esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Fingerprint Minutiae Extraction and Matching Based on Improved SIFT Features
## 3 Proposed Methodology
### Image Preprocessing
#### 3.1.1 Fingerprint Enhancement Using Contextual Filtering  
In this work, an enhancement technique based on contextual iterative filters is designed for preprocessing the fingerprint images, which proceeds in four distinct steps: (i) ROI estimation (ii) ridge-visibility enhancement, (iii) ridge-pattern enhancement, and (iv) image binarization. In the first step, an algorithm based on the local standard deviation [23; 24] is applied to the input fingerprint image for evaluating the local variance of the image intensity, followed by a thresholding operation for removing unwanted finger shadows. Finally, a mask binary image that reliably identifies the fingerprint ROI is obtained. The second step is performed based on a Homomorphic filtering technique, in which the background image \(B_{I}\) is first estimated by applying a morphological opening operation with a mask \(s\) to the input image \(I\). Then, the image \(R_{I}\) representing the fingerprint ridges can readily be obtained as: \(I_{R}=I-B_{I}\). In order to effectively suppress the noise present in the image, a nonlinear equalization is preformed by applying the logarithm to the ridge image \(I_{R}\), yielding an improved image \(I_{L}(x,y)=\log(I_{R}(x,y))\). In the third step, the enhancement of the ridge pattern is performed, similarly to that described in [23], by computing ridge frequency and orientation maps, followed by the application of a bank of Gabor filters optimally tuned to the computed local ridge maps that result in an image \(I_{E}\) containing a minimum amount of background texture details [25]. The fourth and last step in the preprocessing stage is an adaptive image binarization process, which permits effectively reducing the noise in the edges of the ridge pattern described in the image \(I_{E}\), and reliably estimating the potential minutiae points. For obtaining a uniform contrast between ridges and valleys, the logarithm of \(I_{E}\) is taken as: \(I(x,y)=\log(I_{E}(x,y))\). After establishing the histogram of the image \(I_{I}\), a binary image of the ridge pattern \(I_{B}\) is obtained as follows:  
\[I_{B}(x,y)=\begin{cases}0&\text{if }I_{I}(x,y)\leq\underset{i}{\arg\max}(H(i)) \\ 1&\text{otherwise}\end{cases} \tag{1}\]  
Figure 2: A functional block diagram of the automated fingerprint recognizer.  
where \(H\) is the histogram of the image \(I_{I}\). A simplified outline of fingerprint image enhancement based on contextual filters is presented in Figure 3.  
Additionally, some morphological operations (e.g., erosion and closing) are performed to fill out the holes by smoothening the ridges' surface in the fingerprint image and merging the narrow gaps among the fingerprint ridges [1].
## 2 Results
### CdaA and DisA Exhibit Divergent Responses to \(H_{2}o_{2}\)-Induced Mutagenesis  
A previous report showed that strains deficient for CdaA but not for DisA are sensitized to hydrogen peroxide treatment [16]. Therefore, we investigated how the disruption of _disA_, _cdaA_, or both, impacted \(\mathrm{H_{2}O_{2}}\)-induced RifR mutagenesis. Interestingly, while the mutagenesis levels promoted by hydrogen peroxide increased \(\sim\)2.5 times in the _disA_ strain, a decrease of \(\sim\)3.6 times was observed in the _cdaA_ strain, compared to the parental strain (Figure 3).  
The mutagenesis elicited by \(\mathrm{H_{2}O_{2}}\) increased \(\sim\)3.1 times in the strain with double disruption in _disA_ and _cdaA_, compared to the parental strain YB955 (Figure 3). Furthermore, the reintegration of _disA_ or _cdaA_ reestablished the mutation frequencies promoted by the oxidizing agent in the strains with single or double disruption in _disA_ and _cdaA_, demonstrating that the mutagenesis effects were caused by the disruption of these genes (Figure 3). In conjunction, these results unveil divergent functions in the prevention (DisA) or promotion (CdaA) of mutagenic events elicited by the ROS-promoter agent \(\mathrm{H_{2}O_{2}}\), in exponentially growing _B. subtilis_ cells.
## 4 Discussion
### Prediction of Microbial Gene Functions in Silage  
Predicting the functional changes in bacterial communities is crucial for evaluating their impact on silage quality changes. In this study, the relative abundance of KEGG path ways in the bacterial community was analyzed using the PICRUSt2 method. The results revealed that, at the primary pathway level, metabolism was the most prominent category, indicating that microbial metabolism drives the entire fermentation process during silage production. Microorganisms utilize fermentation substrates from corn straw to convert available compounds into new metabolites. At the secondary pathway level, pathways such as global metabolism, carbohydrate metabolism, amino acid metabolism, membrane transport, energy metabolism, and metabolism of cofactors and vitamins showed high relative abundance. Previous studies have demonstrated that these metabolic pathways are closely related to the fermentation quality of silage feed [32]. To explore the metabolic processes further, we delved into the third-level metabolic pathways. Silage fermentation is driven by lactic acid bacteria, which utilize soluble carbohydrates from the raw material to produce lactic acid. As fermentation progresses, lactic acid bacteria generate various secondary metabolites through a range of metabolic pathways. Additionally, we observed significant differences in the metabolic pathways across different treatment groups, particularly in pathways such as metabolic pathways, biosynthesis of secondary metabolites, and microbial metabolism in diverse environments. These variations underscore the impact of different additives on the metabolic processes that shape the fermentation quality and microbial dynamics of the silage.
## 4 Policy Implementation
### Disaster Risk Management  
Given the spatial, and frequently dynamic nature of all aspects of disaster risk (hazard, elements at risk, vulnerability, disaster risk, and disasters) and also of the commonly considered phases of the disaster cycle (mitigation, preparedness, early warning, response, and recovery), remote sensing is superbly suited to study and monitor disaster risk [37]. This, however, does not happen in a policy context that is always equally well established. Hazard and risk assessment are increasingly part of urban and regional planning policies, with spatial analysis, frequently supported by remote sensing data, serving to assess the probability of loss in a given area and for given time periods due to environmental hazards. In particular, policies designed to limit losses due to floods or wildfires are employed and aided by RS data [38, 39]. Remote sensing has also become a critical tool for hazard types that are more difficult to assess, such as seismic hazards. By providing detailed information on the hazard's spatio-temporal characteristics, remote sensing not only supports implementation of policies aimed at loss reduction, such as requirements for adequate construction standards or no-build zones, but also for such policies to be defined in detail in the first place.  
Figure 4: Laser altimetry and sonar bathymetry used to measure beach elevation is used to estimate the volume of sand required to restore the coastal defence function of the beach (Courtesy [PERSON], [33]).  
While RS-based response to natural disasters is naturally _ad hoc_ and case-driven, a policy framework for international support to disaster response based in satellite imagery has been developed. The International Charter \"Space and Major Disasters\", established in 1999, provides for rapid image acquisition and damage map production, and is widely seen as a successful example of international cooperation. However, while the Charter operates within the framework of Space Law, _i.e._, the legally binding Outer Space Treaty, and the United Nations' non-binding Remote Sensing Principles, it employs a best-effort approach with no legal binding, and no liability is assumed for the resulting mapping products [40].
## 5 Distinguishing AI Agents in Autonomous Maritime Systems from Conventional AI Systems
### Cybersecurity, Robustness, and Fault Tolerance  
In autonomous maritime systems, cybersecurity and system resilience are not auxiliary concerns; they are core pillars of operational viability. AI agents embedded in such systems must continuously process and act upon critical data streams in environments that are both physically harsh and digitally vulnerable.  
Figure 3: AI agent vs. conventional AI system comparison (own elaboration).  
Unlike conventional AI systems, which may operate in protected data centers or enterprise environments with robust IT oversight, autonomous vessels are often exposed to remote and untrusted communication environments, including satellite links, open VHF channels, and maritime networks. This makes AI agents vulnerable to a range of threats, such as signal jamming, GPS spoofing, man-in-the-middle attacks, and unauthorized data injection [92].  
To mitigate these risks, maritime AI agents are equipped with multi-layered cybersecurity mechanisms, including the following:  
1. Encrypted communications;
2. Secure authentication protocols;
3. Tamper-proof logging;
4. Real-time anomaly detection for detecting cyber intrusions.  
These safeguards are essential to preserve data integrity because faulty or manipulated sensor inputs can lead to dangerous decisions by the agent, including route deviations, unsafe maneuvers, or regulatory violations [93,94].  
Simultaneously, agents must be built for robustness and fault tolerance in the face of harsh and variable environmental conditions, e.g., extreme temperatures, humidity, saltwater corrosion, and physical shocks. Sensors may fail, connectivity may drop, and internal systems may degrade during long missions. Yet, the AI agent must continue to operate, gracefully degrading performance without collapsing, while autonomously initiating recovery protocols.  
To achieve this, maritime agents are required to perform the following functions:  
1. Monitor their own state via self-diagnostics;
2. Perform redundant computation across subsystems;
3. Fall back on alternative data pathways when one modality fails (e.g., switching from LIDAR to radar in heavy rain) [70,104].  
In contrast, most conventional AI systems benefit from frequent monitoring, retraining, and human-assisted troubleshooting. They are rarely expected to detect and recover from multi-point failures in isolation, without external intervention [92,98].  
Maritime AI agents must be both cyber-resilient and physically robust, capable of maintaining core functionality even in degraded, adversarial, or disconnected states.  
These requirements make them some of the most demanding and high-resilience AI systems in real-world deployment today.
# You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection
## 2 You Only Hear Once (YOHO)
### Loss Function  
Generally, neural networks such as the CRNN that use segmentation-by-classification adopt binary cross-entropy as the loss function. As we modeled the problem as a regression one, we used the sum squared error. Equation (1) shows the loss function for each acoustic class \(c\).  
\[\mathcal{L}_{\mathrm{c}}(\hat{y},y)=\begin{cases}(\hat{y_{1}}-y_{1})^{2}+\\ (\hat{y_{2}}-y_{2})^{2}+(\hat{y_{3}}-y_{3})^{2},&\text{if }y_{1}=1\\ (\hat{y_{1}}-y_{1})^{2},&\text{if }y_{1}=0\end{cases} \tag{1}\]  
where \(y\) and \(\hat{y}\) are the ground-truth and predictions respectively. \(y_{1}=1\) if the acoustic class is present and \(y_{1}=0\) if the class is absent. \(y_{2}\) and \(y_{3}\), which are the start and endpoints for each acoustic class are considered only if \(y_{1}=1\). In other words, \((\hat{y_{1}}-y_{1})^{2}\) corresponds to the classification loss and \((\hat{y_{2}}-y_{2})^{2}+(\hat{y_{3}}-y_{3})^{2}\) corresponds to the regression loss. The total loss \(\mathcal{L}\) is summed across all acoustic classes.
## 3 Results and Discussion
### DPP-4 Inhibitory Activity by the Hydrolysate Obtained from Microcapsule Digestion  
It has been reported that whey protein hydrolysates produced with digestive enzymes such as pepsin or trypsin in short hydrolysis times inhibit in vitro DPP-4 activity [37]. To test the effect of the microcapsule hydrolysates obtained during the intestinal phase and after four hours of digestion, simulating the digestion in human, the DPP-4 test was performed. The DPP-4 enzyme inhibition test was conducted for hydrolysates in both the gastric and intestinal phases at different times. In the gastric phase, negative results were obtained at the initial and final times for the WPI-SA and WPI-CMC systems (data not shown). The same results were obtained at 0 min of the intestinal phase (Figure 8).  
Figure 7: _Cont._  
The results obtained in the intestinal phase (Figure 8) were 44.9% and 43.8% inhibition for 25 \(\mu\)L hydrolysates from empty microcapsules coacervated with SA or with CMC, respectively, at 60 min of hydrolysis. Increasing the hydrolysis time to 120 min, which corresponds to the end of the intestinal phase, the inhibition percentages increased to 54.7% and 53.1% for the capsule hydrolysates with SA and CMC, respectively. On the other hand, for sitagliptin (the reference standard), 51.2% inhibition was obtained at a concentration of 19 nM and no significant difference was observed between hydrolysate for either system at 60 or 120 min hydrolysis. This is a promising result that demonstrates the potential hypoglycemic capacity of the empty capsules after the hydrolysis of the whey protein contained in the wall of the capsules. Consequently, these empty capsules may have a synergistic effect with the peptides or hypoglycemic compounds that can be encapsulated within them.  
Several researchers have reported the inhibition of DPP-4 by whey protein digestion; however, few have determined this phenomenon in vitro. Of these, [PERSON] and [PERSON] [14; 38] reported that individual peptides derived from whey protein generally showed a lower potential than the hydrolysate or the fractions from which they were isolated, suggesting that these peptides act synergistically with each other. For this reason, the fractionation of the hydrolysate obtained in this work was not performed.  
Figure 7: Confocal microscopy images during in vitro digestion: (**a,b**) at the end of the gastric phase, (**c,d**) at the beginning of the intestinal phase, and (**e,f**) at the end of the intestinal phase. Note: blue color, polysaccharides; green color, protein; red color, oil.  
It is important to highlight that the present work has shown that during digestion of the empty capsules, peptides with good inhibitory activity of the DPP-4 enzyme were generated in a simulated digestion that included both the gastric and intestinal phase, unlike the previously reported works that used short time periods and a single enzyme of either gastric or plant origin [15]. Based on the above results, the microcapsules coacervated with CMC were select for the following assays due to an easier coaervation procedure because of the lower viscosity compared with SA.
# EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps
###### Abstract  
Accurate object registration is crucial for precise localization and environment sensing in autonomous driving systems. While real-time sensors such as cameras and radar capture the local environment, high-definition (HD) maps provide a global reference frame that enhances localization accuracy and robustness, especially in complex scenarios. In this paper, we propose an innovative method called enhanced object registration (EOR) to improve the accuracy and robustness of object registration between camera images and HD maps. Our research investigates the influence of spatial distribution factors and spatial structural characteristics of objects in visual perception and HD maps on registration accuracy and robustness. We specifically focus on understanding the varying importance of different object types and the constrained dimensions of pose estimation. These factors are integrated into a nonlinear optimization model and extended Kalman filter framework. Through comprehensive experimentation on the open-source Argoverse 2 dataset, the proposed EOR demonstrates the ability to maintain high registration accuracy in lateral and elevation dimensions, improve longitudinal accuracy, and increase the probability of successful registration. These findings contribute to a deeper understanding of the relationship between sensing data and scenario understanding in object registration for vehicle localization.  
Keywords:high-definition map; registration; visual localization; autonomous driving; cross modality +
Footnote †: journal:  
Article
## 3 Results
A similar result was achieved in extension, with hydrogel augmentation resulting in a 2.3-fold reduction in disc height. In axial rotation and lateral bending, although PU augmentation showed an increase in disc height at the anterior (A), medial (M), and posterior (P) sites, the values were still less than those of the intact state without loadings. Hydrogel augmentation did not restore the disc height but caused less of a reduction than aggressive PTED alone. Figure 5 shows the displacement on the finite element model (model b) when subjected to different motion conditions. The lateral bending condition resulted in the greatest displacement on the model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Body Position** & \multicolumn{4}{c}{**Axial Rotation**} \\ \hline  & **x-Axis (Degree), ML** & **y-Axis (Degree), AP** & **z-Axis (Degree)** & **FJ Gap \({}^{*}\)** \\  & **Direction** & **Direction** & **[\% *]** & **[\% *]** & **FJ Gap \({}^{*}\)** \\ model a (intact) & 0.0 & 0.1 & \(-\)1.5 & - \\ \hline model b (conservative) & \(-0.7\) & \(-1.3\) & \(-1.6\) & \(-12.3\%\) \\  & [undefined] & [1400\%] & [\(-6.7\%\)] & \(-12.3\%\) \\  & \(-4.2\) & \(-0.8\) & \(-1.6\) & \\ model c (aggressive) & [undefined] & [\(-900\%\)] & [\(-6.7\%\)] & \(-90.1\%\) \\ model d (aggressive) & \(-4.2\) & \(-0.8\) & \(-1.6\) & \\ with annular repair) & [undefined] & [\(-900\%\)] & [\(-6.7\%\)] & \(-89.0\%\) \\  & 0.8 & 0.0 & \(-1.4\) & \\ model e (PU) & [undefined] & [\(-100\%\)] & [\(6.7\%\)] & \(-84.5\%\) \\  & \(-2.6\) & \(-1.0\) & \(-1.2\) & \\ model e (Hydrogel) & [undefined] & [\(-1100\%\)] & [\(20\%\)] & \\ \hline
**Body Position** & \multicolumn{4}{c}{**lateral Bending**} \\ \hline  & **x-Axis (Degree), ML** & **y-Axis (Degree), AP** & **z-Axis (Degree)** & **FJ Gap \({}^{*}\)** \\  & **Direction** & **Direction** & **[\% *]** & **[\% *]** & **FJ Gap \({}^{*}\)** \\ \hline model a (intact) & \(-3.3\) & \(-0.2\) & \(-1.2\) & - \\ model b (conservative) & \(-4.6\) & \(-1.7\) & \(-1.2\) & \\ model b (conservative) & [\(-39.4\%\)] & [\(-750\%\)] & [\(0\%\)] & \(-13.1\%\) \\  & \(-6.4\) & \(-2.0\) & \(-1.2\) & \\ model c (aggressive) & [\(-93.9\%\)] & [\(-900\%\)] & [\(0\%\)] & \(-44.2\%\) \\ model d (aggressive) & \(-6.4\) & \(-2.0\) & \(-1.2\) & \\ with annular repair) & [\(-93.9\%\)] & [\(-900\%\)] & [\(0\%\)] & \(-43.3\%\) \\  & \(-1.2\) & 0.2 & \(-1.0\) & \\ model e (PU) & [\(63.6\%\)] & [\(200\%\)] & [\(16.7\%\)] & \(-27.7\%\) \\  & \(-5.4\) & \(-1.7\) & \(-0.9\) & \\ model e (Hydrogel) & [\(-63.6\%\)] & [\(-750\%\)] & [\(25\%\)] & \(-42.6\%\) \\ \hline \hline \end{tabular} \({}^{*}\) FJ gap: as compared to INT. \({}^{*}\) ROM: as compared to intact.  
\end{table} 
Table 3: _Cont._Figure 4: Change in intervertebral disc height at the anterior (A), posterior (P), and medial (M) sides of the disc as a percentage of the intact model (model a, INT) for (**a**) flexion, (**b**) extension, (**c**) axial rotation, and (**d**) lateral bending motions. Figure 5: Displacement on the finite element model (model b) during different motions.
# Designation of the Quality of EGNOS+SDCM Satellite Positioning in the Approach to Landing Procedure
## 4 Results
### Comparison between the Research Method and Analysis of Scientific Knowledge  
The second part of the discussion refers to the influence of the obtained results in the light of the conducted state of knowledge analysis. It is worth noting that the research described in this study concerned more than one SBAS supporting system, similarly as in publications [5; 55; 57; 58]. As the application of a SBAS system in GNSS positioning should enable the control of navigation calculations, it is essential to have verified position readings from a single SBAS solution. Apart from that, a similar type of navigation solution may be found in study [55], where the mathematical model of the application of a Multi-SBAS system in GNSS positioning referred only to the use of code measurement at L1 frequency in the observational equation. Thus, one may say that the authors of [55] used the concept of a Multi-SBAS model to develop a Multi-GNSS solution. Further consideration of the concept of the Multi-SBAS model reveals that, in aviation studies conducted in Poland, a similar solution was proposed in [5]. On the other hand, the analysis of the state of knowledge demonstrated that most aviation studies in Poland were conducted with use of only a single SBAS solution, usually the EGNOS system [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31]. A similar trend is also noticeable in aviation research conducted in Europe, where the EGNOS system was the most commonly used one in navigation [35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46]. What is important in this article, is the fact that it presents a different way to use the SBAS positioning method in aerial navigation. The test results demonstrated that the developed computational strategy proved accurate and efficient in the flight experiment. Thus, it is worth applying the mathematical model of the Multi-SBAS solution in terms of the SBAS positioning method in aviation. To conclude the discussion, it is worth referring directly to the determined quality parameters of SBAS positioning. The comparison of the obtained results of SBAS positioning quality with those presented in publications [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17] allows us to notice that the developed computational strategy for Equations (1)-(9) brings better results or results comparable with those in studies [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17]. In particular, the values of the accuracy and integrity parameters are improved, while the availability and continuity parameters are on a comparable level.