esa-sceva/satcom-chunk-collection huggingface dataset:
# Dissolved Oxygen Modeling by a Bayesian-Optimized Explainable Artificial Intelligence Approach
## 4 Model Application and Results
### Performance Evaluation  
The optimized models are employed to predict the DO levels. The performance metrics of the models are presented in Table 3. A predictive model with CD > 0.75 is often considered 'good' [39; 40]. The established models significantly exceed that threshold with CD > 0.92, indicating high accuracy. At the testing stage, the errors RMSE range from 0.40 to 0.58 and MEA from 0.30 to 0.43, which are minimal. The BO-SVR is the most accurate one, with CD = 0.97. The ensemble approach outperforms its standalone counterpart, which is consistent with a previous study [32].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Hyperparameter** & **Optimal Value** \\ \hline \multirow{3}{*}{SVR} & Box constraint & 797.79 \\  & Epsilon & 0.002 \\  & Kernel scale & 3.88 \\ \hline \multirow{2}{*}{RT} & Minimum leaf size & 1 \\  & Maximum number of splits & 2041 \\ \hline \multirow{3}{*}{Boosting} & Number of learning cycles & 489 \\  & Minimum leaf size & 1 \\  & Maximum number of splits & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters in SVR, RT, and boosting models and their optimal values.  
Figure 4: Bayesian optimization process for hyperparameter tuning.  
Figure 5 compares the predicted and measured DO at the testing phase. The time series comparison demonstrates the satisfactory performance of the models, showing a close match. The scatter plot indicates that all predictions closely and evenly align with the perfect fitting line, especially the BO-SVR forecasting. However, the models exhibit slight overestimation at low DO levels, particularly the BO-Boosting model. Figure 6 shows the cumulative frequency (CF) of the absolute relative error (ABR). The BO-SVR approach results in the smallest errors, with 92% of its predictions exhibiting an ABR \(<\) 10% and 99.4% showing an ABR \(<\) 20%. Under the same error conditions, these numbers are 83.8% and 95.6% for the BO-TR and 86.2% and 94.6% for the BO-Boosting. The mean ABR for the BO-SVR, BO-RT, and BO-Boosting is 4.1%, 5.5%, and 5.7%, respectively. Since the BO-SVR outperforms the other two models, it is used for further analysis.  
Figure 5: Model evaluation: (**a,c,e**) time series comparison and (**b,d,f**) scatter plot of predicted vs. measured DO.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Training Phase**} & \multicolumn{2}{c}{**Testing Phase**} \\ \cline{2-7}  & **CD** & **RMSE (mg/L)** & **MAE (mg/L)** & **CD** & **RMSE (mg/L)** & **MAE (mg/L)** \\ \hline BO-SVR & 0.992 & 0.178 & 0.119 & 0.970 & 0.395 & 0.303 \\ BO-RT & 0.997 & 0.103 & 0.069 & 0.927 & 0.550 & 0.411 \\ BO-Boosting & 0.998 & 0.097 & 0.074 & 0.940 & 0.581 & 0.425 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical performance of Bayesian-optimized ML models in DO estimation.
## 4 Performance Evaluation
Consequently, the channel estimation will not be as efficient as in the first system. Furthermore, in order to investigate  
Figure 5: Performance of the proposed schemes over an Extended Vehicular A (EVA) channel with speed of 100 km/h. how the LDS-F-OFDM and LDS-UFMC can be affected by the mobility, the schemes are also simulated over an EVA channel with a speed of 300 km/h. Figure 5(a),b represents the simulation results for \(\Delta_{f}=15\) kHz and \(\Delta_{f}=30\) kHz, respectively. It can be seen that LDS-F-OFDM shows better performances than LDS-OFDM and LDS-UFMC in low SNR. Moreover, all the techniques demonstrate huge improvements in the second system with \(\Delta_{f}=30\) kHz subcarrier spacing than the first. Thanks to the large subcarrier spacing, the Doppler shift does not impact severely the subcarriers orthogonality. In addition, comparing Figure 4(a) with Figure 5(a), all the techniques face severe performance degradation due to the high mobility of the UEs. Meanwhile, Figure 4(b) with Figure 5(b) presents almost the same BER performances thanks to the large subcarrier spacing. Figure 7 depicts the comparison of the proposed schemes over the EVA channel with different speed limits with both systems. We have chosen to evaluate the schemes also over the EVA channel with a 500 km/h velocity and analyse the degradation of the performance in such an environment. Obviously, the performance decreases due to the high speed, however as shown in Figure 6(b) i.e., with a large subcarrier spacing, both LDS-F-OFDM and LDS-UFMC seem to suffers less. Meanwhile  
Figure 6: Performance of the proposed schemes over an EVA channel with speed of 300 km/h. with \(\Delta_{f}=15\) kHz, all the techniques seem unable to converge at high speed. To improve this, other receivers can be considered such as the JSG and Expectation Propagation Algorithm EPA receivers.
# The Boundary Element Method in Acoustics: A Survey
## 5 Extending the Boundary Element Method in Acoustics
### Vibro-Acoustics
#### 5.3.2 Coupled Fluid-Structure Interaction  
The finite element model in the previous Sub-subsection is directly applicable to a structure when there is no significant coupling between the structure and a fluid. Similarly, the acoustic analysis methods of Sections 4 and 5.1 and Section 5.2 are directly applicable when there is no significant coupling between the fluid and a structure. However, for many dynamical systems, it is appropriate to couple the structural model, of the FEM form outlined in the previous Sub-subsection, with the acoustic model of the fluid with which it is in contact. The finite element method can be applied in both domains, with appropriate properties. In the context of this work, the boundary element method provides the computational acoustic model. The models are coupled together, through continuity of the particle velocity at the interface, and the resultant forcing on the structure is affected by the sound pressure. The discrete coupling is applied at the elements that coincide with the boundary.  
The fluid-structure interaction model with the boundary element method modelling the acoustic domain has been developed and applied over several decades. Expansions on the general method can be found in the following works and the references therein [183; 267; 280; 281; 282; 283]. Applications include the interaction of plates with fluids [245; 284; 23], sandwich panels/lightweight structures [267; 268; 261; 262; 263; 264; 265], sound insulation [283], screens [286], the passenger compartment of an automobile [111], hydrophones [284; 287] and in seismo-acoustics [288].  
In Section 4.2, the acoustic modal analysis of an enclosed fluid was discussed. Similarly, in the previous Section, structural modal analysis via the finite element method was outlined. Of course, when coupling occurs, the eigenvalue analysis need to be applied to the coupled system. For example, a structure typically exhibits different resonant frequencies in vacuo than it does when immersed in a fluid. In the literature, these are often termed the _dry_ and _wet_ modes (at least for structures placed outside of and in water). Modal analysis via the finite element method returns a standard and sparse eigenvalue problem (88), modal analysis by the boundary element method yields a non-linear eigenvalue problem (69) and hence the hybrid coupled FEM-BEM system is also non-linear. There are several reported implementations of coupled fluid-structure modal analysis using the boundary element method [19; 255; 256; 257; 265; 284; 287; 289; 300; 301]. The coupled matrices are generally much larger than for the boundary element method alone and this can be resolved by determining the response in the dry structural modal basis and coupling that with the boundary element model [299; 301].
# Trajectory Planning of Shape-Following Laser Cleaning Robot for the Aircraft Radar Radome Coating
## 2 Materials and Methods  
### Measurement and Preprocessing of Point Cloud Data for Radar Dome Surfaces  
This section presents the development of an automated robot coating laser cleaning system by incorporating 3D sensors, robots, laser cleaning actuators, and computers. The system aims to achieve efficient and precise coating removal through automated laser cleaning. The flowchart of the laser cleaning trajectory planning algorithm is as shown in Figure 1, written using the C++ (c plus plus) programming language. The operational schematic of the system is shown in Figure 2.  
Figure 1: Flow chart of the laser cleaning trajectory planning algorithm.
# Deduplication-Aware Healthcare Data Distribution in IoMT
## 5 Results and Analysis
### Energy Consumption  
In IoMT, collector devices play a critical role in collecting data from wearable devices that are fixed to the human body. These CDs utilize energy to perform data processing, such as deduplication and the transmission of the data to the fog server. Therefore, this is an important metric that needs to be checked. In Figure 9, the energy consumption of the aggregating devices is illustrated. At time t = 0.6, CD1, CD2, and CD3 utilize 0.0046 uJ, 0.0043 uJ, and 0.0051 uJ. The results indicate that collector devices, including CD1 and CD2, utilize 65% additional power as compared to the sensing devices S1, S8, and S12. Furthermore, in the process of deduplication and data transmission, CD3 utilizes 68% more energy.  
Figure 8: Computational overhead.  
Figure 7: Energy efficiency.  
The sensing devices are responsible for frequently taking data readings from the patient's body. This continuous monitoring of data consumes energy. To check the energy consumption at the sensor level, an extensive simulation was performed. The NS-2 tool was used to develop an energy model, and the remaining power data were obtained in a trace record. Afterward, the power utilization was determined through the difference in energy at a particular time t from the trace files that were produced during the simulation. Figure 10 represents the energy consumption of the sensing devices. The x-axis shows the time passed in the simulation, whereas the y-axis represents the energy consumption win micro-Joules. The results show that sensing devices S1, S6, S8, and S12 utilized 0.00296 \(\upmu\)J, 0.00297\(\upmu\)J, and 0.00297 \(\upmu\)J, respectively, when t = 0.4. Initially, for the sensing devices, energy utilization values were obtained. Afterward, they were extracted for the CDs.
_J. Low Power Electron. Appl._**2011**, \(1\), 261-276; doi:10.3390/jlpea1020261  
**Journal of Low Power Electronics and Applications ISSN 2079-9268**  
www.mdpi.com/journal/jlpea/  
_Review_  
**Adaptative Techniques to Reduce Power in Digital Circuits**  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1 and [PERSON] 1,2**  
1 ECE Department, Indian Institute of Science/Bangalore 560012, India;  
E-Mails: [EMAIL_ADDRESS] (N.M.); [EMAIL_ADDRESS] (S.D.);  
[EMAIL_ADDRESS] (A.G.)  
2 Texas Instruments/Bangalore 560012, India  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +91-80-2293-3172; Fax: +91-80-2360-0563.  
_Received: 23 November 2010; in revised form: 13 June 2011 / Accepted: 21 June 2011 / Published: 4 July 2011_  
**Abstract:** CMOS chips are engineered with sufficient performance margins to ensure that they meet the target performance under worst case operating conditions. Consequently, excess power is consumed for most cases when the operating conditions are more benign. This article will review a suite of dynamic power minimization techniques, which have been recently developed to reduce power consumption based on actual operating conditions. We will discuss commonly used techniques like Dynamic Power Switching (DPS), Dynamic Voltage and Frequency Scaling (DVS and DVFS) and Adaptive Voltage Scaling (AVS). Recent efforts to extend these to cover threshold voltage adaptation via Dynamic Voltage and Threshold Scaling (DVTS) will also be presented. Computation rate is also adapted to actual work load requirements via dynamically changing the hardware parallelism or by controlling the number of operations performed. These will be explained with some examples from the application domains of media and wireless signal processing.  
**Keywords:** power adaptation; dynamic power switching; dynamic voltage scaling; dynamic voltage and frequency scaling; adaptive voltage scaling; dynamic voltage and threshold scaling; adaptive hardware parallelism; adaptive computation
## 4 Conclusions  
In conclusion, it should be noted that the standards involving the measurement of reverberation time do not indicate how to average the results or how to determine  
Figure 12: Decay curves from 72 measurements for the frequency of 500 Hz. Each line represents a new measurement repetition.  
Figure 13: Regression function from 72 fragments of decay curves for the frequency of 500 Hz.  
the measurement uncertainty. This is a major difficulty, since the reverberation time is determined from an indirect measurement, i.e., from the decay curve. Of course, there is another measurement method that uses the impulse response of the room, but that is not the study area of this article. Based on the obtained results, it can be concluded that the averaging method of the decay curve significantly affects the results of the estimated reverberation time and the estimation of measurement uncertainty. The difference between the averaging methods for a laboratory room where there is a uniform distribution of the acoustic field is no more than 0.1 s. However, a significant difference can be observed in the estimation of measurement uncertainty.  
For a room with different geometry and different sound absorption of the partitions delimiting the room, i.e., a room with a non-uniform distribution of the acoustic field, the differences in reverberation time are significant. In the example given in this article, it is over 0.3 s. Taking into account the restrictive requirements for the design of, e.g., teaching rooms (T = 0.6 s), the averaging method may affect the qualification of the room. Therefore, the problem is significant. The difference in estimating measurement uncertainty in a room with a non-uniform distribution of the acoustic field is similar to that in a laboratory room. Summing up, the following conclusions can be drawn:  
* The averaging method of decay curves affects the reported reverberation time value. This influence is so significant that it may affect the acoustic qualification of rooms for which the reception of verbal sound and, above all, speech intelligibility are important.
* The proposed averaging method of decay curves using linear regression is innovative and seems to be the best way to estimate the reverberation time based on the measurement of decay curves.
* The applied estimation method of measurement uncertainty using Formula (10) for the estimation of sound decay time averaged by means of the linear regression of decay curves seems to be the most reasonable solution.  
Subsequent works will concern the use of the uncertainties determined in this article to determine acoustic parameters describing the reception of verbal sound.  
**Funding:** Publication supported by the Rector's pro-quality grant. Silesian University of Technology, grant number 03/030/RGJ23/0161.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data are held by the author and made available at the request of the interested party.  
**Conflicts of Interest:** The author declares no conflict of interest.
# Generation of Flat Optical Frequency Comb Using Cascaded Electro-Optic Modulators
###### Abstract  
Optical frequency combs have been widely used in spectrum analysis, coherent optical communication, and accurate distance measurement. We propose a straightforward method to improve the flatness of optical frequency combs. First, we derived the output of the optical signal for the configuration of a cascaded MZM and two _PM_s. Second, we identified the parameter value when the flatness was optimal after traversing different parameter spaces. The optimal flatness conditions could be automatically determined from an existing sample dataset by using neural networks and Bayesian optimization, which significantly reduced the calculation cost. Furthermore, a broad spectrum and low power consumption were also achieved. Finally, the generated optical frequency comb signal was divided into eight carriers with 50 GHz intervals, and the optical transmission system was verified by applying a 16-QAM modulation of 40 GBaud/s to each channel. The constellation diagram proved the feasibility of this optical comb generation scheme.  
Keywords:optical frequency comb; electric-optic modulation; flatness; neural network +
Footnote †: journal: _photonics_
## 5 Discussion
### Discussion on Vertical Displacement Anomaly of Stratum in North Line  
The stratum deformation is affected by the soil properties, which mainly include soil unit weight, compressive modulus, and other factors. There is an abnormal phenomenon in Figure 10, that is, the vertical displacement at 6 m from the ground surface is the maximum. The vertical displacement at 8 m and 10 m depths are relatively close to the shield tunnel and with large disturbance.  
There are two reasons for this phenomenon. The first is that in the synchronous grouting stage, the continuous application of grouting pressure after the shield tail gap filled with grouting slurry will increase the surrounding soil displacement. In the meantime, the transmission of grouting pressure offsets the excavation unloading stress at a certain depth, reducing the vertical displacement at this depth (Figure 21). The blue line indicates the stratum settlement caused by shield tunneling at this depth, the red line indicates the stratum heave caused by grouting at this depth, and the overlapping part is the final settlement value of the stratum.  
The second reason is that the monitored soil stratum is muddy silty clay with a compressive modulus of 2.3 MPa, which is smaller than the compressive modulus of the next layer of silty clay (5.5 MPa) and is easily deformed by external disturbances.  
As the thickness of the silty clay layer with heavy overlying soil is small (silty clay layer is 1.8 m thick, muddy silty clay layer is 6.3 m thick), the vertical displacement is caused by the nature of the stratum and the tunneling environment is relatively limited. Ultimately, the vertical displacement of the stratum at a depth of 6 m is greater than that at a depth of 8 m and 10 m.
## 3 Results and Discussion
### Adsorption measurements  
#### 3.2.1 Types of adsorbent and contact time  
The percentage of dye removal as a function of the contact time when using different types of adsorbent are shown in Figure 4. The dye removal was relatively fast in the initial 30-45 min contact time interval. Further increasing the contact time led to a decreased rate of dye removal as it gradually approached equilibrium at 240, 300, and 90 min for the MCC, chitosan, and Mt-rich clay, respectively. It can clearly be seen that the dye removal on the clay was slower and to a lower extent than on the chitosan or the MCC, respectively. From the zeta potential results at pH 5.5 (Figure 3), even though the dye and clay expressed a negative zeta potential of around \(-\)20.2 mV and \(-\)13.1 mV, respectively,  
Figure 3: Zeta potential versus pH condition.  
the attractive force between the -SO3- groups in the dye structure and the cations in the gap gallery of the clay may overcome the repulsive force and so led to dye binding and removal from the solution.  
The chitosan and MCC exhibited a positive zeta potential of about +3.2 mV and +6.0 mV, respectively, which caused the electrostatic attraction between the dye and these adsorbents. Even though the magnitude of the zeta potential of chitosan was close to that of the MCC, complete dye removal on the MCC was achieved, while only 80% dye removal was obtained on the chitosan. Since the MCC expressed more potential hydrophobic interaction sites (as mentioned above) than the chitosan or clay, this results in more hydrophobic interactions with the dye. Thus, a synergistic combination of electrostatic attraction and hydrophobic interactions results in a higher dye removal via adsorption. From this point, it can be said that the hydrophobic property of the adsorbents is one of the crucial factors in enhancing the adsorption capacity. From its superior adsorption, the MCC was chosen for further study.
## 4 Results
### Model Analysis and Benchmarking

Table 1: A comparison of the performance metrics obtained by Model A.  
Figure 8: CAM visualization highlighting regions contributing to IDC predictions, made by Model A (**A**) and Model (**B**). Warmer colors (red and yellow) indicate areas of higher relevance to predicted class, while cooler color (blue) represent those with less influence. \begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{4}{c}{**Model B: \(\boldsymbol{\lambda}\) = 16 and \(\boldsymbol{\alpha}\) = 0.90 (BCE)**} \\ \hline  & **VGG-16** & **VGG-19** & **ResNet-50** & **DenseNet-121** & \(\boldsymbol{p}\)1 ** \\ \hline Accuracy & 0.7882 \(\pm\) 0.0080 & 0.7931 \(\pm\) 0.0065 & **0.8062 \(\pm\) 0.0045** & 0.7964 \(\pm\) 0.0049 & 0.023 \(\boldsymbol{}^{**}\) \\ Precision & 0.7837 \(\pm\) 0.0075 & 0.7975 \(\pm\) 0.0
# eHealth Integrity Model Based on Permissioned Blockchain +
## 1 Introduction  
Nowadays, healthcare-information systems are becoming increasingly popular worldwide. They have many undeniable advantages compared to paper-based documentation. Several eHealth systems are used during the full healthcare process. Starting from systems for managing patient documentation (e.g., an Electronic Health Record (EHR)), managing organizational issues in a healthcare site (such as patient admission), and ending with financial systems. The basic concept of the EHR is that it is a virtual container for health-related documentation for a subject of care (for a precise definition, see ISO 13606 [1]). The security of every system depends on the used security measures. Because of the rising complexity of eHealth systems, i.e., many interconnected healthcare providers and many healthcare sites, the need to allow EHR integrity verification is rising. An eHealth system should contain a tool allowing to detect any changes in patient medical documentation. One of the most promising technologies that allows implementing EHR integrity is blockchains.  
One really needs to understand the real reasons of using blockchains in eHealth systems. In larger eHealth systems that consist of several different systems managed by different providers, a service should provide transactional transparency of an individual's personal medical record. On the other hand, resistance to data loss has currently been successfully implemented using other mechanisms. Stored data in cloud systems or in professional data centers is usually replicated between a few physicallocations or hard drives. Even when a user sees one logical storage device, redundancy causes a system to be secure against data loss caused by device failure.  
Through the rest of this paper, the term 'eHealth system' means an information system that manages medical data and processes, among others: health-related documents (EHRs), permissions given by a subject of care or other entity that allow access to a specified medical record, audit logs, and other data related to the healthcare process, such as billing information or appointment queues.