esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Controller Parameters Tuning
### Design of Fitness Function  
The purpose of the NLPID controller optimization is to obtain rapid system response without overshoot. Motivated by this requirement, it is critical to design a proper fitness function in the G-PSO algorithm.  
Usually, the ISE, ITSE, IAE, and ITAE are employed in control systems to evaluate the control performance. However, these criteria are designed to describe the integrated control effect, which are not suitable to describe the response speed of the system and overshoot [32]. To overcome this problem, the following fitness function is adopted:  
\[fitness=\int_{0}^{\infty}{[|e(t)|+\omega v+u(t)]dt}+k\cdot t_{u} \tag{19}\]  
where \(e(t)\) is the real-time error of the system, \(\omega v\) is the system overshoot, \(u(t)\) is the output of the NLPID controller, \(t_{u}\) is the rise time, and \(k\) is a constant.  
In Equation (19), \(\omega v\) limits the overshoot of the system. When the system has overshoot, \(\omega v\) increases the value of fitness, while \(\omega v\) is zero when the system has no overshoot. \(u(t)\) is added to the fitness function to ensure a fast and stable control system and avoid over-control [33]. The rise time \(t_{u}\) is to ensure the rapidity of the system response.
## 4 Simulation Model Design
### Model Design for the Case Study  
The model design was based on the three elements, as described in Section 3.4. Therefore, the model would identify and quantify:  
* When an aircraft unit is required at a vertiport and it is not available.
* When an aircraft unit requires a place to land and park at a vertiport and no place is available.  
The simulation model would provide relevant insights as this information, even with real-time and location data collection, could not be obtained. For designing the model, the following simulation assumptions were used:  
* The simulation period was set equal to 1 min.
* The time horizon was selected for 4 days, a total of 5760 periods.
* Every day, there was a redistribution of the aircraft units between vertiports. In the model, this occurred every 1440 periods. This function could be disabled.
* There were 15 vertiports with distances between 100 and 300 km among them. All origin-destination pairs were available by request to customers within the 15 vertiports as potential connections.
* The electric aircraft units had a range of 300 km.
* The flight transportation lead times between the 15 vertiports were calculated with a normal distribution defined by a mean and a standard deviation. However, it was possible to define the mean, the standard deviation, the maximum and the minimum per possible transport route, with a total of 225 combinations.
* If an aircraft unit could not land in a vertiport due to the location having reached its capacity, the aircraft would wait until space became available.
* If a customer flight request for an aircraft unit could not be assigned due to the vertiport being empty, then the customer would wait until an aircraft unit was available in the same vertiport.
* The electric aviation cycle in the simulation model followed the status shown in Figure 11. Between two flights, four cases could occur before the next flight order would be performed; direct to the next flight; charging required; maintenance required; charging and maintenance required.  
Based on the assumptions, the network model of fifteen vertiports was implemented in the simulation model, as shown in Figure 12. Five main characteristics described the network model, the vertiports capacity with ten aircraft units per vertiport, the distance between vertiports, the charging times with a value up to 30 min for a fully charged battery, the quantity of passengers per aircraft with six maximum, and the maintenance parameters for planned and unplanned maintenance:  
Figure 11: Electric Aviation Cycle.  
A central vertiport location was assumed as the main location with more aircraft unit capacity while all other vertiport capacities held ten aircraft units. The model assumed that after each trip, the aircraft unit remained at the vertiport until assigned to a new on-demand request, i.e., flying without passengers was not allowed in the model unless a redistribution for all aircraft units was performed. In this case, flights without passengers were performed to reallocate aircraft units within the network.
## 5 Seismic Performance Analysis  
### Finite Element Model of the Bridges  
The finite element models of the prototype bridge and the new composite tall-pier bridge are established in ABAQUS. For the prototype bridge, the superstructure and piers are simulated by the B31 elements(\"B\"indicates beam element, \"3\" is for three-dimensional beams, and \"1\" is for linear interpolation). The girder is assumed to remain elastic with C50 concrete(\(E_{\mathrm{c}}\) = 30 GPa\({}_{\mathrm{c}}\)/\({}_{\mathrm{c}}\)k = 50 MPa). The reinforcement is defined by the *rebar keyword (i.e., a method to define a integration point with different material fiber in the beam element section). The Kent-Scott-Park model [38] and the USteel01 model (kinematic hardening elastoplastic uniaxial constitutive model) in the PQ-Fiber subroutine developed by the Department of Civil Engineering of Tsinghua University are adopted for the constitutive models of C40(\(E_{\mathrm{c}}\) = 30 GPa\({}_{\mathrm{c}}\)/\({}_{\mathrm{c}}\)k = 40 MPa) unconfined concrete, and the HRB400 (\(E_{\mathrm{s}}\) = 210 GPa\({}_{\mathrm{c}}\)/\({}_{\mathrm{c}}\)y = 400 MPa, and \(\alpha\) = 0.01) reinforcement, as shown in Figure 5, respectively. The basin type bearings are simulated by the link elements. The stiffness is infinite along the fixed direction. In the active direction, the bidirectional elastoplastic model is adopted for the link element.  
For the new composite tall-pier bridge, the superstructure and the basin type bearings adopt the same modeling strategy as the prototype bridge. The four-limb CFST columns and the I-shape steel beams are also modeled by the B31 element. The steel tube is simulated in fiber elements and defined by the *rebar keyword. The USteel01 material in the PQ-Fiber subroutine is adopted for the steel tube. Meanwhile, the confinement effect of the core C50 concrete is considered by the constitutive model proposed by [PERSON] [39]. The EDMSPs are modeled by the four-node reduced integrated shell elements (i.e., S4R elements). The bi-linear plastic material with the Von Mises yield criterion in ABAQUS is used for the Q345 I-shape steel beam and the LYP100 EDMSPs and the yield points are 345 MPa and 100 MPa, respectively. Both of the elastic modulus are 200 GPa. The steel beams and CFST columns are merged into a whole frame. Then, the connections between the EDMSPs and the frame are modeled using the \"tie\" command (i.e., a method to attach an element to the adjacent elements) Furthermore, the boundary condition is simplified as the fixed connection at the pier base. Besides, the effects of structural geometric nonlinearities are considered in the analyses.
## 2 Related Works
In order to prevent nodes with relatively low energy from operating as relays, a protection mechanism is established, and a mobile data collector is provided for data acquisition. Another study [20], based on particle swarm optimization (PSO), proposed a more efficient clustering and aggregation migration (PSO-ECSM) technology strategy that combines energy to address the challenges of cluster head selection and aggregation migration. The PSO-ECSM algorithm evaluates a number of parameters when selecting CH, including the node's residual energy, average energy and energy consumption rate (ECR), distance, node degree, etc. To arrive at the best solution, the algorithm optimizes the values of these parameters. PSO-ECSM also incorporates sink mobility to handle the routing traffic problem in multi-hop networks. To address the challenges posed by HWSNs, an enhanced Hybrid Grey Wolf Optimizer (HMGWO) routing protocol [21] has been proposed. This protocol adopts an advanced approach to optimize the selection of initial clusters, where alternative fitness functions are first constructed specifically for heterogeneous energy nodes. These fitness functions take into account the degree of residual energy of the node, as well as communication distance and other relevant characteristics. By introducing these alternative fitness functions, the protocol aims to improve the accuracy and efficiency of CHs selection. In the HMG protocol, the fitness values of sensor nodes are computed based on these alternative fitness functions and applied as initial weights to the GWO algorithm. However,what sets the HMGW protocol apart is the dynamic adjustment of these weights during the optimization process. These weights are adaptively modified based on the distances between the wolves (representing potential CHs) and the prey (representing the optimal cluster configuration), as well as coefficient vectors associated with the GWO algorithm. This dynamic adjustment mechanism enhances the optimization capability of GWO and ensures the selection of appropriate CHs, enabling efficient resource management and meeting the energy constraints of HWSNs.
# Ambulance Locations in a Tiered Emergency Medical System in a City
## 2 Materials and Methods  
We will start this section by the description of the \(p\)MP, MEXCLP, and ERTM models. All of them are discrete location models [27]. This means that facilities can be located at the selected potential locations only. In practice, an analyst is supposed to select appropriate candidate locations prior to the optimization.  
We introduced a unified notation. The following sets, indices, and parameters are common to all models.  
Sets and indices  
\(I\) set of candidate locations  
\(J\) set of demand points  
\(i\in I\) candidate location  
\(i\in J\) demand point  
\(N_{j}=\ \left\{i\in I:t_{ij}\leq T^{max}\right\}\) set of candidate locations in the neighbourhood of demand point \(j\)  
Parameters  
\(p\) number of ambulances to be sited  
\(q\) probability of an ambulance being unavailable  
\(T^{max}\) the desired service standard (min)  
\(b_{j}\) weight of demand point \(j\) (number of potential patients)  
\(t_{ij}\) shortest travel time of an ambulance driving at all possible speeds from location \(i\) to demand point \(j\) (min)  
Decision variables  
\(x_{i}\) the number of ambulances to be located at site \(i\)  
Except for the location variables, \(x_{i}\), each of the following models needed additional variables to define its specific objective function.
## 6 Conclusions
The results developed by the team under the framework the Antarctic Spanish projects show the potential of this infrastructure for ionospheric monitoring and research purposes at subauroral regions. As far the vertical sounding is concerned, we have the historical series of the key parameters of the channel along more than ten years. The parameters are the delay spread and the Doppler spread and shift as a function of the frequency and the time, as well as the SNR for every frequency and the amount of interference. This database gives us a deep knowledge of the channel and is a great support to design the modulation scheme, the error correcting codes, the interleaver and the rest of parameters that define a complete physical layer. We have also tested some types of modulations that fit well to a slow fading multipath channel. We developed some variations of the classical Direct Sequence Spread Spectrum that are highly robust in low SNR scenarios at the expense of the low bit-rate. The spread spectrum approach does not interfere with other signals in the same band, and it is a good approach for sensors with low data rate. Then, we also tested the multicarrier approach (OFDM) in order to increase the bit-rate using some parallel orthogonal subcarriers at the same time. In practice, OFDM does not ensure a greater capacity in our link, because the available transmitting power is very low and we could use a small amount of subcarriers. Moreover, the effective mean power is even lower due to the PAPR problem, and the need of a linear amplifier increases the cost of the system. Finally, we have tested the Single Carrier approach with Frequency Domain Equalization. The increase of bit-rate is highly remarkable, at the expense of a poorer BER, which can be corrected with the proper codes. Therefore, it is a good candidate for remote sensors that need a higher throughput. Although the best modulations have been already determined, the complete definition of the physical layer, that is, the structure of the frame, the synchronization methods, the coding and interleaving schemes, have not been totally defined yet. At this moment, the hardware design is completed, but the definition of the complete physical layer for the communication of remote sensors in HF is about to be completed.
## 4 KGs Evaluation  
Intrinsic Evaluation (Section 4.1), Extrinsic Evaluation (Section 4.2), Qualitative Evaluation (Section 4.3), and Dataset and Domain-Specific Evaluation (Section 4.4). Overall, the number of publications peaks in 2023 before declining in 2024, but each sub-section follows its own trajectory.  
**Steady or Growing Areas**_Intrinsic Evaluation (Section 4.1)_ gradually increases and stabilizes over time. This steady rise may be attributed to the ongoing need for robust internal metrics--such as accuracy, consistency, and completeness--that allow researchers to benchmark KG models in controlled settings.  
**Peaks and Surges in Specific Years**_Extrinsic Evaluation (Section 4.2)_ and _Dataset and Domain-Specific Evaluation (Section 4.4)_ show a marked surge in 2023, possibly reflecting a heightened focus on real-world applications and specialized datasets during that period. As industries and academic communities sought more tangible results and domain-aligned benchmarks, publications in these areas spiked to address the practical constraints.  
**Fluctuations and Declines** _Qualitative Evaluation (Section 4.3)_ reaches its highest point in 2023, then experiences a decline in 2024. This decline may indicate a shift in research priorities toward more standardized or quantifiable metrics or the saturation of existing qualitative methods. Over time, as certain qualitative approaches mature, interest may wane in favor of new, data-intensive evaluation techniques.  
Overall, these trends underscore how the KG Evaluation landscape evolves in tandem with methodological advances and the practical demands of specific domains. While some approaches--like intrinsic metrics--remain consistently relevant, others experience spikes or dips as researchers adapt to changing expectations for rigorous, real-world performance and domain-driven requirements.
# The State of the Art of the Control Strategies for Single-Phase Electric Springs
## 3 Control Strategies of ES-2
### Single-Phase dq Decoupling Control Strategy  
In ES-2, the current components of SL can be decoupled by the single-phase _dq_ transformation, and both the _d_-axis and _q_-axis currents can be controlled independently. However, if a coordinated control scheme is not provided, the magnitude of the _d_-axis and _q_-axis currents can be of any combination. This will cause conflict on the _d_-axis and the _q_-axis control loops when performing two adjustment functions, such as performing grid voltage and frequency regulation [13]. In response to the problem, a current controller for ES-2 is proposed in [12], as shown in Figure 2.  
Under this control strategy, the _d_-axis and _q_-axis current references are determined by the power angle reference \(\varphi_{sl\_ref}\) and the magnitude reference of the SL current \(\left.\right|I_{sl\_Ref}\left.\right|\), respectively. The current controller is shown in Figure 2 within the dashed rectangle. The controller can only handle power factor correction in an open-loop manner and it lacks the ability to regulate the grid voltage. In order to achieve power factor correction and grid voltage regulation, a PI control loop must be added for closed-loop regulation of the grid voltage, which is shown in Figure 2. It should be noted that when the power angle reference value \(\varphi_{sl\_ref}\) changes, both the _d_-axis and the _q_-axis currents have to be adjusted. Similarly, during grid voltage regulation, these two currents also change simultaneously. It means that there is still a mutual coupling of the two currents in such control, which is the critical disadvantage. Moreover, this control strategy needs a PI controller for grid voltage regulation and two additional PI controllers to regulate the _d_-axis and the _q_-axis currents. Three sets of PI controllers in total make it a large amount of work for parameter designing and debugging.
# Optically Transparent Honeycomb Mesh Antenna Integrated into OLED Light Source
## 4 Experimental Results
### Radiation Patterns Comparison  
The far-field radiation patterns of the non-transparent antenna at 5.27 GHz are shown in Figure 12. Figure 13 shows the far-field radiation patterns of the transparent antenna at 5.16 GHz. In general, the simulated results matched the measured results. The observed deviations were attributed to the manufacturing problems and losses related to the electrical characteristics of the substrate. It was also observed that the antenna had an essentially unidirectional radiation pattern. The measured back radiation could not be evaluated due to the measurement configuration of the anechoic chamber.
# FarpScun: Fully Anonymous Routing Protocol with Self-Healing Capability in Unstable Sensor Networks
## 2 Preliminary  
### On-Demand Route Discovery  
In on-demand routing protocols, the route from the source node to the destination node is only established when the source node needs to communicate with the destination node [15].  
When the network needs to send over data but no available route is found, the source node broadcasts a RREQ (Route REQUest) message to all of its neighbors. After the neighbor node has received the RREQ message, it broadcasts the RREQ message to its neighbors until the destination node is reached. When the RREQ message reaches the destination node, the destination node will answer with a RREP (Route REPly) message. When the source node receives the RREP message, it indicates that the route has been established and it is now ready to communicate.  
Once the node receives the RREP message, the routing table is created. A routing table entry is responsible for the routing to reach a destination IP address. Table 1 shows the routing table structure. In order to keep the route up-to-date, the routing table needs to maintain the destination sequence number, so that the expired or damaged route can be quickly recovered.  
The maintenance of the neighbor node is mainly implemented by regularly broadcasting the \(Hello\) message. When a node is in an active route, it notifies the adjacent node of its own survival by broadcasting a \(Hello\) message to the adjacent node. The node first determines whether or not it is in an active route. If so, it turns on the timer, and it checks every \(HELO\_INTERVAL\) millisecond whether \(Hello\) message or RREQ has been broadcast in the past \(HELO\_INTERVAL\) millisecond, and it broadcasts a new \(Hello\) message for the other case. If the system has not received a \(Hello\) message from the neighbor, then the node considers the neighbor to be invalid and deletes the neighbor from the contacts [16].  
When a link fails, all of the adjacent nodes that directly use the link produce a RERR message, and the nodes are then linked to the other nodes. The other nodes will do the same thing until all of the affected nodes receive the RERR (Route ERRor) message. Whether the source node or intermediate node needs to send data again to the affected destination node after the link failure, it needs to send RREQ message again to discover the route to the destination node [17; 18].
# Robustness Analysis of the Collective Nonlinear Dynamics of a Periodic Coupled Pendulums Chain
## Appendix A  
The functions \(G_{m}\) and \(S\) are chosen according to the number and the positions of the uncertain pendulums in the chain, which is derived from the coupling. Four possible configurations (a-d) are defined, with respect to Section 2.1, as follows:  
**a.**: If the concerned pendulum is deterministic as well as its neighbors, \(G_{m}=2A_{m}\) and \(S=0\). In this case, the complex amplitude equation takes the form  
\[\begin{array}{c}2i\omega_{0}A_{m}^{(1,0)}+i\omega_{0}c_{n}A_{m}+4k_{n}sin[ \frac{q_{m}}{2}]^{2}A_{m}+\frac{3}{4}\alpha_{n}\sum_{j,k,l}A_{j}A_{k}A_{l}^{*} \Delta_{jkl,m}^{(1)}\\ -\frac{1}{(N+1)}f_{n}\ exp(i\sigma T)\sum_{n=1}^{N}sin[nq_{m}]=0,\end{array} \tag{11}\]
**b.**: If the concerned pendulum is deterministic but the previous one is stochastic, \(G_{m}=A_{m}+\widetilde{A}_{m}\) et \(S=-1\). Equation (14) is expressed as  
\[\begin{array}{c}2i\omega_{0}A_{m}^{(1,0)}+i\omega_{0}c_{n}A_{m}+k_{n}\Big{(} 2A_{m}-Cos[q_{m}]\Big{(}A_{m}+\widetilde{A}_{m}\Big{)}\Big{)}\\ +\frac{2k_{n}}{N+1}\sum_{n=1}^{N}sin[nq_{m}]\sum_{x=1}^{N}cos[nq_{x}]sin[q_{x} ]\Big{(}\widetilde{A}_{x}-A_{x}\Big{)},\end{array} \tag{12}\]
**c.**: If the concerned pendulum is deterministic but the following is stochastic, \(G_{m}=A_{m}+\widetilde{A}_{m}\) and \(S=+1\). Equation (14) takes the form  
\[\begin{array}{c}2i\omega_{0}A_{m}^{(1,0)}+i\omega_{0}c_{n}A_{m}+k_{n}\Big{(} 2A_{m}-cos[q_{m}]\Big{(}A_{m}+\widetilde{A}_{m}\Big{)}\Big{)}\\ +\frac{2k_{n}}{N+1}\sum_{n=1}^{N}sin[nq_{m}]\sum_{x=1}^{N}cos[nq_{x}]sin[q_{x} ]\Big{(}A_{x}-\widetilde{A}_{x}\Big{)}\\ +\frac{3}{4}\alpha_{n}\sum_{j,k,l}A_{j}A_{k}A_{l}^{*}\Delta_{jkl,m}^{(1)}- \frac{1}{(N+1)}f_{n}\ exp(i\sigma T)\sum_{n=1}^{N}sin[nq_{m}]=0,\end{array} \tag{13}\]
**d.**: If the stochastic pendulum is concerned, \(\widetilde{A}_{m}\) replaces \(A_{m}\) in Equation (14), \(G_{m}=2A_{m}\) and \(S=0\). Equation (14) is thus written as  
\[\begin{array}{c}2i\omega_{0}\widetilde{A}_{m}^{(1,0)}+i\omega_{0}c_{n} \widetilde{A}_{m}+2k_{n}\Big{(}\widetilde{A}_{m}-cos[q_{m}]A_{m}\Big{)}+\frac {3}{4}\alpha_{n}\sum_{j,k,l}\widetilde{A}_{j}\widetilde{A}_{k}\widetilde{A}_{ l}^{*}\Delta_{jkl,m}^{(1)}\\ -\frac{1}{(N+1)}f_{n}\ exp[i\sigma T]\sum_{n=1}^{N}sin[nq_{m}]=0\end{array} \tag{14}\]  
The number of generated complex equations depends on the number and positions of stochastic pendulums. In fact, the \(N\) equations obtained in deterministic case become \(N(p+q+d)\) in a stochastic one, where \(p\) is the number of stochastic pendulums, \(q\) the number of deterministic pendulumsneighbors of the stochastic ones and \(d=1\) if the structure contains deterministic pendulums having deterministic neighbors and 0 otherwise.
## 3 Problem Formulation  
In this study, we consider a multi-UAV Mobile Edge Computing (MEC) system, where multiple UAVs operate in the air to provide computational offloading services for ground-based mobile devices. Existing research on the tradeoff between latency and energy consumption in multi-UAV MEC systems has been limited in scope. For instance, the authors of [5] focused solely on the weighted system-wide latency and energy consumption of computational offloading among UAV clusters, while [6] only addressed the latency and energy consumption associated with communication and computation within the system. Notably, none of these studies have considered the joint optimization of task processing latency and UAV energy consumption as a weighted sum.  
To address this gap, this paper investigates the problem of task offloading and resource allocation in multi-UAV MEC systems, aiming to minimize the weighted sum of task processing latency and UAV energy consumption. Given the non-convex nature of this problem and the inclusion of discrete variables, we model it as a Markov Decision Process (MDP) and propose a novel task offloading and resource allocation algorithm based on MATD3-TORA. This algorithm jointly optimizes mobile device selection, UAV trajectory planning, UAV CPU frequency adjustment, and task offloading allocation.