esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Monitoring Analysis of Spatiotemporal Effect
#### 3.1.2 Temporal Effectiveness of Surface Settlement  
The surface settlement observed at various distances from the pit during the excavation of the pit is shown in Figure 6. According to on-site monitoring results, as the distance from the pit increased, the settlement outside the pit first increased and then decreased. As shown in Figure 6a, the surface settlement at the west end well increased almost linearly with excavation depth under the sequence of excavation stages, and the maximum surface settlement during excavation was 35 mm. The maximum surface settlement occurred at a distance of 12 m outside the pit, with a settlement rate of \(-1.9\) mm/m per meter of excavation depth. After the excavation was completed, the deformation continued to increase until the base slab was poured, at which point the deformation began to converge and gradually stabilized. Similarly, the ground settlement in the standard section of the pit correlated positively with the depth of excavation and the sequence of excavation, with a sudden change in settlement occurring during the construction of the third steel support, followed by a trend of accelerated deformation over time. After the excavation was completed, the surface settlement continued to increase, reaching over 50 mm. The relationship between surface settlement outside the pit and the maximum horizontal displacement of the diaphragm wall, as shown in Figure 7, indicated a linear correlation. There was a significant correlation between the maximum surface settlement and the maximum horizontal displacement of the diaphragm wall, with an R\({}^{2}\) value of 0.932 for the end well and 0.977 for the standard section. When the horizontal displacement of the diaphragm wall reached approximately 20-30 mm, the surface settlement outside the pit rapidly increased and even underwent abrupt changes, clearly linking ground settlement to the horizontal displacement of the diaphragm wall caused by excavation. These monitoring results indicated that excavation was the main factor causing surface settlement outside the pit, closely related to and controlled by the timing of the excavation. Besides the immediate deformation caused by excavation, the time-dependent deformation of mucky soil was also notably significant.  
Figure 5: Variation rule of maximum horizontal displacement of enclosure wall: (**a**) maximum horizontal displacement of west end well; (**b**) maximum horizontal displacement of standard section wall. RW means retaining wall.  
In deep excavation, excavation unloading caused the lateral horizontal displacement of the diaphragm walls, leading to surface settlement outside the pit. When there was thick mucky soil within the excavation range of the pit, the deformation continuously increased over time, potentially accelerating or even undergoing abrupt change, exhibiting more pronounced time-dependent deformation characteristics, which can easily lead to pit collapse.
# Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio +
## 4 Protocol Analysis and Covert Channel Design
### Analysis of Cover Timing Patterns
#### 4.3.6 Artificial Reconnections (PT15)  
Pattern  
The hidden information is encoded by artificially inducing reconnects of an existing connection between network nodes. As defined in TS 38.323 [10], a PDCP entity and a corresponding PDCP connection are created for every radio bearer used by UE. The covert sender could suspend and re-establish the PDCP session via the RRC layer. If the covert receiver is able to monitor these reconnections at the PDCP layer, the hidden data can be extracted.
# A Review of Electrochemical Sensors for the Detection of Glyated Hemoglobin
## 2 Direct Type Electrochemical HbA1c Sensors
### Amperometric Sensors
fabricated a mixed layer on glassy carbon, which was attached by the redox probe 1,1'-di(aminomethyl)ferrocene (FDMA), followed by covalent attachment of the epitope glycated pentapeptide (GPP), an analogue to HbA1c, to promote competitive inhibition between antibodies and HbA1c [55]. A good linear relationship was observed between the relative faradaic current of FDMA and the concentration of HbA1c, ranging from 4.5% to 15.1% of the total hemoglobin in the serum, without the need for washing or rinsing steps. In addition, the preparation technology of antibodies is complex, time-consuming, and expensive. [PERSON] et al. [56] used a reduced graphene oxide (ERGO)-modified glassy carbon electrode (GCE) as a sensing interface and dropped an anti-HbA1c antibody solution onto the GC/ERGO electrode. A linear relationship was obtained between the DPV response and HbA1c concentrations from 1% to 25%. [PERSON] et al. [57] used a 3-mercaptoethanol-propionic acid (MPA) self-assembled monomolecular membrane to covalently attach anti-HbA1c antibodies. This binding process occurred on the gold electrode surface, which was previously coated with a polyethylene terephthalate (PET) substrate. For samples of HbA1c dissolved in 0.1 M PBS, this sensor had a dynamic range of 7.5-20 ug/mL. For undiluted human serum samples, a linear correlation was observed in the range of 0.1-0.25 mg/mL HbA1c. These results demonstrated that the sensor holds great potential in the treatment of diabetes in the future. **Catalytic-mimetic-based. ** In addition to boronic acid and its derivatives, ferrocene and its derivatives, nucleic acid aptamers, and antibodies, there are some other types of materials suitable as sensitive materials for fabricating electrochemical HbA1c sensors [58]. For example, flexible conductive artificial enzymes can be used for HbA1c detection. Conductive artificial enzyme nanoparticles were prepared by molecular imprinting technology [59]; in the presence of Rhodamine b and 3-aminophenyl boronic acid, Hb and HbA1c were embedded into the molecularly imprinted polymer, and then they were removed to form specific 3D binding sites in the polymer. The catalytic-mimetic HbA1c biosensor based on the lock-key model has good specificity and promotes the redox process. The linear ranges for HbA1c and Hb detection were 0.5-100 mM and 0.45-120 mM, respectively, and only 0.07 uL of the sample was required for one test.
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using Off-Policy
#### 3.2.12. Path Planning I  
In [80], the authors developed a methodology for mobile robot navigation in unknown environments. This methodology overcomes the boundaries inherent in traditional path planning techniques that require extensive knowledge of the environment, which is often unavailable in real-world applications such as emergency rescue and underwater vehicle operation.  
The authors in [80] used a DDQN RL algorithm to estimate the value of actions, helping the robot to select the best action in a given state to maximize rewards. Their technique uses the replay of prioritized experiences (PER) (for more information about the PER, please refer to [92]) to improve robot learning by focusing on experiences with notable  
Figure 12. Architecture of the autonomous navigation system. For more information, please refer to Table 4.  
TD errors. In this work, an ROS was used to allow communication and control between the virtual robots in Gazebo and the DDQN algorithm. Their method for planning the path of mobile robots differs from the standard DQN algorithm. They show that the DDQN with PER achieved faster convergence and required fewer training episodes, especially in complex environments with multiple target points or obstacles. The model consistently achieved a higher success rate in demanding environments with numerous obstacles. For more information on the hardware and software used, please see Table 4.  
The above-mentioned work stresses the need for robots to adapt to dynamic and unexpected conditions by using robot sensors to collect data and discover optimum pathways. The results demonstrate that their approach using DDQN with PER provides a more resilient and efficient method of planning paths for mobile robots in unknown environments. Further refinement of their approach in real-world scenarios, deploying the model in physical robots, and addressing challenges such as sensor noise and real-time computational constraints will be beneficial in future robot systems.
## 3 Results
### Quantification of Nitrite by the Rapid Semi-Quantitative Method  
Salivary nitrite concentrations determined using the rapid test are shown in Table 3.  
Similar to the previous method, the patients with metal braces registered significantly higher values at T\({}_{1}\) and T\({}_{2}\) compared to those with aligners or in the control group (\(p\) = 0.0021). At T\({}_{3}\), nitrite concentrations approached the baseline ones for all patients.  
The regression analysis proved a very good correlation between total nitrite concentration determined by the two methods at all time points (Figure 1).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{2}{c}{**T\({}_{0}\)**} & \multicolumn{2}{c}{**T\({}_{1}\)**} & \multicolumn{2}{c}{**T\({}_{2}\)**} & \multicolumn{2}{c}{**T\({}_{3}\)**} \\ \cline{2-10}  & \(\mu\)**mol/L** & **SD** & \(\mu\)**mol/L** & **SD** & \(\mu\)**mol/L** & **SD** & \(\mu\)**mol/L** & **SD** \\ \hline \multicolumn{10}{c}{**Endogenous nitrite**} \\ \hline
**Metal braces (M)** & 93.26 & 39.47 & 122.61 & 56.80 & 84.43 & 40.62 & 102.13 & 63.11 \\
**Aligners (A)** & 41.12 & 23.05 & 47.02 & 25.00 & 47.88 & 43.22 & 41.15 & 27.23 \\
**Control (C)** & 72.05 & 72.05 & 70.27 & 63.17 & 68.45 & 60.30 & 75.56 & 55.27 \\ \hline \multicolumn{10}{c}{**Total nitrite**} \\ \hline
**Metal braces (M)** & 99.77 & 41.24 & 164.58 & 62.81 & 152.21 & 70.02 & 116.35 & 66.41 \\
**Aligners (A)** & 44.89 & 22.97 & 63.89 & 25.03 & 52.59 & 24.30 & 55.64 & 28.50 \\
**Control (C)** & 73.50 & 73.50 & 74.13 & 63.42 & 76.30 & 65.10 & 84.68 & 56.75 \\ \hline \multicolumn{10}{c}{**Inducible nitrite**} \\ \hline
**Metal braces (M)** & 6.51 & 6.01 & 41.97 & 34.02 & 68.77 & 60.93 & 14.23 & 16.17 \\
**Aligners (A)** & 3.77 & 1.84 & 16.87 & 6.83 & 4.71 & 2.77 & 14.49 & 13.20 \\
**Control (C)** & 2.54 & 2.54 & 3.86 & 2.32 & 7.85 & 10.78 & 9.12 & 8.73 \\ \hline \multicolumn{10}{c}{SD—standard deviation.} \\ \end{tabular}
\end{table}
Table 2: Mean endogenous, total, and inducible nitrite concentrations for the three study groups.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{2}{c}{**T\({}_{0}\)**} & \multicolumn{2}{c}{**T\({}_{1}\)**} & \multicolumn{2}{c}{**T\({}_{2}\)**} & \multicolumn{2}{c}{**T\({}_{3}\)**} \\ \cline{2-10}  & \(\mu\)**mol/L** & **SD** & \(\mu\)**mol/L** & **SD** & \(\mu\)**mol/L** & **SD** & \(\mu\)**mol/L** & **SD** \\ \hline Metallic braces (M) & 92.00 & 37.95 & 146.00 & 85.40 & 143.00 & 53.14 & 103.00 & 55.59 \\ \hline Aligners (A) & 38.00 & 37.95 & 47.00 & 43.47 & 38.00 & 37.95 & 38.00 & 37.95 \\ \hline Control (C) & 91.51 & 91.51 & 83.37 & 43.37 & 68.48 & 68.48 & 47.43 & 47.43 \\ \hline \multicolumn{10}{c}{SD—standard deviation.} \\ \end{tabular}
\end{table}
Table 3: Salivary nitrite concentration by semi-quantitative method for the three study groups.
# A Tiered Control Plane Model for Service Function Chaining Isolation
## 4 Services in the Architecture
### Service Components on the Data Plane
#### 4.3.1 Classification  
Before a packet enters the NFV domain, it must be classified according to what SFC it belongs to. Typically, the classification is based on source IP address and destination (TCP/UDP) port, but it can also be based on any attributes of layer 2 or layer 3 headers. The classifier adds the SFC headers to the packet, utilizing a look-up table of mappings between SFC identifiers and packet classification attributes. Because the packet encryption hides the TCP/UDP ports, this architecture assumes that it is only the classifier in the beginning of an SFC that does the packet classification based on non-SFC headers. Furthermore, SFC forwarding requires that the SFC header uniquely identifies the packet for each SFC hop. On the other hand, SFF proxies require that the SFC headers are removed before the data packets entering the service functions. In order to be compliant with SFF proxies, uniquely identifiable interfaces between the service functions and the SFFs contribute to the re-classification of SFC traffic that is traversing an NSH proxy.  
The result of the classification is a combination of inner and outer SFC identifiers that corresponds to traditional classification rules based on IP. For example: Source IP 82.147.41.42 with destination port 80 is mapped to SFC header 1 with ID 10 and SFC header 2 with ID 20, while source IP 82.147.41.42 with destination port 5060 is mapped to SFC header 1 with ID 10 and SFC header 2 with ID 30.
## 3 Results  
### Identification of Aroma-Active Compounds  
As shown in Table 1, a total of 88 aroma-active compounds were identified by LLE combined with GC-O-MS analysis in four different grades of FJ. A few aroma compounds could not be detected by mass spectrometry due to their low concentrations. But the panelists could clearly perceive the aroma. Then these compounds were inferred by referring to literature, comparing aroma and RIs at first, and finally identified by the authentic standards. In this study, it was first found that 2-heptanone, 2,6-dimethylpyrazine, 3-hexen-1-ol, (Z)-, octyl acetate, 5-methyl furfural and 2(5H)-furanone were the aromactive compounds in LFB. However, their osme values were very low, indicating that they may make fewer contributions to the overall aroma of LFB. By comparison, F30 had the largest number of aroma compounds that could be perceived by the panelists, while F00 had the least. Figure 1 compared the osme values of these aroma compounds, which varied in different samples. The result showed that a majority of aroma compounds presented the highest intensity in F30 while having the lowest in F00. It could be found that high-grade FJ contained more aroma compounds and had higher aroma intensities, which is consistent with the study in different grades of sesame-flavor Baijiu [16]. There were only a few compounds in the low-grade samples (F00, F10) with osme values greater than 4 (none in F00 and 2 in F10). Nevertheless, in F30, 8 aroma compounds were considered to make the most important contributions to the overall aroma as their higher aroma intensity of at least four, which were 3-methylbutyl acetate (fruity), 3-methylbutanol (malty), ethyl hexanoate (fruity), ethyl octanoate (fruity), ethyl phenylacetate (rosy), 2-phenylethyl acetate (floral), \(\beta\)-damascenone (honey), and ethyl 3-phenylpropanoate (floral). The aroma intensities of ethyl acetate (pineapple), 2-methylpropanol (malty), 3-methylbutyl acetate (fruity), 3-methylbutanol (malty), ethyl hexanoate (fruity), dimethyl trisulfide (cabbage), tetramethylpyrazine (nutty), butanoic acid (sweaty), ethyl phenylacetate (rosy), 2-phenylethyll acetate (floral), \(\beta\)-damascenone (honey) and 4-ethylphenol (animal) were all beyond 3 in those four samples. Among these compounds, ethyl acetate, 3-methylbutyl acetate, dimethyl trisulfide, butanoic acid and \(\beta\)-damascenone have been proven to have a significant influence on the final aroma characteristics of LFB [9; 10; 11; 13].
# Evolutionary Game for Content Cache in a mm-Wave-Based Vehicular Fog
## 3 mm-Wave-Based Vehicular Communication Model
### mm-Wave Channel Propagation  
Pathloss model of 60 GHz wireless local area was established for LOS environment based on measurement study [63; 64; 30; 65; 66]. Symbols for the model are shown in Table 1.  
\[L_{d}(dB)=A+20 log_{10}(f*10^{-3})+10\alpha log_{10}(d*10^{3}), \tag{1}\]  
where the \(A\) is 32.5 dB and no shadow factor. \(f\) is an operating frequency (GHz) and \(d\) is a distance between a transmitter and receiver (m) and \(\alpha\) is a pathloss exponent of LOS (e.g., 2).  
In outdoor vehicular communication, additional attenuation from vapour water (\(L_{vup}\)), oxygen(\(L_{O_{2}}\)), and rain (\(L_{R}\)) should be considered. Those atmosphere parameters (dB/km) for further loss are assumed constant for a relatively short communication period in this study.  
\[L_{a}(dB)=d(L_{vap}+L_{O_{2}}+L_{R}). \tag{2}\]  
Total pathloss can be the sum of Equations (1) and (2). For simplicity, we assume the pathloss, \(L_{a}\) from atmosphere condition is static during a short communication period. Therefore, the pathloss is only determined by the distance, \(d\), at a given operational frequency, \(f\) (e.g., 60 GHz). According to LOS or NLOS with obstacles, the pathloss exponent value for the distance is different.  
In the LOS environment, the radio range can be derived by the outage probability with a required signal-to-noise ratio (SNR) threshold, \(T\) of target modulation coding scheme (MCS).  
\[P(PL(d)\leq P_{t}+G_{r}+G_{t}-T-G_{s}), \tag{3}\]  
where \(G_{s}=NF+IL+CB\).  
In above equation, reachable coverage \(d\) is \(PL^{-1}(T_{x}+G_{r}+G_{t}-T-G_{s})\). Except the constant loss (\(G_{s}\)), the maximum range for threshold \(T\) can be rewritten by the Equation (4), where the \(d\) is decided almost by the antenna gain of transmission (Tx) and reception (Rx) (i.e., beam-forming factor) and threshold; in this study, no Tx power control is considered between vehicles.  
\[d=\left(\frac{P_{t}G_{t}G_{r}}{TG_{s}}\right)^{1/\alpha} \tag{4}\]  
where the \(\alpha\) is a pathloss exponent.
## 5 Conclusions  
An efficient semi-analytical ultrasonic crack measurement model based on the Physical Theory of Diffraction (PTD) and an original hybrid asymptotic/numerical approach based on a high-order spectral-like finite element method were recently proposed in the literature. The numerical comparison of the new 3D hybrid model with semi-analytical models for 3D planar embedded defects has been carried out in this study. It highlights important differences in terms of echo amplitude and waveforms for small flaw heights or extensions and the FEM method seems to provide a more physical description in all configurations by simulating phenomena not or badly accounted for by analytical models as corner diffraction, Rayleigh and head waves. When the crack extension is large, the overall validity range of PTD on the flaw half height ka \(>\) (ka)\({}_{\text{max}}\)--where ka is the dimensionless wave number--is wider for P waves (\((\text{ka})_{\text{max}}\in[1,3]\) approximately) than for S waves (\((\text{ka})_{\text{max}}\in[5,10]\) approximately). When the crack height is large, a similar criterion (around ka > 5-10) could be approximately found for the overall PTD validity range with the flaw extension. The PTD limitations are more noticeable when edge diffraction echoes are predominant with respect to specular echoes and when head waves occur; PTD can be valid for smaller ka than those given in the criterion in configurations of specular reflection. The existing PTD code could be improved by accounting for the secondary diffractions due to Rayleigh waves.  
Experimental validations on notches have first confirmed the good prediction provided by PTD for simulating 3D top edge diffraction of large flaws. Measurements of embedded slots carried out at low frequency have shown that the proposed 3D hybrid method leads to a significant improvement for simulating small flaws compared to the wavelength, notably for shear waves. Similar comparisons have been made for surface-breaking cracks and should be the object of a complementary future publication. In-progress improvements and developments of the 3D hybrid method (notably for handling complex-shaped cracks) also have to be evaluated.  
Conceptualization, M.D.; methodology, M.D.; software, M.D. and V.D.; validation, M.D. and G.T.; formal analysis, M.D.; writing--original draft preparation, M.D.; writing--review and editing, M.D. and V.D.; funding acquisition, G.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Volatile Profiles of Vidal Grapes in Weixi Region after On-Vine Dehydration (T5)
#### 3.2.2 AADs  
The AADs were the most abundant and most diverse in both free and bound forms, including 23 benzenoids (or phenylpropanoids) and 15 branched-chain aliphatics. The benzenoids content accounted for 76.79% of the total AADs, which are generated by the metabolism of phenylalanine [4; 27], while the branched-chain aliphatics are derived from the metabolism of branched-chain amino acids such as valine, leucine, and isoleucine [4], including some branched-chain alcohols, aldehydes, and esters.  
Considering the contribution to the final dessert wine aroma, volatile phenols seem to play a greater role in AADs from grapes, as branched-chain aliphatics and most other phenylpropanoids were present in much lower amounts in grape berries than those produced by yeast during alcoholic fermentation [4]. A total of 10 volatile phenols were identified in Vidal grapes (T5), and 98.62% of the total content was present in glycosidically bound forms, indicating the great potential contribution to the dried fruit and caramel note of the resulting wine [4; 11]. Some authors have also observed higher and increased contents of bound volatile phenols in Garnacha Tintorera grapes during artificial post-harvest  
Figure 2: Concentration (**a**) and percentage (**b**) of volatile compounds in Vidal grapes at harvest (T5).  
drying [11]. Based on our data, guaiacol and 4-vinylguaiacol, with a low threshold (1 ug/L and 3 ug/L in water [28], respectively) and smoky, sweet, phenolic, and spicy flavors, can be considered the most valuable compounds of this group in Vidal grapes.  
With the aromatic characteristics of honey and rose, benzylethylaldehyde and 2-phenethyl acetate are two other crucial odorants of phenyl derivatives in Vidal grapes, whose thresholds were detected at 4 ug/L and 250 ug/L in water [28], respectively. Phenylacetaldehyde was present mainly in the free form, which was 10.70 times higher than the bound form, and exceeded its threshold by 20 times. Although the free form of 2-phenethyl acetate was lower than its threshold, its total content was 2.78 times higher than its threshold. Besides phenylacetaldehyde, two important Strecker aldehydes, namely, 2-methylbutanal and 3-methylbutanal (isovaleraldehyde), were also detected at considerably high levels in Vidal grapes at T5. These two compounds were presented completely in free forms in the grapes, with concentration above 50 ug/L. Characterized by malt, cocoa, and almond aromas with extremely low thresholds (less than 1 ug/L) and strong synergistic effects [29], 2-methylbutanal and 3-methylbutanal might contribute directly to the wine aroma.