esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Analysis
#### 2.2.2 Data Quality Assessment  
To confirm that the acquired data are of sufficient quality to train an ML model and contain the expected event-related activity, an analysis of evoked responses was performed. The chosen evoked response was the well-studied VEP [28,29]. More specifically, we investigated whether brain activations related to VEP could be identified when the GO signal was given.  
To investigate the VEPs, the preprocessed raw data were epoched from 300 ms before and 1 s after the GO marker, respectively, with a mean baseline correction applied using the first 300 ms of the signal. The epochs for every run were then averaged to yield evoked responses. For each type of run, i.e., executed movement, imagined movement, and imagined movement with feedback, the evoked data were combined over all sessions for all participants. This resulted in three distinct grand averages for each of those runs. To identify the VEP, the occipital region of interest was investigated, which comprises electrodes O1, O2, and Oz.
# Prediction of Vibration-Mode-Induced Noise of Structure-Acoustic Coupled Systems
## 4 Results and Discussion  
### Coupling Analysis  
#### 4.1.1 Mode Analysis  
Table 3 summarizes the results of eigenvalue analysis of the structure-acoustic coupled FEM model. To show how the coupling effect influences the eigenvalues, the eigenvalues in 12 cases in which the shell and reverberation chamber were independent and coupled were compared. In the Table 3, Bold-underline values showed eigenvalues induced by coupling effect. They were induced by vibration, but their aspects were different from vibration and acoustic.  
The damping ratios were determined by dividing the imaginary part with the real part. The vibration and acoustic mode damping ratios of the shell range from 2.5% to 4.5% and from 0.2% to 0.4%, respectively. The structure-induced eigenvalues (bold and underlined) exhibited decreased natural frequencies and damping ratios, although the differences were extremely small. For the coupled acoustic mode, the natural frequencies were similar, and the difference in the damping ratio was insignificant, except for the low-order acoustic modes. The minor changes in the natural frequency and damping ratio are attributable to changes in the solution of the quartic polynomial function owing to the coupling effect between the acoustic mode and nearby vibration mode. The damping ratio of the acoustic mode increased because of the effect of the damping ratio of the vibration mode, which was 10 times larger.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Domain** & **Item** & **Value** \\ \hline \multirow{3}{*}{FEM Analysis} & Element Type & Tetrahedral \\ \cline{2-3}  & Max Mesh Size & 300 mm \\ \cline{2-3}  & Duration & 10 s \\ \cline{2-3}  & Timestep & 0.1 ms \\ \hline \hline \end{tabular}
\end{table}
Table 2: FEM model and analysis conditions.  
Figure 2: Modeling of the reverberation chamber; **(a)** Perspective (unit: m). The red triangle indicates the point of impact hammer, and the nine black points indicate the accelerometer locations. **(b)** Plane (unit: m). The five blue marks indicate the location of microphones for measuring sound from vibration to verify indoor acoustic characteristic.
###### Abstract  
The use of an instrumented scanning vehicle has become the center of focus for bridge health monitoring (BHM) due to its cost efficiency, mobility, and practicality. However, indirect BHM still faces challenges such as the effects of road roughness on vehicle response, which can be avoided when the vehicle is in a stationary condition. This paper proposes a baseline-free method to detect bridge damage using a stationary vehicle. The proposed method is implemented in three steps. First, the contact-point response (CPR) of the stationary vehicle is computed. Secondly, the CPR is decomposed into intrinsic mode functions (IMFs) using the variational mode decomposition (VMD) method. Finally, instantaneous amplitude (IA) of a high frequency IMF is computed. The peak represents the existence and location of the damage. A finite element model of a bridge with damage is created. The results show that the method can identify the damage location under different circumstances, such as a vehicle with and without damping, different speeds of the moving vehicle, different sizes of damage, and multiple damage. A higher speed was found to provide better visibility of damages. In addition, smaller damage was less visible than wider damage.  
contact-point response; finite element method; intrinsic mode functions; instantaneous amplitude; variational mode decomposition; signal processing; stationary vehicle +
Footnote †: journal: Applied  
Article  
The Use of a Movable Vehicle in a Stationary Condition for Indirect Bridge Damage Detection Using Baseline-Free Methodology
## 2 Retinal Vascular Diseases
### Diabetic Retinopathy (DR)
#### 2.1.1 Detection of DR
[12] compared the detection of microaneurysm in diabetic retinopathy using FA and OCTA. They found that 62% of microaneurysm detected on FA were identified on OCTA. They found that the number of microaneurysm detected per eye was \(11.7\pm 7.1\) on FA versus to \(7.3\pm 3.9\) on OCTA. Moreover, microaneurysm detected by OCTA were mainly in the DCP (\(4.4\pm 2.1\) per eye) versus (\(2.9\pm 2.3\) per eye) in the SCP. [PERSON] et al. [19] also compared the detection of microaneurysm in diabetic retinopathy using FA and OCTA. They found that 75% of microaneurysm detected with FA were identified on OCTA. They found that the number of microaneurysm detected per eye was \(10.0\pm 6.9\) on FA versus to \(6.4\pm 4.0\) on OCTA. Another study by [PERSON] et al. [51] found that OCTA can detect only \(41.0\pm 16.1\%\) of microaneurysm seen on FA images in eyes with DR. The detection of microaneurysm in OCTA and FA has some difference that may be explained by the nature of microaneurysm themselves, as some of them are not completely patent so red blood cells (detected by OCTA) may not pass into the microaneurysm but the smaller fluorescein molecules (detected by FA) still can pass into the microaneurysm [50]. It may also be explained by the fact that blood flow within some microaneurysm was too slow (less than 0.3 mm per second) to be detected by OCTA [12]. A third explanation is that some of the hyperfluorescent dots seen on FA (supposed to be microaneurysm) are actually curved or coiled capillaries as seen on OCTA [51]. This is a notable weakness of OCTA relative to traditional FA.
## 5 Experiments and Results  
### Simulation Environment  
We developed a simulation environment to train and test our deep reinforcement learning method. The robots used were the Turtlebot3 Waffle PI robots [23]. We adapted the simulation environment built by the Turtlebot3 robot team and modified the environment to support multiple robots and goal positions. The simulation environment was built using the Gazebo simulator [24], as shown in Figure 3. The simulation environment was a 4 m \(\times\) 4 m square environment surrounded by a fence. The robots and the goal positions were spawned inside the environment. The robots had fixed starting positions, while the goals' positions were chosen randomly from a set of predefined positions. In the case of training, the set of predefined goals positions held 33 positions, while during testing, it held the same goal positions with 10 extra positions to test for generality. The goal positions were changed when each robot reached a unique goal successfully or when a certain amount of episodes \(E_{g}\) had passed since the last goal positions changed.  
During training, ten simulation environments were launched in parallel. Each of them contained two robots \(N=2\) with a total of 20 robots. Each of the robots had a copy of the policy and value networks and used them for data collection. After the data were collected, the data were sent to a centralized copy of the policy and value networks to be updated. Then, the updated weights were sent back to each copy.  
Figure 1: Policy network architecture.  
Figure 2: Value network architecture.
## 3 Results  
### SWAT Model's Performance in the URB  
As shown by analyzing the SWAT model simulation results, the evolutionary trends of runoff simulated values and measured values were generally consistent (Figure 2a). The model captured the seasonal variation characteristics of runoff, which are higher in summer and absent in winter, in the basin. The runoff from 1981 to 2000 was much higher than that from 2001 to 2012. The highest value, up to 134 m\({}^{3}\)/s, was generated in 1998. The \(NSE\), \(R^{2}\), and percentage bias (\(PBIAS\)) for the calibration periods were 0.62, 0.62, and 18.8%, and for the validation periods, they were 0.71, 0.72, 8.5%, respectively. In this study, the validation period captured the peak runoff better compared to the calibration period. This may be due to the fact that the runoff variation in the study area during the calibration period is more drastic and the runoff process is more complex than that in the validation period, so the simulation results in the validation period are better than those in the calibration period. Moreover, the model-extracted snow area and ET matched the curve trend of MODIS snow and ET, and the correlation coefficients of both were higher than 0.8 (Figure 2b,c).
## 1 Introduction
Effective communication between these systems ensures high-quality sensorimotor signals, directly influencing athletic performance outcomes, while disruptions or inefficiencies in these pathways can lead to reduced performance and increased injury risk [16]. The vestibular system, a key component of NAT, plays a significant role in balance, spatial orientation, and the fine-tuning of motor tasks, all of which are critical for volleyball athletes who must maintain stability during rapid, multi-directional movements [10]. Additionally, NAT's focus on improving neural efficiency may positively affect flexibility, serve speed, and upper limb strength by enhancing neuromuscular responses [17]. This aligns with recent findings that suggest neural conditioning can promote better motor output and coordination, essential for sports performance [18]. The clinical relevance of this study extends beyond performance enhancement. By improving sensorimotor integration, NAT may contribute to injury prevention, recovery, and long-term athletic resilience. Improved neural control over movement patterns may reduce the risk of coordination-related injuries, such as ankle sprains and shoulder strains,which are common in volleyball [19]. Moreover, the potential for NAT to retrain the neural pathways opens new possibilities for rehabilitation, supporting athletes in regaining optimal sensorimotor function post-injury [20]. Uniquely, this study positions NAT as an essential component of athletic conditioning, not merely a supplementary technique. By comparing NAT protocols to traditional training methods, this trial aims to determine whether NAT can produce significant improvements in flexibility, serve speed, upper limb strength, and coordination. The hypothesis is that NAT enhances sensorimotor signal quality and optimizes the neuromuscular pathways, thereby providing a more efficient platform for athletic execution [10]. If validated, NAT could represent a paradigm shift, placing the nervous system at the forefront of training methodologies for volleyball and potentially other sports with complex motor demands. The aim of this study is to validate the theoretical framework of neuro-athletic training (NAT) and address a critical gap in the literature on sports performance by empirically examining NAT's impact on specific volleyball performance metrics, including flexibility, serve speed, and upper limb strength and coordination. The primary hypothesis is that NAT will significantly enhance these volleyball-specific performance metrics compared to traditional training methods by optimizing neuromuscular function, with an emphasis on the nervous system's role in athletic conditioning. A secondary hypothesis suggests that NAT's neurocentric approach could contribute to a more efficient training platform, improving sensorimotor integration and overall athletic performance for elite volleyball players.
## 3 Results and Discussion
### Influence of Marble Hardness Distribution on Surface Glossiness  
Figure 6a shows the average surface glossiness of marble tile after polishing by SG pad with a grain size of 20 \(\upmu\)m. The entire polishing for each marble tile can be roughly divided into two stages: (1) the surface glossiness rise stage, during which the average surface glossiness of each marble tile increases with time, and the SDD of the surface glossiness decreases with time; (2) the surface glossiness decline stage, during which the average surface glossiness of each marble tile decreases with time, and the SDD of the surface glossiness increases with time. The moment when the inflection point of the surface glossiness appears is defined as the optimal polishing time. For each marble tile, the inflection point of the surface glossiness curve occurred at different times (as shown in Figure 6b), which indicated different optimal times of polishing for each marble tile by the SG pad.  
Analyzing combined with the hardness of marble tile in Figure 5, as the hardness SDD of the marble decreases, both the maximum surface glossiness and the surface quality consistency and optimal polishing time rise. To be specific, VO marble tile has the worst consistency of hardness, and the best glossiness it can achieve is the lowest, taking the shortest time during the polishing process; CM marble tile has the best consistency of hardness, the best glossiness it can achieve is the highest, and its optimal polishing time is the longest. Furthermore, the poorer the hardness consistency of marble, the more likely the  
Figure 5: (**a**) Vickers hardness and (**b**) SDD value of the hardness of the marble tiles.  
processing quality is to deteriorate after reaching the best surface glossiness, and the average and SSD of the surface glossiness are significantly reduced and increased, respectively.  
The optimal polishing times for each marble tile using SG pads with grain sizes of 20 \(\upmu\)m, 10 \(\upmu\)m, and 5 \(\upmu\)m were given in Table 2. In every process, the optimal polishing time increases as the SDD of the hardness of the marble tile decreases; furthermore, the optimal polishing time is shorter for the later process using a finer grain SG pad for the same marble tile. The rule above is consistent with the phenomenon presented in industrial production, and VO marble tile is defined as a difficult-to-polish marble by industry.
## 3 Database of Water Content and Hyperspectral Information
### Data Acquisition Method
#### 3.1.1 Determination of Area and GNSS Surveying  
To build the database, experiments were conducted at a university playground that was well compacted and had smooth drainage, similar to a road construction site in the actual compaction stage. Additionally, the flatness of the ground was not important during the experiment process. During the actual subgrade-creation process, compaction is performed using rollers or grinders, and the ground is usually level. This means that it is similar to the experimental environment.  
This ground is granular soil, a type of sand with an optimal water content of 9 to 12%. This material is mainly soil brought in during the process of creating a subgrade at a road construction site. It has no cohesion, and the water content varies depending on the weather (including temperature). First, the area from which the actual water content and hyperspectral information would be extracted was determined. As shown in Figure 3a, the shooting area was set to approximately 20 m in width and 60 m in length, considering the area of the playground. In addition, stakes were driven in to ensure that the area was captured in the orthophotos taken by the drones, and a rectangular area was marked by connecting each corner with a red string. At this time, four coordinates of each corner of the rectangular range were extracted through GNSS surveying. The equipment used was the R4S GNSS System (Trimble, Westminster, CO, USA), and the political surveying accuracy was measured within 3.5 mm. The measurement range and GNSS survey are as shown in Figure 3b.  
Figure 2: Process of data acquisition.
# On the Potential of the RST-FLARE Algorithm for Gas Flaring Characterization from Space
## 3 Results
### Computation of Source Emissive Power  
The emissive power of the flaring sites was assessed by computing the BTE (Equation (2) in Section 2.3.2). Figure 7 shows the BTE values for each year from 2000 to 2016 referred to the previously identified flaring sites (i.e., 314 pixels).  
Looking at Figure 7, it is evident a great increasing of BTE from 2000 to 2003 (when the peak value was measured), followed by a continuous decreasing trend between 2004 and 2016, except for a slight increase in 2013.  
Finally, an overview of how much flaring sites have impacted the region in the long period, in terms of BTE, is shown in Figure 8a. In such an image the BTE was classified in four classes, ranging from <500 K (the lowest level) up to >1500 K (the highest level).  
For onshore areas, the BTE distribution among the four classes is quite similar, with higher intensity for 500-1000 K (31%) and >1500 K (31%) ranges (Figure 8b). On the other hand, for the offshore sites the most of BTE is higher than 1500 K (49%), with only 8% at the lowest BTE level (<500 K). The remaining belong quite similarly to the intermediate levels (25% for the second range and 17% for the third).
# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Performance Baseline and Metric Audit
#### 6.12.3 Multidimensional Class Evaluation  
According to Table 29, this class primarily addresses general concept drift, with one instance explicitly targeting data drift. The focus on general drift highlights how metric-based approaches inherently detect broad shifts in model performance, using performance baselines rather than specific drift types. The identified application contexts predominantly involve healthcare, business process management, and software engineering, reflecting contexts in which performance stability and compliance with predefined standards are critical. Healthcare and business processes particularly benefit from explicit metric auditing to maintain operational accuracy and reliability. Explanations within this class are consistently global, aligning naturally with the concept of performance baselines that encapsulate overall model stability rather than granular instance-level shifts. The audience is primarily data scientists, with partial involvement of end-users, emphasizing that interpreting metric deviations usually requires analytical expertise, though clear visual aids and dashboards can also facilitate user-level comprehension. No quantitative interpretability metrics are explicitly identified. All approaches utilize offline learning modes, demonstrating a clear inclination toward retrospective analysis, typical of performance audits and baseline assessments, which naturally rely on historical data for accurate detection and diagnosis.
# A Flower-Shaped Miniaturized UWB-MIMO Antenna with High Isolation
## 1 Introduction
In [14], a triple bandgap CSRR-loaded EBG structure was inserted near the UWB antenna feedline, encompassing 2.5-12 GHz. The overall size of the proposed MIMO/diversity antenna was 30 \(\times\) 44 mm\({}^{2}\). In addition, a hexagonal slot and a mirrored pair of F-shaped stubs were employed to decrease the mutual coupling. A four-port and overt-leaf-shaped MIMO antenna with coplanar waveguide feeding was proposed in [15] to achieve wideband (12.75-16.05 GHz) by optimizing the ground plane and radiating elements. In addition, a fan-shaped decoupler was inserted in the middle of the back surface of the substrate in sequence, with further low coupling between components to provide more than 20 dB of isolation. However, the above-mentioned design approaches have fundamental flaws, such as complex structure or excessive size. In this work, we demonstrate a compact and unique dual-port UWB-MIMO antenna with an incredibly simple construction. Each radiating element has three flower-like elliptically shaped patches situated 60 degrees apart. On one hand, the branches of the modified ground structure are used to generate multiple frequencies in order to broaden the frequency band through resonance, and on the other hand, these branches are utilized to achieve a high level of isolation by effectively absorbing the current and reducing the mutual coupling between the two radiating patches. The ECC, DG, and TARC of the proposed system are all within an acceptable range. The structural layout of this paper is as follows: Section 2 discusses the proposed UWB-MIMO system's structure, design evolution, parameter analysis, and current distribution. Section 3 presents the proposed MIMO system's simulated and measured performance, including S-parameters, far-field characteristics, and MIMO features. A comparison with literature is provided in Section 4 to emphasize the benefits of the proposed design. The conclusions are detailed in Section 5.
# Optimized Deep Neural Networks for Real-Time Object Classification on Embedded GPUs +
## 4 Optimized Data Transfers Scheme for GPU-Only ConvMM
### Proposed Unified Memory-based Allocations for GPU-Only ConvMM and Other Layers  
Typical desktop systems have the separate host (CPU) and device (GPU) memories as shown in Figure 6. As discussed in the previous section, data transactions between the CPU and the GPU memories are very frequent. The speed of these data transfers is dependent on the PCI express bus. These transfers add the overhead and a lot of complexity to flow of an algorithm as can be compared and visualized by the Figures 1-3. A programmer has to consider these separated memories to define the flow of an algorithm.  
With CUDA 6.0, Unified memory (UM) is introduced which defines a pool of shared memory for the CPU and the GPU as shown in Figure 7. This shared memory can be accessed by both CPU and the GPU. The same allocated variable can be used by a CPU function and a GPU kernel according to the requirement of the program. However, data synchronization is essential after kernel execution.  
Figure 6: Host (CPU) and device (GPU) memories.  
Figure 7: Unified memory.  
Most of the embedded and mobile platforms have physically unified memories, so this includes both GPU as well as CPU requirements. Prior to CUDA v6.0, two copies of data elements were required, one in CPU memory and another in GPU memory. However, by using unified memory scheme, a single copy of trainable parameters is required to be allocated in memory. This can be useful for realization of deep architectures on embedded platforms having low storage capacity.  
In this work, Unified memory is used to realize the optimize flow of deep architectures on embedded platforms.  
As depicted in Figure 3, at least two data transfers are essential for every layer to bring the input data to the GPU for the computations and to move back the results from the GPU to the CPU for the next operation/layer. Using unified memory, the flow of this GPU-only ConvMM and other required layers can be further simplified and optimized as shown in Figure 8. This is done by allocating the inputs and the outputs in the unified memory space from the main program. By using unified memory in the ConvMM and other layers, the output of one layer can be directly fed to the next layer.