esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Series Prediction of Failures in an Industrial Assembly Line Using Artificial Learning
## 4 Results and Discussion
### Error Convergences and Performance Metrics  
It is expected that the convergence curves approach zero with each iteration and exhibit no fluctuations in stability throughout the process. Convergence toward zero indicates that the model's error is consistently decreasing at each iteration. Sudden spikes in the curves, on the other hand, may be a signal of overfitting, often referred to as \"memorisation\".  
Figure 11 illustrates the training error convergence behaviour of six models. The error is measured in terms of RMSE and plotted against the iteration number. Among the models, GRU and LSTM exhibit superior convergence characteristics, with steadily declining error trends and minimal fluctuations throughout the training iterations. Both models achieve the lowest final RMSE, suggesting strong learning capacity and robustness in capturing the temporal dependencies of the data. MLP and CNN-RNN demonstrate acceptable convergence, with error gradually decreasing as iterations proceed. However, their convergence is less stable than that of GRU and LSTM, as indicated by small oscillations and a slower reduction in error. ANFIS and NAR convergence earlier. The curves remain relatively flat throughout the iterations.  
Table 5 presents a comparison of the performance metrics.  
NAR and MLP achieve the lowest MAE (2.592 and 2.122, respectively), MAPE (0.895% and 0.876%), and RMSE (1.107 and 1.418), demonstrating strong accuracy in pointwise prediction. Additionally, MLP has the best performance in terms of ME (1.145) and \(R^{2}_{test}\) (0.9858), suggesting it is most effective at capturing the variance in the target variable.  
Figure 11: Error convergence of models in prediction.  
LSTM and GRU achieve high \(R^{2}_{\mathit{test}}\) scores (0.9166 and 0.9675, respectively), indicating a good generalisation capability despite their relatively higher absolute errors. However, both models require significantly longer calculation durations (1826.525 s for LSTM and 1724.109 s for GRU), making them computationally more expensive.  
ANFIS shows a balanced performance and the highest \(R^{2}_{\mathit{test}}\) (0.9875), suggesting 98.75% of the variance in the dependent variable, though its computational demand (796.826 s) and high number of iterations (1000) may limit its practical deployment. CNN-RNN, in contrast, has the weakest performance with the highest MAE, MAPE, MSE, and RMSE, as well as a relatively high computation time (1004.711 s), despite reaching 100 iterations.  
In summary, MLP and NAR offer a favourable trade-off between accuracy and computational cost, while ANFIS and LSTM provide a strong predictive capacity at the expense of a longer training time. ANFIS demonstrates potential with its high accuracy but requires further tuning. CNN-RNN, however, underperforms relative to its peers across most metrics.
# An Improved Differentiable Binarization Network for Natural Scene Street Sign Text Detection
## 3 Proposed Method
### Improved DBNet for Natural Scene Text Detection
#### 3.2.2 Feature Pyramid Route Enhancement Module  
While strengthening the transmission of high-level semantic information of the model, the effect of low-level features of street sign images for text detection should not be under-estimated. The low-level features contain very important information, such as the location and edge shape of the image. However, the low-level feature map must undergo multiple convolutions and sampling operations compared with the top-level feature map, which leads to the loss of useful information in the low-level feature map during the feature extraction process. In order to make full use of the low-level features and shorten the spatial information transmission distance, making the bottom-level information better propagate to the top level, we added a down-scale enhancement module based on the structure of the original FPN of DBNet. In the up-scale enhancement module of the original FPN, four feature maps, f1, f2, f3, and f4, with sizes of 1/4, 1/8, 1/16, and 1/32 of the input image, respectively, were obtained. In the down-scale enhancement module, the four feature maps obtained from the up-scale enhancement module were sampled and fused to obtain the feature maps fpre1, fpre2, fpre3, and fpre4 with sizes of 1/4, 1/8, 1/16, and 1/32 of the input image, respectively. With fpre3 as an example, f3 obtained a feature map of the same size as fpem2 through the upsampling operation and fused with fpem2 by element summation. The fused feature map was then convolved with a kernel size of 33 and a stride of 2 to generate fpre3 and then fpre4 was generated from fpre3 in the same way. The iterations continued until fpre5 was obtained. In the process of down-scale enhancement, all 3 \(\times\) 3 convolutions applied depthwise separable convolution [30] instead of the original standard convolution, which greatly reduced the increase in the operational parameters and enabled the FPRE to obtain a larger perceptual field with a smaller increase in the number of parameters.  
Figure 6: Schematic diagram of the network structure of the high-level feature enhancement (HLFE) module.
## 3 Numerical Simulation
### Model Validation  
During the excavation of the foundation pit, the displacements of the existing tunnel vault and haunch were closely monitored. Field monitoring data from the right-side tunnel, collected after the completion of the excavation, were compared with corresponding numerical simulation results. This comparison allowed for the validation of the numerical model's rationality by correlating the displacement values of different tunnel sections in the model with the actual measurements.  
Figure 5 illustrates the vertical displacement of the right-side tunnel vault, comparing results from both the numerical simulation and field monitoring. Positive values represent uplift, while negative values represent settlement along the vertical axis. The east side of the tunnel corresponds to the existing station, with the horizontal axis representing the distance from the station's opening. The displacement trend indicates that, upon completion of the foundation pit excavation, the maximum accumulated vertical displacement predominantly occurs at the northern end of the hub area, forming a \"spoon\" shape along the tunnel axis. This pattern highlights the significant disturbance impacts of the excavation on the tunnel within the hub area. Near the northern end, the increase in the curve's slope suggests a localized impact from the excavation. The maximum measured vertical settlement is 13 mm, whereas the maximum settlement in the numerical simulation is 8.6 mm, indicating a relatively close agreement between the two results and validating the reliability of the model. However, due to complex factors, such as additional site excavation and dewatering during actual construction, the vertical displacement curve from the simulation in Figure 5 does not perfectly align with the measured data. Nonetheless, both sources exhibit relatively consistent trends in displacement changes, especially near the northern end of the hub area, where the data align more closely.  
Figure 6 presents the horizontal displacement of the right-side tunnel, comparing numerical simulation and actual monitoring results. Positive values indicate movement toward the pit. The data show a consistent trend in the horizontal displacement of the tunnel haunch, with greater displacement near the northern end of the hub area and lesser displacement farther away. The maximum values observed in the field were 21 mm, while the simulation showed a maximum of 15.29 mm, with both occurring at the northern end, underscoring the localized impact of the excavation. The simulated horizontal displacement values are generally lower than those measured, likely influenced by other construction activities at the site. However, the close approximation and consistency between the measured and simulated results affirm the model's reliability.  
Figure 5: Vertical displacement of tunnel vault.
# GNSS Spoofing Detection Based on Wavelets and Machine Learning
## 5 Conclusions
Since in this research, synthetic spoofing attacks simulated in the \"open-sky\" environment were used, our plan is to include datasets with different propagation environments, which will allow more realistic testing of the proposed approach. This research confirms the reliability of ourapproach in case of intermediate spoofing attacks, and the plan for the future is to test the reliability of the proposed approach in complex sophisticated spoofing attacks. Article conceptualization, K.B. and M.B. ; literature research and data processing, K.B. and M.B. ; methodology, K.B. and M.B. ; validation, K.B. and M.B. ; investigation, K.B. and M.B. ; writing--original draft preparation, K.B. and M.B. ; writing--review and editing, K.B., M.B., and D.B. ; visualization, K.B. and M.B. ; supervision, D.B. ; project management, D.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Data Availability Statement:** OAKBAT datasets were used in this research. The datasets are publicly available. **Conflicts of Interest:** Author [PERSON] was employed by the company Ericsson Nikola Tesla and is a PhD student at the University of Split. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.
## 2 Methods
### Simulation Setup  
The simulations in this study were performed using the program Field II [31,32]. This section describes how the simulations were carried out, and which parameters were used. The transducer, image and phantom parameters are presented in Table 1.  
A 2-D row-column array with integrated apodization was simulated with a center frequency \(f_{0}\) = 3.0 MHz. The first simulation was performed for a straight vessel along the \(x\) direction.  
A 4-cycles sinusoidal pulse was transmitted at \(f_{0}\) from either row or column elements. The emitted ultrasound wave was focused at a line at depth of 30 mm. The pulse repetition frequency \(f_{prf}\) was 5.0 kHz.  
Figure 3: Sequence scheme. \(R_{i}\) are rows and \(C_{i}\) are columns transmissions  
A fixed symmetric Hann window spanning 32 elements was applied as apodization when transmitting the pulse from either row or column elements. In receive, a symmetric Hann window apodization across all elements orthogonal to the transmit aperture was applied for the axial velocity estimator.  
A 20 \(\times\) 20 \(\times\) 20 mm\({}^{3}\) cubic phantom containing a cylindrical blood vessel (Radius = 6 mm) located at 3 cm depth was used. Scatterers inside the cylinder were translated according to a circular symmetric parabolic velocity profile, and scatterers outside the cylinder were stationary. In total, 57,600 scatterers were distributed in the phantom to ensure that more than 10 scatterers per resolution cell were present [33]. The peak velocity \(v_{0}\) in the parabolic flow was set to 0.3 m/s.  
A total of 128 frames for each vessel orientation were simulated. With a sequence length, \(N_{e}\), of 44, a total of 44 \(\times\) 128 emissions were used for each parameter setup. 968 velocity profiles were estimated for the whole volume in both the lateral and transverse direction and 1936 velocity profiles in the axial directions due to the alternating between row and column emissions. 44 emissions in total were made to provide data for estimation of all components of the flow inside the vessel. Simulations were also performed for 75 and 60 degree beam-to-flow angles and 0 and 45 degree azimuth rotation. These were made with the same conditions.
## 3 Integration of Multi-Domain MEC Environments  
The MEC framework is originally defined as an environment managed and administered by a single network operator, which controls a number of edge computing sites defining an area of coverage. From this perspective, Current MEC architecture frameworks do not yet consider a scheme of integration from multiple administrative domains, where different providers offering (totally or partially) MEC capabilities conform an overarching, wider MEC system.  
This section briefly describes the MEC architecture as originally proposed by ETSI.
## 4 Results
### Performance Evaluation Metrics
#### 4.1.2 Average Computational Time (\(T_{com}\))  
For wireless link lifetime prediction, we compute the average computation time per data gathering cycle (\(T_{com}\)) using Equation (38):  
\[T_{com}\ =\frac{1}{\mathcal{K}}\sum_{m=1}^{\mathcal{K}}\sum_{i=1}^{N_{ links}}T_{i} \tag{38}\]  
In Equation (38), \(T_{i}\) is the computational time for predicting the wireless link lifetime of the \(i\)th link in the network, \(N_{links}\) is the total number of wireless links in the network at the \(m\)th data gathering cycle, and \(\mathcal{K}\) is the total number of data gathering cycles. A lower value for the \(T_{com}\) is desirable.
# Effect of Washing Temperature on Adsorption of Cationic Dyes by Raw Lignocellulosic Biomass
###### Abstract  
This study evaluated the potential of using raw _Maclura pomifera_ and wild carob for the treatment of methylene blue (MB) and crystal violet (CV) as part of the search for new, abundant, and cost-effective natural materials applicable for wastewater treatment. Additionally, it explored the impact of washing water temperature on the adsorption performance of these raw organic materials. The physicochemical properties of the materials were characterized using BET, SEM/EDS, and FTIR analyses. The effects of various experimental parameters were investigated through batch adsorption experiments. The results demonstrated that the influence of washing water temperature was material-dependent. For _Maclura pomifera_, the maximum adsorption capacity of methylene blue and crystal violet decreased from 134.4 and 136.6 mg g\({}^{-1}\) for MPC to 67.1 and 90.5 mg g\({}^{-1}\) for MPH. In contrast, the adsorption capacities of wild carob biosorbents remained consistent, with the maximum amounts adsorbed for methylene blue and crystal violet by CC, CW, and CH being close, around 78.8 and 98.9 mg g\({}^{-1}\), respectively, indicating a minimal effect of washing temperature on this material. The adsorption of both dyes onto the adsorbents was positively affected by increasing the pH, contact time, and initial dye concentration and was negatively affected by increasing adsorbent dose or ionic strength. Adsorption isotherms and kinetics were modeled using various mathematical approaches. The kinetic data were accurately described by a pseudo-second-order model, with a significant contribution from intraparticle diffusion. The Sips and Redlich-Peterson models provided the best fit for the adsorption isotherms of both dyes on the biosorbents. These findings confirm that the selected biomaterials are excellent adsorbents for the removal of cationic dyes.  
biosorption; _Maclura pomifera_; wild carob; treatment; wastewater; methylene blue; crystal violet +
Footnote †: journal: Applied Sciences
# A Data Sharing Scheme for GDPR-Compliance Based on Consortium Blockchain
## 5 Analysis and Discussion
### Comparative Analysis
The committee composed of supervisory nodes will endorse the new block to ensure that transaction records will not be tampered with during the block generation process. CEM-PoA ensures data integrity. The consensus protocol used in [15] does not guarantee that internal opponents cannot tamper with the transaction, nor does it use an incentive mechanism, it cannot guarantee that those dishonest nodes will be affected by punishment. In the study [17], there is no specific design for ensuring the integrity of transaction data. The proposed scheme in [19] concentrates on achieving data integrity by separating the identity of each patient from the provider through anonymous metadata. Mutual Authentication: Each party in the consortium blockchain is able to confirm the legal identity of the other party in the network. We use reputation ranking and introduce regulatory authorities to achieve mutual authentication. All service nodes are authenticated before entering the blockchain, the private key generation center ensures that each node in the network has a unique and verifiable identity. Additionally, their reputation behavior is recorded in real-time by ranking. The ADVoCATE in [17] does not authenticate service providers or authorized third parties. The research work in [19] does not use any authentication mechanism. Therefore, mutual authentication between on-chain entities cannot be guaranteed. As can be seen from Table 1, our solution is more suitable for GDPR. The comparison shows that the scheme proposed in this paper can meet multiple security indicators of GDPR-compliance data sharing, which is exactly the goal of our research.
# Some Practical Acoustic Design and Typical Control Strategies for Multichannel Active Noise Control
## 1 Introduction  
Active noise control (ANC) systems have been widely used in both industrial machinery and consumer products. Since noise control usually needs to be executed within an extended area, ANC systems use a number of error microphones and loudspeakers to monitor noise and generate anti-sound, thereby attenuating the unwanted sound propagation over a certain region rather than at several spots. The fundamentals of ANC theory and principles of ANC methods have been well-established over the last 40 years [1; 2; 3]. However, successful applications of the technology are still limited to some specific cases, such as headsets, earplugs, and ventilation ducts where the unwanted noise is reduced only at a single spot or within a certain \"duct-like space\" [4]. With the development in audio and digital signal processing (DSP) equipment [5], and market driving force, active control for noise has been pushed towards a direction where the acoustic field is controlled within an enlarged area such that a real \"quiet zone\" could be created.  
Both the physical and signal processing aspects should be considered to generate a \"quiet zone\". As the first step, the physical design involves several distinct acoustic objectives and the associated optimization of the acoustic system [6; 7; 8; 9]. The physical design determines the best possible performance that an ANC system can achieve. This best possible performance is called the performance upper limit of the ANC system. On the other hand, the control strategies and the corresponding ANC algorithms play a critical role in guaranteeing the ANC system to reach this performance upper limit. To achieve the optimum, the design process should go through both acoustic and algorithmoptimization, conducting computer simulations, and finally implementing and testing a real-time controller to ensure its performance and robustness.  
One of the main aims of this paper is to emphasize the importance of understanding the advantages that acoustic and methodology optimization can create for multichannel ANC systems and a better appreciation of limitations that multichannel ANC systems could have. To this end, we will first discuss in this paper several acoustic design methods for multichannel ANC systems over the last decade, each of which aims to provide practical guidance for the physical design in some popular applications. The multichannel control strategies that use different optimization principles for signal processing are then reviewed and compared. These control strategies include the centralized, decentralized, and diffusion optimization methods. All the studies discussed here are application-oriented, e.g., preventing sound transmission from building openings and compact noise sources, or generating a quiet-zone around heads.
## 3 Predictive Torque Control with Switchable Delta-Star Winding Configuration
### Considerations about Flux, Torque, and Speed Control
#### 3.1.2 Flux Command Limitation  
Suppose that the machine is designed to nominally operate at a line-to-line supply voltage \(U_{\rm n}\) and frequency \(f_{\rm n}\). Neglecting the stator resistance, the nominal stator flux linkage vector magnitude (\(K=2/3\)) can be for a nominally star-connected machine written as  
\[\psi_{1{\rm n}(\Upsilon)}=\frac{U_{\rm n}}{\sqrt{6}\pi f_{\rm n}}, \tag{27}\]  
and for a nominally delta-connected machine as  
\[\psi_{1{\rm n}(\Delta)}=\frac{U_{\rm n}}{\sqrt{2}\pi f_{\rm n}}. \tag{28}\]  
A comparison of (27) and (28) shows that  
\[\psi_{1{\rm n}(\Upsilon)}=\frac{1}{\sqrt{3}}\psi_{1{\rm n}(\Delta)}. \tag{29}\]  
If an inverter-fed machine is designed to operate in a delta-connection, then the IM cannot establish nominal flux at higher speeds when the winding connection is switched from delta to star due to the lack of voltage for countering the back-electromotive force (BEMF). To reach the nominal speed after the winding changeover from delta to star, the excitation flux must be reduced, which leads to lowered torque capability. These limitations, which will be further discussed in the following sections, should be considered in the control algorithm.
## 2 Related Works and Contribution
### Adaptive Sampling  
Adaptive sampling methods allow sensor nodes to adapt their sampling frequency to characteristics of the monitored signal. As a result, they reduce the amount of collected data, decrease number of transmissions and save energy of sensor nodes. According to this approach, the optimal sampling frequency (the time interval between two consecutive samples) is computed online and adapted the sampling to the evolving dynamics of the monitored process [20; 21; 22]. The adaptation of sampling frequency can be performed collectively for a group of sensor nodes, or individually for each node.  
For individual sensor nodes the temporal sampling scheme was proposed based on send-on-delta concept [23]. According to that concept, the data sampling is triggered if the signal deviates by delta defined as the significant change of its value in relation to the most recent sample. In that case, the sensor node does not transmit data readings until they remain within a certain error bound. A dynamic selection of appropriate delta value for the send-on-delta sampling strategy was presented in [24]. That algorithm calculates the delta value for a desired mean transmission rate in real time.  
A real time algorithm for selecting the sampling frequency and reducing the amount of data collected by body sensors was discussed in [25]. The adaptive sampling method was developed by using a neural network to predict the subsequent samples and evaluate their uncertainties. According to that approach, a data reading is carried out by a sensor when the uncertainty of prediction is above a predetermined threshold. It was demonstrated that the method reduces the number of measured samples, but preserves the useful high frequency information contained in the original biomedical signals. That method was implemented for measurements of electromyogram, electrocardiogram, electroencephalogram, and acceleration of body parts.  
The adaptive sensing approach was also used for extending lifetime of a human activity recognition system with accelerometers [26]. In that case, the trade-off between energy saving and accuracy of the activity recognition was maintained by implementing a feedback control algorithm. The idea underlying that algorithm was to use the measured acceleration data as a feedback to adjust the sampling frequency in each sensor node. It was shown that the optimal sensing frequency depends on position of sensor node and activity type.  
In [27] a method was reported for adaptive sampling of electrocardiogram. That method adjusts the sampling frequency by taking into account a medical relevance of the data. The data relevance was evaluated using eye tracking technology. Thus, that method generalises the knowledge from cardiology expert perception of the electrocardiogram. The main objective of that approach was to ensure a good reproducibility of the diagnostic features that are necessary for interpretation of the collected data by experts.