esa-sceva/satcom-chunk-collection huggingface dataset:
# Fine Root Length of Maize Decreases in Response to Elevated CO2 Levels in Soil
## 3 Results
### Development of the Root Diameter Under Different Levels of Elevated Soil CO\({}_{2}\)  
A total of nine sampled root systems from the CK, G1000, and G2000 treatments were scanned. These images were of whole root systems; the numbers of scanned pictures for the CO\({}_{2}\) treatments G1000 and G2000 were 179 and 111, respectively, representing decreases of 3.7% and 40.3%, respectively, compared with the CK treatment (186). The nodal roots of the maize root system could be divided  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Root Parameters** & \begin{tabular}{c} **Mean Values** \\ **CK** \\ \end{tabular} & \begin{tabular}{c} **Mean Values** \\ **Treatments** \\ \end{tabular} & \(t\) & \(n\) & \(p\) & \begin{tabular}{c} **Decrease** \\ **(\%)** \\ \end{tabular} \\ \hline Root length (cm) & 37,924.62 \(\pm\) 3777.91 & \begin{tabular}{c} 35,047.00 \(\pm\) 7606.10 (G1000) \\ \end{tabular} & 0.34 & 3 & 0.75 & — \\ \cline{3-6}  & 20,960.99 \(\pm\) 2860.17 (G2000) & 3.58 & 3 & 0.02\({}^{\text{\#}}\) & 44.73\% \\ \hline Root volume (cm\({}^{3}\)) & 65.34 \(\pm\) 0.53 & \begin{tabular}{c} 71.99 \(\pm\) 12.06 (G1000) \\ \end{tabular} & \begin{tabular}{c} \(-\)0.51 \\ 52.82 \(\pm\) 4.26 (G2000) \\ \end{tabular} & 2.92 & 3 & 0.43 & — \\ \hline Root surface area (cm\({}^{2}\)) & 544.83 \(\pm\) 266.16 & \begin{tabular}{c} 5420.61 \(\pm\) 983.64 (G1000) \\ \end{tabular} & 0.24 & 3 & 0.98 & — \\ \cline{3-6}  & 3585.8735 \(\pm\) 368.70 (G2000) & 4.09 & 3 & 0.02\({}^{\text{\#}}\) & 34.14\% \\ \hline Root diameter (mm) & 0.047 \(\pm\) 0.0022 & \begin{tabular}{c} 0.051 \(\pm\) 0.0016 (G1000) \\ \end{tabular} & \begin{tabular}{c} \(-\)1.6 \\ 0.057 \(\pm\) 0.0030 (G2000) \\ \end{tabular} & \begin{tabular}{c} 3 \\ -2.65 \\ -1.37 \\ \end{tabular} & 3 & 0.06 & — \\ \hline Root dry weight (g) & 19.37 \(\pm\) 2.0 & \
\begin{tabular}{c} 24.65 \(\pm\) 3.3 (G1000) \\ \end{tabular} &
\begin{tabular}{c} -1.37 \\ 18.73 \(\pm\) 1.34 (G2000) \\ \end{tabular} & 0.28 & 3 & 0.80 & — \\ \hline \hline \end{tabular}
\end{table}
Table 2: The values of root length, volume, surface area, diameter, and dry weight in CK and two elevated soil CO\({}_{2}\) treatments. \(t\)-test results for root parameters (mean \(\pm\) SE) and their percentage decreases in CK and leakage treatments. \({}^{\text{\#}}\): The significant \(p\) values, marked with asterisks, indicate significant differences between CK and elevated soil CO\({}_{2}\) treatments (\(p<0.05\)). The \(p\) values between two CO\({}_{2}\) treatments of all root parameter are all greater than 0.5.  
into four nodal-root whorls depending on their initial positions and sequences of growth in the CK and CO2 treatments (Figure 3). The median basal diameter of the first, second, and fourth nodal-root whorls in the CK were significantly smaller than those in the CO2 treatments (Figure 3, \(p\) < 0.05), while the differences in median basal diameter between the second nodal-root whorl lateral roots (LRs) were not significant (Figure 3, \(p\) > 0.05).
# Applications of Water Jet Cutting Technology in Agricultural Engineering: A Review
## 3 Applications of Water Jet Cutting Technology on Different Agricultural Materials
### Fruits
#### 3.2.2 Field Harvesting  
It is a developing direction to use water jet cutting technology to harvest fruits in the field. There are some studies on sugarcane harvesting at present. The chopper used for sugarcane harvesting is easily blocked around the rotating blades, resulting in rapid blade wear, and repeated cutting with dull blades harvesting will damage the stalk [51,52]. Thus, water jet cutting as a non-contact cutting option is proposed to replace the rotating tool.  
[PERSON] et al. [51] used pure water jets with the pressure of 400 MPa and a nozzle diameter of 0.36 mm to cut sugarcane stalks under laboratory conditions. However, specifications for mobile water jet cutting sugarcane were not achieved. To explore the possibility of cutting sugarcane in the field environment, [PERSON] et al. [49] proposed a post-mixing AWJ harvesting method using fine river sand as an abrasive to improve the cutting ability. The jet with a water inlet and a nozzle diameter combination of 0.25 and 0.76 mm at 360 MPa water pressure produced a water flow rate of 1.6 L/min and a power input of 15 kW by the Hoogstrate model [53] and a MATLAB program. The study showed that cutting sugarcane with diameters of 30 and 120 mm required a traverse speed of 1.22 m/s and 0.31 m/s, respectively. It also was found that sugarcane stalks could be completely cut off at a farther target distance by reducing the traverse speed. However, to ensure the thickness of cutting sugarcane and the optimum traverse speed, the target distance should be set to no more than 210 mm when the minimum transverse speed is 0.17 m/s.  
Consequently, water jet cutting technology has realized the post-harvest processing and field harvesting of some fruits. To increase the efficiency and utilization of processing  
Figure 3: Image recognition assisted water jet in removing strawberry calyx ([50], adapted from “Design and testing of an automated high-throughput computer vision guided waterjet knife strawberry calyx removal machine” with permission from Elsevier, 2022).  
machines, intelligent control systems and multi-fruit machines should be designed and manufactured. Cutting sugarcane with water jet under laboratory conditions has been achieved. However, field harvesting is more challenging with complex environments compared with post-harvest processing, so the possibility and applicability of water jet harvesting fruits should be further studied.
## 2 Diversity and Distribution of Rice Phytoalexins
### Diterenoid Phytotalexins in Rice
#### 2.1.3 Oryzalexins  
Oryzalexins A to F (Figure 3A-F) are _ent_-sandaracopimaradiene-type compounds. Oryzalexin A was first isolated in 1983 by [PERSON] et al. [32] from rice leaves infected with _M. grisea_ (Table 1). The in vitro inhibitory activity of oryzalexin A on _M. grisea_ conidial germination, with an ED\({}_{50}\) value of 130 ppm (0.43 mM), was reported by [PERSON] and collaborators [33]. [PERSON] et al. [53] recently studied the accumulation of oryzalexin A in response to UV light in 69 cultivars from the World Rice Core Collection (WRC) [49] and in 10 strains of the wild species _Oryzaa rufipogon_ (the putative wild ancestor of cultivated rice). They found that only ten of the studied cultivars, belonging to both the _japonica_ and _indica_ subspecies, produced oryzalexin A. Moreover, both oyzalexin A-producing and non-producing chemotypes were found in _O. rufipogon_, suggesting that the metabolic pathway involved in the biosynthesis of this compound was inherited from an ancestor of _O. rufipogon_ and was lost multiple times during evolution. They also reported that, in some cultivars, oryzalexin A accumulation is induced by UV light but not by jasmonic acid (JA), while in others, it is induced by JA but not UV light. This indicates that different signal transduction pathways are induced by UV and JA, and in these cultivars, one of these pathways does not operate [53]. Oryzalexins B-F were identified in cultivated rice subjected to pathogen infection or irradiation by UV light, and their antifungal activity against rice pathogens was ascertained [34; 35; 36; 37; 38]. In 1988 [PERSON] and colleagues [54] found a new antifungal substance that was distinct from the previously known phytooleoxins. In 1992 they structurally characterized this new phytoolexin which they named oryzalexin S [55] (Figure 3G). This compound differs from oryzalexins A-F by its semarane-type skeleton [40]. At present, oryzalexins have only been found in cultivated rice and the related wild species _O. rufipogon_ (Table 1).
# Ontology-Driven Semantic Analysis of Tabular Data:
## 4 Software Implementation
### Tabular Data Reader Module  
This module provides the first stage of table analysis. Depending on the file type (CSV, XLSX, PDF), the appropriate module is selected and the table is extracted from the file. If the input file has a CSV or XLSX extension, then it is enough to simply read the table from the file and remove, if any, empty rows and columns that do not carry any information. If processing a PDF file, a search must first be conducted for one or more tables in the document, extract them, and align columns and cells. After that, one can proceed to the next step.  
After extracting the table, the module translates the table into a format that the program will work with in the future. The table in the internal representation of the program is stored in the form of two matrices--a matrix of values and a matrix of data types. In addition, at this stage, the primary classification of data into two types occurs--a string and a number. This will make data analysis easier in the future.  
The type matrix at this stage can have four values: \"Text\", \"Number\", \"Date,\" and an empty value. An empty value is set if the cell does not contain any information, or there are symbols indicating the absence of information, for example, a dash (hyphen, dash). The \"Number\" type is set if the cell contains a number, or its value can be easily reduced to a number (for example, by removing units of measurement).
## 3 Estimation Algorithms Design for Vehicle State Estimation
### Unscented Kalman Filter
The propagated mean calculation: \[\hat{\mathbf{y}}(k|k-1)=\sum_{i=0}^{2L}W_{i}^{(m)}\cdot\mathbf{y}_{i}(k|k-1).\] (33)* The propagated covariance and the Kalman gain calculation: \[\mathbf{P_{y}}(k|k-1)=\sum_{i=0}^{2n}W_{i}^{(c)}.[\mathbf{y}_{i}(k|k-1)-\hat{\mathbf{y}}(k|k -1)]\cdot[\mathbf{y_{i}}\left(k|k-1\right)-\hat{\mathbf{y}}(k|k-1)]^{T}+\mathbf{R_{k}}\] (34) \[\mathbf{P_{xy}}(k|k-1)=\sum_{i=0}^{2n}W_{i}^{(c)}.[\chi_{i}(k|k-1)-\hat{\mathbf{x}}(k| k-1)]\cdot[\mathbf{y}_{i}\left(k|k-1\right)-\hat{\mathbf{y}}(k|k-1)]^{T}\] (35) \[\mathbf{K}(k)=\mathbf{P_{xy}}(k|k-1)\cdot\mathbf{P_{yy}}(k|k-1)^{-1},\] (36) where \(\mathbf{K}(k)\) is the Kalman gain matrix. * Update the vehicle state estimation and state covariance: \[\hat{\mathbf{x}}(k|k)=\hat{\mathbf{x}}(k|k-1)+\mathbf{K}(k)\cdot[\mathbf{y}(k)-\hat{\mathbf{y}}(k|k -1)]\] (37) \[\mathbf{P_{xx}}(k|k)=\mathbf{P_{x}}(k|k-1)-\mathbf{K}(k)\cdot\mathbf{P_{y}}\cdot\mathbf{K}(k)^{T}.\] (38)
## 3 Proposed Modelling Methodology, Simulation Mechanism, and Control Strategy
### Modelling Methodology
#### 3.1.3 Single-Phase DC/AC Inverter Model  
A single-phase full-bide DC/AC voltage source inverter with unipolar pulse width modulation (PWM) switching is modelled in Figure 7, which in charges of the power conversion between DC bus and AC grid/load sides. To maintain the inverter DC input current waveform and to provide stable regulation capability in inverter AC output, a dual-loop control in Figure 8 is used for the inverter. This controller consists of inner voltage loop and outer current loop controls; the former provides the inverter input DC-link voltage regulation, and the latter is used for inverter AC output current control. Error of DC voltage occurs via transfer function \(\mathrm{G_{v}}\) and the current limiter, which is used for inverter capacity limits to obtain current command \(\mathrm{i_{o}}\)* as input of current control loop. Outer current control may let inverter current \(\mathrm{i_{o}}\) follow \(\mathrm{i_{o}}\)* via the integral amplifier to produce reference voltage signal \(\mathrm{v_{fb}}\), then \(\mathrm{v_{fb}}\) compares with measured inverter AC output voltage \(\mathrm{v_{ff}}\) and this finally used to obtain the PWM signals. Some variables present in Figure 8 include \(\mathrm{V_{dcbus}}\) and \(\mathrm{V_{dcbus}}\)* are measured DC voltage at DC link and referenced DC voltage, respectively, \(\mathrm{K_{v}}\) and \(\mathrm{K_{I}}\) are voltage and sensing gains, respectively, \(\mathrm{K_{S}}\) is hall sensor gain, and \(\mathrm{V_{con}}\) is control voltage that input to PWM generator.  
Figure 5: Battery energy storage model.  
Figure 6: Controller design of a bi-directional DC/DC converter.
## 4 Experimental Studies
### Statistical Testing of iaABC
#### 4.1.3 iaABC's Performance on CEC 2022 Benchmark Functions
iaABC also produced the optimal solution for the F3 and F5 basic functions. However, the results show that the algorithm does not have satisfactory stability. The convergence data in Figure 4 suggest that the algorithm struggles to optimize many parameters and is trapped in a local optimum. iaABC could not produce optimal solutions for hybrid functions and composition functions. Since composition functions are composed of functions with different characteristics, their search spaces have very complex waves with high and low frequencies. However, the algorithm has a convergence performance close to 10-D solutions for 20-D solutions. Examining the results shown in Tables 4 and 5, along with the convergence error curves illustrated in Figures 3 and 4, it can be concluded that the iaABC method has a more effective search strategy and faster convergence speed compared to both the standard ABC and aABC methods.
# Angle-Only Cooperative Orbit Determination Considering Attitude Uncertainty
## 2 Attitude and Orbit Determination Model
### Review of Fifth-Order CKF
\begin{split}&\text{A}_{k}=Cholesky(\mathbf{P}_{k-1|k-1})\\ &\mathbf{\chi}_{i,k-1|k-1}=\mathbf{\zeta}_{i}+\mathbf{x}_{k-1|k-1}\\ &\mathbf{\chi}_{k|k-1}^{*}=\mathbf{f}(\mathbf{\chi}_{i,k-1|k-1})\\ &\mathbf{x}_{k|k-1}=\sum_{i=0}^{2n^{2}}w_{i}\mathbf{\chi}_{i,k|k-1}^{*}\\ &\mathbf{P}_{k|k-1}=\sum_{i=0}^{2n^{2}}w_{i}(\mathbf{\chi}_{i,k|k-1}^{*}- \mathbf{x}_{k|k-1})(\mathbf{\chi}_{i,k|k-1}^{*}-\mathbf{x}_{k|k-1})^{\text{T}}+\mathbf{Q} _{k}\end{split}  \tag{31}\]  
ii. Measurement updating  
\[ \begin{split}&\mathbf{A}_{k}=[PERSON](\mathbf{P}_{k|k-1})\\ &\mathbf{\chi}_{i,k|k-1}=\mathbf{\zeta}_{i}+\mathbf{x}_{k|k-1}\\ &\mathbf{z}_{i,k|k-1}^{*}=\mathbf{h}(\mathbf{\chi}_{i,k|k-1})\\ &\mathbf{z}_{k|k-1}=\sum_{i=0}^{2n^{2}}w_{i}z_{i,k|k-1}^{*}\\ &\mathbf{P}_{xx,k|k-1}=\sum_{i=0}^{2n^{2}}w_{i}(\mathbf{\chi}_{i,k|k-1 }^{*}-\mathbf{x}_{k|k-1})(\mathbf{z}_{i,k|k-1}^{*}-\mathbf{z}_{k|k-1})^{\text{T}}\\ &\mathbf{P}_{zz,k|k-1}=\sum_{i=0}^{2n^{2}}w_{i}(z_{i,k|k-1}^{*}- \mathbf{z}_{k|k-1})(\mathbf{z}_{i,k|k-1}^{*}-\mathbf{z}_{k|k-1})^{\text{T}}+\mathbf{R}_{k }\\ &\mathbf{K}_{k}=\mathbf{P}_{xz,k|k-1}\mathbf{P}_{zz,k|k-1}^{-1}\\ &\mathbf{x}_{k|k}=\mathbf{x}_{k|k-1}+\mathbf{K}_{k}(\mathbf{z}_{k}-\mathbf{z}_{k|k -1})\\ &\mathbf{P}_{k|k}=\mathbf{P}_{k|k-1}-\mathbf{K}_{k}\mathbf{P}_{zz, k|k-1}\mathbf{K}_{k}^{\text{T}}\end{split}  \tag{32}\]  
where _Cholesky_(\(\cdot\)) represents the Cholesky decomposition method, \(\mathbf{\chi}_{i,k|k-1}^{*}\) is the Cubature point generated from states and \(\mathbf{z}_{i,k|k-1}^{*}\) represents the Cubature point generated from measurements.
## 2 Methods
### Treatment  
Laser toning was performed using a Q-switched Nd-YAG laser (Helios II; LaserOptek, Sungnam-si, Korea), with a spot size of 6-8 mm and fluence of 2.1-3.5 J/cm\({}^{2}\) at 8-10 Hz. Multiple laser passes were delivered to achieve the clinical endpoint of mild erythema. The laser was applied to the whole face with 3000-5000 shots in a single session. The number of treatment sessions varied according to the patient's needs. All patients were women and received full-face treatment. PIH was diagnosed based on clinical findings and history-taking during each toning session.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Factors** & **(n = 125) Value-x (\%)** \\ \hline Age (years) & \\ Mean & 47.18 \\ Range & 21.00–77.00 \\ \(<\)51 & 83 (66.4) \\
51–60 & 25 (20.0) \\
61–70 & 13 (10.4) \\ \(>\)71 & 4 (3.2) \\ FSPT & \\ Type III & 85 (68.0) \\ Type IV & 27 (21.6) \\ Type V & 13 (10.4) \\ Melasma & \\ No & 81 (64.8) \\ Yes & 44 (35.2) \\ Acne & \\ No & 108 (86.4) \\ Yes & 17 (13.6) \\ ZN & \\ No & 115 (92.0) \\ Yes & 10(8.0) \\ Lentigines & \\ No & 45 (36.0) \\ Yes & 80 (64.0) \\ Pores & \\ No & 85 (68.0) \\ Yes & 40 (32.0) \\ LD & \\ \(<\)750 & 28 (22.4) \\
750–850 & 62 (49.6) \\ \(>\)850 & 35 (28.0) \\ Laser Mode & \\ Fractional & 40 (32.0) \\ Zoom & 85 (68.0) \\ IEL & \\ Erythema & 21 (16.8) \\ Petechiae & 95 (76.0) \\ E mild P & 9 (7.2) \\ PIH & \\ No & 104 (83.2) \\ Yes & 21 (16.8) \\ \hline \hline \end{tabular} Abbreviations: FSPT: Fitzpatrick skin phototype; ZN: Zygomatic Nevus; LD: Laser Dosage; F: Fractional; Z: Zoom; Z+F: Zoom and Fractional; IEL: Immediate Endpoints of Laser; E: Erythema; P: Petechiae; E mild P: Erythema mild Petechiae; PIH: Post Inflammatory Hyperpigmentation; Data from Fitzpatrick RE [12].  
\end{table}
Table 1: Sample characteristics.
## 4 Results and Discussion
### Analysis of the Results of Testing Mortar in the Hard State
#### 4.2.3 Shrinkage  
The results of the total shrinkage of all tested mortar composites as a function of time are shown in the form of a diagram (Figure 9).  
The results of the A-II series testing, which contains micro-reinforcement, show a reduction in shrinkage of only 1.5% on the 126 th day of testing, while in earlier phases (on the 4 th day), shrinkage dilations are 26.2% less compared to the standard. This can be explained by the fact that synthetic fibers are highly effective in the early period, while their modulus of elasticity is lower than that of the mortar, resulting in reduced crack formation and prolonged shrinkage in the later period when strength is higher.  
Figure 8: Functional dependence between the dynamic modulus of elasticity and compressive strength at the age of 28 days.  
Figure 9: Shrinkage esk (%) over a period of 4 months.  
Samples from series B, which contain fly ash, exhibit greater shrinkage compared to samples from series A made with ordinary Portland cement, confirming findings from other authors [28].  
The difference in shrinkage in the early stages is more pronounced in samples from the B series that contain fibers. On the fourth day of testing, the shrinkage dilations of samples from the B-II series are 55.32% lower compared to samples from the B-I series, while on the 126 th day, when the test results have stabilized, the shrinkage is proportionally 1.32% lower, similar to the A series samples.  
Plastic shrinkage usually occurs within the initial few hours after the mortar is applied, and its adverse effects can be mitigated through proper and timely maintenance of the fresh mortar. Considering that samples embedded in models are kept in a humid space (H > 90%) during the first 24 h after application, followed by an additional 48 h in water (T = 20 \(\pm\) 2 \({}^{\circ}\)C), this method mainly eliminates the influence of plastic shrinkage.
## 6 Discussion
### Performance Comparison with the SOTA Methods
For the F1-score, the proposed method improved by 1.33% compared with [PERSON] et al. 's method [33]. For HL, the proposed method significantly improved by 23.4% compared with [PERSON] et al. 's method [33]. The proposed method showed a 4.60% improvement in mAP compared with [PERSON] et al. 's [33] method. For RL, the proposed method significantly improved by 16.09% compared with [PERSON] et al. 's method [33]. Regarding Cov, the proposed method showed a slight enhancement of 0.60% compared with [PERSON] et al. 's [33] method. The proposed method improved by 5.06% in EM compared with [PERSON] et al. 's method [33]. On the other hand, regarding the INBreast dataset, the proposed system outperformed the existing method in the INBreast dataset, with an improvement of 0.99% in the F1-score, 23.1% in Hamming loss, 4.64% in mAP, 20% in ranking loss, 4.45% in the coverage score, and 7.42% in exact match. Overall, the proposed method achieved notable improvements compared with the previous methods. The results in Table 7 indicate that the proposed method using the ConvNeK architecture with two views performs better than the method using both the VGG16 and Efficientnetb3 architectures among all the performance metrics. The higher F1-score, EM, and mAP, and the lower HL, Cov, and RL show that the proposed method achieves better accuracy and precision in predicting the presence of abnormalities in mammograms and classifying breast density. In addition, the ROC curves shown in Figure 7, for each category--density, abnormality type (case), and severity level (pathology)--demonstrate the model's classification effectiveness. For 'Density,' the model's ability to differentiate between multiple density classes yielded an AUC score of 0.91, demonstrating its discriminative power. The 'Abnormality type (case)' category exhibited near-perfect classification with an AUC of 0.99, indicative of the model's exceptional accuracy in case determination. Similarly, the'severity level (pathology)' category showed an AUC of 0.96, reflecting the model's high proficiency in identifying pathological features. These AUC values, significantly exceeding the 0.5 threshold of random chance, underscore the model's potential to provide reliable and accurate diagnostic assistance in mammographic analysis.
# Characteristics of 2.45 GHz Surface-Wave-Sustained Argon Discharge for Bio-Medical Applications
###### Abstract  
Cold atmospheric plasma (CAP) applications in various fields, such as biology, medicine and agriculture, have significantly grown during recent years. Many new types of plasma sources operating at atmospheric pressure in open air were developed. In order to use such plasmas for the treatment of biological systems, plasma properties should fulfil strong requirements. One of the most important is the prevention from heating damage. That is why in many cases, the post-discharge region is used for treatment, but the short living particles in the active discharge zone and reactions with them are missed in that case. We use the active region of surface-wave-sustained argon plasma for biological systems treatment. The previous investigations showed good bactericidal, virucidal, seeds germination and decontamination effects at a short treatment time, but the discharge conditions for bio-medical applications need specific adjustment. A detailed theoretical and experimental investigation of the plasma characteristics and their possible optimization in order to meet the requirements for bio-medical applications are presented in this paper. The length of the plasma torch, the temperature at the treatment sample position and the microwave radiation there are estimated and optimized by the appropriate choice of discharge tube size, argon flow rate and microwave power.  
bio-medical plasma applications; surface-wave-sustained discharge; microwave discharge; cold atmospheric plasma; microwave plasma torch +
Footnote †: journal: Physics Letters B