esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Framework
### Requirements and Assumptions
#### 3.2.2 Assumptions  
Whatever the application type, whatever the communication requirements, SERENA assumes an ideal environment where:  
* _A0_: Any node has a unique identifier in the network.
* _A1_: Any node \(X\) that is not in transmission range of any node \(N\) does not prevent the correct receipt on any link used by \(N\).
* _A2_: links are stable and symmetric.  
Assumption \(A0\) ensures that we can disambiguate node priority by considering node identifier. Assumption \(A1\) means that a node should use for routing and association only links of good quality. We will see in Section 4 the consequences of a violation of this assumption. We will also see how to meet this assumption in a real environment. In Section 5, we will evaluate the impact of color conflicts that can occur when assumption \(A2\) is not met and show how such conflicts can be detected and solved.
## 2 Materials and Methods
### Step III: Classifying Satellite Imagery for Generating LULC Maps
True negative (_TN_) represents the number of negative pixels correctly classified; true positive (_TP_) represents the number of positive pixels correctly classified; false positive (_FP_) represents the number of negative pixels incorrectly classified as positive; and false negative (_FN_) represents the number of positive pixels incorrectly classified as negative. One of the most frequently used criteria in classification is the confusion matrix, along with derived accuracy metrics such as overall accuracy (_OA_), producer's accuracy (_PA_), and user's accuracy (_UA_). The formulas for these accuracy metrics are provided below. _OA_ provides the ratio of correctly predicted pixels to the total number of sample pixels. _PA_ represents the ratio of correctly classified pixels in a specific class to the total number of pixels in the same class. _UA_ represents the ratio of correctly classified pixels in a specific class to the total number of pixels classified as 'belonging' to that class. The formulas for these accuracy metrics are provided in Equations (4)-(6). Another performance measure used was the Kappa coefficient, provided in Equation (7). In the context of classification, this coefficient is typically used to evaluate the agreement between what is predicted by a model and the actual class, while also considering the probability of this agreement occurring by chance. A Kappa coefficient of 1 indicates perfect agreement, 0 indicates agreement that could occur by chance, and \(-1\) indicates perfect disagreement. In Equation (7), \(p_{0}\) represents the relative observed agreement in the classifier, and \(p_{e}\) represents the expected probability of chance agreement. \[OA=(TP+TN)/(TP+FP+TN+FN) \tag{4}\]  
\[PA_{i}=\frac{TP_{i}}{TP_{i}+FN_{i}} \tag{5}\]  
\[UA_{i}=\frac{TP_{i}}{TP_{i}+FP_{i}} \tag{6}\]  
Figure 8: The training and test pixels associated with each image composite: 2007 (**upper left**), 2012 (**upper right**), 2017 (**lower left**), and 2021 (**lower right**). \[\kappa=\frac{p_{0}-p_{e}}{1-p_{e}} \tag{7}\]  
To generate LULC maps for four different years, each comprising five classes, the RF algorithm was employed to run the model that yielded the best prediction results on composite images. Following the classification of all pixels in the images, the resulting images in GEOTIFF format generated on the GEE platform were transferred to the desktop environment and used to create LULC maps for the four years in QGIS 3.34.5 software. These maps facilitated the analysis and mapping of both the LULC distribution for each year and the changes in LULC within each period.
## Comparative Activity of Six Recombinant Stilbene Synthases in Yeast for Resveratrol Production  
**[PERSON] 1\({}^{\copyright}\)**, **[PERSON] 2**, **[PERSON] 3**, **[PERSON] 3\({}^{\copyright}\)**, **[PERSON] 3 and [PERSON] 4\({}^{,}\)*  
Footnote 1: email: www.mdpi.com/journal/applsci  
Received: 12 June 2020; Accepted: 10 July 2020; Published: 15 July 2020  
**Featured Application: This work was focused in the generation of six yeast lines able to produce resveratrol from \(p\)-coumaric acid. Industrial use of these strains may be contemplated.**  
**Abstract:** Resveratrol is a nutraceutical with relevant benefits to human health. This investigation reports on the generation and evaluation of six recombinant yeast lines that produce resveratrol from \(p\)-coumaric acid. The yeast lines contained a single \(p\)-coumaric acid-Co-A ligase from _Plagiochasma appencitulum_ combined with the stilbene synthases from _Parthenocissus henryana_, _Polygonum cuspidatum_, _Morus alba_ var. _atropurpurea_, _Rheum tataricum_, _Vitis viinfera_ and _Anchis hypogaea_. Codon optimized versions of these sequences were inserted in an expression vector flanked by the constitutive PGK and GPD promoters before expression in _Saccharomyces cerevisiae_. Batch fermentation (60 h) revealed that yeast lines had different capacities (\(p<\) 0.01) to produce resveratrol. Slightly acidic pH (6) and concentrations \(<\)100 mg L\({}^{-1}\)\(p\)-coumaric acid improved resveratrol yields. Among the six lines, those containing the stilbene synthases (STS) from _P. cuspidatum_ and _M. alba_ produced up to 39 mg L\({}^{-1}\) using 70 mg L\({}^{-1}\)\(p\)-coumaric acid. On the other hand, lines expressing STS from _V. viinfera_, _A. hypogaea_ and _R. tataricum_ generated resveratrol faster than other lines but accumulated lower amounts at the end of the batch period (27-30 mg L\({}^{-1}\)). The simultaneous consumption of ethanol and \(p\)-coumaric acid corroborates the role of ethanol as a carbon source involved in the conversion of \(p\)-coumaric acid into resveratrol.  
**Keywords:** resveratrol production; 4 CL; STS; engineered yeasts
## Appendix A  
The Tables A1-A3 list descriptive values for all parameters for period perturbation measures (Table A1), amplitude perturbation measures (Table A2) and energy perturbation measures (Table A3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter Name and** & \multirow{2}{*}{**Average**} & \multirow{2}{*}{**Standard Deviation**} & \multirow{2}{*}{**Maximum**} & \multirow{2}{*}{**Minimum**} \\
**Sequence Length** & & & & \\ \hline \multicolumn{5}{c}{**Energy Perturbation Measures (EPM)**} \\ \hline \hline \(EPQ3_{\rm C5}\) & 9.880 & 7.443 & 23.847 & 0.499 \\ \(EPQ3_{\rm C10}\) & 7.768 & 5.175 & 16.266 & 0.443 \\ \(EPQ3_{\rm C20}\) & 7.395 & 4.466 & 14.732 & 1.777 \\ \(EPQ3_{\rm C50}\) & 7.292 & 3.854 & 14.781 & 2.117 \\ \(EPQ3_{\rm C100}\) & 7.295 & 3.701 & 14.341 & 2.528 \\ \hline \(EPF_{\rm C5}\) & 11.089 & 6.934 & 24.424 & 0.869 \\ \(EPF_{\rm C10}\) & 11.108 & 6.517 & 21.952 & 1.209 \\ \(EPF_{\rm C20}\) & 11.233 & 6.209 & 20.983 & 2.812 \\ \(EPF_{\rm C50}\) & 11.384 & 5.526 & 22.090 & 3.340 \\ \(EPF_{\rm C100}\) & 11.392 & 5.381 & 21.629 & 4.210 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Group values of all parameters for Energy Perturbation Measures (EPM).
# Advancement of the DRPE Encryption Algorithm for Phase CGHs by Random Pixel Shuffling
###### Abstract  
This work presents an optical encryption process for various types of information related to 3D worlds (scenes) or 2D images, utilizing Computer-Generated Holograms (CGHs). It also introduces a modification to the Dual Random Phase Encoding (DRPE) encryption algorithm by incorporating pixel shuffling. This proposal enables the use of either a single key for both pixel shuffling and phase mask definition or two independent keys. The latter option is particularly advantageous in applications that require the involvement of two independent agents to retrieve the original plaintext. The dimension of the CGHs determines the size of the keys based on the random generation of values by cryptographically secure algorithms, so the use of arithmetic encryption is proposed for data compression. However, this proposal allows the use of other algorithms described in the literature to generate the shuffle and phase matrices. The complete workflow is described starting from the synthesis of a 3D scene, defined by a mesh of triangles with shape and appearance modeling, or 2D images of any level of geometric or visual complexity using computer graphics; its storage in a CGH, the encryption and decryption process, and finally, the results obtained in the laboratory and by simulation are shown. The similarity between different encryption levels is measured by the Pearson Coefficient to evaluate the results obtained.  
Keywords:Computer-Generated Hologram; optical encryption; double random phase encoding; pixel permutation; secure analysis; 3D computer graphics; random pixel shuffling
# A Digital Bang-Bang Clock and Data Recovery Circuit Combined with ADC-Based Wireline Receiver
## 3 The Proposed DBB-CDR Circuit with an ADC-Based Wireline Receiver  
The 32 Gb/s ADC-based receiver used in this work utilized a continuous-time linear equalizer (CTLE), 4 \(\times\) 8 time-interleaved successive comparison register (TI-SAR) ADCs, four track and hold (T&H) circuits with eight sub-ADCs per track/hold. The 32 TI-SAR ADCs converted the signals into the digital domain and sent them to the DSP. The DSP performed ADC calibration, feedforward equalization (FFE) and decision feedback equalization (DFE). A combination of 1-tap DFE and 8-tap FFE was used in the DSP and the equalized results were discriminated and transmitted. The DSP also sent the signals to the clock data recovery circuit and then the CDR circuit adjusted the sampling phase of the track and hold circuits according to the obtained information. A block diagram of the whole 32 Gb/s ADC-based receiver is shown in Figure 6.
# Encapsulated Probiotics: Potential Techniques and Coating Materials for Non-Dairy Food Applications
## 4 Biomaterials Utilized for Probiotics Encapsulation  
To be an effective encapsulation material (core or wall material), the biomaterial used must be able to protect the encapsulated probiotics along the gastrointestinal tract until reaching the target site (small intestine/large intestine), where they can exert their health-promoting effects. The encapsulation material should only release the encapsulated probiotics when it is exposed and triggered by certain environmental conditions (such as temperature, pH, and enzyme activity). In other words, the capsules containing probiotics should remain protected inside the encapsulation material during the passage through the stomach and only decompose after reaching the target site to release the probiotics. The commonly used biomaterials in probiotic encapsulation include carbohydrates, proteins, and lipids, which will be discussed in detail in the coming subsections. Their specific advantages and limitations in probiotic encapsulation are also summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Properties of Encapsulation** & **Advantages** & **Disadvantages** & **References** \\ \hline \multirow{4}{*}{Spray chilling} & \multirow{4}{*}{\begin{tabular}{c} Produces capsules with sizes of \\ 20-200 \(\mu\)m. \\ Encyclapsules \\ hydrophobic compounds. \\ \end{tabular} } & \begin{tabular}{c} Monodispersity. \\ Fast, continuous, mild process. \\ Low operation cost. \\ Suitable for mass \\ production Promising in \\ controlled release of probiotics. \\ \end{tabular} & \begin{tabular}{c} Low encapsulation efficiency. \\ Rapid release of the \\ encapsulated probiotics. \\ Special storage conditions can \\ be required. \\ \end{tabular} & [43, 44, 48, 64] \\ \hline \multirow{4}{*}{Fluidized bed coating} & \multirow{4}{*}{\begin{tabular}{c} Produces capsules with sizes of \\ 5-5000 \(\mu\)m. \\ Encapsulatelshydrophilic and \\ hydrophobic compounds. \\ \end{tabular} } & \begin{tabular}{c} Mild process. \\ Operation cost. \\ Suitable for mass production. \\ Can provide \\ multi-coating layers. \\ Suitable for temperature- \\ sensitive probiotics. \\ \end{tabular} & \begin{tabular}{c} Thechrique: extrusion, \\ emulsion.Could form a \\ strong gel network by \\ interacting with cationic \\ material (e.g. chitosan). \\ Combination: pectin, \\ starch, chitosan. \\ \end{tabular} & [74, 75] \\ \hline \multirow{4}{*}{Cathosan} & \multirow{4}{*}{\begin{tabular}{c} Cathosan \\ 20-1600 \(\mu\)m. \\ \end{tabular} } & \begin{tabular}{c} Cathosan \\ 20-1600 \(\mu\)m. \\ \end{tabular} & \
\begin{tabular}{c} Cathosan \\ 20-1600 \(\mu\)m. \\ \end{tabular} &
\begin{tabular}{c} Cathosan: extrusion, \\ 20-1600 \(\mu\)m. \\ \end{tabular} \\  & & & & \\ \cline
# Reliability of NAND Flash Memories: Planar Cells and Emerging Issues in 3D Devices
## 2 The NAND Memory Array
### Array Architecture and Layout  
The basic element at the heart of the NAND memory is the floating-gate (FG) transistor, in which a polysilicon island is sandwiched between a gate (or tunnel) oxide and an interpoly dielectric, in turn connected to a control gate. Whenever a charge \(Q\) is stored in the floating gate, simple electrostatics [32] shows that the threshold voltage \(V_{T}\) of the cell becomes  
\[V_{T}=V_{T0}-\frac{Q}{C_{pp}}, \tag{1}\]  
where \(V_{T0}\) is the so-called _neutral_ threshold voltage, corresponding to zero charge on the floating gate, and \(C_{pp}\) is the interpoly dielectric capacitance. As charge cannot easily leak from the isolated floating gate, a non-volatile memory element is achieved. In the NAND array, transistors are connected in series along _strings_, while their control gates, connected across different strings, constitute the wordlines (WLs), each making a _page_, as schematically depicted on the left of Figure 1 (more than one page can be associated to the same WL, but this is irrelevant for our purposes). Strings are connected to the bitlines (BLs) and the common sourceline via select transistors driven by a drain select line (DSL) or a source select line (SSL). This array constitutes a _block_, whose size is a key element in NAND array organization.  
Cross-sections of a current planar NAND array are shown on the right of Figure 1: Figure 1a depicts a typical cut along the WL (green = silicon, red = floating gate, magenta = WL, white = silicon oxide), highlighting the shallow trench isolation used to separate the active area of adjacent devices [33] and the planar structure of the cell, without any wrapping of the control gate along the floating gate sidewalls [34]. Figure 1b depicts instead the cross-section along the string direction. Figure 1c shows the layout of the memory array, which becomes extremely compact thanks to these solutions. In particular, given the feature size \(F\) of a given technology, i.e., the size of the smallest feature that can be created on silicon, the elementary memory cell (shown as a dashed square in Figure 1c) occupies the ideal area of \(4F^{2}\). However, the _effective_ cell area, i.e., total chip area divided by the number of cells, is somewhat larger than that because of the overhead circuitry, string contacts with BLs, dummy cells and select transistors. To decrease such overhead, the number of cells in a NAND string has reached the impressive number of 150 in the latest planar nodes [10].
## 1 Introduction
[PERSON] [14] investigated the feasibility of using classic hydrocyclone separators to achieve the de-bonding of weakly cemented hydrates and found that de-bonding primarily occurs in the cylindrical section of the separator. However, the de-bonding performance of classic hydrocyclone separators does not meet the requirements for commercial extraction, leading to a significant number of hydrates being refilled with sand, thus lowering the hydrate recovery rate. Since the most critical location for de-bonding in hydrocyclone separators is the cylindrical section, increasing the load on the cementing bonds in this  
Figure 1: Three morphologies of natural gas hydrate (NGH) [7]: (**a**) cementation, (**b**) pore filling, and (**c**) load bearing. section and extending the residence time of particles can effectively enhance the de-bonding effect. Nevertheless, improving the de-bonding performance by adjusting the structural parameters of classic hydrocyclone separators is not cost-effective. [PERSON] [15,16] used a combined computational fluid dynamics-discrete element method to study the interaction behavior between hydrate particles, sediment particles, pipelines, and fluids at a pipeline constriction, revealing the mechanism of hydrate migration and deposition at the constricted section of the pipeline. [PERSON] [17,18,19] used a combination of helical and hydrocyclone separators to enhance the performance. The above results indicate that the insufficient residence time of particles constitutes the primary factor contributing to the suboptimal de-bonding performance of classic hydrocyclone separators. This issue can be effectively addressed through the implementation of a combination of helical and hydrocyclone separators, which significantly enhances the residence time of particles. Building on this, this paper proposes a new type of composite in situ separator designed with a tangential inlet to improve its de-bonding performance.
## 4 Spatial Resolution of the Measurements  
In this Section, the aim is to determine the spatial resolution of the measurement by using a 2D model for this problem. This 2D study should confirm the 1D approach developed above. The 1D approach was given for bubble diameters that are an order of magnitude larger than the plate thickness studied: \(D\gg e\). It is important to verify the behavior of the plate with a bubble whose diameter is of the order of the thickness of the plate: \(D\approx e\). The 2D effects in this problem are inherent to the fact that the plate is powered by the direct Joule effect and, therefore, with an internal heat source. The resistance of the plate drives the flux distribution and any change in temperature impacts this distribution and can create inhomogeneities or oppose a temperature change. First, the 2D model is presented with a comparison between the 2D and 1D results for a large bubble (\(D\gg e\)). Then a sensitivity study is carried out to establish the thickness at which a bubble of a fixed size (pressure dependent) can be detected by an IR camera on the dry side (in the case of \(D\approx e\)). Finally, we are interested in the minimum distance between two bubbles for them to be detected independently of each other by an IR camera on the dry side of the plate.
###### Abstract  
The rapid development of the Intelligent connected vehicle (ICV) industry has stimulated technological innovation in energy and communication while also highlighting the need for effective policies and road safety measures. Understanding and addressing road safety issues in the context of ICVs can contribute to ICV development and safe driving. This paper employs a knowledge mapping approach to scientifically and intuitively demonstrate research on the road safety issues of ICV over the last decade. By utilizing bibliometric tools such as CiteSpace, VOSviewer, and SciMAT, a total of 3661 original articles from the Web of Science are examined to explore three aspects. Firstly, the study investigates the collaborative relationships among authors and institutions within the industry. Secondly, it summarizes major research topics by analyzing and clustering keywords. Lastly, the paper identifies research hotspots and predicts future research directions. The findings reveal a dynamic field characterized by close collaboration among diverse institutions, with China and the United States emerging as the most active countries and mathematics and computer science journals becoming mainstream. According to three bibliometric tools, the research topics primarily revolve around three areas: Vehicular ad hoc Networks (VANET), intelligent transportation systems (ITS), and network security. Machine learning and V2X communication are predicted to be essential research topics in the next stage. Research on traffic accidents still has potential as the number of ICVs increases.  
knowledge mapping; intelligent connected vehicle; road safety; traffic accident; bibliometric
## 3 Dynamic Multi-Hop Routing (DMR) Technique
### Energy Dissipation Model  
The DMR protocol employs the energy dissipation model presented in [6]. This model is used to compute the energy required to transmit or receive a K-bit packet. Additionally, this model is applied to calculate the energy consumed by control packets. Here, Equations (2) and (3) are used for transmission and reception, respectively:  
\[E_{Tx}(k,\ d)=\left\{\begin{array}{l}E_{dec}\times k+\varepsilon_{fs}\times k \times d^{2},\ \ \ \ d<d_{o}\\ E_{dec}\times k+\varepsilon_{mp}\times k\times d^{4},\ \ \ \ d\geq d_{o} \end{array}\right. \tag{2}\]  
\[E_{Rx}(k)=E_{elce}\times k \tag{3}\]  
In this context, \(E_{elce}\) denotes the amount of energy required per bit for handling data transmitted or received across the WSN. Both the \(\varepsilon_{fs}\) and \(\varepsilon_{mp}\) parameters correspond to the free-space propagation model and the two-ray propagation model, respectively. It is worth mentioning that \(\varepsilon_{fs}\) is used when the transmission distance is less than \(d_{o}\), while \(\varepsilon_{mp}\) is applied otherwise. The threshold distance \(d_{o}\) is given by Equation (4) [6]:  
\[d_{o}=\sqrt{\varepsilon_{fs}/\varepsilon_{mp}} \tag{4}\]
## 3 Experimental Program
### Test Methods
#### 3.3.2 Hydraulic Tests  
The effective porosity was determined by testing the volume of water displaced by samples according to ASTM C1754/1754M-12 [27]. The sample was firstly oven dried at 110 \({}^{\circ}\)C for 24 \(\pm\) 1 h. Specimens after drying should not be used to determine other properties. Hydraulic tests were conducted after the specimen cooled at room temperature for 1 to 3 h. Then, specimens were immersed in water for up to 24 h. By measuring the difference in the water level before and after immersing the sample, the volume of water repelled by the sample (\(V_{d}\)) can be readily determined. Subtracting \(V_{d}\) from the sample bulk volume (\(V_{b}\)) yields the volume of open pores. The percentage of an effective porosity was expressed as: \(p_{e}=\left(V_{b}-V_{d}\right)/V_{b}\times 100\%\).  
Water permeability of the pervious concrete was measured using the constant head method similar with ASTM D2434 [28], which is shown in Figure 1. To protect from the water leakage between sample and test device, the cylindrical specimen was wrapped with a rubber tube and tightened by circular clamps. Water was allowed into the specimen to obtain a steady state flow. The time in seconds (\(t\)) required for the water in the tuber (\(Q\)) to drop from the top to bottom was recorded. The coefficient of water permeability (\(k\)) in terms of centimeters per second (cm/s) was calculated using Darcy's Law as shown in Equation (1).  
\[k=\frac{QL}{HAt} \tag{1}\]  
where \(k\) is coefficient of permeability; \(Q\) is quantity of water discharged; \(L\) is the height of the specimen; \(H\) is the distance between two water surface; \(A\) is the cross-section area of the specimen; \(t\) is time in seconds.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{3}{c}{**Mass Percentage**} & \multirow{2}{*}{**W-C Ratio**} \\ \cline{2-2} \cline{4-4}  & **C** & & **FA** & **BFS** \\ \hline M1 & 100 & 0 & 0 & 0.35 \\ M2 & 70 & 0 & 30 & 0.35 \\ M3 & 70 & 10 & 20 & 0.35 \\ M4 & 70 & 15 & 15 & 0.35 \\ M5 & 70 & 20 & 10 & 0.35 \\ M6 & 70 & 30 & 0 & 0.35 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cementitious pastes mix.  
Figure 1: Constant-head permeability test.