esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Proposed Unified Performance Metric
#### 2.3.1 Components of the Unified Performance Metric  
The unified performance metric is developed using the four primary evaluation metrics used in our previous paper [13] as follows:  
1. Delay (\(\theta\)): This metric shows the end-to-end delay experienced during data transmission from the sensor node to the base station through multi-hop CH routing. The delay affects data transmission speed, which is crucial in time-sensitive applications or applications requiring low latency. The delay is calculated using Equation (3).
2. Distance (D): The total distance data packets travel from a sensor node until they reach the sink node or the BS are also calculated by utilizing Equations (1) and (2) for the intra-cluster and inter-cluster distance. A lower distance typically represents a more efficient routing, thus reducing energy consumption and transmission time.
3. Residual Energy (\(\xi_{r}\)): The residual energy of each node is also calculated after each simulation round using Equation (9) below, where \(\xi_{o}\) indicates the initial energy, and \(\xi_{cons}\) denotes the consumed energy found with Equation (7) or Equation (8). Energy efficiency is very critical in any WSN application since it directly impacts network lifespan. \[\xi_{r}=\xi_{o}-\xi_{cons}\] (9)
4. Trust (T): The trust for each node, which represents how reliable a particular node is and also represents the integrity of data transmission using that specific node, is calculated using Equation (10), where DT, IT, and ET are calculated using Equations (4)-(6) from the previous section. Higher trust values correlate with more reliable data transmission, reducing the need for retransmissions and improving network efficiency. This is important for WSN applications where the networks are prone to node failure or interference. \[T=\frac{1}{3}(DT+IT+ET)\] (10)
# Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning
## 5 Method
### Feature Extraction  
To handle the complexity and high dimensionality of our clinical dataset--which includes demographic information, vital signs, lab results, and cardiovascular-relatedprocedures--we applied three complementary feature extraction techniques: Principal Component Analysis (PCA), Relief with SelectKBest, and Correlation-Based Feature Selection (CFS).
* PCA helped reduce dimensionality by transforming correlated features into a smaller set of uncorrelated components. This was particularly helpful for compressing lab and vital-sign data without significant loss of information, improving model efficiency and reducing overfitting.
* Relief with SelectKBest was used to identify and retain features most relevant for distinguishing between AAS and non-AAS cases. It works well for clinical data by emphasizing variables that strongly separate the target classes, such as key biomarkers and physiological indicators.
* CFS was applied to select a subset of features that are highly correlated with the target variable (AAS) while minimizing redundancy between features. This ensures that the model benefits from meaningful inputs without duplicating information.  
By combining these methods, we aimed to extract the most informative and non-redundant features, allowing our models to focus on the variables that matter most. This process not only streamlined the dataset for training and evaluation but also supported a deeper understanding of the clinical variables that play a significant role in predicting Acute Aortic Syndrome.
# Verse1-Chorus-Verse2 Structure: A Stacked Ensemble Approach for Enhanced Music Emotion Recognition
## 3 Results and Discussion
### Song Parts Implications for Music Emotion Recognition  
In the context of MER, examining the performance of machine learning models across various song structures--such as Verse 1, Verse 2, Chorus, Whole Song, and Verse1-Chorus-Verse2--provides valuable insights into how emotional content is conveyed and interpreted through both lyrics and potentially audio features.  
Results from Verse 1 and Verse 2 indicate that these song segments contain distinct emotional cues captured by a diverse range of terms, including less frequent ones. As verses often establish narratives or scenes, the nuanced language within them is crucial for discerning the intended emotion. The improved performance metrics at lower MinDF settings suggest that the richness of emotional expression may be linked to the diversity of vocabulary used.  
The Chorus typically encapsulates the primary theme or emotional climax of a song. The observed increase in precision at higher MinDF settings may reflect the repetition of emotionally charged phrases typical in choruses. This implies that the chorus's repetitive nature, aimed at evoking or amplifying the song's core emotional message, is best captured with more frequent terms.  
The robustness of the model across different MinDF settings when analyzing whole songs suggests that both frequent and infrequent terms play significant roles in conveying emotion. The entirety of the song provides a comprehensive emotional narrative, which is not overly sensitive to the frequency of term usage, indicating that emotional content is broadly distributed throughout the song and not confined to specific sections or terms.  
The composite structure of Versel-Chorus-Verse2 exhibits high recall at higher MinDF settings, suggesting that capturing the full spectrum of emotional expression may necessitate focusing on the most common terms across all three sections. The consistent F1 Score across all settings underscores the effectiveness of combining these sections to achieve a balance between precision and recall.  
Different parts of a song contribute uniquely to its overall emotional expression, with verses providing context and narrative, the chorus reinforcing the central emotional theme, and the combination of these elements offering a complete emotional picture. In practical applications, such as playlist creation or album analysis, understanding these nuances is crucial. For instance, in a recommendation system, models tuned to higher MinDF settings might be more effective if a user prefers songs with a specific emotional tone in the chorus. Conversely, for therapeutic or educational purposes where understanding the full narrative is vital, models with lower MinDF settings that capture finer details in verses could be more suitable. Overall, these analyses provide a comprehensive understanding of how to approach emotion recognition in music, emphasizing the importance of considering musical structure and desired outcomes when developing machine learning models for this purpose.
# Unsupervised Domain Adaptation via Stacked Convolutional Autoencoder +
## 3 Related Work
### Autoencoder-Based Methods  
Among deep neural networks, autoencoder-based unsupervised domain adaptation methods have performed well with respect to the no label requirement and fast convergence speed. For example, [PERSON] et al. proposed a stacked denoising autoencoder (SDA) for domain adaptation [43]. A marginalized denoising autoencoder (mSDA) method was proposed for speeding up SDA by two orders of magnitude [19]. [PERSON] et al. introduced non-linear coding by kernelization into the mSDA for domain adaptation [21]. [PERSON] et al. proposed an unsupervised domain adaptation framework with deep autoencoders [22]. In this method, the mSDA is utilized to pre-train the whole framework and two encoding and decoding layers are incorporated to learn more transferable representations between the source and target domains. [PERSON] et al. proposed integration of the manifold regularization term in the objective function [2], involving stacking of two layers of autoencoders to learn more abstract representations for unsupervised domain adaptation. [PERSON] et al. proposed a semi-supervised method using dual autoencoders [1], which extracted more powerful features using two different autoencoders based on mSDA for unsupervised domain adaptation. [PERSON] et al. proposed a face presentation attack detection model using an autoencoder and a multi-layer perceptron [44], which transferred the knowledge of facial appearance between different domains. This domain adaptation method reduced the requirements for large-scale labeled data, which avoided labor-intensive work and reduced costs when training face recognition systems. [PERSON] et al. proposed a deep sparse autoencoder for an imbalanced domain adaptation problem [45], which could adjust the model automatically according to the degree of imbalance to bridge the gap between domains. In this method, a self-adaptive imbalanced cross-entropy loss function is used to highlight minority categories and automatically compensate for training loss bias. In contrast to autoencoder-based methods that rely on application of the classical structure of an autoencoder to learn representations or integrate the regularization term into the objective function, our method introduces convolution and pooling kernels to use local relevance to learn abstract representations for domain adaptation.
## 3 Experimental Design and Analysis of Results
### Comparison of Line Width and Noise Characteristics of FP-ECDL with Two F-P Etalon Precision Levels  
According to the basic principles of ECDL, external cavity feedback has a significant impact on the longitudinal mode characteristics, linewidth, and noise properties of the laser output. For FP-ECDL, the F-P etalon is a crucial component of the external cavity. Therefore, changes in its precision are likely to affect the final laser output performance. The following experimental research focuses on the influence of improved precision in the external cavity F-P etalon on the single longitudinal mode output performance of the laser. As shown in Figure 8, these are the transmittance curves of two coated reflectivity gratings measured by a spectrometer under the same incident light. An increase in reflectivity improves the grating's precision, sharpens the reflection peak, and enhances its suppression effect on light waves outside the central resonant frequency. Due to the use of YOKOGAWA's AQ6370D optical spectrum analyzer with a wavelength resolution of 0.02-0.2 nm in the experiment, it is challenging to distinguish any difference in the single longitudinal mode linewidth formed by the external cavity for two FP etalons based solely on the time-domain spectrum. Beat frequency tests were conducted on the two precision FP-ECDLs using a delayed self-heterodyne linewidth measurement system, as shown in Figure 9. AOM denotes the acousto-optic modulator, and RF is the radiofrequency power supply. Incident light is split into two optical waves by a coupler. One optical wave traverses a delay fiber coil (with a delay time of \(\tau_{\mathrm{d}}\)) and combines with the other optical wave on the opposite side of the coupler, generating a beat frequency signal. This signal is then measured for the linewidth using a photodetector (PD) and a spectrum analyzer (SA). It is important to note that when the laser's linewidth is around \(1\) kHz, the necessary length of the delay fiber can extend to hundreds of kilometers. In such cases, considering previously proposed algorithms for curve fitting in short fiber beat frequency measurement methods becomes essential [23]. This allows the obtention of relatively accurate measurement results without requiring long-distance optical fibers. Figure 8: Transmission curves for two F-P etalons with different precisions. Here is a brief introduction to its testing principle. Assuming the laser's output center frequency is fixed, the spectrum of the laser output will broaden due to the presence of phase noise. Let the output optical field of the laser be represented as  
\[E_{i}(t)=\sqrt{P_{S}}e^{i(\omega t+\varphi(t))}=E(t)e^{i\omega t} \tag{3}\]  
where \(E(t)\) and \(P\) represent the complex amplitude and optical power of the laser optical field, and \(\omega\) is the angular frequency of the output light. \(\varphi(t)\) represents the phase variation.
## 5 Conclusions and Discussion  
In this paper, based on the Duffing oscillator weak signal detection system, the AZV and CSR methods are proposed to accurately estimate the carrier frequency and PAM pseudorandom sequence of the PRBC-PAM signal. In the estimation process, the STFT method is used to identify and obtain the three-valued output of the Duffing oscillator driven by a PRBC-PAM signal. The AZV and the CSR method are designed to adaptively estimate  
Figure 22: Bit error rate for the PAM pseudorandom sequence of different method for different SNRs.  
the carrier frequency and PAM pseudorandom sequence. Several related methods are compared in the experiment and simulation, which include tests on PAM pseudorandom sequences with maximum common ratio deviation, and the results show that the proposed parameter estimation method can still achieve high estimation accuracy of the PRBC-PAM signal at very low SNR. The NRMSE of its carrier frequency estimation reaches 0.01 at the SNR of \(-\)30 dB, while the bit error rate remains at a low level of 11.6%.  
Methodology, M.L., K.W. and J.D.; formal analysis, X.Y. and J.D.; writing--original draft, M.L. and X.H.; funding acquisition, J.D. All authors have read and agreed to the published version of the manuscript.  
The authors would like to acknowledge the National Natural Science Foundation of China (grant No. 62301051 and No. 61973037).  
The data used to support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# Privacy-Preserving Handover Optimization Using Federated Learning and LSTM Networks
## 2 Related
### Federated Learning for Handover Management  
Federated Learning (FL) is an emerging technique that addresses the privacy issues associated with centralized data collection by enabling decentralized model training. Ref. [17] explores the application of federated learning to enhance handover performance in millimeter-wave (mmWave) vehicular networks. Millimeter-wave communication, characterized by high frequency and large bandwidth, offers significant advantages for vehicular networks. However, it also presents challenges such as rapid signal attenuation and poor penetration, leading to frequent handovers to maintain the quality of service (QoS) for mobile users. Ref. [18] introduces a federated learning framework tailored for 6G networks. It addresses the challenges of heterogeneous data and model aggregation in massive IoT environments, enhancing communication efficiency and model robustness.  
In summary, the integration of deep learning and federated learning presents a promising avenue for advancing handover management in next-generation wireless networks. While deep learning offers powerful tools for extracting insights from complex data, federated learning ensures that these insights can be obtained without compromising user privacy. This dual approach is particularly crucial in the era of 5G and beyond, where the volume and variety of data are expected to grow exponentially. The ongoing research in this area continues to push the boundaries of what is possible, paving the way for more intelligent, efficient, and secure handover management solutions.
## 5 Conclusions  
In this work, the optimal allocation of power stacks to an MFCS is determined out based on the lifetime efficiency factor of the fuel cell. For the determined power stacks of the MFCS, integrated subsystem solutions are proposed, including a hydrogen subsystem, an air subsystem, and a thermal management subsystem. The proposed integrated subsystems are specified as follows:  
1. A common rail buffer is added to the integrated air supply subsystem, the function of which is to isolate the air compressor from the fuel cell air demand, providing a degree of buffering and pressure stabilization. It is of interest to note that the volume of the common rail buffer has an impact on the maximum power and energy consumption of the air compressor in the air supply subsystem, as well as the maximum pressure drop of the common rail buffer itself. From the results, it can be concluded that for the  
Figure 15: Temperature variation comparisons of the integrated and distributed thermal subsystems under the CWTVC condition: (**a**) 20 kW; (**b**) 70 kW; (**c**) 120 kW.  
210 kW MFCS, the volume of the common rail buffer chosen as 200 L is an optimal solution and that the choice of compressor power can be varied.
2. In the hydrogen integration supply subsystem, an inlet common rail is added at the anode inlet to stabilize pressure, divert flows, and isolate demands, and an outlet common rail is added at the anode outlet to converge and circulate the gases. This realizes hydrogen supply and exhaust gas circulation for multiple stacks using one hydrogen supply and circulation system. In addition, the integrated hydrogen supply subsystem improves hydrogen utilization and reduces parasitic power.
3. Compared to the distributed thermal subsystem, the integrated thermal subsystem has no advantage in thermal management performance--only in structure. The integrated thermal subsystem possesses advantages in terms of cold start, since this structure is more closely coupled between the coolant circulation pipelines of different stacks.  
Conceptualization, J.G. and S.Z.; methodology, J.G.; software, [PERSON] and Y.L.; validation, [PERSON] and Y.L.; formal analysis, [PERSON]; investigation, [PERSON] and Y.L.; resources, S.Z.; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, W.S. and S.Z.; visualization, [PERSON] and Y.L.; supervision, S.Z.; project administration, S.Z.; funding acquisition, W.S. and S.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
This work was supported by Shanghai TXJS Engineering Technology Co., Ltd., Shanghai REFIRE Technology Co., Ltd. and AVL-List GmbH.  
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest. There is no copyright issue associated with any of the figures.
# An Improved Arc Fault Location Method of DC Distribution System Based on EMD-SVD Decomposition
## 2 Traveling Wave Characteristics in a DC Distribution System
### Arc Fault Characterization of DC Systems  
Arc faults in DC systems can be categorized into series arc faults and parallel arc faults. In this paper, only parallel arc faults are considered. When a parallel arc fault occurs, the arc is directly connected to the earth or connected to the earth through the medium. The arc resistance is affected by some factors, such as arc voltage, arc current, and gap length.  
To simulate the arc fault, the Cassie model was used to simulate the dynamic behavior of the arc burning [27]. Arc resistance is affected by the energy relationship between the arc voltage and arc current.  
The heat balance equation for the arc is shown in Equation (11). \(Q\) is the energy stored in the arc. \(P\) is the dissipated power. \(u\) and \(i\) are the arc voltage and arc current, respectively.  
\[\frac{\mathrm{d}Q}{\mathrm{d}t}=u\cdot i-P \tag{11}\]  
Equation (11) can be changed to quantify the relationship between the rate of change of arc unit conductivity and the change of arc power.  
\[\frac{\mathrm{d}\ln g}{\mathrm{d}t}=\frac{1}{\tau_{c}}(\frac{u^{2}}{E^{2}}-1) \tag{12}\]  
Equation (13) represents the arc resistance for the Cassie model, where \(\tau_{c}\) is the time constant and \(E\) is the arc voltage gradient.  
\[R_{arc}=\mathrm{e}^{\frac{1}{\tau_{c}}\int{(1-\frac{u^{2}}{t^{2}})\mathrm{d}t}} \tag{13}\]
## 1 Introduction
### Objectives  
The reimagined OMSS structural system was modelled parametrically in Grasshopper (Build 1.0.0008), a visual programming tool based on Rhinoceros 3D Version 8 (Rhino 8), mainly used for parametric design and algorithm modelling. Powered by Karamba3D (version 3.1.40918.0), a structural analysis plug-in in Grasshopper, it enables real-time finite element analysis (FEA). The advanced analysis of the reimagined OMSS structural system was conducted using the FEA method. This project utilised Strand7 (version R3.1.3), an FEA software for structural analysis, complemented by the manual verification of the analysis results. The analysis refers to the relevant Australian standards. The bridge deck design was reviewed under AS 5100.1:2017 [5]. The numerical model was analysed under Ultimate and serviceability limit states (ULSs and SLSs) according to AS/NZS 1170.0:2002 for structural design actions [6]. The analysis considers various combinations of actions, including permanent (dead), imposed (live), and wind actions. The member capacity and global deformation of the entire system were examined using the linear static solvers of Strand7. The member sizing process follows AS 4100:2020 for steel structures, based on the results obtained in the linear static analysis [7]. Subsequent examinations focused on crucial parameters, including maximum displacement, principal axial forces, and critical bending moments to evaluate the capacity and feasibility of the proposed model. A new design procedure based on the OMSS design with long-span bridge construction ability is demonstrated. Potential directions for future research are suggested. The objectives of this project are outlined as follows:  
* Employ the parametric design technique through Rhino 8 with Grasshopper to create the three-dimensional numerical model of the OMSS and conduct initial structural optimisation on critical load-bearing frames to enhance design efficiency using the Karamba3D plug-in.
* Conduct a preliminary design of the bridge box girder deck section, operation stages, and site conditions.
* Perform linear static design analysis in Strand7 while applying appropriate load combinations (including permanent (dead), imposed (live), wind actions) as defined in AS/NZS 1170.0:2002.
* Obtain crucial parameters (including maximum displacement, principal axial forces, critical bending moments), verify them manually, and conduct material selection and member sizing according to AS 4100:2020.
* Propose a flowchart describing the new design procedure and possible future research directions.