esa-sceva/satcom-chunk-collection huggingface dataset:
# Depth Retrieval Procedures in Pulsed Thermography: Remarks in Time and Frequency Domain Analyses
### Quantitative Depth Estimation in the Frequency Domain  
Frequency domain analysis using quantitative Pulsed Phase Thermography (PPT) and phase data are of prime interest in thermographic imaging, as, in addition to the enhanced detectability that this technique provides, a depth prediction approach similar to the early detection in time domain analysis has been developed and widely used [34; 35]. This approach consists of identifying the depth of the defect from the frequency for which the phase contrast between a defective and a sound area crosses the zero-contrast line. This technique relies on the thermal diffusion length, i.e., \(\mu=(\alpha/\pi\ell)^{1/2}\) as the depth of the defect can be estimated from a relationship of the form [36]:  
\[z=C\cdot\mu=C\sqrt{\frac{\alpha}{\pi f_{b}}} \tag{2}\]  
where \(C\) is a correlation constant and \(f_{b}\) (Hz) is the blind frequency, defined as the limiting frequency at which the feature of interest presents enough phase contrast for its detection in the frequency spectrum. The above described procedure for quantitative analysis requires the determination of the blind frequency and of the correlation coefficient \(C\), when the material's thermal properties are known. Conventional experimental \(C\) values when using the phase in lock-in experiments range from 1.5 to 2 [37] with a value of \(C=1.82\) typically adapted in experimental studies [38; 39]. PPT results agree with this value range either for homogeneous or composite materials [35; 36]. Thus, when the value of proportionality coefficient is known, the quantitative PPT requires the measurement of the blind frequency. In any other case, to validate the depth results, the accuracy of the correlation coefficient value used in Equation (2) should be graphically evaluated by the variables \(z\) and \(\mu\)[36].
# A Routing Algorithm Based on Real-Time Information Traffic in Sparse Environment for VANETs
## 5 Simulation and Results
### Performance Metrics  
We evaluate the performance of routing protocols using the following performance metrics.  
1. Packet loss rate: This represents the ratio of the number of data packets that are lost to the total number of data packets sent at the source node. \[\text{Packet loss rate}=\frac{P_{t}}{P_{\text{s}}}\times 100\] (18)
2. End-to-end delay: This represents the average delay of packets that are generated at the source node and received successfully at the destination. \[\text{End-to-end delay}=\frac{\sum_{n=1}^{N}D_{n}}{N}\] (19)
3. Network yield: This represents the comprehensive performance of the network. Defined as the ratio of the total packets received at the destination to the total number of packets sent by all nodes of the network. \[\text{Network yield}=\frac{P_{r}}{P_{all}}\] (20)
## 1 Introduction
Here, the reconstruction of the record from shared messages is performed using cloud operators to save computational time and complexity for both healthcare centers and patients. This approach preserves both timeand data in smart healthcare IoT. A real or random model is proposed in [13] for generating the session key for data storage in the cloud. This key is generated after the successful access of the user's wearable device to the cloud. This approach performs well on both the computational and security side for session key protection. In healthcare IoTs, many techniques are used to encrypt and access data [14]. For accessing the data, depending on their responsibilities, attribute-based encryption is used. The information is protected through the group key and it is also transferred and verified through a key matching policy based on their roles. Surveys of security mechanisms used by wearable devices for storing information in the cloud are discussed in [15]. It states that most devices utilize a short-term privacy policy for protecting the data, and this needs to be updated during healthcare monitoring. The recent techniques used in healthcare IoT and their shortcomings are discussed in the following section. The paper is organized as follows. Section 2 highlights the techniques combined with the break-glass procedure in smart healthcare units. Section 3 briefly explains the proposed methodology and its security analysis. Section 4 compares the proposed method performance with the existing lightweight break access. Finally, the paper is concluded with a summary in Section 4.
# A Large Capacity Histogram-Based Watermarking Algorithm for Three Consecutive Bins
## 3 The Proposed Method
### Embedding and Extraction of the Watermark
#### 3.3.1 Watermark Embedding Rules
For instance, given the watermark \"000\", one should modify the original bins \(a\), \(b\), and \(c\) using the formula in the second line of Table 1, so that the pixel number of the modified bins \(a^{\prime}\), \(b^{\prime}\), and \(c^{\prime}\) closes to 1:1:1. In a similar modification/adjustment manner, the proportional relationship of the modified bins \(a^{\prime}\), \(b^{\prime}\), and \(c^{\prime}\) after watermark embedding will close to 1:1:2 if the given watermark is \"001\". Others are all similar to the above, except for a special case, i.e., the watermark \"111\". It can be distinguished by predefining the proportional relationship of the modified bins \(a^{\prime}\), \(b^{\prime}\), and \(c^{\prime}\) as 1:2:3 roughly. With the assistance of this predefinition, one can accomplish the corresponding modification/adjustment before and after watermark embedding by making use of the formula, as shown in the last line of Table 1. Based on the above analysis, the extraction accuracy of the embedded watermark can be ensured. After that, we can embed the 3-bit watermark into each group containing three consecutive bins. To analyze the embedding rules explicitly, an illustration of embedding the 3-bit watermark in eight cases is also shown in Figure 3. Assume that there remains an original group containing three consecutive bins, as shown in Figure 3a; let us analyze the case \(W(i)=2\). Obviously, the original group in Figure 3a does not satisfy the condition in Case 3, shown in the fourth line in Table 1; a part of the pixels in both bins \(a\) and \(c\), shown in Figure 3a, will be modified to \(b\), so that the modified result satisfies \(a^{\prime}=\lfloor(a+b+c)/4\rfloor+\Delta_{1}\), \(b^{\prime}=\lfloor(a+b+c)/2\rfloor+\Delta_{2}\), and \(c^{\prime}=\lfloor(a+b+c)/4\rfloor+\Delta_{3}\); here, \(\lfloor\bullet\rfloor\) represents a down integer function, \(\Delta_{1},\Delta_{2},\Delta_{3}\in[0,1]\) and \(a+b+c=a^{\prime}+b^{\prime}+c^{\prime}+\Delta_{1}+\Delta_{2}+\Delta_{3}\), as shown in Figure 3d. For the watermark embedding process of other cases, the modification process is similar to the above. Figure 3: Illustration of embedding the 3-bit watermark in eight cases. (**a**) The original histogram; (**b**–**i**) the modified histograms for the cases \(W(i)=0,1,2,3,4,5,6,7\), respectively. It is worth noting that for the approximately equal adjacent bins, it may decrease the watermark extraction accuracy when the histogram is fully distorted, but a large number of embedded watermarks can still be extracted if the histogram is slightly changed. In the specific experimental operation, the default value (the case that does not satisfy certain proportional relationships is set as an approximately equal treatment) is used to weaken the fragile relationship for the adjacent bins, which are approximately equal.
# Fabrication of Self-Healable Magnetic Nanocomposites via Diels - Alder Click Chemistry
## 1 Introduction
In this study, a novel self-healing hybrid material system was successfully developed by incorporating iron oxide nanoparticles-multiwalled carbon nanotubes (IONPs-MWCNTs) into a crosslinked PCL-based network. The IONPs-MWCNTs were first synthesized by an \"outside-decorating\" method to connect IONPs to the surfaces of the MWCNTs, followed by a mixing process to combine the IONPs/MWCNTs with 1,1\({}^{\prime}\)-(methylenedi-4,1-phenylene)bismaleimide (BMI) and polycaprolactone-poly(furfuryl glycidyl ether) copolymer (PCLF) which served as a polymeric matrix for the hybrid system. This material could be reversibly crosslinked via DA/retro DA chemistry and exhibit multiple self-healing cycles. Therefore, even complex damages and serious cracks could be efficiently recovered. More importantly, these nanocomposites also exhibited superparamagnetic properties. Thus, the organic/inorganic hybrid material system developed in the current study can be used as a novel material for future electromagnetic applications.
# A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments
## 4 Waveform Selection
### Candidate Waveforms
#### 4.1.3. Sc-Fdp  
Block diagram of SC-FDP massive MIMO systems is shown in Figure 5. The block diagram of this system is similar to OFDM massive MIMO system except that the FFT and IFFT blocks are transferred from the user side to the BS. In fact, SC-FDP is a single-carrier waveform that processes the signal block wise. We should note that in conventional systems the block wise single-carrier waveform was called single-carrier frequency domain equalization (SC-FDE). However, in massive MIMO system since we have frequency domain equalization in the uplink and frequency domain precoding in the downlink of  
Figure 4: Block diagram of OFDM multiple access (OFDMA) massive MIMO system.  
Figure 3: Block diagram of orthogonal frequency division multiplexing (OFDM) massive MIMO system.  
block wise single-carrier, we call this waveform single-carrier frequency domain processing (SC-FDP). We used the term processing to include both equalization and precoding.
## 2 Materials and Methods
### Intervention  
Prior to implant insertion, each patient either received new mandibular full dentures or had their pre-existing mandibular complete dentures refined to enhance the denture fitting. Subsequently, panoramic radiographs, computed tomography (CT) scans, preoperative planning, and surgical guide fabrications were performed. Two implants (Nobel Speedy Groovy RP 4 \(\times\) 10-18 mm, Nobel Biocare, Gothenburg, Sweden) were placed in the interforminal region of each patient using a flapless surgical technique, performed by the same experienced implantologist. Following this, the two groups underwent different treatment regimens.  
Concerning the immediate group, keepers (Magfit, Aichi Steel Co., Aichi, Japan) with a diameter of 4.7 mm were attached to each implant with a torque of 25-30 Ncm. The keepers' height (3.0, 4.0, or 5.5 mm) was adequately chosen based on soft tissue thickness. Afterward, auto-polymerizing acrylic resin (Unifast III, GC, Tokyo, Japan) was used to embed the magnetic assembly (Magfit, Aichi Steel Co., Aichi, Japan) into the intaglio surface of the dentures. To reduce swelling following surgery and prevent the dentures from becoming ill-fitting, patients were told to wear their dentures continuously for 24 h. Participants were asked not to remove their dentures throughout the initial week except for denture cleaning and oral hygiene.  
Regarding patients in the conventional group, two healing abutments were inserted. We then relieved the sides of the denture base surrounding the healing abutments to reduce contact between the denture and the abutments, thus avoiding the stress distribution on the implants. The healing abutments were replaced with keepers of a suitable height three months after the surgery. Magnetic assemblies were picked up similarly to how they were described for the immediate group.
## 5 Numerical Simulation Study of the Iced Wind Turbine Flow Field
### Analysis of Results
#### 5.2.3 Flow Field Analysis  
As shown in Figure 15, the variation of wake vortices on the windward side of the blade for different icing masses is given. The results show that the wake vortices in the flow field are mainly composed of the center vortex and the tip vortex. With the increase in icing mass, the effect of icing on the vortex structure is mainly manifested in the size and number of vortices. The size and number of center vortices and tip vortices increase significantly after icing. This is due to the changes in the airfoil shape and surface roughness values of each blade after icing, resulting in the flow field around the blade becoming unstable. These changes can exacerbate the formation and growth of vortices that spread out to the surroundings, thus affecting the noise performance of the wind turbine.  
Figure 15: Vortex cloud of icing wake on windward side of blade: (**a**) No icing; (**b**) 0.5 kg; (**c**) 1.8 kg.
## 3 Optical Error Analysis
### Optical Errors of FPI  
The main errors in the FPI include optical path errors, non-parallelism errors, non-flatness errors, and coating reflectance errors. The FPI optical path length determines the frequency-shift positions of spectral components; deviations in this parameter directly affect the construction of the coefficient matrix A. Non-parallelism and surface irregularities induce spatially dependent OPD variations across the clear aperture, thereby perturbing the linear relationship defined in Equation (8). Wavelength-dependent fluctuations in the semi-reflective coating's reflectivity alter the harmonic coefficients \(a_{m}\) and further compromise the system's linearity. This section provides a detailed analysis of these error mechanisms and proposes corresponding algorithmic enhancements to mitigate their impacts.
## 1 Introduction
The error of the models mentioned above is little if the number of signals is not too many and overlap probability is not too big, otherwise, the error is large. The reason is that dead time of receiver is thought to be a part of pulse duration for the entire desired signals. In fact, dead time can be regarded as a part of pulse duration only when the desired signal does not overlap with others. It is assumed that desired signal can be received only when it does not overlap with its adjacent pulses and its arrival time is out of dead time. However, the formulas mentioned above cannot calculate the overlap between arrival and dead time rightly, and consequently the error occurs. Dead time is generated by the receiving of front desired signals, but it affects the receiving of subsequent signals since they are thought to be interfered if their arrival times overlap with dead time; however, it is too difficult to judge easily which one of the subsequent signals is in the dead time. Owing to the similarity between the receiving of signal and queueing process, queueing theory is often applied to analyze the characteristics of communication [11; 12; 13]. This paper developed a mathematical model for calculating collision probability between desired signal and intra-system interference based on M/M/1/0 queueing theory model, thus, the collision probability between dead time and subsequent pulse signals is calculated accurately even if the pulse-stream density is high, and consequently intra-system interference can be analyzed accurately. Combining the mathematical model of collision probability for periodic pulse, we built a mathematical model to calculate RE for DME when interfered by intra-system and inter-system signals. The remainder of the paper is organized as follows. In Section 2, we revisited the principles of DME and JTIDS briefly, and focused on the technical characteristics of the two systems that are pertinent to the analysis of interference such as signal structure, pulse duration and PRF. In Section 3, we constructed the flow chart of DME signal firstly, and then defined the evaluation criteria of DME capability. Finally, we built different models to analyze the impact of intra-system and inter-system interference on DME using periodic pulse collision method (PPCM) and theory, respectively. In Section 4, we compared the result from PPCM and theory with that from Monte Carlo simulation, then analyzed RE with respect to capacity, studied capacity of DME when RE and reply rate are limited. In Section 5, we provide conclusions.
## 7 Q-Learning Network Architecture
### Dataset Preparation
Inspect all possible actions \"\(\mathbf{a}_{i}\)\" to move to the new state. 16. Select the best action \(\mathbf{a}_{i}\in\mathbf{A}\), which satisfies the maximum value for the \(\mathbf{Q}\)-value function argmax \(\mathbf{Q}(\mathbf{s},\mathbf{a})\) to move to the new state. 17. Identify the immediate Reward \"\(\mathbf{R}\)\", based on the action implemented to move to the new state. 18. Based on the following: (1) maximum Q-value \(\mathbf{Q}(\mathbf{s},\mathbf{a})\) obtained in (16), (2) the corresponding reward \"\(\mathbf{R}\)\", (3) the discount factor \"\(\mathbf{\gamma}\)\", then \(\mathbf{Q}(\mathbf{s},\mathbf{a})\) can be updated based on bellman's equation \(\mathbf{Q}(\mathbf{s},\mathbf{a})\leftarrow\mathbf{R}+\mathbf{\gamma}\) argmax \(\mathbf{Q}(\mathbf{s},\mathbf{a})\) Outputs
19. Based on the updated \(\mathbf{Q}(\mathbf{s},\mathbf{a})\) values in \(\mathbf{Q}\)-table, the channel coefficients \"\(\mathbf{h}_{ij}\)\" and channel gain \(\left|\mathbf{h}_{ij}\right|^{2}\) can be updated and a new user rate can be calculated and compared to the target rate \"\(\mathbf{R}_{T}\)\". 20. Compute the difference \"\(\mathbf{\Delta Q}\)\" between the updated value function \(\mathbf{Q}_{new}(\mathbf{s},\mathbf{a})\) and the previous \(\mathbf{Q}(\mathbf{s},\mathbf{a})\). 21. Based on (20), \(\mathbf{Q}(\mathbf{s},\mathbf{a})\) value in the \(\mathbf{Q}\)-table can be further updated according to \(\mathbf{Q}(\mathbf{s},\mathbf{a})\leftarrow\mathbf{Q}(\mathbf{s},\mathbf{a})+\alpha\cdot\mathbf{\Delta Q}\)
22. Check whether the terminal state has been reached or the episode has been completed. 23. Compose predicted channel taps \(\hat{h}_{i}\)
```  
**Algorithm 1**Developed \(Q\)-learning Channel Prediction Structure.
## 3 Results and Discussion
### Permeability
#### 3.4.2 Prediction of the Permeability Coefficient from the Consolidation Test  
Table 6 presents the mixture density of the 25 mg/kg and 50 mg/kg arsenic solutions used and the predicted coefficient of permeability using Equation (3). Although the consolidation tests were performed, the results were only considered here to predict the \(k\) value of the soils.  
In the case of the natural soil (0 mg/kg), even when the predicted \(k\) value is 40% lower than the measured one, its value remained in the same order of magnitude. Nevertheless, for the case of the arsenic-contaminated soils, there is a difference of at least two orders of magnitude when comparing the predicted versus the measured \(k\) values.  
According to the \(k\) variation, arsenic-contaminated soils behave like coarse-grained soils. Such behavior is supported by the porosimetry test results that indicated that the pore sizes increased and resulted in higher \(k\) values. However, the predicted \(k\) of the contaminated soils do not reflect such modification, and, consequently, the \(k\) values obtained from the consolidation test do not correspond with the soil behavior.  
Thus, if these results are used in geotechnical design to represent how the liquids are carried out through the soil, it would result in inadequate solutions. So, Equation (3) should be avoided when analyzing contaminated soil samples. Furthermore, additional studies should be performed to fully evaluate the hydraulic conductivity function of the unsaturated soils since it is related to the matric suction of the soil.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Arsenic Concentration (mg/kg)** & \(c_{e}\)**(m\({}^{2}\)/s)** & \(E_{m}\)**(N/m\({}^{2}\))** & \(\rho_{m}\)**(kg/m\({}^{3}\))** & \(k\) **(m/s)** \\ \hline
0 * & 3.15 \(\times\) 10\({}^{-8}\) & 9.1 \(\times\) 10\({}^{5}\) & 1000.00 & 3.2 \(\times\) 10\({}^{-10}\) \\
25 & 1.67 \(\times\) 10\({}^{-8}\) & 1 \(\times\) 10\({}^{6}\) & 1001.10169 & 1.6 \(\times\) 10\({}^{-10}\) \\
50 & 2.79 \(\times\) 10\({}^{-8}\) & 6.8 \(\times\) 10\({}^{5}\) & 1002.03125 & 4.0 \(\times\) 10\({}^{-10}\) \\ \hline \hline \end{tabular}
*Natural soil.  
\end{table}
Table 6: Permeability coefficient values predicted from the consolidation test parameters of the soils.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Arsenic Concentration (mg/kg)** & \(k\)**(m/s)** \\ \hline
0 * & 5.3 \(\times\) 10\({}^{-10}\) \(\pm\) 6.2 \(\times\) 10\({}^{-11}\) ** \\
25 & 2.5 \(\times\) 10\({}^{-8}\) \(\pm\) 4.7 \(\times\) 10\({}^{-9}\) \\
50 & 1.2 \(\times\) 10\({}^{-8}\) \(\pm\) 1.4 \(\times\) 10\({}^{-7}\) \\ \hline \hline \end{tabular}
*Natural soil; ** values in table are reported as average \(\pm\) standard deviation (n = 3).  
\end{table}
Table 5: Permeability values of the soils obtained from the ASTM D5084 [27] falling head test.
# A Consensus-Based Likert-LMPP Model for Evaluating the Earthquake Resistance of Existing Buildings
## 3 Methodology
### Building Vulnerability Parameters (Inputs to the Proposed Model)
#### 3.3.4 Pounding Effects  
Buildings that are not sufficiently separated bound each other during an earthquake because vibration periods differ and vibration amplitudes are not synchronous. Pounding is most detrimental to adjacent buildings at corners, and uneven floor levels aggravate the effect of bounding. High-rise buildings are more likely to be damaged by bounding [53]. A misaligned floor level in an adjacent building is considered the worst-case scenario [27]. To avoid bounding, a building should not be located closer than 4% of its height to an adjacent building to avoid extensive damage [24].
## 2 Materials and Methods  
### Data  
The SST data used in this study were obtained from the National Oceanic and Atmospheric Administration's (NOAA's) Optimum Interpolation SST (OISST) version 2.1 software [35; 36], with a horizontal resolution of \(1/4^{\circ}\) and a temporal resolution of day. By correcting satellite SST biases via additional measurements, such as SSTs from the Argo float and buoy, and the adoption of revised correction methods, such as the ship-buoy SST correction algorithm and the conversion-method of the sea-ice concentration, the quality of this dataset has been significantly improved since 1 January 2016 [35; 36]. Missing values in this long-term climate data from NOAA \(1/4^{\circ}\) daily OISST are filled via interpolation for a spatially complete map of SST, and SSTAs represent departures from normal or average conditions and are computed via the daily OISST minus a 30 year duration climatological mean.  
In this study, we only use the limited database of a global gridded dataset, confined to the EJS and covering the duration from 1 September 1981 to 4 April 2023. The total number of gridded datasets is 1768, covering the whole EJS. In the following analysis, we use only the SSTA database because our goal is to examine the spatiotemporal characteristics of fluctuating behaviors of SST variability.