esa-sceva/satcom-chunk-collection huggingface dataset:
# An Approach to Aid Decision-Making by Solving Complex Optimization Problems Using SQL Queries
## 3 Experimental Design and Results
### First Experiment: Performance of Using SQL Statements
#### 3.1.1 Optimization of the 3 CG for Planar Graphs Using SQL  
The set of random planar graphs was generated using the C++ class library LEDA ([[http://www.algorithmic-solutions.com/index.php](http://www.algorithmic-solutions.com/index.php)]([http://www.algorithmic-solutions.com/index.php](http://www.algorithmic-solutions.com/index.php)), accessed on 10 April 2022). In particular, each instance was generated using the function random_planar_graph(G,N,M). This function first generates a maximal planar graph with \(N\) nodes, \(n\geq 3\), and \(3n-6\) edges. For \(N=1\), a maximal planar graph is formed by a single isolated node. For \(N=2\), the graph consists of two nodes and one edge; for \(N=3\), the graph consists of three nodes and three edges. For \(N>3\), a random maximal planar map with \(N-1\) nodes is constructed first, and then, an additional node is put into a random face. The function first generates a maximal planar graph and then deletes all but \(M\) edges to generate a graph with \(M\) edges.  
Figure 3: BNF syntax of a SQL query that solves coloring problems in graphs.  
The random instance generators were implemented using C++ and compiled with g++. We used a desktop computer with an Intel(R) Core(TM) 2 processor, 3 Gb RAM, the Ubuntu 15.04 operating system, and MySQL 5.5 as the DBMS. Table 2 also shows the results from finding a three-coloring for the random planar graphs. The results are shown in Columns 4 and 5. Column 4 shows the time spent by the DBMS to find a solution if it exists. Column 5 shows if a three-coloring was possible or not for the instances considered. According to the results, only 37 of the 48 instances were three-colorable. The DBMS spent a time of up to 1175.25 s to determine non-three-colorability. On the other side, the DBMS only required at most 41.52 s to determine that there was no solution for the remaining 11 instances.
## 3 Results  
### Data-Model Comparisons and Model Adjustment  
#### 3.1.1 Environmental Conditions  
Simulated daily mean soil temperature data at 10 cm depth from January 2007 to December 2012 were similar to measurement data (Figure 1a, \(y=0.8x\) (measured) + 2.1, \(R^{2}=0.90\)), although they do not quite show the same degree of variation and have a 5-6 \({}^{\circ}\)C lower amplitude range, particularly in earlier years. Soil temperatures simulated at 30 cm depth were in closer agreement with measurements (Figure 1b, \(y=0.9x-0.6\), \(R^{2}=0.97\)).  
Figure 1: Daily mean measured and simulated soil temperature for years 2007–2013 at the Straits Inclosure, Alice Holt Forest. Measured data are from a fixed automatic probe at the Tower Site. Scheme (**a**) temperatures at 10 cm soil depth, (**b**) temperatures at 30 cm soil depth, (**c**) residuals (measured–simulated with PnET) at 10 cm and 30 cm, (**d**) temperatures at 10 cm recorded manually and from fixed probe during 2013–2014 soil gas sampling. Error bars on manual measurements are 1 SD from 24 measurements made each day.  
The differences between temperatures simulated with the PnET and PSIM modules were small (<1 \({}^{\circ}\)C at 10 cm and <0.5 \({}^{\circ}\)C at 30 cm) with near equivalent \(R^{2}\) values. Residual values based on PnET data (Figure 1c) illustrated that the largest differences between measurements and simulations were at 10 cm, mainly in the winter months, when simulated soil temperatures were mostly 1-5 \({}^{\circ}\)C higher than measured. Still, there was a good match between both (linear regression: \(y=1.1x-1.1\), \(R^{2}=0.90\)). At 30 cm depth, simulated temperatures were mostly 1-2 \({}^{\circ}\)C lower than measured, particularly in the winter. It should be noted that there was also good agreement between soil temperature recorded manually at 10 cm depth near the soil chambers during flux measurements and the mean daily data measured nearby with a fixed probe, strengthening the confidence in measured data (Figure 1d).  
Simulated daily mean soil moisture data at 10 cm depth for January 1999 to December 2008 showed a general agreement with available measurements from 1999-2003 (Figure 2), including during the dry summer of 2003 (\(y=0.5x+23.7\), \(R^{2}=0.59\)). However, simulated values were only 2-5% higher in summer and 2-3% lower in winter. Data simulated with PSIM and PnET were similar except during summer 2003 when PSIM simulated lower soil moisture values, closer to measurements. Spikes of high simulated soil moisture (60-65%) from both modules indicate occasions when heavy rainfall has caused surface water to accumulate in the model, although this is not observed at the site, probably because of a slight slope or due to preferential flows that were not visible in the soil profiles used for initialisation.
## 3 Results
### Evaluation of the Reusability Capacity of MIPs  
The reusability capacity of a sorbent is one of the most critical properties that must be tested before a new material can be applied in microextraction [48]. For this reason, the reusability of MIP 1 and MIP 3 was analysed in order to check their potential for future application in SPE.  
First, 50 mg of MIP 2 was loaded with 5 mL of standard solution of BPA (3.12 mg/L) or PG (5.18 mg/L). After the analyte was adsorbed on the MIPs, the concentration of the remanent analyte  
Figure 6: Kinetic study of the retention of BPA (**a**) and PG (**b**) with MIPs and NIPs. Error bars represent \(\pm\)1 standard deviation. Experiments were performed in triplicate.  
in the supernatant was calculated and the amount of the analyte retained in the MIPs was estimated. Knowing the amount adsorbed into the MIP, the percentage of the liberation of the analyte was calculated using four different proportions of eluents (See Figure 7).  
When using the batch method, four possible eluents were applied: acetonitrile, methanol, methanol/acetonitrile (65:35 \(v/v\)), and ethanol/methanol (30:70 \(v/v\)). It was found that the best elution process employed the methanol/acetonitrile mixture, with over 93% elution efficiency (see Figure 7).  
After determining the most suitable mixture of the solvents for the elution of analytes adsorbed in MIPs, the analysis of the reuse of the MIP was continued (See Figure 8). A series of tests of adsorption and desorption of PG and BPA onto MIP 2 was performed in order to evaluate how many consecutive experiments can be carried out using one MIP.
# An Intelligent Weighted Object Detector for Feature Extraction to Enrich Global Image Information
## 3 System Model and Definitions
### Non-Local Neural Networks  
In the convolution operation, the perceptual field size of the convolution is the convolution kernel size, which is generally selected as 3 \(\times\) 3, 5 \(\times\) 5, considering only the local area, making it a local operation. The fully connected neural network is a non-local global operation, but it introduces many parameters, which brings difficulties for optimization.  
Figure 1: Ordinary convolution (1) and GhostModule (2).  
In the non-local neural networks proposed by [PERSON] et al., the output of the designed non-local operation is the same size as the original image, and the calculation Equation (3) is as follows [30].  
\[y_{i}=\frac{1}{C(x)}{\sum_{\forall j}f(x_{i},x_{j})g(x_{j})}. \tag{3}\]  
\(x\) is the input feature, \(y\) is the output feature, and both are of the same dimension. \(f(x_{i},x_{j})\) calculates the correlation between \(x_{i}\) and \(x_{j}\). When calculating the position correlation, the smaller \(f(x_{i},x_{j})\) is, the more distant the two positions are, i.e., \(x_{j}\) has a little influence on the position relationship of \(x_{i}\). \(g(x_{j})\) is used to calculate the feature value of \(x_{i}\) in \(x_{j}\), and \(C(x)\) is the normalization function to ensure that the overall information is unchanged before and after the transformation.  
To simplifying the problem, consider \(g\) as the linear case, that is, \(g(x_{j})=W_{g}x_{j}\), where \(W_{g}\) takes the \(1\times 1\) convolution of the spatial domain or the \(1\times 1\times 1\) convolution of the spatio-temporal domain. \(f\) can take a variety of different forms, including Gaussian, embedded Gaussian, etc., to calculate the correlation between the two points and finally, the overall Equation (4) of the nonlocal block is as follows [30]:  
\[z_{i}=W_{z}y_{i}+x_{i}. \tag{4}\]  
where \(z_{i}\) is the final output with the same dimension as \(x_{i}\). The nonlocal block can be implemented as a plug-and-play module for enhancing the extraction of global feature information.
## 3 Materials and Methods
### GC\(\times\)GC-TOF-MS Analysis of Volatile Arona Compounds  
For GC\(\times\)GC-TOF-MS analysis, a Gerstel MultiPurpose Sampler autosampler (Gerstel GmbH & Co. KG Mulheim an der Ruhr, Germany) with an agitator and SPME fiber was used to extract the volatiles from the EVOO sample vial headspace. The GC\(\times\)GC system consisted of an Agilent 7890 A (Agilent Technologies, Santa Clara, CA, USA) that was equipped with a Pegasus IV time-of-flight mass spectrometer (Leco Corporation, St. Joseph, MI, USA). A VF-Wax column (100% polyethylene glycol) 30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m (Agilent J&W Scientific Inc., Folsom, CA, USA) was used as a first-dimension (1D) column, and a RTX-200 MS-column 1.50 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m (Restek, Bellefonte, PA, USA) was used as a second-dimension (2D) column. The GC system was equipped with a secondary column oven and a non-moving quadjet dual-stage thermal modulator. The injector/transfer line was maintained at 250 \({}^{\circ}\)C. The oven temperature program conditions were as follows: initial temperature of 40 \({}^{\circ}\)C for 4 min., programmed at 6 \({}^{\circ}\)C/min. up to 250 \({}^{\circ}\)C, where it remained for 5 min. The secondary oven was kept 5 \({}^{\circ}\)C above the primary oven throughout the chromatographic run. The modulator was offset by +15 \({}^{\circ}\)C in relation to the secondary oven; the modulation time was 7 s and 1.4 s of hot pulse duration. Such a modulation optimization was adapted for the analysis of minor volatiles. Helium (99.9995% purity) was used as carrier gas at a constant flow of 1.2 mL/min. The MS parameters included electron ionization at 70 eV with the ion source temperature at 230 \({}^{\circ}\)C, detector voltage of 1317 V, mass range of\(m\)/z 35-450, and acquisition rate of 200 spectra/s. Representative contour plots (2D-chromatograms) are reported in Figure S2.  
For GC\(\times\)GC-TOF-MS data, LECO ChromaTOF Version 4.22 software was used for all acquisition control and data processing. Automated peak detection and spectral deconvolution with a baseline offset of 0.8 and signal-to-noise of 100 were used during data treatment. With these settings, it was possible to detect 1479 putative compounds. The identification of VOO volatile aroma compounds was performed by comparing the retention times and mass spectra with those of the pure standards, and with mass spectra from NIST 2.0, Wiley 8, and FFNSC 2 (Chromaleont, Messina, Italy) mass spectral libraries, with a minimum library similarity match factor of 750. Additional identification of volatiles was achieved by comparing the experimental linear temperature retention indices with those that were reported in the literature for 1D-GC. In total, 179 volatile aroma compounds were identified. To account for possible sample-to-sample variation, all of the intensities were normalized to the signal of menthol (internal standard) and corrected for the mass added. The analyses were performed in triplicates, and the average values were used in further data elaboration.
## 2 Materials and Methods
### Artificial Neural Networks  
ANN is a computer learning system inspired by the functioning of the human nervous system. ANNs are made up of artificial neurons (simple information-processing units) displayed in layers. A minimum of two layers of artificial neurons are necessary for the artificial neural network system--input and output layers. The two types of neural networks used for modeling the relationships under study are the multilayer perceptron (MLP) and radial basis function (RBF) neural networks. In an MLP network, the signal is processed from the input layer through one or two hidden layers to the output layer. Connection weights in MLP are adjusted during the training process by a backpropagation algorithm. An RBF network is a feedforward neural network with only one hidden layer. The activation function of neurons in a hidden layer is usually a Gaussian function [56]. The linear neuron is responsible for the calculation of the RBF network's output signal. Radial basis function neural networks have generally better forecasting abilities and faster learning speeds in comparison with other neural networks [57].  
A set of 154 data was acquired from measurements. The dataset was randomly distributed between training and validation sets in the ratio of 80:20. The data were normalized in a range of \(<\)0; 1\(>\) before the training process of the neural models. In this study, both the multilayer perceptron and the radial basis function neural models were developed in the Statistica v. 13 software. The input vector comprised a combination of apparent soil electrical conductivity and magnetic susceptibility measured at depths of 0.5 m and 1 m. As an output model parameter, the compaction of certain soil layers or shear stress was used. The form of the input vector determined the number of nodes in the input layer (four). For each model, 2000 different neural structures were trained. Each neural model was characterized by the type of neural network (MLP or RBF), the number of neurons in the hidden layer, and the initial connection weight matrix. The number of neurons in the hidden layer was changed in a range of 10-40. In the case of the multilayer perceptron network, various transfer functions for the neurons in the hidden and output layers were used (sigmoidal, hyperbolic tangent, and exponential).
# On-the-Fly Olive Tree Counting Using a UAS and Cloud Services
## 3 Materials and Methods
### Performance Metrics  
In order to validate the results, we manually annotated a sequence of 10 images. We compared the trees detected by the application with the reference ground-truth to obtain the number of true positives (TP or correct detections), the number of false positives (FP or false alarms), and the number of false negatives (FN or detection failures). Notice that the number of true negatives (TN) is irrelevant in this application, since there is nothing to count other than trees; thus, the detection of no-tree objects is not within the scope of this study.  
In object detection, this matching between the ground-truth and the detection is not binary in the sense that different ground-truth-to-detection associations can occur [57]: zero-to-one, one-to-zero, one-to-one, many-to-one, one-to-many, and many-to-many (see Figure 9). As our goal is to count trees, if \(M\) trees in the ground-truth are associated with one detected tree (as in Figure 9d), we consider TP = 1 and FN = \(M-1\). In the case that one tree in the ground-truth matches \(N\) detected trees, we consider TP = 1 and FP = \(N-1\). For the general case \(M\)-to-\(N\), where \(M\) trees in the ground-truth match \(N\) detected trees, we consider TP = min(\(M,N\)), FN = \(M-N\) if \(M\) is greater than \(N\) and FP = \(N-M\) if \(N\) is greater than \(M\).  
Figure 8: The architecture of the drone-in-the-cloud solution is able to deliver the relevant information to the end-users on their own devices during the UAV flight.  
Precision, recall, and \(F_{1}\) score are used as classification metrics. Precision, or positive predicted value, indicates the ratio of detected trees that actually shows a correct match with the reference ground-truth (1). Recall, also called true positive rate or sensitivity, measures the ratio of real trees that were correctly detected (2). Thus, high precision indicates a small number of false positives, while high recall indicates a small number of false negatives. The final performance value, the \(F_{1}\) score, is the harmonic mean of the two measures (3).  
\[Precision=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{1}\]  
\[Recall=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{2}\]  
\[F_{1}\ score=\frac{2}{\frac{1}{Precision}+\frac{1}{Recall}} \tag{3}\]
# Scheduling of Industrial Control Traffic for Dynamic RAN Slicing with Distributed Massive MIMO +
## 6 Numerical Experiments
### Experiment Set 1: Periodic Traffic  
In the first set of experiments, we included only periodic traffic, with all background traffic demand rates, both uplink and downlink, set to 0. This reflects cases where there is only control traffic and no other data to be transmitted by the nodes, which is typical for industrial communications if we assume that we have a separate network slice for real-time traffic. For this set of experiments, we tested the effect of the number of nodes (i.e., problem instance size) and how demanding the nodes' traffic profiles are.  
We defined two traffic profiles, one with short periods and one with long periods. We considered industrial processes with a control loop time of between 1 ms and 20 ms, as, for example, in demanding factory automation processes. We therefore set the time slot to 1 ms. Depending on the environment and channel characteristics, the coherence interval may be longer than this, even significantly so; however, for simplicity, we consider the coherence interval and scheduling time slot to be equivalent, i.e., a node must be allocated a pilot in a given slot if it is to transmit or receive in that slot. We exclude processes with a period of 1 ms from the scheduling problem since, if the period is equal to the time slot duration, the node would require a pilot in every time slot, which can be satisfied without any waste of resources with a simple static assignment. We therefore take short periods to be between 2 and 10 ms, and long periods to be between 11 and 20 ms.  
For each number of nodes, we conducted three experiments: one in which all nodes had short periods (1A), one in which all nodes had long periods (1B), and one in which the traffic was mixed, with half the nodes having long periods and half having short periods (1C). In each case, for each node, we generated its period uniformly randomly within the range for its traffic profile. In total, we generated 10 problem instances for each case and took the average and 95% confidence interval across the instances.
# Adaptive Thermal-Aware Routing Protocol for Wireless Body Area Network
## 4 Performance Evaluation
### Simulation Results
#### 4.1.3 Analysis of Maximum Temperature Rise in Node  
Maximum temperature rise is the temperature of the certain node inside the whole network. Figure 18 provides the comparison between ATAR, SHR [26], TARA [25] and MRRP [47]. The simulation results indicate that at initial phase of simulation, all nodes have the same temperature i.e., 0.05 \({}^{\circ}\)C/35 \({}^{\circ}\)C. We ran the simulation for the time period of 1000-6000 s. Initially, the ATAR started with the reading of 0.037 \({}^{\circ}\)C at 1000 second and later gave a maximum reading of 0.039 \({}^{\circ}\)C at 6000 s. The MRRP initially started from 0.046 \({}^{\circ}\)C and at 6000 s it gave a maximum reading of 0.08 \({}^{\circ}\)C. TARA initially started from 0.0602 \({}^{\circ}\)C and at 6000 s it gave a maximum reading of 0.12 \({}^{\circ}\)C. Similarly, SHR initially started from 0.102 \({}^{\circ}\)C and at 6000 s it gave a maximum reading of 0.2 \({}^{\circ}\)C. The high-temperature reading coming from MRRP, TARA and SHR protocol indicates that is also not suitable for implanted nodes. Finally, MRRP, TARA, SHR gives temperature peaks while transmitting data of sensor nodes thus ignoring the temperature factor in its working; whereas the ATAR takes temperature factor in its routing in the implanted nodes.  
The low-temperature peaks for ATAR in the simulation is due to the reason that ATAR always selects the forwarder with minimum available temperature. In case of temperature rise of the forwarder, ATAR selects another forwarder within the next ring level to forward obtained sensor readings.
## 2 Materials and Methods
### Evaluation Indexes
#### 2.3.8 Statistical Analysis of Data  
The membership function comprehensive evaluation method, as a mathematical method of fuzzy comprehensive evaluation, can quantify some factors with unclear boundaries, but they are difficult to quantify. After this, comprehensive evaluation was carried out. In order to obtain a more accurate optimization effect, the membership function comprehensive evaluation method was selected [19]. Then, the principal component analysis (PCA) results and membership function method were combined to comprehensively assess the mulberry drying quality in single-factor tests. The evaluation index system includes the chromatic aberration value, soluble solid content, chewiness, and hardness, that is, the factor set U = (chromatic aberration (\(\mu\)1), soluble solid content (\(\mu\)2), chewiness (\(\mu\)3), elasticity (\(\mu\)4), and Vc (\(\mu\)5)). According to the positive and negative influences on mulberry quality, the index data can be divided into positive indexes (soluble solid content (\(\mu\)2), chewiness (\(\mu\)3), and Vc (\(\mu\)5)) and negative indexes (chromatic aberration (\(\mu\)1) and hardness (\(\mu\)4)). The fuzzy transfer matrix [20] was then built.  
The standardization formula for indexes is  
\[\mu\left(x_{j}\right)=\frac{\left|x_{j}-x_{min}\right|}{x_{max}-x_{min}}\times 1 00\%\ \ \ j=1,2,\,n \tag{3}\]  
where \(x_{j}\) is the jth comprehensive index, \(u(x_{j})\) is the membership function value of the jth comprehensive index, and \(x_{max}\) and \(x_{min}\) are the maximum and minimum of the jth comprehensive index, respectively.  
The Excel 2019 statistical data were chosen. SPSS26.0 was used for variance and correlation analysis. The test design and results analysis were carried out in Design-Expert12.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Score** & **Quality Description and Scoring Criteria** \\ \hline Appearance & \textgreater{}2.5–5 Uniform color, purplish black, and the overall appearance is compact and complete (5 points) & 0–2.5 \\ Sweetness & \textgreater{}2.5–5 & The characteristic sweetness of mulberries is intense \\ (5 points) & 0–2.5 & It essentially has the special sweet taste of mulberry \\ Fragrance & \textgreater{}2.5–5 & The mulberries are fragrant and odorless \\ (5 points) & 0–2.5 & The mulberries have a plain, unpleasant smell \\ Taste & \textgreater{}2.5–5 & Soft, tender, and sweet \\ (5 points) & 0–2.5 & Rough taste \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sensory evaluation standards of the dried mulberry.
## 2 Background and Related Work
### Deep-Learning Background
#### 2.1.3 Cnn  
The CNN was proposed to automatically learn spatial hierarchies of features in tasks, such as image recognition and speech recognition [19], by exploiting the spatial relationships among the pixels in an image. In [20], a CNN is composed of convolutional layers, pooling layers, and fully connected layers, and is trained with the adaptive moment estimation (Adam) optimizer on mini batches [21]. The convolutional layers extract the useful features, while the pooling layers reduce the dimensions of the feature maps. The rectified linear unit (ReLU) is applied as a nonlinear activation function [22], and a dropout layer is used as a regularization method in which the output of each hidden neuron is set to zero with a given probability [23].