esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
After compressed sensing, each SU makes local decision (i.e., \"1\" for presence, \"0\" for absence) and reports to the fusion center (FC), where counting fusion rule is employed to reach a global decision based on the sensing results. It is proven that SU spectral efficiency is unimodal convex function of compressed sensing time \(\tau\). Moreover, inspired by the previous general CSS frame structure, we propose a novel wideband cooperative spectrum sensing (CSS) framework in which each SU reporting duration can be utilized for its following SU sensing. The sensing performance benefits from the novel CSS frame structure because the equivalent sensing time is extended by making full use of reporting time. Additionally, the spatiotemporal CSS (ST-CSS) scheme is designed to achieve space and time diversity gain simultaneously for time-varying channel without introducing additional time overhead. Finally, via computer simulations, it is shown that the proposed CSS framework with optimal settings of sensing time and fusion scheme significantly improves SU spectral efficiency. The rest of this paper is organized as follows. Section 2 describes the system model. Then, Section 3 introduces the novel cooperative spectrum sensing framework. In Section 4, the optimal settings for the novel ST-CSS with counting fusion rule under sufficient protection to PU is presented. Extensive simulation results are provided in Section 5 to validate the performance of the proposed scheme. Finally, conclusions are drawn in Section 6.
# MPFM-VC: A Voice Conversion Algorithm Based on Multi-Dimensional Perception Flow Matching
## 4 Methods
### The Training and Inference Processes of MPFM-VC  
For inference, we use MPFM to predict the vector field, generate the mel spectrogram through ordinary differential equations, and finally generate the speech waveform from the vocoder. The specific inference process is as follows (Algorithm 1):  
```
0:\(MPFM(\theta)\); Vocoder\((\theta)\); input speech \(I_{v}\); voiceprint \(I_{spk}\); and the maximum number of sampling steps \(N_{max}\).
1: Initial time step \(t=0\), time step \(\Delta t=1/N_{max}\)
2: Extracted from the original speech (\(I_{content}\), \(I_{pitch}\), \(I_{energy}\), \(I_{prosody}\))
3: Samples \(z_{0}\) from a random normal distribution \(N(0,1)\)
4:for\(i=1,\ 2,\ \ldots,\ N_{max}\)do
5:\(z_{t+\Delta t}=z_{t}+\Delta t\cdot MPFM(z_{t},t,I_{content},I_{spk},I_{pitch},I_{ energy},I_{prosody})\)
6:\(t=t+\Delta t\)
7:endfor
8:\(O_{mel}=z_{1}\)
9:\(O_{\rho}=Vocoder(O_{mel},I_{pitch})\)
10:return\(O_{\mathbb{P}}\)
```  
**Algorithm 1** The inference process of MPFM-VC.  
During training our MPFM does not predict the mel spectrogram directly, but predicts the vector field by randomly sampling the time step. The specific training process is as follows (Algorithm 2):  
```
0:\(MPFM(\theta)\); dataset \(D_{train}=\left\{(I_{content},I_{spk},I_{pitch},I_{energy},I_{prosody},T_{mel}) \right\}_{m=1}^{M}\); number of training rounds \(N_{iter}\); learning rate \(\eta\);
1:for\(i=1,\ 2,\ \ldots,\ N_{iter}\)do
2: From \(D_{train}\) sample (\(I_{content}\), \(I_{spk}\), \(I_{pitch}\), \(I_{energy}\), \(I_{prosody}\), \(T_{mel}\))
3: Randomly sample \(t\) from \([0,1]\)
4: Samples \(z_{0}\) from a random normal distribution \(N(0,1)\)
5: Calculate \(z_{t}=z_{0}+(z_{1}-z_{0})\cdot t\)
6: Content perturbation training augmentation method: \(l^{\prime}_{content}=M\odot I_{content}+(1-M)\odot I_{blank}\)
7: Forward propagation: \(v(z_{t},t),P_{spk}=MPFM(z_{t},t,l^{\prime}_{content},I_{spk},I_{pitch},I_{ energy},I_{prosody})\)
8: Calculates the loss function: \(L_{total}=L_{FMLLog}-L_{spk}\)
9: Backward propagation: calculate the gradient \(\
abla_{\theta}L_{total}\)
10: Take gradient descent step on \(\theta\leftarrow\theta-\eta\cdot\
abla_{\theta}L_{total}\)
11:endfor
12:return\(MPFM(\theta)\)
```  
**Algorithm 2** The training process of MPFM.
## 3 Experiment and Analyses
### Analyses of Static Experiments  
#### 3.1.1 Accuracy Analyses of Observations  
First, the accuracy of the observation data is analyzed to ensure reliable data, and the main error sources that affect the positioning can be explored. Due to the complexity of the indoor environment, this paper mainly focuses on the static DD pseudorange/carrier phase observations of the zero baseline and short baseline (clock error and antenna phase deviation should be eliminated or weakened by DD). In the case of the zero baseline, it can be considered that the multi-path effect of the rover station and base station is basically the same. The main error which affects positioning is noise. With respect to the short baseline, the impact of the rover and base station is different from each other, so the magnitude of error caused by several positioning error sources can be explored. The static DD pseudorange/carrier phase observations of the zero baseline and short baseline are shown in Figures 4 and 5.  
It can be found that the standard deviation (STD) of the carrier phase observations is very small and stable after DD, and its internal accuracy is high. It shows that if the ambiguities are fixed correctly, the carrier phase observations can be used to achieve high-precision positioning. The overall fluctuation of the DD pseudorange observations is also relatively stable. It indicates the feasibility of positioning. However, its STD reaches the meter level which means that we cannot directly apply it to high-precision positioning (centimeter level). At the same time, some errors still exist in part of the DD pseudorange observations and the weight should be adjusted according to the DD residual.  
Comparing the data of the zero baseline experiment with the short baseline experiment, we can find that the quality of the short baseline observations is far less than the zero baseline due to the very large impact of the multipath and noise in the indoor environment.  
Figure 3: Distribution of pseudolite antennas.  
Figure 4: The double difference observations in the zero baseline.  
Figure 5: The double difference observations in the short baseline.
# Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges
## 4 State of the Art Detection and Mitigation Strategies  
IFA countermeasures in NDN can be classified into several categories based on their core approach. We discuss each category, _(1) rate limiting, (2) PIT management, (3) ML/AI based, (4) reputation/trust systems, and (5) blockchain based_, outlining their key ideas, representative proposals, strengths and weaknesses, and performance evaluation as shown in Table 2.
The experimental results demonstrate the feasibility of this proposed method for the classification of MI-EEG signals and can be applied successfully to BCI systems where the amount of data is large due to daily recording. **Keywords:** EEG; BCI; motor imagery; Common Spatial Pattern (CSP); Wavelet Packet Decomposition (WPD); deep learning; CNN; Long Short-Term Memory (LSTM); merged CNNs
# A Novel Bitrate Adaptation Method for Heterogeneous Wireless Body Area Networks
## 4 Heterogeneous WBAN Simulator
### The Results of Measurements Used in the Simulator
#### 4.2.3 Accuracy of Radio Distance Measurements
The highest standard deviation value of 0.43 m in the LOS conditions was obtained for the chest placement of the MN, which can be explained by the full obstruction of the direct visibility of the MN and RN antennas by the human body. Similarly, the standard deviation equal to 0.66 m was obtained for the NLOS conditions.
## 4 Experiments
### Evaluation of TFRs with Local Descriptors  
We began experiments by testing acoustic features extracted by local descriptors over various time-frequency representations (TFR). In detail, we examined four kinds of TFRs, including Fourier spectrogram, Mel-scaled spectrogram (Mel-spectrogram), constant-Q transform spectrogram (CQT) and Mel-Frequency Cepstral Coefficients (MFCCs), due to their popularity in ASC research. Furthermore, well-developed two-dimensional local descriptors of HLAC, LBP and HOG had been introduced to further characterize spectro-temporal patterns on time-frequency plane. Together, we had 12 combinations of TFRs with local descriptors. Since this evaluation was dedicated to acoustic feature comparison, simple time-averaging was applied to produce feature vector for each audio clip, i.e., \(\mathbf{w}\) was set to \(\mathbf{1}_{N}\) in (9); SVM classifier with Gaussian kernel had been used for multi-class classification. Table 1 summarized all the results, from which we can see the highest ASC precision was achieved by using HOG descriptor over the CQT spectrogram. The performance comparison also revealed that although Mel-spectrogram and MFCCs were widely applied, they are not optimal when working with local descriptors because much local detail information was lost during feature extraction. In addition, we present the class-wise feature distribution of the LITIS Rouen dataset by using CQT spectrogram with HoG features in Figure 8. According to the feature space visualization, the compact clusters are anticipated to achieve higher ASC accuracy, while scattered clusters can be difficult to classify.  
Figure 8: Visualization of Rouen dataset by using CQT+HoG features and t-SNE [23].  
Figure 7: Statistics of acoustic scene recordings collection by categories in Rouen dataset.
## 2 Study Area
### Study Area Setting
#### 2.1.2 Hydrogeological Conditions  
The study area is characterized by a monsoon climate, exhibiting distinct dry and wet seasons. The rainy season typically spans from May to October, while the dry season lasts from November to April of the following year. The multi-year average temperature is 11.1 \({}^{\circ}\)C, with extreme maximum temperatures reaching 31.7 \({}^{\circ}\)C and extreme minimum temperatures dropping to \(-\)10.2 \({}^{\circ}\)C. The multi-year average precipitation is 973.5 mm, with an evaporation rate of 1645.2 mm and a relative humidity of 74%. The multi-year average wind speed is 1.6 m/s, with a maximum wind speed of 16.3 m/s, predominantly from the SSW direction.  
The groundwater in this region primarily derives its recharge from atmospheric precipitation, ultimately discharging into the Lancang River, which serves as the area's lowest drainage reference. Within the landslide area of Cheyipin, groundwater is predominantly composed of water seeping from fractures in the bedrock and water within the pores of the loose layers of the Quaternary system. The sliding zone soil of Cheyiping landslide  
Figure 4: Geological profile of Cheyiping landslide.  
Figure 5: Example of stratigraphic logs taken from a borehole realized in the site. (**a**) Strong toppling bedrock, (**b**) sliding mass (**c**) materials in the sliding zone.  
exhibits the development of multiple relatively impermeable layers. Observations at the borehole sites reveal that some cores near the sliding surface are notably moist, indicating a higher soil moisture content. Analysis suggests the presence of stagnant water near the upper sliding surface of the Cheyiping landslide.  
The Lancang River and the downstream of Cheyiping village are deeply incised by gullies. The depth of the incision in the upstream gully (2# Gully) of Cheyiping village reaches over 30 m, yet no groundwater is exposed, indicating that the groundwater level is buried deeply, constituting deep-seated fractured rock water. The water flow in 3# Gully originates from irrigation in Cheyiping village and from the water channel in the river. Seepage points have been observed in the central part of the landslide in front of the small village, primarily due to water infiltration from the channel, which then percolates underground for a short distance before seeping out onto the surface. During the exploration process, a water injection test was conducted. According to the \"Geological Exploration Code for Hydropower Engineering\" (GB50287-2016) [18], the permeability of rock and soil varies from large to small from the borehole to the bottom of the hole. Permeability is largely influenced by the clay content, with the permeability coefficient of the cover layer ranging from 7.22 \(\times\) 10\({}^{-7}\) to 9.82 \(\times\) 10\({}^{-5}\) m/s, indicating medium to weak permeability. The permeability coefficient of strongly weather mudstone ranges from 8.08 \(\times\) 10\({}^{-7}\) to 2.17 \(\times\) 10\({}^{-6}\) m/s, indicating weak permeability.
# Newly Designed Identification Scheme for Monitoring Ice Thickness on Power Transmission Lines
## 2 Transmission Line Icing Identification  
The main problem in PTLI monitoring based on binocular vision is identifying and extracting iced transmission lines, directly affecting monitoring automation and intelligence. The target detection algorithm identifies and extracts the image of iced transmission lines. When identifying PTLI, the accuracy of edge and line detection algorithms using traditional methods still needs to be improved, and noise interference from PTLI images is quite large. The high similarity in color and texture makes their detection and identification more difficult. Generally, PTLI identification uses the Canny algorithm to detect the edges oficed lines [23; 24], but the result has fractured edges and free points. Identifying PTLI with traditional methods is also not optimal for eliminating background noise and detecting iced lines in dim areas. Therefore, designing a reliable scheme for identifying iced transmission lines is crucial. Table 1 shows the shortcomings of identification methods in previous studies for PTLI scenes. Figure 1 shows the results of PTLI identification using Canny edge detection and the Hough transform. Based on Figure 1, there are areas with fractured edges, and the Hough transform is not optimal for detecting the whole line in the icing line area.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Original Image** & **Result** & **Problem** \\ \hline
1 & & & Fracture edge and \\
2 & & & free point \\ \hline
3 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The shortcomings of the previous method for edge detection of PTLI images.
###### Abstract
AUVs also address the energy efficiency challenges faced by UWSNs, as they can incorporate energy-harvesting mechanisms or rechargeable batteries to extend their operational lifetimes. Moreover, the utilization of USVs for achieving fast maritime wireless communications is being recognized as a promising contributor to future maritime wireless communication systems. This approach presents a viable solution for establishing regenerative maritime non-terrestrial networks. USVs offer the capability to deliver fast wireless communication services to maritime devices without the need for traditional infrastructure coverage or specialized high-gain satellite antennas. In comparison to terrestrial and satellite-based systems, USV-assisted maritime wireless communication have several merits, including cost-effectiveness, robust adaptability to varying environments, support for data relaying and collection, as well as the integration of mobile edge computing capabilities. Remarkably, to deploy AMVs in UWSN, the most challenging issue is to plan the cruising path for each AMV, such that a data harvesting task can be accomplished effectively. Feasible path design holds the potential to enhance transmission performance while ensuring the safety of navigation. Typically, path planning can be conceptualized as an optimization problem with various real-world constraints, including quality of service (QoS), obstacle avoidance, and geographical limitations. Notably, energy efficiency is also a central concern of AMV path planning, necessitating the consideration of factors like surrounding ocean currents or wind patterns to reduce energy consumption. Nevertheless, practical challenges arise due to the unpredictability of meteorological conditions and the inherent uncertainty in weather forecasts, rendering conventional models, such as A* [10] or fast marching [11] ineffective when confronted with the dynamic maritime environment. Furthermore, the intricate communication channels between AMVs and sensor nodes pose substantial hurdles in the realm of path planning. Our previous research [12] has underscored the profound impact of the distance between USVs and sensors on the signal-to-noise ratio (SNR), a pivotal determinant of data transmission efficacy throughout missions. This mechanism is important in achieving efficient data collection, yet it has rarely been considered in path planning. Moreover, the choice of communication proto cols can also have a profound impact. For instance, the communication range should be incorporated into the algorithm when planning routes. The vehicle may need to adjust its path to stay within the communication range if the range is insufficient for data transmission. However, these factors are rarely considered by existing studies. Consequently, path planning for maritime wireless communication remains an open and demanding issue, warranting continued research and attention.
# Addressing ZSM Security Issues with Blockchain Technology
## 7 Assessment of Blockchain and DAG Platforms to Be Used for the ZSM-Solution Implementation
### Candidate Blockchain and DAG Solutions for Our Approach  
#### 7.1.1 Hyperledger Fabric (HLF)  
This blockchain solution is an open-source permitted platform that is established and maintained under the umbrella of the Linux Foundation. HLF's architecture is modular and configurable in order to easily adapt to a wide spectrum of industry use cases. The versatility of this platform makes it ideal for several sectors such as healthcare, supply chain and others, while its ability to support SCs written in general-purpose programming languages (i.e., Java, Go and Node.js) makes it very attractive to organizations. In addition, this platform is permission; therefore, the participants are known to each other which automatically grows a sentiment of security. This means that while the participants may not fully trust one another (they may, for example, be competitors in the same industry), a network can be operated under a governance model [41].  
Another important characteristic of this platform is its ability to support pluggable consensus mechanisms. This feature allows HLF to be effectively customized in order to fit various use cases. For instance, in the ZSM scenario where only known NPs are members of the network, a fully byzantine fault tolerant mechanism might be considered unnecessary and an excessive drag on performance and throughput. In order to maintain high-performancestandards and increase the availability of the solution, a crash fault-tolerant consensus might be the preferred option. This modular architecture allows the platform to rely on well-established toolkits for crash fault-tolerant or byzantine fault-tolerant ordering. Fabric currently offers a crash fault-tolerant ordering service implementation based on the etcd library of the Raft protocol. Moreover, Fabric can leverage consensus protocols that do not require a native cryptocurrency to incent costly mining or to fuel smart contract execution.  
The aforementioned design features make HLF one of the better-performing platforms both in terms of transaction processing and transaction confirmation latency, and it enables privacy and confidentiality of transactions and the smart contracts that implement them. It should be mentioned that many research papers have been published where the performance metrics of the HLF are studied and tested using the Hyperledger Caliper. Authors in [46] scaled HLF to 20,000 transactions per second [41]. Concluding the presentation of HLF, this solution supports the creation and management of tokens and is able to use several oracle mechanisms in order to become suitable for several use cases that demand the interaction of blockchain with the outside world.
# An Inverter Topology for Wireless Power Transfer System with Multiple Transmitter Coils
## 2 System Description and Operation  
### Circuit Configuration  
A block diagram of the proposed system is shown in Figure 1. Multiple transmitter coils are excited by the inverter circuit. Detection switches are placed between the receiver coil and the rectifier circuit. The DC output voltage (V\({}_{\text{out}}\)) is sensed and transmitted to the primary circuit via wireless communication devices. The primary-side controller is used to select the transmitter coil pattern and regulate the output voltage. The secondary-side controller is used to control the detection switches and send feedback data wirelessly. The circuit configuration of the proposed system is shown in Figure 2. The inverter circuit consists of (n+2) MOSFET switches with antiparallel diodes (D\({}_{\text{U}}\), D\({}_{\text{L}}\), D\({}_{\text{1}}\), D\({}_{\text{2}}\),..., and D\({}_{\text{n}}\)), where \"n\" is the number of transmitter coils. The main upper and lower switches are represented by \(S_{\text{U}}\) and \(S_{\text{L}}\), respectively. The excitation of each transmitter coil is controlled by excitation switches, \(S_{\text{1}}\) to \(S_{\text{n}}\). The self-inductance and winding resistance of each transmitter coil are denoted by L\({}_{\text{P1}}\) to L\({}_{\text{Pn}}\) and R\({}_{\text{P1}}\) to R\({}_{\text{Pn}}\), respectively. Compensation capacitors (C\({}_{\text{P1}}\) to C\({}_{\text{Pn}}\)) are connected in series with each transmitter coil. The mutual inductance between coupled coils is represented by M. L\({}_{\text{S}}\) and R\({}_{\text{S}}\) are the self-inductance and winding resistance of the receiver coil, respectively. C\({}_{\text{S}}\) is the series compensation capacitor. A rectifier circuit with four diodes (D\({}_{\text{R1}}\) to D\({}_{\text{R4}}\)) and a filter capacitor (C\({}_{\text{f}}\)) are included on the output. The load is represented by R\({}_{\text{L}}\). The detection switches are locatedin front of the rectifier circuit. When they are on, \(\mathrm{L_{S}}\) and \(\mathrm{C_{S}}\) form a resonant configuration. With a proper switching frequency on the primary side, the secondary circuit experiences a line commutation. The detection switches are designed to exhibit the bidirectional-carrying and bidirectional-blocking characteristics, which make them similar to an ideal switch.  
A combination of two IGBTs (\(\mathrm{S_{A}}\) and \(\mathrm{S_{B}}\)) with external diodes (\(\mathrm{D_{A}}\) and \(\mathrm{D_{B}}\)) in reverse connection is adopted for the detection switches. The reverse diodes provide reverse-blocking capability to prevent the IGBTs being turned on from negative voltages. The current flowing in each transmitter coil are denoted by \(I_{P1}\) to \(I_{Pn}\). The total current in the primary circuit is obtained as:  
\[I_{PT}=I_{P1}+I_{P2}+\ldots+I_{Pn} \tag{1}\]