esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Equipment Setup  
Three-dimensional motion of the lower body was recorded at 240 Hz using an 8-camera infra-red motion capture system (Motion Analysis Corporation, Santa Rosa, CA, USA). To facilitate motion capture, 26 retroreflective markers were placed on the lower body and the shoes. For the human body, the markers were attached to its left and right side at anterior superior iliac spine (ASIS), posterior superior iliac spine (PSIS), mid-dhigh, medial and lateral epicondyles, tibial tuberosity, and fibular head. On the shoes, markers were attached to the fabric at the medial and lateral malleoli, first, second, and fifth metatarsal heads, and the superior part of the posterior calcaneus. To facilitate this process, a tester first palpated the metatarsal head position on the participants before the shoes were worn. The participants were told that the markers will be placed at these specific positions on the shoes. Immediately after the shoes were worn, the tester located the metatarsal head positions with feedback from participants. For the lateral malleolus, the tester aligned the position from the top of the shoe and used a ruler to ensure that the markers were placed at exactly the same marker positions on the other pair of shoes. Two side-by-side force platforms recorded ground reaction forces at 1200 Hz (OR6-7-2000, Advanced Mechanical Technology Inc., Watertown, MA, USA). Surface electromyography (EMG; Trigno(tm) Wireless EMG, Delsys, Inc., Natick, MA, USA) sampled at 1200 Hz was used to record the activation levels of seven muscles in the right lower limb, which were gluteus maximus, vastus medialis, rectus femoris, biceps femoris, tibialis anterior, medial gastrocnemius, and soleus. Motion, force, and EMG signals were recorded simultaneously and synchronized via the Cortex software (version 2.6.2.1169, Motion Analysis Corp, Santa Rosa, CA, USA).
## 2 Related Work & Background
### System Architectures for Industry 4.0  
The fourth industrial revolution (Industry 4.0) signifies the digital integration of traditional manufacturing processes, leading to horizontal and vertical value chain integration [21]. This transformation shifts control from centralized to decentralized structures, impacting various sectors along the value chain. Industry 4.0 architectures leverage key technologies such as Augmented Reality (AR), the Industrial Internet of Things (IIoT), digital twins, autonomous robots, simulations, cybersecurity, and predictive maintenance [23]. These advancements drive efficiency, flexibility, and automation in industrial environments.
## 4 Discussion  
Ensuring the comparability of data from the MSG and Himawari-8 satellites is crucial for the integrity of our study. We took several steps to ensure that the data was consistent and reliable. We used images from both satellites, focusing on similar channels near the 0.6 um spectral band, and re-gridded the images to a common resolution of 3 km to reduce errors. We also applied the Heliosat-2 method, which uses a standardized cloudiness index, to keep our cloud-cover assessments consistent. Despite our efforts, we recognize that some differences might still exist. To address this, we presented our findings using box plots across different quantiles, giving a more comprehensive view of the data and minimizing the impact of any remaining discrepancies.  
Figure 6: Comparison of ground-based and Heliosat-2 GHI estimates in terms of (**A**) R\({}^{2}\), (**B**) MB (in W\(\cdot\)m\({}^{-2}\)), and (**C**) RMSE (in W\(\cdot\)m\({}^{-2}\)) values for diurnal radiation across different months. The blank spaces indicate null values occurring due to the unavailability of ground data. Noticeably, the monsoon months (June-September) in Indian sites (ASO and HON), as well as in EU sites, exhibit higher error values. These months are also characterized by high levels of standard error, as shown below the month labels in the MB and RMSE heatmaps. In contrast, KSA in Saudi Arabia is characterized by high R\({}^{2}\) and low RMSE values.  
A blended method of estimation of all-sky GHI Heliosat-2 is proposed in this study, one adopted from [PERSON] et al. (2017) and [PERSON] et al. (2018), who contributed significantly to the field of solar irradiance modeling, though they focused on different aspects. [PERSON] et al.'s H2-hybrid model emphasizes the importance of cloud cover in its effect on solar irradiance, using a cloudiness index to estimate GHI. On the other hand, [PERSON] et al.'s work enhances this approach by incorporating a wider range of atmospheric data, including aerosols, ozone, and water vapor, through radiative transfer modeling, enabling increasingly precise calculations of spectrally resolved all-sky radiance. These methods complement each other by focusing on both specific and comprehensive atmospheric factors that influence solar irradiance, suggesting the potential of integrated approaches in more accurately modeling and utilizing solar energy data. Notably, cloud cover and aerosols play significant roles in the absorption and diffusion of solar radiation, which further emphasizes the importance of distinguishing between clear-sky and cloudy-sky conditions when evaluating surface solar radiation and air pollution dynamics ([PERSON] et al., 2017). Hence, we move into the following discussion to explore how these integrated approaches perform under varying weather conditions, particularly focusing on estimation biases during cloudy and clear days.
## 1 Introduction
TUBF1 from AFB\({}_{1}\)-contaminated corn [29] and _Pleurotus ostreatus_ GHBBF10 from decomposing tree trunks [30] were also found to degrade AFB\({}_{1}\). Moreover, _Bacillus velezensis_ DY3108 from soil [31], _Aspergillus niger_[32] from feedstuff, _Wickerhamomyces anomalies_ WRL-076 from crops [33], and _Microbacterium proteolytic_ B204 from bovine feces [34] were isolated and found to have the ability to degrade AFB\({}_{1}\). However, there might be unknown aflatoxin-control microorganisms in the marine environment. _Emericella venezuelensis_ was the first organism of marine origin reported to produce aflatoxin [35]. Moreover, so far, there has only been one aflatoxin-inhibiting _Bacillus circulus_ strain from the deep sea reported that could inhibit the growth of aflatoxin fungi hypha and the generation of AFB\({}_{1}\)[36]. Nevertheless, few degrading bacteria of deep-sea origin have been reported. In recent years, _Kocuria rosea_ of deep-sea origin have been reported to degrade AFB\({}_{1}\)[37]; however, the degradation products, degradation pathways, and toxicity of the products have not been further explored and excavated. Since the _Kocuria rosea_ strain was isolated from the deep sea, it was assumed that there might be some degradation products that have not yet been reported. Therefore, this study aimed to identify AFB\({}_{1}\) degradation products, predict degradation pathways, and detect the toxicity of degradation products. Meanwhile, it can also provide new references and inspiration for the future excavation of AFB\({}_{1}\) degradation products and degradation pathways.
# Past, Present and Future Monitoring at the Vallcebre Landslide (Eastern Pyrenees, Spain)
## 2 Monitoring Methods Used in Valleebre and Sample Results
### Noninterferometric GBSAR Monitoring  
The authors of [24; 25] presented the application of the Ground-Based SAR (GBSAR) to landslides. In 2010-2011, the lower part of the Valleebre landslide was monitored using a GBSAR. In this case, we did not use interferometry based on the phase but a new procedure to process the amplitude component of the GBSAR data acquired in discontinuous mode.  
Figure 16: (**a**) the 7 Corner Reflectors appear as bright spots in a 2007 radar amplitude image; (**b**) the concentric circles on the map represent the rate of movement of the CRs during the first half of 2007.  
Figure 17: Evolution of the displacements (cm) of the CRs during the first half of 2007. The relative Line-Of-Sight (L.O.S.) displacement was corrected to the direction of the maximum local slope. Precipitation (mm) is also presented as vertical bars.  
This methodology intends to overcome several well-known drawbacks of the radar interferometry, especially in a landslide environment: loss of coherence; lapse of time between acquisitions; aliasing effect; atmosphere influence, and so on. The use of geometric features of the amplitude images combined with a matching technique is fully described in [7].  
Between February 2010 and September 2011, this technique was applied to the lower unit of the Vallcebre landslide in order to evaluate the performance of the noninterferometric GBSAR approach. In this period, eight measurement campaigns were carried out using the IBIS-L Ku-band GBSAR (Ingegneria Dei Sistemi S.p.A., Pisa, Italy, 2010; Figure 18). In each campaign, 15 small CRs (Figure 19) were deployed in and around the target area (11 inside, 4 outside as reference).  
In parallel to the GBSAR measurements, five Total Station surveying campaigns were carried out to validate the results obtained with the GBSAR. The analysis of the total measurements obtained during the 19 months permitted the validation of the technique. Displacements up to 80 cm were measured in the lower part of the lower unit (Figure 20); the movement of the points almost _en bloc_ corresponds to a translational landslide.  
Figure 19: One of the small Corner Reflectors deployed over the landslide lower unit in order to fix up the vegetation and snow problems. The operator is holding a surveying circular prism, which is observed from a reference point with a Total Station, for validation purposes.  
Figure 18: Picture of the IDS GBSAR system. The synthetic aperture is obtained through the movement of the radar sensor (yellow box) along the rail.
# Dynamic Behavior of Track Bridge on Serviced Long-Span Road/Railway Bridge
## 5 Prediction of Dynamic Behavior of Bridge
### Dynamic Response  
The bridge deck displacement and acceleration with increasing KTX speed (up to 200 km/h) were calculated. The results are shown in Figure 13.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Form**} & \multirow{2}{*}{**Vibration Mode**} & \multicolumn{2}{c}{**2000 (Construction Site)**} & \multicolumn{2}{c}{**2015**} & \multicolumn{2}{c}{**This Study**} \\ \cline{3-6}  & & **Experimental Value** & **Analytical Value** & **Loading Test Result** & **Analytical Value** & **(Analytical Value)** \\ \hline \multirow{3}{*}{Suspension bridge} & 1 st bending & 0.487 & 0.490 & 0.488 & 0.478 & 0.480 \\  & 2 nd bending & 0.810 & 0.831 & 0.824 & 0.779 & 0.725 \\ \cline{1-1}  & 1 st torsional & 1.060 & 1.044 & 1.220 & 1.003 & 0.887 \\ \cline{1-1}  & 2 nd torsional & 1.700 & 1.710 & 1.495 & 1.512 & 0.973 \\ \cline{1-1}  & 1 st bending & - & - & 1.251 & 1.164 & 1.171 \\ \cline{1-1}  & 2 nd bending & - & - & 1.434 & 1.468 & 1.465 \\ \cline{1-1}  & 3 rd bending & - & - & 1.770 & 1.943 & 1.928 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of eigenvalue analysis results.  
Figure 13: \(Cont.\)  
According to the predictions of the dynamic behavior of the bridge, the displacement and acceleration of the bridge deck will increase as speed increases. Regarding the bridge deck displacement, the response increased in both the truss bridge and the suspension bridge as the speed increased beyond 160 km/h. Moreover, the bridge deck acceleration increased for speeds beyond 120 km/h, although the level of increase was marginal.
## 5 DASH7 Communication System Implementation
### The Receiving Process
#### 5.2.2 Demodulation  
At the receiver, and after tuning the LO at the carrier frequency of the received signal, the received digital baseband signal can be expressed as  
\[x(k)=A_{r}e^{i\Phi(k)}\, \tag{20}\]  
where \(A_{r}\) is the received signal's amplitude. The demodulation process of the received signal can be illustrated as follows:  
1. Extracting the phase \(\Phi(k)\) of the baseband signal using a _Complex to Arg_ block: \[\Phi(k)=2\pi h\sum_{k=0}^{n}\alpha(k)+\phi_{e}\,\] (21) where k is the digitized time index, and \(\phi_{e}\) is a constant arbitrary phase due to the phase difference between the transmitter and the receiver.
2. Taking the derivative of \(\Phi(k)\) \[\frac{d\Phi(k)}{dk}=2\pi h(\alpha(k)-\alpha(k-1))\,\] (22) where \(h\) is the modulation index and is equal to the following: \[h=\Delta fT\.\] (23) In order to further decode the packet, the following are required:
3. Time synchronisation and bit decimation must be performed.
4. Payload detection based on the sync word bits.
5. To decode the payload data, further data de-whitening and the optional FEC decoding are required.  
The software implementation of points one and two are shown in Figure 13. The implementation of subsequent points are implemented in Figure 14 and will be discussed. Points 3, 4, and 5 will be discussed in the upcoming sections.  
Figure 12: Fixed-frequency demodulator using recorded data while tuned to channel 0 using the Lo-Rate channel class.
## 3 LB-MPC in Path Tracking of Mobile Platforms
### Learning and Optimizing for Controller Output under Uncertain Disturbances
#### 3.3.2 Learning and Optimizing Controller Output Based on Security Framework
If this new backup solution cannot be found and the planning problem is infeasible, the system can be driven to the secure set \(S^{t}\) along the previously computed trajectory (depicted in brown) [123]. and improving the performance of mobile platforms during the learning process are important to the security control system. The LB-MPC can be used in common situations where the model prediction security filter has uncertainties and needs to learn from data. The general security framework based on Hamilton-Jacobi reachability methods uses approximate knowledge of the system dynamics to satisfy the constraints and minimize the disturbances during the learning process [129]. The Bayesian mechanism is further introduced to improve the security analysis by obtaining new data through the system. The reachability analysis is combined with ML [130]. The reachability analysis maintains the security performance, and the ML improves the system performance. When both control inputs and disturbances are bounded, a secure mandatory control action is required only when the system approaches the boundary of the insecure set. At other times, the system can freely use any other controller. The statistical identification tools are used to identify better prediction models that can deal with state and input constraints and optimize the system performance on the basis of the cost function [131]. The statistical model of the system is established by using the regularity assumption of GP on dynamics [132]. More data are collected from the system and the statistical model is updated, improving the control performance and ensuring the security of the learning process. The proposed security framework removes the security constraints of learning-based control under physical limitations and optimizes the controller by using the model predictive security filters. The combination of model predictive security filters and different types of learning-based control methods not only meets the high-performance requirements of complex systems but also ensures the security in the control process.
## 4 Results and Discussion
### Inter-Method Comparison  
Table 5 presents the test sets MAPE obtained by nine methods based on the results of the optimal training set on the container throughput time series of 20 ports. It can be seen that 17 of the 20 port container throughput time series obtained the minimum MAPE by using GRU. For the remaining three container throughput time series, the minimum MAPE of two series is generated by ARIMA, and the minimum MAPE of one series is generated by Transformer. The possible reason is that the length of the container throughput time series is too short to be explored by methods other than GRU. Similar observations suggest that GRU is able to perform better on certain smaller, less frequent datasets [56,57]. As a method with similar structure to GRU, LSTM performs worse than GRU. The possible reason is that due to the short length of time series used in this study, LSTM cannot obtain enough patterns from too short time series. For the time series of Guangzhou Port, the method to generate the minimum MAPE is ARIMA, but its result is very close to that of GRU, which are 2.2039 and 2.2658, respectively. This indicates that GRU can also produce relatively accurate results, but ARIMA is slightly more accurate.  
In terms of the average MAPE of 20 ports, the best performing method is GRU, followed by CNN and NM. Surprisingly, the simplest NM method ranked third in the forecasting accuracy. Considering the simplicity, convenience, and ease of operation of the NM method, NM can be used for rapid and simple container throughput estimation when computing equipment and services are not available. This finding is consistent with previous studies, which also found that although NM results are not as good as other methods, the accuracy is very close [58]. Another finding is that the average performance of machine learning methods is better than the average performance of traditional methods, 7.89 and 8.39, respectively.  
Figure 13: MAPE and RMSE of Transformers with different number of layers.  
Table 6 presents the test sets RMSE obtained by nine methods based on the results of the optimal training set on the container throughput time series of 20 ports. It can be seen that the results of RMSE are similar to those of MAPE, and the best forecasting method is still GRU. However, there are also slight differences. The number of best performing ARIMA has increased from 2 to 3, and the number of best performing Transformer has increased from 1 to 2. The possible reason is that for some time series, GRU produces larger errors where the actual value is higher, which leads to larger differences. Moreover, because RMSE is highly sensitive to the extreme value of errors, the results of RMSE for some time series are not ideal when GRU performs well in terms of MAPE.
# New Isolated Autochthonous Strains of _S. cerevisiae_ for
## 1 Introduction
The acidity of the grapes is one of the most important parameters influencing the wine fermentation process and organoleptic properties of wine. It is determined by the content of several organic acids, the main of which are tartaric and malic acid [19; 20]. Metabolic activity of the yeast and other microorganisms, mainly lactic acid bacteria, modifies the original profile of organic acids affecting the particular organoleptic features and quality of wine [21; 22]. However, too high a content of organic acids has a negative impact on yeast and the obtained wines are characterized by an improper balance between sugar, acid, and aroma components [23]. In this context, the ability of native strains to adapt to the particular conditions of a specific wine-producing region may be beneficial. Besides organic acids, polyphenols are another important group of compounds responsible for the taste, color, and antioxidant activity of the wine [24; 25; 26]. Wine is a natural source of various bioactive phenols ranging from phenolic acids such as benzoic or cinnamic derivatives to various classes of polyphenic flavonoids such as flavones, flavan-3-ols, flavonols, anthocyanins, and tannins, which are responsible for the wine's pro-health effect [24; 27; 28; 29]. The composition of the phenolic fraction changes throughout the wine-making process [30; 31; 32; 33]. Certain lactic acid bacteria (LAB) and _S. cerevisiae_ strains may interact with these compounds. For example, they can take a part in the degradation of hydroxycinnamic and hydroxybenzoic acids [34; 35]. The metabolic impact of native _S. cerevisiae_ on the wine character, reflecting the typicality of the region was mainly observed in traditional wine-producing countries. Polandbelongs to the cold-climate countries similar to Germany, the Czech Republic, Slovakia, and Austria. The Polish climate is characterized by significant daily and seasonal temperature fluctuations, with the risk of frosts and hail in the spring, which cause differences in the cultivation of grapes and the quality of the wine produced in relation to warmer countries. The excess acidity of the grapes and the low sugar content is the main characteristic of cool-climate regions [30,36]. The cool-climate wines are more delicate and refined in taste, and the higher acidity gives them freshness [37]. The unique features of the local Polish wine contribute to increasing the variety of the wine on the market and are gaining more interest among consumers. The study on the use of autochthonous \(S\). _cerevisiae_ in Polish winemaking is, however, rather limited. Therefore, the aim of this work was to study the effect of the grape variety cultivated in the south-eastern region of Poland and the origin of the used wine yeast strain of \(S\). _cerevisiae_ species on selected physicochemical and aromatic characteristics of wine. The fermentation potential of native and commercial strains was compared, and the regional bio-diversity of \(S\). _cerevisiae_ was evaluated.
# Optimization of a Coherent Dual-Beam Array Feed Network for Aperiodic Concentric Ring Antennas
## 4 Conclusions  
This paper introduced a new dual-beam antenna system for a non-uniform circular concentric ring aperture based on a coherent beamforming network, with scanning and multi-beam shaping capabilities. The global performance of two-beam normalized power patterns, generated by the designed coherent network configuration in an aperiodic and periodic circular concentric ring layout, was contrasted and analyzed. The numerical experiments based on antenna synthesis demonstrate and validate that the designed beamforming network comprises a dual-beam with steering and shaping properties, taking advantage of the layout's aperiodicity, coherent behavior, and isophoric feeding levels, reducing the antenna system's complexity. In the proposed feeding system configuration based on intercalated input signals grouped on C-BFN blocks per ring, numerical results present substantially lower sidelobe levels (\(-17.53\leq\mathrm{SLL}\leq-22.11\) for beam 1 and \(-16.92\leq\mathrm{SLL}\leq-20.61\) on average) and improved directivity values (\(17.95\leq D\leq 19.33\) for beam 1 and \(16.47\leq D\leq 18.73\) for beam 2 on average), showing a significant reduction in the number of control signals (about 50%), i.e., each beam on the two-beam configuration is controlled and scanned with half of input ports, in contrast with the usual feeding system, which requires one feeding input for each output port. A dual-beam coherent network with an aperiodic concentric ring array surpasses its uniform counterpart by up to \(-6.1\) and \(3.5\) dB on average for the sidelobe level and directivity per beam, respectively. Further studies will explore this antenna system further by using full-wave electromagnetic analysis.  
Conceptualization, A.A.; methodology, A.A. and E.S.-N.; software, A.A. and D.H.C.; validation, A.A., E.S.-N., U.P.-R., M.C.-J., F.R.C.-S. and D.H.C.; formal analysis, A.A., E.S.-N. and D.H.C.; investigation, A.A.; writing--original draft preparation, A.A., E.S.-N. and U.P.-R.; writing--review and editing, A.A., E.S.-N., M.C.-J., F.R.C.-S. and D.H.C.; visualization, A.A., U.P.-R. and M.C.-J.; supervision, A.A., E.S.-N. and D.H.C.; project administration, A.A., and E.S.-N. All authors have read and agreed to the published version of the manuscript.  
This research work was partially funded by the Mexican National Council for Science and Technology (CONACYT) with the project Catedras (No. 872) and the Program for Professors' Development (PRODEP) from the Federal Mexican Government.  
The data supporting this research article are available upon request to the corresponding author.  
The authors declare that there is no conflict of interests regarding the publication of this manuscript.