esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multiscale CNN-Based Intrinsic Permeability Prediction in Deformable Porous Media
## 2 Macroscopic Saturated Porous Media Model
### Local Balance Relations  
In the preparation of the governing balance relations that will be used for further discussion, we will make the following simplifying assumptions:  
* Non-polar constituents with symmetric stress tensor (\(\mathbf{T}^{\alpha}=(\mathbf{T}^{\alpha})^{T}\)). Thus, the angular momentum balance equation is always satisfied.
* A quasi-static biphasic model with negligible inertia terms, i.e., \(\rho^{\alpha}\,(\mathbf{v}_{\alpha})^{\prime}_{\alpha}\approx\mathbf{0}\).
* Isothermal conditions, which eliminates the need for the energy balance equation.
* No mass production \(\hat{\rho}^{\alpha}\) between the solid and the fluid phases, i.e., \(\hat{\rho}^{\alpha}\equiv 0\).  
Under these assumptions within the TPM, the constituent balance equations of the fluid-saturated porous media can be expressed as  
* Constituent mass balance: \[(\rho^{\alpha})^{\prime}_{\alpha}+\rho^{\alpha}\,\mathrm{div}\,\mathbf{v}_{ \alpha}\,=\,0\] (6)
* Constituent momentum balance: \[\mathbf{0}\,=\,\mathrm{div}\,\mathbf{T}^{\alpha}+\rho^{\alpha}\,\mathbf{g}+ \hat{\mathbf{p}}^{\alpha}\] (7)  
In Equation (7), \(\mathbf{g}\) is the mass-specific gravitational force, \(\mathbf{T}^{\alpha}\) is the symmetric partial Cauchy stress tensor, and \(\hat{\mathbf{p}}^{\alpha}\) is the direct momentum production (local interaction force between \(\varphi^{S}\) and \(\varphi^{F}\)). For this, the overall conservation of momentum results in \(\hat{\mathbf{p}}^{S}+\hat{\mathbf{p}}^{F}=\mathbf{0}\). Considering \(\alpha=S\) in Equation (6) together with \(\rho^{\alpha\mathrm{R}}=\mathrm{const.}\) yields the solid volume balance equation. Applying an analytical integration to this (see, e.g., [6,46]), we get the solidity as a secondary variable. In particular, we have for \(\varphi^{S}\)  
\[(n^{S})^{\prime}_{S}+n^{S}\mathrm{div}\,\mathbf{v}_{S}=0\quad\xrightarrow{ \text{integration}}\quad n^{S}=n^{S}_{0}\,\det\mathbf{F}^{-1}_{S}\quad \xrightarrow{\text{linearization}}\quad n^{S}\approx n^{S}_{0}\,(1-\mathrm{ div}\,\mathbf{u}_{S}) \tag{8}\]  
with \(\mathbf{F}_{S}=\partial\mathbf{x}/\partial\mathbf{X}_{S}\) being the solid deformation gradient and \(n^{S}_{0}\) the initial volume fraction of \(\varphi^{S}\). Therefore, the direct correlation between the solid deformation \(\mathbf{u}_{S}\) and the porosity (\(n^{F}=1-n^{S}\)) is apparent in Equation (8).
# A Portable Non-Motorized Smart IoT Weather Station Platform for Urban Thermal Comfort Studies
## 4 Efficiency and Performance of the Proposed Weather Station Platform
### Test 2: Data Consistency  
The total number of data points stored on the server and the device was cross-verified by calculating their differences.  
Irrespective of the two scenarios and different built characteristics, no differences were found in the total number of collected data points stored in the device and the server, except for commercial location 4, with a negligible difference of 1 out of 103 data points. This server-side loss of data may be due to a poor internet connection. To overcome this issue in the future, a sequence number was added to the measurement for the automatic deduction of missing server data and to retrieve the same locally.  
Overall, it can be concluded that the device can be used anywhere in Glasgow, as it is extremely consistent in saving data to the server, avoiding potential gaps in the data set.
## 3 Kinematic Analysis
### Human-Robot Hybrid Kinematic Model  
The human-robot hybrid kinematic model could be simplified as a slider-bar linkage as shown in Figure 4. The parallel mechanism of the main motion module is equivalent to a PR mechanism (\(AB\)), and the parallelogram mechanism could be regarded as a link (\(BC\)). \(D\), \(E\) and \(F\) represent the ankle,  
Figure 3: Adduction/abduction (**a**) and ankle (**b**) motion modules.  
Figure 2: Main motion module.  
knee and hip joints of the user. As the user's foot is tied to the foot pedal of the ankle motion module, the linear displacement extending from the ankle joint (\(D\)) to the support shaft (\(C\)) could be regarded as a link (\(CD\)). The lower limb is equivalent to a passive UR mechanism (\(EFD\)), which could rotate in the plane \(XOY\).  
The \(a_{0}\) represents the linear displacement of the movable base; \(\theta_{1}\), \(\theta_{2}\) and \(\theta_{3}\) are joint angles of the robot; \(\theta_{5}\) and \(\theta_{6}\) represent the A/A angle and the F/E angle of the human hip joint; \(\theta_{7}\) represents the F/E angle of the human knee joint. \(\theta_{2}\) is the rotation angle of \(l_{2}\) (\(GB\) as an axis), and \(\theta_{5}\) is the rotation angle of \(l_{5}\) (\(HF\) as an axis). \(l_{1}\) and \(l_{2}\) are the link lengths of the robot; \(l_{3}\) is the length between the point \(C\) and \(D\); \(l_{5}\) and \(l_{6}\) are the lengths of the human thigh and crus; \(X_{F}\) and \(Z_{F}\) represent the position of the human hip in the coordinate system \(\{XOZ\}\). In addition, the lower limb length of the user, the height from the foot sole to the ankle joint and the position of the hip should be measured before training.
## 2 Materials and Methods
### Search Protocol: Data Collection Process  
Previous studies have indicated that an appropriate search protocol is crucial to collect as many relevant and reliable research publications as possible [17]. In this regard, the necessary steps are (1) selecting a reliable database with sufficient coverage; (2) formulating a well-defined search query; and (3) identifying the publications to be included and excluded for quality of bibliometric reviews.  
Taking the transportation industry into account for our study, we focus on how BIM and DTs technologies are applied to digital transformation. In addition, the Web of Science (WoS) Core Collection citations and Scopus databases were considered as the main databases for conducting this review [18]. The initial run of the search query on the topics of the publications (based on title, abstract, keywords, and keywords plus) returned 1081 articles. Then, based on the checking rules, 721 related papers were screened. Lastly, we manually checked the titles and abstracts of the remaining publications to see if they were relevant to the topic. On this basis, 164 papers that had mainly focused on other topics were excluded from the sample. Data collection was completed on 20 July 2022 and was updated on 17 August to avoid data source deviation due to the publication of new papers. Records were exported as plain text files and tab-delimited files using the \"full records and cited references\" option. The search database, search query, and results are shown in Table 1.  
As a result, we chose 493 (five duplicate papers were removed) papers published between 2008 and August 2022 that were eligible for the review. Figure 3 shows the number of publications in the WoS and Scopus databases, respectively. Over the past few years, there have been fluctuating increases in the number of annual publications. 2021 already marked the peak. Moreover, the digitalization of transportation research based on digital twin technology and BIM technology is still an active research hotspot.
## 4 Results
### Three-Dimensional Deformation Field of the Landslide
By contrast, the middle and front parts of the landslide have horizontal and vertical movements, which can be judged as push-type landslides. Error analysis is important for the accuracy of InSAR results. According to Equations (4) and (5), we calculated the 3D deformation decomposition errors. Figure 9 illustrates cumulative errors in the three directions generated by the 3D decomposition. The errors in both the vertical and east-west directions are \(<\)5 cm, which are acceptable considering the annual deformation of the landslide of \(>\)10 cm. While in the north-south direction, the maximum error in this direction inevitably reaches 12 cm due to the near-northern orbit of the SAR satellite. However, the landslide in this study mainly moves in the east-west and vertical directions, so the error in the north-south direction has little impact on the landslide deformation analysis, proving the reliability of the InSAR results. Figure 8: The 3D velocities field obtained from the three-dimensional decomposition method. (**a-c**) The velocities (cm/a) of the vertical, east-west and north-south directions; (**d**) horizontal velocity (cm/a). The red arrow represents a greater horizontal velocity.
# Compact Quad-Port MIMO Antenna with Ultra-Wideband and High Isolation
## 4 Result and Discussion
### Diversity Properties of MIMO Antennas  
In this section, the diversity characteristics of the proposed UWB MIMO antenna are analyzed by the Envelope Correlation Coefficient (ECC), Diversity Gain (DG), Mean Effective Gain (MEG) and Total Active Reflection Coefficient (TRAC).
## 4 Results
### Organ-Level Segmentation
#### 4.1.2 Experiments Utilizing TLS Point Clouds  
The datasets of maize, cotton, rapeseed, and potatoes were utilized to validate the model's application potential for point cloud data acquired using TLS technology. Table 4 presents the segmentation results of these models on TLS point clouds. Focusing on maize and cotton as representative examples, the segmentation results are illustrated in Figure 5.  
Compared to the Pheno4D dataset, there was a general decline in performance metrics. Point cloud data collected in real growing scenarios are typically affected by various environmental factors, such as background clutter and wind. These point clouds often contain noise points that are difficult to filter and occlusions caused by dense planting. Compared to Pheno4D, these datasets present greater challenges for segmentation. ST achieved the best segmentation results across all subjects, while PT secured sub-optimal results in most cases. PointCNN and PAConv yielded comparable segmentation outcomes for both crops, effectively separating the stems and leaves. However, more detailed areas, such as the maize cobs and obscured sections of cotton plants, showed varying levels of misclassification.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Maize**} & \multicolumn{3}{c}{**Tomato**} \\ \cline{2-7}  & **OA** & **mACC** & **mIoU** & **OA** & **mACC** & **mIoU** \\ \hline PointNet & 0.917 & 0.803 & 0.716 & 0.945 & 0.822 & 0.767 \\ PointNet++ & 0.967 & 0.916 & 0.863 & 0.962 & 0.888 & 0.833 \\ PointMLP & 0.974 & 0.950 & 0.896 & 0.963 & 0.892 & 0.839 \\ DGCNN & 0.935 & 0.827 & 0.753 & 0.951 & 0.854 & 0.798 \\ PointCNN & 0.966 & 0.933 & 0.868 & 0.970 & 0.903 & 0.862 \\ PAConv & 0.972 & 0.938 & 0.887 & 0.963 & 0.891 & 0.838 \\ CurveNet & 0.968 & 0.928 & 0.874 & 0.964 & 0.891 & 0.842 \\ PT & 0.981 & 0.959 & 0.919 & **0.971** & **0.914** & **0.868** \\ ST & **0.983** & **0.963** & **0.926** & 0.964 & 0.908 & 0.865 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Segmentation results on Pheno4D. The bolded text indicates optimal performance, while the underlined text denotes the second-best results.  
Figure 4: Segmentation results on Pheno4D. The circled areas indicate incorrectly segmented regions; GT represents the ground truth.
# Development and Application of a Novel Tsunami Monitoring System Based on Submerged Mooring
## 4 Sea Trial Verification and Result Analysis  
### Prototype System Construction  
To verify the effectiveness and feasibility of the system design in this article, a prototype system was built, and sea trials were conducted. The prototype system adopts the Parooscientific 8 CB7000-I pressure sensor (Washington, UT, USA), the IXSEA-Oceano 5000 acoustic release device (Paris, France), and the Soundnine S9 induction coupling device (Washington, UT, USA). The satellite communication device integrates twelve 60-second-frequency BeiDou card (Beijing, China), which can launch data packets of 1 KB immediately to meet the real-time transmission requirements of tsunami raw data. The main configuration of the system is shown in Table 1, and the actual system is shown in Figure 7.  
Figure 6: Release device control mechanism process.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Name** & **Model** & **Main Parameter** \\ \hline
1 & Pressure sensor & Paroscientific \&CB7000-I & Working water depth \(\leq\) 7000 m; accuracy: 0.01\% FS \\
2 & Acoustic release device & DSEA-Oceano Oceano5000 & Working water depth \(\leq\) 6000 m; \\
3 & Anchor weight block & Customization & Communication distance \(\leq\) 10 km \\
4 & Inductive coupling device & Soundnine/S9 & Working water depth \(\leq\) 4500 m \\
5 & Underwater unit & Customization & Diameter of 2 m; height of 1.4 m; reserve buoyancy: 3097 kg; equipped \\  & & with 6 release devices \\
6 & Communication device & BeiDou Second Generation & Integrate twelve 60-second-frequency BeiDou civilian cards with a communication capacity of 1 KB/min \\
7 & Plastic-coated steel cable & Customization & Outer diameter 8.4 mm; inner diameter 6.4 mm; tensile strength \(\geq\) 2 kg \\
8 & Data collector & Customized development & Equipped with 32 GB storage and 8 RS232 interfaces; the operating power consumption is approximately \(\leq\)1 W; and the sleep power consumption is \(\leq\)200 mW \\ \hline \hline \end{tabular}
\end{table}
Table 1: Prototype system hardware configuration.  
Figure 7: Physical diagrams of the prototype system. **(a)** Pressure sensor, **(b)** Acoustic release device, **(c)** Inductive coupling device, **(d)** Small water-surface-communication buoy, **(e)** Shore station data-receiving subsystem.
## 2 Problem Formulation  
In this section, the dynamics we intend to investigate will be presented, while the primary issue that requires resolution is articulated herein. Firstly, let us consider the following relative motion dynamics of inner formation systems:  
\[\ddot{r}_{12}+2n\times\dot{r}_{12}+n\times(n\times r_{12})+\dot{n}\times r= \frac{f_{2}}{m_{2}}-\frac{f_{1}}{m_{1}}+u \tag{1}\]where SC1 and SC2 denote the outer spacecraft and inner spacecraft, respectively, with \(r_{12}=\left[\begin{array}{ccc}r_{1}&r_{2}&r_{3}\end{array}\right]\) representing the vector from SC1 to SC2, \(n=\left[\begin{array}{ccc}n_{1}&n_{2}&n_{3}\end{array}\right]\) representing the angular velocity of SC1, \(f_{1}\) and \(f_{2}\) representing external disturbances acting on SC1 and SC2, respectively, \(m_{1}\) and \(m_{2}\) corresponding to the masses of SC1 and SC2, respectively, and \(u\) representing the control input for the outer spacecraft. It can be observed that \(r_{12}(t)\) comprises both external disturbances and uncertainties, which are collectively represented as \(h_{i}(t)\):  
\[\left\{\begin{array}{l}h_{1}(t)=\Delta a_{1}+2n_{3}r_{2}-2n_{2}r_{3}+{n_{3} }^{2}r_{1}+{n_{2}}^{2}r_{1}-n_{2}n_{1}r_{2}+\dot{n}_{3}r_{2}-n_{3}n_{1}r_{3}- \dot{n}_{2}r_{3}\\ h_{2}(t)=\Delta a_{2}-2n_{3}r_{1}+2n_{1}r_{3}-n_{2}n_{1}r_{1}-\dot{n}_{3}r_{1}+ n_{3}r_{2}+n_{1}^{2}r_{2}-n_{3}n_{2}r_{3}+\dot{n}_{1}r_{3}\\ h_{3}(t)=\Delta a_{3}+2n_{2}r_{1}-2n_{1}r_{2}-n_{3}n_{1}r_{1}+\dot{n}_{2}r_{1}- n_{3}n_{2}r_{2}-\dot{n}_{1}r_{2}+n_{2}^{2}r_{3}+n_{1}^{2}r_{3}\end{array}\right. \tag{2}\]  
where \(\Delta a=\left[\begin{array}{ccc}\Delta a_{1}&\Delta a_{2}&\Delta a_{3} \end{array}\right]^{T}\) represents the term \(\frac{f_{2}}{m_{2}}-\frac{f_{1}}{m_{1}}\), which means the disturbance and uncertainty of the system. Then, the following assumption is proposed.  
**Assumption 1**.: \(h_{i}(t)\) _(\(i=1,2,3\)) is derivable, and its derivative is bounded by some positive constant \(M_{i}\), which means that \(\frac{d|h_{i}(t)|}{dt}\leq M_{i}\)._  
This assumption serves as a fundamental premise in the design of extended observers, enabling us to conduct differential calculations and estimations of the extended state. Building upon this assumption, we elaborate on the key issues that necessitate attention in this article:  
1. To propose an event-triggered extended observer for system (1), based on the output information obtained through event-based sampling, providing estimation for all states as well as external disturbances and uncertainties;
2. To design an output feedback controller using the proposed event-triggered observer, ensuring practical applicability in the presence of bounded disturbances and uncertainties;
3. To determine a minimum interval to ensure the absence of Zeno behavior under the proposed sampling scheme.
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
## 3 Unmanned Aerial Vehicles (UAVs)
### UAV Standardizations  
#### 3.4.1 UAVs 3 GPP Standardization  
In this modern era of technology, UAVs have gained significant attention in LoS and NLoS environments [28; 62]. In 2017, the third-generation partnership group (3 GPP) conducted several studies and issued Release-15 to acknowledge LTE-empowered UAVs. The main objective of these studies was to focus on UAV traffic requirements, channel modeling techniques for air-to-ground propagation perspectives, reuse of current cellular networks to enable UAV communication, LTE support and developments required to integrate LTE into UAVs. The 3 GPP objectives also include the identification of traffic types for which the existing cellular networks must cater for UAV flight over 300 m from ground. Table 6 summarizes these UAV communication requirements which are classified as: (i) synchronization and radio control; (ii) command & control; and (iii) application data [24].
# Multipointer Coattention Recommendation with Gated Neural Fusion between ID Embedding and Reviews
## 4 Experimental Evaluation
### Compared Models  
To explore the performance of our model, MPCAR, we compared MPCAR with a classic algorithm and four recently popular deep learning recommendation algorithms. As shown in Table 3, the classic algorithm MF does not use ID embedding and review information, it only uses rating data as input. DeepCoNN and D-ATT adopt a document-based approach to concatenate multiple reviews of users (item) to obtain a long document as input information, and then extract global interests (features) of users (item). NARRE, MPCN, and the proposed MPCAR model each review individually and then aggregate features of reviews into user (item) features. Review-based approach captures information about a user's preference for a particular item. In addition, among these five deep learning models, NARRE and MPCAR use ID embedding information.  
* Matrix factorization (MF) [4] is a commonly used benchmark. It uses the inner product to represent user and item scores.
* The deep collaborative neural network (DeepCoNN) [5] combines a user review set and an item review set to model users and items through a CNN. It trains the convolutional representation of the user and the item and passes the cascaded embedding into the FM model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Number of Users** & **Number of Items** & **Number of Reviews** \\ \hline Instant Video & 5130 & 1685 & 37,126 \\ Instruments & 1429 & 900 & 10,261 \\ Beauty & 22,363 & 12,101 & 198,475 \\ \hline Cellphone & 27,879 & 10,429 & 194,340 \\ Gourmet Food & 14,681 & 8713 & 151,232 \\ \hline Health & 38,609 & 18,534 & 346,307 \\ Office Products & 4905 & 2420 & 53,237 \\ Baby & 19,445 & 7050 & 160,732 \\ Digital Music & 5541 & 3568 & 64,705 \\ \hline Pet Supplies & 19,856 & 8510 & 157,683 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The statistics of the ten Amazon datasets.  
* Dual attention CNN model (D-ATT) [32], which uses reviews to make recommendations is the latest model based on a CNN. The model is characterized by using two forms of attention (local and global). End-user (item) representations are learned by combining representations learned from local and global attention. The representation of users and items is predicted by scoring using the dot product.
* NARRE [6] uses two parallel neural networks, both of which include a convolutional layer and an attention layer, to capture the usefulness of reviews, to model users, and items.
* MPCN [7] uses a pointer network to learn the characteristics of users and target items from words and reviews and transfers the final representation of users and items to the FM model.
# Spectrum Allocation Based on an Improved Gravitational Search Algorithm
## 4 Spectrum Allocation Based on IGSA  
In order to balance the exploration and exploitation ability, we design an improved algorithm which employs GSA for global optimizing and PSA for local search. In this section, we illustrate the process of IGSA-based spectrum allocation and present the algorithm in Algorithm 1. The process of IGSA-based spectrum allocation is described as below:  
1. Initialization. Generate the system environment; initialize the parameters and constants of IGSA; define the objective function; set the maximum iteration time of GSA and PSA \(T_{g}\), \(T_{p}\), respectively.
2. Population initialization. Build chaotic population for IGSA using the Chebyshev chaotic map (in Section 3.2.1).
3. Exploration. Employ GSA to explore the search space and obtain the close-to-optimal solution.  
* Exploitation. Initialize PSA with the optimal solution obtained from GSA, use PSA to exploit the local area.  
```
Input:\(T\): network topology; \(E(X)\): fitness function; \(k\): initialization constant; \(T_{g}\),\(T_{p}\): the maximum of iteration for GSA and PSA; \(G_{0}\): gravitational constant; \(\xi\): the minimum mesh size; \(N\): population size; Output: the optimum scheme \(X\)
1 encoding by matrix \(L\) of \(T\)
2 initial population with the Chebyshev chaotic map \(X(X_{1}^{1},X_{2}^{1},X_{3}^{1},\ldots,X_{N}^{1})\)
3 for\(t=2\); \(t<T_{g}\); \(t++\)do
4 evaluate the fitness value of \(E(X_{i}^{t})\)
5\(G^{t}\gets G_{0}e^{-\frac{at}{\xi}}\)
6\(f_{worst}^{t}\leftarrow\max_{j\in\{1,2,\ldots,N\}}f_{j}^{t}\)
7\(f_{best}^{t}\leftarrow\min_{j\in\{1,2,\ldots,N\}}f_{j}^{t}\)
8\(M_{i}^{t}\leftarrow\frac{m_{i}^{t}}{\sum_{j=1}^{N}m_{j}^{t}}\)
9\(a_{i,d}^{t}\leftarrow\frac{F_{id}^{t}}{M_{i}^{t}}\)
10 update velocity and position
11 get the agent position with the heaviest mass \(X_{best}^{T_{g}}\) set \(X_{best}^{T_{g}}\) as the initial point \(X_{0}\) of PSA
12 create mesh points
13 while(\(T_{p}\))do
14 evaluate fitness value \(E(X_{i})\)
15 ifmeshsize \(<\xi\)then
16 break
17 else
18 adjust mesh size
19
20 position modification
```  
**Algorithm 1**An Improved Gravitational Search Algorithm.
# Rapid Construction of Aerocapture Attainability Sets Using Sequential Convex Programming
## 5 Numerical Demonstrations
### Aerocapture Attainability Sets with Different Upper Bounds of Bank Angle  
For the optimal aerocapture process, the optimal bank angle profile has a bang-bang control structure. Hence, the bank angle switches between \(\sigma_{\min}\) to \(\sigma_{\max}\). Thus, the bounds of the bank angle are significantly related to the crossrange control ability [28]. In our simulation, the lower bound of the bank angle is fixed, and the aerocapture attainability set influence was analyzed with different upper bounds of bank angle \(\sigma_{\max}\). As shown in Figure 12, the attainability sets are similar with \(\sigma_{\max}=125\) deg, \(165\) deg, but for \(\sigma_{\max}=85\) deg, the aerocapture attainability set significantly contracted. It shows that \(\sigma_{\max}<90\) deg greatly weakens the longitudinal control ability of the vehicle during the atmospheric flight.  
Figure 11: Aerocapture attainability sets with different hypersonic lift-drag ratios.  
Figure 12: Aerocapture attainability sets with different upper bounds of bank angle.