esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Study on the Evolution Mechanism of the Crater under a Millisecond Laser
## 2 Numerical Simulation
### Heat Transfer Governing Equation and Boundary Conditions  
#### 2.1.1 Heat Transfer Governing Equation  
The transient temperature field was controlled by the energy conservation equation and is expressed as  
\[\rho C_{p}^{eq}\frac{\partial T}{\partial t}+\rho C_{p}^{eq}u\
abla T=\
abla.(k\
abla T) \tag{1}\]  
where \(\rho\) is the density, \(T\) is the absolute temperature, \(t\) is the time, and \(k\) is the thermal conductivity.  
The latent heat of solid/liquid phase transformation was treated by the equivalent heat capacity method in this paper, and it was simple and convenient to treat the latent heat with this method without distinguishing the solid-liquid phase [18]. \(C_{p}^{eq}\) is the equivalent specific heat, given by:  
\[C_{p}^{eq}=C_{P}+\frac{L_{m}}{T_{m}}H[(T-T_{m}),\Delta T]+\delta_{m}L_{m} \tag{2}\]  
\[\delta_{m}=\frac{exp[-\frac{(T-T_{m})^{2}}{\Delta T^{2}}]}{\Delta T\sqrt{\pi}} \tag{3}\]  
where \(L_{m}\) is the latent heat of melting, \(T_{m}\) is the melting temperature, \(C_{P}\) is the Specific heat capacity at atmospheric pressure, \(H[(T-T_{m}),\Delta T]\) is the Heaviside smoothing function, \(\delta_{m}\) is the normalized Gaussian function near the melting point where the center of the function is the melting point of the target, and \(\Delta T\) is the temperature range of the transition between the two phases.  
In the simulation, the entire calculation model was regarded as the same phase. To distinguish between the solid, liquid, and the mixed paste area, the volume fraction \(f_{L}\) was used to construct the functions of viscosity \(\mu\), thermophysical parameters \(k\), \(\rho\), and \(C_{P}\).  
\[\mu=[1+(1-f_{L})A_{mush}]\mu_{l} \tag{4}\]  
\[\left\{\begin{array}{c}k=f_{L}k_{l}+(1-f_{L})k_{s}\\ \rho=f_{L}\rho_{l}+(1-f_{L})\rho_{s}\\ C_{p}=f_{L}C_{p,l}+(1-f_{L})C_{p,s}\end{array}\right. \tag{5}\]  
In the formula above, \(\mu_{l}\) is the dynamic viscosity of liquid phase, \(A_{mush}\) is the mushy zone constant, \(k_{s}\), \(k_{l}\), \(\rho_{s}\), \(\rho_{l}\), \(C_{P,s}\), and \(C_{P,l}\) are the thermal conductivity, density, and specific heat capacity at the atmospheric pressure of the solid phase and liquid phase of the material, respectively. The volume fraction \(f_{L}\) changes linearly with temperature in the mushy zone represented as  
\[f_{L}=\left\{\begin{array}{c}1\ \,\ T>T_{l}\\ \frac{T-T_{s}}{T_{l}-T_{s}},\ T_{s}\leq T\leq T_{l}\\ 0\ \,\ T<T_{s}\end{array}\right. \tag{6}\]  
where, \(T_{s}\) is the solid phase temperature and \(T_{l}\) is the liquid phase temperature.
# Reversible Image Processing for Color Images with Flexible Control
## 3 Proposed Method
### Raw Image Recovery
#### 3.2.2 Recovery from Brightness Increase or Decrease  
The proposed method recovers raw images from the brightness increase or decrease function by using the information described in Section 3.1.3(ii). We first describe the procedure for the brightness increase function:  
1. Integrate the histograms of \(Max^{\prime}\) and \(Min^{\prime}\).
2. Obtain the reference bin value and the classification data, which indicate whether the reference bin was empty or not before the increasing process, from the recovery information.
3. Shift the pixels belonging to the bins between the reference and leftmost ones by \(-1\). In the case where the reference bin was not empty before the increasing process, the pixels, which have been integrated in the left adjacent bin, are turned back to the reference bin by referring to the recovery information. Accordingly, the original values of \(Max\) and \(Min\) are recovered.
4. Separate the integrated histogram into \(Max\) and \(Min\) histograms.  
Max_ and _Min_ are recovered by the above processes \(I_{VI}\) times. For the recovery process for the brightness decrease function, the pixels belonging to the bins between the reference and rightmost ones are shifted by \(+1\) in Step 3, and the number of processing times will be \(I_{VD}\).
# Identifying Influential Nodes of Complex Networks Based on Trust-Value
## 3 Experiments and Discussion
### Preliminary Effectiveness Analysis  
To clearly visualize the internal structure of the network, we selected small-scale networks such as Kite [29] and Karate, shown in Figure 5. Table 5 provides the outcome of the top 10 nodes as evaluated by TPR with DC, BC, PageRank, and HITS on Kite and Karate.  
Figure 4: Trend in Kendall coefficient changes with the value of parameter \(k\).  
Table 5 shows that the results of these algorithms for the symmetric Kite network are similar. The influential nodes identified by TPR are consistent with DC. In addition, compared with PageRank, TPR considers nodes 6 and 8 as being more influential than node 2. From the perspective of TPR, the TPR value of node 2 derives from nodes 1 and 3, which have lower TPR value. Node 2 has a small degree, and its similarity with node 3 is not dominant compared to other neighboring nodes of node 3 (nodes 4 and 5). The nodes adjacent to node 6 have excellent ranking and higher TPR values, the degree of node 6 is higher and node 6 is more similar to its neighbors, as is node 8. In addition, node 2 only connects node 3 to a leaf node 1, whereas nodes 6 and 8 are more influential, intuitively, which also proves that TPR is more rational.  
In the Karate network, the top 10 nodes selected by TPR are basically consistent with those selected by other algorithms, with the only difference in the ordering. The top 10 nodes picked by DC, PageRank, TPR are nodes 1, 2, 3, 4, 9, 14, 24, 32, 33, and 34, but BC does not select nodes 4 and 24 but rather nodes 6 and 20. HITS considers node 31 as more important than 24.
###### Abstract
Based on the Google Earth Engine cloud platform, this study used Landsat series images with a spatial resolution of 30 m as the data source to estimate the vegetation cover in the Alar region for the past 30 years from 1990 to 2020 using the image dichotomy method, and explored the spatial and temporal dynamic trends using the CV coefficient of variation and Hurst index, in order to provide a scientific basis for the dynamic monitoring and evaluation of vegetation resources and ecological environment in desert oases.
# A Packet Content-Oriented Remote Code Execution Attack Payload Detection Model
## 1 Introduction
So in this paper, we focus on these three RCE attacks, combining (1) the construction algorithm of the XML entities' UD (use-definition) chain, and (2) 34 features used to represent string operation and the use of sensitive classes/methods in the code, proposing a packet content-oriented RCE attack payload detection model. The contributions of this paper can be summarized as follows:  
* We propose a novel RCE attack payload detection model named PCO-RCEAPD. This model is packet content oriented, so it can quickly discover potential security threats (i.e., XXE, ELi, and IDSER) in the process of network communication. * For the XXE attack, we propose a novel algorithm to construct the UD chain of XML entities. This algorithm tracks the reference process of XML entities in packets and analyzes their sensitive behaviors. * For the ELi and IDSER attack, we slice the code based on the data dependency of expression language and Java code, extract 34 features that describe string operations and the use of sensitive classes/methods, and train a machine learning model to perform detection. The rest of this paper is organized as follows. Section 2 introduces the basic concepts related to the RCE attack. Section 3 introduces the static and dynamic detection technology for malicious code. Section 4 describes the proposed model. Section 5 presents the evaluation results and provides detailed analysis of these results. Section 6 is the summary of this work.
## 2 Materials and Methods
### Neutron Irradiation  
The TRIUMF Neutron Facility (TNF) utilizes a beam dump at the end of one of the 500 MeV beam lines (beam line 1A) from the TRIUMF 500 MeV cyclotron [20]. Neutrons with an energy spectrum from 0 up to 400 MeV are generated by the final beam stop on this beam line from the spallation reaction on an aluminum plate absorber surrounded by a water moderator. The beam is actively monitored via a neutron detector. Over the course of these experiments, the neutron flux measured was \(3.47\pm 0.06\times 10^{6}\) n/(cm\({}^{2}\) s) where n is the number of neutrons with an energy greater than 10 MeV. This corresponds to a dose rate of \(3.16\times 10^{-4}\) Gy/s.  
The beam is accessible by a vertical shaft about 5 m below the local monitoring room. To place the fibers into the beam path, the plastic test placard with the three fibers was mounted onto an additional support plate, which was then lowered into the beam by means of a pulley on tracks. The support plate was marked with an outline of the approximate profile of the beam obtained from previous experiments at TRIUMF. The beam profile was 15.2 cm in the horizontal and 5.1 cm in the vertical direction, respectively. A black plastic bag was used to cover the fibers before placing them in the beam position to reduce ambient background light. Specimens of the three different FBG categories were irradiated to different dose levels (up to 60 Gy). The specimens were monitored before, during and after the irradiation for a shift in frequencies.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Set** & **Radiation** & **Energy [MeV]** & **Dose Steps [Gy]** & **Flux [mGy/s]** \\ \hline
1 & None (control) & 0 & 0 & 0 \\
2 & Neutrons & 0–400 & \(1,5,10,20,35,60\) & fixed \\
3 & Protons & 9 & \(5,10,60\) & \(100,500,1000\) \\
4 & Protons & 13.5 & \(5,10,60\) & \(100,500,1000\) \\
5 & Protons & 20 & \(5,10,60\) & \(100,500,1000\) \\
6 & Protons & 35 & \(5,10,60\) & \(100,500,1000\) \\
7 & Protons & 64 & \(5,10,60\) & \(100,500,1000\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Proton and neutron irradiation experiments.  
Figure 2: (**a**) TRIUMF experimental setup. FBG test specimen at left of image with proton beam line proceeding from right to left. (**b**) Example of UV, FS and RAD grating specimens mounted on plastic placard.
# Numerically Optimized Fourier Transform-Based Beamforming Accelerated by Neural Networks
## 3 Algorithms for Phase Distribution Optimization
### Applying Filtering on Protected Areas  
As mentioned before, the \((u,v)\) domain is split into two main areas: VA and IA, as mandated by the PMS structure dimensions and operating frequency. The regions inside the IA have no immediate effect on the main beams. In contrast, the VA region has a pronounced effect and is subdivided further into the protected and unprotected areas (PAs and UA). One may argue that the significance of points inside PAs decreases when moving away from the PAs centers. To account for this, one can apply a Gaussian filter centered at \((u_{0,i},v_{0,i})\) with the magnitude of unity and the standard deviation \(\sigma=k_{0}d\). Thus, the main beams can be multiplied by such filter, Equation (13), before constructing the cost function to speed up the optimization process:  
\[G_{i}(u,v)=e^{-\frac{(u-u_{0,i})^{2}+(v-v_{0,i})^{2}}{2\sigma^{2}}}. \tag{13}\]  
where \(i\) denotes the beam index. When using such Gaussian filtering, a common multiplier \(G_{i}(u_{ij},v_{ij})\) is included into Equation (12) under the double summation sign.
## 3 System Design
### System Model
#### 3.3.1 The Che Approximation  
Suppose that there is a cache node \(k\) in a network with cache capacity \(M\) (count by chunks). Users request NDO \(c\) from a set \(C\) (\(1<c<C\)) with a large, fixed-size catalog. The requests of \(c\) arrive \(k\) with rate \(\lambda_{c}\) according to a homogeneous Poisson process. The request sequence follows the Independent Reference Model (IRM), which means the request probability for \(c\) is fixed and is independent of past requests. In other word, the popularity of content with a Zipf-like law does not change with time. In the real world, it seems unreasonable that there is no correlation between the request probability and the content itself. However, the correlation can be ignored when the content catalog \(C\) is large enough [22].  
The time average cache-hit probability that the \(c\) can be found in \(k\) can be expressed as:  
\[P_{hit}(c)=P_{in}(c)=1-e^{-\lambda_{c}T_{M}} \tag{1}\]where \(T_{M}\), which is a constant independent of \(c\), is a characteristic time that receiving \(M\) different requests (excluding \(c\)) in \(k.T_{M}\) represents the living time duration for \(c\) in \(k\). The average hit probability of cache node \(k\) is:  
\[P_{hit}^{k}=\sum_{C}\frac{\lambda_{c}}{\Lambda}P_{hit}(c) \tag{2}\]  
where \(\Lambda=\sum_{C}\lambda_{c}\) is the total arrival rate of all content requests. In the above equation, the only unknown value is characteristic time \(T_{M}\), which can be easily obtained with arbitrary precision by a fixed point procedure.
## 2 Silymarin Extraction Methods: From Conventional to Novel
### Solvent Extraction  
Solvent Extraction (SE) stands as a well-established and widely adopted conventional technique for silymarin extraction from milk thistle seeds. This traditional method involves utilising organic solvents to dissolve silymarin from plant material, specifically through the process of grinding or crushing the seeds [24; 29]. The crushed seeds are then mixed with a suitable solvent, allowing for interaction between the solvent and the botanical matrix, followed by separating the extract from the residual plant debris. The choice of solvent is crucial, as solvent polarity significantly impacts the efficiency and selectivity of the extraction process. Polar solvents, like water and ethanol, are effective in extracting polar compounds such as flavonoids and phenolics, owing to their ability to form hydrogen bonds. In contrast, non-polar solvents, such as hexane and chloroform, are better suited for extracting lipophilic compounds like oils and terpen through van der Waals interactions. Additionally, using solvent mixtures can be optimized to target a broader range of compounds [30].  
While solvent extraction is praised for its cost-effectiveness, it has a notable drawback: the potential for lower purity due to the co-extraction of unwanted compounds [31]. Milk thistle seeds contain significant amounts of fatty acids (56% polyunsaturated and 21% monounsaturated), which can become undesired by-products in silymarin production [29]. To address this, the European Pharmacopoeia recommends a two-step extraction method using the Soxhlet apparatus, involving a defatting process with lipophilic solvents before silymarin extraction [32]. Despite these measures, achieving complete solvent removal from the final product remains challenging, which can complicate analysis and impact the accuracy of the extracted silymarin. Other factors influencing the effectiveness of the extraction process include temperature and extraction time; higher temperatures can enhance extraction efficiency but risk degrading sensitive compounds, while longer extraction times may increase yields but also extract unwanted materials. Furthermore, the solvent-to-material ratio and pre-treatment of plant materials, such as drying or grinding, play significant roles in determining the overall effectiveness and selectivity of the extraction process.  
Silymarin extraction from whole and ground seeds, as well as whole and ground pericarp of _S. marianum_, was conducted through maceration at room temperature [33]. A 70% \(v/v\) hydro-alcohol solution was used with a plant-to-solvent ratio of 1:1 and 1:2 for 1, 3, and 7 days. The mentioned method resulted in silymarin yields of 36.23 \(\pm\) 6.38 mg/mL, 38.02 \(\pm\) 1.44 mg/mL, and 40.93 \(\pm\) 1.23 mg/mL when maceration ground whole seeds, whole pericarps, and ground whole pericarps for 7 days at a plant material to solvent ratio of 1:1.
# Microbial and Enzymatic Biodegradation of Plastic Waste for a Circular Economy
## 3 Factors Affecting Plastic Biodegradation  
Plastic biodegradation is affected by the physical and chemical properties of polymers. The main influencing factors are the surface area, molecular weight, hydrophilicity, hydrophobicity, chemical structure, melting temperature, and crystallinity [152]. Furthermore, the hydrophobic properties of plastic polymers inferred by their chemical structures reduce microbial activity by preventing water absorption [153]. For example, plastics such as PE and PS have long hydrophobic chains which resist water penetration, creating a barrier that inhibits microbial colonisation on the plastic's surface [154]. These factors could be broadly classified into extrinsic factors and polymer properties.
# A Discrete Hidden Markov Model for SMS Spam Detection
## 4 Experiment Results and Discussion
### Evaluation Metrics  
The well-known and persuasive evaluation metrics for classification are precision (P), recall (R), F-measure (F1), and accuracy (A) [59]. Their parameters for metrics calculation are shown in Table 3. Among these metrics, accuracy is the most important item [28].  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Number of SMS** & **Percentage of SMS** \\ \hline Spam & 747 & 13.4\% \\ Ham & 4827 & 86.6\% \\ Total & 5574 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: UCI dataset statistics.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Term Frequency** & **Number of Words with the Same Term Frequency** \\ \hline
1 & 9272 \\
2 & 580 \\
3 & 77 \\
4 & 15 \\
5 & 5 \\
6 & 3 \\
7 & 1 \\
8-9 & 0 \\
10 & 1 \\
11–13 & 0 \\
14 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Term frequency in spam and ham SMS set.  
Precision (P) is the fraction of relevant instances among all retrieved instances.  
\[\text{Precision (P)}=\frac{TP}{TP+FP} \tag{9}\]  
Recall (R), called Sensitivity, is the fraction of the total amount of relevant instances that are actually retrieved.  
\[\text{Recall (R)}=\frac{TP}{TP+FN} \tag{10}\]  
F-measure (F1) is the harmonic mean of the precision and recall. It is a balance between precision and recall.  
\[\text{F - Measure (F1)}=\ 2\times\frac{P\times R}{P+R} \tag{11}\]  
Accuracy (A) is the fraction of spam SMS messages that are correctly predicted among all SMS.  
\[\text{Accuracy (A)}=\frac{TN+TP}{TN+TP+FN+FP} \tag{12}\]  
Area under the curve (AUC) is also a well-known criterion for classification. It is the average of the true positive rate (TPR) and false positive rate (FPR). The greater the AUC value, the more accurate the model.  
\[\text{AUC}=\Big{(}\Big{(}\frac{TP}{TP+FN}\Big{)}+\Big{(}\frac{TN}{TN+FP} \Big{)}\Big{)}/2 \tag{13}\]
# Digital Twin-Based Automated Guided Vehicle Scheduling: A Solution for Its Charging Problems
## 3 Principle of the Proposed DTDAS
### Knowledge Support  
#### 3.1.1 AGV Dynamic Energy Consumption Model  
In this article, only the unavailable state caused by insufficient power of the AGV is considered. Other reasons, such as AGV failure, are neglected. Accurate energy consumption calculation is the prerequisite for predicting the availability of the AGV in actual transportation. Therefore, a mathematical model is needed to accurately describe the energy consumption characteristics of AGVs. The traditional scheduling mechanism is also possible to establish an AGV energy consumption model with fixed parameter values. However, the battery used in the AGV system will show different energy consumption characteristics in different states [22]. It is difficult to accurately describe the energy consumption characteristics when the workshop status changes. Digital twin connected with abundant real-time data resources provides data support for the establishment of dynamic energy consumption models. This paper adopts the method of real-time data fitting, as shown in Figure 4. AGV speed, load, and battery state of charge (SOC) are taken as input. AGV's energy consumption rate is taken as the output to establish the AGV's energy consumption model to describe the AGV's energy consumption characteristics at different loads and speeds. It should be noted that the SOC of the AGV battery is an estimated value obtained by current and voltage [23], which is regarded as a given value in this paper to simplify the dynamic energy consumption model. Hence, the dynamic energy consumption model can be expressed as:  
\[\hat{\mu}_{i}=\beta mass_{i}+\kappa v^{2}+\chi \tag{1}\]  
\[\mu=\frac{\Delta SOC}{\Delta t}, \tag{2}\]  
\[e=\mu-\hat{\mu}, \tag{3}\]  
where \(\hat{\mu}\) is the estimated energy consumption rate of transport task \(i\), mass and \(v\) represent AGV load and speed, respectively, \(\beta\), \(\kappa\) are the correlation coefficients, \(\chi\) is offset value. And \(\mu\) is real-time energy consumption rate, \(e\) is the estimated error of the energy consumption rate. The symbols appearing in this paper can refer to the Appendix A.  
When \(e\) is under a certain threshold, this estimation formula is maintained. When \(e\) is greater than this threshold, this formula will be replaced by the formula fitted from the latest data. This data model will provide support for the AGV availability prediction.
## 3 Results and Discussion
### FTIR Measurement  
The secondary structure and functional groups of the _R. hippoglossoides_ collagens showed the five main amide bonds (amides A, B, I, II, and III), visualized in Figure 2D. Regarding amide A, the absorption band is an N-H vibrational coupling group present typically in the intermolecular hydrogen bonds between 3000 and 3500 cm\({}^{-1}\)[56]. In the FTIR spectra it was observed that the amide A peaked at 3319, 3338, and 3315 cm\({}^{-1}\) for _COLRp_1_, _COLRp_II_, and _COLRp_III_, respectively. These values were in the range described to other marine species such as surgeon fish [57] or sea cucumber [58]. Concerning amide B, corresponding to the asymmetric and symmetric stretching of CH\({}_{2}\) groups, it typically presents peaks between 2933 and 2869 cm\({}^{-1}\)[56], and were here detected at 2927 cm\({}^{-1}\) for _COLRp_1_ and 2926 cm\({}^{-1}\) in both other collagens (_COLRp_II_ and _COLRp_III_). Amide I of three collagens displayed the C=O stretching of the carboxyl groups at 1654 cm\({}^{-1}\), 1656 cm\({}^{-1}\), and 1660 cm\({}^{-1}\) for _COLRp_I_, _COLRp_II_, and _COLRp_III_, respectively. According to the literature, the frequencies of amide I bands are in the range of 1600 and 1700 cm\({}^{-1}\)[59]. Furthermore, amide I may participate in the formation of hydrogen bonds between the carbonyl group (C=O) and the N-H group, which is responsible for the triple helical structure [57; 60]. The absorption characteristic of amide II was relative to NH bending vibration coupled with CN stretching between 1500 and 1600 cm\({}^{-1}\). The absorption characteristic of amide II was 1548 cm\({}^{-1}\) for _COLRp_I_ and _COLRp_III_, and 1544 cm\({}^{-1}\) for _COLRp_II_, corresponding to the NH bending of collagen. Regarding the band of amide III, it was observed at 1238 cm\({}^{-1}\), for _COLRp_I_, and _COLRp_III_, and 1240 cm\({}^{-1}\) for _COLRp_II_ and 1238 cm\({}^{-1}\), being related to C-N stretching vibrations and N-H bending, indicating the existence of a helical structure [58].  
The qualitative spectrum of _R. hippoglossoides_ collagen structure provided by FTIR analysis showed peaks of amides and their resemblance to type I collagen. Additionally, FTIR analysis could contribute to understanding the conformation of the collagen molecule. The ratio of the intensity of amide III and the observed signal at 1450 cm\({}^{-1}\) can give information about the presence of triple helix conformation on the structure of collagen molecules when the ratio is near 1 [61]. In this study, _COLRp_I_, _COLRp_II_, and _COLRp_III_ collagens showed 1.17, 1.16, and 1.17 ratios, respectively, confirming the preservation of triple helix conformation in all extracted collagens, being in consonance with CD results. Moreover, characteristic peaks of proteins are N-H bending and C=O, N-H, and C-N stretching, which were also identified in the marine collagens.