esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
Based on the above approach, this paper shows the optimal offset frequency distribution for the two reasonable frequency domains [5 MHz, 25 MHz] and [7 MHz, 21 MHz].  
Table 2 shows the optimal distribution scheme for [5 MHz, 25 MHz], using locking scheme 1, and the alignment is A1. Figure 13 shows its offset frequency transformation curve and the remaining four time-varying beat frequencies curves. It is optimized by narrowing the reasonable frequency domain. The scheme is switched six times in total and the longest one lasting 1459 days. Although the final result increases the number of switches, the increase will not significantly impact the continuous observations within the five years. In addition, we have greatly reduced the overall setting of the offset frequency value, which improves the result of [PERSON] [13] with the same settings, for which some offset frequency values are close to the upper bound of 25 MHz. For the offset frequency setting over a five-year period, a reduction of 20.93% is achieved.  
Table 3 shows the optimal distribution scheme for [7 MHz, 21 MHz], using locking scheme 3, and the alignment is E1. Figure 14 shows two frequency curves. Since all the alignment patterns of scheme 1 cannot find a suitable offset frequency to form a complete distribution scheme. The final solution uses the scheme 3. After low-frequency optimization, the distribution scheme is switched seven times in total and the longest one lasting 1015 days, which substantially improves the results of Barke [9], for which the offset frequency can remain unchanged for up to 177 days and the number of frequency switches can reach 24.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Start** & \(\Delta\)**f1** & \(\Delta\)**f2** & \(\Delta\)**f3** & \(\Delta\)**f4** & \(\Delta\)**f5** & **Last** \\ \hline
0 & 10.57 & \(-\)15.29 & \(-\)15.09 & \(-\)6.50 & \(-\)10.72 & 1459 \\
1459 & 12.34 & 17.02 & 15.89 & \(-\)16.14 & 12.63 & 92 \\
1551 & \(-\)14.44 & \(-\)13.60 & \(-\)8.89 & \(-\)8.31 & \(-\)9.72 & 50 \\
1601 & \(-\)7.93 & 11.63 & 6.07 & 9.96 & 7.35 & 17 \\
1618 & 10.90 & \(-\)14.25 & 12.11 & \(-\)8.94 & \(-\)10.48 & 115 \\
1733 & 11.34 & 11.22 & 9.99 & \(-\)11.03 & \(-\)13.66 & 91 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Offset Frequency Distribution Scheme of 5 MHz–25 MHz.  
Figure 12: Comparison of Different Frequency Band Distribution Schemes After Low Frequency Optimization.
## 2 Theory
### Identification Procedures of the Two Algorithms  
The identification procedures of using the proposed algorithms to identify the stiffness and damping coefficients of each disc are defined in Figure 2.  
Step 1: The unbalance responses at each bearing and other m + 1 unbalance responses should be measured and changed to dimensionless unbalance responses in the frequency domain according to Equation (18). Meanwhile, the rotating speed should also be measured. The inherent parameters, which are the length of the shaft, the mass per unit length of the rotor shaft, the elastic modulus of the shaft and the diameter of the shaft, should be known as prior knowledge and the location of the selected measured points on the shaft should also be used as input.  
Step 2: The matrices H1 and H3 can be calculated according to Equations (4) and (5), respectively. Then, H2 and H4 can be calculated according to Equations (1) and (2), respectively.  
Step 3: Using H2 and the dimensionless unbalance responses in the y direction in the frequency domain, each bearing's main stiffness and main damping coefficients in the y direction can be calculated according to Equation (8). According to Equation (11), each bearing's main stiffness and main damping coefficients in the x direction can be obtained using H4, and the dimensionless unbalance responses in the x direction in the frequency domain. This is the procedure for using Algorithm I.  
Step 4: Change the rotating speed slightly to \(w^{\prime}\) and repeat step 1 to step 3 to obtain H2\({}^{\prime}\) and H4\({}^{\prime}\). Then, using H2, H4, H2\({}^{\prime}\) and H4\({}^{\prime}\), the main stiffness coefficients, the main damping coefficients, the cross-coupled stiffness coefficients and the cross-coupled damping coefficients in both x and y directions are calculated according to Equations (16) and (17). These are the identification procedures of using Algorithm II.  
\[UD=\frac{A}{\pi L}[\cos(\alpha)+i\cdot\sin(\alpha)] \tag{18}\]  
where \(UD\) is the dimensionless unbalance responses in the frequency domain; \(A\) and \(\alpha\) are the amplitude and phase of the unbalance responses in the time domain, respectively.
# Concept for Generating Energy Demand in Electric Vehicles with a Model Based Approach
## 3 Route Data  
In contrast to the approaches presented so far, this work only uses synthetic standard driving cycles (WLTP, NEFC, etc.) for the validation of the model accuracy. For the simulation of the energy flows, realistic routes are used. This method provides a more accurate representation of real driving situations due to their higher level of parameter detail [15]. To generate such routes, not only is the pure route data in the form of longitude and latitude information needed, but additional information is also needed. In the work of [20] the major influencing factors on energy consumption were identified, and from this, the requirements for the necessary additional data for the route are derived. In this work, current traffic data, weather data, topographical data and speed limits are used in addition to the actual route data.  
As a basis for the generation of route data, a route-generating algorithm [21] is used. This takes as input parameters the start and destination coordinates, possibly individual waypoints between the start and destination, and the date of the simulation. Using these input parameters, the route geometry, speed limits, traffic and weather data are generated and combined into a route.  
To obtain the data for the route-generating algorithm, various APIs are utilized. The APIs serve as information sources and return the input data for the route-generating algorithm [21]. The APIs are divided into three categories: data for the visual display of the route (map display), data for the compilation of the route (route data) and additional data like slope, weather or traffic data of the route (additional data). The route-generating algorithm collects this data and, in combination with the user's input data (start and destination of the route, display type and weather/traffic influence), generates the computed route. The schematic of input and output data for the route-generating algorithm is shown in Figure 1. In addition, the route is displayed graphically in the developed _RouteGenerator_ application. The generated data can then be transferred to external programs via an implemented interface. Table 1 shows an overview of available APIs that can be used with the route-generating algorithm.
# A Versatile Resilience Packet Ring Protocol Model for Homogeneous Networks
## 4 Simulation Results
### Comparison between Wired and Wireless Media  
The role of wired and wireless media is well-established. A wired medium can support more bandwidth than a wireless medium [23]. A wireless medium is more flexible than a  
Figure 10: Comparison between packets sent/received and mixed media: (**a**) mixed media, and (**b**) sent/received packets.  
Figure 9: Comparison between wireless and wired media: (**a**) wired/wireless media, and (**b**) wired/wireless and mixed media.  
wired medium. To check wired and wireless media performance, we have collected the packet transmission time of wired and wireless media. We observed that wired media is faster than wireless. The main reason is wireless delay. Figure 10 shows the obtained results. We experimented with different simulation parameters. These parameters have been depicted in Table 3.  
The red color line shows the transmission time in the wireless link. The blue color line shows the transmission time in the wired link. The horizontal line shows the number of packets, and the vertical line shows the time taken by the packet during transmission. In Figure 10a,b, we have also included mixed media, which include both wired and wireless media. This mixed media is similar to wireless media but shows a little bit better performance.  
As a comparison of our work and results with other work, in [14; 24; 25], a case study on the monitor mode passive capturing of WLAN packets in an on-the-move setup, analyzing QoS with multipath routing using real flows dynamic queue (RFDQ) in manet and limiting route request flooding using velocity constraint in multipath routing protocol has been discussed in detail.
## 3 Results and Discussion
Considering a more specific analysis (Figures 6 and 7), the GWP impacts are mostly produced by the silicon feedstock, which includes 50% solar grade silicon (SGS) and 50% electronic grade silicon (EGS), due to the high energy requirement for these processes [5]. Both recycling processes can recover the silicon and reuse it. The thermal treatment can recover crushed silicon cells and reprocess them by ingot growth. The possibility of reusing silicon as raw material or intact wafers to produce new silicon solar cells is beneficial regarding environmental outcomes from the entire process. The recovery of the glass sheet is also advantageous, mostly for the ADP, as this material represents a high percentage of the total weight of the module (approximately 80%) [2]. It is evident that the overall results from the recycling processes are environmentally favourable when compared with the other scenarios included in this study, but some additional aspects should be considered. The ADP impacts for the recycling processes (Supplementary Material) show that the cell production has higher consequences compared to the other EoL scenarios, mainly because, in this study, we did not considered the recovery of other materials besides glass, silver, aluminium and silicon. Lower environmental impacts may be achieved with more complex recycling methods that can recover additional materials and reuse them in new solar cells and modules. The European directive about electronic waste management, which includes PV modules, highlights the importance of recycling potentially harmful substances and rare materials as an environmental solution [33]. Additionally, the substances used during the chemical recycling treatment studied (tetrahydrofuran, o-dichlorobenzene and toluene) present serious issues related to human health and can be risky for fauna and flora. Tetrahydrofuran is a carcinogen and when in contact with humans or animals, can cause severe diseases and even death. This substance is mobile in the environment causing contamination of water, soil and air [39]. The o-dichlorobenzene is not carcinogenic but, otherwise, has the same environmental and health effects [40]. Toluene is also toxic for both humans and animals for acute (short-term) and chronic (long-term) exposures. It can cause several illnesses, but there is inadequate information to assess the carcinogenic potential of this substance. It is an environmentally hazardous material that can affect soil and water causing long-lasting effects in aquatic organisms [41]. Figure 6: Relative environmental impacts for different end-of-life scenarios considering multi-crystalline silicon solar modules: landfill, incineration, reuse and recycling (thermal, chemical and mechanical). Figure 7: Relative environmental impacts for different end-of-life scenarios considering monocrystalline silicon solar modules: landfill, incineration, reuse and recycling (thermal, chemical and mechanical).
# SKVOS: Sketch-Based Video Object Segmentation with a Large-Scale Benchmark +
## 4 SKVOS Model
### Overview  
An overview of our method is shown in Figure 4, where the gray parts represent our vanilla baseline, and the colored parts indicate the new contributions introduced in this paper. Additionally, Algorithm A1 illustrates the complete workflow of the proposed method. Our proposed network is built upon the \"Locate-Propagate-Refine\" framework, which systematically addresses the challenges of sketch-video fusion, mask propagation, and segmentation refinement. Below, we detail the three key components of our framework:  
1. \"Locate\": The primary goal of Section 4.3 is to leverage the sketch to localize the target object in the first frame of the video and generate an initial coarse mask. The core challenge lies in effectively fusing the sketch modality with the video data. To address this, we extend the baseline model by exploring various fusion strategies, enabling robust interaction between the sketch and video modalities.
2. \"Propagate\": In Section 4.4, we propagate the mask across subsequent frames using a memory-based feature matching mechanism. Specifically, we maintain a dynamic memory bank that stores features from previously segmented frames. These features are then utilized to match and guide the segmentation of the target object in the next frame, ensuring temporal consistency throughout the video sequence.
3. \"Refine\": To enhance segmentation accuracy, we introduce two refinement modules. The first module introduced in Section 4.5 aims to improve the spatial-temporal coherence between adjacent frames during propagation. It ensures consistency and smoothness in the representation of objects across frames. The second module proposed in Section 4.6 leverages the sketch as a semantic anchor to facilitate multi-frame learning, enabling the narrowing of the feature differences of the same target object across frames while distinguishing features between different objects.  
Figure 4: The overview of the proposed SKVOS architecture. Initially, a fusion module integrates the sketch annotation with the first frame to accurately localize the target object, serving as a reference indicator. Subsequently, all memory frames, along with the indicator results, are propagated through the memory-based VOS model pipeline. Concurrently, the query frame and adjacent frames are processed by a Temporal Relation Module (TRM), which enhances the space-time coherence between adjacent frames at different scales. Additionally, we introduce Sketch-Anchored Temporal Contrastive Learning (STCL) to reduce feature variations of the same target object under different representations while distinguishing features of different objects. Finally, the fused feature maps are decoded by a hierarchical mask decoder to iteratively refine mask predictions until all frames converge.
## 2 Finite Element Formulations  
### Model of the HDPE Pipes  
Because of the complexity of the crest and valley geometry of an HDPE pipe, the soil grid was irregular when establishing the model, which made it challenging to simultaneously increase the calculation accuracy and efficiency of the model in the actual structure (Figure 2). Therefore, it was necessary to simplify the pipe into a three-dimensional straight-walled pipe.  
As a profiled pipe, the double-wall corrugated pipe is carefully designed with a particular cross-sectional shape of the corrugated exterior wall and straight interior, so its weight is much lower than that of the straight-walled pipe and other profiled pipes with the same hoop stiffness [30]. Under the same installation conditions, the interaction mechanisms between the pipe and soil are similar between straight-walled pipes and double-wall corrugated pipes with the same hoop stiffness [20; 31]. A straight-walled pipe with the same hoop stiffness can be used to replace a profiled pipe in the 2D finite model since field tests have proved the model to be suitable for the deflection and strain of the double-wall corrugated pipe [21; 32; 33]. In this study, the straight-walled pipe was used to simulate the corrugated pipe through the hoop stiffness transformation method, which could better simulate the mechanical response of the pipe under external loads. From the hoop stiffness calculated in Equation (1), the stiffness of a straight-walled pipe is obtained from Equation (2):  
\[S_{P}=EI/D^{3} \tag{1}\]  
\[S_{P}=Et^{3}/12D^{3} \tag{2}\]  
where \(S_{P}\) is the hoop stiffness of a flexible pipe, \(E\) is the elastic modulus of the pipe, \(I\) is the pipe wall moment of inertia per unit length of the longitudinal section, \(t\) is the equivalent wall thickness, and \(D\) is the nominal diameter of the pipe.  
In the simplified double-wall corrugated pipe, the diameter of the pipe remains unchanged, and the wall thickness of the straight-walled pipe is calculated using the hoop stiffness. The corresponding parameters of the simplified double-wall corrugated pipe, i.e., the inner diameter, the wall thickness, and the hoop stiffness, are summarized in Table 1. Although high-density polyethylene is a viscoplastic material, the deformation of the pipe shows linear elasticity in the short term [34]. In the elastic model, the axial displacement of the pipe is constrained, and eight-node reduced-integration shell elements (S8R) are used to simulate the pipes [23; 35]. The material parameters of HDPE pipes suggested by the code of the Technical Specification for Buried Plastic Drainage Pipe Engineering (CJJ) 143-2L10) are shown in Table 2[36].  
Figure 2: Element numbers of double-wall corrugated pipe and straight-walled pipe.
# Short-Range Noncontact Sensors for Healthcare and Other Emerging Applications: A Review
## 5 Future Trends  
Doppler radar has undergone tremendous advancements in the past two decades in nearly all of the technical aspects, including transceiver architecture, antenna optimization, system minimization, chip integration, demodulation techniques, and digital signal processing. Due to the vast potential applications of the technology, we can expect to see the advent of more advances in the years to come. For biomedical applications, random body movement is an issue that prevents Doppler radar from ubiquitous applications. Although some approaches have been proposed to tackle the issue, as discussed in previous sections, more technical advances are needed to solve the body movement issue in some specific use cases such as in-home monitoring. From the system hardware perspective, it is expected to have higher level of chip integration including RF, baseband and the processing unit. As the antenna takes much space, designing a sensor chip at high frequency such as in the millimeter wave range could be an option to minimize the antenna size and even integrate the antenna together on chip or in package. Normally, a Doppler radar can only provide Doppler signature which is the relative motion information, but does not have ranging capability to detect the absolute distance. Other radar techniques, including impulse and FMCW, are good at ranging but not superior at measuring relative motions. Therefore, it is expected to design hybrid radar system that combines the merits of each radar technology. Also, chip integration of the hybrid radar sensor is expected so that the sensor chip could be put into mobile platforms such as smart phone or watch.
# Research on Mechanical Performance of Porous Asphalt Mixture with High-Viscosity Modified Asphalt
## 4 Research on Skid Resistance of Porous Asphalt Mixtures
### Anti-Skid Performance Test and Evaluation Method  
(1) Friction Coefficient Measurement Method  
The British pendulum tester can measure the coefficient of friction of asphalt pavement under wet conditions. It is easy to operate and portable. The British pendulum tester is shown in Figure 18. First, the tester was placed on top of a large, rutted plate specimen molded in the chamber. The tester was then zeroed to ensure that the pointer returned to the zero point after the pendulum was released. Next, the sliding length was calibrated to ensure it was 126 mm. The coefficient of friction is obtained by spraying water at the measurement point and releasing the pendulum, which slides over the road surface.  
(2) Dynamic Friction Coefficient Measurement  
The British pendulum tester can only determine the coefficient of friction of a road surface at low speeds of 20 km/h. To investigate the dynamic friction coefficient of porous asphalt mixtures at high speeds, this study adopted a dynamic friction coefficient tester (DFT) to measure the dynamic friction coefficient of porous asphalt mixtures over the speed range of 0-80 km/h [58]. The DFT is shown in Figure 19. The DFT consists of a freely rotating small disk positioned below the main rotating disk, with three rubber sliders mounted on the small disk. The disk rotates until the rubber sliders reach a linear speed of 80 km/h, at which point the disk is released onto the road surface. The disk continues to rotate until it comes to a stop due to the friction with the road surface. During this process, sensors record the friction between the rubber sliders and the road surface, and a curve of the coefficient of friction versus speed is generated.  
Figure 18: British pendulum tester.
# Temporal Instability Analysis of Injury Severities for Angle and Non-Angle Crashes at Roundabouts
## 6 Results and Discussion
### Analysis of the Characteristics' Effects  
#### 6.1.1 Temporal Characteristics  
As shown in Table A11, the weekend indicator had a significant effect on both angle and non-angle collisions for almost all the analyzed periods (excluding non-angle collisions in 2012-2013). Regarding angle collisions, a time-stable effect of this variable was found. Specifically, crashes occurring on weekends increased the likelihood of no injury or minor injury while decreasing the likelihood of severe injury. Nevertheless, previous research has shown that weekends are more likely to lead to severe crashes [53]. This discrepancy may be since the areas where roundabouts are located typically have lower traffic volumes, especially on weekends. More specifically, there are fewer commuting vehicles, and drivers prefer steady and slow speeds since they are more relaxed on weekends. As a result, weekend crashes in roundabouts are less likely to cause serious injuries.  
For non-angle collisions, there was considerable temporal instability in the impacts of the weekend indicator. Nonetheless, according to the marginal effects in Table A11, non-angle collisions occurring over the weekend showed an overall trend toward more serious injuries over time. Between 2012 and 2019, Florida's total number of tourists increased annually [54]. Especially on weekends, the enormous number of tourists who are unfamiliar with the road will raise the driving risks at the roundabout.