esa-sceva/satcom-chunk-collection huggingface dataset:
# Lightweight Detection Method of Obfuscated Landing Sites Based on the AST Structure and Tokens
## Proposed System
### Lightweight AST
#### 3.2.2 Reduction Rate of the Lightweight AST  
In this section, we calculate the representative minimum node reduction rate (theoretically, per each input JavaScript) when generating lightweight ASTs. The higher reduction rate of the lightweight AST reduces the comparison time. According to the ECMAScript 5 standard, there are expressions and statements that can be repeated in AST. The reduction rate differs depending on how frequently the expressions and statements can be repeated in lower nodes. We set \(E\) and \(S\) as the number of repetitions of expressions and statements, respectively (\(E\), \(S\geq 0\)). When generating an AST, we calculate the node reduction rate of each node type using the following formula in the AST:  
Node reduction rate = #nonstructural node/(#structural node + #nonstructural node)  
We define the smallest minimum value among all these reduction rates as the representative minimum node reduction rate. Table 1 shows the minimum node reduction rates of each node type, as \(E\) and \(S\) vary. Among the nodes shown in the table, SwitchStatement and TryStatement have comparatively lower minimum node reduction rates than the other node types. The smallest minimum value of the two minimum node reduction rates is the representative minimum node reduction rate. The smallest minimum value can be determined depending on both \(E\) and \(S\). Therefore, the representative minimum node reduction rate is given as  
\[ \begin{array}{c}\text{If }E<2S+2,\frac{1}{2S+2},\\ \text{If }E>2S+2,\frac{S+2}{(S+1)(E+2)+(S+2)},\\ \text{If }E=2S+2,\frac{1}{2S+3}=\frac{S+2}{(S+1)(E+2)+(S+2)}\end{array} \] \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of AST Node** & **Nonstructural Node Ratio** & **Type of AST Node** & **Nonstructural Node Ratio** \\ \hline Programs & \(\frac{1}{2S+2}\) & SequenceExpression & \(\frac{1}{E+2}\) \\ \hline Identifier & \(1\) & ExpressionStatement & \(\frac{1}{E+2}\) \\ \hline Literal & \(1\) & BlockStatement & \(\frac{1}{S+2}\) \\ \hline FunctionDeclaration & \(\frac{1}{2}\) & EmptyStatement & \(1\) \\ \hline VariableDeclaration & \(\frac{1}{S}\) or \(\frac{2}{E+4}\) & DebuggerStatement & \(1\) \\ \hline ThisExpression & \(1\) & WithStatement & \(\frac{S+2}{(S+1)(E+1)+(S+2)}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Minimum node reduction rate of each AST node type. Figure 2: Lightweight AST and token extraction algorithm. As shown in Table 2, we analytically compute the representative minimum node reduction rate for 23 different obfuscation types. This result is obtained by first computing the maximum number of expression and statement repetitions and then applying them to the minimum node reduction formula. **Theorem 2**. : _The proposed lightweight AST reduces the structure comparison time with a higher rate than the node reduction rate compared to the conventional AST._
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 4 SMTrust Design and Analysis
### Trust Metrics Identification and Trust Computation  
Trust parameters are vital for the evaluation of a trust-based approach. The trust metrics employed by SMTrust are scrutinized in detail [14]. The important and applicable trust metrics in IoT involve historical observations, energy, direct trust, and recommendations, that is, recommended or indirect trust from neighbouring nodes. SMTrust incorporates mobility of nodes by considering mobility-based metrics in trust computation, including node relocation and link stability.
## 3 Results and Discussion
### Method Validation
#### 3.2.4 Precision Repeatability  
To validate the analytical method, a series of measurements were performed by the same analyst in a short period of time on the same HPLC instrument. The parameters examined were within-day precision (repeatability) and between-day precision (intermediate precision) over three consecutive days. Repeatability was checked over the entire concentration range, at three levels: the lowest, the medium and the highest. For the lowest and the highest concentrations, four repetitions were performed, while for the medium six. For the intermediate precision, the average of the repetitions was calculated, at the three concentration levels between three days. The repeatability results were within the acceptable limits (Table 4).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**APIs**} & \multicolumn{2}{c}{**Repeatability**} & \multicolumn{4}{c}{**Intermediate Precision**} \\ \cline{2-7}  & **Concentration** & **RSD\%** & **Concentration** & **1 st Day** & **2 nd Day** & **3 rd Day** & **RSD\%** \\ \hline \multirow{3}{*}{DIP (UV)} & 1.6 (\(n=4\)) & 0.54 & 1.6 (\(n=4\)) & 0.54 & 0.17 & 0.19 & 0.43 \\  & 16 (\(n=6\)) & 0.25 & 16 (\(n=6\)) & 0.25 & 0.23 & 0.11 & 0.20 \\  & 40 (\(n=4\)) & 0.25 & 40 (\(n=4\)) & 0.25 & 0.78 & 1.53 & 1.75 \\ \hline \multirow{3}{*}{DIP (FLD)} & 0.052 (\(n=4\)) & 0.61 & 0.052 (\(n=4\)) & 0.61 & 1.40 & 1.61 & 1.88 \\  & 0.104 (\(n=6\)) & 0.97 & 0.104 (\(n=6\)) & 0.97 & 0.99 & 1.84 & 1.85 \\  & 0.157 (\(n=4\)) & 0.70 & 0.157 (\(n=4\)) & 0.70 & 0.21 & 0.33 & 0.53 \\ \hline \multirow{3}{*}{ACA (UV)} & 1.6 (\(n=4\)) & 0.86 & 1.6 (\(n=4\)) & 0.86 & 1.00 & 1.64 & 1.63 \\  & 16 (\(n=6\)) & 0.28 & 16 (\(n=6\)) & 0.28 & 0.20 & 0.39 & 1.26 \\  & 40 (\(n=4\)) & 0.39 & 40 (\(n=4\)) & 0.39 & 0.88 & 1.44 & 2.29 \\ \hline \multirow{3}{*}{SAL (UV)} & 1.6 (\(n=4\)) & 0.72 & 1.6 (\(n=4\)) & 0.72 & 0.84 & 1.14 & 1.78 \\  & 16 (\(n=6\)) & 0.58 & 16 (\(n=6\)) & 0.58 & 0.17 & 0.07 & 0.91 \\ \cline{1-1}  & 40 (\(n=4\)) & 0.30 & 40 (\(n=4\)) & 0.30 & 0.80 & 1.55 & 1.25 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Repeatability and intermediate precision analysis.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Analytes**} & **Concentration** & **Equation \({}^{*}\)** & **\%y** & **(R2)** & **LOD** & **LOQ** \\  & **(\%/mL) \({}^{*}\)** & **Interior** & **(\%/mL)** & **(\%/mL)** & **(\%/mL)** \\ \hline DIP (UV 285 nm) & 1.6–40 & y = 67,764.0 \(\pm\) 104.0 x – & 0.08 & 1 & 0.099 & 0.302 \\ DIP (FLD & \multirow{2}{*}{52.3–156.8 \({}^{*}\)} & y = 4353.8 \(\pm\) 67.2 x + & 1.61 & 0.999 & 0.011 & 0.033 \\ \(\lambda_{\text{enc}}/\lambda_{\text{em}}\):285/480 nm) & 1.6–40 & y = 38,234.3 \(\pm\) 197.6 x + & 0.46 & 0.999 & 0.336 & 1.019 \\  & 1.6–40 & y = 15,398.3 \(\pm\) 57.5 x – & 0.42 & 0.999 & 0.243 & 0.736 \\  & 1.6–40 & y = 50,203.2 \(\pm\) 71.2 x + & 0.014 & 1 & 0.092 & 0.279 \\ \hline \multicolumn{6}{c}{\({}^{*}\)Concentration expressed in ng/mL for DIP with FLD detector.} \\ \end{tabular}
\end{table}
Table 3: Linear regression analysis data.
# Design and Analysis of Joint Source-Channel Code System with Fixed-Length Code
## 5 Results and Discussion
### Systems Parameters
In the proposed scheme, the optimized block code with length \(L~{}=~{}6\) is generated by Algorithm 1, and the parameters of the irregular RNSC code are listed in Table 4
## 3 Field Experiments  
During May-June 2008 the first _PAU_-One Receiver was deployed at El Mirador del Balcon, La Aldea de San Nicolas, in the North-West coast of Gran Canaria in the Canary Islands, and gathered for the first time ever L-band radiometric and GNSS-R data, together with oceanographic data (sea surface temperature + sea surface directional spectrum buoys). The field experiment was repeated during the same period of time in 2009 with an improved version of the instrument (_griPAU_) that collected radiometric and GNSS reflectometric data collocated both in time and space using two different antennas with the same 22\({}^{\circ}\) beamwidth (Figure 14).  
Now, we start to understand the relationship between the sea state and the GNSS-R observables (DDMs) and the changes in the brightness temperature. Figure 15 shows the scatter plot of the measured DDM volume (in arbitrary units) _vs._ the SWH for different threshold values. It can be appreciated that increasing the threshold decreases the sensitivity to SWH since a lower volume is being considered. However, this threshold cannot be arbitrarily small, since it has to be above the noise threshold to provide meaningful observations (Figure 4).  
Figure 14: _griPAU_ deployed during the ALBATROSS 2009 field experiment.  
Figure 15: DDM-volume dependence on the SWH for three different thresholds [8].  
The correlation of the instantaneous brightness temperature changes and the instantaneous DDM volumes observed during ALBATROSS 2009 is shown in Figure 16 for incidence angles larger than 55\({}^{\circ}\), since the cliff already imposed a 45\({}^{\circ}\) mask, and incidence angles between 45\({}^{\circ}\) and 50\({}^{\circ}\) were affected by multi-path.  
As it can be appreciated, the behavior is very similar to that shown in Figure 1, including a zero crossing around 55\({}^{\circ}\) degrees at vertical polarization, which suggests that both descriptors (WS in Figure 1 and the volume of the DDM, V\({}_{\rm DDM}\), in Fig 16) are measuring the same phenomena.  
Despite these encouraging results, there is still a long way to go until meaningful physical quantities that can be successfully extracted from satellite data to be used by the oceanographic communities, and they can be used to perform the sea state correction in sea surface salinity retrievals. More extensive data sets need to be gathered and processed.  
The limited GNSS-R data gathered by the UK-DMC satellite and publicly available [23] shows the potential of this technique, and supported the proposal of a _PAU_ secondary payload in SeoSat/INGENIO (Spanish Earth Observation Satellite) [24]. This proposal went through phase A, but did not succeed to pass into phase B due to the accommodation issues with the primary payload raised after a configuration change. Simplified, lighter and less power consuming payloads are currently under development in cooperation with industry and they will be available for future launches of opportunity in micro- or even in pico-satellites.
## 4 Chemical Composition and Structure of _Astragalus_ Polysaccharides  
The monosaccharide compositions of APS obtained from different plant varieties and habitats and using different methods are different. High-performance liquid chromatography (HPLC), gas chromatography (GC), mass spectrometry (MS), and nuclear magnetic resonance (NMR) are commonly used to analyze the chemical composition and structure of APS. Most studies have shown that APS is mainly composed of glucose, in addition to rhamn, galactose, arabin, and other monosaccharides (Figure 1). The main chain contains linked \(\alpha\)-(1\(\rightarrow\)4) glucose residues. The relative molecular mass of APS is 5.6 \(\times\) 10\({}^{3}\)-10\({}^{6}\) Da (Table 1). Monosaccharide composition analysis of an APS sample by HPLC revealed that it was composed of rhamn, glucose, galacturonic acid, and arabin in a molar ratio of 1.19:72.01:5.85:20.95 [24]. Hydrolysis of another APS sample with trifluoroacetic acid to monosaccharides and analysis of the monosaccharide composition by HPLC revealed that this polysaccharide was composed of mannose, rhamn, galacturonic acid, glucose, and arabin in a molar ratio of 0.02:0.05:0.17:1.00:0.18 [25]. [PERSON] et al. [26] determined the monosaccharide composition of APS by capillary GC, and the results showed that it was composed of arabin, fructose, glucose, and mannose in a molar ratio of 1.00:3.24:7.00:0.46. The structures of APS isolated from different varieties of Astragali Radix are also different. Moreover, the chemical structure of polysaccharides influences their biological activities. Thus, [PERSON] et al. [27] analyzed monosaccharides in APS by GC-MS, and the results revealed that the polysaccharide was mainly composed of L-rhannose, L-arabinose, D-xylose, L-xylose, D-ribose, L-ribose, D-galactose, D-glucose, and D-mannose. Furthermore, monosaccharides extracted from a different _A. membranacus_ variety were different. The structure of APS was determined by GC-MS and infrared spectroscopy (IR), and the results showed that the main chain was mainly composed of glucose, xylose, and galactose, and the side chains were composed of glucose, arabin, and galactose [28]. Furthermore, the branching point was composed of glucose, galactose, and arabin, and the terminal residue was glucose. The APS powder for injection, developed by Panhua Pharmaceutical Co., Ltd., is mainly used as a chemotherapy or adjuvant therapy after radiotherapy in cancer patients. It has been reported that the main components of APS are alpha-1,4(1,6)-glucan, arabinose-galactose, rhamnose-galacturonic acid polysaccharide, and arabinose-galactoprotein polysaccharide [29]. These studies have laid a foundation for further analysis of the mechanisms of pharmacological activities of APS. Figure 1: The monosaccharide structure of _Astragalus_ polysaccharides.
###### Abstract  
Plastic waste management is currently a challenge of great importance. The valorization of non-recyclable fractions into carbonaceous adsorbents is an interesting strategy that promotes the circular economy. In this work, a waste-to-adsorbent strategy was pursued with the char from plastic pyrolysis. The char (non-porous, surface area \(\sim\)3 m\({}^{2}\) g\({}^{-1}\)) was activated with chemical activation, with sodium carbonate boosting the textural properties (surface area \(\sim\)418 m\({}^{2}\) g\({}^{-1}\), pore volume 0.436 cm\({}^{3}\) g\({}^{-1}\)), triggering the formation of activated carbon with a large mesoporous (71%). X-ray photoelectron spectroscopy and thermal programmed desorption characterization confirmed the enrichment of the surface with carboxylic groups by treatment with citric acid, with a slight loss of textural properties. The activated carbon showed an enhanced adsorption uptake of lead in water (\(\sim\)52 mg g\({}^{-1}\) functionalized vs. \(\sim\)37 mg g\({}^{-1}\) non-functionalized) and limited influence on the adsorption of acetaminophen. The preparation costs and the consumption cost per unit of removed pollutants confirm the benefits of the activation and functionalization of the original carbonaceous precursor. However, the possible metal lixiviation from plastic additives and the environmental impact according to a life cycle assessment still make this kind of valorization strategy controversial.  
plastic waste char; activated carbon; sodium carbonate activation; citric acid modification; water contaminant removal; characterization; economic and environmental assessment
## 3 Results
### E. coli Adsorption by Biochar  
The effects of vine biochar on _E. coli_ F4+ and F18+ adsorption were analyzed, and the results are shown in Figure 2. The number of _E. coli_ colonies generally decreased after incubation with vine biochar. When _E. coli_ F4+ was incubated with 0, 5, 10, and 20 mg of vine biochar at 37 \({}^{\circ}\)C for two hours, the total number of bacteria was 148.00 \(\pm\) 18.92 \(\times\) 10\({}^{6}\), 78.83 \(\pm\) 7.51 \(\times\) 10\({}^{6}\), 62.42 \(\pm\) 7.19 \(\times\) 10\({}^{6}\), and 44.73 \(\pm\) 7.77 \(\times\) 10\({}^{6}\) cells/mL, respectively. After two hours of contact, the percentage adsorption of _E. coli_ F4+ was at 46.7%, 57.8%, and 69.8%, respectively (Figure 2A). On the other hand, when _E. coli_ F18+ was incubated with 0, 5, 10, and 20 mg of vine biochar at 37 \({}^{\circ}\)C for two hours, the total numbers of bacteria were 157.00 \(\pm\) 7.20 \(\times\) 10\({}^{6}\), 141.00 \(\pm\) 10.66 \(\times\) 10\({}^{6}\), 86.25 \(\pm\) 7.78 \(\times\) 10\({}^{6}\), and 68.92 \(\pm\) 10.27 \(\times\) 10\({}^{6}\) cells/mL, respectively (Figure 2B). The percentage sorption of _E. coli_ F18+ was at the respective concentrations of 10.2%, 45.0%, and 56.1% after two hours of contact (Figure 2B).
# DPtracer: Integrating Log-Driven Accountability into Data Provision Networks
## 4 DPTracer Logger
### Interval Proof
#### 4.3.1 Constructing an Interval Proof  
The construction of an interval proof begins by capturing the log entries for the targets, e.g., \(X_{3}\), \(X_{5}\), and \(X_{6}\) for \(\pi_{3,5,6}\). It sets the highest version among the targets as the anchor  
Figure 5: Interval proof (\(\pi_{3,5,6}\)).  
version of the interval proof. In Figure 5, the anchor version of \(\pi_{3,5,6}\) is 6. The interior elements \(e_{1}\) and \(e_{2}\) are siblings along the path from the targets \(X_{3}\), \(X_{5}\), and \(X_{6}\) to the root. The commitment \(C_{6}\) can be computed from the left branch consisting of \(X_{3}\), \(X_{5}\), and \(e_{1}\) and the right branch consisting of \(X_{6}\) and \(e_{2}\).  
Given a set of target log entries with version numbers \(v_{1},v_{2},\ldots,v_{k}\), the logger constructs an interval proof as follows:  
1. **Identify the anchor version**: determine the highest version among the targets and denote it as \(v_{\max}=\max(v_{1},v_{2},\ldots,v_{k})\).
2. **Collect target nodes**: obtain the leaf elements corresponding to the target log entries.
3. **Find common ancestors**: for each target element, trace the path to the root, checking the sibling nodes along the way, and find the lowest common ancestor.
4. **Build minimal proof set**: combine the paths from all target elements to the root, eliminating duplicate elements. The interval proof includes: * The log entries of the target leaf elements; * The hash values of the sibling elements along the combined paths.
5. **Assemble proof**: assemble the collected log entries and hash values into the interval proof structure along with the anchor version \(v_{\max}\).
## 3 Case Study 1
### Identification and Definition of the Functions  
As suggested by [PERSON] and [PERSON] [57], the use of FRAM for accident investigation starts from the identification and characterization of essential functions. Then, the variability of each function is analyzed based on the accident reports, and its impact on the other functions is evaluated.  
The first step consisted in the definition of essential functions, which are the following:  
* ACC. Patient acceptance: the function that activates the entire process is related to the patient's suitability for the oxygen therapy treatment. Hospital staff has to verify the patient's anamnesis and the request for the treatment issued by a specialist. In this phase, the patient should be informed about the risks related to the treatment, the prohibition of introducing flammable objects into the room, etc. The output of this organizational function is represented by the patient's suitability for the oxygen treatment.
* PREP. Chamber preparation: the hospital staff has to check the correct functioning of the HBOT room, including its safety systems, such as the fire extinguishing devices, as well as the masks/hoods to be used by the patients during the treatment. At the same time, the intervention threshold of the oxygen analyzer has to be set.
* DEV. Provide devices: the hospital staff provides patients with a hood or a mask depending on the type of therapy and verifies they are worn correctly.
* SET. Chamber closing and oxygen settings: once patients are equipped with the mask/hood and are in the correct position inside the chamber, the door is closed, and the HBOT room is set for the treatment.
* RUN. Running treatment: the treatment is performed.
* MON. Monitoring: during the treatment, the hospital staff has to monitor the correct functioning of the system and the proper percentage of oxygen administered. This control is carried out both outside the HBOT room through a control panel and inside the room (usually by a nurse who assists patients).
* END. End treatment: when the treatment time is completed, the staff starts the decompression phase that is concluded with the opening of the chamber door.
# Autonomous Active Tag Using Energy Harvesting Strategies
## 2 Materials and Methods
### Application and Scenarios  
There are many possible scenarios that could be used to test this autonomous active tag, but we decided to validate the design of our device by saving information in the tag related to road maintenance, thanks to our collaboration with a company specialized in this topic. The collaborative project with the company consists of developing intelligent tags (our autonomous active tag) for the identification, monitoring and traceability of road materials.  
Figure 3: (**a**) Tag board; (**b**) Fully assembled autonomous active tag.  
We tested our system in a real environment without a battery. The specific application consists of attaching the device to a road sign with medium/low traffic running at a velocity of 50-80 km per hour, which means that it is exposed to weather conditions. In addition, this application can provide us with insights into its potential application in autonomous driving communications vehicle to vehicle (V2V) and vehicle to infrastructure (V2I), etc., which are topics with a great importance these days. These applications make the idea of autonomous active tags necessary as it is impossible to develop a passive tag for these conditions, where there is no close communication with the reader.
# An Assessment of Anthropogenic CO\({}_{2}\) Emissions by Satellite-Based Observations in China
## 2 Data and Methodology
### Anthropogenic Emission Data  
We collected two datasets of the bottom-up anthropogenic CO\({}_{2}\) emissions. One is the Open-source Data Inventory for Anthropogenic Carbon dioxide (ODIAC) for same years as the used XCO\({}_{2}\) dataset in this study. The other is the CARbon Monitoring for Action (CARMA) power plant database in 2009. The specifications of these data are described in Table 1.  
Figure 1: Number of available retrievals and temporal changes of collected XCO\({}_{2}\) data: (**a**) the number of XCO\({}_{2}\) data points within 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\) grid for 6 years from 2010 to 2015; (**b**) the temporal variation of XCO\({}_{2}\) and the monthly averages.  
The ODIAC emissions data product is a global \(1^{\circ}\times 1^{\circ}\) gridded monthly fossil fuel CO\({}_{2}\) emission inventory, developed based on country level fossil fuel CO\({}_{2}\) emission estimates, fuel consumption statistics, satellite-observed nightlight data, and point source information (geographical locations and emission intensities) from the CARbon Monitoring for Action (CARMA) power plant database (ODIAC2015a, available at [[http://db.cger.nies.go.jp/dataset/ODIAC/](http://db.cger.nies.go.jp/dataset/ODIAC/)]([http://db.cger.nies.go.jp/dataset/ODIAC/](http://db.cger.nies.go.jp/dataset/ODIAC/))). The global nightlight data were used as a geo-referenced, spatial proxy to determine the spatial extent of anthropogenic emissions from line and diffused (area) sources (e.g., road traffic, residential or commercial fuel consumption) [25]. The ODIAC gridded emissions fields defined on a global rectangular (latitude \(\times\) longitude) coordinate are remapped to meet the grids resolutions for each simulation domain.  
Additionally, the CO2 emissions from power plant, which is one of the dominant CO\({}_{2}\) emitting sources, are collected in the study area from the database of Carbon Monitoring for Action (CARMA, available at [[http://carma.org/plant](http://carma.org/plant)]([http://carma.org/plant](http://carma.org/plant))). At the same time, we unify the units of the two sets of emission data to ton, and take the logarithm of two emission data base on 10 (refer to as lgE) to facilitate the calculation.