esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Phase Bias Estimation for BeiDou Satellites Based on Consideration of Orbit Errors
## 4 Results
### PPP-IAR Performance Analysis
After consideration of the orbit errors, the residuals are all within 0.1 cycles for each satellite and for all stations, with the exception of three outliers. No trend between the size of the residual and the distance to the central station of the network is shown. Because the station-specific errors (i.e., troposphere errors and multipath errors) will not present such significant location-specific regulation (see Figure 12) and because these errors will not disappear when the orbit errors are considered (see Figure 13), it can thus be confirmed that the large residuals shown in Figure 3 are caused by large orbit errors and the proposed method can thus work appropriately in eliminating the effects of large orbit errors on NL FCB estimation. Figure 12: A posteriori residuals of the NL ambiguities when using the traditional method with respect to distance to the central station of the network (HALN). Figure 13: A posteriori residuals of the NL ambiguities when using the proposed strategy with respect to the distance to the central station of the network (HALN).
# Smart Bandwidth Assignation in an Underlay Cellular Network for Internet of Vehicles
## 5 Proposed Solution Tests and Validation
### Scenarios and Parameters  
We assume a single cell outdoor system with a carrier frequency of 2 GHz where each RB has a bandwidth of 180 KHz for the uplink communication. Radio resources are organized in 100 RBs following the SC-FDMA (Single Carrier Frequency Divison Multiple Access) scheme used in the UL [40]. According to the agreed 3 GPP V2X deployment scenario for highways [41], a straight highway with 6 lanes in total, 3 in each direction, passes through the cell, and \(M\) V-UEs and \(N\) C-UEs are distributed in the scenario defined to check the proposed solution. The number of V-UEs and C-UEs varies depending on the scenarios simulated explained in Section 5.2 and V-UEs move with a speed of 140 km/h. The used channel models are specified by [42] and the simulation parameters are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Carrier Frequency & 2 GHz \\ Number of RBs & 100 \\ RB bandwidth & 180 kHz \\ Scenario & 1 km highway \\ Number of users & 50 \\ V-UEs velocity & 140 kmph \\ Channel model & Line of Sight \\ V2V coverage radio & 100 m \\ Number of Scenarios & 9 \\ Simulation time & 2 s \\ Number of runs per scenario & 6 \\ Frame size & 1 ms \\ \(P_{RBmax}\) & 125 mW \\ \(P_{a}\) & \(-\)162.5 dBm \\ \(\alpha\) & 0.8 \\ \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters.
## 4 Conclusions  
1. As the interfacial water content increased, the size of the C-S-H/SiO\({}_{2}\) model increased gradually. As the interfacial water layer thickened (increasing from 0 A to 5 A), the number of water molecules invading C-S-H increased gradually, leading to an increase in the difference between the initial size and the balanced size of the model structure.
2. With the injection of interfacial water molecules, the O atoms within the H\({}_{2}\)O molecules easily bonded to Ca\({}^{2+}\) ions, resulting in almost all of the Ca\({}^{2+}\) ions being located back within the C-S-H. As the interfacial water layer thickened, the Ca\({}^{2+}\) ion desorption in the C-S-H surface region became significant, and the number of Ca\({}^{2+}\) ions entering the water layer region increased, leading to a decrease in the interface cohesion.
3. As the interfacial water layer thickened, the interaction energy of the C-S-H/SiO\({}_{2}\) progressively became larger, and the energy ratio (ER) decreased significantly. The same conclusion has also been confirmed in experimental research.
4. The C-S-H/SiO\({}_{2}\) system stress-strain curves with varying water-layer thicknesses were all divided into two stages: the elasticity stage and the failure stage. With an increased interfacial water-layer thickness, the tensile strength \(\sigma_{\mathrm{c}}\) and the residual  
Figure 8: (**a**) The C-S-H/SiO\({}_{2}\) system stress-strain curves; (**b**) tensile strength \(\sigma_{\mathrm{c}}\) and residual strength \(\sigma_{\mathrm{r}}\).  
strength \(\sigma_{\rm r}\) of the C-S-H/SiO\({}_{2}\) system both showed a downward trend. The weakening effect of a low water content on the interface bonding strength was limited, and as the interfacial water content increased, the weakening effect on the C-S-H/SiO\({}_{2}\) was enhanced. This phenomenon has been verified in concrete interfacial bond strength experiments.  
Conceptualization, B.M. and X.H.; methodology, Y.C.; software, Y.C.; validation, B.M. and X.H.; formal analysis, Y.C.; investigation, Y.C.; resources, B.M. and B.Y.; data curation, Y.C.; writing--original draft preparation, Y.C.; writing--review and editing, Y.C. and B.M.; visualization, X.H.; supervision, B.Y.; project administration, B.M.; funding acquisition, B.M. and X.H. All authors have read and agreed to the published version of the manuscript.  
The work was supported by the National Natural Science Foundation of China (No. 12162010), the Science Technology Base and Talent Special Project of Guangxi, China (No. AD19245143), and the Natural Science Foundation of Guangxi, China (No. 2021 GXNSFAA220087).  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare that they have no known competing financial interests or personal relationships that could appear to have influenced the work reported in this paper.
## 3 Method
### System Overview  
The schematic network architecture is showed in Figure 1. It handles these three tasks separately with the utilization of three sub-networks, within which each one of them is capable of jointly training and supervising one another mutually, resolving the scene perception task easier. Additionally, rich occlusion-aware geometric constraints are applied within and between these tasks to supervise one task another mutually. We present a way to employ the nature of occlusion to a greater extent instead of simply filtering it out, by combining it with various geometric constraints.  
During training, we formulate this problem as: Given consecutive frames \(M_{t-1}\), \(M_{t}\) with known camera intrinsic, \(D_{t}\) as per-pixel depth map of each frame is estimated, and \(T_{t-1}\) denotes relative camera motion from \(t-1\) to t, \(F_{f}\) presenting forward optical flow while \(F_{b}\) standing for the backward optical flow. The forward and backward optical flow are used to generalize the occlusion mask to reduce the influence of occlusion when applying rigid geometric constraints. As depth and camera motion are available, we generate point cloud \(P_{t}\) of frame \(M_{t}\) which is a basic element throughout our 3D constraints of the network. The frames are preprocessed as four pyramid scales from \(l_{1}\) to \(l_{4}\). Details of the methods are discussed below.
## 3 Result and Discussion
### Breakdown of the Leakage Vortex Core
#### 3.3.3 Shock Wave on the Breakdown of the Leakage Vortex  
Figure 17 schematically shows the shock wave on the tip-leakage vortex. In this figure, \(\alpha\) is the incident angle of the leakage vortex axis relative to the normal direction of the shock wave and \(\beta\) is the emergence angle of the flow behind the shock wave.  
Figure 16: Relative Vorticity Distribution near the tip leakage vortex core (95% span).  
Figure 15: Velocity distributions for plane normal to flow direction (\(\rm{Ma}_{2}=0.79\), \(\rm{x}\)\(C_{x}=0.85\)).  
Based on the oblique shock correlation [39], the flow turns towards the shock surface after the shock so that the flow angle increases. Therefore, the axis direction of the tip-leakage vortex quickly moves away from the suction side of the blade so that it separates from the suction surface. However, the separated tip-leakage vortex re-attaches to the suction surface, thereby forming a low vorticity region A called \"vorticity separation region\". This phenomenon is attributed to the transverse pressure gradient in the passage.  
Considering the high adverse pressure gradient before and after the shock wave, the flow velocity in the tip-leakage vortex core decreases rapidly. Coupling with its rotating motion, the diameter of the vortex core rapidly expands and forms a low pressure and reversed flow region, which can be considered as the breakdown of the vortex core due to the centrifugal instability of swirling flow [40].  
Based on the performed analysis, it is inferred that in supersonic flow, the leakage vortex breakdown mainly originates from the interaction between the shock wave and the leakage vortex. However, the internal cause is still the centrifugal instability of the vortex as that occurred in the subsonic flow.
## 2 Broadband System
### Wireless Broadband System
#### 2.2.4 Free Space Optics  
As illustrated in Figure 6, an FSO system is an LoS technology in which infrared sources, namely, lasers, are employed to provide optical bandwidth connections. It has been gaining significant attention for a prompt establishment of broadband PtP communication links for rural and urban applications due to its bandwidth and protocol-independent nature. Based on this, FSO systems can be employed for applications like provision of PtP LoS link, last-mile access, metro network extension, wireless backhaul, enterprise connectivity, as well as fiber backup [160; 161; 162]. Furthermore, FSO technology offers advantages such as license-free operation [160; 161; 163; 160], highly secure wireless system [161; 163; 160], ease of deployment [163], full-duplex transmission [163], low installation costs [16], wide communication range [160], immunity to radio frequency interference [161], and extremely high-speed data transmission [163]. Nevertheless, owing to the FSO system PtP LoS link nature, its deployment for the envisaged massive devices in the 5G and beyond broadband networks might not be cost-effective. Besides, it has a number of associated challenges regarding the inability to propagate through obstacles and susceptibility to poor weather conditions and atmospheric effects. The effects can bring about beam wandering and scintillation, which may subsequently result in the fluctuation of the received optical intensity and system outage. Therefore, the degree of the effects can be used to determine the system availability and reliability [161; 163; 164; 165; 166; 167; 168].  
Note that the wavelength ranges of 780-850 nm and 1520-1600 nm are usually employed in the current FSO equipment [169]. Additionally, the employed wavelengths in the scheme significantly assist in atmospheric absorption losses mitigation. For instance, an attenuation of less than 0.2 dB/km is typically experienced by wavelength windows in the region of 850, 1060, 1250, and 1550 nm wavelengths. Remarkably, the 850 nm, as well as 1550 nm transmission windows, that are now being employed in the FSO systems coincide with the standard transmission windows of fiber optic systems. Therefore, to encourage the employment of the existing commercial off-the-shelf (COTS) equipment, most of the viable FSO systems operate at 850 nm and 1550 nm transmission windows [166; 169].  
In addition, the 1520-1600 nm wavelengths are compatible with and can leverage erbium-doped fiber amplifier (EDFA) and COTS wavelength-division multiplexing (WDM) transmission equipment which can help in realizing high-power and high-data-rate systems. Moreover, about 50-65 times more average output power can be transmitted at the wavelengths compared with 780-850 nm for a given eye safety classification due to the low transmission of the human eye at 1520-1600 nm wavelengths [166; 169]. In Section 6.1, we give a further discussion on the FSO system.
## 1 Introduction  
Owing to the COVID-19 pandemic, health policies have mandated the use of masks in many public institutions and workplaces. Children are particularly encouraged to wear masks because of their weak immune systems. However, the limited facial information visible in people wearing masks can affect children's social skills, which are heavily influenced by their ability to read facial expressions [1, 2]. Recent studies have explored methods to identify faces and recognize facial expressions when a mask partially covers the face, particularly when divided into top and bottom parts. For example, one study focused on using the upper facial region for face identification [3]. Another study separated the upper and lower facial regions to derive action units for facial expression recognition [4].  
As will be discussed in detail in Section 2, existing facial expression recognition studies only pursue technical improvements in facial expression recognition. The same goes for face identification technology. These studies on improving accuracy have been developed into studies on the recognition rate of the upper and lower parts of the face according to the COVID-19 pandemic. However, studies comparing the results are lacking. We felt the need to interpret the results of facial expression recognition studies and studies regarding face identification of the upper and lower parts of the face.  
In this study, we aimed to specify the parts of the face that are most important for both face identification and facial expression recognition, as well as to analyze how masks affect the ability to recognize emotions. Specifically, we analyzed the accuracy of eight facial expressions (neutral, happy, sad, surprise, fear, disgust, anger, and contempt) affected by the upper or lower parts of the face. We based our study on a deep learning model trained on a sufficiently large amount of data labeled by experts. Not only does this have the advantage of reducing dependence on the demographic pool of testers, but research [5] has shown that the areas on which deep learning models focus during facial expression recognition are not significantly different from those on which humans focus. Similarly, studies [6; 7] have compared the performance of human and deep learning models in face identification and found them to be similar. Therefore, this study was conducted under the assumption that a sufficiently trained deep learning model can act as a sample of many different people.  
The remainder of this paper is organized as follows: Section 2 provides an overview of related studies, including the models used for face identification and facial expression recognition tasks. Section 3 describes our proposed methods. Section 4 presents an analysis and discussion of the experimental results, and Section 5 concludes the study.
## 1 Introduction
Application-level decisions regarding the virtualized SBAs are correlated with and used to drive the resource provisioning and adjust the workload and traffic to automate the dynamic configuration and deployment of application instances onto available cloud resources. The blueprinting approach promotes service virtualization by allowing an SOA-inspired approach that supports independent layering within a typical cloud stack. For example, a developer can choose to compose services from multiple SaaS providers into a coherent and integrated SBA, which s/he can then synthesize with platform services from one or more PaaS providers, and deploy on a federated cloud infrastructure of multiple IaaS providers. Our previous work [9] has defined a _Blueprint_ as a uniform abstract description of a cloud service offering that hides all specific technical details and complexities to facilitate the SBA developers with the selection, customization and composition of cross-layered cloud services across various syndication channels. It also proposed a structural schema called the _Blueprint Template_ that can be used to describe and customize a blueprint. After releasing the first version of the template within our  
Figure 1: Monolithic _vs_. Syndicated Multi-channel Cloud Delivery Model. industrial partners in the EC's 4 caaSt FP7 project [10], we have received useful comments and feedback that helped us work towards an improved version of the template in this paper. Furthermore, this paper introduces a universal _Blueprint Lifecycle_ that shows how cloud-based services described by blueprints are designed, composed, and ultimately deployed on a cloud infrastructure, which will results in a running SBA in the cloud. The rest of this paper is structured as follows: in Section 2 we introduce a cloud computing scenario that has been defined by the 4 caaSt community and will be used as the running example throughout the paper; Section 3 reviews related work to show why the blueprinting approach is necessary; in Section 4 we elaborate the _Blueprint_ concept, its fundamental structure and different types of blueprint; in Section 5 we propose the _Blueprint Template_ to capture cross-layer cloud service offerings; Section 6 proposes a six-step lifecycle for the blueprinting approach that supports the engineering of SBAs in the cloud; in Section 7 we present our empirical evaluation approach and blueprint prototype implementation; finally, Section 8 summarizes the paper and discusses future issues.
# Leg Trajectory Planning for Quadruped Robots with High-Speed Trot Gait
## 2 Materials and Methods
### Leg Trajectory Planning  
#### 2.4.1. Stance Phase Trajectory Design  
The leg movement of a quadruped robot can be divided into two phases: the stance phase and the swing phase. Consider that high acceleration and extensive loadings will be exerted on the legs during the stance phase. If only the position control is used, it will easily bring a greater rigid impact to the quadruped robot's body, which does not benefit stable walking or running and will also cause damage to the structure. Therefore, the stance and swing phases are separately designed with position control and impedance control. When the leg contacts the ground, an impedance control method will be applied to ensure the flexible contact between the leg and ground, which can reduce the impact effectively [21,22].  
In order to reduce the impact between the leg and ground when the leg leaves or touch the ground, the X-direction motion of the stance phase is designed as a uniform velocity. The formulas of the X-direction of the stance phase are shown as Equations (11) and (12):  
\[S_{ST,x}=V_{desire}t_{ST} \tag{11}\]  
\[T_{ST}=\frac{L}{V_{desire}} \tag{12}\]  
where \(L\) is the leg stride in a gait cycle, \(T_{st}\) is the time of the stance phase, and \(V_{desire}\) is the desired speed of the robot.  
Consider that the cosine function has a good performance in smoothness, so the cosine function, which was also used in other robots [19,23], was used in the trajectory of the stance phase. Considering that the leg will bear an impact and generate deformation when it touches the ground and the impedance control is used to resist impact in the stance phase, it needs a virtual displacement \(\triangle\) to ensure the body stability of quadruped robot. The Y-direction formula of support phase is shown as Equation (13):  
\[S_{ST,y}=-\Delta\times\cos(\pi(\frac{1}{2}-\frac{V_{desire}t_{ST}}{L}))-P_{0} \tag{13}\]
# Internet of Things and Distributed Computing Systems in Business Models
## 4 Theoretical Framework
### Discussion
#### 4.5.2 Complexity and Management  
The match of IoT and DCS implies substantial complexity in terms of system management and coordination. IoT networks consist of numerous interconnected devices, each generating huge quantities of information that must be processed [90]. Managing such a large-scale, heterogeneous network requires advanced infrastructure and sophisticated software to ensure seamless operation and interoperability. Distributed computer systems, which operate by dividing computational tasks across multiple nodes, add another layer of complexity. The complexity of these innovations is illustrated in [PERSON] et al. [91], which indicates that a simple IoT application consisting of 95% status-reporting queries and 5% user queries, and serving 10 billion machines requires 1000 to 100,000 nodes functioning at a 50 ms response rate. Coordinating these nodes, managing their interactions, and ensuring data consistency across the network can be challenging for most managers.
## 3 Results and Discussion  
### Dynamic Response Test of the Control System  
In order to evaluate the actual control effect of the sowing depth measurement and control system, we conducted dynamic tests on the IPRC in the field. The commonly used compaction setting value range of 0-280 N was selected, with a setting interval of 70 N. Four compaction adjustment ranges were selected: 70 N, 140 N, 210 N and 280 N. The data sampling frequency was selected as 10 Hz, the sampling time was 15 s and the test results were obtained, as shown in Figure 14.  
The test results show that when the target compressive force is in the range of 0-280 N, the steady-state error of the system response is less than 10. When the target compressive force is 280 N, the adjustment time and rise time are at a maximum, 6.1 s and 0.64 s, respectively. The overshoot range of the four tests is 18.7%-26.6%, and the average value is 21.8%. The range of the steady-state error is 6.2-10.7, the average value is 8.63 and the error is within 10% when the system is stable. Looking at the curve, it can be seen that the pressure at the end of the system response is slightly reduced, the natural leakage of the system pressure is caused and the introduction of feedback regulation not only eliminates the system static difference but also compensates for the hydraulic pressure leakage. The detailed test result data are shown in Table 1.  
Figure 14: Step response test results of the depth system at different compressive force settings. (a) 0–70 N; (b) 0–140 N; (c) 0–210 N; (d) 0–280 N.  
Compared with other studies on sowing depth control, we provide a more powerful and stable sowing depth measurement and control system for seeders with the help of a tractor hydraulic system. In response testing, for example, [PERSON]'s study [31] used a split hydraulic valve group design with common PID control. The response speed measured under a bench test was 2.69 s, steady-state error 91.5 N and overshoot 2.9%. In this paper, we chose to complete the dynamic response test in the field to be able to predict the actual effect of the control system intuitively. Under the field test, the overshoot was reduced by 25.9% on average, and the response accuracy was improved by 90.6%. This is due to the sowing depth measurement and control algorithm which can adjust itself according to the soil environment, so our system has higher response accuracy.