esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Adsorption
#### 2.5.2 Effect of Contact on MPMs and EDA-MPMs  
The effect of sorption time on the adsorption of Cr(VI) was also investigated from 1 to 210 min under pH = 2, at 298 K and initial concentration of 200 mg/L. As can be clearly seen from Figure 6, the Cr(VI) adsorbability on the magnetic microbeads increased nonlinearly with the increasing time. For MPMs, the sorption of Cr(VI) ion onto the MPMs rose slowly and reached equilibrium after 180 min. For EDA-MPMs, the initial rate was rapid and about 80% uptake of Cr(VI) was achieved during the first 30 min and the remaining amount removal occurred in the following 90 min. In comparison with MPMs, the initial rapid step of Cr(VI) sorption on EDA-MPMs may be attributed to electrostatic attraction. At low pH, the amino groups of the adsorbent were the protonated cations (-NH\({}_{3}\)+ or -NH\({}_{2}\)-) and were bared on the surface of EDA-MPMs which emerged a facilely immediate interaction between Cr(VI) ions and the active. The Cr(VI) sorption capacity of EDA-MPMs will be primarily studied in the following substance.
## 4 Results
### Imbalanced Fault Diagnosis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Dataset1** & **Dataset2** & **Dataset3** & **Dataset4** & **Dataset5** & **Dataset6** & **Dataset7** \\ \hline Testing sample number of & Real & 100 & 100 & 100 & 100 & 100 & 100 \\ each class & Synthetic & 0 & 0 & 0 & 0 & 0 & 0 \\ Training sample number of & Real & 100 & 100 & 100 & 100 & 100 & 100 \\ majority class & Synthetic & 0 & 0 & 0 & 0 & 0 & 20 \\ Training sample number of each & Real & 1 & 1 & 1 & 1 & 1 & 1 \\ minority class & Synthetic & 0 & 1 & 3 & 4 & 9 & 49 & 99 \\ Imbalance ratio & & 100:1 & 50:1 & 25:1 & 20:1 & 10:1 & 2:1 & 1:1 \\ (majority/minority) & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Component of the bearing datasets for diagnosis. To demonstrate the performance of ACGAN-SN in data synthesis, four contrasting methods are used in this paper to demonstrate the superiority of the proposed method. including ACWGAN [33], ACGAN [13], Ro-Sampling [34], and SMOTE [35]. The data synthesis methods all use the original data in Table 4 as the input, and the classification model adopts the MLP and the BiLSTM model proposed in this paper; that is, there are a total of 10 combined algorithms. We used the above 10 algorithms to perform classification tasks on datasets 1-7, for which the MLP-based diagnosis results are shown in Figure (a)a and the LSTM-based diagnosis results are shown in Figure (b)b. We found that compared with the original unbalanced dataset 1, different data synthesis methods have a certain improvement in diagnostic performance in different classification models, but the diagnostic effects of ACWGAN and ACGAN are unstable. RO-Sampling, SMOTE, and ACGAN-SN have a positive effect on the diagnostic accuracy of the classification model. Notably, the proposed ACGAN-SN is of great help for improving the diagnostic performance of the MLP and LSTM classification models. Compared with the comparative data synthesis algorithms, the combined algorithm based on ACGAN-SN and BiLSTM has the best diagnostic accuracy and is the most stable. This shows that the data synthesized by ACGAN-SN have the highest similarity with the original data. To observe the internal operation process of the fault diagnosis model more intuitively, we used t-distributed stochastic neighbor embedding (t-SNE) to visualize the feature distribution. Figure (a)a-e show the t-SNE diagrams of ACGAN-SN, ACWGAN, ACGAN, RO-Sampling, and SMOTE models with an imbalance ratio of 25:1,  
Figure 11: Diagnosis results with different data augmentation approaches: (**a**) MLP-based model and (**b**) BiLSTM-based model. seen from Figure 12, the proposed ACGAN-SN method separates the different fault data well, while the other four methods did not have a good clustering effect on the fault data, which once again proves the advantages of the proposed method.
## 2 Taxonomy of DDoS Attacks
### DDoS Attack Architecture
#### 2.2.1 Volumetric-Based DDoS Attacks  
This category of DDoS attack floods the target network's available bandwidth with huge data packets, thus overwhelming it. The attack saturates the targeted network with abnormally large amounts of malicious traffic to deny service to authorized users. Any server that cannot handle the increased traffic volume can be brought down instantly by such attacks. The generic structure of a conventional volumetric-based DDoS attack is illustrated in Figure 6.  
Figure 4: Volume of DDoS attacks generated through IoT devices in 2020 [40].  
Figure 5: The major categories of DDoS attacks.  
The attackers' goal, as depicted in Figure 6, is to overwhelm the bandwidth of a victim site by sending as much traffic as they can. Attackers mostly exploited amplification strategies, which involve sending brief legitimate requests to a domain name server (DNS) with a spoof source IP address of the victim to overwhelm the target server [42]. Volumetric attacks are recognized by a massive amount of traffic (100 GB/s or more). A reflection medium can be used to produce gigabits of traffic from a small amount of traffic. Examples of volumetric-based DDoS attacks include user datagram protocol (UDP) flooding, internet control message protocol (ICMP) flooding, network time protocol (NTP), simple network message protocol (SNMP) amplifications, character generator (CharGen), Smurf attack, and Fraggle attack, among others. In some of the literature [43; 44], they are referred to as \"bandwidth depletion attacks\".
## 5 Results
Based on the required number of communication rounds, such a network may operate for only a short duration before nodes exhaust their battery power and cease functioning. Integrating LEACH, a hierarchical clustering routing protocol, with FedAvg, FedProx, or FedBuff could help reduce the frequency of direct communication with the base-station, conserving energy and potentially improving network longevity. However, these algorithms still rely on iterative model updates distributed over multiple communication rounds to recognize each pattern, which can exacerbate resource consumption in resource-constrained IoT networks. Moreover, evaluating how hierarchical routing protocols like LEACH influence fairness in model training and data privacy preservation within federated learning could provide deeper insights into their effectiveness. In contrast, the single-shot TFNN approach requires fewer communication rounds, recog  
Figure 3: Number of active nodes while using TFNN and direct-to-sink communications. nizing patterns in each round and inherently preserving energy. Its consistent performance across rounds also highlights its potential as an energy-efficient and privacy-conscious alternative to traditional FL algorithms. In contrast, TFNN offers a single-shot approach to pattern detection. Results from Figure 3 demonstrate that an IoT network using TFNN remains active for 800 rounds before experiencing any node failures. Unlike the multi-round FL algorithms, TFNN recognizes a pattern in each round, making it significantly more efficient. Furthermore, the pattern recognition performance of TFNN remains consistent across a large number of rounds, as its built-in energy efficiency techniques extend the network's operational longevity and enhance its capacity to recognize patterns over time, as shown in Figure 4.  
Figure 4: Accuracy of event recognition when using LEACH in comparison to direct communications to the base-station, while setting \(K=5\) for handwritten digits 0–9, as shown in sub-figures (**a**–**j**), respectively.
# Predictive Modeling of Photovoltaic Energy Yield Using an ARIMA Approach
## 4 Discussion  
In this section, we will present the final forecasts from both models for the specific PV system yield and the total yield.  
Regarding the specific PV system yield, when we compare Figure 7 with Figure 9 (please see Figure 15), we can confidently conclude that the forecast, made by means of the model ARIMA (3,0,4), as suggested by the authors, is way more meaningful than the one suggested by the software. This observation is approved not only by the level of the Normalized BIC but also by the values of all fit statistics given in Tables 5 and 6, respectively. So, we can trust the forecasts produced by the ARIMA (3,0,4) model as reliable predictions for the future development of the specific PV system yield. If these results are not exact, the future yield may lie in the confidence intervals with 95% probability (please see the fourth and fifth columns of Table 11).  
Figure 14: An analysis of real data, approximated values, predicted values, and their confidence intervals for the ARIMA (3,0,5) model: total yield.  
Figure 15: A comparison between the real data, predicted values, and their confidence intervals of the ARIMA (2,0,1) and ARIMA (3,0,4) models: specific PV system yield.  
There exists a \"rule of thumb\" that says the parameters of ARIMA models \(p\) and \(q\) must not be larger than 5, and \(q\) must, in general, be larger than \(p\). These empirical rules are met by the models suggested by the authors. Another reason to trust forecasts is that both time series--specific PV system yield and total yield--are homoscedastic and stationary, which are desired properties and make prediction easier. These characteristics impact the results in such a way that the time series are not differentiated by the models or such that \(d=0\).  
In this paper, we proposed a quantitative approach to forecast the future values of both the specific PV system yield and the total yield using ARIMA models. Our suggested models demonstrated the ability to produce reliable and robust forecasts, making them applicable in various practical scenarios, such as energy management and planning for grid-connected solar photovoltaic systems.  
A limitation of this approach is the necessity for expert intervention to select and calibrate appropriate ARIMA models. However, we presented a straightforward algorithm to enhance the software-generated models, making this method more accessible for practitioners in the field. Moreover, this calibration process is typically required only once, after which the models can be consistently used to generate the necessary forecasts with minimal further adjustment.
## 5 Conclusions  
In this work, an energy-efficient cross-layer routing is accomplished by developing the ECTSA for the MANET. In the ECTSA, the energy, DSR, and mobility are calculated from the network layer. Cross-layer design enables complicated structures which are based on extra data obtained from each state. This ECTSA selects the optimal path by considering four distinct objective functions: residual energy, communication cost, DSR, and mobility. Furthermore, the ECTSA achieved superior performance and provided enhanced decisions over existing routing designs, since it comprises additional data. Additionally, the network contention calculated from the MAC layer and the competition window were adjusted according to the residual energy and contention to minimize the energy consumption of the nodes. From the performance analysis, it is known that the ECTSA achieves a better performance than the CEELBRP and EECRP-PSO. The energy consumption of the ECTSA is 7.1 joules and extends the network lifetime up to 1603 s for 50 nodes, which are fewer  
Figure 10: Analysis of Throughput.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Node Count**} & \multicolumn{5}{c}{**Throughput (Mbps)**} \\  & **CEELBRP [24]** & **EECRP-PSO [25]** & **CLPDM-SI [26]** & **HFF-GSO [28]** & **Proposed ECTSA** \\ \hline
50 & 0.79 & 0.75 & 0.74 & 0.87 & 0.91 \\
75 & 0.77 & 0.72 & 0.77 & 0.85 & 0.88 \\
100 & 0.73 & 0.67 & 0.80 & 0.84 & 0.87 \\
125 & 0.70 & 0.64 & 0.81 & 0.82 & 0.85 \\
150 & 0.68 & 0.62 & 0.76 & - & 0.77 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Performance of Throughput.  
when compared to the CEELBRP and EECRP-PSO. In future, this research will be extended by using hybrid optimization techniques to improve the performances in the MANET.  
The paper investigation, resources, data curation, writing--original draft preparation, writing--review and editing, and visualization were done by M.N.S. The paper conceptualization and software were conducted by V.B. The validation and formal analysis, methodology, supervision, project administration, and funding acquisition of the version to be published were conducted by W.-C.L. and P.B.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 System Design and Implementation
### Implementation
#### 3.3.3 Grove GPS Sensor  
For accurate and timely emergency response services, it is assumed that the exact location of the victims or a collapsed building or similar medical emergencies is known. We used the Grove GPS Breakout board, as shown in Figure 3C, to identify the desired location. It is a cost-efficient and field-programmable gadget, and uses satellite signals to determine the current position, time, and velocity of the OBU. To test the system in an indoor environment, the external antenna needs to be attached with the GPS receiver--otherwise, the GPS receiver may receive deteriorated signals.
# Safe and Sustainable Design of Composite Smart Poles for Wireless Technologies
## 1 Introduction
### Design Drivers for a Smart Pole Structure  
The general, main design drivers, are illustrated in Figure 1. The physical frame, referred to as pole or shaft, is needed primarily to carry the (RF) radios and other electrical devices in a functional and maintenance-friendly way. Since the transmitting and receiving electronics require protection against weather--irrespective if they are fully integrated inside the pole or not--the selected structural materials must possess known, specified interference with RF signals to allow for a dense pole population operating over the specified frequency range. Especially, when customers, such as cities, require for an operational lifetime of 20...50 years for each pole, durability is essential as well.  
For many countries, the safety in terms of vehicle crash must be accounted for in the design of the 5G pole's shaft. The crash design affects primarily the pole sites with a high traffic density. Thus, an electrical vehicle (EV) charging stations or similar services mounted low are not allowed for these sites. The safety and overall sustainable operation of 5G is essential in general [6], since the citizens and their considerations justify the realized 5 GPs at urban areas. The sustainability of the manufacture and minimum usage of material in 5 GPs depends clearly on the realized, future operation time. Whenever material can be recycled, the selection will affect the sustainability in the big picture of future operation [7]. In an even wider perspective, the sustainability of an individual pole and manufacture covers only part of the truth. The thermal management and energy efficiency of the devices attached to the pole have a cardinal role. For certain pole sites, centralized cooling might form an important effect on the over-lifetime carbon and energy foot print of the entire wireless platform.  
For modern, dense-packed electronics [8], thermal properties and heat management are an essential part of the system design to prevent overheating during the anticipated operation. Fluid dynamics and heat transfer are the key fields of science regarding thermal management.  
The state-of-the-art numerical approach for simulating heat transfer with solid-liquid or solid-air interfaces is computational fluid dynamics (CFD). Using CFD simulations, thermal assessment on 5 GPs can also be carried out. In a previous work, air cooling of high-power electronics was investigated inside a tubular pole-like structure [9]. However, the flow control can be challenging in complex environments, such as inside a tight-packed smart pole shaft.
## 5 Lidar Scan Pattern Optimization  
While ideas for improving lidar availability and reliability, as well as other practical considerations, were presented in Section 4, this section focuses on the selection of lidar scan locations upstream of the turbine that result in measurements that best represent the actual wind variables of interest that arrive at the turbine. Lidar scan patterns can be optimized for different control strategies (e.g., collective pitch control for rotor speed regulation, IPC for asymmetric rotor load reduction, yaw control), which require measurements of different wind variables (e.g., rotor effective wind speed, wind shear, wind direction). Therefore, there is generally no single scan pattern that is optimal for all control objectives. Because of the great interest in collective pitch feedforward control for rotor speed regulation in the literature, however, the detailed investigations of scan pattern optimization presented in the rest of this paper will focus on measurements of the rotor effective wind speed.  
For the purposes of the scan pattern optimization discussed here and in Section 6, rotor effective wind speed is defined as the time-varying mean value of the longitudinal \(u\) components of the wind over the rotor disk area. For simplicity, it is assumed that (1) all locations in the rotor disk have equal importance; (2) the response of the rotor is purely a function of the linear combination of wind speeds across the rotor disk; and (3) the transverse \(v\) and vertical \(w\) wind components can be neglected. Perfect alignment between the rotor and the mean wind direction is assumed (i.e., no yaw misalignment or vertical inflow angle).  
The scan pattern optimization performed here is subject to the following potential sources of measurement error: (1) the evolution of the wind as it travels from the measurement location to the rotor plane; (2) the limitation to LOS measurements (i.e., the \"corruption\" of the wind speed measurements by the \(v\) and \(w\) components of the wind, when the \(u\) component of the wind is of interest); (3) range weighting, or the spatial averaging of the wind speeds along the beam direction; and (4) the fact that a scan pattern containing a finite number of points will not perfectly resemble a rotor disk average. All scan patterns are optimized for a rotor diameter of \(D\) = 126 m, the rotor diameter of the NREL 5 MW reference turbine [37].
## 1 Introduction
Therefore, in this paper, a 2D3V IFE-PIC numerical model was established to simulate the near-exit radial-azimuthal (\(r\)-\(\theta\)) plane of HETs. By changing the direction and magnitude of the magnetic field axial component \(B_{z}\), different tilt angles of the magnetic field line are realized. The wall erosion rate and relevant parameters are analyzed under different tilt angles. The study presented in this paper has important implications for the design and optimization of the magnetic field configuration. The organization of the paper is as follows. In Section 2, the relevant model selection, magnetic field setup, and wall erosion calculation method are presented. In Section 3, the ion beam properties and wall erosion under different magnetic field line tilt angles are compared and the associated physical mechanisms are analyzed. The paper is then summarized in Section 4.
# Analysis of LTE-M Adjacent Channel Interference in Rail Transit
## 4 Interference Analysis of LTE-M Ground System
### ACLR Model  
In the uplink, ACIR depends on the ACLR of the UE. The value of the ACLR model varies according to the interval of the adjacent channel band [39]. For a system with any bandwidth, a user in the downlink occupies only one resource block (RB), while all RBs in the uplink are evenly distributed to all users in the scheduling system. There are 50 RBs in 10 MHz bandwidth. If five user terminals are connected in uplink, the number of RBs occupied by a single user is 10 and the bandwidth \(B_{A}\) is the number of RBs occupied by a single user multiplied by the LTE RB width of 180 KHz, where X serves as the step size for simulations, X =... \(-\)10, \(-\)5, 0, 5, 10... dB, as shown in Table 7.  
Figure 9: Interference scenario network topology.
# Name Disambiguation Scheme Based on Heterogeneous Academic Sites
## 3 Proposed Name Disambiguation Scheme  
### Overall Structure  
Traditional name disambiguation schemes, such as rule-based [7] and deep learning-based schemes [13], use structured datasets. In this study, a method for name disambiguation by directly collecting data from multiple operational academic search sites is proposed. The proposed method standardizes affiliation information using a dedicated affiliation table when the affiliation is a university. Then, rules are defined using commonly used metadata to disambiguate names.  
The metadata provided by existing academic search sites is diverse. Name disambiguation schemes do not consider the diversity of metadata provided by each academic search site. To consider this diversity of metadata, a method should selectively execute the name disambiguation scheme based on the input data and exhibit the best performance. Additionally, even if some metadata can be used in the proposed method, if the expected performance is inferior to that of existing schemes, applying the existing methods is more effective. We propose a multi-classifier scheme that considers all these situations. When metadata are input, the multi-classifier is performed based on the rule-based scheme proposed in this paper, as well as the existing deep learning-based name disambiguation method.  
The multi-classifier uses limited metadata from the actual data required by each method to select the most suitable scheme for name disambiguation. The multi-classifier is designed in an expandable manner to consider also new name disambiguation schemes that may emerge in the future.  
Figure 3 presents the overall system architecture of the proposed scheme. The collector gathers research papers, project data, and affiliation information for research outcomes from academic search services. The preprocessor creates a set of name-ambiguity candidates with identical names, considered as subjects in this study, from all the documents collected by the collector. The collected affiliation information is transformed into a standardized form using an affiliation table. Attributes to be used for name disambiguation are then extracted. In the analyzer, the preprocessed attribute data are used to analyze the similarity between all documents of the name-ambiguity candidate group using both the rule-based and deep learning schemes. The analyzer performs calculation simultaneously for the rule-based candidate group similarity and the deep learning-based candidate group similarity. Finally, in the discriminator, the analyzed document similarity data are represented as a distance matrix by using sim2 diss function for clustering execution, and HAC is performed to divide the clusters by unique author documents and to disambiguate names.  
Figure 3: System architecture of the proposed scheme.