esa-sceva/satcom-chunk-collection huggingface dataset:
# Small-Scale Foreign Object Debris Detection Using Deep Learning and Dual Light Modes
## 2 Methods
### Boundary Box Regression Loss Function  
The dataset established in this paper contains mainly small-scale FOD images, where the pixel area is very small compared to the whole image. The default GIoU localization loss function in the YOLOv5 network degenerates into IoU when two boxes have an inclusion relationship, revealing the drawback of the IoU loss function, which is that small distance movements after the inclusion relationship do not reduce loss, leading to convergence difficulties. To improve the model's accuracy in locating foreign objects on airport runways, the default GIoU localization loss function inherent to the YOLOv5 network architecture is supplanted with a CIoU loss function. The distance, overlap rate, scale, and penalty terms between the target and the frame anchor are taken into account by CIoU, which can make the target box regression more stable. A schematic diagram of the Complete CIoU is illustrated in Figure 6. The blue solid box denotes the predicted bounding box, the orange solid box denotes the ground truth bounding box, the white dotted box represents the smallest enclosing bounding box, with c indicating the diagonal distance of the minimum enclosing box. The CIoU equation is defined in (14).  
\[IoU=\left(\frac{b\cap b^{gt}}{b\cup b^{gt}}\right) \tag{10}\]  
\[Loss_{CIoU}=1-\left(IoU-\frac{c+b\cup b^{gt}}{|c|}\right) \tag{11}\]  
\[v=\frac{4}{\pi^{2}}\left(arctan\frac{w}{h}-arctan\frac{w^{gt}}{h^{gt}}\right) ^{2} \tag{12}\]  
\[\alpha=\frac{v}{(1-IoU)+v} \tag{13}\]  
\[Loss_{CIoU}=1-\left(IoU-\frac{\rho^{2}\big{(}b,b^{gt}\big{)}}{c^{2}}-\alpha v\right) \tag{14}\]  
Among them, the prediction box's midpoint and the target box's midpoint are represented by \(b\) and \(b^{gt}\), while \(\rho^{2}(b,b^{gt})\) represents the distance between them. Based on the positional relationship between the two boxes, the box that exactly covers them is defined as the minimum bounding box, with a diagonal length of \(c.v\) reflects the similarity in aspect ratio between the two boxes. \(w\) and \(w^{gt}\) represent the width of the prediction box and target box, respectively. \(h\) and \(h^{gt}\) represent the height of the prediction box and target box, respectively. \(\alpha\) represents the weight function, which is the influencing factor of \(v\). Applying the CIoU loss function can reduce the target positioning error in the small target intrusion detection task of airport runway foreign objects and enhance the fitting ability of the prediction box to the genuine target box.
# A Low-Cost, Small-Size, and Bluetooth-Connected Module to Detect Faults in Rolling Bearings
## 3 Proposed Solution  
In order for the proposed system to detect a faulty bearing and wirelessly notify a remote controller, different hardware and software blocks have to be combined together. As the acoustic noise is exploited, a microphone is required as a transducer, so that its output signal can be sampled and processed by the microcontroller itself. Then, the optimized DEFT has to be executed to evaluate the frequency spectrum of the acquired signal, and the subsequent fault detection algorithm determines whether the rolling bearing is damaged or not. If one or more defects are present, the microcontroller notifies the remote controller by means of the wireless link, which is based on the Bluetooth Low-Energy standard protocol. Moreover, the wireless connection allows the remote controller to access and modify the  
Figure 2: Number of operations (sums and multiplications) required to evaluate the discrete fast Fourier transform (DEFT) according to the classical algorithm (dashed-dot line) and to the optimized one (continuous line).  
configuration of the module on-the-fly, resulting in a highly reconfigurable solution. A schematic view of the proposed module is shown in Figure 3, and an in-depth description of each block will be provided in the remainder of this Section.
# Privacy and Security in Federated Learning: A Survey
## 4 Privacy in Federated Learning
### Privacy-Preserving Defense Strategies
#### 4.2.7 Verification Framework  
VerifyNet [54] is a FL framework that provides the possibility for clients to verify the central server results to ensure its reliability. To preserve privacy, it provides a double masking protocol, which makes it difficult for attackers to infer training data. Moreover, VerifyNet is robust enough to handle clients' dropouts due to the user's battery or hardware issues. However, this framework imposes more communication with the clients, which involves a decrease in performance and additional costs.
# Improved ISR for IEEE 802.11 ah Nonlinearity Compensation via Adjustable Constellation Borders
## 3 Improved ISR  
### Conventional ISR  
As shown in Figure 1, the ISR is an iterative approach to compensate the lost (PA-saturated) samples by updating the prior knowledge both in the data (time) and transformed (frequency) domains [19]. The process can be summarized as following steps.  
1. Estimate the saturation level by specified IBO: \[\hat{A}_{sat}=q_{RMS}\cdot 10^{IBO/20},\;\;q_{RMS}=\sqrt{\sum_{n=0}^{N-1} \lvert q_{n}\rvert^{2}/N},\;\;q_{n}\in\boldsymbol{q},\;\;\boldsymbol{q}= \boldsymbol{\mathcal{F}}_{N}^{H}\boldsymbol{Q}.\] (5)
2. Create time-domain prior knowledge by reserving the non-saturated samples: \[\boldsymbol{K}_{\mathbf{D}}^{ISR}=\boldsymbol{\mathcal{I}}_{\mathbf{D}}^{ISR }q,\;\;\left[\boldsymbol{\mathcal{I}}_{\mathbf{D}}^{ISR}\right]_{(j,j)}= \left\{\begin{array}{ll}1,&q_{n}<\hat{A}_{sat}\\ 0,&q_{n}\geq\hat{A}_{sat}\end{array}\right.,\;\;0\leq j,n\leq N-1.\] (6)
3. Take \(\boldsymbol{q}\) as an initial guess by denoting \(\hat{\boldsymbol{q}}^{(i=\boldsymbol{0})}\).
4. Apply frequency-domain prior knowledge from 802.11 ah (CBW-1) standards (refer to Table 1[19]): \[\begin{array}{ll}\hat{\boldsymbol{q}}^{(i+1)}=\boldsymbol{\mathcal{F}}_{N}^ {H}\big{[}(I-&\boldsymbol{\mathcal{I}}_{T}^{ISR})\boldsymbol{Q}_{HD}^{(i)}+ \boldsymbol{K}_{T}^{ISR}\big{]},\\ &\hat{Q}_{HD}^{(i)}=\underset{\boldsymbol{X}\in\mathcal{M}-\text{QAM}}{\arg \min}\Big{(}\lVert\hat{Q}^{(i)}-\boldsymbol{X}\rVert_{2}\Big{)},\;\;\hat{Q}^ {(i)}\in\text{data subcarriers}.\end{array}\] (7)
5. Apply time-domain prior knowledge: \[\hat{\boldsymbol{q}}^{(i+1)}=\left(I-\boldsymbol{\mathcal{I}}_{D}^{ISR} \right)\hat{\boldsymbol{q}}^{(i+1)}+\boldsymbol{K}_{\mathbf{D}}^{ISR}.\] (8)
6. Return to step 4 for a new iteration.  
Figure 1: System block diagram of OFDM transceiver with ISR.
## 2 Materials and Methods
### Measurement of Neuroprotective Activity of the Extracts  
To observe the neuroprotective activities of the extracts, a 3-(4,5-dimethylthiazol-2-yl)-2,5 -diphenyltetrazolzolium bromide (MTT) assay method was used as follows [21]: First, cells from the HT22 mouse hippocampal cell line (ATCC, Manassas, VA, USA) were inoculated into a well of a 96-well plate at 1.5 \(\times\) 10\({}^{4}\) cells/well. The cells were divided into an experimental group treated with glutamate and another experimental group that was not treated with glutamate. After 200 \(\upmu\)L of _S. platensis_ extract at each concentration or 1 \(\upmu\)g/mL of chlorophyll \(a\) as a positive control was added to the cells, the cells were cultured for 24 h in a CO\({}_{2}\) incubator. Subsequently, an MTT solution at a concentration of 5 \(\upmu\)g/mL was added to each well, and the supernatant was removed after 4 h. Finally, 10 \(\upmu\)L of acid-isopropanol solution (0.04 N HCl in isopropanol) was added to the solution to complete the reaction. The final absorbance of the solution was measured at 565 nm using a microplate reader (Tecan, San Jose, CA, USA). To confirm the neuroprotective effects associated with the antioxidant activity of the extracts, we measured the decrease in reactive oxygen species (ROS) production by HT22 cells by applying various concentrations of the extracts, as follows [21]: HT22 cells were plated in a 48-well plate at 1.5 \(\times\) 10\({}^{4}\) cells/well and incubated at 37 \({}^{\circ}\)C for 24 h. After the cell cultures were established, each _S. platensis_ extract sample and 10 \(\upmu\)L of glutamate were added and further incubated for 8 h. As positive controls, 1 \(\upmu\)M Trolox and 1 \(\upmu\)g/mL chlorophyll \(a\) were administered. Then, 10 \(\upmu\)M of 2\({}^{\prime}\),\({}^{\prime}\)7-dichlor fluorescein (DCF-DA) was added to each well and incubated for 1 h. Finally, 1% Triton X-100 was added to each well, and the solutions were measured at wavelengths of 488 nm and 530 nm. In addition to the measurement of ROS production, the production of Ca\({}^{2+}\) ions by HT22 cells was measured as follows [22]: Calcium ion (Ca\({}^{2+}\)) release from HT22 cells was measured using Fura-2 AM. Before the cells were harvested, each sample was treated with Trolox (positive control) and glutamate followed by the addition of 2 \(\upmu\)M Fura-2 AM. After 2 h, the cells were washed with PBS and treated with 1% Triton X-100. Calcium ions (Ca\({}^{2+}\)) were measured using a fluorescence analyzer at an excitation wavelength of 400 nm and an emission wavelength of 535 nm.
# On the Entropy of Oscillator-Based True Random Number Generators under Ionizing Radiation
## 4 Experimental Results
### TRNG-STR Experimental Results  
For the [PERSON] et al. TRNG [13], the analysis of 5-Mbit bit-streams for each accumulated dose has derived in the p-value distribution showing in Figure 7 for the NIST statistical test suite. The results obtained for this TRNG are very similar to those reported for the [PERSON] et al.'s TRNG [12]. Once again, the TRNG passes all the test until 35 krad(Si) are reached. From this point, a rapid degradation on the output quality can be observed. The same bias than in the RO-TRNG case appears in the bit-stream (Figure 8). In this case, the percentage of 0's in the bit-stream reaches the 59.4% for an accumulated dose of 40.8 krad(Si).  
The analysis of the entropy source as a non-IID source is shown in Table 3. Positive results have been obtained until an accumulated dose of 35 krad(Si). Once again, the min-entropy is set by two estimators (Collision and t-Tuples) because of the bias that appears in the bit-stream.  
All in all, the results obtained for both TRNGs show that before the first failure of a deterministic block of the design (38.1 krad(Si)), a degradation on the statistical properties of both TRNGs is produced. This degradation can compromise the security of applications that depend on the freshness generated by TRNGs. This outcome highlights the necessity of exhaustive testing of TRNGs under ionizing radiation. In addition, the accumulated dose that these devices can support might be revisited where implementing this kind of TRNGs.
## 3 Parametric Studies
Figures 2-4 on the same scale represent these values by dividing the results for the different values of n (Figure 2: n = 2, Figure 3: n = 3, Figure 4: n = 4). It is worth noting that the relationships are linear in most cases. For example, Figure 5 shows the results for Vs = 1000 m/s with a comparison between the various n values, demonstrating that there is a linear dependence between the structural period and the period of the system for rigid soil conditions. Similar trends resulted for Vs = 800 \(-\) 200 m/s. Non-linear trends occur for deformable soils and stiff structures, those cases where SSI effects become fundamental to be considered, as shown in the theoretical models developed by [22]. As an example, Figure 6 compares the results for Vs = 130 m/s for the three selected isolation ratios (n = 2, n = 3 and n = 4). \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Soil** & **Shear Wave Velocity (m/s)** & \multicolumn{2}{c}{**Mass Density**} & \multicolumn{1}{c}{**Shear Modulus (kPa)**} & \multicolumn{1}{c}{**Poisson Ratio**} & \multicolumn{1}{c}{**Typology Based on [31]**} \\  & & **(kN/m3)** & & & & \\ \hline VS1000 & 1000 & 20 & \(2.00\cdot 10^{6}\) & 0.30 & A \\ VS800 & 800 & 20 & \(1.28\cdot 10^{6}\) & 0.30 & A \\ VS700 & 700 & 20 & \(9.80\cdot 10^{5}\) & 0.30 & B \\ VS400 & 400 & 18 & \(2.88\cdot 10^{5}\) & 0.35 & B \\ VS300 & 300 & 16 & \(1.44\cdot 10^{5}\) & 0.35 & C \\ VS200 & 200 & 15 & \(6.00\cdot 10^{4}\) & 0.35 & C \\ VS150 & 150 & 14 & \(3.15\cdot 10^{4}\) & 0.38 & D \\ VS130 & 130 & 13 & \(2.20\cdot 10^{4}\) & 0.38 & D \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Soil properties. Figure 2: Results of the parametric studies (n = 2). Figure 4: Results of the parametric studies (n = 4). Figure 5: Vs = 1000 m/s, comparison between: n = 2, n = 3 and n = 4. Figure 3: Results of the parametric studies (n = 3).
# Research on Path Planning Multiple Mobile Robots Based on the LAPGWO Algorithm
## 4 Results
### Analysis of the Results of the Physical Experiment  
Figure 19 shows the experimental process of the multi-robot system in the warehouse environment using the method proposed in this paper on the physical experiment platform. Among them, a, b, c, and d are the diagrams of four moments during the driving process, respectively.  
Figure 20a shows the moment the robot has just started. At this time, the main driving obstacle for the robot is fixed obstacles. Figure 20b,c represents the main obstacle avoidance moments. During this period, the robot must avoid collisions with obstacles, and it is also when various robots converge. The robots also need to avoid other robots on their own. Figure 20d shows the moment the robot autonomously moves towards the target point after the convergence ends until it reaches the given target point. From the actual driving process, it can be seen that the driving trajectory is consistent with the simulation result in Figure 9e. There is no collision with obstacles and other robots during the driving process. The method proposed in this paper shows promising results in practical applications, and it can achieve the goal of not colliding with obstacles and robots during the driving process and smoothly reaching the given target pointAs shown in Figure 20, from a to d are the operation diagrams at the moments of t1, t2, t3, and t4 during the operation.  
This paper records the actual driving data of each robot. It compares it with the data of the simulation structure to verify the trajectory tracking error of the method proposed in this paper. The specific measurement table of the trajectory tracking error is shown in Table 10.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Serial Number** & **Lateral Error** & **Longitudinal Error** & **Heading Error** & **Total Trajectory Error** &
\begin{tabular}{c} **Maximum** \\ **Instantaneous Error** \\ \end{tabular} & **RMSE (M)** \\ \hline
1 & 0.18 & 0.12 & 2.2 & 0.21 & 0.28 & 0.08 \\ \hline
2 & 0.13 & 0.09 & 1.9 & 0.16 & 0.23 & 0.06 \\ \hline
3 & 0.15 & 0.11 & 2.5 & 0.19 & 0.30 & 0.09 \\ \hline
4 & 0.16 & 0.14 & 2.8 & 0.21 & 0.35 & 0.12 \\ \hline
5 & 0.11 & 0.08 & 1.7 & 0.13 & 0.2 & 0.06 \\ \hline
6 & 0.10 & 0.07 & 1.5 & 0.12 & 0.18 & 0.04 \\ \hline
7 & 0.09 & 0.06 & 1.3 & 0.11 & 0.16 & 0.03 \\ \hline
8 & 0.07 & 0.05 & 1.0 & 0.09 & 0.12 & 0.02 \\ \hline Mean value & 0.124 & 0.090 & 1.863 & 0.153 & 0.228 & 0.063 \\ \hline Standard deviation & 0.035 & 0.028 & 0.614 & 0.043 & 0.084 & 0.033 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Trajectory tracking error measurement table.  
Figure 20: _Cont._
# Infrared Nanosecond Laser Texturing of Cu-Doped Bioresorbable Calcium Phosphate Glasses
## 3 Results and Discussion
### Scan Speed Effect on Morphology  
After establishing the threshold power for the glass modification and its effect on the morphology, it is important to understand the influence of other parameters such as the scan speed. From this perspective, the power-variation study was also repeated for  
Figure 5: Plot of surface-modification characteristics with varying average laser power and at a scan speed of 3 mm/s.  
Figure 6: Profilometer generated 3D view (**a**) and cross-section (**b**) of a modification line written at 19 W and 3 mm/s.  
different scan speeds. At lower speeds, the number of pulses per spot increases for a fixed repetition rate. This impacts the local thermal dynamics and the resulting final modification. In the experiments, the scan speed was set at 1, 3, 5 and 7 mm/s. Figure 7a shows the plot of modification width and height changes for different scan speeds at a fixed average laser power of 19 W. The width and height of the microfeatures steadily decreased with increasing speed. This was expected, as with decreasing speed, more pulses are incident on the substrate at a single spot, generating increased thermal effects. The micrographs of laser modifications at varying scan speeds and at a fixed average power are reported in Figure 7b-d. Crack formation was evident at a speed of 1 mm/s due to greater energy deposition resulting in increased thermal stress. Modifications at the higher scan speed of 5 mm/s, on the other hand, occurred without crack formation but exhibited a wavy outline. As observable from Figure 7, for laser modifications at an average power of 19 W, a scan speed of 3 mm/s was found to be optimal for fabricating neat features.
# A Review on Ultrafast Laser Enabled Excellent Superhydrophobic Anti-Icing Performances
## 3 Anti-Icing Mechanisms of SHSs
### Failure Factors of Superhydrophobic Anti-Icing Surfaces
#### 3.2.2 Surface Durability  
Surface durability plays a key role in long-term practical applications. Among these, micro-nanostructures and low-surface-energy coatings are two crucial factors. For micro-nanostructures, it is well-known that the structures on SHSs are generally fragile and can be abrased easily when subjected to external shear forces [119]. To improve structural durability, much research has conducted [120, 121]. In the best-known study, [PERSON] et al. used armor-like microstructures combined with nanoparticles to realize ultrahigh superhydrophobic durability [87]. However, based on the above analysis, the introduction of armor-like microstructures would result in a decrease in air pocket pressure at low temperatures, eventually causing the failure of anti-icing properties. Hence, the closed-cell structures are not icephobic-durable (Figure 9a). Both room-temperature durability and icephobic durability need to be met for long-term anti-icing applications. Designing the micro-nanostructures to make this comprise is of significance.  
Figure 8: Effect of air pocket stability at low temperatures on anti-icing performances. (**a**) Schematics displaying the triple-phase interface changes during icing processes. (**b**) Phase diagram of air pocket pressure at different temperatures and on the surfaces with different closed/open area ratios. (**c**) Comparison of ice adhesion strengths on different surfaces with different structural types and contact angles. (**d**) Comparison of ice adhesion strengths on the surfaces with different structural types. (Reproduced with permission from [118]. Copyright (2023) Royal Society of Chemistry.)For low-surface-energy coatings, durability is related only to intrinsically hydrophilic materials, such as metals, ceramics, etc. The most common coating substance is fluorosilanes, which can be facilely coated on micro-nanostructures by the methods of vapor deposition or solution immersing [123]. Although excellent superhydrophobicity could be achieved with the methods, the durability of such coatings is usually poor owing to the single molecular layer thickness of the coated fluorosilanes by bonding with hydroxyl groups [82]. [PERSON] et al. measured the surface roughness of the coating on glass [122], where \(n\)-Perfluroeicosane (C\({}_{20}\)F\({}_{42}\), one of the low-surface-energy substances) was vapor deposited. They found that the vapor-deposited layer was single crystal-like with a maximum roughness of only 8 A (Figure 9b). Under external abrasion, the coatings can be easily peeled off to expose the inner hydrophilic sites, eventually leading to the failure of anti-icing properties. Therefore, research on chemical synthesis and coating materials still needs to be made in developing novel durable low-surface-energy coatings.
###### Abstract  
The seismic response of underground liquefied natural gas (LNG) storage tanks has been a significant focus in both academic and engineering circles. This study utilized Ansys (2021R1) to conduct seismic analyses of large-capacity LNG tanks, considering the fluid-structure-soil coupling interaction (FSSI), and it was solved using the Volume of Fluid model (VOF) and Finite Element Method (FEM). The mechanical properties of both the LNG tank structure and soil were simulated using solid elements, and seismic acceleration loads were applied. An analysis of liquefied natural gas was performed using fluid elements within FLUENT. Initially, a modal analysis of the tank was conducted, which revealed lower frequencies for a full-liquid tank (3.193 Hz) compared to an empty tank (3.714 Hz). Subsequently, the seismic responses of both the aboveground and underground LNG tank structures were separately simulated, comparing the acceleration, stress, and displacement of the tank wall structures. The findings indicate that the peak relative displacement of the aboveground empty tank wall is 122 mm, less than that of a full tank (136 mm), while the opposite holds true for underground tanks. The period and wave height of LNG liquid shaking in underground tanks are lower than those in aboveground tanks, which is more conducive to tank safety. The deformation and acceleration of underground tanks are lower than those of aboveground tanks, but the Mises stress is higher. The results indicate that underground LNG tank structures are safer under earthquake conditions.  
underground LNG tank; fluid-soil-structure coupling interaction; seismic response analysis; Ansys simulation; Volume of Fluid model +
Footnote †: journal: Applied Sciences
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 4 Methodology
### Data Collection  
#### 4.3.1 Eye-Tracking Equipment and Measures  
Eye movements were recorded using a mobile eye-tracking device (Tobii Pro Glasses 2, 100 Hz, Tobii, Danderyd, Sweden). The following metrics were collected:  
* Quiet Eye (QE) duration: The final fixation on a specific location or object before the initiation of a motor response.
* Fixation points: X and Y coordinates of gaze fixations.
* Saccades: Rapid eye movements between fixations.
* Pupil dilation: Changes in pupil size during task execution.