.2 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Binning scheme considering the independent variables \(Q^{2},x_{B}\) for the outgoing electron and \(P_{t}^{2},\Phi_{PQ}\) for the positive pion.  
energy spectrum for deuterium with that of solid targets (C, Fe, and Pb) reveals a horizontal shift due to the energy loss in nuclear matter, recognized as the quark energy loss.  
To quantify this shift, incremental energy offsets \(dE\) are applied to the solid target spectra, creating \"shifted distributions\". A statistical test compares each shifted distribution with the deuterium spectrum, iteratively identifying the shift \(dE\) that maximizes the probability of the test, which implies that there is no remaining energy loss after the shift.
## 2 Related Work
### HAR Using Wearable Sensors  
HAR using wearable sensors has been a significant research area for over a decade due to its accessibility, affordability, and portability [9]. Wearable sensor signals are typically favored over video camera signals for different explanations [10]: (1) Wearable sensors circumvent the environmental and static constraints of cameras, which are immobile; (2) Considerable sensors affixed to the body enhance signal accuracy and efficiency; (3) Wearable sensors collect signals for designated objectives, in contrast to cameras that may inadvertently record non-target individuals; (4) Wearable sensors provide enhanced privacy, as video cameras perpetually capture full-body footage during daily activities; (5) Subjects must remain within the fixed field of view of a camera system, limiting mobility and practicality; and (6) Video processing is intricate and expensive.  
Conventional machine learning techniques, such as support vector machine and random forest, have been extensively utilized in HAR tasks [11]. These approaches typically depend on hand-crafted features extracted from time and frequency domains. Although such methods adequately recognize simple physical activities, they often exhibit limitations in capturing complex or subtle movement patterns. Deep learning approaches have significantly advanced HAR. CNNs excel at extracting spatial features from sensor data [12]. Recurrent neural networks (RNNs), especially LSTM networks, effectively capture temporal dependencies in activity sequences [13]. Integrating models that incorporate CNNs and LSTMs utilizes both spatial and temporal data [14].  
Hybrid models integrating one-dimensional CNNs and RNNs have been thoroughly investigated. Our prior research [15] demonstrated that hybrid CNN with LSTM models surpass independent CNN or LSTM models in performance. [PERSON] et al. [16] employed a hybrid model combining CNN and bidirectional LSTM (BiLSTM) networks. Their multi-branch CNN with BiLSTM network can autonomously extract features from raw sensor data without extensive pre-processing. [PERSON] et al. [17] introduced a multi-headed CNN-RNN architecture, assigning a specific CNN head to each sensor. The feature maps created by the CNN are merged and transmitted to the RNN module to detect temporal patterns. [PERSON] et al. [18] introduced a neural network model that employs CNNs and GRUs for automated feature extraction and movement identification.
# Evaluation of Orbital Drift Effect on Proba-V Surface Reflectances Time Series
## 3 Data and Methods
### Method for Real Observations Analysis
#### 3.3.3 Drift Estimation Procedure  
The drifts are estimated with a linear fit of the considered surface reflectance temporal series for each land site and spectral band. In order to minimize the impact of residual atmospheric contamination, outliers were detected and removed from the temporal series, based on the 3-sigma rule; particularly, only values within 3 times the standard deviation of the ensemble of reflectances were retained in the fit. More specifically, each considered temporal series \(y_{i}\), with \(i=1,\,\dots,\,n\) spanning the \(n\) number of retained reflectances, was fitted with a linear curve: \(Y=a+bX\), with the vector of time \(X\), having dimension \(n\) and expressed in days since launch. The estimated drift corresponds to the slope \(b\) of the fitted linear curve, expressed in unit of reflectance (or no unit for NDVI drift) divided by time in days. For the sake of simplicity in notation, the drifts are expressed within the paper in terms of unit of reflectance (or no unit for NDVI) divided by year.
## 5 Results
### Verification of the CPCI Prediction Model
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **Age** \\ **(Year)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Actual** \\ **CPCI** \\ \end{tabular} } & \multicolumn{2}{c}{**3 Data**} & \multicolumn{2}{c}{**4 Data**} & \multicolumn{2}{c}{**5 Data**} & \multicolumn{2}{c}{**6 Data**} \\ \cline{3-10}  & & **Prediction** & **Accuracy** & **PF * & **Prediction** & **Accuracy** & **PF * & **Prediction** \\  & & **CPCI** & **(\%)** & **CPCI** & **(\%)** & **CPCI** & **(\%)** & **Accuracy** & **PF * & **Accuracy** \\ \hline
19 & 4.00 & 3.98 & 99.45 & 4.01 & 99.73 & 4.00 & 99.98 & 3.98 & 99.61 \\ \hline
21 & 3.83 & 3.83 & 99.98 & 3.83 & 99.94 & 3.82 & 99.62 & 3.81 & 99.43 \\ \hline
23 & 3.72 & 3.72 & 99.88 & 3.72 & 99.92 & 3.72 & 99.95 & 3.72 & 99.95 \\ \hline
25 & 3.53 & 3.63 & 97.12 & 3.51 & 99.50 & 3.52 & 99.80 & 3.55 & 99.42 \\ \hline
27 & 3.41 & 3.55 & 95.97 & 3.42 & 99.63 & 3.40 & 99.65 & 3.39 & 99.43 \\ \hline
29 & 3.22 & 3.46 & 92.43 & 3.33 & 96.49 & 3.32 & 96.91 & 3.22 & 99.86 \\ \hline
30 & 3.07 & 3.42 & 88.54 & 3.29 & 92.90 & 3.28 & 93.16 & 3.09 & 99.33 \\ \hline \hline \end{tabular}  
* Note: PF is particle filtering.  
\end{table} 
Table 4: Estimation of prediction accuracy according to the change in the number of time-series points in the same section. Figure 5: Particle filtering prediction of CPCI and 95% confidential interval by 15,000 particles. (**a**) 208 sections (2.08 km). (**b**) 261 sections (2.61 km).
## 3 Results and Discussion
#### 3.1.4 Recommended Thicknesses Ranges for Wall Assemblies and Ceilings/Roofs for Seville and Oslo  
The idea behind this analysis is to find the minimum thickness that is worth applying as a retrofit in the near future (green solid line--Figure 7a--or red solid line--Figure 7b) and in the far future (green dash-dotted line--Figure 7a--or red dash-dotted line--Figure 7b). This objective is achieved by comparing the results against the value for the case that does not have any retrofit measure, i.e., dotted red or green lines.  
As can be seen in Figure 7 for the MRF analysis, this results in a thickness range that differs according to the time interval, as well as the climate change scenario. This is understandable since the indoor conditions will also differ accordingly [34]. For example, in Figure 7b, the recommended thicknesses for the near-future range are between 2.4 and 20 cm, while the recommended thicknesses for the far-future ranges are between 9.9 and 20 cm for RCP 4.5.  
Hence, the recommended thickness ranges that appear in Table 2 are the intersection between ranges for both time frames, i.e., 2.4-20 cm for NF and 9.9-20 cm for FF, which leads to the 10-20 cm range for the mineral wool retrofit (W2) for  
Figure 7: Recommended thickness ranges for mineral wool retrofit if the world evolves as described in RCP 4.5 (a), in the near future (NF) and far future (FF) for MRF, or as described in RCP 8.5 (b), in the near future (NF) and far future (FF) for MRF. \(\text{Im}^{\star}\) is an additional “virtual” thickness that corresponds to a 30 cm layer.  
RCP 8.5 scenario. It is visible that RCP 8.5 is more demanding since shorter thickness ranges are recommended and that Oslo's climate is less demanding when compared to Seville's climate. The thicknesses are rounded up to unit values since these types of insulation layers are normally commercialized in centimeters, and, in this way, they are within the recommended range.  
Table 2 allows us to determine from which thickness onward is worth applying the retrofit measures so that the case study can withstand future conditions in accordance with the parameter that is being analyzed. However, it is also possible to obtain a more compressive thickness range if more than one parameter is taken into account (e.g., if the MRF and the eLM are both considered for the perlite board retrofit (W2) for Seville in RCP 4.5, this means a recommended range of 11-20 cm), but this will also probably lead to a more stringent thickness range [34].
## 2 Mathematical Modeling and Methods
### Transmission Shaft Model  
The HMCVT transmission system consists of a transmission shaft and a planetary row, which can be simplified as shown in Figure 3. According to the transmission principle of HMCVT, the dynamic equation is expressed as Equation (9) [7].  
\[\left\{\begin{array}{l}J_{1}\dot{\omega}_{1}+D_{1}\omega_{1}=T_{1}-\frac{T_{ \text{C1}}}{i_{11}}-\frac{T_{\text{C0}}}{i_{11}}-\frac{T_{\text{C}}}{i_{12}}\\ J_{2}\dot{\omega}_{2}+D_{2}\omega_{2}=T_{5}-T_{h}/(i_{4}i_{5})\\ J_{3}\dot{\omega}_{3}+D_{3}\omega_{3}=\sum\limits_{n=3}^{6}T_{\text{C}n}+T_{h}- T_{OUT}/i_{10}\\ J_{5}\dot{\omega}_{s}+D_{s}\omega_{s}=T_{M}+T_{s}-T_{h}/(i_{4}i_{5})\\ J_{c}\dot{\omega}_{c}+D_{c}\omega_{c}=T_{\text{C0}}+T_{\text{C1}}-\frac{T_{ \text{C3}}}{i_{6}}-\frac{T_{\text{C5}}}{i_{6}}\\ J_{r}\dot{\omega}_{r}+D_{r}\omega_{r}=T_{\text{C0}}+T_{\text{C1}}-\frac{T_{ \text{C4}}}{i_{6}}-\frac{T_{\text{C6}}}{i_{6}}\\ \end{array}\right. \tag{9}\]  
where \(J\) represents the moment of inertia; 1, 2, 3 represent the equivalent axis; \(D\) represents the damping coefficient of the equivalent axis; \(T\) represents the torque; \(C\) represents the clutch; \(i_{x}\) represents the transmission ratio of the \(x\) pair of gear pairs; \(s,c\) and \(r\) represent the sun gear, the planet carrier, ring gear, respectively; \(p\) represents the pump, \(M\) represents the motor; \(h\) represents the pure hydraulic oil circuit.