esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Description and Its Mathematical Modeling
### Quasi Steady-State Analysis  
Let \(I_{1}(t)\), \(I_{2}(t)\), \(V_{o1}(t)\), \(V_{o2}(t)\) and \(I_{g}(t)\) be the quasi stationary values of the averaged state variables. The coordinates of the quasi-equilibrium point can be obtained by equaling to zero all the time derivatives in (2)-(7). In doing so, it is obtained that the quasi steady-state values of the state variables are related to the quasi steady-state duty cycle \(D(t)\) by the following expressions  
\[I_{1}(t) = \frac{I_{g}(t)}{1-D(t)} \tag{8}\] \[I_{2}(t) = -\frac{I_{g}(t)}{D(t)}\] (9) \[V_{o1}(t) = \frac{V_{\rm dc}}{1-D(t)}\] (10) \[V_{o2}(t) = \frac{V_{\rm dc}}{D(t)}\] (11) \[V_{\rm dc}(t) = \frac{v_{g}(t)D(t)(1-D(t))}{2D(t)-1}\] (12) \[I_{g}(t) = I_{\rm mpp}\frac{D(t)(D(t)-1)(2V_{\rm dc}(1-2D(t))+v_{g}D(t)(1- D(t)))}{V_{\rm dc}(2D(t)-1)^{2}} \tag{13}\]  
Since \(v_{g}=V_{o1}-V_{o2}\), using (10) and (11), the voltage gain of the inverter can be expressed as follows  
\[M(D):=\frac{v_{g}}{V_{\rm dc}}=\frac{2D-1}{D(1-D)} \tag{14}\]The conversion gain \(M(D)\) is a continuous odd function of \(D\). At \(D\approx 0.28\) and \(D\approx 0.72\), \(|M(D)|\) is approximately equal to 2.2 which is the maximum gain that will be used in this paper corresponding to peak value of the grid voltage. In the case considered in this study, the grid voltage peak is \(V_{\text{ref}}=230\sqrt{2}\) and the MPP voltage changes between 152 V and 160 V and therefore the maximum inverter gain will vary between 2 and 2.2 approximately at the maximum grid voltage. According to (14), the quasi steady-state \(D(t)\) is given by the following equation:  
\[D(t)=\begin{cases}\dfrac{1}{2}-\dfrac{V_{\text{mpp}}}{v_{g}(t)}+\dfrac{1}{2} \sqrt{1+\dfrac{4V_{\text{mpp}}^{2}}{v_{g}^{2}(t)}}&\text{if $v_{g}(t)>0$,}\\ \dfrac{1}{2}-\dfrac{V_{\text{mpp}}}{v_{g}(t)}-\dfrac{1}{2}\sqrt{1+\dfrac{4V_{ \text{mpp}}^{2}}{v_{g}^{2}(t)}}&\text{if $v_{g}(t)<0$.}\end{cases} \tag{15}\]  
Clearly, the duty cycle is a continuous function in terms of \(v_{g}\) and that regardless of the value of the input voltage \(V_{\text{mpp}}\) one has  
\[\lim_{v_{g}\to 0}D(t)=\dfrac{1}{2} \tag{16}\]  
Figure 5 shows the steady-state waveforms of averaged inductor currents \(\tilde{i}_{1}\) and \(\tilde{i}_{2}\) and the capacitor voltages \(\overline{v}_{o1}\) and \(\overline{v}_{o2}\) by assuming a unity power factor. The inductor currents and capacitor voltage are well balanced and are phase shifted by \(180^{\circ}\) as depicted in Figure 5a,b for both values of power. In the figure, the used grid voltage peak value is \(V_{\text{ref}}=230\sqrt{2}\) V, the DC input voltage is \(V_{\text{dc}}=152\) V and the amplitude of the grid current was adjusted for each used value of power.  
Small-Signal Model of the Inverter under the Differential Peak CMC and Grid Current Controller Design
# A Model for Deriving Trust and Reputation on Blockchain-Based e-Payment System +
## 2 Related Work
### Reputation  
\[Rep_{B.rx}(t)=\begin{cases}0.5,&t=init\\ Rep_{B.rx}(t-1)+\alpha_{A\to B}(td(t))\cdot Rep_{A.tx}(t)\cdot\left(NE_{A.tx}(t)- 0.5\right),&t\
eq init\\ \end{cases} \tag{6}\]  
\[\beta_{A\to B}(t)=\begin{cases}0.05,&NE_{A.tx}(t)\geq DTR_{A\to B}(t-1)\\ 0.25,&NE_{A.tx}(t)<DTR_{A\to B}(t-1)\\ \end{cases} \tag{7}\]  
\[DTR_{A\to B}(t)=\begin{cases}NULL,&\text{if there is no transaction}\\ 0.5,&t=init\text{ and not graded}\\ NE_{A.tx}(t),&t=init\text{ and graded}\\ \beta_{A\to B}(t)\cdot NE_{A.tx}(t)+\left(1-\beta_{A\to B}(t)\right)\cdot DTR_{ A\to B}(t-1),&t\
eq init\\ \end{cases} \tag{8}\]  
\[ITR_{A\to B}(t)=\frac{DTR_{A\to C}(t)\cdot DTR_{C\to B}(t)}{DTR_{A\to C}(t)+DTR_{C \to B}(t)} \tag{9}\]  
The main idea behind our reputation calculation is to not use the absolute rating values themselves (unlike most popular online shopping malls), but to only reflect the relative \"positive\" and \"negative\"opinion values of each review where the value is normalized on a per-reviewer basis. Specifically, every user's reputation is initialized to the _neutral value_ 0.5 at the beginning. This value goes up and down according to the normalized evaluations she receives from others, offset by 0.5, as shown in Equation (6). Concurrently, \(\alpha_{A\to B}\big{(}td(t)\big{)}\) represents the \(TDW\) at the time when [PERSON] evaluates [PERSON] after the original transaction, and the new reputation of evaluatee B is weighted by the reputation of evaluator A. This is based on the intuition that _people tend to trust the evaluation of someone (as evaluator) who has higher reputation_. Last, \(NE\) is offset by 0.5 to use only the positive or negative \"relative\" grading aspect (per evaluator) with respect to \(PEC\) of A. For example, if \(NE\) is greater than 0.5, it means that [PERSON] regards [PERSON] better than others among those that A has evaluated. Of course, the final reputation value will be bounded to [0, 1].
# Secure Delivery Scheme of Common Data Model for Decentralized Cloud Platforms
## 1 Introduction  
In recent years, medical services have shifted from treatment to prevention and management of diseases. The importance of using medical information is on the rise for efficient patient health management [1]. A Hospital Information System (HIS) manages Electronic Medical Record (EMR) that is a digital version of a patient chart containing patient's medical history. Each hospital has a differentstructure of EMR in HIS; hence, each hospital can only conduct research based on the patient data of its own HIS form. However, it is very difficult to draw the ultimate conclusion because the patient groups in each hospital tend to show different characteristics. Data sharing is the fundamental step to gain maximum benefit from research innovations. Clinical studies based on patient data from numerous hospitals can provide more meaningful results. To handle the problem caused by diverse structure of those HIS, needs of integrated Common Data Model (CDM) have been recognized [2].  
Distributed storage can be a solution to the problem of centralized storage that utilizes a distributed ledger and allows data to be stored independently on multiple nodes in the network. The issue of distributed storage is the limitation of storage capacity and processing of network nodes. To overcome these limitations, this paper combines distributed storage and Inter Planetary File System (IPFS), which is a peer-to-peer architecture [3].  
The Secure-Cloud CDM (SC-CDM), designed by us, generates the encrypted CDM with a random key and initial vector and signs it with the public key of the CDM consumer to securely deliver the data before they are uploaded to IPFS. The encrypted CDM is lightweight via IPFS, but is maintained in transactions on a distributed ledger. The proposed system employs an immutable ledger on a message channel basis. It can manipulate and modify the current state of CDM. Finally, we evaluate the fragmented CDM algorithm in terms of elapsed time depending on the number and size of fragments while writing and reading the CDM.  
The main contributions of our work can be summarized as follows:  
1. The SC-CDM is the first approach of demultiplexing with the data confidentiality proof of CDM based on asymmetric key cryptography. We analyze and prove the security of SC-CDM.
2. Compared to previous solutions, we adopt a byte-level challenge with encryption to ensure efficiency while handling a large CDM.
3. The comparison results show that our scheme has the higher performance compared to other schemes.
4. By using X.509 asymmetric key for DID (Decentralized Identifier)-based identity management (signing and using the DID operating, autonomous generation, updating, and removal functions), the risk of personal information exposure would be reduced.
# Model for the Prediction of Rain Attenuation Affecting Free Space Optical Links
###### Abstract  
A model for the prediction of the attenuation induced by rain on free space optical (FSO) links is presented. The proposed methodology is developed, starting from the accurate simulation of the interaction between FSO links (path length up to 5 km) and precipitation maps, from which analytical expressions are then proposed. The model is simple as it only receives the local rainfall statistics for the site of interest as the input, but it is also accurate, as it takes into account the dependence of the attenuation on the drop size distribution (DSD), as well as multiple scattering effects associated with rain, which contributes to reducing the overall predicted attenuation. The proposed model represents a useful tool to dimension FSO links in areas where fog is negligible, i.e., where rain has the highest impact on the link performance.  
free space optics; rain attenuation; multiple scattering; electromagnetic wave propagation Article
## 4 Discussion
### Limitations  
The proposed method demonstrates promising results, but there are still some limitations. For instance, the model misidentifies objects that are similar, and the accuracy of the object boundaries needs to be further enhanced. In future work, we will continue to focus on improving the performance of semantic segmentation by introducing an advanced backbone; thereby, the presented model may attain more precision for the boundary details under more complex situations.
###### Abstract  
Gene therapy aims to silence an oncogene through RNA interference, or replace an abnormal tumour suppressor via gene augmentation. In this study, we intended RNA interference for PRKCA oncogene and gene augmentation for PTEN tumour suppressor with a view to reduce tumour growth in a mouse model of breast cancer. Inorganic carbonate apatite nanoparticles (CA NPs) were utilized to deliver the synthetic siRNA and the purified gene-carrying plasmid DNA both in vitro and in vivo. Effects of PRKCA siRNA- and PTEN plasmid-loaded NPs on viability of MCF-7, MDA-MB-231 and 4T1 breast cancer cells were assessed by MTT assay. The cell viability data in MCF-7 cell line demonstrated that combined delivery of PRKCA specific siRNA and PTEN plasmid with CA NPs had an additive effect to significantly decrease cellular growth compared to individual treatments. In addition, we observed a similar pattern of cumulative influence for combined treatment in triple negative MDA-MB-231 breast cancer cell line. Upon treatment with PRKCA siRNA+PTEN plasmid-loaded NPs, a remarkable decrease in the phosphorylated form of AKT protein of PI3K/ AKT pathway was observed in Western blot, indicative of diminished proliferative signal. Moreover, in vivo study in MCF-7 xenograft breast cancer mouse model demonstrated that the rate of growth and final tumour volume were reduced significantly in the mouse group that received intravenous treatment of PRKCA siRNA+NPs, and PTEN plasmid+NPs. Our findings demonstrated that PRKCA siRNA and PTEN plasmid loaded into CA NPs attenuated breast tumour growth, suggesting their therapeutic potential in the treatment of breast cancer.  
Keywords:RNAi; oncogene; tumour suppressor; gene therapy; nanotherapeutics; PRKCA; PTEN; inorganic nanoparticles +
Footnote †: journal: Applied Sci. 2021, 11, 8133. [[https://doi.org/10.3390/app11178133](https://doi.org/10.3390/app11178133)]([https://doi.org/10.3390/app11178133](https://doi.org/10.3390/app11178133))
# Review of Indoor Positioning: Radio Wave Technology
## 4 Positioning Algorithms
### Maximum Likelihood Algorithm  
The maximum likelihood method is based on the traditional statistical inference principle [171]. This method guesses the location of the target node by minimizing the variance of estimated distance error as shown in Figure 11. This approximation can be  
Figure 10: Min–max-based positioning method.  
implemented using a minimum mean square error (MMSE) standard. However, the performance of this method is unstable considering the quantity of anchor nodes [172].  
A maximum likelihood algorithm is a probabilistic search method. These methods normally make more precise positioning as contrasted with the deterministic method because the deterministic method cannot adjust well to the signal variation. [173; 174; 175].  
In the following, a description for a maximum likelihood algorithm based on RSSI is presented [170]. First, an estimate of distance \(d_{i}\) to each reference device is derived from the RSSI value. Then, the node defines the error \(e_{i}\) between the measured and the actual distance, given by  
\[e_{i}(x_{0},y_{0})=d_{i}-\sqrt{\left(x_{i}-x_{0}\right)^{2}+\left(y_{i}-y_{0} \right)^{2}} \tag{7}\]  
In Equation (7), \(b=(x_{0},y_{0})\) is the unknown position of the target node, and \((x_{i},y_{i})\) the position of the \(i-th\) reference node. This algorithm estimates the target's position by minimizing \(e_{i}\). The unknown node position estimate \(b\) calculated with MMSE estimation is the solution of:  
\[y=Xb. \tag{8}\]  
\[X=\left[\begin{array}{cc}2(x_{k}-x_{1})&2(y_{k}-y_{1})\\ \vdots&\vdots\\ 2(x_{k}-x_{k-1})&2(y_{k}-y_{k-1})\end{array}\right],y=\left[\begin{array}{cc} -x_{1}^{2}-y_{1}^{2}+d_{1}^{2}-\left(x_{k}^{2}-y_{k}^{2}+d_{k}^{2}\right)\\ \vdots\\ -x_{k-1}^{2}-y_{k-1}^{2}+d_{k-1}^{2}-\left(x_{k}^{2}-y_{k}^{2}+d_{k}^{2} \right)\end{array}\right]. \tag{9}\]  
The coordinates \((x_{0},y_{0})\) can be computed by  
\[b=\left(X^{T}X\right)^{-1}X^{T}y. \tag{10}\]  
The detailed mathematical derivations can be found in [171].
# Bayesian Network Demand-Forecasting Model Based on Modified Particle Swarm Optimization
###### Abstract  
With the increasing variety of products, the increasing substitutability of products, and the trend of customized products, the volatility of market demand is increasing, which poses a challenge to make accurate demand forecasting. The Bayesian method is particularly promising and appealing when the data fluctuate greatly. This paper proposes a product-demand forecasting model based on multilayer Bayesian network, which introduces hidden layer variables and volatility factors to meet the time series connection and volatility of the demand data. However, most studies use sampling methods to estimate the parameters. We use Bayesian maximum a posteriori estimation to estimate the model parameters and introduce an improved particle swarm optimization algorithm (MPSO) to optimize the objective function. In order to increase the diversity of the particle population and accelerate the convergence, an adaptive particle velocity, position updating strategy, and nonlinear changing inertia weight are introduced in the algorithm. Finally, RMSE (Root Mean Square Error) and MAPE (Mean Absolute Percentage Error) are used as the evaluation criterion to conduct experiments on six different datasets, and the experimental results are compared with the results of the ARIMA (autoregressive integrated moving average model) method and PSO algorithm. The experimental results show that the method has a good prediction effect. It provides a new idea for demand forecasting in the supply chain.  
demand forecasting; Bayesian inference; particle swarm optimization algorithm +
Footnote †: journal: 2023, 13, 10088. [[https://doi.org/10.3390/app131810088](https://doi.org/10.3390/app131810088)]([https://doi.org/10.3390/app131810088](https://doi.org/10.3390/app131810088))
## 4 Discussion
### Main Findings and Comparison with Previous Studies
This asymmetry could be explained by participants having lower limb dominance [23] or a previous injury [16]. Conversely, it would seem that increasing or decreasing SR may reduce this asymmetry. Unsurprisingly, there is a moderate positive relationship between SH and SA (Tables 5 and 6), given that SH is a component of SA, although it could indicate that SH has a more significant relationship to altering SA. Furthermore, runners who have a lower SR can tend to overstride and increase SL rather than utilising SH [25]. Therefore, gait re-education techniques that alter SH should be considered to alter swing phase biomechanics, for instance, the butt flick. Additionally, a moderately positive relationship was found between SL and SH. Runners may choose to increase SH to allow them to increase swing time and maximise their ability to achieve a higher foot position. A longer swing phase has been associated with better RE [6]. Future studies may want to evaluate the link between temporal parameters and SA and SR manipulation. This is the first study to analyse the sex differences between SAs when SR is manipulated. Regarding SA, no significant difference was found between sex in this study (Table 3). [PERSON] et al. found no significant difference between sexes and SA when running velocity was incrementally increased [13]. Likewise, [PERSON] et al. found no difference between sexes and SA when running at a constant speed of 12 km. With SA comprising of the SH and SL, it is possible that a natural ratio exists between these two measurements that permits sex comparison. From this investigation, we can conclude that SA is comparable between sex when SR and running speeds are altered. Males ran with a significantly longer SL but not higher SH in comparison to females (Table 4). Studies have established that females run with a smaller gait cycle length/height than males [13; 21]. Furthermore, dissimilarities in physique between genders could explain the significant difference in both SL and SH, with males having a mean height of 179.3 cm compared to 164.5 cm in females. It should be noted that the body height was considered as an interactive and covariate factor in our statistical analysis. It is noted in Table 3 that males and females have similar step angles during running. Also, in Tables 5 and 6, it is found that step angle was not linearly correlated with speed and step rate. This indicated that human beings might have specific step angles during running in terms of biomechanically optimum principles, e.g., saving energy, coordinating limbs, smoothing the centre of mass during running, etc. The reason why such a specific range of step angles was used while running is worth exploring in the future. In terms of rehabilitation, the specific range of step angles in this study could be used as a reference for those who have been disabled and would recover while running. Also, this information provides useful information to clinicians in rehabilitation and sport coaches in exercise.
# Fault Inference of Electronic Equipment Based on Multi-State Fuzzy Bayesian Network
###### Abstract  
The aim of this study is to extend the directive function of fault inference in test and diagnosis system for electronic equipment. There are many problems, such as presence of various types of uncertain information in test set of electronic equipment, frequent degenerative faults, complex relationships of modules, multiple fault modes, existence of fuzzy interval in fault state, and interaction of each module. In view of these problems, the total membership degree of faults is commonly synthesized based on weights of multiple test indicators and normal membership degree of a single indicator. On this basis, this study builds the model for inferring fault states of leaf and root nodes based on multi-state triangular fuzzy Bayesian network (BN). Finally, this research carried out feasibility analysis on fault inference of a super-heterodyne receiver, thus verifying the efficiency and applicability of the method proposed in the study.  
multi-state Bayesian network; electronic equipment; fault inference; membership degree of fault +
Footnote †: journal: Applied Sciences
## 3 Case Study
### Comparison with Other Algorithms  
To further verify the prediction accuracy of the improved Informer algorithm, comparisons were made with the RF algorithm and the LSTM network algorithm.
# Toward a Comprehensive Understanding and Evaluation of the Sustainability of E-Health Solutions
## 10 Conclusions  
This study aimed to investigate e-health sustainability in terms of the definitions, dimensions, and aspects, and to develop a sustainability model and assessment method for e-health solutions. The results showed that empirical work related to e-health sustainability models and assessment is scarce. Hence, this paper proposes a sustainability model that encompasses all dimensions and aspects that impact the sustainability of e-health solutions. The proposed e-health sustainability model and assessment method support decision making in adopting new digital technology in healthcare. The sustainability model captures the dimensions and aspects of e-health sustainability and provides a systematic method to evaluate and predict the sustainability of e-health solutions. The model was constructed based on the dimensions and aspects extracted from the SLR conducted in this paper. The model was built and designed through multiple iterations and was validated, in terms of comprehensiveness and applicability, by three experts in healthcare, and their comments were accommodated to refine the model and produce the final and validated version. Furthermore, using the validated version of the model and its assessment method, a case study was conducted on an e-health solution to assess its sustainability. The assessment method considers the singularity of each healthcare context by allowing each dimension to be assigned different weights based on its importance in the context of the evaluation. The contribution of this work is that there is no existing sustainability model that opensthe door for more automation. When practiced, this could be used to help professionals to evaluate the sustainability of e-health solutions. The findings discovered the need for more case studies and experiments on assessing e-health solutions. The sustainability model and its assessment are expected to provide a holistic evaluation and insights about the enablers and challenges in adopting e-solutions. In addition, they are expected to reduce wasted resources, as they support the assessment of the overall sustainability level of an e-solution before adoption or deployment.  
Conceptualization, A.A.; Methodology, M.B.; Formal analysis, A.A.; Investigation, A.A.; Data curation, A.A.; Writing--original draft, A.A.; Writing--review & editing, A.A. and M.B.; Supervision, M.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All data supporting this research results are available on [[https://drive.google.com/drive/folders/IJll0](https://drive.google.com/drive/folders/IJll0) BbmvUa4 qlfMoHkm54 FQLSeixUCSi?usp=sharing]([https://drive.google.com/drive/folders/IJll0](https://drive.google.com/drive/folders/IJll0) BbmvUa4 qlfMoHkm54 FQLSeixUCSi?usp=sharing) (accessed on 9 February 2023).  
We would like to thank King Fahd University of Petroleum and Minerals for supporting this research.  
The authors declare no conflict of interest.
# A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems
## 5 Comparison of The Three Types of Sensor Networks
### Endurance  
_Ranking:_\(\mathrm{SSN}>\mathrm{VSN}>\mathrm{CSN}\)  
_Reasons:_  
The endurance of the sensor nodes is a major property for comparison because it will further influence the Maintenance property and the Data Quality property. A sensor node with energy constraint (e.g., powered by a small capacity battery) requires replacing battery frequently, which increases the burden of maintenance. Moreover, the energy constraint of sensor nodes limits the use of conditioning appliances (e.g., temperature controllers, humidity controllers, gas pumps, _etc._) that help improve the data quality.  
_Explanation:_  
The sensor nodes in CSN systems are with the lowest duration compared to the sensor nodes in VSN systems and SSN systems because they are powered by cellphone or portable batteries. In VSN systems, the sensor nodes are powered by vehicles' batteries. The power supply is guaranteed once the vehicle started. In SSN systems, the sensor nodes are powered by large capacity batteries, energy harvest devices or even power lines. The duration of the sensor nodes can be counted as infinity if they are powered by power lines. Hence, the sensor nodes in SSN systems are with the highest duration compared to the sensor nodes in CSN systems and VSN systems.