esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Sensory Analysis  
Sensory analysis was performed by ten well-trained panelists (five males and five females, with ages ranging from 20 to 45) from the Crops Research Institute at Guangdong Academy of Agricultural Sciences, China. Panelists each received a representative sample of the sweet potatoes and were trained to increase their sensitivity and ability to discriminate between the sensory attributes of the different cultivars. The whole process of sensory evaluation was performed at room temperature. The intact sweet potatoes were steamed for 45 min with a lid on the saucepan to prevent excessive moisture loss and then cut into 1 cm pieces for evaluation. In the process of evaluation, members were not allowed to communicate with each other, the taste of the sample could not be swallowed but spit out, and rinsed with water. The evaluations of each panelist were recorded on the questionnaire. Eight sensory descriptors involving firmness, aroma, sweetness, starchiness, viscosity, coarse texture, fibrous texture, and overall taste were selected to assess the sweet potato samples. The aroma intensity was described from 0 (not aromatic) to 3 (intensely aromatic), and the overall taste was evaluated out of a maximum score of 100. The intensity of other descriptors was evaluated on a 5-point scale from 0 (not perceivable) to 5 (strongly perceivable). Informed consent was obtained from all individual participants included in the study.
## 5 Experimental Validation
### Result and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Energy Error \(\theta\)**} & \multicolumn{3}{c}{**Orthogonal Index \(OI\)**} \\ \cline{2-7}  & **Case 1** & **Case 2** & **Case 1** & **Case 2** & **Case 3** \\ \hline IAVMD & 1.0000 & 0.9989 & 1.0000 & 0.9999 & 0.9999 \\ OVMD & 0.9996 & 0.9925 & 0.9998 & 0.9992 & 0.9993 \\ LMD & 0.9968 & 0.9897 & 0.9838 & 0.9985 & 0.9976 \\ EMD & 0.9908 & 0.9888 & 0.9830 & 0.9878 & 0.9901 \\ WT & 0.7989 & 0.8751 & 0.7291 & 0.7656 & 0.8586 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The correlation coefficient among different approaches in the simulation and experiment signal. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Energy Error \(\theta\)**} & \multicolumn{3}{c}{**Orthogonal Index \(OI\)**} \\ \cline{2-7}  & **Case 1** & **Case 2** & **Case 1** & **Case 2** & **Case 3** \\ \hline IAVMD vs. OVMD & 0.84\% \(\downarrow\) & 0.58\% \(\downarrow\) & 0.68\% \(\downarrow\) & 0.78\% \(\downarrow\) \\ IAVMD vs. LMD & 2.62\% \(\downarrow\) & 1.97\% \(\downarrow\) & 2.59\% \(\downarrow\) & 3.68\% \(\downarrow\) \\ IAVMD vs. EMD & 8.45\% \(\downarrow\) & 9.02\% \(\downarrow\) & 5.67\% \(\downarrow\) & 5.37\% \(\downarrow\) \\ IAVMD vs. WT & 2.47\% \(\downarrow\) & 4.21\% \(\downarrow\) & 3.20\% \(\downarrow\) & 3.55\% \(\downarrow\) \\ \hline \hline \end{tabular} Where \(\downarrow\) denotes the symbol of the reduced percentage.  
\end{table} 
Table 5: The percentage differences of the IAVMD with respect to other approaches in the simulation signal. In a word, by observing the frequency components of the time-frequency representations, we can find that the IAVMD method can excavate a better and more accurate frequency content compared with other approaches (OVMD, LMD, EMD, and WT). Besides, through the above quantification, we can draw the conclusion that the IAVMD method can improve the decomposition precision of the signal to some extent, but that it also brings about an additional computational cost due to an increased preparation stage. If one wants to extend the proposed IAVMD method to the practical engineering application, its time cost needs to be decreased, which is also the focus of our future study.
## 6 Conclusions  
In this paper, a free-space optical field transmission model for a transceiver-integrated fiber-optic LDV is established based on the Diffraction-limited Coherent Imaging Theory. The model integrates the theory of stable light field transmission in weakly guided single-mode fibers, the Johnson Transformation System Theory, and the Fiber Coupling Theory to build an energy utilization model for the rough target fiber-optic LDV transceiver process. The simulation results show that different wavelengths exhibit inconsistent speckle characteristics when applied to the same rough target. A dual-wavelength fiber-optic LDV data acquisition and analysis system based on orthogonal demodulation and dual-channel signal enhancement processing is proposed. The experiments were conducted with laser wavelengths of 1550 nm and 1558 nm for a moving target at an alignment distance of 10 m, verifying the proposed system. The experimental results demonstrate that the dual-wavelength fiber-optic LDV effectively suppresses spike noise during vibration measurement of moving targets and reduces the signal loss probability caused by speckle effects. In engineering applications, the dual-wavelength combination scheme provides higher stability and improved detection resolution. Additionally, the optical field transmission simulation platform can be extended to applications such as laser wind radar, providing an evaluation of the instrument's non-cooperative target measurement capabilities.  
Figure 15: Target plastic box—IQ demodulation velocity results: (**a**) 1550 nm and (**b**) 1558 nm. (**c**) Combination of dual-channel velocities.  
**Author Contributions:** Conceptualization, Y.S.; methodology, X.J. and Y.W.; validation, X.J., Y.S. and Y.W.; writing--original draft preparation, X.J. and Y.S.; supervision, X.K. and W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2022 YFP0706300.  
Not applicable.  
Data Availability Statement: Data will be available from the authors upon reasonable request.  
The authors thank the reviewers and editors for their contributions to this article.  
The authors declare no conflicts of interest.
## 2 Related Works
### Preliminary Conclusions  
There are numerous other mobility based data collection approaches that target one aspect of data collection or another and have different pros and cons. After analyzing all the state-of-the-art solutions regarding mobile data collectors it is possible to conclude that:  
* Approaches using a single radio in the mobile data collector only perform data collection, _i.e._, the mobile node does not communicate with the sink node while on the quest for data collection.
* Approaches targeting simultaneous communication with sensor nodes and sink or base station use at least two different radios on the mobile data collector.  
This motivated us to design an architecture that can communicate with both the WSN and the sink node using the same radio. Our work is different in the way that it focuses on using a dual stack that allows a mobile node, in our case a UAV, with deterministic mobility patterns, to collect data from heterogeneous ground sensor nodes and send telemetry or collected data back to the Sink node using the same communication hardware and without compromising the data collection rate. The details about this architecture are presented in the next section.
# From the Martian Surface to Its Low Orbit in a Reusable Single-Stage Vehicle--[PERSON]
## 3 Results
### Vehicle Subsystems
#### 3.2.4 Communication  
Regarding the communication subsystem of the vehicle, it is critical for the safety of the crew that a communication link with either the base or the node is available at all times during the mission. This means that there must be a system of relay satellites available.  
Figure 10: Simulation of a suborbital flight, as part of a rescue mission, showing the altitude of the rescue vehicle in red and the distance to the vehicle to be saved in blue, over time.  
This is achieved with a constellation of three satellites. These would be in a similar inclination as the orbital node and at an orbital radius of 10,000 km around Mars. This orbit is lower than a Mars stationary orbit, as the distance between the vehicle and the satellites would otherwise cause the space loss to be too high, leading to the communication link not fulfilling the required BER. The Bit Error Rate (BER) requirement is set at \(10^{-7}\), which is slightly stricter than the typical requirement of \(10^{-6}\)[24]. This is done to mitigate the additional noise that will be generated when the signal is cross linked from Charon to the relay satellites and the ground station.  
Regarding the communication of the launch vehicle itself, a similar system as that of the space shuttle is used [25]. Four omnidirectional S-band antennas are placed around the capsule [26], underneath the heat shield. This gives full coverage around Charon during its mission. With this hardware, the communication subsystem can uphold a data rate of 20 Mb/s. The link budgets for the downlink of the spacecraft can be seen in Table 1 for all three different communication links. Because the uplink can by closed by increasing the power output of either the ground, relay or orbital node transmitters, it was not analysed when sizing the communication system of Charon. The ground station is located at the Mars base and is assumed to have an antenna diameter of 6 m. The antenna diameters of the relay satellite and the orbital node are assumed to be 3 m and 2 m, respectively.
# A New Zenith Tropospheric Delay Grid Product for Real-Time PPP Applications over China
## 3 Real-Time ZTD Grid Product Generation
### Real-Time ZTD Grid Product Generation Procedure  
ZTD at GPS stations in CMONOC were estimated in the simulated real-time mode using PANDA package with 5 min interval. For each grid point with horizontal resolution of 0.75\({}^{\circ}\)\(\times\) 0.75\({}^{\circ}\) covering the area of 10\({}^{\circ}\) N\(\sim\)60\({}^{\circ}\) N and 70\({}^{\circ}\) E-140\({}^{\circ}\) E, the GPS stations within 1000 km near the grid point were selected. ZTD at these GPS stations were converted from the station altitude to the grid point surface altitude using the above inverse scale height model which can be expressed as:  
\[ZTD_{c_{i}}(h_{g})=\frac{ZTD_{c_{i}}(h_{c_{i}})}{\exp(-(1/H)_{c_{i}}\cdot h_{ c_{i}})}\cdot\exp(-(1/H)_{c_{i}}\cdot h_{g})=ZTD_{c_{i}}(0)\cdot\exp(-(1/H)_{c_ {i}}\cdot h_{g}) \tag{11}\]  
where the subscripts of \(c_{i}\) and \(g\) denote the station and the grid point, respectively.  
The inverse distance weighted (IDW) method was then applied to calculate the ZTD at the grid point as,  
\[ZTD_{g}(h_{g})=p_{1}\cdot ZTD_{c_{1}}(h_{g})+p_{2}\cdot ZTD_{c_{2}}(h_{g})+ \ldots+p_{n}\cdot ZTD_{c_{n}}(h_{g}) \tag{12}\]  
where \(p_{i}\) is the weight coefficient for each station. In order to make the product easy to use, the ZTD at the grid point surface were eventually converted to MSL using the inverse scale height model.  
For users, ZTD at the nearest four grids around the user were converted from MSL to the user's altitude by inverse scale height model and then IDW method were used for horizontal interpolation to calculate the ZTD at user's location.
## 6 Therapeutic Approaches of Dentin Hypersensitivity
### At-Home Therapeutic Treatments of Dentin Hypersensitivity
#### 6.1.4 Arginine  
Another category comprises toothpastes containing the amino acid arginine (C\({}_{6}\)H\({}_{14}\)N\({}_{4}\)O\({}_{2}\)), recognized for their substantial DH reduction potential and anticariogenic effects [77; 78; 79; 80]. These formulations, containing 8% arginine (arginine bicarbonate), calcium carbonate (CaCO\({}_{3}\)), and 1450 ppm fluoride, establish an alkaline environment that leads to reduced calcium and phosphorus levels on the dentin surface and within the dentin tubules. The arginine works by interacting with calcium ions to create a calcium-rich layer that can occlude the dentin tubules. One notable benefit of arginine-based toothpaste is its potential for long-lasting relief [81].
# Time-Domain Dynamic Modeling and Analysis of Complex Heavy-Duty Gearbox Considering Floating Effect
## 2 LTC Modeling Approach of Heavy-Duty gearbox
### LTC Models of Planetary Gear Set  
Interactive incentives between the gear engagements occur mainly through the meshing forces. The existing LTC model of a planetary gear set is shown in Figure 1a. The translation flexibility is simplified as linear spring dampers in the XY plane, while the flexibility between gear engagement, which is mainly caused by bending deflections and contact deformations of gear teeth, is simplified as a spring damper with equivalent average stiffness along the line of engagement with fixed acting points [12; 15; 25; 26; 27; 28; 29; 30]. Thus, each component is modeled with three degrees of freedom (DOF), and the bearing stiffness and time-invariant meshing stiffness are considered. This method has an inherent defect in that only small angular displacements of gears are permitted, and thus only the transient vibration characteristic of gear pairs can be solved and obtained.  
This paper proposes a novel method for LTC modeling. The elasticity and viscidity of gears, carriers, shafts, bearings, etc., are represented by time-varying stiffness-damping forces, while the bodies of gears, carriers and shafts are all modeled as rigid elements. Thus, a final multibody system of the gear set with consideration of time-varying stiffness, friction, damping, etc. is established. Based on [PERSON]'s third law, the interaction of two bodies can be represented by an action force and an opposite reaction force that are functions related to the corresponding characteristic parameters of the two bodies. According to the basic law on parallel translation of force, forces on the contact point are equal to the combined effects of component force in XY plane and component torque around the Z axis in the center. With these proper transformations, the meshing effect can be modeled as a normal pressure force, represented by a time-varying stiffness spring damper force, and a tangential force, represented by a synthesis of a viscous damping force and a coulomb friction force (Figure 1b). These two kinds of force can eventually be replaced by time-varying component forces and torques with respect to the generalized coordinates on gear centers of the input and output gears respectively. Hence, large angular displacements of gears are allowed, and the entire coupling model can be utilized for the calculation of system dynamic behavior in the time domain.  
The proposed method operates as space-fixed spring dampers with floating acting points, which guarantees that the positions and orientations of spring dampers coincide with the actual meshing forces regardless of changes in the centroids or the angles of the meshing gears. The proposed method is compared with the existing method in Table 1.  
Figure 1: LTC modeling of a planetary gear set: (**a**) using the existing method (**b**) using the proposed method.
# Margin and Shared Proxies: Advanced Proxy Anchor Loss for Out-of-Domain Intent Classification
## 4 Experiments
### Experimental Settings  
Following the settings used in the previous study [10; 11; 12], we randomly sampled only 25%, 50%, and 75% of the IND intents and used them for training. Then, we regarded the remaining unsampled intents as OOD, which are used with sampled intents for validation and testing. We selected the model with the highest F1-ALL performance in the validation set and conducted the test accordingly.  
We utilized the BERT-base model (bert-base-uncased) to generate the intent representation. The max sequence length of BERT is 50, the hidden size is 768, and the scaling factor \(\alpha\) is 64. Moreover, we adapt the AdamW Optimizer, and we set the learning rate to 2 \(\times\) 10\({}^{-5}\) for each sample, 2 \(\times\) 10\({}^{-4}\) for margin proxies, and 2 \(\times\) 10\({}^{-3}\) for centroid and shared proxies. The results of all experiments are expressed as the average of 10 repeated experiments by applying different random seeds in the intent sampling process.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Train** & **Valid** & **Test** & **Intent** \\ \hline CLINC & 15,000 & 3000 & 5700 & 150 \\ SO & 12,000 & 2000 & 6000 & 20 \\ BANKING & 9003 & 1000 & 3080 & 77 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of CLINC, StackOverflow(SO), and BANKING datasets.
# Emulating Cued Recall of Abstract Concepts via Regulated Activation Networks
## 3 RAN Methodology to Simulate Recall Operations
### Recall Demonstration with Toy-Data
#### 3.5.3 Discussion  
The experiments in Sections 3.5.1 and 3.5.2 demonstrate a notable behavior of RAN by simulating the cued recall operation through a Toy-data problem. The intra-Layer learning (i.e., CSRL) is uniquely utilized by RAN modeling to interpret the association among the concepts as inhibitory, excitatory, or neutral. Furthermore, the intra-layer regulation (Algorithm 1) uses intra-layer learning (CSRL) and its interpretations to produce a regulatory effect over the activation of the concepts (at the same layer). The geometric back-propagation operation (Algorithm 2) is a method analogous to remembering something learned in an abstract form and recalling its concrete features. For example, while remembering the abstract concept \"house\", we recall concrete features related to the house, such as \"mother\", \"father\", \"wife\", and \"pets\".  
In the graphs in Figures 8 and 9, we can see that all the trajectories commence from a starting point (red dot) and converge to a point after one thousand iterations. Each point in a trajectory represents a temporal mental state while recalling a concrete concept. Every time a concrete concept (activation vector in layer 0) is recalled, its corresponding abstract concept (at layer 1) is compared with the expected abstract concepts. The difference between expected and observed activation is propagated back as the error to the previously recalled activations at layer 0. In the next instance, the corrected recalled activation at layer 0 repeats the process until one thousand iterations are completed.  
It was observed that without regulation, i.e., 0% \(\rho\), the trajectory converges to a point but with a minimal amount of regulation, and the result improves. For instance, in the graphs in Figure 8, only one abstract concept was being recalled and the results improved when the regulation was introduced. In the two experiments (SCR and MCR), we can see that the two different sets of regulation factors are considered. These sets were obtained empirically, but we can see that the set of the regulation factors for the SCR experiment has a higher value. This is because the GBP algorithm strives to minimizes the error at each abstract concept node at layer 1, and in the geometrical context, similarity cannot be the same for more than one abstract concept. Thus, the trajectory converges to a point, but the result improves when a minimal amount of regulation is induced. In the MCR experiment, the best outcome is observed with little or no regulation because the expected similarity (DoC and E-A) is a non-zero value. The other reason is that these are possible expected similarity vectors, unlike those in the SCR experiments.
# Comparison of Heuristic Algorithms in Discrete Search and Surveillance Tasks Using Aerial Swarms
## 1 Introduction
### Optimal Methods in Discrete Search Tasks  
Although the discrete search task using teams of agents has been shown to be a hard problem to solve optimally, there have been some works addressing the problem by looking for optimal solutions. For instance, in [24], an exact mixed-integer linear programing formulation to solve in an optimal way the search of a discretized area is proposed. Given a mission state defined by the position of the agents and the cells already visited, the best sequence of movements is calculated for a specific time horizon. While the found path is being followed, a new sequence is generated over a receding horizon. Eight possible movements are considered at each decision instant, as well as a prior belief about where the targets are possibly located. The authors remark that the number of possible solutions for \(n\) agents and a time horizon of \(T\) movements is \(8^{nT}\), which reaches prohibitive values quickly. Using a six-core processor, for a grid of 10 \(\times\) 10 cells and considering two and five agents, with time horizons of 10 and 20 steps, optimal solutions are found within one minute (except for one of the cases studied, which needed 142 s). Bigger scenarios and/or more agents are unfeasible to solve.  
In [25], a receding time horizon is also used to optimize on-line the trajectories of a group of UAVs in a discrete bi-dimensional search area. Based on the current state of the agents, the optimizer tries to maximize the reward of finding a target and of exploring the area, while it keeps the energy consumption low. The optimization problem is broken down into local ones assuming communications between the agents, so that it can be solved in a decentralized way. For this procedure, Nash optimality is considered and a particle swarm optimization used as the optimizer tool. Simulations are carried out in a search area of 50 km \(\times\) 50 km (a grid of 500 \(\times\) 500 units), and the results show that although the decentralized optimization does not reach the performance of the centralized one, it requires half of the computational time.