esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Spectral UAV Sensors
### Spatiospectral filter-on-chip cameras  
To address the latter, a modified version of the filter-on-chip camera called COSI Cam has been developed [89]. This sensor no longer uses a small number of spectral filters in a tiled or pixel-wise mosaic arrangement. Instead, a larger number of narrow band filters are used, which are sampled densely enough to have continuous spectral sampling. The filters are arranged in a line-wise fashion, with a \(n\) amount of lines (a small number, five or eight) of the same filter next to each other, followed by \(n\) lines of spectrally adjacent filter bands. In this arrangement, filters on adjacent pixels only vary  
Figure 2: Example images captured by the sequential Rikola Fabry–Pérot Interferometer (FPI) (**left**), multi-point spectrometer CUBERT Firefleye (**center**) and filter-on-chip Imec NIR (**right**) 2D imagers. The excerpt shows one 5 \(\times\) 5 tile used to capture the spectral information.  
slightly in thickness, leading to much cleaner spectral responses than the 4 \(\times\) 4 and 5 \(\times\) 5 pattern. The COSI Cam prototype [89] was the first camera using such a chip, capturing more than 100 spectral bands in the range of 600 nm-900 nm. In a further development, by using two types of filter material on the chip, a larger spectral range of 475 nm-925 nm was achieved (ButterflEYE LS; [90]) with a spectral sampling of less than 2.5 nm.  
Physically, these cameras are filter-on-chip cameras, but their filter arrangement requires a different mode of operation. Their operation includes scanning over an area, and is similar to the operation of a pushbroom camera. Therefore, these sensors are also referred to as spatiospectral scanners. The 2D sensor can be seen as a large array of 1D sensors, each capturing a different spectral band (in fact, \(n\) duplicate lines per band). To capture all of the spectral bands at every location, a new image has to be captured every time the platform has moved the equivalent of \(n\) lines. This is achieved by limiting the flying speed and operating the camera with a high frame rate (typically 30 frames per second (fps)), which means a larger portion of the flying time is used for collecting information. A specialized processing workflow then generates the full image cube for the scene [90].  
Due to their improved design, the radiometric quality of the spectrospatial cameras is better compared to the classical filter-on-chip design. At the same time, their data enables the reconstruction of the 3D geometry similar to other 2D imagers due to the 2D spatial information within the images. [PERSON] et al. [91] showed that a good spatial co-registration can be achieved that also allows extraction of digital surface models (DSMs). The drawback of these systems is that they require large storage capacity and a lower flying speed to obtain full coverage over the target of interest. A further challenge in this kind of sensor is that each band has different anisotropy effects as a result of having different view angles to the object.
## 4 Discussion
### Relationship between WUE and Impact Factor with CWSI  
Figure 9 shows that the correlations between the different vegetation types and impact factors on the Loess Plateau show different patterns of change with the drought index values. The correlation between the ecosystem WUE and NDVI with increasing drought index values (Figure 9a) has the same variation pattern as for the growth trend of the WUE (Figure 7a), and both reach the peak in the interval of the CWSI of 0.3-0.4, which indicates that the NDVI dominates the spatial pattern of the interannual variation in WUE rates in the Loess Plateau. For the different seasonal variations, the correlation between the WUE and NDVI for the ecosystems showed a trend of decreasing and then increasing with increasing drought index values in spring (Figure 9d), and the drought index values were primarily affected by grassland and brush. The link between the WUE and NDVI for each type of plant in the summer revealed a trend of rising and then declining with rising values for the drought index because the proportion of water directly used for the photosynthesis of vegetation to total evapotranspiration gradually decreased with the increasing drought index values [40,41], so the correlation between the ecosystem WUE and NDVI began to decrease. The correlation between the rainfall and ecosystem WUE in the Loess Plateau showed different degrees of negative correlations in the more arid regions in the north (Figure 7b), which was due to the fact that this region is mostly covered with barren grass. The soil water content increases after rainfall but evaporates rapidly, resulting in an increase in total evapotranspiration and difficulty in using soil water for its own photosynthesis [42], so the WUE decreases. The temperature showed a non-significant positive correlation with the ecosystem WUE, and since the temperature affects the stomatal conductance and vegetation photosynthesis mainly through evapotranspiration [43], the correlation between the WUE and temperature is stronger in forests than in several other vegetation types. The negative correlation between the temperature and ecosystem WUE in summer is due to the low precipitation and uneven water heating in the Loess Plateau. As the drought index increases and the temperature rises, this will accelerate the evapotranspiration of the vegetation and reduce the WUE, especially in the scrub, and as the drought index increases, the stronger the negative correlation will be between the temperature and the WUE (Figure 9i).  
Figure 9: Plots of NDVI (**a**,**d**,**g**,**j**), precipitation (**b**,**e**,**h**,**k**), temperature (**c**,**f**,**i**,**j**), and WUE correlation coefficients with the drought index for the Loess Plateau: (**a**–**c**) annual; (**d**–**f**) spring; (**g**–**i**) summer; (**j**–**k**) autumn.
# Approach-Based Analysis on Wireless Power Transmission for Bio-Implantable Devices
## 3 Analysis of Various WPT Approaches
### WPT Based on Role of Impedance Matching Approach and Hybrid Technique
To complete a current loop and supply power to the biomedical implant, two of those pairs of capacitance patches would be required. The operational frequency is determined by inductors connected in series, allowing the L-C combination to resonate. In this system, for the gap called air-kapton, which is between the patches of inductance and patches of capacitance, the data-transferring rate of about 170 kbps and power of up to 90 mW with a PTE of 70% have been attained; for the same construction, with a separation of 3 mm wide bovine tissue samples, the power obtained by the load was 12 mW and the PTE was up to 36%. Hence, comparatively, the proposed work improves the PDL and the efficiency of the system.
## 3 Results and Discussion
### Correlations among Different Parameters  
#### 3.3.1 Oregano  
Pearson's correlation coefficients (r) among the main phenolic compounds and antioxidant capacity of oregano extracts are given in Figure 4. In the oregano extracts, the TPC and FRAP antioxidant assay presented a highly significant correlation of 0.802 (\(p<0.01\)), thus indicating that the phenolic compounds present in _Oregano vulgaris_ act as antioxidants. However, no correlation was observed between the TPC and DPPH radical assay (\(p=0.494\)). The FRAP and DPPH radical assay showed a positive correlation with 0.778 (\(p<0.01\)), indicating that the results of those assays corroborate the high capacity of these species to act as antioxidants.  
Regarding the correlations among individual phenolics, none of the quantified 14 phenolic compounds in thyme were positively or negatively correlated with the TPC or antioxidant assays. However, there was not a significant correlation between the rosmarinic acid and TPC or between this main phenolic compound and antioxidant activity. Contrary to our results, [PERSON] et al. [23] demonstrated a moderate correlation between the DPPH antioxidant activity and rosmarinic acid, but no significant correlation was observed between the FRAP activity and rosmarinic acid content. Also, [PERSON] et al. [24] reported that there is no correlation between the antioxidant capacity measured by the oxygen radical absorbance capacity and the concentration of rosmarinic acid. Thus, although rosmarinic acid is the major phenolic compound detected in oregano extracts, other phenolics and constituents can also be present and, together, act synergistically, providing a strong antioxidant effect.  
A correlation analysis showed some relations among the phenolic compounds. The results demonstrated a moderate negative correlation between rosmarinic acid and the presence of gentistic acid (r = \(-\)0.564, \(p<0.05\)), rutin (r = \(-\)0.524, \(p<0.05\)), and sakuraneetin (r = \(-\)0.472, \(p<0.05\)). Naringenin was strongly correlated with eriodyticiol (r = 0.877, \(p<0.01\)) and sakuraneetin (r = 0.924, \(p<0.01\)), both flavanones, indicating that the presence of one compound from this class is related with the presence of another compound from the same class in similar amounts. Interestingly, the results showed a strong correlation between gentistic acid and sakuraneetin (r = 0.828, \(p<0.01\)).  
Figure 4: Heatmap of the Pearson correlation coefficient (r) matrix between antioxidant capacity and individual phenolic compounds in oregano extracts. The abbreviations used are as follows: TPC—Total Phenol Content, DPPH—antioxidant activity based on 2,2-diphenyl-1-picrylhydrazyl assay, FRAP—antioxidant activity based on ferric reducing antioxidant power assay.
## 2 Methodology  
Figure 1 represents the process used to synthesise the different versions of the vowels [a], [i], and [u]. These were obtained by convolving the glottal source signals with the impulse responses of the vocal tract geometries. As explained in the Introduction, the realistic vocal tract geometries from [5] were used as well as their simplified counterparts with straight mid-line and circular cross-sections (see Section 2.1). With regard to the impulse responses \(h(t)\), those were computed using the 3D FEM acoustic model detailed in Section 2.2. Besides, the glottal source signals \(u_{g}(t)\) were generated by means of a \(R_{d}\) controlled LF model enhanced with aspiration noise, described in Section 2.3. The synthesised acoustic pressure \(p(t)\) for each vowel was finally analysed according to the methodology in Section 2.4.
# The RHO Family GTPases: Mechanisms of Regulation and Signaling
## 2 The RHO Family and the Molecular Switch Mechanism
A characteristic region of RHO family GTPases is the insert helix (amino acids 124-136, RHOA numbering), which may play a role in effector activation and downstream processes [59]. Although the majority of the RHO family proteins are remarkably inefficient GTP-hydrolyzing enzymes, in quiescent cells, they accumulate in an inactive state because GTP hydrolysis by RHO proteins is, on average, two orders of magnitude faster than GDP/GTP exchange [47]. These different intrinsic activities provide the basis for a two-state molecular switch mechanism, which greatly depends on the regulatory functions of GEFs and GAPs. Eleven of the 20 RHO family members possess classical molecular switches, namely, RHOA, RHOB, RHOC, RAC1, RAC2, RAC3, RHOG, CDC42, G25K, TC10, and TCL [47]. Atypical RHO family members including RND1, RND2, RND3, RAC1B, RHOH, WRCH1, RHOD, and RIF have been proposed to accumulate in the GTP-bound form in cells [47]. RND1, RND2, RND3, and RHOH constitute a completely distinct group of proteins within the RHO family (Figure 2) [60], as they do not share several essential amino acids including Gly-12 (RAC1 numbering) in the G1 motif (a phosphate-binding loop or P-loop) and Gln-61 in the G3 motif or switch II region, which are critical in GTP hydrolysis. Thus, they can be considered GTPase-deficient RHO-related GTP-binding proteins [61], RHOD and RIF are involved in the regulation of actin dynamics [41] and exhibit much faster nucleotide exchange than GTP hydrolysis. WRCH1, a CDC42-like protein that has been reported to be a fast-cycling protein, resembles RAC1B, RHOD, and RIF in this regard (Figure 2) [47]. These atypical members do not possess the classical switch mechanism and, therefore, may be regulated through other mechanisms.
# A Study of Order Modification for Reactive Power Support by Wind Farm during Communication Failure
## 2 System Description
### General Framework for Reactive Power Allocation  
The WFMS in this paper adopts a real power prediction and limitation plan. This provides available real power to TSO as an utilizable value. TSO have to maintain power balance between demand and supply with the expected profile of the wind farm, and designates the maximum output power of the WFMS, to set the reference signal for each wind turbine. Each unit in the wind farm can be managed individually, and excessive real power can be regulated by pitch control.  
Figure 2 describes the power control concept in WFMS. Firstly, the TSO sends the required wind farm level quantity to the WFMS. The central controller primarily assigns a maximum profile to each turbine. The WFMS then designates the reference signal as a maximum profile for each single turbine. The turbine then has to follow the limitation signal, before reaching the next stage. Considering the dependence of reactive power control on real power quantity, this control process has the advantage of aiding converter operations in the calculation of reactive power reserve in real time operation. The real power signal is directly utilized in the reactive power assignment process to predict real power loss and voltage variation.  
The allocation plan in Reference [18] considers the reactive power reserve of the entire wind farm. Therefore, a precise capability update plan can enhance usability under practical operating conditions. Added to the composed basic proportional distribution plan, order modification focuses on the main objective function, based on changed system structures.  
Figure 1: Online management network and data flow.
# An Improved Ciphertext-Policy Attribute-Based Encryption Scheme in Power Cloud Access Control
## 5 Security Analysis and Performance Analysis  
### Data Confidentiality and Resist Collusion Attack  
Our PCAC scheme is improved from the CP-ABE scheme, and also has CP-ABE's security properties: data confidentiality and resist collusion attack.  
**Data confidentiality**: File data must be kept confidential to users who do not have access. Additionally, our scheme must be able to defend Chose Plaintext Attack (CPA).  
The proof of our PCAC scheme is similar to [PERSON] [12]. The power cloud only saves ciphertext encrypted by the symmetric key and the encrypted symmetric key, in the encrypt phase. PC does not take part in generation of the secret key. It is hard to recover the plaintext without the secret key. It means that the data saved in power cloud is safe from an outside attacker.  
**Resist collusion attack**: Multiple malicious users cannot combine with each other to decrypt the file ciphertext which a single user cannot decrypt.  
Our PCAC scheme is able to effectively resist collusion attacks. In our scheme, the user does not need to upload his\her own attribute set and only needs to upload his own proof of identity. Therefore, the PC cannot be attacked by forging the attribute set. Additoanlly, in order to prevent to an attacked by forging proof of identity, our PCAC scheme designs Act_Audit phase based on zero-knowledge proof, and is executed before the Gen_key phase, so that malicious users cannot obtain the secret key generated by the CKA.
## 2 Results
### Identification and Expression Verification of JA Signal Pathway Genes under MeJA Treatment  
In Arabidopsis, the JA signal is perceived and transduced via the SCFCOII-JAZ co-receptor complex. The SCFCOII complex includes COI1, ASK2, CULLIN1, Rbx, and E2. The JAZ repressors recruit the co-repressor complex consisting of Novel Interactor of JAZ (NINJA), TOPLESS (TPL), and histone deacetylase (HDA), interact with and repress the transcription activator bHLHzip transcription factor MYC2 (MYC2). The perception of JA-Ile by SCFCOII complex leads to degradation of JAZs via the 26S proteasome, then activate the downstream transcription factors of JA responses [17; 31]. In this study, 24 DEGs associated with JA signal transduction were identified in _M. canadensis_. Of these, 23 DEGs showed up-regulation under MeJA treatment, which encode putative JAZ (9), MYC2 (7), NINJA (6), and S-phase kinase-associated protein 1 (SKP1) (1) (Figure 3). 12 of the 24 DEGs were selected for quantitative real-time PCR (qRT-PCR) to verify the reliability of the RNA-Seq data. The results showed that all the expression patterns of qRT-PCR were quite a good match with the RPKM results of RNA-Seq data (Figure 4). These validation results of qRT-PCR indicated that the RNA-Seq data were quite reliable.  
Figure 2: KEGG pathway enrichment of DEGs induced by MeJA.  
Figure 4: Expression validations of 12 selected JA signal transduction related genes in control and MeJA-treated samples using qRT-PCR. The RPKM values obtained from RNA-Seq data were indicated on the top of each graph.  
Figure 3: Heat maps of the DEGs in JA signal transduction pathway. JAZ, Jasmonate ZIM domain protein; MYC2, bHLHzip transcription factor MYC2; TPL, TOPLESS; NINJA, Novel interactor of JAZ; SKP1, S-phase kinase-associated protein 1.
# The Use of Earth Observation Data for Railway Infrastructure Monitoring--A Review
## 2 Railway Infrastructure Monitoring Use Cases and Satellite Data Potential
### Monitoring of Vegetation Around the Rail Track (VG)  
Identification of the threat to railway sections from surrounding vegetation increases operational safety. One of the main threats is falling trees. To assess that kind of threat, it is important to consider the species composition, as well as the state of health of tree vegetation. Knowing the species composition allows managers to predict what tools will be needed for the maintenance of vegetation. Forest processes related to climate change can be detected by long-term monitoring of specific areas. Apart from the state of vegetation, long-term monitoring also helps identify devastation. The benefits of vegetation monitoring are the increased traffic flow and the reduction of damage to the infrastructure, especially in harsh weather conditions. Unlike conventional methods, such as using trains equipped with LiDAR [33], satellite optical images provide information beyond the row of trees next to the rail track, and they can be used to assess the health of tree vegetation. (e.g., [34; 35]).  
Optical sensors on satellites provide detailed images of Earth's surface, capturing fine-grained views of natural systems and human activities. These sensors work by detecting visible light and other parts of the electromagnetic spectrum, allowing us to observe and monitor various phenomena. Commercial providers such as WorldView-3 and WorldView-4 (Maxar Technologies: Westminster, Colorado, USA), Pleiades Neo (Airbus Defence and Space: Taufkitchen, Germany), and QuickBird (Maxar Technologies) have achieved high resolutions, with some reaching up to 30 cm. There are also several non-commercial optical satellites, which are used for various scientific, environmental, and governmental purposes, such as Landsat series (NASA/USGS), Sentinel-2A and 2B (ESA), and MODIS (NASA). For example, the Sentinel-2 mission, with bands covering near-infrared and red wavelengths, is proving valuable for monitoring vegetation, particularly along railways and roads. In particular, its wide range of vegetation indices, such as the widely used NDVI (Normalized Difference Vegetation Index), enables comprehensive vegetation analysis. The spatial resolution of 10 m, 20 m, and 60 m, depending on the spectrum, allows detailed observations of the monitored areas. With a revisit time of approximately 5 days, the Sentinel-2 mission ensures frequent and timely data acquisition. However, the efficiency of optical data acquisition is susceptible to the challenge of cloud cover.
## 4 Optimal Design
### Optimization Variables and Constraint Conditions
#### 4.2.2 Constraint Conditions  
The constraint conditions can be divided into test cost constraint and test variable actual value range constraint, in which the test cost can be divided into sample cost and test cost. The sample cost is the product of sample unit price and sample number. The test cost includes the depreciation of test box and test equipment, and the consumption of human and power resources, that is, the product of test unit price and test time.  
Therefore, the constraint expression of test cost is as follows:  
\[C_{1}\geq n\cdot C_{d}+C_{0}\cdot\sum\limits_{l=1}^{L}m_{l}\times\Delta t \tag{22}\]  
where \(C_{1}\) is the total cost of the test and \(C_{d}\) is the unit price of the test motorized spindle. \(C_{0}\) is the cost of the test per unit time (including inspection costs).  
The constraints of the actual value range of test variables mainly include stress level, inspection times and the number of samples under each stress level. Among them:  
1. \(S_{i0}<S_{i1}<S_{i2}<S_{i3}\cdots<S_{iL}<S_{imax}\), \(S_{i0}\) is the normal stress when the motorized spindle is working, and \(S_{imax}\) is the ultimate stress when the motorized spindle is working. The ultimate stress can be obtained through the technical requirements of the motorized spindle, and then the highest stress \(S_{iL}\) is selected according to the ultimate stress so as to ensure that the failure mechanism of the motorized spindle remains unchanged at all stress levels;
2. Inspection times \(m_{1}\geq m_{2}\geq\cdots\geq m_{l}\geq 10,m_{1}\geq m/l\), \(\sum\limits_{l=1}^{L}m_{l}=m\), the degradation of motorized spindle is a slow process. In order to ensure the regression fitting accuracy of degradation, the inspection times under each stress level should not be less than 10; under the premise of unchanged failure mechanism, the degradation rate of motorized spindle will accelerate with the increase of stress. In order to collect enough degradation information under each stress level, the inspection times under low stress level should be more than high Stress level.
3. \(n_{l}\geq 5\), in order to ensure the statistical significance of the data obtained in the test, the number of test samples under each stress should not be less than 5.  
By summarizing the above constraints, the mathematical expression of the constraint conditions of the test scheme can be expressed as follows:  
\[s.t.\quad\left\{\begin{array}{c}C_{1}\geq n\cdot C_{d}+C_{0}\cdot\sum \limits_{l=1}^{L}m_{l}\\ S_{i0}<S_{i1}<S_{i2}<S_{i3}\cdots<S_{iL}<S_{imax}\\ m_{1}\geq m_{2}\geq\cdots\geq m_{l}\geq 10,m_{1}\geq\frac{m}{l}\\ n_{l}\geq 5\end{array}\right. \tag{23}\]  
Thus, the optimization problem can be described as:  
\[s.t.\quad\left\{\begin{array}{c}\max\frac{|M(\beta)|}{|M_{2}(\beta)|}\\ C_{1}\geq n\cdot C_{d}+C_{0}\cdot\sum\limits_{l=1}^{L}m_{l}\cdot\Delta t\\ S_{i0}<S_{i1}<S_{i2}<S_{i3}\cdots<S_{iL}<S_{imax}\\ m_{1}\geq m_{2}\geq\cdots\geq m_{l}\geq 10,m_{1}\geq\frac{m}{l}\\ n_{l}\geq 5\end{array}\right.\quad variables:S_{il},n_{l},m_{l} \tag{24}\]