esa-sceva/satcom-chunk-collection huggingface dataset:
# Entropy Analysis of a Flexible Markovian Queue with Server Breakdowns
## 2 Model Formulation and Previous Results
### Entropy Solution Using the Second Moment
: _Some numerical tests are conducted here to see how good is the solution procedure proposed in this subsection. Similarly to the previous subsection, we will refer to the percentage error obtained using the entropy approach with the second moment as PE 2. Then we compare the two approximate solutions using the percentage errors. Let us take a numerical example with the same data in Example 1, that is, \(c=5\), \(\mu_{1}=2\), \(\mu_{2}=5.5\), \(\alpha_{1}=0.05\), \(\alpha_{2}=0.08\), \(\beta_{1}=0.07\), and \(\beta_{2}=0.06\). Table 2 shows the exact solution, the approximate solution obtained in Section 3.1, the approximate solution obtained in this subsection, the percentage errors PE 1 and PE 2, for two different values of the arrival rate \(\lambda\)._  
_When \(\lambda=0.5\), the average of PE 1 is \(0.6032\) and the average of PE 2 is \(0.5119\), and when \(\lambda=5.5\), the average of PE 1 is \(0.5432\) and the average of PE 2 is \(0.4661\). The overall average percentage error of PE 1 and PE 2, respectively, are \(0.5732\) and \(0.4890\), which can be greatly improved in the next subsection._ \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & & \multicolumn{4}{c}{\(c=5\)} \\ \hline \(\lambda\) & \multicolumn{4}{c}{\(\lambda=0.5\)} & \multicolumn{4}{c}{\(\lambda=5.5\)} \\ \hline  & **Exact** & **Appr. 1** & _PE 1_ & **Appr. 2** & _PE 2_ & **Exact** & **Appr. 1** & _PE 1_ & **Appr. 2** & _PE 2_ \\ \hline \(p_{0}\) & 0.7519 & 0.7265 & 0.0338 & 0.6560 & 0.1275 & 0.1077 & 0.2349 & 1.1815 & 0.0391 & 0.6372 \\ \(p_{1}\) & 0.1876 & 0.1794 & 0.0441 & 0.2892 & 0.5414 & 0.1561 & 0.2147 & 0.3748 & 0.0608 & 0.6108 \\ \(p_{2}\) & 0.0465 & 0.0653 & 0.4049 & 0.0523 & 0.1237 & 0.1776 & 0.1438 & 0.1906 & 0.1387 & 0.2191 \\ \(p_{3}\) & 0.0112 & 0.0206 & 0.8346 & 0.0025 & 0.7810 & 0.1799 & 0.1023 & 0.4316 & 0.1112 & 0.3821 \\ \(p_{4}\) & 0.0024 & 0.0065 & 1.6986 & 0.0000 & 0.9858 & 0.1572 & 0.0727 & 0.5373 & 0.0815 & 0.4812 \\ \hline Average & \multicolumn{4}{c}{0.6032} & \multicolumn{4}{c}{0.5119} & \multicolumn{4}{c}{0.5432} & \multicolumn{4}{c}{0.4661} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Initial probability vectors comparison.
# Efficient Estimation of Generative Models Using Tukey Depth
## 2 Approximate Bayesian Computing
The complete algorithm is shown in Algorithm 1. ```
1:\(N\) // Number of iteration
2:\(\mathbf{x}=x_{1},x_{2},\ldots,x_{n}\) // Dataset
3:\(S=S_{1},\ldots,S_{k}\) // Statistics (e.g., mean, variance, covariance)
4:\(\rho(\cdot,\cdot)\) // Metric (e.g., Euclidian distance)
5:\(\widetilde{\theta}=\varnothing\) // The set of accepted approximate posterior samples
6:Method:
7:for\(n\in 1,2,\ldots,N\)do
8:\(\widetilde{\theta}\gets P(\theta)\) // Sample from prior distribution
9:\(\widetilde{\mathbf{x}}=\widetilde{x}_{1},\ldots,\widetilde{x}_{n}\gets P (x|\widetilde{\theta})\) // Sample from generative model
10:if\(\rho(\mathbf{S}(\mathbf{x}),\mathbf{S}(\widetilde{\mathbf{x}}))<\epsilon\)then
11:\(\widehat{\theta}\leftarrow\widehat{\theta}\cup\widetilde{\theta}\) // Add the accepted proposal \(\widetilde{\theta}\) to the set of accepted samples \(\widehat{\theta}\)
12:endif
13:endfor
14:return\(\widehat{\theta}\)
```  
**Algorithm 1** ABC rejection sampling. See e.g., Ref. [7] for a mathematical explanation of why the method is able to generate samples from an approximation of the posterior distribution as well as extensions of the algorithm using, for instance Markov Chain Monte Carlo (MCMC) [39] or Sequential Monte Carlo (SMC) [40]. In contrast to the rejection sampling algorithm, MCMC and SMC propose new values for the unknown parameters, \(\widetilde{\theta}\), by conditioning on the last accepted samples. In ABC MCMC, the most common is to generate proposals from a random walk process. Further, new values data are generated \(\widetilde{\mathbf{x}}\sim P(\cdot\mid\widetilde{\theta})\). The simultaneous proposal of \(\widetilde{\theta}\) and \(\widetilde{x}\) is either accepted or rejected using the common Metropolis-Hastings acceptance probability. A beautiful and necessary property of the ABC MCMC algorithm is that by making such simultaneous proposals, the intractable target distribution \(P\) will not be part of the computation of the Metropolis-Hastings acceptance probability. This is in contrast to the standard. The SMC algorithm is initiated by generating many samples, or particles, from the prior distribution of the unknown parameters. The samples are further sequentially perturbed and filtered to gradually become approximate samples from the desired posterior distribution.
# Towards a Digital Twin for Open-Frame Underwater Vehicles Using Evolutionary Algorithms
## 3 Methodology Description
### Digital Twin Setup  
The first step involves creating an accurate representation by integrating various simulators and models and finding a way to have them communicate with the real vehicle. These models capture different aspects of the vehicle, such as its physical structure, sensors, actuators, electronics, and the environment in which it operates. The main purpose is to ensure that all necessary interactions and behaviors for the tasks that we want to perform are adequately represented, providing a solid foundation for subsequent simulations and analyses. Communication is also a crucial factor, as the simulated and real vehicle need to exchange information during the process.
## 4 Discussion
### Mechanisms of the Retrogressive Landslides  
Loess exhibits high hydraulic sensitivity, readily softening and deforming upon water contact. Changes in the water environment can easily induce loess disasters and form a loess disaster chain. Retrogressive landslides are forms of such disasters, which occur in series at the same location. Landslide deposits may trigger secondary loess landslides or mudflows, which are among the most frequent disasters in China. Many of these loess landslides are induced by multiple factors; however, loss of shear strength is the key factor [5].  
At the beginning of irrigation, the groundwater level of the platform was low, and the slope was stable. Steep loess slopes loosen the loess structure, and cracks are developed at the edge of the platform. These cracks are potential sliding surfaces for subsequent landslides or collapses (Figure 11a), and the safety factor of the slope is in the natural state O (Figure 11b). As the irrigation time increases, surface cracks and caves played a key role  
Figure 10: The evolution process of landslides.  
in facilitating irrigation from unsaturated to saturated zones, causing a steady rise in the groundwater level. Furthermore, the water sensitivity of loess facilitated the expansion of cracks during infiltration. Until the sliding surface is completely penetrated, the safety factor of the slope will develop from the natural state O to the critical state A, and until the landslide occurs at B or C, followed by a landslide. For instant sliding, the time-course change in the safety factor of the slope can be described as OAB. For creep sliding, the time-course change in the safety factor of the slope can be described as OAC (Figure 11b,c). Several studies show that early landslides resulted in the release of a considerable amount of energy on the slope. The alteration of the soil's original structure following the initial sliding modifies its physical properties compared to undisturbed soil. Therefore, after the earlier landslide, the safety factor of the slope will reach D or E. Due to long-term irrigation, the groundwater level is extremely high (Figure 6), and the soil mass has high water content. The soil particles are aligned and rotated, and a large number of fine particles converge. This process induces a sharp increase in pore water pressure and a concomitant decrease in soil strength, followed by subsequent landslides or mudflows. For instant sliding, the time-course change in the safety factor of the slope can be described as DFH or EGJ. For creep sliding, the time-course change in the safety factor of the slope can be described as DFH or EGK (Figure 11c).  
Figure 11: Possible gradual evolution process and mechanism of retrogressive landslides. (**a**) The slope state at point O in Figure 11b; (**b**) The mechanism of retrogressive landslides; (**c**) The slope state at point A, F, G in Figure 11b.
# Positioning by Multicell Fingerprinting in Urban NB-IoT Networks +
## 4 Dataset Description and Analysis
### Measurement Setup  
Measurements were collected using the Rohde & Schwarz (R&S) TSMA6 mobile network scanner [31]. The R&S TSMA6 network scanner is an integrated system comprising an RF receiver, for passive monitoring of downlink control signals transmitted by 3 GPPaccess technologies, and a global positioning system (GPS) receiver, which allows for the association of a geographic information to each measurement. The TSMA6 also includes a Windows-based embedded PC running the ROMES software, used to control and configure the RF and GPS receivers. In addition to the internal RF receiver for passive data acquisition, the TSMA6 can be connected to external devices to collect active data for one or more 3 GPP technologies, which are also controlled by the ROMES software. In the Oslo campaign the setup included an Exelonix NB-IoT USB device for the collection of power consumption and active data for NB-IoT [32], while in the Rome campaign, this device was replaced with a Samsung S20 5G mobile phone, which was used to collect 5G active data. The setup used in the Rome campaign is presented in Figure 1 and is drawn from [33]. In this work, only passive data collected by the built-in RF receiver were used. The TSMA6 was calibrated at the Rohde & Schwarz premises before each measurement campaign as part of its periodic calibration schedule. In addition, the TSMA6 features an advanced autocalibration algorithm, which was independently verified to be highly accurate by multiple research groups [34; 35]. Additional information on the measurement campaigns and on both passive and active data availability can be found in [3; 36; 37] for the Oslo campaign and [33; 38; 39; 40] for the Rome campaign.
## 3 Detailed Description of the Existing Protocols
### Lmpc  
In LMPC, the layered multi path routing approach is used. In this protocol, layers are dividing the network in such a way that layers get closer near the sea surface. This happens to minimize the noise (i.e., ships noise) effect on the sea surface. The network of LMPC consists of the cross (the node on the layer and just coinciding with the layers), normal (simple sensor nodes), source and sink (sonobuoy) nodes with the surface gateways and channel pathways. These sonobuoys act as an embedded system in an underwater environment. The binary tree formation is the main feature of this protocol. This binary tree is generated from the source node as well as from the cross nodes. Multiple copies of data are originated to make reliable data transmission. However, multiple transmission causes several packets to collide. As a result, the energy of the network is dissipated fast and network collapse with the passage of time. The network architecture of LMPC is shown in Figure 8. In this figure, the source node sends multiple copies of data to its forwarder nodes. When these copies reach the next forwarder node, the protocol checks the type of node. If the node is a simple node, it unicasts the DP towards its  
Figure 7: DA in GEDAR.  
potential forwarder node; otherwise, it generates a binary tree and sends multiple copies of data to its next forwarder nodes (here, multiple copies means two copies). This process continues until all the data reaches the destined sink.
## 3 The ILDACP Algorithm
### Power Load Disaggregation
#### 3.3.1 Appliances Identification  
For two one-dimensional time-series \(A^{j}\) and each \(S^{i}\), they are expressed respectively in vector forms as \(A^{j}=\left[x(1),x(2),...,x(t)\right]^{T}\) and \(S^{i}=\left[y(1),y(2),...,y(T)\right]^{T}\), where \(x(m)\) and \(y(n)\) belong \(A^{j}\) and \(S^{i}\).  
We utilize DTW pattern-matching technique to measure the similarity between \(A^{j}\) and each \(S^{i}\). The similarity between two time series is measured by DTW distance. The larger the DTW distance, the lower the similarity between two time series. In our algorithm, DTW distance is calculated according to the following procedure.  
At first, all pairwise distances between \(A^{j}\) and \(S^{i}\) are stored in local cost matrix \(C_{t\times T}\), which is described as:  
\[C_{t\times T}=\left\{C(m,n)\right\}_{m=1,2,...,t,\ \ \ n=1,2,...,T} \tag{4}\] \[C(m,n)=d(x(m),y(n))\ m=1,2,...,t,\ n=1,2,...,T\]  
where \(C(m,n)\) is the element of the \(m\)th row and the \(n\)th column in matrix \(C_{t\times T}\), and \(d(x(m),y(n))\) is the Euclid distance between \(x(m)\) and \(y(n)\), which is \(d(x(m),y(n))=\left|x(m)-y(n)\right|\).  
Secondly, assume that the warping path between \(A^{j}\) and \(S^{i}\) from \((1,1)\) to \((m,n)\) is  
\[p=(p_{1},p_{2},...,p_{k},...p_{l}) \tag{5}\]  
where \(p_{k}=(m_{k},n_{k})\in\{1,2,...,m\}\times\{1,2,...,n\}\).  
The constraints of boundary condition, step size condition and monotonicity condition are shown in the following Equation (6).  
\[\begin{array}{c}p_{1}=(1,1),p_{l}=(m,n)\\ m_{1}\leq m_{2}\leq...\leq m_{l},n_{1}\leq n_{2}\leq...\leq\ n_{l}\\ p_{k+1}-p_{k}\in\{(1,1),(0,1),(1,0)\}\end{array} \tag{6}\]  
where \(l\) is the length of the path. The accumulated cost of warping path \(p\) is described as  
\[D(m,n)=\sum_{k=1}^{l}C(m_{k},n_{k}) \tag{7}\]  
where \(k\in\{1,2,...,l\}\) and \(l\in[\max(m,n),m+n-1]\).  
Finally, we use dynamic programming algorithm according to the recursive rule shown in Equation (8) to search the optimal warping path between \(A^{j}\) and \(S^{i}\).  
\[\begin{array}{c}D(m,n)=C(m,n)+min\{D(m-1,j),D(m-1,n-1),D(m,n-1)\}\\ D(0,0)=0\\ D(m,0)=\infty\ \ \ m>0\\ D(0,n)=\infty\ \ \ n>0\end{array} \tag{8}\]  
The corresponding minimum accumulated distance \(D(t,T)\) is calculated, and finally the DTW distance \(\text{DTW}\left(A^{j},S^{i}\right)=D(t,T)\).  
In this step, we search for the minimum DTW distance between \(A^{j}\) and each signature \(S^{i}\), the appliance with minimum DTW distance is identified as the one of maximum contribution.
# Saturation, Allowed Transitions and Quantum Interference in Laser Cooling of Solids
## 3 Methods and Results  
### Improved Cooling via Saturation of Background Absorption in Yb:LiYF\({}_{4}\)  
Laser cooling was investigated in a crystal of 10% Yb\({}^{3+}\):YLF, and its efficiency was measured by two different methods, namely Differential Luminescence Thermometry (DLT) and Thermal Lens Spectroscopy (TLS). The Yb\({}^{3+}\):YLF sample had dimensions of \(3.4\times 5.1\times 5.6\) mm\({}^{3}\) and was supported by an aerogel disk (Classic Silica Disk, Aerogel Technologies) to minimize the conductive thermal load. A tunable Ti:Sapphire laser (M Squared SolsTiS) was used as the excitation source. To study the intensity dependence, the pump beam radius at the sample was varied by using different focal length lenses and measuring the spot size with a beam profiler (Thorlabs BC106-VIS). When the pump beam was introduced into the sample, temperature changes and thermal lensing were measured by the DLT and TLS methods, respectively. Cooling efficiency was then computed based on these measurements and found to be intensity-dependent.
# A Review: Thermal Stability of Methylammonium Lead Halide Based Perovskite Solar Cells
## 3 Impact of Thermal Stress on Perovskites
### Optical Degradation  
[PERSON] et al. [47] investigated the temperature dependence of the bandgap and energy levels of MAPbI\({}_{3}\) by employing absorbance and photoluminescence (PL) spectroscopy (Figure 6a,b). They showed that both valence band maximum (VBM) and conduction band minimum (CBM) levels were reduced by 110 meV and 77 meV, respectively, when the temperature was increased from 25 \({}^{\circ}\)C to 85 \({}^{\circ}\)C. The bandgap of MAPbI\({}_{3}\) also increased by 33 meV, with an increase in temperature. This provided deeper insights into the relationship between the observed shift in VBM level and thermal expansion of the lattice using density functional theory (DFT) calculations. These results are significant when designing MAPbI\({}_{3}\) solar cells at different operating temperatures. Recently, the effects of heating and cooling on the performance of MAPbI\({}_{3-x}\)Cl\({}_{x}\) perovskite solar cells were investigated by varying the device temperatures from room temperature to 82 \({}^{\circ}\)C and then returning them to room temperature [48]. For this study, temperature-dependent steady-state PL and time-resolved lifetime decay measurements  
Figure 5: Cross-sectional FIB-SEM images of a (**A**) (**a**) A fresh cell and (**b-d**) cells aged at high temperature (55 \({}^{\circ}\)C (85 \({}^{\circ}\)C)) and humidity (50%) for 500 h. Degradations in different layers are denoted as voids in the Spiro-OMeTAD layer (\(\square\)); voids in the perovskite layer (\(\Delta\)); degraded silver layer (\(\bigcirc\)) and formation of particles with higher atomic numbers, likely PbI\({}_{2}\) (\(\lozenge\)). Reprinted with permission from [17].  
were investigated using a Linkam THM S600E system. Interestingly, an irreversible optical and electrical degradation of the perovskite films was observed at 70 \({}^{\circ}\)C. This irreversible degradation continued up to 82 \({}^{\circ}\)C and even when the device was cooled back to below 82 \({}^{\circ}\)C. PL measurements, shown in Figure 6c,d, demonstrate this irreversible degradation. An increase in the disordered phase of the perovskite films (observed by PL measurements) was found to result in a drastic increase in charge trapping and the development of a deeper trap depth. Temperature-dependent lifetime decay measurements were performed and the trapping and detrapping rates were calculated [48] to show a trap depth of 145 meV for temperatures in the range 40-70 \({}^{\circ}\)C. However, a deeper trap depth of 1.1 eV was observed at temperatures over 70 \({}^{\circ}\)C, indicating the formation of deep trap states due to the thermal decomposition of the perovskite layer (Figure 6e,f). [PERSON] et al. also observed a gradual shift in the band-edge of MAPbI\({}_{3}\) at elevated temperature (100 \({}^{\circ}\)C) based on PL and transmittance measurements [49]. These results suggest that the observed intrinsic thermal decomposition of perovskite solar cells at such low temperatures may limit their commercial applications.  
Figure 6: \(Cont.\)
# Routing Schemes in FANETs: A Survey
## 5 Routing Schemes in FANETs
### Proactive
#### 5.2.2 Enhancing Route Stability Based on Link Connectivity  
[PERSON] et al. [124] propose a proactive routing scheme that enables each node to select a next-hop node with the maximum link connectivity time in order to extend coverage in FANETs. The main objectives are to enhance route stability (O.1) as well as to enhance routing performance and QoS (O.3) in FANETs. The routing scheme addresses the challenge of high dynamicity (X.1) of network topology, which causes frequent updates of routing table. This routing protocol is designed for source routing (C.1.2).  
During the neighbor and route discoveries stage, each node broadcasts hello messages, which contains the mobility metrics (M.1) (i.e., nodes' velocity), node identity (M.5) (i.e., node addresses), and the geographical locations (M.3) (i.e., altitude, latitude, and longitude) of one-hop neighboring nodes to one-hop neighboring nodes to form a neighbor set comprised of two-hop neighboring nodes. During the route selection stage, the source node uses mobility metrics (M.1) (i.e., distance and velocity), the geographical location (M.3) of the destination node, and the link expiration time (M.2) between the source node and the destination node to select a route comprised of links with higher expected link expiration time to increase the route lifetime between nodes (i.e., source and destination). During the route maintenance stage, the link expires (or disconnects) and so the source node recalculates the link expiration time of another link in order to reestablish routes.  
The routing scheme is compared with a state-of-the-art scheme that uses hello messages and topology control messages to discover and disseminate link state information. Random waypoint mobility model (A.2) is used in simulation. The routing scheme has been shown to increase QoS (P.1) (i.e., lower outage time, which contributes to lower packet loss rate) and to reduce energy consumption (P.2).
## Abbreviations  
IR Impulse Radio  
UWB Ultra-Wide Band  
PCA Principal Component Analysis  
RPCA Robust Principal Component analysis  
LLM Inexact Augmented Lagrange Multiplier  
EA Exponential Averaging  
O-W Overlapping Windows  
EMW Electromagnetic wave  
Tx Transmitting Antenna  
Rx Receiving Antenna  
SVD Singular Value Decomposition  
TOA Time of arrival
## 3 Data Sources and Preprocessing
### MODIS Dataset  
As Table 2 shows, land cover data are obtained from MCD12Q1, a Level 3 product of the MODIS land cover datasets. This product is derived from the MODIS output first released by the United States National Aeronautics and Space Administration (NASA) at the end of 2008, with processed yearly observation data from the Terra and Aqua satellites applied to depict land cover types. The chosen dataset consist of five land cover classification systems: IGBP global vegetation classification scheme [32]; UMD vegetation classification scheme based on the modified IGBP classification system [33]; LAI/FPAR scheme adopted by MODIS Leaf Area Index and Fractional Photosynthetically Active Radiation (LAI/FPAR) products (MOD15) [34,35]; NPP scheme adopted by the MODIS net primary productivity (NPP) product (MOD17) [36]; and Plant Functional Type (PFT) land cover classification scheme [37]. In our study, five MCD12Q1 data layers updated in 2014 are selected. The time period covered by the chosen data range from 1 January 2010 to 31 December 2010, and the track numbers are h27v05, h27v06, h28v05 and h28v06.