esa-sceva/satcom-chunk-collection huggingface dataset:
# Formal Modeling of IoT and Drone-Based Forest Fire Detection and Counteraction System
## 4 Specifying Model Formally Using VDM-SL
### Static Model
The failure of an alarm is defined in this operation, which takes an alarm as input and reads antopology in the external clause. ```
FailureOfSafarm(alarm:Alarm)failed:bool extrdntopology:[NTtopology] preforallshntinsettopology.snets& existsalnodeinsetshnt.nodes&alnode.alarms=alarm postfailed<>alarm.alid.connection=falseandalarm.nbrs={} andforallshntinsettopology.snets& existsnodeinsetshnt.nodes&PathALGW([node],shnt)=falseandPath=SAL([node],shnt)=false)
```  
The **pre-condition** is described as the input alarm that should exist in the subnets. In the **post-condition**, the alarm is identified as failed if its connection is disconnected with neighbor nodes. The path of the alarm-to-gateway and alarm-to-sensor may be broken. The failure of gateway is identified through this operation _FailureOfGateWay_, which takes _gway_ in the input and reads network topology in the external clause. ```
FailureOfGateway(gways:GateWay)failed:bool extrdntopology:[NTtopology] preforallshntinsettopology.snets& existsgnodeinsetshnt.nodes&gwnode.gateways=gway postfailed<>gway.gwid.connection=falseandgway.nbrs={} andforallcontrolroominsettopology.crooms&existsnodeinsetconbrofrom.nbrs&PathGMCR([node],controlroom)=false)
```  
The presence of an input gateway in the network topology is defined in the **pre-condition**. The **post-condition** specified as the gateway is failed, if the gateway's link is disconnected, and the route from the gateway to the control room is broken. The failure of a drone is specified in this operation; it reads ntopology in the external clause and takes a drone as input. ```
FailureOfDrones(drone:Drones)failed:bool extrdntopology:[NTtopology] preexistsmodeinsettopology.nodes&dnode.drones=drone postfailed<>drone.did.connection=falseanddrone.nbrs={} andforallcontrolroominsettopology.crooms&existsnodeinsetconbrofrom.nbrs&PathDCR([node],controlroom)=false)
```  
In the **pre-condition**, it is described that the input drone exists in the network topology. The **post-condition** defined as the drone is failed if its connection is disconnected, its neighbor set becomes empty, and the path of the drone-to-control room is broken. ```
ReplacementofNodes(model:Node,node2:Node)pos:Pos pretrue postmodel.pos=posandpos.xcor=node2.pos.xcorand pos.ycor=node2.pos.ycor)
```  
When the failure of any node is detected in the network, then it is the control room's responsibility to replace the nodes with new ones. This operation _ReplacementOfNodes_ is applied for all the nodes; it takes two nodes in the input and replaces the location of the failed node with the position of the new node.
# Forecasting the Spread of COVID-19 in Kuwait Using Compartmental and Logistic Regression Models
## 2 Methods
### Logistics Growth Model  
The logistic growth model is a regression model that is widely used in epidemiology mathematical models to estimate the growth and decline rate of pathogens [14]. The model assumes an exponential growth at the beginning of the epidemic, followed by steady increase and finally ending with a declining growth rate. The logistic model is presented by [PERSON] in [15] in Equation (1) as:  
\[\frac{1}{C}\frac{dC}{dt}=r\Big{(}1-\frac{C}{K}\Big{)} \tag{1}\]  
where \(C\) is the number of infected cases, \(r\) defined as the rate of infection and \(K\) is the final epidemic size. Given that the initial condition of \(C(0)=Co\), the number of infected cases is define as:  
\[C=\frac{K}{1+\Big{(}\frac{K-C_{o}}{C_{o}}\Big{)}e^{rt}} \tag{2}\]  
And the maximum growth rate peaks at the time:  
\[t_{p}=\frac{\ln\Big{(}\frac{K-C_{o}}{C_{o}}\Big{)}}{r} \tag{3}\]  
With the peak number of cases defined as:  
\[C_{p}=\frac{K}{2} \tag{4}\]  
And a growth rate at the maximum peak as:  
\[\frac{dC}{dt_{p}}=\frac{rK}{4} \tag{5}\]  
To fit the actual confirmed cases of the infected population, the regression model is written in the form:  
\[C=\frac{b_{1}}{1+b_{2}e^{-b_{3}t}} \tag{6}\]  
where \(b_{i}=b_{i}(t)\) with i = (1, 2,3... n) are estimated parameters from the actual data of the epidemic.
# A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems
## 1 Introduction
It is also applicable to various segments, such as the integration of a large amount of data from devices linked to the IoT context (e.g., smart homes, patient monitoring, Wireless Sensor Networks--WSNs) with cloud services; integration of applications that have multimedia requirements, high density of video and audio traffic, such as applications that use Virtual Reality (VR) and Augmented Reality (AR) technologies; provision of specialized networks with sophisticated slice selection and security mechanisms to provide services for autonomous vehicle networks in different models and topologies (e.g., V2V--Vehicle-to-Vehicle, V2I--Vehicle-to-Infrastructure, V2X--Vehicle-to-Everything) [14; 15; 16]. More than the comprehension of NS, this work contributes to improving the network slice selection process. A framework that implements the NSSF service in vertical and horizontal models is proposed, where the handover decision is shared between the network edge and the User Equipment (UE) at network runtime. The remainder of this paper is organized as follows: Section 2 details its main contributions; Section 3 contextualizes the related work; Section 4 exposes the general problem and the need to use edge computation in the slice selection model; Section 5 presents the proposed solution framework for Network Slicing selection; Section 6 describes the scenario and the methodology used in the experiments, as well as reports and discusses the results obtained. Finally, Section 7 concludes the work.
# Assessment of Fluoride-Releasing Materials in Remineralization of Adjacent Demineralized Enamel
###### Abstract  
This study aimed to evaluate the ability of fluoride-releasing restorative materials to remineralize artificially demineralized proximal enamel adjacent to class II restorations. Fifty-four demineralized enamel lesions were created on extracted premolar teeth, and the baseline lesion depth (LD) and mineral density (MD) were measured using micro-CT. The samples were randomly assigned to one of four groups, each in contact with a class II restorative material: Filtek Z350 XTM (FZ), Cention N(r), Fuji II LC(r)), or Equia Forte(r) (EQ). Finally, post 14 days of pH cycling, measurements were taken. SEM, EDX, and Raman Imaging Microscopy were additionally performed. The results showed significant reductions in LD for all fluoride-releasing materials (CN, FJ, EQ). EQ and CN showed a significantly higher percentage change in LD than FZ. The MD of adjacent demineralized enamel increased significantly in all groups. There was no significant difference in the percentage change in MD between groups. SEM-EDX images revealed greater mineral deposition in fluoride-releasing materials than FZ. The Ca/P ratio of demineralized enamel adjacent to CN was equal to that of EQ, while FJ demonstrated the highest ratio. In conclusion, fluoride-releasing materials exhibited a reduction in the LD of adjacent demineralized enamel and demonstrated Ca/P ratios higher than FZ. CN may be an alternative restorative material to remineralize adjacent demineralized enamel.  
Keywords:alkasite; fluoride-releasing restorative materials; lesion depth; mineral density; proximal demineralized enamel; remineralization +
Footnote †: journal: Applied sciences
# Energy Management of Virtual Power Plant Considering Distributed Generation Sizing and Pricing
## 6 Simulation Results and Discussion
### Effect of Selling Electricity to the Upstream Network  
In Table 11, the effect of the electricity exchanging price to the upstream network in non-peak hours is represented. This issue has been obtained in terms of the optimal size assessment of DGs considering grid connected operation mode.  
According to Table 11, the increase in electricity sales prices to the upstream network will increase the sales of electricity to the upstream network, which will reduce the total operation cost of system and it will raise the revenue.
## 4 Conclusions
The longest computational time taken by the Beta model was approximately 180 min as opposed to the Alpha model's 40 s. When considering that the largest Normalised RMSE across all Runs was 13.9%, the Alpha model is a clear front runner in terms of speed and usability. Nevertheless, the Beta model offers an overall system understanding but is more applicable for analysing transients in the design of any HPES or CAES system.
## 3 Results and Discussion
### Changes in OJIP Transients after Drought and Recovery
An SFI decrease reflects the limited electron transport and  
Figure 3: Chlorophyll \(a\) fluorescence parameters characterizing the PSII functioning: minimal fluorescence intensity, F\({}_{0}\) (**a**), normalized area, S\({}_{\text{m}}\) (**b**), structure – function index, SFI (**c**), fraction of inactivated OEC, V\({}_{\text{K}}\)/V\({}_{\text{J}}\) (**d**), density of reaction centers per excited cross section, RC/CS\({}_{0}\) (**e**) and overall connectivity parameter, \(p\) (**f**) measured in three _Brassica_ seedlings subjected to drought and subsequent recovery. Normalized data are presented as the mean \(\pm\) SD; \(n\) = 7; asterisk (*) represents a significant difference at _p_\(\leq\) 0.05 (ANOVA, HSD). diminished overall photosynthetic activity. It was reported recently that, in sensitive sunflower hybrids, the SFI decreased upon severe drought stress, while in tolerant hybrids, the SFI remained at the control level [101]. An SFI decrease in white cabbage, therefore, suggests that stressful conditions lower the influence of internal factors, thus diminishing reactions in PSII. The ratio of variable fluorescence at times 0.3 and 2 ms (V\({}_{\mathrm{K}}\)/V\({}_{\mathrm{J}}\)) is a reliable indicator of the restrictions at the PSII donor side [81; 99] and can be used as a relative measure of OEC inactivation [102]. Drought stress did not induce significant changes in V\({}_{\mathrm{K}}\)/V\({}_{\mathrm{J}}\) in any _Brassica_ variety (Figure 3d). After recovery, there was a significant increase in the white cabbage compared to the control, while the Chinese cabbage and kale showed no significant changes. An increase in V\({}_{\mathrm{K}}\)/V\({}_{\mathrm{J}}\) is usually the result of the uncoupling of OEC [81; 102]. It was reported recently that UV-B radiation could induce an increase in V\({}_{\mathrm{K}}\)/V\({}_{\mathrm{J}}\) in sensitive and moderately tolerant populations of Scots pine seedlings [81], in rice seedlings exposed to high light, salinity, and PEG- induced drought [99], as well as in _Actinidia_ plants deprived of nitrogen [103]. Our results suggested that drought caused OEC damage in white cabbage, since V\({}_{\mathrm{K}}\)/V\({}_{\mathrm{J}}\) increased after recovery. The fact that the K-band (Figure 1h) showed a positive amplitude corroborated this assumption. Moreover, an increase in RC/CS\({}_{0}\) (Figure 3e) in combination with increased ABS/RC and TR\({}_{0}\)/RC (Figure 2b) confirmed the impairment of OEC in drought-stressed white cabbage. The density of active PSII RCs per excited cross-section (RC/CS\({}_{0}\)) decreased in white cabbage upon drought, and after recovery, it remained as low as it was during stress. On the other hand, Chinese cabbage and kale showed no difference compared to their controls. Differential stressful conditions usually induce the lowering of RC/CS\({}_{0}\) in various species [33; 99; 104; 103], suggesting a lower tolerance to such conditions. Therefore, a decrease in RC/CS\({}_{0}\) suggests an increased susceptibility of white cabbage to drought compared to Chinese cabbage and kale.
## 4 Case Study for Signal Processing of Pulsed Thermography
### Advances of DAC  
[PERSON] et al. [79] proposed depth characterization of delamination defects on concrete's front and back surfaces using a PT-based DAC. Comparative analysis of SNR values was performed by applying thermal contrast, histogram equalization, and PPT and FFT to the raw image. In addition, predictive characterization analysis of the size and depth of delamination defects was performed using linear regression analysis.  
[PERSON] et al. [80] proposed an intelligent diagnostic system based on active infrared thermal imaging for solder joint inspection. TSR and DAC processing techniques were applied to improve the SNR of images acquired by IRT. After that, the hot spots of the solder balls were classified, and statistical features were extracted using the K-means algorithm, and it was suggested that it is an efficient inspection technique. [PERSON] et al. [81] presented factors affecting the heat flux and wind velocity thermal contrast of the surface of the bridge deck by applying the IRT technique to analyze the optimal inspection time for various geometrical properties of delamination. To compare and analyze the data of experiments and simulations, the validity was verified using DAC. [PERSON] et al. [82] proposed a flash lamp's damage characterization of CFRP composites based on IRT. The measured thermal image sequence was evaluated, and comparative analysis was performed using DAC, PPT, and PCT signal processing.
## 1 Introduction
Nevertheless, all these derived features, along with the phenological metrics involve a huge volume of data, which may increase computational complexity with little improvement in accuracy [29]. Several feature selection methods have been proposed [29] to deal with this problem. In [30], various features have been derived from the MODIS time series and best feature selection has been made using the random forest algorithm. LC&CC can also be classified as pixel-based or object-based. Object-based image analysis (OBIA), described by [PERSON], that segmentation of satellite images into homogeneous image segments can be achieved with high-resolution sensors [31]. The various object-based classification has been proposed to produce crop maps using satellite imagery [32; 33; 34]. In this work, we proposed a unique deep neural network architecture for LC&CC, which comprises of Recurrent Neural Network (RNN) that extracts temporal correlations from time series of sentinel-2 data in combination with Convolutional Neural Network (CNN) that analyzes and encapsulate the crops pattern through its filters. The remainder of this paper is organized as follows. Section 2 briefs about related work done for the LC&CC along with an overview of RNN and CNN. Section 3 provides an overview of the raw data collected and exploited during the research. Section 4 provides detailed information on the proposed model and the training strategies. Section 5 contains a complete description of the experiments, results, and discussion along with the comparison with previous state-of-the-art results. Finally, Section 6 draws some conclusions.
## 2 Problem Formulation
### Coverage Model  
The study uses the 0/1 coverage model, which indicates that a space point is either covered and represented as 1 or not covered and represented as 0. In this paper, space points that satisfy Formula (2) are covered, and otherwise are not covered. The degree of coverage for the target space can also be expressed as follows: the degree of coverage for any point in the target space region is \(k\) if at least \(k\) sensor nodes are covering it at the same time. Considering the need to implement the object tracking and localization task as well as the image reconstruction principle, it is required that the moving object be captured by at least two cameras at the same time, i.e., the sensor network degree of coverage \(k\geq 2\) is required. Subsequent modeling and experimental procedures provide scenario-specific \(k\) according to the specific scenario.
## 2 Materials and Methods  
### Participants  
Participants with PD and healthy controls were recruited through Parkinson's New Zealand, community-based gyms, and organizations working with people with PD. Advertisements were placed in gyms, community notice boards, and via social media platforms. Inclusion criteria: the study included participants clinically diagnosed with PD without other neurodegenerative diseases and a score of 23 and above on the Montreal Cognitive Assessment (MoCA) [36]. Exclusion criteria for PD included marked gait impairment or postural instability based on the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III scores [37], a deep brain stimulation implant. Exclusion criteria for all participants included the use of a pacemaker or beta-blockers, the presence of heart conditions, or any other conditions contraindicated for HIIT [6].  
Participants attended on three non-consecutive days. On the first day, participants underwent a cardiopulmonary exercise test (CPET) and on the second and third days they performed boxing sessions. Participants were instructed to avoid exercise for 24 h, abstain from alcohol and caffeine for 12 h, and refrain from consuming a large meal within 3 h of testing. PD participants were tested approximately one hour after medication to optimize their 'on' state. All testing took place at the Auckland University of Technology in the exercise performance laboratory under temperature-controlled conditions (19\(-\)20 \({}^{\circ}\)C).
## 4 Discussion
Among these given the relatively higher sensitivity and specificity, the use of molecular tools like PCR should be preferred in the future studies. Further use of more than one diagnostic modality (combining one of the preliminary tests like CHROMagar with a molecular tool) could in turn increase the reliability of the results. Although all included studies had well-defined cases (histopathological confirmed OSCC) and control (clinically confirmed normal oral mucosa), there were several liabilities due to the lack of adequate matching between the comparison groups. Among the seven studies, only four studies [36; 37; 38; 40] accounted for known confounders (age and gender), while one [35] matched only for gender and the remaining two studies [34; 39] either did not provide any matching information or failed to match the groups. Additional matching for potential confounders was made in only one [36] of the included studies wherein the denture status was considered. The lack of adequate matching resulted in a relatively lower score for the included studies on the Newcastle-Ottawa scale (Table 2). In order to quantitatively assess the association between _Candida_ prevalence, species specificity in OSCC, the odds ratio at 95% confidence interval was calculated for each of the included studies(Table 3). Quantitative assessment was carried out only for those _Candida_ species wherein the odds ratio was available from at least four studies. Forest plots were generated using the overall ratio (at 95% confidence interval) which in turn was calculated from the odds ratio obtained from the individual studies. Only total _Candida_, _CA_, and _NCA_ association with OSCC had odds ratios from at least four studies and were assessed quantitatively. Based on the meta-analysis, a significant association was noted between both total _Candida_ and OSCC. The result confirms the notion that _Candida_ would be higher in the diseased state than the healthy controls. The clinical implication based on total _Candida_ in OSCC is limited without knowing the species specificity. Similar to total _Candida_, even the _CA_ showed a significant association with OSCC. Once again, _CA_ being a commensal, its higher presence in OSCC just confirms its opportunistic nature. To establish a pathogenic aspect to _Candida_ and OSCC, it was necessary to analyze the presence of the pathogenic _Candida_. Although _NCA_ also showed an association with OSCC, the relationship was not significant. The nonsignificant association with _NCA_ questions the notion that _NCA_ could be a potential risk factor for OSCC. As mentioned earlier, since there were limited studies, it was not possible to analyze the association of OSCC with each _NCA_ species individually. Thus, although overall _NCA_ does not have a significant association with OSCC, it is unclear if there could be an _NCA_ species-specific association with OSCC.
## 4 Major Components of Network Slicing System
### Intent-Based Networking (IBN) Tool
#### 4.2.1 User Intents/Contract Design  
The contract design is a very important part of the IBN system because it will define the underlying platform resources requirement in an easy way which is understandable by the network operators. Basically, contract design is the user intent for creating a slice (resource requirements). This user contract design has four major fields: S-NSSAI (single network slice assistance information), architecture ID, up-rate, and down-rate. First, field S-NSSAI is used for identification of the service domain for each slice (URLLC, eMBB, mMTC, etc.) which is a standardized approach provided by 3 GPP. The second field defines the network architecture type; it will be LTE, 5G, LTE advanced, etc.; architecture-ID is different for each network architecture. The last field is to provide QoS parameters up-rate and down-rate data requirements for the requested slice by specifying the bandwidth and latency requirements. The network operators can control the bandwidth, spectrum, and other resources by just specifying the up-rates and down-rates. Thus, the IBN framework has an interactive web-based front-end for network operators where they can define the user contracts by input the information of these four fields. These four important requirements are enough for the IBN framework to generate the service graph which will allocate the network resources (RAN, core) and suitable network functions for a requested slice. The procedure to process a contract with other modules of the IBN framework is shown in Figure 4.  
Figure 4: User intents or contracts design with required attributes.
## 4 Simulation Environment
### Scheduling of Radio Resources, Precoding, and Power Allocation  
We have implemented a centralized radio resource scheduling algorithm, which is placed within the CU-DU. It is based on the concept of a network-centric approach [22]. However, we have adapted it to the UCCF M-MIMO network architecture. The resulting procedure is presented in Algorithm 1. The scheduling algorithm takes as input the exponential average of UE past rates, inter-cluster interference observed by the UE, radio channel coefficients, and a threshold \(\Delta\) in the range \(<0;1)\) for the radio channel correlation coefficient, which signifies the minimum requirement for creating a new MU-MIMO layer. The implemented scheduling algorithm independently allocates each RB to users. The allocation is based on the Proportional Fairness (PF) metric, which is defined as the ratio between the maximum expected throughput at a given RB and the user's past rates. The calculation of the PF metric is performed in steps 2-5 of the scheduling algorithm, resulting in the sequence \(\mathcal{K}\) containing the indices of the UEs (denoted as \(k\)), which are sorted according to their PF metrics. The user with the highest PF metric (first element of the sequence \(\mathcal{K}\)) is given the RB. However, due to the utilization of M-MIMO, the same time-frequency resources can be multiplexed in space. Thus, according to the PF metric, the next UEs are allocated, creating a set of \(allocatedUEs\) with \(N_{\text{L}}\) elements representing spatial layers. This procedure is described in steps 8-25 of the algorithm.