esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The most important challenge of current research in verifying the integrity of digital audio evidence was to provide a control function that would tolerate manipulations of signals with preserved content, but would also identify those that significantly modified the content. For example, the hash value of an altered recording with volume adjustment should be very similar to that of the original recording, but the signal filtered with an LPF should provide a hash value distant from the original. Thus, we proposed a method that summarizes the audio signal, but at the same time identifies its most significant patterns, through the MFCC, PCA and RSA blocks. Since hash values are sensitive to changes in the audio signal, it was necessary to define the BER threshold value between the hash of the original audio and the hash of the manipulated audio in order to provide a binary output in relation to the integrity of the content. The methodology to determine the threshold values was through the cut-off point between FRR and FAR, in such a way as to optimize the number of manipulated audio files that were labelled as authentic versus the number of original audio recordings that were labelled as non-authentic. According to the results, the accuracy in verifying the integrity of the audio recording is greater than 99.8% for the following manipulations: FLAC (sampling frequency: 8 kHz, and 24 bits per sample), MPEG-4 (sampling frequency: 44.1 kHz, and bit rate: 192 kbps), volume adjustment (Gain: \(-6\) dB), resampling (8 kHz \(\to\) 16 kHz \(\to\) 8 kHz), re-quantization (16 bits \(\to\) 32 bits \(\to\) 16 bits), LPF (3 kHz, fifth order), additive noise, cutting and copy-move.  
For future work, we propose the testing of hash values with other types of manipulations such as acoustic impulse responses, and determining of the appropriate BER threshold value.  
Conceptualization, D.R.; Formal analysis, D.M.B.; Investigation, J.V.; Methodology, D.R.; Software, J.V.; Validation, D.M.B.; Writing--original draft, D.R. and J.V.; Writing--review & editing, D.M.B. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the \"Universidad Militar Nueva Granada-Vicerrectoria de Investigaciones\" under the grant IMP-ING-2136.  
The authors declare no conflict of interest.
## 4 Results and Discussions
### Ductility Analysis  
Displacement ductility factor is the ratio of damage displacement \(\Delta_{\text{u}}\) to yield displacement \(\Delta_{\text{y}}\), which is an important index for measuring the deformation capability of a structure. The inter-story drift angles and displacement ductility factors of the main stages are listed in Table 7, where \(\Delta_{\text{cr}}\), \(\Delta_{\text{y}}\), \(\Delta_{\text{max}}\), and \(\Delta_{\text{u}}\) are the cracking, yield, peak, and damage displacements of the specimen, respectively; and \(\theta_{\text{cr}}\), \(\theta_{\text{y}}\), \(\theta_{\text{max}}\), and \(\theta_{\text{u}}\) are the cracking, yield, peak, and damage drift angles, respectively. Table 7 shows the following:  
(1) The displacement ductility factor of the pure steel frame is 3.47. The displacement ductility factors of the specimens are from 2.44 to 2.69 when the cast-in-place RACSWs are infilled. The infilled walls can increase the bearing capacity and initial stiffness of the structure while reducing the yield and damage displacement of the structure. Thus, the ductility of the specimens of infilled cast-in-place RACSWs is reduced. (2) The displacement ductility coefficient in an SPE1 infilled ordinary concrete wall is 1.34 times that of SPE2, indicating that the wall made of recycled coarse aggregate has poor bonding performance and ductility. (3) The inter-story drift angles are from 1/415 to 1/317 at the concrete cracking stage, from 1/116 to 1/114 at the yield stage, and from 1/66 to 1/64 at the peak point, thereby indicating that the specimens of infilled cast-in-place RACSWs have a good deformation capacity. The displacement ductility factor is approximately 10% higher in SPE3 than in SPE2, suggesting that the specimen of end-plate joints is simple to construct and has a good deformation capacity, and the ductility is better in the end-plate joints than in the welded-bolted joints. Figure 14: Strength degradation curves.
## 3 Results  
### Data Labelling and Model Training/Test using First Dataset  
The HAR method for walkability measurement suggested in this study has been validated using the two types of data sets described in the previous section. The suggested LSTM-based DRNN model was trained using 80% of the first data set (64 participants). Then, the rest of the first data set (20% of data) was used to test the training model. While training the model, the binary cross-entropy error between ground truth labels and predicted output labels was used as a cost function because binary cross-entropy error is generally adequate to the backward LSTMs, and the number of label types is two (normal and abnormal gait). The ground truth labels were analyzed by (1) six experts (who were orthopedic surgeons with a specialty in gait analysis) for determining normal and abnormal gaits and (2) the gait detection algorithm [50] for the segmented windows that are fed to the first layer. The labeled data are provided as a one-hot vector.  
There are several popular optimization algorithms, such as SGD, RMSProp, AdaGrad, AdaDelta, Adam, Adamax, and Nadam. Nadam was selected to minimize the cost function among these optimization algorithms by backpropagating its gradient and updating model parameters since Nadam outperformed other optimization algorithms in binary classification [57]. To avoid an overfitting problem, both the dropout technique and the batch normalization technique were used [58; 59].  
Figure 6 presents the accuracy and binary cross-entropy cost of training and testing using the first dataset. As shown in Figure 6a, the gap in accuracy between training and test y is very small. In addition, the binary cross entropies of training and test are very similar, as shown in Figure 6b. The results indicate that the Nadam, the dropout technique, and the batch normalization technique effectively generalize the suggested model and avoid an overfitting problem.  
Figure 6: Accuracy and cost of training and testing using the first dataset: (**a**) accuracies of training (blue line) and test (red line); and (**b**) binary cross-entropy cost between ground truth and predicted labels for both training (blue line) and test (red line).
# A Novel Parallel Auto-Encoder Framework for Multi-Scale Data in Civil Structural Health Monitoring
## 4 Performance Evaluation
In contrary, it can be observed clearly that the proposed method performs the best in damage identification with a very close identified stiffness reduction value against the true value. Besides, SAF produces a considerable false identification at the 13 th element location of the structure. Therefore, the proposed method gives the higher accuracy in the prediction of damage pattern in terms of both the location and severity. Furthermore, to evaluate the models for multiple structural damage cases, following example is chosen from the testing dataset and the predictions are shown in Figure 6. In comparison to \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Baseline Dataset**} & \multicolumn{2}{c}{**Noise Dataset**} \\ \cline{2-5}  & **MSE** & **R-Value** & **MSE** & **R-Value** \\ \hline SAF & \(2.9\times 10^{-5}\) & 0.993 & \(3.2\times 10^{-4}\) & 0.792 \\ Para-AF-0 & \(1.8\times 10^{-5}\) & 0.994 & \(3.0\times 10^{-4}\) & 0.823 \\ Para-AF & \(1.3\times 10^{-5}\) & 0.996 & \(1.7\times 10^{-4}\) & 0.901 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation results for SAF and the proposed methods. the single damage case, the multiple damage cases need higher precision in identifying multiple damages' locations and severities. It can be observed that the proposed approach performs much better in multiple damage cases too, with all the damage locations accurately detected. Furthermore, the identified stiffness reductions are very close to the true values with very small false identifications. In contrast, SAF is not working very well in identifying multiple damages with some significant false identifications appeared at non-damage locations. As shown in the figures above, the identified stiffness reductions of the proposed method are superior to SAF in terms of both the magnitude and the location and this implies the robustness of using the proposed approach in structural elemental stiffness predictions. Therefore, it has been demonstrated that our proposed parallel based approach not only improves the effectiveness but also improves the robustness for damage detection/identification in structural health monitoring against the state-of-the-art approach.
## 4 Results and Discussion
### Variation of the Nusselt Number  
Figure 15a describes the local Nusselt number variation adjacent to the top hot wavy surface with various values of \(Ra\) at \(N=4\), \(A=0.1\), and \(\phi=0.02\). Local heat transfer appears among maximum and minimum rates toward the concave and convex portions of the collector. An increment of the buoyancy parameter (\(Ra\)) points toward an obvious enhancement on the local Nusselt number. It is affected by the high-temperature gradient and energy transporter that related to the high values of \(Ra\). In addition, augmentation of nanoparticle volume fraction tends to boost the nanofluid thermal conductivity, and, in conclusion, an increment of the heat transfer does obtain. Such a behavior is caused higher thermal gradient and energy transport with the rising concentration of nanoparticle volume fraction, as specified in Figure 15b.  
Figure 16 explains the 3-D and 2-D changes of average Nusselt number among \(Ra\) and \(\phi\) at \(A=0.1\). As clearly observed from this figure that the convective heat transfer augments with the increment of both Rayleigh number and nanoparticle volume fraction. Increasing \(Ra\) points through the convective heat transfer, which shows higher thermal gradient with the following of \(\phi\) increasing. More importantly, an evident enhancement is recorded toward the values concerning the average Nusselt number, including raising \(N\) with respect to all values of \(\phi\).  
Figure 15: Variation of local Nusselt number interfaces with the wavy \(W\) for different (**a**) \(Ra\) and (**b**) \(\phi\) at \(N=4\) and \(A=0.1\).
# UVsq-Sat NG, a New CubeSat Pathfinder for Monitoring Earth Outgoing Energy and Greenhouse Gases
## 2 Scientific Objectives  
ECVs are identified as the key variables for understanding and characterizing Earth's climate. Together, they provide a comprehensive view of the evolving global climate system and serve as essential indicators for guiding climate mitigation and adaptation measures, assessing climate risks, attributing climatic events to their root causes, and supporting climate services. The Global Climate Observing System currently identifies 54 ECVs. Satellite data address about 60% of these variables, making satellites a vital component for monitoring and understanding our planet's climate.  
A trailing constellation (multiple satellites orbiting on the same path) of Sun-synchronous satellites would enable an overall shorter revisit period (time elapsed between observations of the same point from a satellite of the train) and hence ECVs observations at different local solar times of the same scenes. It would provide real-time observations on diurnal timescales for all points of the globe, including those that are difficult to access from the ground (polar regions). This cannot be achieved with a single satellite on a Sun-Synchronous Orbit, which samples a specific location at always the same local solar time at a specific location. A single satellite cannot provide any information or constraints on diurnal variations in emissions. It is a major limitation for numerous emission sources. In addition, measurements of these interconnected 'agile' small satellites could be anchored in flight to more accurate measurements of the larger satellites. In other words, it would be possible to use accurate measurements, notably from large satellite platforms, to check the calibration of the small satellites every time the small and large satellites are in coincidence, looking at the same scenes. In return, such a constellation could also relay valuable information, such as the diurnal components of emissions, to the larger satellites for further investigation and detailed analysis. This synergy between CubeSat constellations and large satellite platforms represents a relatively new paradigm for Earth observation.
## 5 Results and Analysis
### Ablation Study  
To verify the impact of the CNN and Transformer modules on the performance of the model proposed in this paper, we conducted ablation experiments on the SEP-28k and PSC-PS-DF datasets. Figure 18 demonstrates the impact of removing the CNN and Transformer modules from the model on the SEP-28k dataset, and Tables 8 and 9 show the results of the ablation experiments on different datasets. First, we evaluated the impact of removing the CNN module on the model. For the Snd, WP, Pro, Intrj, and Bl labels in the SEP-28k dataset, removing the CNN module resulted in a decrease in F1-score of 0.05, 0.04, 0.04, 0.03, and 0.08, respectively. The effect of removing the CNN module on the PSC-PS-DF dataset was not significant, with only a 0.04 decrease in the Bl label. Second, we  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Disfluency** & **Model** & **F1** & _Precision_ & _Recall_ & _Accuracy (\%)_ \\ \hline \multirow{4}{*}{WP} & LSTM & 0.97 & 0.97 & 0.97 & 97.19 \\  & MLP & 0.98 & 0.98 & 0.98 & 97.81 \\  & DisfluencyNet & 0.98 & 0.99 & 0.98 & 98.44 \\  & Ours & 0.98 & 0.99 & 0.98 & 98.44 \\ \hline \multirow{4}{*}{Pro} & LSTM & 0.82 & 0.71 & 0.95 & 78.44 \\  & MLP & 0.82 & 0.71 & 0.97 & 78.44 \\  & DisfluencyNet & 0.83 & 0.83 & 0.83 & 82.57 \\  & Ours & 0.84 & 0.73 & 0.98 & 81.73 \\ \hline \multirow{4}{*}{Intrj} & LSTM & 0.92 & 0.94 & 0.90 & 92.47 \\  & MLP & 0.93 & 0.96 & 0.90 & 93.15 \\  & DisfluencyNet & 0.93 & 0.94 & 0.92 & 93.15 \\  & Ours & 0.94 & 0.97 & 0.92 & 94.52 \\ \hline \multirow{4}{*}{Bl} & LSTM & 0.95 & 0.94 & 0.96 & 95.00 \\  & MLP & 0.96 & 0.95 & 0.98 & 96.11 \\ \cline{1-1}  & DisfluencyNet & 0.97 & 0.98 & 0.97 & 97.50 \\ \cline{1-1}  & Ours & 0.99 & 0.99 & 0.99 & 98.89 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance of baseline models on the PSC-PS-DF dataset.  
Figure 16: Training Loss Curves for Bl.  
removed the Transformer module, and the performance of the model on both the SEP-28k and PSC-PS-DF datasets significantly decreased. For the Snd, WP, Pro, Intrj, and Bl labels in the SEP-28k dataset, the F1-score decreased by 0.08, 0.05, 0.10, 0.13, and 0.13, respectively. For the WP, Pro, and Bl labels in the PSC-PS-DF dataset, the F1-score decreased by 0.01, 0.02, and 0.02, respectively. This indicates that the Transformer module has a significant impact on the overall performance of the model. The ablation study verifies the functions of each module in our model and validates the contributions of each module to the performance of the model.  
Figure 17: Performance of baseline models on the PSC-PS-DF dataset.  
Figure 18: Results of the ablation experiment on the SEP-28k dataset.
# Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio +
## 4 Protocol Analysis and Covert Channel Design
### Design
#### 4.5.7 Countermeasures  
In general, there are three different categories of countermeasures for covert channels: detection, limitation, and elimination. The detection of the reserved/unused pattern and thus the detection of the proposed covert channel is simple if it is possible to capture and read the network traffic of the 5G-air interface. If the protocol exploited by the hiding method is known, a network analyzer like Wireshark can monitor the affected protocol fields. If the network analyzer regularly observes behavior that deviates from the specified values, this is an indication that information is being hidden in the communication. The proposed covert channel can thus be easily detected if the focus of the network analysis is on the reserved bits of the PDCP header. Since these are set to 0 by default, any reserved bit that contains a 1 is suspicious. Based on the network analysis, a traffic normalizer can be used to limit or eliminate the covert channel by normalizing the suspicious bits.
## 5 Feature Extraction
### Symbolic Dynamics  
This paper uses the SD feature extraction algorithm in [26], which is summarized in the following steps: (1) The Poincare plot is traced using a lag of 15, (2) The phase-space represented by the Poincare plot is subdivided into several angular regions as shown in Figure 14, (3) Each region is assigned to a symbol represented by an integer number, 24 angular regions are considered in this application, (4) The symbolic representation of the vibration signal is processed for obtaining the histogram. This is simply attained by counting the number of points in each angular region.  
An example of the histogram obtained for a vibration signal of the reciprocating compressor considering the healthy state P1 is shown in Figure 15. Similarly, the histogram for the faulty state P5 is shown in Figure 16. The array including 24 elements of the histogram is considered as Symbolic Dynamic (SD) feature useful for fault classification. In the faulty case corresponding to P5, the second peak in the histogram has lower amplitude as compared to the healthy case.  
Figure 13: Location of the accelerometers sensors for recording the vibration signals.  
Figure 16: Histogram of symbols for the faulty case P5 of the reciprocating compressor.  
Figure 14: The Poincaré plot is subdivided into 24 angular regions and each region is represented by a symbol.  
Figure 15: Histogram of symbols for the healthy case P1 of the reciprocating compressor.
# Plasmonic Filter and Demultiplexer Based on Square Ring Resonator
## 4 Dual Demultiplexer for Telecommunication Wavelengths  
As the upstream and downstream traffic in ethernet passive optical network (EPON), 1310 nm and 1490 nm are two basic telecommunication wavelengths in present fiber-to-the-home (FTTH) systems [33]. Wavelength division multiplex (WDM) work plays an important role in FTTH systems [34], and plasmonic WDM with ultra-small footprints meets the requirement of future highly integrated and more compact all-optical circuits. Here, the plasmonic dual wavelength demultiplexer is introduced, as shown in Figure 5a. Figure 5b is the drop spectrum for two channels; channel 1 for 1310 nm and channel 2 for 1490 nm. The performance of a waveguide demultiplexer can be assessed by two factors; insertion loss (IL) and cross talk (CT) [35]:  
\[\begin{array}{l}IL=-10\lg(P_{\omega}/P_{i})\\ CT=10\lg(P_{\omega}/P_{u\omega})\end{array} \tag{11}\]  
where \(P_{i}\) is the power in the input waveguide, and \(P_{\omega}\) and \(P_{u\omega}\) represent the drop powers of the desirable and undesirable wavelengths in one channel, respectively. For channel 1 of this structure, \(IL\) = 2.9 dB and \(CT\) = 22.8 dB. For channel 2, \(IL\) = 4.6 dB and \(CT\) = 20.6 dB.  
If the reflector is applied, as shown in Figure 5c, the drop efficiencies will be enhanced, and the corresponding parameters will be: \(IL\) = 2.0 dB and \(CT\) = 23.2 dB for channel 1;  
18.0 dB for channel 2. Compared with previous works [7; 8; 9], this device has comparable performance. Furthermore, it makes possible the application of ring resonators on plasmonic integrated wavelength division multiplex (WDM) systems, which is valuable for designing on-chip integrated circuits.
## 3 Online Processing Parameters Prediction and Reverse Design Framework
### The Reverse Design of Processing Parameters Based on Bayesian Optimization
The overall algorithm execution flow for the online optimization and design of process parameters combining EI-kMeans concept drift detection, OASVR and adaptive learning rate Bayesian optimization is shown in Figure 2. Utilizing this framework allows for online data drift detection and warning in industrial scenarios and the online prediction of equipment processing performance, as well as the online optimization and recommendation of equipment process parameters. Figure 2: The flowchart of OPPD framework, including three main parts: concept drift detection, online machine learning and Bayesian optimization.
## 4 Dataset 2 Austrian Workers' Compensation Board (AUVA)
### Probabilistic Graphical Models (PGMs)  
Probabilistic Graphical Models (PGMs) represent human beliefs about causal dependencies and their strength between a set of random variables (RVs) that describe a phenomenon [46]. A PGM's modeled causal dependencies are depicted as edges in a Directed Acyclic Graph (DAG) where the nodes are visible or hidden RVs; together with the numerical description of each RV, the PGM represents the joint distribution over all RVs. This model does not learn associations between the input factors and the predicted variable (although it makes predictions) but goes beyond that and learns causal dependencies both from the human domain expertise and the available data. Furthermore, it can be used for answering probabilistic queries in cases where evidential information is present and as a generative model as well.  
Figure 15 depicts a PGM trained on the AUVA dataset containing only two of the twenty-six possible features. The age RV has six possible outcomes: \"under 20\", \"20s-30s\", \"30s-40s\", \"40s-50s\", \"50s-60s\", \"over 60\". The season RV is deduced from monthly information, and the inactivity due to sick leave has two possible outcomes: \"minor\" (meaning \(\leq\)3 sick leave days because of the accident) and \"severe\" (>3 sick leave days). The graphical structure of the PGM expresses the prior beliefs of the human domain experts about the causal relationships and dependencies between the RVs. It was assumed that season and age were independent of each other and that the inactivity of the forest worker due to hospitalization was directly caused by those two.  
The training was done with Maximum Likelihood Estimation (MLE) [46], meaning that the parameters of the local distributions in Figure 15 were influenced only by the available data and not from prior knowledge of the human domain expert (a soft and uniform prior). After training was accomplished, the model could be used to provide readable explanations directly from the CPTs. For example, the highlighted row in Figure 15 is the answer to an informative statement:  
If the worker is young (under 20) and the season is winter, then the probability of a severe accident, and thereby more than 3 sick leave days, is 35.8%. The evaluation of long hospitalization times for older people (over 60) can be computed through a marginalization over the season variable; it was 37.9%.  
Figure 14: Decision tree rules (first 2 levels).