esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The study, involving 105 participants in a German public administration post-graduate program, identifies them as the 'creative class' and potential lead users for smart services due to their digital experience. Respondents prioritize addressing citywide mobility challenges, emphasizing the importance of a comprehensive smart strategy. The study suggests integrating various online platforms to meet diverse user needs and underscores key success factors, including comprehensive services, responsiveness, transparency, and data security. Limitations, including potential oversimplification of future user behaviors, are acknowledged. Extending their study in Ref. [20], researchers conducted a web survey in a mid-sized German city to explore citizen preferences for smart city digital services. With 906 valid questionnaires, respondents favored \"Smart Social Services\" and \"Smart Mobility and Smart Infrastructure\". Comparing preferences with smart city executives' estimations revealed significant differences, particularly in the \"Smart Mobility and Smart Infrastructure\" unit. The study emphasizes understanding citizens' expectations, addressing disparities in preferences, and effective communication and adaptation efforts for SC services to align with citizen concerns. In Ref. [21], researchers explored the link between motivation and public participation in urban planning in Wuhan, China. The study, which involved 502 respondents, revealed the positive effects of factors such as citizen interest on the intention to participate, highlighting the need to encourage and address constraints for optimal participation. The research in Ref. [22] investigated the relationship between e-participation and service performance in Jakarta's smart city. Using Qlue MyCity app data, the study found that citizen e-participation enhances problem resolution, particularly for complex issues. While the study emphasizes the potential of smart city platforms, it suggests further research for generalization. Ref. [23] delves into digital projects in South Korean cities, exploring citizens' preferences for smart city initiatives. The study, involving 624 residents, highlighted citizens' prioritization of projects improving daily life, emphasizing the importance of addressing practical issues in smart city planning. The primary objective of Ref. [24] is to assess the current status of smart mobility in Milan, analyzing citizen reviews. The study, using the social, technological, economic, environmental, and political (STEEP) method and strengths, weaknesses, opportunities, and threats (SWOT) analysis, underscored the importance of effective communication and understanding user concerns for managing modern municipal smart mobility systems. Ref. [25] underscores the significance of a structured and stepwise framework for smart city policy transformation, using Helmond, a Dutch city, as a case study. The study stresses the importance of feedback loops, underestimated readiness, and a tailor-made approach, highlighting the need for knowledge management and commitment in smart city initiatives.
# Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey
## 7 Sensors Fusion Techniques Using Deep Learning
### Thermal-Based Sensors  
Even though visible sensors have difficulty functioning when there is a low level of light (i.e., night-time, bad weather), they are the most commonly used sensors for pedestrian and cyclist detection applications [106]. To overcome the shortcomings of visible sensors, thermal cameras could be used in conjunction with visible cameras as, unlike visible cameras, thermal cameras are not significantly affected by ambient lighting [113]. A type of 3D system that could use both visible and thermal information to provide more accurate detection and tracking, known as an RGB-D was proposed in [114; 115]. RGB data provides textural and appearance information of the object being detected, while the depth data (e.g., thermal data) can provide additional information of the shape of the object [106]. This approach was implemented in [106] by fusing an RGB camera with a depth camera, which detects heat signatures using a thermal sensor [116; 117].  
There are two types of thermal sensors that can be used for pedestrian and cyclist detection applications, the Near-IR (infrared) camera and the Far-IR (also known as thermal) camera. Near-IR has wavelength of 0.75-1.3 \(\upmu\)m and Far-IR cameras have a wavelength of 7.5-13 \(\upmu\)m. Pedestrians and cyclists would appear more visible to the thermal cameras than the near-IR, as the pedestrian/cyclist body heat radiates in the long-wavelength (approximately 9.3 \(\upmu\)m), making the thermal camera ideal [108; 110; 118]. The value of the radiation emitted from a human is not particularly affected by other illuminations in the environment (e.g., street-lamps, artificial lighting). This can improve the accuracy of the detector, as demonstrated in [119; 120; 106; 121; 122; 107; 123]. In [123], a commercial visible camera with a resolution 640x480 was used with a thermal camera. Testing was completed during various times of the day and weather conditions (i.e., morning, night, afternoon, rain etc.). In one of the tests, using both visible and thermal cameras, the accuracy of the system was 98.13%. When comparing this result using the visible and thermal cameras separately, the accuracy was 72.11% and 95.91% respectively.  
Comparison of the benefits and drawbacks of visible and thermal cameras, RADAR and LiDAR can be found in Table 3 and Figure 7. Combining the sensor information can offset some of the inefficiencies of the individual sensors, such as a higher detection accuracy throughout the day, even in crowded scenarios.
# Comparative Analysis of Current Control Techniques to Support Virtual Inertia Applications +
## 2 Virtual Inertia and Current Control Techniques
### Model Predictive Controller  
MPC is an advanced control technique used to control power electronic systems. The ease in including system non-linearities with an intuitive control design is the main attraction of the MPC technique [34; 35]. MPC uses a model of the system to predict the future behavior of the controlled variables. The prediction in turn is used by the controller to generate near-optimal control actions by minimizing a cost function [36].  
Figure 5a shows the control structure for current control using MPC. The reference current in the \(dq\) domain is transformed into the \(\alpha\beta\) domain. Similarly, the feedback current, \(i_{abc}\), is also transformed into the \(\alpha\beta\) domain. The MPC controller then uses these signals to generate the gate signals. Figure 5b shows the details of the MPC block. The feedback current is used along with a current prediction model given by Equations (5) and (6) to predict the currents in the next sampling instant [37].  
\[\mathbf{\hat{i}}(k+1)=\frac{1}{rT_{s}+L}[L\mathbf{i}(k)+T_{s}\mathbf{v}(k+1)- T_{s}\mathbf{e}(k+1)] \tag{5}\]  
where \(\mathbf{i}\) is the grid current, \(\mathbf{v}\) is the inverter voltage, and \(\mathbf{e}\) is the grid voltage. The grid voltage can be estimated as:  
\[\mathbf{\hat{e}}(k)=\mathbf{v}(k)+\frac{L}{T_{s}}\mathbf{i}(k-1)-\frac{rT_{s }+L}{T_{s}}\mathbf{i}(k) \tag{6}\]  
An optimization function given by (7) is used within the cost function minimization block that tries to reduce the current tracking error and generates the best switching state [37].  
\[g=|i_{a}^{*}(k+1)-i_{a}(k+1)|+|i_{\beta}^{*}(k+1)-i_{\beta}(k+1)| \tag{7}\]  
Figure 4: Hysteresis current controller. (**a**) Schematic diagram. (**b**) gate signal generation.  
For a three-phase, two-level inverter, the states of each switch in the same leg are complementary to each other. Therefore, there exists only two states for each leg of such an inverter, which is either ON or OFF. Thus, such an inverter has just eight possible switching combinations that need to be considered for the optimization problem [38]. This variation of the MPC is known as finite-control-set MPC (FCS-MPC) [39; 40] or predictive direct control [41]. The MPC is able to exhibit superior dynamics due to the absence of the PWM modulator, but the controller is known to be prone to performance deterioration due to parameter uncertainties, as described in [42].
###### Abstract
Recent studies have demonstrated the successful generation of up to 179 comb lines [35]. This approach involves coupling a laser to an optical microresonator, adjusting the power (\(\left|S\right|^{2}\)), and detuning the (\(\Delta\)) parameters at a resonant frequency to generate multiple wavelengths [36]. This approach offers device stability, reproducibility, and scalability. In [37; 38], the authors propose this approach as an alternative to parallel lasers in WDM systems. This study addresses crucial aspects such as the optical linewidth, power per channel in the comb, and the optical carrier's signal-to-noise ratio. Furthermore, the scalability of a comb source for ultra-high-capacity systems is investigated, as well as the optimization of the above-mentioned parameters. These studies show significant potential in the application of this technology in PONs. However, researchers widely use optical microresonators to transmit information in long-distance networks, where the transmission paths typically have the same distance. The non-uniform distribution of comb wavelengths following a sech2 function poses a problem, as additional optical processing is required to ensure the adequate transmission of OSNR. Existing spectrum allocation methods rely on the uniform spectral power of each wavelength, requiring optical processing before use, which increases the economic costs of implementation. A PON, on the other hand, has a point-to-multipoint structure with variable distances, which allows for the direct utilization of the comb generated by the MRR. The initial concept offers significant advantages, including cost reductions and the potential to incorporate more optical comb wavelengths, thereby increasing the potential of RMLSA methods with WDM. These advantages are critical for last-mile networks, which must meet the demands for flexible network speeds of 50 Gbit/s, 100 Gbit/s, and 400 Gbit/s while accommodating 5G mobile transport, IoT device traffic, spectrum optimization, increased transmission speeds, and other challenges without significantly raising investment costs [2; 3; 7]. However, there is currently no evidence of a spectrum allocation strategy for PON networks using the optical comb of an MRR. This study proposes a methodology for the generation, transmission, and allocation of spectra using an RMLSA-WDM approach with MRR to address the issue. Hence, the work provides the following main contributions. * A method for the deterministic generation of OFCs with a standardized ITU-grid FSR. The values are 12.5 GHz, 50 GHz, 100 GHz, and 200 GHz. * The design of a photonic system with machine learning to select the OFC with the most suitable characteristics for customers with different bandwidth requests and transmission distances. * A methodology for spectrum allocation with multi-level modulation and WDM focus that considers the non-uniformity of the OFC.
# A Virtual Network Resource Allocation Framework Based on SR-IOV
## 5 Evaluation
### Experimental Setup  
We used two DELL R720 servers and one Brocade 80 switch to test and the two servers connected over 10 Gigabit Ethernet. As shown in Table 1, the two servers have basically the same hardware configuration. One server runs the server program by running Apache 2.4.37, and the other server simulates the client to initiate a service request to the server. Although the cluster size is relatively small, our goal is to test the performance of VM scheduling. Our experimental environment can still meet the requirements of our system network performance when testing VM load changes.  
In the process of testing, each time the client initiates a request to the server, and the number of VMs in each server can be adjusted constantly. Each VM can run different types of load, including CPU-intensive load, network-intensive load, and hybrid load. We use _Lookbusy_[34] to simulate CPU-intensive loads, _Netperf_[35] to simulate network-intensive loads, and a combination of _Lookbusy_ and _Netperf_ to simulate hybrid loads. To ensure fairness, we used only one port of the Intel 82599 network card (dual port network card) for testing.
## 2 Model Structure and Description  
In this study, we integrated the P cycle, its interaction with C and N, and feedback into the TRIPLEX-GHG model [63], which was developed based on the Integrated Biosphere Simulator (IBIS) and TRIPLEX [64]. TRIPLEX-GHG includes four submodules [63]: (i) the plant phenology module quantifies the response of different plant functional types (PFTs) to different parameters, capturing PFT differences in phenological processes such as budburst and tree leaf fall period; (ii) the vegetation dynamics module determines the distributions of PFTs based on climatic factors such as cold temperature and growing degree days, and it simulates the dynamic C allocation in leaves, stems and roots for each biome; (iii) the land surface module, developed from the land-surface transfer model [65], describes energy and water exchanges among soil, vegetation and atmosphere; and (iv) the soil biogeochemistry module focuses on exchange, flux and decomposition processes that affect C and N in litter and underground pools. TRIPLEX-GHG also contains a methane (CH\({}_{4}\)) biogeochemical submodule [63] and nitrogen dioxide (N\({}_{2}\)O) submodule [62]. The model has been calibrated, validated, and used to simulate emissions of CH\({}_{4}\)[63; 66; 67] and N\({}_{2}\)O [62; 68] from site-scale to global-scale.  
However, the TRIPLEX-GHG model does not include explicit P cycle processes, their interactions or feedback to C and N processes. The present study covered this gap by integrating important P processes, pools and fluxes into the model (Figure 1), generating the new modeling framework that we call TRIPLEX-CNP.
# A Study on Detection of Malicious Behavior Based on Host Process Data Using Machine Learning
## 4 Experiments
### Experiments Results  
The experimental results were evaluated using the Area Under the Receiver Operating Characteristic Curve (AUROC) to determine how well the models distinguished between normal and malicious behaviors. AUROC is a metric that calculates the area under the ROC Curve, which indicates how well the classification model can distinguish between normal and abnormal data. It is based on the changes in TPR and FPR as the classification threshold of the model is adjusted.  
Table 6 shows the AUROC performance of the datasets used in the experiment. Most of the models used in the experiment showed high performance. KNN, NB, and RF models using the original dataset showed 91-92% performance, while AE and MemAE showed higher performance at 95-96%. Models using the Preprocessed dataset (ADASYN) showed performance ranging from 89-98%, while models using the Preprocessed dataset (SMOTE) showed high performance of 97-98%. The lowest performance difference between KNN and MemAE was only 0.0038, indicating very little difference.  
The Figure 8 shows the ROC curves and AUROC values for each dataset, and the performance evaluation results of the malicious behavior detection models using original dataset, preprocessed dataset (ADASYN), and preprocessed dataset (SMOTE) are presented in Tables 7-9, respectively. The evaluated models include KNN, NB, RF, AE, and MemAE, and the evaluation metrics used to assess the model performance are Precision, Recall, and F1-Score. The results indicate that both the preprocessed datasets (ADASYN and SMOTE) significantly improved the model performance, with all models achieving higher Precision, Recall, and F1-Score values than the original dataset. Notably, in the case of MemAE, the F1-Score for the original dataset was 0.94. However, for the preprocessed dataset (ADASYN), the F1-Score increased to 0.96, and for the preprocessed dataset (SMOTE), the F1-Score increased further to 1.00, demonstrating a significant improvement in the results.
# A Specification-Based IDS for Detecting Attacks on RPL-Based Network Topology
## 6 Conclusions  
This paper aimed at detecting the attacks on the RPL based network topology that jeopardizing the network operational optimality and stability. The considered attacks include the Rank, the Sinkhole, the Local Repair, the Neighbor, and DIS attack. These threats can affect different aspects of network performance, from directly such as increase the end to end delay or create more packet loss, to indirectly like exhausting the resources of the nodes by communicational and computational overhead. Our detection solution involves semi-auto building a specification-based IDS model for  
Figure 7: Extensive capability IDS architecture designed for enabling other robust detecting techniques.  
protecting RPL-based network topology. The main idea is to learn the states, transitions, and relevant statistic based on the analyzing the trace file. The generated model will be integrated in the IDS server, which connects to the network through many cluster interfaces. The simulation results show that our IDS is energy efficiency and applicable for the large-scale networks, while providing high detection rates and accurateness in revealing most of the topology attacks. In the future, with the envisaged IDS architecture in Section 5, we would like to develop a more robust anomaly-based IDS for detecting performance-attacks type of the internal threats, for example, the black hole, selective forwarding or wormhole attack. We believe the extension of IDS capability in storage and computation will allow detection of internal threats more accurately and efficiently.  
We thank our colleagues from Middlesex University and Queen Mary University of London who provided insight and expertise that greatly assisted the research on its early design. We are also grateful to the two anonymous reviewers for their valuable suggestions.  
[PERSON] jointly conceived the study with [PERSON] designed the attack experiment, IDS architecture, profiling model, and prepared the manuscript; [PERSON] implemented the model and experiments, created the analytic model with contributions from [PERSON] and [PERSON]; [PERSON] supervised the analysis on RPL profile and IDS detection, and edited the manuscript; [PERSON] edited the manuscript. All authors have read and approved the final manuscript.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} 6 LoWPAN & IPv6 over Low-power Wireless Personal Area Network \\ DAO & Destination Advertisement Object \\ DIO & Destination Oriented DAG Information Objective \\ DIS & DODAG Information Solicitation \\ FN & False Negative \\ FP & False Positive \\ FPR & False Positive Rate \\ LA & Local Repair Attack \\ LTE & Long-Term Evolution \\ NA & Neighbor Attack \\ IDS & Intrusion Detection System \\ IoT & Internet of Things \\ RA & Rank attack \\ RPL & Routing Protocol for Low Power and lossy network \\ SA & Sinkhole Attack \\ TN & True Negative \\ TP & True Positive Rate \\ \end{tabular}
## 3 Beamforming of the Miniaturized L-Array  
The manifold of the array describes the phenomenon that the sampling signals of the adjacent receiving elements have phase difference due to the different spatial positions. This phase difference is determined by the spatial positions of the elements and the incident direction of the signal. Clearly, if the signals of every channel are added directly, interference weakening will occur except in the direction of vertical upward. In essence, beamforming is to eliminate the phase difference according to the pointing direction, thus the signals of each channel can be added in the same phase to get higher quality in a particular direction.  
Figure 4: (**a**) The layout of the antenna array; (**b**) the actual scene of the antenna array.
# Utilizing Energy Storage for Reliability Solutions in Active Distribution Systems
## 2 Quantification of Reliability Events
The expected value of system-level reliability index is obtained by aggregating the corresponding index of network load points. It should be noted that frequency and duration-based indices are weighted by the number of customers at each load point. Indices for load point reliability profile are quantified using (5)-(8), whereas (9)-(12) provide the estimation of indices used in system-level reliability profile. The reliability profile of a feeder segment of a network can be obtained in a similar fashion as that of system profile considering load points of that particular segment. Figure 1: Distribution network for illustration purpose. * Model contingency (fault) \(c^{x}\). The fault can be further subdivided into three phases (3-ph), line to line (L-L), line to line to ground (L-L-G), and single line to ground (SLG). The temporary failure rate is assumed to be three times the permanent failure rate in this work [2], and the probability distribution of short circuit fault is assumed as follows: 5% 3-ph, 15% L-L, 10% L-L-G, and 70% for SLG [14]. * Pick a contingency and simulate its impact on load points considering all reliability events as described above in Section 2. Assign load points experiencing sags and momentary interruptions to \(L_{\text{sag}}^{x}\) and \(L_{\text{MI}}^{c,x}\). Note \(L_{\text{SI}}^{c,x}\) and \(T_{\text{IS},\text{I}}^{c,x}\) for load points experiencing sustained interruption and the corresponding outage duration. * Update the contribution of contingency toward reliability events experienced by load points with (1)-(4). \[F_{\text{,}\text{,}\text{,}\text{,}\text{,}\text{,}\text{,}\text{,}\text{,} \text  
The corresponding duration of voltage sag (\(D_{\textit{sag}}\)) depends on the short circuit level, fault-clearing times of protective devices, self-extinguishing nature of the fault, and the delay used in protection settings [14; 18]. The probabilistic representation of fault-clearing time associated with a primary protection device can be represented with a probability distribution function (PDF) [18; 20], as depicted by the normal distribution in Figure 2a. Such PDF is discretized into multiple intervals, and a mid-point value is represented as the value of sag duration \((D_{\textit{sag}})\). The magnitude and duration of voltage sag due to a contingency is noted and compared against the sag tolerance curve of customer equipment/process as shown in Figure 2b. In this paper, such sag events that are beyond the sag tolerance curve are reported under 'disruptive sag event' (DSE). This work incorporates a probabilistic approach, similar to works in References [18; 20], to quantify the frequency of DSE as illustrated in Algorithm 1, and takes into account different types of fault, protection settings and resulting duration, and associated probabilities (weightages).
## 5 Experiment and Result Analysis
### Model Evaluation Indicators  
In this study, the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage of error (MAPE) were selected to evaluate the accuracy of the prediction of each model, which was calculated as:  
\[e_{\mathrm{MAE}}=\frac{1}{n}\sum\limits_{i=1}^{n}|y_{i}-\widetilde{y}_{i}| \tag{26}\]  
\[e_{\mathrm{RMSE}}=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}(y_{i}-\widetilde{y }_{i})^{2}} \tag{27}\]  
\[e_{\mathrm{MAPE}}=\frac{100\%}{n}\sum\limits_{i=1}^{n}\abs{\frac{\widetilde{ y}_{i}-y_{i}}{y_{i}}} \tag{28}\]  
where \(n\) is the number of predicted outcomes, \(y_{i}\) is the actual value, and \(\widetilde{y}_{i}\) is the model-predicted value.
## 4 Proposed Method and Procedures
### Feature Extraction
#### 4.4.1 Amplitude Range  
To calculate the amplitude range, first, the maximum and minimum values were found, along with their indices. Then, the amplitude range was defined as the difference between the maximum and the minimum values in the EEG signal.  
\[R=max\ (\text{signal})-min\ (\text{signal}) \tag{3}\]