esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effect Size Analysis  
In addition to examining statistical significance through [PERSON] and [PERSON]'s d signed-rank tests, an effect size analysis was performed to quantify the magnitude of differences between Early Fusion and Late Fusion methods for each of the evaluation metrics. [PERSON]'s d is widely used to determine the standardized mean difference between two groups, providing a clearer notion of the practical significance of the results.  
Table 6 provides the results of both Wilcoxon tests and corresponding [PERSON]'s d values for the performance measures: accuracy, precision, recall, and F1-score. The \(p\)-values of the Wilcoxon tests are utilized to verify whether there is a statistically significant difference in performance between the Early Fusion and Late Fusion methods. [PERSON]'s d values help provide a better understanding of the size of differences.  
Figure 6: Inference time (ms) comparison between Early and Late Fusion meta-classifiers across all metrics.  
* **Precision** is the only metric that has a statistically significant advantage (\(p\) = 0.0177) over Early Fusion, which was confirmed by the Wilcoxon test. [PERSON]'s d for precision, however, is \(-\)0.4074, which is a small effect size, and therefore, although there is significance, the practical effect size of the difference is moderate;
* **Accuracy, Recall, and F1-score** were not statistically distinct (\(p\) > 0.05), as supported by both the Wilcoxon \(p\)-values and the [PERSON]'s d values. For instance, accuracy's [PERSON]'s d is 0.2127, a small positive effect size, while recall's [PERSON]'s d (\(-\)0.5314) and F1-score's [PERSON]'s d (\(-\)0.0578) show non-significant differences between both approaches.  
It is evident from this comparison that Early Fusion and Late Fusion are predominantly of low magnitude in most metrics. The notable result in precision must be interpreted with caution, since the effect size indicates a moderate difference that may not indicate meaningful improvement in actual application.  
Although Late Fusion did not attain a statistically significant improvement in the majority of the measures, the gain in precision in Early Fusion, although statistically significant, is of a smaller practical size according to [PERSON]'s d. These results indicate that Early Fusion is superior regarding precision. Overall comparison between the two methods does not illustrate a clear-cut superiority of one method over the other based on the effect sizes.
## 3 Our Proposed Entity-Linking Method
### Algorithm Description  
Our proposed approach obtains the set of candidate entities and types from the knowledge base to link the mentions from the short text, and then the entity-linking model and entity-typing model are trained in sequence. In the final result of prediction, the candidate entity with the highest score and exceeding the threshold is regarded as the result of the mentioned link; otherwise, the mention is considered as a NIL entity and further assigned to the superordinate type. Finally, the entire process of our model is described in detail by Algorithm 1.  
Figure 5: Entity-typing model.  
```
1:Chinese short text with mentions marked, knowledge base
2:Linked entity \(kb\_id\) or entity type \(et\)
3: Begin
4: Define \(el\_result\) to preserve the relevance score of the entity linking model, \(et\_result\) to store the superordinate type of the entity typing model, and \(K\) as the threshold for determining the relevance of the link;
5: Generate entity linking set \(\mathcal{L}\) and and entity typing set \(\mathcal{T}\) of Chinese short text with mentions marked in the knowledge base by the method of exact match;
6:for\(i=0\) to \(|\mathcal{L}|\)do
7: Obtain the sentence vector representation \(\mathbf{V}\) of text and candidate entity information from RoBERTa;
8: Transform the \(\mathbf{V}\) into a uniformly distributed vector \(\mathbf{F}\) by Equation (4);
9: Use a fully connected layer combined with sigmoid function to predict the correlation of \(\mathbf{F}\) and store the correlation score in \(el\_result\);
10:endfor
11:for\(i=0\) to \(|\mathcal{T}|\)do
12: Obtain the sentence vector representation \(\mathbf{V}\) of text and label information from RoBERTa;
13: Transform the \(\mathbf{V}\) into a uniformly distributed vector \(\mathbf{F}\) by Equation (4);
14: Use a fully connected layer combined with softmax function to predict the entity type of \(\mathbf{F}\) and store the type result in \(et\_result\);
15:endfor
16:for\(i=0\) to \(|el\_result|\)do
17:if\(i\geq K\)then
18: Output the linked entity \(kb\_id\) with the highest score from \(el\_result\);
19:else
20: Output the prediction type \(et\) from \(et\_result\);
21:endif
22:endfor
23: End
```  
**Algorithm 1** Our proposed model for entity linking of Chinese short text
## 2 Materials and Methods
### Deviation of the CAD/CAM-Based Surgical Templates  
First, we combined the surgical template with the CT data at T0 and exported it to DICOM format (T0\({}^{\prime}\)) (Figure 2a). Using the 3D analysis program (OnDemand3D, Cybermed, Seoul, Korea), CT data at T0\({}^{\prime}\) and T1 were imported and superimposed on the basis of the best fit of the cranial base structures (Figure 2b) [31]. To measure the deviation of the template, we established a 3D coordinate system (X, Y, Z) (X, medio-lateral; Y, antero-posterior; Z, supero-inferior) (Figure 2c). The Frankfort horizontal plane (FH plane), which passed through both the orbitale and the right portion, was used as a horizontal reference plane, and the \(x\)-axis was set as the vector from the right to the left orbitale. The midsagittal plane was defined as the plane that is perpendicular to the \(x\)-axis and passed through a nasion. The coronal plane was defined as the plane that passed through the line passing both orbitales and that was perpendicular to the FH plane [31; 32]. To quantify the accuracy of the positioning of the template, we assessed the changes of the 3D coordinates of each conical reference structure from T0\({}^{\prime}\) to T1 (Figure 2d) and calculated the root mean square deviation (RMSD) between T0\({}^{\prime}\) and T1.  
\[\text{RMSD}=\frac{1}{\sqrt{n}}\sqrt{\sum_{i=1}^{n}(RS_{0^{\prime},i}-RS_{1,i} )^{2}}\]  
Figure 1: Design of computer-aided design/computer-aided manufacturing (CAD/CAM)-based surgical templates: (**a**) virtual Le Fort I osteotomy and patient-specific surgical template for Le Fort I osteotomy; (**b**) design of CAD/CAM-based surgical templates for Le Fort I osteotomy; (**c**) fitting of the CAD/CAM-based surgical template on the maxilla.  
For all the data points, \(RS_{0^{\prime},i}\) was the planned position of reference structure, and \(RS_{1,i}\) was the actual position of reference structure. Also, \(n\) refers to the total number of reference structure in each analysis.  
To determine the effect of the contact area between the template and the underlying bone on the positioning accuracy, we obtained the area of inner surface of the template and performed the correlation analysis.
# Research on an Energy Recovery Strategy for Fuel Cell Commercial Vehicles Based on Slope Estimation
## 2 The Model  
### The Vehicle Longitudinal Dynamics Model  
In this study, only the longitudinal dynamic motion of fuel cell commercial vehicles was considered. The force acting on the vehicle on a slope is shown in Figure 2. When driving on a slope, the vehicle is affected by driving force, slope resistance, air resistance, and rolling resistance. Therefore, the longitudinal dynamic equation of the vehicle is:  
\[F_{t}=F_{f}+F_{w}+F_{i}+F_{j} \tag{1}\]  
where \(F_{t}\), \(F_{f}\), \(F_{i}\), \(F_{w}\) and \(F_{j}\) are the driving force, rolling friction force, gradient resistance, aerodynamic resistance and uphill driving force, respectively.  
Among them, the expression for the slope resistance \(F_{i}\) is:  
\[F_{i}=mgsin\alpha \tag{2}\]  
In the equation, g represents the gravitational acceleration, and \(\alpha\) represents the angle of the slope.  
Figure 1: Technical roadmap.  
The expression for the driving force \(F_{t}\) is:  
\[F_{t}=\frac{T_{tq}i_{g}i_{0}\eta_{T}}{r} \tag{3}\]  
In the equation, \(T_{tq}\) is the actual torque input from the engine to the transmission; \(i_{g}\) is the transmission ratio of the gearbox; \(i_{0}\) is the main reducer transmission ration; \(\eta_{T}\) is the mechanical efficiency of the transmission; \(r\) is the rolling radius.  
The expression for the air resistance \(F_{w}\) is:  
\[F_{w}=\frac{1}{2}C_{D}A\rho v^{2} \tag{4}\]  
In the equation, \(C_{D}\) is the air density coefficient, \(A\) is the vehicle frontal area, \(\rho\) is the air density, \(v\) is the speed of the vehicle.  
The expression for the rolling resistance \(F_{f}\) is:  
\[F_{f}=mgf\cos\alpha \tag{5}\]  
In the equation, \(m\) is the mass of the vehicle.  
The nonlinear equation of the vehicle's longitudinal dynamics is expressed as  
\[\dot{v}=\frac{T_{tq}i_{g}i_{0}\eta_{T}}{rm}-g\sin\alpha-\frac{1}{2m}C_{D}A \rho v^{2}-gf\cos\alpha \tag{6}\]  
Based on practical analysis, in highway design standards, the longitudinal slope of the road is not greater than 9%, and the corresponding angle is within 5\({}^{\circ}\). Therefore, in cases of smaller angles, it can be considered that the tangent and sine values of the slope angle are equal. So, the Equation (6) can be changed to:  
\[\dot{v}=\frac{T_{tq}i_{g}i_{0}\eta_{T}}{rm}-gf-\frac{1}{2m}C_{D}A\rho v^{2}-gi \tag{7}\]  
The windward area \(A\) of the fuel cell commercial vehicle used in this article is 6.6454 m\({}^{2}\), and the wind resistance coefficient is generally taken as an empirical value. In this article, \(C_{D}\) is 0.563.  
Figure 2: Analysis of forces on vehicles driving on slopes.
# Navigation in GEO, HEO, and Lunar Trajectory Using Multi-GNSS Sidelobe Signals
## 2 Observation Simulation Strategy  
### GNSS Satellites and Signals  
GNSS constellations were simulated for generating geometric distance with respect to the space vehicles. GPS orbits are generated with GPS almanac from the Analytical Graphics Inc (AGI) server; the orbits of BDS, GLONASS, and Galileo were generated with the HPOP propagator based on nominal constellation parameters [31; 32; 33]. BDS constellation consists of three types of orbits, including GEO, Inclined geo-synchronous orbit (IGSO), and medium earth orbit (MEO). GEO and IGSO are particularly designed to cover Asia, and MEO satellites are distributed globally. The GPS constellation consists of 31 operational satellites allocated in 6 orbital planes. There are 8 satellites from Blocks IIR, 7 from IIR-M, 12 from IIF and 4 from III-A, each block has a different transmitter radiation pattern, which will be described later. Detailed parameters of GNSS constellations are listed in Table 1. Space distribution of them is depicted in Figure 1. The frequency bands of each GNSS constellation used in this paper are listed in Table 2.
## 4 Link Budget  
In this section, the link budget of spaceborne lidar systems will be discussed. We defined realistic orbits based on past and current satellites (see Table 3). Two typical areas were chosen for boreal and tropical forests, which are the main carbon reservoirs. The sites were analyzed during the seasons where the probability of having a significant cloud cover is minimal. In our link budget, we took into account the decrease of the SNR due to the aerosol and cloud optical thicknesses which are derived from operational satellite measurements (MODIS) chosen arbitrary for 2011. The different working hypothesis and the corresponding results are presented in this section.
## 3 Geocasting Based on Local Coverage Areas
### Maintenance: Removing Invalid Coordinates from Local Convex Hulls  
Due to dynamics in network topology, the local convex hull stored in a node can contain coordinates that do no longer reflect the actual coverage area of the node. This might be the case when a node dies/fails or nodes are mobile. To keep the local convex hull up-to-date between two global updates, a timeout mechanism is applied to remove invalid coordinates from the local convex hull. Nodes store a timestamp for each individual coordinate in their local convex hull \(\mathbf{H_{i}}\). The timestamp of a particular coordinate in \(\mathbf{H_{i}}\) is reset when node \(i\) receives a message (_i.e._, HELLO_TO_PARENT) containing the coordinate. But when a coordinate has not been reinforced within the timeout interval, it is removed from the local convex hull \(\mathbf{H_{i}}\). Node \(i\) sends its new convex hull to its parent node in its next HELLO_TO_PARENT message. When the parent hears about the changes, it will update its convex hull accordingly.  
The _timeout interval_ must not be shorter than the interval at which nodes produce and send their convex hulls in HELLO_TO_PARENT message to their parents, otherwise coordinates are removed from the local convex hulls before they are reinforced. If topology changes are frequent, the _timeout interval_ should be short to ensure up-to-date coverage areas. Also, the local convex hull needs to be transmitted to parent nodes at least once per timeout interval.  
To limit resource consumption, such as memory, energy, and bandwidth, of the proposed mechanism for coverage area reporting, compression of convex hulls (_i.e._, approximation of the convex hull with a smaller coordinate set) is an attractive option. When reduction is only applied on copies of convex hulls forwarded to parent nodes, the timeout mechanism of coordinates remains functional without having to e.g., match coordinates to substituted coordinates. In any case, the parent node works with the compressed version. This implies that compressed local convex hulls need to be forwarded to parent nodes within the reinforcement period of coordinates to ensure that substituted coordinates are not removed due to timeout mechanism.  
The timeout mechanism mainly repairs the affected convex hulls when nodes fail/die or move out the coverage area. The main functionality of this mechanism is removing old information. However, it can not totally repair the routing structure when there are moving nodes in the network. With this mechanism, a mobile node can be removed from its previous neighborhood, but it can not be added to its new neighborhood. In our previous work, for the addition of mobile nodes to their new neighborhood in the routing tree (_i.e._, finding a new parent and updating convex hulls), the _global updating_ mechanism described in Section 3.2 has to be executed periodically.
## 5 Analysis of Cracking and Damage in Wharf Structures under Impact Loading  
### Cracking Criteria and Derivation of Damage Evolution  
This research employs the maximum tensile stress criterion as the metric for concrete cracking. According to the provisions of reinforced concrete structure design specification,  
Figure 11: Peak bending moment envelope diagram of crossbeam under various operating conditions.  
Figure 12: The envelope diagram of peak axial force in the crossbeam under various operating conditions.  
Figure 13: The envelope diagram of axial force in pile #6 under various operating conditions.  
C50 concrete possesses a standard compressive strength value of 32.40 MPa and a standard tensile strength value of 2.64 MPa.  
The maximum tensile stress criterion is used to analyze the crack development process by using the fracture criterion to determine whether the crack is unstable or not. It is posited that when the stress at the node or element at the crack tip surpasses the ultimate tensile strength \(f_{t}\), concrete begins to crack. Once the crack emerges, its opening displacement w influences the impact of cohesion. When w escalates to the critical value, cohesion at the crack becomes null. The expression for this criterion is presented as follows:  
\[\sigma_{t}=f_{t} \tag{2}\]  
Damage definition and derivation of damage evolution equation  
When subjected to force, the internal cohesion of a material gradually weakens, causing a continuous breakdown of volumetric units. This process is accompanied by energy dissipation, recognized as damage. Concrete incurs damage after undergoing loading. Presuming the undamaged area to have a volume \(V_{n}\) and sustain an effective stress of \(\sigma_{ij}^{n}\), while the volume in the damaged zone is represented by \(V_{d}\), with the total volume being \(V\). Within the concrete, microcracks are uniformly distributed in all directions. Under these circumstances, the isotropic damage variable \(d\) can be defined as:  
\[\left\{\begin{matrix}d=V_{d}/V\ (0\leq d\leq 1)\\ V=V_{d}+V_{n}\\ 1-d=V_{n}/V\end{matrix}\right. \tag{3}\]  
The effective stress \(\sigma_{ij}^{n}\) can be defined as:  
\[\sigma_{ij}^{n}=\sigma_{ij}/(1-d) \tag{4}\]  
where \(\sigma_{ij}\) is the nominal stress.  
Following the Lemaitre strain equivalence principle, the strain induced by the effective stress \(\sigma_{ij}^{n}\) in the undamaged zone is considered equivalent to the strain caused by the stress \(\sigma\) in the damaged zone. This understanding enables the derivation of the stress-strain relationship within the damaged area:  
\[E=E_{0}(1-d)\ \ (0\leq d\leq 1) \tag{5}\]  
\[\sigma=E\varepsilon \tag{6}\]  
where \(E_{0}\) is the initial elastic modulus; \(E\) is the damage elastic modulus; \(d\) is the damage variable; \(d=0\) indicates that the material is undamaged; \(d=1\) indicates that the material has sustained complete damage; \(0<d<1\) indicates that the material is currently in various stages of damage, ranging from undamaged to complete damage.
## 5 Discussion
When we ran our system on the four videos with different levels of bee traffic, for which we did not obtain human counts, we discovered that some motion detection and classification configurations (e.g., MOG2/VGG16) could distinguish no traffic, low traffic, medium traffic and high traffic in terms of bee motion counts. This observation, while obviously preliminary, raises an important question of how accurately bee motion counts obtained by the proposed method should approximate the human bee motion counts. In some sense, the closeness of approximation may be irrelevant so long as automatically obtained bee motion counts enable the system to distinguish different bee traffic levels in order to track omnidirectional bee traffic over time. More experiments are obviously needed to validate this conjecture. The necessity to conduct more experiments leads to another important question of what ground truth videos are needed and how they should be obtained. The fact that it took us \(\approx\)18 h to obtain human bee motion counts on four videos of live bee traffic indicates that obtaining the ground truth in this manner is very labor intensive. A less expensive approach may be to obtain qualitative human classifications of bee traffic videos as no traffic, low traffic, medium traffic, and high traffic and then to seek ranges of automatic bee motion counts that enable the system to reliably distinguish different bee traffic levels. Our current beekeeper logs capture such variables as drawn comb, liquid honey, eggs, pollen, larvae, capped brood, capped honey, drone brood, and comb. In the future, we plan to add bee traffic estimates to our logs. While accurate visual evaluations of bee traffic levels when bee traffic is very high are difficult and time-consuming, they may provide solid ground truth for videos with low or medium bee traffic levels. To approximate human beekeepers' assessments of bee colony health, future EBM monitors will need robust models capable of correlating digital interpretations of captured sensor data with human beekeeper observations. When such correlations become reasonably accurate, EBM monitors will be able to make more accurate assessments of bee colony health or of environmental states. An important knowledge engineering task ahead is the formalization of human beekeeper observations so that they can be correlated in a meaningful way with digital observations. It remains to be seen whether such formalizations can be generated by computer vision from field pictures of live frames.
## 4 Selection and Analysis of Resonant Compensation Network
### Operating Characteristics of Resonant Compensation Networks
#### 4.2.1 S-S Compensation  
The equivalent circuit of the S-S resonant compensation network is shown in Figure 11, where \(M\) is the mutual inductance between the transmitting coil and the receiving coil, and \(U\) is the input voltage of the circuit. The parameters of the transmitting side include capacitor \(C_{p}\) and transmitting coil \(L_{p}\). The parameters of the receiving side include resonant capacitor \(C_{s}\), receiving coil \(L_{s}\), and load resistor \(R\). To simplify the analysis, the internal resistors of coils were ignored. In the resonant state, the transmitting coil current \(I_{p}\) and the receiving coil current \(I_{s}\) can be calculated as  
\[\left\{\begin{array}{l}I_{p}=\frac{\mathit{l}R}{\omega_{0}{}^{2}M^{2}}\\ I_{s}=\frac{\mathit{d}}{\omega_{0}M}\end{array}\right. \tag{23}\]  
According to Equation (23), the load current is only related to the input voltage \(U\) and mutual inductance \(M\) and is independent of the load. Therefore, the S-S resonant compensation network can achieve CC output. Further calculations can determine that the output power \(P_{o}\) of the S-S system is  
\[P_{o}=I_{s}{}^{2}R=\frac{\mathit{U}^{2}R}{\omega_{0}{}^{2}M^{2}} \tag{24}\]  
Figure 10: High-order resonant compensation networks. (**a**) LCC-S; (**b**) S-LCC; (**c**) LCL-LCL; (**d**) LCC-LCC.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Topologies** & **Parameter Design** & **Output Gain** & **Output Characteristics** & **Freedom** \\ \hline LCC-S & \(\omega_{0}=\frac{1}{\sqrt{L_{\mathrm{c}}}\mathcal{C}_{s}}=\frac{1}{\sqrt{(L_{ p}-L_{\mathrm{c}})C_{p}}}=\frac{1}{\sqrt{L_{\mathrm{c}}}\mathcal{C}_{s}}\) & \(\frac{I_{s}}{M}\) & CV & 1 \\ S-LCC & \(\omega_{0}=\frac{1}{\sqrt{L_{\mathrm{c}}}\mathcal{C}_{s}}=\frac{1}{\sqrt{L_{ \mathrm{c}}}\mathcal{C}_{s}}=\frac{1}{\sqrt{(L_{s}-L_{\mathrm{b}})C_{s}}}\) & \(\frac{I_{s}}{M}\) & CV & 1 \\ LCL-LCL & \(\omega_{0}=\frac{1}{\sqrt{L_{\mathrm{c}}}\mathcal{C}_{s}}=\frac{1}{\sqrt{L_{ \mathrm{p}}}\mathcal{C}_{s}}=\frac{1}{\sqrt{L_{\mathrm{c}}}\mathcal{C}_{s}}= \frac{1}{\sqrt{L_{\mathrm{c}}}\mathcal{C}_{s}}\) & \(\frac{I_{s}}{M\omega_{\mathrm{b}}L_{s}}\) & CC & 0 \\ LCC-LCC & \(\omega_{0}=\frac{1}{\sqrt{L_{\mathrm{c}}}\mathcal{C}_{s}}=\frac{1}{\sqrt{(L_{ p}-L_{\mathrm{c}})C_{p}}}=\frac{1}{\sqrt{L_{\mathrm{c}}}\mathcal{C}_{s}}= \frac{1}{\sqrt{(L_{\mathrm{c}}-L_{\mathrm{b}})C_{s}}}\) & \(\frac{M}{\omega_{\mathrm{b}}L_{s}L_{s}}\) & CC & 2 \\ \hline \end{tabular}
\end{table}
Table 3: Operating characteristics of high-order resonant compensation networks.