esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A new implant design with healing chambers in the threads was analyzed and compared with a conventional implant macrogeometry, both implants models with and without surface treatment. Eighty conical implants were prepared using commercially pure titanium (grade IV) by the company Implaci De Bortoli (Sao Paulo, Brazil). Four groups were performed, as described below: Group 1 (G1), traditional conical implants with surface treatment; group 2 (G2), traditional conical implants without surface treatment (machined surface); group 3 (G3), new conical implant design with surface treatment; group 4 (G4), new conical implant design without surface treatment. The implants were placed in the two tibas (\(n=2\) implants per tibia) of twenty New Zealand rabbits determined by randomization. The animals were euthanized after 15 days (Time 1) and 30 days (Time 2). The parameters evaluated were the implant stability quotient (ISQ), removal torque values (RTv), and histomorphometric evaluation to determine the bone to implant contact (%BIC) and bone area fraction occupancy (BAFO%). The results showed that the implants with the macrogeometry modified with healing chambers in the threads produced a significant enhancement in the osseointegration, accelerating this process. The statistical analyses of ISQ and RTv showed a significative statistical difference between the groups in both time periods of evaluation (\(p\leq 0.0001\)). Moreover, an important increase in the histological parameters were found for groups G3 and G4, with significant statistical differences to the BIC% (in the Time \(1\,p=0.0406\) and in the Time \(2\,p<0.0001\)) and the BAF0% ((in the Time \(1\,p=0.0002\) and in the Time \(2\,p=0.0045\)). In conclusion, the result data showed that the implants with the new macrogeometry, presenting the healing chambers in the threads, produced a significant enhancement in the osseointegration, accelerating the process.  
dental implants; implant macrogeometry; implant stability; osseointegration; removal torque
## 3 Construction of LPTN-Based VML
### Calculation and Classification of TRs  
To determine the parameters requiring identification in the LPTN model, all TRs in the LPTN model (Figure 5) are systematically calculated and classified. This process aims to establish intrinsic relationships between time-varying/ambiguous TRs and operational variables (rotational speed \(n_{s}\) and radial load \(F_{r}\)), ultimately deriving simplified mathematical expressions for parameter identification.
# In-Vitro Experimental Modeling of Oscillatory Respiratory Flow in a CT-Scanned OSAHS Tract
###### Abstract  
Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a highly prevalent respiratory disorder. The knowledge of respiratory flow is an essential prerequisite for the establishment and development of OSAHS physiology, pathology, and clinical medicine. We made the first in-vitro experimental attempt to measure the oscillatory flow velocity in a computed tomography (CT) scanned extra-thoracic airway (ETA) model with OSAHS by using the particle image velocimetry (PIV) technique. In order to mimic respiration flow, three techniques were adopted to address difficulties in in-vitro experimental modeling: (1) fabricating the obstructive ETA measurement section with the CT-scanned data of an OSAHS patient airway; (2) maintaining the measurement accuracy by using the optical index-matching technique; (3) reproducing the oscillatory respiratory flow rates with the compiled clinical data of transient tidal volumes. The in-vitro measurements of oscillatory respiratory flow velocity manifested the time evolution of the complex OSAHS flow patterns, and the potential wall collapse of the ETA model with OSAHS.  
Keywords:in-vitro experimental modeling; CT-scanned OSAHS model; oscillatory respiratory flow rates; particle image velocimetry technique; optical index matching +
Footnote †: journal: Applied Sciences
## 4 Feedback Control System and Field-Null Implementation
### Simulation Results  
By utilizing the closed-loop control system, simulation experiments on coil current FB control can be performed to determine whether the desired field-null configuration can be readily obtained, which form the last step before putting the designed field-null recipes into real HL-3 experiments. A typical example of these simulation experiments is shown in Figure 9, in which the field-null recipe shown by Figure 6a,b is selected as the control target, as such a configuration has not been realized in real experiments. The dashed lines in Figure 9a represent the simulatively controlled coil currents from the feedback control model, while the solid lines represent the target currents obtained by solving the field-null optimization problem, as shown in Figure 6b. It can be seen that the output values of coil currents from the low-pass-filtered PID controller can perfectly match the target current. As shown by Figure 9b, the typical control error is mostly below 0.3 kA for CS, 0.05 kA for PF4 and PF6 and 0.005 kA for PF7 and PF8 coils. This accuracy ensures that the subsequent  
Figure 8: Block diagram of the feedback control algorithm.  
\(|B_{\perp}|\) distribution will not deviate significantly from the designed configuration. This is verified by the calculated \(|B_{\perp}|\) from simulated coil currents, as shown by the contour plots in Figure 9c-e: even at \(t=16\) ms when the total eddy current is-170 kA (see Figure 4d), the calculated field-null configuration still perfectly matches the designed one in Figure 6a, and the area of \(|B_{\perp}|<1\) Gauss is even larger than that at \(t=0\) ms. This demonstrates the high reliability of the field-null optimization method in Section 3.1, as well as the feasibility of the calculated coil current waveforms as control targets in real experiments. In addition, if the field-null configuration could be realized, it may potentially help mitigate the vertically unstable problem in early-stage plasmas due to the midplane-aligned breakdown region, which has a very low value of vertical elongation.  
Figure 9: Evolution of field-null configuration before plasma breakdown. (**a**) The evolution of coil currents, with the solid lines refer to the control targets, and the dashed lines refer to the closed-loop simulation results. Some of the current values has been multiplied by a constant for a clearer view, e.g., “\(10^{4}\)IP\({}_{6}\) in the legend means the curve referring to I\({}_{\text{PF6}}\) is multiplied by 10. (**b**) The tracking error of coil currents, which are defined as the differences between targets and simulation results. For example, \(\text{I}_{\text{CS}}\) (error) \(=\text{I}_{\text{CS}}\) (target) \(-\text{I}_{\text{CS}}\) (sim) (**c**)–(**e**) The \(|B_{\perp}|\) contour plot calculated from the simulated coil currents at t = 0, 8 and 16 ms, respectively. The coils filled with orange and red colors refer to the “unknown” and “known” coils, and those filled with white refer to coils that are not energized, respectively.
# Secure Aviation Control through a Streamlined ADS-B Perception System
## 4 Methodology
### Data Preprocessing  
This subsection will discuss data preprocessing, which outlines the processes we carried out on raw data to prepare it for different operations to be conducted on that data. Data preprocessing is a crucial first stage in our study. The methods have recently been  
Figure 2: An overview of the proposed model.  
modified to aid in training AI and machine learning models by changing the data into a format that can be processed in machine learning and other data science techniques in a way that is quicker and more efficient than using raw data, bearing in mind that these processes are typically applied at the beginning of the machine learning development pipeline to ensure reliable findings. The dataset is available online in CSV files, which are program-generated data files. The data preprocessing consisted of three main processes, outlined below.  
* Data normalization: this mainly consisted of standardizing features for us to be able to utilize it with the machine learning models.
* Data cleansing: we performed data cleansing on the data to make sure there were no errors or duplications while processing the data to avoid bias and conflict in results.
* Data scaling: this process revolved around data encoding, where we changed the results of some selected features from textual values (such as strings and boolean data) to numerical values (integer/float) to be able to use them with the machine learning models.
* Data Shuffling: We used different techniques to randomize the training and testing data sets for more reliable results.  
The procedures used during data preprocessing are outlined below.
* We coded labels to convert categorical columns into numerical values on the icao24 and callsign columns. We also removed the remaining categorical features to decrease the margin of error.
* We converted all data to integer/float values using the one-hot encoding technique to ensure that the machine learning model can understand the data, which creates a one-numeric array to encode categorical information [35].
* We split the dataset into two main splits: one represents all features, and the other represents only the label feature (message type).
* We utilized pandas. drop, pandas.resample, and train_test_split to manipulate the dataset before performing any training or testing on our dataset. We also used multiple balancing techniques to aid with the classification process.
* We produced multiple training and testing subsets in different sizes that we randomly shuffled and generated at each iteration to initiate the learning process.  
As discussed, data preprocessing is a crucial stage in machine learning since the effectiveness of the machine learning model is directly impacted by the data quality. We processed, updated, and cleaned the dataset during data preprocessing so it fits the machine learning model.
## 2 Related Works
### Loss Functions for Semantic Segmentation  
The loss function is mainly an important indicator used to evaluate the training performance by comparing the similarity between the predicted result and the annotated result. In the field of  
Figure 1: Diagram of the 3D U-Net [18] structure.  
semantic segmentation, it is very necessary to select an appropriate loss function. In the past few years, there are many loss functions proposed in the existing research, but only a few works [26] have conducted specific studies on the proposed loss functions. These loss functions can be classified into two types according to different minimization targets.  
The first type is distribution-based loss function, in which the goal is to minimize the difference in the distribution of the model output and the real result. The most basic of this category is _cross entropy_[27], and the others are based on cross entropy transformation. _Cross entropy_ is derived from Kullback-Leibler (KL) divergence [28] and used to measure the difference between two distributions. For general machine learning tasks, the distribution of data is given by the training set. Therefore, minimizing KL divergence is equivalent to minimizing cross entropy. _Cross entropy_ (CE) is formulated as follows:  
\[\textit{Cross Entropy}=-\sum_{c=1}^{C}\bar{Y}_{c}log(Y_{c}) \tag{1}\]  
where C represents the number of target labels in the segmentation result. Weighted _cross entropy_ (WCE) [29] is a general extended form of cross entropy:  
\[\textit{WCE}=-\sum_{c=1}^{C}w_{c}\times\bar{Y}_{c}log(Y_{c}) \tag{2}\]  
_Cross entropy_ loss performs well in most semantic segmentation tasks, but it encounters difficulties when training with uneven sample distribution. To solve this problem, RetinaNet [30] proposed focal loss and used the labeled CE to process the uneven distribution of the foreground and background in the image, which can reduce the loss value of the correct classification category. The formulation of _focal loss_ is:  
\[\textit{Focal loss}=-\sum_{c=1}^{C}(1-Y_{c})^{\gamma}\times\bar{Y}_{c}log(Y_{c}) \tag{3}\]  
where the coefficient \(\gamma\) was introduced to obtain a better training effect by changing the contribution of the positive and negative samples to the loss.  
The another class is area-based loss function, in which the goal is to minimize the mismatched areas between annotated result and model output or to maximize the overlapping area of the annotated result and the model output. The most basic of this category is _dice loss_[31], which is formulated as follows:  
\[\textit{Dice loss}=1-\frac{2\times\left|\bar{Y}\cap Y\right|}{\left|\bar{Y} \right|+\left|Y\right|}=1-\frac{2\times\bar{Y}\cdot Y}{\bar{Y}^{2}+Y^{2}} \tag{4}\]  
The dice loss loss can directly optimize the dice coefficient and is one of the most commonly used segmentation indicators. Unlike cross entropy, it does not need to reweight the unbalanced segmentation tasks.
## 4 Discussion
### Extending Techniques to Determine the Viability
#### 4.2.3 Degradation of Genetic Material and eDNA Amount in the Viability Assessment  
Assessment of the stability of genetic material is another method determining the viability of spherical _H. pylori_ forms being at risk of misinterpretation (Figure 6). In many research articles it has been noticed that the stability of their DNA and RNA is lower than in spiral forms [15; 82; 84; 85]. In coccoid forms, a non-random fragmentation of genetic material occurs, while the amount of DNA and RNA is several times lower compared to spiral forms (Figure 6). Therefore, an observation of the genetic material disintegration of _H. pylori_ may be the result of morphological transformation into spherical forms and not necessarily a genotoxic effect of tested substances.  
An additional aspect worth mentioning is the assessment of eDNA amount as an indicator of the cellular membrane permeability. [PERSON] et al. have shown that in a process of outer membrane vesicles (OMVs) secretion by _H. pylori_, eDNA is released (Figure 6) [86; 87]. This, however, is a physiological process that can play an important role in the _H. pylori_ survival. Protective function of eDNA is associated with a participation in a horizontal gene transfer [88; 89] and the maintenance of biofilm stability [86]. In addition, it has been suggested that the release of OMVs during transformation of _H. pylori_ from a spiral to a coccoid form is associated with a reduction in cell size [24]. The similar process was also detected in VBNC _Vibrio parahaemolyticus_ cells and was related to a starvation-dependent reduction in metabolic activity [90].  
Figure 7: Representative photos of _H. pylori_ cells taken with transmission electron microscopy (TEM). (**A**) Mixed population of spiral/rod-shaped and coccoid cells. (**B**) Population of coccoid cells in different physiological states. (**C**) C coccoid cell after degradation (cell lysis). Symbols: coccoid form with dense cytosol (white arrow), coccoid form with “empty cytosol” (empty arrow), irregularly- shaped coccoid form (white arrowhead), outer membrane vesicles (white stars). Scale bar is equal to 0.2 \(\upmu\)m.
## 4 Results
### Confirmatory Factor Analysis (CFA)  
The current study conducted the CFA by using the maximum likelihood estimation (MLE) (see Table 3). All standardized factor loadings were equal or greater than 0.743 and significant at \(p<0.001\).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Variables** & \begin{tabular}{c} **Factor** \\ **Loading** \\ \end{tabular} & \begin{tabular}{c} **Eigen** \\ **Value** \\ \end{tabular} & \
\begin{tabular}{c} **Explained** \\ **Variance** \\ \end{tabular} &
\begin{tabular}{c} **Cronbach’s a** \\ \end{tabular} \\ \hline
**Perceived risk theory** & & & & \\
**Psychological risk** & & 2.973 & 24.779 & 0.960 \\ PR1 & 0.924 & & & \\ PR2 & 0.949 & & & \\ PR3 & 0.926 & & & \\
**Quality risk** & & 2.850 & 23.746 & 0.964 \\ QR1 & 0.864 & & & \\ QR2 & 0.855 & & & \\ QR3 & 0.888 & & & \\
**Financial risk** & & 2.642 & 22.017 & 0.919 \\ FR1 & 0.880 & & & \\ FR2 & 0.767 & & & \\ FR3 & 0.904 & & & \\
**Time risk** & & 2.263 & 18.862 & 0.880 \\ TR1 & 0.929 & & & \\ TR2 & 0.937 & & & \\ TR3 & 0.907 & & & \\ \hline \hline \end{tabular} Note: Total explained variance = 89.404%, KMO measure of sampling adequacy = 0.861, Bartlett’ test of sphericity (\(p<0.001\)), and measurement scales are presented in Appendix A.  
\end{table}
Table 2: PCA for the PRT in the ISFR context.  
Figure 2: Proposed model revision.  
The results indicated that the model fits the data well (\(\chi^{2}(221)=484.627,\chi^{2}/df=2.193\), \(p<0.001\), IFI = 0.963, CFI = 0.963, TLI = 0.953, and RMSEA = 0.062) [81,83], which is presented in Table 4. The results showed that the internal consistency (CR \(>\) 0.7), convergent validity (AVE \(>\) 0.5), and discriminant validity (correlation2 \(<\) AVE) satisfied the thresholds [84,85].
# Portrait Semantic Segmentation Method Based on Dual Modal Information Complementarity
## 4 Experiments
### Module Ablation Experiments  
#### 4.4.1 Encoder Based Ablation Experiments  
In order to verify the effectiveness of the model encoder, using dual branches for modal information complementation and SK-ASSP as a feature extraction module, ablation experiments were carried out, and the following six groups of networks were set up: Network_1 represents the U-net benchmark model. Network_2 represents the model using U-net basic model and encoder adding gray-scale image input. Network_3 represents a model that uses U-net basic model, the encoder adds grayscale image input, and adds cross-modal complementary module for feature fusion. Network_4 represents a model that adds ASSP module on the basis of Network_3 for feature extraction of RGB image and grayscale image. Network_5 represents a model in which the SK-ASSP module is added to the encoder on the basis of Network_3, and the adaptive ASSP branch is used for feature extraction of RGB image and gray image. Network_6 represents a model in which SK-ASSP module is added to encoder on the basis of Network_3, and adaptive ASSP branches and edge optimization branches are used for feature extraction of RGB and gray images. The experimental results are shown in Table 1:  
According to Table 1, compared with Network_1, Network_2 with grayscale image input increases PA and MIoU by 0.09% and 0.11%, respectively. On the basis of Network_2, Network_3 with the addition of cross-mode complementary module for feature fusion has a more obvious improvement in PA and MIoU by 0.2% and 0.24%, compared with Network_1. Compared with Network_1, Network_4 with ASSP structure has improved both PA and MIoU, which proves that void convolution can effectively improve the segmentation accuracy of the model. Compared with Network_2 in PA and MIoU, Network_5 and Network_6 using SK-ASSP module, which is improved on the basis of ASSP module, have a greater improvement than Network_4. Network_5 has an increase of 0.45% and 0.64% in PA and MIoU compared with Network_4. Network_6 also has 0.2% and 0.32% advantages over Network_4 in PA and MIoU, respectively.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Network**} & \multirow{2}{*}{**Gray Image Input**} & \multirow{2}{*}{**ASSP**} & \multicolumn{2}{c}{**SK-ASSP, Edge**} & \multicolumn{2}{c}{**Cross-Modal**} & \multirow{2}{*}{**PA/\%**} & \multirow{2}{*}{**MIoU/\%**} \\  & & & & **Adaptive ASSP** & & **Optimization** & & **Complementary** \\  & & & & **Branches** & & & **Module** \\ \hline Network\_1 & & & & & & 94.72 & 92.06 \\ Network\_2 & ✓ & & & & & 94.81 & 92.17 \\ Network\_3 & ✓ & & & & & ✓ & 94.92 & 92.30 \\ Network\_4 & ✓ & ✓ & & & & ✓ & 95.54 & 93.12 \\ Network\_5 & ✓ & & & ✓ & & ✓ & 95.99 & 93.76 \\ Network\_6 & ✓ & & & ✓ & ✓ & ✓ & 9
## 3 Methods and Materials
### Model Architecture
#### 3.1.2 Data Pre-Processing  
At this stage, preparing input text for subsequent algorithmic analysis is a critical step in any text-processing procedure. According to the collected data, the pre-processing steps in this research were data cleaning, tokenization, stop word removal, and lemmatization, including part-of-speech (POS) tagging.  
Data Cleaning: where all uppercase characters are converted to lowercase characters, and all extra characters, numbers, and whitespaces were removed. Additionally, any word with less than three characters and high-frequency words was removed. In addition, if the paper was in a language other than English or had no keywords, the full paper was omitted only from the keywords analysis [37].  
Tokenization: to translate the human-readable text into machine-readable format for the purposes of text analysis; these were defined as meaningful units (tokens), hence breaking a stream of textual content up into words and thus forming a set of individual words separated by a line break or punctuation characters [38].  
Stop words removal: each language contains words that are frequently used, also known as noise words, which are the words that provide little information and are rarely needed. The removal of stop words is mainly motivated by the need to enhance execution speed and accuracy. Without the removal of stop words, the similarity may be overloaded with junk words and would render inaccurate results [39].  
Lemmatization: The method of identifying the dictionary forms of words (e.g., learn) given one of its inflected variants (e.g., learning, learns). It is generally followed by a labelling of words using a specific part-of-speech (POS) tagging [40].
# A Sequential Graph Neural Network for Short Text Classification
## 2 Methods
### SGNN Model and ESGNN Model  
With the continuous development of GNNs, GCNs are a simple and widely used message passing algorithm for semi-supervised classification [45]. In one message passing layer, GCNs propagate the features of nodes through average aggregation and transform the features of nodes by linear mapping. Its equation is  
Figure 2: An illustration of constructing a document graph for a real document. \(S1\) and \(S2\) represent the word sequence after preprocessing and after removing the stop words, respectively. We set the sliding window size \(k=2\) in the figure for convenience. \(A\) and X represent adjacency matrix and feature matrix, respectively.  
\[\hat{A}=\hat{D}^{-\frac{1}{2}}\Big{(}A+I_{|V|}\Big{)}\hat{D}^{-\frac{1}{2}} \tag{1}\]  
\[X^{\prime}=\sigma\big{(}\hat{A}XW\big{)} \tag{2}\]  
where \(I_{|V|}\) is the identity matrix, \(\hat{A}=\hat{D}^{-\frac{1}{2}}\Big{(}A+I_{|V|}\Big{)}\hat{D}^{-\frac{1}{2}}\) is the symmetrically normalized adjacency matrix with self-loops, with the diagonal degree matrix \(\hat{D}_{ij}=\sum_{k}\Big{(}A+I_{|V|}\Big{)}_{jk},W\) is the trainable weight matrix, \(\sigma\) is the activation function, such as ReLU, and \(X\in R^{|V|\times d}\) and \(X^{\prime}\in R^{|V|\times d}\) are the feature matrices of the current layer and next layer, respectively [41].  
Recently, it has been found that by decoupling the GCN's feature transformation and propagation and removing the nonlinearities between GCN layers, the improved GCN is more efficient than the traditional GCN in many tasks [46; 47]. Inspired by the above research, we propose SGNN and ESGNN models for short text classification. The model architecture is shown in Figure 3. Since Bi-LSTM can capture bidirectional semantic dependencies, it can well model the sequential information of documents. Therefore, for each document in the short text corpus, Bi-LSTM is used to extract the contextual information between words, learn the unique word representations of each document, and then update the feature matrix of the document graph. Then, we use the simplified GCN to aggregate the features of neighboring nodes on average and update the word node representation. The formulas of the SGNN model are as follows:  
\[H^{(0)}=Bi-LSTM(X) \tag{3}\]  
\[H^{L+1}=\hat{A}H^{(L)} \tag{4}\]  
where \(H^{(L)}\in R^{|V|\times d}\) is the feature matrix of the hidden layer.  
In addition, we extend our model with a branch ESGNN, where in the process of node aggregation, through set \(\alpha=0.1,\ 0.2\ldots\), the initial contextual feature of words is preserved. The formula of the ESGNN is as follows:  
\[H^{L+1}=(1-\alpha)\hat{A}H^{(L)}+\alpha H^{(0)} \tag{5}\]