esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### The HJA-N Model
#### 2.3.5 Aquatic Environment Model  
Most watershed-level studies of nutrient retention and release focus primarily upon terrestrial processes, and most watershed-level ecosystem models maintain this focus, and do not explicitly include nutrient dynamics within near stream areas. Yet it is known that the aquatic and hyporheic environment in small streams can exert significant influence on both the quantity and the forms of exported nutrients [32,33]. The residence time of water and solutes can also be extended based upon hyporheic exchange flows [39]. A 15N addition experiment [40] demonstrated that 32.5% of N added over a 6-week period during the growing season was retained by a second order stream in HJA. HJA-N explicitly includes a set of stocks (Table 2) and flux terms (Table 6) designed to capture the potential role of the aquatic system in regulating the export of terrestrial N fluxes.  
The model makes use of three pools to represent nitrogen in the aquatic environment, and in this version of the model carbon is not accounted for within the aquatic environment. The pools that are included correspond to DON, DIN, and the aquatic biomass. These pools are assumed to represent the combination of the channel and hyporheic zones. The aquatic biomass pool contributes, through a first order respiration model, to both the DIN and DON pools. In addition, gross immobilization of DIN, as an addition input into the aquatic biomass, is also included as a first order term. Nitrogen is lost from the stream system as DON and DIN export, and also through a first order denitrification term. The aquatic biomass also includes a loss rate associated with particulate export, conceptually associated only with the near stream area environment. Inputs of particulate matter from the upslope region are defined based upon the turnover terms (litterfall and mortality) of foliage and woody material.  
The loss of aquatic biomass is treated as a first order rate that is activated only above a discharge-based threshold. At high flows, accumulated biomass is quickly lost from the system, with periods of accumulation during lower flow conditions.
## 1 Introduction
### Security in WMSNs  
Many applications of WMSN have their additional and special requirements in terms of privacy and security, such as military applications, medical care applications, and other video surveillance systems. In addition to the fact that WMSNs are vulnerable to attacks more easily than the wired networks because of their nature as a broadcast medium. In a broadcast medium, adversaries can easily eavesdrop on, intercept, inject, and alter transmitted data [3]. In addition, adversaries are not restricted in using sensor network hardware. They can interact with the network from a distance by using expensive radio transceivers and powerful workstations. In general, there are many types of security attacks on sensor networks such as: jamming, tampering, altered routing information, sinkhole, Sybil, wormholes, acknowledgment spoofing, and hello-flood attacks [4].  
Attacks can be classified into outsider attacks and insider attacks [4]. Outsider attacks are where the attacker has no special access to the sensor network, and the insider attacks are where an authorized participant in the sensor network has gone badly. Insider attacks can be either compromised sensor nodes running malicious code or external devices use stolen key material and data from legitimate nodes to attack the network.  
The resource scarcity, _ad hoc_ deployment, and immense scale of WMSNs make secure communication a particularly challenging problem. Since the primary consideration for sensor networks is energy efficiency, security schemes must balance their security features against the communication and computational overhead required to implement them. Therefore, our focus for designing the security scheme is to implement a lightweight security protocol in terms of energy efficiency and processing complexity in order to secure the communication among the nodes so that every eligible receiver should receive all messages intended for it and be able to verify the integrity of every message as well as the identity of the sender. In this paper, we propose a lightweight distributed key management protocol designed to ensure authentication, integrity and confidentiality, without needing of a central key distribution. Our security scheme is scalable because every node needs to generate a small number of security keys even with deploying a large-scale network, it protects against the majority of outsider attacks, and it resists against insider attacks since the stolen keys will affect only the local area (cannot be used in other clusters). Advanced insider attacks can be eliminated by using simple intrusion detection [5], which is outside the scope of this paper.  
The rest of the paper is organized as the following: Section 2 introduces an overview of existing related works. Section 3 provides network architecture and system model and assumptions. Section 4 describes the cluster-based multipath routing protocol in details. Section 5 presents the security scheme for key management. Section 6 presents the performance evaluation. Finally, Section 7 concludes the paper with summary and directions for future works.
## 6 Suggested Measures to Reduce Energy Consumption and/or Energy Costs
### Legal and Theoretical Framework  
The Portuguese Energy and Climate Plan for the 2021-2030 horizon comprises the ambitious target of reaching a 47% share of energy from renewable sources in gross final consumption in 2030. This plan promotes and dissemates the adoption of renewable energies and its objective is to make Portugal a more self-sufficient country in terms of energy production. The participation of citizens and companies in the energy transition through their investment in renewable energies was facilitated by Decree-law No. 162/2019. This decree-law established and regulated the legal regime applicable to the renewable energy production activity associated with the self-consumption of this renewable energy, namely the production of electricity through production units for self-consumption (UPAC, in Portuguese language). The UPAC are units that have renewable energy as their primary source and whose objective is to satisfy their own electricity needs. UPACs with installed power greater than 30 kW and equal to or less than 1 MW are subject to prior registration for the installation of the UPAC and an operating certificate.  
The implementation of a photovoltaic system requires the dimensioning of different technologies for the system to be functional. Photovoltaic solar modules and photovoltaic inverter(s) are required. A photovoltaic solar module (commonly known as a photovoltaic panel) is the technology that allows it to capture sunlight and convert it into photovoltaic electrical energy. A photovoltaic inverter, on the other hand, allows converting the energy from direct current photovoltaic solar modules into alternating current, which makes it possible to use this energy in different electrical appliances.  
To obtain the environmental analyses, an electricity emission factor of 0.257 tCO2 eq./ MWh was considered, which is based on the moving average of the 5 years preceding 2020 in mainland Portugal [26]. To carry out the financial analysis, an inflation rate of 5% was considered [27].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Specific Consumption** & **Before** & **After** & **Reduction** & **Reduction (\%)** \\ \hline m\({}^{3}\)(n)/kg & 0.337 & 0.2525 & 0.0845 & \\ \hline kgoe/t & 306.7 & 229.8 & 76.9 & \\ \hline \hline \end{tabular}
\end{table}
Table 15: Energy indicator of specific consumption before and after implementation of the measures.
## 3 Research Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No. of Mixture** & \multicolumn{5}{c}{**Shrinkage Ratio \(\varepsilon\times 10^{-4}\)**} \\ \cline{2-7}
**(Table 1)** & **3 h** & **6 h** & **1 Day** & **2 Days** & **3 Days** & **7 Days** \\ \hline
1 & 0.609 & 0.865 & 1.218 & 1.376 & 1.445 & 1.604 \\
2 & 0.653 & 0.890 & 1.221 & 1.342 & 1.383 & 1.507 \\
3 & 0.725 & 0.942 & 1.21 & 1.312 & 1.365 & 1.465 \\
4 & 0.565 & 0.748 & 1.07 & 1.211 & 1.275 & 1.353 \\
5 & 0.564 & 0.718 & 1.035 & 1.163 & 1.225 & 1.325 \\
6 & 0.660 & 0.820 & 1.082 & 1.203 & 1.214 & 1.337 \\
7 & 0.505 & 0.733 & 1.035 & 1.179 & 1.235 & 1.340 \\
8 & 0.528 & 0.75 & 1.031 & 1.153 & 1.212 & 1.304 \\
9 & 0.668 & 0.827 & 1.075 & 1.195 & 1.215 & 1.311 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Shrinkage ratios of concrete and fiber-reinforced concrete in air-dry conditions. After 7 days of hardening, concrete shrinkage does not stop, but the general nature of the change in shrinkage decreases, which confirms the positive influence of variable factors on this characteristic. Regardless of the hardening accelerator amount, fiber-reinforced concretes, with a fiber amount of 50 kg/m\({}^{3}\) and with a fiber amount of 100 kg/m\({}^{3}\), are characterized by significantly less shrinkage compared to unreinforced ones. This is explained by the ability of the fiber framework to keep the structural blocks of the composite from moving when moisture is lost during structure formation [48; 49]. The fact that compositions with fiber amounts of 50 kg/m\({}^{3}\) and 100 kg/m\({}^{3}\) are characterized by almost the same shrinkage can be explained by the fact that with an increase in the amount of dispersed reinforcement amount in the composition, the \(W/C\) mixture increases. Accordingly, the positive influence of the spatial grid of the fiber reinforcement is leveled by the influence of an increased water amount in the concrete mixture. The hardening accelerator amount also affects the shrinkage value of the investigated concretes, but less significantly. Due to the addition of the maximum dosage of Sika Rapid 3, the concrete shrinkage at the age of 7 days is reduced by 3.9% and with the addition of 1.2% of this modifier--by 2.7%. Shrinkage reducing due to the use of a hardening accelerator is explained by the internal stresses in the composite material, arising in the process of structure formation and moisture loss, being restrained by a cement-sand matrix, which is more durable in the early stages of hardening.
## 4 Experimental Settings  
### Datasets  
The dataset utilized in this research, named SubwayBJ, consists of real data from the Beijing subway AFC system along with corresponding information on Beijing holidays and meteorological conditions. The proposed model is then trained and tested using the produced dataset.  
The particulars of the SubwayBJ dataset are illustrated in Table 1. The dataset includes inbound and outbound passenger flow data collected from Beijing Metro Line 13. We selected two time periods from 15 January 2018 to 31 December 2018 and 4 January 2019 to 31 December 2019, before the novel coronavirus pneumonia. The time granularity is set to 30 min, resulting in a total of 16 stations and 30,816 time segments. The shape of the dataset is (30,816,2,4,4), where \"2\" represents inbound and outbound stations, and the last two digits indicate the 4 \(\times\) 4 station distribution network. Additionally, external data including holiday and daily meteorological information for each time segment are also added. The data has a total of 57 days of holidays, 17 weather types, a temperature span of [\(-\)12, 38] and a wind speed range of [1, 61] m/s.  
\begin{table}
\begin{tabular}{c c} \hline
**Dataset** & **SubwayBJ** \\ \hline Date span & 15 January 2018–31 December 2018 \\  & 4 January 2019–31 December 2019 \\ Time & 0:00–24:00 \\ Time granularity & 30 min \\ Space-time grid map size & 4 \(\times\) 4 \\ Holiday numbers & 57 \\ Weather types & 17 \\ \hline \end{tabular}
\end{table}
Table 1: Dataset information.  
Since the \(\tanh\) function (as shown in Formula (11)) is used by the ST-RANet model to limit its output within the range of [\(-\)1, 1], we apply the Min-Max normalization method to prepare the input data. By doing so, we also ensure that the data range is confined to the interval of [\(-\)1, 1]. The corresponding formula for this normalization process can be defined as  
\[x^{\prime}=\frac{x-x_{\min}}{x_{\max}-x_{\min}}\times 2-1 \tag{12}\]  
where \(x\) represents the original passenger flow value, \(x_{\max}\) denotes the maximum value of all passenger flow data, \(x_{\min}\) represents the minimum value, and \(x^{\prime}\) is the resultant normalized value.  
Then each time slice is traversed, and any intervals lacking information related to closeness, period, or trend were removed, resulting in a total of 25,296 time slices. Data from the last 28 days are retained for testing purposes, while the rest of the data were employed for training. Therefore, the test set size is (1344,2,4,4) and the training set size is (23,952,2,4,4), with 10% of the data in the training set being allocated for validation.
# Research on Advancing Radio Wave Source Localization Technology Through UAV Path Optimization
## 4 Results
### Circular Trajectory Placement with a Radius of 100 m
#### 4.1.2 Using Only AOA  
The results of source localization using only AOA are shown in Figures 18-20.  
As shown in Figure 19, the spatial distribution of errors using AOA reveals relatively moderate errors in most areas, though certain locations still exhibit large errors exceeding 30 m. This is likely due to the degradation of angular accuracy in environments with signal reflection or diffraction, where the estimated angle may not correspond directly to the true direction. Figure 20 shows that 90% of the sources are localized within 20 m. While this is a considerable improvement over RSSI-only estimation, a small number of significant outliers remain. This suggests that while angular information provides better directionality, it may still be affected by multipath propagation and shadowing in complex environments.  
Figure 19: Heatmap of localization errors using AOA as the sole feature. AOA yields improved accuracy over RSSI, particularly in line-of-sight regions.  
Figure 18: Optimized UAV placements for AOA-based localization. UAV positions and altitudes were selected to maximize angular estimation effectiveness.  
These findings are in line with previous studies such as [PERSON] et al. [6], where AOA-based localization with UAVs was shown to benefit from careful UAV placement and collaborative sensing strategies.
# Improvement of Power Converters Performance by an Efficient Use of Dead Time Compensation Technique
## 3 Proposed Dead Time Compensation Technique
### Modeling of the Proposed Dead Time Compensation
To achieve the characteristics of an ideal relay, transfer features such as m, \(V_{a}\) are used. Figure 7 also gives a representation of the dead time feedback blocks \(g(i_{a})\), and feed-forward block \(f(\varepsilon)\) of the SPWM inverter model. Owing to the inherent phase lag the feed-forward compensation cannot be ignored. At this stage, the DTC technique can be applied to the 3-phase PWM inverter, an example of which has been demonstrated in Figure 3.  
The compensation of dead time blocks \(g(i_{a})\) (feedback) and \(f(e)\) (feedforward) of the 3 phase SPWM inverter model is presented in Figure 7 schematically. Due to an inherent phase lag, the \(f(\varepsilon)\) (feed-forward compensation) cannot be ignored. Just now at this stage, the DTC technique is ready to be applied on 3-phase PWM inverter for practical implementation which is presented in Figure 3.  
Furthermore, before the dead time compensation method the equation has the following shape:  
\[e=[V_{i}(t)]-V_{c}(t) \tag{25}\]  
After converting the deviation, \(\varepsilon\), to phase voltage, \(V_{a}\); adjusting \(T_{d}\) and \(\varepsilon\) equal to zero; eliminating the factor \(-\left(\frac{\varepsilon}{2}\right)sign(i_{a})\) through feedback factor \(g(i_{a})=\left(\frac{\varepsilon}{2}\right)sign(i_{a})\) application; and applying for the compensation through feedforward for dealing with the inherent phase angle. It is pertinent that the voltage control signal \(V_{i}(t)\) in (25) is equal to \([V_{i}(t)-f(\varepsilon)+g(i_{a})]\) and Equation (25) becomes:  
\[e=[V_{i}(t)-f(\varepsilon)+g(i_{a})]-V_{c}(t) \tag{26}\]  
where \(\varepsilon=V_{i}-V_{c}\) and \(f(e)\) and \(g(i_{a})\) are nonlinear functions. Figure 8: Three-phase SPWM inverter with the proposed DTC method. Figure 9: Load current with/without DTC (**a**) phase 1 (**b**) phase 2 (**c**) phase 3.
# Performance Analysis of Multihop Underground Magnetic Induction Communication
## 2 Underground MI Communication System
### Single-Hop MI Communication Model  
A simple schematic of an MI communication model comprising a source node (Tx) and a transmitter node (Rx) separated by distance \(r\), over which an MI communication link is established, is shown in Figure 2. Both nodes have identical configurations; namely, each node is a coil of diameter \(d\), and serve as magnetic antennas. We considered a standard American Wire Gauge, for which the resistance per unit length, \(R_{0}\), of the loops could vary between \(2\times 10^{-4}\) and \(3\ \Omega/\text{m}\)[14].  
Figure 2: MI communication model for single hop communication.  
In single-hop communication, the transmitter directly induces a voltage in the receiver circuit. The underlying principle of MI communication is that when a time-varying electric signal is induced in the transmitter coil, a time-varying magnetic field is generated around the coil. The coupling of this magnetic field with the neighbouring coil (relay or receiver) induces a voltage in the coil, thereby establishing a communication link between the two coils. This communication link is said to be established on the basis of mutual induction (\(M\)) between the two coils (or nodes), and it can be expressed as [14; 20].  
\[M=\mu\pi N_{k}^{2}\frac{a_{k}^{4}}{2r^{3}}. \tag{1}\]  
Here, \(N_{k}\) is the number of turns of coil \(k\), \(a_{k}\) denotes the radius of the coil, and \(r\) is the internode distance. The impedance \(Z\) of the circuit is given by  
\[Z=R+j\omega L+\frac{1}{j\omega C}, \tag{2}\]  
where \(\omega=2\pi f_{0}\) denotes the angular frequency of the circuit for operation at the resonant frequency \(f_{0}\). For a resonant circuit, the inductance \(L\) and capacitance \(C\) are related as  
\[LC=\frac{1}{\omega^{2}}. \tag{3}\]  
The capacitor is designed to satisfy the resonance condition [20]  
\[j\omega L+\frac{1}{j\omega C}=0 \tag{4}\]  
The received power \(P_{r}\) and transmit power \(P_{t}\) can be evaluated as [14]  
\[\frac{P_{r}}{P_{t}}=\frac{Z\cdot U_{K}^{2}}{\left(Z+Z_{K}+Z_{K}\right)^{2}}. \frac{Z_{K-1}+Z_{K-1}^{\cdot}}{U_{K-1}^{2}}, \tag{5}\]  
where \(Z\) is the load impedance. The subscript \(K\) denotes the receiver while \(K-1\) is used for transmitter. \(U_{K}\) and \(U_{K-1}\) are voltages and \(Z_{K}\) and \(Z_{K-1}\) are the impedances of receiver and transmitter, respectively. In the next section, we develop a multihop communication system on the basis of this single-hop communication model.
## 2 Related Work  
Sales route optimization is a complex and well-studied problem, particularly in the context of logistics and last-mile distribution. It closely aligns with classic combinatorial optimization problems such as the Traveling Salesman Problem (TSP) and the Vehicle Routing Problem (VRP), which are NP-hard and require sophisticated strategies for scalable solutions [1; 2; 3].  
Traditional exact methods--such as integer linear programming and dynamic programming--are often impractical for real-world applications due to exponential computation times. Consequently, metaheuristic techniques, including genetic algorithms (GA), Ant Colony Optimization (ACO), and Tabu Search (TS), have become widely adopted due to their ability to provide near-optimal solutions with manageable computational effort [4, 5, 6].  
Despite their success, basic GA implementations suffer from limitations such as premature convergence and poor adaptability in dynamic environments. To address these challenges, adaptive and hybrid variants have been developed. [PERSON] et al. [7] proposed an adaptive GA with improved performance in time-window-constrained routing problems. Similarly, [PERSON] et al. [9] demonstrated the effectiveness of integrating Tabu Search into GA to enhance local refinement.  
More recent efforts emphasize hybrid metaheuristics and machine learning integration. [PERSON] and [PERSON] [12] proposed an enhanced salp swarm algorithm for solving a permutation flow shop VRP, outperforming GA and simulated annealing in terms of convergence and solution quality. [PERSON] et al. [13] explored various crossover techniques in GA, concluding that a hybrid crossover approach yields superior results for real-world route planning.  
From a learning-based perspective, deep neural networks have been employed for real-time travel time prediction and dynamic routing. [PERSON] et al. [10] introduced a deep learning model that enhances dynamic decision-making in urban travel time estimation. A recent survey by [PERSON] et al. [11] categorized deep learning approaches for solving TSP, including end-to-end, hybrid, and LLM-based architectures, which show promising generalization capabilities for complex routing instances.  
Moreover, the integration of sustainability and multi-objective considerations has gained traction. [PERSON] et al. [14] combined NSGA-II and MOPSO to optimize VRP while accounting for social utility and environmental impact. [PERSON] et al. [15] proposed a VRP framework that incorporates demand urgency and road damage, significantly improving emergency response times in multi-disaster scenarios.  
Despite these advances, many methods still fall short in terms of real-time adaptability and robustness under uncertainty. Our proposed hybrid GA model addresses these limitations by integrating adaptive mutation, Tabu Search, and LSTM-based travel time prediction, offering a scalable and intelligent solution for dynamic sales route optimization.
# Ensemble Classifiers for Network Intrusion Detection Using a Novel Network Attack Dataset +
## 5 Experiments
### A Holistic Approach for IDSs Using Ensemble ML Classifiers  
In this section, we propose an ensemble classifier model (shown in Figure 5) composed of multiple classifiers with different learning paradigms to address the issue of the accuracy and false alarm rates in IDSs. The proposed model is composed of three ML classifiers from various classifier families. The selection of these classifiers is based on the results from the previous section. The selected classifiers are J48 (DT-C4.5), IBK (KNN), and MLP (NN). In the proposed model, the three classifiers work in parallel, and each classifier builds a different model of the data. The outputs of the three classifiers are combined using the majority voting method to obtain the final output of the ensemble model. The selection of the three classifiers is based on the results shown in Table 6.  
Figure 4: Accuracy of each classifier in classifying different classes in GTCS.  
Figure 3: Classification accuracy in the two phases.  
In the ensemble system, each classifier builds a different model of the data based on the preprocessed dataset. To build the models, each classifier was tested using the 10-fold cross-validation technique within the dataset, wherein the dataset is divided into 10 folds or subsets. Any nine subsets were used as training sets, and the remaining subset was used as the test set. More specifically, each fold was analyzed, and the total score results determined the average performance out of the 10 folds. Majority voting is a traditional and common way to combine classifiers.