esa-sceva/satcom-chunk-collection huggingface dataset:
# Modification of Mineral Content in Bread with the Addition of Buckwheat Husk
###### Abstract  
Bread is a staple food and can be enriched with a variety of deficient nutrients in the human diet. This study evaluated the impact of buckwheat hull addition on the mineral content of toasted bread made with wheat bread flour and wholemal bread made with the addition of wholemal wheat flour. Bread samples were prepared with different levels of buckwheat hull addition (1.5%, 3.0%, and 4.5%) and compared with a nonenriched control sample. The impact of buckwheat hull addition on mineral content was determined using atomic absorption spectrometry. In the tested bread samples, the macroelements, in terms of determined quantities, can be ordered as follows: potassium (K) > phosphorus (P) > calcium (Ca) > sodium (Na) > magnesium (Mg); microelements can be ordered as follows: zinc (Zn) > iron (Fe) > manganese (Mn) > copper (Cu). Statistical analysis showed a significant increase (\(p\) < 0.05) in the manganese content in all enriched breads samples, but the most important changes were observed between 1.5 and 4.5% of husk addition. Significant increases were also observed in the Zn, Mg, Ca, Na and K contents depending on the bread type and the level of husk concentration. This study showed the role of food processing methods, such as food enrichment with a functional ingredient--buckwheat husk--in enhancing the nutritional quality of bread. Buckwheat hull addition to bread increased the coverage of daily requirements for several minerals crucial for the proper functioning of the human body. The consumption of a 100 g portion of husk-enriched (4.5%) wholemal bread makes it possible to cover up to almost 70% of the daily requirement for manganese in the adult diet.  
functional food ingredients; food enrichment; wholemal bread; macroelements; microelements; dietary enhancement Article
# Algorithms for Finding Shortest Paths in Networks with Vertex Transfer Penalties
## 3 Shortest Paths with Vertex Transfer Penalties
### Further Expansion Methods  
Although the Kirby-Potts expansion is the most useful for our purposes, we should also note the presence of two other expansion methods used in the literature. The first of these is a more restricted version of Kirby-Potts that has previously been used in studies on bus networks ([PERSON], 2016; [PERSON], 2017; [PERSON], 2018; [PERSON], 2019). In this expansion method each vertex \(v\) of the original graph is represented by a cluster of \(|C^{-}(v)\cup C^{+}(v)|\) dummy vertices.  
Figure 3: (**a**) The graph \(G^{\prime}=(V^{\prime},E^{\prime})\) formed from Figure 2 using the Kirby–Potts expansion method; (**b**) an example cluster of dummy vertices produced using the Kirby–Potts method; and (**c**) the corresponding cluster using the (problematic) restricted expansion method.  
Each vertex in this cluster then corresponds to a different colour, and edges are added between these vertices using weights equivalent to the corresponding transfer penalties. Note, however, that although this restricted method results in smaller graphs than those of Kirby-Potts, it can produce illogical results when the edge weights within a cluster do not obey the triangle inequality. Consider the Kirby-Potts expansion in Figure 3b for example, where a penalty of three is incurred at the vertex when transferring from blue to black. In the corresponding graph produced using the restricted expansion method (Figure 3c) a smaller transfer penalty of two will be identified by transferring from blue to red to black, which is clearly inappropriate when modelling things such as transfers on public transport. (This issue is not noted in any of the above works, although it is avoided due to their use of a constant value for all transfers, thereby satisfying the triangle inequality at each vertex.)  
A further method of graph expansion is due to Winter Winter (2018), who suggests using the line graph of \(G\) (referred to as the \"pseudo-dual\" in the paper) for identifying shortest paths. However, this leads to much larger graphs comprising \(m\) vertices and \(\sum_{v\in V}|E^{-}(v)|\cdot|E^{+}(v)|\) edges. A copy of the original graph \(G\) is also required with this method to facilitate the drawing of routes.
# Systematic Analysis of the Hydrogen Value Chain from Production to Utilization
## 2 Hydrogen Production Technologies
### Electrolysis
#### 2.3.2 Polymer Electrolyte Membrane Water Electrolysis  
PEMEL was first developed in the 1960s, and today it is a commercially available technology for the large-scale production of hydrogen. In PEMEL, water is oxidized on the anode to produce oxygen and protons. The protons migrate to the opposite electrode, where, by reduction, hydrogen is released, as in Figure 9. This technology operates at temperatures of around 30 \({}^{\circ}\)C to 80 \({}^{\circ}\)C, with pressures up to 30 bar, which can be higher for a small-scale production configuration, and presents a stable operation lifetime of about 40,000 to 60,000 h [27; 44; 46].  
A PEMEL cell is composed of two electrodes, with precious metal catalysts, a solid polymer electrolyte, two porous transport layers, and two bipolar plates. A proton exchange  
Figure 8: Schematic representations of the working principle of alkaline water electrolysis (adapted from [44]).  
Figure 9: Schematic representations of the working principle of polymer electrolyte membrane water electrolysis (adapted from [44]).  
membrane, usually made of a polymer with high proton conductivity and chemical stability, such as Nafion or Fumapem, is used as an electrolyte. This membrane enables a high proton conductivity, low crossover of the gases, high operating pressures, and a compacted system design. The two electrodes are directly coated on the proton exchange membrane, the cathode is typically made of carbon-based materials, and the anode is made of precious metals, such as platinum, with both the cathode and anode coated with a catalyst material, such as platinum or another platinum group metal alloy [27]. Information about the state of the art of PEMEL cells can be found in [47].  
When compared with AEL, PEMEL presents higher operating current densities, higher hydrogen purity, a more compact design, and a quicker response [44]. Nonetheless, some disadvantages can be pointed out, such as the high cost of the components [43] and the existing acidic environment originated by the proton-conducting ionomer that contributes to the degradation of the electrodes. The research currently focuses on further increasing the current density, lowering the catalyst loading, reducing the use of expensive materials, and reducing hydrogen production costs [44].
# A Review on Millimeter-Wave Hybrid Beamforming for Wireless Intelligent Transport Systems
## 3 An In-Depth Analysis of the mmWave Hybrid Beamforming Landscape
### Duplex
Using cooperative K-pairs of massive MIMO FD nodes, this study broadens the use of point-to-point FD transmission in the millimeter-wave spectrum. In order to efficiently and affordably handle large MIMO FD networks, a combined technique for weighted sum rate (WSR) maximization and joint hybrid beamforming is used [69]. * **Time-Division Duplex** In a mixed anlog-digital beamforming system, none of the numerous available calibration techniques can be directly used. This is the main reason why large-scale MIMO systems have not caught up to approaches that rely on TDD reciprocity. Methods for evaluating entirely digital devices have been available for almost a decade, but no one has ever managed to crack this code. Full utilization of the beamforming potential is achieved by the TDD hybrid beamforming systems by reciprocity adjustment, which allows them to achieve full CSIT. This paves the way for novel applications of mixed beamforming systems, which provide an alternative to teaching beams using a predetermined set of beams [70]. * **Frequency-Division Duplex** A training scheme for FDD large MIMO systems accurately identifies the training signals and, with this training setup, the aim is to minimize the steady-state channel MSE within the context of Kalman filtering. With the goal of reducing application cost and power use, a transmit precoding technique is employed that reduces the dimensionality training sequence. Then, a hybrid analog-digital beamforming technique that uses a small number of RF chains for digital baseband precoding is subjected to the low-dimensionalrestriction. To achieve this, a uniform linear array (ULA) is applied at the base station in accordance with the Toeplitz distribution theorem. When dealing with large antenna arrays, a deterministic equivalent technique for performance analysis provides a helpful approach to training parameter setting [71].
## 2 Methods and Materials  
### System Overview  
The proposed solder joint quality detection system for aviation plugs is shown in Figure 3. It is mainly composed of two parts: data acquisition and detection approach. The data acquisition is the hardware part of our system and is mainly responsible for moving and taking pictures of the aviation plugs. The detection algorithm is the software part of our system, which consists of two main modules: ROI extraction and quality assessment. They are responsible for providing the distance to fine-adjust the sliding platform and achieving accurate defect detection, respectively.  
The pipeline of our system is illustrated in Figure 4. Overall, we adopt a top-down sequential inspection strategy, i.e., detecting a row of solder joints as soon as it has been welded. Specifically, the proposed inspection system consists of the following steps.  
Step 1: After welding a row of solder joints fixed in the bench clamp, our system moves the aviation plug to the inspection position via the sliding platform and takes pictures via the sampling system.  
Step 2: Using a computer placed in the electronic cabinet, a fine-tuned YOLOv5 model is applied to extract the ROI within the captured image, namely the row of solder joints that have just been soldered and need to be detected.  
Step 3: According to the coordinates of the extracted ROI, the ROI is fine-adjusted to the center of the image via the sliding platform to enhance its imaging clarity.  
Step 4: Another fine-tuned YOLOv5 model performs a quality assessment of solder joints in the adjusted ROI, and the results are displayed on the displayer in real-time.  
Step 5: The aviation plug is moved to the welding position. In addition, if a defect is detected, the solder joints in that row are re-welded, otherwise welding the next row.  
Obviously, the key components of our proposed system are the data acquisition and detection approach, and we describe these in detail below.
## 3 Results and Discussion
### Glutamine Synthease Activity  
Thanks to the activity of nitrate reductase and nitrite reductase, nitrates are reduced to ammonium ions, which are then incorporated into nitrogenous organic compounds. Ammonium ions can be built into amino acids with the participation of glutamate dehydrogenase or in a cycle catalysed by glutamine synthetase (GS) and glutamate synthase (GOGAT) [77; 78; 79]. As part of this study, the activity of glutamine synthetase (GS) was determined in all cases. The results indicate increased enzyme activity in celery vegetables (dill, celery, and parsley) from conventional cultivation, which is associated with a higher level of nitrates in these tissues [Figure 1C]. In contrast to nitrate reductase activity, the positive correlation between glutamine synthetase activity and nitrate levels is presented Figure 3. No linear relationship was observed (\(r\) = 0.1669). Moreover, the correlation's significance was insignificant [Table 2].  
The opposite was true for carrots, beets, and onions, where higher activity was recorded for organic vegetables. The most significant differences in GS activity due to the place of purchase of vegetables were recorded for carrots. The enzyme activity in carrots from conventional cultivation was 20 times higher than the activity of vegetables bought at the market and 5 times higher than the enzyme activity in organically grown vegetables. The enzyme activity was as follows: in the organic food store [48.23 umoles \(\gamma\)-glutamyl hydroxamate \(\times\) h\({}^{-1}\)\(\times\) g\({}^{-1}\) FW], in the supermarket [9.58 umoles \(\gamma\)-glutamyl hydroxamate \(\times\) h\({}^{-1}\)\(\times\) g\({}^{-1}\) FW], and at the local market [2.47 umoles \(\gamma\)-glutamyl hydroxamate \(\times\) h\({}^{-1}\)\(\times\) g\({}^{-1}\) FW]. In the case of beet and onion leaves, no significant differences in enzyme activity were observed due to the origin of the research plant material [Figure 1C]. The activity of both enzymes (NR) and (GS) depended on species and cultivation system [Table 1]. Positive correlations were found between nitrate synthase activity and glutamine synthetase activity [Table 2]. Moreover, the results were statistically significant (\(p\) < 0.05).
# A Flexible FPGA-Based Stochastic Decoder for 5G LDPC Codes
## 4 Proposed Architecture
### Routing Network
#### 4.3.3 Distributor and Re-Distributor  
The distributor takes \(W_{v}=30\) blocks of \(\eta_{v}=56\) bits from SCND and performs a rearrangement process to form \(\eta_{v}=56\) blocks of \(W_{v}=30\) bits. The pipeline registers may optionally latch these blocks before the SVND processes them. After undergoing SVND processing, the resulting \(\eta_{v}=56\) blocks of \(W_{v}=30\) bits are again subjected to a similar rearrangement process by the re-distributor to form \(W_{v}=30\) blocks of \(\eta_{v}=56\) bits.
###### Abstract  
_Listeria monocytogenes_ and _Listeria iwanovii_ are important pathogenic _Listeria_ spp. that cause infections in humans and animals. Establishing a rapid and sensitive method for the simultaneous screening of pathogenic _Listeria_ spp. is of great significance for ensuring food safety. Multiplex polymerase chain reaction (mPCR) has been extensively reported to simultaneously detect several pathogens in food with high sensitivity, but a time-consuming pre-enrichment process is necessary. In this study, we report the usage of surface-modified polyethyleneimine-coated positively charged magnetic nanoparticles (PEI-MNPs) for rapid enrichment of pathogenic _Listeria_ spp. through electrostatic interactions. The enrichment process takes only 10 min with high capture efficiency (more than 70%) at a wide pH range and ionic strength. Combined with mPCR analysis, the PEI-MNPs-mPCR strategy can simultaneously, rapidly, and sensitively detect pathogenic _Listeria_ spp. without a time-consuming pre-concentration process. Under the optimal conditions, the detection limits of \(L\)_monocytogenes_ and \(L\)_. _Iwanovii_ in lettuce were both as low as 10\({}^{1}\) CFU/mL, which was a hundred times lower than that without magnetic enrichment. In conclusion, the magnetic enrichment strategy based on charge interaction combined with mPCR analysis has great application potential in shortening the pre-concentration time of foodborne pathogens and improving the detection sensitivity.  
magnetic enrichment; multiplex PCR; electrostatic interaction; bacteria detection; method sensitivity; food safety Article
# Towards the Internet of Smart Trains:
## 2 Communication Systems in Railway Scenarios
### Wireless Sensor Networks  
As a result of the combination of the latest advances in electronics, networking, and robotics, it is feasible to develop advanced sensor systems for different sectors and applications: energy efficiency [32], Industry 4.0 [33; 34], home automation [35], public safety and defense [36; 37], precision agriculture [38] or transportation [39]. Furthermore, Wireless Sensor Networks (WSNs) have evolved into an integral part of the protection of mission-critical infrastructures [36]. Today, WSNs are used in the scenarios previously described, where sensors can be on top of the train, inside, beside, interacting between railway vehicles and tracks, or even as part of the station infrastructure. An example in the railway station could be the ticket validation equipment based on Low Frequency (LF), 125-135 kHz, and High Frequency (HF), 13.56 MHz RFID bands, or vehicle tagging based on Ultra High Frequency (UHF) RFID solutions [40] in the 865-869 MHz band.  
The sensors make use of the protocols listed in Table 3 to communicate and organize themselves. The information collected is transmitted to APs that utilize more powerful communication technologies, such as the ones cited in the previous sections (e.g., GPRS, Wi-Fi, WiMAX, LTE), to transmit the acquired data to TCCs. In Section 6.2, the applications of WSNs will be further explained.  
\begin{table}
\begin{tabular}{l l l l l l l l l l l l l l} \hline \hline
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Net. Archit.**} & \multicolumn{3}{c}{**Accuracy**} & \multicolumn{3}{c}{**Sensitivity**} & \multicolumn{3}{c}{**Specificity**} & \multicolumn{3}{c}{**AUC**} \\ \cline{2-10}  & **Substudy 1** & **Substudy 2** & **Substudy 1** & **Substudy 2** & **Substudy 1** & **Substudy 2** & **Substudy 1** & **Substudy 2** & **Substudy 1** & **Substudy 2** \\ \hline
[3] & 0.75 \({}^{\rm\pm}\) 0.17 & 0.69 \({}^{\rm\pm}\) 0.11 & 0.72 \({}^{\rm\pm}\) 0.31 & 0.77 \({}^{\rm\pm}\) 0.24 & 0.88 \({}^{\rm\pm}\) 0.20 & 0.74 \({}^{\rm\pm}\) 0.19 & 0.93 \({}^{\rm\pm}\) 0.07 & 0.89 \({}^{\rm\pm}\) 0.10 \\
[8] & 0.78 \({}^{\rm\pm}\) 0.18 & **0.79 \({}^{\rm\pm}\) 0.15** & 0.69 \({}^{\rm\pm}\) 0.31 & **0.80 \({}^{\rm\pm}\) 0.26** & 0.85 \({}^{\rm\pm}\) 0.27 & **0.79 \({}^{\rm\pm}\) 0.42** & 0.94 \({}^{\rm\pm}\) 0.04 & **0.92 \({}^{\rm\pm}\) 0.08** \\
[12] & 0.78 \({}^{\rm\pm}\) 0.20 & 0.73 \({}^{\rm\pm}\) 0.17 & 0.76 \({}^{\rm\pm}\) 0.21 & 0.75 \({}^{\rm\pm}\) 0.33 & 0.80 \({}^{\rm\pm}\) 0.32 & 0.78 \({}^{\rm\pm}\) 0.21 & 0.94 \({}^{\rm\pm}\) 0.06 & 0.91 \({}^{\rm\pm}\) 0.07 \\
[15] & **0.86 \({}^{\rm\pm}\) 0.19** & 0.74 \({}^{\rm\pm}\) 0.08 & **0.82 \({}^{\rm\pm}\) 0.32** & 0.79 \({}^{\rm\pm}\) 0.32 & 0.89 \({}^{\rm\pm}\) 0.17 & 0.68 \({}^{\rm\pm}\) 0.26 & 0.95 \({}^{\rm\pm}\) 0.05 & 0.90 \({}^{\rm\pm}\) 0.06 \\
[5] & 0.76 \({}^{\rm\pm}\) 0.16 & 0.80 \({}^{\rm\pm}\) 0.12 & 0.88 \({}^{\rm\pm}\) 0.21 & 0.73 \({}^{\rm\pm}\) 0.25 & 0.69 \({}^{\rm\pm}\) 0.27 & 0.84 \({}^{\rm\pm}\) 0.21 & 0.93 \({}^{\rm\pm}\) 0.04 & 0.93 \({}^{\rm\pm}\) 0.07 \\
[8] & 0.71 \({}^{\rm\pm}\) 0.22 & 0.71 \({}^{\rm\pm}\) 0.16 & 0.76 \({}^{\rm\pm}\) 0.31 & 0.75 \({}^{\rm\pm}\) 0.29 & 0.67 \({}^{\rm\pm}\) 0.33 & 0.68 \({}^{\rm\pm}\) 0.25 & 0.92 \({}^{\rm\pm}\) 0.07 & 0.90 \({}^{\rm\pm}\) 0.04 \\
[10] & 0.76 \({}^{\rm\pm}\) 0.19 & 0.70 \({}^{\rm\pm}\) 0.20 & 0.68 \({}^{\rm\pm}\) 0.39 & 0.68 \({}^{\rm\pm}\) 0.32 & 0.78 \({}^{\rm\pm}\) 0.22 & 0.66 \({}^{\rm\pm}\) 0.34 & 0.92 \({}^{\rm\pm}\) 0.04 & 0.90 \({}^{\rm\pm}\) 0.10 \\
[12 10] & 0.79 \({}^{\rm\pm}\) 0.15 & 0.71 \({}^{\rm\pm}\) 0.22 & 0.83 \({}^{\rm\pm}\) 0.18 & 0.62 \({}^{\rm\pm}\) 0.38 & 0.83 \({}^{\rm\pm}\) 0.19 & 0.86 \({}^{\rm\pm}\) 0.16 & 0.92 \({}^{\rm\pm}\) 0.09 & 0.90 \({}^{\rm\pm}\) 0.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the evaluated architectures for the multi-layer perceptron neural network (MLPNN) analysis with the test data. Data are presented in the following form: mean \(\pm\) standard deviation. Means in a column with no common superscript letter differ significantly according to [PERSON]’s honest significance test for a value of \(p<0.01\). Substudy 1 has 17 neurons in the input layer (all the neuropsychological tests) and substudy 2 has eight neurons in the input layer (test with more significant differences from Table 2: the Digit Span Backward, the copy of the Rey complex figure and the six subsets of the TAVEC test). The best results for each substudy are in bold. Net. Archit. = Network Architecture.
## 3 Materials and Methods
### Hydrological Model WATEN
To estimate \(ET\), potential crop evapotranspiration \(ETp\) is adjusted from reference evapotranspiration \(ET_{o}\) by local basal crop coefficient values and downscaled according to soil moisture variations. The maximum amount of water retained by the soil and made available to the plant is characterized by the total available moisture (_TAM_). When moisture depletion reaches this value, the plant would not extract any water and transpiration would not occur. The readily available moisture (_RAM_) is a fraction of _TAM_ depending on the crop and its development phase. Soil moisture depletion (\(SMD\cong TAM-S\)), which is the amount of water depleted from the root zone, facilitates the estimation of losses and gain estimates of the water budget [32]. This is expressed as \(S_{i}-S_{i-1} = -(SMD_{i}-\ SMD_{i-1})\). When the depletion level is below _RAM_, \(ET\) reaches potential levels. For a moisture depletion between _RAM_ and _TAM_ levels, \(ET\) is reduced from potential levels (Equation (3)). \[ET = \left\{ \begin{array}{ll}ET_{P},&SMD\leq RAM\\ ET_{P}.\frac{TAM-SMD}{TAM-RAM},&SMD>RAM\end{array} \right. \tag{3}\]  
Considering the fraction of effective precipitation (\(R_{P}\)) and irrigation (\(R_{I}\)), drainage accounts for the fractions of precipitation \(P\)\(\cdot\)\((1-R_{P})\) and irrigation \(I\)\(\cdot\)\((1-R_{I})\) not-efficiently used by the soil-canopy unit. When the soil presents the maximum moisture it can hold, \(SMD\) = 0, the water that is supplied by irrigation or precipitation is drained (Equation (4)). \[D = \left\{ \begin{array}{ll}P\cdot(1-R_{P})+I\cdot(1-R_{I}),&SMD >\ 0\\ P\cdot(1-R_{P})+I\cdot(1-R_{I})+P\cdot R_{P}+I\cdot R_{I}-SMD_{i-1}-ET,&SMD =\ 0\end{array} \right. \tag{4}\]  
Soil moisture depletion \(SMD\) is calculated in Equation (5) with a maximum \(SMD\) = _TAM_. \[SMD = \left\{ \begin{array}{ll}SMD_{i-1}+ET-P\cdot R_{P}-I\cdot R_{I},&0< \ SMD\ <\ TAM\\ TAM,&SMD>\ TAM\end{array} \right. \tag{5}\]  
The model was calibrated through the energy consumption required to pump the drainage discharge from the study area (BXII) to the river. The calibration period covered a decade of study from 2002 to 2012 with a Nash-Sutcliffe coefficient \(e_{2}\) = 0.90 between observed and estimated drainage discharge data.