esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Scheme for Prosumer-Based Energy Sharing  
As indicated in the last section, there are numerous examples in the literature where inter-community energy management and sharing mechanisms are presented. However, prosumer-based intra-community energy sharing is a research area hardly pondered upon. We present a novel optimization-based methodology which incorporates various issues related to prosumer energy management. These issues include optimal utilization of surplus energy in order to minimize shortages, price control, prioritizing the users with shortages according to different criteria etc.  
In this section, details of the proposed PESM methodology have been elaborated. The PESM scheme incorporates multiple prosumers i.e., users who consume as well as generate energy. Although several different architectures are possible, we assume that prosumers are connected to grid and with one another using a local control center as shown in Figure 2. Users normally have renewable energy resources for generation. Typically, some of these users have a shortage of energy at any given time while others have surplus energy. The proposed scheme is devised accordingly and provides a mechanism of sharing surplus energy of some users with those in need. The scheme minimizes demand-supply mismatch and prioritizes users with shortages based on different criteria. We assume that some sort of storage facility is available to the prosumers. Inclusion of the storage in optimization process is one of the possible future enhancements in the proposed scheme.
## 4 Simulation Results
It signifies that the performance benefits are yielded due to adequate compensation of the interferences with minimal noise amplification. Therefore, the simulated results confirm the stated hypothesis. Moreover, these results are validated with an experimental setup provided in the following section.
# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## 3 Methodology
### NDVI Temporal Series at Plot Scale  
Using the S2 images, 17 NDVI maps were derived from the red and infrared bands at each month between September 2017 and December 2018:  
\[\text{NDVI}=\frac{\rho\text{NIR}-\rho\text{RED}}{\rho\text{NIR}+\rho\text{ RED}} \tag{3}\]  
To obtain an NDVI value for each plot at each month, the NDVI pixels within each plot were averaged. Finally, a temporal series of 17 NDVI values were obtained at plot scale.
# Analyzing Data Reduction Techniques: An Experimental Perspective
## 9 Discussion
Also, the accuracy of the reduced data, when compared to the original set, may not be sufficient for all scenarios or applications, emphasizing the need for careful selection of the data reduction technique to be used, considering the specific requirements of the final application. In addition, the test environment, which uses a general-purpose Ubuntu operating system, may introduce some noise into the results. Despite these limitations, our research provides valuable insights into the field and sets a foundation for future studies. Since the algorithms have different characteristics, we split this section into two subsections, one for the lossy data reduction algorithms and the other for the lossless data reduction algorithms. For the analysis, we only consider the results that reduced the original size of the datasets.
# Experimental Research on the Properties of Rock-Filled Concrete
## 3 Experimental Tests for the Properties of Rock-Filled Concrete
### Ultrasonic Test
4 showed that at the depth of 6.3-10.9 m, the water permeability of the dam is far higher than in other parts, which means that there exist vertical interconnected fractures in the area, but with no obvious reflection on ultrasonic velocity. The ultrasonic velocity in borehole No. 6 is generally above 2000 m/s, with an average of 3096 m/s. At the depth of 5.5-6.0 m, the ultrasonic velocity is lower, whereas the permeability of the dam is bad in this place, which indicates that it might exist large defects, voids or relatively loose concrete with low interconnection. As illustrated in Figure 8, the ultrasonic velocity in each borehole shows roughly cyclical fluctuations along the depth direction of the boreholes, which is due to the properties of RFC, which is mainly composed of mutually interlaced rock blocks and SCC, and the ultrasonic velocity in the two kinds of medium shows obvious differences. If defects in the RFC are distributed widely, the ultrasonic velocity is significantly low; on the contrary if RFC is very dense, the ultrasonic velocity can increase significantly. According to a statistical analysis on the dataset with 137 ultrasonic velocity specimens, the average ultrasonic velocity is 2993.3 m/s, and the standard deviation is 650.5 m/s. From the histogram of ultrasonic velocity shown in Figure 9, we can find it approximately conformed to a lognormal distribution and normal distribution. A \(\chi^{2}\) test is applied to determine the optimal distribution type [29]. Since the number of ultrasonic velocity specimens of less than 2024.83 m/s and more than 4328.17 m/s are very few, the specimens in these intervals should be merged. Finally, the specimens are divided into nine intervals. Assuming the specimens conform to a normal distribution, statistic \(\chi^{2}\) = 12.6; and if specimens conform to a lognormal distribution, statistic \(\chi^{2}\) = 3.0. Now, we should calculate the critical statistic \(\chi^{2}\) under the two distribution types. Because the number of specimens is more than 50, and two unknown parameters are contained in the distribution function, the degree of freedom is equal to 6 (i.e., \(f\) = 6). Assuming a level of significance \(\alpha\) = 0.05, under the condition of \(f\) = 6, critical statistic \(\chi^{2}_{0.05}(6)~{}=~{}12.6\). Comparing the \(\chi^{2}\) = 12.6 under normal distribution and \(\chi^{2}\) = 3.0 under lognormal distribution with the critical statistic, the two statistics are not more than it, so the two distribution types can be both accepted. However \(\chi^{2}\) = 3.0 under the lognormal distribution is far less than \(\chi^{2}\) = 12.6 under a normal distribution, therefore the optimal distribution type the specimens fit is a lognormal distribution, with distribution parameters of 7.98, 0.216.
## 2 Materials and Methods  
### Study Site and Data  
The study area was located in the Laonong River watershed in southern Taiwan and covered approximately 117 km\({}^{2}\), as illustrated in Figure 1a. The elevation in the study site ranged from 258 to 1666 m above sea level, measured using the digital elevation model (DEM) modified by [PERSON] et al. [51]. The average elevation was 716 m. The slope range, average slope, and standard deviation were 0-71.2\({}^{\circ}\), 25.84\({}^{\circ}\), and 11.98\({}^{\circ}\), respectively, which indicated that the terrain of the Laonong River watershed is steep. Three geological formations and four soil types were identified in the study area. The geological formations were Lushan, Sanhsia, and Toukoshan. The four soil types were alluvium, colluviums, lithosol, and loam. [PERSON] et al. [34] reported detailed information regarding geological formations and soil types within the study site. The Laonong River watershed is located in the tropical monsoon region, and the annual precipitation is approximately 3400 mm. Therefore, this area is frequently struck by typhoons. For example, Typhoon Morakot caused extensive rainfall in 2009, which resulted in numerous landslides and debris flow in southern Taiwan. An official report [52] indicated that 769 casualties or missing people were directly or indirectly caused by these landslides. Furthermore, Typhoon Morakot caused losses of approximately $526 million because of the damage to agriculture, forestry, and fishery. In particular, a riverside village called Xiaolin (sometimes spelled Shiaolin, Hsiaolin) was destroyed by the landslides and debris flows from a devastating landslide nearby, which caused approximately 500 fatalities.  
A landslide inventory map of Typhoon Morakot revealed deep-seated landslides, as illustrated in Figure 1b. The landslide source, runout, and channel classes were interpreted manually [34] based  
Figure 1: Location of the study site (**a**) and landslide inventory map (**b**).  
on stereo aerial photos and auxiliary data. This study assumed that the used landslide inventory comprised the results of the detection of affected landslide areas extracted from the remotely sensed images. The landslide inventory was also used for quantitative verifications. The 10-m DEM was then analyzed to derive topographic attributes, including the aspect, curvature, elevation, and slope, as illustrated in Figure 2. Furthermore, the landslide inventory map was converted into a grid format of 10 m\({}^{2}\) to match the topographic factors for constructing data mining-based models.  
Figure 2: Topographic layers used in this study. (**a**) Aspect, (**b**) curvature, (**c**) elevation, and (**d**) slope.
## 2 Materials and Methods
### Measuring Dands and AMS Emissions  
Following a previous report [17], the emission fluxes of DSDS and AMS emanating from the skin surface were determined using a passive flux sampler (PFS) in conjunction with gas chromatography-mass spectrometry (GC-MS). Figure 2 shows a schematic of the PFS applied to human skin. The skin surface was not specially treated prior to sampling. The PFS was gently affixed to the skin surface to create a headspace. The DADS and AMS released from the skin move toward and were sampled by the flat adsorbent composed of monolithic silica embedded with activated carbon particles (MonoTrap\({}^{\circledR}\) DCC18, GL Science, Tokyo, Japan). The diffusion length, a distance between the skin surface and the adsorbent in the PFS, was set to 0.80 cm.  
After sampling, the adsorbent was placed into a glass vial and sealed. The trapped volatile compounds were heated and desorbed at 120 \({}^{\circ}\)C using a STRAP headspace sampler (JEOL, Tokyo, Japan) and the desorbed gases in the headspace were introduced into a GC-MS system (JMS-Q1000 GC MkII, JEOL, Tokyo, Japan) by a split mode at the ratio of 10:1. The analytes were separated using an InertCap\({}^{\circledR}\) Pure-Wax column (0.25 \(\upmu\)m film thickness; 30 m \(\times\) 0.25 mm, GL Sciences, Tokyo, Japan) using the following column temperature program: hold for 5 min at 35 \({}^{\circ}\)C, increase at 5 \({}^{\circ}\)C min\({}^{-1}\) to 100 \({}^{\circ}\)C, then immediately to 250 \({}^{\circ}\)C at 25 \({}^{\circ}\)C min\({}^{-1}\) and hold there for 6 min. G1 grade helium (Taiyo Nippon Sasso, Tokyo, Japan) was used as a carrier gas with 1.0 mL min\({}^{-1}\) of flow rate. Sample data were acquired using real-time Selected Ion Monitoring (SIM) mode: \(m/z=41\) for DADS and \(m/z=73\) for AMS. The temperature of the detector interface was held at 250 \({}^{\circ}\)C. The emission fluxes of DADS and AMS at sampling time \(t\), \(E_{t}\) in the unit of ng cm\({}^{-2}\) h\({}^{-1}\) was calculated by:  
\[E_{t}=\frac{W_{t}}{S\cdot T_{t}} \tag{1}\]  
where \(W_{t}\) is the collected amounts (ng) of DADS and AMS at sampling time \(t\), \(S\) is the functional cross-section of the disk-type adsorbent (=0.594 cm\({}^{2}\)), and \(T_{t}\) is the sampling duration (0.5 or 1.0 h). Because no significant contamination was observed in the blank samples, the limit of detection was set at three-times the baseline noise level (\(S/N=3\)), resulting in 8.7 \(\times\) 10\({}^{-3}\) ng cm\({}^{-2}\) h\({}^{-1}\) for DADS and 2.2 \(\times\) 10\({}^{-3}\) ng cm\({}^{-2}\) h\({}^{-1}\) for AMS after 1 h of sampling.  
Figure 2: Schematic of the PFS used to sample DADS and AMS released from the human skin surface. During the sampling, the PFS was gently affixed to the skin surface with surgical tape.  
The total amounts of DADS and AMS emitted during testing following garlic consumption, \(Q\) (ng cm\({}^{-2}\)), were calculated by integrating the obtained emission fluxes multiplied by the sampling duration at 0.5, 1, 2, 3, 6 and 8 h, as follows:  
\[\mathbf{Q}=\mathbf{\Sigma}\mathbf{E_{t}}\cdot\mathbf{T_{t}} \tag{2}\]
## 2 Theory
### Prediction Models
#### 2.2.3 Weights for Methods AVG-CV, -AIC, and -BIC  
In this paper, three types of error rates \(\mathbf{d}\) are used to compute \(\mathbf{w}\) in AVG-CV, -AIC, and -BIC methods.  
* AVG-CV: For the PLSR model with \(r\) LVs, the error rate \(d_{r}\) is the root mean squared error of predictions (RMSEP) estimated on the training data \(\{\mathbf{X},\mathbf{y}\}\) from a random \(K\)-fold (\(K=5\)) CV (RMSEP\({}_{\text{CV}}\)). The \(K\)-fold CV was repeated ten times and \(d_{r}\) was computed by the average of the ten RMSEP\({}_{\text{CV}}\) estimates;
* AVG-AIC: \(d_{r}\) is the Akaike information criterion [15; 16]: \(AIC=\log(SSR)+2\;df\), where \(SSR\) is the sum of the squared residuals computed on the training data \(\{\mathbf{X},\mathbf{y}\}\) and \(df\) the complexity (or \"effective\" dimension or number of degrees of freedom) of the model. The AIC penalty \"\(2\;df\)\" increases when the complexity of the model increases (in contrary to SSR) and counter-balances the optimism of SSR to measure the performance of the model for predicting new observations. When several models are compared (i.e., in this paper, the PLSR models with different numbers \(r\) of LVs), models with the  
Figure 1: Example of weight curves computed from a bi-square function for \(\alpha=0\) and \(\alpha=0.3\). Distances \(d\) were simulated from a Chi-squared distribution (\(\
u=1\;\text{d}\text{f}\)). In the paper, the “distance” is the error rate of the models.  
lowest AICs are considered to be the most performant, as with RMSEP\({}_{\text{CV}}\) in CV. The complexity _df_ is known to be difficult to estimate for PLSR [17; 18; 19; 20]. This is due to the fact that the response variable \(y\) is involved in the computation of the LVs, which is not the case, for instance, for PCR models. Nevertheless, approximations are available and, in particular, several methods are detailed and compared in Leshoff et al. [21]. In the paper, _df_ was computed from the conjugate gradient least square algorithm [22; 23]. Since CV and AIC estimate approximately the same type of prediction error [21; 24], both methods are expected to estimate close weights patterns \(w\) and therefore close results of averaging in Equation (2);
* AVG-BIC: here, \(d_{r}\) is another common parsimony criterion, the Bayesian information criterion (BIC) [25]. In BIC, the AIC penalty constant \"2\" is replaced by \(\log(n)\), where \(n\) is the number of training observations): BIC = \(\log(SSR)+\log(n)\)_df_. Since the penalty added to SSR is increased compared to AIC, BIC is more conservative and selects (by minimal error rate) models with lower dimensions.
# Multi-User Linear Equalizer and Precoder Scheme for Hybrid Sub-Connected Wideband Systems
## 2 System Model
### Receiver Description  
At the receiver we consider a hybrid analog-digital sub-connected architecture, where each RF chain is connected into a group of \(R=N_{rx}/N_{RF}\) antennas, where \(N_{RF}\) is the number of RF chains, as represented in Figure 2. We assume that the number of RF chains is lower than the number of receive antennas, \(N_{RF}\leq N_{rx}\).  
The frequency domain received signal at the \(k\)th subcarrier \(\mathbf{y}_{k}\in\mathbb{C}^{N_{rx}}\) can be written as  
\[\mathbf{y}_{k}=\sum_{u=1}^{U}\mathbf{H}_{u,k}\mathbf{x}_{u,k}+\mathbf{n}_{k }=\sum_{u=1}^{U}\mathbf{H}_{u,k}\mathbf{f}_{u,k}\mathbf{c}_{u,k}+\mathbf{n}_{ k}, \tag{6}\]  
Figure 2: Schematic of the receiver.  
where \(\mathbf{n}_{k}\in\mathbb{C}^{N_{rx}}\) is the zero mean Gaussian noise with variance \(\sigma_{n}^{2}\), and \(\mathbf{x}_{u,k}\) and \(\mathbf{f}_{u,k}\) represent the discrete transmit complex baseband signal and analog precoder of the \(u\)th user at subcarrier \(k\), respectively. We consider a sub-connected hybrid analog-digital multi-user equalizer to efficiently separate the users, as shown in Figure 2. Initially, the signal is processed through the phase shifters modeled by the vector \(\mathbf{w}_{a,r}\in\mathbb{C}^{R}\), where all elements of \(\mathbf{w}_{a,r}\) have equal magnitudes \(\left(\left|\mathbf{w}_{a,r}(n)\right|^{2}=1/N_{rx}\right)\). The overall analog matrix \(\mathbf{W}_{a}\in\mathbb{C}^{N_{rx}\times N_{RF}}\) that represents the connection between each subset of \(N_{rx}/N_{RF}\) antennas and the corresponding \(N_{RF}\) chain, has a block diagonal structure  
\[\mathbf{W}_{a}=\text{diag}\Big{[}\mathbf{w}_{a,1},\dots,\mathbf{w}_{a,r},\dots,\mathbf{w}_{a,N_{RF}}\Big{]},r=1,\dots,N_{RF}. \tag{7}\]  
As in the analog precoder, we also assume that the analog part of the equalizer is constant for all subcarriers.  
After that, the CP is removed on each RF chain and the signal is moved to the frequency domain by applying the DFT operator. Then, the samples of each subcarrier pass through the digital part of the equalizer modeled by matrix \(\mathbf{W}_{d,k}\in\mathbb{C}^{N_{RF}\times U}\). Therefore, the resulting signal at the end of the analog and digital processing equalizer can be written as  
\[\mathbf{\tilde{c}}_{k}=\mathbf{W}_{d,k}^{H}\mathbf{W}_{a}^{H}\mathbf{H}_{ \sigma_{k},k}\mathbf{c}_{k}+\mathbf{W}_{d,k}^{H}\mathbf{W}_{a}^{H}\mathbf{n}_ {k}, \tag{8}\]  
where \(\mathbf{H}_{\sigma_{k},k}=\left[\begin{array}{cccc}\mathbf{H}_{1,k}\mathbf{ f}_{1,k}&\cdots&\mathbf{H}_{U,k}\mathbf{f}_{U,k}\end{array}\right]\in\mathbb{C}^{N _{rx}\times U}\) represents the overall equivalent channel between the \(U\) users and the receiver, and \(\mathbf{c}_{k}\in\mathbb{C}^{U}\) denotes the frequency domain transmitted signal of all users at the \(k\)th subcarrier.  
Finally, the equalized signals are demapped and moved to the time domain by using the inverse DFT, obtaining the estimates \([\mathbf{\tilde{s}}_{u,l}]_{t=0}^{N_{c}-1}\) of the \(u\)th user transmitted \(N_{c}\)-length data block \(\{s_{u,l}\}_{t=0}^{N_{c}-1}\).
# Numerical Simulation of Thermo-Hydro-Mechanical Coupling of Model Test for Nuclear Waste Disposal
## 3 Coupling Model Parameters
### Fluid Parameters and Constitutive Equations  
The Van Genuchten (VG) model is used, and the related parameters are shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline
**Quantity** & **Value** \\ \hline Dry density (\(\rho_{d}\)) & 1700 kg/m\({}^{3}\) \\ Specific heat capacity of solid particles (\(Cs\)) & 879 J/(kg\(\cdot\)K) \\ Poisson ratio (\(\mu\)) & 0.3 \\ Elastic modulus (\(E\)) & 500 MPa \\ Density of solid particles (\(\rho_{s}\)) & 2700 kg/m\({}^{3}\) \\ Coefficient of thermal expansion (\(\beta_{ST}\)) & \(3\times 10^{-6}\) (K\({}^{-1}\)) \\ Hygroscopic expansion coefficient (\(\beta_{h}\)) & \(1.19\times 10^{-2}\) m\({}^{3}\)/kg \\ Permeability (\(k\)) & \(2\times 10^{-20}\) m\({}^{2}\) \\ Bulk strain modulus (\(K_{s}\)) & \(\frac{E}{3(1-2\mu)}\) \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of buffer material.  
Figure 1: The numerical simulation model. (**a**) Geometric model and the boundary; (**b**) model meshing.  
(1) The relative permeability of water bodies [46] is calculated as  
\[\mathrm{k_{Ir}}=\bigg{(}\frac{S-0.05}{0.95}\bigg{)}^{3}\bigg{(}\frac{n}{n_{0}} \bigg{)}^{7} \tag{8}\]  
(2) The dynamic viscosity coefficient of a water body [47] is calculated as  
\[\mu_{1}=1.984\times 10^{-6}\exp\bigg{(}\frac{1825.85}{T}\bigg{)} \tag{9}\]  
(3) The thermal expansion coefficient of a water body is calculated as  
\[\beta_{1}=1.7769\times 10^{-10}T^{3}-5.7556\times 10^{-8}T^{2}+1.1383\times 10^{- 5}T+3.0240\times 10^{-6} \tag{10}\]  
(4) The specific heat capacity of water bodies is calculated as  
\[C_{l}=6872.8945-23.09T+0.069T^{2}-7.35\times 10^{-5}T^{3} \tag{11}\]  
(5) The liquid-phase migration coefficient [48] is calculated as  
\[\omega=\frac{C}{\rho_{l}}\sqrt{\frac{M_{l}}{2\pi RT}} \tag{12}\]  
where \(n_{0}\) is the initial porosity, \(C\) is the adjustment coefficient and its value is \(2\times 10^{-5}\), and the value of \(R\) is 8.3144 J/(mol\(\cdot\)K).
## 1 Introduction
It takes into account wind farm flow field features, and includes a power production optimizer [27] based on the gradient method to help maximize wind farm power production. The power production optimizer is employed to bring up the overall wind farm power output by optimizing the yaw angle or the axial induction factor or even both. How the WFSim can be used to test various active wind farm control algorithms for maximizing the wind farm power production is discussed in [26,28]. Simulation studies are conducted for two wind farm layouts. In the first case, a virtual two-turbine wind farm is considered to investigate the impact of varying the distances between turbines on the power production and to analyze the outcome of using the WRC strategy on the maximization of power. A real-life wind farm configuration of an offshore wind farm is analyzed in the second scenario, which consists of 20 turbines with a spacing between them of roughly 800 m. In this situation, a steady-state optimized power production controller is used to maximize the power output by controlling the yaw angle or axial induction factor of wind farm turbines, or a combination of both. The main contribution of this paper is a preliminary investigation of the impact of WRC and AIC on the power production of a virtual wind farm model of two turbines and a wind farm model  
Figure 1: Wake of wind turbine. of 20 turbines, which simulate a real-life wind farm in the WFSim. Simulations carried out in this study using the WFSim provide dynamic responses in contrast to the work reported in [26; 28] that focuses on the steady state responses. The paper is organized as follows. Section 2 discusses wind farms modeled in WFSim, including details of the two wind farm layouts. In Section 3, wind farm control strategies, i.e., AIC and WRC, are described. In Section 4, the wind farm control strategies are applied to the wind farm layouts, with the simulation results presented. Section 5 draws conclusions and discusses future work.
## 1 Introduction
The presented solution can operate both as an SDR and as a highly integrated radio transceiver, which demands less node resources. Besides, it relies on a system-on-a-chip (SoC) that integrates an ARM Cortex-M3 processor, and a flash-based FPGA fabric, where high-speed processing tasks can be offloaded. The platform supports YetiOS [17], an operating system (OS) that speeds up the development of applications and tests. The power consumption of the platform was measured in the different modes of operation. The results obtained confirm that a state-of-the-art tradeoff between hardware flexibility and energy efficiency was achieved. The rest of the paper is organized as follows. Section 2 presents related works in experimental platforms for CR and edge computing. In Section 3, we introduce our platform, detailing the design decisions and its hardware architecture. Section 4 contains a full description of the resources and methodology used in the platform evaluation. The results obtained in the different tests are presented and discussed in Section 5. Conclusions are offered in Section 6.