esa-sceva/satcom-chunk-collection huggingface dataset:
# Saturation-Based Airlight Color Restoration of Hazy Images
## 2 Dehazing for Color Restoration
### Gray World Hypothesis in the LAB Color Space  
As discussed above, the gray world hypothesis restores color by correcting the reduced R, G, and B channels. However, because the RGB channel also contains the luminance of the image, Equation (4) can cause another color distortion due to excessive white balancing. Therefore, to address this issue with RGB images, the gray world hypothesis using LAB color space has been proposed [20,21].  
CIELAB stands for a three-dimensional color space consisting of three channels, L, a, and b. The L channel represents the brightness component, and the a and b channels represent the saturation components. In particular, the a and b channels allow for the linear process as they are expressed on a grid: the negative value in the a channel is in green, and the positive value is in red; and the negative value in the b channel is blue, and the positive value is in yellow. In other words, higher values of a and b result in a stronger reddish or yellowish color cast, while a typical gray world has values of a and b close to 0.  
Based on the characteristics of LAB, as shown in Equation (6), white balancing in the LAB color space can be performed. Because the saturation and luminance channels are separated in the LAB color space, Equation (6) does not affect L, resulting in little color distortion.  
\[\begin{array}{l}\dot{a}(x)=a(x)-a_{avg}\\ \dot{b}(x)=b(x)-b_{avg}\end{array} \tag{6}\]  
Here, \(a(x)\) and \(b(x)\) represent the \(a\) and \(b\) channel values in LAB, respectively, and \(a_{avg}\) and \(b_{avg}\) are the averages of \(a(x)\) and \(b(x)\).
# Advanced Strategies for Mitigating Particulate Matter Generations in Poultry Houses
## 3 Factors Affecting Dust Generations
### Other Factors
#### 3.6.2 Bird Age, Stocking Density, and Behaviors  
The chickens' activity and dust emission depend on the birds' age in poultry housing [78]. Recent research on pullets found that an increase in pullets' age increases birds' activities and significantly affects or increases dust production (\(p\) < 0.05). Similarly, [PERSON] et al. (2007) also found that increasing broiler age affects PM levels significantly [79]. Chicken activities during feeding mainly increase PM\({}_{10}\) and TSP in the chicken house [80]. However, the perching behaviors and dust bathing in open spaces showed high PM production compared to the feeding and drinking behaviors [78]. Moreover, PM emission is also affected by housing stocking density and bird weight [9]. PM levels are higher with the increase of birds' weight and stocking density.
# Improved MUSIC Method against Range Dimension Deceptive Jamming Based on FDA-MIMO
## 6 Conclusions  
In this paper, an improved MUSIC method against range dimensional deceptive interference is proposed based on the FDA-MIMO radar system. When the false targets deceive the range dimension of the FDA-MIMO radar, there is a mismatch between the equivalent position of the airspace and the position shown by the range bin. Therefore, based on the full study of the existing range dimension deceptive jamming methods, this paper firstly constructs the covariance matrix for the radar received signal, then obtains the signal subspace and noise subspace through eigenvalue decomposition, and then obtains the range parameter information of the desired targets and jamming through spatial-spectral search. Finally, combined with FDA-MIMO adaptive beamforming technology, the problem of range dimension false targets deceptive jamming suppression can be effectively solved. The contribution of this study is to propose a range dimension deceptive jamming suppression method, which provides a new idea for the anti-jamming of the new system radar, and the subsequent study can be combined with multi-domain joint anti-jamming.  
Conceptualization, Y.C. and B.T.; methodology, Y.C. and Y.Z.; software, Y.C. and J.G.; validation, Y.C. and B.T.; data curation, Y.C. and C.W.; writing--original draft preparation, Y.C.; writing--review and editing, Y.C. and B.T.; supervision, C.W.; funding acquisition, J.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of Shaanxi Province under grant 2021 JM-222 and the China Postdoctoral Science Foundation under grant 2019M662257.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Development of a Maintenance and Spare Parts Distribution Model for Increasing Aircraft Efficiency
## 4 Simulation Model Results
### Simulation Design
#### 4.2.5 Model Validation  
A validation of the model was performed using extreme value tests to prove logical responses of the model for maximum and minimum values for the factors. The extreme value test is a method that proves that the model response is plausible when taking extreme values for different input parameters [33]. Some basic physical laws should be observed, for example, when there are no employees the model should not able to meet the spare parts demand. For all the models, we chose the same variables as input and output in order to analyze and validate models. These input variables were the total number of employees, initial stock in the central warehouse, and initial stock in the regional warehouses. From the variation of these variables, the following results can be expected according to the logic of a distribution network:  
* For a lower number of employees, then OTD must be lower and total stock should be higher because the production facility provides more products than the employees are able to process. This is shown in Figure 10, where the blue lines refer to the results over the simulation time for 160 employees, while the red lines are for the results for 140 employees and the green lines for 120 employees. It can be seen that when there are fewer employees, then the OTD decreases while the cumulated spare parts backlog, WIP, and stock in network caused by missing manpower increase. This happens because employees perform the warehouse activities and if there are not enough employees then the spare parts are not ready for delivery on time.
* If the initial stock in the central warehouse is lower, the OTD should be lower and stocks should be lower. As expected, this result was obtained.
* If the initial stock in the regional warehouses is lower, the OTD should be lower and stocks should be lower. As expected, this result was obtained.  
Figure 9: Simulation models developed for the research study (own elaboration).
## 2 Study Area and Datasets  
### Study Area  
Our study was conducted within an agricultural oasis, the Zhangye oasis, which is located in the central reaches of the Heihe River Basin (HRB) and in the arid region of Gansu Province in northwestern China (100.10\({}^{\circ}\)E-100.66\({}^{\circ}\)E, 38.68\({}^{\circ}\)N-39.15\({}^{\circ}\)N). The Zhangye oasis has a semi-arid climate. The annual precipitation is less than 200 mm, but the potential evapotranspiration is approximately 1200-1800 mm yearly, and the annual temperature is approximately 7 \({}^{\circ}\)C [23; 25]. Its terrain is flat, and its elevation is approximately 1480 m. A large portion of the Gobi Desert and the alpine vegetation of the Qilian Mountains are located near the study area (Figure 1). Within the oasis, the land surface is mainly covered by maize, and the growing season lasts from May to September. The artificial oasis is highly heterogeneous, which impacts the thermal-dynamic and hydraulic features. Consequently, monitoring and managing the water resources within the oasis during the crop growing season are urgently needed [26; 27]. The HRB has long served as a test bed for integrated watershed studies,as well as land surface and hydrological experiments [28]. One major objective of the Heihe Watershed Allied Telemetry Experimental Research (HiWATER), namely the Multi-Scale Observation Experiment on Evapotranspiration (HiWATER-MUSOEXE), is to capture the strong land surface heterogeneities and associated uncertainties within a watershed [25; 27; 29]. Two nested observation matrices that covered 30 km \(\times\) 30 km and 5.5 km \(\times\) 5.5 km were in place within the study area from May to September 2012. Within the larger observation matrix, four eddy covariance (EC) systems and one superstation (EC15) which is very high and equipped with a lot of observation system were installed in the oasis-desert ecosystem [25]. Within the oasis, a total of 17 automatic meteorological stations (AMSs) were distributed, including one in a vegetable field (EC01), one in a residential area (EC04), one in an orchard (EC17), and 14 in maize fields (Figure 1).
## 2 Materials and Methods
### Experimental Procedure  
Patients' hand function was evaluated with the Jebsen-Taylor hand function test (JTHFT), a reliable, valid, and responsive measure for assessing hand function after stroke [16]. The JTHFT is one of the most widely used non-diagnostic assessment tools in rehabilitation due to its advantage of providing simple and objective measurements in a short period of time [17,18]. The JTHFT consists of seven specific tasks, each of which is intended to measure a different aspect of hand use in daily activities. These include writing a short sentence (WSS), card turning (CT), picking up small objects (PUSO), simulated feeding (SF), stacking checkers (SC), lifting lightweight objects (LLO) and lifting heavy objects (LHO), as shown in Figure 2. When performing the JTHFT, it is recommended to evaluate the nondominant hand first. This approach helps to establish a baseline for comparison and minimizes the influence of fatigue or learning effects that might occur if the dominant hand were tested first. By starting with the nondominant hand, a more accurate assessment of the individual's hand function can be achieved [19].  
Writing ability was assessed using the Korean alphabet writing test (KAWT), with an inter-rater reliability of r = 0.95 and test-retest reliability of r = 0.65 [20]. The KAWT consists  
Figure 1: The two types of writing aids.  
Figure 2: Jebsen–Taylor hand function test: (**a**) writing a short sentence (WSS); (**b**) card turning (CT); (**c**) picking up small objects (PUSO); (**d**) simulated feeding (SF); (**e**) stacking checkers (SC); (**f**) lifting lightweight objects (LLO); (**g**) lifting heavy objects (LHO).  
of the 14 basic consonants and 10 vowels of the Korean alphabet. The font size for Korean consonants and vowels was set to 48 points, and the squares were set to 2.5 cm \(\times\) 2.5 cm (width \(\times\) height). As shown in Figure 3, examples of letters are provided in the upper box, and letters with auxiliary lines for overwriting are provided in the lower box.  
Upper extremity muscle activity was recorded using Noraxon Desktop Direct Transmission System (Noraxon Inc., Scottsdale, AZ, USA) while performing the JTHFT and KAWT, at a sampling rate of 1500 Hz. Surface electrodes were attached to the flexor carpi radialis (FCR), and extensor carpi radialis longus (ECRL), biceps brachii (BB), triceps brachii (TB), and upper trapezius (UT) on both the paralyzed side (PS) and nonparalyzed side (NPS), which are primarily involved in moving the upper extremity and writing. Before attaching the surface electrodes, the area was cleaned with alcohol to remove skin resistance that occurs during measurement.
## 1 Introduction
### Structure of the Paper  
This paper is organized as follows. Section 2 concisely introduces the required techniques. Forecasting performance evaluation criterion and numerical experimentation are introduced in Sections 3 and 4. In Sections 5 and 6, the results and the superiority of the proposed approach are discussed by using comparisons with other conventional methods. Finally, Section 7 concludes this paper and introduces the direction for future studies.
## 2 Results and Discussion
### Qualitative Characteristics of Fruit
#### 2.4.2 Aroma Profiles
This clustering indicates important differences in terms of the numbers of VOCs specifically induced by the different levels of Se applied at the flowering stage. In addition, the fact that the analysis was repeatable was highlighted by the correct positioning of the different analyzed fruit replicates, with samples from the same treatment being close together. Figure 1: Partial least squares discriminant analysis (PLSDA). Treatment was employed as response variable, while the identified VOCs were used as predictor variables. Variable importance in projection (VIP) scores were used to filter compounds varying the most between different theses, and only compounds with a VIP score higher than 0.8 were included in the analysis. Circles, triangles, and inverted triangles were used to depict control fruit, 1 mg L\({}^{-1}\)- and 1.5 mg L\({}^{-1}\) sodium selenate-treated fruit, respectively. Lipoxygenase pathway-related compounds, such as hexanal, (Z)-3-hexanal and 1-hexanol, were associated with control fruit. Specific C5 and C7 compounds and derivatives instead appeared to be more abundantly accumulated in Se-biofortified fruit. In particular, (E)-2-heptenal and methyl heptenone, together with phenylethyl alcohol, characterized the 1.5 mg Se L\({}^{-1}\)-treated fruit. On the other hand, 1-penten-3-ol, 1-penten-3-one, and (E)-2-pentenal sit, in the PLSDA score plot, closer to the 1.0 mg Se L\({}^{-1}\)-treated samples, which also showed a strict association with guaiacol and methyl salicate. 2-phenylethyl alcohol, guaiacol, and (E)-2-heptenal, together with 1-penten-3-one and (E)-2-pentenal, were identified by [PERSON] et al. [33] as compounds positively correlated with consumer liking and overall flavor intensity, with decreased accumulation in modern cultivars compared to heirloom _S. Iyocperisicum_ varieties. Thus, Se-enriched tomatoes may possibly also benefit from the treatment in terms of organoleptic properties and more attractive traits for consumers. Of particular interest appears to be the increase in methyl salicate characterizing the fruit treated with 1 mg Se L\({}^{-1}\). This compound is one of several phenylpropanoids that significantly contribute to the unique flavor of tomato fruit and is also considered to be better than salicylic acid as the signal molecule transmitted in the systemic acquired resistance (SAR) response [34]. Together with linalool, methyl salicate is involved in the immune response against _Pseudomonas syringae_ bacterial attack [35]. The association of both compounds with selenium treatments rather than with control clusters may indicate a better immunity response of fruit treated with selenium against some bacterial diseases. The association of methylated compounds (methyl salicate and methyl heptanone) with Se-treated fruit may show how selenium affects tomato methyltransferases. In fact, in a proteomics study, [PERSON] et al. [36] demonstrated that methyltransferases are among the differentially expressed proteins in naturally selenium-enriched rice.
## 4 Results and Discussion
### Simulation Results of the Deterministic Model  
Based on the deterministic model, the temperature and thermal stress distributions of the receiver tube wall were obtained and are depicted in Figures 8 and 9, respectively. The results revealed that the temperature distribution of the tube wall was similar to that of the incident solar heat flux distribution, which demonstrated that the incident heat flux distribution had a large influence on the temperature distribution of the tube wall. Consequently, the maximum temperature of the tube wall was 871 K and occurred at the center of the outer sunward tube wall, which was in accord with the peak incident heat flux location.  
The thermal stress distribution of the tube wall was different from that of the temperature distribution because the thermal stress is usually decided by the temperature gradient. As a result, the maximum thermal stress of the tube wall was 204 MPa and was located at the center of the inner sunward tube wall.
## 4 Finite Element Simulations  
To verify the correctness of modeling, we used the finite element software ANSYS Workbench to simulate the proposed CBBM. To ensure comparability among the three models, the default size of the mesh was used. To demonstrate that the pitch stiffness of the CBBM increases, we considered two SBBMs. Based on the proposed CBBM, two compliant SBBMs were established for comparisons. The SBBMs adopted a three-stage design with the structure shown in Figure 9. The bistable fixed limb arms of the SBBM and CBBM had the same end points. SBBM1 and SBBM2 replace a bistable circular beam limb of the CBBM (including two flexible segments and one rigid segment) with a straight beam of the same thickness. The two flexible segments and one rigid segment of SBBM1 have the same horizontal angle. The bistable beam of SBBM1 represents the chord length of the circular bistable beam of CBBM, as shown in Figure 9a. The horizontal angle of the flexible section of SBBM2 is different from that of the rigid section and the three-section bistable beam of SBBM2 corresponds to the chord length of the three-section bistable beam of the CBBM, as shown in Figure 9b.  
The three designs were analyzed and compared using finite element simulations to demonstrate the enhanced pitch stiffness of the CBBM and modeling accuracy. Table 1 lists the geometric parameters of the three evaluated designs.
## 3 Discussion
The functional study of TFs showed that the regulated monoterpene genes in mint were inadequate. The TFs MsYABBY5 and MsMYB were reported to negatively regulate the production of monoterpenes in spearmint [11; 12]. In our transcriptome data, Unigene0018692 was homologous to MsMYB, and was found to be dramatically differently expressed under dark conditions. Interestingly, given that it was regulated by light, the expression pattern of this gene was similar to that of the negatively regulated genes, which were downregulated by dark and upregulated by light (mean RPKM value of 24.78 in control vs. 0.76 under darkness and 5.69 after illumination). These results suggest that other TFs must be more essential in regulating monoterpene gene expression through light signal transduction pathways. Our present work provides a list of candidate genes for further metabolic engineering research and will be helpful in exploring the molecular mechanism of the transcriptional responses of MEP pathway genes to light signals. However, the present RNA-seq results could not determine whether splice variants could vary between the absence and presence light. Full-length transcriptome analyses and more verification research should be carried out in the future. In conclusion, the results of this study prove the effects of light on the density of peltate glandular trichomes and essential oil production in commitment. The low light condition reduced the PGT densities in all five different zones, from the bottom to the top of the mint leaves, by half a percent. We observed dramatically decreased total essential oil and menthol levels, and increased proportions of pulegone (from 0.95 to 8.70%) and piperitone (from 1.23 to 28.31%). Downregulated expression levels of menthol synthesis pathway genes McGpps-L, McGpps-s, McLs, McL3 OH, MciPD, MciPR, and McPR and the monoterpenes biosynthesis genes, as well as the sesquiterpenoids biosynthesis pathway genes, were observed following darkness treatment, and may explain the reduced levels of essential oil produced via the regulatory mechanisms at the transcription level. The identified DEGs in the light signal transduction pathway, and the light-mediated regulation of transcription factors, could be used as effective strategies to achieve increased essential oil yields in future metabolic engineering studies.
## 2 Methodology
### Governing Equations  
In this study, the flow characteristics near the heat sink are influenced by the vibration of the heat sink, resulting in turbulent flow despite the main flow having a relatively low Reynolds number of 1000. To accurately simulate this turbulent flow, various simulation models, including lamina, k-\(\omega\) SST, and large eddy simulation (LES), are used, and the results of those models are compared with experimental and previously published results. Among the different turbulence models, the LES with the dynamic Smagorinsky-Lilly sub-grid model demonstrated good agreement with published experimental measurements.  
Thus, the LES turbulence model was chosen for the numerical simulation and subsequent validation in this study.  
The flow was assumed to be incompressible ideal gas with constant thermo-physical properties. Also, the no-slip condition is employed in the solid-fluid interface of the heat sink models. Thus, the continuity, momentum and energy equations for turbulent flow can be written as below [46]:  
Continuity equation:  
\[\frac{\partial}{\partial x_{i}}(\rho U_{i})=0 \tag{1}\]  
Momentum equation:  
\[\frac{\partial}{\partial t}\big{(}\rho\overline{U}_{i}\big{)}+\frac{\partial }{\partial x_{j}}\big{(}\rho\overline{U}_{i}\overline{U}_{j}\big{)}=-\frac{ \partial\overline{P}}{\partial x_{i}}+\frac{\partial}{\partial x_{j}}\bigg{(} \mu\frac{\partial\overline{U}_{i}}{\partial x_{j}}\bigg{)}-\frac{\partial \tau_{ij}}{\partial x_{j}} \tag{2}\]  
where \(\rho\) represents the density of the working fluid, P represents the pressure, \(U_{i}\) represents the velocity in \(i\)th direction, and \(\tau_{ij}\) represents the sub-grid scale stress which can be written below:  
\[\tau_{ij}=\rho\overline{U_{i}U_{j}}-\rho U_{i}\overline{U}_{j} \tag{3}\]  
Energy equation:  
\[\frac{\partial\big{(}\rho C_{\rm p}T\big{)}}{\partial t}+\frac{\partial\big{(} \rho C_{\rm p}U_{\rm i}T\big{)}}{\partial x_{\rm j}}=\frac{\partial}{ \partial x_{\rm j}}\bigg{(}k\frac{\partial T}{\partial x_{\rm j}}\bigg{)} \tag{4}\]  
where \(T\) represents the temperature, \(k\) represents the thermal conductivity of the fluid, and \(C_{p}\) represents the specific heat capacity of the fluid.
## 2 Review of the MCC and Zero-Attraction (ZA) Technique
### Zero Attracting Technique  
From the update equation in Equation (7), it is found that the MCC system identification method does not consider the sparse-structure information in the sparse system \(\mathbf{w}_{o}(n)\). Furthermore, the MCC-based system identification method can be written as [23; 27]  
\[\widehat{\mathbf{w}}(n+1)=\widehat{\mathbf{w}}(n)+\text{Adaption \ error\ updates}. \tag{8}\]  
In recent years, the zero attracting methods [10; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40] have been widely studied to use the sparsity information in the FIR channels. These zero attracting algorithms are realized by integrating norm penalties, such as \(l_{1}\)-norm and reweighted \(l_{1}\)-norm [10], into the traditional AF algorithms. Thus, the zero attracting sparse MCC-based adaptive filters can be summarized as [10; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40]  
\[\widehat{\mathbf{w}}(n+1)=\underbrace{\widehat{\mathbf{w}}(n)+\text{Adaptive \ error}-\text{update}}_{\text{Zero}-\text{attracting\ MCC}}+\text{Sparsity\ constraint\ term}. \tag{9}\]  
In Equation (9), the sparsity penalty term is usually given by introducing different norm penalties to utilize the prior sparse inform of these sparse systems [10; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40]. Thus, the \(l_{1}\)- and  
Figure 1: A typical adaptive system identification scheme under the MCC method.  
reweighted \(l_{1}\)-norms are considered for exploiting the sparse MCCs to form ZA MCC (ZA-MCC) and reweighting ZA-MCC (RZA-MCC) algorithms [41]. Although the sparse MCCs achieve better estimation performance compared with the traditional MCC algorithm for identifying sparse systems, they are still needed to be improved for practical applications. Thus, we will develop a sparse NMCC algorithm by using the normalization method, proportionate-type method and the zero attracting techniques.