esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Multi-Energy Microgrid Reliability Assessment
### State Model of Equipment Units  
In the microgrid system, the output of units (e.g., PV or CCHP) and different energy load demands have robust time-sequential characteristics. It is challenging to model these characteristics mathematically, which affects the feasibility of any reliability evaluation. A state model of equipment units must be built to simulate the equipment unit states via the Monte Carlo method. Subsequently, the time-sequential characteristics in the microgrid system can be modeled appropriately.  
The Markov two-state models [8] were employed to describe the equipment units of the microgrid system. The normal state of a unit is presented in an exponential distribution. The duration from the unit's normal working state to failure is:  
\[T_{f,k}=-(\frac{1}{\lambda_{k}})\ln u_{k}\,k\in[1,2,\ldots\ldots\,m] \tag{18}\]  
where \(\lambda_{k}\) is the failure rate of \(k\)th equipment unit; and \(u_{k}\) represents the random numbers of uniform distribution in [0,1] interval.  
All of the equipment units are repairable. The failure duration is also an exponential distribution:  
\[T_{r,k}=-(\frac{1}{\mu_{k}})\ln u_{k}\,k\in[1,2,\ldots\ldots\,m] \tag{19}\]  
where \(\mu_{k}\) is the repair rate of \(k\)th equipment unit.
## 2 Materials and Methods  
### Field Surveys  
This study was conducted alongside the Portuguese Coast in 5 areas (Figure 1) with field surveys in 2 different periods between 5 and 26 October 2018 (2018) and between 13 April and 14 May 2019 (2019).  
Water samples were collected with CTD (conductivity, temperature, depth) profiles being acquired along the water column. For the purpose of this study, data from the superficial layer (2-7 m depth) was selected. For the nutrients analysis, the water samples were filtered using 0.45 \(\upmu\)m pore size polycarbonate filters and preserved at a temperature below \(-\)18 \(\lx@math@degree\)C in high-density polyethylene bottles until subsequent analysis at the Hydrographic Institute (IH) laboratories. Total oxidized nitrogen (also known as nitrate + nitrite, \(\upmu\)mol L\({}^{-1}\); NO\({}_{x}\)), nitrites (\(\upmu\)mol L\({}^{-1}\); NO\({}_{2}\)), ammonia (\(\upmu\)mol L\({}^{-1}\); NH\({}_{3}\)), phosphate  
Figure 1: Map of the 5 study areas from the Aquimar project (A—Area A (between Viana do Castelo and Oporto); B—Area B (between Aveiro and Figueira da Foz); C—Area C (Peniche); D—Area D (between Setibal and Sines); E—Area E (Algarve region); (\(\blacksquare\) main cities near the surveyed areas); Wave buoys stations \(\blacksquare\): CSA92D; CSA83D and CSA82D; Currents stations \(\blacksquare\): ADCP1, ADCP2, ADCP3, ADCP4, ADCP5, and ADCP6).  
(PO\({}_{4}\); \(\mu\)mol L\({}^{-1}\)), and silicate (\(\mu\)mol L\({}^{-1}\); SiO\({}_{2}\)) were analyzed. Nitrates (\(\mu\)mol L\({}^{-1}\); NO\({}_{3}\)) were estimated by calculating the difference between NO\({}_{\chi}\)\({}^{-}\) and NO\({}_{2}\)\({}^{-}\). The water samples for chlorophyll \(a\) (\(\mu\)g L\({}^{-1}\)) analysis were filtered using 0.45 \(\mu\)m pore size mixed cellulose ester filters and the filters were preserved at a temperature below \(-\)18 \({}^{\circ}\)C until subsequent analysis at the IH laboratories. pH and dissolved oxygen (mg-O\({}_{2}\) L\({}^{-1}\)) measurements were performed at the ship's laboratory, while temperature (\({}^{\circ}\)C) and salinity data were extracted from the CTD profiles for the desired depths. Water collection was carried out in 265 stations and 249 stations in 2018 and 2019, respectively (Table 1).
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Antenna/** & **Power Dissipation** & **Penetration Distance** & **Application Scenario** \\  & **Electrode Size** & & & \\ \hline Magnetic Induction & Large & Low & Far away & Mine \\ Elastic Wave & Small & High & Near & Mine \\ Earth Electrode & Small & Low & Far away & Subways, Mines, \\ Current Field & & & & Mountain Tunnels, etc. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of characteristics, advantages, and disadvantages of three types of wireless strong-penetration information transmission technologies. ground-penetrating coupled information transmission, and conducted theoretical research and simulation on the propagation of electromagnetic waves at different frequencies in different stratum media. In 2017, Xi'an University of Electronic Science and Technology constructed and analyzed a geotechnical channel model, proposed a new spread spectrum weak signal acquisition algorithm, and designed a direct sequence spread spectrum information transmission system based on the current field. In 2018, China University of Mining and Technology designed a wireless underground magnetic induction information transmission system to achieve short-range wireless data transmission in the underground environment. In 2019, the research team designed an anti-multipath interference information system based on a direct sequence spread spectrum. By using highly random Logistic spread spectrum codes, the anti-multipath interference performance of the tunnel engineering ground-penetrating information system was effectively improved. In 2021, Dongkuo University in Seoul, South Korea, developed a practical deployment model for multi-hop magnetic induction wireless underground sensor networks, and evaluated transmitter-to-receiver performance in terms of the signal-to-noise ratio, channel capacity, and bit error rate [17; 18]. Ground electrode current field communication has the advantages of small antenna size, simple structure, and good flexibility, which can realize a stable communication and long-distance transmission. Relevant research has been carried out continuously both in China and abroad. So far, there are various constraints in the existing technology, which make it incapable of being reliably applied in the closed/collapsed tunnel scenario. This project is mainly aimed at conducting research work on the strong penetration information transmission requirements in complex geological and electromagnetic environments for emergency rescue scenarios in tunnel engineering. The wireless strong penetration information transmission method based on the ground electrode current field has the characteristics of miniaturization, low power consumption, easy on-site deployment, and relatively long penetration transmission distance. It is suitable for rapid deployment in the event of tunnel engineering collapse accidents, achieving emergency rescue information transmission assurance, and is the best information transmission method in this scenario.
# ILRS Reference Point Determination Using Close Range Photogrammetry
## 2 Mathematical Background
### Bundle Adjustment
\begin{array}{c}\Delta x_{i}^{\prime}\\ \Delta y_{i}^{\prime}\end{array} \right)=\left( \begin{array}{c}\Delta x_{ \text{sym},i}^{\prime}\\ \Delta y_{\text{sym},i}^{\prime}\end{array} \right)+\left( \begin{array}{c} \Delta x_{\text{asy},i}^{\prime}\\ \Delta y_{\text{asy},i}^{\prime}\end{array} \right)+\left( \begin{array}{c} \Delta x_{\text{aff},i}^{\prime}\\ \Delta y_{\text{aff},i}^{\prime}\end{array} \right). \tag{10}\]  
The observations are the image coordinates, which are automatically detected and identified by modern software packages. The parameters to be estimated are the spatial coordinates of the object points and the exterior orientation parameters, i.e., the spatial coordinates and orientation parameters of each camera position. This set of parameters is enlarged, if the interior orientation parameters are not known sufficiently, and leads to a self-calibration model [19; 21]. Since Equation (1) is nonlinear, appropriate approximation values are required to estimate the parameters in terms of a least-squares adjustment. The observed image coordinates provide redundant information about the inner geometry of the spatial network but are insensitive in terms of the datum definition, i.e., the definition of the origin, the orientation and the scale of the frame of the object points to be estimated. The resulting normal equation matrix has a rank deficiency \(D\); thus, further conditions are required. A common approach also used in this investigation is to introduce up to \(D=7\) linearly independent condition equations, which do not affect the inner geometry of the network. As shown by [PERSON] [22], such a condition is fulfilled if the estimated coordinates \(\hat{X}_{i}=X_{i}^{0}+dX_{i}\), \(\hat{Y}_{i}=Y_{i}^{0}+dY_{i}\), \(\hat{Z}_{i}=Z_{i}^{0}+dZ_{i}\) are aligned to their approximation values \(X_{i}^{0}\), \(Y_{i}^{0}\), \(Z_{i}^{0}\). The no-net-translation (NNT) conditions, which correspond to the origin of the frame, are obtained by  
\[0 = \sum{dX_{i}}, \tag{11}\] \[0 = \sum{dY_{i}},\] (12) \[0 = \sum{dZ_{i}}. \tag{13}\]  
The no-net-rotation (NNR) conditions, which define the orientation of the frame, are given by  
\[0 = \sum\left(Z_{i}^{0}dY_{i}-Y_{i}^{0}dZ_{i}\right), \tag{14}\] \[0 = \sum\left(X_{i}^{0}dZ_{i}-Z_{i}^{0}dX_{i}\right),\] (15) \[0 = \sum\left(Y_{i}^{0}dX_{i}-X_{i}^{0}dY_{i}\right). \tag{16}\]The no-net-scale (NNS) condition reads  
\[0=\sum\Bigl{(}X_{i}^{0}dX_{i}+Y_{i}^{0}dY_{i}+Z_{i}^{0}dZ_{i}\Bigr{)}. \tag{17}\]  
These datum conditions have to be applied to at least three noncollinear object points, and a Gauss-Markov model (GMM) with constraints can be setup to estimate the unknown parameters ([17] p. 104f).
## 4 Prediction Model of Permeability Coefficient
### Extreme Learning Machine  
ELM is a general estimation of multivariate nonlinear mapping with good generalization performance and learning ability. As a prediction tool, ELM has been extensively used in hydrological and geological problems [37; 38; 39]. The weights between the input and hidden layers are randomly set and kept constant in the ELM strategy, eliminating the back-propagation operation process. At the same time, the system of solving equations is applied to directly determine the connection weights between the implicit layer and the output layer, which significantly improves the generalization ability and learning speed of the model. The input part of the ELM model training sets consists of calcium leaching parameters determined by OD. The output includes the combined head and leakage errors calculated by the FEM simulation at the measurement points.  
Figure 1 shows a neural network with a single hidden layer, where \(\text{I}_{i}(i=1,2,3)\) represents the input layer. \(\text{S}_{i}(i=1,2,3)\) means the single-hidden layer. O is the output layer. \(\Omega_{0}\), \(n\), and \(k_{r}\) are the calcium leaching parameters. \(\text{a}_{ij}=[a_{i1},a_{i2},a_{i3}](i=1,2,3)\) denotes the input weight of the \(i^{\text{th}}\) implied layer unit and \(\text{v}_{i}\) means the weight of the \(i^{\text{th}}\) implied layer output unit. The output layer contains only one unit because of the integration between hydraulic head and leakage. One point to note is that there may be situations where the matrix cannot be inverted since the mapping function is initialized randomly. To solve this problem, the mapping function is chosen to be a Sigmoid function to ensure that the output matrix of the hidden layer achieves full row rank or full column rank.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Test Number**} & \multicolumn{3}{c}{**Permeation Parameters**} & \multirow{2}{*}{**Adaptability Value**} \\ \cline{2-2} \cline{4-5}  & \(\Omega_{0}\) & \(n\) & \(k_{r}\) & \\ \hline
11 & 11,000 & 500 & \(6.4\times 10^{-7}\) & 0.019933 \\
12 & 11,000 & 800 & \(1\times 10^{-7}\) & 0.640415 \\
13 & 11,000 & 1100 & \(4.6\times 10^{-7}\) & 1.239977 \\
14 & 11,000 & 1400 & \(8.2\times 10^{-7}\) & 1.636077 \\
15 & 11,000 & 1700 & \(2.8\times 10^{-7}\) & 2.748317 \\
16 & 14,000 & 500 & \(4.6\times 10^{-7}\) & 0.035461 \\
17 & 14,000 & 800 & \(8.2\times 10^{-7}\) & 0.696786 \\
18 & 14,000 & 1100 & \(2.8\times 10^{-7}\) & 1.298395 \\
19 & 14,000 & 1400 & \(6.4\times 10^{-7}\) & 2.484507 \\
20 & 14,000 & 1700 & \(1\times 10^{-7}\) & 2.892282 \\
21 & 17,000 & 500 & \(2.8\times 10^{-7}\) & 0.038281 \\
22 & 17,000 & 800 & \(6.4\times 10^{-7}\) & 0.702525 \\
23 & 17,000 & 1100 & \(1\times 10^{-7}\) & 1.879253 \\
24 & 17,000 & 1400 & \(4.6\times 10^{-7}\) & 2.515877 \\
25 & 17,000 & 1700 & \(8.2\times 10^{-7}\) & 4.068743 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: A single implicit layer ELM neural network.
# An Integrated Fuzzy C-Means Method for Missing Data Imputation Using Taxi GPS Data
## 2 Literature Review  
In recent years, during the process of analyzing traffic data from various datasets, one problem that has caused extensive concern is the missing traffic value. [PERSON] et al. [19] found that missing traffic flow data occurred frequently when they dealt with long short-term predictions of the traffic flow. Specifically, [PERSON] et al. [20] pointed out that the missing data problem was common in floating sensing and crowdsourcing systems. Similarly, [PERSON] et al. [21] found that raw traffic flow data accumulated by the Intelligent Transportation Systems (ITSs) were typically incomplete, which might render some datasets useless. Thus, to obtain missing values, different imputation methods should be proposed.
## 3 Results
### Contact Angle Measurements  
Equilibrium CA measurement tests were carried out on 15 day aged coquinas both in the absence of CO\({}_{2}\) and using CO\({}_{2}\)-saturated fluids. As CA was measured toward the brine phase, lower CA values indicate a greater water-wet behavior. Figure 5 shows screenshots of the CA measurements, giving evidence of the change in wettability caused by CO\({}_{2}\) dissolution in oil and brine.  
In addition, CA was measured in unaged coquina samples without CO\({}_{2}\) dissolution. Figure 6 illustrates the results of tests including aged and unaged coquinas in the presence or absence of CO\({}_{2}\). It can be observed that, as expected, the unaged rock was water-wet, and aging shifted the wettability toward neutral-wet. In addition, CO\({}_{2}\) injection reduced the CA, increasing the water-wet behavior of the coquina rock sample. These results will be discussed in Section 4.  
Figure 4: Nuclear Magnetic Resonance (NMR) data from the studied coquina sample. (**a**) shows the T2 distribution and (**b**) illustrates the incremental (dashed curve) and cumulative (full curve) pore size distributions. The vertical dashed line shows the microCT pixel size (9.97 \(\upmu\)m). Pores larger than this value are captured in the digital image. By contrast, pores with a radius smaller than this value are under the image resolution.  
Figure 5: Images illustrating contact angle measurements. (**a**) Measurement on an aged rock with no CO\({}_{2}\). (**b**) Measurement on aged coquina using CO\({}_{2}\)-saturated fluids. Images captured after 24 h of experiment.
# LoRaWAN Communication Protocols: A Comprehensive Survey under an Energy Efficiency Perspective
## 3 Communication Protocols
### Energy Efficiency Protocols
#### 3.1.2 Energy Efficiency Protocols Utilizing Dynamic State Transition  
This section is devoted to the presentation of energy efficiency achieving protocols mainly that utilize dynamic state transition. The following publications employ techniques for the optimal switching of operating modes in the devices in order to have the energy efficiency maximization.  
In [48], the authors investigated the application of LoRaWAN in a search and rescue (SAR) operation scenario. They presented a wearable-based SAR (WeSAR) system that is capable of providing location information concerning people prone to becoming lost. The latter were tracked using wearables featuring LoRaWAN technology. In SAR operation, the key design parameter of the IoT network is the maximization of the battery life of the end-device in order to allow the operation team to track and rescue the person-in-need.  
The system presented in [37] uses trilateration and time difference of arrival (TDoA) instead of the more energy consuming geolocation positioning system (GPS). To serve the above scenario, the authors presented an energy efficient mechanism for LoRaWAN that depends on the user's state and wearable's battery level. This mechanism is responsible for the alternation between energy states dynamically over time. The dynamic states that alternate in normal conditions are normal, hibernate, and in cases of emergency it goes to the emergency state. Simulations were conducted using different mobility models and resulted in a decrease in the energy consumption in these models without compromising the packet delivery ratio.
# Recent Advances in Plasmonic Sensor-Based Fiber Optic Probes for Biological Applications
## 6 FOPS Geometrical Configuration and Techniques
### Grating-Assisted Fiber Sensors  
The gratings are photo-inscribed inside the core region and some part of the light is given in the cladding region. This effect has been used to develop the SPR-based FOPs [41]. In what follows, we discuss various types of grating-assisted fiber sensors such as thinned fiber Bragg gratings (ThFBGs), long-period fiber Bragg gratings (LFBGs), and tilted fiber Bragg gratings (TFBGs). Recently, ThFBGs have been established for RI sensing applications with a resolution of 10\({}^{-5}\)-10\({}^{-4}\), as shown in Figure 7d [66]. LFBGs have been proposed to achieve SPR of a single cladding mode at the gold-coated tip of a single mode fiber [67], as shown in Figure 7e. Recently, sensors based on TFBGs have exhibited a sensitivity of 500 nm/RIU in non-plasmon-assisted refractometry and bulk optic SPR sensors, as shown in Figure 7f [68].
## 1 Introduction  
Intelligent and resilient infrastructure and smart cities make up a rapidly emerging field that is redefining the future of urban development and ways of preserving the existing infrastructure against natural hazards. Sensing, and especially networked sensing and monitoring, has been an integral part of a growing field. The analysis and interpretation of a large volume of data (collected by the sensor network or digital images) and the extraction of critical information that can determine the state of health, reliability, and safety, as well as the life cycle assessment of these infrastructures (including feature extraction), require advanced and more realistic computational models to be developed, as well as analysis tools that can predict the behavior of these systems under complex and even hazardous loading environments and identify potential sources of damage and deterioration in real time.  
Over the past several years, a series of artificial-intelligence-based methodologies, including machine learning methods, have been proposed for model updating, diagnostics, data interpretation, and feature extraction for the health monitoring of infrastructure systems. This rapidly emerging field of research has demonstrated superiority within system identification, feature extraction, damage identification, and even the direct response prediction of dynamical systems, and has shown promises for a wide range of practical applications. A typical integrating structural control and health monitoring structure is shown in Figure 1.  
This Special Issue aims to stress the importance of developing and introducing AI-based methodologies for the structural health monitoring of infrastructure systems and the analysis and feature extraction of sensor data.  
The keywords of this Special Issue are as follows:  
* Structural health monitoring;
* Deep learning;
* Artificial intelligence;
* Data analytics;
* Damage detection;
* System identification;
* Feature extraction;
* Machine learning;
* Sensor network;
* Intelligent infrastructure systems.  
Given the interdisciplinary nature of this topic, the proposed Special Issue will be a collection of contributions from scholars in several fields, and will cover topics such as: artificial neural networks; deep learning neural networks; system identification; Big Data in infrastructure systems; optimization; probabilistic methods for SHM combined with AI methods; and dynamic response prediction via AI methodologies.
# Flexible UHF RFID Tag for Blood Tubes Monitoring
## 5 Application of the Proposed Tag as Volume Sensor
However, due to the shift in resonant frequency, this increase in reading range occurs at higher frequencies, out of the frequency band where the RFID UHF system operates. The results obtained in this study at the center frequency of 867 MHz are summarized in Table 3. The results collected in this table clearly indicate that the designed tag can indeed be used as a sensor to detect the blood levels inside a specific clinic tube. A possible method would be to find a dependence equation of the different volume levels of blood inside a clinic tube with the transmitted power or the reading range by means of a linear regression method [40]. Another approach for modelling input/output relationships, would be to use an artificial neural network or a fuzzy technique instead of a linear regression [41]. In this way, it is possible the determination of the volume level of blood inside a clinic tube from only the calculated transmitted power or the reading range, without the need to use any other equipment other than the UHF RFID reader. It is important to point out that the measurements have been performed for the smallest clinic blood tube and using an anechoic chamber. Therefore, they should be confirmed in a future work in a real environment, in order to train the algorithm with more realistic data. Figure 15: Tag power sensitivity obtained for the proposed tag (IC chip: NXP UCODE G2 XL) attached to a clinic tube with different blood volumes. The potential of this tag for the design of new traceability and level sensing systems for blood container tubes using RFID UHF technology has been demonstrated in these last two sections. However, it is important to point out that these systems can only be used by the health industry, if they do not have negative effects on the blood contained within the clinic tube. One of the negative effects would be the overheating of the blood, which would involve discarding the sample collection tube from the system. This can occur when the blood coagulates, since there is a decrease in its electrical conductivity [42]. However, numerical studies have shown that the effects of electromagnetic fields produced by an RFID reader on blood container bags and tubes should not cause overheating during a typical UHF RFID reading cycle [43; 44].