esa-sceva/satcom-chunk-collection huggingface dataset:
# Corrosion Behavior of Zn-Al-Mg-Si Coatings in Sulfur Dioxide-Containing Environment
## 4 Conclusions  
The microstructure of Zn-Al-Mg-Si coatings and their corrosion behavior in a humid environment containing sulfur compounds were investigated. The following conclusions can be drawn from the obtained research results:  
* Zn-Al-Mg-Si coatings have a complex layered structure. Coatings are composed of a diffusion layer of Fe(Al,Si,Zn)\({}_{3}\) intermetallic and an outer layer of Al-rich dendritic and interdendritic areas with Zn/MgZn\({}_{2}\) eutectic, MgZn\({}_{2}\) intermetallic and Si precipitates. The structural components of the outer layer have a different electrochemical character, which leads to the creation of effective links between the structural components of the coating, especially on the top surface of coatings.
* In the sulfur dioxide test with general condensation of moisture, Zn-Al-Mg-Si coatings showed better corrosion resistance than conventional Zn coatings due to the formation  
Figure 9: SEM images and corresponding EDS patterns of the of the cross-section of the Zn-Al-Mg-Si coating after the sulfur dioxide test with general condensation of moisture.  
of favorable corrosion products. In this environment, the coating is locally covered with layered double hydroxides (LDHs) based on divalent cations Mg\({}^{2+}\) and Zn\({}^{2+}\), trivalent cation Al\({}^{3+}\) and SO\({}_{4}\)\({}^{2-}\) anion, while the interdendritic areas of lamellar eutectic are covered with a layer of zinc hydroxysulfate--Zn\({}_{4}\)SO\({}_{4}\)(OH)\({}_{6}\cdot\)5H\({}_{2}\)O. An Al(OH)\({}_{3}\) layer is formed on the surface of Al-rich dendrites, which can constitute a passive protective layer. This configuration of corrosion products changes the electrochemical system of the structural components of the coating and constitutes an effective barrier protection, delaying the corrosion process.
* The addition of Si to the bath effectively limits the excessive thickness of the diffusion layer of coatings, but its precipitation in the outer layer and on the top surface of coatings may promote pitting corrosion due to its cathodic nature and the creation of effective corrosion cells with other structural components of the coatings.  
Conceptualization, H.K.; methodology, H.K. and A.M.; validation, H.K. and A.M.; investigation, H.K. and A.M.; writing--original draft preparation, A.M.; writing--review and editing, H.K.; visualization, H.K. and A.M.; supervision, H.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.
# Survey on Wireless Technology Trade-Offs for the Industrial Internet of Things
## 5 Discussions  
Key specifications of each technology introduced in Section 4 are listed in Tables 2 and 3. Note that not all numbers in Tables 2 and 3 can be taken for granted as they do not depict the trade-offs, but only present theoretical limits. NB-IoT operates in licensed spectrum. It can reach as far as its cellular infrastructure goes, and it is inherently more reliable than technologies that operate in shared spectrum. LoRa, IEEE 802.11 ah and 802.15.4g operate in unlicensed sub-GHz frequency bands, which makes their long range a feature of their physical layers. LoRa operates in 863-870 MHz frequency band in Europe, offering three sub-bands at 864 MHz, 867 MHz and 868 MHz. Five 125 kHz channels are defined in 867 MHz band and three in 864 MHz and 868 MHz band [54; 55]. The three default channels in 868 MHz band are to be implemented by every node, whereas the rest are optional. Wi-Fi HaLow operates in 863-868 MHz band in Europe and defines five 1-MHz channels and two 2-MHzchannels [56]. Considering this, it is clear that Wi-Fi HaLow and LoRa's optional channels overlap in 3 out of 7 Wi-Fi HaLow channels and do not interfere with each other otherwise, which makes their parallel deployments possible. However, Wi-Fi HaLow can severely interfere with 802.15.4g [57], given that they operate in the same bands. The 2.4 GHz unlicensed frequency band is widely utilized today and the technologies that operate in this band must pay special attention to coexistence. WirelessHART, ISA100.11a and 802.15.4e TSCH are all based on the 802.15.4 2.4 GHz PHY layer that operates in sixteen 2 MHz-wide and 5 MHz-spaced channels in 2.4 GHz frequency band. They all employ frequency-hopping to improve the reliability of their transmissions, as they make use of exactly the same spectrum. Given their shorter single-hop ranges, these technologies employ multi-hop topologies to extend their range. Wireless networks have the benefit of mitigating cabling for communication but the question of power cables still remains. Ideally, power cables could also be mitigated when the devices can live long enough drawing the power from the batteries available today. This is feasible for sufficiently large cycle times. Therefore, it is important to also consider the energy consumption. To evaluate the possibility of entirely wireless deployments that do not need power cables, an experimental energy efficiency comparison between LoRa, NB-IoT, IEEE 802.11 ah and IEEE 802.15.4g (Wi-SUN with TSCH) was performed. The experiments combine energy consumption values of state-of-the-art off-the-shelf radios obtained from their data sheet (cf. Table 4), with simulated performance analysis. At the
## 2 Related Works
### IoT Applications in Agriculture  
In 2021, a bibliometric study was carried out on a sample spanning three decades of research, to give an overview of the use of wireless sensor networks in general in agriculture [18]. Their detailed analysis showed that wireless sensor networks are a key element of precision agriculture. The literature on wireless sensor networks (WSNs) revolves around three primary thematic areas, delving into various technologies such as IoT, Artificial Intelligence, unmanned aerial vehicles, RFID, and cloud computing. The interconnections among these key topics underscore the need for deploying WSNs in agriculture and their potential to optimize agri-food processes. Consequently, concerted efforts from scientists, producers and specialists are imperative to enhance the inter-workability of WSN-based agricultural systems and formulate pertinent, adaptive practices and measures. In addition, a bibliometric study of intelligent agricultural environments was performed [19]. Through the appropriate advancement of technologies, the implementation of intelligent systems can facilitate the application of these technologies in the agricultural domain, benefiting, for example, small-scale banana producers by enhancing agricultural efficiency. This, in turn, leads to numerous economic and environmental advantages. Moreover, a bibliometric analysis and review of IoT-based messaging protocols for aquaculture applications was carried out [20]. The results of their work show the scope of research in this area and highlight that protocols such as Hypertext Transmission Protocol (HTTP), Constrained Application Protocol (CoAP), Message Queue Telemetry Protocol (MQTT), MQTT for Sensor Networks (MQTT-SN) and Advanced Message Queuing Protocol (AMQP) are employed for information transfer. These messaging protocols need to consider diverse elements such as the sensing performance limit, storage capability and power usage. In 2023, the authors of the article [21] performed a survey including a bibliometric study on digital agriculture in five West African countries. While the scope of their survey is directly connected to the one of this article, their work did not target all countries of West Africa. It also only focused on digital agriculture, not taking into account environmental aspects and IoT-related aspects. Finally, a review of the state of the art on remote sensing for aquaculture is presented in [22], with emphasis on different aspects: side location, facility mapping, market proximity analysis and roadway infrastructure, epizootic mitigation, meteorological event and flood early warning, environmental pollution monitoring and aquatic ecosystem impact. These last two articles will also be considered in the qualitative analysis and more specifically in Table 3.
# GAT-ABiGRU Based Prediction Model for AUV Trajectory
## 3 Materials and Methods
### Gru  
The gated recurrent unit (GRU), introduced in 2014 [33], features simpler internal operations than LSTM. While LSTM comprises three gates, GRU consists of a reset gate (r) and an update gate (z). GRU eliminates the cell state and instead utilizes the hidden state to transmit information, resulting in fewer parameters and higher efficiency. Despite GRU's numerous simplifications compared to LSTM, it still maintains similar effectiveness. The structure of the GRU network is illustrated in Figure 3.  
The specific computation process of GRU is represented as follows: At any given time, \(x_{t}\) represents the input at that moment, and \(h\) represents the output of the hidden state at that moment. The reset gate \(r_{t}\) plays a crucial role in the GRU, determining how the new input information \(x_{t}\) combines with the previous memory \(h_{t-1}\). By adjusting the value of \(r_{t}\), the GRU can control the degree to which it forgets previous information. When \(r_{t}\) approaches 0, the GRU almost wholly disregards the last memory, making the unit state similar to processing the first input in the sequence. This mechanism allows the GRU to effectively forget previous computations and reset to the initial state when necessary. The formula for the reset gate is:  
Figure 2: GAT structure.  
\[\mathbf{r}_{t}=\sigma(U_{t}x_{t}+W_{t}h_{t-1}+b_{r}) \tag{5}\]  
Here, \(\sigma\) represents the sigmoid activation function, \(U_{r}\) and \(W_{r}\) are weight matrices, \(x_{t}\) is the input at time \(t\), \(h_{t-1}\) is the hidden state output from the previous time step, and \(b_{r}\) is the bias vector.  
The new hidden state \(\overset{\sim}{h}_{t}\) at time t is computed by integrating the current input \(x_{t}\) and the past hidden state \(h_{t-1}\). This is achieved through the following formula:  
\[\overset{\sim}{h}_{t}=tanh\big{(}U_{h}x_{t}+W_{h}(r_{t}\circ h_{t-1})\big{)} \tag{6}\]  
In this formula, \(U_{h}\) and \(W_{h}\) are weight matrices, \(r_{t}\) is the reset gate computed in the previous step, \(\circ\) represents element-wise multiplication, and tanh denotes the hyperbolic tangent function.  
Finally, the update gate \(z_{t}\) determines the rate at which the new hidden state \(\overset{\sim}{h}_{t}\) updates the previous hidden state \(h_{t-1}\). Based on \(z_{t}\), the current state \(h_{t}\) is calculated as follows:  
\[z_{t}=\sigma(U_{z}x_{t}+W_{z}h_{t-1}+b_{z}) \tag{7}\]  
\[h_{t}=z_{t}\circ h_{t-1}+(1-z_{t})\circ\overset{\sim}{h}_{t} \tag{8}\]  
Here, \(U_{z}\), \(W_{z}\), and \(b_{z}\) are weight matrices and a bias vector, respectively.  
This computational procedure enables the GRU to effectively capture temporal dependencies in sequential data, adapt to new information, and selectively update its hidden state, making it well suited for tasks involving sequential data processing, such as natural language understanding and time-series prediction.
## 2 Materials and Methods
### Data Preprocessing  
To eliminate artifacts from the EEG and extract relevant brain activity while minimizing potential noise, we employed the inverse WT as robust method. Briefly, we applied CWT to the original data and used the inverse Continuous Wavelet Transform (iCWT) to reconstruct signals. The iCWT is considered as a band-pass filter; hence, we could reconstruct the signals in a specific frequency range [28], from 2 to 50 Hz. We removed high-frequency noises and the powerline interference generated by electrical devices, mainly the 60 Hz frequency, by establishing an upper threshold of 50 Hz. The baseline and the pulse artifacts (lower frequencies than 1.5 Hz) were eliminated when we established the lower limit at 2 Hz. Muscle artifacts can share frequencies with neurological signals mainly in the \(\alpha\) band; however, they are generally 1 to 2 orders of magnitude larger; these kinds of artifacts were visually inspected and eliminated.  
The application of the iCWT ensured the removal of all non-relevant signals and was able to effectively isolate signals within the \(\delta\), \(\theta\), \(\alpha\), \(\beta\), and \(\gamma\) frequency bands of interest while removing any extraneous signals, whether originating internally or externally [28].  
If certain artifacts persisted after preprocessing, we were confident that they did not alter the results due to the employment of the XWT as a part of the CWA. For a more comprehensive understanding of the methodology and technical details, see [29] and please refers to Appendix A.
# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
## 3 System Architecture for Autonomous Vehicles
### Ultrasonic Sensors  
These sensors use ultrasonic waves and operate in the range of 20-40 kHz [14]. These waves are generated by a magneto-resistive membrane used to measure the distance to the object. The distance is measured by calculating the time-of-flight (ToF) of the emitted wave to the echoed signal. Ultrasonic sensors have very limited range which is generally less than 3 m [47]. The sensor output is updated after every 20 ms [47], making it not compliant with the strict QoS constraints of an ITS. These sensors are directional and provide a very narrow beam detection range [14]. Therefore, multiple sensors are needed to to get a full-field view. However, multiple sensors will influence each other and can cause extreme ranging errors [48]. The general solution is to provide a unique signature or identification code which will be required to discard the echoes of other ultrasonic sensors operating in near-by range [49]. In AVs, these sensors are utilised to measure short distances at low speeds. For example, they are used for SPA and LDWS [50]. Moreover, these sensors work satisfactorily with any material (independent of color), in bad weather conditions and even in dusty environments.  
Figure 4: System Architecture for AVs.
# Caroenoid Biosynthesis in _Fusarium_
## 3 Genes and Enzymes of Caroenoid Metabolism in _Fusarium_
The rhodopsins typically bind retinal as a light-absorbing prosthetic group, providing coherence to the gene organization of the cluster: the genes _carRA_, _carB_, and _carX_ are necessary to produce retinal, which is presumably used by the rhodopsin encoded by the coreguated _carO_ gene. Retinal might be also used for a second rhodopsin, encoded by the gene _opsA_[40]. In addition, retinal might be subject to further chemical reactions. The _Fusarium_ genomes contain a gene for an aldehyde dehydrogenase highly similar to RALDH enzymes converting retinal to retinoic acid in mammals. This enzyme, that was called CarY, was investigated in _F. verticillioides_ and found to exhibit such enzymatic activity in vitro [41]. Targeted mutation of the _carY_ gene in _F. verticillioides_ had no effect on carotenogenesis, but resulted in diverse developmental alterations. However, no retinoic acid could be detected in the control fungal cells and hence the biochemical function of the CarY enzyme in _Fusarium_ has not been solidly established. The _Fusarium_ genes responsible for the conversion of torulene to NX have been recently described. The search for genes encoding other putative carotenoid oxygenases in the _Fusarium_ genome databases led to identify the gene _carT_, whose function was revealed by the presence of a mutation in a torulene accumulating mutant and its genetic complementation with the wild-type _carT_ allele [42]. As biochemical confirmation, purified CarT enzyme efficiently cleaved torulene in vitro to produce \(\beta\)-apo-4\({}^{\prime}\)-carotenal. As additional support, the targeted mutation of its ortholog in _N. crassa_, called _cao-2_ from carotenoid oxygenase, resulted in the block of NX production and the accumulation of torulene [43]. These findings established a new enzymatic class in the carotenoid oxygenase family. The function of the gene _carT_ in _Fusarium_ carotenogenesis, together with those of the genes _carRA_ and _carB_, was also confirmed by targeted gene disruption in _Gibberella zeae_, telecomorph of _Fusarium gaminearum_[44]. The product of CarT, \(\beta\)-apo-4\({}^{\prime}\)-carotenal, requires a further oxidation to produce NX. The responsible gene was first discovered in _N. crassa_ thanks to the study of a yellow mutant, called _ylo-1_. This mutant exhibited a puzzling biochemical phenotype, since it contained a complex carotenoid mixture that varied with the culture conditions and that did not include NX [45; 46]. The gene _ylo-1_ encodes an aldehyde dehydrogenase mediating the last step of NX biosynthesis, as demonstrated in its ability to complement the _ylo-1_ mutation [47] and the capacity of purified YLO-1 protein to convert in vitro \(\beta\)-apo-4\({}^{\prime}\)-carotenal into NX. This finding allowed the identification of the orthologous gene in _F. fujikuroi_, that was called _carD_[48]. The function of _carD_ was confirmed by targeted mutation, which resulted in the lack of NX and the accumulation of unusual carotenoids, interpreted as \(\beta\)-apo-4\({}^{\prime}\)-carotenal derivatives.
## 2 Material and Methods
### Cell Viability and Proliferation Colorimetric Assay  
Cell viability was assessed using the sulforhodamine B (SRB) assay. A total of 3 \(\times\) 10\({}^{3}\) wild type (WT) or SHBG-overexpressing (SHBG_OE) prostate cancer cells were seeded per well in 96-well microtiter plates containing supplemented growth media, and incubated at 37 \({}^{\circ}\)C in humidified 5% CO\({}_{2}\). After 48 h, cell viability was measured following the manufacturer's instructions. After fixing the WT or SHBG_OE prostate cancer cells with 10% trichloroacetic acid (TCA) and carefully washing with ddH\({}_{2}\)O, the cells were stained with 0.4:1 (_w/v_) SRB-acetic acid solution. We carefully washed off unbound SRB dye from the cells using 1% acetic acid thrice, followed by air-drying the plates, and solubilization of bound SRB dye in 10 mM Tris base. For cell proliferation, invitrogen alamarBlue(tm) high sensitivity cell viability reagent (#A50100, Thermo Fisher Scientific Inc., Bartlesville, OK, USA) was used, strictly following the manufacturer's instructions. Briefly, after seeding cells in triplicates with three biological replicas for each assay at each time point (day 1-8), the cells were incubated with alamarBlue(tm) for 2 h at 37 \({}^{\circ}\)C. The number of dye-stained viable/proliferating cells was read at 570 nm absorbance wavelength in the Molecular Devices Spectramax M3 multimode microplate reader (Molecular Devices LLC., San Jose, CA, USA).
# Evaluation of the Long-Term Performances of SMA-13 Containing Different Fibers
## 3 Test Scope and Methods
### Test Methods for Cracking Resistance
#### 3.2.2 SCB Test  
The SCB test is conducted according to AASHTO TP 124 [34]. The test temperature is 25 \({}^{\circ}\)C. This test is designed according to the Fracture mechanics theory. The cross section of the specimen is semicircle-shaped (the radius is 75 mm), and the specimen is pre-cut for a certain length called pre-cut length (the pre-cut length is 15 mm). The difference between the radius of the specimen and the pre-cut length is called the ductile zone length (DZL). \(G_{\text{f}}\) is the fracture energy and it is calculated according to Equation (5), where \(W_{\text{f}}\) is the integral of the load-displacement curve, and \(Area_{\text{lig}}\) is the product of the DZL and the thickness of the specimen (t = 50 mm). \(Fl\) index is adopted to reflect the crack propagation rate and it is calculated using Equation (6), where \(\mid\)m \(\mid\) is the absolute value of the slope at the inflection point after the peak of the loading value. \(Fl\) value is negatively correlated with the crack propagation rate. Four specimens were used to determine the \(Fl\).  
\[G_{\text{f}}=\frac{W_{\text{f}}}{Area_{\text{lig}}}\times 10^{6} \tag{5}\]  
\[FI=\frac{G_{\text{f}}}{\left|\text{m}\right|}\times 0.01 \tag{6}\]  
In this paper, fracture toughness (\(K_{\text{f}C}\)) is also adopted to evaluate the cracking resistance of mixtures. This index is calculated according to AASHTO TP 105 [35], shown as Equation (7).  
\[K_{IC}=\frac{P}{2 rt}\sqrt{\pi a}Y_{I(0.8)} \tag{7}\]  
where: \(P\) is the applied load (MN); \(r\) is the specimen radius (m); \(t\) is the specimen thickness (m); \(a\) is the notch length (m); \(Y_{I(0.8)}\) is the normalized stress intensity factor, calculated with Equation (8).  
\[Y_{I(0.8)}=4.782-1.219(a/r)+0.063\exp\left[7.045(a/r)\right] \tag{8}\]
## 3 Experiments
### Results and Analysis  
This study involved evaluating the performance of an ionospheric scintillation detection model that used the SSA-XGBoost algorithm. This model was compared to SVM and XGBoost algorithms on a test set. The resulting confusion matrices for the SSA-XGBoost, SVM, and XGBoost algorithms are shown in Figure 7, along with accuracy rates of 97.1\(\%\) (SVM), 96.9\(\%\) (XGBoost), and 97.8\(\%\) (SSA-XGBoost), demonstrating that the SSA-XGBoost model, in the context of detecting and classifying ionospheric scintillation, provides superior performance compared to the SVM and XGBoost models.  
The leakage rates of the SVM and XGBoost models for the flicker events are 23.5\(\%\) and 21\(\%\), respectively, while the leakage rate of the SSA-XGBoost model for the flicker events is only 12.9\(\%\), which suggests that the detection model can more accurately capture the features of the flicker events after optimizing XGBoost using the Sparrow Search Algorithm.  
More detailed evaluation metrics are shown in Table 3. The F-score of the SSA-XGBoost-based ionospheric scintillation detection model proposed in this study is 89.2\(\%\)  
Figure 7: Confusion matrix on the test set: (**a–c**) correspond to the SVM, XGBoost, and SSA-XGBoost models.  
which is even better compared to that of the SVM and XGBoost models, indicating that the detection model proposed in this study is more effective. It is worth noting that the performance of the detection model varies with each execution of the model since 75% of the training set is randomly selected from the entire dataset.