esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Analysis
### Simulation Results
#### 5.2.4 Comparison with Traditional Algorithms  
To compare it with the traditional single-satellite navigation and positioning algorithm, because the velocity and attitude are not easy to compare due to the specific carrier characteristics, we focus on the comparison of position error and trajectory error. We selected reference [20] and reference [19] for comparison.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Error** & **Scene** & **Mean** & **STD** \\ \hline Lon (\({}^{\circ}\)) & MEO1 + INS & \(-3.1435\times 10^{-4}\) & \(4.4173\times 10^{-4}\) \\  & MEO1 + INS + altimeter without deviation & \(1.5296\times 10^{-5}\) & \(2.8018\times 10^{-4}\) \\ \hline Lan (\({}^{\circ}\)) & MEO1 + INS & \(-3.33069\times 10^{-4}\) & \(2.1574\times 10^{-4}\) \\  & MEO1 + INS + altimeter without deviation & \(-1.3807\times 10^{-5}\) & \(1.1887\times 10^{-4}\) \\ \hline Alt (m) & MEO1 + INS & 96.0952 & 88.0268 \\  & MEO1 + INS + altimeter without deviation & \(-1.9597\) & 3.5542 \\ \hline \end{tabular}
\end{table}
Table 10: Trajectory error statistics.  
Position Error Comparison  
Since the positioning error caused by the initial error will eventually be corrected by the system and the algorithm, we ignored the impact of the initial error and compared and analyzed the final effect of our algorithm and the algorithm proposed in reference [20]. We collated the positioning accuracy error of our proposed algorithm and the accuracy error proposed in reference [20] in Table 11.  
From Table 11, we can find that, compared with the algorithm proposed in [20], the mean of the position accuracy in the north and east directions is increased by 97.31% and 96.24%, and the STD is increased by 98.52% and 96.40%, respectively, while the mean and STD of the ground accuracy are almost 100% improved (that is, the error is reduced to almost 0). Combined with Table 6, even if there is deviation in the altimeter, for example, the deviation is 30 m, the mean and STD of the down position accuracy can also be increased by 100%. Regardless of whether the mean or STD is considered, in the north, east, and down directions, the error of our algorithm is much smaller than the error of the algorithm proposed in the literature [20], and our algorithm is more suitable for navigation and positioning in challenging situations.  
(2) Trajectory Error Comparison  
The final trajectories of our algorithm and the algorithm proposed in reference [19] were compared and analyzed, and the statistics of relevant results are shown in Table 12.  
From Table 12, we can find that, compared with the algorithm proposed in reference [19], our algorithm can increase the longitude accuracy by 99.94%, latitude accuracy by 96.24%, and altitude accuracy by almost 100%, and the STD increased by 82.31%, 77.69%, and 99.88%, respectively. The accuracy is significantly improved compared to the algorithm proposed in the literature [19], also proving that our algorithm is more suitable for navigation and positioning in challenging situations.
# Preliminary Study on Greywater Treatment Using Nonwoven Textile Filters
## 5 Conclusions  
The following conclusions can be drawn based on the results of this study:  
* The relatively high efficiency of biological treatment of dissolved organic substances (COD\({}_{\text{Cr}}\): 58.8-71.6%; BOD\({}_{5}\): 56.7-79.8%) in greywater on both nonwoven textile filter sets was obtained thanks to the relatively low flow rate (filtration velocity), effective diffusion of atmospheric air into the greywater,
* The relatively high efficiency of mechanical treatment of greywater on both nonwoven textile filter sets (67.0-88.4%) was obtained by reducing the effective pore size of the filtration layer due to high biomass concentration and accumulation of suspended solids,
* No statistically significant difference (95% difference interval) between set I (fed during start-up period with septic tank effluent) and set II (fed during start-up period with greywater) was stated,
* No statistically significant difference (95% difference interval) between four-layer material filter and eight-layer material filter of set I was stated,
* The filters worked, at least in the air-side layer, in aerobic conditions, which was indirectly confirmed by the fact that the observed color of the filter cake on the filter surface was always grey or brown-grey, while the biomass smell was earthly,
* The filters can work practically with very low energy consumption--less than 0.5 kWh m\({}^{-3}\).  
The investigated filters seem to be very useful and attractive technology for greywater reuse e.g., with the objective of its usage for irrigation or toilets flushing thanks to relatively high treatment efficiency, greywater aeration (air-side surface of filter layer has a contact with atmospheric air at very thin film of treated greywater, what is flowing down on this surface), simple construction, easy maintenance, and very low energy consumption.  
Conceptualization, M.S.; methodology, M.S.; investigation, M.S.; data curation, M.S. and T.H.N; statistical analyses, T.H.N.; formal analysis, M.S. and T.H.N.; manuscript writing, M.S. and T.H.N.  
This research received funding: Project No. 005/RID/2018/19 under \"Wielkopolska Regional Initiative of Excellence in the area of life sciences of the Poznan University of Life Sciences\". The project financed under the program of the Minister of Science and Education in 2019-2022.  
The authors acknowledge Mrs [PERSON] from the Department of Hydraulic and Sanitary Engineering for her help in laboratory measurements and Amica S. A. company--for lending the washing machine.  
The authors declare no conflict of interest.
## 12 Conclusions and Future Work  
Assuring security and privacy of PHR data are a key concern and challenging task faced by developers of WBAN applications. Developers have difficulties in assuring security and privacy of WBAN based healthcare applications for a number of reasons which include: lack of knowledge and complexity of the security and privacy standards; lack of understanding of what assets need to be protected in WBAN ecosystems; and difficulty with the identification of appropriate controls and lack of implementation details.  
In this paper, we identified a number of healthcare-related risk management frameworks. However, these risk management frameworks were not directly applicable to WBAN applications because the primary objective of these frameworks is to manage the risk of applications which operate within a HDO's IT-network, whereas WBAN applications may operate in a public, open network using short-range communication media. Furthermore, these frameworks lack a process for selecting controls, lack implementation details for controls, and do not provide any guidance to assure security and privacy for resource constrained sensor devices.  
This paper presents a risk management framework specifically for WBAN applications which addresses the challenges detailed above. The framework was developed in two stages, the alpha version and beta version. The beta version of the framework was developed by considering the suggestions and recommendations received after implementing the alpha version in an industrial setting. We have detailed how the framework addresses the difficulties developers face in assuring security and privacy of WBAN applications, and through implementing the framework within a WBAN development organization we have demonstrated the effectiveness of the security control implementation details provided within the framework.  
Future work is to validate this framework through expert review.  
Conceptualization, P.C.P.; Methodology, P.C.P., J.L. and G.R.; Supervision, J.L., F.M. and G.R.; Writing--original draft, P.C.P.; Writing--review & editing, J.L. and G.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported with the financial support of the Science Foundation Ireland Grant No. 13/RC/2094_2 and co-funded under the European Regional Development Fund through the Southern & Eastern Regional Operational Programme to Lero--the Science Foundation Ireland Research Centre for Software (www.lero.ie access on 30 July 2021).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# On-Board Ship Detection in Micro-Nano Satellite Based on Deep Learning and COTS Component
## 4 The Composition of Ground Demonstration and Verification System
### On-Board Processing Unit  
The processing unit on the satellite is the core of the ground demonstration and verification system. This module uses Jetson TX2 combined with deep learning algorithm to simplify a large amount of remote sensing data into the information required by users, which improves the utilization rate of data and reduces the pressure of data transmission. The code and trained model of proposed scheme need to be stored in the intelligent processing unit before the operation of the system. Since the model and code can be updated or replaced in the information flow unit through the upload channel, the whole framework can also be applied to other high-level tasks, such as other target detection, change detection, terrain classification, etc., which can greatly improve the application ability of micro-nano satellites.
# Cable Force Health Monitoring of Tongwamen Bridge Based on Fiber Bragg Grating
## 2 Monitoring Method and FBG Vibration Sensor
### Design of FBG Vibration Sensor
By combining Equations (2)-(4) and (6), relationship between wavelength shift \(\Delta\lambda\) of the designed FBG sensor and external vibration acceleration can be expressed as:  
\[\Delta\lambda=\frac{6(1-P_{e})\lambda Lm}{Ebh}\cdot a \tag{7}\]  
Equation (7) is the basic measuring principle of the developed FBG vibration sensor. A 'B & K 4808' vibration test system (Bruel & Kjaer, Naerum, Denmark) and a standard 'B & K 4371' piezoelectric accelerometer (Bruel & Kjaer, Naerum, Denmark) were used to measure amplitude-frequency and linearity response of the vibration sensor. Figure 3 shows the typical performance test results for the developed 16 FBG sensors. Figure 3a is the amplitude-frequency curve, which was obtained by exerting different frequencies from 0.5 to 21.5 Hz at an acceleration of 0.2 m/s\({}^{2}\). It can be observed that the resonance frequency is approximately 15 Hz, which exceeds the required 6 Hz and meets monitoring requirements. As shown in Figure 3b acceleration performance test was repeated three times. Average data was fit to obtain sensitivity as 109.667 pm/(m/s\({}^{2}\)), and the measurement showed favorable repeatability of 3.12%. To verify the applicability of the proposed FBG vibration sensor, a contrast experiment has been carried out. A FBG vibration sensor and a 'B & K 4371' piezoelectric accelerometer have been fixed together on the exciter and applied by a sine excitation signal with different frequencies of 2, 5, and 8 Hz, respectively. Figure 4 shows the contrast of the two sensors in responses of time-domain waveforms and frequency spectrums under different excitation signal. From the comparison charts, it can be seen that the FBG vibration sensor is consistent with the piezoelectric accelerometer, which demonstrates the applicability of the proposed sensor. Figure 4: _Cont._  
Figure 3: Performance test of the FBG vibration sensor: **(a)** amplitude-frequency curve; **(b)** acceleration characteristics curve. Figure 5a is the photo of the manufactured FBG vibration sensor, which was fixed on the suspension cable through buckles. Figure 5b is the photo of the sensor installed on the suspension cable.
## 5 Conclusions
\begin{tabular}{l l} \(AM_{R}\) & Added mass due to radiation at the reference point \\ \(\overset{\rightarrow}{a}_{p}\) & Linear acceleration of the structure (m\(\cdot\)s\({}^{-2}\)) \\ \(C_{B}\) & Coefficient of static friction between the seabed and the mooring line \\ \(C^{Hydrostatic}\) & Hydrostatic-restoring matrix \\ DOF & Degrees of freedom \\ \(EA\) & Cross-section axial stiffness (kN) \\ FAST & Fatigue, Aerodynamics, Structures, and Turbulence \\ \(\overset{\rightarrow}{F}_{AM}\) & Total added mass forces from all contributions (kN) \\ \(F_{H}\) & Horizontal components of the effective tension in the mooring line at the fairlead (kN) \\ \(\overset{\rightarrow}{F}_{HS}\) & Hydrostatic forces at the reference node (kN) \\ FOWT & Floating offshore wind turbine \\ \(\overset{\rightarrow}{F}_{RD}\) & Radiation memory-effect force at the reference point (kN) \\ \(F_{V}\) & Vertical component of the effective tension in the mooring line at the fairlead (kN) \\ \(\overset{\rightarrow}{F}_{WAP}\) & Total loads at the platform reference point (kN) \\ \(S\) & Acceleration due to gravity (m\(\cdot\)s\({}^{-2}\)) \\ \(j\) & Number of DOF \\ \(k\) & Discrete-frequency-step counter \\ \(K\) & Radiation kernel from potential flow theory \\ \(K_{ij}\) & Wave radiation-retardation kernel matrix \\ \(L\) & Unstretched line length (m) \\ LC & Load condition \\ MWL & Mean water level \\ \(n\) & Discrete-time-step counter \\ NREL & National Renewable Energy Laboratory \\ OC3 & Offshore code comparison collaboration \\ OC4 & Offshore code comparison collaboration continuation \\ \(S_{\xi}^{2-silated}(\omega)\) & Desired power spectral density of the wave elevation per unit time, this paper uses the P-M spectrum \\ \(t\) & Time (s) \\ TLP & Tension leg platform \\ \(V_{0}\) & Displaced volume of the immersed part of the platform (m\({}^{3}\)) \\ \(W[k]\) & Fourier transform of a white noise time series with zero mean and unit variance \\ \(x\) & Platform motions (m) \\ \(\dot{x}(\tau)\) & Platform velocity (m\(\cdot\)s\({}^{-1}\)) \\ \(x_{F}\) & Cable profile in the horizontal planes at distance \(s\) along the line (m) \\ \(X(\omega,\beta)\) & Wave-excitation force array normalized per unit wave amplitude (kN\(\cdot\)m\({}^{-1}\)) \\ \(z_{F}\) & Cable profile in the vertical planes at distance \(s\) along the line (m) \\ \(\beta\) & Incident wave direction angle (degree) \\ \(\delta_{3}\) & Component of the Kronecker-Delta function \\ \(\rho\) & Fluid density (kg m\({}^{-3}\)) \\ \(\rho_{c}\) & Cable density (kg/m\({}^{3}\)) \\ \(\omega\) & \(\omega\) = \(gA\) (\(\rho_{c}-\rho\)), Weight per unit length in the submerged fluid (N/m) \\ \(\omega_{0}\) & Frequency (Hz) \\ \end{tabular}
# Deep-Learning-Based Stream-Sensing Method for Detecting Asynchronous Multiple Signals
## 6 Evaluation
### Case with the Same SNR for All Mobile Devices  
This section presents the precision and recall with \(d=0\) and \(d=1\) for our ASensNet, AIC, and MDL. The results are obtained with various SNRs, which are 6 dB, 8 dB, 10 dB, 12 dB, and 14 dB. Note that in Wi-Fi, the SNR value of 8 dB corresponds to the minimum receiver sensitivity for the lowest modulation and coding scheme. Hence, this work considers 8 dB as the minimum criterion for stream-sensing schemes to reliably provide good detection performance. In addition, we evaluate those stream-sensing schemes with the SNR of 6 dB to further ensure the performance stability of 8 dB. Table 3-5 present the precision and recall for our ASensNet, AIC, and MDL, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \multicolumn{11}{c}{**Actual Number of Signals**} \\ \hline  & & & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 \\ \hline \multirow{3}{*}{6 dB} & 0 & 1.00 & 1.00 & 1.00 & 0.99 & 0.97 & 0.97 & 0.89 & 0.76 & 0.65 & 0.87 \\  & 1 & - & - & - & 1.00 & 1.00 & 1.00 & 1.00 & 0.99 & 1.00 & 1.00 \\ \hline \multirow{3}{*}{8 dB} & 0 & 1.00 & 1.00 & 1.00 & 0.96 & 0.97 & 0.90 & 0.77 & 0.64 & 0.88 \\  & 1 & - & - & - & 1.00 & 1.00 & 1.00 & 1.00 & 0.99 & 1.00 & 1.00 \\ \hline \multirow{3}{*}{10 dB} & 0 & 1.00 & 1.00 & 1.00 & 0.99 & 0.97 & 0.96 & 0.88 & 0.76 & 0.62 & 0.87 \\  & 1 & - & - & - & 1.00 & 1.00 & 1.00 & 1.00 & 0.99 & 1.00 & 1.00 \\ \hline \multirow{3}{*}{12 dB} & 0 & 1.00 & 1.00 & 1.00 & 0.99 & 0.97 & 0.97 & 0.90 & 0.77 & 0.63 & 0.87 \\  & 1 & - & - & - & 1.00 & 1.00 & 1.00 & 1.00 & 0.99 & 1.00 & 1.00 \\ \hline \multicolumn{11}{c}{**Recall**} \\ \hline  & & & & & & & & & & & & \\ \hline \multicolumn{11}{c}{**The Number of Transmitted Signals**} \\ \hline SNR & d & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 \\ \hline \multirow{3}{*}{6 dB} & 0 & 1.00 & 1.00 & 1.00 & 1.00 & 0.99 & 0.97 & 0.97 & 0.86 & 0.65 & 0.65 \\  & 1 & - & - & - & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 0.99 \\ \hline \multirow{3}{*}{8 dB} & 0 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 0.96 & 0.97 & 0.86 & 0.66 & 0.65 \\  & 1 & - & - & - & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 0.99 \\ \hline \multirow{3}{*}{10 dB} & 0 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 0.97 & 0.96 & 0.84 & 0.65 & 0.63 \\  & 1 & - & - & - & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 0.99 \\ \hline \multirow{3}{*}{12 dB} & 0 & 1.00 & 1.00 & 1.00 & 1.00 & 0.99 & 0.96 & 0.97 & 0.87 & 0.66 & 0.64 \\  & 1 & - & - & - & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 0.99 \\ \hline \multirow{3}{*}{14 dB} & 0 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 0.97 & 0.96 & 0.86 & 0.65 & 0.66 \\  & 1 & - & - & - & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 0.99 \\ \hline \end{tabular}
\end{table} 
Table 2: Example for \(TP_{k\pm d}\), \(FP_{k\pm d}\), and \(FN_{k\pm d}\) with \(k=2\) and \(d=1\).
# Segmentation of Brain Tumors from MRI Images Using Convolutional Autoencoder
## 2 Materials and Methods
### Network Performance Metrics  
The developed network architecture for tumor segmentation is based on the pixel classification method. Therefore, as the network's output, we have a binary image (predicted mask), i.e., \(256\times 256\) pixels, that could be one of two classes, \"1\" for tumor and \"0\" for everything else. In order to evaluate the network performance, we calculated five coefficientsfor each image separately. These coefficients are Accuracy (_Acc_), Specificity (_Sp_), Sensitivity (_Se_), Precision (_Pr_) calculated by formulas presented in Equations (3)-(6), respectively,  
\[Acc=\frac{TP+TN}{TP+TN+FP+FN}*100\% \tag{3}\]  
\[Sp=\frac{TN}{TN+FP}*100\% \tag{4}\]  
\[Se=\frac{TP}{TP+FN}*100\% \tag{5}\]  
\[Pr=\frac{TP}{TP+FP}*100\% \tag{6}\]  
where _TP_ represents true positive pixels, _TN_ true negative, _FP_ false positive, and _FN_ false negative, Table 2.  
One more coefficient used is the _Dice_ coefficient, already mentioned in the previous subsection, Equation (2), only it is transferred to percent.  
The calculated coefficients are presented in table format in the Result section, and the histograms could be found in Appendix A. The table shows the mean and median values of each coefficient.  
As the segmentation of images is performed by the pixel classification method and as the tumors in the images are insufficient in relation to the remaining images, imbalanced classes exist. Metrics used to keep class imbalances negligible are _Se_, _Pr_, and _Dice_ coefficient [31; 32].
# A Multidimensional Data Collection and Edge Computing Analysis Method
## 3 Overall System Architecture and Improved Target Detection Model
### Object Detection and Sensing Data Correction
#### 3.2.1 U-Net-Based Image Super-Resolution Pre-Processing Model Construction
\[L_{loss}=\sum_{i=1}^{M}[\alpha\times MSE(i)+\beta\times(1-SSIM(I))] \tag{3}\]  
\[MSE_{i}=\sum_{p=1}^{N}||Y_{y}(p)-Y_{x}(p)||^{2} \tag{4}\]  
\[SSIM_{i}=\frac{2\mu_{x}(p)\mu_{y}(p)+c_{1}}{\mu_{x}^{2}(p)+\mu_{y}^{2}(p)+c_{1} }\times\frac{2\sigma_{xy}(p)+c_{2}}{\sigma_{x}^{2}(p)+\sigma_{y}^{2}(p)+c_{2}} \tag{5}\]  
Among them, \(M\) represents the total number of training image pairs, \(MSE_{i}\) and \(SSIM_{i}\) represent the mean square error and structural similarity between the ith reconstructed image and the reference image, respectively. \(N\) represents the total number of pixels of the image contained in each image. \(Y_{y}(p)\) and \(Y_{x}(p)\) represent the gray value of the reconstructed image and the reference image at the Pth pixel, respectively. The mean \(\mu_{x}(p)\), \(\mu_{y}(p)\), variance \(\sigma_{x}(p)\), \(\sigma_{y}(p)\), and covariance \(\sigma_{xy}(p)\) in (2) can be obtained by a Gaussian filter with standard variance \(\sigma_{G}(p)\). Its formula is:  
\[\mu_{x}(p)=G_{vG}*P_{x} \tag{6}\]  
\[\sigma_{x}^{2}(p)=G_{vG}*P_{x}^{2}-\mu_{x}^{2}(p) \tag{7}\]  
\[\sigma_{xy}(p)=G_{vG}*(P_{x}\times P_{y})-\mu_{x}(p)\times\mu_{y}(p) \tag{8}\]  
Among them, \(P\) represents the center point pixel of the image block \(P_{x}\). '\(*\)' stands for convolution operation. The calculation process of \(\mu_{y}(p)\) and \(\sigma_{y}^{2}(p)\) is similar to that of \(\mu_{x}(p)\) and \(\sigma_{y}^{2}(p)\). The images acquired at night with subsequent U-Net processing are given in Figure 4,b. It can be seen that the clarity of the image has improved.
## 6 Conclusions  
This paper proposes FP-Deeplab, a novel face parsing network designed to address feature detail loss, boundary blurring, and inadequate context modeling in face semantic segmentation. Built upon the Deeplabv3+ framework, FP-Deeplab integrates two specially designed modules: the Context-Channel Refine Feature Enhancement (CCR-FE) module, which improves multi-scale contextual representation, and the Self-Modulation Attention Feature Integration with Regularization (SimFA) module, which enhances semantic restoration and edge detail preservation during upsampling. Extensive experiments conducted on challenging benchmarks demonstrate that FP-Deeplab achieves competitive or superior performance compared to several state-of-the-art methods, particularly in the segmentation of fine-grained structures and long-tailed categories. Notably, even in the absence of large-scale pretraining (e.g., unlike FaRL), FP-Deeplab maintains robust segmentation accuracy and semantic consistency. Ablation studies confirm the critical role of the CCR-FE and SimFA modules, and backbone comparisons show that the observed improvements are primarily due to the proposed architectural innovations rather than the backbone itself. These findings validate the model's effectiveness and generalizability across complex facial structures.  
In future work, we plan to investigate cross-modal information fusion and adaptive feature guidance mechanisms to further enhance the model's robustness, scalability, and practical deployment in real-world face analysis scenarios.  
Conceptualization, B.Z. and C.S.; Methodology, B.Z., C.S. and Z.L. ([PERSON]); Software, B.Z., C.S. and Z.L. ([PERSON]); Validation, B.Z.; Formal Analysis, J.Y. and Z.L. ([PERSON]); Investigation, X.C.; Writing--Original Draft Preparation, B.Z.; Writing--Review & Editing, B.Z. and C.S.; Visualization, B.Z. and J.Y.; Resources, B.Z. and X.C.; Supervision, X.C.; Project Administration, B.Z.; Funding Acquisition, X.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All code and datasets used in this study are publicly available at [[https://github.com/borry30/FP-Deeplab](https://github.com/borry30/FP-Deeplab)]([https://github.com/borry30/FP-Deeplab](https://github.com/borry30/FP-Deeplab)) (accessed on 26 April 2025).  
We would like to express our sincere gratitude to [PERSON] for her valuable guidance during the course of this study. We also appreciate the open-source community for providing publicly available datasets and tools that supported the development and evaluation of our work.  
The authors declare no conflicts of interest.