esa-sceva/satcom-chunk-collection huggingface dataset:
# Task Assignment of the Improved Contract Net Protocol under a Multi-Agent System
## 1 Introduction
For example, the method of reducing communication traffic in the initial bidding stage depends on historical information, but the lack of historical information in the initial stage means the overall allocation efficiency is not high. Additionally, there are many instances of negotiation communication between the manager agent and the contract agent, and a lot of redundant information is generated in the process of repeated negotiation, which can easily cause network congestion and deadlock. Based on the research of scholars at home and abroad, this study establishes the dynamic response threshold model and pheromone flow model by combining an ant colony algorithm. We have also designed three experiments to simulate the Repast platform. The results of the three experiments are explained in Sections 5.1-5.3. The improved contract net protocol (ICNP) not only has obvious advantages in operation time and communication traffic, but can also effectively improve the rationality of task allocation and the efficiency of task completion, as well as achieve optimal task allocation results.
## 3 Results and Analysis  
### Kinematic Parameters  
For each landslide experiment, the linear displacement transducer (LDT) was used to measure and record the movement of the sliding body in real time. Based on the displacement data and the corresponding time, the velocity and acceleration values were calculated. Displacement, velocity and acceleration are put together in Figure 3. From the beginning to 70 s, the slope movement is in the initial or uniformly-moving stage. The displacement increases slowly and the velocity is below 0.5 mm/s. The acceleration oscillates near 0 and its amplitude is below 0.5 mm/s\({}^{2}\). After 70 s, the displacement curve turns obviously and then rises rapidly. The velocity suddenly increases from a lower level, and reaches the peak value across 4 mm/s at about 80 s. The acceleration fluctuates sharply after 70 s, and changes abruptly at 80 s, reaching the peak value of 1.5 mm/s\({}^{2}\). The occurrence time and curve characteristics of acceleration mutation are very obvious, which may be able to identify the critical change of slope stability.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{Description} & \multicolumn{2}{c}{**Particle Size**} & \multicolumn{2}{c}{**Packing**} \\ \cline{2-6}  & Size Range (mm) & Coefficient of Uniformity & Particle Density (mg\(\cdot\)m\({}^{-3}\)) & Dry Density (mg\(\cdot\)m\({}^{-3}\)) & \multirow{2}{*}{Void Ratio} \\ \hline Granite gravel & 4.1–7.4 & 1.81 & 2.67 & 1.63 & 0.72 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of backfill material.
# Cluster-Based Knowledge Graph and Entity-Relation Representation on Tourism Economical Sentiments
## 3 Methodology  
In this section of the research, a discussion of a detailed stepwise approach is presented as shown in Figure 1. The process starts with collecting the data from Twitter followed by a few crucial components such as cleaning and preprocessing of the data. Then the collected the tweets are changed to numeric vector representations, where an optimized K-means clustering algorithm is used to group similar entities. Cluster-wise knowledge graphs are finally created to visually represent significant relationships in relevance to tourism and economical sentiments.
## 2 Project Overview and Mining Methods  
The study area is a mine in China, where direct mining is risky due to the thickness of the ore body and the depth of its burial. The mining method is sublevel open stoping with backfilling. In this operation, the ore body is firstly divided into different panels along the trend; then, a fixed-width barrier pillar is designed between the panels, and the room stopes and pillar slopes are alternately distributed along the panels perpendicular to the ore body trend, as shown in Figure 1. The mining of the ore body is divided into three steps: The first step is to mine the room stopes under the primary support provided by ore pillars on both sides and then backfill the mined slopes using the cemented tailings backfill material. The second step is to mine the pillar slopes under the support of cemented backfills on both sides and then backfill the mine slopes using the full-tailings backfill material. After completing the previous two steps, most of the mineral resources have been recovered. To further improve the recovery of mineral resources, the third step is to mine the panel barrier pillar slopes, and finally backfill the mined area also using the full-tailings backfill material.  
Although this step-by-step mining process is more complex, it can effectively control the regional stresses and maintain the stability of the stopes throughout the gradual mining and backfilling, and ultimately achieve the goal of less ore loss.  
In Figure 1, \(l_{1}\) is the length of the room stope; \(l_{2}\) is the length of the pillar stope; and \(a\) is the width of the room stope and pillar stope.
## Results and Discussion
### Raman Spectra Analysis  
Raman spectra for GO and nano-PDA@GO-TiO\({}_{2}\) composites are shown in Figure 5. It can be seen from Figure 5 that GO exhibits the D band at 1356 cm\({}^{-1}\) (breathing mode of A\({}_{1g}\) symmetry) and G band at 1587 cm\({}^{-1}\) (E\({}_{2g}\) symmetry of sp\({}^{2}\) carbon atoms), respectively [40]. For PDA@GO-TiO\({}_{2}\), peaks are clearly observed at 1305 and 1544 cm\({}^{-1}\), which can be assigned to the D and G bands. The peaks at 106 cm\({}^{-1}\), 344 cm\({}^{-1}\), 450 cm\({}^{-1}\), and 582 cm\({}^{-1}\) in the PDA@GO-TiO\({}_{2}\) Raman spectrum should be assigned to the E\({}_{g(1)}\), B\({}_{1g(1)}\), A\({}_{1g}\) + B\({}_{1g(2)}\), and E\({}_{g(2)}\) mode of TiO\({}_{2}\), which indicates the successful incorporation of TiO\({}_{2}\) into rGO [41; 42]. It is also found that, after function with PDA, shows red shifting of the D band peak from 1356 cm\({}^{-1}\) to 1305 cm\({}^{-1}\) shows and the G band peak shifts from 1587 cm\({}^{-1}\) to 1544 cm\({}^{-1}\). Red shifting in G and D-bands of PDA@GO-TiO\({}_{2}\) suggests that the reduction of dopamine lead to removal of oxygenated functionalities from GO surface [40; 43; 44]. In addition, the intensity ratio of the D and G bands, ID/IG, increases from 0.89 for GO to 1.04 for PDA@GO-TiO\({}_{2}\). The results show that the grafting of PDA on graphene and the loading of TiO\({}_{2}\) produce more defects and reduces the average size of the sp2 domain [45].
# Design and Multi-Objective Optimization of Fiber-Reinforced Polymer Composite Flywheel Rotors
## Manufacturing Process
### Filament Winding Process  
The wet filament winding process typically requires four different devices: the fiber tensioner, the resin mixing system, the resin bath, and the filament winding machine. These devices are linked and may operate simultaneously. Fibers such as glass, carbon or aramid are stored on creels which are located in the fiber tensioner. Each rim will be wrapped around a mandrel situated on the filament winding machine. To prepare the winding process, the mandrel has to be cleaned and pretreated with a release agent. Further, the tensioner must be stocked with fiber creels, and the resin and the curing agent are blended together in a specific ratio. The resin bath must be preheated to a specific temperature to ensure proper resin viscosity. During the winding process, the electric drive in the winding machine keeps the fibers in motion. The motor is set at a suitable rotational speed. Often the rotational speed is constant, causing the fiber speed to increase as the rim becomes larger. This must be considered when winding a large flywheel. In such cases, it may be necessary to adjust the rotational speed during the process, as otherwise the fiber speed may exceed allowable limits for achieving a quality product, e.g., due to insufficient fiber wetting in the resin bath. Before the fibers reach the mandrel, they dip through the resin bath filled with a mixture of resin and curing agent. The fiber tensioner applies tension to the fibers while they are wound onto the mandrel, ensuring proper part consolidation. After winding, all manufacturing components that are exposed to resin are cleaned in a solvent bath [21].
# Low Cost Circularly Polarized Antenna for IoT Space Applications
## 3 Prototyping and Measurements  
### Prototype Fabrication  
The proposed concept has been fabricated on a low cost substrate that is 1.6 mm-thick FR4 Epoxy with the \(\epsilon_{r}=4.4\) and \(tan\delta=0.02\). The different views of the antenna are shown in Figure 8. The radiating elements are placed at the top board that connect to the feeding circuit and the ground plane at six points by using the 1 mm diameter metallic wires. The three inner points link to the three output ports of feeding circuit, while the three outer points are shorted to the ground plane.
## 3 Results
### CO2 and Climate Effect on iWUE* and LUE Trends  
#### 3.3.1 Atmospheric CO2 Effect on iWUE* and LUE Trends  
The atmospheric CO2 effect on iWUE* and LUE trends is analyzed in Figure 6. We focus on the difference between the trends of S2 and S2-FIXCO2 simulations (Table 1). Hereafter, \(\Delta\)iWUE* refers to the S2-iWUE* trend minus the S2-FIXCO2-iWUE* trend, and \(\Delta\)LUE refers to the S2-LUE trend  
Figure 5: _Cont._  
minus the S2-FIXCO2-LUE trend. We find a positive and high sensitivity of iWUE* and LUE trends to increasing atmospheric CO2, especially in the central African forest. \(\Delta\)iWUE* is 30%-50% per decade (Figure 6A) of the S2-iWUE* trend, indicating that CO2 increase explains about half of the modeled iWUE* trend. The CO2 effects result found in this study, is consistent with previous studies in iWUE* [30, 33, 64]. Outside the central African forest, the CO2 effect on iWUE* trend is generally positive but the statistical significance is weak.  
The LUE trend shows a very high sensitivity to atmospheric CO2 in comparison with the iWUE* trend. \(\Delta\)LUE represents more than 100% of the S2-LUE trend over a large part of the central African forest, where a high and negative LUE trend is predicted if constant atmospheric CO2 is assumed. This high CO2 effect on LUE trend over the central African forest shows a high sensitivity of the ORCHIDEE model LUE to the atmospheric CO2 fertilization effect. Note also that the CO2 effect on the simulated LUE trends is globally positive over the whole continent but not statistically significant outside the central African forest (as seen with CO2 effect on the iWUE* trend).
# Secure and Scalable Internet of Things Model Using Post-Quantum MACsec
## 1 Introduction
We propose a unique approach that is distinct from the existing IoT network platforms for PQC:  
* The confidentiality of L2 TP and MACsec is enhanced, and the utilization of computing resources for PQC-TLS is optimized by improving the SSI's structure. * A strategy to prevent unauthorized IoT device access to the IoT network platform has been proposed for Zerotrust. * Experimental validation has confirmed that SSI-PQM, which incorporates the NTRU algorithm as PQC, is well-suited for IoT devices. The rest of this paper is organized as follows: Section 2 addresses the vulnerabilities in the security of the existing MACsec in a quantum computing environment. The Section 3 elaborates on how MACsec and NTRUs are applied in this study. A detailed description of the methodology for integrating NTRUs with SSI is provided in Section 4. Section 5 evaluates the impact of the NTRU algorithm on computing performance in SSI-PQM. We conclude with a discussion in Section 6.
## 3 The Proposed Methodology
### Action Recognition Using Ddnet  
Once the human skeleton features or keypoints are extracted, then they must be used to recognize the action that they represent. We propose using the double-feature double-motion network (DDNet) [30] as a classifier for the recognition of actions represented by the feature vectors of keypoints extracted using pose estimation models. DDNet is fast because it uses a very lightweight network architecture with only 0.15 million parameters [30], which allows it to operate in real time. Moreover, the relatively fewer parameters mean that it uses a relatively simpler model, which enables it to achieve better generalization performance and detection accuracy compared with the more complex models, as demonstrated in Section 5, where we test it on the University of Central Florida (UCF) sports [18] and on the self-collected HNH datasets.
## 3 A Proposed Robust Scheme Based on Hop-Count Analysis (MHA)
### Route Maintenance in MHA Routing Protocol
#### 3.5.2 Cross-check the Route Paths  
In the MHA protocol, we give an expiration time for every route. All intermediate nodes delete the cached route after expiration to conserve memory. However, in certain environments, the nodes in the network may be static. These routes may persist over time. In order to have better efficiency, when routes of a communication pair are expired but are still in use, the source node unicasts a RACK packet with a new expiration time to the destination node through the valid routes. The intermediate nodes and the destination node then refresh the expiration time in their routing table.  
Consequently, we limit resource usage and recover some overhead that would originally be wasted by launching unneeded route discovery procedures. We also save memory of intermediate nodes that would otherwise be used for keeping idle routes.
## 3 Results of Our Own Ultrasonic Tests  
### Surface Roughness Results  
First, an analysis of measurement errors was carried out. A half confidence interval of 0.95 was used as the error. The tests carried out showed that the error is frequency-dependent. The results are shown in Figure 7. The significant increase in the error for  
Figure 6: Schematic diagram for measuring and determining of the decibel gain drop based on adjacent impulses of ultrasonic wave reflection when measuring materials of different hardness; I—first echo of the ultrasonic longitudinal wave from the reflector (B); II—second echo of the ultrasonic longitudinal wave from the reflector (B); III—third echo of the ultrasonic longitudinal wave from the reflector (B); B—reflector; H\({}_{\text{I}}\)—the height of the first pulse on the flaw detector screen (in this case HI = 80%), H\({}_{\text{II}}\)—the height of the second pulse on the ultrasonic flaw detector screen; H\({}_{\text{III}}\)—the height of the third pulse on the ultrasonic flaw detector screen.  
the highest frequency was probably due to the fact that the attenuation increased with increasing frequency. An ultrasonic probe with a frequency of 20 MHz was used for testing components' small and thin parts (even less than 1 mm). In this study, the same sample (15 mm thick) was tested with each ultrasonic probe listed in Table 2.  
The results of all ultrasonic measurements depending on the frequency of the ultrasonic probe are shown in Figure 8.  
All the results in tabular form are shown in Table 3. It can be seen that for roughness parameters in the range from 0 to 5 \(\upmu\)m, the changes of decibel amplification values are not significant; however, they are significant for larger values of Ra, whereby, it is clear that as the frequency increases, the attenuation of the ultrasonic wave increases as well, which means that there is scattering of the wave on the surface irregularities. Such scattering can have a significant impact on any ultrasonic measurement and therefore on the results of each ultrasonic testing.
# Artificial Intelligence-Empowered Doppler Weather Profile for Low-Earth-Orbit Satellites
## 2 Methodology
### Impact of Doppler on LoRa Satellite  
The study continues to comprehensively analyse the impact of the Doppler effect on the dependability of LoRa satellite connections and determine the latency and latency alteration calculation. We evaluated the occurrences of packet losses by differentiating between Doppler rate and shift. The Doppler rate is due to fluctuations in the velocity of satellites in LEO about an Internet of Things (IoT) device situated on the ground [33].  
The study also determines the propagation delay from the LEO satellites to the Australian ground stations and assesses the propagation delay rate of change. After that, we graph the propagation delay linked to the initial satellite. While it is possible to graph the propagation delay for all satellites, Figures 10 and 12 showcase the initial satellite to provide clarity and streamline the visualisation. Furthermore, it involves evaluating the rate of change of the propagation delay for both satellites also shown in Figures 10 and 12. Subsequently, we plot the propagation to enhance clarity and simplify visualisation.  
Our comprehensive analysis encompasses a range of communication variables and configurations, such as bandwidth and carrier frequency and has successfully pinpointed the necessary parameters for establishing direct connections to low-Earth-orbit (LEO) satellites utilising LoRa technology. These results can be leveraged to guide the selection of ideal parameters for upcoming system designs.  
Specifically, our study demonstrates that the packet delivery rate for the most sensitive spreading factor (SF12) exceeds 91% with 11.5 MHz bandwidth for Globalstar and 145.8 MHz for ISS NAUKA, a carrier frequency of 631 MHz for the ISS located at an altitude of 402.3 km and 500 MHz for Globalstar at an altitude of 1414 km.
## 3 DFIG System with DVR-Based SMES
### Control Strategy of the VSC Converter  
The controllable reference signals of the VSC converter of the DVR are \(V_{dd}\) and \(V_{dq}\) under the d-q rotating reference frame. \(V_{dd}^{*}\) and \(V_{dq}^{*}\) are the normal voltages of the 35 kV line before the faults. To accurately control the output voltage, the LC filter was considered to add into the d-q control loops.  
Figure 4: The DFIG with a DVR-based SMES system connected to the grid.  
Figure 3: Comparison of the stator current curve under theory and simulation.  
Assuming the switching of the VSC converter in Figure 4 are all ideal switches, the mathematical model of the VSC converter in the three-phase stationary coordinate system can be expressed as  
\[\begin{cases}V_{ca}=-L_{d}\frac{d^{z}_{dc}}{dt}+V^{*}_{ca}\\ V_{cb}=-L_{d}\frac{d^{z}_{dc}}{dt}+V^{*}_{cb}\\ V_{cc}=-L_{d}\frac{d^{z}_{dc}}{dt}+V^{*}_{cc}\end{cases}, \tag{13}\]  
where \(V_{ca}\), \(V_{cb}\) and \(V_{cc}\) are the compensated voltages in the 35 kV line; \(V^{*}_{ca}\), \(V^{*}_{cb}\) and \(V^{*}_{cc}\) are the output voltages of the VSC converter; \(i^{*}_{ca}\), \(i^{*}_{cb}\) and \(i^{*}_{cc}\) are the output currents of the VSC converter; and \(L_{d}\) is the inductance of the LC filter.  
In the d-q rotating reference frame, the mathematical equation of the VSC converter can be obtained by  
\[\begin{cases}V_{cd}=-L_{d}\frac{d^{z}_{dc}}{dt}+V^{*}_{cd}-\omega_{s}L_{d}i_{ cq}\\ V_{cq}=-L_{d}\frac{d^{z}_{dc}}{dt}+V^{*}_{cq}+\omega_{s}L_{d}i_{cd}\end{cases}, \tag{14}\]  
where \(V_{cd}\) and \(V_{cq}\) are the compensated voltages in d-q rotating reference frame; \(V^{*}_{cd}\), and \(V^{*}_{cq}\), \(i^{*}_{cd}\), and \(i^{*}_{cq}\) are output voltages and currents of the VSC converter in the d-q rotating reference frame. Additionally, considering the LC filter, \(i^{*}_{cd}\) and \(i^{*}_{cq}\) can be calculated as  
\[\begin{cases}i^{*}_{cd}=i_{cd}-\omega_{s}C_{d}V_{cq}\\ i^{*}_{cq}=i_{cq}+\omega_{s}C_{d}V_{cd}\end{cases}. \tag{15}\]  
By introducing Equation (1) into Equation (2), the following equation can be obtained:  
\[\begin{cases}V^{*}_{cd}=L_{d}\frac{d}{dt}(i_{cd}-\omega_{s}C_{d}V_{cq})+\omega _{s}L_{d}i^{*}_{cq}+V_{cd}\\ V^{*}_{cq}=L_{d}\frac{d}{dt}(i_{cq}+\omega_{s}C_{d}V_{cd})-\omega_{s}L_{d}i^{*} _{cd}+V_{cq}\end{cases}, \tag{16}\]  
Additionally, using the above derivation, the control strategy of the converter can be obtained, as shown in Figure 5.
## 4 Materials and Methods  
### Patient Enrollment and Sample Collection  
Consecutive patients with unresectable IIIC-IV-stage cutaneous melanoma were prospectively evaluated at the Fondazione IRCCS Istituto Nazionale dei Tumori in Milan, Italy, before they started first-line anti-PD-1 immunotherapy (nivolumab or pembrolizumab) or targeted therapy (dabrafenib and trametinib), between September 2019 and December 2020. Inclusion criteria were as follows: (i) age \(\geq\) 18 years and \(\leq\)85 years; (ii) 0, 1, or 2 Performance Status assessment using the ECOG (Eastern Cooperative Oncology Group) Score, which assesses each patient's level of functional status and ability to perform self-care; and (iii) at least one measurable lesion, as assessed via computed tomography (CT) or magnetic resonance imaging (MRI) per Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST v1.1). Subjects with treated brain metastases without MRI evidence of progression with untreated brain metastases, who were neurologically asymptomatic without systemic corticosteroids for at least two weeks prior to cancer therapy, were also included. Exclusion criteria included a history of ocular/uveal melanoma; the presence of active brain metastases, leptomeninegeal disease, autoimmune disease, type I diabetes mellitus, hypothyroidism requiring only hormone replacement, inflammatory bowel disease, celiac disease, or documented food allergy; and prior active cancer in the past three years, except for localized cancers that had been cured and did not recur.  
All enrolled patients were characterized based on their nutritional status, physical activity, and biochemical and inflammatory measures, as described below. Blood and fecal samples were collected from each patient prior to the initiation of therapy. Blood samples were used for complete blood count, including the NLR and immune profile. NLR is an inflammatory biomarker of clinical interest in the prognosis of solid tumors, especially melanoma [17], with values \(\geq\) 4 being associated with decreased overall survival [51]. Fecal samples were immediately stored at \(-\)80 \({}^{\circ}\)C and shipped on dry ice to the Department of Pharmacy and Biotechnology (University of Bologna, Bologna, Italy), where they were stored at \(-\)80 \({}^{\circ}\)C until being processed for microbiome and metabolome profiling.  
At the end of treatment, efficacy was assessed, with the RECIST v1.1 criteria used as the reference standard. Based on their radiographic response, patients were classified as responders (for complete response, partial response, or stable disease for \(\geq\)6 months) or non-responders (for progressive disease).  
The study protocol was approved by the Ethics Committee of the Fondazione IRCCS Istituto Nazionale dei Tumori in Milan (registration number 126/18, 19 July 2018). It was conducted according to the guidelines of the Declaration of Helsinki. Written informed consent was obtained from all patients who participated in the study.