esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Internet of Things Power Meter for Industrial and Domestic Applications
###### Abstract  
Considering the widespread presence of switching devices on the power grid (including renewable energy system inverters), network distortion is more prominent. To maximize network efficiency, our goal is to minimize these distortions. Measuring the voltage and current total harmonic distortion (THD) using power meters and other specific equipment, and assessing power factor and peak currents, represents a crucial step in creating an efficient and stable smart grid. In this paper, we propose a power meter capable for measuring both standard electrical parameters and power quality parameters such as the voltage and current total harmonic distortion factors. The resulting device is compact and DIN-rail-mountable, occupying only three modules in an electrical cabinet. It integrates both wired and wireless communication interfaces and multiple communication protocols, such as Modbus RTU/TCP and MQTT. A microSD card can be used to store the device configuration parameters and to record the measured values in case of network fault events, the device's continuous operation being ensured by the integrated backup battery in this situations. The device was calibrated and tested against three industrial power meters: Siemens SENTRON PAC4200, Janitza UMG-96 RM, and Phoenix Contact EEM-MA400, obtaining an overall average measurement error of only 1.22%.  
internet of things (IoT); power quality; smart grid; smart meter; total harmonic distortion (THD) +
Footnote †: journal: Applied sciences  
utilization of S-transforms or Hilbert-Huang transforms [6]. In the context where multiple disturbances can occur simultaneously in a network, after the identification stage, methods for classifying them are necessary. [PERSON] et al. presented a solution to increase the accuracy of disturbance classification using deep convolutional neural networks [7].  
Energy quality analysis is an important topic even for renewable energy producers, as they can face penalties from grid operators if they introduce imbalances into the system or even have their plants disconnected from the grid for a certain period [8]. [PERSON] [9] measured the power quality parameters in 8 kWp photovoltaic plant connected to the low-voltage distribution network in Egypt and proposed mathematical models to characterize the current total harmonic distortion and the power factor. Several models of commercial solar inverters from multiple manufacturers were tested by [PERSON] et al. [10] to model current distortions and power factor as a function of relative power. This model can be used in the sizing of inverters during the design stage and computerized simulation of photovoltaic plants.  
Grid operators often face challenges in controlling various renewable energy sources due to their variable nature depending on environmental parameters such as sunlight, wind, and water. Forecasting models are used to estimate the power production and demand in the network [11]. Often, meteorological data are used to estimate the power quality parameters, as they play a key role in the forecasting process [12].
## 4 Results and Discussion
### The Companies
Cost reduction (and consequently price reduction) occupies a relatively lower position on the priority scale, given that the competition in the sector is not too fierce. Among the four SC meta-strategies, the SC is closer to the responsive pattern. Focal company C prioritizes cost reduction. The standardization of its product influences this strategic choice, its relative ease of production, and the heightened competition within the sector. The second is quality, due to the difficulty for customers of dealing with dimensional, structural, and appearance errors in raw material supplied to other industries. Flexibility and dependability assume less critical roles, given the company's effective management of substantial inventories to promptly address unexpected demands. Among the four SC meta-strategies, the SC is closer to the efficient pattern. Finally, focal company D prioritizes dependability. This emphasis is underscored by the industrial market's stringent deadlines and narrow delivery windows, in which any lapse could result in a loss of business. Flexibility assumes the secondary position, driven by the swift variations in project features and uncertainties surrounding the definition of standardized batches. Quality follows, as manufacturing errors are difficult to amend by the company's delivery model. Cost reduction, though not irrelevant, takes a less central role, as the packaging cost in industrial deliveries has a less influential role. Among the four SC meta-strategies, the SC is closer to the agile pattern.
# SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach
## 4 Machine Learning Algorithms and Performance Measurements
### Machine Learning Algorithms  
The ML algorithms can be classified as supervised, unsupervised, and semi-supervised. Each class has its own characteristics and applicability. The discussion of all algorithms is beyond the scope of this paper. However, we refer the reader to [19; 20] for detailed technical discussions of these algorithms. In this paper, we use traditional ML algorithms to detect the attacks. Our target is to build supervised machine learning models, and we chose the followings algorithms for attack detection and classification:  
1. Logistic Regression [20].
2. Random Forest [21].
3. Naive Bayes [22].
4. Support Vector Machine (SVM) [23].
5. KNN [24].  
The performance of these algorithms is discussed in Section 6.
# Optimization of Design Parameters in LSTM Model for Predictive Maintenance
## 1 Introduction
An unhealthy stage consists of one or more detailed states with the degradation model of performance triggered by a fault. Finally, prediction and monitoring models are used to calculate the remaining useful life and to monitor the state of machinery via the HI. In these models, there exist design parameters to be considered, and analyzing the effect of these design parameters and optimizing them are important issues for proper execution of predictive maintenance. However, in spite of their importance, there have been few attempts in terms of the optimization of such design parameters. Table 1 summarizes brief information of the design parameters to be considered at each step. Based on these remarks, we intend to suggest an efficient approach for the optimization of design parameters in a predictive maintenance model based on LSTM, which is one of the AI model-based approaches and can overcome limitations of physics and statistical \begin{table}
\begin{tabular}{c c c} \hline \hline
**Steps** & **Design Parameters** & **Examples** \\ \hline Derivation of HI & Feature values used as HI to describe the state of machinery & Elementary statistics (mean, standard deviation), wavelet feature of time series data feature of time series data \\ Definition of HS & Characteristics of degradation model after fault & The number of degradation stages and linear/nonlinear degradation model assigned to each stage \\ Generation of Prediction and Monitoring model & Category of models to predict the state of machinery and hyperparameters of the model & The number of hidden layers and the number of neurons at each layer in deep learning model \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of design parameters in steps for developing a predictive maintenance model. model-based ones. Specifically, in this work, we suggest efficient ways (i) to identify crucial HIs by feature selection among various attributes obtained from raw data of machinery and (ii) to optimize the performance of LSTM-based prediction and monitoring models by exploring the solution space of hyperparameters effectively. The rest of this paper is organized as follows. Section 2 contains a literature survey tackling various models for predictive maintenance and hyperparameter tuning of deep learning models. Section 3 suggests an efficient approach for the optimization of design parameters for an LSTM-based predictive maintenance model. In Section 4, the effectiveness of the proposed method is verified by using a numerical experiment. Finally, Section 5 is a conclusion of our work that briefly explains the contribution of this paper and contains an idea for additional future research work.
# A Tube Linear Model Predictive Control Approach for Autonomous Vehicles Subjected to Disturbances
## 6 Simulation Results and Discussion
### Performance with Uncertain Tire Road Friction Coefficients  
As the surface of the road may contain sand, water, oil pollution, etc., the road friction coefficients may change along the track. In this section, the effectiveness of the tube MPC system under uncertain tire road friction coefficients is demonstrated. The friction coefficient is set to a random value from a uniform distribution over [0.3, 0.7] and by the changes in the simulations. In other words, the nominal friction coefficient is \(\overline{\mu}=0.5\) and its bounds are \(\left|\Delta u\right|_{\text{max}}=0.2\). The results are compared to a traditional MPC without robustness guarantees. A total of 20 trials were run for each controller with random disturbances. Figure 8 shows the trajectory performances of the traditional MPC under friction coefficient disturbances when the autonomous vehicle avoided one obstacle on the road and returned to the road center. In Figures 8 and 9, the blue and red lines represented the trajectories that successfully avoided the obstacle and failed to avoid the obstacle, respectively. Table 4 lists the number of trajectories where the obstacle was avoided successfully when using the proposed tube MPC and traditional MPC methods. The trajectories of the traditional MPC under friction coefficient disturbances were scattered over a large area, and only 16 trials avoided the obstacle effectively. The tube MPC system was carried out under the same friction coefficient disturbance conditions. The trajectory performances of the tube MPC are shown in Figure 9. Compared with Figures 8 and 9, the range of the trajectories of the tube MPC system that were obtained were much tighter, and all of the trajectories successfully avoided the obstacle on the road. The tube MPC system was robust under uncertain tire road friction coefficients.  
Figure 6: Trajectories of the traditional MPC system when under a random state disturbance.  
Figure 7: Trajectories of the tube MPC system when under a random state disturbance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Proposed Tube MPC Method** & **Traditional MPC Method** \\ \hline Number of trajectories & 20 & 14 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The number of trajectories where the obstacle was avoided successfully when using the proposed and traditional methods.
## 3 Deployment of an Application on Serverless Platforms
### SmartSAT Django Web Application  
SmartSAT is a customizable mobile app for San Antonio Transit that provides critical services to transit users. It is similar to other apps like Google Maps, Moovit, and Transit, but SmartSAT is designed specifically to improve the transit experience for lower-income people who rely on San Antonio's public VIA Transit [39]. Its main objective is to provide real-time bus location information to reduce riders' wait times and thus the length of their daily commutes. The application tracks bus locations in real time, ultimately improving the prediction accuracy of bus arrival time. SmartSAT offers a variety of features that are not available on other apps, such as the following:  
* Real-time bus arrival information;* Seat capacity information;
* Instant alert messages on schedule changes;
* Secure data collection and feedback from riders on their commute experience.  
These features can help lower-income people who rely on public transportation to plan their trips more efficiently and avoid delays. They can also help riders to provide feedback to VIA Transit so that the agency can improve its services.  
Google Maps JavaScript API was used to showcase the real-time location of the buses on the selected route. Additionally, the SmartSAT app has the capacity to calculate and display the estimated arrival time at a selected bus stop on the route. The Google Distance Matrix API was used for this purpose, taking into consideration various factors such as traffic patterns, road accidents, and other elements that may impact the trip time. The app currently supports about ten VIA bus routes, but only three were used for the experiment of the performance analysis. Directions API was used to showcase routes based on travel time and modes as well as Places and Geocoding API for addressing current locations.  
To use the app, users first select their desired bus route from the home screen. The app will then load all the bus stops on that route, including the order in which they appear. By clicking on any of the bus stop markers, users can view the estimated arrival time for that stop. The app uses a Postgres database in Google Cloud SQL to maintain data related to users and bus routes.
## 6 Multidrug Resistance of _A. baumannii_
### Genetic Elements Responsible for A. baumannii Multidrug Resistance
Moreover, the physiological level of some cations present within the host promotes the upregulation of genes coding for multidrug efflux pumps [219]. Such regulated changes in efflux pump expression may increase the ability of this pathogen to survive antibiotic challenge. As mentioned, many clinical _A. baumannii_ strains can survive drying for a prolonged period of time. However, when rehydration happens, DNA damage may occur such as various DNA lesions, cross-linking, base removal, and strand breaks. To repair some of the DNA damage, _A. baumannii_ developed an inducible DNA damage response in which RecA plays a major regulatory role in mechanisms involved in stress survival [220]. The RecA protein is involved in DNA damage repair and, consequently, in cellular protection against stresses induced by DNA damaging agents, several classes of antibiotics, and oxidative agents. This response increases mutagenesis and is one of the mechanisms used by _A. baumannii_ to acquire antibiotic resistance, particularly in hospitals under clinically relevant DNA-damaging conditions [221]. In another study, it was shown that _A. baumannii_ cells pretreated at 45 \({}^{\circ}\)C for 30 min were better able to survive a subsequent streptomycin exposure than cells pretreated at 37 \({}^{\circ}\)C. This phenomenon may be explained by the synthesis of misfolded proteins, produced by the streptomycin-disrupted ribosome and inserted into the bacterial membrane. Treating _A. baumannii_ cells with the aminoglycosatile antibiotic streptomycin induces not only ribosomal mistranslation but also expression of the heat-shock proteins DnaK and GroEL, responsible for elimination of aberrant polypeptides, thereby reducing their toxicity to bacterial cells [222]. The emergence of antibiotic-resistant _A. baumannii_ strains may be preceded by the formation of persister cells. The evolution of antibiotic resistance promoted by persistence or tolerance was observed in vitro or in patients in the case of other bacterial species [223; 224; 225; 226; 227]. Persisters, as non-dividing cells, can accumulate de novo mutations via mechanisms independent of DNA replication or via horizontal gene transfer.
# Preliminary Analysis of the Water Quality Status in an Urban Mediterranean River
## 3 Results
### Variations of E. coli Concentrations per Site  
Sampling sites of the Kifisos river had a large variation in _E. coli_ concentrations during the examined period (Figure 7). A comparative assessment was made using the Hazen method, as recommended by WHO 2009, and results are presented in Table 3. The maximum admissible number as set by the EU Directive 2006/7/EC indicative of good quality conditions was exceeded on all occasions, with the highest value found being 117,200 (cfu/100 mL) at the K5 site.  
Figure 5: Box plots per sampling site for the visual representation of the HWQI (HWQI estimated with the use of six water parameters: five nutrient species and DO; colors that indicate the relative water quality conditions: red = bad, orange = poor, yellow = moderate, green = good and blue = high).  
Extremely high values were also found at the K 12 and K 17 sites (100,000 cfu/100 mL), while the lowest median value of _E. coli_ was observed at K MD (Figure 7). These results are in accordance with the nutrient findings mentioned above since there is a tendency for pollution levels to increase from the upstream to the downstream part of the river, with the exception of K 5, which is a small branch of the river that is inside an industrial area and is heavily polluted.  
Moreover, the Pearson correlation coefficient (r) per site was calculated (Table 4) to examine the patterns between _E. coli_ and the physicochemical parameters (T, EC, pH and DO). A strong negative correlation was estimated between EC and _E. coli_ for two (K 12 and K MD) out of five sites. For these two sites, EC and _E. coli_ concentration linear regression models were developed. These models resulted in a coefficient of determination R-squared (R\({}^{2}\)) of 0.63 for the K MD site and an R\({}^{2}\) of 0.15 for K 12.  
Figure 6: Box plots per sampling site for electrical conductivity (blue grading plots) and temperature (yellow grading plots). Light colors represent values above the median and dark colors represent values below the median; box limits indicate the 25 th and 75 th percentiles; whiskers extend to minimum and maximum values.  
TPAHs concentrations also indicated the existence of heavy pollution, and ranged from 17.2 (K MD) to 4541 ng/L (K 5), with a mean value of 764.3 ng/L. Additionally, TPAHs concentrations ranged from 8.1 (K MD) to 1050 ug/L (K 5), with a mean value of 313.8 ug/L (Table 5).
## 2 Description of the Power Converter System Solution for a Cogenerator with an Induction Machine
### Topology of the Battery DC-DC Power Converter  
The battery converter is a bidirectional DC-DC converter, which is designed to convert the low voltage of the battery (48 V or more) into the DC circuit voltage of 600-800 V. The value of this voltage depends on the number of legs of the grid converter. Such a large voltage ratio (700 V/48 V \(\approx\) 14.6) makes the use of a bidirectional non-separated DC-DC buck-boost converter unjustified, due to the need to switch transistors with a very high duty cycle. A DAB converter was used in the project, a diagram of which is shown in Figure 5.  
Figure 4: Scheme of a two-level, three-leg generator power converter. Source: author’s development.  
Figure 5: DAB DC–DC power converter. Source: author’s development.  
On the low-voltage side (battery voltage) 150 V MOSFET transistors are used (LXFN400N15X3, TXS Corporation, Milpitas, CA, USA). On the high-voltage side (DC bus voltage), IGBT transistors with a voltage class of 1200 V are used (FF50R12 RT4, Infineon Technologies, Neubiberg, Germany). The turns ratio of the transformer, Tr, is n1/n2 = 1/14.6.
# On the Optimal Size of Candidate Feature Set in Random forest
## Motivation
### Effect of the Size of Candidate Feature Set  
[PERSON] [10] proposed the size of candidate feature set at each node as \(m\approx log_{2}(p+1)\) in the original article. Later, many researches related to Random forest have generally used the default size of candidate feature set as \(m\approx\sqrt{p}\) in classification problems and \(m\approx p/3\) in regression problems.  
In addition, the default value of \(mtry\) option which controls the size of candidate feature set in R program package named \(randomForest\) is also \(m=floor(\sqrt{p})\) in classification and \(m=floor(p/3)\) in regression. In general, it is empirically known that good performances of Random forest are obtained using those default values. However, there is no existing specific mathematical proofs or theories.  
Meanwhile, bagging is a special case of Random forest where the size of candidate feature set is \(m=p\). In other words, bagging uses the full feature set at each node of trees. In addition, Random forest generally outperforms bagging in terms of test accuracy [14]. Therefore, we can presume that the performance of Random forest can be affected by the size of candidate feature set. To investigate it, we conducted an experiment using 4 real or artificial datasets. Table 1 provides the description of datasets.  
The design of the experiment is as follows. We used random 60% of the dataset as training set for fitting, and random 40% of the dataset as test set for evaluating. With the training data, a Random forest model is constructed using the fixed feature set size \(m\). We explored all possible values of \(m\), such as \(m=1\), 2,..., \(p\). Finally, we calculated the classification accuracy of each Random forest model using the test data.  
As in Breiman [10], we set the number of trees in Random forest to 100, but it is not limited in general. Other parameter values in Random forest package were set to the default ones. For a reliable comparison, we repeated the whole experiment process 100 times to reduce the effect of favorable training or test datasets. The repeated process will also reduce the sampling bias due to unbalanced class frequencies.  
The result of the experiment is summarized in Figure 1. The horizontal axis refers to the size of candidate feature set and the vertical axis refers to the test accuracy. In addition, points in the figure represents the average of the test accuracy obtained from 100 repetitions. The filled point indicates the optimal size of candidate feature set which shows the highest test accuracy and the crossed point indicates the default size of candidate feature set.  
As a result, firstly, we found that the performance of Random forest was highly affected by the size of candidate feature set. Secondly, the default size of candidate feature set did not assure the best performance of Random forest. In addition, the optimal size of candidate feature set is quite different for each data.
# Radar Voxel Fusion for 3D Object Detection
## 3 Methodology
### Inclement Weather  
We expect the fusion of different sensor modalities to be most beneficial in inclement weather, which deteriorates the quality of the output of lidar and camera sensors. We analyze the nuScenes data set for frames captured in such environment conditions. At the same time, we make sure that enough input data, in conjunction with data augmentation, are available for the selected environment conditions to realize a good generalization for the trained networks. We filter the official nuScenes training and validation sets for samples recorded in rain or night conditions. Further subsampling for challenging conditions such as fog is not possible for the currently available data sets. The amount of samples for each split is shown in Table 1. We expect the lidar quality to deteriorate in the rain scenes, whereas the camera quality should deteriorate in both rain and night scenes. The radar detection quality should be unaffected by the environment conditions.