esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Concentration of Fermentation Volatiles in Samples Collected during RO-EP Treatment of Wine  
The concentrations of volatile acids, esters and alcohols present in wine, retentate, permeate and strip water samples are shown in Table 4. It should be noted that these concentrations do not represent absolute quantities of volatiles since the volume of wine, permeate and strip water are not the same. Therefore, mass flow values were calculated (Table 5) to better demonstrate changes in fermentation volatiles.  
It is evident from Table 4 that all of the fermentation volatiles measured were capable of permeating the RO membrane. Different proportions of each volatile were distributed in the retentate and permeate fractions but fermentation volatiles were typically present at higher concentrations in retentate compared with permeate. Following EP treatment of permeate, the concentrations of most volatiles decreased, suggesting they were being removed. This notion was supported by their detection in strip water. As a consequence, volatile concentrations in dealcoholized wines were generally lower than the levels observed in wine prior to RO-EP treatment. As outlined above, this outcome may also reflect shifts in the equilibrium between ester formation and hydrolysis due to the removal of ethanol. This was consistent with an earlier study that reported decreases in ester concentrations of up to 60% in red wines following alcohol removal using a polypropylene membrane [34]. A separate study also observed a significant loss of esters (ethyl octanoate, ethyl acetate and isoamyl acetate) following RO-EP treatment of wine to remove alcohol [35].
# Cross-Correlation and Fractal Analysis in the Images Diatoms Symmetry
## 2 Materials and Methods
### Two-Dimensional Cross-Correlation  
Cross-correlation is a common signal processing used as a similarity measure through the comparison between time series as a function of the displacement of one with respect to the other. However, this signal processing must be applied to compare two images [37], in this case, called \"2D-cross-correlation\" (CC) [38]. In this study, the CC was used to find similarities between two images, comparing one diatom image with the same image modified by symmetry operations and between two different diatom images.
## 3 Results and Discussions
### Final Products Characterization
#### 3.2.2 Free Sugars Content of Final Products  
Dough sugars are subjected to transformations during baking, so the temperature gradient between the core and the dough surface is responsible for the behavior of the starch as follows: in the center, being lower temperature, the starch becomes sticky and with a colloidal structure, thus forming the crumb. At the surface, the higher temperature causes dextrinization and caramelization of the available sugars. Volatile gases and compounds are already depleted, and the Maillard reaction between sugars and amino acids leads to the formation of new compounds, responsible for the organoleptic properties typical of the manufactured assortment [63].  
The free sugars content of the final baked products increased as BW and SF flour percentages increased due to their richer chemical composition in simple sugars (Table 3) and also as a possible consequence of two reactions: enzymatic hydrolysis of starch and fructans during kneading and fermentation of dough and due to thermal degradation during baking process, which lead to the formation of lower sugars, especially maltose and sucrose [63]. Samples with BW addition registered an augmentation of glucose, sucrose, maltose and fructose content; while in buns supplemented with SF, glucose was reduced, but fructose increased (Table 7).  
To the best of our knowledge, no studies have been found in the literature regarding the content of free sugars in baked goods with the addition of these two unconventional flours.
## 4 REEMR: Reliable Energy-Efficient Multi-Level Routing Algorithm
### REEMR Algorithm Analysis  
REEMR is a distributed algorithm, where the nodes decide to be cluster heads according to the local neighbor nodes.  
**Proposition 1**: _The control overhead complexity of cluster formation in REEMR is \(O(N)\), where N is the number of nodes._  
PROOF. At the beginning of each round, every node first broadcasts a message ADV_E to calculate the centrality and the number of its neighbor, and the maximal total ADV_E overhead is \(N\). Whereafter according to the cluster head declaring time, each node transmits the message JOIN or ADV_HEAD. We assume \(k\) cluster heads are chosen, and then they broadcast \(k\) ADV_HEAD message to let the sensor nodes know about their presence. In the meantime, the other \(N-k\) cluster member nodes broadcast JOIN messages. Hence, the total message overhead is \(N+k+N-k=2N\) and the control overhead complexity is \(O(N)\).  
**Proposition 2**: _The upper bound of the time complexity of the proposed fuzzy reasoning algorithm is \(O(nm)\), where \(n\) is the number of places, and \(m\) is the number of transitions._  
PROOF. For a place \(p_{i}\), the maximum number of transitions in its postset is \(m\), so the maximum computation times of the truth degree for its IRS are \(m\). For \(n\) places, their maximum computation  
Figure 9: The sprouting tree.  
times are \(n\times m\). Therefore, the upper bound of the time complexity of the fuzzy reasoning algorithm is \(O(nm)\).  
In REEMR, the nodes with higher residual energy, more neighbor nodes and less centrality are more likely to be elected as cluster heads. The node whose degree of reliability is less than the corresponding threshold of transition will not flood any data. Therefore the route is reliable and is not easily broken.
## 3 Results and Discussion
### Effect on the Physical Characteristics
#### 3.1.1 Effect on the Porosity  
The average porosity of the tested sample versus the number of F-T cycle is shown in Figure 6. From that, the porosity presented an increasing trend with an increase in the F-T cycle number. After 30 F-T cycles, the average porosity of the tested sandstone increased by 13.7%, suggesting an increase in the volume of pores within the sandstone sample. This means that irreversible damages occurred in the samples during the process of freezing or thawing [23], and these damages within the rocks embodied the work that was done by the frost heave force. Further, the change of stress path within the samples during loading due to the existence of defect spaces explained the different failure modes with the number of F-T cycles as presented in Figure 5. Besides, during cyclic F-T tests, some mineral components on the pore wall of the sample dissolved in pore water, leading to damage and deterioration of the weak pore wall [13]. Thus, crystallization pressures that were generated by the ice and exerted on the pore walls caused the generation of more pore space, which in turn leads to the decrease of the overall (tensile) strength of the rock.
# Cooperative Power-Domain NOMA Systems: An Overview
## 1 Introduction
The evolution of mobile wireless systems with multiple access technologies is discussed in [14] and depicted in Figure 2. The 5G and B5G technologies are able to provide solutions for different and mission-critical applications, such as massive machine type communication (mMTC) [15], enhanced mobile broadband (eMBB) [16], and ultra-reliability and low latency communication (URLLC) [17], as per International Telecommunication Union (ITU) [18]. The first commercial version of a 5G system based on 3G Partnership Project (3 GPP) release has 15 standards. The new generation of networks is likely to have additional improvements to enable advanced services and applications [19]. Because of the scarcity of wireless spectrum resources, desired system capacity remains a major issue for the future mobile communication generations. By superposing the signals of multiple users in the same orthogonal resource block (RB), NOMA is recognized to be an effective method for supporting the needs of large-scale access [20]. In addition, in MIMO with a larger antenna order, and its special case, i.e., multiple-input single-output (MISO) could achieve a higher capacity. The MIMO-NOMA systems can take advantage of the extent in both the space and power domains, thus enhancing system spectrum efficiency even more.
# Enhancing Autonomous Vehicle Navigation with a Clothoid-Based Lateral Controller
## Appendix A  
Calculating the Frechet distance in real-time implementation involves finding a discrete approximation of the continuous Frechet distance between two trajectories. Here is a simplified explanation of how it can be found:  
1. Discretize the Curves: 1.1 First, you need to discretize the two curves, \(P(t)\) and \(Q(t)\), into a set of points. These points represent positions of the objects at specific time intervals or parameter values. 1.2 For example, you can represent each curve as an array of points: \(P=[(x1,y1),\)\((x2,y2),\ldots]\) and \(Q=[(u1,v1),\)\((u2,v2),\ldots]\). 2. Dynamic Programming Matrix: 2.1 Create a matrix, often referred to as the Frechet distance matrix, to store intermediate results for calculations. 2.2 The matrix is typically of size (m + 1) \(\times\) (n + 1), where m is the number of points in curve \(P\) and n is the number of points in curve \(Q\). 2.3 Initialize the matrix with a value that represents \"infinity\" for all entries except the top-left corner, which should be initialized to 0. 3. Fill the Matrix: 3.1 Use dynamic programming to fill the matrix by calculating the Frechet distance for all pairs of points in curves \(P\) and \(Q\). 3.2 Start from the top-left corner and proceed row by row, column by column.  
For each cell (i, j) in the matrix, calculate the distance between points \(P[i]\) and \(Q[j]\).  
Update the cell \((i,j)\) with the maximum value of the following:  
The distance between \(P[i]\) and \(Q[j]\).  
The minimum distance in the adjacent cells to the left, above, and diagonally top left of the cell \((i,j)\).  
Continue this process until you reach the bottom-right corner of the matrix.  
Final Result:  
The value in the bottom-right corner of the matrix represents the Frechet distance between curves \(P\) and \(Q\).  
The logic behind this approach is to find the minimum \"closeness\" between the two curves while allowing both to be traversed continuously. By considering all possible pairs of points between the two curves and using dynamic programming to efficiently compute the minimum distance, you can approximate the Frechet distance.
## 5 Taxonomy of Technology-Enabled Smart City Applications in 6G Networks
### Internet of Things (IoT)
#### 5.1.1 Internet of Vehicles (IoV)  
The IoV is a distributed network of mobile vehicles that have sensing, computing, and Internet Protocol (IP)-based communication capabilities [76]. The global IoV market is projected to reach $208,107 million by 2024 from $66,075 in 2017, with a GAGR of 18% between 2018 and 2024 [77]. The IoV network interconnects vehicles with pedestrian and urban infrastructure facilities such as the cloud and Roadside Units (RSUs). The IoV includes six types of communications for vehicles to receive and transmit data as shown in Figure 16: (1) Vehicle-to-Vehicle (V2V), (2) Vehicle-to-Infrastructure (V2I), (3) Vehicle-to-Roadside (V2R), (4) Vehicle-to-Sensors (V2S), (5) Vehicle-to-Cloud (V2C), and (6) Vehicle-to-Pedestrian (V2P). Several vehicular applications have been developed for the IoV such as an intelligent parking system, real-time navigation, traffic and accident alert, facial recognition for autonomous driving, cooperative adaptive cruise control, and traffic signal violations. These applications have strict data rate and latency requirements that should be supported by the underlying 6G networks. For instance, autonomous driving involving multiple sensors may require a total data rate of 1 Gbps for V2V and V2X communications [78].  
Figure 13: Machine Learning (ML)- and Deep Learning (DL)-based botnet detection for Internet of Things (IoT).  
Furthermore, it requires a reliability of 99.999% [79], which cannot be obtained with the existing wireless communication systems. In addition, vehicular applications such as infotainment, e-toll collection, collision warning, autocruise, AR map navigation, and co-operative stability control have stringent latency requirements of 500 ms, 200 ms, 100 ms, 20 ms, 5 ms, and 1 ms, respectively [80]. The 6G networks should consider the issues of the limited spectrum, high latency, and low reliability prevailing in the current vehicular standards, i.e., IEEE 802.11p [81].  
Figure 14: Taxonomy of smart city applications in 6G based on underlying technologies.
# Toward Low Time Fragmentation of Equipment: A Double-DQN Based TT&C Task Planning Approach
## 4 Problem Transformation  
Observing the above optimization problem P, we can see that this problem is a multi-objective combinatorial optimization problem and has been proven to be NP-hard [18]. To solve the TT&C task-planning problem more conveniently and effectively, this paper first transforms the problem into a graph theory problem, and then uses machine learning to solve the problem. Specifically, a task-planning conflict graph \(CG(V,E)\) is constructed, and the vertex \(v_{i}^{\alpha}\) in the graph represents the task feasible scheme \(y_{i}^{\alpha}\), i.e., \(V=\big{\{}v_{i}^{\alpha}\big{|}y_{i}^{\alpha}\in Y\big{\}}\). The edges in \(CG\) represent the conflicted relationship between different feasible task feasible schemes. For any \(v_{i}^{\alpha},v_{n}^{\beta}\in V\), a conflict between them must meet one of the following two conditions:  
(1) \(v_{i}^{\alpha}\) and \(v_{n}^{\beta}\) present the two different feasible schemes of the same task, i.e., \(i=n\).  
(2) There is a conflict between the feasible schemes corresponding to \(v_{i}^{\alpha}\) and \(v_{n}^{\beta}\). That is to say, there is \(y_{n}^{\beta}\in O(y_{i}^{\alpha})\) for \(v_{i}^{\alpha}\).  
It means that there is a conflicted relationship between two vertices, namely, \(\left(v_{i}^{\alpha},v_{n}^{\beta}\right)\in E\).  
The above two conditions correspond to the constraints C1 and C2 in problem P C1 represents the conflicted relationship between different feasible schemes of the same TT&C task. C2 indicates the conflicted relationship between feasible schemes of different TT&C tasks. Therefore, solving the optimization problem P is equivalent to solving the independent set problem on the graph.
## 4 Experimental Results
### Event of 4 July 2018  
In Figure 7, the received powers by the stations \"S2\(-\)Torcia\", \"PZS1\", \"Ufficio Ingr\", and \"Pala Eolica\" are shown along with the rainfall intensity estimated from the radar and the RG for the event on 4 July 2018. In order to allow for an easy comparison of data, the SRS measurements are shown as \(P_{R}(t)-P_{R}(t_{0})\), where \(P_{R}(t)\) is the received power's time series and \(P_{R}(t_{0})\) is the received power at the start of the displayed time window. In Figure 8 the estimates of rainfall intensity returned by the stations \"S2\(-\)Torcia\", \"PZS1\", \"Ufficio Ingr\", and \"Pala Eolica\" are shown along with the ones returned by the radar and the RG for the event on 4 July 2018. Finally, the estimation errors are reported in Table 2.  
Figure 7: Received power variation \([\mathrm{dBm}]\) at the sites (**a**) S2\(-\)Torcia, (**b**) PZS1, (**c**) Pala Eolica, and (**d**) Ufficii Ingr on 4 July 2018. The signal power variations for channels A and B refer to the right \(y\)-axis, whereas the rain intensities \(\mathcal{R}\) provided by the radar and the RG refer to the left \(y\)-axis.
# Compact Midwave Imaging System: Results from an Airborne Demonstration
## 2 Materials and Methods
### Instrument
#### 2.1.6 Electronics  
CMIS camera electronics (Detector Board, Digital Board, and Power Board) were implemented in a small form-factor chosen specifically for CubeSat compatibility. Components were selected and the boards were laid out to accept either low-cost commercial components for the airborne model or their high-reliability equivalents for the spacecraft model.
## 4 Evolution of Cadmium Transporters and Their Functions in Cadmium Accumulation
### Function of Cd Transporters
#### 4.2.4 CDFs  
CDF proteins, also known as MTPs, are a family of heavy metal transporters involved in the transport of Zn\({}^{2+}\), Cd\({}^{2+}\), and Co\({}^{2+}\)[128]. They have been identified in diverse organisms, including bacteria, fungi, animals, and plants. The CDFs of plant cells, generally known as MTPs [129], are known to mediate the heavy metal efflux from the cytoplasm either to the extracellular space or to vacuoles and organelles [130]. MTPs consist of seven phylogenetic groups, with Zn-CDFs in groups 1 (MTP1-MTP4), 5 (MTPS), and 12 (MTP12); Fe/Zn-CDFs in groups 6 (MTP6) and 7 (MTP7); and Mn-CDFs in groups 8 (MTP8) and 9 (MTP9-MTP11) [131]. In rice, OsMTP1 has been demonstrated to be a PM-localized transporter involved in the translocation of Cd and other heavy metals in both roots and shoots [132]. Other MTPs, such as TgMTP1 from _Thlaspi goesingense_[133], CsMTP1 and CsMTP4 from _Cucumis sativus_[134], and CitMTP1 from _Citrus sinensis_[129], have been proven to be involved in the sequestration of Cd into vacuoles or its efflux from root cells.
# Opportunistic and On-Demand Network Coding-Based Solutions for LPWAN Forwarding
## 2 Related Works
### Intermediate Forwarder  
We can use other end-devices or place a relay between the end-device and the gateway for the intermediate forwarder. Hare [9] topology is similar to a hierarchical tree that uses end-devices close to the gateway as the intermediate node. This topology requires time synchronization, such as time division multiple access, to synchronize Transmission \(Tx\) and Reception \(Rx\) between the parent and its children. Similarly, the authors in [13; 14; 28] also propose a solution that requires a parent to forward a packet and time synchronization to avoid a collision. However, both approaches will pressure the parents because they have to listen and forward several packets from their children.  
The authors of [10; 11; 12] use a relay to forward a packet from the end-device to the gateway. Although adding a relay incur a financial cost, this does not put pressure on the other end-device, and we can exclude the end-device from the routing discovery. Besides, this relay is more economical than putting an extra gateway as it is cheaper and does not require internet access.
## 3 Results and Discussion
### Simulation Results
#### 3.3.2 Analysis of Evaluation Indices  
Figure 13 presents the ADPI distribution in the EDTS velocity diagram at different air inlet spacings. In the occupied zone, approximately 1500 nodes were used for the  
Figure 11: Velocity distribution contour in the plane z =1.1 m with different air inlet spacings.  
Figure 12: Temperature distribution contour in the plane z = 1.1 m with different air inlet spacings.  
calculations of the ADPI. The ADPI values were all above 90% at different supply air inlet spacings, indicating that SV could be in full compliance with the stipulation of the ADPI in ASHRAE 113-2013 [54]. The results also indicated the good thermal comfort performance of SV. Most of the nodes were concentrated in the upper left corner of the square frame, indicating that the room with SV effectively improved the thermal neutral temperature at a low air velocity. When the supply air inlet spacing was greater than 500 mm, the ADPI value did not increase significantly, indicating that when the spacing was greater than a certain value, the ADPI value was only slightly affected.  
Figure 13: ADPI distribution in EDTS–velocity diagram with different air inlet spacings.  
Figure 14 shows that the lowest mean air age occurred when the air inlet spacing was 800-1100 mm, indicating that better air distribution and air quality in the occupied zone were created when the spacing was within this range. A jet with a smaller air inlet spacing was more concentrated with a higher air velocity, which lowered the local age of the air. However, the local mean age of air in the area on the sides of the room was significantly greater than in other areas owing to insufficient air movement, resulting in a greater mean age of air in the entire occupied zone. The jets from large supply air inlet spacings were isolated from each other, forming an area between the jets that could not be covered, leading to a weak airflow and greater age of air. Although the index was lower in the area through which the isolated jet passed, the mean age of air in the entire room was also greater.  
Figure 15 shows the variation in the index values with different air inlet spacings. As the supply air spacing increased from 500 mm, the ADPI value started to increase gradually, reaching a maximum when the spacing reached 800 mm before fluctuating. The change trend of the EUC was similar; however, the maximum value occurred when the spacing was 950 mm. The LMAA value changed little when the spacing increased to 650 mm and then decreased sharply to 430 s at a spacing of 950 mm. When the spacing exceeded 950 mm, the LMAA rebounded to 450 s as the spacing increased. The optimal values of ADPI, ECU, and LMAA correspond to air inlet spacings of 800 mm, 950 mm, and 950 mm, respectively. Thus, it was impossible to specify the optimum inlet spacing directly. A comprehensive optimal supply air inlet spacing can be obtained through multi-objective optimization analysis.  
Figure 14: Contour of local mean air age in the plane z = 1.1 m with different air inlet spacings.
###### Abstract  
Time-synchronization technology can provide a common notion of time among the participating nodes on a network. This is essential not only for protocol operation for time-critical services but also for the time stamp for information included in the message. Precise time information can be very crucial for such things as autonomous driving because there are various sensor measurements from multiple cameras, and radio detection and ranging (radar) and light detection and ranging (LiDAR) are used for perceiving the current situation via sensor fusion. A well-known synchronization method, IEEE 1588, denoted as the precision time protocol (PTP), can be used for various applications. For in-vehicle networks of autonomous cars, we have to consider that the network may be comprised of subnetworks based on different protocols such as controller area network (CAN) and Ethernet. However, implementing PTPs on such heterogeneous vehicle networks causes several problems. First, the PTP procedure must be modified to be implement on a CAN network. Second, to calculate the delay and offset for PTP, the processing delay that occurs during message conversion must be considered. In this paper, we propose a synchronization method for CAN-Ethernet networks to solve these problems. The performance of the proposed synchronization method is evaluated by experiments on a real CAN-Ethernet network.  
time synchronization; CAN-Ethernet network; in-vehicle network; IEEE 1588; precision time protocol +
Footnote †: journal: Accepted: 9 December 2020; Published: 11 December 2020