esa-sceva/satcom-chunk-collection huggingface dataset:
# A Gap Waveguide-Based Compact Rectangular Waveguide to a Packaged Microstrip Inline Transition
## 7 Conclusions  
Two simple inline microstrip to standard rectangular waveguide transitions based on a Chebyshev transformer implemented in gap waveguide technology have been studied in this paper. Both designs are suitable for analytical design, which simplifies the whole procedure. Besides, the configurations used in this work avoid any kind of soldering and losses caused in the H-plane cuts of the waveguide, thanks to band-gap created by the bed of nails which surrounds the gap waveguide. Therefore, as contact between the upper and bottom part is not required, the fabrication tolerances can be relaxed. This, plus the fact that intricate shapes are avoided, reduces their cost with respect to other configurations [20; 26; 27].  
In the first configuration, since the substrate covers the whole bottom metallic block, problems associated to substrate cutting with a specific shape are avoided, which simplifies manufacturing. However, the first steps of the transition must be replaced for ridge gap waveguide in order to avoid higher-order mode propagation, which adds complexity to the design process. Evenly, thanks to the microstrip line taper which ensures good contact with the ridge gap waveguide, the transition is more tolerant to misalignment.  
The second transition suppresses the microstrip taper transition, but at the expense of using a narrow ridge gap waveguide section which makes the circuit less tolerant to misalignments. However, in this case the substrate does not cover the whole gap between the bed of nails and the lid. This way, any possibility of higher-order mode propagation is avoided and the design procedure is simplified. Finally, in order to prove these concepts, W-band back-to-back transitions were validated and a comparison with the state-of-the-art is shown in Table 4 featuring similar performances. These transitions are, therefore, a good alternative to other designs.  
Figure 13: Analysis of tolerances for a misalignment of the microstrip on the x-direction in second prototype (Figure 1b): (**left**) insertion loss and (**right**) return loss.  
**Author Contributions:** Investigation: [PERSON], [PERSON], [PERSON] and [PERSON]; writing--original draft: [PERSON]; writing--review and editing: [PERSON]. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Spanish Ministry of Economy and Competitiveness, project numbers TEC2013-47753-C3-1-R, TEC2016-76997-C3-1-R and PID2019-109984 RB-C43.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Numerical Simulation of Heat Transfer Performance of a Dimpled Tubular Heat Exchanger
## 2 Numerical Simulation
### Grid and Calculation Conditions
#### 2.2.3 Computational Boundary Condition  
The inner tube is a high temperature fluid, and the annulus space is a low temperature fluid. Water flow medium was used for numerical calculation, and 20 G was selected as the pipe wall material, and its physical parameters were shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Symbol**} & \multirow{2}{*}{**\(\varphi\)1**} & \multirow{2}{*}{**\(\varphi\)2**} & **Depth of** & **Size of** & **Size of** & **Tube** & **Axial Pitch** & **Included** \\  & & & **Concave** & **Outer** & **Tube** & **Inner Tube** & **L** & **P** & **Angle 0** \\ \hline
6–11 & 6 & 11 & 3 & & & & & \\ \hline
8–13 & 8 & 13 & 4 & & & & & \\ \hline
10–15 & 10 & 15 & 5 & \(\varphi\)121 \(\times\) 14 & \(\varphi\)73 \(\times\) 11 & 1000 & 20 & 60\({}^{\circ}\) \\ \hline
12–17 & 12 & 17 & 6 & & & & & \\ \hline
14–19 & 14 & 19 & 7 & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structure and size of dimpled tube.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Meshes** & **1.67 Million** & **2.25 Million** & **3.05 Million** & **4.25 Million** \\ \hline Nusselt number & 198.28 & 223.18 & 238.42 & 244.19 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Nusselt number for different grids.  
Figure 2: Computational grid.  
The temperature of water medium is between 15-273 \({}^{\circ}\)C, and the density is between 790-1008 kg/m\({}^{3}\) under 20 MPa pressure. Therefore, the physical parameters of water medium are given by multi-point linear interpolation method in numerical simulation [9].  
The boundary parameters are determined and calculated according to the actual working conditions, and the specific Settings are shown in Table 4. Inlet of inner tube is set to velocity inlet.  
Steady-state numerical calculation is adopted, and the specific discrete scheme and algorithm are shown in Table 5.  
When the absolute value of energy residual is stable below 1 \(\times\) 10\({}^{-6}\) and other terms are lower than 1 \(\times\) 10\({}^{-6}\), it is judged to be convergent. The energy equation has an underrelaxation factor of 0.5, and the rest remain default in Fluent.
## 8 Future Challenges for the Enhancement of ED Limitations
### Research Challenges Related to the ED Degradation at Low SNRs  
ED performance significantly depends on an estimate of the noise level in terms of precision and reliability. Since this estimate is used for calculating SNR at the position of SU, the evaluation of the influence of different DT adaptation levels for versatile NU levels was presented in the previous section. According to the results presented in Figures 9-12 and Figures 15-17, an additional relevant disadvantage of the ED method is the lack of possibility to reliably sense PU signals for low SNRs at the location of SU (Table 4), which was also proven in Refs. [66; 67].  
According to the results of the simulations presented previously, if the noise power contributes to the overall SNR in such a way that the SNR is below a specific level known as the SNR wall [68], the energy detector cannot sense a difference between the PU signal and the somewhat higher noise power. This works independently of the sensing duration length or the used sample quantity [55]. Since measuring NU is a demanding process due to the constant variations of NU over time, ED techniques require a mechanism that constantly estimates NU in order to accurately compute the overall noise power.  
This estimation can be done using OFDM signal guard bands or by means of a channelized radiometer in the frequency domain [69; 70]. The channelized radiometer divides the overall frequency spectrum into several channels and then combines the energy from each separate channel using a radiometer. The cell averaging concept presented in Ref. [68] was used for the realisation of channelized radiometer when CFAR ED strategies are considered [70]. An estimation concept based on an adaptive noise level was offered in Ref. [67], as a consequence of the errors detected in the assessment of noise power [56]. This concept was based on classification algorithms of multiple signals, which can split the noise and signal subspaces and estimate the noise floor. An approach based on the DT selection according to the real measurements of the noise level power present in the received signal during SS was proposed in Ref. [65]. The results showed an enlargement in the detection probability compared to those based on ED with a static threshold.  
Nevertheless, new approaches offering more reliable and accurate noise level estimation for SS using ED are missing. Such novel approaches which will increase the precision of the noise level estimation can contribute to the improvement of signal detection at weaker SNRs (Table 4).
## 3 Experiments
### Evaluation Metrics  
To comprehensively evaluate the performance of the segmentation network, we employed four widely used evaluation metrics for the semantic segmentation of remote sensing building images in the experiment. These metrics are Precision (P), Mean Pixel Accuracy (MPA), Mean Intersection over Union (MIoU), and Frequency Weighted Intersection over Union (FWIoU). Their definitions are as follows:  
\[\text{P}=\frac{\text{BB}}{\text{BB}+\text{NB}} \tag{14}\]  
\[\text{MPA}=\frac{\frac{\text{BB}}{\text{BB}+\text{NB}}+\frac{\text{NN}}{\text{ NN}+\text{BN}}}{2} \tag{15}\]  
\[\text{MIoU}\ =\ \frac{\frac{\text{BB}}{\text{BB}+\text{BN}+\text{NB}}+\frac{ \text{NN}}{\text{NN}+\text{BN}+\text{NB}}}{2} \tag{16}\]  
\[\text{FWIoU}\ =\frac{\text{BB}+\text{BN}}{\text{BB}+\text{BN}+\text{NB}+\text{ NN}}\cdot\big{(}\frac{\text{BB}}{\text{BB}+\text{BN}+\text{NB}}\big{)}+\frac{ \text{NN}+\text{NB}}{\text{BB}+\text{BN}+\text{NB}+\text{NN}}\cdot\Big{(} \frac{\text{NN}}{\text{NN}+\text{BN}+\text{NB}}\Big{)} \tag{17}\]  
In the above formulas, BB represents the number of pixels correctly predicted as building targets; BN denotes the number of pixels incorrectly detected as background targets; NB refers to the number of pixels incorrectly detected as building targets; and NN represents the number of pixels correctly detected as background targets.  
In advance, to evaluate the building edge detection capabilities of each model, this paper introduces two evaluation metrics (border IoU and Hausdorff distance), which are defined as shown in the following equations.  
\[\text{border IoU}=\frac{\text{BB}}{\text{BB}+\text{BN}+\text{NB}} \tag{18}\]  
\[\text{HD}(\text{A},\text{B})\ =\ \text{max}\big{(}\text{sup}_{\text{a}\in\text{ Ai}}\text{inf}_{\text{b}\in\text{B}}\text{d}(\text{a},\text{b}),\text{sup}_{\text{b}\in \text{Bin}}\text{f}_{\text{a}\in\text{A}}\text{d}(\text{a},\text{b})\big{)} \tag{19}\]  
where A and B represent the sets of predicted and ground truth edge points, respectively, and d denotes the Euclidean distance.
# Performance Evaluation Model of Landport and Seaport Collaboration Using the Support Vector Machine
## 5 Case Analysis
### Corresponding Countermeasures  
1. Signing the cooperation agreement  
The customs of the city where the dry port is located and the Entry Inspection and Quarantine Bureau of the seaport city have signed more reciprocal cooperation agreements to develop dry ports and extend the seaport services to dry ports. For goods imported from seaports and transported to dry ports, they can be declared, inspected and released at dry ports; for the goods transported from the dry port to the seaport, the inspection declaration and other exit work can be carried out at the dry port, and no re-inspection is required after the goods are transported to the seaport.  
2. Cargo priority at the dry port  
The flow direction of goods can be implemented by giving priority to release and loading and unloading, and delimiting special areas to improve the efficiency and smoothness of goods.  
1. Warehouse: an appropriate warehouse area shall be set aside as a dedicated warehouse for the dry port.
2. Parking lot: this is convenient for the management of vehicles, and a certain number of special vehicles are established in the existing parking lot.
3. Establish a dry-port information platform.  
The efficient operation of the dry port is inseparable from the close cooperation with customs declaration and other aspects to ensure the timely and accurate information flow of incoming and outgoing goods. The waterless port information platform will integrate the logistics information and customs management platform, and become the center of information communication between various parties.  
4. Most favorable sea-port treatment  
The most-favored-nation treatment for sea ports includes streamlining customs procedures, offering special trade financing terms, entering into trade cooperation agreements, and other measures designed to attract a greater volume of trade activities to the port. This serves to bolster the port's competitiveness, stimulate economic development, and foster trade cooperation.  
1. The most favorable agreement can be signed between the dry port and the seaport to provide convenient, efficient and low-cost services for the goods of both parties and form the mutual adsorption effect.
2. The goods shall be graded, and those with high risk coefficient shall be inspected with emphasis; those with a medium risk coefficient shall be spot-checked according to a certain proportion; a direct release policy shall be adopted for those with a low risk coefficient.
## 5 Performance Study
### Simulation Results
#### (3) Number of data flows
(5) Effect of parameter \(\xi\) on the performance of RoCoMAR  
As discussed in Section 4.1, the value of parameter \(\xi\) (xi) affects the performance of RoCoMAR. In order to understand the effects in detail, we collect the average throughput and average delay of RoCoMAR over different values of \(\xi\) in the simulation scenario where the load of each data flow is set to 900 Kbps. The maximum speed of group and member movements are also set to 20 m/s. The value of \(\xi\) varies from 0 to 0.6. As shown in Figure 15, the average throughput of RoCoMAR decreases as the value of \(\xi\) grows. This is because a larger value of \(\xi\) results in a low possibility of placing a new relay node even some degree of performance gain is expected. Figure 16 shows the average end-to-end delay of RoCoMAR over different values of \(\xi\). As shown in Figure 16, the delay of RoCoMAR mostly keeps stable as the value of \(\xi\) varies. The these results indicate the variation of \(\xi\) does not have a significant effect on the end-to-end delay of RoCoMAR. Figure 14: Effects of of transmission range on average end-to-end delay. Figure 15: Effects of value of \(\xi\) on average throughput of RoCoMAR.
# Simulative and Experimental Investigation of Vibration Transfer Path at Gearboxes
## 3 Simulation Model
### Gear Pair  
In this work, the helical gear pair is modeled as one spring element connected with nodes 3 and 9 of the shafts. Figure 3 shows several coordinate systems for the calculation of the gear mesh stiffness matrix in a global coordinate system with index K. The ZE coordinate system is the system of the tooth mesh, which means that the direction of the spring element is identical with ZEX. To achieve the II coordinate system, the ZE coordinate system should first rotate with the helix angle \(\upbeta\) of the gears around the \(z\)-axis in the positive direction. To achieve the I coordinate system, the II coordinate system should then be rotated with the operating pressure angle \(\alpha_{\text{wt}}\) around the \(y\)-axis in the positive direction. Finally, a rotation of 90 degrees around the \(z\)-axis in the negative direction achieves the global coordinate system. Based on these rotations, the transfer matrix from ZE to the global coordinate system can be calculated [27].  
The gear mesh stiffness matrix in the ZE coordinate system [27] is as follows:  
\[\begin{pmatrix}f_{x1}\\ f_{y1}\\ f_{z1}\\ f_{x2}\\ f_{y2}\\ f_{z2}\end{pmatrix}=\begin{pmatrix}c&0&0&-c&0&0\\ 0&0&0&0&0&0\\ 0&0&0&0&0&0\\ -c&0&0&c&0&0\\ 0&0&0&0&0&0\\ 0&0&0&0&0&0\\ \end{pmatrix}\cdot\begin{pmatrix}u_{x1}\\ u_{y1}\\ u_{z1}\\ u_{x2}\\ u_{y2}\\ u_{z2}\end{pmatrix}, \tag{2}\]  
Figure 3: Several coordinate systems for calculation of gear stiffness matrix, modified to [27]. (**a**) Global representation; (**b**) coordinate transformation in several steps.  
where \(f\) and \(u\) are the force and displacement of points A and B on the base circle of the gears and \(c\) is the stiffness of the spring element, which is equal to the gear mesh stiffness and has been calculated using the FVA-program DZP [25]. The stiffness matrix in the ZE coordinate system has been transferred to the global coordinate system by multiplying the transfer matrices that were described before.  
The stiffness matrix after the coordinate transformation still presents the force and displacement of points A and B. Hence, it is necessary to convert it by multiplying an additional matrix so that the stiffness matrix corresponds to the center point of the gears [26]. In the simulation model, the center points are nodes 3 and 9 in Figure 2.
# Hierarchical Federated Learning for Edge-Aided Unmanned Aerial Vehicle Networks
## 1 Introduction
### Contributions  
The main contributions of our work are two-fold as follows:  
* We develop a novel and high-performing FL scheme, namely, the hierarchical FL algorithm, for the edge-aided UAV network that works well in real-world scenarios with non-i.i.d data distributions (i.e., highly skewed feature and label distributions). In this algorithm, we make an innovative idea to employ commonly shared data at the edge servers to effectively solve the divergence issue caused by the non-i.i.d. nature. In practice, this idea is realizable since the commonly shared data can be made or constructed on the edge servers offline by collecting exemplary data samples from the UAVs. We also present an effective method to hierarchically aggregate the local models of both the UAVs and the edge servers for the global model update.
* We present extensive numerical results under various degrees of non-i.i.d data distributions, especially including several extreme situations with label distribution skew in order to demonstrate the superiority and effectiveness of the proposed hierarchical FL algorithm, compared to other baseline FL algorithms. From the numerical results, we also provide useful and insightful guidelines on how the hyperparameters of the hierarchical FL can be set and used in practical UAV networks with edge servers.
## 4 Stability Analysis
### Sliding Mode Attitude following Control  
**Theorem 1**.: _Consider the dynamic model described in (15) and the control for attitude following in (29). The attitude following error dynamics is exponentially stable if the switching gain satisfies the following condition._  
\[\lambda_{m}(K_{1})\geq\Big{(}\|\widetilde{H}\|\|\tilde{r}_{r}\|+\|\widetilde{C} \|\|\dot{r}_{r}\|+\|\tau_{d}\|\Big{)}+\varepsilon_{1} \tag{45}\]  
_where \(\varepsilon_{1}\) is a positive constant._  
**Proof.**\(\widehat{H}\) and \(\hat{C}\) represent the nominal \(H\) and \(C\), where \(\widetilde{H}=H-\widehat{H}\) and \(\ \widetilde{C}=C-\hat{C}\).  
The rotational dynamics can be expressed as follows:  
\[\widehat{H}(q)\tilde{r}+\hat{C}(r,\dot{r})\dot{q}=\Psi^{T}(\tau+\tau_{d})+h_{ 1}(r,\dot{r},\tilde{r}) \tag{46}\]  
\[h_{1}(r,\dot{r},\tilde{r})=-\widetilde{H}(r)\tilde{r}-\widetilde{C}\big{(}r, \dot{r}\big{)}\dot{r}\]  
Define  
\[\dot{r}_{r}=\dot{r}_{d}-\Lambda_{1}(r-r_{d}) \tag{47}\]  
and  
\[s_{1}=\dot{r}-\dot{r}_{r}=\dot{r}-\dot{r}_{d}+\Lambda_{1}(r-r_{d}) \tag{48}\]  
It follows from (48) that  
\[\dot{s}_{1}=\tilde{r}-\ddot{r}_{r}=\tilde{r}-\ddot{r}_{d}+\Lambda_{1}\big{(} \dot{r}-\dot{r}_{d}\big{)} \tag{49}\]  
Now, we propose the control  
\[\tau=\Psi^{-T}\big{[}\dot{H}\tilde{r}_{r}+\hat{C}\dot{r}_{r}-K_{1}\;SGN(s_{1} )-K_{3}s_{1}-K_{4}\tilde{r}\big{]} \tag{50}\]  
where \(\ \tilde{r}=r-r_{d}\) and \(K_{1},\ K_{3},\ \text{and}\ K_{4}\) are diagonal matrices. \(\square\)  
Define the Lyapunov function  
\[V_{1}=\frac{1}{2}s_{1}^{T}M(r)s_{1}+\frac{1}{2}\tilde{r}^{T}K_{4}\tilde{r} \tag{51}\]  
Using (50) and taking derivative of \(V_{1}\) yield  
\[\dot{V}_{1}=s_{1}^{T}H\dot{s}_{1}+\frac{1}{2}s_{1}^{T}\dot{H}s_{1}+\tilde{r}^ {T}K_{4}\dot{\tilde{r}} \tag{52}\]  
If the switching gain meets the condition as follows  
\[\lambda_{m}(K_{1})\geq\Big{(}\|\widetilde{H}\|\|\tilde{r}_{r}\|+\|\widetilde{C }\|\|\dot{r}_{r}\|+\|\tau_{d}\|\Big{)}+\varepsilon_{1} \tag{53}\]  
where \(\varepsilon_{1}\) is a positive constant.  
From (52), we have  
\[\dot{V}_{1}\leq-\varepsilon_{1}\;s_{1}^{T}SGN(s_{1})-s_{1}^{T}K_{3}s_{1}- \tilde{r}^{T}K_{4}\tilde{r}<0\;,\;s_{1}\
eq 0 \tag{54}\]  
The following adaptation law can replace the switching gain \(K_{1}\)  
\[K_{1}=diag([k_{1}\;\;k_{2}\;\;k_{3}]) \tag{55}\]  
\[k_{i}(t)=k_{ci}|\eta_{i}|+k_{mi}\]where \(k_{ci}>0,\ k_{mi}>0\) and \(\eta_{i}\) is the obtained by filtering the \(sgn(s_{1i})\) using a low-pass filter  
\[\zeta_{i}\ \dot{\eta}_{i}+\eta_{i}=sgn(s_{1i}),\ \eta_{i}(0)=0 \tag{56}\]  
where \(\zeta_{i}\) is a positive constant.
# Grating-Assisted Fiber to Chip Coupling for SOI Photonic Circuits
## 5 Lowering Footprint
### Linear Grating Based Compact Tapers
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Paper Designs** & **Length (\(\upmu\)m)** & \begin{tabular}{c} **Initial Width** \\ **(\(\upmu\)m)** \\ \end{tabular} & \
\begin{tabular}{c} **Coupling** \\ **Efficiency** \\ **Theoretical** \\ \end{tabular} & **Remarks** \\ \hline \multirow{4}{*}{Adiabatic Paper} & Linear [39] & 150\(-\)500 & 10\(-\)0.5 & 65\%–99\% & Tradeoff between the taper length and coupling efficiency due to the adiabatic transition \\ \cline{2-6}  & Exponential [40] & 200 & 10\(-\)0.5 & 70\% & Tradeoff between the taper length and coupling efficiency due to the adiabatic transition \\ \cline{2-6}  & Parabolic [41] & 200 & 10\(-\)0.5 & 98.3\% & \\ \cline{2-6}  & Efficient Adiabatic [42] & 120 & 10\(-\)0.5 & 98.3\% & \\ \hline \multirow{4}{*}{Non-Adiabatic} & Complex Non-adiabatic [43] & 15.4 & 10\(-\)0.56 & 70\% & \\ \cline{2-6}  & & & & -1 dB (TE) & Complexity in \\ \cline{2-6}  & Lens-assisted [44] & 20 & 10\(-\)0.45 & -5 dB (TM) & Fabrication \\ \cline{2-6}  & Discontinuous [45] & - & 10\(-\)0.45 & 90\% & \\ \hline \multirow{4}{*}{Non-Adiabatic} & Compact Taper [46, 47, 48] & 15 & 10\(-\)0.5 & 92\% &
\begin{tabular}{c} Robust to \\ Fabrication errors, \\ Broadband, Efficient \\ \end{tabular} \\ \cline{2-6}  & & & & & \\ \cline{1-1} \cline{2-6}  & & & & \\ \cline{1-1} \cline{2-6}  & & & & \\ \cline{1-1} \cline{2-6}  & & & & \\ \cline{1-1} \cline{2-6}  & & & & \\ \cline{1-1} \cline{2-6}  & & & & \\ \cline{1-1} \cline{2-6}  & & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Various Adiabatic and Non-Adiabatic Tapers on SOI proposed in literature. Figure 11: SEM image of a compact taper along a linear grating coupler for 1.55 \(\upmu\)m TE polarization for (a) SOI ridge waveguide (b) SOI wire waveguide.
# Real-Time Indoor Positioning Based on BLE Beacons and Pedestrian Dead Reckoning for Smartphones
## 3 Methodology
### Particle Filter
#### 3.4.3 Particle Weight Update  
Once the condition that triggers the BLE-based positioning is satisfied, i.e., \(N(P_{i}>P_{th})>N_{th}\), the observation based on BLE-based positioning is obtained \(\mathbf{X}_{k+1}^{B}=[x_{k+1}^{B}~{}y_{k+1}^{B}]^{\mathrm{T}}\). Based on the observation, we update the particle weights according to a Gaussian \(k\)-pseudo-distribution:  
\[w_{k+1}^{(i)}=\frac{1}{\sqrt{2\pi\delta}}\exp\left(-\frac{(\mathbf{X}_{k+1}^{( i)}-\mathbf{X}_{k+1}^{B})^{\mathrm{T}}(\mathbf{X}_{k+1}^{(i)}-\mathbf{X}_{k+1}^{ B})}{2\delta^{2}}\right), \tag{18}\]  
where \(\delta\) is the positioning error of the BLE-based method. After computing all particle weights, we normalize them to ensure that all weights sum up to one:  
\[\tilde{w}_{k+1}^{(i)}=\frac{w_{k+1}^{(i)}}{\sum_{j=1}^{m}w_{k+1}^{(i)}}. \tag{19}\]