esa-sceva/satcom-chunk-collection huggingface dataset:
# Method of Multilevel Adaptive Synthesis of Monitoring Object Knowledge Graphs
## 8 Case Study
### Initial Models of the Monitoring Object  
Based on network statistics for routers, stationary, and mobile devices, the rules have been generated in order to assess the traffic. In defining the rules, the following models were used:  
* Model of subscriber devices, describing their state and behavior in terms of hours per day and working/non-working days.
* Statistical model of the movement of mobile devices between the coverage areas of routers, in terms of hours per day and working/non-working days.
* Static crash model for routers when the aggregate traffic exceeds the limit. To assess the state of the routers, the following are defined:
* Three ranges for the values of the total traffic on the router at a point in time--normal traffic (green zone), increased traffic (yellow zone, when all requests are processed, but there is not enough performance margin), and critical traffic (some or all requests cannot be processed).
* Three ranges for the number of mobile devices registered on the router at a time--normal number (green zone), increased number (yellow zone, when all requests from devices are processed, but the performance margin is not enough) and critical number (part of requests from devices or all, cannot be processed).
## 3 Literature Review
Two methods were used to accomplish this; the first was the position-prediction method, which improves the greedy forwarding strategy by selecting optimal routes to forward the data, and the second is dynamic beaconing, which uses the slow-start algorithm to change the beaconing interval depending on data traffic load and node mobility rather than the periodic beaconing technique. Similarly, [PERSON] and [PERSON] [48] suggested an FL-QN GPSR routing protocol based on an intelligent fuzzy logic control system with fixed beaconing in VANET. The proposed routing protocol incorporates two criteria to determine the appropriate next-hop node for packet forwarding: neighbor node and node speed. On the other hand, [PERSON] et al. [49] proposed an ABNT technique for geographic routing in UAVs. To keep the neighbor data table up to date, the strategy combines adaptive beaconing (AB) and neighbor timeout (NT). NT is calculated dynamically by utilizing mobility characteristics. The fuzzy logic system uses numerous characteristics related to node mobility, remaining node energy and traffic load to determine the beaconing rate, which reduces beacon overhead, latency, delivery loss rate and energy usage. Finally, the study in [50] focuses on developing the Greedy Perimeter Stateless Routing (GPSR) protocol for FANETs. The approach is known as the Utility-Function-based Greedy Perimeter Stateless Routing (UFGPSR). The suggested method optimizes the greedy forwarding strategy by taking into account various critical factors of UAVs, including residual energy ratio, distance degree, movement direction, connection risk degree and speed. The proposed UFGPSR uses a utility function on these parameters to improve routing performance by choosing the best next hop within the transmission range. In this paper, the GPSR is enhanced by using a dynamic fuzzy-beaconing algorithm termed AFB-GPSR in a MANET network in order to provide more adaptability to the rapid changes in node topology. The adaptive beaconing mechanism based on intelligent fuzzy logic is proposed instead of traditional periodic beaconing mechanism. Our strategy considers the network parameters of the node speed, one-hop neighbors' density, and network link stability. In brief, this technique effectively enhances GPSR performance by using a new real-time uncertainty fuzzy logic scheme to choose the best next-hop in GPSR routing.
## 3 System Model
### Mobile Devices
#### 3.2.2 Local Execution  
The local execution queue is a first-in-first-out queue. That is, tasks arriving in the queue earlier will also be executed earlier. Each mobile device has \(p(m_{i})\) flops of processing power. As a result, given task \(t_{i}\), the amount of time required to run the task locally is:  
\[d_{l,e}(t_{i})=d(t_{i})/p(m_{i}) \tag{1}\]  
Additionally, the amount of time required for the task to wait in the local execution queue is:  
\[d_{l,eq}(m_{k},t_{i})=\alpha.d_{l,e}(t_{c})+\sum_{t_{j}\in q(m_{k})}d_{l,e}(t_ {j}) \tag{2}\]  
where \(\alpha\) is the ratio of the currently executing task that is executed, \(t_{c}\) is the currently executing task, and \(m_{k}\) is the corresponding mobile device. If no task is currently executing, the value of term \(\alpha.d_{l,e}(t_{c})\) will be zero.  
As a result, if a task \(t_{i}\) is to be executed locally, it will require a \(d_{l,et}(t_{i})\) amount of time:  
\[d_{l,et}(m_{k},t_{i})=d_{l,e}(t_{i})+d_{l,eq}(m_{k},t_{i}) \tag{3}\]  
The energy consumption of executing a task \(t_{i}\) at mobile device \(m_{j}\) is  
\[e_{e}(m_{j},t_{i})=c_{e}(m_{j}).d(t_{i}) \tag{4}\]  
where \(c_{e}(m_{j})\) is the energy consumption in joules for performing one floating point operation in the mobile device \(m_{j}\).
## 2 Materials and Methods
### Physical-Layer Security Analysis: Non-Colluding Eavesdroppers
#### 2.2.2 Asymptotic Probability of Non-Zero Secrecy Capacity
That is, as \(\lambda_{e}\) rises, there is a greater probability of having a closer eavesdropper to [PERSON]. Additionally, the privacy of the shared information is under a higher risk when selecting the first closest eavesdropper \((i=1)\) as opposed to selecting the second closest one \((i=2)\). The reason is that the first closest eavesdropper is more probable to have better channel conditions compared to the other farther eavesdroppers. This figure proves the significance of considering random locations for the eavesdroppers, rather than being fixed at specific locations and distances from the transmitter. Figure 8 presents a comparison between colluding and non-colluding eavesdroppers with a density of \(\lambda_{e}=0.1\). In the case of non-colluding eavesdroppers, the security declines as the number of antennas rise. Moreover, comparing the case of non-colluding eavesdroppers for \(K=2\) with the colluding eavesdroppers case, it is noted that although the number of non-colluding eavesdroppers is greater, the interception and decoding ability of the colluding eavesdroppers is stronger. Hence, the privacy of information is more vulnerable when colluding eavesdroppers exist in the network. This leads to the realization that further countermeasures should be adopted at the main channel in the presence of colluding eavesdroppers. As a final investigation, Figure 9 demonstrates the probability of non-zero secrecy capacity for different values of the average received SNR at \(E\) (\(\bar{\gamma_{E}}\)) for the non-colluding eavesdroppers case. The results clearly demonstrate how the system's privacy behaves as the eavesdroppers' channel quality improves. Particularly, fixing the legitimate receiver received SNR, the privacy of the shared information is severely compromised as \(\bar{\gamma_{E}}\) takes high values. After a certain limit, the \(P_{nsc}\) approaches its asymptotic degree, i.e., lowest value. That is, the curve reaches a value of zero as \(\bar{\gamma_{E}}\rightarrow\infty\). Indeed, this is in agreement with the asymptotic results obtained in (30). Figure 8: The intercept probability \((P_{int})\) versus the average received SNR at \(E\). For the main channel, \(\kappa=1,\mu=1\), and for the wiretap channel, \(\kappa_{e}=1,\mu_{e}=1\). \(\bar{\gamma_{B}}=5\) dB, \(N=2\), \(n_{e}=1\), \(\alpha=2\), \(\lambda_{e}=0.1\), and \(i=1\).
# Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology
## 3 Implementation of MBSS Technique Using SDR Technology  
One of the main motivations of the work, previously developed by the authors [12], is to propose a MBSS technique that is both (i) adequate for correctly detecting a primary user and (ii) having a computational complexity allowing real-time implementation in a wireless communications environment. Low-cost devices easy to install and program, such as the SDR devices explained above, are considered. Hence, Figure 1 shows the general scenario for the MBSS using low-cost SDR devices to know the occupation of a wide spectrum interval.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Device** & **HackRF One** & **RTL-SDR** & **LimeSDR Mini** \\ \hline Frequency range & 1 MHz\(\sim\)6 GHz & 22 MHz–22 GHz & 10 MHz–3.5 GHz \\ RF bandwidth & 20 MHz & 3.2 MHz & 30.72 MHz \\ Sample depth & 8 bit & 8 bit & 12 bit \\ Sample rate & 20 MSPS & 3.2 MSPS & 30.72 MSPS \\ Tx channels & 1 & 0 & 1 \\ Rx channels & 1 & 1 & 1 \\ Duplex & Half & - & Full \\ Transmit power & \(-\)10 dBm + (15 dBm @ 2.4 GHz) & - & Max 10 dBm (depending on frequency) \\ \hline \hline \end{tabular}
\end{table}
Table 1: SDR device characteristics [31], (MSPS, mega samples per second).  
Figure 1: General description of implemented MBSS scenario.  
In general, the implemented scenario consists of four big blocks: a sliding time window, an estimator of the power spectrum density, a block for reduction in impulsive noise, and a block to determine frequency edges and detection of PUs. Basically, each device collects information from the environment (i.e., each one working as an \(SU\)), and this information is processed by the estimator of the power spectral density (PSD) to get the spectrum in different bands. Thereafter, this PSD is modified to eliminate noise, especially impulsive noise, through an MRA (discrete wavelets). Lastly, the last block allows deciding if a \(PU\) is present or not using discrete wavelets, machine learning, and the Higuchi fractal dimension. Next, each block is described in detail.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 3 Security Protections in IoT Environments
### Challenges and Future Directions in IoT Security
#### 3.8.2 Future Directions  
The following research and innovation to be undertaken as future directions will overcome the aforementioned challenges to IoT security, and lay the foundation for a robust and trusted IoT ecosystem.  
* _Lightweight Encryption_. Lightweight encryption algorithms for resource-constrained devices are a key future direction for IoT security. These algorithms need to secure data while minimizing the impact on device performance to improve the overall security of the IoT system [90]. Efficient and lightweight authentication is also one of the future directions of security research, which is important because verifying the identity of the user prevents sensitive data leakage and improves the performance of IoT networks.
* _Artificial Intelligence-Driven Security_. Traditional encryption methods are often utilized to address security and privacy issues in IoT networks; however, the nature of IoT nodes makes it impossible for existing methods to support the architecture of an entire complex and large IoT network, in part due to resource constraints and the large amount of real-time data generated by IoT devices. One may apply machine learning and deep learning solutions to IoT devices and networks, aiming to optimize the overall security of the system by learning statistical information collected from sensors. For instance, IoT systems may adopt deep learning to train a model that automatically identifies malicious behaviors to improve the self-protection performance.
* _Blockchain-based Security._ Blockchain technology provides decentralized and tamper-proof data management, which is suitable for security and tamper-proof requirements in the IoT. Thus, the blockchain ensures trust between devices and provides traceable data records that help prevent data tampering and malicious access and can effectively protect customers from data privacy breaches [91].
* _Privacy-Protection Technologies._ IoT devices provide personalized services by collecting and analyzing user data, which poses the risk of privacy leakage. Therefore, the future development of IoT security will inevitably focus more on privacy-protection techniques.
# A Machine Learning Approach to Identifying Risk Factors for Long COVID-19
## 1 Introduction
A history of severe acute COVID-19 infection with a high symptom burden or requiring hospital or ICU admission has also been found to place patients at higher risk of Long COVID-19 [5; 7; 8; 10; 11]. Interestingly, a protective effect has been observed for COVID-19 vaccination and the use of antiviral medication for acute infection [7; 10; 12]. A lower risk of Long COVID-19 has also been reported with the recent Omicron COVID-19 variant and greatest risk with the earliest variants [5; 7; 13]; whilst this has typically been observed in association with the effect of vaccination, there is some evidence for its effect independent of vaccination timing [11]. Studies have also begun exploring social risk factors for developing Long COVID-19; however, data in this domain are much less robust as compared to those for acute COVID-19. There is emerging evidence for lower socioeconomic status and certain minority ethnic groups including Hispanic, African American and mixed ethnicity being associated with an increased risk of Long COVID-19 [5; 7; 11; 14]. However, the literature at present is conflicting, highlighting the need for further investigation; an analysis of 10 UK longitudinal studies, for example, demonstrated similar findings for demographic and clinical risk factors but no association with socioeconomic status and lower risk of Long COVID-19 in minority ethnic groups and lower education levels [15]. Much of the existing literature that examines risk factors for Long COVID-19 analyses electronic health record or online cohort data to discover differences in baseline characteristics and associations by logistic regression and other statistical analyses; studies that utilise machine learning to develop predictive models are few. One US study that utilised XGBoost machine learning techniques to identify predictors for Long COVID-19 using electronic health record data reported similar findings to the literature with the most significant predictors in the model being middle age, having common chronic health conditions, and markers of severe acute infection [16]. The findings of two similar studies, one employing both logistic regression and random forest analysis on electronic health record data, and another utilising a gradient boosting classifier on healthcare data from German primary care centres, yielded similar predictors [17; 18]. Such studies hold promising prognostic value that can support clinical decision-making; expanding to include sociodemographic predictors can also influence targeted public health interventions. This study thus aimed to produce a machine learning algorithm to identify key risk factors for Long COVID-19 in a community-based sample from which a broad and detailed set of potential health-related and sociodemographic factors can be analysed.
# Satellite-Based Estimation of Carbon Dioxide Budget in Tropical Peatland Ecosystems
## 3 Results
### GPP in Tropical Peatlands  
The daily average and the standard deviation of the modified GPP\({}_{\text{robust}}\) were 8.91 and 1.21 g C/m\({}^{2}\)/day, respectively, at the Palangkaraya \(DF\) site from 2002 to 2018 (Figure 8). The original data from the \(DF\) flux tower were 9.5 and 0.74 g C/m\({}^{2}\)/day for the daily average and the standard deviation, respectively. Before the modification of the GPP in this study, although the daily average was low (6.26 g C/m\({}^{2}\)/day), the standard deviation of the MODIS GPP was relatively high (2.57 g C/m\({}^{2}\)/day). The area's average of the monthly GPP was 39.15 Mt C/month, and its standard deviation was 1.09 Mt C/month). The mean annual GPP ranged from 2100 to 2400 g C/m\({}^{2}\)/year (Figure 9).
# Common Gabor Features for Image Watermarking Identification
## 4 Results and Discussion
### Performance Comparison of Methods  
As illustrated in Table 2, the proposed method is compared to other state-of-the-art image watermarking approaches [35; 36; 37; 38]. The spatial domain is used in both [37; 38] watermarking detection methods. The techniques [37; 38] are based on singular value (SV) decomposition and shell based pixel selection, respectively. The transform domain is used in both [35; 36] watermarking detection methods. The techniques [35; 36] are based on discrete wavelet transform (DWT) and discrete shearlet transform (DST), respectively. The present approaches in [35; 36] have the maximum accuracy. However, as seen in Table 2, they have the disadvantage of being time intensive. The fundamental reason is that the transform domain is being used. Our proposed outperforms other spatial based watermarking methods [37; 38]. Figures 12 and 13 show how the proposed method, which does not use the transform domain, outperforms other current methods in terms of TPR and FNR rates. In comparison to both of other current spatial and transform domain techniques, the proposed method had a maximum TPR of roughly 93.71 percent and a minimum FNR of around 6.29 percent.
# A New Orbiting Deployable System for Small Satellite Observations for Ecology and Earth Observation
## 3 Study Cases
### Application 3: Coastal Area Monitoring
#### 3.3.1 Ocean Color  
The ocean color (OC) is determined by the concentration of the phytoplankton, organic materials, and sediments, as well as anthropogenic materials such as sewage and fertilizers, which provide a nutrient base for organisms in the coastal ocean (e.g., [136; 144; 145; 146; 147]). It is therefore a key variable to investigate the primary productivity in the upper layers of the coastal zones, together with the phenomena such as the mesoscale eddies (circular currents on the ocean spanning 10 to 100 km in diameter, which persist from a few days up to months), fronts (boundaries between distinct water masses), upwellings (where deep cold water rises to the surface), and internal waves (e.g., [148; 149; 150; 151; 152]).  
To study the OC, two types of instruments are needed: (i) multispectral radiometers, sensitive to a narrow, discrete wavelength band, ranging between 0.94 and 2.26 \(\upmu\)m, and (ii) imaging spectrometers, sampling across the spectrum with a defined spectral resolution.  
Small satellites can also be a powerful option to monitor narrow regions at high spatial resolutions, in various spectral ranges [135], e.g., coastal areas to track anthropogenic and pollutant compounds, on a daily basis and at a ground resolution of a few hundred meters [153; 154]. For instance, OC measurements are the main goal of CubeSats, such as SeaHawk-1 (belonging to the SOCON constellation) [155] and ZACUBE-2 [156], with ground resolutions better than about a hundred of meters.
## 5 Experiments
### Sliding Mode Controllers Chattering Analysis  
The chattering of the sliding mode controller mainly comes from the sign function. Too much chattering is not conducive to the tracking control, and may cause the controller to crash. Through experiments, it was found that adjusting the size of \(\epsilon_{1}/\epsilon_{2}\) can effectively suppress chattering. However, suppressing chattering to a certain extent will affect the accuracy of the controller, resulting in a larger tracking error. Therefore, while ensuring a certain tracking accuracy, chattering with little amplitude variation is acceptable. The test results of \(\epsilon_{1}/\epsilon_{2}\) from large to small are shown in Figure 11.
# Solving Task Scheduling Problems in Dew Computing via Deep Reinforcement Learning
## 5 Reinforcement Learning for Dew Computing
### Problem Definition: Task Scheduling in Dew Computing  
We tackle the problem of distributing jobs in a Dew environment. The Dew environment consists of a set of devices connected to a local network. Some of these devices are _IoT devices_ that have an unlimited power supply (e.g., Raspberry Pis and personal computers) and others are _mobile devices_ with a limited battery (e.g., tablets and smartphones). In addition, one node in the local network is assigned as the _scheduler_. The scheduler's purpose is to receive jobs and distribute them among the devices, as shown in Figure 2.  
Once a job is assigned to a device, the time that it takes to complete the job (and how much battery it consumes) depends on the features of the job and the current state of the device. For instance, since some devices have limited battery, a job might not be completed if the job is assigned to a device with a low battery level. Users might also interact with the devices, and the local network might suffer from congestion issues. The scheduler must consider all these factors in order to distribute jobs effectively.  
In more detail, assigning a job to a device adds that job to the device's job queue. Then, the device will keep running the jobs in its queue, one by one, until either completing all of them or running out of battery. Once a device runs out of battery, all the jobs in its current queue are discarded. If a job is assigned to a device that has no battery, that job is also discarded. In our experiments, if a job is discarded, it cannot be reassigned to a different device. This forces the scheduler to be extra careful when deciding to assign a job to a battery-dependent device.  
To evaluate the effectiveness of a given job distribution, we use the _giga-instructions per second (GIPS)_ that are completed in the Dew environment:  
\[\text{GIPS}(t_{0},t)=\frac{\sum_{j\in J_{c}}j\text{.ops}}{t-t_{0}}, \tag{2}\]  
where \(J_{c}\subseteq J\) is the subset of jobs that were completed within the time interval \([t_{0},t]\) and \(j\).ops is the number of giga-operations executed when processing job \(j\in J_{c}\). Intuitively, GIPS is a measurement of how many operations are completed by a unit of time. The goal of the scheduler is then to distribute the jobs so that the GIPS are maximized in the Dew environment.  
In this work, we assume that the Dew environment is fixed. That means that no device leaves or enters the local network during the period that we evaluate the system. We discuss how to extend our model to work in non-fixed Dew environments in Section 7.