esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characterization of Microcapsules
#### 3.2.4 FTIR Analysis  
The FTIR spectra were shown in Figure 4. In the region below 1116 cm\({}^{-1}\), the microcapsules revealed the same characteristic peaks as AG, while the Mix. was similar to PRJSR. A peak at 1698 cm\({}^{-1}\) due to COOH stretching [31, 32] only appeared in PRJSR and Mix. but not in the microcapsules. The group peaks (marked as A in Figure 4) only appeared in PRJSR, Mix., and microcapsules, indicating the characteristic peaks of PRJSR. The peak at 1640 cm\({}^{-1}\) in GE corresponded to NH\({}^{3+}\), and the peak at 1608 cm\({}^{-1}\) in AG corresponded to COO\({}^{-}\). However, in the microcapsules, there was a peak at 1652 cm\({}^{-1}\), which confirmed that these two groups stimulated interactions [26, 33, 34]. The results confirmed that PRJSR was successfully embedded by GE and AG. In PRJSR, a peak at 1608 cm\({}^{-1}\) also appeared, which might indicate that some fatty acids still existed in the form of sodium salt.
## 5 Design and Implementation  
### Overall Design  
Based on the above insights, we designed a prototype system, FLuaScan, which takes IoT firmware as initial input and ultimately outputs suspicious vulnerability points, along with their data flow details. As shown in Figure 1, FLuaScan consists of three tightly coupled modules.  
As indicated by  in Figure 1, the handler location module automatically extracts Lua applications from the firmware. Then, FLuaScan utilizes heuristic methods based on the characteristics described in Section 4 to identify the dispatch mode and locate service handlers.  
Then, FLuaScan carries out a progressive taint analysis ( in Figure 1) starting from these service handlers. During the analysis process, our system divides multiple Lua files into segments and performs taint analysis in a segmented manner.  
The final module is the graph-based vulnerability search module ( in Figure 1). It records the taint flows from module  and organizes them as a graph based on their relationships. Then, FLuaScan utilizes graph search algorithms to identify the execution paths in dangerous functions that accept user input without sanitization and reports them as potential vulnerability points.
# Interface Direct Shear Tests on JEZ-1 Mars Regolith Simulant
## 3 Experimental Setup
### Interface Direct Shear Test  
In this paper, the mechanical behavior of the interface between JEZ-1 simulant and steel is investigated by a novel interface direct shear device (Figure 4a). This interface  
Figure 3: Steel plates for the interface direct shear test: (**a**) smooth and (**b**) rough surfaces.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Plate** & \(R_{\text{a}}\) (\(\upmu\)m) & \(R_{\text{max}}\) (\(\upmu\)m) & \(d_{50}\) (\(\upmu\)m) & \(R_{\text{n}}\) (-) & **Surface** \\
**Number** & & & & & **Rough** \\ \hline \#1 & 1.0 & 7.9 & 57 & 0.14 & Smooth \\ \hline \#2 & 45.1 & 277 & 57 & 4.86 & Rough \\ \hline \hline \end{tabular}
\end{table}
Table 3: Roughness parameters of the steel plates.  
direct shear apparatus has the capability of conducting shear tests under different loading conditions. It was employed to perform experiments and to characterize the mechanical parameters of the soil-structure interface in the laboratory [11; 15; 17; 18; 19]. The interface direct shear box is divided into two parts, i.e., the upper and the bottom parts (Figure 4b,c). The dimension of the upper part is 100 mm \(\times\) 100 mm and can contain a JEZ-1 sample with a maximum initial height of 50 mm. The bottom part has a dimension of 140 mm \(\times\) 100 mm \(\times\) 11 mm in order to accommodate the steel plate. More details about the interface direct shear apparatus are available in [15].  
The experimental program presented in this section consisted of two phases: (1) direct shear, and (2) interface direct shear. Direct shear tests on the JEZ-1 simulant were firstly performed with the interface machine to get the shear results of soil-soil. Three direct shear tests were performed at normal stresses of 25, 50, and 75 kPa. The normal stresses for the interface direct shear tests with smooth and rough plates are 25, 50, 75, and 100 kPa. After the consolidation phase on the sample had been finished, a displacement-controlled shear was performed with a shearing rate of 0.5 mm/min to a maximum horizontal displacement of 10 mm.  
The samples for the (interface) direct shear tests were prepared by the dry tamping method rather than other sample preparation techniques, such as wet tamping [20; 21; 22], slurry deposition [15; 17; 23; 24; 25; 26; 27; 28], due to the water was not considered in this paper. The average initial sample height for direct shear and interface direct tests are 31.59 mm and 27.58 mm, respectively. Since very loose or very dense Martian regolith is not likely to encounter in the in-situ conditions, the average values of density (\(\rho_{\text{a}}\)) and relative density (\(D_{\text{r}}\)) are 1.84 g/cm\({}^{3}\) and 86.28%, therefore all the samples for the two kinds of shear tests are dense [6; 8; 29].
## 4 Methodology
### Cluster Analysis  
A cluster analysis has been employed to identify group membership for each station in this study. Cluster analysis is a technique to group similar individuals into a number of cluster based on the observed values of some variables for each individual. In our case, the clusters have been calculated through the Biplot-coordinates (K-means method, Eucliden distance). K-means [41, 42] is a clustering algorithm very popular for cluster analysis in data mining. K-means is an iterative algorithm that tries to partition the data set into \(K\) predefined disjoint clusters in such a way that each observation belongs to only one group. The algorithm consists of the following steps:  
1. To fix the number \(K\) of cluster to be used.
2. To initialize the \(K\) centroids.
3. Each station is assigned to the cluster with the nearest centroid. In our case, the distance between the stations and the centroids is calculated using the Euclidean distance.
4. To recalculate the centroids for each cluster.
5. To repeat the process until the assignments no longer change.
# Enzymatic Synthesis of Biobased Polyesters and Polyamides
## 2 Polyesters
These liquid crystalline polyesters are generally characterized by a rod-like molecular structure, rigidness of the long axis, and strong dipoles [55]. Aromatic polyesters are good candidates for thermotropic main-chain polymers due to the highly rich aromatic (mesogenic) fragments, and the low inter-chain forces because of the relatively low energy of association of the ester groups. Generally speaking, polyesters can be produced via two methods: (1) step-growth polycondensation of diols and diacid/diesters, or hydroxyacids/hydroxyesters; and (2) ring-opening polymerization of cyclic monomers (lactones, cyclic diesters and cyclic ketene acetals) and cyclic oligomers. Both of these two methods have some merits and also suffer from some drawbacks. On the one hand, the building blocks for step-growth polycondensation are generally easily obtained at a relatively cheap price. However, elevated reaction temperatures (150-280 \({}^{\circ}\)C), long reaction times, high vacuum condition, heavy metal catalysts and a precise stoichiometric balance between monomers are normally required for polycondensation. In addition, side-reactions and volatilization of monomers may occur at elevated temperatures or under high vacuum [50; 57]. On the other hand, removal of by-products is not required by ring-opening polymerization and, therefore, high molecular weight products can be obtained under relatively mild conditions in a matter of minutes. Besides, side reactions can be greatly suppressed during ring-opening polymerization. However, extra synthesis steps and heavy metal catalysts are often required for the preparation of the starting materials, cyclic monomers and cyclic oligomers. Moreover, polyesters can be also synthesized by other methods such as polyaddition of diepoxides to diacids [58], and acyclic diene metathesis (ADMET) polymerization of diene monomers containing ester bonds in the main chain [59]. At present, some biobased polyesters are already commercially available, including fully biobased PLA, PHAs, and poly(ethylene furanoate) (PEF), partially biobased PBS, PET, PTT and poly(butylene adipate-\(\alpha\)-terephthalate) (PBAT), and so on (Table 1) [49; 60; 61; 62; 63; 64; 65; 66; 67; 68]. However, polymers including polyesters, polyamides and other types, are still mainly derived from petroleum oils. The production capacity of biobased polymers represented only a 2% share of the total polymer production in 2013 and will increase to 4% by 2020 [3].
## 3 Proposed Switched Triple-Band LNA  
To provide coverage for a wide range of wireless communication services, three spectrums will be covered with up to 400, 400, and 800 MHz from 2.3-2.7, 3.4-3.8, and 5.1-5.9 GHz, respectively, which include TD-LTE Band 40-41, Band 42-43, mid-band 5G, and the unlicensed 5-GHz band. To achieve 5 dB noise figure specifications [24], the 2 dB margin of the noise figure is appreciated when the effects of process, voltage, and temperature (PVT) variations can be estimated by simulation [25]. Consequently, the target noise figure of the proposed LNA is less than 3 dB with sufficient gain in 2-, 3-, and 5-GHz bands.  
The triple-band LNA can reduce the chip area considerably by using a dual-band input network with an additional switched component. As shown in Figure 8, we designed the LNA to utilize a double-peak single-notch network with an additional switch as the load impedance to have the same characteristics of the input network. The additional resistance \(R_{B}\) was adopted to simultaneously accomplish noise power reduction. The transistor M\({}_{5}\) with a 50 \(\Omega\) resistive load R\({}_{1}\) was employed to achieve output matching for testing purposes. A decrease of 0.71/0.67/0.64 dB in the noise figure was attained at 2.5/3.5/5.2 GHz due to the use of \(R_{B}\).  
Figure 8: The proposed triple-band low-noise amplifier (LNA) with the load network design and the noise reduction resistor, the component values \(R_{\rm B}=8\) k\(\Omega\), \(R_{\rm g}=5.2\) k\(\Omega\), \(R_{1}=50\)\(\Omega\), \(C_{1}=588\) fF, \(C_{2}=3.8\) pF, \(C_{3}=3.8\) pF, \(C_{4}=388\) fF.  
Figure 7: Simulated noise figure with and without the additional resistance \(R_{B}=8\) k\(\Omega\). (**a**) Dual band when the switch is on, (**b**) single band when the switch is off.
## 3 Results and Discussion
### Biological Activity of AmPS  
3.2.1. Antidiabetic Potential  
\(\alpha\)-glucosidase and \(\alpha\)-amylase are enzymes that participate in the degradation of starch, oligosaccharides, and disaccharides into monosaccharides, which, in turn, leads to increased blood glucose levels. Decreasing such enzymes' activity modulates hyperglycemia after eating, which is an early metabolic disturbance that appears in diabetes [28]. Acarbose is a commonly known \(\alpha\)-glucosidase inhibitor that reduces postprandial blood glucose spikes by delaying the digestion of carbohydrates within the small intestine; however, it exerts some side effects, mainly flatulence, abdominal distension, or diarrhea. Therefore,  
Figure 2: \({}^{1}\)H NMR spectrum of the crude polysaccharides from _A. mella_.  
searching for new natural inhibitors seems reasonable. To determine the antidiabetic potential of polysaccharides from _A. mellea_, their impact on \(\alpha\)-glucosidase and \(\alpha\)-amylase were assayed. It was found that AmPS exhibited remarkable \(\alpha\)-glucosidase inhibitory activity (EC\({}_{50}\) 0.73 mg/mL), which was even higher than in the case of the standard compound acarbose (EC\({}_{50}\) 1.37 mg/mL). However, no activity of AmPS against \(\alpha\)-amylase was detected (Table 2). There are also previous reports available on an in vivo study of the antidiabetic activity of polysaccharides from _A. mellea_. [PERSON] et al. [29] demonstrated that a polysaccharide fraction from _A. mellea_ administered orally to type 2 diabetic rats decreased fasting blood glucose, improved glucose intolerance, and mitigated insulin resistance. Another study revealed that a neutral polysaccharide of honey mushroom, described as mannogalactoglucan, increased insulin sensitivity, lowered blood glucose, modulated lipid metabolism, and protected damaged pancreatic islets in db/db mice [8]. Our findings indicate that polysaccharides from edible _A. mellea_ may be a potential anti-hyperglycemic food that affects postprandial blood glucose level due to the inhibition or delay of glucose production from carbohydrates. Including mushrooms such as _A. mellea_ in the diet can be helpful in the prevention of diabetes.
###### Abstract  
Accurate and reliable estimation of gross primary productivity (GPP) is of great significance in monitoring global carbon cycles. The fraction of absorbed photosynthetically active radiation (FAPAR) and vegetation index products of the Moderate Resolution Imaging Spectroradiometer (MODIS) are currently the most widely used data in evaluating GPP. The launch of the Ocean and Land Colour Instrument (OLCI) onboard the Sentinel-3 satellite provides the FAPAR and the OLCI Terrestrial Chlorophyll Index (OTCI) products with higher temporal resolution and smoother spatial distribution than MODIS, having the potential to monitor terrain GPP. OTCI is one of the red-edge indices and is particularly sensitive to canopy chlorophyll content related to GPP. The purpose of the study is to evaluate the performance of OLCI FAPAR and OTCI for the estimation of GPP across seven biomes in 2017-2018. To this end, OLCI FAPAR and OTCI products in combination with insitu meteorological data were first integrated into the MODIS GPP algorithm and in three OTCI-driven models to simulate GPP. The modeled GPP (GPP\({}_{\text{OLCI-FAPAR}}\) and GPP\({}_{\text{OTCI}}\)) were then compared with flux tower GPP (GPP\({}_{\text{EC}}\)) for each site. Furthermore, the GPP\({}_{\text{OLCI-FAPAR}}\) and GPP derived from the MODIS FAPAR (GPP\({}_{\text{MODIS-FAPAR}}\)) were compared. Results showed that the performance of GPP\({}_{\text{OLCI-FAPAR}}\) was varied in different sites, with the highest R\({}^{2}\) of 0.76 and lowest R\({}^{2}\) of 0.45. The OTCI-driven models that include APAR data exhibited a significant relationship with GPP\({}_{\text{EC}}\) for all sites, and models using only OTCI provided the most varied performance, with the relationship between GPP\({}_{\text{OTCI}}\) and GPP\({}_{\text{EC}}\) from strong to nonsignificant. Moreover, GPP\({}_{\text{OLCI-FAPAR}}\) (R\({}^{2}\) = 0.55) performed better than GPP\({}_{\text{MODIS-FAPAR}}\) (R\({}^{2}\) = 0.44) across all biomes. These results demonstrate the potential of OLCI FAPAR and OTCI products in GPP estimation, and they also provide the basis for their combination with the soon-to-launch Fluorescence Explorer satellite and their integration with the Sentinel-3 land surface temperature product into light use models for GPP monitoring at regional and global scales.  
Keywords:gross primary productivity; MODIS FAPAR; OLCI FAPAR; OLCI Terrestrial Chlorophyll Index; eddy covariance +
Footnote †: journal: remote sensing
## 3 Strategies to Enhance the Biosensor Performance
### Potential Nanomaterials for Sensor Surface Modification  
With the rapid development of nanotechnology in the past few years, various nanomaterials, such as gold nanoparticles, graphene and carbon nanotubes, have been widely used in the design of biosensors as transduction substrates to enhance sensing performance. Nanomaterials characteristics, such as nano-scale size, larger surface area and higher conductivity, aid in signal amplification [138]. Besides that, the inherent large surface areas of nanomaterials can enhance the loading effect caused by the bioreceptor [139], thereby improving the sensitivity and stability of the biosensor.  
In SPR-based sensors, metal nanoparticles (e.g., gold nanoparticles) have been commonly incorporated on the sensor surface [140; 141]. As nanoparticles possess unique physical, electronic and chemical properties, large surface area and high free surface energy, biomolecules (e.g., antibody) are strongly adsorbed onto the surface. This improved the SPR detection limit from the nM range to the pM range [142]. The sensitivity improvement by nanomaterials also depends on the type of structures such as nanoshells, nanospheres, nanorods and nanowires. Compared to other shapes, high aspect ratio nanorods (NRs) offer a higher sensitivity to refractive index changes [143; 144]. When NRs are used in a sandwich assay format, concentrations in the fM range can be detected [145; 146]. In another strategy, dual nanomaterials (e.g., NRs and quasi-spherical nanoparticles (qsNPs)) have yielded atomolar range (aM) detection, which is about a 10-fold enhancement in sensitivity compared to sensor employing a single type of nanoparticles [141].  
In addition, two-dimensional materials such as graphene are widely used in SPR, FET and electrochemical based biosensors. The high surface area to volume ratio and carbon-based ring structures of graphene enhances the adsorption of biorecognition elements onto the sensor surface [147; 148]. Adding graphene-based materials on the gold film of SPR sensors allow detection down to fg/mL of mass change [149]. Graphene and reduced graphene oxide possess excellent electronic properties, which allow them to be used as sensor surface material in FET [150] and electrochemical sensors [151]. Due to the excellent electronic and adsorption properties of these materials, the LOD of the FET biosensor reported in [147] is 1 fM and 10 fM for the detection of the Japanese encephalitis and avian influenza virus, respectively. However, graphene yields a small current on-off ratio in FETs, which limits its sensitivity [136]. A large on-off ratio is required to reduce static leakage current for the sensor. To achieve this, two-dimensional semiconducting transition metal dichalcogenides (TMDCs) (e.g., molybdenum disulfide (MoS2) nanoflakes, tungsten disulfide (WS2) nanosheets) and black phosphorus (BP) can be explored. TMDCs have a larger bandgap than graphene and excellent characteristics such as large surface area and high electron transfer [152; 153]. BP has adjustable band gap value and high carrier mobility [154; 155].
## 4 Emerging Lightweight Security Approaches
### Symmetric Encryption--Lightweight AES Approaches  
For IoT systems, researchers are working to develop lightweight versions of AES. The AES algorithm is a symmetric cipher. Decryption can be achieved by performing the inverse of these operations and the same secret key.
# Comparison and Combination of Mobile and Terrestrial Laser Scanning for Natural Forest Inventories
## 6 Discussion
For MLS campaigns aimed at determining relevant parameters of forest inventories and tree interactions, the distance of plot centers from the trajectory is crucial. The tree species of the stand also has an influence. While pure coniferous stands provide a clear view of the trunks, deciduous or mixed forest stands often show the disadvantage of occlusions in the trunk space due to low branches or tapering. To capture younger trees well, they should be close to the trajectory. To investigate this aspect more closely, a higher number of plots with at least 250-300 trees is necessary. These should be positioned within the 35 m corridor with different distances from the plotcenters to the trajectory. Pure coniferous plots as well as deciduous and mixed forest plots should also be included. The recording time should be limited to the leaf-off vegetation period if the focus is on the derivation of standard inventory parameters. However, for analysing ecological interactions between individual trees, especially when using QSMs, the resolution of MLS scans will likely be too low. This becomes particularly apparent when comparing QSMs generated from MLS data to those generated from TLS data. Many branches that are important for the interpretation of the interaction between tree individuals are not modeled.
## 2 Materials and Methods
### Radial Fourier-Mellin Signatures Through Hilbert Transform
If the intensity of a neighboring pixel is greater than or equal to the central pixel, its position is assigned a value of 1; otherwise, it is assigned 0. After comparing all pixels, a binary sequence is formed. This binary sequence is then converted into a decimal value by multiplying each position by its corresponding weight (decimal value) and summing the results. The final _LBP_ value is used to label the central pixel. Figure 4 illustrates the _LBP_ calculation for a pixel with \(P=8\) neighboring pixels. To compute the Local Binary Pattern (_LBP_) for a grayscale image, the following equation is used:  
\[LBP(x_{c},y_{c})=\sum_{p=0}^{P-1}s(I_{p}-I_{c})2^{p}, \tag{15}\]  
where \((x_{c},y_{c})\) represents the central pixel's coordinates, \(P\) is the number of neighboring pixels, \(I_{P}\) is the intensity of the neighboring pixel, \(I_{c}\) is the intensity of the central pixel, and \(s\) is a step function defined as:  
\[s(x)=\left\{ \begin{array}{ll}1,&x\geq 0\\ 0,&\textit{another value}\end{array} \right.. \tag{16}\]  
The uniform _LBP_ (_LBP-U_) is a variant of the standard _LBP_ that reduces the dimensionality of the characteristic vector and provides rotational invariance. An _LBP_ is considered uniform if it has at most two transitions between 0 and 1 in the binary sequence. For example, patterns like 11111111 (0 transitions), 11111000 (1 transition), and 11001111 (2 transitions) are uniform, whereas 11010110 (6 transitions) and 11001001 (4 transitions) are non-uniform. In an eight-pixel neighborhood, 256 patterns can be generated, of which 58 are uniform. These uniform patterns are assigned unique labels (1-58), while all non-uniform patterns are grouped under a single label (59). The _LBP-U_ technique was employed in this study. Figure 4: Illustration of the Local Binary Pattern (_LBP_) computation. The surrounding \(3\times 3\) neighborhood is compared for each central pixel to determine binary threshold values. The resulting binary sequence is converted into a decimal code to label the central pixel, capturing local texture patterns. After calculating the uniform _LBP_ for each pixel, a histogram of _LBP_ values is constructed to represent the texture distribution for both RGB and grayscale images \(LBP_{R}\), \(LBP_{G}\), \(LBP_{B}\), and \(LBP_{Gray}\). These histograms are then concatenated to form 444 components, creating one-dimensional object signatures (Figure 3g). Figure 5 exemplifies this procedure applied to the R channel.
## 2 Related Work and Research Methods
### Loss Function  
A loss function involving a classification loss and a localization loss is encompassed by object detection tasks. The loss function is used to compute the difference between predicted objects and ground truth, effectively assessing the accuracy of object localization. A higher level of alignment between the predicted and actual positions is indicated by a smaller loss value, signifying more accurate position predictions. However, inaccurate positioning when tracking the drilling rig target can be caused by factors such as vibration and drill sticking. Therefore, a need exists to optimize the calculation of positioning coordinates in order to enhance the accuracy of the positioning process. Efforts focused on addressing these challenges and refining the positioning algorithms can lead to an improved precision in tracking the drilling rig target.