esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 System Architecture and Design Procedures
### IoT Device Energy Consumption  
IoT devices play a key role in data collection for environmental monitoring and security applications. Optimising their performance involves considering factors like individual voltage range, power needs, and energy consumption. Ensuring sufficient captured energy  
Figure 9: I-V and P-V characteristic curve of the perovskite-on-Si tandem solar cell used on this study.  
is vital to sustain device operation and lifespan in energy harvesting systems. The total energy required by an IoT device (\(E_{IoT}\)) is expressed as follows:  
\[E_{IoT}=P_{IoT}\times t_{IoT}. \tag{34}\]  
\(P_{IoT}\) represents the power consumption of the IoT device, and \(t_{IoT}\) denotes its operation time. The operation time of the IoT device, \(t_{IoT}\), considering multiple modes, such as sleep (\(t_{s}\)), standby (\(t_{sb}\)), power-off (\(t_{po}\)), power storing (\(t_{ps}\)), etc., can be obtained as follows:  
\[t_{IoT}=\tau_{o}-(t_{s}+t_{sb}+t_{po}+t_{ps}+t_{1}+t_{2}+\ldots+t_{n}). \tag{35}\]  
In (35), \(\tau_{o}\) represents the maximum operation time (24 h) for the IoT device. \(t_{1},\ t_{2}\) and \(t_{n}\) are the duration of other power modes such as communication, processing, and other overpower modes. For a multi-mode active IoT device, the energy budget balance can be determined as follows:  
\[E_{IoT}=E_{s}+E_{sb}+E_{p}+E_{ps}+E_{1}+\ldots+E_{n}, \tag{36}\]  
where \(E_{s}\), \(E_{sb}\), \(E_{p}\), \(E_{ps}\), and \(E_{n}\) represent the IoT energy during sleep mode at \(t_{s}\), standby mode at \(t_{sb}\), power-off mode at \(t_{po}\), power-storing mode at \(t_{ps}\), and other modes at \(t_{n}\), respectively.  
The hybrid RF-PSC system needs to produce energy from its surroundings consistently to ensure the continuous operation of IoT devices. Moreover, the generated energy must meet or surpass the power requirements of IoT devices, as shown in (37). The lifetime of an IoT device (\(T_{lifetime}\)) indicates its operational duration before ceasing to function, depending on its data gathering and measurement activities [56]. The formula used to calculate the network lifetime is expressed as (38).  
\[E_{hybride}\geq E_{IoT}, \tag{37}\]  
\[T_{lifetime}=\frac{(E_{i}-E_{w})}{E_{c}+P_{c}\times R_{p}}. \tag{38}\]  
In (38), \(E_{i}\) refers to the initial energy of the device, \(E_{w}\) is the wasted energy, \(E_{c}\) is the consumed energy, \(P_{c}\) is the overall power consumed, and \(R_{p}\) is the rate at which packets are received.
## 1 Introduction
A deep learning model, GATE Net, based on graph convolutional networks (GCNs), was proposed in [18], which is trained using supervised contrast learning for labeling designs containing random insertion triggers using only the corresponding netlist. A new model-based dynamic convolution neural network (DCNN) architecture was developed in [19], which includes three convolutional and pooling layers for automatically extracting invariant and relevant features from IC images. A new graph learning (GL)-based method for detecting node HTs, called NHTD-GL, was proposed in [20]. As more and more companies join the chip manufacturing sector, the need for flow and verification of chip designs after they are completed is also on the rise. However, unlike large companies, which have the financial resources to spend large sums of money on scanning electron microscopes (SEMs) to facilitate the subsequent verification process, small- and medium-sized companies are forced to outsource this step to professional facilities, albeit at a high cost. In essence, this is because the availability of validation tools is based on high-quality IC images. This creates a contradiction: the high difficulty of obtaining high-quality images and the indispensability of noiseless high-quality IC images. This contradiction is also one of the biggest obstacles between academia and industry. A low-cost and easily achievable alternative is, therefore, worth exploring. The aim of this paper is to propose an algorithm for the recovery of low-quality image information for subsequent detection tasks. As the approach is pragmatic, the data used in the experiments will take into account the following practices and common situations:  
1. Unlike SEM, which uses a narrowly focused high-energy electron beam to scan the sample and sweep the image elements from left to right and from top to bottom on a sample to obtain a high-precision, full-range image in a single pass, the workflow of a normal camera with a microscope is still to capture different partial views several times and finally combine them into a complete large image. 2. For the sensor on the camera, the unstable temperature and light source of the acquisition environment can bring about changes in the signal-to-noise ratio (SNR), and images with low SNRs often contain a variety of noises. 3. Image data may be corrupted when uploading from the camera to the server, such as due to multiplicative noise caused by unsatisfactory channels. To cope with the images obtained in the above scenario, this paper proposes a fused representation clustering framework called High-Frequency Low-Rank Subspace Clustering (HFLRSC), where high frequency means the High-Frequency Texture Component (HFTC). In HFLRSC, A fused subspace learning framework that incorporates an HFTC and low-rank constraints is proposed. Experiments on simulated data show that the method can effectively process low-quality IC images and achieve better performance than the benchmark and existing methods.
# Channel-Hopping Using Reinforcement Learning for Rendezvous in Asymmetric Cognitive Radio Networks
## 4 Simulation Results
### Simulation Results for Symmetric Cases  
First, we conducted experiments on the symmetric cases. While this is not the main focus of this paper, comparing the performance of reinforcement learning-based algorithms in symmetric cases, which have been commonly addressed in previous studies [6; 13], can indirectly provide insight into the performance and exploration patterns of the proposed algorithm.  
In the symmetric case, since all channels are shared channels, the important factor is the total number of channels. Therefore, we compare each algorithm when the number of channels is 20, 40, 60, 80, and 100. For each case, we independently conducted 15,000 tests (episodes) and compared the methodologies based on the average performance of each case. Initially, all entries for each state were set to zero. As a result, the first rendezvous attempt essentially selects actions randomly, following a uniform distribution. Additionally, since there is a method to apply the M-sequence in symmetric cases, the comparison will also include experiments using the M-sequence.  
Figure 6(a) shows the performance comparison of each algorithm. In symmetric scenarios, the algorithm designed using M-sequence demonstrates the best EUTR performance.  
Figure 6: Average rewards over the number of episodes.  
While the reinforcement learning-based algorithm shows slightly lower performance with about a 10% difference compared to the M-sequence algorithm, it achieves significantly better ETTR--about half that of the JS algorithm. Considering that the M-sequence-based algorithm is difficult to apply in asymmetric scenarios, the substantial performance gap between the reinforcement learning algorithm and the JS algorithm suggests that the effectiveness of the reinforcement learning-based approach could become more pronounced in such environments.
# A Dual-Encoder-Condensed Convolution Method for High-Precision Indoor Positioning
## 4 Experimental Results and Discussion
### Positioning Accuracy  
In this section, we set up two experiments to verify the stability and performance differences of DECC. The positioning performance of urban canyons and indoor scenes is shown in Figure 7a,b, and the ablation experiment is shown in Figure 8a,b.  
2. _Exp. 2: Ablation Experiment for the DECC_--In order to verify the performance of DECC, we carry out the burning experiment in an indoor scene. First, we take the full version of DECC as the control group, and DECC with only the Dual-Encoder structure as the control group 1, and with only the Conv-for-Sum module as control group 2.  
As shown in Figure 8b, we can observe that the control group achieves positioning accuracy with an average error of 0.26 m, while the accuracies of control groups 1 and 2 are 17.2 m and 18.1 m, respectively. The performance has increased by 16.72 m and 17.62 m, respectively.  
Moreover, in terms of standard deviation, the standard deviation of the control group is 0.28 m, which is significantly higher than those of control groups 1 and 2 (5.2 m and 6.0 m). The results show that due to the superiority of the model, the full DECC has great advantages in positioning accuracy and stability.  
In summary, our proposed method achieves good results for both complex indoor environments and open urban canyon scenarios.
## 2 Building Domain Dictionaries and Dictionary Matching Methods
### Domain Dictionary Construction
#### 2.1.1 Statistical Method  
Firstly, each corpus was pre-processed. Then, in each corpus, the top 50 words with the highest Term Frequency-Inverse Document Frequency (TF-IDF) [35,36] values were selected. These words were grouped into a set \(W=\{W_{i}\mid 1\leq i\leq 50\}\), where \(W_{i}\) represents the \(i\)-th word among these words. Subsequently, the normalized word frequency \(tf_{i}\) of each word was calculated. If the condition \(tf_{i}\geq n\) is met (where n is a predefined threshold), the word \(W_{i}\) is added to the domain dictionary. Finally, these domain words were subjected to manual screen ing and validation by experts in the respective domains to ensure the creation of a more accurate domain-specific dictionary.
## 5 Conclusions  
Our study analyzed high-resolution RTP aeromagnetic anomaly data around the northern SCS and then applied the magnetic 3-D L\({}_{\rm p}\) norm regularization inversion method with the constraint of multiple deep seismic reflection data to construct a high-quality 3-D susceptibility model of the crust in this area. We focused on the 3-D distribution characteristics of the Mesozoic volcanic arc, which provides important support for understanding the deep structure of the northern SCS. The following key conclusions can be drawn:  
1. The mixed L\({}_{\rm p}\) norm regularization of magnetic inversion produces a more focused model than conventional regularizations, and the multiple deep seismic reflection data provides effective constraints on the magnetic inversion, thereby significantly reducing the non-uniqueness of the inversion;
2. While previous studies have suggested the presence of a Mesozoic volcanic arc in the northern SCS, our model provides new insights to refine the spatial-distributed and compositional characteristics of the arc. The high-magnetic anomaly belt observed in the northern SCS, trending northeastward from the Dongsha Rise to Taiwan Island, is confirmed to be associated with this volcanic arc, which is primarily located within the lower crust at depths ranging from 20 to 28 km;
3. The high-magnetism belt in the lower crust revealed in this study indicates a volcanic arc connecting the forearc basin on the northern margin of the SCS, providing evidence for the formation of the Paleo-Pacific plate subduction system. This supports the previously proposed model of Paleo-Pacific subduction in the northern SCS.  
Conceptualization, L.G.; investigation, Z.C.; writing--original draft preparation, Z.C.; writing--review and editing, L.G.; visualization, Z.C.; supervision, L.G.; project administration, L.G.; and funding acquisition, L.G. All authors have read and agreed to the published version of the manuscript. This work is supported by the National Science and Technology Major Project of China (2024 ZD1000403) and the National Natural Science Foundation of China (41974101). And The APC was funded by the corresponding author [PERSON]. Not applicable. Not applicable. Not applicable. Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request. We thank researcher [PERSON] at Sun Yat-Sen University for valuable discussions on the tectonics of the SCS. We also thank [PERSON], a postdoctoral researcher at the University of British Columbia in Canada, for his help and discussions on the magnetic inversion method. We sincerely thank the editor and the anonymous reviewers for their valuable comments and suggestions. The authors declare no conflict of interest.
# An Improved Weighted and Location-Based Clustering Scheme for Flying Ad Hoc Networks
## 3 Preliminaries
### Cluster Head Selection
#### 4.2.3 Relative Mobility  
In FANETs, the relative mobility of nodes in a cluster should be controlled within a certain range to guarantee the stability and robustness of the network. The nodes with fast moving speed are not eligible to be candidates for CH selection. As mentioned above, the UAVs are equipped with IMU and GPS. In this work, a method is proposed to calculate the relative mobility of UAVs in a 3D coordinate system, by using the location and speed information. The movement state of node \(i\) and \(j\) is shown in Figure 4, and the speed differences between \(i\) and \(j\) in \(x\), \(y\) and \(z\) axes are given as the following:  
\[\Delta v_{x}=v_{i}\cos\theta_{i}\cos\psi_{i}-v_{j}\cos\theta_{j}\cos\psi_{j} \tag{19}\]  
\[\Delta v_{y}=v_{i}\cos\theta_{i}\sin\psi_{i}-v_{j}\cos\theta_{j}\sin\psi_{j} \tag{20}\]  
\[\Delta v_{z}=v_{i}\sin\theta_{i}-v_{j}\sin\theta_{j} \tag{21}\]  
The average speed difference between \(i\) and its neighbor nodes can be calculated using the following equations:  
\[\overline{v_{ix}}=\frac{\sum\limits_{j=1}^{Nd_{i}}\left(v_{i}\cos\theta_{i} \cos\psi_{i}-v_{j}\cos\theta_{j}\cos\psi_{j}\right)}{Nd_{i}} \tag{22}\]  
\[\overline{v_{iy}}=\frac{\sum\limits_{j=1}^{Nd_{i}}\left(v_{i}\cos\theta_{i} \sin\psi_{i}-v_{j}\cos\theta_{j}\sin\psi_{j}\right)}{Nd_{i}} \tag{23}\]  
\[\overline{v_{iz}}=\frac{\sum\limits_{j=1}^{Nd_{i}}\left(v_{i}\sin\theta_{i}-v_ {j}\sin\theta_{j}\right)}{Nd_{i}} \tag{24}\]  
Figure 4: Relative geometric parameters of UAVs.  
\[\overline{v_{i}}=\sqrt{\overline{v_{ix}}^{2}+\overline{v_{iy}}^{2}+\overline{v_{iz}} ^{2}} \tag{25}\]  
The variances of speed difference between \(i\) and its neighbor nodes along the \(XYZ\) axes are calculated as  
\[\sigma_{ix}{}^{2}=\frac{\sum\limits_{j=1}^{Nd_{i}}(\overline{v_{ix}}-\Delta v_{ x})^{2}}{Nd_{i}},\sigma_{iy}{}^{2}=\frac{\sum\limits_{j=1}^{Nd_{i}}(\overline{v_{ iy}}-\Delta v_{y})^{2}}{Nd_{i}},\sigma_{iz}{}^{2}=\frac{\sum\limits_{j=1}^{Nd_{i}}( \overline{v_{iz}}-\Delta v_{z})^{2}}{Nd_{i}} \tag{26}\]  
The average variance of speed difference between node \(i\) and its neighbor nodes is  
\[\overline{\sigma}_{i}^{2}=\frac{\sigma_{ix}^{2}+\sigma_{iy}^{2}+\sigma_{iz}^{2 }}{3} \tag{27}\]  
Finally, the relative mobility is normalized [20] by  
\[M_{i}=e^{-(\overline{\sigma}_{i}^{2}+\overline{v}_{i}^{2})} \tag{28}\]
## 2 Description of the Proposed Method
### Determination of Phonation Features for Stress Detection  
For stress recognition in the speech, spectral properties such as MFCC together with prosodic parameters (jitter and shimmer) and energetic features such as Teager energy operators (TEO) are mostly used [31; 32]. In the frame of the current experiments, we use four types of parameters for analysis of the phonation signal:  
1. Prosodic features containing micro-intonation components of the speech melody F0 given by a differential contour of a fundamental frequency F0\({}_{\text{DIFF}}\), absolute jitter \(J_{\text{abs}}\) as an average absolute difference between consecutive pitch periods \(L\) measured in samples, shimmer as a relative amplitude perturbation \(AP_{\text{rel}}\) from peak amplitudes detected inside the \(n\)th signal frame, and signal energy \(Ent_{\text{TK}}\) for \(P\) processed frames calculated as \[Ent_{\text{TK}}=abs\left(\frac{1}{P-2}\sum_{n=1}^{P-2}TEO(n)\right),\] (4) where the Teager energy operator is defined as \(TEO=x(n)^{2}-x(n-1)\cdot x(n+1)\).
2. Basic spectral features comprising the first two formants (\(F_{1}\), \(F_{2}\)), their ratio (\(F_{1}\)/\(F_{2}\)) and 3-dB bandwidth (\(B3_{1}\), \(B3_{2}\)) calculated with the help of the Newton-Raphson formula or the Bairstow algorithm [33], and \(H1\)-\(H2\) spectral tilt measure as a difference between \(F_{1}\) and \(F_{2}\) magnitudes.
3. Supplementary spectral properties consisting of the center of spectral gravity, i.e., an average frequency weighted by the values of the normalized energy of each frequency component in the spectrum in [Hz], spectral flatness measure (SFM) determined as a ratio of the geometric and the arithmetic means of the power spectrum, and spectral  
Figure 3: Block diagram of the fusion procedure to obtain the final stress evaluation rate.  
entropy (SE) as a measure of spectral distribution quantifying a degree of randomness of spectral probability density represented by normalized frequency components of the spectrum.
4. Statistical parameters that describe the spectrum: spectral spread parameter representing dispersion of the power spectrum around its mean value (\(S_{\text{SPREAD}}=\sum^{2}\)), spectral skewness as a 3 rd order moment representing a measure of the asymmetry of the data around the sample mean (\(S_{\text{SKEW}}=E(x-\mu)^{3}/\sigma^{3}\)), and spectral kurtosis being a 4 th order moment as a measure of peakiness or flatness of the shape of the spectrum relative to the normal distribution (\(S_{\text{KURT}}=E(x-\mu)^{4}/\sigma^{4}-3\)); in all cases \(\mu\) is the first central moment and \(\sigma\) is the standard deviation of spectrum values \(x\), and \(E(t)\) represents the expected value of the quantity \(t\).
## 2 Lateral-Line System and Artificial Lateral-Line System  
### Lateral Lines of Aquatic Organisms  
Fish and aquatic amphibians live in underground rivers, in dark lakes, on the seafloor and in murky waters where light is absent. In this environment, the visual perception  
Figure 1: Main content and order of this paper.  
system is greatly limited. However, in order to better complete the complex behaviors of feeding, avoiding enemies, reproduction, clustering, navigation and communication, some fish and aquatic amphibians have evolved a special sensory system [27], namely the lateral line.  
From the micrograph, the basic unit of the sensory organ of the lateral-line system is the neuromast, which is composed of hair cells, supporting cells and mantle cells, as shown in Figure 2b. Hair cells have cilia on their tips, and the cilia are coated with a gel secreted by Sertoli cells and mantle cells and from the cupula structure together. Ciliated fascicles are the receptor units of the thalamus cells. When fluid flows over the surface of the fish, it causes the cilia bundles to deflect or vibrate. Hair cells convert mechanical stimulation into electrophysiological signals and transmit them to the central nervous system.  
In terms of function, there are two types of colliculus, namely superficial neuromasts and canal neuromasts. The ductal-nerve colliculus is generally distributed in the lateral canal below the surface of the fish's body. The lateral-line canal is filled with liquid and connected to the water through the lateral-line pore. When the pressure is different between the two sides of the lateral-line pore, it causes the fluid inside the lateral-line canal to flow, which causes the hair cells to swing and triggers the nerves in the neuromast to respond, as shown in Figure 2c. It is generally believed that the ducted-nerve colliculus is sensitive to high-frequency information, such as the acceleration of fluid, by sensing the pressure difference between two adjacent lateral pores to sense the flow information of fluid parallel to the body axis. Therefore, the canal neuromast senses the flow information of fluid parallel to the body axis by sensing the pressure difference between two adjacent lateral pores. The canal neuromast is sensitive to high-frequency information, such as fluid acceleration.  
Lateral neuromasts are numerous and spread throughout the fish's surface in a specific pattern, as is shown in Figure 2a. Typically, there are seven lateral-line branches in the head, and two pairs of lateral line branches in the trunk and caudal. Superficial neuromasts are distributed around the canal neuromasts, and the arrangement of superficial neuromasts and canal neuromasts is at an angle of about 90\({}^{\circ}\). The special structure and distribution characteristics of the lateral lines of fish help them to learn the temporal and spatial changes of surrounding water and collect hydrodynamic information in an all-round way.
## 3 Quantum Circuit Scheduling Problem  
Quantum gate scheduling and layering have been identified as critical components in quantum algorithm optimization, particularly for Hamiltonian simulation tasks [64], where circuit depth reduction and hardware resource efficiency are prioritized.  
The target Hamiltonian is systematically decomposed into quantum gate operations through a structured methodology that leverages its inherent spectral attributes and interaction topologies, as formalized in Equation (1). This decomposition process involves the analysis of the Hamiltonian's spectral properties, including eigenvalue distributions and symmetry characteristics, alongside the interaction patterns encoded in its connectivity graph to generate efficient gate sequences. The resulting sequences are constructed to preserve unitary dynamics while minimizing circuit depth and gate complexity, adhering to physical constraints such as qubit connectivity and coherence time limitations. This ensures a high-fidelity realization of the target dynamics, addressing key challenges in quantum circuit design for practical hardware implementations.  
\[H=Z_{0}Z_{1}+Z_{1}Z_{2}+Z_{2}Z_{3}+Z_{3}Z_{4}+X_{0}+X_{3} \tag{1}\]The 5-qubit simulation architecture shown in Figure 1a provides the foundational hardware configuration for circuit implementation. Fundamental quantum operations including CNOT and Hadamard (H) gates are identified in Figure 1c, demonstrating the basic components of the quantum circuit. Initial qubit dependency relationships presented in Figure 1b reveal that CNOT\(q_{0},q_{3}\) and CNOT\(q_{0},q_{1}\) cannot be executed in parallel due to shared access to Qubit \(q_{0}\). Concurrent operations are enabled through the coordinated scheduling of CNOT\(q_{0},q_{3}\), CNOT\(q_{1},q_{4}\), and H\(q_{2}\) in Layer \(l_{0}\), which maintain independent qubit utilization patterns.  
Through systematic layering optimization, the dependent gates CNOT\(q_{0},q_{1}\) and CNOT\(q_{2},q_{4}\) are allocated to Layer \(l_{1}\), while CNOT\(q_{1},q_{3}\) is isolated in Layer \(l_{2}\) to resolve cross-layer conflicts. The optimized quantum circuit architecture illustrated in Figure 1d achieves depth reduction by implementing hardware topology-aware scheduling. This improvement is accomplished through coordinated consideration of qubit coupling constraints during the compilation process, where gate sequences are reordered according to both dependency relationships and physical connectivity limitations.  
The scheduling methodology confirms that parallel execution capabilities can be maximized through structured analysis of operational conflicts and hardware-specific constraints. Recent quantum compiler studies have demonstrated similar approaches for enhancing circuit efficiency through adaptive layering techniques [65]. Such optimizations become increasingly critical as quantum algorithms grow in complexity, requiring meticulous synchronization of computational requirements with device-specific architectural characteristics.