esa-sceva/satcom-chunk-collection huggingface dataset:
# High Voltage Ride through Strategy of Wind Farm Considering Generator Terminal Voltage Distribution
## 2 Voltage Distribution Characteristics under a Wind Power Transmission Fault
### Analysis of Generator Terminal Voltage Distribution in a Wind Farm
The simulation results based on the topology in Figure 3 (simulation parameters are the same as provided in Section 5) of the voltage distribution in the wind farm are shown in Figure 6 and Table 1.  
The simulation results show that the generator terminal voltage distribution of wind turbines on the same collection line has the following characteristics: the further the wind turbine is from the busbar, the higher the generator terminal voltage of the wind turbine is, and the smaller the voltage difference between two adjacent wind turbines is. In the case of a voltage swell fault caused by DC blocking, even if the voltage of the grid-connection point is stabilized within the normal operating range through the reactive power compensation, the generator terminal voltage of the wind turbine at the end of the collection line is very likely to exceed the normal operating range, which will cause trip-off accidents. Therefore, it is necessary to fully consider the generator terminal voltage of each wind turbine in the wind farm during the voltage swell period to avoid trip-off accidents happening at the end of the collection line. It should be noted that during low-voltage ride-through (LVRT) periods, wind turbines at the head of the line have lower generator terminal voltages, which are more likely to trip-off from the grid. In a LVRT scenario, selecting the grid-connection point as the reactive power compensation reference point, and considering the recovery of the grid-connection point, can ensure the safety of all wind turbines. However, wind turbines at the end of the line have higher generator terminal voltages in the HVRT, which are more likely \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Collection** & & & & **U/p.u.** & & \\
**Line** & & & & & & \\ \hline Line 1 & 0.9981 & 1.0029 & 1.0072 & 1.0106 & 1.0123 & — & — \\ Line 2 & 0.9975 & 1.0073 & 1.0157 & 1.0228 & 1.0264 & — & — \\ Line 3 & 0.9977 & 1.0041 & 1.0094 & 1.0128 & 1.0143 & 1.0165 & 1.0224 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The generator terminal voltage distribution in the wind farm at 2 s.  
Figure 6: Generator terminal voltage distribution in the wind farm. (**a**) Line 1. (**b**) Line 2. (**c**) Line 3.  
to trip-off from the grid. Therefore, selecting the grid-connection point as the reactive power compensation reference point cannot ensure the safety of all wind turbines during HVRT periods.
# rStaple: A Robust Complementary Learning Method for Real-Time Object Tracking
## 4 Proposed rStaple Tracking Method
### Implementation Details  
The proposed rStaple is summarized in Algorithm 1. In this paper, the model of the translation filter is normalized to a fixed size \(150\times 150\). The learning rate of translation filter \(\eta_{cf}\), the learning rate of scale filter \(\eta_{s}\), and the learning rate of color histogram \(\eta_{hist}\) are set to 0.01, 0.025 and 0.04, respectively. The learning rate parameters determine how much target information the tracker can learn in the next frame; too high a learning rate will lead to overlearning of the change of target area and a tendency to drift under the situations of occlusion and being out of view. For scale estimation, the scale factor \(\sigma\) is set to 1.02 and the number of scales \(N\) in the scale pool is 33. The update thresholds \(\beta_{1}\) and \(\beta_{2}\) of \(\mathit{APCE}\) and \(\mathit{F}_{\max}\) in the adaptive model update are 0.57 and 0.38, respectively. The thresholds affect when to perform the model update; if the thresholds are inappropriate, wrong update timing will lead to tracking failure. The parameter \(p_{0}\) is set to 0.71 and \(r\) in histogram enhancement is 0.82. If \(p_{0}\) is too high and \(r\) is too low, the enhancement will be weak and not beneficial to the performance. Otherwise, the color histogram part will excessively impact the tracker result. We empirically determine these parameters and fix them throughout all the experiments. The parameter of learning rate \(\eta\) is determined by Staple and the parameters in scale estimation refer to DSST [21]. Furthermore, we preset the update thresholds and parameters in the histogram enhancement with a few random initial values. After, these values were tested in 10 video sequences which are not included in OTB2013 [10] and OTB2015 [11] to avoid overfitting. Finally, we further fine-tune the values with good performance to obtain the most suitable choice.  
```
1:Initial bounding box \(b_{1}=(x_{1},y_{1},w_{1},h_{1}),F_{T},F_{S}\)
2:Estimated target bounding box \(b_{t}=(x_{t},y_{t},w_{t},h_{t})\)
3:repeat
4: Crop out an image patch z from frame t at the center of \((x_{t-1},y_{t-1})\) and extract its fHOG and CN features; //Translation estimation
5: Compute the response map \(r_{t}(z)\) with \(F_{T}\) and \(r_{hist}(z)\), estimate target position \((x_{t},y_{t})\); //Scale estimation
6: Construct scale pyramid at estimated target position \((x_{t},y_{t})\), infer the best scale \(s_{t}\) with \(F_{S}\); //Adaptive model update
7: Compute \(F_{\max}\) and APCE of \(r_{t}(z)\);
8:if\(F_{\max}\) and APCE satisfy the condition then
9: Update \(F_{T}\) and \(F_{S}\);
10:endif
11:until end of video sequence.
```  
**Algorithm 1** Outline of our proposed tracking algorithm
# Multi-Robot Formation Control Based on CVT Algorithm and Health Optimization Management
###### Abstract
Secondly, considering that there are obstacles in the real environments where robots perform tasks, this study also verified the formation maintenance experiment in an obstacle environment. Under the premise of ensuring the uniform distribution of the global density function, the density value corresponding to the obstacle position was reduced to ensure that the centroid was not divided into the obstacle area. That is, the characteristics of the CVT algorithm were used to realize the repulsive force field that drives the robot to avoid obstacles. It was necessary to set up additional potential field functions to complete the collision avoidance and obstacle avoidance control of formation robots, which significantly improved the flexibility and scalability of the algorithm. Finally, the health optimization management algorithm was used to maximize the endurance of unhealthy robots under the premise of minor changes to the original framework, so that the system could deal better with unforeseen damage such as mechanical failures and emergencies, making the formation more robust when performing tasks in different scenarios. The rest of this article is organized as follows. Section 2 provides the CVT-based multi-robot formation control algorithm and cost function optimization process, and gives a proof of the stability of the algorithm. Section 3 introduces the health optimization management algorithm used in this study and the insertion construction method used when the robot's health is exhausted. Section 4 presents the numerical multi-robot formation simulation and health optimization management optimization under the control of the algorithm presented in this paper, to verify the effectiveness of the algorithm. In Section 5, the robot platform is verified in a laboratory scenario to illustrate the practicability of the algorithm presented in this paper. Section 6 presents the conclusions and discusses future work.
## 5 Earth System Models
### Roots  
Another component of ESMs that is oversimplified is root forms and function. For example, most root algorithms in ESMs consist of a fixed rooting depth and distribution [93]. This constrains water uptake to the root zone (weighted by effective root fraction in each layer) and does not allow differential water uptake from soil layers due to changes in root kinetics and morphology, nor does it allow changes in root distribution or depth to increase the water available to the plant. Water uptake is generally a function of plant demand (for evapotranspiration needs), root distribution, and soil water content [121]. The most common model approach to determine water limitation is to calculate a water stress scalar, which can be a function of soil water matric potential, matric potential when stomata are open or closed, or soil water content [121]. That water stress scalar is then multiplied by physiological variables such as transpiration, stomatal conductance, photosynthetic capacity, and maintenance respiration to capture water stress on plants. One approach to improve the evapotranspiration in the CLM is to add root hydraulic redistribution [122], which can transport water either up or down the soil column. Hydraulic redistribution does not influence root growth or distribution, but it does enhance water uptake by shifting the water distribution in the root zone.  
Nitrogen uptake (when included) is often less complex in ESMs; most N uptake is from a bulk N mineralization pool that depends on supply and demand rather than root biomass or profile, although some recent work has focused on uptake as a function of root biomass [106]. Essentially, plants are given every opportunity to extract all available N necessary for growth. Although this allows an implicit metric for allowing roots an opportunity to adjust their uptake, it is not realistic and lacks feedbacks on the biogeochemistry from changes in the root profile.  
Recently, studies focused on modeling root growth to maximize various plant traits have been conducted. For example, [PERSON] and [PERSON] [123] used optimization techniques for root growth in the TIN-based Real-Time Integrated Basin Simulator coupled to the Vegetation Generator for Interactive Evolution (tRIBS + VEGGIE) model to maximize plant transpiration. The improved root parameterizations allowed more root C to be allocated to soil layers with high soil moisture, increased water uptake, and decreased plant water stress. Another study by [PERSON] et al. [124] altered the vertical distribution of root mass to maximize N uptake. However, the underlying limitation in all studies is that they focus on maximizing only one limiting resource, namely, water or N. This limits the studies' relevance to environments that share those resource limitations so they may not reflect the future state of those regions or be suitable for global application.
## 3 Proposed Scheme
### Overview of Proposed SDPCACM Corresponding to NDN
#### 3.2.2 A Single SDN Controller Scenario  
The inter-domain handover is supervised via a single controller. Therefore, herein, we demonstrate a scenario when an MC switches from AP-1 to AP-2, AP-3, and AP-4 accessing data packets from NAR-1 to a different NAR-2 surrounded by the reach of a single SDN network controller, or a scenario where the source of content and MC are participants or belong to the same controller. Next, the controller modifies the routing table, comprising the ID of MCs, interface, and content name, to keep itself aware of this movement via notifications. Furthermore, it sends updated new flow rules to the NDN access router (NAR). Once the NAR modifies its routing table according to updated flow entries, then the packets are destined to the MC, which is shown in Figure 5.
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 2 Related Work
### Comparison with Existing Approaches
#### 2.3.3 Reinforcement Learning for Scheduling Optimization  
[PERSON] et al. [18] proposed a deep RL-based scheduling method using Deep Q-Learning Network (DQN) agents for complex job shop environments, particularly in semiconductor manufacturing. Their method trains cooperative RL agents in simulation environments to optimize scheduling efficiency. [PERSON] et al. [19] developed a Deep Reinforcement Learning (DRL) approach, SRP-DRL, for cloud task scheduling, focusing on load balancing and reducing task response time by adapting to varying workloads.  
In Parallel Machine Scheduling (PMS), [PERSON] et al. [20] proposed a DRL-based PMS framework, DPMS, to address the limitations of static optimization in handling machine failures and job variations. By modeling PMS as a Markov Decision Process (MDP), DPMS dynamically adjusts job assignments using Deep Reinforcement Learning (DRL). It employs a meta-dispatching rule system to optimize scheduling policies in real time, demonstrating superior performance over traditional heuristics (e.g., SPT, LPT, MLB) and metaheuristics (e.g., GA) in dynamic environments.  
Unlike these studies, which rely solely on RL for dynamic scheduling, our approach combines fault prediction through deep learning with SMT-based scheduling optimization, focusing on proactive fault management alongside adaptive scheduling. By integrating Predictive Scheduling (fault anticipation) and Proactive Scheduling (preemptive job reassignment), our framework ensures both efficiency and reliability in dynamic production settings.
# A Review of the Network Arch Bridge
## 4 Research on Network Arch Bridges
### Hanger Arrangement  
The hanger arrangement of a network arch bridge is the oldest research topic for such structures, but as spans, rise-to-span ratios, materials used and bridge cross-sections are diverging from the ideas of [PERSON] et al. [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18], the issue of hanger arrangement appears in a series of other studies [50; 51; 52; 53; 54; 57]. Papers published on the hanger arrangement and not mentioned so far include [70], where [PERSON] and [PERSON] make a summary of their doctoral studies, showing the advantages and disadvantages of five proposed hanger arrangements in terms of both structural response and user-friendliness. In ref. [71], another group presented a comparison for a concrete-filled steel tubular arch between the classical vertical disposition of the hangers: the fan arrangement and the network arrangement with parallel inclined hangers. A comparison between a 90 m tied-arch bridge with vertical hangers, three different Nielsen configurations of the hangers, and the network hanger configuration for parallel hangers with a slope angle 50-70\({}^{\circ}\) is given in ref. [72]. The case of a 100 m tied-arch bridge inclined inward by 15\({}^{\circ}\) is given in [73], where a comparison is made between an arch with vertical hangers varying in number from 5 to 50 and a network arrangement with parallel inclined hangers. The skew angle of the hangers was set to 40\({}^{\circ}\), and the number of hangers varied from 40 to 85. The research presented was part of a doctoral thesis [74], and the configuration studied was used as the configuration of a road bridge opened to traffic in 2022, the Bridge at Ulmeni across the Somes River, in Romania. In ref. [75], a comparison between different hanger arrangements for a proposed road bridge with a span of 80 m with circular hollow cross-sections is given. The configurations studied were for 20, 22 and 24 hangers with a constant angle increase of 1.5\({}^{\circ}\) and a radial arrangement for 20, 22 and 24 hangers with an angle of 38\({}^{\circ}\) between the hanger and the radius. Another study [76] showed that a parabolic arch with two sets of radial hangers had good behavior in terms of resistance for a small rise-to-span ratio, compared to elliptical arches that showed poor performance in both strength and serviceability.  
In any bridge design, a great deal of time is spent in finding the optimal configuration given the required span and other constraints. Two different groups of researchers worked to find optimization models for NABs. In ref. [77], an optimization model based on a three-step algorithm is given, with the remark that the procedure used does not ensure, a priori, a global optimum. In ref. [78], another optimized design using a global optimization algorithm (EVOP) was presented. EVOP was interfaced with Ansys to evaluate the structural response of the bridge. It was observed that a reduction of 38-40% in the total cost could be achieved for both circular and parabolic arches in the case of optimized design.
# Reversal of Photoinduced Bending of Crystals Due to Internal Refraction of Light
## 4 Model of the Interaction of Light with a Crystal
### Simulation Results
#### 4.3.2 Square Cross Section  
To check the influence of the shape of the crystal's cross section and the orientation of its faces to the light, a numerical solution of the problem was obtained for a square crystal in the case of normal light incidence on a flat face (square orientation), or light incidence on the side edge of the crystal, along the diagonal of the cross section (diamond orientation). Figure 11 shows the dependences \(\Omega_{r}(\mu h)\) for crystals of different thicknesses and orientations towards the light, as well as the theoretical dependence for square orientation, following from BBL law, according to Equation (20);  
\[\Omega_{\mathrm{BBL}}\ =\ \frac{1}{h^{2}}\int\limits_{0}^{h}\left(\frac{h}{2}-x \right)e^{-\mu x}dx. \tag{20}\]  
Figure 11: \(\Omega_{r}(\mu h)\) dependences for crystals with square-shaped section of different thicknesses and orientation to the light (\(\lambda=400\) nm). The arrows show the direction of the incident light.  
Figure 10: Dependence of \(\Omega_{r}\) on crystal thickness at constant absorption \(\mu^{-1}=10\)\(\upmu\)m for \(\lambda=400\) nm.  
As can be seen, crystals with a thickness of more than 1 \(\upmu\)m in a square orientation qualitatively follow the dependence for BBL law, predominantly bending in the usual direction determined by light absorption. Reducing the thickness to values \(h\)-\(\lambda\) leads to the reversal of bend direction, similar to the case of a circular section. This effect is caused by the diffraction of the incident wave on a thin crystal leading to an efficient light penetration from the side faces. The behavior of crystals with a sub-wavelength thickness becomes qualitatively similar to the behavior of crystals in the diamond orientation discussed below. Due to diffraction effects, the wave ceases to distinguish the orientation of very thin crystals.  
The diamond orientation is characterized by a strong refraction effect due to the oblique incidence of light on the face of the crystal. As a result, the bend reversal effect is observed, which is even larger in amplitude than in the case of the circular cross section. \(\Omega_{r}(j\mu)\) curves are poorly dependent on crystal thickness for the diamond orientation, which is apparently due to the small contribution of the corner region of cross section to intensity moment, compared to the round shape.  
Thus, the same crystals can exhibit a normal or reversed bending direction, depending on the orientation of the crystal to the light, i.e., whether the light is incident on a flat face or on a side edge. Under normal irradiation of wide crystal faces, the usual direction of bending is observed, well described by the standard BBL law, Equation (20).
In the realm of passivity-based control, research on disturbance rejection controllers is relatively sparse. [PERSON] introduced an inner-loop controller for robotic systems, generating compensation signals by estimating aggregated disturbances defined by object uncertainty and external perturbations, thus achieving tracking error control and enhancing the robustness of passive controllers [20]. [PERSON] demonstrated new findings on reducing mismatched disturbances in pH systems via control by interconnection (Cbl) [21]. Controllers were designed to suppress constant disturbances originating from non-passive outputs. In the port-Hamiltonian framework, the disturbance suppression problem for permanent magnet synchronous motors was addressed by incorporating integral actions and changing coordinates. [PERSON] investigated the issue of grid imbalance in Vienna rectifiers, proposing a passive control approach with inherent disturbance rejection capabilities [22]. [PERSON] designed a PD control law for rotating flexible spacecraft, achieving disturbance rejection in a port-Hamiltonian approach [23]. However, there is as yet no specific design for disturbance rejection control in EMFF systems in the port-Hamiltonian framework. This article derives the Lagrangian for a two-spacecraft EMFF within the port-Hamiltonian structure, utilizing Euler-Lagrange equations. Leveraging these equations, we introduce the dynamics of EMFF. Inspired by the IDA-PBC paradigm, we have designed a controller specifically for the dynamics of a two-satellite EMFF. Based on the nonlinear disturbance observer technique, the robust composite control strategy is designed. Simulation tests are conducted on a given Circular Orbit reference trajectory. The contributions of this study are outlined below:  
1. This study presents the EMFF dynamics for two satellites within the pH framework, preserving the system's inherent nonlinear characteristics;
2. Guided by the tIDA-PBC concept, a controller tailored to the dynamics of two-satellite EMFF is designed;
3. Utilizing the nonlinear disturbance observer technique, a robust composite control strategy is formulated. The structure of this paper is outlined as follows: Section 2 presents the dynamic model of the two-satellite electromagnetic formation within the port-Hamiltonian framework. Section 3 proposes the magnetic dipole composite passivity-based controller, aimed at achieving trajectory tracking and disturbance rejection. Then, numerical simulation results are discussed in Section 4. Lastly, Section 5 provides valuable conclusions derived from the study.
# Private and Secure Distribution of Targeted Advertisements to Mobile Phones
## 6 Evaluation
### User Privacy against Other Users
#### 6.2.3. Agent Collects Ads  
When appearing within the proximity of an Ad-Dealer, the Agent forwards all of his/her stored ARMs, including his/her own. The Ad-Dealer fetches the requested adverts, encrypts them with different keys of his/her own choosing and he/she organizes them in Bundles. For every ARM, the Ad-Dealer then creates an ADM (Advert Delivery Message), which contains information that will allow the recipient to locate his/her ads within the appropriate Bundle and decrypt them. Finally, the ADMs are encrypted with \(K_{i}^{user}\) and sent back to the Agent. The Agent is able to decrypt the ADMs that are responses to his/her own ARMs, since he/she knows \(K_{i}^{Agent}\), but the Requester's ADMs remain private as the Agent cannot decrypt them without knowing the \(K_{i}^{Requester}\).