esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Dataset Details  
In this section, the experimental validation of the AJODL-DSSEM model was tested using two open access datasets, namely the IHEPC [25] and ISO-NE datasets [26]. The IHEPC dataset encompasses 2,075,259 readings collected in a house located in Seeaux, Paris. The dataset collected power consumption for four years (from 16 December 2006 to 26 November 2010) in a home in France. The dataset holds nine attributes, such as data, time, global active power, global reactive power, voltage, global intensity, and sub metering one, two, and three. Next, the ISO-NE dataset collected hourly time-series data from 2012 to 2016, for a total of five years (43,915 samples), and was employed for model training. Similarly, one year (2017) of hourly data (8783 samples) was used for testing purposes. The dataset comprises a total of 14 features, where a feature called \"SYSLOAD\" was undertaken as the target label, and the dry bulb column represented the temperature in degrees Fahrenheit, among other data-time features.
## 3 Identification and Quantification of Volatile and Semi-Volatile Compounds in Sparkling Wines
### Sample Preparation
#### 3.1.6 QuEChERS  
Another method used in sample preparation is QuEChERS (Quick, Easy, Cheap. Effective, Rugged, and Safe). Originally developed for the detection of pesticide residues in fruits and vegetables, this technique has since been adapted to various complex matrices, including wine. Its popularity lies in its simplicity and efficiency, as well as in the minimal solvent consumption and reduced sample handling required [100]. According to the International Organisation of Vine and Wine (OIV-MA-AS323-08) [101], the QuEChERS protocol for pesticide residue analysis in wine involves extraction with acetonitrile, followed by a salting-out partition step using magnesium sulfate and sodium chloride, buffered with citrate salts. The extract is then cleaned up through dispersive solid-phase extraction (d-SPE) using an amino-propyl sorbent and additional magnesium sulfate to remove matrix interferences. To ensure the stability of the extract before instrumental analysis, a small amount of formic acid is added. The final extract can then be analyzed by GC-MS or LC-MS/MS [101].  
While there are currently no reports specifically addressing the use of QuEChERS for the extraction of pesticide residues in sparkling wines, the technique has demonstrated promising results in related matrices. For instance, [PERSON], [PERSON] et al. [100] successfully optimized and validated a QuEChERS-based method for the determination of pesticide residues in grapes, musts, and still wines. Given the similarities in matrix composition, QuEChERS could also be a suitable and effective alternative for sparkling wine analysis, particularly in quality control procedures where multi-residue detection is required [100].  
To support the selection of the appropriate sample preparation technique according to the characteristics of volatile compounds and analytical objective, a decision diagram is presented in Figure 3. This figure summarizes the methodologies described in Sections 3.1.1-3.1.6 and illustrates their suitability depending on compound classes, sensitivity, and matrix complexity. Visual guidance is useful for researchers who aim to optimize extraction efficiency
## 3 Results and Discussion
### Mechanical Properties  
In order to investigate the mechanical behavior of the TOCgel/ZnO composite film, tensile tests were performed on the both samples (TOCgel, composite and ZnO). As shown in Figure 9A, the tensile curve of pure ZnO is not shown due to the poor mechanical properties of ZnO powders and the high brittleness of the ZnO film.  
Indeed, Interfacial interaction between ZnO and TOCgel appears to be an important factor affecting the mechanical properties of the composite film. In our work, results showed that the incorporation of ZnO within the fibrous network of the TOCgel enhances the mechanical behavior of the composite film through the well-dispersed state and strongest interactions of ZnO onto cellulose fibers surfaces. Of course, the mechanical behavior of the composite remained lower comparatively to that of the pure  
Figure 9: Tensile stress-train behaviors of pure TOCgel and the TOCgel/ZnO composite films (**A**); and optical image of the composite film (**B**).  
TOCgel. However, the composite film maintains some flexibility (bending up to 120\({}^{\circ}\)) as shown in the optical image (Figure 9B). In conclusion, by making a coating of ZnO flower-like superstructures onto the flexible TOCgel matrix, flexibility can be introduced without loss of its photocatalytic properties.
## 3 Modeling, Simulation and Analysis  
### Modelling and Simulation of the Unit Cell  
As the basic elements of the proposed metasurface, a rectangular slot is adopted to couple with the travelling magnetic field energy in the cavity and radiates one polarized component of the objective waves. A horizontal slot along the \(x\)-axis radiates a y-polarized wave, accordingly, a vertical slot radiates the x-polarized wave. By assembling two kinds of units, the circularly polarized waves can be generated and steered flexibly. Thus, the modelling and simulation of the single unit is of significance and needs to be studied first.  
The radiation feature of a single slot unit can be studied by a microstrip transmission line mimicking a surface wave excitation. By analyzing the results of scatter parameters and radiation patterns, the original size of a slot unit can be decided. In Figure 2a, an electromagnetic simulation model of a slot unit is built up, in which a slot along the \(y\)-axis is patterned in the top layer of the transmission line. This model is composed of a grounded substrate with a size of (\(L_{s}=49.7\) mm \(*\)\(W_{s}=46.15\) mm \(*\)\(h=1.542\) mm) and a microstrip with a width of \(W_{m}=3.55\) mm. An air box is built up and two wave-ports are created on its outside surfaces. The size of the wave-port is (\(W_{p}=11W_{m}*\)\(h_{p}=6h\)) and the thickness of the ground is \(t=0.035\) mm. The model of a microstrip transmission line patterned in a slot unit performs a full-wave simulation in the ANSYS Electromagnetics Suite software. For the simulation, a radiation boundary is applied to the air box with a size of (\(L_{s}*\)\(W_{s}*\)\(8h\)) to mimic an infinitely large free space. As the input port, port 1 is excited by the source with a power of 1 W and port 2 is set as the output port. By using the full-wave simulation, the reflection coefficient \(|S11|\) and the transmission coefficient \(|S21|\)of this model can be obtained. The former (\(|S11|\)) denotes the ratio of reflection power to the input power of port 1, while the latter (\(|S21|\)) represents the ratio of output power of port 2 to the input power of port 1. As shown in Figure 2c, \(|S11|\)remains below -6 dB during the operation band and \(|S21|\) is below \(-1.2\) dB during the same bandwidth, demonstrating that most of the energy is converted into radiation. The 3D radiation pattern of the unit model and 2D radiation patterns at the planes of \(\varphi\) = 0\({}^{\circ}\) and \(\varphi\) = 90\({}^{\circ}\) are illustrated in Figure 2b,d, respectively, the maximum total gain of which is 2.6 dB.
# Cooperative Localization Using Distance Measurements for Mobile Nodes
###### Abstract  
This paper considers the two-dimensional (2D) anchorless localization problem for sensor networks in global positioning system (GPS)-denied environments. We present an efficient method, based on the multidimensional scaling (MDS) algorithm, in order to estimate the positions of the nodes in the network using measurements of the inter-node distances. The proposed method takes advantage of the mobility of the nodes to address the location ambiguity problem, i.e., rotation and flip ambiguity, which arises in the anchorless MDS algorithm. Knowledge of the displacement of the moving node is used to produce an analytical solution for the noise-free case. Subsequently, a least squares estimator is presented for the noisy scenario and the associated closed-form solution derived. The simulations show that the proposed algorithm accurately and efficiently estimates the locations of nodes, outperforming alternative methods.  
localization; sensor network; multidimensional scaling; position ambiguity +
Footnote †: journal: Sensors
# A Review of Smart Grid Anomaly Detection Approaches
## 3 Results and Analysis
### Devices for Detecting Anomalies  
Compact and affordable devices, such as the Raspberry Pi, have proven to be useful in identifying abnormalities in an SG by analyzing data collected from PMUs. These devices can detect two types of anomalies: (1) constraint anomalies, which refer to measured values that fall outside a predetermined acceptable range determined through heuristic methods, such as voltage or current; and (2) temporal anomalies, which refer to rapid oscillations or changes in measured values within a specific time frame [28].  
The use of a single board computer (SBC) in a decentralized, heterogeneous architecture to keep the computational load at acceptable levels for lower-power chipsets demonstrates that anomalies can be detected at real-time speeds [31].  
Big Data technologies and techniques are present in the detection of anomalies in electricity consumption based on Big Data analytical techniques and machine learning in industrial wireless sensors (IWSN) installed in the power grid. K-NN is employed in data mining and training [51].  
The anomalies can range from harmless impedance changes at some network termination to pronounced electrical faults, also considering the degradation of physical  
\begin{table}
\begin{tabular}{c c c} \hline \hline _Scenario_ & _Method/Technique_ & _Objective_ \\ \hline The SG is the target of diverse types of cyber-attacks. & Real-time anomaly detection framework, unsupervised machine learning, Boltzmann machine, Dynamic Bayesian Networks, PRISM, Markov chains, and decision tree. & Identify and detect anomalies \\ \hline \hline \end{tabular}
\end{table}
Table 10: Cyber-attack detection.  
components over time. Information about such anomalies in distribution networks can be collected using power line modems as network sensors to distinguish between localized faults, load impedance changes, and distributed faults [5].  
The frequency of data collection in smart meters for early anomaly detection makes these devices increasingly relevant as specialized elements in detection [42].
## 3 Controller Design
### State Observer Design  
For the sliding-mode controller as well as for the linear state-feedback controller, time derivatives of the measured electrical power, in Equation (12), are needed additionally. Unfortunately, they cannot be measured, so they are reconstructed by a Luenberger observer, shown in Figure 8. This observer was developed with the help of the approximate system in Equations (27) and (28).  
To determine the observer gains, the principle of duality is used, which implies that the same design methodology can be applied for state-feedback controller and observer synthesis. Hence, the observer gain is also designed by placing the closed-loop poles  
\[\det(s\cdot\mathbf{I}-(\mathbf{A}-\mathbf{h}\cdot\mathbf{c}))\overset{!}{=} \prod_{i=1}^{n=3}(s-s_{i,\text{d},\text{O}}) \tag{33}\]  
in the complex left half-plane with respect to Hurwitz stability. Note that the eigenvalues \(s_{i,\text{d},\text{O}}\) of the observed system should always be faster than the eigenvalues \(s_{i,\text{d}}\) of the controlled system [24].  
**Remark 5**.: _Alternative estimation approaches are Kalman filter-based derivative estimators, sliding-mode differentiators, and algebraic derivative estimation schemes [12]._
## 3 Results and Discussion
Pre-Si samples have a lower \(M_{s}\) value and a greater degree of substitution of Si than post-Si samples. However, the magnitude of the difference in \(M_{s}\) among these samples is highly dependent on the sintering temperature. At a sintering temperature of 1200 \({}^{\circ}\)C, the \(M_{s}\) of the post-Si sample is closer to that of the no-Si sample than that of the pre-Si sample, as the driving force of Si diffusion into the interior of the grains in the post-Si sample is not high enough to produce an appreciable effect. At a sintering temperature of 1250 \({}^{\circ}\)C, the \(M_{\rm s}\) of the post-Si sample is closer to that of the pre-Si sample than that of the no-Si sample. These data show that the Si atom has a significantly higher driving force for diffusion at 1225 \({}^{\circ}\)C than that at 1200 \({}^{\circ}\)C. The driving force for diffusion is high enough to achieve full diffusion of Si at 1250 \({}^{\circ}\)C. It can be assumed that there is a homogeneous distribution of Si in the pre-Si grains and the \(M_{\rm s}\) values of pre-Si and post-Si samples are approximately the same. Concerning the \(H_{\rm c}\) of the samples, it was found that the post-Si method is more effective than the pre-Si method, as higher \(H_{\rm c}\) values were obtained for post-Si samples. However, both post-Si and pre-Si samples exhibit greatly enhanced values compared to no-Si samples. As shown in Figure 2, the average grain size between the samples prepared by pre-Si and post-Si methods is not significantly different. As previously mentioned, it was observed that Si substitution into Fe sites extrudes Fe out of the matrix and forms excess Fe\({}_{2}\)O\({}_{3}\), which prohibits grain growth. The \(H_{\rm c}\) of the samples mostly depends on the grain size. Figure 6: M-H curves of no-Si, pre-Si, and post-Si SCLFCO samples sintered at **(a)** 1220 \({}^{\circ}\)C, **(b)** 1225 \({}^{\circ}\)C, and **(c)** 1250 \({}^{\circ}\)C.
# Analyzing the Performance of WBAN Links during Physical Activity Using Real Multi-Band Sensor Nodes
## 3 Results
### Sitting Scenario  
The same measurements as in the standing scenario are presented for the sitting scenario in the following figures. Figure 7 shows the average RSSI of all received packets for each of the transmit power levels of the different transceivers and sender node locations. The sub-1 GHz bands perform very similarly to the standing scenario, with the 868 MHz frequency being more affected by the increase in distance of the ankle location. However, for the 2.4 GHz band, in this scenario, the wrist location gives a better performance in terms of RSSI than the ankle one, oppositely to the standing case. This is because, in this scenario, the wrist is placed on top of a desk and there is no body tissue obstructing the signal path, only the table material. This supports the hypothesis given in the previous section: the 2.4 GHz band is much more negatively affected by body parts in the signal path than the other frequencies, at least in this indoor environment and these experiment conditions.  
The calculated PRR metric of the sitting scenario is presented in Figure 8. As can be inferred from the RSSI graph, both sub-1 GHz bands perform as expected and similarly to the standing scenario. On the other hand, the 2.4 GHz PRR curves show the intermittent losses referred to in the standing scenario, with the ankle location performing much worse in terms of PRR. This is in line with the aforementioned hypothesis that the 2.4 GHz band is more negatively affected by body tissue in the signal path. In the sitting scenario, the hip-ankle path is partially obstructed by the subject's legs and chair, whereas in the standing scenario the path between the nodes is clearer. Additionally, multipath fading is likely to have a strong impact on this scenario and sender location, given the experiment environment in which the desk is close to two walls (Figure 2d).  
Figure 7: Average RSSI in the sitting scenario.
# Blade Shape Optimization and Analysis of a Propeller for VTOL Based on an Inverse Method
## 2 Modeling
### Blade Element Momentum Theory
#### 2.2.2 2D Airfoil Aerodynamics Surrogate Model  
In this paper, the airfoils used for the sections of the propeller were obtained from the affine transformation of a Clark Y airfoil, as shown in Figure 4.  
The accuracy of 2D airfoil aerodynamics has a direct impact on the BEMT. The Reynolds number, Mach number and angle of attack are the three main parameters influencing the 2D aerodynamics of a certain airfoil. In addition, different from the general design and optimization of a propeller, a VTOL propeller usually encounters a large angle of attack flow near the propeller root during low advance ratio flight conditions. For this reason, an accurate large angle of attack aerodynamic model is required. Traditional methods to capture large angle of attack aerodynamics are mostly empirical such as the Viterna Corrigan method [29] and the Montgomeryie model [30]. Although they have the advantage of simplicity, they are most suitable for specific airfoils and flow conditions. A back-propagation artificial neural network (BP-ANN) trained using large amounts of samples obtained from 2D CFD simulations provides an approach to describe airfoil aerodynamics with a wide range of angle of attack.  
Figure 4: Affine transformation of Clark Y airfoil.  
Figure 3: Smoothing modification to ensure first-order continuity.  
The Latin hypercubic sampling (LHS) method was adopted to select the sample angle of attack (\(\alpha=-15\sim 75^{\circ}\)), Reynolds number (\(Re=5\times 10^{4}\sim 3\times 10^{6}\)), airfoil relative thickness (\(t_{rel}=0.03\sim 0.4\)) and Mach number (\(M_{a}=0.1\sim 0.7\)). The 2D CFD simulations using structural grids (Figure 5) and Spalart-Allmaras (S-A) turbulence model were then conducted to generate the database; i.e., \(C_{L}(\alpha,Ma,Re,t_{rel})\) and \(C_{D}(\alpha,Ma,Re,t_{rel})\). A BP-ANN with one input layer, one hidden layer and one output layer was trained using the Bayesian regulation method based on the airfoil aerodynamic database. The inputs of the BP-ANN were \(\alpha,Ma,Re,t_{rel}\) and the outputs were \(C_{L}\) and \(C_{D}\). The number of neurons of the hidden layer was selected as 13 after several adjustments. The regression of the trained BP-ANN surrogate model is shown in Figure 6. The mean squared error of the training result was \(2.87\times 10^{-4}\), which validated the precision of the BP-ANN surrogate model.
# FPGA Implementation of a Higher SFDR Upper DDFS Based on Non-Uniform Piecewise Linear Approximation
## 4 Experimental Result
### Performance Evaluation of Single-Core DDFS
Compared to [12; 25], our proposed DDFS has significant advantages in SFDR, clock frequency, and resource consumption. It is foreseeable that the SFDR will be further improved if we further reduce the MAE. Figure 8: The relationship between SFDR and the number of segments \(s\) of DDFS based on different PWL approximation method. The red curve represents the SFDR measured by the proposed DDFS, while the blue curve is the upper bound of the SFDR inferred in [21; 22]. \begin{table}
\begin{tabular}{c c c} \hline
**MAE** & **Segments** & **SFDR (dB)** \\ \hline
0.01 & 4 & 44.94 \\
0.001 & 10 & 69.64 \\
0.0001 & 31 & 91.41 \\
0.00005 & 44 & 96.58 \\
0.00002 & 70 & 105.47 \\
0.00001 & 100 & 114.04 \\ \hline \end{tabular}
\end{table} 
Table 3: Averaged SFDR of DDFS under different MAE.
## 3 Material and Methods
### HMP-Coffee Model
#### 3.2.1 Contextual Knowledge Phase
Flowchart on the left: shows the general process steps: “Remove stop words” and “Frequency Words” to identify a list of concepts by the query string. 2. Flowchart on the right: shows the result for the HMP-Coffee model. Figure 5: Venn diagram of the identified concepts. The colors blue, grey, and green represent the regions by query strings Small-scale Coffee Production (SCP), Economic Coffee Profitability (ECP), and Small-scale Coffee Profitability (SCPR) region 25 words. The 24 words located in the overlap regions are selected as HMP-Coffee brainstorming (words in bold). **In the fine granularity step**, we used the concepts obtained from brainstorming to create an HMP-Coffee conceptual hierarchical model following two activities: _Relationship Analysis_ to identify relationships among concepts and _Model Structuration_ to organize the information in a hierarchical structure. Relationship Analysis: In order to define the levels of relationship and generality between the brainstorming concepts and profitability, we applied the Repertory Grid technique. This technique places a concepts group in the rows and columns, and uses experts' opinions to assign the score to the cell representing the relationship/generality [34]. For building up HMP-Coffee, we created two grids as follows:  
The first grid determines the relationship between profitability (i.e., objective variable--ov) and each brainstorming concept. The grid's rows contain the 24 concepts, and the columns include three variables, namely Production Cost, Production Volume, and Market (renamed as \"global variables--gv1, gv2, gv3\"). We defined these global variables by adopting the formal definition of profitability, proposed by the ICO and the Colombian National Federation of Coffee Growers (FNC) [18; 35], which calculates profitability as the difference between incomes (associated with Production Volume and Market) and the investment (associated with the Production Cost) [18]. Each cell of Grid 1 on Table 3 contains the expert panel's average score to the corresponding relationship. Each expert provided scores on a 1 to 4 scale, where (1) No direct relation, (2) Low relation, (3) Medium relation, and (4) High relation. In Grid 1, the green cells highlight the most
## 3 An Overview of Existing Works
### Correlation between First-Hop Channels and Correlation between Second-Hop Channels  
Recently, [PERSON] et al. has studied the joint impact of the correlations between the first-hop channels and between the second-hop channels on the performance of a multiple AnF relay cooperative system with the BRS scheme [21]. The system model is schematically illustrated in Figure 2c, in which the source-destination channel is assumed to be unavailable. The assumption on the channel correlations is shown in Table 2, Model C. More specifically, the authors assume that \(\left\{\rho_{SR_{k}}^{SR_{l}}\right\}_{\begin{subarray}{c}l\lambda=1\\ j\
eq k\end{subarray}}^{K}=\rho_{SR}\) and \(\left\{\rho_{R_{k}D}^{R_{i}D}\right\}_{\begin{subarray}{c}l\lambda=1\\ j\
eq k\end{subarray}}^{K}=\rho_{RD}\). By exploiting the axioms of probability and the principle of inclusion and exclusion for probability, the authors derive a lower bound on the system outage probability under Rayleigh fading. In order to gain further insights on the effect of the considered correlation on the system DO, the authors provide a closed-form expression as well as the corresponding asymptotic approximation of the outage probability of a two-relay system. They point out that even though the system performance is considerably degraded in the high SNR region when the correlation coefficients are sufficiently large, yet less than one, the system still achieves a DO equal to the number of relays. We should keep in mind that [21] considers the identical correlation coefficient case, and the cases with unequal channel correlation coefficients still remain unsolved.