esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Generalization vs. Total Sample Size  
As mentioned in Section 4.1, the trained classifier did not satisfactorily generalize with respect to the left-out subjects--having an almost perfect validation accuracy while yielding a lower testing accuracy. As pointed out, one potential reason could be the lack of diversity on training data to provide a big enough learning space for vocal fatigue. The results from Section 4.2.1 also indicated that although more features could lead to better validation accuracy, it aggravated the issue with the generalization. So, in order to investigate the influences of sample sizes we repeated the experiment in Section 4.2, but this time varies the amount of used subject pairs in each experiment from 20 to 40. The classification results were plotted in terms of their testing accuracy in Figure 5. These results demonstrated a clear linear relationship between the total amount of training data being used (as adding the subject pair) and the resulting accuracy. Evidently, if more than 40 pairs were available, the results would further improve.  
Finally, as a last investigation on the generalization capability of the classifier, we performed a 90% left-out experiment on the 40 age- and skinfold-matched subjects. In this case, we randomly selected 10% of the samples (i.e., between 10 and 15 samples) from the testing subject 'left-out' to be included in training and validation. The results are reported in Table 7. As the table shows, by adding only 10-15 additional training samples (w.r.t. the total size of 6472 samples), the classification performance in testing was significantly improved from 62.36% to 83.93% in accuracy. Similarly, the sensitivity went up from 0.72 to 0.85, and the specificity from 0.53 to 0.83.  
Figure 5: Progression of the proposed protocol for the sEMG classification of vocal fatigue as the total number of matched subjects increases.
## 6 MBES Backscatter Angular Response Features  
Backscatter angular response approaches use the angular response curves (ARCs) to extract statistical parameters that describe their overall structure and sometimes even directly link them to modelled responses of certain sediment granulometries. This work followed the approach of [65]. ARC statistics are extracted from seafloor patches, defined as average per 2.5\({}^{\circ}\) angular bin (range of grazing angles) of several consecutive sonar pings, set here to 30. The stacked angular responses are then divided in three angular ranges: Near, Far and Outer. Finally, slopes and intercepts are extracted for each range per channel (port and starboard) (Figure 7a). In our case ARCs are calculated directly from the recorded raw data files and they are corrected for transmission losses (spreading and absorption) and for the volume scattering due to the ensonified footprint following [66].  
Factor Analysis application to the ARCs, using the consecutive stacks of pings as cases and the respective angular bins as variables, clearly revealed 3 angular ranges with highly correlated responses. Those ranges, defined as Near: 0-25\({}^{\circ}\), Far: 25-45\({}^{\circ}\) and Outer: 45-65\({}^{\circ}\) (Figure 7b), nearly validates the work in [65], which defined them as Near: 0-25\({}^{\circ}\), Far: 25-55\({}^{\circ}\) and Outer: 55-85\({}^{\circ}\).This way, a calibration is realized, especially in the Far and Outer ranges, so that those ranges are better adapted to our dataset.  
Figure 7: Left (**a**): Angular response ranges as defined in [65]. Right (**b**): angular ranges as proposed by FA (factor analysis) loadings of all stacked ARCs constructed from the MBES data.  
Following the above findings, slopes and intercepts where extracted from the ranges identified by Factor Analysis application, finally constructing an ARC derived feature vector of 6 features (Figure 5).
# A 100 Gbps OFDM-Based 28 GHz Millimeter-Wave Radio over Fiber Fronthaul System for 5G
## 4 Remote Radio Head (RRH) System
For IQ compensation, the DSP unit implements the Gram-Schmidt orthogonalization procedure which results in a new pair of orthogonal signals given as, \(\mathrm{I}^{\mathrm{O}}(\mathrm{t})\) and \(\mathrm{Q}^{\mathrm{o}}(\mathrm{t})\) expressed as:  
\[\mathrm{I}^{\mathrm{o}}(\mathrm{t})=\frac{\mathrm{r}_{\mathrm{I}}(\mathrm{t})} {\sqrt{\mathrm{P}_{\mathrm{I}}}},\mathrm{Q}^{\mathrm{o}}(\mathrm{t})=\frac{ \mathrm{Q}^{\prime}(\mathrm{t})}{\sqrt{\mathrm{P}_{\mathrm{Q}}}},\mathrm{Q}^{ \prime}(\mathrm{t})=\mathrm{r}_{\mathrm{Q}}(\mathrm{t})-\frac{\rho\cdot\mathrm{ r}_{\mathrm{I}}(\mathrm{t})}{\sqrt{\mathrm{P}_{\mathrm{I}}}} \tag{16}\]  
where, \(\mathrm{P}=\mathrm{E}\big{\{}\mathrm{r}_{\mathrm{I}}(\mathrm{t}),\mathrm{r}_{ \mathrm{Q}}(\mathrm{t})\big{\}}\) is the correlation coefficient, \(\mathrm{E}\{.\}\) is the ensemble average operator. \(\mathrm{P}_{\mathrm{I}}=\mathrm{E}\big{\{}\mathrm{r}^{2}_{\mathrm{I}}(\mathrm{ t})\big{\}}\) and \(\mathrm{P}_{\mathrm{Q}}=\mathrm{E}\Big{\{}\mathrm{Q}^{\prime 2}(\mathrm{t})\Big{\}}\). For non-linear compensation, the DSP unit implements a digital backpropagation method based on the inverse non-linear Schrodinger equation expressed as;  
\[\frac{\partial\mathrm{E}}{\partial(-\mathrm{z})}=(\mathrm{D}+\mathrm{N}) \mathrm{E} \tag{17}\]  
where E is the complex field of the received signal, D is the differential operator accounting for linear effects (chromatic dispersion and attenuation) and N is the non-linear operator which are given as;  
\[\mathrm{D}=\frac{\mathrm{j}}{2}\cdot\beta_{2}\cdot\frac{\partial^{2}}{ \mathrm{dt}^{2}}-\frac{\alpha}{2} \tag{18}\]  
Figure 5:0  
where, \(\alpha\) is the attenuation factor, \(\beta_{2}\) is the group velocity dispersion and \(\gamma\) is the non-linear parameter. In the receiver system, the demodulated IQ signal from the quadrature modulated is first filtered by a LPCR-Off filter before IQ and non-linearity compensation. The resulting signal is demodulated by the OFDM demodulator. The PSK or QAM sequence decoder is finally used to demodulate the OFDM symbol and the BER analyzer is used to estimate the BER and QF.
## 4 Experiment Setup  
### Dataset  
In the experiments, we used a public database presented by [PERSON] et al. in [30] (Valentini for short). This database consists of 30 speakers from the Voice Bank corpus [31], where 28 speakers were used for the training set and 2 for the test set. The training set has 11,572 clean-noisy utterances pairs. The noisy conditions include 10 noise types (8 noises from the DEMAND database [32] and 2 artificial noises) at signal-to-noise ratio (SNR) of 0, 5, 10 and 15 dB. There are 824 clean-noisy utterances pairs in the test set, including 5 noise types from the DEMAND database that are different from the training set [32], and the SNR is set to 2.5, 7.5, 12.5 and 17.5 dB.  
Furthermore, to evaluate the denoising performance of our model under unseen noise in the Valentini dataset, we chose clean speech of 2 speakers from the clean Valentini test set [30], and the noise generated by the vibration of objects collected by special equipment. These objects, which were irradiated by special equipment, include laptop bag, briefcase, foam board, desk calendars, potted green dill, clothes and pillows. The SNR is set to \(-5\), 0, 5 and 10 dB, and a total 560 noisy sentences (560-Testset, for short) were obtained for testing.
## 3 Long-Term Losses of Prestress
The shrinkage of concrete is affected by such factors as time, concrete compressive strength, member shape, relative humidity, type of cement, and temperature. Equation (9) was proposed by ACI 209R [36] to incorporate the time-dependency of \((\varepsilon_{sh})_{t}\) for the moist-curing condition. \[(\varepsilon_{sh})_{t}=\frac{t}{35+t}(\varepsilon_{sh})_{u}, \tag{9}\]  
where \(t\): time after 7 days of moist curing (days) and \((\varepsilon_{sh})_{u}\): ultimate shrinkage strain with the recommended value of \(780\times 10^{-6}\) m/m. Various correction factors are applied to reflect other effects on the shrinkage [36]. In comparison, ACI 423.10R [34] suggested \((\varepsilon_{sh})_{u}\) by incorporating a few correction factors in Equation (10). The final shrinkage loss can be obtained by using \((\varepsilon_{sh})_{u}\) instead of \((\varepsilon_{sh})_{t}\) in Equation (8). The shrinkage that occurs after the end of initial wet curing and before tensioning is not considered in the calculation of prestress loss. \[(\varepsilon_{sh})_{u}=8.2\times 10^{-6}K_{sh}(1-0.0024V/S)(100-RH), \tag{10}\]  
where \(K_{sh}\): correction factor considering the time between the end of initial wet curing and tensioning, \(V/S\): volume to surface ratio (mm), and \(RH\): relative humidity (%). On the other hand, whereas the relaxation loss can be estimated using various predictive equations [33; 34], it cannot be measured by using strain-based sensors, such as Smart Strand, because the relaxation indicates the reduction in tendon stress under constant strain. The long-term losses can also be obtained in a combined manner using Equation (11), specified in Eurocode 2 [38]. The notations used in the original Expression (5.46) of Eurocode 2 [38] were partly modified in Equation (11) to be concise and consistent. \[\Delta f_{p,CR+SH+R}=\frac{E_{p}(\varepsilon_{sh})_{t}+0.8\Delta f_{pR}+nC_{ t}f_{c}}{1+n\frac{A_{p}}{\Delta c}\Big{(}1+\frac{A_{p}}{\Delta c}\varepsilon_{p} ^{2}\Big{)}(1+0.8C_{t})}, \tag{11}\]where \(\Delta f_{pR}\): relaxation loss, all other notations were defined earlier. The original form of \(e_{p}{}^{2}\) in Equation (11) can be revised to \(e_{p}e_{sm}\) in order to further enhance the accuracy, as explained above for Equation (6). However, \(f_{c}\) in Equation (11) should be calculated based on \(P_{i}\) instead of 0.9\(P_{i}\) because the long-term variation of \(P_{i}\) is already accounted for in the derivation of Equation (11) and is incorporated in the denominator of this equation. The detailed calculation methods of \((\varepsilon_{sh})_{t}\) and \(C_{t}\) in Equation (11) are different from those of the aforementioned ACI 209R. The Korean design code for highway bridges [39] also uses this form of Equation (11), but \((\varepsilon_{sh})_{t}\) and \(C_{t}\) are differently calculated.
# Evaluation of CYGNSS Observations for Snow Properties, a Case Study in Tibetan Plateau, China
To the best of our knowledge, there is no reported research on snow cover using CYGNSS data. In this study, the CYGNSS data in the Tibetan Plateau from 1 January 2018 to 31 December 2019 are selected for snow cover analysis. This provides a new method for expanding the research field of satellite-borne CYGNSS, and provides a unique remote sensing method for the study of snow characteristics on the Tibetan Plateau. With the advantages of CYGNSS, using it in the remote sensing of the surface snow depth will improve the temporal resolution of snow depth data, and reduce the effects of background brightness temperature and radio frequency interference (RFI). The data description and processing method are presented in Section 2. The analysis using CYGNSS surface reflectivity (SR) and the surface reflectivity-difference ratio (SR-DR factor) are carried out in Section 3. Finally, the conclusions are given in Section 4.
# Defect Prediction for Capacitive Equipment in Power System
## 5 Experiment Results and Discussion
### Results of Defect Level Prediction  
The defect level prediction model serves to further predict the potential levels of defects. This model categorizes the defects into levels of urgency, critical, general, and others utilizing the same evaluation indicators as the defect occurrence prediction model. The prediction results of the defect level prediction models on the testing set, employing one-hot encoding, are presented in Table 7. The prediction results of the five models utilizing WOE encoding are displayed in Table 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Linear & 0.79 & 0.98 & 0.69 & 0.86 \\ XGBoost & 0.93 & 0.98 & 0.89 & 0.93 \\ SVM & 0.83 & 0.98 & 0.73 & 0.89 \\ MLP & 0.88 & 0.98 & 0.87 & 0.92 \\ RF & 0.96 & 0.98 & 0.97 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation of the defect occurrence prediction models based on WOE encoding.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Linear & 0.72 & 0.95 & 0.64 & 0.82 \\ XGBoost & 0.85 & 0.98 & 0.84 & 0.90 \\ SVM & 0.74 & 0.98 & 0.70 & 0.83 \\ MLP & 0.86 & 0.98 & 0.83 & 0.91 \\ RF & 0.92 & 0.98 & 0.92 & 0.94 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation of the defect occurrence prediction models based on one-hot encoding.  
After contrasting the data in both tables, we observed a notable enhancement in classification accuracy across all five algorithms for defect level prediction with the application of WOE encoding. Moreover, when compared to other algorithms, RF exhibited the most robust overall classification performance in predicting defect levels. The significance of the defect level prediction model lies in its ability to assist personnel in performing the precise maintenance of capacitive devices.
# Around-View-Monitoring-Based Automatic Parking System Using Parking Line Detection
## 3 Proposed Method
### Parking Line Detection
#### 3.1.3 Parking Slot Tracking  
After recognizing a parking slot, the parking location is determined and parking is performed. The parking slot is selected by the driver. Automatic parking is performed through path planning and tracking. During automatic parking, the vehicle moves and inevitably occludes the parking lines. In particular, there are cases where the corners and parking lines, which are important in this study, are not visible. As Figure 7 shows, during parking, the parking line may not be recognized in various circumstances, such as when a parking line or corner point is occluded. This highlights the importance of tracking the parking slot while parking to ensure proper parking control. Therefore, the approach proposed in this paper uses a method to track the parking slot so that the parking location information cannot be lost even while parking is in progress [35].  
Figure 6: Parking slot corner true and false data examples: (**a**) extraction of corner point candidates, and (**b**) true and false corner point examples.  
Figure 7: Examples of parking lines occluded by the vehicle.  
Therefore, the proposed method tracks the parking slot and is configured to provide information about the parking slot even when it is not covered or recognized during parking. For this, an algorithm to track the parking slot was employed.  
To track the parking area, both corners of the parking area and the distance between them are used. The corners are tracked using a Kalman filter. The state matrix of the Kalman filter is as follows.  
\[x=[L_{x}\ L_{y}\ R_{x}\ R_{y}\ \Delta L_{x}\ \Delta L_{y}\ \Delta R_{x}\ \Delta R_{y}]. \tag{2}\]  
In Equation (2), \(L_{x}\) and \(L_{y}\) are the coordinates of the left corner of the parking area, \(\Delta L_{x}\), \(\Delta L_{y}\) represent the derivative at the left corner coordinates, \(R_{x}\) and \(R_{y}\) are the coordinates of the right corner, and \(\Delta R_{x}\) and \(\Delta R_{y}\) represent the derivative at the right corner. In addition, because the distance between both points is fixed, this condition is used for tracking. When one point is occluded, the location and distance to the other point is used for tracking.  
It is important to maintain the information about the parking area for vehicle control during actual automatic parking. Figure 8 shows how this is achieved. As shown, as parking proceeds, the parking line is almost completely occluded, but the parking line is detected because of the tracking algorithm.
# Emergence of Nano-Dentistry as a Reality of Contemporary Dentistry
## 4 Discussion
### Conservative Nano-Dentistry
#### 4.1.2 Tooth Restoration Procedures with Nanomaterials
Glass Ionomer Cement (GIC) originated in the middle of the 20 th century, as a biocompatible, cost effective, tooth-colored restorative material and is constantly evolving. Considering its unique ability to bond to the tooth structure without the use of any bonding agent coupled with fluoride releasing potential, GIC has gradually emerged as the material of choice for various applications in the field of dentistry [52]. The glass ionomers with nanoparticles are called nanoinomers. The glass ionomer cement is widely used on the basis of its chemical binding to the tooth surface. Nanomers and nanoclusters are added to fluoroduminosilicate glass [53]. The nanoinomer produces aesthetic and fluoride releasing properties. The nanoglass ionomer has high translucency and optical properties compared to the conventional GIC [54]. Ketac N-100 is the first resin-modified glass ionomer cement designed based on the nano-filler technology. Ketac N-100 nanoinomer represents a combination of the fluoroluminosilicate technology and the concept of nanotechnology encountered in FiltekTM Supreme Universal Restorative preparation [55]. Incorporation of nano-sized particles in powder-modified nanoglass ionomers improves their mechanical properties [56]. For the first time, it was assumed by [PERSON] et al. that doping conventional GICs with nano-sized glass particles can decrease the setting time and enhance the compression strength and elastic-modulus [57].
# Text Classification Model Enhanced by Unlabeled Data for LaTeX Formula
## 3 Unlabeled Data Enhanced Text Classification
### Multi-Rounds Teacher-Student Training Approach  
The classification model should be trained for multi-rounds since the pseudo-labels from the single-round self-training are not accurate enough. The multi-rounds teacher-student training approach is proposed, where the Teacher model generates the pseudo-labels to retrain the Student model, and the Student model updates the Teacher model on the next training round.  
The role of the Teacher model is to help and supervise the training of a better Student model. It can reduce the prediction instability caused by an immature model and improve its classification accuracy.  
Figure 3 shows the training approach, and the training process is as follows:  
1. TP-BERT is pretrained by the domain and task unlabeled data \(\{\widetilde{x}_{1},\widetilde{x}_{2},\ldots\widetilde{x}_{i},\ldots, \widetilde{x}_{n}\}\);
2. For the first time, UL-BERT (Teacher) is fine-tuned based on TP-BERT by the original labeled data \((x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{n},y_{n})\);
3. By way of the DPP method, a high confidence label will be selected as an available pseudo-label \(\widetilde{y}_{i}^{p}\), which can be expressed as: \[\widetilde{y}_{i}^{p}=DPP(\widetilde{x}_{i}^{p});\] (5)
4. The UL-BERT model (Student) is fine-tuned on TP-BERT using the new dataset \(\left(\widetilde{x}_{i}^{p},\widetilde{y}_{i}^{p}\right)\) and the original training data \((x_{i},y_{i})\). The cross-entropy loss function \(L_{s}\) from both data is given by: \[L_{s}=-\frac{1}{m}\sum_{j=1}^{m}\sum_{i=1}^{l}y_{i}^{j}logp_{j}^{j}-\frac{1}{k }\sum_{j=1}^{n}\sum_{i=1}^{l}\widetilde{y}_{i}^{j}log\widetilde{p}_{j}^{j},\] (6)where \(m\) and \(k\) are the batch size of the label data and pseudo-label data, which are selected from \((\widetilde{x_{n}},\widetilde{y_{n}})\); \(\widetilde{y}\) is the pseudo-label and \(\widetilde{p}\) is the pseudo-labeled data predicted label;
5. If the F1 score of the test dataset (by UL-BERT Student model) is better than the one (by UL-BERT Teacher model), the Teacher model will be updated by the Student model;
6. Repeat steps 3-5 until the F1 score of the test dataset of the Student model no longer increases.
# Multi-Device to Multi-Device (MD2 MD) Content-Centric Networking Based on Multi-RAT Device
## 1 Introduction
The remainder of this paper is organized as follows: Section 2 introduces D2D communication, CCN, and Bluetooth Beacon, and identifies the problems arising in an MD2 MD CCN environment. Section 3 proposes a means of transmitting and receiving device information using Bluetooth Beacon, and presents some example scenarios in which this method could be applied. Section 4 evaluates and analyzes the performance of the proposed method as realized in a testbed based on the Raspberry Pi. Finally, Section 5 concludes the paper.
## 1 Introduction  
Nowadays, one of the major health goals is to achieve and maintain a desirable body weight, and this is accompanied by high awareness of the types of diets used for weight control. Meal replacements are substitutes for a wholesome dish in the form of a snack, bar or powder for making a drink or soup. They should be palatable, portion-controlled, exclude the need to choose and prepare foods, and eliminate food varieties that can spur over-consumption of energy [1, 2]. Incorporating meal replacements (1-2 per day) into traditional lifestyle interventions is effective in treating overweight or obese patients [3, 4]. Among other food trends, organic food products are considered to have higher nutritional value, compared to conventional ones. As well, organic plant raw materials contain fewer nitrates and pesticide residues [5].  
A high-protein bar is an example of meal replacement. Its production should include the use of high-quality raw materials that bring biologically active substances, i.e., the basic nutrients, as well as non-nutritive compounds naturally occurring in the raw material or in the product subjected to the technological process that affects physiological and metabolic functions of the body [6]. Previous research conducted by [PERSON] et al. [7] concerned the development of bar recipes made from organic plant-origin material (cereal, dried fruits, nuts, chocolate and others), as well as whey protein concentrate (WPC). The protein content was dependent on the product composition. Muesli and coconut bars contained a high amount of proteins in total (at least 20% energy from proteins according to Regulation (EC) No 1924/2006 [8]. All bars were characterized by a high scavenging capacity of DPPH (2,2-diphenyl-1-picryl-hydrazyl-hydrate) free radicals. However, some organic ingredients were contaminated with _Bacillus cereus_, and these bacteria were also found in muesli and pumpkin bars.  
Although these organic bars had low water activity (aw) (0.63-0.74), which has clear advantages concerning controlling the growth of foodborne pathogens, there are nevertheless some major concerns. Desiccation stress is a common event in natural and food industry environments that significantly affects bacterial viability, but they can resist this [9; 10].  
The study aimed to assess the safety characteristics of organic high-protein during storage at ambient and refrigerated temperatures. Moreover, an attempt was made to define the date of minimum durability and the storage conditions of the bars. Selected microbiological and chemical indicators were evaluated.
## 2 Software-Defined GNSS Receiver
### Bit Boundary Detection  
Once the BeiDou receiver keeps tracking the carrier phase and the code offset of the incoming signal, the next phase is to detect the bit boundary and then to wipe off the NH code. The purpose of bit boundary detection is to avoid integration across a data bit-edge which might cause errors in the navigation message detection. Algorithms for the bit boundary detection can be found in [16; 21; 22]. The Histogram Method, for instance, senses the bit sign changes and keeps a statistic of their position. But this approach will not work with the BeiDou D1 signal due to the presence of NH code. The sign changes in this code within the data bit boundary would in fact be detected as data bit changes affecting the statistics that this method uses for the bit boundary detection. On the other hand, as the data bits are now modulated with the NH code, a simple correlation of the incoming NH code modulated data with the locally generated NH code can then be used to estimate the bit edge. The index with a maximum correlation peak of 20 will be perfectly aligned with the NH code, and it can then be used as the bit boundary index. In case of BeiDou D2 signal, a histogram-based approach is implemented for D2 bit edge detection.