esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Area**} & \multirow{2}{*}{**RSLR RCP 2.6 (m)**} & **RSLR RCP 8.5** & **RCP 2.6** & **RCP 2.6** & **RCP 8.5** & **RCP 8.5** \\  & & **(m)** & **2050 (km\({}^{2}\))** & **2100 (km\({}^{2}\))** & **2050 (km\({}^{2}\))** & **2100 (km\({}^{2}\))** \\ \hline Catania plain & 2050: 0.15 \(\pm\) 0.03 & 2050: 0.20 \(\pm\) 0.04 & & & & \\  & 2100: 0.33 \(\pm\) 0.06 & 2100: 0.72 \(\pm\) 0.13 & 1.21 & 2.634 & 1.762 & 4.092 \\ Augusta & 2050: 0.31 \(\pm\) 0.04 & 2050: 0.36 \(\pm\) 0.05 & & & & \\  & 2100: 0.74 \(\pm\) 0.07 & 2100: 1.12 \(\pm\) 0.13 & 0.275 & 0.668 & 0.433 & 1.659 \\ Siracusa & 2050: 0.31 \(\pm\) 0.04 & 2050: 0.36 \(\pm\) 0.05 & & & & \\  & 2100: 0.74 \(\pm\) 0.07 & 2100: 1.12 \(\pm\) 0.13 & 0.393 & 0.951 & 0.544 & 2.378 \\ Asinaro river & 2050: 0.47 \(\pm\) 0.04 & 2050: 0.52 \(\pm\) 0.05 & & & & \\ mouth & 2100: 1.13 \(\pm\) 0.07 & 2100: 1.52 \(\pm\) 0.13 & 0.015 & 0.021 & 0.017 & 0.043 \\ Vendicari & 2050: 0.47 \(\pm\) 0.04 & 2050: 0.52 \(\pm\) 0.05 & 0.675 & 1.047 & 0.837 & 1.528 \\ Vendicari & 2100: 1.13 \(\pm\) 0.07 & 2100: 1.52 \(\pm\) 0.13 & 0.675 & 1.047 & 0.837 & 1.528 \\ Marzamemi & 2050: 0.15 \(\pm\) 0.04 & 2050: 0.2 \(\pm\) 0.05 & 0.007 & 0.013 & 0.010 & 0.045 \\  & 2100: 0.33 \(\pm\) 0.07 & 2100: 0.72 \(\pm\) 0.13 & & & & \\ \hline \multicolumn{2}{c}{**Total extension**} & 2.58 & 5.33 & 3.60 & 9.75 \\ \hline \end{tabular}
\end{table} 
Table 2: Expected RSRL (m) and extension (km\({}^{2}\)) of the exposed flooded areas for 2050 and 2100 A.D. for RCP 2.6 and RCP 8.5 emission scenarios. If sea-level will continue to rise at accelerated rates, it is reasonable to expect a corresponding accelerated erosion that will strike severely the coastal system. The increasing coastal hazard will facilitate the marine flooding with subsequent impacts of the coastal zone, including saltwater contamination of surface and underground waters, wetland losses, and increased flooding which may lead to relevant socioeconomic impacts. An additional factor, acting in combination with RSRR, is the occurrence of extreme marine events connected to storm and tropical-like system (Medicanes). Several models showed that the future storm events will be more intense than today [117; 118; 119; 120], and the combined effect of wave flooding and sea-level rise could increase the coastal vulnerability, with loss of surfaces greater than those shown in Figures 10-15. Moreover, the intensity of tropical-like system is increasing in the Mediterranean [121; 122; 123; 121], leading to extreme marine events that in conjunction with RSRR could determine several meters of inland flooding.
## 1 Introduction
Increased attention has recently been given to the fractionation into more homogeneous preparations using organic solvents, ionic liquids, or ultrafiltration [37; 38; 39; 40; 41; 42; 43; 44; 45]. For example, [PERSON] et al. [38] investigated sequential solvent fractionation of heterogeneous bamboo organosolv lignin. The starting lignin and obtained fractions were compared in terms of functional groups and molecular weight distribution. It was concluded that sequential solvent fractionation can be a useful method to prepare homogeneous lignin with desirable functional groups for further processing. [PERSON] et al. [40] have reported a precipitation fractionation method for kraft lignin based on aqueous organic solvents. In this approach, moisture containing lignin can be subjected to fractionation directly without initial drying, which is a great advantage for large-scale applications. It was shown that the molar mass and functionalities of the precipitated lignin fractions depended on the ratio between solvent and water in the precipitation step. [PERSON] et al. [41] compared the impact of sequential solvent fractionation on the molecular weight, composition, and contents of functional groups of industrial softwood and hardwood kraft lignins. In this work, it was demonstrated that the antioxidant activity, chemical structure, heating values, and thermal and adsorption properties of lignin can be tuned by such processing. The results obtained provide useful information for targeted uses of lignin raw material. [PERSON] et al. [43] investigated ultrafiltration and selective precipitation as two different methods for lignin fractionation and confirmed that both fractionation processes influence the properties of the obtained lignin. [PERSON] et al. [45] investigated fractionation of technical lignin using ionic liquids for further application as antioxidants. It was shown that fractionated lignins with improved polydispersity resulted in higher antioxidant activity as compared to non-fractionated lignins. [PERSON] et al. [46] produced a wide range of lignin fractions by ultrafiltration of black liquor using ceramic membranes with a different Mw cut-off. Investigation of the thermal properties of such fractions revealed the possibility to tailor certain parameters by choice of membrane. Lignin fractions with well-defined characteristics could be a predictive tool for the development of high-performance lignin-based systems [47; 48; 49]. Polymer porous microspheres obtained by a suspension-polymerization method have great potential in the sorption processes [17]. The addition of lignin into the polymeric system results in more eco-friendly materials [31; 50]. In our previous work [50] on the synthesis of microspheres, we used a commercial kraft lignin and low molecular weight kraft lignin obtained by ultrafiltration of industrial black liquor using a ceramic membrane with Mw cut-off of 5 kD. The specific surface area of these materials decreased with increasing lignin content. The main aim of this work is to investigate the impact of lignin fractionation on the properties of the biopolymeric microspheres obtained.
# Temporal Encoding to Reject Background Signals
## 2 Results
### Time Correlation Encoding Scheme
With this, the discrete form of Equation (1) is:  
\[g(\tau_{j})=\sum_{i=1}^{N_{s}-n_{stop}}\sum_{l=0}^{k-1}f_{i}f_{i+n_{start}+(j-1 )k+l}. \tag{2}\]  
As \(f_{i}\) is a binary value, and the output from the SPAD is a transistor-transistor logic (TTL) signal, the summation could be implemented with simple logic circuits. Encoding data in \(g(\tau_{j})\) has the potential to allow data transfer at exceptionally low light levels, and in the presence of significant background illumination. To detect correlations, the receiver requires the detection of a single photon from each optical pulse. Such conditions allow average received power to be extremely low, in the range of \(\mathrm{pW}\). The trade-off in this transmission scheme is that the data rate is expected to be relatively modest, as the optical signal must be repeated several times in order to generate a distinguishable signal in \(g(\tau_{j})\). There are several potential ways to encode data in \(g(\tau_{j})\), with parallels to on-off keying (OOK), PPM or pulse amplitude modulation (PAM). Here, we consider the simplest form of encoding, OOK, where data can be encoded using a single pulse time separation. On transmission of the symbol \"1\", pulses are transmitted continuously with a fixed time separation \(T=40\) ns, corresponding to a repetition rate of 25 MHz, so \(g(\tau_{j})\) will show a peak at \(\tau=T\). This time separation has been chosen as the dead time of the SPAD used is 35 ns. On transmission of the symbol \"0\" no pulses are transmitted, producing only background counts in \(g(\tau_{j})\). A schematic of the expected waveforms is shown in Figure 1b. With pulse width \(t_{pulse}=5\) ns, deliberately less than \(\tau_{d}\), only one signal photon can be detected from each pulse, indicated by the blue SPAD signals in Figure 1b. In reality, the detection rate will be less than one per pulse, and pulses can also be missed if they are received during the dead time after a noise pulse, indicated in red. Time correlation of the measured events from the SPAD is performed over a data interval, producing a histogram with peaks at 40 ns intervals for transmission of a \"1\", and a background correlation level for transmission of a \"0\", determined by ambient background light and detector dark count rate. Applying a threshold to the histogram bin generated for each symbol at a delay of 40 ns allows decoding of the binary stream. This threshold will have to be sufficient to reject correlation counts from background and dark count correlations. A crucial feature of this method is that it is robust to temporal jitter between the transmitter and receiver. Synchronisation of the system can be easily achieved by using an embedded clock in the transmitted data, as discussed in Section 4.2.
## 4 Discussion  
Among the most interesting worked-out result belongs to the generated distortion component during the transient of the harmonic supply linear load (mode M1a Figure 11d), while at steady state, it is zero. In the case of long system time constants (lightly loaded large motors, induction furnaces) and the need for precise compensation, it is, therefore, necessary to compensate for deformation power, although we consider a balanced network and linear RL load. Under rectifier supply system Mode #2, in the case of the apparent power component (Figure 20a), nearly the same apparent powers of 3- and 5- phase systems are generated at steady-state \(t\in\langle 0.0;0.1\rangle\) despite DC powers being different. Distortion components of all connections are rather high due to impulse current taken from the network. Therefore, the power factors are rather poor. In Mode #3, under non-harmonic inverter supply, distortion components of all connections are rather high due to the impulse character of the voltages produced by VSI inverters. Therefore, the power factors are also rather poor. In the case of the Mode #4 version \(\mathit{a}^{\prime}\), the harmonic network supplies an unbalanced load. The current \(I_{N}\) flows through the neutral wire despite the voltage zero-sequence being zero. It flows from an unbalanced load to a symmetrical network. Mode #4 version, \(\mathit{b}^{\prime}\), the unbalanced harmonic network supplies the symmetrical linear load. The active power of the load is proportionally higher, Figure 29b, and the zero-sequence component \(\mathit{Po}\) is added to the phase active power.  
If compensating reactive blind power under linear RL load using static compensator SC (Figure 31a), the distortion power component has still remained during transient, Figure 31b,c. SC compensator comprises capacitors switched on by antiparallel thyristors at zero voltage crossing.  
Figure 31: Using switched capacitors (**a**) for compensating reactive blind power (**b**) under linear RL load and distortion power (**c**).  
If compensating reactive power (blind and distortion) under linear RL load using an active PAF compensator (Figure 32a), the distortion power component is compensated already during transient, Figure 32b,c. The PAF compensator comprises 3-, 5-, and 2-phase voltage inverters and DC link capacitor C.  
In this case of using an active PAF compensator, if the transient was significantly longer than the time period (e.g., ten times), the deformation power would be fully compensated already during the transient state.
# Applying Deep Learning to Medical Imaging: A Review
## 4 Deep Learning for Specific Medical Imaging Modalities
### Optical Coherence Tomography (OCT)
#### 4.5.2 Challenges and Future Directions  
One of the challenges in applying DL techniques to OCT imaging is the limited availability of annotated datasets, particularly for rare diseases. Moreover, OCT images suffer from speckle noise and low signal-to-noise ratio, which can affect the performance of DL models. Therefore, developing robust DL models that can handle these challenges is an important future direction. Additionally, developing transferable models that can handle variations in imaging protocols and patient populations is also an important future direction.
## 3 System Descriptions and Assumptions
### Cache Chord Mechanism  
In this section, we present a decentralized real-time resource access mechanism by orchestrating the network resources of MEC server clusters in the edge layer, utilizing a DHT-based consistent hashing ring network topology. Our discussion focuses on three key aspects: mapping, indexing, and updating for the Cache Chord.
## 5 Results and Discussion
### Load-Bearing Capacity  
Figure 4 plots the relationship between loading force and deflection measured at the center of the horizontal span of the extreme tensile fibers. The red color with markers indicates the curves for the linearly elastic model. In contrast, the blue color with markers represents the elastic-plastic model of laminated veneer behavior obtained from the numerical analyses. The green color with markers indicates the results obtained from the theoretical model. The black curves represent the experimental results obtained from [16]. The green and red curves exhibit linear behavior throughout the test range. The models labeled FEM-  
Figure 4: _Cont._  
The theoretical model obtained a slightly higher accuracy of the maximum loading force for non-strengthened elements, with a quotient of 1.03 in comparison to the numerical model. In the case of strengthened beams, a numerical model is typically more accurate than a theoretical model. The relative error in estimating the maximum force is approximately 5%, and the deflection is approximately 5% to 10%. The differences are significant only for beams strengthened with ultra-high modulus sheets. This phenomenon can be attributed to the low elongation value at the point of material failure. Consequently, the material is subjected to preliminary failure, as elucidated in [16]. It is crucial to acknowledge that the theoretical and numerical models apply only when failure is initiated in the wood by a fracture due to tension or crushing in the compression zone. Similarly, as previously stated in [28], these models are not applicable in the event of shear failure. The analytical model demonstrated comparable accuracy in estimating the maximum bending moment for full-size non-strengthened and strengthened laminated veneer lumber beams with CFRP composites, as reported in [27]. The transformed cross-section method provided considerably less accuracy when estimating the so-called rupture modulus [30].  
In most instances, the estimated deflection values are less than the experimental values (Table 6).  
Figure 4: Comparison of experimental [16], theoretical, and numerical load versus deflection curves, for series: (**a**) LVL; (**b**) A; (**c**) AU; (**d**) G; (**e**) GU; (**f**) C; (**g**) CU; (**h**) CH; (**i**) CHU.
# Research on Secure Community Opportunity Network Based on Trust Model
## 3 Trust Community Routing Algorithm
### Node Trust Degree Measurement
For instance, in a network that prioritizes the overall behavior of nodes, the value of \(w_{1}\) can be elevated. The direct trust degree comprehensively evaluates the historical forwarding behavior of the relay node towards other nodes in the network, its reception behavior of messages from the designated node, and its transmission behavior of messages provided by the designated node. Such a comprehensive evaluation enables a more accurate prediction of subsequent forwarding behavior within social opportunistic networks. **Definition 3**. : _Indirect trust._  
In social opportunistic networks, aside from evaluating the node's own trustworthiness towards neighboring nodes, it is imperative to also account for the trustworthiness of nodes within the node's local community towards neighboring nodes. This study proposes utilizing the average trust value of the same node within the node's local community as the node's indirect trust value. The calculation formula is delineated as follows:  
\[iTrust_{(A,B)}=\frac{1}{n}\sum_{i\in\{A.local\}\cap\{B.local\}}Trust_{(A,i)} \tag{4}\]  
where \(n\) represents the count of identical nodes in the local community of both node \(A\) and node \(B\), while \(Trust_{(A,i)}\) signifies the comprehensive trust value of node \(A\) towards the identical node \(j\)within the local community.
## 4 Results and Analysis
### Factors Affecting the Surface Velocity of Glaciers in Svalhard  
Glacier surface velocity can intuitively reflect glacier motion, and is also an important indicator when evaluating glacier stability under the background of global warming.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Nature of Glacier** & **Length/km** & **Area/km\({}^{2}\)** & **Peak Flow Velocity/(m/Day)** & **Flow Direction** \\ \hline  
locked extrusion flow & 14.34 & 76.57 & 4.39 & South by east 70.9\({}^{\circ}\) \\
25 & Blocked extrusion flow & 20.96 & 234.46 & 3.08 & South by east 62.7\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: SNR and RMS errors in non-glacial areas of the two image pairs of the results.  
Figure 4: **(a)** Flow velocity of the Svalbard wide-area glacier. **(b)** Corresponding slope information of Svalbard wide-area glacier.  
Therefore, it is of great scientific value to monitor the surface velocity of wide-area glaciers. In this section, the Monacobreen glacier located in the northwest fjord of Spitsbergen Island in Svalbard is taken as an example to discuss and analyze the factors affecting the flow velocity on the surface of the glacier. The glacier flows north from the Isachsenfonna ice sheet to Liefdefjorden, with a length of about 40 km and an area of 394 km\({}^{2}\). It is the largest typical leapfrom glacier on the island. The flow velocity of this glacier began to surge in 1990, and the peak flow velocity during the transition period occurred in January 1994, with a flow velocity greater than 5 m/day and an average flow velocity of 4.5 m/day [31]. Recently, the Monacobreen glacier has entered a new cycle [32]. Figure 5 shows the cumulative displacement of the Monacobreen glacier obtained by processing Sentinel-1 data in 2021 using the time-series POT technique with a time interval of 36 days.
# Nanomaterials-Based Ion-Imprinted Electrochemical Sensors for Heavy Metal Ions Detection: A Review
## 3 Application of Nanomaterials in HMIIECSs
### Gold Nanomaterials  
Gold nanomaterials (AuNPs) have quasi-spherical polycrystalline gold nanostructures with diameters ranging from 5 to 100 nm, which have excellent properties and unique advantages (such as high catalytic activity, good biocompatibility and fast electron transfer speed, high stability, facile synthesis method, naked eye visibility, shape- and size-dependent optical properties, and direct surface modification) [120; 121]. More interestingly, the inherent properties of AuNPs can be controlled by tweaking their shape, size and chemical environment. In addition, AuNPs of different shapes, such as spheres, rods, triangles, cubes and nanowires, have been prepared by various synthesis techniques [122]. [PERSON] et al. prepared a new electrochemical sensor (see Figure 11 for the preparation process) based on IIP and AuNPs for selective and sensitive determination of Cd\({}^{2+}\) in the actual samples [123]. In this study, the IIP was surface imprinted on the silicon sphere to increase the quantity of the imprinting site and enhance the adsorption performance of the imprinted material by selecting thiosemicarbamide functionalized CS and MAA as functional monomers, and doped with AuNPs, which widened the electrochemically active site and accelerated the transfer of electrons from the reaction center to the electrode surface. The electrochemical behaviors of the IIP composites were characterized by CV, DPV and EIS, and the results showed that the as-prepared sensor exhibited good selectivity to the target Cd\({}^{2+}\). DPV was used to discuss the relationship between the peak current and Cd\({}^{2+}\) concentration under optimal conditions. As shown in Figure 12, the current increased linearly with Cd\({}^{2+}\) logarithmic concentration from \(10^{-9}\) to \(10^{-4}\) M. The correlation
## 3 The Adopted SHM Strategy  
This section describes the SHM strategy used to evaluate SAE's performance, summarized in two main steps:  
1. Training phase 1. Data organization. An input training matrix \(A\) with \((P\times M)\) elements is created, \(P\) being the number of dynamic tests randomly selected for the training phase, where \(M\) is the sampled signals' length. Thus, matrix \(A\) is formed by arranging each selected vector \(x\) in a row of the input matrix. In other words, matrix \(A\) gathers the dynamic measurements. 2. Data characterization using SAE. This task is conducted in an unsupervised way via SAE, using matrix \(A\). The penalization function \(Z(x,g(f(x)))\) is minimized and, for each vector \(x\), a corresponding vector \(h\) is obtained. An SAE output matrix \(B\) with its \((P\times K)\) elements is organized with the output vectors \(h\). It can be said that matrix \(B\) \"collects\" the feature vectors. The minimization is performed through a feedforward backpropagation algorithm employing the Scaled Conjugate Gradient (SCG) optimization method [41]. Furthermore, hyperparameters, such as the sparsity proportion \(\rho\), sparsity regularization \(\beta\) and weight regularization \(\lambda\), were selected empirically. These parameters are related to the sparse penalty function defined in Section 2 and assist in the determination of the best solution by the SAE [42]. 3. Data classification using SVM. Although the signal characterization is unsupervised, the data classification model's training process is supervised, since the pattern recognition herein is carried out using classical SVM. Hence, matrix \(B\) and its respective targets (referring to the structural conditions) are utilized for training the SVM. In this case, the SVM model is constructed using the Radial Basis Function (RBF) kernel and considers the one-against-one strategy  
Figure 2: Sparse Auto-Encoder network.  
to solve the multi-class problems. The parameters \(\sigma\) and \(C\) (regularization terms from the RBF kernel and maximization formulation, respectively) were estimated through an exhaustive search procedure known as Grid Search [43] applying the 10-fold cross-validation [44].
2. Evaluation phase. Assuming that the SVM models are well-trained and achieve acceptable classification rates, the performance of the proposed SHM strategy is directly linked to the SAE's capability to extract features from the dynamic signals adequately. The current step is developed by evaluating the set of dynamic measurements that were not used in the previous phase, gathered in matrices \(C\) and \(D\). Matrix \(C\), containing \(Q\) vectors \(x\) (being \(N=P+Q\), where \(N\) is the number of available dynamic signals), is presented to the trained SAE network, resulting in a matrix \(D\) with the respective vectors \(h\). Finally, matrix \(D\) is presented to the trained SVM, whose output is the structural condition. Figure 3 schematically illustrates the two main steps of the SHM strategy.