esa-sceva/satcom-chunk-collection huggingface dataset:
# Brain Tumor Detection with Deep Learning Methods' Classifier Optimization Using Medical Images
###### Abstract  
It is known that, with the development of artificial intelligence science in recent years, it has started to be used in all areas of life. Due to the increase in diseases that threaten human life, such as epidemics and cancer, more attention has been paid to research in this field. Especially in the field of biomedical image processing, very successful results have been obtained in recent years with the use of deep learning methods. For this study, MR images are utilized to diagnose brain tumors. To assist doctors and radiologists in automatic brain tumor diagnosis and to overcome the need for manual diagnosis, a brain MR image automated classification system is being developed. The data used in the study are open access data obtained from the Kaggle library. This paper presents a novel approach for classifying brain MR images utilizing a dataset of 7022 MR images. To give an unbiased evaluation of the dataset, it is divided into a 40% test and 60% training set. Respectively, VGG, ResNet, DenseNet and SqueezeNet architectures are trained and used for feature extraction from brain MRI images. In order to classify the extracted features, machine learning methods (Support Vector Machines, K-Nearest Neighbors, Naive Bayes, Decision Tree, Linear Regression Analysis) are applied first, then an ensemble learning method is applied and the best validation method is selected. In addition, parameter optimization is applied to the trained CNN algorithms. In order to develop the proposed methods, the Python software program was used in the training and testing phases of the models, and the classification success rates were mutually evaluated. Among the results found, it can see that the ResNet architecture reached 100% accuracy. The data obtained as a result of the study were compared with the results of similar studies. In conclusion, the techniques and methods applied highlight their effectiveness in accurately classifying brain MRI images and their potential to improve diagnostic capabilities.  
deep learning image processing convolutional neural networks classification; brain tumor +
Footnote †: journal: Medical Imaging  
10 December 2023  
Revised: 7 January 2024  
Accepted: 8 January 2024  
Published: 12 January 2024
# Credible Navigation Algorithm for GNSS Attack Detection Using Auxiliary Sensor System
## 4 Performance Evaluation
### Simulation Results  
In order to validate the performance of the proposed algorithm ACNA, the GNSS jump or slow-change attacks are simulated on the data set \(\Psi_{1}\) and \(\Psi_{2}\). The simulation of GNSS jump attacks are applied to \(\Psi_{2}\) to obtain \(\widetilde{\Psi}_{2}\). The simulation of GNSS slow-change attacks are applied to \(\Psi_{2}\) to obtain \(\widetilde{\Psi}_{3}\). Additionally, the simulation of GNSS jump and slow-change attacks are applied to \(\Psi_{2}\) to obtain \(\widetilde{\Psi}_{4}\).  
GNSS jump attacks are injected into data set as follows:  
\[\widehat{\rho}^{\text{GJ}}\text{=}\rho^{\text{G}}+\Delta\rho^{\text{J}}, \forall\Delta\rho^{\text{J}}\sim N(20,0.1) \tag{28}\]  
where \(\widehat{\rho}^{\text{GJ}}\) represents the position after GNSS jump attacks are injected, \(\rho^{\text{G}}\) represents the GNSS position, and \(\Delta\rho^{\text{J}}\) (random interference is added with an average of 20 m and a variance of 0.1) represents the position disturbance caused by GNSS jump attacks in the location domain. As can be seen from Equation (28), the jump attack injects big errors to GNSS signals.  
GNSS slow-change attacks are injected into data set as follows:  
\[\widehat{\rho}^{\text{GS}}\text{=}\rho^{\text{G}}+\sum_{i=T}\Delta\rho^{\text {S}}_{i},\forall\Delta\rho^{\text{S}}\sim N(0.5,0.05) \tag{29}\]  
where \(\widehat{\rho}^{\text{GS}}\) represents the position after GNSS slow-change attacks are injected, and \(\Delta\rho^{\text{S}}_{i}\) (random interference is superimposed per second with an average of 0.5 m and a variance of 0.05) represents the positional disturbance at time \(i\) generated by GNSS slow-change attacks in the location domain. As can be seen from Equation (29), the slow-change attack injects small errors to GNSS signals slowly and continuously during each second.  
Under such a condition, slowly injected errors can influence the filter gradually. In GNSS jump attack simulation (data set \(\widetilde{\Psi}_{2}\)), the segment from 150 s to 300 s of the data set is exposed to GNSS jump attacks defined in Equation (28). In GNSS slow-change attack simulation (data set \(\widetilde{\Psi}_{3}\)), the segment from 150 s to 300 s of the data set is exposed to GNSS slow-change attacks defined in Equation (29). Additionally, in the jump and slow-change attack simulation (data set \(\widetilde{\Psi}_{4}\)), the segment from 50 s to 100 s of the data set is exposed to GNSS jump attacks defined in Equation (28) and the segment from 150 s to 200 s of the data set is exposed to GNSS slow-change attacks defined in Equation (29).
# A Membrane Modified with Nitrogen-Doped TiO2/Graphene Oxide for Improved Photocatalytic Performance
## 3 Results and Discussion
### The X-Ray Diffraction Analysis of Modified Membrane  
Figure 4A was an XRD spectrum of graphite oxide, in which a characteristic peak corresponding to the (001) plane of the graphite oxide layered structure appears in the vicinity of 20 = 10\({}^{\circ}\) and the (002) plane characteristic peak of graphite disappears, which indicated that most of the graphite had been oxidized. The XRD pattern of the PVDF microfiltration membrane modified by different photocatalysts was shown in Figure 4B. The four catalysts supported on the surface were mainly of the anatase TiO\({}_{2}\) crystal structure; 20 was equal to 25.3 (101), 37.9 (004), 48.1 (200), 54 (105, 211), 62.7 (204), 69.1 (116, 220), and 75 (215). The doping of N and GO had no significant effect on the crystal structure of nano-titanium dioxide. There was no characteristic diffraction peak of graphite oxide in Figure 4B, which may have been due to the orderly layering of graphite oxide due to the ultrasonic dispersion and subsequent hydrothermal treatment. The structure was destroyed and partially reduced graphene  
Figure 3: Scanning electron microscopy (SEM) image of the modified membrane surface: (**a**) Polyvinylidene Fluoride (PVDF) membrane; (**b**) TiO\({}_{2}\)/PVDF membrane; (**c**) nitrogen-doped TiO\({}_{2}\) (NT)/PVDF membrane; (**d**) TiO\({}_{2}\)-graphene oxide (TG)/PVDF membrane and (**e**) nitrogen-doped TiO\({}_{2}\)/graphene oxide NTG/PVDF membrane.  
oxide was formed, TiO\({}_{2}\) particles were formed on the surface of the graphene oxide sheet layer, which hinders the ordered packing of the graphene oxide layer sheet.
## 5 Target Polarization Imaging Experiment  
The experiment uses the long-wave infrared polarization detector to obtain the polarization image of the target in real-time. This experiment aims to test the effectiveness of infrared polarization technology in distinguishing natural objects from artificial objects, metal, and botany and provide effective support for target identification and material evidence searches. Figure 11 is a schematic diagram of a long-wave infrared polarization imaging device.  
Figure 12 is a traditional visible light image and a long-wave infrared polarization image of a scene about 500 m away. It can be seen from the polarization degree image in Figure 12b that the contrast between people in action and the background is very high due to their radiation. The sidelines of the sports field ground can also be clearly seen. The metal plates on the fence are very obvious, and the polarization effects of car windows and car bodies parked outside the fence are apparent. From the polarization angle image in Figure 12c, it can be observed that the detailed contour information of the target is highlighted. With the reduction of the imaging distance, the detailed information of the target is more abundant, especially the eaves carving lines near are clear. The experimental results show that long-wave infrared polarization imaging can detect and identify targets at the same time and improve the contrast of targets. The infrared polarization imaging technology combining polarization information and infrared radiation information can improve the operating range of the imaging system.  
Figure 11: Schematic of long-wave infrared polarization imaging.  
Based on the advantages of infrared polarization imaging detection technology, this paper uses three methods to combine long-wave infrared and visible near-infrared. The fusion image flow is shown in Figure 13. The preliminary fusion of infrared and intensity images is achieved using wavelet transform [19]. The purpose of fusion rule 1 is to complete the fusion of low-frequency information of two source images, and fusion rule 2 is to achieve the fusion of high-frequency components of two source images. Then use the colour migration algorithm to enhance the pseudo colour of the image to make the target scene more prominent [20].  
Figure 12: Distant scenery image. (**a**) Visible light image, (**b**) long-wave infrared polarization image, (**c**) long-wave infrared polarization angle image.  
Figure 13: Framework flow chart of the fused image.  
Figure 14 shows the fused image. The fusion image results show that the imaging method combining visible near-infrared and long-wave infrared can not only image clearly under low illumination at night but also restore the original colour of the scene to a certain extent for easy observation and significantly improve the contrast of the target.
## 4 Proposed Scheme
### Signature Verification  
Verification involves the use of the public key \(K_{pub}\):  
* Mathematical validity: It is verified that \[\sigma^{e}(mod\ n)=M,\] where \(e\) is the public exponent of \(K_{pub}\).
* Confirmation of the parameter \(T\): The Algorithm 1 verifies that the level of anonymity specified by \(T\) is respected.
* Verification result: If both conditions are met, the signature \(\sigma\) is considered valid.
# Scale-Free Features in Collective Robot Foraging
## 2 Methods
### Data Analysis
#### 2.4.3 Correlation Measures  
To examine the presence of correlations between the support for the power law distribution (i.e., the value of \(\rho_{q}\)) and the swarm performance it is important to use an appropriate correlation measure. One of the most prominent correlation measures is the _Pearson_ correlation coefficient [58; 59]. It evaluates the quality of a linear association between two distributions. In essence, it calculates the covariance of the mean values of two distributions, over the root of their standard deviations. It is closely related to linear regression and does not require the data to be normally distributed. Despite its mathematical simplicity it is an appropriate correlation measure for many distributions and, therefore, is widely used [60; 61; 62; 63].  
However, one could argue that the Pearson correlation coefficient is not ideal for skewed distributions with strong outliers. Popular alternatives are the _Spearman's rank_ and the _Kendall's tau_ correlation coefficients [62; 63; 64; 65]. Both are based on generating ranked distributions by assigning a rank to each variable with respect to its value. The correlation coefficient is then given as a measure of the association between the two ranked distributions. Consequently, both correlation metrics are robust to outliers and suitable for non-linear distributions.  
Although both correlation measures commonly return very similar results, Kendall's tau handles _ties_ (i.e., cases in which there is no difference between the ranks) in a mathematically more straightforward way. More precisely, Kendall's tau returns the density difference between _concordant_ and _discordant_ pairs. Consider two vectors of length \(n\), \((x_{1},x_{2},...,x_{n})\) and \((y_{1},y_{2},...,y_{n})\). Concordant pairs are pairs of data points that satisfy \(sgn(x_{i}-x_{j})sgn(y_{i}-y_{j})>0\) (where \(sgn\left(z\right)\) is a sign-function equal to +1 if \(z>0\), \(-1\) if \(z<0\) and 0 if \(z=0\)); similarly, discordant pairs satisfy \(sgn(x_{i}-x_{j})sgn(y_{i}-y_{j})<0\). Furthermore, ties are pairs for which \(x_{i}=x_{j}\) or \(y_{i}=y_{j}\). Hence, with \(n_{c}\) (\(n_{d}\)) as the number of concordant (discordant) pairs, respectively, and \(n_{x}\) (\(n_{y}\)) as the number of ties in \(x\) (\(y\)), respectively, the Kendall's tau (also known as the Kendall's tau-b) is given by [66]:  
\[\tau_{Kendall}=\frac{n_{c}-n_{d}}{\sqrt{n_{c}+n_{d}+n_{x}}\sqrt{n_{c}+n_{c}+n_{ y}}}, \tag{11}\]  
with  
\[n_{c}=\sum_{i,j}\delta_{i,j}^{(c)}\,\ n_{d}=\sum_{i,j}\delta_{i,j}^{(d)}\,\ n_{x}=\sum_{i,j}\delta_{i,j}^{(x)}\,\ n_{y}=\sum_{i,j}\delta_{i,j}^{(y)}\, \tag{12}\]  
where  
\[\delta_{i,j}^{(c)}=\begin{cases}1,&\text{if }sgn(x_{i}-x_{j})sgn(y_{i}-y_{ j})>0\\ 0,&\text{else}\end{cases} \tag{13}\]  
\[\delta_{i,j}^{(x)}=\begin{cases}1,&\text{if }x_{i}=x_{j}\\ 0,&\text{else}\end{cases}\]  
and similarly, for \(\delta_{i,j}^{(d)}\) and \(\delta_{i,j}^{(y)}\).
# Joint Radar and Communications Waveform Design Based on Complementary Sequence Sets
## 3 JRC-CWG and DR-JRC-CWG Design
### Dr-JRC-CWG Design  
In Section 3, we have designed the JRC-CWG under the assumption that the relative radial velocity \(v\) between the JRC system and the target is known or can be ignored. However, the accurate \(v\) is hard to be obtained, or \(v\) cannot be ignored in practice. The range sidelobe level of the received JRC-CWG will increase if \(v\) cannot be compensated at the radar receiver. To decrease the range sidelobe level of the received JRC-CWG with Doppler shift, a Doppler-resilient JRC-CWG, i.e., the DR-JRC-CWG is devised in this section.
## 3 Results  
### Validation of the Proposed DBIM-FISTA Algorithm  
This subsection presents results from simple imaging scenarios where only a stroke-like target is unknown, with the aim to validate our 3-D DBIM-FISTA implementation. We consider both CST-calculated and experimental data and compare our results with 2-D scenarios where the target is placed at the same height as the antenna ring used by the 2-D algorithm. The 2-D DBIM-FISTA can perform well for these scenarios, as it deals with a detectable scattered signal and an inverse problem of much fewer unknowns. Although the number of iterations required for convergence will be different for each 2-D and 3-D imaging scenario, we have selected a fixed number of 20 iterations for this comparison. To study the impact of keeping the number of iterations fixed, we have compared results with a much greater number of iterations for \"Case I\" presented in Section 3.1.1. We note that all of our reconstructions estimate 3D volume distributions, but we only show the 2D axial slice results, as we have limited array coverage along the \(z\)-axis, and we also want to compare directly 2D and 3D simulations along the axial planes.  
The true dielectric constant values \(\Re(\varepsilon_{r})\) of the numerical and experimental phantoms are shown in Figure 4.  
The root mean square error (\(RMSE\)) of the xy-slice reconstruction contrast is used to compare the 2-D and 3-D reconstructions, defined as  
\[RMSE=\sqrt{\frac{1}{N_{r}}\sum_{p=1}^{N_{r}}e_{\varepsilon}^{2}(p)}, \tag{37}\]  
where \(N_{r}\) is the number of voxels inside the reconstruction region, \(p\) is the index of the voxel, and \(e_{\varepsilon}\) is the difference between the real part of relative permittivity of reconstruction \(\varepsilon_{r}(p)\) and true values of \(\varepsilon_{gt}(p)\), i.e.,  
\[e_{\varepsilon}=\varepsilon_{r}(p)-\varepsilon_{gt}(p). \tag{38}\]  
The max error of the xy-slice reconstruction contrast is defined as  
\[e_{max}=max|e_{\varepsilon}(p)|, \tag{39}\]and the relative error is defined as the relative norm between the residual errors at the \(K\)-th and first DBIM iteration,  
\[RE=\frac{\|b_{K}\|_{2}}{\|b_{1}\|_{2}}. \tag{40}\]
## 2 Materials and Methods
### Subjects  
Twenty-five male college students, physically active and familiar with aerobic activities on a treadmill, participated in this study. Individuals were invited through announcements made at the university and in a fitness center where the study was conducted over the course of six months. As inclusion criteria, participants needed to have a minimum of 2 years of aerobic training experience and perform high-intensity exercises a minimum of twice a week, with at least 150 min of moderate or vigorous aerobic activities executed. This study excluded those with a recent history of injury due to the potential interference on running performance, low adherence to the exercise program (i.e., an interval of more than three days between sessions), or the use of any ergogenic substance that could potentially interfere with the study results.  
All individuals were invited to have their questions clarified after signing the consent form. This study was approved by the ethics committee of the university (#045.2010). Using the statistical package G-Power (Free Version 3.0.5) for analysis, \"ANOVA Repeated Measures Between Factors\", with two measures and three groups, the sample size considered an error of 5%, statistic power of 80%, and an effect size of 0.60 (moderate) (19), resulting in 24 participants. Considering sample loss and significant abandonment when conducting chronic experimental models, a greater number of participants were recruited, expecting a loss of around 20% to 30%. Table 1 contains the anthropometry, body composition, physiological parameters, and performance of the groups investigated.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Variables**} & **GT\({}_{1\%}\)** & **GT\({}_{10\%}\)** & **CON** \\ \cline{2-4}  & **(n = 9)** & **(n = 8)** & **(n = 8)** \\ \hline Age years (SD) & 26 (5) & 28 (3) & 26 (3) \\
**Anthropometry and Body Composition** & & & \\ Body mass kg (SD) & 79.5 (9.8) & 84.0 (13.6) & 82.2 (10.5) \\ Height cm (SD) & 178.0 (7.4) & 175.1 (5.9) & 177.3 (6.5) \\ BF \% (SD) & 13.5 (4.3) & 14.7 (3.6) & 12.5 (4.2) \\
**Physiologic variables** & & & \\ VO\({}_{2\max}\) mL kg\({}^{-1}\)-min\({}^{-1}\) (SD) & 52.6 (4.4) & 54.1 (4.1) & 54.7 (5.6) \\ H\({}_{\max}\) ppm (SD) & 193 (7) & 191 (10) & 193 (10) \\ \hline \hline \end{tabular} Subtitle: GT\({}_{1\%}\)—running group on 1% slope; GT\({}_{10\%}\)—running group on 10% slope; CON—control group; SD–standard deviation; HR\({}_{\max}\)—maximum heart rate; BF—body fat.  
\end{table}
Table 1: Average (SD) of the subjects’ characteristics, training sessions, physiological parameters, and performance.
## 3 Results and Discussion
### HIP-Treated Fullerite C\({}_{60}\) Study
The O 1s peak (right inset in Figure 6) is symmetric and the separation of O 1s signals associated with different oxygen-containing groups and adsorbed water molecules is not possible. From a comparison of the peak areas of the C 1s and O 1s XPS spectra of the C\({}_{60}\) powder treated with HIP, it was found that the ratio of the number of oxygen atoms participating in the formation of oxygen-containing groups and the adsorption of water molecules on the sample surface to the number of carbon atoms, is about of 3%. The presence of a broad band at a BE of \(\sim\)290 eV in C 1s XPS spectrum explains the appearance of a wide shoulder at the low-energy side of the \(\sigma^{\star}\) absorption band in C 1s NEXAFS spectrum of the MWCNTs and HIP-treated fullerite (Figure 6) as the contribution of C1s \(\rightarrow\)\(\pi^{\star}\) transitions in the O=C\(-\)O group [43]. The analysis of the XPS and NEXAFS spectroscopy data suggests that with an increase in temperature during the HIP treatment, the destruction of the fullerite C\({}_{60}\) molecular structure occurs with the formation of C\({}_{60}\) dimers at a temperature of 1170 K, as well as the fusion of fullerene molecules into nanocapsules with a graphene surface and the formation of a new material at a temperature of 1270 K. A comparison between the C 1s NEXAFS spectra of this material and MWCNTs allows to assert that the obtained material is not a disoriented graphite phase, but a set of arbitrary oriented closed graphene surfaces connected with covalent bond, in which carbon atoms have the high environment stoichiometry as in graphene.
## 4 Results and Discussion  
### Flow around Two Stationary Cylinders  
Considering that the spacing ratio along the flow direction affects the generation of the gap flow, the flow around two stationary cylinders with roughness strips in tandem with different spacing ratios under Reynolds number \(Re=2000\)-14,000 is numerically simulated in this section. The spacing ratio for gap flow is also discussed. Four representative Reynolds numbers (\(Re=2000\), 6000, 10,000, 14,000) were selected for detailed analysis.
# Temporal Variations Dataset for Indoor Environmental Parameters in Northern Saudi Arabia
## 4 Dataset Description
### Measures of Dispersion  
The followings are dispersion measures and their definitions.  
* Range: The difference between the maximum and minimum values;
* Variance: The average of the squared differences from the mean;  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Measure**} & **Light** & **Temperature** & **Humidity** & **Pressure** & **Attitude** \\  & **(Lumen (Im))** & **(\({}^{\circ}\)C)** & **(\%)** & **(Pascal (Pa))** & **Attitude** \\ \hline mean & 265.865881 & 36.96486 & 11.775421 & 89,448.598429 & 1039.161420 \\ \hline Median & 102.00 & 37.10 & 11.00 & 89,455.00 & 1038.56 \\ \hline Mode & 0.0 & 36.6 & 11.0 & 89,326.0 & 1050.45 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Central tendency analysis.  
* Standard Deviation: The square root of the variance, representing the dispersion or spread of the data;
* Interquartile Range (IQR): The range between the first quartile (25 th percentile) and the third quartile (75 th percentile).  
Table 5 provides further insights into the variability and spread of each of the measures (light, temperature, humidity, pressure, and attitude) mentioned.  
Light: The standard deviation is large (305.72), which implies significant variability in the light data. The large range (856) further underscores this variation. The variance is quite high, confirming the large spread in the data. The interquartile range (IQR), which measures the statistical spread between the 25 th and 75 th percentile, is 565, which is also considerable. The minimum is 0 (possibly indicating periods of darkness), while the maximum is 856.  
Temperature: The standard deviation is 1.64, indicating a relatively small variation in temperature. The range (10.9) and IQR (1.9) also suggest that most temperature values are close to the mean. The minimum and maximum temperatures recorded are 29.3 and 40.2 degrees, respectively.  
Humidity: The standard deviation (1.83) and variance (3.34) suggest moderate variability in humidity. The range (15) and IQR (1) indicate that most of the data points lie near the mean. The humidity values lie between 10 and 25 units.  
Pressure: Pressure shows a significant variation, indicated by a standard deviation of 140.65 and a large range (900). The variance is also quite high. However, the IQR is 186.75, which suggests a moderate spread of values around the median. The minimum and maximum recorded pressure values are 88,982 and 89,882, respectively.  
Attitude: The standard deviation (12.95) indicates a moderate spread in attitude. The range (82.62) and IQR (17.22) confirm this spread. The minimum and maximum recorded attitude values are 999.59 and 1082.21, respectively.  
In summary, light and pressure appear to have the highest variability. Temperature shows the least variability, with most values clustering around the mean. It would be useful to pair these insights with the previous set of descriptive statistics to have a more comprehensive understanding of each measure's distribution.