esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Photonic Technologies
### Interferometric Techniques  
#### 3.2.1 Moire Interferometry  
Moire interferometry is an experimental technique based on the superposition of two such similar, but slightly different, line arrays that cause interference between the arrays. The interference results in the formation of moire fringes. There is a definite relationship between the pitches of the two arrays and the spacing of the fringes. Therefore, given the pitch of one of the arrays and the distance between the moire fringes, the pitch of the other array can be calculated.  
The Moire interferometry technique was used to study strain patterns through tooth structures. [PERSON] _et al._[51] have studied the mechanical load across the dentin-enamel junction (DEJ), generated by changes in humidity of the hard tissues of teeth, as dentin and enamel, and [PERSON] _et al._[52] have mapped the in-plane strain distribution of human tooth crowns under compression and studied the relation between the strain distributions within teeth structures. The manner in which the resulting strain is distributed within the tooth is related to its structure, showing differences between enamel, dentin, and at their interface, the dentin-enamel junction. Their findings were consistent with the hypothesis that within the dentin there are structural adaptations for transferring and minimizing stress. [PERSON] _et al._[53] developed a sensitive measuring method, based in Moire fringe interferometry with a resolution of 0.19 mm, to enable the direct evaluation of gingival swelling.
# An Efficient Quantized Message Passing Receiver Design for SCMA Systems
## 3 Proposed Quasi-Uniform Quantizer for SCMA System
### Complexity Analysis  
From the above discussion, it is obvious that the difference between the original Log-MPA and the Max-log MPA lies on the resource node updating, i.e., Equations (8) and (12). It is straightforward that the message exchange in (8) consists of a large number of exponential and logarithmic operations, while the RN updating in (12) only contains a comparison operation, which significantly reduces the computational cost of the original SCMA detector. On the other hand, the computational complexity of the Max-log MPA is identical to that of the Sub-log MPA in each iteration. However, the Sub-log MPA in Section 3.2 needs a lower number of iterations to converge due to its utilization of the latest updating messages in the current iteration, which subsequently reduces the computational cost of the Max-log MPA. Based on (14) and (16), it is clear that the storage overhead of the Sub-log MPA is identical to that of the Max-log MPA.
# Effect of Different Gradings of Lightweight Aggregates on the Properties of Concrete
## 1 Introduction
Among several characteristics of aggregates, the grading (or particle size distribution) of lightweight aggregates is a very important factor because it can affect the workability and cost of concrete [18]. In particular, it is desirable for lightweight concrete to contain large volume of lightweight aggregates to reduce the density and to minimize the use of cement. For this purpose, the use of appropriate grading of aggregates is required to include large volume of lightweight aggregates in the specimen. The main objectives of this study can be summarized as follows: (1) production of lightweight aggregate concrete (LWC) with very low density (less than 500 kg/m\({}^{3}\)) by maximizing lightweight aggregate volume using different gradings of lightweight aggregates (LWA); (2) evaluation of the material responses using experimental and numerical approaches; and (3) investigation of the effect of different gradings on the material characteristics and properties. Here, Liaver(r) (Ilmenau, Germany), an expanded glass granulate, is used as lightweight aggregates for lightweight concrete specimens according to its very low density and water absorption characteristics; this material is produced by being ground, granulated, distended and vitrified in a rotary kiln at 750 \({}^{\circ}\)C to 900 \({}^{\circ}\)C, and this produces very light granules measuring between 100 \(\upmu\)m and 16 mm grain diameter. [19]. Different gradings following the modified Andreasen and Andersen dense packing model [20; 21; 22; 23] are adopted to produce the lightweight specimens with densely packed Liaver(r) particles. Here, the target volume of lightweight aggregates is more than 70% of the concrete volume. To investigate the effect of the gradings on the material properties, elastic modulus, compressive strength, and thermal conductivity values of lightweight concrete specimens are evaluated using experimental and numerical approaches; experimental tools, such as testing compressive strength (Toni Technik, Berlin, Germany) and thermal conductivity by the transient plane source method (Hot Disk, Goteborg, Sweden), are adopted to measure the mechanical and thermal properties, respectively. For the numerical lightweight specimens, random packing models of particles with different size distributions are adopted here [24; 25; 26], and their responses are computed using finite element (FE) analysis. In addition, X-ray computed tomography (CT) is utilized to investigate the inner structures of the lightweight concrete specimen without destruction of the samples; the aggregate structures are investigated by incorporating a probabilistic description method, the lineal-path function, to describe the size characteristics of the lightweight aggregates within the specimens. Then, the correlation of the gradings and the material properties is discussed.
# Prediction of Temperature Distribution for Previous Cement Concrete Pavement with Asphalt Overlay
## 2 Mathematical Model of Pavement Temperature Field  
### Governing Equation  
The time-dependent one-dimensional temperature function of the \(j\)th layer in an \(n\)-layered existing cement concrete pavement with asphalt overlay is assumed to be \(T_{j}(z,t)\), which varies with the depth (\(z\)) and time (\(t\)). The material is considered to be isotropic, uniform and continual, and is temperature-independent in each layer.  
Ignoring the horizontal heat conduction of the pavement, the heat conduction equation for the pavement temperature field is as follows:  
\[\frac{\partial T_{j}(z,t)}{\partial t}=\alpha_{j}\frac{\partial^{2}T_{j}(z,t) }{\partial z^{2}},\;H_{j-1}<z<H_{j}, \tag{1}\]  
where \(z\) represents the depth (m); \(t\) represents the time (s); \(T_{j}(z,t)\) represents the temperature field of the \(j\)th layer (\({}^{\circ}\)C); \(\alpha_{j}\) represents the thermal diffusivity of the \(j\)th layer (m\({}^{2}\)/h); and \(H_{j}=\sum_{k=1}^{j}h_{k}\), \(j=1,2,\ldots,n-1,H_{0}=0\), \(H_{n}=\infty\), \(h_{k}\) represents the thickness of the \(k\)th layer (m).
## 2 Problem Description
### Assumptions  
According to previous studies and engineering experience, several reasonable assumptions and simplifications are made to simplify the EOS scheduling problem:  
* Only the spot targets are considered in this paper, and the polygon targets can be viewed as multiple independent spot targets. Each spot target only needs to be observed with one pass.
* The dynamic missions are assumed to arrive in batch style, which indicates that dynamic scheduling is only triggered when a certain number of dynamic tasks are submitted, rather than being triggered by one or a few tasks.
* All Earth observation satellites have enough memory and power on each orbit.
* Relay satellites and ground stations are assumed to be sufficient resource to download all observation data. Therefore, the download scheduling for the observation data is not considered.
# Adaptive Energy Management in 5G Network Slicing: Requirements, Architecture, and Strategies
## 4 Energy Management Model Mathematical Formulation
### Hardness of the Problem  
The energy-aware resource allocation for service processing in the context of 5G network slicing, considering the finite energy supply, an intra-slice priority scheme, and different management strategies with the aim of minimizing energy consumption through optimal energy utilization is equivalent to the objective of multi-dimensional multi-choice knapsack problem of choosing the most valuable items of a set of classes (one item per class) without overloading the knapsack [35]. The literature has proven that the complexity linked to this kind of problem is \(\mathcal{NP}\)-hard. Establishing an analogy with our proposal, the multidimensional behavior is given by the power and time parameters of services, and the multiple-choice feature corresponds to the selection of a specific time shifting value and/or quality degradation level from a possible set of options. Thus, we can conclude that the optimal adaptive energy management in 5G network slicing falls into the \(\mathcal{NP}\)-hard classification.
# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 3 Method
### Dynamic Motion Model
#### 3.4.3 Data-Driven Noise Adjustment  
A key limitation of traditional Kalman filters lies in their fixed noise covariance matrices, which are unable to adapt to dynamic changes in target motion states. To address this issue, we propose an adaptive noise adjustment mechanism, where the process noise matrix \(\mathbf{Q}\) and observation noise matrix \(\mathbf{R}\) are dynamically updated based on real-time matching results as follows:  
\[\mathbf{Q}^{t}=\mathbf{Q}^{t-1}\cdot\bigg{(}1+\frac{\|\mathbf{v}^{t}-\mathbf{ v}^{t-1}\|}{v_{\text{ref}}}\bigg{)},\quad v_{\text{ref}}=10\,\text{pixel}/\text{ frame} \tag{17}\]  
\[\mathbf{R}^{t}=\mathbf{R}_{0}\cdot\Big{(}2-\text{conf}^{t}\cdot e^{-\text{AR} _{\text{dev}}}\Big{)},\quad\text{AR}_{\text{dev}}=|\text{AR}_{\text{det}}- \text{AR}_{\text{hist}}| \tag{18}\]  
Here, \(\mathbf{Q}\) and \(\mathbf{R}\) are the process and observation noise matrices, respectively. The process noise matrix \(\mathbf{Q}\) is updated based on the magnitude of the target's velocity change, reflecting the degree of abruptness in its motion. The observation noise matrix \(\mathbf{R}\) is adjusted based on the detection quality, taking into account both detection confidence and the consistency of the target's aspect ratio with the historical data.  
This adaptive adjustment mechanism modifies the filter parameters based on two key metrics as follows: (1) the rate of velocity change, reflecting abruptness in target motion states; and (2) detection quality assessment, combining detection confidence and the historical consistency of the target's aspect ratio. When detection results are reliable, the system reduces observation noise to rely more on current observations; conversely, during rapid target direction changes or unstable detection, it automatically increases process or observation noise to maintain prediction stability.
## 5 Heat Transfer around an Oscillating Cylinder in Non-Newtonian Fluid Flow
### Effects of the Oscillating Frequency  
Here, in order to study the effects of the oscillating frequency on the heat transfer around an oscillating cylinder in a uniform flow, the oscillating amplitude is fixed at \(H^{*}=1.0\) with \(\text{Re}=100\). Two more oscillating frequencies \(f^{*}=0.8\) and \(1.2\) are considered for comparison. The time-averaged Nusselt numbers and amplitudes are presented in Table 6. The results show that the increasing oscillating frequency of the cylinder enhances the heat convection, resulting in larger averaged Nusselt numbers and amplitudes for both shear-thinning and shear-thickening fluids. However, the enhancement in the shear-thinning fluid is much more significant than that in the shear-thickening fluid.  
The instantaneous vorticity and temperature contours for \(n=0.6\) are presented in Figure 11. Similar Karman-street type wake is observed in all cases. It is found that the increasing oscillating frequency of the cylinder tends to increase the vertical distance between the two adjacent vortices.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(f^{*}\) & \(n=0.6\) & \(n=1.0\) & \(n=1.4\) \\ \hline
0.8 & 6.326, 0.283 & 5.776, 0.224 & 5.408, 0.163 \\
1.0 & 6.677, 0.431 & 5.993, 0.295 & 5.563, 0.262 \\
1.2 & 6.901, 0.591 & 6.152, 0.421 & 5.567, 0.351 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Time-averaged Nusselt number (\(Nu\)) and its amplitude (\(\Delta Nu\)) for forced convection heat transfer from an oscillating cylinder to power-law fluids at \(\text{Re}=100\), \(\text{Pr}=1.0\), and \(H^{*}=1.0\).  
Figure 10: Instantaneous temperature contours at \(\text{Re}=100\), \(\text{Pr}=1.0\), \(H_{0}=1.0\) and \(n=0.6\). (**a**) \(0T\), (**b**) \(T/4\), (**c**) \(T/2\) and (**d**) \(3T/4\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(H^{*}\) & \(n=0.6\) & \(n=1.0\) & \(n=1.4\) \\ \hline
1.0 & 6.677, 0.431 & 5.993, 0.295 & 5.563, 0.262 \\
0.5 & 6.368, 0.115 & 5.786, 0.086 & 5.394, 0.067 \\
0.25 & 6.296, – & 5.683, – & 5.312, – \\ \hline \hline \end{tabular}
\end{table}
Table 5: Time-averaged Nusselt number (\(Nu\)) and its amplitude (\(\Delta Nu\)) for forced convection heat transfer from an oscillating cylinder to power-law fluids at \(\text{Re}=100\), \(\text{Pr}=1.0\), and \(f^{*}=1.0\).  
Specifically, the vortices shed at \(f^{*}=0.8\) are almost in a line (as marked in Figure 11a) as oscillating frequency increases, and the vortices tend to separate vertically (as marked in Figure 11c). The temperature contours show that the heat transfer in the fluid exactly follows these vortices.
# Efficient Reversible Data Hiding Scheme for AMBTC-Compressed Images
## 3 Proposed Scheme
### Extracting Phase  
After obtaining the code stream \(CS\), the receiver can extract the embedded secret message \(S\) exactly and recover the AMBTC-compressed image precisely. The detail of the extracting phase is shown as follows.  
Step 1: Extract the guide information \(IN\) from the code stream \(CS\). According to \(IN\), the information of the embedded message \(S\), \(CS_{\rm H}\), \(CS_{\rm L}\), \(CS_{\rm B}\), the size of the cover image, and the value of \(m\) are determined.  
Step 2: Read one bit indicator \(ind\) from the codestream \(CS_{\rm H}\) (or \(CS_{\rm L}\)).  
Step 3. If \(ind=0\), this means that no secret message is extracted. Then, eight consecutive bits are read and converted to decimal value as the reconstructed mean value \(SH_{\rm i}\) (or \(SL_{\rm i}\)). Step 4: If \(ind=1\), read one next bit \(d\) as the sign of the reconstructed difference value \(D\). Then, read \(m\) consecutive bits and convert them to decimal values as the reconstructed difference value \(D\). Three next bits \(b_{1}b_{2}b_{3}\) are read as three extracted secret bits and concatenated into the secret message \(S\). Then, the value of \(d\) is considered to reconstruct the mean value as follows.  
If \(d=1\), the reconstructed mean value \(SH_{\rm i}\) (or \(SL_{\rm i}\)) is calculated using Equation (5).  
\[\begin{array}{l}SH_{i}=SH_{i-1}-D\\ SL_{i}=SL_{i-1}-D\end{array} \tag{5}\]  
If \(d=0\), the reconstructed mean value \(SH_{\rm i}\) (or \(SL_{\rm i}\)) is calculated using Equation (6).  
\[\begin{array}{l}SH_{i}=SH_{i-1}+D\\ SL_{i}=SL_{i-1}+D\end{array} \tag{6}\]  
where \(2\leq i\leq M\times N\). The reconstructed mean value \(SH_{\rm i}\) (or \(SL_{\rm i}\)) is sent to the corresponding high or low mean tables.  
Step 5: Steps 2 to 4 are implemented repeatedly until all values of the two mean tables are reconstructed completely.  
Once the extracting algorithm is finished, the secret message \(S\) is extracted exactly. Moreover, the two mean tables are recovered accurately. Obviously, the original AMTBC-compressed image is recovered without any distortion. Following the example of the embedding phase shown in Figure 6, Figure 7 shows the example of the proposed extracting phase. First, the first bit, \(ind=0\), is read from code stream \(CS_{\rm H}\). According to Step 3, the consecutive eight bits, \(10011110\), are read and converted to decimal value as \(SH_{\rm i}=158\). The next indicator bit, \(ind=1\), is read from \(CS_{\rm H}\). According to Step 4, read one bit \(d=1\), then four subsequent bits, \(0001\), are read and converted to a decimal value as \(D=1\). Three next bits, \(100\), are read as three extracted secret bits, \(b_{1}b_{2}b_{3}\). Since \(d=1\), according to Step 4, the reconstructed mean value \(SH_{\rm i}\) is calculated as \(SH_{i}=SH_{i-1}-D=158-1=157\). The extracting phase is terminated when the entire bits in the code stream \(CS\) are processed completely.  
Figure 7: Example of the extracting algorithm for high mean table: (**a**) codestream \(CS_{H}\); (**b**) high mean table \(H\).
# Feasibility Assessment of a Fine-Grained Access Control Model on Resource Constrained Sensors
## 5 Performance Evaluation
### Performance Metric Modeling
#### 5.1.2 Energy Consumption Definition and Measuring Method  
Regarding the energy cost measurement, the analysis focuses on the energy consumption of the CDS due to the exchange of five mandatory messages as well as two additional and occasional messages, which are the one-way key chain initiator message pair. Therefore, the energy consumed by the transmission and reception of bits over the air as well as the message processing consumption is considered.  
For the measurement of the processing energy consumption, two timestamps are inserted at the sensor's side: timestamps at the beginning and end of the message processing software code. Once the time to process each message is measured, a constant instantaneous power consumption (\(P_{C}\)) provided by the manufacturer in the data-sheet is considered to compute the energy consumption of each message.  
For the computation of the power consumption due to the transmission and reception of each message, the involved message lengths in bytes and packet fragmentation are computed (considering 50 bytes as the longest IEEE 802.15.4 plus UDP/6 LowPAN headers). The lengths of the messages exchanged during the authentication and authorization protocol range from 15 to 63 bytes. Enclosing the policy in the \(HID\_CM\_IND\) message (33 bytes), which is one of the smallest messages, implies the minimum fragmentation of 6 LowPAN IPv6 packets over IEEE 802.15.4 links.  
Additionally, the constant reception and transmission power consumption rates provided by the manufacturer in the data-sheet and a constant propagation bit rate are also considered. Table 7 shows the test-lab real-world (non-optimal) network data bit rate and the different instantaneous power consumption values used for the analysis. Note that these power consumption values correspond to a MEMSIC IRIS mote (XM2110 CA) powered with a \(3V\) power supply [37].  
Finally, the power consumption is calculated as the sum of the individual power consumptions of each of the involved messages in the sensor.
## 1 Introduction
The novelty lies in analyzing the response of reference targets in a pulse-by-pulse fashion, such that additional information concerning the variation of key quantities within the illumination time of each target can be leveraged in the estimation of propagation direction dependent calibration corrections. As will be argued in the next section, this approach is especially suited in the context of multi-channel SAR and DBF. A more general advantage of basing the calibration directly on raw data, rather than focused imagery, is that it ensures independence from the SAR processor. Modern SAR processors are highly complex and it is often difficult to distinguish calibration issues that arise at the sensor level from errors introduced by the processor in focusing the SAR imagery. Decoupling the processor from the task of calibration reduces complexity and can help pinpoint issues in the SAR processor. Calibration results do not transfer to the focused SAR imagery as expected in the presence of processing artifacts. The remainder of the paper is organized as follows: Section 2 provides an overview of the state of the art in external SAR sensor calibration and indicates the contributions of the proposed approach. Section 3 introduces the reference target response model used and describes how the SAR data in calibration acquisitions are analyzed to quantify any mismatch between the measured and the expected target response. Section 4 describes how a rigorous optimization process connects the measured response mismatch to appropriate calibration corrections, while Section 5 illustrates and validates the approach using real multi-channel SAR data and finally Section 6 concludes the paper.
## 3 Results Analysis
### Influence of Reinforcement on Horizontal Stress in Slope  
Figure 7 shows the horizontal stress profiles within a depth range of 5 m in the slope, as indicated by the location \"1-1\" in Figure 1. The horizontal stress profiles within this depth range exhibit an inverted \"V\" shape, with the magnitude of increase becoming larger as the rainfall intensity increases. A sharp change in horizontal stress occurs within the depth range of 1-2 m. The impact of reinforcement on horizontal stress differs in the surface layer. For rainfall conditions of Case 1 and Case 2, where the rainfall intensity is low, the horizontal stress profiles are nearly overlapped regardless of reinforcement. However, for Case 3 and Case 4, where the rainfall intensity is relatively high, the horizontal stress profiles show separation. At a depth of 0 m for Case 3, the reinforced horizontal stress is reduced by 26.3%. At a depth of 0.9 m, the reinforced horizontal stress increases by 2.9%. At a depth of 1.9 m, the reinforced horizontal stress decreases by 1.4%. For Case 4, at a depth of 0 m, the reinforced horizontal stress increases by 12.0%. At a depth of 0.9 m, the reinforced horizontal stress increases by 16.68%. At a depth of 1.9 m, the reinforced horizontal stress decreases by 0.3%.  
The top of the \"V\" shape corresponds to the shallow layer (weathered layer) and the interface between the intermediate layer (non-weathered layer). The sliding tendency in the shallow layer increases with increasing rainfall intensity. As the rainfall ends, the expansion deformation of the slope's weathered layer reaches its maximum, leading to a significant difference in expansion deformation between the weathered layer and the non-weathered layer. This results in a large sharp change in horizontal stress at the interface between the two layers. Compared to the stress generated by the differential expansion deformation, the resistance provided by vegetation reinforcement is limited. Therefore, when the rainfall intensity is low, the horizontal stress profiles with and without reinforcement tend to overlap. However, as the rainfall intensity increases to Case 3 and  
Figure 7: Horizontal stress in the slope.  
Case 4, the sliding tendency in the shallow layer becomes more significant, and some of the stress is transmitted to the non-weathered layer through the root system. Thus, the effect of reinforcement is more pronounced in Case 3 and Case 4 compared to Case 1 and Case 2. Vegetation reinforcement can alter the stress field in the slope, but the changes are limited.