esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Petrological Classification and Petrogenesis
Combined with the lack of both Neoarchean to Palaeoproterozoic inherited zircons and metamorphic wall rock enclaves in the Dazeshan granitoids and their dark enclaves, which are common in Late Jurassic (166-149 Ma) and middle Early Cretaceous granitoids (132-122 Ma), it is most likely that almost all the crust-derived components contained in the region of the magma source were melted, except for few Neoproterozoic to Palaeozoic inherited zircons, indicating their melting at higher crustal levels, likely due to the removal of the Archean lithospheric mantle as a consequence of delamination (Figure 12) [29, 65]. In addition, the Dazeshan granitoids show the homogeneous Sr-Nd isotopic compositions, as are also indicated by other late Early Cretaceous granitoids (118-110 Ma), including the Changshannan pluton [64] and Gushan pluton [66]. Therefore, magma homogenization could have been obtained in the magma chamber at the crust base, and a MASH (melting, assimilation, storage, and homogenization) process [101] was responsible for the Dazeshan granitoids. Figure 12: Schematic tectonic model illustrating the generation of the high Ba-Sr Dazeshan granitoids and their dark enclaves (see text for details). Adapted with permission from [33, 47, 52]. MASH—melting, assimilation, storage, and homogenization; SCLM—subcontinental lithospheric mantle; SOB—Sulu orogenic belt; NCC—North China craton; YC—Yangtze craton; CC—continental crust; WQYF—Wulian-Qingdao-Yantai Fault; TF—Tan-Lu fault. In summary, on the basis of the petrography, major and trace element geochemistry, geochronology and Sr- and Nd-isotope data of the Dazeshan granitoids and their microgranular dark enclaves, a MASH process followed by the liquid immiscible is envisaged for the origin of late Early Cretaceous granitoids in the Jiaodong Peninsula (Figure 12). It is suggested that at c. 120 Ma, partial melting of ancient continental lower crust in the Jiaodong Peninsula (mainly the Neoarchean-Palaeoproterozoic basement rocks in the Jiaobei terrane) containing a subduction-related material, which resulted from the addition of the enriched subcontinental lithospheric mantle-derived melts, generated the major melts of the Dazeshan granitoids. Then, the mantle-derived basic melts were assimilated by the crust-derived acid melts, forming the homogeneous magma chamber at the crust base (Figure 12). During the homogeneous magma ascent, the single melt split into two immiscible liquids due to the variations in densities, viscosities and temperatures, with one granitic liquid producing Dazeshan granitoids, whereas the other intermediate melt formed the dark enclave (Figure 12).
# Design of Cislunar Navigation Constellation via Orbits with a Resonant Period
## 4 Simulation Analysis
### DOP Value Distribution Analysis of Resonant Constellations  
Taking the resonant constellation with optimal DOP performance in the combined region as a case study, this section investigates the spatiotemporal evolution of DOP values for both the near-Earth and lunar regions. To analyze the spatial distribution characteristics of DOP, the near-Earth region was redefined as a series of concentric multi-spherical regions with radii ranging from 10,000 km to 100,000 km. Similarly, the lunar region was redefined as concentric spheres with radii spanning 2000 km to 11,000 km, with longitude and latitude intervals refined to 10\({}^{\circ}\). As established in Section 4.1.3, the optimal configuration comprises L2 southern/northern Halo orbits and L4/L5 vertical orbits, with detailed orbital parameters provided in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Rank** & **Resonant Configuration** & \begin{tabular}{c} **Baseline** \\ **Period** \\ \end{tabular} & \begin{tabular}{c} **Resonance** \\ **Ratio** \\ \end{tabular} & \
\begin{tabular}{c} **Mean** \\ **DOP** \\ \end{tabular} &
\begin{tabular}{c} **DOP SD** \\ \end{tabular} \\ \hline
1 & L2 NH-L2 SH-L4V-L5V & 1.5715 & 1:1:4:4 & 10.72 & 24.53 \\
2 & L2 NH-L2 SH-L4P-L5P & 1.6352 & 1:1:4:4 & 10.86 & 27.70 \\
3 & L2 SH-L1 NH-L4V-L5V & 2.0946 & 1:1:3:3 & 15.20 & 50.58 \\
4 & L2 SH-L1 NH-L4P-L5P & 2.1801 & 1:1:3:3 & 16.84 & 92.86 \\
5 & L2 NH-L1 SH-L4P-L4P & 2.1801 & 1:1:3:3 & 16.84 & 92.86 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dominant configurations and spatiotemporal average DOP values in the combined region.  
Figure 7: Schematic diagram of optimal resonant constellation configurations and time-averaged DOP distribution in the combined region.  
Table 5 indicates that the constellation's baseline period is 1.57146, with a resonant period of 6.28584, corresponding to a resonant ratio of 1:1:4:4. Orbital propagation was performed using a time step of 0.01, with numerical integration spanning one resonant period.
## 6 Results and Discussion
### Effect of Traffic Redistribution  
Alternative future scenarios were simulated to assess the potential impact of a different traffic distribution to that assumed in Scenario B (Figure 3). The alternative scenarios considered a different use of the bypasses in terms of the portion of traffic using them compared to the total for each section. The scenarios described in Table 11 were taken into account, starting from the most favorable Scenario B1, where all traffic takes the bypasses, to the least favorable Scenario B9, where all traffic remains on the existing route.  
Simulations have shown that the predicted number of accidents can be reduced by between 23% and 53% by varying the proportion of traffic using bypasses, as shown in Figure 4. This is because even if no vehicles use the bypass (Scenario B9), there is still a safety benefit from the road layout rehabilitation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multicolumn{2}{c}{**Scenario A**} & \multicolumn{2}{c}{**Scenario B**} \\ \cline{2-5}  & **Existing** & **Existing** & **Rehabilitated** & **Bypasses** \\ \cline{2-5}  & **Road** & **Road** & **Road** & **Road** \\ \hline
**Segments** & \(N_{predicted,s}\) & 16.64 & 1.84 & 6.20 & 2.30 \\
**Intersections** & \(N_{predicted,i}\) & 5.34 & 0.32 & 1.35 & - \\
**Total** & \(N_{predicted,tot}\) & 21.98 & & 12.02 & \\ \hline \multicolumn{5}{c}{**Yearly crash reduction**} & \multicolumn{2}{c}{**9.96**} \\ \multicolumn{5}{c}{**\% crash reduction**} & \multicolumn{2}{c}{**45\%**} \\ \hline \hline \end{tabular}
\end{table}
Table 10: Number of predicted crashes for future scenarios.  
Figure 4: Effect of traffic redistribution on crash reduction.
## 3 MIMO Antenna Configuration Analysis
### Inverted Structure Placement  
In this case, the two-port MIMO antenna is designed in such a way that the second element is placed parallel to the first element but on the opposite side of substrate, as given in Figure 15. The total dimension of the suggested two-port MIMO antenna is M\({}_{\text{X4}}\)\(\times\) M\({}_{\text{Y4}}\) = 20.5 mm \(\times\) 12 mm. The hardware prototype is also fabricated to verify the software generated results. The distance between the two elements in this case is G\({}_{4}\) = 6 mm and fixed at this value to reduce mutual coupling between two elements.  
Figure 16a depicts the transmission and reflection co-efficient of the suggested antenna. It can be noted from the figure that the suggested antenna two-port MIMO configuration operates at 4.5 GHz wideband ranging from 25.5-30 GHz. The antenna offers a return loss maximum of \(-\)20 dB. The suggested antenna offers S\({}_{21}\) and S\({}_{12}\) of \(<\)\(-\)40 dB at operational bandwidth, which is much higher than the acceptable value. Moreover, the software predicated and hardware measured results show much similarity, which implies that the antenna is a good candidate for future wideband devices operating over a mm-wave spectrum.  
Figure 13: Comparison of results of MIMO antenna with orthogonal placement of element (**a**) scattering parameters (**b**) axial ratio.  
Figure 16: Comparison of MIMO antenna with inverted structure placement of element (**a**) scattering parameters (**b**) axial ratio.  
Figure 14: Comparison among gain and numerically calculated radiation efficiency of MIMO antenna with orthogonal placement of element.  
Figure 15: Schematic of MIMO antenna for opposite elements.  
The axial ratio bandwidth (ARBW) of the suggested antenna is given in Figure 16b. The antenna offers circular polarization of both left hand (LHCP) and right hand (RHCP). From the figure, it is clear that the antenna offers wide band AR of 3.1 GHz ranging from 25.2-28.3 GHz and 2.6 GHz ranging from 25.2-27.8 GHz for RHCP and LHCP, respectively. The figure also shows that there is no major difference between software predicated and prototype tested results, which makes the suggested antenna a good applicant for future mm-wave applications operating over circular polarization.  
Figure 17 shows the gain versus frequency as well as the radiation efficiency plot of the suggested antenna. It can be noted from the figure that the antenna offers gain > 7.8 dBi at operating bandwidth with a peak value of 8.75 dBi at 28.5 GHz. On the other hand, the antenna offers a radiation efficiency > 97.5% at operational band with a maximum value of 99% at 30 GHz. Moreover, the figure makes it clear that there is no major difference between software and hardware results, which makes the suggested design an excellent candidate for future high gain and high efficiency application.
## 2 Related Works
### Clustering Sensor Nodes Based on Non-Computational Intelligence  
Many methods for sensor node clustering based on non-computational intelligence have been proposed. These methods group sensor nodes into several clusters and select a CH for each cluster. The CH can gather data from other sensor nodes within the same cluster and send the aggregated data to the sink. This effectively saves energy and prolongs the network lifetime.  
For clustering of sensor nodes based on non-computational intelligence, LEACH is a very popular clustering routing protocol for WSNs proposed by [PERSON] et al. [9]. In LEACH, some sensor nodes are randomly selected as CHs. Each CH aggregates data from the sensor nodes within its own cluster, and then sends the data to the sink. LEACH has the advantage of increasing energy efficiency, but, its problem is to randomly select cluster heads, sensor nodes with more residual energy and less residual energy have the same probability of being selected as CH. If sensor nodes with less residual energy are selected as CHs, then they die quickly. To solve this problem, a stable election protocol (SEP) for clustered heterogeneous WSNs is proposed by [PERSON] et al. [29]. In SEP, a certain percentage of sensor nodes are equipped with more energy than other sensor nodes. The selection of CHs is based on the election weight according to the remaining energy of each node. However, neither LEACH nor SEP takes into account the location and distance information of sensor nodes. Therefore, a quadrant-based routing protocol (Q-LEACH) is proposed by [PERSON] et al. [30]. In Q-LEACH, the sensing region is divided into four quadrants, and the area within each quadrant is regarded as a cluster. The CH of each cluster communicate with each other to determine the shortest route between the source and the destination. The Q-LEACH protocol prolonged the network lifetime while increased congestion in WSNs. [PERSON] and [PERSON] [31] proposed a cross layer-low energy adaptive clustering hierarchy model (CL-LEACH) to save the energy of sensor nodes. CL-LEACH considered the residual energy of sensor nodes for CH selection, which preserves the overall energy. In addition, some scholars proposed the grid-based sensor node clustering strategy [10; 11; 12]. For the grid-based clustering method, the sensing region is regularly divided into several subregions, and each subregion is regarded as a cluster, which can avoid the redundant data and long-distance communications. But the grid-based clustering strategies lead to an imbalance in the number of sensor nodes between clusters, thereby resulting in an imbalance in the energy consumption between CHs.
## 4 FON Function--Ant Colony Optimization Routing in Network
### Design of ACO Packet  
The packet format is depicted in Figure 10. Each field is described in Table 5. These _walks_, _path_, _visited_ are similar in that these fields record an array of nodes but these fields have their own purpose. _walks_ record all nodes so it is useful when debugging. _path_ field is used to know shortest path. _visited_ field comes from [20] and prevents revisit and the graph cycle.  
Whenever an ant packet visits a node, the size of the packet increases. So the maximum number of visited nodes depends on the value of _init_ttl_. _init_ttl_ having an upper bound as (8):  
\[\begin{array}{l}\text{FON Header Size }+\text{ ACO Fixed Field Size }+n walks*4\text{ Bytes }+npath\\ *4\text{ Bytes }+nvisted*4\text{ Bytes }+ndists*2\text{ Bytes }<=\text{ MTU}\end{array} \tag{8}\]  
In the Ethernet network, the default MTU is 1500 Bytes. But _nwalks_, _npath_, _nvisted_, _ndists_ are not fixed so _init_ttl_ cannot be calculated. But in the worst case, _init_ttl_ can be calculated. Assume that the ant visits a new node always. Then (9) holds true. Finally, Equation (10) can be obtained:  
\[nwalks=npath=nvisted=ndists=init\_ttl\text{, When worst case} \tag{9}\]  
\[\begin{array}{l}\text{\emph{init\_ttl}}<=91.5\end{array} \tag{10}\]  
So in the Ethernet network, the ant packet can visit at least 91 nodes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Field** & **Description** \\ \hline _source_ & The node that generates an ant packet. \\ _destination_ & The node that an ant packet should reach. \\ _direction_ & It means the type of ant. There are two types of ant; _forward_ is a request \\  & packet. _backward_ is a response packet. \\ _init_ttl_ & It is an initial time-to-live. Using this filed, each node can calculate the \\  & distance between _source_ and current node. \\ _cur_ttl_ & Current time-to-live. \\ _nwalks_ & The length of _walks_ field. \\ _npath_ & The length of _path_ field. \\ _nvisted_ & The length of _visited_ field. \\ _ndists_ & The length of _dists_ field. \\ _padding_ & This field is a 2 bytes padding for a 4 bytes-align. \\ _walks_ & The array of FON ID. This field records all nodes that ant (packet) dropped \\  & by. So, this field includes all nodes that backtracked nodes. \\  & The array of FON ID. This field records all nodes that ant (packet) dropped \\ _path_ & by, but does not include backtracked node. Using this field, the shortest \\  & path from _source_ to _destination_ can be calculated. \\  & _Visited_ field is the array of node IDs and where all visited nodes will be \\ _visited_ & recorded in. It is used to confirm that duplicate visits have been made and \\  & to prevent the graph cycle. \\  & _Visited_ field is the array of the distance. Each element has 2 byte length. \\ _dists_ & And nth element is the distance between source and nth visited’ element. \\  & In other words, _dists_[i] == Dist(source, _visited_[i]) where i is the index of array. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Description of ant packet fields.
## 4 Experiment and Evaluation
### Motion Vector Evaluation  
In this study, we introduce the Ground Plane Odometry (GPO) of a low-profile pallet robot using ortho-rectified GPI, a virtual top-view image. GPO is performed on the assumption that a virtual top-view camera captures the flat ground plane. If this assumption is correct, ideally, all the motion vectors on an ortho-rectified GPI will have the same direction and length. Figure 17a shows an example of the ortho-rectified GPI and motion vectors via the KLT optical flow. Figure 17b shows the distribution of the motion vectors in the \(\left(\overline{dx},\ \overline{dy}\right)\) space. As seen in the figure, the distribution of the motion vectors is almost uniformly distributed, and we take the median vector as the robot motion between frames.  
In this study, we assume that the median vector is the motion of the robot. Using the median vector is more accurate than using the mean vector, which is the average of all the motion vectors. Because motion vectors can contain some outliers due to incorrect motion tracking, the mean vector is rather biased from the real robot motion. If the mean vector is erroneous due to the outlier, the final robot odometry is also affected by the erroneous motion vector. To compare the performance of GPO using the median and the mean vectors, we have three experiments using short trajectories. Table 3 shows a comparison of GPO results using median and mean vectors. The table shows the minimum, maximum, and average values of the relative translation error of GPO. Three experimental trajectories for comparison are shown in Figure 18. This experiment shows that using the median vector is more robust to noise and more accurate than using the mean vector.  
Figure 16: (**a**) Optitrack’s optical pose tracking device V120 Trio; (**b**) Four optical markers attached to the pallet robot.  
Figure 17: (**a**) Unfiltered motion vectors obtained from the ground plane image (**b**) A \(\left(\overline{dx},\ \overline{dy}\right)\) plot of motion vectors.
# A Low-Cost IoT-Based System to Monitor the Location of a Whole Herd
## 2 Materials and Methods
### Testbeds  
Two commercial farms have been used as testbeds to validate the performance of the proposed solution under different livestock production conditions.  
Both testbeds are situated in Andalusia, Spain. Andalusia is a semi-arid Mediterranean region where rain is scarce and irregularly distributed over the year. Average temperature is 18 \({}^{\circ}\)C and annual precipitation ranges 400-600 mm. Herbs are the main forage resource, basically in fall and spring when rainfall occurs [27]. Moreover, both farms are located in \"dehesas\", which are defined as agrosilvopastoral systems, originated from the clearing of holm oak and cork woodlands, mostly used for livestock farming [28].  
The first farm was situated in Northern Cordoba (38.49\({}^{\circ}\) N, 5.11\({}^{\circ}\) W). The validation study was carried out with a \"Merina\" sheep herd composed of 50 ewes. From July to October 2018, for 100 consecutive days, 25 randomly selected ewes were fitted with GPS collars and another 25 with BLE tags. During the experiment, ewes grazed free-range across 70 hectares. The same experimental protocol was used in the second farm, located in Northern Huelva (37.96\({}^{\circ}\) N, 6.25\({}^{\circ}\) W), with a \"Negra Andaluza\" beef cattle herd. Twenty-five cows were fitted with GPS collars and another 25 with BLE tags. In this case, beef cows grazed free-range on 300 hectares from November 2018 to February 2019. Both \"Merina\" and \"Negra Andaluza\" are autochthonous breeds, highly adapted to the production conditions of \"dehesa\" farms.
# A Non-Fungible Token and Blockchain-Based Cotton Lint Traceability Solution
## 5 Implementation Details  
In this section, we present the exhaustive testing and validation of the proposed solution. The debugging and functional verification of the smart contract are designed and implemented in the Ethernet blockchain environment using the Remix IDE development tool and the Solidity programming language. All system participants and smart contracts are identified through uniquely assigned Ether addresses, and the specific mapping relationships are as shown in Table 1. The experiment covers four key phases: the NFT minting process; the auction mechanism; the ownership transfer operation; and the quality certification session.  
The seed cotton buyer can only send a request to participate in the auction through the market SC. The buyer can notify the stakeholders by entering the EA of the seed cotton owner and the seed cotton token ID to execute the BuySeedCotton function to successfully issue an event, as shown in Figure 6, which shows the log of the buyer's successful bids on this batch of seed cotton. The end of the seed cotton auction is determined by whether or not the auction amount is higher than the preset amount, and the auction time must be reached within a specified period; see Figure 7 for the auction final winner log. After the end of the normal auction, the seed cotton owner executes the EndAuction function to announce the final winner and will wait to transfer the NFT ownership to the winner. The seed cotton owner ships the seed cotton by executing the SeedCottonShip function, waits for the buyer to confirm receipt of the shipment and then initiates the success shipment event, which changes the ownership of the digital twin seed cotton NFT to the buyer and notifies all participants of the transfer of ownership of the seed cotton, as shown in Figure 8.  
Figure 5: A demonstration of seed cotton owners successfully casting seed cotton NFT logs through seed cotton rolls.  
Figure 6: The purchaser’s success in bidding on this batch of seed cotton logs.  
Figure 7: The log of the highest bidder at the end of the auction.  
The process used by the MSAB to cast NFT inspection certificates for cotton lint is similar to the process used to cast NFTs for seed cotton. The lint inspection certificate includes a quality inspection and weight inspection. Each bale of cotton lint obtains its own unique quality certification certificate. The certificate is uploaded to the IPFS. Then, the return of the metadata address is required for the generation of the cotton lint inspection certificate twin NFT. Upon executing the CertificateBound function, the lint NFT and lint inspection certificate NFT binding process begins, as shown in Figure 9. Once the binding is complete, a complete cotton lint quality traceability chain is formed to ensure the quality of the cotton lint with reliability certification.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 7 BackCom: Range Extension and Reliability
### Coverage and Range Improvement
#### 7.1.4 Relaying for BackCom  
Relaying is a procedure to extend the communication range of BackCom devices. This technique enables some nodes to assist other nodes while carrying information in the network. Figure 37 delineates a simple concept of relaying for Amb-BackCom devices [93]. They consider backscatter tags to relay information from the source tag to the destination tag. Due to the full-duplex mode of relaying tags, the data can be sent simultaneously during the reception of signals. Two cases are considered in this relaying scheme [93]. In the first case, the selected relay tag has its own data to transmit; in the other case, it does not. The time period is divided into two blocks, and information is carried from source to relay, and from relay to destination, in each half time block, respectively.  
A passive two-hop relaying scheme via BackCom in cooperative wireless networks was proposed by [PERSON] et al. [176]. Their scheme maximizes the throughput by jointly optimizing the WPT and relay strategies. [PERSON] et al. proposed the three-time-slot decode-and-forward relay system for Amb-BackCom [177]. Amb-BackCom-assisted wirelessly powered relaying was suggested in [178]. This technique utilizes hybrid relay in a dual-hop relaying system with widely distributed energy sources. Similarly, a relay selection scheme for cooperative BackCom enables communication with out-of-coverage devices using Hybrid-AP [179]. A team of MIT researchers introduced the \"RFLY\" relay to extend the range of backscatter tags with the help of drones [180]. They also addressed the problem of self-interference and localization, by preserving the phase information of the reflected signals.