esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Multi-Fidelity Monte Carlo Method
### Data-Driven Low-Fidelity Models of Nonlinear Partial Differential Equations
#### 2.3.1 POD Coupled with DEIM  
Consider a set of snapshots \(\{\mathbf{y}(t_{1}),\dots,\mathbf{y}(t_{M})\}\in R^{N}\) and the corresponding snapshot matrix \(\mathbf{Y}=[\mathbf{y}(t_{1}),\dots,\mathbf{y}(t_{M})]\in R^{N\times M}\). Let \(\mathbf{V}=[v_{1},\dots,v_{n}]\in R^{N\times n}\) be the POD basis in \(n\) dimensional space. The POD basis vector \(\mathbf{V}\in R^{N\times n}\) of the matrix \(\mathbf{V}\) is the \(n\) largest left singular vectors of the matrix \(\mathbf{Y}\). The POD reduced-order system is obtained by Galerkin projection [22; 26]:  
\[\frac{d}{dt}\widetilde{\mathbf{y}}(t)=\widetilde{\mathbf{A}}\widetilde{\mathbf{ y}}(t)+\mathbf{V}^{\mathbf{T}}\mathbf{F}(\mathbf{V}\widetilde{\mathbf{y}}(t))+ \widetilde{\mathbf{B}}(\mathbf{u}(t)), \tag{14}\]  
where the reduced operator is \(\widetilde{\mathbf{A}}=\mathbf{V}_{n}^{T}\mathbf{A}\mathbf{V}_{n}\in R^{n \times n}\), the reduced input operator is \(\widetilde{\mathbf{B}}=\mathbf{V}_{n}^{T}\mathbf{B}\in R^{n\times p}\) and state vector is \(\mathbf{y}(t)=\mathbf{V}\widetilde{\mathbf{y}}(t)\).  
We use POD method to construct low-fidelity models. Although the dimension of solving complex systems can be reduced, the complexity of higher order nonlinear terms may be as challenging as the original equation. DEIM [30; 33] provides an effective calculation method for evaluating nonlinear terms.  
Select \(m\in N\) vectors as columns stored in DEIM as base vector \(\mathbf{U}\in R^{N\times m}\). The nonlinear terms are approximately \(\mathbf{F}(\mathbf{y}(t))=\mathbf{U}\mathbf{c}(t)\), where \(\mathbf{U}=[t_{1},\dots,t_{m}]\) and \(\mathbf{c}(t)\) are the corresponding coefficient vectors. Then DEIM selects \(m\) pairs of different interpolation points \(p_{1},\dots,p_{m}\in\{1,\dots,N\}\) and assumes that DEIM interpolation point matrix \(\mathbf{P}=[\mathbf{e}_{p_{1}},\dots,\mathbf{e}_{p_{m}}]\in R^{N\times m}\), where \(\mathbf{e}_{i}\in\{0,1\}^{N}\) is the \(i\)-th standard unit vector. The coefficient vector \(\mathbf{c}(t)\) can be uniquely determined by \(\mathbf{P}^{T}\mathbf{F}(\mathbf{y}(t))=\mathbf{P}^{T}\mathbf{U}\mathbf{c}(t)\). The DEIM interpolation points of nonlinear function \(F\) are composed of tuples \((\mathbf{U},\mathbf{P})\) defined by DEIM basis \(\mathbf{U}\) and DEIM interpolation point matrix \(\mathbf{P}\). The DEIM approximate of nonlinear function \(F\) is estimated at the state vector \(\mathbf{y}(t)\) as:  
\[\mathbf{F}(\mathbf{y}(t))\approx\mathbf{U}(\mathbf{P}^{T}\mathbf{U})^{-1} \mathbf{P}^{T}\mathbf{F}(\mathbf{y}(t)), \tag{15}\]  
By coupling POD (14) and DEIM (15), the POD-DEIM low-fidelity models are:  
\[\widetilde{\mathbf{A}}\widetilde{\mathbf{y}}(t)+\mathbf{V}^{T}\mathbf{F}( \mathbf{U}(\mathbf{P}^{T}\mathbf{U})^{-1}\mathbf{P}^{T}\mathbf{F}(\mathbf{y}( t)))+\widetilde{\mathbf{B}}(\mathbf{u}(t))=0. \tag{16}\]
## 3 Ferry Node Ranking Clustering Algorithm (FNRCA)
### Cluster Head Selection Process  
After the initial formation of clusters and based on the information collected through the first dispatching round of the ferry, nodes in each cluster are ranked by the BS based on their position with respect to the checkpoint they are attached to and on their current energy level. This information is dispatched back to the nodes through the next ferry visit. Nodes with the maximum residual energy and minimum distance will be chosen as a cluster head based on NodeRanking \((En,Dn)\) where:  
\[[Dn(i)=Min(D(i,Closest_{CP})),(En(i)=Max(ResidualEnergy(i))] \tag{1}\]where:  
\[\left|\right.D(i,\mathit{closest}_{cp})\left|\right.=\sqrt{(Xi-Xcp)^{2}+(Yi-Ycp) ^{2}} \tag{2}\]  
and ResidualEnergy (\(En\) (\(i\))) is the current energy level of node i, \(D(i,\mathit{closest}_{cp})\) is the Euclidean distance of node i to the closest checkpoint. Given a particular deployment region of interest, \(X_{i}\) and Y\({}_{i}\) are the \(X\) and \(Y\) positions of node i. \(X_{cp}\) and Y\({}_{cp}\) are the \(X\) and \(Y\) positions of the closest checkpoint on the sensing field.  
A cluster head in each cluster will be changed when its energy level reaches a pre-defined threshold or a calculated value and not every sensing round. This will make it possible for \(i\)th node to continuously play the role of a cluster head for multiple sensing rounds and thus save any wasted energy for control and exchanged messages used in replacing it. In the next two subsections we will discuss more the placement of the checkpoints and the stopping time the ferry will spend at each checkpoint.
## 3 Results and Discussion
### Construction of a Dynamic Load Model for River Ice Impacted Bridge Piers
#### 3.4.1 Peak Impact Force  
After the ice discharge flow velocity reaches the critical value, the size of Fmax is independent of the velocity and no longer increases. In this model, only the most unfavorable situation of river ice impact damage under high flow velocity is considered, and the maximum force when the river ice velocity is 1 m/s is taken as the peak impact force in the ice loading model, and the relationship between the river ice porosity and the extreme value of the impact force is obtained by fitting, as shown in Figure 11.  
The simulation results clearly indicate that the ice force on the pier is negatively correlated with the porosity of the river ice, and with the increase in the porosity, the ice load reduction gradually decreases, which is in line with the strength of the river ice affected by the porosity. This nonlinear trend is consistent with the fact that the uniaxial compressive strength of ice is affected by porosity and that the change in ice strength with increasing porosity is the main reason for the impact force.  
Figure 10: Ice discharge damage pattern with different porosities: (**a**) 5%; (**b**) 10%; (**c**) 15%; (**d**) 20%; (**e**) 25%; (**f**) 30%.  
According to the statistical study of a large amount of data from sea ice compressive strength tests by previous scholars and summarizing the results of previous researchers [27], the relationship between compressive strength and porosity under the same strain rate mainly has the following expression:  
\[\sigma_{c}=A(1-\sqrt{p/B}) \tag{14}\]  
\[\sigma_{c}=A(1-\sqrt{p}) \tag{15}\]  
\[\sigma_{c}=A(1-p)^{B} \tag{16}\]  
where \(\sigma_{c}\) is the standardized value of the compressive strength of ice, and \(A\) and \(B\) are coefficients related to the strain rate. From the above expression, the compressive strength of the ice material is negatively correlated with the porosity or the square root of the porosity, which is corroborated by the DEM results of this study.  
The porosity coefficient is introduced as a correction coefficient into the ice pressure calculation method stipulated in the Chinese General Specification for Highway Bridge and Culvert Design (JTG D60-2015), so the expression of the porosity-corrected one is as follows:  
\[F_{\text{max}}=mC_{t}btR_{ik}p_{0} \tag{17}\]  
where \(F_{\text{max}}\)--standard value of calculated ice pressure (KN);  
\(m\)--shape coefficient of ice-facing surface of bridge piers and pilings;  
\(C_{t}\)--ice temperature coefficient;  
\(b\)--projected width of bridge pier and pile facing ice surface (m);  
\(t\)--river ice thickness (m);  
\(R_{ik}\)--ice compressive strength standard value (KN/m)  
\(p_{0}\) versus river ice porosity \(p\) is given as follows:  
\[p_{0}=1.08e^{-7.4p}-0.064 \tag{18}\]
# A Statistical Approach to Detect Jamming Attacks in Wireless Sensor Networks
## 8 Conclusions  
In this paper, we proposed a stepwise approach for detecting different forms of jamming attacks, where the jamming detector algorithm is deployed on the cluster head to detect attacks in the member nodes and also on the base stations to detect attacks in the cluster heads using the packet IAT metric. This metric is used to detect an abrupt change in packet sequence caused by a situation of jamming attacks using the EMWA algorithm. To demonstrate the efficiency of our proposed method, we conducted a trace-driven experiment using EWMA to detect changes in traffic flow during situations of both non-jamming and jamming attacks. We evaluated our work using a non-jamming trace and the three different variations of jamming attacks from the CRAWDAD dataset. Results obtained show that our proposed method can efficiently detect the presence of a jamming attack with little or no overhead in WSN.  
Our detection approach can be implemented on sensor nodes deployed in areas such as military battle fields, healthcare and mission-critical events, where sensed information needs to be transmitted in real time devoid of error. In the future, we plan to extend the evaluation of our proposed method using other datasets and deploy in real-world environment.  
The contribution distribution of the authors are as follows: Conceptualization, O.O.; Methodology, O.O.; Software, A.S.A.; Validation, O.O.; Formal Analysis, O.O., A.S.A. and G.P.H.; Investigation, O.O.; Resources, A.S.A. and G.P.H.; Data Curation, O.O.; Writing-Original Draft Preparation, O.O.; Writing-Review & Editing, O.O. and A.S.A.; Visualization, O.O., A.S.A. and G.P.H.; Supervision, A.S.A. and G.P.H.; Project Administration, A.S.A. and G.P.H.; Funding Acquisition, A.S.A. and G.P.H.  
The authors would like to thank the anonymous reviewers for providing a constructive feedback. Despite their invaluable assistance, any errors remaining in this paper are solely attributed to the authors. This research is funded by the Advanced Sensor Networks SARChI Chair program, co-hosted by University of Pretoria (UP) and Council for Scientific and Industrial Research (CSIR), through the National Research Foundation (NRF) of South Africa.  
The authors declare no conflict of interest.
## 3 Test Results and Analysis  
### Qualitative Description of Fracture Propagation  
The thickness of the CT slice of the coal mass sample was set to 2 mm, and 50 CT slice images were obtained from a single sample. Given the paper length limitation, a slice with typical fracture distribution characteristics was selected (Table 1). A binary graph [24] was obtained to analyze the fracture characteristics. The micro-fractures of rock samples (scale = 0.001 mm) are typically difficult to identify because of the low-resolution scale of the CT scanner (medical = 0.3 mm and industrial = 0.01 mm). The average CT number method is currently the most widely used damage detection CT method. The CT number is the relative value of the linear attenuation coefficient of a tissue structure in a CT image, and the unit is HU. The CT number reflects the density of the object being scanned, and the density is directly proportional to the CT number. Therefore, fracture and mineral particles in coal mass are important factors that affect the CT number [11]. As shown in Figure 3, the fracture of the coal mass in Shuangma developed rapidly, and the average CT number of the coal medium was distributed between 150 and 305 HU. The average CT number of the principal fracture location was distributed between 50 and 225 HU, while that of the mineral particles was distributed between 325 and 500 HU. The CT number in the same sample's cross-section exhibited a complex distribution. The distribution of CT number exhibited multiple peak values and had the random distribution characteristics of diffusion, but did not follow a conventional statistical distribution law. The overall average CT number decreased as the sampling locations of different samples were closer to abandoned oil wells, whereas the micro-fractures increased. This indicates that coal mass damage was not caused by a single factor, but by multiple fracture sources, mineral particles, and gas pressure. In summary, as the sampling location approached the oil well disaster source, the opening degree, density, and volume of the coal mass sample fracture showed an increasing trend, and the sensitivity of the average CT number to the principal fracture location and mineral particles also significantly increased. However, the gas pressure leads to crack development and the material density of the coal body decreases, and the average CT number showed a decreasing trend. The diffusion of high-pressure oil and gas into the coal mass resulted in the increase in the fracture density and volume ratio, which allowed the accumulation of oil and gas in the coal and oil resources co-storage area. This phenomenon was the basis of coal mass instability and the formation of oil and gas disasters.
## 3 Related Work
In [16], the authors divide the received data into two categories of popular and non-popular data based on a calculated threshold where the non-popular data are placed near the consumers, and the popular data are placed at the shortest path that contains a maximum of users that request the same data. We believe that this type of strategy is better than the first type for limited network environments. The main reason is that, in these strategies, many types of data can be cached, especially the less requested data. Yet, these strategies remain limited because they do not provide a robust classification model for the data, as they classify the data only using their frequencies, and do not focus on the content. Furthermore, the proposed strategies do not provide a control factor that matches the demands of consumers and the needs of the managers of these networks.
# Cyanobacteria as Chassis for Industrial Biotechnology: Progress and Prospects
###### Abstract
This review discusses the recent developments in the field of cyanobacterial genetic modification in the past five years and how these tools can be employed to increase the efficiency of cyanobacteria in industrial biotechnology applications. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fuel Molecule** & **Species Engineered** & **No. of Genes** & **Reference** \\ \hline Ethanol (C\({}_{2}\)H\({}_{3}\)OH) & _Synechocystis_ sp. PCC6803 & 2 & [20] \\ _Synechococcus elongatus_ PCC7942 & 2 & [21] \\ _Anabena_ sp. PCC7120 & & & Algenol Biofuels Company \\
1-Butanol (C\({}_{4}\)H\({}_{3}\)OH) & _Synechococcus elongatus_ PCC7942 & 5 & [22] \\ Isobutanol (C\({}_{4}\)H\({}_{3}\)OH) & _Synechococcus elongatus_ PCC7942 & 5 & [17] \\ _Synechocystis_ sp. PCC6803 & 2 & [23] \\ Ethylene (C\({}_{2}\)H\({}_{4}\)) & _Synechococcus elongatus_ PCC7942 & 1 & [8; 24] \\ Isobutyraldehyde (C\({}_{4}\)H\({}_{3}\)O) & _Synechococcus elongatus_ PCC7942 & 5 & [17] \\ Isoborne (C\({}_{3}\)H\({}_{8}\)) & _Synechocystis_ sp. PCC6803 & 1 & [16] \\ Free fatty acids (C\({}_{10}\)-C\({}_{18}\)) & _Synechocystis_ sp. PCC6803 & 5 & [25] \\ Fatty alcohols (C\({}_{16}\)/C\({}_{18}\)) & _Synechocystis_ sp. PCC6803 & 1 & [26] \\ _n_-alkanes (CnH2n) & _Synechococcus_ sp. PCC7002 & 2 & Joule Unlimited Company \\ _Thermsysmicroboccus elongatus_ BP-1 & & Joule Unlimited Company \\ _Synechocystis_ sp. PCC6803 & & & Joule Unlimited Company \\ Hydrogen (H\({}_{2}\)) & _Synechococcus elongatus_ PCC7942 & 1 & [27] \\ Sesquietrepenes (C\({}_{15}\)H\({}_{24}\)) & _Synechocystis_ sp. PCC6803 & & [28] \\ Squalene & _Synechocystis_ sp. PCC6803 & Inactivation of gene _slr_2089 & [29] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Current reports of biofuel production in genetically engineered cyanobacteria. The number of transgenes required is indicated, although in several cases additional genes were introduced to increase productivity.
# IoT-Enabled Smart Cities: Evolution and Outlook
## 3 Key Technologies Developed
### Sensor and Actuator Technologies
For example, while city traffic monitoring is typically addressed using road side cameras [44] and inductive loops [45] embedded in the road, many cities are using the information collected from sensors in vehicles, usually public vehicles like buses [46] or garbage trucks [47] to have more detailed and fine-grained information. Additionally, some aspects like road surface conditions cannot be correctly monitored through stationary sensors. On the downside, mobile nodes introduce new challenges and problems, mainly related to the connectivity guarantee. Additionally, for the design of the network, it is important to consider the paths that the vehicles will follow in order to consider how well the sensors they are equipped with will cover the monitored area. * **Crowd-sourced sensors:** Crowd sensing basically means outsourcing the monitoring of the environment to the crowd. In cities, this mainly relates to the capacity of leveraging the abundance of digital devices that are equipped with sensors. Citizens' smart phones are the most commonly used ones, and also the ones providing rich information. Crowd sensing is typically catalogued into two main types: participatory sensing [48; 49] and opportunistic sensing [50; 51]. In participatory sensing, the users are directly involved in the sensing action. For example, in [52] citizens reported issues like broken public infrastructures (e.g., streetlights, benches, bins, etc.) or cultural activities (e.g., street art, festivals, etc.). In opportunistic sensing, the users might be unaware of the sensing of the smart phone. For example, the solution presented in [53] is designed to monitor the urban traffic and road surface conditions and an estimation of air quality and PM2.5 in cities is presented in [54].
## 3 Taguchi and Artificial Neural Network (ANN) Methods
Equations (7)-(9) give the condition of the smaller the better (STB), the larger the better (LTB), and the nominal the best (NTB) [17; 18]. \[\frac{S}{N}=-10\,\log\,\left[\frac{1}{n}\,\sum_{i=1}^{n}\left(y_{i}^{2}\right)\right] \tag{7}\]  
\[\frac{S}{N}=-10\log\left[\frac{n}{i=1}\left(\frac{1}{y_{i}^{2}}\right)\right] \tag{8}\]  
\[\frac{S}{N}=-10\log\left[\frac{y^{2}}{s^{2}}\right] \tag{9}\]  
In Equations (7)-(9) \(n\), \(y_{i}\), \(y\), and \(s^{2}\) are the number of experiments, \(i\)th observation values, average of values, and variances of data respectively. In this study, Equation (7) is employed to achieve THD below 1%. The Artificial Neural Network (ANN) is an intelligent computing system that was developed by [PERSON] and [PERSON] [19]. The principle of the ANN works by imitating the capability of the human brain to learn from observations and to generalize by abstractions [20]. The ANN network consists of three layers, namely input layer, output layer, and hidden layer, where the neurons are accommodated [21]. The data obtained from the observation are collected in the input layer, useful information that is used to solve the non-linear problems is collected in the output layer, and the hidden layers are there to link the input layer and output layer and to process the data from the input layer [22]. Due to its successful ability to solve non-linear problems, the ANN is applied in many different applications, as described in [23]. Figure 11 shows the illustration of the ANN network, in this case, obtained based on reference [24], which is used to compare the result of the Taguchi optimization. From Figure 11, the input layer consists of three neurons in which neuron number 1 is for the air gap length (A), neuron number 2 is for the offset angle (B), and neuron number 3 is for the stator yoke height (C). Hidden layer 1 consists of six neurons and hidden layer 2 consists of two neurons. The output layer consists of one neuron to collect the useful information from the hidden layer to predict THD. The ANN tool of Matlab R2020 is used to implement the structure of the ANN network in Figure 11. The network type of a feed-forward backpropagation with a trained function of Levenberg-Marquardt (LM) and a logsig transfer function for two hidden layers are used to predict THD. Input and target data obtained from the Taguchi orthogonal array are automatically divided by the ANN tool into three groups: training data (75%), testing data (12.5%), and validation  
Figure 11: Structure of the Artificial Neural Network (ANN) used in this case. data (12.5%). The quality of the ANN network is evaluated by the percentage of prediction error formulated by Equation (10) [22]. \[\%\ error=\left|\frac{Experimental\ data-ANN\ data}{Experimental\ data}\right|*100\ \% \tag{10}\]  
In the case of (10), experimental data are data obtained from the Taguchi combination and ANN data are obtained from THD prediction of the ANN network.
## 1 Introduction  
The prevalence of heart failure is approximately 1-2% of the adult population in developed countries, and \(\geq\)10% of patients are \(>\)70 years of age. The lifetime risk of heart failure at the age of 55 is 33% for men and 28% for women [1]. HFrmEF is a separate entity as of 2016. The prevalence of HFrmEF in patients with heart failure is 10-25%. HFrmEF can be described as an intermediate clinical entity between HFrEF and HFpEF, more similar to HFrEF, including a high prevalence of coronary artery disease, a lower prevalence of CKD and a lower representation of women in this population. With HFpEF, there are more similarities in terms of higher prevalence of hypertension, higher BMI and lower natriureticepeptides. Some studies have suggested that medications designed for patients with HFrEF could be used effectively in patients with HFmrEF. Furthermore, HFmrEF patients were borderline in the representation of atrial fibrillation or diabetes mellitus, which were more frequent in HFpEF. HFmrEF patients had a lower risk of cardiovascular events but a similar risk of non-cardiovascular events, in contrast to HFpEF patients.  
HFmrEF is a heterogeneous group based on the instantaneous measurement of EF in its dynamic evolution. Some patients may experience a decrease due to e.g., myocardial infarction and conversely an increase in LV EF after the introduction of medical, device or reperfusion therapy. For example, in SwedeHF during follow-up of patients with HFmrEF, only 38% remained in this group. Of HfmrEF patients, 37% moved to the HFrEF group and 25% improved EF and moved to the HFpEF category during follow-up.  
In patients with HFmrEF, phenotyping or aetiology of heart failure, including new imaging techniques, is important to determine the correct treatment. Pharmacological treatment of HFmrEF has only weak recommendations, based on post hoc analyses and subgroup analyses of heart failure studies already performed that included patients with HFmrEF. Ideally, studies in a special HFmrEF population should be generated, but these are unlikely to be sponsored by industry but will be publicly funded.  
It gave us grounds to conduct the current analysis aiming to compare the pharmacotherapy, comorbidities and outcome of patients with HFrEF and HFmrEF.
# Groundwater Prediction Using Machine-Learning Tools
## 4 Groundwater Prediction Methodology
### Monthly Groundwater Data Set  
The dataset used in this study consists of 174 monthly groundwater satellite images between March 2002 and May 2019. Each image has a size of \(360\times 180\) pixels, and provides a color-coded representation of the \(\Delta\)TWS of the earth's land surface. A sample image from the dataset is provided in Figure 2. The images were originally obtained as part of the GRACE survey conducted by the U.S. National Aeronautical and Space Administration (NASA). The actual data was obtained from the Physical Oceanography Distributed Active Archive Center (PO. DAAC) website [6].  
Figure 1: Flowchart showing the implementation process.  
Figure 2: A sample full image of the GRACE groundwater dataset used in this research [6].  
Using this dataset posed some serious challenges. Several monthly images in the dataset were missing. Neglecting these months would disrupt the periodicity/seasonality in the data, which is a key aspect of the data. Further reducing the dataset was not feasible, since the number of images is already on the small side for machine-learning applications. Finding better methods for dealing with missing images is an ongoing research topic.