esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Agronomic Parameters  
Tomatoes conditions and fruits were monitored for 6 weeks. Climatic conditions, temperature, ETo and VPD were the usual ones in the area in spring, suitable for tomato cultivation (Figure 1). Temperature increased slightly around DOY 140 until values greater than 40 \({}^{\circ}\)C but, most of the experiment maximum were between 30-35 \({}^{\circ}\)C. Minimum temperatures were greater than 10 \({}^{\circ}\)C at the beginning and slightly increase during the experiment until 20 \({}^{\circ}\)C. ETo and VPD presented a similar pattern to temperature. Minimum values were measured at the beginning of the experiment, and progressive increase occurred from DOY 120.  
Table 1 presents data of applied water and water status from the first week of harvest. The amount of applied water in RDI treatment at the first week was approximately half of the control. Applied water in the control was increased according to ETo along the period of harvest until 614 mm. The increase of applied water in RDI was slower than control with periods of no irrigation. At the end of the harvest (6 th week) RDI was 47.2% of the applied water compared to the control. However, crop water status did not show significant differences in water potential measurements or stress integral. The only significantly different parameter was in the last week of crop monitorization. In the same way, the weekly harvest in grams per each plant was similar in both irrigation treatments because although there are some oscillations in the values, no statistically significant differences were observed. Maximum yield was measured at Week 4 in both treatments. Cumulative yield was also not significantly different between treatments (3014.37 control vs. 2709.25 RDI g plant\({}^{-1}\)) with a slight trend of reduction in RDI around 10%. These results are similar to those shown in previous works [21] but with lower levels of stress, which have resulted in 10.1% less but not significant cumulative production in the 6 weeks of experiment (3014.37 control vs. 2709.25 RDI g plant\({}^{-1}\)). Water stress level in RDI treatment was more positive than \(-0.91\) MPa of minimum midday water potential, which was likely an adequate threshold during harvest. [PERSON] et al. [22] reported a great decrease of yield with water stress level more negative than \(-1\) MPa over the whole season.
# Quality-Aware Resource Model Discovery
## 4 Implementation
### Architecture  
Our approach has been implemented as a Python application with an easy-to-use graphical user interface. Figure 5 depicts the architecture of the implemented tool, and it is composed mainly of three layers: _Preparation_, _Analysis_, and _Visualization_ and _Result_. Each layer has several modules which perform specific roles.  
The preparation layer has a role in preparing the data suitable for applying the proposed method. This layer is composed of the _Import_ module, which loads event logs  
Figure 4: An Example of the simplified models with different conditions.  
Figure 5: The architecture of the implemented tool.  
into the implemented tool and the _Preprocessing_ module that performs preprocessing methods such as data error and imperfect data handling.  
The analysis layer is the main component in our architecture that performs our approach, including discovering transition systems, the superior and inferior case identification, and model simplification. The _Model Mining_ module produces a transition system from the preprocessed event log with the selected state and event representation functions. The _SCs/ICs Identification_ module classifies cases into the superior, inferior, and moderate groups with user's involvement. The _Statistical Analysis_ module determines what resources are included in the superior, inferior, and moderate class based on the correlation with the quality, respectively. Lastly, the _Model Simplification_ module streamlines the model for clear visualization.  
The visualization and result layer presents the final outcomes to users. The _Visualization_ module creates the interactive viewer, composed of filtering and highlighting capabilities, and envisions the outcomes to be delivered. The _Export_ module helps to save the network for later usage.  
Our tool provides a graphical user interface, i.e., GUI, to achieve user-friendliness. Users deliver the required parameters through GUI, which are defined in our methods, including preprocessing options, state and event representation functions, statistical parameters, and simplification options. In addition, in the interactive visualization panel, filtering and highlighting methods are provided with GUI to provide more precise visualization, allowing users to fine-tune the complexity of the resulting model. Figure 6 presents the example of the visualization panel of the implemented tool. The derived result is shown at the top of the panel, and filtering and highlighting options are placed at the bottom. Based on these options, the model can be simplified and effectively visualized. The details for the visualization of the model are provided in Section 4.2.
## 3 Experiment Results and Discussion
### Flow Field Simulation and Analysis by CFD Method
The relationship between the output power and the average lift of the FWR is shown in Figure 17b where the equivalent output power of the motor defined as below,  
\[P_{eq}=\left(0.5\cdot T_{fu}\cdot P_{fu}+0.5\cdot T_{fd}\cdot P_{fd}\right) \left/\left(0.5\cdot T_{fu}+0.5\cdot T_{fd}\right)\right. \tag{6}\]  
Figure 16: Variation of the pressure and vorticity in a flapping cycle in case 4.  
where \(T_{fu}=1/f_{u}\) and \(T_{fd}=1/f_{d}\) means the corresponding flapping period of the flapping frequency \(f_{u}\) and \(f_{d}\); \(P_{fd}\) and \(P_{fu}\) stands for the power output of the motor with rotation speed \(60\cdot f_{u}\) and \(60\cdot f_{d}\) which can be found from Figure 17a. According to Figure 17b, it shows that in the same power, the average lift force produced by the FWR in the VFF cases is greater than that in the CFF cases. Moreover, larger lift difference between the CFF and VFF cases with larger \(\left|f_{d}-f_{u}\right|\) is found, and in particular, the lift more than doubled in the VFF case 4 (3 & 1 Hz) compared with that in the CFF cases (1.3 Hz) of the same power output. From the above results, it is found that the motor provides adequate torque for the FWR to obtain VFF for the investigation although the flapping frequency (3 Hz) is relatively low due to the limited motor power. Consequently, the maximum lift produced by the FWR is only 43% of the FWR model weight (50 g), and even smaller than the motor mass 28 g. Obviously, it is impractical for this model to achieve hovering flight unless the model weight can be reduced significantly. Although it is feasible to reduce the component weight, understanding how to improve the power to weight ratio (efficiency) of the ultrasonic motor is beyond the scope of the current study.
## 1 Introduction
In contrast, [PERSON] et al. [40], [PERSON] et al. [41] and [PERSON] et al. [42], molded the specimens with modified Proctor (resulting in lower specimen compaction) and tested them using a triaxial configuration. In general, the use of RAP as a base layer material is a sustainable rehabilitation technique that reduces costs. However, accurately characterizing its behavior is essential for ensuring its effectiveness. The performance of RAP depends on several factors, including the stress within the pavement layer structure [43; 41] and the intrinsic properties of RAP, such as binder content, specific gravity, and parent rock [44]. These factors significantly affect the mixture's performance in terms of resilient modulus (RM) and permanent deformation (PD) test results, which in turn impacts the overall accuracy of pavement response predictions. Additionally, variability in mechanical properties can arise from factors such as particle size distribution, type of RAP, compaction method, type and content of active filler, and bitumen stabilizing agent [2; 45; 46; 47; 48; 39]. Furthermore, [PERSON] et al. [39] observed that curing time, curing temperature and compaction in CRAMs with emulsified asphalt recycling agent do not follow a standardized protocol; therefore, it is difficult to carry out a more in-depth comparative analysis. This lack of standardization reinforces the need to understand the behaviour of these mixtures. In this context, [PERSON] et al. [23] concluded that the resilient modulus and PD based on repeated load triaxial tests can be considered predictive methods for the behavior of milled material in base layers. These tests provided initial data for interpreting the behavior of emulsified treated base, offering a potential solution to the challenges highlighted by [PERSON] et al. [39]. In addition, [PERSON] et al. [49] also highlighted the importance of using repeated load triaxial tests to evaluate the mechanical behavior of emulsion-stabilized soil in real-world scenarios. Despite the potential of CRAMs, there is still a significant gap in understanding how different curing times affect their mechanical behavior in repeated load triaxial tests. Mostexisting research still focuses on analysis in specific laboratory conditions and through specific tests of asphalt mixtures, which may not adequately reflect performance in real traffic conditions. Therefore, it is crucial to investigate the influence of curing times on the mechanical properties of CRAMs, especially with regard to resilience modulus and permanent deformation in repeated load triaxial tests, in order to guarantee adequate and long-lasting performance in paving base layers. Therefore, the aim of this study is to evaluate the influence of different curing times on the behavior of CRAMs through resilient modulus (RM) and permanent deformation (PD) tests, in both repeated load triaxial tests, for application base layer.
## 3 Silicon Germanium Waveguides and Nonlinear Applications
### Linear and Nonlinear Optical Characterization
#### 3.1.3 Two-Photon Absorption  
A TPA coefficient measurement for a waveguide of width of 2 \(\upmu\)m and varying Ge concentration was carried out using the inverse transmission method [25], as shown for the first time in [55]. Figure 8 shows the inverse transmission (ratio of input to output power) as a function of the input peak power for the three different Ge concentrations and for a waveguide width of 2 \(\upmu\)m. A clear increase in TPA is observed when the Ge concentration increases. By adopting a linear fit to these measurements [6], the TPA coefficient values were found to be 1.16 cm/GW, 1.24 cm/GW and 1.5 cm/GW for an increasing Ge concentration from 10% to 30%. A comparative theoretical and experimental discussion of TPA properties in SiGe waveguides is presented in detail in [55]. By combining the obtained values for both the Re\(\{\gamma\}\) (Section 3.1.2) and Im\(\{\gamma\}\) (this section) it is possible to calculate the FOM (as defined in Equation (4)) for the three different Ge concentrations, obtaining 0.53, 0.43 and 0.58 for Ge concentration of 10%, 20% and 30%, respectively. Even though they are not as high as in the case of amorphous silicon, these values are slightly higher than what reported for crystalline silicon waveguides [3].  
Figure 8: A plot of the inverse transmission vs. coupled input peak power measurement for a waveguide of 2 \(\upmu\)m width and varying Ge concentrations [55].  
Figure 7: A comparison of experimental and numerical results of the nonlinear coefficient measurement as a function of the waveguide width for varying Ge concentration [55].  
Re[\(\gamma\)], Im{\(\gamma\)} and FOM values are reported in the following Table 4 for three selected SiGe waveguide configurations [55].
# Vulnerability Detection and Classification of Ethereum Smart Contracts Using Deep Learning
## 1 Introduction
As part of our contribution, we hope to emphasize that instruction-based tools now in use for detecting and addressing attacks, vulnerabilities and threats targeted at smart contracts are not as efficient and capable of detecting unknown and more sophisticated attacks and suffer from lack of flexibility and long detection time. In addition, we highlight the best algorithm for binary and multi-label classification to detect and classify efficient vulnerabilities in smart contracts, by categorizing a dataset that contains 12,253 smart contracts. The main contributions of this study to the field of smart contract vulnerability detection and classification using deep learning include the following:  
1. Novelty in Binary Classification: to the best of the author's knowledge, this study is the first known research to explore binary classification for Ethereum smart contract vulnerabilities using deep learning algorithms. 2. Effectiveness in Classification Approach: to the best of the author' knowledge, and according to experimental results, this study outperforms the currently available literature on multi-label classification for Ethereum smart contract vulnerabilities using deep learning algorithms. 3. Real-world Dataset and Algorithm Evaluation: this work utilizes a novel, real-world smart contract dataset for classification using five deep learning algorithms (ResNet50, VGG19, DenseNet201, KNN, and RF). This allows for a comprehensive evaluation of their effectiveness based on real-world data 4. Innovative Data Representation: this research introduces a unique approach of converting smart contracts' bytecode into RGB and grayscale images. Then, the effectiveness of this image conversion process in achieving optimal accuracy and F1-score for vulnerability detection is verified. This paper is organized as follows. In Section 2, the relevant previous literature is listed, and the requirements of this research, including the concepts and resources used, are stated. Section 3 describes the proposed models for binary and multi-label classification and the explanation and representation of the dataset used for images and features. Section 4 highlights the presentation and discussion of the results and the proposed models. Finally, concluding remarks and recommendations for future work are emphasized in Section 5.
## 1 Introduction
### Motivation  
Although many meaningful works have paid attention to the EH-WSNs routing protocol design [10; 11; 12; 13; 14; 15; 16], the energy efficiency problem is not resolved well. In detail, most of the existing studies do not fully consider the energy harvesting rate and the impact of channel features on energy consumption, as well as the energy wastage caused by buffer constraint. It should be also noted that if the status of the data buffer fails to be considered in the protocol design, the congestion, transmission time prolonging and energy-wasting issues would easier to be caused [17]. When the node has insufficient residual energy (enters the sleep mode) and the buffer is not empty (there are still packets in the buffer), it inevitably causes the transmission time of this part of packets to be prolonged. As a result, it is necessary to design a brand new routing protocol that comprehensively takes the energy state and buffer occupancy into account.  
On the engineering application aspect, EH technique has been used to ease the issues of energy constraint and improve the energy balance in traditional WSN. Currently, EH-WSN has achieved great commercial success in many fields. Numerous EH-WSN based IoT devices are widely used in our lives, such as Smart Home, 5G/B5G and 6G communication, Wise information technology of med (WITMED), intelligent wearable devices, etc., which contribute to the development of society and lead our daily affairs to be convenient and intelligent. In addition, since the nodes of EH-WSN can extract energy from the surroundings, environmental pollution caused by battery replacement and network maintenance costs are correspondingly reduced. This is in line with the internationally promoted rules of 3R (reducing, reusing and recycling). Especially in our country, China, the widespread use of this technology is meaningful for achieving the goal of Carbon Neutrality [18] in the future. Hence, a thorough study of the EH-WSN routing algorithm design has practical value for constructing IoT systems.
###### Abstract  
Real-time hybrid method is an economical and efficient test method to evaluate the dynamic behavior. The purpose of this study is to develop the computational algorithm and to prove the reliability of a real-time hybrid control system. For performing the multi-direction dynamic test, three dynamic actuators and the optimized real-time hybrid system with new hybrid simulation program (FEAPH) and a simplified inter-communication were optimized. To verify the reliability and applicability of the real-time hybrid control system, 3-DOF (3 Degrees of Freedom) non-linear dynamic tests with physical model were conducted on a steel and concrete frame structure. As a ground acceleration, El Centro and Northridge earthquake waves were applied. As a result, the maximum error of numerical analysis is 13% compared with the result of shaking table test. However, the result of real-time hybrid test shows good agreement with the shaking table test. The real-time hybrid test using FEAPH can make good progress on the total testing time and errors. Therefore, this test method using FEAPH can be effectively and cheaply used to evaluate the dynamic performance of the full-scale structure, instead of shaking table and full-scale test.  
Keywords:real-time; hybrid control system; dynamic test; three-degree of freedom +
Footnote †: journal: Applied Sciences
# Constrained Dynamic Mean-Variance Portfolio Selection in Continuous-Time
## Appendix A
### The Proof of Theorem 3  
**Proof.** Combining Theorem 2 and Lemma 2, we could easily obtain the optimal solution for the HJB Equation (14) as follows:  
\[u^{*}(t) =\frac{V_{x}(t,x)}{V_{xx}(t,x)}\hat{L}(t)\mathbf{1}_{\frac{V_{x}(t,x )}{V_{xx}(t,x)}\geq 0}+\Big{|}\frac{V_{x}(t,x)}{V_{xx}(t,x)}\Big{|}L(t)\mathbf{1}_{ \frac{V_{x}(t,x)}{V_{xx}(t,x)}<0}\] \[=\frac{V_{x}(t,x)}{V_{xx}(t,x)}\hat{L}(t)\mathbf{1}_{x(t)-\lambda \rho(t)\geq 0}+\Big{|}\frac{V_{x}(t,x)}{V_{xx}(t,x)}\Big{|}\hat{L}(t) \mathbf{1}_{x(t)-\lambda\rho(t)<0}, \tag{16}\]  
where \(V_{xx}(t,x)\
eq 0\). Indeed, if we let \(V_{xx}(t,x)=0\), it has \(V_{x}(t,x)=constant\). However, this result conflicts with Lemma 2, since \(V(t,x)\) is strictly decreasing on \((-\infty,\lambda\rho(t)]\) and strictly increasing on \((\lambda\rho(t),+\infty)\), for each fixed \(t\in[0,T]\).  
Recall that if there are no strategy constraints, the optimal solution for the HJB Equation (14) is  
\[u^{*}(t)=-[\sigma(t)\sigma(t)^{\prime}]^{-1}b(t)\frac{V_{x}(t,x)}{V_{xx}(t,x)}.\]  
Thus, if we define  
\[B(t)= \sigma(t)\sigma(t)^{\prime}\hat{L}(t)\mathbf{1}_{\frac{V_{x}(t,x) }{V_{xx}(t,x)}\geq 0}+[-\sigma(t)\sigma(t)^{\prime}\hat{L}(t)]\mathbf{1}_{ \frac{V_{x}(t,x)}{V_{xx}(t,x)}\leq 0}\] \[= \sigma(t)\sigma(t)^{\prime}\hat{L}(t)\mathbf{1}_{x(t)-\lambda \rho(t)\geq 0}+[-\sigma(t)\sigma(t)^{\prime}\hat{L}(t)]\mathbf{1}_{x(t)-\lambda \rho(t)<0},\]  
it is not hard to see that solving the HJB Equation (14) is equivalent to solve the following auxiliary HJB equation without strategy constraints  
\[\begin{cases}V_{t}+\inf_{u(t)}\{\frac{1}{2}V_{xx}u(t)^{\prime} \sigma(t)\sigma(t)^{\prime}u(t)+V_{x}[r(t)x+u(t)^{\prime}B(t)]\}=0\\ V(T,x)=(x-\lambda)^{2}\end{cases} \tag{17}\]  
More specifically, the solution of (17) is equal to the one of (14). In fact, the solution to the HJB Equation (17) is also the value function associated with the following problem:  
\[(\tilde{\mathcal{A}}_{m\
u}(\lambda)) \min_{\{u(t)\}\stackrel{{ T-1}}{{\underset{t=0}{ }{\rightarrow}}}}\mathrm{E}[x(T)-\lambda]^{2}\] \[(s.t.) dx(t)=(r(t)x(t)+B(t)^{\prime}\pi(t))dt+u(t)^{\prime}\sigma(t)dW(t).\]  
This problem has been widely studied in the literature and can be solved by the stochastic optimal control or martingale method (see, [3; 5]). The optimal portfolio policy of problem (\(\tilde{\mathcal{A}}_{m\
u}(\lambda)\)) is  
\[u_{B}^{*}(t)=-[\sigma(t)\sigma(t)^{\prime}]^{-1}B(t)^{\prime}[x(t)-\lambda\rho (t)].\]  
Therefore, the optimal control of mean-variance problem with cone constraints is (15).
## 2 Results and Discussion
### Identification of Genes Involved in Biosynthesis of Other Terpenoids  
Besides genes related to iridoid biosynthesis, the 454 sequence assembly also contains a number of genes encoding enzymes involved in the formation of other terpenoids, among them, the transcripts involved in biosynthesis of triterpenoids, triterpenoids and their derivatives, such as farnesyl diphosphate synthase (FPPS) and geranylgeranyl diphosphate synthase (GGPPS), are well represented (Supplementary File 1). In plants, triterpenoids form the basis for biologically important compounds such as retinal, phytol, carotenoids and gibberellins, which display important functional roles; triterpenoids are precursors to saponins and steroids, the latter are components of membranes in most organisms. Among these genes, the genes that encode enzymes phytoene synthase (PSY), geranylgeranyl reductase (GGR) and squalene synthase (SQS) are located at the major branch points of the central terpenoid pathway and occupy a particular position in control of product distributions. PSY is a key enzyme of the carotenoid biosynthetic pathway that has also been shown to be involved in ABA formation under abiotic stresses [36]. GGR reduces free geranylgeranyl diphosphate (GGPP) to phytil diphosphate, which provides the side chain to chlorophyls, tocopherols, and plastoquinones [37, 38]. SQS, which catalyzes a reductive dimerization of two farnesyl diphosphate (FPP) molecules into squalene, is a key enzyme capable of diverting carbon flow specifically to the biosynthesis of sterols [39]. Four unigenes assembled from 162 reads were annotated as SQS, indicating that they are highly expressed in the tuberous root (Supplementary File 1). Apart from SQS, a relatively large amount of enzyme sequences representing five enzyme families were mapped to the steroid biosynthetic pathway according to the standard KEGG pathway, which implied that a large portion of triterpenoids are channeled towards the steroid pathway. The sequences and annotation information explored in this study will also be a good resource for understanding the biosynthesis of other terpenoids in _R. glutinosa_.
# Requirements for Crafting Virtual Network Packet Captures
## 4 Use Cases
### Testing  
The network is a fundamental and crucial element in modern environments. The high-speed connection of involved systems as well as the correct transport of the information is one of the most relevant tasks. Bringing new devices into the network has to be tested in order to minimise the risk of unwanted results. Such devices, hardware-based as well as virtual, change the internal packet flow, which results in a different behaviour. So, testing a new device in a real-world scenario is necessary in modern infrastructures, but most of the environments do not provide such an opportunity. Creating network packets with real-world information in real time supports the testing and implementation of new devices. The generated packets, either by a tool such as _Encapcap_ or by a different tool that is able to provide virtual network packet captures, can be replayed or sent in real time into a given network device, and the resulting behaviour can be analysed in detail in a simulated network environment to ensure the intended processing. A slow or dissimilar packet flow might not produce the same behaviour as high-speed packet transfers with valid network data, so the precision and performance as well as the awareness are crucial requirements in this use case. We evaluated this process by replaying a generated packet capture into a  
Figure 2: Example of incompatibility to RFC.  
firewall implementation to analyze the forwarding process of the device. By this, we were able to detect minor misconfigurations in the complex ruleset of the firewall. Without such a testing process, these errors would have persisted into production mode. As a result, this testing opportunity is similar to gray box testing [61].