esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Soil Salinity and Land Use  
Agricultural practices play a critical role in salt salinity distribution [(76)]. The spatial relationship between soil salinity and land use types in this study indicates that paddy fields and field crops dominate the study area. This finding is similar to the previous studies. [PERSON] [(81)] studied the spatial modeling and prediction of soil salinization using SaltMod in a GIS environment in the Nong Suang sub-district, Kham Thale So district, Nakhon Ratchasima, Thailand, by measuring ECe and pH in 51 study areas at three levels: 0-30, 30-60, and 60-100 cm. The SaltMod was used to model temporal changes in salinization over two decadal (20 years) periods. The pattern indicates the effect of physiographic conditions on salinity, as a major part of the southwestern side is dominated by ridges and the northeastern side by flood plains, lateral vales, and terraces. This pattern can also be associated with land use types, as the latter side is dominated by paddy fields, while cassava and maize are cultivated southwestern.  
[PERSON] et al. [(82)] studied land use and hydrological management with the Isaac Catchment Hydrogeological and Agricultural Model (ICHAM), an integrated model at a regional scale in Northeast Thailand. The ICHAM consists of a series of interconnected worksheets, which cover different aspects of the salinity management system. It brings knowledge of hydrogeology, agronomy, and farm economics, but further ground studies are needed before making confident recommendations about changing land use in particular areas. This finding shows how salinization can be managed, the general direction to take, and the approximate magnitude of land use change needed. The simulation results show that salinity will almost double in the next 30 years if current land use is maintained for a catchment in northeast Thailand.
# RACF: A Multimodal Deep Learning Framework for Parkinson's Disease Diagnosis Using SNP and MRI Data
## 2 Materials and Methods
### Dataset and Preprocessing
#### 2.1.1 SNP Preprocessing  
Raw SNP data from NeuroX arrays underwent quality control using PLINK: removing SNPs with missingness \(>5\%\), samples with genotyping rate \(<95\%\), sex chromosomes, mitochondrial SNPs, and variants with MAF \(<0.05\) or Hardy-Weinberg equilibrium \(p\)-value \(<1\times 10^{-4}\). Genotype imputation was performed via the Sanger Imputation Server (SHAPEIT2 phasing, Minimac4 imputation), retaining high-confidence SNPs (INFO \(\geq 0.5\)). Post-quality control, 442 samples were retained, with 424 samples having matched sMRI data.
# Evaluating MPTCP Congestion Control Algorithms: Implications for Streaming in Open Internet
## 3 Results and Discussion
### Result Analysis
#### 3.1.3 Protocol Delay  
LIA with BBR exhibited the highest average protocol delay, a result that was unexpected given its low mean drop rate. This anomaly might be attributed to BBR's embedded reluctance to prompt throughput change, which can lead to temporarily longer queuing delays. In turn, wVegas paired with Reno at the path level registered the highest maximum protocol delay, rendering it less suitable for the applications that prioritize low latency, such as those in the tactile Internet.  
Figure 3 gives a more granular view of the protocol delay, specifically plots (b), (d), and (f). Across all three SPTCP CC algorithms--Reno, Cubic, and BBR--fluctuations in the protocol delay are evident. The maximum values hover around 135 ms, with occasional peaks reaching 150 ms, whereas the minimum values stabilize near 60 ms. The mean is approximately 95 ms, aligning closely with the average derived from all the experiments, as detailed in Table 3. In the BBR plot, a lone peak of around 275 ms points out abnormal temporary traffic conditions.
# Audio Fingerprint Extraction Based on Locally Linear Embedding for Audio Retrieval System
## 4 Experimental Results and Analysis
### Results and Analysis
#### 4.2.1 Influence of Nearest Frames Number \(K\) on Retrieval Performance  
The algorithm is sensitive to the selection of \(K\) and different \(K\) has great influence on the retrieval result. The influence of different \(K\) is analyzed in each group reduction and the optimal \(K\) is selected for the experiment. Generally \(K\) is between 3 and 8. Table 2 shows the retrieval results corresponding to different \(K\) under the condition that the horizontal sub-region is reduced by 1 dimension.  
As can be seen in Table 2, when \(K\) is set to 3, the best retrieval performance is obtained. Through the same experimental steps, the selection of \(K\) value under different conditions is determined and the optimal \(K\) value is applied in the subsequent experiment. It is worth noting that the influence of \(K\) value on the retrieval results is not always monotonous, but the higher its value means the increase of  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dimension** & **Group Type** & **Comparison** \\ \hline
1–7 & Horizontal & 1a–1b, 1b–1c, 1c–1d, 1d–1e, 1e–1f, 1f–1g, 1g–1h \\
8–10 & Vertical & 2a–2b, 2b–2c, 2c–2d \\
11–14 & Central & 3a–3b, 3b–3c, 3c–3d, 3d–3a \\
15–18 & Marginal & 4a–4b, 4c–4d, 4e–4f, 4g–4h \\
19–22 & Extended & 4a + 4b–(4c + 4d), 4c + 4d–(4e + 4f), 4e + 4f–(4g + 4h), 4g + 4h–(4a + 4b) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Non-reduced fingerprint extraction.  
algorithm complexity. Therefore, this paper comprehensively considers both the retrieval results and the algorithm complexity to select the most appropriate \(K\) value.
## Results  
### MANT Accuracy  
The omnibus analysis revealed a significant main effect of intervention (F(1396) = 2.92, \(p\) = 0.024) with participants in the berry condition performing more accurately than placebo. This main effect was qualified by an intervention x session interaction trend (F(2400) = 2.472, \(p\) = 0.086) along with a trend towards significance for session (F(2400) = 2.92, \(p\) = 0.055). Post hoc analysis of the intervention \(\times\) session interaction revealed more accurate performance in the berry condition in comparison to placebo at 6 h following intervention (\(p\) = 0.003). Furthermore, accuracy following placebo reduced significantly between 2 h (\(M\) = 0.96) and 6 h, and between 4 h and 6 h (both \(p\) < 0.001) whereas performance in the berry condition remained constant across the day (see Figure 3a).  
A significant intervention x congruency interaction was also revealed (F(2405) = 27.6, \(p\) < 0.001); post hoc analysis found no difference between interventions for the congruent and neutral trials, however on the more cognitively demanding incongruent trials, participants in the berry condition performed more accurately than placebo. Additional post-hoc analysis further qualified this finding  
Figure 3: (**a**) Mean correct responses (\(\pm\)SE of the mean) as a function of intervention and session, showing a significant reduction in accuracy for placebo at 6 h in comparison to 2 and 4 h and significantly better performance in the berry condition in comparison to placebo at 6 h. (**b**) Mean correct incongruent trial responses (\(\pm\)SE of the mean) as a function of intervention and session showing a significant reduction in accuracy for placebo at 6 h in comparison to 2 and 4 h and significantly better performance in the berry condition in comparison to placebo at 6 h. Baseline performance, included as a covariate in the analysis, is shown on both graphs separated by the dotted line. # \(p\) < 0.01, \(\dagger\) \(p\) < 0.001.  
by revealing no difference between interventions on the incongruent trials at 2 and 4 h following intervention, however, berry performance was significantly more accurate on incongruent trials at 6 h in comparison to placebo (\(p\) = 0.002). Furthermore, placebo intervention accuracy on incongruent trials reduced significantly between 2 h and 6 h, and 4 h and 6 h (both \(p\) < 0.001) whereas performance in the berry condition remained constant across the day (see Figure 3b). This finding indicates the effects of the intervention x session interaction described above were primarily driven by poorer placebo performance on the more cognitively demanding incongruent trials.
# Online Coal Identification Based on One-Dimensional Convolution and Its Industrial Applications
## 3 Preliminaries  
### Maximum Information Coefficient (MIC)  
The MIC was proposed by [PERSON] et al. [29] based on information entropy to analyze the degree of linear and nonlinear dependencies among different features, which can be used for redundant feature rejection. MIC has been widely used for the extraction of linear and nonlinear feature variables.  
The advantages of MIC are as follows:  
1. Universality: applicable to all types of data, regardless of data distribution issues.
2. Autonomy: automatically mines the relationship among different features.
3. Robustness: strong anti-interference ability, not affected by outliers and missing values.
4. Interpretability: MIC results are in the range of [0, 1], allowing the strength of the correlation to be visualized.  
The MIC primarily aims to discretize two features in space and divide them into several intervals to calculate the joint probability and determine the correlation.  
Given a dataset: Consider two discrete random variables \(X=\{x_{1},x_{2},...,x_{n}\}\) and \(Y=\{y_{1},y_{2},...,y_{n}\}\), where \(n\) is the number of samples. The mutual information of \(X\) and \(Y\) can be calculated by using the following equations:  
The formula for calculating mutual information is as follows :  
\[I(x;y)=\int p(x,y)\log_{2}\frac{p(x;y)}{p(x)p(y)}dxdy \tag{1}\]  
The calculation formula for MIC is as follows:  
\[mic(x;y)=\max_{a\
eq b<B(n)}\frac{I(x;y)}{\log_{2}\min(a,b)} \tag{2}\]  
where \(a\) and \(b\) are the number of intervals partitioned in space; \(B(n)\) is the interval partition coefficient with the value of \(B(n)=n^{0.6}\), and \(n\) is the amount of data. The MIC calculation process is shown in Algorithm 1.  
```
Input: A data matrix \(\mathbf{D}\) Output: Selected k features \(\mathbf{\hat{D}_{i}}=(\hat{d}_{i1},\hat{d}_{i2}...\hat{d}_{in})\), \(i=1,2,\ldots,k\); // One of the random feature in \(\mathbf{D}\)
1\(\mathbf{i}x=\mathbf{D}_{\mathbf{k}}=(d_{k1},d_{k2}...d_{kn})\), \(k=1,2,\ldots,n\); // Another random feature in \(\mathbf{D}\)
2\(\mathbf{j}=\mathbf{D}_{\mathbf{1}}=(d_{1l},d_{l2}...d_{ln})\), \(i=1,2,\ldots,n\); Step 1: Compute \(I(x;y)\) according to Equation (3) Step 2: Compute \(mic(x;y)\) according to Equation (4) Step 3: Sort the result \(mic(x;y)\) in descending order Step 4: Select the best k features as the output result. \(\mathbf{\hat{D}_{i}}=(\hat{d}_{i1},\hat{d}_{i2}...\hat{d}_{in})\), \(i=1,2,\ldots,k\);
```  
**Algorithm 1** Feature Selection through Maximum Information Coefficient.
## Materials and Methods
### Immunofluorescence and F-Actin Localization  
Fluorescence analysis was performed in cells grown until semi-confluence. HaCaT cells were cultured into 8-well u-slides (ibidi GmbH, AmKlopferspitz19, D-82152 Martinsried, Germany) and exposed or not to simulated microgravity for adaptation or recovery experiments as described in \"Simulated microgravity and cell exposure section\". At the end of incubation, the cells were fixed with 4% paraformaldehydehehyde for 10 min at 4 \({}^{\circ}\)C and washed twice for 10 min with PBS. The cells were permeabilized for 30 min using PBS, 3% BSA (Bovine Serum Albumin, Santa Cruz Biotechnology), 0.1% Triton X-100 (Sigma-Aldrich), followed by mouse anti-E-cadherin C-terminal end monoclonal antibody (BD Transduction Laboratoriestm, Cat# 610181), and mouse anti vinculin (Santa Cruz, Cat sc-73614 1:50 dilution).  
The cells were washed with PBS and incubated for 1 h at room temperature with the secondary antibody (FITC-AffiniPure donkey anti mouse IgG Cat# 715-095-150, RRID: AB_2340792 purchased from Jackson Immuno Research Labs, 1: 200 dilution). TO-PRO3 iodide fluorescent dye 642/661 (1:5000 in PBS, Invitrogen, cat. T3605, Carlsbad, CA, USA) for nuclei staining and rhodamine phalloidin (Invitrogen Molecular Probes Eugene 1:40 dilution) for F-Actin visualization were utilized. Then, the slides were washed with PBS and mounted with 0.1 mM Tris-HCl at pH 9.5: glycerol (2:3). Negative controls were processed in the same conditions besides primary antibody staining. Finally, immunological was analyzed with a Leica confocal microscope TCS SP2 (Leica Microsystems Heidelberg GmbH, Mannheim, Germany) equipped with Ar/ArKr and He/Ne lasers. The laser line was at 488, 543, and 633 nm for FITC, rhodamine, and TO-PRO-3 excitation, respectively. The images were scanned under a 20\(\times\) or 40\(\times\) oil immersion objective. E-cadherin quantification was performed using an optical spatial series with a step size of 1 \(\upmu\)m. Fluorescence intensity (SUM (I), a.u arbitrary units) was evaluated in sized Regions Of Interest (ROI) that included group of cells. For each ROI, the analyzed cell number was determined counting cell nuclei and the SUM (I) of the ROI was normalized versus the cell number. We counted about 500 cells for each sample. The laser intensity and confocal parameters were maintained at the same values to perform 1g and RPM series. The background threshold of fluorescence was determined by the use of negative control in which the primary antibody was omitted. For depth analyses, an optical spatial series with a step size of 5 \(\upmu\)m were recovered. Colocalization analysis was effectuated at the level of periphery of the cell and in an internal compartment at level of cell-cell contact. The fluorescence intensity of vinculin and actin (SUM (I)) at these regions was also quantified. All analyses were performed using Leica Confocal software. Data were analyzed with Sigma Plot Software and Pearson coefficient was used for correlation analysis.
## Data and Methodology
### Land Surface Temperature
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**1992**} & \multicolumn{3}{c}{**1993**} & \multicolumn{3}{c}{**1994**} & \multicolumn{3}{c}{**1995**} & \multicolumn{3}{c}{**1996**} & \multicolumn{3}{c}{**1997**} & \multicolumn{3}{c}{**1998**} & \multicolumn{3}{c}{**1999**} & \multicolumn{3}{c}{**2000**} & \multicolumn{3}{c}{**2001**} \\ \hline
**MA** & **w** & **c** & **w** & **c** & **w** & **c** & **w** & **c** & **w** & **c** & **w** & **c** & **w** & **c** & **w** & **c** & **w** & **c** \\ \hline Atlanta & 13 & 19 & 27 & 17 & 16 & 18 & 19 & 15 & 28 & 17 & 25 & 20 & 30 & 24 & 33 & 35 & 41 & 39 & 40 & 36 \\ Boston & 17 & 25 & 22 & 20 & 22 & 23 & 22 & 17 & 18 & 13 & 15 & 17 & 21 & 18 & 35 & 37 & 42 & 40 & 42 & 48 \\ Chicago & 18 & 17 & 23 & 18 & 28 & 20 & 23 & 13 & 16 & 12 & 21 & 19 & 20 & 21 & 35 & 42 & 40 & 43 & 41 & 35 \\ Houston & 19 & 11 & 19 & 13 & 16 & 18 & 15 & 15 & 20 & 15 & 21 & 19 & 19 & 14 & 23 & 34 & 36 & 34 & 38 & 31 \\ San Francisco & 18 & 17 & 23 & 18 & 28 & 20 & 23 & 13 & 16 & 12 & 21 & 19 & 20 & 21 & 35 & 42 & 40 & 43 & 41 & 45 \\ Phoenix & 20 & 14 & 28 & 25 & 23 & 21 & 21 & 21 & 34 & 34 & 33 & 29 & 31 & 25 & 33 & 43 & 53 & 50 & 47 & 50 \\ \hline  & \multicolumn{3}{c}{**2002**} & \multicolumn{3}{c}{**2003**} & \multicolumn{3}{c}{**2004**} & \multicolumn{3}{c}{**2005**} & \multicolumn{3}{c}{**2006**} & \multicolumn{3}{c}{**2007**} & \multicolumn{3}{c}{**2008**} & \multicolumn{3}{c}{**2009**} & \multicolumn{3}{c}{**2010**} & \multicolumn{3}{c}{**2011**} \\ \hline
**MA** & **w** & **c** & **w** & **c** & **w** & **c** & **w** & **c** & **w** & **c** & **w** & **c** & **w** & **c** & **w** & **c** \\ \hline Atlanta & 31 & 34 & 42 & 37 & 51 & 42 & 49 & 31 & 44 & 38 & 48 & 31 & 45 & 38 & 49 & 42 & 56 & 50 & 56 & 41 \\ Boston & 38 & 34 & 43 & 40 & 43 & 40 & 50 & 40 & 47 & 43 & 50 & 37 & 45 & 34 & 46 & 40 & 48 & 40 & 46 & 41 \\ Chicago & 36 & 42 & 48 & 42 & 43 & 40 & 48 & 45 & 46 & 37 & 53 & 37 & 44 & 31 & 45 & 40 & 52 & 37 & 46 & 28 \\ Houston & 35 & 34 & 33 & 31 & 40 & 33 & 41 & 32 & 36 & 34 & 33 & 30 & 39 & 40 & 38 & 33 & 39 & 33 & 38 & 31 \\ San Francisco & 36 & 42 & 48 & 42 & 43 & 40 & 48 & 45 & 46 & 37 & 53 & 37 & 44 & 31 & 45 & 40 & 52 & 37 & 46 & 28 \\ Phoenix & 41 & 42 & 56 & 57 & 59 & 54 & 63 & 49 & 57 & 52 & 57 & 42 & 67 & 57 & 67 & 61 & 64 & 60 & 68 & 57 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of Landsat images available in the Climate Engine (CE) platform for each of the investigated U.S. MA, respectively in the warm (w) period (March, April, May, June, July, August) and cold (c) period (September, October, November, December, January, February). The orbits of Landsat satellites are solar synchronous, and passages are between 10:00 and 10:30 mean local time at the Equator, in order to provide maximum illumination with minimum water vapor (haze and cloud build-up) [44].
## 3 Results
### Correlation between Grassland NPP and Meteorological Factors
shows a negative correlation with temperature, and only 7.59% of the total grassland area shows a positive correlation coefficient of greater than 0.3. The areas with negative correlation are mainly concentrated in the north side of the Greater Khingan Range. When the influence of precipitation and sunshine duration is controlled, 79.19% of the total grassland area shows a certain improvement in the positive correlation between NPP and temperature, mostly in the north side of the Greater Khingan Range. Although Inner Mongolia is located at high latitudes, a higher temperature would cause more evaporation in arid areas with little precipitation, which would then make the natural environment more disadvantageous for the growth of grassland. In contrast, in the south side of the Greater Khingan Range, due to the humid climatic conditions, the warmer environment, to a certain degree, is conducive to the increase of NPP. The correlation between NPP and sunshine duration also shows spatial heterogeneity (Figure 12e,f) and most areas are weakly correlated. The proportion of grassland NPP showing positive correlation and negative correlation with sunshine duration is 51.18% and 48.82%, respectively. Only 6.34% of the grassland has a negative correlation of greater than 0.3, and 10.93% has a positive correlation of greater than 0.3. However, the partial correlation is positive in most grassland areas, accounting for 87.41%, with 47.66% of the total grassland having a positive correlation of greater than 0.3. A small amount of negative partial correlation is found in west Xilingol and west Inner Mongolia. Due to the arid climate in Inner Mongolia, the sunshine leads to increased soil water evaporation, which affects the growth of the grassland. In addition, the sunshine time might be less affected by the undulating terrain in Inner Mongolia than precipitation and temperature, and there is no obvious difference in the correlation and partial correlation between the two sides of the Greater Khingan Range. In general, at the scale of a single year, precipitation has the most obvious effect on grassland NPP in Inner Mongolia. Most of the grassland shows a strong positive correlation between NPP and precipitation. The second factor is the length of sunshine duration, showing a relatively weak positive correlation. The third factor is temperature. The influence of temperature is different on the different sides of the Greater Khingan Range.
## 3 Transformative Impact and Ethical Implications of AI in Healthcare
### Implementing Ethical AI in Healthcare
#### 3.3.4 Differential Privacy: Advanced Techniques in Healthcare AI  
Differential Privacy (DP) is a critical privacy-preserving technique that has gained significant traction in healthcare AI due to its ability to provide strong privacy guarantees. It functions by introducing a calculated amount of random noise, either to the data itself or to the outputs of algorithms, thereby making it statistically challenging for attackers to deduce sensitive information about any individual in the dataset. This method is advantageous for sharing insights from health data, such as statistical summaries or AI model parameters, while safeguarding patient confidentiality. The core principle of DP is to ensure that the presence or absence of any individual's data do not substantially alter the outcome of an analysis. This feature is vital in healthcare settings where the sensitivity of patient data necessitates the utmost levels of confidentiality. DP's application extends to various algorithms, including but not limited to boosting, principal component analysis, support vector machines, and deep learning. It is also effectively employed in Federated Learning scenarios involving decentralized data processing across multiple entities, such as hospitals or research institutions, to prevent indirect data leakage--a prevalent issue in collaborative environments [19].  
Despite its benefits, it is crucial to acknowledge that DP is inherently a 'lossy' technique. While it excels at enhancing privacy, it can simultaneously lead to reduced accuracy of predictions or analyses due to the added noise. This inherent trade-off between privacy and utility is a pivotal consideration in the deployment of DP within healthcare AI. As privacy protection intensifies with increased noise, the usefulness of the data for research and the accuracy of AI models may be compromised. To counterbalance this, researchersoften integrate DP with other methods like Secure Multi-party Computation (SMC) to balance privacy and data utility [19]. This hybrid approach is designed to control the escalation of noise with the number of participants, thus maintaining both privacy and the practical value of the data. DP presents a robust framework for preserving individual privacy in healthcare AI applications, particularly in distributed data processing contexts. Its capability to prevent data leakage must be carefully weighed against the potential diminution in prediction accuracy and data utility. Therefore, meticulous implementation and, where necessary, incorporating additional privacy-preserving techniques are essential to optimizing the benefits of DP in healthcare AI [10].
## 5 Result and Discussion
### Discussion  
Considering the resource usage (CPU and GPU utilization), object accuracy, latency, and power consumption measured through the evaluation metrics, the opinion of using deep learning frameworks for the Jetson AGX Xavier 16 GB platform is as follows. If object detection services and research are conducted based on TensorFlow, the use of TF-TRT seems to have the best performance, and if service and research are conducted on SoC-type mobile devices (Android, IOS), it is most efficient to use TF-Lite. Finally, if you do not have enough knowledge of TensorFlow, it seems that you can efficiently conduct object detection service and research using pure TRT.  
TRT is a software development kit (SDK) that enables fast inference for GPUs developed by Nvidia. When the model is converted to TF-TRT and TRT, the object accuracy is slightly lower than that of the existing model, but it can provide efficiency in terms of latency and power consumption that is relatively low when compared to the desktop environment in the low specification of the Nvidia Jetson platform. However, TRT is a technology that can be used not only by the Nvidia Jetson platform but also by GPUs with Tensor Cores. In other words, if Tensor Cores are included in a high-performance GPU (e.g., RTX 3090) that does not use the Jetson Nvidia platform, you can directly build and use the TRT [42]. In addition, if you use a high-performance GPU, it is an environment using a desktop, and the power supply responsible for power is likely to be stable, it is not necessary to detect objects through TRT through relatively low object accuracy and reduced power consumption. Therefore, if deep learning-based object detection is performed on the Jetson Nvidia platform or in a general desktop environment, it is thought that the measurement results presented in this paper can efficiently conduct service and research on object detection.