esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Robot Motion Control
#### 2.5.2 Positioning  
To accomplish proper positioning of the articulated forceps before insertion, the robot followed the operator's desired motion through forces/torques exerted over the commanding handle attached to the robot. An admittance control scheme was used to enable compliant motion of the manipulator [28]. The implemented admittance controller measured the externally applied forces/torques via the 6-DOF F/T sensor and generated the target velocity from the following admittance model bounded within the saturation limits:  
\[F_{h}=M_{v}\dot{V}_{ee}+D_{v}V_{ee} \tag{1}\]  
where \(F_{h}\in\Re^{6}\) is the external force/torque command, \(V_{ee}\in\Re^{6}\) and \(\dot{V}_{ee}\in\Re^{6}\) are the desired EE velocity and acceleration, respectively, and \(M_{v}\) and \(D_{v}\in\Re^{6\times 6}\) are constant positive definite diagonal matrices that represent the desired virtual mass and damping, respectively. The velocity and force vectors consisted of linear and angular components, respectively, as:  
\[V_{ee}=\begin{bmatrix}\dot{p}_{ee}\\ \omega_{ee}\end{bmatrix},\quad\text{and}\quad F_{h}=\begin{bmatrix}f_{h}\\ \tau_{h}\end{bmatrix}. \tag{2}\]  
The desired pose of the robot was then obtained by integrating the forceps tip target velocity. Figure 9 depicts a block diagram of the proposed 6-DOF cooperative force controller based on admittance control. Given the force/torque command applied by the surgeon \(f_{h}\) and \(\tau_{h}\), the discretized desired velocity of the forceps tip (EE) is obtained as follows:  
\[{}^{B}V_{ee[k]}=\frac{M_{v}{}^{B}V_{ee[k-1]}+\Delta T^{B}F_{h[k]}}{M_{v}+ \Delta TD_{v}} \tag{3}\]  
Figure 8: Software architecture of the proposed robotic surgical system.  
where \(k\) is a discrete time step and \(\Delta T\) is the control period. We can then obtain the translational motion of the forceps tip as:  
\[{}^{B}p_{ee[k]}={}^{B}p_{ee[k-1]}+\Delta T^{B}\dot{p}_{ee[k]}. \tag{4}\]  
For the rotational motion, we used the relationship between the time derivative of the rotation matrix \(R\) and the angular velocity \(\omega\),  
\[\dot{R}_{ee}=S(\omega_{ee})R_{ee} \tag{5}\]  
where \(S(\omega_{ee})\in\Re^{3\times 3}\) is the angular velocity tensor (skew-symmetric matrix). The desired orientation of the EE can be computed by integrating (5) as:  
\[{}^{B}R_{ee[k]}={}^{B}R_{ee[k-1]}e^{S(\Delta T\omega_{ee[k]})}. \tag{6}\]  
By using the Rodrigues rotation formula, we obtain:  
\[{}^{B}R_{ee[k]}={}^{B}R_{ee[k-1]}(I+\sin\sigma\hat{S}+(1-\cos\sigma)\hat{S}^{2}) \tag{7}\]  
where \(\hat{S}\) is the normalized skew-matrix and \(\sigma\) is the magnitude of the rotation vector \(\theta=\Delta T\omega_{ee}\):  
\[\sigma=\left\|\Delta T\omega_{ee}\right\|,\quad\text{and}\quad\hat{S}=\frac{S (\Delta T\omega_{ee})}{\sigma}. \tag{8}\]  
Note that we ensured the orthogonality of the resulting rotation matrix with QR decomposition using Householder reflections.  
Figure 9: Block diagram of the force based cooperative control.
## 5 Challenges and Future Research Trends
### Enhancing Estimation with Limited Data  
The estimation accuracy of deep-learning-based methods highly depends on the quantity and quality of training and testing data. However, obtaining channel data can be challenging in some special communication scenarios. With limited or no data available, the performance of these methods often suffers. Therefore, it is necessary to combine multiple approaches in channel estimation algorithms. In this case, integrating transfer learning [97] or meta-learning [98] with deep learning could be an effective strategy to enhance the channel estimation accuracy.
# STOD: Towards Scalable Task-Oriented Dialogue System on MultiWOZ-API
## 5 Experiment and Analysis
### Multi-Domain Dialogue Modeling
#### 5.1.4 Further Analysis  
API Query Accuracy  
We evaluated the precision, recall, and F1 of the generated API queries in terms of whether the API query needs to be called (Whether Call API), whether the called API is correct (API Correctness), and whether the parameters of the called API are accurate (Args Accurateness). As shown in Table 4, models tend to predict more API queries. Among the turns that need to call API, in our model, all were predicted to call API. But about 15% of the turns predicted to call the wrong APIs, and about 30% of the turns predicted to call the right API but generate the wrong API call parameters. Further, we respectively analyzed API call prediction error types and API parameter prediction error types of three different API types. As shown in Figure 3a, 67% turns predict fewer APIs. As shown in Figure 3b, the API parameters predicted errors of book APIs and get_attr APIs, which have few parameters, which were all mostly caused by the API parameters being wrong, and find APIs, which have more parameters, were prone to generating more or fewer parameters.  
\begin{table}
\begin{tabular}{l c c c c} \hline  & **Success** & **Coherency** & **Fluency** & **Average** \\ \hline Agreement & 0.65 & 0.72 & 0.82 & 0.73 \\ \hline Galaxy & 1.1 & 1.3 & 1.7 & 1.37 \\ MSTOD (ours) & **1.5** & **1.6** & **1.8** & **1.63** \\ \hline \end{tabular}
\end{table}
Table 3: Human evaluation results.  
Since MSTOD incorporates API queries and DB results in the previous and current turns to generate user responses, in the end-to-end setting, we replace the golden API queries and DB results with the generated API queries and corresponding DB results. To measure the effect of API query accuracy, we use the golden API queries and DB results in previous turns and all turns to test the model. As shown in Table 5, if MSTOD tasks golden API information in previous turns, the Inform, Success, and BLEU will increase consistently. And if MSTOD tasks golden API information in all turns, Inform and BLEU will increase further, but the Success will drop a bit. Thus, the accuracy of API information in previous turns is more important for the model performance.
# Routing Design for Pipe System Considering Natural Frequency
## 4 A Novel Path Planning Method for Pipe Arrangement with Considering Natural Frequency  
This study aims to enhance the quality of pipe routing and ensure the pipes can withstand operational loads by considering natural frequency. Throughout the derivation of natural frequency [26], it becomes evident that calculating the natural frequency for a pipe model with a fixed geometric shape and centerline is not time-consuming. However, directly applying the natural frequency calculation algorithm to the pipe's routing algorithm poses challenges. This is because natural frequency calculation necessitates knowledge of the pipe's geometric model. In deterministic algorithms, the final centerline of the pipe is not known during the routing. Heuristic algorithms can provide the centerline and the geometric model of the pipe. During iterations, the centerline of the pipe changes. As a result, the pipe that requires natural frequency evaluation is not a single determined pipe but thousands of undetermined pipes with varying centerlines. This complexity makes it challenging to incorporate natural frequency considerations into heuristic algorithms for the existing pipe routing methods.  
To overcome these challenges, this paper introduces a novel path planning method that consider the natural frequencies of pipes. To address the limitation of considering natural frequency during the iterative process, a numerical surrogate model is developed to predict pipe natural frequencies. An evaluation function is formulated, integrating weighted values of natural frequency and pipe length. Finally, the effectiveness of the proposed algorithm is demonstrated through experiments on two well-designed examples and its application in an engine.  
Figure 6: Routing space model by adaptive decomposition.
# Energy Performance of LR-FHSS: Analysis and Evaluation
## 3 Overview of LoRaWAN and LR-FHSS
### LR-Fhss
Subsequent physical channels are chosen in a pseudo-random fashion, which guarantees an even carrier distribution. These parameters are summarized in Table 3 for the EU863-870 band [7]. In contrast to LoRa channels, as long as LR-FHSS packets stay within the designated bandwidth of the GW, they can be demodulated. Prior knowledge of certain frequencies or channel hopping sequences is not required. This enables several transmitters to operate simultaneously with distinct channel hopping sequences if the GW is able to listen to the whole channel bandwidth at the same time [7]. In contrast to original LoRaWAN use cases, this enables for the simultaneous reception of hundreds of packets, which increases the complexity of signal recognition at the receiver but makes it appropriate for networks with high device density (e.g., satellite-scale).
## 2 Background Study
### Multisensor Data Fusion  
Sensor fusion is a technique that combines data from various sensors to produce information superior to what would be possible from any single sensor. This rapidly growing field is increasingly applied across diverse sectors, including biomedical, aerospace, and environmental systems. The benefits of sensor fusion are manifold, offering deeper analysis, enhanced system resilience, and more precise predictions with reduced uncertainty. Multisensor data fusion, in particular, integrates observations from multiple sensors, both similar and different, to create a more comprehensive and robust depiction of a target environment or process. This approach has recently gained significant traction, with applications in navigation, robotics, and industrial automation, among others. It leverages the redundancy of measurements to improve system robustness and accuracy, leading to better performance of integrated multisensory systems. The fusion process encompasses various related fields, such as signal processing, sensor management, and artificial intelligence techniques.
## 3 Wake-Up Radio Communication Protocols
### Single Hop and Multi-Hop Wake-Up Network
#### 3.1.3 Wake-Up Multi-Hop (Relay)  
This type of wake-up radio is used in multi-hop networks. It can simultaneously serve as a wake-up signal receiver and transmitter, and hence can relay the wake-up signal to a next hop. The wake-up radio check to determine the wake-up packet is intended for it and taps the wake-up signal to harvest enough energy to become activated. The wake-up radio sends the interrupt trigger to the main radio to perform its sending and/or receiving function. However, the wake-up radio relays the wake-up call to the next hop address when it reads that the address on the signal packet is destined for another node. The relay of a wake-up signal is performed by the wake-up radio transmitter without waking up the main radio. If the node (known as an intermediate relay node) detects an event where a signal is to be forwarded, it packs the signal information into a packet, and delivers the packet to a next hop via multi-hop relaying. After forwarding the wake-up signal, the node usually sets an internal timer to avoid starting a new data transmission before the packet forwarding from the source to the destination node is expected to complete. The wake-up multi-hop process is shown in Figure 6. The nodes need to have hardware with transceiver capability that can receive and send a wake-up signal.  
Figure 5: Passive wake-up reception process.  
Figure 6: Wake-up relay process.
## 3 Comparative Flexural Test of FRP-Concrete Hybrid Beam
### Test Observations of the Two Beams  
For Beam 1, the interfacial connection was designed to be mainly provided by two rows of steel bolts, and the width of the contact surface between FRP and in situ cast concrete was the same as the FRP upper flange. The steel bolts in Beam 1 and Beam 2 had a yield strength of 480 MPa (Grade M6.8) and 880 MPa (Grade M10.8), respectively. With an increase to the load at about 55 kN, the interfacial slip recorded by the dial gauges at the beam ends occurred suddenly, at the same time a loud noise was heard. At about 70 kN, two cracks sprouted at the bottom of the concrete slab at the locations beneath the two load points, respectively, and then propagated upward. At 238 kN, a snapping noise at the interface was heard. Steel bolts in the shear span were broken due to the shear force, and this failure mode was the same as the push-out specimens in Figure 2.  
Figure 4: Cross section of (**a**) Beam 1, and (**b**) Beam 2 (units in mm, not to scale).  
For Beam 2, the interfacial connection was designed to be provided by two rows of steel bolts and two SIP forms (see Figure 4), so the contact surface between the FRP and in situ cast concrete was much larger than that of Beam 1. At about 90 kN, two cracks sprouted at the bottom of the concrete slab at locations beneath the two symmetric load points, respectively, and then extended upward. At about 200 kN, the interfacial slip recorded by the dial gauges occurred suddenly, and a loud noise was heard at the same time. At about 206 kN, a longitude crack was found in the joint area of the FRP web and flange. At about 250 kN, a longitude horizontal crack alongside the fiber direction was found in the FRP web. At about 306 kN, the crack became wider, resulting in failure, thus the concrete was crushed on the top of the bending section just beside the north loading point (Figure 5).  
The load responses of deflection of the two beams are shown in Figure 6a. The ultimate capacity of Beam 2 was 306 kN, which was 28.6% higher than the capacity of Beam 1 (238 kN). When the load was less than 200 kN, the deflection of Beam 1 was always lower than Beam 2. Thus, it can be concluded that greater capacity and higher flexural rigidity can be obtained by using SIP forms. The reason, according to Figure 6b, is that Beam 1 had a smaller slip, which means that a higher degree of composite action was obtained. This finding well matched the push-out tests in this paper. It was interesting to see in Figure 6a that at about 205 kN, there was a plateau in the load-deflection curve of Beam 1, which was caused by the dramatic slip along the FRP-concrete interface. Additionally, when the mid-span deflection exceeded 60 mm, the slope of the load-mid-span deflection curve had a nearly fixed value.  
Figure 5: Failure mode for Beam 2. (**a**) Loading of beam 2; (**b**) Concrete cracks beneath the loading point; (**c**) Mid-span concrete crushing (from top view); (**d**) Mid-span concrete crushing (from side view); and (**e**) The slip and separation of the SIP-concrete interface at the beam end.
## 3 Legal Challenges Posed by Mega-Constellations to Existing International Space Law Regime
### Transfer of Satellite Ownership in Orbit
However, achieving a global consensus will be difficult given the competing interests involved. In summary, the legal status of mega-constellations is currently highly uncertain and unresolved under existing space law frameworks. Significant international cooperation and new binding regulations will likely be required to manage their impacts and implications going forward. Against this background, the next section will assess the applicability of mega-constellations under the principles of equitable access and non-appropriation, as the core principles in this study. Assessing Applicability of Mega-Constellations in Light of Principles of Equitable Access and Non-Appropriation
# BDS-3 Triple-Frequency Timing Group Delay/Differential Code Bias and Its Effect on Positioning
## 2 Data and Methods
### Triple-Frequency Positioning Model of BDS-3
#### 2.2.2 IF123  
The code and phase observation models for the IF123 combinations from frequencies \(i\), \(j\) and \(k\) are expressed as:  
\[\left\{\begin{array}{l}p_{r,IF_{ijk}}^{s}=e_{i}\cdot p_{r,i}^{s}+e_{j}\cdot p _{r,j}^{s}+e_{k}\cdot p_{r,k}^{s}\\ l_{r,IF_{ijk}}^{s}=e_{i}\cdot l_{r,i}^{s}+e_{j}\cdot l_{r,j}^{s}+e_{k}\cdot l_{r, k}^{s}\end{array}\right. \tag{6}\]  
with:  
\[\left\{\begin{array}{l}e_{i}+e_{j}+e_{k}=1\\ e_{i}+\mu_{j}\cdot e_{j}+\mu_{k}\cdot e_{k}=0\\ e_{i}^{2}+e_{j}^{2}+e_{k}^{2}=min\end{array}\right. \tag{7}\]  
where \(e_{i}\), \(e_{j}\) and \(e_{k}\) are the combined coefficients of the IF123 model, which satisfy the constraints of distance invariance, elimination of ionospheric delay and noise minimization.  
From Equation (7), we can further obtain:  
\[\left\{\begin{array}{l}e_{i}=\frac{p_{i}^{2}+p_{k}^{2}-\mu_{j}-\mu_{k}}{2(p _{i}^{2}+p_{k}^{2}-\mu_{j}\cdot\mu_{k}-\mu_{j}-\mu_{k}+1)}\\ e_{j}=\frac{p_{i}^{2}+p_{j}\cdot\mu_{k}-\mu_{j}+1}{2(p_{i}^{2}+p_{k}^{2}-\mu_{j }\cdot\mu_{k}-\mu_{j}-\mu_{k}+1)}\\ e_{k}=\frac{p_{i}^{2}+p_{j}\cdot\mu_{k}-\mu_{k}+1}{2(p_{i}^{2}+p_{k}^{2}-\mu_{ j}\cdot\mu_{k}-\mu_{j}-\mu_{k}+1)}\end{array}\right. \tag{8}\]  
The UCDs and UPDs of receivers and satellites in Equation (6) can be expressed as:  
\[\left\{\begin{array}{l}b_{IF_{ijk}}^{s}=e_{i}\cdot b_{i}^{s}+e_{j}\cdot b_{i }^{s}+e_{k}\cdot b_{k}^{s}\\ b_{r,IF_{ijk}}=e_{i}\cdot b_{r,i}+e_{j}\cdot b_{r,j}+e_{k}\cdot b_{r,k}\\ B_{IF_{ik}}^{s}=e_{i}\cdot B_{i}^{s}+e_{j}\cdot B_{j}^{s}+e_{k}\cdot B_{k}^{s}\\ B_{r,IF_{ijk}}=e_{i}\cdot B_{r,i}+e_{j}\cdot B_{r,j}+e_{k}\cdot B_{r,k}\end{array} \right. \tag{9}\]Similarly, three different frequencies, \(i\), \(j\) and \(k\), after correcting the satellite clock offset, form the unique code and phase observation models, as follows:  
\[\left\{\begin{array}{l}p^{s}_{r,I\,F_{ijk}}=u^{s}_{r}\cdot x+dt_{r,I\,F_{ijk}}+m ^{s}_{r}\cdot Z_{r}-\overline{b}^{s}_{IF_{ijk}}+\epsilon^{s}_{r,I\,F_{ijk}}\\ l^{s}_{r,I\,F_{ijk}}=u^{s}_{r}\cdot x+dt_{r,I\,F_{ijk}}+m^{s}_{r}\cdot Z_{r}+ \overline{N}^{s}_{IF_{ijk}}+\epsilon^{s}_{r,I\,F_{ijk}}\end{array}\right. \tag{10}\]  
with:  
\[\left\{\begin{array}{l}dt_{r,IF_{ijk}}=dt_{r}+\overline{b}_{r,I\,F}=dt_{r}+( e_{i}\cdot b_{r,i}+e_{j}\cdot b_{r,j}+e_{k}\cdot b_{r,k})\\ \overline{N}^{s}_{IF_{ijk}}=e_{i}\cdot\hat{N}^{s}_{r,j}+e_{j}\cdot\hat{N}^{s} _{r,j}+e_{k}\cdot\hat{N}^{s}_{r,k}+B_{r,IF_{ijk}}-B^{s}_{IF_{ijk}}+b^{s}_{IF}- \overline{b}_{r,IF}\end{array}\right. \tag{11}\]  
where \(dt_{r,I\,F_{ijk}}\) is the receiver clock offset that absorbs the triple-frequency IF receiver's UCDs; \(\overline{b}^{s}_{IF_{jk}}\) denotes the satellite UCDs for the \(i\), \(j\) and \(k\) IF combinations after correcting for the satellite clock offset; and \(\overline{N}^{s}_{IF_{ijk}}\) denotes the \(i\), \(j\) and \(k\) IF combinations floating ambiguity.
## 4 Experimental Design and Setup
### Prediction Accuracy Measures
#### 4.3.2 Statistical Tests and Effect Size  
Test of significance is used in this study to validate the results according to a defined hypothesis. We carried out a one-way analysis of variance (ANOVA) F test [53] on the performance of AUC. We selected ANOVA because there are more than one pair (i.e., four prediction models). Therefore, this test is performed to investigate if there exists a significant difference between the group population means (i.e., performance of the prediction models). Statistical significance is defined in this study as (\(\alpha=0.05\)) and we evaluate and compare the \(p\)-value with this defined value. If the \(p\)-value is smaller than \(\alpha\) then the H\({}_{0}\) (the null hypothesis, that there is not a statistically significant difference between all the group population means) is rejected.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Predicted Class (No)** & **Predicted Class (Yes)** \\ \hline Actual class (No) & True negatives (TN) & False positive (FP) \\ \hline Actual class (Yes) & False negative (FN) & True positive (TP) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Confusion matrix.  
In addition, to further understand the strength of a result, the effect size is used in this study too, which is considered an essential component to complement significance tests [54]. From the various effect size measures introduced in the literature, eta-squared (\(\eta^{2}\)) is selected in this study because this is a suitable measure for ANOVA [54]. [PERSON] proposed the standard classifications of the effect sizes, which are small (\(\approx\)0.01), medium (\(\approx\)0.06), and large (\(\approx\)0.14) [55]. Equation ((5)) computes the value of \(\eta^{2}\).  
\[\eta^{2}=\frac{SS\text{ effect}}{SS\text{ total}} \tag{5}\]  
SSeffect is the sum of squares of the effect, and SStotal is the total sum of squares [54]. Additionally, If H\({}_{0}\) is rejected, we apply multiple comparisons using a plot chart of [PERSON]'s confidence intervals [53] to identify which pairs are significantly different. If a confidence interval does not include 0 then the pair is significantly different.
## 2 Analytical Methodology
### Cfd-Dem
#### 2.1.2 Discrete Particle Phase  
The motion of the discrete particle phase was evaluated by the DEM solver provided by the STAR-CCM+ code in this research. In the DEM solver, particle translation and rotation are considered based on [PERSON]'s law of motion, with no mesh requirement.  
The governing equation for simulating the particle translation is subjected to the particle linear momentum conservation, given below [32]:  
\[m_{i}\frac{d\overset{\rightarrow}{V}_{i}}{dt}=m_{i}\overset{\rightarrow}{S}+ \sum_{j=1,j\
eq i}(\overset{\rightarrow}{F}_{cont,ij})+\overset{\rightarrow }{F}_{f,i}, \tag{5}\]  
where \(m_{i}\) [kg] and \(\overset{\rightarrow}{V}_{i}\) [m/s] are the mass and the velocity of the particle \(i\). \(\overset{\rightarrow}{F}_{f,i}\) [kg\(\cdot\)m/s\({}^{2}\)] is the fluid-particle force on the particle \(i\), and is explained in Section 2.1.3. \(\overset{\rightarrow}{F}_{cont,ij}\) is the contact force from particle \(j\) to particle \(i\), evaluated by the contact model (spring-dashpot model), referring to the particle properties (e.g., Young's modulus, Poisson's ratio, static friction coefficient, rolling resistance coefficient, and particle size). The detailed explanations of the spring-dashpot model applied in this research can be found in our previous research (regarding the CFD-DEM method's validation, which is also conducted based on the code of STAR-CCM+ [35]).  
The equation of the energy conservation for the discrete particle phase can be expressed as below [32]:  
\[m_{i}C_{p,i}\frac{dT_{i}}{dt}=\sum_{j=1,j\
eq i}(Q_{cond,ij})+Q_{s,i}+Q_{f,i}, \tag{6}\]  
where \(C_{p,i}\) [J/kg\(\cdot\)K] and \(T_{i}\) [K] are the particle \(i\)'s specific heat and temperature. \(Q_{f,i}\) [W] is the heat transfer term from the fluid phase to the particle \(i\), which will be explained in Section 2.1.3. \(Q_{s,i}\) [W] is the heat source term of the particle \(i\), such as the generated decay heat in the debris particle. \(Q_{cond,ij}\) [W] represents the heat conduction from the particle \(j\) to \(i\), and it can be obtained as below [32]:  
\[Q_{cond,ij}=4\gamma_{c}k_{eq}(T_{j}-T_{i}), \tag{7}\]  
where \(\gamma_{c}\) [m] is the contact area radius. \(k_{eq}\) [W/m\(\cdot\)K] is the equivalent thermal conductivity between the particle \(i\) and \(j\) in the form of:  
\[\frac{1}{k_{eq}}=\frac{1}{k_{i}}+\frac{1}{k_{j}}, \tag{8}\]  
where \(k_{i}\) [W/m\(\cdot\)K] and \(k_{j}\) [W/m\(\cdot\)K] are the thermal conductivities of particle \(i\) and \(j\), respectively.