esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling Non-Equilibrium Dynamics and Saturable Absorption Induced by Free Electron Laser Radiation
## 2 Model for Ultrafast Transmission Measurements  
### Three-Channel Model  
In our recent work [16], we developed a phenomenological three-state model which is able to reproduce saturation phenomena related to the increased transmission in the high fluence regime. We refer to the original publication [16] for some more details on the computational model. In brief, the model describes the variation of the density of occupation numbers \(N_{1}\), \(N_{2}\) and \(N_{3}\) of three exemplary many-body states (ground \(|1\rangle\), excited \(|2\rangle\) and an intermediate relaxed state \(|3\rangle\)) by a set of rate equations with proper constraints:  
\[\frac{dN_{1}(z,t)}{dt}=\frac{g(z,t)I(z,t)}{h\
u}+\frac{N_{2}(z,t)}{\tau_{21}}+ \frac{N_{3}(z,t)}{\tau_{31}} \tag{1}\]  
\[\frac{dN_{2}(z,t)}{dt}=-\frac{g(z,t)I(z,t)}{h\
u}-\frac{N_{2}(z,t)}{\tau_{21}} -\frac{N_{2}(z,t)}{\tau_{23}} \tag{2}\]  
\[\frac{dN_{3}(z,t)}{dt}=\frac{1}{\tau_{23}}N_{2}(z,t)-\frac{1}{\tau_{31}}N_{3}( z,t) \tag{3}\]  
\[g(z,t)=\sigma(T)\left(N_{2}(z,t)-N_{1}(z,t)\right) \tag{4}\]  
\[N=N_{1}(z,t)+N_{2}(z,t)+N_{3}(z,t) \tag{5}\]  
In this set of equations, the occupation numbers depend thus on the photon field intensity \(I(z,t)\) at time \(t\) and position \(z\) (along the direction of propagation of the pulse), the photon absorption cross-section \(\sigma\) at given photon energy \(h\
u\), and on the relaxation times \(\tau\) between the various states. In this formalism, \(g(z,t)\) is an effective time- and space-dependent absorption coefficient--possibly temperature-dependent--that can be considered constant for linear absorption (Lambert-Beer law) at a given temperature \(T\). From the condition of conservation of the total number of the states Equation (5), we just need to solve couples of rate equations.  
The set of rate equations are coupled with the transport condition of the incoming pulse, within the classical electrodynamics limit:  
\[\frac{dI(z,t)}{dz}+\frac{1}{c}\frac{dI(z,t)}{dt}=g(z,t)I(z,t). \tag{6}\]  
Within the model described by the equations above, absorption and stimulated emission by laser radiation involve transitions between ground \(|1\rangle\) and excited \(|2\rangle\) states. The relaxed state \(|3\rangle\) represents the ensemble of all possible relaxed states reached by decay of state \(|2\rangle\). It can decay to state \(|1\rangle\) by emitting a photon or through other processes.
## 1 Introduction
The results show that this technique can retrieve spatially quasi-continuous deformation maps, even in areas characterized by the absence of good quality reflectors (mainly buildings and infrastructures). Moreover, through a careful interferogram analysis and selection, it is possible to characterize the evolution of landslide displacement through time and its relationship with the rainfall regime.
## 2 Materials and Methods
### Preparation of Coal Gangue Slurry  
The fractal dimension \(D\) is utilized to quantify the irregularity of complex shapes, indicating the extent of their effective occupation within a given space. Through the fractal formula of grading gangue particle size, given different fractal dimensions (\(D\)), different grading curves can be obtained. Then, the passing percentage of each particle size of the aggregate can be calculated. The authors of reference [36] deduced the aggregate particle size distribution equation based on fractal theory:  
\[P(d_{\rm i})\ =\ \frac{M_{\rm i}}{M_{\rm t}}\ =\ \frac{d_{\rm i}^{3-D}-d_{\rm min }^{3-D}}{d_{\rm max}^{3-D}-d_{\rm min}^{3-D}} \tag{1}\]  
In this formula, \(P(d_{\rm i})\) is the mass fraction of the interval particle size distribution; \(M_{\rm i}\) is the aggregate mass with a particle size between \(d_{\rm i}\) and \(d_{\rm min};M_{\rm t}\) is the total mass of the aggregate, \(D\) represents the fractal dimension of the aggregate particle size distribution, \(d_{\rm max}\) denotes the maximum particle size of the aggregate particles, and \(d_{\rm min}\) refers to the minimum particle size of the aggregate particles. When the minimum particle size \(d_{\rm min}\) = 0, Equation (1) can be simplified as:  
\[P(d_{\rm i})\ =\ (\frac{d_{\rm i}}{d_{\rm max}})^{3-D} \tag{2}\]  
The fractal dimension serves as a critical parameter for characterizing the distribution properties of particles. In order to better understand and optimize the particle size characteristics of coal gangue particles and their influence on the subsequent treatment process, it is necessary to clarify the mass distribution ratio of particles with different particle sizes. The coal gangue was sieved into five particle sizes (<0.15 mm, 0.15-0.30 mm, 0.30-0.60 mm, 0.60-1.18 mm, and 1.18-2.36 mm), and the passing rate of each sieve was determined by Formula (2). The cumulative passing rate for each particle size is shown in Figure 2. The obtained data were converted into mass ratios for each particle size range. Subsequently, seven groups of slurry with different particle size fractal dimensions were prepared, precisely 2.30, 2.40, 2.45, 2.50, 2.55, 2.60, and 2.70. The solid mass fraction of the gangue slurry is controlled at 75%.  
Figure 1: Material characteristics of gangue. (**a**) Gangue XRD pattern; (**b**) SEM image of gangue.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Chemical Composition** & **SiO2** & **Al2O3** & **Fe2O3** & **Na2O** & **CaO** & **MgO** & **K2O** & **TiO2** & **Others** \\ \hline Mass fraction (\%) & 59.77 & 22.51 & 6.03 & 0.43 & 1.35 & 2.33 & 4.88 & 1.73 & 0.97 \\ \hline \end{tabular}
\end{table}
Table 1: Main chemical composition of Materials.
## 4 Observation Results  
### Water Flow State  
Under various flood discharge conditions, the free water surface at the inlet of the open channel is generally stable, and there is no apparent adverse flow pattern. There are vertical axis vortices near both sides of the access gate groove of the sluice gate, as shown in Figure 5a. There is no noticeable vibration near the gate area. As the discharge of the open channel increases, a hydraulic jump will be made at a discrete distance behind the gate as water flows out of the sluice gate. The head of the hydraulic jump is mainly located between the gate piers, and the tumbling of water in this area gradually intensifies due to discharging water flowing into the bottom of the stilling basin and mixing with air during rapid flow. When the discharge rate in the open channel is low, the flow state in the stilling basin is stable, and the free water surface fluctuation appears in a local area but is generally stable. Backflow will be formed in some areas of the stilling basin, as shown in Figure 5b,g.  
Figure 5: Discharge flow state in the open channel under various flood discharge conditions: **(a)** FDC No.1; **(b)** FDC No.2; **(c)** FDC No.5; **(d)** FDC No.7; **(e)** and **(f)** FDC No.9; **(g)** FDC No.11; **(h)** FDC No.13.  
With an increase in the discharge rate in the open channel, for example, over 2000 m\({}^{3}\)/s, discharging water into the stilling basin starts to tumble violently. The turbulence of the flow intensifies, creating a more fully aerated, milky white fluid. The plane backflow phenomenon in the stilling basin disappears, with obvious surging waves formed, particularly on both sides of the stilling basin, as shown in Figure 5c-e. The spray of the highest surging wave may sometimes exceed the top of the left guide wall. As the discharge rate in the open channel increases, the free water surface near the continuous ridge begins to fall clearly, as can be seen in Figure 5f,h. Sometimes a second fall would form after the continuous ridge. The discharging water surface tends to become smooth and steady after a certain distance downstream of the open channel, meeting well with the natural flow of the riverbed downstream.
## 3 Flotation Behavior  
Surface roughness is a significant factor regarding the physical heterogeneity of non-ideal surfaces. Various grinding methods are commonly employed to refine particles, but they also produce different levels of surface roughness that can impact the flotation behavior. The relationship between the surface roughness produced by these grinding methods and flotation behavior (specifically flotation recovery and flotation rate) has been investigated. To study the relationship between the surface roughness and flotation behavior without altering particle shapes and sizes, researchers utilize etching and abrasion methods. Some scholars also combine grinding and etching or abrasion methods to compare the effects of surface roughness and shape factors on flotation behavior. The findings indicate that the surface roughness greatly affects both the flotation recovery and flotation rate, although some conclusions are contradictory. Flotation recovery refers to the mass percentage of the target component extracted from the raw components. Numerous scholars have demonstrated that surface roughness plays an important role in flotation recovery. Most of these studies conclude similar results, that an increased surface roughness enhances the max flotation recovery of various minerals, such as talc [93; 95], spodumene [64], calcite [1], magnesite, [3; 37], quartz [41], hematite [39], graphite [38], malachite [2], cassiterite [68], and sphalerite [31], as summarized in Table 3. Specifically, the surface roughness effectively improves flotation recovery, particularly at low collector concentrations. However, the influence of surface roughness on flotation recovery decreases as the collector dosage increases (Figure 3). Conversely, some researchers have reported reverse results, that a smoother surface can improve the floatability of minerals like quartz [32; 33], calcite [32; 33; 72], talc [32; 33; 69], barite [32; 33; 46; 62], pyrite [66; 96], coking coal [79], molybdenite [97], chalcopyrite [98], and spodumene [65] (Table 3). \begin{table}
\begin{tabular}{p{108.4 pt} p{108.4 pt} p{108.4 pt}} \hline \hline
**Minerals** & **Particle Size/\(\upmu\)m** & **Flotation Results** \\ \hline talc, quartz & 106–150 & The talc and quartz particles with a higher angularity and roughness exhibited better floatability. \\  & & The max flotation recovery of spodumene particles \\ spodumene [64] & 38–74 & increased from 40.3\% to 80.7\% while increasing the Ra value from 0.87 nm to 1.14 nm. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Flotation results of different minerals with varying surface roughness.
# Joint Location-Allocation Model for Multi-Level Maintenance Service Network in Agriculture
## 5 Case Study
### The Performance of Benders Decomposition Approach  
The Benders decomposition approach is analysed in this section. First, the cases in Figures 6 and 7 are solved by the Gurobi solver, which cannot be obtained in less than 12 h. Especially for some cases in Figures 6 and 7, the optimal solutions cannot be obtained in less than 24 h. But for the Benders decomposition approach, all optimal solutions can be obtained in 3 h.  
In addition, the Gurobi and Benders decomposition solvers are applied to solve several cases with different sizes. The calculation time using different solution methods is shown in Table 2. As shown in Table 2, the calculation time of the Gurobi solver is shorter than that of the Benders decomposition algorithm only when the number of service units is less than 50. When the number of service units is greater than 50, the solution time using the commercial Gurobi solver will be much greater than that of the Benders decomposition algorithm. Even when the number of service units exceeds 200, the Benders decomposition algorithm can still obtain the optimal solution in 3 h, while the optimal solution cannot be obtained within 24 h using the commercial Gurobi solver.
## 5 Description of Data Fusion Using the Extended Kalman Filter  
Let us carry out a fusion of data using the extended Kalman filter combining COG (\(k\)) and SOG (\(k\)) measurements, changes in submergence \(\Delta z\) (\(k\)) carried out every one second with 3D coordinate system measurements of the USBL system, carried out at different time intervals (in some moments \(k+1\)). Figure 6: Change of parameters of mean error ellipse after covering a distance of 200 and 500 m.  
Let us assume that the results of the USBL measurement system will create the so-called vector of observation, \(\mathbf{z}\left(k+1\right)=\left[ \begin{array}{cc}r\left(k+1\right)&\alpha \left(k+1\right)&\beta\left(k+1\right)\end{array} \right]^{\mathsf{T}}\), described by the function [30]:  
\[\mathbf{h}\left(\mathbf{x}\left(k+1\right),\,^{\ast}\,\left(k+1\right)\right)= \left[ \begin{array}{c}f_{r}\left(k+1\right)\\ f_{a}\left(k+1\right)\\ f_{\beta}\left(k+1\right)\end{array} \right]=\left[ \begin{array}{c}r\left(k+1 \right)\\ \alpha\left(k+1\right)\\ \beta\left(k+1\right)\end{array} \right]+\left[ \begin{array}{c}v_{r}\left(k+ 1\right)\\ v_{a}\left(k+1\right)\\ v_{\beta}\left(k+1\right)\end{array} \right] \tag{15}\]  
where  
\[r\left(k+1\right)=\sqrt{\left(x_{1}\left(k+1\right)-x\left(k+1\right)\right)^ {2}+\left(y_{1}\left(k+1\right)-y\left(k+1\right)\right)^{2}+\left(z_{1}\left( k+1\right)-z\left(k+1\right)\right)^{2}}\]  
\[\alpha\left(k+1\right)=\text{arctg}\frac{x_{1}\left(k+1\right)-x\left(k+1 \right)}{y_{1}\left(k+1\right)-y\left(k+1\right)}\]  
\[\beta\left(k+1\right)=-\text{arctg}\frac{z_{1}\left(k+1\right)-z\left(k+1 \right)}{\sqrt{\left(x_{1}\left(k+1\right)-x\left(k+1\right)\right)^{2}+ \left(y_{1}\left(k+1\right)-y\left(k+1\right)\right)^{2}}}\]  
\(v_{r}\left(k+1\right)\), \(v_{a}\left(k+1\right)\), \(v_{\beta}\left(k+1\right)\)--values of measurement errors (with zero mean normal distribution); \(x\left(k+1\right)\), \(y\left(k+1\right)\), \(z\left(k+1\right)\)--fixed coordinates of transceiver position; \(x_{1}\left(k+1\right)\), \(y_{1}\left(k+1\right)\), \(z_{1}\left(k+1\right)\)--variable coordinates of the transponder position (BAUV). And on the basis of COG \(\left(k\right)\), SOG \(\left(k\right)\) measurements and the submergence changes \(\Delta z\left(k\right),\) vector \(\mathbf{x}\) of BAUV's 3D position coordinates, using the function \(\mathbf{f}\left(\mathbf{x}\left(k\right),\mathbf{u}\left(k\right),\mathbf{w} \left(k\right)\right)\):  
\[\mathbf{x}\left(k+1\right)\,=\left[ \begin{array}{c}f_{x}\left(k+1\right)\\ f_{y}\left(k+1\right)\\ f_{z}\left(k+1\right)\end{array} \right]=\left[ \begin{array}{c}x\left(k \right)\\ y\left(k\right)\\ z\left(k\right)\end{array} \right]+\left[ \begin{array}{c}\Delta t\left(k \right)\cdot\mathsf{SOG}\left(k\right)\cdot\mathsf{sinCOG}\left(k\right)\\ \Delta t\left(k\right)\cdot\mathsf{SOG}\left(k\right)\cdot\mathsf{cosCOG} \left(k\right)\\ w_{\mathrm{Z}}\left(k\right)\end{array} \right]+\left[ \begin{array}{c}w_{ \mathrm{x}}\left(k\right)\\ w_{\mathrm{x}}\left(k\right)\\ w_{\mathrm{Z}}\left(k\right)\end{array}
# Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
## 6 Discussion
### Limitations and Future Work  
While Adaptive-DecayRank achieves strong performance, it requires more robust fine-tuning of the decay factor to optimize results across different datasets. Additionally, although it effectively detects anomalies in dynamic graphs, it does not leverage deep feature learning, as seen in graph neural networks (GNNs). Future work will explore integrating **self-supervised learning** to improve anomaly detection in complex graph structures. We also aim to optimize performance further using **GPU acceleration**, enabling even faster real-time analysis for large-scale networks. Lastly, we will extend our methodto more diverse domains, including Autonomous Vehicle (AV) security detection, medical data analysis, and industrial IoT security.
## 3 Influence of Structural Parameters of the Magnetic Circuit on Magnetic Induction Intensity
### Effect of Damped Channel Gap on Magnetic Induction Strength  
While keeping the values of the other parameters in the magnetic circuit structure constant, the width of the damping channel was varied within the range of 0.6 to 2 mm, with intervals of 0.2 mm. This allowed for the establishment of a parameterized model for simulation. A parametric model was built for simulation. As depicted in Figure 8, the viewing path was established along the centerline of the damped channel to record the variation curve of the magnetic induction intensity value about the gap of the damped channel. Additionally, the average magnetic induction intensity curve was derived, along with a cloud diagram illustrating the magnetic induction intensity under different damped channel gaps.  
Figure 9 illustrates a symmetrical distribution of magnetic induction intensity along the damping channel. Within the range of 0 to 6 mm, which constitutes the effective working length of the damping channel, the magnetic induction intensity remains notably stable. In the region of approximately 6 mm to 27 mm, the damping channel approaches the pole coil, resulting in magnetic induction levels nearing zero. This area, however, does not impact the operational performance of the magnetorheological damper. With an increase in the width of the damping channel, the cross-sectional area of the peripheral region expands, leading to a dispersion of the magnetic field within the damping channel. Consequently,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Parts** & **Materials** \\ \hline A1 & Piston rod cavity & Vacuums \\ A2 & Piston rod & 20 steel \\ A3 & Coil & Copper \\ A4 & Damped channel & MRF-J25T \\ A5 & Cylinder & 20 steel \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model parts and materials.  
the magnetic induction strength shows a gradual decrease, as shown in Figure 10. When the damping channel width becomes too large, it leads to a reduction in the magnetic induction strength across the MRF gap. To achieve a greater damping force, it is more effective to have a smaller damping channel gap. Given the settling attributes inherent to MRF, a narrow damping channel width can result in channel blockage. This leads to a substantial escalation in viscous damping force, surpassing the Coulomb damping force by a significant margin and consequently diminishing the adjustability coefficient. Therefore, in the course of designing the damping channel gap, it becomes imperative to account for its influence on both the adjustability coefficient and the resultant output damping force. As a judicious choice, a gap in the vicinity of 1 mm is deemed appropriate.  
Figure 10: Plot of magnetic induction intensity clouds corresponding to different lengths of damped gaps.  
Figure 9: Simulation results of damping gap with different lengths. (**a**) Plot of magnetic induction intensity versus distance; (**b**) plot of average magnetic induction intensity versus distance.
## 1 Introduction
Later, the security analysis is made when linear, differential, algebraic, and brute-force attacks are applied [20]. To evaluate the resistance of AICLDH to the differential attack, we employ the Number of Pixels Change Rate (NPCR), Unified Average Changing Intensity (UACI), and Avalanche Criteria (AC) parameters. Furthermore, since the AICLDH construction uses an ElGamal asymmetric cryptosystem, the discrete logarithm problem is considered for its security analysis [21]. It tries to find the \(a\) value in equation \(\beta=a^{a}\mod p\) when \(\beta\), \(\alpha\)\(y\)\(p\) values are known. In this sense, many algorithms have been developed, called generic algorithms, to find the value \(a\)[22], as well as the Pohlig--Hellman attack [23]. In Section 6, we analyzed the complexity of these algorithms. For evaluation purposes, cipher images are damaged with the additive, multiplicative, and occlusion noises [24] and one proposed using the \(\chi^{2}\) distribution. As a result of these attacks, a sharpness loss is produced over the images. This work proposes measuring it with the Similarity Parameter (SP) for each color: red, green, and blue. The cipher quality evaluation includes the encryption of totally black and totally white images, as well as other works measuring it [25]. The distribution of this work begins with Section 1, which corresponds to the introduction and some related works. Section 2 provides a theoretical description of the materials and methods applied in the proposal. Section 3 shows the building elements that conform to the proposed cryptosystem as well as the quality test. Section 4 presents all the noise attacks considered for a damage simulation to the encrypted images. In addition, it includes the median filter 5 \(\times\) 5 application to improve the image quality after the attack and its measure with the SP parameter. The results are shown in Section 5, while their discussion and analysis are presented in Section 6. Finally, Section 7 contains the conclusions and future works.
## 4 Discussion
### Accuracy of Measurement with an Acoustic Camera  
Mean sizes of individuals detected with the acoustic camera were smaller than those of individuals caught during the scientific fishing survey. Such a difference where estimated sizes with the acoustic camera were slightly lower than sizes of captured fish was observed previously [18; 53; 54]. Three reasons could explain this difference: (1) measurements with an acoustic camera depend on the angle of the fish in the acoustic beam (Figure 3) and the degree of reflectiveness of fishtails; (2) Measurement errors could also be due to the active behavior of the fish that does not allow a suitable image to measure [18], and (3) measurement of the fish on deck may be biased upward because some may measure by following the contour of the fish rather than measuring the straight line distance. Such an error could increase the size of the fish by approximately 5%, whereas measurements of acoustic images are always a straight line.  
Nevertheless, regardless of the method of measurement (direct measurement on deck or measurement via acoustic camera), patterns of size distribution were similar: small individuals around Remire islands and larger ones around the Grand Connetable Island MPA. Even if sizes were underestimated with the acoustic camera, this method still allows scientific studies on temporal and spatial patterns of fish size, giving a measure of relative size rather than absolute size (the same protocol must be used) and has the advantage of being more efficient than fishing surveys. In addition, with large fish like goliath groupers, acoustic size estimates provide confidence in identification, which minimizes counting error.
# SASC: Secure and Authentication-Based Sensor Cloud Architecture for Intelligent Internet of Things
## 3 The Proposed Secure and Authentic Sensor Cloud Architecture
### Cloud-Based Data Security Algorithm
Both the public and private keys are mathematically related but not the same and provide robust authentication against impersonation attacks. Furthermore, only the public key is globally known via a publicly accessible directory while the private key is to keep secret. The robust authentication of the cluster heads is performed by the BS, which separates the original data \(D_{U_{i}}\) and the appended data \(AD\). The original data \(D_{U_{i}}\) is XORed with the corresponding cluster head's public key \(k_{i}\) to produce the MAC, which is matched against the value of appended data \(AD\). If the match is true, the BS acknowledges the verification and authentication process of the cluster head \(U_{i}\) and proceeds the transmission and reception activities, else, the BS ignores the data from the cluster heads \(U_{i}\) and stops further communications.