esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This study presents a novel \(\varphi\)-OTDR technique that integrates OFDM and NLFM pulse modulation. The application of OFDM-NLFM signals addresses the challenges related to long-distance sensing, particularly in terms of limited frequency response bandwidth. By increasing the repetition frequency of the probing light through frequency multiplexing, this approach enhances spectral efficiency and expands the system's frequency response spectrum. Additionally, it enables more advanced frequency division techniques within the receiver's confined bandwidth. Fine-tuning the degree of nonlinearity in frequency modulation improves sidelobe suppression, reducing interference between adjacent signals and enhancing the system's SNR.  
In summary, our investigation demonstrates optical fiber sensing with a 65 MHz frequency bandwidth, with multiplexing of four distinct frequency components using OFDM-NLFM signals. This innovative approach achieves an 11 m spatial resolution over a 16.3 km optical fiber span while covering a frequency response range from 1 to 10 kHz. These empirical findings highlight the substantial potential of this technique in extending the frequency response capabilities of \(\varphi\)-OTDR systems, paving the way for its integration in diverse  
Figure 10: (**a**) The time-domain signal of the vibration applied to PZT1; (**b**) the corresponding phase-recovered frequency-domain signal; (**c**) the time-domain signal of the vibration applied to PZT2; (**d**) the corresponding phase-recovered frequency-domain signal.  
optical fiber sensing applications. Future studies may explore further advancements in signal processing and parameter optimization to maximize performance and applicability.  
All authors contributed substantially to the manuscript. Conceptualization, Z.L. and X.Y.; methodology, X.Y.; software, Y.Z. ([PERSON]); validation, Z.L., Z.X. and X.Y.; formal analysis, X.Y.; investigation, Z.L.; resources, Z.L.; data curation, Y.Z. ([PERSON]); writing--original draft preparation, Z.L.; writing--review and editing, Z.L.; visualization, Z.L.; supervision, Y.H., Y.Z. ([PERSON]) and X.Y.; project administration, Y.Z. ([PERSON]); funding acquisition, Y.Z. (Yangan Zhang) and X.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by State Key Laboratory of Information Photonics and Optical Communications (BUPT) (Grant No. IPOC2021 ZT14), P. R. China; The Funds for Creative Research Groups of China (Grant No. 62021005).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
We thank the reviewers for their valuable feedback and the editors for their devoted guidance.  
The authors declare no conflict of interest.
### Background and Literature Review
#### 1.2.3 Redirection Algorithm  
A redirection algorithm in Cloud-CDN is a set of instructions that determine how to redirect a client's request in a network to one of the selective edge servers. Analysis of CDN performance related to the redirection mechanism was studied by [PERSON] et al. (2018). Several algorithms were used, including the Geographical algorithm Domain Name Server (GeoDNS), Round Robin (RR), Weighted Round Robin (WRR), and Least Connection (LC). The study aimed to evaluate the performance of these algorithms in terms of response time and load balancing. The results of the study showed that the GeoDNS algorithm performed the best in terms of response time, and in terms of load balancing, the WRR algorithm performed the best. However, the choice of algorithm may depend on the specific needs of a particular application or network. [PERSON] et al. (2019) focused on the key issue of server selection. Therefore, the selection process is one of the key technologies in distributed systems, including CDN, Mobile CDN, and Cloud Computing. The server selection mechanism is designed to support user mobility and maximize user-perceived service quality. The results show that the delay in the packets has been reduced and the quality of the video streaming service has improved. This proposal provided efficiency and scalability for the mobile CDN system. [PERSON] et al. (2019) discussed the guarantee of QoE for video streaming on the cloud. To ensure quality of experience, video streaming over the Internet requires a high bandwidth. However, the Internet does not guarantee latency and packet loss. They integrated Load Balancing Routing Protocol (LBRP) as an adaptive routing protocol for SDN-Based Clouds, which is to enhance the user experience of video streaming. The protocol achieves routing by taking into account various parameters, including link capacity and re-routing non-real-time traffic towards under-utilized links. LBRP is able to optimize the distribution of network bandwidth, improving the throughput and reducing the delay of the video stream. The proliferation of CDN services has made it challenging to manage basic network resources effectively. [PERSON] et al. (2019) proposed a machine learning-based approach, which provides an effective solution for detecting CDN domain names and acceleration nodes accurately. The method leverages the basic principles and workflow of CDN and considers multiple features and attributes of CDN services. The proposed approach is essential for ensuring the security and reliability of the network and enabling effective resource management. [PERSON] et al. (2020) discusses Information-Centric Networking (ICN) as a potential design for the future internet, which uses content names instead of host addresses, de
## 3 Experiments and Results  
To validate the performance of the proposed algorithm, five experiments were performed. Firstly, the accuracy of the proposed classifiers was tested based on the 900 labeled testing samples to investigate its robustness. Secondly, 200 images captured at vineyard under different lighting conditions were selected as the testing images on which the proposed algorithm was applied. Thirdly, the proposed method's robustness against adjacent and occlusion conditions were investigated and analyzed. Fourthly, the performance of the proposed algorithm was compared with other a bunch detection methods from the aforementioned literature. Finally, the interactive performance of the proposed algorithm was investigated. In order to facilitate the analysis of the experimental data, the false negative rate (FNR), false positive rate (FPR) and true positive rate (TPR) were used to evaluate the result of experiments, which were defined by Equations (12)-(14). All experiments of the algorithm developed in this study were performed on Matlab(R2015a) with an Intel (R)Core(TM)i5-3230M [EMAIL_ADDRESS] GHz:  
\[\text{FNR}=\frac{\text{False negative}}{\text{False negative}+\text{Correctly done }}\times 100\% \tag{12}\]  
\[\text{FPR}=\frac{\text{Missed done}}{\text{Total count}}\times 100\% \tag{13}\]  
\[\text{TPR}=\frac{\text{Correctly done}}{\text{Total count}}\times 100\% \tag{14}\]
# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 4 Experiments and Results
### Improved ML Models
#### 4.3.3 RF Results

Table 8: Test set validation results of a random forest model for the two study scenarios. Figure 24 is generated from the results of the RF experiment #4 (Table 8) and depicts a moderate positive relationship. The regression line is fitted around the cluster of points, indicating a good model fit. Figure 21: Graph showing the random forest decision tree nodes and splits. Figure 22: Random forest model fit over actual EPA NO\({}_{2}\) values for training/testing sets throughout the year using all features of a single power plant (Alabama) dataset. Figure 23: Random forest model fit over actual EPA NO\({}_{2}\) values for training/testing sets throughout the year using all features for all coal-fired power plants second scenario. Figure 25 is generated from the last experiment in Table 7 and shows a strong positive correlation between actual and predicted EPA NO\({}_{2}\) values indicating a good model fit. Figure 26 displays a feature importance graph that the random forest model deemed the most important based on the feature's correlation score with EPA NO\({}_{2}\). It ranked \"dayofyear\" as the most important due to the trend associated with the time axis and the correlation this feature has with the other input variables. TROPOMI NO\({}_{2}\) came second, while another time feature, \"dayofmonth\" came third. This model determined that MERRA-2 features were not significantly correlated with EPA NO\({}_{2}\) and thus has ranked them last. Figure 24: Random forest actual vs. predicted EPA NO\({}_{2}\) correlation plot using TROPOMI and date features for the study’s first scenario. Figure 25: Random forest actual vs. predicted EPA NO\({}_{2}\) correlation plot using TROPOMI and date features for the study’s second scenario. Different MERRA-2 temporal datasets were used in Experiments #6 through #10. With daily or weekly MERRA-2 data, this model did not produce greater benefits. The results of the daily MERRA-2 experiments were close to those of the monthly MERRA-2 experiments, however, they were a little behind. This is attributable to the model's low correlation with the target EPA NO\({}_{2}\), which resulted in little performance improvement. RF experiment #4 (Table 8) lists the error rate of 34.10%, with the MSE being identical to experiment #3 (Table 8) using TROPOMI NO\({}_{2}\) feature only. The inclusion of MERRA-2 and date features for this model had little performance boost. RF experiment #12 (Table 8) produced a low error rate of 15.92% by utilizing a grid search to tune the hyperparameters. Similar to the LSTM model, it is worth noting that this model was somewhat biased toward a narrow range of EPA NO\({}_{2}\) values, between 0.08 and 0.10, due to the averaging required to avoid EPA NO\({}_{2}\) data overlap across date features.
# Cooperative Multi-Objective Optimization of DC Multi-Microgrid Systems in Distribution Networks
## 4 Cooperative Multi-Objective Optimization Model
### Cooperative Multi-Objective Optimization Objective Function  
The main goal of optimization is to reduce the daily economic total cost of MMGS. MMGS discussed in this model consists of multiple MGs, which are assumed to be owned by a single operator. Another goal of the model is the lowest network loss of the distribution network, which can be obtained through the outer-loop model. Therefore, the objective function to minimize the total economic cost of the entire system can be expressed as:  
\[f=C_{ETC}=C_{OTC}+C_{WTC} \tag{5}\]  
where \(f\) is the main goal of the cooperative optimization, \(C_{ETC}\) is the economic total cost of MMGS. \(C_{OTC}\) is the operating total cost of MMGS, \(C_{WTC}\) is the energy loss cost of the MMGS that is obtained from the outer-loop model, where  
\[C_{WTC}=C_{il}+C_{co} \tag{6}\]  
\[C_{co}=E_{CO}\bullet k_{c} \tag{7}\]  
\[E_{CO}=(W_{S}^{G}-W_{S}^{B})\bullet\Delta t\bullet e_{c} \tag{8}\]  
\[C_{il}=(W_{S}^{G}-W_{S}^{B})\bullet\Delta t\bullet k_{il} \tag{9}\]  
\[E_{C}=\sum_{m=1}^{M}E_{Clm}+E_{CO} \tag{10}\]  
\(C_{il}\) and \(C_{co}\) are the network loss cost and carbon emissions cost caused by the increase in the distribution network loss in the outer-loop model, respectively. \(E_{CO}\) is the carbon emissions generated by the distribution network. \(W_{S}^{G}\) is the total daily operating network loss of the distribution network when MMGS is integrated into the distribution network and runs. \(W_{S}^{B}\) is the total daily operating network loss when there is no MMGS access, which is a fixed value also called the original baseline loss. \(k_{il}\), \(e_{c}\), \(k_{c}\) are fixed factors, \(k_{il}\) is the loss cost coefficient of the distribution network, \(e_{c}\) is the carbon emissions factor, \(k_{c}\) is the carbon cost factor. \(\Delta t=1\) h. \(E_{C}\) is the total carbon emissions of MMGS and the distribution network, \(E_{Clm}\) is the carbon emissions generated by \(m\)-th MG in the inner-loop model, \(M\) is the number of MGs in the MMGS.  
Since \(C_{OTC}\) and \(W_{S}^{G}\) are the optimization targets of the inner-loop model and the outer-loop model, respectively, the objective functions of the inner-loop model and the outer-loop model are set as follows:  
\[f_{1}=\text{min}C_{OTC} \tag{11}\]  
\[f_{2}=\text{min}W_{S}^{G} \tag{12}\]  
where \(f_{1}\) and \(f_{2}\) are the objective functions of the inner-loop model and the outer-loop model, respectively.  
Through (5)-(12),\(f\) can be expressed as:  
\[f=f_{1}+(f_{2}-W_{S}^{B})\bullet\Delta t\bullet[e_{c}\bullet k_{c}+k_{il}] \tag{13}\]  
Figure 4: Process of the cooperative multi-objective optimization.
# Advances in Biomaterials for Breast Reconstruction
## 3 Scaffold Guided Breast Tissue Engineering  
### Three Dimensional (3D) Bioprinting Technique for Breast Tissue Regeneration  
Three dimensional (3D) bioprinting is a valuable tool for regenerative medicine and tissue engineering. It involves fabricating complex bioactive structures with cell-laden bioink under the guidance of computer-aided design (CAD) in a layer-by-layer fashion (Figure 4) [70]. The most frequently used 3D printing techniques for breast tissue regeneration are inkjet printing, extrusion-based bioprinting (EBB), light-mediated stereolithography (SLA), and digital light processing (DLP). Inkjet printing is the most commonly used bioprinting method which provides cell viability via thermal or piezoelectric processes [71]. EBB is capable of handling bioink of a high viscosity using a fluid-dispensing system, which has been mostly utilized in the bioprinting of adipose tissue [72].  
Several studies have investigated structural concepts to obtain the most suitable 3D model for breast tissue engineering. [PERSON] et al. researched the ideal additive biomanufacturing of scaffold model for breast reconstruction using a 3D bioprinting technique [73]. In the study, the model was focused on two independent structures. The external structure was designed for the integrity of the scaffold and minimized direct stress transfer to newly formed tissue; the internal structure was designed as tissue guidance that provides an appropriate microenvironment in connection with native tissue by adjusting pore size and channel structure.
## 2 Methodology
### Event Filtering and Expert Knowledge Extraction Modules: Structure and Operation  
To address the twin challenges of detecting sensor anomalies linked to quality degradation and extracting information from the labels chosen by the human experts, this part of the system was built using a two-module architecture. The design of these modules was driven by the need for flexibility, clarity, and scalability, leading to a final structure comprising two distinct but complementary pipelines.
# From Bottom of Sea to Space: Quo Vadis IoT? So What About Security?
## 5 Future Roadmap
### A Future Look-Ahead: The Promise of AI
#### 5.2.2 AI-Assisted Secure IoT Extensions
To the authors' best knowledge, this is the first time deep learning has been implemented in the encryption of images in the IoMT. AI-Assisted Secure Internet of Drones  
Drones collect a lot of information such as location and HD images. When connected, they are able to create a large network of distributed information. This makes it paramount for this kind of network to maintain data integrity, security, and privacy. To protect these elements, the authors of [243] propose a blockchain-based security mechanism that allows drones to communicate securely. The blockchain protocol consists of three phases. Registration of a drone or device on the blockchain is the first phase. The second phase is to verify the identity of a device or drone trying to access the blockchain. This is performed through the use of a zero-knowledge proof protocol. The last phase is the transaction phase, which allows for devices to securely communicate over the network. Before any block can be added to the blockchain, it must be mined first to check for data tampering. A mining node is selected using a deep learning-based selection miner algorithm using a deep Boltzmann machine. The scheme was tested using a variety of performance metrics and shown to be a promising way for IoD to securely communicate. Another relevant research has been reported in [244], where the authors argued that the aggregation of raw data gathered by individual drones in the fog node can incur privacy leakage in Fog-aided Internet of Drones. They proposed a method based on Federated Learning (FL) for local training in drones and sharing training model parameters in the fog in order to preserve data privacy. They augmented their method with techniques for power control in drones in order to keep privacy from being divulged to ground eavesdroppers via wiretapping and parameter analysis during the FL training process.
# Research on a Partial Discharge Expert System for the Diagnosis of Damaged Transformation Equipment
## 2 Materials and Methods
### Hardware Architecture
#### 2.1.2 Analog-to-Digital Converter  
The local raw discharge waveform measurement system employs the Analog Devices Inc. (ADI) AD9226 high-speed analog-to-digital converter (ADC) due to the approximate 10 MHz frequency of the raw discharge waveform. The AD9226 analog-to-digital conversion module is depicted in Figure 3. The AD9226 operates with a single power supply and features a high-performance amplifier and reference voltage source, offering a voltage resolution of up to 12 bits and a sampling rate of up to 65 MS/s. It can be applied in various applications such as ultrasound, imaging, and communication systems, making it suitable for multi-channel multiplexing systems for detecting voltage ranges in high-frequency continuous signals. It can also perform single-channel sampling at the Nyquist rate and has a rated temperature range of \(-40\)\({}^{\circ}\)C to \(+85\)\({}^{\circ}\)C.
## 5 Numerical Experiments
### Results and Discussion  
To assess the efficiency of _MOWOATS_ as a multi-objective optimizer, _MOWOATS_ is tested on multiple benchmark problems, which include both bi-objective and tri-objective problems. We use the _ZDT_[20], _DTLZ_[20], and _CEC2009_[21] benchmark test functions. These test functions are considered to be examples of the most challenging _MOPs_, for they generate search spaces with different shapes of _PF_, such as convex, non-convex, discontinuous, and multi-modal shapes. Evaluating the quality of generated solutions is made by comparing them with respect to the true _PF_ of the problem. There are two main characteristics that can efficiently evaluate the generated solutions: convergence to the true _PF_ and the distribution of solutions over the solution space. The _IGD_ metric is used to evaluate both the convergence of solutions to the true _PF_ and the spread of solutions over solution space [33]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Definition** & **Value** \\ \hline _MaxIt_ & Maximum number of iterations & 1000 \\ _Np_ & Population size & 100 \\ _Max_NonImprove_ & Maximum number of iterations without improvement & 15 \\ _max_EL_ & Maximum number of solutions stored in elite list & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main Parameter values of _MOWOATS_. First, the performance of _MOWOATS_ algorithm is compared with several alternate algorithms: MOGWO [13], _MOPSO_, and a Multi-Objective _EA_ based on Decomposition (_MOEA/D_) over the _CEC2009_ test functions according to _IGD_ metric, defined as:  
\[IGD=\frac{\sqrt{\sum_{i=1}^{n}d_{i}^{2}}}{n}, \tag{26}\]  
where \(n\) represents the number of true _PF_, \(d_{i}\) corresponds to the Euclidean distance between the \(i\)th solution in _PF_ and generated solutions from _MOWOATS_. Table 2 presents the average, median, standard deviation, worst, and best _IGD_ values computed from 30 independent runs. Results in Table 2 show the superiority of _MOWOATS_ over those competitor algorithms for the whole set of test functions, consistently. It reaches high convergence rates to the true _PF_ for all the functions, and the low values of the standard deviation of the results are a consequence of the stability of the algorithm.
## 4 Mineralogy of Rock Samples in the Survey Area  
For mineralogical and textural studies of the sampled rocks, the results of petrographic studies and X-ray diffraction (XRD) analysis of rocks collected from the study area are summarized in Table 1 and shown in Figure 2. Samples for XRD tests were prepared from rock powders from the samples used for uniaxial compressive strength (UCS) tests after their failures. The XRD analysis equipment is XPET-PRO from Malvern Panalytical Ltd. (located in Malvern, United Kingdom). Scanning interval of XRD tests was 0.02\({}^{\circ}\) (20), and scanning range is from 5\({}^{\circ}\) to 80\({}^{\circ}\). Post processing of the XRD results is conducted using Siroquant program (version 4.0) from Sietronics Pty Ltd. (located in Perth, Australia) In thin section inspection of rocks from Location A, the major constituent minerals were dolomite, calcite, quartz, and opaque minerals were observed. The main rock texture consisted of fine-grained crystals, while there were also calcite crystals of 0.5 mm in size. Equigranular dolomite was also observed as the result of diagenesis or alteration. Some calcite crystals showed cleavage planes. Fine quartz grains showing straight extinction were observed. Due to diagenesis, some calcite had been replaced with dolomite. Some calcite crystals around 0.3 mm in size showed twinning (Figure 2). In XRD analysis (Table 1), the rocks in Location A were classified as calcareous dolomite, consisting of 31.3% calcite, 44.5% dolomite, and 24.2% quartz.  
The rocks in Location B consisted of quartz crystals, a small amount of diopside, and opaque minerals. Mineral crystals were mostly quartz crystals of 0.2-0.3 mm on average, and the remaining space was filled with fine-grained crystals. Quartz crystals showing straight extinction and undulose extinction were both observed. The quartz crystals showed a sutured appearance, while there was also a small amount of fine-grained, poikilitic diopside. In XRD analysis, the rocks were classified as quartzite, consisting of 95.3% quartz and 4.7% dickite.
# A Robust UWSN Handover Prediction System Using Ensemble Learning
## 4 The Proposed System
### Dataset Generation
Each node has a signal strength, which is measured by the Friis transmission equation (Equation (7)) as follows:  
\[P_{r}=P_{t}G_{t}G_{r}\bigg{(}\frac{\lambda}{4\pi d}\bigg{)}^{2} \tag{7}\]  
where \(P_{r}\) and \(P_{t}\) are the signal power of the receiver (i.e., sensor node) and transmitter (i.e., the buoy), respectively; \(G_{r}\) and \(G_{t}\) are the antenna gains of the receiver and transmitter, respectively; \(d\) represents the distance between the receiver and the transmitter; and \(\lambda\) is the wavelength.