esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 CFD Simulations  
### Computational Domain and Grid  
Since the construction of the cavern group is divided into two contract sections, the computational domain of this paper is the first section, including cavern 1 to cavern 4, shafts, the construction roadway, and the connecting roadway. In order to compare the air quality between the original ventilation scheme and the improved ventilation scheme, the 3D modelling software Space Claim 17.0 was first used to establish the geometry of forced ventilation and axial-flow gallery ventilation by using the shaft as shown in the Figure 5. The X direction is the axis direction, the Y direction is the width direction, the Z direction is the height direction of the cavern, and the three-dimensional dimension is exactly the same as the actual project.  
Then, the ICEM 17.0 was employed to generate the grids of the geometry. In consideration of the computing scale, stability and convergence of the simulation, a regular hexahedron grid with a quality of 0.5 and 0.52 was used in the geometrical model meshing. In general, quality values above 0.3 are acceptable for most solvers [23]. The grid density near the outlet of the air duct was properly increased due to its large airflow velocity and pressure gradient. Figure 6 shows the meshing details of the local geometrical model of the two ventilation schemes. In the forced ventilation scheme, the mesh size of the longitudinal direction (X direction) is 1.15 m, and in the cross section (Y direction), it ranges from 0.2 m to 0.6 m, with a total of 1,755,658 elements. In the axial-flow gallery ventilation scheme, a longitudinal mesh size from 0.5 m to 1 m and cross mesh size from 0.3 m to 0.6 m were selected, which includes 2,645,122 elements.  
Figure 4: Field test of ventilation environment in the caverns: (**a**) Test of air velocity; (**b**) Test of dust; (**c**) Air quality after ventilation.  
Figure 5: Three-dimensional geometrical model: (**a**) Forced ventilation scheme; (**b**) Axial-flow gallery ventilation scheme.
# Multi-Scale Flame Situation Detection Based on Pixel-Level Segmentation of Visual Images
## 4 Experiments
### Experimental Result and Analysis
#### 4.5.4 Inversion Results of Static Flame Parameters  
Based on the preceding analysis, Flame-SeaFormer is selected to invert the static flame parameters: flame height, width, and area. Five combustion processes are chosen for analysis, capturing one image frame every 1 s. The inversion results are depicted in Figure 13. Due to variations in the duration of the different combustion processes, the endpoint coordinates of the five curves differ.  
Figure 11: Multi-scale flame segmentation results on Our_flame_smoke dataset. (**a–c**) represent the original image sequences of three burning processes, respectively. The next line of each sequence corresponds to the flame segmentation result. The flame pixels are highlighted in green, while the background pixels are uncolored and consistent with the original image.  
Figure 12: Segmentation results of negative samples. (**a–d**) are four negative samples selected from two datasets, where (**a,b**) are from the Our_flame_smoke dataset. (**c,d**) are from the FLAME dataset. (**e–h**) are their corresponding segmentation results.  
Among the three subplots, the flame height exhibits the most distinct pattern. From 0-100 s, the flames in the five combustion experiments burned rapidly. The values of the three static flame parameters reach peaks at around 100 s. Subsequently, each static parameter remains stable or fluctuates slightly, indicating the vigorous combustion stage. After 300 s, groups 1, 3, 4, and 5 entered the decay and extinction phase, with flames gradually decreasing in intensity. The second experiment lasted longer, entering the third stage of combustion at around 400 s. Flame-SeaFormer accurately calculates reasonable flame parameters for all individual images at different combustion moments. A typical study conducted by [PERSON] et al. investigated the upward spread of a flame on an FRNC N2 XCH cable using a thermocouple [32]. The burn length (i.e., flame height) was treated as a function of time, with the curve shown in Figure 14. A comparison between the image-based Flame-SeaFormer model's results and the physical device's measurements in Figure 14 reveals a consistent flame development pattern. The consistency provides strong evidence for the reliability and efficiency of the gained flame-burning parameters by the model. In urgent fire scenarios, Flame-SeaFormer shows great promise for the real-time sensing of multi-scale flame information and the analysis of flame propagation properties.
## 5 Experimental Evaluation
### Comparative Analysis and Key Findings  
SMART DShot achieves significant improvements across all key metrics compared to baseline systems, demonstrating substantial advances in multiple domains simultaneously. Table 7 presents a consolidated performance comparison between the baseline CRC-based system and SMART DShot, highlighting improvements in detection accuracy, timing precision, response time, and system overhead.  
Our comprehensive evaluation establishes five critical findings that validate SMART DShot as a transformative solution. HATC achieves a success rate of 31.01%, demonstrating an improvement of 26.5% through intelligent algorithm selection with statistically significant performance gains. The utilization of FPGA resources below 2.5% enables integration into existing systems without significant redesign while maintaining excellent timing margins.  
Security enhancement provides 88.3% attack detection rate (95% CI: [87.1%, 89.5%]) with 2.3% false positives (95% CI: [1.9%, 2.7%]), offering comprehensive protection against sophisticated threats. Statistical significance is confirmed through rigorous validation with 160 simulation runs and bootstrap analysis, ensuring reliable performance under diverse conditions. The implementation of FPGA on the PolarFire SoC demonstrates practical feasibility for immediate deployment.  
These results conclusively validate SMART DShot as a practical solution to enhance motor control security while maintaining strict real-time performance requirements, establishing a new paradigm for intelligent embedded security systems with immediate practical deployment potential.
# The Influence of Sensor Size on Acoustic Emission Waveforms--A Numerical Study
## 2 Numerical Simulation  
Numerical simulations are conducted with commercially available software (Wave 2000) [19]. It computes displacement vectors by solving 2D elastic wave equations using a method of finite differences. The specific acoustic equation that is simulated is:  
\[\rho\frac{\partial^{2}u}{\partial^{2}t}=\ \bigg{[}\mu+h\frac{\partial}{ \partial t}\bigg{]}\
abla^{2}u+\ \bigg{[}\lambda+\mu+\varphi\frac{\partial}{ \partial t}+\ \frac{h}{3}\ \frac{\partial}{\partial t}\bigg{]}\
abla(\
abla\cdot u) \tag{1}\]  
where: \(u\) is the displacement vector (consisting of two vertical \(u_{x}\) and \(u_{y}\) components), \(\rho\) is the density (kg/m\({}^{3}\)), \(\lambda\) and \(\mu\) are the first and second Lame constants (Pa), \(h\) and \(\varphi\) are the \"shear\" and \"bulk\" viscosity (Pa\(\cdot\)s) and \(t\) is time (s) [19].  
The simulated two-dimensional geometry is given in Figure 1. The physical properties were chosen close to concrete: longitudinal wave velocity 4300 m/s, shear velocity 2350 m/s and density 2300 kg/m\({}^{3}\). Concrete was chosen as the most representative example of widely applied structural material used in bulk geometries, as metals and composites are usually formed in plates and the propagation conditions differ.  
Three receivers with different size were used, namely 20 mm (called \"long\" sensor), 5 mm (\"medium\" sensor) and 1 mm (\"point\" sensor). The receiver sizes are chosen to simulate the commonly used sensors in AE field, while the smallest sensor of 1 mm was used as a reference. The receivers are set at the top side of the geometry shown in grey color in Figure 1.  
The acoustic source was placed 50 mm away from the receivers at three different positions: on the surface, vertically beneath the receiver and diagonally (Figure 1). Under this configuration, the effect of the directionality of the source can also be assessed. The source was 1 mm long and produced a vertical displacement along its length. An actual source of AE is considered as a step function, thus very short in time, governed by the movement of the crack front (and the related oscillation around the new equilibrium position) [20]. The basic excitation was selected as two cycles of five different frequencies, namely 1 MHz, 500 kHz, 200 kHz, 100 kHz and 50 kHz aiming to cover large range of wavelengths. A discussion on the influence of the excitation duration is also included later. The simulated time was up to 100 \(\upmu\)s resulting in a waveform length of 5194 samples and a sampling interval of 0.01925 \(\upmu\)s or inversely sampling rate of 52 MHz. The space resolution was 0.1 mm, which was quite dense even for the case of highest frequency of 1 MHz with Rayleigh wavelength 2.35 mm.
## 3 Results and Discussion
### Comparisons of Different Models  
It has been shown that, in the elastic-plastic regime, the use of standard deeply notched SENB with crack depth of a/W = 0.5 specimens provide a conservative assessment of fracture toughness for both weld metal and because of the high constraint that is associated with this specimen geometry. Use of specimen geometries and loading modes associated with lower constraint (e.g., SENT specimens), allow for improved estimates of fracture toughness to be made that are appropriate for the assessment of circumferential flaws in pipe girth welds. The resistance curves of all the models mentioned above are plotted together in Figure 11. Three different mismatched cases from under-match to even-match and over-match are considered herein for comparison. A minor influence of geometric models on the crack initiation toughness can be found for all these three mismatched cases. With the increase of \(M_{y}\) (e.g., \(M_{y}\) = 1.0 and 1.3), the difference in resistance curves for pipes (\(a\)/t = 0.2, 0.5) and the corresponding SENT specimens (same crack depth, e.g., \(a\)/W = 0.2, 0.5) tends to be distinct. It can also be seen that the results from SENB specimen is obviously more conservative than that from SENT with respect to the pipes for all three mismatched cases considered in this study, which therefore can validate that the SENT specimen is a good representation of circumferentially cracked pipes for the fracture mechanics testing in engineering critical assessment and an alternative to the conventional standard SENB specimen, similar findings have also been discussed for circumferentially cracked pipes, as can be seen in reference [17].
# Efficient Position Change Algorithms for Prolonging Driving Range of a Truck Platoon
###### Abstract
However, the results from our previous work [9] shows that the number of position changes is considered relatively high and may increase significant fuel consumption during the position change operations. When considering the realistic scenario, one of the reasons for extra fuel consumption is an unsteady velocity caused by frequent acceleration and breaking. Even though the total driving range can be higher, one position change means trucks have to change their positions, thus wasting fuel energy in adjusting the velocity eventually. Our primary motivation is to maintain the maximum increased driving distance achieved with the position change methodology, yet with much less position change count. In this paper, we mathematically formulate the position change problem as an optimization problem. Furthermore, the heuristic algorithms proposed in [9] are revised in a more rigorous manner so that they can be applied to more general cases. This paper also proposes an improved position change algorithm that promises a smaller number of position changes, achieving more efficient fuel-saving while still achieving the highest driving range. Basically, trucks with the higher remaining fuel capacity move to the front of the platoon such that all trucks arrange in descending order of fuel left. We notice that the position arrangement may have similar patterns over and over, since the platoon already shares fair fuel consumption. This urges us to reorganize the arrangement pattern where truck platooning only switches to each pattern once with no duplicates to reduce the number of position changes. The simulation-based evaluation shows that the number of position changes is significantly reduced compared to the original algorithm. Thus, it can significantly decrease fuel costs per freight transportation, thus enhancing fuel economy and providing much more traffic efficiency. To the best of our knowledge, our work is the first to bring position change methodology into truck platooning technology. The contributions of the paper can be summarized as follows. * We formulate the position change problem as an optimization problem in a more rigorous manner. We define the assumptions and the objects more clearly. * We revise two previously proposed position change algorithms based on the formulated optimization problem. * We propose a new objective, which is the number of position changes, and a new heuristic algorithm for the new objective. The rest of the paper is organized as follows. In Section 2, we describe the background and review the related works. Section 3 proposes several heuristic position change algorithms, which focus on prolonging the driving range and reducing the number of position changes. We present the evaluation results of the proposed algorithms in Section 4, and discuss the computational complexity in Section 5. Lastly, the paper is concluded in Section 6.
# Seismic Velocity Anomalies Detection Based on a Modified U-Net Framework
## 2 Materials and Methods
### Seismic Data Preparation  
Firstly, a set of two-dimensional layered velocity models with anomalies are generated; the models include high or low speed anomaly regions. An acoustic wave equation forward modeling scheme is employed for producing seismic data based on the generated velocity models. The detailed settings are listed here.  
* Background velocity can be simple layered model or including faults.
* Anomalies are simplified to some elliptical regions, with random center coordinates and major/minor axis length.
* Masks for the designed anomalies are generated simultaneously.
* At most, two anomalies are located in each model, either inside one layer or crossing multiple layers.
* Seismic data generated by different shots are recorded.
* All the data are separated into two parts for training and testing.
## 4 Electrochemical and Bio-Electrochemical Applications
### Electrode Material for Microbial Fuel Cells (MFCs)  
Microbial fuel cells (MFCs) have emerged as a promising approach that integrates wastewater treatment with concurrent energy recovery [54; 55]. One of the developments is using SDB to improve MFC anodes for wastewater treatment and energy recovery simultaneously. SDB has shown potential as an electrode material in MFCs due to its electrical conductivity, large surface area, and biocompatibility [56]. [PERSON] et al. [57] reported that SDB anodes made by pyrolyzing activated sludge achieved a power density of approximately 2.17 W/m\({}^{2}\) and a current density of 5.99 A/m\({}^{2}\) under an optimized temperature, which was about three times higher than a plain carbon cloth anode. The hierarchical porosity and enriched graphitic structure of SDB could facilitate electron transfer and support a thick electroactive biofilm. Enriching anodic microbial community electroactive bacteria and boosting key SDBs for extracellular electron transfer are one of major reasons for MFCs conducting with SDB anodes. In practical terms, using SDB as electrode material to create MFC electrodes can cut costs (replacing expensive carbon felt or graphite) while enhancing the treatment of organics. A recent study focused on utilizing SDB as electrode materials in microbial fuel cell-constructed wetland (MFC-CW) configurations, revealing its capacity to boost both denitrification processes and bioelectricity output. SDB could improve cathode electrochemical performance and anode biocompatibility, achieving 95.85% nitrogen removal and 9.05 mW/m\({}^{2}\) power density in SDB-based MFC-CWs [58]. [PERSON] et al. [59] constructed wetland-MFC systems that used SDB as both the filler medium and electrode, achieving >95% nitrogen removal along with power generation. These results highlight SDB as a highly effective anode material for bio-electrochemical treatment systems.
## 6 Algorithms and Frameworks in Federated Learning
### Federated Averaging (FedAvg)
#### 6.1.2 Limitations and Challenges of FedAvg  
FedAvg faces challenges like differing data distributions across devices and asynchronous updates, which can impact model accuracy and introduce biases. Hyperparameter tuning is critical; misconfiguration can hinder model performance. Additionally, the distributed nature of FedAvg raises security concerns, as sharing model updates across networks can expose the system to adversarial attacks [83].
## 2 Materials and Methods
### GET Co-Delivery of pDNA Encoding Human Insulin and Glucokinase In Vitro  
Initial gene electrotransfer experiments were performed in vitro, utilizing C2C12 myoblasts and a BTX ECM 830 Square Wave Electroporation System (BTX, Holliston, MA, USA), with a single pulse parameter and four experimental groups, along with two control groups. Based on previous studies [4], 6 monophasic pulses were applied at 130 V, each with a duration of 100 \(\upmu\)s and an interval of 250 ms. The pulse length, voltage, and interval were measured with a TBS2000 Series Digital Oscilloscope (Tektronix, Beaverton, OR, USA) (Figure 1B).  
Myoblasts were suspended in electroporation buffer, placed in a 1 mm parallel plate cuvette, and plated at a density of 3.4 \(\times\) 10\({}^{3}\) cells/cm\({}^{2}\) in a 24-well plate. Nanoplasmid(r) DNA encoding for the following groups was resuspended with each sample in the cuvette: 10 \(\upmu\)g Insulin-DDK, 10 \(\upmu\)g Glucokinase-DDK, 10 \(\upmu\)g Insulin-DDK, and 10 \(\upmu\)g Glucokinase-DDK on separate plasmids, as well as 10 \(\upmu\)g of pDNA encoding Insulin-myc-Glucokinase-DDK on the sample plasmid. The two control groups included myoblasts with 10 \(\upmu\)g of Nanoplasmid(r) encoding for Insulin-myc-Glucokinase-DDK and no pulses or myoblasts with no pDNA. Following thorough resuspension, cuvettes were pulsed and allowed 5 min undisturbed to ensure full membrane recovery. The groups were then plated in 1 mL of high-glucose Dulbecco's Modified Eagle medium (DMEM), 10% fetal bovine serum,  
Figure 1: The in vitro pulse parameter is illustrated with a broken axis, along with 6 monophasic pulses with a length of 100 \(\upmu\)s and a 250 ms interval (**A**). A single representative waveform was recorded by an oscilloscope to ensure a square waveform with the correct voltage and pulse length was induced (**B**).  
1% antibiotic antimitotic solution, L-Glutamine, and sodium pyruvate. The effects of co-delivery of insulin and glucokinase were also observed in the low- and high-glucose media conditions to model transfected cells' response to environmental variability. The glucose concentration of the high-glucose complete media was 400 mg/dL, while the concentration of low-glucose media was 100 mg/dL.  
Following GET, media glucose levels of each group were measured and normalized to complete media that were not exposed to cells each day for seven days using GlucCellTM(r) (Esco VacciXcell, Singapore). An ultrasensitive human insulin ELISA (Alpco, Macedon, NY, USA) was used according to manufacturer instructions to measure the secretion of insulin into the media.
## 3 Experiment and Results
### Study Area and Data Set  
Three different study areas and data sets are used in this paper. The optical images of Figure 2(a2,b2,c2) are from Google Earth. Figure 2(a3,b3,c3) is the true ground map, which is derived from the distribution vector of buildings and the interpretation of optical remote sensing images taken at the same time.  
The first study data were obtained by E-SAR. E-SAR is an experimental SAR developed in Germany with optional vertically polarized or horizontally polarized antennas. We conducted experiments using the airborne L-band fully polarimetric E-SAR data of Oberpfaffenhofen, Germany, obtained in July 1999. The azimuth of the image has been conducted with 2-look processing; the image size is \(1408\times 1540\) pixels; and the azimuth resolution and ground resolution are both about 3 m. The image mainly includes land types such as buildings, woodlands, farmland, and roads. The E-SAR image and the real surface are shown in Figure 2(a1,a3).  
The second study data were obtained by GF-3. GF-3 is a C-band SAR satellite platform developed by China. This study used PolSAR data acquired on 9 December 2016 in Huashan, Wuhan, China. The GF-3 image size is \(550\times 650\) pixels, which means that the azimuth resolution and ground are both about 8 m. The image and the real surface are shown in Figure 2(b1,b3). The third study's data was obtained by RADARSAT-2. RADARSAT-2 is a C-band SAR satellite platform developed in Canada. This study uses PolSAR data acquired on December 7, 2011, in Wuhan, China. The image size is 450 \(\times\) 550 pixels, the azimuth resolution is 5 m, and the ground resolution is 4.7 m. The image and the real surface are shown in Figure 2(c1,c3).
# The Role of Electron Trajectories in XUV-Initiated High-Harmonic Generation
## 4 Discussion
### Time Delay between Short and Long Trajectories  
The spectral separation between long and short trajectory HHG signals enables a comparison of their sub-cycle oscillations with XUV-IR delay. The interference of quantum paths from different ionization channels, corresponding to the harmonics of the incident XUV pulse train, leads to oscillations of the HHG signal with \(\Delta t\). The phase information of these oscillations was used to reconstruct the temporal buildup of the electron wavepacket in the presence of the strong IR field [24]. To this end, the oscillations of the XUV-initiated HHG signal were analyzed around \(\Delta t=0\) where short trajectories dominate. In the present work, we focus on the sub-cycle oscillation delays between the long and short trajectories, which are present when \(\Delta t\) is tuned to the rising or falling edge of the IR pulse. A clear shift is visible in H19 and H21, where the XUV-IR oscillations of the short trajectories lag behind those of the strongly shifted long trajectories. Figure 4g shows the spectra zoomed into H21 for an XUV-IR delay of \(-3\) IR cycles. Here, we find a delay of about 450 as between the short trajectories and the blue-shifted long trajectories. This value is in qualitative agreement with the CCTSM calculations. Assuming that the same ionization channels are responsible for short and long trajectories, we find a delay of about 250 as for an instantaneous intensity of \(6.5\times 10^{13}\,\mathrm{W}\,\mathrm{cm}^{-2}\), with the short trajectories lagging behind the long ones. This observation is consistent with the fact that the ionization times of the long trajectories precede those of the short trajectories in the three-step picture.