esa-sceva/satcom-chunk-collection huggingface dataset:
# Adapted B-Spline Quasi-Interpolation for Approximating Piecewise Smooth Functions
## 2 Preliminaries
### Using the Smoothness Indicator to Define a New Adapted Approximation  
Returning to the problem of approximating a piecewise smooth function, we now describe the method for selecting the appropriate weight at each location \(x_{i}\).  
The first step is to identify the interval containing the discontinuity. This is achieved using the following approach, assuming a uniform grid:For each point \(x_{i}\), we compute a smoothness indicator \(I_{i}\):  
\[I_{i}=|\triangle^{2}f(x_{i})|,\]  
where \(\triangle^{2}f(x_{i})\) is the central second difference, defined as  
\[\triangle^{2}f(x_{i})=f(x_{i+1})-2f(x_{i})+f(x_{i-1}).\]  
The value of \(I_{i}\) is of size \(O(h^{2})\) at smooth regions of \(f\), and O(1) if \((x_{i-1},x_{i+1})\) contains a discontinuity of \(f\). Consequently, large values of \(I_{i}\) indicate regions of significant variation and are useful for detecting discontinuous intervals. We use second-order differences, rather than first-order, to also capture singularities such as jumps in the first derivative.  
If \(I_{j}\) exceeds a predetermined threshold, we conclude that a discontinuity exists within the interval \([x_{j},x_{j+1}]\). Once the interval of discontinuity is identified, we apply quasi-interpolation operator (14) while ensuring that the discontinuous region is excluded.  
We select the weights \(\{w_{i}\}\) in quasi-interpolation operator (14) from the three available options (15)-(17), so that the resulting operator depends only on function values from one side of a discontinuity. The selection is carried out as follows:  
For each \(x_{i}\), we compute the three values,  
\[V^{1}=I_{i},\;\;V^{2}=I_{i+1}+I_{i+2},\;\;V^{3}=I_{i-2}+I_{i-1}.\]  
Let \(k\in\{1,2,3\}\) be the index for which \(V^{k}\) is minimal. We then set \(w_{i}=w_{i}^{k}\) in (14). Notably, the procedure naturally selects \(w_{i}^{1}\equiv w_{i}^{Sym}\) in smooth regions.  
The resulting modified operator is referred to as the **adapted quasi-interpolation operator**, denoted by \(Q^{Adapt}(f)\).
## 3 Data and Methodology
### Evaluation Measures  
A useful visual representation of the classification results is the Confusion (Alternatively Contingency) Matrix [45]. The totals of correct predictions are presented under \"True\" area, while incorrect predictions totals are presented under \"False\" area. The outcomes of the prediction process are divided into Positive (\(P\)) and Negative (\(N\)) classes, hence this research tackles a binary classification problem. The class of interest is the positive class (i.e., a client has subscribed term deposit, \(y=yes\)) and the other is a negative class (i.e., a client not interested in a term deposit, \(y=no\)). To clarify more the predictions of the classification process, they are divided such that a True Positive (\(TP\)) and True Negative (\(TN\)) are considered correct predictions that match actual facts in the testing dataset. While False Positive (\(FP\)) and False Negative (\(FN\)) are incorrect classifications, meaning an actual negative classified as positive and actual positive classified as negative, respectively. Table 5 illustrates the organization of the classification results in the Confusion Matrix.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted Willingness to**} \\  & & **Take a Term Deposit** \\ \hline \multirow{3}{*}{Actual Willingness to} & \multicolumn{2}{c}{Willing to} & \multicolumn{2}{c}{Not interested} \\ \cline{2-4}  & Willing to & \(TP\) & \(FN\) \\ \cline{2-4}  & Not interested & \(FP\) & \(TN\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confusion Matrix.  
Furthermore, several performance metrics are derived from the confusion matrix to illustrate more the performance of the prediction model. Some of the metrics that are adopted by this research are listed in Table 6.
# A Modified Iterative Algorithm for Numerical Investigation of HIV Infection Dynamics
## 3 Application
### MNIM for the HIV Infection Model  
Let us first apply the Laplace transform on both sides of Equations (1)-(3) and using the initial conditions (4) and model parameters discussed in Table 1.  
\[\begin{array}{l}\Im(X(t))=\frac{1}{s+\alpha-r}\Big{[}\Big{\{}\frac{p}{s}+x_ {0}-\frac{r}{T_{\max}}\Im\big{(}X^{2}(t)\big{)}\Big{\}}\\ \quad-\Big{\{}\frac{r}{T_{\max}}\Im(Y(t)X(t))+k(Z(t)X(t))\Big{\}}\Big{]}\\ \Im(Y(t))=\frac{1}{s+\beta}\{y_{0}+k\Im(Z(t)\ X(t))\}\end{array} \tag{77}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Remark** \\ \hline \(p\) & Production rate of healthy T cells from bone marrow and thymus \\ \(\alpha\) & Uninfected T cell natural turn-over rate \\ \(r\) & Healthy T-cell growth rate through mitosis \\ \(T_{\max}\) & CD4\({}^{+}\) T cells maximum concentration level in the body \\ \(k\) & The rate of infection \\ \(\beta\) & Natural turn-over rates of infected T cells \\ \(N\) & Number of virus particles assumed to be produced by infected T cells \\ \(q\) & Virus particle natural turnover rate \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of parameters used in the HIV infection model.  
\[\Im(Z(t))=\frac{1}{s+q}\{z_{0}+N\beta\Im(Y(t))\} \tag{78}\]  
Again, applying the inverse Laplace transform on both sides of Equations (76)-(78), we get  
\[X(t)=\Im^{-1}\Big{[}\frac{1}{s+a-r}\Big{\{}\frac{p}{s}+x_{0}-\frac{r}{T_{\max}} \Im\big{(}X^{2}(t)\big{)}-\frac{r}{T_{\max}}\Im(Y(t)X(t)) \tag{79}\]  
\[-k\Im(Z(t)X(t))\}\Big{]} \tag{80}\]  
\[Z(t)=\Im^{-1}\bigg{\{}\frac{1}{s+\beta}(z_{0}+N\beta\Im(Y(t)))\bigg{\}} \tag{81}\]  
In view of (47)-(58), solutions are obtained by using MNIM, as follows  
\[X_{0}(t)=\Im^{-1}\bigg{\{}\frac{1}{s+\alpha-r}\Big{(}\frac{p}{s}+x_{0}\Big{)} \bigg{\}} \tag{82}\]  
\[Y_{0}(t)=\Im^{-1}\bigg{\{}\frac{y_{0}}{s+\beta}\bigg{\}} \tag{83}\]  
\[Z_{0}(t)=\Im^{-1}\bigg{\{}\frac{z_{0}}{s+q}\bigg{\}} \tag{84}\]  
\[X_{1}(t)=-\Im^{-1}\Big{[}\frac{1}{s+a-r}\Big{[}\frac{r}{T_{\max}}\big{\{}\Im \big{(}X_{0}^{2}(t)\big{)}+\Im(Y_{0}(t)X_{0}(t)) \tag{85}\]  
\[+k\Im(Z_{0}(t)X_{0}(t))\}\big{]} \tag{86}\]  
\[Y_{1}(t)=\Im^{-1}\bigg{[}\frac{k}{s+\beta}\big{\{}\Im(Z_{0}(t)X_{0}(t))\big{\}} \bigg{]} \tag{87}\]  
\[X_{2}(t)=-\Im^{-1}\Big{[}\frac{1}{s+a-r}\Big{[}\frac{r}{T_{\max}}\Big{\{}\Im( X_{1}(t)+X_{0}(t))^{2} \tag{88}\]  
\[+k\Im(Z_{0}(t)X_{0}(t))\}\Big{]} \tag{89}\]  
\[Z_{2}(t)=\Im^{-1}\bigg{[}\frac{N\beta}{s+q}\big{\{}\Im(Y_{1}(t))\big{\}}\bigg{]} \tag{90}\]  
The next successive iterations can be obtained in a similar manner.  
Therefore,  
\[X(t)=\sum_{i=0}^{2}X_{i}(t) \tag{91}\]  
\[Y(t)=\sum_{i=0}^{2}Y_{i}(t) \tag{92}\]  
\[Z(t)=\sum_{i=0}^{2}Z_{i}(t) \tag{93}\]  
For the computation purpose we have taken the 2-iterations of MNIM solutions.
# Study on the Fire Resistance of Axially Restrained H-Shaped Steel Beams Under Real Fire
## 4 Correction of Critical Temperature Calculation Formula of Steel Beam
### Limitations of the Formula  
The simplified formula of critical temperature proposed in this paper is based on the study of a Q460 high-strength steel H-beam under specific fire and boundary conditions. The error of the calculation results is less than 5%, but the applicability is limited. The formula derivation depends on the high-temperature performance parameters of Q460 steel, and the applicability of other steel grades needs to be verified. The research is only for the H-beam section, and its temperature distribution and buckling behavior are specific, and the applicability to other cross-section forms needs to be studied. The formula is based on a specific axial and rotational constraint stiffness ratio setting, and the applicability may be limited when the actual boundary conditions change. In complex cases, it is recommended to supplement the correction coefficient or use numerical simulation to verify.
###### Abstract  
This paper reports the preparation and characterization of thermosensitive poly(N-isopropylacrylamide) (PNIPAM)/magnetite nanoparticles in various conditions. The nanoprecipitation conditions address the impact of the temperature on PNIPAM/magnetite nanoparticle features due to the thermosensitive character of PNIPAM. Hybrid nanoparticles with desired features (size, size distribution, agglomeration, and release profile) are prepared by nanoporeipitation in nonsolvent (acetone) at various temperatures. These nanoparticles are targeted as nanocarriers to deliver doxorubicin in breast cancer cells. Therefore, three temperatures, below the LCST (lower critical solution temperature), around the LCST, and above the LCST, were chosen as the main parameters within nanoprecipitation. Besides temperature, another major parameter drives the nanoparticles' features: polymer solution concentration. In this regard, two variable parameters were used to study the characteristics of developed hybrid nanoparticles. After preparation, the hybrid nanoparticles were subjected to morphological and size distribution investigation by SEM and DLS. The doxorubicin loading and release measurements were also performed to reveal the behavior of the nanoparticles. Finally, the unloaded and loaded hybrid nanoparticles were biologically assessed within a cancer cells line (MCF7) in terms of biocompatibility, cancer cell viability, and cell morphology.  
P NIPAM; magnetite; nanoparticles; doxorubicin; breast cancer; drug delivery Article
# A Method for Broken Rotor Bars Diagnosis Based on Sum-Of-Squares of Current Signals
## 5 Conclusions  
In this paper, a simple SS method is proposed for the fault diagnosis of broken rotor bars of IMs. The SS method only needs to calculate the SS signal of three-phase current signals. In the spectrum of the SS signal, the signature frequency components are more obvious. The effectiveness of this method is verified by the experimental data and comparison with other popular methods. Through experimental data analysis, this method is shown to have the following advantages:  
1. The SS method is effective for broken rotor bar diagnosis under different load conditions and different rotation speeds of the MFS test stand.
2. By comparing with the popular methods, including [PERSON]'s vector modulus, HT, TEO, and d-q transform, the effect of the SS method and [PERSON]'s vector modulus is the best of them. Theoretically, the SS method and [PERSON]'s vector modulus have the same effectiveness.
3. The principle of the SS method is simple and the computation cost is relatively smaller than many popular methods.  
However, due to the limitation of experimental conditions, this paper does not discuss the effectiveness of the SS method in BRBs incipient fault detection, and the fault detection threshold for the quantification of BRBs is also not studied. Both of them are of great significance for accurately estimating the health conditions of IMs. In our future studies, we will focus on research into these challenges.  
[PERSON] conceived and designed the experiments, analyzed the experimental results, and wrote the first draft of the manuscript. [PERSON] proposed the theoretical system of the method, critically revised the manuscript, and approved the final version of the manuscript. [PERSON], [PERSON], [PERSON], and [PERSON] revised the manuscript and developed the structure of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Grant No. 51975576), Defense Industrial Technology Development Program (Grant No. WDZC20195500305), and the National Natural Science Foundation of China (Grant No. 51475463).  
Thanks to our younger brother [PERSON] for his great support during the experiment. Thanks also go to the anonymous reviewers and the editor for their helpful comments and constructive suggestions.  
The author declares no conflict of interest.  
Figure 9: Comparison of computation cost between the SS method and other algorithms.
# Design and Analysis of Joint Source-Channel Code System with Fixed-Length Code
###### Abstract  
As the demands of multimedia and data services increase, efficient communication systems are being investigated to meet the high data rate requirements. Joint source-channel coding (JSCC) schemes were proposed for improving overall system performance. However, existing JSCC systems may suffer a symbol error rate (SER) performance loss when residual source redundancy is not fully exploited. This paper presents a novel, low-complexity JSCC system, which consists of a fixed-length source block code and an irregular convolutional channel code. A simple approach is proposed to design source codes that minimize the SER of source detection and guarantee the convergence of iterative source-channel decoding (ISCD). To improve the waterfall performance of ISCD, the channel code is optimized by using the extrinsic information transfer (EXIT) chart and the concept of irregular code. The channel code is constituted by recursive non-systematic convolutional (RNSC) subcodes. The weights of subcodes are optimized to make the EXIT curves of the channel decoder and the source decoder well-matched, and therefore, a near-capacity performance is achieved. Simulation results show that the proposed system achieves more than 1 dB gains and 0.3 dB gains compared to the separate source-channel code system and the other optimal JSCC systems, respectively. Additionally, the performance of the proposed system is within 1 dB deviation from the Shannon limit capacity.  
joint source-channel code; fixed-length code; irregular code; extrinsic information transfer chart; iterative decoding +
Footnote †: journal: Information  
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This implementation facilitates monitoring patients' conditions remotely, providing a safe diagnosis tool for doctors and physicians. This study is organized as follows. In Section 2, a critical review of recent literature is presented. In Section 3, the wearable is described providing the specifications of instrumental sensors for its development. Section 4 illustrates the system architecture while also providing significant benefits of the approach. Besides, the algorithmic process adopted is presented. Section 5 includes the results stemming from the present study. Lastly, in Section 6, concluding remarks and discussion on future research steps are provided.
## 2 Materials and Methods
### Model Simulations Based on Texas A&M Beaumont Rice Model  
As stated above, the rice aeration and insect population model developed by Texas A&M Beaumont [18] was used because there are no comparable easy-to-use web-based models for stored wheat. The model allows the user to specify parameters for bin size, depth of grain mass, type of aeration fan, and starting grain temperature. The input values chosen were a bin height of 7.3 m, diameter of 3.7 m, depth of grain 6.1 m, 1 m of headspace, and a 30 hp Sukup (www.sukup.com) fan. The bin dimensions were chosen to represent a large farm bin or a small elevator silo, with a capacity of about 340 MT. This model, similar to previous models, assumes unlimited growth and development of the insect population from a starting value. Thus, starting values must be low otherwise the numbers are so high that they may not be biologically meaningful. The starting value of _S. oryzae_ was 0.04 mixed-sex adults/27.2 kg of wheat, which equilibrates to 1.5 adult/metric ton as the starting value. These adults were assumed to enter the bins on 1 August. The temperature patterns produced through aeration, and the resulting effects on _S. oryzae_ population development, Birch [21] can be used to describe the difference between unaerated and aerated wheat. The maximum and minimum datasets for each site were run through the aeration component of the model, which then outputs the temperatures in 5 levels for unaerated and aerated grain: top, \(\frac{1}{4}\) down from the top, middle, \(\frac{3}{4}\) from the top, bottom, and the average temperature in the bin. The insect model predicts populations in an unspecified level, and the average per kg, based on a specified starting level. Because the insect component is paired with the aeration component, only the average bin temperatures between unaerated and aerated grain are reported for the simulations.
# An Analysis of Visibility Requirements and Reaction Times of Near-Field Projections
## 2 Materials and Methods
### Statistical Methods and Data Analysis
The selected data was analyzed through statistical evaluation methods to determine statistically significant differences between the individual independent parameters. The chi-square test (\(\chi^{2}\)) analyzes the distribution and the corresponding significances between the compared data [37]. The chi-square test is a statistical test procedure to declare statements about the relationship between variables that are either nominally or ordinally scaled. This present study used nominally scaled variables. Additionally, Cramer's V with the effect size V (see (5)) measures the strength of the correlation between two nominally scaled variables. For an effect size \(V\) < 0.2, the result has a weak effect even though the results are significant; for \(0.2<V<0.6\) the results have a moderate effect; and for \(V>0.6\) the results are considered to have a strong effect [37; 38]. A significance level of \(p=0.05\) was applied to all tests. \[V=\sqrt{\frac{\chi^{2}}{n\cdot m}} \tag{5}\]  
where:  
* \(\chi^{2}\) test statistic;
* \(n\) sample size;
* \(m\) degrees of freedom (min of: number of columns\({}^{-1}\)/number of rows\({}^{-1}\)).
# Retinal OCT Images: Graph-Based Layer Segmentation and Clinical Validation +
## 3 Results
### Performance Evaluation
#### 3.3.2 Segmentation Accuracy with Respect to Manual Segmentation  
The six intra-retinal layers segmented by the graph-based automated algorithm were manually re-segmented or the erroneous regions were corrected by the expert clinician using the graphical user interface described above. Figure 9 illustrates the manual segmentation of six intra-retinal layers of a sample macular SD-OCT image from the test dataset.  
Table 4 summarizes the mean thicknesses from automated segmentation and manual segmentation, respectively, for each of the six segmented layers. The mean error is reported as the difference between the layer thickness from manual segmentation and that from automated segmentation. The negative (-) sign denotes that the layer thickness from automated segmentation has a larger value than that from manual segmentation. No sign indicates a positive value, which signifies that the value of layer thickness from manual segmentation is greater than that from automated segmentation. The accuracy of the segmentation of each layer has also been determined and reported from Equation (5) as previously described.  
The automated graph-based segmentation algorithm demonstrated impressive speed and accuracy in segmenting the retinal layers and computing their mean thicknesses. The algorithm took approximately 5 s to produce the final output, including the segmented image and the layer thicknesses. In contrast, manual segmentation followed by thickness computation using the graphical user interface took about 10 min. Table 5 shows the average computation time for segmenting the macular SD-OCT images using automated and manual techniques.  
Figure 9: A sample macular SD-OCT image from the test dataset illustrating the manual segmentation by an expert clinician.
## 6 Experiments Results and Discussion
### RNNs Models Results  
#### 6.2.1 LSTM Model Results  
The LSTM model's accuracy, recall, precision, and F1 score are shown in Table 7. The worst experiment percentage is underlined, while the highest/best accuracy is bold, as shown in the table.
## 3 The Proposed Method
### Representation of the Environment Where the UAV Operates  
The initial step in the PP process is to represent the UAV's moving/flying environment from a real environment map with the help of relevant geometrical shapes. Generally, it is a process of dividing \(W\) into obstacle-free regions (\(\xi_{free}\)) and the obstacle regions (\(\xi_{obstacles}\)). An example of \(\xi_{free}\) and \(\xi_{obstacles}\) is presented in Figure 2, in which black and yellow regions represent the \(\xi_{free}\) and the \(\xi_{obstacles}\) regions, respectively. The obstacles in \(W\) can be modeled with sufficient accuracy by leveraging geometric shapes, such as cubes, rectangles, cylinders, circles, polygons, prisms, etc. In our work, obstacle modeling from a raw environment map is carried out with the help of 3D convex polyhedrons having six faces each. The minimum height (e.g., \(z_{min}\)) of each obstacle is 0, and the three other dimensions are random. To generate a convex-obstacles set, we extract the digital map's elevation readings in accordance with digital environment data standards, and we find a convex-hull to accomplish the obstacle modeling task. After the real map's processing, we create a map with fixed convex-polyhedron 3D obstacles. The beginning location of the mission is denoted with \(s\) (i.e., a 3D point), where \(s=(x_{s},y_{s},z_{s})\). The target location is denoted with \(t\) (also a 3D point), where \(t=(x_{t},y_{t},z_{t})\). Taking into account \(W\)'s representations, the UAV profile information, and path searching from a 3D environment, the objective of the proposed PP method is to find good-quality paths with the least computing complexity. The proposed PP method fulfills the stated assertions by finding a path from high-probability regions of the map, and the UAV is considered a single 3D point, just like \(s\) or \(t\).
# Real-Time Performance Measurement Application via Bluetooth Signals for Signalized Intersections
## 2 Materials and Methods
### Bluetooth-Based Traffic Analysis Detector Application Layout Structures  
In the method applied at Refik Cesur Intersection, Bluetooth traffic analysis sensors are placed as seen in Figure 6.  
Sensors are placed in the middle of the going and coming directions of the intersection arms. Thus, traffic data of vehicles from all traffic flows are obtained. Field installation of Bluetooth traffic analysis sensors is shown in Figure 7.  
Figure 6: Bluetooth traffic analysis sensor layouts.  
Figure 7: Bluetooth traffic analysis sensor field installation.  
Data can be detected for all traffic flows according to the layout plan of Bluetooth traffic analysis sensors. The detected MAC addresses are received by Bluetooth sensors and the data are sent to the center. MAC addresses sent to the center are matched according to the detected sensors. Thus, parameters such as vehicle routes, Bluetooth detection times, travel times, and delays can be determined. With this structure, a real-time performance measurement system is created. The scientifically biggest innovation of this structure is that it creates an infrastructure for performance-enhancing algorithms by using the detected parameters as feedback data in the fully adaptive signaling system. Since the performance measurement in this study is made in real time with real traffic data, it will make the management of fully adaptive signaling systems more efficient. As seen in Figure 6, Bluetooth data can be collected in the control center, and automatic results are detected and then sent as real-time feedback to the intersection control device that manages the fully adaptive signaling system.  
Bluetooth traffic analysis sensor configurations corresponding to traffic flows are shown in Table 5.