esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study and Parameter Optimization of a Magnetic Coupled Piezoelectric Energy Harvester
## 2 Modeling and Analysis
### Simulation Analysis of Different Modes  
This section will conduct a preliminary study of the different classifications of the structure based on the lumped parameter model. The nonlinear harvester with magnetic oscillator can be classified into two types according to attraction and repulsion, and two types according to the piezoelectric beam as a higher frequency beam or a lower frequency beam. Therefore, there are four different modes of structure named low/high beam with attraction/repulsion. The normalized simulation results are shown in Figure 3.  
From the results of Figure 3, the hardening property is exhibited at the configuration of repulsion, and the system frequency band is shifted to the high frequency. Conversely, the softening characteristic appears at the configuration of attraction, and the system frequency band moves toward the low frequency. \"Low beam repulsion mode\" and \"High beam attraction mode\" exhibit high peak performance and widen a certain frequency band in the direction of frequency band shift. \"Low beam attraction mode\" and \"High beam repulsion mode\" exhibit a double degree-of-freedom feature. The specific results are evaluated as summarized in Table 2.  
In the subsequent study of this paper, \"Low beam repulsion mode\" will be analyzed in detail for the following reasons:  
* The magnitude of the magnetic force is inversely proportional to the fourth power of the distance. The magnets at close distances are easy to fit at the attraction configuration, and cannot be automatically separated. This causes difficulties in actual assembly and application. Therefore, the repulsion mode is selected for analysis.
* The energy is proportional to the square of the amplitude. Compared with the double degree-of-freedom characteristic, the high performance is more efficient in practical applications. Thus, \"Low beam repulsion mode\" is selected with higher amplitude and bandwidth than the conventional configuration without tuning.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Beam Classification** & **Low Frequency** & **High Frequency** \\ \hline \multirow{2}{*}{Attraction} & Softening & Softening \\  & Double degree-of-freedom & High Performance \\ \hline \multirow{2}{*}{Repulsion} & Hardening & Hardening \\  & High Performance & Double degree-of-freedom \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of different model features.  
Figure 3: Output voltage simulation curve in different modes: (**a**) Low beam; (**b**) High beam.
## 5 Discussion
The PSNR family methods and the Histogram Comparisons at 0.01 and 0.001 assigned higher values to the LQ results, while the G-SSIM family methods and the Histogram Comparison at 0.1 assigned higher values to the HQ results. For the side-ball phantom, the visual similarity is ambiguous. However, the Low-Quality image contains more red spots than the High-Quality image. Figure 11 compares the similarity results for the side-ball phantom. This phantom scored benchmark errors for all the examined GQA methods, with LQ similarity consistently higher than HQ similarity. Since the visual determination of the closest reconstruction is challenging, interpreting the similarity of the results is also ambiguous. The uncertain performance of the benchmark error-prone reconstructions for the last two phantoms (rod and side-ball) suggests that some benchmark errors in the dataset originated from instances where certain High-Quality images were, in fact, less similar to their phantoms than the corresponding Low-Quality images. Electrical Capacitance Tomography systems often produce images that can be blurry, distorted, and lack clarity. These image quality issues can hinder accurate interpretation and decision making in real-time monitoring and diagnosis of industrial processes. By analyzing the graph structure of the 2D/3D ECT image, GQA can identify and flag artifacts like ringing, blurring, or ghosting. This early detection can trigger offline and online corrective actions, such as adjusting ECT sensor calibration or re-acquiring broken or noisy data. GQA modules can be integrated into existing ECT systems as standalone components or as part of a larger image-processing pipeline. GQA metrics can also be used to support and prepare online ECT industrial process monitoring by optimizing the regularization parameter in image reconstruction algorithms. They can help in selecting the most appropriate image reconstruction model. By comparing the quality of images produced by different models, the best-performing one can be chosen for the online scheme. By assessing the quality of reconstructed images for different parameter values, the optimal setting can be determined. GQA can also guide 3D image enhancement techniques, such as contrast enhancement, sharpening, or noise reduction, by identifying areas that require improvement, which can be added to the 3D image-processing pipeline. GQA techniques can be computationally intensive, especially for real-time applications, so efficient algorithms and GPU-based hardware acceleration techniques can be used to reduce processing time and achieve an acceptable image frame rate.
# SKVOS: Sketch-Based Video Object Segmentation with a Large-Scale Benchmark +
## 5 Experiments
### Ablation Study
#### 5.3.2 Further Studies of Cross-Q  
We also explore alternative approaches to the function in Equation (7) to further examine how to fuse sketch and frame features in our top-performing design, Cross-Q,  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Fusion Designs**} & \multicolumn{2}{c}{**Level**} & \multirow{2}{*}{\(\mathcal{J}\&\mathcal{F}\)} & \multirow{2}{*}{\(\mathcal{J}\)} & \multirow{2}{*}{\(\mathcal{F}\)} & \multirow{2}{*}{\(\mathcal{J}\&\mathcal{F}\)} & \multirow{2}{*}{\(\mathcal{J}\&\mathcal{F}\)} & \multirow{2}{*}{\(\mathcal{J}\)} & \multirow{2}{*}{\(\mathcal{F}\)} \\ \cline{2-2} \cline{6-9}  & **Visual** & & & & & & & & \\ \hline (a) Concatenation & raw pixel & raw pixel & 74.1 & 72.1 & 76.0 & - & - & - \\ \hline (b) Convolution weight & Decoder & HyperNet & 19.6 & 20.1 & 19.0 & - & - & - \\ \hline \multirow{6}{*}{(c) Cross-KV/Q} & res5 & GAP & 19.6 & 16.2 & 23.1 & 55.1 & 54.1 & 56.1 \\  & res4 & res4 & 56.6 & 55.5 & 57.6 & 74.3 & 72.3 & 76.3 \\ \cline{1-1}  & res5 & res5 & 67.1 & 65.4 & 68.7 & 74.8 & 72.8 & 76.8 \\ \cline{1-1}  & multi-level & res4 & 57.3 & 55.9 & 58.6 & 74.7 & 72.7 & 76.7 \\ \cline{1-1}  & multi-level & res5 & 55.8 & 54.4 & 57.1 & 74.9 & 72.9 & 77.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Sketch-based VOS model evaluated on the Sketch-YouTube-VOS validation set. GAP indicates global average pooling. Decoder means the convolution layer of segmentation head of the decoder. HyperNet means the weight generated by HyperNet.  
As shown in Table 6, all options effectively combine sketch and visual features. Notably, concatenating the query and weighted value leads to an improvement, achieving the best performance with a \(\mathcal{J}\)&\(\mathcal{F}\) score of 75.44, using res5 visual and sketch features.
## 4 Results and Discussion
### Discussion
#### 4.5.3 Comparison with Existing Classification Systems  
This study's four-level hierarchical framework differs from and advances existing fluvial classification systems in key aspects. The hierarchical framework proposed in this study differs from and improves upon existing fluvial classification systems in several key aspects. [PERSON]'s Architectural Element Analysis [27,28] focuses on identifying architectural elements (e.g., channel, bar, floodplain) and their bounding surfaces but lacks a clear hierarchical breakdown of microscale features (e.g., lithofacies) and macroscale genetic associations (e.g., element combinations across temporal stages). In contrast, this micro-macro four-level hierarchical framework directly connects 14 lithofacies (Table 1) to macroscale elements (e.g., downstream accreting bars) via sedimentary processes. For example, the planar cross-bedded conglomerate (Gp) is linked to downstream accretion through paleocurrent data (Figure 3), a causal relationship not emphasized in [PERSON]'s system. [PERSON] and [PERSON] [7] proposed a \"story-element-archetype\" system primarily for meandering rivers, emphasizing boundary surface hierarchies but neglecting quantitative relationships between morphological parameters (e.g., bankfull depth vs. width). The hierarchical analysis approach, tailored to braided rivers, adds lithofacies as the foundational level and establishes empirical equations (e.g., W\({}_{\text{b}}\) = 56.025 d\({}_{\text{m}}\)\({}^{0.9656}\)) that enable quantitative reconstruction of paleochannel scales--an advantage for reservoir modeling. Traditional \"Lithofacies Association\" Methods focusing on lithofacies combinations [5] often overlook genetic links between elements and temporal evolution. Element associations in this study explicitly document genetic relationships (contemporaneous vs. non-contemporaneous) and sedimentary sequences, providing a dynamic model of braided river evolution rather than static facies descriptions.
# Performance Evaluation of Active Noise Control for a Real Device Casing
## 3 Experimental Verification
### Control System  
The control system was implemented using a distributed system with four dSPACE DS1104 boards, each realizing individual task of the control algorithm [17]. All boards have synchronized Analog to Digital Converters (ADCs) and Digital to Analog Converters (DACs) using an external trigger. All cards communicate with each other and the host through a PCI (peripheral component interconnect) bus. The sampling frequency was equal to 2 kHz. To drive the actuators, Monacor STA-850D amplifiers were used, together with the analogue reconstruction filters, custom-made for the dSPACE DS1104 boards employed. Table 1 shows the nominal parameters of the control algorithm. The number of parameters in the secondary path models \(N_{S}\) was selected based on impulse responses of the secondary paths. The number of parameters in the feedback model \(N_{F}\) was selected based on impulse responses of the path to the reference microphone.  
The normalized leaky LMS algorithm step size, \(\mu_{n}\), and leak factor, \(\gamma\), here selected based on the performance of the control system. The leakage introduced by the \(\gamma\) parameter enhances robustness of the algorithm [27]. The noise control system performance was measured with reproducible noise  
Figure 5: Laboratory setup.  
generated by a loudspeaker placed inside the drum. A recorded spinning noise at 1200 rpm was used to evaluate the noise reduction.  
Figure 6 shows the average sound pressure level (SPL) at error and monitor microphones for a different \(\mu_{n}\). The SPLs were measured after 15 min of operation of the control system. The averaging was performed on sound pressure power and then converted to sound pressure level. The optimal \(\mu_{n}\) range for such adaptation time is around 0.0003 to 0.002.  
Figure 7 shows the average SPLs at error and monitor microphones for a different \(\gamma\). The SPLs were measured after 15 min of operation of the control system. The optimal \(\gamma\) range is around 0.0005 to 0.001. The leakage allows for finding better power distribution for different actuators. Without a leak, the system is under-determined, because the number of actuators is larger than the number of errors. Additionally, for some actuators the control signal limits were reached without leakage. The leak has a higher positive effect on monitoring microphones compared to error microphones.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(N_{S}\) & \(N_{W}\) & \(N_{F}\) & \(N_{I}\) & \(N_{E}\) & \(P\) & \(\mu\) & \(\gamma\) & \(\zeta\) & \(C_{0}\) to \(C_{2}\) & \(C_{3}\) \\ \hline
128 & 160 & 128 & 256 & 4 & 4 & 0.001 & \(10^{-3}\) & \(10^{-9}\) & 3 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nominal parameters of the control algorithm.  
Figure 6: Average SPL at error and monitor microphones for reproduced spinning noise at 1200 rpm for a different \(\mu_{n}\) after 15 min, \(\gamma=0.0001\).
## 4 Numerical Validation
Figure 5: Effect of step frequency and crowd size on dynamic responses: (**a**) the larger mass ratio \(\rm{Mp/Ms}\) = 0.5 induces the slightly larger vibrational displacement under the uniform non-resonant excitation rate \(f_{\rm s}\) = 1 Hz; however the resonant excitation rate \(f_{\rm s}\) = 2 Hz induces the noteworthy larger vibrational displacement amplitude under the same mass ratio \(M_{\rm p}/M_{\rm s}\) = 0.5; (**b**) the larger mass ratio \(M_{\rm p}/M_{\rm s}\) = 0.5 induces the slightly larger vibrational acceleration under the uniform non-resonant excitation rate \(f_{\rm s}\) = 1 Hz; but the resonant excitation rate \(f_{\rm s}\) = 2 Hz induces the remarkable larger vibrational acceleration amplitude under the same mass ratio \(M_{\rm p}/M_{\rm s}\) = 0.5. Figure 6: Effect of mass ratio between crowd and structure on dynamic properties: (**a**) the larger mass ratio between crowd and structure induces the lower frequency of structure under resonant excitation; (**b**) the larger mass ratio between crowd and structure results the larger damping of structure under resonant excitation. To further study the effect of crowd size, dynamic property peaks, including the frequency minimum peak, damping ratio maximum peak, and acceleration maximum peak were plotted, as shown in Figure 8. Along with the increase of mass ratio between crowd and structure, the frequency peak approximately decreases linearly, as shown in Figure 8a, which also happens to the damping ratio peak, as shown in Figure 8b. In Figure 8c, the acceleration peak shows a faster increase when the mass ratio \(M_{p}/M_{s}\) is less than 0.3. When the \(M_{p}/M_{s}\) is larger than 0.3, the acceleration approximately increases along with the increase of \(M_{p}/M_{s}\). The influence of crowd size on model characteristics is consistent with measurement results [3]. The model property formulas induced by crowd are obtained under the assumption that all pedestrians move consistently and that they follow a uniform distribution on the structure, which is also the most unfavorable situation for the control of structural vibration. However, the above descriptions do not happen frequently in real walking behaviors. Additionally, interactions between pedestrians may alleviate the worst situation. The proposed model formulas caused by crowds may give a more significant indication of human behaviors. Thus, a reduction coefficient needs to be incorporated into the model formulas and its definition needs experimentation, with the dynamic measurement of structure under the walking crowd in further research.
# Enhanced Multiple Speakers' Separation and Identification for VOIP Applications Using Deep Learning
## 4 Experiments
### Network Configuration  
#### 4.2.1 For Separation  
Based on the validation set, we choose hyper parameters. The input kernel size L was 8 and the preliminary convolutional layer had 128 filters. A seven-second audio fragment sampled at 8 kHz is used. The design used of b = 6 MULCAT blocks, with 128 neurons in each LSTM layer. We extract the STFT for the speaker model using a 20 ms window, a stride of 10 ms, and a Hamming window.
# Energy Efficient Dynamic Symmetric Key Based Protocol for Secure Traffic Exchanges in Smart Homes
## 1 Introduction
There is also a requirement to negotiate the session key among the communicating entities utilized to encrypt the exchanged packets [6]. Unfortunately, the conventional authentication and key agreement protocols have high computational requirements such as power consumption, memory, and processing capacity. In addition, some of them have design flaws that result in leakages of sensitive data. To address power constraints in smart home IoT devices, the Long-Range (LoRa) technology known as Low-Power Wide-Area Network (LPWAN) has been implemented. As one of the LPWAN technologies, the Long-Range Wide-Area Network (LoRaWAN) uses very little power for long-range communication and is, therefore, highly efficient [21]. In addition, LoRaWAN offers open standard specifications and hence is crucial for networking hybrid autonomous communication architectures [22]. Another important LPWAN technology is the Narrow Band IoT (NB-IoT) that is heavily deployed in 3 GPP cellular systems. It has high throughput and low complexities and can therefore help extend the battery lifetime of IoT devices. In addition, it provides better performance in terms of enhanced channel quality [23], long-range, high capacity, and low power [24]. In general, LPWAN technologies have salient capabilities such as low-cost, long-range, low energy consumption, the transmission of low volumes of data, and support for a high number of devices. As such, these LPWAN technologies can play crucial roles in IoT applications such as smart homes. Although LPWAN offers admirable features that render them applicable in smart home deployments, there are many security issues that need to be solved. For instance, LoRaWAN has numerous privacy and security vulnerabilities that can be utilized by adversaries to compromise the privacy of transmitted data, availability, and authentication [25]. For instance, its Activation by Personalization (ABP) activation mode uses static secret keys and addresses, which are stored in the end devices. Consequently, side-channeling through power analysis can retrieve these secrets and launch further attacks such as impersonation and spoofing. On its part, NB-IoT requires a large infrastructure and proprietary license [24]. Therefore, NB-IoT becomes costly to implement in realtime. In addition, lack of physical security, poor application, end-point security, and weak authorization and authentication are some challenges that are yet to be solved in NB-IoT [26]. It is evident that conventional IoT technologies, security protocols, and standards are unable to uphold privacy and security in smart homes [11]. Several hacks and software flaws have led to a lack of public confidence in smart home networks. As such, the design of efficient and secure message authentication protocols is still an open challenge.
## 1 Introduction
### Frequency-Selective Surfaces  
In parallel, the technical literature in the field of electrical engineering has seen substantial work on frequency-selective surfaces (FSS), proposing different designs, applications, models, and prototypes. FSSs can be defined as spatial filters: devices that block electromagnetic waves in specific frequency bands while allowing others to pass. This selective insulation is akin to acoustic insulation, but instead of attenuating mechanical sound waves, FSSs mitigate electromagnetic waves. While traditional building materials provide some degree of electromagnetic insulation, they lack the frequency selectivity that FSSs offer. Existing materials cannot selectively block specific frequency bands while allowing others to pass as though the barrier were not there. The ability to tune frequencies is what makes FSSs a compelling alternative or complement to conventional materials.  
FSS designs typically involve geometric combinations of conductive and dielectric surfaces. For aesthetic purposes, the color of these surfaces can be customized, provided the materials maintain their electromagnetic properties. FSSs thus emerge as advanced materials capable of providing electromagnetic insulation in building facades or internal partitions, either as visible components or integrated into the multi-layer wall structures. In fact, FSSs can be installed in any type of building. Depending on the architectural design, they can be added as a discrete layer within the wall (hidden among other materials like acoustic insulation, thermal protection, bricks, etc.) or form part of the external facade design.
# Parameter Identification Methods for Hyperelastic and Hyper-Viscoelastic Models
## 2 Parameter Identification Method for the Hyperelastic Model
### The Object Function for Parameter Identification  
Previous studies have shown that the constitutive parameters of a hyperelastic model identified from a particular deformation mode may be invalid for other modes. For example, [PERSON] et al. claimed that the parameters identified from the uniaxial test data fail in predicting biaxial or planar tension responses [PERSON] et al. (2010). So, it is of much significance to take the four types of experiment data listed above into consideration. This goal can be achieved by the least square procedure to make the deviations of the experiment data and the fitted data least. The object function is defined as  
\[min:S_{1}=\sum\limits_{q=1}^{4}\omega_{q}(\sum\limits_{i=1}^{n_{q}}{({(f_{q})}_ {i}-{(\sigma_{q})}_{i})}^{2}) \tag{11}\]  
where \(\omega_{q}\) is the weight of different experiment type, if there exists no experiment data for a specific type, we just need to set its \(\omega_{q}\) to be zero, if we want to emphasize the experiment data of a specific type, we can set its \(\omega_{q}\) larger than others'. \(n_{q}\) represents the number of the experiment data for each type of experiment, \({(f_{q})}_{i}\) is the theoretical value which is calculated by Equations (5)-(7) and (9), \({(\sigma_{q})}_{i}\) is the experiment data.
# A Traceable and Verifiable Tobacco Products Logistics System with GPS and RFID Technologies
## 3 The Proposed Scheme
### Verification Phase (Auditor-End)  
In particular, government agencies will regularly check whether there are counterfeit tobacco products on the market that have not been approved or authenticated by the CA. Figure 13 shows the audit mechanism flow to verify the tobacco products. The detailed steps are as follows:  
1. AU uses the MR to scan the TID of tobacco products randomly from the company such as a retailer, logistics, distributor, or manufacturer.
2. The application will send the selected tobacco ID, company ID, and its role type to CA.
3. CA triggers a chaincode \"Verification\" in the BCC to verify the legality of tobacco products, where the chaincode is shown in Algorithm 6.
4. BCC returns the legality result to the CA.
5. CA responds to the result to the AU, if it is not legal, then the AU enforces the law on the illegal tobacco product and company.
## 9 Quickest Spectrum Sensing
### Multiple Channels and Bayesian Case with Partial Observation  
When there are multiple frequency channels (e.g., in DTV systems, there are multiple available frequency bands; if wider frequency band, e.g., 1000 MHz, is open for cognitive radio systems, we can divide the wide band into multiple frequency bins and consider each bin as a channel). It may be difficult for the secondary user to monitor all these channels since it requires high sampling rate and may not be supported by current commercial analog-digital converters (ADC). Therefore, the secondary user can monitor a subset of channels simultaneously and needs to jump across different channels. For simplicity of analysis, we assume that only one channel can be monitored at a time.  
For simplicity, we consider \(M\) channels and assume that the secondary user can monitor one channel at a time. We denote the observation distributions over channel \(m\) with and without primary user by \(P_{1m}\) and \(P_{0m}\), respectively. Again, we assume that there are no primary users over the \(M\) channels at the beginning. At time slot \(t\), primary user may emerge over channel \(m\) with _a priori_ probability(\(\rho\) is known to secondary users; thus the quickest detection is Bayesian) and the activities over different channels are mutually independent (note that the geometrical distribution of primary user emergence coincides with a two-state Markov model in which the probability of transmitting from idle to busy is \(\rho\)). We also assume that it requires \(d_{s}\) time slots for switching between two channels.