esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analytical Protocol Parameters

Table 2: Linear dynamic range (LDR), linear equation, correlation coefficient (r), limit of detection (LOD), and limit of quantification (LOQ) of each compound investigated in this study. 300 ng mL\({}^{-1}\), concentrations: in the first case, the recoveries vary between 93.4% (BPA) and 101.1% (DiBP), whereas in the second case they vary between 95.7% (DEP) and 104.5% (DEHP). The intra-day errors are between 3.6% and 7.4%, whereas the inter-day errors are less than 9.3%. Figure 6 shows the gas chromatogram of mixing PAE/BPA standard solution at the concentration of 50 ng mL\({}^{-1}\) of each compound, whereas Figure 7 shows the gas chromatograms of (a) drinking water sampled from plastic bottles and (b)the same sample spiked with the mixed PAE/BPA solution (50 ng mL\({}^{-1}\) of each compound). As can be seen, the chromatograms  
For a complete analytical characterization, a comparison was carried out between our main results and those reported in the literature; in particular, recoveries, LODs and LOQs were compared between different studies performed in the last decade for the simultaneous analysis of PAEs and BPA in food/beverage matrices [71; 72; 73; 74; 75; 76; 77; 78; 51] (Table 4). As can be seen, the main basic PAEs analyzed in most papers are DEP, DBP, and DEHP together with BPA, whereas only a few papers show a more complete PAE speciation. According to the analytical parameters, our recoveries are acceptable compared to the other studies; in addition, the LODs and LOQs are adequate for determining such compounds in the water matrix, although [PERSON] et al. [52] achieved very low LODs and LOQs. It should be noted that very good LODs and LOQs were achieved by [PERSON] et al. [75]; PAEs and bisphenols (A and S) were simultaneously analyzed at very low levels (0.8-15.5 and 2.3-46.9 ng L\({}^{-1}\), with good recoveries ranging between 95% and 109%). These parameters are better than those obtained by the authors; it should be noted however that they were reached using a different methodology (SPE) and, essentially, by ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS), a very expensive instrumentation. Furthermore, the matrix is different from that investigated in this study. A similar consideration could be drawn by other authors. Finally, it should be noted that the paper by [PERSON] et al. [77] shows interesting LODs and LOQs, but that the paper is focused only on the determination of five PAEs (with no BPA).
# A Complex Network Important Node Identification Based on the KPDN Method
## 5 Results and Analysis
### Analysis of Real Networks
#### 5.1.2 Size of Real Network from \(10^{4}\) to \(10^{5}\)
As shown in Table S3 of Supplementary File S3, across the five datasets of scale \(10^{4}\sim 10^{5}\), compared with other classic algorithms, the KPDN method can decompose the original network more rapidly by removing fewer nodes.
# A Sentiment-Aware Contextual Model for Real-Time Disaster Prediction Using Twitter Data
## 3 Material and Methods
### A Fusion of Loss Functions  
In this subsection, we explore the options of loss functions. We considered two individual loss functions including the binary cross-entropy (BCE) loss and the Focal Loss. In addition, we employed a fusion strategy as suggested in [51] to combine the two losses, which resulted in performance improvement.  
Since the disaster Tweet detection task is a typical binary classification problem, it is intuitive to utilize the BCE loss as shown in Equation (3) below.  
\[L_{BCE}=-\frac{1}{m}\sum_{i=1}^{m}(y_{(i)}log(\hat{y}_{(i)})+(1-y_{(i)})log(1- \hat{y}_{(i)})), \tag{3}\]  
in which \(m\) is the training set size, and \(y_{(i)}\) and \(\hat{y}_{(i)}\) denote the ground truth and the predicted class for the \(i\)th sample in the dataset, respectively.  
Meanwhile, considering the imbalanced sample distribution, this study also employs Focal Loss, defined in Equation (4).  
\[L_{FL}=-\frac{1}{m}\sum_{i=1}^{m}(y_{(i)}\alpha(1-\hat{y}_{(i)})^{\gamma}+(1-y_ {(i)})(1-\alpha)\hat{y}_{(i)}^{\gamma}log(1-\hat{y}_{i})), \tag{4}\]  
where \(\gamma\) is a coefficient that controls the curve shape of the focal loss function. Using Focal Loss with \(\gamma>1\) reduces the loss for well-classified examples (i.e., with a prediction probability larger than 0.5) and increases loss for hard-to-classify examples (i.e., with a prediction probability less than 0.5). Therefore, it turns the model's attention towards the rare class in case of class imbalance. On the other hand, a lower \(\alpha\) value means that we tend to give a small weight to the dominating or common class and high weight to the rare class. By fusing the focal loss and the BCE loss in a certain ratio, we obtain Equation (5), in which \(\beta_{1}\) and \(\beta_{2}\) specify the fusion weights.  
\[L_{mix}=\beta_{1}L_{BCE}+\beta_{2}L_{FL} \tag{5}\]
## 5 Impact of the Collimator Characteristics  
### I-V Curves  
In this work, three different collimator designs were studied. The first series of experiments deals with the collimator material: graphite vs molybdenum. For the two other series the collimator is made of graphite. The second series evaluates the impact of the FC collection solid angle upon the ion current density. The last experiments investigate the influence of having the collimator screened from the ion beam. For all FC configurations the geometry and properties of the collector are unchanged. Configuration G.10.E, see Figure 4, is taken here as the reference. A current density profile is measured for all FC configurations. The thruster was operated at 200 V and 250 V with a fixed discharge current \(I_{d}=0.66\) A. The discharge power is 132 W and 165 W, respectively. The heat load on the thruster channel walls prevents the operation of the ISCT200 thruster above 250 V at 0.66 A.  
Prior to FC measurements, current-voltage characteristic curves must be acquired to verify the proper design and functioning of the probe. Figure 8 shows I-V traces recorded at various angles in the plume of the ISCT200 Hall thruster firing at 250 V and 0.765 mg/s anode mass flow rate, see Figure 4. Measurements were carried out 27.4 cm downstream the thruster exit plane. A broad negative potential range must be swept to get an accurate picture of the FC behavior. Faraday cups provide reliable information upon the ion current as electrons are screened and sheath expansion effect and SEE are cancelled. Consequently, the ion branch, the part of the I-V curve where the probe is negatively biased, should be horizontal. This constant ion current is termed the saturation current [10]. The potential to apply to the collector is determined from the ion current saturation extracted from I-V curves. The latter is used to measure the current density angular distribution profile of the EP plume, see Sections 5.3 and 5.4. In Figure 8, the collected ion current does not depend upon the cup voltage at large angles. The slow increase in ion current with the voltage at small angles finds its origin in an increase of secondary electron emission.
## 3 Experiments and Validations  
### Experiments of Physical-Layer Communication  
MFSK modulation is adopted in the physical layer. Before modulation, the information bits are processed with CRC validation, scrambling, convolutional coding, and interleaving. The data frame structure is shown in Figure 9, where the hyperbolic frequency modulation (HFM) signal with 6 KHz bandwidth and length of 25.6 ms is for synchronization and Doppler compensation. The HFM signal is followed with silence time, forward training symbols, data symbols, and time-reversal training symbols. The parameter list of MFSK modulation is presented in Table 1.  
The experiments are conducted in an indoor pool. We assemble two nodes, and the distance between the transducer and hydrophone of the two nodes is about 2 m. Node 1 sends the sensor data, and its ARM process transfers the data to the DSP process. After the MFSK modulation, the DSP generates the waveform to be transmitted, as shown in Figure 10. The whole waveform has 110080  
Figure 8: The processing flow of the DSP process.  
Figure 9: The frame structure (contains two data frames).
## 2 Materials and Methods
### Failure Probability Bounds  
Using system reliability analysis, the typical approach is to define the failure probability bounds. This is the way in which the SYSREL program estimates the reliability of the structure. For series system failure, probability bounds are defined as follows [42]:  
* First-order bound: \[\max P(F_{i})\leq P_{f}\leq\sum\limits_{i=1}^{n}P(F_{i})\] (25)
* Second-order bound: \[P_{f}=\left\{\begin{aligned} &\geq P(F_{1})+\sum\limits_{i=2}^{n}\max \left[0,P(F_{i})-\sum\limits_{j=1}^{i-1}P\left(F_{i}\cap F_{j}\right)\right]\\ &\leq\sum\limits_{i=1}^{n}P(F_{i})-\sum\limits_{i=2}^{n}maxP\left(F _{i}\cap F_{j}\right)\end{aligned}\right.\] (26) In the above, \(F\) means failure event, \(P_{f}\) is the probability of failure for the whole system. For parallel system failure, probability bounds are defined as follows [42]:
* First-order bound: \[0\leq P_{f}\leq\min P(F_{i})\] (27)
* Second-order bound: \[0\leq P_{f}\leq\underset{ij}{n}min\left\{P\left[F_{i}\cap F_{j}\right]\right\}\] (28)  
Figure 2: Linearization of limit state functions for: (**a**) series system, (**b**) parallel system.
# OGSM: A Parallel Implicit Assembly Algorithm and Library for Overlapping Grids
## 3 Parallel Implicit Assembly Method
### Parallel Hole-Cutting Based on Two-Step Nodes Classification Scheme
#### 3.3.1 Preliminary Screening Step: Wall Distance Calculation
\] (12) In addition, each cuboid capsule \(S^{i_{m}}\) has a property parameter indicating whether \(S^{i_{m}}\) is active and the initial value is set to \(TRUE\). Note that \(d(p,N_{w}^{i_{m}})\) is initialized to \(10^{40}\). 2. Search for the cuboid capsule \(S^{i_{s}}\) that makes \(d(p,S^{i_{s}})\) obtain its minimum among all the active cuboid capsules as shown in Figure 8. Whenever a suitable \(S^{i_{s}}\) is found, the index corresponding \(i_{s}\) and \(d(p,S^{i_{s}})\) are recorded. It is very likely that the capsule \(S^{i_{s}}\) contains the desired closest wall node, and a further verification is performed in _Step_ 3. Otherwise, the routine goes directly to _Step_ 4. 3. Update the value of \(d(p,N_{w}^{i_{m}})\) as \[ \begin{cases}d(p,N_{w}^{i})=\min(d(p,N_{w}^{i_{m}}),d(p,N_{w}^{i_{m}}))\\ d(p,N_{w}^{i_{s}})=\min(d(p,q)\mid q\in N_{w}^{i_{s}})\end{cases} \] (13) and reset the property parameter of \(S^{i_{s}}\) to \(FALSE\). If the cuboid capsule \(S^{i_{m}}\) satisfies the condition \[d(p,S^{i_{m}})>d(p,N_{w}^{i})\] (14) then its property parameter will be assigned to \(FALSE\). The routine goes back to _Step_ 2 and keeps on searching the **active** cuboid capsules. 4. After the above steps are completed, the smallest distance \(d(p,N_{w}^{i})\) is obtained. **(c) Calculating \(ds\) and \(dt\) in parallel**  
As shown in Figure 6, \(p\in N^{i}\) is an arbitrary node of source grid \(SG\), and \(L\) target grids \(TG^{j}(1\leq j\leq L)\) may overlap with \(SG\). According to the domain decomposition strategy \(T^{2}\) illustrated in Figure 3, \(SG\) can be treated with process-group \(P^{0}\) and \(TG^{j}\) with \(P^{j}\). The parameters \(ds\) and \(ts\) can be calculated as follows. 1. The solid wall nodes \(N^{i}_{w}\) of \(SG\) are collected and shared among processes in the group \(P^{0}\). Similarly, each process in \(P^{j}\) also has the whole solid-wall nodes for component grid \(TG^{j}\). 2. All the processes calculate the distance \(d^{j}(0\leq j\leq L)\) from \(p\) to the wall boundary in parallel. 3. Equation (7) indicates that \(ds\) is actually \(d^{0}\) relevant to \(P^{0}\), and \(dt\) is the minimum of the \(L\)\(d^{j}\)s.  
Since the total number of solid wall nodes is much smaller than that of nodes on the whole grid, the current communication cost in the calculation of wall distance is greatly reduced compared to that of Ref. [12]. Figure 8: Diagram of recursive-box algorithm for wall distance calculation. Figure 7: Schematic diagram of distance from a given node to a cuboid capsule.
## New Programmable CMOS Fuzzifier and C2V Circuits
### Main Blocks and Circuits  
The systematic blocks of proposed Fuzzifier for this purpose are shown in Figure 3. The improved work is done using differential blocks which consist of transistors that are scaled with a different coefficient of \(W\)/\(L\)'s. In the left column circuit of differential block (Figure 4) the gates of transistors are connected to (\(V_{in}\)) either together or alone via control switches. In the same manner, Figure 4 (right) shows the right circuit of differential block. The scaled transistors in any column of right blocks can be connected to the reference voltage (\(V_{REF}\)) through control switches.  
Figure 2: Drain current variation of differential pair _versus_ input voltage (\(V_{in}-V_{REFi}\)).  
The proposed design has basically a similar operation discussed previously. As we mentioned, increasing _W/L_ ratio of scaled transistors cause to decrease \(\Delta V_{0}\) in current constant [9]. For these reasons the difference voltage of \(2\sqrt{2}\Delta V_{0}\) between two right block(i) and block(i+1) would be minimal. This benefit makes it possible to use a lower power supply, high swing, and consequently more stages and numbers of MFs.  
With these results and using the basic of differential pair circuit of Figures 1 and 4, the proposed Fuzzifier circuit is completed as shown in Figure 5. This circuit is capable of obtaining Gaussian membership function shapes with tuning reference voltages in least quantity (\(V_{REF2}-V_{REF1}=2\sqrt{2}\Delta V_{0}\)).  
Increasing different reference voltages and distancing them from each other, cause to have Trapezoidal shapes. Different MFs slopes can be achieved by changing transistor sizes in 1, 2, 4, and 8 _W/L_ ratios using control switches. The Triangular membership functions are also resulted with setting different reference voltages in least quantity and using high control switches.  
In contrast to conventional works [1, 10, 11, 12, 13], the proposed circuit is very simple and flexible in designing Layout (Figures 6 and 7) and processing fuzzy data, respectively. It can also generate three different shape types of equally spaced symmetrical membership functions. This work is applicable in VLSI implementations without any complexity and extra hardware.  
Figure 4: Left and right circuits for a differential block of fuzzifier stages.  
Figure 3: Main blocks.  
Figure 5: Proposed Fuzzifier circuit with input crisp voltage of \(V_{in}\), reference voltages, control switches, and output fuzzy currents of \(I_{I}\) to \(I_{t}\).  
Figure 6: Layout of Fuzzifier circuit (of Figure 5 Left).  
Figure 7: Layout of proposed Fuzzifier circuit (from Figure 5).
## 6 Results
### Results for OOK  
The first experiment was to analyze the effect of OOK modulation frequency concerning distance. The number of stripes detected from the frame is the evaluation parameter. For each frequency and distance, an average of 5 tests is considered. As shown in Figure 10, it is observed that for the shorter distance, the number of stripes detected is more and drastically decreases with the increase in the distance. It is majorly because of the decrease in the Rol region while stripe width stays constant. Thus, an optimal trade-off between frequency, distance, and size of light aperture is required. The number of detectable stripes in OOK modulation is directly proportional to the data rate.  
The other experiment was to analyze the relation between stripe width and distance. As shown in Figure 11, it was observed that stripe width is inversely proportional to frequency. However, it was also observed that there was no significant visible stripe for very low frequencies and greater distances; this is due to the size of the aperture and the number of pixels it occupied in the image/video. Here, the stripe width remains constant, but identification of RoI based on the implemented pre-processing techniques fails as the distance increases; this is majorly due to the absence of the brightest blob in the image as the transmitter appears to be overlapped by a dark stripe. Also, the decrease in the size of stripe width is observed to follow the elliptic curve, where the curve reaches its minimum at the frequency of 2000 Hz for a fixed distance of 20 cm and starts ascending again. This behavior was observed due to the phenomenon of pixel spreading which results in stripes with smaller widths spreading and causing boundary detection error.  
The steps involved in pre-processing and identification of the number of stripes and stripe width are shown in Figure 12. Here, two cases with the same OOK frequency but varying distances are shown. The number of stripes detected is dependent on the distance,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Exposure & ISO3200 & Contrast & 100 \\ Frame Rate & 30 FPS & Brightness & 100 \\ Resolution & 160 \(\times\) 96 & Ambient Light & 50–120 lux \\ \hline \hline \end{tabular}
\end{table}
Table 2: Camera Parameter.  
Figure 9: SNR vs. Distance vs. Ambient Light.  
whereas the width of the stripe remains almost identical. It was an important observation as it defined a reliable feature to identify frequencies in BFSOOK.  
Figure 11: OOK Modulation Frequency vs. Stripe Width.  
Figure 12: Number of Lines vs. width vs. distance.  
Figure 10: OOK Modulation Distance vs. Detectable Stripes for various frequencies.  
lower bit rate, the rate of change in BER concerning distance is comparatively slower than higher frequencies; this is because detection of LED State under lower frequency is better compared to a higher frequency. Thus, in OOK modulation, as the number of bits represented per frame is proportional to the distance between transmitter and receiver, the accuracy decreases at a higher data rate due to frame loss.
## 2 Materials and Methods  
### Specimens Collection and Experimental Groups Assignment  
An in vitro randomized trial was schematized in order to obtain the approval of the Ethical and Research Committee of the institution in which the investigation was carried out. The sample size calculation was also determined before performing the study (CIPI/006/14). The following exclusion criteria were applied: open apex canals and/or with radicular caries, calcified canals, internal or external root resorption, cracks and/or fractures, previously endodontically treated root, and canals with double curvature; after applying these exclusion criteria, 40 human mandibular molars were selected. The methodology used was described in previous studies [13]. Calculus was eliminated (ultrasonic tip mounted on a Suprasson P5 Booster(r) (Satelec) (Aeteon, Merignac, France)) and specimens were stored in a 2% thymol solution. Crowns and distal roots of the mandibular molars were discarded and the working length of the mesiobuccal canals was standardized (13 to 15 mm).  
A size 8 K-file (Dentsply Maillefer, Ballaigues, Switzerland) was introduced into the canals and the working length was established as follows: once the file was visible in the apical foramen, 0.5 mm was subtracted from this length. A number was assigned to each specimen and they were radiographed to establish their angle of maximum curvature, as described in previous studies [14]. According to [PERSON] et al. [15], the angle of curvature was measured using AutoCad 2014(r) (Autodesk Inc, San Rafael, CA, USA) for this purpose. Specimens with angles of curvature between 20\({}^{\circ}\) and 90\({}^{\circ}\) were included in the study, arranged based on this parameter, and divided into 2 groups of 20 samples each via stratified random sampling. A group name was randomly assigned: BC with sonic activation (BC+) and BC without sonic activation (BC\(-\)).
## 1 Introduction
### Mapping Lava Flows
The role of the pre- and post-eruptive images has been shown to be crucial in the previous well-established techniques since the differential information between them is highly related to the newly emplaced lava flow. Pre- and post-eruptive optical remote images could be used to manually trace new lava flows, but differences in the acquisition times lead to differences in the environmental condition of the imaged area, thus making this task hard to automate. Nowadays, machine learning techniques have been widely exploited in image processing tasks [37; 38; 39; 40]. Among them, artificial neural network models (ANNs) have been shown to be well-suited for complex data classification [41; 42; 43]. ANNs have demonstrated excellent performance in different tasks [44; 45; 46], such as pattern recognition, in a wide variety of scientific and industrial applications [47; 48; 49; 50]. In the design of an ANN classifier, training data have to be representative of the phenomena under investigation. However, big amount of data are not always available, thus other strategies to limit the size of the training dataset may be necessary [51]. Cascading different techniques represents a valuable solution to enhance the learning performance [52; 53]. Here, we present a novel classifier based on machine learning techniques designed to classify pixels related to recent lava flows from pre- and post-eruptive multispectral images combined with pre-eruptive digital topography. We utilized the Multispectral Imager (MSI) onboard Sentinel-2 satellite and the high-resolution topographic data derived from tri-stereo Pleiades-1 satellite imagery. A k-medoids unsupervised clustering is used as a strategy to minimize the ANN training dataset size. The proposed ANN is trained by using the \"Bayesian regularization\" training function [54], thus the ANN will be referred to as BNN (i.e., Bayesian Neural Network). In particular, a small number of points belonging to the cooled lava flow are used to feed the BNN and obtain a 2D map. This approach is semi-automatic and was applied as a support instrument to map active lava flows of several recent effusive eruptions at Mount Etna.
# Effect of ElectroSpark Process Parameters on the WE43 Magnesium Alloy Deposition Quality
## 3 Results and Discussion  
### Microstructure of WE43 Base Material  
OM and SEM micrographs of the base material in transverse section (XZ plane) are shown in Figure 2. The alloy microstructure consisting of \(\alpha\)-Mg grains (Figure 2a), amounts of intermetallic eutectic phase which mainly aggregate at the grain boundaries (Figure 2a-c) and some precipitates of cuboid-shaped distributed within the matrix and around the eutectic (see the arrow in Figure 2b). The SEM-EDS map scan (Figure 3) reveals that the eutectic phase contains mainly Nd while the elements Y and Zr are more concentrated in cuboid-shaped phase. Indeed, Nd is concentrated in the intermetallic eutectic phase due to the low solubility in the Mg matrix [35], on the contrary Zr is distributed also in the Mg matrix. The XRD measurements indicate (Figure 4) that the substrate is mainly composed of five phases: \(\alpha\)-Mg (Card N\({}^{\circ}\)35-0821), Mg\({}_{24}\)Y\({}_{5}\) (Card N\({}^{\circ}\)31-0817), Mg\({}_{41}\)Nd\({}_{5}\) (Card N\({}^{\circ}\)45-1031), MgY and \(\beta\) (Mg\({}_{14}\)Nd\({}_{2}\)Y) phase. In the electrode microstructure three main compounds were identified: \(\alpha\)-Mg (Card N\({}^{\circ}\)35-0821), Mg\({}_{24}\)Y\({}_{5}\) (Card N\({}^{\circ}\)31-0817), Mg\({}_{12}\)Nd (Card N\({}^{\circ}\)17-0401). In the XRD patterns, the Zr-rich phase has not been detected due to low volume fraction [36].  
The micro-hardness (HV\({}_{0.3/15}\)) of the electrode and substrate alloy was \(78\pm 1\) and \(75\pm 4\) HV, respectively.  
Figure 2: Microstructure of WE43: (**a**) Optical Microscope (OM) of the base material unattached showing the \(\alpha\)-Mg grain structure and grain boundary eutectic structures, (**b**) backscattered electron (BSE) image illustrating the cuboidal intermetallic particles (precipitates) and the grain boundary eutectic structures (**c**) BSE image enlarged of the eutectic structure.