esa-sceva/satcom-chunk-collection huggingface dataset:
# Identification of Bottlenecks in Passenger Handling Processes Using Data-Driven Tools
## 3 Development and Excel-Based Implementation of the Simulation Model
### Simulation of the Check-In Process
Several average system characteristics are also evaluated during the check-in process, including average waiting time in minutes, total duration of the check-in process in hours, average handling time per passenger in minutes, and the number of passengers handled at each service channel. The check-in simulation results are presented in Figure 4, which illustrates the handling process of the first twenty passengers at two check-in desks. The passenger's arrival at the registration zone is computed as the sum of their arrival time at the airport and the individual movement duration to the check-in area. These values are then sorted in ascending order and assigned to a corresponding passenger index. All passengers are subsequently sorted in ascending order based on this value to reflect the actual queuing sequence. The model then simulates the allocation of passengers to two parallel check-in desks. For each arriving passenger, the simulation determines whether one of the desks is available at the moment of their arrival. If at least one desk is free, the passenger is processed immediately. However, if both desks are still occupied, the passenger must wait until one becomes available. This principle is implemented in the simulation model by comparing two values--the end times of the previous passengers handled at different check-in desks (the earliest end time is selected) and the current passenger's arrival time at the registration zone. The larger of these values is taken as the start time of the passenger's service, since service cannot begin before the passenger arrives at the station or if both desks are still occupied. End time is calculated as the sum of the start time and service duration. This implementation increases the code complexity but more accurately reflects the real passenger handling process. Additionally, the code accounts for cases where one passenger is served longer than multiple passengers at the other desk. Therefore, determining the correct service start time requires comparing the arrival time of a passenger with the end time of the passenger who was served earlier. The model is designed to compare the last ten recorded values. If a situation arises where one passenger is served longer than ten  
Figure 4: Example of check-in process simulation. other passengers at the second desk, the code will display an \"error\" during the simulation. Since this is an automated system, pressing \"fn + f9\" will trigger a new number generation. The simulation model also evaluates the total time passengers spend in the queueing system, defined as the difference between the end time of service and the time of arrival at the station. Additionally, the waiting time is tracked, defined as the difference between the start time of service and the passenger's arrival time at the check-in desk. Both indicators are automatically converted into minutes for consistency and easier evaluation of system performance across different scenarios.
# Global-Entropy Driven Exploration with Distributed Models under Sparsity Constraints
## 1 Introduction
Static processes are of particular interest for SAR applications [15] or height profile estimation in general [16]. Therefore, we aim to represent these processes with a weighted sum of static point spread functions, i.e., kernels, which are also referred to as kernel methods. The contribution of this work is manifold and is summarized in the following. * We propose a swarm exploration system, which processes the obtained sensor measurements online and processes these distributively. * We apply two distribution paradigms: SOE and SOF. * We present how to achieve a joint entropy of a distributed estimation by means of a consensus algorithm. * We show the performance gain of our system in simulations with real data. The structure of this paper is as follows. In Section 2, we first show the centralized approach followed by the two distribution paradigms: SOE and SOF. Afterward, we present the main contribution of this paper in Section 3, where we show the entropy-driven exploration schemes for a centralized, SOE and SOF approach. In Section 4, we present the performed simulations. Finally, we draw the conclusions in Section 5. Throughout the paper, we use the following notation. Matrices are written in capital bold letters \(\mathbf{\Gamma},\mathbf{P},\dots\). Vectors are written in lower case and bold \(\mathbf{\gamma},\mathbf{p},\dots\). Constant scalar values are capital letters, e.g., \(\Gamma,P\), and scalars are lowercase \(\gamma,p,\dots\). We also use sets, which are denoted in calligraphic letters \(\mathcal{P},\dots\).
## 5 Conclusions  
Using Landsat 8 OLI remote sensing images combined with measured Chl-a concentrations, the GBDT model was used to study the coastal waters of the Beibu Gulf in Guangxi and analyze the spatial-temporal distribution of the Chl-a concentration. The main research results are as follows:  
1. Compared with the performance of different models, the GBDT model can significantly improve the accuracy of Chl-a concentration inversion, proving that it can be a new method for remote sensing inversion of the water quality parameters. When B4, B3 + B4, B3, B1 - B4, B2 + B4, B1 + B4, and B2 - B4 were considered the characteristic variables of the GBDT model, the inversion accuracy of the model was the highest (MAE = 0.998 \(\upmu\)g/L, MAPE = 19.413%, RMSE = 1.626 \(\upmu\)g/L, and R\({}^{2}\) = 0.778).
2. The spatial distribution of the Chl-a concentration was highest in the nearshore and lowest in the offshore waters in the Beibu Gulf in Guangxi. The Chl-a concentration was highest in the summer, and the concentration in autumn was lower, while concentrations in spring and winter were the lowest. The ranking of Chl-a concentrations, from high to low, across multiple bays was as follows: Naniu River Estuary Bay, Dafeng River Estuary Bay, Qinzhou Bay, Beihai Pearl Harbor, and Fangcheng Bay.  
Limited by the revisiting period of the satellite and the quality of the satellite images, the data used to train and validate the GBDT model may be relatively small in size, which may have influenced the inversion accuracy of the model. To estimate and learn the spatial and temporal distribution of Chl-a concentrations more precisely, an increased amount of data may be included in future work in combination with multi-source satellite remote sensing data.  
Resources, H.Y.; writing--original draft preparation, Y.H.; formal analysis, Y.W.; data curation, W.Z.; visualization, K.W. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Date** & **Temperature** & **Wind Direction** & **Wind Strength** \\ \hline
14 April 2015 & 14.3 & SE & \textless{}3 \\
23 October 2015 & 27.2 & S & \textless{}3 \\
3 June 2016 & 31.2 & S & \textless{}3 \\
28 December 2016 & 18 & N & 1 \\
14 February 2017 & 17.44 & N & 1 \\
2 March 2017 & 17.22 & SW & 1 \\
28 October 2017 & 25.72 & N & 3–4 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Climate data of the sampling point (GX10) on the sensing date of the satellite.
## 1 Introduction
### Research Motivation (the Need to Predict DDoS Attacks)  
Intrusion detection systems (IDSs) are used in cyber security to identify and remove harmful activity [5]. As the number of malicious attacks continues to grow dramatically, IDSs are tasked with the responsibility of preventing such attacks from wreaking havoc on a broad region of cyberspace. DDoS attacks are a significant security risk in Iot environments. According to statistics from Amazon Web Services, the largest DDoS attack to date occurred in February 2020. The attack's highest network data is estimated to reach 2.3 Tbps. The hackers chose to exploit seized CLDAP webservers (Connection-less Lightweight Directory Access Protocol webservers), a protocol that is a substitute to LDAP, which is also used to manage public catalogs. Prior to this 2.3 Tbps DDoS attack in February 2020, the second most significant DDoS attack was the 1.3 Tbps DDoS attack on GitHub, which included delivering 126.9 million traffic packages every second [1]. As a consequence, there is an imperative necessity to develop a DDoS attack detection system for efficient classification of incoming traffic into \"attack\" or \"normal\" categories.  
Deep learning (DL) is a novel field of computer science that uses a sophisticated collection of feature embedding techniques to automatically learn from past data and accurately anticipate outcomes [6]. It has been successfully utilized in a variety of applications throughout the years, including stock market prediction [7], assessment of student performance [8], predictive modeling [9], and text categorization [10], and many others [5]. Data analysts are driven to build practical solutions that might assist network administrators to effectively forecast DDoS attacks in the field of DDoS attack detection [5]. As a result, for  
Figure 1: A botnet-driven DDoS attack.  
effective DDoS attack prediction, it is critical to explore and use state-of-the-art hybrid DL models on DDoS data.
## 3 Analytical and Experimental Results
### Microstructure and Fiber Content
Contrary to the idealized illustration in Figure 2, the actual distances between the lines of the printing path vary slightly. As a result, the adjacent printed filaments overlap to varying degrees and gaps sometimes occur. Figure 7 shows micrographs of the top view of two adjacent printed filaments. In Figure 7, a gap occurs between the printed filaments, leading to a less dense local microstructure and, thus, to more voids. In Figure 7, on the other hand, the printed filaments overlap, resulting in a denser microstructure with significantly fewer voids. Figure 6: Microstructure of a 3D printed CF/PA6 specimen. (**a**) illustrates the printing path and the cross-section location, shown in (**b**), where the layer-wise specimen structure is visible. A layer consists of multiple filaments deposited next to each other. The micrograph’s location is shown in (**c**). Areas with interlayer voids, other voids, pure matrix, and irregular fiber distribution are visible. Figure 7: Micrographs of one selected area of one bending test specimen (top view). In (**a**) there is a visible gap between two printed filaments, in (**b**) the filaments overlap. The density of voids strongly depends on the way filaments are deposited.
# A Data-Driven Model for the Energy and Economic Assessment of Building Renovations
## 2 Materials and Methods
### Data Sources and Preprocessing  
The data used in this study are extracted from a comprehensive dataset of residential buildings in 2022 and 2023, consisting of detailed building characteristics, energy consumption indicators, such as total, heating, cooling, and domestic hot water (DHW), and temporal consumption data. Additionally, economic data related to retrofit interventionsare collected from the Lazio region price list [29], which included average costs and estimated savings of various renovation measures. Initial preprocessing included the cleaning of missing values: numerical fields are filled with column means, while categorical fields are imputed using the mode. Outlier analysis is conducted and data normalization is applied where required, particularly for cost and return computations. Monthly consumption trends were averaged and derived variables such as surface-to-volume ratio and percentage of glass surface were calculated. This pie chart shows the average distribution of energy consumption components across buildings (Figure 1):  
* Heating dominates, accounting for approximately 60-70% of total energy use.
* DHW and cooling contribute smaller shares.  
This insight reinforces that retrofit strategies targeting heating systems and insulation will likely have the greatest impact on reducing energy consumption.
## 4 Experimental Results  
This section evaluates and compares the proposed AMF-MSPF framework with the TSHT of [27] and PFKBOT of [24]. The choice of selecting these reference approaches, is because they employ multi-features and an adaptive model updating techniques similar, in some way, to our multi-feature framework. The experiments have been conducted on frames of different sizes and processing is done using an Intel Core i5 2.60 GHZ with 4 GB RAM. To illustrate the efficiency of the proposed AMF-MSPF, we applied it to video sequences having full occlusions, abrupt intensity changes, and clutter background introduced by the moving object. These challenging sequences evidently makes the experiments very difficult. The accuracy and robustness of the proposed AMF-MSPF are tested on the WalkByShop1 cor, CAVIAR, and PETS video data sets. Table 2 highlights some of the important characteristics of these sequences.  
The reason for selecting these data sets is because they have rich characteristics such as full occlusion, clutter, and abrupt illumination change. The computer vision research community widely uses these data sets to evaluate their algorithms. The prime goal, of the simulations, is to track manually initialized regions of interest during long video sequences under clutter background and occlusions by intensity. The ground truth locations of the objects are recorded manually for each sequence. Red, blue, green and yellow depicts the results of the ground truth, proposed AMF-MSPF, PFKBOT, and TSHT respectively. For a quantitative analysis F-SCORE, false alarm rate (FAR) and root mean square error (RMSE) are evaluated for all the sequences under test, as shown in Table 3.  
Precision (P) and recall (R) are given by Equations (26) and (27):  
\[\mathrm{P}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}} \tag{26}\]  
\[\mathrm{R}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}} \tag{27}\]  
where, TP, FP, FN and TN are the number of true positives, false positives, false negatives, and true negatives respectively. F-SCORE becomes trivial if we know P and R that is given by Equation (28):  
\[\mathrm{F\_SCORE}=2*\frac{\mathrm{P*R}}{\mathrm{P+R}} \tag{28}\]  
Similarly, FAR is calculated as the ratio between the number of negative event wrongly categorized as positive and the total number of actual negative events using Equation (29).  
\[\mathrm{FAR}=\frac{\mathrm{FP}}{\mathrm{TN+FP}} \tag{29}\]
## Results and Discussion
### TEM Analysis  
The morphologies of GO and nano-PDA@GO-TiO\({}_{2}\) composites were observed by TEM, as shown in Figure 7a-e. It can be seen from Figure 7a,b that GO that exhibits a typical translucent sheet structure with curls and wrinkles in the middle portion, which may be due to a deformation during peeling or a superposition effect of GO sheets. As shown in Figure 7c,d, the PDA@GO layer structure can effectively prevent the aggregation of TiO\({}_{2}\) nanoparticles and the decoration of TiO\({}_{2}\) nanoparticles has good dispersibility on the surface of PDA@GO. From Figure 7e, it can be seen that the nano-TiO\({}_{2}\) particles on the surface of PDA@GO have good crystallinity and lattice spacing, the relatively obvious lattice spacing of 0.352 nm, which are in good agreement with the nano-TiO\({}_{2}\) particles in XRD at 2\(\theta\) = 25.2\({}^{\circ}\) (101) crystal plane. In addition, the sedimentation test of GO and nano-PDA@GO-TiO\({}_{2}\) composites  
Figure 6: X-ray photoelectron spectroscopy (XPS) spectra of (**a**) GO, (**b**) PDA@GO-TiO\({}_{2}\), (**c**) GO/C1s, (**d**) PDA@GO-TiO\({}_{2}\)/C1s, (**e**) PDA@GO-TiO\({}_{2}\)/N1s, and (**f**) PDA@GO-TiO\({}_{2}\)/Ti2p.  
in deionized water, as shown in Figure 7a,c, which presents the dispersing ability of the GO and nano-PDA@GO-TiO\({}_{2}\) composites in deionized water. In this test, GO and PDA@GO-TiO\({}_{2}\) are dispersed in deionized water with ultrasonication for 45 min and then the dispersions are storage without any disturbance for same times. As shown in Figure 7a,c, GO has deposited into the bottom of the bottle completely after 3 h of storage, while there is no obvious stratification for the nano-PDA@GO-TiO\({}_{2}\) composites solution at all the time, indicating that the dispersing ability of the nano-PDA@GO-TiO\({}_{2}\) composites in deionized water is significantly. This may be attributed to that covalent grafting exists between the active sites of the GO and DA molecules, which promoting the GO is reduced to Graphene, and improving the dispersing ability of the nano-PDA@GO-TiO\({}_{2}\) composites in deionized water.
# Dual-Neighborhood Tabu Search for Computing Stable Extensions in Abstract Argumentation Frameworks
## 7 Conclusions  
This paper introduces a dual-neighborhood tabu search (DNTS) algorithm to solve the single stable extension (SE-ST) problem within abstract argumentation frameworks. It implements a dual-neighborhood strategy incorporating a fast neighborhood evaluation method. In addition, by introducing techniques such as tabu and perturbation, this algorithm is able to jump out of the local optimum, which significantly improves the performance of the algorithm. The DNTS algorithm was experimentally validated on a series of random instances generated by the AFGen generator provided by ICCMA, demonstrating significant effectiveness. Comparative results with the u-toksa and HAYWOOD algorithms reveal that DNTS achieves superior best run times in most of the randomly generated instances, though it slightly lags in worst-case scenarios. Nevertheless, the efficient performance of DNTS in processing complex instances enables it to surpass both u-toksa and HAYWOOD in overall performance, evidencing that DNTS has raised the upper limit of solving efficiency, yet there is still room for improvement in terms of stability.  
As the size of instances increases, the number of instances correctly solved by the DNTS algorithm shows a declining trend, but it still outperforms the HAYWOOD algorithm, which is of the same class of local search algorithms. This experimental result not only confirms the feasibility and advantages of the DNTS algorithm in computing stable extensions within abstract argumentation frameworks but also highlights the need for further optimization in terms of the number of solutions the algorithm can find.  
Future work will explore methods to enhance the stability of the DNTS algorithm and attempt to apply it to solving other extension problems such as complete extension, preferred extension, etc., to fully assess its application potential and scalability.  
[PERSON] conceived and developed the main ideas of the study; [PERSON] conducted all the experiments and wrote the initial draft of the manuscript under the supervision of [PERSON] and [PERSON]; [PERSON] provided oversight and guidance to ensure the overall quality of the study; [PERSON] organized the statistics of the experiments. [PERSON] provided insightful ideas during the discussion. All authors contributed to the refinement of the final manuscript. All authors have read and agreed to the published version of the manuscript.  
The research was supported by the National Natural Science Foundation of China (Grant No. 6201203) and the Key R&D Program of Hubei Provincial Department of Science and Technology (Grant No. ZZCXHZYF2022000815).  
Not applicable.  
The instances and code used in this paper can be found at [[https://github.com/trues-d/dnts](https://github.com/trues-d/dnts)]([https://github.com/trues-d/dnts](https://github.com/trues-d/dnts)) (accessed on 28 Jun 2024).  
The authors declare no conflicts of interest.
# Policy-Based Smart Contracts Management for IoT Privacy Preservation
## 5 Discussion and Conclusions
Additionally, our approach benefits from the private channels provided by the Hyperledger platform, enabling private communication between the user and other nodes based on predefined policies. Depending on the user's preferences, the model selects the appropriate channel for communication with other nodes. In future work, we plan to implement a forensic layer to detect any violations of Service Level Agreements (SLAs) between users and providers. Since blockchain data are generally trusted, we only need to ensure the admissibility of client-side records. This will require developing new metrics to provide proof of evidence admissibility [56]. Additionally, we intend to explore policy-based smart contract management, which involves using a dedicated programming language to create smart contracts based on obligations and rights [57]. The primary benefit of this approach is the ability to enforce smart contract compliance with legal requirements and ensure that they incorporate necessary legal concepts and properties [58]. Lastly, we plan to enable the service providers to extend their networks globally, which, consequently, requires a scalable solution that facilitates user registration or migration to the Ethereum platform, which provides permissionless registration. Ethereum is widely used for its permissionless communication features, making it a favorable candidate, especially with existing studies showing its power to deal with scalability issues [59; 60]. Conceptualization, M.R., A.A., M.M. and N.K. ; Methodology, M.R., A.A. and N.K. ; Software, A.A. and M.M. ; Validation, A.A. and M.M. ; Formal analysis, A.A.; Writing--original draft, A.A and M.M. ; Writing--review & editing, M.R. and A.A.; Visualization, A.A and M.M. ; Supervision, M.R. and N.K. ; Project administration, M.R. All authors have read and agreed to the published version of the manuscript. This research was funded by King Khalid University grant number RGP2/428/46. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. **Acknowledgments:** The authors extend their appreciation to the Scientific Research at King Khalid University for funding this work through the large group Research Project under grant number RGP2/428/46.
# A Small-Sample Scenario Optimization Scheduling Method Based on Multidimensional Data Expansion
## 3 XGBoost PV Power Prediction Method Considering Data Imbalance in Small-Sample Scenarios
### Optimized Scheduling Objective Functions for Integrated Energy Systems
In the equation, \(a_{i}\), \(b_{i}\), and \(c_{i}\) represent the quadratic, linear, and constant term coefficients of the operating cost for DG unit \(i\), respectively; \(\Delta t\) is the discretized interval length of the operating time; \(\lambda\) is the discount factor; \(\overline{P}_{i}^{G}\) and \(\underline{P}_{i}^{G}\) denote the maximum and minimum power generation limits of distributed generator (DG) unit \(i\), respectively; \(RU_{i}\) and \(RD_{i}\) are the ramp-up and ramp-down capabilities of DG unit \(i\); \(\overline{P}_{j}^{B}\) and \(\underline{P}_{j}^{B}\) represent the maximum charging and discharging power limits of energy storage system (ESS) \(j\); \(\underline{E}_{j}^{B}\) and \(\underline{E}_{j}^{B}\) are the maximum and minimum levels of its state of charge (SOC); \(\overline{P}^{C}\) is the maximum limit of the import/export power of the main grid; \(\beta\) is the electricity sales coefficient; \(\eta_{B}\) is the energy exchange efficiency of the ESS; \(\sigma_{1}\) and \(\sigma_{2}\) are the reward adjustment coefficient and constraint penalty coefficient, respectively; \(\rho_{t}\) is the electricity price at time \(t\); \(P_{m,t}^{V}\) is the active power output of the photovoltaic (PV) system at time \(t\); and \(P_{k,t}^{L}\) is the active power demand of load \(k\) at time \(t\). Among the continuous variables, \(P_{i,t}^{G}\) is the active power output of DG unit \(i\) at time \(t\); \(P_{j,t}^{B}\) is the charging/discharging power of ESS \(j\) at time \(t\); \(SOC_{j,t}^{B}\) is the state of charge of ESS \(j\) at time \(t\); \(P_{t}^{N}\) is the import power of the main grid at time \(t\); and \(\Delta P_{t}\) is the active power imbalance at time \(t\). The binary variable \(u_{i,t}\) indicates the operating state of DG unit \(i\) at time \(t\) (\(1\) for running, \(0\) for shutdown).