esa-sceva/satcom-chunk-collection huggingface dataset:
# A Virtual Network Resource Allocation Framework Based on SR-IOV
## 4 Basic Idea and Implementation
### VF Scheduler  
The VF Scheduler module is located in the privileged domain Dom0 and is responsible for the global scheduling of the VF, and all VMs are placed in the two queues of PQ and GQ. The purpose of classifying VMs is to be able to quickly identify the type of VM and efficiently allocate VF resources dynamically. Among them, PQ is a _red-black tree_, which stores network-intensive VMs, while GQ stores other types of VMs, including disk I/O-intensive and CPU-intensive VMs. To improve the network performance of network-intensive VMs and improve the overall network performance of the system, VMs in PQ will always use VF, while VMs in GQ work with para-virtualized NICs. Since the effective bandwidth acquired by each VF is inversely proportional to the total number of configured VFs, by limiting the number of VFs, it is ensured that there is sufficient bandwidth allocated for non-network-intensive VM. The number of VFs is set according to the specific environment, such as the number of VMs, the size of available bandwidth, and so on.  
To ensure the network performance of the system, the purpose of VF scheduling is to make the network-intensive VM always use VF, so the VM in the PQ should be dynamically adjusted. Initially all VMs are put into the GQ, and then the VM is inserted into the PQ according to the network density. When the VM network density in the PQ is lower than that of the VM in GQ, the VM in the PQ is moved to the GQ and the VF is deprived, and the VM with the most network density in the GQ is selected to be inserted into the PQ and allocate the remaining VFs. The VF and para-virtualized NIC switching during VF dynamic allocation uses the technique of Section 3.  
Figure 6: The process of receiving data packets by SR-IOV NIC.  
We combine I/O factor _io_degree_ and network factor _net_degree_ to determine the degree of network-intensive VMs. For hybrid VMs (that is, VMs with dense network and CPU), we prefer to assign VF to network-intensive VMs, but we cannot distinguish the two by using network factor _io_degree_ alone. For this reason, we first use I/O factor _io_degree_ to classify VMs into I/O-intensive and non-I/O-intensive VMs, and then use network factor _io_degree_ to distinguish network-intensive VMs from I/O-intensive VMs. Specifically, letting each VM's I/O factor _io_degree_ be compared to a threshold (according to a specific environment setting) can divide the VM into I/O-intensive and non-I/O-intensive in a linear time. Then I/O-intensive VMs are sorted according to network factor _io_degree_ from large to small, and the network-intensive VMs are judged according to the number of the remaining number of assignable VMs, and the VFs are allocated according to bandwidth requirement of the VM. In this way, in order to improve the overall network performance of the system, the network-intensive VM in the PQ dynamically allocates the VF with a time complexity of \(O(\log n)\), where \(n\) is the number of network-intensive VMs in the PQ.
# Integrated Adaptive Cruise Control with Weight Coefficient Self-Tuning Strategy
## 2 Modeling of Integrated Car-Following System
### Longitudinal Dynamics Model  
As illustrated in Figure 4, three state variables are defined: the distance error \(\Delta d\), speed error \(\Delta v\) and derivative of the longitudinal acceleration (jerk for short) \(j\), which can be described as  
\[\left\{\begin{array}{l}\Delta d=d-d_{des}\\ \Delta v=v_{p}-v_{x}\\ j=\dot{a}_{x}\end{array}\right. \tag{8}\]  
where \(d\) is the actual inter-vehicle distance, \(d_{des}\) is the desired inter-vehicle distance, \(v_{p}\), \(a_{p}\) and \(v_{x}\), \(a_{x}\) are the longitudinal speed/acceleration of the preceding vehicle and the following vehicle respectively.  
With respect to \(d_{des}\), the constant time headway (CTH) spacing policy [17] is employed, which changes linearly with the speed of the following vehicle.  
\[d_{des}=\tau_{h}v_{x}+d_{0} \tag{9}\]  
where \(\tau_{h}\) is the nominal time headway and \(d_{0}\) is the zero-speed clearance.  
In addition, the decoupled longitudinal dynamics of the vehicle (see Figure 3) is still nonlinear, mainly due to the nonlinearity of the engine torque maps, time-varying gear position, as well  
Figure 4: Sketch of longitudinal car-following.  
Figure 3: Disturbance decoupling via a state-feedback.  
as aerodynamic drag force [18]. In order to linearize the longitudinal dynamics of the vehicle, we introduce a first-order inertial system [5], whose transfer function is described as  
\[H(s)=\frac{K_{L}}{T_{L}\cdot s+1} \tag{10}\]  
where \(K_{L}\) is the system gain and \(T_{L}\) is the time constant.  
Integrating Equations (8)-(10), a four state space model for longitudinal car-following dynamics can be formulated as  
\[\mathbf{x}_{1}(k+1|k)=\mathbf{A}_{1}\mathbf{x}_{1}(k)+\mathbf{B}_{1}u_{1}(k)+\mathbf{G}_{1}w_{1}(k) \tag{11}\]  
where \(\mathbf{x}_{1}(k)=[\Delta d(k),\,\Delta v(k),\,a_{x}(k),\,j(k)]^{T}\), \(u_{1}(k)=a_{x,des}(k)\), \(w_{1}(k)=a_{p}(k)\), \(k\) represents the \(k\)th sampling point, \(\mathbf{A}_{1},\mathbf{B}_{1}\) and \(\mathbf{G}_{1}\) are the state matrices, mathematically expressed as  
\[\mathbf{A}_{1} =\left[\begin{array}{ccc}1&T_{s}&-\tau_{h}T_{s}&0\\ 0&1&-T_{s}&0\\ 0&0&1-T_{s}/T_{L}&0\\ 0&0&-1/T_{L}&0\\ \end{array}\right]\] \[\mathbf{B}_{1} =[0,\,\,0,\,\,T_{s}\cdot K_{L}/T_{L},\,\,K_{L}/T_{L}]^{T}\] \[\mathbf{G}_{1} =[0,\,\,T_{s},\,0,\,\,0]^{T}\]
# Research on Multi-Physical Field Coupling Simulation of Local Electrochemical Machining
## 3 Simulation Result Analysis
### The Influence of Cathode End Width on Machining Accuracy  
The purpose of simulation is to improve machining accuracy. For local electrochemical machining, one essential issue that needs to be addressed is controlling the joint marks, and the choice of cathode end width \(L\) is the key to reduce the joint mark. According to Figure 12a, for \(L\) = 3.3 mm, the cathode is fed to the final position when t = 744 s, but the raised joint mark is still retained on the joint area due to the width \(L\) of the cathode end being narrow.  
In order to reduce the joint mark and improve the machining accuracy, the anode shaping processes of the cathode end with different widths \(L\) was simulated. Figure 13 shows the final convex contour obtained by simulation under different cathode end widths \(L\) (3.4 mm-3.9 mm). It can be seen that the raised area rapidly decreases with the increase in cathode end width. When \(L\) = 3.7 mm, the raised area basically disappears. However, when the width \(L\) exceeds 3.7 mm, there will be a certain sunken area, which will continue to grow larger as the width \(L\) continues to increase. Figure 14 shows the up and down deviation between the simulated contours at the joint area with the target circle contour under different cathode end widths \(L\) (3.4 mm-3.9 mm). When \(L\) = 3.7 mm, the maximum deviation at the joint area can be controlled at about 0.02 mm, which is the best cathode end width.  
Figure 12: Localized electrochemical machining results when \(L\) = 3.3 mm.  
Figure 13: Convex contour at the joint area with different widths of cathode end.
## 5 Experimental Verification
### Derailment  
The yaw suspensions of the scaled IRW can be disconnected, as shown in Figure 24. The experimental results in Figure 25 demonstrate that if the IRW rides at low speeds without any yaw suspension, the states of the IRW will all diverge, which means that the IRW will ride towards one side of the rail until the wheel flange contacts with the rail profile, and the IRW will not come back to the center of the rail track again. The derailment tendency caused by the gravitational restoring moment discussed in Section 2.2 is verified.  
Figure 24: The scaled IRW without any yaw suspension.  
Figure 25: Experiment results of the IRW without any yaw suspension at average rotational speed 200 r/min. (**a**) Lateral displacement; (**b**) yaw angle; (**c**) differential wheel rotational speed; (**d**) \(y-\psi\) phase diagram.  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Symbol** & **Definition** & **Value** \\ \hline \(J_{Wx}\) & Wheel\({}^{\prime}\)s inertia moment in the \(x\) direction & 3.08 \(\times\) 10\({}^{-3}\) kg\(\cdot\)m\({}^{2}\) \\ \(J_{Wy}\) & Wheel\({}^{\prime}\)s inertia moment in the \(y\) direction & 3.91 \(\times\) 10\({}^{-3}\) kg\(\cdot\)m\({}^{2}\) \\ \(J_{Wz}\) & Wheel\({}^{\prime}\)s inertia moment in the \(z\) direction & 3.08 \(\times\) 10\({}^{-3}\) kg\(\cdot\)m\({}^{2}\) \\ \(b\) & Half lateral distance between the left and right wheels & 0.083 m \\ \(r_{0}\) & Nominal wheel radius & 0.04 m \\ \(\lambda\) & Equivalent wheel–rail tread conicity at the center position & 0.1 \\ \(d_{\rm r}\) & Nominal roller diameter & 0.6 m \\ \(m_{z}\) & Extra vertical loads & 5 kg \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._It is necessary to re-install the yaw suspensions onto the IRW and re-conduct the experiment at the same speed. All the states of the IRW converge, as shown in Figure 26, which shows the same dynamic characters as the simulation results in Section 4.1.
## 5 Discussion
For local deformation comparison, the photogrammetry method can better capture the local changes of soft clay in real time, especially in radial deformation, which is closer to the true value due to the consideration of the refraction effect. However, it should be noted that the comparison is based on specific experimental conditions and soilsamples. The performance and accuracy of these methods may vary in different geological environments and for different types of soft clay. Future research could focus on further improving the photogrammetry method and, for example, developing more advanced image-processing algorithms to enhance the accuracy of deformation measurement and exploring the use of multi-sensor fusion technology to supplement and verify the data obtained by photogrammetry. In numerical simulation, more complex and accurate soil models can be developed, considering factors such as soil microstructure, anisotropy, and the influence of environmental factors on soil properties. Additionally, more experimental studies on a larger scale and with different types of soil are needed to comprehensively validate the methods and results of this study, providing more reliable references for geotechnical engineering design and construction. Moreover, the triaxial photogrammetry method in this study can measure volume changes, which is a significant advancement. By analyzing the images captured during the triaxial test, the surface deformation of the soil specimen can be obtained, and the total and localized volume changes can be calculated. This is crucial for understanding the deformation mechanism of soft clay. When the soil is under load, the volume change reflects the internal structural adjustment of the soil particles and the change in pore space. In traditional triaxial tests, measuring local volume changes is difficult due to factors like non-homogeneity and end constraints. However, the photogrammetry method can overcome these problems. It can track the movement of the soil surface in real time and, through image-processing algorithms, calculate the volume change corresponding to different parts of the soil specimen. This volume change data can be further combined with stress-strain analysis to more comprehensively understand the mechanical behavior of soft clay, such as its compressibility and shear-induced volume change characteristics. This paper is currently studying the soft soil in China's coastal areas. For different soft soils around the world, this method uses non-contact measurement to solve the problem of disturbing soil samples and avoids the limitations of traditional triaxial measurement. It provides a convenient method for other researchers and engineering practices to investigate undisturbed soft soil. The triaxial photogrammetry method not only has theoretical value but also has certain guiding significance for engineering practice.
## 5 Experiments and Analysis
### Algorithm Comparison  
The two algorithms are compared in terms of computation time, and the results are presented in Table 6. The INIIDA shows a significant improvement in speed, which is basically approximately 30% of the NSGA-II-Dijkstra algorithm. This demonstrates the effectiveness of the INIIDA algorithm in reducing computational time.  
For a practical case, some liquid terminal represents a type of small-scale berths. As an example, consider two berths and designated tanks with a relatively stable selected pipeline. The algorithm can quickly find the optimal path, allowing the PAVP to be solved efficiently using a simple algorithm. On the other hand, when dealing with mixed berths that include a significant number of valves and pipelines, and as  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Quantity of** & **Iterations** & \
\begin{tabular}{c} **Time of NSGA-** \\ **II-Dijkstra** \\ \end{tabular} & **Time of INIIDA** &
\begin{tabular}{c} **Speed Increase** \\ **\%** \\ \end{tabular} \\ \hline
10 & 100 & 1:02:19 & 00:18:43 & 69.97\% \\
10 & 500 & 2:01:31 & 00:45:23 & 62.65\% \\
10 & 1000 & 11:47:15 & 03:09:57 & 73.14\% \\
27 & 100 & 1:01:36 & 00:22:35 & 63.33\% \\
27 & 500 & 5:00:58 & 01:32:18 & 69.33\% \\
27 & 1000 & 10:05:41 & 03:27:57 & 65.67\% \\
42 & 100 & 1:48:12 & 00:38:25 & 64.49\% \\
42 & 500 & 8:09:58 & 02:51:26 & 65.01\% \\
42 & 1000 & 17:02:58 & 05:41:07 & 66.65\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Result of computational time between two algorithms.  
Figure 11: Optimal solution Gantt chart of mixed berths.  
vessels increases, the PAVP requires an improved algorithm for effective analysis. The superiority of the INIIDA algorithm is particularly evident in large-scale mixed berths.
## 2 Related Work
### Healthcare Resource Allocation Approaches  
Several works have targeted healthcare-specific IoT service delivery; however, many fail to address optimization of latency and delivery rate under dynamic network and service conditions.  
[PERSON] et al. [PERSON] et al. (2018) propose a hybrid architecture for secure and energy-efficient healthcare monitoring; however, they primarily focus on security and encryption overhead. [PERSON] et al. [PERSON] et al. (2019) introduced an FL-based offloading model for healthcare, improving privacy; however, it lacks the flexibility to adapt to various latency and delivery demands. [PERSON] et al. [PERSON] et al. (2017) present a DL-based framework for continuous patient monitoring; however, it lacks scalability and rate adaptiveness, limiting its performance under fluctuating workloads. Furthermore, [PERSON] et al. [PERSON] et al. (2019) employ IOTA distributed ledgers to support data integrity in fog-based systems; however, their work does not engage with predictive or rate-adaptive provisioning strategies necessary for workload balancing in high-traffichealthcare settings. Although these studies offer domain-specific contributions, they typically optimize isolated performance objectives (e.g., privacy, energy, latency), leaving a critical gap in unified frameworks that jointly optimize multiple QoS parameters.  
In summary, although recent advances in resource allocation, traffic prediction, and edge-enabled computing have yielded significant progress, most existing approaches do not meet the compound requirements of modern smart healthcare, namely, the need for real-time responsiveness, joint latency-throughput optimization, and scalable deployment across hierarchical IoT layers. Many systems rely on static configurations, centralized schedulers, or domain-specific assumptions that limit their adaptability under volatile healthcare workloads. Table 1 summarizes key features of recent techniques compared to ML-RASPF, highlighting that our framework uniquely supports joint latency-rate optimization and outperforms previous methods in adaptability, predictive control, and architectural coverage. In contrast, ML-RASPF bridges this critical research gap by offering a modular, learning framework that integrates supervised forecasting and reinforcement-learning-based rate control. Its ability to proactively manage traffic surges and dynamically allocate resources across mist, edge, and cloud layers positions it as a scalable and robust solution for next-generation healthcare service provisioning.  
These gaps motivate the development of ML-RASPF, an ML-based, rate-adaptive framework designed to jointly optimize latency and service delivery rate across mist-edge-cloud layers. ML-RASPF uses LSTM-based traffic forecasting and RL-based rate control in a convex optimization structure, which addresses limitations of prior models and delivers a lightweight, scalable, and QoS-aware provisioning mechanism for real-time healthcare IoT services.
## 4 System Design and Analysis
### Optimal Network Utilization Mechanism
#### 4.2.3 Heuristic Somun algorithm  
The goal of long flow scheduling is to find the best candidate from all optional paths for each long flow [43]. The heuristic long flow scheduling algorithm HSA is described in Algorithm 2. The inputs of HSA are long flow set \(F\) and path set \(P\), and the output of HSA is the mapping solution set \(M\). Firstly, HSA initializes the mapping set \(M\) and polls the set \(F\). Secondly, When the set \(F\) is not empty, it means that a long flow is detected and the long flow needs to be scheduled. HSA calculates the \(SRU\) for each path based on the source and destination addresses of the long flow. If there is a suitable path that satisfies the constraints and is available for allocation, HSA deletes this long flow from \(F\), and records the matching relationship between the long flow and the path in \(M\). Finally, HSA updates the path information, outputs the mapping relationship, and generates a flow table for delivery.  
HSA's time complexity is O(\(n\)). The speed of HSA is related to the size of long flow set \(F\) and path set \(P\). The processing of the HSA is extremely simple, which means that the HSA does not need to consume too much computing resources of the controller, and can quickly generate routing policies.
## 3 Results and Discussion
### Effect of the Interface Defect Density  
Because the holes and electron carriers generated by light must pass through the interface to reach each electrode, the interface defect density is important for device performance. The effects of the ZnO/PAL (CsPbI\({}_{\mathrm{x}}\)Br\({}_{\mathrm{3-x}}\)) interface and PAL (CsPbI\({}_{\mathrm{x}}\)Br\({}_{\mathrm{3-x}}\))/NiO\({}_{\mathrm{x}}\) interface defect density were investigated in the interface defect density range of 10\({}^{8}\)-10\({}^{14}\) cm\({}^{-3}\). Figure 11a,b illustrates the current density-voltage (_J-V_) curves for the ZnO/PAL and PAL/NiO\({}_{\mathrm{x}}\) interfaces, respectively.  
Figure 10: Recombination current density with varying PAL defect densities.  
Figure 9: Current density–voltage (_J–V_) curves for various PAL defect densities.  
In the case of the ZnO/PAL interface, as the interface defect density increased from \(10^{8}\) cm\({}^{-3}\) to \(10^{14}\) cm\({}^{-3}\), each of the output parameters (V\({}_{\rm\infty}\), J\({}_{\rm\infty}\), FF, and PCE) decreased from 1.06 V, 25.6 mA/cm\({}^{2}\), 76.0%, and 20.5% to 0.96 V, 25.0 mA/cm\({}^{2}\), 72.4%, and 17.3%, respectively.  
In the case of PAL/NiO\({}_{\rm\infty}\), as the interface defect density increased from \(10^{8}\) cm\({}^{-3}\) to \(10^{14}\) cm\({}^{-3}\), the V\({}_{\rm\infty}\), J\({}_{\rm\infty}\), FF, and PCE decreased from 1.13 V, 25.6 mA/cm\({}^{2}\), 81.8, and 23.7% to 1.01 V, 23.8 mA/cm\({}^{2}\), 56.0, and 13.4%, respectively.  
Figure 11: Current density–voltage (J–V) curves for varying interface defect densities at (a) ZnO/PAL and (b) PAL/NiO\({}_{\rm\infty}\) interfaces.  
Even if the carriers have a diffusion length that can reach each the electrode without recombination, severe recombination can occur because of an increase in the interface defect density [47]. Therefore, when stacking layers, a reduction in the interface defect density is recommended.  
In this analysis, corresponding values of 10\({}^{11}\) cm\({}^{-3}\) or less were considered because both interface defect densities exhibited sharp decreases in efficiency above 10\({}^{12}\) cm\({}^{-3}\). Therefore, the interface defect density was fixed at 10\({}^{11}\) cm\({}^{-3}\) for both the ZnO/PAL and PAL/NiO\({}_{\rm x}\) interfaces. Following this optimization process, the V\({}_{\rm oc}\), J\({}_{\rm sc}\), and FF obtained were 1.09 V, 25.58 mA/cm\({}^{2}\), and 82.89%, respectively, and as a result, a maximum PCE of 23.07% was realized.
## 3 Results
### Spatiotemporal Concentration Image Sequence of the 0.25 mL/min Methane Leak Scenario  
The spatiotemporal sequence of methane concentration images captures the 0.25 mL/min methane leak scenario over a duration of 6 s or 60 images at a frame rate of 10 Hz. These sequences, obtained using the three different methods, are presented in Figure 6 for DAS-2f and in Appendix C for DAS-F and -f, respectively. The methane contrast between the leakage gas and ambient methane, relative to the spatial noise in the image, is in line with the previously described advantages of DAS-f and DAS-2f over DAS-F. The localization of the leak at the center of the image, even for methane leak rates as low as 0.25 mL/min, can be observed in every concentration image of the sequences. By analyzing the concentration sequences, additional details can be extracted. Particularly, DAS-f and DAS-2f reveal the spatiotemporal dynamics of the gas plume around the leak. It becomes apparent that the gas cloud originating from the leak is not stationary, even in controlled laboratory conditions without significant atmospheric disturbances such as wind. These results highlight the practical detection of small leak rates, which requires high spatial resolution, as well as low spatiotemporal concentration noise. This necessity arises from the immediate dilution of methane from the leak into the surrounding ambient air.  
According to Figure 7, the noise equivalent concentration (NEC) image is given by the pixelwise standard deviation of the last second of the concentration sequence (10 images, average time span of 1 s or bandwidth 1 Hz) of the 6 s sequence, as presented in Figure 6 and Figures 3 and 4 in Appendix C. The mode values of the three NEC images for the  
Figure 6: The spatiotemporal sequence of methane concentration images for DAS-2f displays the 0.25 mL/min methane leak scenario within 6 s (60 images) at a frame rate of 10 Hz.  
different methods are also documented in Figure 7 and show that DAS-f and DAS-2f are up to twice as powerful as DAS-F. The temporal variations in concentration values or NEC within this last second of the image sequence can be approximated to 2 ppm\({}^{\text{t}}\)m or even lower for DAS-f and DAS-2f.