esa-sceva/satcom-chunk-collection huggingface dataset:
# Precise Feature Removal Method Based on Semantic and Geometric Dual Masks in Dynamic SLAM
## 3 Methodology  
This paper presents a visual SLAM system capable of handling dynamic environments. The proposed system is based on the monocular ORB-SLAM3 framework, with a focus on elimination of dynamic features to improve localization and mapping accuracy in dynamic scenes. As depicted in Figure 1, the overall framework of the system includes modules for image input, feature point extraction, semantic segmentation, dynamic feature point filtering, and pose optimization. The core contribution of this paper is centered on dynamic feature point filtering and dynamic region updating. Starting with image frame input, which serves as the carrier of the original visual information, the system utilizes the ORB-SLAM3 framework algorithm to extract feature points, providing a foundation for subsequent operations. Concurrently, the YOLOv11 neural network model is employed for semantic segmentation to generate segmentation masks that contain prior information about the location of dynamic content such as pedestrians and vehicles. Based on these segmentation masks derived from semantic segmentation along with feature points extracted using geometric constraints, a further in-depth analysis is conducted to precisely differentiate outliers into static and dynamic points. The overall process of the proposed system includes the following main steps:  
Image Input: Receives original video frames as the starting point for SLAM processing.  
Feature Point Extraction: Uses ORB-SLAM3 to extract feature points from each frame.  
Semantic Segmentation: Utilizes YOLOv11 to perform semantic segmentation, creating masks for potential dynamic objects.  
Mask Processing: Performs morphological dilation on the generated masks to enhance the capture precision of dynamic object boundaries.  
Dynamic Feature Point Filtering: Combines a dual-mask strategy with geometric information to effectively filter out dynamic feature points.  
Pose Optimization: Improves camera localization accuracy through PnP optimization calculations and dynamically updates the feature point set to support subsequent frame processing.
# Seismic Observations in Bucharest Area with a Raspberry Shake Citizen Science Network
###### Abstract  
Technological advancements and the appearance of low-cost Raspberry Shake seismographs have enabled the development of citizen science seismic networks in many areas worldwide. These networks can help reduce seismic risk and increase citizens' understanding of seismology and earthquakes. Such a network exists in Bucharest, one of the cities in Europe that are struck and affected by strong Vrancea earthquakes. The paper aims to show that data from such networks can be used in both outreach programs and research studies. There are presented, for the first time, seismic observations collected over two years beginning in the summer of 2020 in the Bucharest area based on the low-cost seismometers from the citizen science Raspberry Shake network. A significant number of earthquakes from the Vrancea region were recorded by the Bucharest Raspberry Shake Seismic Network (BRSSN). Some of them were felt by Bucharest inhabitants. The National Institute for Earth Physics in Magurele (Romania) organizes educational events that promote geosciences among the population and presents the tools at its disposal for a better understanding of earthquakes and their effects, contributing this way to the development of the concept of citizen science. Citizens are the first witnesses to seismic events and the citizen science seismic network provides them with the first direct information about the event via web apps available for any internet-connected device. Their involvement as non-professional participants helps in providing data for scientists via questionnaire forms to improve scientific research for earthquake assessment. Since citizen seismometers are installed in urban areas, an analysis of the ambient seismic noise (ASN) was performed in addition to the analysis of recorded seismic events. The analysis indicates that the level of seismic noise is mainly controlled by human activities. At the same time, for one citizen seismometer installed in a school in Bucharest, the results show patterns of noise variations due to students' activity.  
citizen seismology; citizen science; earthquakes; Raspberry Shake; seismicity Article
## 3 Distributed Drive Vehicles Based on MCSRCKF
### Experimental Results and Analysis
#### 4.2.2. Serpentine Condition  
For more comprehensive verification of the algorithm's adaptive capability, in the strong nonlinear limit condition, select the serpentine condition of continuous turning with an adhesion coefficient of 0.85 and the initial vehicle driving speed set to 95 km/h. Equations (58) and (59) express the initial process noise and the measurement noise, and the equations shown below express the initial state vector values with their corresponding covariance matrices:  
\[x_{0}=[95/3.6,0,0,95/3.6/R_{0},95/3.6/R_{0},95/3.6/R_{0},95/3.6/R_{0}] \tag{64}\]  
\[P=diag([0.01,0.01,0.01,0.01,0.01,0.01,0.01]) \tag{65}\]  
The driving trajectory, import, and observed signals of the estimator are described in Figure 5, and the simulation results of estimating the driving state for distributed drive vehicles by the CKF, SCKF, and MCSRCKF are compared in Figure 6.  
Figure 5: Serpentine condition: (**a**) vehicle driving trajectory; (**b**) front wheel angle; (**c**) longitudinal acceleration; (**d**) lateral acceleration.  
Figure 6: Simulated vehicle state results in case of the serpentine condition: (**a**) longitudinal speed; (**b**) lateral speed; (**c**) yaw rate; (**d**) Rotation speed of left front wheel; (**e**) Rotation speed of right front wheel; (**f**) Rotation speed of left rear wheel; (**g**) Rotation speed of right rear wheel.  
In the serpentine condition, the simulation result curves of longitudinal speed, lateral speed, and yaw rate are respectively compared in Figure 6a-c. As is clear from the figures, the CKF and SCKF follow the true values poorly because the vehicle system in continuous turning at high speed exhibits strong nonlinear characteristics in the dramatically changing driving environment, and the measurement accuracy of the on-board sensors is reduced, which is prone to generating outlier data, resulting in large errors at the peak. In particular, the SCKF is heavily contaminated by non-Gaussian noise, and the estimation performance deteriorates sharply, with large fluctuations and gradual dispersion in the filtering results at the 6 th second.  
From Table 4, the RMSE of the lateral speed estimates obtained by the SCKF reaches 3.36, the average absolute error of the MCSRCKF in longitudinal speed estimation is 95.1% lower than that of the CKF, and the lateral speed and yaw rate decreased by 55.6% and 81.3%, respectively. And the simulation results of MCSRCKF basically match with the real value curve, which can maintain tracking consistency even when the vehicle makes continuous turning operations and only has small fluctuations near the true value, which can rapidly and accurately convert to the true value, showing the highest estimation accuracy, the strongest robustness, and the best real-time performance. Compared with the double-shift condition, the improvement in the estimation accuracy of MCSRCKF in the serpentine condition is greater. Therefore, we can conclude that the MCSRCKF is better in the strong nonlinear limit condition with large deviations and exhibits more powerful convergence ability.
## 2 Optimization Problem and the Method of Finding the Optimal Solution
### Artificial Ant Colony Algorithm  
Artificial Ant Colony (ACO) is an algorithm inspired by the behavior of ants. At the beginning, the algorithm was designed for discrete problems such as graph [23]. Then, different versions were designed for problems dealing with continuous functions [24]. It is a model of searching for food by ants. If the source of food is found, the ant returns to the nest leaving a pheromone trace that helps to return to the source. Unfortunately, the amount of pheromone is reduced over time due to its evaporation. The ant \(\overline{x}^{m}\) moves towards the selected individual from the population. The probability of selecting the \(j\)-th individual in the population is determined as  
\[p_{j}=\frac{\left(1+\exp\left(f\left(\overline{x_{j}}\right)\right)\right)}{ \sum\limits_{r=1}^{n}\left(1+\exp\left(f(\overline{x_{r}})\right)\right)}. \tag{8}\]  
Calculating probability, a direction of movement is selected by choosing a colony \(c\) as  
\[c=\begin{cases}\max\limits_{i=1,2,\ldots,n}(p_{i}),&q\leq q_{0}\\ C&q>q_{0}\end{cases}, \tag{9}\]where \(q_{0}\in\langle 0,1\rangle\) is a parameter and \(q\) is random value in range \(\langle 0,1\rangle\) and \(C\) is a random ant in population. After choosing the parent colony \(c\), a Gaussian sampling is done. Using the density function, the scattering of pheromones in the entire space is modeled by the following equation  
\[g(x_{i}^{m},\mu,\sigma)=\frac{1}{\sigma\sqrt{2\pi}}\exp\left(-\frac{(x_{j}-\mu )}{2\sigma^{2}}\right), \tag{10}\]  
where \(x_{i}^{m}\) is the specific coordinate for the \(m\) ant \(\overline{x^{m}}=(x_{1}^{m},\ldots,x_{n}^{m})\) and \(\mu=x_{i}^{j}\) so it is the coordinate for the selected \(j\)-th ant, and \(\sigma\) is the mean distance between the coordinate data of a points \(\overline{x^{m}}\) and \(\overline{x^{j}}\) calculated as  
\[\sigma=\xi\sum_{r=1}^{n}\frac{|x_{r}^{m}-x_{r}^{j}|}{k-1}, \tag{11}\]  
where \(\xi\) is the evaporation rate.  
Then, the \(m\) ant population is generated in \(N(\mu_{i},\sigma_{i})\), and the worst \(m\) individuals are deleted.  
The more detailed description of ACO algorithm is presented in Algorithm 2.  
```
1: Start,
2: Define fitness function \(f(\cdot)\),
3: Create an initial population of ants,
4: Evaluate all individuals in the population,
5: Define \(\xi\), \(n\), \(m\) and the number of iteration \(T\),
6:\(t:=0\),
7:while\(t<T\)do
8:for each ant do
9: Calculate the probability using Equation (8),
10: Find the best nest by Equation (9),
11: Determine Gaussian sampling according to Equation (10),
12: Create \(m\) new solutions and destroy \(m\) the worst ones,
13:endfor
14:\(t++\),
15:endwhile
16: Sort individuals according to \(f(\cdot)\),
17: Return the best ant,
18: Stop.
```  
**Algorithm 2**Ant Colony Optimization Algorithm
## 2 Materials and Methods
### Methodology  
The selected plant samples (_P. stratiotes, S. molesta_, and _E. crassipes_) were collected from within the premises of Universiti Tenaga National (UNITEN). The research was performed as described by [16], but with adjustments in the acclimatization period and retention time. Eighty grams of the fresh _P. stratiotes, S. molesta_, and _E. crassipes_ was cultivated in three separate hydroponic ponds filled with the treated secondary domestic wastewater from the STP as shown in Figure 2. The plants were allowed to acclimatize in the system for 5 days prior to the start of the sampling study. Sampling was carried out for a period of 14 days with an interval of 2 days for each retention time (6, 12, and 24 h). The influent wastewater samples were collected directly from the STP secondary treatment point. Similarly, the effluent samples were collected from each of the hydroponic tanks. The collected wastewater samples were transferred to the environmental laboratory for water quality analysis in terms of turbidity, phosphate, ammoniacal nitrogen, and nitrate according to standard methods for the scrutiny of water and wastewater. The procedures used for carrying out the analysis are described in the subsequent section. The readings were recorded in triplicates and the average results obtained were expressed as mean \(\pm\) standard deviation. Analysis of variance (ANOVA) IBM SPSS (New York, NY, USA) version 25 package was used to evaluate the significance of the differences and the \(t\)-test. Furthermore, a \(p\)-value of less than 0.05 was considered statistically significant, which indicates strong evidence against the null hypothesis, as there was less than a 5% probability the null was correct; in contrast, a \(p\)-value greater than 0.05 was considered statistically insignificant, indicating strong  
Figure 1: Map of West Malaysia showing the location of the study area.  
evidence for the null hypothesis [17]. The dilution factor and percentage reduction efficiency was calculated using the following equations [18]:  
\[\begin{array}{c}\text{Dilution factor}=\frac{\text{Total volume of diluted sample}}{\text{ Volume of wastewater sample}}\,\\ \text{Reduction efficiency}\ (\%)=\frac{\text{C}i-\text{C}c}{\text{C}i}\ \times 100. \end{array}\]  
where \(Ci\) is the influent concentration and \(C\epsilon\) is the effluent concentration
# Nondestructive Tomographic Imaging of Rust with Rapid THz Time-Domain Spectroscopy
###### Abstract  
In this study, we developed a rapid nondestructive tool for testing rust spread in a metal covered by a paint layer by using a THz time-domain spectroscopy system at a speed of 100 Hz/pixel. Time-of-flight imaging helps identify rust formation by exclusively obtaining the reflection from the steel below the paint surface. The use of frequency-selective imaging allows us to manipulate the contrast in rust imaging. Higher contrast is generally obtained when monitoring using the higher frequency component. In addition, we monitored the spread of rust in a steel plate under the influence of two different chemical solutions: NaCl and acid. We found that in the early stages, the decrease in THz reflection was governed by the high-frequency components due to the formation of lepidocrocite, whereas the low-frequency component develops as the proportion of hematite increases with time.  
t +
Footnote †: journal:  
into the rapid time-delay technique for the efficient inspection of defects in packaged chips, which provides information on the type of defects together with their locations [28]. Although THz imaging is a powerful tool for nondestructive mapping of conductive objects, the THz-TDS system has rarely been considered for NDT of rust embedded in the paint layer, particularly at imaging speeds higher than 100 Hz/pixel.  
In this study, we report high-speed nondestructive imaging of rust on a metal plate covered by paint coatings. Using the THz-TDS system incorporated with galvano-mirror scanning, we obtained TOF imaging of the rust formation at a speed of 100 Hz/pixel. The 3D information was analyzed with respect to both the temporal and spectral domains. We also monitored the spread of rust when the steel plate was influenced by ionic and acidic solutions.
applied sciences  
A Phantom Study to Investigate Robustness and Reproducibility of Grey Level Co-Occurrence Matrix (GLCM)-Based Radiomics Features for PET [PERSON] \({}^{\dagger}\)\({}^{\clubsuit}\)  
Department of Biomedical Engineering, College of Engineering, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia; [EMAIL_ADDRESS] or [EMAIL_ADDRESS]  
\(\dagger\) Submission is extension of conference paper: [PERSON] Grey Level Co-occurrence Matrix (GLCM) as a Radiomics Feature for Artificial Intelligence (AI) Assisted Positron Emission Tomography (PET) Images Analysis. _Materials Science and Engineering Conference Series_, **2019**, _646_, 12047, doi:10.1088/1757-899X/646/1/012047.  
Quantitation and classification of heterogeneous radiotracer uptake in Positron Emission Tomography (PET) using textural features (termed as radiomics) and artificial intelligence (AI) has the potential to be used as a biomarker of diagnosis and prognosis. However, textural features have been predicted to be strongly correlated with volume, segmentation and quantization, while the impact of image contrast and noise has not been assessed systematically. Further continuous investigations are required to update the existing standardization initiatives. This study aimed to investigate the relationships between textural features and these factors with \({}^{18}\)F filled torso NEMA phantom to yield different contrasts and reconstructed with different durations to represent varying levels of noise. The phantom was also scanned with heterogeneous spherical inserts fabricated with 3D printing technology. All spheres were delineated using: (1) the exact boundaries based on their known diameters; (2) 40% fixed; and (3) adaptive threshold. Six textural features were derived from the gray level co-occurrence matrix (GLCM) using different quantization levels. The results indicate that homogeneity and dissimilarity are the most suitable for measuring PET tumor heterogeneity with quantization 64 provided that the segmentation method is robust to noise and contrast variations. To use these textural features as prognostic biomarkers, changes in textural features between baseline and treatment scans should always be reported along with the changes in volumes.  
radiomics; PET tumor heterogeneity; texture analysis; GLCM; noise and contrast; segmentation
# Extensible Hook System for Rendezvous and Docking of a CubeSat Swarm +
## 5 Discussion  
As stated in this paper, the proposed EHS will support RVD and flight formation, saving propellant and energy. Moreover, the obtained results demonstrate that it is feasible to provide the required energy to move the CubeSat and the EHS by commercial actuators in a LEO environment.  
The proposed EHS (Figure 3) is able to reach up to 5 m of distance, allowing both CubeSats to be connected, even when they are up to 10 m from each other. However, this limited configuration provides some drawbacks related to the size and mass of the EHS. In particular, if the EHS was made of aluminum, its mass would be around 2 kg without considering electronics and motors, which could increase this mass to 2.5-3 kg. Considering that the CubeSat would require four EHSs, its mass could reach up to 12 kg. Considering the available payload mass for a 16U CubeSat is 18-20 kg, around half of its weight should be dedicated to the EHS. Based on this statement, the best recommendation would be to reduce the maximum distance of the EHS to 2.5 m. In this case, the mass would be almost the half, with a total mass around 6-8 kg, considering 5 m distance between CubeSats as a  
Figure 8: Required torques and forces to move the CubeSats and hold them while the EHS is deployed.  
Figure 9: Required EHS joint forces and torques (F/T).  
feasible solution. Using this reduced configuration, the CubeSat would be configured in a long 4U format, or even in a 2U format, occupying less space.
## 2 Methodology
### Theoretical Calculation Methodology
#### 2.3.4 Theoretical Methods for Punch-Through Assessment  
Punch-through predominantly occurs in stiff-over-soft stratified sediments when spudcan penetration destabilizes the overlying stratum, inducing uncontrolled penetration into weaker underlying layers. Such failure is directly related to abrupt reductions in the bearing capacity factor \(N_{c}\). This study addressed punch-through evaluation for two typical seabed soil profiles.  
For stiff-over-weak clay conditions (Figure 11a), the punch-through capacity at the top of the stiff layer was calculated based on the punching shear failure criterion [21]:  
\[q_{u,top}=\frac{3H}{B}c_{u,s}+N_{c}s_{c}\bigg{(}1+0.2\frac{D+H}{B}\bigg{)}c_{ u,w}+p^{\prime}_{0} \tag{12}\]  
where \(q_{u,top}\) denotes the ultimate vertical bearing capacity at the top of the stiff clay layer (kPa); \(q_{u,w}\) represents the bearing capacity of the underlying weak clay (kPa); \(c_{u,s}\) and \(c_{u,w}\) signify the undrained shear strength of the stiff clay and weak clay layers, respectively (kPa); \(D\) corresponds to the penetration depth from the seabed to the spudcan base (m); and \(H\) indicates the vertical distance from the spudcan base to the weak clay layer (m). The punch-through criterion is defined as \(q_{u,top}>q_{u,w}\).  
For sand-over-weak clay conditions (Figure 11b), the load-spreading method was employed to evaluate the punch-through potential. This approach assumes that the spud-can load applied to the upper (stiff) layer spreads downward, generating an \"equivalent bearing pressure\" at the top of the underlying weak clay layer. Punch-through occurs if this pressure exceeds the bearing capacity of the weak clay. The equivalent bearing pressure is calculated as [26]  
\[q_{u,e}=q_{u,s}-\gamma^{\prime}H-6c_{u,u}\frac{H}{\gamma^{\prime}B^{2}}\big{(} \gamma^{\prime}H+2p_{0}^{\prime}\big{)} \tag{13}\]  
where \(q_{u,e}\) stands for the equivalent bearing pressure at the weak clay layer (kPa); \(q_{u,s}\) represents the ultimate bearing capacity of the sand layer beneath the spudcan (kPa); and \(c_{u,u}\) denotes the undrained shear strength of the weak clay layer (kPa). Punch-through occurs if \(q_{u,e}>q_{u,w}\).
## 2 Materials and Methods
### Analysis of Volatile Compound Profile  
Solid-phase microextraction (SPME) before gas chromatography coupled with mass spectrometry (GC/MS) was applied to determine volatile compounds (VCs) in meat samples. For SPME extraction of VCs from 5 g of sample, a triple-coated fiber DVB/CAR/PDMS (divinylbenzene/carboxy/polydimethylsiloxane; 10 mm length, 50/30 mm thickness, Supelco, Bellefonte, PA, USA) was used. The extraction was achieved at 40 \({}^{\circ}\)C by exposing SPME fiber in the sample's headspace at 40 \({}^{\circ}\)C for 40 min. The details about conditions of VC extraction are described previously [45; 46]. Separation was performed on GC/MS (6890N GC, 5975 MS Agilent Technologies, Santa Clara, CA, USA) with HP--5 MS column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m film thickness, 5%--diphenyl--95%--polydimethylsiloxane; Agilent, Santa Clara, CA, USA) and run in splitless mode. Carrier gas was He at a flow rate of 0.9 mL/min. The details of the temperature oven program are described in previous papers [45; 46]. The MS was set to electronic impact mode (EI) 70 eV with the source temperature at 230 \({}^{\circ}\)C and an ion scanning range of 33-350 \(m/z\) (amu). Volatile compounds were identified by comparing the Kovats' retention indexes (K.I.) and mass spectrum with those reported in the NIST Mass Spectral Search Program (NIST.08 and Wiley 8 th Ed. Libraries) with <80% as a cutoff to match compounds. The Kovats' retention indexes (K.I.) were calculated from the retention times of C6--C20 n--alkanes (Sigma--Aldrich, Poznan, Poland).  
Quantities of VCs were reported as a relative percentage of the total peak area [53,54]. The analyses were carried out in triplicates.