esa-sceva/satcom-chunk-collection huggingface dataset:
# Bio-Inspired Energy-Efficient Cluster-Based Routing Protocol for the IoT in Disaster Scenarios
## 6 Time Complexity Analysis
### DEEC Structure and Time Complexity  
The DEEC protocol selects cluster heads probabilistically based on the residual energy of nodes, with higher-energy nodes more likely to be chosen. It operates in a distributed manner, with local decisions for cluster formation and cluster head selection. DEEC functions in rounds, where each round includes cluster head selection, cluster formation, data collection, and transmission. After each round, the system resets, and the process repeats based on the updated energy levels and network conditions. The structure of the DEEC algorithm is as follows:  
* Cluster Formation: The nodes form clusters based on energy levels. Each node evaluates its potential as a cluster head and communicates with its neighbours. This process typically involves a series of messages between nodes, and the time required is \(O(n^{2})\).
* Cluster Head Selection: Neighbour nodes decide whether to become cluster heads based on their energy levels and other criteria. This process involves evaluating and comparing energy levels across all nodes, which takes \(O(n)\) time for each node.
* Data Aggregation and Communication: This involves collecting and aggregating data within clusters. For K clusters with m nodes per cluster, this has a complexity of \(O(K.m)\). The total time complexity for I iterations is \(O(T.(k.m).n^{2}\).
# Design and Analysis on Decoupling Techniques for MIMO Wireless Systems in 5G Applications
## 2 Mutual Coupling Issue of MIMO Systems
### Negative Effects of Mutual Coupling  
The impact of mutual coupling on MIMO systems is like a coin with two sides, containing both beneficial and detrimental aspects. Of these, detrimental impacts are the focus of this paper. Firstly, the mutual coupling has a significant effect on the average bit error ratio (BER) of MIMO systems, especially at separations 0.3-0.5 wavelength of antenna elements [17]. Additionally, in terms of radiation pattern [18; 19; 20], a distortion of radiation pattern occurs in MIMO antennas compared to the isolated radiation pattern of each element. Additionally, in terms of direction-of-arrival (DOA) [21; 22; 23], the estimation accuracy of DOA may degrade substantially, especially when the MIMO systems operate at high frequency or very high frequency because the array manifold is often affected by mutual coupling. Lastly, the high capacity of MIMO can be estimated with bias if various coupling receivers are in the terminal [24; 25; 26]. When the receivers spacing is around 0.35-0.5 wavelength, the received SNR is dropped by the effect of mutual coupling, which leads to the actual channel capacity being lower than expected.  
To sum up, the influences of mutual coupling on MIMO systems can be recognized from the following two aspects. As for output terminals, the desired EM field is generated by the currents with a particular frequency. Due to the tight distance, the transmitting antennas are easily affected by EM fields which are generated by their currents and adjacent antennas. Thus, the phase and amplitude of the original current are changed by the superposition of different EM fields. As for input terminals, when the input signals are transmitted to the receiving port, the current is not only induced, but also radiated again. Therefore, the essence of the signal obtained at the receiving port is the vector sum of the EM wave, which is generated by output terminals and the re-radiated fields. We can see that it is necessary to overcome the detrimental effects of mutual coupling in MIMO systems.
## 3 Method
### Image Preprocessing  
Image preprocessing was a prerequisite which involved radiometric calibration, geometric rectification, and atmospheric correction. Radiometric calibration was to convert the digital numbers recorded by the sensor to radiance. Because HJ-IRS data has a spatial resolution of 150 m, it needs to be resampled at 30 m in order to match HJ-CCD data before geometric correction. Nearest Neighbor was selected as the resampling method due to its capacity in preserving the original spectral characteristics. The resampled HJ-IRS data were layer-stacked with HJ-CCD data for further processing.  
The calibrated and resampled multispectral images were then geometrically rectified to a standard Landsat-8 Operational Land Imager (OLI) image, which had already been geo-corrected with a topographic map with a scale of 1:50000. The resulting root mean square error (RMSE) of the rectified image was less than 0.2 pixels which showed good accuracy.  
As for atmospheric correction, by-band 6S model [40] was adopted to retrieve remote sensing reflectance from HJ-1B multispectral data. A Mid-Latitude Summer atmospheric model was used while the aerosol type was set to be continental. Other parameters such as the altitude and wavelength of the image sensor and the imaging time can be acquired through the header files of HJ-1B data. The visibility for the 6S model was set to be 36.8 km which was obtained from the local meteorological bureau. The retrieved remote sensing reflectance was then used for MESMA to derive sub-pixel fraction maps.  
Figure 2: Workflow of this study.
# The Net Influence of Drought on Grassland Productivity over the Past 50 Years
## 3 Results and Discussion  
The Spatial and Temporal Characteristics of Precipitation and Temperature Changes over the Past 50 Years  
It is necessary to analyze the trend of precipitation and temperature changes to further investigate the net influence of drought on NPP because the influence of drought in this study is mainly affected by precipitation and temperature changes. As shown in Figure 6a, there has been no significant change in precipitation (\(p>0.05\)) in Inner Mongolia over the past 50 years, with a slight decrease of 2.2 mm/10a, while the temperature raised significantly with a value of 0.358 \({}^{\circ}\)C/10a (Figure 6b). Some researchers have also indicated that precipitation changed indistinctively, while the temperature rose significantly in the arid and semi-arid regions of North China, with an increased value of 0.04 \({}^{\circ}\)C/10a [43]. However, Figure 7 shows that the annual average of precipitation in eastern regions is higher than in western regions, and it decreased from southeast regions to northwest regions. The precipitation increased in the meadows and desert grassland regions over the past 50 years, while it decreased in the northeast, southeast, and middle small areas of typical grassland regions. The are significant variations in the range of precipitation changes, with about 37.8% of the region with an increase, and 62.2% of the region with a decrease, which is similar to the trend of drought over the past 50 years. The precipitation of the Inner Mongolia grassland in recent decades has shown a change trend, in which the west has more precipitation and the east has less precipitation, divided by middle Inner Mongolia, with asymmetrical spatial distribution. In particular, desert grassland precipitation increased obviously in the autumn, the number of rainy days were reduced, single rainfalls increased, and the rainfall seasons changed significantly.  
Figure 6: The trends of annual mean precipitation and temperature in Inner Mongolia over the past 50 years: (**a**) Precipitation; (**b**) temperature.  
The annual average temperature in Inner Mongolia showed that the temperature in the western regions is highest, followed by the southeast regions and north regions. There is a change trend of increased temperature in the whole region, especially the north and middle-west regions with significant increases. However, the temperature change has relatively small variations, with about 45.8% of the region with 0.2 \({}^{\circ}\)C increments, and 53.9% of the region with 0.1-0.2 \({}^{\circ}\)C increments. The regions with increased temperature are mainly distributed in the north, west, and middle areas of typical grassland, and the north of the meadow steppe, which may be beneficial for grass growth in meadow steppes in cold regions and harmful to grass growth in typical grassland.
## 5 Future Research Work  
* Service Migration: In the future, we will try to analyze the detrimental effects of service migration on various factors, such as traffic for different computer node types, service migration cost, service migration performance deterioration, and data transmission. We will also devise a plan to balance service migration's advantages and disadvantages.
* Security Concern: Security concerns and standardization are significant challenges to dynamic load balancing and orchestration. While orchestration provides dynamic allocation and mobility for IoT nodes, it will require further hopping of data that may cause security breaches that have not been thoroughly examined yet. There are also many heterogeneous devices in a fog environment, so it is not easy to provide a standard procedure, and less effort has been expended towards such standardization. These aspects can also be considered for future research. Besides these future plans, blockchain integration in fog computing and its effect on load-balancing mechanisms can also be studied in detail [38].
* Machine Learning Application: Traditional load-balancing approaches are generally not very efficient for dynamic conditions. Machine learning (ML) can be suitable for implementing dynamic load balancing when conditions change swiftly. Some research has been done in this domain, but further research is needed, especially in heterogeneous environments. A detailed study to find the best-suited ML approach in such conditions is needed as well [40].
## 4 Results and Discussion
### Flow Depth Models Analysis and Optimal Model Selection
The geostatistical analysis of results for the test model, considering the distance from the riverbank, showed very similar trends (Figures S1 and S2) to those related to the 500-year return period. Therefore, the scatter plot of Figure S1 shows that the best fit with the control (or benchmark) model was linked to [PERSON]'s \(n\) value in the range of 0.014-0.016. The results of the box plot (Figure S2), which were the same as for 500-year return period models, showed differences in the best fit that was linked to the distance to the riverbank. As discussed above, the best fit for shorter distances was obtained with a lower [PERSON]'s \(n\) value (about 0.011), while for distances equal to or greater than 500 m, the model that offered the best results was possibly the one with a [PERSON]'s \(n\) value of 0.016. As for the 500-year return period, the test model linked to the HDCM approach showed an overestimation of the flow depth values for all distances to the riverbank. For all hydraulic models related to the 100-year return period, an increase in uncertainty could be observed at further distances from the riverbank, which was due to the smaller flooding area at the floodplain (and the consequent lower number of sample points at further distances) than the one related to the 500-year return period models. In general, we observed that the best-fitting [PERSON]'s \(n\) value increased slightly from the methodological developed model (500-year return period peak flow) to the methodological test model (100-year return period peak flow). However, taking this into account, the optimal range of [PERSON]'s \(n\) value from 0.014 to 0.016 could be defined. Within this range of [PERSON]'s \(n\) value, the flow depth errors in the river floodplain were drastically reduced from the model without bathymetry data and a \"normal\" (0.027 in the study site) [PERSON]'s \(n\) value. The results from the two peak flows considered in the present assessment point towards the validation of our methodological approach, and the usefulness of using a calibrated and reduced values of Manning's \(n\) coefficient where the topography of the riverbed is not available and its acquisition lies outside the economic budget of flood risk managers.
## 7 Open Issues and Opportunities for Future Research  
The management architecture for future networks and services has attracted extensive research interest from both academia and industry. In addition to the standardization work reviewed in previous sections, the open source community is also actively involved in developing platforms for network management and service orchestration. Open Source MANO (OSM) ([[https://osm.etsi.org/](https://osm.etsi.org/)]([https://osm.etsi.org/](https://osm.etsi.org/)) (accessed on \(1\) February 2021))is an ETSI-hosted project to develop an open-source MANO software stack. ONAP ([[https://www.onap.org/](https://www.onap.org/)]([https://www.onap.org/](https://www.onap.org/)) (accessed on \(1\) February 2021)) is a comprehensive open source platform for orchestration, management, and automation of composite network-cloud/edge services. The architecture for network service management and orchestration has been investigated in various research projects, for example the NGPaaS project ([[http://ngpaas.eu](http://ngpaas.eu)]([http://ngpaas.eu](http://ngpaas.eu)) (accessed on \(1\) February 2021)) and the 5 GTANGO project ([https://www.5 gtango.eu/](https://www.5 gtango.eu/) (accessed on \(1\) February 2021)), both sponsored by the 5G PPP research initiative. Numerous results of academic research on applying ML/analytics techniques for network management have also been published in the literature, for example [45; 46; 47].  
Although encouraging progress has been made toward a holistic architectural framework that integrates various ML/data analytics techniques for intelligent and autonomous management in future networks, there are still some open issues that need to be further studied, thus offering opportunities for future research and development. The discussions in this section focus on open issues and research directions particularly relevant to management for supporting network-cloud/edge convergence in cloud-native future networks from an architectural perspective.
## 3 System Description  
### Overall System Architecture  
The overall system architecture of a typical term and pre-term monitoring system [24] for mothers is discussed in this Section. The system is depicted in Figure 3.  
Wearable sensors are unobtrusively attached to the patient; these detect the EMG signals, which contain indicators of the term/pre-term condition. Raw EMG data (straight lines) obtained from the sensors in each channel are transmitted to the wearable device (e.g., wearable watch), where they  
Figure 3: Architecture of a pre-term monitoring system.  
Figure 2: Example EMG signal interval.  
are encoded to be efficiently transmitted through our proposed compression module. The algorithm developed for the compression module is described in Section 3.2.  
The compressed EMG signal is sent to the smart device, which contains the transmission direction and communication control module. The network environment used here is usually BLE. The Transmission & Communication module decides where the signals or messages should be sent. If the EMG signal shows no signs of pre-term delivery, it is sent to the database via the cloud network. In this case, the used network may be 4 G (LTE). If the EMG signal shows signs of pre-term delivery, it is sent to the hospital server.  
In the hospital server, the visual User Interface (UI) module decodes the compressed EMG data into a user-friendly format, and presents it to the clinician. Clinical decisions or feedbacks are then sent to the smart device (message and control communications are depicted as dotted lines in Figure 3). Finally, the potential patient may receive clinical prevention services through the smart device UI; for example, warning of the risk of pre-term delivery. This facilitates screening for prematurity, allowing precautions to be taken; for example, appropriate interventions after premature birth, preventing the development of complex illness [25] due to a pre-term birth.  
An important note that should not be confused is that in the telemonitoring situations for maternal patients shown in Figure 3, real-time interaction is not implemented between the healthcare professional and patient. Physicians are only alerted by the system when abnormality is detected, and vice versa, and they only provide feedback to patients when necessary. Real-time communication is only applied between wearable devices and smart devices (BLE transmission).
## 4 Experimental Evaluation and Performance Analysis
### Discussion  
Aiming at the balance between the benefits of cloud manufacturing platforms and the needs of users in the EMRS problem of the cloud manufacturing environment, this paper proposes a dynamic pricing model that considers the maintenance time, the maintenance cost of the cloud platform, and the maintenance price of users. According to the characteristics of maintenance resources in the cloud manufacturing environment that are different from ordinary maintenance, this paper divides maintenance resources into physical resources and virtual resources, and pricing is carried out according to the quality of the maintenance resources and the completion time. In order to better take into account the interests of the cloud manufacturing platform and users and ensure the loyalty of users under the conditions of the cloud platform benefits, the concept of satisfaction is proposed.  
Experiments were designed to analyze the influence of different weights on the maintenance time and maintenance cost valued by cloud manufacturing platforms and the impact on the maintenance time and maintenance price valued by users. The research in this paper provides certain guidelines for the actual production process. The experimental results show that the EMRS model in this paper can satisfy the interests of cloud manufacturing platforms and users at the same time, which has great advantages.  
Theoretically, this paper fills the gap in EMRS research in the cloud manufacturing environment, proposes a pricing model based on physical resources and virtual resources, builds a model by integrating the E-CARGO model and fuzzy mathematics, and solves it by using a genetic algorithm, achieving good results and providing beneficial ideas for future research on such problems. In practical applications, the theoretical results can be used to price the equipment maintenance tasks of cloud manufacturing platforms, balancing platform interests and user satisfaction, thus helping manufacturers achieve long-term development. The research in this paper provides some guidance for the actual production process.  
The case study in this paper is an experimental simulation based on an actual situation that may be different from an actual industrial production situation. In the equipment maintenance process of the actual cloud manufacturing platform, there may still be some situations that we do not intend. For example, our research is based on research under general production conditions, and situations of resource scarcity and resource surplus are not considered enough. In cases of insufficient resources or excess demand, users can only reduce their expectations of the maintenance completion time and solve this problem by reducing the allocation and queuing of maintenance resources. We need to consider more deeply and comprehensively the allocation of equipment maintenance resources in various cases, study the cooperative relationships between different manufacturing objects, and establish a more perfect EMRS model.  
Figure 11: Platform benefits and user satisfaction under different weights.
###### Abstract  
In this paper, utilising real-internet traffic data, we modified a popular network emulator to better imitate real network traffic and studied its subjective and objective implications on QoE for cloud-gaming apps. Subjective QoE evaluation was then used to compare cross-correlated QoS metric with the default non-correlated emulator setup. Human test subjects showed different correlated versus non-correlated QoS parameters affects regarding cloud gaming QoE. Game-QoE is influenced more by network degradation than video QoE. To validate our subjective QoE study, we analysed the experiment's video objectively. We tested how well Full-Reference VQA measures subjective QoE. The correlation between FR QoE and subjective MOS was greater in non-correlated QoS than in correlated QoS conditions. We also found that correlated scenarios had more stuttering events compared to non-correlated scenarios, resulting in lower game QoE.  
network emulation; NetEm; quality of experience (QoE); quality of service (QoS); packet delay; packet loss ratios (PLR); cloud-gaming; stuttering events; cross-correlation +
Footnote †: journal:  
Article
## 5 Results, Analysis, and Discussion
### Propagation Mechanism  
According to the proposed multipath propagation model and the group velocity in Figure 1, the arrival time of each mode wave is calculated to identify the received wave packet and mode. The arrival time can be estimated using the numerical simulation (\(T\)s) and the theoretical calculation (\(T\)c). The results obtained by the two methods are compared to prove the effectiveness and consistency. The arrival times of each interested mode wave scattered from the stiffener with \(f_{\rm c}=150\) kHz and \(h=32\) mm are marked in Figure 11. The incident L(0,1) mode, reflected L(0,1) mode, and transmitted L(0,1) mode can be easily distinguished in Figure 11, which proves that determining the installation position of sensors based on the proposed model in Section 2.3 is feasible.  
As seen in Figure 11a, three modes are identified: the incident L(0,1) mode, the reflected L(0,2) mode (converted from the incident L(0,1) mode, denoted as L'(0,2)/L(0,1)), and the reflected L(0,1) mode from the incident L(0,1) mode (denoted as L'(0,1)/L(0,1)). In Figure 11b, the first group of waves is the converted L(0,2) mode, which is produced when the incident L(0,1) mode interacts with the stiffener, denoted as L(0,2)/L(0,1). Additionally, the second mode is the transmitted L(0,1) mode from the incident L(0,1) mode, denoted as L(0,1)/L(0,1).  
Table 3 shows the comparison of the arrival time obtained using both the numerical simulation and the theoretical calculation. The estimated arrival times obtained by the two methods are in close agreement because of the presented less time difference. Accordingly,  
Figure 11: Reflection and transmission waveforms with \(f_{\rm c}=150\) kHz and \(h=32\) mm: (**a**) reflection; (**b**) waveform.  
Figure 10: The partial transmitted waveform in the \(z\)-direction acquired by T (\(f_{\rm c}=150\) kHz).  
the mechanism about the single-frequency AE signal mainly in L(0,1) mode propagating in the cylindrical shell with the stiffener can be summarized, as shown in Figure 12.  
It can be noticed that the L(0,1) mode wave propagating to the bottom of the stiffener has a \"T-shaped\" transformation to result in the reflected wave (L\({}^{\prime}\)(0,1)/L(0,1)), the transmitted wave (L(0,1)/L(0,1)), and the wave propagating alongside the stiffener. In contrast, mode conversion appears and produces a little L(0,2) mode wave when the incident L(0,1) mode wave interacts with the stiffener. The produced L(0,2) mode wave mainly propagates in two directions, namely the reflected wave (L\({}^{\prime}\)(0,2)/L(0,1)) and the transmitted wave (L(0,2)/L(0,1)). These mode waves also propagate alongside the stiffener, while their energy is negligible. The wave propagating alongside the stiffener is reflected at the stiffener's end and then goes back into the cylindrical shell where mode conversion appears again to produce the reflected waves (L\({}^{\prime\prime}\)(0,1)/L(0,1) and L\({}^{\prime\prime}\)(0,2)/L(0,1)) and transmitted waves (L\({}^{\prime\prime\prime}\)(0,1)/L(0,1) and L\({}^{\prime\prime\prime}\)(0,2)/L(0,1)) with less energy.