esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### EMEP Air Quality Model  
The EMEP model version rv_33 has been used in this work. It is an off-line regional transport chemistry model [27], used to study the impact of road transport emissions on air quality over Europe. The domain stretches from \(-15.05^{\circ}\) to \(36.95^{\circ}\) longitude and \(30.05^{\circ}\) to \(71.45^{\circ}\) latitude with a horizontal resolution of \(0.1^{\circ}\)\(\times\)\(0.1^{\circ}\) longitude/latitude. The model has 20 vertical levels, with the first level around 45 m. The model uses meteorological initial conditions and lateral boundary conditions from the European Centre for Medium Range Weather Forecasting (ECMWF-IFS). The meteorological year used was 2015. The gas-phase chemistry was based on the evolution of the so-called \"EMEP scheme\" as described in [27] and references therein. The chemical scheme couples sulphur and nitrogen chemistry to photochemistry, using about 140 reactions between 70 species. Aqueous phase chemistry describes the SO\({}_{2}\) oxidation by ozone and H\({}_{2}\)O\({}_{2}\), and catalysis of O\({}_{2}\) in clouds, to form sulphate. An important pathway of aerosol nitrate formation is through the hydrolysis of N\({}_{2}\)O\({}_{5}\) on wet aerosol surfaces that converts NOx into HNO\({}_{3}\)[27]. The EMEP model has two size fractions for aerosols, fine aerosol (PM\({}_{2,5}\)) and coarse aerosol (PM10-2.5). The aerosol components presently accounted for are SO\({}_{4}^{-}\), NO\({}_{3}^{-}\), NH\({}_{4}^{+}\), anthropogenic primary PM, and sea salt.  
The MARS equilibrium module is used to calculate the partitioning between gas and fine-mode aerosol phase in the system of SO\({}_{4}^{-}\), HNO\({}_{3}^{-}\), NO\({}_{3}^{-}\), NH\({}_{3}^{-}\), and NH\({}_{4}^{+}\)[27]. More information on the gas and aerosol partitioning is given in [27], Section 7.6. Regarding secondary organic aerosols (SOA), the EmChem09 soa scheme is used, which is a simplified version of the so-called volatility basis set (VBS) approach. The main differences between the VBS schemes and EmChem09 soa is that all primary organic aerosol (POA) emissions are treated as non-volatile in EmChem09 soa. This is done to keep the emission totals of both PM and VOC components the same as in the official emission inventories. The semi-volatile biogenic and anthropogenic SOA species are assumed to oxidise (also known as ageing process) in the atmosphere by OH reactions. This will lead to a reduction in volatility for the SOA, and thereby increase partitioning to the particle phase. More information on SOA is given in [28].  
Detailed information on the meteorological driver, land cover, model physics, and chemistry are described in [PERSON] et al. [27] and in the EMEP Status Report 2017 [29].
# Unmanned Aerial Vehicles Rescue System Design and Traffic Model Planning
## 2 Materials and Methods
### Establishment of Traffic Planning Model
#### 2.4.1 Transportation Planning Model Based on Benefit Maximization  
According to the maximum rescue distance of the UAV disaster response system, the locations should be determined to follow the following four principles:  
(1) Each delivery location was within at least one ISO cargo container of the UAV disaster response system's maximum rescue distance.  
When \(u_{ij}=1\), otherwise \(u_{ij}=0\).  
\[\sum\limits_{j=1}^{3}u_{ij}\geq 1,(i=1,2,3,4,5). \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Exterior**} & \multicolumn{3}{c}{**Interior**} & \multicolumn{3}{c}{**Door Opening**} \\ \cline{2-9}  & **Length** & **Width** & **Height** & **Length** & **Width** & **Height** & **Width** & **Height** \\ \hline
20\({}^{\prime}\)Standard & 20\({}^{\prime}\) & 8\({}^{\prime}\) & 8\({}^{\prime}\)6\({}^{\prime\prime}\) & 19\({}^{\prime}\)3\({}^{\prime\prime}\) & 7\({}^{\prime}\)8\({}^{\prime\prime}\) & 7\({}^{\prime}\)10\({}^{\prime\prime}\) & 7\({}^{\prime}\)8\({}^{\prime\prime}\) & 7\({}^{\prime}\)5\({}^{\prime\prime}\) \\ Dry Container & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Standard ISO Container Dimensions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Drone Cargo Bay Type** & **Length (in)** & **Width (in)** & **Height (in)** & \\ \hline
1 & 8 & 10 & 14 & Top Loaded \\
2 & 24 & 20 & 20 & Top Loaded \\ \hline \hline \end{tabular}
\end{table}
Table 5: Drone Cargo Bay Packing Configuration/Dimensions by Type.  
Among them,  
\(u_{ij}=1\): Delivery Location can get help from location \(j\).  
\(u_{ij}=0\): Delivery Location cannot get help from location \(j\).  
(2) As many delivery points as possible could be handled by each ISO container of the UAV disaster response system.  
\[max\sum\limits_{i=1}^{5}u_{ij},(j=1,2,3). \tag{10}\]  
(3) Adjacent ISO cargo containers' distance was smaller than the maximum flight distance of the UAV.  
\[u_{j}=1,(j=1,2,3). \tag{11}\]  
Among them,  
\(u_{j}=1\): Adjacent ISO cargo containers' distance is smaller than the flight distance of the UAV.  
\(u_{j}=0\): Adjacent ISO cargo containers' distance is not less than the flight distance of the UAV.  
(4) The maximum number of roads could be detected in the maximum range.  
\[max\sum\limits_{k}\vec{u}_{jk},(j=1,2,3). \tag{12}\]  
Among them,  
\(\vec{u}_{jk}=1\): location. \(j\) can detect road k.  
\(\vec{u}_{jk}=0\): location. \(j\) can't detect road k.  
The best location to determine is on the coast.  
After giving the objective function and constraint conditions, the multi-objective programming model with the maximum detection road and the maximum rescue capability as the objective function could be obtained:  
\[\begin{array}{c}max\sum\limits_{i=1}^{5}u_{ij},(j=1,2,3)\\ max\sum\limits_{i}\vec{u}_{jk},(j=1,2,3)\\ s.t.\left\{\begin{array}{c}3\\ j=1\\ u_{j}=1,(j=1,2,3)\end{array}\right.\\ \end{array} \tag{13}\]
# Intrusion Detection Method Based on Preprocessing of Highly Correlated and Imbalanced Data
## 1 Introduction
### Objectives and Contribution  
The purpose of this paper is to develop an intrusion detection method based on the preprocessing of highly correlated and imbalanced data.  
The main objectives of this study include the following:  
* Addressing classification challenges related to imbalanced data, where a small number of intrusion detection examples can lead to critical false negatives.
* Developing techniques for reducing the feature space when dealing with highly correlated data.
* Evaluating the effectiveness of different classifiers as base models and optimizing their parameters.
* Proposing an intrusion detection method based on the preprocessing of highly correlated and imbalanced data.  
This paper is organized as follows: Section 2 presents the methodology of research. Section 3 describes the model construction, which involves data collection and preprocessing, feature engineering, model selection, training, and evaluation. In order to study the effectiveness of using these methods, their software models were developed in the Collab Python environment. Sections 4 and 5 discuss the main results of the investigation and present conclusions.
# Biological Production of 3-Hydroxpropionic Acid: An Update on the Current Status
## 1 Introduction  
The use of fossil resources for the production of fuels, chemicals, and materials has caused serious environmental problems, which--together with their imminent depletion--has made the establishment of renewable alternative production methods an important priority. For this reason, development of technologies for the establishment of biorefineries for the production of fuels, chemicals, and materials from renewable resources has been actively pursued to replace the use of fossil resources. Of the different molecules that are projected to be produced through biorefinery-based strategies, 3-hydroxypropionic acid (3 HP) holds an important position.  
3 HP is one of the key building-block chemicals. In the list issued in 2004 and updated in 2010, the United States Department of Energy (DOE) recognized it as one of the 12 top building-block chemicals that can be produced from biomass, ranking it in third position among the molecules selected [1]. It contains two functional groups (a carboxyl group and a \(\beta\)-hydroxyl group), which makes it attractive to serve as an excellent versatile platform for the production of a variety of high added-value chemicals through chemical modification reactions [1]. The compounds that can be produced from 3 HP include 1,3-propanediol, acrylic acid, acrylamide, acrylonitrile, propiolactone, malonic acid, homopolymers, and heteropolymers [2,3]. These compounds have a broad range of applications, and can be used for the production of adhesives, polymers, plastic packaging, fibers, cleaning agents, and resins. Until now,they have mainly been produced by the petrochemical industry from fossil resources. However, this is not sustainable, and there is an urgent need to switch to economically and ecologically sound production using renewable resources. Production of these substances from 3 HP is a sustainable solution in itself, but it is also important that the production of 3 HP itself should take place from renewable resources. For example, it has been estimated that production of acrylic acid from 3 HP, which is otherwise produced from glycerol, may be 50% cheaper than petroleum-based acrylic acid production, with a 75% reduction in greenhouse gas emissions [4]. Much research is being devoted to the biotechnological production of 3 HP from renewable resources. To achieve this goal, an intense research effort has been made (1) to identify novel biochemical pathways; and (2) regarding the metabolic engineering of microbial strains to transform them to cell factories capable of producing 3 HP (the two main renewable carbon sources being glycerol and sugars). Moreover, attempts to increase 3 HP production titers are also being made through improvements in cultivation techniques. These intense research activities have meant that a lot of research work has been published in the past four years. The aim of the current review is to provide an update on the progress that has occurred in the biotechnological production of 3 HP after 2013, as an update to our previous review article on this matter [3].
# Routing Attacks Detection in 6 LoWPAN-Based Internet of Things
## 5 Experiment Setup and Results
### Analysis of the Captured Data
The final routing dataset provided in Table 4 includes upwards of 6893 instances with 46 characteristics. Nodes within the range of the malicious node experienced a significant increase in energy consumption, with the percentage difference between energy consumed during \begin{table}
\begin{tabular}{c c} \hline \hline
**Data Type** & **Number of Received Samples** \\ \hline Without Attacks Data & 2200 \\ \hline Hello Flood Attack, & 130 \\ \hline Data-DoS/DDoS Attack & 150 \\ \hline Wormhole Attack & 130 \\ \hline RPL Rank Attack & 120 \\ \hline Blackhole Attack & 200 \\ \hline Version Attack & 140 \\ \hline Sinkhole Attack & 120 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Dataset collection of simulation in real-time. \begin{table}
\begin{tabular}{c c} \hline \hline  & (1) Reboots, (2) Node, (3) Received, (4) Max Inter Packet Time (5) \\ Node Info Graph & (10) RTmetric, (11) ETX, (10) Churn, (12) Listen Power, (13) Beacon \\ Interval, (14) Transmit Power, (15) Avg inter-packet Time, (16) \\ Power, (17) CPU Power, (18) On-Time, (19) Listen Duty Cycle, (20) \\ Min Inter-Packet Time \\ \hline Power-based Graph & (1) Average Power, (2) Radio Duty Cycle, (3) Instantaneous Power, (4) Power History \\ \hline Network Graph & (1) Received (Per Node), (2) Latency, (3) Received (Over Time), (4) \\ Node (Over Time), (5) Avg Routing Metrix (Over Time), (6) ETX \\ (Over Time), (7) Next Hop (Over Time), (8) Network Hops (Per \\ Node), (9) Routing Metric (Over Time), (10) Neighbors, (11) \\ Beacon Interval, (12) Network Hops (overtime) \\ \hline Topological Graph & (1) Serial Console, (2) Node Control, (3) Network Graph \\ \hline Sensors Graph & (1) Average Temperature, (2) Temperature, (3) Battery Voltage, (4) \\ Battery Indicator, (5) Relative Humidity, (6) Light 1, (7) Light 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Characteristics of the Cooja simulator. the attack simulation and the reference simulation being approximately 2000%. Nodes in close proximity to the malicious node can consume up to 20 times more energy. The attack directly affects nodes within the range of the malicious node, and there is a correlation between attacker-victim distance and power consumption. All power markers, except for Low-Power Mode, showed significant increases with slight variations depending on the distance of the node to the malicious node.
## 3 Transit Offset and Transit Tilt Error Model  
In ideal geometrical construction, the two axes of the laser tracker are orthogonal to each other and intersect at a point that serves as the origin for the spherical coordinate system. In some cases however, the two axes may not be orthogonal and may not intersect. Transit tilt refers to the tilt of the transit axis relative to the standing axis. Transit offset refers to the transit axis being offset from its ideal position for the standing axis and not intersecting it.  
As shown in Figure 2, the original ( nominal) coordinate system is defined as \(O\)-\(XYZ\), the nominal transit axis is defined as \(X\) axis, and the standing axis is defined as \(Z\) axis. The transit axis transfers to the \(X^{\prime}\) axis due to the transit offset error (\(OO^{\prime}\)) and transfers to the \(X^{\prime\prime}\) axis due to the transit tilt error (\(i\)). In the original (nominal) coordinate system \(O\)-\(XYZ\), the true distance is \(D\), and the vertical and horizontal angles are defined as \(H\) and \(V\), respectively. The measured distance and angles are defined as \(Dm\), \(Hm\) and \(Vm\), respectively. The correction distance and angles are defined as \(\Delta D\), \(\Delta H\), and \(\Delta V\), respectively.  
The transit offset (\(OO^{\prime}\)) produces an error in the measured distance (\(OA\)); the correction in the distance is given by \(\Delta D=OO^{\prime}\cdot cos(Vm)\), while the transit offset produces an error in the measured vertical angle(\(ZPO^{\prime}P^{\prime}\)). The correction in the vertical angle is given by \(\Delta V=OO^{\prime}\cdot sin(Vm)/Dm\). The transit tilt (\(i\)) produces an error in the measured horizontal angle (\(ZBOB\)); the correction in the horizontal angle is given by \(\Delta H\approx i\cdot cot(Vm)\). Therefore, the true distance from the laser tracker to the target can be expressed as \(D=Dm+\Delta D\), the true vertical angle is \(H=Hm+\Delta H\), and the true horizontal angle is \(V=Vm+\Delta V\).  
Figure 1: The working principle of laser tracker.
###### Abstract
For the optimization procedure, this inverse modeling utilized the gradient descent (GD) method to find the best estimate (i.e., hydrocarbon depth) based on the forward Gaussian process (GP) regression model. As mentioned before, GP acts as the alternative forward modeling technique as it is capable of being a surrogate model for the complicated mathematical model that requires high computational time to solve [15]. Researchers in [16] also stated that GP is an elegant and simple approach,  
Figure 1: Illustration of the seabed logging (SBL) application in the deep marine environment. The source is towed by a ship and the electromagnetic (EM) receivers are placed on the seafloor beforehand. The target reservoir is in the sedimentary rock. The low-frequency EM waves travel in all directions (straight arrow: direct wave; dotted arrow: air wave; dashed arrow: guided wave). where it performs well in modeling nonlinear problems. Mean squared error (MSE) was used as the objective function (i.e., loss function) in the optimization. The main focus of this work was to find the depth of hydrocarbon of observational EM responses, whose hydrocarbon layer was assumed to be present but with an unknown depth. This work is novel in two ways. First, it presents the integration of a stochastic process (the GP regression model acts as the EM forward model) and an indirect search algorithm (the iterative GD method as the acquirer of minimizer) in one inversion methodological flow. Second, applying this proposed inverse modeling for predicting the input parameter of the potential high-resistive bodies (e.g., hydrocarbon depth) is a new procedure or approach in SBL data processing. This attempt could be very beneficial to de-risk hydrocarbon exploration in the offshore environment. Details of the GP and GD approaches are discussed in the next section.
## 5 Experimental Results
### Synchronization and Performance of the Backhaul Link in the D&F Relay Node
For example, when signal 1, LOS environment, and MMSE equalization were taken into account, the PDSCH of the D&F relay node achieved an EVM (dB) level of \(-42.19\) dB at around \(\mathrm{P_{\mathbf{BS}}}=15\) dBm while, without the MMSE technique, the PDSCH of the relay node reached \(-33.43\) dB for the same \(\mathrm{P_{\mathbf{BS}}}\), such that a performance gain of 8.76 dB was achieved by the MMSE technique. Furthermore, comparing the results of Figure 14, it can be observed that the 64-QAM signal constellation presented less error  
Figure 8:2 Median EVM at physical downlink control channel for two signals, LOS and NLOS environment, and equalization step under real test. than 256-QAM in both measurement environments. In the case of the LOS channel and \(\mathrm{P_{\mathbf{BS}}}=7\,\mathrm{dBm}\), a 1.85 dB performance gain was successfully achieved by the PDSCH 64-QAM when compared to the EVM of the PDSCH 256-QAM modulation scheme.
## 3 Algorithm Design
### Establishment of Profit Function
#### 3.2.1 Revenue Function Improvement  
The traditional SU's revenue function \(P_{i}\) is expressed as:  
\[P_{i}=r_{i}k_{i}b_{i} \tag{3}\]where \(r_{i}\) represents the revenue per unit transmission rate of \(SU_{i}\); and \(k_{i}\) represents the spectral efficiency of \(SU_{i}\) transmissions. According to the [PERSON]'s theorem, the expression of \(k_{i}\) is:  
\[k_{i}=lb(1+K\gamma_{i}) \tag{4}\]  
In Equation (4), \(\gamma_{i}\) represents the signal-to-noise ratio (SNR) of \(SU_{i}\); and \(K\) represents a constant determined by the receiver's bit error rate (BER) threshold:  
\[K=\frac{1.5}{\ln\frac{0.2}{B_{1}^{tar}}} \tag{5}\]  
Furthermore, \(B_{i}^{tar}\) represents the target BER off the \(SU_{i}\). The transmission rate can be dynamically adjusted according to the channel quality with the adaptive modulation technique [14].  
In Equation (3), the traditional revenue function is determined by the allocated bandwidth of the SU and it increases with the addition of the bandwidth, which is a monotone linear function. In fact, the revenues obtained by the SU are non-linear with the allocated spectrum. According to the elastic business model [15], SUs receive higher unit revenue when they are allocated less spectrum communication to meet basic communication requirements. However, with an increase in the allocated spectrum, the SU revenues will reduce and the willingness to continue to lease the spectrum will reduce. Essentially, the SU's revenue and the allocated spectrum should be logarithmic. Therefore, a new revenue function is proposed:  
\[P_{i}=\sigma_{i}\ln(1+r_{i}k_{i}b_{i}) \tag{6}\]  
In Equation (6), use \(\sigma_{i}\) to represent the spectrum demand factor of the SU, and its value reflects the degree of demand for the spectrum of the SU. In addition, the total amount of SU allocated spectrum should not exceed the number of spectrum allocated by the PU at each stage.
# Radar-Based Target Tracking Using Deep Learning Approaches with Unscented Kalman Filter
## 1 Introduction
### Challenges with Assuming Standard Deviation: Exploring Alternative Approaches  
In Ref. [23], one challenge described with assuming standard deviation is that it can lead to underestimation or overestimation of the uncertainty in the system state. If the true standard deviation exceeds the assumed value, the filter may underestimate the uncertainty and produce overconfident estimates. On the other hand, if the true standard deviation is smaller than the assumed value, the filter may overestimate the uncertainty and produce overly conservative estimates. Overall, the wrong assumption of standard deviation limits the filter's ability to effectively capture the system's nonlinear behavior [8]. Suppose researchers without expertise or materials attempt to assume the standard deviation within data. In that case, they risk making inaccurate or unreliable estimates, leading to severe consequences in applications where accuracy and reliability are critical. To solve this problem, the author proposed a deep learning method to estimate these standard deviations, which provides many benefits, such as accurate, robust, and unbiased predictions. It can process large amounts of data, be updated, and be reused across multiple scenarios, while human intuition can be hard to replicate.  
The main problem of effectively predicting dynamic systems is the focus of this research. It is concentrated on enhancing the target state prediction in particular. The authors acknowledge that the covariance matrix, an essential part of many prediction models, may be difficult to reliably estimate using conventional approaches. The interesting aspect of the paper is how different deep learning models are combined with the UKF to anticipate dynamic systems. Incorporating deep learning models facilitates a data-driven methodology for estimating the covariance matrix, an undertaking frequently arduous in conventional techniques. The authors are able to anticipate target states with a high degree of accuracy and dependability by fusing deep learning with UKF. For this particular application, the research presents a comparative examination of CNN, GRU, LSTM, and MLP models, showing that CNN and GRU perform better in terms of accuracy and dependability. Section 2 gives a detailed explanation of materials and methods. The detailed simulation analysis and results are given in Section 3, followed by a conclusion in Section 4.