esa-sceva/satcom-chunk-collection huggingface dataset:
# Synchronous Rectification Method for CLLC Resonant Converters Based on State-Trajectory Models
## 5 Conclusions  
In this paper, a state-trajectory model is introduced into the CLLC resonant converter, and a novel synchronous rectification (SR) strategy specifically designed for the CLLC resonant converter is proposed. The proposed SR strategy requires only the detection of the input voltage, output voltage, and output current, eliminating the need for additional auxiliary circuits. To validate the effectiveness of the proposed strategy, an 800 W converter prototype was constructed and experimentally tested. The results demonstrate that the proposed SR algorithm achieves high accuracy across a wide range of operating conditions, including both steady-state and dynamic processes. Compared to uncontrolled rectification, the proposed synchronous rectification method significantly improves efficiency, with a rated efficiency of 97.38%. Overall, the proposed method exhibits high accuracy and efficiency, providing an effective solution for the performance optimization of CLLC resonant converters. Future work could explore the adaptability of this method to asymmetric CLLC  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Accuracy** & \
\begin{tabular}{c} **Application** \\ **Complexity** \\ \end{tabular} & **Operating Range** &
\begin{tabular}{c} **Additional** \\ **Sensors** \\ \end{tabular} \\ \hline FHA method [2, 13, 14] & low & simple & restricted & no \\ Accurate time-domain methods [16] & high & complex & wide & no \\ Simplified time-domain approach [21] & moderate & moderate & wide & no \\ Frequency-domain fitting methods [20] & moderate & simple & restricted & no \\ Voltage detection methods [7, 8, 9, 10] & high & complex & wide & yes \\ Current detection methods [11, 12] & high & complex & wide & yes \\ The methodology proposed & high & simple & wide & no \\ \hline \hline \end{tabular}
\end{table}
Table 3: Typical fault settings for distribution networks.  
Figure 20: The efficiency of the synchronous finishing scheme proposed in this paper is compared with the SR scheme in [16] at different output voltages for rated power.  
topologies and AI-based real-time state-trajectory prediction to enhance its application potential under dynamic operating conditions.  
Conceptualization, Z.S.; methodology, C.F.; methodology, Q.Y.; software, C.R.; writing--original draft preparation, T.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article.  
The authors declare no conflicts of interest.
## 5 Experimental Verification and Result Analysis
### Simulation and Comparison Experiment  
The model is compared with the A* search algorithm, RRT algorithm, and QL algorithm, while the rolling online algorithm is used to supplement the traditional algorithm. The comparison results are shown in Figure 3. In order to adapt to a four-way proxy, the A* search algorithm combines a linear function and the Manhattan distance as the heuristic function. The RRT algorithm selects the nearest node to join the tree after obstacle judgment. The QL algorithm combines the average rolling algorithm to complete the test, which is different from the proposed method. All algorithms only search online on the map without being trained beforehand. From Figure 3, it can be seen that the A* search algorithm cannot adapt to real-time scenarios, and the agent is easily trapped in deadlock situations, resulting in its inability to complete tasks. Due to the lack of a target decision process, the generalization ability of the RRT algorithm could be improved in different scenarios, and the algorithm often cannot continue due to stuttering during runtime. The QL algorithm and the proposed method can complete planning and produce coverage search paths, but the proposed method performs better when searching for the target.  
Statistical analyses are conducted on the path-planning results for final coverage, repeated coverage, and step size to evaluate the above algorithms. Table 1 shows the quantitative evaluation results. It shows that the search path produced by the proposed method performs better when searching for the target. In addition, the algorithm is evaluated from the perspective of searchability. As shown in Figure 4, the proposed method has the fastest convergence speed. This indicates that the agent in the proposed method catches the target preferentially. Traditional path-planning algorithms are prone to becoming stuck in local optima, especially when dealing with autonomous navigation tasks  
Figure 3: The planning results of different algorithms in three scenarios (from top to bottom: A* algorithm, RRT algorithm, QL algorithm, and our algorithm). The orange arrow in the figure represents the search path. The green arrow represents the movement of the target. The yellow star represents the final capture position.  
in unknown maps. In contrast, reinforcement learning algorithms have more substantial autonomous planning capabilities. Our improved algorithm has a faster convergence speed in the autonomous planning process and can effectively handle searching for dynamic targets in unknown maps.
## 2 Materials and Methods
### Data Reduction  
A complete cycle of the right pole is characterized by the interval from one pole touch-down to the next, with the cycle length measured as the distance between these two points and the cycle rate defined as the frequency of cycles per second. The poling phase encompasses the duration from the ski pole's initial contact with the ground to its departure, whereas the swing phase spans from this departure to the subsequent pole touch-down.  
From the images provided by the two synchronized cameras, the Fastmove 3D Motion software was employed to identify the distal point of the ski pole and various body points, including the head, temporomandibular joint, neck (the midpoint between the upper sternum and the seventh cervical vertebra), and the centers of major joints (shoulder, elbow, wrist, hip, knee, and ankle), along with the heel, toe, and point of hand grip on the pole. From these data, the angles of the trunk, hip, knee, ankle, shoulder, elbow, and the angular velocities of these joints, as well as the movement of the center of mass (CoM) were calculated. The shoulder/hip angles were defined as the angle between the projection of the upper arm/thigh vector on the sagittal plane and the longitudinal axis of the trunk. The elbow joint angle is the angle between the upper arm vector and the forearm vector, while the knee joint angle is the angle between the thigh vector and the shank vector. The angular velocity is the first derivative of the angle with respect to time, with units of \({}^{\circ}\)/s. The detailed calculation methods and illustration of these parameters are described in the existing literature [29]. Fastmove represents a cutting-edge, AI-driven system for the automatic capture of human movement, offering proven accuracy in analyzing biomechanical data [30].  
The tilt angle of the ski pole was defined as the angle between the line extending from the hand grip to the pole's distal end and the vertical Z-axis within the global coordinate system. During both the poling and swing phases, the range of motion for the trunk, elbow, hip, and knee joints, as well as their respective angular velocities, were meticulously calculated. The coordinates of the CoM were determined using the refined Zatsiorsky-Seluyanov human inertia parameters [31], and its vertical displacement was normalized to body height.
# Intelligibility and Listening Effort of Spanish Oesophageal Speech +
## 4 Experiment 2: Listening Effort for High-Intelligibility Oesophageal Speech  
### Materials and Methods  
#### 4.1.1 Experimental Design  
Based on our preliminary intelligibility and self-reported LE experiment (see Experiment 1, Section 3), we had the chance to probe further into our data by designing a study specifically aimed at measuring LE. The aim was to investigate the differences in LE for a set of HS and OS speakers that have comparable intelligibility. As pointed out in [31], even highly intelligible OS speech was found to have different LE ratings. Therefore, this is a methodological decision in order to rule out that observed effects are due to differences in intelligibility.  
The experiment was designed for an Electroencephalography (EEG)-based LE measurement. We aimed to record EEG data of participants while they listened to OS and HS to investigate if there are differences in the LE correlates of brain activity. Along with measuring the EEG data, we also collected subjective LE ratings from the listeners. As the participants had an EEG cap on, the stimuli were played on a loudspeaker, and not on headphones. Here, we present the LE ratings findings. EEG data is still being analysed and will be presented in the future.  
Figure 3: Word Error Rates (WER) for Human Speech Recognition (HSR) and Automatic Speech Recognition (ASR) for oesophageal (OM1, OM2, OM3, OF1) and healthy (HM1, HF1) speakersIn addition to the LE experiment, a separate intelligibility test was conducted to replicate the results of Experiment 1 in a laboratory setting. This time we asked the participants to listen to the sentence and repeat out loud what they heard. This is less taxing for the participant as they do not have to type their responses. Also, this resulted in speedier responses and hence less effort from the listeners' side in memorising the sentence. The advantage of oral response is that typing errors can be excluded as a confounding factor for WER. However, this involves post-processing, i.e., the task of transcription of their oral responses to text to calculate WER.
# Model Predictive Control Algorithm for Converter Based on a Convolutional Neural Network
## 2 Neural Network Controller for Converters and Model Switching Strategy
### Neural Network Controller for Converters
Therefore, it is necessary to train corresponding neural network controller models for different load types, resulting in a set of neural network controllers with identical network structures but different parameters. During online control, the corresponding neural network controller is selected based on the identified load type. Furthermore, the complexity of the FCSMPC algorithm designed during the offline training phase does not affect the real-time computational quantities of the neural network controller. This implies that the online computational quantities of the neural network controller remain fixed and are unaffected by the complexity of the designed FCSMPC algorithm, thus achieving decoupling between the converter FCSMPC algorithm design and the algorithm's computational quantities. During the online control phase of the neural network, corresponding neural network controller parameters are selected based on the system's load type. The control variables calculated by the neural network, using the sampled values of system states, are then applied to the converter system to achieve real-time control. Therefore, to enhance the  
Figure 2: Structure diagram of 2L-VSI. adaptability of the neural network controller, it is necessary to identify the converter load types in time. In conventional load-type identification methods based on voltage-current relationships, at least one cycle of voltage and current signals is required; this leads to issues such as computational complexity, low accuracy, and vulnerability to interference [39]. To address these limitations and achieve rapid and accurate identification of converter load types, this article proposes a load-type identification method for converters based on a CNN.
## 6 Implementation and Evaluation
### Preparation Time for ADL Measurement  
In the proposed device network, tag devices that measure the use of household goods are not always active. Instead, the tag device activates its internal sensors only when someone is in the location where the tag device is installed. Therefore, the tag device has the advantage of reducing standby power consumption when no one is in a room. Due to this tag device activation process, the preparation time from when a user enters a specific room to when the tag device can measure the state of an appliance should be considered. We measured the time for all tag devices to be activated for ADL measurement when the user with a wearable device enters the space where the stationary device is installed. Figure 8 illustrates the results. The stationary device is wirelessly connected with multiple tag devices using BLE communication and can connect with up to seven tag devices. As the number of tag devices connected to the stationary device increases, the preparation time for ADL measurement tends to increase. However, even if the stationary device is connected to the maximum number of tag devices in a unit space, the preparation time of the tag devices is less than 1700 ms. The results demonstrate that the devices in the network are sufficient to track a user's activities after entering the space.
## 4 Experiment and Results
### Evaluation Metrics
The Brier score measures the overall accuracy of probability forecasts by calculating the mean squared error (MSE) between predicted probabilities and actual outcomes, as shown in Formula (1): \(\mathrm{p_{i}}\) is the probability that the model predicts sample i as the positive class, \(\mathrm{o_{i}}\) is the actual label of sample i, and N is the total number of samples. The ECE quantifies the degree of miscalibration in the predicted probabilities by dividing them into several bins and calculating the difference between the average predicted probability and the actual positive rate within each bin. The overall error is then obtained by taking a weighted average, as shown in Formula (2): \(\mathrm{\mid B_{m}\mid}\) is the number of the sample of the m-th probability interval, \(\mathrm{avg_{m}}\) (\(\mathrm{p}\)) is the average predicted probability within this interval, and \(\mathrm{acc_{m}}\) is the actual accuracy (proportion of positive cases) within this interval. The Brier score is complemented by a 95% confidence interval, which also quantifies the uncertainty of the model and is computed using bootstrap sampling. By combining these uncertainty metrics, we can more comprehensively assess the reliability of the model, ensuring better predictive performance and robustness in real-world scenarios. 6. Brier score = \(\frac{1}{\mathrm{N}}\)\(\sum_{i=1}^{\mathrm{N}}(\mathrm{p_{i}}-\mathrm{o_{i}})^{2}\) (1) \[\mathrm{ECE}=\sum_{m=1}^{\mathrm{M}}\frac{\mathrm{\mid B_{m}\mid}}{\mathrm{N}} \mathrm{\mid avg_{m}}(\mathrm{p})-\mathrm{acc_{m}}\mathrm{\mid}\] (2)
7. To further enhance the interpretability of the model, we use normalized mutual information (NMI) [45] to quantify the relationship between deep features and radiomics features. Mutual information is a commonly used metric to measure the amount of shared information between two variables, revealing their correlation, especially when the relationship between the two types of data is not explicitly clear. To avoid biases caused by differences in data scale and feature count, we chose NMI, which normalizes the mutual information values to a range of [0,1], ensuring a fair comparison between different feature sets.
# Context-Aware Enhanced Application-Specific Handover in 5G V2X Networks
## 4 Results and Discussion
* CAP includes the stay time, which determines how long a UE remains in an SC before attempting an HO. This prevents fast-moving UEs from transferring to HO frequently, thereby preventing unnecessary HOs and a ping-pong effect. CAP clearly maintains HO stability across a wide range of UE velocities. * CAP evaluates Current SC loads before allowing HO. This is a form of proactive load management that load-balances a network and prevents HO failures due to congestion. * Using stay time reduces transient or unstable HOs. * CAP implements a policy-based approach to adjust weights according to the application requirements of a UE (context). This optimizes HOs to not rely only on signal metrics, but also on QoS criteria to ensure better user experience and session continuity. Using static weights will not allow the adoption of dynamic network conditions.
# Communication Fault Maintenance Decision of Information System Based on Inverse Symmetry Algorithm
## 1 Introduction
In order to make the communication technology reflect the ideal practicability, it is necessary to focus on detailed analysis of various common fault problems. Understanding their common types and basic causes can make processing and control methods more reasonable. It can reduce the possibility of accident threat and increase the application value of communication technology. more reasonable methods and methods for processing control, reduce the probability of accident threats, and promote the application value of communication technology. At present, there is much research on communication faults in information systems. Related scholars have proposed a new scheme that combines fuzzy sets and fuzzy inference methods with fault diagnosis, using fuzzy sets to represent voltage and current characteristic information when faults occur and using fuzzy reasoning to improve the accuracy and availability of diagnostic results. At the same time, a fuzzy set learning platform was developed to alleviate the problem of knowledge acquisition of expert systems. Other researchers use network communication technology to solve system fault diagnosis problems and segment system faults through rule representation, providing decision-making schemes according to human logical thinking rules, which is easy for people to understand. However, this method takes a long time to analyze and has poor real-time performance. The method in [7] optimizes the deployment of near-space communication systems and introduces a multi-objective evolutionary algorithm to segment the internal space of the communication system, obtaining a relatively stable signal through effective partitioning, effectively removing interference, and performing fault location. However, due to the complicated process, the fault location time of this method is long, and the efficiency is not good. Reference [8] proposed an optimization method for fault diagnosis of high-voltage circuit breakers in power supply systems, which diagnosed high-voltage circuit breaker faults through a probabilistic neural network algorithm. The energy of vibration signal is quantified, and the causes and types of faults are analyzed. The method has high accuracy, but this method has poor timeliness. In order to solve the problem of difficult decision-making of information system fault maintenance, this paper studies the information system communication fault maintenance decision-making based on the inverse symmetry algorithm. This method first realizes the information system based on the inverse symmetry algorithm. Based on this information system, it studies the communication fault diagnosis method and maintenance decision-making and puts forward ideas for the stable operation of the information system.
## 1 Introduction
Researchers focus on collaboratively utilizing edge computing nodes and cloud computing centers on satellites to achieve distributed processing of tasks, reduce latency, and reduce network load. Although some scholars have studied resource allocation algorithms under cloud-network integration, the following challenges still exist:  
1. [label=(0)]
2. Different applications have greatly different requirements for performance indicators, such as latency, bandwidth, and reliability. As the number of users and service demands grow, on-demand allocation of resources becomes particularly important. Therefore, in a cloud-network integration environment, how to ensure that satellite and cloud resources are fully utilized while meeting the differentiated needs of users is a major challenge. 3. Computing, storage, and communication resources on satellites are limited, and their availability may vary depending on satellite operating status, energy supply, and other factors. Therefore, in a cloud-network integration environment, how to design intelligent and adaptive resource allocation strategy that adapts to dynamic changes to more effectively optimize the utilization of satellite resources is a major challenge. To address the challenges mentioned above, we study a virtual network mapping algorithm under cloud-network integration, and propose a VNE algorithm based on QoS demand satisfaction. The contributions of this paper are summarized as follows:  
1. [label=(0)]
2. We abstract the topology of the satellite network under cloud-network integration, and introduce QoS satisfaction. By calculating the QoS satisfaction during virtual network mapping, we can measure whether the mapping method meets the needs of users. 3. We use reinforcement learning algorithm to design a VNE algorithm, which can adapt to the dynamically changing satellite network conditions. By dynamically adjusting the virtual network mapping, the algorithm makes it more flexible to adapt to the changes of satellite network, so as to better meet the QoS requirements. The structure of this paper is arranged as follows. We describe related work on satellite network resource allocation and virtual network mapping algorithms in Section 2. In addition, the system model of satellite network is established in Section 3. In Section 4, we introduce the specific algorithm flow of SN-VNE in detail. The SN-VNE algorithm performance evaluation results and conclusions are described in Sections 5 and 6, respectively.
## 3 Results and Discussion
### Characterization of the Membranes
#### 3.2.2 Identification of Lignin by Color Measurement  
The L\({}^{*}\) (lightness/darkness), a\({}^{*}\) (greenness/redness), b\({}^{*}\) (blueness/yellowness), and ISO brightness (R457 C) values of the fabricated membranes were measured and used as a tool to evaluate the lignin content used in membrane fabrication. As can be seen in Figure 3, the virgin membrane (without lignin) expresses the highest brightness (highest L\({}^{*}\) and R457 C) and the lowest degree of blueness (b\({}^{*}\)). With a successive increase of lignin dosage in the dope solution, the brightness and b\({}^{*}\) values of fabricated membranes reduced and increased remarkably, respectively. The reduction of brightness (the degree of yellowing) as a result of lignin and lignocellulosic compounds is mostly associated with the absorption of UV light by lignin chromophoric functional groups such as phenolics, hydroxyl, and carbonyl groups and double bonds that interact with UV light [44].  
The results reflect the fact that the induction of lignin in the polymer solution leads to the formation of a darker membrane. These results are in agreement with [PERSON] et al. [45] and [PERSON] [46], who report the formation of a darker edible film as a result of lignin and phenolic compounds.