esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The HyPlant imaging spectrometer is a high-performance airborne instrument consisting of two sensor modules. The DUAL module records hyperspectral data in the spectral range from 400-2500 nm, which is useful to derive biochemical and structural plant properties. In parallel, the FLUO module acquires data in the red and near infrared range (670-780 nm), with a distinctly higher spectral sampling interval and finer spectral resolution. The technical specifications of HyPlant FLUO allow for the retrieval of sun-induced chlorophyll fluorescence (SIF), a small signal emitted by plants, which is directly linked to their photosynthetic efficiency. The combined use of both HyPlant modules opens up new opportunities in plant science. The processing of HyPlant image data, however, is a rather complex procedure, and, especially for the FLUO module, a precise characterization and calibration of the sensor is of utmost importance. The presented study gives an overview of this unique high-performance imaging spectrometer, introduces an automatized processing chain, and gives an overview of the different processing steps that must be executed to generate the final products, namely top of canopy (TOC) radiance, TOC reflectance, reflectance indices and SIF maps.  
Keywords:HyPlant; hyperspectral; automatized processing chain; sun-induced chlorophyll fluorescence; SIF; SIF retrieval; airborne imaging spectrometer; FLuorescence Explorer; FLEX
## 2 Circuit Description
### Tester Circuits: A2V and T2V Converters
Meanwhile, the T2V converter ensures precise range detection by converting the ToF into a proportional voltage. Digital circuits, including the latch and control block, help mitigate notorious walk errors by generating accurate STOP pulses. Previously, various TDCs have been suggested to achieve precise time-interval measurements [14; 15; 16]. Yet, they mostly require complex algorithms. However, the proposed simple T2V converter provides a more simplified solution for accurate time-interval measurement, effectively replacing previous TDCs. In addition, the output levels can be mapped onto binary codes using a thermometer-to-binary circuit, which can then be used with an FPGA for further post-processing. Figure 5: (a) A block diagram of the test circuitry for the proposed OTA and (b) a schematic diagram of the utilized PDH circuit.
# Vitamin D and Rehabilitation after Stroke: Status of Art
## 3 Genetic and Epigenetic of Vitamin D Pathway
### Epigenetic
_CYP27B1_ encodes for calcidiol-1\(\alpha\)-hydroxylase, which acts at the level of the liver and kidneys so that the observed discrepancy in the relationship to plasma 25(OH)D could depend on the type of tissue analyzed. The _CYP24A1_ gene is located on chromosome 20q13.2 (chr20:52769988-52790516) and its promoter region contains a CpG island (136 CpG sites) spanning between +1263 and \(-470\) positions with respect to the start site of the gene [88]. Several regulatory sequences are located within this genomic region, including two VDREs (VDRE 1 and VDRE 2), a vitamin-stimulating element (VSE) and SP1-binding sites [86]. DNA methylation of _CYP24A1_ is highly variable and tissue-specific, both in health and diseases [48; 41]. [PERSON] et al. according with [PERSON] et al. reported that _CYP24A1_ methylation levels are negatively associated with plasma levels of 25(OH)D [31; 32], but, when correcting for vitamin D intake, this correlation is lost, suggesting that methylation levels of _CYP24A1_ are influenced by its response to increased availability of plasma 25(OH)D.  
The _VDR_ gene, located on the chromosome 12q13.11 (chr12:48235320-48298814), contains four potential promoter regions that are well-conserved [90]. One of the most studied of these promoters contains a CpG island (71 CpG sites) spanning between +168 and +723 positions with respect to the start site of the gene. In some tumors, such as breast tumors, the methylation levels of the _VDR_ gene are significantly higher compared with normal breast tissue [55], while in others, such as colon cancer or parathyroid tumors, there are no differences in the methylation levels of _VDR_[54; 56]. In peripheral blood (PBCs) cells from healthy subjects, the _VDR_ promoter methylation is positively associated with plasma 25(OH)D status [32]. Indeed, a hypermethylation of _VDR_ in response to high plasma levels of 25(OH)D suggests a regulation mechanism based on a negative feedback loop, where a greater availability of the ligand reduced the expression of the receptor to maintain homeostasis of vitamin D metabolism. Finally, few studies investigated the association between vitamin D status and global DNA methylation, producing controversial data [91; 92].
## 6 Conclusions  
In this paper, a submarine landslide susceptibility assessment was carried out using unsupervised machine learning models in the Yellow River Estuary, China. Nine influential factors were selected to analyze the susceptibility of submarine landslides based on terrain data and remote sensing images. We used different unsupervised machine learning models to classify landslide risk and discussed the accuracy of the model and the importance of a single factor. The main conclusions are as follows:  
1. Unsupervised machine learning models can be used to study and assess submarine landslide susceptibility and provide high accuracy.  
Figure 10: The normalized score of clustering results changed with different influence parameters. The normalized results were obtained by dividing the new scores of eight parameters by the scores of nine parameters.  
2. Results using the spectral clustering method have the highest accuracy among k-means, spectral clustering, and hierarchical clustering after testing with both internal validation measures and external validation measures.
3. In this study area, the order of importance of submarine-landslide-influencing factors is as follows: liquefaction, water depth, wave height, soil strength, sediment type, erosion, maximum current velocity of the bottom, slope angle, and human engineering activities. In different research areas, the importance of each impact factor is different, which needs specific analysis.  
Due to the complexity of the elements impacting the submarine-landslide-hazard triggers and the difficulties of monitoring submarine landslide sites, it is challenging to determine the precise location and hazard information of each landslide in the research region. Currently, unsupervised machine learning can only be conducted using limited data for semiquantitative description. For a more in-depth examination of submarine landslide hazards, accurate training data is necessary. In the future, we must, therefore, place a greater emphasis on the collection of field data for undersea landslide identification and monitoring.  
Conceptualization, Y.S. ([PERSON]); methodology, X.D.; program, X.D.; validation, Y.S. ([PERSON]) and Z.X.; resources, Z.S.; data curation, Z.S.; writing--original draft preparation, X.D.; writing--review and editing, X.D.; visualization, X.D.; supervision, Y.S. ([PERSON]); project administration, X.D. and Y.S. ([PERSON]); funding acquisition, X.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Foundation item: The National Natural Science Foundation of China under contract NO. 42102326; the Basic Scientific Fund for National Public Research Institutes of China under contract NO. GY0222Q05; and The Shandong Provincial Natural Science Foundation, China under contract NO. ZR2020 QD073.  
Not applicable.  
Data Availability StatementData that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Chromatography and Mass Spectrometry of ISP Extracts
On the one hand, this suggests higher activity of endoproteases and exoproteas at the higher temperature of 12 \({}^{\circ}\)C, as compared with lower temperatures. On the other hand, temperature changes may affect not only proteolysis, but also the extraction of oligo- and polypeptides from meat to brine during marination. Changes in the brine composition cannot be thus considered as results of proteolysis only. The 2-4 kDa fraction dominated the meat and the brine, followed by 4-6 kDa, and 0.5-2 kDa (Table 5). An increase in the marinating temperature had no effect on the proportion of fractions \(>\) 8 kDa in meat, while a temperature of 7 \({}^{\circ}\)C favored a higher proportion of 4-6 and 6-8 kDa fractions, at the expense of 2-4 and 0.5-2 kDa fractions, than using 2 and 12 \({}^{\circ}\)C. All brines contained fewer polypeptides with a mass to charge ratio within the range 4-8 kDa than meat extracts did. There was no significant difference between the brine and meat extracts in their content of material within other segments of \(m/z\), annotated in Table 5. In contrast, the meat to brine ratio for the fractions \(<\)4 kDa decreased with an increasing marinating temperature from 1.0-1.06 to 0.75-0.77, in contrast to fractions \(>\)4 kDa, where the meat to brine ratio changed from 0.52-1.06 to 1.59-4.47. This means that the higher marinating temperature promotes both higher protease activity, and a greater diffusion of peptides from the meat into the brine. [PERSON] et al. [44], using the SDS-PAGE method, found that the brine was dominated by the 10-20 kDa protein fraction, but after membrane fractionation of the brine, a higher total amino acid content was found in the nanofiltration fraction than in the ultrafiltration fraction. All samples contained only a slight amount of material with a high molecular mass (\(m/z\) exceeding 10 kDa). This may suggest that the main compounds extracted to the brine during marinating, as well as during post-marinating extraction, were proteolysis products. RP-HPLC and MALDI-ToF mass spectrometry may be recommended as tools for monitoring proteolysis, as alternatives to polyacrylamide gel electrophoresis (SDS-PAGE), which is more commonly used for this purpose [45]. Both HPLC and MS may serve for simultaneous detection compounds in a broad range of molecular masses, from oligopep
# Research Progress and Future Prospects of Key Technologies for Dryland Transplanters
## 2 Classification of Transplanters
### Fully Automatic Transplanter
#### 2.3.2 Fully Automatic Carpet-Seedling Transplanter  
Most existing fully automatic transplanters handle vegetable plug seedlings, but current plug-seedling transplanters suffer from issues like inconsistent seedling picking positions of the picking arms and \"seedling dropout\", which significantly affect transplanting efficiency. Thus, researchers have adapted the block seedling cutting-picking principle from rice transplanters to design vegetable blanket-seedling transplanters. [PERSON] et al. [84] developed a vegetable transplanter based on the YR-6D rice transplanter chassis, through improving the transplanting mechanism and integrating components like a soil compaction device. [PERSON] et al. [40] utilized the blanket-seedling cultivation method for rapeseed to design the 2 ZY-6 rapeseed carpet-seedling transplanter (Figure 10a), capable of performing slotting, transplanting, and soil compaction in a single pass. [PERSON] [85] developed a tractor-powered rapeseed blanket-seedling transplanter with an integrated picking-planting mechanism (Figure 10b), effectively addressing challenges of difficult land preparation, poor seedling insertion, and low seedling standing rate in rice stubble fields with sticky soil and full straw incorporation.  
In summary, research on carpet-seeding vegetable transplanters can be divided into two directions. One is based on the rice transplanter chassis as described in references [40] and [86], which achieves the transplantation of vegetable carpet seedlings by improving the insertion mechanism and adding trenching and soil compaction devices. The other research direction is the traction-type combined transplanter that integrates land preparation, ridge formation, planting, and soil compaction devices as described in reference [87]. The transplanter modified from the rice transplanter chassis has a simpler structure and better adaptability to the working environment, but the quality of the operation is affected by the soil preparation effect. The joint transplanter is equipped with a ridge-raising device, which can obtain standard ridges that meet the transplanting requirements. The transplanting effect is good, but the structure is more complex.  
Figure 9: Fully automatic plug-seedling transplanter. (**a**) Traction double-row fully automatic transplanter [80]. (**b**) Automatic transplanter for densely planted vegetables [81]. (**c**) Flexible-disc automatic transplanting machine for scallions [82]. (**d**) Side dense transplanting machine for sweet potato seedlings [83].
## 2 Preliminaries  
### Hardware Trojans  
HTs are malicious circuits designed to alter the original chip/circuit functionality, leak secret information, or even cause the permanent failure of the chip on which they are integrated at runtime. As shown in Figure 1, HTs can be inserted by untrusted foundries and third-party IP core vendors at different stages of FPGA and ASIC (Application Specific Integrated Circuit) design development and manufacturing. HTs consists of two parts: a trigger and a payload. The trigger usually corresponds to a rare data input (sequence), while the payload is the activity that causes a data leak or fault when the HT is triggered. For example, in communication systems, a chip infected with HT can leak sensitive data to the outside world when a specific combination or sequence of input signals is transmitted [17].  
To prevent/alleviate the damage of the HTs to the circuit, we must first make sure whether the HTs exist in ICs. Hence, there are a large number of studies focusing on detecting HTs in ICs. The methods for detecting HT fall into two main categories: (1) dynamic detection; and (2) static detection. The dynamic detection method is designed to detect HT circuits inserted by untrusted foundries during the manufacturing process [4; 5; 6; 7; 8; 9]. The static detection methods use testability-based structural features extracted from IC design files to match HTs' features [10; 11; 12; 13; 14]. Since attackers may create special types/hard-to-activate HTs, there is no complete guarantee that HT circuits can be identified during the detection phase. Once the HTs are activated at runtime, it is common practice to replace the device infected with HT. However, in complex and dangerous applications, manual replacement/repair of hardware devices is almost impossible. Therefore, it is necessary to adopt intelligent mitigation mechanisms to deal with HT attacks in the ICs.  
To date, intelligent mitigation mechanisms aiming at HT attacks are only available for programmable devices or virtual reconfigurable architectures. The circuit infected by HTs can be changed by reconfiguration to eliminate HTs. However, relevant research in this area is still in the exploratory stage. [PERSON], [PERSON] et al. proposed a mechanism to mitigate HT for VRC implemented on field-programmable gate arrays (FPGAs) [18]. The circuits are autonomously and periodically reconfigured to eliminate the impact of HT on the circuit. [PERSON], [PERSON] et al. proposed a security mapping approach, dynamic resource management based on security values, to enhance CGRA against HTs by selectively protecting PEs [19]. However, this approach is based on a triple modular redundancy architecture to mitigate HTs, which has a high resource overhead and is difficult to mitigate in the presence of multiple HTs. In this paper, an EA-based HT mitigation mechanism is proposed exploratively on CGRA. HT attack mitigation is achieved by reconfiguring the circuit to avoid the use of HT-I PEs.  
Figure 1: FPGA/ASIC design flow and potential insertion of HTs.
# Advancing Data Privacy in Cloud Storage: A Novel Multi-Layer Encoding Framework
## 3 Privacy-Protecting Attributes Classification for the Storage of Cloud Data
### Management of Performance
#### 3.5.1 Performance Standards
Scalability considerations play a crucial role in influencing encryption and decryption timings. The multi-layer encoding framework must be adept at handling increased data volumes and user demands in real-world scenarios. Effective scalability prevents bottlenecks, preserving optimal encryption and decryption timings and ensuring the overall security of the system. Adaptability to varying workloads is essential for maintaining optimized encryption and decryption timings across diverse scenarios. Real-world usage patterns fluctuate, and an adaptive system can sustain efficiency under different conditions, ensuring consistent security improvements. Continuous monitoring and optimization are imperative for sustained system performance. Regular assessments of performance metrics facilitate the identification of potential bottlenecks or inefficiencies, allowing continuous optimization based on real-world performance data. This iterative process ensures that encryption and decryption timings are consistently improved, aligning with evolving security requirements in dynamic environments. Lastly, adherence to service-level agreements (SLAs) is indispensable. The system's performance, encompassing encryption and decryption timings, must consistently meet the defined SLAs, underscoring the reliability and effectiveness of the multi-layer encoding framework in real-world usage scenarios.
# Additively Manufactured Polymers for Electronic Components
## 4 Applications in the Electronic Field of Additively Manufactured Polymers
### Sensors
#### 4.1.1 Strain Sensors  
Strain gauge devices serve to quantitatively translate applied strain or deformations into variations in resistance, capacitance, or piezoelectricity [142]. Traditional strain gauges typically consist of a sensitive grid (referred to as the active layer), a substrate, a covering layer, and lead wires. When strain is applied to the sensitive grid, the original geometry of the foil pattern is changed, leading to a subsequent change in its electrical resistance. The sensitive grid, which transforms surface strain into a change of resistance, is often made of thin alloy wires or foil traces, such as copper-nickel. These traces are patterned in tortuous ways and placed on polymeric substrates, e.g., polyimide, epoxy, or fibre glass-reinforced epoxy.  
However, traditional foil-based strain gauges are gradually becoming outdated due to their rigidity and susceptibility to failure under high strain rates [143]. Ensuring strong adhesion between conventional strain gauges and the structural materials of interest presents its own set of challenges [144]. Direct-write methods, instead, offer an excellent alternative for replacing conventional sensors [139; 145]. They enable direct printing of functional material components, including strain gauge patterns and insulating backing layers, onto the structural platform. Furthermore, AJP demonstrates its versatility in creating next-generation strain sensor designs on unconventional substrate platforms, including intricate 3D-printed parts [146], soft structural textiles [143], and temperature-sensitive materials like PVC conduits [147] and store-bought bandages [148].
## 1 Introduction
[PERSON] _et al._[22] validated the TRMM Microwave Imager (TMI) 2A12, 3B42V6, 3B43V6, and Global Precipitation Climatology Center (GPCC) with the monthly precipitation data collected over 10 years (2001-2010) from 22 precipitation gauges distributed over Malaysia. Both studies showed that 3B43V6 performs well over Malaysia, with a \(\pm\)15% error bias at monthly scale. However, these studies have compared only one or two SPPs, thus limiting their conclusions. There are several other available SPPs that need to be tested such as Global Precipitation Climatology Project (GPCP), PERSIANN, and CMORPH. Moreover, the accuracy assessment of these SPPs is also required at daily time steps. More comprehensive comparisons of various SPPs can not only provide guides on the selection of better products over Malaysia for local application, but also offer insight into the strengths and weakness of different satellite products over this typical tropical climate zone, enabling further improvement of satellite products. The objective of this study was to evaluate the performance of five different SPPs: TRMM Near-Real Time (3B42 RT), TRMM 3B42 version 7 (3B42V7), GPCP 1 Degree Daily (GPCP-1 DD), CMORPH, PERSIANN-Climate Data Record (PERSIANN-CDR) (which was only released in Jun 2014) [23], and a ground-based precipitation product (GPP), the Asian Precipitation - Highly-Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE). The accuracy of these products was evaluated by using 342 rain gauges at daily, monthly, seasonal, and annual scale from 2003 to 2007, which included the 2006-2007 flood. To our knowledge, this study presents a more comprehensive evaluation across Malaysia than was previously available in terms of the numbers of gauge stations, evaluated satellite products, and the evaluated aspects. Moreover, this study presents the first attempt to evaluate the rain-detection ability of each of the SPPs over Malaysia.
# Dynamic Node Privacy Feature Decoupling Graph Autoencoder Based on Attention Mechanism
## 5 Method Details
### Node Privacy Ranking by Attention
Compared with other node importance assessment methods, such as node centrality measures [23] based on static topological structure, the use of the attention mechanism can adapt to the topological structure and task requirements of different graphs. Meanwhile, by extending the attention mechanism, comprehensive nonlinear relationships and high-order dependencies can be captured. Finally, the loss function of the privacy-preserving graph autoencoder (9) can be further expressed as:\[L_{p}=\mathbb{E}_{q_{p_{p}}(\widetilde{Z_{p}}|H)}\Big{[}\log p_{\theta_{p}}(P| \widetilde{Z_{p}})\Big{]}-\text{KL}\Big{[}q_{\theta_{p}}(\widetilde{Z_{p}}|A,X) \parallel P(\widetilde{Z_{p}})\Big{]}, \tag{15}\]  
The detailed steps of the node privacy importance ranking algorithm are shown in Algorithm 1. ```
0: Adjacency matrix \(A\), node features \(X\), training epochs epoch\({}_{p}\), learning rate \(\eta_{p}\), partial observed privacy information \(P\)
0: Node privacy importance matrix \(\alpha\in\mathbb{R}^{n\times 1}\)
1: Initialize network parameters \(\theta_{p}\), \(\theta_{f}\), \(\varphi_{p}\)
2:for\(i=1\) to epoch\({}_{p}\)do
3: //Generate privacy-preserving latent representation \(Z_{p}\leftarrow\theta_{p}(A,X)\)
4: //Generate queries, keys and values through attention layer \(Q\gets W_{q}Z_{p}\), \(K\gets W_{k}Z_{p}\), \(V\gets W_{v}Z_{p}\)
5: //Normalized attention matrix \(\text{attn}_{p}\leftarrow\text{softmax}\Big{(}\frac{QK^{T}}{\sqrt{d_{k}}}\Big{)}\)
6: //Weighted aggregation of neighbor information \(\widetilde{Z}_{p}\leftarrow\text{attn}_{p}\cdot V\)
7: //Predicted privacy information \(P=\varphi_{p}(P)\)
8: //Compute loss \(L_{p}\leftarrow\mathbb{E}_{q_{p_{p}}(\widetilde{Z}_{p}|H)}\Big{[}\log p_{ \varphi_{p}}(P|\widetilde{Z}_{p})\Big{]}-\text{KL}\Big{[}q_{\theta_{p}}( \widetilde{Z}_{p}|A,X)\parallel P(\widetilde{Z}_{p})\Big{]}\)
9: //Update parameters via gradient descent \(\theta_{p}\leftarrow\theta_{p}-\eta_{p}\frac{\partial L_{p}}{\partial\varphi_{p}}\) \(\varphi_{p}\leftarrow\varphi_{p}-\eta_{p}\frac{\partial L_{p}}{\partial\varphi_ {p}}\)
10:endfor
11: //Compute node privacy importance score \(\alpha_{p}\leftarrow\sum_{i=1}^{n}\text{attn}_{p}[\cdot,j]\), \(\forall j\in\{1,2,\ldots,n\}\)
12: return \(\alpha_{p}\)
```  
**Algorithm 1** Node Privacy Importance Ranking Algorithm