esa-sceva/satcom-chunk-collection huggingface dataset:
# Noise-Assessment-Based Screening Method for Remote Photoplethysmography Estimation
## 3 Method
### Feature Extraction
#### 3.3.2 Illumination Noise Assessment  
To quantify the illumination noise within the region of interest (ROI) pertaining to facial analysis, the ROI was partitioned into nine distinct areas, as illustrated in Figure 7.  
The evaluation of illumination noise was conducted by computing the average pixel values for both the entire facial region and the nine segmented facial areas. To assess the temporal fluctuations in illumination within each area, standard deviation and the difference between the maximum and minimum values were calculated based on the time series of average pixel signals. The standard deviation of the time series data (\(X_{1}\) to \(X_{n}\)) representing the average brightness values for each area was determined using the following formula:  
\[\sigma_{k}=\sqrt{\frac{\sum_{\gamma=1}^{n}\left(X_{\gamma}-\underline{X} \right)^{2}}{n}},\ (k=1,2,3,\cdots,10) \tag{4}\]  
In the equation, \(X\) represents the average value of the time series data from \(X_{1}\) to \(X_{n}\), while \(k\) denotes the specific area of the face under consideration. The calculation of the difference between the maximum and minimum values in the time series data for the average brightness values (\(X_{1}\) to \(X_{n}\)) of each area was performed using the following formula:  
\[I_{k}=Max(X_{k})-Min(X_{k}),(k=1,2,3,\cdots,10),X_{k}=\{X_{1},\ X_{2},\ \cdots,\ X_{n}\} \tag{5}\]  
Figure 7: Divided areas for illumination noise assessment feature extraction.  
Hence, the assessment of illumination noise entails considering both the standard deviation and the difference between the maximum and minimum values. Consequently, the resulting illumination noise indicator comprises a total of 20 vectors.
## 7 Conclusions  
In this paper, we have developed a complete understanding of the dynamics and problems of MPTCP over IEEE 802.11_ay_ networks. We have demonstrated several ways to capture the impact due to the mobility of devices on MPTCP and exploit it while scheduling packet transmissions amongst multiple 802.11_ay_ links from the transport layer. We have found that the requirement is usually to transmit group of packets with low  
Figure 11: Throughputs in Gbps over time of LD-MPSP and DALIA users in the 802.11_ay_ links as observed at the devices using five links EDMG-SC 802.11_ay_ networks with spatial streaming (up to eight).  
Figure 12: Impacts of varying file size downloads (from 2 MB to 8 MB) on the perceived network utilities when devices are using seven links EDMG-SC 802.11_ay_ networks with spatial streaming (up to eight) (several runs with LIA, DALIA and LD-MPSP using the extended setup of Figure 6).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**MCS** & **EDMG SC** & **EDMG OFDM** \\ \hline Maximum TCP Throughput & 28.96 Gbps & 30.95 Gbps \\ Mobility TCP Throughput & 9.6 Gbps & 9.25 Gbps \\ Mobility MPTCP Throughput & 16.36 Gbps & 16.15 Gbps \\ Maximum MPTCP Throughput & 41.32 Gbps & 42.13 Gbps \\ Mobility LD-MPSP Throughput & 37.16 Gbps & 36.95 Gbps \\ Maximum LD-MPSP Throughput & 56.20 Gbps & 61.01 Gbps \\ \hline \hline \end{tabular}
\end{table}
Table 3: Throughputs of various cases with four bonded channels (Cf., Table 1).  
latency. Thus, a more significant factor to be considered from the user's perspective is the aggregate data transfer completion time rather than the per-packet delay. We have developed a delay analysis model based on stochastic multipath scheduling of packets and hybrid beamforming of 802.11\(ay\) links, which enables us to design a novel MPTCP that considers packets inside congestion windows and their relationship to hybrid beamforming and device selection. More importantly, we have shed light on a data-driven TRA-Layer design platform. For example, we demonstrate how a multi-armed bandit can achieve delay balancing (with and without federated learning) and how to schedule replicated packets for short and longer connections opportunistically. A detailed investigation of the impact of low delay with forwarding error correction, 802.11 support for MPTCP sources, and extending the design over WiFi 7 are topics of our future study.  
Conceptualization, S.R.P. and M.M.; Methodology, S.R.P. and M.M.; Software, S.R.P.; Validation, S.R.P.; Formal analysis, S.R.P. and M.M.; Investigation, S.R.P. and M.M.; Resources, S.R.P. and M.M.; Data curation, S.R.P. and M.M.; Writing--original draft, S.R.P.; Writing--review & editing, M.M.; Visualization, S.R.P.; Supervision, M.M.; Funding acquisition, S.R.P. and M.M. All authors have read and agreed to the published version of the manuscript.  
This research was jointly funded by the Faculty of Science, Engineering and Built Environment (FSEBE/Deakin) and the school of IT (SIT/Deakin) Deakin University internal research grants and the Comcast/USA innovation fund 2022.  
The authors declare no conflict of interest.
## 4 Analysis and Comparison
### Measure Metrics  
The performance of the packet detection algorithm can be measured by three indicators: the probability of detection \(P_{d}\), the probability of false alarm \(P_{f}\), and the probability of misdetection \(P_{m}\)[24]. False alarm is less severe than misdetection, since missing packets result in data loss. A good algorithm should achieve a high \(P_{d}\), which is related to \(P_{f}\) and \(P_{m}\). The algorithm designers must balance them properly. There are two main factors that affect these three indicators: threshold setting and decision rule. The detailed discussions are as follows.
## 4 Conclusions  
Based on a separation rate of 50%, the GHG emissions from collection, transportation, and disposal of MSW in Beijing with current mixed MSW management system and source-separated MSW management system in future were investigated. The results show that the source-separated MSW management system realizing the integration of the MSW management system with the renewable resource recovery system has environmental benefits, and GHG emissions from MSW using the dichotomy and the trichotomy separation method were lower than those under the existing approach. In particular, using the trichotomy method, the optimal approach was obtained after the separation of kitchen waste and recyclables followed by the incineration of the residue waste, which could achieve an emission reduction benefit of 70.82%. The GHG emission reduction benefits brought by electricity generation from incineration of waste and recycling of recyclables were even greater than the amount of emissions. Changing the separation rate revealed that the optimal model in this paper was robust. Therefore, for the purpose and requirements of national GHG emission reduction, the integration of the two systems is environmentally feasible.  
In addition, the comparative analysis of the activities in the whole life cycle of MSW management revealed that the GHG emissions from collection and transportation were the smallest, and the GHG emissions from landfill were the largest compared with incineration and composting, which was the opposite of some researches results from the economic perspective. Therefore, future research can integrate the economic and environmental benefits and compare the comprehensive cost on mixed MSW management system and source-separated MSW management system, which may lead to different results.  
Writing: [PERSON], [PERSON]; Providing case and idea: [PERSON], [PERSON]; Revising and editing: [PERSON]-[PERSON], [PERSON]-M.Z. and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
Supported by the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (No. 2015 BAK39B00) and the Funds for First-class Discipline Construction (XX1802-5).  
Figure 13: GHG emissions from MSW transportation and its proportion of total emissions.  
Acknowledgments: Provincial and Municipal Co-construction Project Fund of Zhongshan City and Guangdong Province. The authors declare no conflict of interest. References References References
## 3 Ultra-High-Speed 1.2 Tb/s OTU and 200 Gb/s ICS OTU
It comprises two single-polarization modulated signals in horizontal and vertical polarizations, which are combined by a polarization beam combiner (PBC). And this sensing method is based on monitoring and analyzing the SOP of the received signals, thus it will not occupy extra transmission channels and has almost no impact on data transmission.
# A Multi-Scale Deep Back-Projection Backbone for Face Super-Resolution with Diffusion Models
## 5 Experiment
### Ablation Experiments on Face Images  
To illustrate the necessity of multi-scale information, we compared DBPNs at different single scales on the same face dataset as in Section 5.2.  
We trained different DBPN groups for 10 epochs and tested the 10 th epoch on the first three images (a), (b), and (c) in FFHQ. Figure 9 shows the reconstruction results for each group.  
Figure 8: Reconstruction results of some of the algorithms in Table 1 are shown. (The 5 th and 6 th images in FFHQ are selected as examples).  
Figure 9: Image reconstruction results of training 10 epochs when using different-scale DBPN as the base network, where (**a**–**c**) represent the first three images of the dataset FFHQ.  
For Figure 9, the HR column is the reference HR image; the Interpolation column is the reconstruction with linear interpolation only, while \"Scale 8\", \"Scale 4\", and \"Scale 2\" represent the reconstruction with DBPN scales of 8, 4, and 2, respectively; and \"Scale 2-4-8\" is the reconstruction with multiple scales in parallel. It is evident that the reconstruction of \"Scale 8\" is notably subpar, exhibiting considerable noise while displaying rough outlines.  
The single-scale reconstructions of Scale 2 and Scale 4 are satisfactory, but they are blurry in detail compared to the multi-scale DBPN. For example, the eyes of Figure 9a,c, and the mouth of Figure 9b.  
To quantitatively illustrate the effect of DBPN groups at several different scales, we calculated the FID, average PSNR, and average SSIM between HR and reconstructed images, as shown in Table 2.  
We observe that the multi-scale DBPN, despite having a slightly lower PSNR, achieves the best performance in both SSIM and FID for the same number of training iterations. The image reconstructions in Figure 9 also show the benefits of using multi-scale.
###### Abstract  
The Direct Broadcast Network (DBNet) provides near-real-time delivery of low-earth-orbiting (LEO) meteorological satellites to operational numerical weather prediction (NWP) systems that need short data cut-off times to allow for the assimilation of the most recent satellite measurements. The NWP model requires timely delivery of observations including atmospheric temperature, humidity, and surface wind vectors. The World Meteorological Organization (WMO) Space Program (WSP) recommends the data latency of no more than 20 min for the satellite measurements. Currently, not all DBNet stations are delivering satellite data within the 20-min time frame. In this study, the forecast impact of the observations of LEO satellite sounders with data latency of 20 min or less was evaluated using the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). Reducing the data latency up to 5 min increases the number of LEO infrared (IR) and microwave (MW) sounder observations delivered to the NCEP GFS data assimilation system by more than 20%. Overall, this study demonstrates a positive impact on the global weather forecasts when the IR and MW sounder data are delivered by 20 min anywhere in the world. Additional forecast benefits are not obvious for shorter data latency. Results from this study support the WSP recommendation of 20-minute data latency.  
data latency; low-earth-orbit satellite; data assimilation; numerical weather prediction 2020 IEEEexample:BSTcontrol
# Resilience in the Internet of Medical Things: A Review and Case Study
## 6 A Case Study: Remote Patient Monitoring of a Heart Patient in the IoMT Networks
### Failure Scenario in a General Layerwise Architecture  
Figure 4 presents a failure scenario with a red circle sign in a general layerwise architecture during the remote monitoring of heart patients. Patients can be monitored by using different types of sensors at the thing layer. A number of patients are equipped with IoMT devices in the thing layer. The medical data have to be transmitted from the patients to the clinician or medical staff at the hospital. These critical data are transferred through the gateway layer and network layer, and finally stored at the cloud layer so that the clinician can access his authorized data [99]. However, several failures at links and devices may occur during this transmission.  
* Failure at thing layer: battery failure of IoMT devices.
* Failure at gateway layer: fault occurrence at home router.
* Failure at network layer: failure at ISP infrastructure; floods, roadworks, etc., in local areas; overloaded routers.
* Failure at cloud layer: unauthorized access, denial of services, server downtime, power issues in data center, etc.  
So, these are the possibilities of the faults and failures in the general layerwise architecture of IoMT networks during the remote monitoring of heart patients. Figure 4 also illustrates which particular capabilities of resilience are required at which point in the IoMT networks, for example, route optimization, resource management, traffic classification, and global view are required at the network layer, whereas access control through authorization and authentication is required at the thing, gateway, and cloud layers. The combination of SDN, ML, and MSA technologies can potentially provide these capabilities of resilience in data communication at different points in the architecture of IoMT networks.  
Figure 4: Failure scenario in a general layerwise architecture.
## 3 Results and Discussion
### Correlation of Aerosol Properties over Saudi Arabia  
To better understand the temporal and spatial distribution characteristics of aerosols over Saudi Arabia, the correlation among aerosol parameters was studied during spring, summer, autumn, and winter for different regions. The correlation between AOD\({}_{1}\) and HTH\({}_{1}\) in regions I, II, and III is shown in Figure 17. As shown in Figure 17, there exists a weak positive correlation between HTH\({}_{1}\) and AOD\({}_{1}\) in the three regions in the daytime, but the correlation is not significant (R\({}^{2}\) < 0.5), indicating that the aerosol concentration is not high. Furthermore, Figure 17 shows that except autumn, there was a significant correlation between HTH\({}_{1}\) and AOD\({}_{1}\) in the three regions at nighttime (R\({}^{2}\) > 0.6), indicating that the aerosol concentration in the three regions was higher at nighttime. This pattern can be associated with the downward movement of the upper aerosol layer due to the lower temperature at nighttime than that observed during the daytime. Moreover, most aerosols are concentrated in the bottom layer, being affected by dust storms, which further increases the aerosol concentration in the bottom layer. This observation is consistent with that reported in the previous studies [18; 24; 34; 45].  
Figure 18 displays the correlation between TAH and N in the daytime and nighttime, respectively. They all exhibit a positive correlation; that is, the greater the value of N, the greater the value of TAH. This is consistent with the logical hypothesis that the greater the number of aerosol layers (N), the thicker the atmosphere, leading to the attainment of a higher TAH. These results are similar to those of studies conducted in Northeast China [51] and Pakistan [12]. It should be noted that the correlation between TAH and N at nighttime was greater than that during the daytime, indicating that the accumulation in the upper layer at nighttime was weaker than that in the daytime, and the TAH value was relatively stable.  
As shown in Figure 19, there is a significant negative correlation between PAOD\({}_{1}\) and N in the three regions of Saudi Arabia. Except for region I, the correlation was low at nighttime in winter (R\({}^{2}\) = 0.7), and the correlation of the other two regions in each season was greater than 0.96. Since PAOD\({}_{1}\) is the proportion of AOD of the lowest aerosol layer, more aerosol layer (N) leads to a smaller value of PAOD\({}_{1}\), which is supported by data obtained from previous studies [58].  
Figure 17: The correlation between HTH\({}_{1}\) and AOD\({}_{1}\) over Saudi Arabia from 2007 to 2019. (**a**,**d**) represent region I, (**b**,**e**) represent region II, and (**c**,**f**) represent region III.
# Research on Mechanical Behavior of Geogrid-Soil Interface Under Rainfall Infiltration
## 3 Results and Discussion
### Effect of Infiltration Time on Geogrid-Soil Interface Characteristics  
Figure 10 shows the shear stress-shear displacement curves for different working conditions at rainfall infiltration times of 10 min, 20 min, and 30 min, respectively. It can be seen from the figure: at different infiltration times, geogrid-reinforced soil samples still show the characteristics of shearing strain-softening. The peak shear stress of the shear stress-shear displacement curve is distributed between shear displacement of 10-20 mm, which is about 3% to 7% of the length of the shear surface. This is due to the fact that with the increase in infiltration time, the amount of rainfall infiltration increases, the friction of sand particles decreases, the interaction force between the particles is weakened, and the shear strength decreases.  
Figure 10: _Cont._  
Figure 11 shows the peak shear stress-shear displacement curves of geogrid reinforced specimens at normal stresses of 50 kPa, 100 kPa, 150 kPa, a shear rate of 1.5 mm/min, and rainfall infiltration times of 10 min, 20 min, 30 min. The data from the shear test results are fitted and analyzed, and the results are shown in Table 5.  
As can be seen from Table 5, the length of the infiltration time has an effect on the values of the cohesion (c) and the friction angle (\(\varphi\)). When the shear rate is 1.5 mm/min, the cohesion is reduced by 45.5%, and the friction angle is reduced by 22.9%. The variation shows that the longer the infiltration time, the smaller the values of c and \(\varphi\). This is because the prolonged rainfall leads to an increase in soil moisture, a thickening of the bonded water film between soil particles, weakened capillary action, and a reduction in substrate suction [28, 29, 30]. In addition, the lubricating effect of water makes the occlusion between soil particles ineffective, which results in the decrease in cohesion with the increase in infiltration time. The friction angle of soil reflects the frictional properties, including sliding friction, occlusal friction, and particle crushing force [31]. The sliding friction and occlusal friction of the soil decreases with the increase in moisture content.  
Figure 11: Peak shear stress–shear displacement curves under different rainfall infiltration times.  
Figure 10: Shear stress–shear displacement curves under different infiltration time. (**a**) J-A-1.0; (**b**) J-B-1.0; (**c**) J-C-1.0; (**d**) J-A-1.5; (**e**) J-B-1.5; (**f**) J-C-1.5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Infiltration** \\ **Time/min** \\ \end{tabular} } & \multicolumn{2}{c}{**Strength Index**} & \multicolumn{1}{c}{**Fitting**} \\ \cline{2-4}  & **Cohesion (c)/kPa** & **Friction Angle** & **Correlation** & **Relationship** \\ \hline
10 & 16.7 & 42.0 & 0.999 & y = 0.899x + 16.7 \\
20 & 12.5 & 36.6 & 0.998 & y = 0.742x + 12.5 \\
30 & 9.1 & 32.4 & 0.992 & y = 0.635x + 9.1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Strength index under different infiltration time.
# Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System
## 3 System Model
### Network Scenario
\begin{table}
\begin{tabular}{c c} \hline \hline
**Training Process** & **Parameter Descriptions** \\ \hline \multirow{2}{*}{Round 1} & Total Service Types \\  & Average Data Volume \\  & Network Congestion \\ \hline \multirow{2}{*}{Round 2} & Network Side Transmission Power \\  & Average Delay of the Network \\  & User Amount in the Coverage \\ \hline \multirow{2}{*}{Round 3} & Average Data Amount \\  & MS Location Information \\  & States of Three Networks \\ \multicolumn{2}{c}{Records of Past Resource Decisions} \\ \end{tabular}
\end{table} 
Table 1: Summary of parameters in the three training processes. Figure 3: The diagram contains three service types (three slices) for two users. Here, user 1 is located in countryside industrial areas away from the cellular network, so the eMBB service is mapped to the satellite network, while mMTC is mapped to the drone network. Figure 2: Distributed network decision-making structure. In Figure 2, the allocation decision-making process is given, consisting of three aspects, package and access strategy analysis at the MS side, slice management at the side of the core network controller and the location analysis provided by navigation systems (GNSS). Figure 2 is consistent with what is described in the previous chapters, and we further elaborate it from the perspective of model training. In the initial training stage, the DP-DQN model will first be trained based on the reinforcement learning method, and then in the process of continuous iterative update of the initial model, data from Channel Monitor will be used to improve the adaptability of the model. Then, the secondary training will be performed on each network node before the final deployment of the model. On the client side, the data to be sent are packaged by Message Package block, analyzed in the Type Analysis module and given a priority. At the same time, the current MS location information is obtained by the MS location module. Each time data are sent, the above information is obtained, and combined with the final resource allocation result and system feedback, the MS side will form a data pool. Subsequent edge DP-DQN models will be trained based on this.