esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proof-of-Concept Methodology
### Data Analysis  
For the laboratory bioassays, we used the software PoloPlus (version 1.0, LeOra Software Company, Petaluma, CA) to create log concentration-probit curves and calculate LC\({}_{50}\) values with 95% confidence intervals. Any death in the negative and solvent controls was accounted for in the program and any round with <80% adult SHB survival in these two groups was excluded. Rounds with <100% mortality of the positive controls were also excluded from the analysis. We excluded two concentrations of coumaphos and one concentration of fipronil as outliers when calculating LC\({}_{50}\) values due to pipetting errors and abnormally high mortality.  
We constructed a generalized linear model for our field trial with a negative binomial distribution for both live and dead SHB counts. In both models, we selected SHB counts (either live or dead) as our response variable and treatment type (acetone, acetamiprid, fipronil) as our predictor fixed effect variable. All models were constructed in R (version 4.1.1) with glmnmTMB::glmnmTMB [23]. We visually assessed various model assumptions using simulated residuals in DHARMa::simulateResidals [24]. We calculated the significance of our models with a Type II Wald chi-square test using car::Anova [25]. Additionally, we constructed pairwise comparisons of estimated marginal means (EMMs) with a Tukey adjustment using emmeans::emmeans [26]. For treatments with only zero values, we compared the overlap of 95% confidence intervals based on a negative binomial distribution to determine significance [27].
# VNF-Enabled 5G Network Orchestration Framework for Slice Creation, Isolation and Management
## 6 Conclusions  
The proposed research focused on three key aspects of resource allocation in 5G mobile networks: slice creation, slice isolation, and slice management. The prepared performance dataset addressed slice creation using machine learning techniques and discovered that slice creation using the Random Forest technique produced comparatively better accuracy than other techniques. Slice isolation was achieved using the SIRA scheme, which uses NC-SIRA and LC-SIRA for node allocation and link establishment. For node allocation, NC-SIRA used a PROMETHE-II-based MCDM technique, while LC-SIRA used [PERSON]'s algorithm. In addition, slice management was handled by the SMART scheme, which is responsible for four key aspects of resource allocation: dynamic slicing, reporting, inner slicing, and priority provisioning. The main goal of SMART is to turn a failed NSR slice into a success by using an inner slicing approach based on Alien Node provisioning concepts. The proposed schemes are put to the test under various network operating conditions with varying PI and NSRs. Furthermore, the life time of the NSRs is taken into account, allowing for effective dynamic slicing and priority provisioning. Also, the NSRs' security concerns and minimal SLA have been properly addressed by incorporating constraints into the resource allocation process. Finally, the effectiveness of the proposed scheme is supported by the literature findings. The scope of the proposed work can be enhanced to address further SLA and security issues. It can also be extended to address the mobility and energy management of user equipment.  
Writing (original draft) and Methodology: T.S. and S.V.; writing (review & editing): [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C2014333).  
This study did not report any data.  
The authors declare no conflict of interest.
# Use of Social Media Data in Disaster Management: A Survey
## 6 Information Dimension
### Temporal
Further, during a disaster, normalised activity levels, rates of original content creation, and rates of content rebroadcasting must be considered to identify the hardest-hit areas in real time. The number of tweets during the Christchurch, New Zealand earthquakes were analysed over time in a window of five minutes in [41]. The analysis indicated that when an earthquake with a magnitude of 4.2 or stronger occurred at a particular time, it correlated with a spike in the number of tweets over that time frame. Another crucial factor to be considered while choosing the time frame for social media data collection is the type of disaster. For disasters like landslides, floods, and storms, we may be able to capture some of the warning signs for these events from social media posts before the actual occurrence of these events, whereas for other events, such as wildfires and earthquakes, the posts relevant or related to them may surface only after the occurrence of these events. [PERSON] et al. [42] analysed wildfire-related tweets of some of the major wildfires that occurred in San Diego County, USA with respect to space, time, content, and network by collecting Twitter data from the day when the first wildfire occurred until the date when most of these wildfires were 100% contained. The temporal evolution of wildfire-related tweets obtained using different keywords, with and without the location, gave an insight into the time lag taken for the spreading of the information. Furthermore, as [PERSON] and [PERSON] mentioned in [43], the durations of the impacts of these events also affect the temporal and contextual variation in the data related to these events. For instance, real-time and post-event data can be utilised for disaster response and recovery, whereas pre-event data can be utilised for preparedness and planning. A case study to analyse the social media text during and after the 2012 Beijing rainstorm was described in [3], where the authors performed time-series decomposition of the data to identify the overall trend and variations with respect to different developmental stages of the event, as well as the cyclical trends of microblogging activity. They concluded that the trend analysis of text streams for different topics over time corresponded well with different development stages of the event. For example, texts related to the event increased in the week after the rainstorm, following which they began to slowly subside, and, finally, faded out.
# A Hybrid Deep Learning Model Based on FFT-STL Decomposition for Ocean Wave Height Prediction
## 2 Model Design
### Decomposition Submodel  
#### 2.1.1 Fast Fourier Transform for Dominant Frequency Extraction  
Before decomposing the wave height data, accurately extracting the dominant frequency of the signal is crucial to identify and analyze the periodic characteristics of the fluctuations, thus providing an important basis for subsequent signal modeling and trend prediction. Fast Fourier Transform (FFT) is a widely used and effective tool in spectrum analysis, which converts a time-domain signal into a frequency-domain representation [25]. It effectively decomposes the frequency components of a signal, revealing the dominant frequency and other key frequency characteristics. This can effectively distinguish periodic fluctuations from random noise and enhance the extraction of fluctuation patterns.  
The raw wave height time series were subjected to Fourier transform analysis to extract T and dominant frequency components. For a discrete time series, \(x(n)\) (with \(n=0\), \(1\),..., \(N-1\)) is defined by  
\[X(k)=\sum_{n=0}^{N-1}x(n)e^{-j\frac{2\pi}{N}kn},k=0,1,...,N-1 \tag{1}\]  
Once the Fourier coefficients \(X(k)\) are calculated, we can obtain the power spectral density (\(PSD\)) by taking the square of these coefficients:  
\[PSD(k)=|X(k)|^{2} \tag{2}\]  
Through power spectral density analysis, a quantitative framework is obtained to characterize the frequency-domain energy distribution of the wave height time series, thereby identifying the main periodicities governing the oscillatory behavior of the signal.  
Figure 1: Wave height prediction technology roadmap.  
By systematically sorting the values \(PSD\), the most prominent frequency components are identified as dominant frequencies, corresponding to the highest energy contribution within the spectrum. The inverse of the dominant frequency defines the characteristic periodicity, which is then used to dynamically parameterize the seasonal window in the seasonal and trend decomposition using STL.
## 2 Methods
### Screening and Selection  
From an initial pool of 800 records (WoS = 312, IEEE Xplore = 156, Scopus = 274, PubMed = 58), 112 duplicates were removed, leaving 688 records for title/abstract screening. Two independent reviewers screened records against the inclusion criteria, achieving high inter-rater reliability ([PERSON]'s \(\kappa\) = 0.84). After screening, 245 articles underwent full-text evaluation, with 85 excluded for reasons including method mismatch (\(n\) = 43), domain mismatch (\(n\) = 24), and language (\(n\) = 18). The final 160 articles addressed three research questions: (1) What are the dominant control methodologies? (2) How do AI, digital twins, and 5G transform robot control? (3) What technical, ethical, and environmental challenges persist? A PRISMA 2020 flow diagram (Figure 1) summarizes the process.
## 2 Materials and Methods
### Assessments
#### 2.4.3 Countermovement Jump (CMJ)  
Each athlete performed three CMJ recorded using the CAPSTONE software version 1.13.4 (PASCO(r), Roseville, CA, USA) on two force platforms PS-2142 (PASCO(r) Scientific, Roseville, CA, USA) with a sampling rate of 1000 Hz [23], and one minute rest between attempts, under the supervision of physical activity sciences professional. The subjects placed their hands on their hips throughout the jump. Before the evaluation, a warm-up was performed, divided into three blocks: a general block, which consisted of performing joint mobility exercises, as well as dynamic stretches and ballistic stretches with emphasis on the lower limbs. This was followed by a specific block where low-impact jumps were performed on a coordination ladder, performing two sets of five repetitions with a one-minute rest between sets, and the final block, where they performed the CMJ jump three times. The entire warm-up lasted approximately 10 min. The week prior to the assessments, a familiarization session with the jumping technique was carried out. Prior to the intervention period, the reliability of the countermovement jump (CMJ) assessment was evaluated using a test-retest design. Each participant performed two CMJ testing sessions separated by 48 h under identical conditions. The intraclass correlation coefficient (ICC, two-way mixed model, absolute agreement, single measure ICC (3,1)) was calculated to assess relative reliability for jump height, propulsive impulse, and peak power. The standard error of measurement (SEM), coefficient of variation (CV), and technical error (TE) were also calculated to determine absolute reliability. The kinetic and kinematic variables analyzed based on existing literature were as follows [2; 3; 27]: (i) peak power; (ii) propulsive impulse (concentric); and (iii) jump height.  
The reliability results of the CMJ assessment presented significant differences for propulsive impulse (\(p=0.03\)) in the test-retest comparison. The jump height presented excellent reliability during the test-retest (ICC of 0.91), the propulsive impulse presented excellent reliability during the test-retest (ICC of 0.97), and likewise the peak power also presented excellent reliability during the test-retest (ICC 0.98). The coefficient of variation for the variables analyzed ranged between 12% and 17%. The standard error of the mean was similar for jump height (SEM 0.24) and propulsive impulse (SEM 0.89), being greater for peak power (SEM 16.7). The technical error (TE) was 1.13 cm for jump height, 3.56 Ns for propulsive impulse, and 79.6 w for peak power.
# Inverse Halftoning Methods Based on Deep Learning and Their Evaluation Metrics: A Review
## 1 Introduction  
Digital halftoning is a special image transformation technology that can convert continuous-tone images into halftone images with only 1 bit per pixel. Different from the general binary image, the halftone image can be perceived as a continuous-tone image when it is viewed from a certain distance because of the low filtering character of the human visual system. Halftone images are widely used in many fields, such as the printing industry [1], telemedicine [2], Internet of Things (IoT) [3], and the textile industry [4]. The common application case of halftone image is for bi-level output devices such as printing press, printers, copiers, fax machines, plasma display panels, and so on. Another application field is in that the halftone image can be used as an image compression mode where there is not enough storage space and ultra-low power image acquisition is needed, such as in telemedicine and IoT [2,3]. Inverse halftoning is an inverse process of halftoning that is used to restore continuous-tone images from their halftone versions. This inverse process is needed because typicalimage processing techniques are suitable to continuous-tone images but are very difficult to halftone images. Nowadays, inverse halftoning is required in many practical applications, such as image compression, image restoration, watermarking, high dynamic range imaging, and so on. Specifically, halftone images printed in newspapers, magazines, or books are often copied by photographing or scanning for further applications, and these copied halftone images with halftone dot noises and Moire artifacts need to be restored as clear and high quality continuous-tone images. Figure 1 gives an illustration of halftoning and inverse halftoning, where the halftone image in Figure 1b is generated from Figure 1a using the error diffusion digital halftoning method, and the image in Figure 1c is restored from Figure 1b by our designed inverse halftoning method. In this section, we briefly introduce halftoning and inverse halftoning technologies.
## 3 Simulation Experiments
### Comparison of Different Denoising Methods  
To validate the superiority of the PSO-optimized VMD method, a comparison was performed under identical conditions for the simulated signals between the proposed PSO-optimized VMD method and traditional algorithms such as EMD, ITD, and VMD. To quantitatively analyze the advantages of the proposed method, the NCC and SNR were used as metrics for evaluating the effectiveness of the denoising.  
In the traditional VMD algorithm, through multiple experimental analyses, the parameter combination [K, \(\alpha\)] = [4, 2000] was ultimately selected as the optimal parameter for the simulated impulse noise signal; [K, \(\alpha\)] = [5, 2500] was chosen as the optimal parameter for both the simulated square wave signal and the simulated triangular wave noise signal; and [K, \(\alpha\)] = [3, 2000] was selected as the optimal parameter for the simulated periodic noise signal. The experimental results of denoising the four types of simulated strong cultural noise signals using the aforementioned denoising methods are quantitatively compared in Table 4.  
The results indicate that the PSO-optimized VMD method demonstrates improvements in both NCC and SNR compared to the traditional algorithms, such as EMD, ITD, and VMD, exhibiting superior denoising performance. Taking the simulated impulse noise signal as an example, the PSO-optimized VMD method improved the NCC by 0.024, 0.035, and 0.019 compared with the traditional algorithms EMD, ITD, and VMD, respectively; and increased the SNR by 2.225, 2.446, and 1.964, respectively. Other types of simulated noise signals also exhibited similar denoising effects. This suggests that the proposed PSO-optimized VMD method has significant advantages in suppressing noise for simulated strong cultural noise signals, effectively extracting the noise profile characteristics while enhancing the signal's signal-to-noise ratio.
## 2 Facility Description  
### Facility  
This paper concerns the modeling and simulation of a particular newly built facility, namely the DBR30 two-stage light gas gun facility at the Institute of Mechanics Chinese Academy of Sciences (CAS). The facility consists of a detonation tube, a pump tube, a launch tube, and a target chamber, as shown in Figure 1. These components have inner diameters of 150 mm, 108 mm, 30 mm, and 1800 mm, respectively. The lengths of each section are shown in Figure 1.  
Diaphragm I separates the detonation tube and the pump tube, and diaphragm II separates the pump tube and the launch tube; both are scored steel diaphragm. The grooves in the form of a cross with different depths are designed to adjust the critical rupture pressure of the diaphragm and minimize fragmentation. Diaphragm II is designed to have a rupture pressure of 45 MPa or 75 MPa in the present study. An ignition tube is placed at the upstream end wall of the drive tube. The mixture in the ignition tube is initially ignited by electrical sparks. A high-temperature jet is formed and propagating into the driving section, thereby igniting the detonable gas directly. This will lead to a stable detonation wave traveling downstream in the detonation tube, behind which a Taylor wave follows and the products are at high temperature and pressure.  
Piezoelectric pressure transducers (CY-YD-211, made by SINOCERA PIEZOTRONICS, INC (Yangzhou, Jiangsu, China)), with a resonant frequency of more than 100 kHz, are mounted on the tube sidewall to record the pressure histories. The signals from the sensors are acquired by a signal conditioner and are processed on a PC-based data acquisition system at a sampling rate of 2 MHz. The transducers are labeled as P1 to P9 in the present study. P1 is located in the detonation tube and the others are placed in the pump tube. The origin of the coordinate system is located on the left side of the facility, and \(x\)-axis is in the longitudinal direction of the device. Thus, the coordinates of P1-P9 are \(x_{1}\) = 8.21 m, \(x_{2}\) = 10.55 m, \(x_{3}\) = 12.65 m, \(x_{4}\) = 18.65 m, \(x_{5}\) = 24.65 m, \(x_{6}\) = 28.25 m, \(x_{7}\) = 28.54 m, \(x_{8}\) = 30.54 m, and \(x_{9}\) = 30.83 m, respectively.
## 2 Device Structure, Design and Fabrication Methods
The lateral gratings along the 1.2-\(\upmu\)m-wide strips have pitches of 599.7, 602, 604 and 606.7 nm, a duty factor \(\sim\)0.75 and a grating depth \(\sim\)1.75 \(\upmu\)m, which results in the coupling coefficient \(\kappa\sim\) 30 cm\({}^{-1}\) according to the finite element analysis (FEA) simulation, as shown in Figure 1c. After the process of the deep gratings and waveguides, a planarization process of silica was adopted instead of a benzocyclobutene (BCB) planarization process described in the article of [PERSON], A.K. The process was significantly simplified as shown in Figure 3. A 2-\(\upmu\)m-thick SiO\({}_{2}\) supporting layer was deposited by PECVD, whose surface topography is shown in Figure 2b. SiO\({}_{2}\) on top of the ridge waveguide was removed by CHF\({}_{3}\) ICP after self-alignment exposure, as shown in Figure 2c. Then Ti/Pt/Au p-type electrodes and Ti/Au n-type electrodes were deposited, respectively. Laser chips with a cavity length of \(\sim\)350 \(\upmu\)m were cleaved out of the wafer. High reflection (HR) film with reflectivity of 98.66% and anti-reflection (AR) film with reflectivity of 0.37% were deposited on two ends of lasers respectively by evaporation after annealing to increase the output power of the facet.
# Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI
## 6 Future Directions
### Dynamic Model Adaptation and On-Device Training  
The majority of edge AI solutions rely on static, pre-trained models that seldom adapt after deployment. However, real-world conditions are rarely static; input distributions can drift over time as environments change or user behaviors shift [61]. Therefore, techniques for dynamic model adaptation, such as runtime pruning or partial retraining, are expected to gain traction. By allowing models to adjust their parameters or structure based on real-time feedback, devices can maintain higher accuracy without frequent server communications [81]. The advent of incremental and transfer learning for microcontrollers remains an active research frontier, as training steps themselves can be computationally taxing. Nevertheless, approaches like federated or split learning could enable incremental updates, provided that algorithms are carefully optimized for local hardware constraints [82].
# Robust and Secure Data Transmission Using Artificial Intelligence Techniques in Ad-Hoc Networks
## 4 Proposed Work
### Artificial Bee Colony (ABC)
Input: Final Route from S to D and Mobile Sensor Nodes Properties
2. Output: Optimized Mobile Sensor Nodes Properties
3. Start Properties Optimization
4. To optimized the FR, ABC Algorithm is used
5. Set up basic parameters of ABC: Population of Bee (\(\mathrm{P_{b}}\))--Number of Sensor Nodes
6. Final Route (FR)--Route from \(\mathrm{N_{S}}\) to \(\mathrm{N_{D}}\)
7. Fitness Function: \(\mathrm{F(f)}=\left\{ \begin{array}{ll}1;&\text{if $\mathrm{NP_{PROP}}$ $< Threshold_{PROP}}\\ 0;&\text{Otherwise}\end{array} \right.\)
8. Where \(\mathrm{NP_{PROP}}\) are the properties of the node such as energy consumption, delay, etc. 9. In the fitness function, \(\mathrm{NP_{PROP}}\): are properties of current sensor nodes which are in FR and Threshold\(\mathrm{{}_{PROP}}\) is the threshold properties of all communicating sensor nodes which are defined based on energy and distance
10. Calculate Length of Route in terms of R Length
11. Set, Optimized Nodes Properties, \(\mathrm{ON_{PROP}}\)=[]
12. For i in rang of R Length
13. \(\mathrm{EgE}=\mathrm{F(f)}=\mathrm{N_{PROP}}\) // Current Bee from \(\mathrm{P_{b}}\)
14. \(\mathrm{O_{BEE}}=\mathrm{Threshold_{PROP}}\) // // Mean of all \(\mathrm{P_{b}}\)
15. \(\mathrm{F(f)}=\mathrm{Fit~{}Fun~{}(\mathrm{EgE}_{EE},\mathrm{O_{BEE}})}\)
16. \(\mathrm{ON_{PROP}}=\mathrm{ABC~{}(F(f),\mathrm{F(i)})}\)
17. End--For
18. Returns: \(\mathrm{ON_{PROP}}\) as an Optimized Mobile Sensor Nodes Properties
19. End--Function
```  
**Algorithm 2**ABC Route Optimization
# A Novel Fractional Fourier Transform-Based ASK-OFDM System for Underwater Acoustic Communications
## 5 System Model  
This section presents the proposed FrFT-based OFDM system for underwater communication. Figure 3 presents a block diagram of the system model. This article considers the Rician fading channel model, as it was found to provide a good match for underwater channels [8; 9].  
The structure of the system is similar to the ordinary OFDM system, where the IFFT and FFT blocks are replaced by IDFrFT and DFrFT blocks. The system uses a \(M\)-ary ASK modulator/demodulator. An advantage of \(M\)-ary FrFT-ASK-OFDM is that it achieves high power and saves bandwidth. That is, it provides a good application for the UWA channel which is bandwidth-limited. The cyclic prefix (CP) is added to the transmitted signal in order to suppress the ISI, which is due to multipath fading. OFDM suffers greatly from the effect of CFO which induces inter carrier interference (ICI). Furthermore, the effect of CFO becomes more severe as the number of subcarriers increases and BER increases to a large extent. However, this is not the case in FrFT-based OFDM, where ICI can be extremely suppressed by using IFrFT and FrFT instead of IFFT and FFT.  
Reported parameters for OFDM systems presented in the literature based on actual applications were not fixed. A summary of the range of common parameters is as follows. Carrier frequencies ranged from 8 kHz to 24 kHz and bandwidths ranged from 3 kHz up to 50 kHz. The number of subcarriers \(N\) reported in the literature varied, and values were up to 2048 sub-carriers. Sub-carrier spacings were on the order of tens of Hertz up to hundred Hertz [13; 16; 17; 18].