esa-sceva/satcom-chunk-collection huggingface dataset:
# A Three-Dimensional Elastoplastic Constitutive Model for Geomaterials
## 7 Numerical Implementation
### The Mixed Complementarity Problem and its GSPC Algorithm  
The elastoplastic constitutive relation in the rate form can be expressed as:  
\[\dot{\boldsymbol{\sigma}}=\boldsymbol{D}^{e}\Big{(}\dot{\boldsymbol{\epsilon }}-\dot{\boldsymbol{\epsilon}}^{p}\Big{)} \tag{38}\]  
where \(\dot{\boldsymbol{\sigma}}\), \(\dot{\boldsymbol{\epsilon}}\), and \(\dot{\boldsymbol{\epsilon}}^{p}\) represent the stress, strain, and plastic strain vector, respectively; \(\boldsymbol{D}^{e}=D_{ijkl}^{e}\) represents the elastic matrix. Considering the time discretization on the interval \([0,T]\), the relationship between the strain rate \(\dot{\boldsymbol{\epsilon}}\) and \(T\) is as follows:  
\[\dot{\boldsymbol{\epsilon}}=\frac{\Delta\boldsymbol{\epsilon}}{T} \tag{39}\]  
where \(\Delta\boldsymbol{\epsilon}\) denotes the incremental strain vector. To establish the complementary equation of the elastic-plastic problem, the backward Euler algorithm is used to rewrite the constitutive Equation (38) as follows:  
\[\boldsymbol{\sigma}=\boldsymbol{\sigma}^{e}-\boldsymbol{\sigma}^{p} \tag{40}\]  
where \(\boldsymbol{\sigma}^{e}\) represents the test stress, which can be written as:  
\[\boldsymbol{\sigma}^{e}=\boldsymbol{\sigma}_{0}+\boldsymbol{D}^{e}\Delta \boldsymbol{\epsilon} \tag{41}\]  
where \(\boldsymbol{\sigma}_{0}\) denotes the total stress at the beginning of the load phase; the \(\boldsymbol{\sigma}^{p}\) expression is as follows:  
\[\boldsymbol{\sigma}^{p}=\boldsymbol{D}^{e}\lambda\frac{\partial f}{\partial \boldsymbol{\sigma}} \tag{42}\]  
The mixed complementarity issue that the plastic constitutive integral defines may be expressed as follows:  
\[\left\{ \begin{array}{l}0\leq\lambda\bot f_{f}(\boldsymbol{\sigma},\lambda) \geq 0\\ f_{E}(\boldsymbol{\sigma},\lambda)=0\end{array} \right. \tag{43}\]  
This problem is abbreviated as MiCP(\(f_{1}\),\(f_{2}\)); \(f_{1}\) and \(f_{E}\) are defined as follows:  
\[\left\{ \begin{array}{l}f_{1}(\boldsymbol{\sigma},\lambda)=-f(\boldsymbol{ \sigma})\\ f_{E}(\boldsymbol{\sigma},\lambda)=\boldsymbol{\sigma}+\boldsymbol{\sigma}_{p}- \boldsymbol{\sigma}_{e}\end{array} \right. \tag{44}\]  
For the mixed complementarity problem, [PERSON] et al. [8] designed a GSPC algorithm based on the Gauss-Seidel idea and projection-contraction algorithm. The GSPC algorithm is easy to program, can converge for any size of strain increment, and does not need to calculate the potential function's Hessian matrix. The computational efficiency is higher than that of the return mapping algorithm. The GSPC algorithm's code is as follows:  
\[
# A Model-Driven Approach for Solving the Software Component Allocation Problem
## 5 Methodology
### Meta-Models  
The meta-model defining a component allocation problem is depicted in Figure 4. An _AllocationProblem_ has a number of _Components_ and _Units_. Each of the _Components_ and _Units_ has a name. In addition, an _AllocationProblem_ has a number of _Resources_, and each _Resource_ has a name.  
Each _Unit_ has a certain capacity for every _Resource_ it provides. This is represented by the _ResourceAvailability_ elements in the _AllocationProblem_. Each _ResourceAvailability_ defines the amount of the capacity of a _Unit_ for a given _Resource_. In addition, an _AllocationProblem_ is composed of a number of _ResourceConsumption_ elements. Each _ResourceConsumption_ defines the amount of _Resource_ consumed by a given _Component_ on a given _Unit_.  
Figure 3: Overview of the proposed framework for solving CAPs.  
Furthermore, an _AllocationProblem_ is composed of a number of _TradeOffWeight_ elements which define the trade-off weights assigned to the different resources. These trade-off weights are important in the case of a single-objective optimization of CAP. An _AllocationProblem_ can also have a number of _AllocationConstraints_ and _AntiAllocationConstraints_ which define the architectural constraints in a CAP. An _AllocationConstraint_ element linking a _Component_ to a _Unit_ represents the constraint that this component must be allocated on that unit. Conversely, an _AntiAllocationConstraint_ element linking a _Component_ to a _Unit_ represents the constraint that this component must not be allocated on that unit.  
The solution set meta-model is shown in Figure 5. A _SolutionSet_ is composed of a number of _Solutions_. Each _Solution_ has an id and is composed of _Mappings_. Each _Mapping_ maps a component to a unit. The optimal solutions to a CAP are represented using a _SolutionSet_. If there is no solution, the _SolutionSet_ is empty.
## 4 Light Intensity- and Spectrum-Associated Adjustment of Metabolism
### Light Control of Carbohydrate Metabolism
#### 4.1.2 The Effect of Light Quality on Carbohydrate Metabolism  
The light spectrum is also an important regulator of carbohydrate metabolism since the low R:FR ratio increased this process by the induction of photosynthetic capacity in soybean seedlings [139]. The observed change may be a result of the activation of Rubisco, the key enzyme of carbon assimilation (Figure 4). This hypothesis was confirmed for other spectral ranges in tomato and sweet pepper [27; 140; 141] since BL- and red-blue-light (RBL)-grown plants had increased amount/activity of Rubisco compared to WL grown plants. However, the use of RL alone resulted in a lower amount or decreased activity of Rubisco [27; 140; 141], indicating the specific effect of different spectral ranges and their ratios on Rubisco. GAPDH activity was also enhanced by BL and RBL, and it was decreased by RL in sweet pepper and tomato [27]. However, [PERSON] et al. [140] found in tomato seedlings that GAPDH activity was significantly increased by monochromatic RL treatment and decreased in monochromatic BL and also in RBL compared to WL. These conflicting results may be explained by the species-specific effect of the various spectral ranges, and the developmental stage and cultivation conditions may also modify their influence. This presumption is also confirmed by the effect of RL and BL and their mixture on the activities of FBP, SBP, and RPK in various plant species [140; 142; 143].  
Carbohydrate catabolism was also influenced by the light spectrum since the compounds of the TCA cycle were enriched among the 99 metabolites (DEMs) (42 upregulated and 57 downregulated) detected in different amounts in RL and BL in _Mesona chinensis_[144]. Some RL-responsive metabolites from the TCA cycle like succinate or oxaloacetate, participating in the stomatal opening, were also identified in _Arabidopsis_[145]. In addition, the activity of the key TCA cycle enzyme, 2-oxoglutarate dehydrogenase (2-OGDH), was also influenced by the light spectrum (Figure 4) [146]. Both BL and RL could diminish 2-OGDH activity in maize, but RL was more efficient, and its effect could be reversed by FRL. A different transcriptional regulation of 2-OGDH isoenzymes was also shown in response to RL and BL in maize and _Arabidopsis_. In _Arabidopsis phyA_ knockout mutants, the effect of RL was reduced only on _OGDH2_ transcription, but _phyB_ mutants showed a wild type-like expression of _OGDH1_ and _OGDH2_, which observation indicates the specific regulatory effects of various phytochromes on this enzyme [146].
# Advances in High-Power, Ultrashort Pulse DPSSL Technologies at HiLASE
###### Abstract  
The development of kW-class diode-pumped picosecond laser sources emitting at various wavelengths started at the HiLASE Center four years ago. A 500-W Perla C thin-disk laser with a diffraction limited beam and repetition rate of 50-100 kHz, a frequency conversion to mid-infrared (mid-IR), and second to fifth harmonic frequencies was demonstrated. We present an updated review on the progress in the development of compact picosecond and femtosecond high average power radiation sources covering the ultraviolet (UV) to mid-IR spectral range at the HiLASE Center. We also report on thin-disk manufacturing by atomic diffusion bonding, which is a crucial technology for future high-power laser development.  
Keywords: diode-pumped solid-state lasers (DPSSL); high average power lasers; Yb:YAG; mid-infrared radiation; thin-disk laser; higher harmonic generation; picosecond pulses; UV generation; optical parametric amplifier; thin-disk bonding +
Footnote †: journal: Applied
## 2 Materials and Methods
### Test Program
#### 2.3.3 Test Instrumentation  
Load, displacement, and the number of cycles of the actuator were recorded by the fatigue testing machine, and a real-time load versus time curve was displayed to facilitate test monitoring. Strains were measured to capture the stress distribution of the test specimen. Altogether, 25 strain gauges, including nine strain rosettes and 16 unidirectional strain gauges, were mounted on specimen plate surfaces. To avoid any possible damage from welding and transportation, we mounted all of the strain gauges after the completion of the specimen installation. Because of limited operation space inside the box specimen, most of the measuring points were distributed on the outside surface of the girder webs. The arrangement of strain gauges is schematically shown in Figure 6; designations in brackets are for the lower support plate.  
For observing possible fatigue cracking, conventional methods such as visual inspections and digital images were used for crack detection during the test. In addition, acoustic emission technology was employed in real time to monitor possible crack initiation and propagation during cyclic loading. A photo of the acoustic emission system is presented in Figure 7. Four acoustic emission sensors were installed on the surfaces of the test specimen, with monitoring points 3 and 4 mounted on the bottom plate of the box girder, as shown in Figure 5, and monitoring points 1 and 2 inside the box girder, as shown in Figure 6. Each sensor is capable of monitoring an area within a radius of 30 cm near the measurement point.
# Effect of Plasma Surface Modification on Print Quality of Biodegradable PLA Films
## 2 Materials and Methods
### Plasma Treatment  
Plasma activation was performed using a vacuum chamber with a Diener Nano low-pressure plasma system (Diener Electronic, Altensteig, Germany). One half of PLA films was exposed to low pressure oxygen plasma (100% O\({}_{2}\)), while the other half was treated with argon plasma (100% Ar). The device was operated at the following settings: radio frequency 40 kHz, power 1000 W, pressure 0.5 mbar (50 Pa), gas pressure control, fixed gas delivery time 2 min and exposure time 2 min. Activation time and pressure were determined based on previously completed studies [21; 22; 29; 30] as optimal for obtaining high surface free energy (SFE) values.
## 2 Study Area  
Our area of study focuses on mainland Japan (except Okinawa and small islands) (Figure 1). Japan is made up of over 7000 contiguous islands from the northeast to the southwest, with a total length of approximately 3000 km. Total land area is approximately 377,900 km\({}^{2}\). Over 60% of the land is recognized as mountainous (over 2000 m in altitude) and 12% as hilly. The total forested area makes up approximately 67% [3], the majority of which is located around the aforementioned mountain or hill areas. Climatic conditions in Japan vary widely due to its geographical location; its lands stretch from a southern latitude of 30 \(\mathrm{\SIUnitSymbolN}\) (Okinawa omitted) to a northern latitude of 45 \(\mathrm{\SIUnitSymbolN}\). This range, together with the ocean currents, gives each region a different climate. Because of the variety in topography and climatic conditions, Japan has a great diversity of vegetation both horizontally and vertically. For example, there are over 6000 species of Pteridophytes, each of which has evolved and adapted to its specific location [17]. The dominant coniferous tree types in Japan are the Japanese cedar (_Cryptomeria japonica_) and cypress (_Chamaecyparis obtusa_) trees, which occur widely from the north to the south. As a whole, coniferous and broadleaf forests are distributed in approximately a 50/50 ratio, while 80% of coniferous forests tend to be planted forests that were part of a large plantation plan that lasted from the 1950s to the 1970s. On the other hand, the broadleaf forests are primarily natural (over 90%), and few deciduous or evergreen broadleaf trees have been planted. Broadleaf hardwoods are dominant in the central region, while mixed deciduous evergreen forests or evergreen broadleaf forests can be observed in the temperate south zones.  
Remote sensing data for Chiba Prefecture was used for a detailed analysis of statistical data, so a brief explanation is given. Chiba is located on the east coast of Japan along the Pacific Ocean, just east of the Tokyo metropolitan area, where the peninsula juts out. Climatic conditions include a warm oceanic climate, which is a condition with high humidity and high precipitation in summer, and low humidity and low precipitation in the winter. However, comparing the southern and northeastern regions of Chiba,  
Figure 1: Study Area, Japan and its eight major districts.  
the climate is warm throughout the year in contrast to the inland area, which shows more diversity such as larger temperature drops in the winter [18]. Annual precipitation is highest in the southern area, with more than 2000 mm, and is next highest in the northern area, which has approximately 1400 mm to 1600 mm. The difference in precipitation clearly shows the distribution of the forests, which are more dense in the south and sparse in the north [18]. A characteristic of Chiba is that it is one of the flattest regions in Japan; the average land altitude is 46 m above sea level, and there are no mountains higher than 500 m in altitude.
# End-to-End Object Detection with Enhanced Positive Sample Filter
## 2 Related Work
### End-to-End Object Detector  
Many approaches are proposed to establish fully end-to-end detection pipelines. One stream of approaches formulate object detection as a set prediction problem, which are also called query-based approaches. DETR [13] firstly introduces transformer architecture to object detection and makes the decoder directly output sparse detection results without any post-processing. The input of the encoder is a set of learnable object queries, which model the relations between each object and global image context. Due to the dense information interaction manner, DETR [13] suffers from high computation complexity, slow convergence duration and relatively poor performance on small objects. Then, deformable DETR [30] is proposed to limit the attention field of each query to a small set of sampling locations around a reference point. Additionally, some other variants of DETR [31; 32; 33; 34] also make efforts to improve DETR.  
However, these transformer-based methods require large amounts of training data if deployed in practical applications, which involves large development and computation costs. Meanwhile, Sparse-RCNN [14] proposes a sparse set of proposal boxes to replace the dense candidates from RPN and makes extracted RoI features interact with associated proposal features in dynamic heads for a final prediction. Thus, the whole pipeline is in a pure sparse manner. Based on Sparse-RCNN, ref. [35] proposes a progressive prediction method to improve the performance in handling crowed scenes. Though it achieves a high performance, industrial applications still expect a simpler and more efficient detection pipeline. To this end, OneNet [11] and DeFCN [12] attempt to realize end-to-end detection in a one-stage framework without resorting to a self-attention mechanism or multiple-stage refinement. They utilize one-to-one label assignment strategies on one-stage detectors, where each GT instance is assigned only one positive training sample according to the results of classification and regression simultaneously. Since one-to-one assignment may inevitably introduce ambiguity and bring challenges to feature representation learning, DeFCN [12] proposes auxiliary loss and 3D max filtering to help training, which achieve better performances than OneNet [11].  
These methods make it possible to perform end-to-end detection in one-stage frameworks, but there is still room for improvement. To meet the high efficiency requirements of industrial deployment, we built an end-to-end detector in a one-stage anchor-free manner. We utilized the one-to-one label assignment strategy as in DeFCN [12] to preliminarily discard NMS. To further improve the detection performance, we proposed an Enhanced Positive Sample Filter (EPSF) to conduct an adaptive selection among candidate predictions of each GT instance.
## 2 Methodology
### The State-of-the-Art Methods
#### 2.2.2 Online Region-Based Active Contour Model (Oracm)  
The ORACM is introduced as representing the level set with region-based ACM. The models do not require any parameters. Compared to traditional ACMs, the ORACM requires less time without a change in the segmentation accuracy. In each iteration, the ORCAM performs block thresholding. Rigorous boundaries and small particles that do  
Figure 5: An example of global segmentation property of the C-V model. (**a**) shows the initial contour and (**b**) shows the segmentation result of the C-V method, which cannot extract all boundaries in the image.  
not belong to the object and are produced outside of this process. To tackle this problem, morphological operations such as closing and opening are applied. The level set function, \(\phi(x)\), is initialized to constants, which have different signs such as \(-1\) and \(+1\) inside and outside the contour. A simple and efficient level set updating formulation is used in ORACM as follows:  
\[\frac{\partial\phi}{\partial t}=H(\text{SPF}(I(x)))\cdot\phi(x), \tag{8}\]  
where \(H(.)\) is the Heaviside function, and \(SPF(.)\) is the signed pressure function defined as follows:  
\[\text{SPF}(I(x,y))=\frac{I(x,y)-\frac{c_{1}-c_{2}}{2}}{\text{max} \Big{(}\Big{|}I(x,y)-\frac{c_{1}-c_{2}}{2}\Big{|}\Big{)}}, \tag{9}\]  
where \(I\) and \(\phi\) show an input image and the current level set, respectively. Two parameters, \(c_{1}\) and \(c_{2}\), have the same definitions as (20) and (21), respectively. The advantages of ORACM are as follows:  
* Decreasing efficiency without changing the accuracy of the image segmentation process;
* Accurate segmentation of all object regions in both inside and outside for medical, real, and synthetic images with holes, complex backgrounds, weak edges, and high noise.  
The disadvantage of ORACM is that it supports only bimodal segmentation of piecewise constant intensity distribution. Therefore, the application of ORACM is limited to the cases satisfied the above constraints. It should be noted that two types of ORACM have been conducted by researchers called ORACM1 and ORACM2 which consider ORACM without or with the morphological operations, respectively.
# Detecting Reinforced Concrete Rebars Using Ground Penetrating Radars
## 3 Experimental program  
### Specim Design and Fabrication  
Four reinforced concrete (RC) slabs were created and tested to verify the efficiency of the in-house developed software. Three specimens, S1-1, S1-2, and S1-3, have the same dimensions of 914 \(\times\) 610 \(\times\) 89 mm (length \(\times\) width \(\times\) thickness), as depicted in Figure 1a. These slabs contain three different rebar diameters (#3, #5, and #7) situated at the same depth with an identical spacing of 203 mm. The fourth RC slab (specimen S2) measuring \(1143\times 356\times 178\) mm (width \(\times\) length \(\times\) thickness) (Figure 1b) was constructed to investigate the impact of overlap reflected signals from rebars with small spacings. Specim S2 includes six rebars with varying sizes, ranging from #3 to #8, embedded at the same depth and with an identical spacing of 127 mm. Ready-mix concrete from in-transit mixers was used to cast the RC slab specimens. The specimens were then cured and tested in laboratory conditions.
## 3 Results  
The improved GPP estimation model considered the effect of CO\({}_{2}\) fertilization resulting from differences in CO\({}_{2}\) within the canopy and leaves, which can effectively represent the spatio-temporal variation in GPP and realize a 93.32 g C/m\({}^{2}\)/year error reduction compared to the existing model (Figure 3a,b). The improved GPP estimation model performed better for both types of validation data (Figure 3c-f), exhibiting lower RMSE values at the global FLUXNET stations (CASA model: RMSE = 248.95 g C/m\({}^{2}\)/year; improved GPP estimation model: RMSE = 183.09 g C/m\({}^{2}\)/year) and the collected stations in the literature (CASA model: RMSE = 531.22 g C/m\({}^{2}\)/year; improved GPP estimation model: RMSE = 433.44 g C/m\({}^{2}\)/year). Therefore, the improved GPP estimation model indicated an increase in accuracy in vegetation productivity and carbon sequestration after considering the uptake of atmospheric CO\({}_{2}\) through stomata into leaf cells, which was closer to the observed data from the flux sites.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **V\({}_{\text{min}}\)** & **V\({}_{\text{max}}\)** & **Weight** \\ \hline TEM & \(-40\)\({}^{\circ}\)C & \(30\)\({}^{\circ}\)C & \(1\) \\ PRE & 0 mm & 1000 mm & \(1\) \\ SRA & 100 MJ/m\({}^{2}\) & 1000 MJ/m\({}^{2}\) & \(1\) \\ NDVI & \(-1\) & \(1\) & \(1\) \\ CO\({}_{2}\) & 340 ppm & 420 ppm & \(1\) \\
1 & 0 & 200 & \(1\) \\ LUCC & 1 & 17 & \(1\) \\ TEMopt & \(-15\)\({}^{\circ}\)C & \(40\)\({}^{\circ}\)C & \(1\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: List of the environmental factors for sensitivity analysis.  
Figure 3: Validations of GPP estimation from 2003 to 2015 using the CASA model and the improved GPP estimation model based on the reference GPP from FLUXNET and the literature. (**a,b**) show all the reference data, (**c,d**) show the FLUXNET site data, and (**e,f**) show the data collected from the literature.  
To assess the efficacy of the GPP dataset in the Qinghai-Tibet Plateau, MODIS GPP products were used for comparison. The comparison results indicated that the improved GPP estimation model accurately estimated inter-annual GPP at all sites (Figure 4). The GPP dataset has achieved better performances (R\({}^{2}\) = 0.68 and RMSE = 406 g C/m\({}^{2}\)/year) than MODIS GPP (R\({}^{2}\) = 0.49 and RMSE = 522.56 g C/m\({}^{2}\)/year). Therefore, the improved GPP estimation model indicated a considerable accuracy improvement compared to the GPP estimation algorithms for MODIS products.
# An Integrated DQN and RF Packet Routing Framework for the V2X Network
## 3 Design of the IDRF System
### Offline Phase
#### 3.1.3. Estimation of the Average Vehicle Speeds for Each Road Segment within Different Periods  
After rectifying and classifying the actual vehicle trajectories, we can obtain comprehensive vehicular information, including the time and coordinates at which the vehicle is at the road segment and intersection. Therefore, we can calculate the speed for each vehicle on every road segment. In terms of the calculation method, we partition one day into eight time periods [33], focusing on those with relatively higher traffic densities: 07:00-09:00, 12:00-13:00, 17:00-19:00, and 22:00-23:00. Next, utilizing historical trajectory data, we calculate the estimated average speeds for each road segment within different periods (Figure 12).