esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Analysis
### Analysis of Detection Results for Partial-Band Interference  
In the simulation experiment, since partial-band interference is generated by white Gaussian noise passing through a bandpass filter, the resulting partial-band interference exhibits attenuation in both the side lobes and the passband. To address these issues, we  
Figure 5: Detection probability of single-tone and multi-tone interference.  
define the detection probability of partial-band interference as the ratio of the number of times the correctly detected interference frequencies exceed 80% of the frequencies within the interference range to the total number of Monte Carlo simulation runs.  
Figure 6 illustrates the detection probability curves for different bandwidth interferences using the three algorithms. When RBW = 10%, the detection performance of the CME, FCME, and the proposed algorithm is nearly identical. When RBW = 50%, the proposed algorithm performs about 3 dB better than the FCME algorithm, while the CME algorithm completely fails. This is because the CME algorithm assumes that all samples are free from interference when calculating the initial threshold, and as the interference bandwidth increases, the initial threshold becomes too high, making it unable to detect the interference signal. When RBW = 80%, the proposed algorithm still maintains good detection performance, whereas the CME and FCME algorithms nearly fail completely. Additionally, it can be observed that the detection performance of the algorithms decreases as the relative bandwidth increases.
# Complex Background Reconstruction for Novelty Detection
## 1 Introduction
Second, we propose a similarity metric network to measure the similarity between the sample and its reconstructed one. This network extracts more discriminant features as the basis for judging similarities and dealing with complex backgrounds and distributed discrete samples more effectively. As highlighted in Figure 1, the uppermost group showcases the input images. The intermediate group depicts the reconstructed images exploiting L2 loss, whereas the lowermost group exhibits the reconstructions obtained using our solution. It is discernible that in the left segment, all the samples correspond to autonomous vehicles, which can be considered as standard instances. Conversely, the right segment contains a multitude of novel samples, including airplanes, trucks, and animals, etc. Notably, in both cases, it is evident that our solution improves the background reconstruction of the respective samples. In this work, we propose a method for novelty detection. First, we propose a new reconstruction network (RNet) based on MMD-GAN [11] for the reconstruction of images. Second, we propose a similarity metric network (MNet) to learn the distance metric between latent representations of original images and reconstructed ones, which is used to determine whether the image is a novelty. In our method, RNet is trained on a dataset with only normal samples, and MNet takes samples and their reconstructed images generated by RNet as inputs. The goal of the whole model is to minimize the distance between normal samples and their reconstructed ones. At the testing stage, the detection result is based on the distance between the original sample and its reconstructed image. Our contributions are three-fold. * We propose a reconstruction network that not only can learn the features of the target, but also the distribution of the background. Reconstructing background information can reduce the interference of the background information on reconstructed objects
* We propose a Siamese neural network with mutual information to learn the similarity between original images and their reconstructions. This method not only can extract more discriminant features about objects than the previous method but can also obtain background context information. Therefore, the target classification accuracy is significantly improved. Experiments on challenging datasets with complex backgrounds demonstrate the superiority of the proposed model.
## 4 Numerical Application
### Robustness Analysis
#### 4.3.2 Noise Analysis  
In a practical seismic data inversion, noise is an inevitable interference factor; thus, evaluating the robustness of the FAFWI method under various noise conditions is important. We incorporated Gaussian white noise with a standard deviation of \(\sigma=2\sigma_{0}\) into the noise-free observational data of the Marmousi model, where \(\sigma_{0}\) denotes the standard deviation of the noise-free data.
## 3 Results
### Results of the AdvEx-Based Analyses
#### 3.2.1 Results of the AdvEx Analysis  
Table 5 lists the results of the AdvEx analysis applied to a GPP and its components. As seen in columns 2 and 3 of Table 5, the total exergy destruction of 16.9 MW is from 72% endogenous and 28% exogenous sources. This means that the interaction between components is weak. In addition to that, the majority of exergy destruction of all components except pumps is endogenous, which means it arises from the components themselves. The highest endogenous exergy destruction amount belongs to CON 2 with 1.65 MW. It is followed by VAP 2 with 1.61 MW, CON 1 with 1.55 MW, and TURB 2 with 1.24 MW. Furthermore, for CON 1, TURB 2, and VAP 2, the difference in endogenous and exogenous exergy destruction is very wide. Specifically, CON 1 is a component selected as having a low capacity. With its use over time, the polluting and blinding effect of geothermal water reduces the efficiency of the condenser. Hence, the endogenous exergy destruction is wider. The pumps (PUs 1 and 2) are negatively affected due to the pressure drop in the condensate. In TURB 2, partly due to low turbine efficiency, inlet and outlet temperature differences, leaks and pressure drops, the endogenous part is higher, with 1.24 MW compared to the exogenous. The endogenous exergy destruction of VAP 2 in Level II is more than that of VAP 1 in Level I. The reason for this is that the temperature of the geothermal fluid coming out of VAP 1 is very low. Compared to Level I, the total endogenous exergy destruction in Level II is 1.12 MW more than in Level I. Importance can be given to improving the components in Level II. A further decomposition process is applied to evaluate the interaction and improvement potential between components in the plant. Thus, the endogenous and exogenous parts of the inevitable exergy destruction are listed in columns 6 and 7 of Table 5. From this table, 75% of the 9.3 MW total unpreventable exergy destruction is endogenous and 25% is exogenous. Except for turbines, vaporizers, and recuperators, the outer part of the inevitable exergy destruction of all components is higher than the inner part.
# Seismic Performance of LRS-FRP-Concrete-Steel Tubular Double Coupling Beam
## 4 Seismic Performance
### Strength Degradation  
Three reverse cycles were carried out under each displacement level. With the cycle increase, the corresponding bear capacity of the specimen decreases. Usually, the coefficient of strength degradation \(\lambda_{i}\) is used to reflect the degree of strength reduction, which can be calculated as follows [32]:  
\[\lambda_{i}=\frac{V_{j}^{i}}{V_{j}^{1}} \tag{2}\]  
where \(j\) is the number of main cycles starting from the skeleton curve; \(i\) is the number of sub-cycle at each main cycle \(j\); \(V_{j}^{1}\) corresponds to the strength on the skeleton curve, and \(V_{j}^{i}\) is the strength at sub-cycle \(i\).  
Figure 12 represents all specimens by showing the coefficient of strength degradation decreasing when the cycles increased under constant deformation. Relatively speaking, the phenomenon between these coefficients presents an obvious difference based on the deformation increase.  
For the RC-DCB, specimen only two displacement levels were carried out due to the ductility shortcoming. Meanwhile, the coefficient only exhibited a monotone decreasing phenomenon with the displacement increase due to the damage accumulation.  
For the other four specimens, as shown in Figure 12b-e, the coefficient tends to decrease first and then increase. Compared to the specimens embedded with a steel tube, the coefficient of the LRS-DCB specimen even shows two troughs, which also reflect the difference between the two systems: (1) the first trough indirectly reflects the peak strength of the LRS-DCB specimen. Before this trough, the damage accumulation of the LRS-DCB specimen was mainly caused by concrete, the concrete-steel interface, and yielding of reinforcement, which is similar to that of the RC-DCB specimen because the PET-FRP constraint had not been activated. After that, the further damage of concrete was limited by the increasing constraint stress provided by the PET-FRP material, which resulted in the value of the coefficient recovered. However, the constraint stiffness decreased when the strain of PET-FRP material reached the second stage in Figure 3a, then the damage  
Figure 12: Strength degradation of all specimens: (**a**) DCB; (**b**) LRS-DCB; (**c**) DST-DCB-89; (**d**) DST-DCB-76; (**e**) DST-DCB-89P.  
accumulation during the subsequent cycles increased again. (2) Relatively speaking, there is only one trough in those specimens embedded with steel tubes. Notice that the values of \(\lambda_{i}\) corresponding to the trough of LRS-DCB are similar to each other, which means that the relatively larger coefficients corresponding to the trough of the DST-DCB specimens were caused by the slippage of the concrete-tube interface and the buckling of the steel tube. After that, the constraint effect provided by the steel tube and PET-FRP sheet limited the further damage of concrete, which resulted in the decrease of the coefficient. Unlike the performance of LRS-DCB, the coefficient tends to show a monotone increase after the trough. This indicates that the state of the specimens tends to be stable.
## 2 Technological Background and Related Work
### Related Work
For data mappings, kMAF migrates one memory page to the node where the frequency of faults for this page is exclusive, so the page is mostly accessed from that node. This reduces remote memory access. For methods using page fault to trigger migrating the faulted page to the same node as the faulting thread, the induced faults reduce performance. Existing studies also use page faults on the same page table for all threads for one multithreaded process to determine the memory access pattern for data mappings or thread mappings. Since several threads of a multithreaded process may fault one page, [PERSON] et al. [8] indicated that this might result in an inaccurate estimation of the working-set of individual threads for one multithreaded process performance is decreased in terms of thread mappings. They then proposed a solution that uses the multi-view address space (MVAS). If MVAS is switched on while one multithreaded process runs, one individual page table is created for each of its threads. The memory access pattern for different threads can be separately tracked in different page tables until MVAS is switched off for the multithreaded process. MVAS does not incur extra page faults, so it can support those studies [11; 13; 14] that use page faults to perform page migrations to reduce remote memory access. [PERSON] et al. [9] studied the placement of threads and data on NUMA nodes and the asymmetry of interconnecting links for nodes connected by links of different bandwidths. A dynamic thread and memory placement algorithm was developed in Linux to minimize contention for asymmetric interconnect links and maximize bandwidth between communicating threads. [PERSON] et al. [10] also studied the effect of hardware asymmetry. The AMPS scheduler is implemented in the Linux kernel to support asymmetric multicore architectures for which cores in the same processor have different performances. The Linux kernel is modified to track the memory usage for each thread on each node and predicts the migration overhead for a thread. Threads are migrated to faster cores when they are under-utilized. However, if the predicted migration overhead is too high or the thread is in the memory allocation phase, this thread cannot be migrated across nodes.
## 2 Materials and Methods
### Accuracy of the Results  
To calculate the a priori horizontal error of a block with 10 GCPs at the edges of Scenario A, Equation (1) proposed by [58] (p. 265) can be used. This equation was conceived for aerial photographs measured with analytical stereoplotters and precision stereocomparators with 8 GCPs at the edges of the block.  
The accuracy of the results using UAV-DAP depends on the images' side lap and end lap, the distribution of the GCPs, and the distribution of the block.  
\[\sigma_{\text{B,I. mean}}=(0.83+0.05n_{s})\cdot\sigma_{\text{M,I}}. \tag{1}\]where  
\(\sigma_{\text{B,L mean}}\) is the estimated horizontal accuracy of the block (L = XY);  
\(n_{s}\) is the number of forward strips in the block, and  
\(\sigma_{\text{M,L}}\) is the accuracy in a single model (estimated horizontal accuracy of a single model), and is the parameter described in Equation (2):  
\[\sigma_{\text{M,L}}=\pm 6\mu\cdot m_{b} \tag{2}\]  
where \(6\mu=0.0006\), and \(m_{b}\) is the denominator of the photo scale.  
\[m_{b}=\frac{h}{c} \tag{3}\]  
where \(h\) is the flying height above ground and \(c\) is the focal length.  
The a priori vertical error of a block with 10 GCPs at the edges of Scenario A can be calculated with Equation (4).  
\[\sigma_{z}=\pm 6\%\cdot h \tag{4}\]  
where \(\sigma_{z}\) is the height error.  
Equation (1) can be rewritten for digital photogrammetry with digital sensors as follows in Equation (5):  
\[\sigma_{\text{B,L mean}}=\mu_{\text{xy}}\cdot\sigma_{\text{M,L}} \tag{5}\]  
where \(\mu_{\text{xy}}\) denotes the weight coefficients depending on the layout of the GCP and the image network after flight planning.  
For the calculation of the vertical accuracy a priori, we can use the following Equation (6):  
\[\sigma_{\text{BZ}}=\mu_{\text{Z}}\cdot\sigma_{\text{Z}} \tag{6}\]  
where \(\mu_{\text{z}}\) denotes the vertical weight coefficients.  
The root mean square error (RMSE) Equation (7) is calculated from the differences between the coordinates of the points measured on the ground with GNSS equipment and the coordinates obtained in the photogrammetric adjustment [65].  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}\text{ei}^{2}}{n}} \tag{7}\]  
where ei is the difference of each point for the given direction (original position--computed position).
## 3 Results and Discussions
### Aggregate Quality Tests
#### 3.1.1 Kim Test  
The results of the Kim test for the three test specimens are shown in Figure 9. It can be observed that the colored asphalt mixture's deformation strength, with a value of 3.02 MPa on the average, is significantly superior compared to the other materials with average values of 2.60 MPa and 2.41 MPa for typical SMA and colored asphalt with pigment, respectively.  
The interlock provided by mudstone aggregate offers substantial support against deformation. As illustrated in Figure 4, the multifaceted shape of mudstone aggregates enhances the aggregate-to-aggregate interaction. Additionally, based on the flatness rate of the mudstone aggregates, the rounded shape of these aggregates contributes to their resistance to deformation. In asphalt pavement, flat and elongated aggregates tend to permit plastic deformation due to their shape and the lack of interlock with other aggregates. In contrast, rounded aggregates are more likely to maintain their form under traffic load due to their effective interaction with other aggregates. Also, the mudstone aggregate's high stability and resistance to abrasion, as shown in Table 6, are also factors in the increased resistance to deformation of the colored asphalt pavement. However, test results indicate that the addition of pigment adversely affects the deformation resistance of colored asphalt pavement. When pigment is incorporated into the asphalt binder, the stiffness of the binder decreases, thereby reducing its resistance to rutting.
## 3 Transient Response Simulation
### Simulation Modeling  
In the study of the transient calculation model of the propulsion shaft system in Section 2.1, it has been understood that during the simplification process of the propulsion shaft system, components such as cylinders, flywheels, and propellers are treated as lumped mass points. According to the characteristics of the experimental ship's propulsion shaft system, the rotor of the half-type structured shaft-driven generator is directly fitted onto the propulsion shaft system without bearing support, and it is also treated as a concentrated mass point for transient simulation calculation. The model diagram based on the classical frequency domain lumped-parameter method is depicted in Figure 2. The equivalent parameter model data used at this time are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Equipment** & **Parameter** & **Equipment** & **Parameter** & \\ \hline  & model & 8G95 ME-C & & moment of inertia & 498,093 kg\({}^{2}\) \\  & maximum continuous & 45,300 kW & & mass & 80,105 kg \\  & output power & 80 r/min & & number of blades & 5 \\  & number of cylinders & 8 & & diameter & 10.1 m \\ main engine & cylinder bore diameter & 950 mm & & model & Geislinger \\  & firing order & 1-8-3-4-7-2-5-6 & vibration & torsional stiffness & 71 MNm/rad \\  & piston stroke & 3460 mm & damper & instantaneous elastic & 1150 kNm \\ \hline flywheel & mass & 9331 kg & & continuous elastic torque & 768 kNm \\  & moment of inertia & 35,000 kg\({}^{2}\) & & mass & 11,200 kg \\ \hline \hline \end{tabular}  
* The above table lists the main equipment parameters for the time domain transient simulation calculation and experimental verification of the ship’s propulsion shaft system in this paper.  
\end{table}
Table 1: Relevant parameters of the propulsion shaft system.
## 4 Results and Discussion
### Modal Analysis of Numerical Results: Identification of the Excited Modes
#### 4.1.2 Effect of the Depth of the Source  
In this part, a point source modelling a crack-opening mode 1 (source D\({}_{1}\)) is located on the midplane and at two different distances, 0.25 mm and 0.95 mm, away from the midplane of the EPL3 sample. Figure 10 shows the 2 DFT of the signals. When the source is located on the midplane, only the longitudinal modes are excited (Figure 10a). However, when the source is located away from the midplane, the external load ceases to be symmetric and a flexural mode (F\({}_{11}\)) starts to be stimulated (Figure 10b). When the source is located further away from the midplane, close to the surface (Figure 10c), the longitudinal modes are very attenuated, and the flexural mode F\({}_{11}\) is predominant. The further the source is away from the midplane and the closer to the surface, the more flexural modes are stimulated in comparison to longitudinal modes for the same source. As already pointed out by [PERSON] [54], the z-position of AE sources influences the ratio of simulated modes and therefore the spectral content of the signal.  
Figure 9: 2 DFT (wavenumber k versus frequency f) of the numerical signals obtained applying a point force, whose temporal evolution is a chirp, on the surface of EPL3 (**a**), EPL16 (**c**), and EPL80 (**e**) geometries. Superimposed and on the side of each 2 DFT, the theoretical modes are shown for bar (**b**), beam (**d**), and plate (**f**). These modes were calculated with a dedicated resolution algorithm using the semi-analytical discontinuous Galerkin method. In (**b**), the theoretical longitudinal, torsional and flexural modes of a beam have been superimposed to the semi-analytical curves in three different colors, while in (**f**), the analytical solutions of the Lamb modes of an infinite plate have been superimposed in red and blue.  
Figure 10: _Cont._
## 5 Advanced MPPT Techniques
### Hybrid MPPT Techniques
#### 5.3.3 FLC-P&O Algorithm  
The FLC-P&O hybrid MPPT algorithm combines FLC with the P&O method to optimize MPPT in PV systems. The FLC dynamically adjusts the perturbation step size based on system inputs such as changes in power and voltage, improving the system's responsiveness to varying environmental conditions like fluctuating irradiance and temperature. Once the fuzzy controller sets the optimal step size, the P&O method fine-tunes the voltage to track the MPP by perturbing and observing power changes. This hybrid approach enhances tracking efficiency, reduces oscillations around the MPP, and adapts better to variable conditions. However, it introduces increased complexity, requires careful tuning of fuzzy rules, and demands more computational power, making it more resource-intensive compared to simpler algorithms [99].
## 3 Results and Discussion
### Bio-Delignification and Chemical Delignification Processes
As a whole, pretreatments on lignocellulosic matters based on microbial technology are perceived to be promising and low-impact methods for improving process efficiency, but their effective application at a plant-scale has been a subject of controversy due to the many conflicting literature reports about how they can be conveniently used to enhance the AD process. For example, a recent techno-economic analysis carried out by [PERSON] et al. [71] underlined that bio-delignification is not economically feasible when it is realized as a pretreatment for bioethanol production, because in that case, bio-delignification had to be preceded by a sterilization step followed by enzymatic hydrolysis to obtain sugars to be fermented to ethanol. In the case of bio-delignification used as a former step of biogas production, hydrolysis is carried out directly during the AD and sterilization is not needed. Moreover, there are no cost to feedstock, as it is already a collected waste, and there is no need to wash it with pure water, because of the absence of toxic residues or toxic byproducts. Overall, it seems a promising technique of pretreatment in order to increase the quantity of fermentable sugars to biogas. The addition of glucose in the culture medium during bio-delignification has probably favored the production of laccase and peroxidase as lignolytic enzymes and promoted the growth of _B. adusta_ on the easily available glucose rather than inducing cellulolytic activity, which would have reduced the amount of fermentable sugars in the subsequent AD process [72]. In fact, as reported by [PERSON] et al. [73], _B. adusta_ exhibits its maximum cellulolytic activity on the 6 th day of culture, when lignocellulosic material is the sole carbon source. In the conditions maintained in our experiments, we can exclude a significant cellulose concentration depletion due to the growing metabolism of _B. adusta_ in the 7 days of pretreatment. For the subsequent 30 days, the anaerobic conditions for biogas production, not favorable to fungal growth, provoked fungal death, and so there is no cellulose depletion, making DA available to the organic material of cells debris. Compared to the untreated GW, delignification treatments modified the TS and VS content of GW, as shown in Table 3.  
A consistent loss of both TS and VS occurred with sodium hydroxide treatment and at a lesser extent in the case of the other chemical treatments, whereas the bio-delignification did not significantly alter the characteristics of GW. Moreover, without water flushing for biomass washing, all the soluble matter was available for AD.