esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Sulfoxaflor has activity against piercing-sucking insects [39]. The efficacy of spinetoram + sulfoxaflor was inconsistent in suppressing the nymphal stages of _B. tabaci_[11; 40]. The maximum registered label rate for XXpire in the US is 192.6 g/ha [41], and XXpire high rate used in the current study was 245.2 g/ha, which is the off-label rate. Additionally, the spray volume typically used in the ornamentals is 935.4 L/ha. In the current study, higher spray volumes of 1870.8 and 3741.6 L/ha were used for low (foliar) and high (spench) foliar sprays, respectively. The results indicated that higher foliar spray volume (spench) effectively reduced nymphal densities of _B. tabaci_ even at a low XXpire rate, 140.1 g/ha. These data suggest that spinetoram + sulfoxaflor can provide alternative options for _B. tabaci_ control when the application method is modified to higher spray volume and installation of spray nozzle tip that delivers more insecticide solution than a typical nozzle tip would deliver. It is worth noting that sulfoxaflor is placed in group 4 as neonicotinoids [17]; thus, the use of XXpire, especially outdoors, should be consistent with the product label to reduce exposure to pollinators. Improved insecticide coverage will refine the integrated pest management program for _B. tabaci_ in the greenhouse. There are many reasons to support this statement. First, better coverage improves the insecticide efficacy by exposing most insects to the applied insecticide [42]. This can be especially true if the primary mode of exposure is by contact. In the current study, the spinetoram component of XXpire primarily works by contact exposure. The sulfoxaflor component of XXpire is typically translaminar and efficacious on piercing-sucking insect pests, such as whitelfies. Dinotefuran (Zylam) is known for its systemic activity within the plant, but it is also effective as a contact poison. Thus, improved coverage of both XXpire and Zylam should improve their efficacy against _B. tabaci_. Second, contact exposure is more efficacious if the behavior of the target insect involves active movement on the leaf surface. The adults and first instars of _B. tabaci_ may wander on the foliage until they settle at a spot on the leaf surface. In addition, all stages of _B. tabaci_ are mostly found on the abaxial leaf surface, suggesting that insecticide coverage on the abaxial leaf surface is essential. These two behaviors of _B. tabaci_ likely helped improve XXpire and Zylam when applied with greater water volume. Third, greater insecticide coverage may have helped reduce the development of resistance to these insecticides through improved efficacy. Finally, the improved coverage and efficacy of insecticide likely reduced the population of _B. tabaci_ below threshold levels, which provided opportunities for biological control agents to suppress the residual _B. tabaci_ population post-insecticide application. However, the effects of insecticide coverage on specific biological control agents present on the plants during insecticide application warrant more research.
## 4 Regression Analysis and Model Verification  
### Regression Analysis  
In order to determine the specific influence degree of the above three factors on the SMD of the circular-hole rotating core micro-fog nozzle, generally, high-order polynomials are used to predict the relationship between independent variables and dependent variables. However, the preliminary analysis of the data by Statistical Product and Service Solutions (SPSS) software (version 24.0.0.0) showed that if the ternary cubic polynomial was used for analysis, the principle of the consistent variance in the group could not be satisfied, and the so-called variance analysis could not be carried out at all. Therefore, the relationship between the droplet size, \(D_{[3,2]}\), and the independent variable outlet diameter, (D), and the water supply pressure (P) and the axial distance (S) can be regarded as a ternary quadratic polynomial. According to the McLaughlin expansion, the multiple regression model was:  
\[D_{[3,2]}=b_{0}+b_{1}S+b_{2}D+b_{3}P+b_{11}S^{2}+b_{22}D^{2}+b_{33}P^{2}+b_{12 }SD+b_{13}SP+b_{23}DP \tag{1}\]  
The overall significance test of the droplet size regression model by the SPSS multiple regression analysis is shown in Table 4, where R\({}^{2}\) is the determination coefficient and F is the statistical verification value.  
Figure 5: Comprehensive mean and range of three factors: (**a**) axial distance and SMD; (**b**) outlet diameter and SMD; (**c**) water supply pressure and SMD.  
As can be seen from Table 4, the coefficient of determination, R\({}^{2}\), of the regression model, with the introduction of the correction term in the established multiple regression model, was 0.802, which indicated that the degree of closeness between the dependent variate and the independent variate of the model was high and that the degree of explanation for the change of the dependent variable was described and explained by the multiple linear regression equation. The p of the overall significance test of the regression model was less than 0.001, which was far less than the set significance level of \(\alpha\) = 0.05, and its significance had an obvious influence, which indicates that the overall regression model was very significant, and the hypothesis of the regression model was correct. The regression equation of the established multiple regression model was:  
\[\begin{split}\mathrm{D}_{[3,2]}=2269.243-15.594\mathrm{S}-2342.5 24\mathrm{D}-216.007\mathrm{P}+0.193\mathrm{S}^{2}+1314.13\mathrm{D}^{2}\\ -1.942\mathrm{P}^{2}+0.955\mathrm{SD}-3.053\mathrm{SP}+21.15 \mathrm{DP}\end{split} \tag{2}\]
## 2 Materials and Methods  
### Model Driven Service Engineering Architecture  
*Model-driven>> engineering (MDE) is a software engineering methodology that considers models to be the core asset of the software development process as explained in Reference [11]. Model-driven>> architecture (MDA) [12] and model-driven>> interoperability (MDI) [13] are MDE initiatives. They aim to tackle, respectively, architecture and interoperability problems at earlier stages. From the same perspective, researchers proposed a unified methodology, <<model-driven>> service engineering architecture (MDSEA), based on MDA/MDI and designed to overcome their lack of modeling and developing services [10]. One of the main benefits of MDSEA is the definition of the three types of service system components (IT, Organization/Human, and Physical Means) and their integration at the highest level of the modeling phase. Therefore, in comparison to other <<model-driven>> approaches (MDA/MDI), the goal of MDSEA is not only IT-oriented implementation, but all types of resource implementation.  
As shown in Figure 1, MDSEA is a three-layer methodology:  
* Business system model (BSM): This top layer gives a general model of the system (top BSM). Leading down from this (bottom BSM) is the breakdown of the three resource domains, giving the details for each individually;
* <<Technology-independent>> model (TIM): This level focuses on describing the operational details of the system, including the three components, without detailing any implementation specificities;
* <<Technology-specific>> model (TSM): This level focuses on describing the specificities of each resource implementation.  
A detailed explanation of each level of MDSEA is given in Reference [10].  
There are different dimensions that can be addressed when using enterprise modeling to model a system: Processes, Products/Services, Organization, Information, and Environment, according to Reference [14]. This paper focuses on enterprise processes modeling only.
## 2 Materials and Methods
### Theoretical Considerations
#### 2.2.5 Determining the IFD for Emission Classes F1B and F2B  
Denoting \(K_{MF}\) the modulator frequency conversion constant, and \(\Delta\omega=K_{MF}X_{m}\)--the angular frequency deviation, the general form of these frequency-modulated signals is described by the following Equation (21):  
\[s_{MF}(t)=A_{p}\cos\bigl{[}\omega_{p}t+\varphi(t)\bigr{]}=A_{p}\cos\biggl{[} \omega_{p}t+\Delta\omega\int_{0}^{t}f(\tau)d\tau\biggr{]}\Delta\omega\int_{0}^ {t}f(\tau)d\tau, \tag{21}\]  
In the case of F1B and F2B emission classes, the actual information is transmitted by varying the frequency of the carrier signal. Therefore, an amplitude limiter is inserted before the demodulator. The power of the input signal will be given by the relation (16) and that of the input noise by the relation (3).  
The demodulated signal will be proportional to the time variation of the signal's total phase \(d\Phi/dt\). If we note with \(K_{DF}\) the demodulator constant or efficiency, the respective powers of the output signal and the output noise are given by Equations (22) and (23):  
\[P_{SoutF1B}=\overline{\left[\Delta\omega_{M}f(t)\right]^{2}}=\Delta\omega_{M }^{2}\overline{f^{2}(t)}=K_{DF}^{2}K_{MF}^{2}X_{m}^{2}\overline{f^{2}(t)}, \tag{22}\]  
\[P_{ZoutF1B}=\frac{K_{D}^{2}}{A_{p}^{2}}\cdot\frac{1}{2\pi}\int\limits_{\omega _{c}-\pi B}^{\omega_{c}+\pi B}\omega^{2}\cdot N_{0}d\omega=\frac{2}{3}\frac{K _{D}^{2}\cdot N_{0}B^{3}}{A_{p}^{2}}. \tag{23}\]  
The result for the improvement factor is expressed by relationship (24):  
\[\rho_{F1B}=\frac{K_{DF}^{2}K_{MF}^{2}X_{m}^{2}\overline{f^{2}(t)}}{\left(2/3 \right)\cdot\left(K_{DF}^{2}\cdot N_{0}B^{3}/A_{p}^{2}\right)}\cdot\frac{2N_ {0}B}{A_{p}^{2}}=3\cdot\frac{K_{MF}^{2}X_{m}^{2}}{B^{2}}\cdot\overline{f^{2}( t)}=3\cdot D^{2}\cdot\overline{f^{2}(t)}, \tag{24}\]  
where D is the deviation ratio.
# Assessing the Potential of PlanetScope Imagery for Iron Oxide Detection in Antimony Exploration
## 3 Materials and Methods
### Data  
The PS image was acquired on 28 August 2024. The PS image was obtained through the Planet Explore gallery (developers.planet.com). The image was already provided in surface reflectance, so no atmospheric correction was needed. PS, operated by Planet, is a constellation of around 130 satellites with a ground sample distance (GSD) of 3 m. The  
Figure 2: Methodological workflow adopted in this study. The process is divided into two main stages: (1) spectral data acquisition and analysis and (2) satellite image processing, including band ratio calculations and relative comparison.  
SuperDove instrument (PSB.SD), launched in 2021, enhanced the original PS capabilities by expanding the available bands from the initial four (blue, green, red, and near-infrared (NIR)) to eight, with the addition of green I, yellow, coastal blue, and the red edge band [30]. The Sentinel-2 image was acquired on 2 October 2023. The image was downloaded from the Copernicus Data Space website. The Sentinel-2 Multispectral Instrument (MSI) has four bands with 10 m of ground sample distance (GSD), six bands with 20 m of GSD, and three bands with 60 m of GSD [31]. The ASTER image was captured on 14 September 2003. The ASTER has three bands in the visible and NIR with 15 m of GSD, six bands in the SWIR with a GSD of 30 m, and four bands in the thermal infrared region (TIR) with 90 m of GSD [32]. The 2003 ASTER image was selected as it is one of the few cloud-free scenes with full spectral coverage before the SWIR sensor failure in 2008. Although the SWIR bands were not used in this study, the same image will be used in future work where SWIR data will be essential for mapping other alteration minerals such as clays. The Landsat 9 image was acquired on 202 October 2. Landsat 9 is equipped with the Operational Land Imager 2 (OLI-2) and the Thermal Infrared Sensor 2 (TIRS-2), which provide data across 11 spectral bands. The OLI-2 sensor captures data in the visible, near-infrared (NIR), and shortwave infrared (SWIR) regions with a GSD of 30 m for most bands. The panchromatic band has a GSD of 15 m. The TIRS-2 sensor acquires thermal infrared data in two bands with a native GSD of 100 m [33].  
One of the goals of this research is to compare the potential of PS satellites in mapping ferric iron (Fe\({}^{3+}\)) with that of Sentinel-2 and ASTER satellites. Table 1 presents the correlation between the bands of these satellites and those of the PS satellite.
## 3 Results and Discussion
### Effect of Enzyme Loading  
Biosensors were prepared in the manner described earlier (see biosensor fabrication and storage) with the enzyme LOX at loadings of 15 to 15,000 units per biosensor. These biosensors were initially investigated with solutions of linoleic acid over the concentration range 20 to 200 uM, using a supporting electrolyte solution comprised of PBS pH 7.0, at a fixed temperature of 37 \({}^{\circ}\)C. Calibration graphs were constructed and it was shown that these plots were non-linear, exhibiting Michaelis Menten type behaviour. For all loadings studied the greatest change in steady state current occurred between 20-100 uM, this was followed by a gradual increase to 200 uM. For comparison, the steady state currents were measured at 100 uM and the following values were obtained: 0.903 uA for 15 units of LOX; 0.471 uA for 150 units of LOX; 0.306 uA for 1500 units of LOX; 0.095 uA for 15,000 units of LOX. The decrease in sensitivity with increase in loading is probably due to a decrease in the rate of diffusion of the fatty acid through the outer reaction layer, and lower diffusion rate of the hydroperoxide produced in this layer, towards the CoPC-SPCE surface.  
As we wanted our biosensor to have a high sensitivity for trace FFA determinations, we selected 15U to study concentrations between 2 and 20 uM, at the same pH and temperature as above. We found that the response for this un-optimised biosensor was linear between 2 and 10 uM and the sensitivity was 0.0085 uA uM-1. We also carried a similar study with \(\alpha\)-linolenic acid and the linear range and sensitivity were found to be 2 to 20 uM and 0.0111 uA/uM, respectively. These initial studies were encouraging and demonstrated the possibility of measuring low concentrations of both linoleic and \(\alpha\)-linolenic acid. Having deduced the optimum enzyme loading, further studies were next performed to further optimise the biosensor performance and these are discussed in the following sections.  
Figure 5: Hydrodynamic voltammograms obtained for: (white) \(\alpha\)-linolenic acid and (black) linoleic acid both a concentration of 100 uM; the supporting electrolyte consisted of 0.1 M phosphate buffer pH 7.0 containing 0.1 M NaCl, using a cobalt phthalocyanine screen-printed carbon electrode with 15,000 units of lipoxygenase.
# Real-Time Egress Model for Multiplex Buildings under Fire Based on Artificial Neural Network
## 4 Egress Model for Multiplex Buildings in Fire  
### Egress Model Algorithm  
In this study, an egress model that can provide optimal evacuation routes for occupants when a fire occurs in multiplex buildings was proposed based on the ANN model developed. Figure 16 shows the flowchart of the proposed egress model. First, the visibility and concentrations of oxygen, carbon monoxide, and carbon dioxide are measured using sensors installed at each compartment in the event of a fire, and the data are then entered into the ANN model to estimate ASET for each compartment. Later, the optimum routes that ensure safety can be provided in real time based on the ASET of each compartment and drawings for the building.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Case No.**} & \multicolumn{2}{c}{**Safe Time, s**} & \multirow{2}{*}{**Safe Time, s (ANN)**} & \multirow{2}{*}{**Error, s**} & \multirow{2}{*}{**Error, \%**} \\  & **(FDS)** & & & \\ \hline
1 & 13.8 & 14.7 & 0.9 & 6.0 \\
2 & 60.2 & 57.8 & 2.4 & 4.1 \\
3 & 7.1 & 6.4 & 0  
The ANN-based egress model is an algorithm in which the position of each measurement sensor installed in a multiplex building is set to each node, and the ASET at each node in the event of a fire is calculated to induce evacuation to the node with the highest level of safety among the adjacent nodes. In this case, it is essential to compare movement time between nodes and ASET. The movement time between the nodes (\(t_{move}\)) can be calculated in consideration of occupants' walking speeds and distance between the nodes:  
\[t_{move}=D/F_{w} \tag{4}\]  
where \(D\) is the movement distance between the nodes, and \(F_{w}\) is the occupant's walking speed, which is taken to be 1.272 m/s based on the results of research conducted by [PERSON] [28]. Note that in this research, the occupant was assumed to be elderly (i.e., age = 70, sex = female) in a conservative manner, which certainly can always be adjusted depending on the purpose of the analysis. If the movement time between specific nodes is less than the ASET predicted using the ANN model, i.e., \(t_{move}\leq\) ASET, the evacuation route is determined as the path between the nodes. Even in this case, a route with a large margin of safety (i.e., ASET \(-t_{move}\)) is preferentially selected over others. The repetition of this calculation procedure can lead to the optimum evacuation route to ensure the safety of occupants, as shown in Figure 17.  
Figure 16: Flowchart of the egress model.
# Hybrid Methods for Selecting Precast Concrete Suppliers Based on Factory Capacity
## 4 Sensitivity Analysis
### Sensitivity to Changes in Criteria Weights  
The criteria weights derived from the SWARA method are based on expert opinions and represent a significant source of subjectivity. To assess the model's sensitivity to these weights, a series of experiments were performed. The analysis focused on adjusting the weight of the most influential criterion, C6 (Easy to communicate), which originally weighted 0.171 (as shown in Table 5). In each scenario, the weight of C6 was increased by 10%, 20%, and 30%, while the weights of the remaining eight criteria were proportionally reduced to maintain a sum of 1.0. This simulates a situation where a decision-maker places even greater importance on the top criterion. Equation (12), used to redistribute the weights, is as follows:  
\[w_{j_{new}}=w_{j_{old}}\times\frac{1-w_{j_{new}}}{1-w_{j_{old}}} \tag{12}\]  
where \(w_{j_{new}}\) is the new weight of the adjusted criterion (C6) and \(w_{j_{new}}\) is the recalculated weight for any other criterion. The final WASPAS scores and resulting supplier rankings for each scenario are presented in Table 17.  
The results from Table 17 demonstrate that the model remains remarkably stable in the face of changes in the weight of the most important criterion. Even when the weight of C6 was increased by up to 30%, the final ranking of all five suppliers remained unchanged. The top supplier (A3) and the worst-performing supplier (A2) held their positions consistently. This stability indicates that the performance differences between the suppliers are significant enough to withstand moderate shifts in criteria priorities. For decision-makers, this suggests that the recommendation of Supplier A3 is robust and not merely an artifact of a specific, narrowly defined set of expert weights.
## 4 Use of the Controller-Driven OppNet Architecture to Manage Congestion
### Control Layer Tailored for Congestion Control  
The control layer proposed in Section 3.4 has been adapted to use a congestion policy to efficiently limit the number of messages in the network to limit congestion. This congestion policy dynamically determines the replication limit for a newly created message based on the network congestion perception of a controller.  
As a network congestion measure, we use the node's buffer occupancy rate (\(o\)):  
\[o=\frac{\sum_{i=1}^{n}sizeof(g_{i})}{b} \tag{5}\]  
where \(n\) is the number of buffered data messages, _sizeof_ is the function that returns a message size in bytes, \(g_{i}\) is a buffered message, and \(b\) is the buffer capacity in bytes.  
The generic closed-loop control system in Figure 3, for the use case of congestion control, is specialized in Figure 5 with the slight difference that the controller assumes calculating the difference between the reference input (\(\mathfrak{r}\)) and the controlled variable (\(\mathfrak{c}\)). The controller's reference input (\(\mathfrak{r}\)) is an optimal buffer occupancy congestion interval. This interval consists of a range of buffer occupancy rates, defined by a lower and upper bound, \(o_{min}\) and \(o_{max}\), respectively. The buffer occupancy is considered optimal when the buffer is neither underused nor close to its maximum capacity.  
The controller feeds from the buffer occupancy measurements (\(o\)) received from contacted nodes and uses those measurements to calculate a buffer occupancy prediction (\(o_{t+n}\)) following Algorithm 2.
# Experimental Investigation into the Number of Phases in Debris Flows
###### Abstract  
Controversial number of phases in debris-flow masses involves almost all areas of debris-flow research. In this study, we experimentally investigated the number of phases in fully developed debris flows using six sediments with three maximum diameters of up to 30 mm from two representative debris-flow deposits in China. Fluid escape tests, displacement experiments, relative motion experiments, and rheometrical tests were conducted using 12 slurries prepared with the sediments. The results from four types of experiments show that debris flows are close to one-phase flow and far from two-phase flow. Under both gravity and artificial hydraulic and mechanical forcing, no relative motion of water or fluid composed of water and fine-grained particles and solids occurs in both the experimental flows and static slurries. This suggests that the water and solids in debris flows move together as a single fluid, and are deposited by \"freezing\". The rheological behavior of the experimental debris flows is similar to that of Bingham materials. This indicates that debris flows may be approximated as continuous, homogeneous, isotropic fluids. In conclusion, debris flows behave more like monophasic flow, are far from biphasic flow, and should be treated as one- rather than as two-phase flow.  
debris flow; number of phases; relative motion; phase separation; excess water pressure; rheology +
Footnote †: journal: Applied sciences