esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Management of Virtual Power Plant Considering Distributed Generation Sizing and Pricing
## 3 Mathematical Formulation  
Figure 1 shows the proposed microgrid structure that interacts with the electricity market. As shown in this figure, the considered system includes wind turbine units, solar cells, electrolytic reservoirs, hydrogen fuel storage tanks, fuel cells, batteries, and Direct current (DC)/Alternative current (AC) converters and loads (in the proposed model, the load types are considered as non-interruptible). In an autonomous microgrid, distributed energy sources should be able to balance their production and consumption with sufficient capacity and proper operation, while islanded from the upstream network. In the proposed model of this paper, resources will only sell energy on the day-ahead market to the upstream distribution network due to the compatibility with the objective function target (cost minimization/profit maximization).  
Where in the above figure we have:  
* \(P_{PV}\): Output power of each solar unit (kW)
* \(P_{WT}\): Output power of each wind turbine (kW)
* \(P_{Gen}\): Total power generated by renewable energy units (kW)
* \(P_{Gen\text{-}ele}\): Transmission power from renewable sources to electrolyzer (kW)
* \(P_{Gen\text{-}but}\): Transmission power from renewable sources to battery (kW)
* \(P_{Gen\text{-}inv}\): Transmission power from renewable sources and battery to DC/AC converter (kW)
* \(P_{\text{}ele\text{-}Tank}\): Transmission power from electrolyzer to hydrogen tank (kW)
* \(P_{\text{}h\text{-}int}\): Transmission power from battery to DC/AC converter (kW)
* \(P_{\text{}tank\text{-}FC}\): Transmission power from hydrogen tank to fuel cell (kW)
* \(P_{FC\text{-}inv}\): Transmission power from fuel cell to DC/AC converter (kW)
* \(P_{\text{}int\text{-}loud}\): Transmission power from DC/AC converter to load and upstream network (kW)
* \(P_{\text{}sale}\): Wholesale power sold to up-stream network (kW)
# Rapid-Detection Sensor for Rice Grain Moisture Based on NIR Spectroscopy
## 2 Materials and Methods
### Chemometric Analysis  
In this paper, partial least squares (PLS) and competitive adaptive reweighted squares (CARS) were used to model and analyze the NIR spectral data, as well as to determine the characteristic waveband sensitive to rice grain moisture. The Polynomial fitting Model was applied to fit the mathematical relationship between the reflected voltage value and the true moisture content of rice grain samples. In this study, data analysis was based on MATLAB R2014a (The MathWorks, Inc., Natick, MA, USA, 2014).
## 3 Data Pre-Processing
In order to estimate the statistical characteristics of measurement error, firstly, the measurement error data are accumulated, then the statistical characteristics of the error are estimated by Variational auto-encoding (VAE) algorithm, and the relationship between the statistical characteristics of the measurement error and working distance is fitted by support vector regression (SVR).
# Energy-Efficient Wireless Communication Strategy for Precision Agriculture Irrigation Control
## 4 Results and Discussion
### Power Consumption  
To estimate the power consumption in the sensor nodes, we conducted current measurements during the different operating states of the node; we also computed the time spent in each state during the sampling events. Table 3 shows the obtained measurements' average values for each one of the five identified states.  
**Data acquisition**. Refers to the actions required for the three sensor reads, including a pulse to energize each sensor, which should be stabilized for at least 100 ms before the measurement and 50 ms after the reading.  
**Data transmission**. The microcontroller sends data frames to the RF transceiver containing address and payload information. Then, the transceiver validates the frames, extracts and complements the transmitting information, and finally transmits the data and waits for each packet's success/failure status. Transmission retries are automatically executed as required.  
**Data receiving**. Upon data reception, the RF transceiver identifies the source address of each received packet, sends an acknowledgment message, and then composes the receiving data frame to send it to the microcontroller for further processing.  
**Data processing**. Includes all the processing and calculation activities executed by the microcontroller, such as obtaining the average soil moisture value, creating data frames for transmissions, extracting values from receiving data frames, executing the next sampling instance estimation algorithm, and preparing and recovering from the standby mode.  
**Standby mode**. Refers to the energy-saving mode for the microcontroller and the RF transceiver.  
Based on the previous measurements and considering the number of communication events registered in each sensor node during the four-week experimental evaluation period, the estimated average power consumption in Watts is shown in Figure 10. In addition, we  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Operation State** & **Current** & **Time** \\ \hline Data Acquisition & 30 mA & 450 ms \\ Data Transmission & 360 mA & 210 ms \\ Data Receiving & 75 mA & 210 ms \\ Data Processing & 20 mA & 0.1 ms \\ Standby Mode & 0.57 mA & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: The current measurements in the sensor node for each operation state.  
Figure 9: Periodical sampling (h = 80 min) state variable evolution and sampling periods.  
also present the irrigation water consumption for each case, since it is a critical performance parameter and is heavily influenced by the sampling strategy.  
The flexibility provided by the aperiodical approaches \(\gamma_{1}\) and \(\gamma_{2}\) that sampled only when necessary reduced around 20% in power consumption and irrigation water compared to the 10-min and 80-min periodical implementations, respectively.
# Efficient Conjunctive Keywords Search over Encrypted E-Mail Data in Public Key Setting
## 3 Preliminaries
### Bilinear Map  
The definition of the bilinear map was introduced in Reference [2]. Let \(G_{1}\), \(G_{2}\) be two cyclic groups of lager prime order \(q\). A bilinear pairings map \(\hat{\varrho}:G_{1}\times G_{1}\to G_{2}\) can be defined, satisfying the following properties:  
1. Bilinear: \(\hat{\varrho}(u^{a},v^{b})=\hat{\varrho}(u,v)^{ab}\),where \(u,v\in G_{1}\) and \(a,b\in Z_{A}^{*}\) ;
2. Non-degenerate: \(\hat{\varrho}\) does not send all pairs of points in \(G_{1}\times G_{1}\) to the identity in \(G_{2}\). If \(g\) is a generator of \(G_{1}\) then \(\hat{\varrho}(g,g)\) is a generator of \(G_{2}\);Computable: There is an efficient algorithm to compute \(\hat{e}(u,v)\), for any \(u,v\in G_{1}\).  
A bilinear pairing map satisfying three properties above is reckoned as an admissible bilinear map. An efficient admissible bilinear map can be constructed by using the Weil pairing or the Tate pairing proposed in Reference [29].
# Design of 4 \(\times\) 1 Power Beam Combiner Based on MultiCore Photonic Crystal Fiber
## 4 Simulation Results  
The multicore PCF 4 \(\times\) 1 power combiner structure was simulated using RSoft Photonics CAD software (5.1.5, RSoft, Ossiting, NY, USA), based on BPM.  
Figure 4a shows the transmission of four Gaussian sources at a 1 \(\upmu\)m wavelength at \(z=0\), with a normalized power value of 0.25. Figure 4b shows the light coupling between the central core to the other four cores at \(z=1\) mm. Figure 4c shows that four Gaussian sources are combined to one source at \(z=2.6\) mm, with 99.6% of the total power.  
Figure 4: \(Cont.\)  
Figure 3: Refractive index profiles of the 8 \(\times\) 1 power combiner: (**a**) \(xz\) plane at \(y=0\); (**b**) \(yz\) plane at \(x=0\); (**c**) \(xy\) plane at \(z=0\).  
The proposed device can also act as a two dimensional (2D) 2 \(\times\) 1 power combiner, as shown in Figure 5a,b.  
In addition, a MATLAB script code, combined with BPM simulations, was developed to examine the sensitivity of the proposed device to the wavelength variation of the laser sources around 1 \(\upmu\)m. Figure 6 shows power attenuation around the central wavelength (1 \(\upmu\)m).  
Figure 4: The 4 \(\times\) 1 power combiner of the optical signals (\(\uplambda\) = 1 \(\upmu\)m) of three \(xy\) cross sections: (a) \(z\) = 0 mm, (b) \(z\) = 1 mm; (c) \(z\) = 2.6 mm.  
Figure 5: Two dimensional (2D) 2 \(\times\) 1 power combiner of the optical signals (\(\uplambda\)= 1 \(\upmu\)m): (a) \(yz\) plane; (b) \(xz\) plane.  
Figure 6: Normalized power in the central core at \(z\) = 2.6 mm as a function of wavelength.  
It can be noticed from Figure 6 that the bandwidth (FWHM) of the 4 \(\times\) 1 power combiner is about 220 nm in the 900-1120 nm range. Such a bandwidth implies that this combiner may be suitable for tunable lasers around a wavelength of 1000 nm, and can support broadband sources. This provides many benefits, due to the fact that within this range, one can use optical lasers such as ytterbium doped fiber laser, which is highly useful for high power and parametric applications.  
This device can be also used in cascade configuration for obtaining a higher ratio of combining. For example, we used two units of the 4 \(\times\) 1 power combiner and connected them to a 2 \(\times\) 1 combiner, in order to obtain an 8 \(\times\) 1 power combiner. Figure 7a shows the transmission of eight Gaussian sources at a 1 \(\upmu\)m wavelength at \(z=0\), with a normalized power value of 0.125. Figure 7b shows the combining of eight sources to two at \(z=2.6\) mm. Figure 7c shows that eight Gaussian sources are combined to one single mode at \(z=5.8\) mm.  
Figure 7: _Cont._
## 4 Discussion
### The Mechanism of Petrophysical Differences  
#### 4.2.1 Density and Porosity  
The density and porosity evolution of shale is a comprehensive and complex process under sedimentary compaction, mineral composition, and organic matter [49]. It is generally acknowledged that compaction is brought about through the closer spacing of grains [50]. And the increase of clay content could result in the decrease of pore spaces between brittle mineral grains. On the contrary, the thermal evolution of the organic matter would generate much liquid hydrocarbon in the low maturity, leading to the oil saturation of about 70-80% with an oil density of 0.78-0.89 g/cm\({}^{3}\), or generate dry gas in the high to the over-maturity stage with many organic pores [1,51].  
The continental shale has much higher clay content (41.3% on average) and lower organic maturity (Ro: 0.7-1.1%) compared to the marine shale [27]. However, the continental shale has a significantly lower density (2.12 g/cm\({}^{3}\) on average) than the marine shale (2.55 g/cm\({}^{3}\) on average). It is obvious that the high content of clay minerals did not significantly increase the density and decrease the porosity of the continental shale, which is mainly related to the following two reasons: (1) Based on pyrolysis experiments of shale samples from the Yanchang Formation, the total oil yield is 6.98 mg/g on average, and the formation oil density is 0.75 g/cm\({}^{3}\)[52]. After calculation, the volume of liquid hydrocarbon  
Figure 9: Pore size distribution based on N\({}_{2}\) adsorption of marine shale (**a**) and continental shale (**b**). There are both unimodal (MS-1, MS-4) and bimodal (MS-2, MS-3, MS-5, MS-6) distributions in marine shale, and all samples present unimodal distribution in continental shale.  
bon (0.00933 cm\({}^{3}\)/g) could occupy 23% of the total pore volume of the continental shale (0.0404 cm\({}^{3}\)/g), effectively preventing further compaction. In contrast, the organic matter in the marine shale with high maturity eventually produced large amounts of organic pores, which were easily compacted and disappeared during the depositional, leading to the increase of the rock density. (2) Different types of kerogens have various activation energy values to transform bitumen and then crack into hydrocarbon. The initial pyrolysis temperature of type II kerogen is lower than that of type I, leading to the hydrocarbons being generated earlier in the source rock with type II kerogen during the deposition [53]. The kerogen in the continental shale of the Yanchang Formation is mainly type II [54], while the type I kerogen is mainly distributed in the marine shale of the Longmaxi Formation [55], indicating that continental shale could generate hydrocarbons at a lower temperature [56]. Hence, continental shale can produce liquid hydrocarbons earlier to resist compaction during compaction, which contributes to lower density.
# Effect of Data Augmentation on Deep-Learning-Based Segmentation of Long-Axis Cine-MRI
## 2 Materials and Methods
### Datasets  
#### 2.1.1 Annotation Rules  
For training the models, two datasets containing Cine-MRI LAX 2-chamber and 4-chamber images were utilized, comprising 668 and 1007 examinations, respectively. The variation in the dataset sizes is partially attributed to the nonavailability of anatomical ground truths for some examinations. For each Cine-MRI study, end-diastolic (ED) and end-systolic (ES) phases of the cardiac cycle were visually selected and annotated under the supervision of an expert physicist. The segmentation task in the 2-chamber view focused on the left ventricle cavity (LVC), the myocardium (MYO) and the cavity of the left atrium (LA). For the 4-chamber view, three additional classes were used: the cavities of the right ventricle (RV) and the right atrium (RA), along with the aortic root (AO). The aortic root class was introduced to address cases where the aorta is visible on the image in a 4-chamber orientation. The absence of a consistently present aortic root class across all samples contributes to the segmentation complexity in the 4-chamber view.  
In both orientations, specific rules based on anatomy were established. The LVC must be disconnected from the background and enveloped by the MYO class. Additionally, the LVC must also be in direct contact with the LA at the level of the atrioventricular valve. If present, the AO class must also be in direct contact with the left ventricle cavity at the level of the aortic valve.
# Acoustic Rocket Signatures Collected by Smartphones
## 3 Results
### Falcon Heavy  
The Falcon Heavy is a super-heavy-lift launch vehicle manufactured by SpaceX. The first stage of its launch is powered by twenty-seven liquid-fueled Merlin 1D engines. It is represented in ASTRA by 40 signals originating from eight individual launches. The five waveforms collected from flight FH-003 are shown in Figure 8. Compared to signals collected at short range from Artemis I, the FH-003 signals at the 23.0 and 24.5 km ranges appear to have significantly less impulsive onsets and longer durations (\(\sim\)100-200 s), and the leading small transients (i.e., short-duration changes in amplitude and/or frequency) seen in the Artemis I data are not present at the closest station. The peak separations seen in the Artemis I data as range increases are also observed here; however, while this pattern was apparent only past the 30 km range in the Artemis I data, it is observed in all but the closest FH-003 signal.  
We once again select one signal from each range class to illustrate identified time-frequency characteristics of Falcon Heavy signals. In Figure 9, we see that the increase in power at the start-aligned arrival time estimate is gradual when compared with data from Artemis I, and the high-power bands near 45 and 100 Hz observed in the Artemis I data are not present. The short-range signals still contain more higher-frequency content than the longer-range signals, however, with most of the power over 10 Hz being lost to attenuation as the signal propagates. We once again note the Doppler shift observed at all stations, but it is most clearly illustrated by comparing panels (d) and (f) in Figure 10. The leading transient seen in the longer-range stations has a characteristic frequency near 6 Hz.  
Figure 8: Normalized, start-aligned waveforms of all signals collected from launch FH-003 of SpaceX’s Falcon Heavy. Labels on the left-hand \(y\)-axis indicate the estimated propagation distance of each signal.
## 5 Experiments
### Reconstruction Results
#### 5.3.2 Real Experiment Dataset Reconstruction  
To further substantiate the network efficacy in a real experimental environment, we utilized a real experimental system to collect the red blood cell dataset and compared results of different reconstruction methods, as illustrated in Figure 5. As shown in Figure 5, our method produces clearer visual outcomes on real data, while the aberration reconstruction is also comparable to that of traditional FPM pupil aberration reconstruction.  
Figure 4: Visual comparison of different methods’ reconstruction results (the values in bold are the best measurements in each group).  
Figure 3: Visual comparison of SFPT simulation reconstruction results under various noise variances.  
We further collected a variety of samples such as fibers, human vein cells and plexus. Moreover, we compared their reconstruction results in the two stages of the SFPT, as illustrated in Figure 6. As shown in Figure 6, the supervised learning in the second stage effectively enhances the visual fidelity of the reconstructed amplitude and phase. The clearer phase reconstruction accurately reflects the refractive index distribution of the cells, thereby indirectly revealing cellular metabolic activity, pathological states, and physiological processes such as proliferation or apoptosis.
# Effect of Aggregate Type on the Shear Behavior of Reinforced Lightweight Concrete Beams
## 6 Limitations and Future Studies  
In this study, we explored the shear capacity of ALWAC beams under monotonic loading and confirmed that the type of aggregate does not significantly affect shear resistance as it is. However, ALWAC beams exhibited inferior capacity, showing more extensive crack propagation under identical loading conditions compared to NWC beams. Notably, there were discrepancies between the measured shear strength values and those predicted by existing codes and guidelines, underscoring the urgent need for further research into the structural performance of ALWAC structures.  
Future research should examine how the properties of lightweight aggregates, such as density and water absorption, influence the mechanical behavior of ALWAC. The integration of polymers and other emerging materials might enhance the flexibility and crack resistance of ALWAC beams. Long-term studies are essential to assess the impact of environmental factors like moisture, temperature fluctuations, and chemical exposure on the aging of these beams. Moreover, developing advanced predictive models that incorporate a range of material and loading factors and potentially integrating artificial intelligence and machine learning could significantly improve prediction accuracy. Testing methods should also be expanded to include various stress types, such as cyclic, seismic, and impact loads, to better understand ALWAC's behavior under real-world conditions. Comprehensive evaluations of the environmental impacts of different lightweight aggregates and the benefits of using polymers in ALWAC are crucial, aiming to balance structural efficiency with environmental sustainability. Promoting the use of recycled and local materials will further advance ALWAC technology, leading to the creation of more sustainable and resilient structures.  
Lastly, this study focused on the effect of LWA type and the presence of stirrups on the shear behavior of LWAC beams. However, several other crucial design parameters--including beam height, cross-section geometry, aggregate dimensions, and strength-reduction factors--significantly influence the structural performance of reinforced LWAC beams and merit further investigation. These parameters are pivotal in determining the beams' load-carrying capacity and enhancing ductility. Specifically, beam height and cross-section geometry affect the bending and shear capacities, while aggregate strength may influence the ability to resist shear forces. Additionally, factors like reinforcement detailing, which include the spacing and diameter of reinforcement bars, play a crucial role in controlling crack widths and improving ductility.
## 1 Introduction
The unsupervised method was used to split the data into classes. Later on, the supervised method was applied for classification of new data. [PERSON] and [PERSON] [24] designed a neural network-based analysis method to classify matrix cracking and fiber pull-out of fiber reinforced ultra-high-performance concrete samples during split cylinder testing. The network was trained on different tests in which one failure mode was dominant. [PERSON] et al. [25] compared three clustering algorithms for the investigation of failure in carbon fiber reinforced plastics: k-means, self organising map combined with k-means, and competitive neural network. It was found that the second method was most effective. [PERSON] et al. [26] obtained the best results with a hierarchical model to cluster indentation-induced damages in laminated composites. Work on corroded RC samples has been performed by [PERSON] et al. [27] who combined a set of algorithms to remove environmental noise during corrosion monitoring of a concrete beam. However, clustering has not been performed so far to distinguish between AE sources such as the corrosion process itself and concrete cracking. Previous work of the authors focused on source characterization [5], and filtering and localization of AE signals [28]. It was found that damage due to corrosion could be detected and localized. However, dedicated filtering was required. At the onset of macro-cracking, a decrease in both peak and center frequency was noticed. It was also found that frequency ranges of AE sources are overlapping. Therefore, source identification based on frequency content may be challenging. A hierarchical clustering algorithm has been developed, applied on small-scale mortar samples, and verified by means of micro-CT images [29]. On this sample scale, the algorithm showed promising results. This paper aims to scale up the clustering algorithm to automatically differentiate AE signals recorded during the accelerated corrosion process of RC prisms. Moreover, the algorithm is applied on real-size RC beams with varying corrosion zones and AE setups. The novelty of the paper lies in the upscaling and application of the signal-based AE clustering algorithm, which has not been performed so far for corrosion monitoring in RC, and the validation of the developed clustering algorithm on an extensive set of experimental results.
## 6 Proposed MAC Protocol for Wireless Healthcare Environments--WhMAC
### Frequency Considerations  
Once a MN starts forming a network, it chooses a channel randomly and listens for a beacon. The beacon signal is sent using higher power than intra-BAN communication, and it is used for TDMA synchronising and MN-to-MN localisation. In general, it should be of higher range than intra-BAN communication. If a channel is free, the MN starts sending beacons, looks at the desired scenario and sends wake-up signals to desired sensors, with command packets using the free frequency for connection. Of course, if nearby sensors are connected to a different patient they should reject the wake-up signals with different WBAN IDs if they detect them. After this, the sensors are in a \"stand-by\" mode, paired with the MN, waiting for a new wake up signal to change the state if needed.  
If there is a need for constant monitoring, the MN occupies a free channel, and the patient is usually immobilised to get clear signals. From then, on a regular basis, or if packet error increases, the MN should stop TDMA and check the channel. If it detects that beacons from another MN on the same channel are getting stronger (another WBAN on the same frequency is approaching), the frequency should be changed to a random, free channel, using WUp \(+\) command. Additionally if there is an emergency situation, there should be free channels that are dedicated only for this types of situations.