esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Overview of 5G-Enabling Technologies
### Key 5G-Enabling Technologies
#### 5.1.3 Device-to-Device (D2D) Communication  
D2D communication is one of the novels enabling technologies in the 5G network that enables the direct connection of devices, typically during congestion or at cell borders, resulting in an ad hoc mesh network where the intermediary devices might act as a relay for other devices. It is considered the primary technology for establishing point-to-point communications to achieve better coverage area and greater data rates for 5G user devices [4]. D2D communication offers a variety of services which include, but are not limited to, safety, traffic offloading, backhaul network, cellular coverage expansion, battery consumption reduction, reliable communication, and location-based proximity services. Other benefits include power management, spectrum efficiency, better coverage, capacity expansion via radio resource reuse, and decreased overheads due to lowering base stations, device exchanges, and data centers [15; 89]. Further benefits of D2D data transmission are that it will be efficient at unloading traffic and improving frequency and space reuse to offer direct communication among local devices without requiring network infrastructure.  
D2D communication can improve overall network efficiency by potentially reusing the same frequency resources as traditional infrastructure-based communication [90]. Notwithstanding the several advantages, device-to-device communication also has its challenges; it introduces a variety of interference into the network [91]. Non-negligible intra-cell interference is one of the challenges. Because of the presence of other transmitters, the device's proximity can potentially cause interference. To manage interference, several solutions, such as power regulation, network coding, or interference avoidance multi-antenna transmission, can be used [11]. D2D communication scenarios are shown in Figure 6.
# Wavelength-Switchable Ytterbium-Doped Fiber Laser Based on All-Fiber Lyot Interferometer Filter
reported a fused tapered-fiber Mach-Zehnder interferometer (MZI) for wavelength-tunable YDFL generation, and the single-wavelength tuning ranged from 1026.7 nm to 1046.7 nm [20]. From these reports, it is evident that achieving wavelength-tunable ytterbium-doped fiber laser outputs typically involves methods such as all-fiber interference structures, fiber gratings, and nonlinear fiber components. Notably, both all-fiber and non-all-fiber filtering structures, including fiber Sagnac rings, fiber Fabry-Perot cavities, high-birefringence fibers, and polarizing gratings, have been utilized. However, these structures often exhibit complexity and fragility, frequently requiring specialized nonfiber devices, which compromises system integrity. Additionally, there is a need for further improvement of the wavelength tuning range and stability. As one type of comb filter, the fiber Lyot filter is usually composed of a polarization-maintaining fiber and a polarization controller [21]. The Lyot filter has the advantages of a compact structure, a high modulation intensity, a flexible and adjustable free spectral range, etc., and has been widely used in signal filtering and fiber sensing technology. [PERSON] et al. reported wavelength-tunable holmium- and bismuth-doped fiber lasers based on the Lyot filter, and 1.3 and 2 um band multi-wavelength lasers were realized, respectively [22; 23]. [PERSON] et al. reported a switchable erbium-doped fiber laser (EDFL) using an embedded Lyot filter, and the output wavelengths can be switched among single-, dual-, triple-, quad-, and quintuple-wavelength outputs [24]. [PERSON] et al. reported a multi-wavelength EDFL incorporating a Lyot filter and a nonlinear optical loop mirror. The proposed laser can emit 12 stable lasing lines, and the side-mode suppression ratio (SMSR) is greater than 25 dB [25]. [PERSON] et al. demonstrated a wavelength-switchable thulium-doped fiber laser based on a parallel Lyot filter, and up to four and nine lasing lines were generated based on wavelength spacings of 0.9 nm and 1.8 nm, respectively [26]. As mentioned above, in order to realize 1 \(\upmu\)m wavelength-band wavelength-switchable laser generation, this paper proposes and demonstrates a YDFL based on an all-fiber Lyot filter, achieving tunable outputs of single-, double-, triple-, and quadruple-wavelength lasers--a system on which few studies have been reported.
# Enhancing Malware Detection via RGB Assembly Visualization and Hybrid Deep Learning Models
## 3 Materials and Methods
### EfficientNetB0  
EfficientNet represents a family of convolutional neural networks specifically optimized to achieve high performance while maintaining parameter efficiency. In this study, we employ the EfficientNetB0 variant as a feature extractor [26; 33; 34; 35], leveraging its ImageNet pretrained weights for transfer learning. The model is implemented without its fully connected layers (include_top = False) and utilizes global average pooling to extract high-level spatial features. To enhance domain adaptation, the final 20 layers were unfrozen during fine-tuning, allowing the network to adjust its deeper feature representations specifically for our malware classification task.
# A Multi-Objective Evaluation Method for Smart Highway Operation and Management
## 4 Methods for Assigning Weights to Evaluation Indicators
### Minimum Deviation Method  
The commonly employed method is the linear weighting approach, which necessitates the determination of subjective preference coefficients, thus introducing a level of subjectivity into the evaluation outcomes. To effectively utilize attribute weight information acquired from diverse subjective and objective weighting methods, minimizing deviations in the weighted weights derived from these methods is crucial. Hence, this study employs the method of minimum deviation for the comprehensive calculation of indicator weights [61].  
Step 1: Given the total number of \(q\) methods for calculating the weights of the indicators, of which there are 1 and \(q\)-1 for the subjective and objective methods, respectively, the weight vector is expressed as follows:  
\[\begin{array}{l}u_{k}=(u_{k1},u_{k2},\cdots,u_{km})^{T};\\ \sum\limits_{i=1}^{m}u_{ki}=1;k=1,2,\cdots,q^{\cdot}\end{array} \tag{23}\]  
Step 2: The utilization of a deviation function allows for a comprehensive consideration of the objectivity of decision-making and the subjective opinions of decision-makers. It aims to minimize the weight deviations obtained from various methods, and the reasonable weight vector is represented as \(w=(w_{1},w_{2},\ldots,w_{m})^{T}\). The constructed single-objective optimization model can be expressed as follows:  
\[\begin{array}{l}minJ=\sum\
olimits_{k=1}^{l}\sum\
olimits_{j=1}^{m}a_{k}f_{ j}(u_{k})+\sum\
olimits_{k=l+1}^{q}\sum\
olimits_{j=1}^{n}a_{k}g_{j}(u_{k}) \\ \text{s. }t.\sum\
olimits_{i=1}^{m}w_{i}=1;\;w\geqslant 0\end{array} \tag{24}\]  
In Equation (24), \(J\) represents the objective function to be solved. \(a_{k}\) represents the weight coefficients obtained from various weighting methods. \(a_{j}\) represents the weights obtained using the \(j\)-th weighting method. \(f_{j}(u_{k})\) and \(g_{j}(u_{k})\) are the deviation functions for subjective and objective weighting methods, respectively.  
Step 3: The lower the deviation in weight for each weighting method, the higher the effective utilization rate of the obtained weight information. Therefore, the constructed model can be transformed into Equation (25).  
\[\begin{array}{l}minJ=\sum\limits_{j=1}^{n}\sum\limits_{k=1}^{q}\sum\limits_{ i=1}^{m}\left(a_{k}u_{ki}-a_{j}u_{ij}\right)^{2}\\ \text{s. }t.\sum\limits_{k=1}^{q}a_{k}=1;a_{k}\geqslant 0;k\in[1,q]\end{array} \tag{25}\]  
Step 4: Combining the necessary conditions for the existence of extremum in the Lagrange function, \(L\) is the established Lagrange function. To solve it, take the following derivative:  
\[\begin{array}{l}\frac{\partial L}{\partial a_{k}}=qa_{k}\sum\limits_{i=1}^{ m}u_{ki}^{2}-\alpha_{1}\sum\limits_{i=1}^{m}u_{1i}u_{ki}-\\ \cdots-a_{q}\sum\limits_{i=1}^{m}u_{qi}u_{ki}+\frac{\lambda}{2}=0\\ \frac{\partial L}{\partial\lambda}=\sum\limits_{k=1}^{q}a_{k}-1=0\end{array} \tag{26}\]
# Weigh-in-Motion Method Based on Modular Sensor System and Axle Recognition with Neural Networks
## 4 Results and Discussion in Weighing on Modular WIM System
### Test Site Modular System Comprehensive Test
#### 4.1.2 Performance Comparison of Modular System Consistency  
To compare the consistency of the three WIM systems, their weights were recorded five times, respectively, while maintaining a constant speed of 20 km/h. The standard deviation and error fluctuation amplitude were selected as the evaluation parameters. The error fluctuation amplitude was defined as the difference between the maximum and minimum errors among the five times. The results are presented in Tables 6 and 7.  
Figure 8: Weighing error statistics of vehicles. (**a**) Two-axle vehicle; (**b**) four-axle vehicle; (**c**) six-axle vehicle.  
\begin{table}
\begin{tabular}{l c c c c c} \hline
**Trial Number** & **1–5** & **6–10** & **11–15** & **16–20** & **21–25** & **26–30** \\ \hline Speed (km/h) & 5 & 10 & 15 & 20 & 25 & 30 \\ \hline \end{tabular}
\end{table}
Table 5: The relationship between the trial number and the speed.  
It can be seen from Tables 6 and 7 that the standard deviation and error fluctuation amplitude of the five-platform modular system were the smallest. Compared with the five-platform modular system, the standard deviation and error fluctuation amplitude of the three-platform modular system were up to 11.71 times and 10.95 times, respectively. The one-platform system increased by 15.53 times and 17.4 times, respectively. It was demonstrated that the modular system exhibited significant advantages over the independent system in terms of weighing consistency. In the three-platform modular system and one-platform system, both the standard deviation and error fluctuation amplitude increased significantly with an increase in the axle number. By contrast, the five-platform modular system exhibited only slight fluctuations, and was more stable and reliable. This is mainly due to the advantages provided by the whole-vehicle weighing method and sufficient weighing time.
# AUV-Aided Optical--Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning
## 6 Results and Discussion
### The Convergence Performance  
To demonstrate the convergence of the AoI optimization algorithm for AUV data collection, in Figure 3, we show the variation of the cumulative reward, where the X-axis represents the number of iterations trained and the Y-axis presents the cumulative reward. It can be seen that in the early stages of training, the cumulative reward values are very low due to the high chance of \(\epsilon\)-greedy random exploration. As the training period continues to increase, the reward value gradually increases and stabilizes.
# A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches
## 7 Open Source Approaches
### OSC Community Labs  
OSC Community Labs or OSC Labs are integration and testing platforms offered by OSC. OSC labs provide the hardware and software resources for OSC members to test their software. OSC labs' software follows the O-RAN specification. Currently, there are three OSC labs [44]. The first lab is located in New Jersey and is maintained by AT&T. It is the first laboratory in OSC and focuses on building an E2E environment using open-source software to demonstrate operational RAN. OSC New Jersey Lab has been accessible since early 2021 with the goal that is to creating a comprehensive environment where a functional Radio Access Network (RAN) can be showcased using open-source software. The second laboratory is located in San Jose and is hosted by China Mobile to maintain simplex and duplex instances of O-Cloud, allowing for seamless interconnection with the Bedminster Lab. This enables the sharing of testing tools and supports a continuous integration (CI)/continuous delivery (CD)/continuous testing (CT) pipeline built upon the XTesting framework. The third one is Taiwan Lab, which is maintained by the National Taiwan University of Science and Technology (NTUST) and National Yang Ming Chiao Tung (NYCU) and is supported by Chunghwa Telecom (CHT) and the 'B5G/6G Software Technology' project funded by the Ministry of Science and Technology, Taiwan. The primary purpose is to replicate the environment of the Bedminster Lab, with a specific focus on integrating O-DU and conducting tests related to security [136]. Figure 20 shows the logical resources owned by each lab. OSC labs are important platforms for open-source RAN integration and testing. OSC labs can be leveraged for future research in improving open RAN.
###### Abstract
In particular, the lack of joint modeling of initial state-of-charge and time-of-use price response strategies limits the ability of wind/storage systems to achieve coordinated optimization between profitability and robustness. While existing studies have explored wind/storage scheduling through multi-scenario optimization, demand-side coordination, or rolling intraday control, many of them assume a fixed _SOC_ and do not explicitly incorporate risk constraints. In contrast, this study focuses on day-ahead scheduling and introduces a unified framework that integrates non-parametric modeling of forecast errors, Copula-based scenario generation, initial _SOC_ optimization, and CVaR-based risk control. This approach captures both the economic and risk dimensions of decision-making under uncertainty, distinguishing it from prior works that either overlook initial _SOC_ flexibility or treat uncertainty in a simplified manner. The main contributions of this study are as follows:  
1. Based on historical wind power forecasting errors, kernel density estimation is employed to fit the marginal distributions, while a Gaussian Copula function is used to capture the temporal correlation among forecasting errors across different time periods. This enables the generation of wind power output scenarios with sequential dependence. Representative scenarios are then extracted using K-means clustering to enhance scenario representativeness and modeling accuracy;
2. The initial _SOC_ of the energy storage system is introduced as a decision variable in the scheduling framework. By jointly considering electricity sales revenue, operation and maintenance costs, penalties for schedule deviations, and time-of-use pricing, the model enables economic arbitrage through charging and discharging during peak and off-peak price periods, thereby improving overall system profit and operational flexibility;
3. A CVaR-based risk control mechanism is incorporated into the multi-scenario optimization framework to effectively constrain profit losses under extreme scenarios, thereby enhancing the robustness and risk controllability of the scheduling strategy under high uncertainty. The remainder of this paper is organized as follows: Section 2 introduces the modeling of wind power forecasting errors and the generation of representative scenarios. Section 3 develops the day-ahead optimization scheduling model for the wind/storage system, with a detailed description of the objective function, constraints, and the CVaR-based risk control mechanism. Section 4 presents case studies based on multi-scenario wind power outputs, in which simulation experiments are conducted under different optimization strategies, initial \(SOC\) settings, and CVaR confidence levels. Section 5 concludes the paper and outlines directions for future research.
# A Novel Optimal Control Strategy of Four Drive Motors for an Electric Vehicle
## 2 Simulation Platform and Optimal Energy Management
### Electric Vehicle Simulation Platform
#### 2.1.4 Lithium Battery Model  
The lithium battery pack serves as the power source for the vehicle's drive motor. Based on data from ADVISOR(r) (2002, NREL's advanced vehicle simulator), a relationship between the lithium battery voltage and internal resistance is constructed, and the State of Charge (SOC) of the lithium battery is calculated based on the results obtained. Regarding the operating temperature of the lithium battery, this study's model sets the battery temperature to a fixed value and does not consider changes in battery temperature under  
Figure 4: Efficiency curve of the drive motor.  
actual usage conditions, which would vary with usage conditions and operating time. The accuracy of lithium battery charge estimation impacts the overall vehicle dynamics and energy management strategies. Typically, the internal resistance is shown in Formula (3):  
\[R_{b}=R_{b}(SOC_{b},t_{b}) \tag{3}\]  
where \(R_{b}\) is the equivalent internal resistance of the lithium battery; \(SOC_{b}\) is the state of charge for the lithium battery; \(t_{b}\) is the lithium battery temperature. SOC represents a range from 0 to 1 (0-100%), and its calculation method is shown in Formula (4):  
\[SOC_{b}=\frac{SOC_{int}Q-\int\frac{I_{b}}{\eta_{b}}dt}{Q} \tag{4}\]  
where \(Q\) is the rated capacity of the lithium battery; \(I_{b}\) is the discharge current of the lithium battery; \(\eta_{b}\) is the charge-discharge efficiency of the lithium battery. \(\eta_{b}\) is a function of the relationship between \(V_{OC}\) and \(I_{b}\):  
\[\eta_{b}=\eta_{b}(V_{OC},I_{b}) \tag{5}\]  
The discharge current of the lithium battery calculation method is as shown in Formula (6):  
\[I_{b}=\frac{V_{OC}-\sqrt{{V_{OC}}^{2}-4\times P_{b}\times R_{b}}}{2\times R_{b}} \tag{6}\]  
where \(V_{OC}\) is the open circuit voltage of the lithium battery, a function of \(SOC_{b}\) and \(I_{b}\); \(P_{b}\) is the discharge power of the battery to the drive motor. The calculation method for the load voltage of the battery during discharge is shown in Formula (7):  
\[V_{b}=V_{OC}-I_{b}\times R_{b} \tag{7}\]  
The calculation of dynamic resistance performance uses the internal resistance method, which can be determined through experimental data on energy storage batteries, anticipating the change in internal resistance of each battery at different SOC levels, and obtaining the open circuit voltage values at various SOCs.
# Comparison of PCR Primers for Analyzing Dentirifying Microorganisms in the Hyporheic Zone
###### Abstract  
In this study, the specific amplifications of six denitrification-associated genes using PCR(Polymerase Chain Reaction) primer sets were compared. Thereafter, the PCR primer sets that were determined to be suitable for each denitrification-associated gene were used to test samples from sixteen aqueous environments (three from groundwater, three from stream water, and ten from hyporheic zone water). The specific amplification was determined using PCR primer sets for denitrification-associated genes and nucleic acids from eleven types of strains. _NosZ_ was the most frequently amplified gene from the nucleic acid of type, with a specific band seen in all eleven strains. The specific band amplification and PCR time of the strains were analyzed to select one PCR primer set for each gene. The selected PCR primer sets were used to analyze sixteen samples from the aqueous environments in which denitrifying microorganisms were expected to be present. Specific bands of _narG_, _nitS_, and _nosZ_ were most frequently observed in the hyporheic water samples. The results showed that microorganisms containing _nitG_ (involved in the reduction of nitrate to nitrite to nitrite), _nitS_ (involved in the reduction of nitrite to nitric oxide), and _nosZ_ (involved in the reduction of nitrous oxide to nitrogen gas) were the most abundant in the hyporheic zone samples used in this study.  
hyporheic zone; denitrification-associated gene; polymerase chain reaction; nitrogen cycle +
Footnote †: journal: Applied Sciences
## 1 Introduction  
Generally, supercomputing and grid computing have been used to process data-intensive or complex problems in parallel. Most studies on large-scale job processing using supercomputing or grid computing have been focused on the development of task partition algorithms, such as K-means, particle swarm optimization, and cat swarm optimization. However, resources in networks or servers used in most companies or institutions are not used to the maximum, and become idle. Their mean operation rates have been measured at less than 20% to 30%. In addition, as much attention has been paid to the Fourth Industrial Revolution throughout the world, the importance of cloud computing that can analyze and process a large amount of unstructured data has also increased.  
To accommodate exponentially increasing user requirements and efficiently use computing resources, cloud computing integrates a single physical resource into multiple logical resources or many physical resources into a single logical resource based on virtualization technology. Thus, studies on the use of cloud computing have been conducted to process large-scale jobs in a distributed manner. As described above, if computing resources are provided using logically created virtual machines based on a virtualization technique, the parallel processing of large-scale jobs, which is not possible in existing systems, can be achieved, and requirements of space and hardware costs can be reduced [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12].  
However, a survey by Amazon EC2 found that the mean use rate of virtual machines was 7.3% per week, and a survey by IBM found that 30% to 40% of all virtual machines held by cloud computing service providers were idle for a long period of time. When virtual machines created through virtualization techniques to overcome the limitations of existing physical servers are not used for a long period of time, the unused computing resources are wasted [13; 14; 15; 16].  
Thus, this study proposes an idle virtual machine management scheme that provides additional service functions by using idle virtual machines within OpenStack, which is a cloud computing open-source project, as a form of infrastructure as a service that supports multiple platforms.
## 3 Methodology
#### 3.2.3 Simulation Verification  
During the scanning process, response speed is a critical factor in improving scanning speed. To validate the effectiveness of the controller designed in this paper, simulations of the response speed were first conducted in Simulink using traditional PID and Fuzzy PID controllers. A \(1^{\circ}\) step input signal was applied, and the output responses of the two controllers were compared. As shown in Figure 10, when the PID control algorithm was used in the position loop controller, the steady-state time of the output response was 0.25 s, with an overshoot of 9.5%. When the Fuzzy PID control algorithm was used in the position loop controller, the steady-state time of the output response was 0.07 s, with almost no overshoot. Comparing these results, it was found that the response speed was improved by 25.7% when the Fuzzy PID control algorithm was used. Therefore, it can be concluded that this algorithm has better dynamic performance and can enhance the response speed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**ec**} & \multicolumn{5}{c}{**e**} \\ \cline{2-6}  & **NB** & **NS** & **ZO** & **PS** & **PB** \\ \hline
**NB** & PS & NB & NB & NB & PS \\
**NS** & ZO & NB & NB & NS & ZO \\
**ZO** & ZO & NS & NS & NS & ZO \\
**PS** & PB & ZO & ZO & ZO & PB \\
**PB** & PB & PS & PS & PS & PB \\ \hline \hline \end{tabular}
\end{table}
Table 4: Control-rule table for \(\Delta K_{d}\).  
Figure 10: Step response.