esa-sceva/satcom-chunk-collection huggingface dataset:
Comparative analysis between simulation outcomes and experimental data concerning flange-forming force highlights that optimal results manifest when both elevated temperature and strain rates are considered within the material's flow curve. Moreover, the study undertakes simulations for C45 billet tubes, varying upsetting ratios (\(H0/D0\)), (\(S0/D0\)), and the punch's pitch angle (\(\alpha\)), with the intent of addressing optimization challenges pertinent to the flange-forming force. This endeavor culminates in the development of a mathematical model representing the intricate interplay between the flange-forming force and geometric parameters (\(H0/D0\), \(S0/D0\), and \(\alpha\)).
## 3 Results
### Optimized Parameters  
We used the MCMC method to estimate parameters for the revised EC-LUE model based on ground-GPP data. To examine the influence of the EC footprint on model accuracy at different spatial resolutions, we optimized the model parameters (\(\varepsilon_{\text{max}}\), \(\theta\) and VPD\({}_{0}\)) separately, using Landsat-footprint NDVI and MODIS-footprint NDVI as driving factors. The model parameter calibrations used the same meteorological data, with a key difference; one used EC footprints derived from Landsat NDVI data at 30 m spatial resolution, and the other used EC footprints derived from MODIS NDVI data at 500 m spatial resolution. We generated two sets of optimized parameters for the revised EC-LUE model, both at 30 m spatial resolution (Landsat) and 500 m spatial resolution (MODIS), respectively. The 30-m optimized parameter values differed greatly from the 500-m optimized parameter values for grassland, wetland, savanna, shrubland, and cropland, but differed only slightly for forest ecosystems (Table 1). This reflected the difference between the Landsat-footprint NDVI and the MODIS-footprint NDVI.
## 2 Materials and Methods
### Stability Study
#### 2.4.1 Particle Size  
Laser diffraction analysis was conducted to determine the particle size and distribution of the samples (Fresh, and after 1 month and 3 months) using a Mastersizer Scirocco 2000 (Malvern Instruments Ltd., Worcestershire, UK). The refractive index for KTP was set at 1.592. A dry dispersion unit was employed, with 0.5-1.0 g of the sample placed in the feeder tray. The air pressure was set to 3.0 bar, with the vibration feed at 75%. Each sample was measured in triplicate. Particle size distribution was represented by D[0.1] (indicating the size which below 10% of the volume distribution falls), D[0.5] (median particle size), and D[0.9] (indicating 90% of the volume distribution is below this size). Furthermore, the span of the size distribution was calculated using Equation (2).  
\[\text{Span}=\frac{\text{D[0.9]--D[0.1]}}{\text{D[0.5]}} \tag{2}\]
## 3 An Analysis of Application of Steam Injectors in a Boiler Regenerative Feed Water System
### Application of Steam Jet Injectors in Complex Regenerative Feed Water Systems
#### 3.2.2 Application of Two-Stage Regenerative Injectors
The use of injectors allows, through the selection of the ejection ratio, to optimize the operation of the exchangers, selecting the pressure of steam supplying the exchanger as close as possible to the saturation pressure, for the assumed temperatures. Figure 11 shows a variant of a complex system consisting of five heat exchangers. The system uses two single-stage injectors and one two-stage injection unit. The state parameters in individual control planes are presented in Table 6.  
The mathematical model of a complex system is described by the system of Equations (18) and (11)-(12). \[\left\{ \begin{array}{l}\dot{m}_{34}(i_{34}-i_{35})-\dot{m}_{15}(i_{15}-i_{14 })=0\\ \dot{m}_{31}(i_{31}-i_{32})-\dot{m}_{14}(i_{14}-i_{13})=0\\ \dot{m}_{34}i_{35}+\dot{m}_{32}i_{32}+\dot{m}_{28}i_{28}+m_{22}i_{24}+\dot{m}_{ 17}i_{17}+\dot{m}_{11}i_{11}=\dot{m}_{12}i_{12}\\ \dot{m}_{34}+\dot{m}_{32}+\dot{m}_{28}+\dot{m}_{22}+\dot{m}_{17}+\dot{m}_{11}= \dot{m}_{12}\\ \dot{m}_{22}(i_{22}-i_{23})-\dot{m}_{11}(i_{11}-i_{10})=0\\ \dot{m}_{17}(i_{17}-i_{18})-\dot{m}_{11}(i_{10}-i_{9})=0\end{array} \right. \tag{18}\]  
The calculated efficiency of the system is:  
\[\eta_{CRreg2}=\frac{\dot{m}_{1}(i_{1}-i_{4})-\dot{m}_{2}(i_{2}-i_{4})-\dot{m}_ {3}(i_{3}-i_{4})}{\dot{m}_{1}(i_{1}-i_{15})}=0.37631 \tag{19}\]  
Figure 11: A thermal flow diagram of a complex regenerative cycle. with the regeneration degree of the system equal to:  
\[\varepsilon_{CRreg2}=\frac{\eta_{CRreg2}-\eta_{CRref}}{\eta_{CRref}}=0.882\% \tag{20}\]  
The use of additional heat exchangers and a two-stage injector unit allows for heating of the boiler feed water to the temperature of 125 \({}^{\circ}\)C, thus reducing the required mass flow of the bleed steam for the last heat exchanger. However, due to the low ejection ratio on injectors of the fourth and fifth stages, the recovery of waste energy in the form of a mass flow of the drawn turbine exhaust steam is negligible and does not significantly increase the efficiency. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Control Plane**} & **P abs** & **t** & **i** & **\(\hat{m}\)** \\  & **[barA]** & [\({}^{\circ}\)C] & **[kJ/kg]** & **[kg/s]** \\ \hline  
6 & 0.0261 \\ \hline \hline \multirow{4}{*}{Assumed Ejection Ratio} & **6** 1 & 0.500 & \multirow{4}{*}{} & \multirow{4}{*}{} \\ \cline{2-2} \cline{4-4}  & **6** 2 & 0.167 & \\ \cline{1-1} \cline{2-2} \cline{4-4}  & **6** 3 & 0.143 & \\ \cline{1-1} \cline{2-2} \cline{4-4}  & **6** 4 & 0.167 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Thermodynamic parameters of the working fluid in the control planes of a complex regenerative cycle.
###### Abstract  
_Trichoderma_ spp. are known as biocontrol agents of fungal plant pathogens and have been recognized as a potential source of bioactive metabolites. The production of antimicrobial substances from strains _T. atroviride_ (TS) and _T. asperellum_ (IMI 393899) was investigated. The bioactivity of 10- and 30-day culture filtrate extracted with ethyl acetate was assessed against a set of pathogenic fungi and oomycetes. The 30-day extracts of both strains had significant cytotoxic effects against the tested pathogens, with values of minimum fungicidal concentration (MFC) ranging between 0.19 and 6.25 mg/mL. Dual culture assay (direct contact and nondirect contact) and the percentage inhibition of radial growth (PIRG) was calculated. The highest PIRG values were 76% and 81% (direct contact) withIMI 393899 and TS, respectively. Nondirect contact does not show inhibition on any of pathogens tested, indicating that the inhibition is not due to the secretion of volatile substances. Culture filtrates were analyzed by GC-MS and HPLC-Q-TOF-MS for the identification of volatile organic compounds (VOCs) and nonvolatile organic compounds (nVOCs), respectively. Seven classes of VOCs and 12 molecules of nVOCs were identified. These results indicate that these strains of _Trichoderma_ had antimicrobial activities and they are potential natural sources of compounds with biological activity.  
_Trichoderma asperellum; Trichoderma atroviride_; bioactive metabolites; biological control; plant pathogens +
Footnote †: journal: Fungal
# Collision Avoidance Geographic P2P-RPL in Multi-Hop Indoor Wireless Networks
###### Abstract  
In home and building automation applications, wireless sensor devices need to be connected via unreliable wireless links within a few hundred milliseconds. Routing protocols in Low-power and Lossy Networks (LLNs) need to support reliable data transmission with an energy-efficient manner and short routing convergence time. IETF standardized the Point-to-Point RPL (P2P-RPL) routing protocol, in which P2P-RPL propagates the route discovery messages over the whole network. This leads to significant routing control packet overhead and a large amount of energy consumption. P2P-RPL uses the trickle algorithm to control the transmission rate of routing control packets. The non-deterministic message suppression nature of the trickle algorithm may generate a sub-optimal routing path. The listen-only period of the trickle algorithm may lead to a long network convergence time. In this paper, we propose Collision Avoidance Geographic P2P-RPL, which achieves energy-efficient P2P data delivery with a fast routing request procedure. The proposed algorithm uses the location information to limit the network search space for the desired route discovery to a smaller location-constrained forwarding zone. The Collision Avoidance Geographic P2P-RPL also dynamically selects the listen-only period of the trickle timer algorithm based on the transmission priority related to geographic position information. The location information of each node is obtained from the Impulse-Response Ultra-WideBand (IR-UWB)-based cooperative multi-hop self localization algorithm. We implement Collision Avoidance Geographic P2P-RPL on Contiki OS, an open-source operating system for LLNs and the Internet of Things. The performance results show that the Collision Avoidance Geographic P2P-RPL reduced the routing control packet overheads, energy consumption, and network convergence time significantly. The cooperative multi-hop self localization algorithm improved the practical implementation characteristics of the P2P-RPL protocol in real world environments. The collision avoidance algorithm using the dynamic trickle timer increased the operation efficiency of the P2P-RPL under various wireless channel conditions with a location-constrained routing space.  
point-to-point routing; geographic routing; trickle algorithm; routing convergence time +
Footnote †: journal: 2020 IEEE International Conference on Communications and Information Theory
# Laser R-Test for Angular Positioning Calibration and Compensation of the Five-Axis Machine Tools
## 2 System Uncertainty Analysis and Calibration
### Calibration  
#### 2.2.1 Translational Axis Calibration  
The location of point read from the CNC machine tool controller should be close to the actual location; the more accurate the read location is from the machine tool, the more accurate value of angular error is achieved later on. The compensating error process for translational axes was implemented by using the 5530 Laser Calibration System of Keysight Technologies on three axes, XYZ, as shown in Figure 4. The results before and after compensation are shown in Figures 5-7. The error on the \(X\)-axis after compensation is equal to 1 \(\upmu\)m approximately, and the error is 2 \(\upmu\)m approximately on the \(Y\)-axis and \(Z\)-axis. These values seem to be safe with the chosen values in uncertainty analysis before, and with these results they are in agreement with each other.  
Figure 4: Implementing the 5530 Laser Calibration System on the CNC machine.
# Optimization of Active Distribution Network Operation with SOP Considering Reverse Power Flow
###### Abstract  
As the penetration of distributed renewable energy increases, the phenomenon of bidirectional power flow in distribution networks becomes increasingly severe. Traditional regulation devices like OLTC (on-load tap changer) and CB (capacitor bank) cannot effectively mitigate reverse power flow in distribution networks due to their limitations. The transmission capacity of the distribution network under reverse power flow is approximately 50% of the rated capacity of the OLTC, leading to issues such as voltage limit violations and high wind and solar curtailment rates. This paper proposes a method for calculating the reverse power flow delivery capacity of distribution networks, quantitatively describing the distribution network's delivery limits for reverse power flow. Based on this, a joint optimization model for multiple distribution networks with an SOP is established. The SOP is utilized to share reverse power flow delivery capacity among multiple distribution networks, enhancing operational economy and increasing the accommodation of the DG. Finally, the method's effectiveness and correctness are verified in the IEEE 33-node system. The results validate that while joint operation does not enhance the reverse flow transmission capacity of a single distribution network, it can, through the shared reverse flow transmission capacity approach, elevate the reverse flow transmission capacity to approximately 70% during the majority of time periods.  
distribution network; soft open point; power electronic devices; reliability evaluation method; mixed integer linear programming model +
Footnote †: journal: Applied sciences  
definition network; soft open point; power electronic devices; reliability evaluation method; mixed integer linear programming model
## 3 Materials and Methods
### Framework Overview
#### 3.2.1 Global Feature Extraction  
Due to the different sizes of the area in each county used for crop yield prediction, it is not feasible to directly feed different sizes of images that contain an entire county into the network to learn deep features. The strategy adopted in [15] is to extract image patches of the same size from each county. As a result, the county with a larger area can only partially be included in the extracted image patch. Furthermore, for the county with a smaller area, the extracted image includes not only this county but also part of other regions. However, [PERSON] et al. [1] designed a new dimension reduction algorithm based on the assumption of permutation invariance, which can map a high-dimensional image with different sizes into a histogram with equal dimensions without losing information.  
Thus, we perform the transformation using the same procedure as [1], and the operation process of the global feature extraction is shown in Figure 3. Firstly, at every time t, we extract the multispectral images, which is the minimal matrix containing each county. Then, the land cover information is used to mask the pixels that are not masked as croplands. Accordingly, the values of these pixels are set to 0. Thirdly, we discretize the pixel values of each band \(I_{k}^{(t)},k=1,\cdots,d\) in the image \(I^{(t)}\) into \(b\) bins and produce a b-bins histogram \(h_{k}^{(t)}\in R^{b},k=1,\cdots,d\). And \(d\) is the number of the bands for each image. By concatenating all \(h_{k}^{(t)}\) into \(H^{(t)}\), the \(b\times d\) histogram matrix \(H^{(t)}\) is produced per image and regarded as a compact representation of the original multispectral image. Thus, the desired mapping \(\left(I^{(1)},\cdots,I^{(T)}\right)\to y\) is converted to the desired mapping \(\left(H^{(1)},\cdots,H^{(T)}\right)\to y\). The sequential nature of the inputs \(\left(H^{(1)},\cdots,H^{(T)}\right)\) suggests the use of temporal models. And we stack \(\left(H^{(1)},\cdots,H^{(T)}\right)\) into the 3D histogram \(\Psi\in R^{b\times d\times T}\) as the input of the 3D convolutional neural network in the proposed method.  
Figure 2: The architecture of the proposed framework.  
Figure 3: The operation process of the global feature extraction.
# Routing Selection Algorithm for Mobile Ad Hoc Networks Based on Neighbor Node Density
## 3 Node-Density-Based Routing Method
### Nd-Aodv  
In the AODV route discovery process, when a node receives a route request (RREQ) and determines that it is not the destination node and does not have a route to the destination in its routing table, it will add its own information and then forward the RREQ to its neighbor nodes. However, ND-AODV modifies this route discovery process. In [6], ND-AODV introduces a parameter called \"\(k\)\" to determine if a node has too many neighbor nodes, indicating whether the node is in a high-density environment. The optimal value for \"\(k\)\" was determined to be 11 in the experiments presented in [6]. When the number of neighbor nodes exceeds the value of \"\(k\)\" there is a probability that the node will not forward the RREQ. To calculate the probability \(p_{i}\) of the forwarding RREQ of the \(i\) node:  
\[p_{i}=min(1,\frac{k}{Nb_{i}}) \tag{1}\]  
where \(Nb_{i}\) is the number of neighbor nodes.  
In the ND-AODV route discovery process, when a node receives an RREQ and completes the destination node information check, it will then further assess whether the number of its neighbor nodes exceeds the \"\(k\)\" value. If the number is not greater than \"\(k\)\", the node will forward the RREQ. However, if the number of neighbor nodes is greater than \(k\), the node will calculate a new forwarding probability \(p_{i}\). Subsequently, it randomly generates a value \(p_{r}\) within the range of \((0,1)\). If \(p_{r}\) is less than \(p_{i}\), the node will forward the RREQ; otherwise, it will discard the RREQ and not forward it, as shown in Figure 4.
# Background Subtraction for Moving Object Detection in RGBD Data: A Survey
## 6 Comparisons
### Comparisons on the RGB-D Object Detection Dataset  
Performance comparisons on the RGB-D Object Detection dataset are reported in Table 6. Here, values for the two weak color and depth classifiers (\(\text{CL}_{\text{C}}\) and \(\text{CL}_{D}\)) and the weighted color and depth classifier (\(\text{CL}_{W}\)) by [PERSON] and [PERSON] [17], the four-dimensional MoG model (MoG4D) by [PERSON] et al. [42], the combined RGB and depth ViBe model (ViBeRGB+D) by [PERSON] et al. [47], and the combined RGB and depth MoG model (MoGRGB+D) by [PERSON] et al. [48] are those reported in [17]. Values for the RGBD-KDE algorithm by [PERSON] et al. [80] and the KDE algorithm using only color (C-KDE) and only depth (D-KDE) are those reported in [80]. Values for the AMDF (Adaptive Multi-Cue Decision Fusion) algorithm by [PERSON] et al. [69], the RFBS (Refinement Framework for Background Subtraction) algorithm by [PERSON] et al. [20], the EC-RGBD algorithm by [PERSON] et al. [71], the enhanced classifier (MoG-RegPRE) by [PERSON] et al. [62], the GSM\({}_{\mathit{LIB}}\) and GSM\({}_{\mathit{UF}}\) algorithms by [PERSON] et al. [32], and the SAFBS algorithm by [PERSON] et al. [75] are those reported by the related authors. Good performance can be achieved for color camouflage (ColCamSeq) and shadows (ShSeq), as well as for sequence GenSeq (see Figure 4), which combines different issues (color shadows, color and depth camouflage, and noisy depth data). On the other hand, depth camouflage (DCamSeq) seems to be a problem for most of the methods using depth.
## 4 Results and Comparisons with Other Surface Water Products
### Comparisons with MODIS/Terra-Derived Inundation Maps
Second, the MODIS mixed pixels include water surfaces, vegetation surface and bare soil, and the percentage of each surface type within the pixel is not quantified. Figure 11: Time series of the surface water detected by SAR (red) and MODIS data (black), over the Mekong Delta (Latitude [9.8\({}^{\circ}\)N–11.3\({}^{\circ}\)N]; Longitude [104.75\({}^{\circ}\)E–107\({}^{\circ}\)E]), for 2015. Two hypotheses are tested for the MODIS mixed pixels: 50% inundated (**top Panel**), and 25% inundated (**bottom Panel**).
## 3 Results and Discussion
### Thermal Stability of the Composite PCM  
The thermal stability of the composite PCM was measured by TGA. Figure 9 shows the TG curve of the composite PCM (Sample S8). One can see that the onset decomposition temperatures of the composite PCM at a heating rate of 5 and 10 K/min are \(\sim\)894 \({}^{\circ}\)C and \(\sim\)885 \({}^{\circ}\)C, respectively [47]. When the temperature is over 900 \({}^{\circ}\)C, it starts to decompose rapidly. The residual substance of the composite PCM (S8) after the TG test (heated over 950 \({}^{\circ}\)C) was analyzed by XRD to understand the decomposition products. In Figure 10, one can see that the three substances are the main decomposition products which have remained, which are MgO, Na\({}_{2}\)MgSiO\({}_{4}\), and K\({}_{2}\)MgSiO\({}_{4}\). [PERSON] et al. [48] have studied the decomposition of Na\({}_{2}\)CO\({}_{3}\) and found that it starts to decompose slowly after 850 \({}^{\circ}\)C. [PERSON] et al. [49] have found that K\({}_{2}\)CO\({}_{3}\) starts to decompose slowly after 900 \({}^{\circ}\)C. In addition, the decomposition of Na\({}_{2}\)CO\({}_{3}\) and K\({}_{2}\)CO\({}_{3}\) occurs in two consecutive steps: (1) A\({}_{2}\)CO\({}_{3}\) (l) = A\({}_{2}\)O (s) + CO\({}_{2}\) (g); (2) A\({}_{2}\)O (l) = 2A (g) + \(\frac{1}{2}\) O\({}_{2}\) (g); where A stands for Na\({}_{2}\)CO\({}_{3}\) or K\({}_{2}\)CO\({}_{3}\). Therefore, it can be deduced that decomposition products like Na\({}_{2}\)O and K\({}_{2}\)O could react with SiC and MgO at high temperatures. As a result, the reaction has produced Na\({}_{2}\)MgSiO\({}_{4}\) and K\({}_{2}\)MgSiO\({}_{4}\). However, the chemical reaction during decomposition still needs to be further studied. From the preliminary study of the thermal stability given above, this composite could stand at a relatively high temperature (\(\sim\)850 \({}^{\circ}\)C). Long-term stability of this material and the degradation kinetics during decomposition can be a meaningful subject for future study [50].  
Figure 8: DSC and Cp curves of the composite PCM (Sample S8).  
Figure 9: TG curve of the composite PCM.
# Proposal of a Design Procedure for Steel Frames with Viscoelastic Dampers
## 5 Application to a Case Study
### Numerical Model for Design Analyses  
Due to the in-plan and in-elevation regularity of the buildings, a two-dimensional numerical model is considered. Further, due to the double symmetry of the plan, only a perimetral and a central frame are included in the model, as shown in Figure 9. To simulate the rigid diaphragm effect due to the slabs, all the nodes of the two frames belonging to the same floor are constrained to have the same translational displacements. All members are modeled as elastic elements.  
Columns are constituted by the same profile along the height of two stories and column-to-column connections are assumed to be rigid and full-strength. Column-to-foundation connections are fixed, except for the case of C0 columns, which are pin-connected to the foundation. With the sole exception of the braced beam, which is pinned at the ends, beams-to-columns connections are fixed.  
In the model to use for design analyses, the VED-brace sub-assemblage consists of two truss members in the chevron configuration. Note that in the horizontal direction, the stiffness of the VED-brace assemblage is given by the equivalent brace model discussed in Section 3.2, while in the vertical direction, the stiffness of the VED-brace assemblage is the same as the stiffness of the sole braces. Thus, to properly estimate the axial force in the braces, internal forces due to gravity loads in the seismic combination and due to seismic actions are separately evaluated considering two different structural models. In the model for gravity loads analysis, the actual cross-section of the chosen steel profile is used for braces. In the model for response spectrum analysis, the cross-sectional area of the braces is evaluated using the equivalent brace model. Internal forces resulting from the two structural models are then combined.  
The seismic action is represented by the average elastic response spectrum obtained from artificial ground motion records for different values of the equivalent viscous damping ratio \(\xi\), namely 3%, 5%, 10%, 15%, and 20% of the critical damping. Spectra corresponding to intermediate values of \(\xi\) are obtained by linear interpolation. The set of 10 artificial ground motions used to generate the spectra is the same set used to perform nonlinear dynamic analyses (NLDA) and discussed in Section 6. The use of average spectra instead of the EC8 spectrum is preferred to simplify the comparison with the results of NLDA.