esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Chlorophyll fluorescence (ChIF) parameters serve as non-destructive indicators of vegetation photosynthetic function and are widely used as key input parameters in photosynthesis-fluorescence models. The rapid acquisition of the spatiotemporal dynamics of ChIP parameters is crucial for enhancing remote sensing applications and improving carbon cycle modeling. While hyperspectral reflectance offers a promising data source for estimating ChIP parameters, previous studies have relied primarily on spectral indices derived from specific datasets, which often lack robustness. In this study, we simultaneously monitored ChIP parameters and spectral reflectance in leaves from different species, growth stages, and canopy positions within a temperate deciduous forest. We developed a data-driven partial least squares regression (PLSR) model by integrating fractional-order derivative (FOD) spectral transformation with multiple feature selection methods to predict ChIP parameters. The results demonstrated that FOD spectra effectively improved prediction accuracy compared to conventional PLSR attempts. Among the feature selection algorithms, the least absolute shrinkage and selection operator (LASSO) and stepwise regression (Stepwise) methods outperformed others. Furthermore, the LASSO-based PLSR model that used low-order (<1) FOD spectra achieved high predictive performance for NPQ (R\({}^{2}\) = 0.60, RPD = 1.60, NRMSE = 0.16), \(\Phi\)P (R\({}^{2}\) = 0.73, RPD = 1.94, NRMSE = 0.11), \(\Phi\)N (R\({}^{2}\) = 0.62, RPD = 1.62, NRMSE = 0.12), and \(\Phi\)F (R\({}^{2}\) = 0.54, RPD = 1.48, NRMSE = 0.15). These findings suggest that the integration of FOD spectral transformation and appropriate feature selection enables the simultaneous estimation of multiple ChIP parameters, providing valuable insights for the retrieval of ChIP parameters from hyperspectral data.  
chlorophyll fluorescence; feature selection; fractional-order derivative; hyperspectral; partial least squares regression Article
## 4 Algorithm Design
### MSMTAG Model  
The MSMTAG model is denoted by \(G=(V,E)\), where \(V\) is the set of vertices and \(E\) is the set of edges between vertices. Each vertex \(v_{p}\in V\) represents an aggregation task. An aggregation task can contain only one meta-task, i.e., each meta-task itself can be viewed as an aggregation task containing only itself. For any two vertices \(v_{p}\) and \(v_{q}\) in Graph \(G\), an edge connection relationship exists between vertex \(v_{p}\) and vertex \(v_{q}\) if the meta-task contained in \(v_{p}\) and the meta-task contained in \(v_{q}\) satisfy the satellite resource constraint shown in Equation (2) and the observation time window constraint shown in Equation (3) with each other. There may be multiple edge connection relationships between vertex \(v_{p}\) and vertex \(v_{q}\), each of which corresponds to one aggregation of the meta-tasks contained in \(v_{p}\) and \(v_{q}\), as shown in Figure 2.  
All edges between vertex \(v_{p}\) and \(v_{q}\) form a set \(E_{p,q}\), and the more elements there are in the set \(E_{p,q}\), the more aggregation options are available between vertex \(v_{p}\) and vertex \(v_{q}\). Figure 3 depicts the aggregation graph \(G\) containing five vertices and seven edges.  
Vertex \(v_{1}\) indicates an aggregation task containing meta-tasks \(T_{1}\) and \(T_{2}\); vertex \(v_{2}\) indicates an aggregation task containing meta-tasks \(T_{3}\) and \(T_{4}\); vertex \(v_{3}\) indicates an aggregation task having only meta-task \(T_{5}\); vertex \(v_{4}\) indicates an aggregation task containing meta-tasks \(T_{6}\) and \(T_{7}\); vertex \(v_{5}\) indicates an aggregation task containing only meta-task \(T_{8}\); and edge \(E_{1,2}\) indicates that vertices \(v_{1}\) and \(v_{2}\) are aggregated in one way under satellite resource \(S_{k}\); edge \(E_{1,3}\) indicates that vertices \(v_{1}\) and \(v_{3}\) are aggregated in one way under satellite resource \(S_{j}\); edge \(E_{1,5}\) indicates that vertices \(v_{1}\) and \(v_{5}\) are aggregated in two ways under satellite resource \(S_{j}\) and in one way under satellite resource \(S_{k}\); edge \(E_{2,5}\) indicates that vertices \(v_{1}\) and \(v_{5}\) are aggregated in three ways under satellite resource \(S_{j}\); edge \(E_{3,4}\) indicates that vertices \(v_{3}\) and \(v_{4}\) are aggregated in two ways under satellite resource \(S_{k}\);edge \(E_{3,5}\) indicates that vertices \(v_{3}\) and \(v_{5}\) exist in one aggregation mode under satellite resource \(S_{k}\); edge \(E_{4,5}\) indicates that vertices \(v_{4}\) and \(v_{5}\) exist in one aggregation method under satellite resource \(S_{j}\).  
The initial MSMTAG model contains only one meta-task per vertex, and the edges between the vertices are constructed by judging whether the time windows between the vertices' corresponding meta-tasks satisfy the task aggregation constraints shown in Equations (2)-(4), and the specific construction algorithm is shown in Algorithm 1.
_Appl. Sci._**2015**, \(5\), 1076-1085; doi:10.3390/app5041076  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
The Preparation and Application of Dendrimer Modified CdTe/CdS Near Infrared Quantum Dots for Brain Cancer Cells Imaging  
**[PERSON] 1, [PERSON] 2,*, [PERSON] 2, [PERSON] 1, [PERSON] 2 and [PERSON] 2**  
1 Department of Neurology, Pudong People's Hospital, Shanghai 201299, China;  
E-Mails: [EMAIL_ADDRESS] (Q.B.) [EMAIL_ADDRESS] (J.C.)  
2 Department of Radiology, Pudong People's Hospital, Shanghai 201299, China;  
E-Mails: [EMAIL_ADDRESS] (H.S.); [EMAIL_ADDRESS] (X.X.);  
[EMAIL_ADDRESS] (F.W.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel./Fax: +86-21-5898-1990.  
Academic Editor: [PERSON]  
_Received: 26 August 2015 / Accepted: 27 October 2015 / Published: 10 November 2015_  
**Abstract:** The most notable obstacle of brain cancer diagnosis is the inability of delivering imaging agents across the blood-brain barrier (BBB). Recently, quantum dots (QDs) has been demonstrated as an ideal image agent for brain imaging due to their ultra-small size for crossing BBB. The plolyamidoamine dendrimers modified CdTe/CdS core/shell near-infrared (NIR) region QDs was successfully synthesized in aqueous solution, and then was characterized by UV-vis absorption, photoluminescence (PL) spectroscopy, dynamic light scattering (DLS), X-ray powder diffraction (XRD) and high-resolution transmission electron microscopy (HR-TEM), _etc_. Our results reveal that the dendrimers modified CdTe/CdS QDs exhibits good water-dispersity and stable NIR fluorescence in various biological environments. In addition, this NIR QDs demonstrates a good biocompatibility and sensitive photoluminescence responses in brain tumor cell imaging. In a word, this type of dendrimers modified NIR CdTe/CdS QDs has huge potential applications in brain imaging.  
**Keywords:** noninvasive cancer diagnosis; brain imaging; nanotechnology; near infrared; quantum dots
## 2 Background and Related Work
### Neural Machine Translation
#### 2.1.2 The NMT Structure  
The most commonly used NMT approach is the \"Embed-Encode-Attend-Decode\" paradigm, which is illustrated in Figure 1. When the encoder receives one source sentence,it reads the source sentence word by word and compresses the variable-length sequence into a fixed-length vector. This process is encoding, i.e., the encoder converts words in the source sentence into word embedding. These word embeddings are then processed by neural layers and converted to representations that capture contextual information. These contextual representations are called the encoder representations. The decoder uses an attention mechanism, the encoder representations, and previously generated words to generate the decoder representations, which in turn are used to generate the next target word. The encoder and decoder can be of RNN [13], CNN [14], or self-attention and feed-forward [15].  
While NMT has shown great potential in capturing the dependencies inside the sequence, it still suffers a huge performance reduction when the input sentences are too long. This is due to the limited feature representation ability in a fixed-length vector. Thus, the attention mechanism came into being. It works as an intermediate component between Encoder and Decoder, which facilitates the word correlation in a dynamic manner (Figure 1). As a matter of fact, the inspiration for applying the attention mechanism on NMT comes from human behavior in reading and translating text data: human beings often read text repeatedly to mine the word dependency within the sentence.
## 2 Materials and Methods
### Experimental Setup  
The performance of the proposed CS-MRI reconstruction approach was evaluated using a dataset comprising T2-weighted brain MR images extracted from the IXI database [30]. In this study, 7427 and 7460 images (256 \(\times\) 256 resolution) of 57 subjects each were randomly selected for CNN training and testing, respectively. Experiments were performed for Cartesian and radial undersampling masks of 10%, 20%, 30%, and 40%, as indicated in Figure 3[21].  
To evaluate the performance of the proposed iterative method, the (1) standalone image-based U-net and (2) noniterative k-space correction [23] methods were adopted for comparison.  
1. The standalone image-based U-net was trained by the same architecture as that of this study, expressed by the following: \[\theta=\underset{\theta}{\operatorname{argmin}}\|x_{true}-f_{\theta}\big{(} \big{|}\mathcal{F}^{-1}(y_{0})\big{|}\big{)}\|.\] (4)
2. The noniterative k-space correction method was implemented based on [PERSON]'s method [23], expressed by the following: \[z=f_{\theta}\big{(}\big{|}\mathcal{F}^{-1}(y_{0})\big{|}\big{)},\] (5) \[x=\big{|}\mathcal{F}^{-1}\big{(}y_{0}+\mathcal{F}(z)\circ\overline{\mathbf{R}} \big{)}\big{|}\] (6)  
Here, \(x\) denotes the output of the noniterative k-space correction method that corresponds to the first output of the proposed iterative method.  
To ensure a fair comparison, the architecture and hyperparameters of the CNNs of other methods were set to be identical to those considered in the proposed approach. The PSNR and SSIM values were calculated to evaluate the performance of the methods at Cartesian and radial sampling masks of different sampling rates (10%, 20%, 30%, and 40%) [31]. The statistical significance was tested by conducting a paired \(t\)-test.  
Figure 2: CNN architecture used for representing a priori information in the proposed method. The number of channels is denoted above each box, and pixel sizes appear on the left; arrows denote different operations.
## 3 Nutritional and Bioactive Components of Sprouted Grains
### Enhanced Bioavailability of Micromutrients  
Germination not only alters macronutrient profiles but also enhances the bioavailability of essential micronutrients, including vitamins and minerals. These improvements are largely attributed to enzymatic activation, degradation of antinutritional factors, and structural remodeling of the grain matrix, all of which enhance extractability and physiological relevance.  
Significant increases in water-soluble B vitamins have been observed across various grains following germination. In sprouted oats, thiamine (vitamin B1) and riboflavin (vitamin B2) levels increased markedly, alongside improvements in \(\beta\)-glucan, amino acids, and antioxidant capacity [26]. Similar patterns were reported in quinoa, though with variety-specific responses. For instance, the JQ-R2 variety showed the highest thiamine content at 36 h (1.247 mg/100 g dry weight), while JQ-R2 and JQ-W4 exhibited 4-fold to 5-fold increases in riboflavin. Pyridoxine (vitamin B6) also increased in most varieties, although it was undetectable in certain ones (e.g., JQ-W3 and JQ-B2) at specific time points [38]. These findings underscore the importance of genetic background and germination timing in shaping vitamin profiles.  
Germination also increases fat-soluble vitamin E compounds, particularly \(\alpha\)-tocopherol, \(\alpha\)-tocotrienol, and \(\beta\)-tocopherol, in brown rice cultivars (e.g., Guichao2 and Guanghong6) --especially at 96 h after soaking, the final stage of germination analyzed--when their contents reached the highest levels [39]. These lipid-soluble antioxidants contribute to the nutritional enhancement of sprouted grains and may support oxidative stress regulation and insulin signaling, which are relevant in metabolic diseases.  
However, not all grains or vitamins respond positively to germination. A study on sorghum cultivar BRS 330 reported both beneficial and adverse effects: while riboflavin levels increased, thiamine and pyridoxine content declined significantly, with retention ranging from only 3.8% to 50.2%. Additionally, locopherol and locotrienol levels decreased, and certain flavonoids, such as 3-deoxyanthocyanidins and flavones, showed reduced stability during germination [50]. These divergent outcomes likely reflect the biochemical diversity of grain species, influenced by factors such as seed composition, enzymatic activity, and environmental conditions during cultivation and germination [51].  
Taken together, these findings suggest that while germination can enhance the bioavailability of key micronutrients--notably thiamine, riboflavin, and vitamin E--the effects are highly grain- and context-dependent. A deeper understanding of these patterns is essential for developing sprouted grain-based products tailored to specific nutritional goals, such as managing IR and metabolic dysregulation.
# Application Layer Protocol Identification Method Based on ResNet
## 2 Related Work
### Protocol Format  
#### 2.4.1 BGP Protocol  
BGP is a protocol used for exchanging routing information between Autonomous Systems (ASs). Its message structure is mainly used for establishing connections, exchanging routing information, and error notifications. It includes OPEN and NOTIFICATION messages.  
OPEN Message: Used to initialize the BGP connection, containing information such as the BGP version, Autonomous System Number (ASN), and Hold Time. Through the OPEN message, BGP peers can confirm each other's protocol version and capabilities.  
NOTIFICATION Message: Used to report errors or exceptional situations. For example, the \"Cease\" error code indicates that the connection was rejected, usually due to some configuration issue or connection incompatibility.
## 3 Numerical Simulations and Discussion
### Application of Adjustment Point
#### 3.3.2 Discussion  
From the above, the identification errors are almost equal to zero when the set error is zero by using the proposed adjustment point. Moreover, the identification errors are also almost equal to zero; although, the set measured error is (5%, 5 deg). This indicates that if the errors of all measured unbalance responses are equal, the bearing coefficients will be identified accurately. Therefore, the repeatability precision of each measuring channel of the unbalance response measurement system is very important for Algorithm I and Algorithm II.  
In addition, when the two methods are used for rolling bearings, the identification errors of the main stiffness coefficients are smaller than those of the main damping coefficients. While for the cross-coupled coefficients, the identification errors of the cross-coupled stiffness coefficients are higher than that of the cross-coupled damping coefficients. When Algorithm II is used for oil journal bearings, the identification errors of the main stiffness coefficients are bigger than those of the main damping coefficients. Moreover, the identification errors of the cross-coupled stiffness coefficients are bigger than those of the cross-coupled damping coefficients. Hence, for Algorithm II used for rolling bearings, the stiffness coefficients of rolling bearings can be better identified than the damping coefficients. Whereas, when Algorithm II is used for journal bearings, the damping coefficients can be better identified than the stiffness coefficients. The reason is that the numerical calculation errors of computers, such as rounding error and calculation accuracy, have little influence on big numbers and a great influence on small numbers.
## 2 Related Work
### Vehicle Detection
#### 2.1.2 Two-Stage Method  
As CNN and deep learning have become increasingly popular, a number of deep learning-based detection models have been proposed. In general, there are two types of deep learning-based detectors: one-stage and two-stage. A two-stage detector is composed of two phases: (1) region proposal (2) classification and localization. In the first phase, an arbitrary number of objects are identified, and in the second phase, these objects are classified and located. R-CNN is the first CNN-based two-stage detector. Fast R-CNN [15], Faster R-CNN [1], and Mask R-CNN [16] are well-known models developed based on R-CNN that are either more accurate or more efficient.  
A more accurate object localization and recognition system could be achieved by executing two separate tasks for region proposal and localization. However, inference speed is sacrificed, which leads to not meeting the real-time requirement in real traffic scenarios.
# Machine Learning Techniques for Non-Terrestrial Networks
## 5 Machine Learning General Aspects
### Machine Learning Methods Taxonomy  
Machine learning techniques can be classified in three main branches: (i) supervised learning, (ii) unsupervised learning and (iii) reinforcement learning. In Figure 12, the three types are depicted, highlighting their inputs and outputs.
## 5 Simulation and Results
### Simulation Results and Analysis for Highway Scenarios
The simulation results indicate that the design of the performance evaluation system based on fuzzy logic in highway scenarios is effective, achieved by analyzing the characteristics of the scene and the user requirements. Here, the energy consumption factor is considered the most crucial, and the system balances the BER and spectral efficiency factors while prioritizing energy consumption. The simulation results demonstrate that the optimal index parameters determined through the fuzzy logic output indeed represent the point of maximum system performance, enabling adaptive parameter regulation. To further verify the BER performance advantages of our proposed fuzzy logic adaptive OFDM-IM parameter optimization method, we conduct an additional set of comparative experiments. Previously, in a fixed urban traffic scenario, we used fuzzy logic to select four optimal parameter combinations: (N, K, M) = (4, 2, 4), (8, 4, 4), (12, 5, 4), (16, 6, 4). To increase the persuasiveness of the results, this supplementary experiment compares these optimal combinations with the typical parameter settings commonly used in traditional OFDM-IM systems. From [36], we select the widely adopted parameter sets (N, K, M) = (16, 13, 8) and (4, 3, 16) as benchmarks for conventional IM-OFDM. Under identical channel conditions, we plot the BER versus Eb/N0 curves. As shown in Figure 16, compared to the traditional OFDM-IM benchmark parameters, our fuzzy logic-optimized parameter combinations \begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameter** & **Configured Value** \\ \hline Rician Factor R & 20 dB \\ \hline Reflection Coefficient \(\mu\) & 0.2 \\ \hline Number of Subcarriers N & 4/8/12/16 \\ \hline Number of Active Subcarriers K & 1-N \\ \hline Modulation Order of Constellation M & 2/4/8/16 \\ \hline SNR Value & 15 dB \\ \hline Total OFDM Subcarriers U & 64 \\ \hline Number of Subblocks G & U/N \\ \hline Bandwidth & 10 MHz \\ \hline Length of the CP \(N_{\text{CP}}\) & 16 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Simulation parameters for highway scenarios. consistently achieve lower BERs across the entire Eb/N0 range. In the medium-to-high Eb/N0 region, our optimal combinations reduce the BER by roughly an order of magnitude relative to the traditional schemes. This indicates that our method not only achieves the comprehensive optimization of the energy efficiency, spectral efficiency, and BER in specific Eb/N0 scenarios, but also demonstrates significant advantages in a more general BER vs. Eb/N0 comparison framework. Through this comparative experiment, we further validate the applicability and superiority of the proposed method in practical vehicular network communication scenarios. Figure 16: Performance comparison of BER versus Eb/N0 between adaptive OFDM-IM and conventional OFDM-IM systems. Figure 15: Output results chart for highway scenario. The star indicates the maximum point of the Y value.
# Passive Power Line Communication Filter Design and Benchmarking Using Scattering Parameters
## 4 Additional Testing
### Surge  
The test should be carried out according to the relevant surge testing standard [54] under the following conditions:  
* Filter operating condition: circuits should be energized with reference voltage +5% without any load connected to the output of the circuit;
* Cable length between the surge generator and filter: 1 m;
* Tested in differential mode (line to line) and, if ground connection is available, common mode (line/neutral and ground);
* Phase angle: pulses to be applied at 60\({}^{\circ}\) and 240\({}^{\circ}\) relative to the zero crossing of the mains supply;
* Test voltage on the mains lines: 4 kV;
* Generator source impedance: 2 \(\Omega\);
* Number of pulses: five positive and five negative;
* Repetition rate: maximum 1/min.