esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Discussion
### The Bit Error Rate (BER) Measurement  
The experimental setup is shown in Figure 8. The transmission quality of the proposed fiber laser is verified by 10 Gb/s, 231-1 pseudorandom binary sequence (PRBS), non-return-to-zero (NRZ) format. The home made fiber laser with 59.86 dB OSNR and original  
Figure 6: (**a**) The fiber laser linewidths are 4.97 KHz and 1.46 KHz in a partial un-shielded condition, and (**b**) The comparison of fiber laser linewidths of fully shielded and partially un-shielded conditions.  
Figure 7: The linewidth stable measurement of a fiber laser.  
launched power of 1.4 mW was used. It was then amplified by an EDFA located after the EOM. All the fibers used in the transmission system are SMF. The extinction ratio (ER) of the EOM is 40 dB. Figure 9a shows the blue, red and green curves of the original fiber laser, fiber laser with filter and fiber laser being amplified, respectively. The measured BER is \(10^{-9}\) for 10 Gb/s modulation speed when the receiving power is \(-16.2\) dBm, as is shown in Figure 9b.
# Optimization and Application of Drilling Parameters Based on Gas-Solid Coupling Simulation
## 3 Numerical Simulation  
### Geometric Modeling and Boundary Conditions  
The three-dimensional geometric model was simplified to a two-dimensional planar model using COMSOL numerical simulation software. A square model with a side length of 10 m was established. The boundaries around the gas seepage field model were set as zero flux (used to simulate impermeable geologic barriers (e.g., dense rock formations, faults, or geologic formations) to stop gas flow), the left and right boundaries were set as roller supports (used to simulate a model that is constrained on both sides by the surrounding rock (e.g., horizontal geopathic stress effects) but can settle or expand in the vertical direction due to gas extraction), the upper boundary was applied with a constant load (vertical pressure applied to the top of the model to simulate self-weight or tectonic stresses in the overlying rock formation) of 6.3 Mpa, the lower boundary was set as a fixed support (acts on the bottom of the model to simulate the support of the upper strata by a stable rock layer unaffected by mining), and the boundaries of the extraction boreholes were set as the Dirichlet boundary conditions (used to simulate the active extraction of gas by negative extraction pressure in extraction boreholes).  
In order to improve the accuracy and efficiency of the simulation, a triangular mesh was used to refine the model. Figure 1 shows the geometric model and boundary conditions.
## 2 Exploring SDN, NFV, Policies, ML, Network Slicing and Telecom Security
### Network Slicing
#### 2.3.1 The Need for Network Slicing  
The emergence of these diverse use cases underscores the need for a more flexible network architecture capable of dynamically adapting to accommodate their contrasting requirements. Network slicing, a key technology within 5G and beyond, is a potential solution. Network slicing allows network operators to carve out virtual slices from the shared physical infrastructure, each tailored to the specific needs of a particular use case. This enables the concurrent support of eMBB, URLLC, and mMTC applications within a single network, ensuring optimal performance for each category. The role of network slicing in addressing the diverse QoS requirements of various applications, implicitly highlighting the limitations of traditional networks in providing such differentiated services is being discussed in several works [27; 47]. The proliferation of diverse use cases with conflicting requirements necessitates a paradigm shift in network design. Traditional one-size-fits-all networks are ill equipped to handle the intricate demands of eMBB, URLLC, and mMTC applications. Network slicing emerges as a promising solution, offering network operators the flexibility to create virtualized slices tailored to each use case's specific needs. Further research and development efforts are crucial to fully realise the potential of network slicing and pave the way for a future where diverse applications coexist and thrive within a single, adaptable network infrastructure.
## 3 Results and Discussions
### Proposed Adsorption Mechanism of IBP  
The proposed adsorption mechanism of IBF onto the adsorbents is shown in Figure 5. Determining the adsorption mechanism(s) is very important. Biodegradation and adsorption are the two ways of removing pharmaceutical compounds which mainly depend on the chemical structure and their operating conditions [49]. [PERSON] et al. (2021) [50] also mentioned that functional groups, size and charge of the adsorbent plays a role in determining the adsorption mechanism. There are several mechanisms for the adsorption of ibuprofen on adsorbents such as \(\pi\)-\(\pi\) interactions, hydrophobic, electrostatic hydrogen bonds and cation exchange [49]. Plant materials contain lignocellulosic materials such as cellulose, hemicellulose and lignin [51]. The structure of lignocellulosic materials consists of oxygen-rich functional groups such as (CO), (OH) and (COOH) which might be good candidates for adsorption processes [52]. Additionally, activated carbons developed from lignin materials have revealed promising results for adsorption of different toxic ions including pharmaceutical pollutants [51]. Based on the literature of [PERSON] et al. (2021) [49], the functional groups determined during FTIR analyses in this study suggest that they were involved in the adsorption process, hence the proposed adsorption mechanism in Figure 5.
## 4 Results and Discussion
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**ML Techniques** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Boosting & 95.44 & 94.22 & 96.66 & 98.01 \\ Bagging & 93.62 & 91.22 & 92.55 & 94.44 \\ Voting & 94.66 & 94.70 & 96.77 & 95.34 \\ Rotation Forest & 91.20 & 91.88 & 92.24 & 90.40 \\ XGBoost (EL Method) & 98.00 & 98.90 & 98.90 & 98.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison with other ensemble methods. Figure 10: Evaluation of various ML methods during testing phase. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**ML Techniques** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline LR & 95.00 & 96.30 & 97.56 & 96.93 \\ KNN & 91.00 & 91.18 & 96.47 & 94.75 \\ RF & 90.00 & 93.10 & 96.42 & 94.73 \\ DT & 88.00 & 91.95 & 94.11 & 93.01 \\ SVM & 94.00 & 95.69 & 97.80 & 96.73 \\ XGBoost (EL Method) & 98.00 & 98.90 & 98.90 & 98.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance in classification of CKD. compared to other algorithms in classifying the instances of the records of the dataset into \"ckd\" and \"notckd.\"  
All the performance measures reported in this study are statistically different compared to the other studies, as shown in Table 7.
## 4 Results
### Accuracy Analysis on Natural and Marked Check Points
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Statistics**} & \multirow{3}{*}{**Detector**} & \multicolumn{6}{c}{**The Statistic of the Rmse On Detected Natural and Marked Check Points**} \\ \cline{3-8}  & & \multicolumn{6}{c}{**[mm]-Scenario A**} \\ \cline{3-8}  & & \multicolumn{6}{c}{Test site I - natural check points} & \multicolumn{6}{c}{Test site I - marked check points} \\ \cline{3-8}  & & \multicolumn{6}{c}{Carteographic transformation} \\ \cline{3-8}  & & Spherical & Orthoimage & Mercator & Spherical & Orthoimage & Mercator \\ \hline \multirow{3}{*}{**Avg**} & FAST & 4.2 & 3.8 & 3.8 & 4.2 & 6.7 & 6.7 \\  & SURF & 4.3 & 3.9 & 6.7 & 4.8 & 6.3 & 4.8 \\ \hline \multirow{3}{*}{**Min**} & FAST & 3.9 & 3.6 & 3.8 & 0.3 & 2.4 & 2.4 \\  & SURF & 4.0 & 3.8 & 3.8 & 4.0 & 2.2 & 0.3 \\ \hline \multirow{3}{*}{**Max**} & FAST & 4.6 & 4.1 & 4.2 & 9.2 & 13.1 & 13.1 \\  & SURF & 4.4 & 4.2 & 12.5 & 11.4 & 12.8 & 9.9 \\ \hline \multirow{3}{*}{**Statistics**} & \multirow{3}{*}{**Detector**} & \multicolumn{6}{c}{Test site II-natural check points} & \multicolumn{6}{c}{Test site II-marked check points} \\ \cline{3-8}  & & \multicolumn{6}{c}{Carteographic transformation} \\ \cline{3-8}  & & Spherical & Orthoimage & Mercator & Spherical & Orthoimage & Mercator \\ \hline \multirow{3}{*}{**Avg**} & FAST & 2.7 & 5.3 & 3.0 & - & - & - \\  & SURF & 4.3 & 3.9 & 2.9 & - & - & - \\ \hline \multirow{3}{*}{**Min**} & FAST & - & - & - & - & - & - \\  & SURF & - & - & - & - & - & - \\ \hline \multirow{3}{*}{**Max**} & FAST & - & - & - & - & - & - \\  & SURF & - & - & - & - & - & - \\ \hline \multirow{3}{*}{**Statistics**} & \multirow{3}{*}{**Detector**} & \multicolumn{6}{c}{Test site III-natural check points} & \multicolumn{6}{c}{Test site III-marked check points} \\ \cline{3-8}  & & \multicolumn{6}{c}{Carteographic transformation} \\ \cline{3-8}  & & Spherical & Orthoimage & Mercator & Spherical & Orthoimage & Mercator \\ \hline \multirow{3}{*}{**Avg**} & FAST & 4.9 & 6.6 & 4.7 & 3.6 & 3.5 & 3.1 \\  & SURF & 4.9 & 6.4 & 6.0 & 3.6 & 9.1 & 3.1 \\ \hline \multirow{3}{*}{**Min**} & FAST & - & 6.6 & - & - & 3.0 & - \\  & SURF & - & 6.0 & - & - & 3.0 & - \\ \hline \multirow{3}{*}{**Max**} & FAST & - & 7.3 & - & - & 3.9 & - \\  & SURF & - & 15.7 & - & - & 15.1 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The statistics of the RMSE values on natural and marked check points for all test sites (scenario A).
# EliNAC: Autoencoder Approach for Electronic Invoices Data Clustering
## 3 ELINAC: ELICronic INvoices Autoencoder Clustering  
This section presents ELINAC, a service for clustering short-text data in NF-es that uses an autoencoder for faster results. ELINAC's purpose is to aid auditing of transactions documented in NF-e, clustering similar data by short-text descriptions and making anomaly detection in numeric fields easier. For this purpose, ELINAC transforms the data and creates clusters based on an autoencoder model. Therefore, ELINAC generates results that auditors can use to analyze NF-es or similar data in a time-efficient manner.  
For a better understanding of the proposed service, Figure 1 shows an overview of how ELINAC works, from the NF-e emission until the auditing. After the extraction of the data from a regulatory agency, the NF-e dataset goes through the process of removing unneeded information from the description field (Label A, Figure 1), which is filled with human input and contains a significant amount of noise and is very unbalanced [42; 43; 44; 45]. The data are now better suited for the next step (Label B, Figure 1), where the autoencoder training generates a reconstruction value based on how similar the short-text description is to the learned features. The reconstruction error output from the autoencoder has significant variations to represent different data, and that variation is the threshold that defines a new cluster.  
The clustering phase of ELINAC (Label C, Figure 1) is performed by an algorithm that searches for the ideal threshold value that represents the necessary variations for the number of clusters requested as a parameter. The clustering is performed in conjunction with the analysis phase (Label D, Figure 1) since it can be repeated more times depending on the generated clusters. One of the advantages of ELINAC is that when the number of clusters is unknown, the clustering can be performed multiple times in a short time since the data does not need to be retrained in the autoencoder model.  
This way, if the analysis presents clusters too specific or too broad, the clustering algorithm can receive a different number of clusters and output the new result almost immediately. Every step in ELINAC is essential for adequate results: the autoencoder cannot generate similar reconstruction error values if the data are not standardized correctly, and if the autoencoder does not produce values with a perceivable variance, the clustering and analysis phase will not generate correct clusters.
# Lung Ultrasound Imaging, a Technical Review
## 3 Ultrasound Multiple-Scattering Characterization  
As discussed in the introduction, one of the main obstacles to conventional B-mode lung imaging is the multiple scattering of ultrasound waves by the millions of air-filled alveoli in the parenchyma. This leads to more complex propagation paths and impairs the linear relationship between propagation time and propagation distance, which is the basis of conventional ultrasound imaging. However, when an ultrasound wave propagates in a complex medium such as the lung parenchyma, each scattering event is also the opportunity for the wave to accumulate quantitative information on the microstructure of the medium. In a highly scattering medium such as the parenchyma, ultrasound waves diffuse rather than propagate. It is possible to take advantage of this. In fact, by quantitatively characterizing the properties of the diffusive phenomenon, it is possible to extract properties of the scatterers (the air-filled alveoli) and their spatial distribution, which can be diagnostically relevant. As an example, diseases such as pulmonary edema and pulmonary fibrosis will increase the distance between healthy air-filled alveoli on average, by increasing the volume of fluid buildup or fibrotic tissue. In pulmonary edema, the increase in interstitial fluid leads to an increase in the size of interstitial spaces. In pulmonary fibrosis, the interstitium will contain increased amounts of collagen and inflammatory cells. If air-filled alveoli are scatterers to the ultrasound wave, both diseases will effectively increase the mean distance between scatterers, thereby affecting the diffusion of the wave in the parenchyma (Figure 5).  
Figure 4: Ultrasound images obtained during patients investigation using an open research platform (on the left) and clinical scanner (on the right). The result in terms of detection and localization as obtained by the computer aided system in also shown.  
Figure 5: Blue = air-filled alveoli. (**Top**): ultrasound wave into normal lung experiences multiple scatters. Diffusion will be slow (**Middle**): Fibrotic lungs have smaller alveoli, thicker alveolar walls, and more tissue between alveoli, so diffusion will be faster. (**Bottom**): Edematous lung. Some alveoli may be fluid filled (white) which will further increase the distance between scatterers.  
In a diffusive medium, the diffusion constant (D) measures the rate of growth of the diffusive halo. If the scatterer encounters are rare (diseased lung, low density of healthy air-filled alveoli), the wave will diffuse rapidly in the parenchyma, propagating straight in between the rare scattering events, leading to large values of D. In a healthy parenchyma on the contrary, scattering events will occur more often. The rate at which the wave will make progress will be lower, leading to lower values of D, by definition of the diffusion constant (Figure 6).  
This new paradigm can be exploited for the quantitative characterization of the lung parenchyma. To do so, D can be estimated using a conventional ultrasound probe, as follows.
## 3 Materials and Methods  
### Dataset  
In this study, audio recordings selected from the Turkish section of the publicly available Mozilla Common Voice (MCV) dataset are utilized [25]. MCV is a dataset created through contributions from volunteers globally. This dataset contains a total of 30,329 h of speech data, of which 19,916 h have been verified. The dataset covers 120 languages as of February 2024. Each recording is stored in a unique MP3 file, and demographic data for the recordings, such as age, gender, and accent, are stored in a text file. Participants can log in to the system via the Common Voice website or iPhone application and contribute to the dataset as either speakers or listeners. Speakers voice the sentences that appear on the screen, while listeners verify the previously voiced sentences using a simple voting method. Speeches are considered valid if they are heard by at least two listeners and receive a positive vote by the majority. Conversely, speeches are considered invalid if they receive a negative vote by the majority. Records that have not yet received a sufficient number of valid or invalid votes are grouped as \"other\". The datasets for each language are split into training, development, and testing sections, and only valid recordings are included in these sections. In the datasets, the gender information of the speakers is categorized into male, female, and other. Additionally, the age information is categorized into seven groups with ten-year age intervals, such as twenties, thirties, forties, and so on.  
A total of 113 h of verified speech records in the Turkish dataset version 16.1 is used in the study. However, some of these records are missing gender and age information. Additionally, some of the recordings have been down-voted by at least one listener, even though they are verified. In the study, records that have received at least one down-vote, lack age or gender information, or have \"other\" defined as their gender are excluded from the dataset. Additionally, there is an imbalance in the number of records across the different age groups, with some having a high number of records and others having a low number. To address this issue and prevent biased results, a balanced dataset is created by randomly selecting an equal number of records from each age group. Unfortunately, there were no speech samples that met the criteria for males in their seventies and eighties, so these groups were excluded from the dataset. As a result, speeches from ten groups remained in the final dataset, consisting of a total of 6700 speeches. This dataset was used in the development of the model proposed in the study.
# A Hybrid Vibration Isolator Based on Elastomeric and Electromagnetic Restoring Force
## 6 Results and Discussions
### Experimental Setup  
To experimentally evaluate the characteristics of the described isolator, a test rig was realized (Figures 5 and 6) in accordance with the scheme in Figures 2 and 3. The lower plate of the isolator was connected to a horizontal slide driven by a linear mechanical actuator able to impose a periodic motion with assigned amplitude and frequency; the slide motion was detected by means of a displacement laser sensor (OptoNCDT 1420 by Micro-Epsilon, Ortenburg, Germany). The upper plate was connected to the fixed frame by means of a load cell (mod. 6210S, by Dytran, Chatsworth, CA 91311, USA) to measure the force that the lower plate transmitted to the upper one.  
Figure 5: Scheme of the test rig.  
With the aim of simulating the presence of the object to be isolated, in all the tests, a ballast mass of 90 kg was added. The results of these tests are described in the following paragraphs.
## 3 Stray Current Corrosion Morphology, Measurement Method, and Influencing Factors
### Corrosion Morphology of AC Stray Current  
In contrast to DC stray current corrosion, AC stray current leads to a different corrosion morphology. [PERSON] found that when the AC stray current density is no more than 100 A/m\({}^{2}\), the steel corrosion is dominated by uniform corrosion; however, as the AC stray current density increases, the steel specimen surface starts to become uneven, and the corrosion morphology changes. The phenomenon of uniform corrosion turning into pitting corrosion occurs, and at this time, the corrosion becomes more localized. When the AC stray current density reaches 150 A/m\({}^{2}\), there is obvious localized corrosion pitting [35]. [PERSON] et al. analyzed the corrosion morphology and products of steel by SEM and EDS. They found that as the interference current increases, the corrosion degree becomes more intense, and the corrosion products are mainly iron oxides [36].  
In terms of morphology, [PERSON] found that the corrosion products of AC corrosion are mainly magnetite (Fe\({}_{5}\)O\({}_{4}\)) mixed with soil to form soil crust. Moreover, the products reduce the soil resistivity and enlarge the damaged coating surfaces, accelerating the corrosion rate; thus, the corrosion process is essentially autocatalytic. In addition, [PERSON] found that at low current density, the presence or absence of AC interference has little influence on the SCC of pipeline steel. When the current density is high, corrosion pits will be generated near the fracture surface, which is also the local corrosion (pitting) that accelerates the generation of microcracks, as mentioned above.  
[PERSON] observed that low-carbon steel corrosion without an AC power supply and negative protection is much better than that without negative protection; however, as the AC current density increases, the pH of the solution becomes higher. The higher cathodic protection potential is better than the lower cathodic protection potential [37; 38; 39].
## 5 Numerical Results and Discussions
### Surface Pressure Distribution  
Figure 10 shows the distribution of the circumferential pressure coefficient on the surface at different sections along the water depth when the temporary pier is submerged at a depth of 20 m. The pressure around the upstream pier is symmetrically distributed with respect to x = 0\({}^{\circ}\). According to the flow field characteristics of the upstream pier, although there is a vortex behind the upstream pier, this area has little influence on the pressure of the upstream pier. Therefore, the pressure around the upstream pier column is still distributed symmetrically along the central axis, and the pressure in the negative pressure area on both sides of the upstream pier and behind the pier does not change much. For the midstream pier, due to the influence of the vortex shedding behind the upstream pier column, the incident point of the water flow on the midstream pier changes, resulting in the maximum positive pressure appearing around \(-140^{\circ}\) (left front) of the pier column. For the downstream pier, the position of the maximum positive pressure is near \(-150^{\circ}\) (left front), where the pressure is not much different to the maximum pressure at the middle pier. In addition, the pressure of the downstream pier is less than that of the midstream pier, so that the resistance of the downstream pier is greater than that of the midstream pier. From Figure 10b,c, it can be seen that the pressure fluctuations around the middle and downstream pier are more obvious than those around the upstream pier, mainly due to the regular vortex shedding generated by the front pier columns. The circumferential pressure coefficient of the midstream pier in the section at d = 8 m fluctuated significantly, which is due to the influence of the bottom boundary on the fluid, resulting in uneven distribution of flow velocity along the water depth, leading to different shedding situations of the vortex at different depths. Therefore, the circumferential pressure coefficient distribution of the midstream pier varies significantly along the water depth.