esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Photosynthetic Pigments and Chlorophyll Fluorescence Parameters  
Homogenized samples (\(n\) = 9) were suspended in 3 mL of 95% ethanol at 4 \({}^{\circ}\)C for 1 h. in the dark. At the end of the incubation, the samples were centrifugated at 800\(\times\)\(g\) for 10 min to remove the cell debris. Chlorophylls and carotenoid detection were performed with a spectrophotometer to measure the absorbances of the supernatants at 664 nm (chlorophyl a), 648.6 nm (chlorophyll b), and at 470 nm (carotenoids). Photosynthetic pigments amount was determined according to [PERSON] [46] and expressed as \(\mu\)g/mg f.w.  
Imaging of chlorophyll fluorescence parameters was performed after 30 days of growth, to evaluate the activity of the photosynthetic apparatus following the protocol described by [PERSON] et al. [10]. Chlorophyll fluorescence was measured with a MINI-Imaging-PAM (Walz, Germany), on the last fully expanded leaf of four representative plants per treatment. To determine F\({}_{0}\) (minimum fluorescence) and F\({}_{\mathrm{m}}\) (maximum fluorescence) leaves were maintained in the dark for 30 min. Then, leaves were adapted to a photosynthetic photon flux density (PPFD) of 55 \(\mu\)moles m\({}^{-2}\) s\({}^{-1}\) for 10 min until the steady-state condition was reached. Maximum fluorescence (F\({}_{\mathrm{m}}\)) and steady-state fluorescence (F\({}_{\mathrm{s}}\)), during actinic illumination, were detected through a saturation light pulse. Saturation pulse images and values of the chlorophyll fluorescence were captured. Fluorescence parameters were calculated with the following formulas:  
\[\mathrm{F_{v}}/\mathrm{F_{m}}\] (Maximal quantum efficiency of PSII photochemistry) = (F\({}_{\mathrm{m}}-\mathrm{F_{0}}\))/F\({}_{\mathrm{m}}\)  
\[\Phi\mathrm{PSII}\] (Quantum efficiency of PSII photochemistry) = (F\({}_{\mathrm{m}^{\prime}}-\mathrm{F_{s}}\))/F\({}_{\mathrm{m}^{\prime}}\)  
NPQ (Non-photochemical quenching) = (F\({}_{\mathrm{m}}-\mathrm{F_{m}}\))/F\({}_{\mathrm{m}^{\prime}}\)  
ETR (Electron transport rate) = \(\Phi\mathrm{PSII}\) * PPFD * 0.5 * Abs  
(0.5 = light absorbed by PSII antennae; Abs = apparent absorptivity of leaf surface)
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 2 Principles of Grounding Systems  
### Purpose of Grounding Systems  
Grounding systems in electrical installations are essential to ensure personnel safety, system reliability, and equipment protection. These systems serve multiple purposes, each with specific technical requirements to maintain a safe and functional electrical network.
## 2 Study Region and Data
### Statistics Metrics  
Relative bias (RB), root-mean-squared error (RMSE), correlation coefficient (CC), probability of detection (POD), false alarm ratio (FAR) and critical success index (CSI) are used in this study to evaluate the performance of TMPA products. RB and CC are dimensionless, while RMSE is in mm. All the above statistics are computed pixel by pixel. Compared with rain gauge observations, the number of hits (A), false alarms (B), and misses (C) can be computed, and then statistic metrics POD, FAR and CSI can be computed with A, B, and C. More detail is available in [1].
# Smart Black Box 2.0: Efficient High-Bandwidth Driving Data Collection Based on Video Anomalies
## 5 Experiments
### Dataset  
The SBB is designed for high-bandwidth data collection in long-term driving where on-board storage is limited. Therefore, SBB performance evaluation requires a large, high-quality video dataset, which contains both normal driving data, as well as events-of-interest (EOIs). To our best knowledge, there is currently no single dataset that satisfies all these requirements. The BDD100K dataset [32] is one of the largest high-quality driving video datasets and contains 100,000 video clips covering about \(\sim\)1100 driving hours. The DoTA dataset [15] is the largest and newest high-quality video dataset for traffic anomalies and contains 4677 anomalous video clips. We combined the 10,000 validation videos in the BDD100K dataset and randomly sampled and interspersed 500 anomalous video clips from the DoTA dataset, resulting in a large testing video with \(\sim\)4,000,000 frames at 10 FPS. The frames from BDD100K were compressed using OpenCV with JPEG quality 85 in order to eliminate the difference in image size between BDD100K and DoTA. This combined dataset contains over 100 h of driving video at \(1280\times 720\) resolution with 0.5% of frames being anomalous, meeting our requirements for a large, high-quality, and mostly non-anomalous dataset. Note that the ST*anomaly class was not included in this combined dataset, as its rarity in the DoTA dataset led to no ST* clips being sampled.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c} \hline \hline  & **ST** & **AH** & **LA** & **OC** & **TC** & **VP** & **VO** & **OO** & **ST*** & **AH*** & **LA*** & **OC*** & **TC*** & **VP*** & **VO*** & **OO*** \\ \hline Likelihood & 0.011 & 0.057 & 0.054 & 0.023 & 0.163 & 0.012 & 0.010 & 0.089 & 0.010 & 0.091 & 0.104 & 0.081 & 0.207 & 0.010 & 0.011 & 0.070 \\ \hline Normalized info.measure & 0.977 & 0.635 & 0.633 & 0.816 & 0.395 & 0.957 & 0.995 & 0.525 & 1.0 & 0.521 & 0.491 & 0.546 & 0.342 & 1.0 & 0.990 & 0.576 \\ \hline \hline \end{tabular}
\end{table}
Table 4: DoTA Anomaly Class Probabilities and Values. An anomaly label with “*” indicates an event where the ego car is not involved (i.e., non-ego); otherwise, the event is ego-involved.
## 2 Materials and Methods
### The Data Assimilation System  
EnKF, an approximated version of the traditional Kalman filter based on Monte Carlo simulations, was used as the DA technique in this experiment. In the EnKF, the state probability distribution is represented by a sample (or ensemble), propagated forward in time and updated when a new observation becomes available [14; 17; 54]. The EnKF technique has flexibility in treating errors in model equations and parameters and is particularly suitable for nonlinear problems, such as soil dynamics [20; 55]. Twenty ensemble members are generated by perturbing the atmospheric forcing following the same methodology presented in [24; 26]. The precipitation and shortwave radiation are perturbed via multiplicative error models, whereas an additive perturbation is used for longwave radiation.  
GLASS LAI observations are perturbed via an additive model with a standard deviation of 0.1. Following [PERSON] et al. [24], leaf mass is also updated with LAI in this experiment. The observation perturbations for the SMAP product follow an additive model, with zero mean and a standard deviation of 0.04 m\({}^{3}\)/m\({}^{3}\). Additionally, a cumulative distribution function (CDF) matching was applied prior to the SMAP DA as a scaling strategy to remove any existing bias between the satellite observations and model estimation, following the approach developed by [PERSON] et al., 2004 [56].  
The model was spun up for 15 years (2001-1 April 2015) to set the system to an initial equilibrium. A total of four experiments were run: (i) an open-loop (OL) simulation, in which no assimilation is performed; (ii) a DA run that only merges GLASS LAI; (iii) a DA run that only merges SMAP soil moisture; and (iv) a DA run that jointly merges GLASS LAI and SMAP soil moisture.
# Electrodegradation of Selected Water Contaminants: Efficacy and Transformation Products
## 3 Results and Discussion  
### Development of Methods for Parent Compounds and Their Transformation Products  
#### 3.1.1 Method Development for Dibutyl Phthalate and Its TPs  
The chromatographic method was developed on a Kinetex XB-C18 column with the available standards for DBP and some of its TPs: phthalic acid, monomethyl phthalate, dimethyl phthalate, and monobutyl phthalate. DAD quantitation of each analyte was performed at its absorption maximum. For all analytes, the method was linear in the concentration range from 0.1 (or 0.9 mg/L) to 15 mg/L with \(R^{2}>0.9989\); LODs were 0.02-0.26 mg/L, and intra-day RSDs were 0.5-1.2%. For the LC-MS/MS method, formic acid was added to acetonitrile instead of MQ to enhance the ionisation. ESI in positive mode provided enhanced ionisation and was optimized for quantitation purposes at the voltage of 3800 V except for phthalic acid (4000 V). The monitored SRM transitions were at the following \(m/z\): DBP 190.0 \(\rightarrow\) 149.0; monobutyl phthalate 148.8 \(\rightarrow\) 120.8; dimethyl phthalate 163.0 \(\rightarrow\) 133.0; monomethyl phthalate 162.9 \(\rightarrow\) 76.9; and phthalic acid 149.0 \(\rightarrow\) 121.0. For all analytes, the method was linear in the concentration range from 0.005-0.1 mg/L to 5 mg/L with \(R^{2}>0.997\) (except phthalic acid with \(R^{2}\) 0.9887); LODs were 0.002-0.029 mg/L, and intra-day RSDs were 0.7-1.3%.  
Samples after electrodeposition were not directly amenable to LC-MS/MS analysis because of the high concentration of supporting electrolytes and low concentrations of TPs. Therefore, the SPE method was optimized with the focus on more polar TPs. Two sorbents were tested: LC-8, which is a reverse-phase sorbent suitable for nonpolar and moderatelypolar compounds, and HLB, with the ability to bind both polar and nonpolar compounds. As expected, HLB performed slightly better for all analytes except DBP (a recovery of 50.2% for HLB and 53.7% for LC-8). A huge difference in favour of HLB, however, was seen with phthalic acid, with recoveries of 53.8% for HLB and 18.3% for LC-8. As the focus of the research was on TPs, we decided to further optimize the method using HLB cartridges. Other optimized parameters were the elution solvent (pure methanol or its mixtures with acetonitrile), pH and ionic strength of the sample, and wash-out with MQ water. No significant influences on the recoveries were observed for these parameters, except that ionic strength increased the recovery for phthalic acid and decreased it for DBP. Therefore, in the final method, methanol (2 mL) was used as the elution solvent, and no pH adjustment was performed on the samples. The wash-out step was included because of the high electrolyte concentration in the samples, which also made additional ionic strength adjustments redundant. The recoveries were 85.1% for phthalic acid, 44.8% for DBP, and 92.3-93.5% for the other analytes with a repeatability of 1.2-9.4%.
# IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses
## 2 Taxonomy of Deep Learning for IoT-IDS Logic
### Intrusion Detection System (IDSs)  
Many studies have proposed, developed, and empirically evaluated different approaches for IDSs [21; 22; 23; 24; 25; 26]. There are primarily four different categories as shown in Figure 1: (1) anomaly-based intrusion-detection system (AD-IDS), (2) signature-based intrusion-detection system (S-IDS), (3) hybrid-based intrusion-detection system (Hybrid  
Figure 2: Passive and active threats in the IoT system.  
IDS), and (4) specification-based IDS. AD-IDS depends on established known patterns for normal behavior. Behavior outside the realm of \"normal\" is considered anomalous, thus causing some sort of warning or alert. S-IDS relates to the known pattern (signature) of malicious traffic to detect attacks. The zero-day (unknown; never been seen before) attack cannot be detected by S-IDSs. Specification-based IDS and hybrid-IDS attempt to leverage complementary capabilities by integrating the first two types (AD-IDS and S-IDS). ML and DL algorithms are good examples of the core capability used in AD-IDS. The snort tool is an excellent example of S-IDS [27; 28; 29]. Other important considerations and details related to IDSs are examined in Section 5.
# Road Scanner: A Road State Scanning Approach Based on Machine Learning Techniques
## 5 Experiments
### Classification Models  
#### 5.2.1 Artificial Neural Networks  
Artificial Neural Networks (ANNs) [23] are computer systems that take their cue from the brain's biological neural networks. Artificial neurons are the building blocks of an ANN.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Speed** & **acc\_x** & **acc\_y** & **acc\_z** & **gyro\_x** & **gyro\_y** & **gyro\_z** & **Status** \\ \hline
13.57 & \(-\)0.021148682 & \(-\)0.97845 & 0.22049 & \(-\)0.02143 & \(-\)0.07219 & 0.047946 & smooth \\ \hline
13.57 & \(-\)0.094284058 & \(-\)1.04622 & 0.134445 & \(-\)0.04775 & \(-\)0.07095 & 0.02635 & smooth \\ \hline \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) \\ \hline
8.11 & \(-\)0.512405396 & \(-\)0.91891 & 0.08551 & 0.496783 & 0.543487 & \(-\)0.20743 & potholed \\ \hline
8.11 & 0.346130371 & \(-\)0.97647 & 0.704208 & 0.172343 & 0.016928 & \(-\)0.21046 & potholed \\ \hline \end{tabular}
\end{table}
Table 2: A sample of the data used in the experiments.  
Figure 6: The steps from in situ data collection to model deployment.  
Similar to brain synapses, these ties carry information between neurons via what we call edges. The signal strength at an edge connection is affected by the weight. Typically, the importance of edges weight shifts as learning progresses. Neurons frequently group into layers. Different layers may modify their inputs in different ways. Signals move through the layers from the input layer (the first layer) to the output layer (the last layer). The ANNs' main advantages are as follows: (1) it is a self-adaptive data-driven approach, and (2) it is a non-linear model, making it adaptable in modeling real-world problems [24]. The general structure of an ANN is presented in Figure 7.
# New and Efficient Algorithms for Producing Frequent Itemsets with the Map-Reduce Framework
## Mining Closed Frequent Itemsets with Map-Reduce
### The Algorithm  
#### 3.2.1 Overview  
Our algorithm is iterative, where each iteration is a Map-Reduce job. The inputs for iteration \(i\) are:  
1. \(\mathcal{D}\), the transaction database
2. \(C_{i-1}\), the set of the closed frequent itemsets found in the previous iteration (\(C_{o}\), the input for the first iteration, is the empty set).  
The output of iteration \(i\) is \(C_{i}\), a set of closed frequent itemsets that have a generator of length \(i\). If \(C_{i}\
eq\phi\), then another iteration, \(i+1\), is performed. Otherwise, the algorithm stops. As mentioned earlier, each iteration is a Map-Reduce job (line 7 in Algorithm 1, see details in algorithms 2, 3 and 4), comprised of a map phase and a reduce phase. The map phase, which is equivalent to the \(g\) function, emits sets of items called _closure generators_ (or simply _generators_). The reduce phase, which is equivalent to the \(f\) function, finds the closure that each generator produces, and decides whether or not it should be added to \(C_{i}\). Each set added to \(C_{i}\) is paired with its generator. The generator is needed for the next iteration.  
The output of the algorithm, which is the set of all closed frequent itemsets, is the union of all \(C_{i}\)s.  
Before the iteration begins, we have discovered that a pre-process phase which finds only the frequent items greatly improves performance, even though another Map-Reduce job is executed, and this data must be shared among all mapper tasks. This Map-Reduce job simply counts the support of all items and keeps only the frequent ones.  
The pseudo-code of the algorithm is presented below (Algorithm 1). We provide explanations of the important steps in the algorithm.  
```
Input:\(minSup\): user-given minimum support \(\mathcal{D}\): the database of transaction Output: all closed frequent itemsets \(f\_items\leftarrow\texttt{findFrequentItems}(\mathcal{D},minSup)\) ; \(C_{0}\leftarrow\{\phi\}\); \(i\gets 0\) ; repeat \(i++\) ; \(C_{i}\leftarrow\) MapReduceJob(\(\mathcal{D},minSup,C_{i-1},f\_items\)) ; until\(C_{i}=\phi\); return\(\bigcup\limits_{j=1}^{i}C_{j}\) ;
```  
**Algorithm 1**Main: Mine Closed Frequent Itemsets.
# Influence of Selected Model Parameters on the Electromagnetic Levitation Melting Efficiency
## 2 Materials and Methods
### Calculation Model  
As described above, the model was implemented in three-dimensional space. High-level tools dedicated to determine the coupling widening towards the top is not available in the program used. Therefore, coil geometry was manually designed. The wire leading from the power source was based on a straight line. Beyond it was where the wire began coiling, and each full turn of the wire was divided into 4 parts. All of them are based on circle functions with a different center position and a growing radius. The concept is shown in Figure 3. Before the last turn, a change in direction follows. Afterwards, the wire was linked back to the source of power by a straight line.  
As is shown in Figure 4, the box shape of the surroundings was adopted. Its edges were almost two times longer than the inductor's height. The batch inside the inductor was modeled as a ball. Although the simulation time of such a model is quite significant (approximately 1 to 1.5 h), it provides more credible results with comparison to the 2D coaxial model.  
The distance between adjacent nodes of the Finite Elements Method (FEM) depends on the depth of penetration of the electromagnetic field (1) in the chosen material type. The tetrahedral finite element was used.  
\[\delta=\sqrt{2/(\omega*\pi*\sigma*\mu)} \tag{1}\]  
* \(\delta\)--field penetration,
* \(\omega\)--angular frequency,
* \(\sigma\)--conductivity,
* \(\mu\)--magnetic permeability.  
Figure 3: The concept of coil geometry.
# On the Optimal Calculation of the Rice Coding Parameter
## 1 Introduction  
In this article, we study the calculation of the optimal Rice coding parameter. A Rice code is a parameterised Variable-Length Code (VLC)--proposed by Rice in [1; 2]--that works in the following way.  
Rice encoding: For a given parameter \(r\) and source data \(n_{i}\), the following two values are calculated:  
\[q_{i}=\left\lfloor\frac{n_{i}}{2^{r}}\right\rfloor\text{, and} \tag{1a}\] \[m_{i}=n_{i}\mod 2^{r}\text{, where} \tag{1b}\]  
\(\lfloor x\rfloor\) is the floor function: the function that takes as input a real number and returns as output the greatest integer less than or equal to \(x\). Similarly, we will use \(\lceil x\rceil\) for the ceiling function: the function that takes as input a real number and returns as output the smallest integer higher than or equal to \(x\). The function \(a\mod b\) refers to the modulo operation: the remainder after the integer division of \(a\) by \(b\).  
The Rice encoding for \(n_{i}\) can be constructed in three steps:  
1. append a bit set to one to the output stream, if \(n_{i}\) is a positive number; or a bit set to zero, otherwise,
2. encode the value of \(q_{i}\) in unary code and append it to the first bit, and finally,
3. encode the value of \(m_{i}\) as an unsigned integer using only \(r\) bits.  
The unary code of a value \(x\) is a sequence of \(x\) bits, all set to the value one plus one additional bit--a delimiter bit--set to zero.  
The reader shall note that the encoding operation can be efficiently implemented in CPUs using bit shift and bit masking operations solely, without the need of floating-point operations.  
Rice decoding: The decoder must know the Rice coding parameter \(r\) that was used for encoding. The following method is used to obtain the values of \(q_{i}\) and \(m_{i}\) from the encoded stream.  
1. read the first bit, and determine the sign of the encoded value: one for a negative sign, zero for a positive sign,
2. count the number of consecutive bits set to one (let \(q_{i}\) be that number),
3. discard the delimiter bit (a bit set to zero), and
4. let the following \(r\) bits be the value of \(m_{i}\).  
Once the values of \(q_{i}\) and \(m_{i}\) are known, the original value \(n_{i}\) is calculated as:  
\[n_{i}=m_{i}+q_{i}\cdot 2^{r}. \tag{2}\]  
The reader shall note that the decoding operation can be efficiently implemented in CPUs using bit shift and addition operations solely, without the need of floating-point operations.
## Abbreviations  
\begin{tabular}{l l} \(P_{e}\) & Engine power \\ \(P_{e\max}\) & Engine maximum output power \\ \(\Omega_{\rm e}\) & Engine output speed \\ \(\Omega_{\rm e0}\) & Engine output speed maximum power \\ \(T_{e}\) & Engine torque \\ \(i_{o}\) & Variable speed transmission ratio \\ \(i_{p}\) & Parallel wheel train transmission ratio \\ \(T_{out}\) & Transmission system output torque \\ \(T_{MR}\) & Rotor torsion torque \\ \(L_{MR}\) & Rotor lift force \\ \(Fue\) & Fuel flow \\ \(\rho_{\rm a}\) & Air density \\ \(v\) & Blade cross-section velocity \\ \(c_{e}\) & Blade equivalent chord length \\ \(\theta(Pit)\) & Rotor pitch angle \\ \(\alpha\) & Inflow angle \\ \(C_{L}\) & lift coefficient \\ \(C_{D}\) & Drag coefficient \\ \(\phi\) & Flow speed in the blade section \\ \(\omega_{MR}\) & Rotor angular velocity \\ \(v_{c}\) & Air inflow velocity \\ \(v_{ind}\) & Rotor-induced velocity \\ \(i_{1}\) & Low gearshift transmission ratio \\ \(i_{1}^{\prime}\) & High gearshift transmission ratio \\ \(z\) & Number of gear teeth \\ \(T_{in}\) & Transmission system input torque \\ \(\tau_{o}\) & One-way clutch torque \\ \(k_{\rm c1}\) & Nonlinear stiffness \\ \(\tau_{f}\) & Friction clutch torque \\ \(F_{n}\) & Positive pressure acting on the piston \\ \(\mu_{d}\) & Coefficient of dynamic friction \\ \(r_{e}\) & Equivalent radius of the friction plate \\ \(n\) & Number of friction surfaces \\ \(N_{b}\) & Number of Blade \\ \(R\) & Rotor Radius \\ \(\sigma\) & Rotor Solidity \\ \(I_{MR}\) & Flap Moment of Inertia \\ \(r_{s}\) & Sun base circle radius \\ \(r_{p1}\) & Wheel planet base circle radius \\ \(r_{p2}\) & Pinion planet base circle radius \\ \(r_{r1}\) & Ring r\({}_{1}\) base circle radius \\ \(r_{r2}\) & Ring r\({}_{2}\) base circle radius \\ \(Hyd\) & Clutch hydraulic pressure \\ \(\Delta\) & Overshot \\ \(H\) & High gear \\ \(L\) & High gear \\ \end{tabular}