esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research and Analysis of the Cracking Mechanism  
During the construction phase of shield tunnels, the causes of segment cracking are mainly the magnitude of the jack thrust, a change in the thrust direction, the nonuniform distribution of cylinder pressure, and the occurrence of segment misalignment [26; 27]. As shown in Figures 1 and 2, when the jack thrust F1 exceeds the segment's ultimate bearing capacity or segment attitude deviation occurs, the segment will crack to different degrees. In addition to the small linear radius of the tunnel design, other reasons leading to the abnormal attitude deviation of the segment are as follows: (1) there is a certain inclination angle between the thrust direction of the and normal direction of the segment torus, and the tangential component force F3 produces a moment M; (2) the cylinder exerts uneven pressure on the segment; (3) misalignment of the segment occurs. Therefore, in view of segment fracture control, both domestic and foreign codes put forward clear requirements based on the fracture width of reinforced concrete structures. In addition, 16 loading cylinders at the site are divided into four zones, which are represented by four colored arrows in Figure 1.  
According to the introduction above, the existing numerical research results [24; 25] cannot quantitatively reflect the fracture width value, and the calculation results cannot be checked directly based on the structural design specification \"fracture width checking calculation\". Therefore, the fracture \"bandwidth\" is introduced in this paper to quantitatively describe the \(w\)-\(\epsilon_{cr}\) relationship, as shown in Equation (1).  
\[h_{c}=\frac{w}{\epsilon_{cr}} \tag{1}\]  
where \(h_{c}\) is the fracture \"bandwidth\", representing the standard distance of the dispersion from the fracture width \(w\) to the cracking strain \(\epsilon_{cr}\).  
The finite element analysis software Diana adopted in this paper provides three methods for describing the fracture \"bandwidth\": (1) the Rots model; (2) the Govindjee model; and (3) user-specified values. The Rots model determines the fracture \"bandwidth\" based on the cell size, and the Govindjee model determines the fracture \"bandwidth\" based on the cracking direction. Since the direction of segment cracking is unknown, the cell size is set to 0.5 m during meshing. Therefore, the Rots model is adopted in this paper to determine the fracture \"bandwidth\".
# Distributed Software Build Assurance for Software Supply Chain Integrity
## 6 Experimental Analyses
### Computing Performances  
**Computational Latency:** A user needs to perform leaf node construction (hashing all the files of the Ethereum software, compiler output, and SBOM document) and then finally generate the non-leaf nodes up to the Merkle root to generate a proof using the Merkle tree in Figure 4. The leaf-node construction corresponds to the bottom lines for the hash computations of the leaf nodes (ID, \(b,d\), and the source files \(F\)) in Figure 4, while the non-leaf node construction takes the preceding hash outputs as the inputs to progress upward. The leaf-node construction is the dominant factor of the computing overhead because of the longer sizes and the processing for ID, \(b,d\), and the source files \(F\). In our measurements, the leaf nodes construction and the non-leaf nodes construction take 1082 ms or 1.082 s and 14.48 ms, respectively, resulting in a proof generation latency of 1096 ms or 1.096 s. On the other hand, the proof verification takes only 0.4254 ms. Therefore, the computing overhead including the proof generation and verification is 1097 ms or 1.097 s for the software build assurance of the Ethereum software artifacts. To compare, the Ethereum software download takes 157 s and 371 s for compilation, which are two orders of magnitude greater than our online scheme overhead of 1.097 s. Both the software download/build and the user-side proof generation and verification occur online.  
**Storage, Memory, and Processing:** Though each Ethereum software version can have different \(b\) values (thus different \(p\)) based on the operating systems and processors, a user needs to store the Merkle tree of only the Ethereum software version it is using. For the latest Ethereum software version (Archanes (v1.13.4)), the Merkle tree size is 197.42 kilobytes with a total of 1972 files, including the SBOM document and compiler output. Moreover, in our experiment, it takes only 3-5% RAM and 1-2% CPU usage for Proof generation and verification.
# Hyperspectral Imaging for the Detection of Bitter Almonds in Sweet Almond Batches
## 2 Materials and Methods
### Pixel-by-Pixel Classification  
Previous to the pixel-by-pixel validation, to carry out an exploratory analysis of the images, PCA was applied to the images of the mixtures obtained after having removed the background. This algorithm reduces spectral dimensionality by converting the huge amount of data from the hypercube into a limited set of scores and loadings. The PC images and the loading vectors for the first three principal components (PC1, PC2, PC3) were analysed for their interpretation. For this goal, the mean centre was performed as a pre-processing method [22].  
Then, once the PCA was carried out and the global model for the classification between sweet and bitter almonds was developed, to simulate the actual situation in the industry, where the identification of each individual bitter almond included in the processed batches is necessary, the pixel-by-pixel prediction of the mixtures was carried out. To test whether the bitter almonds had been correctly identified, 2 almonds for each category (sweet and bitter) were used as a control in each mixture. Finally, the accuracy of the models was calculated as the percentage of correctly classified mixtures.
# Separable Data-Hiding Scheme for Encrypted Image to Protect Privacy of User in Cloud
## 5 Conclusions  
In this paper, we proposed a new scheme of separable data-hiding schemes for encrypted images based on block compressive sensing (SDH-EI-BCS). First, the protected private image is compressed and encrypted using a measurement matrix, which is known as an encryption key. Then, some additional data can be hidden into the four least significant bits of measurement using the data-hiding key during the process of encoding. With an encrypted image that contains hidden data, the receiver can extract the hidden data or decrypt/reconstruct the protected private image based on the key that he/she possesses. This scheme has features of flexible compression and anti-data-loss. Moreover, the reconstruction of the image and the extraction of data are separate. Also, compared  
Figure 11: Cropping test in our proposed scheme with different cropping ratios: (**a**) 50% cropping, PSNR = 28.65 dB; (**b**) 30% cropping, PSNR = 30.04 dB; (**c**) 20% cropping, PSNR = 30.59 dB; (**d**) 10% cropping, PSNR = 31.10 dB.  
with the previous work of SDH-EI-CS, our proposed scheme reduces the complexity of the scheme and achieves better performance in compression, anti-data-loss, and hiding capacity. So, the proposed scheme provides convenience and security for the processing, storage, and the transmission of users' data in cloud computing. However, due to the distortion of the images, our proposed scheme cannot be used in medical, military, or other areas that require the reconstruction of images without distortion. In future work, we will focus on the research of separable reversible data-hiding scheme for encrypted image in order to provide a security guarantee for user data in every fields.  
Conceptualization, L.L. and Y.-Q.S.; Methodology, C.-C.C.; Software, L.L.; Supervision, L.W.; Validation, L.W., Y.-Q.S. and C.-C.C.; Writing--original draft, L.L.; Writing--review and editing, C.-C.C.  
This research received no external funding.  
This work was supported in part by The National Natural Science Foundation of China (No. 61540009), Shanxi Province Natural Science Foundation (No. 201801D121129). The first author was supported by a scholarship from China Scholarship Council (CSC).  
The authors declare no conflict of interest.
## 3 Results  
In this section, we conduct comprehensive experiments on nine publicly available datasets to evaluate the effectiveness of the proposed DGA method in comparison with five state-of-the-art approaches. Prior to presenting the results, we provide a detailed explanation of the parameter settings and evaluation metrics used in the experiments. The experimental outcomes are subsequently analyzed to demonstrate the performance, robustness, and advantages of our method. All experiments were implemented using Python 3.8 and executed on a Windows 10 system running on a server equipped with an Intel Core i5-8250U CPU and 12.0 GB of RAM.
# Design and Evaluation of a New Fuzzy Control Algorithm Applied to a Manipulator Robot
## 7 Simulation Results  
This section presents the simulation results of the fuzzy and PID control strategies. Furthermore, the test trajectories used are shared, and then the performance of the designed controllers is assessed through three performance indicators, namely Residual Mean Square (RMS), Residual Standard Deviation (RSD), and Index of Agreement (IA).
## 7 Simulation Results
### Specific Case for a Human Subject  
Figure 6 shows the selected transmission power and the associated RSSI for a specific human subject doing a sequence of 12 postures under different power control schemes. It can be observed that both algorithms, reactive and predictive, respond appropriately by increasing the power transmission level when the RSSI value drops below the safety threshold (red line in \(-80\) dBm), and they decrease the power transmission level when the RSSI has a value over the upper safety threshold (red line in \(-75\) dBm). The range of RSSI values is divided into three zones delimited by red line thresholds. The minimum RSSI value required to maintain the link reliability is \(-80\) dBm. The aim of the TPC policy is to maintain the RSSI within the safety region defined by the red line thresholds, with the minimal energy consumption. The choice of these thresholds is based on the results obtained by our experimental characterization.  
All schemes exhibit considerable fluctuation in their transmission power, since the channel conditions vary quite rapidly. We can see that when we use the maximum transmission power level (0 dBm), the RSSI for both links shows a large variation, whereas when the TPC schemes are used, the RSSI can maintain a relatively stable level (which it is translated into an improvement of the QoS for the communication link). Link 1 is the most critical case, because even for the maximum transmission level, the RSSI value can fall under \(-85\) dBm for some postures.  
The reactive scheme shows 5.5% and 3.22% average packet loss and energy savings of 9% and 30.4% for Link 1 and Link 2, respectively, in comparison with the maximum transmission power level. The  
Figure 6: Transmission power and associated RSSI under maximal, reactive and predictive schemes.  
predictive scheme shows 5.1% and 2.5% average packet loss and energy savings of 9.5% and 30.3% for Link 1 and Link 2, respectively, in comparison with the maximum transmission power level. In conclusion, both power control schemes have a similar behavior, and both provide significant energy savings by adjusting the transmission power level to the channel requirements.
## 3 Research Results
### Impacts of Land Use Type Changes on CS  
Between 2000 and 2020, the LU conversion area of BRB reached 1933.22 km\({}^{2}\) (Table 4), and the LU conversion area was as follows: grassland (945.91 km\({}^{2}\)) > arable land (419.11 km\({}^{2}\)) > woodland (545.66 km\({}^{2}\)). During this process, grassland CS showed a decreasing trend and was mostly converted into woodland, followed by grassland. The cultivated land and woodland are basically in mutual conversion, among which the CS of cultivated land is reduced by 342.35 tons, and the CS of woodland is reduced by 178.55 tons. The area of arable land converted to woodland accounts for 82.31% of the total conversion amount of arable land, and the area of woodland converted to farmland accounts for 72.39% of the total conversion amount of woodland. Additionally, the transformation between cultivated land and forestland exceeds that between other land types, resulting in a more significant impact on ecosystem services. In addition, the conversion area of water bodies and other land types is relatively small, and the impact on CS is weak. In general, shifts in LU types have a considerable impact on ecosystem CS, predominantly reflected in grassland, cultivated land, forestland, and built-up areas, with the alterations exhibiting a complex pattern of various combinations.  
Figure 6: Characteristics of CS changes at different stages of BRB, (**a**) is the CS space change from 2000 to 2010, (**b**) is the CS space change from 2010 to 2020, and (**c**) is the CS space change from 2000 to 2020.
## 3 Experimental Results and Analysis
### Evaluation Metrics
#### 3.2.1 Hypervolume  
The main objective is to measure the extent of the solution set's dominance over the target space. A higher hypervolume value for the solution set suggests broader dominance within the target space, indicating a superior distribution. Computing the hypervolume entails establishing a reference point (r). The values of this reference point must surpass those of all solutions across each dimension in the non-dominated solution set under evaluation. Once established, the area between all solutions and the reference point can be determined, as depicted in Figure 8, where A-C are non-dominated solutions. In this research, the reference point (r) is defined as (200, 700), denoting that the delay time and energy consumption of all solutions derived from the methods are below 200 s and 700 W, respectively. Consequently, the area between each solution set and the reference point is computed.
# Biocompatibility of Membranes Based on a Mixture of Chitosan and _Lythri herba_ Aqueous Extract
## 3. Results
### Biological Analysis
#### 3.3.4. Bacterial Adhesion Inhibitions Assay of CS-Ly-Based Membranes  
The adhesion of _Staphylococcus aureus_, _Enterococcus faecalis_ and _Klebsiella pneumoniae_ bacteria on the tested membranes was compared with the materials used as standards (medical steel and silicone elastomer) (Figure 7).  
Figure 6: The thrombin generation after CS 1 and CS1–Ly mixture membranes were incubated in modified plasma; medical steel (MS), a highly reactive reference material; the values are the averages of two recordings; the result of the negative control was \(\leq\)2000 \(\Delta\)mU/mL/min (assay criteria kit).  
Figure 7: The number of bacterial colonies (CFU/mm\({}^{2}\)\(\times\) 10\({}^{3}\)) recorded using the spot method (24 h).  
These results show that all mixed CS-Ly membranes showed higher adhesiveness compared with the chitosan membranes (CS 1 and CS 0.5) without the _Lythri herba_ extract. The chitosan membranes recorded several CFU/mm\({}^{2}\) colonies of approximately 4 \(\times\) 10\({}^{3}\), a value very close to that of the standard materials tested (medical steel, silicone elastomer).  
The inhibition of the adhesiveness of the microorganisms on the membranes with an Ly content was not noticed. Correlations between the different concentrations of the extract and the recorded effects were also not observed. For the CS 1-Ly membranes, the adhesiveness of the _K. pneumoniae_ bacterial species was two times lower compared with the other bacteria, which suggests the importance of chitosan concentration in fixing these Gram-negative bacteria. In the case of Gram-positive bacteria, a lower attachment of _S. aureus_ species compared with _E. faecalis_ could be noted on the surface of CS 0.5-Ly membranes. Therefore, the observations indicate that the adhesiveness of the microorganisms under investigation was influenced by two key factors: the concentration of the polymer and the specific type of bacteria. The presence of the plant extract did not align with the initial assumptions regarding its potential to reduce bacterial adhesiveness. The enhancement of the biopolymer-induced phenomenon was not found in the presence of the extract.
## 2 Method
### SFST Method
As for the second-order Doppler effect and the Earth's rotation influence, they are expressed as [43]  
\[\frac{\Delta f}{f_{e}}=\frac{\phi_{s}-\phi_{e}}{c^{2}}-\frac{|\vec{v}_{e}-\vec{ v}_{s}|^{2}}{2c^{2}}-\frac{\vec{r}_{se}\cdot\vec{a}_{e}}{c^{2}}+O(c^{-3}), \tag{7}\]  
where \(\phi_{s}-\phi_{e}\) is the gravitational potential difference between the satellite and the ground station; \(\vec{v}_{e}\) and \(\vec{v}_{s}\) are the velocities of the ground station and the satellite (spacecraft), respectively; \(\vec{r}_{se}\) is the vector from the satellite to the ground station; \(\vec{a}_{e}\) is the centrifugal acceleration vector of the ground station; and \(O(c^{-3})\) denotes terms of higher order than \(c^{-2}\). On the right-hand side of Equation (7), the second term denotes the second-order Doppler shift predicted by the special relativity, and the third term represents the effect of the Earth's rotation during the signal's propagation period. If we omit the higher-order terms \(O(c^{-3})\), Equation (7) holds only at the accuracy level of \(10^{-15}\)[45]. In this case, we do not need to consider other influential factors, such as the residual ionospheric effects, tidal effect, etc. However, to achieve a one-centimeter level measurement in height, we must consider higher-order terms until \(O(c^{-4})\) and various influential factors for satellite-ground microwave links. We derived a theoretical formula that holds at accuracy levels better than \(10^{-18}\), which is expressed as [34]  
\[\frac{\Delta\phi_{es}}{c^{2}}\equiv\frac{\phi_{s}-\phi_{e}}{c^{2}}=\frac{ \Delta f}{f_{e}}-\frac{v_{s}^{2}-v_{e}^{2}}{2c^{2}}-\sum_{i=1}^{4}q^{(i)}+ \Lambda f+\delta f+O(c^{-5}), \tag{8}\]  
where \(\Lambda f\) is the sum of all correction terms (it contains corrections of ionospheric and tropospheric effects, tidal effects, and influences of celestial bodies); \(\delta f\) is the sum of all error terms; and \(q^{(i)}\) (\(i=1,2,3,4\)) are quantities related to the positions and velocities of the ground station and satellite, second Newtonian potential, vector potential, and higher-order post-Newtonian terms. The order terms higher than \(O(c^{-5})\) are safely omitted. For detailed expressions of the relevant quantities, refer to [34]. Based on Equation (8), when the output frequency shifts, \(\Delta f\) is measured, and relevant quantities (such as the position, speed, and acceleration of ground station and satellite) are given, the gravitational potential difference \(\phi_{es}\) can be obtained. We also discussed how to determine the geopotential difference between two ground stations that are connected to the same satellite simultaneously [34]. Since the satellite can serve as a \"bridge\" to connect the two ground sites, the geopotential difference between these two sites can be obtained. Simulation experiments show that the precision of the geopotential difference between two ground sites determined by the SFST method is about 2\(\sim\)5 cm in height [34], under the assumption that the accuracy of the OACs is about \(1\times 10^{-18}\), which is achievable currently [17; 18; 19].
# Phytosterols in Seaweeds: An Overview on Biosynthesis to Biomedical Applications
## 4 Health Benefits and Biomedical Applications of Phytosterols
### Phytosterols as Anti-Inflammatory Agents
The activated p38 pathway triggers the production of pro-inflammatory mediators (IL-1\(\upmu\)g, TNF-\(\alpha\), IL-6, and COX-2) that direct the remodeling of connective tissue in pathological conditions by encoding adherent proteins along with other inflammatory-related molecules [107]. Another study found that fucosterol, isolated from _U. pimmatifida_, reduced the formation of pro-inflammatory cytokines (iNOS, TNF-\(\alpha\), and IL-6) and, subsequently, inhibited LPS-stimulated NO production by suppressing the activation of NF-\(\kappa\)B and the phosphorylation of p38 MAPK in macrophages [108]. Phytosterols present in seaweed have been shown to be neuromodulators in the central nervous system (CNS). By stimulating synapse remodeling, improving neuroinflammation, and suppressing neurotoxic protein accumulation in the CNS, seaweed-derived phytosterols can be considered an exciting therapy for the treatment of neurodegenerative diseases [13]. [PERSON]'s disease is attributed to the accumulation of the toxic protein amyloid-\(\beta\) (A\(\beta\)) in the CNS. Although A\(\beta\) is crucial for neuronal survival and synaptic plasticity, high concentrations eventually lead to cell death and neurotoxicity [109]. [PERSON] and colleagues reported that fucosterol also protects against A\(\beta\)-associated neuroinflammation via attenuating the production of inflammatory mediators in A\(\beta\)-induced microglial cells [68]. A\(\beta\) peptide was found to trigger pro-inflammation through the activation of p38 MAPK [110]. Chronic microglial activation by A\(\beta\) stimulated the expression of inflammatory mediators such as TNF-\(\alpha\), IL-1\(\beta\), IL-6, NO, and prostaglandin E2, which could lead to the neuroinflammation known as Alzheimer's disease [111; 112]. Also, the anti-inflammatory agent fucosterol has been shown to be a non-competitive inhibitor of the enzyme \(\beta\)-secretase, which is important for the production of toxic A\(\beta\) monomers [113]. Saringosterol, another important seaweed-derived phytosterol, has been found to diminish the formation of neuronal A\(\beta\) while triggering microglia-mediated A\(\beta\) clearance [114]. The brown seaweed _S. fusiforme_ contains high ranges of 24(S)-saringosterol. Saringosterol reduced the load of A\(\beta\) plaque and improved cognitive performance in an AD animal model [113]. However, with the exception of saringosterol, seaweed-derived fucosterol, fucoxanthin, and fucoidan have been reported to act against memory deficits [115]. Overall, these findings highlight the anti-inflammatory properties of seaweed-derived phytosterols.
## 5 Discussion
### Immediate Experience Sampling Method (IESM) Performance  
IESM was used by participants to evaluate each input interaction immediately following the action. Designing data collection in this way guarantees precise and real-time ground truth. This accurate UX data could be used to teach the vehicle the driver's preferred driving pattern and will increase driver satisfaction. In the first scenario participants preferred to overtake the slow car rather than slowing behind it. The reasons they gave for this selection included saving time, preference for driving fast, or they wanted the vehicle to take quicker actions. For the second scenario, most participants preferred to change the route rather than change lanes to avoid an accident. They said changing the route is safer and helps to avoid an expected traffic jam. Involving drivers in the loop and asking their opinions about vehicle action will increase feelings of control, which decreases by default when driving an AV [17; 18]. Responding to a 5-point Likert scale question, participants rated the importance of using IESM to teach the vehicle their driving preferences at (\(M=4.18\), \(SD=1.16\)), which is relatively high.