esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Canopy Height
#### 3.2.3 Forest Types  
To assess CASAL's canopy height inversion capability across forest types, footprints were classified into the three following categories: broadleaf, coniferous, and mixed forests. Table 8 presents correlation coefficients and RMSE values between RH90 and ALS-Hmax for each forest type. CASAL demonstrated the highest accuracy in mixed forests, evidenced  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Vegetation Cover** & **Number** & \(R^{2}\) & **Bias (m)** & **RMSE (m)** & **MAD (m)** \\ \hline
0.2–0.4 & 78 & 0.441 & 1.49 & 4.06 & 3.75 \\
0.4–0.6 & 268 & 0.598 & \(-0.56\) & 3.12 & 2.76 \\
0.6–0.8 & 4558 & 0.495 & \(-0.38\) & 2.69 & 2.06 \\
0.8–0.9 & 7015 & 0.502 & \(-0.30\) & 2.61 & 2.07 \\ \(>\)0.9 & 2601 & 0.397 & \(-0.43\) & 2.89 & 2.28 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Accuracy of CASAL inversion of canopy height under different vegetation cover categories.  
Figure 10: Scatter plots comparing CASAL-RH90 and ALS-Hmax canopy height estimates across five different categories of vegetation cover: (**a**) 0.2–0.4, (**b**) 0.4–0.6, (**c**) 0.6–0.8, (**d**) 0.8–0.9, and (**e**) \(>\)0.9. The least-squares regression line (black) and 1:1 reference line (red) are superimposed for comparative analysis.  
by maximum \(R^{2}\) values and minimum RMSE and MAD values. Broadleaf forests showed relatively high accuracy, while coniferous forests exhibited the lowest performance among the three types. These differences primarily stem from structural variations among forest types, where the dense foliage and multi-layered canopies characteristic of coniferous forests attenuate LiDAR signals more significantly than the heterogeneous structure of mixed forests, which facilitates better echo discrimination.
# Stimuli-Responsive Polymeric Nanosystems for Controlled Drug Delivery
## 2 Typical Polymers for Controlled Drug Delivery
### PLGA Polymers  
The poly (D,L-lactic-co-glycolic acid) (PLGA) polymer is another extensively studied aliphatic polyester with ester bond in the backbone, which can be hydrolyzed in water [8; 47]. The PLGA with a diverse range of molecular weights and lactic to glycolide ratios can be synthesized by ring-opening polymerization. This polymer has been widely used as building blocks to develop nanoparticles for drug delivery applications, and the biocompatibility and blood circulation time can be largely improved when modified with PEG, the clinically approved polymer, to obtain PLGA-PEG copolymers [48]. The versatility and solubility of PEG motif allows for modification with various functional ligands at the terminal ends, leading to the established nanosystems with specific functions [49; 50; 51]. Additionally, the viscous gels that are responsive to temperature can be prepared based on PLGA triblock copolymers with ester linkages in the backbone, which is a promising strategy to develop thermo-responsive drug delivery nanocarriers [52].
# Seismic Identification and Characterization of Deep Strike-Slip Faults in the Tarim Craton Basin
## 5 Fault Identification through Attribute Fusion
### Attribute Fusion Based on Image Processing  
The ant tracking and amplitude gradient attributes were combined to obtain high-precision and accurate fault images. First, the grid subdivision method was used to divide the amplitude gradient attribute image into many small cells. To better reflect the law of data change, a relatively dense grid was used. Then, the wavelet transform was applied to fully highlight the characteristics of the fault structure; the spatial frequency could be localized for analysis, and the signal (function) could be gradually refined at multiple scales through the expansion and translation operation to distinguish fine faults [58]. Then, because K-means can cluster similar data based on the distance between data points, the data were classified into two clusters through K-means clustering: fault clusters and non-fault clusters [59]. Moreover, the locations of the attribute points (fault data points) were extracted. The data points of the fault cluster were set to 1, and the data points of the non-fault cluster were set to 0 (Figure 9a). Then, the ellipsoid expansion method was applied to treat the attribute points that were higher than the threshold value. Due to the anisotropy of the ellipsoid, the original normal line may not be perpendicular to the  
Figure 8: Fault identification comparison of preferred attributes. (**a**) Map of the ant track. (**b**) Fault interpretation of the ant tracking attribute. When applied to heterogeneous deep carbonate reservoirs in cratonic basins, the tracking attribute exhibits limitations, such as reduced recognition ability for carbonate faults and challenges in identifying micro-faults. (**c**) Map of the amplitude gradient attribute. (**d**) Fault interpretation (red lines) of the amplitude gradient attribute. The amplitude gradient attribute successfully reflects the trends and locations of these faults, whereas the ant tracking attribute often exhibits excessive disorder and interferes with the accurate determination of branch fault locations.  
new ellipsoid after expansion (Figure 9b). By setting the structural unit, the range of the extracted attribute points could be expanded according to geological theory to obtain the data body of the fault location.  
To integrate the ant tracking attribute with the data body of the fault location, a uniform grid size was applied to process the ant tracking attribute. The \"or and no\" rule was employed to multiply the ant tracking attribute and the fault location data body, resulting in a data body with limited fault location information. Subsequently, the data volume and amplitude gradient attribute were combined using the wavelet transform method, yielding a new fusion attribute. This fusion attribute precisely identifies the fault's location and offers a detailed representation of the fault through high resolution.
# A Synthetic Aperture Radar-Based Robust Satellite Technique (RST) for Timely Mapping of Floods
## 5 The Methodology
### RST Methodology Implementation  
The historical series of S1-GRD images with VH polarization was used as the input to implement the RST methodology. As described above, such an activity encompasses two main steps: (i) the identification of the expected values at the pixel level (i.e., temporal mean and standard deviation) for the investigated signal (i.e., the backscattering \(\sigma\)0); (ii) and the ALICE index computation for all the S1 images useful to characterize the analyzed flood events.  
In Figure 3, two examples of the ten maps (one temporal mean map plus one standard deviation map for five events) produced in the first step are shown. These maps refer to the two outputs produced for analyzing the Zaragozza Ebro River event (December 2021). They were obtained by analyzing at the pixel levels all S1 GRD IW data collected in the calendar month of December from 2014 to 2023. Similar maps, which are not shown for the sake of brevity, were produced for all other events.  
Using these maps as reference signals, the ALICE maps for all images in Table 2 were computed, highlighting values higher than two, whose probability of occurrence was less than 3% for inherent construction [29; 50]. These maps were used as inputs in the next step to produce the final flood maps.  
Figure 3: _Cont._
## 3 Results and Discussion  
### Peptides to Treat Metabolic Syndrome  
Proteins from foods are digested in the gastrointestinal tract by multiple proteases. Total protein is digested to di- and tripeptides before being absorbed by small intestine cells. Only 10% of the protein remains as di- and tripeptides after digestion by peptidases of the brush border and enterocytes [19]. Thus, we only considered peptides with a maximum length of three amino acids to construct a protein that can release these peptides. The BIOPEP database showed 258 ACEI peptides and 287 DPPIV inhibitor peptides. These peptides were sorted according to their IC\({}_{50}\) and evaluated in gastrointestinal digestion (SGD). ACEI peptides that showed resistance to SGD and presented the highest activity were selected. These were IW (isoleucine-tryptophan), VY (valine-tyrosine), IY isoleucine-tyrosine, EY (glutamate-tyrosine), and DG (aspartate-glycine). Except for EY, all peptides presented in vivo activity despite being hydrophobic [20; 21; 22; 23]. These have been described as unstable during gastrointestinal digestion [24]. For protein design, we selected the tripeptide IPI (isoleucine-proline-proline) as a DPPIV inhibitor because it is the most powerful peptide against this enzyme [25]. Based on the review by [PERSON] [26], the antioxidant dipeptide CG [27] and the lipid-lowering dipeptide DE [28] were also chosen as potential peptides against metabolic syndrome (Table 1). [PERSON] [9] also recently reviewed peptides with the potential to treat metabolic syndrome, including the aforementioned peptides. This work suggests that the dipeptides IY and VY are potential peptides to treat hypertension in metabolic syndrome. Most of the selected peptides considered for protein design are uncharged or hydrophobic. This could favor their bioavailability and absorption through the transporter PepT1 and paracellular transport [29].  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Peptide** & **Activity** & **IC50 (\(\upmu\)M)** & **In Vivo** & **Reference** \\ \hline
**VY** & ACEI & 7.1 & ✓ & [30] \\
**VW** & ACEI & 1.4 & x & [31] \\
**IW** & ACEI & 4.7 & ✓ & [20] \\
**IY** & ACEI & 2.69 & ✓ & [32] \\
**EY** & ACEI & 2.68 & x & [33] \\
**DG** & ACEI & 12.3 & ✓ & [22] \\
**IPI** & DPPIV Inhibitor & 1.1 \(\upmu\)g/ml & x & [26] \\
**GW** & Antioxidant & - & x & [34] \\
**DW** & Antioxidant & - & x & [35] \\
**CG** & Antioxidant & - & x & [24] \\
**DE** & Lipid-lowering & - & ✓ & [28] \\ \hline \end{tabular}  
* In vivo: \(\checkmark\) Indicates peptides with a reported in vivo activity; x indicates no reported in vivo activity. ACEI, angiotensin-converting-enzyme inhibitors, DPPIV, dipeptidyl-perpidase 4.  
\end{table}
Table 1: Biopeptides selected to design a protein for metabolic syndrome treatment.
## 4 Results and Discussion
### Energy Base DCMG Results  
With regard to the DCMG tested in the laboratory, it was possible to test the connection and disconnection of a 4.8 kW AC load, as shown in Figure 12. It can be seen that the voltage on the DC bus remains stable at around 310 V. When the load is switched off and then on, the voltage peaks at around 367 V on the DC bus and then stabilizes. As there is no control layer on the DC bus, which is only controlled by the bidirectional AC/DC inverter, when the load is disconnected it stops acting and a closed loop is formed between the PV system and the BESS and therefore it is speculated that the peak voltage is the nominal voltage of the PV system. Since this system was designed to supply loads on the AC side, it does not have a specific controller for the DC bus, so connecting loads directly to it could become a problem.  
It was also possible to check the behavior of the DC bus voltage in terms of its response to variations in irradiance while the load is active, as can be seen in Figure 13. The DC bus voltage takes on effective values of around 308 V, with a ripple of approximately 10 V. It can also be seen that the voltage level increases and decreases slightly as the power produced increases and decreases, respectively, which shows that when the inverter is connected the system performs well when dealing with power variations.
## 3 Experimental Results
### Comparison of Multi-Focus Image (i.e., Leaf)  
The evaluation of the sixth multi-focus image is the leaf, which is illustrated in Figure 14. Figure 14 represents the original image. Figure 14 illustrates left-focused and right-focused images, respectively. The term \"left-focused image\" refers to the fact that the image's left side is focused, while the right side is not. The right side of the image is focused while the left is not. Figure 14 shows the process of creating the all-in-focus image after the method has been successfully implemented. The following parameters are computed to evaluate the proposed methodology performance: _E(F)_, _AG_, _CC_, \(Q^{ABF}\), _SSIM_, \(Q_{E}\), and _SF_. Finally, the performance of the proposed approach is compared to that of other methods previously published in the literature. The results of the comparison are shown in Tables 30-32 of the report. The letter X indicates that metrics are not calculated for the article depicted in the image. According to the literature [21; 24; 30], the proposed method is more successful than those approaches, and the best outcomes of methods are indicated in bold.
## 4 Two Real Case Studies
### The Second Case Study: Operations Strategies of Fars Province Pharmaceutical Distributing Companies
#### 4.2.1 The Main Results of Applying the Interactive Algorithm for the Second Case Study  
To find DMU targets for the second case study, the interactive algorithm, including two interactive stages (Figure 1), was applied. In applying the first interactive stage (Appendix A, Appendix A.1), non-radial model 12 was composed and solved 13 times. The results show that only 2 DMUs (about 15%) lay on the efficient frontier, and 11 DMUs did not (Appendix A, Table A.1). By applying the second interactive stage (Appendix A, Tables A.2-A.6), \(\text{DMU}_{07}\) target was obtained, but \(\text{DMU}_{04}\) and \(\text{DMU}_{10}\) target were not obtained. The brief results of applying the interactive algorithm (Figure 1) for all DMUs are shown in Table 14.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**DMUj** & **I1** & **I2** & **O1** & **O2** & **O3** \\ \hline DMU01 & 1 & 1 & 8 & 7 & 8 \\ \hline DMU02 & 1 & 2 & 8 & 6 & 6 \\ \hline DMU03 & 3 & 4 & 8 & 7 & 4 \\ \hline DMU04 & 2 & 5 & 5 & 6 & 5 \\ \hline DMU05 & 1 & 1 & 5 & 5 & 6 \\ \hline DMU06 & 2 & 3 & 6 & 8 & 7 \\ \hline DMU07 & 3 & 4 & 4 & 3 & 6 \\ \hline DMU08 & 3 & 3 & 5 & 5 & 3 \\ \hline DMU09 & 2 & 2 & 3 & 6 & 4 \\ \hline DMU10 & 6 & 4 & 7 & 7 & 2 \\ \hline DMU11 & 5 & 4 & 4 & 7 & 4 \\ \hline DMU12 & 5 & 5 & 4 & 7 & 6 \\ \hline DMU13 & 5 & 3 & 6 & 4 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Data of the second case study.  
As seen in the last column of Table 14, applying the interactive algorithm for the second case study can be categorized into four cases. As shown in Table 14, DMU\({}_{01}\) and DMU\({}_{06}\) are members of M set and the target of these DMUs are themselves (case 1). The obtained targets by solving step 1 of the STEM model and via [PERSON]'s opinions of DMU\({}_{02}\), DMU\({}_{03}\), DMU\({}_{05}\), DMU\({}_{07}\), DMU\({}_{09}\), and DMU\({}_{11}\) are DMU\({}_{01}\). By applying step 1 or 2 of STEM, the best compromise solutions for these DMUs are found (DMU\({}_{01}\)) via [PERSON]'s opinions (case 2). By solving step 1 of the STEM model, DMU\({}_{01}\) is obtained as the target of the rest DMUs. But the target of these DMUs via [PERSON]'s opinions are DMU\({}_{06}\) (case 3 and 4). For DMU\({}_{04}\), DMU\({}_{08}\), and DMU\({}_{13}\), a compromise solution is not found because there is only one similar unsatisfied objective function in step 1 and the first iteration of step 2 of STEM (case 3). The best compromise solution is not obtained for DMU\({}_{10}\) because \(\beta_{1}\), 1 = 1,..., 5 is not calculable for the first iteration of step 2 of STEM (case 4). The required repetition times of composing and solving non-radial model 14, depends on [PERSON]'s opinions about suitable target for each considered DMU\({}_{o}\). In Appendix A, finding the target of a DMU in each case are described.
# Transmission Efficiency of Cycloid-Pinion System Considering the Assembly Dimensional Chain
###### Abstract  
The rotary vector reducer is the core component of industrial robots, and the transmission efficiency is undoubtedly an important indicator of transmission performance. In addition, the assembly dimensional chain leads to clearance between parts, which can have a certain impact on the transmission efficiency. During previous studies, this effect was often ignored. Firstly, the cycloid tooth profile is a relatively large collection of points. Therefore, a more efficient tooth profile model is employed as the basis for the calculation. Secondly, the contact between the cycloid and the pinion is determined by experimental observation to be a point contact rather than a theoretical line contact. Moreover, the dynamics and friction loss models of the cycloid-pinion system are constructed. Finally, the assembly dimensional chain is introduced into the different structural designs. The clearance results were obtained with the extreme value method. The results show that the addition of the pinion sleeve allows the system to have a smoother drive process and a more efficient transmission; this was verified with ADAMS.  
cycloid-pinion system; cycloidal profile model; efficiency model; pinion sleeve; structural optimization design +
Footnote †: journal: Medical Imaging  
1211917. [[https://doi.org/10.3390/app122311917](https://doi.org/10.3390/app122311917)]([https://doi.org/10.3390/app122311917](https://doi.org/10.3390/app122311917))
## 3 Topology Optimization and Parametric Analysis
### Topology Optimization Settings  
1. Design variable: Unit density of all the nodes on the three reflector surfaces;
2. Establish responses: The total displacement of all the nodes on the reflector surfaces, the volume fraction of the reinforced ribs relative to the volume of the reflector surfaces;
3. Basic and total thickness: The basic thickness equals 20 mm, which is the thickness of the reflector surface, the total thickness equals to 40 mm. The difference between the total and basic thickness is the height of the reinforced ribs;
4. Response constraint and boundary constraint condition: Upper value was 0.3 for the volume fraction, the centers of the two bolts were set as fixed constraints;
5. Objective: The maximum thermal deformation among design variables, which means the maximum value of the total displacement of the nodes;
6. Minimum and maximum member sizes: The minimum member size refers to the allowable minimum scale in regions with a unit density of 1 in the optimization results. The minimum member size must be greater than two times the average size of the elements. The maximum member size constraint indicates that the dimensions in all directions in regions with a unit density of 1 in the optimization results cannot all exceed this size. The maximum member size must be greater than twice the minimum member size. For this setting, the minimum member size equaled 40 mm and the maximum member size equaled 80 mm.
# Enhancing Route Guidance with Integrated V2X Communication and Transportation Systems: A Review
## 1 Introduction
This underscores the need for an integrated approach that considers both the algorithmic and communication aspects to enhance the reliability and efficiency of real-time route guidance systems (RGSs) in practical traffic networks. Consequently, there is a pressing need for advanced systems that seamlessly integrate contemporary communication technologies with transportation networks to deliver optimal route solutions for road users. The rest of of this review is organized as follows. Section 2 details the methodology used in this review, explaining the approach taken for analysis. Following that, Section 3 highlights previous studies on route guidance systems, examining application requirements and use cases. Section 4 then shifts the focus to research on vehicular communication, evaluating the strengths and weaknesses of various technologies and studies. Building on this, Section 5 investigates the integration of route guidance with vehicular communication, highlighting key contributions and areas in need for improvement. Section 6 presents a proposed framework for this integration and outlines potential directions for future research. Finally, the review is concluded in Section 7.
## 5 Synthetic Data Example  
Through this illustration, the superior SSGLR malfunction prevention methodology's efficiency-focused KPCA-KDE is evaluated through recognizing faults in the process. The KPCA-KDE-based SSGLR model is assessed in magnitude unity mean shift condition, using two illustrative examples: the modeled simulated data collection [40; 41] and the Tennessee Eastman Process benchmark (TEP). Three fault detection metrics are used to measure efficiency, fault detection rate (FDR), false alarm rate (FAR), and average run length (ARL\({}_{1}\)).
# A Comparison of an Adaptive Self-Guarded Honeypot with Conventional Honeypots
###### Abstract  
To proactively defend computer systems against cyber-attacks, a honeypot system--purposely designed to be prone to attacks--is commonly used to detect attacks, discover new vulnerabilities, exploits or malware before they actually do real damage to real systems. Its usefulness lies in being able to operate without being identified as a trap by adversaries; otherwise, its values are significantly reduced. A honeypot is commonly classified by the degree of interactions that they provide to the attacker: low, medium and high-interaction honeypots. However, these systems have some shortcomings of their own. First, the low and medium-interaction honeypots can be easily detected due to their limited and simulated functions of a system. Second, the usage of real systems in high-interaction honeypots has a high risk of security being compromised due to its unlimited functions. To address these problems, we developed Asgard an adaptive self-guarded honeypot, which leverages reinforcement learning to learn and record attacker's tools and behaviour while protecting itself from being deeply compromised. In this paper, we compare Asgard and its variant Midgard with two conventional SSH honeypots: Cowrie and a real Linux system. The goal of the paper is (1) to demonstrate the effectiveness of the adaptive honeypot that can learn to compromise between collecting attack data and keeping the honeypot safe, and (2) the benefit of coupling of the environment state and the action in reinforcement learning to define the reward function to effectively learn its objectives. The experimental results show that Asgard could collect higher-quality attacker data compared to Cowrie while evading the detection and could also protect the system for as long as it can through blocking or substituting the malicious programs and some other commands, which is the major problem of the high-interaction honeypot.  
a _Applied_  
18 April 2022  
Accepted: 19 May 2022  
Published: 21 May 2022  
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