esa-sceva/satcom-chunk-collection huggingface dataset:
# Computer-Aided Diagnosis of Alzheimer's Disease via Deep Learning Models and Radiomics Method
## 1 Introduction
### Alzheimer's Disease
#### 1.1.2 Multimodality Diagnosis of Alzheimer's Disease  
Multimodal diagnosis is an exhibition of the structural and functional imaging of brain images by a variety of imaging devices. By comparing the differences between various images and healthy brain image templates, the result reflects the image characteristics of Alzheimer's disease. It has been shown that in diagnosis and prognosis of Alzheimer's disease, multi-modal use may improve the performance more than a single modal [7]. In 2019, [PERSON] et al. [8] focused on how to make the best use of multimodal neuroimaging and genetic data for Alzheimer's disease diagnosis. They proposed a three-stage deep feature learning and fusion framework, and proved the superiority of using multimodality data through experiments. In 2021, for Alzheimer's disease diagnosis with incomplete modalities, [PERSON] et al. [9] proposed an Auto-Encoder, which can complement the missing data in the kernel space. This can help solve the problem of incomplete medical data. Such research promotes the development of multimodal technology in the field of Alzheimer's disease.  
The project intends to use T2-weighted Magnetic Resonance Imaging (T2-MRI) and positron emission tomography (PET). The lesions of T2-MRI in [PERSON]'s disease focus on atrophy of brain regions such as hippocampus, amygdala, and entorhinal cortex. Studies have confirmed [10] that the hippocampus atrophy is earlier than other clinical symptoms of the disease, which is an early critical characteristics performance of [PERSON]'s disease and can be used as an early specific and sensitive predictor. The only way that the brain gains energy is glucose uptake. Accordingly, the extent of [PERSON]'s disease can be determined according to differences in glucose uptake in various parts of the brain. 18F-flurodeoxyglucose (18F-FDG) PET imaging shows a phenomenon in patients with [PERSON]'s disease where the area of both sides of the temporal lobe and parietal lobe demonstrates a symmetrical decrease in glucose metabolism, and with the deteriorating of condition, the phenomenon spreads from the temporal lobe to the other cortical areas [11]. PET uses a unique imaging technique to present metabolic changes of glucose in the brain, which can be used to map the distribution of the lesions. In Figure 2, two different modality images of Alzheimer's disease patients are displayed.  
Figure 1: Difference between healthy brain and AD (Alzheimer’s disease) patients (adapted from ref [6]).  
Figure 2: (**a**) T2-weighted MR (Magnetic Resonance) image of AD. (**b**) PET (Positron Emission Tomography) image of AD.
## 3 Results
### Bivariate LISA Analysis  
Once the independence of the indicators was analyzed from the point of view of spatial autocorrelation, a bivariate analysis was carried out between each of the indicators and the damage indicator of the buildings. To do this the level of local correlation in the two-dimensional approach was calculated using the Anselin Local Moran's statistic (LISA) [30] with a 95% confidence level. The values obtained for the bivariate analysis from ArcGIS are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**GIS Indicators** & \(I_{SS}\) & \(I_{SC}\) & \(I_{SAC}\) & \(I_{IHC}\) & \(I_{IARD}\) & \(I_{BA}\) & \(I_{SF}\) & \(I_{LDB}\) \\ \hline Global Moran’s Index & 0.39 & 0.45 & 0.71 & 0.45 & 0.36 & 0.47 & 0.63 & 0.38 \\ \(z\)-score & 15.5 & 22.3 & 69.9 & 27.6 & 12.1 & 22.7 & 61.1 & 18.8 \\ \(p\)-value & 0.01 & 0.01 & 0.01 & 0.01 & 0.01 & 0.01 & 0.01 \\ \hline \end{tabular}
\end{table}
Table 1: Evaluation of Global Moran’s I statistic for the different GIS indicators.  
The aggregate bivariate analysis clearly shows that there is no two-dimensional correlation at the spatial level between the buildings' antiquity indicator \(I_{BA}\) and their damage level, nor was significant geostatistical correlation found between the designed seismological indicator \(I_{SF}\) and the level of damage to buildings \(I_{LDB}\). The rest of the indicators present, to a greater or lesser extent, a certain globally positive two-dimensional correlation. However, a more detailed analysis is necessary in order to specify whether this correlation occurs only at the level of the mean values from a numerical point of view, or if a coherent and homogeneous behavior for the spatial detailed perspective of distribution actually occurs.
# Minimizing Maximum Feature Space Deviation for Visible-Infrared Person Re-Identification
## 4 Experiments and Analysis
### Role of Minimizing Feature Space Maximum Deviation Loss Function
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\(\lambda\)} & \multicolumn{3}{c}{**Visible to Infrared**} & \multicolumn{3}{c}{**Infrared to Visible**} \\ \cline{2-7}  & **Rank1 (\%)** & **mAP (\%)** & **mINP (\%)** & **Rank1 (\%)** & **mAP (\%)** & **mINP (\%)** \\ \hline
0 & 85.12 & 80.44 & 68.67 & 84.22 & 79.16 & 65.75 \\
0.25 & 90.94 & 85.25 & 73.26 & 89.81 & 83.72 & 69.58 \\
0.5 & **92.19** & **85.95** & **73.86** & **90.65** & **84.38** & **70.73** \\
1 & 90.18 & 81.49 & 65.46 & 88.27 & 79.23 & 61.65 \\
2 & 88.02 & 79.71 & 63.97 & 86.45 & 78.48 & 61.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance comparison of using different weights of MMFSD loss function (i.e., \(\lambda\) in Equation (9)) on the RegDB dataset. Equation (9)) on the RegDB dataset. Figure 4: The CMC curves in different MMFSD’s weights (i.e., \(\lambda\) in Equation (9)) on RegDB dataset of (**a**) visible-to-infrared retrieval and (**b**) infrared-to-visible retrieval. Figure 3: The CMC curves in different MMFSD’s weights (i.e., \(\lambda\) in Equation (9)) on SYSU-MM01 dataset of (**a**) all-search mode and (**b**) indoor-search mode. By comparing with the results on the SYSU-MM01 and RegDB datasets, one can see that the best \(\lambda\) value of RegDB is bigger than that of SYSU-MM01. Furthermore, comparing with Figures 3 and 4, we can find that the CMC curves' variation on RegDB dataset is larger than that on SYSU-MM01 dataset with adjusting the \(\lambda\). This is because, compared with SYSU-MM01 dataset which suffer from multiple intra-class variation (e.g., pose variation, view variation and modal variation), the modal discrepancy on RegDB dataset is the most significant problem. Therefore, it further demonstrates the FSMD's outstanding performance on deal with modal discrepancy.
## 2 Islanding Detection Methods
### Signal Processing Based Methods
#### 2.5.1 Fourier Transformer Method  
In this technique, the frequency domain is used to extract the signal's features at particular frequency levels. To provide quick and effective islanding detection techniques, a variety of techniques, including discrete Fourier transform, fast Fourier transform, and short-time Fourier transform, are used. However, this method's primary drawbacks are its low-frequency resolution and sparse spectrum estimation [38].
# Transfer Learning Approach for Human Activity Recognition Based on Continuous Wavelet Transform
## 4 Results
### Model Selection Results
In this study, the DenseNet121 architecture, which is DenseNet with 121 trainable layers, yielded the best results when trained on the Morlet-256 configurations, outperforming the ResNet and InceptionNet-based models, which indicates the potential of the DenseNet-based models to address the HAR problems and perform the 2D transformed signals classification. Considering the CWT configuration, using a scalogram set with the Morlet wavelet and the scale values from 0 to 256 resulted in the best model performance, indicating that the usage of the Morlet wavelet may represent the wearable sensors' signals more accurately than the commonly used Mexican hat. However, it was observed that the Morlet wavelet requires a broader scale value range to represent low-frequency signal characteristics than the Mexican Hat wavelet, whose classification results are not significantly affected by a change in the scale value range. Figure 5 depicts the confusion matrix of the classification conducted using the selected model. As seen in Figure 5, the cluster of classification errors consists of the classes stand, sit, and talk-sit. All of these classes represent static activities, which are difficult to differentiate. Therefore, the construction of HAR classification models that consider such static activities separately is a promising area for future research and can significantly improve classification accuracy. Figure 4: The classification accuracy results of five tries for each tested combination.
## 2 RASMR for Big Data Analysis under GLM
### RASMR with the Delta Ratio Split
Generate the mean representative for each data block to form the initial weighted data: \(\mathcal{D}^{(0)}=\{(n_{k},\tilde{\mathbf{X}}_{k}^{(0)}=\tilde{\mathbf{X}}_{k },\ \tilde{y}_{k}^{(0)}=\tilde{y}_{k})\}_{k=1}^{K}\); Implement the IRLS on \(\mathcal{D}^{(0)}\) for the initial estimate \(\hat{\beta}^{(0)}\); for\(t=1,\ldots,T\)do for\(k=1,\ldots,K\)do (1) Compute \(\eta_{i}=\mathbf{X}_{i}^{T}\tilde{\beta}^{(t-1)}\) for each \(i\in I_{k}\); (2) Split \(\mathcal{D}_{k}\) by the sign of \(\eta_{i}\) and the sign of \(y_{i}-G(\eta_{i})\); (3) Suppose \(\tilde{\mathcal{D}}_{k}\) is split to \(\{\mathcal{D}_{kl},l=1,\ldots,m_{k}\}\) with index blocks \(I_{kl}\subseteq I_{k}\) for\(\alpha k\)do (3.1) compute \(\tilde{y}_{kl}^{(t)}\) by (2); (3.2) solve (3) for\((\tilde{y}_{kl}^{(t)}\)); (3.3)while\(\tilde{y}_{kl}^{(t)}\) is not uniquedo  compute \(\tilde{y}_{kl-MAX}^{(t)}=\underset{\tilde{y}_{kl}}{\arg\max}\ v(\tilde{y}_{kl}) [\tilde{y}_{kl}^{(t)}-G(\tilde{y}_{kl})]\tilde{y}_{kl}\) ;  split \(\mathcal{D}_{kl}\) further by the sign of \(\eta_{i}-\tilde{y}_{kl-MAX}^{(t)}\), and then return to (3.1);  end for (3.4) compute \(\tilde{\mathbf{X}}_{kl}^{(t)}\) by (4) ; (3.5) compute \(\tilde{\mathbf{X}}_{kl}\), and the delta ratio \(\tilde{\delta}_{kl}=\left\|\tilde{\mathbf{X}}_{kl}^{(t)}-\tilde{\mathbf{X}}_{ kl}\right\|/\Delta_{kl}\) ; (3.6)while\(\tilde{\delta}_{kl}>\delta_{0}\)do  split \(\mathcal{D}_{kl}\) by the sign of \(\eta_{i}-\tilde{y}_{kl}\), where \(\tilde{y}_{kl}=n_{kl}^{-1}\sum_{i\in I_{kl}}\eta_{i}\), and then return to (3.1);  end for  end for  Implement the IRLS on the weighted dataset \(\mathcal{D}^{(t)}=\{(n_{kl},\tilde{\mathbf{X}}_{kl}^{(t)},\tilde{y}_{kl}^{(t) }),k=1,\ldots,K;l=1,\ldots,m_{k}\}\) to obtain \(\hat{\beta}^{(t)}\);  end for  Report \(\hat{\beta}=\hat{\beta}^{(T)}\)
```  
**Algorithm 2**RASMR algorithm with the delta ratio split.
# Radiometric Comparison within the Sentinel-1 SAR Constellation over a Wide Backscatter Range
## 3 Results
### Very Low Radar Backscatter (Calm Water Region at Lake Constance)  
A distributed target with a very low radar backscatter and a low SNR can be found, e.g., within calm waters. For the study, a region of 1000 by 700 pixels (range, azimuth) located at Lake Constance (centered at 47.55\({}^{\circ}\)N, 9.49\({}^{\circ}\)E) is selected which is covered by subswath IW1 (Figure 7).  
The averaged radar brightness for this area is evaluated and the VH channel results are depicted as a timeline in Figure 8 for S-1A (blue) and S-1B (red). Outliers may exit due to bad weather conditions or strong winds which have an impact on the water surface disturbing the calm water conditions and leading to significant higher backscatter power for these acquisitions. Therefore, results with radar brightness higher than 1 dB compared to the mean value are marked with open symbols and are not considered for the calculations of mean values and standard deviations.  
Although the same observation area is evaluated for both SAR instruments, the average radar brightness differs between S-1A (\(-\)20.12 dB) and S-1B (\(-\)21.27 dB). The significant higher difference of 1.15 dB (compared to \(\sim\)0.2 dB found for the Amazon rainforest) for targets with low SNR seems  
Figure 8: Observed radar brightness \(\beta^{0}\) for the VH polarization channel as a function of acquisition time covering the selected region within Lake Constance for S-1A (blue squares) and S-1B (red squares). Outliers (open squares) with higher backscatter values may appear from a more rough water surface due to strong winds or bad weather conditions. The annotated noise level (lines) available since July 2018 shows the low SNR for the target area (\(\sim\)1 dB) and the different noise levels for S-1A and S-1B.  
Figure 7: Distributed target (red rectangle) selected within IW1 burst acquired over Lake Constance. The background elevation map was derived from TanDEM-X 90 m DEM using GMT.  
to be related to the different noise levels for S-1A and S-1B for these acquisitions as discussed in the next section. The noise level is annotated in the related SAR product. The noise product quality is significantly improved since SAR processor version 2.90 [32] which has been operationally available since summer 2018. For the selected observation area, an average noise level of \(-20.87\) dB for S-1A and \(-22.12\) dB for S-1B are extracted from the SAR annotation products. The evaluated backscatter and noise values are summarized in Table 4. The radiometric stability over time is expressed by the standard deviations with 0.18 dB for S-1A and 0.25 dB for S-1B. These values are similar to the ones found for the Amazon rainforest.
## 5 Illustrative Examples
### Regarding the Fixed-Fixed Structure  
In the proposed model the set of sectional deformation modes were derived through cross-section analysis of a cantilevered structure. One might consider that if they were a positive natural characteristic of the cross-section, they should be equally applicable in modeling a thin-walled structure with different displacement constraints. To check this, another example was carried out with the structure in Figure 4, with the two ends completely restrained.  
Table 2 presents the natural frequencies of the first six modes derived from the present model and the ANSYS shell theory, and their relative differences \(\delta_{i}\). Similarly, the results of present model were obtained with forty elements, while the ANSYS analysis was carried out with 1120 Shell 181 4-node elements, distributed as forty elements, along the length, and twenty-eight over the cross-section.  
As expected, the results in Table 2 show that the natural frequencies derived from the present model were very close to those from the ANSYS shell theory, with relative differences smaller than 4% for the studied modes. The relative errors exceeded expectation, as they were even smaller than those of a cantilevered thin-walled structure. The reason behind this was worthy of further study.  
Besides, Figure 14 provides the comparison concerning the 1 st-6 th modal shapes, which reconfirmed the good agreement with the ANSYS shell theory. These facts proved that the proposed model, with a set of sectional deformation modes derived from a cantilevered structure, could accurately reproduce three-dimensional behaviors of thin-walled structures with their two ends fixed. Therefore, it is reasonable to deduce that the set of sectional deformation modes are equally applicable to thin-walled structures with different constraints, provided the cross-section is the same. It supports the idea that the set of sectional deformation modes, identified with the present simplified approach, are an inherent property of the thin-walled cross-section. In this sense, the derived model possesses stronger applicability and generality.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mode**} & **Present Model** & **ANSYS Shell** & **Relative Error** \\ \cline{2-4}  & \(f_{i}\) **(Hz)** & \(f_{Ai}\) **(Hz)** & \(\delta_{i}\) **(\%)** \\ \hline
1 st & 197.00 & 196.67 & 0.17 \\
2 rd & 208.69 & 216.30 & \(-\)3.52 \\
3 rd & 226.88 & 232.36 & \(-\)2.36 \\
4 th & 242.34 & 249.06 & \(-\)2.70 \\
5 th & 276.91 & 271.31 & 2.06 \\
6 th & 294.35 & 295.06 & \(-\)0.24 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the first six natural frequencies of the fixed–fixed thin-walled structure.
# The Role of Plant Growth-Promoting Rhizobacteria (PGPR) in Mitigating Plan's Environmental Stresses
## 2 Properties and Potential of Plant Growth-Promoting Rhizobacteria (PGPR)
### Nitrogen Fixation  
Nitrogen (N) is also an essential element for plant growth. Although atmospheric nitrogen is present in high quantities (78%), it is not found in the form available for absorption by the roots from the soil. This is mainly due to the biological nitrogen fixation (BNF) mediated by an enzymatic reaction catalyzed by the enzyme nitrogenase, transforming the atmospheric N\({}_{2}\) into ammonia (NH\({}_{3}\)). BNF can be exerted by bacteria living in symbiotic association with legumes or higher plants, such as alder (_Alnus_ spp.), feeding the associated nitrogen-fixing bacteria, or it can also occur in a non-symbiotic relationship through heterotrophic or autotrophic bacteria free-living in the rhizosphere [61; 62]. Among the free-living (non-symbiotic) bacteria, we remember the cyanobacteria (or blue-green algae), _Anabaena_ and _Nostoc_, and genera, such as _Azotobacter_, _Beijerinckia_, and _Clostridium_. Among the symbiotic bacteria, there are the genera _Rhizobium_, associated with leguminous plants; _Frankia_, associated with some dicotyledonous species; and some _Azospirillum_, associated with grasses [48; 63]. In Leguminosae, colonization and subsequent invasion of rhizobia leading to active nodules containing symbiotic nitrogen-fixing bacteria can occur through root hairs or wounds in root tissue or even through intact plant cells [64].
# Rare Earth Elements Uptake by Synthetic Polymeric and Cellulose-Based Materials: A Review
## 8 Cellulosic Materials for RE Capture
### Polymer-Grafted Cellulose Materials  
Moreover, a study involving carboxylated NCC/polyethyleneimine (PEI) s-IPN showed a capture capacity up to 120 mg/g of REEs and suggested selectivity toward heavy REEs at pH values near neutral [92]. The nature of the interaction between PEI and carboxylated NFC suggests low carboxylic acid availability due to entangled carboxylated NFC to the crosslinked PEI by ionic interactions between the carboxylic acid functionality present and ammonium groups of PEI. It seems that PEI amino chelating groups were favored due to near-to-neutral pH conditions, which keep nitrogen coordinating groups available for REE uptake. As previously mentioned, nitrogen-chelating materials are not suitable for this type of application due to the acidic conditions found in mining wastewaters and AMD.  
On the other hand, poly (amidoxime)-grafted-cellulose capture up to 262 mg/g of La (III) at pH 6.0 and comparing the results for the other REEs evaluated suggest selectivity toward light REEs [93]. The reported capture capacity could be explained by the coordinative properties of the amidoxime moiety present in the graft polymer, which consists of both oxime and amide groups. Nevertheless, the possible capture capacity for REEs under acidic conditions found in mining wastewaters and AMD could be drastically lower, making amidoxime unsuitable for the intended application due to protonation of both amine and oxime moieties, as previously stated.  
Furthermore, another study involving microcrystalline cellulose and derived grafted polymers with tetraethylenepentamine (TEPA) or poly(carboxymethyl) cellulose (PCMC) showed up to 38, 101, and 107 mg/g capture capacity for La (III), respectively, at pH 3.0-4.0 [94]. These materials are promising candidates for REE capture from mining wastewaters and AMD, especially PCMC, due to the high development related to carboxymethylcellulose, which already has many industrial and commercial applications [103].
# Evaluation of Anti-Wear Properties of Metalworking Fluids Enhanced with Halloysite Nanotubes
## 2 Materials and Methods  
### Materials  
Nanofluids were prepared by adding HNTs (purchased from Sigma-Aldrich, St. Louis, MO, USA) in concentrations of 0.01, 0.05, and 0.10 wt. % within a MO and a SF. These concentrationswere selected based on a previous study by our group [33]. Figure 1a shows a micrograph with the morphology on HNTs obtained by scanning electron microscopy (SEM). Dimension measurements of HNTs obtained by SEM are observed in Figure 1b. Elemental analysis of HNTs in Figure 1c shows Al, Si, and O in concentrations of 26%, 26%, and 43%, respectively. Also, Table 1 presents the properties of nanoparticles and MWFs. Nanoparticles were dispersed in both MWFs using an Ultra Turraz T10 homogenizer for 5 min at room temperature (25 \({}^{\circ}\)C), followed by 5 min of ultrasonication with a Cole-Parmer 750-watt ultrasonic homogenizer.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Materials** & \multicolumn{2}{c}{**Properties**} \\ \hline
**Metalworking fluids** & **Density (15 \({}^{\circ}\)C)** & **Viscosity (40 \({}^{\circ}\)C)** \\ \hline Semi-synthetic fluid (SF) & 0.970 g/cm\({}^{3}\) & 75.0 mm\({}^{2}\)/s \\ \hline Mineral oil (MO) & 0.871 g/cm\({}^{3}\) & 33.2 mm\({}^{2}\)/s \\ \hline \multicolumn{2}{c}{**Nanoparticle**} \\ \hline Halloysite clay nanotubes (HNTs) & Morphology: tubular; diameter: 28.5–135 nm \(\times\) length: 1–3 \(\upmu\)m \\ \hline \multicolumn{2}{c}{**Test blocks**} \\ \hline AISI 1018 & Dimensions: 6.35 \(\times\) 10 \(\times\) 15.75 mm, 71 HRB, \(R_{a}\) = 0.45 \(\upmu\)m \\ Chemical composition: 0.14–0.20\% C, 0.60–0.90\% Mn, \textless{}0.04\% P, 0.05\% S, Fe Balance \\ \hline \multicolumn{2}{c}{**Test rings**} \\ \hline AISI D2 &
\begin{tabular}{c} Diameter: 35 mm, 58 HRC, \(R_{a}\) = 0.65 \(\upmu\)m \\ Chemical composition: 1.5511.5\% Cr, 0.90\% Mo, 0.80\% V, Fe Balance \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties.  
Figure 1: **(a)** Scanning electron micrograph of halloysite nanotubes (HNTs); **(b)** Micrograph of HNTs at higher magnification indicating nanoparticle dimensions; **(c)** elemental analysis of HNTs showing high concentrations of Al, Si, and O.  
Figure 2 shows the hydrodynamic diameter size distribution of HNTs over time for MO and SF nanofluids, where measurements were taken after following the dispersion method. These tests were performed with a Malvern Zetasizer Nano ZS using dynamic light scattering after 3, 6, and 9 min. In Figure 2a the HNTs hydrodynamic diameter size distribution increases rapidly with time for MO nanofluids, suggesting lower stability in this fluid, likely due to its lower viscosity (33.2 mm\({}^{2}\)/s) compared to SF (75.0 mm\({}^{2}\)/s). For SF nanofluids the size distribution remains stable with very similar curves (Figure 2b). Figure 2c shows a plot of the average HNTs hydrodynamic diameter size over time, indicating the lower stability in MO.