esa-sceva/satcom-chunk-collection huggingface dataset:
# WAID: A Large-Scale Dataset for Wildlife Detection with Drones
## 5 Discussion
### Research Contributions  
Considering the current landscape of wildlife detection in aerial imagery and aiming to drive advancements in the field of wildlife research, this study contributes to two main aspects. Firstly, the proposed enhancements address the challenge of detecting small objects in aerial imagery using deep learning. The incorporation of channel attention mechanisms aids in better feature extraction for small objects, consequently enhancing the detection of small animals in aerial images. This approach introduces novel insights and solutions to advance the development of wildlife detection algorithms tailored to aerial imagery. Secondly, this research introduces the Wildlife Aerial Image Dataset (WAID), which fills a significant gap in the domain of wildlife monitoring using aerial imagery by offering a multi-category public dataset. This dataset serves as a valuable resource for wildlife research and conservation efforts, providing a benchmark for evaluating and improving wildlife detection algorithms. In combination, these contributions provide a strong foundation for the development of techniques and methodologies focused on wildlife detection in aerial imagery, which in turn can have broader implications for conservation efforts, ecological research, and environmental monitoring.
###### Abstract  
In this study, full-scale fire tests and finite element (FE) analyses are conducted to investigate the fire resistance performance of hollow-core slabs (HCSs) manufactured using the extrusion method. The deflection of the HCS specimens and the temperature distribution in the section according to the fire exposure time are measured and analyzed comprehensively, and the test results are compared with the FE analysis results. In addition, parametric analyses are conducted on 21 cases with the HCS depth, span length, hollow ratio in a section, cover thickness of concrete, and load ratio (i.e., the ratio of the external load to the ultimate load) as variables, based on which the fire resistance performance of the HCS according to each variable is investigated. The analysis results show that the load ratio is a key factor governing the fire resistance behavior of HCSs, whereas the effects of the cover thickness of concrete and the hollow ratio in a section are relatively slight within the range of variables examined in this study.  
hollow-core slab; precast concrete; prestressed concrete; fire resistance performance; nonlinear finite element analysis +
Footnote †: journal: Applied Sciences
###### Abstract  
The rapid expansion of Edge and Industrial Internet of Things (IIoT) systems has intensified the risk and complexity of cyberattacks. Detecting advanced intrusions in these heterogeneous and high-dimensional environments remains challenging. As the IIoT becomes integral to critical infrastructure, ensuring security is crucial to prevent disruptions and data breaches. Traditional IDS approaches often fall short against evolving threats, highlighting the need for intelligent and adaptive solutions. While deep learning (DL) offers strong capabilities for pattern recognition, single-model architectures often lack robustness. Thus, hybrid and optimized DL models are increasingly necessary to improve detection performance and address data imbalance and noise. In this study, we propose an optimized hybrid DL framework that combines a transformer, generative adversarial network (GAN), and autoencoder (AE) components, referred to as Transformer-GAN-AE, for robust intrusion detection in Edge and IIoT environments. To enhance the training and convergence of the GAN component, we integrate an improved chimp optimization algorithm (IChoA) for hyperparameter tuning and feature refinement. The proposed method is evaluated using three recent and comprehensive benchmark datasets, WUSTL-IIoT-2021, EdgeIIoTset, and TON_IoT, widely recognized as standard testbeds for IIoT intrusion detection research. Extensive experiments are conducted to assess the model's performance compared to several state-of-the-art techniques, including standard GAN, convolutional neural network (CNN), deep belief network (DBN), time-series transformer (TST), bidirectional encoder representations from transformers (BERT), and extreme gradient boosting (XGBoost). Evaluation metrics include accuracy, recall, AUC, and run time. Results demonstrate that the proposed Transformer-GAN-AE framework outperforms all baseline methods, achieving a best accuracy of 98.92%, along with superior recall and AUC values. The integration of IChoA enhances GAN stability and accelerates training by optimizing hyperparameters. Together with the transformer for temporal feature extraction and the AE for denoising, the hybrid architecture effectively addresses complex, imbalanced intrusion data. The proposed optimized Transformer-GAN-AE model demonstrates high accuracy and robustness, offering a scalable solution for real-world Edge and IIoT intrusion detection.  
Industrial Internet of Things; intrusion detection; transformer; generative adversarial network; autoencoder; chimp optimization algorithm +
Footnote †: journal:  
Article
# A Hybrid CNN-GRU-LSTM Algorithm with SHAP-Based Interpretability for EEG-Based ADHD Diagnosis
## 3 Materials and Methods
### Dataset Collection
The reader can observe a clear difference in heightened delta-band power, particularly in frontal channels Fp1 and Fp2, commonly related to hypoactivation of the prefrontal cortex. Furthermore, alpha activity in ADHD children appears more focused over the temporopaarietal areas (F8, T8, and P7), indicating the potential compensatory activation of secondary cognitive networks. Excessive beta and gamma activity observed in regions such as T7, P4, and O2 may reflect heightened neural variability and difficulty in maintaining attention over long periods. In Figure 3, the radial plots of the control group and ADHD group are compared using graph-theoretical metrics computed on binarized correlation matrices, with values ranging between 0.5 and 8. All the figures show six important measures of the brain functional network topology: average node degree (degree), betweenness centrality (betweenness), closeness centrality (closeness), global efficiency (efficiency), modularity (modularity), and network density (density). In the top-left diagram (threshold = 0.5), both groups exhibit relatively high node degree and network density, indicating the presence of numerous weak but stable connections across brain regions. The control and ADHD groups show similar values for most metrics; however, the control group demonstrates slightly higher global efficiency and density, while the ADHD group shows marginally higher modularity, suggesting more pronounced network clustering. As the threshold increases to 0.6 (top-right diagram), differences between the groups become more evident. The ADHD group experiences a noticeable decline in global efficiency, while modularity increases. This indicates that excluding weaker connections results in a less integrated but more fragmented functional network for the ADHD group, characterized by more distinct modu  
Figure 3: Functional network comparison across thresholds in ADHD and control groups. (bottom-left diagram), both groups show a sharp drop in degree and density, with the effect being more pronounced in the ADHD group. Concurrently, modularity in the ADHD group continues to increase, reaching a significant peak. The combination of decreased global efficiency and increased modularity supports the hypothesis of insufficient integration along with increased local clustering in the brain network associated with ADHD. Lastly, at the top level (0.8, depicted in the bottom-right figure), the networks are very sparse. Only the strongest links are left, with the weaker connections removed predominantly. In the ADHD group, node degree and density both converge towards zero, and modularity is still high, but efficiency reaches near-zero values. In contrast, the control group maintains slightly higher values, reflecting the preservation of a few inter-regional connections and greater resistance to the removal of weak links.
# An InSAR Interference Fringe-Matching Algorithm Based on Mountain Branch Points
###### Abstract  
Interferometric synthetic aperture radar (InSAR) is an integrated navigation technique that can be used for aircraft positioning and attitude retrieval, regardless of weather conditions. The key aspect of the entire system is interference fringe matching, which has not been extensively researched in existing literature. To address this gap, this paper proposes a terrain-feature-based interference fringe-matching algorithm. The proposed algorithm first extracts mountain line features from the interference fringes and identifies mountain branch points as key points for feature matching. A threshold is set to eliminate false detections of mountain branch points caused by phase mutation. Matching is then carried out by combining the mountain line features and curvature design feature descriptors of the area around a branch point. The proposed algorithm is verified using interference fringe data obtained from an actual flight experiment in Inner Mongolia, China, compared to a reference interference fringe dataset with errors. The results show that, under the condition of position error, the proposed algorithm yields 72 matching inliers with a precision of 0.103, a recall of 0.128, and an F1-Score of 0.144. Compared to traditional algorithms, our proposed algorithm significantly improves the problem of mismatch and opens up new possibilities for downstream interference fringe matching navigation technology. Furthermore, the proposed algorithm provides a new approach for remote sensing image matching using terrain features.  
interference fringe; image matching; matching navigation; mountain feature
# Influence of Lubrication Systems on the Fatigue Strength of Bolted Joints
## 2 Materials and Methods  
### Test Material and Lubrications  
In order to evaluate the lubricant influence, a bolt M8x55 in strength class 8.8 with basecoat KL100 was selected as an example, which was combined with a steel nut ISO 4032 M8-10 in the fatigue test (Figure 2).  
Figure 1: Concept—systematical investigation program.  
This application-oriented selected zinc flake coating was provided with different topcoats. The following lubrication systems were considered:  
1. Without lubrication (reference)
2. Silicate sealant with integrated lubricant VH351 GZ
3. Oil, Castrol Rustilo 66 VCI
4. Bonded coating, DF 977S (microGLEIT(r))
5. Bonded coating, LS 855S (microGLEIT(r))
6. Grease, LP 475 (microGLEIT(r))
7. Grease, LP 410 (microGLEIT(r))  
The reference for all further tests was the first variant without lubrication. This silicate sealant with integrated lubricant is the usual combination of basecoat and topcoat for zinc flake coatings. The topcoat represents an additional passive corrosion protection to zinc flake coating and contains dry lubricants, such as polytetrafluoroethylene (PTFE), rigidly integrated into the silicon structure of the binder. The coating thickness was approximately 2 \(\upmu\)m.  
Castrol Rustilo 66 VCI corrosion protection oil is an oxidation-resistant and solvent-free oil that forms a thin lubricating film completely covering the thread. Homogeneous wetting was generated by immersion. The resulting oil film thickness was approximately 2 \(\upmu\)m.  
The selected bonded coating systems contained either white solid lubricants (DF 977S) or metallic solid lubricants such as molybdenum disulfide MoS\({}_{2}\) (LS 855S). The bonded coating DF 977S formed a dry, barely visible dry sliding film and was applied as a spray. The thickness of the sliding film was about 0.5 to 3.0 \(\upmu\)m.  
The bonded coating LS 855S was also applied as a spray and cured at room temperature after evaporation of the solvent. The result was a gray-black bonded coating film with a film thickness of approximately 3 \(\upmu\)m. Compared with bonded coating DF 977S, the solid lubricants were integrated more strongly in the inorganic binder matrix.  
Further lubricant variants were the selected greases LP 475 and LP 410. LP 410 is the classic molybdenum disulfide-containing assembly paste based on a mineral oil and a combination of solid lubricants. The grease is characterized by high pressure resistance and a temperature application range up to 450 \({}^{\circ}\)C. The grease LP 475 is based on a mixture of synthetic oil and solid lubricants. It is used in high-temperature applications up to 1200 \({}^{\circ}\)C. Example applications include turbine bolts, exhaust or heating systems. The greases were applied to the bolt thread by brush before assembly and created an effective separating layer to the nut thread.
# Research Trends in Collaborative Drones
## 3 Countering Drone Threats
### Problem Description  
The problem is threefold:  
1. _Spotting and Following._ When do drones enter a given airspace? Are they friends or adversaries? What tasks are drones specially fitted for? Are they armed? Loaded? What is their kind? How many rotors do they have? Are they small? Micro? Are they equipped with infrared cameras? Are drones autonomous or under the command and control of a human pilot? What are the whereabouts of spotted drones?
2. _Intention determination._ What is the drones' mission? Are they looking around infrastructures? Searching? Are they preparing for a strike? What is their target? Are they  
Figure 2: Distributed architecture to report faulty drones using anonymous ephemeral identifiers. In contrast to the approach depicted in Figure 1, drones exchange lists of ephemeral identities and locally update their status without the necessity of central intermediary services. The main differences with respect to the centralized version are in Steps 2 and 3, which are conducted now in a machine-to-machine mode.  
taking pictures? Shooting videos? What strategies or algorithms are they following to achieve their mission?
3. _Riposte._ How to respond to the presence of adversarial drones? What are the effective countermeasures?  
The swarm dimension, drones acting in a collaborative group to achieve their goal, and the increasing degree of elaborateness of drones compound the problem. The environmental conditions are also challenging, since drones are equipped with sensors of various types and have onboard computing resources that enable embedding advanced algorithms and artificial intelligence, rendering them reactive and adaptable.
## 2 Communication Framework Description
### Protocol Routine
These nodes, equipped with advanced sensors, continuously gather data and remain prepared to transmit this information as per the communication protocol.
# Optoelectronic Instrumentation and Measurement Strategies for Optical Chemical (Bio)Sensing
## 3 Spectroscopic Techniques
### Measurement of Luminescence Lifetimes  
Unfortunately, the direct measurement of luminescence intensity can be affected by a series of fluctuations that are independent of the analyte, which means that this approach must sometimes be replaced by other techniques that provide greater reliability for chemical measurements. One possibility is to measure the lifetime of the luminescent emission. This technique is conditioned by the availability of chemical sensors that undergo a change in the lifetime after an interaction with the analyte This technique is conditioned by the availability of chemical sensors that undergo a change in the lifetime after an interaction with the analyte. An additional limitation is the complexity of the measurement system, particularly for the measurement of very small lifetimes (ns).  
Equation (6) describes only the permanent luminescence regime. If the excitation light is an impulse function, the luminescence decays according to:  
\[I=I_{0}\cdot e^{(-t/\tau(X))} \tag{7}\]  
where \(I_{0}\) is the initial intensity of luminescence (\(t=0\)) and \(\tau\) is the time constant, called the luminescence lifetime. It is possible to use the lifetime as a parameter to identify the concentration of a given substance provided that that substance, although not luminescent, attenuates the emission of the luminescent indicator (luminophore). In this type of sensor, the analyte reacts with the luminescent indicator, attenuating the luminescent emission, as shown in Figure 9 [40].  
Figure 8: Instrumentation setup for an extrinsic optical fiber sensor used for measuring light intensity.  
Figure 9: Dynamic response of a luminescent sensor to a pulse of light.  
This mechanism has been used to measure very low concentrations of oxygen, halides, and various metals [41; 42; 43]. Lifetime data are independent of the luminescent sensor concentrations and changes in excitation light, sensor size, geometry, or other more complex chemical phenomena. The lifetimes of the indicators must be long enough (\(\upmu\)s) so that they can be measured with low-cost instrumentation. An additional complication occurs when the decay times of the luminescence do not fit a monoexponential, whereby more processing will be required to achieve a better fit. Once the lifetimes are known, the concentration of the analyte can be calculated using the well-known Stern-Volmer (SV) equation [36], which linearly relates lifetimes to concentrations:  
\[\frac{\tau_{0}}{\tau}=1+K_{SV}[C] \tag{8}\]  
where \(K_{SV}\) is the \(SV\) constant, \(\tau_{0}\) is the remaining lifetime in the absence of the quencher, and \([C]\) is the analyte concentration. Dynamic attenuation affects the lifetimes of excited states, being very useful for measuring substances that although are not luminescent, inhibit the luminescence of a chemical indicator. The measurement of the lifetime can be performed in the frequency domain or in the time domain.
###### Abstract  
The aims of this study were to analyze the within-group weekly metabolic power average (wMPA), weekly acceleration zones (wAcZ), and weekly deceleration zones (wDrZ), in starter and non-starter professional soccer players, based on different periods of the full season (pre-, early-, mid-, and end-season). Twenty-one professional soccer players (age, 28.3 \(\pm\) 3.8 years; height, 181.2 \(\pm\) 7.1 cm; body mass, 74.5 \(\pm\) 7.7 kg; BMI, 22.6 \(\pm\) 1.0 kg.m\({}^{2}\)) were monitored during the full season in the highest level of the Iranian Premier League. WMPA, wAcZ, and wDrZ at three different zones (wAcZ1, wAcZ2, wAcZ3, wDrZ1, wDrZ2, wDrZ3) were collected using Global Positioning System. ANOVA was applied to analyze within-group changes across the different periods of the full season. In general, starters presented higher values in the beginning of the season (pre- and early-season) and lower values at mid- and end-season. Specifically, starters showed significant differences in early- vs. mid-season for wMPA, wAcZ1, and wDrZ1 (\(p\) < 0.05), plus early- vs. end-season for wMPA and wAcZ1 (\(p\) < 0.01). In addition, non-starters showed higher values in the pre-season that decreased until the end-season for wMPA, wAcZ1, and wDrZ1. Regarding wAcZ2, wDrZ2, wAcZ3, and wDrZ3, non-starters presented higher values in early-season that decreased until the end-season. Specifically, non-starters showed significant differences in pre- vs. mid-season for wMPA, wAcZ1, and wDrZ1; pre- vs. end-season for wMPA, wDrZ1, wDrZ2, and wDrC23; early- vs. mid-season for wMPA, wAcZ1, wDrZ1, wAcZ2, and wDrC22; early- vs. end-season for wMPA, wAcZ1, wDrZ1, wAcZ2, wDrC21, wDrZ2, wAcZ3, and wDrZ3; and mid- vs. end-season for wMPA, wAcZ1, and wAcZ3 (\(p\) < 0.05). In conclusion, starters were revealed to have higher values in pre- and early-season, and lower values in mid- to end-season, while non-starters were revealed to have lower load values in pre- and end-season and higher load values in early- and mid-season. This study provides extra insights over the season for starters and non-starters, respectively. Data can be used by coaches to better training periodization across soccer season.  
acceleration; deceleration; GPS; performance; football; external monitoring; WIMU  
Accepted: 20 July 2021
## 3 Sentinel-1: The Mission  
Despite the great potentiality offered by WARM-SAR and similar projects, the scaling and the capillary diffusion of the derived products is still constrained by technical and economical limits, especially in low-income countries, where most of the potential beneficiaries of the proposed products can not sustain the cost of commercial data.  
The aim of the Sentinel mission is to support the Copernicus program, offering a significant scientific opportunity thanks to: (i) a broad variety of sensing methods, (ii) the compatibility with past ESA mission (in the case of SAR this holds for ERS and ENVISAT) [29], and (iii) the distribution policy, which will guarantee free, full and open access to data. Therefore, the capacity of acquiring, interpreting and processing Sentinels' data will be crucial for the success of projects involving geographical information.  
Sentinel-1 is a two-SAR satellite constellation designed to guarantee global coverage with a revisit time of 6 days. The first satellite (Santinel-1A) was launched on 3 April 2014. A 12-meter long radar working in C-band was successfully deployed and is starting to acquire images all over the world. Sentinel-1A is placed in a near-polar, sun-synchronous orbit with a 12-day repeat cycle and 175 orbits per cycle. Both the satellites of the Sentinel-1 constellation share the same orbit plane with a 180\({}^{\circ}\) orbital phasing difference.  
Sentinel-1 is designed to work with four different possible operative modes: (i) stripmap, (ii) wave, (iii) interferometric wide swath and (iv) extra wide swath. It supports dual polarization acquisition (\(\mathrm{HH+HV}\) or \(\mathrm{VV+VH}\)) for all the modes, with the exception of the wave mode (see Table 1). The medium resolution of the stripmap is very close to the resolution of the images used for the development of the products introduced in Section 2. In the following sections, also with the use of a SAR raw signal simulation, we present a study of the expected performances of Sentinel-1 in the design and development of the products presented in the frame of the WARM-SAR project.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Mode** & **Swath (km)** & **Resolution (m \(\times\) m)** & **Polarization** \\ \hline Stripmap & 80 & \(5\times 5\) & Dual \\ Wave & 20 & \(5\times 5\) & Dual \\ Interferometric WS & 250 & \(5\times 20\) & Dual \\ Extra WS & 400 & \(20\times 40\) & Single \\ \hline \end{tabular}
\end{table}
Table 1: Sentinel-1 operative modes.
# A Programmable ROADM System for SDM/WDM Networks
###### Abstract  
This paper proposed and evaluated a programmable ROADM system for MCF-based SDM/WDM networks. The proposed ROADM system employing both bypass connection and Route-and-Select wavelength switching enables adaptable virtual topology in optical networks by dynamically configuring bypass connection cores. The simulation results confirmed this ROADM system could provide acceptable performance with an around 10-20% reduction in the total cost including the number of ports and WSSs by comparing with a fully flexible SDM/WDM ROADM system, which cannot be implemented due to the required extremely high-port-count WSSs.  
optical network; multicore fibre; programmable ROADM; bypass connection