esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Traffic Management in Next-Generation Networks
### Paper Organization  
As illustrated in Figure 1, the rest of this paper is organized as follows. In Section 2, we present the main ML and DL-based models that have been used in the SDN environment and their strengths/weaknesses. Then, we present a comparison and summary table for the reviewed ML/DL algorithms. Section 2.3 presents a review of dimensionality reduction techniques, their advantages/disadvantages, and then we analyze how these techniques can be used to achieve the high performance of learning algorithms that ultimately improve the performance of the model. The main purpose of Section 3 is to provide a comprehensive survey of ML/DL applications with SDN for network traffic management/analysis. This survey also aims to highlight the list of challenges and issues of using ML/DL over SDN as presented in Section 4. Finally, Section 5 outlines the conclusions of this paper. For better comprehension, we summarize definitions of the abbreviations that will be used in this paper in Table 2.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Topic**} & \multicolumn{4}{c}{**Scope**} \\ \cline{3-6}  & & **DL** & **Other ML Models** & **DR** & **SDN** \\ \hline
[13] & A survey of deep learning and its applications. & ✓ & & & \\ \hline
[11] & A brief survey of SDN-based network intrusion detection system using machine and & ✓ & ✓ & & ✓ \\  & deep learning approaches. & & & & \\ \hline
[14] & A tutorial on deep learning. & ✓ & & & \\ \hline
[6] & An overview of AI techniques in the context of SDN. & ✓ & ✓ & & ✓ \\ \hline
[4] & A survey of ML/DL applications in SDN. & ✓ & ✓ & & ✓ \\ \hline
[15] & A brief survey of feature selection/extraction methods. & & & ✓ & \\ \hline
[16] & A survey of the procedure of feature selection and its application. & & & ✓ & \\ \hline
[17] & A survey of the software-defined wireless sensor networks. & & & & ✓ \\ \hline
[10] & A survey of the ML-based model applied to fundamental problems in networking & & ✓ & & \\ \hline Our paper & A survey of DR, ML and DL in the SDN environment. & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of existing surveys related to ML/DL, dimensionality reduction (DR), and SDN.
# The Improved A* Algorithm for Quadrotor UAVs under Forest Obstacle Avoidance Path Planning
## 4 Discussion
The APF algorithm, the improved A* algorithm proposed in the literature [38], and the improved A* algorithm proposed in this paper yielded shorter paths and generated smoother trajectories in comparison to the traditional A* algorithm. However, the APF algorithm and the improved A* algorithm proposed in the literature [38] exhibited collision risks as the path trajectory was closer to the edge of the obstacle. Moreover, the algorithm proposed in the literature [38] did not consider the effect of changing weights on path search, thus leading to a longer path and larger total turns. The APF algorithm took more search time, and its lack of global perception led to an increased probability of falling into a local optimum. In contrast, the improved A* algorithm in this paper reduced the path length and search time while ensuring the safety of obstacle avoidance, significantly reducing the total turning angle, improving the smoothness and continuity of UAV flight, reducing energy consumption, and proving to be more suitable for the operating environment of plantation forests. To summarize, this study aims to enhance the obstacle avoidance and endurance capability of UAVs in unstructured plantation forests. It may provide new insights for improving the safety and efficiency of UAV autonomous flight, which can broaden the range of applications for UAVs. In the future, we plan to optimize the obstacle avoidance algorithm further by combining it with other intelligent algorithms to plan optimal paths for UAVs in dynamic environments and extend its application to other scenarios.
# Advanced Methods for Photovoltaic Output Power Forecasting: A Review
## 1 Introduction
Advanced methods based on artificial intelligence (AI) techniques and machine learning (ML) [22] include artificial neural networks (ANNs), k nearest neighbor (kNN), extreme learning machine (ELM), support vector machine (SVM), etc. These methods, that do not need any information regarding the PV systems, have some advantages typical of statistical approaches. They are used when measurements from the field are available, and basically for short-term applications [23]. Hybrid approaches combine one of the above-mentioned advanced methods with one physical or statistical approach. This type of techniques presents a very good forecasting accuracy as they benefit from the combination of two well performing techniques [24; 25]. In the literature, there are models based on the use of on-site measurements, models relying on weather forecast data, and models that combine on-site historical measurements with weather forecasts [8]. The latter are more and more used and belong to the hybrid category. There are a good number of reviews on PV power, and the most recent ones are reported in Table 1. The present work differs from the ones already available in the literature, as the main objective in this case is to review the field of advanced techniques. This review offers the first comprehensive comparison of the most relevant techniques for PV power forecasting base on machine learning (ML), deep learning (DL), and hybrid methods. These methods are validated with external data (data not used in model development) to verify the capability of the model to forecast power with good accuracy, when it is mentioned in the referenced papers.
## 4 Implementation Details of Field Arithmetic
### Field Addition and Subtraction over \(\mathbb{F}_{p}\)  
The curve Ted127-glv4 uses a Mersenne-like prime of the form \(p=2^{127}-5997\). An efficient field addition/subtraction method for this scenario was proposed by [PERSON] et al. [16]. Let \(0\leq a,b<p=2^{127}-5997\). Field addition over \(\mathbb{F}_{p}\) can be computed by \(c=a+b\pmod{p}=((a+5997)+b)-carry\cdot 2^{127}-(1-carry)\cdot 5997\), where \(carry=0\) if \(a+b+5997<2^{127}\). Otherwise, \(carry=1\). The result is bounded by \(p\) because, if \(a+b+5997<2^{127}\), then \(a+b<2^{127}-5997\), whereas if \(a+b+5997\geq 2^{127}\), then \((a+b+5997)\pmod{2^{127}}=a+b-p<p\). Because \(a+5997<2^{127}\), addition does not require carry propagation. Note that subtraction with \(carry\cdot 2^{127}\) can be efficiently implemented by clearing the 128-th bit of \((a+5997)+b\).  
Similar to field addition, field subtraction over \(\mathbb{F}_{p}\) can be computed by \(c=a-b\pmod{p}=(a-b)+borrow\cdot 2^{127}-borrow\cdot 5997\), where \(borrow=0\) if \(a\geq b\), otherwise, \(borrow=1\). Addition with \(borrow\cdot 2^{127}\) can be implemented by clearing the 128-th bit of \(a-b\).  
Figure 1: The implementation hierarchy of four-dimensional Gallant-Lambert-Vanstone and Galbraith-Lin-Scott (GLV-GLS) scalar multiplication.
# Data Synchronization: A Complete Theoretical Solution for Filesystems
## 6 Synchronization by Conflict Resolution
### The Synchronization Algorithm  
The input of the synchronization algorithm is \(\left(A^{o},B^{o}\right)\), which is a pair of refluent canonical command sets. The goal is to find, using successive conflict resolution steps, a merger command set \(M\) that fits Definition 6. In addition, we aim to show that our algorithm can produce _any_ of the mergers allowed by the definition, and only valid mergers. To achieve this, the algorithm accepts a pre-set merger \(M^{o}\) as an optional argument. Whenever the algorithm needs to make an otherwise arbitrary choice, \(M^{o}\) is used to choose one of the possibilities. While choosing any of the possibilities will generate a valid merger, guiding the choice will guarantee that \(M^{o}\) will be generated.  
According to the discussion in Section 6.1, the synchronization algorithm has two main components. The first one, called Algorithm 1, reduces the general problem to the special case when the input command sets have no common elements. Properties of the merger set proved in Proposition 12 provide the foundation of the reduction. The second component, called Algorithm 2, assumes that its input command sets \(A^{o}\) and \(B^{o}\) are disjoint so that it can rely on the intrinsic properties of the conflict graph when generating the merger \(M\). The two algorithms are visualized in Figures 6 and 7, respectively. The detailed description is followed by the proofs of the correctness and a short discussion on time and space complexity.  
**Algorithm 1** (Synchronization in the general case).: Let \(C=A^{o}\cap B^{o}\). Execute Algorithm 2 with the command sets \(A^{o}\smallsetminus C\) and \(B^{o}\smallsetminus C\). If \(M^{o}\) is specified, then it must satisfy \(C\subseteq M^{o}\); in this case pass \(M^{o}\smallsetminus C\) as the optional input to Algorithm 2. When Algorithm 2 returns \(M^{\prime}\), return \(M=C\cup M^{\prime}\).
## 2 Materials and Methods
### Small RNA Library Construction and Sequencing  
Each sample of tissue consisted of a pool of three biological replicates. Total RNA of the sample was extracted using the Trizol Kit (Promega, Madison, WI, USA) according to the manufacturer's instructions. Subsequently, DNase I (RNase-free) (TaKaRa, Kyoto,Japan) was used to remove DNA, and the concentration and integrity of clean RNAs were evaluated by a 2100 Bioanalyzer at 230 nm and 280 nm. A total amount of 1.5 \(\upmu\)g RNA per sample was used as input material for sample preparations. Sequencing libraries were generated using NEBNext(r) UltraTM small RNA Sample Library Prep Kit for Illumina(r) (NEB, Ipswich, MA, USA) following the manufacturer's recommendations.  
Briefly, small RNAs were separated on PAGE gel from total RNAs. After ligation of 3\({}^{\prime}\) and 5\({}^{\prime}\) adaptors, the small RNA fragments were used for reverse transcription and PCR amplification. Subsequently, the RNA fragments were purified by the AMPure XP system (Beckman Coulter, Beverly, MA, USA) and the library quality was assessed on the Agilent Bioanalyzer 2100 system (Agilent Technologies, Palo Alto, CA, USA). Finally, the RNA library from each sample was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kit v4-cBot-HS (Illumina, San Diego, CA, USA), and deep sequenced on an Illumina Hiseq 2500 platform according to the manufacturer's instructions.
## 1 Introduction
The implementation of an all-optical 4-bit parity generator and checker holds promise for applications where speed, efficiency, and data integrity are critical. This includes high-speed optical communication networks, optical computing, and scenarios demanding real-time data processing. The system's reliance on all-optical components not only enhances processing speed but also reduces energy consumption and electromagnetic interference, contributing to a more sustainable and robust information processing infrastructure. In recent studies, notable advancements have been made in the realm of CR-SOAs, with several works highlighting the design of combinational systems. These innovations extend beyond the conventional and include the development of a CR-SOA-based half adder [12; 13], half subtractor [14], digital-to-analog converter [15], latches operating at an impressive speed of 100 Gb/s [16], header recognition also at 100 Gb/s [17], and header processor [18]. This growing body of research underscores a pivotal stride towards the realization of all-optical data processing systems that are not only faster but also significantly more efficient. The exploration of CR-SOA-based logic systems represents a noteworthy evolution in optical computing. By achieving functionality at ultra-high speeds, such systems pave the way for enhanced data processing capabilities, promising a transformative impact on diverse applications. The demonstrated feasibility of CR-SOA integration in various combinational circuits establishes a foundation for the broader deployment of all-optical processing methods. Throughout this paper, we delve into the underlying principles of CR-SOA-based MZI, elucidate the design considerations for an all-optical 4-bit parity generator and checker at 120 Gb/s, and discuss the potential applications and benefits of such a system in the realm of high-speed data communication and processing. The study further explores the impact of key data signals and CR-SOA parameters on the quality factor (QF) metric. The results showcase the CR-SOAs' capability to cascade and create more complex logic circuits, underscoring the adaptability and promise of this inventive methodology in pushing forward the domain of all-optical data processing.
## 1 Introduction
At the same time, the research on wake effects using the numerical simulation method is gradually increasing around the world [21; 22]. For example, [PERSON] [23] used the Large Eddy Simulation (LES) method to simulate the wake of wind turbines and then studied the relationship between the wake and power loss. [PERSON] et al. [24] simulated the wind turbine in a yaw state by LES, used the coupling of the Nonlinear Vortex Lattice Method (NVLM) and time-accurate Vortex Particle Method (VPM), and compared it with the measurement results for verification. [PERSON] [20] compared the results obtained by large eddy simulation with those obtained by actual wind tunnel experiments to increase the accuracy of the results and data. [PERSON] [25] and others used OpenFOAM (using the dynamic type solver pimpleDyMFoam) software to simulate the wind farm array to study the impact of various parameters of the wind farm on power generation efficiency, and pointed out that the spacing and incident angle of wind turbines have a significant impact on the power generation of the wind farm. [PERSON] [26] used OpenFOAM to simulate the operation of wind turbine arrays and study the layout of the wind turbine array with the highest power generation efficiency (OpenFOAM solvers are based on the Finite Volume Method (FVM) and the fluid flow is governed by the Navier-Stokes equations). [PERSON] [27] used OpenFOAM to simulate the wake of a wind turbine in Mexico under different inflow conditions. The above algorithms or numerical simulations show that wind farms with different hub heights have a positive effect on their power generation. In this paper, the influence of adding a row of small wind turbines on the power of large wind turbines downstream is studied by numerical calculation. Section 2 of the paper details an overview of the simulation and wind turbine layouts. Section 3 discusses the results of downstream wind turbine power when a layout of small wind turbines was placed at the middle of two large turbines. Lastly, Section 4 summarizes the main outcomes of the study.
# Generalized Chirp Spread Spectrum for Underwater Acoustic Communications
## 5 Conclusions  
In this paper, we proposed the G-CSS technique to enhance underwater acoustic communications and compared its performance with conventional up/down-CSS and LoRa-CSS through both simulations and ocean experiments. In simulation, under East Sea channel conditions at 4 dB SNR, G-CSS achieved a BER of \(3\times 10^{-4}\)--substantially lower than the \(2\times 10^{-2}\) and \(8\times 10^{-3}\) obtained by up/down-CSS and LoRa-CSS, respectively. Under the harsher West Sea conditions, G-CSS maintained a BER of \(1\times 10^{-3}\), while the other methods experienced degradations by factors of up to 11.4 and 66.7. Ocean experiments further confirmed these advantages: for modulation orders of \((8,2)\) and \((8,16)\), G-CSS reduced BER by approximately 3.8 and 2.5 times versus up/down-CSS, and by 2.5 and 2.3 times versus LoRa-CSS. Notably, G-CSS enhances frequency resource flexibility by adjusting the modulation order independently at the symbol's start and end, ensuring high resilience to noise and multipath fading. These results underscore the potential of G-CSS to complement or replace existing methods in underwater communication environments.  
Conceptualization, J.K.; methodology, J.K.; validation, S.H. and H.L.; formal analysis, J.K., B.S., Y.K. and H.L.; investigation, S.H.; data curation, J.K. and B.S.; writing--original draft preparation, J.K.; writing--review and editing, Y.K. and H.L.; visualization, B.S.; supervision, Y.K. and H.L. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP)-Innovative Human Resource Development for Local Intellectualization program grant funded by the Korea government (MSIT) (IITP-2024-RS-2024-00436765, 50%), and this work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (NRF-2022M3J9A107887612, 50%).  
Data are contained within the article.  
The authors declare no conflicts of interest.
## 4 Simulation Results  
In this simulation, we compare the position estimation results from a ground-sensor-mimicking fixed sensor, a non-optimized UAV sensor, and an optimized UAV sensor. First we discuss an optimized route where the UAV is restricted to follow a circular orbit in Section 4.1. Then, the restriction is removed and an optimized free-path orbit is investigated in Section 4.2.
# Adaptive Probabilistic Optimization Approach for Vibration-Based Structural Health Monitoring
## 5 Summary and Future Work  
An adaptive damage detection algorithm has been presented in this paper to identify damage characteristics by using the frequency response function. The main conclusions from this study can be summarized as follows:  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Stage 1**} & \multicolumn{4}{c}{**Stage 2**} \\ \hline  & **Time (s)** & & \(P_{d}\)**(\%)** & & **Time (s)** & & \(P_{d}\)**(\%)** \\ \hline  & \(\overline{x}\) & \(S_{x}\) & \(\overline{x}\) & \(S_{x}\) & \(\overline{x}\) & \(S_{x}\) & \(\overline{x}\) & \(S_{x}\) \\ \hline Case 1 & 6.70 & 0.5 & 75.2 & 10.5 & 6.97 & 0.43 & 89.2 & 0.5 \\ Case 2 & 7.65 & 0.19 & 74.6 & 2.4 & 6.40 & 0.32 & 70.5 & 2.3 \\ Case 3 & 6.64 & 0.69 & 69.6 & 2.3 & 6.57 & 0.68 & 70.9 & 3.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Computational time and probability of damage existence: \(\overline{x}\)—sample mean, \(S_{x}\)—sample standard deviation.  
Figure 7: Computational time of simulated cases.  
* The algorithm has been specifically used to calculate the probability of damage existence along with the probabilities associated with the crack size, as well as crack location.
* The adaptive algorithm developed in this study has been evaluated for robustness through three simulated cases with various damage characteristics and different levels of noise.
* In all three cases, the algorithm has been successful in predicting damage even in the presence of a high level of noise and even for relatively small damage.  
Multiple variables associated with the algorithm such as the weighting factor, constraint levels, etc. can be established by the user based on prior data or experience with the structure that is being evaluated for SHM. It should be mentioned that this study has been limited to the use of the frequency response data from a single location on a simply supported beam with only one type of defect. Future work will include the application of the probabilistic algorithm that has been developed in this study to large and complex truss and frame structures, as well as structures made of composite materials. Other common types of defects such as disbonds, kissing bonds, voids, and delamination will also be investigated in the future. Since sensor location is expected to play a substantial role in the identification of damage characteristics, sensor placement will be integrated into the framework of the proposed algorithm in future studies.  
Conceptualization, H.A. and S.K.; methodology, H.A. and S.K.; software, H.A.; validation, H.A.; formal analysis, H.A.; investigation, H.A. and S.K.; data curation, H.A.; writing--original draft preparation, H.A. and S.K.; writing--review and editing, H.A. and S.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Portable Protein and Fat Detector in Milk Based on Multi-Spectral Sensor and Machine Learning
## 3 Results
### Instrument Validation Results  
Figure 8 shows the results for Test Set 1. The MAE and MSE of the protein model illustrated in Figure 8a were 0.0086 and 0.0012, respectively. The _MAE_ and _MSE_ of the fat model illustrated in Figure 8b were 0.0077 and 0.0015, respectively. There were still individual samples with large differences between the true and predicted values, which may have been induced by the measurement process. The overall forecast had high accuracy and a relatively small error. The XGBoost model showed better generalization in the case of the unknown milk sample data used in the training process compared to the other models.  
However, the model did not learn all the milk samples. To further test the predictive ability of the model for new samples, two milk samples, Milk-1 and Milk-2, from Test Set 2 were used. The results are shown in Figure 9. The dataset contains 40 unlearned milk samples with an actual protein content of 3.3 and 3.5 g/100 mL and an actual fat content of 4.6 and 1.9 g/100 mL for the two milk samples.  
The protein model illustrated in Figure 9a predicted more accurate results for Milk-1 and Milk-2. The average predicted values were very close to the mean of the actual values of 3.3004 and 3.4976 g/100 mL, respectively. The _MAE_ and _MSE_ of the model were 0.0660 and 0.0109, respectively. This indicates that the average prediction error of the model was relatively small, and the difference between this value and the true value was small.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & \(R^{2}\) & _MAE_ & _MSE_ \\ \hline \multirow{2}{*}{LR} & Protein & 0.1809 & 0.1806 & 0.0541 \\  & Fat & 0.8624 & 0.2290 & 0.0946 \\  & Protein & 0.1165 & 0.1866 & 0.0584 \\  & Fat & 0.8112 & 0.2739 & 0.1300 \\  & Protein & 0.6405 & 0.1155 & 0.0237 \\  & Fat & 0.5792 & 0.1270 & 0.0278 \\  & Protein & 0.7905 & 0.0847 & 0.0134 \\  & Fat & 0.9919 & 0.0959 & 0.0160 \\  & Protein & 0.9703 & 0.0136 & 0.0020 \\  & Fat & 0.9951 & 0.0172 & 0.0035 \\  & Protein & 0.9864 & 0.0048 & 0.0009 \\  & Fat & 0.9994 & 0.0079 & 0.0013 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the performance of different models.  
Figure 8: Protein model prediction results for Test Set 1 (**a**) and fat model prediction results for Test Set 1 (**b**).  
The prediction results of the fat model illustrated in Figure 9b were more accurate for Milk-1 and Milk-2, with mean prediction values of 4.5861 and 1.9017 g/100 mL, respectively. The _MAE_ and _MSE_ of the model were 0.0580 and 0.0072, respectively. This indicates that the average prediction error of the model and the difference between this value and the true value were small.  
The mean error of the two milk samples was relatively low, indicating good generalization to the new samples and good accuracy of the XGBoost measurement model. By testing the samples and comparing the data, our designed detection instrument can achieve the expected accuracy.