esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Antioxidant Potential  
Mushrooms are rich in low molecular weight antioxidants, mainly phenolic acids. Such compounds are mainly present in free form and can be easily extracted with both water and organic solvents. However, some antioxidants bind to larger molecules and become less or non-extractable [57]. These compounds are often bound to mushroom derived polysaccharides and affect their antioxidant capacities. All the isolated WSP exhibited antioxidant activity in both assays (Figure 3). The highest antioxidant parameters were noticed in the case of the control samples (23.53 \(\pm\) 0.30 \(\upmu\)moles of TE/g dw for ABTS and 12.29 \(\pm\) 0.87 \(\upmu\)moles of TE/g dw for FRAP). Antioxidant activity of _L. edodes_ polysaccharides was reported before and this study confirms earlier findings [16; 35; 58]. Blanching caused a significant drop in FRAP test (7.59 \(\pm\) 0.75 \(\upmu\)moles of TE/g dw), while no relevant change in ABTS parameter was noticed. Boiling resulted in the decrease of ABTS activity (20.83 \(\pm\) 1.24 \(\upmu\)moles of TE/g dw), whereas no further change in the case of FRAP test was observed. The most profound changes caused blanching and fermenting (7.78 \(\pm\) 1.50 \(\upmu\)moles of TE/g dw for ABTS and 3.56 \(\pm\) 0.33 \(\upmu\)moles of TE/g dw for FRAP). The results clearly showed that aqua-thermal processing may have negative impact on antioxidant parameters of polysaccharides. Previous findings showed that antioxidant activity of polysaccharides is related to total phenolics and protein content [59; 60; 61]. In this study, a high correlation was observed between ABTS values and total phenolics (R = 0.93) as well as protein content (R = 0.85). In the case of the FRAP parameter, the correlation coefficients were similar to ABTS (R = 0.96 and R = 0.82, respectively). Therefore, the reason for the drop in antioxidant capacity is very likely caused by the removal of phenolics and protein. Further studies should be carried out to isolate and identify antioxidant compounds which are bound to polysaccharide matrix by covalent bonds [57].
## 2 Materials and Methods
### Investigation
#### 2.3.2 Electrical Resistivity Tomography (ERT)  
Electrical Resistivity Tomography (ERT) is a widely utilized method in geophysics, relying on the transmission of current through electrodes positioned along defined profiles. It correlates measured resistivity with various geological characteristics of the terrain, such as lithology, structural arrangement, moisture content, and water presence [21; 22; 23; 24].  
At the Selanac landslide site, ERT was employed to ascertain its geometric features, including the depth of the initial block, eroded and deposited material, and accumulated debris. Given the occurrence's dimensions, material heterogeneity, and the terrain's inaccessibility, traditional geotechnical investigation methods were impractical, making ERT the most suitable option. Measurements across all profiles were conducted using a '4-point light HP resistivity meter' (LGM Lippmann-GERMANY) with 20-60 electrodes. This system reduces the measurement time. All operational electrodes along the profile were connected to the resistivity meter via multi-core cables. For each individual measurement, the resistivity meter selects four electrodes and measures the apparent resistivity [22]. This research was conducted in November 2015 by the expert team from the Faculty of Mining and Geology, Department of Geophysics.  
Validation was conducted by comparing a single cross-section corresponding to the position of the ERT5 profile in the deposition zone, as well as the difference in DTM heights. ERT5 (Figure 5) is located in the deposition zone of the transported material. It was not possible to determine the lateral boundaries, but the thickness of the accumulated material was estimated. For ERT5, the maximum thickness was estimated at 15-20 m(Figure 6).  
Figure 4: Map obtained by overlapping two epochs of the Digital Terrain Model (DTM).
# A Method to Reduce Eddy Current Loss of Underwater Wireless Power Transmission by Current Control
## 4 Electromagnetic Field Simulation Analysis and Discussion
### Change Current Phase Difference
(**a**) \(\Delta\varphi\) = 90.00\({}^{\circ}\), \(\eta\) = 0.8021; (**b**) \(\Delta\varphi\) = 110.40\({}^{\circ}\), \(\eta\) = 0.8297; (**c**) \(\Delta\varphi\) = 130.80\({}^{\circ}\), \(\eta\) = 0.8435; (**d**) \(\Delta\varphi\) = 135.01\({}^{\circ}\), \(\eta\) = 0.8444; (**e**) \(\Delta\varphi\) = 140.25\({}^{\circ}\), \(\eta\) = 0.8392; (**f**) \(\Delta\varphi\) = 150.02\({}^{\circ}\), \(\eta\) = 0.8145. It can be obtained that the energy transmission efficiency of the system changes with the current phase difference, as shown in Figure 11.  
It can be seen from the simulation results that the different phases of the primary and secondary currents have little effect on the distribution of electric field strength in the area outside the coil, but it has a weakening effect on the area between the two coils. This is because the field strength outside the two coils mainly originates from the contribution of the coils themselves, while the field strength between the two coils contributes to the two coils together. With the increase in the current phase difference, the resultant field strength between the coils gradually decreases, thereby reducing the eddy current loss and improving the efficiency of the system. As shown in Figure 11, the efficiency is increased by 4.2% in the range of 90\({}^{\circ}\)-135\({}^{\circ}\). However, as the phase difference continues to increase, the secondary side is in a non-resonant state, resulting in a decrease in power factor accordingly, and the overall efficiency of the system will decrease, as shown in the interval of 135\({}^{\circ}\)-150\({}^{\circ}\) degrees in Figure 11. Therefore, it can be seen that within a certain range, increasing the current phase difference between the primary side and the secondary side can reduce the eddy current loss and improve the energy transmission efficiency of the system.
# Applying Real-Time Travel Times to Estimate Fire Service Coverage Rate for High-Rise Buildings
## 3 Case Studies
### Estimation Scenarios  
Traffic conditions are constantly changing, and the driving speed of fire trucks will also change at different times. In general, the traffic is the smoothest at around 04:00 a.m. (early in the morning) [8, 40, 41]. At around 08:00 and 18:00, there are the most vehicles on the road, and the traffic is very heavy, which often leads to traffic congestion. In addition, the traffic conditions on weekdays and non-weekdays are varied. At around 12:00 a.m., the traffic is neither smooth nor heavy. Therefore, 30 estimation scenarios in a complete week (from 2019-12-02 00:00 a.m. to 2019-12-09 00:00 a.m., including five weekdays and two non-weekdays) were set. At least four estimation scenarios (04:00 a.m., 08:00 a.m., 12:00 a.m., and 18:00 p.m.) were set every day. In the dynamic estimation model, \(T\)_TCR_(\(C\), \(\Delta T\)) should be calculated with a complete evaluation time-period (from the first evaluation scenario to the last evaluation scenario). Therefore, another two estimation scenarios (2019-12-02 00:00 a.m. and 2019-12-09 00:00 a.m.) were set. Obviously, the accuracy of estimation results can be improved by setting more scenarios with a shorter time interval. This will consume more computing resources, and increase the number of calls to the Baidu Map web service API [17]. However, the limit of the calling number makes it difficult to set too many scenarios.
# Effect of Dynamic Loading Conditions on Maximizing Energy Dissipation of Metallic Dampers
## 3 Dynamic Analysis of Static Optimization
### Finite Element Model  
FE analysis was conducted for each iteration to calculate the changing amount of energy dissipation resulting from the change in shape. The design conditions were assumed to be as simple as possible for this optimization. Generalized properties were selected with reference to ASTM A36 steel, which is a common mild steel. The yield strength was 250 MPa, the Poisson ratio was 0.26, and the modulus of elasticity was 200 GPa. The material was also assumed to have completely plastic behavior. This elementary constitutive model was determined to simplify the optimization problem, considering the complex loading conditions of the proposed optimization problem, such as dynamic excitation, which will be discussed later. Eight-node brick elements (C3D8) were used, and the failure of the material was not considered. The mesh size was approximately 10 mm, and the total number of elements was roughly 5000. These values were determined through initial parameter studies and set to demand as little computational cost as possible in providing data independent of the mesh size. The shape optimization was based only on the correspondence to a uniaxial seismic load.  
Figure 4: Specifications of the control points.  
Under vertically fixed conditions, a cyclic load was applied horizontally to the top of the prismatic metallic damper. Through this assumption of the loading condition, the developed damper worked in a way similar to general shearing dampers, such as shear slit dampers and shear panel dampers. The cyclic loading scheme was controlled with various conditions for several cases with different strokes. In total, there were four cases with different scales of multi-cycled strokes (Figure 5). Following a previous study ([PERSON] et al., 2014), each case had nine cycles of strokes, which was determined by considering the computational speed. Conventionally, a loading scheme is utilized by gradually increasing the displacement in a single multi-cycle condition. However, through these various loading schemes, it was possible to determine whether the optimum shape varied with respect to the required capacity.  
Here, the loading conditions were controlled with a static load, i.e., the displacement. However, to evaluate how the optimized damper will behave under real dynamic circumstances, the model should be considered with the acceleration. Therefore, further optimization cases with dynamic FE analyses were conducted. The dynamic optimization process also followed the general optimization process described in this section, with some changes to the boundary and the loading conditions. The specific modifications are explained in Section 4.1.
## 4 Discussion and Conclusions
In case 4, as CVB selects frequencies with cyclic impulses, one may positively recognize the presence of damage. In this paper we have also checked the results for the real vibration signal. The real data analysis (which corresponds to case 4 of the simulated data) has highlighted several important issues:  
* The spectral smoothness index appeared to be numerically sensitive. If the signal is long enough and the values are mostly small in the absolute value then the value of the spectral smoothness index is infinite--this is visible as yellow areas in the map corresponding to the smoothness index. * Infogram is based on two components, SE and SES. The first one is sensitive for the non-cyclic impulses while the SES indicates the cyclic behaviour of the signal. However, when we take their mean value, then infogram does not give the expected information about the local damage, because the scoring works in favour of non-cyclical impulses. Thus, the results obtained for the real (and simulated) signal indicate that the classical infogram needs to be improved to be more sensitive for the cyclic impulses related to damage than for the non-cyclic ones, which usually have bigger amplitudes. The weighted infogram can be introduced. This is the plan for our future research. * The kurtosis-based selectors fail as predicted, due to the fact that the kurtosis is very sensitive to outliers, which in the considered cases are the random, non-cyclic impulses with relatively high amplitudes (higher than the cyclic informative impulses). If the amplitudes of impulses are higher, then the value of the kurtosis grows. * For the presented real data, the amplitude and number of non-cyclical impulses are not very large, thus such selectors as Alpha and CVB selectors, as well as the spectral Gini index, can be successfully used. However, if these values increase (as we have in case 4 for simulated data) then it seems that CVB selector outperforms the other techniques. As the future work, we also plan to undertake additional study related to the influence of the number and amplitudes of the non-cyclic impulses on the selectors' results. Currently, this has been done for kurtosis [55] and CVB selector [49]. Such analysis could be interesting for other methods considered in this paper as well. Conceptualization, methodology, writing-original draft preparation, J.H.-S., A.W., R.Z. ; software, validation, formal analysis, visualization, J.H.-S.; data curation, investigation, resources, supervision, writing-review and editing, A.W., R.Z. ; project administration, funding acquisition, A.W., R.Z. All authors have read and agree to the published version of the manuscript. This research and APC was funded by EIT RawMaterials GmbHHunder Framework Partnership Agreement No. 18253 (OPMO - Operation monitoring of mineral crushing machinery). The authors declare no conflict of interest. Figure 16: Normalized results of the informative frequency band selection for 7 different methods for the signal \(s_{2}\). Figure 16: Normalized results of the informative frequency band selection for 7 different methods for the signal \(s_{3}\).
## 3 Comparative Parameters and Dimensional Analysis
### Relative Axial Force  
Based on the research needs concerning the load response mechanism of the secondary air system following shaft failure, it is essential to analyze the variation in axial loads within the flow path under sudden change boundaries and to investigate the influence mechanisms of the flow path's geometric parameters. When accounting for the effects of inertial forces within the cavity and the inlet and outlet areas, the axial forces acting on the two end faces of the cavity will vary. For the purposes of this study, the end face closer to the atmosphere is designated as the A end face, while the end face farther from the atmosphere is designated as the B end face, as illustrated in Figure 7a. The variations in the axial forces \(F_{A}\) and \(F_{B}\) at the two end faces during the response process are analyzed, summarizing the influence mechanisms of the flow path's geometric parameters on the evolution of axial loads under sudden boundary conditions. Here, \(F_{A}\) represents the axial force at the A end face, and \(F_{B}\) represents the axial force at the B end face, as indicated in Equations (10) and (11).  
\[F_{A}=\overline{P_{A}}\cdot S_{A} \tag{10}\]  
\[F_{B}=\overline{P_{B}}\cdot S_{B} \tag{11}\]  
\(\overline{P_{A}}\)--Average pressure at the A end face; \(\overline{P_{B}}\)--Average pressure at the B end face; \(S_{A}\)--Area of the A end face; \(S_{B}\)--Area of the B end face.  
The initial axial forces on each end face are non-dimensionalized with respect to \(F_{A}\) and \(F_{B}\), as expressed in the following equations.  
\[\overline{F_{A}}=\frac{F_{A}}{\overline{P_{A0}}\cdot S_{A}} \tag{12}\]  
\[\overline{F_{B}}=\frac{F_{B}}{\overline{P_{B0}}\cdot S_{B}} \tag{13}\]  
\[\Delta\overline{F}=\overline{F_{A}}-\overline{F_{B}} \tag{14}\]  
\(\overline{P_{A0}}\)--Initial average pressure at the A end face; \(\overline{P_{B0}}\)--Initial average pressure at the B end face; \(\overline{F_{A}}\)--Relative axial force at the A end face; \(\overline{F_{B}}\)--Relative axial force at the A end face; \(\Delta\overline{F}\)--Difference in relative axial forces between the A and B end faces.
## 7 Conclusions  
In this study, we presented a new hybrid-composite approach to improve the quality and intelligibility of speech for hearing aids users (called HC-DDAEs). The proposed method was divided  
Figure 12: **The HASQI results (average measures for the test set). Higher values present better results (A) White noise enhancement; (B) pink noise enhancement; (C) train noise enhancement; (D) bubble noise enhancement; and (E) restaurant noise enhancement.**  
Figure 13: **The** HASPI results (average measures for the test set). Higher values present better results (A) White noise enhancement; (B) pink noise enhancement; (C) train noise enhancement; (D) bubble noise enhancement; and (E) restaurant noise enhancement.  
into two stages: 1) The filtering stage, which uses the Wiener filter to reduce the noise components and to generate a clean speech; 2) a composite of three levels of DDAEs with different windows lengths of one, three, and five frames, for DDAE1, DDAE2, and DDAE3, respectively. For observing the improvement in performance, we adopted multi-objective learning technology. Two kinds of experiments were presented in this paper, in the first experiment; five types of noises were used (i.e., white, pink, train, babble, and restaurant noises) and were added to the training set by corrupting random training speech signals at four SNR levels (i.e., 0, 5, 10, 15 dB). In contrast, in the second experiment, 600 speech signals/gender were mixed with eight noise types (i.e., train, babble, car, exhibition hall, restaurant, street, airport, and railway station noises). We conducted an extensive evaluation of the speech intelligibility and quality produced by the proposed method based on three well-known evaluation metrics, which were applied to two HFHL audiograms. We compared the results with the Wiener filter and the single DDAE networks separately and performed tests to assess the statistical significance in enhancing noisy speech signals. Based on our experimental results, we concluded that the HC-DDAEs approach yields higher HASPI and HASQI scores than the Wiener filter and the individual DDAE methods under most of the test conditions. Thus, the proposed method could provide better speech intelligibility and quality for individuals with hearing loss. More noise classes with of the seven sensorineural hearing losses will be evaluated in the future.  
[PERSON] developed the algorithm, conducted the experiments and participated in writing the paper. [PERSON] supervised the overall work and reviewed the paper. [PERSON] review the paper. All authors have read and agreed to the published version of the manuscript. This work was partially supported by the National Key Research and Development Program of China (No.2017 YFB1402102), the National Natural Science Foundation of China (No.11772178, No.11502133, No.11872036, and No.61701291), the Fundamental Research Fund for the Central Universities (No.GK201801004), the China Postdoctoral Science Foundation-funded project (No.2017M613053) and the Shaanxi Natural Science Foundation Project (No.2018 JQ6089). The authors declare no conflict of interest.
###### Abstract  
Background: Accurate and reliable classification models play a major role in clinical decision-making processes for prostate cancer (PCa) diagnosis. However, existing methods often demonstrate limited performance, particularly when applied to small datasets and binary classification problems. Objectives: This study aims to design a fine-tuned deep learning (DL) model capable of classifying PCa MRI images with high accuracy and to evaluate its performance by comparing it with various DL architectures. Methods: In this study, a basic convolutional neural network (CNN) model was developed and subsequently optimized using techniques such as L2 regularization, Tanh activation, dropout, and early stopping to enhance its performance. Additionally, a pyramid-type CNN architecture was designed to simultaneously evaluate both fine details and broader structures by combining low- and high-resolution information through feature maps extracted from different CNN layers. This approach enabled the model to learn complex features more effectively. For performance comparison, the developed fine-tuned enhanced pyramid network (FT-EPN) model was benchmarked against models such as Vgg16, Vgg19, Resnet50, InceptionV3, Densenet121, and Xception, which were trained using transfer learning (TL) techniques. It was also compared to next-generation models such as vision transformer (ViT) and MaxViT-v2. Results: The developed fine-tuned model achieved an accuracy rate of 96.77%, outperforming pre-trained TL models and next-generation models like ViT and MaxViT-v2. Among the TL models, Vgg19 achieved the highest accuracy rate at 92.74%. In comparison, ViT achieved an accuracy of 93.55%, while MaxViT-v2 achieved an accuracy of 95.16%. Conclusions: This study presents an optimized FT-EPN model to enhance the performance of DL models for PCa classification, offering a reference solution for future research. This model provides significant advantages in terms of classification accuracy and simplicity and has been evaluated as an effective solution in clinical applications.  
prostate cancer; fine tuning; CNN; transfer learning; classification; deep learning Article
## 5 Review of the Original GN-AK Protocol  
The GN-AK protocol, introduced by [PERSON] and [PERSON], is an elliptic curve-based authenticated key agreement scheme that enables two communicating parties--commonly denoted as \(T\) (the initiator) and \(R\) (the responder)--to establish a shared session key while mutually authenticating each other. This section outlines the structure, cryptographic principles, and detailed message flow of the original GN-AK protocol.  
The GN-AK protocol is designed to satisfy several critical properties required of a secure key agreement scheme:  
* Mutual authentication: Both \(T\) and \(R\) must confirm each other's identities.
* Key agreement: The protocol must result in both parties computing the same session key \(K\).
* Session freshness: Each session should be based on pseudorandom values to ensure forward secrecy.
* Resistance to impersonation and replay attacks: The protocol must use cryptographic techniques that prevent adversaries from masquerading as legitimate participants.  
Let us define the following:  
* \(E(\mathbb{F}_{p})\): An elliptic curve defined over a finite field.
* \(P\), \(Q\in E(\mathbb{F}_{p})\): Publicly agreed curve points.
* \(SK_{T}\), \(PK_{T}\): Secret and public key pair of \(T\).
* \(SK_{R}\), \(PK_{R}\): Secret and public key pair of \(R\).
* \(d_{T}\), \(d_{R}\): pseudorandom session-specific scalars generated by \(T\) and \(R\).
* \(h(\cdot)\): A cryptographic hash function (e.g., SHA-1).
* \(\mathrm{Enc}_{SK}(\cdot)\), \(\mathrm{Dec}_{PK}(\cdot)\): Asymmetric encryption and decryption.