esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Samples and Experiments
\begin{table}
\begin{tabular}{c c c c c} \hline \hline - & \#1 & \#2 & \#3 & \#4 \\ \hline In composition (\%) & 4.5 & 5.5 & 6.5 & 7.5 \\ \(V_{\mathrm{F}}\) (V) & 3.68 & 3.53 & 3.51 & 3.47 \\ \(R_{\mathrm{S}}\) (\(\Omega\)) & 2.0 & 1.9 & 1.8 & 1.7 \\ \(n_{\mathrm{min}}\) & 2.7 & 1.9 & 1.8 & 1.6 \\ \(A\) (\(\times\)10\({}^{7}\) s\({}^{-1}\)) & 3.24 & 1.72 & 1.00 & 0.62 \\ \(E_{\mathrm{g}}\) (eV) & \(\sim\)3.26 & -3.22 & -3.18 & \(\sim\)3.15 \\ \(\Delta E_{\mathrm{C}}\) (eV) & \(\sim\)0.50 & \(\sim\)0.54 & \(\sim\)0.58 & \(\sim\)0.62 \\ \(F_{\mathrm{PZ}}\) (MV/cm) & \(-\)0.8 & \(-\)0.9 & \(-\)1.0 & \(-\)1.1 \\ \(\lambda_{\mathrm{300}}\) (mm) & 379.0 & 386.6 & 392.9 & 397.2 \\ \(\lambda_{\mathrm{1}}\) (nm) & 374.6 & 382.8 & 389.8 & 395.0 \\ Redshift (nm) & 4.4 & 3.8 & 3.1 & 2.2 \\ \(S_{\mathrm{10}}\) (a.u.) & 2.1 & 1.7 & 1.5 & 1.3 \\ \(S_{\mathrm{450}}\) (a.u.) & 0.68 & 0.76 & 0.84 & 0.86 \\ LEE (\%) & 6.65 & 7.71 & 7.78 & 7.84 \\ IQE\({}_{\mathrm{max}}\) (\%) & 23.2 & 48.0 & 64.1 & 74.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Device characteristics of the near-ultraviolet (NUV) LED samples under study. \(V_{\mathrm{F}}\): Forward-bias voltage at 220 mA (20 A/cm\({}^{2}\)); \(R_{\mathrm{S}}\): Series resistance; \(n_{\mathrm{min}}\): Minimum ideality factor; \(A\): The Shockley-Read—Hall (SRH) recombination coefficient; \(E_{\mathrm{g}}\): Bandgap energy calculated from Vegard’s Law, where the bandgap energies of GaN, InN, AlN, and bowing parameters of InGaN and AlGaN are 3.44, 0.72, 6.28, 1.3, and 0.8 eV, respectively; \(\Delta E_{C}\): Conduction-band offset; \(F_{\mathrm{PZ}}\): Piezoelectric field; \(\lambda_{\mathrm{350}}\): Peak wavelength at 350 mA; \(\lambda_{\mathrm{1}}\): Peak wavelength at 1 mA; \(S_{\mathrm{10}}\): S value at 10 mA; \(S_{\mathrm{450}}\): S value at 450 mA, which represents carrier overflow; LEE: Relative light extraction efficiency; IQE\({}_{\mathrm{max}}\): Maximum internal quantum efficiency (IQE). Figure 1: Schematic structure of the light-emitting diode (LED) samples under study.
## 6 Performance Evaluation
### Simulation Setting and Performance Metrics
#### 6.1.3 Evaluation Metrics  
With the message statistics report by the ONE simulator, we can calculate the number of created messages, number of messages that have been delivered, number of messages that have been relayed, average delay of messages, and average number of hops a message has passed. Accordingly, four performance metrics, successful delivery rate, transmission overhead ratio, average latency, and average hop count, are examined.  
Let \(|M_{s}|\) be the number of original messages that are generated by source nodes, \(|M_{d}|\) be the number of distinct messages delivered to the destinations in the network, and \(|M_{f}|\) be the total number of times by forwarding messages between any two relay nodes in a network.  
* Successful message delivery rate: This metric indicates the rate of the number of original messages created in the network to the number of distinct messages that were successfully received by their destinations during the simulation. With \(|M_{s}|\) and \(|M_{d}|\), the measure turns out to the value of \(\frac{|M_{d}|}{|M_{s}|}\).
* Transmission overhead ratio: This metric is the ratio of the total times any original messages and replicas were transferred between intermediate nodes to the total amount  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **TVCM** & **NCCU** \\ \hline Number of nodes & 50 & 116 \\ Map size & \(1500\times 1500\) m\({}^{2}\) & \(3764\times 3420\) m\({}^{2}\) \\ Simulation time & 24 h & 24 h \\ Time-to-live (TTL) duration & 1 to 10 h & 1 to 10 h \\ Message size & 100 KB & 100 KB \\ Buffer size & 10 MB & 10 MB \\ Message creation intervals & 300 s & 300 s \\ Transmission speed & 2 MB/s & 2 MB/s \\ \(W_{0}\) in MPMF & 0.6 & 0.5 \\ \(\epsilon\) in MPMF & 50, 100, and 150 & 60 \\ \(\alpha\) in MPMF & 0.5 & 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters.  
of distinct messages received by their destinations during the simulation. With \(|M_{f}|\) and \(|M_{d}|\), the overhead ratio is given as \(\frac{|M_{f}|}{|M_{d}|}\).
* Latency: This metric indicates the duration from the time instance which a message is generated by a source node to the time instance which the message is successfully received by its destination.
* Hop count: This measure is equal to the number of hops that a message has passed through during its delivery to the destination.
###### Abstract  
The capacity aging of lithium-ion energy storage systems is inevitable under long-term use. It has been found in the literature that the aging performance is closely related to battery usage and the current aging state. It follows that different frequency regulation services, C-rates, and maintaining levels of SOC during operation will produce different battery aging rates. In the simulations, the researchers used real frequency data to generate SOC curves based on the Taiwan frequency regulation services under different C-rates and different battery SOC target levels. Then, the aging formula of lithium iron batteries (LiFePO\({}_{4}\) battery, LFP battery) and the proposed improved rainflow counting algorithm were used. The capacity aging situation and economy under different usage scenarios were analyzed. The simulation results showed that using a high C-rate and a low SOC level had a higher net profit, and the income of dReg was more than that of sReg. The SOC of BESS has an important impact on the life cycle. Keeping the SOC at a lower level will help prolong the life cycle and increase the net income. In dReg0.5, maintaining the SOC at 30% would yield 8.5% more lifetimes than 50%, 20.6% more lifetimes than 70%.  
battery energy storage system (BESS); frequency regulation service; battery degradation; automatic frequency control +
Footnote †: journal: Applied Sciences  
with different ancillary services and control methods. In this way, this research can help market managers formulate reasonable bidding prices and can help market participants make the best investment decisions.  
The aging speed of a battery affects the profit of the system operators due to differences in battery life. The purpose of this paper is to establish a battery aging model based on the SOC curves simulated by different frequency modulation modes and the ratio of different rated capacity to total battery energy, find out its aging characteristics, and evaluate battery aging in each situation.
## 3 Common Latitude Power Series Expansions with Geodetic Latitude as Variable
### Power Series Expansion of Rectifying Latitude with Geodetic Latitude as Variable  
According to the literature [30], the meridian arc length can be expressed as an elliptic integral  
\[X=\int\limits_{0}^{B}M\,dB=a\Big{(}1-e^{2}\Big{)}\int\limits_{0}^{B}\Big{(}1- e^{2}\sin^{2}B\Big{)}^{-3/2}dB, \tag{3}\]  
where \(M\) is the radius of curvature in the meridian at the calculation point. Assuming the constant term  
\[k_{0}=1+\frac{3}{4}e^{2}+\frac{45}{64}e^{4}+\frac{175}{256}e^{6}+\frac{11025} {16384}e^{8}+\frac{43659}{65536}e^{10} \tag{4}\]  
then the definition of rectifying latitude \(\psi(B)\) is  
\[\psi(B)=\frac{X}{R}=\frac{X}{a(1-e^{2})k_{0}}=\frac{\int\limits_{0}^{B}\big{(} 1-e^{2}\sin^{2}B\big{)}^{-\frac{3}{2}}dB}{k_{0}}=\frac{\int\limits_{0}^{B} \big{(}1-e^{2}\sin^{2}B\big{)}^{-\frac{3}{2}}dB}{1+\frac{3}{4}e^{2}+\frac{45} {64}e^{4}+\frac{175}{256}e^{6}+\frac{11025}{16384}e^{8}+\frac{43659}{65536}e^ {10}} \tag{5}\]  
which cannot be solved by integration. The conventional solution is to expand the integrated function using [PERSON]'s binomial theorem, transform the power form of trigonometric function into the double angle form, and then integrate it term by term. This process is long and complicated, especially when high precision is required. In addition, higher orders are difficult to calculate and errors increase at these orders. However, using Mathematica (hereafter, other calculations also were completed with Mathematica, but we do not repeat that fact further), the power series expansion of the rectifying latitude \(\psi(B)\) can be expressed as  
\[\psi(B)=B+\alpha_{2}\sin 2B+\alpha_{4}\sin 4B+\alpha_{6}\sin 6B+\alpha_{8}\sin 8 B+\alpha_{10}\sin 10B. \tag{6}\]  
The coefficients in Equation (6) are expanded in terms of \(e\) up to \(e^{10}\) and in terms of \(n\) up to \(n^{5}\), which gives in Table 1.
## 4 The Integration of the Streamed and Crack Growth Signals
### Signal Decomposition  
When the _SNR_ of combined signal is equal to 1.0, the AE features are controlled by the noise signal characteristics if the AE features are extracted using the entire streamed waveform. The AE features are extracted by dividing the streamed AE signal into sections of 1 ms duration. The positions of unusual data representing AE power, peak frequency, and frequency centroid are determined by the  
Figure 8: The schematic of signal summation.  
Figure 9: The comparison of embedded cracking streamed signals, (**a**) _SNR_ = 7.5, (**b**) _SNR_ = 4.0, (**c**) _SNR_ = 2.0, and (**d**) _SNR_ = 1.0.  
outlier rule, according to the mean and standard deviations. The upper and lower limits are defined as \(\overline{x}\pm 3\sigma\), where \(\overline{x}\) is mean value and \(\sigma\) is standard deviation. The data points outside the boundary limits are defined as outliers. Two examples are shown in Figures 10 and 11. The \(SNR\) is adjusted as 1.0 and the cracking signals are embedded at 74 ms and 120 ms, respectively. The data at the boundary of lower and upper bound limits present only in individual feature plots are not considered as outliers. For instance, there is a data point at the AE power plot near 140 ms, which is not present in the frequency centroid plot. The data points with the highest divergence from the mean and the limits and the present in both AE power and frequency centroid are considered to represent the presence of crack related signal. While the AE power and the frequency centroid can successfully identify the position of crack signal, the peak frequency is still influenced by the streamed signal. The decomposition of each streamed waveform into a window of typical AE signal allows for identifying the crack signal that is embedded into the background noise.  
Figure 11: The outlier detection of AE power, peak frequency and frequency centroid of original streamed signal and embedded streamed signal (\(SNR\) = 1.0, location of cracking signal: 120).  
Figure 10: The outlier detection of AE power, peak frequency and frequency centroid of original streamed signal and embedded streamed signal (\(SNR\) = 1.0, location of cracking signal: 74).
# Detection of Welding Defects Using the YOLOv8-ELA Algorithm
## 3 Experiments and Results
### Ablation Experiments  
To verify the effectiveness of the improved components in enhancing the model's detection performance, ablation experiments were conducted to compare the detection outcomes of the improved modules. The accuracy and speed of the model detection were evaluated using the overall [EMAIL_ADDRESS] value obtained on the test set. Additionally, the impact of the improved modules on the model complexity was assessed by measuring the number of parameters and floating-point operations (FLOPs). Figure 13 illustrates the training curves for [EMAIL_ADDRESS] over 200 epochs on the training set, showing that the improved model consistently outperforms the original model in this metric.  
Figure 12: Comparison of the heat map effects. The green regions indicate pore defects; the orange regions represent tungsten slag inclusion defects; and the pink regions correspond to incomplete penetration defects.  
Figure 13: [EMAIL_ADDRESS] change curve.  
In Table 5, the results of the ablation experiments are presented. Group 0 shows the performance of the original YOLOv8 network without any enhancements. Group 1 incorporates the HS-FPN structure, resulting in a significant reduction in both the floating-point operations and the model parameters, while concurrently enhancing the accuracy. Building on Group 1, Group 2 implements the ELA attention mechanism in place of the CA attention mechanism, resulting in a 1% accuracy gain compared to the original model. Group 3 further incorporates the SimAM attention module, resulting in a 1.3% increase in the [EMAIL_ADDRESS], alongside a reduction in the parameter count and an improved detection speed.
# IoT Socioeconomicenvironmental Assessment Instrument: Validation Process Applying Delphi Method
## 3 Results  
### Dimensions and Constructs of Original Assessment Tool  
The original assessment tool was developed by applying the systematic literature review conducted in the Web of Science and Elsevier's Scopus databases with the search terms ('Sustainable Manufacturing' OR 'Sustainable Production' OR 'Sustainable Operations') as \"TOPIC\" from WoS or \"TITLE-ABS-KEY\" from Scopus--meaning title, abstract and keyword--in addition to term \"Internet of Things\" as \"TOPIC\" or \"TITLE-ABS-KEY\", respectively. The PRISMA [27] was applied, as detailed in the works of [PERSON] et al. [25; 26]. The initial search was conducted on 14 April 2022; however, it was reviewed on 27 September 2023.  
The original assessment tool [25; 26] is an instrument that facilitates a comprehensive exploration of respondents' perspectives, structured into three dimensions and twelve constructs:  
* The IoT technology dimension seeks to examine the extent to which industrial organizations recognize and integrate IoT within both strategic and operational contexts. This includes an analysis of IoT technology expectations, the technological capabilities, IoT integration into processes, data management and the challenges and barriers to its adoption. The objective is to assess how effectively IoT is implemented in production processes or supply chains, identifying its potential, strengths and limitations in driving circularity and sustainability.
* The environmental dimension evaluates an organization's engagement to sustainability, particularly regarding the role of IoT in environmental monitoring, resource management and waste reduction. This assessment involves analyzing procedures, indicators and IoT applications related to ecological impact, aiming to identify areas of strength and improvement in environmentally sustainable and CE approaches.
* The social dimension focuses on the organization's engagement with IoT in relation to workforce management, partnerships and broader social responsibility initiatives. It examines assessment procedures, performance indicators and the application of IoT in enhancing workplace conditions and stakeholder relationships. The purpose is to identify how IoT contributes to social sustainability, highlighting both opportunities and areas for further development.  
These dimensions and related constructs together provide a comprehensive evaluation of IoT's role in promoting sustainability and circularity practices. While 'IoT technology dimension' assesses how IoT contributes to strategic and operational contexts, the environmental and social dimensions explore IoT impact within ecological and workforce management/partnerships contexts.
## 3 Results  
### Testing Noah-MP Model at Kuwei Site  
In order to evaluate the simulation effect of the Noah-MP model on a single point, we first drive the model based on the meteorological observation data of Kuwei site from September 2013 to April 2014, and verify it based on the observed snow depth and snow water equivalent. As can be seen from Table 2, Noah-MP can combine more than 20,000 optional parameterization schemes. [PERSON] et al. [40] tested these parameterization schemes at a site in the Altai Mountains, which is also located in the Irtysh River Basin, and the results show SFC and STC have the greatest influence on the simulation results of snow depth and snow water equivalent. At the Kuwei site, we also tested the SFC and STC parameterization schemes, and obtained four simulation results (Figure 2a,b). It can be seen from Figure 2a,b that different SFC and STC schemes have little influence on the simulation results during the snow accumulation period, but have a great influence on the simulation results during the ablation period. During the ablation period, the effect of SFC scheme on the simulation results was smaller than that of STC; when STC uses a fully implicit scheme, the error of simulated snow depth and snow water equivalent is greater than that of a semi-implicit scheme. The ALB and SNF parameterizations schemes were also tested at the Kuwei site. From the results obtained (Figure 2c-f), ALB mainly affects the melting process of snow, while SNF mainly affects the accumulation process. However, compared with STC, ALB and SNF have much less influence on the snow cover process. Furthermore, the simulated snow depth of all combined schemes is less than the observed value. In this study, SR50 sensor with a resolution of 1 cm is used for snow depth observation at the Kuwei site. However, in actual monitoring, especially in mountainous areas with complex terrain, the error of the SR50 sensor may be much higher than 1 cm. This may also be an important reason for the difference between the Noah-MP-simulated and the observed snow depth during the snow accumulation and melting period.
## 2 Design Section  
### Antenna Configuration and Design Consideration  
The configuration of the proposed CSRR-fed SIW cavity-backed fractal patch antenna is illustrated in Figure 1. It consists of three metallization layers and a stack of two substrates with thicknesses of \(h_{1}\) and \(h_{2}\) and relative dielectric constants of \(\varepsilon_{r1}\) and \(\varepsilon_{r2}\), respectively. Many via holes spaced along the rectangular opening are drilled in the substrate, thus constituting SIW cavity, which is the backing of the radiating element (patch surrounded by a slot). A [PERSON] fractal geometry radiation patch is printed on the top metallization layer, which is fed by two longitudinal and transverse CSRR slots etched on the middle metallization layer. The microstrip feed lines are on the bottom layer of the lower substrate. The proposed antenna is printed on two substrates with the same size, 40 \(\times\) 40 mm\({}^{2}\). Two layers have the same substrate material, F4B, with the relative dielectric constant of \(\varepsilon_{r1}=\varepsilon_{r2}=2.65\), the thickness of \(h_{1}=h_{2}=0.8\) mm, and the loss tangent of \(tan\delta=0.002\). All vias have the same diameter of 0.3 mm, and the spacing between each is 2.2 mm [3]. The detailed geometrical parameters are illustrated in Figure 1.
# Simultaneous Detection of Nine Key Bacterial Respiratory Pathogens Using Luminex xTAG(r) Technology
## 3 Results
### Application to Clinical Samples  
A total of 86 clinical BALF specimens were analyzed using the MPLT assay on the Luminex 200 analyzer. The results were confirmed by the simplex real-time PCR for the nine pathogens. The MPLT assay successfully detected and differentiated between all nine target respiratory pathogens. Compared with the results of the simplex real-time PCR, the consistency of MPLT assay exceeded 98.8% for all nine target pathogens (Table 4).  
For _S. pneumoniae_, 8 of 86 clinical BALF samples were considered to be positive by MPLT assay, while 7 of 86 clinical BALF samples were detected as positive by the simplex real-time PCR assay. Furthermore, the sample which was considered to be positive by the MPLT assay but negative by the real-time PCR assay was then confirmed as a true positive by 16S rDNA sequence analysis. Coinfections were identified in 10 samples for both MPLT assay and real-time PCR assay.  
The MPLT assay and the simplex real-time PCR assay both identified the same number of positive samples for _S. aureus_, _H. influenzae_, _M. pneumonia_, _P. aeruginosa_ and _K. pneumoniae_.  
The obtained sensitivity and specificity of this MPLT assay relative to those of real-time PCR were as follows: 87.5% and 100% for _S. pneumoniae_, and 100% and 100% for _S. aureus_, _H. influenzae_, _M. pneumonia_, _P. aeruginosa_ and _K. pneumoniae_, respectively.  
Figure 3: Sensitivity of the nine target bacteria in MPLT assay. Correlation of _Ct_ values derived from the real-time PCR assay and different bacterial concentration. **(A)**_S. pneumoniae_; **(B)**_M. catarrhalis_; **(C)**_S. aureus_; **(D)**_S. pyogenes_; **(E)**_H. influenzae_; **(F)**_M. pneumoniae_ (DNA); **(G)**_Legionella_ spp._; **(H)**_P. aeruginosa_; **(I)**_K. pneumoniae_. Cut-off cMFI = 3.0. All samples were performed in triplicate. If the results from a duplicate sample analysis exceeded the cut-off cMFI, the sample was defined as MPLT-positive.
# Fog Computing: Enabling the Management and Orchestration of Smart City Applications in 5G Networks
## 5 Evaluation Results
### Fog-Cloud Infrastructure Analysis
Moreover, our proposal only requires 1.46% of the available bandwidth between the fog node and the cloud node, while in a traditional centralized solution, on average, six-times the available bandwidth is needed (627.2%) only for a single fog node to transmit all data samples from only one IoT sensor, which makes it impossible for centralized cloud solutions to comply with smart city use cases. The end-to-end delay in case an abnormal sample is detected is lower in our solution since the first-level analysis is executed on fog nodes. On average, our solution needs 0.33 milliseconds to send an alarm or notification to the IoT sensor, while a traditional approach requires 0.46 milliseconds, but with an enormous amount of network bandwidth.
## 2 Materials and Methods
### Methods
#### 2.3.2 The Implementation of the CS-DINEOF Algorithm
This stage embedded a coordinate-value data division strategy, which involved defining the stratification levels for the annual average chl-a concentration and subsequently dividing the Bohai Sea region into \(N\) subregions, assuming that the data within each subregion would share similar spatiotemporal patterns. Specifically, the stratification levels were determined based on two key rules: (1) preserving the stratified spatial pattern of chl-a concentration and (2) ensuring balanced pixel numbers across the subregions as much as possible. Stage 3: Dataset Missing Rate Check. As mentioned in Section 2.2, images with a high SMR and pixels with a high TMR need to be excluded for further calculations. Before CS-DINEOF reconstruction, 3 rounds of checks were required to ensure that every sub-dataset and data point could provide valid information: (1) Subregions' SMR check: For each image, the SMR of each subregion was calculated. Only images where all subregions' SMRs were less than 95% could be retained. Since GOCI provides 8 images per day, if more than 1 image in a day passed this check, only the image with the lowest SMR was kept. (2) TMR check: For all images passing the SMR check, the TMR of each pixel in the time series was calculated, and pixels with a TMR higher than 95% were excluded (values set to NaN). (3) Subregions' SMR recheck: This step ensured that after the exclusion of a certain number of pixels, each image and subregion in the dataset still held valid data. Stage 4: Stratified Reconstruction. DINEOF reconstruction was independently executed for each sub-dataset, yielding distinct optimal EOFs and corresponding RMSEs. The reconstructed sub-datasets were then systematically merged according to their positions in the original dataset, generating the final output of the CS-DINEOF process.