esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Stage Dynamic Transmission Network Expansion Planning Using LSHADE-SPACMA
## 5 Simulation Results and Analyses
### West Delta Network (WDN) System Planning
#### 5.2.2 Multi-Stage Dynamic Planning for WDN up to 2040 without Security N-1  
In this section, MSDTNEP of WDN without N-1 security constraint is employed up to year 2040. The actual peak load data of WDN from the year 2008 to 2015 are used in the load forecasting process [24]. The load forecasting up to year 2040 using ANFIS, linear, parabolic and exponential trends is presented in Figure 4. The results show that ANFIS has the lowest mean absolute error (MAE) and the best results; therefore, it is applied in this work. The predicted loads from 2016 to 2040 using ANFIS are shown in Table 6.  
MSDTNEP without including an N-1 security constraint for WDN from 2016 up to 2040 is shown in Table 7. The planning period is divided into five stages and each stage comprises five years. The considered interest rate is 0.2. The dynamic planning results in adding three circuits in the first and second stages, two circuits in the third stage, and four circuits in the fourth and fifth stages to guarantee that the power system operates economically and reliably. The results also show that the generation station's capacity at bus 53 should be increased to 1315.7 MW to meet the possible load demand in 2040. Dynamic planning of WDN in 2030 (third stage) shows that total circuits added from 2016 to 2030 are eight; however, the static plan in Section 5.2.1 gives seven circuits. As in dynamic planning, each stage depends on previous stages. In the second and third stages, six new routes are added to ensure reliable operation for the power system and meet each bus's load demand in these periods. Hence, these routes are imposed in the planning of the third stage.
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline
**Day** & **28-February** & **1-March** & **2-March** & **3-March** & **4-March** & **5-March** & **6-March** & **7-March** & **8-March** & **9-March** & **10-March** \\ \hline
28-February & & \(-0.12\) & \(-0.15\) & \(0.08\) & \(-0.03\) & \(0.23\) & \(0.07\) & \(-0.21\) & \(-0.14\) & \(0.01\) & \(0.06\) \\
1-March & 0.12 & & \(-0.03\) & \(0.2\) & \(0.09\) & \(0.35\) & \(0.18\) & \(-0.09\) & \(-0.02\) & \(0.13\) & \(0.18\) \\
2-March & 0.15 & 0.03 & & \(0.23\) & \(0.12\) & \(0.38\) & \(0.22\) & \(-0.06\) & \(0.01\) & \(0.16\) & \(0.21\) \\
3-March & \(-0.08\) & \(-0.2\) & \(-0.23\) & & \(-0.11\) & \(0.15\) & \(-0.02\) & \(-0.29\) & \(-0.22\) & \(-0.08\) & \(-0.02\) \\
4-March & 0.03 & \(-0.09\) & \(-0.12\) & \(0.11\) & \(0.26\) & \(0.1\) & \(-0.18\) & \(-0.1\) & \(0.04\) & \(0.09\) \\
5-March & \(-0.23\) & \(-0.35\) & \(-0.38\) & \(-0.15\) & \(-0.26\) & & \(-0.17\) & \(-0.44\) & \(-0.37\) & \(-0.23\) & \(-0.17\) \\
6-March & \(-0.07\) & \(-0.18\) & \(-0.22\) & \(0.02\) & \(-0.1\) & \(0.17\) & & \(-0.27\) & \(-0.2\) & \(-0.06\) & \(0\) \\
7-March & 0.21 & 0.09 & 0.06 & 0.29 & 0.18 & \(0.44^{\star}\) & \(0.27\) & & \(0.07\) & \(0.22\) & \(0.27\) \\
8-March & 0.14 & 0.02 & \(-0.01\) & 0.22 & 0.1 & \(0.37\) & \(0.2\) & \(-0.07\) & & \(0.14\) & \(0.2\) \\
9-March & \(-0.01\) & \(-0.13\) & \(-0.16\) & 0.08 & \(-0.04\) & \(0.23\) & \(0.06\) & \(-0.22\) & \(-0.14\) & & \(0.06\) \\
10-March & \(-0.06\) & \(-0.18\) & \(-0.21\) & 0.02 & \(-0.09\) & \(0.17\) & & \(0\) & \(-0.27\) & \(-0.2\) & \(-0.06\) & \\ \hline \end{tabular}
\end{table} 
Table 8: Post hoc test for all pairwise comparisons of LMF-HRV-correlation mean between days for the United Kingdom’s group (* \(p<0.05\)). Figure 2: Mean LMF-HRV-correlations by Day and group (Abbreviations CA, LT, NZ, SA and UK correspond to California, Lithuania, New Zealand, Saudi Arabia and United Kingdom, respectively). Note that the only day of study period where all the groups’ HRV was positively correlated with the LMF was on the day 6, which was the day of the HLI and the correlations between the HRV and LMF for all the groups were substantially higher on that day. The pattern of differences can be seen most clearly in Figure 2. Intriguingly, the only day of study period where all the groups HRV was positively correlated with the LMF was on the day 6, which was the day of the HLI and the correlations between the HRV and LMF for all the groups was substantially higher on that day. Figure 3: Mean LMF-HRV-correlation by Day and group.The day with the black color indicates day 6 which is the day all the groups did the 15-min Heart Lock-In at the same time. Pairwise significance is noted by Day# labels above each bar. It can be seen, for example, that in the Lithuanian group, day 6 (HLI day) was significantly different from all other days in the study period. Day 11 was different from three of the other days (2, 6 and 8), etc.
## 2 Materials and Methods: Ten-Koh Mission Description
### Satellite Platform  
Ten-Koh was developed over a period of 16 months mainly at Kyushu Institute of Technology (Kyutech), while some payload instruments were designed and developed in parallel in Australia, Bulgaria, and the USA. Ten-Koh is based on the platform of a previous small deep-space probe Shinen-2 [15], developed and launched by Kyutech in 2014. The main structure of Ten-Koh is composed of a CFRP composite shell with a rigid internal load-bearing structure made of aluminum alloy (Al 6061-T6).  
The satellite platform was based on several components and architecture topologies used by Shinen-2. However, the design and integration of most of the platform subsystems has been completely redefined in order to suit the new mission objectives. The main goal when adapting the Shinen-2 platform architecture for the purpose of Ten-Koh was to reuse as many of the heritage electronic components as possible in order to reduce the risk of component failure and thus increase system reliability. The development time was also shortened by reusing the same structure, which assured both successful environmental testing and removed the need for devoting a significant effort to design a new structure. Figure 1 shows the Ten-Koh satellite in its in-orbit configuration.  
The satellite platform is formed of the following subsystems:  
* OBC--on-board computer and data handling subsystem;
* COMM--communication subsystem;
* EPS-- electrical power subsystem; and
* ADS-- attitude determination subsystem.  
Due to the short time allowed for the development and limited resources, attitude control was not included in the satellite and the use of any mechanisms was discarded, even though they were being considered until the preliminary design review (PDR). A passive attitude control based on permanent magnets was not possible due to the influence on the CPD and magnetometer readings. Figure 2 shows the block diagram of the satellite platform subsystems and the main interfaces between them. Each of the platform subsystems is described below.
# Research on ECG Signal Classification Based on Hybrid Residual Network
## 3 Experimental Results and Discussion
### Ablation Studies
We also replaced the preprocessing method with EWT (empirical wavelet transform) to verify the effectiveness of our preprocessing approach. While EWT did not achieve the highest accuracy, it demonstrated excellent performance in other evaluation metrics, such as the specificity (99.59%) and sensitivity (98.45%). This could be attributed to EWT's ability to adaptively decompose the signal into meaningful components, effectively capturing subtle patterns and reducing false positives. However, its computational complexity and reliance on clean signal quality might limit its practical application in real-time scenarios. Notably, under the same preprocessing conditions, the hybrid model requires 68 s per epoch for training, compared to only 44 s per epoch for the traditional CNN model. However, the performance of the traditional CNN falls short of the hybrid model, demonstrating that the hybrid model achieves a better balance between computational efficiency and accuracy. Specifically, the hybrid model leverages depthwise separable convolution to significantly reduce computational complexity while maintaining high classification performance. This design effectively balances computational complexity and model effectiveness. Overall, the Hybrid Residual Network proposed in this paper attains superior outcomes in ECG signal categorization via systematic improvements. The preprocessing steps ensured high-quality input signals, residual connections enhanced the model's feature \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Experiment** & **Preprocessing** & **Classifier** & **Accuracy (\%)** & **Sensitivity (\%)** & **Specificity (\%)** & **PPV (\%)** & **F1 Score (\%)** \\ \hline Baseline & No & CNN & 98.24 & 94.85 & 99.21 & 88.71 & 91.53 \\ Baseline & Our method & CNN & 98.72 & 94.75 & 99.41 & 92.52 & 93.61 \\ Baseline & Our method & ResNet & 98.98 & 96.92 & 99.51 & 93.22 & 94.97 \\ Baseline & EWT & Hybrid & 98.41 & 98.45 & 99.59 & 89.77 & 93.68 \\ Baseline & Our method & Hybrid & 99.09 & 97.56 & 99.53 & 95.18 & 96.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation metrics for ablation experiments after adding individual components of the algorithm (the preprocessing method labeled as “Our method” refers to the preprocessing approach proposed in this study). extraction proficiency, and depthwise separable convolution further boosted computational efficiency.
## 2 Materials and Methods
### Adsorption Kinetics  
Sandstone core was crushed to small pieces and then sieved to achieve a uniform particle size in the range of 0.5-1.0 mm. Approximately 1 g of this rock was placed in several glass vials together with 9 g of the optimized formulation (water with 1 wt% eutectic and 0.5 wt% NaCl). The vials were then shaken under controlled conditions (atmospheric pressure and 298.15 K) in a Selecta Boxcult orbital shaker. To determine the kinetic behavior of the adsorption process, a sample was taken from each vial at different times. All samples were centrifuged at 1000 rpm during 30 min in an Ortoalresa Digicen 21 R centrifuge. The supernatant was filtered and analyzed using an Agilent UV-Vis 8453 spectrophotometer. The apparatus was previously calibrated at a wavelength of 215 nm. The surfactant adsorption was calculated as follows [24]:  
\[q_{t}=(C_{0}-C_{e})\cdot\frac{V_{s}}{m} \tag{2}\]  
where \(q_{t}\) is the surfactant adsorption density (mg/g rock) at a certain time \(t\) (it becomes \(q_{e}\) when the system reaches the equilibrium), \(C_{0}\) is the initial concentration of surfactant in the solution (mg/L), \(C_{e}\) is the concentration of surfactant in the solution after reaching equilibrium (mg/L), \(V_{s}\) is the volume of solution added originally (L), and \(m\) is the mass of crushed rock (g).  
Two commonly used kinetic models were evaluated to describe the adsorption behavior. The pseudo-first-order kinetic model, developed by [PERSON] in 1898 [25], is mathematically expressed as:  
\[\log(q_{e}-q_{t})=\log(q_{e})-\frac{K_{1}}{2.303}t \tag{3}\]  
where \(q_{e}\) represents the equilibrium adsorption density (mg/g rock) and \(K_{1}\) is the rate constant (h\({}^{-1}\)). As an alternative, the linearized pseudo-second-order kinetic equation was also tested:  
\[\frac{t}{q_{t}}=\frac{1}{q_{e}K_{2}}+\frac{t}{q_{e}} \tag{4}\]  
where \(K_{2}\) introduces the rate constant of the pseudo-second-order model (h\({}^{-1}\)).
## 2 Proposed Circuit Configuration and Operation Analysis  
### Circuit Topology  
Figure 1 shows the proposed circuit configuration of the LED driver. The half-bridge converter consists of two active switches (\(S_{1}\), \(S_{2}\)), a transformer (\(T_{1}\)), a capacitor (\(C_{b1}\)), a diode rectifier (\(D_{1}\)-\(D_{4}\)), an output inductor (\(L_{o}\)), and an output capacitor (\(C_{o}\)). The diodes \(D_{SI}\) and \(D_{S2}\) are the intrinsic diodes of \(S_{1}\) and \(S_{2}\), respectively. \(L_{m}\) represents the mutual inductance of the transformer \(T_{1}\). \(C_{b1}\) is used to eliminate the dc-offset current in the transformer. \(S_{1}\) and \(S_{2}\) are operated by the control scheme of ASPWM to dim the LEDs.  
A boost converter performs the function of power-factor correction which consists of an inductor \(L_{PFC}\) and the lower arm switch \(S_{2}\). By operating the boost converter in discontinuous-conduction mode (DCM), the peak value of the inductor current \(i_{LPFC}\) will follow the track of the rectified input voltage \(v_{in}\). The frequency of \(i_{LPFC}\) is far greater than the input-line frequency. The inductor \(L_{f}\) and the capacitor \(C_{f}\) form a low-pass filter which is used to filter out the high frequency component of \(i_{LPFC}\). In this way, the input current can be close to a sinusoidal waveform and in phase with the input-line voltage, thus achieving a high power factor. The magnetizing current in \(L_{m}\) can be used to remove the energy accumulated on parasitic capacitors of \(S_{1}\) and \(S_{2}\) to attain ZVS operation. Thus, the switching losses can be drastically reduced, and the operating efficiency can be promoted.
## 4 Conclusions  
This paper presents a novel subway passenger flow prediction model based on the Variational Mode Decomposition (VMD) method, an improved TimesNet model, and the SARIMA model. The model addresses the shortcomings of traditional mathematical approaches, which struggle to capture the complex features of subway passenger flow, especially outliers and seasonal components. Additionally, the model proposes solutions  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Time Period** & **Morning Rush Hour** & **Evening Rush Hour** & \multicolumn{2}{c}{**Normal Time**} \\ \hline Metrics & MAE & RMSE & MAE & RMSE & MAE & RMSE \\ Proposed & 0.3626 & 0.4513 & 0.5400 & 0.6774 & 0.1897 & 0.2522 \\ Only SARIMA & 1.4445 & 1.6875 & 1.1110 & 1.311 & 0.3353 & 0.3927 \\ Only I\_TimesNet & 0.5666 & 0.6847 & 0.8192 & 0.9922 & 0.2865 & 0.3368 \\ VMD-I\_TimesNet & 0.6389 & 0.6998 & 0.4078 & 0.5046 & 0.2819 & 0.3903 \\ VMD-SARIMA & 0.4207 & 0.5825 & 0.4303 & 0.5358 & 0.2593 & 0.3001 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model rationalization result in detailed time period.  
to the challenges deep learning models face when predicting seasonal components and handling off-peak flow data.  
The model's structure starts with the VMD method, which decomposes the passenger flow data into multiple frequency components. After the seasonality judgment module, components are directed either to the SARIMA module or the TimesBlock for separate prediction. Finally, the predictions are synthesized to reconstruct the overall passenger flow. This hybrid approach utilizes real-world data from the Hangzhou subway system to validate the model's performance.  
In terms of the results, the proposed model achieves strong prediction performance with MAE and RMSE values of 0.2253 and 0.3178, respectively. Compared to baseline models such as Informer, Crossformer, and Autoformer, our model provides the best prediction accuracy and stability. The proposed approach demonstrates superior performance, especially during peak and off-peak hours, making it a valuable tool for urban transportation planning and optimization. In conclusion, this hybrid subway passenger flow prediction model offers a practical solution for urban transportation systems, contributing to the intelligent and data-driven management of urban mobility.
###### Abstract  
We present a spatial frequency domain multiplexing method for extending the imaging depth range of a spectral domain optical coherence tomography (SDOCT) system without any expensive device. This method uses two galvo scanners with different pivot-offset distances in two independent reference arms for spatial frequency modulation and multiplexing. The spatial frequency contents corresponding to different depth regions of the sample can be shifted to different frequency bands. The spatial frequency domain multiplexing SDOCT system provides an approximately 1.9-fold increase in the effective ranging depth compared with that of a conventional full-range SDOCT system. The reconstructed images of phantom and biological tissue demonstrate the expected increase in ranging depth. The parameters choice criterion for this method is discussed.  
fiber optics imaging; medical and biological imaging; optical coherence tomography +
Footnote †: journal: Applied Physics Letters B
## 6 Conclusions  
The OSWS offers a groundbreaking approach to weather monitoring, harnessing low-cost sensors to overcome challenges posed by high costs and the need for expert personnel inherent in existing technologies. Its capacity for real-time monitoring and forecasting of pivotal environmental variables using the ARIMA model stands utmost for agriculture, transportation, and air quality monitoring. Its open-source nature further amplifies data accessibility, fostering informed decisions essential in our current climate-changing era.  
The design and implementation of the OSWS laid the groundwork for the future evolution of cost-effective and efficient monitoring systems. As we move ahead, focusing on enhancing data transmission security, diversifying communication interfaces, and enduring diverse environmental conditions will be pivotal in refining the OSWS, aiming to bolster environmental sustainability and foster informed decision-making amidst escalating ecological challenges.  
Conceptualization, A.R., P.P., A.M. ([PERSON]) and A.M. ([PERSON]); Methodology, A.R., P.P. and O.M.; Software, A.R., P.P. and A.M. ([PERSON]); Validation, A.R., P.P. and O.M.; Formal analysis, A.R., P.P., O.M., A.M. ([PERSON]) and A.M. ([PERSON]); Investigation, A.R., P.P., O.M. and A.M. ([PERSON]); Resources, P.P.; Data curation, A.R., P.P. and A.M. ([PERSON]); Writing--original draft, P.P. and A.M. ([PERSON]); Writing--review & editing. A.R. and A.M. ([PERSON]); Visualization, A.R.; Supervision, P.P., A.M. ([PERSON]) and A.M. ([PERSON]); Project administration, P.P.; Funding acquisition, A.M. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
The authors declare no conflict of interest.
# Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications
## 8 Future Directions in 6G Technology Development
### AI-Enabled Networking  
It is expected that the world of communication will benefit from the potential 6G wireless networks in the form of worldwide coverage and services with variable levels of QoS. Sixth generation networks equip the whole network with storage and computing capabilities spread from the central part of the cloud to its edge or to the remote device at the client's end. The principal role of the AI technique in hyper-intelligent 6G networking is quite apparent. The fundamental networking processes can be understood by conducting more investigations on logical AI solutions. Hybrid centralized-distributed AI configurations support better utilization of the information gathered from IoT devices and the processing capability of these devices. It is also feasible to prepare a learning model through the worldwide server at central clouds and satellites. However, it must be noted that data transfer from IoT devices to a central server in one go may not be possible at times. Hence, progressive, hierarchical data transfer is adopted whereby data are transferred from end devices to edge nodes and global nodes.
## 1 Introduction and Research Gap
Nevertheless, the majority of studies investigate factors to prevent cyberattacks from technical perspectives for the digital crime problem [15; 16; 17], but there are insufficient studies that investigate factors relevant to organizational factors and the role of leadership in cybersecurity awareness in leveraging cybersecurity behavior in banks and business organizations [18; 19]. Therefore, this gap needs to be addressed in this research. Moreover, cyberattack factors play a major role in showing evidence of the business impact on the world and society that is shaped by the influence of cyberattacks as a digital threat [20]. Furthermore, almost all businesses are starting to learn about technology transformation, focusing on the skills possessed by venture capitalists in the business innovative process for safety and protection [21]. Therefore, there is a need to study the role of skilled leaders in financial organizations in the UAE in preventing cyberattacks and investigate other human factors related to the individual in financial organizations in the UAE in preventing cyberattacks [1; 16; 22; 23]. In line with that, this study has three bold research objectives. First, to propose a model of cyberattack prevention for financial organizations in UAE. Second, to identify and examine the factors that prevent individuals from being victims of cyberattacks in finance organizations in the UAE. Third, to examine the cybersecurity leadership mediating role between the investigated factors of cybersecurity protection and cyberattack prevention. Substantially, in addition to employing and examining cyberattack preventive measures based on protection motivation theory, this research introduces and examines newly emerged cyberattack preventive measures, which are highly relevant to the banking sector and that can be generalized to other financial institutions and big business organizations. The new proposed preventive measure variables were derived from related research and will be presented in the next subsection in the form of three variables illustrated as follows: two independent variables, (a) frequent organizational training (b) frequent government alerting; and one mediating variable known as (c) cybersecurity leadership. This paper is organized so that the next section of this article will include the background information on the research and related studies. Next is a description of how the research framework and hypotheses were developed. Then, the applied research method to develop and validate the instrument to collect data is presented. The analysis of the data and the findings are then presented and discussed. The study is ended with a conclusion and the implications of the research.
## 1 Introduction
### Resource Management in Network Function Virtualization  
Here, a use-case is presented which can utilize the concept of the SDN-with-knowledge paradigm in a network setting called network function virtualization [12]. It is a relatively new approach in which network applications, such as firewalls and load balancers, are implemented virtually, called as virtual network functions, without the use of extra hardware [10]; in fact, they are implemented utilizing general hardware.  
The management of resources in NFV is very difficult and one of the most researched topics because of its vast applications in Data Centers [12]. The SDN-with-knowledge paradigm has the potential to address resource management problems in NFV [7]. KP with the aid of machine learning techniques can be considered as a new pillar in modeling the resource requirement of VNFs, hence improving the overall performance of the system.