esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Cooking Methods on Bioactive Pigments in Purple Carrots (_Daucus carota_ L.)
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Peak**} & **Fresh** & **Freeze-Drying** & **Boiling** & **Steaming** & **Roasting** & **Roasting** & **Roasting** \\  & **(mg\(\cdot\)g\({}^{-1}\) DW)** & **(mg\(\cdot\)g\({}^{-1}\) DW)** & **(mg\(\cdot\)g\({}^{-1}\) DW)** & **(mg\(\cdot\)g\({}^{-1}\) DW)** & **(mg\(\cdot\)g\({}^{-1}\) DW)** & **(mg\(\cdot\)g\({}^{-1}\) DW)** & **(mg\(\cdot\)g\({}^{-1}\) DW)** \\ \hline
1 & 0.22 \(\pm\) 0.09 & 83.06 \(\pm\) 0.03 & 0.89 \(\pm\) 0.02 & 3.35 \(\pm\) 0.01 & 0.67 \(\pm\) 0.01 & 8.62 \(\pm\) 0.21 & 16.93 \(\pm\) 0.11 \\ \hline
2 & 0.66 \(\pm\) 0.11 & 51.60 \(\pm\) 0.20 & 0.16 \(\pm\) 0.04 & 1.06 \(\pm\) 0.16 & 0.36 \(\pm\) 0.01 & 2.45 \(\pm\) 0.47 & 3.95 \(\pm\) 0.19 \\ \hline
3 & 0.96 \(\pm\) 0.20 & 380.39 \(\pm\) 0.03 & 0.78 \(\pm\) 0.06 & 7.03 \(\pm\) 0.25 & 2.40 \(\pm\) 0.01 & 26.36 \(\pm\) 0.14 & 48.13 \(\pm\) 0.03 \\ \hline
4 & 9.76 \(\pm\) 0.13 & 1017.23 \(\pm\) 0.01 & 3.26 \(\pm\) 0.14 & 20.32 \(\pm\) 0.05 & 10.19 \(\pm\) 0.01 & 52.23 \(\pm\) 0.01 & 115.66 \(\pm\) 0.18 \\ \hline
5 & 0.96 \(\pm\) 0.03 & 405.11 \(\pm\) 0.10 & 0.64 \(\pm\) 0.18 & 3.63 \(\pm\) 0.21 & 1.89 \(\pm\) 0.01 & 14.09 \(\pm\) 0.35 & 13.55 \(\pm\) 0.07 \\ \hline Total & 12.56 & 1937.39 & 5.72 & 35.40 & 15.51 & 103.75 & 198.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Effect of the cooking on anthocyanins identified and quantified by HPLC-DAD.
## 4 Experimental Setup
### Parameter Assignment  
In our experiment, a parameter is needed to be specified, i.e., the quantity of groups \(k\) in the step of feature clustering analysis. As suggested by prior research [28; 29], retaining 15% of the original features made the model achieve the same or even better prediction performance than the model using original features for the SE task; therefore, in this work, we set \(k\) as 89 \(\times\) 15% = 14.
# Automatic Refactoring Approach for Asynchronous Mechanisms with CompletableFuture
## 4 Empirical Research
### Method Framework  
The framework diagram for the automated refactoring method aimed at asynchronous mechanisms is shown in Figure 4. In the automated refactoring process, the first step is to transform the source program into an AST (Abstract Syntax Tree) and Jimple IR (a three-address intermediate representation provided by the static analysis tool Soot). We use the visitor pattern to traverse the AST to obtain specific syntax nodes, which we then map to the corresponding Jimple IR model. Concurrently, we analyze the executor inheritance structure. Next, we perform program analysis on the Jimple IR to check preconditions, starting with verifying if the Future variable's type matches the Future interface type. After obtaining the results of the executor inheritance structure analysis (executor types in the program that meet the refactoring conditions), it then determines whether the executor object type in the current traditional asynchronous construction statement satisfies the refactoring conditions through executor type analysis. Finally, the refactoring checks if the future object calls the cancel(true) method and if there are any type checks and operations on the future object in the program. Traditional asynchronous constructs that meet the precondition conditions are refactored by applying a refactoring template to modify the AST and complete the refactoring.
# Unique Features of Nonlocally Nonlinear Systems with Oscillatory Responses
## 5 Perturbation-Iteration Method  
In the nonlocally nonlinear system with oscillatory responses governed by Equation (7), the Hermite-Gauss-like solitons with arbitrary peak numbers are hardly obtained by the usual numerical algorithms such as the Newton-conjugate-gradient method. Therefore, we developed a special numerical method by which we called the perturbation-iteration method to solve this problem [38]. The key idea behind the perturbation-iteration method is to treat the NNLSE as a perturbed model of the harmonic oscillator, and soliton solutions can be obtained by perturbation theory in quantum mechanics [46]. The procedure for the perturbation-iteration method is briefly reviewed. Substituting the following soliton solutions:  
\[u_{n}(x,z)=\sqrt{A_{n}}\psi_{n}(x)\exp(-i\beta_{n}z)\ (n=0,1,2,3\ldots) \tag{29}\]  
into the NNLSE (7) yields the following:  
\[\left[-\frac{1}{2}\frac{d^{2}}{dx^{2}}-A_{n}\int_{-\infty}^{\infty}R(x-\xi) \psi_{n}^{2}(\xi)d\xi\right]\psi_{n}(x)=\beta_{n}\psi_{n}(x), \tag{30}\]  
where \(A_{n}\) is a coefficient related to the power of soliton, \(\psi_{n}(x)\) and \(\beta_{n}\) are, respectively, the transverse distribution and propagation constant of soliton and \(n\) is the order of soliton. We can treat Equation (30) as a perturbed model of the harmonic oscillator:  
\[\left[-\frac{1}{2}\frac{d^{2}}{dx^{2}}+\frac{1}{2\mu^{4}}x^{2}+f_{n}(x)\right] \psi_{n}(x)=E_{n}\psi_{n}(x), \tag{31}\]  
where \(\psi_{n}(x)\) and \(E_{n}\) are, respectively, eigenfunctions and eigenvalues, \(f_{n}(x)\) is the perturbation compared to the potential of the harmonic oscillator. Starting from the perturbed  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \(n=0\) & \(n=1\) & \(n=2\) & \(n=3\) & \(n=4\) & \(n\geq 5\) \\ \hline \(s=-1\) & \((1.05,+\infty)\) & \((1.41,+\infty)\) & \((1.10,+\infty)\) & \((1.10,+\infty)\) & \((1.10,1.61)\) & no \\ \(s=1\) & \((0.05,0.78)\) & \((0.38,0.78)\) & \((0.42,0.45)\) & \((0.48,0.49)\) & no & no \\ \hline \hline \end{tabular}
\end{table}
Table 2: The ranges of \(\sigma_{0}\) within which the HG–type solitons are stable.  
Figure 14: Dependencies of the power \(P_{c}\) on the degree of nonlocality \(\sigma_{0}\) (**a**,**c**) and the propagation constant \(b\) on the degree of nonlocality \(\sigma_{0}\) (**b**,**d**) for the multi–peak solitons. (**a**,**b**) and (**c**,**d**) for \(s=-1\) and \(s=1\), respectively.)model of the harmonic oscillator, we determine the \"minimum\" perturbation, then use the formal expression of infinite-order perturbation expansions to numerically calculate the eigenfunctions and eigenvalues of the perturbed model and iterate this perturbation to obtain multipeak solitons with enough high accuracy.  
This form of perturbation-iteration method developed for the \((1+1)\)-dimensional NNLSE also has been extended to the \((1+2)\)-case [47]. In fact, the method we developed might also be extended to the numerical integration of the Schrodinger equations in any type of potentials.
# Role of Forster Resonance Energy Transfer in Graphene-Based Nanomaterials for Sensing
###### Abstract  
Forster resonance energy transfer (FRET)-based fluorescence sensing of various target analytes has been of growing interest in the environmental, bioimaging, and diagnosis fields. Graphene-based zero- (OD) to two-dimensional (2D) nanomaterials, such as graphene quantum dots (GQDs), graphene oxide (GO), reduced graphene oxide (rGO), and graphdiyne (GD), can potentially be employed as donors/acceptors in FRET-based sensing approaches because of their unique electronic and photoluminescence properties. In this review, we discuss the basics of FRET, as well as the role of graphene-based nanomaterials (GQDs, GO, rGO, and GD) for sensing various analytes, including cations, amino acids, explosives, pesticides, biomolecules, bacteria, and viruses. In addition, the graphene-based nanomaterial sensing strategy could be applied in environmental sample analyses, and the reason for the lower detection ranges (micro- to pico-molar concentration) could also be explained in detail. Challenges and future directions for designing nanomaterials with a new sensing approach and better sensing performance will also be highlighted.  
graphene; quantum dots; graphene oxide; graphdiyne; FRET; fluorescence; sensing +
Footnote †: journal: Applied Physics Letters B
# A Service-Efficient Proxy Mobile IPv6 Extension for IoT Domain
## 4 Implementation and Evaluation Measures  
For assessment and comparison of the proposed PMIP6 protocol extension with the existing RSS-based PMIPv6 extensions, mathematical equations are derived for comparing the efficiency. For measuring the potential integration of the proposed scheme in IoT, equations are implemented by developing an application. The main purpose of the application is to provide an easy means for the user to provide input parameters and generate output results and assess the performance for fulfilling the requirements of 6G adopted IoT. Users can easily set different parameters and their values such as RSS checking, the velocity of nodes, etc., along with the customization of output file generation. The details of the parameters, their default values, and their description are shown in Table 1. The application measures handover latency, signaling required, and the required buffering and stores these results in an Excel file. Excel can be used for meaningful interpretations for incorporating more features and functionality, and the application will be updated and refined in future installments.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameters Taken** & **Symbol** & **Default Values** \\ \hline The radius of the cell & R & 100 m \\ Number of MNs & \(\delta\) & 20 \\ Cost of HI message & C\({}_{\text{HI}}\) & 52 bytes \\ Cell crossing rate & \(\mu_{c}\) & Equation (2) \\ The delay occurs from L2 down to L2 up & \(t_{L2}\) & 0.02 s \\ Hopes between two MAGs & H\({}_{\text{MAGs}}\) & 04 \\ Cost of L2 report & C\({}_{\text{L2 report}}\) & 40 bytes \\ The velocity of the nodes & v & [5\(\ldots\), 50] \\ No of hope between MAG and LMA & H\({}_{\text{LMA\_MAG}}\) & 06 \\ Cost of IHR message & C\({}_{\text{IHR}}\) & 52 bytes \\ Measuring time & T & 30 s \\ HI message transmission time & t\({}_{\text{HI}}\) & 0.03 s \\ Number of MAGs around a MAG & N & 20 \\ Cost of HACK message & C\({}_{\text{HACK}}\) & 52 bytes \\ PBU message transmission time & t\({}_{\text{PBA}}\) & 0.06 s \\ Delay before sending RS message & t\({}_{\text{w-data}}\) & 0.001 s \\ Delay before sending RS message & RWS & 0.01 s \\ Time for scanning nMAG & t\({}_{\text{scan}}\) & 0.02 s \\ HI message transmission time & t\({}_{\text{HACK}}\) & 0.03 s \\ Cost of sending MAG’s location information & C\({}_{\text{loc\_MAG}}\) & 40 bytes \\ FReq message transmission time & t\({}_{\text{FReq}}\) & 0.03 s \\ Session length & \(\Delta\)s-E(s) & 20 bytes \\ PBA message transmission time & t\({}_{\text{PEU}}\) & 0.06 s \\ Session rate & \(\Delta\)s-E(s) & 0.01/s \\ Delay for L2 down since MAG or MN detects that RSS is below the threshold & \(\varnothing\) & 0.02 \\ The probability of RSS error & P\({}_{\text{e}}\) & Rate of RSS error \\ FRep message transmission time & t\({}_{\text{FRep}}\) & 0.03 \\ RS transmission time & t\({}_{\text{RS}}\) & 0.01 s \\ \hline \end{tabular}
\end{table}
Table 1: Parameters and variables used for analysis.
# Fog-Based Two-Phase Event Monitoring and Data Gathering in Vehicular Sensor Networks
## 5 Experimental Study
### Impact of Factors
#### 5.4.4 Threshold Adjustment  
TPEG adopts the 2 LTA strategy to adaptively adjust the thresholds at the node and the RSU levels to suppress sensing and data upload operations. According to Equation (13), the threshold \(t_{0}\) is dynamically set according to \(m\), which is the expected number of readings received by the RSU for the event-checking decision. Threshold \(\tau_{1}\) would increase with the incremental factor \(\Delta\) according to Equation (14) and would decrease with the time of epochs passed according to Equation (15). \(\tau_{1}\) is initially set to 0.4, \(m\) to 3 and \(TU\) to 10,000 through parameter tuning. Figure 13 shows the change of \(\tau_{1}\) with time, and Table 3 shows the impact of \(\Delta\) for TPEG. When \(\Delta\) is larger, e.g., 0.1, \(\tau_{1}\) goes up with time more quickly and obviously. When \(\Delta\) goes up from 0.03-0.1, the recall goes down from 0.9554 to 0.8929, the message transmission decreases from \(618.77\times 10^{4}\) to \(532.22\times 10^{4}\) and the average detection time goes up from 24.73 min to 37.55 min. This is because larger \(\Delta\) leads to larger \(\tau_{1}\), which leads to a lesser number of event-checking procedures, which in turn leads to a smaller amount of data transmissions and some misses and delay of event detections. When \(\Delta\) is in the medial, e.g., 0.05, \(\tau_{1}\) sways accordingly to time. Its recall, transmissions and detection time are 0.94, \(578.38\times 10^{4}\) and 25.65, respectively, which are in the medial range of all the results.  
Figure 12: Impact of sensing interval on the average detection time.  
Figure 13: Change of threshold \(\tau_{1}\) with time.
# A Novel Cooperative Opportunistic Routing Scheme for Underwater Sensor Networks
## 3 Opportunistic Void-Avoidance Routing Protocol
### Routing Algorithm
#### 3.5.2 Packet Advancement  
To specify the priority of relaying nodes, we define a fitness factor, \(\alpha\), which represents the depth difference between sender's depth, \(D_{s}\), and receiver's depth, \(D_{r}\), in a normalised value as follows:  
\[\alpha=\frac{D_{s}-D_{r}}{R}\quad(-1\leq\alpha\leq 1) \tag{7}\]where \(R\) is the transmission range of sensor nodes. According to the fitness factor, a relay node with a lower depth has higher priority to relay the packets as it is closer to the surface where the sink is located. The negative value of the fitness factor indicates that the receiver node is located below the sender, perhaps due to the presence of a void area in the routing path. In contrast to the majority of greedy routing protocols, OVAR gives these kinds of nodes (with a higher depth than the sender and maybe having a higher geographical distance to the sink) the chance to participate in the packet forwarding to bypass the void areas. However, these nodes still can be prioritised based on the lower depth due to the fact that the packet most likely should be relayed upward over the next step to become closer to the final destination on the surface. In order to use this value in our calculations, we further normalise this value to be placed in the range [0,1] as follows:  
\[\beta=\frac{1}{2}(\alpha+1)\quad(0\leq\beta\leq 1) \tag{8}\]  
Now, we can explain three phases of our routing algorithm: forming the adjacency graph and selecting the best forwarding set, adjusting the number of forwarding nodes in the forwarding set and, finally, holding time calculation. Algorithm 1 details the two phases of OVAR: forwarding set selection and adjusting the number of forwarding nodes.  
```
1:procedureForwardPacket(\(R_{i}\), P)
2:if forwarding timer expiredthen
3:\(F(R_{i})=\emptyset\)
4:\(L(R_{i})=\{n_{k}|1\leq k\leq c\}\)
5:\(G(L(R_{i}))=NeighGraph\big{(}L(R_{i}),Table(R_{i})\big{)}\)
6:\(F(R_{i})=ForwardingSetSelect\big{(}G(L(R_{i}),\Phi^{*})\)
7:for\(j=1\) to \(r\)do
8: Calculate \(\mathit{EEPA}(F,j)\)
9:\(j_{max}=\underset{j}{\arg\max}\)\(\mathit{EEPA}(F,j)\)
10:for all\(j>j_{max}\)do
11:\(F(R_{i})=F(R_{i})-n_{j}\)
12:\(P.ForwardingList\gets F(R_{i})\)
13: Forward P
14:else
15: Drop P
16:endif
17:endprocedure
```  
**Algorithm 1** OVAR routing algorithm.
# An Evaluation of Machine Learning Models for Forecasting Short-Term U.S. Treasury Yields
## 2 Literature Review
### Machine Learning Approaches  
Recent advances in ML have enabled researchers to explore more flexible, data-driven methods for financial forecasting. Decision trees and ensemble methods like random forests (RFs) have been successfully applied to bond and stock market prediction due to their ability to model complex, nonlinear relationships and interactions among variables [6; 9]. RF, in particular, reduces overfitting risks common in single decision trees by averaging multiple models trained on different data subsets.  
Neural networks, especially multilayer perceptron (MLP) models, have also demonstrated promise in financial forecasting tasks, including yield prediction [10; 11]. These models can capture highly nonlinear and high-dimensional relationships but require careful tuning and large datasets. While they are powerful, their lack of interpretability, often referred to as the \"black box\" problem, can be a limitation for economic policy applications.  
Several studies have compared traditional statistical models with ML models in forecasting yields or macroeconomic indicators. For instance, [PERSON] and [PERSON] [12] found that ML methods consistently outperformed traditional regression models in terms of predictive accuracy for interest rate forecasting. Similarly, [PERSON] [13] demonstrated that ensemble learning models provided more robust yield predictions, particularly during volatile economic conditions.
## 3 Current Status of the Circuit Component in the MMW Regime
### Power Amplifier (PA)
This is an important issue in MMW PA design. The effect of combing transistors to gain higher output power may not be significant for the high combing loss from transformers. According to aforementioned combing techniques, the difficulties in gaining higher output power is to reduce the loss of the passive components by combining multi-metal layers or investigating different ground shield patterns, which are utilized to isolate the lossy substrates and the passive components. In addition, with the demanding of the complex digital modulation schemes like quadrature phase-shift keying (QPSK), n-quadrature amplitude modulation (n-QAM), and orthogonal frequency-division multiplexing (OFDM), linearity of the PA becomes more important than last two decades. For improving the linearity, a bias mechanism is proposed an implemented to a cascode configuration. This contributes to the improvement of the power added efficiency (PAE) at output 1 dB compression point (P1 dB) [85]. Except for the method in [85], a V-band PA adopts with the proposed pre-distortion linearizer using body bias technique is successfully implemented with improved P1 dB, PAE at P1 dB and the third order intermodulation distortion (IMD3) [86]. Regarding different specifications of the target systems, the correspondent circuit topologies can be utilized to implement the PAs. Meanwhile, this shows the possibility of the integration of the PAs with the MMW front-end system in silicon-based technologies.
## 4 Numerical Simulation Results and Analysis
### Simulation Results of Temperature Field  
To clarify the temperature change of stored grain pile, the distribution of the temperature field in the bulk grain pile at different time points was studied and analyzed. Figure 7  
Figure 6: Comparison of simulated and experimental temperature at different measuring points of bulk grain pile: (**a–c**) 50 kPa (**d–f**) 100 kPa (**g–i**) 200 kPa.  
shows the middle plane temperature distribution nephogram of the bulk grain pile at different time points with a vertical pressure of 50 kPa. The temperature distribution of the bulk grain pile in the model warehouse is uniform when the simulated storage time is 1 h. Due to the combined effect of heat conduction and natural convection, the temperature of the grain pile is redistributed. The temperature of the bulk grain pile near the high-temperature boundary wall is higher than the initial temperature and forms a high-temperature region, and the temperature of the bulk grain pile near the low-temperature boundary wall forms a low-temperature region. The results show that the isotherm was nearly vertical, and the wall temperature exhibited a rectangular distribution around the control temperature for 24 h. With the increase in the storage time and heat transfer from the high-temperature region to the low-temperature region, the area of the high-temperature region increases, and the area of the low-temperature region decreases. The simulated grain storage (for 72 h) isotherm is almost \"s\"-shaped, and the temperature distribution of the high and low-temperature regions is trapezoidal. Considering the 23.75 \({}^{\circ}\)C isotherm as an example, the dotted line in Figure 7 is the vertical line where the temperature difference between the top of the storage structure and the bottom of the storage structure is the largest, and the abscissa of the vertical line is at the top of the storage structure. The main reason for the temperature difference at each point on the vertical line is the influence of natural convection on the heat transfer of the bulk grain pile. The temperature difference between the top and bottom of the storage structure is 1.0 \({}^{\circ}\)C for 24 h. With increase in storage time, the influence of natural convection on the heat transfer of the bulk grain pile increases, and the temperature difference increases. At 48 h, the temperature difference is 1.87 \({}^{\circ}\)C, and at 72 h, the temperature difference is 2.75 \({}^{\circ}\)C.  
In the process of the solid heat conduction in an ideal state, heat is transferred from the high-temperature to the low-temperature region in parallel. A grain pile is typically a porous medium. Under static storage conditions, the temperature gradient causes natural convection. The hot air near the high-temperature boundary wall diffuses from the bottom to the top of the grain pile, while the heat is transferred upward simultaneously. The area of the high-temperature region exhibits a wide top and narrow bottom, while the area of the low-temperature region exhibits a narrow top and wide bottom.