esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Minimum Nonlinearity PL Design Guidelines
### Extension of the IMPM to Account for Nonlinearities  
The Internal Multi-Ports Method (IMPM) is an efficient way to characterize the behavior of a reconfigurable device for a set of switch states, requiring only one FWEM simulation at setup. After setup, formulations yielding results equivalent to circuital simulations allow to determine the effects of the terminating loads on the fundamental antenna parameters. A simple extension to the IMPM is added to account for the power delivered to the fed ports and to the switches, becoming a useful indicator of power efficiency and possible excitation of nonlinearities. Expressions to calculate loaded port parameters, radiated field and port power are briefly summarized. The setup FWEM simulation is performed to obtain the reference network scattering parameters \(S\in\mathbb{C}^{N_{p}\times N_{p}}\), and the field radiated when the nth port is fed with unit amplitude (and the others are terminated in matched loads) \(\bar{E}_{n}(\theta,\phi)\), \(n\in\{1\ldots N_{p}\}\), where \(N_{p}=N_{A}+N_{B}\), \(N_{A}\), and \(N_{B}\) are the number of total, fed (external), and loaded (internal) ports, respectively. The matrices and vectors involved in the formulation can be partitioned to index the external or internal ports, \(A,B\) subscripts are used to denote this partitioning. Figure 10: Flow chart with the main elements of the design methodology. From the defining relation of the total S-matrix \(\mathbf{b}=\mathbf{S}\)\(\mathbf{a}\) in terms of the power wave amplitudes \(a_{m}=v_{m}^{+}/\sqrt{ZZ_{cm}}\), \(b_{m}=v_{m}^{-}/\sqrt{ZZ_{cm}}\) (\(v_{m}^{\pm}\) is the peak amplitude of the incoming/outgoing voltage wave at the mth port) for ports with possibly different characteristic impedances, the block partition described above results in:  
\[\left[ \begin{array}{c}\mathbf{b_{A}}\\ \mathbf{b_{B}}\end{array} \right]=\left[ \begin{array}{cc}\mathbf{S_{AA}}& \mathbf{S_{AB}}\\ \mathbf{S_{BA}}&\mathbf{S_{BB}}\end{array} \right]\left[ \begin{array}{c} \mathbf{a_{A}}\\ \mathbf{a_{B}}\end{array} \right] \tag{7}\]  
Noting that only input ports have a source signal \(\mathbf{a_{A}}\), while for the loaded ports the incoming signals are actually reflections on the load terminations, obtainable from \(\mathbf{\Gamma_{B}}\), the diagonal matrix telling the reflection coefficient of the terminating loads, we can write:  
\[\left[ \begin{array}{c}\mathbf{b_{A}}\\ \mathbf{b_{B}}\end{array} \right]=\left[ \begin{array}{cc}\mathbf{S_{AA}}& \mathbf{S_{AB}}\\ \mathbf{S_{BA}}&\mathbf{S_{BB}}\end{array} \right]\left[ \begin{array}{c} \mathbf{a_{A}}\\ \mathbf{\Gamma_{B}}\mathbf{b_{B}}\end{array}
###### Abstract  
This study investigates a hybrid microelectromechanical system (MEMS) acoustic resonator through a hybrid approach to combine capacitive and piezoelectric transduction mechanisms, thus harnessing the advantages of both transducer technologies within a single device. By seamlessly integrating both piezoelectric and capacitive transducers, the newly designed hybrid resonators mitigate the limitations of capacitive and piezoelectric resonators. The unique hybrid configuration holds promise to significantly enhance overall device performance, particularly in terms of quality factor (\(Q\)-factor), insertion loss, and motional impedance. Moreover, the dual-transduction approach improves the signal-to-noise ratio and reduces feedthrough noise levels at higher frequencies. In this paper, the detailed design, complex fabrication processes, and thorough experimental validation are presented, demonstrating substantial performance enhancement potentials. A hybrid disk resonator with a single side-supporting anchor achieved an outstanding loaded \(Q\)-factor higher than 28,000 when operating under a capacitive drive and piezoelectric sense configuration. This is comparably higher than the measured \(Q\)-factor of 7600 for another disk resonator with two side-supporting anchors. The hybrid resonator exhibits a high \(Q\)-factor at its resonance frequency at 20 MHz, representing 2-fold improvement over the highest reported \(Q\)-factor for similar MEMS resonators in the literature. Also, the dual-transduction approach resulted in a more than 30 dB improvement in feedthrough suppression for devices with a 500 nm-thick ZnO layer, while hybrid resonators with a thicker piezoelectric layer of 1300 nm realized an even greater feedthrough suppression of more than 50 dB. The hybrid resonator integration strategy discussed offers an innovative solution for current and future advanced RF front-end applications, providing a versatile platform for future innovations in on-chip resonator technology. This work has the potential to lead to advancements in MEMS resonator technology, facilitating some significant improvements in multi-frequency and frequency agile RF applications through the original designs equipped with integrated capacitive and piezoelectric transduction mechanisms. The hybrid design also results in remarkable performance metrics, making it an ideal candidate for integrating next-generation wireless communication devices where size, cost, and energy efficiency are critical.  
Keywords:lateral extensional mode; piezoelectric transducers; atomic layer deposition (ALD); thin-film piezoelectric on substrate (TPoS) resonators; microelectromechanical system (MEMS); quality factor +
Footnote †: journal: Applied Sciences
## 2 Background and Methods
### Importance-Based Feature Selection  
In this study, feature selection is used to reduce the input dimension while maintaining the performance of ML. It is also known as feature extraction, and as an important tool for data preprocessing in data mining, it has been applied to reduce the number of input features by creating new and more representative feature combinations. This process reduces the number of features without causing a large loss of information [34].  
An importance-based feature selection is applied in the proposed framework. The feature importance represents the information of how important or useful each feature that constructs of the trees of XGBoost and RF. A higher score means higher importance and more relative to the output prediction. It is a ranking list based on the \"information gain\" of the feature when it is used in trees. The gain is calculated as the decreasing entropy after splitting on a variable:  
\[Gain(T,X)=Entropy(T)-Entropy(T,X) \tag{3}\]  
where \(T\) is a target variable, \(X\) is the feature to be split on and \(Entropy()\) is the entropy calculated after the data is split on feature \(X\) in the tree.  
Figure 4: Generic representations of XGBoost algorithm.  
To predict a specific feature, all other features are ranked. Then, we repeat the ranking procedure for all the 117 body features to be predicted and select the top 20 features that rank the highest for predicting all the 117 features. The selected 20 features, instead of all the 117 features, can be considered as the input to the ML methods. The validation of the proposed feature selection scheme will be conducted in the next section.
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 1 Introduction
This paper addresses the critical need for seamless integration of satellite and terrestrial network operations to achieve the coverage and performance goals envisioned for 6G, proposing a framework that bridges operational gaps and enhances interoperability between these traditionally distinct domains. The paper contributes with the following: (1) characterizing the distinct methodologies for satellite and network operations and identifying their integration gaps and challenges; (2) a novel and integrated management framework for operating satellite-based NTN named the CMS; (3) presenting several architectures using the novel management framework and featuring sporadic connectivity and persistent connectivity scenarios; and (4) evaluating the proposed architectures with the integrated management framework against the identified integration challenges. The remainder of this paper is structured as follows. Section 2 surveys the proposed architectures in the literature for STIN and describes the management of satellite and network operations. Section 3 proposes the novel integrated management framework to operate a satellite-based NTN. Section 4 describes the design of different control architectures enabled by the CMS, covering sporadic and persistent connectivity constellations. Section 5 evaluates the key enabling features of the proposed management framework and operational architectures, and how they address the identified gaps and novel challenges that emerge from integrating both operational management methodologies. Finally, Section 6 concludes this work.
## 4 Implementation and Evaluation
### Evaluation
#### 4.2.3 Analysis of Computational Overhead and Memory Usage  
During the experiment, the experimental data may be affected by CPU performance, network bandwidth, number of nodes, and data IO speed. The computing power of the CPU will affect the execution time of the consensus algorithm of the node and also affect the time of the node to generate and verify the DID. Network bandwidth affects the speed at which nodes send and receive transactions to the blockchain network, which increases block processing time and reduces system performance. The number of nodes increases the amount of data in the network, and as the number of nodes increases so does the time it takes to synchronize blockchain data between nodes. The data I/O speed affects the time the node takes to process the data in the transaction pool, thereby reducing the block time.  
After the framework is deployed to IoT devices, the central server responsible for managing resource-constrained IoT devices has resource constraints and performance constraints. Resource-constrained IoT devices are responsible for collecting data and delivering it to the central server, which participates in the blockchain network as a blockchain node.  
In terms of computational overhead and energy consumption, since we incorporate decentralized identity to ensure trust issues among users in the blockchain network, the difficulty of reaching consensus on each block can be reduced. This greatly reduces the computing overhead and energy consumption of nodes, and also shortens the time for reaching consensus between nodes.  
In terms of memory usage, nodes need to maintain a transaction pool and DSS locally. The transaction pool includes transaction requests sent to the blockchain network, and the DSS stores the transaction flow process of the data type. The maximum block size is 1 MB, including a fixed block header of 80 B, and the transaction body is responsible for storing transactions. The size of transactions is usually around 250 B, which means that a block can store up to 4000 transactions. Therefore, a node only needs to have the ability to store more than 4000 transactions to perform block construction work normally and participate in the blockchain network normally. In the early stages of blockchain network operation, memory consumption is extremely low for the burden on nodes.
# Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges
## 5 Applications and Use Cases of NDN  
Different NDN application domains, such as IoT deployments, wireless sensor networks (WSNs), and other constrained environments, face unique IFA challenges and have inspired specialized defense strategies. Here, we examine how IFA defenses are applied or adapted in IoT (including smart homes, healthcare, and industrial IoT) and WSNs.
# New Optimal Design of Multimode Shunt-Damping Circuits for Enhanced Vibration Control
## 3 Results and Discussion
### Solution of Optimization Problem: Model 3  
Finally, the optimization problem defined by Equation (56) is solved with the resistance values \(R_{i}\) and the capacitance values \(C_{i}\) considered as design variables. Given the capacitances, the inductance values \(L_{i}\) of the simplified shunt circuit are determined by Equation (35). The lower and upper limits for the resistances and the capacitances vary, the same as in model 1 and are given in Table 3. The optimal values of the design variables, along with the calculated inductances, are given in Table 7. As we can see, the required inductance values for this model are even larger relative to model 2 and considerably larger compared to the ones of model 1. The magnitude frequency response of the tip displacement, before and after shunt damping, are presented in Figure 10. For the purpose of comparison, the corresponding magnitude frequency response of model 1 is also illustrated simultaneously in this figure. It can be observed from this figure that the amplitude reduction for model 1 is higher than that of model 3. It means that model 1 yields better results than the model 3. Using the values of the shunt circuit from Table 7, the resonant amplitudes for the second and third modes are reduced by 24.07 dB and 44.22 dB, respectively. Compared with the corresponding reduction obtained when the shunt system is tuned according to the proposed method of model 1, these reductions are obvious lower.  
Figure 11 shows the iteration process of optimization. It can be seen from the figure, that the process converges after generation 5, when the fitness function remains quasi constant. It takes about 15 min to carry out the 38 iterations. Again, the algorithm stops before the maximum iteration is reached, since the relative changes in objective function are less than the tolerance (10\({}^{\circ}\)-6).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(R_{2}\) (k\(\Omega\)) & 16.34 \\ \(R_{3}\) (k\(\Omega\)) & 5.43 \\ C\({}_{2}\) (\(\mu\)F) & 0.001 \\ C\({}_{3}\) (\(\mu\)F) & 0.001 \\ \(L_{2}\) (\(H\)) & 235.36 \\ \(L_{3}\) (\(H\)) & 29.59 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Shunt parameters for model 3.  
Figure 10: Simulated frequency response: undamped response (blue —), damped response—model 3 (red).
# Effective Seed Scheduling for Directed Fuzzing with Function Call Sequence Complexity Estimation
## 5 Evaluation
### Utility for Symbolic Execution-Based Fuzzer (RQ4)  
In this experiment, we show the promise of SEZZ in another fuzzer, where we integrate SEZZ into symbolic execution seed scheduling. Symbolic execution is known to incur a high overhead during path constraint collection and solving [33,34]. Hence, in symbolic execution, scheduling promising seeds is crucial to its performance [26,35]. To perform this experiment, we use the symbolic executor from BugRedux [36]. BugRedux is a directed white-box fuzzer based on KLEE [37], which takes as its input a sequence of program statements, and generates as its output a test case that exercises that sequence and crashes the program. Since our goal was to show the utility of SEZZ for symbolic execution seed scheduling, we only modified its seed-scheduling algorithm and called it BugRedux*. We did not modify BugRedux's symbolic executor logic. We ran BugRedux* against the default seed scheduler in BugRedux on five subjects on its own dataset using the same experimental setup, starting configuration, and timeouts. Regarding the arithmetic mean over the five runs, Table 5 shows that BugRedux* improves the speed to expose the vulnerabilities by 1.87\(\times\). Hence, this shows the potential promise of SEZZ for seed scheduling in non-evolutionary fuzzing settings. However, we note that our results are preliminary and inconclusive. We leave a detailed evaluation for future work.
## 6 Energy-Efficient, Priority-Based Forwarding (EPF)
### Packet Formats  
Four types of packets are used in the EPF. Controller ID packets (C_ID pkt) contain information on the controller ID. This packet is broadcasted once at the start of the topology. New nodes in the network can communicate with neighbor nodes and send their information to the controller node after syncing with it. Local information packets (L_info pkt) have information on the node's ID, the node's residual energy, and the content names, which are broadcasted to the controller node and neighbor nodes for the population of the GIT and LIT. Interest packets in EPF (EPF I_pkt) have a naming scheme in which some priority criteria is presented: the path information, requested content name, and the nonce for the unique identifier of the packet. Data packets in EPF (EPF D_pkt) have the same structure as EPF I_pkt with addition of Producer's signature. Figure 9 shows the packet formats for EPF.
# Optimization Techniques for a Distributed In-Memory Computing Platform by Leveraging SSD
## 3 Optimization Techniques for the Spark Platform
### RDD Caching Policy  
The Spark platform offers diverse RDD caching options involving main memory and disks. Basically, the default option is the MEMORY_ONLY, where the RDD is maintained in the storage space described in Section 3.2 as a non-serialized Java object. If this storage space is insufficient for holding all RDDs, some of them would be evicted from the main memory based on a pre-defined cache replacement policy. However, whenever a non-cached RDD is required for task processing, this RDD should be re-created based on the _lineage_ information which can result in substantial performance degradation in this MEMORY_ONLY caching policy.  
Besides the MEMORY_ONLY option, Spark provides alternative MEMORY_AND_DISK, DISK_ONLY, and OFF_HEAP options. The MEMORY_AND_DISK option stores RDDs in the non-volatile disk when the storage space is not enough to store all required RDDs. Disks can consist of HDDs or SSDs; however, normal spindle disks have relatively poor read/write throughput, so that the overall execution time can be longer than that of the MEMORY_ONLY caching option. To address this problem, we can effectively leverage SSDs, which can potentially reduce the overall job completion time compared to the normal HDD-based approach.  
The DISK_ONLY option stores RDDs only in non-volatile storage devices such as HDDs or SSDs, i.e., not in the main memory. A cluster which does not have sufficient amount of available memory can achieve good performance with this option. In this case, since RDD is stored only in disk media, the shuffle space can be extended instead of using the storage space of the memory. As a result, when running an application such as PageRank, which generates a relatively large amount of shuffle data, we can observe better performance than in the MEMORY_ONLY case.  
The OFF_HEAP option enables Spark to use off-heap space, which is outside of the Java garbage collector's management. Thus, if we use off-heap space, we have to deal with complicated memory operations such as allocation/deallocation and serialization/deserialization. Therefore, for practical purpose, we do not use the OFF_HEAP configuration.
###### Abstract  
This paper investigates the aeroelastic flutter and vibration reduction of functionally graded (FG) multilayer graphene nanoplatelets (GPLs) reinforced composite plates with piezoelectric patch subjected to supersonic flow. Activated by the control voltage, the piezoelectric patch can generate the active mass and active stiffness that can accordingly increase the base plate's stiffness and mass. As a result, it changes the GPLs reinforced plate's dynamic characteristics. The motion equation of the plate-piezoelectric system is derived through the Hamilton principle. Based on the modified Halpin-Tsai model, the effects of graphene nanoplatelets weight fraction and distribution pattern on the dynamic behaviors of the plate are numerically studied in detail. The result illustrates that adding a few amounts of graphene nanoplatelets can effectually enhance the aeroelastic properties of the plates. Two kinds of control strategies, including the displacement and acceleration feedback control, are applied to suppress the occurrence of the flutter of the plate. It shows that the displacement and acceleration feedback control can improve the critical flutter Mach number of the plate by attaching active stiffness and active mass, respectively. Furthermore, the combined displacement and acceleration feedback control has a better control effect than that of considering only one of them.  
graphene nanoplatelets; piezoelectric material; aeroelastic flutter; feedback control 1
## 4 Numerical Modeling
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Designation** & **Value** & **Dimension** \\ \hline Number of elastic elements & i & 4 & – \\ Weight of the small spacecraft body & m\({}_{\rm K}\) & 3 & t \\ Mass of the elastic element & m\({}_{1}\) & 50 & kg \\ Length of the elastic element & 1 & 5 & m \\ Diagonal components of the inertia tensor & l\({}_{\rm xx}\) & 1 & \\ in the principal bound coordinate system & l\({}_{\rm yy}\) & 1.5 & t \(\times\) m\({}^{2}\) \\ The maximum distance of the internal & & & \\ environment point of the small spacecraft & r & 0.5 & m \\ from its center of mass & & & \\ Width of the elastic element & b & 0.5 & m \\ Thickness of the elastic element & h & 6 & mm \\ Material of the elastic element & – & MA–2 & – \\ Young’s modulus & E & 42 & GPa \\ Thermal conductivity & \(\lambda\) & 96.3 & W/(m \(\times\) K) \\ Thermal expansion coefficient & \(\alpha\) & \(2.6\times 10^{-4}\) & \(\mu\)m/(m \(\times\) K) \\ External heat flux & Q & 1.4 & kW/m\({}^{2}\) \\ Vacuum temperature & T\({}_{\rm C}\) & 3 & K \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameter values of the small spacecraft used in numerical simulation [35]. Figure 2: The dependence of inertia force on temperature shock for the simulated small spacecraft. In this case, we get the results shown in Figure 4.  
The analysis of Figure 4 shows its fundamental similarity with the results obtained in [34] without taking into account the initial deflection in the framework of the one-dimensional thermoelasticity problem (Figure 5 in [34]). However, due to the greater length of the flexible elements in this case, it was possible to realize larger values of the initial deflection without a stability loss than in the previous case (Figure 2). Here, the dependence of the inertia force on the initial deflection is more clearly seen. The potential energy of the initial deformation contributes to the achievement of the large values of inertia force after the initial stage of the temperature shock. The initial stage of the inertia force dynamics of both numerical simulation examples is a more rapid dynamic phenomenon and practically does not depend on the initial deflection (Figures 2 and 4).