esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on Properties of Ultrasonic Coupling Agent with Graphene in NDT
## 4 Experimental Results  
### Microscopic Image of the Hydrogel-Graphene Composite Coupling Agent  
As shown in Figure 3 and Table 1, Figure 3a is the micrograph with a mass fraction of graphene of 0.25%. There is less graphene in the picture and the agglomeration is not obvious. The graphene can be evenly dispersed into the hydrogel. When the mass fraction of graphene was 0.5%, the phenomenon of graphene agglomeration presented in the microscopic diagram became obvious, and larger agglomeration (maximum diameter: 7.4 \(\upmu\)m) was formed. The quantity and density increased, and the dispersion was not relatively uniform. When the mass fraction of graphene is 0.75%, the diameter of the agglomerated particles (2.7 \(\upmu\)m) formed in the dispersion are similar to that (2.5 \(\upmu\)m) of 0.25%. The agglomeration is not obvious, and the dispersion uniformity is relatively good. When the mass fraction of graphene is 1%, the diameter of agglomeration particles increases significantly (average diameter: 5.1 \(\upmu\)m). The agglomeration of large particles formed from graphene in the dispersion will increase the acoustic absorption of graphene, thus affecting the propagation efficiency of ultrasonic wave and reducing the energy propagated into specimens. However, when the graphene content is relatively low, the acoustic impedance of the composite coupling agent rarely changes, so the propagation efficiency of ultrasonic wave cannot be optimized well. In terms of graphene dispersion, when the mass fraction of graphene is 0.75%, the propagation of sound waves is most favorable.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Mass Fraction of Graphene** & **0.25\%** & **0.5\%** & **0.75\%** & **1\%** \\ \hline Maximum particle diameter (\(\upmu\)m) & 5.7 & 7.4 & 7.6 & 11.2 \\ Minimum particle diameter (\(\upmu\)m) & 1.0 & 1.0 & 1.0 & 1.9 \\ Average particle diameter (\(\upmu\)m) & 2.5 & 3.2 & 2.7 & 5.1 \\ \end{tabular}
\end{table}
Table 1: Graphene agglomerated particle diameter in micrograph of a hydrogel–graphene coupling agent.  
Figure 2: Schematic diagram of experimental platform.
## 1 Introduction  
Both unmanned aerial vehicles (UAVs) and unmanned underwater vehicles (UUVs) are widely applied in civil and military fields. However, a single UAV or UUV cannot go on multidomain missions, such as mapping of isolated water regions, multidomain oceanic monitoring, and inspections of submersed structures and ship hulls [1]. As a result, a hybrid aerial underwater vehicle (HAUV) that can operate both underwater and aerially would be helpful to accomplish such tasks. Thus, interest has risen in HAUVs.  
In recent years, HAUV research has focused more on two subsets of HAUV: HAUVs based on fixed-wing or rotary-wing UAVs combined with UUVs' underwater functionality. [PERSON] et al. first achieved aerial flight, underwater cruising, and cross-domain transition with one vehicle [2]. Later, [PERSON] et al.'s work added an art water rotor to optimize underwater motion on multi-domain missions [3]. As for rotary-wing HAUVs, which have shown good control potential for multidomain emissions, they were first put forward by [PERSON] et al. in 2014 [4]. Later, some works combined rotary-wing UAVs with underwater rotors to allow underwater operation [5, 6, 7]. These HAUVs showed controllable and smooth operation; nevertheless, they are limited by their UAV-based structures. Our previous work was inspired by gliders, which have proven to be the most efficient UUVs [8]. Our HAUV was a combination of underwater gliders (UGs) and UAVs. We showed the functionality of underwater gliders in both vertical and level flight.  
Many works on HAUV have been performed recently. However, most of them focus on the control system and structural design. Few studies on path planning for HAUVsin multidomain environments have been proposed. These vehicles have different work modes in various domains, and are supposed to conform to various constraints in different environments. Therefore, they require copmlex path planning and trajectory generation strategies compared to other vehicles, such as UAVs and USVs. Indeed, the trajectory planning for HAUV is of great significance. Therefore, we propose a way to grasp and cluster the information from the environment and find an energy-saving mission trajectory for an HAUV.
# Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application
## 6 Future Direction and Real-World Implementation  
While the findings from this study provide insight on the potential applications of swarm robots in mining operations, the simulations used also present certain limitations when considering real-world implementation. Future efforts aimed at addressing these issues will focus primarily on transitioning from simulation-based research to physical applications. This will involve real-world validation through small-scale physical testing and a hybrid simulation-real-world approach. In the next stage of this research, small-scale functional prototypes of swarm robots based on the ant, firefly and honeybee models that have been tested in simulation will be built. The testing environments for these prototypes will be controlled and replicated to represent key aspects of a real mining site, such as the distribution of ore differences in the terrain and potential obstacles. In real-world mining environments, several challenges could affect the performance of swarm robots. Imperfect sensors, for example, may not be 100% accurate in detecting ore or navigating complex terrains, as they are in the simulation. Real mining sites might lead to complications such as wheel traction inefficiency, slipping wheels, and difficulties in precise localization. Furthermore, environmental factors like dust and humidity could affect the functionality of sensors and actuators, requiring frequent recalibration and maintenance.  
Physical testing will provide important information that is critical to improving algorithms and the model's ability to adapt to changing situations. Also, a hybrid strategy will be adopted to integrate the data from the real-world testing into the simulation environment to produce a more accurate and dynamic testbed. This hybrid setup will allow to iteratively improve the swarm model and control algorithm by regularly updating the simulation with real data. By simulating a range of dynamic scenarios that may be difficult to replicate in physical testing, such as unexpected failures or variations in ore quality, the robustness of models will be improved. This will ensure the adequate performance of the models in both controlled and uncontrolled situations. This will contribute to further development and validation of the simulation results, ultimately leading to more reliable and efficient swarm robotics solutions for mining applications.
## 4 Experiments Results and Discussions
### Comparative Analysis with Different Crack Detection Algorithms  
To comprehensively evaluate the crack detection performance of MSE-Faster R-CNN, four other detection methods are used for comparison. In this part, VGG-19 network is set as the benchmark network, while Faster R-CNN, YOLOv4, Single Shot MultiBox Detector (SSD) [39], MMTL-NET [40], SegNet [41], and MSE-Faster R-CNN methods are used as target detection models for experiments. The location accuracy of different kinds of cracks is listed in Table 3. Among the methods, the proposed scheme has the best result (0.898). This shows that MSE-Faster R-CNN outperforms the original Faster R-CNN, and can better ensure the safety and economy of gas turbine operation. YOLOv4 is another state-of-art deep network, and it outperforms YOLOv3 with respect to detection accuracy as well as speed. It has a better result than Faster R-CNN (0.819). Unlike Faster R-CNN, YOLOv4 is a one-stage detector which also uses max-pooling and a mish activation to improve the accuracy. However, the feature extraction network of YOLOv4 is so deep that the information will be lost during training. The multi-scale model proposed in this paper fuses the convolution kernels of different visual fields, which enriches the diversity of features. Thus, the accuracy of the proposed method is higher than YOLOv4. The accuracy of the SSD algorithm is not as good as the previous deep learning algorithms (0.783). This might be because SSD are not sensitive enough to detect internal cracks. MMTLNET is a transfer learning network with adversarial training for 3D whole heart segmentation and it can extract multi-modality features and obtain the final segmentation results by fusing MRI and CT images. However, when the intersections of two or more foreground objects are not obvious like the pores and tiny cracks, its accuracy will be greatly reduced. Like the results shown in the last column, the accuracy of detection of small scale cracks decreases 0.2. Similar results also appear on SegNet, which is a state-of-art surface crack segmentation method, as discussed in the introduction. It can also be seen that when the crack length increases, the accuracy of each algorithm increases. This may be because the crack length and opening and closing degree in the large-scale crack dataset are more obvious than those of small-scale cracks.  
Figure 6: Confusion matrix of small scale crack detection using MSE-Faster R-CNN.  
Table 4 shows the comparison of crack detection accuracy of various algorithms. The conclusion is close to Table 3: the algorithm proposed in this paper still achieves the best performance (0.806). It can be seen that the detection accuracy of each algorithm has decreased compared with the positioning accuracy, which is also consistent with the conclusions in Table 2. In addition, the detection results of large-scale crack data set are better than small-scale cracks, which is still in line with the previous conclusions.
# An Optimal Online Resource Allocation Algorithm for Energy Harvesting Body Area Networks
## 5 Performance Analysis
### Optimality of the Proposed Algorithm  
In Theorem 4, we analyse the optimality of the proposed resource allocation algorithm. We show the performance gap between the user utility achieved by the proposed algorithm and optimal utility.  
**Theorem 4**: _We define \(\overline{O}\) and \(O^{*}\) as the proposed algorithm user utility and optimal utility, and the performance gap between them is satisfied:_  
\[\overline{O}\geq O^{*}-\frac{B}{V}. \tag{18}\]  
Proof.: See proof of theorem 4. \(B\) is a constant. There is a \(\frac{B}{V}\) performance gap between the user utility achieved by proposed algorithm and optimal utility. It is easy to know that the user utility increases with the increase of \(V\), and illustrate that \(V\) means how much we put an emphasis on user utility. It also shows that user utility function is a concave function.
# EC-Kad: An Efficient Data Redundancy Scheme for Cloud Storage
## 1 Introduction
In this work, we propose a highly available cloud storage solution called \"EC-Kad\" which is aimed at reducing storage redundant computing overhead and improving the reliability of data recovery. The main contributions of this work are as follows. First, we introduce an encoding-based approach for data file redundancy and fault tolerance, A Cauchy matrix with good numerical stability is used as the coefficient matrix to implement the encoding and decoding of data files in cloud storage systems, which ensures data integrity and availability even when storage nodes fail. Second, a Kademlia [13] algorithm is used to optimize the retrieval and recovery efficiency of data blocks during user read operations, thereby significantly enhancing the overall performance of the cloud storage system. Finally, we implement our proposed \"EC-Kad\" scheme on the Hadoop cloud storage platform. The experimental results demonstrate that by combining the two strategies mentioned above, our proposed \"EC-Kad\" solution not only minimizes redundant storage computational overhead but also maintains high data availability and reliability, making it well suited for modern cloud environments. The remainder of this paper is organized as follows. Section 2 briefly discusses the related works. Section 3 presents the cloud storage system model based on erasure codes. Section 4 presents our experimental results. Finally, in Section 5, we conclude the paper.
## 2 Development of Proposed Controller
### Fractional-Order PI-PD Controller  
Fractional-order PI-PD (FOPI-PD) controllers are advanced versions of traditional controllers that use fractional-order computation. These controllers employ \(\lambda\) and \(\mu\) parameters to enhance the control performance of systems with complex dynamics. The \(\lambda\) parameter of a FOPI controller represents the fractional order of the integral action, which enables the controller to perform non-integer orders of integration to capture long-term memory effects and improve control performance. By adapting \(\lambda\), the FOPID controller can adjust the integral action to the specific dynamics of the controlled system. Tuning \(\lambda\) enables the FOPI controller to handle systems with non-linearity, time delays, and memory effects, depending on system complexity, desired control goal, and error signal characteristics. The FOPI-PD controller's block diagram is shown in Figure 2. The control signal of the FOPI-PD controller is given as follows:  
\[U(s)=\bigg{(}K_{p}+\frac{K_{i}}{s^{\lambda}}\bigg{)}E(s)+\bigg{(}1+K_{d}s^{ \mu}\bigg{)}Y(s),\;0<\lambda,\mu<1. \tag{4}\]  
The \(\mu\) parameter of the FOPD controller represents the fractional order of the derivative and controls the rate of change of the fractional-order error signal. The \(\mu\)-th order fractional derivative captures the system dynamics with memory effects and allows FOPD controllers to respond to complex behaviours not well captured by integer-th-order derivatives. The choice of \(\mu\) depends on the dynamics and control requirements of the particular system. By adequately tuning \(\mu\), FOPD controllers can effectively control systems with nonlinear and time-varying behaviour, improving control accuracy and stability. In the FOPI controller, the \(\lambda\) introduces additional degrees of freedom and \(\mu\) parameters, making it more flexible and adaptable than the traditional PI-PD controller. By customizing the fractional order of the integral and derivative actions, these controllers can better capture the complex system's complex behaviour and improve control performance. Various tuning, optimization, or model-based approaches can be used to determine appropriate parameter values for a particular control application. Stability, responsiveness, and robust control gains should be tuned to the system's specific characteristics to achieve the desired control performance.
# Large Field-Size Elliptic Curve Processor for Area-Constrained Applications
## 3 Proposed Crypto Processor Architecture  
Figure 1 shows the block diagram of our proposed elliptic curve point multiplication architecture. It includes data buffers, a memory block, an arithmetic unit and a control unit. These essential components of our architecture are highlighted with different colors in Figure 1. The data buffers contain input parameters loaded from the outside of the accelerator circuit. The memory block stores the intermediate and the final results after the computation. The arithmetic unit of the proposed accelerator executes the modular operations (addition, square, multiplication and inversion) of layer one of ECC. The control unit generates the corresponding control signals to implement the elliptic-curve PM operation. More insight details are given in the succeeding sections.
# Wide-Band Wide-Beam Circularly-Polarized Slot-Coupled Antenna for Wide-Angle Beam Scanning Arrays
## 1 Introduction
Therefore, the main novelties of this work consist of (_a_) the design of a new wide-beam _CP_ radiator exploiting an aperture coupling feeding mechanism to significantly widen the impedance bandwidth and overcome spurious radiation, narrowband operation, and more complex manufacturing of probe-fed layouts in the literature [20], (_b_) the formulation of the arising synthesis problem, unlike the parametric _trial-and-error_ approach used in [20], as a global optimization enabling a more effective control of the _CP_ in the complete radiating semi-sphere and a proper impedance matches within the user-defined wide bands, (_c_) its efficient solution by means of a customized system-by-design (_SbD_) methodology, and (_d_) the wide-band assessment of the suitability of the _WWCS_ for implementing large planar _WASAs_, differently from [20] where only the single radiator is considered. The manuscript is organized as follows. Section 2 describes the layout of the _WWCS_ radiator. The _SbD_-based synthesis strategy, which is used for the synthesis of this radiating element, is detailed in Section 3. A representative example, which is concerned with a _LHCP_ design, is illustrated in Section 4 to numerically assess, via full-wave (_FW_) simulations along with a tolerance analysis, the effectiveness of the proposed radiator when implementing wide-band _WASA_s. Finally, the experimental assessment of the designed _WWCS_ radiator, carried out on a _PCB_-manufactured prototype, is shown in Section 5. Eventually, some conclusions and final remarks are presented in (Section 6).
# Data-Driven Analysis of Student Engagement in Time-Limited Computer Laboratories
## 4 Materials and Methods
### Preliminaries  
We first define the preliminary concepts of sequence and sequential databases. Sequential pattern mining in compliance with [46].  
Let \(I\) be a set of all items. An itemset is a subset of items in \(I\). A sequence, \(s\), is an ordered list of itemsets. A sequence, \(s\), is denoted by \(\langle s_{1}s_{2}\ldots s_{l}\rangle\), where \(s_{j}\in I\), \(1\leq j\leq l\) is an itemset. \(s_{j}\) is also denoted by _element_ of the sequence, consisting of a set of items (\(x_{1}x_{2}\cdot x_{m}\)), where \(x_{k}\in I\), \(1\leq k\leq m\). For the sake of brevity, hereafter we will omit the brackets when \(m=1\). An item occurs at most once in an element of a sequence, but can occur multiple times in different elements of the same sequence. An \(l\)-sequence, i.e., a sequence of length \(l\), is a sequence where the number of instance of occurring items is \(l\). \(\alpha=\langle\alpha_{1}\alpha_{2}\ldots\alpha_{l}\rangle\) is a subsequence of another sequence, \(\beta=\langle\beta_{1}\beta_{2}\ldots\beta_{l}\rangle\), denoted by \(\alpha\sqsubseteq\beta\), if there exist integers \(1\leq j_{1}\leq j_{2}\ldots\leq m\) such that \(\alpha_{1}\sqsubseteq\beta_{j_{1}}\), \(\alpha_{2}\sqsubseteq\beta_{j_{2}}\), \(\ldots\), \(\alpha_{n}\sqsubseteq\beta_{j_{n}}\).  
A sequence database, \(S\), is a set of tuples, \(\langle sid,s\rangle\), where \( sid\) is the sequence identifier and \(s\) is a sequence. A tuple, \(\langle sid,s\rangle\), contains subsequence \(\alpha\) if \(\alpha\sqsubseteq s\). The absolute support of subsequence \(\alpha\) in \(S\), denoted by \(\sup_{S}(s)\), is the number of tuples containing \(\alpha\). The relative support is the fraction of tuples containing \(\alpha\).  
Given a sequence database, \(S\), and a minimum support threshold, \(\mathtt{minsup}\), the sequential pattern mining task entails extracting all the subsequences, \(\alpha\), in \(S\) whose \(\sup_{S}(\alpha)\geq\mathtt{minsup}\), i.e., it focuses on discovering all the _frequent subsequences_ in the sequence database.  
Figure 1: Designed pipeline.  
Whether or not the occurrences of the sequence elements are timestamped, i.e., \(t_{j}\) is the timestamp at sequence \(s_{j}\), we can enforce additional constraints into the sequential pattern mining process (beyond enforcing the support threshold):  
* \(\mathsf{mingap}\): the minimum time gap between consecutive elements of a sequence;
* \(\mathsf{maxgap}\): indicates the the maximum time gap between consecutive elements of a sequence;
* \(\mathsf{maxwinsize}\): the maximum temporal duration of the overall sequence.  
When not otherwise specified, time gaps and window sizes are expressed in minutes. By varying the values of \(\mathsf{mingap}\), \(\mathsf{maxgap}\), and \(\mathsf{maxwinsize}\), it is possible to focus the exploration on sequences with varying temporal periodicity.
###### Abstract  
This study presents an analytical and experimental approach to enhance cantilever-based piezoelectric energy harvesters by optimizing thickness distribution. Using a gradient projection algorithm within a state-space framework, the unimorph beam's geometry is tailored while constraining the first natural frequency. The objective is to amplify axial strain within the piezoelectric layers, thereby increasing electric polarization and maximizing the conversion efficiency of mechanical vibrations into electrical energy. The steady-state response under harmonic base excitation at resonance was modeled to evaluate the harvester's dynamic behavior against uniform-thickness counterparts. Results show that the optimized beam achieves significantly higher output voltage and energy harvesting efficiency. Simulations reveal effective strain concentration in regions of high piezoelectric sensitivity, enhancing power generation under resonant conditions. Two independent experimental setups were employed for empirical validation: a non-contact laser vibrometry system (Polytec 3D) and a first resonant base excitation setup. Eigenfrequencies matched within 5% using a Polytec multipath interferometry system, and constant excitation tests showed approximately 30% higher in optimal shapes electrical potential value generation. The outcome of this study highlights the efficacy of geometric tailoring--specifically, non-linear thickness shaping--as a key strategy in achieving enhanced energy output from piezoelectric harvesters operating at their fundamental frequency. This work establishes a practical route for optimizing unimorph structures in real-world applications requiring efficient energy capture from low-frequency ambient vibrations.  
piezoelectric energy harvesting; electromechanical output maximization; optimal thickness shaping; cantilever unimorph; gradient projection method