esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hyper Heuristic Algorithm to Solve the Low-Carbon Location Routing Problem
## 5 Simulation Experiment and Analysis
### Experiments and Analyses
#### 5.3.2 The Impact of Time Window on Cost and Carbon Emissions  
Table 5 shows the calculation results of the _LCTW_ examples with four different time windows (\(\lambda\in\{1,1.5,2,2.5\}\)). The results were used to analyze the effects of time windows on _TC_, _RC_, _PF_, _CO\({}_{2}\)_, \(D\), and _CWT_. In Table 5, \(\Delta\)_TC_, \(\Delta\)_RC_, \(\Delta\)_PF_, \(\Delta\)_CO\({}_{2}\)_, \(\Delta\)_D_, and \(\Delta\)_CWT_ are the amount of change in _TC_, _RC_, _PF_, _CO\({}_{2}\)_, \(D\), and _CWT_ when \(\lambda=1\). The bold font is the amount of increase, and the non-bold font is the amount of reduction.  
From the results in the table, it can be seen that when \(\lambda=1.5\), it can reduce the average _TC_ by 3.04%, _RC_ by 2.48%, _PF_ by 9.91%, _CO\({}_{2}\)_ by 1.59%, \(D\) by 4.70%, and _CWT_ by 6.22%; when \(\lambda=2\), it can reduce the average _TC_ by 4.86%, _RC_ by 3.23%, _PF_ by 31.59%, _CO\({}_{2}\)_ by 0.33%, \(D\) by 4.77%, and _CWT_ by 30.13%; when \(\lambda=2.5\), it can reduce the average _TC_ by 6.59%, _RC_ by 4.92%, _PF_ by 42.44%, _CO\({}_{2}\)_ by -1.12%, \(D\) by 3.74%, and _CWT_ by 43.35%.  
Figure 5 is a graph showing the trend in the average values of the six types of parameters under the four types of \(\lambda\) values. Figure 5 shows that \(\Delta\)_TC_, \(\Delta\)_RC_, \(\Delta\)_PF_, and \(\Delta\)_CWT_ gradually increased with an increase in the \(\lambda\) value, but _CO\({}_{2}\)_ gradually reduced from 1.59% to 1.22%. In addition, \(\Delta\)_D_ increased by 0.07% when \(\lambda\) increased from 1.5 to 2, but decreased sharply by 1.3% when \(\lambda\) increased from 2 to 2.5. This indicates that the time window has a great influence on the choice of distribution center and service vehicle.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Instance**} & \multicolumn{6}{c}{\(\lambda\)**= 1**} & \multicolumn{6}{c}{\(\lambda\)**= 1.5**} \\ \cline{2-13}  & **TC** & **RC** & **PF** & **CO\({}_{2}\)** & **D** & **CWT** & \(\Delta\)**TC** & \(\Delta\)**RC** & \(\Delta\)**PF** & \(\Delta\)**CO\({}_{2}\)** & \(\Delta\)**D** & \(\Delta\)**CWT** \\ \hline  
*5.52** & 53.06 & 13.08 & 13.63 & 56.91 & **4.56** & **
# Establishing a Geological Knowledge Base for Braided River Deltas Using Google Earth
## 3 Results and Discussion
### Sedimentary Parameter Distribution Characteristics
#### 3.1.2 Amplitude Distribution  
Figure 5a shows Saloum delta's 80% amplitudes! 2800 m (41%! 700 m). Figure 5b displays Rufiji River's 80% amplitudes! 1200 m. Figure 5c indicates Lena River's 80% amplitudes! 3000 m (46% in 1000-2000 m). Figure 5d shows Poyang Lake's 80% amplitudes! 2000 m. Figure 5e illustrates Yukon-Kuskokwim delta's 80% amplitudes! 3000 m. Figure 5f presents Orinoco's 80% amplitudes! 3000 m (35% in 1000-2000 m). Figure 5g shows Wax Lake's amplitude distribution. Figure 5h displays Po River's 80% amplitudes! 1500 m.  
The development of high-amplitude deltas is closely associated with strong seasonal flooding or steep topographic slopes, where rapid sediment unloading under such conditions tends to form thick gravel-sand bodies. Flume tank experiments by [PERSON] et al. showed that high-amplitude delta fronts are often accompanied by multi-cycle sedimentary complexes, consistent with the rapid accumulation process under strong hydrodynamic conditions [35]. In contrast, low-amplitude deltas are controlled by gentle topography or stable currents, leading to uniform sediment distribution and the formation of sheet-like sandbodies. [PERSON] et al.'s global synthesis of deltas indicated that such environments favor the development of low-relief deposits, which matches the uniform amplitude distribution observed in Po River sediments [29]. The 46% proportion of 1000-2000 m amplitudes in the middle Lena River reflects periodic stacking caused by a cyclic sediment supply, consistent with [PERSON] et al.'s observations on channel evolution in high-sediment-load river deltas [34].  
As a comprehensive response to depositional energy and topographic control, amplitude characteristics provide critical insights for subsurface reservoir characterization: high-amplitude areas indicate the development of thick sandbodies under strong hydrodynamic conditions, while low-amplitude areas signal extensive sandbody distribution in stable depositional environments. Together, these features offer geological constraints for the prediction and evaluation of delta reservoirs.
# Statistical Analysis and Modeling for Optical Networks
## 4 A Brief Introduction to Optical Networks
### Classification of Optical Networks  
Optical networks can be categorized based on various factors, with transparency being a key classification criterion. In communication engineering, transparency refers to the ability of the physical medium--optical fiber in optical networks to support end-to-end data communication regardless of bit rates and signal formats [1]. Based on this, optical networks are classified as transparent, translucent, and opaque. Transparent optical networks allow signals to pass without any electrical conversion, maintaining full optical transmission. Translucent optical networks, or partially transparent networks, permit some electrical regeneration at certain nodes, balancing transparency with signal quality enhancement. Opaque optical networks, on the other hand, require full electrical conversion at each node, impacting scalability but ensuring signal integrity. These classifications are depicted in Figure 1.  
This classification also relates to the types of repeaters used along network links, which are categorized as 1R, 2R, and 3R regenerators [79]. First, 1R repeaters use optical amplifiers for simple signal boosting. Next, 2R repeaters incorporate regeneration and reshaping, necessitating optical-electrical-optical (O-E-O) conversions. Finally, 3R repeaters perform regeneration, reshaping, and re-timing, further enhancing signal quality through multiple O-E and E-O conversions. These differences influence the data rates that OTNs can support across links. Despite these variations, modern traffic grooming techniques optimize data transmission rates, ensuring that overall network performance remains efficient and scalable [1].
## 4 Design of the Controller
### Supply Pressure Regulator  
Generally, the supply pressure of an EHSS remains constant and is often set to a large value to ensure that the system produces sufficient force. Since the inlet and the outlet of a hydraulic cylinder are controlled by the same spool when the load force is small, the supply pressure results in a large pressure drop at a valve's orifice and increases the system temperature.  
The VSP-SMEHSS proposed in this paper breaks the mechanical connection between the inlet and the outlet, whereas the supply pressure changes with the change in load. When the load force is small, the supply pressure becomes small, due to which the pressure drop at a valve's orifice decreases. Since the back pressure of the hydraulic cylinder can be controlled separately, the back pressure is set to a smaller value to reduce throttling losses of the outlet orifice, thus achieving energy-saving effects.  
In this paper, an electro-hydraulic proportional variable pump was used as a hydraulic source. The output pressure of the variable pump is proportional to the input signal. Considering the hysteresis characteristics of the variable pump, the output pressure can be expressed using Equation (14):  
\[p_{pump}(t)=k_{p}\cdot u_{p}(t-t_{0}), \tag{14}\]  
where \(p_{pump}\) is the output pressure, \(u_{p}\) is the input signal and \(t_{0}\) is the response time of the variable pump. For the position servo system, the minimum required pressure \(p_{s1}\) is given by Equation (15):  
\[p_{s1}=\frac{f_{L}+\lambda_{f}}{A_{1}}+\frac{A_{2}}{A_{1}}p_{2}+\Delta P_{s1}+ \Delta P_{1}, \tag{15}\]  
where \(f_{L}\) is the load force, \(p_{2}\) is the back pressure, \(\Delta P_{1}\) is the adjustment margin and \(\Delta P_{s1}\) is the pressure drop across the valve's orifice.  
For the pressure servo system, the required minimum supply pressure \(p_{s}\) is given by Equation (16):  
\[p_{s2}=P_{d}+\Delta P_{s2}+\Delta P_{2}, \tag{16}\]  
Figure 5: Schematic of the controller.  
where \(P_{d}\) is the reference signal and \(\Delta P_{2}\) is the adjustment margin. Therefore, the control law of an oil SPR is expressed using Equation (17):  
\[u_{3}(t)=k_{p}\cdot max[p_{s1}(t+t_{0}),p_{s2}]. \tag{17}\]  
It should be noted that the maximum acceleration of the load is given by Equation (18):  
\[a=A_{1}\Delta P_{1}/m. \tag{18}\]  
According to Equation (18), it is known that a larger \(\Delta P_{1}\) can generate a larger acceleration, which means that the load can quickly track the command signal. However, a large \(\Delta P_{1}\) will result in a higher \(p_{s1}\), which means that the system will generate a larger throttling loss. Similarly, a larger \(\Delta P_{2}\) results in a higher \(p_{s2}\). Therefore, the choices of \(\Delta P_{1}\) and \(\Delta P_{2}\) requires a trade-off between the system dynamics and the energy-saving effects.
# Analysis of Wafer Warpage in Diamond Wire Saw Slicing Sapphire Crystal
Part of the cutting heat is carried away by air and coolant, and about one-third to one-half of the heat is absorbed by the crystal [5]. The time difference between the heated parts of the slice results in uneven temperature distribution, causing the wafer to warp and deform under the action of thermal expansion and contraction. It is difficult to measure the temperature distribution in the cutting area during the diamond wire saw cutting process. [PERSON] et al. [12] used experimental methods to measure the temperature in the cutting zone of a free abrasive multi-wire saw, but were unable to determine the temperature field distribution throughout the entire cutting process. Finite element simulation is an effective method for analyzing the temperature field and thermal deformation of sawing [13; 14]. Based on the theory of thermoelasticity, a finite element model for thermal analysis of diamond wire saw cutting sapphire crystals was established in this paper, and the relationship between the temperature field and thermal deformation displacement field of the wafer and the process parameters was analyzed. The influence of wafer thickness, saw wire diameter, saw wire speed, and feed rate on wafer warpage was studied using the simulation model based on the node thermal deformation displacement field of the as-sawn sapphire C-plane. The research results provide a reference for understanding the variation of wafer warpage during sawing and optimizing sawing process parameters. By using the simulation calculation model of wafer warpage established in this paper, the influence of various process parameters on the warpage of sapphire wafers can be analyzed, the warpage of sapphire wafers cut by diamond wire saw under different process conditionscan be calculated, and rapid prediction of sapphire wafer warpage can be achieved. The use of this simulation calculation model can avoid conducting multiple sawing experiments, saving costs such as time, manpower, and financial resources. In this paper, the theoretical basis of temperature field analysis for sawing is introduced in Section 2, the details of finite element modeling are introduced in Section 3, and sawing experiments are conducted in Section 4. In Section 5, the simulation results of the temperature field and thermal deformation of the wafers are analyzed in Section 5.1, and the rationality of the simulation calculation model is verified by experimental results in Section 5.2. Then, based on the established simulation calculation model, the wafer warpage under different sawing process conditions is calculated and analyzed in Section 5.3. Finally, the research conclusions are summarized in Section 6.
## 3 Proposed Method
### YOLOv3 Coupled with Patch-Wise Detection Strategy
After the patch extraction, the annotations were re-calculated and saved in a.CSV file. As an example, consider an image of size 1360 \(\times\) 800 pixels image shown in Figure 5(a), it has one traffic sign with bounding box annotations [763, 426, 812, 473]. Its extended coordinates will be [363, 26, 1212, 873]. All of the coordinates are within dimension limits of the training image except the last one exceeds 800. Therefore, it is constrained to 800. The patch obtained from the aforementioned image is shown at the bottom of Figure 5(a). Similarly, the test images were forward into the network in the form of patches of size 400 \(\times\) 400 pixels as illustrated in Figure 5(b). A 400 \(\times\) 400 pixels-sized window slides over the entire image with a stride of 100 pixels. The portion of the image captured in the window is cropped and saved as shown at the bottom of Figure 5(b). The window slides to the next position with a stride of 100, as shown by the blue border square image. This image patch-wise cropping continues until the window reaches the right border of the image. A new position was attained in the y-direction with a stride of 100 as shown by the green square in the image. The sliding and cropping continue along the x-axis. This process was repeated until the complete image was covered by the sliding window. These patches were forwarded into the network for detection. Once the detection is complete, the patches were recombined into a single image to re-create the original test image. Figure 5: Effect of larger image resizing on traffic sign features. Figure 6: Patch-wise detection strategy (**a**) train image patch (**b**) test image patches. The proposed patch-wise training helped in retaining the fine features of traffic signs, which were lost because of the re-sizing of an input image to a lesser number of pixels. The proposed method improved recall percentage by 20% and subsequent detection accuracy by 13% as compared with the default rescaling.
# IFE-Net: An Integrated Feature Extraction Network for Single-Image Dehazing
## 3 The Proposed Method
### Network Design  
The architecture of the proposed IFE-Net contains three essential parts: (i) fused filters of different sizes concatenated them together to form a multiscale feature block; (ii) an attention mechanism composed of channel attention, pixel attention, and texture attention; and (iii) a bilateral constrained rectifier linear unit (BCReLU). As illustrated in Figure 1, the input image is first passed to the multiscale feature extraction block to produce multiscale features. Next, we process multiscale features using an attention mechanism block. The combination of the multiscale feature block and attention mechanisms module forms the \(D(x)\) estimation block. Finally, we employ BCReLU to perform nonlinear regression on \(D(x)\), thus obtaining the clean image.  
Figure 1: Overall architecture of IFE-Net.
# Mechanical Activation of Smectine-Based Nanocomposites for Creation of Smart Fertilizers
## 3 Results
### Nutrient Release Characteristics of Composites  
The release of nitrogen from the composites was studied by the soil column leaching experiment (Figure 6). The release of ammonium (NH4\({}^{+}\)) from the composites was faster in the first 7 days than in the remaining period (Figure 6A). The ammonium content in the leached solution was almost similar in the first few days for all composites. After the first week, the composite S3N2 pm11 released higher NH4\({}^{+}\) relative to others. The leaching rate of ammonium was stable after the 28 th day for all samples, which was followed by the overlapping leaching curves (Figure 6A). The urea control released ammonium at the fastest rate at each stage of the experiment. The release of nitrates took place at different rates during the experiment (Figure 6B). The nitrate was leached in four steps, namely: up to the 7 th day, from the 7 th to 21 st day, from the 21 st to 28 th day, and from the 28 th to 56 th day. However, the urea control released the most nitrate at all stages. The composite S3N2 pm11 released nitrate at the slowest rate. The rate of release of nitrate stabilized after the 28 th day for all samples. The total nitrogen was released in four stages, following the similar fashion of nitrate release (Figure 6C).  
Figure 5: TG patterns of smectite-urea composites compared with smectite. The weight losses of the intercalated nitrogen in intervals of 300–450 \({}^{\circ}\)C and 450–590 \({}^{\circ}\)C.  
Figure 6: Cumulative release of (**A**) NH4\({}^{+}\), (**B**) NO3\({}^{-}\), and (**C**) total nitrogen during the soil leaching test. Note the sharp decrease in the steepness of curves after 6 to 7 days. The statistical significance of every parameter indicates at the \(p\) = 0.05 level. Bars represent standard deviation.
# IOF-Tracker: A Two-Stage Multiple Targets Tracking Method Using Spatial-Temporal Fusion Algorithm
###### Abstract
This allows the tracker to fully utilize both the temporal and spatial features of targets during the association and matching phase, thereby our approach can enhance the tracking performance of the tracker. The main contributions of this paper are as follows:  
* To obtain the overall motion trend of the target and to suppress noise interference, we calculate the average optical flow vectors of each pixel within the detection and prediction regions. The average optical flow can improve the robustness of motion estimation. * To address the issue that most trackers underutilize target motion features during multi-object tracking, we introduce the temporal feature represented by optical flow and the spatial feature indicated by R-IoU (Reverse of Intersection over Union). By fusing these temporal and spatial features, we develop a model that enhances the tracking performance of the tracker. * We integrate the R-IoU and optical flow feature fusion model into a two-stage association tracking framework. Experimental results show that our IOF-Tracker multi-object tracking method significantly improves the tracking performance. On the MOT17 dataset, our method's HOTA score reaches 64.9. At the time of submission, our method ranks first on the MOT17 dataset.
## 2 Materials and Methods  
### Finite Element Simulation  
The finite element method (FEM) simulation provides an accurate flow stress analysis and a quicker method to optimize the process parameters during forming. Hence, reducing the production cost and time [15]. The metal flow pattern obtained from the simulation analysis is of great advantage in the manufacture of high-quality products. In this study, DEFORM-3D software was used to investigate the influence of forming parameters on the flow stress behavior of titanium alloys (Ti-4.5 Al-1V-3 Fe, Ti-6 Al-1V-3 Fe, and Ti-6 Al-4V). The general relationship between these process parameters (strain-\(\varepsilon\), strain rates-\(\dot{\varepsilon}\) and temperature-T) and flow stress (\(\sigma\)) is as given in Equation (1) [16].  
\[\overline{\sigma}=\overline{\sigma}\ \big{(}\overline{\epsilon},\ \dot{ \epsilon},\ T\big{)} \tag{1}\]  
The flow behavior of titanium alloys was studied by inputting the constitutive parameters derived from experimental flow stress data [17; 18; 19; 20]. The constitutive parameters used in this analysis are given in Table 1. The software input window is shown in Figure 1. The FEM simulation analysis uses a rigid viscoplastic formulation approach [16]. The governing equations of the metal-forming process are widely covered in the literature [16]. The die and workpiece design was carried out using the in-built software modeling module. The workpiece dimensions were similar to those used in the physical laboratory simulation. Table 2 shows the simulation parameters and conditions. The sample reduction in height was from 12 to 6.3 mm, \(\varepsilon\approx\) 0.64 for the low strain rate (0.1 to 10/s) simulations. A total strain of \(\sim\)1.4 was used for the high strain rate (20 to 10,000/s) simulation.
## Materials and Methods
### Numerical Modelling
#### 2.3.4 Defect Configuration and Loading Sequence  
Three common pipeline dent-crack defect configurations were investigated. The first defect configuration investigated was a pipe with an unrestrained dent-crack defect subjected to internal pressure. The denting load was applied through the indenter as a displacement. The indenter was released from the surface of the cracked pipe after loading, to create the unrestrained dent-crack and the flawed pipe was then subjected to internal pressure to propagate the crack. The second defect configuration was a pipe with a restrained dent-crack subjected to internal pressure. The indenter was left on the pipe surface after applying denting load and internal pressure was then applied to propagate the crack. This configuration simulates a pipe dented and cracked by a hard object on which it is placed during construction. The last configuration was a pipe with a restrained dent-crack defect, subjected to maximum allowable operating pressure (MAOP) of the pipe. The indenter was gradually released from the pipe surface with the pipe still at MAOP. The objective was to assess whether releasing the indenter at MAOP would propagate the dent-crack. Often in the field, an initially restrained dent is unrestrained circumstantially. In such a scenario, getting the maximum safe operating pressure that does not propagate an existing crack as the restraint is released becomes paramount. Figure 4 shows the steps used to vary the defect configurations investigated in this study. Different permutations of the steps produce different defect and loading scenarios in the pipe. Unrestrained dent-crack defects under internal pressure were realized using steps 1-4. Restrained dent-crack under internal pressure was created using steps 1, 2, and 4, eliminating step 3 altogether. Finally, the third scenario (release of indenter at MAOP) was simulated using steps 1, 2, 4, and 3, in that order, with the amount of pressure used in step 4 being the MAOP of the pipe. Parametric studies were conducted for the three defect scenarios by varying denting pressure, dent depth, and crack dimensions.  
Figure 3: Showing: (**a**) Model geometry; (**b**) Mesh distribution in the model.