esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Port Vessel Detection System
### Tracking Path Analysis
To overcome the occlusion problem, other local trajectory constraints must be considered, allowing the trajectory to be incomplete in the event that the target is occluded, disappears, or is not detected. In this study, it is assumed that each vessel is moving at a uniform speed due to their idle motion characteristics in the image and the fact that their directions do not change abruptly [27; 28; 29; 30]. The speed of each vessel is calculated based on the coordinate information obtained from the real-time detection of the target vessel. The vessel motion velocity \(v_{i}^{k}\) is taken as the motion assumption condition of the path coherence function \(\Phi\). Therefore, the trajectory constraint of the vessel is set as follows:  
\[\Phi<\Phi_{max} \tag{7}\]  
Figure 2.0  
Figure 2.1  
where \(\Phi_{max}\) is the maximum value of the path coherence function, Equation (8) represents any two consecutive trajectory points \(X_{i}^{k}\), and the displacement of \(X_{i}^{k+1}\) must be less than the preset threshold \(d_{max}\). In Equation (9), when the target trajectory is non-complete, the next frame position of a target \(i\) is predicted according to the target motion velocity \(v_{i}^{k}\) and the video frame rate \(t\).
## 4 Joint Preprocessing Techniques for MU-Network MIMO
### Mu-Jzf  
The MU-JZF scheme enables complete suppression of interference, but it demands higher transmitter power for the jointly pre-coding process. To derive the preprocessing matrix, we stack up the \(N_{CEU}\) received vector in (12) to form  
\[\mathbf{Y}\equiv\Big{[}\mathbf{Y}^{(0)^{T}},\mathbf{Y}^{(1)^{T}},\ldots, \mathbf{Y}^{(N_{CEU}-1)^{T}}\Big{]}^{T}=\boldsymbol{\Lambda}\mathbf{F} \mathbf{d}+\mathbf{W}, \tag{20}\]  
where \(\boldsymbol{\Lambda}\equiv\Big{[}\boldsymbol{\Lambda}^{(0)^{T}},\boldsymbol{ \Lambda}^{(1)^{T}},\ldots,\boldsymbol{\Lambda}^{(N_{CEU}-1)^{T}}\Big{]}^{T}\), \(\mathbf{F}\equiv\Big{[}\mathbf{F}^{(0)},\mathbf{F}^{(1)},\ldots,\mathbf{F}^{( N_{CEU}-1)}\Big{]}\) and \(\mathbf{W}\equiv\Big{[}\mathbf{W}^{(0)^{T}},\mathbf{W}^{(1)^{T}},\ldots, \mathbf{W}^{(N_{CEU}-1)^{T}}\Big{]}^{T}\) are the \(N_{CEU}\Big{|}\Im^{(u)}\Big{|}N_{R}\times 3\Big{|}\Im^{(u)}\Big{|}N_{T}\) aggregate channel matrix, aggregate preprocessing matrix and \(N_{CEU}\Big{|}\Im^{(u)}\Big{|}N_{R}\times 1\) aggregate noise vector for the entire network MIMO, respectively. Since the aggregate channel matrix \(\boldsymbol{\Lambda}\) is a random matrix, it has full rank with probability one. With ZF criterion, the square \(\Big{(}N_{CEU}\Big{|}\Im^{(u)}\Big{|}N_{R}\times N_{CEU}\Big{|}\Im^{(u)}\Big{|} N_{R}\Big{)}\) matrix product \(\boldsymbol{\Lambda}\mathbf{F}\) in (20) must be an identity matrix. This requires that the aggregate channel matrix \(\boldsymbol{\Lambda}\) have a full row rank of \(N_{CEU}\Big{|}\Im^{(u)}\Big{|}N_{R}\)[22]. As a result, this leads to the inequality \(N_{CEU}N_{R}\leq 3N_{T}\), which is usage constraint of MU-JZF. To satisfy this constraint, each \(\Big{|}\Im^{(u)}\Big{|}N_{R}\times 3\Big{|}\Im^{(u)}\Big{|}N_{T}\) per-CEU channel matrix \(\boldsymbol{\Lambda}^{(u)}\) of \(\boldsymbol{\Lambda}\) must be full row rank of \(\Big{|}\Im^{(u)}\Big{|}N_{R}\) and \(K^{(u)}\) (the number of data symbols sent to CEU \(u\)) will be equal to \(\Big{|}\Im^{(u)}\Big{|}N_{R}\).  
Then the \(3\Big{|}\Im^{(u)}\Big{|}N_{t}\times K\) aggregate preprocessing matrix \(\mathbf{F}\) in (20) is the following pseudo-inverse of aggregate channel matrix \(\mathbf{\Lambda}\):  
\[\mathbf{F}=\mathbf{\Lambda}^{H}\Big{(}\mathbf{\Lambda}\mathbf{\Lambda}^{H} \Big{)}^{-1}. \tag{21}\]  
Applying (21) to (20), the received signal vector of (12) becomes the \(K^{(u)}\) equivalent parallel channels:  
\[Y_{k}^{(u)}=d_{k}^{(u)}+W_{k}^{(u)},\;k=0,1,\ldots,K^{(u)}-1. \tag{22}\]
# Event Knowledge Graph: A Review Based on Scientometric Analysis
## 2 Background Knowledge
### Event Relation Extraction  
The relations among events are extracted to compose the edges connecting the nodes (i.e., event entities) in event knowledge graphs. Since the relations are various and complex, especially those expressed in natural language, the pattern-matching-based approach has been widely used for such purposes by predefining a set of specific phrases as constraint rules to extract event relations. Table 2 lists some commonly used types of event relations, their meaning, and the phases for extraction, including causal relation, consequent relation, conditional relation, and concurrency relation.  
The pattern-matching-based method is easy to use, but it highly depends on human-made rules with less flexibility. As deep learning methods emerge, which show great potential for information mining, a number of deep learning models have been trained to extract and infer event relations in recent years. For instance, [PERSON] et al. [31] incorporated the knowledge from the ConceptNet [32] and increased attention to contextual information, based on which an event mention masking mechanism was designed to uncover causal relationships in the historical text data. Experimental results showed that this method was effective and exhibited strong robustness in cross-subject applications. [PERSON] et al. [33] took the temporal relation extraction as a classification task that concatenated word vectors, part-of-speech vectors, and dependency relation vectors as features imported into the bidirectional Long Short-Term Memory (LSTM) model. [PERSON] et al. [34] proposed a joint-learning model that identified event entities and temporal relations at the same time by sharing event representations to reduce error transmission between the two steps. With the same purpose, an improved framework was constructed to enhance the performance of deep neural networks through the use of probability-based distribution constraints constructed using domain knowledge. This approach also applied the Lagrangian relaxation method to the task of temporal relation extraction, achieving optimal performance [35].  
There may exist inconsistency, incompleteness, and redundancy among the extracted event entities and event relations. Measures such as entity alignment and coreference analysis are implemented to event knowledge fusion to finalize the event knowledge graph construction. The event knowledge graph G can be represented as G = (V, R), where V refers to a set of events and R refers to a set of event relations. The fused event knowledge can be organized as triples to be stored, managed, and visualized in a graph database (e.g., Neo4j database).
# Non-Orthogonal eMBB-URLLC Radio Access for Cloud Radio Access Networks with Analog Fronthauling
## 3 Analog Fronthaul Signal Processing
### Radio Resource Mapping over Fronthaul Channels
**_The easiest practical implementation of the proposed analog radio resource mapping at the EN is by grouping the subcarriers onto a specific frequency portion of the cable, as described in [18; 19; 20; 22]. As an example, let us assume that the EN is equipped with 5 antennas, that each antenna receives a 20-MHz radio signal, and that the analog fronthauling disposes of 4 links with 100 MHz bandwidth each. In this case, the above references have shown that it is possible to freely map, or to replicate, in an all-analog fashion the 5 \(\times\) 20 MHz bands onto the overall 4 \(\times\) 100 MHz = 400 MHz fronthaul bandwidth. This example corresponds to a special case of the model studied in this paper, obtained by setting \(\mu=1\)i.e., the whole radio signal bandwidth received at the ENs is mapped/replicated over the analog fronthauling. More generally, this paper posits the possibility to carry out the fronthaul mapping at a finer granularity, i.e., at a subcarrier level. In this case, filtering operations would in practice be mandatory in order to extract groups of subcarriers. This operation can be implemented in principle still by analog filters, whose design is left as future works._
# Greenhouse Temperature Prediction Based on Time-Series Features and LightGBM
## 4 Experiment
### Feature Engineering
The difference features only represent the change regularity of environmental factors at the last time or earlier and do not consider the general regularity of environmental factors in the present time range. To reflect the general trend of features, we generated statistical features according to all the data information in a time interval. As an example, if a statistical feature within a time interval is generated, a window size of 30 is needed in the training set, and the mean value, minimum value, maximum value, median, and other attributes of each feature are calculated using the sliding window algorithm [32; 33]. As examples, Temperature(outdoor)_min is the minimum value of the outdoor temperature in the sliding window, Humidity(indoor)_mean is the minimum value of the indoor humidity in the sliding window, Pressure(outdoor)_max is the maximum value of the outdoor air pressure in the sliding window, and Pressure(outdoor)_median is the median of the outdoor air pressure in the sliding window. Statistical features reflect the general trend of environmental factors based on difference features but do not combine relationships between various environmental factors. The relationship between a single environmental factor and indoor temperature may be linear, whereas combined environmental factors and the indoor temperature may have a relationship that is not simply linear. Combined features help us to express the nonlinear relationship between features and labels. Therefore, the combination information of environmental factors was introduced as cross-features (combination features), which can be information obtained by multiplication or taking the Cartesian product of environmental factors. As examples, the feature  
Figure 3: Change in indoor temperature over time. Humidity(indoor_outdoor)_quotient is the quotient of the outdoor humidity and indoor humidity, the feature Temp(outdoor)_Hum(outdoor)_quotient is the quotient of the outdoor temperature and outdoor humidity, and the feature Pre(outdoor)_Hum(indoor)_quotient is the quotient of indoor humidity and outdoor air pressure. All the difference and statistical features mentioned above are time-series features. The difference features can be described as the gap between the current time and the previous or earlier time, which allows the model to learn the change trend of environmental factors. Statistical characteristics (e.g., the mean, minimum, maximum, and median) describe the general trend of environmental factors in the time interval. These features are conducive to reducing the effect of outliers in the training process. The difference features, statistical features, and cross-features were input into the LightGBM model for preliminary feature selection. The ranking of the feature importance after selection from high to low is shown in Figure 4 (where only the top 20 features are displayed).
## 5 Simulation of Active Detumbling
From Figures 20 and 21, it is clearly observed that due to the decrease of the precession angular velocity, the cycle of the nutation angle of the space debris became longer and the nutation angle was reduced after impact. In both cases, the trend of the angular velocity of space debris before and after impact is shown in Figure 22, showing that mechanical impact active detumbling effectively attenuated the triaxial angular momentum of space debris, but with different levels. Therefore, the effect of mechanical impact active detumbling was successfully proven. Taking Case (2) as an example, the changing trend of space debris after impact was demonstrated. Figure 23 shows a comparison of space debris before and after impact. Figure 24 shows a comparison of the two-dimensional displacement of space debris before and after impact. The effect of mechanical impact active detumbling is directly demonstrated in Figures 23 and 24. The effect of active detumbling was therefore proven once again. This simulation verified that the impact reduces the triaxial angular momentum of space debris simultaneously and the direction of the combined angular momentum does not change during impact, reflecting the high efficiency of mechanical impact active detumbling. The efficiency of normal force is much greater than that of friction force. In this paper, the feasibility of mechanical impact active detumbling was verified through simulation experiments, thereby laying the foundation for mechanical impact active detumbling research.
## 3 Results and Discussion
### Reflectance, Absorbance, Transmittance and External Quantum Efficiency  
The optical properties of the HIT solar cell and its external quantum efficiency are calculated using the cell's parameters as shown in Table 5. Figure 8 shows the absorbance spectrum (Abs) in red, the reflectance (Refl) spectrum of the front side in black, the transmittance (Tras) spectrum in blue, and the external quantum efficiency (EQE) in green. The solid lines are the values calculated when the transparent conductive layer is ITO, while the dashed lines correspond to one graphene layer as a transparent conductive layer. The cell with graphene as TCO has a larger reflectance in the 300-1200 nm wavelength range, so the cell's absorbance is lower. Transmittance is the same in the whole range, regardless of which transparent electrode is being analyzed. Regarding the EQE in the ultraviolet range, the values calculated with graphene are higher than the values calculated with ITO, and in the visible range, the values calculated with ITO are higher than the values calculated with graphene. In the infrared range, they have the same value.
# Dynamic Multiple Junction Selection Based Routing Protocol for VANETs in City Environment
## 4 Simulation Setup and Result Analysis
### Simulation Results and Discussion
#### 4.3.3. Routing Overhead  
Figure 7 shows the routing-overhead of all the three protocols against the number of the nodes. With an increase in vehicular density, there is an increase in routing overhead for all the protocols. This is because the amount of control messages depends on the number of vehicles. DMJSR has least routing overhead as compared to GSR, E-GyTAR and TFOR. GSR incurs higher routing overhead because of generation of more beacon messages. It obtains the locations of its neighbor by generating a higher amount of beacons messages as compared to other routing protocols. While in E-GyTAR the routing overhead increases because of its improper junction selection mechanism that causes local optimum in the absence of directional density of vehicles. TFOR also produces more overhead because of its junction selection mechanism. Additionally, maintaining two-hop neighbor information in greedy forwarding brings more routing overhead for TFOR in highly dense environments like traffic jams.  
Figure 6: End-to-end delay vs. the number of nodes (@5 packets/second).
## 3 Materials and Methods
### Prediction
#### 3.1.2 Random-Forest Algorithm  
Although the decision-tree approach, suggested by [PERSON] [25], can be more useful, it builds a very deep and complex tree that suffers from high variance and overfitting due to high depth. Therefore, it requires pruning techniques. [PERSON] [2] introduced \"The random forest algorithm\" to reduce overfitting in the decision tree. This reduction was made by building an ensemble of \(M\) trees [26]. To obtain M decision trees in the RF algorithm, suppose \(X_{1},X_{2},\ldots,X_{p}\) and \(y_{1},y_{2},\ldots,y_{N}\) are explanatory variables and response variables, respectively. A subset of features is randomly selected, \(mtry=\frac{\mathbb{P}}{\mathbb{S}}\). The predictor space is divided into J distinct and non-overlapping regions, \(R_{1},R_{2},\ldots,R_{j}\). Then, for every observation that falls into the \(R_{j}\) region, the same prediction is made, which is the mean of the response values for the observations in \(R_{j}\). The predictor and cut point are selected. Finally, a tree is obtained that has minimum RSS. The aim of building the decision trees in the RF is to minimize the equation  
\[RSS(j,s)=\sum_{i=1}^{N_{1}}\sum_{x_{i}\in R_{1}}\left(y_{i}-\hat{y}_{R_{1}} \right)^{2}+\sum_{i=1}^{N_{2}}\sum_{x_{i}\in R_{2}}\left(y_{i}-\hat{y}_{R_{2} }\right)^{2} \tag{3}\]  
where \(R_{1}=\left\{x_{i}|x_{i}\leq s\right\}\) and \(R_{2}=\left\{x_{i}|x_{i}\rangle s\right\}\). \(\hat{y}_{R_{2}}=\frac{1}{N_{2}}\sum_{x_{i}>s}y_{i}\) and \(\hat{y}_{R_{1}}=\frac{1}{N_{1}}\sum_{x_{i}<s}y_{i}\) are the mean response for the training observations in \(R_{2}\) and \(R_{1}\), respectively. \(N_{1}\), \(N_{2}\), and s are the number of samples in \(R_{2}\), \(R_{1}\), and the cut point, respectively.  
Figure 2 shows the random-forest algorithm, the steps of which can be expressed as follows:  
1. Generate bootstrap datasets \(D_{1},D_{2},\ldots,D_{M}\) from the original D dataset;
2. Construct trees according to the bootstrap dataset. (In this step the tree growth is without pruning. The mtry node is selected from the predictors as a separator).
3. Generate \(M\) trees \(T_{1},T_{2},\ldots,T_{M}\).  
4. Extract \(M\) predicted tree \(T_{1}(z),\ldots,T_{M}(z)\).
5. Final prediction for the whole \(M\) regression tree as: \[\overline{y}=\frac{1}{M}\sum_{i=1}^{M}T_{i}(z)\] (4)
# Emission of Terpenoid Compounds from Rice Plants
## 3 Results
### Flux above the Rice Paddy  
Two BVOCs, \(\alpha\)-pinene and methyl salicate, were detected in the gases collected using the REA method. Linalool was not detected in any of the sampled gases. The concentrations of \(\alpha\)-pinene and methyl salicate in the upward and downward air samples were low and were reported as \(<\)4 nmol m\({}^{-3}\) in all cases (Figures S1-S4). The calculated fluxes of the two compounds from the \"Koshihikari\" paddy were very low and were reported as \(<\)0.03 nmol m\({}^{-2}\) s\({}^{-1}\) in most cases (Figure 4). The accumulated data (103 datasets) across two years provided mean \(\pm\) 95% confidence intervals of \(\alpha\)-pinene and methyl salicate fluxes of 0.006 \(\pm\) 0.004 nmol m\({}^{-2}\) s\({}^{-1}\) and 0.006 \(\pm\) 0.002 nmol m\({}^{-2}\) s\({}^{-1}\), respectively. These ranges did not include a negative value range, suggesting that the two compounds were emitted from rice paddies. No clear diurnal variation in fluxes was observed for either compound. Using the accumulated data, the relationship of the two compounds against leaf temperature and PPFD were examined (Figure 5). No clear relationships were found in any of these cases. Therefore, we did not apply the light- and temperature-dependent G93 models [23] to determine the basal flux of \(\alpha\)-pinene under standard conditions (30 \({}^{\circ}\)C leaf temperature and 1000 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\) PPFD). Instead, the average flux of \(\alpha\)-pinene (0.006 nmol m\({}^{-2}\) s\({}^{-1}\)) was used for further consideration in the Discussion section. Except for \(\alpha\)-pinene, no terpenoids were detected in the sampled gases.  
Figure 3: Relationships between linalool emission rate of rice against mean (**a**) and maximum wind velocity (**b**). The mean wind velocity was averaged for 2 h between 2 and 4 h before the measurements. The maximum wind velocity was the maximum value of 1 min averages recorded 2–4 h before the measurements.
# Assignment Approach for Electric Vehicle Charging Using Traffic Data Collected by SUMO
## 3 Proposed Solution
### Assignment Algorithm  
The system was represented by an optimization model using linear programming for optimal assignment and decision-making. The goal was to assign each EV to an appropriate CS while keeping the EV's pin SOC as high as possible when the CS location was reached. By consuming a small amount of power while reaching the recommended CS, EV drivers are more confident that the risk of failure (without power) is minimized. There are many constraints to this goal, including resource constraints, such as energy remaining in the electric battery, availability of a free CS, and the energy provided by charging stations; location constraints, such as the location of each EV and CS, the distance between them,  
Figure 1: System overview.  
road traffic, and the nature of the routes; and time constraints, such as the time needed to reach the CS.  
The assignment problem was a scientific analysis method in which we expressed the objective and constraints by linear equations. This allowed us to assign (\(n\)) individuals to establish (m) tasks. In our work, the objective was to satisfy the demand in terms of energy at the smart grid level and reduce the occupation time at the charging station. Therefore, the proposed solution was to route an EV, according to its needs, to a charging station while consuming the minimum energy.  
We proposed that each EV was characterized by:  
* The distance given by the GPS.
* The state of charge (SOC) of the remaining battery.
* The driving mode of the driver and accessories.
* The capacity and autonomy of the battery.  
Additionally, for each charging station, we granted the following parameters:  
* The number of charging stations.
* The charging power of each station and its position.  
Another very important parameter in our modeling that impacted energy consumption was the infrastructure conditions, especially between the position of the EV and the charging station. Each path was defined by several characteristics, including traffic conditions (traffic jam/not), urban area, etc. We assumed \(Alloc\) (\(i,j\)) to be the assignment matrix of a number (\(n\)) of EVs to a number (\(m\)) of load stations, such that \(m<n\) for \(1\leq i\leq n\) and \(1\leq j\leq m\).  
\[Alloc(i,j)=\begin{pmatrix}SOC_{f_{11}}&\dots&SOC_{f_{1m}}\\ \vdots&\ddots&\vdots\\ SOC_{f_{n1}}&\dots&SOC_{f_{nm}}\end{pmatrix}\]