esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Antioxidant Activities
#### 2.5.2. DPPH Free Radical Inhibition Activity  
The antioxidant activity of DPPH (radical 1,1-diphenyl-2-picrylhydrazyl) was determined according to [PERSON] et al. [23]. The propolisis extract was diluted (1:1000 \(w/v\)) using methanol, homogenized until complete solubility using a vortex, then the extracts were centrifuged at 18,510\(\times\) g for 15 min at 4 \({}^{\circ}\)C ([PERSON], Z 36 HK, Gosheim, Germany). One hundred mL of a stock solution of 0.2 mM DPPH (7.9 mg of DPPH were dissolved and adjusted to 100 mL with 80% methanol) was prepared to generate DPPH\({}^{+}\) free radicals, and the mixture was kept under magnetic stirring for 2 h in total darkness. Before starting the reading of the extracts, the DPPH\({}^{+}\) reagent was standardized to an absorbance of 0.7 \(\pm\) 0.01 using 80% methanol. Two and half mL of the standardized DPPH\({}^{+}\) reagent was added to 0.5 mL of the supernatant, and they were incubated for 1 h in the absence of light. Samples were read at 517 nm with a spectrophotometer (Jenway 6715, Ware, UK), and methanol was used as a blank. The antioxidant activity was expressed as mg equivalents of gallic acid in 100 g of extract, according to the following equation. All samples were analyzed in triplicate.  
Antioxidant activity (mg GA/100 g) = (GA \(\times\) V \(\times\) DF \(\times\) 100)/(Sample weight (g)) where:  
GA = Gallic acid concentration from the standard curve (mg/mL);  
V = Sample volume (mL);  
DF = Sample dilution factor.
# Study on Burr Formation and Tool Wear in Drilling CFRP and Its Hybrid Composites
## 3 Results and Discussion
### Burr Formation  
The carbon fiber composites drilling is prone to produce many unique features, such as cracks, uncut fibers, fiber pull-out and delamination. Additionally, these uncut fibers are called burr. In general, burr often formed on the exit surface of drilled holes after the drilling process. The burr shape is irregular, and it cannot be removed easily using a punching process, due to the withdrawal in the feed direction of a drill bit [41; 42]. [PERSON] et al. [43] reported that the burr cannot be completely removed in the drilling process, even through the repeated optimization of drilling parameter, it can reduce the burr formation. Although the burr formed during the drilling process usually does not have a negative impact on the mechanical properties of the carbon fiber composite parts, the complete removal of them requires some additional machining operations, resulting in an increase in processing costs. In this study, CFRP and C-AFRP were drilled using an 8 mm diameter carbide drill with a point angle of 90\({}^{\circ}\), while the spindle speed and feed rate were 2000 rpm and 200 mm/min, respectively. The burr is generated on the exit side of the hole in the drilling of CFRP and C-AFRP, as shown in Figure 7. It can be clearly observed in Figure 7 that more burrs are generated by drilling C-AFRP than that of drilling CFRP, and the clear yield and necking phenomena of aramid fibers are also observed. In addition, on the whole, more or larger burrs are formed as the number of drilling holes increases. The reason may be attributed to the bending-dominated failure mode governing the chip separation in this circumference region, and the high-fiber recession to cutting edges in the laminate bottom. Both of these coupled effects on carbon and aramid fibers drilling lead to the evidence that many irregular-fractured fibers are generated [44]. Moreover, other researchers also found that large burr defects are formatted by the tensile force during the cutting process, and few burr defects are found when the fiber is in a shear stress state. [PERSON] et al. [45] reported that the angle between the cutting direction and the fiber orientation plays an important role in burr formation--the vibration of workpiece and the rise of outlet temperature are  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Sample** & **No.** & \begin{tabular}{l} **Tensile** \\ **Strength(MPa)** \\ \end{tabular} & \
\begin{tabular}{l} **Tensile** \\ **Modulus(GPa)** \\ \end{tabular} &
\begin{tabular}{l} **Tensile** \\ **Strain(\%)** \\ \end{tabular} \\ \hline \multirow{4}{*}{CFRP} & 1 & 431.78 & 21.68 & 2.08 \\  & 2 & 425.66 & 21.96 & 2.08 \\  & 3 & 436.17 & 21.43 & 2.23 \\  & Average value & 431.20 \(\pm\) 4.31 & 21.69 \(\pm\) 0.22 & 2.13 \(\pm\) 0.07 \\  & 1 & 737.73 & 56.05 & 1.27 \\  & 2 & 705.31 & 55.38 & 1.26 \\  & 3 & 761.2 the other reasons--and, secondly, the vibration of the workpiece and the increase in the outlet temperature are another reason.
# Sequential Recommendations on GitHub Repository
## 3 Dataset and Models
### Baseline Recommendation Models
#### 3.2.1 GRU4 Rec  
The GRU4 Rec [44] model focuses on modeling model interaction sequences in session-based scenarios. The backbone structure of GRU4 Rec uses a variant of RNN, gated recurrent units (GRUs), which is a more sophisticated version of RNN that effectively alleviates the vanishing gradient problem of an RNN with an update gate and a reset gate. The gates in GRU mainly learn to control the amount of information that is required to update and forget the hidden state of the unit. For instance, a reset gate allows control of how much information to remember from the previous state. Similarly, an update gate allows control of how much of the new state is a copy of the old states. Considering **W** and **U** as the learned weight at each gate and the information of the hidden gate as \(h\), the update gate is calculated with input \(x\) into the network at a time step \(t\),  
\[z_{t}=\sigma(\textbf{W}_{z}x_{t}+\textbf{U}_{z}h_{t-1}), \tag{1}\]  
and the reset gate is determined by  
\[r_{t}=\sigma(\textbf{W}_{r}x_{t}+\textbf{U}_{r}h_{t-1}), \tag{2}\]  
where sigmoid \(\sigma(\cdot)\) is an activation function for the non-linear transformation. The memory content that stores information from the previous gate using the reset gate is given as follows:  
\[\hat{t}_{t}=tanh(\textbf{W}x_{t}+r_{t}\odot\textbf{U}h_{t-1}), \tag{3}\]  
where \(\odot\) is an element-wise product of elements. The determination of what to store in the current memory content within the past and current steps by final linear interpolation is determined as  
\[h_{t}=z_{t}\odot h_{t-1}+(1-z_{t})\odot\hat{h}_{t}. \tag{4}\]  
The GRU4 Rec outperforms the RNN-based algorithms; the basic GRU4 Rec's architecture is shown in Figure 2. In this illustration, \(L\) represents the previous items and \(T\) represents the target item sequences within the user interaction sequence \(Su\), respectively, and **E** represents the embedding matrix of the \(L\) previous consecutive interactions. To capture inter-dependencies, [PERSON] et al. [44] further improved recommendation accuracy by introducing new ideas, such as TOP1 losses [44], Bayesian Personalized Ranking loss [45], and mini-batch negative sampling.
# An Enhanced Privacy-Preserving Authentication Scheme for Vehicle Sensor Networks
## 5 Improved Scheme
### Secure Communication  
The secure communication procedure is performed between two trusted vehicles \(OBU_{i}\) and \(OBU_{j}\) when they intend to interact with each other. The secure communication procedure is shown in Figure 5 and the steps are described as follows.  
* After completing the login procedure, \(OBU_{i}\) retrieves \(PSK=E_{i}\oplus h(PW_{i}).x_{i}=Z_{i}\oplus h(PW_{i})\), then it generates a random number \(r_{i}\) and computes the messages \(AID_{i}=ID_{i}\oplus h(r_{i}P_{pub_{i}})\), \(T=r_{i}P\), \(u_{i}=T+PSK\cdot P\), and \(M_{1}=h(T||ID_{i}||AID_{i})\), where \(E_{i}\) was obtained from a previous authentication procedure.
* \(OBU_{i}\to OBU_{j}\): A secure communication request (i.e., \(AID_{i}\), \(u_{i}\), \(M_{1}\)) is sent to \(OBU_{j}\) by \(OBU_{i}\).
* Upon receiving (i.e., \(AID_{i}\), \(u_{i}\), \(M_{1}\)), \(OBU_{j}\) uses \(PSK\) to retrieve \(T\) from \(u_{i}\) and then computes \(ID_{i}=AID_{i}\oplus h(x_{j}T)\), and checks whether \(M_{1}\) is equal to \(h(T||ID_{i}||AID_{i})\). The request  
Figure 4: General authentication procedure.  
will be rejected if this equality does not holds. Otherwise, \(OBU_{j}\) randomly chooses \(r_{j}\) and computes  
\[\begin{array}{l}AID_{j}=ID_{j}\oplus h(r_{j}P_{pub_{j}}),\\ R=r_{j}P,\\ u_{j}=R+PSK\cdot P,\\ s=r_{j}P_{pub_{j}}+x_{j}T,\\ k=h(T||R||P_{pub_{j}}||P_{pub_{j}}||s),\\ M_{2}=h(ID_{j}||k).\end{array}\]  
* \(OBU_{j}\to OBU_{i}\): \(OBU_{j}\) returns the response messages (i.e., \(AID_{j}\), \(u_{j}\), \(M_{2}\)) to \(OBU_{i}\).
* After receiving the messages \(\{AID_{j},u_{j},M_{2}\}\), \(OBU_{i}\) verifies whether \(OBU_{j}\) is trusted: \(OBU_{i}\) computes \(R=u_{j}-PSK\cdot P\), \(ID_{j}=AID_{j}\oplus h(x_{i}R)\), \(s=r_{i}P_{pub_{j}}+x_{i}R\) and \(k=h(T||R||P_{pub_{j}}||P_{pub_{j}}||s)\), and then checks whether \(M_{2}=h(ID_{j}||k)\) holds. If so, \(OBU_{i}\) computes a reply message \(M_{3}=h(u_{j}||k)\). Otherwise, the process is terminated.
* \(OBU_{i}\to OBU_{j}\): \(OBU_{i}\) sends \(M_{3}\) to \(OBU_{j}\).
* After receiving the message \(\{M_{3}\}\), \(OBU_{j}\) checks whether \(M_{3}=h(u_{j}||k)\) holds. if so, the two trusted vehicles can communicate securely using \(k\). Otherwise, \(OBU_{j}\) terminates the process.
## 4 Materials and Methods  
Ag/Ge/AlN NMLs were deposited on 2\({}^{\prime\prime}\) epi-polished \(\alpha\)-Al\({}_{2}\)O\({}_{3}\) single-crystalline substrates (sapphire-C (0001)) by DC unbalanced magnetron sputtering in a high vacuum chamber (base pressure \(<\)10\({}^{-8}\) mbar) from three 2\({}^{\prime\prime}\) confocally arranged targets of pure Ag (99.99%), Al (99.99%) and Ge (99.999%), in a reactive atmosphere with a flux volume ratio of 7N\({}_{2}\)/15 Ar sccm (N\({}_{2}\) used only for AlN deposition). The power applied were: 50 W for Ag and Ge with a deposition rate of 4.3 nm/min and 21.4 nm/min respectively; 200 W for Al with deposition rate of 2.9 nm/min. Prior to deposition, eventual surface contamination on the \(\alpha\)-Al\({}_{2}\)O\({}_{3}\) substrates (mostly adventitious carbon) was removed by Ar\({}^{+}\) sputter cleaning for 5 min applying a RF bias of 100 V. First, a 10 nm-thick AlN buffer layer was deposited using reactive sputtering on the cleaned substrate. For the sequential samples, a building block composed of a Ge layer (nominal thickness: 1 nm) followed by a Ag layer (nominal thickness 10 nm) and an AlN layer (nominal thickness 10 nm) were deposited for 20 times, composing the NML structure. Two batches of the sequential samples were grown: with and without the application of a RF bias (62 V) to the substrate during deposition. Using an RF-bias during deposition increases the energy of the incoming ions leading to a higher defect-density of the grown layer stack [18]. The as-prepared samples were then annealed at 200, 400 and 700 \({}^{\circ}\)C in flowing Ar and in air atmosphere for 30 min.  
The Bruker D8 Discover X-ray diffractometer operated in Bragg- Brentano geometry, was applied to measure 2-\(\theta\) scans for the as-deposited and annealed samples. Diffraction patterns were recorded in 2\(\theta\) range from 10\({}^{\circ}\) to 90\({}^{\circ}\) using Cu K \(\alpha_{1,2}\) radiation at 40 KV and 40 mA. Pole figures were acquired for the Ag \(\langle 111\rangle\) and Al \(\langle 103\rangle\) family of planes. Stress and texture measurements were recorded at RT on ex situ annealed samples. Stress analysis was carried out using the Crystalline Group Method (CGM) [30], suitable for highly textured systems, like in this case. In the case of textured films, it is assumed that all crystallites having the same orientation form a unique crystal. This implies that every crystallite shows the same stress state (Reuss averaging scheme). Due to its specific geometry,a thin film deposited onto a substrate is free to expand or contract along the film normal direction [31]. We applied this method to the Ag \(\{111\}\)\(\langle\)211\(\rangle\) series of reflections.  
High-resolution SEM analysis was performed in plane and in cross-sectional views using a Hitachi S-4800 instrument equipped with a Bruker XFlash 6\(|\)60 energy dispersive X-ray (EDX) detector. Cross-sectional cuts were prepared by using a Hitachi IM4000 Ar ion milling system applying an acceleration voltage of 6 kV, a discharge voltage of 1.5 kV and a swing angle of \(\pm 30^{\circ}\).
# Application of Dimension Extending Technique to Unified Hardening Model
## 4 Numerical Examples
### Verification of Different Stress Paths
#### 4.1.5 Complex Loading Conditions  
To further validate the feasibility of the dimension-extending technique for the UH model, complex loading condition is considered, which is the true triaxial test for Fujii-nomori clay. The loading path on the \(\pi\)-plane is \(OAB\overset{\rightarrow}{OCDOE}\), as shown in Figure 11. \(\overset{\rightarrow}{OA}\) is the compression path with \(\sigma_{y}\) being the major principal stress; \(\overset{\rightarrow}{AB}\) is the extension path with \(\sigma_{y}/\sigma_{z}=\sigma_{1}/\sigma_{3}=3\); \(\overset{\rightarrow}{BO}\) is the unloading path; \(\overset{\rightarrow}{OC}\) is the compression path with \(\sigma_{x}\) being the major principal stress; \(\overset{\rightarrow}{CD}\) is the path with \(\sigma_{x}/\sigma_{y}=\sigma_{1}/\sigma_{3}=3\); \(\overset{\rightarrow}{DO}\) is the unloading path; \(\overset{\rightarrow}{OE}\) is the path with \(\sigma_{z}\) being the major principal stress.  
Figure 12 shows the stress-strain curve, from which it can be seen that the numerical calculation results are in good agreement with the experimental data.  
Figure 10: Comparison of true triaxial numerical results with experimental results for equal \(p\) and equal \(b\) paths. (**a**) \(b=0.268\); (**b**) \(b=0.5\); (**c**) \(b=0.732\).
## 5 Simulation and Analysis  
To validate the performance of the LFC system with AGFTSMC, the model of a two-area LFC system with AGFTSMC, which incorporates an ESS and PV generation, is established using the MATLAB/Simulink R2022a toolbox. For the above LFC system model under load disturbances, the frequency deviations are analyzed based on their response curves. To further demonstrate the effectiveness of the proposed method, the results are compared with other controllers, such as the PID controller, SMC controller, and GFTSMC controller.
# Remote Sensing of Forest Burnt Area, Burn Severity, and Post-Fire Recovery: A Review
## 4 Results and Discussions
### Classification Algorithms  
#### 4.3.1 Feature Extraction  
Choosing appropriate variables (classifiers) can be considered the crucial step for successful classification. The commonly used variables include spectral signatures, vegetation indices, transformed images, textural or contextual information, multitemporal images, multisensor im  
included iterative self-organizing data analysis (ISODATA), self-organization cluster analysis, object-based analysis and k-means clustering [232; 254; 255; 256; 257; 258; 283; 288; 298; 306; 308; 344; 345; 346; 347; 348]. The hybrid (mix of several approaches) classification was utilized in 11 of the reviewed studies [95; 288; 339; 349].  
The \"Manual\" group of studies were those that manually produced BA classes and BS mapping through digitizing polygons, using vegetation and spectral indices, extracting features, hotspot detection, and change analysis in forest patterns [350; 351; 352; 353; 354; 355; 356; 357; 358; 359; 360; 361; 362; 363; 364; 365; 366]. The burned area extraction and dating (BAED) algorithm [367], multiscale curvature classification [233], spectral angle mapper classification [155; 200; 317], discriminant analysis [282], and rare class prediction in the absence of true labels [368] classification methods were used less than three times, and, therefore, they were included in the \"Other\" group of methods.  
In the last decade, the GEOBIA method has emerged based on image-objects segmentation and classification rather than pixels, which increased the land-cover studies with the use of high spatial resolution remote sensing imagery [369]. Of the total BA studies, 9% used OBIA/GEOBIA classification with the application of ground truth data and regional forest maps to validate the classification accuracy [370; 371; 372]. Application of the GEOBIA improves detection and fuzzy logic classification accuracy of BA [147; 215; 259; 278; 373; 374]. In addition, the object-based approach, despite the inherent computational costs, was reported to be less time-consuming than the pixel-based approach [299]. However, pixel-based approaches are still more widely used by researchers.
# A Survey on Moving Target Defense: Intelligently Affordable, Optimized and Self-Adaptive
## 1 Introduction
### Survey Methodology  
We used the thesis database (Scopus database, occasionally assisted by the Web of Science) and specific keywords (such as moving target defense, machine learning, etc.) to search and determine an extensive range of the literature review papers (LRPs) that were initially related [2]. One most straightforward way to achieve this is to filter the papers' date (we have been looking for papers for nearly 20 years), publishing platform, field, and so on with the filter that comes with the database, including journal articles and conference papers. By browsing abstracts, keywords, and objective statements of pieces of the literature, we could determine their relevance. Then, the results of rough reading based on inductive coding and of intensive reading based on co-occurrence analysis were presented. The papers were selected based on three main criteria: whether they (i) provided new machine learning techniques or ideas on moving target defense, (ii) had a high degree of completion and reproducibility, and (iii) had the ability to meet the demands of affordability, optimization, and self-adaptation.  
We initially collected around 150 published papers with relevant keywords in the thesis database, in addition to many online articles, with each paper carefully reviewed and summarized. Then, we analyzed around 80 papers in this survey based on the selection criteria mentioned above. Additionally, by then, we had come up with a system flow of what our survey should be similar to, as shown in Figure 2.  
The selected papers were then grouped into three categories: (i) MTD-related techniques, (ii) intelligent MTD-related techniques, and (iii) surveys that concentrated on MTD development. It needs to be noted that these three categories were not mutually exclusive. We believe all the papers selected were state-of-the-art at that moment. We selected them to follow our systematic flow.  
* Papers in (i) were put forward because their defense methods showed better effectiveness than corresponding traditional defense or their defense methods could be improved into intelligent MTD techniques and be compared with the latter ones.
* Papers in (ii) were put forward because their defense methods could achieve more significance than previous ones. We collected these papers and summarized three categories of them, _towards affordable_, _towards optimized_, and _towards self-adapting_, respectively. Or we could also regard them as development trends of intelligent MTD based on their commonalities. We analyzed what these papers had achieved and what they had not accomplished to approximately represent the status quo of the entire category.
* Papers in (iii) demonstrated different categories of MTD techniques, and we classified them into various levels, which helped us to comprehend the development of MTD in a more comprehensive way.
# Re-Pair in Small Space +
## 2 Sequential Algorithm
### Trade-Off Computation  
Using the frequency tables, we present a solution with a trade-off parameter:  
Lemma 2: _Given an integer \(d\) with \(d\geq 1\), we can compute the frequencies of the \(d\) most frequent bigrams in a text of length \(n\) whose characters are drawn from an alphabet of size \(\sigma\) in \(\mathcal{O}(\max(n,d)n\lg d/d)\) time using \(2d\big{|}\lg(\sigma^{2}n/2)\big{|}+\mathcal{O}(\lg n)\) bits._  
Proof: Our idea is to partition the set of all bigrams appearing in \(T\) into \(\lceil n/d\rceil\) subsets, compute the frequencies for each subset, and finally, merge these frequencies. In detail, we partition the text \(T=S_{1}\cdots S_{\lceil n/d\rceil}\) into \(\lceil n/d\rceil\) substrings such that each substring has length \(d\) (the last one has a length of at most \(d\)). Subsequently, we extend \(S_{j}\) to the left (only if \(j>1\)) such that \(S_{j}\) and \(S_{j+1}\) overlap by one text position, for \(1\leq j<\lceil n/d\rceil\). By doing so, we take the bigram on the border of two adjacent substrings \(S_{j}\) and \(S_{j+1}\) for each \(j<\lceil n/d\rceil\) into account. Next, we create two frequency tables \(F\) and \(F^{\prime}\), each of length \(d\) for storing the frequencies of \(d\) bigrams. These tables are at the beginning empty. In what follows, we fill \(F\) such that after processing \(S_{i}\), \(F\) stores the most frequent \(d\) bigrams among those bigrams occurring in \(S_{1},\ldots,S_{i}\) while \(F^{\prime}\) acts as a temporary space for storing candidate bigrams that can enter \(F\).  
With \(F\) and \(F^{\prime}\), we process each of the \(n/d\) substrings \(S_{j}\) as follows: Let us fix an integer \(j\) with \(1\leq j\leq\lceil n/d\rceil\). We first put all bigrams of \(S_{j}\) into \(F^{\prime}\) in lexicographic order. We can perform this within the space of \(F^{\prime}\) in \(\mathcal{O}(d\lg d)\) time since there are at most \(d\) different bigrams in \(S_{j}\). We compute the frequencies of all these bigrams in the complete text \(T\) in \(\mathcal{O}(n\lg d)\) time by scanning the text from left to right while locating a bigram in \(F^{\prime}\) in \(\mathcal{O}(\lg d)\) time with a binary search. Subsequently, we interpret \(F\) and \(F^{\prime}\) as one large frequency table, sort it with respect to the frequencies while discarding duplicates such that \(F\) stores the \(d\) most frequent bigrams in \(T[1..jd]\). This sorting step can be done in \(\mathcal{O}(d\lg d)\) time. Finally, we clear \(F^{\prime}\) and are done with \(S_{j}\). After the final merge step, we obtain the \(d\) most frequent bigrams of \(T\) stored in \(F\).  
Since each of the \(\mathcal{O}(n/d)\) merge steps takes \(\mathcal{O}(d\lg d+n\lg d)\) time, we need: \(\mathcal{O}(\max(d,n)\cdot(n\lg d)/d)\) time. For \(d\geq n\), we can build a large frequency table and perform one scan to count the frequencies of all bigrams in \(T\). This scan and the final sorting with respect to the counted frequencies can be done in \(\mathcal{O}(n\lg n)\) time.