esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis  
### Young's Modulus  
The values of Young's moduli for the groups with different treatment methods are presented in Figure 3. The average Young's modulus for the control group (G1) is 40.23 GPa. The average Young's modulus for G2 is 40.65 GPa, which is similar to the original value. The Young's moduli of G3 tonalite specimens are reduced relative to the G1 group but they show no significant difference at different freezing temperatures. The average values of Young's moduli are 66.6%, 72.1% and 73.1% of original value following freeze-thaw treatment at \(-30\,^{\circ}\mathrm{C}\), \(-40\,^{\circ}\mathrm{C}\) and \(-50\,^{\circ}\mathrm{C}\). The results show that freeze-thaw weathering at different freezing temperatures does not significantly influence the [PERSON]'s modulus of tonalite. This can be attributed to the low porosity of the tonalite specimens.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Group** & **Method** \\ \hline G1 & No treatment (1 group) \\ \hline G2 & Immersed in HNO\({}_{3}\) (pH = 1) for 60 days (1 group) \\ \hline G3 & Subjected to 30 freeze-thaw cycles at \(-30\,^{\circ}\mathrm{C}\), \(-40\,^{\circ}\mathrm{C}\) and \(-50\,^{\circ}\mathrm{C}\) (3 groups) \\ \hline G4 & Immersed in HNO\({}_{3}\) (pH = 1) for 60 days, then subjected to 30 freeze-thaw cycles at \(-30\,^{\circ}\mathrm{C}\), \(-40\,^{\circ}\mathrm{C}\) and \(-50\,^{\circ}\mathrm{C}\) (3 groups) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Groups of specimens treated by different methods.  
Figure 2: Temperature variation of freeze-thaw cycles for tonalite specimens.  
Again, the [PERSON]'s moduli for G4 specimens are reduced relative to the G1 group but they do not show a distinct difference at different temperatures following the freeze-thaw treatment. The average values of [PERSON]'s moduli are 30.6%, 29.4% and 30.3% of the original value after freeze-thaw treatment at \(-\)30 \({}^{\circ}\)C, \(-\)40 \({}^{\circ}\)C and \(-\)50 \({}^{\circ}\)C respectively. It can be concluded that the loss of [PERSON]'s modulus of tonalite specimens subjected to the combined effects of chemical corrosion and cyclic freeze-thaw weathering is more significant than the result for each of the two treatments. The chemical effect amplifies the loss of the [PERSON]'s modulus which results from freeze-thaw weathering. However, compared with the Young's modulus of samples after under the combined action of acid and freeze-thaw, the discreteness of the Young's modulus of the samples under freeze-thaw only is greater. This is because a large number of colloids were produced in the sample under the action of acid solution, making the internal structure of the sample more stable.
## 4 Results and Discussion  
### Electrical Characterization of the Device  
The IDT actuators have been characterized experimentally in order to extract the frequencies at which the maximum FPW generation effectiveness is expected. Figure 4a,b show the admittances Y\({}_{\text{A-B}}\) and Y\({}_{\text{A-C}}\) measured between A and B while C was floating, and between A and B electrically shorted and C, respectively.  
The resonant frequencies of the two configurations, i.e., the frequencies where the conductance reaches a maximum [25; 26], are at about 6.7 and 8.0 MHz for Y\({}_{\text{A-B}}\), and at about 6.0 and 7.2 MHz for Y\({}_{\text{A-C}}\). These frequencies are expected to correspond to the propagation of FPW modes in the substrate [27; 28].  
A tailored electronic interface has been developed to drive the actuators. Figure 5 shows a block diagram of the interface. The excitation signals were generated by a programmable direct digital synthesizer (DDS, AD9959) that allowed us to excite the actuators with two independent channels and provided the possibility of setting both the common excitation frequency and the phase difference between the channels.  
Dedicated power amplifiers (ADA4870) have been adopted as the output stage to properly drive the actuators. The electronic interface excites the actuators with a sinusoidal signal with peak-to-peak amplitude and frequency of up to 20 V and 15 MHz, respectively.  
Figure 4: Measured admittance Y\({}_{\text{A-B}}\) (**a**) and Y\({}_{\text{A-C}}\) (**b**) as functions of frequency. The plots show resonances at frequencies around 6.7 and 8.0 MHz (**a**) and around 6.0 and 7.2 MHz (**b**).
# Image-Based Structural Health Monitoring: A Systematic Review
## 4 Discussion
### Roles and Importance of Components and Parameters
#### 4.3.2 Detection and Sizing of Deep and Multiple Damages  
Through the use of a faster R-CNN-based structural visual inspection in a database with 2366 images (with 500,375 pixels) labeled for five types of damages--concrete crack, steel corrosion with two levels (medium and high), bolt corrosion, and steel delamination--it is possible to achieve the near-real-time simultaneous detection of multiple types of damages [69].  
Metallic surface defect identification is a critical method for managing industrial product quality. Existing defect datasets, however, are inaccessible for the deployment of the detection algorithm due to the limited data volume and blemish classifications. In a straightforward way, with regard to defect categories, image quantity, and data scale, the new dataset GC10-DET for large-scale metallic surface defect identification faces significant difficulties. Additionally, standard detection methods are inefficient and inaccurate in the complicated real-world context. This calls for the development of a revolutionary end-to-end defect detection network (EDDN) based on the single shot multibox detector. The EDDN model can handle flaws of various scales. Additionally, a strong negative mining approach is designed to address the issue of data equity, and a few data augmentation techniques are advocated to enrich the training data for the problem of exclusive data gathering. Subsequently, extensive experiments demonstrate that combining GC10-DET with EDDN can meet accuracy requirements for metallic defect recognition [70].  
The use of guided wave imaging (GWI) in SHM is an effective way to determine the size of the defect and its location inside a particular structure [71]. Machine learning is used for automatic defect sizing and localization, which is useful for non-destructive testing (NDT) methods in SHM. The convolutional neural network (CNN) is a subset of machine learning, which is primarily used for image recognition and processing, and they used it as a basis for their inversion strategy. Only simulations of guided wave signals that are subsequently subjected to the delay and sum method are employed with CNN. Although the delay-and-sum (DAS) GWI method is used to produce pictures linked to piezoelectric sensor data, it is still very difficult to automate guided waves imaging to diagnose various sorts of structures. However, GWI is useful and practical for evaluating the state of a structure, particularly for defect magnitude and localization, which are essential in SHM.
## 4 Results and Discussion
### The Contact Angle Effect  
The contact angle was measured from the displaced fluid. All the contact angles were larger than 90\({}^{\circ}\) in this part to simulate the imbibition process.  
Figure 9 shows the fluid distributions when the displacing fluid breaks through at the outlet for the contact angles of 162\({}^{\circ}\), 150\({}^{\circ}\), 135\({}^{\circ}\), and 120\({}^{\circ}\). It is observed that the invading paths of the wetting fluid do not change significantly except for a few branches. An obvious difference can be seen at the contact angle of 135\({}^{\circ}\) where the bottom finger advances furthermost. Figure 10 shows the evolution of the displacing fluid for different contact angles. The case of \(\theta=120^{\circ}\) has the lowest wetting fluid saturation but its difference from the others is very small. It can also be seen from Figure 6 that the contact angle does not have a big impact on the saturation. Therefore, the contact angle has a limited effect on the efficiency of displacement in the micro-model of Berea sandstone.  
Figure 8: Evolution of the displacing fluid saturation for the interfacial tension of 0.0005 mu-ts\({}^{-2}\), 0.015 mu-ts\({}^{-2}\) and 0.03 mu-ts\({}^{-2}\).  
Figure 9: Fluid distributions at breakthrough for a wetting fluid invading a porous medium initially saturated with a non-wetting fluid. The wetting fluid (indicated in blue) is injected from the left side and the values of the contact angle are 162\({}^{\circ}\), 150\({}^{\circ}\), 135\({}^{\circ}\), and 120\({}^{\circ}\), respectively.
# Fast-Convergence Reinforcement Learning for Routing in LEO Satellite Networks
## 4 Simulation Results
Each satellite node has limited resources, so the communication capacity of the whole network is also limited. The FRL-SR algorithm considers the resource utilization of satellite nodes as an optimization objective. When a satellite node selects the next hop, it takes the queue length of neighboring nodes as a consideration parameter and forwards the packets to the satellite with sufficient communication resources first, which improves the resource utilization of the network and forwards more packets per unit time. Considering Figures 5-7 together, we observe that the FRL-SR algorithm transmits a higher number of successful packets in the same amount of time with a smaller average  
Figure 6: Comparison of the average delay of the FRL–SR algorithm and the Dijkstra algorithm with 5000 initial packages. Figure 7: Comparison of the number of successfully delivered packets by FRL–SR algorithm and Dijkstra algorithm. delay per packet. This is enough to show the advantages of the FRL-SR algorithm proposed in this paper over the traditional satellite routing algorithm. \[s=\sqrt{\frac{\sum(x-M)^{2}}{n}} \tag{9}\]  
Figure 8 shows a comparison of the load balance of nodes in the network. We use the population standard deviation to express the load balance of the network, as shown in Equation (9), where M is the mean of the data and n is the total number of nodes in the network, which is a commonly used parameter to describe the degree of discreteness of the system. By observing the simulation results, we can observe that, under the same user request conditions, the FRL-SR algorithm has a better load balancing effect than the Dijkstra algorithm. The former can make full use of the resources of each node for routing, while the Dijkstra algorithm is more likely to cause network congestion. Based on the above simulation results, we conclude that the FRL-SR algorithm has lower network latency and higher data throughput, which is more suitable for satellite networks with variable network status. In addition, the FRL-SR algorithm also has good load balancing characteristics, and it considers both satellite link status and satellite load when making data forwarding decisions, avoiding network congestion. The Dijkstra algorithm only blindly forwards packets to ports with good link status, causing congestion on some network links.
# Analyzing TCP Performance in High Bit Error Rate Using Simulation and Modeling
## 4 Analysis and Validation of Results
### Theoretical Maximum Throughput
#### 4.3.3 TCP Window Size  
TCP throughput T_TCP depends on the round-trip time (\(RTT\)) and the TCP receiver window (_rround_). It is not possible to go any faster than the window size offered by the receiver, divided by the RTT [46].  
\[T_{TCP}\leq\frac{rwnd}{RTT} \tag{10}\]  
Thus, with an \(RTT\) of 0.04 s (as used in the simulation) and _rwnd_ of six MSS (8760, see Table 2, Receive Buffer).  
\[T_{TCP}\leq\frac{8760}{0.04}=219,000\;\mathrm{bytes/s} \tag{11}\]  
This correlates well with the throughput of 200,000 bytes/s (see Equation (7)), as recorded by the Riverbed Modeler. \(RTT\) in the simulation is expected to be slightly higher because of the processing delays on the end systems.
## 3 VQA Model Design
### Data Preparation and Cross-Validation Strategy  
To ensure that the sensitivity analysis is not biased by a single, possibly fortuitous, division of the LIVE-YT-HFR data, the 480 video sequences were repartitioned into multiple, statistically independent training/validation pairs before each ANFIS run. First, a stratified five-fold cross-validation scheme was generated: after permuting the sample indices with a fixed pseudo-random seed, the dataset was divided into five folds of equal size (96 samples per fold). For each fold \(k\) (1... 5), the \(k\)-th subset served as the checking (validation) set, while the remaining four folds were used for training, so every video sequence appeared exactly once in validation and four times in training. Secondly, 30 additional random 80/20 hold-out splits were produced. For each repetition \(r\), a new permutation of the indices was drawn, while the first 80% of samples were assigned to the training set and the remaining 20% to the validation set.  
In total, 35 distinct partitions (five cross-validation and 30 random splits) were obtained. During the sensitivity study, each candidate clustering configuration was evaluated on each split, and the resulting performance metrics were averaged, as discussed in the next section.
## 3 System Model
### Computation Model  
To represent the task offloading strategies, we set indicator variables, i.e., \(y_{i,0}\), \(y_{i,1}\), \(y_{i,j}\in\{0,1\}\), \(\forall i\in\mathcal{N}\), and \(\forall j\in\mathcal{M}\). When task \(i\) is completed by local computing, \(y_{i,0}=1\), otherwise, \(y_{i,0}=0\). When task \(i\) is completed by local satellite edge computing, \(y_{i,1}=1\), otherwise, \(y_{i,1}=0\). When task \(i\) is completed by neighboring satellite edge computing, \(y_{i,j}=1\), otherwise, \(y_{i,j}=0\).  
When \(y_{i,0}=1\), the task computing delay of IoT device \(i\) is:  
\[D_{i,0}=\frac{c_{i}a_{i}}{f_{i,0}}, \tag{3}\]  
where \(f_{i,0}\) is the computing capacity of IoT device \(i\) (cycles per second).  
When \(y_{i,1}=1\), the task completion delay of IoT device \(i\) includes the delay with which IoT device \(i\) offloads task \(i\) to the local satellite, the propagation delay between IoT device \(i\) and the local satellite, and the delay that the local satellite's MEC server computes task \(i\).  
\[D_{i,1}=\frac{a_{i}}{r_{i,1}}+\frac{2d_{i,1}}{c}+\frac{c_{i}a_{i}}{f_{i,1}}, \tag{4}\]  
where \(f_{i,1}\) is the computing resource allocated to task \(i\) by MEC server of the local satellite (cycles per second).  
When \(y_{i,j}=1\), the task completion delay of IoT device \(i\) includes the delay that IoT device \(i\) offloads task \(i\) to the local satellite, the propagation delay between IoT device \(i\) and the local satellite, the delay with which the local satellite migrates task \(i\) to neighboring satellite \(j\), the propagation delay between the local satellite and neighboring satellite \(j\), and the delay with which neighboring satellite \(j\)'s MEC server computes task \(i\).  
\[D_{i,j}=\frac{a_{i}}{r_{i,1}}+\frac{2d_{i,1}}{c}+\frac{a_{i}}{r_{1,j}}+\frac{2 l_{1,j}}{c}+\frac{c_{i}a_{i}}{f_{i,j}}, \tag{5}\]  
where \(f_{i,j}\) is the computing resource allocated to task \(i\) by the MEC server of neighboring satellite \(j\) (cycles per second).  
In all of the cases above, the delay of IoT devices downloading task computing results from the local satellite or neighboring satellites is neglected, because the size of output resulting data obtained after task computation is usually much smaller than the size of input data [33].
## 3 Materials and Methods  
The computation-based modeling of radio wave attenuation over the atmosphere is the stepwise application of relevant radio propagation models, data, and procedures to effectively and prognostically estimate the losses of the propagated radio signals that have been induced by the atmospheric constituents.  
A semi-empirically based method that combines empirical atmospheric data and appropriate signal attenuation propagation models has been engaged in this study. The MATLAB software interface platform has been explored to accomplish the computerized coding of scripts, computations, and extrapolative graphical display of the results obtained from experimental measurements. Moreover, the extrapolative attenuation estimation results and the performance analysis have been accomplished by engaging different computation and modeling parameters from those often utilized in Earth-satellite and terrestrial communications.
## 3 Results and Discussion
### Pharmacological Activities
#### 3.3.3 Anti-Inflammatory Potential  
Table 4 illustrates the results of the anti-inflammatory activity evaluation using the method of inducing inflammation in the rat paw using carrageenan. These include plant extracts that have been shown to have anti-inflammatory properties, though the effect varies depending on the part of the plant used and the dose used. SEFR at 400 mg/kg showed an anti-inflammatory effect (86%) comparable to the inflammation-inhibiting effect of indomethacin (92%); SEFE at the two doses also showed an anti-inflammatory effect, but to a lesser extent than SEFR. [PERSON] and colleagues studied the anti-inflammatory properties of _S. elaegnifolium_[19]. A few species of the _Solanum_ genus have been shown in scientific studies to have anti-inflammatory properties [44; 45; 46]. Furthermore, studies have shown that a hydro-ethanolic extract of the aerial parts of _Solanum lyocarpum_ has anti-inflammatory activity against carrageenan-induced paw inflammation [47]. The anti-inflammatory capacity of plant extracts depends on their phytochemical composition. The phytochemicalal study revealed the presence of quercetin in large quantities, and this molecule demonstrated anti-inflammatory activity via the mechanisms of regulation of COX-2 protein expression, inhibition of NF activation, and inhibition of NO production [48].  
Figure 5: Glycemic postprandial response in normal rats to S. elaegnifolium extract and acarbose): (**A**) effect of leaf extract, and (**B**) effect of fruit extract. ***\(p\) < 0.001 as compared to the control.
# Study of System Integral Energy Efficiency of a Hybrid Pneumatic Power System
## 2 Materials and Methods
### Efficiency
#### 2.3.2 Compressor Efficiency  
The definition of isentropic efficiency in a compressor is the ratio of work input to the isentropic process, to the work input to the actual process between the same inlet and outlet pressures.  
\[\eta_{C}=\frac{\text{Isentropic compressor work}}{\text{Actual compressor work}}=\frac{W_{s}}{W_{a}} \tag{4}\]  
where \(\eta_{C}\) is the isentropic efficiency; \(W_{s}\) is the isentropic compressor work; \(W_{a}\) is the actual compressor work; \(W_{a}\) and \(W_{s}\) can be determined from the energy balance of the pumps.  
During an enthalpy change, energy associated with the gas flowing through the compressor are negligible. The energy balance of the compressor becomes:  
\[\dot{W}=-\dot{m}(\text{h}_{2}-\text{h}_{1}) \tag{5}\]  
The isentropic efficiency of a compressor becomes:  
\[\eta_{c}\equiv(h_{2s}-h_{1})/(h_{2a}-h_{1}) \tag{6}\]In the HPPS, \(W_{s}\) equals the output of ICE-W (work of one cycle). \(W_{a}\) equals the energy stored in the air tank. This energy can be calculated by the theory of storage thermodynamics. For a process from an initial condition A to a final condition B, with constant absolute temperature T = T\({}_{A}\) = T\({}_{B}\). Additionally, if the outside of the container maintains a constant pressure that equals the initial pressure \(P_{A}\), the external pressure will reduce the available energy. The equation to calculate the energy stored in an air tank is as follows:  
\[W_{a}=W=P_{A}P_{B}ln\frac{P_{A}}{P_{B}}+(V_{A}-V_{B})P_{A}=P_{A}V_{A}ln\frac{P_ {A}}{P_{B}}+(P_{B}-P_{A})V_{B} \tag{7}\]  
where \(W\) is the energy stored in the air tank (Watt), \(P_{A}\) is the absolute pressure in the air tank (N/m\({}^{2}\)), \(V_{A}\) is the volume of the air tank (m\({}^{3}\)), \(P_{B}\) is the absolute pressure at state B (N/m\({}^{2}\)), and \(V_{B}\) is the volume at state B. (m\({}^{3}\)).
###### Abstract  
The International Maritime Organization (IMO) emphasizes that shipweck accidents frequently occur at sea and advocates for the safe recovery of shipwcks. This paper examines the case of the Korean \"Sewol\" ferry salvage, where two lifting barges were symmetrically utilized to retrieve a substantial shipweck. The dynamic analysis of the salvage operation is based on the computational fluid dynamics (CFD) approach. The main investigation covers two fundamental physical parameters: the motion response of the lifting barges and shipweck and the tension response of the lifting cables. Using the parameters of the maximum absolute value (MA), root mean square (RMS), and coefficient of variation (CV), a unified criterion is established to quantitatively evaluate the safety of the salvage operation under different working conditions. The study demonstrates that by carefully considering the enhancement of safety and stability for the three vessels involved in the salvage process and by optimizing the safety performance of the lifting cables, suitable operating windows are determined at wave intervals of (\(115^{\circ},155^{\circ}\)) and (\(205^{\circ},245^{\circ}\)). Under most working conditions, curves illustrating the maximum tensions of lifting cables No. 1-15 and No. 16-30 show a distribution with a \"middle part drooping\" shape. The placement of connecting cables on the water's surface at 1.1-1.2 times the salvage spacing between the two lifting barges or the arrangement of inclined lifting cables underwater proves advantageous in constraining the motion response of the three vessels. Reinforcing the lifting cables at the bow and stern ends is recommended. This study presents a methodology for salvaging a shipweck using two lifting barges, which can be used as a reference for designing related salvage approaches.  
Keywords:shipwreck salvage; dynamic analysis; safety assessment; motion response; tension response; twin barges +
Footnote †: journal: Applied Sciences