esa-sceva/satcom-chunk-collection huggingface dataset:
# Load-Balancing Routing for LEO Satellite Network with Distributed Hops-Based Back-Pressure Strategy
## Appendix A  
Proof of Lemma 1.: Let \(M_{n}^{c}(t)\) be packets number of flow \(c\) formed at \(n\) during \([0,t]\). By dividing time into discrete points, we have  
\[M_{n}^{c}(t)=\sum_{\tau=0}^{t}m_{n}^{c}(t) \tag{1}\]  
where \(m_{n}^{c}(t)\) is packets number formed at \(n\) at time slot \(\tau\). Accumulated destination hops delay of \(M_{n}^{c}(t)\) is defined as \[H(M_{n}^{c}(t))=\sum\limits_{\tau=0}^{t}H(m_{n}^{c}(\tau)) \tag{2}\]  
Let \(F_{ab}^{c}(t)\) be packets number of \(c\) transmitted over \((a,b)\) up to time slot \(t\). At temporal point \(\tau\), packets number of flow \(c\) over \((a,b)\) is \(f_{ab}^{c}(\tau)\). Accumulated destination hops delay of \(F_{ab}^{c}(t)\) is expressed as  
\[H(F_{ab}^{c}(t))=\sum\limits_{\tau=0}^{t}H(f_{ab}^{c}(\tau)) \tag{3}\]  
Consequently,  
\[H\Bigg{(}\sum\limits_{\tau=0}^{t}X_{n}^{c}(\tau)\Bigg{)}=H\Bigg{(}\sum\limits _{b\in N}F_{nb}^{c}(t)\Bigg{)}=\sum\limits_{b\in N}H(F_{nb}^{c}(t)) \tag{4}\]  
\[H\Bigg{(}\sum\limits_{\tau=0}^{t}Y_{n}^{c}(\tau)\Bigg{)}=H\Bigg{(}\sum\limits _{a\in N}F_{an}^{c}(t)\Bigg{)}=\sum\limits_{a\in N}H(F_{an}^{c}(t)) \tag{5}\]  
Considering queue backlog of \(n\) (\(Q_{n}^{c}(t)\)),  
\[M_{n}^{c}(t)-Q_{n}^{c}(t)=\sum\limits_{b\in N}F_{nb}^{c}(t)-\sum\limits_{a\in N }F_{an}^{c}(t) \tag{6}\]  
To satisfy requirement of network stability [40], there must exist finite value \(V\) which ensures  
\[\frac{Q_{n}^{c}(t^{\prime})}{t^{\prime}}\leq\frac{V}{t^{\prime}}\leq\varepsilon \tag{7}\]  
If network is stable, for \(\varepsilon>0\) and \(t^{\prime}\geq V/\varepsilon\),  
\[\frac{M_{n}^{c}(t^{\prime})}{t^{\prime}}-\sum\limits_
# Situation Assessment of Air Traffic Based on Complex Network Theory and Ensemble Learning
## 3 Methods
### Ensemble Learning  
A single classification learning method is usually difficult to fully mine for all the classification knowledge of datasets. It will cause problems such as the poor generalization of the classification model and insufficient classification ability. Ensemble learning can synthesize the advantages of various classification learning methods to obtain a more accurate and reasonable classification result. The basic process of ensemble learning is shown in Figure 3. The training database is input into classification learning models to obtain classification datasets. Classification datasets are integrated into an integrated dataset. Finally, the ensemble learning model is trained by the integrated dataset.  
In the base classifier generation, the training database \(TD=\{\mathbf{x}|\mathbf{x}_{1},\mathbf{x}_{2}\ldots\mathbf{x}_{n}\}\) is trained on classification learners \(C_{1}\), \(C_{2}\), \(\ldots\), \(C_{t}\). In base classifier integration, an integrated database \(ED=\{C_{1}(\mathbf{x}),C_{2}(\mathbf{x}),\cdots,C_{t}(\mathbf{x})\}\) can be obtained by classification learners \(C_{1}\), \(C_{2}\), \(\ldots\), \(C_{t}\). And the ensemble learning model \(C(\mathbf{x})\) can be built by training \(ED\).  
According to the base classifiers' type [27; 28; 29], ensemble learning contains a homogeneous ensemble and heterogeneous ensemble. The heterogeneous ensemble has an advantage in improving the accuracy and generalization level of the classification model. Therefore, we choose four different base classifiers. According to reference [30; 31], we can obtain a higher classification accuracy by choosing the k-nearest neighbor, Bayes, BP neural network, and SVM. And it can also ensure that the calculation time is in an acceptable range.
## 2 Related Work
### Task Offloading Methods  
Task offloading is a pivotal method for enhancing terminal computing capabilities and reducing load in satellite networks by transferring tasks from resource-limited devices to satellites or ground cloud platforms. This approach aims to achieve a balanced distribution of computational load and maximize resource utilization. Recent studies have explored various offloading strategies, including dynamic offloading based on network conditions, task allocation based on user demands, and intelligent scheduling based on computational requirements. These strategies seek to improve task processing efficiency, reduce delays, and ensure quality of service for users.  
For example, [PERSON] et al. [25] developed an autonomous partial offloading system for delay-sensitive computation tasks in multiuser IIoT MEC systems, aiming to minimize delay and enhance quality of service (QoS). Similarly, [PERSON] et al. [26] introduced a scheduling optimization method that incorporates an atomic task offloading model based on maximum flow theory alongside a dynamic caching model. Addressing varying task demands, [PERSON] et al. [27] proposed a deep reinforcement learning-based approach that divides the offloading and resource allocation problem into two subproblems, optimizing resource allocation using the Salp Swarm Algorithm (SSA). [PERSON] et al. [28] propose an optimized distributed computation offloading scheme with dependency guarantees in vehicular edge networks, leveraging deep reinforcement learning to improve task scheduling, minimize system latency, and enhance privacy while outperforming existing offloading methods.  
Furthermore, multi-level task offloading schemes have been developed to integrate different resource management strategies, thereby enhancing flexibility and efficiency [29; 30; 31]. For instance, [PERSON] [29] formulated a collaborative task offloading and resource scheduling framework as a Markov Decision Process (MDP) and deployed deep reinforcement learning (DRL) agents for decision-making. Similarly, [PERSON] et al. [30] presented an optimal task offloading and resource allocation strategy that minimizes the weighted sum of the energy consumption and task execution time of wireless devices (WDs).  
Despite these improvements, current task offloading methods often assume stable network environments and consistent computational demands, neglecting the impact of resource fluctuations and topology changes.
# Pilot-Scale Fenton-like System for Wastewater Treatment Using Iron Mud Carbon Catalyst
## 1 Introduction
However, the activation of dual oxidants by Fenton iron peat catalysts still presents a research gap. Moreover, the research on heterogeneous Fenton-like systems based on H\({}_{2}\)O\({}_{2}\) is mainly in the laboratory stage, lacking engineering application exploration, and it is difficult to evaluate its practical application value. Therefore, it is crucial to explore the removal performance of COD in actual wastewater by FSC activation for the dual oxidants process at the pilot scale, which promotes the industrial application of heterogeneous Fenton-like technology. Ferrate (Fe(VI)) has attracted attention in treating heavy metals, recalcitrant organics, and antibiotic resistance gene spread due to its multifunctional properties (oxidation, flocculation, disinfection) [16]. In this study, a demonstration line was built to prepare the sludge-based catalyst via pyrolysis. FSC was obtained using the demonstration line. Ferrate (Fe(VI)) was introduced into the FSC/H\({}_{2}\)O\({}_{2}\) system, and the FSC/Fe(VI)/H\({}_{2}\)O\({}_{2}\) system was constructed to remove the COD in the secondary effluent of the sewage treatment plant at the pilot scale. The effects of the ratio and total concentration of the Fe(VI)/H\({}_{2}\)O\({}_{2}\) oxidant and solution pH on COD degradation were investigated. The mechanism of the FSC/Fe(VI)/H\({}_{2}\)O\({}_{2}\) system was revealed via catalyst characterization, quenching experiment, and an electron paramagnetic resonance (EPR) test. In addition, the actual application effect of the system was evaluated after 65-day COD monitoring. Finally, the catalyst preparation cost and pollutant treatment benefits are evaluated.
# Greening the NetFPGA Reference Router
## 5 Experiments
### Fixed Traffic Experiments
Thus, the hardware RTT measurements do not need to launch out-of-band Internet Control Message Protocol (ICMP) echo requests (pings) nor embed timing information in application traffic. In the RTT measurements, the experimental setup is somewhat different from the lab setup in Figure 9. All four ports are used to measure the RTT: two ports of the PG are connected directly to one another, and the RTT is measured to provide a baseline RTT reference as RTT\({}_{0}\); the other two ports of the PG are connected to the RR or the FSR under test; and the RTT is measured as RTT\({}_{1}\). The RTT is calculated as the difference between RTT\({}_{1}\) and RTT\({}_{0}\). Figure 12 reports the RTT of the RR and the FSR under different operating frequencies and different packet sizes. Experimental results indicate that a higher operating frequency and a smaller packet size could give rise to a shorter round trip time. Compared to the smaller-sized packet, the larger-sized packet has a longer RTT because it takes longer time to buffer and process. For example, a packet 64 bytes long has a 2 us RTT, while a packet 1500 bytes long has a 16 us RTT with the same operating frequency of 125 MHz for the RR. The traffic characteristics and the operating frequency also affect the packet loss. Figure 13 presents the packet loss of the RR and the FSR under the same 1500-byte packet stream with different operating frequencies and different aggregated input traffic bit rates. Experimental results indicate that, for both the RR and the FSR, the packet loss is proportional to the traffic bit rate. Besides, a higher operating frequency could result in lower packet loss. Figure 12: Round trip time of the RR and the FSR under different frequencies and packet sizes. Figure 13: Packet loss of the RR and the FSR under different frequencies and traffic bit rates.
## 5 Experimental Verification
### Selective Power Transfer at Static Load Condition  
Initially, the separation of the Tx coil and the receiving section is maintained at 5 c.m, and the source and Tx resonant frequency are changed from 1 MHz to 3 MHz to select the Rx 1 to Rx 4. The load coil voltages are measured as shown in Figure 16. Figure 16a represents load-1 and load-2 voltage waveform, when the Tx coil is operated at 1 MHz the voltage at load-1 is 4.8 V and the other load voltages are less than 0.4 V.  
Figure 16: Measured load voltage of selective WPT system. (**a**) \(f\) = 1 MHz; (**b**) \(f\) = 1.5 MHz; (**c**) \(f\) = 2 MHz; (**d**) \(f\) = 3 MHz.  
Figure 15: Experimental setup of proposed MRWPT system.  
Similarly, when the Tx frequency is changed from 1 MHz to 1.5 MHz, 2 MHz and 3 MHz the respective load coil voltage is increased to more than 4.5 V and other non-resonant load coil voltages are below 0.5 V as shown in Figure 16b-d. It shows that the selective uniform power transfer is accomplished with the very minimal amount of cross coupling. Under the static condition, the effect of frequency splitting also not present in the load voltages as the measured voltage consists of the only single frequency component.
###### Abstract  
The unprecedented growth of today's cities together with increased population mobility are fueling the avalanche in the numbers of vehicles on the roads. This development led to the new challenges for the traffic management, including the mitigation of road congestion, accidents, and air pollution. Over the last decade, researchers have been focusing their efforts on leveraging the recent advances in sensing, communications, and dynamic adaptive technologies to prepare the deployed road traffic management systems (TMS) for resolving these important challenges in future smart cities. However, the existing solutions may still be insufficient to construct a reliable and secure TMS that is capable of handling the anticipated influx of the population and vehicles in urban areas. Along these lines, this work systematically outlines a perspective on a novel modular environment for traffic modeling, which allows to recreate the examined road networks in their full resemblance. Our developed solution is targeted to incorporate the progress in the Internet of Things (IoT) technologies, where low-power, embedded devices integrate as part of a next-generation TMS. To mimic the real traffic conditions, we recreated and evaluated a practical traffic scenario built after a complex road intersection within a large European city.  
smart city; Internet of Things; embedded devices; genetic algorithm; optimization +
Footnote †: journal: Sensors
# Displacement Patterns and Predictive Modeling of Slopes in the Bayan Obo Open-Pit Iron Mine
## 2 Data and Methodology
### Methodology
#### 2.3.5 Model Prediction Evaluation  
Prediction accuracy was quantified using root mean square error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R\({}^{2}\)):  
RMSE is the square root of the average of the squared differences between the predicted and true values, measuring the deviation between predictions and actual values, as shown in Equation (14).  
\[\text{RMSE}=\sqrt{\frac{1}{\text{N}}\sum_{i=1}^{\text{N}}\left(\mathbf{y}_{i}- \hat{\mathbf{y}}_{i}\right)^{2}} \tag{14}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Optimization** & **Maximum** & **Initial Learning** & **Learning Rate** & **Learning Rate** \\
**Algorithm** & **Number of Epochs** & **Rate** & **Drop Factor** & **Drop Period** \\ \hline Adam & 1000 & \(5\times 10^{-3}\) & 0.1 & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter settings for the GRU model.  
MAE is the average of the absolute differences between predicted and true values, quantifying the average magnitude of prediction errors, as shown in Equation (15).  
\[\text{MAE}=\frac{1}{N}\sum_{i=1}^{N}\left|y_{i}-\hat{y}_{i}\right| \tag{15}\]  
R\({}^{2}\) measures the model's ability to explain the variability in the target variable, with a range of [0, 1]. A value closer to 1 indicates better model fit, as shown in Equation (16).  
\[R^{2}=1-\frac{\sum_{i=1}^{N}\left(y_{i}-\hat{y}_{i}\right)^{2}}{\sum_{i=1}^{N} \left(y_{i}-\overline{y}\right)^{2}} \tag{16}\]  
where y\({}_{i}\) is the i-th true value, \(\hat{y}_{i}\) is the i-th predicted value, \(\overline{y}\) is the mean of true values, and N is the total number of samples.
# Traffic Signal Optimization for Multiple Intersections Based on Reinforcement Learning
###### Abstract  
In order to deal with dynamic traffic flow, adaptive traffic signal controls using reinforcement learning are being studied. However, most of the related studies are difficult to apply to the real field considering only mathematical optimization. In this study, we propose a reinforcement learning-based signal optimization model with constraints. The proposed model maintains the sequence of typical signal phases and considers the minimum green time. The model was trained using Simulation of Urban MObility (SUMO), a microscopic traffic simulator. The model was evaluated in the virtual environment similar to a real road with multiple intersections connected. The performance of the proposed model was analyzed by comparing the delay and number of stops with a reinforcement learning model that did not consider constraints and a fixed-time model. In a peak hour, the proposed model reduced the delay from 3 min 15 s to 2 min 15 s and the number of stops from 11 to 4.7 compared to the fixed-time model.  
traffic signal optimization; reinforcement learning; adaptive traffic signal control; multiple intersections; Deep Q-network +
Footnote †: journal: Applied Sciences
# Identification of Bottlenecks in Passenger Handling Processes Using Data-Driven Tools
## 4 Discussion
Additionally, other approaches can be introduced, such as random assignment of passengers to stations, which can serve as a control scenario for comparing the efficiency of different methods. These scenarios allow for testing extreme situations where passenger behavior is unpredictable and provide valuable data for designing optimal assignment strategies. The model can further be expanded to include various types of check-in processes. Currently, passengers at airports can check in using three methods--online check-in, self-service kiosks, or staffed check-in desks. The model can simulate the choice of the check-in process using binomial or multinomial distributions. A binomial distribution assigns a probability to whether passengers choose online check-in or a staffed desk, while a multinomial distribution can include self-service kiosks and assign different probabilities to each type of station based on statistical data or passenger preferences. This approach allows modeling real-world situations at airports where the choice of check-in process depends on passenger preferences, queue lengths, or station availability. Based on the model outputs, situations causing system overload can be identified, and appropriate solutions proposed. In addition to increasing the number of service stations or staff, additional self-service systems could be considered to reduce the burden on staffed check-in desks. Expanding the model to include these elements allows for analyzing system behavior under different configurations and determining optimal solutions for improving the efficiency of passenger handling processes. Although the current version of the model does not include all processes, such as immigration or customs control, its expansion to incorporate these elements would enable the creation of a comprehensive tool for strategic planning and process optimization at airports or constructing a comprehensive digital twin. Such a model could be used not only to identify bottlenecks or support strategic airport planning but also provide an experimental environment for testing infrastructural changes, evaluating staff allocation plans, or simulating policy interventions (e.g., changes in security protocols or boarding procedures). In summary, the simulation model is already a useful exploratory tool, and its development potential is considerable. With the integration of Monte Carlo techniques, macro-automated testing, and additional behavioral or structural logic, the model can evolve into a versatile decision-support platform capable of guiding operational improvements and innovation in airport passenger handling.
# DGA Domain Name Classification Method Based on Long Short-Term Memory with Attention Mechanism
###### Abstract  
Currently, many cyberattacks use the Domain Generation Algorithm (DGA) to generate random domain names, so as to maintain communication with the Communication and Control (C&C) server. Discovering DGA domain names in advance could help to detect attacks and response in time. However, in recent years, the General Data Protection Regulation (GDPR) has been promulgated and implemented, and the method of DGA classification based on the context information, such as the WHOIS (the information about the registered users or assignees of the domain name), is no longer applicable. At the same time, acquiring the DGA algorithm by reversing malware samples encounters the problem of no malware samples for various reasons, such as fileless malware. We propose a DGA domain name classification method based on Long Short-Term Memory (LSTM) with attention mechanism. This method is oriented to the character sequence of the domain name, and it uses the LSTM combined with attention mechanism to construct the DGA domain name classifier to achieve the rapid classification of domain names. The experimental results show that the method has a good classification result.  
security; DGA classification; attention mechanism; LSTM +
Footnote †: journal: Applied Sciences
## 5 Case Study
### Simulation Results about Consuming Res  
First, we calculate the pure load of the whole system, which is shown as Figure 4. The pure load represents the normal load minus RES, which will be the actual power output of the conventional generations. However, we can find that the ramp rate is beyond the maximum or minimum rate of conventional generations from time slot 6 to time slot 10 and time slot 14 to 16 due to the sharp decrease and increase of RES. In practice, the ISO has to abandon RES to ensure a balance if there is no DR. This case is shown as Figure 5. Ramp rate constraints of conventional generations can be ensured, while a large amount of RES has to be abandoned to help conventional generations adjust.  
Figure 4: Pure load curve and RES curve.  
Figure 5: Conventional generation curve and RES curve through abandoning RES to get balance.  
To help consume more RES and get more social welfare, LSE will exceed the proposed DR to help in adjusting. The proposed DR scheme is tested, and its result is shown as Figure 6. Because of the adjusting of DR customers, all the RES can be totally consumed and the ramp rate of conventional generations can also be ensured.  
In practice, RES usually has an opposite trend with the customer load, which results in violent fluctuation for the pure load. The proposed CDL-based DR scheme is able to promote customers changing their load profile and resulting in the load curves having a closer trend with RES. In order to illustrate this issue, we can define a variable \(\rho\) to evaluate two time series' similarity according to [54]. Assume that \(X_{1}\) and \(X_{2}\) are two time series with the same length, and we transfer each series to 0 to 1. For example,  
\[X_{1}^{*}=\frac{X_{1}-\min(X_{1})}{\max(X_{1})-\min(X_{1})} \tag{30}\]  
and \(X_{2}\) can be transferred to \(X_{2}^{*}\), too. Then, \(\rho\) is define as the variance of the difference value series of \(X_{1}^{*}\) and \(X_{2}^{*}\). A larger value of \(\rho\) means smaller similarity and larger opposite trend of the two time series. In order to evaluate similarity of a load curve to the RES curve, we let the load curve be \(X_{1}\) and RES curve be \(X_{2}\). Firstly, the total customer load curve without DR is set to \(X_{1}\); we can work out that \(\rho=0.387\). If the CDL-based DR scheme is implemented, the value of \(\rho\) will decrease to 0.354. This means the total customer load curve is changed to be closer to the RES curve under the proposed DR scheme. Then, each DR customer's load curve is set to \(X_{1}\) one by one and values of \(\rho\) are shown in Figure 7. After implement of DR, \(\rho\) for each customer changes to become smaller, which means every customers' load curve also becomes closer to the RES curve in the process of DR.  
Figure 6: Conventional generation curve and RES curve through the proposed DR scheme to get balance.  
Figure 7: The value of \(\rho\) for each customer.