esa-sceva/satcom-chunk-collection huggingface dataset:
# The Role of Extracellular Matrix (ECM) Adhesion Motifs in Functionalised Hydrogels
### Hydrogels as Sophisticated, Biomimetic Materials  
Hydrogels are three-dimensional (3D), crosslinked, fibre networks composed of a water soluble polymer [4]. They have desirable physiochemical properties which contribute to their many biomedical applications, such as drug delivery, implant coatings and tissue engineering [5]. Hydrogels are formed through physical or chemical crosslinking of 3D fibre networks [6]. Physical crosslinking connects fibres through hydrogen bonding, electrostatic interactions, or hydrophobic interactions to form secondary alpha (\(\alpha\)) helical or beta (\(\beta\)) sheet structures [7]. Chemically crosslinked hydrogels contain covalent bonds induced by polymerisation reactions such as enzyme polymerisation and photo-polymerisation [7]. Important features of hydrogels include their biocompatibility, biomimetic nature towards the ECM and high water content [8]. Mechanical properties such as mesh size and swelling, along with degradation rate, play important roles in biomaterial stability [9].  
Hydrogels are excellent cell culture matrices due to their ability to provide a 3D culture environment rather than a 2D environment, enhancing their ability to accurately mimic the ECM [10]. They can be prepared from ECM materials such as collagen or fibronectin to produce hydrogels with enhanced biocompatibility [10]. Additionally, the physical properties of hydrogels themselves can influence how the material resembles the ECM. For instance, mesh size and molecular porosity control the passage of nutrients and waste through the hydrogel matrix, resembling how these components travel through the native ECM [9]. Crosslinked hydrogels can result in biomimetic properties which resemble that of soft tissues, such as viscoelasticity and interstitial flow [11]. Hydrogels also contain large volumes of water, much like the ECM, and are often described as water-swollen polymer networks [12]. The hydrophilic nature of hydrogels is associated with the presence of polymeric backbone motifs containing carboxyl, amine and hydroxyl groups [13].
## 4 Methodology  
### Direct Problem Solution Method  
The dimensionless model, consisting of Equations (10)-(16), was addressed using two distinct methods. Firstly, the Method of Lines (MOL) was employed, which entails converting the partial differential equation (PDE) into a set of ordinary differential equations (ODEs) (refer to Supplementary Materials). These ODEs were then numerically solved using the ode15s subroutine within a computational code developed in Matlab (R2022a). Secondly, the pdepe subroutine of the Matlab program was utilized, enabling the PDE to be directly solved without the requirement for prior reduction to a system of ODEs.
# Testing Multi-Frequency Low-Cost GNSS Receivers for Geodetic Monitoring Purposes
###### Abstract  
Global Navigation Satellite System (GNSS) technology is widely used for geodetic monitoring purposes. However, in cases where a higher risk of receiver damage is expected, geodetic GNSS receivers may be considered too expensive to be used. As an alternative, low-cost GNSS receivers that are cheap, light, and prove to be of adequate quality over short baselines, are considered. The main goal of this research is to evaluate the positional precision of a multi-frequency low-cost instrument, namely, ZED-F9P with u-blox ANN-MB-00 antenna, and to investigate its potential for displacement detection. We determined the positional precision within static survey, and the displacement detection within dynamic survey. In both cases, two baselines were set, with the same rover point equipped with a low-cost GNSS instrument. The base point of the first baseline was observed with a geodetic GNSS instrument, whereas the second baseline was observed with a low-cost GNSS instrument. The results from static survey for both baselines showed comparable results for horizontal components; the precision was on a level of 2 mm or better. For the height component, the results show a better performance of low-cost instruments. This may be a consequence of unknown antenna calibration parameters for low-cost GNSS antenna, while statistically significant coordinates of rover points were obtained from both baselines. The difference was again more significant in the height component. For the displacement detection, a device was used that imposes controlled movements with sub-millimeter accuracy. Results, obtained on a basis of 30-min sessions, show that low-cost GNSS instruments can detect displacements from 10 mm upwards with a high level of reliability. On the other hand, low-cost instruments performed slightly worse as far as accuracy is concerned.  
monitoring; low-cost instrumentation; displacements; GNSS +
Footnote †: journal: Sensors
# Autonomous Navigation of Robots: Optimization with DQN
## 4 Study Case  
One of the primary tasks commonly performed by mobile robots involves obstacle avoidance, where optimization criteria such as minimizing cost and traversing the shortest distance within a limited timeframe are considered. Traditionally, this process required continuous monitoring and intervention from operators. However, advancements in technology have led to the automation of this process in recent years.  
This study focuses on the development of a control system based on machine learning for the Turtlebot3 Burger mobile robot. Specifically, it aims to simulate the training process for trajectory planning, which includes obstacle avoidance and collision prevention. As depicted in Figure 5, the proposed architecture integrates multiplatform systems such as ROS, enabling seamless message transmission among different components of the system. Additionally, the Gazebo simulator and the Deep Q learning algorithm are employed to facilitate autonomous learning through reinforcement.  
The DQN agent selects the action based on the maximum Q-value, and this is reflected in the environment. Then, the obtained reward is stored in the replay memory, which is used because the network should not be trained with the current value; instead, this information must be stored in a buffer that allows for the sampling of the batches of experience, either random or prioritized, so that the network can readjust itself. Finally, after there are enough examples in memory, the agent is updated randomly, thanks to  
Figure 4: Node communication model in ROS.  
Figure 5: Study case architecture.  
the previously performed sampling. ROS is used to launch the simulation and the DQN node, which allows for easy communication based on the publication and subscription of messages through topics, where the observed state \(S\), the subsequent state \(S^{\prime}\), the reward \(R\) and the action \(A\) are transmitted, which are part of the batch for the reinforcement learning algorithm.  
The objective of this work is to use machine learning in a mobile robot within a simulated environment to solve mobile and fixed obstacle avoidance through a multiplatform and open-source system. The control algorithm is developed as a node and includes some layers within its programming as a neural network, which allows it to obtain the state from the environment, calculate the reward, and establish an action based on the stored results.
# A Small-Sample Text Classification Model Based on Pseudo-Label Fusion Clustering Algorithm
## 2 Related Work
### Pseudo-Label Method  
The pseudo-labeling approach [28] can increase the size of the training dataset and improve the model performance by labeling unlabeled data as labeled data. t-sne visualization of embedding in the paper of [PERSON] et al. [29] clearly demonstrates the effect of the pseudo-label method. It is mentioned in that paper that using only pseudo-label methods may be more efficient compared to the semantic drift that most data augmentation methods introduce. [PERSON] et al. [30] used uncertainty-aware pseudo-label selection (UPS) combined with uncertainty estimation and negative learning to reduce the model's noise effectively, but this method relies heavily on the predictive power of the original model and does not utilize a large amount of unlabeled data, and there is an upper limit to the prediction accuracy. When using pseudo-labeling for training, some noisy data may be misclassified, leading to a degradation of model performance, and pseudo-labeling is an unsupervised method that cannot solve the problem of label noise.  
In conclusion, pseudo-labeling is an effective method to improve model performance when the dataset size is small, and there is insufficient labeled data. However, it is necessary to choose the appropriate model and parameters according to the actual situation to avoid introducing noisy data and to note the possible errors caused by pseudo-labeling.
## 4 Simulation Results
### Applications of KC200 GT PV Module  
#### 4.2.1 Case 1: SD Model of KC200 GT PV Module  
In this instance, the recommended MGA is used to extract the KC200 GT PV module's SD model features. The five unknown SD model parameters for various inspirational approaches that produced the greatest results in the experiment are listed in Table 4. The findings demonstrate that, in terms of competitiveness, the suggested MGA performs better than the comparable methods. This indicates that the optimal RMSE value of  
Figure 8: Overall discrepancies in currents and powers between the measured and simulated for the DD model of the R.T.C. France cell using MGA.  
Figure 7: (**a**) I-V and (**b**) P–V characteristics for the DD of the R.T.C. France cell using the suggested MGA.  
\(7.548013\times 10^{-3}\) is achieved by the suggested MGA. Furthermore, the table shows the PV-derived electrical parameters using the established optimization techniques; these include the Growth optimizer (GO) [56], energy valley optimizer (EVO) [56], and Hazelnut tree search (HTS) algorithm [56].  
Figure 9a,b display the measured and simulated I-V and P-V characteristics for the SD model. It is observable that the data produced by the suggested MGA technique closely resemble the data acquired by experimentation, suggesting that the MGA technique was successful in producing power and current at a range of voltage levels. The simulation and measured current absolute errors range from \(1.26882\times 10^{-8}\) to \(5.61013\times 10^{-5}\), while the simulation and measured power absolute errors range from 0 to \(2.0897\times 10^{-1}\), as shown in Figure 10a,b.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Applied Technique** & **HTS** & **EVO** & **GO** & **MGA** \\ \hline \(I_{\text{P}\text{H}}\) (A) & 8.190356368 & 8.214036069 & 8.192967279 & 8.206335844 \\ \hline \(R_{\text{sh}}\) (\(\Omega\)) & 53.11148143 & 13.69688319 & 15.10392113 & 9.197019679 \\ \hline \(R_{\text{S}}\) (\(\Omega\)) & 0.004417871 & 0.00428964 & 0.004709892 & 0.004659152 \\ \hline \(I_{\text{S1}}\) (A) & 1.12138 \(\times\)\(10^{-7}\) & 1.42038 \(\times\)\(10^{-7}\) & 4.31808 \(\times\)\(10^{-8}\) & 4.81328 \(\times\)\(10^{-8}\) \\ \hline \(\eta_{1}\) & 1.309576425 & 1.326768155 & 1.244345573 & 1.251560118 \\ \hline RMSE & 0.01799763 & 0.023069893 & 0.008515347 & 0.007548013 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Acquired parameters for the KC200 GT PV module (the SD model) using MGA compared to others.  
Figure 10: _Cont._  
Figure 9: (a) I–V and (b) P–V characteristics for the SD of the KC200 GT PV module using the suggested MGA.
## 3 Method Proposal to Analyze AST n-Tools Combinations
### Metrics Selection  
The selected metrics are widely accepted in others works [8; 9; 10; 28; 73; 76] and in the work of [PERSON] and [PERSON] [77] that analyzes distinct metrics for different levels of the criticality of web applications. The metrics used in this methodology are:  
* Precision (1). Proportion of the total TP detections: \[\mathit{TP}\ /\ (\mathit{TP}+\mathit{FP})\] (1) where \(\mathit{TP}\) (true positives) is the number of true vulnerabilities detected in the code and \(\mathit{FP}\) (false positives) is the number of vulnerabilities detected that, really, do not exist.
* True positive rate/Recall (TPR) (2). Ratio of detected vulnerabilities to the number that really exists in the code: \[\mathit{TP}\ /\ (\mathit{TP}+\mathit{FN})\] (2)where \(TP\) (true positives) is the number of true vulnerabilities detected in the code and \(FN\) (false negatives) is the total number of existing vulnerabilities not detected in the code.
* False positive rate (FPR) (3). Ratio of false alarms for vulnerabilities that do not really exists in the code: \[TN\ /\ (TN+FP)\] (3) where \(TN\) (true negatives) is the number of not detected vulnerabilities that do not exist in the code and \(FP\) (false positives) is the total number of detected vulnerabilities that do not exist in the code.
* F-measure (4) is harmonic mean of precision and recall: \[\frac{(2\ x\ \text{precision}\ x\ \text{recall})}{(\text{precision}+\text{recall})}\] (4)
* F\({}_{\beta}\)-Score (5) is a particular F-measure metric for giving more weight to recall or precision. For example, a value for \(\beta\) of 0.5 gives more importance to precision metric, however a value or 1.5 gives more relevance to recall precision: \[\left(1+\ \beta^{2}\right)x\ \frac{\text{precision}\ x\ \text{recall}}{(\left( \beta^{2}\ x\ \text{precision}\right)+\text{recall})}\] (5)
* Markedness (6) has as a goal to penalize the ratio of true positives considering the ratio of false positives and the ratio of false negatives. \[\frac{TP*TN-FP*FN}{\sqrt{(TP+FP)*(TP+FN)*(TN+FP)*(TN+FN)}}\] (6)
* Informedness (7) computes all positive and negative cases found are reported, and for each true positive the result is increased in a ratio of 1/P while if not detected it is decreased in a ratio of 1/N, where P is the number of positives (test cases with vulnerability) and N is the number on Negatives (test cases with no vulnerability). \[\frac{TP}{TP+FP}+\frac{TN}{FN+TN}-1=\frac{TP}{TP+FP}-\frac{FN}{FN+TN}\] (7)
## 6 Conclusions  
In conclusion, this study demonstrates the substantial impact of modifying autoencoder architectures and training methods on anomaly detection across various domains. We validated our hypothesis through rigorous experimentation that tailored modifications to latent space and cost functions significantly enhance model performance. The results showed that autoencoders can detect network anomalies with high sensitivity and specificity and exhibit robustness in adequately adjusting normal and anomalous data.  
Our comparative analysis of autoencoder models, including Variational Autoencoders and those with modified cost functions like M-ELBO, revealed that incorporating domain-specific enhancements results in marked improvements in detection accuracy. For instance, the modified M-ELBO function demonstrated its ability to differentiate between normal and anomalous samples, achieving sufficient results for practical application. The internal comparative analysis was the primary focus, ensuring the models met the required metrics without venturing into comparisons with more complex state-of-the-art (SOTA) algorithms. This decision was based on the observation that the achieved metrics were adequate for the intended use case, validating the practical effectiveness of our approach.  
Our findings suggest that with these modifications, autoencoders can be tailored to achieve higher sensitivity and specificity, making them powerful tools not just in network security but also in other fields where anomaly detection is critical, such as healthcare [53; 54; 55; 56; 57], finance [28; 58; 59], and industrial manufacturing [60; 61; 62]. This general applicability underscores the versatility of autoencoders when their architecture and training methods are carefully designed.  
This study aligns with recent advancements, such as EFS-YOLO [47; 63] and MEFPNet, emphasizing lightweight and robust architectures for practical applications in diverse domains. Integrating such approaches could further enhance the adaptability and efficiency of anomaly detection systems.  
This research validates the hypothesis that autoencoders' targeted architectural and training modifications can significantly enhance their anomaly detection capabilities. The results highlight the practical utility of these models, marking a significant contribution to machine learning and its applications in network security and beyond.  
Investigation, T.W. and D.J.; methodology, T.W. and D.J.; supervision, Z.P.; validation, Z.P.; resources T.W. and D.J.; writing--original draft, T.W. All authors have read and agreed to the published version of the manuscript.  
This research work was supported and funded by the Military University of Technology under research project no. UCB/22-747/2024 on Application of artificial intelligence methods to cognitive spectral analysis, satellite communications, watermarking, and deepfake technology.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.
# Fatigue Assessment of Explosive Bolts Considering Vibration of Fixtures
###### Abstract  
In order to comprehensively evaluate the safety and reliability of a missile, this paper gives a process of how to perform fatigue assessment for release devices--such as explosive bolts--considering road roughness during its carrying on different classes of roads. Firstly, displacement power spectral density (PSD) function is used to fit models of road surfaces of Class A, C, and E. Taking the surface models as simulations, multi-body dynamics analyses with code ADAMS are performed to obtain acceleration responses of different points on the missile. Loading these accelerations, the corresponding stress distribution of explosive bolts is achieved with FEM code ABAQUS. These loads are counted by rainflow method. Finally, constant life curves with different survival rates are employed to get the S-N curves at different stress ratios and stress concentration coefficients, and fatigue lives of explosive bolts are assessed based on the stress-life method. Afterward, the effect of pretension force on the vibration load is obtained. This paper provides a fatigue evaluation procedure for release devices, which is helpful to raise the reliability of release devices for both design and service procedures.  
explosive bolt; fatigue assessment; fatigue life; stress-life method Article
# Optical Design for Laser Diode Scanner Headlamp with
## 5 Conclusions  
We proposed an optical scanning system design for a pixelated system of a laser diode-based headlamp with a simple prism added. In the case of mirror scanning with a sinusoidal function, e.g., to implement the Lissajous pattern, the optical power in the edge became larger than that in the center area, which was disadvantageous. When the optical power distribution was formed in this manner, it was difficult to use the maximum optical intensity from the light source because of the inefficient headlamp distribution. To improve this optical power distribution, we proposed an optical scanning system with a simple prism and discussed a design method to implement it. The optical system was optimized assuming that the object of the optical system viewed through the prism shifted in the \(x\)-axis direction by geometric optical calculation. The optimized design of the optical system was verified by placing a PD on the phosphor plane and measuring the optical power. Using the PD, the lighting time in the center or edge can be measured; subsequently, the relative optical power can be calculated. The optical power distributions between the center and edge of the designed system, and the system without a prism were compared. We observed that the optical power at the center of the designed optical scanning system was approximately 5.4 times greater than that at the edge. Through experimental verification, we confirmed that the proposed scanning system had a higher optical power distribution in the center than at the edge, which is meaningful for the implementation of an efficient  
Figure 11: Normalized optical energy measured in designed scanning system with prism and scanning system without prism according to beam spot position at phosphor plane.  
Figure 12: Distributions of light illuminated on phosphor plane by (**a**) designed scanner system and (**b**) scanning system without prism.  
pixelated system for headlamps. Therefore, using the proposed optical scanner, a brighter headlamp can be configured in a simple form because the light intensity distribution can be efficiently configured, unlike the existing pixelated system.  
Conceptualization, H.C.; methodology, H.C., W.-S.L.; validation, W.-S.L., B.S.H.; data curation, W.-S.L.; writing--original draft preparation, H.C.; writing--review and editing, J.L.; project administration, W.-C.K., J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT), grant number NRF-2019R1G1A1009520 and NRF-2019R1F1A1058411.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### Patients and Tissue Specimens  
The research was performed on formalin-fixed paraffin-embedded tissue (FFPE) specimens from 68 patients with PDAC who were operated at the Department of Liver and General Surgery, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun (Poland). Histopathological diagnosis for each tumor sample was confirmed by two independent pathologists, based on the hematoxylin and eosin (HE) staining in the Department of Clinical Pathomorphology, Collegium Medicum in Bydgoszcz. All tumors were reclassified according to the standardized TNM 8 th edition classification of The American Joint Committee on Cancer (AJCC) criteria. To avoid excessive study complexity, the cohort included only adenocarcinomas, and all other histological types were excluded from the series. All clinical data were systematized and analyzed in detail. The study group included 68 patients (34 female, 34 male) with an average age of 63 years (range 43-81). The most common tumor location was the head of the pancreas (\(n\) = 60, 88.24%). In turn, pancreatic body and tail tumors were less frequent (\(n\) = 8, 11.77%). Considering a histological grade, 5 samples were classified as differentiated (G1; 7.35%), 55 as moderately differentiated (G2; 80.88%), and 8 as poorly differentiated (G3; 11.77%) cancer tissue. Regarding pathologic T stage, the study group consisted of 10 T1 (14.71%), 43 T2 (63.24%), 8 T3 (11.77%), 2 T4 (2.94%) and 5 Tx (7.35%) cases. Thirty-six (52.94%) patients were characterized by a positive and 30 (44.12%) by a negative lymph node status (no data for 2 patients). Perineural invasion was present in 42 (61.77%) patients, while vascular invasion was confirmed for 23 (37.10%) patients. Data concerning postsurgical survival were available for 62 patients, and the median follow-up time was 1427 days. The study was carried out using the same tissue specimens as in our previously published study [25], whereby the final follow-up was extended to 6 September 2020. The control tissue (\(n\) = 64) was from 54 of the 68 PDAC patients with the adjacent normal pancreatic tissue and another 10 specimens from normal peritumoral tissue of other PDAC patients. The study protocol has been approved by The Ethics Committee of Nicolaus Copernicus University in Torun, Ludwik Rydygier Collegium Medicum in Bydgoszcz (approval number KB 342/2020).