esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mitigation Scheme for SAR
### Adaptive Filtering
#### 3.3.2 Time-Frequency Filtering  
Time-frequency representations are commonly used to analyze or characterize non-stationary signals whose energy varies in time and frequency. Based on the analysis of the time-frequency characteristic, an interference suppression filter combined with the constant false alarm rate algorithm is designed in [59]. In [32] and [60], time-frequency reconstruction and the mask technique are proposed to filter the RFI. To achieve super resolution of the time-frequency representation and reduce the signal loss, iterative adaptive approach (IAA) [61; 62] features the adaptive RFI estimation and is believed to be more compatible with the time-variant RFI. [PERSON] et al. proposed using delay-Doppler iterative decomposition algorithm on the Wigner distribution [63]. Generally, the time-frequency representation is very suitable for differentiating the RFI and SAR echoes, in which the sparse, non-overlapping feature could be maximized for RFI separation and suppression. These methods have large computation burden due to time-frequency transform, and it should be noted that only a reversible time-frequency transform could be used.
## 3 Methods
### Optimization Procedure  
This section will provide more details about the optimization procedure for obtaining the dust particle size and dust density from the measurements. As illustrated in Figure 2 and the previous sections, we have obtained two measured quantities \(\left(\Delta\hat{n}_{\mathrm{e}},\hat{T}\right)\), which can be directly compared to theoretically determined values \(\left(n_{\mathrm{d}}Z_{\mathrm{d}},T\right)\) using Equations (5) and (11), respectively. For the sake of clarity, the experimentally measured quantities are denoted with a hat symbol, while the quantities derived from theory are not.  
The optimization procedure works as follows: (i) For a wide range of \(a\) and \(n_{\mathrm{d}}\), the theoretical values of \(\left(n_{\mathrm{d}}Z_{\mathrm{d}},T\right)\) are calculated. Then, (ii) the relative errors with respect to the measured values \(\left(\Delta\hat{n}_{\mathrm{e}},\hat{T}\right)\) are calculated using \(\chi_{\mathrm{mcrs}}=\left|\frac{n_{\mathrm{d}}Z_{\mathrm{d}}-\Delta\hat{n}_{ \mathrm{e}}}{\Delta\hat{n}_{\mathrm{e}}}\right|\) and \(\chi_{\mathrm{ext}}=\left|\frac{T-\hat{T}}{T}\right|\). Figure 4 shows a qualitative visualization of the relative errors for a single data point in the measured time trace. The red curve in the plot represents pairs of \(\left(a,n_{\mathrm{d}}\right)\) for which the goodness of fit with respect to the MCRS measurements is high, i.e., the relative MCRS error \(\chi_{\mathrm{mcrs}}\) is low. The blue curve corresponds to a region for which the goodness of fit with respect to the extinction measurements is high, i.e., the relative extinction error \(\chi_{\mathrm{ext}}\) is low. Since both of the diagnostics are measured in a time-synchronized fashion and therefore probe the same sample of dust, the most probable \(\left(a,n_{\mathrm{d}}\right)\) pair lies on the intersection of the two regions; where both \(\chi_{\mathrm{mcrs}}\) and \(\chi_{\mathrm{ext}}\) are low, which is indicated with the magenta region in Figure 4. In the example of Figure 4, this would result in a dust particle radius of \(a=110\,\mathrm{nm}\) and a dust density of \(n_{\mathrm{d}}=1.19\times 10^{14}\,\mathrm{m}^{-3}\), as indicated by the black circle.
## 4 Proposal
### Communication Technologies and Elements in the Vehicular Network  
The main elements communicating in the system are intersection nodes, vehicles and roadside sensors deployed over the road infrastructure. The vehicles are communication capable devices that also count with a GPS-enabled device in such a way the vehicle knows its actual position and the position of its destination node, and can communicate with other vehicles through the 802.11p technology. It also communicates with the intersection node for retrieving the information associated to its route and to report when the vehicle enters in a congestion state. The streets are seen as static elements with characteristics as distance and capacity, and the roadside sensors are capable of collecting data from the environment and transmit it to the intersection node (_i.e._, vehicle counting and velocity measurements).  
Intersections nodes are seen as active elements and checkpoints, so are responsible for storing data collected from the roadside sensors to establish the congestion state, and can communicate with vehicles through 802.11p technology. Intersections also communicate with other intersections nodes forming _ad-hoc_ networks through the 802.11p for short separation with the next node, or 802.16 for large separations, or through 3 GPP communications as 2G, 3G and LTE. Figure 13 shows the supporting communication infrastructure:The route assignment and recalculation service is going to be in each node, meaning that every intersection node is capable of assign the routes for vehicles in his neighborhood without relying in a central entity. The congestion data associated to the vehicular network is constructed through cooperation between nodes and roadside sensors, and it is shared among all nodes, so there is no need that a single entity recalculates the congestion state of the whole vehicular network every time a vehicle requests its route or affects the streets it uses. When a vehicle enters the range of the next intersection node, it transmits its route intentions and the intersection node assigns the set of streets the vehicle is going to follow according to the network congestion state, as shown in the Figure 14. Figure 15 shows the recalculation process: the recalculation service is triggered when the roadside sensors information reports the congestion exceeds a threshold, or when a vehicle reports it is unable to move. The recalculation message travels to the source intersection node in the street and that intersection node searches and assigns an alternative route to all vehicles in his range in the previous street.  
Figure 14: Communication between vehicle and intersection node.  
Figure 13: Supporting communication infrastructure.
## 3 Test Results and Discussion  
### Electrical Resistivity  
The electrical conductivity test results are shown in Figures 2-4. The experimental results were expressed as the average value measured from three specimens, and the standard deviation is indicated on the bar graph.  
The effect of the functionalized MWCNTs and GNPs on the electrical resistance is shown in Figure 2. Except for the specimens with the MWCNT mixture ratio of 0.06% and the hybrid mixture ratio of 0.12%, the functionalized specimens showed lower electrical resistance in all cases. The difference in electrical resistance between the functionalized and nonfunctionalized specimens became larger as the mixture rate was increased to \(\geq\)0.30%. Improved electrical conductivity, along with the functionalization of MWCNTs, was observed more frequently compared with the GNPs. The electrical resistance of MWCNTs was reduced by an average of 36% in functionalized specimens with a mixture ratio of \(\geq\)0.30% and by an average of 22% for GNPs. The standard deviation for the electrical resistance of the functionalized specimens was smaller than for the nonfunctionalized  
Figure 1: Noncovalent functionalization of MWCNTs and GNPs: (**a**) ultrasonication of MWCNTs or GNPs with melamine in DMF, (**b**) ball milling, and (**c**) washing.  
Figure 3 shows the electrical resistance changes with respect to the content of MWCNTs and GNPs. The decrease in electrical resistance was generally large up to a content of 0.30% but slowed thereafter. In nonfunctionalized specimens, the electrical resistance changes were insignificant at \(\geq\)0.60%. However, although small, the electrical resistance notably continued to decrease in the functionalized specimen. In nonfunctionalized specimens, the GNP was 9.6 \(\Omega\) m smaller than MWCNT on average, except for the 0.06% content. However, in functionalized specimens, the MWCNT was an average of 12.7 \(\Omega\) m smaller than the GNP at the content of \(\geq\)0.30%. At a relatively high content, the dispersion effect of the MWCNT appeared to be larger compared with the GNP as a result of functionalization.  
Figure 4 shows the hybrid effect on the electrical resistance of the MWCNTs and GNPs. In the case of hybrid mixing of one-dimensional MWCNTs and 2D GNPs, the electrical resistance was higher than or similar to that of individual mixing, regardless of the functionalization. This result was contrary to the expected effect that hybrid incorporation would provide an advantage in forming conductive networks. Therefore, it was concluded that differently shaped nanomaterials interfered with one another upon dispersion within the cement matrix.
## 5 Deformation Analysis and Reconstruction
### Deformation Analyses  
The aforementioned prototype model test has been analyzed and verified for the rigid body motion of the FCRA. Herein, the panels and shell of the back frame are taken for flexible deformation analysis. According to the physical size of the prototype model, the diameter of two side panels and the longitudinal length of the shell are 250 mm and 75 mm, and the thickness of the panels and shell is 3 mm. The material density of the back frame is 1.2 \(\times\) 10\({}^{3}\) kg/m\({}^{3}\) with an elastic modulus of 3.2 GPa and a Poisson's ratio of 0.35.  
The three-dimensional finite element model of the back frame is established in ABAQUS (refer to Figure 12). To facilitate the mesh division and the application of boundary constraints, a small semi-circular hole is opened at the center of two side panels, and fixed constraints are placed at the hole. Considering the weight of the back frame, the weight of the trolley is equivalent to four concentrated forces applied at the contact point between the wheels and the tracks. The range of water pressure is calculated by Equation (1) and the hydrostatic pressure is applied to the outer surface of the back frame.  
To obtain accurate reference solutions, the high-fidelity mesh discretization in Figure 12 is performed with 2352 quadrilateral shell elements and 14,112 DOFs. The FEM analyses of the back frame at four rotational motion states recorded in Figures 8 and 9 are carried out as the quasi-static sampling moments for the following deformation reconstructions. The contours of total displacement for different rotation angles are presented in Figure 13.  
Figure 12: Finite element model of back frame.  
From the results of deformation analysis in four different rotation states, the deformation of the panels and shell parts in the back frame becomes greater when the rotation angle \(\varphi\) of the FCRA becomes larger. Comparing the displacement deformation contour maps, only the deformation at \(\varphi=0^{\circ}\) shows a significant difference. The reason for this phenomenon may be that the positions of the concentrated force are not accurate enough, which leads to the rotation trend of the FCRA and some larger deformations in non-positive elevation states.
# Topological Qubits as Carriers of Quantum Information in Optics
## 5 Conclusions  
It has been shown that winding number, a topologically significant quantity, can be used as a coding parameter for the carrying of quantum information in linear optical systems, the most common realm for quantum communication. In particular, it was shown that winding number can be coded in several physical quantities of such systems, including quantum electromagnetic states of spin, momentum, orbital angular momentum, polarization of beams of particles propagating in free-space, optical fiber, beam splitters, and optical multiports. By taking two winding number values as bit values, the photons considered are seen as carriers of winding number qubits, because bit pairs have become qubit pairs by being encoded into a pair of superposable quantum states in a two-dimensional complex Hilbert space. Making use of the topologically-induced robustness of winding number qubits opens new possibilities for building error-protection into information processing systems.  
**Funding:** This research was supported by the National Science Foundation EFRI-ACQUIRE grant no. ECCS-1640968, AFOSR grant no. FA9550-18-1-0056, and by the Northrop Grumman NG Next.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 30 cm Model Cavity Simulation
### Post-Treatment Processes
#### 4.2.3 Evacuation Process  
To ensure SC operation, the accelerator requires a vacuum environment. Simulating the evacuation process involves applying atmospheric pressure to the cavity's surface, calculating resultant deformations, and determining the cavity's frequency after deformation.  
Utilizing the same simulation model, calculation modules, and material properties employed during the cooling process, the Solid module now addresses stress and deformation induced by atmospheric pressure on the cavity surface. The equations governing this process are as follows:  
\[\
abla\cdot\mathrm{S}+F_{V}=0, \tag{11}\]  
\[\mathrm{S}\cdot\mathbf{n}=-p\mathbf{n}, \tag{12}\]  
where \(\mathbf{n}\) represents the normal direction of the surface, and \(p=1.01\times 10^{5}\) Pa denotes atmospheric pressure.  
The DG and EMW modules function consistently with their roles in the cooling process simulation. The results indicate that the maximum deformation of the niobium cavity during evacuation to a vacuum is 1.28 mm, predominantly occurring on the vanes.  
\begin{table}
\begin{tabular}{c c} \hline
**Property** & **Value** \\ \hline Density (g/cm\({}^{3}\)) & 8.57 \\ Young Modulus (GPa) & 105 \\ Poisson Ratio & 0.4 \\ Thermal Expansion (K\({}^{-1}\)) & \(7.3\times 10^{-6}\) \\ Thermal Conductivity (W/(m-K)) & 54 \\ \hline \end{tabular}
\end{table}
Table 4: The material properties of niobium [42].  
Figure 20: The simulation result of the frequency and deformation distribution of the niobium cavity after cooling process.  
The frequency of the deformed cavity is 164.35 MHz, reflecting a decrease of 430 kHz compared to the cavity frequency after cooling (refer to Figure 21).  
The total frequency changes given by the simulation processes are listed in Table 5. Observing the outcomes, it is evident that following a sequence of post-treatment procedures, the cavity's frequency has experienced a net increase of 1.77 MHz. This indicates that in order to achieve the final frequency of 162.5 MHz, the initial 30 cm cavity frequency should be set to 160.73 MHz. Additionally, it will be helpful to design stiffeners in positions with large deformation to enhance the overall stability of the cavity.
## 2 Literature Survey
### QoS Routing  
Currently there are a lot of multimedia applications and traffic has grown significantly, so the QoS performance in routing becomes more and more important for a multicast sensor network. The routing protocols such as OSPF (Open Shortest Path First) which is created by the Dijkstra algorithm is widely used in network routing protocols for computing a routing table inside a sub-network to get a shortest transmission path [7, 8].  
According to [6], the QoS requirements in routing can be classified into two categories:  
* Link constraints: the restrictions on the use of link to form a routing tree, such as bandwidth, link capacity, or buffer of each link.
* Path constraints (or tree constraints): the restrictions in the perspectives of the whole multicast tree. For example, the end-to-end delay from source to destination.  
The goal of QoS routing is to find a feasible path (tree) with sufficient available resources to address the QoS requirements for sensor nodes in a wireless sensor network [3], as well as to achieve as much efficiency in resource utilization as possible.  
Delay, bandwidth, delay jitter, throughput, or packet loss ratio are the QoS measurements of a routing strategy of a network link. In addition, the cost of a link in the multicast tree can be defined in dollars or as a function of the buffer or bandwidth utilization. In previous research, determining the available feasible paths of the optimization problem and finding the lowest-cost feasible solution is also considered. [PERSON] and [PERSON] have conducted a survey of various QoS routing algorithms; these can be divided into three broad classes: (1) source routing algorithms, (2) distributed routing algorithms, and (3) hierarchical routing algorithms. [PERSON] proposes a QoS-Aware Multicast Routing Protocol (QMRP) for non-additive metrics in which he wishes to discover a feasible path with enough requested link bandwidth and buffer space management [9]. In [10], [PERSON], [PERSON], and [PERSON] introduce new single mixed metrics for multi-constraint routing. In order to adequately reduce routing complexity, QoS routing may discard some potentially useful information in the process. Nevertheless, from an operational standpoint, those mentioned above are usually taken into account.
## 3 Security and Privacy-Preserving Solutions for IoE
### Integration of Different Security Layers  
In the IoE ecosystem, security has become too multidimensional to be ensured by individual solutions alone. Therefore, the recent literature shows an increasing trend toward solutions that employ multiple security layers simultaneously. Technologies such as AI, Blockchain, Edge Computing, and Quantum-Resilient techniques are increasingly positioned as complementary tools across various application domains. These technologies provide more comprehensive and effective security solutions not only individually but also when used in combination [20; 85; 87; 105].
# A Wideband Power Amplifier in 65 nm CMOS Covering 25.8 GHz-36.9 GHz by Staggering Tuned MCRs
## 5 Conclusions  
This article presents a wideband power amplifier in 65 nm CMOS covering the bandwidth from 25.8 GHz to 36.9 GHz. A symmetrical output MCR is designed to achieve wideband optimum load impedance for the power amplifier. To flatten the small-signal bandwidth of the power amplifier, staggered interstage and input MCRs are custom-designed. A high-pass form MCR2 and an asymmetrical MCR1 satisfying \(\omega_{1}/\omega_{2}=Q_{1}/Q_{2}\) are used to successfully compensate and stagger the Z21 shape of MCR3 and MCR4. To increase the output power, the two-way current combining method is used via a microstrip transmission line power combiner and splitter. Three common-source stages with neutralization capacitors are used to improve power gain and stability and to stagger the Z21 of each MCR conveniently. Benefiting from the above-mentioned design considerations, the power amplifier achieves both wideband small- and large-signal performance. A peak small-signal gain of 25.87 dB with 3 dB bandwidth from 25.8 GHz to 36.9 GHz is achieved. From 26 GH to 36 GHz, a \(P_{sat}\) of \(17.44\pm 0.4\) dBm with a \(22.59\pm 1.78\%\)\(PAE_{max}\) and an \(OP_{1 dB}\) of \(13.78\pm 0.31\) dBm under the 1.1 V supply voltage are realized. These results indicate that this power amplifier is competitive on the applications of wideband millimeter-wave communication.  
Figure 28: **(a)** Post-simulated S-parameters of the designed power amplifier at ss corner and **(b)** the large-signal performance at ss corner from 26 GHz to 36 GHz.  
Figure 27: **(a)** Post-simulated S-parameters of the designed power amplifier at ff corner and **(b)** the large-signal performance at ff corner from 26 GHz to 36 GHz.  
**Author Contributions:** **Conceptuation, Z.W.; methodology, Z.W.; validation, Z.W.; software, Z.W. and X.W.; formal analysis, Z.W.; investigation, Z.W.; writing-original draft preparation, Z.W.; writing-review and editing, Z.W., X.W. and Y.L.; project administration X.W. and Y.L. All authors have read and agreed to the published version of the manuscript.** Funding: This work was supported by National Natural Science Foundation of China under grant 61574165.**  
**Data Availability Statement:** **Not applicable.**
## 3 Controller Design
### PI Control with Feedforward Control  
First, the power control is realized with a PI controller according to the transfer function  
\[G_{\text{PI}}(s)=K_{\text{R}}\cdot\frac{T_{\text{I}}\cdot s+1}{T_{\text{I}} \cdot s} \tag{5}\]  
combined with a simplified dynamic feedforward control, as shown in Figure 4.  
Usually, the integral term in the PI controller already leads to robustness against static gain errors [17]. However, it needs to be chosen in such a way that a compromise is found between damping and fast transient responses, because of the influence of the parameter \(T_{\text{I}}\) on the overall phase reserve. Basically, it is set equal to the largest plant time constant of Equation (2). For the simplified dynamic feedforward control design, the SOFC system is approximated by  
\[G_{\text{S,PT1}}(s)=\frac{4.119\times 10^{5}}{s+1.279\times 10^{-2}} \tag{6}\]  
resulting in  
\[S_{\text{ff}}(s)=G_{\text{S,PT1}}(s)^{-1} \tag{7}\]  
as the dynamic feedforward control.  
**Remark 2**.: _The gain \(K_{\text{R}}\) needs to be kept small enough so that actuator saturations are not reached which may provoke undesirable integrator wind-up phenomena._  
**Remark 3**.: _As shown in both simulations and experiments, the PI controller has the drawback in the case of non-modeled nonlinearities and deviations between the plant and the design model, in which a compensation for this mismatch can only take place by the integral control action after a sufficiently large tracking error has occurred._
## 2 Background
### Lidar System Optimization for Control Applications
#### 2.4.1 Optimizing Lidars by Assessing Controller Performance
Assuming a hub-mounted CW lidar with three rotating beams, the authors found that blade loads were reduced to nearly the same level as with frozen turbulence for a preview distance of 126 m (\(\sim\)3 \(D\)). At this preview distance, the spatial averaging along the lidar beam was found to effectively filter out the high-frequency structures in the wind that change the most due to wind evolution.
## 2 System Model
### Adaptive Extended Kalman Filter Algorithm  
The relative range measurements obtained by UWB sensors could have an adverse effect on CL due to the NLOS errors. Although we can set a large-ranging noise covariance matrix to reduce the adverse effect in NLOS conditions, the ranging noise covariance matrix might vary due to environmental influences. It was not appropriate to set a fixed nominal value for the ranging noise covariance matrix. In the study, the Sage-Husa adaptive filter algorithm was utilized to estimate the ranging noise.  
The innovation of the extended Kalman filter is expressed below:  
\[\widetilde{\mathbf{Z}}_{ij}(k|k-1)=\mathbf{Z}_{ij}(k)-h_{ij}(\hat{\mathbf{X} }_{i}(k|k-1),\hat{\mathbf{X}}_{j}(k|k-1)) \tag{42}\]  
The adaptive filter algorithm of the range measurement noise variance matrix can be formulated as follows:  
\[\hat{\mathbf{R}}_{r}(k)=(1-\beta(k))\hat{\mathbf{R}}_{r}(k-1)+\beta(k)( \widetilde{\mathbf{Z}}_{ij}(k|k-1)\widetilde{\mathbf{Z}}_{ij}^{T}(k|k-1)- \mathbf{H}_{r}(k)\mathbf{P}_{r}(k|k-1)\mathbf{H}_{r}^{T}(k)) \tag{43}\]  
\[\beta(k)=\frac{\beta(k-1)}{\beta(k-1)+b} \tag{44}\]  
where \(\beta(0)=1\), \(b\) is the forgetting factor that satisfies \(0<b<1\).  
To maintain the positive definiteness of \(\hat{\mathbf{R}}_{r}(k)\), the threshold boundary values \(\mathbf{R}_{\text{rmx}}\) and \(\mathbf{R}_{\text{rm}}\) were selected. We defined the discriminant equation as:  
\[\varepsilon=\widetilde{\mathbf{Z}}_{ij}(k|k-1)\widetilde{\mathbf{Z}}_{ij}^{T} (k|k-1)-\mathbf{H}_{r}(k)\mathbf{P}_{r}(k|k-1)\mathbf{H}_{r}^{T}(k) \tag{45}\]  
Therefore, \(\hat{\mathbf{R}}_{r}(k)\) can be modified to:  
\[\hat{\mathbf{R}}_{r}(k)=\begin{cases}\mathbf{R}_{\text{rmx}}&(\varepsilon> \mathbf{R}_{\text{rmx}})\\ (1-\beta(k))\hat{\mathbf{R}}_{r}(k-1)+\beta(k)\mathbf{R}_{\text{rm}}&( \varepsilon<\mathbf{R}_{\text{rm}})\\ (1-\beta(k))\hat{\mathbf{R}}_{r}(k-1)+\beta(k)\varepsilon&(others)\end{cases} \tag{46}\]  
We drew a corresponding algorithm block diagram for the proposed DCLAEKF algorithm, as shown in Figure 4.