esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Limitations of AIDMs When Applied with the Presence of GSCI
### Active Method-Impedance Measurement Type
Hence, the useful bandwidth loses the sub-harmonics area and starts on the local load resonance, which matches with the fundamental grid frequency \(\omega_{3}\). Then, it is possible to redefine a new DFBW as:  
\[DFBW^{\prime}=\sqrt{\frac{L_{RL}}{L_{G}}} \tag{10}\]  
Figure 8: Bloc diagram of the PCC impedance transfer function. The asymptotes shown in Figure 9 are directly related to the weakness of the grid and to the quality factor \(q\) of the resonant load. Figures 10 and 11 illustrate this behavior. In order to facilitate the analysis of how the asymptotes are shifted, a resonant load set at \(R_{RL}=1.76\)\(\Omega\), \(C_{RL}=1.8\) mF, and \(L_{RL}=5.6\) mH will be considered, i.e., a 30-kW/phase resonant load. For the strong grid case, it is assumed \(L_{G}=30\) mH and \(R_{G}=5\) m\(\Omega\), while for the weak grid case, \(L_{G}=300\) mH and \(R_{G}=50\) m\(\Omega\) are taken into account. Figure 10a shows the Bode diagrams of Figure 8 for both connected and disconnected scenarios assuming the mentioned 30-kW/phase resonant load, the strong grid case, and a quality factor \(q\) set to one (the value suggested in [7; 11; 12]). It can be observed that the most sensitive change is near \(\omega_{3}\) (314.16 rad/s for the applied case). This responds to the assumed fundamental grid frequency in the paper. Around the utility angular frequency, it is expected that the grid-disconnected impedance will be higher than the grid-connected one in terms of gain. For higher frequencies, the detection could be unattainable. This is demonstrated in Figure 10b,c, where it can be seen how the PCC impedance does not change at 350 Hz (\(\approx\)2200 rad/s). This example allows deducing that the optimal multiple of the rated frequency for detection is 100 Hz (628.32 rad/s). In addition, it should be noted that the DFBW not only evolves with the weakness or strength of the mains, but also with \(q\). As can be observed in Figure 11a, the DFBW is reduced for weak grids. This is due to the vertical displacement (upwards) of the grid resistance asympt  
Figure 10: Frequency and impedance change analysis of the PCC impedance transfer function for a strong grid scenario and 30-kW/phase with \(q=1\) resonant load. (**a**) Bode diagram (magnitude). (**b**) Simulations of a PCC impedance shift after a grid disconnection. (**c**) PCC simulation test impedances on the Bode diagram. Figure 9: Conceptual asymptotic magnitude Bode diagram of the PCC impedance. DFBW, detection frequency bandwidth. displacement (towards the left) of the grid inductance asymptote. Besides, considering Figure 11b, it can seen that the higher the resonant load quality factor, the narrower the DFBW. This is because of displacement towards the right of the load inductance asymptote and towards the left shift of the load capacitor asymptote.
# Efficient Resource Allocation for Beam-Hopping-Based Multi-Satellite Communication Systems
## 4 Online Resource Allocation Scheme  
In the previous section, we propose an offline solution assuming that complete information about the channel gains is available. As discussed before, this assumption is too optimistic for most of practical scenarios. Therefore, in this section, we design an online resource allocation scheme through the dynamic programming algorithm.
## 2 Numerical Analysis
### Mesh Independence Analysis  
Mesh density or mesh resolution plays an important role in mesh quality and therefore in the accuracy of the final CFD result. Additionally, mesh density also affects the calculation time. Therefore, to ensure the accuracy of the final CFD result and the reasonableness of the computation time, a mesh independence analysis was performed. Three simulations using three various mesh densities, including coarse, medium, and fine meshes, were performed to analyze the mesh independence of the solution. The simulations were performed in parallel using an Intel(R) Xeon(R) CPU X5690 @ 3.47 GHz, 16-core, 32-thread processor, 32 GB RAM Windows workstation. Table 7 shows the properties of the meshes with the respective calculation time in the mesh independence analysis. A comparison between the final CFD results using these three mesh densities is shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Boundary Names** & **Boundary Types** & **Specific Conditions/Values** \\ \hline \hline \begin{tabular}{c} Piston \\ Liner \\ Cylinder head \\ Segment cut \\ \end{tabular} & \begin{tabular}{c} Movable wall \\ Layering wall \\ Stationary wall \\ Cyclic \\ \end{tabular} & \begin{tabular}{c} Temp./297 \({}^{\circ}\)C \\ Temp./197 \({}^{\circ}\)C \\ Temp./297 \({}^{\circ}\)C \\ \end{tabular} \\ \hline \begin{tabular}{c} **Initial Conditions** \\ \end{tabular} & \begin{tabular}{c} **Values** \\ \end{tabular} \\ \hline \begin{tabular}{c} Scavenging air temp. \\ Scavenging air press. \\ SPC \\ EVO \\ \end{tabular} & \begin{tabular}{c} 46 \({}^{\circ}\)C \\ 2.2 Bar \\ 30 CAD ABDC \\ 30 CAD BBDC \\ Pilot fuel \\ \end{tabular} & \begin{tabular}{c} \\ 2.2 Bar \\ 30 CAD ABDC \\ 12 CAD BTDC \\ 10 CAD BTDC \\ TDC \\ TDC \\ \end{tabular} \\ End of injection (EOI) & \
\begin{tabular}{c} Gaseous fuel \\ \end{tabular} &
\begin{tabular}{c} 20 CAD ATDC \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 6: Boundary and initial conditions for the CFD numerical simulations.  
Figure 5: Comparison of the simulation results among various mesh densities: (**a**) In-Cylinder pressure; (**b**) In-Cylinder temperature; (**c**) NO mass fraction.  
The comparison showed the good agreement of the final CFD results between the medium and fine mesh cases. Therefore, the medium mesh was chosen for simulations in this study because it gave accurate CFD results that were the same as the fine mesh but in a shorter calculation time.
# Feasibility Investigation of SIW Cavity-Backed Patch Antenna Array for Ku Band Applications
At first, simple screws were employed to connect the two substrates. In the final prototypes, a prepreg press compression molding process was employed to stack the multi-layer PCB. The design was performed by employing the CST Microwave Studio(r) software, and the SIW antenna prototypes were fabricated with the PCB process in the SOMACIS plant. A complete feasibility investigation is illustrated by reporting all of the steps toward the 16 \(\times\) 16 antenna array optimization. Excellent characteristics, comparable or slightly better than those reported in [18; 26], were obtained. In particular, the same gain but with a wider band compared with [26], was obtained when the same number of array elements was considered. The 16 \(\times\) 16 antenna array exhibited high gain, close to G = 29.2 dBi, and good return losses in a wide frequency region including the whole Direct Broadcast Satellite (DBS) band (i.e., in the range 10.7-12.7 GHz) and over A practically squint-less main lobe was measured. Therefore, a 16 \(\times\) 16 antenna array could be of practical interest in actual communication systems. Different materials have been considered in order to choose the best trade-off between the engineered prototypes. The paper is organized as follows. Section 2 presents the design of the single cavity backed antenna; Section 3 shows the design of the antenna arrays; Section 4 describes the fabrication and the characterization of the prototypes; Section 5 details the feasibility and discussion on the material choice; and Section 6 presents our conclusions.
# Depth Segmentation Approach for Egocentric 3D Human Pose Estimation with a Fisheye Camera
## 5 Results
### Effect of Referencing Depth Map with Segmentation  
The impact of the SegDepth module was evaluated on three architectures: our proposed method, xR-EgoPose, and Mo2 Cap2. The results in Table 2 show that referencing depth and segmentation features consistently improve the mean per joint position error (MPJPE) across all architectures and action types. When referencing depth information alone, xR-EgoPose achieved a 7.1% improvement in MPJPE, Mo2 Cap2 showed a 10.1% improvement, and our architecture showed a 4.7% improvement compared to the baseline. Adding segmentation features, in addition to depth, resulted in performance gains,with MPJPE reductions of 10.9%, 10.2%, and 8.2% for xR-EgoPose, Mo2 Cap2, and our method, respectively. These results suggest that depth information provides additional spatial context that can help resolve ambiguities inherent in 2D heatmap-based methods. The segmentation features improve performance by isolating relevant body regions and reducing noise, which is beneficial for accurate joint localization. The SegDepth module provides certain poses involving dynamic movements, occlusions, and extended limb positions. Among these, the upper stretching, lower stretching, and walking poses showed the most improvement. In the upper stretching pose, the integration of depth and segmentation features reduced MPJPE by 11.8% for xR-EgoPose and 12.2% for Mo2 Cap2. This improvement reflects the module's ability to enhance spatial localization in stretching poses with significant limb movement. For lower stretching, xR-EgoPose and Mo2 Cap2 showed MPJPE reductions of 12.0% and 11.6%, respectively, demonstrating the module's effectiveness in capturing leg positions and resolving spatial ambiguities in motion. In walking poses, xR-EgoPose and Mo2 Cap2 showed improvements of 13.6% and 10.6%, respectively, in the utility of depth and segmentation features in addressing occlusion issues and maintaining spatial consistency in motions. The qualitative comparisons presented in Figure 5 provide additional insight into the improvements achieved by the SegDepth module. The results show accurate joint localization and smoother pose reconstruction when depth and segmentation features are referenced. For Mo2 Cap2, our proposed method resolves depth ambiguities. For example, when depth and segmentation features are used, the reconstructed poses show a reduction in errors related to limb length discrepancies, especially in walking or stretching actions. The estimated leg lengths are closer to the ground truth compared to the baseline, showing the effect of depth information in maintaining consistent proportions. Similarly, the reconstructed poses for xR-EgoPose show improved accuracy in reflecting realistic joint movements for actions involving significant upper-body bending.
# Pitfalls in the Detection of Hearing Loss via Otoacoustic Emissions
## 4 Discussion
This weakness may give rise to disagreement between different systems when different classification criteria are used (e.g., if the criterion is that 3 of 5, or 2 of 5, frequency bands have to reach a certain SNR). Generally, it can be said that problems with incorrect pass/refer classifications come from ears with downward sloping audiograms. Although our work looked at particular systems, we think the results have more general significance. We did not intend to promote or disparage any of the examined systems; we just evaluated the systems that are in use in our clinic. This paper shows there are significant differences between results obtained by different systems, even from the same manufacturer. This implies there is a need for more normative papers involving OAE data that compare different systems. There is some basic knowledge of what the pass/refer rates are in UNHS programs, but the results presented here indicate that the rate depends substantially on which device is used. It would be interesting to compare the results of UNHS programs made on different devices in different clinics or countries, similar to the study of [PERSON] et al. [15]. Another problem is that instructions for the devices often fail to provide details about how the OAE signal is detected and classified. For example, the Accuscreen systems tested here give only vague descriptions of noise-weighted averaging and counting of significant signal peaks. Although screening devices are usually aimed at UNHS, it is worth mentioning that screening devices are often used for assessing the hearing status of adults in the clinic as well as in research [33], and the Accuscreen device has been tested by the U.S. Food and Drug Administration (FDA) for use on all age groups [30]. In order to compare the three OAE systems, only the highest quality OAE data were considered, i.e., data recorded from adult subjects. Since the same hardware and software algorithms are used to collect neonatal and adult data, the findings of this study can be extended to other types of populations (neonates, toddlers, etc.). For example, the data from Tables 2 and 3 indirectly suggest that the Accuscreen evaluation algorithm deliberately uses a rather narrow frequency response, capturing information mainly from the 1-2 kHz band. This implies that hearing losses in other octave bands will not be detected and the inevitable outcome of an evaluation will be a pass. Nevertheless, in order to improve the current OAE evaluation criteria, additional studies that compare different OAE devices and their performances in different age groups are needed.
# Boosting of Denoising Effect with Fusion Strategy
###### Abstract  
Image denoising, a fundamental step in image processing, has been widely studied for several decades. Denoising methods can be classified as internal or external depending on whether they exploit the internal prior or the external noisy-clean image priors to reconstruct a latent image. Typically, these two kinds of methods have their respective merits and demerits. Using a single denoising model to improve existing methods remains a challenge. In this paper, we propose a method for boosting the denoising effect via the image fusion strategy. This study aims to boost the performance of two typical denoising methods, the nonlocally centralized sparse representation (NCSR) and residual learning of deep CNN (DnCNN). These two methods have complementary strengths and can be chosen to represent internal and external denoising methods, respectively. The boosting process is formulated as an adaptive weight-based image fusion problem by preserving the details for the initial denoised images output by the NCSR and the DnCNN. Specifically, we design two kinds of weights to adaptively reflect the influence of the pixel intensity changes and the global gradient of the initial denoised images. A linear combination of these two kinds of weights determines the final weight. The initial denoised images are integrated into the fusion framework to achieve our denoising results. Extensive experiments show that the proposed method significantly outperforms the NCSR and the DnCNN both quantitatively and visually when they are considered as individual methods; similarly, it outperforms several other state-of-the-art denoising methods.  
image denoising; denoising effect; boosting; image fusion; pixel intensity; global gradient +
Footnote †: journal: Applied Physics Letters  
19 April 2020; Accepted: 29 May 2020; Published: 1 June 2020
## 2 Material and Methods
### Modeling Soil Water Balance and Daily Course of Relative Water Content  
Soil water storage integrated over the rooting depth (\(SWS_{obs}\)), that is \(ca\). 4.5 m [29], was measured, during the vegetative periods, at approximately monthly interval, with a neutron moisture gauge in 1984-1986 [30] and since July 1998. Discrete measurements were interpolated at a daily time step with a soil water balance model proposed in [31] and further used in [32]. Soil water storage and soil water potential were related by a Campbell-type retention curve [33] whose parameters are strongly dependent on soil texture [16]. The drainage curve relating depth drainage to soil water storage depends on the stone content over the whole-soil profile [34]. The model was driven by daily values of incoming solar radiation, minimal and maximal temperature and rainfall amount. Comparison of measured against simulated soil water storages, in mm, and predawn leaf water potentials, in MPa, displayed very good agreement. Leaf water potential values came from discrete measurements performed on the study site [35]. Reduced major axis (RMA) regressions yielded \(\mathrm{SWS_{sim}=\alpha_{RMA}\,SWS_{obs}+\beta_{RMA}\,}\) with \(\alpha_{RMA}\pm\mathrm{standard-error\,}(\mathrm{SE})=0.94\pm 0.03\), \(\beta_{RMA}\pm\mathrm{SE}=6.0\pm 4.4\), \(\mathrm{R^{2}=0.93}\), \(\mathrm{F=1137}\), \(p<0.0001\) and \(\mathrm{n=91}\). For the predawn potential, \(\psi_{\mathrm{pdsim}}\)\(=\alpha_{RMA}\,\psi_{\mathrm{pdobs}}+\beta_{RMA}\,\) with \(\alpha_{RMA}\pm\mathrm{SE}=0.93\pm 0.05\), \(\mathrm{B_{RMA}\pm\mathrm{SE}=-0.09\pm 0.09}\), \(\mathrm{R^{2}=0.840}\), \(\mathrm{F=273.3}\), \(p<0.0001\) and \(\mathrm{n=54}\). The continuous daily course of soil relative water content, \(RWC\), was derived from \(\mathrm{SWS_{sim}}\) divided by the soil water storage at field capacity. We choose to fix it at 205 mm. It corresponds to the value we observed after 2 days of free drainage after a substantial rain event falling in cool wet period. For characterizing the whole-year water limitation, we calculated the water stress integral (WSI) as the yearly sum of \(\psi_{\mathrm{pdsim}}\). For days with \(\mathrm{RWC}\geq 1\), \(\psi_{\mathrm{pdsim}}\) is fixed to \(-0.03\) MPa. The WSI are expressed in MPa day.
## 2 Materials and Methods
### Sample Preparation  
The tester has strict requirements for the morphology of the test material. In order to ensure the scientific rigor and validity of the rock mechanics test, it is recommended to carry out the pre-preparation of the test specimens according to ISRM requirements so as to improve the quality of the test data and provide a reliable basis for the study of the mechanical properties of the rock and its engineering applications.  
Based on the purpose and requirements of the test, samples free of cracks, voids, or other visible defects were selected from the parent core, and the shape and size of the specimens were required to meet the specifications of the experimental equipment to avoid boundary effects (the top and bottom of the specimens were maintained to be less than 0.02 mm non-parallel). Figure 2a illustrates the selection of test specimens, with three specimens chosen as representatives due to their density and wave speed being near the batch mean; another four specimens were randomly selected for the fatigue test. Figure 2b-d present the measurements of wave speed, mass, and size of the specimens, respectively, which are foundational for selecting the test specimens. The measuring equipment is an NM-4A non-metallic ultrasonic testing analyzer. The physical parameters of the specimens are detailed in Table 1.  
\begin{table}
\begin{tabular}{l c c c c c} \hline
**No.** & \begin{tabular}{c} **Mass** \\ **[g]** \\ \end{tabular} & **High [mm]** & **Diameter [mm]** & **Density [g/m3]** & \
\begin{tabular}{c} **Axial Longitudinal** \\ **Wave Velocity [m/s]** \\ \end{tabular} &
\begin{tabular}{c} **Radial Longitudinal** \\ **Wave Velocity [m/s]** \\ \end{tabular} \\ \hline X10 & 675.1 & 100.29 & 49.82 & 3.45 & 4811 & 3906 \\ X15 & 675.6 & 99.63 & 50.09 & 3.44 & 4630 & 3676 \\ X49 & 676.0 & 100.15 & 50.19 & 3.41 & 4690 & 3788 \\ X43 & 718.9 & 100.30 & 50.23 & 3.62 & 4314 & 3472 \\ X48 & 635.9 & 100.15 & 50.19 & 3.41 & 4690 & 3788 \\ X53 & 645.6 & 99.78 & 50.03 & 3.29 & 4606 & 3472 \\ X54 & 658.8 & 100.57 & 49.58 & 3.39 & 4438 & 3205 \\ \hline \end{tabular}
\end{table}
Table 1: Physical parameters of diorite specimens (experimental part).  
Figure 1: Analysis of the microstructure of skarm rock. (**a**) Polarization microscope micrograph of the sample crushed block processed into a cast thin film, Gr (Garnet), Di (Diopside), Act (Actinolite), Q (Quartz); (**b**) analysis results of the powder XRD test after sample failure.
# A Packet Content-Oriented Remote Code Execution Attack Payload Detection Model
## 5 Experimental Results and Analysis
### Results and Limitations Analysis  
In the preceding sections, we evaluate the detection effect of the proposed model from various perspectives. Specifically, for the ELi and IDSER detector, our focus is on identifying malicious expressions and Java code. The ELi and IDSER detector performs well in detecting ELi, but its effect is declined slightly in detecting IDSER. However, machine learning models trained with integrated ELi and IDSER data perform well in detecting both ELi and IDSER simultaneously as expected. For the XXE detector, it can construct the UD chain of XML entities well and complete the detection based on the integrity of the chain and the behavior of the chain's tail node. However, this model still has the following limitations:  
Figure 12: A false positive case of the XXE detector.  
Figure 11: A false negative case of the XXE detector.  
* The construction of the UD chain for XML entities may introduce false positives, although these false positives have little impact on the final judgment of security practitioners.
* The XXE detector has false negatives when analyzing the behavior of UD chain nodes. Therefore, we suggest providing it with a list of sensitive behaviors to improve its detection accuracy.
* Because the dataset is collected in the experimental environment, it may lack representation of the real production environment. This could result in incomplete feature analysis and extraction, thus affecting the accuracy of the machine learning model.
* It is difficult to determine the success of an attack solely based on the request and response packets. We can only make a preliminary judgment by checking if a complete UD chain can be constructed and if a string can be parsed by the interpreter. This may bring false positives to practitioners.
## 2 Materials and Methods
### Main Machine Learning Algorithms
#### 2.2.1 Convolutional and Artificial Neural Networks  
Convolutional neural networks (CNN) and artificial neural networks (ANN) have carved a niche for themselves in the realm of atmospheric science, exhibiting progress in both spatial and temporal data analysis [34]. CNNs are renowned for their ability to understand spatial hierarchies and associations. This makes them especially suitable for analyzing the satellite imagery obtained from remote sensing, and they have become a powerful tool for detection and classification tasks in various domains, ranging from computer vision to natural language processing [35]. Using convolutional layers, they are designed to capture spatial nuances such as cloud formations, storm patterns, and aerosol distributions [36]. For example, CNNs have shown efficacy in classifying cloud types from satellite images and predicting the intensity of tropical cyclones based on specific visual patterns [37]. However, ANNs, with their layered architecture, are instrumental in deciphering intricate relationships in large datasets. In atmospheric studies, ANNs are often used to forecast the weather, model climate, and identify specific meteorological events [38]. For example, training ANNs on historical weather data enables them to recognize patterns and relationships between disparate atmospheric variables, facilitating the prediction of future weather conditions. Furthermore, the adaptive nature of ANNs allows the identification and localization of atmospheric phenomena such as aerosol layers, cyclone centers, and storm fronts using datasets such as radar measurements or lidar scans [39]. It is essential to note that the efficacy of CNNs, ANNs, and machine learning techniques in general is intrinsically related to the quality and completeness of the training data and the architectural nuances tailored for specific tasks. This nexus of neural network technology and atmospheric science continues to foster advances that further our understanding of the dynamic atmosphere of the Earth.