esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Natural and Human Factors on Dryland Vegetation in Eurasia from 2003 to 2022
## 3 Results
### Response Characteristics of NDVI, GPP and SIF to Natural Factors in Eurasian Drylands  
To reveal the impact of natural factors on vegetation temporal variability, we conducted partial correlation analyses between detrended vegetation activity factors (normalized vegetation index (NDVI), gross primary productivity (GPP) and solar-induced chlorophyll fluorescence (SIF)) and natural factors (mean annual temperature (MAT), mean annual precipitation (MAP), soil moisture (SM), atmospheric vapor pressure deficit (VPD) and terrestrial water storage (TWS)). We found that the partial correlation coefficients between the three indicators representing vegetation activity, namely NDVI, GPP and SIF, exhibited similar spatial patterns with natural factors (Figure 6). In summary, the interannual variability of vegetation temporal variability showed a stronger correlation with changes in SM compared to MAT, MAP, VPD and TWS (Figure 6). The positive influence of SM on SIF (33.47\(\%\)) was stronger compared to its influence on the NDVI (14.23\(\%\)) or GPP (21.65\(\%\)) (Figure 6g-i). Additionally, VPD had a significantly negative impact on SIF (14.23\(\%\)) compared to its impact on NDVI (11.27\(\%\)) or GPP (12.73\(\%\)) (\(p<0.05\)) (Figure 6j-l).  
Regions with a position relationship MAT with vegetation temporal variability (NDVI, GPP and SIF) were located in WSB (W. Siberia) (Figure 7a). Meanwhile, the strongest negative impacts of VPD on vegetation temporal variability (NDVI, GPP and SIF) were mainly distributed in WSB (Figure 7d). The positive impacts of MAP on NDVI, GPP and SIF were in WSB, ESB (E. Siberia), ECA (E. C. Asia) and TIB (Tib  
Figure 6: Spatial patterns of partial correlation coefficient between NDVI, GPP, SIF and natural factors. (**a**,**d**,**g**,**j**,**m**) show the partial correlation coefficient between normalized difference vegetation index (NDVI) and mean annual temperature (MAT), mean annual precipitation (MAP), soil moisture (SM), vapor pressure deficit (VPD) and terrestrial water storage (TWS), respectively. (**b**,**e**,**h**,**k**,**n**) show the partial correlation coefficient between gross primary productivity (GPP) and MAT, MAP, SM, VPD and TWS, respectively. (**c**,**f**,**i**,**l**,**o**) show the partial correlation coefficient between solar-induced chlorophyll fluorescence (SIF) and MAT, MAP, SM, VPD and TWS, respectively.  
results presented the opposite findings in WCE (West and Central Europe) and the MED (Mediterranean) (Figure 7b). The positive impacts of SM on NDVI, GPP and SIF in WCE, the MED, WCA (W. C. Asia), TIB, EAS (E. Asia), ARP (Arabian Peninsula) and SAS (S. Asia) were found. Opposite findings were found in EEU (E. Europe), WSB and ESB for NDVI (Figure 7c). In regions where TWS is positively correlated with vegetation temporal variability (NDVI, GPP and SIF) are mainly distributed in the EEU, the MED, WSB, ECA and ARP, the correlation of TWS is lower in WCE (Figure 7e).
## 2 Experiments and Methods
### Battery Testing Bench  
To complete the model parameter identification, the testing bench shown in Figure 2 was established. It is composed of a NEWARE (the NEWARE Co., Limited, Shenzhen, China) battery tester system 4000 (BTS4000) with eight independent channels, an intermediate machine, and a host computer on which NEWARE BTS Software v7.5.6 and MATLAB R2010a are installed.  
Figure 1: The first-order resistor-capacitor (RC) (**a**) and second-order RC (**b**) equivalent circuit models.  
Figure 2: NEWARE BTS4000 battery testing bench.  
The battery tester is used to load the programmed current profiles on the battery cells within a range of voltage 0-10 V and current \(-6\) to 6 A, and the measuring errors of its voltage and current sensors are both less than 0.5%. The intermediate machine is mainly responsible for the network connection, receiving the control command from the host computer, controlling the lower battery cycler, and uploading the real-time test data. The host computer is employed to control and monitor the cycler via Ethernet cables, as well as to store the voltage and current acquired by the sensors. The tested battery cells are representative 18650 cylindrical cells whose specifications mainly include 2350 mAh nominal capacity, 3.7 V operating voltage, 4.2 V charging limit voltage, 2.7 V discharge limit voltage, and 10 A maximum continuous discharging current. During the test, the battery current and the voltage were recorded every second, while they were recorded every 100 milliseconds during the hybrid pulse power characterization (HPPC) test. The important parameters of the experiment are summarized in Table 1.
# Underwater Degraded Image Restoration by Joint Evaluation and Polarization Partition Fusion
## 2 Underwater Degraded Image Restoration Methods
### \(I_{min}\), \(I_{max}\) Pre-Processing  
As turbidity increases, the target region \(S(x,y)\) becomes fuzzy, and the interference \(B(x,y)\), caused by the turbid medium, and the ratio (sharpness) \(\gamma=S(x,y)/B(x,y)\) become smaller, causing low contrast in the image. As in reference [18], the imaging quality in turbid environments can eventually be improved by utilizing the polarization orthogonal  
Figure 2: Overall flowchart.  
image contrast enhancement preprocessing method proposed in this paper [18]. Since there is a polarization relationship between two orthogonally polarized images, contrast enhancement of each of the two orthogonally polarized images may destroy this polarization relationship, which would make the polarization characteristics not guaranteed.  
\[P(x,y)=\frac{I_{max}(x,y)-I_{min}(x,y)}{I_{max}(x,y)+I_{min}(x,y)} \tag{16}\]  
Polarizability is the most important and basic polarization parameter, and Equation (16) is the determinant [18]. This is also known as the Michelson contrast, which uses the maximum and minimum luminances (\(I\)). So, in order to maintain the polarization relationship, we only need to process one of the orthogonal images by the contrast enhancement method, and then obtain the other image according to the polarization relationship between the two images. In this paper, histogram stretching is performed on polarized orthogonal images; in the image's gray-level histogram, it stretches the narrower gray level intervals to both ends. The contrast of the whole image is increased to achieve the effect of image enhancement.  
\[A=min\big{[}I_{minpro}(x,y)\big{]} \tag{17}\]  
\[B=max\big{[}I_{minpro}(x,y)\big{]} \tag{18}\]  
\[I_{minpro}(x,y)=\left\{\begin{array}{cc}0&f\ (x,y)\ll A\\ \frac{255}{B-A}(I_{minpro}(x,y)-A)&B>f\ (x,y)>A\\ 255&f\ (x,y)\gg B\end{array}\right. \tag{19}\]  
where \(A\) is the maximum gray level, \(B\) is the minimum gray level, \(f(x,y)\) is the original image, and \(I_{minpro}(x,y)\) is the stretched image.  
\[I_{maxpro}(x,y)=\frac{1+P(x,y)}{1-P(x,y)}I_{minpro}(x,y) \tag{20}\]  
There exists an intrinsic polarization relationship between the original orthogonally polarized image \(I_{min}\), \(I_{max}\) pairs, as shown in Equation (16), and the polarization degree \(P(x,y)\) can be derived from Equation (6), and the same polarization relationship should be maintained between the preprocessed images. In addition, as in reference [18], due to the large information attenuation in turbid media, the \"darkest\" (\(I_{min}\)) image suffers from less backward scattering of light than the \"brightest\" (\(I_{max}\)) image [18]. In contrast, target light accounts for the largest percentage. Therefore, it is more effective to directly preprocess \(I_{min}\) for contrast enhancement. Combined with Equation (20), the preprocessed \(I_{max}\) can be obtained.
# MIMO Over-the-Air Computation for Distributed Estimation
## 2 MIMO-AirComp Fundamentals  
We consider \(K\) wireless nodes, each having a set of data \(\mathbf{y}_{k}=[y_{k,1},\ldots,y_{k,L_{y}}]^{\top}\in\mathds{R}^{L_{y}},\forall k \in\{1,\ldots,K\}\) and a single FC as depicted in Figure 1. This paper assumes a centralized star network topology, where all \(K\) nodes contend to send data to the FC, which is the data sink. Each node generates \(L_{y}\) heterogeneous sensing measurements. Multiple antenna arrays are equipped on all devices, where each node has \(N_{\text{tr}}\) transmit antennas, whereas the FC has \(N_{\text{tr}}\) receive antennas. Assuming symbol-level synchronization, all nodes concurrently send their symbol vectors via their arrays.  
The application aims to compute \(L_{f}\) target functions using all of the set of data \(\mathcal{D}=\{y_{k,l}\mid y_{k,l},\forall k=1,\ldots,K,l=1,\ldots,L_{y}\}\)  
\[\mathbf{f}(\mathcal{D}):\mathds{R}^{K\times L_{y}}\rightarrow\mathds{R}^{L_{f} }\,. \tag{1}\]  
In theoretical computer science, every real-valued multivariate function can be represented in its nomographic form as a function of a finite sum of univariate functions [15; 23].  
**Definition 1** (Nomographic Function).: _The function \(r_{l}(d_{1,l},\ldots,d_{K,l}),\forall l\in\{1,\ldots,L_{d}\}\) is a nonographic function, if there is a set of preprocessing functions \(\alpha_{k,l}:\mathds{R}\rightarrow\mathds{R},\forall k\in\{1,\ldots,K\}, \forall l\in\{1,\ldots,L_{d}\}\) and a postprocessing function \(\beta_{l}:\mathds{R}\rightarrow\mathds{R},\forall l\in\{1,\ldots,L_{d}\}\), such that_  
\[r_{l}(d_{1,l},\ldots,d_{K,l})=\beta_{l}\left(\sum_{k=1}^{K}\alpha_{k,l}(d_{k,l} )\right),\forall l\in\{1,\ldots,L_{d}\}\,. \tag{2}\]  
The AirComp allows the devices to simultaneously transmit their signal vectors to harness the superposition property of the interference in order to compute the nomographic function [13,14]. Thus, it theoretically completes the computation of the nomographic function of the distributed data in one single time unit (i.e., in a symbol level).  
Any continuous function of \(n\) variables can be represented as a summation form with a maximum of \((2n+1)\) nomographic component functions [23]. In other words, the AirComp requires at most \((2n+1)\) channels to compute any function of \(n\) variables. However, many emerging applications still face fundamental technical challenges due to the stringent throughput/delay requirements over the limited spectrum bandwidth. In Section 4, we explicitly convert the target function of the distributed estimation to the nomographic function with proper processing functions.
# Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System +
## 2 Background to Interference Monitoring
### Classification
However, these methods are burdened by several challenges, including expensive representative training datasets and parameter tuning, overfitting risks, and a need for more generalizability.
# A New Subject-Sensitive Hashing Algorithm Based on MultiRes-RCF for Blockchains of HRRS Images
## 3 The Proposed Method
### Structure of the Improved Blockchain Based on Subject-Sensitive Hashing  
The structure of the improved blockchain is shown in Figure 1. The most obvious improvement is using two kinds of hashing methods, subject-sensitive hashing and SHA-1, instead of just a cryptographic hash function. The subject-sensitive hashing is used to calculate the digital digest of the HRRS image, that is, the subject-sensitive hash sequence; SHA-1 is used to ensure the security of the blockchain. Furthermore, we propose a subject-sensitive hashing algorithm dedicated to this method, which will be discussed in detail in Section 3.2.  
As shown in Figure 1, the structure of the block header of the improved blockchain is the same as that of the conventional blockchain, but the block body has specific differences. The block header contains the following information:  
1. The root hash is the hash value of the block body. If there is only one HRRS image, the root hash can use a subject-sensitive hash sequence instead of compressing multiple subject-sensitive hash sequences with SHA-1.
2. The previous block hash is the hash sequence of the previous block header, which is calculated by SHA-1 to ensure the security of the blockchain itself.
3. Other information in the block header includes additional information such as timestamps and the version number.  
In the block body, the hash sequence of each HRRS image is calculated by subject-sensitive hashing instead of a cryptographic hash. After the subject-sensitive hash sequence of the HRRS image is compressed by SHA-1 (other cryptographic hash functions can also be used), the final hash value obtained is used as the root hash of the block header.
## 1 Introduction
Furthermore, it has been highlighted that the block permutation and co-XOR (BPCX) encryption algorithm, which is frequently employed in this type of methodology, exhibits vulnerabilities against certain known-plaintext attacks (KPAs) [26]. In summary, the design objectives of our scheme were to endow it with complete reversibility, separability, a substantial embedding capacity, and robust resistance against the KPA outlined in [26]. Leveraging AQP and MSB prediction, we introduce a novel RDH-EI approach. In the encryption phase, pixel bit modulation is incorporated, to bolster the security of the BPCX encryption algorithm. Throughout the data embedding process, tagging bits serve to denote the state of the pixel block, optimizing the utilization of inter-pixel redundancy and, thereby, elevating the embedding capacity. Our experimental findings demonstrate the proposed approach's efficacy in withstanding the KPA attack outlined in [26]. Additionally, our method achieved maximum embedding capacity for 8056 images, with an average embedding rate of 2.4731 across 10,000 tested images, surpassing the results reported in [13; 14]. In summary, the primary contributions of this paper can be outlined as follows:  
* The paper integrates adaptive quadtree partitioning and MSB prediction, to propose an innovative RDH-EI technique that overcomes the KPA vulnerability of the BPCX algorithm in RDH-ED and improves the embedding capacity. * During the encryption phase, Arnold permutation is introduced for pixel bit modulation, enhancing the security of the BPCX encryption algorithm and defending against KPA attacks. * Throughout the data embedding process, tagging bits serve to denote the state of the pixel block, optimizing the utilization of inter-pixel redundancy and, thereby, elevating the embedding capacity. The remainder of this paper is structured as follows: Section 2 offers a concise overview and analysis of the methodologies presented by [13; 14]. Section 3 delves into the detailed exposition of our proposed method. Our experimental outcomes are presented in Section 4, while Section 5 concludes the paper with a summary.
## 4 Comparative Study and Error Quantification  
### Comparative Methods  
The comparison models are hybrid models from recently published journal papers. They include Slime Mould Algorithm-optimized Random Forest (RF) [39], Snake Optimizer-optimized BiGRU [40], and Equilibrium Optimizer-optimized Support Vector Regression (SVR) [41]. Detailed information about them is shown in Table 3.  
Figure 3: Hybrid modeling process of RIME-XGBoost.  
These three comparison models cover a variety of machine learning methods and optimization algorithms. The setup ensures model diversity. RF is a classic ensemble learning method suitable for modeling high-dimensional data. It has strong robustness and anti-overfitting capabilities [42]. BiGRU belongs to deep learning and is particularly suitable for processing sequential data. It can capture long-term dependencies in the data [43]. SVR is suitable for small-sample modeling and establishing nonlinear mappings. It uses kernel functions to map from low-dimensional data to high-dimensional spaces, solving complex problems [44].  
Therefore, by comparing different combinations of optimization algorithms and models, this study comprehensively evaluates the performance of RIME-XGBoost. It verifies its effectiveness across different scenarios and data scales. Moreover, the comparison models are all from published journal papers, enhancing persuasiveness and credibility.
## 2 Results
### Antimicrobial Activity of Allium Species  
Considering the well-recognized antimicrobial activity of _Allium_[5], we found it of interest to assess how the different drying methods can impact such an effect. The in vitro minimum inhibitory concentrations (MICs) of the different dried _Allium_ specimen extracts were determined against _Bacillus subtilis_ growth. Results are revealed for different inhibition levels for the two different species. In particular, freeze-dried garlic extract exhibited a stronger growth inhibitory effect with a smaller IC\({}_{50}\) value (IC\({}_{50}\) 2.1 \(\pm\) 1.3) compared to freeze-dried onion extracts (IC\({}_{50}\) 5.5 \(\pm\) 1.9). With regards to drying influence on garlic activity, a lower inhibition effect was observed for microwave-dried extracts (IC\({}_{50}\) 4.5 \(\pm\) 2.0), whereas air-dried extracts showed an IC\({}_{50}\) > 50 \(\mu\)g/mL. Similar attenuation in the antimicrobial effect of _A. cepa_ in response to drying was observed more dramatically, with no effect found for both microwave-dried and air-dried extracts. No antimicrobial effect at our assay conditions was detected for the commercially dried garlic product \"Tomex\".  
Figure 5: UPLC/MS based PCA score plot derived from modelling _A. sativum_ specimens (**A**) and _A. cepa_ red cv. specimens (**C**) one at a time separately to assess the effect of drying on metabolites composition (\(n\) = 3); The loading plot from _A. sativum_ model (**B**) and _A. cepa_ red cv. specimens (**D**) shows the most variant masses detected using UPLC/MS and contributing to the samples segregation. Metabolites are denoted with \(m/z\)/retention time (sec) pair and identifications are discussed in the text.
## 3 Results and Discussion
On the other hand, BE3 (5.0%) showed over 93% adsorption of chlorophylls and over 86% adsorption of carotenoids, which is also unfavorable to the quality of oils. The descriptive analysis showed that the most advantageous choice for CP oils would be using chemically modified BE based on kerolite-hydrated magnesium silicate in the amount of 2.5% of the adsorbent mass to the oil volume. In turn, unmodified magnesium bentonite should be used for bleaching HP oils in the same dose. To check and systematize the impact of the BEs interaction force on the tested oil variants and their adsorption efficiency, principal component analysis (PCA) was performed separately for the CIE-Lab color measurement method and the content of natural dyes. The statistical analysis began with verifying color changes using the CIE-Lab method. Figure 1a,b shows the results of the analysis performed. As shown by [PERSON] et al. [38], the color of oils in the CIE-Lab method depends on the pre-treatment of the raw material. Thermal treatment of the raw material intensifies the oils' color, making them more challenging to discolor. PCA analysis of the results of color measurement using the CIE-Lab method shows that all BEs have different strengths of effective influence on the color change of the tested oils (Figure 1a). Moreover, BE5, BE6, and BE7 bleaching earths can change the color of oils, especially to lighten them. Referring to the work of [PERSON] et al. [39], chemical modification significantly affects the ability to discolor oils, distinguishing BE6 and BE7 from other BEs used in the research. Based on this, it can be concluded that their use to change color will be most justified, but in strictly controlled doses. Figure 1b shows that the mass of added bleaching earth is crucial in determining the effectiveness of color change in the CIE-Lab method, which is further confirmed by the work of [PERSON] et al. [40] Doses of 2.5% by mass of BE5, BE6, and BE7 showed little ability to change the color of the tested oils (gray field in Figure 1b), but doses of 5.0% showed the greatest effectiveness in this matter (blue field in Figure 1b). [PERSON] et al. [41] showed that slight changes in the oil color determined by the CIE-Lab method occurred during repeated oil filtration. Referring to the conducted research, it can be concluded that some BEs in small doses (i.e., BE7; 2.5%, Finola CP) do not show the proper adsorption capacity to carotenoid dyes. To change the color of the tested oils, it is best to use the BEs mentioned above in an amount within the range of 2.5% to 5.0%, which will make the oils appropriately clear, and their color will be characterized by a higher b* index, which, according to [PERSON] et al. [42], is a value desired by consumers for whom the yellowness of the oil is an indicator of product quality. Another analyzed factor was the impact of BEs on the content of natural dyes in the tested oils. Figure 2a,b shows the results of the analysis performed.
# Applications of Deep Learning to Neurodevelopment in Pediatric Imaging: Achievements and Challenges
## 4 Discussion
### Challenges and Future Directions
#### 4.2.3 Difficulty in Interpreting Deep Learning Results  
Deep learning has been criticized for its \"black-box nature\" which poses challenges for the interpretability and explainability of trained models, and thus brings concerns to medical decision-making. The deep learning system must provide the rationale behind the decision-making process to make trustworthy predictions [171]. Various approaches have been proposed to interpret deep learning algorithms. One of the common methods is the graph-based visualization approach, which identifies the critical regions for predicting results based on activation maps derived from model weights [172; 173]. Study [92] applied such an approach to identify the brain regions where children with ADHD differed from controls. The attention mechanism which focuses selectively on information of interest also plays a vital role in the interpretability of deep learning [174]. Functional connectivity differences between ADHD patients and healthy controls were identified using deep self-attention factorization in the study [90]. There are some other techniques for interpretation such as feature importance and analyzing trends and outliers in predictions. However, studies in this review have not utilized such techniques. Deep model interpretation provides crucial information for understanding brain functions and neurodevelop, which is of great importance for pediatric neuroimaging studies. Interpretability should be one of the research focuses in future neuroimaging studies.