esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Classification Architecture  
### Classification Methodology  
In urban classification, there are usually four classes: a pedestrian class and three vehicle classes. We can go about running forecasts by relying on multiple algorithms in one overall simulation. Rulex possesses various algorithms to choose from, such as neural networks (NN), k-nearest neighbor (KNN), decision trees, support vector machines, and logic learning machine (LLM), all of which can be used for classification. However, since the adopted methodology applies multiple ML algorithms in a cascaded setup and tests multiple arrangements, which can have a large number of forecasts,we included just NN and LLM. This was adopted since NN is a widely used ML algorithm and also because LLM is the commercial algorithm of Rulex. Furthermore, for NN, a multi-layer perceptron (MLP) arrangement was applied using the back propagation algorithm [18]. However, since the vehicle class can be split into 3 sub-classes, namely, cars, trucks, and motorcycles, and since there is no need to differentiate between them in real-world scenarios, we took advantage of this fact by applying a sub-class-based tree structure, where the classes are nodes and the algorithms are branches. The forecasts of the tree-based method can lead to improved validation results by setting different weights in each forecast. Therefore, we can choose a convenient split ratio between training and testing data, separately for each forecast, where the split method used is holdout validation. It is also possible to use different classification algorithms in each forecast on each branch. This is useful in case the adopted algorithm is not generating the expected results for the dataset at hand. Figure 1 presents such a tree structure, where the leaves are the final classification outputs.  
As presented, different types of algorithms were applied to the final branches of the tree since they possess the lowest success rate. Therefore, the prediction accuracy is optimized by varying weights, data splitting, and, more critically, the algorithm used for classification. The further we go down the tree, the harder it gets to differentiate between classes.
# Focused and TSOM Images Two-Input Deep-Learning Method for Through-Focus Scanning Measuring
## 4 Discussion
### Influence of the Shift of the Gold Lines  
In practical application, the gold line cannot always be placed in the middle part of the focused image. The applicability of Focus & TSOM-CNN when the position of the gold line on the image is shifted should be proved. In the experiment, two test sets with shifted gold lines in the images are collected and used to test the model, each set contains 7400 gold line images in different shift modes (as shown in Figure 14).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**The Evaluation Index** & **Value** \\ \hline Standard deviation of the average RMSE, nm & 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The uncertainties of our model.  
Figure 13: The MSE and RMAE of Focus & TSOM-CNN in multiple tests. (**a**) MSE and (**b**) RMAE.  
Table 3 shows the test results of Focus & TSOM-CNN on two shifted gold line sets and a no shift set, the latter can be considered as the standard case. Compared with the standard case where all gold lines are in the middle, the MSE and MAE of the two shifted sets has increased, but the MSE is still within 10 nm\({}^{2}\) and the MAE is still within 3 nm, indicating that the model has good applicability to the case that nano-lines are not located in the middle of the input images.  
It must be pointed out that the result is largely due to the translation invariance of CNN. If the gold lines make more complex change rather than simply shift in the input images, Focus & TSOM-CNN will no longer be applicable. To solve this problem, it is necessary to expand the datasets or use methods of rotating target detection in the network, which is one of the future research directions.
## 1 Introduction  
The reconstruction of severe atrophy or defects on the alveolar bone resulting from a pathologic lesion is a clinically challenging indication for ridge augmentation [1]. High osteogenic potency bone substrates have been used for the severe defects, such as autogenous bone and demineralized dentin matrix with rhBMP-2 (Auto-DDM/ rhBMP-2, Korea Tooth Bank, Korea) [2, 3]. The common complication of the ridge augmentation is wound dehiscence, which is caused mainly by a lack of soft tissue and tension of the flap [1, 4]. The exposed grafts could be contaminated, leading to infection and the loss of grafted bone [4]. On the other hand, the earlier intervention, such as membrane removal or re-suture, can adversely affect graft stability [5]. For sufficient bone healing, graft stability should be secured during the vascularization and remodeling period.  
Several dressing materials have been developed for the oral cavity, which is an unfavorable condition, including humidity, normal flora, dynamic muscle movement, and divided into the three categories: (1) zinc oxide and eugenol, (2) zinc oxide without eugenol, and (3) neither zinc oxide nor eugenol [6]. An effective dressing should have a short setting time, antibacterial ability, and high adhesion and retention properties. With eugenol, however, the dressing material can cause tissue inflammation and allergic reactions [7]. Zinc oxide without eugenol has been used widely as a periodontal dressing owing to its high mechanical strength and retention property. On the other hand, the mechanical property prevents application by the patient and maintenance oral hygiene from plaque accumulation. To overcome these disadvantages, neither zinc oxide nor eugenol was developed as an attachable band-type dressing material with enhanced availability, and is composed of mainly with ethyl cellulose and two surfaces: adhesion surface (tocopherol acetate, Carbomer 940 and hydroxyethyl cellulose) toward the gingiva, and protective surface (ethyl cellulose and castor oil) with a film form. On the other hand, its repetitive application on the exact surgical area to block bacterial invasion is difficult for the patients to perform themselves.  
An ideal conservative treatment has not been established to maintain proper oral hygiene and allow secondary healing on the wound dehiscence after ridge augmentation. This case series suggests an effective conservative method using an oral wound dressing material with an omnivec splint and presents three cases for the wound dehiscence after ridge augmentation.
# Density Self-Adaptive Hybrid Clustering Routing Protocol for Wireless Sensor Networks
## 3 The Design of DAHC Protocol
### Communication Model  
In this paper, the calculation of energy consumption uses the same radio model as stated in [11]. In this model, to transmit an \(l\)-bit message from transmitter node to receiver node over a distance \(d\), the respective consumption is:  
\[\begin{array}{ll}E_{Tx}(l,d)&=E_{Tx,elec}(l)+E_{Tx,amp}(l,d)\\ &=\left\{\begin{array}{l}l\times E_{elec}+l\times\varepsilon_{fp}\times d^ {2},d<d_{0}\\ l\times E_{elec}+l\times\varepsilon_{mp}\times d^{4},d\geqslant d_{0}\end{array} \right.\\ \\ E_{Rx}(l)=E_{Rx,elec}(l)=l\times E_{elec}\end{array} \tag{2}\]  
where \(d_{0}\) is set as \(d_{0}=\sqrt{\varepsilon_{fp}/\varepsilon_{mp}}\), \(E_{elec}\) (per bit) is the energy dissipation to run either the transmitter or the receiver circuitry, and it depends on factors, such as the digital coding, modulation, filtering, and spreading of the signal. Additionally, \(\varepsilon_{fs}\) and \(\varepsilon_{mp}\) are the amplification coefficients of the power-amplifier in the free space and the multipath fading channel models, respectively. With the communication model, the node calculates the energy consumption based on the length of information and the distance between transmitter and receiver. If the distance is less than \(d_{0}\), the overhead is calculated according to Equation (2), otherwise it is computed according to Equation (2).
## 2 Materials and Methods
### Total Phenol and Flavonoid Quantification  
The total phenolic content (TPC) was estimated as the gallic acid equivalent (GAE) per dried weight of the sample and expressed as the mg-GAE/g-DW, per the method described by [PERSON] et al. (2000) [21]. A standard gallic acid solution was prepared by dissolving 10 mg of gallic acid in 10 mL of methanol and then diluted to desirable concentrations in methanol. Next, 0.5 mL of gallic acid solution or the fermented extract was mixed with 1 mL of Folin-Ciocalteu phenol reagent and then neutralized with 1 mL of 7.5% Na\({}_{2}\)CO\({}_{3}\) solution for 3 h. After centrifugation, the absorbance of the supernatant in the mixture was measured at 760 nm against a blank solution (reagents except extract or gallic acid) by using a UV-Vis spectrophotometer (UV-2600i, Shimadzu, Japan). The calibration curve of absorbance (\(y\)) versus gallic acid concentrate (\(x\)) had the equation \(y=0.0518x+0.0684\) (\(R^{2}=0.9992\)).  
The total flavonoid content (TFC) was estimated as the quercetin equivalent (QE) per dried weight of the sample and expressed as mg-QE/g-DW in accordance with the method described by [PERSON] et al. (2018) with little modification [5]. A standard quercetin solution was prepared by dissolving 40 mg of quercetin in 10 mL of methanol and then diluted to desirable concentrations in methanol. Next, 1 mL of quercetin solution or the fermented extract was mixed with 4 mL of distilled water, 0.3 mL of 5% NaNO\({}_{2}\), and 0.3 mL of 10% AlCl\({}_{3}\) solution. After 10 min of stirring, 2 mL of 1 M NaOH and 2.4 mL of distilled water were added in turn, and the resulting mixture was gently stirred. After 30 min, the absorbance of the mixture was measured at 415 nm against a blank solution (reagents minus the extract or quercetin) by using a UV-Vis spectrophotometer. The calibration curve of absorbance (\(y\)) versus the concentration of quercetin (\(x\)) had the equation \(y=0.0753x+0.0412\) (\(R^{2}=0.9951\)).
# Real-Time Wave Energy Converter Control Using Instantaneous Frequency
## 4 WEC Control Using Estimated Instantaneous Frequency
### Fixed Parameter PTO Control  
To establish a baseline for comparison and validate the effectiveness of the impedance matching technique applied to the state-space model of RM3, a period-sweeping control test was conducted. The test considers a sea state with an incoming wave spectrum characterized by a dominant period of 9 s, while the PTO control parameters are tuned separately for different assumed periods, as listed in the first column of Table 2.  
This approach allows for the evaluation of how PTO tuning affects energy absorption when the control period does not match the actual dominant wave period. The results demonstrate that maximum energy absorption occurs when the PTO damping and stiffness parameters are tuned to match the dominant period of the incoming wave spectrum.  
The implementation is performed using a state-space model of the WEC. The Simulink model used for this study is shown in Figure 10, representing the dynamics of the RM3 WEC system.  
To optimize PTO control in real-time, a dedicated sub-block for optimal control estimation is designed, as illustrated in Figure 11. This block computes the optimal PTO damping and stiffness control based on the estimated instantaneous frequency, ensuring efficient energy conversion under varying wave conditions. The calculation of these parameters follows the impedance matching technique to maximize energy absorption.  
To enhance stability, the real-time frequency estimate is saturated to within a range of frequencies of interest for a typical sea state, as shown in Figure 12. In Figure 11, this function is seen as the \"Saturation\" block in the center of the diagram.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline Control Period (s) & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 \\ \hline CWR (\%) & 14.49 & 19.21 & 22.95 & 24.71 & 24.79 & 23.13 & 19.28 & 12.34 \\ \hline \end{tabular}
\end{table}
Table 2: CWR comparison across different control tuning periods. The incoming wave spectrum has a dominant period of 9 s, while the PTO control parameters are tuned to the values corresponding to the listed periods.  
Figure 10: Block diagram of RM3 system using state-space modeling.
# Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns
## 3 Preliminaries
### Adaboost  
AdaBoost is a widely used boosting algorithm that constructs an ensemble by performing multiple iterations each time with different instance weights and adjusts adaptively to the errors returned by classifiers from previous iterations [66; 67]. Changing the weights of training instances in each iteration forces the learning algorithms to put more emphasis on instances that were incorrectly classified previously and less emphasis on instances that were correctly classified previously. In other words, weights of misclassified instances are increased, whereas weights of correctly classified instances are decreased. This will ensure misclassification errors for these misclassified instances count more heavily in the next iterations. AdaBoost uses the predictions of multiple weak classifiers and gives a final prediction through combined voting on techniques. Weak classifiers as originally defined by [PERSON] and [PERSON] are classifiers that perform a little better than random guessing [68].
# Secure Internal Data Markets
## 3 Security Issues of Internal Data Markets
### Data Volume  
Global data markets are built to accommodate a high volume of diverse data sets and have to be capable of managing large amounts of transactions. Internal data markets may be built to completely different specifications, thus also changing the attack surface and manageability of the system as a whole.
# An Efficient On-Chip Data Storage and Exchange Engine for Spaceborne SAR System
## 4 Hardware Implementation
### Register Control for Custom Engines  
Our entire system architecture is designed and implemented using register control signals to configure parameters, status feedback, and process control for all engines. All custom engines share a set of register access requests, address lines, and data lines. The interface for the custom engine register access control signals is provided in Table 3. After setting the read or write request and register address of the custom engine that needs to be accessed, as well as the register write data during write access, all custom engines can receive the register access information. Then, within each custom engine, the accurate register that is being accessed is determined through absolute address, and the register access response signal is generated accordingly. The VC709 FPGA register control path and data path are shown in Figure 14. Data download and data upload control receiving raw data and sending processed data, respectively, and the data bit width is 256 bit. The control signal request broadcast shares register control signals to the custom engine in the form of broadcast transmission. The control signal response selection is used to select the output signal of each custom engine register interface, and to accurately select the read or write response signal of the accessed register and the read data during read access.  
Figure 13: Schematic diagram of address mapping hardware architecture.  
The register settings of the DDR3_TOP module and the Dline_Buf module in the superscalar pipeline data storage exchange engine are shown in Tables 4 and 5, respectively. Through the control of these registers, the parameterized configuration and imaging process control of the superscalar pipeline data storage and exchange engine can be realized. The main control terminal can also monitor the working status of each customized engine for subsequent operations. In addition, to improve the scalability of the data storage exchange engine, registers can be added or modified according to the design plan.  
Figure 14: Schematic diagram of register control path and data path for VC709 FPGA.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Signal Name** & **Input/Output** & **Bit Width** & **Description** \\ \hline Wreq & Input & \(1\) & Write request signal for register \\ Waddr & Input & \(14\) & Write address signal for register \\ Wdata & Input & \(32\) & Write data signal for register \\ Wack & Output & \(1\) & Write response signal for register \\ Rreq & Input & \(1\) & Read request signal for register \\ Raddr & Input & \(14\) & Read address signal for register \\ Rdata & Output & \(32\) & Read data signal for register \\ Rack & Output & \(1\) & Read response signal for register \\ \hline \end{tabular}
\end{table}
Table 3: The interface table of signals for accessing and controlling the registers of the custom engines.
# An Efficient Multistage Approach for Blind Source Separation of Noisy Convolutive Speech Mixture
## 4 Results and Discussion  
This section provides the performance evaluation of the proposed work using the Matlab simulation tool. First, we use real speech signals from the TIMIT database [41] to generate noisy convolutive mixed signals using a simulated room model and then apply the proposed multistage algorithm to see its effectiveness.
# Automatic Wireless Signal Classification: A Neural-Induced Support Vector Machine-Based Approach
## 4 Simulation Results and Discussion
### Performance of NSVM with Different N  
The performance of the proposed schemes with different numbers of symbols is analyzed in this section. The overall averaged accuracy with symbol Number \(N\) = 2048, 1024 and 512 is plotted in Figure 8. The performance achieved reasonable degradation in classification accuracy as the number of space decreased. Another observation made here is to relate the performance of the proposed algorithm NSVM with higher order modulation. It can be seen from Figure 9 that the performance of the proposed QAM16 and QAM64 is compared with different symbol numbers. The proposed scheme performed better for higher order modulation due to more information being extracted from the constellation map by CNN, as well as a greater number of symbols.  
Figure 8: Performance of NSVM with different sampling points.  
Figure 7: The performance of NSVM for different individual modulation schemes.  
Figure 9: Performance of NSVM for QAM16 and QAM64 with \(N\) = 1024, 2048 sampling points.
# Image Segmentation Approaches for Weld Pool Monitoring during Robotic Arc Welding
## 2 The Structured Light Monitoring System  
Figure 1 shows the configuration of the popular weld pool monitoring system that has been adopted in References [9; 15; 16; 17; 18; 19; 20; 21; 22; 23]. The major parts of the system include two point grey cameras, C1 and C2, and one Lasiris SNF with the wavelength at 635 nm. A linear glass polarizing filter with the wavelength from 400 nm to 700 nm is mounted on camera C2 to remove the strong arc light. The structured light laser line pattern is projected by the Lasiris SNF laser generator onto the weld pool surface and reflected onto the diffusive imaging plane \(P1\). The calibrated camera C2 views the reflected laser lines from the back side of \(P1\), which is made up of a piece of glass and a piece  
of high-quality paper. To facilitate the computation, \(P1\), the \(YZ\) plane and the image plane of the calibrated camera \(C2\) are set to be parallel to each other. During calibration, the laser lines are projected by the Lasiris SNF laser onto a horizontal diffusive plane and camera \(C1\) is used to calculate the length of the straight laser lines in the world coordinate system. Then the horizontal diffusive plane is replaced by a horizontal mirror plane, which reflects the laser lines onto the vertical diffusive plane \(P1\). The calibrated camera \(C2\) is used to calculate the length of the imaged straight laser lines in the world coordinate system. Both camera \(C1\) and camera \(C2\) are not attached to the welding platform to avoid the vibration generated during the welding process.
# Automatic Digital Modulation Classification Based on Curriculum Learning
## 2 Signal Model and Relative Theories  
### Signal Models  
The received modulated signal can be expressed as:  
\[x(t)=\left(A_{i}+jA_{q}\right)e^{j(2\pi(f_{c}+\Delta f)t+\Delta\theta)}, \tag{1}\]  
where \(A_{i}\) and \(A_{q}\) are the in-phase and quadrature components of IQ modulation, respectively, \(f_{c}\) is the carrier frequency, \(\Delta f\) is the offset of carrier frequency and \(\Delta\theta\) is the phase offset. \(A_{q}=0\) in ASK and FSK, and \(f_{c}\) is a variable in FSK. For PSK, the amplitude of modulated signal is fixed but the phase is variable. Therefore, both \(A_{i}\) and \(A_{q}\) are varied while \(\left|A_{i}+jA_{q}\right|\) is fixed in PSK. QAM is a hybridization of ASK and PSK, whose amplitude and phase are variables. These features provide possibility for us to classify the modulation type, so that the original signal can be recovered accurately. However, the emerged noise in signal transmission often leads to signal distortion, which imposes a big obstacle in the recovery of the original signal.  
Among various noises, additive white Gaussian noise (AWGN) and Rayleigh fading are two most common noises. Therefore, we built models to test the performance of our method in the two above-mentioned noisy environments. Firstly, since AWGN cannot cause the amplitude attenuation and phase offset on signal, the received signal can be expressed as:  
\[r(t)=\left(A_{i}+jA_{q}\right)e^{j(2\pi(f_{c}+\Delta f)t+\Delta\theta)}+n(t), \tag{2}\]where \(n(t)\) is the additive white noise obeying the zero-mean Gaussian distribution. This model is an effective model to depict the propagation of wired signal, satellite signal and deep space radio frequency communication signal.  
Rayleigh fading describes the amplitude attenuation and Doppler shift induced by reflection, refraction, scattering and relative motion between the receiver and the transmitter in the propagation of wireless signal. Once a signal passes through a wireless channel, its amplitude becomes random and its envelope obeys the Rayleigh distribution. According to the central limit theorem, the amplitude of received signal approaches to the zero-mean Gaussian distribution. Since there is no line of sight in Rayleigh channel, the received signal is composed of multi signals suffering reflection, refraction or scattering. Hence, the received signal can be described as:  
\[\begin{array}{l}r(t)=\sum_{k=1}^{n}a_{k}(t)\big{(}A_{i}+jA_{q}\big{)}e^{j(2n( \zeta_{i}+\Delta f)(t-\tau_{k}(t))+\Delta\theta)}+n(t)\\ =\big{(}A_{i}+jA_{q}\big{)}e^{j(2\pi(\zeta_{i}+\Delta f)t+\Delta\theta)}\sum_{k= 1}^{n}a_{k}(t)e^{-j2\pi\tau_{k}(t)}+n(t),\end{array} \tag{3}\]  
where \(n\) means the number of paths, \(a_{k}(t)\) is the path gain of the \(k\)-th path and \(\tau_{k}(t)\) is the path gain of the \(k\)-th delay.