esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Grid Architecture for Rural Distribution Networks: Application to a Spanish Pilot Network
## 1 Introduction
This device, which is based on power electronics and secondary batteries, permits actually routing power between each of the phases of the three-phase distribution system, thus ensuring the required network power balance and quality. The whole formulation of the innovative algorithms in the management plane can be found in [30; 31; 32]. Thus, this paper mainly focuses on the transformations evolving the power plane: such actuations evolving the network into a smart new one. The management plane is secondarily addressed by quantifying the algorithms performance managing the network under different operational scenarios. It should be noted that the aim is to assess the potentiality of including a set of new Smart Grid technologies and power electronics in different parts of the network but not to specifically quantify the impact such technologies into the global grid performance. Therefore, the present work, instead of deeply discussing the mathematics presenting the management algorithms, opts for describing in a holistic way all actuations to evolve the distribution network into a smart new one. These actuations cover the management but also other updates on the power plane, i.e., telecommunication network and innovative power electronics with embedded storage. In addition, as a difference with reference [31], the present paper depicts a study case evaluating the impact of the above-mentioned actuations around the inclusion of power electronics to evolve the rural grid, while study cases in [31] focus on the management of the whole smart grid. The work presented in this paper is framed into the European research project Smart Rural Grid (SRG). The project key impacts are directly related with the delivery of significant cost and investment savings in rural electricity distribution (by overall reducing the percentage of electricity lost during electricity distribution and the gap between electricity generated and electricity consumed). They also refer to allowing for an increased potential to inherently accommodate the integration and distribution of renewable energy sources. Moreover, it enables new operation modes that guarantee the continuity of supply and reliability even in the case of the external failure conditions [18; 26; 28; 30; 31; 32]. The contents of this paper have been structured into five main sections. First, Section 2 presents the typical characteristics of rural distribution networks, also focusing on that for the adopted network. Second, Section 3 introduces the proposed architecture (both addressing management and power planes) evolving the rural network. Third, Section 4 describes the new operation modes for the network enabled by the new architecture. Fourth, Section 5 develops a study case that shows the performance of the new architecture (focusing on the power electronics included to provide advanced services). Finally, the main conclusions of the work are presented in Section 6.
# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
## 6 Lessons Learned, Open Issues, and Research Challenges
### Open Issues and Research Challenges
#### 6.2.3 Localization to Predict Dynamic Topology  
The accuracy of the localization techniques accelerates autonomous execution and enhances the robustness of FANETs. An accurate localization technique supports many core network services such as topology control, collision avoidance, trajectory planning, and routing. In QL-based position-aware routing protocols, location accuracy is a key factor for predicting a better topology. GPS is the most common localization system used in FANETs owing to its wide coverage, flexibility, and convenience. In wide and open outdoor environments, GPS signals are adequately received, and UAVs can localize them in global coordinates. However, in many circumstances, such as bad weather and long-distance radio communication, the GPS signal may be absent (indoor) or inadequate owing to several causes, such as urban NLoS scenarios and enemy-controlled airspaces.  
In such scenarios, infrastructure-independent cooperative localization (CL) can be used, where a group of mobile UAVs with communication capabilities use relative distance measurement via different types of range-based and range-free methods to jointly estimate the position and orientation of all UAVs. Optimization techniques such as semidefinite programming [63], gray wolf optimization (GWO) [3], particle swarm optimization (PSO) [13], and multi-dimensional scaling [64] aid in estimating the accurate location with less measurement in a noisy scenario. The single-shot CL incorporated with filtering techniques, such as extended Kalman filtering [65; 66] and Monte Carlo localization [5] can produce continuous state estimation utilizing an onboard sensor, such as an odometer and IMU, which provides better localization accuracy. Ultrawideband localization techniques provide better noise-free ranging in an NLoS environment [5]. Recently, the prediction of location and mobility were well studied in FANETs to improve the routing performance in highly mobile FANETs [67]. In addition, according to our earlier discussion, an UAV formation controller updates the mobility of UAVs in the next state based on the mobility in the current state. Thus, QL-based MDP formulation can be used to design a routing protocol with mobility prediction.
## 3 Results
### Real-Time Prediction of Sea Water Elevation and Warning  
The monitoring system has been installed and put into operation at the KaStela Bay test site to provide real-time measurement of wind speed, wind direction and barometric pressure. The observed data is used for the real-time prediction of sea level based on the developed algorithm for the estimation of sea level heights and information obtained from the monitoring system, which has been integrated into the algorithm. The real-time prediction of sea level values is obtained based on tidal fluctuations, barometric pressure values and wind characteristics.  
Resulting sea level values are compared with coastline elevation in order to identify the potential danger of flooding and issue on-time warnings. Figure 14 shows wind characteristics and barometric pressure as observed during a period of 7 days during February 2022. The data containing barometric pressure along with wind direction and speed were collected into a database for the application. The data were used as an input for an algorithm to estimate sea level height.  
Figure 15 shows sea water elevation as a consequence of each of the components (tidal effect, barometric pressure and wind-generated waves) as well as absolute sea water elevation as a superposition of the abovementioned components for the period from 15 December 2021-7 February 2022. The results are given for one randomly selected zone along the study area to demonstrate the capacity of the proposed procedure to assess the sea water elevation. The estimated water surface elevation is referred to the HVRS71 vertical reference datum determined on the basis of mean sea level, as stated in the Methodology section.  
Figure 14: LoRaWAN-obtained observed time series: (**a**) wind speed, (**b**) wind direction and (c) barometric pressure.  
Figure 13: Modeled wave heights along the control line—incident directions: SE and SSW.  
If the resulting sea level value estimated from the algorithm exceeds the coastline elevation, the warning is immediately issued by the mobile application to alert the endangered population. Flood hazard warnings in the mobile application are primarily focused on citizens' safety and their classification is based on the particular threat level. Threat levels are based on tests of the instability of a human body in floodwater and different stability thresholds [37]. Following the results of the tests, the first threat level, issuing a yellow alert, is defined for flood water depth on the coastline up to 20 cm, which is mostly threatening for children. The second level of threat corresponds to an orange warning, which is now becoming dangerous for adults and is issued when the water depth is between 20 cm and 50 cm. Finally, an exceedance of flood water depth over 50 cm on the coastline corresponds to the third threat level. In this case, severe injuries can be caused by flood water and the red warning for the population is issued.
## 4 Data Enhancement Experimental Methodology
### Classification Algorithms for Enhanced NSL-KDD  
For the classification training on the enhanced NSL-KDD dataset, various classification algorithms were used in this study, including machine learning algorithms KNN [18], SVM [19], random forest [20], and XGBoost [21]; and neural network models MLP [22] and CNN [23]. As there were 41 features in the NSL-KDD dataset, and the CNN algorithm mainly dealt with the image recognition of two-dimensional data, the CNN model could not be directly used to train the feature data of NSL-KDD. In order to convert the NSL-KDD feature data into a two-dimensional matrix form, this experiment removed one feature and turned it into 40 features, which were converted into a 5 \(\times\) 8 two-dimensional matrix form. In data preprocessing, we also had to convert the non-numerical features into numerical data and used regularization methods to uniformly scale all the features to the interval [0, 1].
## 4 Analysis Example and Methods
### Frequency-Domain Characteristic Analysis and Experimental Verification of the FSI Dynamic Model
#### 4.1.2 Modal Verification Test  
The modal experiment generally uses a hammer or the exciter as the excitation device. The experimental object in this paper is the BHP, which belongs to light and small equipment. The hammer is used as the excitation equipment and its specific technical parameters are shown in Figure 6a. Considering the effect of the oil on the pipeline, the oil is sealed in the pipeline. And the BHP filled with oil is suspended by a string with small rigidity. The acceleration sensor is stuck to the middle of the pipeline, as shown in Figure 6b. Then, the left end (inlet end) of the pipeline is knocked by the calibrated hammer to produce the frequency response curve.  
The modal experiment needs to collect the input signal and the output signal (acceleration signal). The collected input/output signals are transformed into the frequency domain using Fourier transform. Then, according to Equations (32)-(34), the auto-power spectrum of the input signal, the auto-power spectrum of the output signal, and the cross-power spectrum of the input-output signal are obtained, respectively. Finally, the frequency response curve for the axial velocity of the BHP is obtained by Equation (35), as shown in Figure 7.  
\[G_{\mathrm{xx}}=\frac{1}{N}\sum_{i=1}^{N}F_{i}^{x}F_{i}^{x^{*}} \tag{32}\]  
Figure 5: The curve on the effect of the friction term on the axial velocity of the BHP.  
Figure 6: Modal test. (**a**) Hammer used in the modal test. (**b**) The tested pipeline.  
\[G_{\text{yy}} =\frac{1}{N}\sum\limits_{i=1}^{N}F_{i}^{y}F_{i}^{y^{*}} \tag{33}\] \[G_{\text{xy}} =\frac{1}{N}\sum\limits_{i=1}^{N}F_{i}^{y}F_{i}^{x^{*}}\] (34) \[H =\frac{G_{\text{xy}}}{G_{\text{xx}}} \tag{35}\]  
where \(F\) is the data in the frequency domain after the Fourier transform, \(N\) is the number of sampling points, superscript \(*\) represents the complex conjugate, subscript x is the input signal, and subscript y is the output signal.  
The comparison results for the experiment and simulation are shown in Table 4. The error between them is less than 5%. The validity of the FSI 14-equation dynamic model considering friction coupling is verified.
# Optimization-Based Approaches to Control of Probabilistic Boolean Networks
## 3 Polynomial Optimization Approach
### Reduction to a Polynomial Optimization Problem  
Consider reducing Problem 1 to a polynomial optimization problem. Then, the following theorem was obtained in [24].  
**Theorem 2**.: _Problem 1 is equivalent to the following polynomial optimization problem:_  
find \[E[x(k+1)]\in\mathcal{R}^{n},\ \ u(k)\in\mathcal{R}^{m},\ \ k=0,1, \ldots,N-1\] min Cost function (4) subject to System \[\Sigma_{i}\], \[i=1,2,\ldots,n,\] \[x(0)=x_{0},\] \[u_{i}(k)(u_{i}(k)-1)=0.\]  
This theorem is immediately obtained from the following two properties: (i) the relation \(E[u(k)]=u(k)\) holds; (ii) the fact that \(u_{i}(k)\) is a binary variable is equivalent to the constraint \(u_{i}(k)(u_{i}(k)-1)=0\). We remark here that this constraint is non-convex. From the practical viewpoint, it is desirable to relax this constraint to \(0\leq u_{i}(k)\leq 1\). Furthermore, from Theorem 1, we see that \(E[x(k+1)]\in[0,1]^{n}\) always holds. Hence, we set \(E[x(k+1)]\in\mathcal{R}^{n}\) in the above polynomial optimization problem. Finally, \(E[x(k+1)]\) may be eliminated from decision variables in the above problem, but its elimination is not easy. Hence, \(E[x(k+1)]\) is included in a decision variable of the above problem.  
A polynomial optimization problem can be solved by using e.g., SparsePOP [45], which is available on MATLAB. In SparsePOP, polynomials can be directly described.
## 5 Experiments and Discussion
### Experimental Datasets and Settings
#### 5.1.2 Comparison Methods  
We compared TRSA to the following baselines:  
* **DTC [8]:** This method utilizes multi-dimensional statistical features from the four perspectives of text content, user characteristics, forwarding behavior, and communication mode, and implements decision trees to determine the truthfulness of information;
* **HSA-BLSTM [49]:** HSA-BLSTM is a hierarchical neural network model used to describe the semantic features of different levels of rumor events (a rumor event is composed of source information and multiple forwarded or commented posts, and each post is composed of words);
* **SVM-TS [50]:** This method utilizes SVMs with linear kernel function to model temporal features for false information;
* **DTCA [21]:** This model considers user comments as an evidence source for the truthfulness judgment of a claim and uses a co-attention network to enhance the semantic interactions between evidence and source information;
* **BERT-Emo [35]:** BERT-Emo uses a pretrained language model to obtain the text semantic representation and the emotions difference between an information publisher and their audience;
* **GLAN [22]:** GLAN is a novel neural network model that can corporately model local semantic features and global propagating features;
* **BiGCN [23]:** BiGCN is a two-layer graph convolutional network model used to capture the bidirectional propagating structure of information. It also integrates source post information into each layer of the GCN to enhance the impact of source information;
* **DDGCN [31]:** DDGCN is a dynamic graph convolution neural network model used to capture the characteristics of the information propagation structure and knowledge entity structure at each point in time. Since our model only concentrates on the contents and social contexts, we do not introduce a dynamic knowledge structure.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Statistical Indicators** & **PHEME** & **CED** \\ \hline Source Tweets & 2402 & 3387 \\ Comments/rep & 30,723 & 1,275,179 \\ Users & 20,538 & 1,064,970 \\ Fake & 638 & 1538 \\ True & 1067 & 1849 \\ Uncertain & 697 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of datasets.
# Routing Protocol for Heterogeneous Wireless Sensor Networks Based on a Modified Grey Wolf Optimizer
## 4 Proposed Algorithm
### Proposed Algorithm
#### 4.3.1 Selection of Initial Clusters  
The protocol uses the energy of the node and the distance from the node to the BS as parameters to select the initial clusters of the network, thereby fixing and limiting the number of CHs in each round, so that the number of CHs in each round is even. The use of the distance parameter makes the distribution of CHs in the network more uniform. The following are the rules used for selecting the initial clusters. The set of alive sensor nodes is divided into \(m\) (\(m\) is the number of desired clusters, which equals \(N/p\), where \(N\) is the number of sensor nodes, and \(p\) is the portion of CHs) equal subsets according to the ascending result of the fitness value of the sensor nodes. In each subset, the sensor node that is closest to the middle point is selected as the initial CH. Finally, each node is added to the nearest CH according to the Euclidean distance to form the initial cluster. The node's fitness value is calculated according to the distance from the node to the BS and residual energy:  
\[F_{1}=\left\{\begin{array}{rl}&a_{1}\times\frac{E_{r}}{E_{i}}+(1-a_{1})\times \left(\frac{d_{MAXBS}-d_{MAXBS}}{d_{MAXBS}-d_{MINBS}}\right),\ E_{r}>0\text{ and the node is the normal node}\\ &(1-a_{1})\times\frac{E_{r}}{E_{i}}+a_{1}\times\left(\frac{d_{MAXBS}-d_{MINBS} }{d_{MAXBS}-d_{MINBS}}\right),\ E_{r}>0\text{ and the node is the advanced node}\\ &0\,\ E_{r}\leq 0\end{array}\right., \tag{12}\]  
where \(a_{1}\) is the weight, \(E_{i}\) is the initial energy of the node, \(E_{r}\) is the residual energy of the node, \(d_{toBS}\) is the distance from the node to the BS, and \(d_{MAXBS}\) and \(d_{MINBS}\) are, respectively, the maximum and minimum distances between a sensor node and the BS.
# Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion
## 3 Methods for Path Loss Prediction  
As mentioned above, any supervised learning algorithm can be used for the path loss prediction. In this section, we will introduce some popular models, such as ANN, SVR, and decision tree, and evaluate their prediction performance by means of the measured data.
## 4 Security Analysis and Numerical Simulation
### Asymptotic Security Analysis
When the modulation variance is relatively large, the secret key rate in the case of homodyne detection is larger than that in the case of heterodyne detection, and the difference of the secret key rate between them gradually decreases as the modulation variance decreases, which explains why the performance of the protocol in the two different detection cases in Figures 5 and 6 is not much different. By comparing Figures 9 and 10, we can also find that the modulation variance range of the eight-state protocol is much wider than that of the four-state protocol, and the optimal modulation variance and maximum secret key rate of the eight-state protocol are both larger than that of the four-state protocol under the same conditions. Through the comparative analysis above, we can conclude that the lower is the complexity of the seawater channel, the better is the performance of the CV-QKD system. Besides, the performance of the eight-state protocol is much better than that of the four-state protocol, and homodyne detection is slightly better than heterodyne detection.
## 1 Introduction
However, only the flow velocity inside the weld pool and on the weld pool surface under a specific welding condition was discussed. In another paper, the influence of shielding gas flow rate was discussed in detail. The results showed that with varying shielding gas flow rate, the convection inside the weld pool changes, causing welding defects such as undercut [19]. [PERSON] et al. calculated the material flows inside the weld pool with variable polarity plasma arc (VPPA) welding for aluminum using both experimental and numerical simulation approaches [20; 21]. The results indicated that, in this case, the velocity inside weld pool is much lower than with steel and the weld pool length is very short in comparison to steel. In recent works, a novel variant of the KPAW process was developed for welding aluminum [22; 23]. The results showed that using a hybrid welding process between VPPA welding and metal inert gas (MIG) welding can reduce the porosity, which is the most common issue in welding aluminum. Even though many publications are related to this welding process, the weld pool formation mechanism and the behavior of material flow in relation to the main driving forces are not fully understood. To provide clarification, we focused on determining the influence of Marangoni and shear forces on the weld pool formation through changing the shielding gas composition. The weld pool convection was investigated by observing the movement of slight zirconia particles on the weld pool surface with an HSVC. The temperature distribution on the surface along the keyhole wall was measured to evaluate the Marangoni force acting on the weld pool.
# Investigation of Gentamicin Release from
###### Abstract  
Polydopamine (PDA), being highly reactive in nature, has acquired great attention in multi-disciplinary fields. Owing to its fascinating properties, including its biocompatible, non-toxic and readily bio-degradative nature, we investigated the drug loading and release behavior, using an aminoglycoso antibiotic gentamicin (G) as a model drug. The gentamicin was loaded into the PDA nanoparticles (NPs) via an in situ polymerization method. The release kinetics of the gentamicin was then studied in pH 3, 5 and 7.4. Two batches with varied gentamicin loadings, G-PDA NPs 1:1 (with approx. 84.1% loaded gentamicin) and G-PDA NPs 0.6:1 (with approx. 72.7% loaded gentamicin), were studied. The drug release data were analyzed by LC-MS. The PDA showed good stability in terms of gentamicin release at alkaline pH over a period of seven days. The negative surface charge of PDA at pH 7.4 makes a strong bond with gentamicin, hence preventing its release from the PDA NPs. However, at pH 5 and 3, the amine groups of PDA are more prone towards protonation, making PDA positively charged, hence the repulsive forces caused the gentamicin to detach and release from the G-PDA NPs. Consequently, approx. 40% and 55% drug release were observed at pH 5 and 3, respectively, from the G-PDA NPs 1:1. However, the drug released from G-PDA NPs 0.6:1 was found to be one half as compared to the G-PDA NPs 1:1, which is obvious to the concentration gradient. These findings suggested that the in situ loading method for gentamicin could provide drug release over a period of seven days, hence defending the drug's efficacy and safety challenges. Furthermore, two kinetic models, namely the Ritger-Peppas and Higuchi models, were implemented to determine the drug release kinetics. Curve fitting analysis supported our findings for the drug release kinetics which are followed by PDA structural changes in response to pH.  
polydopamine; drug delivery; gentamicin; drug release kinetics; kinetic models +
Footnote †: journal: Applied Physics Letters