esa-sceva/satcom-chunk-collection huggingface dataset:
# Analyzing the Nonlinear Performance of Miniature Loudspeakers in Consideration of the Creep Effect
## 3 Experimental Validation
### Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Sample A**} & \multicolumn{2}{c}{**Sample B**} & \multicolumn{2}{c}{**Sample C**} \\ \cline{3-8}  & & **NL-NC Model** & **NL-C Model** & **NL-NC Model** & **NL-C Model** & **NL-NC Model** & **NL-C Model** \\ \hline \multirow{3}{*}{Clip 01} & Total & 0.0321 & 0.0086 & 0.0216 & 0. 0091 & 0.0251 & 0.0047 \\  & \textless{}500 Hz & 0.0320 & 0.0083 & 0.0215 & 0.0088 & 0.0248 & 0.0034 \\  & \textgreater{}500 Hz & 0.0031 & 0.0024 & 0.0024 & 0.0022 & 0.0035 & 0.0032 \\ \hline \multirow{3}{*}{Clip 02} & Total & 0.0343 & 0.0107 & 0.0238 & 0.0102 & 0.0270 & 0.0067 \\  & \textless{}500 Hz & 0.0341 & 0.0104 & 0.0236 & 0.0099 & 0.0267 & 0.0059 \\ \cline{1-1}  & \textgreater{}500 Hz & 0.0029 & 0.0026 & 0.0029 & 0.0025 & 0.0034 & 0.0032 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The RMSE values for nonlinear displacement waveforms. In the experiments mentioned above, the secondary parameters \(K_{2}\) and \(R_{2}\) for creep were determined by the small-signal displacement data measured under linear conditions. To verify the appropriateness of these parameters for the nonlinear model, the displacement transfer function curves were measured and predicted under both linear and nonlinear conditions, taking sample A as an example (Figure 7). The measurement data shown in Figure 7a were acquired at an RMS voltage of 0.2 V to ensure the sample remained in a linear state. In contrast, the measurement data shown in Figure 7b were obtained under an RMS voltage of 2 V to ensure the sample was in a nonlinear state. The theoretical predictions under both conditions were made using the same values of \(K_{2}\) and \(R_{2}\). The predictions of the  
Figure 6: Comparison of the measured and predicted THD and SPL values: (**a**) THD of sample A, (**b**) SPL of sample A, (**c**) THD of sample B, (**d**) SPL of sample B, (**e**) THD of sample C, and (**f**) SPL of sample C.  
NL-C model in Figure 7a were expected to align more closely with the actual measurement data since the creep parameters were derived from the linear measurement data. Due to the model's limitations, within the frequency range of 40-200 Hz, the predicted displacements were slightly lower than the actual measurements. Comparing the nonlinear displacement transfer function curves in Figure 7b shows that the curve predicted by the NL-C model is consistently more accurate than the one predicted by the NL-NC model. At frequencies below 50 Hz, the predictions of the NL-C model exceeded the actual measurements, and deviations were also observed in the middle frequency range of 200-1000 Hz. In summary, the predictions of the NL-C model were still markedly superior to those of the NL-NC model. Considering the data shown in Figure 7 and the previous comparisons of the predicted and measured displacement waveforms, the method used for rapidly estimating creep parameters from linear displacement data is acceptable for practical applications.
# A Laser-Based Method for the Detection of Honey Adulteration
## 2 Materials and Methods  
### Samples  
A total of 15 honeys of different floral origins (e.g., fir, thyme, multiforal, and pine) were used in this study and were previously characterized by pollen analysis and conventional physicochemical parameters as determined by the European and Greek legislation. They are listed in Table 1. Adulterated honey samples were prepared by mixing a fir, a thyme, and a multifloral origin honey with different amounts of glucose syrup (C\({}_{6}\)H\({}_{4}\)O\({}_{7}\)). The honey samples were mildly heated in a water bath, and then, they were mixed with the preweighed amounts of glucose while a magnetic stirrer was used for homogenization of the mixtures. Thus, adulterated honey samples of 10, 20, 30, 40, 50, 60, 70, 80, and 90%(\(w/w\)) were obtained. The unadulterated honey samples corresponded to 0% \(w/w\), and glucose syrup samples corresponded to 100% \(w/w\).  
In total, 45 samples (i.e., the initial 15 honeys, 27 adulterated samples, and 3 glucose syrup samples) were used for classification purposes following two different approaches. Initially, the data were split into two sets: one for training and one for testing the algorithm. The training set consisted of 33 samples: 10 honey samples, 21 adulterated honey samples (7 samples being adulterated fir honey, 7 samples being thyme adulterated honey, and 7 samples being adulterated multifloral honey), and 2 glucose samples. The test set consisted of 12 samples: 5 honey samples, 6 adulterated honey samples (2 from each honey, i.e., fir, thyme, and multifloral, which were chosen randomly), and 1 glucose sample.  
Following the second approach, the data were split into two sets: the first set containing 30 samples used for training the algorithms, and the second set containing 15 samples for testing and validation. The training set contained 10 honey samples, 18 adulterated honey samples (9 adulterated fir honey samples and 9 thyme adulterated honey samples), and 2 glucose samples. The test set consisted of 5 honey samples, 9 adulterated honey samples (resulting from the adulteration of the multifloral honey), and 1 glucose sample.  
The above methodology was chosen to assess the effectiveness of the detection of honey adulteration by LIBS technique by means of two approaches presenting increasing degree of difficulty. In the first case, the algorithm was trained considering all three adulterated honeys (fir, thyme, and multifloral) and was tested on 6 adulterated samples (2 different adulterations randomly selected from each one), while in the second case, the whole set of the 9 adulterated multifloral honey samples were used for prediction. The increasing difficulty arises from the fact that in the second approach, the algorithm was not trained with any adulterated multifloral honey sample, while it was asked to recognize and predict them correctly.
## 4 Artificial Neural Networks
### Performance Evaluation of the CNN  
The performance of the proposed CNN was evaluated using the augmented database in Table 2. Figure 8 shows the performance of the CNN for 500 epochs. The black line in Figure 8 represents the training accuracy, and it can be seen to start with an accuracy of 50.8%, to rapidly increase to 95.8% until 50 epochs, and to further increase to 99% at 250 epochs, thus confirming that the training reaches saturation. The red line indicates the test accuracy, which starts at 58.58% in the first epoch, increases rapidly to 94.04% until 90 epochs, and reaches saturation at about 280 epochs with a gradual increase to 98.54%. Subsequently, at about 225 epochs, there is no further improvement in the performance. When the epoch number was 90 \(\leq\) epoch \(\leq\) 300, the accuracy was 95.58% \(\pm\) 1.28%, and when the epoch number was 300 \(<\) epoch \(\leq\) 500, the accuracy was 98.57% \(\pm\) 0.66%. If 90 epochs are considered as the saturation point, then the accuracy is 97.19% \(\pm\) 1.8%. Figure 9 illustrates the evaluation of the defect classification accuracy of the proposed CNN.  
Figure 8: CNN training performance with the augmented database.  
The probability of accurately classifying a signal without defects was 99.83%, and the probability of incorrect classification as defect at 2 mm below the surface was 0.17%. Correct classification was performed for defects that were 1 mm below the surface; the probability of incorrect classification of the defects at 2 mm below the surface as defects at 1 mm below the surface was 1%, and the probably of correct classification of the defects at 2 mm below the surface was 98.83%. In most cases, it is confirmed that the prediction performance for defect classification was almost accurate.
# Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting +
## 3 Methodology
### Baseline Models  
We utilized several popular traffic forecasting models for the estimation of the comparative performance of the transferred models. The list of baseline models includes the following:  
* Naive forecasts,
* Conventional ARIMA models,
* Conventional VAR models,
* Sparse VAR models in two specifications: with sparsity, controlled using travel time or cross-correlation.  
Naive forecasts and conventional ARIMA models are widely used as univariate (non-spatial) baseline models in traffic forecasting. The conventional VAR model, applied to a vector of traffic values \(Y_{t}\) at spatial locations, is a data-driven approach to learn linear spatial relationships between time series:  
\[Y_{t}=\sum_{\text{h}=1}^{\text{P}}\Phi_{h}Y_{t-\text{h}}+\varepsilon_{t}, \tag{11}\]  
where \(\Phi_{h}\) is a set of \(k\times k\) weight matrices for every lag \(h=1,\dots,p\).  
The conventional VAR specification is extremely flexible and parameter-rich, so for a large dimensionality (citywide traffic forecasting) it suffered from the well-known curse of the dimensionality problem. Sparse VAR specifications, where some coefficients of \(\Phi_{h}\) are forced to be 0, usually demonstrate better forecasting performance [54]. We utilized the sparse specification of VAR, based on local spatial neighborhoods defined above, and introduced it into the model as a set of \(S_{h}\) matrices:  
\[s_{ij,h}(r)=\left\{\begin{array}{c}1,if\;j\in NB(i,r),\\ 0,\;otherwise,\end{array}\right. \tag{12}\]  
Figure 2: General architecture of graph convolutional neural networks (GCNN).  
Definition of spatial neighborhoods depends on the distance metric \(d_{ij}\). In addition to the travel time-based metric, we utilized the cross-correlation:  
\[NB(i,r)=\Big{\{}j:Corr\Big{(}y_{i,t},y_{j,t-h}\Big{)}>\theta\;and\;i\
eq j\Big{\}}, \tag{13}\]  
where \(\theta\) is a trained threshold value.  
Given the set of regularizing matrices \(S_{h}\), the sparse VAR specification is expressed as  
\[Y_{t}=\sum_{h=1}^{\mathbb{P}}(\Phi_{h}\circ S_{h})Y_{t-h}+\varepsilon_{t}, \tag{14}\]  
where \(\Phi_{h}\circ S_{h}\) is the entry-wise product of the matrices. Further, we refer the sparse VAR specification based on the travel time definition of spatial neighborhoods as SpVAR-tt, and the specification based on cross-correlations, as SpVAR-cc.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 5 Evaluation Protocols and Metrics
### Benchmark Results
#### 5.4.4 ActivityNet Captions Dataset: Method Performance Overview  
On the ActivityNet Captions Dataset, numerous methods have been assessed using the BLEU, METEOR, CIDEr, and ROUGE-L metrics. As presented in Table 6, several approaches have reported results for this dataset, with certain models demonstrating notable performances. Here, we provide an overview of the best-performing methods alongside their published scores.  
Encoder-decoder based approaches: Encoder-decoder frameworks have exhibited promising outcomes on the ActivityNet Captions Dataset. Among the methods listed, COST [41] stands out with a BLEU score of 11.88, METEOR score of 15.70, CIDEr score of 29.64, and ROUGE-L score of 6.11. This approach leverages collaborative mechanisms for improved video captioning. Another noteworthy method is V5 JM-Net [22], which achieves competitive scores, particularly in the METEOR and ROUGE-L metrics. Earlier works, such as VC-FF [113] and JSRL-VCT [96], also employed encoder-decoder architectures but obtained lower scores compared to more recent methods. This underscores the advancements made in video description techniques over time.  
Transformer-based approaches: Transformer-based methods have gained traction due to their capacity to capture long-range dependencies in video sequences. Among the transformer-based approaches listed, COOT [37] performs notably well, with a BLEU score of 17.43, METEOR score of 15.99, CIDEr score of 28.19, and ROUGE-L score of 31.45. This method effectively utilizes transformers for video captioning tasks. Other transformer-based methods, such as SART [114] and TextKG [32], also show promising results. However, it is worth noting that some transformer-based approaches, like BMT-V+sm [115], report lower scores. This suggests that the effectiveness of transformers in video description tasks on the ActivityNet Captions Dataset may be influenced by the specific architectural choices and implementation details.  
Attention-based approaches: Attention-based methods have been extensively used in video descriptions to focus on relevant video frames and features when generating captions. However, the performance of these methods on the ActivityNet Captions Dataset varies. EC-SL [116] reports relatively modest scores, with a BLEU score of 1.33, METEOR score of 7.49, CIDEr score of 21.21, and ROUGE-L score of 13.02. This method emphasizes the use of attention mechanisms for improved caption generation. Other attention-based methods, such as WS-DEC [117] and Bi-SST [118], also report lower scores compared to encoder-decoder and transformer-based approaches. This variability in performance may be attributed to differences in the attention mechanisms employed or the overall design of the methods. Overall, while attention-based methods have shown potential, further improvements are needed to achieve competitive results on the ActivityNet Dataset.
## 3 Results and Discussion
### Temperature-Dependent Shift of Modal Properties  
An experimental modal analysis is performed at nine different temperatures and the resulting frequency response spectra can be found in Figure 5. We observe an increase of all eight eigenfrequencies and an apparent stiffening of the rotor due to temperature decrease. This is due to the change of the mechanical properties of the resin system leading to relative changes of a maximum of 15%. The relative frequency deviation for the first five mode shapes is presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Measurement**} & **Temperature** & **EF 1** & **EF 2** & **EF 3** & **EF 4** & **EF 5** \\ \cline{2-7}  & **(°C)** & **87.6 Hz** & **164.9 Hz** & **278.3 Hz** & **418.0 Hz** & **486.5 Hz** \\ \hline
**M01** & 19.9 & 0.00\% & 0.00\% & 0.00\% & 0.00\% & 0.00\% \\
**M03** & 9.8 & 4.29\% & 3.52\% & 2.61\% & 2.05\% & 0.48\% \\
**M05** & \(-\)0.2 & 8.23\% & 6.79\% & 5.08\% & 4.12\% & 1.38\% \\
**M07** & \(-\)10.2 & 12.02\% & 9.99\% & 7.64\% & 6.23\% & 2.53\% \\
**M09** & \(-\)20.2 & 15.30\% & 12.20\% & 10.11\% & 8.39\% & 3.78\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Relative shift of the first five natural frequencies (EF 1 to EF 5) under different temperatures; the values are peak estimates of the averaged response spectra.  
Figure 5: Response spectra of measurements conducted at different temperatures.
# A Numerical Analysis of the Nonlinear Dynamics of Shimmed Conical Gas Foil Bearings
## 2 Theory
### Coupling of the Domains  
The fluid film and foil structure domains are coupled through the film height. The film height can be calculated from the nominal clearance, the displacement of the rotor and the deformation of the foil structure.  
\[\widetilde{h}(\theta)=\frac{c(\theta)}{c_{0}}-\cos\left(\varphi\right)\cos \left(\theta\right)\varepsilon_{\rm x}-\cos\left(\varphi\right)\sin\left( \theta\right)\varepsilon_{\rm y}+\sin\left(\varphi\right)\varepsilon_{\rm z}+ \widetilde{w} \tag{6}\]  
The nominal clearance is equal around the circumference of the conical bearing but can be modified by using shims between the bearing sleeve and the bump foil. In this work the modified nominal clearance \(c(\theta)\), due to the use of three shims with the thickness \(t_{\rm s}\), is described by Equation (7) (see ref. [9]).  
\[c(\theta)=c_{0}-\frac{t_{\rm s}}{2}(1+\cos\left(3\;\theta\right)) \tag{7}\]  
The movement of the rotor described by Equation (5) is coupled with the Reynolds equation (Equation (3)) through the fluid film forces \(F_{\rm x,y,z,F}\) resulting from the pressure distribution in the air gap.  
\[\begin{bmatrix}F_{\rm xF}\\ F_{\rm yF}\\ F_{\rm zF}\end{bmatrix}=\begin{bmatrix}-\sin\left(\varphi\right)\cos\left( \varphi\right)\\ -\sin\left(\varphi\right)\cos\left(\varphi\right)\\ \sin^{2}\left(\varphi\right)\end{bmatrix}p_{\rm a}\varepsilon_{1}^{2}\int_{ \zeta=-\zeta_{0}}^{\zeta_{1}}\int_{\theta=0}^{2\pi}\!\left(\frac{\psi}{ \widetilde{h}}-1\right)\begin{bmatrix}\cos\left(\theta\right)\\ \sin\left(\theta\right)\\ 1\end{bmatrix}\mathrm{d}\theta\mathrm{d}\zeta \tag{8}\]  
These equations yield the problem mentioned in the introduction. For a rotor without a radial displacement and with a uniform bearing clearance, Equation (6) yields the partial derivative \(\frac{\partial\widetilde{h}}{\partial\theta}=0\). Assuming an ambient pressure in the bearing clearance, the partial derivatives of the pressure yield \(\frac{\partial\widetilde{p}}{\partial\theta}=0\) and \(\frac{\partial\widetilde{p}}{\partial\zeta}=0\). This assumed state satisfies the Reynolds equation (Equation (1)). In this case, the rotational speed of the rotor does not lead to an increase of stationary pressure in the bearing clearance independent of an axial displacement and the bearing can therefore not provide any axial support.
## 6 Experiments and Results
### Performance Evaluation of the DMOGWO Algorithm
#### 6.4.1 Performance Comparisons in the Initial Static Problem  
This section focuses on the static DFJSP without variable processing times to comprehensively evaluate the performance of the proposed algorithm. All comparative algorithms are independently run 20 times on each benchmark instance, and the average HV, IGD, and C values are computed. A Wilcoxon signed-rank test [44] with a significance level of 0.05 is carried out to assess the significant differences in the results. The Wilcoxon test is a non-parametric statistical method that does not rely on distributional assumptions and is effective in assessing significant differences between algorithms. In Tables 3-6, the signs \"+/ -/=\" indicate that DMOGWO performs significantly better than the compared algorithm, significantly worse than the compared algorithm, or shows no significant difference to the compared algorithm, respectively. The best values are highlighted in bold but it is important to note that bold values indicate only the best numerical performance, not necessarily statistical significance. The results are presented in Tables 3 and 4.  
its superior performance in solving the static MODFJSP. Specifically, for the HV metric, while DMOGWO shows slightly lower values than NSGA-II on instances MK01 and MK02, the differences are not statistically significant, indicating comparable performances in these cases. Similarly, for the C metric, DMOGWO performs slightly worse than MOGWO on MK02, but the difference also fails to reach statistical significance. In comparison to MOGWO, DMOGWO has several advantages. First, DMOGWO employs a unique social hierarchy strategy where each wolf selects different leader wolves, whereas MOGWO uses a fixed leader wolf for all individuals, which can lead to local convergence. Second, DMOGWO adopts a discrete encoding and position update mechanism that better fits the static MODFJSP, while MOGWO combines continuous and discrete encoding, which may not fully capture the problem's discrete characteristics.  
Overall, despite its comparable or slightly inferior performance in certain instances, DMOGWO demonstrates a more balanced performance across the three key metrics, generating higher-quality Pareto solutions. These findings confirm the effectiveness of DMOGWO in addressing the static MODFJSP.
## 1 Introduction
In this paper, a hierarchical federated learning architecture combining blockchain in a cloud-edge-end architecture is proposed, and an efficient aggregation method and a multi-objective node selection method adapted to this architecture are proposed to construct a secure and trustworthy hierarchical federated learning framework, BHFL. In order to improve the efficiency of the node selection algorithm, a highly efficient node selection algorithm based on reinforcement learning is proposed to obtain the optimal selection scheme quickly. The specific contributions of this paper are as follows:  
1. A hierarchical federated learning architecture combining blockchain with a cloud-edge-end architecture is proposed, which reduces the communication overhead through hierarchical federated learning, reduces the introduction overhead of blockchain through cloud-edge-end resource collaboration, and realizes the organic integration of blockchain and federated learning under the cloud-edge-end architecture. 2. A Decentralized Hierarchical Aggregation Process (DHAP) is proposed, containing a decentralized model evaluation process as well as a model aggregation algorithm. Among them, the model aggregation algorithm uses the first proposed semi-asynchronous aggregation algorithm with a fixed training time on the edge side and the synchronous aggregation algorithm on the cloud side while strengthening the link between edge aggregation and cloud aggregation. 3. Facing multiple objectives such as system performance, statistical performance, and security, the node selection problem is modeled as a Markov Decision Process (MDP) using key influencing factors such as data quality and model accuracy, and a DQN-based reinforcement learning algorithm is proposed to achieve an efficient solution for the selection strategy. 4. The test results using multiple datasets and network models show that the method proposed in this paper can effectively reduce the communication overhead and improve the system efficiency on the one hand. On the other hand, it can effectively filter malicious nodes and improve system robustness. In addition, the proposed DQN-based node selection algorithm can efficiently realize the selection of the optimal policy, which is suitable for practical application scenarios.
## 3 Results and Discussion
### Typical Node Failures  
Among the common problems detected during the operation of the deployment, the most frequent were those that led to the exhaustion or destruction of the battery. The usual reasons were (i) overheating due to the high temperatures reached inside the box in summer, (ii) dirt or ageing of the solar panels and (iii) natural ageing of the batteries as they reached the end of their lifespan.  
Figure 16 presents the evolution of the battery voltage of node 23. As can be seen, between 7 and 27 March there was a prolonged discharge due to the ageing of the panel, which stopped charging (this was later verified in a field intervention). Once the solar  
Figure 14: Typical behavior of the battery voltage in a node without operational problems.  
Figure 15: Battery voltage (node 25) vs. daily average radiation (source: [[http://siam.imida.es/](http://siam.imida.es/)]([http://siam.imida.es/](http://siam.imida.es/)) Agro-Meteorological Weather Station CA12-La Palma, accessed on 6 April 2020). Cloudy days.  
panel was replaced on the 27 th, the node started charging again. It is worth noting that from this graph it is possible to extract very interesting information: due to the failure of the panel, the node showed an autonomy of approximately 20 days, which validates the theoretical calculations indicated in the section of consumption and autonomy estimation of the nodes.  
Figures 17 and 18 depict failures in two other nodes due to dirt on the solar panels (mainly dust and bird droppings). In the case of node 5 (Figure 17), it can be seen that since 15 March, there were successive loads that did not reach the upper threshold. From 21 March, the panel did not supply the required current to increase the battery charge, whose voltage started a downward trend until 25 March, the day on which the panel was cleaned, and the node recovered. In the case of node 4 (Figure 18), the node never managed to reach the upper threshold because the panel current was not sufficient, so on 24 March, the voltage fell below the minimum operating voltage and the node stopped transmitting.  
Figure 16: Evolution of the battery voltage of node 23.  
Figure 17: Evolution of the battery voltage of a node with a dirty panel (node 05).