esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The lasers on each side project their beams on the screen on the opposite side, and a camera near the screen captures an image of the screen. By calculating the positions of the laser beams, the relative displacement between two sides can be estimated. In a follow-up study conducted by the same research group, a 2-DOF manipulator was introduced to an increased range of the displacement measurement. In the case of a visually servoed paired structure light system, the displacement can be estimated with an error within 0.2 mm and 0.2 deg, but the installation of a relatively heavy sensing system on port structures and mobile platforms is required. Therefore, in this paper, a displacement measurement method using the fixed intrinsic parameter of the camera is applied to measure the displacement of 6-DOF between the camera and the fixed/port structure; with its use, the movement of the port structure based on fixed structure can be measured. In this paper, the floating port structure was assumed to be a rigid body, and it was assumed that there was no deformation in shape. Since the displacement estimation of the structure is highly dependent on the camera-intrinsic parameter, in this paper, the intrinsic parameter is tuned based on the given measured translational and rotational displacements using a genetic algorithm. An indoor model experiment and an outdoor field image-based experiment were performed, and the results of the experiments confirmed that translational and rotational displacements are estimated more precisely  
Figure 1: Critical damage to the facilities. after calibrating the intrinsic parameters of the vision sensor, and the proposed technique is applicable to the field. The remainder of the paper is organized as follows. In Section 2, the translational and rotational displacement estimation method using a vision sensor is described. The application of the genetic algorithm for tuning the camera-intrinsic parameters is introduced in Section 3. To validate the performance and applicability of the proposed method, the experimental tests using model structures and captured image with a drone are conducted and the results are discussed in Section 4. Conclusions and further research directions are discussed in Section 5.
## 3 Feature Selection Algorithms
### Empirical Mode Decomposition
#### 4.2.3 Support Vector Machines (SVMs)
The experimental results showed that the proposed system performed damage localization in a five-story laboratory model with response data from two accelerometers: one for the basement and the other from the roof of the structure. [PERSON] et al. [126] presented a novel SHM framework for damage identification in smart structures. DWT, autoregressive models, and SVM were combined in this proposal, in which damage-sensitive feature extraction is performed from wavelet-based AR time series models. The detection results showed that the proposed system effectively detected structural damage in a three-story structure. [PERSON] and [PERSON] [114] proposed a genetic algorithm to select the optimal hyperparameters of the SVM. Figure 4 shows the diagram with the proposed structural damage detection methodology based on the combination of a SVM and a GA.Mode shape and frequency ratios were used as input data for the SVM model. In the results, the combination of the GA-SVM damage identification method showed an accuracy of 98.16% for the case of single, double and triple damage elements, outperforming other methods, such as RBF networks and GA-BP networks.
## 5 Stability Analysis of TSWs
### Analysis of Influencing Factors  
Based on the analysis of the mechanical model in Section 3, it can be seen that the stress distribution of the TSW is influenced by factors such as the position of the lateral  
Figure 6: Yield zone cloud under different LP conditions. (**a**) LP = 5 m; (**b**) LP = 10 m; (**c**) LP = 15 m; (**d**) LP = 20 m; (**e**) LP = 25 m.  
load center, lateral load width, TSW structural parameters, in situ stress magnitude and direction, and rock mechanical properties. In the Jubankeng tungsten mine residual ore recovery project, the mining depth and rock properties are fixed; so, the magnitude of in situ stresses and rock mechanical parameters remain constant. Therefore, this study primarily focuses on analyzing the impact of variables such as lateral load position (LP), lateral load width (LW), sidewall thickness (ST), sidewall length (SL), and sidewall height (SH) on the TSW stress distribution, further determining the MPS and yield zone distribution under different conditions. The parameters required for the mechanical model calculations are all derived from Section 4.2.
# Fed-Hetero: A Self-Evaluating Federated Learning Framework for Data Heterogeneity
## 3 Notations and Preliminaries
### Federated Learning
#### 3.1.1 Local FL Model  
The local model is trained on data from individual clients and local accuracy measures the performance of a model trained specifically on a client's local data. Let:  
* \(D_{\text{m}}\): Local dataset of a client (with \(n\) samples).
* \(y_{i}\): The actual label for the \(i\)-th sample in \(D_{\text{local}}\).
* \(\hat{y_{i}}\): Predicted label for the \(i\)-th sample by the local model.  
The formula for calculating local accuracy \(A_{\text{local}}\) is  
\[A_{\text{local}}=\frac{1}{n}\sum_{i=1}^{n}\mathbb{I}(y_{i}=\hat{y}_{i})\times 100 \tag{2}\]  
where \(\mathbb{I}(y_{i}=\hat{y}_{i})\) is a binary function that outputs \(1\) if the predicted label matches the true label and \(0\) otherwise.
## 2 Materials and Methods
### Data-Acquisition Experiment  
Due to the novelty of the RD \(\alpha\) smart gloves, it was necessary to create a dataset that would serve as a basis for model implementation. Thus, to construct a labeled dataset, a data-acquisition experiment was conducted. Members from local kickboxing gyms and the national team (currently active athletes of different sex, age, height, and experience level) were invited to repeatedly perform either individual striking techniques or a set of technique sequences, each consisting of four individual techniques executed immediately one after the other, with approximately 5 s break between techniques and 30 s breaks between sets. This approach was selected to include as much variation as possible in the data since the execution of a strike can vary when it is executed in sequence compared to when it is executed in isolation. Also, this best represents strike instances produced in a real-world scenario, e.g., a training session or combat since both options (individual and sequence) can occur. For the same reason, distance and stances or exact execution patterns were not predetermined by the researchers. The previously described striking techniques were all executed on four different target types (with approximately 5 min break to subjective physical recovery, between the executions on each target): punching bag (commercial standing bag), punch pads (held by an experienced coach with the instructions to \"hold for but not hit against\" each technique), gloves (used as punch pads) and a concrete wall which were further divided into two groups (valid and invalid target) for the classification. The valid targets are therefore selected to represent the targets most commonly used in a conventional training session. During the data-acquisition experiment,each of the strike samples was labeled unanimously by two domain experts (>10 years of coaching in international events and minimum second level of national trainer education) with the respective label for the striking technique and the target object. Labeling is a crucial part of data acquisition since it allows the application of supervised machine learning which is more reliable and suitable for classification tasks than unsupervised machine learning that does not require labeled data. It is noted that due to the predetermined order of techniques, they predominantly represented an additional measure of data quality. The data-acquisition experiments' (n = 13) inclusion criteria allowed for healthy participants who at least practiced kickboxing or boxing for at least three months (evaluated through a questionnaire). Some athletes also had experience in other combat sports disciplines. The characteristics of the participants are shown in Table 1. The data of the data-acquisition experiment was used as input for the classification algorithms to train, optimize and test the classification models. Figure 3 shows the setup of the experiment depicting one participant executing the jab technique and the respective components.
###### Abstract  
Estimating the remaining useful life (RUL) of components is a crucial task to enhance reliability, safety, productivity, and to reduce maintenance cost. In general, predicting the RUL of a component includes constructing a health indicator (_HI_) to infer the current condition of the component, and modelling the degradation process in order to estimate the future behavior. Although many signal processing and data-driven methods have been proposed to construct the _HI_, most of the existing methods are based on manual feature extraction techniques and require the prior knowledge of experts, or rely on a large amount of failure data. Therefore, in this study, a new data-driven method based on the convolutional autoencoder (CAE) is presented to construct the _HI_. For this purpose, the continuous wavelet transform (CWT) technique was used to convert the raw acquired vibrational signals into a two-dimensional image; then, the CAE model was trained by the healthy operation dataset. Finally, the Mahalanobis distance (_MD_) between the healthy and failure stages was measured as the _HI_. The proposed method was tested on a benchmark bearing dataset and compared with several other traditional _HI_ construction models. Experimental results indicate that the constructed _HI_ exhibited a monotonically increasing degradation trend and had good performance in terms of detecting incipient faults.  
health indicator; performance degradation assessment; deep learning; vibration monitoring; bearing; remaining useful life; digital twin +
Footnote †: journal: Applied  
Article
# Universally Composable Oblivious Transfer with Low Communication
## 5 UC-Secure 1-out-of-N OT Protocol  
In this section, we expand the 1-out-of-2 OT protocol to a 1-out-of-N protocol with a minor tweak. The 1-out-of-N OT protocol is shown in the Table 5. In the 1-out-of-N OT ideal function, the S inputs \((M_{1},\ldots,M_{n})\), where \(n>2\), then the R inputs choice bit \(\sigma\in[1,n]\). Finally, the R outputs \(M_{\sigma}\).  
Proof of Security
1. The first step is to prove that the protocol can guarantee the parties' privacy. From Theorem 1, it is clear that \(B=b^{\prime}\) and \(B=b^{\prime}+T_{\sigma-1}\) are indistinguishable, so the receiver's privacy is secure. From Theorem 2, the receiver only gets the information he chooses, so the sender's privacy is secure.
2. The next step is to prove that the protocol can be simulated as \(\mathcal{F}_{\mathcal{OT}}\). When the S is corrupted, the \(\mathcal{S}\) records the random oracle machine queries in the specific format. Then, the \(\mathcal{S}\) uses these records to obtain the real input from the sender and inputs it into the ideal function. When the \(R\) is corrupted, the \(\mathcal{S}\) receives the real input \(\sigma\) from the receiver through the records of the random oracle machine. Then, the \(\mathcal{S}\) inputs it to the ideal function. Finally, in the same way as Theorem 3, the real execution and the ideal environment from the perspective of \(\mathcal{Z}\) are indistinguishable. Hence, the OT protocol can be simulated as \(\mathcal{F}_{\mathcal{OT}}\).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Alice** & **Bob** \\ \hline \multicolumn{3}{l}{**Parameter Generation:**} \\ \(seed_{a}\leftarrow\mathcal{U}\big{(}\big{(}0,1\big{)}^{256}\big{)}\) & \(a\gets gen(seed_{a})\) \\ \(a\gets gen(seed_{a})\) & \(s^{\prime}\leftarrow\beta_{\mu}\big{(}R_{q}^{l\times 1}\big{)}\) \\ \(s\leftarrow\beta_{\mu}\big{(}R_{q}^{l\times 1}\big{)}\) & \(s\gets\beta_{\mu}\big{(}R_{q}^{l\times 1}\big{)}\) \\ \(T_{0}=\big{\{}0\big{\}}^{l}\) & \(v^{\prime}=haus\big{(}s^{\prime},\varepsilon_{p},\varepsilon_{p}\big{)}+h_{1}\) \\ \(T_{i}\leftarrow\mathcal{U}\big{(}R_{q}^{l\times 1}\big{)},i\in[1,n-1]\) & \(c=bits\big{(}v^{\prime},\varepsilon_{p}-1,\varepsilon_{t}\big{)}\) \\ \(A=bits\big{(}as+h,\varepsilon_{q},\varepsilon_{p}\big{)}\) & \\ \hline \multicolumn{3}{l}{**Make a choice:**} \\ \hline \multicolumn{3}{l}{**Key derivation:**} \\ \(For\ every\ i\in[1,n]:\) & \\ \(v_{i}=(B-T_{i})^{T}bits\big{(}s,\varepsilon_{p},\varepsilon_{p}\big{)}+h_{1}\) & \\ \(sk_{i}=bits\Big{(}v_{i}-2^{\varepsilon_{p}-\varepsilon_{t}-1}c+h_{2},\varepsilon _{p},1\Big{)}\) & \\ \hline \multicolumn{3}{l}{**Encryption:**} \\ \(C_{i}=Enc(Hash(sk_{i}),M_{i}),i\in[1,n]\) & \(\begin{array}{l}C_{1},C_{2},C_{2},\\ \end{array}\) & \(\begin{array}{l}\textbf{Decryption:}\\ M_{\sigma}=Dec(C_{\sigma},sk)\end{array}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: UC-secure 1-out-of-N OT protocol.
# Environment-Aware Knowledge Distillation for Improved Resource-Constrained Edge Speech Recognition
## 4 Experimental Setup
### Datasets  
To train our models, we use the LibriSpeech corpus [23] as the dataset of clean speech utterances. It consists of 960 h of audiobook recordings with a 16 kHz sampling rate derived from the LibriVox project. As we are interested in performing data augmentation and evaluating environmental awareness, we use noise signals present in the MUSAN [25] and UrbanSound8K [30] datasets to corrupt the signals from the LibriSpeech dataset during the training stage. These datasets contain approximately 15 h of recordings in a wide variety of categories. UrbanSound8K contains 8732 labeled sound excerpts of urban sounds from 10 distinct classes, such as engine idling, car horn, and siren. We removed the children playing and street music categories to focus on non-speech-like noise sources in this analysis. Moreover, we use the noise portion of MUSAN, which contains 929 files of assorted noise types, including office-like noises, as well as ambient sounds, such as car idling, thunder, wind, footsteps, and rain. This portion of the dataset does not include recordings with intelligible speech. However, some recordings include crowd and babble noises with indistinct voices. All of the utterances are resampled to 16 kHz.  
A room impulse response (RIR) dataset is also used, namely the Big Impulse Response Dataset (BIRD) [31], comprising simulated room impulse responses corresponding to rooms of various sizes and absorption coefficients, with RT60 values ranging from 140 ms to 1 s. The train set used for reverberation consists of approximately 35,000 simulated RIRs sampled from the BIRD dataset. Half of these samples have a lower reverberation time (RT60 smaller than 500 ms) and the other half have a higher reverberation time (RT60 greater than 500 ms).  
At test time, combined with the LibriSpeech test set, two additional datasets are used to test the model performance under unseen conditions. The first is the Acoustic Scene Classification from the Detection and Classification of Acoustic Scenes and Events (DCASE) Challenge of 2020, namely DCASE2020 [32]. The dataset consists of 64 h of audio recordings  
Figure 3: Diagram of the proposed Environment-Aware DistilHuBERT pipelines for (**a**) noisy and (**b**) reverberant environments.  
in 10 acoustic scenes, recorded with four different recording devices in 12 different cities. For reverberation, we use a different subset of the BIRD dataset comprising approximately 4000 simulated room impulse responses. Again, the signals are equally split between low and high RT60 values.
# Fracture Threshold Analysis and Parameter Matching of Cut-Out Induced Bolts for Subway Couplers
## 4 Fracture Threshold Optimization  
### Problem Definition  
Due to the inability of the bolts in the test to meet the requirements of the overload protection device, it is necessary to optimize the structural parameters of the bolts. During the optimization process, the following conditions must be met: the fracture force of the bolts needs to have a corresponding matching relationship with the coupler parameters. An excessively large fracture force can lead to vehicle structure damage and excessive vehicle deceleration, while an excessively small fracture force can cause the bolts to fail either during or before the coupler's operation. However, it is difficult to determine the breaking moment of the bolts solely from its fracture force, so the fracture time of the bolts needs to be considered in  
to find the global optimal solution. As shown in Figure 22, the genetic algorithm refers to all design points in each iteration as a population. When generating the initial population for the first time, random design points are first created as the initial generation population. The response and objective form the fitness of each design point, and most design points are eliminated based on their fitness ranking. In the remaining high-fitness design points, crossover operations that exchange design variables and mutation operations that cause random changes in some design variables are performed to generate the next-generation population for iteration. By continuously repeating the above operations, the fitness of the population increases, gradually approaching the global optimal solution.  
Using the genetic algorithm with parameter settings as shown in Table 3, the optimal fracture force of the bolts for each generation of the population obtained after optimization is shown in Figure 23. Through optimization using the genetic algorithm, the final determination was made that the depth of the bolt cut-out zone should be 1.37 mm, and the width should be 17.74 mm.
## 3 Experimental Results and Analysis  
### Load-Displacement Behaviours  
The load-displacement curves for each void slab specimen group are shown in Figures 6-8. Figure 6 presents the load-displacement curves for specimens with a 30% void ratio. These curves exhibit a characteristic shear failure pattern, evident in the rapid decrease of the slope after reaching the peak shear strength. This behaviour is consistent across all specimens with the same hollow core ratio, indicating uniformity in their failure mode.  
Similar shear failure patterns were observed in specimens with higher hollow core ratios (35% and 40%). Figures 7 and 8, respectively, illustrate the load-displacement curves for these specimens. As with the 30% hollow-core ratio group specimens, these curves display a pronounced decrease in slope following the achievement of peak shear strength. This consistency across different void ratios strengthens the suggestion of a potential trend, where increasing hollow core ratios might influence the failure mechanism of the void slabs.  
Figure 5: Test setup for the specimen.  
Figure 6: The load–displacement curve on a hollow core ratio of 30%.  
In the load-displacement curves presented in Figure 8, the results demonstrate a consistent shear failure pattern across all tested hollow core ratios (30%, 35%, and 40%). This behaviour, characterised by a rapid decrease in slope after peak shear strength is reached, indicates a potential influence of the hollow core ratio on the failure mechanism of the void slabs.
# Machine Learning Enabled Performance Prediction Model for Massive-MIMO HetNet System
## 4 Conclusions  
This paper introduced an ML approach to predict the performance of a MIMO HetNet system considering a multi-cell scenario. The performance metrics considered in this paper are \(CP\) and \(ASE\), which are stochastic functions of the network parameters. Two separate multivariate linear regression models have been trained for the network performance metrics, with network parameters as the input attributes. The generalization ability trained models have been evaluated numerically based on the percentage of the error margin and R\({}^{2}\) score. The error margin for \(CP\) and \(ASE\) are found to be 12.06% and 11.32%, respectively, which are within the tolerable range for practical application. The R\({}^{2}\) scores for \(CP\) and \(ASE\) are 0.754 and 0.749, respectively, which are closed to 1, and are found to be satisfactorily high. The visual inspection-based performance evaluation is done using actual vs. predicted output plots. For both models, the scatter points linearly match with the respective trend lines, showing the worth of fitness. During practical deployments of 5G and B5G networks, the application of this model could be very valuable in the precise planning of network and capital expenditures. This model would help the network provider to estimate the quality of service for a given network configuration. The study of other parameters that influence the network performance and their inclusion in the model may be considered as a future direction of research.  
Figure 4: Simulated vs. predicted values of the coverage probability with a trend line.  
Figure 3: Simulated vs. predicted values of the area spectral density (ASE) in bits/sec/Hz/km\({}^{2}\) with a trend line.  
**Author Contributions:** S.B., concept and setup preparation, design of system model, text and plot preparation, and review; S.R.S., methodology creation, performance prediction model selection, analysis and simulations supervision, and text editing; V.P., overview of model validation, final model preparation, data preparation, text editing, and review. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The APC was funded by the Bulgarian Science Fund research project KP06-N27/3.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Acknowledgments:** Authors from Technical University of Sofia were supported by research project KP06-N27/3 \"Resource self-configuration and management in ultra-dense networks with user centric wireless access\" of the Bulgarian Science Fund.  
**Conflicts of Interest:** The authors declare no conflict of interest.