esa-sceva/satcom-chunk-collection huggingface dataset:
# MAG Welding Tests of Modern High Strength Steels with Minimum Yield Strength of 700 MPa
###### Abstract  
The modern high strength steel plates have an excellent combination of strength and toughness based on micro-alloying and complex microstructure. Retaining this combination of properties in the weld zone is a major challenge for applications in high-demanding structural construction. This work investigates the weldability of three different modern high strength steel plates, with a thickness of 8 mm. Two of the test materials were produced by a thermo-mechanically controlled process (TMCP) and one by a quenching and tempering method (Q&T). Two-passes MAG (metal active gas) welding was used with four different heat inputs. The tests implemented on all the materials included tensile, hardness profiles (HV5), Charpy-V impact toughness tests, and microstructure analysis using scanning electron microscope (SEM). For one of the TMCP steels, some extended tests were conducted to define how the tensile properties change along the weld line. These tests included tensile tests with digital image correlation (DIC), and 3-point bending tests. The most notable differences in mechanical properties of the welds between the materials were observed in Charpy-V impact toughness tests, mostly at the vicinity of the fusion line, with the Q&T steel more prone to embrittlement of the heat affected zone (HAZ) than the TMCP steels. Microstructural analysis revealed carbide concentration combined with coarse bainitic structures in HAZ of Q&T steel, explaining the more severe embrittlement. During the tensile tests, the DIC measurements have shown a strain localization in the softest region of the HAZ. Increasing the heat input resulted in earlier localization of the strain and less maximum strength. The tensile properties along the weld line were investigated in all welding conditions, and the results emphasize relevant and systematic differences of the yield strength at the transient zones near the start and end of the weld compared with the intermediate stationary domain.  
high strength steel; weldability; thermo-mechanically controlled process; quenched and tempered; metal active gas welding process; Charpy-V; tensile; digital image correlation; bending; hardness; microstructure +
Footnote †: journal: Applied Physics Letters
###### Abstract  
The manuscript is a presentation of the combined effect of magnetic and electric field on unsteady flow of Maxwell nanofluid over a stretching surface with thermal radiations. The flow of Maxwell nanofluid is assumed to be in an unsteady state. The basic governing equations changed to a group of differential equations, using proper similarity variables. The obtained modeled equations are nonlinear and coupled. An optimal approach is used to acquire the solution of the modeled problem analytically. The effects of electric field, magnetic field and thermal radiations on Maxwell nanofluid are the main focus in this study. The impact of the Skin friction on velocity profile, Nusselt number on temperature profile and Sherwood number on concentration profile are studied numerically. The influential behavior of the unsteady parameter \(\lambda\), magnetic parameter \(M\), electric parameter \(E\), radiation parameter \(Rd\), Maxwell parameter \(\beta\), thermophoresis parameter \(Nt\), Prandtl number Pr, Schmidt number \(Sc\), space dependent coefficient \(A\) and temperature dependent coefficient \(B\) on the velocity \(f(h)\), concentration \(\phi(\eta)\) and temperature \(\theta(\eta)\) are analyzed and studied. The consequences are drawn graphically to see the physical significance of the problem.  
nanofluid; unsteady flow; stretching sheet; thermal radiations; electric field; magnetic field; HAM +
Footnote †: journal: Applied Physics Letters B
# A Deep Learning Approach for Network Intrusion Detection Using a Small Features Vector
## 3 Background Theoretical and Conceptual Framework  
### Artificial Neuron  
An artificial neuron is the basic building block with which to construct a neural network. It is simply a computational unit that performs a computation based on other connected units. As shown in the Figure 1, each neuron is connected to the input vector X. This vector is composed of scalar values \(\mathrm{x}_{1},\mathrm{x}_{2},\ldots,\mathrm{x}_{d}\), where subscript d refers to the size of vector X. An artificial neuron reads the information from vector X and performs a particular computation, which dictates its value. The computation of a neuron can be decomposed into two steps: pre-activation and activation. The pre-activation function is computed by multiplying the weight of each connection \(\mathrm{w}_{1}\), \(\mathrm{w}_{2},\ldots,\mathrm{w}_{d}\) with the corresponding input value; then, the bias term \(\mathrm{b}\) is added. An activation function defines the output of the neuron \(\mathrm{g}[\mathrm{a}(\mathrm{X})]\). It is computed over the pre-activation value \(\mathrm{a}(\mathrm{X})\).
# A Trustworthy Communication Hub for Cyber-Physical Systems
## 5 Solutions of the CPS Communication Hub  
### Services of the CPS Hub  
An experimental realization of the CPS communication hub services are communication spaces and presence services, information services, and trust services; Figure 12. The realization of communication spaces and presence services relies on the application of presence and contact list management related features of extensible messaging and presence protocol (XMPP) technology [8; 9]. The information sharing services relies on the application of publish/subscribe capabilities of message queue based telemetry transport (MQTT) protocol [13; 14]. The trust services rely on the contact list management related features of XMPP, capabilities to define access control policies with eXtensible access control markup language (XACML) [22], and external authorization services; Safax [23]. The experimental realization of the referred services is clarified in the following sections.  
Figure 11: Example attachments of physical CPS resources to the CPS hub.
## 4 Analysis of Security and Performance  
A VANET is vulnerable to various malicious attacks, including masquerading attacks, forgery attacks and reply attacks. To ensure the privacy of vehicles, the proposed scheme must support anonymity, confidential communication, and conditional un-traceability. When a legal vehicle makes a malicious attack, it will be traced and revoked.
## 4 Results
### EEG Sensitivity to Different Fact-Extraction Methods
#### 4.1.3 Based on Global Field Power (GFP)  
GFP values reflect the overall activity level of EEG signals within specific time windows [40]. Higher GFP values typically indicate stronger brain electrical activity, while lower values denote weaker activity. In this section, we calculated the GFP values of factual and non-factual words extracted using different methods across three time windows, as shown in Table 6.  
Throughout the entire time window, factual words extracted using the Entity method exhibited the highest GFP values. This indicates that the overall brain activity level is strongest when processing these types of words. The result might suggest that such words pose more significant cognitive challenges, requiring more cognitive resources for processing, or they may trigger more complex cognitive proce  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Type of Words** & **Overall** & **N400** & **P600** \\ \hline All words & 0.000528 & 0.000525 & 0.000524 \\ Dependency factual words & 0.000522 & 0.000520 & 0.000520 \\ Dependency non-factual words & 0.000532 & 0.000530 & 0.000527 \\ Entity factual words & 0.000542 & 0.000533 & 0.000538 \\ Entity non-factual words & 0.000524 & 0.000523 & 0.000520 \\ Pos factual words & 0.000525 & 0.000524 & 0.000523 \\ Pos non-factual words & 0.000530 & 0.000527 & 0.000525 \\ TF-IDF textual words & 0.000513 & 0.000510 & 0.000507 \\ TF-IDF non-factual words & 0.000537 & 0.000535 & 0.000535 \\ \hline \hline \end{tabular} Overall: 0–1500 ms; N400: 250–500 ms; P600: 500–1000 ms.  
\end{table}
Table 6: The GFP of different types of words.  
Figure 7: EEG signals obtained using the extraction method of TF-IDF.  
brain. The N400 time window (250-500 ms) is associated with semantic understanding. During this phase, non-factual words extracted via the TF-IDF method elicited the strongest electrical activity in the brain, suggesting that these words stimulate more brain activity during semantic processing than other types. Additionally, factual words extracted using the Entity method also showed the second highest GFP values, indicating that these words also activate the brain to a certain extent regarding semantic understanding. The P600 time window (500-1000 ms) is primarily related to syntactic processing. In this stage, factual words extracted using the Entity method again exhibited the highest GFP values, suggesting that these words might have particular importance or complexity in syntactic understanding, thereby inducing stronger brain electrical activity. These findings may reveal differences in cognitive processing among different types of words and how the brain dynamically adjusts its processing strategies based on the attributes of words, such as factuality. This information is invaluable for understanding the neural mechanisms of language processing.
## 4 Mode Properties and Broadband Characteristics
### Bending Loss  
For fiber in the actual transmission process, the loss generated by bending will decrease the quality of signal transmission. The bending loss of each eigenmode is obtained from the imaginary part of the effective index, shown as follows [33],  
\[Bending\ Loss\ =\ \frac{20\cdot 2\pi\cdot Im(n_{\text{eff}})}{ln(10)\cdot\lambda} \tag{3}\]  
The DBT-MREC-MMF is asymmetric. The bending loss is different for different bending directions. The bending losses along the \(x\) and \(y\)-axis are calculated here. As shown in Figure 14, at the bending radius of 24, 22, and 10 mm along the \(x\) or \(y\)-axis, the number of eigenmodes reduces to 20, 18, and 16, respectively. Under an identical bending radius, the bending loss value of the low-order mode is lower than that of the high-order mode. When \(R\) is 30 mm, the bending loss along the \(x\) and \(y\)-axis is below \(10^{-4}\) dB/m. Numerical results show that the proposed fiber has excellent bending resistance. When \(R\) is larger than 30 mm, the proposed fiber supports 22 eigenmodes, and the corresponding maximum bending losses of the fiber along the \(x\) and \(y\)-axis are \(8.05\times 10^{-4}\) and \(3.789\times 10^{-4}\) dB/m, respectively.
## 6 Implementation Scenarios
### Few to Moderate Obstacles  
The effect on downlink communication becomes more noticeable when moving to locations with more obstacles or obstacles of intermediate height. Barriers such as buildings or trees cause signal reflections, diffraction, and partial obstructions. In this scenario, U4 is located in a zero-coverage spot, where there is NLoS with respect to BS1. While adopting BS handover will provide the user with their intended signal, the transmission latency will be extremely high; this is because finding a different BS with a LoS with U4 will cause extra overload in the network.  
On the other hand, using the conventional T/R RIS will successfully deliver the signal for U4, but there is a higher level of power loss with moderate coverage. This is because of the large distance between the user and the conventional T/R RIS. Alternatively, it could be because the user is in the transmission area, so the allocated signal power will be very low, making it impossible for the signal to reach the target U4; as a result, this will affect the coverage area and reduce it.  
During such situations, the HRIS adapts its surface configuration in real-time to minimise the impact of obstacles, optimising the transmission routes and compensating for any signal degradation. The new HRIS-DMA will enable the HRIS to transmit and reflect the incident signal simultaneously without any power loss.  
Notably, the HRIS-DMA successfully remodels the signal power and phase and then redirects the signal to U4 while the user is in the transmission area. It can be noticed here that the main focus is on the transmission area because the current state-of-the-art system splits the signal power in the conventional T/R RIS, which is the main drawback. The HRIS-DMA solves this drawback effectively and surpasses the conventional T/R RIS, especially for the application of the new wireless communication generation, 6G. Notwithstanding the difficulties, the system consistently sustains dependable downlink transmission, utilising the HRIS-DMA adaptability to improve the signal's strength and overcome insignificant obstacles.
# Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data
## 2 Study Sites and Datasets  
### Study Sites  
We analyzed around 9 months of data, from 1 September 2018 to 31 May 2019 over two selected sites located in metropolitan France (Figure 1). The criteria of choice for these two sites was that they are agricultural areas, and are likely to be affected by freeze episodes. The first study site (48.86\({}^{\circ}\)N, 3.17\({}^{\circ}\)E; Site A, Figure 1 left) is situated near Paris and is comprised of 1001 agricultural parcels, with a surface area of 8943 ha, and mean parcel surface of 8.9 ha. In this study site, 80% of the parcels contain cereal grains, 17% meadows, and the rest are orchards and vineyards (roughly 3%). The second study area (48.33\({}^{\circ}\)N, 7.43\({}^{\circ}\)E; Site B, Figure 1 right) is situated near Strasbourg and is comprised of 11,578 agricultural parcels, with a mean parcel surface area of 1 ha. Here, 68% of parcels contain orchards and vineyards, 23.5% cereal grains, and the rest are meadows. Figure 2 shows the temperature profiles over the two study sites for the period between 1 September 2018 and 31 May 2019. The temperature profiles were acquired from the nearest weather station network to each site, which are \(\sim\)3 km and \(\sim\)17 km away from study sites A and B, respectively. Study site A (Figure 2a) shows seven freezing episodes when the temperatures fall below 0 \({}^{\circ}\)C and reach a minimum of \(-\)3 \({}^{\circ}\)C. Six of these freezing episodes occur from late fall (29 October 2018) to the end of the winter season (11 February 2019), and one freezing episode in the middle of the spring season (12 April 2019). Study site B (Figure 2b) shows eleven freezing episodes (temperatures \(<\) 0 \({}^{\circ}\)C and reach a minimum of \(-\)6 \({}^{\circ}\)C) in the period from 13 November 2018 to 15 February 15 2019.
# Microbial Bioremediation and Biodegradation of Petroleum Products--A Mini Review
## 3 Microbial Diversity and Ecology
### Microbial Influential Factors
#### 3.2.2 Concentration  
The over-concentration of excess nutrients can curtail the biodegradation process. [PERSON] et al. [40] found that the concentration of the pollutant had a particular pressure on petroleum-degrading organisms. The high PAH levels were restricting the development of microorganisms that built up a response against PAHs concerning the alterations of mycelia and sporulation pigmentation and the structure of the cell membrane. [PERSON] et al. [41] conducted on the various sources of carbon to produce lipase by Mucor racemosus, Lasiodiplodia theebromae, and Penicillin chrysogenum showed that cellulose and sucrose induced the highest activity in the mentioned species. Additionally, the study discovered that yeast extract was a superior key contributor to high lipase levels. Moreover, the pyrene-degrading efficiency and the strain growth were improved in comparison to the control experiment after 7 and 14 days of incubation by Hypocrea/Trichoderma using pyrene as a carbon source when 0.1% sucrose or lactose or 0.02% yeast extract was studied during PAHs degradation [40].
# Software Integration of Power System Measurement Devices with AI Capabilities
## 2 Monitoring Devices
### Power Quality Monitors (PQMs) and Power Quality Analyzers (PQAs)  
The IEEE Std C37.2-2008 standard [31] defines a PQM as a device capable of monitoring the electrical parameters used in power quality (PQ) analysis, specifically those parameters that determine compliance or non-compliance with supply quality regulations. These parameters include RMS voltage and current values, frequency values, power, phase unbalance, an harmonics and interharmonics in an electrical signal. Along with these, parameters of other nature may also appear. An \"extended\" version of this type of equipment is the Power Quality Analyzer (PQA), which further processes the data, checking whether the measurements comply with the standards or not. PQMs and PQAs usually also include an event recorder apart from their trend data. The difference between a PQM and a PQA is subtle; however, a PQA can be regarded as an enhanced version of a PQM. In general, the particular parameters they measure and the way they are treated will strongly depend on the objectives when installing a meter.  
All measurements collected by a PQM are derived from those collected by Current Transformer (CT) and Power Transformer (PT) modules, along with additional measurements that require further processing, such as harmonic measurements and Total Harmonic  
Figure 1: Example of a distribution network with measurement devices.  
Distortion (THD) calculation. In three-phase devices, most measurements are presented three times. Moreover, these measurements are often shown with different time aggregations, significantly increasing the amount of available data. As a result, the volume of trend data can be quite substantial. There is no regulation or standard that indicates which parameters should be chosen, although IEEE Std 1159-2019 [32] gives some recommendations.
# A Method to Produce vsiRNAs in Plants with Cross-Kingdom Gene Silencing Capacity
## 2 Materials and Methods
### Determination of the Relative Expression of Krt18 in Macrophages  
Twenty-four hours after transfection, the relative expression level of the Krt18 gene in macrophages from BALB/c mice was determined. Three mouse macrophage cultures were used: the first one was only treated with the transfecting agent. A mouse macrophage culture was transfected with 10 ug of sRNA from plants infected with pEuMV-YP:Krt18 and, as a negative control, 10 ug of sRNA from plants infected with pEuMV-YP:\(\Delta\)AV1:chl were used. Total RNA was extracted in the presence of TRIzol reagent(tm) (Invitrogen), followed by RNA purification using the Direct-zoI(tm) miniprep Plus kit (Zymo Research). After treatment with DNase (Ambion Inc., Austin, TX, USA), reverse transcription of total RNA was performed using a Revert Aid(tm) H Minus First strand cDNA Synthesis Kit (Thermo Scientific), following the manufacturer's instructions. Real-time PCR was performed using 5 ng of cDNA and the primers Krt18-F 5\({}^{\prime}\)-TTGCCGCCGATGACTTTAGAG-3\({}^{\prime}\) and Krt18-R 5\({}^{\prime}\)-GATTCCACCCATTCCCGCCA-3\({}^{\prime}\). All quantifications were normalized to the housekeeping gene glyceraldehyde 3 phosphate dehydrogenase (GAPDH), whose primers are gapdh-R 5\({}^{\prime}\)-GGCATGGACTGTGCTATGA-3\({}^{\prime}\) and gapdh-F 5\({}^{\prime}\)-TTCACCACTGGAGAAGGC-3\({}^{\prime}\), using the Maxima SYBR Green qPCR Master Mix (Thermo Scientific). The relative expression was determined by the 2\({}^{-\Delta\)ACt method. Data are expressed as the mean of three biological replicates with three technical replicates, and the significance was analyzed with a two-tailed Student's \(t\)-test.
## 4 Results
### Mean Annual VI and Variability
#### 4.2.1 Mean Annual VI
The locations of the three transects are marked in Figure 4.  
The relationships between the VIs for main vegetation types in the MODIS land cover product are depicted in Figure 6 and summarized in Table 2. All of the VIs show significant linear relationships between each other (\(p<0.05\)) with varying correlations in different vegetation types. There is a strong linear relationship (\(R>0.94\)) between the NDVI and SAVI for each vegetation type. The highest correlations were found in areas of cropland and grassland. NDVI and SAVI have a moderate correlations (\(R=0.55\)) with EVI in areas of woody savannas. For grassland and mixed forest, the relationship between NDVI, SAVI and EVI are weakest (\(R=0.29\) to \(0.40\)). The SAVI and EVI have a stronger linear relationship than NDVI with EVI. Figure 6 shows the scatter plots between the mean annual VIs over the 2001-2012 period. The relationship between EVI and NDVI and SAVI exhibited a high scattering of sampling points. The EVI variations corresponding to each SAVI and NDVI are mostly caused by the variation of the blue band, since EVI values rely on the blue reflectance in addition to red and NIR. For evergreen needleleaf forest, mixed forest, woody savannas and savannas, scattering samples values are observed comparing NDVI with SAVI and EVI at value ranges from 0.2 to 0.6. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vegetation Type** & **NDVI and SAVI** & **NDVI and EVI** & **SAVI and EVI** \\ \hline
**Evergreen Needleleaf Forest** & 0.9422 & 0.4093 & 0.4916 \\
**Mixed Forest** & 0.9773 & 0.3675 & 0.4032 \\
**Open Shrubland** & 0.9867 & 0.4163 & 0.4585 \\
**Woody Savannas** & 0.9949 & 0.6504 & 0.6630 \\
**Savannas** & 0.9849 & 0.5447 & 0.5717 \\
**Grassland** & 0.9999 & 0.2886 & 0.2886 \\
**Cropland** & 0.9998 & 0.4095 & 0.4096 \\
**Cropland/Natural Vegetation Mosaic** & 0.9987 & 0.5149 & 0.5196 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The Pearson’s correlation coefficients of mean annual VIs for 2001–2012 for different vegetation types. Figure 6: Scatter plots of the mean annual VIs for the years 2001–2012 among the three VIs for different vegetation types.
# A Multi-Hop Clustering Mechanism for Scalable IoT Networks
###### Abstract  
It is expected that up to 26 billion Internet of Things (IoT) equipped with sensors and wireless communication capabilities will be connected to the Internet by 2020 for various purposes. With a large scale IoT network, having each node connected to the Internet with an individual connection may face serious scalability issues. The scalability problem of the IoT network may be alleviated by grouping the nodes of the IoT network into clusters and having a representative node in each cluster connect to the Internet on behalf of the other nodes in the cluster instead of having a per-node Internet connection and communication. In this paper, we propose a multi-hop clustering mechanism for IoT networks to minimize the number of required Internet connections. Specifically, the objective of proposed mechanism is to select the minimum number of coordinators, which take the role of a representative node for the cluster, i.e., having the Internet connection on behalf of the rest of the nodes in the cluster and to map a partition of the IoT nodes onto the selected set of coordinators to minimize the total distance between the nodes and their respective coordinator under a certain constraint in terms of maximum hop count between the IoT nodes and their respective coordinator. Since this problem can be mapped into a set cover problem which is known as NP-hard, we pursue a heuristic approach to solve the problem and analyze the complexity of the proposed solution. Through a set of experiments with varying parameters, the proposed scheme shows 63-87.3% reduction of the Internet connections depending on the number of the IoT nodes while that of the optimal solution is 65.6-89.9% in a small scale network. Moreover, it is shown that the performance characteristics of the proposed mechanism coincide with expected performance characteristics of the optimal solution in a large-scale network.  
Internet of Things; multi-hop cluster; IoT network; scalability; optimization 1