esa-sceva/satcom-chunk-collection huggingface dataset:
# Tracking and Monitoring System Based on LoRa Technology for Lightweight Boats
## 5 Test-Bench
### Scenario  
As explained before, the designed and implemented system is devoted to monitor and report the evolution of races of light and small boats. In this case, we installed our LoRa-enabled monitoring devices on-board of Optimist Class sailboats (Figure 5) in order to track part of their training sessions in the port of Vigo (Spain) (Figure 6). Concretely, we monitored a series of training sessions of 1 h of duration with maximum transmission distances of 1 km inside the port. As will be detailed in the following section, the boats were equipped with a set of sensors and a LoRa transceiver with the aim of enabling the transmission of their position and other additional metrics to a base station placed on the roof of a building in the port (Figure 7). The nature and inherent limitations of these boats pose important energetic and payload restrictions, so both the equipment and the transmitted data were optimized to accomplish with these requirements. Due to the big attention that these races are receiving during the last times, a tracking platform through the Internet has also been developed. Thus, the base station uploads the received data to a service hosted in the cloud that presents the race metrics to the users by means of a web-based dashboard (Figure 8). Observe that this dashboard automatically updates the position and other metrics for each boat, so the race can be followed live with a certain delay added by the LoRa link. This delay appears because, as explained above, LP-WAN technologies offer low-data rates that introduce certain latency in the data transmission, although this delay is assumable for the application under consideration. This fact will be deeply discussed in Section 6. In the following, we examine the technical details of the mentioned deployment.  
Figure 4: LP-WAN-based proposed architecture.  
Figure 5: Tracking module on-board a participant boat.  
Figure 6: Test scenario in the port of Vigo (Spain).  
Figure 7: Base station.  
Figure 8: Dashboard for the online race monitoring.
# High-Risk AI Systems--Lie Detection Application
## 2 EU AI Act  
The misuse of artificial intelligence (AI), which has grown dramatically in the last few years, is a major growing concern, according to European Union Agency for Cybersecurity (ENISA) [3]. Artificial intelligence (AI) is used in an increasing range of business applications in many sectors as it offers new chances to address decision-making challengesintelligently and automatically. The related advantages are substantial. However, project teams will also need to deal with new threats that come with AI's growth. It is evident in many projects that these new concerns are related to cybersecurity as well as privacy, primarily when AI is employed for creative endeavors that involve the processing of personal data. The incredible powers, significant hazards, quick implementation, tremendous adoption, and endless controversy surrounding foundation models like ChatGPT are reshaping society [4]. To mitigate risks to health, safety, and fundamental rights, and generally to regulate artificial intelligence (AI), the EU has just cleared the world's first omnibus legal framework for AI, the EU AI Act [5]. The Act was formally approved on 13 March 2024, and despite being titled \"Act\", it takes the form of an EU Regulation (2024/1689). This framework lays out requirements (such as cybersecurity and privacy) for AI systems deemed high risk. Organizations such as OpenAI and Google, which supply foundation models, are formally obligated to comply with these requirements of the Act. We assess the fact that critical vendors of foundation models are not yet following these proposed standards.  
The AI Act encompasses both basic AI systems that follow set rules and have been used in many different situations, as well as more advanced machine learning systems that can make decisions on their own and can be used in flight control, pacemakers, and industrial settings. The Commission's comprehensive approach encompasses nearly all systems currently utilizing AI or anticipated to utilize AI in the future, including customized pricing, advertising, feed algorithms, connected IoT systems, autonomous vehicles, and applications that facilitate recruitment and other business functions.  
Under the framework, each AI application or system will be classified into one of the following four risk categories (Figure 1):  
* unacceptable risk
* high risk
* limited risk
* minimal risk  
Depending on the assessed risk level, distributors, importers, providers, and end-product designers may be subject to strict regulations. As the risk escalates, the requirements for an AI application/system become increasingly rigorous.
## 3 Experimental Results and Discussion
### Porosity and Porosimetric Curves
In addition, Figure 8 shows that, in general, moist-cured concretes are to the left of the air-cured concretes. This means that the ambient moisture influences the morphology of the voids, in such a way that the higher the moisture, the smaller the voids are. An interesting parameter related to the pore size distribution is the nominal maximum pore size (NMPS), defined as the pore length corresponding to a cumulative pore volume of 90%. This parameter is analogous to the \"nominal maximum aggregate size\" or NMAS, commonly used in coarse aggregates. Table 3 shows the values of NMPS for all the series. It can be observed in Table 3 that, under the same curing conditions, the NMPS of fiber-reinforced concrete is greater than the one of plain concrete. In consequence, fiber-reinforced concrete retains more pore volume and also larger voids. In addition, moist-cured concretes show smaller values of NMPS than air-cured, meaning that a higher environmental moisture helps to obtain smaller voids. These results agree with the ones previously shown. \begin{table}
\begin{tabular}{c c} \hline \hline
**Mixture and Curing Condition** & **NMPS (mm)** \\ \hline SFRC. Moist-Cured & 3.67 \\ SFRC. Air-Cured & 6.41 \\ PC. Moist-Cured & 2.55 \\ PC. Air-Cured & 4.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Nominal maximum pore size (NMPS). Figure 8: Porosity curves along the depth: (**a**) SFRC moist-cured, (**b**) SFRC air-cured, (**c**) PC moist-cured and (**d**) PC air-cured.
# Spectrum and Energy Efficiency of Uplink Massive MIMO System with D2D Underlay
## 6 Conclusions  
We have derived the accurate closed-form expressions of the outage probability and average achievable rate for a heterogeneous network consisting of an uplink massive MIMO and a D2D link. The influence of the power of CUEs is investigated for the D2D and the CUEs. Constrained by the performance of the D2D link and CUEs, the EE of the massive MIMO system is maximized by jointly optimizing the number of BS antennas and the transmit power of CUEs. Meanwhile, the optimization problem is solved by a one-dimensional search with low complexity. Numerical results show the tightness of our closed-form expressions and reveal the impacts of various parameter settings. In particular, it is interesting to study some extensions of this work similarly to [21; 22; 23].  
Figure 6: Comparison between our proposed method and the exhaustive search.  
According to the law of large numbers, we have \(\lim\limits_{N\to\infty}\frac{|\mathbb{h}|^{2}}{N}=1\), so it is straightforward that  
\[S_{ck}\approx P_{\mathrm{c}}\beta_{ck}N. \tag{10}\]  
Combining (11) and (10), the theorem can be proved.
## 4 Evaluation Model Based on AH-Entropy and Normal Cloud Model
### Cloud Model-Related Theories
#### 4.1.2 Digital Features of Normal Cloud Model  
According to the definition in Section 4.1.1, the normal cloud model encompasses three numerical features: \(Ex\), \(En\), and \(He\). Among these, \(Ex\) represents the expectation, \(En\) denotes the entropy, and \(He\) signifies the entropy of entropy, known as hyper entropy. A degenerate cloud drops into a normal distribution when \(Ex=0\). If \(Ex=0\) and \(En=0\), then \(x=Ex\) and \(\mu(x)=1\). A larger \(He\) indicates a heavier tail in the distribution of the random variable \(x\). \(Ex\) serves as the central value of the cloud droplet distribution in the domain space, representing the qualitative concepts most accurately. \(En\) measures the uncertainty of qualitative concepts, determined by the randomness and fuzziness of the concept. It not only reflects the probability degree of cloud droplets but also mirrors the fuzziness of qualitative concepts. Hyper entropy \(He\) is an uncertainty measure of entropy \(En\), determined by the randomness and fuzziness of entropy, primarily reflecting the aggregation of uncertainty in qualitative concepts [30]. The cloud image comprises a specific number of cloud droplets, as shown in Figure 2. In this study, the number of cloud droplets is set at 3000, determined through a comparison of cloud images composed of varying numbers of cloud droplets.
## 6 Results
### Bin Estimate MSE as a Function of SNR (Hann)  
In this second study, the performance of the XQIFFT and other QIFFT-derived methods is compared to the state-of-the-art direct methods of [PERSON] [14] and [PERSON] [15] using again a length-4096 Hann window. Here, we choose \(p=0.22917\) to minimize \(\text{E}[|e_{\mathcal{K}}\left(\mathcal{K}\right)|]\) as we found earlier. As before, the complex sinusoids are corrupted by additive white Gaussian noise. For each SNR from \(-40\) to 120,in intervals of \(5\) dB, \(20,000\) sinusoids are tested with bin offsets of \(-0.5\) to \(+0.5\). The MSE of bin estimate is plotted against SNR for each method and the Cramer-Rao bound (CRB) in Figure 9a.  
This plot shows a \"thresholding\" effect at low SNR (below \(-15\) dB). All of the tested methods use as their coarse estimate the maximum of the magnitude spectrum. The thresholding effect arises when the DFT samples are so noisy that the coarse estimate no longer reliably picks the DFT bin closest to the true fractional bin index \(\mathcal{K}\). At low SNRs, the MSE of each method is dominated by noise--as SNR increases, the variance decreases. At high SNRs, the variance of each method is dominated by its systematic bias--noise no longer contributes much to the MSE so the traces \"flatten out.\"  
For this particular window, the length-4096 Hann window, Figure 9a shows that Candan has the best performance at high SNRs (above 30 dB), followed by Duda, followed by the XQIFFT. However, at low SNRs (between \(-15\) and 30 dB), the XQIFFT has better performance than both Duda and Candan and gets closer to the Cramer-Rao bound--a zoomed detail on this region is shown in Figure 9b.
## 2 Bioactive Compounds Commonly Found in Green-Colored Fruits and Vegetables
### Glucosinolates
#### 2.3.2 Isothiocyanates: Sulforaphane General Characteristics, Structure, and Food Sources  
Sulforaphane is one of the most promising diet-derived chemoprevertive agents [135] belonging to the ITC group of organosulfur compounds. It exists as its biologically inactive precursor, glucoraphanin, a storage form in cruciferous vegetables, being the most abundant in broccoli (Table 1). This precursor belongs to the group of phytochemicals called glucosinolates, which contain a sugar moiety in their structure, and is rapidly converted to sulforaphane by the enzyme myrosinase [136]. The chemical name of sulforaphane is 4-methylsulfonylbutyl isothiamate or 1-isothiocyanate-4-methylsulfonylbutane, and its chemical structure is shown in Table 2.  
Bioavailability  
Sulforaphane has the highest bioavailability among phytochemicals, with its absolute bioavailability being approximately 80% [137]. It is converted from its inactive precursor glucoraphanin to its active form by the enzyme myrosinase, whose amount and activity are closely related to its own bioavailability. When plant cells are damaged, the enzyme myrosinase is released, which interacts with glucoraphanin to produce sulforaphan, which is metabolized in vivo via the mercapturic acid pathway, triggered by a spontaneous reaction between the electrophilic -N=C-S group and glutathione. The end products of sulforaphane metabolism are sulforaphane-cysteine and sulforaphane-N-acetyl-l-cysteine, and the main metabolite is sulforaphane-N-acetyl-l-cysteine [138].
# The Effect of Railway Projects Increasing Safety on the Frequency of Occurrences
## 4 Discussion  
From the analyzed data, it can be seen that the implementation of constructions resulted in a decrease in the number of occurrences by 4.63% on average, and when considering the average increase in traffic performance in the monitored period, it is a total of 9.15%. The average societal cost of one occurrence amounts to CZK 511,564 (expressed at the 2021 price level), with the fact that this value will increase over time as a result of GDP growth and thus also the standard of living of the population of the Czech Republic.  
The incidence of the occurrence was evaluated based on statistics in relation to the length of the line or its traffic load. The results show that one occurrence occurred every 30,966 km of track, and after track reconstruction there was one occurrence for every 32,470 km of track.  
Within the monitored projects, the average length of the reconstructed section was 16.74 km per monitored project. The average savings resulting from the reduction in the probability of the occurrence amounted to an average of 12,804 CZK/year per project, with average socio-economic costs before implementation of CZK 276,509 and CZK 263,705 after construction.  
In relation to traffic performance, the average of one occurrence for every 693,725.56 km per year was calculated. After the modernization or reconstruction of the line, one occurrence should occur on average at 726,548.4 km per year.  
A suitable topic for the discussion would be a comparison of the achieved results of the presented research with the situation abroad. Scientific publications deal with the issue of occurrences on railways quite widely; however, in most cases, the research is focused on specific aspects causing occurrences, such as unauthorized access to a railway crossing or the influence of the load on selected railway equipment on the occurrence of failures. However, as part of the search, data in the same or similar structure were not found in scientific publications. The presented results can serve as a starting point for further research and a possible international comparison.  
As part of the research, an answer was sought to a research question related to investments into rail transport and its safety. To answer the question, current valid methodologies for evaluating of the effectiveness of transport investments [2], the database of extraordinary events [33], and many other sources were used as sources. The research question was formulated as follows:  
RQ1: To what extent can the frequency and impact of occurrences be reduced through the modernization of the railway infrastructure?  
The evaluation of the research question was carried out on the basis of a comparison of the development of the number of extraordinary events on 33 monitored projects involving the reconstruction of 552 km of railway tracks.  
From the analyzed data, it can be seen that the implementation of constructions resulted in a decrease in the number of occurrences by 4.63% on average, and even by 9.15% when considering the average increase in traffic performance in the monitored period.
## 4 Results and Discussion
### Influence of the Number of Nozzles at the Bottom of the Internal Bit for Non-Coring Drilling  
During non-coring drilling, a water jet will be formed after drilling fluid exits the nozzles of both the external and internal drill bits. When the jet collides with rocks at the bottom of borehole, it can produce impact dynamic pressure and a vortex, which is beneficial for increasing the rock breaking area and rock free surface, thus improving the rock-breaking effect. Therefore, the numbers and arrangement of nozzles on the internal non-coring bit are key factors for bit function. To analyze the flow characteristics of the non-coring modular bit with different nozzles arranged on its internal bit, a group of non-coring modular bits was designed. The outside diameter of the external bit is 216 mm, and the numbers of nozzles on the internal bit are set at 1, 2, 3, 4, and 5, respectively, with a diameter of 12 mm. Figure 8 shows the layout of the nozzles on the internal non-coring bit.  
With the inlet flow rate set at 300 L/min, the velocity curve and pressure curve are displayed in Figure 9. These results are obtained through the utilization of the CFD simulation model for analyzing drilling fluid dynamics within non-coring bits with different nozzle layouts. According to the curve, drilling fluid velocities at the bit outlet remain largely unchanged, with values at around 0.705 m/s, when increasing the number of the nozzles on the internal non-coring bit. Meanwhile, with the number of nozzles on the internal non-coring bit increasing, the pressure at the bit inlet decreases gradually. Because increasing the number of nozzles on the internal non-coring bit will reduce the size and strength of the teeth on the bit, the teeth on bits will be prone to wear, thus shortening the service life of the bit. In this group of simulations, four nozzles on the internal non-coring bit are recommended, in which case relatively low pressure loss can be achieved.  
Figure 8: Nozzle layout on the internal drill bit for non-coring drilling: (**a**) one nozzle on the internal non-coring bit; (**b**) two nozzles on the internal non-coring bit; (**c**) three nozzles on the internal non-coring bit; (**d**) four nozzles on the internal non-coring bit; (**e**) five nozzles on the internal non-coring bit.  
Figure 7: Velocity and pressure with different clearances between the external bit and internal bit.
## 4 Experiment
### Experiment Variables
#### 4.2.2 Outcome Spaces  
The outcome spaces the robot learns are hierarchically organized:  
* \(\Omega_{0}=\{(x_{0},y_{0})\}\): positions touched on the table;
* \(\Omega_{1}=\{(x_{1},y_{1})\}\): positions of the first object;
* \(\Omega_{2}=\{(x_{2},y_{2})\}\): positions of the second object;
* \(\Omega_{3}=\{(x_{1},y_{1},x_{2},y_{2})\}\): positions of both objects;
* \(\Omega_{4}=\{(f,l,b)\}\): burst sounds produced;
* \(\Omega_{5}=\{(f,l,b,t)\}\): maintained sounds produced.  
The outcome space is a composite and continuous space (for the physical setup \(\Omega=\bigcup_{i=0}^{5}\Omega_{i}\), for the simulation setup \(\Omega=\bigcup_{i=0}^{4}\Omega_{i}\)), containing subspaces of 2 to 4 dimensions. Multiple interdependencies are present between tasks: controlling the position of either the blue object (\(\Omega_{1}\)) or the green object (\(\Omega_{2}\)) comes after being able to touch the table at a given position (\(\Omega_{0}\)); moving both objects (\(\Omega_{3}\)) or making a sound (\(\Omega_{4}\)) comes after being able to move the blue and the green object, the maintained sound (\(\Omega_{5}\)) is the most complex task of the physical setup. This hierarchy is shown on Figure 2.  
Our intuition is that a learning agent should start by making a good progress in the easiest task \(\Omega_{0}\), then \(\Omega_{1}\), \(\Omega_{2}\). Once it mastered those easy tasks, it can reuse that knowledge to learn to achieve the most complex tasks \(\Omega_{3}\) and \(\Omega_{4}\). We will particularly focus on the learning of the \(\Omega_{4}\) outcome space and the use of the procedure framework for it. Indeed in this setting, the relationship between a goal outcome in \(\Omega_{4}\) and the necessary positions of both objects (\(\Omega_{1},\Omega_{2}\)) to reach that goal are not linear. So with this setting, we test if the robot can **learn a non-linear mapping between a complex task and a procedural space**. Finally for the physical version, we see if the robot can reach and explore the most complex task \(\Omega_{5}\) in the absence of an allocated teacher.
# Probiotics in the Sourdough Bread Fermentation: Current Status
## 5 Enzymes in Sourdough
### Transfeases  
The more expressive metabolic activities of microbial communities during fermentation are acidification, leaving, and flavor formation, all related to the metabolism of carbohydrates [159]. Transfeas are enzymes that catalyze the transfer of specific functional groups from one molecule to another. Glycosyltransferase (EC 2.4) are involved in the biosynthesis of exopolysaccharideamides in sourdough. It is an enzymatic group produced by the LAB from sourdough microbiota, such as _Lactobacillus, Leuconostoc_ spp., _Leuconostoc citreum_, and _Weissella_ species. Glucansucrases are glycosyltransferases from LAB that split sucrose. The resulting glucose builds EPS-type \(\alpha\)-glucan polymers such as dextran, mutant, alternan, and reuteran. Fructanusucrase, found in Gram-negative and Gram-positive bacteria, is another glycosyltransferase that synthesizes levan (levansucrase) or inulin (inulosucrase). The inulosucrase is observed only in LAB [160; 161].  
EPS formed during sourdough fermentation influences the dough's viscoelastic properties and improves the texture and shelf-life. In this context, EPS can replace hydrocolloids used as bread improvers and meet consumer demands for reduced use of food additives [162].  
Adding a glucosyltransferasenare B, made by _Streptococcus thermophilus_ with the activity of 4,6-\(\alpha\)-glucanotransfarese in the preparation of wheat flour dough, results in bread with better health benefits. This enzyme increased the number of short branches and percentage of (\(\alpha\)1\(\rightarrow\)6) linkages in starch, resulting in slow starch digestibility and low retrogradation properties. It also reduced hardness, gumminess, and chewiness, indicating improvements in texture [92].  
Transglutaminase (TGase, EC 2.3.2.13) catalyzes the cross-linking of proteins between the \(e\)-amino group of a lysine residue and the \(\gamma\)-carboxamide (acyl) group of a glutamine residue. Which results in an intra- or intermolecular bridge highly resistant to proteolysis, which leads to an increase in the structure and texture of protein substrates [163; 164]. It is an important enzyme for the food industry, where studies report that its application improves dough handling properties, increases bread stability, and volume. The protein polymers resulting from the action of TGase can modify the rheological properties of gluten, transforming a weak gluten into a strong one [75].  
Figure 3: General scheme of bread sourdough fermentation and the major enzymes involved. EPS: Exopolysaccharide.