esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Evaluation  
To quantitatively assess the effectiveness of the proposed augmentation framework, we adopted a series of evaluation metrics and comparative strategies centered on model generalization, classification accuracy, and inter-class discriminability.  
The primary evaluation metric is top-\(1\) classification accuracy on a held-out test set, ensuring comparability across models and datasets. All models were trained under identical settings with and without augmentation to isolate the contribution of synthetic data. For further validation, we utilized confusion matrix analysis to visualize class-level performance and to identify reductions in misclassification errors, particularly for semantically or visually similar gestures.  
In addition, we computed relative accuracy gains across multiple architectures and training budgets to evaluate consistency and scalability. The inclusion of temporal models (e.g., TCN) and attention-based architectures (e.g., Transformer, Informer) provides a broad perspective on how different inductive biases interact with the augmented data.  
This evaluation protocol enables us to not only confirm the overall effectiveness of the augmentation strategy, but also to uncover architecture-specific benefits and gesture-specific improvements. These findings are further analyzed in the subsequent discussion section.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Original Data Accuracy** & **Augmented Data Accuracy** \\ \hline CNN3D & 85\% & 90\% \\ TCN & 83\% & 89\% \\ Transformer & 90\% & 92\% \\ Informer & 91\% & 94\% \\ Sparse Transformer & 89\% & 92\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test set classification accuracy (%) for all models trained on original vs. augmented datasets.  
Figure 9: Comparison of classification performance between models trained on raw and IK-augmented gesture data. (**a**) Confusion matrix of the model trained on raw data, where several classes exhibit misclassification. (**b**) Confusion matrix of the model trained on IK-augmented data, illustrates improved class separability with augmented data, showing stronger diagonal dominance and reduced inter-class confusion.
# Controlled Drainage Effectiveness in Reducing Nutrient Outflow in Light of Climate Changes
## 2 Materials and Methods
### Climate Data  
The daily maximum and minimum air temperature, as well as precipitation data, were obtained from the ISIMIP Repository for this study. A selection of three global climate models (GCMs) (Table 3) was made for a one-time horizon to the near future (2021-2050). The future climate projections were based on the SSP370 scenario for the Poznan meteorological station (45 km from the field) [37]. This particular scenario, known as \"regional rivalry\", assumes an expected radiative forcing level of 7 W m\({}^{-2}\) by 2100, with a focus on regional dynamics, while global issues are given less priority [38]. The data used in this study were bias-corrected at a resolution of 0.5\({}^{\circ}\) grid cell [37]. The average values of these parameters were obtained from data from grid cells around Poznan and then compared to the simulated values of the historical period (1971-2000) and the observed values from the Institute of Meteorology and Water Management-National Research Institute (IMGW-PIB).  
Figure 2 shows the temporal development of variables, describing the climate conditions, especially historical precipitation during the years 1971-2000, which were used to choose models for the simulations. The average annual precipitation recorded for the Poznan meteorological station from the analyzed multi-year period is 507 mm. For the selected GFDL, MPI, and UKESM models, it is 582, 599, and 583 mm, respectively.  
Figure 3 shows the temporal variation of precipitation in the near future of 2021-2050 for the SSP370 scenario for Poznan. The annual mean value of precipitation in the used future scenario is 651, 626, and 620 mm for the GFDL, MPI, and UKESM models, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Analyses**} & \multirow{2}{*}{**Methodology**} & \multirow{2}{*}{**ISO Standard Method**} & \multicolumn{2}{c}{**Concentration [mg/L]**} \\ \cline{3-4}  & & & **Average** & **SD** \\ \hline Nitrate nitrogen (NO\({}_{3}\)–N) & spectrophotometric method & PN-EN 26777:1999 [35] & 42.33 & 17.33 \\ Orthophosphate (PO\({}_{4}\)) & spectrophotometric method & PN-EN 1189:2000 [36] & 1.66 & 1.19 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Nutrient concentrations determined in drainage water samples from the facility.  
Figure 2: Annual mean precipitation for the Poznan meteorological station and historical projected data are presented for GFDl, MPI, and UKESM climate change models for 1971–2000.
# Reliability Enhancement Driven by ANN for Lighting Control System in Highway Tunnels
###### Abstract
The task of tunnel lighting is to constantly provide the same level of safety and comfort for road traffic in the tunnels as on open roads. However, it is both uneconomical and technically difficult to keep the tunnel lighting environment close to the natural light outside the tunnel. Subject to the fact that the natural light conditions cannot be changed, it is necessary to construct good artificial lighting by technical means to realize the harmony and unity of natural lighting conditions with artificial lighting conditions, so as to achieve the smooth transition in to and out of tunnel light environments to ensure that road conditions ahead are visible to drivers, and that driving safety and comfort are maintained. In the meantime, tunnel lighting systems should supply drivers with complete visual comfort and avoid hidden dangers caused by inadequate visual information at every stage of the journey. Here, these stages are classified into different lighting zones according to drivers' visual needs, which consist of access, threshold, transition, interior and exit zones [5].
## 5 Experimental Results
### Experimental Platform  
The experimental system combines multi-objective optimization scheduling algorithms with 3D visualization to display pipeline and task status in real time via the HMI, supporting dynamic adjustments and resource optimization (see Figure 9a). The 3D model operation and control were developed using C# programming language, combined with an interactive HMI. Operators can rotate the model, respond to button commands, and simulate valve operations to realistically replicate the actual edible oil pipeline system's operational scenarios. As shown in Figure 9b, the HMI of the decision-making system includes sections for \"New Tasks\", \"Pending Tasks\", \"Active Tasks\", and \"Parameter Information\". It supports task input, list management, real-time status display, and system notifications for operations and task updates.
## WSNs Data Acquisition by Combining Hierarchical Routing Method and Compressive Sensing  
**[PERSON] 1,2,3,*, [PERSON] 1,+, [PERSON] 1,+, [PERSON] 1 and [PERSON] 1,2,3**  
* Nanjing University of Posts and Telecommunications, Nanjing 210003, China; E-Mails: [EMAIL_ADDRESS] (C.H.); [EMAIL_ADDRESS] (F.Z.); [EMAIL_ADDRESS] (H.Z.); [EMAIL_ADDRESS] (S.S.)
* Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210003, China
* Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA
* These authors contributed equally to this work.
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +608-422-9099; Fax: +86-25-8586-6433.  
_Received: 8 June 2014; in revised form: 22 August 2014 / Accepted: 26 August 2014 / Published: 9 September 2014_  
**Abstract:** We address the problem of data acquisition in large distributed wireless sensor networks (WSNs). We propose a method for data acquisition using the hierarchical routing method and compressive sensing for WSNs. Only a few samples are needed to recover the original signal with high probability since sparse representation technology is exploited to capture the similarities and differences of the original signal. To collect samples effectively in WSNs, a framework for the use of the hierarchical routing method and compressive sensing is proposed, using a randomized rotation of cluster-heads to evenly distribute the energy load among the sensors in the network. Furthermore, \(LI\)-minimization and Bayesian compressed sensing are used to approximate the recovery of the original signal from the smaller number of samples with a lower signal reconstruction error. We also give an extensive validation regarding coherence, compression rate, and lifetime, based on an analysis of the theory and experiments in the environment with real world signals. The results show that our solution is effective in a large distributed network, especially for energy constrained WSNs.  
**Keywords:** compressive sensing; wireless sensor networks; sparse representation; hierarchical routing method; energy efficiency
# Prototype Network for Few-Shot Hazard Assessment of Vehicle Lane Changes in Risk Field
## 3 Risk Level Assessment of Vehicle Lane Change Based on Field Strength Theory  
The driving risk field is a mathematical model analogous to a \"physical field\", and it quantitatively describes the driving risk of a vehicle based on the dynamic traffic information of the vehicle's surroundings. This research originated from the trajectory-planning method of mobile robots based on the concept of an artificial potential field proposed by [PERSON] [37]. Since then, the concept of a \"field\" has been widely concerned with the control and decision-making applications of mobile robots [38; 39; 40; 41]. When a vehicle is in a driving state, the driver usually keeps a certain driving distance from the surrounding traffic participants. When the workshop distance is less than the driving safety threshold, the driver will take such measures such as braking deceleration or emergency obstacle avoidance to avoid collision with traffic participants. This is analogous to considering the presence of this virtual \"field\" in the traffic environment, just as particles with the same charge repel each other in the electric field.  
As an effective means of traffic safety assessment, the field of traffic risk has the advantages of strong real-time performance and abundant traffic information, which can be used to describe, make decisions with, and control comprehensive traffic risks in complex scenarios. On the basis of identifying the vehicle lane-change risk, this paper describes the impact of each factor on traffic risk from the perspective of field strength. Moreover, the key factors of lane-change risk are studied and analyzed from the perspective of field strength.
# Enhancing Kazakh Sign Language Recognition with BiLSTM Using YOLO Keypoints and Optical Flow
## 4 Proposed Method  
In this project, a system for recognizing Kazakh Sign Language was developed that combines YOLOv8-Pose, Bi-LSTM, and the optical flow algorithm--Farneback. The main goal of the system is accurate and reliable gesture recognition using keypoints and dynamic characteristics of movements. Recent studies, such as [20], have demonstrated the effectiveness of deep learning approaches for sign language recognition. Their work focuses on translating Saudi Sign Language using neural networks, confirming the relevance of keypoint-based and sequence-processing methods. Inspired by such approaches, our system integrates YOLO-based keypoint detection with temporal modeling using Bi-LSTM to enhance dynamic gesture recognition.  
The method is based on a combination of keypoint detection and optical flow analysis, followed by processing of sequences using a recurrent neural network. At the first stage, a video stream with gestures is fed to a detector (YOLO) based on a convolutional neural network, which extracts bounding boxes and keypoints of hands. Then, the optical flow around the keypoints is calculated, which allows the pixel shift between frames to be fixed. After that, the obtained features, including the coordinates of the keypoints and their dynamics, are transferred to bidirectional long short-term memory, which processes temporal dependencies and forms the final gesture prediction.  
Thus, the method combines spatial and temporal features for more accurate gesture recognition (Figure 4).
# Passive Location for 5G OFDM Radiation Sources Based on Virtual Synthetic Aperture
## 2 Theoretical Analyses of Typical Source Localization Methods
### Time Difference of Arrival (TDOA)  
TDOA as a relatively mature localization technology has been applied to various radio navigation systems. The principle is that there are spatially separated base stations and by comparing the time difference of the signal arrival to each base station, a hyperbola can be made with the base station as the focus and the distance difference as the long axis, thus the intersection point of the hyperbola is the location of the signal source. The TDOA system requires high synchronization between each base station [16].  
Suppose there are \(M\) base stations (satellites) to receive the same source signal, and the location of the station \(i\) is \(\mathbf{p}_{i}=(x_{i},y_{i})^{T}\), the signal source location is \(\mathbf{p}_{o}=(x,y)^{T}\). The received signal of the base station \(i\) is  
\[u_{i}(t)=s(t-d_{i})+v_{i}(t),\ i=1,2,\cdots,M \tag{2}\]  
where \(s(t)\) is the source signal, \(d_{i}\) is the time delay of source signal arrival at \(i\)th base station, \(v_{i}(t)\) is the additive Gaussian white noise, and the signal and noise are assumed to be independent.  
With the 1 st base station as the reference base station, the time difference between the source signal arrival at other base stations and the reference base station is  
\[\begin{split} d_{i1}=d_{i}-d_{1},\ i=2,3,\cdots,M\\ \mathbf{d}=[d_{21},d_{31},\cdots,d_{M1}]^{T}\in\mathbf{R}^{(M-1)\times 1} \end{split} \tag{3}\]  
Then, set out the system of equations using time delay \(\mathbf{d}\) and the geometric relation between base stations and target source. We can solve the system of equations by the least square iterative algorithm (e.g., Taylor) or obtain the analytical solution by linearization method (e.g., Chan, Fang) [27].  
TDOA has higher location accuracy, but it is only suitable for the wideband signals and has high requirement for time synchronization.
## 6 Limitations and Future Directions
### Potential of Retrieval-Augmented Generation (RAG)  
Retrieval-augmented generation (RAG) offers a promising approach to address these challenges by enhancing language models with real-time access to external knowledge sources. A RAG pipeline combines a retrieval system with a generative language model, allowing it to dynamically fetch relevant information from structured knowledge bases, such as ICD taxonomies, and integrate it into its outputs.  
In the context of SQL query generation, an RAG workflow would begin by analyzing the user's natural language query to identify key terms requiring resolution. The retrieval system then queries an external medical terminology database, fetching the appropriate ICD codes or related taxonomy entries. This retrieved information would inform the generation process, ensuring accurate alignment between the user's intent and the SQL schema.  
This approach has several advantages. Grounding outputs in real-time knowledge retrieval reduces errors caused by the limitations of pre-trained knowledge. It also enables dynamic adaptation to updates in ICD taxonomies, ensuring consistent query accuracy over time. In addition, RAG systems can help resolve ambiguous queries by using contextual information to select the most relevant terms.
###### Abstract  
Background: Toll-like receptors (TLRs) and nucleotide-binding oligomerization domain (NOD)-like receptors (NLRs) are innate, damage-associated molecular patterns (DAMP) sensors. Their expressions in human periodontal resident cells and reactions toward irritations, such as hypoxia and lipopolysaccharide-backaride (LPS), remain not well characterized. This cross-sectional study aimed to investigate and characterize TLRs, NOD1/2 and NLRP1/2 expressions at the dentong-gingival junction. Methods: Immunohistochemistry screening was carried out on periodontal tissue biopsies sections, while selected DAMP sensors signal and protein expression under _Escherichia coli_ LPS (2 ug/mL) and/or hypoxia (1% O\({}_{2}\)), 24 h, by human gingival keratinocytes (HGK) or fibroblasts (HGF) were investigated. Results: Positive TLR1/2/4/5/6, NOD1/2 and NLRP1/2 immunostaining were observed in healthy and periodontitis biopsies with apparently more pocket epithelial cells positive for TLR2, TLR4 and NOD1/2. TLR1-6, NOD1/2 and NLRP1/2 messengers were detected in gingival/periodontal biopsies as well as healthy HGK and HGF explants. LPS and/or hypoxia induced signals and protein upregulation of NOD2 in HGKs or TLR1/6 and NOD2 in HGFs. Conclusion: Transcripts and proteins of TLR1/2/4/5/6, NOD1/2 and NLRP1/2 were expressed in human periodontal tissue in health and disease. Putting all observations together, NOD2, perhaps with TLR1/2/4/6, might be considered key, damage-associated molecular pattern sensors on periodontal resident cells.  
Escherichia coli; gingiva; hypoxia; lipopolysaccharide-backaride; NLR proteins; periodontitis; toll-like receptors +
Footnote †: journal: Applied Sciences
Further applications of blind signatures may emerge together with the introduction of the European Digital Identity Wallet (EDIW) according to eIDAS 2.0 regulation [7]. (In fact, the consensus of the cryptographic community is that anonymous credentials protocols should be used to implement the EDIW [8], while, as we mentioned earlier, blind signatures are often their main technical component). The eIDAS 2.0 regulation aims to provide space for certificates of attributes and identification/authentication schemes that reveal only the information that is strictly required for a given purpose. For example, an EDIW should be able to prove that its holder is of legal age, e.g., to let them purchase alcoholic beverages. At the same time, neither the seller nor the issuer of the EDIW should be able to learn the buyer's identity according to the data-minimization security paradigm. Attribute certificates issued with blind-signature protocols may solve this problem of privacy-preservingprotecting age verification. A similar situation may involve certain sensitive healthcare services, where the health insurance company should be able to check the eligibility of a medical transaction but not necessarily the patient's identity.  
In many countries, there are strict rules regarding privacy protection. The GDPR (General Data Protection Regulation) [9] of the European Union states that all information systems must implement privacy protection _by design_ and _by default_. Moreover, implementing these principles must be demonstrable (_accountability_ principle). Thus, deploying a well-researched technical solution like a blind-signature scheme may be a pragmatic option where demonstrating compliance with the GDPR rules is cheap, easy, and non-disputable.  
Sometimes, the situation enforces rapid progress in privacy protection on a global scale, as it was the case with contact-tracing systems during the COVID-19 pandemic. At this time, the need arose to create a method of contact tracing to help identify potentially infected people and alert them before they infect more people. Phone apps notifying users of exposure have been created to address that problem. Of course, such a system had to be developed in a privacy-preserving manner, as information about who interacts with whom can be critical for personal and public security. This privacy-by-design approach was the key feature of the exposure notification privacy-preserving analytics system [10], developed jointly by Apple and Google. As long as users' smartphones follow the protocol, the users do not have to worry that the generated data will be misused, say, by criminals. This was the key acceptance condition for this technology in countries like Germany, where residents care a lot about their privacy.
###### Abstract  
In this work, the development of a _software-in-loop_ platform to carry out Underwater Wireless Sensor Network (UWSN) simulations using a real-time STNAAG 5066 stack is presented. The used protocol stack is part of a real-world implementation of an underwater wireless node based on ElectroMagnetic (EM) Underwater Radio Frequency Communication (EM-URFC), framed within Spanish Government's project HERAKLES. The main objective of this work was to assess the suitability of this _software-in-loop_ approach for carrying out realistic UWSN simulations. In addition to a detailed description of the simulation process, several simulations considering an illustrative network topology are performed, analyzing the impact of different critical parameters on the network performance. The conclusions suggest that the developed _software-in-loop_ platform is suitable to carry out UWSN network tests using a real-world implementation of the STNAAG 5066 stack. Moreover, other real-time protocol stacks may be easily adapted with minor modifications.  
electromagnetic underwater wireless sensor network; underwater communications; STNAAG 5066; _software-in-loop_
## 1 Introduction
### Motivation  
The main motivation of this work is to develop a novel technique that is able to help law enforcement agencies (LEA) to conduct forensics in mobile devices that have security protections enabled, since these protections prevent LEA to properly collect evidence and analyze them in case of crimes.  
It is important to remember that criminals all over the world use communication devices such as mobile phones with security measures applied to them. Such mechanisms make it very hard to forensics specialists to bypass them and acquire evidence that helps in the solution of criminal cases.
## 3 Numerical Simulation of Warm Forging-Finishing of Spiral Bevel Gear
### Simulation and Result Analysis of Warm-Forging-Finishing of Spiral Bevel  
#### 3.3.1 Influence of Forging Striking Speed on Forging Formation  
Under the condition of not changing other process parameters, three different forging striking speeds, namely 10 mm/s, 20 mm/s and 30 mm/s, were selected to analyze the effective strain and effective stress at each reference point of the forging workpiece, and the variation curve is shown in Figure 11.  
Figure 10: Distribution of tooth reference points.  
Figure 9: Effective strain field quantities.  
From Figure 11, it can be seen that, from the overall trend of the curve, the faster the forging striking speed is, the greater the effective strain at the reference point is and the smaller the effective stress is. Seen from the point of view of the gear as a whole, the effective strain and the effective stress of the large end of the spiral bevel gear are obviously larger than those of the middle and the small end of the gear, which is due to the formation of flash at the large end of the gear, so the deformation of the metal material at this part is larger. Because of the existence of the forging flash, the flow of metal material here requires a greater forging impact force, so the force at the root of the tooth is larger than that at other parts. From all parts of the gear, the effective stress and effective strain at the tooth top of the spiral bevel gear forging are greater than those at the tooth root. It should be noted that when the forging speed is set at 30 mm/s, the effective strain and effective stress at the tooth top of the forging have a large mutation.  
In a word, the higher the forging striking speed is, the smaller the effective force the forging bears in the forging process and the faster the deformation speed is, which is conducive to the full deformation of the forging and can also improve the stress situation of the forging die. Therefore, a relatively higher forging blow speed can be selected in the actual production.