esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Nutritional Value and Composition of _Lemma minor_
minor_ (% protein in dry mass) is presented in Table 3.  
An experiment with a diet for broiler chicken that contained 50% _L. minor_ reported a reduced ileal digestibility of amino acids, which suggests that the composition of the diet should be optimized to improve the digestibility of protein in general [7]. This reduced digestibility may be caused by a very high level of _L. minor_ inclusion in the diet. A mixture of _L. minor_ and _L. gibba_ contained the following vitamins: beta-carotene, 14.5-29.4 mg/kg, Retinol, <700 IU/kg, Tiamine, <0.15 mg/kg, Riboflavin, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Amino Acids** & **[PERSON] et al. [50]** & **[PERSON] et al. [51]** & **[PERSON] et al. [46]** & **[PERSON] et al. [2]** \\ \hline histidine & 1.18 & 2.06 & 1.50 & 0.89 \\ \hline serine & 3.84 & 4.76 & 4.10 & 2.35 \\ \hline arginine & 3.94 & 8.14 & 4.80 & 3.06 \\ \hline glycine & 3.90 & 5.46 & 4.60 & 2.86 \\ \hline aspartic acid & 7.90 & 18.97 & 8.20 & 3.71 \\ \hline glutamic acid & 9.41 & 11.08 & 9.80 & 6.43 \\ \hline threonine & 3.42 & 4.46 & 4.00 & 1.92 \\ \hline alanine & 4.88 & 5.34 & 5.10 & 2.88 \\ \hline proline & 3.47 & 4.06 & 3.80 & 1.25 \\ \hline lysine & 3.99 & 5.90 & 5.00 & 2.68 \\ \hline tyrosine & 2.09 & - & 3.10 & 1.91 \\ \hline valine & 4.40 & 5.46 & 4.60 & 2.66 \\ \hline isoleucine & 3.33 & 4.29 & 3.70 & 2.04 \\ \hline leucine & 5.78 & 8.42 & 7.30 & 4.13 \\ \hline phenylalanine & 3.34 & - & 4.40 & 2.57 \\ \hline cysteine & 1.12 & - & 0.90 & 0.38 \\ \hline methionine & 1.37 & - & 1.60 & 0.86 \\ \hline tryptophan & 1.35 & - & - & 0.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Amino acid profile (g/100 g crude protein) of _L. minor_. 0.47-1.24 mg/kg, Niacin, 5.87-9.54 mg/kg, Pantothenic acid 0.80-1.47 mg/kg, Pyridoxine, 0.58-0.95 mg/kg, Biotin, 89.3-109.0 ug/kg, Polate, 233-338 ug/kg, Cyanocobalamin, <2.5-16.3 ug/kg, Ascorbic acid, <5-16.1 mg/kg, alpha-Tocopherol, 7.5-18.0 mg/kg, Phyloquinone, 216-452 ug/kg [41]. Concerning fatty acids, a study reported that _L. minor_ cultivated in exterior conditions had more polyunsaturated fatty acids (37.13 g/100 g) and less saturated fatty acids (33.75%), compared to _L. minor_ cultivated indoors (21.96 and 41.63 g/100 g, respectively) [26]. [PERSON] et al. [50] reported that the ratio of omega n-6 polyunsaturated fatty acids (PUFAs) to omega n-3 PUFAs of _L. minor_ cultivated in the natural environment in ponds is 0.844, which is considered more favorable than the ratio of omega n-6/n-3 of _L. minor_ cultivated under artificial conditions in laboratories, which was 2.86. Other studies have reported the presence of some anti-nutritional compounds such as oxalate and phytate in _L. minor_, which should be controlled and reduced, previously to its application in animal feed, if it were necessary [47]. On the other hand, _L. minor_ is also a rich source of bioactive compounds such as carotenoids and phenolic compounds (mainly flavonoids, phenolic acids, and glucosinolates) which are responsible for its antioxidant activity [47].
# Real-Time Security Health and Privacy Monitoring for Saudi Highways Using Cutting-Edge Technologies
###### Abstract  
Kingdom of Saudi Arabia (KSA) highways hold the record for having the straightest, longest highways in the world. Since the country's major population centers are dispersed across the country and due to the country's geography, which includes valleys, deserts, and mountains, among other landscapes, these highways connect the many cities of the kingdom and neighboring nations. However, it is still challenging to provide emergency assistance in a timely way in the case of accidents, such as first aid, medical aid, police protection, etc. The transport ministry is actively working on improvements and safety features for the drivers. This research proposes a CET (cutting-edge technologies)-based model named the real-time security, health, and privacy monitoring model for passenger safety (RTSHPMP) for securing the traveler's safety and privacy besides medical and legal help. The vehicle will be equipped with IoT-based front-back cameras to collect real-time data and share it with the cloud using 5G network. The local and national trusted authorities (TAs) will monitor the collected cloud data and inform the government machinery (police, first aid, fire brigade, hospitals) in the case of an accident. In addition, the data collected through other vehicles on the road at the time of the incident will help supply evidence linked to the accident. The RTSHPMP was evaluated with the help of a case study, and the results show that it provides an efficient and secure mechanism for traveler safety on Saudi highways at the time of need.  
highways; KSA; IoT; 5G; cloud +
Footnote †: journal: Applied Sciences  
18 January 2022  
Accepted: 11 February 2022  
Published: 19 February 2022
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 7 Multi-User Multiple-Input Multiple-Output (MU-MIMO)
### MU MIMO in Wi-Fi 6  
Wi-Fi 6 supports uplink and downlink MU MIMO. A combination of OFDMA and MU MIMO is also possible. Downlink MU MIMO enables an access point to send data to multiple stations at the same time. The new standard improves Wi-Fi 5 to support up to eight downlink spatial streams. The access point can serve multiple stations that have fewer antennas, e.g., an access point with eight antennas is capable of serving up to four stations with two antennas. The access point periodically learns the target stations' locations using channel sounding and then directly beamforms the data toward the destination. It transmits the data using the HE MU PPDU format (see Section 4.1.4).  
Uplink MU MIMO is a newly added feature in Wi-Fi 6 to support high-bitrate applications and content streaming. It exploits MIMO systems to allow up to eight stations to transmit frames simultaneously to the same access point. Similar to uplink OFDMA, the access point initiates the transmission by a Trigger Frame (TF). Then, the stations transmit data in the HE TB PPDU format (see Section 4.1.4).
## 4 Classification of Routing Protocols
### QoS-Aware Routing Protocols
#### 4.1.2 Reinforcement Learning based Routing Protocol with QoS Support (RL-QRP)  
In [33], the authors proposed a reinforcement learning-based routing protocol with QoS support, using geographic information and a distributed Q-learning algorithm where the optimal routes can be found through experiences and rewards. The tiny bio-medical sensor nodes, implanted inside the body or attached with the body, forward the sensed data to sink nodes deployed at fixed positions. After collecting the data packets from the bio-medical sensor nodes, the sink node(s) forward them to the medical server for further real-time monitoring and diagnosis.  
In this scheme the packet delivery ratio and end-to-end delay are the main QoS metrics. In the Q-learning algorithm, each sensor node receives a reward, either positive or negative, after forwarding a data packet to its neighbor. The reward along with the expected future reward, updates the Q-value of the sensor node, which will be used for the future decisions. Sensor nodes exchange the Q-values with its one-hop neighbors to learn about their optimal routes. The sensor nodes can use the neighbor sensor nodes Q-value information to predict the expected future reward. Each sensor node considers its Q-values list as its routing table.  
The authors of this scheme use Random Waypoint Mobility Model (RWMM) for the mobile sensor nodes, where the sensor nodes can only move to the chosen random destinations and will stay there for a predefined time. RL-QRP uses the neighbor nodes' Q-values and geographic information to find out the optimal routes while energy which is one of the major constraints of wireless sensor networks, is not considered at all.
# Reversible Circuit Synthesis Time Reduction Based on Subtree-Circuit Mapping
## 4 BDD-Based Synthesis of Reversible Circuits
The BDD-based algorithm of reversible circuit synthesis implemented in [27] is shown in Figure 4. First of all, the BDD of the input Boolean function is constructed; thereafter, an iterative BDD reordering algorithm followed by the application of BDD reduction rules is applied. Once a small-sized BDD is generated, it undergoes the mapping phase (which is the core of our work), where each node is substituted by a cascade of Toffoli gates following a pre-constructed look-up table. Finally, the synthesized reversible circuit that realizes the input Boolean function is outputted from this algorithm. In the mapping phase, the BDD is traversed in a Depth-First Search (DFS) fashion, such that each node is substituted into a cascade of reversible gates. The algorithm investigates all variations of BDD nodes and maps each node to its corresponding reversible gates according to a previously prepared look-up table, as shown in Table 2 below. However, this process is a principal contributor to the entire synthesis time due to its linear relation with the number of nodes in the BDD. Figure 3: Binary Decision Diagram (BDD) representation of the Boolean function \(f=x_{1}\oplus x_{2}\cdot x_{3}\)[25]. _Appl. Sci._**2020**, _10_, 4147 \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Node** & **Circuit** & **Node** & **Circuit** \\ \hline \hline low(\(f\)) & high(\(f\)) & low(\(f\)) & low(\(f\)) & high(\(f\)) \\ \hline \hline low(\(f\)) & high(\(f\)) & low(\(f\)) & low(\(f\)) & high(\(f\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: One-to-one mapping from BDD nodes to cascades of reversible gates. Figure 4: BDD-based synthesis for reversible circuits following the algorithm published in [27].
## 1 Introduction
[PERSON] et al. considered cutting parameters and tool wear values as variables and used time-frequency domain analysis to extract important features of vibration signals as input for neural network pattern recognition. They then established a wear condition monitoring model through the adaptive learning of a BP neural network [25]. Compared with a probabilistic neural network, the BP neural network has excellent ability to process classification issues, which are widely used in the field of fault diagnosis [26]. The classification accuracy of the BP neural network is affected by the weight, threshold, and the number of hidden layers [27]. Meanwhile, the BP neural network has the shortcoming of rapid convergence; it is also easy for this network to fall into the minimum value. These problems can be solved using intelligent optimization algorithms [28]. [PERSON] et al. used the adaptive particle swarm optimization (PSO) algorithm to overcome the shortcomings of BP neural networks, which are easily trapped in the local optimum and have a long training time [29]. The particle swarm optimization (PSO) algorithm has characteristics such as rapidity and robustness, which are widely used in the optimization of the network parameters of the BP neural network. The purpose of this research is focused on the problem of woodworking tool wear condition monitoring and proposes a limiting arithmetic average filtering method and PSO-BP neural network algorithm. The method includes three processing stages. In stage one, the spindle power signals under different spindle speeds, depths of milling and tool wear conditions were collected. In stage two, the limiting arithmetic average filtering method is used to process the spindle power signal and extract the features of the woodworking tool wear conditions. Finally, the spindle speed, depths of milling, features and tool wear conditions were used as sample vectors, and subsequently, the PSO-BP neural network algorithm was used to establish a woodworking tool wear monitoring model through the sample to achieve high-precision monitoring of woodworking tool wear conditions under different milling parameters.
## 3 Results and Discussion
### Extraction of Solar Module Parameters from the Measured Dark I-V Curves
The important point is also to compare the I-V curves of the same module in different periods along its life, because a check of possible shifts or changes in the curves or in the extracted parameters along time could reveal degradation processes being started or affecting the performance of the module. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Module** & \(\mathbf{J_{0}}\) **[A/cm\({}^{2}\)]** & \(\mathbf{I_{0}}\) **[A]** & \(\mathbf{n}\) & \(\mathbf{R_{sh}}\) & \(\mathbf{R_{s}}\) & \(\mathbf{R_{s}}\) & \(\mathbf{\left[\Omega\right]}\) \\  & & & & **[k\(\Omega\) cm\({}^{2}\)]** & **[k\(\Omega\)]** & **[\(\Omega\)\(\cdot\)cm\({}^{2}\)]** & **[\(\Omega\)\(\cdot\)cm\({}^{2}\)]** & **[\(\Omega\)\(\cdot\)cm\({}^{2}\)]** \\ \hline
1 & 7.87 \(\times\) 10\({}^{-9}\) & 1.23 \(\times\) 10\({}^{-6}\) & 1.73 & 24.79 & 11.42 & 1.17 & 0.54 \\
2 & 4.22 \(\times\) 10\({}^{-9}\) & 6.60 \(\times\) 10\({}^{-7}\) & 1.61 & 3.75 & 1.73 & 0.51 & 0.24 \\
3 & 3.64 \(\times\) 10\({}^{-10}\) & 5.69 \(\times\) 10\({}^{-8}\) & 1.38 & 12.33 & 5.68 & 1.29 & 0.59 \\
4 & 2.83 \(\times\) 10\({}^{-11}\) & 4.42 \(\times\) 10\({}^{-9}\) & 1.17 & 9.56 & 4.41 & 0.99 & 0.46 \\
5 & 2.15 \(\times\) 10\({}^{-9}\) & 3.36 \(\times\) 10\({}^{-7}\) & 1.55 & 7.72 & 3.56 & 0.49 & 0.23 \\
6 & 7.57 \(\times\) 10\({}^{-10}\) & 1.18 \(\times\) 10\({}^{-7}\) & 1.41 & 4.15 & 1.91 & 1.00 & 0.46 \\
7 & 3.98 \(\times\) 10\({}^{-12}\) & 6.22 \(\times\) 10\({}^{-10}\) & 1.04 & 2.64 & 1.21 & 1.45 & 0.67 \\
8 & 3.68 \(\times\) 10\({}^{-9}\) & 5.75 \(\times\) 10\({}^{-7}\) & 1.56 & 2.02 & 0.93 & 0.34 & 0.16 \\
9 & 4.00 \(\times\) 10\({}^{-9}\) & 6.25 \(\times\) 10\({}^{-7}\) & 1.63 & 89.47 & 41.23 & 0.54 & 0.25 \\
10 & 7.49 \(\times\) 10\({}^{-6}\) & 1.17 \(\times\) 10\({}^{-3}\) & 3.05 & 2.15 & 0.99 & 0.86 & 0.40 \\
11 & 3.91 \(\times\) 10\({}^{-9}\) & 6.11 \(\times\) 10\({}^{-7}\) & 1.57 & 60.57 & 27.91 & 0.47 & 0.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Absolute and referred to unit area main parameters of the one exponential model (single- diode model) of each module obtained using the 2/3 Diode Fit software. The values referred to unit area correspond to the cell current density \(J_{0}\) [A/cm2] and cell shunt \(R_{sh}\) [k\(\Omega\)\(\cdot\)cm\({}^{2}\)] and series \(R_{s}\) [\(\Omega\) cm\({}^{2}\)] resistances. The absolute values of current and resistance included in the table correspond to the module parameters, and they have been obtained from the cell values referred to unit area.
## 2 Materials and Methods
### Quantitative Evaluation of Image Quality  
Each filter was applied to a low-dose CT phantom image to calculate the noise level and spatial resolution. To measure the noise level, the contrast-to-noise ratio (CNR) and  
Figure 1: Simplified scheme of adaptive search window NL-TV filter using the gray-level difference (GLD) in low-dose CT.  
coefficient of variation (COV) were calculated. The formulae for the two parameters are as follows [46]:  
\[CNR=\frac{\left|S_{target}-S_{background}\right|}{\sqrt{\sigma_{target}^{2}+ \sigma_{background}^{2}}}, \tag{5}\]  
\[COV=\frac{S_{target}}{\sigma_{target}}, \tag{6}\]  
Here, \(S_{target}\) and \(S_{background}\) denote the signal intensity in the region of interest (ROI) of target and background areas, respectively; \(\sigma_{target}\) and \(\sigma_{background}\) represent the standard deviation values of the target and background areas, respectively. The ROI for calculating CNR and COV is shown in Figure 2a; the target ROI is indicated as \"ROIA\" and the background ROI is indicated as \"ROIB\".  
The spatial resolution was evaluated by selecting the most appropriate edge area in the CT image of the 610-03 part of the CIRS phantom. After obtaining the edge spread function (ESF), the normalized line spread function (LSF) was expressed and the final spatial resolution was evaluated using the sigma value. The measurement procedure is as follows: Select a clear edge in the image. This edge should have a high contrast (i.e., a metal edge, the boundary of a phantom). Make sure that the edge is aligned in one direction in the footage, which is usually vertical or horizontal. Extract a linear profile in the direction perpendicular to the edge. Then, extract multiple profiles and average them to reduce noise and improve the signal-to-noise ratio (SNR). Align the data around the position of the edge. Here, interpolation is used to generate continuous data to refine the sampling near the edge  
Figure 2: (**a**) Low-dose CT image acquired by marking ROIs for CNR and COV evaluation. Resulting images obtained by applying (**b**) TV, (**c**) NL-TV, and (**d**) adaptive NL-TV filters to the low-dose CT image in (**a**). The proposed adaptive NL-TV filter was effectively applied in the red arrow area (smallest diameter hole).  
to improve spatial resolution by reducing the sample interval. From this, an accurate ESF is obtained and the LSF is derived by calculating the first derivative. Because computing the derivative can be noisy, you can pre-fit the ESF with Gaussian smoothing or other filtering techniques.
# EpiInfer: A Non-Markovian Method and System to Forecast Infection Rates in Epidemics
## 2 Related Work
### Models That Use Information About Meetings
#### 2.2.2 Contact Tracing-Based Models  
Contact tracing-based models offer detailed person-to-person analysis by identifying who interacted with whom during each encounter. [PERSON] et al. [17] combine agent-based modeling to simulate epidemic dynamics with probabilistic contact tracing with Belief Propagation and Simple Mean Field algorithms to identify superspreaders and reconstruct transmission pathways. They find that probabilistic contact tracing methods improve epidemic containment by efficiently detecting superspreaders, enhancing backward and multi-step tracing, and reducing social and diagnostic costs compared to traditional contact tracing approaches.  
[PERSON] et al. [18] propose a multi-hop contact tracing strategy, which identifies and monitors direct contacts of an infected individual but also traces secondary, tertiary, and further levels of contacts within the transmission network. By using the Microscopic Markov Chain Approach (MMCA), the model simulates disease transmission dynamics and evaluates the effectiveness of multi-hop tracing in controlling outbreaks. Their study shows that multi-hop contact tracing enhances epidemic containment, and their proposed mathematical framework enables faster and more scalable computation compared to traditional Monte Carlo simulations.  
[PERSON] and [PERSON] (2019) rely on contact tracing data to detect and estimate heterogeneity in susceptibility. Likelihood ratio tests are used to evaluate the fit of competing susceptibility models, and maximum likelihood estimation (MLE) is included to estimate model parameters. They conclude that contact tracing data alone are sufficient to detect and quantify the heterogeneity in susceptibility, allowing early identification of differences in infection likelihood among individuals, which leads to improvements in epidemic modeling and public health interventions.  
[PERSON] et al. (2018) propose what they call a Next-Generation Matrix (NGM) framework which combines contact matrices, derived from diary-based contact survey data, with previously known epidemiological parameters such as the transmission probability and the duration of infectiousness. The largest eigenvalue of the NGM represents the basic reproduction number. The study enhances the estimation of the basic reproduction number by incorporating professional contact data and refining age-specific interaction patterns.
# Integration and Field Evaluation of an IoV System for Enhancing Road Safety
## 4 Evaluation Plan and Field-Testing Approach
### Methodology and Evaluation Framework of the User Trials  
The described system has been evaluated in the field through user trials carried out in two (2) rounds in a semi-controlled traffic environment based on the FESTA methodology as described in the FESTA handbook [32]. Figure 6 reflects the methodology used for the evaluation of the system.  
The functions of the system are defined and matched to the use cases of the project [33] and the definition of the research questions that need to be answered was drawn up along with the evaluation objectives. These were linked to the real-life scenarios and necessary measurement tools to conclude the evaluation plan and the required installations. The functionalities of the system, their connection to the project use cases and the defined research questions are not presented in this paper as their description is throughout presented in the respective Deliverable EED1 [34].  
To evaluate the overall performance of the system, a two-fold methodology was developed based on (a) objective measurement tools, based on multiple data logging mechanisms incorporated within every component of the system that record every event and (b) subjective evaluation tools consisting of questionnaires addressed to the users before and after the conduction of the tests, which have been selected to evaluate subjectively, but with objective weighted criteria, the user's experience. In each pilot test, a sequence of asynchronous events is created, those describing the conditions and those describing the movement of the vehicles. Each event is converted into the appropriate message and transferred between the subsystems either directly or by using the cloud. Each generated message includes the timestamp of its creation. This creates a chain of events, which is related to one another. The time of creation and reception of these messages is offered as the basic tool for evaluating the performance of the system regarding the timely information of the users about the upcoming situation on the road.  
Figure 6: The ODOS2020 evaluation framework.