esa-sceva/satcom-chunk-collection huggingface dataset:
# Parallel-Structure Deep Learning for Prediction of Remaining Time of Process Instances
## 3 Proposed Method
### Network Architecture
#### 3.3.1 Multi-Layer Perceptron with a Data-Aware Entity-Embedding Layer
```
1:
2:Input:
3:\(L\): Event log, \(\{\sigma_{l}:i=1,\ldots,K\}\),
4:\(SOP\): Set of prefixes, \(\{p_{i}:i=1,\ldots,N\}\), sorted by its \(\sigma_{T}\)
5:Output:
6:\(DAE\): Data-Aware Events
7:Begin
8:
9:\(DAE\leftarrow\varnothing\)
10:for\(i\gets 1\ldots N\)do
11:\(row\leftarrow\varnothing\)
12:for\(j\gets 1\ldots max\_length\)do
13:\(tuple\leftarrow\varnothing\)
14:\(tuple\gets tuple\parallel\rho_{act}(i,j)\)
15:for\(k\gets 1\ldots|D|\)do
16:\(tuple\gets tuple\parallel\rho_{att}(i,j,k)\)
17:endfor
18:\(row\gets row\parallel tuple\)
19:endfor
20:\(DAE\gets DAE\parallel row\)
21:return\(DAE\)
```  
**Algorithm 1** DAEE Build-Up from SOP. Algorithm 1 shows how to build up the fixed-size vector data-aware events from the SOP. We now introduce some functions: \(\rho_{act}\) returns the activity at row \(i\) and at the \(j\)-th event in the SOP, while \(\rho_{att}\) returns the \(k\)-th data attribute at row \(i\) and at the \(j\)-th event. It is noted here that data attributes include only the categorical variables, just like in the SOP. Before the data-aware events input enters the MLP part, it is passed to the embedding layer, where the value of each vector will be updated in line with the training process of the network. The issue of how to determine the number of dimensions in the embedding layer is still being researched [30]. Therefore, we determine it with an arbitrary number, which is high, so that the network can learn a good representation compared with having only a single number, but also not too high, which would cause loss of representation (so that the result would be the just same as that with one-hot encoding). As for the continuous variable input, it is received by precisely one neuron. We concatenate all of the output vectors of the embedding layer and input neurons that handle continuous variables before finally forwarding them to the MLP. We obtained an acceptable performance using this model in comparison with other machine-learning models, such as deep learning with one-hot encoding [31]. In the MLP part, empirically, the deeper (the more the hidden layers of) the network, the better the generalization ability. Therefore, we used four layers heuristically while performing trial and error experiments (also known as the babysitting method) with other options. As for the number of neurons in each hidden layer, there are no fixed rules regarding this determination, but we followed a best practice: the number of neurons in the hidden layer is between the number of neurons in the input layer and that in the output layer, and the number of neurons in the hidden layer that is closer to the output layer is less than the number of neurons in the hidden layer closer to the input layer.
# Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network
## 4 Problem Define and Experimental Design  
### Problem Statement  
The purpose of this paper is to demonstrate and investigate feature selection as a framework to detect DDoS in 5G network traffic. We explain it in two parts: defining the problem statement and presenting the objective function of this experiment. Finding the most influential features related to 5G network traffic can be formally given as follows:  
Let X be a dataset containing n pairs (\(x_{i}\), \(y_{i}\)) of 5G network traffics, where \(x_{i}\in\) X is a data sample described by m input features, and target variable \(y_{i}\) corresponds to its multi-class.  
\[\begin{array}{l}\text{X}=\{(x_{1},y_{1}),(x_{2},y_{2}),\,\ldots,(x_{n},y_{n})\} \text{, where n is the number of samples}\\ y_{i}\in\{y_{1},y_{2},\,\ldots,y_{k}\}\text{, where k is the total number of classes}\end{array} \tag{1}\]  
Assume that feature vector \(F\) denotes a full feature set, \(f\) is a feature, and selected feature subset vector \(S\) denotes the set of subset variables, respectively. The first objective function \(g(x)\), identifies irrelevant, redundant features in 5G networks and finds the best representative subset of features to improve machine learning accuracy. Given a feature set \(F=\{f_{i}\ |\ \ i=1\ \ldots\ m\}=\{f_{1},f_{2},\,\ldots,f_{m}\}\), find a subset \(S_{n}=\{f_{1},f_{2},\,\ldots,f_{n}\}\), with \(n<m\), that optimizes an objective function \(g(x)\), where m is the number of input features and n is the number of subset features, respectively. Therefore \(g(x)\) maximizes the learner's ability to classify the target class.  
\[S_{n}=\operatorname*{arg\,max}_{f_{i}\in F}(g(f_{i};Y;M)),\text{ where }M\text{ is leanerd model} \tag{2}\]  
The second objective function, Complexity (\(F\), \(t\), \(M\)), minimizes the runtime of the training phase to reduce the cost of detecting DDoS attacks. For each feature selection method, the runtime (\(t\)) of the training phase can be measured to analyze the massive volume of DDoS traffic and compare detection costs.  
\[C(F,t,M)=\operatorname*{arg\,min}[C(F,t,M)-C(S_{n},t,M)] \tag{3}\]  
where \(F\) is a full feature set, \(S\) is a selected feature set, \(M\) is the learner, and \(t\) is the training time (second) of the training data (D).
# Classroom Expression Recognition Based on Deep Learning
## 2 Related Works
### Educational Applications of Facial Expression Recognition
#### 2.3.1 Application in Online Teaching  
For multiple participants in online classes, it is often difficult for teachers to detect students' emotional changes one by one. This expression recognition system provides teachers with emotion recognition to help them better understand students' emotional states and optimize classroom interactions. Specific application scenarios include:  
Personalized teaching: Students' facial expressions can be monitored. By analyzing students' emotional fluctuations (such as anxiety, confusion, boredom, etc.), teachers can understand students' learning status and adjust the classroom rhythm and teaching content. For example, the system will display students' emotions in the form of graphs on the screen. If multiple students show anxiety or confusion when explaining a certain knowledge point, the teacher can adjust the teaching content or involve students through interactive question-and-answer sessions to improve classroom interaction and learning outcomes.  
Emotional monitoring: By analyzing facial expressions in the classroom environment, students' concentration can be judged. For classes with a large number of students, teachers can understand the emotional state of the entire class through the system, providing more data support and help for teachers, students, and parents.
## 3 Experimental Results
### Misting System
#### 3.2.2 Ultrasonic Transducer Mist Spraying  
In this experiment, the efficiency of the ultrasonic transducer misting system was evaluated through tests designed to assess the difference in cultivation performance across a 6 m long growing area with and without the use of guiding tubes. As shown in Table 2, the setup including guiding tubes resulted in uniformly distributed misted nutrients across the cultivation area, leading to notably healthier plant growth.  
On the other hand, in the scenarios without guiding tubes, plant growth significantly declined further away from the misting point, especially on the side opposite to the misting point where growth issues were prominently worse. These results suggest that even in ultrasonic misting systems, the introduction of external air can cause backflow issues, significantly obstructing the efficient delivery of nutrient mist across the growing area.  
Nevertheless, the introduction of guiding tubes in the ultrasonic mixing setup allowed for consistent and healthy plant growth throughout the cultivation area. This uniform nutrient distribution demonstrates the potential for enhanced cultivation performance through optimized control of growing conditions and nutrient solutions. Future research should explore fine-tuning the cultivation environment and optimizing nutrient mixtures to maximize growth efficiency based on these system configurations.
# Eco-Efficient Mobility in Smart City Scenarios
## Personal Vehicles and C-ITS
### Gap Analysis  
As seen, the range of novel services from which personal vehicles will benefit is extensive. Therefore, we are facing a new scenario in which the integration of this range of personal mobility devices within smart cities and C-ITS ecosystems becomes a real demand from the society. However, research on communications and telematics in personal vehicles is notably limited so far. Previous works usually put two-wheelers at the same level of pedestrians, with the requirement differences and clear consequences that this implies [32]. Some papers have proposed the use of the cellular infrastructure for exchanging data with servers that emit safety warnings in case of potential collision with vulnerable road users [33]. Given the latency introduced by this technology, other proposals have adopted WiFi to reduce the system response time. However, due to the high power consumption of these technologies, some works have suggested the use of Zigbee [18] or Bluetooth [19], although the short communication range of these technologies certainly limits its general application in widespread vehicular scenarios. Finally, other authors bet on vehicular-specific communications, using IEEE OCB. However, interference issues should be considered in highly-crowded scenarios such as the urban one, together with strident battery constraints of personal mobility devices [34].  
In the light of the previous discussion, an important set of challenges to be addressed is observed to provide eco-efficient personal vehicles with appropriate communication capabilities:  
* Lack of specific hardware/software designs for bicycles, scooters, and similar vehicles.
* Efforts are focused on cellular or IEEE 802.11 OCB technologies, but not on hybridizing different communication technologies, adopting a Multi Radio Access Technology (multi-RAT) approach.
* The interconnection of these vehicles with IPv6 networks has not been addressed, which is crucial for their full integration in the Future Internet.
* The use of standardized protocols is not generalized, which is of prominent importance given the long-term life-cycles of the target vehicles.
* To the best of the authors' knowledge, there are no works exploiting the use of LPWAN technologies for personal vehicle monitoring, tracking, or eventual notification delivery.
* There are few proposals fostering transport intermodality and sustainable mobility options.
* Considering personal mobility devices as part of the future crowd-sensing ecosystem implies new IoV capabilities and sensory OBU platforms not present at the moment.  
In the following, we present a wide overview of the vehicular communication landscape, discussing how some specific solutions can cover the identified gaps, as shown in Figure 2.
## 4 Experimental Methods and Results
### Experimental Methods and Results of the Proposed Framework
#### 4.3.1 Comparisons Result between the EMD-LSTM Model and Other Models
\begin{table}
\begin{tabular}{c l c c c c} \hline \hline
**IMFs** & **Input features** & **MAE** & **RMSE** & **MAPE** & \(\mathbf{R^{2}}\) \\ \hline \multirow{3}{*}{1} & IMF & 3.0886 & 3.7415 & 1.4874 & 0.0186 \\  & IMF + TIs & 3.8096 & 4.7048 & 2.7588 & \(-\)0.5518 \\  & IMF + PCA & 2.8581 & 3.5379 & 1.3411 & 0.1225 \\ \hline \multirow{3}{*}{2} & IMF & 1.3146 & 1.5134 & 2.8782 & 0.7544 \\  & IMF + TIs & 1.9184 & 2.2518 & 2.8495 & 0.4562 \\  & IMF + PCA & 0.7432 & 1.0220 & 1.3655 & 0.8880 \\ \hline \multirow{3}{*}{3} & IMF & 0.8446 & 1.0072 & 0.6026 & 0.9867 \\  & IMF + TIs & 0.8597 & 1.0726 & 0.5518 & 0.9849 \\  & IMF + PCA & 0.5220 & 0.6367 & 0.3536 & 0.9947 \\ \hline \multirow{3}{*}{4} & IMF & 0.5653 & 0.8389 & 0.2236 & 0.9960 \\  & IMF + TIs & 1.1520 & 1.5367 & 0.1784 & 0.9865 \\  & IMF + PCA & 0.3113 & 0.4243 & 0.0622 & 0.9990 \\ \hline \multirow{3}{*}{5} & IMF & 0.4080 & 0.4232 & 0.3565 & 0.9972 \\  & IMF + TIs & 1.1124 & 1.4344 & 1.1847 & 0.9679 \\  & IMF + PCA & 0.6061 & 0.6862 & 0.6483 & 0.9927 \\ \hline \multirow{3}{*}{6} & IMF & 0.2432 & 0.3083 & 0.0914 & 0.9948 \\  & IMF + TIs & 0.7971 & 0.9648 & 2.1934 & 0.9489 \\ \cline{1-1}  & IMF + PCA & 0.9819 & 1.0147 & 3.9208 & 0.9435 \\ \hline \multirow{3}{*}{7} & IMF & 0.0275 & 0.0309 & 0.0000 & 1.0000 \\ \cline{1-1}  & IMF + TIs & 2.4216 & 2.4945 & 0.0025 & 0.9414 \\ \cline{1-1}  & IMF + PCA & 0.7128 & 0.8066 & 0.0007 & 0.9939 \\ \hline \end{tabular}
\end{table} 
Table 7: Results of IMFs prediction from EMD-LSTM with additional input feature. Figure 10: Results of IMF prediction using only IMF value (**a**) and principal component (**b**). Figure 9: The MAE result applies an additional input feature to each IMF of the EMD-LSTM.
## 3 Adaptive Rule Based Controller
### Offline Optimization of Dynamic Programming  
In the DP-based controller, the control variable is the power of the battery, \(P_{bat}\), which is depicted in Equation (3), and the SOC of the battery, \(SOC_{bat}\), and the ultra-capacitor, \(SOC_{uc}\), are selected as the state variables, as shown in Equation (4).  
\[u=\{P_{bat}(t)\} \tag{3}\]  
\[x=\{SOC_{bat},SOC_{uc}\} \tag{4}\]  
The discretization step of the optimization \(dt\) is 1 s and the state transition can be calculated by Equations (5) and (6).  
\[SOC_{bat}(k+1)=SOC_{bat}(k)-\frac{\Big{(}U_{bat}-\sqrt{U_{bat}^{2}-4R_{bat}P_{ bat}(k)}\Big{)}dt}{2R_{bat}Q_{rate}\cdot 3600} \tag{5}\]  
\[SOC_{uc}(k+1)=SOC_{uc}(k)-\frac{\Big{(}U_{uc}-\sqrt{U_{uc}^{2}-4R_{uc}P_{uc}(k )}\Big{)}dt}{2R_{uc}C(U_{uc\_max}-U_{uc\_min})} \tag{6}\]  
where \(U_{bat}\) and \(R_{bat}\) represent the voltage and the resistance of the battery, respectively. Similarly, the voltage, resistance, and power of the ultra-capacitor are labeled by \(U_{uc}\), \(R_{uc}\), and \(P_{uc}\), respectively. \(C\) presents the capacity loss of the battery. \(U_{uc\_max}\) and \(U_{uc\_min}\) denote the maximum and minimum of the ultra-capacitor voltage.  
The indexes that greatly reflect the economic performance of HESS are the life and the electricity consumption of the battery. Therefore, in this paper, the capacity loss of the battery, \(C\), and the electricity depletion of the battery, \(\Delta SOC\), which are depicted in Equations (7) and (8), are selected as the control objectives.  
\[C =\frac{I_{bat}}{Q_{rate}} \tag{7}\] \[\Delta SOC =\frac{I_{bat}\times dt}{3600Q_{rate}} \tag{8}\]  
Actually, in a certain state, the discrete step, \(dt\), and the nominal capacity of battery, \(Q_{rate}\), are determined. Thus, the optimal decision of the DP is the same when Equation (7) or Equation (8) is selected to be the cost function. In this paper, the cost function \(J\) is computed by the following:\[J=\sum_{k=0}^{N-1}\frac{I_{bat}}{Q_{rate}} \tag{9}\]
## 3 Results
### Identification of Candidate Genes for RppSLN  
We compared the sequences in the candidate region of the susceptible recurrent parent N531 and the resistant introgression line N531_R (Figure 3a). N531 only contains one R gene (_NR5_) (Data set 2), an allele of _Zmays10G000440_. To further confirm the candidate genes, we conducted real-time quantitative polymerase chain reaction (RT-qPCR) analysis detecting the expression levels of these genes (_Zmays10G000430_, _Zmays10G000440_, and _NR5_) between N531_R and its NIL line N531_S at 0 h, 12 h, 24 h, 48 h, and 96 h after infection (Supplementary Materials S1). The results indicated that the relative expression levels of _Zmays10G000430_ and _Zmays10G000440_ were synergistic expression patterns in N531_R (Figure 3b). However, _NR5_ lacks a 2 kb sequence in the 3'-UTR that is shared by _Zmays10G000440_, which might explain why _NR5_ lost its resistance. _NR5_ expression levels after induction were not significantly changed, suggesting that the absence of the 3'-UTR  
Figure 2: Fine mapping of _RppSLN_. (**a**) The resistant inbred line N531_R and susceptible inbred lines F35 and hybrid F\({}_{1}\) (N531_R\(\times\)F35). Scale bar: 30 cm. (**b**) A major southern corn rust-related QTL located in the F\({}_{2}\) population of N531_R/F35. LOD, logarithm of odds. (**c**) A large-scale isolation of 2253 F\({}_{2}\) individual plants combined with a genotyping linkage analysis delimited _RppSLN_ to a 38 kb interval flanked by the markers W4 and W6. The _RppSLN_ region contains two putative NLR genes, which encode NBS-LRR proteins. Green and yellow segments represent N531_R and F35 alleles, respectively. Phenotypes are provided next to the haplotype. R, resistant; S, susceptible.  
sequence may impact its function in conferring resistance (Figure 3c). A comparison was made with the published K22 inbred line reference genome [32], which identified three R genes in the target confidence interval (_R1_, _R2_, _R3_). Among these, _R3_ was confirmed to be the resistance gene _RppK_ for SCR. Further analysis revealed that _Zmays10G000430_ and _Zmays10G000440_ correspond to the K22 alleles _R1_ and _R3_, respectively (Figure 3a).
## 2 Materials and Methods
### Overview, Design Principles, Details
#### 2.1.2 Entities, State Variables, and Scales  
Entities of this model are three classes of agents: several user agents, a governing agent, and a registrar agent. State variables of user agents are named _threshold_ and _decision_. Each user agent's _threshold_ is a real number between 0 and 1, which is predefined at the beginning of each simulation, remains constant throughout the simulation, and is visible to that user agent alone. User agents' _decisions_ are binary variables that change throughout the simulation and are visible to all agent classes upon request. State variables of the governing agent are named _signal_, _state_, \(Q\), and _policy_, which change throughout the simulation. Except for _signal_, all other variables of the governing agent are known to itself only. _Signal_ is a binary variable. _State_ is a two-dimensional variable with non-negative integer values. \(Q\) is a table with a real value for each _state_ and _signal_ combination. _Policy_ is a table with a real number between 0 and 1 for each _state_. The registrar's state variables are named _nLast_ and _nSum_, which are non-zero integers that change throughout the simulation and are visible to all agent classes upon request. User agents and the governing agent are the main entities of the model. Registrar is an auxiliary agent that is meant to make the model easier to understand and serves as a mediator of information. This abstract model does not have a spatial dimension, and time in the model is measured with dimensionless time steps.
# Arsenic (V) Removal by an Adsorbent Material Derived from Acid Mine Drainage Sludge
## 2 Materials and Methods
### Arsenic Adsorption Batch Test
#### 2.3.4 Adsorption from Environment Samples of Acid Mine Drainage  
The capacity of the adsorbent to extract arsenic from an environmental sample of acid mine drainage (AMD) was evaluated. Acid mine drainage was collected from a mine drainage collection facility located in a gold mine. The AMD contained 291 \(\pm\) 19 \(\mu\)g/L of As, 871 \(\pm\) 102 \(\mu\)g/L of Zn, 210 \(\pm\) 50 \(\mu\)g/L of Cd, and 65 \(\pm\) 8.7 \(\mu\)g/L of Fe.  
An arsenic stock solution was mixed with the environmental sample of AMD to achieve an initial arsenic concentration of 50 mg/L. Then, 2.5 mL of aqueous solution containing 1000 mg/L of As(V) was added to 47.5 mL AMD. The pH was adjusted to 6.0 by adding 0.1 M HCl and NaOH. Then, 0.05 g of adsorbents were added to this 50 mL solution, reflecting a 1 g/L dosage of adsorbent. A solution sample was taken at 48 h, and arsenic concentrations were determined as described in Section 2.2.
## 2 Analysis Methods
### Irregular Wave Analysis  
Time history of the irregular wave was generated through the superposition of a number of linear regular waves. This study applied the two-parameter Pierson-Moskowitz (PM) spectrum to express the real sea wave condition using the following equation [37].  
\[S(\omega)=\frac{173H_{\mathrm{S}}^{2}}{T_{P}^{4}\omega^{5}}\exp\left(-\frac{6 92}{T_{P}^{4}\omega^{4}}\right) \tag{18}\]  
where \(S\) is the sea spectrum, \(H_{\mathrm{S}}\) is the significant wave height, and \(T_{P}\) is the peak period. Each wave amplitude can be obtained as follows:  
\[A(\omega_{i})=\sqrt{2S(\omega_{i})d\omega(\omega_{i})} \tag{19}\]  
where \(d\omega\) is the frequency interval.  
Under the irregular wave conditions, the wave elevation at an arbitrary location can be obtained by superposing the regular waves as follows:  
\[\eta(t)=\sum_{i=1}^{Nw}A_{i}\cos(k_{i}(x\cos\alpha_{i}+y\sin\alpha_{i})-\omega _{i}t+\varepsilon_{i}) \tag{20}\]where \(\eta\) is the wave elevation, \(Nw\) is the total number of regular waves, and \(\varepsilon\) is the random phase angle. The frequency interval between regular waves was randomly set to prevent the repeatability of the time history of wave elevation [38].  
The time ramp function was applied to gradually increase the wave elevation and external force acting on the structure to avoid the rapid response of the numerical simulation and the instability of calculation. The ramp function is expressed as Equation (21), and \(T_{ramp}\) of 50 s was applied.  
\[ramp(t)=\left\{\begin{array}{ll}1&for\ t\ >\ T_{ramp}\\ 1-\cos\left(\pi\frac{t}{2T_{ramp}}\right)&for\ t\ \leq\ T_{ramp}\end{array}\right. \tag{21}\]
# Random Access Using Deep Reinforcement Learning in Dense Mobile Networks
## 4 RL Based Selection of TRxPs for RA
### TRxPs Search and Selection  
(1) The cell search procedure is initiated when a user has a buffer to send/receive data to/from the TRxPs. Cell search and selection criteria depend on the users' RSRP and reference signal received quality (RSRQ) measurements. Based on these measurements, users can select from the available TRxPs and monitor system parameters through the system information block (SIB2) signal. A user can attach to a selected TRxP given that its current RSRP measurement is higher than a threshold value provided according to the following equation.  
\[\text{RSRP}>\text{RSRP}_{\text{min}}. \tag{9}\]