esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Simulated Case Study
#### 3.1.4 Tracking DSFs in the Time Domain to Detect Nonlinearity
As stated before, for failure mechanisms with limited stiffness deterioration, the capacity to pick up nonlinearities in short windows is key. The three variations of the toy model, defined by changing the stiffness degradation parameter of the Bouc-Wen spring, are used to assess the correlation of DSFs with respect to the Park and Ang index (\(DI_{PA}\)) (see Figure 14). Two DSFs that are derived from post-earthquake AVs, \(\Delta f\) (Figure 14, top left) and \(TAC\) (Figure 14, top center), are very sensitive to stiffness degradation: for the model with very weak stiffness deterioration (\(\delta\eta=10^{-5}\)), the residual damage is small, and thus, the nonlinearity is not detected. In addition, the high performance for OMA-based DSFs to detect and quantify nonlinearity, shown in Figure 8, would significantly drop when considering multiple models with changing hysteretic properties, as evidenced by the large scatter obtained for the change in natural frequency, \(\Delta f_{1}\), in Figure 14 (top left). Figure 13: DSFs evaluated on short time windows for a nonlinear response with low stiffness degradation (**left**), high stiffness degradation (**center**), and a linear response (**right**) to the Montenegro earthquake. Tracking the DSFs over time allows us to separate peak and residual nonlinearities. When using DSFs that can be split into short moving time windows, residual damage and nonlinearity can be addressed separately (see Figure 13). To detect (peak) nonlinearity, the 90-percentile value of the DSF values derived for the windows defining the GM (see Figure 2 bottom) may be used. The 90-tile values of \(TAC\) (Figure 14, top right) and \(WLC\) (Figure 14, bottom left), scale well with the \(DI_{PA}\), irrespective of the stiffness degradation parameter. This feature is important to detect nonlinearity that may weaken a structure but not result in residual changes. Finally, two DSFs that pinpoint nonlinearity from the entire GM signal are evaluated: \(CE\) (Figure 14, bottom center) and \(CohAC\) (Figure 14, bottom right). While both features scale with \(DI_{PA}\), the lack of differentiation between the three models reveals their dependence on nonlinearity alone and the related incapacity to detect residual damage. In addition, the CohAC fails to pick up nonlinearity for GM signals that correspond to short earthquakes, such as Northridge, Marche, and Amatrice (see Figure 4).
## 2 Background and Related Work
### Modelling Obstructions  
City models benefit multiple applications such as archaeology [8], transport projects [9], pedestrian behaviour studies [10], Building Information Modelling [11], thermal emissions surveys [12], map updating/change detection [13] or flood defences/town planning [14]. The open standard format, CityGML [15] defines different qualities of structure in terms of the Level Of Detail (LOD), a ranking that goes from 0-4. LOD0 is a 2.5D digital surface model (DSM). An LOD1 city model is a block model without roof structures. LOD2 adds roof forms to each building. LOD3 accurately represents the design and dimensions of each building face and roof, whereas LOD4 also models the interior or each building. The more accurate or realistic the model, the longer it will take to create. This paper will present a method to minimise the time spent creating models for GNSS survey planning.
# Study on Mechanism of Static Blasting-Induced Hard Rock Fracture Expansion
## 4 Numerical Simulation Analysis of Static Blasting Fracturing Influencing Factors
### Simulation Analysis of Cracking of Double-Hole Specimens with Different Influencing Factors
#### 4.2.2 Influence of Hole Spacing on Specimen Cracking  
(1) Influence of hole spacing on crack expansion and rupture state of specimens  
Figure 25 illustrates the impact of different hole spacing on the fracture behavior of double-hole specimens. The crack areas for the three specimens were measured as 43.5 mm\({}^{2}\), 38.7 mm\({}^{2}\), and 35.2 mm\({}^{2}\), respectively. The findings revealed that when the spacing between double holes was small, the stress between the holes was more concentrated, resulting in a faster transfer of tensile stress and quicker extension of the transferring crack. Furthermore, there was a positive correlation observed between the hole spacing and cracking efficiency.  
Moving on, Figure 26 displays the variation in the number of internal cracks in double-hole specimens under expansion pressure with different hole spacings. The graph demonstrates a positive relationship between the hole spacing and the time it takes for crack initiation. Conversely, there is a negative correlation between the number of crack expansions and the hole spacing.  
Figure 24: Changing the law of expansion pressure of double-hole specimen under different hole diameters.  
The change rule of expansion pressure under different hole spacing and the size of fracturing expansion pressure  
Figure 27 illustrates the change in expansion pressure for double-hole specimens with different hole spacings. Figure 27a shows that the peak expansion trend for each specimen is essentially the same, with the peak expansion pressure ranging from 43.9 MPa to 46.1 MPa.  
Figure 26: Variation curves of the number of expansion fracture cracks in double-hole specimens with different hole spacings.  
Figure 25: Expansion cracking pattern and crack distribution characteristics of double-hole specimens with a different hole spacing.  
Furthermore, Figure 27b presents the expansion pressure size at the time of double-hole penetration and at the time of fracturing. It is observed that as the distance between the center holes increases, the crack expansion time between the two holes lengthens. Additionally, a stronger fracturing impact leads to a higher through-expansion pressure.
# Information Criteria for Signal Extraction Using Singular Spectrum Analysis: White and Red Noise
## 3 Modifications of Information Criteria for the Case of Hankel Noise
### Case of Zero Signal  
Information criteria allow one to consider the absence of a signal as one of the models. In this case, in the form of the criterion (4), the mean square of the values of the initial time series serves as the estimate of \(\sigma^{2}\).  
Accordingly, the signal values are \(0\). Recall that the ESTER and SAMOS criteria do not consider the case \(r=0\). Formally, criteria ESTER and SAMOS return \(-\infty\) in the case \(r=0\), so the value \(r=0\) will never provide the maximum for these criteria.
## 2 Theoretical Model and Hypotheses
### Information Security Marketing and Information Security Intention  
Intention is the concept of having a scheme to accomplish a particular purpose [15]. Information security intention can be described as a specific employee's specific willing behavior related to the organization's information security. Internal marketing involves facets of security that can effectively influence behavioral intention through training, rewards, socialization, participatory decision-making, participation, and communication formalization [31]. The implementation of internal marketing is often seen as a set of activities designed to optimize internal processes within the organization [32], and ISM enhances employees' ISI. ISM is envisioned as a behavioral instrumental approach, similar to an internal-directed marketing strategy that focuses on meeting employees' needs and improving their security-related intentions [10]. This study was mainly in line with this view because ISM influences employees' ISI to protect their organization's information assets, which is the basis for the following hypothesis:  
**Hypothesis 1**.: _ISM is positively related to ISI in the container shipping industry._
## 5 Void Analysis and Mortar Void Percent
### 3D Void Analysis  
The sliced images were reconstructed as a 3D model, and the total voxel number of the coarse aggregate, mortar (without voids) and voids were plotted. A three-dimensional model of AC-13 with voids is shown in Figure 7. The voids, after measuring tests and CT scanning, were compared, and the results are shown in Table 4. In this section, all of the mortar mentioned had an NMPS of 2.36 mm for research consistency and one NMPS mortar was enough to clarify the different parts of the mixture [32]. Then, the air void content of the three laboratory manufactured asphalt mixtures were calculated using Equation (7). The density of the mixture samples was tested by the surface dry method, and the theoretical maximum density was tested by the vacuum method. The two methods follow the \"Standard Test Methods of Bitumen and Bituminous Mixtures for Highway Engineering\". Table 5 shows the voxel statistic results of air void, mortar and coarse aggregate. And Table 4 shows that the CT scanned air void content matched the tested air void content.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mixture**} & \multicolumn{4}{c}{**Air Void Content**} \\ \cline{2-5}  & **Designed** & **Tested** & **Sanned** & **Deviation** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline AC-13 & 4 & 4.1 & 4.2 & 0.1 \\ AC-20 & 4.2 & 4.2 & 4.1 & \(-\)0.1 \\ AC-25 & 4.5 & 4.4 & 4.6 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Air void content, coarse aggregate volume and deviation of the CT and test results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Mixture**} & \multicolumn{4}{c}{**Air Void**} & \multicolumn{2}{c}{**Mortar (without Voids)**} & \multicolumn{2}{c}{**Coarse Aggregate**} \\ \cline{2-5}  & **Voxel** & **Volume** & **Voxel** & **Volume** & **Voxel** & **Volume** \\  & **(mm\({}^{3}\))** & **Voxel** & **(mm\({}^{3}\))** & **Voxel** & **(mm\({}^{3}\))** \\ \hline AC-13 & \(1.08\times 10^{7}\) & 15,646.3 & \(8.97\times 10^{7}\) & 129,641.1 & \(1.57\times 10^{8}\) & 227,244.5 \\ Percent (\%) & 4.2 & & 34.8 & & 61 \\ \hline AC-20 & \(1.58\times 10^{7}\) & 22,842.0 & \(1.27\times 10^{8}\) & 183,293.0 & \(2.43\times 10^{8}\) & 350,986.6 \\ Percent (\%) & 4.1 & & 32.9 & & 63 \\ \hline AC-25 & \(1.57\times 10^{7}\) & 22,721.8 & \(9.87\times 10^{7}\) & 142,752.1 & \(2.27\times 10^{8}\) & 328,478.1 \\ Percent (\%) & 4.6 & & 28.9 & & 66.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Calculation results of the air voids, mortar and coarse aggregate volume.  
Figure 7: Illustration of the 3D model of the voids for AC-13.  
The aggregates and asphalt parts in the mortar (without void) occupied a larger volume percent in AC-13 than that in AC-20, and the aggregates and asphalt parts in the mortar occupied a larger volume percent in AC-20 than that in AC-25. The coarse aggregate volume percent of AC-13 was less than AC-20, and the coarse aggregate volume percent of AC-20 was less than AC-25. The larger the mixture's NMPS, the less the mortar, and the larger the mixture's NMPS, the larger the coarse aggregate percent.
# A Symbolic Encapsulation Point as Tool for 5G Wideband Channel Cross-Layer Modeling
## 3 Methods Applied
### Finite State Markov Chain Constitution
#### 3.3.2 Channel Matrix Derivation  
Let us associate an AMC scheme \(\omega\) to the mobile user if its current \(\mathbf{r}\) falls in region k, \(\mathbf{r}\in(\mathbf{r}_{\omega-1}^{b},\mathbf{r}_{\omega}^{b}]\), \(\mathbf{r}_{\omega}^{b}\) denoting the boundary signal level for \(\forall\omega\), used to select AMC scheme. The channel assumption is that transitions are smooth or made only in neighboring regions,  
\[p_{k,k^{\prime}}=0\text{ and }p_{k,k+1}=0,\ \forall\left|k-k^{\prime} \right|>1 \tag{18}\]  
To determine transition probability to \"out of state \(k\)\", we perform simply  
\[p_{k\alpha}=p_{k,k-1}+p_{k,k+1}-j_{k,k-1,k+1} \tag{19}\]  
Figure 3: State classification model: (**a**) LCR classification; (**b**) Two-state Markov model.  
where \(j_{k,k-1,k+1}\) is the probability that both transitions happened at one time. Assuming that for one user, signal can transit only to one state in the same time instant, we have \(j_{k,k-1,k+1}=0\). Following steps from [35], denote cumulative density function of state k with \(CDF_{k}\) and set  
\[p_{k,k+1}=\frac{LCR_{k+1}\cdot\tau}{CDF_{k}} \tag{20}\]  
\[p_{k,k-1}=\frac{LCR_{k}\cdot\tau}{CDF_{k}} \tag{21}\]  
We now derive our probabilities by substituting (20) and (21) in (19),  
\[p_{ko}=\frac{LCR_{k+1}\cdot\tau}{CDF_{k}}\ +\ \frac{LCR_{k}\cdot\tau}{CDF_{k}}= \frac{(LCR_{k+1}+LCR_{k})\cdot\tau}{CDF_{k}} \tag{22}\]  
As rows in the (10) summary give a probability of 1, then  
\[p_{k}=1-\frac{(LCR_{k+1}+LCR_{k})\cdot\tau}{CDF_{k}} \tag{23}\]  
In the same way,  
\[p_{\alpha k}=p_{k-1,k}+p_{k+1,k}-j_{k-1,k+1,k}=p_{k-1,k}+p_{k+1,k} \tag{24}\]  
\[p_{\alpha k}=\frac{LCR_{k}\cdot\tau}{CDF_{k-1}}\ +\ \frac{LCR_{k+1}\cdot\tau}{CDF_{k+1}} \tag{25}\]  
\[p_{o}=1-\left(\frac{LCR_{k}}{CDF_{k-1}}\ +\ \frac{LCR_{k+1}}{CDF_{k+1}}\right)\cdot\tau \tag{26}\]  
Here, we shortly look back to parameters \(\tau\) and \(CDF_{k}\). First, this presents a transmission time interval (fixed 1 ms in LTE) or multiple OFDM symbols (SLIV in 5G NR), as mentioned, and within channel response remains invariant. Channel response may change from interval to interval, as assumed. Signals in 5G are transmitted in bursts, so we can take this time as the duration of one burst. Another viewpoint is to consider it as packet transmission time, where \(\tau=Packet\_length/Symbol\_rate\). The second parameter is one of first-order statistics expressed as  
\[CDF_{k}=\int\limits_{L_{k}}^{L_{k+1}}prob(r)dr \tag{27}\]  
After completing all the elements, we conclude our observation, leaving space for further exploitation of the presented concept for QoS metrics evaluation.
## 6 Experimental Simulation and Analysis of the Results
### Influence of Channel Fading on The Network  
Figure 11a presents the first network node death of the four protocols for different path loss exponents. Based on the linear change of the four curves, the PEAFC-CL protocol is superior to the other three protocols; its superiority is better embodied with the increase in the path loss exponent, which only embodies the advantage of regarding transmission loss as the reference for CH selection and member nodes selecting cluster sets in the PEAFC-CL protocol. Along with the increase in the path loss exponent, the variation trend of the first network node death under the PEAFC-CL protocol tends to be gradual, whereas the variation in slope of the other three protocols increases gradually. Figure 11b shows the packet loss rate (PLR) of the four protocols for different numbers of distributed nodes. The overall trend illustrates that the PLR of the four protocols increases with varying degrees with increases in the number of nodes because the increase in node quantity will cause increases in the data volume and PLR according to probability statistics. The PLR is influenced by network layers and bears important relations with network topology. In the LEACH protocol, a CH is formed  
Figure 10: (**a**) Number of nodes still alive _vs._ the number of rounds; (**b**) Round when the first node dies _vs._\(\xi\)  
by a comparison between a random number and threshold, and the randomness causes an unstable network topology; thus, the PLR is highest for LEACH. With network coverage as the object, the CNF-Degree protocol guarantees the relationship between a CH and member node and neglects the important relationship between a CH and the BS; thus, the PLR is superior to the LEACH protocol but inferior to EBACR and PEAFC-CL. Considering network energy consumption, EBACR starts with channel fading, but it only considers the influence of the physical layer on the network relative to PEAFC-CL.  
The introduction of the channel efficiency factor in PEAFC-CL not only stabilizes the influence of the CH formation number on the PLR from the network layer but also considers the condition of the bottom channel, which benefits from the fact that cross-layer design provides relevant information from the physical layer and the MAC layer to the network layer. Therefore, the improved network layer increases the data delivery rate. Figure 12 analyzes the PLR in PEAFC-CL under various channel fading intensities and shows that the PLR increases with increases in the channel fading intensity, which is consistent with the above theoretical analysis.
# An Image Processing Method for Measuring the Surface Area of Rapeseed Pods
## 3 Results and Analysis
### Analysis of Measurement Efficiency  
The efficiency of measurement was tested using modeling and detection samples (81 pods) during the precision analysis of the experiment. Using the [PERSON] formula and quasi-cylinder side area methods, we performed the calculations by only measuring the pod length and width with similar efficiency. The [PERSON] formula was the only method that was selected as the representative for testing. The 81 pods were measured using image processing, flattening scanning, and [PERSON] formula methods. Each method was conducted by the same person and timed with a stopwatch. Using the image processing method, the time for placing and shooting was 172 s. This value excluded the preparation time, such as preliminary numbering, which was the same as those used in the following methods. The time for subsequent image processing was 162 s, and the measurement time was 334 s. For the flattening scanning method, the common time of flattening and plastic wrapping, scanning, and measurement were 1620 s, 405 s, and 2025 s, respectively. The length and width measurement of pods using the Clark formula method was 567 s. Since data post-processing with these three methods could be performed in batches, the post-processing time was not recorded, and the operation time was the total measurement time. The analysis of the \"efficiency test\" used above found that the image processing method was the most efficient, followed by the Clark formula method. The flattening scanning method was the least efficient.
## 4 Conclusions  
In summary, SMM equipped with a Mach-Zehnder interferometer to allow for 50 \(\Omega\) free detection system and probing high impedance semiconducting materials was applied to electrically characterize P3 HT as a reference semiconducting polymer in a Metal-Insulator-Semiconductor structure. The microwave probing method operating in a 2 to 20 GHz spectral range was shown to be sensitive to variations of electrical properties in the semiconductor upon negative DC tip bias. Provided the operating setpoint is appropriately adjusted in the vicinity of a chosen interferometric fringe to ensure enough sensitivity (\(|f_{\text{OP}}-f_{0}|<\) 100 kHz) and avoid cross-talking artifacts (\(|f_{\text{OP}}-f_{0}|>\) 10 kHz), PNA phase and amplitude signal variations could be simultaneously and independently correlated to the conductive and capacitive mechanisms modifying the properties of P3 HT upon bias. High frequencies occurred to be more efficient for the study of intrachain conductive mechanisms associated with the formation of polarons along polymer chains and generated from carrier injection upon bias, and for the determination of locally charged transport mechanisms and associated mobilities. Low frequenciesprobing locally across the whole material appeared more suitable to explore trapping mechanisms inherent to the morphology of organic semiconductors. This study evidenced the versatility of SMM but also the necessary constraints for efficient experimental protocols to carry out high resolution non-contact electrical characterization of organic semiconductors, to be useful both for the fundamental understanding of physical mechanisms at stake in these complex materials and for the industrial development of organic electronics.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/22/8234/s1](http://www.mdpi.com/2076-3417/10/22/8234/s1)]([http://www.mdpi.com/2076-3417/10/22/8234/s1](http://www.mdpi.com/2076-3417/10/22/8234/s1)), Figure S1: Complement to interferometric profiles of Figure 2, Figure S2: Complement to interferometric and time profiles of Figure 2, Figure S3: additional interferometric and associated time profiles at 10.46944 GHz, Figure S4: Schematic of the variations of the tip sample interactions varying microwave frequency and probed bias, Figure S5: interferometric and associated time profiles at 3.5709 GHz, S6: Interferometer modeling.  
Conceptualization, O.D., D.T. and D.M.; methodology, O.D., D.T. and D.M. validation, O.D., D.T. and D.M.; formal analysis, O.D., D.T. and D.M.; investigation, O.D., D.T. and D.M.; resources, O.D., D.T.; writing--original draft preparation, O.D.; writing--review and editing, O.D., D.T. and D.M.; supervision, O.D.; All authors have read and agreed to the published version of the manuscript.  
This work was supported by the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 761036 (MMAMA project) and by the National Research Agency (ANR) under the project EQPY ExCELSiOR.  
The authors declare no conflict of interest.
# Self-Adjustment Energy Efficient Redeployment Protocol for Underwater Sensor Networks
## 8 Performance Evaluation
### The Coverage Rate  
Figure 7 compares the coverage rate achieved with the 3D-DVFA protocol without taking into consideration the water currents and the proposed protocol SA-DVFA. The target is to study the effect of considering the water currents in one of the most important sensor network performance parameters, which is the coverage rate. Based on the simulation results, we notice that the coverage rate achieved by our protocol is 98%, the same coverage rate achieved by 3D-DVFA which is a very acceptable rate from the application point of view. In addition, considering the water currents in our protocol does not affect the time needed for the underwater sensor nodes to be deployed in the network area. This shows that our protocol did not reduce the good performance achieved by the 3D-DVFA protocol. Indeed, SA-DVFA takes into account the water currents to readjust the direction of the sensor node displacement to reach the final destination calculated by the 3D-DVFA algorithm.  
\begin{table}
\begin{tabular}{c c} \hline Area size & (3000 m \(\times\) 3000 m \(\times\) 1000 m) \\ \hline Number of sensor nodes & 400 \\ \hline simulation time & 3000 s \\ \hline Transmission range & 500 m \\ \hline Sensing range & 250 m \\ \hline Sensor Speed (\(V_{\text{is}}\)) & 2.5 m/s \\ \hline k & 0, 1, 2, 3, 4, 5 \\ \hline \multirow{6}{*}{Water currents speed (\(V_{\text{rec}}\)) based on the water depth} & (0–200 m) 2 m/s \\  & (200–400 m) 1.7 m/s \\ \cline{1-1}  & (400–600 m) 1.3 m/s \\ \cline{1-1}  & (600–800 m) 0.9 m/s \\ \cline{1-1}  & (800–1000 m) 0.5 m/s \\ \hline \end{tabular}
\end{table}
Table 2: Simulation Parameters.
## 2 Proposed Time-Based Sensor
### Process, Voltage, and Temperature Variations  
The time-based sensor employs a current source to generate two proportional voltages and measures the time-constants by monitoring the discharge period. As long as the ratio of the charging periods is precise, the proportional voltages are accurate, leading to precise time-constant values. Despite the absolute value of the current source varies due to process, voltage, and temperature (PVT) variations, the approach remains robust as it primarily relies on the accuracy of the on-chip clock rather than requiring a precise on-chip voltage or current reference. Furthermore, given the sensor's brief operational duration, we can safely assume that voltage and temperature remain stable without experiencing transient variations across the circuit's operation.