esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of a Helium-Nitrogen RF Plasma Jet on Onion Seed Germination
## 3 Results and Discussion
The stimulation of the seeds with cold plasma for 60-480 s at a 50-mm distance between the electrode and the seeds yielded higher values of the germination parameters than the shorter stimulation (2-15 s) with the shorter distance. At the distance of 50 mm, there was a statistically significant increase in the germination energy value in all samples, in comparison with the control. The germination rate increased upon cold plasma exposure for 240 s. It was also greater than in the case of the helium and air gas mixture for the same tested treatment times [28]. The shorter seed stimulation with the lower distance reduced the germinability parameter. The highest energy and germination capacity were achieved by seeds stimulated for 240 s with cold plasma. In comparison with the control sample, the germination energy and germination capacity increased by 22% and 8%, respectively. Figure 3: Fraction of germinated onion seeds after pre-sowing plasma treatment. (SD mean: control—2.44 2 s; 20 mm—2.02 5 s; 20 mm—1.86 10 s; 20 mm—1.38 15 s; 20 mm—1.98 60 s; 50 mm—2.02 120 s; 50 mm—1.95 240 s; 50 mm—1.57 480 s; 50 mm—2.32). Figure 2: Comparison of the seeds’ surface before and after plasma treatment, 1000 magnification. In accordance with presented data, scientific literature provides reports on the application and positive effects of cold plasma on crop seeds. [PERSON] et al. [30] reported that tomato seedling growth was improved by atmospheric pressure plasma treatment. The bloom times, the height, the caulis, the extent of the plants, and the average weight, length, and diameter of each fruit were increased distinctly. The tomato yields of plasma-treated plants were larger than the untreated samples. [PERSON] et al. [31] and [PERSON] et al. [32] concluded that plasma treatment significantly improved the wheat germination rate. Cold plasma gives the possibility of thermal damage avoidance and has a positive impact on the seed germination and plant yield of _Oryza sativa_[33], _Glycine max._ L. [17] and _Lavatera thuringiaca_ L. [20]. Under the influence of discharge plasma generated in dielectric barrier discharge, [PERSON] et al. [34] noted an improvement in the germination parameters and growth of maize seeds (_Zea mays_ L.). The obtained results show an increase in wettability, resulting in a better water uptake and in the enhancement of growth parameters. Plasma source provides significant technical advantages and application potential for seed surface finishing without the use of hazardous chemicals.
# Analysis of Potent Odour-Active Volatile Thioids in Foods and Beverages with a Focus on Wine
## 3 Thiol Isolation--Extraction and Derivatisation
### Derivatisation Approaches
#### 3.2.2 Derivatisations for LC Analysis of Thiols
Reagents for stable isotope labelled derivatisation can not only enhance the stability and detectability of thiols, just like the conventional reagents, but also provide advantages in facilitating untargeted identification and targeted quantitation based on the characteristic mass differences between the unlabelled/labelled derivative pair that are easily distinguishable by MS [55]. Stable isotope labelled derivatisation reagent pairs that have been used for thiol analysis in beverages include d\({}_{0}\)/d\({}_{7}\)-\(\omega\)-bromoacetonylquinolinium bromide (d\({}_{0}\)/d\({}_{7}\)-BQB) for beer (Table 1, Entry 18) [43; 49] and d\({}_{0}\)/d\({}_{4}\)-acridone-10-ethyl-N-maleimide (d\({}_{0}\)/d\({}_{4}\)-AENM) for wine (Table 1, Entry 19) [44]. Both of the reagents utilise a reactive group (bromide for BQB, maleimide in AENM), an ionisable group, and an isotopically labelled group in one of the pairs. BQB derivatisation consists of rather simple sample preparation steps, which only involve drying and derivatisation (60 \({}^{\circ}\)C, 60 min, pH = 3.5) [49]. AENM derivatisation is faster (40 \({}^{\circ}\)C, 10 min, pH = 7.4), but it requires a lengthy \(p\)-HMB-based SPE step before derivatisation [44]. AENM-thiol adducts were reported to be stable at room temperature for at least three days [44]. Despite the advantages of stable isotope labelled derivatisations, the reagent pairs have to be synthesised, which could be a potential downside of these approaches.
# Double-Peaked Mid-Infrared Generation Based on Intracavity Difference Frequency Generation
###### Abstract  
It was reported that a double-peaked mid-infrared laser was generated based on an intracavity difference frequency generation (DFG). The double-peaked pump source was achieved by suppressing the intensity at the central wavelength of the pump source. The double-peaked mid-infrared generation had a double-peaked spacing of 23-37 nm, and the full width at half-peak (FWHM) of the peaks was up to 30 nm. It was demonstrated that a tilted Bragg fiber grating (TFBG) with a specific transmission spectral depth and bandwidth can generate a double-peaked mid-infrared laser with controllable double-peaked spacing. It was the first generation of a double-peaked mid-infrared laser based on a near-infrared pumping spectral modulation. Furthermore, the double-peaked mid-infrared generation was tunable, and the FWHMs of the peaks were controllable by an intracavity DFG. It had a high potential for the monitoring and absorption of gas molecules by differential absorption lidar (DIAL).  
spectral modulation; nonlinear optics; infrared lasers; fiber lasers; nonlinear crystals +
Footnote †: journal: Applied  
Article
# Electronic Voting System Using an Enterprise Blockchain
## 3 The Case of Electronic Voting  
### Challenges  
It seems to be necessary to improve the process of voting and counting, as well as the voter registration and validation process. Some of the most frequent flaws in electoral systems are the hacking of electronic voting devices and the manipulation of votes. Some articles [1; 3; 6; 7; 8; 9; 10; 11; 12; 13; 19] have already been written on how to implement a fraud-proof electronic voting system. However, despite the benefits that blockchain can bring to voting, there is still some skepticism about such a model; the most significant doubt relates to accessibility since, in most cases, voters are required to have a digital device with Internet access, such as a mobile phone, a tablet, or a computer. Although decentralization helps to solve many of these problems, the system cannot prevent all possible types of electoral fraud.  
Another challenge is the scalability of blockchain-based solutions. Scalability is the basic feature of traditional distributed systems, but it is hard to reach in blockchain due to decentralization requirements. Some factors that affect the performance of blockchain transactions are consensus mechanisms, network size, and transaction verification. Bitcoin and Ethereum 1.0 technologies are limited with respect to the rate of transactions per second [20; 21], in public domain scenarios. Such a limitation can degrade the performance of a system, which is why some research [7; 10] has focused on permissioned blockchain. Given that performance and scalability are extremely important to the Hyperledger Consortium, they decided to create the Performance and Scalability Working Group (PSWG [[https://wiki.hyperledger.org/display/PSWG/Performance+and+Scale+Working+Group](https://wiki.hyperledger.org/display/PSWG/Performance+and+Scale+Working+Group)]([https://wiki.hyperledger.org/display/PSWG/Performance+and+Scale+Working+Group](https://wiki.hyperledger.org/display/PSWG/Performance+and+Scale+Working+Group)) (accessed on 23 October 2021) to discuss, research, and identify key metrics related to the performance and scalability of blockchain and blockchain-related technologies. Several research papers have been published studying and testing the performance capabilities of Hyperledger Fabric. The latest advancement scaled HF to 20,000 transactions per second [22].
## 1 Introduction
One notable advantage of our proposed model is that MMD computation can leverage the samples' unconditional distribution by utilizing the vast number of unlabeled samples in the target domain, which is common for collected datasets in internal networks. * Additionally, we apply soft classification to the unlabeled data, using the classification sub-network to compute MMD over classes, thereby aiming to align conditional distributions between domains more effectively [20]. * To validate the effectiveness and generalizability of our approach, we conduct multiple transfer learning tasks between diverse datasets, including the widely used NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets [25]. Through rigorous experimentation, we demonstrate substantial improvement in cross-domain attack detection accuracy in various learning scenarios, validating the efficacy of our proposed method. As the proposed method eliminated the requirement for massive labeled data in the target network by transferring knowledge from heterogeneous source networks, it lays a good foundation for the application of deep transfer learning in internal network attack detection. The remainder of this paper is organized as follows: Section 2 provides an overview of related works in the fields of network attack detection, transfer learning, and deep learning techniques. Section 3 details the methodology and architecture of our proposed model. Section 4 presents the experimental setup and evaluation results and discusses the findings and analyzes the performance of the model. Finally, Section 5 concludes the paper with a summary of contributions and highlights potential future research directions.
# Self-Organizing Maps to Assess the Recycling of Waste in Ceramic Construction Materials
## 3 Results and Discussion
### Industrial Extruded Clay Mixtures with WS Incorporation
Each map corresponding to one variable (component) should be compared to the map representing the distribution of the pilot-plant clay mixtures with WS-FSD incorporation presented in Figure 4a; hexagons in the same place on different component planes correspond to the same map unit. The colours indicate the value of the component in the weight vector of each unit of the map, according to the colour bars on the right. A SOM analysis has been made in order to compare the four industrial bricks that incorporates WS with the six semi-industrial prototypes with only WS addition enabling the detection of sample clusters. The data set results of the 12 variables of the semi-industrial prototypes and industrial bricks with only WS addition have been grouped in a SOM trained map (Figure 5a) based onto \"range\" normalization of data. The 4 \(\times\) 4 (16 neurons) map, equal to the size of the 16-unit map proposed by the algorithm [59] result in the values of QE = 0.566 and TE = 0. Figure 5b shows the component planes of the SOM for the 12 properties of the tested samples. Industrial bricks up to 20% of WS are grouped in the same luster I separately to the others. Inside this Cluster I, IC4 and IC4W20 are located in the same neuron, while IC1 is in a different neuron. All samples in this cluster show high density and MOR with low OP and WA values; in addition, show relatively low release values of the studied metals with the best results to the IC4 samples (Figure 5b). Cluster II, III and IV grouped samples with increasing amount of WS from 10% to 40%. The industrial sample IC1W30 is located in cluster IV, together with semi-industrial samples with higher WA content. It is in the same neuron of semi-industrial sample with a higher WS content (SC1W35), mainly due to the high release values of Cr and Mo, caused by the reducing conditions in the industrial oven [63; 64]. Samples of clusters II, III and IV exhibit worse technical properties and higher Ba, Cr and Mo released as we go forward in the order cluster II to III and to IV. Bricks incorporating up to 20% Waelz slag (IC4W20) show optimum technological properties and metals release behaviour, reducing the waste landfilled as well as the energy requirements involved in the firing process and therefore contributing to the circular economy. However, higher additions of WS (IC1W30) lead to worse properties in comparison to the same dosage on a semi-industrial scale (SC1W30).
# A Novel Energy-Efficient Contention-Based MAC Protocol Used for OA-UWSN
## 3 OA-UWSN MAC Protocol Design
### Data Transmission, Reception, and Confirmation  
This paper combines the advantages of both optical and acoustic communication modes and proposes an OA-CMAC communication mechanism. How node A and node B of the lower layers transmit data to the upper node is illustrated as an example to describe the data sending, receiving, and confirming mechanisms. As shown in Figure 3, the specific steps are as follows:  
1. The mobile node is bound to a pressure sensor, which can measure its depth and send an acoustic interrogation signal at a certain frequency. This signal can reach the water surface directly at Time 0, or it can be forwarded by the fixed node and reach the water surface at Time 1. The time difference and the measured depth are used to locate the mobile node [22];
2. The lower nodes A and B need to send acoustic RTS1 and RTS2, respectively, to perform an acoustic handshake. The acoustic RTS (Request to Send) includes information such as location so that the next optical handshake can be completed more smoothly;
3. After receiving the first RTS1, the upper node immediately replies with an acoustic CTS (Clear to Send). If all the nodes receive the CTS, they can know which nodes are competing to the channel, and the uncommitted nodes perform postponed access and wait for the channel to be idle;
4. After successful competition, the lower node A sends an optical RTS to perform an optical handshake. After receiving the RTS, the upper node responds with an optical CTS to confirm whether the communication mode is optical or acoustic;
5. If the optical handshaking is not completed within the time slot, the node that competes in the channel through the acoustic handshake performs acoustic communication with the upper node. If the two instances of handshaking are successful, optical data communication is performed. At this time, the upper node broadcasts a busy-tone signal through the acoustic signal to notify all lower nodes that it is busy, and the node that succeeded in competition performs optical communication according to the communication mode, modulation, and coding information determined by the optical handshaking;
6. After the end of one frame of data transmission, the upper node first sends an ACK (Acknowledgement) confirmation signal to the lower node. Then the upper node broadcasts a free signal through the acoustic signal, and all lower nodes recompete for the next frame of information transmission.  
Each frame is subjected to an acoustic and optical handshake, and the position information of the mutual communication node is transmitted during the acoustic handshake. If the node moves and the position information changes, the optical alignment is realigned in the optical handshake phase.  
Figure 2: Superframe structure.
_Applied_  
_sciences_  
_Article_  
**Weightage Effect during Back-Calculation of**  
**Rock-Mass Quality from the Installed Tunnel Support**  
**in Rock-Mass Rating and Tunneling Quality**  
**Index System**  
**[PERSON] 1, [PERSON] 1,2, [PERSON] 2, [PERSON] 1,3 and [PERSON] 1,***  
1 Department of Civil and Environmental Engineering, Hanyang University, ERICA Campus, Sangnok-gu, Ansan 15588, Korea; [EMAIL_ADDRESS] (J.K.); [EMAIL_ADDRESS] (H.R.); [EMAIL_ADDRESS] (A.M.N.)  
2 Department of Mining Engineering, Baluchistan University of Information Technology Engineering and Management Sciences (BUITEMS), Quetta 081, Pakistan; [EMAIL_ADDRESS]  
3 Department of Geological Engineering, Baluchistan University of Information Technology Engineering and Management Sciences (BUITEMS), Quetta 081, Pakistan  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +82-31-400-5147; Fax: +82-31-409-4104  
Received: 17 April 2019; Accepted: 16 May 2019; Published: 19 May 2019  
**Abstract:** In extensively used empirical rock-mass classification systems, the rock-mass rating (_RMR_) and tunneling quality index (\(Q\)) system, rock-mass quality, and tunnel span are used for the selection of rock bolt length and spacing and shotcrete thickness. In both systems, the rock bolt spacing and shotcrete thickness selection are based on the same principle, which is used for the back-calculation of the rock-mass quality. For back-calculation, there is no criterion for the selection of rock-bolt-spacing-based rock-mass quality weightage and shotcrete thickness along with tunnel-span-based rock-mass quality weightage. To determine this weightage effect during the back-calculation, five weightage cases are selected, explained through example, and applied using published data. In the _RMR_ system, the weightage effect is expressed in terms of the difference between the calculated and back-calculated rock-mass quality in the two versions of _RMR_. In the \(Q\) system, the weightage effect is presented in plots of stress reduction factor versus relative block size. The results show that the weightage effect during back-calculation not only depends on the difference in rock-bolt-spacing-based rock-mass quality and shotcrete along with tunnel-span-based rock-mass quality, but also on their corresponding values.  
**Keywords:**\(RMR\); \(Q\); tunnel support; back-calculation; weightage
## 2 Operation and Control
### Advanced Electrical Equipment  
Equipment with prediction and fault information is interesting, since in this way the operation of the system can be improved. Potential subareas are:  
* The field equipment must be updated in a simple way, and the information that it sends must be easily interpretable. Therefore, it is necessary to work on a unified standardization.
* The energy flows must be improved, and in this sense, the new Flexible Alternating Current Transmission System (FACTS) will be necessary.
* As already mentioned, field teams must have elements of self-diagnosis and prediction.  
After the review of the works, the author has considered grouping them in the following subsections: The Improvement, Development and Unification of Electrical Equipment Accessibility Standards, Incorporation of Power Electronics Control Equipment, The Development of Prediction and Self-Diagnosis Systems
## 5 Cross-Referencing Bloom-Filter-Based Data Integrity Framework
### Memory Overhead Analysis
#### 5.3.2 Memory Requirements of CRBF-Based Data Integrity Solution  
The memory requirements of the proposed data integrity framework are analyzed in this section.  
Considering the same dataset as that described in Section 5.3.1, the total number of entries per Bloom filter in the cross-referencing Bloom-filter-based data structure is as shown in Table 3.  
Therefore, the total memory required by the proposed data integrity solution is the sum of the memory overhead for the DLBF, DSBF, and MDBF, and the same can be represented as follows:  
\[m=-\frac{(C\times S)lne}{ln2^{2}}+-\frac{(N)lne}{ln2^{2}}+-\frac{(N)lne}{ln2 ^{2}} \tag{10}\]  
Assuming that the total number of files in the dataset (\(S\)) is equal to the number of objects per file (\(N\)) and that the constant value \(C\) is much smaller than \(N\), then the total bits required can be approximated as  
\[m=-\frac{(3N)lne}{ln2^{2}}\approx N \tag{11}\]  
From Equation (11), we can observe that CRBF-based integrity framework has a linear memory requirement. This means that the amount of memory needed increases linearly with the number of elements in the dataset (i.e., \(O(N)\)). Conversely, Equation (9) suggests that the standard Bloom-filter-based integrity solution has a quadratic memory requirement. This means that memory usage grows quadratically with the number of elements in the dataset (i.e., \(O(N^{2})\)). Therefore, the proposed data integrity framework efficiently reduces the memory and storage footprint requirements.
## 2 Methodology
### A Concrete Temperature Prediction Model Based on SSA-ISVR
#### 2.2.2 SVR Theory  
It is often challenging to acquire substantial sample data during the early stages of construction. Compared to other models, the support vector regression (SVR) model can construct accurate models with minimal training samples while maintaining insensitivity to outliers. Therefore, this study employed SVR for predictive modeling.  
SVR is a regression algorithm based on support vector machines (SVMs), and its core principle involves identifying an optimal hyperplane to partition the data. This hyperplane is used to fit the data points so that all training samples reside within a certain distance, denoted as \(\varepsilon\)[31], which determines the model's accuracy and generalization capability. If the difference between a sample's predicted value and its actual value is less than \(\varepsilon\), the sample is regarded as correctly predicted. Furthermore, SVR introduces slack variables to allow certain points to violate the \(\varepsilon\)-region constraint, thereby enhancing the model's robustness. SVR can be formulated as a quadratic programming problem with linear constraints, as shown in Equations (1) and (2):  
\[\min_{w,b_{\hat{x}_{i}}\hat{y}_{i}}=\frac{1}{2}\|w\|^{2}+C\sum_{i=1}^{m}\bigl{(} \hat{\xi}_{i},\hat{\xi}_{i}\bigr{)} \tag{1}\]  
\[s.t.\left\{\begin{array}{l}f(x_{i})-y_{i}\leq\varepsilon+\xi_{i}\\ y_{i}-f(x_{i})\leq\varepsilon+\hat{\xi}_{i}\\ \xi_{i}\geq 0,\hat{\xi}_{i}\geq 0,i=1,2,\cdots,m\end{array}\right. \tag{2}\]  
Using the Lagrange multiplier method, the constraints in Equation (1) can be transformed into equality conditions, and a kernel function is introduced to represent the mapping from a lower-dimensional space to a higher one. This ultimately converts the linearly constrained quadratic programming problem into a dual optimization problem [32], as depicted in Equations (3) and (4):  
\[\min_{a,a^{*}}=\frac{1}{2}\sum_{i=1}^{n}\sum_{j=1}^{n}Q_{ij}(a_{i}-a_{j}*)(a_ {j}-a_{j}*)+\sum_{i=1}^{n}\varepsilon(a_{i}+a_{i}*)-\sum_{i=1}^{n}y_{i}(a_{i}- a_{i}*) \tag{3}\]  
\[s.t.0\leq a_{i},a_{i}^{*}\leq C,\sum_{i=1}^{n}(a_{i}-a_{i}^{*})=0,i=1,\ldots,n \tag{4}\]  
Since the objective function of the dual optimization is convex, it ensures that a global optimal solution can be found. Additionally, by employing various kernel functions (such as linear kernels, polynomial kernels, and radial basis function kernels), the data can be mapped to a higher-dimensional space, facilitating the identification of linear relationships in this space, which is particularly useful for handling nonlinear regression problems. Moreover, SVR exhibits some tolerance to noise, as it defines parameters like allowable error and slack variables, making the model insensitive to small errors. Consequently, SVR is highly suitable for the requirements of the temperature prediction of pouring blocks with significant time-varying characteristics.