esa-sceva/satcom-chunk-collection huggingface dataset:
# Research into Robust Federated Learning Methods Driven by Heterogeneity Awareness
## 4 Experiment
### Ablation Experiment  
To evaluate the contribution of each component to the overall performance, the following ablation settings are designed:  
Method1: Heterogeneity Loss: Removes the heterogeneity-aware loss mechanism.  
Method2: Center Constraint: Removes the feature center constraint term.  
Method3: Weighted Aggregation: Replaces heterogeneity-weighted aggregation with standard averaging.  
Method4: The complete method with all modules included.  
Figure 6: Loss comparison plot on different datasets (alpha = 0.1).  
Table 4 summarizes the ablation study outcomes. It is evident that each module within the proposed heterogeneity-aware framework plays a significant role in improving the model's performance. The complete method (Method4) achieves the best results in both accuracy (53.74%) and loss (1.3683), demonstrating the synergistic effect of the designed mechanisms in handling non-IID scenarios.  
Method1, which removes the heterogeneity loss component, performs the worst, with an accuracy of only 47.63% and the highest loss of 1.6466. This highlights the importance of modeling and perceiving client feature distributions as a key factor for ensuring models' stability.  
In Method2, the removal of the feature center constraint leads to a drop in accuracy to 52.14%, indicating that feature alignment helps mitigate local feature drift and enhances the generalization ability of the model.  
Method3 substitutes the heterogeneity-weighted aggregation with a simple averaging strategy, resulting in a decreased accuracy of 51.31%. This demonstrates that incorporating client heterogeneity as a weighting factor during aggregation can more effectively coordinate local model updates and improve the global model's integration.  
In conclusion, each of the three mechanisms is indispensable. Together, they form a robust framework that supports a high performance in highly heterogeneous federated learning environments.
## 4 Experiments and Results  
### Corpus Description  
In order to evaluate the proposed approach, we conducted experiments with the JIBO humanoid robot which has 3 full-revolute axes [2] as a brief specification of the robot introduced in Table 1. We mainly consider the scenario in which humans interact with the robot while the robot shakes its head and body. To simulate noisy environments for the humanoid robot, we recorded the ego-noises using the single microphone located at the front side of the head under the anechoic environment. These noise signals involve two types of the ego-noise including fan and movement noises. The first type of the ego-noise labeled \"Motor Off \" only considers the fan noise, while the second type labeled as \"Motor On\" considers both the fan and movement noise. The mixing was conducted at various signal-to-noise ratio (SNR) levels including 5 dB and 15 dB with 1 m distance between the speaker and robot. Walking robots typically use a motor with a large torque, so low SNR levels between \(-\)5 dB and 0 dB are assumed [4]; a walking robot like ASIMO [23] requires motors with large torque because they run, climb stairs or avoid obstacles like human. However, since the JIBO robot is a social robot for home, its motion is limited and it uses a motor with lower torque. Furthermore, social robots generally operate in an indoor environment, so there is little external noise. Therefore, extreme conditions like an SNR level less than 0 dB were not considered. These mixtures were then used to train and evaluate the ego-noise robust ASR algorithms described above. Our experiments were performed on the LibriSpeech database [24] which is composed of three distinct training subsets of 100,360, and 500 h of speech respectively, representing a total training set of 960 h of read English speech. In our experiments, we used 100 h subset and further augmented the database via simulating the ego-noises that we recorded in reality. We evaluated the models on the test sets provided by the LibriSpeech corpus: test-clean and test-other. The difference between \"clean\" and \"other\" is the quality of the audio and its corresponding transcription. The quality of the \"clean\" is higher than that of the  
Figure 4: Framework of the proposed ASR system employing latent features.  
\"other\" [24]. The waveform sampling rate of the corpus and the recorded noises was set to 16 kHz. We then evaluated the proposed algorithm in terms of WER under the aforementioned environments. Since we focus on the ego-noise in this work, the ambient noise was excluded, but, the acoustic noise can be successfully addressed in a unified DNN framework in the end.
## 1 Introduction
Unlike traditional 3D non-rigid transformation algorithms, this method does not rely on unstructured or high-resolution grids and does not involve a large number of computational operations and complex metamodeling processes. The deduction time of this model is nearly 200 times faster than traditional FEM methods. * This method infers the changed shape of an object based on its initial shape, the control point data in their initial state, and changes in the control point data. Using non-contact measurements to obtain control point coordinates instead of the original force magnitude and direction reduces the complexity of the model. Our method has the advantages of low computational complexity, low grid dependency, and high real-time performance. * We propose a CNN gate recurrent unit self-attention mechanism (CNN-GRU-SA)-based multi-input single-output derivation network. The network first uses the CNN feature extraction module to extract the initial shape. Subsequently, the network introduces the GRU to obtain the features of the initial and changed control points. The extracted features are then fused according to the self-attention mechanism. The output features are extracted through the convolutional layers. * We measure and compare the deduction time of the FEM method and the deep learning-based method to verify the real-time performance of the proposed method. We also compare the network with several other neural network architectures to demonstrate its applicability and optimality. This paper is organized as follows. In Section 2, we introduce the specific process of establishing the measurement field, initialization, and real-time update of the derivation model based on multi-control point perception data. In Section 3, we introduce the three stages of the CNN-GRU-SA derivation network, namely input feature extraction, self-attention mechanism fusion, and feature output. In Section 4, we present the training process and derivation results and analyze and discuss the results. In Section 5, we conclude the study and discuss the scope for future research.
# Comparison of Various Factors Affected TID Tolerance in FinFET and Nanowire FET
## 3 Results and Discussion
When the doping was changed to \(1\times 10^{18}\) cm\({}^{-3}\), the change in \(\mathrm{V_{T}}\) was less in the 5 ch-NW than that in the FinFET as less of the interface and oxide traps were generated in the case of the former [29]. As channel doping increased, 10 M radiation appears to have significantly reduce \(\mathrm{V_{T}}\), however, this can only attributed to the increase in the absolute value of the initial amount. In fact, very slight difference was observed when calculating the percentage variation of the aforementioned amount. As presented in Table 4, the \(\mathrm{V_{T}}\) variation percentage was found to be similar between the channel doping of \(1\times 10^{16}\) cm\({}^{-3}\) and \(1\times 10^{18}\) cm\({}^{-3}\) for both the FinFET and 5 ch-NW. Thus, channel doping had little or no influence on the TID effect in the FinFET and 5 ch-NW. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**FinFET (\(\Delta\mathrm{V_{T}}\))**} & \multicolumn{2}{c}{**5 ch-NW (\(\Delta\mathrm{V_{T}}\))**} \\ \cline{2-5} Channel doping & \(1\times 10^{16}\) cm\({}^{-3}\) & \(1\times 10^{18}\) cm\({}^{-3}\) & \(1\times 10^{16}\) cm\({}^{-3}\) & \(1\times 10^{18}\) cm\({}^{-3}\) \\ Initial & 329 mV & 345 mV & 402 mV & 411 mV \\
10 Mrad & 312 mV & 329 mV & 388 mV & 398 mV \\ \(\Delta\mathrm{V_{T}}\) (\%) & 5.16\% & 4.63\% & 3.48\% & 3.16\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: \(\mathrm{V_{T}}\) variation according to the channel doping. Figure 8: The simulation results according to channel doping. (**a**) FinFET, (**b**) Sch-NW.
# Tiny Machine Learning Battery State-of-Charge Estimation Hardware Accelerated
## 2 Related Works
### Artificial Neural Networks
Given the sequential nature of lithium battery data, some papers experimented specific CNN designed to capture temporal dependencies in the dataset. [PERSON] et al. studied the effectiveness of dilated, causal 1D convolutional layers in estimating the SoC. After testing the feasibility of the proposed model, the authors suggested further investigations toward the use of compression techniques on this type of ANN [57]. Other works [58; 59; 60] employed other advanced deep neural architectures, without, however, analysing their memory footprint and their number of parameters. Both of them are crucial aspects for EVs as they are usually equipped with in-vehicle computing platforms with low computational power. [PERSON] et al. [61] modeled an embedded environment consisting of two Raspberry PI boards, one for acquiring data and the other for calculating the SoC, to check whether the chosen algorithm met the real-time requirements of estimation. The authors concluded that under conditions of reduced computational resources, it was adequate to use the EKF instead of the UKF, as the latter shown a 12.4% higher computational cost on average. [PERSON] et al. [62] tested the performance of the CNN and GRU after optimising the size of the two models using TensorFlow Lite [63] and STM32 Cube.AI [64]. The evaluation of the models was conducted in terms of accuracy and average inference time on an STM32F429 ZI discovery board. From the results achieved in estimating the SoC, the work suggested a 1D CNN model quantized through the ST Edge AI Developer Cloud ([https://stm32 ai-cs.st.com/home](https://stm32 ai-cs.st.com/home) (accessed on 13 July 2024)) framework. Minimal research has deepened the joint estimation and MCU deployability performances of other DL architectures for SoC estimation under conditions of reduced computational and memory capacities. This suggested the need to further explore the usability of these advanced models on low-power platforms. Following the analysis of many studies, this paper proposed to compare on the same dataset tiny ANN of the following topologies: FNN, LSTM, GRU, temporal convolutional network (TCN), Legendre memory units (LMU). This because each of these approaches were claimed to achieve best results under certain conditions or by processing a given dataset. [PERSON] et al. [65] verified the validity of using CNN for sequential problems by introducing TCN. [PERSON] et al. proposed a new memory cell for recurrent networks in their paper, the LMU [66], claimed that it could efficiently deal with long temporal dependencies. For these reasons, TCNs and LMUs were investigated by this work too.
### Seismocardiogram  
The SCG signal presents the vibration due to the pressure within the body and the acceleration of the blood during the diastole and systole of the cardiac cycle [15; 16]. Compared with BCG and ECG signals, SCG waveform is more responsive to the changes in heart function, because its signal is directly caused by heart vibration [17; 18]. The SCG monitoring position is usually set near the sternum of the human body, as shown in Figure 2. The signal is measured from the dorsal ventral direction, from the front of the subject's chest to the back [19; 20; 21; 22]. Figure 1 shows a typical ECG, head-to-foot BCG, and tri-axial SCG. The SCG records the accelerations of the chest wall and is thus presented in units of milligram. In the SCG signal from AO to AC, the heart is in systole, while in the rest periods, the heart is in diastolic [23]. [PERSON]'s team [15; 24; 25] carried out research on the physiological waveform of SCG signal and the opening and closing of the corresponding heart aortic valve, to mark the start and end time of cardiac systolic and diastolic periods, reflect the strength of myocardial contraction, and accurately mark abnormal information in cardiac activities [19].  
The research of SCG started from the 1960s [26]. This technology was initially used in space programs to monitor health of astronauts, and then gradually applied to clinical medicine [14]. The development of SCG was very similar to that of BCG, both declined due to limitations of technology development, and have risen again for their technical potential [26].
## 4 Results  
For the discrete blood pressure prediction, all nine machine learning models were trained and then evaluated on the testing dataset. Results from training the machine learning models are shown in Table 2. Each algorithm was evaluated on the MAE for the unseen test data, the acceptable proportion (percentage of predictions with an error less than 10 mmHg, as per the standard discussed in Section 2), and the coefficient of determination (\(R^{2}\)). Based off of the standard for blood pressure prediction, the passing population should be at least 85% for the model to be considered adequate. The \(R^{2}\) value indicates how well-correlated the predicted blood pressure is with the actual blood pressure [27]. It is used because a large proportion of the blood pressures fall within a relatively narrow range. \begin{table}
\begin{tabular}{l l} \hline \hline \# & **Description** \\ \hline
1 & 3 fully connected 1024 layers with batch normalisation, ReLU, and 0.2 dropout, using Pearson correlation regression layer \\
2 & 2 layers with 1024 bilstm units, with 0.2 dropout factors, with a standard regression layer \\
3 & 2 layers with 1024 bilstm units, with 0.2 dropout factors solved using the Adam optimiser, with a standard regression layer \\
4 & 2 layers with 1024 bilstm units, with 0.2 dropout factors, using the Pearson correlation regression layer \\
5 & 2 layers with 1024 lstm units, with 0.2 dropout factors, using a standard regression layer \\
6 & 2 layers with 1024 lstm units, with 0.2 dropout factors, using the Pearson correlation regression layer \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of various neural network architectures for waveform regression. Row 1 corresponds to the final model chosen. Figure 3: Waveform neural network architecture. FC refers to a fully connected layer. A model that outputs the mean blood pressure may appear to do well, based off of the MAE and passing population, but is of no use. Such a model would have an \(R^{2}\) close to 0, indicating that the two variables are independent and uncorrelated [27]. \(R^{2}\) is not calculated by squaring another parameter and can yield a negative result in circumstances when the best-fit line exhibits a worse fit than a horizontal line [27]. Results are further discussed in Section 5. From the three models that performed best on SBP, DBP, and MAP, respectively, a scatter plot is presented in Figure 4. This shows the predicted blood pressure against the actual blood pressure. The main performance metric used for the waveform network is Pearson's \(r\) value, with a higher value corresponding to higher correlation. The final model, consisting of three hidden layers of 1024 neurons and a Pearson correlation regression layer, achieved a strong \(r\) value of 0.864 across the testing data. This result, as well as the results from the other models discussed in Section 3.5.2, are shown in Table 3.
## 2 Related Works
### Blockchain-Based Publisher Subscribers  
The study presented in [5] introduces 'Pub-SubMCS', a privacy-preserving decentralized framework for MCS systems. Using a publish-subscribe model enables data sharing, allowing subscribers to match existing data requests or create new tasks with specific requirements. While the pub-sub-model conserves resources, it exacerbates sensing issues, addressed by Pub-SubMCS through smart contract-based access control. This ensures the early identification and penalization of malicious workers. Data privacy and validationare ensured through blockchain by employing data transformation techniques, such as normalization, and utilizing the Pearson correlation coefficient measure to assess similarity in collected sensor data. However, the Pearson correlation alone may not suffice for assessing similarity in collected sensor network data, particularly in real-world scenarios where identical data types may be unavailable. In such cases, the inclusion of _sensor associations_ become crucial for selecting alternative data sources. This approach ensures a more comprehensive measure of similarity, accommodating situations where the exact data sought may not be present.  
Trinity [15] pioneered the integration of the Publisher-Subscriber model with blockchain. However, it presumed data accuracy without addressing the challenge of storing substantial volumes on the blockchain. Unlike Trinity's decentralized approach, which uses multiple brokers, this research sets the broker directly on blockchain technology, offering a more streamlined and integrated solution.  
Other recent blockchain-based publisher-subscriber models presented in [35; 36], [13; 37; 38], and [14; 39; 40] mainly focused on authentication, data privacy, and improving the quality of service. However, the quality assessment of the data itself remains unaddressed. Another critical aspect of the smart broker with blockchain is that the blockchain needs to be permissioned. It does not need currency, and the consensus mechanism can be proof-of-work or similar. To be most effective, permissioned peer publishers' static information, for example, identification details, can be verified in the real world, at least initially.
## 5 Conclusions  
This paper proposed a new security ANM framework for optimal operational planning in active distribution networks using a flexible hybrid VSC-based AC/DC system (containing MVDC interfaces) while upholding a security of supply condition. The proposed (ANM) scheme was described and applied to a modified urban 75-bus UK generic distribution network. The proposed ANM scheme was compared using three configurations: Base case (only AC), P2P (with point-to-point VSCs), and with the proposed configuration (MVDC). The advantage of the ANM framework was that it could guarantee a security of supply at all times akin to the reliability criterion applied to transmission systems (i.e., \(N-1\)).  
The results clearly proved that the proposed approach with the proposed hybrid AC/DC configuration improved the operation of the distribution system. The proposed MVDC configuration reduced demand shifting and DG curtailment when compared to normal AC or hybrid P2P configurations while upholding continuity of supply. The MVDC interface also reduced the power loadings of the lines and facilitated the tap changer operation burden, since it provided seamless load  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Item** & \multicolumn{2}{c}{**Hybrid Network Configurations**} \\ \cline{2-3}  & **P2P** & **MVDC** \\ \hline Total VSC station cost & \$456,000 & \$456,000 \\ Total power lines cost & - & \$196,500 \\ Total investment cost & \$456,000 & \$652,500 \\ The NPV at 30 year & \$1,349,334 & \$1,403,315 \\ Payback period & 7.6 years & 9.5 years \\ \hline \hline \end{tabular}
\end{table}
Table 5: An estimated investment cost and return on investment.  
balancing between multiple feeders. The use of the proposed MVDC interface also showed significant savings to the DSO, when compared to the P2P configuration with the same number of VSCs.  
Nevertheless, further work could be carried out to estimate the exact geographical locations of the MVDC placements. This is however highly dependent on specific networks and the topological and geographical constraints.  
Conceptualisation, A.A.I. and B.K.; methodology, A.A.I., B.K., and J.R.; validation, B.K. and J.R.; formal analysis and investigation, A.A.I.; writing-original draft preparation, A.A.I. and B.K.; writing-review and editing, A.A.I., B.K., and J.R.; supervision and project administration, B.K.; funding acquisition, A.A.I.  
[PERSON] was funded by Universiti Kebangsan Malaysia through projects GGPM-2019-031 and GUP-2018-024. The work of [PERSON] was supported by Madrid Regional Government through projects PRICAM-CM (Ref. S2013/ICE-2933) and PROMINT-CM (Ref. S2018/EMT-4366). The APC was funded by GUP-2018-024.  
The authors declare no conflict of interest.
## 2 System and Channel Models
### Atmospheric Turbulence  
According to the discussion of atmospheric turbulence in the introduction, it is evident that it influences optical signal transmission. The Malaga model is universal in describing atmospheric turbulence because it can be converted to other common atmospheric turbulence models by adjusting its parameters [17], and it has higher accuracy than other distribution models. Meanwhile, the PDF of \(I_{at}\) based on the Malaga fading model can be expressed as  
\[f_{I_{at}}(I_{at})=\frac{A}{2}\sum_{i=1}^{b}\mu_{i}I_{at}^{\frac{4\pi i}{2}-1}G _{0,2}^{2}\left[\frac{ab}{xb+Y}I_{at}\Big{|}_{\frac{\pi^{2}}{2},-\frac{\pi^{2 }}{2}}\right], \tag{4}\]  
where  
\[A\triangleq\frac{2a^{\frac{4}{2}}}{\kappa^{1+\frac{5}{2}}\Gamma (a)}\left(\frac{xb}{xb+Y}\right)^{b+\frac{5}{2}}, \tag{5a}\] \[\mu_{i}\triangleq\binom{b-1}{i-1}\frac{(xb+Y)^{1-\frac{i}{2}}}{ (i-1)!}\left(\frac{Y}{\kappa}\right)^{i-1}\left(\frac{a}{b}\right)^{\frac{i}{2}},\] (5b) \[\kappa=\varsigma(1-\rho),\] (5c) \[Y=\chi+\varsigma\rho+2\sqrt{\varsigma\rho\chi}\cos\Delta\phi, \tag{5d}\]  
where \(a\) and \(b\) denote two fading parameters associated with atmospheric turbulence, and higher values of \(a\) and \(b\) indicate weakened atmospheric turbulence. Note that \(b\) must be a natural number. \(\kappa\) indicates the average power of the scattered components received by the off-axis eddies, \(\rho\) indicates the ratio of the scattering components mixed into the LOS part to the total scattering components, subject to \(0\leq\rho\leq 1\), and \(\varsigma\) denotes the average power of all scattered components. Y denotes the average power of the coherent parts, \(\chi\) indicates the average power of the LOS part, and \(\Delta\phi\) denotes the difference of the definite phase between the LOS part and the scattered components that is combined with the LOS part. Additionally, \(G_{p,q}^{m,n}[\cdot]\) is the Meijer's G function, defined in [27].