esa-sceva/satcom-chunk-collection huggingface dataset:
# Progressive Pruning of Light Dehaze Networks for Static Scenes
## 5 Discussion  
Our experiments demonstrate that scene-specific fine-tuning significantly enhances dehazing performance in fixed camera environments. The fine-tuned models outperformed the pretrained model on their respective scenes, indicating that adapting to specific scene characteristics allows the model to better handle unique features and haze patterns.  
The channel importance analysis revealed that not all channels contribute equally to dehazing performance. Channels with higher importance scores were often associated with features unique to the specific scene, such as structural details or textures prevalent in the environment. This insight supports the effectiveness of importance-based pruning, as it allows us to retain critical channels while removing less significant ones.  
Our progressive pruning algorithm successfully reduced the model size with minimal impact on dehazing quality. The progressive pruning approach ensured that the model maintained performance above the predefined PSNR threshold. The slight decrease in PSNR and SSIM in pruned models is an acceptable trade-off considering the benefits of reduced computational requirements.  
However, we observed that reductions in model size did not proportionally decrease inference time. This suggests that other factors, such as hardware limitations or software inefficiencies, may be influencing processing speed. Future work could explore optimization techniques at the implementation level to further improve inference time.  
The pruned models maintained robustness across varying haze conditions and lighting changes in a static scenes. This consistency is crucial for real-world applications where environmental conditions fluctuate. The models' ability to generalize within the context of a static scene demonstrates the practicality of our approach.  
One limitation of our study is that we focused on fixed camera environments. While this is a common scenario in surveillance and monitoring systems, extending our methods to dynamic scenes or moving cameras would increase the applicability of our approach. Additionally, exploring more advanced pruning techniques, such as structured pruning or quantization, could yield further improvements in efficiency.
## 3 Methods
### Micro-Genetic Algorithm
#### 3.2.2 Fitness Function  
In micro-GA optimization, we need to define an appropriate fitness function. Because the GA uses only one fitness function, we should be cautious about organizing a fitness function. In this study, we defined a normalized \(RMSE\) as the fitness function to obtain an appropriate assessment for variables with different scales. Since our experiments focus on sea breeze forecasting, the fitness function is based on surface meteorological variables and vertical wind profile, which are critical data for sea breeze forecasting. The fitness function is defined as follows:  
\[Fitness\ Function=NRMSE_{sfc}+NRMSE_{ver} \tag{1}\]  
where \(NRMSE_{sfc}\) and \(NRMSE_{ver}\) are defined as  
\[NRMSE_{sfc}=NRMSE_{T_{2m}}+NRMSE_{RH_{2m}}+NRMSE_{WS_{10m}}+NRMSE_{WD_{10m}} \tag{2}\]  
\[NRMSE_{ver}=NRMSE_{WS}+NRMSE_{WD}. \tag{3}\]  
Here, \(NRMSE\) can be calculated by normalizing \(RMSE\) by the standard deviation of observations (\(\sigma\)) as  
\[NRMSE=\frac{RMSE}{\sigma}, \tag{4}\]  
and \(RMSE\)s are given by  
\[RMSE=\sqrt{\frac{1}{K\times S\times T}\sum_{k=1}^{K}\sum_{s=1}^{S}\sum_{t=1}^ {T}(\ F_{\text{{\em fst}}}-O_{\text{{\em fst}}}\ )^{2}} \tag{5}\]  
Figure 3: The flow chart describing the combinational optimization process from the WRF-\(\upmu\)GA system.  
for the surface variables (\(T_{2m}\), \(RH_{2m}\), \(WS_{10m}\), and \(WD_{10m}\)) and  
\[RMSE=\sqrt{\frac{1}{K\times T\times L}\sum_{k=1}^{K}\sum_{t=1}^{T}\sum_{l=1}^{L} \left(F_{ktl}-O_{ktl}\right)^{2}} \tag{6}\]  
for the vertical wind profile (\(WS\) and \(WD\)). Here, subscripts _sfc_ and _ver_ indicate surface variables and wind variables at vertical levels; \(K\), \(S\), \(T\), and \(L\) denote the number of the sea breeze cases, observational stations, observations with an interval of 1 h, and model vertical levels below 1 km, respectively (\(K\) = 3, \(S\) = 8, T = 11, L = 6); F and O indicate the prediction and observation, respectively; subscripts \(T_{2m}\), \(RH_{2m}\), \(WS_{10m}\), and \(WD_{10m}\) indicate 2 m temperature, 2 m relative humidity, 10 m wind speed, and 10 m wind direction, respectively; subscripts \(WS\) and \(WD\) mean the wind speed and direction at vertical levels, respectively. Note that the smaller fitness function indicates the higher prediction performance. To calculate \(RMSE\) for the wind direction, we applied the method by [PERSON] et al. [54]. The \(RMSE\) for the wind direction is calculated using the difference \(\Delta d\) between the simulated (model) and observed (obs) wind directions are given by  
\[\Delta d=\left\{\begin{array}{cl}d_{model}-d_{obs}&if\ d_{model}-d_{obs}\ \leq\ |180|\\ d_{model}-d_{obs}-360&if\ d_{model}-d_{obs}\ >180\\ d_{model}-d_{obs}+360&if\ d_{model}-d_{obs}<-180\.\end{array}\right. \tag{7}\]  
Therefore, the values of \(\Delta d\) are in the range [\(-180^{\circ}\), \(180^{\circ}\)] and the \(RMSE\) is defined as  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}\left(\Delta d_{i}\right)^{2}}{n}} \tag{8}\]  
where n is the number of pairs of simulated and observed wind directions.
## 5 Discussion
Strictly speaking, the Simulations 1.2a/-b and 2.2a/-b therefore consider a pressure wave with a propagation velocity that is too high. It is important to note, however, that despite the high propagation velocity, the simulations considering a pressure wave result in elastic behavior, while the comparable Simulations 1.3a/-b considering a shock wave result in plastic deformation. This is because the instantaneous and abrupt pressure increase in the shock wave leads to rapid loading of the structure, while the pressure wave causes a slower rise in pressure. Therefore, in terms of structural response, a shock wave usually generates higher stresses than an impulse and peak pressure congruent pressure wave, less because of the velocity of wave propagation and more because of the instantaneous increase in pressure. The selection of an appropriate scenario is crucial for accurately simulating the structural response. It has been observed that a shock wave scenario tends to yield conservative results, implying a higher level of safety in comparison to the impulse-congruent pressure wave scenario. However, it is important to note that this observation is based on a single calculation example. Furthermore, it raises the question of whether pursuing conservative results, which may potentially lead to a design with excessive material usage, is advisable in times of limited resources. In addition, the present work assumes a constant tank wall thickness over the tank height. This is an accurate hypothesis for a representative structure. The construction of a real tank can be planned with a graduated wall thickness with thinner sheets in the upper tank area. The developed calculation method implies material reserves so that thinner steel sheets can also withstand the stresses. A further controversially discussed topic is roof loading. The simulation results show that the side of the tank roof that is oriented frontally towards the explosion source exhibits the highest stresses in the tank roof. Both in the case of roof compression and roof suction loading, this part is subjected to the highest load, as the largest compression/suction coordinates occur here. For the roof panel itself, higher stresses result when a compressive load is applied than when a suction load is applied despite the static preload due to internal design pressure (Table 3, e.g., comparison of Simulations 1.2a and 2.2a). The reason for this is as follows: the tank wall/roof corner of steel tanks is always designed as a so-calledcompression ring in accordance with current design regulations. It can be up to twice as thick or thicker than the tank wall and the roof plate. This results in a kind of restraint for the tank wall and the roof at this point. Compressive loads on the tank wall and roof produce bending deformation with strong restraints, as illustrated in Figure 15a, while a compressive load on the tank wall with simultaneous roof suction load causes less restraint, a smoother bending figure (Figure 15b), and a smaller stress level. As expected, the anchor forces are larger when a roof suction load is applied compared to when a roof compression load is applied.
## 3 Materials and Methods
### Survey Operational Mode  
Photographs were taken at around 50 m from the cliff face (Figure 3a), which resulted in a mean pixel size of 1.60 cm/pixel with the Nikon Reflex camera and 1.93 cm/pixel with the Huawei Smartphone. Some extra photographs were taken from a closer position to limit occlusions, particularly as one section of the cliff is obstructed by a rocky outcrop. In general, not being too close to the cliff face is important for guaranteeing a good GNSS reception, a problem that is accentuated in cases where photogrammetry relies on GCPs attached to the cliff face.  
Figure 3: (**a**) Measured camera stations for the tests using Reflex Nikon D800 camera and Huawei Smartphone, respectively. For the Huawei smartphone, positions were recorded using both the internal GNSS system (automatic geotag) and the RTK GNSS-assisted method. (**b**) Example of a photograph taken at a new position, at the beginning of the series of photographs. (**c**) Example of dense point cloud reconstructed in Agisoft PhotoScan Pro® for the Huawei Smartphone dataset.  
For measuring the entire cliff face, the setup was moved to different camera stations. For each station, approximately five to ten photographs are collected according to a \"fan-shaped capture\" (i.e., spinning the camera around the vertical axis [9]) and the station position is measured by RTK GNSS using a 30-s average. The system is then moved to the next station, without being dismantled. For each new position, a first photograph is taken in order to indicate the number of the station and make associating the correct position to each photograph easier (cf. Section 3.3 and Figure 3b). Using this approach optimizes the image overlap, so that each point is captured by more than nine photographs.  
For our tests, two different camera models were used (Table 1). In the case of the Huawei Y5 Smartphone camera, which is equipped with an internal GNSS, we also tested the possibility of using the image geotags provided by the smartphone. Table 2 summarizes the main characteristics for each test. The total number of collected photographs is larger for the Reflex camera than for the Smartphone camera. This is due to the image format (3/2 for the Reflex camera versus 16/9 for the Smartphone camera) paired with a shorter focal length for the Smartphone camera, meaning that the desired surface coverage with a sufficient image overlap is achieved with fewer photographs with the Smartphone. The duration of a survey (including assembly of the system) was about 30 min.
## 2 Materials and Methods
### Gas Chromatography-Mass Spectrometry (GC-MS) and Gas Chromatography Olfactory (GC-O)  
The GC-MS analysis was performed on an Agilent Technologies-6890N GC coupled with Agilent 5973I Mass Selective Detector (Agilent Technologies, Santa Clara, CA, USA). The GC-MS system was equipped with a DB-WAX capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.15 \(\upmu\)m) (Agilent Technologies, Santa Clara, CA, USA). Helium was used as a carrier gas at a constant flow rate of 1.0 mL/min. The oven temperature was initially set at 40 \({}^{\circ}\)C for 2 min, then heated to 160 \({}^{\circ}\)C by 6 \({}^{\circ}\)C/min, and finally to 225 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min, maintained for 10 min. The mass spectrometry was operated in the electron impact (EI) mode at 70 eV and screened from 33 to 450 \(m/z\). Library WILEY 275L was used to identify the volatile compounds for fermentation samples. The concentration of the identified volatile compounds was calculated by the relative peak area between the internal standard and the analyte [17].  
The equation is followed by  
\[\mathrm{C_{2}}\ (\upmu\mathrm{g/L})=\mathrm{C_{1}}\ (\upmu\mathrm{g/L})\ \times(\mathrm{A_{2}/A_{1}}) \tag{1}\]  
\(\mathrm{C_{1}}\ (\mathrm{A_{1}})\): concentration (peak area) of the internal standard, \(\mathrm{C_{2}}\ (\mathrm{A_{2}})\): concentration (peak area) of the identified volatile compound  
The GC-O system shared the same equipment and analysis condition on the GC with the GC-MS. An olfactory detection port was equipped (OPD-3, Gerstel, Linthicum, MD, USA) on the GC. Once the analyte was separated by the GC, the column fluent was split by 1:1 to the mass spectrometry and the sniffing port individually. The odor descriptions of volatile compounds in the GC-O were followed by [PERSON] et al. [18] with modifications. Three trained panelists evaluated the odor intensity by indicating strong (S), medium (M), and weak (W), and the sniffed retention indices and the notes of the odor were recorded as well.
# A Comparison Study on the Metabolites in PC-3, RWPE-1, and Chrysin-Treated PC-3 Cells
## 3 Results
### Metabolic Difference between Groups RWPE-1, PC-3, and Chrysin-Treated PC-3 Cells  
The chrysin-treated PC-3 cell group was similar to the RWPE-1 group in 23 (e.g., arabinitol, arabitol, mannitol, myoinositol, valine, leucine, isoleucine(1), isoleucine, proline, glycine, pyroglutamic acid, propionic acid, 2-keto-3-methylpentanoic acid, 2-keto-isoacarproic acid, 2-pyrrolidone-5-carboxylic acid, oxalic acid, succinic acid, malic acid, 2-hydroxylutamic acid, tagtofuranose, fructopyranose, glucose, and urea) of 35 metabolites. Therefore, when considering the results, the PC-3 group was distinctively different from the RWPE-1 and chrysin-treated PC-3 groups.
## 3 Results  
### Study Area  
To evaluate the adaptability and reliability of the proposed method, we applied it to a simulated mine, which is shown in Figure 2a. The simulated mine is located in Taian City, Shandong Province, China. In addition, the method is also used in a real coal mine, and the working face and the roadway of the real coal mine are presented in Figure 2b,c, respectively. The coal mine is located in Jining City, Shandong Province, China. It can be seen from Figure 2b that the working face of the coal mine has the characteristics of a narrow working space (about 180 m in length and 15 m in width), mechanical equipment (shearer and hydraulic prop), poor visual clarity, and high temperature.  
To gauge the quality of the registration accuracy and performance across different methods, we employed two metrics: the root mean square error (RMSE) and operation time, where the RMSE can be calculated by Equation (13).  
\[E_{RMS}=\sqrt{\frac{\sum_{i=1}^{n}(p_{i}-q_{i})}{n}} \tag{13}\]
# Optimization Design Method of Pipe-Insulating Joints Based on Surrogate Model and Genetic Algorithm
## 3 Results
### Surrogate Model-Genetic Algorithm Optimization Results
When the optimization target is set to \(\sigma\leq 0.9\sigma_{\text{max}}\), the maximum stress is located at the lower edge of the chamfer. When the optimization target is within the range of \(\sigma_{\text{max}}\leq\sigma\leq 1.2\sigma_{\text{max}}\), the maximum stress is located at the upper edge of the chamfer. Target stress, optimized maximum stress, and right flange cross-sectional area are counted as shown in Table 8. The surrogate model predicted stress is the stress calculated by the surrogate model established in Section 2 for the optimized dimensions. The actual maximum stress is the maximum stress calculated by the finite element software. The cross-sectional area \(S\) is equivalent to the self-weight, with a smaller cross-sectional area indicating a lower self-weight, where \(S_{0}\) represents the cross-sectional area of the right flange in the initial design dimensions, which is 28,214 mm\({}^{2}\). As shown in Table 8, the genetic algorithm established in this paper, based on a surrogate model, can provide suitable design parameters for the right flange with a relatively high degree of accuracy according to the optimization targets. The maximum stress obtained from finite element analysis based on the optimized dimensional parameters has a maximum relative error of 8.98% and an average error of 4.63% compared to the optimization target. Furthermore, the maximum stress of the right flange predicted by the surrogate model based on the optimized dimensions has a maximum error of 5.33%  
Figure 11: Stress of the right flange under different optimization targets. compared to finite element calculations. When the optimization target for the maximum stress of the right flange is set to the current design material's yield strength of 485 MPa, the dimensions derived from the optimization algorithm in this study result in a 64.89% reduction in cross-sectional area compared to the existing design, significantly reducing the manufacturing cost and self-weight of the insulating joint. The results for the optimization targets of \(\sigma\leq 1.1\sigma_{\text{max}}\) and \(\sigma\leq 1.2\sigma_{\text{max}}\) indicate that when the material strength exceeds 485 MPa, despite an increase in the material stress limit, the change in the optimized cross-sectional area is minimal. This suggests that the effect of using high-strength materials to reduce the self-weight and cost of the right flange is far less significant than that achieved by optimizing the cross-sectional dimensions. Conversely, when the optimization target is below \(0.9\sigma_{\text{max}}\), the cross-sectional dimensions of the right flange remain larger than the current design even after optimization with the genetic algorithm. Therefore, the material currently used in the design is considered reasonably appropriate. Still, the cross-sectional dimensions can be optimized using the algorithm presented in this study, and the dimensions of the right flange can be optimized by the algorithm in this study.
## 2 Experimental Procedure
### Corrosion Test  
Specimens from the ECAP samples were used to study the corrosion properties. Each specimen was metallographically polished. The polished specimens were further tested to obtain their corrosive properties, using potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) in 3.5% NaCl solution, using a potentiostat. The required chemical solutions were prepared using commercial NaCl with distilled water. Note that before proceeding with the genuine potentiodynamic tests, the open-circuit potential test was performed using the abovementioned test solution for 60 min to achieve a consistent state. Subsequently, a potentiodynamic examination was performed. These tests were completed by utilizing an ordinary three-cathode cell with a graphite bar and a saturated calomel anode (SCE). In these experiments, a polished sample was used as a working electrode; graphite was used as a counter electrode; and an SCE was used as a reference electrode. The polarization curves were generated at the interval from \(-\)1000 to + 1200 mV SCE at a constant scan rate of 1 mV/s. Electrochemical impedance spectroscopy was executed over a wide frequency range of 100 kHz to 0.01 Hz.
## 2 Study Area and Data  
Section 2.1 is a brief description of the study area. Section 2.2 introduces VIIRS data, one of the data sources for estimating emissions, and makes a comparative analysis with MODIS data that is widely used at present. Himawari-8 satellite with higher time resolution for fitting Gaussian Curves is presented in Section 2.3. Land-use-type data used to distinguish different land types is described in Section 2.4. Section 2.5 describes other datasets for validation, such as GFAS and FINN data.
###### Abstract  
Background. Mechanical models of the human ankle complex are used to study the stabilizing role of ligaments. Identification of ligament function may be improved via image-based personalized approach. The aim of this study is to compare the effect of the ligament origin and insertion site definitions obtained with different magnetic resonance imaging (MRI) modalities on the mechanical behaviour of a dynamic model of the ankle complex. Methods. MRI scans, both via 1.5 T and 3.0 T, were performed on a lower-limb specimen, free from anatomical defects, to obtain morphological information on ligament-to-bone attachment sites. This specimen was used previously to develop the dynamic model. A third ligament attachment site mapping scheme was based on anatomical dissection of the scanned specimen. Following morphological comparison of the ligament attachment sites, their effect on the mechanical behaviour of the ankle complex, expressed by three-dimensional load-displacement properties, was assessed through the model. Results. Large differences were observed in the subtolar ligament attachment sites between those obtained through the two MRI scanning modalities. The 3.0 T MRI mapping was more consistent with dissection than the 1.5 T MRI. Load-displacement curves showed similar mechanical behaviours between the three mappings in the frontal plane, but those obtained from the 3.0 T MRI mapping were closer to those obtained from dissection. Conclusions. The state-of-the-art 3.0 T MRI image analysis resulted in more realistic mapping of ligament fibre origin and insertion site definitions; corresponding load-displacement predictions from a subject-specific model of the ankle complex showed a mechanical behaviour more similar to that using direct ligament attachment observations.  
ankle complex modelling; MRI; ligament origin and insertion; tibo-talar joint; subtolar joint 2022125087. [[https://doi.org/10.3390/app12105087](https://doi.org/10.3390/app12105087)]([https://doi.org/10.3390/app12105087](https://doi.org/10.3390/app12105087))