esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Section
### Analysis of VvRiLinNer Expression by Real Time PCR  
Analysis of gene expression was performed by Real time PCR using the SYBR green method on an ABI 7300 Real-Time System (Applied Biosystems, Foster City, CA, USA). RNA isolation and cDNA preparation were conducted as described above. The synthesized cDNA was quantified and all the tested samples were adjusted to the same concentration. Each Real time PCR reaction (30 \(\upmu\)L) contained 15 \(\upmu\)L SYBR(r) Premix Ex Taq(tm), 0.6 \(\upmu\)LBox Reference Dye (50\(\times\)) (TaKaRa, Shiga, Japan), 13.5 \(\upmu\)L ddt\({}_{2}\)O, 0.5 \(\upmu\)L cDNA and 1 \(\upmu\)L primer mixture (forward primers and reverse primers, 10 \(\upmu\)M). A 162-bp fragment in the 3' region of the coding sequence was amplified by using specific primers designed by PerlPrimer ([[http://perlprimer.sourceforge.net/](http://perlprimer.sourceforge.net/)]([http://perlprimer.sourceforge.net/](http://perlprimer.sourceforge.net/))) [47]. The primers used were as follows: forward primer (5'-ATGACAATCAGAGCTTTCCACAC-3') and reverse primer (5'-AAGAAACG AAGAGACAATCCTG-3'). _VvUbiquitin_ (GenBank No. AY684131) that is stably expressed in grape berry was selected as the internal control, and primers for it were _Ubiquitin_ F (5'-GTGATTATT GAGCCATCCTT-3') and _Ubiquitin_ R (5'-AACCTCCAATCCAGTCATCTAC-3'). The specification of the primers was verified on the basis of the following two facts: firstly, the dissociation curve for a pair of primers presented a single peak, and secondly, the fragment size of PCR product was consistent with the theoretical prediction, and the fragment sequence was shown to be correct through nucleotide sequencing.  
For real time PCR, the template cDNA was denatured at 95 \({}^{\circ}\)C for 30 s followed by 40 cycles of amplification with 94 \({}^{\circ}\)C for 10 s, 60 \({}^{\circ}\)C for 31 s [48]. As to each grape sample, three independent RNA extraction replicates were done and each replicate was performed with two amplification reactions. The average cycle threshold (\(C\)t) of _VvUbiquitin_ at each sampling point was determined and transcript abundance of the target gene, normalized to _VvUbiquitin_, was calculated using the formula \(2^{-\upalpha C}\), where \(\upalpha C\)t = \(C_{\text{target}}-C_{\text{VvUbiquitin}}\). The mean and the standard derivation (SD) were estimated after \(2^{-\upalpha C}\) calculation.
## 4 Experiment Results
### Bit Error Rate  
The bit error rate (BER) performance of OFDM and OCDM in the 800 m shallow water acoustic channel is shown in Figure 18. OCDM shows a higher BER within the 0-10 dB SNR range in contrast to OFDM, indicating inferior performance. However, as SNR increases, OCDM's performance starts to surpass that of OFDM LS. Furthermore, at an SNR of 12.5 dB, OCDM MMSE has already outperformed OFDM LMMSE, which is obvious at higher SNRs. For instance, the BER has been reduced by 35.1% and 36.5% at 15 dB and 20 dB, respectively, compared to OFDM LMMSE. OCDM equalization causes a series of data determination errors due to a frequency point estimation error. Therefore, the performance of OCDM is not as good as OFDM under the low SNR region, but the performance of OCDM has surpassed OFDM under medium and high SNR. Comparing the deep learning method with traditional methods, for OFDM DL equalization, the signal experiences a slightly higher BER than OFDM LMMSE within the 0 to 2.5 dB SNR range. Specifically, the BER of OFDM DL is approximately 15.1% higher than that of OFDM LMMSE method at 0 dB. However, beyond this range, OFDM DL consistently outperforms OFDM LMMSE at 2.5-20 dB. Moreover, at 20 dB, OFDM DL attains a performance level that is on par with that of OCDM MMSE. In the case of OCDM DL equalization, the signal performs better across the entire SNR range when compared to traditional ZF and MMSE methods. Due to frequency point estimation error leading to a series of chirp determination errors, the BER performance of OCDM DL is inferior to that of OFDM LMMSE and OFDM DL methods in the low SNR range of 0-5 dB, but is superior to the OFDM LS after 2.5 dB. When the SNR is higher than 7.5 dB, the improvement of OCDM DL is remarkable, which is the best among the six equalization methods, the BER has been reduced by approximately 53% compared to the OFDM DL method at 20 dB.  
Figure 17: Mean square error measurement in traditional and deep learning approaches for OFDM and OCDM at 3000 m distance.  
The BER at 300 m and 3000 m are shown in Figures 19 and 20, respectively. It can be seen from the three sets of channel models that the BER performance of OCDM is inferior to that of the OFDM system at low SNR region. However, with increasing SNR, OCDM achieves lower BER compared to the OFDM system. When deep learning is applied to OFDM and OCDM systems, the performance is superior to their respective traditional methods, with more significant improvements observed in the OCDM system. Under medium to high SNR region, OCDM outperforms the OFDM system in terms of BER performance.  
Figure 19: Comparison of BER between traditional and deep learning approaches for OFDM and OCDM at 300 m distance.  
Figure 18: Comparison of BER between traditional and deep learning approaches for OFDM and OCDM at 800 m distance.
# A Review of Non-Destructive Damage Detection Methods for Steel Wire Ropes
## 3 Non-Destructive Detection Method for Steel Wire Ropes
### Electromagnetic Detection Method
#### 3.1.2 Magnetization Method  
The magneizer is an important component of WR non-destructive detection system. Its excitation structure and mode will produce different magnetization effects [21]. The commonly-used magnetization methods mainly include the coil electromagnetic magnetization method and permanent magnetization method.  
Thus far, [PERSON] et al. [22] analyzed the different WR magnetization effect of different magnetization methods by finite element method and obtained the optimum size of feed-through magnetizer. In [23], the equipment was operated according to the principle of strong magnetic detection, which realized magnetization saturation by magnetizing the wires with a pre-magnetic head. [PERSON] et al. [24] improved the traditional current excitation device, so that the excitation intensity of the device can be controlled by adjusting the excitation power supply and the coil. [PERSON] et al. [25] designed an excitation device that can restrain the fluctuation of lift-off distance and can improve the structure of the detection device. [PERSON] et al. [26] studied whether AC magnetization in U-yoke would cause eddy current disturbance field. The results showed that the axial component of MFL of crack was independent of excitation frequency and intensity.  
In the practical application, for thin WR in strong electromagnetic interference environment, [PERSON] et al. [27] proposed an electromagnetic non-destructive testing method. In this method, a simplified magnetic circuit was proposed to excite WR, and the defect detection of thin WR in electromagnetic interference environment was realized. A new WR tester was established based on MLF principle in [28], and each arc segment subtends an angle of 22.5 degrees at the center by cutting two rings of NdFeB along axial direction into 32 equal arc segments, which were then parallelly magnetized in magnetizer. To detect WR damage in coal mine, [PERSON] et al. [29] proposed an opening electric magnetized through the magnetic control for a C-like electric loop-coil, which has an opening structure capable of directly encompassing and centering the endless object in it. The magnetizer can solve problems, i.e., installation inconvenience and on-line detection difficulty in the non-destructive testing of coal mine wire rope fixed at both ends.
# A Study of Cislunar-Based Small Satellite Constellations with Sustainable Autonomy  
[PERSON]  
1 The Department of Aerospace Engineering, Korea Advanced Institute of Science and Technology,  
Daejeon Campus, Daejeon 34141, Republic of Korea 1  
[PERSON]  
1 The Department of Aerospace Engineering, Korea Advanced Institute of Science and Technology,  
Daejeon Campus, Daejeon 34141, Republic of Korea 1  
Footnote 1: email: {hjocochoong_bang_1  
The target applications that are focused on through this research in the frame of the Cislunar space, which includes the Earth and Moon's orbital regions with specific values for defined forms of technology utilization, are precisely determined. For the Earth, disaster management is taken into consideration, which is at a critical stage at this node of global reforms [17; 18]. Research in this direction is commendable [19; 20], and a lot has to be done further in the span of the Cislunar space. For the Moon, it is for promoting, designing, and developing sustainable systems for effective communication relays and safe navigation. With Chandrayaan-3 [21; 22], the stride towards the South Pole landing is intensifying, and this needs creatively unique methods to solve critical problems of tracking and environment perturbations.  
Autonomy is an essential focus of this research. There is an immense need to do multiple tasks in a very particular situation that cannot be handled by ground control solely. This needs to be synergized with autonomous operations and systems governing the mission objectives thoroughly and with precision. This is the need of the hour in the Cislunar space, as developing ground for autonomy will enhance overall accessibility and sustainability in the long run. Several references have been studied to understand and develop strategies that will efficiently help develop autonomy in the Cislunar domain with focus on both Earth and Lunar operating environments [23; 24; 25].  
This paper is an enhanced and extended version of [16], and sincere efforts have been made to definitively express the key outcomes of this research and its futuristic need. Theustenance of the Cislunar economy and its operations is only possible if there are well-defined, rigorously tested, and well-evaluated methodologies for the small satellite constellations. This will be of major importance in assisting and achieving major tasks in the form of applications. For Earth, it is mainly to assist disasters and reduce casualties as much as possible, as well as to assist missions like Artemis near the Moon. All these need to be achieved by surviving and analyzing the space environment significantly.  
Figure 1: Cislunar space operations—current trends [16].
## 2 Materials and Methods
### HPLC-DAD Analysis of Secondary Metabolites  
A raw assessment of the content of secondary metabolites in prickly pear seeds was performed by using an Agilent 1260 HPLC binary pump coupled to a 1260 diode array detector (DAD). As stationary phase, a Thermo Eclipse ODS Hypersil C18 column (150 \(\times\) 4.6 mm, 5 \(\upmu\)m) was used. The mobile phase consisted of a mixture of solvent A (water + 0.1% formic acid; Merck) and solvent B (acetonitrile + 0.1% formic acid; Merck). The elution gradient was 0-5 min: 95% A; 10 min; 90% A; 35 min, 50% A; 45 min, 95% A; 65 min, 95% A. The injection volume was 20 \(\upmu\)L and the flow rate was 1 mL/min. DAD was operating at detection wavelengths = 280 nm and 292 nm. Prior to analysis, all the solutions of extracts and standards were filtered over 0.22 \(\upmu\)m Millipore membranes to avoid damage to the column and to limit interference due to impurities.  
Peak identification of phenolic compounds was performed by comparing the retention times (R.T.) of eluted compounds with those of reference standards (Merck). Quantification of secondary metabolites was performed by using calibration curves of gallic acid and quercetin, obtained by analyzing standard solutions in concentration range 0.02-0.08 mg/mL.
## 3 Results and Discussion
### Ecotoxicological Studies
#### 3.2.3. Algaltoxkit F-Surface Water  
Algaltoxkit F is commonly used in ecotoxicological research [54; 55] and can be used in the evaluation of water toxicity in water bodies. The calculation of LC50 was possible in the case of algae over 24, 48, and 72 h (Table 7). According to the rule, the higher the value of LC50, the less toxic the sample (to the tested algae) is. In all samples, the toxicity was slightly higher with time, although the lowest values were obtained for S2, which is in line with other studies; the LC50 [%] value for both site S1 and site S3 was around 30% for the diluted sample, which suggest that these samples are not highly toxic in comparison with S2. Studies on the toxicity towards water organisms have not been conducted here before, although the assessment of health risks based on the metal concentrations in the water and soil in the former plant has been performed before [45; 56]. Both studies suggest that there is an impact on health. In both studies, the level of chromium was found to be dangerous and carcinogenic in humans in the affected area.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sampling Sites** & **Dilution [\%]** & \multicolumn{2}{c}{**Mortality Rate [\%]**} \\ \cline{3-4}  & & **24 h** & **48 h** \\ \hline \multirow{5}{*}{S1} & control & 0 & 0 \\ \cline{2-4}  & 6.25 & 5 & 5 \\ \cline{2-4}  & 12.5 & 0 & 0 \\ \cline{2-4}  & 25 & 0 & 0 \\ \cline{2-4}  & 50 & 5 & 20 \\ \cline{2-4}  & 100 & 0 & 35 \\ \hline \multirow{5}{*}{S2} & control & 0 & 0 \\ \cline{2-4}  & 6.25 & 5 & 15 \\ \cline{2-4}  & 12.5 & 0 & 25 \\ \cline{2-4}  & 25 & 0 & 35 \\ \cline{2-4}  & 50 & 0 & 35 \\ \cline{2-4}  & 100 & 0 & 40 \\ \hline \multirow{5}{*}{S3} & control & 0 & 0 \\ \cline{2-4}  & 6.25 & 0 & 0 \\ \cline{2-4}  & 12.5 & 0 & 0 \\ \cline{2-4}  & 25 & 0 & 0 \\ \cline{2-4}  & 50 & 0 & 0 \\ \cline{2-4}  & 100 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The average mortality rate of _D. magna_ for the studied surface water from sites S1 “Blekitna”, S2 “Huta”, and S3 “Mata Szwecja” [%].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**LC50 [\%]** & **24 h** & **48 h** & **72 h** \\ \hline S1 & 33\% & 25\% & 16\% \\ \hline S2 & 3\% & 0.03\% & - \\ \hline S3 & 30\% & - & 29\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: The results of LC50 [%] for Algaltoxkit F for the studied surface water from sites S1 “Blekitna”, S2 “Huta”, and S3 “Mata Szwecja”.
# Surface Defect Detection Methods for Industrial Products: A Review
## 3 Surface Defect Detection Method of Industrial Products Based on Deep Learning
### Supervised Method
#### 3.1.4 Fully Convolutional Networks  
In an FCN, an end-to-end image segmentation method, all layers in the network are convolutional layers; the network mainly uses three technologies: Convolutional, Upsample and Skip Layer; label map can be directly obtained by making the network do pixel-level prediction. One of the core ideas is the deconvolution Layer with increased data size so that accurate results can be output.  
Literature [47] proposed an algorithm that uses a deep neural network to combine Autoencoder and FCN to distinguish keyboard light leakage defects from dust. The proposed method was tested in a test set composed of 1632 images, and the false positive rate of light leakage defect was reduced from 6.27% to 2.37%. Literature [48] designed a complete system for automatic identification and diagnosis of insulator strings, which combined different deep learning-based components, including one insulator string segmentation component and two diagnostic components for missing and damaged insulator disc units, respectively. Literature [49] proposes a method for defect segmentation of solar cell electroluminescence (EL) images; this method uses FCN with a specific architecture of U-net, which can obtain the defect segmentation map in one step; compared with the method of repeatedly executing CNN sliding window, this method obtains similar results. Literature [50] combines FCN with Faster RCNN to design a deep learning model based on FCN for defect detection of tunnels; this model can accurately and quickly detect defects such as stains, leaks, and blockages of pipelines.
# Enhancing Autonomous Vehicle Navigation with a Clothoid-Based Lateral Controller
## 6 Results and Discussion
### Comparative Analysis of Clothoid-Based Lateral Controller and Existing MPC-Based Lateral Controller  
This section presents a comparative analysis between the proposed clothoid-based lateral controller and an existing improved model predictive control (MPC)-based lateral controller [46].  
The analysis reveals that the proposed clothoid-based lateral controller is closely competitive with the existing MPC-based lateral controller in terms of path-tracking accuracy as  
Figure 16: (**a**) Tracked speed vs. arc length. (**b**) Steering angle vs. time plot for the Zandvoort racetrack.  
Figure 15: (**a**) Tracked trajectory of the Zandvoort racetrack. (**b**) CTE vs. time plot.  
shown in Figure 17. It can be seen that the clothoid controller slightly delays in converging the error to zero when compared to the MPC approach. This observation may be ascribed to the differential testing conditions; the MPC controller was calibrated using a car model in CARSIM, which likely presents a less-complex steering trajectory than that required for a semi-tractor trailer. Nevertheless, the clothoid controller demonstrates a comparable level of tracking accuracy, as shown in Figure 17b.  
It is also to be noted that the MPC study did not specify the vehicle speed during trajectory tracking. To infer this, we extrapolated the approximate speed from their control effort (steering angle) versus time plot. The speed maintained by our clothoid-based controller, depicted in Figure 18a, is consistent despite the challenging curvature of the track. Further insights are gained from examining the steering angle versus time plot for the clothoid controller, as illustrated in Figure 18b. Although there are peaks in the steering angle, indicative of active responses required to maintain the designated path, the overarching pattern demonstrates the controller's proficiency in delivering smooth and measured steering inputs that align with the sustained velocities.
# Parallel fuzzy minimals on GPU
## 1 Introduction
In this scenario, serial executions are not feasible, and their parallelization is mandatory. These issues are often solved using algorithms based on K-Means [12; 13], the most popular hard partition algorithm. Many parallel versions were proposed, and the initial solutions were based on the message passing method. Quite recently, new solutions have been based on other techniques and platforms, e.g., OpenMP, GPUs, and Intel Xeon Phi [14]. Likewise, fuzzy clustering algorithms also demand parallel solutions to explore large datasets. The most adopted fuzzy clustering algorithm has an extensive literature about its parallelization [15; 16]. However, these proposals do not change the requirements of this algorithm, i.e., a prior knowledge about the number of groups in the dataset. This has an exponential impact on the performance with the problem size, since several executions are needed to get the sought solution. In order to overcome these issues, an algorithm called fuzzy minimals (FM) was proposed in [17]. This algorithm uses fuzzy sets theory to find clusters without any prior knowledge about the number of groups, the size or format of the dataset. Furthermore, the authors proved that this solution meet all requirements that a good classification algorithm should have (e.g., scalability, adaptability, stability, etc). Besides the different approaches for clusterization that have been proposed, the technological advances in microelectronics led to improvements in graphical accelerators. These improvements, in a relatively short period of time, became the driving force behind thousands heavy computing problems. Some of these applications based on fuzzy sets, provides solutions for image segmentation to help medical doctors diagnosing illnesses, simulation of wildfires, modeling geological phenomena, etc. On the other hand, other applications have only explored GPU's capabilities to speedup their solution up to a few hundred times their sequential code [18; 19]. In this paper, we consider the introduction of two novel parallel implementations of the FM, which addresses frequent challenges in clustering algorithms [20; 21; 22; 23]. More precisely, our study is focused on the parallel implementation on GPU, exploiting its massively parallel architecture to deliver high performance at a low cost. We compare this solution both with the traditional sequential version and with a new parallel version using MPI. Numerical simulations showed that our solution on GPU provides a significant speedup over the sequential version. In fact, the found solution on GPU gives results at least as fast as than the expensive computing environments needed to run the MPI version. The remainder of this paper is organized as follows. Section 2 presents some preliminaries on the FM algorithm and GPU. Section 3.1 is intended to motivate our parallel fuzzy solution on GPU. Section 4 shows the numerical results with a synthetically developed dataset and two real-world benchmarks. Finally, Section 5 outlines the main results of this paper, open problems and possible future developments.
# On Channel Estimation in LTE-Based Downlink Narrowband Internet of Things Systems
## 3 NB-IoT Downlink System Model  
A complete NB-IoT downlink system block diagram with NRS sequence, related channel estimation, and equalization along with wireless channel and associated noise are shown in Figure 3, according to the downlink signal processing chain described in [36; 37; 38]. Here, narrowband physical downlink shared channel (NPDSCH) is used as the leading data-bearing channel by the downlink transmitter (i.e., LTE eNB). Input binary information reaches to the coding unit as a single transport block for a number of resource blocks (RB) in every downlink cell. The scheduling of RB is arranged according to [36]. According to 3 GPP [37], the NPDSCH data processing sequence contains transport block cyclic redundancy check (CRC) attachment (e.g., 24 bits with generator polynomial \(g_{CRC24A}(D),g_{CRC24B}(D)\), 16 bits and 8 bits with generator polynomial \(g_{CRC16}(D)\) and \(g_{CRC8}(D)\) 1/3 rate-based tail-biting convolutional coding, respectively. Finally, NPDSCH gets the input after the rate matching operation.