esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Fault detection in the landing gear retraction/extension hydraulic system is difficult due to uncertainties in component parameters and sensor measurement values. This work lies in the introduction of linear fractional transformation technology and uncertainty analysis theory for the construction of the diagnostic bond graph of the landing gear retraction/extension hydraulic system. Thus, interval analytical redundancy relations can be derived as well as fault signature matrices. By using the fault signature matrix, existing faults can be detected and isolated preliminary. Furthermore, interval analytical redundancy relations can be used to detect system faults in detail. The analysis results of the failure cases of the internal and external leakage of the actuator and landing gear selector valve reversing stuck show that compared to the traditional analytical redundancy relations, this method takes into account the negative factors of uncertainty, so it can effectively reduce missed diagnosis and misdiagnosis; compared to the traditional absolute diagnostic threshold, the interval diagnostic threshold is more accurate and sensitive.  
fault detection; retraction/extension (R/E) hydraulic system; bond graph-linear fractional transformation technique; interval analytic redundancy relations; uncertainty; fault signature matrix; residuals; thresholds +
Footnote †: journal: Applied Sciences  
Accepted: 21 September 2022  
Published: 26 September 2022
## 2 Materials and Methods  
### Data Retrieval and Identification of TPSs  
The proteome data of the sequenced Labiatae plants were downloaded from [[http://www.ndctcm.org/shujukuijieshao/2015-04-23/27.html](http://www.ndctcm.org/shujukuijieshao/2015-04-23/27.html)]([http://www.ndctcm.org/shujukuijieshao/2015-04-23/27.html](http://www.ndctcm.org/shujukuijieshao/2015-04-23/27.html)) (_Salvia militorrhiza_) [24], [[http://caps.ncbs.res.in/Ote/](http://caps.ncbs.res.in/Ote/)]([http://caps.ncbs.res.in/Ote/](http://caps.ncbs.res.in/Ote/)) (_Ocimum tenuiforum_) [25], [[http://ocri-genomics.org/Sinbase/](http://ocri-genomics.org/Sinbase/)]([http://ocri-genomics.org/Sinbase/](http://ocri-genomics.org/Sinbase/)) (_Sesamum indicum_) [26], and [[http://gigadb.org/dataset/100463](http://gigadb.org/dataset/100463)]([http://gigadb.org/dataset/100463](http://gigadb.org/dataset/100463)) (_Salvia splendens_) [27] (Accessed data: 21 July 2020). For the identification of TPSs, the TPS specific Pfam N-terminal domain model (PF01397) and C-terminal domain model (PF03936) were downloaded from the Pfam website ([[http://pfam.xfam.org/](http://pfam.xfam.org/)]([http://pfam.xfam.org/](http://pfam.xfam.org/))) [28]. Then, an HMM search (v3.1b2) [29] was conducted to search the proteome using the PF01397 and PF03936 domain model data as queries. Candidate genes with both N-terminal and C-terminal domains were considered as complete TPSs and used for further analysis. The _Arabidopsis_ TPS sequences were downloaded from TAIR ([[https://www.arabidopsis.org/](https://www.arabidopsis.org/)]([https://www.arabidopsis.org/](https://www.arabidopsis.org/))) (Accessed data: 21 July 2020). The genome data of _M. longifolia_ were downloaded from Mint Genomics Resource ([[http://langelabtools.wsu.edu/mgr/](http://langelabtools.wsu.edu/mgr/)]([http://langelabtools.wsu.edu/mgr/](http://langelabtools.wsu.edu/mgr/))) (Accessed data: 5 May 2020). The assembly of the _M. longifolia_ genome contains 12 large scaffolds encompassing 462.6 Mb, which is consistent with the previously reported genome size (400-500 Mb) [22]. The new assembly corresponds to at least 92.5% of the predicted genome size. Due to the lack of gene prediction of the _M. longifolia_ genome sequence assembly, a BLAT-based method was used to identify TPSs in _M. longifolia_ genome sequence assembly [30]. The protein query set representing the TPS family used for BLAT was constructed based on the PF01397 and PF03936 seed sequences. The target sequences and flanking sequences in the _M. longifolia_ genome sequence were extracted and then imported to Genscan for gene prediction [31]. The conserved N-terminal and C-terminal domains of _M. longifolia_ TPSs were confirmed on the SMART website ([[http://smart.embl-heidelberg.de/](http://smart.embl-heidelberg.de/)]([http://smart.embl-heidelberg.de/](http://smart.embl-heidelberg.de/))).
# OneMap Drought Monitoring Analysis Based on Statistical Models
## 2 Materials and Methods
### Multi-Drought Indices
#### 2.2.1 Meteorological Drought Indices  
Most drought indices based on meteorological factors have been developed and used for meteorological and agricultural monitoring[PERSON] Among the meteorological drought indices available, the SPI was selected for use because data can be obtained to calculate it, it is easy to calculate, and it is monitored by government ministries. SPI, developed by [PERSON] et al. [12; 21], the most widely used drought index, is based on the fact that drought begins with a decrease in precipitation that causes water shortages compared to relative water demand. SPI is calculated through a statistical analysis process, representing the effect of individual water sources on drought by setting the time unit as a cumulative series, such as 3, 6, 9, 12, and 24 months, and calculating the precipitation shortage per cumulative time unit. The SPI calculated for a specific time unit is used to monitor various drought conditions according to each time unit. For instance, short time units such as 1 and 3 months can be used to evaluate short-term drought, and longer time units such as 12 and 24 months can be used to assess long-term drought. Table 2 below shows a classification of drought conditions according to the SPI range.
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Sum of Squares** & \begin{tabular}{c} **Degrees of** \\ **Freedom** \\ **(df)** \\ \end{tabular} & **Mean Square** & \
\begin{tabular}{c} **Fisher Test** \\ **(F)** \\ \end{tabular} & \(p\)**-Value** &
\begin{tabular}{c} **Effect Size** \\ **(\(\eta^{2}\))** \\ \end{tabular} \\ \hline \multicolumn{6}{c}{Willingness to consume} \\ \hline Plant-based vs. meat product & 2.328 & 1 & 2.328 & 0.861 & 0.354 & 0.002 \\ “High protein” Nutrition & 12.249 & 1 & 12.249 & 4.531 & 0.034 & 0.009 \\ Claim vs. no claim & & & & & & \\ Source of protein/claims & & & & & & \\ interaction & 0.437 & 1 & 0.437 & 0.162 & 0.688 & \textless{}0.001 \\ Residuals & 1400.272 & 518 & 2.703 & & & \\ \hline \multicolumn{6}{c}{Environmentally friendly} \\ \hline Plant-based vs. meat product & 92.907 & 1 & 92.907 & 38.681 & \textless{}0.001 & 0.069 \\ “High protein” Nutrition & 15.993 & 1 & 15.993 & 6.658 & 0.010 & 0.012 \\ Claim vs. no claim & & & & & & \\ Source of protein/claims & & & & & & \\ interaction & 0.056 & 1 & 0.056 & 0.024 & 0.878 & \textless{}0.001 \\ Residuals & 1244.159 & 518 & 2.402 & & & \\ \hline \multicolumn{6}{c}{Natural} \\ \hline Plant-based vs. meat product & 34.604 & 1 & 34.604 & 15.518 & \textless{}0.001 & 0.029 \\ “High protein” Nutrition & 5.299 & 1 & 5.299 & 2.376 & 0.124 & 0.004 \\ Claim vs. no claim & & & & & & \\ Source of protein/claims & & & & & & \\ interaction & 0.431 & 1 & 0.431 & 0.193 & 0.660 & \textless{}0.001 \\ Residuals & 1155.094 & 518 & 2.230 & & & \\ \hline \multicolumn{6}{c}{High quality} \\ \hline Plant-based vs. meat product & 2.701 & 1 & 2.701 & 1.643 & 0.200 & 0.003 \\ “High protein” Nutrition & 11.875 & 1 & 11.875 & 7.227 & 0.007 & 0.014 \\ Claim vs. no claim & & & & & & \\ Source of protein/claims & & & & & & \\ interaction & 0.161 & 1 & 0.161 & 0.098 & 0.754 & \textless{}0.001 \\ Residuals & 851.204 & 518 & 1.643 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: ANOVA analysis.
# Vehicular Visible Light Communication for Intersection Management +
## 4 V2X Cooperative System Evaluation
### Traffic Signal Phasing: V2X Communication
The main benefit was to improved safety, since it separates potential conflict points where vehicles, pedestrians, and bicyclists may cross paths. The separation of traffic flow on the north/south street allows Intersection 1 to handle a greater volume of traffic and operate with less delay. The pedestrian Phase will begin early and the number of phases in Intersection 2 was reduced. The wait times are shorter because fewer vehicle traffic signal phases means less time stopping at the intersections. Additionally, through synchronization of the two signalized intersections, the corridor travel times is improved on both the major and side streets. Results show that the vehicles' arrival is controlled and scheduled to cross the intersections in order to reduce traffic delay. The pose analysis is also used to allocate delays between left turns and forward movements. As a final remark, traffic light split coordination using the V-VLC request-response concept facilitates traffic circulation, promoting smooth movement along the network, forming platoons with efficient speeds, preventing the formation of queues, avoiding congestion and delays. This is also an effective way to reduce excessive fuel consumption and preserve the environment through minimal air pollution. To evolve towards real implementation, the performance of the V-VLC system still needs improvement, namely the distance between conflicting vehicles along with the trajectories of other opposing vehicles should also be monitored and optimized.
# Agricultural Evolution: Process, Pattern and Water Resource Effect
## 4 Discussion
### Water Resource Effect Caused by Cultivated Land Changes  
Based on cultivated land area and water consumption per unit area, the water consumption of cultivated land in different period was calculated in our study. The values of water consumption per unit area of paddy field and dry farmland in the study area were sourced from [PERSON] et al. [48]. Results indicated that net water consumption of cultivated land increased from 529,048.97 m\({}^{3}\)/km\({}^{2}\) in 1990 to 775,852.29 m\({}^{3}\)/km\({}^{2}\) in 2015, mostly contributed by paddy expansion (Table 8). Water consumption of paddy field increased from 151,328.68 m\({}^{3}\)/km\({}^{2}\) in 1990 to 351,852.24 m\({}^{3}\)/km\({}^{2}\) in 2015 while that of dry farmland increased from 377,720.29 m\({}^{3}\)/km\({}^{2}\) in 1990 to 424,000.05 m\({}^{3}\)/km\({}^{2}\).  
Dry farmland is still the dominant landscape in the West Songneng Plain. However, the area growth of paddy field in the past cannot be ignored. The water consumption per unit area of paddy field is about six times that of dry farmland. Paddy expansion has brought considerable pressure on water resources. Studies have indicated that yearly precipitation decreased by 14.7 mm per decade during 1950-2000 [17] and cultivated land expansion has brought drought in the West Songnen Plain [49]. More large dams and reservoirs have been constructed in the West Songnen Plain to irrigate cultivated land in recent decades [47; 50]. These dams and reservoirs increased the food yield while also caused serious land salinization and alkalinization in the study area [18; 51]. For example, the building of Xingfu Dam in early 1970s was the main reason for rapid increase of salinized land from 1954 to 1975 in the northern part of Da'an County, which is located in the middle of the Songnen Plain [51]. Agricultural crops usually consume a lot of water resources, especially rice which grows on flooded soil, leading to an effect on regional water security. In this study, our results also indicated that agricultural expansion caused an increase in water consumption by 529,048.97 m\({}^{3}\)/km\({}^{2}\) in the West Songnen Plain. For future sustainability, irrigation mechanisms should be standardized and water use efficiency should be improved based on cultivated land distribution. In the future, according to evaluation of regional water and land carrying capacity comprehensively, the rational configuration of water and soil resources should be carried out to achieve ecological and sustainable development.
# Advancements in Flame-Retardant Systems for Rigid Polyurethane Foam
## 3 Additive-Type Flame Retardants
### Addition of Phosphorus-Nitrogen-Based Flame Retardants  
Nitrogen-containing flame retardants (N-FRs) are another class of commonly used flame retardants in the RPUF matrix that work by releasing nitrogen species in the gas phase during combustion, which can act as diluents fuels, oxygen, and free radicals in the flame [59]. The most used N-FRs in RPUF include melamine, melamine cyanurate,melamine phosphate, and guanidine derivatives. These N-FRs can provide excellent an flame retardancy performance in RPUF, especially when used in combination with phosphorus-containing flame retardants. The addition of N-FRs can also improve other properties of RPUF, such as its mechanical properties and thermal stability, while minimizing the smoke and toxic gas released during combustion. [PERSON] et al. [(2018)] investigated the smoke suppression mechanism of melamine in rigid polyurethane foam using a smoke density chamber, cone calorimetry, and a Py/GC-MS analysis.  
A new phosphorus-nitrogen intorescent flame retardant (DPPM) was synthesized by [PERSON] et al. [(2018)]. The addition of only 9% DPPM was sufficient to enable RPUF to achieve a UL-94 V-0 rating and a limit oxygen index of 29%. Hex(phosphitehydroxylmethylphenoxyyl) cyclotriphosphazene (HPHPICP, depicted in Figure 5) is another flame retardant that has been successfully synthesized and incorporated into rigid polyurethane foam [(2018)]. HPHPICP contains multifunctional groups that introduce crosslinking into the foam structure, thereby improving its thermal stability and compressive strength. The addition of HPHPCP to DPPM-RPUF resulted in an LOI of 29.5% and a UL-94 V-0 rating, which can be attributed to the surface pyrolysis of RPUF.
# Flexible Dispatch for Integrated Power and Gas Systems Considering Power-to-Gas and Demand Response
## 3 Flexible Economic Dispatch Model
#### 3.1.1 Objective Function of Main Problem  
The day-ahead economic dispatch model for power systems is to minimize the total operation costs. Including system operating costs \(C_{main1}\) (the operating and start-up/shutdown costs of coal-fired units; the gas fuel and start-up/shutdown costs of gas-fired units; the P2G facility operation cost; the wind power curtailment cost; the load shedding cost) and adjustable capacity allocation costs \(C_{main2}\) (generator units; P2G facility; demand respond):  
\[\min C_{main}=C_{main1}+C_{main2} \tag{8a}\] \[\begin{array}{rl}C_{main1}&=\sum\limits_{t=1}^{T}\left[\sum\limits_{i\in \Omega_{c},i\
eq\Omega_{cCT}}(f(P_{i,t})\cdot u_{i,t}+SU_{i,t}+SD_{i,t})\right. +\left.\sum\limits_{i\in\Omega_{cCT}}(W_{GT,t}+SU_{i,t}+SD_{i,t})\right.\\ &+\left.\sum\limits_{p\in\Omega_{cPIC}}C_{p2g,p}P_{p2g,p,t}+\sum\limits_{k\in \Omega_{\rm Wcourt,k}}C_{\rm wcourt,k}\Delta P_{w,k,t}+\sum\limits_{b\in\Omega _{\rm B}}C_{\rm lcunt,b}\Delta P_{b,t}\right]\\ \\ C_{main2}&=\sum\limits_{t=1}^{T}\left[\sum\limits_{i\in\Omega_{c}}\left(C^{u }_{g,i}\cdot f^{u}_{g,i,t}+C^{d}_{g,i}\cdot f^{d}_{g,i,t}\right)\right.+\left. \sum\limits_{p\in\Omega_{\rm P2G}}\left(C^{u}_{p2g,p}\cdot f^{u}_{p2g,p,t}+C^{ d}_{p2g,p}\cdot f^{d}_{p2g,p,t}\right)\right.\\ &+\left.\sum\limits_{m\in\Omega_{\rm Dba}}\sum\limits_{j=1}^{I_{a}}f^{u}_{dr,m,j,t}\cdot\rho^{u}_{m,j}+\sum\limits_{q\in\Omega_{\rm Dba}}\sum\limits_{j=1 }^{I_{a}}f^{d}_{dr,q,j,t}\cdot\rho^{d}_{q,j}\right]\end{array} \tag{8c}\]
# Data-Free Area Detection and Evaluation for Marine Satellite Data Products
## 2 Materials and Methods
### Date Collection  
#### 2.2.1 GOCI, MODIS and OLCI L-1 Data  
Korea successfully launched the world's first geostationary ocean color satellite called Communication Ocean and Meteorological Satellite (COMS) on 26 June 2010. The GOCI on top of COMS has eight bands with a range of 0.4-0.9 \(\upmu\)m and a spatial resolution of 500 m. COMS has a high temporal resolution and can generate eight remote sensing images from 8:00 a.m. to 3:00 p.m. (Beijing time) every day at time intervals of 1 h. Ocean color data can be obtained at the regional scale for the East Asian seas around the Korean Peninsula [34], and their high temporal resolution is exceptional for ocean color monitoring [35]. In this study, we selected COMS_GOCI_L1B and COMS_GOCI_L2A as the research data. COMS_GOCI_L1B, with its complete information, was used as the primary data without calibration and spatial correction processing. By contrast, COMS_GOCI_L2A, which includes the data-free area, was used as the secondary ocean color product data, as provided by GOCI.  
MODIS, the most important sensor in the U.S. Earth Observation System, was successfully launched on 4 May 2002, onboard the two satellites of Terra and Aqua. Its 36 bands range from 0.4 to 14.38 \(\upmu\)m, with a spatial resolution of 250/500/1000 m and a temporal resolution of 1 day. Owing to the excellent imaging capability and early public time of MODIS, its data are widely used in research in various industries [36, 37, 38]. Among them, MOD02 KM is the Level 1 data, with a spatial resolution of 1000 m and complete information. Moreover, L2_LAC_OC is the full-resolution ocean color product data with local area coverage.  
The OLCI onboard the Sentinel 3 satellite was launched on 16 February 2016. Its 21 bands ranging from 0.4 to 1.02 \(\upmu\)m, with a spatial resolution of 300 m and a temporal resolution of 2 days. The excellent spatial and temporal resolution and relatively good spectral resolution of OLCI have gradually been accepted by the public, and an increasing number of researchers have scientifically processed the OLCI data [39, 40, 41]. Here, the selected datasets were OL_1_EFR and OL_2_WFR. OL_1_EFR is the uncalibrated full-resolution atmospheric radiation with complete information, whereas OL_2_WFR is the full-resolution ocean color product with the data-free area. Table 1 summarizes the data used in the study and the corresponding information.  
Figure 1: Location of the study area in the Yellow Sea and Bohai Sea.
# Long-Term Analysis of Wave Climate and Shoreline Change along the Gulf of California
## 4 Results  
### Long-Term Shoreline Change  
The long-term shoreline analysis (Figure 4 and Table 3) showed that the major part of the coastline in the Gulf of California falls under the erosion category. The state with a major percentage of shoreline subject to erosion was Sonora (87.7% of its total shoreline), followed in descending order by Sinaloa (77.1% of its total shoreline), Baja California Sur (B.C.S.) (55.3% of its total shoreline) and Baja California (B.C.) (43.7% of its total shoreline). Erosion was mainly found to occur around the mouths of dammed rivers and in areas with high population densities, as well as agricultural and aquacultural land uses (Figure 4). The most prominent erosion hotspots were observed on the northern coast of B.C. (Figure 4a), on the northern and southern coasts of Sonora (Figure 4c), and along the coast of Sinaloa (Figure 4d). Accretion was mostly noticed along the coasts of B.C. and B.C.S. (except the north coast) (Figure 4a,b).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Statistics** & **B.C.S.** & **B.C.** & **Sonora** & **Sinlao** &
\begin{tabular}{c} **Regional** \\ **(Total)** \\ \end{tabular} \\ \hline Mean shoreline change (m/year) & \(-0.1\) & \(-2.7\) & \(-4.6\) & \(-1.2\) & \(-2.2\) \\ Minimum shoreline change (m/year) & \(-4.8\) & \(-43.4\) & \(-78.1\) & \(-50.7\) & \(-78.1\) \\ Maximum shoreline change (m/year) & \(9.1\) & \(18.1\) & \(14.1\) & \(81.3\) & \(81.3\) \\ Mean accretion rate (m/year) & \(0.7\) & \(1.0\) & \(1.7\) & \(7.5\) & \(1.9\) \\ Mean erosion rate (m/year) & \(-0.8\) & \(-7.5\) & \(-5.5\) & \(-3.8\) & \(-4.2\) \\ Standard deviation (m/year) & \(1.0\) & \(9.2\) & \(10.6\) & \(9.8\) & \(8.6\) \\ Shoreline length that records erosion (km) & \(558.2\) & \(275.0\) & \(958.7\) & \(491.7\) & \(2283.6\) \\ Shoreline length that records accretion (km) & \(451.7\) & \(354.9\) & \(134.4\) & \(146.4\) & \(1087.4\) \\ Shoreline part subjected to erosion with respect to the total study area (\%) & \(16.6\) & \(8.2\) & \(28.4\) & \(14.6\) & \(67.8\) \\ Shoreline part subjected to accretion with respect to the total study area (\%) & \(13.4\) & \(10.5\) & \(4.0\) & \(4.3\) & \(32.2\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Long-term shoreline change statistics at state and regional scales.  
Figure 4: Long-term shoreline change maps: (**a**) B.C.; (**b**) B.C.S.; (**c**) Sonora; (**d**) Sinlao.
## 6 Applications of Antimicrobial Peptides in Active Packaging
### Future Scope of AMP Derived from Insects in Food Packaging  
The incorporation of AMPs harvested from insects into packaging material could be helpful in preventing food deterioration. Defensis has antimicrobial activity against _Staphylococcus aureus_, which is one of the commonly found pathogenic microorganisms in food, such as meat products and dairy products [86]. Cecropins B and cercopin P1 have shown inhibitory activity against _E. coli_[87; 88] which is found in milk, meat and raw vegetables. Inhibitory activity of attacain against Gram-negative bacteria was observed in research [89]. Drosocin has shown resistivity against M. Luteus, which is found in milk and goat's cheese [90]. Antimicrobial activity of diptericins against _E. coli_ K12, Erwinia hericola T and Erwinia carotivora 113 was observed [91]. Erwinia sp. is usually found on tomato fruit causing bacterial soft rot [92]. Metchnokwin inhibits growth of fungi and Gram-positive bacteria [93]. Bacterial disease of rice can be inhibited using melittin [94]. The antimicrobial activity of persulcatusin against MRSA (methicillin-sensitive _S. aureus_) is highest among all AMPs [95]. MRSA is one of the major causes of infections that are usually present in animal-originated foods [96; 97]. Minimally processed foods, such as cold-pressed non-alcoholic apple cider, milk, raw meat and mixed salad leaves, are the vehicles for the transmission of Cryptosporidium parvum, which can be inhibited by Pyrrhocoricin [98; 99]. Studies focused on impregnation of such peptide isolated from the insects into the packaging matrix have great potential towards reducing food deterioration due to microbial contamination.
## 5 Discussion
The high-energy particles in the strong radiation environment will damage the detector and electronic equipment, resulting in decreased performance and a shortened life. Therefore, it is necessary to use anti-radiation materials and implement anti-radiation reinforcement of the electronic equipment to protect it [25; 26]. Moreover, due to the direct exposure of the system to space, the temperature change is extremely severe, which poses a great challenge to the thermal control design. It is necessary to use efficient thermal control materials and complex thermal management systems to ensure that the working temperature of the instrument and equipment is appropriate. In addition, when the Sun enters the field of view, the instrument will also suffer from complex and strong stray radiation interference, which aggravates the difficulty of the observation task. In addition to the above problems, in the future iteration, it is necessary to focus on solving the calibration problem of the instrument before or after the task starts, optimizing the mechanical design and improving its data processing capability.