esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Grapes  
Grapes (_Vitis vinifera_ L.) of the Portuguese variety Arinto of Lisbon wine region provenance were harvested by hand at the optimum ripening stage and stored at 10 \({}^{\circ}\)C until their arrival at the pilot-plant winery located in Gouveia (Portugal). Upon arrival, the grape material was weighted and separated into similar batches before processing. The grapes were divided into 9 batches, each with about 167 kg, in which the only variation referred to the treatment applied: (a) 3 batches for control, (b) 3 batches treated with commercial-grade juice yield increasing enzymes and (c) 3 batches PEF-treated with monopolar-shaped pulses (1 kV/cm and 2 kJ/kg).  
Testing PEF Parameters: Optimizing Preliminary Experiments  
Prior to conducting the experiment at an industrial scale, preliminary assays were performed using a batch treatment chamber for the application of PEFs, using a linear parallel plate electrode (P2P: plate-to-plate) configuration, at a laboratory scale (EnergyPulse Systems, Lisbon, Portugal).  
To accomplish this, batches of 200 g of destemmed grapes were weighed and crushed to simulate a typical vinicification process and divided into six groups, each corresponding to a specific treatment type. Every experiment was carried out in triplicate. The range of parameters considered in each protocol of this preliminary experience was based not only on results published by other researchers but also taking into account the cost-benefit tradeoff between yield increase, equipment design requirements and energy efficiency [8; 22].  
Before undergoing PEF treatment, grapes were gently crushed, causing the skin to rupture, replicating the destemmer/crusher process. Afterwards, the PEF treatment was applied, and the grape mash went through a two-phase process. The first phase consisted of collecting the free run juice volume. In the second phase, the remaining grape mash was subjected to low pressure by being manually pressed using a river, representing the juice yield obtained by pressing. The optimization of _cuvee_ yield being the principal focus of this work, only yield optimization was assessed a priori. The measure was performed with the aid of a graduated cylinder (\(\pm\)0.5 mL). The different PEF protocols and results obtained in this assay are presented in Table 1.  
Based on the obtained results, Protocol B was found to be the most effective PEF treatment, as it resulted in the highest increase in free run _cuveee_ must (+36.8%) compared to the control, along with a 17.6% increase in total _cuveee_ yield. It is noteworthy that the rise in free run _cuveee_ yield was accompanied by a reduction in _cuveee_ press yield (\(-6.6\%\)). This suggests that PEFs may not only facilitate the extraction of greater volumes of must but also aid in obtaining higher-quality juices, a characteristic associated with _cuveee_ musts.
## 3 Results  
### The Experiment of Measuring Positioning Range  
Figure 2 shows the block diagram and experimental setup for the proposed VLC positioning range-testing system respectively. In this system, the drive signals are generated by the arbitrary waveform generator (AWG) (Tektronix AWG520, Tektronix, Shanghai, China) with an offline Matlab program. After the hardware electrical amplifier, the resulting waveform coupled with the direct current is applied to the LED (Cree, XLamp XP--G Q5, CREE, Durham, NC, USA). Two commercially available PINs (HAMAMATSU S10784, HAMAMATSU, Beijing, China, effective photosensitive area: 7 mm\({}^{2}\), and 0.45 A/W sensitivity with a \(-3\) dB bandwidth of 300 MHz at 660 nm) are used for detecting the light signal. At the transmitters, up-sampling by a factor of four is employed and the original bit sequence is mapped into complex symbols of 16 QAM (quadrature amplitude modulation). In this experiment, the data rate is 10 Kbit/s, and the sample rate of the oscilloscope (Tektronix, MDO 3022, Tektronix, Shanghai, China) is 50 KS/s. The offline digital signal processing (DSP) is applied to down-sample and demodulate the sampled signal by oscilloscope (OSC). The vertical distance between the LED and receivers is \(d\), and \(l\) is the distance between the two receivers. In order to obtain the minimum positioning range at different vertical distances, we fix the LED and change \(l\) (up to 1.2 m) on the transmitting plane.  
In the proposed VLC positioning range-testing system, we measured the bit error rate (BER) of two receivers when \(d\) is 0.5 m, 1 m and 1.8 m. Figure 3 shows the BER versus horizontal offset \(x\) while \(d\) is 0.5 m, 1 m and 1.8 m. The effective positioning condition is that the BERs of the two receivers are both below \(3.8\times 10^{-3}\). As shown in Figure 3, the smaller the vertical distance is, the wider the positioning range. In addition, when \(d\) is greater than 1.8 m, only one receiver can receive the positioning signal at the same time. Positioning can be realized only when two receivers both calculate the RSS, so the experimental result means the target cannot be positioned anywhere when the distance is beyond 1.8 m. To sum up, the effective positioning range of our system is 1.8 m when the maximum distance of two receivers is 1.2 m.  
Figure 2: **(a)** The block diagram of the proposed VLC positioning range-testing system. EA: electrical amplifier, DC: direct current, OSC: real-time oscilloscope; **(b)** The schematic of experimental setup; **(c)** The experimental setup of measuring positioning range.
# A Secure and Efficient Authentication Scheme for Fog-Based Vehicular Ad Hoc Networks
## 4 Proposed Protocol
### User Revocation and Re-Registration  
If \(V_{i}\) is compromised, \(CS\) deletes \((ID_{i},x_{i})\) from its database. Then, login attempts with the previous smart card are rejected.  
\(CS\) allows \(V_{i}\) to re-register through registration. \(V_{i}\) re-registers using the same identity and an updated password. Afterward, \(CS\) assigns a new random number, \(x_{i}^{new}\), and stores it in the database with \(V_{i}\)'s \(PID_{i}\).
###### Abstract
The deeply-cutting channels of the outer fan mainly develop a one bouncing segment-one suspension segment-one transitional zone pattern reflecting the conversion from high-density turbidity current to low density turbidity current [11]. After studying the grain size probability accumulative curve characteristics of the Paleogene delta depositional environment in Huimin Depression, the researchers believed that distributary channel microfacies in the delta plain developed typical bi-segment or tri-segment patterns, while the subaqueous distributary channel microfacies of the delta front mainly developed one bouncing segment-one suspension segment-one transitional zone pattern and low-slope multi-segments pattern [12]. There have two patterns of estuary sand bar microfacies: high-slope one bouncing segment-one suspension segment-one transitional zone pattern and one bouncing segment-one suspension segment -one transitional zone pattern [12]. At the same time, the authors also studied the fan delta sedimentary environment. They believed that the fan delta plain subfacies mainly developed 3 patterns: unpatched arc pattern, typical bi-segment pattern or tri-segment pattern, and fine-grained high-slope transitional pattern [12]. The subaqueous distributary channel microfacies of fan-delta front subfacies are mainly developed with one- segment and multi-segment patterns reflecting the gravity current and typical bi-segment pattern or tri-segment pattern reflecting the fluvial deposition [12]. Estuary sand bar microfacies are mainly developed with three patterns: low-slope tri-segments pattern, high-slope multi-bouncing segments-one suspension segment pattern and high-slope one bouncing segment-one suspension segment-one transitional zone pattern [12]. As regards sandy debris flow, some scholars have conducted exploration and research, but there is no perfect grain size probability curve model for sandy debris flow at present, and the method of using grain size probability curve to identify sandy debris flow is not very mature [13; 14; 15]. Therefore, the purpose of this study is to collect core samples from the 2 nd member of Sangonghe Formation in Qianshao area for laser grain size analysis, to study the type and characteristics of grain size probability curve, and comprehensively identify the genetic type of sand bodies. Combined with the study of conventional logging facies and core facies markers, the sedimentary facies type of the study area will be determined. It will provide a reference for the discrimination of sedimentary microfacies types in other similar areas in the hinterland of Junggar Basin. At the same time, this study will contribute to enriching the grain size probability curve patterns of different sedimentary facies types and strengthening the application of grain size probability curve in sedimentary facies discrimination.
## 4 Performance Test of SOADS
### Performance Test of Frequent Subsequence Miner  
To evaluate the performance of the Frequent Subsequence Miner, the 10 GB event log, which stores about 9 million events, is increased to 500 GB, and the time for discovering subsequence patterns were measured at every 100 GB increase. First, 10 GB of event logs was stored in Hbase. The processing time for deriving the subsequence pattern from event logs using the original PrefixSpan algorithm was measured. Next, the processing time for deriving the subsequence pattern using the PrefixSpan algorithm with the _RELATIONAL DEPENDENCY TABLE_ was measured. Each measurement was repeated 30 times and the average processing time was recorded. Figure 14 summarizes the performance experiment of the Frequent Subsequence Miner.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Server Type** & **CPU** & **RAM** & **HDD** & **OS** \\ \hline
**Configuration** & 3.6 GHz \(\times\) 8 & 64 GB & 512 GB & Ubuntu 16.04 \\
**Server** & 2.2 GHz \(\times\) 8 & 64 GB & 512 GB & Ubuntu 16.04 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of servers.  
The test results show that the PrefixSpan algorithm using _RELATIONAL DEPENDENCY TABLE_ outperforms the original PrefixSpan algorithm to derive the subsequence pattern, as shown in Figure 15a. Also in Figure 15a,b shows, the gap in subsequence pattern derivation time increases linearly as the size of the event log increases. This means that as the amount of data increases, prefixspan using _RELATIONAL DEPENDENCY TABLE_ is more advantageous.
###### Abstract  
The collapse settlement of granular soil, which brings about considerable deformations, is an important issue in geotechnical engineering. Several factors are involved in this phenomenon, which makes it difficult to predict. The present study aimed to develop a model to predict the collapse settlement and coefficient of stress release of sandy gravel soil through evolutionary polynomial regression (EPR). To achieve this, a dataset containing 180 records obtained from a large-scale direct shear test was used. In this study, five models were developed with the secant hyperbolic, tangent hyperbolic, natural logarithm, exponential, and sinusoidal inner functions. Using sand content (SC), normal stress (\(\sigma_{n}\)), shear stress level (SL), and relative density (Dr) values, the models can predict the collapse settlement (\(\Delta\)H) and coefficient of stress release (CSR). The results indicated that the models developed with the exponential functions were the best models. With these models, the values of \(R^{2}\) for training, testing, and all data in the prediction of collapse settlement were 0.9759, 0.9759, and 0.9757, respectively, and the values of \(R^{2}\) in predicting the coefficient of stress release were 0.9833, 0.9820, and 0.9833, respectively. The sensitivity analysis also revealed that the sand content (SC) and relative density (Dr) parameters had the highest and lowest degrees of importance in predicting collapse settlement. In contrast, the Dr and SC parameters showed the highest and lowest degrees of importance in predicting the coefficient of stress release. Finally, the conducted parametric study showed that the developed models were in line with the results of previous studies.  
evolutionary polynomial regression; collapse settlement; coefficient of stress release; sandy gravel soil; modelling Article
## 2 Materials and Methods
### Remote Sensing Data
#### 2.2.2 SIF Data  
TROPOSIF refers to the satellite-based SIF product retrieved from the TROPOMI spectrometer on board the Sentinel-5 Precursor satellite [29]. It provides ungridded Level-2B daily aggregated data, where daily aggregation is achieved by scaling the TROPOMI observations (local solar time 13:30) with daily length scale factors [29]. The SIF retrieval employs a data-driven approach based on principal component analysis and is applied within two fitting windows (743-758 and 735-758 nm). Due to its robustness against atmospheric influences compared to the 735-758 nm SIF [29], the 743-758 nm daily average SIF product was exclusively utilized, filtering out observations with cloud fractions exceeding 0.2 and viewing zenith angles exceeding 60\({}^{\circ}\) and solar zenith angles exceeding 70\({}^{\circ}\). TROPOSIF data were aggregated into 0.05\({}^{\circ}\) grid cells every 8 days.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**VI Name** & **Formula** & **Reference** \\ \hline CIr & \(\frac{\text{B}\text{T}}{\text{B}\text{B}\text{B}}\,-\,1\) & [PERSON] et al. [37] \\ MTCI & \(\frac{\text{B}\text{T}}{\text{B}\text{B}\text{B}}\,-\,\text{B}\text{B}\) & Dash and Curran [38] \\ CSI & \(2.5\,*\,\text{B}\text{B}\,-\,\text{B}\text{B}\,+\,\text{B}\text{B}\,+\, \text{B}\text{B}\,+\,\text{B}\text{B}\,+\,\text{B}\text{B}\,+\,\text{B}\,+\, \text{B}\,+\,\text{B}\,+\,\text{B}\,+\,  
The eSIF data, created by [PERSON] et al. [27], provide global SIF data with an 8-day temporal resolution and a spatial resolution of 0.05\({}^{\circ}\). They combine TROPOSIF data with information on the near-infrared radiation reflected by vegetation (NIRvP). Notably, the eSIF data demonstrate reduced angular dependence and an improved signal-to-noise ratio compared to the original TROPOSIF data. As a result, the eSIF data are better equipped for capturing seasonal variations in GPP under environmental stress conditions [27].
## 3 Kinetic Analysis Methodology
### Generalized Models for Kinetic Analysis of CaO Carbonation
#### 3.4.1 Chemical Reaction Control Region: Intrinsic Reaction Rate Model  
The kinetic control region of the carbonation reaction is generally considered to be limited by heterogeneous surface chemical reaction kinetics, with the driving force for the reaction being the difference between bulk CO\({}_{2}\) pressure and equilibrium CO\({}_{2}\) pressure [56]. This region is typically described by the kinetic reaction rate constant \(k_{s}\) (m\({}^{4}\) mol\({}^{-1}\) s\({}^{-1}\)), which is considered to be an intrinsic property of the material [7].  
In this paper, the carbonation of P-CaO is modeled using the grain model used by [PERSON] et al. to determine the intrinsic rate constants of the CaO-CO\({}_{2}\) reaction [56]. Under kinetic control, the reaction rate, \(R\) (min\({}^{-1}\)), is described by:  
\[R=\frac{dX}{dt{(1-X(t))}^{2/3}}=3r \tag{14}\]  
where \(r\) is the grain model reaction rate (min\({}^{-1}\)), which is assumed to be constant over the kinetic control region. In integral form the reaction rate can be expressed as:  
\[\left[1-{(1-X(t))}^{1/3}\right]=r\times t \tag{15}\]Plotting 1-(1-\(X(t)\))\({}^{\frac{1}{3}}\) versus \(t\) will produce a straight line plot due to the constant reaction rate. Values of \(r\) can be determined for each isothermal experiment and are taken to represent the true reaction rate at the zero conversion point [56], i.e.:  
\[r_{0}=r \tag{16}\]  
The specific reaction rate can also be expressed in power law form as:  
\[R=3r(1-X(t))^{-1/3}=56k_{\mathrm{s}}\big{(}P_{CO_{2}}-P_{CO_{2,0}}\big{)}^{n}S \tag{17}\]  
where \(k_{\mathrm{s}}\) is the intrinsic chemical reaction rate constant (mol m\({}^{-2}\) s\({}^{-1}\) kPa\({}^{-n}\)), \(P_{CO_{2}}-P_{CO_{2,0}}\) is the difference between the equilibrium and partial pressure of CO\({}_{2}\), \(n\) is the reaction order, and \(S\) is the specific surface area (m\({}^{2}\) g\({}^{-1}\)). [PERSON] et al. determined that at CO\({}_{2}\) partial pressures greater than 10 kPa, the reaction order \(n\) is zero-order [56]. At time 0, the following relationships are therefore established:  
\[k_{\mathrm{s}}=k_{0}exp\bigg{(}\frac{-E_{a}}{RT}\bigg{)} \tag{18}\]  
where \(k_{0}\) is the pre-exponential factor (mol. m\({}^{2}\) s\({}^{-1}\)). An Arrhenius plot of slope ln\(r_{0}\) vs 1/\(T\) can therefore be produced to determine the kinetic parameters \(E_{a}\) and \(k_{0}\):  
\[ln_{r_{0}}=ln\bigg{(}\frac{56k_{0}S_{0}}{3}\bigg{)}-\frac{E_{a}}{RT} \tag{19}\]  
The intrinsic chemical reaction rate constant \(k_{\mathrm{s}}\) (calculated using Equation (18)) can be converted to the rate constant \(k\) (min\({}^{-1}\)) by means of:  
\[k=k_{\mathrm{s}}SM_{\mathrm{CaO}} \tag{20}\]
# A Method for Energy Balance and Data Transmission Optimal Routing in Wireless Sensor Networks
###### Abstract  
Wireless sensor networks are widely used in many fields. Nodes in the network are typically powered by batteries. Because the energy consumption of wireless communication is related to the transmission distance, the energy consumption of nodes in different locations is different, resulting in uneven energy distribution of nodes. In some special applications, all nodes are required to work at the same time, and the uneven energy distribution makes the effective working time of the system subject to the node with the largest energy consumption. The commonly used clustering protocol can play a role in balancing energy consumption, but it does not achieve optimal energy consumption. This paper proposes to use the power supply line to connect the nodes to fully balance the energy. The connection scheme with the shortest power line length is also proposed. On the basis of energy balance, the method of transmitting data with the best hop count is proposed, which fully reduces the power consumption of the data transmission. The simulation results show that the proposed method can effectively reduce the energy consumption and prolong the lifetime of wireless sensor networks.  
wireless sensor network; energy balance; optimal routing; clustering routing protocol +
Footnote †: journal: Sensors
## 4 Discussion
### Coherence of Temperature Distribution, Energy Deposition, Cut Kerf Properties, and Cutting Result
ated sets (see Table 6). The heat conduction losses into the basic material are locally high compared to the amount of deposited energy. In these areas, the temperature drops and the viscosity increases, which in turn leads to aggravated melt ejection. This sustains the inhomogeneous temperature distribution within the melt pool. The position of the highest temperature shifts toward the start of the cut front and is not congruent with the position of the highest average values (see Figure 17d). This indicates very high fluctuation in the viscosity of the melt pool, spatially as well as temporally. Altogether, this destabilizes the melt ejection. As a cutting result, set #4 (Table 4) still enables a feasible cutting speed, but the surface roughness and the burr attachment degraded due to the partly aggravated melt ejection. A drawback in terms of surface roughness is also obtained for set #3. It represents the experimental setup with the highest amplitude. Compared to set #2, this also results in a higher beam velocity. As visible in Figure 18c, the temperature for the evaluated submatrix \(\overline{\text{T}_{\text{p,p}}}(\text{t})\) drops temporarily below melting temperature compared to set #1 and set #2. In this case, the heat conduction losses temporarily exceed the dissipated laser energy, with the consequence of partial temperature loss. It results in a temporarily higher viscosity and, as stated before, an insufficient melt ejection. In turn, this causes the higher surface roughness.
## 3 Experimental Section
### Data Processing  
Non-processed MS data from GC-TOF-MS analysis were exported in NetCDF format, which was generated by the chromatography processing and mass spectral deconvolution software (Leco ChromaTOF version 2.32; LECO, MI, USA), to MATLAB 7.0 and MATLAB2011b (Mathworks, MA, USA), where all data pretreatment procedures, such as smoothing, alignment, time-window setting and hierarchical multivariate curve resolution (H-MCR), were carried out [79]. The resolved mass spectra were matched against the reference mass spectra by using the NIST mass spectral search program from the NIST/EPA/NIH mass spectral library (Data Version: NIST 08, Software Version 2.0) [34], Terpenoid library, Identification of Essential Oil Components by Gas Chromatography/Mass Spectrometry (the 4 th Edition) commercial library [35] and VocBinbase [36]. Our custom software was also used for peak annotation written in Java. The metabolites were defined  
\begin{table}
\begin{tabular}{l l l l} \hline SPME fiber & Conditioning & Conditioning & Inlet \\  & temperature & ( time (h) & temperature ( \\  & \({}^{\circ}\)C) & & \({}^{\circ}\)C) \\ \hline
100-\(\upmu\)m PDMS & 250 & 0.5 & 220 \\
30-\(\upmu\)m PDMS & 250 & 0.5 & 220 \\
7-\(\upmu\)m PDMS & 320 & 0.5 & 220 \\
85-\(\upmu\)m polyacrylate & 280 & 1 & 220 \\
60-\(\upmu\)m PEG & 240 & 0.5 & 220 \\
75-\(\upmu\)m CAR/PDMS & 300 & 1 & 280 \\
85-\(\upmu\)m CAR/PDMS & 300 & 1 & 280 \\
65-\(\upmu\)m PDMS/DVB & 250 & 0.5 & 250 \\
50/30-\(\upmu\)m & 270 & 0.5 & 250 \\ DVB/CAR/PDMS & & & \\ \hline \end{tabular}
\end{table}
Table 3: Conditioning temperature and time for the SPME fibers used in the study.  
as annotated metabolites by comparison with RI values calculated by alkane standard mixtures (C8-20) and mass spectra from these three libraries. Peaks that showed a match factor of \(\geq\)800 in the mass spectral library were selected for annotation. Data were normalized by dividing each peak area by the peak area of \(n\)-tridecane and metabolite identifiers were organized using MetMask [80].
## 3 Results and Discussion
### Phylogenetic Analysis  
A near-complete 16S rRNA gene sequence (1500 bp) was obtained from the PCR amplification and sequencing. Similarity analysis on the 16S rRNA gene sequence revealed that OS4T shared the highest similarity with _J. aquimarina_ GSW-M26T (96.04%). Moreover, OS4T shared 94.98% similarity with the type species of genus _Jannaschia_-strain _J. helgolandensis_ 14858T on the 16S rRNA gene sequence. The ANIs of OS4T to 14858T and GSW-M26T were 77.69% and 78.93%, respectively. In silico DDH analysis showed that the estimated DDHs on a generalized linear model were 20.10% and 19.20%, respectively. Phylogenetic analysis showed that strain OS4T was placed nearest to _J. aquimarina_ GSW-M26T within the genus _Jannaschia_ (Figures 3 and S1). Altogether, both the 16S rRNA comparison analysis and the phylogenetic relationships indicate that strain OS4T is more likely a novel species of the genus _Jannaschia_. The G + C content was determined to be 72.7 mol% for strain OS4T.