esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Mechanical Behavior  
Five effective tensile tests were conducted to obtain the reproducibility of the experimental results and the corresponding load versus displacement curves are shown in Figure 6. From Figure 6, a nonlinear bend of the curves is observed at low tensile load levels, which may be related to matrix plasticity and cracking [44]. With the tensile load increasing, composite specimens exhibit quasi-brittle linear behavior until their ultimate failure. The average tensile failure load and standard deviation are 19.54 kN and 0.34 kN, respectively. Table 2 lists the specific mechanical properties and the AE data. Comparing the test results of the five specimens, it is found that the AE parameters like count,  
Figure 5: (**a**) Typical AE waveforms. (**b**) The relationship between amplitude and duration of the AE signals.  
amplitude, and frequency of the five specimens are in agreement. Therefore, one typical specimen was chosen to analyze the damage mechanism and AE response for the composites.  
In order to describe the inner damage of the specimen, the fractured areas were observed with the help of SEM. Figure 7 shows the morphology of the fractured specimen. The image of the whole fractured specimen is shown in Figure 7a, which clearly demonstrates that the specimen is completely broken into two pieces. From Figure 7b, a large number of delaminations were observed and the obvious matrix crack can be seen. It can be attributed to the inter-laminar separation resulting from the shear stress transfer [45]. From Figure 7c, fiber pull-out and fiber damage can be clearly observed and fiber breakage appears as a brittle fracture. In addition, it is found that the mixed failure mode sustains the balance in the composite specimen.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Failure Load**} & \multicolumn{3}{c}{**AE Parameters**} \\ \cline{2-7}
**Material** & \begin{tabular}{c} **Actual** \\ **Load (kN)** \\ \end{tabular} & \begin{tabular}{c} **Mean Value** \\ **(kN)** \\ \end{tabular} & \begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & \
\begin{tabular}{c} **AE Count** \\ **Aptitude (dB)** \\ \end{tabular} &
\begin{tabular}{c} **Average** \\ **Frequency (kHz)** \\ \end{tabular} \\ \hline \hline Specimet-1 & 19.23 & & & 2489 & 59.09 & 193.60 \\ Specimet-2 & 20.12 & 19.54 & 0.34 & 2791 & 59.15 & 203.17 \\ Specimet-3 & 19.40 & & & 2791 & 64.32 & 175.89 \\ \hline Specimet-4 & 19.39 & & & 2130 & 71.54 & 177.03 \\ Specimet-5 & 19.59 & & & 3140 & 69.79 & 178.14 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of measured mechanical properties and corresponding AE characteristics.  
Figure 6: The tensile loading curve versus displacement for the composite specimen.  
Figure 7: **(a)** The image of the whole fractured sample. (**b,c**) SEM micro-photographs of the fractured composite: delamination and matrix crack; fiber breakage and fiber pull out.
## 2 Related Work
Other studies have used a hybrid DL model that is based on CNNs and RNNs for spatial and temporal feature extraction to improve attack classification. In [34], the authors used a CNN for feature selection since it could provide fast feature selection to support real-time analysis. For threat classification, the authors used one of the variants of the LSTM model: weight-dropped LSTM (WDLSTM). The proposed hybrid model showed good performance in terms of execution time. [PERSON] et al. [35] studied the effect of CNN in threat classification and intrusion detection system (IDS). The authors investigated different hybrid DL models with CNNs including CNN-LSTM, CNN-GRU, and CNN-RNN, and the model implementing CNN-LSTM outperformed the other models. Moreover, the authors highlighted that selecting a minimum set of features for threat classification degraded the performance of the classification. Therefore, DL models can perform well in terms of feature selection. In [36], the authors proposed a hierarchical model based on CNN-LSTM. The authors used stacked CNN layers for spatial features learning using image classification and then stacked LSTM for temporal features learning. Similarly, in [20], the authors proposed an LuNet model based on CNN-LSTM. The authors discussed how stacking LSTM layers after CNN layers could drop some of the temporal features. Thus, the authors proposed the LuNet block, which consists of LSTM layer stacked after the CNN layer, and they then stacked the LuNet block in multiple layers to improve classification performance and lower the FPR. As shown in Table 1, different network traffic benchmark datasets have been used to analyze the low-level IoC such as UNSW-NB15, NSL-KDD, and KDD Cup 99. For IoT attack classification, the BoT-IoT dataset has been used in multiple studies to evaluate the performance of proposed models. Different ML and DL models, such as the SVM, CNN, and LSTM, have been used to analyze threats and provide accurate results, and the CNN-LSTM hybrid model has been used in multiple studies to improve threat classification performance. In terms of the CTI for smart cities, multiple papers, including [24; 25], have analyzed the threats pattern based on network traffic. Additionally, in [37], the authors proposed a trustworthy privacy-preserving secured framework (TP2 SF) for smart cities; the authors used the optimized gradient tree boosting system (XGBoost) and blockchain, and they evaluated the proposed framework on two datasets: BoT-IoT and TON_IoT. DDoS is one of the challenging threats in a smart city that has been studied by different researchers, who have proposed methods to analyze IP addresses and track the sources to prevent this attack or to identify the behavior of the network when there is overload traffic. Data theft, which can be described as privacy and identity theft, is another threat that has been studied by
## 2 Materials and Methods  
### Plant Material and Culture Conditions  
The analysis of shoot multiplication efficiency was conducted using a medium developed by [PERSON] and [PERSON] (MS) [37], supplemented with agar-agar (8.0 g \(\times\) dm\({}^{-3}\)(Sigma-Aldrich, St. Louis, MO, USA). The medium was enriched with kinten (KIN--cytokinin containing furfuryl moiety) at concentrations of 0.25, 0.5, 1.0, 2.0 mg \(\times\) dm\({}^{3}\), marked accordingly as MS + KIN 0.25, MS + KIN 0.5, MS + KIN 1, MS + KIN 2, or 6-benzylaminopurine (BAP--cytokinin with an aromatic benzyl group) at a concentration of 0.25, 0.5, 1.0, 2.0 mg \(\times\) dm\({}^{3}\) marked accordingly as MS + BAP 0.25, MS + BAP 0.5, MS + BAP 1, MS + BAP 2. MS medium without phytohormones served as the control (MS \"0\"). Before autoclaving at 121 \({}^{\circ}\)C for 20 min, the pH was set at 5.8. The medium was prepared in glass jars with a total volume of 0.5 dm\({}^{3}\), and the volume of the medium was 0.05 dm\({}^{3}\). A single _S. bacialensis_ plant was used to initiate the in vitro culture, which was also used for molecular analyses, referred to as the donor plant (DP) (Figure 1A). The plant came from Experimental Farm of the University of Life Sciences in Lublin. Preparation of plant material to start in vitro culture was carried out according to the methodology provided by [PERSON] and [PERSON] [38] with modifications. Briefly, shoots were washed under tap water for an hour, rinsed for 15 min in distilled water, then sterilized with 70% ethanol for 15 s and 1% sodium hypochlorite for 5 min; they were subsequently washed three times (5 min each) in sterile distilled water. The nodal segments (5-7 mm) were excised and placed on MS medium (\"0\") to stabilize the culture (Figure 1B,C). The prepared medium was used to initiate the culture: under aseptic conditions using a laminar flow hood, shoots from the in vitro culture (MS \"0\") were cut into segments, and the obtained nodal explants were transferred to jars with 8 explants each. For all substrate variants and the control, 6 jars were analyzed in each of three replicates. The explants were cultured under 16 h light photoperiod (40 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\)) in growth room at 21 \({}^{\circ}\)C \(\pm\) 2 \({}^{\circ}\)C. After 4 weeks, the number of propagated shoots, their average length (mm), average number of nodes, fresh weight of the regenerants (g), and the intensity of callus tissue formation were recorded. The analysis of variance (ANOVA), Duncan's test, and Pearson correlation coefficient were carried out in Statistica 13.1 software, and the significant difference between each treatment method was determined with a significance level of 5% (\(p\leq 0.05\)).
# Protograph LDPC Code Design for Asynchronous Random Access +
## 5 Conclusions
#### Acronyms  
The following abbreviations are used in this manuscript:  
5G fifth generation of mobile networks ACRDA asynchronous contention resolution diversity ALOHA APP a posteriori probability ARA accumulate repeat accumulate AWGNadditive white Gaussian noise CDF cumulative distribution function CNcheck node CRA contention resolution ALOHA CRCcyclic redundancy check CRDSA contention resolution diversity slotted ALOHA CSAcoded slotted ALOHA DAMA demand assigned multiple access DSA diversity slotted ALOHA ECRA enhanced contention resolution ALOHA eMBB enhanced mobile broadband EXIText extrinsic information transfer FECforward error correction GEO geostationary orbit ICinterference cancellation IRCRA irregular repetition contention resolution ALOHA IRSA irregular repetition slotted ALOHA LDPC low-density parity-check LLR log-likelihood ratio LT Luby transform M2 Mmachine-to-machine MACmedium access MFmatched filter MF-TDMAmulti-frequency time division multiple access MRC maximal-ratio combining mMTCmassive machine-type communications PDFprobability density function PERpacket error rate PLRpacket loss rate PRACHphysical random access channel QPSKQuadrature amplitude shift keying RArandom access RVrandom variable SAslotted ALOHA SB Shannon bound SCselection combining SICsuccessive interference cancellation SINRsignal-to-interference and noise ratio SNRsignal-to-noise-plus-interference ratio SNRsignal-to-noise ratio TDMAtime division multiple access TStime sharing VFvirtual frame VNvariable node WERword error rate
## 3 Filtering and Combining the Light
### OH-Emission Suppression  
Ground-based NIR spectroscopy faces the problem of bright NIR background due to a series of narrow (\(\sim\)0.1 nm) emission lines (telluric lines) from atmospheric hydroxyl (OH) molecules [37]. Background subtraction is very difficult, due to short-timescale fluctuations in the brightness of these lines, making observations of faint sources impractical. This problem is pronounced in the J (1100-1300 nm) and H bands (1400-1700 nm). Sending this background-dominated light to a low/moderate resolution spectrograph (with \(\delta\lambda>0.05\) nm) leads to scattering of the bright background which is difficult to remove after the fact. An improved background subtraction requires suppression of these lines before it interacts with the dispersing optics. A promising way to suppress these emission lines before dispersion with high rejection ratio and low loss is using Bragg gratings in single-mode fibers [30].  
Bragg gratings act via variations in the refractive index, leading to constructive interference of the backward propagating wave of a certain frequency, thus that frequency is reflected backwards. A complex grating reflects a desired profile in wavelength, i.e., a filter can be constructed using this concept. Such a filter can be implemented in four ways: a) Individual SMFs [38], b) a multi-core bundle of single mode fibers [39; 40], c) on-chip waveguide Bragg gratings [41], and d) 3D photonic Bragg gratings in a glass block using ULI [42]. So far, Bragg gratings on individual SMFs has been demonstrated on sky with \(\sim\)100 notches, with a rejection ratio of \(\sim\)1000 at a notch-width \(\delta\lambda\sim 0.15\) nm [30; 43].  
A key challenge of creating notches at precise locations for on-chip and fiber-based Bragg-gratings is the need to control temperature, since the refractive index is a sensitive function of temperature [44]. It is easier to control the temperature on a compact chip than on extended fibers. With on-chip Bragg-gratings, however, there is an issue of polarization dependence with on-chip rectangular waveguides. Even with square waveguides, small dissimilarities in the upper and lower cladding can lead to shifts in the notch wavelengths. Hence, fabrication process control is critical. These shortcomings are currently being alleviated significantly by improving the fabrication recipes and device modeling [41; 45; 46]. Considering the potential of combining the filtering and dispersion steps on the same chip (as shown in Figure 2), this line of ideas is worth exploring.  
On-chip ring resonators offer another way to suppress the telluric lines, where each line is suppressed by an individual ring [47]. However, due to small ring radii required (e.g., \(<\)10 \(\upmu\)m in the NIR band), the bending losses tend to be high. This necessitates the use of high index contrast platforms (e.g., \(Si\) on \(SiO_{2}\)), which, due to high confinement of the modes, face low coupling efficiency with SMFs. This problem can be alleviated with the use of mode-matching tapers.
## 3 Methods
### Study on the Seasonal Variation Characteristics of CI
#### 3.1.3 Improved Dynamic Threshold Method  
The dynamic threshold method adopts a dynamic ratio that is closely tied to the amplitude of seasonal variations in the time series, effectively reducing the uncertainty in phenological parameter estimations [63]. When the CI time series descends or ascends to a specific percentage of the annual CI amplitude, it is marked as the SOS or EOS, respectively, whereas for the NDVI, the process is the reverse, which indicates that a largerNDVI value tends to have a smaller CI value for a vegetation canopy. As a vegetation structural parameter, the CI is affected mainly by hotspot reflectance [23; 24] and mixed pixel heterogeneity [20], leading to complex noise anomalies in the time series, even after fitting, compared with the NDVI. To address this, an improved dynamic threshold method was developed based on the characteristics of the CI time series. For noise with a CI fluctuation amplitude less than 0.1 of the ipsilateral amplitude, the SOS or EOS is defined as the mean value of multiple moments corresponding to a certain percentage of the current year's amplitude during ascent or descent, further reducing noise. In addition, [PERSON] et al. [64], who proposed the dynamic threshold method, suggested that the threshold value of the SOS is approximately 20%, but the selection of the threshold value varies according to the vegetation index, land cover type and other factors. Therefore, this study broadens the threshold range, from 10% to 90%, based on the characteristics of CI time series fluctuations, using the MCD12Q2 product for validation to determine the optimal threshold for estimating phenological parameters from the CI and NDVI time series.
## 2 Hardware of Transient Luminous Event (TLE) Observation System
### Board Configuration
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multicolumn{1}{c}{**Parts**} & \multicolumn{1}{c}{**Specifications**} & \multicolumn{1}{c}{**Functions**} \\ \hline \multicolumn{1}{c}{Processor (Intel Cyclone II EP2C50)} & \multicolumn{1}{c}{50,528 logic elements} & \multicolumn{1}{c}{(FPGA) Implement TLE observation algorithm} \\ \multicolumn{1}{c}{Processor (Intel Cyclone II EP2C50)} & \multicolumn{1}{c}{594,432 total RAM bits} & \multicolumn{1}{c}{(Niso-II) System control, interface and data handling} \\ \multicolumn{1}{c}{} & \multicolumn{1}{c}{86 embedded multipliers} & \multicolumn{1}{c}{} \\ \multicolumn{1}{c}{} & \multicolumn{1}{c}{4 PLLs, 294 I/O pins} & \\ \hline \multirow{4}{*}{Detector} & MaPMT & 8 \(\times\) 8 multi-node & Sense photons from TLE \\  & 18.1 mm \(\times\) 18.1 mm effective area & & \\  & Pin-hole lens & 28.16 mm focal length, \(\phi\) 0.3 mm aperture & 35\({}^{\circ}\) field of view (FOV) \\  & DDC264 & Data rates up to 68 GPS with 20-bit resolution & Measure the output current of MaPMT \\  & DAC & 16-bit resolution, SPI interface & Gain control of MaPMT \\  & High voltage supply & 0 \(\times\) 1000 VDC output voltage & Supply voltage for MaPMT \\ \hline \multirow{4}{*}{Camera} & CMOS image sensor & 1/2.5 inch optical format & Obtain 640 \(\times\) 480 images of TLE with 8-bit grayscale \\  & 2592 Hz \(\times\) 1944 V active imager size & & \\  & 2.2 μm \(\times\) 22 μm pixel size & & \\  & Up to 96 96 Mb/s data rate & & \\  & Fixed-local length lens & 8.0 μm focal length, f/1.4/+16, C-mount & 35\({}^{\circ}\) field of view \\ \hline \multirow{4}{*}{Memory} & SDRAM 512 Mb & 32 M \(\times\) 16, 143 MHz, 54-ball TF-BGA & For Niso-II software \\  & SDRAM 64 Mb & 4 M \(\times\) 16, 166 MHz, 54-ball TF-BGA & For the camera IP \\  & Flash memory & EPCS(16 \(\times\) 6 Mb, 8-GMC) & Programming the processor \\ \hline \multirow{4}{*}{Power} & 3.3 V (External) & 1.2 V & For the processor and relevant parts \\  & 5 V (External) & 5 V (External) & For the detector and relevant parts \\  & 1.8 V 2.8 V & For the CMOS image sensor \\  & 4.096 V & Reference voltage for the DDC264 \\ \hline Interface & PC/104 non-stackthrough connector, RS-422 serial interface & \\  & Size & 90.5 mm \(\times\) 95.5 mm \(\times\) 76 mm (including lens housing) \\  & Weight & Approximately 450g (including lens housing) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hardware Specification Summary of the TLE Observation System.
## 4 Experimental Validation
### Data Acquisition  
The data acquisition and assessment systems consist of five accelerometers, a load cell, a data acquisition instrument, and a computer, as shown in Figure 2. The DH-1A202E voltage output accelerometers were adopted in the experiment. The accelerometers have the characteristics of a no zero offset and strong anti-interference ability, which is suitable to measure low-frequency vibration. The sampling frequency, measuring range, sensitivity, dimensions and weight of the accelerometers are 20 Hz, 50 m\(\cdot\)s\({}^{-2}\), 10 mV/m\(\cdot\)s\({}^{-2}\), \(\Phi\)31.5 \(\times\) 34 mm and 172 g, respectively. Meanwhile, a load cell was installed on the hydraulic jack, and the sampling frequency of the load cell is 1 Hz. The detailed layout of the sensors is shown in Figure 2. Five accelerometers were installed 10 m away from the loading side, with a space of 20 m. The data acquisition instrument converted the voltage signals into digital signals and then saved them in the computer for analysis.  
The loading process of the experiment was divided into three stages: loading, slow unloading, and fast unloading. The loading force was measured by the load cell, as shown in Figure 4. Firstly, the longitudinal loading tension increased gradually from 2255.8 kN to 2458.1 kN in 27 s. Then, the loading tension decreased with a slow unloading speed from 2458.1 kN to 2450.3 kN in 135 s. Finally, the loads drop rapidly to 2305.9 kN in 17 s.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Length (m)**} & **Mass per Unit** & **Specification** & **Anchorage** & **Protective Sleeve** \\  & **(kg/m)** & **(mm)** & **Length (m)** & **Material** \\ \hline
191.9 & 22.0 & 127 \(\times\)\(\Phi\)5.2 & 1.0 & Polyethylene \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the experimental cable.  
Figure 3: Cross-section details of experimental cable.  
Figure 2: Experimental set-up.  
Using the accelerometers, the in-plane dynamic responses of the cable were collected, as shown in Figure 5. Except for the loads and environment noise, no other excitation force was applied to the structural system. Therefore, the acceleration conforms to free vibration with the amplitude of gradual attenuation.  
Figure 4: Loading tension measured by the load cell.  
Figure 5: Acceleration of the cables by (**a**) Sensor 1, (**b**) Sensor 2, (**c**) Sensor 3, (**d**) Sensor 4, and (**e**) Sensor 5.
## 2 Materials and Methods
### Study Selection and Data Extraction
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Nr.** & **Study** & **Elastography** & **CKD** & **Healthy** & **Kidney** & **CKD vs.** & **Correlation** & **AUROC/Cut-** \\
**Method** & **Patients** & **Subjects** & **SWS in** & **CRTD** & **Group** & **Coffier Kidney** & **Offier** \\
11. & [PERSON] et al., 2014 [21] & VTQ & 163 & 32 & Low & Yes & Correlated & Performed \\ \hline
12. & [PERSON] et al., 2014 [22] & VTQ & 120 & 30 & High & Yes & - & Performed \\ \hline
13. & [PERSON] et al., 2014 [23] & VTQ & 309 & 14 & Low & - & Correlated & - \\ \hline
14. & [PERSON] et al., 2014 [24] & VTQ & 45 & - & No relationship & - & - & - \\ \hline
15. & [PERSON] et al., 2014 [25] & VTQ & 76 & - & High & Yes & - & Performed \\ \hline
16. & [PERSON] et al., 2015 [26] & VTQ & 46 & 58 & Low & - & Correlated & - \\ \hline
17. & [PERSON] et al., 2015 [27] & VTQ & 20 & - & Low & Yes & - & Performed \\ \hline
18. & [PERSON] et al., 2015 [28] & VTQ & 90 & 39 & Low & Yes & - & - \\ \hline
19. & [PERSON] et al., 2016 [29] & VTQ & 76 & 79 & Low & Yes & - & - \\ \hline
20. & [PERSON] et al., 2017 [30] & VTQ & 80 & 84 & Low & Yes & Correlated & - \\ \hline
21. & [PERSON] et al., 2017 [31] & VTQ & 30 & & & & & \\  & 2017 [31] & VTQ & pediatric & 38 & Low & Yes & - & Performed \\ \hline
22. & [PERSON] et al., 2017 [32] & VTQ & - &
\begin{tabular}{c} 264 \\ healthy \\ children \\ \end{tabular} & No relationship & - & - & - & - \\ \hline
23. & [PERSON] et al., 2018 [33] & VTQ & 187 & - &
\begin{tabular}{c} No relationship \\ tionship \\ \end{tabular} & - & - & - \\ \hline
24. & [PERSON] et al., 2018 [34] & VTQ &
\begin{tabular}{c} 90 \\ idiopathic \\ nephrotic \\ syndrome \\ \end{tabular} & 30 & High & Yes & - & Performed \\ \hline
25. & [PERSON] et al., 2019 [2] & VTQ & 146 & 39 & Low & Yes & - & Performed \\ \hline
26. & [PERSON] et al., 2015 [35] & VTQ &
\begin{tabular}{c} 73(biopsies \\ of kidney \\ donors \\ before \\ transplant) \\ \end{tabular} & - & Low & - & Correlated & - \\ \hline
27. & [PERSON] et al., 2021 [36] & VTQ &
\begin{tabular}{c} 30 acute \\ glomerulu- \\ linephri- \\ ties \\ \end{tabular} & 30 & High & Yes & - & - \\ \hline
28. & [PERSON] et al., 2021 [37] & VTQ & 80 & 50 & Low & Yes & Correlated & Performed \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._The results obtained using different types of elastography are not comparable, and no correlation tables are available, so out of the 37 titles found during the original search, only 21 studies that used point shear wave elastography (Virtual Touch Quantification-VTQ system) were compared. We were primarily looking for a specific comparison of CKD patients to a control group (mean measures of KSWS and their standard deviation (SD)), also for a statistically significant correlation coefficient between eGFR and kidney stiffness, as well as AUC and a cut-off value of KSWS potentially useful for the diagnosis of CKD. Finally, 11 studies (11 full-length publications) involving 1214 CKD patients versus 781 healthy controls examined using VTQ to determine renal stiffness were evaluated. (Figure 1.)