esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Design Results  
Figure 9 illustrates the frequency response of the three designed controllers and the benchmark controller. The single \(\mathcal{H}_{\infty}\) voltage controller has a bigger gain for low frequencies, lower resonant peak and lower frequency gain than the other controllers. The current frequency response of a single \(\mathcal{H}_{\infty}\) voltage controller is lower for all frequencies without any resonant peak.  
To analyse the response of the controllers and their robustness, the designed controllers have been tested in three different scenarios, namely open-circuit islanded WTG, islanded WTG with rated-load and WTG connected to an ideal grid (DR-connected)  
Figure 10 shows the response of the designed controllers to a unit sinusoidal reference in the three considered scenarios (ISL no-load, ISL with rated-load and DR-connected).  
It has been found that the time response of the three cascaded controllers is very similar. Therefore, only the response of the \(PR_{\mathcal{H}_{\infty}}\) controller is shown for clarity. The cascaded controllers have a faster response in the islanded scenarios. Still, when the WTG is connected to the DR (similar to connecting to a grid with a low impedance), the cascaded controller achieves a settling time of 200 ms.  
\begin{table}
\begin{tabular}{c} \hline \(\mathcal{H}_{\infty}\)**Weights** \\ \hline \(W_{err}\): \(K_{Low}=1\); \(W_{err-res}\) (before apply (7): \(K=0.2\), \(b=1539.0\), \(a=3.078\) ; \\ \(W_{High:}\): \(K=0.36\), \(b=3614.0\), \(a=903.6\); \(\alpha=1.1\) \\ \(W_{u}\): \(K=0.2\); \(W_{i}\): \(K=0.02\); \(W_{j}\): \(K=0.02\); \(W_{p}\): \(K=1\) \\ \hline \end{tabular}
\end{table}
Table 5: Weight parameters of the single-loop voltage control.  
Figure 9: Frequency response of the designed controllers (left: \(K_{V}\); right: \(K_{I}\)).  
In the case of the \(\mathcal{H}_{\infty}\) voltage controller, the response in the three scenarios is very similar. An a posteriori analysis, using the normalised coprime factor uncertainty margin [17], shows the robustness level of the designs to the considered parametric uncertainties.  
Table 6 shows the achieved margins for the designed controllers. The \(\mathcal{H}_{\infty}\) voltage controller can be considered the most robust, as it achieves better margins for islanded no-load and grid-connected operation, and almost the same margin for islanded rated-load operation, than the cascaded \(\mathcal{H}_{\infty}\) controller.
# Numerical Study of the Flow and Blockage Ratio of Cylindrical Pier Local Scour
## 3 Numerical Method
### Initial and Boundary Conditions  
To validate the numerical model in the clear-water case, we set the problem using the PIV measurements by [PERSON], 2018 [18]. These measurements were performed close to the bed with a vertical resolution of \(\Delta z_{PIV}=8\times 10^{-4}\) m. To avoid the influence of waves on the scouring process, a plexiglass sheet is put over the water surface to prevent a free surface. Due to the flow conditions, the Froude number \(Fr=U/(gh)^{2}\) is small, so implementing a rigid roof as a surface boundary condition is appropriate. The experimental configuration produces a flow with a mean velocity \(U\approx 0.16\) m/s and friction velocity \(u_{\tau}\approx 8\pm 1\) mm/s. As \(\text{Re}_{\tau}\approx 5.4\) in this case, according to [PERSON]'s formulation (Equation (13)), the flow approaching the obstacle is in the transitional regime, but very  
Figure 2: (**a**) Horizontal view of the mesh used for the numerical simulations. (**b**) Detail of the cells near the cylinder.  
close to being hydraulically smooth. Naturally, as the flow approaches the obstacle and its friction velocity increases, the flow regime is consolidated as transitional.  
For the clear-water simulations, we adjust the Swamee profile (14) to the PIV data while confirming that the friction velocity is conserved, and then we apply the profile to the inflow boundary condition. As a result, considering \(D=0.01\) m, the pier Reynolds number in the numerical model is \(\mathrm{Re}_{D}\approx 2000\).  
For the live-bed simulations, the Swamee profile (14) is adjusted to the flow velocity and pier size described in [PERSON] et al., 2005 [4]. The mean velocity in this case is \(U=0.46\) m/s, the pier diameter is \(D=0.1\) m, and, therefore, the pier Reynolds number is \(\mathrm{Re}_{D}=46\),000.  
In the present study, the same parameters are used in all the simulations for sediment density, fluid sediment, grain size, sand roughness, bed porosity, critical Shields number for a horizontal slope, and static and dynamic friction coefficients. The parameters used in the tests are summarized in Table 2.  
From the value of \(\theta_{c_{0}}\), it follows that the critical friction velocity for the incipient motion of a solid particle on a flat bed is given by Equation (16).  
\[u_{\mathrm{c}crit}=\left[\theta_{c_{0}}gd(\rho_{s}/\rho-1)\right]^{1/2}=1.27 \times 10^{-2}\ \mathrm{m/s}. \tag{16}\]  
Therefore, the ratio \(u_{\mathrm{\tau}}/u_{\mathrm{c}crit}\) is equal to 0.72, i.e., clear-water conditions.
# Regional Seismicity of the Northeastern Tibetan Plateau Revealed by Crustal Magnetic Anomalies
###### Abstract  
The northeastern Tibetan Plateau (NETP) is located at the front of the northeastward expansion of the Tibetan Plateau and is a tectonically active region with complex faults and intense seismicity. In this study, based on the high-order geomagnetic field model EMM2017, the crustal magnetic anomalies and Curie point depths (CPDs) in the NETP and adjacent areas were investigated. The relationship between the magnetic anomalies, CPDs, and seismic activity was assessed. The results show that strong earthquakes occur mainly in areas where the magnetic anomalies are negative or have a strong-to-weak transition. The CPD is located at 18-42 km. In the NETP, a shallow CPD corresponds to high heat flow. In contrast, in surrounding areas, a deep CPD corresponds to low heat flow. The northeast area from Bayan Har to the Qilian orogenic belt, and the region with a deep CPD in the Qaidam Basin, record the northeastward flow of the Tibetan Plateau. High-magnitude earthquakes are associated with depth changes in the CPD and areas with a shallow CPD. The frequent seismic activity in the NETP can be attributed to the northeastward flow of the Tibetan Plateau caused by a deep heat flux. The results can be used as a reference for the prediction of strong regional earthquakes.  
EMM2017; crustal magnetic anomaly; curie point depth; seismic activity; northeastern Tibetan Plateau
## 3 Discussions and Results
### Application to a Fuel Tank Inerting Process
To determine the time to inert, we consider that the advection term acts physically in the same direction as the TW-speed \(\lambda\). This assumption is realistic as the forced convection drives the nitrogen to all tank areas where the gas is free to flow. Nonetheless, once the nitrogen is in complicated tank shapes, it moves by diffusion only. Hence, the forced convection (\(a\)) and the diffusive TW (\(\lambda\)) are summative terms that act to reach the inert condition in all tank areas (both free paths and difficult geometries). Thus, the time to obtain the inerted configuration under similar operational conditions compared to the A320 aircraft is:  
\[t=\frac{x-\xi}{a+\lambda}=\frac{(3.88-0.004)\,\mathrm{m}}{(0.061+0.0125)\, \mathrm{m}/\mathrm{min}}=52.7\,\mathrm{min}. \tag{45}\]  
Globally, at \(t=52.7\) min, the tank will reach a value of 9% of oxygen in all areas. This value is overly pessimistic compared to the value provided in Figure 6, in which the value of 9% of oxygen is obtained at \(t=44\) min. Nonetheless, the values of oxygen in Figure 6 are only provided for different locations where the oxygen sensors are placed. Therefore, the modelled value of \(t=52.7\) min, shall be seen as an approach in which the diffusion acts as per the travelling wave front and tip to cover the whole domain. It is particularly relevant to observe that the scale for the tank dimension (\(x=3.88\) m) is much higher than the scale of the TW spatial variable \(\xi\). This fact can be explained in view of the TW features. The front and the tip represent the diffusion acting along the wave so that \(\xi\) represents a dimension of the wave interface where the front and the tip are confined.
## 5 Proposed Scheme  
In the proposed scheme, we assume that the D2D pairs in inner cell region reuse the resources of cellular users in the edge cell region and vice versa. However, the reuse of cellular resources by the D2D pairs in the same section of the cell is not allowed. The main objective of the proposed scheme is to mitigate the interference generated due to deployment of D2D communication into conventional cellular networks. In this section, we provide an analytical expression for interference caused by D2D communications while reusing the same cellular resource by two-pairs of D2D simultaneously. The operational flow of the proposed resource allocation scheme with FFR of 2 is presented in Algorithm 1.  
```
1. Get all active cellular users and D2D pairs
2. Set \(P_{C}=P_{C}^{max}\) and \(P_{D}=P_{D}^{max}\)
3. for t=1 to RB do
4. Calculate \(\gamma_{j}\), \(\gamma_{f}\), and \(\gamma_{k}\)
5. Calculate \(P_{i}^{out}\), \(P_{i}^{out}\), and \(P_{k}^{out}\)
6. Calculate \(\tilde{T}_{C}=Wlog_{2}(1+\gamma_{j})\) and \(T_{D}=W[log_{2}(1+\gamma_{j})+log_{2}(1+\gamma_{f})]\)
7. if \((T_{C}\geq\ T_{C}^{min},\ T_{D}\geq\ T_{D}^{min})\) then
8. Calculate \(T_{overall}=\sum\limits_{c=1}^{N}T_{c}+\sum\limits_{D=1}^{M}T_{D}\)
9. else if \((T_{C}<\ T_{C}^{min},\ T_{D}<\ T_{D}^{min})\) then
10. recalculate \(\gamma_{j}\), \(\gamma_{f}\), and \(\gamma_{k}\) and repeat the step 6
11. end if end for
```  
**Algorithm 1**Working of the proposed resource allocation scheme.  
Figure 1: Uplink interference scenarios: (**a**) interference from cellular user to D2D-Rx and (**b**) interference from D2D-Tx to eNB. D2D, device-to-device; eNB, evolved Node B.
# Combined Fusion Rules in Cognitive Radio Networks Using Different Threshold Strategies
## 3 System Model  
Assume a cognitive radio network (CRN) with a single PU and \(N\) multiple CRs as depicted in Figure 3. All CRs are assumed to use energy detector as a spectrum sensing approach. The CRs are grouped into multiple clusters according to their geographical locations. A central entity, i.e., FC, is coordinates the CRN, controls the transmission of the CRs, and selects a cluster head (CH) for each cluster in the CRN. Based on the largest reporting gain in the cluster, the CH is selected. Other CRs in the cluster are called cluster members (CMs). As aforementioned, the CRs within the cluster are assumed to experience the same radio conditions, i.e., have identical average SNR of the received PU signal since they are located near each other and the distance between them is much smaller compared with their average distance to the PU. The CMs forward their local observations to the CH through a error-free reporting channel, i.e., perfect reporting channel, because all CRs in the cluster are geographically closed to each other.  
This system has two fusion levels, a cluster level and a CRN level. In a cluster level, a CH collects local observations from its CMs, combines them with a certain fusion rule to formulate a cluster decision, and forwards the cluster decision to the FC through a reporting channel. The CRN level  
Figure 3: Cognitive radio network with multiple clusters.  
consists of multiple clusters. In a CRN level, FC collects clusters' decisions, fuses them to extract the global decision about the existence of the PU, and then disseminates back the global decision to the CHs. It is noteworthy that reporting channel between FC and CHs is assumed imperfect because clusters are far apart from the FC, and each cluster has different and independent average SNR of the PU signal due to its geographical location.  
It is assumed that the clusters are located at different distances from the PU and that the PU SNR at the cluster level is defined as [27]:  
\[\gamma_{c}=\frac{P_{PU}}{\sigma^{2}}\frac{\delta}{R^{\alpha}} \tag{12}\]  
where \(\delta\) is the path loss constant, \(\alpha\) is the path loss exponent, \(P_{PU}\) is the transmit power of the PU, \(\sigma^{2}\) is the noise variance and \(R\) is the average distance between the cluster center and the PU.
## 2 Materials and Methods
### The Mill Accessibility and Analysis of Monitoring Paths and Technologies
In this case, the inner ring is fixed and is constrained to the mill cage. For temperature monitoring of working rolls, sensors can be installed inside the roll. It is known that fiber optics sensors can be embedded in the roll geometry, in close proximity to the surface, and the interrogation module, together with additional modules required to perform the pre-analysis and dispatch of signal, can be installed at one end of the roll on their cross-section, just in front of access exploited by the operators [39]. Figure 2: Back bearing assembly of a cluster mill [44]. Main monitoring activity will include the measurement of pressure applied by rolls on the rolled product, vibration amplitude of the mill cage, temperature of inner elements, and spin speed, at least of some rolls. Those measurements are performed every rolling pass at different speeds. The aim is to detect the mill dynamic behavior in operation at both the lower and higher frequency range. Particularly, the MCM will detect the chattering phenomenon, occurring at a higher frequency, usually appearing at some kHz, in cold rolling (1 to 10 approximately and depending on the mill layout and size). Those MCM targets often motivate mounting some accelerometers on the mill cage to measure vibration and some load cells on working rolls. Nevertheless, an additional measurement at bearings should be welcome to prevent failures. The technology of capacitive MEMS sensors, already tested in this field [49], does not look effective because of the huge load applied (several MN at least) and of high temperature. Exploiting the fiber optics technology for direct measurement of the mill cage vibration and for monitoring load, temperature, and damage in bearings seems promising. Fiber optics are never affected by problems related to electromagnetic compatibility, while, for instance, self-sensing piezoelectric sensors [50], which have been already proposed for monitoring the inner ring vibration in bearings, look poorly compatible with loading conditions because of their material brittleness. In contrast, fiber optics are free from ECM, thermal, and loading issues when suitably applied to monitored elements, as is herein described.
## 4 Key Design Requirements and System Components
### Layer 5: Storage Capsules (SCs)  
In Layer 5, the storage capsules (SCs) are custom-built and isolated storage and computational cloud containers hosted in federated cloud environments. The SCs function as avatars representing NW subscribers in the federated wireless communication NW. By doing so, storage capsules play a crucial role in protecting the digital identities of subscribers, ensuring identity credentials are protected from unauthorized access and duplication. This mechanism, orchestrated by NW brokers, ensures that each new subscriber is assigned a unique SC, thereby eliminating the risk of redundancy and enhancing the overall security framework of the NW.
# Considering Inter-Frequency Clock Bias for BDS Triple-Frequency Precise Point Positioning
## 1 Introduction
The contribution starts with the description of materials and methods, including the estimation approach for IFCB and the development of triple-frequency PPP model with consideration of IFCB. Subsequently, we show and analyze our results in terms of space segment and navigation signals, data acquisition, characteristics of IFCB and performance evaluation of triple-frequency PPP. Finally, we summarize the main points and conclusions.
# Maximization of Average Achievable Rate for NOMA-UAV Dual-User Communication System Assisted by RIS
## 4 Proposed Algorithm  
In the previous section, we derive the optimization problem. Due to the presence of coupling variables and non-convex constraints, problem \(\mathrm{P}(1)\) presents a significant challenge. Therefore, we dissect the problem into three distinct sub-problems: the optimization of RIS phase shifts, UAV transmission power, and UAV flight trajectories.
It also proposes a linear active disturbance rejection controller for controlling the position and attitude of the underwater robot based on this model. The convergence of the extended state observer in the active disturbance rejection controller and the stability of the closed-loop control system are demonstrated using the Lyapunov method. 2. A novel control method, named DDPG-LADRC, has been introduced to address disturbances in linear systems by integrating the Deep Deterministic Policy Gradient (DDPG) algorithm with an active disturbance rejection control approach. This method focuses on optimizing the extended state observer through the DDPG algorithm, enabling the observer to sustain optimal performance under varying external disturbances during both position and attitude control of Remotely Operated Vehicles (ROVs). Through real-time adjustments of control parameters, the performance of the extended state observer (LESO) is enhanced, thereby improving the system's resilience to disturbances and enhancing control accuracy in intricate underwater settings. 3. Based on a nonlinear underwater robot model, numerical simulations have confirmed the efficacy of the approach. The simulation results first compared three control algorithms: PID, fixed-parameter LADRC, and DDPG-LADRC, and finally included NESO-DSMC for comparison. Through analysis, the proposed method has been verified to have significant advantages in terms of transient performance, control accuracy, and robustness. The subsequent sections of this article are structured as follows: Section 2 introduces a nonlinear model for the underwater robot, Section 3 outlines the design of the LADRC controller for the robot, and Section 4 introduces the DDPG-LADRC algorithm and analyzes the convergence of the extended state observer and the stability of the closed-loop control. Section 5 presents the results and analysis of numerical simulations, and lastly, a conclusion is offered.
## 2 Problem Formulation and Model
### UAV Dynamical Model  
For simplicity, it is assumed that each UAV is equipped with a low-level flight control system [20; 21; 22; 23] that holds constant altitude and speed. A general flight control system--which includes the attitude angles (roll, pitch, and yaw) controller and fast loop attitude angle rates controller--can follow the guidance inputs to generate control surface deflections. In general, the guidance inputs include the speed (\(v\)), the vertical flight path angle (\(\gamma\)) and the horizontal flight path angle (\(\chi\)). In this paper, we assume that the UAVs move on a fixed altitude with a constant cruising speed. Thus, we only need to designate the horizontal flight path angle (\(\chi^{c}\)), which is the sole guidance input to this low-level flight control system. The two-dimension motion of a UAV in a horizontal plane is thus described by:  
\[\left(\begin{array}{c}\dot{x}_{i}\\ \dot{y}_{i}\\ \dot{x}_{i}\end{array}\right)=f(\mathbf{x}_{i},\mathbf{u}_{i})=\left(\begin{array}{ c}v_{i}\cos\chi_{i}\\ v_{i}\sin\chi_{i}\\ \alpha_{\chi}(\chi_{i}^{c}-\chi_{i})\end{array}\right) \tag{1}\]  
Figure 1: The scenario where a fixed base station utilizes multi-UAVs for tracking a ground moving target.  
where \(\mathbf{x}_{i}=[x_{i},y_{i},\,v_{i},\,x_{i}]^{\mathrm{T}}\) denotes the \(x\) and \(y\) components of the planar position, the speed, and the horizontal flight path angle of UAV \(A_{i}\), respectively. Additionally, \(\alpha_{\chi}\) is the control gain, and \(\mathbf{u}_{i}=\chi_{i}{}^{c}\) is the desired horizontal flight path angle of \(A_{i}\), which is the guidance input for the low-level flight control system of the UAV: \(\dot{\chi}_{i}\) is the turning rate, which is constrained by the following dynamic limits:  
\[-\omega_{\mathrm{max}}\leq\dot{\chi}_{i}\leq\omega_{\mathrm{max}} \tag{2}\]  
The continuous UAV model in Equation (1) can be discretized by Euler integration method:  
\[\mathbf{x}_{i}(k+1)=\mathbf{f}_{\mathrm{u}}(\mathbf{x}_{i}(k),\,\mathbf{u}_{i}(k))=\mathbf{x}_{i}(k )+T_{\mathrm{s}}\mathbf{f}(\mathbf{x}_{i}(k),\,\mathbf{u}_{i}(k)) \tag{3}\]  
where \(T_{\mathrm{s}}\) is the discrete sampling time.
## 1 Introduction  
The primitive element of all digital circuits (i.e., for computing, signal processing, etc.) is a \"binary switch\" which has two stable states encoding the binary bits 0 and 1. Computing and digital signal processing tasks are carried out by flipping such switches back and forth between the two states. As a result, for a given algorithm and a given computing architecture, the energy cost and speed of a digital computational task are determined by the energy dissipation and the switching delay of the switches. Therefore, it has become common practice to benchmark digital switches on the basis of their 'energy-delay product', which is the product of the energy dissipated during switching and the switching time [1].  
This approach, unfortunately, ignores the fact that usually the less energy we dissipate (or the faster we try to switch), the more error-prone the switch becomes. As a result, any saving in energy or computational time gained by employing switches with a lower energy-delay product may be offset by the additional resources that would be needed for error correction. In this paper, we show the direct relation between energy dissipation and error-resilience with three examples, a field effect transistor, a nanomagnetic switch flipped with current induced spin-transfer-torque [2] and a non-linear mirror.
## 5 Experimental Setup
### Comparison Heuristics
#### 5.2.1 Manually-Designed Heuristic  
The Weighted Objective (WO) heuristic has been designed to tackle CCUS3O instances. It belongs to the space of low-level heuristics (defined in Algorithm 1), where the scoring function \(f_{u}\) depends on three values (one per CCUS3O objective).  
\[f_{u}(v)=\sum\limits_{o\in\mathcal{O}}a_{uv}^{(o)}\]  
with  
\[\begin{split} a_{uv}^{(time)}&=W-dist(pos(u),v)- dist(v,P_{u}[0])\\ a_{uv}^{(rate)}&=W\cdot x_{uv}^{(rate)}\\ a_{uv}^{(com)}&=\min\left(W,W+D^{com}-\min\limits_ {u^{\prime}\in U\backslash\{u\}}dist(v,pos(u^{\prime}))\right)\end{split}\]  
The value of \(W\) is set arbitrarily. For the coverage time objective, the weight linearly decreases according to the distance of the path from the current position to the initial position passing by the vertex to be evaluated. The weight of the coverage rate objective is equivalent to the state variable of the vertex to evaluate. For the connectivity objective, the weight depends on the distance to the closest UAV from the vertex to evaluate. If the latter distance is lower than \(D^{com}\), i.e., is in the communication range of a UAV, then the weight is maximum; otherwise, it linearly decreases according to that distance. Even though the quality of WO is not guaranteed, it makes it possible to compare the performance of the heuristic generated by QLHH-II with behaviour more relevant than a random process.