esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Deterioration Features and Data Description for East Japan  
### Deterioration Features of East Japan  
The most popular road bridge structure in Japan is the steel girder bridge with reinforced concrete slab. An reinforced concrete (RC) deck slab consists of four main layers: the concrete slab, the waterproof layer, the asphalt surface layer, and the asphalt base layer (Figure 1). Since a waterproof layer is not mandatory in Japan, waterproof layers are not installed in at least 20% of RC deck slabs in national roads. The total thickness of the two asphalt layers is around 70 mm to 80 mm.  
These structural features are standardized to resist the normal Japanese environment and a certain degree of climatic variation. Moreover, this design is durable enough to handle the mechanical damage caused by traffic loads. However, it does not have enough resistance to severe environments. One degradation phenomenon often observed is rust juice from cracks caused by de-icing salt. Normally, NaCl is used for de-icing. In Japan, the problem of de-icing salt damage increased after the prohibition of studded tires on April 1991. Two years before this prohibition, the amount of de-icing salt used on national roads in East Japan was around 4500 t/year. From 2006 to 2008, this figure increased by a factor of 4.1, to around \(19,500\) t/year in average, or \(12.11\) t per km of road (Efron, 1977). The Japanese design code for bridges does not account for such high salt concentrations, and thus they pose a threat to existing bridges.  
To date, there have been many examples of complex deterioration in bridges in East Japan. Figure 2 gives a typical example, which was provided by the Ministry of Land Infrastructure and Transport East Japan Regional Development Bureau. The bridge was used for 36 years, and the average traffic volume was only 12,000 vehicles per day, but serious deterioration occurred [17]. The proposed mechanism is as follows. As [PERSON] (1987) found, when salt water enters the crack, freezing and thawing, alkali silica reaction (ASR) and rebar corrosion begin to occur. Therefore, cement paste parts are seriously damaged. As a consequence, bending and shear capacity are significantly reduced, and shear punching failure takes place [18]. Additionally, soluble components such as Ca(OH)\({}_{2}\) and Na\({}_{2}\)SO\({}_{4}\) dissolve in water and combine with carbon dioxide in the atmosphere, resulting in efflorescence. The appearance of efflorescence indicates a high probability of crack penetration and water invasion; thus, it is an important degradation indicator in concrete inspection.
# Biological Activities of Selected Medicinal and Edible Plants Aqueous Infusions
###### Abstract  
This study investigated the phenolic profile and biological activities (antioxidant, antimicrobial, anti-hyperglycemic and antiproliferative) of aqueous infusions prepared from the following medicinal and edible plants: _Geranium macrorrhizum_ L., _Verbscum thapsus_ L., _Ononis spinosa_ L., _Achillea millefolium_ L., and _Polygonum aviculae_ L. Despite their long-term usage and numerous scientific results on various solvent extracts of selected plants, they are still un-explored in terms of certain aspects of in vitro biological activities, especially in the form of aqueous infusions, which are a very common form of consumption of medicinal plants. The phenolic analysis of the selected infusions was carried out using the HPLC-DAD method and showed the highest content of total phenols in _G. macrorrhizum_ and _P. aviculae_, the highest total flavonoid content in _P. aviculae_, and a high content of procyanidins in _G. macrorrhizum_. The highest antioxidant activity according to the three methods (DPPH, FRAP and Rancimat) was achieved by _G. macrorrhizum_ and _A. millefolium_. The aqueous infusions of the selected plants showed no antimicrobial activity against _Escherichia coli_ ATCC 25922 and _Staphylococcus aureus_ ATCC 25923. The highest anti-hyperglycemic activity by inhibition of the enzymes \(\alpha\)-glucosidase and \(\alpha\)-amylase and the highest antiproliferative activity against MD-MBA-231, A549 and T24 cells was obtained from _G. macrorrhizum_.  
Keywords:antioxidant activity; antimicrobial activity; anti-hyperglycemic activity; antiproliferative activity; phenolic compounds; herbal infusions +
Footnote †: journal: Applied sciences
## 1 Introduction
### Research Objective  
The research presented in Section 1.4 focuses on concrete products and concrete time estimation. However, there is a lack of information in the mentioned sources on how to proceed in the application of PAs. In addition, the suitability of the model is evaluated based on the Correctly Classified Instances (CCI), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), or Root Relative Squared Error (RRSE). These metrics are useful in research where the results come from using different PAs with different training setups for model building and where performances of models are compared. Comparing models is possible if they use the same data.  
However, the goal of SMEs is to find models for different product groups with limited data. For this reason, our research introduces the risk metric that takes into account both: the accuracy of the modeling (CCI for classification and RAE for regression) and the data used. In the following, the methodology of a DM application is proposed. It includes the selection of a suitable PA for the time estimation model based on experiments and the evaluation of the model based on a risk metric. The proposed methodology is tailored to the conditions of SMEs and it is integrated into the CISPDM in the following steps: see Figure 2 in the color blue:  
* Job type categorization to increase data based on similarity criteria;
* Model generation process;
* Model evaluation based on a defined risk metric.
## 2 Mathematical Model
### Objective Function
#### 2.1.1 Charging Time of an EV  
The charging time of an electric car depends on several factors, including the battery capacity, the charging power, and the initial state of charge of the battery. The charging time for full charging of an EV as a function of initial state of charge (SOC) can be calculated as Equation (2).  
\[t(i)=\frac{(1-SOC_{i})*ES_{i}}{P_{v}} \tag{2}\]  
where \(SOC_{i}\) is the initial SOC of the vehicle \(i\), \(ES_{i}\) is the battery capacity of EV \(i\), and \(P_{v}\) is the power rate with which EV is charged. We are considering three levels of SOC for batteries, as depicted in Table 1, and the parking lot's input-output power will exhibit three stages, similar to the illustration in Figure 1[1].  
It is worth noting that the charging process does not always happen at a constant rate. Typically, charging follows a curve where the speed is faster when the battery's SOC is low, and it slows down as the battery approaches full capacity. This adjustment in charging speed is managed by the battery management system to ensure the battery's health and safety are optimized. Moreover, certain electric vehicles are compatible with different charging speeds, including slower AC charging and faster DC fast charging. The charging  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Initial SOC** & **Number of Vehicles** \\ \hline SOC1 (0.3) & n1 \\ SOC2 (0.45) & n2 \\ SOC3 (0.7) & n3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Initial SOC of available vehicles in the parking lot.  
Figure 1: Input-output power modeling of the parking lots.  
duration can vary significantly based on the type of charging station and the capabilities of the EV. With advancements in EV technology, enhancements in charging infrastructure, and progress in battery chemistry, it is probable that charging times for electric vehicles will further improve.
## 5 Conclusions  
Optical image analysis characterization for all ironmaking feedstock materials needs to be of the highest quality if it is to be used to better predict downstream processing performance. Mineral and textural characterization of iron ore allows for the improved prediction of downstream processes such as beneficiation and sintering. The complex petrology of iron ore sinter also needs to be accurately characterized for sinter quality optimization. Finally, structural/textural characterization of coke is needed to best understand the connection between coke structure/texture, parent coal blend composition and final coke quality.  
CSIRO optical image analysis package Mineral4/Recognition4 allows for the high-quality segmentation of phases in different materials using multi-thresholding and textural identification. In particular, it is capable of segmenting phases with the same reflectivity, but different morphology.  
During coke characterization, Mineral4 successfully segments the two types of IMDC, unreacted and partially reacted, and RMDC. Segmentation of unreacted IMDC uses three comprehensive textural identification methods: bulk identification of IMDC, porous IMDC identification and identification of \"washed out\" IMDC, and finally combines them in one map. A similar approach, based on two methods, is used for partially reacted IMDC segmentation.  
For sinter characterization, textural identification in Mineral4 allows for the segmentation of primary and secondary hematite, based on association of secondary hematite with certain other melt-precipitated phases. It also allows for the segmentation of SPCA-I from SFCA, by taking into account the micro-porous structure of the former.  
In iron ore characterization, textural identification enables the segmentation of different morphologies of hematite, such as microplaty hematite and martite. Used in combination with multi-thresholding it can reliably segment dark siliceous goethite with reflectivity overlapping with that of epoxy.  
This article provides detailed descriptions of textural identification algorithms utilized by Mineral4 for ironmaking-related characterization. These and similar algorithms can also be applied in other image analysis tasks where morphological segmentation is required.  
Conceptualization, E.D.; methodology, E.D. and A.P.; software, A.P. and E.D.; validation, E.D.; formal analysis, E.D. and A.P.; investigation, E.D.; data curation, E.D.; writing--original draft preparation, E.D.; writing--review and editing, E.D. and A.P.; visualization, E.D.; supervision, E.D.; project administration, E.D. All authors have read and agreed to the published version of the manuscript.  
This research was fully funded by CSIRO.  
The authors wish to thank CSIRO Carbon Steel Futures group staff for valuable suggestions and help during this work. We would like to express our personal acknowledgment to [PERSON] for his useful corrections, comments and critical revision of this paper, and to [PERSON] for sharing some images.  
The authors declare no conflict of interest.
# Using Geospatial Information to Map Yield Gain from the Use of _Azospirillum brasilense_ in Furrow
## 2 Method
### Acquisition of Yield Values  
The yield data were directly obtained from the vector files of harvest provided by the machinery, by measuring the yield from measurements of mass and volume of the harvested product (Figure 2A,D). The harvester had load cells to measure the harvested load. Yield was estimated in tons per hectare at each meter of harvest.  
Figure 1: (**A**) Location of the experiments in the municipality of Patos de Minas, MG e (**B**) Br1 top and Br2 bottom.  
Figure 2: Data of corn yield. (**A,D**)—Spatial distribution of corn yield samples after harvest differentiated by the application of _A. brasilense_ (TS) or without treatment (STD), for areas Br1 and Br2, respectively. (**B,E**)—Yield data after filtering outliers for simulation of yield surfaces by Kriging for areas Br1 and Br2, respectively. (**C,F**)—Yield data for yield simulation by images for areas Br1 and Br2, respectively.  
The yield measurements were associated with a GNSS receiver, machine displacement, and cutting width systems. Additional sensors were also present in the sensor platform of the machines. These devices included humidity sensors, rotation sensors, and inclinometers that assisted the internal calibrations of the mechanisms of generation of georeferenced yield data.  
The Differential Global Positioning System (DGPS) was used for positioning by GNSS. In this system, the relative positioning of the harvester occurs based on the correction of information obtained by a GPS receiver by the use of data from one or more fixed GPS receivers. In Brazil, a network of support towers receives the GPS signals and transmits the corrected signals through radio transmitters. For agricultural activities in which continuous positioning in real-time is required, the DGPS positioning provides coordinates with an accuracy of 1 to 3 m [17].
## 1 Introduction  
Urban expansion, also called \"urban sprawl,\" is defined as \"the spreading of urban development (e.g., houses, shopping centers) on undeveloped lands near a city\" or \"the rapid expansion of the geographic extent of cities and towns, often characterized by low-density residential housing, single-use zoning, and increased reliance on private automobiles for transportation\" [1, 2]. In general, urban expansion has a close relationship with urban development, infrastructure improvement, population growth, etc. [3].  
Coarse particle, defined as particulate matter 10 (PM\({}_{10}\)), consists of particles with a diameter of 10 \(\upmu\)m or less [4, 5]. PM\({}_{10}\) is one of the main components of air pollution, and it also results in various environmental impacts (e.g., atmospheric pollution) and human health impacts (e.g., chronic respiratory diseases) [6, 7]. In particular, exposure to a high PM\({}_{10}\) concentration can cause a number of significant health impacts, ranging from coughing to high blood pressure, heart attack, stroke, and lung cancer [8].  
Previous studies found that urban expansion generally has a significant impact on air pollution because more human activities that can cause air pollution (e.g., vehicular traffic) are expected in urban areas [9]. [PERSON] (2007) assessed the relationship between urban expansion and air quality [10]. [PERSON] and [PERSON] (2014) investigated the effect of compact urban development on air quality [11]. [PERSON] et al. (2018) assessed the relationship between urban air pollution and urban form, seasonality, and city size [12].  
To protect the population from respiratory diseases and to prevent public health disasters due to PM\({}_{10}\) concentration, the main causes of PM\({}_{10}\) concentration in the city should be identified. Limited research has been conducted, however, to identify the main causes of PM\({}_{10}\) concentration in each city in South Korea. In general, urban expansion is regarded as the main cause of urban air pollution owing to the urban development activities accompanying it. This research aims to assess the relationship between urban expansion and PM\({}_{10}\) concentration by monitoring the 10 years (from 2007 to 2017) of urban expansion and the annual PM\({}_{10}\) concentrations using multi-temporal spatial datasets acquired in Daegu, South Korea and the machine learning technique. First, the expanded urban areas were detected from the multi-temporal Landsat satellite images using the machine learning technique. Then the annual PM10 concentrations were calculated using the long-term measured PM10 data. Finally, the relationship between urban expansion and PM\({}_{10}\) concentration was assessed by counting the pixels representing the expanded urban areas and computing variation of the annual PM10 concentrations, respectively.
## 3 Experimental Analysis
### Component Prediction
#### 3.4.1 Trend Component Prediction Based on LSTM  
In the proposed combined method, the trend component is input into the LSTM model. Within the internal structure of LSTM, the load data at each time step undergo processing through the input gate, forget gate, and output gate. These gating units determine the cell state and hidden state at the current time step. At the same time, the data at each time step, together with the hidden state obtained from the previous time step, are used to update the cell state and pass it to the next time step. Ultimately, LSTM utilizes this information to predict the trend component of future loads. In the trend component prediction based on LSTM, the number of LSTM layers is set to three, with 64 units per layer, and the activation function is set to tanh.  
Figure 5 shows the prediction results of the trend component of the test set load data for the first two weeks (336 h). It can be seen that the LSTM model demonstrates a smooth and accurate prediction of the trend component, with a high degree of fit between the actual and predicted value curves.  
Figure 4: Values of the original load and its components for the previous 336 h.  
Figure 5: Predicted results of trend component for the previous 336 h.  
To further illustrate the accuracy of LSTM in predicting the trend component, Figure 6 shows the MAPE values of the trend component of the load data at each moment in the first two weeks (336 h). It demonstrates MAPE values ranging between 0 and 0.003, indicating a high level of prediction accuracy. Furthermore, the curve of the MAPE value is notably smooth.
## 2 System Architecture
### Digital Component  
The digital component of the proposed system includes one Ettus SDR Universal Software Radio Peripheral (USRP) N310 from National Instruments and one Host-PC (Personal Computer). The USRP is a network SDR equipment (uses gigabit ethernet interface) which has four independent transmitting and receiving channels, operating at a frequency that can be chosen from 10 MHz up to 6 GHz, with up to 100 MHz of bandwidth. This equipment is responsible for digitally generating any required signal for the desired application and modulating it with a RF microwave carrier. The baseband signal is digitally decomposed into two orthogonal components that feed a phase and quadrature modulator. The block diagram of the signal modulation performed inside the USRP is shown in Figure 3. At the USRP output, the resulting modulated intermediate frequency (IF) signal is chosen to have a 3.84 GHz carrier frequency (fc). An equivalent reverse process is applied to the IF reception signals. The received IF signal is demodulated in phase and quadrature, generating I/Q samples that are later sent out over a 10 GbE connection to the HOST-PC for processing. This equipment is controlled by the HOST-PC, where a Python script based on the USRP hardware drivers (UHD) library was developed.  
The N310 architecture is divided into two segments, as shown in Figure 3: a pair of daughterboards, each based on the Analog Devices AD9371 transceiver, and one motherboard composed by one Arm Cortex A9 and one FPGA (Kintex-7). In this work, all the core digital signal processing is performed in the USRP from the selection of frequency of the local oscillator, the sample rates, decimation/interpolation and the selection of appropriate filter bandwidth. The remaining processing of the In-phase and Quadrature (IQ) samples is performed on the PC. Regarding the communication between the HOST-PC and the USRP, the UHD library used in the PC to control USRP parameters is illustrated in Figure 4, which provides a set of important functions for the communication. Figure 4 focuses with high detail on the block diagram of the software structure developed on the PC to calibrate the system and manage the beamforming. A Python program adapted to the USRP N310 was created, to control the generated and received waveform signals. In this script, eight threads were created, each one  
Figure 2: Block diagram of the proposed SDR beamforming system.  
independently controlling each channel of the USRP. The process starts by requesting samples from the USRP, encoding them with the channel number and sending them over the Transmission Control Protocol/Internet Protocol (TCP/IP) socket. In this case, the Matlab program is the client, which for each channel decodes the samples in an IQ vector. A calibration routine was also developed, calculating the phase and amplitude differences existing among the RF frontend channels.
# SMS: A Secure Healthcare Model for Smart Cities
## 4 Proposed SMS Framework
### Role of IoT in SMS  
IoT-enabled devices are primarily responsible for sensing and collecting data about the patient and their surroundings. This data sensing, collection, and transfer to hubs are governed by typical IoT protocols. Some of these protocols include [67; 68; 69; 70]:  
MQTT: This is a very lightweight protocol responsible for sharing information between two devices. It supports \"machine-to-machine (M2M)\" communication and works on the principle of the \"publish-subscribe\" communication pattern usually using TCP/IP [67].  
OMA-DM: This is a device management protocol responsible for managing the devices connected in an IoT network. It works in the client server model. OMA-DM works in sessions. A typical session comprises two phases. The first phase is known as the \"setup,\" while the second phase is known as the \"management\" phase. The setup phase contains authentication and device information, while in the management phase, the commands issued by DM server are executed by DM clients [67].  
6 LowPAN: This provides a low-power wireless connection over IPv6. It is used to provide end-to-end connectivity for the transfer of data in a WSN. It has an inbuilt encryption and compression mechanism that takes care of the security and speed of the data transferred [67].  
CoAP: CoAP is primarily used for low-resourced devices like sensors nodes. They can provide multicast support, minimal overhead, and ease of usage [67].  
XMPP: This stands for \"Extensible Messaging and Presence Protocol'. It is mainly used in \"real-time communication\" like \"instant messaging,\" \"voice and video calls,\" chatting, etc.  
ZigBee: This is a protocol that uses the IEEE 82.15.4 standard and is used for low-powered sensor devices. They are highly cost-effective and perform very well in several varieties of applications like home automation, personal health monitoring, etc. [67].  
The SMS proposes to use an integration of these protocols to effectively capture, store, and transfer the data sensed by the sensors.
# Maintenance Management of a Transmission Substation with Optimization
## 5 Results of Optimal RCM Maintenance
### Optimization Process  
The progress of optimization process versus iterations is shown in Figure 5. It can be seen that the optimization process converged to the optimal solution after approximately 700 iterations.  
Seeking an optimal solution in the optimization process is conditioned by the values of individual objective functions and their weights. Individual objective functions, of course, cannot converge to their own optimums, since the optimization process is oriented to the common criterion that represents the correlation of individual criteria.  
Ten independent calculations of optimization algorithm runs were performed for each SA variant. The average value and standard deviation of the objective function were computed on the basis of these runs (Table 5). The use of an optimization algorithm without SA indicates a dispersion of the results of 10 independent runs. The results of both other variants with the use of SA, on the other hand, indicate the reliability and robustness of the optimization algorithm, since there was no dispersion of the results of 10 independent optimization runs (standard deviation equals zero). All results are related to the optimization with SA.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Control Parameter** & **Value** & **Description** \\ \hline \(VTR\) & \(1\times 10^{-3}\) & value-to-reach \\ \(D\) & 62 & number of parameters \\ \(NP\) & 600 & population size \\ _Strategy_ & 7 & strategy DE/rand/1/bin \\ CR & 0.7/self-adaptive & crossover probability \\ \(F\) & 0.6/self-adaptive & differential weighting factor \\ _itermax_ & 1000 & maximum number of iterations \\ \hline \hline \end{tabular}
\end{table}
Table 4: Control parameters of DE.